LEARNING ANALYTICS DASHBOARDS ARE AT THE CORE OF THE LAK VISION TO INVOLVE THE HUMAN INTO THE DECISION-MAKING PROCESS. THE KEY FOCUS OF THESE DASHBOARDS IS TO SUPPORT BETTER HUMAN SENSE-MAKING AND DECISION-MAKING BY VISUALISING DATA ABOUT LEARNERS TO A VARIETY OF STAKEHOLDERS. EARLY RESEARCH ON LEARNING ANALYTICS DASHBOARDS FOCUSED ON THE USE OF VISUALISATION AND PREDICTION TECHNIQUES AND DEMONSTRATES THE RICH POTENTIAL OF DASHBOARDS IN A VARIETY OF LEARNING SETTINGS. PRESENT RESEARCH INCREASINGLY USES PARTICIPATORY DESIGN METHODS TO TAILOR DASHBOARDS TO THE NEEDS OF STAKEHOLDERS, EMPLOYS MULTIMODAL DATA ACQUISITION TECHNIQUES, AND STARTS TO RESEARCH THEORETICAL UNDERPINNINGS OF DASHBOARDS. IN THIS PAPER, WE PRESENT THESE PAST AND PRESENT RESEARCH EFFORTS AS WELL AS THE RESULTS OF THE VISLA19 WORKSHOP ON "VISUAL APPROACHES TO LEARNING ANALYTICS" THAT WAS HELD AT LAK19 WITH EXPERTS IN THE DOMAIN TO IDENTIFY AND ARTICULATE COMMON PRACTICES AND CHALLENGES FOR THE DOMAIN. BASED ON AN ANALYSIS OF THE RESULTS, WE PRESENT A RESEARCH AGENDA TO HELP SHAPE THE FUTURE OF LEARNING ANALYTICS DASHBOARDS.
LEARNING ANALYTICS IS A MELTING POT FOR A MULTITUDE OF RESEARCH FIELDS AND ORIGIN OF MANY DEVELOPMENTS ABOUT LEARNING AND ITS ENVIRONMENT. THERE IS A SERIOUS HYPE OVER THE CONCEPTS OF LEARNING ANALYTICS, HOWEVER, CONCRETE SOLUTIONS AND APPLICATIONS ARE COMPARABLY SCARCE. OF COURSE, DATA RICH ENVIRONMENTS, SUCH AS MOOCS, COME WITH STATISTICAL ANALYTICS DASHBOARDS, ALTHOUGH THE EDUCATIONAL VALUE IS OFTEN LIMITED. PRACTICAL SOLUTIONS FOR SCENARIOS IN DATA-LEAN ENVIRONMENTS OR FOR SMALL-SCALE ORGANIZATIONS ARE RARELY ADOPTED. THE LA4S PROJECT IS DEDICATED TO GATHER PRACTICAL SOLUTIONS, PROVIDE A TOOL BOX FOR PRACTITIONERS, AND PUBLISH A COOK BOOK WITH CONCRETE LEARNING ANALYTICS RECIPES FOR EVERYONE.
POLICY FOR LEARNING ANALYTICS JOINS A STREAM OF INITIATIVES AIMED AT UNDERSTANDING THE EXPANDING WORLD OF INFORMATION COLLECTION, STORAGE, PROCESSING AND DISSEMINATION THAT IS BEING DRIVEN BY COMPUTING TECHNOLOGIES. THIS PAPER OFFERS A INFORMATION POLICY PERSPECTIVE ON LEARNING ANALYTICS, JOINING WORK BY OTHERS ON ETHICS AND PRIVACY IN THE MANAGEMENT OF LEARNING ANALYTICS DATA [8], BUT EXTENDING TO CONSIDER HOW ISSUES PLAY OUT ACROSS THE INFORMATION LIFECYCLE AND IN THE FORMATION OF POLICY. DRAWING ON PRINCIPLES FROM INFORMATION POLICY BOTH INFORMS LEARNING ANALYTICS AND BRINGS LEARNING ANALYTICS INTO THE INFORMATION POLICY DOMAIN. THE RESULTING COMBINATION CAN HELP INFORM POLICY DEVELOPMENT FOR EDUCATIONAL INSTITUTIONS AS THEY IMPLEMENT AND MANAGE LEARNING ANALYTICS POLICY AND PRACTICES. THE PAPER BEGINS WITH A BRIEF SUMMARY OF THE INFORMATION POLICY PERSPECTIVE, THEN ADDRESSES LEARNING ANALYTICS WITH ATTENTION TO VARIOUS CATEGORIES OF CONSIDERATION FOR POLICY DEVELOPMENT.
RECOGNIZING THE NEED FOR ADDRESSING THE RATHER FRAGMENTED CHARACTER OF RESEARCH IN THIS FIELD, WE HAVE HELD A WORKSHOP ON LEARNING ANALYTICS FOR WORKPLACE AND PROFESSIONAL LEARNING AT THE LEARNING ANALYTICS AND KNOWLEDGE (LAK) CONFERENCE. THE WORKSHOP HAS TAKEN A BROAD PERSPECTIVE, ENCOMPASSING APPROACHES FROM A NUMBER OF PREVIOUS TRADITIONS, SUCH AS ADAPTIVE LEARNING, PROFESSIONAL ONLINE COMMUNITIES, WORKPLACE LEARNING AND PERFORMANCE ANALYTICS. BEING CO-LOCATED WITH THE LAK CONFERENCE HAS PROVIDED AN IDEAL VENUE FOR ADDRESSING COMMON CHALLENGES AND FOR BENEFITING FROM THE STRONG RESEARCH ON LEARNING ANALYTICS IN OTHER SECTORS THAT LAK HAS ESTABLISHED. LEARNING ANALYTICS FOR WORKPLACE AND PROFESSIONAL LEARNING IS NOW ON THE RESEARCH AGENDA OF SEVERAL ONGOING EU PROJECTS, AND THEREFORE A NUMBER OF FOLLOW-UP ACTIVITIES ARE PLANNED FOR STRENGTHENING INTEGRATION IN THIS EMERGING FIELD.
THIS PAPER PRESENTS THE RESULTS OF A REVIEW OF EIGHT POLICIES FOR LEARNING ANALYTICS OF RELEVANCE FOR HIGHER EDUCATION, AND DISCUSSES HOW THESE POLICIES HAVE TRIED TO ADDRESS PROMINENT CHALLENGES IN THE ADOPTION OF LEARNING ANALYTICS, AS IDENTIFIED IN THE LITERATURE. THE RESULTS SHOW THAT MORE CONSIDERATIONS NEED TO BE GIVEN TO ESTABLISHING COMMUNICATION CHANNELS AMONG STAKEHOLDERS AND ADOPTING PEDAGOGY-BASED APPROACHES TO LEARNING ANALYTICS. IT ALSO REVEALS THE SHORTAGE OF GUIDANCE FOR DEVELOPING DATA LITERACY AMONG END-USERS AND EVALUATING THE PROGRESS AND IMPACT OF LEARNING ANALYTICS. MOREOVER, THE REVIEW HIGHLIGHTS THE NEED TO ESTABLISH FORMALISED GUIDELINES TO MONITOR THE SOUNDNESS, EFFECTIVENESS, AND LEGITIMACY OF LEARNING ANALYTICS. AS INTEREST IN LEARNING ANALYTICS AMONG HIGHER EDUCATION INSTITUTIONS CONTINUES TO GROW, THIS REVIEW WILL PROVIDE INSIGHTS INTO POLICY AND STRATEGIC PLANNING FOR THE ADOPTION OF LEARNING ANALYTICS.
IT IS ARGUABLE THAT THE CHIEF AIM OF LEARNING ANALYTICS IS TO USE ANALYTICS FOR MEANINGFUL PURPOSES IN LEARNING AND TEACHING CONTEXTS, AND THAT RESEARCH IN THE FIELD SHOULD ADVANCE THIS CAUSE. HOWEVER THE FIELD DOES NOT PRESENT A SINGLE CLEAR UNDERSTANDING OF WHAT CONSTITUTES QUALITY IN LEARNING ANALYTICS RESEARCH. IN THIS PAPER WE PRESENT THE PRAGMATIC INQUIRY FOR LEARNING ANALYTICS RESEARCH (PILAR) METHOD AS ONE APPROACH TO CONDUCTING LEARNING ANALYTICS RESEARCH. RATHER THAN CREATING A NEW METHOD, WE REINTRODUCE AN OLD METHOD TO A NEW FIELD, DRAWING ON THE PRAGMATIC MAXIM, PROPOSED BY CHARLES SANDERS PEIRCE AS A PRINCIPLE FOR MAKING IDEAS CLEAR. OUR INSTANTIATION OF THE PRAGMATIC MAXIM REQUIRES THE RESEARCHER TO SITUATE LEARNING ANALYTICS RESEARCH WITHIN A CLEARLY DEFINED LEARNING CONTEXT AND TO CONSIDER THE ANALYTICS IN TERMS OF THE PRACTICAL EFFECTS ON LEARNING. WE PROPOSE THREE ESSENTIAL ELEMENTS AND A FIVE STEP PROCESS FOR ADDRESSING THEM IN RESEARCH. AFTER PRESENTING PILAR WE ADDRESS TWO POTENTIAL LIMITATIONS OF THE APPROACH, AND CONCLUDE WITH SOME IMPLICATIONS FOR ITS FUTURE USE IN LEARNING ANALYTICS RESEARCH.
THIS WORKSHOP EXPLORES HOW DATA LITERACY IMPACTS ON LEARNING ANALYTICS BOTH FOR PRACTITIONERS AND FOR END USERS. THE TERM DATA LITERACY IS USED TO BROADLY DESCRIBE THE SET OF ABILITIES AROUND THE USE OF DATA AS PART OF EVERYDAY THINKING AND REASONING FOR SOLVING REAL-WORLD PROBLEMS. IT IS A SKILL REQUIRED BOTH BY LEARNING ANALYTICS PRACTITIONERS TO DERIVE ACTIONABLE INSIGHTS FROM DATA AND BY THE INTENDED END USERS, SUCH THAT IT AFFECTS THEIR ABILITY TO ACCURATELY INTERPRET AND CRITIQUE PRESENTED ANALYSIS OF DATA. THE LATTER IS PARTICULARLY IMPORTANT, SINCE LEARNING ANALYTICS OUTCOMES CAN BE TARGETED AT A WIDE RANGE OF END USERS, SOME OF WHOM WILL BE YOUNG STUDENTS AND MANY OF WHOM ARE NOT DATA SPECIALISTS. WHILST DATA LITERACY IS RARELY AN END GOAL OF LEARNING ANALYTICS PROJECTS, THIS WORKSHOP AIMS TO FIND WHERE ISSUES RELATED TO DATA LITERACY HAVE IMPACTED ON PROJECT OUTCOMES AND WHERE IMPORTANT INSIGHTS HAVE BEEN GAINED. THIS WORKSHOP WILL FURTHER ENCOURAGE THE SHARING OF KNOWLEDGE AND EXPERIENCE THROUGH PRACTICAL ACTIVITIES WITH DATASETS AND VISUALISATIONS. THIS WORKSHOP AIMS TO HIGHLIGHT THE NEED FOR A GREATER UNDERSTANDING OF DATA LITERACY AS A FIELD OF STUDY, ESPECIALLY WITH REGARD TO COMMUNICATING AROUND LARGE, COMPLEX, DATA SETS.
THIS DEMONSTRATION INTRODUCES THE CONNECTED LEARNING ANALYTICS (CLA) TOOLKIT. THE CLA TOOLKIT HARVESTS DATA ABOUT STUDENT PARTICIPATION IN SPECIFIED LEARNING ACTIVITIES ACROSS STANDARD SOCIAL MEDIA ENVIRONMENTS, AND PRESENTS INFORMATION ABOUT THE NATURE AND QUALITY OF THE LEARNING INTERACTIONS.
TO SUPPORT ETHICAL CONSIDERATIONS AND SYSTEM INTEGRITY IN LEARNING ANALYTICS, THIS PAPER INTRODUCES TWO CASES OF APPLYING THE VALUE SENSITIVE DESIGN METHODOLOGY TO LEARNING ANALYTICS DESIGN. THE FIRST STUDY APPLIED TWO METHODS OF VALUE SENSITIVE DESIGN, NAMELY STAKEHOLDER ANALYSIS AND VALUE ANALYSIS, TO A CONCEPTUAL INVESTIGATION OF AN EXISTING LEARNING ANALYTICS TOOL. THIS INVESTIGATION UNCOVERED A NUMBER OF VALUES AND VALUE TENSIONS, LEADING TO DESIGN TRADE-OFFS TO BE CONSIDERED IN FUTURE TOOL REFINEMENTS. THE SECOND STUDY HOLISTICALLY APPLIED VALUE SENSITIVE DESIGN TO THE DESIGN OF A RECOMMENDATION SYSTEM FOR THE WIKIPEDIA WIKIPROJECTS. TO PROACTIVELY CONSIDER VALUES AMONG STAKEHOLDERS, WE DERIVED A MULTI-STAGE DESIGN PROCESS THAT INCLUDED LITERATURE ANALYSIS, EMPIRICAL INVESTIGATIONS, PROTOTYPE DEVELOPMENT, COMMUNITY ENGAGEMENT, ITERATIVE TESTING AND REFINEMENT, AND CONTINUOUS EVALUATION. BY REPORTING ON THESE TWO CASES, THIS PAPER RESPONDS TO A NEED OF PRACTICAL MEANS TO SUPPORT ETHICAL CONSIDERATIONS AND HUMAN VALUES IN LEARNING ANALYTICS SYSTEMS. THESE TWO CASES DEMONSTRATE THAT VALUE SENSITIVE DESIGN COULD BE A VIABLE APPROACH FOR BALANCING A WIDE RANGE OF HUMAN VALUES, WHICH TEND TO ENCOMPASS AND SURPASS ETHICAL ISSUES, IN LEARNING ANALYTICS DESIGN.
IN THE LAST FEW YEARS, THERE HAS BEEN A GROWING INTEREST IN LEARNING ANALYTICS (LA) IN TECHNOLOGY-ENHANCED LEARNING (TEL). GENERALLY, LA DEALS WITH THE DEVELOPMENT OF METHODS THAT HARNESS EDUCATIONAL DATA SETS TO SUPPORT THE LEARNING PROCESS. RECENTLY, THE CONCEPT OF OPEN LEARNING ANALYTICS (OLA) HAS RECEIVED A GREAT DEAL OF ATTENTION FROM LA COMMUNITY, DUE TO THE GROWING DEMAND FOR SELF-ORGANIZED, NETWORKED, AND LIFELONG LEARNING OPPORTUNITIES. A KEY CHALLENGE IN OLA IS TO FOLLOW A PERSONALIZED AND GOAL-ORIENTED LA MODEL THAT TAILORS THE LA TASK TO THE NEEDS AND GOALS OF MULTIPLE STAKEHOLDERS. CURRENT IMPLEMENTATIONS OF LA RELY ON A PREDEFINED SET OF QUESTIONS AND INDICATORS. THERE IS, HOWEVER, A NEED TO ADOPT A PERSONALIZED LA APPROACH THAT ENGAGES END USERS IN THE INDICATOR DEFINITION PROCESS BY SUPPORTING THEM IN SETTING GOALS, POSING QUESTIONS, AND SELF-DEFINING THE INDICATORS THAT HELP THEM ACHIEVE THEIR GOALS. IN THIS PAPER, WE ADDRESS THE CHALLENGE OF PERSONALIZED LA AND PRESENT THE CONCEPTUAL, DESIGN, AND IMPLEMENTATION DETAILS OF A RULE-BASED INDICATOR DEFINITION TOOL TO SUPPORT FLEXIBLE DEFINITION AND DYNAMIC GENERATION OF INDICATORS TO MEET THE NEEDS OF DIFFERENT STAKEHOLDERS WITH DIVERSE GOALS AND QUESTIONS IN THE LA EXERCISE.
A MIXED METHODS APPROACH WAS UNDERTAKEN IN THIS EXPLORATORY STUDY TO BETTER UNDERSTAND HOW LEARNERS PERCEIVE AND UTILIZE LEARNING ANALYTICS VISUALIZATIONS DURING ONLINE DISCUSSIONS ACTIVITIES. INTERNAL CONDITIONS SUCH AS GOAL ORIENTATION AND NUMERACY WERE MEASURED ALONGSIDE THE EXTERNAL CONDITIONS CREATED BY THE DISCUSSION STRUCTURE AND LEARNING ANALYTICS. OUR RESULTS EMPHASIZE KEY FACTORS THAT SHOULD BE CONSIDERED WHEN DESIGNING LEARNING ANALYTICS TOOLS.
LEARNING ANALYTICS PROMISES TO SUPPORT ADAPTIVE LEARNING IN HIGHER EDUCATION. HOWEVER, THE ASSOCIATED ISSUES AROUND PRIVACY PROTECTION, ESPECIALLY THEIR IMPLICATIONS FOR STUDENTS AS DATA SUBJECTS, HAS BEEN A HURDLE TO WIDE-SCALE ADOPTION. IN LIGHT OF THIS, WE SET OUT TO UNDERSTAND STUDENT EXPECTATIONS OF PRIVACY ISSUES RELATED TO LEARNING ANALYTICS AND TO IDENTIFY GAPS BETWEEN WHAT STUDENTS DESIRE AND WHAT THEY EXPECT TO HAPPEN OR CHOOSE TO DO IN REALITY WHEN IT COMES TO PRIVACY PROTECTION. TO THIS END, AN INVESTIGATION WAS CARRIED OUT IN A UK HIGHER EDUCATION INSTITUTION USING A SURVEY (N=674) AND SIX FOCUS GROUPS (26 STUDENTS). THE STUDY HIGHLIGHT A NUMBER OF KEY IMPLICATIONS FOR LEARNING ANALYTICS RESEARCH AND PRACTICE: (1) PURPOSE, ACCESS, AND ANONYMITY ARE KEY BENCHMARKS OF ETHICS AND PRIVACY INTEGRITY; (2) TRANSPARENCY AND COMMUNICATION ARE KEY LEVERS FOR LEARNING ANALYTICS ADOPTION; AND (3) INFORMATION ASYMMETRY CAN IMPEDE ACTIVE PARTICIPATION OF STUDENTS IN LEARNING ANALYTICS.
LEARNING ANALYTICS COURSES AND DEGREE PROGRAMS BOTH ON- AND OFFLINE HAVE BEGUN TO PROLIFERATE OVER THE LAST THREE YEARS. AS A RESULT OF THIS GROWTH IN INTEREST FROM STUDENTS, UNIVERSITY ADMINISTRATORS, RESEARCHERS AND INSTRUCTORS WE BELIEVE IT IS A GOOD TIME TO REVIEW HOW THESE EDUCATIONAL EFFORTS ARE IMPACTING THE FIELD, HOW SYNERGY BETWEEN INSTRUCTORS MIGHT BE DEVELOPED TO GREATER SERVE THE FIELD AND WHAT KINDS OF BEST PRACTICES COULD BE DEVELOPED.
IT IS OF HIGH RELEVANCE TO THE LAK COMMUNITY TO EXPLORE BLENDED LEARNING SCENARIOS WHERE STUDENTS CAN INTERACT AT DIVERSE DIGITAL AND PHYSICAL LEARNING SPACES. THIS WORKSHOP AIMS TO GATHER THE SUB-COMMUNITY OF LAK RESEARCHERS, LEARNING SCIENTISTS AND RESEARCHERS FROM OTHER COMMUNITIES, INTERESTED IN UBIQUITOUS, MOBILE AND/OR FACETO-FACE LEARNING ANALYTICS. AN OVERARCHING CONCERN IS HOW TO INTEGRATE AND COORDINATE LEARNING ANALYTICS TO PROVIDE CONTINUED SUPPORT TO LEARNING ACROSS DIGITAL AND PHYSICAL SPACES. THE GOALS OF THE WORKSHOP ARE TO SHARE APPROACHES AND IDENTIFY A SET OF GUIDELINES TO DESIGN AND CONNECT LEARNING ANALYTICS SOLUTIONS ACCORDING TO THE PEDAGOGICAL NEEDS AND CONTEXTUAL CONSTRAINTS TO PROVIDE SUPPORT ACROSS DIGITAL AND PHYSICAL LEARNING SPACES.
LITTLE IS KNOWN ABOUT THE PROCESSES INSTITUTIONS USE WHEN DISCERNING THEIR READINESS TO IMPLEMENT LEARNING ANALYTICS. THIS STUDY AIMS TO ADDRESS THIS GAP IN THE LITERATURE BY USING SURVEY DATA FROM THE BETA VERSION OF THE LEARNING ANALYTICS READINESS INSTRUMENT (LARI) [1]. TWENTY-FOUR INSTITUTIONS WERE SURVEYED AND 560 RESPONDENTS PARTICIPATED. FIVE DISTINCT FACTORS WERE IDENTIFIED FROM A FACTOR ANALYSIS OF THE RESULTS: CULTURE; DATA MANAGEMENT EXPERTISE; DATA ANALYSIS EXPERTISE; COMMUNICATION AND POLICY APPLICATION; AND, TRAINING. DATA WERE ANALYZED USING BOTH THE ROLE OF THOSE COMPLETING THE SURVEY AND THE CARNEGIE CLASSIFICATION OF THE INSTITUTIONS AS LENSES. GENERALLY, INFORMATION TECHNOLOGY PROFESSIONALS AND INSTITUTIONS CLASSIFIED AS RESEARCH UNIVERSITIES-VERY HIGH RESEARCH ACTIVITY HAD SIGNIFICANTLY DIFFERENT SCORES ON THE IDENTIFIED FACTORS. WORKING WITHIN A FRAMEWORK OF ORGANIZATIONAL LEARNING, THIS PAPER DETAILS THE CONCEPT OF READINESS AS A REFLECTIVE PROCESS, AS WELL AS HOW THE IMPLEMENTATION AND APPLICATION OF ANALYTICS SHOULD BE DONE SO WITH ETHICAL CONSIDERATIONS IN MIND. LIMITATIONS OF THE STUDY, AS WELL AS NEXT STEPS FOR RESEARCH IN THIS AREA, ARE ALSO DISCUSSED.
DATA SOLUTIONS IN THE TEACHING AND LEARNING SPACE ARE IN NEED OF PRO-ACTIVE INNOVATIONS IN DATA MANAGEMENT, TO ENSURE THAT SYSTEMS FOR LEARNING ANALYTICS CAN SCALE UP TO MATCH THE SIZE OF DATASETS NOW AVAILABLE. HERE, WE ILLUSTRATE THE SCALE AT WHICH A LEARNING MANAGEMENT SYSTEM (LMS) ACCUMULATES DATA, AND DISCUSS THE BARRIERS TO USING THIS DATA FOR IN-DEPTH ANALYSES. WE ILLUSTRATE THE EXPONENTIAL GROWTH OF OUR LMS DATA TO REPRESENT A SINGLE EXAMPLE DATASET, AND HIGHLIGHT THE BROADER NEED FOR TAKING A PRO-ACTIVE APPROACH TO DIMENSIONAL MODELLING IN LEARNING ANALYTICS, ANTICIPATING THAT COMMON LEARNING ANALYTICS QUESTIONS WILL BE COMPUTATIONALLY EXPENSIVE, AND THAT THE MOST USEFUL DATA STRUCTURES FOR LEARNING ANALYTICS WILL NOT NECESSARILY FOLLOW THOSE OF THE SOURCE DATASET.
WE CONDUCTED A LITERATURE REVIEW ON SYSTEMS THAT TRACK LEARNING ANALYTICS DATA (E.G., RESOURCE USE, TIME SPENT, ASSESSMENT DATA, ETC.) AND PROVIDE A REPORT BACK TO STUDENTS IN THE FORM OF VISUALIZATIONS, FEEDBACK, OR RECOMMENDATIONS. THIS REVIEW INCLUDED A RIGOROUS ARTICLE SEARCH PROCESS; 945 ARTICLES WERE IDENTIFIED IN THE INITIAL SEARCH. AFTER FILTERING OUT ARTICLES THAT DID NOT MEET THE INCLUSION CRITERIA, 94 ARTICLES WERE INCLUDED IN THE FMAL ANALYSIS. ARTICLES WERE CODED ON FIVE CATEGORIES CHOSEN BASED ON PREVIOUS WORK DONE IN THIS AREA: FUNCTIONALITY, DATA SOURCES, DESIGN ANALYSIS, PERCEIVED EFFECTS, AND ACTUAL EFFECTS. THE PURPOSE OF THIS REVIEW IS TO IDENTIFY TRENDS IN THE CURRENT STUDENT-FACING LEARNING ANALYTICS REPORTING SYSTEM LITERATURE AND PROVIDE RECOMMENDATIONS FOR LEARNING ANALYTICS RESEARCHERS AND PRACTITIONERS FOR FUTURE WORK.
LEARNING ANALYTICS (LA) STUDIES THE LEARNING PROCESS IN ORDER TO OPTIMIZE LEARNING OPPORTUNITIES FOR STUDENTS. ALTHOUGH LA HAS QUICKLY RISEN TO PROMINENCE, THERE REMAIN QUESTIONS REGARDING THE IMPACT LA HAS MADE TO DATE. TO EVALUATE THE EXTENT THAT LA HAS IMPACTED OUR UNDERSTANDING OF LEARNING AND PRODUCED INSIGHTS THAT HAVE BEEN TRANSLATED TO MAINSTREAM PRACTICE OR CONTRIBUTED TO THEORY, WE REVIEWED THE RESEARCH PUBLISHED IN 2011-2018 LAK CONFERENCES AND JOURNAL OF LEARNING ANALYTICS. THE REVIEWED STUDIES WERE CODED ACCORDING TO FIVE DIMENSIONS: STUDY FOCUS, DATA TYPES, PURPOSE, INSTITUTIONAL SETTING, AND SCALE OF RESEARCH AND IMPLEMENTATION. THE CODING AND SUBSEQUENT EPISTEMIC NETWORK ANALYSIS INDICATES THAT WHILE LA RESEARCH HAS DEVELOPED IN THE AREAS OF FOCUS AND SOPHISTICATION OF ANALYSES, THE IMPACT ON PRACTICE, THEORY AND FRAMEWORKS HAVE BEEN LIMITED. WE HYPOTHESIZE THAT THIS FINDING IS DUE TO A CONTINUING PREDOMINANCE OF SMALL-SCALE TECHNO-CENTRIC EXPLORATORY STUDIES THAT TO DATE HAVE NOT FULLY ACCOUNTED FOR THE MULTIDISCIPLINARITY THAT COMPRISES EDUCATION. FOR THE FIELD TO REACH ITS POTENTIAL IN UNDERSTANDING AND OPTIMIZING LEARNING AND LEARNING ENVIRONMENTS, THERE MUST BE A PURPOSEFUL SHIFT TO MOVE FROM EXPLORATORY MODELS TO MORE HOLISTIC AND INTEGRATIVE SYSTEMS-LEVEL RESEARCH. THIS NECESSITATES GREATER EFFORT APPLIED TO UNDERSTANDING THE RESEARCH CYCLES THAT EMERGE WHEN MULTIPLE KNOWLEDGE DOMAINS COALESCE INTO NEW FIELDS OF RESEARCH.
LEARNING ANALYTICS INVOLVE THE MEASUREMENT, COLLECTION, ANALYSIS, AND REPORTING OF DATA ABOUT LEARNERS AND THEIR CONTEXTS, IN ORDER TO UNDERSTAND AND OPTIMIZE LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS. SINCE EMERGING AS A DISTINCT FIELD IN 2011, LEARNING ANALYTICS HAS GROWN RAPIDLY, AND EARLY ADOPTERS AROUND THE WORLD ARE DEVELOPING AND DEPLOYING THESE NEW TOOLS. THIS PAPER REPORTS ON A STUDY THAT INVESTIGATED HOW THE FIELD IS LIKELY TO DEVELOP BY 2025 IN ORDER TO RECOMMEND ACTIONS TO THOSE CONCERNED WITH THE IMPLEMENTATION OF LEARNING ANALYTICS. THE STUDY USED A POLICY DELPHI APPROACH, PRESENTING A RANGE OF FUTURE SCENARIOS TO INTERNATIONAL EXPERTS IN THE FIELD AND ASKING FOR RESPONSES RELATED TO THE DESIRABILITY AND FEASIBILITY OF THESE SCENARIOS, AS WELL AS ACTIONS THAT WOULD BE REQUIRED. RESPONSES WERE RECEIVED FROM 103 PEOPLE FROM 21 COUNTRIES. RESPONSES WERE CODED THEMATICALLY, INTER-RATER RELIABILITY WAS CHECKED USING COHEN'S KAPPA COEFFICIENT, AND DATA WERE RECODED IF KAPPA WAS BELOW 0.6. THE SEVEN MAJOR THEMES IDENTIFIED WITHIN THE DATA WERE POWER, PEDAGOGY, VALIDITY, REGULATION, COMPLEXITY, ETHICS, AND AFFECT. THE PAPER CONSIDERS IN DETAIL EACH OF THESE THEMES AND ITS IMPLICATIONS FOR THE IMPLEMENTATION OF LEARNING ANALYTICS.
OVER THE PAST DECADE, THE INCREASING USE OF LEARNING ANALYTICS OPENED THE POSSIBILITY OF MAKING DATA-DRIVEN DECISIONS FOR IMPROVING STUDENT LEARNING. DRIVEN BY THE STRONG UNIVERSITY ADOPTION OF LEARNING ANALYTICS, MOST EARLY LEARNING ANALYTICS RESEARCH FOCUSED ON ISSUES SPECIFIC TO TERTIARY EDUCATION. WITH THE BROADER ADOPTION OF EDUCATIONAL TECHNOLOGIES IN PRIMARY AND SECONDARY EDUCATION AND THE EMERGENCE OF NEW CLASSROOM-FOCUSED TECHNOLOGIES, THERE HAS BEEN A GROWING AWARENESS OF THE POTENTIALS OF LEARNING ANALYTICS FOR SUPPORTING STUDENTS AND DIAGNOSING THEIR LEARNING PROGRESS IN PRE-UNIVERSITY CONTEXTS. THIS SPECIAL SECTION FOCUSED ON INVESTIGATING, DEVELOPING, AND EVALUATING STATE-OF-THE-ART LEARNING ANALYTICS APPROACHES WITHIN PRIMARY AND SECONDARY SCHOOL SETTINGS. IN THIS EDITORIAL, WE SUMMARIZE THE PAPERS OF THE SPECIAL SECTION AND DISCUSS THE CHALLENGES AND OPPORTUNITIES FOR LEARNING ANALYTICS WITHIN THE SCHOOL CONTEXT. WE CONCLUDE WITH THE DISCUSSION AROUND THE OPPORTUNITIES FOR FUTURE WORK AND THE IMPLICATIONS OF THIS SPECIAL SECTION FOR THE FIELD OF LEARNING ANALYTICS.
STUDENT'S LEARNING HAPPENS WHERE THE LEARNER IS, RATHER THAN BEING CONSTRAINED TO A SINGLE PHYSICAL OR DIGITAL ENVIRONMENT. IT IS OF HIGH RELEVANCE FOR THE LAK COMMUNITY TO PROVIDE ANALYTICS SUPPORT IN BLENDED LEARNING SCENARIOS WHERE STUDENTS CAN INTERACT AT DIVERSE LEARNING SPACES AND WITH A VARIETY OF EDUCATIONAL TOOLS. THIS WORKSHOP AIMS TO GATHER THE SUB COMMUNITY OF LAK RESEARCHERS, LEARNING SCIENTISTS AND RESEARCHERS IN OTHER AREAS, INTERESTED IN THE INTERSECTION BETWEEN UBIQUITOUS, MOBILE AND/OR CLASSROOM LEARNING ANALYTICS. THE UNDERLYING CONCERN IS HOW TO INTEGRATE AND COORDINATE LEARNING ANALYTICS SEEKING TO UNDERSTAND THE PARTICULAR PEDAGOGICAL NEEDS AND CONTEXT CONSTRAINTS TO PROVIDE LEARNING ANALYTICS SUPPORT ACROSS DIGITAL AND PHYSICAL SPACES. THE GOALS OF THE WORKSHOP ARE TO CONSOLIDATE THE CROSS-LAK SUB-COMMUNITY AND PROVIDE A FORUM FOR IDEA GENERATION THAT CAN BUILD UP FURTHER COLLABORATIONS. THE WORKSHOP WILL ALSO SERVE TO DISSEMINATE CURRENT WORK IN THE AREA BY BOTH PRODUCING PROCEEDINGS OF RESEARCH PAPERS AND WORKING TOWARDS A JOURNAL SPECIAL ISSUE.
THIS POSTER SETS OUT THE BACKGROUND AND DEVELOPMENT OF THE LACE EVIDENCE HUB, A SITE THAT GATHERS EVIDENCE ABOUT LEARNING ANALYTICS IN AN ACCESSIBLE FORM. THE POSTER ALSO DESCRIBES THE FUNCTIONALITY OF THE SITE, SUMMARISES ITS QUANTITATIVE AND THEMATIC CONTENT TO DATE, AND ASSESSES THE STATE OF EVIDENCE. IN ADDITION, IT ENCOURAGES PEOPLE TO ADD TO AND MAKE USE OF THE HUB.
THE LEARNING ANALYTICS AND POLICY (LAP) WORKSHOP EXPLORES AND DOCUMENTS THE WAYS IN WHICH POLICIES AT NATIONAL AND REGIONAL LEVEL ARE SHAPING THE DEVELOPMENT OF LEARNING ANALYTICS. IT BRINGS TOGETHER REPRESENTATIVES FROM AROUND THE WORLD WHO REPORT ON THE CIRCUMSTANCES IN THEIR OWN COUNTRY. THE WORKSHOP IS PRECEDED BY AN INFORMATION GATHERING PHASE, AND FOLLOWED BY THE AUTHORING OF A REPORT. THE ASPIRATIONS, ACHIEVEMENTS AND CONSTRAINTS IN THE DIFFERENT COUNTRIES ARE CONTRASTED AND DOCUMENTED, PROVIDING A VALUABLE RESOURCE FOR THE FUTURE DEVELOPMENT OF LEARNING ANALYTICS.
STUDIES IN THE FIELD OF LEARNING ANALYTICS (LA) HAVE SHOWN STUDENTS' DEMOGRAPHICS AND LEARNING MANAGEMENT SYSTEM (LMS) DATA TO BE EFFECTIVE IDENTIFIERS OF "AT RISK" PERFORMANCE. HOWEVER, INSIGHTS GENERATED BY THESE PREDICTIVE MODELS MAY NOT BE SUITABLE FOR PEDAGOGICALLY INFORMED INTERVENTIONS DUE TO THE INABILITY TO EXPLAIN WHY STUDENTS DISPLAY THESE BEHAVIORAL PATTERNS. THEREFORE, THIS STUDY AIMS AT PROVIDING EXPLANATIONS OF STUDENTS' BEHAVIORS ON LMS BY INCORPORATING DISPOSITIONAL DIMENSIONS (E.G., SELF-REGULATION AND EMOTIONS) INTO CONVENTIONAL LEARNING ANALYTICS MODELS. USING A COMBINATION OF DEMOGRAPHIC, TRACE, AND SELF-REPORTED DATA OF EIGHT CONTEMPORARY SOCIAL-COGNITIVE THEORIES OF EDUCATION FROM 1,069 STUDENTS IN A BLENDED INTRODUCTORY QUANTITATIVE COURSE, WE DEMONSTRATE THE POTENTIAL OF DISPOSITIONAL CHARACTERISTICS OF STUDENTS, SUCH AS PROCRASTINATION AND BOREDOM. OUR RESULTS HIGHLIGHT THE NEED TO MOVE BEYOND SIMPLE ENGAGEMENT METRICS, WHEREBY DISPOSITIONAL LEARNING ANALYTICS PROVIDE AN ACTIONABLE BRIDGE BETWEEN LEARNING ANALYTICS AND EDUCATIONAL INTERVENTION.
THIS PAPER PRESENTS THE LEARNING ANALYTICS INDICATOR REPOSITORY (LAIR), AN INTERACTIVE WEB-BASED APPLICATION THAT ALLOWS THE EXPLORATION OF LEARNING ANALYTICS APPROACHES. FROM SCIENTIFIC PUBLICATIONS IN THE FIELD OF LEARNING ANALYTICS, WE EXTRACTED THE STAKEHOLDERS, METRICS, PLATFORMS AND INDICATORS, AND TRANSFORMED THEM INTO A DIRECTED GRAPH REPRESENTATION. THE LAIR ALLOWS FILTERING BY THESE COMPONENTS AND PROVIDES A LIST OF PUBLICATIONS WHERE THE APPROACHES CAN BE FOUND. WE INVITE OTHER RESEARCHERS TO CONTRIBUTE TO THIS REPOSITORY.
ISSUES RELATED TO ETHICS AND PRIVACY HAVE BECOME A MAJOR STUMBLING BLOCK IN APPLICATION OF LEARNING ANALYTICS TECHNOLOGIES ON A LARGE SCALE. RECENTLY, THE LEARNING ANALYTICS COMMUNITY AT LARGE HAS MORE ACTIVELY ADDRESSED THE EP4LA ISSUES, AND WE ARE NOW STARTING TO SEE LEARNING ANALYTICS SOLUTIONS THAT ARE DESIGNED NOT ONLY AS AN AFTERTHOUGHT, BUT ALSO WITH THESE ISSUES IN MIND. THE 2ND EP4LA@LAK16 WORKSHOP WILL BRING THE DISCUSSION ON ETHICS AND PRIVACY FOR LEARNING ANALYTICS TO A THE NEXT LEVEL, HELPING TO BUILD AN AGENDA FOR ORGANIZATIONAL AND TECHNICAL DESIGN OF LA SOLUTIONS, ADDRESSING THE DIFFERENT PROCESSES OF A LEARNING ANALYTICS WORKFLOW.
LEARNING ANALYTICS, THE ANALYSIS AND REPRESENTATION OF DATA ABOUT LEARNERS IN ORDER TO IMPROVE LEARNING, IS A NEW LENS THROUGH WHICH TEACHERS CAN UNDERSTAND EDUCATION. IT IS ROOTED IN THE DRAMATIC INCREASE IN THE QUANTITY OF DATA ABOUT LEARNERS AND LINKED TO MANAGEMENT APPROACHES THAT FOCUS ON QUANTITATIVE METRICS, WHICH ARE SOMETIMES ANTITHETICAL TO AN EDUCATIONAL SENSE OF TEACHING. HOWEVER, LEARNING ANALYTICS OFFERS NEW ROUTES FOR TEACHERS TO UNDERSTAND THEIR STUDENTS AND, HENCE, TO MAKE EFFECTIVE USE OF THEIR LIMITED RESOURCES. THIS PAPER EXPLORES THESE ISSUES AND DESCRIBES A SERIES OF EXAMPLES OF LEARNING ANALYTICS TO ILLUSTRATE THE POTENTIAL. IT ARGUES THAT TEACHERS CAN AND SHOULD ENGAGE WITH LEARNING ANALYTICS AS A WAY OF INFLUENCING THE METRICS AGENDA TOWARDS RICHER CONCEPTIONS OF LEARNING AND TO IMPROVE THEIR TEACHING.
THIS WORKSHOP AIMS TO PROMOTE STRATEGIC PLANNING FOR LEARNING ANALYTICS IN HIGHER EDUCATION THROUGH DEVELOPING INSTITUTIONAL POLICIES. WHILE ADOPTION OF LEARNING ANALYTICS IS PREDOMINANTLY SEEN IN SMALL-SCALE AND BOTTOM-UP PATTERNS, IT IS BELIEVED THAT A SYSTEMIC IMPLEMENTATION CAN BRING THE WIDEST IMPACT TO THE EDUCATION SYSTEM AND LASTING BENEFITS TO LEARNERS. HOWEVER, THE SUCCESS OF IT HIGHLY DEPENDS ON THE ADOPTED STRATEGY THAT MEETS THE NEEDS OF VARIOUS STAKEHOLDERS AND SYSTEMATICALLY PUSHES THE INSTITUTION TOWARDS ACHIEVING ITS TARGETS. IT IS IMPERATIVE TO DEVELOP A LEARNING ANALYTICS POLICY THAT ENSURES A PRACTICE THAT IS VALID, EFFECTIVE AND ETHICAL. THE WORKSHOP INVOLVES TWO COMPONENTS. THE FIRST COMPONENT INCLUDES A SET OF PRESENTATIONS ABOUT THE STATE OF LEARNING ANALYTICS IN HIGHER EDUCATION, DRAWING ON RESULTS FROM AN AUSTRALIAN AND A EUROPEAN PROJECT EXAMINING INSTITUTIONAL LEARNING ANALYTICS POLICY AND ADOPTION PROCESSES. THE SECOND COMPONENT IS AN INTERACTIVE SESSION WHERE PARTICIPANTS ARE ENCOURAGED TO SHARE THEIR MOTIVATIONS FOR ADOPTING LEARNING ANALYTICS AND THE DIVERSITY OF CHALLENGES THEY PERCEIVE IMPEDE ANALYTICS ADOPTION IN THEIR INSTITUTION. USING THE RAPID OUTCOME MAPPING APPROACH (ROMA), PARTICIPANTS WILL CREATE A DRAFT POLICY THAT ARTICULATES HOW THE VARIOUS CHALLENGES CAN BE ADDRESSED. THIS WORKSHOP AIMS TO FURTHER DEVELOP OUR UNDERSTANDING OF HOW LEARNING ANALYTICS OPERATES IN AN ORGANIZATIONAL SYSTEM AND PROMOTE A CULTURAL CHANGE IN HOW SUCH ANALYTICS ARE ADOPTED IN HIGHER EDUCATION.
MULTIMODAL LEARNING ANALYTICS (MMLA) CAPTURES, INTEGRATES AND ANALYZES LEARNING TRACES FROM DIFFERENT SOURCES IN ORDER TO OBTAIN A MORE HOLISTIC UNDERSTANDING OF THE LEARNING PROCESS, WHEREVER IT HAPPENS. MMLA LEVERAGES THE INCREASINGLY WIDESPREAD AVAILABILITY OF DIVERSE SENSORS, HIGH-FREQUENCY DATA COLLECTION TECHNOLOGIES AND SOPHISTICATED MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TECHNIQUES. THE AIM OF THIS WORKSHOP IS TWOFOLD: FIRST, TO EXPOSE PARTICIPANTS TO, AND DEVELOP, DIFFERENT MULTIMODAL DATASETS THAT REFLECT HOW MMLA CAN BRING NEW INSIGHTS AND OPPORTUNITIES TO INVESTIGATE COMPLEX LEARNING PROCESSES AND ENVIRONMENTS; SECOND, TO COLLABORATIVELY IDENTIFY A SET OF GRAND CHALLENGES FOR FURTHER MMLA RESEARCH, BUILT UPON THE FOUNDATIONS OF PREVIOUS WORKSHOPS ON THE TOPIC.
THE EFFECTIVENESS OF USING LEARNING ANALYTICS FOR LEARNING DESIGN PRIMARILY DEPENDS UPON TWO CONCEPTS: GROUNDING AND ALIGNMENT. THIS IS THE PRIMARY CONJECTURE FOR THE STUDY DESCRIBED IN THIS PAPER. IN OUR DESIGN-BASED RESEARCH STUDY, WE DESIGN, TEST, AND EVALUATE TEACHER-FACING LEARNING ANALYTICS FOR AN ONLINE INQUIRY SCIENCE UNIT ON GLOBAL CLIMATE CHANGE. WE DESIGN OUR LEARNING ANALYTICS IN ACCORDANCE WITH A SOCIOCONSTRUCTIVISM-BASED PEDAGOGICAL FRAMEWORK, CALLED KNOWLEDGE INTEGRATION, AND THE PRINCIPLES OF LEARNING ANALYTICS IMPLEMENTATION DESIGN. OUR METHODOLOGY FOR THE DESIGN PROCESS DRAWS UPON THE PRINCIPLE OF THE ORCHESTRATING FOR LEARNING ANALYTICS FRAMEWORK TO ENGAGE STAKEHOLDERS (I.E. TEACHERS, RESEARCHERS, AND DEVELOPERS). THE RESULTING LEARNING ANALYTICS WERE ALIGNED TO UNIT ACTIVITIES THAT ENGAGED STUDENTS IN KEY ASPECTS OF THE KNOWLEDGE INTEGRATION PROCESS. THEY PROVIDED TEACHERS WITH ACTIONABLE INSIGHT INTO THEIR STUDENTS' UNDERSTANDING AT CRITICAL JUNCTURES IN THE LEARNING PROCESS. WE DEMONSTRATE THE EFFICACY OF THE LEARNING ANALYTICS IN SUPPORTING THE OPTIMIZATION OF THE UNIT'S LEARNING DESIGN. WE CONCLUDE BY SYNTHESIZING THE PRINCIPLES THAT GUIDED OUR DESIGN PROCESS INTO A FRAMEWORK FOR DEVELOPING AND EVALUATING LEARNING ANALYTICS FOR LEARNING DESIGN.
INSTITUTIONAL IMPLEMENTATION OF LEARNING ANALYTICS CALLS FOR THOUGHTFUL MANAGEMENT OF CULTURAL CHANGE. THIS INTERACTIVE HALF-DAY WORKSHOP RESPONDS TO THE LA LITERATURE DESCRIBING THE BENEFITS AND CHALLENGES OF INSTITUTIONAL LA IMPLEMENTATION BY OFFERING PARTICIPANTS AN OPPORTUNITY TO LEARN ABOUT AND BEGIN PLANNING FOR A PROGRAM TO ACTIVELY ENGAGE FACULTY AS LEADERS OF DATA EXPLORATION AROUND THE THEME OF 'STUDENT SUCCESS'. THIS SESSION WILL SHARE EXPERIENCES FROM FIVE INSTITUTIONS ACTIVELY ENGAGED IN FOSTERING LEARNING ANALYTICS COMMUNITIES (LAC) BY IDENTIFYING KEY ISSUES, SHARING LESSONS LEARNED, AND CONSIDERING STRUCTURAL FRAMEWORKS THAT ARE TRANSFERABLE TO OTHER INSTITUTIONAL CONTEXTS. STRUCTURED DISCUSSION AND ACTIVITIES WILL ENGAGE PARTICIPANTS IN DEVELOPING AN ACTION PLAN FOR ESTABLISHING AN LAC ON THEIR OWN CAMPUS.
LEARNING ANALYSTS OFTEN CLAIM TO MEASURE LEARNING, BUT THEIR WORK HAS ATTRACTED GROWING CONCERN ABOUT WHETHER OR NOT THE MEASURES ARE SUFFICIENTLY ACCURATE, FAIR, RELIABLE, AND VALID, WITH UTILITY FOR EDUCATORS AND INTERPRETABLE BY THEM. THIS PAPER CONSIDERS THESE ISSUES IN THE LIGHT OF PRACTICES OF SCHOLARS IN MORE ESTABLISHED FIELDS, EDUCATIONAL MEASUREMENT PARTICULARLY. THE FOCUS IS ON WHAT REALLY MATTERS ABOUT METHODOLOGIES FOR MEASURING LEARNING, INCLUDING FOUNDATIONAL ASSUMPTIONS ABOUT THE NATURE OF LEARNING, WHAT IS UNDERSTOOD BY THE TERM 'MEASURED', THE CRITERIA APPLIED WHEN ASSESSING QUALITY OF DATA, THE STANDARDS OF PROOF REQUIRED TO ESTABLISH VALIDITY, RELIABILITY, GENERALIZABILITY, UTILITY AND INTERPRETABILITY OF FINDINGS, AND ASSUMPTIONS ABOUT LEARNERS AND LEARNING UNDERLYING DATA MODELING TECHNIQUES USED TO ABSTRACT MEANING FROM THE DATA. THIS PAPER ARGUES THAT, FOR LEARNING ANALYTICS TO TAKE ITS PLACE AS A FULLY-FLEDGED MEMBER OF THE LEARNING SCIENCES, IT NEEDS SERIOUSLY TO CONSIDER HOW TO MEASURE LEARNING. METHODOLOGY CRAFTED AT THE INTERFACE OF MEASUREMENT SCIENCE AND LEARNING ANALYTICS MAY BE OF SUFFICIENT INTEREST TO CREATE A NEW SUBFIELD OF SCHOLARSHIP - DUBBED HERE 'METRILYTICS' - TO MAKE A DISTINCTIVE CONTRIBUTION TO THE SCIENCE OF LEARNING.
AN IMPORTANT RESEARCH PROBLEM IN LEARNING ANALYTICS IS TO EXPEDITE THE CYCLE OF DATA LEADING TO THE ANALYSIS OF STUDENT PROGRESS AND THE IMPROVEMENT OF STUDENT SUPPORT. FOR THIS GOAL IN THE CONTEXT OF SOCIAL LEARNING, WE PROPOSE A PIPELINE THAT INCLUDES DATA INFRASTRUCTURE, LEARNING ANALYTICS, AND INTERVENTION, ALONG WITH COMPUTATIONAL MODELS FOR INDIVIDUAL COMPONENTS. NEXT, WE DESCRIBE AN EXAMPLE OF APPLYING THIS PIPELINE TO REAL DATA IN A CASE STUDY, WHOSE GOAL IS TO INVESTIGATE THE POSITIVE EFFECTS THAT GOAL-SETTING STUDENTS HAVE ON THEIR PEERS, WHICH SUGGESTS WAYS IN WHICH WE MIGHT FOSTER THESE SOCIAL BENEFITS THROUGH INTERVENTION.
WHILE THERE IS HIGH POTENTIAL IN USING LEARNING ANALYTICS TO PROVIDE EDUCATIONAL INSTITUTIONS AS WELL AS TEACHERS AND LEARNERS WITH ACTIONABLE INFORMATION AND IMPROVE LEARNING EXPERIENCES, CURRENTLY ONLY VERY FEW LEARNING ANALYTICS TOOLS ARE ACTUALLY USED IN EDUCATIONAL INSTITUTIONS. IN THIS PAPER, WE INTRODUCE MORPH, A PLATFORM THAT FACILITATES THE INTEGRATION OF LEARNING ANALYTICS MODULES AND TOOLS INTO INSTITUTIONAL LEARNING SYSTEMS. MORPH PROVIDES A ROBUST DISTRIBUTED ARCHITECTURE WHICH COMBINES BATCH, STREAM AND REAL-TIME DATA PROCESSING USING A PARALLEL PROCESSING MODEL TO ENABLE AND SUPPORT EFFICIENT PROCESSING OF LARGE AMOUNTS OF DATA. FURTHERMORE, IT PROVIDES COMMON MANAGEMENT AND ADMINISTRATION FEATURES THAT ENABLE THE SEAMLESS INTEGRATION OF LEARNING ANALYTICS RESEARCH MODULES AND TOOLS INTO EXISTING INSTITUTIONAL LEARNING SYSTEMS.
DESPITE STRONG INTEREST IN LEARNING ANALYTICS (LA), ADOPTION AT A LARGE-SCALE ORGANIZATIONAL LEVEL CONTINUES TO BE PROBLEMATIC. THIS MAY IN PART BE DUE TO THE LACK OF ACKNOWLEDGEMENT OF EXISTING CONCEPTUAL LA MODELS TO OPERATIONALIZE HOW KEY ADOPTION DIMENSIONS INTERACT TO INFORM THE REALITIES OF THE IMPLEMENTATION PROCESS. THIS PAPER PROPOSES THE FRAMING OF LA ADOPTION IN COMPLEXITY LEADERSHIP THEORY (CLT) TO STUDY THE OVERARCHING SYSTEM DYNAMICS. THE FRAMING IS EMPIRICALLY VALIDATED IN A STUDY ANALYSING INTERVIEWS WITH SENIOR STAFF IN AUSTRALIAN UNIVERSITIES (N=32). THE RESULTS WERE CODED FOR SEVERAL ADOPTION DIMENSIONS INCLUDING LEADERSHIP, GOVERNANCE, STAFF DEVELOPMENT, AND CULTURE. THE CODED DATA WERE THEN ANALYSED WITH LATENT CLASS ANALYSIS. THE RESULTS IDENTIFIED TWO CLASSES OF UNIVERSITIES THAT EITHER I) FOLLOWED AN INSTRUMENTAL APPROACH TO ADOPTION - TYPICALLY TOP-DOWN LEADERSHIP, LARGE SCALE PROJECT WITH HIGH TECHNOLOGY FOCUS YET DEMONSTRATING LIMITED STAFF UPTAKE; OR II) WERE CHARACTERIZED AS EMERGENT INNOVATORS-BOTTOM UP, STRONG CONSULTATION PROCESS, BUT WITH SUBSEQUENT CHALLENGES IN COMMUNICATING AND SCALING UP INNOVATIONS. THE RESULTS SUGGEST THERE IS A NEED TO BROADEN THE FOCUS OF RESEARCH IN LA ADOPTION MODELS TO MOVE ON FROM SMALL-SCALE COURSE/PROGRAM LEVELS TO A MORE HOLISTIC AND COMPLEX ORGANIZATIONAL LEVEL.
HOW LEARNING DISPOSITION DATA CAN HELP US TRANSLATING LEARNING FEEDBACK FROM A LEARNING ANALYTICS APPLICATION INTO ACTIONABLE LEARNING INTERVENTIONS, IS THE MAIN FOCUS OF THIS EMPIRICAL STUDY. IT EXTENDS PREVIOUS WORK (TEMPELAAR, RIENTIES, & GIESBERS, 2015), WHERE THE FOCUS WAS ON DERIVING TIMELY PREDICTION MODELS IN A DATA RICH CONTEXT, ENCOMPASSING TRACE DATA FROM LEARNING MANAGEMENT SYSTEMS, FORMATIVE ASSESSMENT DATA, E-TUTORIAL TRACE DATA AS WELL AS LEARNING DISPOSITIONS. IN THIS SAME EDUCATIONAL CONTEXT, THE CURRENT STUDY INVESTIGATES HOW THE APPLICATION OF CLUSTER ANALYSIS BASED ON E-TUTORIAL TRACE DATA ALLOWS STUDENT PROFILING INTO DIFFERENT AT-RISK GROUPS, AND HOW THESE AT-RISK GROUPS CAN BE CHARACTERIZED WITH THE HELP OF LEARNING DISPOSITION DATA. IT IS OUR CONJECTURE THAT ESTABLISHING A CHAIN OF ANTECEDENT CONSEQUENCE RELATIONSHIPS STARTING FROM LEARNING DISPOSITION, THROUGH STUDENT ACTIVITY IN E-TUTORIALS AND FORMATIVE ASSESSMENT PERFORMANCE, TO COURSE PERFORMANCE, ADDS A CRUCIAL DIMENSION TO CURRENT LEARNING ANALYTICS STUDIES: THAT OF PROFILING STUDENTS WITH DESCRIPTORS THAT EASILY LEND THEMSELVES TO THE DESIGN OF EDUCATIONAL INTERVENTIONS. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE FIELD OF LEARNING ANALYTICS HAS EVOLVED OVER THE PAST DECADE TO PROVIDE NEW WAYS TO VIEW, UNDERSTAND AND ENHANCE LEARNING ACTIVITIES AND ENVIRONMENTS IN HIGHER EDUCATION. IT BRINGS TOGETHER RESEARCH AND PRACTICE TRADITIONS FROM MULTIPLE DISCIPLINES TO PROVIDE AN EVIDENCE BASE TO INFORM STUDENT SUPPORT AND EFFECTIVE DESIGN FOR LEARNING. THIS HAS RESULTED IN A PLETHORA OF IDEAS AND RESEARCH EXPLORING HOW DATA CAN BE ANALYSED AND UTILISED TO NOT ONLY INFORM EDUCATORS, BUT ALSO TO DRIVE ONLINE LEARNING SYSTEMS THAT OFFER PERSONALISED LEARNING EXPERIENCES AND/OR FEEDBACK FOR STUDENTS. HOWEVER, A CORE CHALLENGE THAT THE LEARNING ANALYTICS COMMUNITY CONTINUES TO FACE IS HOW THE IMPACT OF THESE INNOVATIONS CAN BE DEMONSTRATED. WHERE IMPACT IS POSITIVE, THERE IS A CASE FOR CONTINUING OR INCREASING THE USE OF LEARNING ANALYTICS, HOWEVER, THERE IS ALSO THE POTENTIAL FOR NEGATIVE IMPACT WHICH IS SOMETHING THAT NEEDS TO BE IDENTIFIED QUICKLY AND MANAGED. AS MORE INSTITUTIONS IMPLEMENT STRATEGIES TO TAKE ADVANTAGE OF LEARNING ANALYTICS AS PART OF CORE BUSINESS, IT IS IMPORTANT THAT IMPACT CAN BE EVALUATED AND ADDRESSED TO ENSURE EFFECTIVENESS AND SUSTAINABILITY. IN THIS EDITORIAL OF THE AJET SPECIAL ISSUE DEDICATED TO THE IMPACT OF LEARNING ANALYTICS IN HIGHER EDUCATION, WE CONSIDER WHAT IMPACT CAN MEAN IN THE CONTEXT OF LEARNING ANALYTICS AND WHAT THE FIELD NEEDS TO DO TO ENSURE THAT THERE ARE CLEAR PATHWAYS TO IMPACT THAT RESULT IN THE DEVELOPMENT OF SYSTEMS, ANALYSES, AND INTERVENTIONS THAT IMPROVE THE EDUCATIONAL ENVIRONMENT.
FEEDBACK IS IMPORTANT FOR ANY ACTIVITY, AND LEARNING IS NO EXCEPTION. WHEREAS ASSESSMENT CAN GIVE SUMMATIVE FEEDBACK ABOUT THE PROFICIENCY OF THE LEARNING, LEARNING ANALYTICS CAN GIVE A MUCH FINER LEVEL OF FEEDBACK ABOUT THE LEARNING PROCESS. LEARNING ANALYTICS CAN HELP IN IDENTIFYING THE EFFECTIVENESS OF LEARNING ELEMENTS, CAN HELP IN ENGAGING STUDENTS, CAN GUIDE TEACHERS IN THE PREPARATION AND DEPLOYMENT OF THE TEACHING ACTIVITY. IN THIS PAPER, WE PRESENT A NUMBER OF DIFFERENT INITIATIVES CARRIED OUT AT UC3M THAT INCLUDE ELEMENTS OF LEARNING ANALYTICS FOR DIFFERENT PURPOSES.
REFLECTIVE WRITING HAS BEEN WIDELY RECOGNIZED AS ONE OF THE MOST EFFECTIVE ACTIVITIES FOR FOSTERING STUDENTS' REFLECTIVE AND CRITICAL THINKING. THE ANALYSIS OF STUDENTS' REFLECTIVE WRITINGS HAS BEEN THE FOCUS OF MANY RESEARCH STUDIES. HOWEVER, TO DATE THIS HAS BEEN TYPICALLY A VERY LABOR-INTENSIVE MANUAL PROCESS INVOLVING CONTENT ANALYSIS OF STUDENT WRITINGS. WITH RECENT ADVANCEMENTS IN THE FIELD OF LEARNING ANALYTICS, THERE HAVE BEEN SEVERAL ATTEMPTS TO USE TEXT ANALYTICS TO EXAMINE STUDENT REFLECTIVE WRITINGS. THIS PAPER PRESENTS THE RESULTS OF A STUDY EXAMINING THE USE OF THEORETICALLY-SOUND LINGUISTIC INDICATORS OF DIFFERENT PSYCHOLOGICAL PROCESSES FOR THE DEVELOPMENT OF AN ANALYTICS SYSTEM FOR ASSESSMENT OF REFLECTIVE WRITING. MORE PRECISELY, WE DEVELOPED A RANDOM-FOREST CLASSIFICATION SYSTEM USING LINGUISTIC INDICATORS PROVIDED BY THE LIWC AND COH-METRIX TOOLS. WE ALSO EXAMINED WHAT PARTICULAR INDICATORS ARE REPRESENTATIVE OF THE DIFFERENT TYPES OF STUDENT REFLECTIVE WRITINGS.
LEARNING ANALYTICS CAN BRIDGE THE GAP BETWEEN LEARNING SCIENCES AND DATA ANALYTICS, LEVERAGING THE EXPERTISE OF BOTH FIELDS IN EXPLORING THE VAST AMOUNT OF DATA GENERATED IN ONLINE LEARNING ENVIRONMENTS. A TYPICAL LEARNING ANALYTICS INTERVENTION IS THE LEARNING DASHBOARD, A VISUALISATION TOOL BUILT WITH THE PURPOSE OF EMPOWERING TEACHERS AND LEARNERS TO MAKE INFORMED DECISIONS ABOUT THE LEARNING PROCESS. RELATED WORK HAS INVESTIGATED LEARNING DASHBOARDS, YET NONE HAVE EXPLORED THE THEORETICAL FOUNDATION THAT SHOULD INFORM THE DESIGN AND EVALUATION OF SUCH INTERVENTIONS. IN THIS SYSTEMATIC LITERATURE REVIEW, WE ANALYSE THE EXTENT TO WHICH THEORIES AND MODELS FROM LEARNING SCIENCES HAVE BEEN INTEGRATED INTO THE DEVELOPMENT OF LEARNING DASHBOARDS AIMED AT LEARNERS. OUR ANALYSIS REVEALED THAT VERY FEW DASHBOARD EVALUATIONS TAKE INTO ACCOUNT THE EDUCATIONAL CONCEPTS THAT WERE USED AS A THEORETICAL FOUNDATION FOR THEIR DESIGN. FURTHERMORE, WE REPORT FINDINGS SUGGESTING THAT COMPARISON WITH PEERS, A COMMON REFERENCE FRAME FOR CONTEXTUALISING INFORMATION ON LEARNING ANALYTICS DASHBOARDS, WAS NOT PERCEIVED POSITIVELY BY ALL LEARNERS. WE SUMMARISE THE INSIGHTS GATHERED THROUGH OUR LITERATURE REVIEW IN A SET OF RECOMMENDATIONS FOR THE DESIGN AND EVALUATION OF LEARNING ANALYTICS DASHBOARDS FOR LEARNERS.
IN THIS POSTER, WE DESCRIBE THE DESIGN OF A UNIVERSITY COURSE WITH A BLENDED LEARNING CHARACTER. LEARNING ANALYTICS WERE USED BOTH WITHIN THE COURSE TO FACILITATE EFFECTIVE TEACHER-STUDENT INTERACTION, AS WELL AS AFTER THE COURSE TO EXAMINE PATTERNS BETWEEN STUDENTS' ACTIVITIES DURING THE COURSE AND THEIR PERFORMANCE ON THE TEST AND THE GROUP ASSIGNMENT AT THE END OF THE COURSE.
WHERE IS THE EVIDENCE FOR LEARNING ANALYTICS? IN PARTICULAR, WHERE IS THE EVIDENCE THAT IT IMPROVES LEARNING IN PRACTICE? CAN WE RELY ON IT? CURRENTLY, THERE ARE VIGOROUS DEBATES ABOUT THE QUALITY OF RESEARCH EVIDENCE IN MEDICINE AND PSYCHOLOGY, WITH PARTICULAR ISSUES AROUND STATISTICAL GOOD PRACTICE, THE 'FILE DRAWER EFFECT', AND WAYS IN WHICH INCENTIVES FOR STAKEHOLDERS IN THE RESEARCH PROCESS REWARD THE QUANTITY OF RESEARCH PRODUCED RATHER THAN THE QUALITY. IN THIS PAPER, WE PRESENT THE LEARNING ANALYTICS COMMUNITY EXCHANGE (LACE) PROJECT'S EVIDENCE HUB, AN EFFORT TO RELATE RESEARCH EVIDENCE IN LEARNING ANALYTICS TO FOUR PROPOSITIONS ABOUT LEARNING ANALYTICS: WHETHER THEY SUPPORT LEARNING, SUPPORT TEACHING, ARE DEPLOYED WIDELY, AND ARE USED ETHICALLY. SURPRISINGLY LITTLE EVIDENCE IN THIS STRONG, SPECIFIC SENSE WAS FOUND, AND VERY LITTLE WAS NEGATIVE (7%, N=123), SUGGESTING THAT LEARNING ANALYTICS IS NOT IMMUNE FROM THE PRESSURES IN OTHER AREAS. WE EXPLORE THE EVIDENCE IN ONE PARTICULAR AREA IN DETAIL (WHETHER LEARNING ANALYTICS IMPROVE TEACHING AND LEARNERS SUPPORT IN THE UNIVERSITY SECTOR), AND SET OUT SOME OF THE WEAKNESSES OF THE EVIDENCE AVAILABLE. WE CONCLUDE THAT THERE IS CONSIDERABLE SCOPE FOR IMPROVING THE EVIDENCE BASE FOR LEARNING ANALYTICS, AND SET OUT SOME SUGGESTIONS OF WAYS FOR VARIOUS STAKEHOLDERS TO ACHIEVE THIS.
THIS PAPER INTRODUCES A LEARNING ANALYTICS POLICY DEVELOPMENT FRAMEWORK DEVELOPED BY A CROSS-EUROPEAN RESEARCH PROJECT TEAM - SHEILA (SUPPORTING HIGHER EDUCATION TO INTEGRATE LEARNING ANALYTICS), BASED ON INTERVIEWS WITH 78 SENIOR MANAGERS FROM 51 EUROPEAN HIGHER EDUCATION INSTITUTIONS ACROSS 16 COUNTRIES. THE FRAMEWORK WAS DEVELOPED USING THE RAPID OUTCOME MAPPING APPROACH (ROMA), WHICH IS DESIGNED TO DEVELOP EFFECTIVE STRATEGIES AND EVIDENCE-BASED POLICY IN COMPLEX ENVIRONMENTS. THIS PAPER PRESENTS THREE CASE STUDIES TO ILLUSTRATE THE DEVELOPMENT PROCESS OF THE SHEILA POLICY FRAMEWORK, WHICH CAN BE USED TO INFORM STRATEGIC PLANNING AND POLICY PROCESSES IN REAL WORLD ENVIRONMENTS, PARTICULARLY FOR LARGE-SCALE IMPLEMENTATION IN HIGHER EDUCATION CONTEXTS.
THIS ARTICLE INTRODUCES LEARNING ANALYTICS DASHBOARDS THAT VISUALIZE LEARNING TRACES FOR LEARNERS AND TEACHERS. WE PRESENT A CONCEPTUAL FRAMEWORK THAT HELPS TO ANALYZE LEARNING ANALYTICS APPLICATIONS FOR THESE KINDS OF USERS. WE THEN PRESENT OUR OWN WORK IN THIS AREA AND COMPARE WITH 15 RELATED DASHBOARD APPLICATIONS FOR LEARNING. MOST EVALUATIONS EVALUATE ONLY PART OF OUR CONCEPTUAL FRAMEWORK AND DO NOT ASSESS WHETHER DASHBOARDS CONTRIBUTE TO BEHAVIOR CHANGE OR NEW UNDERSTANDING, PROBABLY ALSO BECAUSE SUCH ASSESSMENT REQUIRES LONGITUDINAL STUDIES.
THE USERS OF THE WORLD WIDE WEB PRODUCE DATA CONTINUOUSLY. THIS HAPPENS IN VARIED AREAS SUCH AS TRADING ON LINE, PRODUCT RATINGS, SUPPORT AND USE OF SERVICES, AND MANY MORE, COMPRISING DISTANCE EDUCATION. THE EVER INCREASING AMOUNT OF SUCH DATA CAN MAKE ANALYSIS AND EXTRACTION OF MEANINGFUL INFORMATION PROGRESSIVELY HARDER, AND SOPHISTICATED ANALYSIS TECHNIQUES ARE TO BE USED TO EXTRACT ADDED VALUE FROM DATA. MANY COMPANIES DO COLLECTION AND ANALYSIS OF DATA WITH THE PURPOSE TO DEVELOP THEIR MARKETING STRATEGIES. IN THE FIELD OF EDUCATION, AND DISTANCE EDUCATION IN PARTICULAR, DATA COLLECTED THROUGH ONLINE LEARNING MANAGEMENT SYSTEMS (LMSS) CAN PROVIDE A GREAT RESOURCE, AND A STRONG CHALLENGE, FOR THE ANALYSIS OF LEARNING PROCESSES, THE DESIGN OF TRAINING PATHS, AND THE UPDATING AND PERSONALIZATION OF LEARNING ENVIRONMENTS. WHILE, ON THE ONE HAND, THERE IS AN INCREASING DEMAND BY EDUCATIONAL INSTITUTIONS TO MEASURE, DEMONSTRATE, AND IMPROVE THE RESULTS ACHIEVED IN DISTANCE LEARNING, ON THE OTHER HAND THE LOGIC OF TRADITIONAL REPORTING INCLUDED IN LMS PLATFORMS DOES NOT SATISFY THAT GROWING NEED. LEARNING ANALYTICS IS THE ANSWER TO THE NEED FOR OPTIMIZATION OF LEARNING THROUGH THE TECHNIQUES OF ANALYSIS OF DATA PRODUCED BY LEARNING PROCESSES, INVOLVING ALL STAKEHOLDERS OF THE SYSTEM. IN THIS PAPER WE SHOW AND DISCUSS A BRIEF STATE OF THE ART OF MODELS OF LEARNING ANALYTICS PRESENTED IN THE LITERATURE.
THIS PAPER DESCRIBES SEAMLESS LEARNING ANALYTICS METHODS OF VASCORLL (VISUALIZATION AND ANALYSIS SYSTEM FOR CONNECTING RELATIONSHIPS OF LEARNING LOGS). VASCORLL IS A SYSTEM FOR VISUALIZING AND ANALYZING THE LEARNING LOGS COLLECTED BY THE SEAMLESS LEARNING SYSTEM, WHICH SUPPORTS LANGUAGE LEARNING IN THE REAL-WORLD. AS FAR, SEVERAL STUDIES HAVE BEEN MADE IN THE SEAMLESS LEARNING ENVIRONMENTS IN ORDER TO BRIDGE FORMAL LEARNING OVER INFORMAL LEARNING. HOWEVER, THEIR FOCUS WAS THE IMPLEMENTATION OF THE SEAMLESS LEARNING ENVIRONMENT IN EDUCATION. THIS STUDY FOCUSES ON VISUALIZING AND ANALYZING LEARNING LOGS COLLECTED IN THE SEAMLESS LEARNING ENVIRONMENT. THIS PAPER DESCRIBES HOW OUR ANALYTICS COULD CONTRIBUTE TO BRIDGING THE GAP BETWEEN FORMAL AND INFORMAL LEARNING. AN EXPERIMENT WAS CONDUCTED TO EVALUATE 1) WHETHER OUR DEVELOPED VASCORLL IS EFFECTIVE IN CONNECTING THE WORDS LEARNED IN FORMAL LEARNING TO THE ONES LEARNED IN INFORMAL LEARNING, 2) WHICH SOCIAL NETWORK ALGORITHM IS EFFECTIVE TO ENHANCE LEARNING IN THE SEAMLESS LEARNING ENVIRONMENT. TWENTY INTERNATIONAL STUDENTS PARTICIPATED IN THE EVALUATION EXPERIMENT, AND THEY WERE ABLE TO INCREASE THEIR LEARNING OPPORTUNITIES BY USING VASCORLL. IN ADDITION, IT WAS FOUND THAT THE BETWEENNESS CENTRALITY IS USEFUL IN FINDING CENTRAL WORDS BRIDGING FORMAL AND INFORMAL LEARNING.(1)
THIS ARTICLE PROPOSES A FRAMEWORK OF QUALITY INDICATORS FOR LEARNING ANALYTICS THAT AIMS TO STANDARDISE THE EVALUATION OF LEARNING ANALYTICS TOOLS AND TO PROVIDE A MEAN TO CAPTURE EVIDENCE FOR THE IMPACT OF LEARNING ANALYTICS ON EDUCATIONAL PRACTICES IN A STANDARDISED MANNER. THE CRITERIA OF THE FRAMEWORK AND ITS QUALITY INDICATORS ARE BASED ON THE RESULTS OF A GROUP CONCEPT MAPPING STUDY CONDUCTED WITH EXPERTS FROM THE FIELD OF LEARNING ANALYTICS. THE OUTCOMES OF THIS STUDY ARE FURTHER EXTENDED WITH FINDINGS FROM A FOCUSED LITERATURE REVIEW.
OPEN LEARNING ANALYTICS (OLA) IS A RELATIVELY NEW BRANCH OF LEARNING ANALYTICS (LA) WHICH EMERGED DUE TO THE GROWING DEMAND FOR SELF-ORGANIZED, NETWORKED, AND LIFELONG LEARNING OPPORTUNITIES. OLA DEALS WITH LEARNING DATA COLLECTED FROM VARIOUS LEARNING ENVIRONMENTS AND CONTEXTS, ANALYZED WITH A RANGE OF ANALYTICS METHODS, AND FOR DIFFERENT STAKEHOLDERS WITH DIVERSE INTERESTS AND OBJECTIVES. THIS DIVERSITY IN DIFFERENT DIMENSIONS OF OLA IS A CHALLENGE WHICH NEEDS TO BE ADDRESSED BY ADOPTING A PERSONALIZED LEARNING ANALYTICS (PLA) MODEL. CURRENT IMPLEMENTATIONS OF LA MAINLY RELY ON A PREDEFINED SET OF QUESTIONS AND INDICATORS WHICH IS NOT SUITABLE IN THE CONTEXT OF OLA WHERE THE INDICATORS ARE UNPREDICTABLE. IN THIS PAPER WE PRESENT THE GOAL - QUESTION INDICATOR (GQI) APPROACH FOR PLA AND PROVIDE THE CONCEPTUAL, DESIGN, IMPLEMENTATION AND EVALUATION DETAILS OF THE INDICATOR ENGINE COMPONENT OF THE OPEN LEARNING ANALYTICS PLATFORM (OPENLAP) THAT ENGAGES END USERS IN THE INDICATOR GENERATION PROCESS BY SUPPORTING THEM IN SETTING GOALS, POSING QUESTIONS, AND SELF-DEFINING INDICATORS.
A MAJOR PROMISE OF LEARNING ANALYTICS IS THAT THROUGH THE COLLECTION OF LARGE AMOUNTS OF DATA WE CAN DERIVE INSIGHTS FROM AUTHENTIC LEARNING ENVIRONMENTS, AND IMPACT MANY LEARNERS AT SCALE. HOWEVER, THE CONTEXT IN WHICH THE LEARNING OCCURS IS IMPORTANT FOR EDUCATIONAL INNOVATIONS TO IMPACT STUDENT LEARNING. IN PARTICULAR, FOR STUDENT-FACING LEARNING ANALYTICS SYSTEMS LIKE FEEDBACK TOOLS TO WORK EFFECTIVELY, THEY HAVE TO BE INTEGRATED WITH PEDAGOGICAL APPROACHES AND THE LEARNING DESIGN. THIS PAPER PROPOSES A CONCEPTUAL MODEL TO STRIKE A BALANCE BETWEEN THE CONCEPTS OF GENERALIZABLE SCALABLE SUPPORT AND CONTEXTUALIZED SPECIFIC SUPPORT BY CLARIFYING KEY ELEMENTS THAT HELP TO CONTEXTUALIZE STUDENT-FACING LEARNING ANALYTICS TOOLS. WE DEMONSTRATE AN IMPLEMENTATION OF THE MODEL USING A WRITING ANALYTICS EXAMPLE, WHERE THE FEATURES, FEEDBACK AND LEARNING ACTIVITIES AROUND THE AUTOMATED WRITING FEEDBACK TOOL ARE TUNED FOR THE PEDAGOGICAL CONTEXT AND THE ASSESSMENT REGIME IN HAND, BY CO-DESIGNING THEM WITH THE SUBJECT EXPERTS. THE MODEL CAN BE EMPLOYED FOR LEARNING ANALYTICS TO MOVE FROM GENERALIZED SUPPORT TO MEANINGFUL CONTEXTUALIZED SUPPORT FOR ENHANCING LEARNING.
PURPOSE THE PURPOSE OF THIS PAPER IS TO DEMONSTRATE A PROCESS OF FACULTY UTILIZATION OF LEARNING ANALYTICS BY EVALUATING STUDENTS' COURSE OBJECTIVE ACHIEVEMENT RESULTS TO ENABLE STUDENT REFLECTION, STUDENT REMEDIATION AND FACULTY CURRICULUM EVALUATION. DESIGN/METHODOLOGY/APPROACH UPON THE COMPLETION OF A BACKWARD CURRICULUM DESIGN PROCESS, THE AUTHORS UTILIZED LEARNING ANALYTICS TO IMPROVE ADVISING, STUDENT REFLECTION, REMEDIATION AND CURRICULUM EVALUATION. THE LEARNING MANAGEMENT SYSTEM INCORPORATED THE LEARNING ANALYTICS TOOL TO ASSIST THE LEARNING ANALYTICS PROCESS. THE COURSE FACULTY, STUDENT ADVISORS AND STUDENTS UTILIZED THE LEARNING ANALYTICS THROUGHOUT THE ACADEMIC YEAR. FINDINGS UNLIKE RELYING MERELY ON STUDENT GRADES AND OTHER PROXY INDICATORS OF LEARNING, THE LEARNING ANALYTICS TOOL PROVIDED IMMEDIATE AND DIRECT DATA TO MULTIPLE STAKEHOLDERS FOR ADVISING, STUDENT REFLECTION, STUDENT REMEDIATION AND COURSE CURRICULUM EVALUATION. THE AUTHORS BELIEVE IT WAS A MEANINGFUL ENDEAVOR. IT ENABLED MEANINGFUL CONVERSATIONS FOCUSING ON COURSE LEARNING OBJECTIVES AND PROVIDED DETAILED INFORMATION ON EACH STUDENT. THE LEARNING ANALYTICS TOOL ALSO PROVIDED DETAILED INFORMATION REGARDING WHICH AREAS FACULTY NEEDED TO IMPROVE IN THE CURRICULUM. ORIGINALITY/VALUE MOST OF THE LITERATURE ON LEARNING ANALYTICS PRESENT THE CASES THAT ADMINISTRATORS UTILIZED LEARNING ANALYTICS TO MAKE HIGHER LEVEL DECISIONS AND RESEARCHERS TO EXPLORE THE FACTORS INVOLVED IN LEARNING. THIS PAPER PROVIDES CASES TO FACULTY REGARDING HOW LEARNING ANALYTICS CAN BENEFIT THE FACULTY AND THE STUDENTS.
IN BLENDED LEARNING SCENARIOS, EVIDENCE NEEDS TO BE GATHERED FROM DIGITAL AND PHYSICAL SPACES TO OBTAIN A MORE COMPLETE VIEW OF THE TEACHING AND LEARNING PROCESSES. HOWEVER, THESE SCENARIOS ARE HIGHLY HETEROGENEOUS, AND THE VARYING DATA SOURCES AVAILABLE IN EACH PARTICULAR CONTEXT CAN CONDITION THE ACCURACY, RELEVANCE, INTERPRETABILITY AND ACTIONABILITY OF THE LEARNING ANALYTICS (LA) SOLUTIONS, AFFECTING ALSO THE USER'S SENSE OF AGENCY AND TRUST IN SUCH SOLUTIONS. TO AID STAKEHOLDERS IN MAKING USE OF LEARNING ANALYTICS, WE PROPOSE A PROCESS TO INVOLVE TEACHERS IN CUSTOMIZING MULTIMODAL LA (MMLA) SOLUTIONS, ADAPTING THEM TO THEIR PARTICULAR BLENDED LEARNING SITUATION (E.G., IDENTIFYING RELEVANT DATA SOURCES AND METRICS). SINCE MEASURING THE ADDED VALUE OF ADOPTING AN LA SOLUTION IS NOT STRAIGHTFORWARD, WE ALSO PROPOSE A CONCRETE METHOD FOR DOING SO. THE RESULTS OBTAINED FROM TWO CASE STUDIES IN AUTHENTIC, BLENDED COMPUTER-SUPPORTED COLLABORATIVE LEARNING SETTINGS SHOW AN IMPROVEMENT IN THE SENSITIVITY AND F1 SCORES OF THE CUSTOMIZED MMLA SOLUTION. ASIDE FROM THESE QUANTITATIVE IMPROVEMENTS, PARTICIPANT TEACHERS REPORTED BOTH AN INCREMENT IN THE EFFORT INVOLVED, BUT ALSO INCREASED RELEVANCE, UNDERSTANDING AND ACTIONABILITY OF THE RESULTS.
INVOLVING TEACHERS IN THE DESIGN OF TECHNOLOGY-ENHANCED LEARNING ENVIRONMENTS IS A USEFUL METHOD TOWARDS BRIDGING THE GAP BETWEEN RESEARCH AND PRACTICE. THIS IS ESPECIALLY RELEVANT FOR LEARNING ANALYTICS TOOLS, WHEREIN THE PRESENTATION OF EDUCATIONAL DATA TO TEACHERS OR STUDENTS REQUIRES MEANINGFUL SENSE-MAKING TO EFFECTIVELY SUPPORT DATA-DRIVEN ACTIONS. IN THIS PAPER, WE PRESENT TWO CASE STUDIES CARRIED OUT IN THE CONTEXT OF TWO RESEARCH PROJECTS IN THE USA AND SPAIN WHICH AIMED TO INVOLVE TEACHERS IN THE CO-DESIGN OF LEARNING ANALYTICS TOOLS THROUGH PROFESSIONAL DEVELOPMENT PROGRAMS. THE RESULTS OF A CROSS-CASE ANALYSIS HIGHLIGHT LESSONS LEARNED AROUND CHALLENGES AND PRINCIPLES REGARDING THE MEANINGFUL INVOLVEMENT OF TEACHERS IN LEARNING ANALYTICS TOOLING DESIGN.
THIS PAPER REPORTS ON RESEARCH CONDUCTED IN A PROJECT NAMED LISA WHICH AIMS AT SUPPORTING LEARNERS THROUGH LEARNER-CENTERED LEARNING ANALYTICS USING PHYSIOLOGICAL SENSOR DATA AS WELL AS ENVIRONMENTAL SENSORS. WE PRESENT THE CONCEPT AND A PROTOTYPICAL REALIZATION OF A MOBILE SENSOR DEVICE USED IN LISA.
GAME LEARNING ANALYTICS HAS A GREAT POTENTIAL TO PROVIDE INSIGHT AND IMPROVE THE USE OF GAMES IN DIFFERENT EDUCATIONAL SITUATIONS. HOWEVER, IT IS NECESSARY TO CLEARLY ESTABLISH WHAT THE LEARNER'S REQUIREMENTS ARE AND TO SET REALISTIC EXPECTATIONS ABOUT THE LEARNING PROCESS AND OUTCOMES. APPLICATION OF GAME LEARNING ANALYTICS REQUIRES PEDAGOGICALLY INFORMED POLICIES THAT SETTLE THE LEARNING GOALS AND RELATE THEM TO ANALYSIS AND VISUALIZATION; AND A SUPPORTING INFRASTRUCTURE THAT PROVIDES THE MECHANISM ON TOP OF WHICH IT IS EXECUTED. BOTH CONCERNS CAN BE ADDRESSED SEPARATED: ON THE ONE HAND, THERE IS A LEARNING ANALYTICS MODEL (LAM) WHICH DESCRIBES HOW THE ANALYSIS IS CARRIED OUT, INTERPRETED AS LEARNING, AND PRESENTED TO STAKEHOLDERS; AND ON THE OTHER HAND, AN UNDERLYING ANALYTICS SYSTEM THAT CONCENTRATES ON PERFORMANCE, SECURITY, FLEXIBILITY AND GENERALITY. AN IMPORTANT ADVANTAGE OF THIS SEPARATION IS THAT IT ALLOWS LAM AUTHORS TO CONCENTRATE ON THEIR AREA OF EXPERTISE, LIMITING THEIR EXPOSITION TO THE ACTUAL MECHANISM USED UNDERNEATH. HOWEVER, LAMS BUILT FOR A SINGLE GAME FAIL TO ACCOUNT FOR THE FREQUENT CASE WHERE GAMES AND THEIR ANALYTICS ARC AGGREGATED INTO LARGER, OVERARCHING PLOTS, GAMES OR COURSES. THIS WORK DESCRIBES AN EXTENSION TO AN EXISTING GAME LEARNING ANALYTICS SYSTEM, USED IN RAGE AND BEACONING H2020 PROJECTS, WHICH MANAGES MULTILEVEL ANALYTICS THROUGH IMPROVEMENTS TO BOTH POLICY AND MECHANISM; AND INTRODUCES META-LEARNING ANALYTIC MODELS, WHICH CHARACTERIZE LEARNING IN HIERARCHICAL STRUCTURES.
LEARNING ANALYTICS OPEN UP A COMPLEX LANDSCAPE OF PRIVACY AND POLICY ISSUES, WHICH, IN TURN, INFLUENCE HOW LEARNING ANALYTICS SYSTEMS AND PRACTICES ARE DESIGNED. RESEARCH AND DEVELOPMENT IS GOVERNED BY REGULATIONS FOR DATA STORAGE AND MANAGEMENT, AND BY RESEARCH ETHICS. CONSEQUENTLY, WHEN MOVING SOLUTIONS OUT THE RESEARCH LABS IMPLEMENTERS MEET CONSTRAINTS DEFINED IN NATIONAL LAWS AND JUSTIFIED IN PRIVACY FRAMEWORKS. THIS PAPER EXPLORES HOW THE OECD, APEC AND EU PRIVACY FRAMEWORKS SEEK TO REGULATE DATA PRIVACY, WITH SIGNIFICANT IMPLICATIONS FOR THE DISCOURSE OF LEARNING, AND ULTIMATELY, AN IMPACT ON THE DESIGN OF TOOLS, ARCHITECTURES AND PRACTICES THAT NOW ARE ON THE DRAWING BOARD. A DETAILED LIST OF REQUIREMENTS FOR LEARNING ANALYTICS SYSTEMS IS DEVELOPED, BASED ON THE NEW LEGAL REQUIREMENTS DEFINED IN THE EUROPEAN GENERAL DATA PROTECTION REGULATION, WHICH FROM 2018 WILL BE ENFORCED AS EUROPEAN LAW. THE PAPER ALSO GIVES AN INITIAL ACCOUNT OF HOW THE PRIVACY DISCOURSE IN EUROPE, JAPAN, SOUTH-KOREA AND CHINA IS DEVELOPING AND REFLECTS UPON THE POSSIBLE IMPACT OF THE DIFFERENT PRIVACY FRAMEWORKS ON THE DESIGN OF LA PRIVACY SOLUTIONS IN THESE COUNTRIES. THIS RESEARCH CONTRIBUTES TO KNOWLEDGE OF HOW CONCERNS ABOUT PRIVACY AND DATA PROTECTION RELATED TO EDUCATIONAL DATA CAN DRIVE A DISCOURSE ON NEW APPROACHES TO PRIVACY ENGINEERING BASED ON THE PRINCIPLES OF PRIVACY BY DESIGN. FOR THE LAK COMMUNITY, THIS STUDY REPRESENTS THE FIRST ATTEMPT TO CONCEPTUALISE THE ISSUES OF PRIVACY AND LEARNING ANALYTICS IN A CROSS-CULTURAL CONTEXT. THE PAPER CONCLUDES WITH A PLAN TO FOLLOW UP THIS RESEARCH ON PRIVACY POLICIES AND LEARNING ANALYTICS SYSTEMS DEVELOPMENT WITH A NEW INTERNATIONAL STUDY.
WE PROPOSE THAT THE DESIGN AND IMPLEMENTATION OF EFFECTIVE SOCIAL LEARNING ANALYTICS (SLA) PRESENT SIGNIFICANT CHALLENGES AND OPPORTUNITIES FOR BOTH RESEARCH AND ENTERPRISE, IN THREE IMPORTANT RESPECTS. THE FIRST IS THAT THE LEARNING LANDSCAPE IS EXTRAORDINARILY TURBULENT AT PRESENT, IN NO SMALL PART DUE TO TECHNOLOGICAL DRIVERS. ONLINE SOCIAL LEARNING IS EMERGING AS A SIGNIFICANT PHENOMENON FOR A VARIETY OF REASONS, WHICH WE REVIEW, IN ORDER TO MOTIVATE THE CONCEPT OF SOCIAL LEARNING. THE SECOND CHALLENGE IS TO IDENTIFY DIFFERENT TYPES OF SLA AND THEIR ASSOCIATED TECHNOLOGIES AND USES. WE DISCUSS FIVE CATEGORIES OF ANALYTIC IN RELATION TO ONLINE SOCIAL LEARNING; THESE ANALYTICS ARE EITHER INHERENTLY SOCIAL OR CAN BE SOCIALISED. THIS SETS THE SCENE FOR A THIRD CHALLENGE, THAT OF IMPLEMENTING ANALYTICS THAT HAVE PEDAGOGICAL AND ETHICAL INTEGRITY IN A CONTEXT WHERE POWER AND CONTROL OVER DATA ARE NOW OF PRIMARY IMPORTANCE. WE CONSIDER SOME OF THE CONCERNS THAT LEARNING ANALYTICS PROVOKE, AND SUGGEST THAT SOCIAL LEARNING ANALYTICS MAY PROVIDE WAYS FORWARD. WE CONCLUDE BY REVISITING THE DRIVERS AND TRENDS, AND CONSIDER FUTURE SCENARIOS THAT WE MAY SEE UNFOLD AS SLA TOOLS AND SERVICES MATURE.
THIS RESPONSE REVIEWS THE ARTICLE ENTITLED "ETHICAL OVERSIGHT OF STUDENT DATA IN LEARNING ANALYTICS: A TYPOLOGY DERIVED FROM A CROSS-CONTINENTAL, CROSS-INSTITUTIONAL PERSPECTIVE" (WILLIS ET AL. IN EDUC TECHNOL RES DEV 64(5):881-901. 10.1007/S11423-016-9463-4, 2016) FROM A POLICY PERSPECTIVE. THE PAPER SUMMARIZES THE TYPOLOGY OF LEARNING ANALYTICS AND ITS CORE IDEAS PROPOSED IN WILLIS ET AL. (2016), BUT ALSO HIGHLIGHTS THE CHALLENGES CONSIDERING THE THIRD-PARTY POLICIES OF DATA PRIVACY AND USAGE. ADDITIONALLY, THE PAPER ADDRESSES THE CORE ETHICAL PRINCIPLES DECLARED IN CORRIN ET AL. (ACCESS ONLINE 26, 2019) AND SUGGESTS USING THESE PRINCIPLES AS UNDERPINNINGS WHEN CONSIDERING THE ETHICS OF LEARNING ANALYTICS AND SEEKING EFFECTIVE SOLUTIONS TO BEST ENSURE ETHICAL PRACTICES OF IMPLEMENTING LEARNING ANALYTICS. THE ESTABLISHMENT OF A COMPREHENSIVE DATA GOVERNANCE SYSTEM BY INSTITUTIONS OF HIGHER EDUCATION IS RECOMMENDED.
WHEN DESIGNING LEARNING ANALYTICS TOOLS FOR USE BY LEARNERS WE HAVE AN OPPORTUNITY TO PROVIDE TOOLS THAT CONSIDER A PARTICULAR LEARNER'S SITUATION AND THE LEARNER HERSELF. TO AFFORD ACTUAL IMPACT ON LEARNING, SUCH TOOLS HAVE TO BE INFORMED BY THEORIES OF EDUCATION. PARTICULARLY, EDUCATIONAL RESEARCH SHOWS THAT INDIVIDUAL DIFFERENCES PLAY A SIGNIFICANT ROLE IN EXPLAINING STUDENTS' LEARNING PROCESS. HOWEVER, LIMITED EMPIRICAL RESEARCH IN LEARNING ANALYTICS HAS INVESTIGATED THE ROLE OF THEORETICAL CONSTRUCTS, SUCH AS MOTIVATIONAL FACTORS, THAT ARE UNDERLYING THE OBSERVED DIFFERENCES BETWEEN INDIVIDUALS. IN THIS WORK, WE CONDUCTED A FIELD EXPERIMENT TO EXAMINE THE EFFECT OF THREE DESIGNED LEARNING ANALYTICS VISUALIZATIONS ON STUDENTS' PARTICIPATION IN ONLINE DISCUSSIONS IN AUTHENTIC COURSE SETTINGS. USING HIERARCHICAL LINEAR MIXED MODELS, OUR RESULTS REVEALED THAT EFFECTS OF VISUALIZATIONS ON THE QUANTITY AND QUALITY OF MESSAGES POSTED BY STUDENTS WITH DIFFERENCES IN ACHIEVEMENT GOAL ORIENTATIONS COULD EITHER BE POSITIVE OR NEGATIVE. OUR FINDINGS HIGHLIGHT THE METHODOLOGICAL IMPORTANCE OF CONSIDERING INDIVIDUAL DIFFERENCES AND POSE IMPORTANT IMPLICATIONS FOR FUTURE DESIGN AND RESEARCH OF LEARNING ANALYTICS VISUALIZATIONS.
THE RESEARCH TRIES TO EXPLORE NEW COLLEGE ENGLISH TEACHING AND LEARNING MODES BASED ON LEARNING ANALYTICS IN BIG DATA ERA. UNDER THE THEORIES OF SECOND LANGUAGE ACQUISITION AND LEARNING ANALYTICS, THIS ARTICLE HOLDS THAT IT IS VITAL AND INDISPENSABLE TO APPLY LEARNING ANALYTICS TO COLLEGE ENGLISH TEACHING AND LEARNING BASED ON THE TWO TYPICAL CASES OF WIDE APPLICATION OF CORRECTS A COOL WEB AT COLLEGES AND UNIVERSITIES AND THE PILOT PROJECT-THE BLENDED TEACHING AND LEARNING METHOD CONDUCTED AT UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA. WITH THE HELP OF THE SOFTWARE SUCH AS LANMUYUN, ISMART, RESEARCHER CAN GET MORE SCIENTIFIC AND EFFECTIVE DATA TO PROMOTE PERSONALIZED LEARNING ACTIVITY AND ALSO OPTIMIZE TEACHING MODES AND TEACHING METHODS AS WELL. THE PAPER ENDS WITH SOME IMPLICATIONS OF THE PRESENT RESEARCH FOR COLLEGE ENGLISH LANGUAGE TEACHING AND LEARNING IN CHINA.
THE FIELD OF LEARNING ANALYTICS HAS THE POTENTIAL TO ENABLE HIGHER EDUCATION INSTITUTIONS TO INCREASE THEIR UNDERSTANDING OF THEIR STUDENTS' LEARNING NEEDS AND TO USE THAT UNDERSTANDING TO POSITIVELY INFLUENCE STUDENT LEARNING AND PROGRESSION. ANALYSIS OF DATA RELATING TO STUDENTS AND THEIR ENGAGEMENT WITH THEIR LEARNING IS THE FOUNDATION OF THIS PROCESS. THERE IS AN INHERENT ASSUMPTION LINKED TO LEARNING ANALYTICS THAT KNOWLEDGE OF A LEARNER'S BEHAVIOR IS ADVANTAGEOUS FOR THE INDIVIDUAL, INSTRUCTOR, AND EDUCATIONAL PROVIDER. IT SEEMS INTUITIVELY OBVIOUS THAT A GREATER UNDERSTANDING OF A STUDENT COHORT AND THE LEARNING DESIGNS AND INTERVENTIONS THEY BEST RESPOND TO WOULD BENEFIT STUDENTS AND, IN TURN, THE INSTITUTION'S RETENTION AND SUCCESS RATE. YET COLLECTION OF DATA AND THEIR USE FACE A NUMBER OF ETHICAL CHALLENGES, INCLUDING LOCATION AND INTERPRETATION OF DATA; INFORMED CONSENT, PRIVACY, AND DEIDENTIFICATION OF DATA; AND CLASSIFICATION AND MANAGEMENT OF DATA. APPROACHES TAKEN TO UNDERSTAND THE OPPORTUNITIES AND ETHICAL CHALLENGES OF LEARNING ANALYTICS NECESSARILY DEPEND ON MANY IDEOLOGICAL ASSUMPTIONS AND EPISTEMOLOGIES. THIS ARTICLE PROPOSES A SOCIOCRITICAL PERSPECTIVE ON THE USE OF LEARNING ANALYTICS. SUCH AN APPROACH HIGHLIGHTS THE ROLE OF POWER, THE IMPACT OF SURVEILLANCE, THE NEED FOR TRANSPARENCY, AND AN ACKNOWLEDGMENT THAT STUDENT IDENTITY IS A TRANSIENT, TEMPORAL, AND CONTEXT-BOUND CONSTRUCT. EACH OF THESE AFFECTS THE SCOPE AND DEFINITION OF LEARNING ANALYTICS' ETHICAL USE. WE PROPOSE SIX PRINCIPLES AS A FRAMEWORK FOR CONSIDERATIONS TO GUIDE HIGHER EDUCATION INSTITUTIONS TO ADDRESS ETHICAL ISSUES IN LEARNING ANALYTICS AND CHALLENGES IN CONTEXT-DEPENDENT AND APPROPRIATE WAYS.
WITH THE INCREASING DEVELOPMENT AND ADOPTION OF DIGITAL TECHNOLOGIES FOR EDUCATION, MORE DATA GATHERED FROM EDUCATIONAL CONTEXTS ARE BEING ANALYZED TO GIVE ACTIONABLE INSIGHTS TO STAKEHOLDERS. AS A DATA-DRIVEN APPROACH FOR BETTER UNDERSTANDING AND OPTIMIZING LEARNING AND THE LEARNING ENVIRONMENT, LEARNING ANALYTICS HAS THE POTENTIAL TO CONTRIBUTE TO SMART LEARNING. HOWEVER, CURRENT LEARNING ANALYTICS LACKS KNOWLEDGE AWARENESS, AN IMPORTANT COMPONENT IN SMART LEARNING. THIS PAPER DRAWS UPON RESEARCH IN THE DOMAIN OF SMART LEARNING, REFLECTS ON CURRENT RESEARCH ON METHODS AND PROCESSES IN LEARNING ANALYTICS, AND PROPOSES A FRAMEWORK FOR KNOWLEDGE-AWARE LEARNING ANALYTICS FOR SMART LEARNING. (C) 2019 THE AUTHORS. PUBLISHED BY ELSEVIER B.V.
LEARNING ANALYTICS IS A NEW RESEARCH STREAM DEVELOPED FROM OTHER PREVIOUS RELATED STREAMS SUCH AS ACADEMIC ANALYTICS AND EDUCATIONAL DATA MINING. WE CONDUCTED A LITERATURE REVIEW ON RECENT PUBLICATIONS RELATED TO DATA ANALYTICS IN EDUCATION IN VARIOUS JOURNALS AND CONFERENCES. WITH THE REFERENCE MODEL PROPOSED BY CHATTI ET AL (2012), WE PROPOSED A REFINED MODEL BY INTRODUCING THE SECOND LAYER ELEMENTS TO TWO OUT OF THE FOUR DIMENSIONS AND PROPOSE CHANGES TO THE LIST OF OPTIONS OF SOME DIMENSIONS. WE THEN ANALYSED PUBLICATIONS OF THE INITIAL THREE YEARS (2014-2016) IN THE JOURNAL OF LEARNING ANALYTICS BASED ON THE REFINED MODEL. WE DISCOVERED FIVE TRENDS IN LEARNING ANALYTICS APPLICATIONS INCLUDING EXTENDING THE DATA LANDSCAPE TO NON-ACADEMIC DATA AND SHIFTING OF OBJECTIVES AND ANALYSIS METHODS. FINALLY, WE PROPOSED FOUR FUTURE RESEARCH DIRECTIONS IN THIS AREA AND RATIONALISED THE RELATIONSHIP BETWEEN DIMENSIONS IN THE MODEL.
LEARNING ANALYTICS HAS BECOME A MAJOR RESEARCH AREA RECENTLY. IN PARTICULAR LEARNING INSTITUTIONS SEEK WAYS TO COLLECT, MANAGE, ANALYZE AND EXPLOIT DATA FROM LEARNERS AND INSTRUCTORS FOR THE FACILITATION OF FORMAL LEARNING PROCESSES. HOWEVER, IN THE WORLD OF INFORMAL LEARNING AT THE WORKPLACE, KNOWLEDGE GAINED FROM FORMAL LEARNING ANALYTICS IS ONLY APPLICABLE ON A COMMODITY LEVEL. SINCE PROFESSIONAL COMMUNITIES NEED LEARNING SUPPORT BEYOND THIS LEVEL, WE NEED A DEEP UNDERSTANDING OF INTERACTIONS BETWEEN LEARNERS AND OTHER ENTITIES IN COMMUNITY-REGULATED LEARNING PROCESSES - A CONCEPTUAL EXTENSION OF SELF-REGULATED LEARNING PROCESSES. IN THIS PAPER, WE DISCUSS SCALING CHALLENGES FOR COMMUNITY LEARNING ANALYTICS, GIVE BOTH CONCEPTUAL AND TECHNICAL SOLUTIONS, AND REPORT EXPERIENCES FROM ONGOING RESEARCH IN THIS AREA.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE OPENED NEW EDUCATIONAL POSSIBILITIES FOR LEARNERS AROUND THE WORLD. MOST OF THE RESEARCH AND SPOTLIGHT HAS BEEN CONCENTRATED ON A HANDFUL OF GLOBAL, ENGLISH-LANGUAGE PROVIDERS, BUT THERE ARE A GROWING NUMBER OF REGIONAL PROVIDERS OF MOOCS IN LANGUAGES OTHER THAN ENGLISH. IN THIS WORK, WE HAVE PARTNERED WITH THIRTEEN MOOC PROVIDERS FROM AROUND THE WORLD. WE APPLY A MULTI-PLATFORM APPROACH GENERATING A JOINT AND COMPARABLE ANALYSIS WITH DATA FROM MILLIONS OF LEARNERS. THIS ALLOWS US TO EXAMINE LEARNING ANALYTICS TRENDS AT A MACRO LEVEL ACROSS VARIOUS MOOC PROVIDERS, WITH A GOAL OF UNDERSTANDING WHICH MOOC TRENDS ARE GLOBALLY UNIVERSAL AND WHICH OF THEM ARE CONTEXT-DEPENDENT. THE ANALYSIS REPORTS PRELIMINARY RESULTS ON THE DIFFERENCES AND SIMILARITIES OF TRENDS BASED ON THE COUNTRY OF ORIGIN, LEVEL OF EDUCATION, GENDER AND AGE OF THEIR LEARNERS ACROSS GLOBAL AND REGIONAL MOOC PROVIDERS. THIS STUDY EXEMPLIFIES THE POTENTIAL OF MACRO LEARNING ANALYTICS IN MOOCS TO UNDERSTAND THE ECOSYSTEM AND INFORM THE WHOLE COMMUNITY, WHILE CALLING FOR MORE LARGE SCALE STUDIES IN LEARNING ANALYTICS THROUGH PARTNERSHIPS AMONG RESEARCHERS AND INSTITUTIONS.
THE WIDE RANGE OF DATA PRODUCED BY PARTICIPANTS IN LEARNING PROCESSES HAS LED TO INCREASED INTEREST IN THE COLLECTION AND ANALYSIS OF DATA TO SUPPORT DATA-DRIVEN DECISION MAKING AT ALL LEVELS OF EDUCATIONAL INSTITUTIONS. THE PAPER IS DEVOTED TO THE EMERGING FIELD OF LEARNING ANALYTICS THAT HAS BEEN RESEARCHED TO IDENTIFY ALL STAKEHOLDERS AND THEIR INTERESTS IN THE LEARNING ANALYTICS RESULTS. IT PRESENTS A COMPREHENSIVE APPROACH TO LEARNING ANALYTICS IN THE FIELD OF HIGHER EDUCATION FROM THE PERSPECTIVE OF ALL DIFFERENT STAKEHOLDERS, WHICH AIMS TO IMPROVE ITS METHODS OF APPROACHING AND ANALYZING LEARNING DATA. ON THE BASIS OF A LITERATURE REVIEW IN THE FIELD AND AN INVESTIGATION OF REQUIREMENTS FOR QUALITY EVALUATION OF LEARNING IN HIGHER EDUCATION THE CORRESPONDING STAKEHOLDER GROUPS ARE IDENTIFIED (STUDENTS, TEACHERS, PROGRAMME MANAGERS, DEANS, RECTORS, MEMBERS OF QUALITY COMMISSIONS AND EXTERNAL QUALITY EXPERTS) AND 6 MODELS (1 MODEL PER EACH STAKEHOLDER GROUP) FOR DATA COLLECTION AND PERSONALIZED AND MEANINGFUL ANALYSIS ARE PROPOSED FOR THE NEEDS OF LEARNING ANALYTICS. EACH MODEL CONSISTS OF MEASURABLE INDICATORS ALLOWING THE RELEVANT STAKEHOLDER TO TRACK DATA FOR STUDENTS' LEARNING OR TRAINING FOR DIFFERENT PURPOSES, E.G. MONITORING, ANALYSIS, FORECAST, INTERVENTION, RECOMMENDATIONS, ETC., BUT FINALLY TO IMPROVE THE QUALITY OF LEARNING AND TEACHING PROCESSES. IN THE NEXT STAGES OF THE STUDY, THE DEVELOPED MODELS WILL BE VERIFIED WITH THE HELP OF REPRESENTATIVES FROM THE VARIOUS STAKEHOLDERS AND THEN ON THAT BASIS SOFTWARE TOOLS FOR LEARNING ANALYTICS AUTOMATION WILL BE DEVELOPED.
PERSONALIZATION IS CRUCIAL FOR ACHIEVING SMART LEARNING ENVIRONMENTS IN DIFFERENT LIFELONG LEARNING CONTEXTS. THERE IS A NEED TO SHIFT FROM ONE-SIZE-FITS-ALL SYSTEMS TO PERSONALIZED LEARNING ENVIRONMENTS THAT GIVE CONTROL TO THE LEARNERS. RECENTLY, LEARNING ANALYTICS (LA) IS OPENING UP NEW OPPORTUNITIES FOR PROMOTING PERSONALIZATION BY PROVIDING INSIGHTS AND UNDERSTANDING INTO HOW LEARNERS LEARN AND SUPPORTING CUSTOMIZED LEARNING EXPERIENCES THAT MEET THEIR GOALS AND NEEDS. THIS PAPER DISCUSSES THE PERSONALIZATION AND LEARNING ANALYTICS (PERLA) FRAMEWORK WHICH REPRESENTS THE CONVERGENCE OF PERSONALIZATION AND LEARNING ANALYTICS AND PROVIDES A THEORETICAL FOUNDATION FOR EFFECTIVE ANALYTICS-ENHANCED PERSONALIZED LEARNING. THE MAIN AIM OF THE PERLA FRAMEWORK IS TO GUIDE THE SYSTEMATIC DESIGN AND DEVELOPMENT OF EFFECTIVE INDICATORS FOR PERSONALIZED LEARNING.
THE ABILITY TO WORK COLLABORATIVELY IN INTERCULTURAL VIRTUAL TEAMS, IS CONSTANTLY GAINING IMPORTANCE FOR THE LABOUR MARKET. VIRTUAL MOBILITY ENABLES STUDENTS TO ACQUIRE THE NECESSARY INTERCULTURAL TEAMWORK SKILLS WHILE REMAINING LOCALLY INTEGRATED INTO THEIR REGULAR STUDIES. BUT STILL, INTERNATIONAL VIRTUAL COLLABORATIVE LEARNING SCENARIOS DEMAND MUCH TIME AND EFFORT FOR PLANNING AND COORDINATION WHICH BINDS RESOURCES. THE SUPPORT CONCEPTS FOR SUCH COLLABORATIVE VIRTUAL LEARNING GROUPS ARE ALSO RESOURCE-INTENSIVE, BECAUSE LEARNERS SHOULD BE ACCOMPANIED BY QUALIFIED E-TUTORS TO OPTIMISE LEARNING RESULTS BOTH AT INDIVIDUAL AND GROUP LEVEL. CLASSICAL SUMMATIVE TESTS AND EXAMS ARE RATHER UNSUITABLE FOR THE ASSESSMENT OF COLLABORATION AS EXPECTED LEARNING OUTCOME. THESE ARRANGEMENTS ALSO NEED NEW FORMATIVE ASSESSMENT FORMS, AS PARTICIPANTS NEED ACTIVE AND ONGOING FEEDBACK. A MEANINGFUL ASSESSMENT OF LEARNING PROCESSES AND OUTCOMES SHOULD NOT ONLY BE BASED ON THE OBSERVATION OF 'SOFT' FACTORS BUT SHOULD ALSO BE COMPLEMENTED BY 'HARD', FIXED, AUTOMATICALLY MEASURABLE, QUANTITATIVE INDICATORS. TO GAIN THESE HARD INDICATORS THE RESEARCH PROJECT ISLA - INDICATOR-BASED SOCIAL LEARNING ANALYTICS WAS LAUNCHED. THIS PAPER PRESENTS THE PROCEDURE FOR IMPLEMENTATION AS WELL AS VIRTUAL PRESENCE, CONTENT CREATION AND RELATIONSHIPS WITHIN THE COMMUNITY AS FIRST DERIVED INDICATORS AND THEIR PROTOTYPICAL VISUALISATION IN A LEARNING ANALYTICS DASHBOARD.
CONTEMPORARY SYSTEMS FOR SUPPORTING DIGITAL LEARNING ARE CAPABLE OF COLLECTING A WIDE RANGE OF DATA ON LEARNER BEHAVIOURS. THE EMERGING SCIENCE AND TECHNOLOGY OF LEARNING ANALYTICS SEEKS TO USE THIS INFORMATION TO IMPROVE LEARNING OUTCOMES AND SUPPORT INSTITUTIONAL ASSESSMENT. IN THIS WORK WE EXPLORE THE POTENTIAL FOR THE SPATIAL DIMENSION IN LEARNING ANALYTICS, AND WE EVALUATE A PROTOTYPE GEOVISUALIZATION SYSTEM DESIGNED TO SUPPORT WHAT WE CALL SPATIAL LEARNING ANALYTICS. A USER EVALUATION WITH GEOGRAPHERS AND EDUCATORS WAS CONDUCTED TO CHARACTERIZE THE USABILITY AND UTILITY OF OUR PROTOTYPE SPATIAL LEARNING ANALYTICS SYSTEM. BY HELPING US UNDERSTAND WHAT OUR PROTOTYPE SYSTEM DOES AND DOES NOT DO WELL, WE ARE ABLE TO SUGGEST A VARIETY OF NEW WAYS IN WHICH FUTURE SPATIAL LEARNING ANALYTICS SYSTEMS CAN BE DEVELOPED. RESUME LES SYSTEMES ACTUELS UTILISES POUR FACILITER L'APPRENTISSAGE NUMERIQUE SONT CAPABLES DE COLLECTER UN TRES GRAND NOMBRE DE DONNEES SUR LE COMPORTEMENT DES APPRENANTS. LES SCIENCES ET TECHNIQUES EMERGENTES EN ANALYSE DE L'APPRENTISSAGE CHERCHENT A UTILISER CES INFORMATIONS POUR AMELIORER LES RESULTATS D'APPRENTISSAGE ET POUR AIDER L'EVALUATION INSTITUTIONNELLE. DANS CE TRAVAIL NOUS EXPLORONS LE POTENTIEL DE LA DIMENSION SPATIALE DANS L'ANALYSE DE L'APPRENTISSAGE ET NOUS EVALUONS UN PROTOTYPE DE SYSTEME DE GEOVISUALISATION CONCU POUR FACILITER CE QUE NOUS APPELONS L'ANALYSE SPATIALE D'APPRENTISSAGE UNE EVALUATION DES UTILISATEURS PAR DES GEOGRAPHES ET DES EDUCATEURS A ETE MENEE POUR CARACTERISER L'UTILISABILITE ET L'UTILITE DE NOTRE PROTOTYPE DE SYSTEME D'ANALYSE SPATIAL D'APPRENTISSAGE. EN NOUS AIDANT A COMPRENDRE CE QUE NOTRE SYSTEME FAIT CORRECTEMENT ET CE QU'IL NE FAIT PAS CORRECTEMENT, NOUS SOMMES CAPABLE DE PROPOSER UNE VARIETE DE NOUVELLES FACONS QUI POURRONT ETRE INTEGREES DANS LE DEVELOPPEMENT DE FUTURS SYSTEMES D'ANALYSE SPATIAL D'APPRENTISSAGE.
IN THIS STUDY THE AUTHORS CONDUCTED AN EMPIRICAL, BIBLIOMETRIC ANALYSIS OF CURRENT LITERATURE IN LEARNING ANALYTICS. THE AUTHORS PERFORMED A CITATION NETWORK ANALYSIS AND FOUND THREE DOMINANT CLUSTERS OF RESEARCH. A QUALITATIVE THEMATIC REVIEW OF PUBLICATIONS IN THESE CLUSTERS REVEALED DISTINCT CONTEXT, GOALS, AND TOPICS. THE LARGEST CLUSTER FOCUSED ON PREDICTING STUDENT SUCCESS AND FAILURE, THE SECOND LARGEST ON USING ANALYTICS TO INFORM INSTRUCTIONAL DESIGN, AND THE THIRD ON CONCERNS IN IMPLEMENTING LEARNING ANALYTICS SYSTEMS. THE AUTHORS SUGGEST THAT FURTHER COLLABORATION WITH EDUCATIONAL TECHNOLOGY RESEARCHERS AND PRACTITIONERS MAY BE NECESSARY FOR LEARNING ANALYTICS TO REACH ITS INTERDISCIPLINARY GOAL. THE AUTHORS ALSO NOTE THAT LEARNING ANALYTICS CURRENTLY DOES NOT OFTEN TAKE PLACE IN K-12 SETTINGS, AND THAT THE BURDEN OF CREATING LEARNING INTERVENTIONS STILL SEEMED TO RESIDE MAINLY WITH PRACTITIONERS.
THIS ARTICLE PRESENTS THE IMPLEMENTATION OF LEARNING ANALYTICS IN A UNIVERSITY WITH THE AIM OF TESTING THE EXTRACTION OF KNOWLEDGE FROM DATABASES OF TWO SYSTEMS USED IN DISTANCE EDUCATION, AIMING TO PROVIDE SUPPORT FOR THE MANAGEMENT OF THE DISTANCE EDUCATION AS A WAY TO UNDERSTAND STUDENTS' DIFFICULTIES. THEREFORE, IT WAS VALIDATED THE APPLICABILITY OF LITERATURE REFERENCE MODEL FOR THE IMPLEMENTATION THE LEARNING ANALYTICS. THE DATA WERE EXTRACTED FROM THE SYSTEM OF ACADEMIC MANAGEMENT AND FROM THE ENTRANCE EXAM TO UNIVERSITIES AND WERE CHOSEN THE INTEGRATION DATA, DATABASE AND ANALYTICAL TOOLS. ON THE PRESENTED RESULTS, THE MODEL HAS BEEN SUCCESSFULLY APPLIED, GUIDING THE IMPLEMENTATION OF LEARNING ANALYTICS AT THE UNIVERSITY. THE CHOSEN TOOLS FACILITATED THE DEPLOYMENT AND, BROUGHT BENEFITS TO UNIVERSITY.
IN THIS PAPER WE PRESENT A LEARNING ANALYTICS CONCEPTUAL FRAMEWORK THAT SUPPORTS ENQUIRY-BASED EVALUATION OF LEARNING DESIGNS. THE DIMENSIONS OF THE PROPOSED FRAMEWORK EMERGED FROM A REVIEW OF EXISTING ANALYTICS TOOLS, THE ANALYSIS OF INTERVIEWS WITH TEACHERS, AND USER SCENARIOS TO UNDERSTAND WHAT TYPES OF ANALYTICS WOULD BE USEFUL IN EVALUATING A LEARNING ACTIVITY IN RELATION TO PEDAGOGICAL INTENT. THE PROPOSED FRAMEWORK INCORPORATES VARIOUS TYPES OF ANALYTICS, WITH THE TEACHER PLAYING A KEY ROLE IN BRINGING CONTEXT TO THE ANALYSIS AND MAKING DECISIONS ON THE FEEDBACK PROVIDED TO STUDENTS AS WELL AS THE SCAFFOLDING AND ADAPTATION OF THE LEARNING DESIGN. THE FRAMEWORK CONSISTS OF FIVE DIMENSIONS: TEMPORAL ANALYTICS, TOOL-SPECIFIC ANALYTICS, COHORT DYNAMICS, COMPARATIVE ANALYTICS AND CONTINGENCY. SPECIFIC METRICS AND VISUALISATIONS ARE DEFINED FOR EACH DIMENSION OF THE CONCEPTUAL FRAMEWORK. FINALLY THE DEVELOPMENT OF A TOOL THAT PARTIALLY IMPLEMENTS THE CONCEPTUAL FRAMEWORK IS DISCUSSED.
THE IN-DEPTH APPLICATION OF NEW TECHNOLOGIES HAVE BROUGHT ABOUT AN EXPLOSION OF EDUCATIONAL DATA AND PEOPLE ARE PLACING MORE AND MORE EMPHASIS ON LEARNING ANALYTICS. AGAINST THIS BACKGROUND, HOW TO MAKE BETTER USE OF THESE DATA AND DISPLAY THEM IN A VISUALIZED MANNER HAS BECOME AN URGENT ISSUE IN APPLYING LEARNING ANALYTICS TO EDUCATION AND LEARNING PROCESS. THIS PAPER INTRODUCED THE CURRENT RESEARCH STATUS AND RELEVANT CASES OF LEARNING ANALYTICS VISUALIZATION, BROUGHT FORWARD LEARNING ANALYTICS VISUALIZATION MODEL, AND THEN DISPLAYED THE DESIGN CONNOTATION OF LEARNING ANALYTICS VISUALIZATION FROM THE PERSPECTIVES OF DIFFERENT STAKEHOLDERS (STUDENTS, TEACHERS AND ADMINISTRATORS).
THIS PAPER AIMS TO LINK STUDENT FACING LEARNING ANALYTICS DASHBOARDS (LADS) TO THE CORPUS OF RESEARCH ON OPEN LEARNER MODELS (OLMS), AS BOTH HAVE SIMILAR GOALS. WE CONDUCTED A SYSTEMATIC REVIEW OF LITERATURE ON OLMS AND COMPARED THE RESULTS WITH A PREVIOUSLY CONDUCTED REVIEW OF LADS FOR LEARNERS IN TERMS OF (I) DATA USE AND MODELLING, (II) KEY PUBLICATION VENUES, (III) AUTHORS AND ARTICLES, (IV) KEY THEMES, AND (V) SYSTEM EVALUATION. WE HIGHLIGHT THE SIMILARITIES AND DIFFERENCES BETWEEN THE RESEARCH ON LADS AND OLMS. OUR KEY CONTRIBUTION IS A BRIDGE BETWEEN THESE TWO AREAS AS A FOUNDATION FOR BUILDING UPON THE STRENGTHS OF EACH. WE REPORT THE FOLLOWING KEY RESULTS FROM THE REVIEW: IN REPORTS OF NEW OLMS, ALMOST 60% ARE BASED ON A SINGLE TYPE OF DATA; 33% USE BEHAVIORAL METRICS; 39% SUPPORT INPUT FROM THE USER; 37% HAVE COMPLEX MODELS; AND JUST 6% INVOLVE MULTIPLE APPLICATIONS. KEY ASSOCIATED THEMES INCLUDE INTELLIGENT TUTORING SYSTEMS, LEARNING ANALYTICS, AND SELF-REGULATED LEARNING. NOTABLY, COMPARED WITH LADS, OLM RESEARCH IS MORE LIKELY TO BE INTERACTIVE (81% OF PAPERS COMPARED WITH 31% FOR LADS), REPORT EVALUATIONS (76% VERSUS 59%), USE ASSESSMENT DATA (100% VERSUS 37%), PROVIDE A COMPARISON STANDARD FOR STUDENTS (52% VERSUS 38%), BUT LESS LIKELY TO USE BEHAVIORAL METRICS, OR RESOURCE USE DATA (33% AGAINST 75% FOR LADS). IN OLM WORK, THERE WAS A HEIGHTENED FOCUS ON LEARNER CONTROL AND ACCESS TO THEIR OWN DATA.
THE DEMAND FOR OPEN LEARNING ANALYTICS (OLA) HAS GROWN IN RECENT YEARS DUE TO THE INCREASING INTEREST IN THE USAGE OF SELF-ORGANIZED, NETWORKED, AND LIFELONG LEARNING ENVIRONMENTS. HOWEVER, PLATFORMS THAT CAN DELIVER AN EFFECTIVE AND EFFICIENT OLA ARE STILL LACKING. MOST OLA PLATFORMS CURRENTLY AVAILABLE DO NOT CONTINUOUSLY INVOLVE END-USERS IN THE INDICATOR DEFINITION PROCESS AND FOLLOW DESIGN PATTERNS WHICH MAKE IT DIFFICULT TO EXTEND THE PLATFORM TO MEET NEW USER REQUIREMENTS. THESE LIMITATIONS RESTRICT THE SCOPE OF SUCH PLATFORMS WHERE USERS REGULATE THEIR OWN LEARNING PROCESS ACCORDING TO THEIR NEEDS. IN THIS PAPER, WE DISCUSS THE OPEN LEARNING ANALYTICS PLATFORM (OPENLAP) AS A STEP TOWARD AN ECOSYSTEM THAT ADDRESSES THE INDICATOR PERSONALIZATION AND PLATFORM EXTENSIBILITY CHALLENGES OF OLA. OPENLAP FOLLOWS A USER-CENTERED LEARNING ANALYTICS APPROACH THAT INVOLVES END-USERS IN THE PROCESS OF DEFINING CUSTOM INDICATORS THAT MEET THEIR NEEDS. MOREOVER, IT PROVIDES A MODULAR AND EXTENSIBLE ARCHITECTURE THAT ALLOWS THE EASY INTEGRATION OF NEW ANALYTICS METHODS AND VISUALIZATION TECHNIQUES.
THIS STUDY AIMS TO CONTRIBUTE TO RECENT DEVELOPMENTS IN EMPIRICAL STUDIES ON STUDENTS' LEARNING STRATEGIES, WHEREBY THE USE OF TRACE DATA IS COMBINED WITH SELF-REPORT DATA TO DISTINGUISH PROFILES OF LEARNING STRATEGY USE [3-5]. WE DO SO IN THE CONTEXT OF AN APPLICATION OF DISPOSITIONAL LEARNING ANALYTICS IN A LARGE INTRODUCTORY COURSE MATHEMATICS AND STATISTICS, BASED ON BLENDED LEARNING. BUILDING ON OUR PREVIOUS WORK WHICH SHOWED MARKED DIFFERENCES IN HOW STUDENTS USED WORKED EXAMPLES AS A LEARNING STRATEGY [7, 11], THIS STUDY COMPARES DIFFERENT PROFILES OF LEARNING STRATEGIES WITH LEARNING APPROACHES, LEARNING OUTCOMES, AND LEARNING DISPOSITIONS. ONE OF OUR KEY FINDINGS IS THAT DEEP LEARNERS WERE LESS DEPENDENT ON WORKED EXAMPLES AS A RESOURCE FOR LEARNING, AND THAT STUDENTS WHO ONLY SPORADICALLY USED WORKED EXAMPLES ACHIEVED HIGHER TEST SCORES.
QUALITY ASSURANCE IN ANY ORGANIZATION IS IMPORTANT FOR ENSURING THAT SERVICE USERS ARE SATISFIED WITH THE SERVICE OFFERED. FOR HIGHER EDUCATION INSTITUTES, THE USE OF SERVICE QUALITY MEASURES ALLOWS FOR IDEOLOGICAL GAPS TO BE BOTH IDENTIFIED AND RESOLVED. THE LEARNING ANALYTIC COMMUNITY, HOWEVER, HAS RARELY ADDRESSED THE CONCEPT OF SERVICE QUALITY. A POTENTIAL OUTCOME OF THIS IS THE PROVISION OF A LEARNING ANALYTICS SERVICE THAT ONLY MEETS THE EXPECTATIONS OF CERTAIN STAKEHOLDERS (E.G., MANAGERS), WHILST OVERLOOKING THOSE WHO ARE MOST IMPORTANT (E.G., STUDENTS). IN ORDER TO RESOLVE THIS ISSUE, WE OUTLINE A FRAMEWORK AND OUR CURRENT PROGRESS TOWARDS DEVELOPING A SCALE TO ASSESS STUDENT EXPECTATIONS AND PERCEPTIONS OF LEARNING ANALYTICS AS A SERVICE.
OPEN LEARNING ANALYTICS (OLA) IS AN EMERGING CONCEPT IN THE FIELD OF LEARNING ANALYTICS (LA). IT DEALS WITH LEARNING DATA COLLECTED FROM MULTIPLE ENVIRONMENTS AND CONTEXTS, ANALYZED WITH A WIDE RANGE OF ANALYTICS METHODS TO ADDRESS THE REQUIREMENTS OF DIFFERENT STAKEHOLDERS. DUE TO THIS DIVERSITY IN DIFFERENT DIMENSIONS OF OLA, THE LA DEVELOPERS AND RESEARCHERS FACE NUMEROUS CHALLENGES WHILE DESIGNING SOLUTIONS FOR OLA. THE OPEN LEARNING ANALYTICS PLATFORM (OPENLAP) IS A FRAMEWORK THAT ADDRESSES THESE ISSUES AND LAYS THE FOUNDATION FOR AN ECOSYSTEM OF OLA THAT AIMS AT SUPPORTING LEARNING AND TEACHING IN FRAGMENTED, DIVERSE, AND NETWORKED LEARNING ENVIRONMENTS. IT FOLLOWS A USER-CENTRIC APPROACH TO ENGAGE END USERS IN FLEXIBLE DEFINITION AND DYNAMIC GENERATION OF PERSONALIZED INDICATORS. IN THIS PAPER, WE ADDRESS A SUBSET OF OLA CHALLENGES AND PRESENT THE CONCEPTUAL AND IMPLEMENTATION DETAILS OF THE ANALYTICS FRAMEWORK COMPONENT OF OPENLAP, WHICH FOLLOWS A FLEXIBLE ARCHITECTURE THAT ALLOWS THE EASY INTEGRATION OF NEW ANALYTICS METHODS AND VISUALIZATION TECHNIQUES IN OPENLAP TO SUPPORT END USERS IN DEFINING INDICATORS BASED ON THEIR NEEDS IN ORDER TO EMBED THE RESULTS INTO THEIR PERSONAL LEARNING ENVIRONMENT.
IN THIS PAPER, WE POSE THE QUESTION, CAN THE TRACKING AND ANALYSIS OF THE PHYSICAL MOVEMENTS OF STUDENTS AND TEACHERS WITHIN A PRACTICE-BASED LEARNING (PBL) ENVIRONMENT REVEAL INFORMATION ABOUT THE LEARNING PROCESS THAT IS RELEVANT AND INFORMATIVE TO LEARNING ANALYTICS (LA) IMPLEMENTATIONS? USING THE EXAMPLE OF TRIALS CONDUCTED IN THE DESIGN OF A LA SYSTEM, WE AIM TO SHOW HOW THE ANALYSIS OF PHYSICAL MOVEMENT FROM A MACRO LEVEL CAN HELP TO ENRICH OUR UNDERSTANDING OF WHAT IS HAPPENING IN THE CLASSROOM. THE RESULTS SUGGEST THAT MULTIMODAL LEARNING ANALYTICS (MMLA) COULD BE USED TO GENERATE VALUABLE INFORMATION ABOUT THE HUMAN FACTORS OF THE COLLABORATIVE LEARNING PROCESS AND WE PROPOSE HOW THIS INFORMATION COULD ASSIST IN THE PROVISION OF RELEVANT SUPPORTS FOR SMALL GROUP WORK. MORE RESEARCH IS NEEDED TO CONFIRM THE INITIAL FINDINGS WITH LARGER SAMPLE SIZES AND REFINE THE DATA CAPTURE AND ANALYSIS METHODOLOGY TO ALLOW AUTOMATION.
IN RECENT YEARS, LEARNING ANALYTICS HAS BECOME A HOT TOPIC WITH MANY INSTITUTES DEPLOYING LEARNING MANAGEMENT SYSTEMS AND LEARNING ANALYTICS TOOLS. IN THIS PAPER, WE INTRODUCE LEARNING ANALYTICS PLATFORMS THAT HAVE BEEN ESTABLISHED IN TWO TOP NATIONAL JAPANESE UNIVERSITIES. THESE INITIATIVES ARE PART OF A BROADER RESEARCH PROJECT INTO CREATING WIDE-REACHING LEARNING ANALYTICS FRAMEWORKS. THE AIM OF THE PROJECT IS TO SUPPORT EDUCATION AND LEARNING THROUGH RESEARCH INTO EDUCATIONAL BIG DATA ACCUMULATED ON THESE PLATFORMS. WE ALSO DISCUSS THE FUTURE DIRECTION OF OUR RESEARCH INTO LEARNING ANALYTICS PLATFORMS. THIS INCLUDES INTRODUCING A MODEL IN WHICH LEARNING ANALYTICS TOOLS AND THE RESULTS OF RESEARCH CAN BE SHARED BETWEEN DIFFERENT EDUCATION INSTITUTES.
LEARNING ANALYTICS IS A DYNAMIC INTERDISCIPLINARY FIELD THAT ENCOMPASSES EDUCATIONAL SCIENCES AND STATE-OF-THE-ART TECHNOLOGY, METHODS AND SYSTEMS FROM VARIOUS FIELDS OF COMPUTING SUCH AS DATA SCIENCE, DATA VISUALIZATION, SOFTWARE ENGINEERING, HUMAN-COMPUTER INTERACTION, STATISTICS, ARTIFICIAL INTELLIGENCE, WITH VARIOUS STAKEHOLDERS, E.G. INSTRUCTORS, STUDENTS, DEPARTMENT AND COLLEGE ADMINISTRATORS, PRACTITIONERS, UNIVERSITY TOP MANAGERS, COMPUTER SCIENTISTS, IT EXPERTS, AND SOFTWARE DEVELOPERS. DESPITE SOME CURRENT ACHIEVEMENTS AND INITIAL DEVELOPMENTS IN LEARNING ANALYTICS, WE ARE STILL IN A VERY EARLY STAGE OF DEVELOPMENT OF SOPHISTICATED TECHNOLOGIES AND WELL-THOUGHT PRACTICES, TOOLS AND APPLICATIONS IN THIS FIELD AS WELL AS UNDERSTANDING THE IMPACT OF LEARNING ANALYTICS ON (A) STUDENT LEARNING AND PRIVACY, AND (B) FACULTY INSTRUCTION AND AUTONOMY. THIS PAPER PRESENTS THE UP-TO-DATE RESEARCH FINDINGS AND OUTCOMES OF A MULTI-ASPECT PROJECT ON SMART LEARNING ANALYTICS AT BRADLEY UNIVERSITY (USA). IT DESCRIBES THE OBTAINED RESEARCH OUTCOMES ABOUT STUDENT PERCEPTION AND ATTITUDE TO LEARNING ANALYTICS ON AN ACADEMIC COURSE LEVEL AND CORRESPONDING LEARNING ANALYTICS-BASED PEDAGOGY.
HUMAN-CENTRED SOFTWARE DESIGN GIVES ALL STAKEHOLDERS AN ACTIVE VOICE IN THE DESIGN OF THE SYSTEMS THAT THEY ARE EXPECTED TO USE. HOWEVER, THIS IS NOT YET COMMONPLACE IN LEARNING ANALYTICS (LA). CO-DESIGN TECHNIQUES FROM OTHER DOMAINS THEREFORE HAVE MUCH TO OFFER TO LA, IN PRINCIPLE, BUT THERE ARE FEW DETAILED ACCOUNTS OF EXACTLY HOW SUCH SESSIONS UNFOLD. THIS PAPER PRESENTS THE RATIONALE DRIVING A CARD-BASED CO-DESIGN TOOL SPECIFICALLY TUNED FOR LA, CALLED LA-DECK. IN THE CONTEXT OF A PILOT STUDY WITH STUDENTS, EDUCATORS, LA RESEARCHERS AND DEVELOPERS, WE PROVIDE QUALITATIVE AND QUANTITATIVE ACCOUNTS OF HOW PARTICIPANTS USED THE CARDS. USING THREE DIFFERENT FORMS OF ANALYSIS (TRANSCRIPT-CENTRIC DESIGN VIGNETTES, CARD-GRAPHS AND TIME-ON-TOPIC), WE CHARACTERISE IN WHAT WAYS THE SESSIONS WERE "PARTICIPATORY" IN NATURE, AND ARGUE THAT THE CARDS SUCCEEDED IN PLAYING VERY SIMILAR ROLES TO THOSE DOCUMENTED IN THE LITERATURE ON SUCCESSFUL CARD-BASED DESIGN TOOLS.
IN RECENT YEARS, IT HAS BEEN A GROWING INTEREST IN THE PHENOMENON OF LEARNING ANALYTICS. MOST EFFORTS HAVE BEEN FOCUSED ON LEARNING PLATFORMS, DUE TO THE HUGE AMOUNT OF INFORMATION THEY PROVIDE ABOUT STUDENT ACTIVITY. HOWEVER, AS OTHER AUTHORS ALSO STATE, IT IS IMPORTANT TO POINT OUT THAT MUCH OF THIS ACTIVITY OCCURS BEYOND SUCH PLATFORMS. THIS WORK AIMS AT PRESENTING A SET OF GUIDELINES TO SELECT AND COMBINE SEVERAL INFORMATION SOURCES IN ORDER TO INCREASE THE IMPACT AND BENEFITS OF LEARNING ANALYTICS. THESE GUIDELINES HAVE BEEN APPLIED TO DIFFERENT EXPERIENCES ON TWO COURSES OF THE BACHELOR DEGREE IN INFORMATICS ENGINEERING.
THIS PAPER IS FOCUSING ON LEARNING ANALYTICS AS A MEANS OF DESCRIBING A TOOL BASED ON MARKET SEGMENTATION AND MACHINE LEARNING THAT WILL MAKE CLUSTER COMPARISONS AND FIND CORRELATIONS. ALTHOUGH SEGMENTATION IS A TERM THAT IS WIDELY USED IN PREDICTIVE MARKETING, IT CAN SUCCESSFULLY APPLIED IN EDUCATION AND PROVIDE INSIGHTS IN ORDER TO CREATE AN ENHANCED VIRTUAL LEARNING ENVIRONMENT (VLE). FOR THIS REASON, WE ARE EXAMINING TWO CLUSTERING TECHNIQUES IN ORDER TO SEE IF THEY CAN BE USED TOWARDS THE DEVELOPMENT OF THIS TOOL THAT WILL OPTIMIZE THE VIRTUAL LEARNING ENVIRONMENT. FOR THIS PROJECT WE ARE USING THE OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD). THIS DATA SET CONTAINS MORE THAN 10 MILLION ROWS INTRODUCING IN A WAY BIG DATA IN EDUCATION. THESE DATA INCLUDE THE INTERACTION OF STUDENTS WITH THE VLE IN SEVEN SELECTED COURSES (MODULES). BY PERFORMING CLUSTERING ANALYSIS ON THEM, WE CAN CREATE NEW GROUPS AND CATEGORIES THAT CAN BE SUBSEQUENTLY USED IN ORDER TO COME UP WITH USEFUL SUGGESTIONS BASED ON PERSONALIZED LEARNING TECHNIQUES.
ADVISORS ASSIST STUDENTS IN DEVELOPING SUCCESSFUL COURSE PATHWAYS THROUGH THE CURRICULUM. THE PURPOSE OF THIS PROJECT IS TO AUGMENT ADVISOR INSTITUTIONAL AND TACIT KNOWLEDGE WITH KNOWLEDGE FROM PREDICTIVE ALGORITHMS (I.E., MATRIX FACTORIZATION AND CLASSIFIERS) SPECIFICALLY DEVELOPED TO IDENTIFY RISK. WE USE A PARTICIPATORY ACTION RESEARCH APPROACH THAT DIRECTLY INVOLVES KEY MEMBERS FROM BOTH ADVISING AND RESEARCH COMMUNITIES IN THE ASSESSMENT AND PROVISIONING OF INFORMATION FROM THE PREDICTIVE ANALYTICS. THE KNOWLEDGE GAINED FROM PREDICTIVE ALGORITHMS IS EVALUATED USING A MIXED METHOD APPROACH. WE FIRST COMPARE THE PREDICTIVE EVALUATIONS WITH ADVISORS EVALUATIONS OF STUDENT PERFORMANCE IN COURSES AND ACTUAL OUTCOMES IN THOSE COURSES WE NEXT EXPOSE AND CLASSIFY ADVISOR KNOWLEDGE OF STUDENT RISK AND IDENTIFY WAYS TO ENHANCE THE VALUE OF THE PREDICTION MODEL. THE RESULTS HIGHLIGHT THE CONTRIBUTION THAT THIS COLLABORATIVE APPROACH CAN GIVE TO THE CONSTRUCTIVE INTEGRATION OF LEARNING ANALYTICS IN HIGHER EDUCATION SETTINGS.
HIGHER EDUCATION INSTITUTIONS AND INVOLVED STAKEHOLDERS CAN DERIVE MULTIPLE BENEFITS FROM LEARNING ANALYTICS BY USING DIFFERENT DATA ANALYTICS STRATEGIES TO PRODUCE SUMMATIVE, REAL-TIME, AND PREDICTIVE INSIGHTS AND RECOMMENDATIONS. HOWEVER, ARE INSTITUTIONS AND ACADEMIC AS WELL AS ADMINISTRATIVE STAFF PREPARED FOR LEARNING ANALYTICS? A LEARNING ANALYTICS BENEFITS MATRIX WAS USED FOR THIS STUDY TO INVESTIGATE THE CURRENT CAPABILITIES OF LEARNING ANALYTICS AT HIGHER EDUCATION INSTITUTIONS, EXPLORE THE IMPORTANCE OF DATA SOURCES FOR A VALID LEARNING ANALYTICS FRAMEWORK, AND GAIN AN UNDERSTANDING OF HOW IMPORTANT INSIGHTS FROM LEARNING ANALYTICS ARE PERCEIVED. THE FINDINGS REVEAL THAT THERE IS A LACK OF STAFF AND TECHNOLOGY AVAILABLE FOR LEARNING ANALYTICS PROJECTS. WE CONCLUDE THAT IT WILL BE NECESSARY TO CONDUCT MORE EMPIRICAL RESEARCH ON THE VALIDITY OF LEARNING ANALYTICS FRAMEWORKS AND ON EXPECTED BENEFITS FOR LEARNING AND INSTRUCTION TO CONFIRM THE HIGH HOPES THIS PROMISING EMERGING TECHNOLOGY RAISES.
MORE AND MORE LEARNING IN HIGHER EDUCATION SETTINGS IS BEING FACILITATED THROUGH ONLINE LEARNING ENVIRONMENTS. STUDENTS' ABILITY TO SELF-REGULATE THEIR LEARNING IS CONSIDERED A KEY FACTOR FOR SUCCESS IN HIGHER EDUCATION. LEARNING ANALYTICS OFFER A PROMISING APPROACH TO BETTER SUPPORT AND UNDERSTAND STUDENTS' LEARNING PROCESSES. THE PURPOSE OF THIS STUDY IS TO INVESTIGATE STUDENTS' EXPECTATIONS TOWARDS FEATURES OF LEARNING ANALYTICS SYSTEMS AND THEIR WILLINGNESS TO USE THESE FEATURES FOR LEARNING. A TOTAL OF 20 UNIVERSITY STUDENTS PARTICIPATED IN AN INITIAL QUALITATIVE EXPLORATORY STUDY. THEY WERE INTERVIEWED ABOUT THEIR EXPECTATIONS OF LEARNING ANALYTICS FEATURES. THE FINDINGS OF THE QUALITATIVE STUDY WERE COMPLEMENTED BY A QUANTITATIVE STUDY WITH 216 PARTICIPATING STUDENTS. THE FINDINGS SHOW THAT STUDENTS EXPECT LEARNING ANALYTICS FEATURES TO SUPPORT THEIR PLANNING AND ORGANIZATION OF LEARNING PROCESSES, PROVIDE SELF ASSESSMENTS, DELIVER ADAPTIVE RECOMMENDATIONS, AND PRODUCE PERSONALIZED ANALYSES OF THEIR LEARNING ACTIVITIES. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
BOTH, MOOCS AND LEARNING ANALYTICS, ARE TWO EMERGENT TOPICS IN THE FIELD OF EDUCATIONAL TECHNOLOGY. THIS PAPER SHOWS THE MAIN CONTRIBUTIONS OF THE EMADRID NETWORK IN THESE TWO TOPICS DURING THE LAST YEARS (2014-2016), AS WELL AS THE PLANNED FUTURE WORKS IN THE NETWORK. THE CONTRIBUTIONS IN THE FIELD OF THE MOOCS INCLUDE THE DESIGN AND AUTHORING OF MATERIALS, THE IMPROVEMENT OF THE PEER REVIEW PROCESS OR EXPERIENCES ABOUT TEACHING THESE COURSES AND INSTITUTIONAL ADOPTION. THE CONTRIBUTIONS IN THE FIELD OF LEARNING ANALYTICS INCLUDE THE INFERENCE OF HIGHER LEVEL INFORMATION, THE DEVELOPMENT OF DASHBOARDS, THE EVALUATION OF THE LEARNING PROCESS, OR THE PREDICTION AND CLUSTERING.
THIS POSTER EXPLORES HOW TO DEVELOP A WORKING FRAMEWORK FOR STEM EDUCATION THAT USES BOTH HUMAN ANNOTATED AND MACHINE DATA ACROSS A PURPOSE-BUILT LEARNING ENVIRONMENT. OUR DUAL APPROACH IS TO DEVELOP A ROBUST FRAMEWORK FOR ANALYSIS AND INVESTIGATE HOW TO DESIGN A LEARNING ANALYTICS SYSTEM TO SUPPORT HANDS-ON ENGINEERING DESIGN TASKS. DATA FROM THE FIRST USER TESTS ARE PRESENTED ALONG WITH THE FRAMEWORK FOR DISCUSSION.
EDUCATIONAL DATA CONTAINS VALUABLE INFORMATION THAT CAN BE HARVESTED THROUGH LEARNING ANALYTICS TO PROVIDE NEW INSIGHTS FOR A BETTER EDUCATION SYSTEM. HOWEVER, SHARING OR ANALYSIS OF THIS DATA INTRODUCE PRIVACY RISKS FOR THE DATA SUBJECTS, MOSTLY STUDENTS. EXISTING WORK IN THE LEARNING ANALYTICS LITERATURE IDENTIFIES THE NEED FOR PRIVACY AND POSE INTERESTING RESEARCH DIRECTIONS, BUT FAILS TO APPLY STATE OF THE ART PRIVACY PROTECTION METHODS WITH QUANTIFIABLE AND MATHEMATICALLY RIGOROUS PRIVACY GUARANTEES. THIS WORK AIMS TO EMPLOY AND EVALUATE SUCH METHODS ON LEARNING ANALYTICS BY APPROACHING THE PROBLEM FROM TWO PERSPECTIVES: (1) THE DATA IS ANONYMIZED AND THEN SHARED WITH A LEARNING ANALYTICS EXPERT, AND (2) THE LEARNING ANALYTICS EXPERT IS GIVEN A PRIVACY-PRESERVING INTERFACE THAT GOVERNS HER ACCESS TO THE DATA. WE DEVELOP PROOF-OF-CONCEPT IMPLEMENTATIONS OF PRIVACY PRESERVING LEARNING ANALYTICS TASKS USING BOTH PERSPECTIVES AND RUN THEM ON REAL AND SYNTHETIC DATASETS. WE ALSO PRESENT AN EXPERIMENTAL STUDY ON THE TRADE-OFF BETWEEN INDIVIDUALS' PRIVACY AND THE ACCURACY OF THE LEARNING ANALYTICS TASKS.
IN THIS PAPER WE PROPOSE AN OVERVIEW OF LEARNING ANALYTICS APPROACHES IN THE CONTEXT OF OPEN EDUCATION IN ITS MOST RECENT DEVELOPMENTS RELATED TO MASSIVE OPEN ONLINE COURSES (MOOCS) AND LINKED DATA (LD), BOTH STRICTLY INTERCONNECTED TO THE OPEN EDUCATIONAL RESOURCES (0ERS) CONCEPT. THE MOOCS HAVE DEVELOPED AS A SEAMLESS INTEGRATION OF DIFFERENT SOLUTIONS AND SERVICES WHICH HAVE APPEARED ON THE WEB IN RECENT YEARS TO ENABLE THE OPENING OF COURSES TO HUNDREDS OR THOUSANDS OF PARTICIPANTS ON THE INTERNET. ACTUALLY MOOCS COMBINE, AMONGST OTHERS, THE NETWORKING POTENTIALS OFFERED BY THE SOCIAL WEB (DEVELOPED AROUND THE WEB 2.0 PORTALS), THE EASE OF ACCESS TO OERS, THE INFORMAL LEARNING OPPORTUNITIES WHICH CHARACTERIZE THE LEARNERS' AUTONOMY IN IDENTIFYING THEIR OWN PATTERNS TO KNOWLEDGE, THUS CHANGING THE WAY LEARNING OCCURS. THE HUGE AMOUNT OF DATA PRODUCED DURING A MOOC SUGGESTS THAT LEARNING ANALYTICS TECHNIQUES COULD BE EXTREMELY USEFUL TO SUPPORT THE LEARNING AND TEACHING PROCESSES DURING THE COURSE. THE LINKED DATA CAN BE EXPLOITED IN THE EDUCATIONAL FIELD. IN FACT, LD OFFERS EDUCATIONAL BENEFITS FOR TEACHING AND LEARNING BY PROVIDING INTEGRATION MECHANISMS OF HETEROGENEOUS RESOURCES FROM DIVERSE REPOSITORIES AND SOURCES AND BY SUPPORTING AUTOMATIC SEARCH TECHNIQUES THAT PROVIDE LEARNERS WITH EDUCATIONAL RESOURCES SEMANTICALLY RELATED TO A SPECIFIC KNOWLEDGE FIELD. AS A CONSEQUENCE, THE HUGE AMOUNT OF DATASETS OF LD CAN BE EXPLOITED FOR EDUCATIONAL PURPOSES, THUS BECOMING AN ALTERNATIVE SOURCE OF OPEN EDUCATIONAL RESOURCES. THE RECENT EVOLUTION OF LD AND ITS DATASETS HAS HAD A REMARKABLE INFLUENCE ON LEARNING ANALYTICS AS WELL, SO THAT LEARNING ANALYTICS IS NOT LIMITED TO NUMERIC DATA, BUT IT HAS TO TAKE INTO ACCOUNT DATA AND THEIR SEMANTIC REPRESENTATION. THE AIM OF THIS PAPER IS THEREFORE TO HIGHLIGHT THROUGH THE ANALYSIS OF THE RECENT LITERATURE - HOW LEARNING ANALYTICS CAN ENHANCE LEARNING EXPERIENCES IN OPEN EDUCATION SETTINGS BASED ON MOOCS AND LD.
RECENTLY, LEARNING ANALYTICS (LA) HAS DRAWN THE ATTENTION OF ACADEMICS, RESEARCHERS, AND ADMINISTRATORS. THIS INTEREST IS MOTIVATED BY THE NEED TO BETTER UNDERSTAND TEACHING, LEARNING, INTELLIGENT CONTENT, AND PERSONALIZATION AND ADAPTATION. WHILE STILL IN THE EARLY STAGES OF RESEARCH AND IMPLEMENTATION, SEVERAL ORGANIZATIONS (SOCIETY FOR LEARNING ANALYTICS RESEARCH AND THE INTERNATIONAL EDUCATIONAL DATA MINING SOCIETY) HAVE FORMED TO FOSTER A RESEARCH COMMUNITY AROUND THE ROLE OF DATA ANALYTICS IN EDUCATION. THIS ARTICLE CONSIDERS THE RESEARCH FIELDS THAT HAVE CONTRIBUTED TECHNOLOGIES AND METHODOLOGIES TO THE DEVELOPMENT OF LEARNING ANALYTICS, ANALYTICS MODELS, THE IMPORTANCE OF INCREASING ANALYTICS CAPABILITIES IN ORGANIZATIONS, AND MODELS FOR DEPLOYING ANALYTICS IN EDUCATIONAL SETTINGS. THE CHALLENGES FACING LA AS A FIELD ARE ALSO REVIEWED, PARTICULARLY REGARDING THE NEED TO INCREASE THE SCOPE OF DATA CAPTURE SO THAT THE COMPLEXITY OF THE LEARNING PROCESS CAN BE MORE ACCURATELY REFLECTED IN ANALYSIS. PRIVACY AND DATA OWNERSHIP WILL BECOME INCREASINGLY IMPORTANT FOR ALL PARTICIPANTS IN ANALYTICS PROJECTS. THE CURRENT LEGAL SYSTEM IS IMMATURE IN RELATION TO PRIVACY AND ETHICS CONCERNS IN ANALYTICS. THE ARTICLE CONCLUDES BY ARGUING THAT LA HAS SUFFICIENTLY DEVELOPED, THROUGH CONFERENCES, JOURNALS, SUMMER INSTITUTES, AND RESEARCH LABS, TO BE CONSIDERED AN EMERGING RESEARCH FIELD.
THE PURPOSE OF THIS STUDY WAS TO EXAMINE STUDENT PERCEPTIONS OF PRIVACY PRINCIPLES RELATED TO LEARNING ANALYTICS. PRIVACY ISSUES FOR LEARNING ANALYTICS INCLUDE HOW PERSONAL DATA ARE COLLECTED AND STORED AS WELL AS HOW THEY ARE ANALYZED AND PRESENTED TO DIFFERENT STAKEHOLDERS. A TOTAL OF 330 UNIVERSITY STUDENTS PARTICIPATED IN AN EXPLORATORY STUDY CONFRONTING THEM WITH LEARNING ANALYTICS SYSTEMS AND ASSOCIATED ISSUES OF CONTROL OVER DATA AND SHARING OF INFORMATION. FINDINGS INDICATE THAT STUDENTS EXPECT LEARNING ANALYTICS SYSTEMS TO INCLUDE ELABORATE ADAPTIVE AND PERSONALIZED DASHBOARDS. FURTHER, STUDENTS ARE RATHER CONSERVATIVE IN SHARING DATA FOR LEARNING ANALYTICS SYSTEMS. ON THE BASIS OF THE RELATIONSHIP BETWEEN THE ACCEPTANCE AND USE OF LEARNING ANALYTICS SYSTEMS AND PRIVACY PRINCIPLES, WE CONCLUDE THAT ALL STAKEHOLDERS NEED TO BE EQUALLY INVOLVED WHEN LEARNING ANALYTICS SYSTEMS ARE IMPLEMENTED AT HIGHER EDUCATION INSTITUTIONS. FURTHER EMPIRICAL RESEARCH IS NEEDED TO ELUCIDATE THE CONDITIONS UNDER WHICH STUDENTS ARE WILLING TO SHARE RELEVANT DATA FOR LEARNING ANALYTICS SYSTEMS.
LEARNING ANALYTICS IS A RECENT INNOVATION THAT HOLDS PROMISE FOR IMPROVING RETENTION IN FULLY ONLINE PROGRAMS. HOWEVER, ONLY A FEW CASE STUDIES EXIST TO SHOW MODELS FOR AND OUTCOMES OF THE IMPLEMENTATION OF LEARNING ANALYTICS SYSTEMS. THIS PAPER REPORTS ON A LEARNING ANALYTICS IMPLEMENTATION IN A FULLY ONLINE, MULTIDISCIPLINARY PROGRAM DESIGNED FOR NONTRADITIONAL STUDENTS USING A PILOT PLANNING GROUP WITH STAKEHOLDERS FROM VARIOUS ROLES. THE PROCESSES FOR SELECTING REPORTS, CREATING COMMUNICATION STRUCTURES, AND EVALUATING OUTCOMES ARE OUTLINED. OVERALL, FACULTY AND ADVISORS WERE POSITIVE ABOUT THE PROJECT AND FOUND THE REPORTS TO BE HELPFUL. THE RESULTS SUGGEST THAT THE ACTIONS MOST OFTEN TRIGGERED BY LEARNING ANALYTICS REPORTS WERE EMAILS TO STUDENTS. EVALUATION RESULTS SUGGEST THAT THE IMPLEMENTATION OF THE LEARNING ANALYTICS PROGRAM AND THE INTERVENTIONS ENACTED HAD A POSITIVE IMPACT ON STUDENT SUCCESS, THOUGH WE ACKNOWLEDGE THAT IT IS DIFFICULT TO ISOLATE THE IMPACT OF THE LEARNING ANALYTICS TOOL ITSELF. WE ALSO ADDRESS SEVERAL CHALLENGES THAT CAME ALONG WITH THE IMPLEMENTATION OF LEARNING ANALYTICS INCLUDING UNDERSTANDING THE EFFICACY OF INTERVENTIONS, DATA SECURITY, AND ETHICS.
IN ONLINE LEARNING ENVIRONMENTS, LEARNERS ARE OFTEN REQUIRED TO BE MORE AUTONOMOUS IN THEIR APPROACH TO LEARNING. IN SCALED ONLINE LEARNING ENVIRONMENTS, LIKE MASSIVE OPEN ONLINE COURSES (MOOCS), THERE ARE DIFFERENCES IN THE ABILITY OF LEARNERS TO ACCESS TEACHERS AND PEERS TO GET HELP WITH THEIR STUDY THAN IN MORE TRADITIONAL EDUCATIONAL ENVIRONMENTS. THIS EXPLORATORY STUDY EXAMINES THE HELP-SEEKING BEHAVIOUR OF LEARNERS ACROSS SEVERAL MOOCS WITH DIFFERENT AUDIENCES AND DESIGNS. LEARNING ANALYTICS TECHNIQUES (E.G., DIMENSION REDUCTION WITH T-SNE AND CLUSTERING WITH AFFINITY PROPAGATION) WERE APPLIED TO IDENTIFY CLUSTERS AND DETERMINE PROFILES OF LEARNERS ON THE BASIS OF THEIR HELP-SEEKING BEHAVIOURS. FIVE HELP SEEKING LEARNER PROFILES WERE IDENTIFIED WHICH PROVIDE AN INSIGHT INTO HOW LEARNERS' HELP-SEEKING BEHAVIOUR RELATES TO PERFORMANCE. THE DEVELOPMENT OF A MORE IN-DEPTH UNDERSTANDING OF HOW LEARNERS SEEK HELP IN LARGE ONLINE LEARNING ENVIRONMENTS IS IMPORTANT TO INFORM THE WAY SUPPORT FOR LEARNERS CAN BE INCORPORATED INTO THE DESIGN AND FACILITATION OF ONLINE COURSES DELIVERED AT SCALE.
ALTHOUGH THERE HAVE BEEN NUMEROUS STUDIES COMMITTED TO MOOCS INTEGRATED WITH LEARNING ANALYTICS, FEWER OF THEM HAVE SYSTEMATICALLY REVIEWED THE RELATED LITERATURE. USING CLUSTERING TECHNIQUES, BIBLIOGRAPHIC NETWORK VISUALIZATION, CONTENT ANALYSIS, AND STARLITE METHOD, THIS STUDY SYSTEMATICALLY REVIEWS THE LITERATURE IN TERMS OF LEARNING ANALYTICS MODELS, PLATFORMS OF MOOCS, EFFECT OF LEARNING ANALYTICS, EFFECT OF ENGAGEMENT, SELF-REGULATION, MOTIVATION, AND ONLINE INTERACTIONS, AS WELL AS OTHER INFLUENCING FACTORS OF THE EFFECTIVENESS OF MOOCS INTEGRATED WITH LEARNING ANALYTICS. IT PROVIDES CONSTRUCTIVE SUGGESTIONS FOR DESIGNERS, RESEARCHERS, LEARNERS, AND INSTRUCTORS OF MOOCS SO AS TO LOWER DOWN LEARNER DROPOUT RATES, INCREASE COMPLETION RATES, ENHANCE LEARNER ENGAGEMENT, AND BETTER LEARNING OUTCOMES. FUTURE RESEARCH INTO MOOCS COULD FOCUS ON THE IMPROVEMENTS ON LEARNING ANALYTICS BECAUSE LEARNING ANALYTICS COULD EXERT AN ESSENTIAL INFLUENCE ON THE EFFECTIVENESS OF MOOCS.
LEARNING ANALYTICS FOCUSES ON COLLECTING, CLEANING, PROCESSING, VISUALIZATION AND ANALYZING TEACHING AND LEARNING RELATED DATA OR METRICS FROM A VARIETY OF ACADEMIC SOURCES. OUR VISION FOR ENGINEERING OF SMART LEARNING ANALYTICS - THE NEXT GENERATION OF SYSTEMS AND TOOLS FOR LEARNING ANALYTICS - IS BASED ON THE CONCEPT THAT THIS TECHNOLOGY SHOULD STRONGLY SUPPORT "SMARTNESS" LEVELS OF SMART ACADEMIC INSTITUTIONS SUCH AS ADAPTIVITY, SENSING, INFERRING, ANTICIPATION, SELF-LEARNING, AND SELF-ORGANIZATION. THIS PAPER PRESENTS THE UP-TO-DATE FINDINGS AND OUTCOMES OF RESEARCH, DESIGN AND DEVELOPMENT PROJECT AT THE INTERLABS RESEARCH INSTITUTE AT BRADLEY UNIVERSITY (PEORIA, IL, U.S.A.) THAT IS FOCUSED ON CONCEPTUAL MODELING OF SMART LEARNING ANALYTICS SYSTEMS, INCLUDING IDENTIFICATION OF GOALS, OBJECTIVES, FEATURES AND FUNCTIONS, MAIN COMPONENTS, INPUTS AND OUTPUTS, HIERARCHICAL AND SMARTNESS LEVELS, MATHEMATICAL METHODS AND ALGORITHMS FOR THOSE SYSTEMS. AGILE SOFTWARE ENGINEERING APPROACH HAS BEEN USED FOR A DEVELOPMENT OF A SERIES OF SOFTWARE PROTOTYPES TO VERIFY THE DESIGN AND DEVELOPMENT PROCESS AND VALIDATE THE OBTAINED OUTCOMES FOR SMART LEARNING ANALYTICS SYSTEMS.
DESPITE THE RECOGNITION OF THE NEED TO INCLUDE PRACTITIONERS IN THE DESIGN OF LEARNING ANALYTICS (LA), ESPECIALLY TEACHER INPUT TENDS TO COME LATER IN THE DESIGN PROCESS RATHER THAN IN THE DEFINITION OF THE INITIAL DESIGN AGENDA. THIS PAPER PRESENTS A CASE STUDY OF A DESIGN PROJECT TASKED WITH DEVELOPING LA TOOLS FOR A READING GAME FOR PRIMARY SCHOOL CHILDREN. TAKING A CO-DESIGN APPROACH, WE USE THE INSPIRATION CARDS WORKSHOP TO PROMOTE MEANINGFUL TEACHER INVOLVEMENT EVEN FOR PARTICIPANTS WITH LOW BACKGROUND IN DATA LITERACY OR EXPERIENCE IN USING LEARNING ANALYTICS. WE DISCUSS OPPORTUNITIES AND LIMITATIONS OF USING THE INSPIRATION CARDS WORKSHOPS METHODOLOGY, AND PARTICULARLY INSPIRATION CARDS AS A DESIGN TOOL, TO INFORM FUTURE LA DESIGN EFFORTS.
THE DISCOURSE AND PRACTICE OF DATA GOVERNANCE IN LEARNING ANALYTICS, AND THE ETHICS WHICH INFORM IT, ARE CONFUSED AND CONTRADICTORY. THIS CAN BE ELUCIDATED BY CONSIDERING THE COMING TOGETHER OF TWO CONTRASTING RESEARCH TRADITIONS: ACADEMIC RESEARCH AND OPERATIONS RESEARCH.
STUDENT ENGAGEMENT WITHIN THE DEVELOPMENT OF LEARNING ANALYTICS SERVICES IN HIGHER EDUCATION IS AN IMPORTANT CHALLENGE TO ADDRESS. DESPITE CALLS FOR GREATER INCLUSION OF STAKEHOLDERS, THERE STILL REMAINS ONLY A SMALL NUMBER OF INVESTIGATIONS INTO STUDENTS' BELIEFS AND EXPECTATIONS TOWARDS LEARNING ANALYTICS SERVICES. THEREFORE, THIS PAPER PRESENTS A DESCRIPTIVE INSTRUMENT TO MEASURE STUDENT EXPECTATIONS (IDEAL AND PREDICTED) OF LEARNING ANALYTICS SERVICES. THE SCALES USED IN THE INSTRUMENT ARE GROUNDED IN A THEORETICAL FRAMEWORK OF EXPECTATIONS, SPECIFICALLY IDEAL AND PREDICTED EXPECTATIONS. ITEMS WERE THEN GENERATED ON THE BASIS OF FOUR IDENTIFIED THEMES (ETHICAL AND PRIVACY EXPECTATIONS, AGENCY EXPECTATIONS, INTERVENTION EXPECTATIONS, AND MEANINGFULNESS EXPECTATIONS), WHICH EMERGED AFTER A REVIEW OF THE LEARNING ANALYTICS LITERATURE. THE RESULTS OF AN EXPLORATORY FACTOR ANALYSIS AND THE RESULTS FROM BOTH AN EXPLORATORY STRUCTURAL EQUATION MODEL AND CONFIRMATORY FACTOR ANALYSIS SUPPORTED A TWO-FACTOR STRUCTURE BEST ACCOUNTED FOR THE DATA PERTAINING TO IDEAL AND PREDICTED EXPECTATIONS. FACTOR ONE REFERS TO ETHICAL AND PRIVACY EXPECTATIONS, WHILST FACTOR TWO COVERS SERVICE FEATURE EXPECTATIONS. THE 12-ITEM STUDENT EXPECTATIONS OF LEARNING ANALYTICS QUESTIONNAIRE (SELAQ) PROVIDES RESEARCHERS AND PRACTITIONERS WITH A MEANS OF MEASURING OF STUDENTS' EXPECTATIONS OF LEARNING ANALYTICS SERVICES.
THIS PAPER TREATS ACTIVITIES OF A RESEARCH PROJECT ABOUT IOT (INTERNET OF THINGS) SECURITY EDUCATION. IN THIS PROJECT, THE AUTHORS PLAN TO PROVIDE SPOC (SMALL PRIVATE ONLINE COURSE), MOOC (MASSIVE OPEN ONLINE COURSE) AND EDUCATION GAMES (SERIOUS GAMES) ABOUT IOT SECURITY. ONE OF THE KEY CHALLENGES IS THE DEVELOPMENT OF EDUCATIONAL MATERIALS RELATED TO IOT SECURITY. AS THE FIRST ACTIVITY OF THE PROJECT, THE AUTHORS ARE BUILDING A COMPREHENSIVE DATABASE REGARDING VARIOUS KINDS OF IOT THREATS BASED ON LINKED DATA, AND ALSO DEVELOPING AN AUTHORING TOOL THAT ENABLES PROFESSORS/TEACHERS TO EASILY MAKE EDUCATIONAL MATERIALS ABOUT IOT SECURITY USING THE DATABASE. AS ITS SECOND ACTIVITY, THE AUTHORS ARE DEVELOPING A WEB-BASED MATERIAL VIEWER SUPPORTING LEARNING ANALYTICS. FOR LEARNING ANALYTICS, THE WEB-BASED MATERIAL VIEWER SHOULD HAVE THE FUNCTIONALITY TO COLLECT LEARNERS' ACTIVITY DATA AND TO STORE THEM IN A DATABASE. THE AUTHORS HAVE ALREADY PROPOSED TWO VISUALIZATION TOOLS FOR LEARNING ANALYTICS FOR SUCH DATA. SO, IN THIS PAPER, THE AUTHORS PROPOSE A LEARNING ANALYTICS FRAMEWORK FOR IOT SECURITY EDUCATION AS THE INTEGRATION OF THE AUTHORING TOOL BASED ON LINKED DATA, THE WEB-BASED MATERIAL VIEWER SUPPORTING LEARNING ANALYTICS AND THE VISUALIZATION TOOLS FOR LEARNING ANALYTICS.
LEARNING ANALYTICS, AS A RAPIDLY EVOLVING FIELD, OFFERS AN ENCOURAGING APPROACH WITH THE AIM OF UNDERSTANDING, OPTIMIZING AND ENHANCING LEARNING PROCESS. LEARNERS HAVE THE CAPABILITIES TO INTERACT WITH THE LEARNING ANALYTICS SYSTEM THROUGH ADEQUATE USER INTERFACE. SUCH SYSTEMS ENABLES VARIOUS FEATURES SUCH AS LEARNING RECOMMENDATIONS, VISUALIZATIONS, REMINDERS, RATING AND SELF-ASSESSMENTS POSSIBILITIES. THIS PAPER PROPOSES A FRAMEWORK FOR LEARNING ANALYTICS AIMED TO IMPROVE PERSONALIZED LEARNING ENVIRONMENTS, ENCOURAGING THE LEARNER'S SKILLS TO MONITOR, ADAPT, AND IMPROVE THEIR OWN LEARNING. IT IS AN ATTEMPT TO ARTICULATE THE CHARACTERIZING PROPERTIES THAT REVEALS THE ASSOCIATION BETWEEN LEARNING ANALYTICS AND PERSONALIZED LEARNING ENVIRONMENT. IN ORDER TO VERIFY DATA ANALYSIS APPROACHES AND TO DETERMINE THE VALIDITY AND ACCURACY OF A LEARNING ANALYTICS, AND ITS CORRESPONDING TO LEARNING PROFILES, A CASE STUDY WAS PERFORMED. THE FINDINGS INDICATE THAT EDUCATIONAL DATA FOR LEARNING ANALYTICS ARE CONTEXT SPECIFIC AND VARIABLES CARRY DIFFERENT MEANINGS AND CAN HAVE DIFFERENT IMPLICATIONS ON LEARNING SUCCESS PREDICTION.
AN ONGOING CHALLENGE FOR LEARNING ANALYTICS RESEARCH HAS BEEN THE SCALABLE DERIVATION OF USER INTERACTION DATA FROM MULTIPLE TECHNOLOGIES. THE COMPLEXITIES ASSOCIATED WITH THIS CHALLENGE ARE INCREASING AS EDUCATORS EMBRACE AN EVER GROWING NUMBER OF SOCIAL AND CONTENT-RELATED TECHNOLOGIES. THE EXPERIENCE API (XAPI) ALONGSIDE THE DEVELOPMENT OF USER SPECIFIC RECORD STORES HAS BEEN TOUTED AS A MEANS TO ADDRESS THIS CHALLENGE, BUT A NUMBER OF SUBTLE CONSIDERATIONS MUST BE MADE WHEN USING XAPI IN LEARNING ANALYTICS. THIS PAPER PROVIDES A GENERAL OVERVIEW TO THE COMPLEXITIES AND CHALLENGES OF USING XAPI IN A GENERAL SYSTEMIC ANALYTICS SOLUTION - CALLED THE CONNECTED LEARNING ANALYTICS (CLA) TOOLKIT. THE IMPORTANCE OF DESIGN IS EMPHASISED, AS IS THE NOTION OF COMMON VOCABULARIES AND XAPI RECIPES. EARLY DECISIONS ABOUT VOCABULARIES AND STRUCTURAL RELATIONSHIPS BETWEEN STATEMENTS CAN SERVE TO EITHER FACILITATE OR HANDICAP LATER ANALYTICS SOLUTIONS. THE CLA TOOLKIT CASE STUDY PROVIDES US WITH A WAY OF EXAMINING BOTH THE STRENGTHS AND THE WEAKNESSES OF THE CURRENT XAPI SPECIFICATION, AND WE CONCLUDE WITH A PROPOSAL FOR HOW XAPI MIGHT BE IMPROVED BY USING JSON-LD TO FORMALISE RECIPES IN A MACHINE READABLE FORM.
THIS STUDY AIMS TO IDENTIFY THE CONCEPTUAL STRUCTURE AND THE THEMATIC PROGRESS IN LEARNING ANALYTICS (EVOLUTION) AND TO ELABORATE ON BACKBONE/EMERGING TOPICS IN THE FIELD (MATURITY) FROM 2011 TO SEPTEMBER 2019. TO ADDRESS THIS OBJECTIVE, THIS PAPER EMPLOYS HIERARCHICAL CLUSTERING, STRATEGIC DIAGRAMS AND NETWORK ANALYSIS TO CONSTRUCT THE INTELLECTUAL MAP OF THE LEARNING ANALYTICS COMMUNITY AND TO VISUALIZE THE THEMATIC LANDSCAPE IN THIS FIELD, USING CO-WORD ANALYSIS. OVERALL, A TOTAL OF 459 PAPERS FROM THE PROCEEDINGS OF THE LEARNING ANALYTICS AND KNOWLEDGE (LAK) CONFERENCE AND 168 ARTICLES PUBLISHED IN THE JOURNAL OF LEARNING ANALYTICS (JLA), AND THE RESPECTIVE 3092 AUTHOR-ASSIGNED KEYWORDS AND 4051 MACHINE-EXTRACTED KEY-PHRASES, WERE INCLUDED IN THE ANALYSES. THE RESULTS INDICATE THAT THE COMMUNITY HAS SIGNIFICANTLY FOCUSED IN AREAS LIKE MASSIVE OPEN ONLINE COURSES AND VISUALIZATIONS; LEARNING MANAGEMENT SYSTEMS, ASSESSMENT AND SELF-REGULATED LEARNING ARE ALSO BASIC TOPICS, YET TOPICS LIKE NATURAL LANGUAGE PROCESSING AND ORCHESTRATION ARE EMERGING. THE ANALYSIS HIGHLIGHTS THE SHIFT OF THE RESEARCH INTEREST THROUGHOUT THE PAST DECADE, AND THE RISE OF NEW TOPICS, COMPRISING EVIDENCE THAT THE FIELD IS EXPANDING. LIMITATIONS OF THE APPROACH AND FUTURE WORK PLANS CONCLUDE THE PAPER.
THIS PAPER ARGUES BASED ON EVIDENCE FROM THE LITERATURE THAT LEARNING ANALYTICS, WHEN UNDERTAKEN BY HIGHER EDUCATION INSTITUTIONS, IS NOT CONSIDERED WITHIN A HOLISTIC KNOWLEDGE MANAGEMENT STRATEGY, WHICH COULD PROVIDE SIGNIFICANT IMPROVEMENT TO THE OUTCOMES OF LEARNING ANALYTICS. PARTICULARLY, A SYNTHESIS OF KNOWLEDGE EXTRACTION VIA LEARNING ANALYTICS AND APPROPRIATE HANDLING OF SUCH KNOWLEDGE VIA KNOWLEDGE MANAGEMENT IS NOT TYPICALLY IMPLEMENTED IN HIGHER EDUCATION PRACTICES, BUT IT CONSTITUTES A PROMISING PATH TO IMPROVING IT, AND EVENTUALLY CONTRIBUTES TO IMPROVING LEARNING SERVICES. ESSENTIALLY, KNOWLEDGE MANAGEMENT CAN SUPPORT IMPROVEMENTS AND INNOVATION IN ANALYTICS TOOLS, TRANSLATE AN ORGANISATION'S STRATEGIC VISION INTO ACTION, AND ENABLE SHARING OF INFORMATION AMONG DIFFERENT ACTORS. THESE ARE ALL NECESSARY REQUIREMENTS FOR EFFECTIVE LEARNING ANALYTICS.
EFFORTS TO REALIZE THE POTENTIAL OF LEARNING ANALYTICS (LA) TO CONTRIBUTE TO IMPROVING STUDENT LEARNING AND LEARNING DESIGN HAVE BROUGHT IMPORTANT ADVANCES. A REVIEW OF SUCCESSFUL CASES OF LEARNING ANALYTICS APPLICATIONS REVEALS THAT 1) THERE IS A TIGHT COUPLING BETWEEN THE LEARNING OUTCOME (LO) GOALS, TASK SEQUENCE DESIGN, AND THE LEARNING ANALYTICS AND FEEDBACK IN EACH CASE, AND 2) THE LEARNING ANALYTICS TO BE DEPLOYED AND THE FEEDBACK TO BE PROVIDED TO LEARNERS AND/OR TEACHERS ARE INTEGRAL TO THE LEARNING DESIGN (LD) RATHER THAN CONSTRUCTED AFTER THE LD IS COMPLETED. LEARNING DESIGN FRAMEWORKS IN THE LITERATURE HAVE FOCUSED ON GENERIC LEARNING TASK TAXONOMIES AND ARE UNABLE TO SCAFFOLD LA-INTEGRATED LD PRACTICE. THIS PAPER PROPOSES A MULTILEVEL FRAMEWORK FOR LA-INTEGRATED LD, WHICH PROVIDES A HIERARCHICALLY NESTED MULTILEVEL STRUCTURE FOR THE DESIGN OF LD AND LA ELEMENTS BASED ON 60 STEM CURRICULUM UNITS COLLECTED FROM AUTHENTIC CLASSROOMS. THE FRAMEWORK INCLUDES A DESIGN PROCESS MODEL IN THE FORM OF A LEARNING DESIGN TRIANGLE AND THE CONCEPT OF LEARNING ANALYTICS INTEGRATED CURRICULUM COMPONENT DESIGN PATTERNS (LA-CCDP). OPERATIONALIZATION OF THE FRAMEWORK IS ILLUSTRATED USING ONE STEM CURRICULUM UNIT. THIS FRAMEWORK CAN BE ADOPTED FOR PROFESSIONAL LEARNING AND TECHNOLOGY DEVELOPMENT TO SUPPORT LA-INTEGRATED LD PRACTICES.
MANY UNIVERSITIES CHOOSE THE C PROGRAMMING LANGUAGE (C) AS THE FIRST ONE THEY TEACH THEIR STUDENTS, EARLY ON IN THEIR PROGRAM. HOWEVER, STUDENTS OFTEN CONSIDER PROGRAMMING COURSES DIFFICULT, AND THESE COURSES OFTEN HAVE AMONG THE HIGHEST DROPOUT RATES OF COMPUTER SCIENCE COURSES OFFERED. IT IS THEREFORE CRITICAL TO PROVIDE MORE EFFECTIVE INSTRUCTION TO HELP STUDENTS UNDERSTAND THE SYNTAX OF C AND PREVENT THEM LOSING INTEREST IN PROGRAMMING. IN ADDITION, HOMEWORK AND PAPER-BASED EXAMS ARE STILL THE MAIN ASSESSMENT METHODS IN THE MAJORITY OF CLASSROOMS. IT IS DIFFICULT FOR TEACHERS TO GRASP STUDENTS' LEARNING SITUATION DUE TO THE LARGE AMOUNT OF EVALUATION WORK. TO FACILITATE TEACHING AND LEARNING OF C, IN THIS ARTICLE WE PROPOSE A SYSTEM-LAPLE (LEARNING ANALYTICS IN PROGRAMMING LANGUAGE EDUCATION)-THAT PROVIDES A LEARNING DASHBOARD TO CAPTURE THE BEHAVIOR OF STUDENTS IN THE CLASSROOM AND IDENTIFY THE DIFFERENT DIFFICULTIES FACED BY DIFFERENT STUDENTS LOOKING AT DIFFERENT KNOWLEDGE. WITH LAPLE, TEACHERS MAY BETTER GRASP STUDENTS' LEARNING SITUATION IN REAL TIME AND BETTER IMPROVE EDUCATIONAL MATERIALS USING ANALYSIS RESULTS. FOR THEIR PART, NOVICE UNDERGRADUATE PROGRAMMERS MAY USE LAPLE TO LOCATE SYNTAX ERRORS IN C AND GET RECOMMENDATIONS FROM EDUCATIONAL MATERIALS ON HOW TO FIX THEM.
WITH THE COMING OF THE ERA OF BIG DATA, IT IS THE INEVITABLE TREND OF THE DEVELOPMENT OF MODERN EDUCATION TO PROVIDE SERVICE FOR EDUCATORS BY THE ANALYSIS OF BIG DATA. LEARNING ANALYTICS AS THIS EMERGING TECHNOLOGY, WILL BE APPLIED TO EDUCATION TEACHING AND CHANGE THE TRADITIONAL TEACHING PATTERN, IT CAN EFFECTIVELY IMPROVE THE EFFICIENCY OF EDUCATION TEACHING. THROUGH MINING VALUABLE DATA OF LEARNERS AND EFFECTIVELY UTILIZING THESE DATA IN THE LEARNERS' LEARNING PROCESS, THE LEARNING INTEREST AND EFFICIENCY OF LEARNER CAN BE IMPROVED AS WELL AS THE TEACHING METHODS OF EDUCATORS CAN BE DEVELOPED. THIS ARTICLE WILL INTRODUCE LEARNING ANALYTICS AND CONSTRUCT THE CORRESPONDING MODEL. THROUGH THE APPLICATIONS OF LEARNING ANALYTICS IN PRACTICAL TEACHING, THE RESULTS SHOW THAT THE NEW TEACHING METHOD HAS BETTER EFFECT IN THE PRACTICAL TEACHING.
THIS PAPER PRESENTS A RESEARCH ON THE EFFECTIVENESS OF DIGITAL LEARNING IN ORGANISATIONS AND HOW IT CAN BE MEASURED. THIS RESEARCH AIMS TO BRIDGE THE GAP BETWEEN SCIENTIFIC AND PRACTICE-ORIENTED RESEARCH ABOUT DIGITAL LEARNING. THE FOCUS WILL BE ON LEARNING ANALYTICS AND MEASURING ITS IMPACTS ON ORGANISATIONS WITH THE GOAL TO IDENTIFY THE POTENTIAL OF DIGITAL LEARNING FOR ORGANISATIONS. THIS RESEARCH IS BASED ON A SYSTEMATIC LITERATURE REVIEW AND A SURVEY ANALYSIS TO ANSWER THE FOLLOWING RESEARCH QUESTIONS: WHAT ARE THE CONTEXTS IN WHICH DIGITAL LEARNING CAN TAKE PLACE? WHAT ARE THE MAIN METRICS WHICH ALLOWS MEASURING THE EFFICIENCY OF DIGITAL LEARNING IN ORGANISATIONAL CONTEXTS? IS THERE A POSITIVE INFLUENCE OF THE DIGITAL LEARNING CONTEXTS SUPPORTED BY ORGANISATIONS, ON SKILLS DEVELOPMENT AND IMPACTS ON PERFORMANCE ANALYTICS THAT MEASURE THE IMPACTS IN ORGANISATIONS? AND WHAT IS THE DIGITAL LEARNING ANALYTICS THAT MEASURES THE IMPACTS ON ORGANISATIONS? WHAT IS THE DIGITAL LEARNING ANALYTICS THAT MEASURES THE IMPACTS ON ORGANISATIONS? THE RESULTS SHOWED THAT DIGITAL LEARNING ANALYTICS CAN ENHANCE THE EFFICACY OF THE LEARNING PROCESS IMPACT IN ORGANISATIONS AND THAT CONTEXTS FOR LEARNING SUPPORTED ON MOBILE TECHNOLOGIES, TABLET AND SMARTPHONE APPLICATIONS BECOME MORE AND MORE POPULAR AMONG THE EMPLOYEES. ADDITIONALLY, THE RESULTS HELPED TO CREATE A MODEL OF LEARNING ANALYTICS AND IDENTIFY IMPACTS OF THE LEARNING PROCESS.
THIS PAPER PRESENTS A RESEARCH ON THE EFFECTIVENESS OF DIGITAL LEARNING IN HIGHER EDUCATION AND HOW THIS CAN BE MEASURED. THIS RESEARCH AIMS TO BRIDGE THE GAP BETWEEN SCIENTIFIC AND PRACTICE-ORIENTED RESEARCH ABOUT DIGITAL LEARNING. THE FOCUS WILL BE ON LEARNING ANALYTICS WITH THE GOAL TO IDENTIFY THE POTENTIAL OF DIGITAL EDUCATION FOR UNIVERSITIES. TO ACHIEVE THIS GOAL, WE CONDUCTED A LITERATURE REVIEW OF WHICH HAVE RESULTED IN THE FOLLOWING RESEARCH QUESTIONS: WHAT ARE THE HIGHER EDUCATION CONTEXTS IN WHICH DIGITAL LEARNING CAN TAKE PLACE? WHAT ARE THE PRIMARY METRICS WHICH ALLOWS MEASURING THE EFFICIENCY OF DIGITAL LEARNING IN HIGHER EDUCATION CONTEXTS? THE METHODOLOGICAL APPROACH OF THE RESEARCH WAS QUALITATIVE, AND THE MAIN TECHNIQUE TO COLLECT AND ANALYZE DATA WAS CONTENT ANALYSIS FROM THE LITERATURE REVIEW OF PAPERS ON DIGITAL LEARNING CONTEXTS AND DIGITAL LEARNING ANALYTICS. THE RESULTS OF THE PRESENT STUDY SHOW THAT DIGITAL LEARNING ANALYTICS CAN ENHANCE THE EFFICACY OF THE IMPACT OF THE LEARNING PROCESS IN ORGANIZATIONS AND THAT CONTEXTS FOR LEARNING SUPPORTED ON MOBILE TECHNOLOGIES, TABLET AND SMARTPHONE APPLICATIONS BECOME MORE AND MORE POPULAR AMONG THE STUDENTS.
THE CURRENT PAPER DEBATES UPON THE IMPORTANCE OF LEARNING ANALYTICS, BY UNDERLINING ITS PLACE IN CURRENT RESEARCH DIRECTIONS AND ITS INFLUENCE IN RESHAPING EDUCATION WITH THE AID OF CUTTING-EDGE TECHNOLOGIES. DEFINITIONS, SUCCESSFUL EXAMPLES OF LEARNING ANALYTICS TOOLS, AS WELL AS THE CHALLENGES OF IMPLEMENTING THEM ARE REVISED. A SPECIAL ATTENTION IS AWARDED TO LEARNING ANALYTICS PERFORMED IN SOCIAL LEARNING ENVIRONMENTS, DUE TO THEIR POPULARITY AND WELL-ESTABLISHED VALUE. IN ORDER TO INVESTIGATE THE IMPORTANCE OF LEARNING ANALYTICS TO END-USERS (STUDENTS AND TEACHERS), A SURVEY BASED ON TWO QUESTIONNAIRES WAS PERFORMED AND REVEALED THE FACT THAT ADVANCED LEARNING ANALYTICS FEATURES ARE HIGHLY REQUESTED IN ONLINE ENVIRONMENTS. THE QUESTIONNAIRES WERE FILLED BY 59 IT TEACHERS AND MORE THAN 390 IT STUDENTS, FROM VARIOUS COUNTRIES, FACT THAT DEMONSTRATES THE GLOBAL SIGNIFICANCE OF THE STUDY. ALSO, A MODEL OF DEVELOPING A FEASIBLE SOCIAL LEARNING SOLUTION WITH LEARNING ANALYTICS FEATURES IS PROPOSED, SHAPING THUS THE FEATURE RESEARCH DIRECTIONS.
LEARNING ANALYTICS IS A RAPIDLY GROWING AREA OF INTEREST IN THE HIGHER EDUCATIONAL INSTITUTIONS WORLDWIDE. ITS EFFECTIVE UTILIZATION DEPENDS ON THE PROPER SELECTION OF STRATEGY OF THE LEARNING ANALYTICS ARCHITECTURE IMPLEMENTATION. THE PAPER COMPARES SEVERAL LEARNING ANALYTICS ARCHITECTURES AND DESCRIBES THEIR MAIN BUILDING COMPONENTS. CONSEQUENTLY, THE PROPOSAL AND CURRENT STATE OF THE LEARNING ANALYTICS ARCHITECTURE IMPLEMENTATION AT THE UNIVERSITY ENVIRONMENT IS DISCUSSED. THE CONCLUSION PROVIDES SEVERAL RECOMMENDATIONS, OPEN QUESTIONS AND TASKS, WHICH ROSE DURING THE IMPLEMENTATION PROCESS AND REQUIRE THE FUTURE RESEARCH.
THE BIG DATA REVOLUTION IS AN EXCITING OPPORTUNITY FOR UNIVERSITIES, WHICH TYPICALLY HAVE RICH AND COMPLEX DIGITAL DATA ON THEIR LEARNERS. IT HAS MOTIVATED MANY UNIVERSITIES AROUND THE WORLD TO INVEST IN THE DEVELOPMENT AND IMPLEMENTATION OF LEARNING ANALYTICS DASHBOARDS (LADS). THESE DASHBOARDS COMMONLY MAKE USE OF INTERACTIVE VISUALISATION WIDGETS TO ASSIST EDUCATORS IN UNDERSTANDING AND MAKING INFORMED DECISIONS ABOUT THE LEARNING PROCESS. A COMMON OPERATION IN ANALYTICAL DASHBOARDS IS A 'DRILL-DOWN', WHICH IN AN EDUCATIONAL SETTING ALLOWS USERS TO EXPLORE THE BEHAVIOUR OF SUB-POPULATIONS OF LEARNERS BY PROGRESSIVELY ADDING FILTERS. NEVERTHELESS, DRILL-DOWN CHALLENGES EXIST, WHICH HAMPER THE MOST EFFECTIVE USE OF THE DATA, ESPECIALLY BY USERS WITHOUT A FORMAL BACKGROUND IN DATA ANALYSIS. ACCORDINGLY, IN THIS PAPER, WE ADDRESS THIS PROBLEM BY PROPOSING AN APPROACH THAT RECOMMENDS INSIGHTFUL DRILL-DOWNS TO LAD USERS. WE PRESENT RESULTS FROM AN APPLICATION OF OUR PROPOSED APPROACH USING AN EXISTING LAD. A SET OF INSIGHTFUL DRILL-DOWN CRITERIA FROM A COURSE WITH 875 STUDENTS ARE EXPLORED AND DISCUSSED.
THIS DOCUMENT PRESENTS THE MAIN TOPICS AND OBJECTIVES OF THE SEVENTH EDITION OF THE TRACK ON LEARNING ANALYTICS WITHIN THE 2019 INTERNATIONAL CONFERENCE TECHNOLOGICAL ECOSYSTEMS FOR ENHANCING MULTICULTURALITY (TEEM'19). THE INTRODUCTORY ARTICLE ALSO DETAILS THE REVIEW PROCESS FOLLOWED FOR SUBMISSION OF CONTRIBUTIONS AND GIVES AN OVERVIEW OF THE ACCEPTED SUBMISSIONS. THE DOCUMENT REFLECTS ABOUT CURRENT ISSUES IN RESEARCH ON LEARNING ANALYTICS, FROM THE AUTHORS' VIEWS AND EXPERIENCE AS TRACK CHAIRS IN PREVIOUS EDITIONS.
MASSIVE OPEN ONLINE COURSE (MOOC) IS A POPULAR WAY FOR OFFERING ONLINE COURSES, GLOBALLY, BY THE EDUCATION PROVIDERS. WITH LARGE NUMBER OF LEARNERS ENROLLED IN THE COURSES, IT BECOMES DIFFICULT TO HELP THE LEARNERS, INDIVIDUALLY, IN ACHIEVING LEARNING OBJECTIVES. BUT, LARGE AMOUNT OF DATA GETS GENERATED FROM ACTIVITIES PERFORMED BY THE LEARNER. LEARNING ANALYTICS USES THE LEARNER-GENERATED EDUCATIONAL DATA TO IMPROVE THE LEARNING PROCESS. CURRENTLY, MOOCS PROVIDE LIMITED SUPPORT TO THE LEARNERS FOR LEARNING ANALYTICS. IN THIS PAPER, AN ANALYSIS OF FEATURES OFFERED TO THE LEARNERS BY DIFFERENT MOOC PLATFORMS FOR LEARNING ANALYTICS, IS PRESENTED. FOR THIS, FEATURES OF SEVERAL KEY PLATFORMS ARE IDENTIFIED AND FURTHER CATEGORIZED FOR ANALYSIS PURPOSES. THE COMPARATIVE ANALYSIS HELPS DURING SELECTION OF A PLATFORM ON BASIS OF THE ANALYTICAL FEATURES SUPPORTED BY THEM. ALSO, A RECOMMENDATION IS PROVIDED FOR ENHANCEMENT OF LEARNING ANALYTICS CAPABILITIES OF MOOC PLATFORMS.
THIS IS A PROPOSAL FOR ORGANIZING A MULTIMODAL LEARNING ANALYTICS (MLA) DATA CHALLENGE AS PART OF THE WORKSHOP OFFERING OF THE LEARNING ANALYTICS AND KNOWLEDGE (LAK) CONFERENCE. IT EXPLAINS THE MOTIVATION OF THE EVENT, ITS OBJECTIVES, TARGET GROUPS, EXPECTED FORMAT, ORGANIZATION, DISSEMINATION STRATEGY AND SCHEDULE.
THE WIDESPREAD ADOPTION OF LEARNING ANALYTICS (LA) AND EDUCATIONAL DATA MINING (EDM) HAS SOMEWHAT STAGNATED RECENTLY, AND IN SOME PROMINENT CASES EVEN BEEN REVERSED FOLLOWING CONCERNS BY GOVERNMENTS, STAKEHOLDERS AND CIVIL RIGHTS GROUPS ABOUT PRIVACY AND ETHICS APPLIED TO THE HANDLING OF PERSONAL DATA. IN THIS ONGOING DISCUSSION, FEARS AND REALITIES ARE OFTEN INDISTINGUISHABLY MIXED UP, LEADING TO AN ATMOSPHERE OF UNCERTAINTY AMONG POTENTIAL BENEFICIARIES OF LEARNING ANALYTICS, AS WELL AS HESITATIONS AMONG INSTITUTIONAL MANAGERS WHO AIM TO INNOVATE THEIR INSTITUTION'S LEARNING SUPPORT BY IMPLEMENTING DATA AND ANALYTICS WITH A VIEW ON IMPROVING STUDENT SUCCESS. IN THIS PAPER, WE TRY TO GET TO THE HEART OF THE MATTER, BY ANALYSING THE MOST COMMON VIEWS AND THE PROPOSITIONS MADE BY THE LA COMMUNITY TO SOLVE THEM. WE CONCLUDE THE PAPER WITH AN EIGHT-POINT CHECKLIST NAMED DELICATE THAT CAN BE APPLIED BY RESEARCHERS, POLICY MAKERS AND INSTITUTIONAL MANAGERS TO FACILITATE A TRUSTED IMPLEMENTATION OF LEARNING ANALYTICS.
AS LEARNERS MOVE FROM ONE LEARNING ENVIRONMENT TO ANOTHER, THERE IS A KEY NECESSITY OF TAKING WITH THEM A PROOF OF PREVIOUS LEARNING ACHIEVEMENTS OR EXPERIENCES. IN MOST CASES, THIS IS EITHER EXPRESSED IN TERMS OF RECEIPT OF SCORES OR A CERTIFICATE OF COMPLETION. WHILE THIS MAY BE SUFFICIENT FOR ENROLLMENT AND OTHER ADMINISTRATIVE DECISIONS, IT POSES SOME LIMITATIONS TO THE DEPTH OF LEARNING ANALYTICS AND CONSEQUENTLY A SLOW ONBOARDING PROCESS. ALSO, WITH DIFFERENT INSTITUTIONS HAVING THEIR LEARNING DATA ISOLATED FROM EACH OTHER, IT BECOMES MORE DIFFICULT TO EASILY ACCESS A LEARNER'S LEARNING HISTORY FOR ALL LEARNING ACTIVITIES ON OTHER SYSTEMS. IN THIS PAPER, WE PROPOSE A BLOCKCHAIN BASED APPROACH FOR CONNECTING LEARNING DATA ACROSS DIFFERENT LEARNING MANAGEMENT SYSTEMS (LMS), LEARNING RECORD STORES (LRS), INSTITUTIONS AND ORGANIZATIONS. LEVERAGING ON UNIQUE PROPERTIES OF BLOCKCHAIN TECHNOLOGY, WE ALSO PROPOSE SOLUTIONS TO ENSURING LEARNING DATA CONSISTENCY, AVAILABILITY, IMMUTABILITY, SECURITY, PRIVACY AND ACCESS CONTROL.
AS LEARNING ANALYTICS ACTIVITY HAS INCREASED, A VARIETY OF ETHICAL IMPLICATIONS AND CONSIDERATIONS HAVE EMERGED, THOUGH A SIGNIFICANT RESEARCH GAP REMAINS IN EXPLICITLY INVESTIGATING THE VIEWS OF KEY STAKEHOLDERS, SUCH AS ACADEMIC STAFF. THIS PAPER DRAWS ON ETHICS-RELATED FINDINGS FROM AN AUSTRALIAN STUDY FEATURING TWO SURVEYS, ONE OF INSTITUTIONAL LEADERS (N = 22) AND ONE OF ACADEMIC STAFF (N = 353), AS WELL AS A SET OF FOLLOW-UP INTERVIEWS (N = 23) WITH ACADEMIC LEVEL STAFF. A SELF-SELECTING SAMPLE OF PARTICIPANTS WAS ASKED ABOUT THE ETHICAL ISSUES THEY SEE AS IMPORTANT IN LEARNING ANALYTICS AND ABOUT THE TYPES OF ETHICAL PRINCIPLES OR CONSIDERATIONS THEY THOUGHT SHOULD GUIDE LEARNING ANALYTICS USE. DATA SHOWED PARTICIPANTS' VIEWS DID TEND TO ALIGN WITH ESTABLISHED ETHICAL PRINCIPLES, THOUGH THE LANGUAGE USED TO EXPRESS THIS VARIED WIDELY. BUILDING ON, AND IN RESPONSE TO, BOTH THE DATA AND THE LITERATURE REVIEW THE PAPER PROPOSES AN ETHICAL DECISION MAKING FRAMEWORK THAT ENCOURAGES INSTITUTIONAL LEADERS AND THOSE INVOLVED IN IMPLEMENTING LEARNING ANALYTICS TO CONSISTENTLY APPLY AND DOCUMENT ETHICAL DECISION MAKING PROCESSES AS PART OF AN OVERALL APPROACH TO DEVELOPING WELL-ALIGNED AND TRANSPARENT INSTITUTIONAL POLICIES AND BROADER ETHICAL LITERACY.
STUDY BACKGROUND AND SIGNIFICANCE OF INFORMATION INSTRUCTION MODE BASED ON LEARNING ANALYTICS IS ELABORATED IN THIS THESIS. EMPHATICALLY DESIGN KEY OF INFORMATION INSTRUCTION MODE BASED ON LEARNING ANALYTICS IS ELABORATED. IT CAN BE SEEN THAT FLIPPEDCLASSROOM INSTRUCTION MODE REFORMATION CAN BE CARRIED OUT ON INSTRUCTION SYSTEM BASED ON INFORMATION TECHNOLOGY BY INDUCING LEARNING ANALYTICS. SO GOOD ATMOSPHERE FOR STUDY INSTRUCTION AFTER SCHOOL IS BUILT, AND SUPPORT IS SUPPLIED FOR DESIGN, INNOVATION, PRACTICE AND TURNING OVER TO THINK OF INFORMATION INSTRUCTION MODE.
LEARNING IN THE WORKPLACE IS CRUCIAL IN HIGHER ENGINEERING EDUCATION, SINCE IT ALLOWS STUDENTS TO TRANSFER KNOWLEDGE AND SKILLS FROM UNIVERSITY TO PROFESSIONAL ENGINEERING PRACTICE. LEARNING ANALYTICS ENDEAVORS IN HIGHER EDUCATION HAVE PRIMARILY FOCUSED ON CLASSROOM-BASED LEARNING. RECENTLY, WORKPLACE LEARNING ANALYTICS HAS BECOME AN EMERGENT RESEARCH AREA, WITH TARGET USERS BEING WORKERS, STUDENTS AND TRAINERS. WE PROPOSE TECHNOLOGY FOR WORKPLACE LEARNING ANALYTICS THAT ALLOWS PROGRAM MANAGERS OF HIGHER ENGINEERING EDUCATION PROGRAMS TO GET INSIGHT INTO THE WORKPLACE LEARNING OF THEIR STUDENTS, WHILE ENSURING PRIVACY OF STUDENTS' PERSONAL DATA BY DESIGN. USING A DESIGN-BASED AGILE METHODOLOGY, WE DESIGNED AND DEVELOPED A CUSTOMIZABLE WORKPLACE LEARNING DASHBOARD. FROM THE EVALUATION WITH PROGRAM MANAGERS IN THE COMPUTING DOMAIN, WE CAN CONCLUDE THAT SUCH TECHNOLOGY IS FEASIBLE AND PROMISING. THE PROPOSED TECHNOLOGY WAS DESIGNED TO BE GENERALIZ ABLE TO OTHER (ENGINEERING) DOMAINS. A NEXT LOGICAL STEP WOULD BE TO EVALUATE AND IMPROVE THE PROPOSED TECHNOLOGY WITHIN OTHER ENGINEERING DOMAINS.
LEARNING ANALYTICS HAS THE POTENTIAL TO IMPACT STUDENT LEARNING, AT SCALE. EMBEDDED IN THAT CLAIM ARE A SET OF ASSUMPTIONS AND TENSIONS AROUND THE NATURE OF SCALE, IMPACT ON STUDENT LEARNING, AND THE SCOPE OF INFRASTRUCTURE ENCOMPASSED BY 'LEARNING ANALYTICS' AS A SOCIO-TECHNICAL FIELD. DRAWING ON OUR DESIGN EXPERIENCE OF DEVELOPING LEARNING ANALYTICS AND INDUCTING OTHERS INTO ITS USE, WE PRESENT A MODEL THAT WE HAVE USED TO ADDRESS FIVE KEY CHALLENGES WE HAVE ENCOUNTERED. IN DEVELOPING THIS MODEL, WE RECOMMEND: A FOCUS ON IMPACT ON LEARNING THROUGH AUGMENTATION OF EXISTING PRACTICE; THE CENTRALITY OF TASKS IN IMPLEMENTING LEARNING ANALYTICS FOR IMPACT ON LEARNING; THE COMMENSURATE CENTRALITY OF LEARNING IN EVALUATING LEARNING ANALYTICS; INCLUSION OF CO-DESIGN APPROACHES IN IMPLEMENTING LEARNING ANALYTICS ACROSS SITES; AND AN ATTENTION TO BOTH SOCIAL AND TECHNICAL INFRASTRUCTURE.
THIS CHAPTER LOOKS INTO EXAMINING RESEARCH STUDIES OF THE LAST FIVE YEARS AND PRESENTS THE STATE OF THE ART OF LEARNING ANALYTICS (LA) IN THE HIGHER EDUCATION (HE) ARENA. THEREFORE, WE USED MIXED-METHOD ANALYSIS AND SEARCHED THROUGH THREE POPULAR LIBRARIES, INCLUDING THE LEARNING ANALYTICS AND KNOWLEDGE (LAK) CONFERENCE, THE SPRINGERLINK, AND THE WEB OF SCIENCE (WOS) DATABASES. WE DEEPLY EXAMINED A TOTAL OF 101 PAPERS DURING OUR STUDY. THEREBY, WE ARE ABLE TO PRESENT AN OVERVIEW OF THE DIFFERENT TECHNIQUES USED BY THE STUDIES AND THEIR ASSOCIATED PROJECTS. TO GAIN INSIGHTS INTO THE TREND DIRECTION OF THE DIFFERENT PROJECTS, WE CLUSTERED THE PUBLICATIONS INTO THEIR STAKEHOLDERS. FINALLY, WE TACKLED THE LIMITATIONS OF THOSE STUDIES AND DISCUSSED THE MOST PROMISING FUTURE LINES AND CHALLENGES. WE BELIEVE THE RESULTS OF THIS REVIEW MAY ASSIST UNIVERSITIES TO LAUNCH THEIR OWN LA PROJECTS OR IMPROVE EXISTING ONES.
VIDEO LEARNING ANALYTICS ARE NOT OPEN TO EDUCATION STAKEHOLDERS, SUCH AS RESEARCHERS AND TEACHERS, BECAUSE ONLINE VIDEO PLATFORMS DO NOT SHARE THE INTERACTIONS OF THE USERS WITH THEIR SYSTEMS. NEVERTHELESS, VIDEO LEARNING ANALYTICS ARE NECESSARY TO ALL RESEARCHERS AND TEACHERS THAT NEED TO UNDERSTAND AND IMPROVE THE EFFECTIVENESS OF THE VIDEO LECTURE PEDAGOGY. IN THIS PAPER, WE PRESENT AN OPEN VIDEO LEARNING ANALYTICS SERVICE, WHICH IS FREELY ACCESSIBLE ONLINE. THE VIDEO LEARNING ANALYTICS SERVICE (NAMED SOCIALSKIP) FACILITATES THE ANALYSIS OF VIDEO LEARNING BEHAVIOR BY CAPTURING LEARNERS' INTERACTIONS WITH THE VIDEO PLAYER (E.G., SEEK/SCRUB, PLAY, PAUSE). THE SERVICE EMPOWERS ANY RESEARCHER OR TEACHER TO CREATE A CUSTOM VIDEO-BASED EXPERIMENT BY SELECTING: 1) A VIDEO LECTURE FROM YOUTUBE, 2) QUIZ QUESTIONS FROM GOOGLE DRIVE, AND 3) CUSTOM VIDEO PLAYER BUTTONS. THE OPEN VIDEO ANALYTICS SYSTEM HAS BEEN VALIDATED THROUGH DOZENS OF USER STUDIES, WHICH PRODUCED THOUSANDS OF VIDEO INTERACTIONS. IN THIS STUDY, WE PRESENT AN INDICATIVE EXAMPLE, WHICH HIGHLIGHTS THE USABILITY AND USEFULNESS OF THE SYSTEM. IN ADDITION TO INTERACTION FREQUENCIES, THE SYSTEM MODELS THE CAPTURED DATA AS A LEARNER ACTIVITY TIME SERIES. FURTHER RESEARCH SHOULD CONSIDER USER MODELING AND PERSONALIZATION IN ORDER TO DYNAMICALLY RESPOND TO THE INTERACTIVITY OF STUDENTS WITH VIDEO LECTURES.
THE INCREASING PROGRESS IN UBIQUITOUS TECHNOLOGY MAKES IT EASIER AND CHEAPER TO TRACK STUDENTS' PHYSICAL ACTIONS UNOBTRUSIVELY, MAKING IT POSSIBLE TO CONSIDER SUCH DATA FOR SUPPORTING RESEARCH, EDUCATOR INTERVENTIONS, AND PROVISION OF FEEDBACK TO STUDENTS. IN THIS PAPER, WE REFLECT ON THE UNDEREXPLORED, YET IMPORTANT AREA OF LEARNING ANALYTICS APPLIED TO PHYSICAL/MOTOR LEARNING TASKS AND TO THE PHYSICALITY ASPECTS OF 'TRADITIONAL' INTELLECTUAL TASKS THAT OFTEN OCCUR IN PHYSICAL LEARNING SPACES. BASED ON DISTRIBUTED COGNITION THEORY, THE CONCEPT OF INTERNET OF THINGS AND MULTIMODAL LEARNING ANALYTICS, THIS PAPER INTRODUCES A THEORETICAL PERSPECTIVE FOR BRINGING LEARNING ANALYTICS INTO PHYSICAL SPACES. WE PRESENT THREE PROTOTYPES THAT SERVE TO ILLUSTRATE THE POTENTIAL OF PHYSICAL ANALYTICS FOR TEACHING AND LEARNING. THESE STUDIES ILLUSTRATE ADVANCES IN PROXIMITY, MOTION AND LOCATION ANALYTICS IN COLLABORATIVE LEARNING, DANCE EDUCATION AND HEALTHCARE TRAINING.
SERIOUS GAMES HAVE PROVEN SEVERAL ADVANTAGES WHEN USED IN EDUCATION IMPROVING STUDENTS LEARNING. HOWEVER, GAMES ARE STILL COMPLEX TO DEPLOY IN THE CLASS FOR AVERAGE TEACHERS. MANY TEACHERS STILL DO NOT SEE GAMES AS A POWERFUL TOOL TO IMPROVE THEIR TEACHING WORK. TO THIS END, IT IS ESSENTIAL TO HUMANIZE THE GAME TECHNOLOGY MAKING THE USE OF GAMES MORE TRANSPARENT TO TEACHERS IN A WAY THAT THEY GET THE BENEFITS AND AVOID MOST OF THE GAME DEPLOYMENT COMPLEXITY. WE CONSIDER THAT GAME LEARNING ANALYTICS IS ONE OF THE KEYS TO HELP TEACHERS IN THE APPLICATION OF SERIOUS GAMES IN THE CLASSROOMS. GAME LEARNING ANALYTICS ALLOWS TO CAPTURE DATA FROM STUDENTS' INTERACTIONS WITH GAMES AND DERIVE INFORMATION THAT SIMPLIFY TEACHERS' TASKS. DOING IT IN A TRANSPARENT WAY WITHIN THE GAME ENVIRONMENT (I.E. STEALTH ASSESSMENT) CAN PROVIDE EVIDENCE-BASED DATA ABOUT THE LEARNERS' KNOWLEDGE AT EACH POINT OF TIME. COMBINING BOTH GAME LEARNING ANALYTICS IN NEAR REAL-TIME AND OFFLINE, AND STEALTH ASSESSMENT FOR GAMES, IT COULD BE POSSIBLE TO LEVERAGE THEIR USE IN CLASSROOM SETTINGS AT REAL-TIME MAKING THEIR USE EASIER FOR TEACHERS.
THE OPEN UNIVERSITY OF JAPAN (OUJ) AND NATIONAL INSTITUTE OF INFORMATICS (NII) HAVE STARTED COLLABORATIVE STUDY FOR LEARNING ANALYTICS. OUJ IS THE LARGEST DISTANCE EDUCATION INSTITUTION IN JAPAN. IN THE ACADEMIC YEAR OF 2015, OUJ LAUNCHED INTERACTIVE ONLINE COURSES. ONE OF THE ESSENTIAL FEATURES OF ONLINE COURSES IS THE POSSIBILITY TO COLLECT AND ANALYZE EDUCATIONAL BIG DATA. TO UTILIZE SUCH EDUCATIONAL DATA, OUJ AND NII HAVE DEVELOPED LEARNING ANALYTICS PLATFORM FOR OUJ ONLINE COURSES. IN THIS PAPER, THE FEATURES OF THE SYSTEM ARE OVERVIEWED.
LEARNING ANALYTICS CAN PROVIDE MANY OPPORTUNITIES FOR IMPROVING LEARNING EXPERIENCE AND PERFORMANCE IN HIGHER EDUCATION. IN ADDITION TO GRADES, MANY OTHER DATA CAN BE COLLECTED AND ANALYSED DURING THE TEACHING-LEARNING PROCESS. E-LEARNING SYSTEMS SEEMS TO BE THE EASIEST MEAN OF COLLECTING LEARNING RELATED DATA. ALTHOUGH E-LEARNING SYSTEM ARE ALREADY WIDELY USED IN HIGHER EDUCATION, LEARNING ANALYTICS SOLUTION ARE STILL NOT IMPLEMENTED. THE PURPOSE OF THE CURRENT RESEARCH IS TO EXPLORE WHAT CAN BE REASONS BEHIND THIS PHENOMENON, HOW MUCH TEACHERS ARE AWARE OF STUDENT LEARNING ANALYTICS RELATED EXPECTATIONS AND WHICH ELEMENTS AND POINTS OF LEARNING ANALYSIS SHOULD BE INCLUDED IN A BLENDED LEARNING TRAINING. A PILOT COURSE WILL BE LAUNCHED IN THE MOODIE E-LEARNING SYSTEM. MOST OF THE DATA RECORDED BY THE SYSTEM - LIKE DATA CONCERNING TIME SPENT WITH READING COURSE MATERIALS, ASSIGNMENT SCORES, OR SELF-EVALUATION TEST RESULTS ETC CAN BE RELEVANT FOR LEARNING ANALYSIS. THE QUESTION IS HOW TO APPLY THIS DATA IN A MEANINGFUL AND REASONABLE MANNER. OUR AIM IS TO DEVELOP AND DEMONSTRATE THE USE OF A LEARNING ANALYTICS FRAMEWORK FOR EVALUATING THE LEARNING OUTCOMES OF A PARTICULAR COURSE. IN ORDER TO EXPLOIT THE BENEFITS OF LEARNING ANALYTICS AN INTEGRATED TOOLSET MUST BE DEVELOPED THAT CAN IDENTIFY, COLLECT AND THEN PROCESS DATA BASED ON THE VARIOUS REQUIREMENTS. IN THIS RESEARCH HIGH EMPHASIS WILL BE PUT ON VISUALIZATION SOLUTIONS (E.G.: KNOWLEDGE NETWORK, WORKFLOW DASHBOARDS AND RANKINGS) FOR SUPPORTING STUDENTS AND TEACHERS TOO.
THE WIDESPREAD SUCCESS OF MASSIVE OPEN ONLINE COURSES IN HIGHER EDUCATION REQUIRES EVEN FURTHER DEVELOPMENT OF LEARNING ANALYTICS CUSTOMIZED TO THIS FRAMEWORK. ONE OF THE FEATURES OF THE LEARNING ANALYTICS IS THE INTEGRATION AND COMBINATION OF TECHNIQUES TO TRACE USER BEHAVIOR IN ORDER TO CREATE A `CUSTOMIZED' LEARNING. THE AIM OF THIS PAPER IS TO PROPOSE MULTIVARIATE DATA ANALYSIS METHODS TO DEFINE THE USERS' AND THE LEARNING PROFILE. THE ANALYSIS REFERS TO THE COURSES OFFERED BY THE PLATFORM FEDERICA.EU AT UNIVERSITY OF NAPLES, FEDERICO II.
PILARES (SMART LEARNING ANALYTICS PLATFORM TO ENHANCE PERFORMANCE IN SECONDARY EDUCATION) IS A RESEARCH PROJECT AIMED AT DEVELOPING A LEARNING ANALYTICS PLATFORM FOR SPANISH SECONDARY SCHOOLS' BLENDED LEARNING FOCUSED PRIMARILY AT MICRO LEVEL (STUDENTS, TEACHERS, TUTORS AND FAMILIES). IN THIS PAPER WE CONTEXTUALIZE THE GOAL OF THE PILARES PROJECT IN THE WIDER PANORAMA OF LEARNING ANALYTICS WITH REFERENCES TO THE SITUATION IN THE SPANISH CONTEXT.
LEARNING ANALYTICS IS AN EMERGING FIELD OF RESEARCH, MOTIVATED BY THE WIDE SPECTRUM OF THE AVAILABLE EDUCATIONAL INFORMATION THAT CAN BE ANALYSED TO PROVIDE A DATA-DRIVEN DECISION ABOUT VARIOUS LEARNING PROBLEMS. THIS STUDY INTENDS TO EXAMINE THE RESEARCH LANDSCAPE OF LEARNING ANALYTICS TO DELIVER A COMPREHENSIVE UNDERSTANDING OF THE RESEARCH ACTIVITIES IN THIS MULTIDISCIPLINARY FIELD, USING SCIENTIFIC LITERATURE FROM THE SCOPUS DATABASE. AN ARRAY OF STATE-OF-THE-ART BIBLIOMETRIC INDICES IS DEPLOYED ON 2811 PROCURED PUBLICATION DATASETS: PUBLICATION COUNTS, CITATION COUNTS, CO-AUTHORSHIP PATTERNS, CITATION NETWORKS AND TERM CO-OCCURRENCE. THE RESULTS INDICATE THAT THE FIELD OF LEARNING ANALYTICS APPEARS TO HAVE BEEN INSTANTIATED AROUND 2011; THUS, BEFORE THIS TIME PERIOD NO SIGNIFICANT RESEARCH ACTIVITY CAN BE OBSERVED. THE TEMPORAL EVOLUTION INDICATES THAT THE TERMS 'STUDENTS', 'TEACHERS', 'HIGHER EDUCATION INSTITUTIONS' AND 'LEARNING PROCESS' APPEAR TO BE THE MAJOR COMPONENTS OF THE FIELD. MORE RECENT TRENDS IN THE FIELD ARE THE TOOLS THAT TAP INTO BIG DATA ANALYTICS AND DATA MINING TECHNIQUES FOR MORE RATIONAL DATA-DRIVEN DECISION-MAKING SERVICES. A FUTURE DIRECTION RESEARCH DEPICTS A NEED TO INTEGRATE LEARNING ANALYTICS RESEARCH WITH MULTIDISCIPLINARY SMART EDUCATION AND SMART LIBRARY SERVICES. THE VISION TOWARDS SMART CITY RESEARCH REQUIRES A META-LEVEL OF SMART LEARNING ANALYTICS VALUE INTEGRATION AND POLICY-MAKING.
THE MAJORITY OF ALGORITHMS USED IN THE DATA ANALYSIS ARE DESIGNED ACCORDING TO THE CAPACITIES OF POWER AND FLEXIBILITY RATHER THAN FOR ITS SIMPLICITY AND ARE TOO COMPLEX TO USE IN THE EDUCATIONAL CONTEXT. THE OBJECTIVE OF THIS PAPER IS TO PRESENT A LITERATURE REVIEW ON THE LEARNING ANALYTICS IN HIGHER EDUCATION: PROBLEMS, LIMITATIONS, TECHNIQUES AND TOOLS USED. THE SYSTEMATIC LITERATURE REVIEW METHODOLOGY WAS USED TO ANSWER THREE RESEARCH QUESTIONS ON THE BASIS OF SCIENTIFIC PUBLICATIONS. THIS PAPER CONCLUDES THAT IT MUST IMPLEMENT, ADAPT OR DEVELOP ALGORITHMS FOR THE EDUCATIONAL CONTEXT AND MUST BE BUILD TOOLS FOR THE ANALYSIS OF EDUCATIONAL DATA WITH INTUITIVE INTERFACES AND EASY TO USE.
THIS ARTICLE SUMMARIZES THE METHODS, OBSERVATIONS, CHALLENGES AND IMPLICATIONS FOR EXPLORATORY ANALYSIS DRAWN FROM TWO LEARNING ANALYTICS RESEARCH PROJECTS. THE CASES INCLUDE AN ANALYSIS OF A GAMES-BASED VIRTUAL PERFORMANCE ASSESSMENT AND AN ANALYSIS OF DATA FROM 52,000 STUDENTS OVER A 5-YEAR PERIOD AT A LARGE AUSTRALIAN UNIVERSITY. THE COMPLEX DATASETS WERE ANALYZED AND ITERATIVELY MODELED WITH A VARIETY OF COMPUTATIONALLY INTENSIVE METHODS TO PROVIDE THE MOST EFFECTIVE OUTCOMES FOR LEARNING ASSESSMENT, PERFORMANCE MANAGEMENT AND LEARNER TRACKING. THE ARTICLE PRESENTS THE RESEARCH CONTEXTS, THE TOOLS AND METHODS USED IN THE EXPLORATORY PHASES OF ANALYSIS, THE MAJOR FINDINGS AND THE IMPLICATIONS FOR LEARNING ANALYTICS RESEARCH METHODS.
THIS WORK AIMS TO DEFINE AND PRESENT SOME SCENARIOS TO USE LEARNING ANALYTICS WITH FLIPPED CLASSROOM MODELS. WE SELECTED SOME IMPORTANT INDICATORS AND VISUALIZATIONS WIDELY USED IN LEARNING ANALYTICS AND ANALYZE HOW TO APPLY THEM IN DIFFERENT TYPES OF FLIPPED CLASSROOM. BESIDES, WE SPECIFY SOME ACTIVITIES THAT STUDENTS AND TEACHERS CAN DO IN THESE TYPES OF FLIPPED CLASSROOM BASED ON THE USE OF LEARNING ANALYTICS IN ORDER TO IMPROVE THE LEARNING PROCESS. WE ALSO DISCUSS THE ADVANTAGES OF THE COMBINATION OF LEARNING ANALYTICS WITH THE FLIPPED CLASSROOM, IN COMPARISON WITH ONLY USING THE FLIPPED CLASSROOM.
DEPLOYING LEARNING ANALYTICS THAT SIGNIFICANTLY IMPROVE LEARNING OUTCOMES REMAINS A CHALLENGE. MOTIVATION HAS BEEN FOUND TO BE RELATED TO ACADEMIC ACHIEVEMENT AND IS ARGUED TO PLAY AN ESSENTIAL ROLE IN EFFICIENT LEARNING. WE DEVELOPED A LEARNING ANALYTICS DASHBOARD AND DESIGNED AN INTERVENTION THAT RELIES ON GOAL ORIENTATION AND SOCIAL COMPARISON. SUBJECTS CAN SEE A PREDICTION OF THEIR FINAL GRADE IN A COURSE AS WELL AS HOW THEY PERFORM IN COMPARISON TO CLASSMATES WITH SIMILAR GOAL GRADES. THOSE WITH ACCESS TO THE DASHBOARD ENDED UP MORE MOTIVATED THAN THOSE WITHOUT ACCESS, OUTPERFORMED THEIR PEERS AS THE COURSE PROGRESSED AND ACHIEVED HIGHER FINAL GRADES. OUR RESULTS INDICATE THAT LEARNER-ORIENTED DASHBOARDS ARE TECHNICALLY FEASIBLE AND MAY HAVE TANGIBLE BENEFITS FOR LEARNERS.
THIS PAPER FOCUSES ON LEARNING ANALYTICS FRAMEWORK BEHIND MATH ELEMENTS MATHEMATICS GAME. THE GAME, WAS INTRODUCED AT ECGBL 2012. THE NOVELTY VALUE OF THIS DESIGN STUDY IS IN DEVELOPMENT PROCESS OF VISUALIZING AND DATA MINING TECHNOLOGIES BEHIND THE LEARNING ANALYTICS. THE ANALYTIC TOOLS PROVIDE 1) EASY ACCESS TO FOLLOW PROGRESS AND 2) REAL TIME ANALYSIS ON THE LEARNING PROCESS. THE ANALYTICS GIVES FAST AND EASY TO UNDERSTAND VIEW INTO LEARNING PROCESS, STILL SUPPORTING THE STORY, THE GAME PLAY AND MOTIVATION TOWARDS GAME PLAY. IN THIS PAPER WE SHOW THE USER CENTERED DEVELOPMENT PROCESS, THE IMPROVEMENTS DONE ACCORDING TO USER FEEDBACK AND OPEN THE FUTURE RESEARCH FOCUSES.
SELF-REGULATED LEARNING (SRL) CAN PREDICT ACADEMIC PERFORMANCE. YET, IT IS DIFFICULT FOR LEARNERS. THE ABILITY TO SELF-REGULATE LEARNING BECOMES EVEN MORE IMPORTANT IN EMERGING ONLINE LEARNING SETTINGS. TO SUPPORT LEARNERS IN DEVELOPING THEIR SRL, LEARNING ANALYTICS (LA), WHICH CAN IMPROVE LEARNING PRACTICE BY TRANSFORMING THE WAYS WE SUPPORT LEARNING, IS CRITICAL. THIS SCOPING REVIEW IS BASED ON THE ANALYSIS OF 54 PAPERS ON LA EMPIRICAL RESEARCH FOR SRL IN ONLINE LEARNING CONTEXTS PUBLISHED BETWEEN 2011 AND 2019. THE RESEARCH QUESTION IS: WHAT IS THE CURRENT STATE OF THE APPLICATIONS OF LEARNING ANALYTICS TO MEASURE AND SUPPORT STUDENTS' SRL IN ONLINE LEARNING ENVIRONMENTS? THE FOCUS IS ON SRL PHASES, METHODS, FORMS OF SRL SUPPORT, EVIDENCE FOR LA AND TYPES OF ONLINE LEARNING SETTINGS. ZIMMERMAN'S MODEL (2002) WAS USED TO EXAMINE SRL PHASES. THE EVIDENCE ABOUT LA WAS EXAMINED IN RELATION TO FOUR PROPOSITIONS: WHETHER LA I) IMPROVE LEARNING OUTCOMES, II) IMPROVE LEARNING SUPPORT AND TEACHING, III) ARE DEPLOYED WIDELY, AND IV) USED ETHICALLY. RESULTS SHOWED MOST STUDIES FOCUSED ON SRL PARTS FROM THE FORETHOUGHT AND PERFORMANCE PHASE BUT MUCH LESS FOCUS ON REFLECTION. WE FOUND LITTLE EVIDENCE FOR LA THAT SHOWED I) IMPROVEMENTS IN LEARNING OUTCOMES (20%), II) IMPROVEMENTS IN LEARNING SUPPORT AND TEACHING (22%). LA WAS ALSO FOUND III) NOT USED WIDELY AND IV) FEW STUDIES (15%) APPROACHED RESEARCH ETHICALLY. OVERALL, THE FINDINGS SHOW LA RESEARCH WAS CONDUCTED MAINLY TO MEASURE RATHER THAN TO SUPPORT SRL. THUS, THERE IS A CRITICAL NEED TO EXPLOIT THE LA SUPPORT MECHANISMS FURTHER IN ORDER TO ULTIMATELY USE THEM TO FOSTER STUDENT SRL IN ONLINE LEARNING ENVIRONMENTS.
THIS PAPER INTRODUCES BOOKLOOPER [1] AND TIME-TUNNEL [2] AS LEARNING ANALYTICS TOOLS FOR VISUALIZING E-LEARNING ACTIVITY DATA SUCH AS HOW LONG AND HOW OFTEN STUDENTS LOOK AT EACH PAGE OF AN E-LEARNING MATERIAL SUCH AS POWERPOINT SLIDES. BOOKLOOPER ENABLES TO COLLECT THESE ACTIVITY DATA. THE ANALYSIS OF E-LEARNING ACTIVITY DATA IS IMPORTANT FOR TEACHERS OR INSTRUCTORS TO UNDERSTAND STUDENTS' LEARNING STATES AND THE DIFFICULTY LEVELS OF E-LEARNING MATERIALS. BY LOOKING BACK AT THEIR INSTRUCTIONAL WAYS FROM ANALYSIS RESULTS, IT WILL BE POSSIBLE TO MODIFY AND MAKE THEM MORE EFFICIENT. SO, IN THIS PAPER, THE AUTHORS PROPOSE THE-LEARNING ANALYTICS USING TIME-TUNNEL BY VISUALIZING THE E-LEARNING ACTIVITY DATA AND CLARIFY ITS USEFULNESS BY SHOWING VISUALIZATION EXAMPLES OF ACTUAL DATA COLLECTED BY BOOKLOOPER.
PURPOSE - THE PURPOSE OF THIS PAPER IS TO PROVIDE AN OVERVIEW OF THE DEVELOPMENT OF AN INSTITUTION WIDE APPROACH TO LEARNING ANALYTICS AT THE UNIVERSITY OF WOLLONGONG (UOW) AND THE INCLUSION OF LIBRARY DATA DRAWN FROM THE LIBRARY CUBE. DESIGN/METHODOLOGY/APPROACH - THE STUDENT SUPPORT AND EDUCATION ANALYTICS TEAM AT UOW IS TASKED WITH CREATING POLICY, FRAMEWORKS AND INFRASTRUCTURE FOR THE SYSTEMATIC CAPTURE, MAPPING AND ANALYSIS OF DATA FROM THE ACROSS THE UNIVERSITY. THE INITIAL DATA SET INCLUDES: LOG FILE DATA FROM MOODLE SITES, LIBRARY CUBE, STUDENT ADMINISTRATION DATA, TUTORIALS AND STUDENT SUPPORT SERVICE USAGE DATA. USING THE LEARNING ANALYTICS DATA WAREHOUSE UOW IS DEVELOPING NEW MODELS FOR ANALYSIS AND VISUALISATION WITH A FOCUS ON THE PROVISION OF NEAR REAL-TIME DATA TO ACADEMIC STAFF AND STUDENTS TO OPTIMISE LEARNING OPPORTUNITIES. FINDINGS - THE DISTINCT ADVANTAGE OF THE LEARNING ANALYTICS MODEL IS THAT THE SELECTED DATA SETS ARE UPDATED WEEKLY, ENABLING NEAR REAL-TIME MONITORING AND INTERVENTION WHERE REQUIRED. INCLUSION OF LIBRARY DATA WITH THE OTHER OFTEN DISPARATE DATA SETS FROM ACROSS THE UNIVERSITY HAS ENABLED DEVELOPMENT OF A COMPREHENSIVE PLATFORM FOR LEARNING ANALYTICS. FUTURE WORK WILL INCLUDE THE DEVELOPMENT OF PREDICTIVE MODELS USING THE RAPIDLY GROWING LEARNING ANALYTICS DATA WAREHOUSE. PRACTICAL IMPLICATIONS - DATA WAREHOUSING INFRASTRUCTURE, THE SYSTEMATIC CAPTURE AND EXPORTING OF RELEVANT LIBRARY DATA SETS ARE REQUISITE FOR THE CONSIDERATION OF LIBRARY DATA IN LEARNING ANALYTICS. ORIGINALITY/VALUE - WHAT WAS NOT ANTICIPATED FIVE YEARS AGO WHEN THE VALUE CUBE WAS FIRST REALISED, WAS THE DEVELOPMENT OF LEARNING ANALYTIC SERVICES AT UOW. THE CUBE AFFORDED UNIVERSITY OF WOLLONGONG LIBRARY CONSIDERABLE ADVANTAGE: THE FRAMEWORK FOR DATA HARVESTING AND ANALYSIS WAS ESTABLISHED, READY FOR INCLUSION WITHIN LEARNING ANALYTICS DATA SETS AND SUBSEQUENT REPORTING TO FACULTY.
LEARNING ANALYTICS COLLECT SENSITIVE DATA FROM STUDENTS. IN SOME CASES, THE ETHICS BEHIND THE USE OR ACCESS BY THIRD PARTIES ARE NOT CLEAR. THIS SITUATION RAISES ADVERSE REACTIONS AND FEELINGS OF FEAR THAT GENERATES A NEGATIVE PERCEPTION TOWARDS THE USE OF LEARNING ANALYTICS. IN CONSEQUENCE, IT IS QUESTIONED WHETHER PRIVACY AND SECURITY CAN BE PRESERVED WHEN COLLECTING EDUCATIONAL DATA. AS A RESULT, SOME POLICIES AND GOOD PRACTICES ARE SET TO FRAME HOW STUDENT DATA SHOULD BE USED IN THE APPLICATION OF THIS ANALYTICAL APPROACH. ITS OBJECTIVE IS TO INCREASE CONFIDENCE IN THE APPLICATION OF LEARNING ANALYTICS. HOWEVER, SOME OF THESE LEGAL ACTIONS, WHICH ARE LIMITED TO THE AREAS IN WHICH THEY ARE ORIGINATED, ARE THE RESULT OF ALLEGATIONS OF DATA LEAKAGE. HENCE, THESE INITIATIVES CAN DO LITTLE TO INSURE THE USE OF SENSITIVE DATA OF STUDENTS IN UNKNOWN SITUATIONS. TO ENSURE CONTINUITY AND AN INCREASE OF CONFIDENCE IN THE APPLICATION OF LEARNING ANALYTICS IS NECESSARY TO BIND TO LEGALITY A NEW APPROACH TO SAFEGUARD PRIVACY OF STUDENTS' DATA IN ITS CURRENT AND FUTURE USES. CONSIDERING THE ABOVE, IT IS POSSIBLE TO ADD A TECHNOLOGICAL LAYER ABOVE THESE POLICIES THAT ENSURES ITS VIABILITY. SOME EMERGING TECHNOLOGIES SUCH AS BLOCKCHAIN AND SMART CONTRACTS ARE STRONG CANDIDATES TO ENSURE PRIVACY AND SECURE SENSIBLE DATA OF STUDENTS. THE USE OF SMART CONTRACTS ALLOWS THE AUTOMATION OF LEGAL ACTIONS SO THAT THEY ARE EXECUTED AS SOON AS IRREGULARITIES IN THE USE OR DATA COLLECTION ARE DETECTED. IN THIS WORK, WE PROPOSE A SERIES OF ACTIONS TO PRESERVE THE IDENTITY OF STUDENTS AND SECURE THEIR DATA WITH EMERGING TECHNOLOGIES SUCH AS BLOCKCHAIN.
MOOC RESEARCH IS TYPICALLY LIMITED TO EVALUATIONS OF LEARNER BEHAVIOR IN THE CONTEXT OF THE LEARNING ENVIRONMENT. HOWEVER, SOME RESEARCH HAS BEGUN TO RECOGNIZE THAT THE IMPACT OF MOOCS MAY EXTEND BEYOND THE CONFINES OF THE COURSE PLATFORM OR CONCLUSION OF THE COURSE TIME LIMIT. THIS WORKSHOP AIMS TO ENCOURAGE OUR COMMUNITY OF LEARNING ANALYTICS RESEARCHERS TO EXAMINE THE RELATIONSHIP BETWEEN PERFORMANCE AND ENGAGEMENT WITHIN THE COURSE AND LEARNER BEHAVIOR AND DEVELOPMENT BEYOND THE COURSE. THIS WORKSHOP INTENDS TO BUILD AWARENESS IN THE COMMUNITY REGARDING THE IMPORTANCE OF RESEARCH MEASURING MULTI-PLATFORM ACTIVITY AND LONG-TERM SUCCESS AFTER TAKING A MOOC. WE HOPE TO BUILD THE COMMUNITY'S UNDERSTANDING OF WHAT IT TAKES TO OPERATIONALIZE MOOC LEARNER SUCCESS IN A NOVEL CONTEXT BY EMPLOYING DATA TRACES ACROSS THE SOCIAL WEB.
EDUCATIONAL BIG DATA (EBD) COME FROM VARIOUS TYPES OF EDUCATIONAL SYSTEMS SUCH AS LMS, CMS, ITS, SLS, AND MOOCS. HOW TO SHARE AND INTEROPERATE EDUCATIONAL BIG DATA FOR LEARNING ANALYTICS (LA) HAS GARNERED A LOT OF RESEARCH ATTENTION. IN THE PAPER, WE PROBE INTO THE PROCESS OF LEARNING ANALYTICS, OUTLINE THE DATAFLOW OF EBD, DEFINE THE LEARNING ANALYTICS OBJECT, AND PROPOSE A NEW DATA FORMAT MODEL FOR THE LEARNING ANALYTICS OBJECT TO ACTUALIZE THE SHARING AND INTEROPERABILITY OF LEARNING ANALYTICS.
LEARNING ANALYTICS CAN HELP HIGHER EDUCATIONAL INSTITUTIONS IMPROVE LEARNING. ITS ADOPTION, HOWEVER, IS A COMPLEX UNDERTAKING. THE LEARNING ANALYTICS CAPABILITY MODEL DESCRIBES WHAT 34 ORGANIZATIONAL CAPABILITIES MUST BE DEVELOPED TO SUPPORT THE SUCCESSFUL ADOPTION OF LEARNING ANALYTICS. THIS PAPER DESCRIBED THE FIRST ITERATION TO EVALUATE AND REFINE THE CURRENT, THEORETICAL MODEL. DURING A CASE STUDY, WE CONDUCTED FOUR SEMI-STRUCTURED INTERVIEWS AND COLLECTED (INTERNAL) DOCUMENTATION AT A DUTCH UNIVERSITY THAT IS MATURE IN THE USE OF STUDENT DATA TO IMPROVE LEARNING. BASED ON THE EMPIRICAL DATA, WE MERGED SEVEN CAPABILITIES, RENAMED THREE CAPABILITIES, AND IMPROVED THE DEFINITIONS OF ALL OTHERS. SIX CAPABILITIES ABSENT IN EXTANT LEARNING ANALYTICS MODELS ARE PRESENT AT THE CASE ORGANIZATION, IMPLYING THAT THEY ARE IMPORTANT TO LEARNING ANALYTICS ADOPTION. AS A RESULT, THE NEW, REFINED LEARNING ANALYTICS CAPABILITY MODEL COMPRISES 31 CAPABILITIES. FINALLY, SOME CHALLENGES WERE IDENTIFIED, SHOWING THAT EVEN MATURE ORGANIZATIONS STILL HAVE ISSUES TO OVERCOME.
LEARNING ANALYTICS IS AN EMERGING FIELD FOCUSED ON ANALYZING LEARNERS' INTERACTIONS WITH EDUCATIONAL CONTENT. ONE OF THE KEY OPEN ISSUES IN LEARNING ANALYTICS IS THE STANDARDIZATION OF THE DATA COLLECTED. THIS IS A PARTICULARLY CHALLENGING ISSUE IN SERIOUS GAMES, WHICH GENERATE A DIVERSE RANGE OF DATA. THIS PAPER REVIEWS THE CURRENT STATE OF LEARNING ANALYTICS, DATA STANDARDS AND SERIOUS GAMES, STUDYING HOW SERIOUS GAMES ARE TRACKING THE INTERACTIONS FROM THEIR PLAYERS AND THE METRICS THAT CAN BE DISTILLED FROM THEM. BASED, ON THIS REVIEW, WE PROPOSE AN INTERACTION MODEL THAT ESTABLISHES A BASIS FOR APPLYING LEARNING ANALYTICS INTO SERIOUS GAMES. THIS PAPER THEN ANALYZES THE CURRENT STANDARDS AND SPECIFICATIONS USED IN THE FIELD. FINALLY, IT PRESENTS AN IMPLEMENTATION OF THE MODEL WITH ONE OF THE MOST PROMISING SPECIFICATIONS: EXPERIENCE API (XAPI). THE EXPERIENCE API RELIES ON COMMUNITIES OF PRACTICE DEVELOPING PROFILES THAT COVER DIFFERENT USE CASES IN SPECIFIC DOMAINS. THIS PAPER PRESENTS THE SERIOUS GAMES XAPI PROFILE: A PROFILE DEVELOPED TO ALIGN WITH THE MOST COMMON USE CASES IN THE SERIOUS GAMES DOMAIN. THE PROFILE IS APPLIED TO A CASE STUDY (A DEMO GAME), WHICH EXPLORES THE TECHNICAL PRACTICALITIES OF STANDARDIZING DATA ACQUISITION IN SERIOUS GAMES. IN SUMMARY, THE PAPER PRESENTS A NEW INTERACTION MODEL TO TRACK SERIOUS GAMES AND THEIR IMPLEMENTATION WITH THE XAPI SPECIFICATION.
CURRENT LEARNING ANALYTICS (LA) SYSTEMS ARE PRIMARILY DESIGNED WITH UNIVERSITY STAFF MEMBERS AS THE TARGET AUDIENCE; VERY FEW ARE AIMED AT STUDENTS, WITH ALMOST NONE BEING DEVELOPED WITH DIRECT STUDENT INVOLVEMENT AND UNDERTAKING A COMPREHENSIVE EVALUATION. THIS PAPER DESCRIBES A HEFCE FUNDED PROJECT THAT HAS EMPLOYED A VARIETY OF METHODS TO ENGAGE STUDENTS IN THE DESIGN, DEVELOPMENT AND EVALUATION OF A STUDENT FACING LA DASHBOARD. LA WAS INTEGRATED INTO THE DELIVERY OF 4 UNDERGRADUATE MODULES WITH 169 STUDENT SIGN-UPS. THE DESIGN OF THE DASHBOARD USES A NOVEL APPROACH OF TRYING TO UNDERSTAND THE REASONS WHY STUDENTS WANT TO STUDY AT UNIVERSITY AND MAPS THEIR ENGAGEMENT AND PREDICTED OUTCOMES TO THESE MOTIVATIONS, WITH WEEKLY PERSONALISED NOTIFICATIONS AND FEEDBACK. STUDENTS ARE ALSO GIVEN THE CHOICE OF HOW TO VISUALISE THE DATA EITHER VIA A CHART-BASED VIEW OR TO BE REPRESENTED AS THEMSELVES. A MIXED-METHODS EVALUATION HAS SHOWN THAT STUDENTS' FEELINGS OF DEPENDABILITY AND TRUST OF THE UNDERLYING ANALYTICS AND DATA IS VARIABLE. HOWEVER, STUDENTS WERE MOSTLY POSITIVE ABOUT THE USABILITY AND INTERFACE DESIGN OF THE SYSTEM AND ALMOST ALL STUDENTS ONCE SIGNED-UP DID INTERACT WITH THEIR LA. THE MAJORITY OF STUDENTS COULD SEE HOW THE LA SYSTEM COULD SUPPORT THEIR LEARNING AND SAID THAT IT WOULD INFLUENCE THEIR BEHAVIOUR. IN SOME CASES, THIS HAS HAD A DIRECT IMPACT ON THEIR LEVELS OF ENGAGEMENT. THE MAIN CONTRIBUTION OF THIS PAPER IS THE TRANSPARENT DOCUMENTATION OF A USER CENTRED DESIGN APPROACH THAT HAS PRODUCED FORMS OF LA REPRESENTATION, RECOMMENDATION AND INTERACTION DESIGN THAT GO BEYOND THOSE USED IN CURRENT SIMILAR SYSTEMS AND HAVE BEEN SHOWN TO MOTIVATE STUDENTS AND IMPACT THEIR LEARNING BEHAVIOUR.
THIS ARTICLE CONSIDERS HOW TEACHERS AND UNIVERSITY ADMINISTRATORS CAN USE A SIGNIFICANT AMOUNT OF DATA STORED IN THE INFORMATION SYSTEMS OF INSTITUTIONS. INTELLIGENT ANALYSIS OF THESE LEARNING PROCESSES IS OF GREAT USE IN THE HIGHER EDUCATION SYSTEM. THE USE OF LEARNING ANALYTICS (LA) BY A LARGE NUMBER OF HIGHER EDUCATIONAL INSTITUTIONS SHOWS THE INTEREST AND PARTICIPATION OF UNIVERSITIES IN THIS MATTER. LEARNING ANALYTICS CAN TELL A LOT ABOUT THE PROGRESS OF STUDENTS AND THE ENVIRONMENT IN WHICH LEARNING TAKES PLACE. INTELLECTUALIZATION OF EDUCATIONAL ANALYTICS WILL HELP PROVIDE PREDICTIVE MODELS THAT CAN SERVE AS A BASIS FOR QUALITY ASSURANCE AND QUALITY IMPROVEMENT. THIS ARTICLE GIVES AN IDEA OF THE CURRENT LEVEL OF LA DEVELOPMENT AT THE INTERNATIONAL LEVEL. THE ARTICLE ALSO DRAWS CONCLUSIONS ABOUT THE PROBLEMS AND LIMITATIONS ASSOCIATED WITH LEARNING ANALYTICS (LA). THE EXISTING EXPERIENCE HAS BEEN STUDIED, AND THE CONCLUSION HAVE BEEN MADE ABOUT THE EXISTING LIMITATIONS THAT PREVENT THE WIDER USE OF LA.
SUMMER BRIDGE PROGRAMS ARE DESIGNED TO IMPROVE RETENTION AND ACADEMIC SUCCESS AMONG AT-RISK POPULATIONS IN POSTSECONDARY EDUCATION BY FOCUSING ON SUCCESSFUL SKILLS, BEHAVIORS, AND HIGH IMPACT PRACTICES THAT PROMOTE ACADEMIC PERFORMANCE. RECENT RESEARCH ON THESE PROGRAMS HAS FOCUSED PRIMARILY ON HOW STUDENTS' INCOMING DEMOGRAPHICS AND PRIOR ACADEMIC PERFORMANCE PREDICT ACADEMIC PERFORMANCE AT THE UNIVERSITY LEVEL. THIS STUDY INVESTIGATED CHANGES IN STUDENTS' ACADEMIC MOTIVATION ORIENTATIONS OVER THE COURSE OF ONE BRIDGE PROGRAM, AND HOW A LEARNING ANALYTICS-BASED INTERVENTION WAS EMPLOYED BY ACADEMIC ADVISORS TO INFORM THEIR FACE-TO-FACE MEETINGS WITH STUDENTS. THE RESULTS OF OUR STUDY SHOW THAT STUDENTS' MASTERY ORIENTATION DECREASED OVER THE COURSE OF THE BRIDGE PROGRAM, AND INDICATE THAT STUDENTS' EXPOSURE TO DISPLAYS OF THEIR ACADEMIC PERFORMANCE NEGATIVELY PREDICTS THIS CHANGE. THE FINDINGS SUGGEST THAT STUDENT PERCEPTIONS OF THEIR GOALS AND FORMATIVE PERFORMANCE NEED TO BE CAREFULLY CONSIDERED IN THE DESIGN OF LEARNING ANALYTICS INTERVENTIONS SINCE THE RESULTING TOOLS CAN AFFECT STUDENTS' INTERPRETATIONS OF THEIR OWN DATA AS WELL AS THEIR SUBSEQUENT ACADEMIC SUCCESS. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
LEARNING ANALYTICS CAN IMPROVE LEARNING PRACTICE BY TRANSFORMING THE WAYS WE SUPPORT LEARNING PROCESSES. THIS STUDY IS BASED ON THE ANALYSIS OF 252 PAPERS ON LEARNING ANALYTICS IN HIGHER EDUCATION PUBLISHED BETWEEN 2012 AND 2018. THE MAIN RESEARCH QUESTION IS: WHAT IS THE CURRENT SCIENTIFIC KNOWLEDGE ABOUT THE APPLICATION OF LEARNING ANALYTICS IN HIGHER EDUCATION? THE FOCUS IS ON RESEARCH APPROACHES, METHODS AND THE EVIDENCE FOR LEARNING ANALYTICS. THE EVIDENCE WAS EXAMINED IN RELATION TO FOUR EARLIER VALIDATED PROPOSITIONS: WHETHER LEARNING ANALYTICS I) IMPROVE LEARNING OUTCOMES, II) SUPPORT LEARNING AND TEACHING, III) ARE DEPLOYED WIDELY, AND IV) ARE USED ETHICALLY. THE RESULTS DEMONSTRATE THAT OVERALL THERE IS LITTLE EVIDENCE THAT SHOWS IMPROVEMENTS IN STUDENTS' LEARNING OUTCOMES (9%) AS WELL AS LEARNING SUPPORT AND TEACHING (35%). SIMILARLY, LITTLE EVIDENCE WAS FOUND FOR THE THIRD (6%) AND THE FORTH (18%) PROPOSITION. DESPITE THE FACT THAT THE IDENTIFIED POTENTIAL FOR IMPROVING LEARNER PRACTICE IS HIGH, WE CANNOT CURRENTLY SEE MUCH TRANSFER OF THE SUGGESTED POTENTIAL INTO HIGHER EDUCATIONAL PRACTICE OVER THE YEARS. HOWEVER, THE ANALYSIS OF THE EXISTING EVIDENCE FOR LEARNING ANALYTICS INDICATES THAT THERE IS A SHIFT TOWARDS A DEEPER UNDERSTANDING OF STUDENTS' LEARNING EXPERIENCES FOR THE LAST YEARS.
ASSESSMENT AND LEARNING ANALYTICS BOTH COLLECT, ANALYSE AND USE STUDENT DATA, ALBEIT DIFFERENT TYPES OF DATA AND TO SOME EXTENT, FOR VARIOUS PURPOSES. BASED ON THE DATA COLLECTED AND ANALYSED, LEARNING ANALYTICS ALLOW FOR DECISIONS TO BE MADE NOT ONLY WITH REGARD TO EVALUATING PROGRESS IN ACHIEVING LEARNING OUTCOMES BUT ALSO EVALUATIVE JUDGMENTS ABOUT THE QUALITY OF LEARNING. LEARNING ANALYTICS FALL IN THE NEXUS BETWEEN ASSESSMENT OF AND FOR LEARNING. AS SUCH IT HAS THE POTENTIAL TO DELIVER VALUE IN THE FORM OF (1) UNDERSTANDING STUDENT LEARNING, (2) ANALYSING LEARNING BEHAVIOUR (LOOKING TO IDENTIFY NOT ONLY FACTORS THAT MAY INDICATE RISK OF FAILING, BUT FOR OPPORTUNITIES TO DEEPEN LEARNING), (3) PREDICTING STUDENTS-AT-RISK (OR IDENTIFYING WHERE STUDENTS HAVE SPECIFIC LEARNING NEEDS), AND (4) PRESCRIBING ELEMENTS TO BE INCLUDED TO ENSURE NOT ONLY THE EFFECTIVENESS OF TEACHING, BUT ALSO OF LEARNING. LEARNING ANALYTICS HAVE UNDERLYING DEFAULT POSITIONS THAT MAY NOT ONLY SKEW THEIR IMPACT BUT ALSO IMPACT NEGATIVELY ON STUDENTS IN REALISING THEIR POTENTIAL. WE EXAMINE A SELECTION OF DEFAULT POSITIONS AND POINT TO HOW THESE POSITIONS/ASSUMPTIONS MAY ADVERSELY AFFECT STUDENTS' CHANCES OF SUCCESS, DEEPENING THE UNDERSTANDING OF LEARNING.
THE POSSIBILITIES OF LEARNING ANALYTICS AS A TOOL FOR EMPOWERING TEACHERS AND EDUCATORS HAVE CREATED A STEEP INTEREST IN HOW TO PROVIDE SO-CALLED ACTIONABLE INSIGHTS. HOWEVER, THE LITERATURE OFFERS LITTLE IN THE WAY OF DEFINING OR DISCUSSING WHAT THE TERM "ACTIONABLE INSIGHT" MEANS. THIS SELECTIVE LITERATURE REVIEW PROVIDES A LOOK INTO THE USE OF THE TERM IN CURRENT LITERATURE. THE REVIEW POINTS TO A DOMINANT PERSPECTIVE IN THE LITERATURE THAT ASSUMES THE PERSPECTIVE OF A RATIONAL ACTOR, WHERE ACTIONABLE INSIGHTS ARE TREATED AS INSIGHTS MINED FROM DATA AND SUBSEQUENTLY ACTED UPON. IT ALSO FINDS EVIDENCE OF OTHER PERSPECTIVES AND DISCUSSES THE NEED FOR CLARIFICATION OF THE TERM IN ORDER TO ESTABLISH A MORE PRECISE AND FRUITFUL USE OF THE TERM.
VIRTUAL LEARNING ENVIRONMENTS ARE WIDELY USED AS THEY SIMPLIFY AND IMPROVE THE TEACHING AND LEARNING PROCESSES. AS PART OF THE RESEARCH PROJECT AIX - FUTURE TEACHING & LEARNING, WE WANT TO DEVELOP A LEARNING ANALYTICS APPLICATION TO EVALUATE THE GENERATED LOGGING DATA OF OUR LEARNING ENVIRONMENT MOODLE. THE APPLICATION NEEDS TO PROVIDE COMPREHENSIVE STATISTICS AND REPORTS TO SUPPORT THE TEACHING AND LEARNING PROCESSES. THE APPLICATION WILL ALLOW A VARIETY OF COMPLEX RESEARCH QUESTIONS TO BE ANSWERED WHICH HELP THE LECTURERS TO IMPROVE THE LEARNING PROCESS. MOODLE ITSELF OFFERS BASIC LEARNING ANALYTICS FUNCTIONALITY, BUT THE LOGS ALLOW A MORE DETAILED ANALYSIS OF HOW LEARNERS AND TEACHERS USE THE SYSTEM. IN THIS PAPER, WE PRESENT HOW MOODLE STORES ITS LOGGING DATA AND EXPLAIN WHY THE EXISTING DATA MODEL IS NOT WELL SUITED FOR LEARNING ANALYTICS PURPOSES. THEREFORE, WE INTRODUCE OUR OWN DATA MODEL AND EXPLAIN HOW THE MOODLE LOGGING DATA CAN BE CONVERTED INTO OUR FORMAT. IN ADDITION, WE EXTEND THE DATABASE STRUCTURE TO ALLOW THE STORING OF ARBITRARY DATA WHICH CAN BE LINKED TO LEARNERS AND CLASSES. THIS FLEXIBILITY ALLOWS STORING DATA FROM SYSTEMS OTHER THAN MOODLE, WHICH MAKES IT POSSIBLE TO USE META DATA LIKE EXAM GRADES FOR LEARNING ANALYTICS. WE CLAIM THAT THE PRESENTED MODEL IS WELL SUITED FOR STORING THE LOGGING DATA OF VIRTUAL LEARNING ENVIRONMENTS AND THAT IT PROVIDES A GOOD BASIS FOR A LEARNING ANALYTICS APPLICATION.
INTERVENTION HAS BEEN CLAIMED TO BE THE GREATEST CHALLENGE IN LEARNING ANALYTICS. AS THE PROVISION OF JUST-IN-TIME AND PERSONALISED SUPPORT FOR LEARNERS, INTERVENTION HAS YET TO BE WIDELY IMPLEMENTED IN LEARNING ANALYTICS PRACTICES. THIS PAPER REVIEWS INTERVENTION PRACTICES IN HIGHER EDUCATION IN 23 CASE STUDIES. THE CASES WERE CATEGORISED INTO FOUR TYPES - DIRECT MESSAGE, ACTIONABLE FEEDBACK, CATEGORISATION OF STUDENTS, AND COURSE REDESIGN - ACCORDING TO THE NATURE OF THE METHODS OF INTERVENTION; AND THE INTERVENTION METHODS WERE SUMMARISED. MOST OF THE INTERVENTION CASES BELONGED TO THE FIRST TWO TYPES. DIRECT MESSAGE INVOLVES CONTACTING AT-RISK STUDENTS VIA CHANNELS SUCH AS EMAILS OR PHONE CALLS TO ENCOURAGE THEIR PARTICIPATION, PROVIDE ADDITIONAL LEARNING RESOURCES, OR REMIND THEM OF DEADLINES. ACTIONABLE FEEDBACK INVOLVES THE PROVISION OF SUGGESTIONS OR INFORMATION FOR STUDENTS TO HELP THEM UNDERSTAND THEIR PERFORMANCE AND POSSIBLE WAYS OF IMPROVING IT. ONLY A FEW CASES WERE IDENTIFIED FOR THE OTHER TWO TYPES OF INTERVENTION, WHICH INVOLVE CATEGORISATION OF STUDENTS INTO DIFFERENT GROUPS BASED ON THEIR RISK LEVELS FOR TAKING SPECIFIC REMEDIAL ACTIONS FOR EACH GROUP, AND REDESIGNING THE COURSE STRUCTURE OR CONTENTS BASED ON THE ANALYSIS OF DATA. THE RESULTS OF THIS REVIEW SERVE TO FACILITATE THE FORMULATION OF INTERVENTION STRATEGIES FOR HIGHER EDUCATION INSTITUTIONS WHICH PRACTISE LEARNING ANALYTICS.
LEARNING ANALYTICS ALLOWS TO DESCRIBE, DIAGNOSE, PREDICT AND PRESCRIBE LEARNING, ESPECIALLY IN HIGHER EDUCATION. TWO THOUSAND EIGHT HUNDRED FIFTY-THREE PAPERS RELATED TO LEARNING ANALYTICS ARE STORED IN THE SCOPUS BIBLIOGRAPHY DATABASE FROM 2014 UNTIL THE MIDDLE OF 2019, EVIDENCING THE IMPORTANCE AND INCREASING INTEREST IN THIS LINE OF RESEARCH. THIS RESEARCH DISCOVERS SPECIFIC CHARACTERISTICS OF INVESTIGATIONS IN LEARNING ANALYTICS AND ANSWERS THE GENERAL QUESTION: WHAT IS THE ACTUAL STATE OF LEARNING ANALYTICS RESEARCH IN ECUADOR? THIS RESEARCH USES A SYSTEMATIC MAPPING TO ANSWER FOUR RESEARCH QUESTIONS ABOUT INDEXED PRODUCTION IN LEARNING ANALYTICS BY ECUADORIAN AUTHORS. THIS STUDY HAS BEEN DONE FROM 2014 UNTIL JUNE 2019 (11 SEMESTERS). EIGHTY-SIX ARTICLES ABOUT LEARNING ANALYTICS WERE FOUND IN RRAAE, SCOPUS, WOS AND IEEE. SIXTY-EIGHT REPORTS WERE DOWNLOADED, ARRANGED AND ANALYSED AFTER REMOVING DUPLICATES, APPLYING INCLUSION, EXCLUSION AND QUALITY CRITERIA. THE METHODOLOGY USED IS REPLICABLE BY THE RESEARCHERS INTERESTED IN ESTABLISHING A BASELINE IN GENERAL AND IN PARTICULAR IN LEARNING ANALYTICS.
THIS PAPER PRESENTS FOUR CATEGORIES OF LEARNING ANALYTICS TOOLS: DASHBOARDS, AD HOC TOOLS, TOOLS FOR ANALYSIS OF SPECIFIC ISSUES, AND LEARNING ANALYTICS FRAMEWORKS, AND DETAILS THE CHARACTERISTICS OF A SELECTION OF TOOLS WITHIN EACH CATEGORY: (1) MOODLE DASHBOARD AND MOODLE DEFAULT REPORTING TOOL; (2) INTERACTIONS AND TEAMWORK ASSESSMENT TOOL; (3) SNAPP, GRAPHFES AND MOODLE ENGAGEMENT ANALYTICS; AND (4) VELA AND GISMO. THE STUDY INVESTIGATES HOW THESE TOOLS CAN BE APPLIED TO THE ANALYSIS OF COURSES BY USING REAL DATA FROM A COURSE THAT MADE INTENSIVE USE OF FORUMS, WIKIS, WEB RESOURCES, VIDEOS, QUIZZES AND ASSIGNMENTS. THE DISCUSSION THAT FOLLOWS POINTS OUT HOW THE DIFFERENT TOOLS COMPLEMENT EACH OTHER, AND SUGGESTS THE IMPLEMENTATION OF BASIC DASHBOARDS IN LEARNING PLATFORMS AND THE USE OF EXTERNAL FRAMEWORKS FOR LEARNING ANALYTICS.
RESEARCH IN LEARNING ANALYTICS IS PROLIFERATING AS SCHOLARS CONTINUE TO FIND BETTER AND MORE ENGAGING WAYS TO CONSIDER HOW DATA CAN HELP INFORM EVIDENCE-BASED DECISIONS FOR LEARNING AND LEARNING ENVIRONMENTS. WITH WELL OVER A THOUSAND ARTICLES PUBLISHED IN JOURNALS AND CONFERENCES WITH RESPECT TO LEARNING ANALYTICS, ONLY A HANDFUL OR ARTICLES EXIST THAT ATTEMPT TO SYNTHESIZE THE RESEARCH. FURTHER, A META-REVIEW OF THOSE ARTICLES REVEALS A LACK OF CONSISTENCY IN THE SCOPE OF INCLUDED STUDIES, THE CONFLUENCE OF EDUCATIONAL DATA MINING ACTIVITIES AND "BIG DATA" AS A PARAMETER FOR INCLUSION, AND THE REPORTING OF ACTUAL STRATEGIES AND ANALYTIC METHODS USED BY THE INCLUDED STUDIES. TO FILL THESE GAPS WITHIN EXISTING REVIEWS OF LEARNING ANALYTICS RESEARCH, THIS METASYNTHESIS FOLLOWS PROCEDURES OUTLINED BY COOPER TO REVEAL DEVELOPMENTS OF LEARNING ANALYTICS RESEARCH. THE RESULTS INCLUDE A NUMBER OF METRICS SHOWING TRENDS AND TYPES OF LEARNING ANALYTIC STUDIES THROUGH 2017 THAT INCLUDE WHICH FIELDS ARE PUBLISHING AND TO WHAT EXTENT, WHAT METHODS AND STRATEGIES ARE EMPLOYED BY THESE STUDIES, AND WHAT DOMAINS REMAIN LARGELY YET UNEXPLORED.
THE PRESENT WORK DESCRIBES THE CHALLENGES FACED DURING THE DEVELOPMENT OF A COUNTRYWIDE LEARNING ANALYTICS TOOL FOCUSED ON TRACKING THE TRAJECTORIES OF URUGUAYAN STUDENTS DURING THEIR FIRST THREE YEARS OF SECONDARY EDUCATION. DUE TO THE LARGE-SCALE OF THE PROJECT, WHICH COVERS AN ENTIRE NATIONAL EDUCATIONAL SYSTEM, SEVERAL CHALLENGES AND CONSTRAINTS (BOTH TECHNICAL AND LEGAL) WERE FACED DURING ITS CONCEPTION AND DEVELOPMENT. THIS PAPER PRESENTS THE DESIGN DECISIONS AND SOLUTIONS FOUND TO ADDRESS OR MITIGATE THE PROBLEMS FOUND, WITH THE CURRENT STATE OF THE PROJECT. EARLY RESULTS POINT OUT THE FEASIBILITY OF FINDING MEANINGFUL PATTERNS IN THE AVAILABLE DATA (USING DATA MINING TECHNIQUES) WHICH CAN BE EMBEDDED INTO A PROTOTYPE FOR TRACKING THE STUDENTS SCHOLAR TRAJECTORY.
DEPENDING ON THEIR MOTIVATIONAL DISPOSITIONS, STUDENTS CHOOSE DIFFERENT LEARNING STRATEGIES AND VARY IN THEIR PERSISTENCE IN REACHING LEARNING OUTCOMES. AS LEARNING IS MORE AND MORE FACILITATED THROUGH TECHNOLOGY, ANALYTICS APPROACHES ALLOW LEARNING PROCESSES AND ENVIRONMENTS TO BE ANALYZED AND OPTIMIZED. HOWEVER, RESEARCH ON MOTIVATION AND LEARNING ANALYTICS IS AT AN EARLY STAGE. THUS, THE PURPOSE OF THIS QUANTITATIVE SURVEY STUDY IS TO INVESTIGATE THE RELATION BETWEEN STUDENTS' MOTIVATIONAL DISPOSITIONS AND THE SUPPORT THEY PERCEIVE THROUGH LEARNING ANALYTICS. FINDINGS INDICATE THAT FACETS OF STUDENTS' GOAL ORIENTATIONS AND ACADEMIC SELF-CONCEPT IMPACT STUDENTS' EXPECTATIONS OF THE SUPPORT FROM LEARNING ANALYTICS. THE FINDINGS EMPHASIZE THE NEED TO DESIGN HIGHLY PERSONALIZED AND ADAPTABLE LEARNING ANALYTICS SYSTEMS THAT CONSIDER STUDENTS' DISPOSITIONS AND NEEDS. THE PRESENT STUDY IS A FIRST ATTEMPT AT LINKING EMPIRICAL EVIDENCE, MOTIVATIONAL THEORY, AND LEARNING ANALYTICS.
OBSERVATIONAL DATA CAN BE USED TO ILLUMINATE DIFFERENT AREAS OF TEACHING AND LEARNING PROCESS AND ENRICH LEARNING ANALYTICS DATA. MAJORITY OF LESSON OBSERVATION TOOLS PROVIDE OBSERVATIONAL DATA THAT IS NOT COMPLIANT WITH LA DATASETS. THE PAPER PRESENTS OBSERVATA - A TABLET COMPUTER APPLICATION FOR CONTEXT-AWARE SEMANTIC ANNOTATIONS OF SIGNIFICANT EVENTS DURING REAL TIME LESSON OBSERVATIONS. DURING THE DEMO-SESSION WE EXPECT THE PARTICIPANTS TO ENGAGE IN THE DISCUSSION AND PROVIDE FEEDBACK ON THE PROTOTYPE.
IN ORDER TO FURTHER THE FIELD OF LEARNING ANALYTICS (LA), RESEARCHERS AND EXPERTS MAY NEED TO LOOK BEYOND THEMSELVES AND THEIR OWN PERSPECTIVES AND EXPERTISE TO INNOVATE LA PLATFORMS AND INTERVENTIONS. WE SUGGEST THAT BY CO-CREATING WITH THE USERS OF LA, SUCH AS EDUCATORS AND STUDENTS, RESEARCHERS AND EXPERTS CAN IMPROVE THE USABILITY, USEFULNESS, AND DRAW GREATER UNDERSTANDING FROM LA INTERVENTIONS. WITHIN THIS ARTICLE WE DISCUSS THE CURRENT LA ISSUES AND BARRIERS AND HOW CO-CREATION STRATEGIES CAN HELP ADDRESS MANY OF THESE CHALLENGES. WE FURTHER OUTLINE THE CONSIDERATIONS, BOTH PRE- AND DURING INTERVENTIONS, WHICH SUPPORT AND FOSTER A CO-CREATED STRATEGY FOR LEARNING ANALYTICS INTERVENTIONS.
LEARNING ANALYTICS REFERS TO A SYSTEMATIC PROCESS INVOLVING MEASURING, COLLECTING, ANALYZING AND REPORTING DATA ABOUT LEARNERS WITH THE AIM OF FULLY UNDERSTANDING HOW BEST LEARNING ENVIRONMENTS CAN BE OPTIMIZED TO INCREASE EFFICIENCY. THE AIM OF THIS STUDY IS TO UNDERSTAND THE FACTORS CONTRIBUTING TO THE LEARNING ANALYTICS ADOPTION BY UNIVERSITY STUDENTS IN NORTH CYPRUS. PARTICIPANTS COMPRISED OF STUDENTS FROM THREE UNIVERSITIES IN NORTH CYPRUS. 718 VALID QUESTIONNAIRES INVOLVING ITEMS FROM THE ADOPTED UTAUT (UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY) MODEL WAS USED IN THE STUDY. THE RESULTS HAVE SHOWN THAT THERE WAS A WEAK NEGATIVE CORRELATION BETWEEN PERFORMANCE EXPECTANCY AND TECHNOLOGY USE INTENTION IMPLYING THAT WHEN STUDENTS ARE AWARE OF HOW A TECHNOLOGY OPERATES AND IF IT SATISFIES THEIR REQUIREMENTS, THEN THEY WILL BE READY TO ADOPT LEARNING ANALYTICS. THERE WAS ALSO A NEGATIVE WEAK CORRELATION BETWEEN EFFORT EXPECTANCY AND TECHNOLOGY USE INTENTION. A POSITIVE WEAK CORRELATION BETWEEN SOCIAL INFLUENCE AND TECHNOLOGY USE INTENTION WAS OBSERVED WHILE THERE WAS A NEGATIVE WEAK CORRELATION BETWEEN TECHNOLOGY USE INTENTION AND TECHNOLOGY USE BEHAVIOR IMPLYING THAT WHEN A STUDENTS HAVE INTENTIONS OF USING LEARNING ANALYTICS, THEY SHOW A POSITIVE BEHAVIOR TOWARDS THE TECHNOLOGY. THE STUDY ALSO SHOWS THAT THERE WAS ALSO MODERATE POSITIVE CORRELATION BETWEEN TECHNOLOGY ANXIETY AND TECHNOLOGY USER BEHAVIOR. THIS STUDY IS CONSIDERED TO BE OF GREAT BENEFIT AND PRACTICAL IMPLEMENTATION TO RESEARCHERS, INSTRUCTORS, STUDENTS, UNIVERSITIES AND THE MINISTRY OF EDUCATION.
LEARNING ANALYTICS (LA) IS A MULTIDISCIPLINARY AREA OF RESEARCH, WHERE EDUCATION, STATISTICS, AND TECHNOLOGY EXPERTS COLLABORATE WITH OTHER DISCIPLINES TO GET INSIGHTS FROM LEARNING DATA SETS. THIS INVOLVES A CONTINUOUS CYCLE OF GATHERING DATA FROM TEACHERS' AND STUDENTS' INTERACTIONS, FILTERING AND TRANSLATING IT TO PROPER FORMATS, USING A WIDE RANGE OF ANALYSIS TECHNIQUES AND STARTING AGAIN AFTER TAKING ADVANTAGE FROM THE RESULTS FOUND. CONSIDERING THE VARIETY OF EXPERTISE INVOLVED, THE NEED FOR SHARING KNOWLEDGE AND EXPERIENCES IS HIGHLIGHTED IN RELEVANT FORUMS SUCH AS THE LEARNING ANALYTICS AND KNOWLEDGE CONFERENCE. THE SAME HAPPENS AT SPANISH LEVEL WITH THE LEARNING ANALYTICS SUMMER INSTITUTE, SPAIN AND THE SPANISH NETWORK OF LEARNING ANALYTICS. RESEARCH WORKS PRESENTED IN THIS SPECIAL ISSUE SHOW THE ABILITY OF ANALYZING AND IMPROVING LEARNING AND TEACHING PROCESSES THROUGH LA. THESE THREE PAPERS ADDRESS DIFFERENT EDUCATIONAL CHALLENGES, SUCH AS ASSESSING MASSIVE COLLABORATIVE PROJECTS, EVALUATING THE ACCESSIBILITY AND USABILITY OF OPEN EDUCATIONAL RESOURCES AND MAPPING THEM WITH THE IMS CALIPER STANDARD, AND USING SOCIAL NETWORK ANALYSIS TO EVALUATE THE SOCIO-REGULATION SKILLS AMONG STUDENTS.
LEARNING ANALYTICS IS A TECHNOLOGY THAT EMPLOYS PATERNALISTIC NUDGING TECHNIQUES AND PREDICTIVE MEASURES. THESE TECHNIQUES CAN LIMIT STUDENT AUTONOMY, MAY RUN COUNTER TO STUDENT INTERESTS AND PREFERENCES, AND DO NOT ALWAYS DISTRIBUTE BENEFITS BACK TO STUDENTS-IN FACT SOME HARMS MAY ACTUALLY ACCRUE. THE PAPER PRESENTS THREE CASES OF PATERNALISM IN LEARNING ANALYTICS TECHNOLOGIES, ARGUING THAT PATERNALISM IS AN ESPECIALLY PROBLEMATIC CONCERN FOR HIGHER EDUCATION INSTITUTIONS WHO ESPOUSE LIBERAL EDUCATION VALUES. THREE GENERAL RECOMMENDATIONS ARE PROVIDED THAT WORK TO PROMOTE STUDENT AUTONOMY AND CHOICE MAKING AS A WAY TO PROTECT AGAINST RISKS TO STUDENT ACADEMIC FREEDOM.
LEARNING ANALYTIC IMPLEMENTATIONS ARE INCREASINGLY BEING INCLUDED IN LEARNING MANAGEMENT SYSTEMS IN HIGHER EDUCATION. WE LAY OUT SOME CONCERNS WITH THE WAY LEARNING ANALYTICS - BOTH DATA AND ALGORITHMS - ARE OFTEN PRESENTED WITHIN AN UNPROBLEMATIZED BIG DATA DISCOURSE. WE DESCRIBE SOME POTENTIAL PROBLEMS WITH THE OFTEN IMPLICIT ASSUMPTIONS ABOUT LEARNING AND LEARNERS - AND INDEED THE TENDENCY NOT TO THEORIZE LEARNING EXPLICITLY - THAT UNDERPIN SUCH IMPLEMENTATIONS. FINALLY, WE DESCRIBE AN ATTEMPT TO DEVISE OUR OWN ANALYTICS, GROUNDED IN A SOCIOMATERIAL CONCEPTION OF LEARNING. WE USE THE DATA OBTAINED TO SUGGEST THAT THE RELATIONSHIPS BETWEEN LEARNING AND THE DIGITAL TRACES LEFT BY PARTICIPANTS IN ONLINE LEARNING ARE FAR FROM TRIVIAL, AND THAT ANY ANALYTICS THAT RELIES ON THESE AS PROXIES FOR LEARNING TENDS TOWARDS A BEHAVIORIST EVALUATION OF LEARNING PROCESSES.
IN THIS PAPER A LEARNING ANALYTICS DASHBOARD FOR MOOCS IS PROPOSED. IT VISUALISES THE PROGRESS OF LEARNERS' ACTIVITY TAKING INTO ACCOUNT NAVIGATION, SOCIAL INTERACTIONS AND INTERACTION WITH EDUCATIONAL RESOURCES. THIS APPROACH WAS TESTED WITH THE MOOCS CREATED BY THE UNIVERSITY AUTONOMA OF MADRID (SPAIN) IN THE EDX PLATFORM. NOWADAYS, THE DASHBOARD IS BEING IMPROVED TAKING INTO ACCOUNT THE RECEIVED FEEDBACK FROM MOOCS INSTRUCTORS AND ASSISTANTS. FINALLY, A NEW VERSION IS PRESENTED TO WORK ALONG WITH EDX AND OPEN EDX.
LEARNING ANALYTICS IS THE MEASUREMENT OF STUDENT PROGRESS BY THE COLLECTING, ANALYSIS AND REPORTING DATA IN THE LEARNING ENVIRONMENT. LEARNING ANALYTICS METHODS TRY TO FIND OUT THE DEPENDENT PATTERN IN DATASET GATHERED. LEARNING ANALYTICS IMPROVE STUDENT OUTCOMES IN SEVERAL WAYS, FIRST, USING LEARNING ANALYTICS LEADS TO MEASURE STUDENT SUCCESS CORRECTLY. WITH THIS INFORMATION STUDENTS CAN FIND ACCURATE TEACHING TECHNIQUES AND SUPPORT THEMSELVES. ALSO, IT GIVES PROPER AND FASTER FEEDBACK ABOUT LEARNING TECHNIQUE TO THE STAKEHOLDERS (PRINCIPALS, TEACHERS, PARENTS). THE SCOPE AND THE AIM OF LEARNING ANALYTIC PROJECTS MAY DIFFER FOR DIFFERENT ORGANIZATIONS. SELECTING THE RIGHT LEARNING ANALYTIC PROJECT IS CRUCIAL FOR THE OVERALL SUCCESS OF THE LEARNING PROCESS. DESPITE THEIR BENEFITS, WHILE SELECTING THE LEARNING ANALYTIC PROJECTS NOT ONLY FINANCIAL BENEFITS BUT ALSO VARIOUS FACTORS INCLUDING PRIVACY, ACCESS, TRANSPARENCY, SECURITY, ACCURACY, RESTRICTIONS, AND OWNERSHIP SHOULD BE TAKEN INTO ACCOUNT. YET, EVALUATING THESE FACTORS IS NOT EASY SINCE THEY INVOLVE UNCERTAINTIES. THEREFORE, THE SELECTION OF LEARNING ANALYTIC PROJECTS IS A COMPLEX PROCESS THAT INCLUDES VARIOUS UNCERTAINTIES. IN THIS STUDY, WE UTILIZE AN SPHERICAL FUZZY TOPSIS APPROACH FOR SELECTING LEARNING ANALYTICS PROJECTS THIS METHOD ENABLED US DEFINING THE UNCERTAINTIES WITH INDEPENDENT PARAMETERS.
IN RECENT YEARS, THE EVOLUTION OF RESEARCH IN THE DOMAIN OF EDUCATION FOCUSES ON LEARNING ANALYTICS WHICH PROVIDES VITAL INSIGHTS ON EDUCATION OUTCOME BY COLLECTING VALUABLE DATA ABOUT LEARNING PERSON OR/AND INSTITUTIONS. THE MAIN OBJECTIVE OF THIS ARTICLE IS TO INSPECT THE RESEARCH LITERATURE DISSEMINATED ON LEARNING ANALYTICS PREDICTIVE MODELS IN THE LAST FIVE YEARS. THIS WORK DISCUSSES A DETAILED STUDY ON THE LEARNING ANALYTICS BY CATEGORIZING BASED ON PREDICTION ALGORITHMS, DATA SET USED, AND FACTORS PRIORITIZED FOR PREDICTION. THIS PAPER ALSO EXPLAINS THE VARIOUS APPLICATIONS, LIFE CYCLE MODELS OF LEARNING ANALYTICS AND THE VITAL CHALLENGES THAT COULD IMPROVE THE OVERALL PERFORMANCE OF THE LEARNING ANALYTICS SYSTEM. THIS WORK MAY PROVIDE COMPREHENSIVE DETAILS ON LEARNER ANALYTICS AND ITS POSSIBLE LINE OF FUTURE RESEARCH FOR THE RESEARCHERS TO PROPOSE NOVEL MODELS TO APPEND RESEARCH VALUE TO THE DOMAIN. (C) 2020 THE AUTHORS. PUBLISHED BY ELSEVIER B.V.
MASSIVE OPEN ONLINE COURSES (MOOC) ALLOWS TEACHING AND LEARNING FOR EVERYONE. THIS MEANS THAT PEOPLE FROM ANY LEARNING BACKGROUND CAN JOIN ANY OF THE COURSES OFFERED THROUGH MOOC PLATFORMS. ALTHOUGH LEARNING MATERIALS ARE OFFERED FOR FREE, LEARNING RETENTION AND LEARNING ENGAGEMENT WERE FOUND TO BE CONSISTENTLY LOW ALTHOUGH SOME MOOC ARE OFFERED BY WELL-KNOWN INSTRUCTORS. MANY RECENT STUDIES TRIED TO UNDERSTAND THE SUITABLE INSTRUCTIONAL DESIGN IN MOOC TO IMPROVE LEARNING ENGAGEMENT AND RETENTION. THIS STUDY IS AN EXPLORATORY STUDY TO EVALUATE THE POTENTIAL OF USING LEARNING ANALYTICS TO IMPROVE INSTRUCTIONAL DESIGN IN MOOC. DATA WERE COLLECTED FROM A MOOC OFFERED FOR TWO CONSEQUENT YEARS IN A PUBLIC UNIVERSITY IN MALAYSIA. THE IMPACT OF LEARNING ANALYTICS ON MOOC INSTRUCTIONAL DESIGN WAS ALSO DISCUSSED.
LEARNING ANALYTICS (LA) HAS BECOME A PROMINENT PARADIGM IN THE CONTEXT OF EDUCATION LATELY WHICH ADOPTS THE RECENT ADVANCEMENTS OF TECHNOLOGY SUCH AS CLOUD COMPUTING, BIG DATA PROCESSING, AND INTERNET OF THINGS. LA ALSO REQUIRES AN INTENSIVE AMOUNT OF PROCESSING RESOURCES TO GENERATE RELEVANT ANALYTICAL RESULTS. HOWEVER, THE TRADITIONAL APPROACHES HAVE BEEN INEFFICIENT AT TACKLING LA CHALLENGES SUCH AS REAL-TIME, HIGH PERFORMANCE, AND SCALABLE PROCESSING OF HETEROGENEOUS DATASETS AND STREAMING DATA. AN INTERNET OF THINGS (IOT) SCALABLE, DISTRIBUTED AND HIGH PERFORMANCE FRAMEWORK HAS THE POTENTIAL TO ADDRESS MENTIONED LA CHALLENGES BY EFFICIENT CONTEXTUALIZATION OF DATA. IN THIS PAPER, COALA, A SMART LEARNING ANALYTICS CONCEPTUAL MODEL IS PROPOSED TO IMPROVE THE EFFECTIVENESS OF LA BY UTILIZING AN IOT-BASED CONTEXTUALIZATION FRAMEWORK IN TERMS OF PERFORMANCE, SCALABILITY, AND EFFICIENCY.
THIS PAPER PROPOSES A NOVEL APPROACH TO BUILD AND DEPLOY LEARNING ANALYTICS DASHBOARDS IN MULTIPLE LEARNING ENVIRONMENTS. EXISTING LEARNING DASHBOARDS ARE BARELY PORTABLE: ONCE DEPLOYED ON A LEARNING PLATFORM, IT REQUIRES CONSIDERABLE EFFORT TO DEPLOY THE DASHBOARD ELSEWHERE. WE SUGGEST CONSTRUCTING DASHBOARDS FROM LIGHTWEIGHT WEB APPLICATIONS, NAMELY WIDGETS. OUR APPROACH ALLOWS TO PORT DASHBOARDS WITH NO ADDITIONAL COST BETWEEN LEARNING ENVIRONMENTS THAT IMPLEMENT OPEN SPECIFICATIONS (OPENSOCIAL AND ACTIVITYSTREAMS) FOR DATA ACCESS AND USE WIDGET APIS. WE PROPOSE TO FACILITATE REUSE BY SHARING THE DASHBOARDS AND WIDGETS VIA A CENTRALIZED ANALYTICS REPOSITORY.
USE OF UNIVERSITY STUDENTS' EDUCATIONAL DATA FOR LEARNING ANALYTICS HAS SPURRED A DEBATE ABOUT WHETHER AND HOW TO PROVIDE STUDENTS WITH AGENCY REGARDING DATA COLLECTION AND USE. A CONCERN IS THAT STUDENTS OPTING OUT OF LEARNING ANALYTICS MAY SKEW PREDICTIVE MODELS, IN PARTICULAR IF CERTAIN STUDENT POPULATIONS DISPROPORTIONATELY OPT OUT AND BIASES ARE UNINTENTIONALLY INTRODUCED INTO PREDICTIVE MODELS. WE INVESTIGATED UNIVERSITY STUDENTS' PROPENSITY TO CONSENT TO LEARNING ANALYTICS THROUGH AN EMAIL PROMPT, AND COLLECTED RESPONDENTS' PERCEIVED BENEFITS AND PRIVACY CONCERNS REGARDING LEARNING ANALYTICS IN A SUBSEQUENT ONLINE SURVEY. IN PARTICULAR, WE STUDIED WHETHER AND WHY STUDENTS' CONSENT PROPENSITY DIFFERS AMONG STUDENT SUBPOPULATIONS BYSENDING OUR EMAIL PROMPT TO A SAMPLE OF 4,000 STUDENTS AT OUR INSTITUTION STRATIFIED BY ETHNICITY AND GENDER. 272 STUDENTS INTERACTED WITH THE EMAIL, OF WHICH 119 ALSO COMPLETED THE SURVEY. WE IDENTIFIED THAT INSTITUTIONAL TRUST, CONCERNS WITH THE AMOUNT OF DATA COLLECTION VERSUS PERCEIVED BENEFITS, AND COMFORT WITH INSTRUCTORS' DATA USE FOR LEARNING ENGAGEMENT WERE KEY DETERMINANTS IN STUDENTS' DECISION TO PARTICIPATE IN LEARNING ANALYTICS. WE FIND THAT STUDENTS IDENTIFYING ETHNICALLY AS BLACK WERE SIGNIFICANTLY LESS LIKELY TO RESPOND AND SELF-REPORTED LOWER LEVELS OF INSTITUTIONAL TRUST. FEMALE STUDENTS REPORTED CONCERNS WITH DATA COLLECTION BUT WERE ALSO MORE COMFORTABLE WITH USE OF THEIR DATA BY INSTRUCTORS FOR LEARNING ENGAGEMENT PURPOSES. STUDENTS' COMMENTS CORROBORATE THESE FINDINGS AND SUGGEST THAT AGENCY ALONE IS INSUFFICIENT; INSTITUTIONAL LEADERS AND INSTRUCTORS ALSO PLAY A LARGE ROLE IN ALLEVIATING THE ISSUE OF BIAS.
IDIOGRAPHIC METHODS HAVE EMERGED AS A WAY TO EXAMINE INDIVIDUAL BEHAVIOR BY USING SEVERAL DATA POINTS FROM EACH SUBJECT TO CREATE PERSON-SPECIFIC INSIGHTS. IN THE FIELD OF LEARNING ANALYTICS, SUCH METHODS COULD OVERCOME THE LIMITATIONS OF CROSS-SECTIONAL GROUP-LEVEL DATA THAT MAY FAIL TO CAPTURE THE DYNAMIC PROCESSES THAT UNFOLD WITHIN EACH INDIVIDUAL LEARNER AND LESS LIKELY TO OFFER RELEVANT PERSONALIZED LEARNING OR SUPPORT. IN THIS STUDY, WE PROVIDE A DEFINITION OF IDIOGRAPHIC LEARNING ANALYTICS AND WE EXPLORE THE POSSIBLE POTENTIALS OF THIS METHOD TO ZOOM IN ON THE FINE-GRAINED DYNAMICS OF A SINGLE STUDENT. SPECIFICALLY, WE MAKE USE OF GAUSSIAN GRAPHICAL MODELS -AN EMERGING TREND IN NETWORK SCIENCE- TO ANALYZE A SINGLE STUDENT'S DISPOSITIONS AND DEVISE INSIGHTS SPECIFIC TO HIM/HER. OUR FINDINGS OFFER A PROOF OF CONCEPT OF THE POTENTIAL OF THIS NOVEL METHOD IN REVEALING PERSONALIZED VALUABLE INSIGHTS ABOUT STUDENTS' SELF-REGULATION. WHILE OUR SPECIFIC FINDINGS APPLY TO A SINGLE STUDENT, OUR METHOD APPLIES TO EVERY STUDENT REGARDLESS OF CONTEXT.
A LEARNING ANALYTICS DASHBOARD ENABLES TEACHERS AND STUDENTS TO MONITOR AND REFLECT ON THEIR ONLINE TEACHING AND LEARNING PATTERNS. THIS STUDY WAS A REVIEW OF PRIOR STUDIES ON LEARNING ANALYTICS DASHBOARDS TO SHOW THE NEED TO DEVELOP AN INSTRUMENT FOR MEASURING DASHBOARD SUCCESS. AN EARLY VERSION OF THE INSTRUMENT BASED ON THE FRAMEWORK OF KIRKPATRICK'S FOUR LEVELS OF EVALUATION WAS REVISED THROUGH EXPERT REVIEWS AND EXPLORATORY FACTOR ANALYSIS. THE INSTRUMENT CONTAINS FIVE CRITERIA: VISUAL ATTRACTION, USABILITY, LEVEL OF UNDERSTANDING, PERCEIVED USEFULNESS, AND BEHAVIORAL CHANGES. THE VALIDITY OF THE INSTRUMENT WAS SUBSEQUENTLY TESTED WITH FACTOR ANALYSIS. A TOTAL OF 271 SAMPLES FROM STUDENTS WHO UTILIZED A LEARNING ANALYTICS DASHBOARD FOR ONE SEMESTER WERE COLLECTED AND ANALYZED USING STRUCTURAL EQUATION MODELING. IN THE MODEL WITH FAIR FIT, THE VISUAL ATTRACTION AND USABILITY OF THE DASHBOARD SIGNIFICANTLY AFFECTED THE LEVEL OF UNDERSTANDING, AND LEVEL OF UNDERSTANDING AFFECTED PERCEIVED USEFULNESS, WHICH IN TURN SIGNIFICANTLY AFFECTED POTENTIAL BEHAVIOR CHANGES. THE FINDINGS OF THIS STUDY HAVE IMPLICATIONS FOR DESIGNERS WHO WANT TO DEVELOP SUCCESSFUL LEARNING ANALYTICS DASHBOARDS, AND FURTHER RESEARCH IS SUGGESTED RELATED TO MEASURING THE CROSS VALIDITY OF THE EVALUATION INSTRUMENT TO BROADEN ITS USAGE.
IN RECENT YEARS, THE APPLICATION OF TECHNOLOGICAL INNOVATION IN HIGHER EDUCATION HAS BECOME MORE AND MORE WIDELY SPREAD, AND TECHNOLOGICAL INNOVATION HAS BEEN IMPROVING THE LEVEL OF EDUCATION. IN THE RESEARCH OF HIGHER EDUCATION WITH INNOVATION TECHNOLOGY, ONE OF THE MAIN FOCUSES IS ON THE DYNAMIC DATA WHICH CAN LAY A FOUNDATION FOR THE ANALYSIS OF EDUCATIONAL ACTIVITIES BY LEARNING ANALYTICS. THE DYNAMIC DATA CREATED BY TECHNOLOGICAL INNOVATION WILL BECOME THE KEY BASIS FOR ANALYTICAL RESEARCH AND DEVELOPMENT IN HIGHER EDUCATION. THE METHODS AND ANALYSIS RESULTS OF LEARNING ANALYTICS WILL DIRECTLY AFFECT DECISION-MAKING AND STRATEGY ABOUT HIGHER EDUCATION. IN THIS PAPER, WE USE BIBLIOMETRIC AND VISUALISATION METHODS TO REVIEW THE LITERATURE, IN ORDER TO HIGHLIGHT THE DEVELOPMENT OF LEARNING ANALYTICS IN HIGHER EDUCATION. USING BIBLIOMETRIC ANALYSIS, OUR STUDY DEPICTS THE DEVELOPMENT PROCESS OF THE MAIN METHODS USED IN LEARNING ANALYTICS, AND SUMMARISES THE CURRENT SITUATION IN THIS FIELD, WHICH INCREASES THE LEVEL OF UNDERSTANDING PROVIDED BY THOSE STUDIES. FINALLY, WE SUMMARISE THE RESEARCH HOTSPOTS AND STUDY TRENDS, WHICH WILL BE USEFUL FOR FUTURE STUDY IN THIS FIELD.
THIS PAPER INTRODUCES A LEARNING ANALYTICS POLICY AND STRATEGY FRAMEWORK DEVELOPED BY A CROSS-EUROPEAN RESEARCH PROJECT TEAM - SHEILA1 (SUPPORTING HIGHER EDUCATION TO INTEGRATE LEARNING ANALYTICS), BASED ON INTERVIEWS WITH 78 SENIOR MANAGERS FROM 51 EUROPEAN HIGHER EDUCATION INSTITUTIONS ACROSS 16 COUNTRIES. THE FRAMEWORK WAS DEVELOPED ADAPTING THE RAPID OUTCOME MAPPING APPROACH (ROMA), WHICH IS DESIGNED TO DEVELOP EFFECTIVE STRATEGIES AND EVIDENCE-BASED POLICY IN COMPLEX ENVIRONMENTS. THIS PAPER PRESENTS FOUR CASE STUDIES TO ILLUSTRATE THE DEVELOPMENT PROCESS OF THE SHEILA FRAMEWORK AND HOW IT CAN BE USED ITERATIVELY TO INFORM STRATEGIC PLANNING AND POLICY PROCESSES IN REAL WORLD ENVIRONMENTS, PARTICULARLY FOR LARGE-SCALE IMPLEMENTATION IN HIGHER EDUCATION CONTEXTS. TO THIS END, THE SELECTED CASES WERE ANALYZED AT TWO STAGES, EACH A YEAR APART, TO INVESTIGATE THE PROGRESSION OF ADOPTION APPROACHES THAT WERE FOLLOWED TO SOLVE EXISTING CHALLENGES, AND IDENTIFY NEW CHALLENGES THAT COULD BE ADDRESSED BY FOLLOWING THE SHEILA FRAMEWORK.
PURPOSE THIS PAPER AIMS TO REVIEW AND IDENTIFY THE MAJOR PATTERNS AND TRENDS IN LEARNING ANALYTICS PRACTICES IN HIGHER EDUCATION INSTITUTIONS. THE REVIEW COVERS THE CHARACTERISTICS OF THE INSTITUTIONS, AS WELL AS THE CHARACTERISTICS AND OUTCOMES OF THE LEARNING ANALYTICS PRACTICES. DESIGN/METHODOLOGY/APPROACH THIS RESEARCH COLLECTED LITERATURE PUBLISHED IN 2011-2016 WHICH REPORTED LEARNING ANALYTICS PRACTICES FROM SCOPUS AND GOOGLE SCHOLAR, COVERING A TOTAL OF 47 INSTITUTIONS, AND CATEGORISED THE INFORMATION ABOUT THE RELEVANT INSTITUTIONS AND PRACTICES. FINDINGS THE RESULTS SHOW THAT MOST OF THE INSTITUTIONS WERE PUBLIC ONES IN THE USA AND THE UK OF VARIOUS SIZES AND OFFERING DIFFERENT LEVELS OF STUDY. THE LEARNING ANALYTICS PRACTICES WERE MAINLY INSTITUTION-WIDE, APART FROM A SMALL NUMBER FOCUSING ON SELECTED COURSES. THE PURPOSES OF THE PRACTICES WERE MAINLY TO ENHANCE THE EFFECTIVENESS OF LEARNING SUPPORT AND ADMINISTRATION, FOLLOWED BY FACILITATING STUDENTS' LEARNING PROGRESS. THE MOST COMMON TYPES OF DATA COLLECTED FOR THE PRACTICES WERE STUDENTS' ACADEMIC BEHAVIOURS AND THEIR BACKGROUND INFORMATION. POSITIVE OUTCOMES WERE REPORTED FOR A MAJORITY OF THE PRACTICES, AND THE MOST FREQUENT ONES BEING AN INCREASE IN COST-EFFECTIVENESS AND UNDERSTANDING OF STUDENTS' LEARNING BEHAVIOURS. OTHER OUTCOMES INCLUDED THE IMPROVEMENT OF STUDENT RETENTION, TIMELY FEEDBACK AND INTERVENTION, SUPPORT FOR INFORMED DECISION-MAKING AND THE PROVISION OF PERSONALISED ASSISTANCE TO STUDENTS. ORIGINALITY/VALUE THE RESULTS PROVIDE AN OVERVIEW OF THE USE OF LEARNING ANALYTICS IN THE HIGHER EDUCATION SECTOR. THEY ALSO REVEAL THE TRENDS IN LEARNING ANALYTICS PRACTICES, AS WELL AS FUTURE RESEARCH DIRECTIONS.
THIS ARTICLE SERVES AS A REFLECTION ON THE PAPERS ACCEPTED FOR THIS SPECIAL ISSUE. ALTHOUGH 2011 SAW THE FIRST LEARNING ANALYTICS CONFERENCE, THE FIELD DRAWS FROM A LONG HISTORY OF RESEARCH IN ASSOCIATED DISCIPLINES SUCH AS PSYCHOLOGY, STATISTICS, EDUCATION AND COMPUTER SCIENCE. HOWEVER, THE NOVELTY OF LEARNING ANALYTICS RESEARCH STEMS FROM THE ACCESS AND ANALYSIS OF LARGE, YET GRANULAR, DATA SOURCES THAT ARE GENERATED FROM STUDENT INTERACTIONS IN ONLINE ACTIVITIES. THE VAST ARRAY AND VOLUME OF ACCESSIBLE LEARNING DATA HAVE PROMULGATED NEW APPROACHES TO UNDERSTANDING AND MEASURING LEARNING. AS RESEARCHERS PURSUE SUCH ALGORITHMICALLY GENERATED INSIGHTS, THERE ARE PARALLEL CHALLENGES TO INDIVIDUAL ETHICS AND PRIVACY AND A BROADER CONSIDERATION OF THE IMPACT OF AN INCREASED QUANTIFICATION OF EDUCATION. THIS SPECIAL ISSUE ON THE ROLE OF LEARNING ANALYTICS IN OPEN, FLEXIBLE, AND DISTANCE LEARNING (OFDL) ENVIRONMENTS AIMS TO BRING ALTERNATE PERSPECTIVES TO BEAR ON HOW SUCH ANALYSES ARE IMPROVING LEARNING WHILE STILL ADDRESSING SOCIAL AND CULTURAL CHALLENGES.
WITH THE RAPID DEVELOPMENT IN WEARABLE TECHNOLOGY, WEARABLE DEVICES INTEGRATING WITH VARIOUS SENSORS HAVE BEEN BROADLY APPLIED IN DIFFERENT AREAS. YET THERE IS SELDOM ANY PREVIOUS STUDY WHICH FOCUSES ON APPLYING WEARABLE DEVICES AND DEEP LEARNING IN LEARNING ANALYTICS. THIS PAPER CONSIDERS A SOPHISTICATED REAL-TIME LEARNING ANALYTICS PLATFORM FOR ANALYZING STUDENTS' LEARNING STATES AND LEARNING ACTIVITIES WITH WEARABLE DEVICES AND DEEP LEARNING. DURING THE EXPERIMENTAL PERIOD OF THIS PLATFORM, STUDENTS WILL RECEIVE INSTANT NOTIFICATIONS FROM AN INTELLIGENT MOBILE APPLICATION WHEN THEIR HEART RATE ARE OUT OF THEIR NORMAL RANGE SO THAT THE ACTUAL LEARNING ACTIVITIES CONDUCTED BY STUDENTS CAN BE COLLECTED TO TRAIN DEEP LEARNING MODELS FOR RECOGNIZING THEIR LEARNING ACTIVITIES. AT THE SAME TIME, STUDENTS CAN ENJOY THE SLEEPING MONITORING AND THE EXERCISE MONITORING FUNCTIONALITIES PROVIDED BY THE SMART WATCHES IN THIS PLATFORM. THE RESULTS OF THE INTERVIEWS CONDUCTED AFTER THE EXPERIMENT FOR THIS PLATFORM DEMONSTRATE THAT 89% OF STUDENTS THINK THAT THIS PLATFORM IS USEFUL FOR THEIR DAILY LIVES AND 65% OF STUDENTS REPORT THAT THIS PLATFORM BRINGS POSITIVE EFFECTS ON THEIR LEARNING IN DIFFERENT ASPECTS. MORE IMPORTANTLY, THIS WORK SHEDS LIGHTS ON THE POSSIBILITY OF APPLYING WEARABLE DEVICES IN LEARNING ANALYTICS TO IMPROVE THE LEARNING EFFECTIVENESSES AND LIFE QUALITIES OF STUDENTS.
FEEDBACK HAS BEEN IDENTIFIED AS THE MOST POWERFUL MODERATOR FOR SUPPORTING LEARNING. LEARNING ANALYTICS HAVEN BEEN RECOGNIZED FOR OPPORTUNITIES FOR PROVIDING TIMELY AND INFORMATIVE FEEDBACK TO LEARNERS WHEN THEY NEED IT. THIS STUDY SEEKS TO INVESTIGATE LEARNERS' PERCEPTIONS AND EXPECTED BENEFITS OF DIFFERENT FORMS OF LEARNING ANALYTICS FEEDBACK FROM DIFFERENT SOURCES. IN A QUASI-EXPERIMENTAL STUDY INCLUDING 230 STUDENTS, FOUR EXPERIMENTAL GROUPS WERE CONFRONTED WITH FIVE LEARNING SCENARIOS RECEIVING DIFFERENT LEARNING ANALYTICS FEEDBACK. FINDINGS INDICATE THAT PERCEIVED BENEFITS FROM LEARNING ANALYTICS FEEDBACK VARIES ACROSS DIFFERENT DELIVERY SOURCES AND REQUIRES INFORMATIVE RECOMMENDATIONS. ACCORDINGLY, DESIGNING AND IMPLEMENTING FEEDBACK IN LEARNING ANALYTICS SYSTEMS IS MORE COMPLEX THAN JUST PROVIDING VISUALIZATIONS OF BEHAVIORAL DATA.
LEARNING ANALYTICS HAS BECOME A HOT RESEARCH TOPIC IN THE FIELD OF ICT IN EDUCATION IN RECENT YEARS. BASED ON 623 JOURNAL PAPERS (PUBLISHED FROM 2011 TO 2018) ABOUT LEARNING ANALYTICS IN WEB OF SCIENCE CORE COLLECTION, THIS STUDY EXPLORED RESEARCH HOTSPOTS AND TRENDS OF LEARNING ANALYTICS BY THE BIBLIOMETRIC METHOD AND MAPPING KNOWLEDGE. TIME RANGE, REGION DISTRIBUTION, MAIN RESEARCH DIRECTIONS OF THE LITERATURE, AND MAPPING KNOWLEDGE WERE USED TO EXPLORE THE MAIN TRENDS OF THE LEARNING ANALYTICS. A FURTHER SUMMARY AND REFLECTION WERE GIVEN TO PROVIDE THE REFERENCE FOR DEEP STUDY AND PRACTICAL EXPLORATION OF LEARNING ANALYTICS.
TO SUCCESSFULLY IMPLEMENT LEARNING ANALYTICS (LA) SYSTEMS WITHIN HIGHER EDUCATION, WE NEED TO ENGAGE ADMINISTRATORS, FACULTY, AND STAFF ALIKE. THIS PAPER IS BY AND PRIMARILY FOR PRACTITIONERS. WE SUGGEST IMPLEMENTATION STRATEGIES THAT CONSIDER THE HUMAN FACTOR IN ADOPTING NEW TECHNOLOGIES BY ANALYZING THE VIABILITY OF OUR LEARNING ANALYTICS FELLOWS PROGRAM (LAFP), WHERE FACULTY ARE EMPOWERED AS AGENTS OF INSTITUTIONAL CHANGE. THIS PROGRAM DIRECTLY ADDRESSES KNOWN BARRIERS TO THE USE OF LA, DEALING WITH CULTURE MANAGEMENT, ADOPTION, AND SUSTAINABILITY. THE FELLOWS PROGRAM ENGAGES FACULTY IN INQUIRY ABOUT STUDENT SUCCESS, PROVIDING THEM WITH A VIEW OF THE STUDENT EXPERIENCE THROUGH INSTITUTIONAL DATA. FACULTY, WITH THEIR KNOWLEDGE OF STUDENTS AND PROGRAMS AS WELL AS THEIR RESEARCH EXPERTISE, ARE WELL-POSITIONED TO ADVANCE LA EFFORTS ON OUR CAMPUSES. IN OUR CASE, FACULTY ARE ALSO THE END USERS OF THEIR FINDINGS, AND ARE ABLE TO PROVIDE INPUT INTO THE DESIGN OF THE ANALYTICAL TOOLS CREATED FOR THEM. EXPANDING ON A PAPER PRESENTED AT THE LAK 18 CONFERENCE (REHREY, GROTH, FIORINI, HOSTETTER, & SHEPARD, 2018), WE DESCRIBE THE RATIONALE FOR THE IMPLEMENTATION STRATEGY, REFLECT ON THE EFFECTIVENESS OF THIS STRATEGY BY ANALYZING SELF-REPORTS FROM OUR LAFP, AND CONSIDER THE BROADER IMPACTS OF THIS APPROACH FOR THE FUTURE.
IN THE 21ST CENTURY, WITH THE GROWING IMPORTANCE OF EDUCATION AND THE INCREASE USE OF INTERNET, LEARNING MANAGEMENT SYSTEMS (LMS) OR VIRTUAL LEARNING ENVIRONMENTS (VLE) HAVE BECOME A MUST AMONGST LEARNERS AND INSTRUCTORS. ONE OF THE MAIN OBJECTIVES OF THESE PLATFORMS IS TO IMPLEMENT TECHNOLOGY WITH TRADITIONAL LEARNING METHODS SO AS TO ENHANCE THE EFFECTIVENESS OF LEARNING AND TEACHING PARTICULARLY IN LARGE COHORTS OF STUDENTS. BUT DOES THAT REALLY HELP TO IMPROVE THE PROCESS OF LEARNING? TO BE ABLE TO ANSWER, LEARNING ANALYTICS IS PERHAPS THE RIGHT TOOL. THIS AREA OF STUDY IS CONCERNED WITH THE IMPROVEMENT OF LEARNING THROUGH THE ANALYSIS OF MASSIVE AMOUNT OF DATA AND INFORMATION GENERATED BY THE TEACHING AND LEARNING PROCESS. THIS PAPER DEMONSTRATES A PROTOTYPE IMPLEMENTATION OF LEARNING ANALYTICS, NAMED TRUESIGHT LEARNING AND EVENTUALLY SHOWS HOW IT CAN BE USED TO PREDICT THE LEARNING OUTCOMES OF LEARNERS AND TO IDENTIFY THE LEARNERS THAT ARE AT RISK AND THAT WILL PERHAPS NEED SPECIAL ATTENTION AND DOUBLE EFFORT.
WITH THE POPULARITY TAKEOFF OF THE LEARNING ANALYTICS AREA DURING THE LAST DECADE, NUMEROUS RESEARCH STUDIES HAVE EMERGED AND PUBLIC OPINION HAS ECHOED THIS TREND AS WELL. HOWEVER, THE FACT IS THAT THE IMPACT THE FIELD HAS HAD IN PRACTICE HAS BEEN QUITE LIMITED, AND THERE HAS BEEN LITTLE TRANSFER TO EDUCATIONAL INSTITUTIONS. ONE OF THE POSSIBLE CAUSES IS THE HIGH COMPLEXITY OF THE FIELD, AND THAT THERE ARE NO CLEAR IMPLEMENTATION PROCESSES; THEREFORE, IN THIS WORK, WE PROPOSE A PRAGMATIC IMPLEMENTATION PROCESS OF LEARNING ANALYTICS IN FIVE STAGES: 1) LEARNING ENVIRONMENTS, 2) RAW DATA CAPTURE, 3) DATA TIDYING AND FEATURE ENGINEERING, 4) ANALYSIS AND MODELLING AND 5) EDUCATIONAL APPLICATION. IN ADDITION, WE ALSO REVIEW A SERIES OF TRANSVERSE FACTORS THAT AFFECT THIS IMPLEMENTATION, LIKE TECHNOLOGY, LEARNING SCIENCES, PRIVACY, INSTITUTIONS, AND EDUCATIONAL POLICIES. THE DETAILED PROCESS CAN BE HELPFUL FOR RESEARCHERS, EDUCATIONAL DATA ANALYSTS, TEACHERS AND EDUCATIONAL INSTITUTIONS THAT ARE LOOKING TO START WORKING IN THIS AREA. ACHIEVING THE TRUE POTENTIAL OF LEARNING ANALYTICS WILL REQUIRE CLOSE COLLABORATION AND CONVERSATION BETWEEN ALL THE ACTORS INVOLVED IN THEIR DEVELOPMENT, WHICH MIGHT EVENTUALLY LEAD TO THE DESIRED SYSTEMATIC AND PRODUCTIVE IMPLEMENTATION.
HIGHER EDUCATION INSTITUTIONS HAVE STARTED USING BIG DATA ANALYTICS TOOLS. BY GATHERING INFORMATION ABOUT STUDENTS AS THEY NAVIGATE INFORMATION SYSTEMS, LEARNING ANALYTICS EMPLOYS TECHNIQUES TO UNDERSTAND STUDENT BEHAVIORS AND TO IMPROVE INSTRUCTIONAL, CURRICULAR, AND SUPPORT RESOURCES AND LEARNING ENVIRONMENTS. HOWEVER, LEARNING ANALYTICS PRESENTS IMPORTANT MORAL AND POLICY ISSUES SURROUNDING STUDENT PRIVACY. WE ARGUE THAT THERE ARE FIVE CRUCIAL QUESTIONS ABOUT STUDENT PRIVACY THAT WE MUST ADDRESS IN ORDER TO ENSURE THAT WHATEVER THE LAUDABLE GOALS AND GAINS OF LEARNING ANALYTICS, THEY ARE COMMENSURATE WITH RESPECTING STUDENTS' PRIVACY AND ASSOCIATED RIGHTS, INCLUDING (BUT NOT LIMITED TO) AUTONOMY INTERESTS. WE ADDRESS INFORMATION ACCESS CONCERNS, THE INTRUSIVE NATURE OF INFORMATION-GATHERING PRACTICES, WHETHER OR NOT LEARNING ANALYTICS IS JUSTIFIED GIVEN THE POTENTIAL DISTRIBUTION OF CONSEQUENCES AND BENEFITS, AND ISSUES RELATED TO STUDENT AUTONOMY. FINALLY, WE QUESTION WHETHER LEARNING ANALYTICS ADVANCES THE AIMS OF HIGHER EDUCATION OR RUNS COUNTER TO THOSE GOALS.
TEACHER INQUIRY IS IDENTIFIED AS A KEY GLOBAL NEED FOR DRIVING THE CONTINUOUS IMPROVEMENT OF THE TEACHING AND LEARNING CONDITIONS FOR LEARNERS. HOWEVER, SPECIFIC BARRIERS (MAINLY RELATED TO TEACHERS' DATA LITERACY COMPETENCES), CAN DEFER TEACHERS FROM ENGAGING WITH INQUIRY TO IMPROVE THEIR TEACHING PRACTICE. TO ALLEVIATE THESE BARRIERS AND SUPPORT TEACHER INQUIRY, THE CONCEPT OF TEACHING AND LEARNING ANALYTICS (TLA) HAS BEEN PROPOSED, AS A COMPLEMENTING SYNERGY BETWEEN TEACHING ANALYTICS AND LEARNING ANALYTICS. TEACHING AND LEARNING ANALYTICS AIMS TO PROVIDE A FRAMEWORK IN WHICH THE INSIGHTS GENERATED BY LEARNING ANALYTICS METHODS AND TOOLS CAN BECOME MEANINGFULLY TRANSLATED FOR DRIVING TEACHERS' INQUIRY TO IMPROVE THEIR TEACHING PRACTICE, CAPTURED THROUGH TEACHING ANALYTICS METHODS AND TOOLS. IN THIS CONTEXT, TLA HAVE BEEN IDENTIFIED AS A RESEARCH CHALLENGE WITH SIGNIFICANT PRACTICAL IMPACT POTENTIAL. THIS CHAPTER CONTRIBUTES THE FIRST SYSTEMATIC LITERATURE REVIEW IN THE EMERGING RESEARCH FIELD OF TEACHING AND LEARNING ANALYTICS. THE INSIGHTS GAINED FROM THE SYSTEMATIC LITERATURE REVIEW AIM TO (A) TRANSPARENTLY OUTLINE THE EXISTING STATE-OF-THE-ART FOLLOWING A STRUCTURED ANALYSIS METHODOLOGY, AS WELL AS (B) ELICIT INSIGHTS AND SHORTCOMINGS WHICH COULD INFORM FUTURE WORK IN THE TEACHING AND LEARNING ANALYTICS RESEARCH FIELD.
THE ANALYSIS OF DATA COLLECTED FROM THE INTERACTION OF USERS WITH EDUCATIONAL AND INFORMATION TECHNOLOGY HAS ATTRACTED MUCH ATTENTION AS A PROMISING APPROACH FOR ADVANCING OUR UNDERSTANDING OF THE LEARNING PROCESS. THIS PROMISE MOTIVATED THE EMERGENCE OF THE NEW RESEARCH FIELD, LEARNING ANALYTICS, AND ITS CLOSELY RELATED DISCIPLINE, EDUCATIONAL DATA MINING. THIS PAPER FIRST INTRODUCES THE FIELD OF LEARNING ANALYTICS AND OUTLINES THE LESSONS LEARNED FROM WELL-KNOWN CASE STUDIES IN THE RESEARCH LITERATURE. THE PAPER THEN IDENTIFIES THE CRITICAL TOPICS THAT REQUIRE IMMEDIATE RESEARCH ATTENTION FOR LEARNING ANALYTICS TO MAKE A SUSTAINABLE IMPACT ON THE RESEARCH AND PRACTICE OF LEARNING AND TEACHING. THE PAPER CONCLUDES BY DISCUSSING A GROWING SET OF ISSUES THAT IF UNADDRESSED, COULD IMPEDE THE FUTURE MATURATION OF THE FIELD. THE PAPER STRESSES THAT LEARNING ANALYTICS ARE ABOUT LEARNING. AS SUCH, THE COMPUTATIONAL ASPECTS OF LEARNING ANALYTICS MUST BE WELL INTEGRATED WITHIN THE EXISTING EDUCATIONAL RESEARCH.
ADAPTIVE LEARNING AND LEARNING ANALYTICS ARE POWERFUL LEARNING TOOLS ON THEIR OWN. SCHOLARS HAVE REPORTED OUTCOMES ON COMBINING THEM, BUT THE LACK OF A SUMMARY FROM THESE STUDIES PREVENTS STAKEHOLDERS FROM HAVING A CLEAR VIEW OF THIS COMBINATION. IN THIS PAPER, WE CONSIDER THE KEY DIMENSIONS OF LEARNING ANALYTICS APPLICATIONS IN ADAPTIVE LEARNING, IN ORDER TO SUGGEST A PROPER REFERENCE FRAMEWORK THAT SERVES AS THE BASIS TO SYSTEMATICALLY REVIEW THE LITERATURE. THE FINDINGS SUGGEST THAT INTERESTING RESEARCH WORK HAS BEEN CARRIED OUT DURING THE LAST YEARS ON THE TOPIC. YET, THERE IS A CLEAR LACK OF STUDIES (A) ON SCHOOL EDUCATION AND IN TOPICS OUTSIDE STEM AND (B) THAT DO NOT FOCUS SOLELY ON THE (SELF-)REFLECTION OF STUDENTS OR TUTORS. FINALLY, THE MAJORITY OF THE STUDIES MERELY CONCENTRATES ON NARROW MEASURES OF LEARNING LIKE STUDENT PERFORMANCE. A NICHE AREA TAKING INTO ACCOUNT MORE COMPLEX STUDENT BEHAVIORS, LIKE COLLABORATION, IS EMERGING.
A LEARNING ANALYTICS TOOL THAT FACILITATES TEACHERS IN MONITORING STUDENTS' LEARNING PROCESS AND ENABLES THEM TO EARLY RECOGNIZE THE SIGNS OF FAILURE WAS DEVELOPED. THE TOOL WAS ADDED TO AN E-LEARNING SYSTEM BASED ON MOODLE OPERATED BY A PRIMARY SCHOOL. TWO PARALLEL CLASSES (I.E., AN EXPERIMENTAL GROUP AND A CONTROL GROUP) OF STUDENTS WERE INVOLVED IN THE RESEARCH FOR DURATION OF ONE SEMESTER. THE STATISTICAL TEST RESULTS SHOWED THAT BETTER LEARNING IMPROVEMENT OCCURRED TO THE EXPERIMENTAL GROUP WITH AN INCREASE IN THE AVERAGE MARKS BY 5.3 (OUT OF 100 POINTS). THE USE OF LEARNING ANALYTICS HAS HELPED IMPROVE THE EFFECTIVENESS OF THE STUDENTS' LEARNING PROCESS.
THE ADVANCED LEARNING ANALYTICS RESEARCH OF THE LAST YEARS CONVERGES WITH THE INDUSTRY DEMAND TO ENHANCE FAMOUS LEARNING MANAGEMENT SYSTEMS WITH LEARNING ANALYTICS CAPABILITIES PROMOTING THE EFFICIENCY OF HIGHER EDUCATION. THE EXPLOITATION OF BIG VOLUME LEARNING DATA, IS A CRITICAL CHALLENGE FOR THE DESIGN OF PERSONALIZED CURRICULA AND LEARNING EXPERIENCES. THE PURPOSE OF THIS RESEARCH PAPER IS TO COMMUNICATE A FRAMEWORK FOR LEARNING ANALYTICS AIMING TO SUPPORT THE INTEGRATED MANAGEMENT OF END TO-END LEARNING DATA. WE PRESENT THE RESEARCH FOUNDATIONS OF A RESEARCH PROTOTYPE FOR THE INTEGRATION OF A LEARNING ANALYTICS DASHBOARD: THE AMBA PROTOTYPE WITH FAMOUS LEARNING MANAGEMENT SYSTEMS. FINALLY, WE PRESENT THE MAIN FINDINGS OF AN EMPIRICAL STUDY THAT PROVES THE CAPACITY OF LEARNING ANALYTICS TO ENHANCE THE LEARNERS' ECOSYSTEM WITH VALUE ADDING LEARNING SERVICES. THE PROPOSED FRAMEWORK EXPLOITS COGNITIVE COMPUTING FOR THE ENHANCEMENT OF DECISION MAKING IN EDUCATION BY PROVING THE CAPACITY OF LEARNING ANALYTICS TO REVEAL HIDDEN PATTERNS OF LEARNERS' BEHAVIOUR AND ATTITUDE. (C) 2018 PUBLISHED BY ELSEVIER LTD.
THE GROWING PRACTICE OF STUDENTS AS PARTNERS (SAP) HAS SPARKED NUMEROUS CONVERSATIONS IN HIGHER EDUCATION ABOUT THE ROLES STUDENTS DO AND SHOULD PLAY IN SHAPING THE FUTURE. SAP SCHOLARS CONTEND THAT BY ENGAGING WITH STUDENTS IN MEANINGFUL PARTNERSHIP, UNDERPINNED BY VALUES SUCH RECIPROCITY, STUDENTS CAN HAVE DEEPER AND MORE MEANINGFUL LEARNING EXPERIENCES. SIMILAR IN THIS GOAL, THE FIELD OF LEARNING ANALYTICS ALSO STRIVES TO CREATE DEEPER AND MORE MEANINGFUL LEARNING EXPERIENCES FOR STUDENTS, HOWEVER, THE CURRENT APPROACH DIFFERS. STUDENTS AND OTHER STAKEHOLDERS SUCH AS TEACHERS ARE RARELY PART OF THE PROCESS IN LEARNING ANALYTICS, AND THIS DIVIDE, AND LACK OF COMMUNICATION, BETWEEN DESIGNERS AND USERS, HAS LED TO SEVERAL CRITICAL ISSUES IN THE AREA. THEREFORE, THIS PAPER WILL DISCUSS THREE ONGOING ISSUES WITHIN LEARNING ANALYTICS AND DRAW ON SAP VALUES AND GUIDING PRINCIPLES, SUCH AS RECIPROCITY, EMPHASIS ON THE PROCESS, AND SHARED RESPONSIBILITY TO QUESTION THE CURRENT WAY LEARNING ANALYTICS PERCEIVES STUDENTS. BY ARGUING FOR A CHANGE IN PERSPECTIVE AND THE ADOPTION OF SAP IN LEARNING ANALYTICS, WE WILL FURTHER ADD ON GROWING LITERATURE ABOUT THE IMPORTANCE OF UNIVERSITIES TO BECOME TRANSPARENT AND COLLABORATIVE COMMUNITIES.
SOFTWARE IS AN IMPORTANT ASPECT IN THE MODERN WORLD BECAUSE IT USES BY EVERYONE AND EVERYWHERE. THEREFORE SOFTWARE ENGINEERING EDUCATION GETS MORE IMPORTANT IN THE COMPUTER SCIENCE CURRICULA BUT IT HAS LAPSES TO PRODUCE GOOD SOFTWARE ENGINEERS TO THE INDUSTRIES REQUIREMENTS. LEARNING ANALYTICS HELPS THE STUDENTS TO IMPROVE THEIR LEARNING ACTIVITIES. THIS WAS ANALYZED AMONG THE SOFTWARE ENGINEERING STUDENTS HOW THE LEARNING STYLE INFLUENCES IN GATHERING KNOWLEDGE. FORTY SIX QUESTIONNAIRES WERE DISTRIBUTED TO THE FIRST YEAR MCA STUDENTS IN THE DEPARTMENT OF COMPUTER APPLICATIONS, BHARATHIAR UNIVERSITY, COIMBATORE, INDIA FOR THIS STUDY.
PROGRAMMING IS A COMPLEX LEARNING ACTIVITY THAT INVOLVES COORDINATION OF COGNITIVE PROCESSES AND AFFECTIVE STATES. THESE ASPECTS ARE OFTEN CONSIDERED INDIVIDUALLY IN COMPUTING EDUCATION RESEARCH, DEMONSTRATING LIMITED UNDERSTANDING OF HOW AND WHEN STUDENTS LEARN BEST. THIS ISSUE CONFINES RESEARCHERS TO CONTEXTUALIZE EVIDENCE-DRIVEN OUTCOMES WHEN LEARNING BEHAVIOUR DEVIATES FROM PEDAGOGICAL INTENTIONS. MULTIMODAL LEARNING ANALYTICS (MMLA) CAPTURES DATA ESSENTIAL FOR MEASURING CONSTRUCTS (E.G., COGNITIVE LOAD, CONFUSION) THAT ARE POSITED IN THE LEARNING SCIENCES AS IMPORTANT FOR LEARNING, AND CANNOT EFFECTIVELY BE MEASURED SOLELY WITH THE USE OF PROGRAMMING PROCESS DATA (IDE-LOG DATA). THUS, WE AUGMENTED IDE-LOG DATA WITH PHYSIOLOGICAL DATA (E.G., GAZE DATA) AND PARTICIPANTS' FACIAL EXPRESSIONS, COLLECTED DURING A DEBUGGING LEARNING ACTIVITY. THE FINDINGS EMPHASIZE THE NEED FOR LEARNING ANALYTICS THAT ARE CONSEQUENTIAL FOR LEARNING, RATHER THAN EASY AND CONVENIENT TO COLLECT. IN THAT REGARD, OUR PAPER AIMS TO PROVOKE PRODUCTIVE REFLECTIONS AND CONVERSATIONS ABOUT THE POTENTIAL OF MMLA TO EXPAND AND ADVANCE THE SYNERGY OF LEARNING ANALYTICS AND LEARNING DESIGN AMONG THE COMMUNITY OF EDUCATORS FROM A POST-EVALUATION DESIGN-AWARE PROCESS TO A PERMANENT MONITORING PROCESS OF ADAPTATION.
AS THE FIELDS OF LEARNING ANALYTICS AND LEARNING DESIGN MATURE, THE CONVERGENCE AND SYNERGIES BETWEEN THEM BECOME AN IMPORTANT AREA FOR RESEARCH. COLLECTING AND COMBINING LEARNING ANALYTICS COMING FROM DIFFERENT CHANNELS CAN CLEARLY PROVIDE VALUABLE INFORMATION IN DESIGNING LEARNING. HENCE, THIS PAPER INTENDS TO SUMMARIZE THE MAIN OUTCOMES OF A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL EVIDENCE ON LEARNING ANALYTICS FOR LEARNING DESIGN. THE SEARCH WAS PERFORMED IN SEVEN ACADEMIC DATABASES, RESULTING IN 38 PAPERS INCLUDED IN THE MAIN ANALYSIS. THE REVIEW DEMONSTRATES ONGOING DESIGN PATTERNS AND LEARNING PHENOMENA THAT IMPROVE LEARNING, BY PROVIDING MORE COMPREHENSIVE BACKGROUND OF THE CURRENT LANDSCAPE OF LEARNING ANALYTICS FOR LEARNING DESIGN AND ITS IMPACT ON THE CURRENT STATUS OF LEARNING TECHNOLOGIES. CONSEQUENTLY, FUTURE RESEARCH SHOULD CONSIDER HOW TO CAPTURE AND SYSTEMATIZE LEARNING DESIGN DATA. MOREOVER, IT SHOULD EVALUATE AND DOCUMENT WHAT LEARNING DESIGN CHOICES MADE BY EDUCATORS USING WHAT LEARNING ANALYTICS TECHNIQUES INFLUENCE LEARNING EXPERIENCES AND LEARNING PERFORMANCES OVER TIME.
MULTIMODAL LEARNING ANALYTICS (MMLA) HAS HUGE POTENTIAL FOR EXTENDING THE WORK BEYOND TRADITIONAL LEARNING ANALYTICS FOR THE CAPABILITIES OF LEVERAGING MULTIPLE DATA MODALITIES (E.G. PHYSIOLOGICAL DATA, DIGITAL TRACING DATA). TO SHED A LIGHT ON ITS APPLICATIONS AND ACADEMIC DEVELOPMENT, A SYSTEMATIC BIBLIOMETRIC ANALYSIS WAS CONDUCTED IN THIS PAPER. SPECIFICALLY, WE EXAMINE THE FOLLOWING ASPECTS: (1) ANALYZING THE YEARLY PUBLICATION AND CITATION TRENDS SINCE THE YEAR 2010; (2) RECOGNIZING THE MOST PROLIFIC COUNTRIES, INSTITUTIONS, AND AUTHORS IN THIS FIELD; (3) IDENTIFYING THE COLLABORATIVE PATTERNS AMONG COUNTRIES, INSTITUTIONS, AND AUTHORS, RESPECTIVELY; (4) TRACING THE EVOLVING PROCEDURE OF THE APPLIED KEYWORDS AND DEVELOPMENT OF THE RESEARCH TOPICS DURING THE LAST DECADE. THESE ANALYTIC TASKS WERE CONDUCTED ON 194 CAREFULLY SELECTED ARTICLES PUBLISHED SINCE 2010. THE ANALYTICAL RESULTS REVEALED AN INCREASING TREND IN THE NUMBER OF PUBLICATIONS AND CITATIONS, IDENTIFIED THE PROMINENT INSTITUTIONS AND SCHOLARS WITH SIGNIFICANT ACADEMIC CONTRIBUTIONS TO THE AREA, AND DETECTED THE TOPICS (E.G. CHARACTERIZING LEARNING PROCESSES USING MULTIMODAL DATA, IMPLEMENTING UBIQUITOUS LEARNING PLATFORMS) THAT RECEIVED THE MOST ATTENTION. FINALLY, WE ALSO HIGHLIGHTED THE CURRENT RESEARCH HOTSPOTS ATTEMPTING TO INITIATE POTENTIAL INTERDISCIPLINARY COLLABORATIONS TO PROMOTE FURTHER PROGRESS IN THE AREA OF MMLA.
LEARNING ANALYTICS PROVIDES METHODS FOR MEASURING, COLLECTING AND ANALYZING DATA ABOUT LEARNERS AND THEIR CONTEXTS, FOR THE PURPOSE OF UNDERSTANDING AND OPTIMIZING THE LEARNING PROCESS AND ITS ENVIRONMENTS. IN RECENT YEARS, MOST RESEARCH HAS BEEN CONDUCTED REGARDING METHODOLOGIES OR TECHNOLOGICAL CHALLENGES OF LEARNING ANALYTICS. LITTLE RESEARCH HAS BEEN DONE REGARDING SOFTWARE ENGINEERING AND THE SOFTWARE IMPLEMENTATION OF LEARNING ANALYTICS FOR THE USE IN LEARNING MANAGEMENT SYSTEMS. THIS MAKES IT CURRENTLY DIFFICULT OR EVEN IMPOSSIBLE FOR RESEARCHERS AND TEACHERS TO USE CURRENT RESEARCH RESULTS WITHOUT WRITING SOFTWARE ON THEIR OWN. IN THIS PAPER, WE FIRST PRESENT EXISTING LEARNING ANALYTICS TOOLS FOR THE LEARNING MANAGEMENT SYSTEM MOODLE. WE DISCUSS DIFFERENT IMPLEMENTED METHODOLOGIES AND HOW EACH SOFTWARE INTEGRATES INTO THE LEARNING PLATFORM. AFTER THAT, WE PRESENT THE POSSIBLE APPROACHES TO DEVELOP LEARNING ANALYTICS SOFTWARE FOR MOODLE. BASED ON THE PRESENTED SOFTWARE, WE EXPLAIN THE ADVANTAGES AND DISADVANTAGES OF THE DIFFERENT DEVELOPMENT APPROACHES. WHILE SOME APPROACHES ALLOW A DEEP INTEGRATION INTO THE LEARNING MANAGEMENT SYSTEM BUT ARE FOCUSED ON ONLY THE MOODLE SYSTEM, OTHER APPROACHES CAN TRACK THE USER OVER VARIOUS DIFFERENT SYSTEMS. LASTLY, WE DESCRIBE OUR OWN LEARNING ANALYTICS SOFTWARE IMPLEMENTATION FOR MOODLE. IT IS DEVELOPED AS PLUGIN FOR THE PLATFORM ALLOWING IT TO BE EASILY INSTALLABLE AND WELL INTEGRATED INTO MOODLE FROM A USER PERSPECTIVE. FURTHERMORE, ITS FUNCTIONALITY CAN BE EXTENDED BY INSTALLING ADDITIONAL PLUGINS. THIS MAKES IT POSSIBLE TO EXTEND THE FUNCTIONALITY BY WRITING MINIMAL AND FOCUSED PLUGINS. IN THE PAPER, WE EXPLAIN HOW THE SOFTWARE IS INTEGRATED INTO MOODLE AS WELL AS HOW PLUGINS CAN BE DEVELOPED TO EXTEND ITS FUNCTIONALITY. WE DISCUSS OUR SOFTWARE DESIGN DECISIONS AND EXPLAIN THEIR BENEFITS.
THE DOCUMENT DESCRIBES OUTLINES THE THEME AND MAIN OBJECTIVES SET FOR THE SIXTH EDITION OF THE TRACK ON LEARNING ANALYTICS WITHIN THE 2018 INTERNATIONAL CONFERENCE TECHNOLOGICAL ECOSYSTEMS FOR ENHANCING MULTICULTURALITY (TEEM' 18), DETAILS THE REVIEW PROCESS, PROVIDES AN OVERVIEW OF THE ACCEPTED SUBMISSIONS AND REFLECTS ABOUT CURRENT ISSUES IN RESEARCH ON LEARNING ANALYTICS, FROM THE AUTHORS' VIEWS AND EXPERIENCE AS TRACK CHAIRS IN PREVIOUS EDITIONS.
LEARNING ANALYTICS CAN BE USED TO ENHANCE STUDENT ENGAGEMENT AND PERFORMANCE IN ONLINE COURSES. USING LEARNING ANALYTICS, INSTRUCTORS CAN COLLECT AND ANALYZE DATA ABOUT STUDENTS AND IMPROVE THE DESIGN AND DELIVERY OF INSTRUCTION TO MAKE IT MORE MEANINGFUL FOR THEM. IN THIS PAPER, THE AUTHORS REVIEW DIFFERENT CATEGORIES OF ONLINE ASSESSMENTS AND IDENTIFY DATA SETS THAT CAN BE COLLECTED AND ANALYZED FOR EACH OF THEM. TWO DIFFERENT DATA ANALYTICS AND VISUALIZATION TOOLS WERE USED: TABLEAU FOR QUANTITATIVE DATA AND MANY EYES FOR QUALITATIVE DATA. THIS PAPER HAS IMPLICATIONS FOR INSTRUCTORS, INSTRUCTIONAL DESIGNERS, ADMINISTRATORS, AND EDUCATIONAL RESEARCHERS WHO USE ONLINE ASSESSMENTS.
THIS SUMMARY DESCRIBES THE 1ST INTERNATIONAL WORKSHOP ON MULTIMODAL LEARNING ANALYTICS. THIS AREA OF STUDY BRINGS TOGETHER THE TECHNOLOGIES OF MULTIMODAL ANALYSIS WITH THE LEARNING SCIENCES. THE INTERSECTION OF THESE DOMAINS SHOULD ENABLE RESEARCHERS TO FOSTER AN IMPROVED UNDERSTANDING OF STUDENT LEARNING, LEAD TO THE CREATION OF MORE NATURAL AND ENRICHING LEARNING INTERFACES, AND MOTIVATE THE DEVELOPMENT OF NOVEL TECHNIQUES FOR TACKLING CHALLENGES THAT ARE SPECIFIC OF EDUCATION.
THE LEARNING ANALYTICS (LA) DISCIPLINE ANALYZES EDUCATIONAL DATA OBTAINED FROM STUDENT INTERACTION WITH ONLINE RESOURCES. MOST OF THE DATA IS COLLECTED FROM LEARNING MANAGEMENT SYSTEMS DEPLOYED AT ESTABLISHED EDUCATIONAL INSTITUTIONS. IN ADDITION, OTHER LEARNING PLATFORMS, MOST NOTABLY MASSIVE OPEN ONLINE COURSES SUCH AS UDACITY AND COURSERA OR OTHER EDUCATIONAL INITIATIVES SUCH AS KHAN ACADEMY, GENERATE LARGE AMOUNTS OF DATA. HOWEVER, THERE IS NO GENERALLY AGREED-UPON DATA MODEL FOR STUDENT INTERACTIONS. THUS, ANALYSIS TOOLS MUST BE TAILORED TO EACH SYSTEM'S PARTICULAR DATA STRUCTURE, REDUCING THEIR INTEROPERABILITY AND INCREASING DEVELOPMENT COSTS. SOME E-LEARNING STANDARDS DESIGNED FOR CONTENT INTEROPERABILITY INCLUDE DATA MODELS FOR GATHERING STUDENT PERFORMANCE INFORMATION. IN THIS PAPER, WE DESCRIBE HOW WELL-KNOWN LA TOOLS COLLECT DATA, WHICH WE LINK TO HOW TWO E-LEARNING STANDARDS - IEEE STANDARD FOR LEARNING TECHNOLOGY AND EXPERIENCE API - DEFINE THEIR DATA MODELS. FROM THIS ANALYSIS, WE IDENTIFY THE ADVANTAGES OF USING THESE E-LEARNING STANDARDS FROM THE POINT OF VIEW OF LEARNING ANALYTICS.
LEARNING ANALYTICS PROMISE SIGNIFICANT BENEFIT TO ONLINE EDUCATION PROVIDERS THROUGH IMPROVED, BETTER-TARGETED STUDENT SERVICES. MUCH HAS BEEN WRITTEN ABOUT THE POTENTIAL OF ANALYTICS AND HOW THEY MIGHT BE TECHNICALLY IMPLEMENTED, AND VARIOUS ETHICAL CONSIDERATIONS ARE PUBLISHED HIGHLIGHTING THE SIGNIFICANT POTENTIAL RISK OF GATHERING, MANIPULATING AND APPLYING STUDENT DATA TO EDUCATION. DEVELOPING AN ETHICAL POSITION PROVIDES INSTITUTIONS WITH AN OPPORTUNITY TO CRITICALLY APPRAISE THEIR ASSUMPTIONS AND PRACTICE HOWEVER HOW BEST TO ARTICULATE ONE IS FAR FROM CLEAR. THIS CASE STUDY OUTLINES THE DEVELOPMENT OF AN ETHICAL FRAMEWORK AT OPEN POLYTECHNIC OF NEW ZEALAND AND DEMONSTRATES HOW INTERNATIONAL ETHICS GUIDELINES FOR LEARNING ANALYTICS HAVE SHAPED PRACTICE AND INFORMED POLICY DEVELOPMENT. FOUR STAGES TO DEVELOPING AN ETHICS POSITION IN EDUCATION INSTITUTIONS WERE FOLLOWED: 1) EXAMINING EXISTING INSTITUTIONAL POLICY, 2) CATEGORISING STUDENT DATA, 3) ADDRESSING ETHICS CONCERNS IN A STRUCTURED WAY AND 4) APPROVING AN ETHICS POSITION AND EXTENDING PRACTICE. THE PAPER CONCLUDES WITH SUGGESTIONS FOR THOSE DEVELOPING ETHICS POSITIONS FOR IMPLEMENTING LEARNING ANALYTICS TO HELP TOWARDS A SUCCESSFUL OUTCOME.
THE PENETRATION OF SOCIAL MEDIA IN EVERYDAY LIFE HAS RESULTED IN A CORRESPONDING GROWTH IN THE AVAILABILITY OF LARGE DATA-SETS, SO CALLED 'BIG DATA', EMERGENT FROM TRACKING AND/OR MINING OF USER INTERACTIONS IN ONLINE SETTINGS. IT IS NOW POSSIBLE TO BETTER SUPPORT (AND EVEN PREDICT) LEARNER/S ACHIEVEMENT THROUGH ANALYSIS OF THEIR INTERACTIONS WITHIN ONLINE LEARNING ENVIRONMENTS. LEARNING ANALYTICS INVOLVES THE MEASUREMENT, COLLECTION, ANALYSIS AND REPORTING OF ONLINE DATA ABOUT LEARNERS AND THEIR CONTEXTS, FOR THE PURPOSES OF UNDERSTANDING, ADAPTING AND OPTIMIZING THE PROCESS OF LEARNING AND/OR THE ENVIRONMENTS IN WHICH IT OCCURS. IN THE CONTEXT OF EDUCATION, THE ANALYSIS OF THIS DATA HAS THE POTENTIAL TO HELP US LEARN MORE ABOUT LEARNERS' FRAMES OF REFERENCE, LEARNING STYLES, PREFERRED MODES OF LEARNING AND PATTERNS OF BEHAVIOR IN LEARNING ENVIRONMENTS. THIS PAPER CONSIDERS LEARNING ANALYTICS IN ONLINE LEARNING ECOLOGIES, AND POSITS THE RHIZOME/RHIZOMATIC THINKING INTRODUCED BY DELEUZE AND GUATTARI AS INSPIRATION FOR RE-CONCEPTUALISING THE PROCESSES OF LEARNING ANALYTICS IN THE 21ST CENTURY.
CYBERSECURITY EXERCISES ARE GAINING IN POPULARITY IN UNIVERSITY CURRICULA AND PROFESSIONAL TRAINING PATHS AND ARE SEEN AS AN EFFECTIVE TEACHING METHOD. SUCH EXERCISES PROVIDE DIGITAL DATASETS TO FACILITATE A LEARNING ANALYTICS APPROACH SUCH AS BY USING THE TRACES THAT LEARNERS LEAVE BEHIND TO IMPROVE THE LEARNING PROCESS AND ENVIRONMENT. WHILE THERE ARE VARIOUS LEARNING MEASUREMENT EFFORTS FROM DIGITAL DATASETS IN THE EXISTING LITERATURE, A HOLISTIC LEARNING ANALYTICS APPROACH INCORPORATED INTO CYBERSECURITY EXERCISES IS STILL LACKING. WE PROPOSE A PRACTICAL REFERENCE MODEL FOR INCORPORATING A LEARNING ANALYTICS APPROACH INTO THE CYBERSECURITY EXERCISE LIFE-CYCLE. TO FACILITATE THIS APPLICATION, WE HAVE PERFORMED AN EXTENSIVE REVIEW OF EXISTING ACADEMIC RESEARCH ON APPLYING LEARNING ANALYTICS IN THE CONTEXT OF CYBERSECURITY EXERCISES. WE SPECIFICALLY FOCUS ON THE LEARNING INDICATORS USED TO MEASURE EMPIRICAL IMPACT AND TRAINING EFFECTIVENESS THAT COULD INDICATE ACHIEVEMENT OF DEFINED LEARNING OUTCOMES. THIS REFERENCE MODEL AND OVERVIEW OF EXISTING LEARNING ANALYTICS USE CASES AND LEARNING METRICS IN VARIOUS TYPES OF EXERCISES CAN HELP EDUCATORS, ORGANISERS AND CYBER RANGE DEVELOPERS. THIS RESULTS IN MORE ADAPTIVE EXERCISE DESIGN AND MEASUREMENT USING EVIDENCE BASED DATA AND CONNECTS DIGITAL LEARNING TRACES TO SKILLS AND COMPETENCIES.
LANGUAGE E-LEARNING, AN ONLINE LANGUAGE LEARNING MODE, COMPLETELY TRANSFORMS THE TRADITIONAL LEARNING AND TEACHING MODE. HOWEVER, WITH THE COMING OF BIG DATA ERA, IT NOT ONLY ENJOYS SOME BENEFITS, BUT ALSO IS CONFRONTED WITH GREAT CHALLENGES. THE ARTICLE FIRST CONCLUDES THE REFORMS BIG DATA BROUGHT TO THE WORLD, AND THEN INTRODUCES THE DEFINITION, KEY ELEMENTS, THE APPLYING MODEL, MAIN ANALYZING METHODS AND TOOLS OF LEARNING ANALYTICS. FINALLY, THE ARTICLE FULLY SHOWS THE IMPLEMENTATION OF LEARNING ANALYTICS TO THE STUDY OF LANGUAGE E-LEARNING. THROUGH THE STUDY OF LEARNING ANALYTICS, THE DESIGNER OF LANGUAGE E-LEARNING CAN LEARN THE LEARNERS' LEARNING BEHAVIORS AND PROVIDE THE EFFICIENT LEARNING MATERIAL, TOOLS AND SYSTEMS.
IN A CONTEXT WHERE LEARNING MEDIATED BY TECHNOLOGY HAS GAINED PROMINENCE IN HIGHER EDUCATION, LEARNING ANALYTICS HAS BECOME A POWERFUL TOOL TO COLLECT AND ANALYSE DATA WITH THE AIM OF IMPROVING STUDENTS' LEARNING. HOWEVER, LEARNING ANALYTICS IS PART OF A YOUNG COMMUNITY AND ITS DEVELOPMENTS DESERVE FURTHER EXPLORATION. SOME CRITICAL STANCES CLAIM THAT LEARNING ANALYTICS TENDS TO UNDERPLAY THE COMPLEXITY OF TEACHING-LEARNING PROCESSES. BY MEANS OF BOTH A BIBLIOMETRIC AND A CONTENT ANALYSIS, THIS PAPER EXAMINES THE PUBLICATION PATTERNS ON LEARNING ANALYTICS IN HIGHER EDUCATION AND THEIR MAIN CHALLENGES. 385 PAPERS THAT WERE PUBLISHED IN WOSCC AND SCIELO INDEXES BETWEEN 2013 AND 2019 WERE IDENTIFIED AND ANALYSED. LEARNING ANALYTICS IS A VIBRANT AND FAST-DEVELOPING COMMUNITY. HOWEVER, IT CONTINUES TO FACE MULTIPLE AND COMPLEX CHALLENGES, ESPECIALLY REGARDING STUDENTS' LEARNING AND THEIR IMPLICATIONS. THE PAPER CONCLUDES BY DISTINGUISHING BETWEEN A PRACTICE-BASED AND MANAGEMENT-ORIENTED COMMUNITY OF LEARNING ANALYTICS AND AN ACADEMIC-ORIENTED COMMUNITY. WITHIN BOTH COMMUNITIES, THOUGH, IT SEEMS THAT THE FOCUS IS MORE ON ANALYTICS THAN ON LEARNING.
SEVERAL TOOLS TO SUPPORT LEARNING PROCESSES BASED ON EDUCATIONAL DATA HAVE EMERGED FROM RESEARCH ON LEARNING ANALYTICS (LA) IN THE LAST FEW YEARS. THESE TOOLS AIM TO SUPPORT STUDENTS AND INSTRUCTORS IN DAILY ACTIVITIES, AND ACADEMIC MANAGERS IN MAKING INSTITUTIONAL DECISIONS. ALTHOUGH THE ADOPTION OF LA TOOLS IS SPREADING, THE FIELD STILL NEEDS TO DEEPEN THE UNDERSTANDING OF THE CONTEXTS WHERE LEARNING TAKES PLACE, AND OF THE VIEWS OF THE STAKEHOLDERS INVOLVED IN IMPLEMENTING AND USING THESE TOOLS. IN THIS SENSE, THE SHEILA FRAMEWORK PROPOSES A SET OF INSTRUMENTS TO PERFORM A DETAILED ANALYSIS OF THE EXPECTATIONS AND NEEDS OF DIFFERENT STAKEHOLDERS IN HIGHER EDUCATION INSTITUTIONS, REGARDING THE ADOPTION OF LA. MOREOVER, THERE IS A LACUNA IN RESEARCH ON STAKEHOLDERS' EXPECTATIONS FROM LA OUTSIDE THE GLOBAL NORTH. THEREFORE, THIS PAPER REPORTS ON THE FINDINGS OF THE APPLICATION OF INTERVIEWS AND FOCUS GROUPS, BASED ON THE SHEILA FRAMEWORK, WITH STUDENTS AND TEACHING STAFF FROM A BRAZILIAN PUBLIC UNIVERSITY, TO INVESTIGATE THEIR PERCEPTIONS OF THE POTENTIAL BENEFITS AND RISKS OF USING LA IN HIGHER EDUCATION IN THE COUNTRY. FINDINGS INDICATE THAT THERE IS A HIGH INTEREST IN USING LA FOR IMPROVING THE LEARNING EXPERIENCE, IN PARTICULAR, BEING ABLE TO PROVIDE PERSONALIZED FEEDBACK, TO ADAPT TEACHING PRACTICES TO STUDENTS' NEEDS, AND TO MAKE EVIDENCE-BASED PEDAGOGICAL DECISIONS. FROM THE ANALYSIS OF THESE PERSPECTIVES, WE POINT TO OPPORTUNITIES FOR USING LA IN BRAZILIAN HIGHER EDUCATION.
MANY ANALYTIC TOOLS HAVE BEEN DEVELOPED TO DISCOVER KNOWLEDGE FROM STUDENT DATA. HOWEVER, THE KNOWLEDGE DISCOVERY PROCESS REQUIRES ADVANCED ANALYTICAL MODELLING SKILLS, MAKING IT THE PROVINCE OF DATA SCIENTISTS. THIS IMPEDES THE ABILITY OF EDUCATIONAL LEADERS, PROFESSORS, AND ADVISORS TO ENGAGE WITH THE KNOWLEDGE DISCOVERY PROCESS DIRECTLY. AS A RESULT, IT IS CHALLENGING FOR ANALYSIS TO TAKE ADVANTAGE OF DOMAIN EXPERTISE, MAKING ITS OUTCOME OFTEN NEITHER INTERESTING NOR USEFUL. USUALLY THE OUTCOME PRODUCED FROM SUCH ANALYTIC TOOLS IS STATIC, PREVENTING DOMAIN EXPERTS FROM EXPLORING DIFFERENT HYPOTHESES BY CHANGING DATA MODELS OR PREDICTIVE MODELS INSIDE THE TOOL. WE HAVE DEVELOPED A FRAMEWORK FOR INTERACTIVE AND EXPLORATORY LEARNING ANALYTICS WHICH BEGINS TO ADDRESS THESE CHALLENGES. WE ENGAGED IN DATA EXPLORATION AND HYPOTHESES GENERATION WITH OUR UNIVERSITY DOMAIN EXPERTS BY CONDUCTING TWO FOCUS GROUPS. WE USED THE FINDINGS OF THESE FOCUS GROUPS TO VALIDATE OUR FRAMEWORK, ARGUING THAT IT ENABLES DOMAIN EXPERTS TO EXPLORE THE DATA, ANALYSIS AND INTERPRETATION OF STUDENT DATA TO DISCOVER USEFUL AND INTERESTING KNOWLEDGE.
IN THE COURSE OF THE LAST FEW YEARS, MANY EDUCATIONAL AND RESEARCH COMMUNITIES HAVE BEEN DEEPLY INVESTED IN THE DEVELOPMENT OF LEARNING ANALYTICS. LEARNING ANALYTICS MEASURES THE EFFECTIVENESS AND EFFICIENCY OF LEARNING ENVIRONMENTS, IN ORDER TO UNDERSTAND THE NEEDS OF LEARNERS AND TO IMPROVE THE TEACHING PROCESS. THE RESEARCH PRESENTED IN THIS PAPER USES PARALLEL PARTICLE SWARM OPTIMIZATION (PPSO) MECHANISM TO ANALYZE AND PREDICT A DYNAMIC LEARNING PATH FOR LEARNERS BASED ON COMPETENCE AND META-COMPETENCE VALUES OBSERVED IN A LEARNING ENVIRONMENT. THE PROPOSED SYSTEM IS ABLE TO AUTO-CONFIGURE AND AUTO-CUSTOMIZE ITSELF TO OFFER PERSONALIZED AND INDIVIDUALIZED INSTRUCTION, AND CALCULATE AN OPTIMAL LEARNING PATHWAY FOR LEARNERS. FURTHERMORE, IT PROVIDES ON-DEMAND AND ADAPTIVE SUPPORT FOR LEARNERS BASED ON THEIR NEEDS. EXPERIMENTAL EVALUATIONS -CARRIED OUT WITHIN A JAVA PROGRAMMING COURSE - DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED SYSTEM.
A SMART CLASSROOM INTEGRATES THE DIFFERENT COMPONENTS IN A TRADITIONAL CLASSROOM, BY USING DIFFERENT TECHNOLOGIES AS ARTIFICIAL INTELLIGENCE, UBIQUITOUS, AND CLOUD PARADIGMS, AMONG OTHERS, IN ORDER TO IMPROVE THE LEARNING PROCESS. ON THE OTHER HAND, THE LEARNING ANALYTICS TASKS ARE A SET OF TOOLS THAT CAN BE USED TO COLLECT AND ANALYZE THE DATA ACCUMULATED IN A SMART CLASSROOM. IN THIS PAPER, WE PROPOSE THE DEFINITION OF THE LEARNING ANALYTICS TASKS AS SERVICES, WHICH CAN BE INVOKED BY THE COMPONENTS OF A SMART CLASSROOM. WE DESCRIBE HOW TO COMBINE THE CLOUD AND MULTI-AGENT PARADIGMS IN A SMART CLASSROOM, IN ORDER TO PROVIDE ACADEMIC SERVICES TO THE INTELLIGENT AND NON-INTELLIGENT AGENTS IN THE SMART CLASSROOM, TO ADAPT AND RESPOND TO THE TEACHING AND LEARNING REQUIREMENTS OF STUDENTS. ADDITIONALLY, WE DEFINE A SET OF LEARNING ANALYTICS TASKS AS SERVICES, WHICH DEFINES A KNOWLEDGE FEEDBACK LOOP FOR THE SMART CLASSROOM, IN ORDER TO IMPROVE THE LEARNING PROCESS IN IT, AND WE EXPLAIN HOW THEY CAN BE INVOKED AND CONSUMED BY THE AGENTS IN A SMART CLASSROOM.
WHAT IS LEARNING ANALYTICS, AS A PRACTICE ACROSS DIFFERENT SETTINGS AND FOR DIFFERENT PURPOSES? THIS CHAPTER ADDRESSES THIS QUESTION BY CONSIDERING DIVERSE ISSUES FOR THE PRACTICE OF LEARNING ANALYTICS, INCLUDING EVIDENCE, METHODS, AND ITS SYSTEMIC NATURE. IT DISCUSSES WHAT WAYS LEARNING ANALYTICS IS NEW WHEN COMPARED TO OTHER WAYS IN WHICH DATA ARE USED IN EDUCATION. IT CONSIDERS EVIDENCE FOR THE IMPACT OF LEARNING ANALYTICS AND THE RELATIONSHIP OF POLICIES TO ITS EXPECTATIONS, USES, AND IMPLEMENTATIONS. IT SHOWS HOW LEARNING ANALYTICS CAN OPERATE AT A PROGRAMMATIC LEVEL TO ADDRESS LARGE PROBLEMS AND ALSO IN A MICRO LEVEL OF ANALYTIC EVENTS OCCURRING WHEN NEW DATA CAN BE COLLECTED. IT SHOWS HOW LEARNING ANALYTICS IS A DESIGN SCIENCE IN THE SERVICE OF EVIDENTIARY PURSUITS WHERE ANALYSTS MAKE IMPORTANT DECISIONS ABOUT WHAT DATA TO COLLECT AND HOW TO ASSEMBLE DATA TO ANSWER QUESTIONS. THIS CROSS-CUTTING VIEW PROVIDES INSIGHTS FOR THE FURTHER DEVELOPMENT OF THIS EMERGING ACTIVITY IN EDUCATIONAL RESEARCH AND PRACTICE.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE RECENTLY EMERGED AS A REVOLUTION IN EDUCATION. DUE TO THE HUGE AMOUNT OF USERS, IT IS DIFFICULT FOR TEACHERS TO PROVIDE PERSONALIZED INSTRUCTION. LEARNING ANALYTICS COMPUTER APPLICATIONS HAVE EMERGED AS A SOLUTION. AT PRESENT, MOOC PLATFORMS PROVIDE LOW SUPPORT FOR LEARNING ANALYTICS VISUALIZATIONS, AND A CHALLENGE IS TO PROVIDE USEFUL AND EFFECTIVE VISUALIZATION APPLICATIONS ABOUT THE LEARNING PROCESS. AT THIS PAPER WE REVIEW THE LEARNING ANALYTICS FUNCTIONALITY OF OPEN EDX AND MAKE AN OVERVIEW OF OUR LEARNING ANALYTICS APPLICATION ANALYSE. WE PRESENT A USABILITY AND EFFECTIVENESS EVALUATION OF ANALYSE TOOL WITH 40 STUDENTS TAKING A DESIGN OF TELEMATICS APPLICATIONS COURSE. THE SURVEY OBTAINED VERY POSITIVE RESULTS IN A SYSTEM USABILITY SCALE (SUS) QUESTIONNAIRE (78.44/100) IN TERMS OF THE USEFULNESS OF VISUALIZATIONS (3.68/5) AND THE EFFECTIVENESS RATIO (92/100) OF THE ACTIONS REQUIRED FOR THE RESPONDENTS. THEREFORE, WE CAN CONCLUDE THAT THE IMPLEMENTED LEARNING ANALYTICS APPLICATION IS USABLE AND EFFECTIVE.
COMPUTERS ARE NECESSARY AND UNAVOIDABLE PART OF THE MODERN LIFESTYLE. SOFTWARE IS THE KEY FACTOR TO SATISFY ALL THE DESIRES AND KEEP UP THIS ROCK STATUS. SOFTWARE ENGINEERS ARE THE PEOPLE WHO DEVELOP THE SOFTWARE TO SATISFY THE USER NEEDS AND MAKE THEIR WORK EASIER. SOFTWARE ENGINEERS ARE ACADEMICALLY MOLD UP TO THE INDUSTRY REQUIREMENTS THROUGH THE PROPER SOFTWARE ENGINEERING EDUCATION. HENCE SOFTWARE ENGINEERING EDUCATION PLAYS AN IMPERATIVE ROLE IN COMPUTER EDUCATION, BUT IT FALLS SHORT TO FABRICATE THE GENIUS SOFTWARE ENGINEERS TO SATISFY THE INDUSTRIES NEED. TO OVERCOME THESE ISSUES RESEARCHERS PROPOSED NUMBER OF SOFTWARE ENGINEERING LEARNING/TEACHING METHODS TO EGG-ON STUDENTS TO REAP THEIR DEPTH KNOWLEDGE IN SOFTWARE ENGINEERING, ALBEIT THESE ALLUSIONS DOES NOT UTTERLY CONQUER THIS DECISIVE ISSUE SINCE THE SUGGESTED APPROACHES DOES NOT MEET WITH THE STUDENT'S LEARNING STYLE. LEARNING ANALYTICS PLAYS A VITAL ROLE TO IMPROVE THE STUDENTS LEARNING ACTIVITIES. THIS PAPER DESCRIBES THE SOFTWARE ENGINEERING STUDENTS' REQUIREMENTS THROUGH LEARNING ANALYTICS AND PROPOSES A TEACHING/LEARNING TOOL TO ENGAGE THE STUDENTS LEARNING ACTIVITIES TO OVERCOME SUCH ISSUES AND INSPIRE THEM IN GATHERING SOFTWARE ENGINEERING KNOWLEDGE.
IN THE LAST FIVE YEARS, LEARNING ANALYTICS HAS PROVED ITS POTENTIAL IN PREDICTING ACADEMIC PERFORMANCE BASED ON TRACE DATA OF LEARNING ACTIVITIES. HOWEVER, THE ROLE OF PEDAGOGICAL CONTEXT IN LEARNING ANALYTICS HAS NOT BEEN FULLY UNDERSTOOD. TO DATE, IT HAS BEEN DIFFICULT TO QUANTIFY LEARNING IN A WAY THAT CAN BE MEASURED AND COMPARED. BY CODING THE DESIGN OF C-LEARNING COURSES, THIS STUDY DEMONSTRATES HOW LEARNING DESIGN IS BEING IMPLEMENTED ON A LARGE SCALE AT THE OPEN UNIVERSITY UK, AND HOW LEARNING ANALYTICS COULD SUPPORT AS WELL AS BENEFIT FROM LEARNING DESIGN. BUILDING ON OUR PREVIOUS WORK, OUR ANALYSIS WAS CONDUCTED LONGITUDINALLY ON 23 UNDERGRADUATE DISTANCE LEARNING MODULES AND THEIR 40,083 STUDENTS. THE INNOVATIVE ASPECT OF THIS STUDY IS THE AVAILABILITY OF FINE-GRAINED LEARNING DESIGN DATA AT INDIVIDUAL TASK LEVEL, WHICH ALLOWS US TO CONSIDER THE CONNECTIONS BETWEEN LEARNING ACTIVITIES, AND THE MEDIA USED TO PRODUCE THE ACTIVITIES. USING A COMBINATION OF VISUALIZATIONS AND SOCIAL NETWORK ANALYSIS, OUR FINDINGS REVEALED A DIVERSITY IN HOW LEARNING ACTIVITIES WERE DESIGNED WITHIN AND BETWEEN DISCIPLINES AS WELL AS INDIVIDUAL LEARNING ACTIVITIES. BY REFLECTING ON THE LEARNING DESIGN IN AN EXPLICIT MANNER, EDUCATORS ARE EMPOWERED TO COMPARE AND CONTRAST THEIR DESIGN USING THEIR OWN INSTITUTIONAL DATA.
LEARNING ANALYTICS AND GAMIFICATION ARE EXAMPLES OF SOME STRATEGIES THAT HAVE BEEN APPLIED IN EDUCATIONAL CONTEXT AS AN ATTEMPT TO REDUCE MOTIVATIONAL PROBLEMS. WHILE LEARNING ANALYTICS SEEK TO ANALYZE LEARNERS AND THEIR CONTEXTS IN ORDER TO UNDERSTAND AND IMPROVE THE TEACHING-LEARNING PROCESS, GAMIFICATION APPLIES ALL SUITABLE GAME ELEMENTS IN LEARNING ENVIRONMENTS TO MOTIVATE AND ENGAGE STUDENTS. THIS WORK AIMS TO INTEGRATE LEARNING ANALYTICS TECHNIQUES AND GAME ELEMENTS IN A VIRTUAL LEARNING ENVIRONMENT AND VERIFY IF THIS INTEGRATION INCREASED STUDENTS' INTERACTION AND SATISFACTION. THROUGH A CONTROLLED EXPERIMENT, 77 UNDERGRADUATE STUDENTS WERE DIVIDED INTO TWO GROUPS (I.E., EXPERIMENTAL AND CONTROL) AND THEIR PARTICIPATION IN AN ONLINE COURSE WAS MONITORED. AS A RESULT, THE SATISFACTION WAS HIGHER FOR THE EXPERIMENTAL GROUP, ALTHOUGH THERE WAS NO SIGNIFICANT DIFFERENCE BETWEEN THE GROUPS IN RELATION TO THEIR INTERACTION.
TEXTBOOK COSTS HAVE SKYROCKETED IN RECENT YEARS, PUTTING THEM BEYOND THE REACH OF MANY STUDENTS, BUT THERE ARE OPTIONS WHICH CAN MITIGATE THIS PROBLEM. OPEN TEXTBOOKS, AN OPEN EDUCATIONAL RESOURCE, HAVE PROVEN CAPABLE OF MAKING TEXTBOOKS AFFORDABLE TO STUDENTS. THERE HAVE BEEN FEW EDUCATIONAL DEVELOPMENT AS PROMISING AS THE DEVELOPMENT OF OPEN TEXTBOOKS TO LOWER COSTS FOR STUDENTS. WHILE THE LAST FIVE YEARS HAVE WITNESSED UNPARALLELED INTEREST AND SIGNIFICANT ADVANCES IN THE DEVELOPMENT AND DISSEMINATION OF OPEN TEXTBOOKS, ONE IMPORTANT ASPECT HAS, UNTIL NOW, REMAINED UNEXPLORED: THE PRAXIS OF LEARNING ANALYTICS FOR EXTRACTING INFORMATION REGARDING HOW LEARNERS INTERACT AND LEARN WITH OPEN TEXTBOOKS, WHICH IS CRUCIAL FOR THEIR EVALUATION AND ITERATIVE IMPROVEMENT PROCESS. LEARNING ANALYTICS OFFERS A FASTER AND MORE OBJECTIVE MEANS OF DATA COLLECTION AND PROCESSING THAN TRADITIONAL COUNTERPARTS, SUCH AS SURVEYS AND QUESTIONNAIRES, AND MOST IMPORTANTLY WITH THEIR CAPABILITY TO PROVIDE DIRECT EVIDENCE OF LEARNING, THEY PRESENT THE OPPORTUNITY TO ENHANCE BOTH LEARNER PERFORMANCE AND ENVIRONMENT. WITH SUCH BENEFITS ON OFFER, IT IS HARDLY SURPRISING THAT THE OPTIMISM SURROUNDING LEARNING ANALYTICS IS MOUNTING. HOWEVER, IN PRACTICE, IT HAS BEEN POINTED OUT THAT THE TECHNOLOGY TO DELIVER ITS POTENTIAL IS STILL VERY MUCH IN ITS INFANCY, WHICH IS TRUE IN THE CASE OF OPEN TEXTBOOKS. AGAINST THIS BACKGROUND, THE MAIN AIM OF OUR STUDY WAS TO DEVELOP A PROTOTYPE OPEN TEXTBOOK LEARNING ANALYTICS SYSTEM TO TRACK INDIVIDUAL LEARNERS' ONLINE AND OFFLINE INTERACTIONS WITH THEIR OPEN TEXTBOOKS IN ELECTRONIC PUBLICATION (EPUB) FORMAT, AND TO PRESENT ITS DEVELOPMENTAL WORK AS BUILDING BLOCKS FOR FUTURE DEVELOPMENT IN THIS AREA. WE CONCLUDE WITH A DISCUSSION OF THE PRACTICAL IMPLICATIONS OF OUR WORK AND PRESENT DIRECTIONS FOR FUTURE WORK.
LEARNING ANALYTICS RESEARCH HAS OFTEN BEEN TOUTED AS A MEANS TO ADDRESS CONCERNS REGARDING STUDENT RETENTION OUTCOMES. HOWEVER, FEW RESEARCH STUDIES TO DATE, HAVE EXAMINED THE IMPACT OF THE IMPLEMENTED INTERVENTION STRATEGIES DESIGNED TO ADDRESS SUCH RETENTION CHALLENGES. MOREOVER, THE METHODOLOGICAL RIGOR OF SOME OF THE EXISTING STUDIES HAS BEEN CHALLENGED. THIS STUDY EVALUATES THE IMPACT OF A PILOT RETENTION PROGRAM. THE STUDY CONTRASTS THE FINDINGS OBTAINED BY THE USE OF DIFFERENT METHODS FOR ANALYSIS OF THE EFFECT OF THE INTERVENTION. THE PILOT STUDY WAS UNDERTAKEN BETWEEN 2012 AND 2014 RESULTING IN A COMBINED ENROLMENT OF 11,160 STUDENTS. A MODEL TO PREDICT ATTRITION WAS DEVELOPED, DRAWING ON DATA FROM STUDENT INFORMATION SYSTEM, LEARNING MANAGEMENT SYSTEM INTERACTIONS, AND ASSESSMENT. THE PREDICTIVE MODEL IDENTIFIED SOME 1868 STUDENTS AS ACADEMICALLY AT-RISK. EARLY INTERVENTIONS WERE IMPLEMENTED INVOLVING LEARNING AND REMEDIATION SUPPORT. COMMON STATISTICAL METHODS DEMONSTRATED A POSITIVE ASSOCIATION BETWEEN THE INTERVENTION AND STUDENT RETENTION. HOWEVER, THE EFFECT SIZE WAS LOW. THE USE OF MORE ADVANCED STATISTICAL METHODS, SPECIFICALLY MIXED-EFFECT METHODS EXPLAINED HIGHER VARIABILITY IN THE DATA (OVER 99%), YET FOUND THE INTERVENTION HAD NO EFFECT ON THE RETENTION OUTCOMES. THE STUDY DEMONSTRATES THAT MORE DATA ABOUT INDIVIDUAL DIFFERENCES IS REQUIRED TO NOT ONLY EXPLAIN RETENTION BUT TO ALSO DEVELOP MORE EFFECTIVE INTERVENTION APPROACHES.
PURPOSE THIS PAPER AIMS TO PRESENT A REVIEW OF CASE STUDIES ON THE USE OF LEARNING ANALYTICS IN SCIENCE, TECHNOLOGY, ENGINEERING, (ARTS), AND MATHEMATICS (OR STE[A]M) EDUCATION. IT COVERS THE FEATURES AND TRENDS OF LEARNING ANALYTICS PRACTICES AS REVEALED IN CASE STUDIES. DESIGN/METHODOLOGY/APPROACH A TOTAL OF 34 CASE STUDIES PUBLISHED FROM 2013 TO 2018 REPORTING RELEVANT LEARNING ANALYTICS PRACTICES WERE COLLECTED FROM SCOPUS AND GOOGLE SCHOLAR FOR ANALYSIS. THE FEATURES AND TRENDS OF PRACTICES WERE IDENTIFIED THROUGH A COMPARISON OF THE FIRST (2013-2015) AND THE SECOND PERIOD (2016-2018). FINDINGS THE RESULTS SHOWED AN INCREASING ADOPTION OF LEARNING ANALYTICS IN STE(A)M EDUCATION, PARTICULARLY IN THE USA AND EUROPEAN COUNTRIES AND AT THE TERTIARY LEVEL. MORE SPECIFIC TYPES OF DATA HAVE BEEN COLLECTED FOR THE LEARNING ANALYTICS PRACTICES, AND THE DATA RELATED TO STUDENTS' LEARNING PROCESSES HAS ALSO BEEN MORE FREQUENTLY USED. THE TYPES OF STE(A)M LEARNING PRACTICES HAVE BECOME MORE DIVERSIFIED, WITH TECHNOLOGY ENHANCEMENT FEATURES INCREASINGLY INTRODUCED. THE OUTCOMES OF THE CASE STUDIES REFLECT THE OVERALL BENEFITS OF LEARNING ANALYTICS AND ADDRESS THE SPECIFIC NEEDS OF STE(A)M EDUCATION. THERE HAVE ALSO BEEN FEWER TYPES OF LIMITATIONS ENCOUNTERED IN THE LEARNING ANALYTICS PRACTICES OVER THE YEARS, WITH UNKNOWN CORRELATION AMONG VARIABLES, SMALL SAMPLE SIZE AND LIMITED DATA BEING THE MAJOR TYPES. ORIGINALITY/VALUE THIS STUDY REVEALS THE IMPLEMENTATION OF LEARNING ANALYTICS IN RELATION TO THE CONTEXTS AND NEEDS OF STE(A)M EDUCATION. THE FINDINGS ALSO SUGGEST FUTURE WORK FOR EXAMINING THE ADOPTION OF LEARNING ANALYTICS TO COPE WITH THE DEVELOPMENT OF STE(A)M AND, IN PARTICULAR, HOW THE SUCCESSFUL EXPERIENCE OF LEARNING ANALYTICS IN OTHER DISCIPLINES COULD BE TRANSFERRED TO STE(A)M.
AS MASSIVE OPEN ONLINE COURSES (MOOCS) GENERATE A HUGE AMOUNT OF LEARNING ACTIVITY DATA THROUGH ITS THOUSANDS OF USERS, GREAT POTENTIAL IS PROVIDED TO USE THIS DATA TO UNDERSTAND AND OPTIMIZE THE LEARNING EXPERIENCE AND OUTCOME, WHICH IS THE GOAL OF LEARNING ANALYTICS. BUT FIRST, THE DATA NEEDS TO BE COLLECTED, PROCESSED, ANALYZED AND REPORTED IN ORDER TO GAIN ACTIONABLE INSIGHTS. TECHNICAL CONCEPTS AND IMPLEMENTATIONS ARE RARELY ACCESSIBLE AND THEREFORE THIS WORK PRESENTS AN ARCHITECTURE HOW LEARNING ANALYTICS CAN BE IMPLEMENTED IN A SERVICE-ORIENTED MOOC PLATFORM. TO ACHIEVE THAT, A SERVICE BASED ON EXTENSIBLE SCHEMA-AGNOSTIC PROCESSING PIPELINES IS INTRODUCED FOR THE HPI MOOC PLATFORM. THE APPROACH WAS EVALUATED REGARDING ITS SCALABILITY, EXTENSIBILITY, AND VERSATILITY WITH REAL-WORLD USE CASES. ALSO, DATA PRIVACY WAS TAKEN INTO ACCOUNT. BASED ON FIVE YEARS OF RUNNING THE SERVICE IN PRODUCTION ON SEVERAL PLATFORM DEPLOYMENTS, SIX DESIGN RECOMMENDATIONS ARE PRESENTED WHICH CAN BE UTILIZED AS BEST PRACTICES FOR PLATFORM VENDORS AND RESEARCHERS WHEN IMPLEMENTING LEARNING ANALYTICS IN MOOCS.
THIS PAPER REPORTS A DESIGN SCIENCE RESEARCH (DSR) STUDY THAT DEVELOPS, DEMONSTRATES AND EVALUATES A SET OF DESIGN PRINCIPLES FOR INFORMATION SYSTEMS (IS) THAT UTILISE LEARNING ANALYTICS TO SUPPORT LEARNING AND TEACHING IN HIGHER EDUCATION. THE INITIAL SET OF DESIGN PRINCIPLES IS CREATED FROM THEORY-INSPIRED CONCEPTUALISATION BASED ON THE LITERATURE, AND THEY ARE EVALUATED AND REVISED THROUGH A DSR PROCESS OF DEMONSTRATION AND EVALUATION. WE EVALUATED THE DEVELOPED ARTEFACT IN FOUR COURSES WITH A TOTAL ENROLMENT OF 1,173 STUDENTS. THE DEVELOPED DESIGN PRINCIPLES FOR LEARNING ANALYTICS INFORMATION SYSTEMS (LAIS) TO ESTABLISH A FOUNDATION FOR FURTHER DEVELOPMENT AND IMPLEMENTATION OF LEARNING ANALYTICS TO SUPPORT LEARNING AND TEACHING IN HIGHER EDUCATION.
LEARNING ANALYTICS IS THE ANALYSIS OF LEARNING DATA FOR OPTIMISING LEARNING AND LEARNING ENVIRONMENTS. A NUMBER OF MODELS OR FRAMEWORKS FOR LEARNING ANALYTICS HAVE BEEN PROPOSED, WHICH FOCUS ON THE PROCESS OF ANALYTICS. MOTIVATED BY FRAMEWORK DEVELOPMENTS IN OTHER AREAS, SUCH AS SYSTEMS DEVELOPMENT AND IT MANAGEMENT WE PROPOSE TO VIEW LEARNING ANALYTICS THROUGH THE LENS OF DESIGN SCIENCE. WE IDENTIFY A SET OF ARTEFACTS WHICH EXTEND THE EXISTING LEARNING ANALYTICS FRAMEWORK ALONG A SECOND DIMENSION. INCORPORATING A LEARNING MODEL BASED ON INTERACTION THEORY, THE EXTENDED FRAMEWORK AND ARTEFACTS ARE APPLIED TO A CASE STUDY OF BUSINESS COMPUTING STUDENTS STUDYING CUSTOMER RELATIONSHIP MANAGEMENT IN A CLOUD COMPUTING ENVIRONMENT. THE STUDY SHOWS THE ARTEFACTS TO BE USEFUL IN EXTENDING THE DESCRIPTIVE ABILITY OF THE ANALYTICS FRAMEWORK. THE SIGNIFICANCE OF THE WORK IS THAT IT PROVIDES A VIEW OF ANALYTICS THROUGH THE LENS OF DESIGN SCIENCE. IN THIS WAY THE EXTENDED FRAMEWORK PROVIDES A NUMBER OF ADVANTAGES FOR THE APPLICATION OF LEARNING ANALYTICS. THE FRAMEWORK ALSO CONTRIBUTES TO LEARNING ANALYTICS RESEARCH BY EXPANDING THE ANALYTICS VOCABULARY AND PROVIDING TOOLS FOR FURTHER RESEARCH.
THIS PAPER IS IN RESPONSE TO THE MANUSCRIPT ENTITLED "STUDENT PERCEPTIONS OF PRIVACY PRINCIPLES FOR LEARNING ANALYTICS" (IFENTHALER AND SCHUMACHER, STUDENT PERCEPTIONS OF PRIVACY PRINCIPLES FOR LEARNING ANALYTICS. EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT, 64(5), 923-938, 2016) FROM A PRACTICE PERSPECTIVE. LEARNING ANALYTICS (THE USE OF DATA SCIENCE METHODS TO GENERATE ACTIONABLE EDUCATIONAL INSIGHTS) HAVE GREAT POTENTIAL TO IMPACT LEARNING PRACTICES DURING THE SHIFT TO DIGITAL. IN PARTICULAR, THEY CAN HELP FILL A CRITICAL INFORMATION GAP FOR STUDENTS CREATED BY AN ABSENCE OF CLASSROOM-BASED CUES AND THE NEED FOR INCREASED SELF-REGULATION IN THE ONLINE ENVIRONMENT, HOWEVER THE ADOPTION OF LEARNING ANALYTICS IN EFFECTIVE, ETHICAL AND RESPONSIBLE WAYS IS NON-TRIVIAL. IFENTHALER AND SCHUMACHER (2016) PRESENT IMPORTANT FINDINGS ABOUT STUDENTS' PERCEPTIONS OF LEARNING ANALYTICS' USEFULNESS AND PRIVACY, SIGNALING THE NEED FOR A STUDENT-CENTERED PARADIGM, BUT STOP SHORT OF ADDRESSING ITS IMPLICATIONS FOR THE CREATION AND ADOPTION OF LEARNING ANALYTICS TOOLS. IN THIS PAPER WE ADDRESS THIS LIMITATION BY DESCRIBING THE THREE SPECIFIC SHIFTS NEEDED IN CURRENT LEARNING ANALYTICS PRACTICE FOR ANALYTICS TO BE ACCEPTED BY AND EFFECTIVE FOR STUDENTS: (1) INVOLVE STUDENTS IN THE CREATION OF ANALYTIC TOOLS MEANT TO SERVE THEM; (2) DEVELOP ANALYTICS THAT ARE CONTEXTUALIZED, EXPLAINABLE AND CONFIGURABLE; AND (3) EMPOWER STUDENTS' AGENCY IN USING ANALYTIC TOOLS AS PART OF THEIR LARGER PROCESS OF LEARNING. THESE SHIFTS ARE CURRENTLY IN DIFFERENT STAGES OF MATURITY AND ADOPTION IN MAINSTREAM LEARNING ANALYTICS PRACTICE. THE PRIMARY IMPLICATION OF THIS WORK IS A CALL TO ACTION FOR RESEARCHERS AND PRACTITIONERS TO RETHINK AND RESHAPE HOW STUDENTS PARTICIPATE IN THE CREATION, INTERPRETATION AND IMPACT OF LEARNING ANALYTICS.
IN THIS PAPER, WE PRESENT THE RESULTS OF AN EXPLORATORY STUDY IN WHICH THE REQUIREMENTS AND (CONFLICTING) INTERESTS OF THE VARIOUS STAKEHOLDERS REGARDING THE DEVELOPMENT AND IMPLEMENTATION OF LEARNING ANALYTICS APPLICATIONS ARE DISCUSSED. FURTHERMORE, WE EXPLAIN HOW THEY RELATE TO THE PROCESS OF LEARNING ANALYTICS, FOR WHICH A NEW MODEL IS PRESENTED. THE FINDINGS, BASED ON A WORKSHOP WITH VARIOUS STAKEHOLDERS, DEMONSTRATE THAT INTERESTS CLASH IN THE AREAS OF PRIVACY, DATA OWNERSHIP, TRUST AND INTERFERENCE, THE PREVAILING EDUCATIONAL CULTURE, STANDARDS AND INTEROPERABILITY, COMPETITION AND REGULATION. THE BIGGEST OBSTACLES ARISE IN THE FIELDS OF PRIVACY AND DATA OWNERSHIP. PERSONAL DATA ENABLES BETTER SERVICES AND INNOVATION FROM PRODUCERS OF LEARNING MATERIALS AND LEARNING TRACKING SYSTEMS. HOWEVER, LEARNERS AND THEIR PARENTS ARE EXPECTED TO DEMAND CONTROL OVER THEIR PERSONAL DATA. THIS CALLS FOR THE APPLICATION OF THE 'PRIVACY BY DESIGN' PRINCIPLE. ANOTHER IMPORTANT TOPIC IS HUMAN-MACHINE INTERACTION: THE DEGREE OF AUTONOMY OF A LEARNING ANALYTICS APPLICATION, AND THE IMPLICATIONS FOR THE TEACHER'S ROLE IN THE LEARNING PROCESS.
VOCATIONAL EDUCATION AND TRAINING (VET) REMAIN OVERLOOKED IN LEARNING ANALYTICS (LA) RESEARCH. THIS SYSTEMATIC LITERATURE REVIEW, USING FOUR DATABASES AND OTHER SOURCES, WAS CARRIED OUT BY ANALYZING SELECTED 60 ARTICLES (2012-2017) TO STUDY THE LEVELS AND STAGES OF EDUCATION THAT THE REVIEWED LA LITERATURE EXAMINED. THE REVIEW INDICATED THAT MOST OF THE ANALYZED PAPERS FOCUSED ON THE COURSE LEVEL, FOLLOWED BY STUDENT AND INSTITUTION LEVELS IN HIGHER EDUCATION. FEW EMPIRICAL STUDIES HAVE ADDRESSED LA USE DURING THE VET STAGE, PARTICULARLY AT THE GOVERNMENTAL LEVEL. WE ALSO CONSIDERED ETHICAL CONCERNS AND RECOMMENDATIONS FOR FURTHER LA DEVELOPMENT IN VET. IT IS SUGGESTED TO USE LA IN KNOWLEDGE TRANSFER AND INTEGRATION BETWEEN THE CLASSROOM AND WORKPLACE.
A FEW YEARS AFTER THE EMERGENCE OF LEARNING ANALYTICS AS A RELEVANT TOPIC IN EDUCATIONAL RESEARCH, WHICH HAS BEEN ACCOMPANIED BY SEVERAL PUBLICATIONS DEVOTED TO THE THEME, WE STILL SEE HOW THE DIFFERENT TOPICS SEEM TO ALTERNATE BOTH CONVERGING AND DIVERGING TRENDS. THUS, WHEREAS SOMETIMES DIFFERENT APPROACHES SEEM TO CONVERGE INTO THE STUDY OF THE SAME ISSUE OR SITUATION, OTHER TIMES THE SAME APPROACHES ARE APPLIED TO SOLVE DIFFERENT PROBLEMS IN A DIVERGENT WAY. THIS SITUATION BECOMES MORE COMPLEX WITH THE ADDED INCLUSION OF NEW TECHNOLOGICAL ADVANCES AND ANALYSIS TECHNIQUES, AS A RESULT OF WHICH THE EXTENSION OF THE SCOPE OF LEARNING ANALYTICS AS A DISCIPLINE MAY BE PERCEIVED AS ENDLESS OR BOUNDLESS. THIS IS AN INTRODUCTION TO THE TRACK ON LEARNING ANALYTICS WITHIN THE TECHNOLOGICAL ECOSYSTEMS FOR ENHANCING MULTICULTURALITY 2015 CONFERENCE, WHICH AIMS TO ADDRESS SOME OF THE CURRENT CONCERNS ON LEARNING ANALYTICS AND HELP CLEARING THE HORIZON. THIS INTRODUCTORY PAPER PROVIDES AN OVERVIEW OF THE RATIONALE OF THE TRACK'S PROPOSAL, INCLUDING DIFFERENT TOPICS OPEN FOR DISCUSSION; THE INTRODUCTION IS FOLLOWED BY AN INSIGHT OF THE SUBMISSION MANAGEMENT AND PARTICIPANTS' SELECTION PROCESS. THEN, A DETAILED SUMMARY OF THE MANUSCRIPTS ACCEPTED FOR PARTICIPATION IN THE CONFERENCE IS PRESENTED.
LEARNING ANALYTICS IMPROVES THE TEACHING AND LEARNING PROCEDURES BY USING THE EDUCATIONAL DATA. IT USES ANALYSIS TOOLS TO CARRY OUT THE STATISTICAL EVALUATION OF RICH DATA AND THE PATTERN RECOGNITION WITHIN DATA. THIS CHAPTER, FIRSTLY, DESCRIBES FOUR LEARNING ANALYTICS METHODS IN EDUCATIONAL INSTITUTIONS. SECONDLY, IT PROPOSES A LEARNING ANALYTICS APPROACH FOR JOB SCHEDULING ON CLOUD SERVERS, CALLED LAJOS. THIS APPROACH APPLIES A LEARNING-BASED MECHANISM TO PRIORITISE USERS' JOBS ON SCHEDULING QUEUES. IT USES THE THREE BASIC ATTRIBUTES "IMPORTANCE LEVEL", "WAITING TIME" AND "DEADLINE TIME" OF VARIOUS JOBS ON CLOUD SERVERS. THE CLOUD BROKER ACTS AS A TEACHER AND LOCAL SCHEDULERS OF CLOUD SITES ACT AS STUDENTS. THE BROKER LEARNS TO LOCAL SCHEDULERS HOW TO PRIORITISE USERS' JOBS ACCORDING TO THE VALUES OF THEIR ATTRIBUTES. IN THE DEPLOYMENT PHASE, THE EFFECT OF THE ABOVE ATTRIBUTES ON THE SYSTEM THROUGHPUT IS STUDIED SEPARATELY TO SELECT THE BEST ATTRIBUTE. IN THE SERVICE PHASE, USERS' JOBS ARE PRIORITISED BY COMPUTER SYSTEMS ACCORDING TO THE SELECTED ATTRIBUTE. SIMULATION RESULTS SHOW THAT THE LAJOS APPROACH IS MORE EFFICIENT COMPARED TO SOME OF THE JOB SCHEDULING METHODS IN TERMS OF SCHEDULE LENGTH AND SYSTEM THROUGHPUT.
LEARNING ANALYTICS (LA) HAS BEEN APPLIED TO VARIOUS LEARNING ENVIRONMENTS, THOUGH IT IS QUITE NEW IN THE FIELD OF COMPUTER ASSISTED LANGUAGE LEARNING (CALL). THIS ARTICLE ATTEMPTS TO EXAMINE THE APPLICATION OF LEARNING ANALYTICS IN THE UPCOMING BIG DATA AGE. IT STARTS WITH AN INTRODUCTION AND APPLICATION OF LEARNING ANALYTICS IN OTHER FIELDS, FOLLOWED BY A RETROSPECTIVE REVIEW OF HISTORICAL INTERACTION BETWEEN LEARNING AND MEDIA IN CALL, AND A PENETRATING ANALYSIS ON WHY PEOPLE WOULD GO TO LEARNING ANALYTICS TO INCREASE THE EFFICIENCY OF FOREIGN LANGUAGE EDUCATION. AS APPROVED IN PREVIOUS RESEARCH, NEW TECHNOLOGY, INCLUDING BIG DATA MINING AND ANALYSIS, WOULD INEVITABLY ENHANCE THE LEARNING OF FOREIGN LANGUAGES. POTENTIAL CHANGES THAT LEARNING ANALYTICS WOULD BRING TO CHINESE FOREIGN LANGUAGE EDUCATION AND RESEARCHES ARE ALSO PRESENTED IN THE ARTICLE.
REFLECTION IS CRITICAL FOR ADOLESCENTS' PROBLEM SOLVING AND LEARNING IN GAME-BASED LEARNING ENVIRONMENTS (GBLES). YET CHALLENGES EXIST IN THE LITERATURE BECAUSE MOST STUDIES LACK A THEORETICAL PERSPECTIVE AND CLEAR OPERATIONAL DEFINITION TO INFORM HOW AND WHEN REFLECTION SHOULD BE SCAF-FOLDED DURING GAME-BASED LEARNING. IN THIS PAPER, WE ADDRESS THESE ISSUES BY STUDYING THE QUANTITY AND QUALITY OF 120 ADOLESCENTS' WRITTEN REFLECTIONS AND THEIR RELATION TO THEIR LEARNING AND PROBLEM SOLVING WITH CRYSTAL ISLAND, A GBLE. SPECIFICALLY, WE (1) DEFINE REFLECTION AND HOW IT RELATES TO SKILL AND KNOWLEDGE ACQUISITION; (2) REVIEW STUDIES EXAMINING REFLECTION AND ITS RELATION TO PROBLEM SOLVING AND LEARNING WITH EMERGING TECHNOLOGIES; AND (3) PROVIDE DIRECTION FOR BUILDING REFLECTION PROMPTS INTO GBLES THAT ARE ALIGNED WITH THE LEARNING GOALS BUILT INTO THE LEARNING SESSION (E.G., LEARN ABOUT MICROBIOLOGY VERSUS SUCCESSFULLY SOLVE A PROBLEM) TO MAXIMIZE ADOLESCENTS' REFLECTION, LEARNING, AND PERFORMANCE. OVERALL, OUR FINDINGS EMPHASIZE HOW IMPORTANT IT IS TO EXAMINE NOT ONLY THE QUANTITY OF REFLECTION BUT ALSO THE DEPTH OF WRITTEN REFLECTION AS IT RELATES TO SPECIFIC LEARNING GOALS. WE DISCUSS THE IMPLICATIONS OF USING GAME-LEARNING ANALYTICS TO GUIDE INSTRUCTIONAL DECISION MAKING IN THE CLASSROOM.
LEARNING ANALYTICS (LA) SITS AT THE CONFLUENCE OF MANY CONTRIBUTING DISCIPLINES, WHICH BRINGS THE RISK OF HIDDEN ASSUMPTIONS INHERITED FROM THOSE FIELDS. HERE, WE CONSIDER A HIDDEN ASSUMPTION DERIVED FROM COMPUTER SCIENCE, NAMELY, THAT IMPROVING COMPUTATIONAL ACCURACY IN CLASSIFICATION IS ALWAYS A WORTHY GOAL. WE DEMONSTRATE THAT THIS ASSUMPTION IS UNLIKELY TO HOLD IN SOME IMPORTANT EDUCATIONAL CONTEXTS, AND ARGUE THAT EMBRACING COMPUTATIONAL "IMPERFECTION" CAN IMPROVE OUTCOMES FOR THOSE SCENARIOS. SPECIFICALLY, WE SHOW THAT LEARNER-FACING APPROACHES AIMED AT "LEARNING HOW TO LEARN" REQUIRE MORE HOLISTIC VALIDATION STRATEGIES. WE CONSIDER WHAT INFORMATION MUST BE PROVIDED IN ORDER TO REASONABLY EVALUATE ALGORITHMIC TOOLS IN LA, TO FACILITATE TRANSPARENCY AND REALISTIC PERFORMANCE COMPARISONS.
AN IMPORTANT GOAL OF LEARNING ANALYTICS (LA) IS TO IMPROVE LEARNING BY PROVIDING STUDENTS WITH MEANINGFUL FEEDBACK. FEEDBACK IS OFTEN GENERATED BY PREDICTION MODELS OF STUDENT SUCCESS USING DATA ABOUT STUDENTS AND THEIR LEARNING PROCESSES BASED ON DIGITAL TRACES OF LEARNING ACTIVITIES. HOWEVER, EARLY IN THE LEARNING PROCESS, WHEN FEEDBACK IS MOST FRUITFUL, TRACE-DATA-BASED PREDICTION MODELS OFTEN HAVE LIMITED INFORMATION ABOUT THE INITIAL ABILITY OF STUDENTS, MAKING IT DIFFICULT TO PRODUCE ACCURATE PREDICTION AND PERSONALIZED FEEDBACK TO INDIVIDUAL STUDENTS. FURTHERMORE, FEEDBACK GENERATED FROM TRACE DATA WITHOUT APPROPRIATE CONSIDERATION OF LEARNERS' DISPOSITIONS MIGHT HAMPER EFFECTIVE INTERVENTIONS. BY PROVIDING AN EXAMPLE OF THE ROLE OF LEARNING DISPOSITIONS IN AN LA APPLICATION DIRECTED AT PREDICTIVE MODELING IN AN INTRODUCTORY MATHEMATICS AND STATISTICS MODULE, WE MAKE A PLEA FOR APPLYING DISPOSITIONAL LEARNING ANALYTICS (DLA) TO MAKE LA PRECISE AND ACTIONABLE. DLA COMBINES LEARNING DATA WITH LEARNERS' DISPOSITION DATA MEASURED THROUGH FOR EXAMPLE SELF-REPORT SURVEYS. THE ADVANTAGE OF DLA IS TWOFOLD: FIRST, TO IMPROVE THE ACCURACY OF EARLY PREDICTIONS; AND SECOND, TO LINK LA PREDICTIONS WITH MEANINGFUL LEARNING INTERVENTIONS THAT FOCUS ON ADDRESSING LESS DEVELOPED LEARNING DISPOSITIONS. DISPOSITIONS IN OUR DLA EXAMPLE INCLUDE STUDENTS' MINDSETS, OPERATIONALIZED AS ENTITY AND INCREMENTAL THEORIES OF INTELLIGENCE, AND CORRESPONDING EFFORT BELIEFS. THESE DISPOSITIONS WERE INPUTS FOR A CLUSTER ANALYSIS GENERATING DIFFERENT LEARNING PROFILES. THESE PROFILES WERE COMPARED FOR OTHER DISPOSITIONS AND MODULE PERFORMANCE. THE FINDING OF PROFILE DIFFERENCES SUGGESTS THAT THE INCLUSION OF DISPOSITION DATA AND MINDSET DATA, IN PARTICULAR, ADDS PREDICTIVE POWER TO LA APPLICATIONS.
LEARNING ANALYTICS AND KNOWLEDGE (LAK) AND EDUCATIONAL DATA MINING (EDM) ARE TWO OF THE MOST POPULAR VENUES FOR RESEARCHERS AND PRACTITIONERS TO REPORT AND DISSEMINATE DISCOVERIES IN DATA-INTENSIVE RESEARCH ON TECHNOLOGY-ENHANCED EDUCATION. AFTER THE DEVELOPMENT OF ABOUT A DECADE, IT IS TIME TO SCRUTINIZE AND COMPARE THESE TWO VENUES. BY DOING THIS, WE EXPECTED TO INFORM RELEVANT STAKEHOLDERS OF A BETTER UNDERSTANDING OF THE PAST DEVELOPMENT OF LAK AND EDM AND PROVIDE SUGGESTIONS FOR THEIR FUTURE DEVELOPMENT. SPECIFICALLY, WE CONDUCTED AN EXTENSIVE COMPARISON ANALYSIS BETWEEN LAK AND EDM FROM FOUR PERSPECTIVES, INCLUDING (I) THE TOPICS INVESTIGATED; (II) COMMUNITY DEVELOPMENT; (III) COMMUNITY DIVERSITY; AND (IV) RESEARCH IMPACT. FURTHERMORE, WE APPLIED ONE OF THE MOST WIDELY-USED LANGUAGE MODELING TECHNIQUES (WORD2VEC) TO CAPTURE WORDS USED FREQUENTLY BY RESEARCHERS TO DESCRIBE FUTURE WORKS THAT CAN BE PURSUED BY BUILDING UPON SUGGESTIONS MADE IN THE PUBLISHED PAPERS TO SHED LIGHT ON POTENTIAL DIRECTIONS FOR FUTURE RESEARCH.
THE IMPORTANCE OF TEACHERS IN ONLINE LEARNING IS WIDELY ACKNOWLEDGED TO EFFECTIVELY SUPPORT AND STIMULATE LEARNERS. WITH THE INCREASING AVAILABILITY OF LEARNING ANALYTICS DATA, ONLINE TEACHERS MIGHT BE ABLE TO USE LEARNING ANALYTICS DASHBOARDS TO FACILITATE LEARNERS WITH DIFFERENT LEARNING NEEDS. HOWEVER, DEPLOYMENT OF LEARNING ANALYTICS VISUALISATIONS BY TEACHERS ALSO REQUIRES BUY-IN FROM TEACHERS. USING THE PRINCIPLES OF TECHNOLOGY ACCEPTANCE MODEL, IN THIS EMBEDDED CASE-STUDY, WE EXPLORED TEACHERS' READINESS FOR LEARNING ANALYTICS VISUALISATIONS AMONGST 95 EXPERIENCED TEACHING STAFF AT ONE OF THE LARGEST DISTANCE LEARNING UNIVERSITIES BY USING AN INNOVATIVE TRAINING METHOD CALLED ANALYTICS4ACTION WORKSHOP. THE FINDINGS INDICATED THAT PARTICIPANTS APPRECIATED THE INTERACTIVE AND HANDS-ON APPROACH, BUT AT THE SAME TIME WERE SKEPTICAL ABOUT THE PERCEIVED EASE OF USE OF LEARNING ANALYTICS TOOLS THEY WERE OFFERED. MOST TEACHERS INDICATED A NEED FOR ADDITIONAL TRAINING AND FOLLOW-UP SUPPORT FOR WORKING WITH LEARNING ANALYTICS TOOLS. OUR RESULTS HIGHLIGHT A NEED FOR INSTITUTIONS TO PROVIDE EFFECTIVE PROFESSIONAL DEVELOPMENT OPPORTUNITIES FOR LEARNING ANALYTICS.
RECENT DEVELOPMENTS IN LEARNING ANALYTICS HAVE ATTRACTED MUCH ATTENTION FROM RESEARCHERS AND PRACTITIONERS, AS WELL AS DIVERSE STAKEHOLDERS, IN EXPLORING THE POTENTIAL FOR LEARNING ANALYTICS TO ENHANCE LEARNING AND TEACHING PRACTICES. HENCE, THIS PAPER PRESENTS A LITERATURE REVIEW OF THE USE OF THE LEARNING ANALYTICS INTERVENTION IMPLEMENTED IN VARIOUS EDUCATIONAL INSTITUTIONS IN ORDER TO IMPROVE STUDENTS' SUCCESS AND ENGAGEMENT IN THEIR LEARNING, PARTICULARLY FOR AT-RISK STUDENTS. THE AIM OF THIS PAPER IS TO PROVIDE AN OVERVIEW OF HOW LEARNING ANALYTICS INTERVENTION HAS BEEN UTILISED IN THE EDUCATIONAL INSTITUTION, THE TYPES OF THEORY APPLIED IN THE INTERVENTION AND WHAT TOOLS ARE USED IN THE INTERVENTION. ADDITIONALLY, THE APPLICATION OF LEARNING ANALYTICS INTERVENTION, ITS PURPOSE, EFFECTS AND EXAMPLES ARE ALSO DISCUSSED IN THIS PAPER. THE FINDINGS SHOW THAT MOST OF THE LEARNING ANALYTICS INTERVENTIONS HAVE AN IMPACT ON STUDENTS' SUCCESS IN THEIR LEARNING, ALTHOUGH SOME OF THEM HAVE LITTLE IMPACT OR NO SIGNIFICANT EFFECT, AND VARIOUS LEARNING ANALYTICS TOOLS AND STRATEGIES ARE DISCOVERED. LASTLY, IT IS SUGGESTED THAT FURTHER LEARNING ANALYTICS INTERVENTIONS SHOULD BE DEVELOPED IN ORDER TO MEET THE NEEDS OF STUDENTS.
A KEY NOTION CONVEYED BY THOSE WHO ADVOCATE FOR THE USE OF DATA TO ENHANCE INSTRUCTION IS AN AWARENESS THAT LEARNING ANALYTICS HAS THE POTENTIAL TO IMPROVE INSTRUCTION AND LEARNING BUT IS NOT CURRENTLY REACHING THAT POTENTIAL. GIBBONS (2014) SUGGESTED THAT A LACK OF LEARNING FACILITATED BY CURRENT TECHNOLOGY-ENABLED INSTRUCTIONAL SYSTEMS MAY BE DUE IN PART TO THE NATURAL TENDENCY OF MANY DESIGNERS TO FOCUS ON THE SURFACE LAYERS OF INSTRUCTION (I.E., THE CONTENT AND CONTROL LAYERS), WHILE FAILING TO ADEQUATELY DESIGN THE INTERNAL (LESS VISIBLE) ASPECTS (E.G., THE DATA MANAGEMENT LAYER). IN THIS PAPER, WE OUTLINE PHASES IN THE DESIGN PROCESS RELATED TO THE DATA MANAGEMENT LAYER THAT SHOULD BE CONSIDERED WHEN INTEGRATING LEARNING ANALYTICS INTO A TECHNOLOGY-ENABLED LEARNING SYSTEM.
LEARNERS HAVE DIFFERENT NEEDS AND ABILITIES; TEACHERS HAVE THE AMBITION TO INTERVENE BEFORE IT IS TOO LATE. HOW MAY E-LEARNING SYSTEMS SUPPORT THIS? LEARNING ANALYTICS MAY BE THE ANSWER BUT THERE IS NOT A GENERAL-PURPOSE MODEL TO ADOPT. MANY LEARNING ANALYTICS TOOLS EXAMINE DATA RELATED TO THE ACTIVITIES OF LEARNERS IN ON-LINE SYSTEMS. RESEARCH EFFORTS IN LEARNING ANALYTICS TRIED TO EXAMINE DATA COMING FROM LMS TRACKS IN ORDER TO DEFINE PREDICTIVE MODEL OF STUDENTS' PERFORMANCES AND FAILURE RISKS AND TO INTERVENE TO IMPROVE THE LEARNING OUTCOMES. THE ANALYTICAL METHODS ARE WIDELY USED BUT NO THEORETICAL REFERENCES ARE CLEAR. IN THIS PAPER, WE TRIED TO DEFINE A PREDICTION MODEL FOR LEARNING ANALYTICS. IN PARTICULAR, WE ADOPTED A MOODLE-BASED LMS IN A BLENDED COURSE AND COLLECTED ALL DATA OF MORE THAN 400 UNDERGRADUATE STUDENTS IN TERMS OF RESOURCE ACCESSES AND EXAM PERFORMANCES. THE MODEL WE DEFINED WAS ABLE TO IDENTIFY THE LEARNERS AT RISK DURING THEIR LEARNING PROCESSES ONLY BY ANALYSING THEIR NAVIGATION PATHS AMONG THE CONTENTS.
THIS BRIEF PAPER DEVELOPS A SERIES OF PROVOCATIONS AGAINST THE CURRENT FORMS OF LEARNING ANALYTICS THAT ARE BEGINNING TO BE IMPLEMENTED IN HIGHER EDUCATION CONTEXTS. THE PAPER HIGHLIGHTS A NUMBER OF WAYS IN WHICH LEARNING ANALYTICS CAN BE EXPERIENCED AS DISCRIMINATORY, OPPRESSIVE AND ULTIMATELY DISADVANTAGING ACROSS WHOLE STUDENT POPULATIONS, AND CONSIDERS THE LIMITATIONS OF CURRENT EFFORTS WITHIN EDUCATIONAL DATA SCIENCE TO INCREASE AWARENESS OF 'ETHICS' AND 'SOCIAL GOOD'. THIS CULMINATES IN A STARK CHOICE: IS IT POSSIBLE TO SUBSTANTIALLY IMPROVE THE FIELD OF LEARNING ANALYTICS AS IT CURRENTLY STANDS, OR SHOULD WE ABANDON IT IN FAVOUR OF NEW FORMS OF APPLYING DATA SCIENCE THAT ARE ALIGNED WITH THE EXPERIENCES OF NON-CONFORMING 'LEARNERS' AND UN-CATEGORIZABLE FORMS OF 'LEARNING'?
THERE IS A GROWING INTEREST IN THE USE OF LEARNING ANALYTICS IN HIGHER EDUCATION INSTITUTIONS. LEARNING ANALYTICS ALSO APPEAR TO HAVE THE POTENTIAL TO BE USED TO PROVIDE PERSONALIZED FEEDBACK AND SUPPORT IN ONLINE LEARNING. HOWEVER, WHEN THE LITERATURE IS EXAMINED, THE USE OF LEARNING ANALYTICS FOR THIS PURPOSE APPEARS AS A GAP TO BE INVESTIGATED. THIS RESEARCH AIMS TO EXAMINE THE OPINIONS OF PRE-SERVICE TEACHERS ABOUT THE PERSONALIZED RECOMMENDATION AND GUIDANCE FEEDBACK BASED ON LEARNING ANALYTICS. THE RESEARCH WAS CARRIED OUT ON 40 PRE-SERVICE TEACHERS IN THE COMPUTER I COURSE, WHICH WAS CONDUCTED ACCORDING TO THE FLIPPED LEARNING MODEL FOR 12 WEEKS. THROUGHOUT THE RESEARCH PROCESS, PERSONALIZED FEEDBACK BASED ON LEARNING ANALYTICS WAS PROVIDED BY RESEARCHER (THE RESEARCHER IS ALSO THE TEACHER OF THE COMPUTER I COURSE) TO PRE-SERVICE TEACHERS AT THE END OF EACH WEEK. ACCORDINGLY, THE STUDENTS' WEEKLY LEARNING MANAGEMENT SYSTEM (LMS) OBTAINED LEARNING ANALYTICS RESULTS FROM THE LOG DATA RELATED TO THEIR USAGE BEHAVIOR. THEN, THE RESEARCHER PREPARED PERSONALIZED RECOMMENDATION AND GUIDANCE MESSAGES BASED ON LEARNING ANALYTICS RESULTS. LEARNING ANALYTICS RESULTS AND RELATED RECOMMENDATIONS AND GUIDANCE MESSAGES WERE SENT VIA LMS (FROM THE MESSAGING TOOL) AS FEEDBACK. THIS PROCESS WAS DONE FOR EACH PRE-SERVICE TEACHER BY THE RESEARCHER EVERY WEEK DURING THE RESEARCH PROCESS. THE DATA OF THE RESEARCH WERE OBTAINED WITH A SEMI-STRUCTURED OPINION FORM AND CONTENT ANALYSIS WAS MADE IN THE ANALYSIS OF THE DATA. AS A RESULT OF THE RESEARCH, BENEFICIAL ASPECTS AND LIMITATIONS OF PERSONALIZED RECOMMENDATION AND GUIDANCE FEEDBACK BASED ON LEARNING ANALYTICS FROM THE PERSPECTIVE OF PRE-SERVICE TEACHERS WERE REVEALED. IN LINE WITH THE RESULTS OBTAINED FROM THE RESEARCH, VARIOUS SUGGESTIONS WERE MADE FOR THE DESIGN AND USE OF FEEDBACK MESSAGES BASED ON LEARNING ANALYTICS.
INTERVENTION HAS LONG BEEN PRACTISED IN HIGHER EDUCATION TO PROVIDE ASSISTANCE FOR AT-RISK OR UNDERACHIEVING LEARNERS. WITH THE DEVELOPMENT OF LEARNING ANALYTICS, THE DELIVERY OF INTERVENTION HAS BEEN INFORMED BY DATA-DRIVEN APPROACHES TO IDENTIFY LEARNERS' PROBLEMS AND PROVIDE THEM WITH JUST-IN-TIME AND PERSONALISED SUPPORT. HOWEVER, INTERVENTION HAS BEEN CLAIMED TO BE THE GREATEST CHALLENGE IN LEARNING ANALYTICS AND HAS YET TO BE WIDELY IMPLEMENTED. THIS PAPER REVIEWS 24 CASE STUDIES OF LEARNING ANALYTICS INTERVENTION IN HIGHER EDUCATION. THE CASES WERE CATEGORISED AND SUMMARISED ACCORDING TO THEIR OBJECTIVES, THE DATA USED, THE INTERVENTION METHODS, THE OUTCOMES OBTAINED AND THE CHALLENGES ENCOUNTERED. THE RESULTS SHOW THAT INTERVENTION PRACTICES HAVE FOCUSED MOST FREQUENTLY ON INCREASING STUDENTS' STUDY PERFORMANCE, OFFERING PERSONALISED FEEDBACK AND IMPROVING STUDENT RETENTION. THE FREQUENT TYPES OF DATA INVOLVED STUDENTS' ONLINE LEARNING BEHAVIOURS, STUDY PERFORMANCE, DEMOGRAPHICS AND COURSE SELECTION INFORMATION. THE MOST COMMONLY USED INTERVENTION METHODS INVOLVED OFFERING PERSONALISED RECOMMENDATIONS AND VISUALISING LEARNING DATA. THE INTERVENTIONS HAVE LED TO OUTCOMES SUCH AS ENHANCING STUDY PERFORMANCE, RETENTION AND COURSE REGISTRATION, AS WELL AS PRODUCTIVITY AND EFFECTIVENESS IN LEARNING AND TEACHING. THE CHALLENGES COVERED A WIDE RANGE OF ASPECTS, INCLUDING THE SCALABILITY OF INTERVENTION, CONDITIONS FOR IMPLEMENTING INTERVENTION, LIMITATIONS OF THE CHANNELS FOR DELIVERING INTERVENTION AND THE EVALUATION OF INTERVENTION EFFECTIVENESS. THE RESULTS SUGGEST THAT LEARNING ANALYTICS INTERVENTION HAS THE POTENTIAL TO FURTHER EXTEND ITS SCOPE OF PRACTICES TO SERVE A WIDER RANGE OF PURPOSES, BUT MORE STUDIES ON THE EMPIRICAL EVIDENCE, EVEN WITH NULL OR NEGATIVE RESULTS, ARE NEEDED TO SUPPORT ITS LONG-TERM EFFECTIVENESS AND SUSTAINABILITY.
LEARNING ANALYTICS HAS ACQUIRE SIGNIFICANCE ON THE EDUCATIONAL FIELD DUE TO IT DISCOVERS HIDDEN PATTERNS ONCE THE DATA IS PROCESSED AND INTEGRATED IN REAL CASE OF USE. ADDITIONALLY IT OFFERS TO STAKEHOLDER'S SUPPORT TO IMPROVE THEIR TASK INTO EDUCATIONAL CONTEXT. AUTHORS INTEGRATED LEARNING ANALYTICS TECHNIQUES WITH A DECISION SUPPORT SYSTEM IN A PROPOSAL TO HELP EDUCATIONAL INSTITUTION'S ADMINISTRATORS IN DEVELOPING DECISION MAKING PROCESS AND COLLATERALLY IMPROVE STUDENT PERFORMANCE. IN THIS PAPER, AUTHORS DEFINE A DECISION SUPPORT SYSTEM AND DESCRIBE ITS USAGE IN A EDUCATIONAL INSTITUTION. THE FUNCTIONALITIES OF LEARNING ANALYTICS ARE PRESENTED AND THE INTEGRATION TO HELP DEPARTMENTS ADMINISTRATORS TO IMPROVE DECISION IMPACT.
THIS ARTICLE DESCRIBES THE USE OF VARIOUS LEARNING ANALYTICS (LA) TOOLS IN THE LEARNING MANAGEMENT SYSTEM (LMS). USING THE LA TOOLS CONSISTS OF FOUR STEPS: COLLECTING DATA AND CHOOSING THE APPROPRIATE LA METHODS, CHOOSING THE LA TOOLS, TESTING THE LA TOOLS, AND EVALUATING THE RESULTS. THE MAIN CONCLUSIONS OF THIS ARTICLE ARE THAT LA TOOLS ARE STILL TOO DEPENDENT ON THE DATA FORMAT AND THE LMS VERSION, AND THAT TEACHERS FIND USING THIS TYPE OF TOOL USEFUL, BUT STILL DIFFICULT TO UTILIZE
LEARNING ANALYTICS IS THE MEASUREMENT, ANALYSIS, AND REPORTING OF DATA ABOUT LEARNERS AND THEIR CONTEXTS, FOR THE PURPOSES OF UNDERSTANDING AND OPTIMIZING LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS. CIVITAS LEARNING HAS CREATED A SUITE OF LEARNING ANALYTICS TOOLS AND EMPLOYED EARLY VERSIONS OF THIS SOFTWARE AT VALENCIA COLLEGE IN ORLANDO, FLORIDA. WE EXPLORE THE USE OF THIS TOOL IN THREE INSTRUCTORS' ONLINE COURSES.
WE ARE STILL DESIGNING EDUCATIONAL EXPERIENCES FOR THE AVERAGE STUDENT, AND HAVE ROOM TO IMPROVE. LEARNING ANALYTICS PROVIDES A WAY FORWARD. THIS COMMENTARY DESCRIBES HOW LEARNING ANALYTICS-BASED APPLICATIONS ARE WELL POSITIONED TO MEANINGFULLY PERSONALIZE THE LEARNING EXPERIENCE IN DIVERSE WAYS. IN SO DOING, LEARNING ANALYTICS HAS THE POTENTIAL TO CONTRIBUTE TO MORE EQUITABLE AND SOCIALLY JUST EDUCATIONAL OUTCOMES FOR STUDENTS WHO MIGHT OTHERWISE BE SEEN THROUGH THE LENS OF THE AVERAGE STUDENT. UTILIZING BIG DATA, GOOD DESIGN, AND THE INPUT OF THE STAKEHOLDERS, LEARNING ANALYTICS TECHNIQUES AIM TO DEVELOP APPLICATIONS FOR THE SOLE PURPOSE OF REDUCING THE CLASSROOM SIZE TO 1. OVER TIME, THESE DIGITAL INNOVATIONS WILL ENABLE US TO DO AWAY WITH A MODEL OF EDUCATION THAT TEACHES TOWARD THE NON-EXISTENT AVERAGE STUDENT, REPLACING IT WITH ONE THAT IS MORE SOCIALLY JUST-ONE THAT ADDRESSES THE INDIVIDUAL NEEDS OF EVERY STUDENT.
IN HIGHER EDUCATION INSTITUTIONS, A HIGH NUMBER OF STUDIES SHOW THAT THE USE OF PREDICTIVE LEARNING ANALYTICS CAN POSITIVELY IMPACT STUDENT RETENTION AND THE OTHER ASPECTS WHICH LEAD TO STUDENT SUCCESS. PREDICTIVE LEARNING ANALYTICS EXAMINES THE LEARNING DATA FOR INTERVENING OR IMPROVING THE PROCESS ITSELF THAT POSITIVELY REFLECTS ON STUDENT PERFORMANCE. IN OUR SURVEY, WE ARE CONSIDERING THE MOST RECENT RESEARCH PAPERS FOCUSING ON PREDICTIVE LEARNING ANALYTICS AND HOW THAT AFFECTS THE FINAL STUDENT OUTCOME IN EDUCATIONAL INSTITUTIONS. THE PROCESS OF PREDICTIVE LEARNING ANALYTICS, SUCH AS DATA COLLECTION, DATA PREPROCESSING, DATA MINING, AND OTHERS, HAS BEEN ILLUSTRATED IN DETAIL. WE HAVE IDENTIFIED FACTORS THAT AFFECT STUDENT PERFORMANCE. SEVERAL MACHINE LEARNING APPROACHES HAVE ALSO BEEN COMPARED TO PROVIDE A CLEAR VIEW OF THE MOST SUITABLE ALGORITHMS AND TOOLS USED FOR IMPLEMENTING THE LEARNING ANALYTICS.
IN A GROWING WORLD OF BIGDATA LEARNING ANALYTICS, TREMENDOUS QUANTITIES OF DATA STREAMS ARE COLLECTED AND ANALYZED BY VARIOUS ANALYTICS SOLUTIONS. THESE DATA ARE CRUCIAL IN PROVIDING THE MOST ACCURATE AND RELIABLE ANALYSIS RESULTS, BUT AT THE SAME TIME THEY CONSTITUTE A RISK AND CHALLENGE FROM A SECURITY STANDPOINT. AS FIRE NEEDS FUEL TO BURN, SO DO HACKING ATTACKS NEED DATA IN ORDER TO BE "SUCCESSFUL". DATA IS THE FUEL FOR HACKERS, AND AS WE PROTECT WOOD FROM FIRE SOURCES, SO DO WE NEED TO PROTECT DATA FROM HACKERS. LEARNING ANALYTICS IS ALL ABOUT DATA. THIS PAPER DISCUSSES A MODULAR, AFFORDABLE SECURITY MODEL THAT CAN BE IMPLEMENTED IN ANY LEARNING ANALYTICS PLATFORM TO PROVIDE TOTAL PRIVACY OF LEARNERS' DATA THROUGH ENCRYPTION MECHANISMS AND SECURITY POLICIES AND PRINCIPLES AT THE NETWORK LEVEL.
DATA-DRIVEN LEARNING IN COMBINATION WITH EMERGING ACADEMIC AREAS SUCH AS LEARNING ANALYTICS (LA) HAS THE POTENTIAL TO TAILOR STUDENTS' EDUCATION TO THEIR NEEDS [1]. THE AIM OF THIS ARTICLE IS TO PRESENT A WEB-BASED TRAINING PLATFORM FOR PRIMARY SCHOOL PUPILS WHO STRUGGLE WITH THE ACQUISITION OF GERMAN ORTHOGRAPHY. OUR OBJECTIVE IS THE IMPROVEMENT IN THEIR WRITING AND SPELLING COMPETENCES. THE FOCUS OF THIS ARTICLE IS ON THE DEVELOPMENT OF THE PLATFORM AND THE DETAILS CONCERNING THE REQUIREMENTS AND THE DESIGN OF THE USER INTERFACE (UI). IN COMBINATION WITH LEARNING ANALYTICS, THE PLATFORM IS EXPECTED TO PROVIDE DEEPER INSIGHT INTO THE PROCESS OF SPELLING ACQUISITION. FURTHERMORE, ASPECTS OF LEARNING ANALYTICS WILL HELP TO DEVELOP THE PLATFORM, TO IMPROVE THE EXERCISES AND TO PROVIDE BETTER MATERIALS IN THE LONG RUN.
RYAN BAKER PRESENTED IN A LAK 2019 KEYNOTE A LIST OF SIX GRAND CHALLENGES FOR LEARNING ANALYTICS RESEARCH. THE CHALLENGES ARE SPECIFIED AS PROBLEMS WITH CLEARLY DEFINED SUCCESS CRITERIA. EDUCATION IS, HOWEVER, A DOMAIN FULL OF ILL-DEFINED PROBLEMS. I ARGUE THAT LEARNING ANALYTICS RESEARCH SHOULD REFLECT THIS NATURE OF THE EDUCATION DOMAIN AND FOCUS ON LESS CLEARLY DEFINED, BUT PRACTICALLY ESSENTIAL ISSUES. AS AN ILLUSTRATION, I DISCUSS THREE IMPORTANT CHALLENGES OF THIS TYPE: ADDRESSING INHERENT TRADE-OFFS IN LEARNING ENVIRONMENTS, THE CLARIFICATION OF METHODOLOGICAL ISSUES, AND THE SCALABILITY OF SYSTEM DEVELOPMENT.
IF LEARNING ANALYTICS ARE TO DIRECTLY BENEFIT STUDENTS' LEARNING RATHER THAN SIMPLY INFORM BROAD POLICY DECISIONS, THEY MUST BE USED BY ACADEMICS IN THE MIDST OF BUSY AND FRAGMENTED LIVES. THIS PAPER TAKES AN ECOLOGICAL OR ACTION-ORIENTED PERSPECTIVE OF THE USE OF LEARNING ANALYTICS IN HIGHER EDUCATION, DRAWING ON RESEARCH SOURCES IN PSYCHOLOGY, HUMANCOMPUTER INTERACTION AND VISUAL ANALYTICS. WE UNPACK THE CIRCUMSTANCES DURING THE LEARNING INTERACTIONS OF ACADEMICS WITH COURSE MATERIALS AND STUDENTS WHERE ANALYTICS COULD TRIGGER OR INFLUENCE ACTION. THIS LEADS TO A FRAMEWORK BASED AROUND DIFFERENT ACADEMIC TIMESCALES, AND THE STRATEGIES FOR SYNCHRONISING THE RECOGNITION OF NEED WITH THE POTENTIAL FOR EXECUTION OF TEACHING AND LEARNING INTERVENTIONS.
LEARNING ANALYTICS (LA) IS A RECENT RESEARCH FIELD, IN WHICH BUSINESS INTELLIGENCE AND ANALYTICS TECHNIQUES ARE APPLIED TO LEARNERS AND THEIR CONTEXTS, WITH THE PURPOSE OF ACQUIRING A GREATER INSIGHT ABOUT THE ENTIRE LEARNING PROCESS (INCLUDING OUTCOMES). IN THIS TALK, WE EXPLORE THE LA LANDSCAPE, DELVING INTO THE DEFINITIONS, TECHNIQUES, CHALLENGES, AND LESSONS LEARNED.
LEARNING ANALYTICS IS A RESEARCH AREA THAT TARGETS THE PROBLEM OF TRACKING AND EVALUATING STUDENTS LEARNING PROGRESS. IN CASE OF EDUCATIONAL GAMES, TRADITIONAL ANALYTIC TOOLS CAN PROVE INSUFFICIENT. ELECTRONIC GAMES ARE DYNAMIC, PACKED WITH ACTION AND LEARNING IS INTEGRAL PART OF GAMEPLAY. SUCH A SPECIFIC LEARNING ENVIRONMENT REQUIRES A SPECIFIC REAL-TIME ANALYTICAL TOOL THAT WILL ADEQUATELY MATCH THE DYNAMIC GAME ENVIRONMENT. THIS PAPER PRESENTS A NEW FORM OF SPECIFIC VISUALIZATION TOOL FOR EDUCATORS TO TRACK STUDENTS LEARNING PROGRESS IN REAL-TIME, WHILE IN GAMEPLAY SESSION. THE TOOL IS BASED ON THE SPECIALLY DESIGNED STUDENTS LEARNING MODEL. SAME TOOL CAN BE USED BY STUDENTS TO TRACK THE GAME PROGRESS. USE OF THIS TOOL PROVIDES EDUCATORS WITH REAL-TIME TRACKING OF STUDENTS LEARNING AND ENABLES THEM TO REACT AND INFLUENCE THE OVERALL LEARNING PROCESS.
LEARNING ANALYTICS REPRESENTS A RESEARCH DISCIPLINE, WHICH DEALS WITH A GATHERING, MEASURING, AND ANALYSIS OF AVAILABLE EDUCATIONAL DATA ABOUT STAKEHOLDERS FOR UNDERSTANDING AND OPTIMIZING OF THE LEARNING PROCESS AS WELL AS THE WHOLE ENVIRONMENT, WHERE THE LEARNING PROCESS TAKES PLACE. ALTHOUGH SEVERAL APPROACHES AND FRAMEWORKS FOR LEARNING ANALYTICS SUCCESSFUL ADOPTION AND IMPLEMENTATION AT THE HIGHER EDUCATIONAL INSTITUTIONS ARE PROPOSED IN THE LITERATURE, MEANINGFUL IMPLEMENTATION OF THE LEARNING ANALYTICS STILL FACES SEVERAL CHALLENGES AND ENCOUNTERS MANY OBSTACLES. THE PAPER SUMMARIZES THE MAIN CHALLENGES AND ISSUES, WHICH MODERN UNIVERSITIES SHOULD TAKE INTO ACCOUNT IN CASE THEY PLAN TO ADOPT SOME USEFUL FEATURES OF THE LEARNING ANALYTICS AT DIFFERENT LEVELS OF THE UNIVERSITY MANAGEMENT. CONSEQUENTLY, IT ANALYSES A CURRENT STATE OF READINESS OF THE UNIVERSITY FOR LEARNING ANALYTICS IMPLEMENTATION. AS A RESULT, A STRATEGY FOR THE LEARNING ANALYTICS ADOPTION AND IMPLEMENTATION IS PROPOSED.
THE DEVELOPMENT OF SKILLS IN THE ENGINEERING EDUCATION IS ONE OF THE ISSUES THAT GENERATE GREATER INTEREST AT PRESENT. THANKS TO LEARNING ANALYTICS, WE FOUND AN EXCELLENT OPPORTUNITY TO OFFER A QUALITY COMPETENCE ASSESSMENT OF OUR ENGINEERING STUDENTS. RESEARCH IN LEARNING ANALYTICS CURRENTLY FOCUSES ON APPLYING THESE TECHNIQUES TO FIND OUT HOW THE STUDENT LEARNS AND TO IMPROVE TEACHING/LEARNING PROCESSES. A KEY ASPECT IN IMPROVING THESE PROCESSES IS THE ASSESSMENT OF GENERAL COMPETENCES, WHICH CONSTITUTES KEY LEARNING IN ENGINEERING STUDENTS AND HAS THUS BEEN IDENTIFIED AS A NEED THAT CAN BE MET BY LEARNING ANALYTICS. THIS ARTICLE PRESENTS TWO RELATED STUDIES CONDUCTED AT THE UNIVERSITY OF DEUSTO. THE FIRST STUDY WANTS TO SHOW THAT IT IS POSSIBLE TO CARRY OUT AN ASSESSMENT OF THE PROJECT MANAGEMENT COMPETENCE THROUGH THE ANALYSIS OF THE DATA THAT IS OBTAINED WHEN THE STUDENTS INTERACT WITH CERTAIN TOOLS FOR THE MANAGEMENT OF PROJECTS. IN THIS SENSE, IN THE FIRST STUDY CONDUCTED WITH 93 STUDENTS IN THE ACADEMIC YEAR 2014-2015, IT COMPARES THE AUTOMATIC ASSESSMENT PERFORMED WITH LEARNING ANALYTICS AND THE MANUAL ASSESSMENT CARRIED OUT BY THE TEACHER. ANOTHER OBJECTIVE OF THIS FIRST STUDY IS TO COMPARE THE VALIDITY AT THE TIME TO ASSESS THE PROJECT MANAGEMENT COMPETENCE OF THE THREE TECHNOLOGICAL TOOLS USED IN THE STUDY. IN THE SECOND STUDY CONDUCTED WITH 227 STUDENTS IN THE ACADEMIC YEAR 2015-2016, AN ASSESSMENT MODEL IS DESIGNED BASED ON ANALYTICAL DATA THAT IS EXTRACTED FROM EVEN MORE COMPLEX TECHNOLOGICAL TOOLS. IN THIS SECOND STUDY THE OBJECTIVE IS TO DEMONSTRATE THAT THE USE OF LEARNING ANALYTICS ASSESSMENT TO CARRY OUT CONTINUOUS MONITORING AND PROVIDE FEEDBACK TO THE STUDENTS, DIRECTLY INFLUENCES THEIR CAPACITY TO MANAGE A PROJECT AND THEREFORE, LEADS TO AN IMPROVEMENT IN THEIR RESULTS. THE MODEL DESIGNED IN BOTH STUDIES FOR ANALYSIS IS DESCRIBED IN THIS PAPER, IN ADDITION TO THE METHODOLOGY AND RESEARCH CARRIED OUT.
LEARNING ANALYTICS (LA) DASHBOARDS HELP RAISE STUDENT AND TEACHER AWARENESS REGARDING LEARNER ACTIVITIES. IN BLOG-SUPPORTED AND INQUIRY-BASED LEARNING COURSES, LA DATA IS NOT LIMITED TO STUDENT ACTIVITIES, BUT ALSO CONTAINS AN ABUNDANCE OF DIGITAL LEARNER ARTEFACTS, SUCH AS BLOG POSTS, HYPOTHESES, AND MIND-MAPS. EXPLORING PEER ACTIVITIES AND ARTEFACTS CAN HELP STUDENTS GAIN NEW INSIGHTS AND PERSPECTIVES ON LEARNING EFFORTS AND OUTCOMES, BUT REQUIRES EFFORT. TO HELP FACILITATE AND PROMOTE THIS EXPLORATION, WE PRESENT THE LESSONS LEARNT DURING AND GUIDELINES DERIVED FROM THE DESIGN, DEPLOYMENT AND EVALUATION OF FIVE DASHBOARDS.
OPEN LEARNING ANALYTICS (OLA) AIMS TO MEET DIVERSIFIED NEEDS FOR INSIGHTS INTO DIFFERENT STAKEHOLDERS' EFFORTS TO IMPROVE LEARNING AND LEARNING CONTEXTS INTEGRATING HETEROGENEOUS LEARNING ANALYTICS TECHNIQUES. FROM AN ABSTRACT POINT OF VIEW, OLA ALIGNS WELL WITH THE IDEAS OF OPEN AND DISTANCE EDUCATION INSTITUTIONS, OF WHICH SHANGHAI OPEN UNIVERSITY (SOU) IS A LEARNING CHINESE REPRESENTATIVE. THE PAPER REPORTS ON THE DESIGN OF AN OLA FRAMEWORK FOR SOU, BASED ON DIFFERENT USERS' SERVICE DEMANDS AND THE DIVERSE SOURCES OF DATA AND MULTIPLE PLATFORMS IN USE AT THE UNIVERSITY. THE PROPOSED ARCHITECTURE IS BASED ON A DISCUSSION OF THE GENERAL CHARACTERISTICS OF OLA ARCHITECTURE. THE FINAL MODEL IS ACHIEVED THROUGH AN ITERATIVE DEVELOPMENT METHOD.
THIS PAPER PRESENTS THE RESULTS OF INTEGRATING LEARNING ANALYTICS INTO THE ASSESSMENT PROCESS TO ENHANCE ACADEMIC INTEGRITY IN THE E-LEARNING ENVIRONMENT. THE GOAL OF THIS RESEARCH IS TO EVALUATE THE COMPUTATIONAL-BASED APPROACH TO ACADEMIC INTEGRITY. THE MACHINE-LEARNING BASED FRAMEWORK LEARNS STUDENTS' PATTERNS OF LANGUAGE USE FROM DATA, PROVIDING AN ACCESSIBLE AND NON-INVASIVE VALIDATION OF STUDENT IDENTITIES AND STUDENT-PRODUCED CONTENT. TO ASSESS THE PERFORMANCE OF THE PROPOSED APPROACH, WE CONDUCTED A SERIES OF EXPERIMENTS USING WRITTEN ASSIGNMENTS OF GRADUATE STUDENTS. THE PROPOSED METHOD YIELDED A MEAN ACCURACY OF 93%, EXCEEDING THE BASELINE OF HUMAN PERFORMANCE THAT YIELDED A MEAN ACCURACY RATE OF 12%. THE RESULTS SUGGEST A PROMISING POTENTIAL FOR DEVELOPING AUTOMATED TOOLS THAT PROMOTE ACCOUNTABILITY AND SIMPLIFY THE PROVISION OF ACADEMIC INTEGRITY IN THE E-LEARNING ENVIRONMENT.
THE RAPID DEVELOPMENT OF LEARNING ANALYTICS MAKES IT DIFFICULT FOR THE READERSHIP OF RESEARCH LITERATURE TO GAIN A STRUCTURED OVERVIEW OVER THE DIFFERENT TYPES OF DATA AVAILABLE AND SUBJECT TO THE APPLICATION OF LEARNING ANALYTICS TECHNIQUES OR METHODS. THIS SPECIAL ISSUE REUNITES SIX EXAMPLES OF APPLICATION OF DIFFERENT LEARNING ANALYTICS APPROACHES USING VARIOUS DATA TYPES, AIMING TO ACHIEVE DIFFERENT GOALS, AND EMPLOYING DIFFERENT INSTRUMENTS AND METHODS: EYE TRACKING, AUTOMATED ONLINE DIALOG ANALYSIS, SURVEY DATA FROM SCHOOL ECOSYSTEMS, LOG DATA ANALYSIS AT INDIVIDUAL AND COLLABORATIVE LEVEL, AND VISUAL LEARNING ANALYTICS APPLIED TO INTERNET-OF-THINGS DATA. THESE CASE STUDIES ARE PROVIDED IN THE FRAMEWORK OF THE OBSERVED PROCESS DATA TRANSFORMATION ANALYSIS OUTPUT PATTERN OF PRACTICES. BRIEF CONCLUSIONS PERTAINING TO ADVANTAGES, LIMITATIONS AND FUTURE WORK ARE DRAWN.
LEARNING ANALYTICS DEALS WITH THE DATA THAT OCCURS FROM STUDENTS' INTERACTION WITH ICT: COLLECTING DATA, ANALYZING AND REPORTING THAT CAN INFLUENCE LEARNING AND TEACHING. ANALYSIS OF VALIDITY AND RELIABILITY OF ASSESSMENT LAGS BEHIND OTHER APPLICATIONS OF LEARNING ANALYTICS. MATHEMATICAL MODELING OF LEARNING ANALYTICS FOR ASSESSMENT, ESPECIALLY FOR PEER-ASSESSMENT, IS PRESENTED IN THE PAPER. IN ADDITION, STUDENTS' RECOGNITION OF ADVANTAGES AND DISADVANTAGES OF PEER-ASSESSMENT IS ANALYZED AND CATEGORIZED. FINALLY, IMPLEMENTATIONS OF RELIABILITY CHECK OF PEER-ASSESSMENT IN MOODLE WORKSHOP MODULE ARE EXPLAINED.
AS PART OF ITS ACTIVITIES TO PROMOTE EDUCATIONAL INNOVATION AMONG LOCAL SCHOOLS, TALLINN UNIVERSITY'S CENTER FOR INNOVATION IN EDUCATION ORGANIZES LEARNING SCENARIOS FOR STUDENTS AT DIFFERENT LEVELS OF PRIMARY AND SECONDARY EDUCATION. THE PAPER GIVES AN INSIGHT INTO THE POSSIBILITIES OF USING LEARNING ANALYTICS IN THE PROCESS OF DESIGNING SUCH PEDAGOGICAL INTERVENTIONS. IT DESCRIBES THE FIRST STAGES OF A DESIGN-BASED RESEARCH PROCESS, IN WHICH RESEARCHERS AND TEACHER-TRAINERS CO-CREATE EDUCATIONAL INTERVENTIONS IN AN AUTHENTIC EDUCATIONAL SETTING. THE AIM OF THIS INITIAL RESEARCH WAS TO ANALYZE HOW THE INTEGRATION OF LEARNING ANALYTICS HELPS TO MOVE A PEDAGOGICAL INTERVENTION FROM THE PILOTING PHASE TO LATER IMPLEMENTATION. IN THIS PARTICULAR CONTEXT LEARNING ANALYTICS PROVIDED INFORMATION ABOUT STUDENT COLLABORATION, TASK QUALITY, AND ENGAGEMENT PROFILES COMING FROM DIFFERENT DATA SOURCES, THAT COULD BE ANALYZED INDIVIDUALLY, GROUP AND TASK WISE. THE OUTPUT OF THESE ANALYSES NOT ONLY MADE THE LEARNING PROCESS AND EFFECTIVENESS MORE VISIBLE FOR TEACHERS, BUT ALSO PROVIDED ACTIONABLE FEEDBACK ON HOW TO IMPROVE THE DESIGN OF SUCH COMPLEX LEARNING SCENARIOS, WHERE DIFFERENT LEVELS OF ENGAGEMENT AND FORMS OF COLLABORATION TAKE PLACE. THESE LEARNING ANALYTICS TECHNIQUES ARE NOT INTENDED AS A SUBSTITUTE FOR STRONG QUALITATIVE CONTENT ANALYSIS, BUT RATHER AS A COMPLEMENT, AS THE COMBINATION OF BOTH PROVIDES SUFFICIENT INFORMATION TO DEVELOP COMPLEX LEARNING SCENARIOS WHICH SUPPORT MEANINGFUL LEARNING AND ACTIVE COLLABORATION.
IN HIGHER EDUCATION, LEARNING ANALYTICS IS TRENDING FOR TRANSFORMING TEACHING BEHAVIOR, UNCOVERING INFORMATION ON THE HUMAN LEARNING PROCESS, UNDERSTANDING FACTORS FOR SMART LEARNING ENVIRONMENTS, AND ADDRESSING ARTIFICIAL INTELLIGENCE ISSUES IN EDUCATION. DURING THE LAST DECADE, LEARNING ANALYTICS HAS BEEN PRACTICED IN MANY EDUCATIONAL INSTITUTIONS WORLDWIDE, RESULTING IN MANY SUCCESS STORIES. THIS PAPER IS BASED ON A PRE-SELECTED 49 ARTICLES PUBLISHED IN THE LAST DECADE WHERE LEARNING MANAGEMENT SYSTEMS AND SMART LEARNING ENVIRONMENTS ARE USED TO COLLECT DATA AND ANALYZE USING DATA-DRIVEN METHODS TO SUPPORT LEARNING ANALYTICS PRINCIPLES. HOWEVER, AN EMPHASIS IS GIVEN TO 15 ARTICLES FOUND BETWEEN 2017 TO 2020 IN ASIA, NORTH AND SOUTH AMERICA, THE UK, AND EUROPEAN HIGHER EDUCATIONAL CONTEXTS AS JISC 2017 REPORT BRIEFED ON THE EARLIER PRACTICES. THE FINDINGS FROM THE 15 INTERNATIONAL PRACTICES ARE DISCUSSED AND VISUALIZED USING THE GEO-VISUALIZATION METHOD.
MOBILE LEARNING HAS REACHED A CONSIDERABLE LEVEL OF MATURITY IN RECENT YEARS, AND ITS ROLE IS WIDELY ACKNOWLEDGED IN SCHOOL CONTEXTS, UNIVERSITY, VOCATIONAL TRAINING, FORMAL AND NON-FORMAL LEARNING SETTINGS, AND MORE GENERALLY AS AN OPPORTUNITY FOR LIFELONG LEARNING. DESPITE ITS MATURITY, EVALUATION OF MOBILE LEARNING REMAINS AN OPEN RESEARCH ISSUE, ESPECIALLY AS REGARDS THE ACTIVITIES THAT TAKE PLACE OUTSIDE THE CLASSROOM. IN THIS CONTEXT, LEARNING ANALYTICS CAN PROVIDE ANSWERS, AND OFFER THE APPROPRIATE TOOLS TO ENHANCE MOBILE LEARNING EXPERIENCES. IN RECENT YEARS LEARNING ANALYTICS HAS BEEN HIGHLY SUCCESSFUL IN DIFFERENT CONTEXTS, BUT MOBILE LEARNING EXHIBITS PARTICULAR CHARACTERISTICS RELATED TO THE TECHNOLOGIES USED, STUDENT MOBILITY, THE POSSIBILITY OF HAVING LOCALIZED DATA AND INFORMATION AND THE SOCIAL DYNAMICS THAT CHARACTERIZE THE CONTEXT IN WHICH LEARNING TAKES PLACE. IN THIS PAPER WE PROPOSE AN INNOVATIVE APPROACH TO SUPPORT ANALYTICS OF LEARNERS' ACTIVITIES IN A MOBILE LEARNING SETTING BASED ON THE SEMANTIC WEB PARADIGM AND ON THE SEMANTIC RELATIONSHIPS EXPRESSED IN THE LINKED OPEN DATA CLOUD. MELOD, A MOBILE ENVIRONMENT FOR LEARNING WITH LINKED OPEN DATA, IS ALSO INTRODUCED AS A DEMONSTRATOR FOR THE IDEAS ILLUSTRATED IN THE PAPER. POTENTIALS AND PITFALLS OF THE PROPOSED APPROACH, BOTH FOR TEACHERS AND LEARNERS, ARE REPORTED IN THE CONCLUSIONS.
THE ABILITY OF LEARNING ANALYTICS TO IMPROVE THE LEARNING/TEACHING PROCESSES IS WIDELY RECOGNIZED. IN THIS PAPER, THE LEARNING ANALYTICS ARCHITECTURE DEVELOPED AT THE DIGITAL CONTENT PRODUCTION CENTER OF THE TECHNICAL UNIVERSITY OF CARTAGENA (SPAIN) IS PRESENTED. THIS ARCHITECTURE CONTRIBUTES TO THE FIELD OF LEARNING ANALYTICS IN TWO ASPECTS: IT ALLOWS FOR DASHBOARD CUSTOMIZATION AND IMPROVES THE EFFICIENCY OF THE ANALYSIS OF LEARNERS INTERACTION DATA. EVENTS RESULTING FROM LEARNERS INTERACTION ARE CAPTURED AND STORED IN CALIPER STANDARD FORMAT, TO BE FURTHER PROCESSED INCREMENTALLY TO ALLOW DASHBOARDS TO BE SHOWN WITHOUT DELAY TO TEACHERS. CUSTOMIZATION IS CONSIDERED A MANDATORY REQUIREMENT FOR LEARNING ANALYTICS TOOLS, HOWEVER, ALTHOUGH SOME PROPOSALS HAVE RECENTLY BEEN MADE, A GREATER RESEARCH EFFORT IN THIS TOPIC IS NECESSARY. IN THE PRESENT WORK, THIS REQUIREMENT IS ADDRESSED BY DEFINING A DOMAIN-SPECIFIC LANGUAGE (DSL) THAT ALLOWS TEACHERS TO CUSTOMIZE DASHBOARDS. THIS LANGUAGE ALLOWS TO EXPRESS INDICATORS (LOGICAL EXPRESSIONS) THAT CLASSIFY STUDENTS INTO DIFFERENT GROUPS DEPENDING ON THEIR PERFORMANCE LEVEL. THE PAPER ALSO SHOWS HOW OUR LEARNING ANALYTICS APPROACH WAS EVALUATED WITH A COURSE THAT APPLIES A FLIPPED CLASSROOM METHOD, AND HOW IT COMPARES TO THE MOST RELEVANT RELATED WORKS THAT HAVE BEEN PUBLISHED.
THE TECHNOLOGY APPLIED TO EDUCATIONAL CONTEXTS, AND SPECIALLY THE LEARNING PLATFORMS, PROVIDES STUDENTS AND TEACHERS WITH A SET OF TOOLS AND SPACES TO CARRY OUT THE LEARNING PROCESSES. INFORMATION RELATED TO THE PARTICIPATION AND INTERACTION OF THESE STAKEHOLDERS WITH THEIR PEERS AND WITH THE PLATFORM IS RECORDED. IT WOULD BE USEFUL TO EXPLOIT THIS INFORMATION IN ORDER TO MAKE DECISIONS. HOWEVER THIS IS A COMPLEX ACTIVITY MAINLY BECAUSE OF THE HUGE QUANTITY OF INFORMATION STORED. THIS WORK PRESENTS A VISUAL LEARNING ANALYTICS SYSTEM THAT MAKES POSSIBLE THE EXPLOITATION OF THAT INFORMATION. THE SYSTEM INCLUDES SEVERAL TOOLS THAT HELP TO ANALYZE USERS' INTERACTION ATTENDING TO DIFFERENT DIMENSIONS, SUCH AS: WHEN INTERACTION IS CARRIED OUT, WHICH ARE MORE IMPORTANT CONTENTS FOR USERS, HOW THEY INTERACT WITH OTHERS, ETC. THIS SYSTEM HAS BEEN TESTED WITH THE SUBJECT INFORMATION RECORDED DURING FIVE ACADEMIC YEARS. FROM THIS ANALYSIS IT IS POSSIBLE TO SHOW THAT VISUAL LEARNING ANALYTICS MAY HELP TO IMPROVE EDUCATIONAL PRACTICES.
SNOLA IS A THEMATIC NETWORK OF EXCELLENCE RECOGNIZED BY THE SPANISH MINISTRY OF ECONOMY AND COMPETITIVENESS COMPOSED OF THE MAIN SPANISH RESEARCHERS IN THE FIELD OF LEARNING ANALYTICS. THIS NETWORK EMERGED IN 2013 FOCUSING MAINLY ON THE TECHNOLOGY UNDERLYING LEARNING ANALYTICS DEVELOPMENT, BUT ALSO INTERESTED ON THE INTEGRATION OF OTHER VIEWS AND DISCIPLINES THAT GIVE THE NETWORK A WIDER SCOPE. DURING THESE YEARS, SNOLA HAS CARRY OUT NUMEROUS ACTIONS WITH THE GOAL OF PROMOTING THE COLLABORATION ON THIS FIELD THE DIFFUSION OF KNOWLEDGE AND INITIATIVES, THE COLLECTION OF RESOURCES AND THE PROVISION OF BROAD SUPPORT REMARKABLE EXAMPLES OF THESE EFFORTS HAVE BEEN LASI SPAIN EVENTS, WORKSHOPS AT TEEM CONFERENCES OR THE NUMEROUS WEBINARS. IN THIS CASE, THE EDUCON 2018 HAS GIVEN US THE OPPORTUNITY TO ORGANIZE THIS SPECIAL SESSION WHERE 5 PAPERS HAVE BEEN SELECTED FOR PRESENTATION.
LEARNING ANALYTICS IS COMMONLY USED IN LEARNING MANAGEMENT SYSTEMS TO IMPROVE LEARNING. THIS PAPER PRESENTS A METHOD TO USE LEARNING ANALYTICS IN OPEN EDUCATIONAL RESOURCES. THE VISITING LOGS IN AN OPEN EDUCATIONAL RESOURCES SYSTEM - THEOL (TSINGHUA EDUCATION ONLINE) COURSE UNION SYSTEM ARE CATEGORIZED BY VISITORS' AND RESOURCES' UNIVERSITY PROPERTIES (REGION, EDUCATIONAL LEVEL AND DISCIPLINE TYPE). THE DIFFERENCE BETWEEN RESOURCES' VISITED COUNT AND VISITORS' VISITING COUNT OF EACH CATEGORY IS USED TO MEASURE THE COMPREHENSIVE INDEX OF RESOURCES' RICHNESS AND POPULARITY OF THIS CATEGORY. CHI-SQUARE TEST AND RATIO OF EACH CATEGORY ARE USED TO SHOW THE DIFFERENCE OF DIFFERENT CATEGORY VISITORS PREFERRING DIFFERENT CATEGORY RESOURCES. THE ANALYSIS RESULTS CAN GUIDE ADMINISTRATORS ON HOW TO SHARE EDUCATIONAL RESOURCES EFFICIENTLY.
THE IMPLEMENTATION OF THE BOLOGNA PROCESS HAS INTRODUCED CONTINUOUS ASSESSMENT PROCESSES, PRIMARILY FORMATIVE ASSESSMENT, IN MANY SPANISH UNIVERSITIES. IN SUCH KIND OF SCENARIOS, RUBRICS PROVIDE MANY BENEFITS THAT CAN REMARKABLY BE IMPROVED USING VISUAL LEARNING ANALYTICS TECHNIQUES. THIS PAPER PRESENTS EVALVIS, A SYSTEM THAT ENHANCES FEEDBACK BASED ON RUBRIC ASSESSMENT AND VISUAL LEARNING ANALYTICS. EVALVIS PROVIDES DIFFERENT VISUALIZATIONS FOR STUDENTS AND TEACHERS. THE VISUALIZATIONS INCLUDED IN EVALVIS HAVE BEEN DESIGNED UPON IDENTIFICATION OF VISUALIZATION NEEDS OF STUDENTS AND TEACHERS. THE SYSTEM PROVIDES STUDENTS WITH INFORMATION ABOUT THEIR INDIVIDUAL LEARNING PROCESS AND PERFORMANCE, INCLUDING THE CAPABILITY TO CONTEXTUALIZE THEIR INFORMATION INTO THE GROUP. EVALVIS ALSO ALLOWS THE TEACHING STAFF TO MONITOR DIFFERENT GROUPS OF STUDENTS AND COMPARE THEIR EVOLUTION AND PERFORMANCE. THE ENRICHED FEEDBACK PROVIDED BY RUBRICS AND VISUALIZATIONS WILL HELP USERS TO BETTER UNDERSTAND AND IMPROVE THE LEARNING AND ASSESSMENT PROCESSES IN ORDER TO IMPROVE THEM. EVALVIS HAS BEEN EVALUATED TO CHECK WHETHER IT COVERS THE IDENTIFIED NEEDS OBTAINING VERY GOOD RESULTS.
THIS PAPER CONSIDERS THE DATA SCIENCE AND THE SUMMARIES SIGNIFICANCE OF BIG DATA AND LEARNING ANALYTICS IN EDUCATION. THE WIDESPREAD PLATFORM OF MAKING HIGH-QUALITY BENEFITS THAT COULD BE ACHIEVED BY EXHAUSTING BIG DATA TECHNIQUES IN THE FIELD OF EDUCATION IS CONSIDERED. ONE PRINCIPAL ARCHITECTURE FRAMEWORK TO SUPPORT EDUCATION RESEARCH IS PROPOSED.
THE EMERGENCE OF PERSONALISED DATA TECHNOLOGIES SUCH AS LEARNING ANALYTICS IS FRAMED AS A SOLUTION TO MANAGE THE NEEDS OF HIGHER EDUCATION STUDENT POPULATIONS THAT ARE GROWING EVER MORE DIVERSE AND LARGER IN SIZE. HOWEVER, THE CURRENT APPROACH TO LEARNING ANALYTICS PRESENTS TENSIONS BETWEEN INCREASING STUDENT AGENCY IN MAKING LEARNING-RELATED DECISIONS AND 'DATAFYING' STUDENTS IN THE PROCESS OF COLLECTING, ANALYSING AND INTERPRETING DATA. THIS ARTICLE PRESENTS A STUDY THAT EXPLORES STAFF AND STUDENT EXPERIENCE OF AGENCY, EQUITY AND TRANSPARENCY IN EXISTING DATA PRACTICES AND EXPECTATIONS TOWARDS LEARNING ANALYTICS IN A UK UNIVERSITY. THE RESULTS SHOW A NUMBER OF INTERTWINED FACTORS THAT HAVE CONTRIBUTED TO THE TENSIONS BETWEEN ENHANCING A LEARNER'S CONTROL OF THEIR STUDIES AND, AT THE SAME TIME, DIMINISHING THEIR AUTONOMY AS AN ACTIVE AGENT IN THE PROCESS OF LEARNING ANALYTICS. THIS ARTICLE ARGUES THAT LEARNER EMPOWERMENT SHOULD NOT BE AUTOMATICALLY ASSUMED TO HAVE TAKEN PLACE AS PART OF THE ADOPTION OF LEARNING ANALYTICS. INSTEAD, THE INTERWOVEN POWER RELATIONSHIPS IN A COMPLEX EDUCATIONAL SYSTEM AND THE INTERACTIONS BETWEEN HUMANS AND MACHINES NEED TO BE TAKEN INTO CONSIDERATION WHEN PRESENTING LEARNING ANALYTICS AS AN EQUITABLE PROCESS TO ENHANCE STUDENT AGENCY AND EDUCATIONAL EQUITY.
SERIOUS GAMES HAVE ALREADY PROVED THEIR ADVANTAGES IN DIFFERENT EDUCATIONAL ENVIRONMENTS. COMBINING THEM WITH GAME LEARNING ANALYTICS CAN FURTHER IMPROVE THE LIFE-CYCLE OF SERIOUS GAMES, BY INFORMING DECISIONS THAT SHORTEN DEVELOPMENT TIME AND REDUCE DEVELOPMENT ITERATIONS WHILE IMPROVING THEIR IMPACT, THEREFORE FOSTERING THEIR ADOPTION. GAME LEARNING ANALYTICS IS AN EVIDENCE-BASED METHODOLOGY BASED ON IN-GAME USER INTERACTION DATA, AND CAN PROVIDE INSIGHT ABOUT THE GAME-BASED EDUCATIONAL EXPERIENCE PROMOTING ASPECTS SUCH AS A BETTER ASSESSMENT OF THE LEARNING PROCESS. IN THIS ARTICLE, WE REVIEW OUR EXPERIENCES AND RESULTS APPLYING GAME LEARNING ANALYTICS FOR SERIOUS GAMES IN THREE DIFFERENT SCENARIOS: (1) VALIDATING AND DEPLOYING A GAME TO RAISE AWARENESS ABOUT CYBERBULLYING, (2) VALIDATING THE DESIGN OF A GAME TO IMPROVE INDEPENDENT LIVING OF USERS WITH INTELLECTUAL DISABILITIES AND (3) IMPROVING THE EVALUATION OF A GAME ON FIRST AID TECHNIQUES. THESE EXPERIENCES SHOW DIFFERENT USES OF GAME LEARNING ANALYTICS IN THE CONTEXT OF SERIOUS GAMES TO IMPROVE THEIR DESIGN, EVALUATION AND DEPLOYMENT PROCESSES. BUILDING UP FROM THESE EXPERIENCES, WE DISCUSS THE RESULTS OBTAINED AND PROVIDE LESSONS LEARNT FROM THESE DIFFERENT APPLICATIONS, TO PROVIDE AN APPROACH THAT CAN BE GENERALIZED TO IMPROVE THE DESIGN AND APPLICATION OF A WIDE RANGE OF SERIOUS GAMES IN DIFFERENT EDUCATIONAL SETTINGS.
INSTITUTIONS OF HIGHER EDUCATION ARE FACING CONTEMPORARY CHALLENGES. AND BIG DATA CAN BE USED FOR THE HIGHER EDUCATION PRACTICE. APART FROM THE INTRODUCTION OF THE RELEVANCE OF BIG DATA TO HIGHER EDUCATION, ANALYTICAL APPROACHES SUCH AS LEARNING ANALYTICS IS PROVIDED FOR THE ANALYSIS AND EXPLOITATION OF BIG DATA IN HIGHER EDUCATION. IT IS HELPFUL FOR BIG DATA TO PROVIDE INSIGHTS TO SUPPORT STUDENT LEARNING NEEDS. AND THE POTENTIAL OF LEARNING ANALYTICS FOR QUALITY IMPROVEMENT IN LEARNING PROCESS IS ALSO DISCUSSED IN DETAIL.
OVER THE LAST DECADE THE DEPLOYMENT AND USE OF LEARNING ANALYTICS HAS BECOME ROUTINE IN MANY UNIVERSITIES AROUND THE WORLD. THE ABILITY TO ANALYSE THE WAY STUDENTS INTERACT WITH TECHNOLOGY HAS DEMONSTRATED SIGNIFICANT VALUE FOR PROVIDING INSIGHTS INTO STUDENT LEARNING AND THERE ARE NOW A WIDE RANGE OF USES FOR LEARNING ANALYTICS IN EDUCATION. FROM USE AS A DIAGNOSTIC TOOL, TO A METHOD FOR PREDICTION, LEARNING ANALYTICS IN HIGHER EDUCATION HAS AN EMPHASIS ON A WIDE RANGE OF OUTCOME MEASURES, INCLUDING STUDENT RETENTION, PROGRESSION, ATTAINMENT, PERFORMANCE, MASTERY, EMPLOYABILITY AND ENGAGEMENT. IN EXPLORING HOW LEARNING ANALYTICS CAN IMPROVE LEARNING PRACTICE BY TRANSFORMING THE WAYS WE SUPPORT LEARNING PROCESSES, THIS EDITORIAL HIGHLIGHTS SOME OF THE LEARNING ANALYTICS RESEARCH THAT HAS BEEN PUBLISHED IN AJET TO DATE.
AS TECHNOLOGY PROGRESSES, UNDERSTANDING ONLINE LEARNER'S BEHAVIOR IS THE KEY FOR SUCCESSFUL IMPLEMENTATION OF DIGITAL LEARNING. THE MORE WE KNOW ABOUT LEARNERS, THE MORE TAILORED LEARNING EXPERIENCES WILL BE. ANALYZING THE LEARNER'S PREFERENCES WILL HELP TO PREDICT THE BEHAVIOR OF LEARNER'S SO THAT REQUIRED SUPPORT AND GUIDANCE CAN BE PROVIDED ON TIME. FOR ACHIEVING THIS MUCH MORE EFFICIENT SYSTEMS ARE REQUIRED TO PROCESS ALL TYPES OF THE LEARNER INTERACTIONS WITH THE LEARNING MANAGEMENT SYSTEM DURING THEIR EACH LEARNING ACTIVITY. THIS PAPER DISCUSSES THE IMPORTANCE OF LEARNING ANALYTICS, AND PRESENTS ITS USAGE IN ELEARNING. IT DESCRIBES HOW LEARNING ANALYTICS CAN HELP THE LEARNERS, INSTRUCTORS AND INSTITUTIONS TO LOOK INTO ONGOING LEARNING PROCESSES AND HOW THE ONLINE EDUCATIONAL PROCESSES CAN BE IMPROVED. IT ALSO DEPICTS ABOUT THE PROPOSED SYSTEM HOW THE LEARNING STYLES CAN BE USED FOR IMPROVING LEARNING EXPERIENCE.
ETHICS IS A PROMINENT TOPIC IN LEARNING ANALYTICS THAT HAS BEEN COMMENTED ON FROM CONCEPTUAL VIEWPOINTS. FOR A BROAD RANGE OF EMERGING TECHNOLOGIES, SYSTEMATIC LITERATURE REVIEWS HAVE PROVEN FRUITFUL BY PINPOINTING RESEARCH DIRECTIONS, KNOWLEDGE GAPS, AND FUTURE RESEARCH WORK GUIDANCE. WITH THESE OUTCOMES IN MIND, WE CONDUCTED A SYSTEMATIC LITERATURE REVIEW OF THE RESEARCH ON ETHICAL ISSUES THAT HAVE BEEN EMPIRICALLY APPROACHED IN THE LEARNING ANALYTICS LITERATURE. IN OUR FINAL ANALYSIS, 21 ARTICLES PUBLISHED IN THE PERIOD 2014-2019 MET OUR INCLUSION CRITERIA. BY ANALYZING THIS DATA, WE SEEK TO CONTRIBUTE TO THE FIELD OF LEARNING ANALYTICS BY 1) CHARACTERIZING THE TYPE OF EMPIRICAL RESEARCH THAT HAS BEEN CONDUCTED ON ETHICS IN LEARNING ANALYTICS IN THE CONTEXT OF HIGHER EDUCATION, 2) IDENTIFYING THE MAIN ETHICAL AREAS ADDRESSED IN THE SELECTED LITERATURE, AND 3) PINPOINTING KNOWLEDGE GAPS.
PREVIOUS RESEARCH HAS SHOWN THAT TEAMWORK BETWEEN STUDENTS UNDERPINS THE COMMUNICATION INTERACTIONS AMONG TEAM MEMBERS, AND THESE INTERACTIONS ARE UNDERSCORED IN THE WORK ENVIRONMENT, JOB QUALITY, WORK OUTCOME AND, OF COURSE, GRADES. ANALYSING THE INTERACTIONS AMONG THE MEMBERS OF A TEAM USING A LEARNING ANALYTICS SYSTEM ALLOWS FOR A FORMATIVE EVALUATION THAT INDICATES THE PROGRESS OF EACH TEAM MEMBER AND TAKING REMEDIAL ACTIONS IF APPROPRIATE PROGRESS HAS NOT BEEN MADE. THIS PAPER USES A LEARNING ANALYTICS SYSTEM TO STUDY INTERACTIONS BETWEEN STUDENTS AND DETECT THE VALUES AND ATTITUDES DEMANDED OF A LEADER BY SOCIETY. THE RESULTS OF THIS ANALYSIS ARE KEYS FOR AVOIDING CORRUPTION AND WRONG PRACTICES AND CAN EVEN PROVIDE A SOLUTION TO GLOBAL INTERCULTURAL TROUBLES. IN THIS STUDY, A VALIDATED QUESTIONNAIRE OF AUTHENTIC LEADERSHIP WAS GIVEN TO 78 TEAM MEMBERS IN A UNIVERSITY CONTEXT; THE INFLUENCE OF SOME VALUES AND ATTITUDES ON LEADERSHIP IS PROVED WITH GRADES; AND A LEARNING ANALYTICS SYSTEM WAS USED TO ANALYSE INFORMATION THAT COULD PREDICT A LEADER'S BEHAVIOUR DURING THE DEVELOPMENT OF TEAMWORK.
MOST OF THE APPROACHES OF LEARNING ANALYTICS RELY ON A PREDEFINED SET OF INDICATORS, AND MOST OF THE TOOLS USED TO ANALYSE LEARNING TRACES FOCUS ON A SPECIFIC ASPECT, THUS LACKING IN FLEXIBILITY TO PROVIDE MULTIPLE ANALYSIS. IN THIS PERSPECTIVE, LEARNING ANALYTICS TOOLS MUST BE MORE FLEXIBLE IN ORDER TO SUPPORT PERSONALIZED LEARNING ANALYTICS APPROACHES IN WHICH INDICATORS ARE DEFINED BY USERS. IN THIS PAPER, WE PRESENT A WORKFLOW MANAGEMENT TOOL AIMED AT SUPPORTING THE DEVELOPMENT OF LEARNING ANALYTICS APPLICATIONS SPECIFICALLY DESIGNED TO DEAL WITH STUDENTS' INTERACTIONS IN ONLINE LEARNING MANAGEMENT SYSTEMS. A USE CASE IN WHICH THE TOOL HAS BEEN USED TO DETECT STUDENTS AT RISK OF DROPOUT IS ALSO ILLUSTRATED.
IN THIS ARTICLE WE REPORT ON THE FINDINGS OF A PROJECT FUNDED BY THE AUSTRALIAN OFFICE FOR LEARNING AND TEACHING AND ENTITLED "LEARNING ANALYTICS: ASSISTING UNIVERSITIES WITH STUDENT RETENTION." WHILE THIS PROJECT WAS PRIMARILY FOCUSED ON RETENTION AS A POTENTIAL OUTCOME OF LEARNING ANALYTICS, ITS APPLICATION COULD BE RELATED TO THE BROADER CONCEPT OF STUDENT SUCCESS. STUDENT SUCCESS ALLOWS FOR A FOCUS ON PEDAGOGY AND THE USE OF LEARNING ANALYTICS FOR THE IMPROVEMENT OF LEARNING AND TEACHING WITH A FIRM SCHOLARLY EVIDENCE BASE. THE DATA GATHERED FOR THE PROJECT PROVIDE THE BACKGROUND FOR A DISCUSSION ABOUT THE POTENTIAL OF LEARNING ANALYTICS TO INFORM THE PRACTICE OF THE SCHOLARSHIP OF TEACHING AND LEARNING. A CASE STUDY DEMONSTRATES THE POTENTIAL OF THIS APPROACH. OVERALL, CLEAR PEDAGOGICAL QUESTIONS ARE IMPORTANT IN THE APPLICATION OF LEARNING ANALYTICS TO THE SCHOLARSHIP OF TEACHING AND LEARNING, AND WE SUGGEST POTENTIAL WAYS TO EXPLORE PEDAGOGICAL QUESTIONS WITH BIG DATA METHODS.
LEARNING MANAGEMENT SYSTEMS ARE OFTEN SEEN AS ENABLERS FOR LEARNING ANALYTICS AS THEY GENERATE DATA AND HELP TO ANALYSE SUCH DATA. IN THE CASE OF THE POPULAR LEARNING MANAGEMENT SYSTEM MOODIE, HOWEVER, ALMOST NO LEARNING ANALYTICS FUNCTIONALITIES ARE PROVIDED. IN THIS REPORT, A METHOD WILL BE DEMONSTRATED HOW LEARNING MATERIALS CAN BE INTEGRATED INTO MOODIE COURSES SO THAT THEY PRODUCE INTERACTION DATA. SUCH DATA CAN BE COLLECTED AND USED AS INPUT FOR LEARNING ANALYTICS.
WITH THE INCREASE IN AVAILABLE EDUCATIONAL DATA, IT IS EXPECTED THAT LEARNING ANALYTICS WILL BECOME A POWERFUL MEANS TO INFORM AND SUPPORT LEARNERS, TEACHERS AND THEIR INSTITUTIONS IN BETTER UNDERSTANDING AND PREDICTING PERSONAL LEARNING NEEDS AND PERFORMANCE. HOWEVER, THE PROCESSES AND REQUIREMENTS BEHIND THE BENEFICIAL APPLICATION OF LEARNING AND KNOWLEDGE ANALYTICS AS WELL AS THE CONSEQUENCES FOR LEARNING AND TEACHING ARE STILL FAR FROM BEING UNDERSTOOD. IN THIS PAPER, WE EXPLORE THE KEY DIMENSIONS OF LEARNING ANALYTICS (LA), THE CRITICAL PROBLEM ZONES, AND SOME POTENTIAL DANGERS TO THE BENEFICIAL EXPLOITATION OF EDUCATIONAL DATA. WE PROPOSE AND DISCUSS A GENERIC DESIGN FRAMEWORK THAT CAN ACT AS A USEFUL GUIDE FOR SETTING UP LEARNING ANALYTICS SERVICES IN SUPPORT OF EDUCATIONAL PRACTICE AND LEARNER GUIDANCE, IN QUALITY ASSURANCE, CURRICULUM DEVELOPMENT, AND IN IMPROVING TEACHER EFFECTIVENESS AND EFFICIENCY. FURTHERMORE, THE PRESENTED ARTICLE INTENDS TO INFORM ABOUT SOFT BARRIERS AND LIMITATIONS OF LEARNING ANALYTICS. WE IDENTIFY THE REQUIRED SKILLS AND COMPETENCES THAT MAKE MEANINGFUL USE OF LEARNING ANALYTICS DATA POSSIBLE TO OVERCOME GAPS IN INTERPRETATION LITERACY AMONG EDUCATIONAL STAKEHOLDERS. WE ALSO DISCUSS PRIVACY AND ETHICAL ISSUES AND SUGGEST WAYS IN WHICH THESE ISSUES CAN BE ADDRESSED THROUGH POLICY GUIDELINES AND BEST PRACTICE EXAMPLES.
WHEN USED FOR EDUCATION, GAMES CAN INCREASE STUDENTS' MOTIVATION AND ENGAGEMENT AND PROVIDE A MORE AUTHENTIC LEARNING ENVIRONMENT WHERE THEY CAN APPLY KNOWLEDGE, MAKING THEM ESPECIALLY SUITED TO SCHOOLS. HOWEVER, ACTUAL APPLICATION OF SUCH (SERIOUS) GAMES IN SCHOOLS IS STILL LIMITED. TEACHERS STILL CONSIDER THAT USING GAMES IS A COMPLEX PROCESS THAT THEY DO NOT FULLY MASTER AND THAT REQUIRES EXTRA EFFORT FROM THEM. WE CONSIDER THAT SIMPLIFYING TEACHERS' TASKS WHEN DEPLOYING GAMES IS CRUCIAL TO PROMOTE THEIR USE. IN CLASSROOM SCENARIOS, TEACHERS CAN GREATLY BENEFIT FROM KNOWING WHAT IS HAPPENING AS A SERIOUS GAME IS BEING PLAYED. GAME LEARNING ANALYTICS (GLA) IS THE PROCESS OF COLLECTING, ANALYZING AND DISPLAYING STUDENT INTERACTION DATA WITH THE GAMES TO IMPROVE THE EDUCATIONAL EXPERIENCE. GLA CAN BE USED BOTH AT REAL-TIME, PROVIDING TEACHERS WITH INFORMATION WHILE THEIR STUDENTS ARE STILL PLAYING, AND OFFLINE, INSPECTING ALREADY-FINISHED GAME SESSIONS. IN BOTH CASES, ANALYTICS IS ONLY USEFUL WHEN IT MANAGES TO BRIDGE THE GAP BETWEEN LARGE COLLECTIONS OF INTERACTION DATA AND PEDAGOGICALLY SOUND INSIGHT. ANALYTICS DASHBOARDS SHOULD THEREFORE PROVIDE NOT ONLY COMPLETE BUT MEANINGFUL AND EASY-TO-UNDERSTAND INFORMATION, CONSIDERING THAT TEACHERS WILL MOST PROBABLY NOT KNOW ALL THE DETAILS OF THE ANALYSES PERFORMED UNDERNEATH. IN THIS PAPER, WE REVIEW OUR EXPERIENCES ON GAME LEARNING ANALYTICS DASHBOARDS FOR TEACHERS, AND DESCRIBE SOME OF THE STEPS WE HAVE TAKEN TO IMPROVE OUR DASHBOARDS.
HIGHER EDUCATION INSTITUTIONS ARE DEVELOPING THE CAPACITY FOR LEARNING ANALYTICS. HOWEVER, THE TECHNICAL DEVELOPMENT OF LEARNING ANALYTICS HAS FAR EXCEEDED THE CONSIDERATION OF ETHICAL ISSUES AROUND LEARNING ANALYTICS. WE EXAMINED HIGHER EDUCATION ACADEMICS' KNOWLEDGE, ATTITUDES, AND CONCERNS ABOUT THE USE OF LEARNING ANALYTICS THOUGH FOUR FOCUS GROUPS (N = 35). THEMATIC ANALYSIS OF THE FOCUS GROUP TRANSCRIPTS IDENTIFIED FIVE KEY THEMES. THE FIRST THEME, 'FACILITATING LEARNING', REPRESENTS ACADEMICS' PERCEPTIONS THAT, WHILE CURRENTLY UNREALIZED, THERE COULD BE SEVERAL BENEFITS TO LEARNING ANALYTICS THAT WOULD HELP THEIR STUDENTS. THREE THEMES; 'WHERE ARE THE ETHICS?', 'WHAT ABOUT THE STUDENTS!', AND 'WHAT ABOUT ME!' REPRESENTED ACADEMICS' PERCEPTIONS OF HOW LEARNING ANALYTICS COULD POSE SOME CONSIDERABLE DIFFICULTIES WITHIN A HIGHER EDUCATION CONTEXT. A FINAL THEME 'LET'S MOVE FORWARD TOGETHER' REFLECTED THAT DESPITE SOME CHALLENGES AND CONCERNS ABOUT LEARNING ANALYTICS, ACADEMICS PERCEIVED SCOPE FOR LEARNING ANALYTICS TO BE BENEFICIAL IF THERE IS COLLABORATION BETWEEN ACADEMICS, STUDENTS, AND THE UNIVERSITY. THE FINDINGS HIGHLIGHT THE NEED TO INCLUDE ACADEMICS IN THE DEVELOPMENT OF LEARNING ANALYTICS POLICIES AND PROCEDURES TO PROMOTE THE SUITABILITY AND WIDESPREAD ADOPTION OF LEARNING ANALYTICS IN THE HIGHER EDUCATION SECTOR.
THE APPLICATION OF LEARNING ANALYTICS TECHNIQUES TO LOG DATA FROM LEARNING MANAGEMENT SYSTEMS (LMS) HAS RAISED INCREASING INTEREST IN THE PAST YEARS. ADVANCES IN THIS FIELD INCLUDE THE SELECTION OF ADEQUATE INDICATORS AND DEVELOPMENT OF RESEARCH FRAMEWORKS. HOWEVER, MOST RESEARCH HAS FOCUSED ON INDIVIDUAL STUDENTS, WHICH HAS HAMPERED THE DEVELOPMENT OF LEARNING ANALYTICS FOR TEAM ASSESSMENT IN COLLABORATIVE LEARNING CONTEXTS. FROM A FOUR-DIMENSIONAL VIEW OF TEAMWORK, THIS STUDY PROPOSES A SET OF LOG DATA-BASED INDICATORS TO FACILITATE GROUP ASSESSMENT IN PROJECT-BASED LEARNING COURSES, AND IDENTIFY RELEVANT PREDICTORS OF FINAL PROJECT RESULTS.
THE AIM OF THIS PAPER IS TO SHOW HOW MULTIMODAL LEARNING ANALYTICS (MMLA) CAN HELP UNDERSTAND HOW ELEMENTARY STUDENTS EXPLORE THE CONCEPT OF FEEDBACK LOOPS WHILE CONTROLLING AN EMBODIED SIMULATION OF A PREDATOR-PREY ECOSYSTEM USING HAND MOVEMENTS AS AN INTERFACE WITH THE COMPUTER SIMULATION. WE REPRESENT STUDENT MOTION PATTERNS FROM FINE-GRAINED LOGS OF HANDS AND GAZE DATA, AND THEN MAP THESE OBSERVED MOTION PATTERNS AGAINST LEVELS OF STUDENT PERFORMANCE TO MAKE INFERENCES ABOUT HOW EMBODIMENT PLAYS A ROLE IN THE LEARNING PROCESS. RESULTS SHOW FIVE DISTINCT MOTION SEQUENCES IN STUDENTS' EMBODIED INTERACTIONS, AND THESE MOTION PATTERNS ARE STATISTICALLY ASSOCIATED WITH INITIAL AND POST-TUTORIAL LEVELS OF STUDENTS' UNDERSTANDING OF FEEDBACK LOOPS. ANALYSIS OF STUDENT GAZE ALSO SHOWS DISTINCTIVE PATTERNS AS TO HOW LOW- AND HIGH-PERFORMING STUDENTS ATTENDED TO INFORMATION PRESENTED IN THE SIMULATION. USING MMLA, WE SHOW HOW STUDENTS' EXPLANATIONS OF FEEDBACK LOOPS LOOK DIFFERENTLY ACCORDING TO CLUSTER MEMBERSHIP, WHICH PROVIDES EVIDENCE THAT EMBODIMENT INTERACTS WITH CONCEPTUAL UNDERSTANDING.
THIS PAPER ATTEMPTS TO UNDERSTAND THE BROADER IMPLICATIONS FOR INTEGRATING LEARNING ANALYTICS IN E-LEARNING SYSTEMS. THIS IS ALSO THE COMMENCEMENT OF A COLLABORATIVE EFFORT FOR DEVELOPING RESEARCH BASES AROUND LEARNING ANALYTICS BETWEEN THE UNIVERSITY OF MAURITIUS AND THE UNIVERSITY OF KWAZULUNATAL. THE FOCUS ON E-ASSESSMENTS IS DELIBERATE: THE TENDANCY IN BOTH OUR UNIVERSITIES TO APPRAISE STUDENT LEARNING AS SUMMATIVE AND QUANTITATIVE DOES NOT REFLECT CONTEMPORARY WORKPLACE AND SCHOLARLY NEEDS WHERE LEARNING PROCESSES ARE MORE VALUABLE THAN OUTCOMES. THE NECESSITY FOR EPISTEMOLOGICAL RETHINKING AND DEVELOPING SYSTEMS BASED ON CONTEXTUAL REQUIREMENTS IS IMPORTANT, WHETHER IT IS RELATED TO CURRICULUM DEVELOPMENT OR WORKSHOP ORGANISATION OR EVEN CONDUCTING A CLASS. AFTER A SURVEY OF THE LITERATURE ON THE POTENTIALS AND HYPES AROUND LEARNING ANALYTICS, WE LOOK TOWARDS THE LITERATURE LINKING LEARNING ANALYTICS, DATA MINING AND E-ASSESSMENT FOR EDUCATORS TO INTERFACE MORE EFFECTIVELY AND EFFICIENTLY WITH THE LMS PLATFORM. WE CONCLUDE THIS CONCEPT-PAPER WITH A WORKSHOP OUTLINE WHICH CAN HELP EDUCATIONAL TECHNOLOGISTS ORGANISE PRACTICAL, SUSTAINABLE AND MEANINGFUL HANDS-ON SESSIONS WITH EDUCATORS.
KNOWLEDGE MANAGEMENT ADOPTION IN HIGHER EDUCATION INSTITUTIONS IS STILL LIMITED. ESSENTIALLY, HIGHER EDUCATION INSTITUTIONS HAVE NOT DEVELOPED THEIR KNOWLEDGE MANAGEMENT APPROACHES TO AN ADVANCED LEVEL AS OPPOSED FOR INSTANCE TO BUSINESS AND INDUSTRY. THE INCREASING USE OF TECHNOLOGY IN LEARNING PRACTICES HOWEVER, HAS BROUGHT NEW TECHNOLOGY-ENHANCED LEARNING SOLUTIONS THAT PROVIDE AN OPPORTUNITY BY LINKING THIS TANGIBLE BASIS TO THE (OFTEN) NON-TANGIBLE ELEMENTS OF A KNOWLEDGE MANAGEMENT PROCESS. THIS PAPER MAINTAINS THE SIGNIFICANCE OF UTILISING LEARNING ANALYTICS FOR KNOWLEDGE MANAGEMENT IMPLEMENTATION IN HIGHER EDUCATION INSTITUTIONS. IT ARGUES THAT KNOWLEDGE MANAGEMENT IS ESSENTIAL TO SUPPORT IMPROVEMENTS AND INNOVATION OF ANALYTICAL PROCESSES, WHICH ARE ESSENTIAL FOR ROBUST LEARNING ANALYTICS. MOREOVER, LEARNING ANALYTICS IS REQUIRED TO BE EMBEDDED IN THE ORGANISATION'S STRATEGIC VISION, AND KNOWLEDGE MANAGEMENT CAN HELP AN ORGANISATION TRANSLATE ITS STRATEGIC VISION INTO ACTION. AS SUCH, THE MAPPING BETWEEN THESE INSTITUTIONS' PRODUCTS AND THE INDUSTRY NEEDS MIGHT STILL BE LACKING. LEARNING ANALYTICS, GIVEN THE INTRODUCTION AND SPREAD OF VIRTUAL LEARNING ENVIRONMENTS, PROVIDES AN UNPRECEDENTED OPPORTUNITY FOR HIGHER EDUCATION INSTITUTIONS TO IMPROVE THEIR KNOWLEDGE MANAGEMENT INITIATIVES. THIS IS BELIEVED TO BRIDGE THE GAP BETWEEN MANUFACTURING AND ITS EDUCATION.
E-LEARNING SYSTEMS NOWADAYS FACE THE CHALLENGE OF KEEPING UP WITH A DIVERSE SET OF USERS WITH DIFFERENT LEARNING NEEDS AND ABILITIES. IN ORDER TO PROVIDE BETTER INSIGHTS TO THE TEACHER WE MUST DEVELOP MORE EFFICIENT TUTORING SYSTEMS THAT CAN PROCESS THE LARGE AMOUNT OF DATA GENERATED FROM THE STUDENT INTERACTION WITH THE SYSTEM. SO FAR TRADITIONAL ANALYSIS TOOK INTO CONSIDERATION STRUCTURED DATA TO PROVIDE FEEDBACK TO THE TUTOR. DATA ANALYTICS, BY EXAMINING PATTERNS AND CORRELATIONS, TRY TO TRANSFORM DATA IN A WAY THAT CAN BE USED FOR DECISION MAKING. SIMILARLY LEARNING ANALYTICS ANALYZE EDUCATIONAL DATA OBTAINED FROM STUDENT INTERACTION WITH THE SYSTEM. LEARNING ANALYTICS CAN BE OF GREAT BENEFIT TO INTELLIGENT TUTORING SYSTEMS (ITSS). IN THIS PAPER WE PROPOSE AN ANALYTICS TOOL THAT GATHERS INFORMATION FROM THE STUDENT INTERACTION WITH THE SYSTEM AND FEEDS THEM TO A SEARCH ENGINE IN ORDER TO PROVIDE BETTER INSIGHTS TO THE TUTOR.
A COMPETENCY-BASED CURRICULUM INVOLVES AN OUTCOME-BASED APPROACH FOR CULTIVATING GRADUATES' CORE COMPETENCIES REQUIRED FOR SPECIFIC PROFESSIONS. A CURRICULUM COMMITTEE DEFINES GRADUATES' OUTCOMES OF CORE COMPETENCIES, DESIGNS A CURRICULUM TO CULTIVATE CORE COMPETENCIES, EVALUATES GRADUATES' OUTCOMES, AND REFLECTS ON AND REGULATES THE CURRICULUM. HOWEVER, CURRICULUM COMMITTEES LACK SYSTEMATIC EVALUATIVE INFORMATION FOR REFLECTION. LEARNING ANALYTICS, AN EMERGING DATA-DRIVEN ANALYTICS OF EDUCATIONAL DATA, COULD BE APPLIED TO ASSIST CURRICULUM COMMITTEES IN REFLECTION. THIS STUDY PROPOSES COMPETENCY-BASED LEARNING ANALYTICS, INCLUDING SEVEN ANALYTIC TOOLS, TO ANALYZE CURRICULA AND GRADUATES' ACADEMIC RECORDS TO ASSIST CURRICULUM COMMITTEES IN REFLECTING (1) OBJECTIVES, COMPETENCIES, AND CURRICULUM DESIGN OF COMPETENCY-BASED CURRICULA AND (2) FACULTY TEACHING AND STUDENT LEARNING. THE PROPOSED LEARNING ANALYTICS WERE CONDUCTED ON 14 DEPARTMENTS OF A UNIVERSITY. THIS STUDY REPORTS THE CURRICULUM COMMITTEES' EIGHT PRACTICAL REFLECTIONS OF CURRICULA, FACULTY TEACHING, AND STUDENT LEARNING. THIS STUDY ILLUSTRATES POTENTIAL APPLICATIONS AND IMPACT OF COMPETENCY-BASED LEARNING ANALYTICS ON COMPETENCY-BASED CURRICULA.
TO ASSIST HIGHER EDUCATION INSTITUTIONS IN MEETING THE CHALLENGE OF LIMITED STUDENT ENGAGEMENT IN THE IMPLEMENTATION OF LEARNING ANALYTICS SERVICES, THE QUESTIONNAIRE FOR STUDENT EXPECTATIONS OF LEARNING ANALYTICS (SELAQ) WAS DEVELOPED. THIS INSTRUMENT CONTAINS 12 ITEMS, WHICH ARE EXPLAINED BY A PURPORTED TWO-FACTOR STRUCTURE OF "ETHICAL AND PRIVACY EXPECTATIONS" AND "SERVICE FEATURE EXPECTATIONS." AS IT STANDS, HOWEVER, THE SELAQ HAS ONLY BEEN VALIDATED WITH STUDENTS FROM UK UNIVERSITY, WHICH IS PROBLEMATIC ON ACCOUNT OF THE INTEREST IN LEARNING ANALYTICS EXTENDING BEYOND THIS CONTEXT. THUS, THE AIM OF THE CURRENT WORK WAS TO ASSESS WHETHER THE TRANSLATED SELAQ CAN BE VALIDATED IN THREE CONTEXTS (AN ESTONIAN, A SPANISH, AND A DUTCH UNIVERSITY). THE FINDINGS SHOW THAT THE MODEL PROVIDED ACCEPTABLE FITS IN BOTH THE SPANISH AND DUTCH SAMPLES, BUT WAS NOT SUPPORTED IN THE ESTONIAN STUDENT SAMPLE. IN ADDITION, AN ASSESSMENT OF LOCAL FIT IS UNDERTAKEN FOR EACH SAMPLE, WHICH PROVIDES IMPORTANT POINTS THAT NEED TO BE CONSIDERED IN FUTURE WORK. FINALLY, A GENERAL COMPARISON OF EXPECTATIONS ACROSS CONTEXTS IS UNDERTAKEN, WHICH ARE DISCUSSED IN RELATION TO THE GENERAL DATA PROTECTION REGULATION (2018).
RECENTLY, LEARNING ANALYTICS HAVE PLAYED A VITAL ROLE IN PREDICTING AND MONITORING STUDENTS' PERFORMANCE. OUR WORK-IN-PROGRESS PAPER SUGGESTS, THIS DATA COULD BE FURTHER ANALYSED TO ASCERTAIN THE ALIGNMENT BETWEEN INSTITUTIONAL STRATEGIC GOALS AND STAFF PERFORMANCE APPROACHES IN HIGHER EDUCATION INSTITUTIONS. WE EXTEND THIS MODEL TO INSTITUTIONAL PERFORMANCE BASED ON KEY PERFORMANCE INDICATORS. WE CONTEND THAT LEARNING ANALYTICS COULD ACT AS A CRUCIAL MECHANISM IN ACHIEVING GOAL CONGRUENCE BETWEEN HIGHER EDUCATION INSTITUTIONS' STRATEGY AND PERFORMANCE AND NOT LIMITED TO THE STUDY OF STUDENTS' PERFORMANCE. THIS STUDY IS AIMED AT FINDING HOW THIS COULD BE APPLIED TO INSTITUTIONAL LEVEL GOAL ALIGNMENT.
THE PRESENT LEVEL OF THE TECHNOLOGY DEVELOPMENT GIVES VARIOUS OPPORTUNITIES TO IMPLEMENT METHODS NEVER USED OR SEEN BEFORE. DIFFERENT SOLUTIONS EMERGE, AND ONE OF THE MOST CRITICAL GOALS FOR THE SCIENTIFIC SOCIETY IS TO ESTABLISH A STRONG LINK OF THE NEW TECHNOLOGY WITH THE METHODS USED TRADITIONALLY IN PARTICULAR AREAS. IN THIS ARTICLE WE WOULD LIKE TO DISCUSS HOW TWO RELATIVELY NEW APPROACHES TO EDUCATION PROCESS, LEARNING ANALYTICS (LA) AND SERIOUS GAMES (SG), CAN INTERRELATE BETWEEN EACH OTHER WITH REFERENCE TO THE CLASSROOM IMPLEMENTATION. WE WILL REVIEW THE ROLE OF LA IN THE CONTEXT OF SG AND VICE VERSA AND DISCUSS POSSIBLE AND ACTUAL LIMITATION THAT COULD CONFINE THE POSITIVE RESULTS OF SUCH INTERRELATION.
PRESENT RESEARCH AND DEVELOPMENT OFFER VARIOUS LEARNING ANALYTICS TOOLS PROVIDING INSIGHTS INTO DIFFERENT ASPECTS OF LEARNING PROCESSES. ADOPTION OF A SPECIFIC TOOL FOR PRACTICE IS BASED ON HOW ITS LEARNING ANALYTICS ARE PERCEIVED BY EDUCATORS TO SUPPORT THEIR PEDAGOGICAL AND ORGANIZATIONAL GOALS. IN THIS PAPER, WE PROPOSE AND EMPIRICALLY VALIDATE A LEARNING ANALYTICS ACCEPTANCE MODEL (LAAM) OF FACTORS INFLUENCING THE BELIEFS OF EDUCATORS CONCERNING THE ADOPTION A LEARNING ANALYTICS TOOL. IN PARTICULAR, OUR MODEL EXPLAINS HOW THE USAGE BELIEFS (I.E., EASE-OF-USE AND USEFULNESS PERCEPTIONS) ABOUT THE LEARNING ANALYTICS OF A TOOL ARE ASSOCIATED WITH THE INTENTION TO ADOPT THE TOOL. IN OUR STUDY, WE CONSIDERED SEVERAL FACTORS THAT COULD POTENTIALLY AFFECT THE ADOPTION BELIEFS: I) PEDAGOGICAL KNOWLEDGE AND INFORMATION DESIGN SKILLS OF EDUCATORS; II) EDUCATORS' PERCEIVED UTILITY OF A LEARNING ANALYTICS TOOL; AND III) EDUCATORS' PERCEIVED EASE-OF-USE OF A LEARNING ANALYTICS TOOL. BY FOLLOWING THE PRINCIPLES OF TECHNOLOGY ACCEPTANCE MODEL, THE STUDY IS DONE WITH A SAMPLE OF EDUCATORS WHO EXPERIMENTED WITH A LOCO-ANALYST TOOL. OUR STUDY ALSO DETERMINED SPECIFIC ANALYTICS TYPES THAT ARE PRIMARY ANTECEDENCE OF PERCEIVED USEFULNESS (CONCEPT COMPREHENSION AND SOCIAL INTERACTION) AND EASE-OF-USE (INTERACTIVE VISUALIZATION). (C) 2012 ELSEVIER LTD. ALL RIGHTS RESERVED.
IN THIS PAPER, THE RESEARCHERS PROPOSE A CONCEPTUAL FOR SYSTEM ARCHITECTURE OF LEARNING ANALYTICS PROCESS IN THE INTELLIGENT LEARNING ENVIRONMENT. WITHIN THIS CONCEPT, TODAY'S COMPETITIVE BUSINESS ENVIRONMENT NEED FOR BUSINESSES IN ORDER TO IMPLEMENT THE MONITOR AND ANALYZE THE USER-GENERATED DATA ON THEIR OWN AND THEIR COMPETITORS. THE ACHIEVEMENT OF COMPETITIVE ADVANTAGE IS OFTEN NECESSARY TO LISTEN TO AND UNDERSTAND WHAT CUSTOMERS ARE SAYING ABOUT COMPETITORS' PRODUCTS AND SERVICES. NOT ONLY PERSONALITY ANALYTICS BUT ALSO THE CONCEPTUAL DESCRIPTION CAN CAPTURE AN INTELLIGENT LEARNING ENVIRONMENT, AND IT IS THE ANALYTIC TOOLS THAT ARE USED TO IMPROVE LEARNING AND EDUCATION. THE RESEARCHERS ALSO DISCUSS HOW LEARNING ANALYTICS IS DEVELOPED IN DIFFERENT FIELDS. IT CLOSELY TIED TO, A SERIES OF OTHER FIELDS OF STUDY INCLUDING BUSINESS INTELLIGENCE, WEB ANALYTICS, ACADEMIC ANALYTICS, EDUCATIONAL DATA MINING, AND ACTION ANALYTICS. THE RESEARCHERS BELIEVE THAT CONCEPTUAL OF PERSONALITY ANALYTICS IN THE INTELLIGENT LEARNING ENVIRONMENT CAN PLAY AN ESSENTIAL ROLE IN MANAGING AND ANALYZING PERSONALITY AND CONTRIBUTE TO THE CONCEPT OF PERSONALITY ANALYTICS IN THE INTELLIGENT LEARNING ENVIRONMENT. THE RESULTS OF THIS RESEARCH COULD BE SUMMARIZED AS FOLLOWS: LEARNING ANALYTICS PROCESS SHOULD BE USED AS MEASURING AND COLLECTING DATA ABOUT LEARNERS AND LEARNING WITH THE AIM OF IMPROVING TEACHING AND LEARNING PRACTICE THROUGH ANALYSIS OF THE DATA. BY ACHIEVING THIS PROCESS, IT SHOULD COLLECT DATA TO REPORT OR ANALYZE THE HAPPENING ABOUT THE LEARNER. THEN, INSTRUCTORS MONITOR LEARNING WHAT IS HAPPENING NOW, WHILE AS LEARNING ANALYTICS SHOULD GET WHAT IS GOING TO HAPPEN IN THE FUTURE FOR LEARNERS. FINALLY, INSTRUCTORS TAKE ACTION TO FEEDBACK LEARNERS.
LEARNING ANALYTICS, BEING A VERY ACTIVE FIELD OF RESEARCH, RECENTLY HAS A NUMBER OF SIGNIFICANT CONTRIBUTIONS WHERE DATA RELATED TO STUDENTS' DEMOGRAPHICS, LEARNING AND BEHAVIOUR IS BEING USED TO PREDICT STUDENT PERFORMANCE. IN HIGHER EDUCATION INSTITUTES MOST OF THESE MODELS ARE BASED ON STATISTICAL AND MACHINE LEARNING TECHNIQUES. THE BIGGEST CHALLENGE THAT CAN BE SEEN IN ALMOST ALL THESE RESEARCH EFFORTS IS THE INEFFECTIVE PORTABILITY OF PREDICTION MODEL ACROSS MULTIPLE COHORTS EVEN IN THE SAME INSTITUTE. WE PROPOSE TO USE HYBRID FUZZY-STATISTICAL MODEL FOR PREDICTING STUDENT PERFORMANCE BASED ON BEHAVIOUR RELATED PREDICTORS IN A PARTICULAR COHORT. WE HAVE RECENTLY STARTED THIS PROJECT SO PRELIMINARY DETAILS OF OUR MODEL BASED ON FUZZY EXPERT SYSTEM ARE PROVIDED. THE MODEL IS PROPOSED FOR ENHANCING TRADITIONAL STATISTICAL MODELS WITH FUZZY SETS/FUZZY LOGIC TO OVERCOME THE LIMITATIONS OF COMMONLY USED STATISTICAL TECHNIQUES AND DELIVER A STRONGER SYSTEM WITH COMBINED ADVANTAGES OF BOTH DOMAINS.
SOCIAL NETWORK LEARNING ANALYTICS AIMS TO EXTRACT USEFUL INFORMATION TO IMPROVE THE LEARNING PROCESS, BUT THE VARIETY OF LEARNING MANAGEMENT SYSTEMS MAKES THIS TASK BURDENSOME AND DIFFICULT TO MANAGE. THIS STUDY SHOWS HOW GEPHI, A GENERAL-PURPOSE, OPEN-SOURCE SOCIAL NETWORK ANALYSIS APPLICATION, CAN BE USED BY INSTRUCTORS AND INSTITUTIONS TO EXTRACT AND VISUALIZE RELEVANT INFORMATION THAT IS COMMONLY HIDDEN OR DIFFICULT TO OBSERVE FOR COURSE COORDINATORS AND TEACHERS. THE EMPIRICAL CASE STUDY USES DATA FROM ONE CROSS-CURRICULAR COURSE WITH 656 STUDENTS AT THE OPEN UNIVERSITY OF CATALONIA (UOC) AND SHOWCASES THE USE OF GEPHI AS A SOCIAL NETWORK LEARNING ANALYTICS TOOL. THE STUDY FURTHER DISCUSSES THE POTENTIAL OF SOCIAL NETWORK LEARNING ANALYTICS TO IMPROVE ONLINE INSTRUCTION BY VISUALIZATION OF EDUCATIONAL DATA.
DESPITE THE UBIQUITY OF LEARNING IN THE EVERYDAY LIFE OF MOST WORKPLACES, THE LEARNING ANALYTICS COMMUNITY ONLY HAS PAID ATTENTION TO SUCH SETTINGS VERY RECENTLY. ONE PROBABLE REASON FOR THIS OVERSIGHT IS THE FACT THAT LEARNING IN THE WORKPLACE IS OFTEN INFORMAL, HARD TO GRASP AND NOT UNIVOCALLY DEFINED. THIS PAPER SUMMARIZES THE STATE OF THE ART OF WORKPLACE LEARNING ANALYTICS (WPLA), EXTRACTED FROM A SYSTEMATIC LITERATURE REVIEW OF FIVE ACADEMIC DATABASES AS WELL AS OTHER KNOWN SOURCES IN THE WPLA COMMUNITY. OUR ANALYSIS OF EXISTING PROPOSALS DISCUSSES PARTICULARLY ON THE ROLE OF DIFFERENT CONCEPTIONS OF LEARNING AND THEIR INFLUENCE ON THE LA PROPOSALS' DESIGN AND TECHNOLOGY CHOICES. WE END THE PAPER BY DISCUSSING OPPORTUNITIES FOR FUTURE WORK IN THIS EMERGENT FIELD.
DIGITAL LEARNING GAMES ARE USEFUL EDUCATIONAL TOOLS WITH HIGH MOTIVATIONAL POTENTIAL. WITH THE APPLICATION OF GAMES FOR INSTRUCTION THERE COMES THE NEED OF ACKNOWLEDGING LEARNING GAME EXPERIENCES ALSO IN THE CONTEXT OF EDUCATIONAL ASSESSMENT. LEARNING ANALYTICS PROVIDES NEW OPPORTUNITIES FOR SUPPORTING ASSESSMENT IN AND OF EDUCATIONAL GAMES. WE GIVE AN OVERVIEW OF CURRENT LEARNING ANALYTICS METHODS IN THIS FIELD AND REFLECT ON EXISTING CHALLENGES. AN APPROACH OF PROVIDING REUSABLE SOFTWARE ASSETS FOR INTERACTION ASSESSMENT AND EVALUATION IN GAMES IS PRESENTED. THIS IS PART OF A BROADER INITIATIVE OF MAKING AVAILABLE ADVANCED METHODOLOGIES AND TOOLS FOR SUPPORTING APPLIED GAME DEVELOPMENT.
THE EVOLUTION OF MASSIVE OPEN ONLINE COURSES PLATFORMS (MOOC'S PLATFORMS) IN THE LAST YEARS WAS ARISEN. THE SIX YEARS OF MOOC, STARTING FROM THE FIRST MOOC UNTIL NOW ARE SIX YEARS WITH HUNDREDS OF EDUCATIONAL INSTITUTIONS INVOLVED IN MOOC, MILLIONS OF STUDENTS, THOUSANDS OF COURSES AND HOURS OF VIDEO RECORDS. THE MOOC CONCEPT STARTED IN 2008 WITH THE "CONNECTIVISM AND CONNECTIVE KNOWLEDGE" COURSE HELD BY GEORGE SIEMENS AND STEPHEN DOWNES. IN THIS ARTICLE, THE AUTHOR EXAMINES THE HIGH INCREASE OF MOOC PLATFORMS FROM THEIR APPEARANCE UNTIL NOW IN BOTH SEGMENTS - GENERAL AND PARTICULAR. THE ARTICLE EXAMINES THE LARGEST GLOBAL MOOC PLATFORMS - WITH THE LARGEST INCREASE, MOST STUDENTS ENROLLED, MOST INSTITUTION PARTNERS OR MOST ACTIVE COURSES. BASED ON THE LAST EVOLUTION OF MOOC, THE PROPOSES OF THIS ARTICLE IS TO PREDICT THE NEXT EVOLUTION OF MOOC IN MEDIUM TERM. THE LEARNING ANALYTICS IS ALSO A FIELD WITH GROWTH IN THE LAST YEARS. IT'S PURPOSE IS TO PREDICT AND ADVISE ON LEARNING. THE ARTICLE ANALYZES THE EVOLUTION OF THE FIELD AND THE CONTRIBUTIONS MADE BY IT IN THE DEVELOPMENT OF MOOC WITH RELEVANT DATA GATHERED FROM PRESENT COURSES. THE ARTICLE SUMMARIZES THE MOST IMPORTANT ASPECTS OF THE EVOLUTION OF LEARNING ANALYTICS.
A STUDY CONCERNING LEARNING ANALYTICS IS PRESENTED. THIS AREA CONSISTS OF THE COLLECTION AND ANALYSIS OF DATA GENERATED BY STUDENTS AND THEIR ITERATIONS WITH THE PURPOSE OF UNDERSTANDING AND OPTIMIZING LEARNING. BY USING DATABASES, A REFERENTIAL REVIEW OF THE LAST FIVE YEARS IS PROPOSED TO IDENTIFY ASPECTS RELATED TO THE GROWTH OF THIS APPROACH AND ITS FIELDS OF APPLICATION IN HIGHER EDUCATION. THE VOLUME OF RELATED RESEARCH IS INCREASING DUE TO THE NEED TO INVESTIGATE MORE ACCURATE PREDICTIVE MODELS AND NEW ALGORITHMS IN THE FIELD OF DATA SCIENCE.
MODERN EDUCATION IS BENEFITTED FROM USING LEARNING MANAGEMENT SYSTEMS (LMS) ENABLING AUTOMATIC COLLECTION OF LARGE AMOUNT OF DATA ABOUT STUDENTS' ACTIVITIES, PROGRESS AND RESULTS. HOWEVER, CURRENTLY THERE IS A LACK OF ENOUGH MEANS FOR TEACHERS TO EFFECTIVELY ANALYZE AND ASSESS RESULTS FROM SUCH DATA. THIS PAPER PRESENTS A RESEARCH AIMING TO DESIGN AND DEVELOP A NEW LEARNING ANALYTICS (LA) SOLUTION WHICH USES THE EXISTING LMS AS DISTRIBUTED SYSTEMS AND STORAGES OF DATA THROUGH USER-ORIENTED SERVICES. IT INTEGRATES THE BEST FEATURES FROM LA AND DATA VISUALIZATION APPLICATIONS, FROM SERVICE COMPOSITION PLATFORMS EMPOWER USERS TO BUILD COMPLEX BUSINESS PROCESSES. USING TEACHER-ORIENTED SERVICES DATA IS RETRIEVED DIRECTLY FROM THE RELEVANT LMS AS A RESPONSE TO EDUCATOR'S REQUEST AND ARE VISUALIZED IN APPROPRIATE WAY FOR FAST AND EASY ADOPTION. THE PAPER DESCRIBES THE NEW APPROACH AND FUNCTIONALITIES OF THIS SOLUTION AND ENDS WITH SOME CHALLENGES AND CONCLUSIONS.
THIS PAPER DRAWS ON VISUALISATION APPROACHES IN LEARNING ANALYTICS, CONSIDERING HOW CLASSROOM VISUALISATIONS CAN COME TOGETHER IN PRACTICE. WE SUGGEST AN OPEN LEARNER MODEL IN SITUATIONS WHERE MANY TOOLS AND ACTIVITY VISUALISATIONS PRODUCE MORE VISUAL INFORMATION THAN CAN BE READILY INTERPRETED.
THE FIELD OF STUDY WITHIN WHICH THIS WORK IS PLACED IS THAT OF DATA PRODUCED WITHIN DIGITAL LEARNING ENVIRONMENTS, A FIELD OF RESEARCH NOW KNOWN AS LEARNING ANALYTICS (LA). IN PARTICULAR, THE AIM IS TO INVESTIGATE THE RELATIONSHIP BETWEEN THE STANDARD PSYCHOMETRIC PROPERTIES OF THE TEST QUESTIONS AND THE INFORMATION OBTAINED FROM THE LOG FILES PRODUCED DURING ITS ADMINISTRATION, ON A LARGE SCALE, BY COMPUTER. THE RESULTS OF THIS TYPE OF SURVEY CAN HELP TO MAKE VISIBLE THE INTERSECTIONS BETWEEN FORMATIVE ASSESSMENT AND SUMMATIVE ASSESSMENT AND TO RENEW, IN THIS WAY, THE EVALUATION PRACTICES OF A RAPIDLY EXPANDING SECTOR SUCH AS DIGITAL EDUCATION.
THIS ARTICLE ANALYZES THE CONCEPT OF VALIDITY TO SET OUT KEY FACTORS BEARING ON CLAIMS ABOUT VALIDITY IN GENERAL AND PARTICULARLY REGARDING LEARNING ANALYTICS. BECAUSE USES OF TRACE DATA IN LEARNING ANALYTICS ARE INCREASING RAPIDLY, SPECIFIC CONSIDERATION IS GIVEN TO RELIABILITY OF TRACE DATA AND THEIR ROLE IN CLAIMING VALIDITY FOR INTERPRETATIONS GROUNDED ON TRACE DATA. THIS ANALYSIS REVEALS THE ESSENTIAL AND INESCAPABLE ROLE OF THEORY IN DECIDING WHAT TRACE DATA SHOULD BE GATHERED AND HOW TRACE DATA CAN CONTRIBUTE TO RECOMMENDATIONS FOR IMPROVING LEARNING, ONE MAIN GOAL FOR GENERATING AND USING LEARNING ANALYTICS.
THIS PAPER PRESENTS A STUDY THAT FIRST OF ALL, CHARACTERIZES DECISION CRITERIA IN ORDER TO SELECT A TYPE OF MOBILE APPLICATION TO BE DEVELOPED. NEXT, THE STUDY FOCUSES ON THE COMPARISON OF TECHNOLOGICAL ALTERNATIVES FOR DISPLAYING LEARNING ANALYTICS ON MOBILE DEVICES. THIS PAPER PRESENTS A COMPARISON OF TOOLS TO DEVELOP ANALYTIC VISUALIZATIONS TAKING INTO ACCOUNT GENERAL ASPECTS, AND SOME SPECIFIC CHARACTERISTICS. THEN, THE STUDY SUPPORTS DECISION MAKING IN MOBILE APPLICATIONS DEVELOPMENT USING LEARNING VISUAL ANALYTICS. MOBILE APPLICATION PROTOTYPES WERE IMPLEMENTED USING THE DIFFERENT STUDIED TECHNOLOGIES. IN THESE PROTOTYPES, LEARNING ANALYTICS SUPPORT A PROCESS OF COMPETENCE QUALIFICATIONS IN A HIGHER EDUCATION COURSE ON THE EQF. DIAGRAMS USED IN ANALYTICS SHOW THE RESULTS IN PERCENTAGES OR SUMMATIONS OF THE LEVELS OF COMPETENCE REACHED BY SOME STUDENTS IN THE COURSE. A SERIES OF TESTS ARE PRESENTING DIFFERENT CONCLUSIONS ABOUT THE EFFICIENT USE OF THESE TECHNOLOGIES, AND THE TYPE OF MOBILE APPLICATION TO DEVELOP.
SOCIAL MEDIA SITES ARE INCREASINGLY BEING ADOPTED TO SUPPORT TEACHING PRACTICE IN HIGHER EDUCATION. LEARNING ANALYTICS (LA) DASHBOARDS CAN BE USED TO REVEAL HOW STUDENTS ENGAGE WITH COURSE MATERIAL AND OTHERS IN THE CLASS. HOWEVER, RESEARCH ON THE BEST PRACTICES OF DESIGNING, DEVELOPING, AND EVALUATING SUCH DASHBOARDS TO SUPPORT TEACHING AND LEARNING WITH SOCIAL MEDIA HAS BEEN LIMITED. CONSIDERING THE INCREASING USE OF TWITTER FOR BOTH FORMAL AND INFORMAL LEARNING PROCESSES, THIS PAPER PRESENTS OUR DESIGN PROCESS AND A LA PROTOTYPE DASHBOARD DEVELOPED BASED ON A COMPREHENSIVE LITERATURE REVIEW AND AN ONLINE SURVEY AMONG 54 HIGHER EDUCATION INSTRUCTORS WHO HAVE USED TWITTER IN THEIR TEACHING.
IN THIS POSTER, WE DESCRIBE THE AIM AND CURRENT ACTIVITIES OF THE EARLI-CENTRE FOR INNOVATIVE RESEARCH (E-CIR) "MEASURING AND SUPPORTING STUDENT'S SELF-REGULATED LEARNING IN ADAPTIVE EDUCATIONAL TECHNOLOGIES" WHICH IS FUNDED BY THE EUROPEAN ASSOCIATION FOR RESEARCH ON LEARNING AND INSTRUCTION (EARLI) FROM 2015 TO 2019. THE AIM IS TO DEVELOP OUR UNDERSTANDING OF MULTIMODAL DATA THAT UNOBTRUSIVELY CAPTURE COGNITIVE, META-COGNITIVE, AFFECTIVE AND MOTIVATIONAL STATES OF LEARNERS OVER TIME. THIS DEMANDS FOR A CONCERTED INTERDISCIPLINARY DIALOGUE COMBINING FINDINGS FROM PSYCHOLOGY AND EDUCATIONAL SCIENCES WITH ADVANCES IN COMPUTER SCIENCES AND ARTIFICIAL INTELLIGENCE. THE PARTICIPANTS IN THIS E-CIR ARE LEADING INTERNATIONAL RESEARCHERS WHO HAVE ARTICULATED DIFFERENT EMERGING PERSPECTIVES AND METHODOLOGIES TO MEASURE COGNITION, METACOGNITION, MOTIVATION, AND EMOTIONS DURING LEARNING. THE PARTICIPANTS RECOGNIZE THE NEED FOR INTENSIVE COLLABORATION TO ACCELERATE PROGRESS WITH NEW INTERDISCIPLINARY METHODS INCLUDING LEARNING ANALYTICS TO DEVELOP MORE POWERFUL ADAPTIVE EDUCATIONAL TECHNOLOGIES.
LEARNING ANALYTICS IS A RAPIDLY EVOLVING RESEARCH DISCIPLINE THAT USES THE INSIGHTS GENERATED FROM DATA ANALYSIS TO SUPPORT LEARNERS AS WELL AS OPTIMIZE BOTH THE LEARNING PROCESS AND ENVIRONMENT. THIS PAPER STUDIED STUDENTS' ENGAGEMENT LEVEL OF THE LEARNING MANAGEMENT SYSTEM (LMS) VIA A LEARNING ANALYTICS TOOL, STUDENT'S APPROACH IN MANAGING THEIR STUDIES AND POSSIBLE LEARNING ANALYTIC METHODS TO ANALYZE STUDENT DATA. MOREOVER, EXTENSIVE SYSTEMATIC LITERATURE REVIEW (SLR) WAS EMPLOYED FOR THE SELECTION, SORTING AND EXCLUSION OF ARTICLES FROM DIVERSE RENOWNED SOURCES. THE FINDINGS SHOW THAT MOST OF THE ENGAGEMENT IN LMS ARE DRIVEN BY EDUCATORS. ADDITIONALLY, WE HAVE DISCUSSED THE FACTORS IN LMS, CAUSES OF LOW ENGAGEMENT AND WAYS OF INCREASING ENGAGEMENT FACTORS VIA THE LEARNING ANALYTICS APPROACH. NEVERTHELESS, APART FROM RECOGNIZING THE LEARNING ANALYTICS APPROACH AS BEING A SUCCESSFUL METHOD AND TECHNIQUE FOR ANALYZING THE LMS DATA, THIS RESEARCH FURTHER HIGHLIGHTED THE POSSIBILITY OF MERGING THE LEARNING ANALYTICS TECHNIQUE WITH THE LMS ENGAGEMENT IN EVERY INSTITUTION AS BEING A DIRECTION FOR FUTURE RESEARCH.
EDUCATION IS BEING POWERED BY TECHNOLOGY IN MANY WAYS. ONE OF THE MAIN ADVANTAGES IS MAKING USE OF DATA TO IMPROVE THE LEARNING PROCESS. THE MASSIVE OPEN ONLINE COURSE (MOOC) PHENOMENON BECAME VIRAL SOME YEARS AGO, AND WITH IT MANY DIFFERENT PLATFORMS EMERGED. HOWEVER MOST OF THEM ARE PROPRIETARY SOLUTIONS (I.E. COURSERA, UDACITY) AND CANNOT BE USED BY INTERESTED STAKEHOLDERS. AT THE MOMENT OPEN EDX IS PLACED AS THE PRIMARY OPEN SOURCE APPLICATION TO SUPPORT MOOCS. THE COMMUNITY USING OPEN EDX IS GROWING AT A FAST PACE WITH MANY INTERESTED INSTITUTIONS. NEVERTHELESS, THE LEARNING ANALYTICS SUPPORT OF OPEN EDX IS STILL IN ITS FIRST STEPS. IN THIS PAPER WE PRESENT AN OVERVIEW AND DEMONSTRATION OF ANALYSE, AN OPEN SOURCE LEARNING ANALYTICS TOOL FOR OPEN EDX. ANALYSE INCLUDES CURRENTLY 12 NEW VISUALIZATIONS THAT CAN BE USED BY BOTH INSTRUCTORS AND STUDENTS.
LEARNING ANALYTICS CAN PROVIDE POWERFUL TOOLS FOR TEACHERS IN ORDER TO SUPPORT THEM IN THE ITERATIVE PROCESS OF IMPROVING THE EFFECTIVENESS OF THEIR COURSES AND TO COLLATERALLY ENHANCE THEIR STUDENTS' PERFORMANCE. IN THIS PAPER, WE PRESENT THE THEORETICAL BACKGROUND, DESIGN, IMPLEMENTATION, AND EVALUATION DETAILS OF ELAT, A LEARNING ANALYTICS TOOLKIT, WHICH ENABLES TEACHERS TO EXPLORE AND CORRELATE LEARNING OBJECT USAGE, USER PROPERTIES, USER BEHAVIOR, AS WELL AS ASSESSMENT RESULTS BASED ON GRAPHICAL INDICATORS. THE PRIMARY AIM OF THE DEVELOPMENT OF ELAT IS TO PROCESS LARGE DATA SETS IN MICROSECONDS WITH REGARD TO INDIVIDUAL DATA ANALYSIS INTERESTS OF TEACHERS AND DATA PRIVACY ISSUES, IN ORDER TO HELP THEM TO SELF-REFLECT ON THEIR TECHNOLOGY-ENHANCED TEACHING AND LEARNING SCENARIOS AND TO IDENTIFY OPPORTUNITIES FOR INTERVENTIONS AND IMPROVEMENTS.
IN THIS PAPER, WE INTRODUCE THE RESULTS OF A STUDY CONCERNING THE USEFULNESS OF LEARNING ANALYTICS TOOLS IMPLEMENTED IN THE TUT CIRCLE, A SOCIAL MEDIA ENHANCED LEARNING ENVIRONMENT, FROM THE TEACHERS' POINT OF VIEW. ACCORDING TO THE RESULTS OF THE STUDY, LEARNING ANALYTICS TOOLS WITH INTERACTIVE VISUALIZATIONS CAN HELP TEACHERS UTILIZE DATA ABOUT STUDENTS' ACTIONS RECORDED IN LOG FILES OF LEARNING ENVIRONMENTS FOR DIDACTICAL PURPOSES. THE TOOL CAN HELP TEACHERS TO MAKE DECISIONS REGARDING POTENTIAL PEDAGOGICAL STRATEGIES, INSTRUCTIONAL GUIDANCE, ACTIONS, AND INTERVENTIONS THAT MAY BE USED TO SUPPORT STUDENT PARTICIPATION AND ACTIVITY. THE ABILITY TO ACCESS REAL TIME INFORMATION ABOUT STUDENTS' ACTIONS, COMPARE STUDENTS' ACTIVITIES DURING THE COURSE, AND IDENTIFY STUDENTS WHO MIGHT BE AT RISK OF FAILING OR DROPPING OUT ARE OF SIGNIFICANT BENEFIT TO THE TEACHERS. HOWEVER, IN ORDER TO SUPPORT TEACHERS IN OBTAINING MORE PRACTICAL VALUE FROM THE LEARNING ANALYTICS TOOLS THE CONTROLS FOR SPECIFYING THE DATA AND MANIPULATING VIEWS NEED TO BE DEVELOPED FURTHER.
THE ARTICLE COVERS THE METHODS OF DEEP LEARNING FOR EDUCATION DATA MINING (EDM) AND LEARNING ANALYTICS (LA). THE AUTHORS HAVE REVIEWED THE RESEARCHES ON EDM AND LA THAT HAVE EMPLOYED THE CLASSICAL APPROACHES OF ARTIFICIAL NEURAL NETWORKS (ANNS). THE AUTHORS COME TO CONCLUSION THAT LA AND EDM PRESENT INSUFFICIENT VOLUME OF RESEARCH ON THE APPLICATION OF A NEW AND MORE PROFOUND TECHNOLOGY OF ARTIFICIAL NEURAL NETWORKS - DEEP LEARNING. THE NEW METHODS ARE DESCRIBED AND THE RESEARCH ON LA AND EDM THAT EMPLOYS DEEP LEARNING IS STUDIED
IN RECENT TIMES, ONLINE LEARNING PLATFORMS GET MORE AND MORE ATTENTION AND THE NUMBER OF COLLECTED DATA IS GROWING. LEARNING ANALYTICS IS A VALUABLE OPPORTUNITY TO GAIN SPECIFIC INFORMATION FOR A BETTER UNDERSTANDING OF STUDENT'S LEARNING BEHAVIOUR AND TO IMPROVE THEIR LEARNING SUCCESS. IN THIS WORK, THE COLLECTED DATA OF THE ONLINE LEARNING PLATFORM WWW. MORE-ONLINE. AT IS ANALYSED AND FIRST RESEARCH RESULTS ARE PRESENTED. THE MAIN OBJECTIVE IS TO ANALYSE THE USAGE BEHAVIOUR OVER A SCHOOL YEAR AND TO SHOW THE DIFFUSION OF THE ONLINE PLATFORM AMONG PROVINCES IN AUSTRIA, DIFFERENT SCHOOL TYPES AND OTHER CHARACTERISTICS. FURTHERMORE, THE CONTENT OF THE ONLINE PLATFORM IS PUT UNDER CLOSER EXAMINATION TO ENABLE DECISIONS ABOUT THE EFFICIENCY AND EFFECTIVENESS OF DIFFERENT TYPES OF EXERCISES.
DESPITE THE POTENTIAL OF LEARNING ANALYTICS FOR PERSONALIZED LEARNING, IT IS SELDOM USED TO SUPPORT COLLABORATIVE LEARNING PARTICULARLY IN FACE-TO-FACE (F2F) LEARNING CONTEXTS. THIS STUDY USES LEARNING ANALYTICS TO DEVELOP A DASHBOARD SYSTEM THAT PROVIDES ADAPTIVE SUPPORT FOR F2F COLLABORATIVE ARGUMENTATION (FCA). THIS STUDY DEVELOPED TWO DASHBOARDS FOR STUDENTS AND INSTRUCTORS, WHICH ENABLED STUDENTS TO MONITOR THEIR FCA PROCESS THROUGH ADAPTIVE FEEDBACK AND HELPED THE INSTRUCTOR PROVIDE ADAPTIVE SUPPORT AT THE RIGHT TIME. THE EFFECTIVENESS OF THE DASHBOARDS WAS EXAMINED IN A UNIVERSITY CLASS WITH 88 STUDENTS (56 FEMALES, 32 MALES) FOR 4 WEEKS. THE DASHBOARDS SIGNIFICANTLY IMPROVED THE FCA PROCESS AND OUTCOMES, ENCOURAGING STUDENTS TO ACTIVELY PARTICIPATE IN FCA AND CREATE HIGH-QUALITY ARGUMENTS. STUDENTS HAD A POSITIVE ATTITUDE TOWARD THE DASHBOARD AND PERCEIVED IT AS USEFUL AND EASY TO USE. THESE FINDINGS INDICATE THE USEFULNESS OF LEARNING ANALYTICS DASHBOARDS IN IMPROVING COLLABORATIVE LEARNING THROUGH ADAPTIVE FEEDBACK AND SUPPORT. SUGGESTIONS ARE PROVIDED ON HOW TO DESIGN DASHBOARDS FOR ADAPTIVE SUPPORT IN F2F LEARNING CONTEXTS USING LEARNING ANALYTICS.
MASSIVE OPEN ONLINE COURSE (MOOC) SYSTEMS HAVE BECOME PREVALENT IN RECENT YEARS AND DRAW MORE ATTENTION, A.O., DUE TO THE CORONAVIRUS PANDEMIC'S IMPACT. HOWEVER, THERE IS A WELL-KNOWN HIGHER CHANCE OF DROPOUT FROM MOOCS THAN FROM CONVENTIONAL OFF-LINE COURSES. RESEARCHERS HAVE IMPLEMENTED EXTENSIVE METHODS TO EXPLORE THE REASONS BEHIND LEARNER ATTRITION OR LACK OF INTEREST TO APPLY TIMELY INTERVENTIONS. THE RECENT SUCCESS OF NEURAL NETWORKS HAS REVOLUTIONISED EXTENSIVE LEARNING ANALYTICS (LA) TASKS. MORE RECENTLY, THE ASSOCIATED DEEP LEARNING TECHNIQUES ARE INCREASINGLY DEPLOYED TO ADDRESS THE DROPOUT PREDICTION PROBLEM. THIS SURVEY GIVES A TIMELY AND SUCCINCT OVERVIEW OF DEEP LEARNING TECHNIQUES FOR MOOCS' LEARNING ANALYTICS. WE MAINLY ANALYSE THE TRENDS OF FEATURE PROCESSING AND THE MODEL DESIGN IN DROPOUT PREDICTION, RESPECTIVELY. MOREOVER, THE RECENT INCREMENTAL IMPROVEMENTS OVER EXISTING DEEP LEARNING TECHNIQUES AND THE COMMONLY USED PUBLIC DATA SETS HAVE BEEN PRESENTED. FINALLY, THE PAPER PROPOSES THREE FUTURE RESEARCH DIRECTIONS IN THE FIELD: KNOWLEDGE GRAPHS WITH LEARNING ANALYTICS, COMPREHENSIVE SOCIAL NETWORK ANALYSIS, COMPOSITE BEHAVIOURAL ANALYSIS.
PURPOSE THE PURPOSE OF THIS PAPER IS TO DESCRIBE EXAMPLES OF THE APPLICATION OF LEARNING ANALYTICS (LA), INCLUDING THE ASSESSMENT OF SUBJECT GRADES, IDENTIFYING SUBJECTS THAT NEED REVISION, STUDENT SATISFACTION AND COHORT COMPARISONS, TO PROGRAM CURRICULUM REVIEW. DESIGN/METHODOLOGY/APPROACH EXAMPLES OF ANALYSES THAT ADDRESS SPECIFIC QUESTIONS THAT A CURRICULUM REVIEW WISHES TO ADDRESS ARE PROVIDED, TOGETHER WITH EXAMPLES OF VISUALIZATIONS FROM THE ANALYSES TO AID INTERPRETATION. FINDINGS THE RESULTS SHOW THAT USING LA AS A PART OF CURRICULUM REVIEW CAN PROVIDE INSIGHTS NOT POSSIBLE WITH THE TRADITIONAL CURRICULUM REVIEW METHODS AND CAN YIELD USEFUL AND ACTIONABLE INSIGHTS. RESEARCH LIMITATIONS/IMPLICATIONS THE WORK IN THIS PAPER ILLUSTRATES ANOTHER IMPORTANT APPLICATION FOR LA AND DEMONSTRATES THE VALUE THIS APPROACH HAS FOR INFORMING CURRICULUM ENHANCEMENT AT THE PROGRAM LEVEL. PRACTICAL IMPLICATIONS THE ANALYSES DESCRIBED PROVIDE INSIGHTS NOT POSSIBLE WITH TRADITIONAL CURRICULUM REVIEW METHODS. HOWEVER, THE CHALLENGE REMAINS TO DEVELOP ANALYTIC TOOLS THAT CAN ASSIST TEACHERS TO CONDUCT LA INDEPENDENTLY. ORIGINALITY/VALUE LA HAVE BEEN USED TO PREDICT GRADES OR IDENTIFY AT-RISK STUDENTS (GAEVIC ET AL., 2016), BUT THERE IS LITTLE RESEARCH ON ITS USE FOR CURRICULUM EVALUATION (MENDEZ ET AL., 2014). THIS PAPER ADDRESSES THIS GAP AND PROVIDES EXAMPLES OF ITS APPLICATION IN PROGRAM CURRICULUM REVIEW AND THE INSIGHTS IT CAN PROVIDE.
WITH THE DEVELOPMENT OF BLENDED LEARNING, IT HAS BEEN WIDELY APPLIED IN TEACHING PROCESS. HOWEVER, IN THE CONTEXT OF BLENDED LEARNING, TUTOR ARE NOT EASY TO ACQUIRE LEARNING PROCESS OF STUDENTS AND TO MAKE GOOD USE OF LEARNER' BEHAVIORS RECORDS. THEREFORE, THEY CANNOT TIMELY RECEIVE FEEDBACK AND EFFECTIVELY PERFORM EVALUATION BASED ON TRADITIONAL INDICATORS AND METHOD. TO ADDRESS THIS PROBLEM, THIS PAPER APPLIES LEARNING ANALYTICS AND STUDIES THE LEARNING BEHAVIOR RECORDS IN BLENDED LEARNING, I.E., INTEGRATING LEARNING ANALYTICS TO CONSTRUCT AN EVALUATION MODE THROUGH THE EVALUATION THEORY, TYPES, SUBJECT, AND APPLIES THE MODE TO THE SPECIFIC TEACHING PRACTICE. THIS CAN PROVIDE GUIDANCE FOR TUTORS AND HELP THEM MAKE BETTER EFFECTIVE AND OBJECTIVE EVALUATION.
SOCIAL LEARNING ANALYTICS INTRODUCES TOOLS AND METHODS THAT HELP IMPROVING THE LEARNING PROCESS BY PROVIDING USEFUL INFORMATION ABOUT THE ACTORS AND THEIR ACTIVITY IN THE LEARNING SYSTEM. THIS STUDY EXAMINES THE RELATION BETWEEN SNA PARAMETERS AND STUDENT OUTCOMES, BETWEEN NETWORK PARAMETERS AND GLOBAL COURSE PERFORMANCE, AND IT SHOWS HOW VISUALIZATIONS OF SOCIAL LEARNING ANALYTICS CAN HELP OBSERVING THE VISIBLE AND INVISIBLE INTERACTIONS OCCURRING IN ONLINE DISTANCE EDUCATION. THE FINDINGS FROM OUR EMPIRICAL STUDY SHOW THAT FUTURE RESEARCH SHOULD FURTHER INVESTIGATE WHETHER THERE ARE CONDITIONS UNDER WHICH SOCIAL NETWORK PARAMETERS ARE RELIABLE PREDICTORS OF ACADEMIC PERFORMANCE, BUT ALSO ADVISES AGAINST RELYING EXCLUSIVELY IN SOCIAL NETWORK PARAMETERS FOR PREDICTIVE PURPOSES. THE FINDINGS ALSO SHOW THAT DATA VISUALIZATION IS A USEFUL TOOL FOR SOCIAL LEARNING ANALYTICS, AND HOW IT MAY PROVIDE ADDITIONAL INFORMATION ABOUT ACTORS AND THEIR BEHAVIORS FOR DECISION MAKING IN ONLINE DISTANCE LEARNING. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
THIS INTRODUCTION TO THE SPECIAL ISSUE ON LEARNING ANALYTICS PROVIDES AN OVERVIEW OF THE AREA, ACKNOWLEDGING THE RESEARCH TRADITIONS IT EMERGES FROM, SUCH AS COMPUTER-SUPPORTED COLLABORATIVE LEARNING, ACADEMIC ANALYTICS, AND EDUCATIONAL DATA MINING, AND THE WAY THE FIELD AIMS TO BRIDGE FROM TECHNOLOGICAL INNOVATION TO LEARNING PURPOSES. THE INTRODUCTION PROVIDES EXAMPLES OF AREAS AND EDUCATIONAL STAKEHOLDERS WHO ARE SERVED BY AND CAN BENEFIT FROM LEARNING ANALYTICS INITIATIVES, REFERRING THROUGHOUT TO THE ARTICLES IN THIS SPECIAL ISSUE.
THE INFORMATION AND COMMUNICATION TECHNOLOGIES CHANGES HOW WE INTERACT WITH OTHERS AND WITH THE INFORMATION. IT CAN BE REALLY ACCESSED AT ANYTIME AND ANYWHERE. FUTURE PROFESSIONALS SHOULD BE READY FOR THIS REALITY WHICH REQUIRES CHANGES IN TRADITIONAL TEACHING AND LEARNING METHODS. CHALLENGE BASED LEARNING IS AN EXAMPLE OF THEM. THIS METHOD POSES CHALLENGES TO STUDENTS THAT THEY SHOULD SOLVE EMPLOYING THE TECHNOLOGY THEY USE DURING THEIR DAILY LIFE. THE EVALUATION OF CHALLENGES SOLUTIONS SHOULD TAKE INTO ACCOUNT STUDENTS' FINAL OUTCOMES BUT ALSO THE INTERACTIONS THAT TAKE PLACE BETWEEN THEM. THIS COULD BE VERY HARD GIVEN THE WIDE CHOICE OF TOOLS THAT STUDENTS CAN APPLY. LEARNING ANALYTICS TOOLS COULD BE A SOLUTION. THIS PAPER REVIEWS AND CLASSIFIES THE TOOLS APPLIED IN SEVERAL CHALLENGE BASED LEARNING EXPERIMENTS AND DESCRIBES DIFFERENT POSSIBILITIES TO APPLY LEARNING ANALYTICS. FROM THIS RESEARCH, IT IS POSSIBLE TO CONCLUDE THAT LEARNING ANALYTICS CAN BE APPLIED IN CHALLENGE BASED LEARNING CONTEXTS, BUT IT IS DESIRABLE TO USE A SINGLE PLATFORM TO GROUP THE TOOLS EMPLOYED TO SOLVE THE CHALLENGE.
LEARNING ANALYTICS (LA) CAN BE DATA DRIVEN: THE PROCESS IS ORIENTED ESSENTIALLY BY DATA AND NOT ACCORDING TO A THEORETICAL BACKGROUND. IN THIS CASE, RESULTS CAN BE, SOMETIMES, NOT EXPLOITABLE. THIS IS THE REASON WHY SOME LA PROCESSES ARE THEORY DRIVEN: BASED ON A PRIORI KNOWLEDGE (APK), ON A THEORETICAL BACKGROUND. HERE, WE INVESTIGATE THE RELATIONSHIP BETWEEN APK AND LA. WE PROPOSE A "2-LEVEL FRAMEWORK" THAT CONSIDERS LA AS A LEVEL 2 LEARNING PROCESS AND INCLUDES FIVE COMPONENTS: STAKEHOLDERS, GOALS, DATA, TECHNICAL APPROACHES AND FEEDBACKS. BASED ON THIS FRAMEWORK, A SAMPLE OF LA RELATED WORKS IS ANALYZED TO EXHIBIT HOW SUCH WORKS RELATE LA WITH APK. WE SHOW THAT MOST OF THE TIME THE APK USED FOR LA IS THE LEARNING THEORY SUSTAINING THE STUDENT'S LEARNING. HOWEVER, IT CAN BE OTHERWISE AND, ACCORDING TO THE GOAL OF LA, IT IS SOMETIMES FRUITFUL TO USE ANOTHER THEORY.
EDUCATIONAL PROCESSES TAKE PLACE IN PHYSICAL AND DIGITAL PLACES. TO ANALYSE EDUCATIONAL PROCESSES, LEARNING ANALYTICS (LA) ENABLE DATA COLLECTION FROM THE DIGITAL LEARNING CONTEXT. AT THE SAME TIME, TO GAIN MORE INSIGHTS, THE LA DATA CAN BE COMPLEMENTED WITH THE DATA COMING FROM PHYSICAL SPACES ENABLING MULTIMODAL LEARNING ANALYTICS (MMLA). TO INTERPRET THIS DATA, THEORETICAL GROUNDING OR CONTEXTUAL INFORMATION IS NEEDED. LEARNING DESIGNS (LDS) CAN BE USED FOR CONTEXTUALISATION, HOWEVER, IN AUTHENTIC SCENARIOS THE AVAILABILITY OF MACHINE-READABLE LD IS SCARCE. WE ARGUE THAT CLASSROOM OBSERVATIONS (COS), TRADITIONALLY USED TO UNDERSTAND EDUCATIONAL PROCESSES TAKING PLACE IN PHYSICAL SPACE, CAN PROVIDE THE MISSING CONTEXT AND COMPLEMENT THE DATA FROM THE COLOCATED CLASSROOMS. THIS PAPER REPORTS ON A CO-DESIGN CASE STUDY FROM AN AUTHENTIC SCENARIO THAT USED CO TO MAKE SENSE OF THE DIGITAL TRACES. IN THIS PAPER WE POSIT THAT THE DEVELOPMENT OF MMLA APPROACHES CAN BENEFIT FROM CODESIGN METHODOLOGIES; THROUGH THE INVOLVEMENT OF THE END-USERS (PROJECT MANAGERS) IN THE LOOP, WE ILLUSTRATE HOW THESE DATA SOURCES CAN BE SYSTEMATICALLY INTEGRATED AND ANALYSED TO BETTER UNDERSTAND THE USE OF DIGITAL RESOURCES. RESULTS INDICATE THAT CO CAN DRIVE SENSE-MAKING OF LA DATA WHERE PREDEFINED LD IS NOT AVAILABLE. FURTHERMORE, CO CAN SUPPORT LAYERED CONTEXTUALISATION DEPENDING ON RESEARCH DESIGN, RIGOUR AND SYSTEMATIC DOCUMENTATION/DATA COLLECTION EFFORTS. ALSO, CO-DESIGNING THE MMLA SOLUTION WITH THE END-USERS PROVED TO BE A USEFUL APPROACH.
MANY LEARNING ANALYTICS TECHNIQUES TO CAPTURE THE LEARNER'S REAL-TIME RESPONSES ARE COMPUTATIONALLY INTENSIVE FOR RUNNING ON MOBILE DEVICES. IN THIS PAPER, WE PROPOSE A PERSONALIZED LEARNING PLATFORM NAMED THE PETAL FACILITATED BY AN EFFICIENT LEARNING ANALYTICS APPLICATION TO DETECT LEARNERS' LEVEL OF ATTENTIVENESS AND THE PROXIMITY OF THEIR EYES TO MOBILE DEVICES AND THEN ALERT LEARNERS TO BE MORE ATTENTIVE OR TO KEEP ACCEPTABLE DISTANCE FROM A DEVICE. DATA PRIVACY IS GUARANTEED BY PASSWORD-PROTECTED ACCOUNTS. BEING THE FIRST ATTEMPT, A PROTOTYPE OF OUR PETAL APPLICATION IS CAREFULLY BUILT AND EVALUATED ON ANDROID TABLETS, WITH MANY PROMISING DIRECTIONS FOR FUTURE EXTENSIONS.
ACQUIRING THE TEAMWORK COMPETENCY IS FUNDAMENTAL NOWADAYS, IN ORDER TO GUARANTEE A CORRECT WORKING PERFORMANCE FOR INDIVIDUALS. THIS MEANS THAT A GREAT DEAL OF IMPORTANCE IS BEING GIVEN TO THIS ACTIVITY IN EDUCATIONAL CIRCLES. NEVERTHELESS, EVALUATING THE DEVELOPMENT OF TEAMWORK INDIVIDUALLY IS NOT SIMPLE, GIVEN THAT ON MANY OCCASIONS THERE IS NO OBJECTIVE EVIDENCE TO STUDY. INFORMATION AND COMMUNICATION TECHNOLOGIES APPLIED TO EDUCATIONAL CONTEXTS ENABLE ACCESS TO INFORMATION THAT CAN HELP IN THIS ANALYSIS. HOWEVER, IT IS STILL COMPLEX DUE TO THE LARGE AMOUNT OF INFORMATION THAT NEEDS TO BE CONSIDERED. THIS STUDY PROPOSES INDICATORS BASED ON THE INTERACTION BETWEEN LEARNING AGENTS (STUDENT STUDENT AND ACTIVE PASSIVE). THE EXPLORATION OF THESE INDICATORS CONTRIBUTES TO THE ASSESSMENT OF THE INDIVIDUAL DEVELOPMENT WITHIN THE TEAMWORK CONTEXT. THE ANALYSIS CARRIED OUT IN THIS STUDY DEMONSTRATES THAT THERE IS A DIRECT RELATION BETWEEN THESE INTERACTIONS AND FINAL GRADING CORRESPONDING TO INDIVIDUAL ASSESSMENT OF TEAMWORK ACTIVITIES BY TEACHERS. ADDITIONALLY, A LEARNING ANALYTICS SYSTEM IS INTRODUCED AS SUPPORT FOR THE CHALLENGING TASK THAT TEACHERS FACE IN EVALUATING AND MONITORING INDIVIDUAL PROGRESS WITHIN TEAMWORK. THE INFORMATION PROVIDED BY THE LEARNING ANALYTICS SYSTEM AND TIMELY INFORMATION EXTRACTION ALLOW PREVENTING PROBLEMS, CARRYING OUT CORRECTIVE MEASURES AND MAKING DECISIONS TO IMPROVE THE LEARNING PROCESS OF TEAMWORK. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
IS LEARNING ANALYTICS A MOVEMENT THAT SEEKS TO REBALANCE THE EFFECTS OF HIGHER EDUCATION'S APPARENT BLINDNESS TO PRIVILEGE, ITS UNEQUAL ACCESS REGIMES AND PERSISTENT RETENTION AND ATTAINMENT GAPS THROUGH A MORE SKILFUL AND STRATEGIC USE OF STUDENT DATA? OR IS IT PART OF A LARGER PROJECT TO SURVEIL STUDENTS AND STAFF IN HIGHER EDUCATION, IN PURSUIT OF GREATER EFFICIENCY AND CONTROL? BOTH PERSPECTIVES ARE ALIVE AND WELL IN DEBATES SURROUNDING HIGHER EDUCATION'S CHANGING RELATIONSHIP WITH ITS STUDENTS. THE SYSTEMATIC INSTITUTIONAL USE OF STUDENT-GENERATED DATA KNOWN AS LEARNING ANALYTICS IS RAISING PRACTICAL, METHODOLOGICAL AND ETHICAL QUESTIONS, WHICH ARE YET TO BE ANSWERED. HOWEVER, A PROPOSED FRAMEWORK FOR ASSESSING AND COMPARING THE QUALITY OF LEARNING AND TEACHING IN THE UK IS POISED TO USE SUCH DATA AS ONE OF ITS METRICS. LEARNING ANALYTICS AND ITS RELATIONSHIP TO STUDENT ENGAGEMENT IS EXPLORED THROUGH THE FIRST KNOWN RESEARCH TO UTILISE AN ADAPTATION OF KUH'S NATIONAL SURVEY OF STUDENT ENGAGEMENT WITH PEOPLE STUDYING MASSIVE OPEN ONLINE COURSES. CONTRASTING PERSPECTIVES ARE OFFERED BY SIEMEN'S THEORY OF CONNECTIVIST LEARNING AND FOUCAULT'S NOTION OF THE PANOPTICON. IF THE POTENTIAL OF ANALYTICS IS TO BE REALISED IN TERMS OF MEANINGFUL QUALITY IMPROVEMENT, QUESTIONS REMAIN CONCERNING ETHICS, TRUST, ITS ROLE IN ENGAGEMENT IN LEARNING, AND THE WAYS IN WHICH POLICY MIGHT EFFECTIVELY SAFEGUARD THE LONGER-TERM INDIVIDUAL AND COLLECTIVE INTERESTS OF STUDENTS.
IN THIS PAPER WE WILL DESCRIBE THE STEPS NEEDED FOR THE IMPLEMENTATION OF LEARNING ANALYTICS TOOLS IN A MASSIVE OPEN ONLINE COURSE (MOOC) BASED ON LEARNING MANAGEMENT SYSTEM MOODLE AND INTENDED FOR LANGUAGE LEARNING. MOOC IN QUESTION IS INTENDED FOR LEARNING OF THE CROATIAN LANGUAGE AT THE COMMON EUROPEAN FRAMEWORK OF REFERENCE FOR LANGUAGES LEVEL A1, AND IT WAS STARTED IN 2017 IN COOPERATION OF THE FACULTY OF HUMANITIES AND SOCIAL SCIENCES UNIVERSITY OF ZAGREB AND THE CENTRAL STATE OFFICE FOR CROATS ABROAD. IT HAS OVER 4400 PARTICIPANTS (NOVEMBER 2019) AND THE INTERFACE IS IN ENGLISH AND SPANISH LANGUAGE. SINCE LEARNING ANALYTICS TOOLS HAVE BEEN INTRODUCED TO MOODLE, THAT SET OF TOOLS WILL BE USED FOR THE ANALYSIS. DATA COLLECTED BY THE LEARNING MANAGEMENT SYSTEM WILL BE COMBINED WITH THE INFORMATION GATHERED THROUGH THE MATOMO (EX. PIWIK) WEB ANALYTICS PLATFORM. WE WILL ALSO PRESENT THE INITIAL RESULTS, AS WELL AS PREDICTIVE MODELS THAT WERE TESTED (BOTH PRE-MADE AND CUSTOM). IMPLEMENTATION WILL INCLUDE STAKEHOLDERS FROM BOTH INSTITUTIONS: MANAGEMENT, COURSE TEACHERS AND MENTORS, DIGITAL CONTENT CREATORS AND INSTRUCTIONAL DESIGNERS, AND IT SUPPORT PERSONNEL. THE USE OF LEARNING ANALYTICS TOOLS IS INTENDED FOR THE EVALUATION, ANALYSIS AND PREDICTION OF THE PARTICIPANT DROP-OUT, AS WELL FUTURE REDESIGN OF THE CURRENT MOOC STRUCTURE / DELIVERY.
GAMIFICATION IS ONE OF THE MOST USED TECHNIQUES TO IMPROVE ACTIVE PARTICIPATION AND ENGAGEMENT IN DIFFERENT KINDS OF CONTEXTS. THE USE OF GAME TECHNIQUES IS EFFECTIVE IN PUSHING SUBJECTS TO BE INVOLVED IN AN ACTIVITY. SINCE THE EARLY CHILDHOOD, INDEED, THE PROMISES OF REWARDS ARE USEFUL TO AFFECT SPECIFIC BEHAVIORS. ON THE OTHER HANDS, THE LEARNING ANALYTICS HAVE BEEN LARGELY IMPLEMENTED IN EDUCATION IN ORDER TO IMPROVE THE ASSESSMENT AND THE SELF-ASSESSMENT OF STUDENTS, ABOVE ALL IN E-LEARNING SETTINGS. THE RESEARCH PRESENTED IN THIS WORK AIMS AT COMBINING GAMIFICATION TECHNIQUES AND LEARNING ANALYTICS TO IMPROVE THE ENGAGEMENT IN UNIVERSITY COURSES. THE PAPER DESCRIBES A MODEL OF GAMIFICATION AND A LEARNING DASHBOARD DEFINED BASED ON DATA IN MOODLE E-LEARNING PLATFORM. A PILOT TEST OF AN APP ANDROID IN WHICH BOTH THE SOLUTIONS HAVE BEEN IMPLEMENTED POINTED OUT PROMISING RESULTS.
CLOUD COMPUTING INSTRUCTION REQUIRES HANDS-ON EXPERIENCE WITH A MYRIAD OF DISTRIBUTED COMPUTING SERVICES FROM A PUBLIC CLOUD PROVIDER. TRACKING THE PROGRESS OF THE STUDENTS, ESPECIALLY FOR ONLINE COURSES, REQUIRES ONE TO AUTOMATICALLY GATHER EVIDENCE AND PRODUCE LEARNING ANALYTICS IN ORDER TO FURTHER DETERMINE THE BEHAVIOR AND PERFORMANCE OF STUDENTS. WITH THIS AIM, THIS PAPER DESCRIBES THE EXPERIENCE FROM AN ONLINE COURSE IN CLOUD COMPUTING WITH AMAZON WEB SERVICES ON THE CREATION OF AN OPEN-SOURCE DATA PROCESSING TOOL TO SYSTEMATICALLY OBTAIN LEARNING ANALYTICS RELATED TO THE HANDS-ON ACTIVITIES CARRIED OUT THROUGHOUT THE COURSE. THESE DATA, COMBINED WITH THE DATA OBTAINED FROM THE LEARNING MANAGEMENT SYSTEM, HAVE ALLOWED THE BETTER CHARACTERIZATION OF THE BEHAVIOR OF STUDENTS IN THE COURSE. INSIGHTS FROM A POPULATION OF MORE THAN 420 ONLINE STUDENTS THROUGH THREE ACADEMIC YEARS HAVE BEEN ASSESSED, THE DATASET HAS BEEN RELEASED FOR INCREASED REPRODUCIBILITY. THE RESULTS CORROBORATE THAT COURSE LENGTH HAS AN IMPACT ON ONLINE STUDENTS DROPOUT. IN ADDITION, A GENDER ANALYSIS POINTED OUT THAT THERE ARE NO STATISTICALLY SIGNIFICANT DIFFERENCES IN THE FINAL MARKS BETWEEN GENDERS, BUT WOMEN SHOW AN INCREASED DEGREE OF COMMITMENT WITH THE ACTIVITIES PLANNED IN THE COURSE.
ALTHOUGH IT IS FREQUENTLY CLAIMED THAT LEARNING ANALYTICS CAN IMPROVE SELF-EVALUATION AND SELF-REGULATED LEARNING BY STUDENTS, MOST LEARNING ANALYTICS TOOLS APPEAR TO HAVE BEEN DEVELOPED AS A RESPONSE TO EXISTING DATA RATHER THAN WITH A CLEAR PEDAGOGICAL MODEL. AS A RESULT THERE IS LITTLE EVIDENCE OF IMPACT ON LEARNING. EVEN FEWER LEARNING ANALYTICS TOOLS SEEM TO BE INFORMED BY AN UNDERSTANDING OF THE SOCIAL CONTEXT AND SOCIAL PRACTICES WITHIN WHICH THEY WOULD BE USED. AS A RESULT, THERE IS VERY LITTLE EVIDENCE THAT LEARNING ANALYTICS TOOLS ARE ACTUALLY IMPACTING ON PRACTICE. THIS PAPER DRAWS ON RESEARCH IN SELF-REGULATED LEARNING AND IN THE SOCIAL PRACTICES OF LEARNING AND ASSESSMENT TO CLARIFY A SERIES OF DESIGN ISSUES WHICH SHOULD BE CONSIDERED BY THOSE SEEKING TO DEVELOP LEARNING ANALYTICS TOOLS WHICH ARE INTENDED TO IMPROVE STUDENT SELF-EVALUATION AND SELF-REGULATION. IT PRESENTS A CASE STUDY OF HOW THESE DESIGN ISSUES INFLUENCED THE DEVELOPMENT OF A PARTICULAR TOOL: THE LEARNING COMPANION.
THE CURRENT PAPER DEBATES UPON THE IMPORTANCE OF LEARNING ANALYTICS, BY UNDERLINING ITS PLACE IN THE CURRENT RESEARCH TRENDS AND ITS INFLUENCE IN RESHAPING EDUCATION WITH THE AID OF CUTTING-EDGE TECHNOLOGIES. DEFINITIONS, EXAMPLES OF LEARNING ANALYTICS TOOLS, AS WELL AS THE CHALLENGES OF IMPLEMENTING THEM ARE REVISED. A SPECIAL ATTENTION IS AWARDED TO LEARNING ANALYTICS PERFORMED IN SOCIAL LEARNING ENVIRONMENTS, DUE TO THEIR POPULARITY AND WELL ESTABLISHED VALUE. IN ORDER TO INVESTIGATE HOW LEARNING ANALYTICS IS PERCEIVED IN THE DANUBE REGION COUNTRIES, A SURVEY WAS PERFORMED AND REVEALED ITS ROLE FOR THE SUCCESS OF A SOCIAL LEARNING PLATFORM WITHIN THIS PART OF EUROPE: ADVANCED LEARNING ANALYTICS WAS ACKNOWLEDGED TO BE AMONG THE MANDATORY FEATURES. THE MAIN RESEARCH INSTRUMENTS WERE TWO ONLINE QUESTIONNAIRES, WHICH WERE REFINED USING LITERATURE MINING AND STRUCTURED INTERVIEWS. THE QUESTIONNAIRES WERE FILLED BY 59 IT PROFESSORS AND MORE THAN 390 IT STUDENTS, FROM VARIOUS COUNTRIES, FACT THAT DEMONSTRATES THE REGIONAL SIGNIFICANCE OF THE STUDY.
TECHNOLOGY SUPPORTED LEARNING IS NOWADAYS OFTEN BASED ON HETEROGENEOUS ENVIRONMENTS THAT ENCOMPASS NOT ONLY ONE APPLICATION AND ALSO POSSIBLY INVOLVE DIFFERENT DEVICES. THIS CREATES SPECIFIC CHALLENGES FOR DATA ANALYSIS AND THUS FOR LEARNING ANALYTICS. IN THIS PAPER, WE PROPOSE A FRAMEWORK TO CREATE REUSABLE LEARNING ANALYTICS COMPONENTS THAT ARE PORTABLE TO DIFFERENT TARGET PLATFORMS. IN THIS APPROACH, THE LOGIC OF EACH ANALYSIS COMPONENT IS SPECIFIED IN A SEPARATE WEB-BASED VISUAL ENVIRONMENT (OR "WORKBENCH") FROM WHERE IT IS LATER EXPORTED TO THE TARGET ENVIRONMENTS FORM OF A GADGET-BASED DASHBOARD. WE DEMONSTRATE THIS MECHANISM IN THE CONTEXT OF THE GO-LAB PORTAL FOR ACCESSING REMOTE LABORATORIES IN STEM LEARNING SCENARIOS. AN EXAMPLE SHOWS HOW SUCH ANALYTICS GADGETS CAN BE USED TO SUPPORT COLLABORATION INSIDE THE CLASSROOM.
THIS ARTICLE PRESENTS THE CASE OF THE LEARNING ANALYTICS ARCHITECTURE (LARC) DATASET, A COLLABORATIVE EFFORT AT THE UNIVERSITY OF MICHIGAN TO DEVELOP A COMMON AND EXTENSIBLE TOOL USING ADMINISTRATIVE DATA AND DESIGNED PRIMARILY FOR LEARNING ANALYTICS RESEARCHERS TO INVESTIGATE ENROLLED STUDENTS' ACADEMIC CAREERS, DEMOGRAPHICS, AND RELATED TEACHING AND LEARNING OUTCOMES. THE INSTITUTIONAL CONTEXT PRIOR TO THE CREATION OF THE DATASET AND THE RATIONALE, DESIGN, DEVELOPMENT, AND MAINTENANCE INVOLVED IN CREATING LARC ARE ALL DETAILED. ALSO DISCUSSED ARE THE PROCEDURES FOR ACCESS, DOCUMENTATION, AND ENSURING THE CONTINUED USABILITY AND RELEVANCE OF THE DATASET FOR A GROWING LEARNING ANALYTICS AND DATA SCIENCE RESEARCH COMMUNITY. THE AUTHORS CONCLUDE THE CASE DESCRIPTION WITH RECOMMENDATIONS FOR INSTITUTIONS SEEKING TO REPLICATE THIS EFFORT.
THE GOAL OF OUR RESEARCH WAS SET ON HOW TO USE LEARNING ANALYTICS FOR POSSIBLE IMPROVEMENT OF UNIVERSITY LEARNING OUTCOMES OF MATHEMATICS. IN THE DESCRIBED RESEARCH, WE USE A COMBINATION OF ACTION RESEARCH APPROACHES AND LEARNING ANALYTICS. WE HAVE SOLVED PROBLEMS THAT ARISE FROM OUR PEDAGOGICAL PRACTICE AS IS USUAL IN ACTION RESEARCH BUT USING QUANTITATIVE METHODS THAT ARE USED IN LEARNING ANALYTICS. THE RESEARCH HAS HAD TWO MAIN OBJECTIVES. THE FIRST WAS TO FIND OUT WHETHER THE ENTRY TEST COULD BE USED AS AN INDICATOR OF SUCCESS IN THE MATHEMATICS I COURSE. THE SECOND PRIMARY OBJECTIVE WAS TO FIND OUT WHETHER IT IS POSSIBLE TO INFLUENCE THE PREDICTED STUDENT RESULTS BY APPROPRIATE A DURING-TERM INTERVENTION. TO VERIFY THE STATED HYPOTHESES, WE USED A STATISTICAL APPROACH AND METHODS USING THE SPSS SOFTWARE. THE AIS ACADEMIC INFORMATION SYSTEM WAS USED AS A DATA SOURCE FOR RESEARCH. THE RESULTS OF THE RESEARCH SHOWED THAT OUR ENTRY TEST COULD BE USED AS AN INDICATOR OF THE STUDENT'S FUTURE RESULT IN MATHEMATICS I COURSE (COURSE SUCCESS INDICATOR WAS STATISTICALLY SIGNIFICANT AT THE SIGNIFICANT LEVEL 0.1 (P=0.066)). ON THE OTHER HAND, THE RESULTS OF THE RESEARCH DID NOT CONFIRM THE SIGNIFICANT IMPACT OF THE INTERVENTION IN THE FORM OF ADDITIONAL SEMINARS (P=0.800) ON THE SUCCESS OF THE STUDENTS IN THE OVERALL ASSESSMENT OF THE COURSE. ALTHOUGH WE HAD EXPECTED THAT THE FINAL COURSE GRADE IN COMPARISON TO OUTCOME OF THE PREDICTION COULD BE AFFECTED BY APPROPRIATE INTERVENTION, GAINED RESULT IS VALUABLE TO US AS WELL. WE HAVE RECEIVED INFORMATION ON THE NEED TO CHANGE THE INTERVENTION SO THAT WE CAN INCREASE THE SUCCESS OF STUDENTS IN THE MATHEMATICS COURSES AND CONSEQUENTLY ALSO INCREASE THE SUCCESS OF STUDY PROGRAMS AT THE TECHNICAL UNIVERSITY.
CURRENT TRENDS IN HIGHER EDUCATION INVOLVE THE INTEGRATION OF FACE-TO-FACE LEARNING, ONLINE LEARNING AND THE USE OF INFORMATION TECHNOLOGIES THAT SUPPORT DIVERSE ASPECTS OF THE LEARNING-TEACHING PROCESS. ONE OF THE MAIN ASPECTS CONSIDERED IN ALL THE EDUCATIONAL TENDENCIES IS THE USE OF EFFECTIVE ASSESSMENT PROCESSES THAT ENTAIL A CONTINUOUS ANALYSIS OF STUDENTS' RESULTS IN ORDER TO DETECT ANOMALIES AND PROVIDE ADEQUATE FEEDBACK SO AS TO SOLVE THEM. HOWEVER, IN LEARNING-TEACHING ENVIRONMENTS WHERE THERE IS A GREAT AMOUNT OF HETEROGENEOUS INFORMATION, THIS MUST BE INTEGRATED IN ORDER TO BE ADEQUATELY ANALYZED. THIS PAPER PRESENTS COBLE, A TOOL THAT MAKES POSSIBLE THE INTEGRATION OF INFORMATION FROM DIFFERENT SOURCES TO ANALYZE IT USING VISUAL LEARNING ANALYTICS TECHNIQUES. COBLE INCORPORATES A FEEDBACK MODULE THAT PROVIDES BOTH TEACHERS AND STUDENTS WITH VISUAL INFORMATION RELATED TO THE ASSESSMENT PROCESS IN ORDER TO FACILITATE THEIR DECISION-MAKING PROCESSES. THE PROVIDED VISUALIZATIONS CAN BE ADAPTED TO THE REQUIREMENTS OF EACH COURSE OR USER. THE EVALUATION OF COBLE HAS BEEN CARRIED OUT USING IT IN A REAL BLENDED LEARNING ENVIRONMENT WHERE DIFFERENT TEACHERS HAVE INTEGRATED INFORMATION FROM DIFFERENT SOURCES (MOODLE, PERSONAL SPREADSHEETS, ETC.), RELATED TO THE PERFORMANCE OF STUDENTS. NEXT, BOTH TEACHERS AND STUDENTS USED COBLE AND ITS VISUALIZATIONS TO EXTRACT INFORMATION REGARDING THE ASSESSMENT PROCESS. THE RESULT OF THE SYSTEM'S EVALUATION HAS BEEN VERY SATISFACTORY, OBTAINING A GOOD ACCEPTANCE OF THE DIVERSE USERS INVOLVED.
A UNIVERSITY CAMPUS IS COMPRISED OF SCHOOLS AND FACULTIES ATTENDED BY STUDENTS WHOSE PRIMARY INTENTION IS TO LEARN AND ULTIMATELY GRADUATE WITH THEIR DESIRED QUALIFICATION. FROM THE MOMENT STUDENTS APPLY TO A UNIVERSITY AND THEREAFTER GAIN ACCEPTANCE AND ATTEND THE CAMPUS THEY CREATE A UNIQUE DIGITAL FOOTPRINT OF THEMSELVES WITHIN THE UNIVERSITY IT SYSTEMS. STUDENTS' DIGITAL FOOTPRINTS ARE A SOURCE OF DATA THAT IS OF INTEREST TO GROUPS INCLUDING TEACHERS, ANALYSTS, ADMINISTRATORS AND POLICY MAKERS IN THE EDUCATION, SOCIOLOGY, AND PEDAGOGY DOMAINS. LEARNING ANALYTICS CAN OFFER TOOLS TO MINE SUCH DATA PRODUCING ACTIONABLE KNOWLEDGE FOR PURPOSES OF IMPROVING STUDENT RETENTION, CURRICULUM ENHANCEMENT, STUDENT PROGRESS AND FEEDBACK, AND ADMINISTRATIVE EVOLUTION. IN THIS PAPER, WE SUMMARISE THREE ONGOING LEARNING ANALYTICS PROJECTS FROM AN IRISH UNIVERSITY, DEMONSTRATING THE POTENTIAL THAT EXISTS TO ENHANCE HIGHER EDUCATION PEDAGOGICAL APPROACHES. FIRST YEAR STUDENTS OFTEN STRUGGLE WITH MAKING THE TRANSITION INTO UNIVERSITY AS THEY ADAPT TO LIFE AND STUDY AT A HIGHER EDUCATION INSTITUTION. THE RESEARCH PROJECTS IN THE AREA OF LEARNING ANALYTICS AT OUR INSTITUTION FOCUS ON: IMPROVING TEST PERFORMANCE USING ANALYTICS FROM A GENERAL-PURPOSE VLE LIKE MOODLE, IDENTIFYING STUDYING GROUPS AND THE PERFORMANCE PEER EFFECT USING ON-CAMPUS GEOLOCATION DATA, AND DETECTING LOWER-PERFORMING OR AT-RISK STUDENTS ON PROGRAMMING MODULES.
IDENTIFYING NON-THRIVING STUDENTS AND INTERVENING TO BOOST THEM ARE TWO PROCESSES THAT RECENT LITERATURE SUGGESTS SHOULD BE MORE TIGHTLY INTEGRATED. WE PERFORM THIS INTEGRATION OVER SIX SEMESTERS IN A FIRST YEAR EXPERIENCE (FYE) COURSE WITH THE AIM OF BOOSTING STUDENT SUCCESS, BY USING AN INTEGRATED CLOSED-LOOP LEARNING ANALYTICS SCHEME THAT CONSISTS OF MULTIPLE STEPS BROKEN INTO THREE MAIN PHASES, AS FOLLOWS: ARCHITECTING FOR COLLECTION (STEPS: DESIGN, BUILD, CAPTURE), ANALYZING FOR ACTION (STEPS: IDENTIFY, NOTIFY, BOOST), AND ASSESSING FOR IMPROVEMENT (STEPS: EVALUATE, REPORT). WE CLOSE THE LOOP BY ALLOWING LATER STEPS TO INFORM EARLIER ONES IN REAL-TIME DURING A SEMESTER AND ITERATIVELY YEAR TO YEAR, THEREBY IMPROVING THE COURSE FROM DATA-DRIVEN INSIGHTS. THIS PROCESS DEPENDS ON THE PURPOSEFUL DESIGN OF AN INTEGRATED LEARNING ENVIRONMENT THAT FACILITATES DATA COLLECTION, STORAGE, AND ANALYSIS. METHODS FOR EVALUATING THE EFFECTIVENESS OF OUR ANALYTICS-BASED STUDENT INTERVENTIONS SHOW THAT OUR CRITERION FOR IDENTIFYING NON-THRIVING STUDENTS WAS SATISFACTORY AND THAT NON-THRIVING STUDENTS DEMONSTRATED MORE SUBSTANTIAL CHANGES FROM MID-TERM TO FINAL COURSE GRADES THAN ALREADY-THRIVING STUDENTS. LASTLY, WE MAKE A CASE FOR USING EARLY PERFORMANCE IN THE FYE AS AN INDICATOR OF OVERALL PERFORMANCE AND RETENTION OF FIRST-YEAR STUDENTS.
RECENTLY, THE POSSIBILITIES FOR LEVERAGING "BIG DATA" IN RESEARCH AND PEDAGOGY HAVE GIVEN RISE TO THE GROWING FIELD OF "LEARNING ANALYTICS" IN ONLINE EDUCATION. WHILE MUCH OF THIS WORK HAS FOCUSED ON QUANTITATIVE METRICS, SOME HAVE CALLED FOR CRITICAL PERSPECTIVES THAT INTERROGATE SUCH DATA AS AN INTERPLAY BETWEEN TECHNICAL INFRASTRUCTURES AND CONTINGENT SOCIAL PRACTICES. FOLLOWING SUCH CALLS, THIS ARTICLE CONCEPTUALIZES "LEARNING ANALYTICS" AS AN ASSEMBLAGE OF TECHNICAL, DESIGNED, AND SOCIOCOGNITIVE DIMENSIONS. DRAWING ON DELANDA'S ARTICULATION OF ASSEMBLAGE THEORY, WE EXAMINE THE WAYS ONLINE LEARNING UNFOLDS WITHIN AND ACROSS THESE SCALES BY USING ILLUSTRATIVE QUANTITATIVE AND QUALITATIVE DATA-CLICK-DATA, USER-GENERATED CONTENT, AND STUDENT INTERVIEWS-FROM THREE ONLINE HIGHER EDUCATION COURSES. WE CONSIDER HOW INSIGHTS GENERATED FROM SUCH A STANCE MIGHT CONTRIBUTE TO CRITICAL PERSPECTIVES ON HOW POWER CIRCULATES IN ONLINE LEARNING ENVIRONMENTS-A FRAMEWORK WE CALL "CRITICAL LEARNING ANALYTICS." WE CONCLUDE BY OFFERING SOME POSSIBILITIES FOR WHICH SUCH A FRAMEWORK MIGHT BE PUT TO USE-NOT ONLY TO MAP LEARNING ANALYTICS AS ASSEMBLAGE, BUT ALSO TO IMAGINE HOW THEY MIGHT BE ASSEMBLED OTHERWISE TO PROMOTE MORE ETHICAL INSTRUCTION AND MORE EQUITABLE STUDENT FLOURISHING.
FOR SERVICE IMPLEMENTATIONS TO BE WIDELY ADOPTED, IT IS NECESSARY FOR THE EXPECTATIONS OF THE KEY STAKEHOLDERS TO BE CONSIDERED. FAILURE TO DO SO MAY LEAD TO SERVICES REFLECTING IDEOLOGICAL GAPS, WHICH WILL INADVERTENTLY CREATE DISSATISFACTION AMONG ITS USERS. LEARNING ANALYTICS RESEARCH HAS BEGUN TO RECOGNISE THE IMPORTANCE OF UNDERSTANDING THE STUDENT PERSPECTIVE TOWARDS THE SERVICES THAT COULD BE POTENTIALLY OFFERED; HOWEVER, STUDENT ENGAGEMENT REMAINS LOW. FURTHERMORE, THERE HAS BEEN NO ATTEMPT TO EXPLORE WHETHER STUDENTS CAN BE SEGMENTED INTO DIFFERENT GROUPS BASED ON THEIR EXPECTATIONS TOWARDS LEARNING ANALYTICS SERVICES. IN DOING SO, IT ALLOWS FOR A GREATER UNDERSTANDING OF WHAT IS AND IS NOT EXPECTED FROM LEARNING ANALYTICS SERVICES WITHIN A SAMPLE OF STUDENTS. THE CURRENT EXPLORATORY WORK ADDRESSES THIS LIMITATION BY USING THE THREE-STEP APPROACH TO LATENT CLASS ANALYSIS TO UNDERSTAND WHETHER STUDENT EXPECTATIONS OF LEARNING ANALYTICS SERVICES CAN CLEARLY BE SEGMENTED, USING SELFREPORT DATA OBTAINED FROM A SAMPLE OF STUDENTS AT AN OPEN UNIVERSITY IN THE NETHERLANDS. THE FINDINGS SHOW THAT STUDENT EXPECTATIONS REGARDING ETHICAL AND PRIVACY ELEMENTS OF A LEARNING ANALYTICS SERVICE ARE CONSISTENT ACROSS ALL GROUPS; HOWEVER, THOSE EXPECTATIONS OF SERVICE FEATURES ARE QUITE VARIABLE. THESE RESULTS ARE DISCUSSED IN RELATION TO PREVIOUS WORK ON STUDENT STAKEHOLDER PERSPECTIVES, POLICY DEVELOPMENT, AND THE EUROPEAN GENERAL DATA PROTECTION REGULATION (GDPR).
THE EMERGENCE OF THE LEARNING ANALYTICS (LA) FIELD CONTEXTUALISED THE CONNECTIONS IN VARIOUS DISCIPLINES AND THE EDUCATIONAL SECTOR, ACTED AS A STEPPINGSTONE TOWARD THE REFORMATION OF THE EDUCATIONAL SCENERY, THUS PROMOTING THE IMPORTANCE OF PROVIDING USERS WITH ADAPTIVE AND PERSONALISED LEARNING EXPERIENCES. AT THE SAME TIME, THE USE OF AUGMENTED REALITY (AR) APPLICATIONS IN EDUCATION HAVE BEEN GAINING A GROWING INTEREST ACROSS ALL THE EDUCATIONAL LEVELS AND CONTEXTS. HOWEVER, THE EFFORTS TO INTEGRATE LA TECHNIQUES IN IMMERSIVE TECHNOLOGIES, SUCH AS AR, ARE LIMITED AND SCARCE. THIS INADEQUACY IS MAINLY ATTRIBUTED TO THE DIFFICULTIES THAT GOVERN THE COLLECTION AND INTERPRETATION OF THE PRIMARY DATA. TO DEAL WITH THIS SHORTCOMING, WE PRESENT THE "AUGMENTED REALITY LEARNING ANALYTICS" (ARLEAN) ETHICAL FRAMEWORK, TAILORED TO THE SPECIFIC CHARACTERISTICS THAT AR APPLICATIONS HAVE, AND FOCUSED ON VARIOUS LEARNING SUBJECTS. THE CORE OF THIS FRAMEWORK BLENDS THE TECHNOLOGICAL, PEDAGOGICAL, AND PSYCHOLOGICAL ELEMENTS THAT INFLUENCE THE OUTCOME OF EDUCATIONAL INTERVENTIONS, WITH THE MOST WIDELY ADOPTED LA TECHNIQUES. IT PROVIDES CONCRETE GUIDELINES TO EDUCATIONAL TECHNOLOGISTS AND INSTRUCTIONAL DESIGNERS ON HOW TO INTEGRATE LA INTO THEIR PRACTICES TO INFORM THEIR FUTURE DECISIONS AND THUS, SUPPORT THEIR LEARNERS TO ACHIEVE BETTER RESULTS.
THIS PAPER COMPARES APPROACHES TO VISUALISING DATA FOR USERS IN EDUCATIONAL SETTINGS, CONTRASTING VISUAL LEARNING ANALYTICS AND OPEN LEARNER MODELS. WE CONSIDER THE ROOTS OF EACH, AND IDENTIFY HOW EACH FIELD CAN LEARN FROM EXPERIENCES AND APPROACHES OF THE OTHER, THEREBY BENEFITING BOTH.
THIS PAPER AT HAND DESCRIBES THE DESIGN AND IMPLEMENTATION OF AN ANALYTICS SERVICE TO RETRIEVE LIVE USAGE DATA FROM STUDENTS ENROLLED IN A SERVICE-ORIENTED MOOC PLATFORM FOR THE PURPOSE OF LEARNING ANALYTICS (LA) RESEARCH. A REAL-TIME AND EXTENSIBLE ARCHITECTURE FOR CONSOLIDATING AND PROCESSING DATA IN VERSATILE ANALYTICS STORES IS INTRODUCED.
NOWADAYS, THE DIGITAL LEARNING ENVIRONMENT HAS REVOLUTIONIZED THE VISION OF DISTANCE LEARNING COURSE DELIVERY AND DRASTICALLY TRANSFORMED THE ONLINE EDUCATIONAL SYSTEM. THE EMERGENCE OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAS EXPOSED WEB TECHNOLOGY USED IN EDUCATION IN A MORE ADVANCED REVOLUTION USHERING A NEW GENERATION OF LEARNING ENVIRONMENTS. THE DIGITAL LEARNING ENVIRONMENT IS EXPECTED TO AUGMENT THE REAL-WORLD CONVENTIONAL EDUCATION SETTING. THE EDUCATIONAL PEDAGOGY IS TAILORED WITH THE STANDARD PRACTICE WHICH HAS BEEN NOTICED TO INCREASE STUDENT SUCCESS IN MOOCS AND PROVIDE A REVOLUTIONARY WAY OF SELF-REGULATED LEARNING. HOWEVER, THERE ARE STILL UNRESOLVED QUESTIONS RELATING TO THE UNDERSTANDING OF LEARNING ANALYTICS DATA AND HOW THIS COULD BE IMPLEMENTED IN EDUCATIONAL CONTEXTS TO SUPPORT INDIVIDUAL LEARNING. ONE OF THE MAJOR ISSUES IN MOOCS IS THE CONSISTENT HIGH DROPOUT RATE WHICH OVER TIME HAS SEEN COURSES RECORDED LESS THAN 20% COMPLETION RATE. THIS PAPER EXPLORES LEARNING ANALYTICS FROM DIFFERENT PERSPECTIVES IN A MOOC CONTEXT. FIRST, WE REVIEW EXISTING LITERATURE RELATING TO LEARNING ANALYTICS IN MOOCS, BRINGING TOGETHER FINDINGS AND ANALYSES FROM SEVERAL COURSES. WE EXPLORE META-ANALYSIS OF THE BASIC FACTORS THAT CORRELATE TO LEARNING ANALYTICS AND THE SIGNIFICANT IN IMPROVING EDUCATION. SECOND, USING THEMES EMERGING FROM THE PREVIOUS STUDY, WE PROPOSE A PRELIMINARY MODEL CONSISTING OF FOUR FACTORS OF LEARNING ANALYTICS. FINALLY, WE PROVIDE A FRAMEWORK OF LEARNING ANALYTICS BASED ON THE FOLLOWING DIMENSIONS: DESCRIPTIVE, DIAGNOSTIC, PREDICTIVE AND PRESCRIPTIVE, SUGGESTING HOW THE FACTORS COULD BE APPLIED IN A MOOC CONTEXT. OUR EXPLORATORY FRAMEWORK INDICATES THE NEED FOR ENGAGING LEARNERS AND PROVIDING THE UNDERSTANDING OF HOW TO SUPPORT AND HELP PARTICIPANTS AT RISK OF DROPPING OUT OF THE COURSE.
THIS ARTICLE CONSIDERS THE DEVELOPING FIELD OF LEARNING ANALYTICS AND ARGUES THAT TO MOVE FROM SMALL-SCALE PRACTICE TO BROAD SCALE APPLICABILITY, THERE IS A NEED TO ESTABLISH A CONTEXTUAL FRAMEWORK THAT HELPS TEACHERS INTERPRET THE INFORMATION THAT ANALYTICS PROVIDES. THE ARTICLE PRESENTS LEARNING DESIGN AS A FORM OF DOCUMENTATION OF PEDAGOGICAL INTENT THAT CAN PROVIDE THE CONTEXT FOR MAKING SENSE OF DIVERSE SETS OF ANALYTIC DATA. WE INVESTIGATE ONE EXAMPLE OF LEARNING DESIGN TO EXPLORE HOW BROAD CATEGORIES OF ANALYTICSWHICH WE CALL CHECKPOINT AND PROCESS ANALYTICSCAN INFORM THE INTERPRETATION OF OUTCOMES FROM A LEARNING DESIGN AND FACILITATE PEDAGOGICAL ACTION.
LEARNING ANALYTICS CONSISTS OF GATHERING AND ANALYZING DATA FROM STUDENTS IN ORDER TO UNDERSTAND COMPLEX ASPECTS OF THE LEARNING PROCESS AND PROMOTE ITS IMPROVEMENT. CURRENTLY, TO THE BEST OF OUR KNOWLEDGE, THERE IS A LACK OF TOOLS AIMED AT DISPLAYING MULTIMODAL DATA IN AN INTEGRATED WAY FOR GENERAL PURPOSE ANALYSIS. IN THIS PAPER, WE PRESENT A FREE SOFTWARE TOOL BASED ON THE MICROSOFT KINECT SENSOR FOR AUTOMATIC CAPTURE, IDENTIFICATION, AND VISUALIZATION OF TEN BODY POSTURES FOR POSTERIOR ANALYSIS. IT IS ALSO POSSIBLE TO INCORPORATE THE IDENTIFICATION OF NEW POSTURES IF NECESSARY. LEARNING AND RECOGNITION IS BASED ON THE ADABOOST ALGORITHM. POSTURE RECOGNITION REACHED ACCURACY RATES AS HIGH AS 80% FOR 8 OF THE 10 IDENTIFIED POSTURES. CONCERNING THE SOFTWARE USABILITY, A HEURISTIC EVALUATION WITH THREE SPECIALISTS WAS PERFORMED, AS WELL AS A USABILITY TEST WITH FIVE VOLUNTEER STUDENTS. RESULTS INDICATED THAT THE SOFTWARE INTERFACE, BASED ON THE METAPHOR OF A VIDEO EDITOR, MAY ALLOW ITS EFFECTIVE USE BY END USERS, THOUGH SOME ADJUSTMENTS ARE STILL NECESSARY, SUCH AS THE TERMINOLOGY USED IN SOME COMMANDS AND THE HELP SYSTEM.
THIS PAPER ADDRESSES THE IMPLICATIONS OF COMBINING LEARNING ANALYTICS AND SERIOUS GAMES FOR IMPROVING GAME QUALITY, MONITORING AND ASSESSMENT OF PLAYER BEHAVIOR, GAMING PERFORMANCE, GAME PROGRESSION, LEARNING GOALS ACHIEVEMENT, AND USER'S APPRECIATION. WE INTRODUCE TWO MODES OF SERIOUS GAMES ANALYTICS: IN-GAME (REAL TIME) ANALYTICS, AND POST-GAME (OFF-LINE) ANALYTICS. WE ALSO EXPLAIN THE GLEANER FRAMEWORK FOR IN-GAME ANALYTICS AND DESCRIBE A PRACTICAL EXAMPLE FOR OFF-LINE ANALYTICS. WE CONCLUDE WITH A BRIEF OUTLOOK ON FUTURE WORK, HIGHLIGHTING OPPORTUNITIES AND CHALLENGES TOWARDS A SOLID UPTAKE OF SGS IN AUTHENTIC EDUCATIONAL AND TRAINING SETTINGS.
LEARNING ANALYTICS IS A GROWING TREND IN ALL EDUCATION. THE DIGITAL FOOTPRINT STUDENTS LEAVE ON LEARNING MANAGEMENTS SYSTEM AND OTHER DIGITAL PLATFORMS TELL ABOUT STUDENTS' STUDYING HABITS, MATERIAL USAGE AND TIME CONSUMPTION IN STUDIES. THE NUMBER OF LOG EVENTS IN MOODLE LOG WAS FOUND TO CORRESPOND TO THE STUDYING EFFORT AND CORRELATE NICELY WITH THE FINAL GRADE. LEARNING ANALYTICS OFFER A VIEW TO STUDYING AND LEARNING ACTIVITIES, BUT IT IS NOT THE WHOLE TRUTH. LEARNING MANAGEMENT SYSTEM CANNOT RECORD SUCH ACTIVITIES AS READING THE COURSE BOOK OR CARRYING OUT CALCULATIONS ON PAPER. THEREFORE, IN THIS STUDY LEARNING ANALYTICS DATA IS SUPPLEMENTED WITH DATA OF WHATSAPP MESSAGING, LEARNING OUTCOMES AND SELF-EVALUATION. ACCORDING TO THE WHATSAPP DATA, THE MESSAGING TOOK PLACE MOSTLY OUTSIDE NORMAL WORK HOURS, BETWEEN 9 PM AND 10 PM. THE STUDENTS PRESENTED THEIR QUESTIONS PRIVATELY TO THE TEACHER RATHER THAN IN COURSE'S GENERAL DISCUSSION FORUM.
LEARNING ANALYTICS IS A GROWING TREND IN ALL EDUCATION. THE DIGITAL FOOTPRINT STUDENTS LEAVE ON LEARNING MANAGEMENTS SYSTEM AND OTHER DIGITAL PLATFORMS TELL ABOUT STUDENTS' STUDYING HABITS, MATERIAL USAGE AND TIME CONSUMPTION IN STUDIES. THE NUMBER OF LOG EVENTS IN MOODLE LOG WAS FOUND TO CORRESPOND TO THE STUDYING EFFORT AND CORRELATE NICELY WITH THE FINAL GRADE. LEARNING ANALYTICS OFFER A VIEW TO STUDYING AND LEARNING ACTIVITIES, BUT IT IS NOT THE WHOLE TRUTH. LEARNING MANAGEMENT SYSTEM CANNOT RECORD SUCH ACTIVITIES AS READING THE COURSE BOOK OR CARRYING OUT CALCULATIONS ON PAPER. THEREFORE, IN THIS STUDY LEARNING ANALYTICS DATA IS SUPPLEMENTED WITH DATA OF WHATSAPP MESSAGING, LEARNING OUTCOMES AND SELF-EVALUATION. ACCORDING TO THE WHATSAPP DATA, THE MESSAGING TOOK PLACE MOSTLY OUTSIDE NORMAL WORK HOURS, BETWEEN 9 PM AND 10 PM. THE STUDENTS PRESENTED THEIR QUESTIONS PRIVATELY TO THE TEACHER RATHER THAN IN COURSE'S GENERAL DISCUSSION FORUM.
CURRENTLY, COMPETENCE-BASED LEARNING IS BECOMING VERY POPULAR AND SYSTEMS THAT SUPPORT THIS PROCESS ARE NEEDED. TECHNOLOGY ENHANCED EDUCATIONAL SYSTEMS CAN NOTABLY IMPROVE THE TEACHING AND LEARNING PROCESSES IN THE CONTEXT OF COMPETENCE-BASED LEARNING. IN THIS PAPER, WE PRESENT A SYSTEM CALLED COBLE (COMPETENCE-BASED LEARNING ENVIRONMENT) THAT SUPPORTS COMPETENCE-BASED LEARNING AND COMBINES VISUAL LEARNING ANALYTICS AND RECOMMENDATION ASPECTS IN ORDER TO PROMOTE THE STUDENTS' AND LECTURERS' SELF-REFLECTION ABOUT THE LEARNING AND TEACHING PROCESSES.
NOWADAYS, THE BIG DATA IS BECOMING MORE AND MORE IMPORTANT IN THE FIELD OF EDUCATION. THE HUGE AMOUNTS OF DATA GENERATED BY THE PARTICIPANTS IN THE LEARNING PROCESSES LED TO INCREASED INTEREST IN THE COLLECTION AND ANALYSIS OF STUDENT DATA TO SUPPORT AND OBTAIN INSIGHT INTO THE STUDENTS' LEARNING ACTIVITIES. THIS FORMED A NEW RESEARCH FIELD, KNOWN AS LEARNING ANALYTICS. THE PAPER FOCUSES ON SOLUTIONS OF SOME PROBLEMS RELATED TO TECHNOLOGICAL CONSIDERATIONS IN LEARNING ANALYTICS. IT PROPOSE A SET OF INDICATORS THAT LEARNING ANALYTICS SHOULD PROVIDE TO HELP STUDENTS TO MEET THEIR LEARNING GOALS AND TO IMPROVE THEIR SUCCESS. THE MOBILE APPLICATION MOBILE LAP THAT ALLOWS STUDENTS TO TRACE OUT THE VALUES OF THE INDICATORS DURING TRAINING IS DEVELOPED. USING MOBILE LAP STUDENTS ARE ABLE TO KEEP TRACK ON THEIR OWN ACTIVITY, SUCCESS RATE, ADHERENCE TO THE LEARNING SCHEDULE.
LEARNING ANALYTICS IS A NEW FIELD IN EDUCATION WHOSE ADOPTION CAN BRING BENEFITS FOR TEACHING AND LEARNING PROCESSES. HOWEVER, MANY HIGHER EDUCATION INSTITUTIONS MAY NOT BE READY TO START USING LEARNING ANALYTICS DUE TO CHALLENGES SUCH AS ORGANIZATIONAL CULTURE, INFRASTRUCTURE, AND PRIVACY. IN THIS CONTEXT, MATURITY MODELS (MMS) CAN SUPPORT INSTITUTIONS TO SYSTEMATIZE THEIR PROCESSES, ENABLING THEM TO PROGRESS SUCCESSIVELY IN THE LEARNING ANALYTICS ADOPTION. MMS ARE USED IN DIFFERENT FIELDS TO SUPPORT THE IMPROVEMENT OF PROCESSES, DESCRIBING THEM IN TERMS OF MATURITY LEVELS, AND IDENTIFYING ENHANCEMENTS THAT COULD LEAD AN ORGANIZATION TO HIGHER LEVELS OF SUCH MATURITY. THUS, THIS PAPER PRESENTS AN OUTLINE OF A MM FOR LEARNING ANALYTICS ADOPTION IN HIGHER EDUCATION INSTITUTIONS, DESCRIBING ITS LEVELS AND AREAS, TOGETHER WITH ITS DEVELOPMENT METHODOLOGY.
THIS PAPER PRESENTS A LEARNING ANALYTICS SYSTEM THAT HAS BEEN EXTENDED TO ADDRESS MULTIPLE DOMAINS (WRITING AND CODING) FOR A BREADTHWISE EXPANSION. THE SYSTEM HAS ALSO BEEN INFUSED WITH ANALYTICS SOLUTIONS TARGETING COMPETENCE, GRADE PREDICTION AND REGULATION TRAITS, THUS OFFERING DEEPER INSIGHTS. OUR EXPERIENCES IN EXTENDING THE BREADTH AND DEPTH OF THE ANALYTICS SYSTEM HAVE BEEN DISCUSSED. THE DISCUSSION INCLUDES ELABORATION ON TWO TYPES OF SENSORS TO TRACK THE WRITING AND THE CODING EXPERIENCES OF STUDENTS. THE DESIGN OF A DASHBOARD FOR TEACHERS TO MONITOR THE PERFORMANCE OF THEIR CLASSROOMS AND ADVOCATE REGULATION ACTIVITIES IS ALSO INCLUDED. THE DISCUSSIONS LEAN MORE ON THE SIDE OF TEACHERS, PARENTS, AND ADMINISTRATORS, THAN ON THE SIDE OF STUDENTS.
THE PAPER PRESENTS A STUDY IN THE AREA OF LEARNING ANALYTICS - A SCIENTIFIC FIELD THAT IS STILL IN A RELATIVELY EARLY STAGE OF STUDY IN BULGARIA. THE STUDY AIMS TO OFFER, DEVELOP AND TEST SOFTWARE TOOLS FOR LEARNING ANALYTICS THAT WILL IMPROVE THE LEARNING OUTCOMES IN THE FIELD OF HIGHER EDUCATION. USING DATA MINING METHODS FOR ANALYSIS THE DATA PRODUCED IN THE LEARNING PROCESS (EXTRACTED FROM DIFFERENT DATA SOURCES), INTELLIGENT SOFTWARE TOOLS WILL BE DEVELOPED TO IMPROVE THE QUALITY OF OUTCOMES IN DIFFERENT ELEMENTS OF THE LEARNING PROCESS (LEARNING, TEACHING, MONITORING, ETC.), AND FOR THE NEEDS OF DIFFERENT STAKEHOLDER GROUPS.
THE PURPOSE OF THIS ARTICLE IS TO REPORT ON THE FINDINGS OF A USABILITY STUDY OF A LEARNING ANALYTICS TOOL DEVELOPED FOR THE ECLASS LEARNING MANAGEMENT SYSTEM AT THE UNIVERSITY OF ALBERTA. THE STUDY WAS CONDUCTED WITH 39 GRADUATE STUDENTS USING AND INTERACTING WITH A LEARNING ANALYTICS TOOL. THE STUDY DESIGN CONSISTED OF WATCHING AN ONLINE TUTORIAL ON HOW TO USE AND INTERACT WITH A LEARNING ANALYTICS TOOL, COMPLETING A NUMBER OF TASKS, AND, FINALLY, COMPLETING USABILITY SURVEY QUESTIONNAIRES. THE MAJORITY OF STUDENTS FOUND THE TOOL EASY TO USE, EASY TO LEARN, AND VISUALLY APPEALING. IT WAS ALSO NOTICEABLE THAT 62% OF THE PARTICIPANTS FELT THAT THEY GAINED NEW INSIGHT INTO THEIR INTERACTION WITH DIGITAL LEARNING OBJECTS AND 72% FELT THAT THIS WOULD PROVIDE THEM WITH INSIGHT AS TO HOW THEY COULD IMPROVE THEIR INTERACTION WITH LEARNING OBJECTS. CONCERNS WERE RAISED BY STUDENTS IN RELATION TO THE POTENTIAL FOR MISINTERPRETATION OF STUDENT DATA BY INSTRUCTORS.
THIS ARTICLE SETS OUT TO EXPLORE LEARNING DESIGN AND LEARNING ANALYTICS AS CENTRAL CONSTRUCTS IN HIGHER EDUCATION. BOTH TERMS REFER TO IDEAS AND FRAMEWORKS OF RELEVANCE FOR ACCOUNTABILITY AND QUALITY ENHANCEMENT. WHILE THE CONSTRUCTS INVOLVE A RANGE OF MULTIFACETED AND COMPLEX THEMES, THIS STUDY CONCENTRATES ON TASK DESIGN AS A KEY COMPONENT IN SCIENCE AND ENGINEERING EDUCATION. TASKS AND PROBLEMS ARE USED INTERCHANGEABLY, BOTH REFERRING TO STUDENT ACTIVATING RESOURCES. THE PURPOSE OF THE STUDY IS FIRST TO RAISE AWARENESS OF DESIGN BY INTRODUCING SETS OF FRAMEWORKS OF RELEVANCE TO SCIENCE AND ENGINEERING. SECOND, SOME EVIDENCE IS INCLUDED TO DEMONSTRATE THE APPLICABILITY OF CATEGORIES OF PROBLEM DESIGN TO INFORM LEARNING ANALYTICS.
THE VALUE OF TECHNOLOGY LIES NOT ONLY WITH THE SERVICE OR FUNCTIONALITY OF THE TOOL, BUT ALSO WITH ITS SUBSEQUENT VALUE TO THE PEOPLE WHO USE IT. NEW LEARNING ANALYTICS ( LA) SOFTWARE AND PLATFORMS FOR CAPTURING DATA AND IMPROVING STUDENT LEARNING ARE FREQUENTLY INTRODUCED; HOWEVER, THEY SUFFER FROM ISSUES OF ADOPTION AND CONTINUED USAGE BY STAKEHOLDERS. SCHOLARS HAVE PREVIOUSLY SUGGESTED THAT IT IS NOT ENOUGH TO INTRODUCE STAKEHOLDERS (E.G., TEACHERS AND STUDENTS) TO LA TECHNOLOGIES; THEY MUST ALSO BE A PART OF THE LA CREATION AND DESIGN PROCESS. IN THIS PAPER, WE WILL CONTINUE THE ONGOING WORK TO CLARIFY AND COMPARE DIFFERENT APPROACHES OF HUMAN-CENTRED DESIGN THROUGH AN OVERVIEW OF PARTICIPATORY FRAMEWORKS IN LA (CO-DESIGN, CO-CREATION). WE WILL ALSO PRESENT A CASE STUDY USING AN LA TOOL USED AND DEVELOPED WITHIN SEVERAL AUSTRALIAN UNIVERSITIES THAT WAS UTILIZED OVER SIX YEARS AS AN EXAMPLE OF HOW LA DESIGNERS CAN CO-CREATE DYNAMIC PLATFORMS WITH TEACHERS. IMPLICATIONS OF PARTICIPATORY DESIGN FRAMEWORKS IN LA WILL ALSO BE PRESENTED THROUGH A DISCUSSION OF THE COSTS, CHALLENGES, AND BENEFITS OF ADOPTING HUMAN-CENTRED DESIGN.
RECENTLY THERE HAVE BEEN SOME EFFORTS, ALBEIT LIMITED, TO IDENTIFY THE OPTIMAL USE OF THE AVAILABLE ARRAYS OF DATA IN HIGHER EDUCATION FOR MANY PURPOSES, SUCH AS TO IDENTIFY THOSE STUDENTS WHO ARE AT RISK AND TO EVALUATE STUDENT DROPOUT RATES. IN THE LITERATURE, THE TWO MAIN TERMS FOR THE EFFORTS MADE TO IDENTIFY PRACTICAL WAYS OF MAKING USE OF HIGHER EDUCATION DATA ARE ACADEMIC ANALYTICS AND LEARNING ANALYTICS. FOR THE MOST PART, THESE EFFORTS TEND TO FOCUS MORE ON THE TRADITIONAL OR NON-MOBILE STUDENTS' DATA, WHICH ARE PRE-STORED IN MANY UNIVERSITY SYSTEMS, SUCH AS THEIR PERSONAL DETAILS OR TRACKING INFORMATION TAKEN FROM STUDENTS' LEARNING ACTIVITIES. HOWEVER, LITTLE ATTENTION HAS BEEN PAID TO THE DATA OF MOBILE LEARNERS. IN THIS PAPER, THE POSSIBLE BENEFITS OF USING LEARNING ANALYTICS TECHNIQUES TO ENHANCE MOBILE AND PERVASIVE LEARNING ENVIRONMENTS ARE THEORETICALLY EXAMINED. FURTHERMORE, WE DESCRIBE OUR EARLY EFFORTS TOWARDS REALISING THE SIGNIFICANCE OF LEARNING ANALYTICS IN THE MOBILE LEARNING ENVIRONMENT.
SERIOUS GAMES ADOPTION IS INCREASING, ALTHOUGH THEIR PENETRATION IN FORMAL EDUCATION IS STILL SURPRISINGLY LOW. TO IMPROVE THEIR OUTCOMES AND INCREASE THEIR ADOPTION IN THIS DOMAIN, WE PROPOSE NEW WAYS IN WHICH SERIOUS GAMES CAN LEVERAGE THE INFORMATION EXTRACTED FROM PLAYER INTERACTIONS, BEYOND THE USUAL POST-ACTIVITY ANALYSIS. WE FOCUS ON THE USE OF: (1) OPEN DATA WHICH CAN BE SHARED FOR RESEARCH PURPOSES, (2) REAL-TIME FEEDBACK FOR TEACHERS THAT APPLY GAMES IN SCHOOLS, TO MAINTAIN AWARENESS AND CONTROL OF THEIR CLASSROOM, AND (3) ONCE ENOUGH DATA IS GATHERED, DATA MINING TO IMPROVE GAME DESIGN, EVALUATION AND DEPLOYMENT; AND ALLOW TEACHERS AND STUDENTS TO BENEFIT FROM ENHANCED FEEDBACK OR STEALTH ASSESSMENT. HAVING DEVELOPED AND TESTED A GAME LEARNING ANALYTICS PLATFORM THROUGHOUT MULTIPLE EXPERIMENTS, WE DESCRIBE THE LESSONS THAT WE HAVE LEARNT WHEN ANALYZING LEARNING ANALYTICS DATA IN THE PREVIOUS CONTEXTS TO IMPROVE SERIOUS GAMES.
MONITORING ONLINE COLLABORATION IS A CRUCIAL PART TO ENSURE THE SUCCESS OF ONLINE SOCIAL LEARNING ENVIRONMENTS (SLE) IN EDUCATION. WHILE THERE HAVE BEEN EFFORTS TO ANALYZE AND EVALUATE ONLINE COLLABORATION, MOST WORKS DO NOT OFFER AN IMPLEMENTATION OF NEW CONCEPTS. BY APPLYING SOCIAL LEARNING ANALYTICS ON AN ACTIVE SLE IN A DESIGN SCIENCE METHODOLOGY FRAMEWORK, INDICATORS FOR ONLINE COLLABORATION ARE IDENTIFIED IN A LITERATURE BASED AND DATA DRIVEN ANALYSIS, WHILE SETTING THREE MAIN OBJECTIVES: OBJECTIFYING THE MONITORING PROCESS, ESTABLISHING COMPARABLE INDICATORS AND SIMPLIFYING THE CONFLICT IDENTIFICATION. WE PROPOSE VISUALIZATIONS OF THESE INDICATORS TO E-TUTORS RESPONSIBLE FOR MONITORING ONLINE COLLABORATION IN HIGHER EDUCATION COURSES. E-TUTORS TEST THE IMPLEMENTED VISUALIZATIONS FOR ONE SEMESTER IN A HIGHER EDUCATION COURSE ESTABLISHING A PROOF OF CONCEPT. SUBSEQUENTLY, THE E-TUTORS EVALUATE THE UTILITY OF THE VISUALIZATIONS, REGARDING THE SET OBJECTIVES. FINALLY, LIMITATIONS OF THE INDICATORS ARE DISCUSSED AND FUTURE APPLICATIONS IN EDUCATION ARE PROPOSED.
INDUSTRY HAS CALLED UPON ACADEMIA TO BETTER PREPARE COMPUTER SCIENCE GRADUATES FOR TEAMWORK, ESPECIALLY IN DEVELOPING THE SOFT SKILLS NECESSARY FOR COLLABORATIVE WORK. HOWEVER, THE TEACHING AND ASSESSMENT OF TEAMWORK IS NOT EASY, WITH INSTRUCTORS BEING PRESSED FOR TIME AND A LACK OF TOOLS AVAILABLE TO EFFICIENTLY ANALYSE STUDENT TEAMWORK, WHERE LARGE COHORTS ARE INVOLVED. WE HAVE DEVELOPED A TEAMWORK DASHBOARD, FOUNDED ON LEARNING ANALYTICS, LEARNING THEORY AND TEAMWORK MODELS THAT ANALYSES STUDENTS' ONLINE TEAMWORK DISCUSSION DATA AND VISUALISES THE TEAM MOOD, ROLE DISTRIBUTION AND EMOTIONAL CLIMATE. THIS TOOL ALLOWS EDUCATORS TO EASILY MONITOR TEAMS IN REAL-TIME. EDUCATORS MAY USE THE TOOL TO PROVIDE STUDENTS WITH FEEDBACK ABOUT TEAM INTERACTIONS AS WELL AS TO IDENTIFY PROBLEMATIC TEAMS. WE PRESENT A CASE STUDY, TRIALING THE DASHBOARD ON ONE UNIVERSITY COMPUTER SCIENCE COURSE AND INCLUDE REFLECTIONS FROM THE COURSE LECTURER TO DETERMINE ITS UTILITY IN MONITORING ONLINE STUDENT TEAMWORK.
LEARNING ANALYTICS (LA) IS A SIGNIFICANT AREA OF THE ENHANCED LEARNING TECHNOLOGY THAT HAS BEEN EMERGED DURING THE LAST DECADE. IN THIS PAPER, WE PRESENT A CLOUD ADAPTED WORKFLOW E-ASSESSMENT SYSTEM, CALLED CLOUD-AWAS. THIS SYSTEM MAKES USE OF THE LEARNING ANALYTICS IN ORDER TO TURN LEARNERS INTO MORE EFFECTIVE AND BETTER LEARNERS. HENCE, CLOUD-AWAS COULD BE SEAMLESSLY INTEGRATED INTO ANY LEARNING MANAGEMENT SYSTEM. THIS SYSTEM PROVIDES A GENERIC E-ASSESSMENT WORKFLOW WHICH IS ADAPTED TO LEARNER'S PROFILES. WE HAVE STARTED BY CREATING A LEARNER PROFILE ONTOLOGY BASED ON EXTRACTION DATA FROM E-ASSESSMENT ACTIVITIES, FILE LOG AND PERSONAL INFORMATION. THEN, WE HAVE DEFINED THREE ADAPTATION ACTIONS: ADD ACTIVITY, EDIT ACTIVITY, AND DELETE ACTIVITY, APPLIED ON THE WORKFLOW ASSESSMENT AND USING INFORMATION EXTRACTED FROM LEARNER PROFILE ONTOLOGY INSTANCES. EACH ACTION IS APPLIED ACCORDING TO A NUMBER OF CONDITIONS. FINALLY, WE PRESENT SOME RESULTS OF THE EMPIRICAL EVALUATION OF CLOUD_AWAS. (C) 2016 WILEY PERIODICALS, INC.
NOWADAYS, THE IMPORTANCE OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS IN HIGHER EDUCATION INSTITUTIONS IS BEING RECOGNISED. THE ANALYSIS OF UNIVERSITY CAREERS AND OF STUDENT DROPOUT PREDICTION IS ONE OF THE MOST STUDIED TOPICS IN THE AREA OF LEARNING ANALYTICS. FROM THE PERSPECTIVE OF ESTIMATING THE LIKELIHOOD OF A STUDENT DROPPING OUT, WE PROPOSE AN INNOVATIVE STATISTICAL METHOD THAT IS A GENERALISATION OF MIXED-EFFECTS TREES FOR A RESPONSE VARIABLE IN THE EXPONENTIAL FAMILY: GENERALISED MIXED-EFFECTS TREES (GMET). WE PERFORMED A SIMULATION STUDY IN ORDER TO VALIDATE THE PERFORMANCE OF OUR PROPOSED METHOD AND TO COMPARE GMET TO CLASSICAL MODELS. IN THE CASE STUDY, WE APPLIED GMET TO MODEL UNDERGRADUATE STUDENT DROPOUT IN DIFFERENT COURSES AT POLITECNICO DI MILANO. THE MODEL WAS ABLE TO IDENTIFY DISCRIMINATING STUDENT CHARACTERISTICS AND ESTIMATE THE EFFECT OF EACH DEGREE-BASED COURSE ON THE PROBABILITY OF STUDENT DROPOUT.
THE AFFORDANCES OF LEARNING ANALYTICS (LA) ARE BEING INCREASINGLY HARNESSED TO ENHANCE 21ST CENTURY (21C) PEDAGOGY AND LEARNING. RELATIVELY RARE, HOWEVER, ARE USE CASES AND EMPIRICALLY BASED UNDERSTANDINGS OF STUDENTS' ACTUAL EXPERIENCES WITH LA TOOLS AND ENVIRONMENTS AT FOSTERING 21C LITERACIES, ESPECIALLY IN SECONDARY SCHOOLING AND ASIAN EDUCATION CONTEXTS. THIS PAPER ADDRESSES THIS KNOWLEDGE GAP BY 1) PRESENTING A FIRST ITERATION DESIGN OF A COMPUTER-SUPPORTED COLLABORATIVE CRITICAL READING AND LA ENVIRONMENT AND ITS 16-WEEK IMPLEMENTATION IN A SINGAPORE HIGH SCHOOL; AND 2) FOREGROUNDING STUDENTS' QUANTITATIVE AND QUALITATIVE ACCOUNTS OF THE BENEFITS AND PROBLEMATICS ASSOCIATED WITH THIS LEARNING INNOVATION. WE FOCUS THE ANALYTIC LENS ON THE LA DASHBOARD COMPONENTS THAT PROVIDED VISUALIZATIONS OF STUDENTS' READING ACHIEVEMENT, 21C LEARNING DISPOSITIONS, CRITICAL LITERACY COMPETENCIES AND SOCIAL LEARNING NETWORK POSITIONING WITHIN THE CLASS. THE PAPER AIMS TO PROVIDE INSIGHTS INTO THE POTENTIALITIES, PARADOXES AND PATHWAYS FORWARD FOR DESIGNING LA THAT TAKE INTO CONSIDERATION THE VOICES OF LEARNERS AS CRITICAL STAKEHOLDERS.
THE RICH DATA THAT MASSIVE OPEN ONLINE COURSES (MOOCS) PLATFORMS COLLECT ON THE BEHAVIOR OF MILLIONS OF USERS PROVIDE A UNIQUE OPPORTUNITY TO STUDY HUMAN LEARNING AND TO DEVELOP DATA-DRIVEN METHODS THAT CAN ADDRESS THE NEEDS OF INDIVIDUAL LEARNERS. THIS TYPE OF RESEARCH FALLS INTO THE EMERGING FIELD OF LEARNING ANALYTICS. HOWEVER, LEARNING ANALYTICS RESEARCH TENDS TO IGNORE THE ISSUE OF THE RELIABILITY OF RESULTS THAT ARE BASED ON MOOCS DATA, WHICH IS TYPICALLY NOISY AND GENERATED BY A LARGELY ANONYMOUS CROWD OF LEARNERS. THIS PAPER PROVIDES EVIDENCE THAT LEARNING ANALYTICS IN MOOCS CAN BE SIGNIFICANTLY BIASED BY USERS WHO ABUSE THE ANONYMITY AND OPEN-NATURE OF MOOCS, FOR EXAMPLE BY SETTING UP MULTIPLE ACCOUNTS, DUE TO THEIR AMOUNT AND ABERRANT BEHAVIOR. WE IDENTIFY THESE USERS, DENOTED FAKE LEARNERS, USING DEDICATED ALGORITHMS. THE METHODOLOGY FOR MEASURING THE BIAS CAUSED BY FAKE LEARNERS' ACTIVITY COMBINES THE IDEAS OF REPLICATION RESEARCH AND SENSITIVITY ANALYSIS. WE REPLICATE TWO HIGHLY-CITED LEARNING ANALYTICS STUDIES WITH AND WITHOUT FAKE LEARNERS DATA, AND COMPARE THE RESULTS. WHILE IN ONE STUDY, THE RESULTS WERE RELATIVELY STABLE AGAINST FAKE LEARNERS, IN THE OTHER, REMOVING THE FAKE LEARNERS' DATA SIGNIFICANTLY CHANGED THE RESULTS. THESE FINDINGS RAISE CONCERNS REGARDING THE RELIABILITY OF LEARNING ANALYTICS IN MOOCS, AND HIGHLIGHT THE NEED TO DEVELOP MORE ROBUST, GENERALIZABLE AND VERIFIABLE RESEARCH METHODS.
THIS PAPER DESCRIBES A LEARNING ANALYTICS PERSPECTIVE FOR DESIGNING TO IMPLEMENT A SEAMLESS LEARNING ENVIRONMENT. SEAMLESS LEARNING HAS BEEN FOCUSED ON SUPPORTING LEARNING ACROSS FORMAL AND INFORMAL LEARNING CONTEXTS, INDIVIDUAL AND SOCIAL LEARNING AND PHYSICAL WORLD AND CYBERSPACE. MAJORITY OF THE CURRENT RESEARCHES HAVE REALIZED A SEAMLESS LEARNING ENVIRONMENT BY USING THE TECHNOLOGIES SUCH AS SMART-PHONE AND GPS AT SCHOOLS OR UNIVERSITIES. HOWEVER, UTILIZATION OF THE COLLECTED LEARNING LOGS STILL REMAINS A CHALLENGE YET TO BE EXPLORED. IN THIS STUDY, TO CONSTRUCT A SEAMLESS LEARNING ENVIRONMENT, THIS STUDY DEVELOPED A SYSTEM THAT INTEGRATED A DIGITAL TEXTBOOK SYSTEM CALLED AETEL WITH A UBIQUITOUS LEARNING SYSTEM CALLED SCROLL. THE SYSTEM ENABLES LEARNERS TO BRIDGE THE LEARNING BETWEEN DIGITAL TEXTBOOK LEARNING AND REAL-LIFE LEARNING. TO ANALYZE AND VISUALIZE THE RELATIONSHIPS BETWEEN THEM, THIS STUDY DEVELOPED AN INNOVATIVE SYSTEM CALLED VASCORLL 2.0 (VISUALIZATION AND ANALYSIS SYSTEM FOR CONNECTING RELATIONSHIPS OF LEARNING LOGS). AN EXPERIMENT WAS CONDUCTED TO EVALUATE WHETHER VASCORLL 2.0 CAN INCREASE LEARNERS' LEARNING OPPORTUNITIES. AS A RESULT, THEY WERE ABLE TO INCREASE THEIR LEARNING OPPORTUNITIES BY USING VASCORLL 2.0. IT CONTRIBUTED TO ENHANCING LEARNING ACTIVITIES IN THE SEAMLESS LEARNING ENVIRONMENT BY UTILIZING THE COLLECTED LEARNING LOGS WITH WELL-DESIGNED ANALYSIS AND VISUALIZATION APPROACHES.
EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) RESEARCH HAVE EMERGED AS INTERESTING AREAS OF RESEARCH, WHICH ARE UNFOLDING USEFUL KNOWLEDGE FROM EDUCATIONAL DATABASES FOR MANY PURPOSES SUCH AS PREDICTING STUDENTS' SUCCESS. THE ABILITY TO PREDICT A STUDENT'S PERFORMANCE CAN BE BENEFICIAL FOR ACTIONS IN MODERN EDUCATIONAL SYSTEMS. EXISTING METHODS HAVE USED FEATURES WHICH ARE MOSTLY RELATED TO ACADEMIC PERFORMANCE, FAMILY INCOME AND FAMILY ASSETS; WHILE FEATURES BELONGING TO FAMILY EXPENDITURES AND STUDENTS' PERSONAL INFORMATION ARE USUALLY IGNORED. IN THIS PAPER, AN EFFORT IS MADE TO INVESTIGATE AFOREMENTIONED FEATURE SETS BY COLLECTING THE SCHOLARSHIP HOLDING STUDENTS' DATA FROM DIFFERENT UNIVERSITIES OF PAKISTAN. LEARNING ANALYTICS, DISCRIMINATIVE AND GENERATIVE CLASSIFICATION MODELS ARE APPLIED TO PREDICT WHETHER A STUDENT WILL BE ABLE TO COMPLETE HIS DEGREE OR NOT. EXPERIMENTAL RESULTS SHOW THAT PROPOSED METHOD SIGNIFICANTLY OUTPERFORMS EXISTING METHODS DUE TO EXPLOITATION OF FAMILY EXPENDITURES AND STUDENTS' PERSONAL INFORMATION FEATURE SETS. OUTCOMES OF THIS EDM/LA RESEARCH CAN SERVE AS POLICY IMPROVEMENT METHOD IN HIGHER EDUCATION.
INCREASINGLY, HIGHER EDUCATION INSTITUTIONS ARE EXPLORING THE POTENTIAL OF LEARNING ANALYTICS TO PREDICT STUDENT RETENTION, UNDERSTAND LEARNING BEHAVIORS, AND IMPROVE STUDENT LEARNING THROUGH PROVIDING PERSONALIZED FEEDBACK AND SUPPORT. THE TECHNICAL DEVELOPMENT OF LEARNING ANALYTICS HAS OUTPACED CONSIDERATION OF ETHICAL ISSUES SURROUNDING THEIR USE. OF PARTICULAR CONCERN IS THE ABSENCE OF THE STUDENT VOICE IN DECISION-MAKING ABOUT LEARNING ANALYTICS. WE EXPLORED HIGHER EDUCATION STUDENTS' KNOWLEDGE, ATTITUDES, AND CONCERNS ABOUT BIG DATA AND LEARNING ANALYTICS THROUGH FOUR FOCUS GROUPS (N = 41). THEMATIC ANALYSIS OF THE FOCUS GROUP TRANSCRIPTS IDENTIFIED SIX KEY THEMES. THE FIRST THEME, "UNINFORMED AND UNCERTAIN," REPRESENTS STUDENTS' LACK OF KNOWLEDGE ABOUT LEARNING ANALYTICS PRIOR TO THE FOCUS GROUPS. FOLLOWING THE PROVISION OF INFORMATION, VIEWING OF VIDEOS AND DISCUSSION OF LEARNING ANALYTICS SCENARIOS THREE FURTHER THEMES: "HELP OR HINDRANCE TO LEARNING," "MORE THAN A NUMBER," AND "IMPEDING INDEPENDENCE"; REPRESENTED STUDENTS' PERCEPTIONS OF THE LIKELY IMPACT OF LEARNING ANALYTICS ON THEIR LEARNING. "DRIVING INEQUALITY" AND "WHERE WILL IT STOP?" REPRESENT ETHICAL CONCERNS RAISED BY THE STUDENTS ABOUT THE POTENTIAL FOR INEQUITY, BIAS AND INVASION OF PRIVACY AND THE NEED FOR INFORMED CONSENT. A KEY TENSION TO EMERGE WAS HOW "PERSONAL" VS. "COLLECTIVE" PURPOSES OR PRINCIPLES CAN INTERSECT WITH "UNIFORM" VS. "AUTONOMOUS" ACTIVITY. THE FINDINGS HIGHLIGHT THE NEED THE NEED TO ENGAGE STUDENTS IN THE DECISION MAKING PROCESS ABOUT LEARNING ANALYTICS.
LEARNING ANALYTICS IS AN INTERDISCIPLINARY AND INCLUSIVE FIELD, A FACT WHICH MAKES THE ESTABLISHMENT OF METHODOLOGICAL NORMS BOTH CHALLENGING AND IMPORTANT. THIS COMMUNITY-BUILDING WORKSHOP INTENDS TO CONVENE METHODOLOGY-FOCUSED RESEARCHERS TO DISCUSS NEW AND ESTABLISHED APPROACHES, COMMENT ON THE STATE OF CURRENT PRACTICE, AUTHOR PEDAGOGICAL MANUSCRIPTS, AND CO-DEVELOP GUIDELINES TO HELP MOVE THE FIELD FORWARD WITH QUALITY AND RIGOR.
THE KHAN ACADEMY PLATFORM ENABLES POWERFUL ON-LINE COURSES IN WHICH STUDENTS CAN WATCH VIDEOS, SOLVE EXERCISES OR EARN BADGES. THIS PLATFORM PROVIDES AN ADVANCED LEARNING ANALYTICS MODULE WITH USEFUL VISUALIZATIONS FOR TEACHERS AND STUDENTS. NEVERTHELESS, THIS LEARNING ANALYTICS SUPPORT CAN BE IMPROVED WITH RECOMMENDATIONS AND NEW USEFUL HIGHER LEVEL VISUALIZATIONS IN ORDER TO TRY TO IMPROVE THE LEARNING PROCESS. IN THIS PAPER, WE DESCRIBE OUR ARCHITECTURE FOR PROCESSING DATA FROM THE KHAN ACADEMY PLATFORM IN ORDER TO SHOW NEW HIGHER LEVEL LEARNING VISUALIZATIONS AND RECOMMENDATIONS. THE DIFFERENT INVOLVED ELEMENTS OF THE ARCHITECTURE ARE PRESENTED AND THE DIFFERENT DECISIONS ARE JUSTIFIED. IN ADDITION, WE EXPLAIN SOME INITIAL EXAMPLES OF NEW USEFUL VISUALIZATIONS AND RECOMMENDATIONS FOR TEACHERS AND STUDENTS AS PART OF OUR EXTENSION OF THE LEARNING ANALYTICS MODULE FOR THE KHAN ACADEMY PLATFORM. THESE EXAMPLES USE DATA FROM AN UNDERGRADUATE PHYSICS COURSE DEVELOPED AT UNIVERSIDAD CARLOS III DE MADRID WITH MORE THAN 100 STUDENTS USING THE KHAN ACADEMY SYSTEM.
LEARNING ANALYTICS AND DIGITAL BADGES ARE EMERGING RESEARCH FIELDS IN EDUCATIONAL SCIENCE. THEY BOTH SHOW PROMISE FOR ENHANCING STUDENT RETENTION IN HIGHER EDUCATION, WHERE WITHDRAWALS PRIOR TO DEGREE COMPLETION REMAIN AT ABOUT 30 % IN ORGANISATION FOR ECONOMIC COOPERATION AND DEVELOPMENT MEMBER COUNTRIES. THIS INTEGRATIVE REVIEW PROVIDES AN OVERVIEW OF THE THEORETICAL LITERATURE AS WELL AS CURRENT PRACTICES AND EXPERIENCE WITH LEARNING ANALYTICS AND DIGITAL BADGES IN HIGHER EDUCATION WITH REGARD TO THEIR POTENTIAL IMPACT ON STUDENT RETENTION TO ENHANCE STUDENTS' FIRST-YEAR EXPERIENCE. LEARNING ANALYTICS INVOLVES MEASURING AND ANALYZING DYNAMIC STUDENT DATA IN ORDER TO GAIN INSIGHT INTO STUDENTS' LEARNING PROCESSES AND OPTIMIZE LEARNING AND TEACHING. ONE PURPOSE OF LEARNING ANALYTICS IS TO CONSTRUCT PREDICTIVE MODELS TO IDENTIFY STUDENTS WHO RISK FAILING A COURSE AND THUS ARE MORE LIKELY TO DROP OUT OF HIGHER EDUCATION. PERSONALIZED FEEDBACK PROVIDES STUDENTS WITH INFORMATION ABOUT ACADEMIC SUPPORT SERVICES, HELPING THEM TO IMPROVE THEIR SKILLS AND THEREFORE BE SUCCESSFUL IN HIGHER EDUCATION. DIGITAL BADGES ARE SYMBOLS FOR CERTIFYING KNOWLEDGE, SKILLS, AND COMPETENCIES ON WEB-BASED PLATFORMS. THE INTENTION IS TO ENCOURAGE STUDENT PERSISTENCE BY MOTIVATING THEM, RECOGNIZING THEIR GENERIC SKILLS, SIGNALING THEIR ACHIEVEMENTS, AND CAPTURING THEIR LEARNING PATHS. THIS ARTICLE PROPOSES A MODEL THAT SYNTHESIZES LEARNING ANALYTICS, DIGITAL BADGES, AND GENERIC SKILLS SUCH AS ACADEMIC COMPETENCIES. THE MAIN IDEA IS THAT GENERIC SKILLS CAN BE REPRESENTED AS DIGITAL BADGES, WHICH CAN BE USED FOR LEARNING ANALYTICS ALGORITHMS TO PREDICT STUDENT SUCCESS AND TO PROVIDE STUDENTS WITH PERSONALIZED FEEDBACK FOR IMPROVEMENT. MOREOVER, THIS MODEL MAY SERVE AS A PLATFORM FOR DISCUSSION AND FURTHER RESEARCH ON LEARNING ANALYTICS AND DIGITAL BADGES TO INCREASE STUDENT RETENTION IN HIGHER EDUCATION.
LEARNING ANALYTICS IS AN IMPORTANT TREND IN EDUCATION. IN CONVENTIONAL CLASSROOM SETTINGS, HOWEVER, A SOUND BASIS OF DIGITAL DATA FOR ANALYTICS IS LACKING. THEREFORE, IT IS IMPORTANT TO DEVELOP THE METHODOLOGIES AND TECHNOLOGIES TO UTILIZE THE SCATTERED AND HETEROGENEOUS BITS OF AVAILABLE DATA AS EFFECTIVE AS POSSIBLE. IT IS ALSO IMPORTANT TO DEPLOY SIMPLE AND USABLE TOOLS TO TEACHERS THAT COULD HELP THEM WITHIN THE CONTEXT CONDITIONS AND CONSTRAINTS OF THEIR DAILY WORK. IN THIS PAPER, WE INTRODUCE A PROTOTYPICAL APPROACH FOR LEARNING ANALYTICS IN THE CLASSROOM AND A SIMPLE DATA COLLECTION TOOL NAMED FLOWER TOOL. THIS TOOL ENABLES COLLECTING AND COMPARING STUDENTS' SELF-ASSESSMENTS WITH TEACHER-LEAD ASSESSMENTS AND THE RESULTS OF EXTERNAL TESTS. IN A FIRST FIELD STUDY, WE GATHERED FEEDBACK FROM STUDENTS AND TEACHERS ABOUT THE APPROACH, INDICATING A STRONG ACCEPTANCE AND A NUMBER OF POTENTIAL ADVANTAGES FOR THE ASSESSMENT AND REFLECTION PROCESSES IN THE CLASSROOM.
SOFTWARE POCKETS AN INDISPENSABLE ROLE IN THE MODERN LIVING STYLE TO MAKE THEIR WORK EASY. SOFTWARE ENGINEERS ARE THE PEOPLE WHO DEVELOP THE SOFTWARE TO COMPLY WITH THE USER NEEDS AND MAKE THEM JOYFUL. SOFTWARE ENGINEERING EDUCATION IS THE PLACE WHERE THE SOFTWARE ENGINEERS MOLD UP ACADEMICALLY FOR THE SOCIETY'S REQUIREMENT. AS A RESULT, SOFTWARE ENGINEERING EDUCATION GRABS THE ESSENCE IN THE COMPUTER EDUCATION, ALBEIT IT FALL SHORTS TO COOK UP THE GENIUS TO MEET WITH THE INDUSTRIES NECESSITY. TO OVERCOME THESE ISSUES, RESEARCHERS SUGGESTED NUMBER OF SOFTWARE ENGINEERING LEARNING/TEACHING METHODS TO EGG ON STUDENTS TO ACQUIRE THEIR PROFUNDITY KNOWLEDGE IN SOFTWARE ENGINEERING. EVEN THOUGH THE SUGGESTIONS DO NOT UTTERLY SURMOUNT THIS CRUCIAL ISSUE SINCE THE SUGGESTED APPROACHES DID NOT ATTRACT THE MAJORITY OF THE STUDENTS AND TOUCH THEIR LEARNING STYLE. LEARNING ANALYTICS PLAYS A VITAL ROLE TO IMPROVE THE STUDENTS LEARNING ACTIVITIES. LEARNING STYLE AND LEARNING ENGAGEMENT ARE THE KEY FACTORS FOR THE LEARNING ANALYTICS. THIS PAPER FOCUSES ON LEARNING STYLE AND LEARNING ENGAGEMENT OF THE SOFTWARE ENGINEERING STUDENTS. THIS WORK CLASSIFIES THE STUDENTS IN LINE WITH THEIR LEARNING STYLE AND IDENTIFIES THE NEEDS OF THE SOFTWARE ENGINEERING STUDENTS USING LEARNING ENGAGEMENT FINALLY PROPOSED THE MODEL TO INSPIRE THE STUDENTS TO OVERCOME SUCH ISSUES AND MOTIVATE THEM IN GATHERING SOFTWARE ENGINEERING KNOWLEDGE.
MAJOR ISSUES CURRENTLY RESTRICTING THE USE OF LEARNING ANALYTICS ARE THE LACK OF INTERPRETABILITY AND ADAPTABILITY OF THE MACHINE LEARNING MODELS USED IN THIS DOMAIN. INTERPRETABILITY MAKES IT EASY FOR THE STAKEHOLDERS TO UNDERSTAND THE WORKING OF THESE MODELS AND ADAPTABILITY MAKES IT EASY TO USE THE SAME MODEL FOR MULTIPLE COHORTS AND COURSES IN EDUCATIONAL INSTITUTIONS. RECENTLY, SOME MODELS IN LEARNING ANALYTICS ARE CONSTRUCTED WITH THE CONSIDERATION OF INTERPRETABILITY BUT THEIR INTERPRETABILITY IS NOT QUANTIFIED. HOWEVER, ADAPTABILITY IS NOT SPECIFICALLY CONSIDERED IN THIS DOMAIN. THIS PAPER PRESENTS A NEW FRAMEWORK BASED ON HYBRID STATISTICAL FUZZY THEORY TO OVERCOME THESE LIMITATIONS. IT ALSO PROVIDES EXPLAINABILITY IN THE FORM OF RULES DESCRIBING THE REASONING BEHIND A PARTICULAR OUTPUT. THE PAPER ALSO DISCUSSES THE SYSTEM EVALUATION ON A BENCHMARK DATASET SHOWING PROMISING RESULTS. THE MEASURE OF EXPLAINABILITY, FUZZY INDEX, SHOWS THAT THE MODEL IS HIGHLY INTERPRETABLE. THIS SYSTEM ACHIEVES MORE THAN 82% RECALL IN BOTH THE CLASSIFICATION AND THE CONTEXT ADAPTATION STAGES.
TO BRIDGE THE DIGITAL SKILLS GAP, WE NEED TO TRAIN MORE PEOPLE IN SOFTWARE ENGINEERING TECHNIQUES. THIS PAPER REPORTS ON A PROJECT EXPLORING THE WAY STUDENTS SOLVE TASKS USING COLLABORATIVE DEVELOPMENT PLATFORMS AND VERSION CONTROL SYSTEMS, SUCH AS GITLAB, TO FIND PATTERNS AND EVALUATION METRICS THAT CAN BE USED TO IMPROVE THE COURSE CONTENT AND REFLECT ON THE MOST COMMON ISSUES THE STUDENTS ARE FACING. IN THIS PAPER, WE EXPLORE LEARNING ANALYTICS APPROACHES THAT CAN BE USED WITH GITLAB AND SIMILAR TOOLS, AND DISCUSS THE CHALLENGES RAISED WHEN APPLYING THOSE APPROACHES IN SOFTWARE ENGINEERING EDUCATION, WITH THE OBJECTIVE OF BUILDING A PIPELINE THAT SUPPORTS THE FULL LEARNING ANALYTICS CYCLE, FROM DATA EXTRACTION TO DATA ANALYSIS. WE FOCUS IN PARTICULAR ON THE DATA ANONYMISATION STEP OF THE PROPOSED PIPELINE TO EXPLORE THE AVAILABLE ALTERNATIVES TO SATISFY THE DATA PROTECTION REQUIREMENTS WHEN HANDLING PERSONAL INFORMATION IN ACADEMIC ENVIRONMENTS FOR RESEARCH PURPOSES.
THIS PAPER SHOWS HOW INFORMATION ABOUT THE STUDENT'S LEARNING BEHAVIOR MAY BE COLLECTED AND EVALUATED BY USING A SELF-LEARNING MODULE. BASED ON THIS EVALUATION, SOME USE CASES ARE CONSIDERED FOR A FURTHER DEVELOPMENT OF THE SELF-LEARNING MODULE. IN THIS PAPER THESE USE CASES ARE PRESENTED FROM THE TEACHER'S AND THE STUDENT'S POINT OF VIEW. THEIR ADVANTAGES AND IMPLICATIONS ARE DISCUSSED. THE PAPER CLOSES WITH A SHORT DISCUSSION OF RISKS AND POTENTIAL DISADVANTAGES OF LEARNING ANALYTICS.
WE PRESENT A SYSTEMATIC LITERATURE REVIEW OF THE EMERGING FIELD OF VISUAL LEARNING ANALYTICS. WE REVIEW EXISTING WORK IN THIS FIELD FROM TWO PERSPECTIVES: FIRST, WE ANALYZE EXISTING APPROACHES, AUDIENCES, PURPOSES, CONTEXTS, AND DATA SOURCES BOTH INDIVIDUALLY AND IN RELATION TO ONE ANOTHER THAT DESIGNERS AND RESEARCHERS HAVE USED TO VISUALIZE EDUCATIONAL DATA. SECOND, WE EXAMINE HOW ESTABLISHED LITERATURE IN THE FIELDS OF INFORMATION VISUALIZATION AND EDUCATION HAS BEEN USED TO INFORM THE DESIGN OF VISUAL LEARNING ANALYTICS TOOLS AND TO DISCUSS RESEARCH FINDINGS. WE CHARACTERIZE THE REVIEWED LITERATURE BASED ON THREE DIMENSIONS: (A) CONNECTION WITH VISUALIZATION BACKGROUND; (B) CONNECTION WITH EDUCATIONAL THEORY; AND (C) SOPHISTICATION OF VISUALIZATION(S). THE RESULTS FROM THIS SYSTEMATIC REVIEW SUGGEST THAT: (1) LITTLE WORK HAS BEEN DONE TO BRING VISUAL LEARNING ANALYTICS TOOLS INTO CLASSROOM SETTINGS; (2) FEW STUDIES CONSIDER BACKGROUND INFORMATION FROM THE STUDENTS, SUCH AS DEMOGRAPHICS OR PRIOR PERFORMANCE; (3) TRADITIONAL STATISTICAL VISUALIZATION TECHNIQUES, SUCH AS BAR PLOTS AND SCATTER PLOTS, ARE STILL THE MOST COMMONLY USED IN LEARNING ANALYTICS CONTEXTS, WHILE MORE ADVANCED OR NOVEL TECHNIQUES ARE RARELY USED; (4) WHILE SOME STUDIES EMPLOY SOPHISTICATED VISUALIZATIONS, AND SOME ENGAGE DEEPLY WITH EDUCATIONAL THEORIES, THERE IS A LACK OF STUDIES THAT BOTH EMPLOY SOPHISTICATED VISUALIZATIONS AND ENGAGE DEEPLY WITH EDUCATIONAL THEORIES. FINALLY, WE PRESENT A BRIEF RESEARCH AGENDA FOR THE FIELD OF VISUAL LEARNING ANALYTICS BASED ON THE FINDINGS OF OUR LITERATURE REVIEW.
THE AIM OF LEARNING ANALYTICS IS TO USE AVAILABLE DATA FROM DIFFERENT SYSTEMS AND DATA BASES IN ORDER TO SUPPORT STUDENTS AND TEACHERS IN LEARNING AND TEACHING PROCESSES. IN ORDER TO ENSURE THAT MAIN GROUPS OF USERS GET WHAT THEY NEED THE MOST BY USING LEARNING ANALYTICS, THERE IS AN IMPERATIVE TO DEVELOP FEASIBLE NEEDS ANALYSIS METHODOLOGY AS WELL AS TO PERFORM THE NEEDS ANALYSIS ACCORDING TO IT. THERE ARE FAR LESS RESEARCH AND PREDICTION ABOUT THE ROLE AND IMPLEMENTATION OF LEARNING ANALYTICS IN PRE-TERTIARY EDUCATION (SCHOOLS) THAN IN HIGHER EDUCATION. THE RESEARCH PRESENTED IN THIS PAPER WAS PERFORMED IN PRIMARY AND SECONDARY SCHOOLS IN CROATIA. FINAL RESULTS ARE PRESENTED IN FORM OF MOST RELEVANT QUESTIONS POSED BY STUDENTS AND TEACHERS THAT LEARNING ANALYTICS SYSTEM SUPPOSED TO ANSWER.
THE CONSTRUCTION OF KNOWLEDGE THROUGH COMPUTATIONAL PRACTICE REQUIRES TO TEACHERS A SUBSTANTIAL AMOUNT OF TIME AND EFFORT TO EVALUATE PROGRAMMING SKILLS, TO UNDERSTAND AND TO GLIMPSE THE EVOLUTION OF THE STUDENTS AND FINALLY TO STATE A QUANTITATIVE JUDGMENT IN LEARNING ASSESSMENT. THE FIELD OF LEARNING ANALYTICS HAS BEEN A COMMON PRACTICE IN RESEARCH SINCE LAST YEARS DUE TO THEIR GREAT POSSIBILITIES IN TERMS OF LEARNING IMPROVEMENT. BOTH, BIG AND SMALL DATA TECHNIQUES SUPPORT THE ANALYSIS CYCLE OF LEARNING ANALYTICS AND RISK OF STUDENTS' FAILURE PREDICTION. SUCH POSSIBILITIES CAN BE A STRONG POSITIVE CONTRIBUTION TO THE FIELD OF COMPUTATIONAL PRACTICE SUCH AS PROGRAMMING. OUR MAIN OBJECTIVE WAS TO HELP TEACHERS IN THEIR ASSESSMENTS THROUGH TO MAKE THOSE POSSIBILITIES EFFECTIVE. THUS, WE HAVE DEVELOPED A FUNCTIONAL SOLUTION TO CATEGORIZE AND UNDERSTAND STUDENTS' BEHAVIOR IN PROGRAMMING ACTIVITIES BASED IN SCRATCH. THROUGH COLLECTION AND ANALYSIS OF DATA GENERATED BY STUDENTS' CLICKS IN SCRATCH, WE PROCEED TO EXECUTE BOTH EXPLORATORY AND PREDICTIVE ANALYTICS TO DETECT PATTERNS IN STUDENTS' BEHAVIOR WHEN DEVELOPING SOLUTIONS FOR ASSIGNMENTS. WE CONCLUDED THAT RESULTANT TAXONOMY COULD HELP TEACHERS TO BETTER SUPPORT THEIR STUDENTS BY GIVING REAL-TIME QUALITY FEEDBACK AND ACT BEFORE STUDENTS DELIVER INCORRECTLY OR AT LEAST INCOMPLETE TASKS. (C) 2018 ELSEVIER B.V. ALL RIGHTS RESERVED.
ONLINE TEACHING ENVIRONMENTS ACQUIRE EXTREMELY HIGH GRANULARITY OF DATA, BOTH ON USERS' PERSONAL PROFILES AND ON THEIR BEHAVIOUR AND RESULTS. LEARNING ANALYTICS (LA) IS OPEN TO NUMEROUS POSSIBLE RESEARCH SCENARIOS THANKS TO THE DEVELOPMENT OF TECHNOLOGY AND THE SPEED OF DATA COLLECTION. ONE CHARACTERISTIC ELEMENT IS THAT THE DATA ARE NOT ANONYMOUS, BUT THEY REPRODUCE A PERSONALIZATION AND IDENTIFICATION OF THE PROFILES. IDENTIFIABILITY OF THE STUDENT IS IMPLICIT IN THE TEACHING PROCESS, BUT ACCESS TO ANALYTICS TECHNIQUES REVEALS A FUNDAMENTAL QUESTION: "WHAT IS THE LIMIT?" THE ANSWER TO THIS QUESTION SHOULD BE PRELIMINARY TO ANY USE OF DATA BY STUDENTS, TEACHERS, INSTRUCTORS AND MANAGERS OF THE ONLINE LEARNING ENVIRONMENTS. IN THE PRESENT DAY, WE ARE ALSO EXPERIENCING A PARTICULAR MOMENT OF CHANGE: THE EFFECTS OF THE EUROPEAN GENERAL DATA PROTECTION REGULATION (GDPR) 679/2016, THE GENERAL REGULATION ON THE PROTECTION OF PERSONAL DATA THAT AIMS TO STANDARDIZE ALL NATIONAL LEGISLATION AND ADAPT IT TO THE NEW NEEDS DICTATED BY THE EVOLVING TECHNOLOGICAL CONTEXT. THE OBJECTIVE OF THIS WORK IS TO PROPOSE A THREE-POINT CHECKLIST OF THE QUESTIONS CONNECTED TO THE MANAGEMENT AND LIMITS OF TEACHERS' USE OF DATA IN LEARNING ANALYTICS AND STUDENTS' RIGHT OF TRANSPARENCY IN THE CONTEXT OF HIGHER DIGITAL EDUCATION, TO TAKE INTO ACCOUNT BEFORE CONDUCTING RESEARCH. TO THIS END, THE PAPER CONTAINS AN EXAMINATION OF THE LITERATURE ON PRIVACY AND ETHICAL DEBATES IN LA. WORK CONTINUES WITH LEGISLATIVE REVIEW, PARTICULARLY THE ITALIAN PATH, AND THE DISCUSSION ABOUT ONLINE DATA MANAGEMENT IN OUR CURRENT UNIVERSITIES' TWO CONTEXTS: TECHNOLOGY AND LEGISLATION.
IN THE FIELD OF LEARNING ANALYTICS (LA), ACTIONABLE INSIGHT FROM LA DESIGNS TENDS TO BE A BUZZWORD WITHOUT CLEAR UNDERSTANDINGS. AS THE TEACHER IS A KEY STAKEHOLDER, WHAT TEACHER-ACTIONABLE INSIGHTS CAN BE DERIVED FROM LA DESIGNS? TOWARDS PROVIDING GREATER CLARITY ON THIS ISSUE, WE CONCRETIZE A TAXONOMY OF LA DECISION SUPPORT FOR TEACHER-ACTIONABLE INSIGHTS IN STUDENT ENGAGEMENT. FOUR TYPES OF DECISION SUPPORT ARE CONCEIVED IN THIS TAXONOMY WITH RELEVANT TEACHER IMPLICATIONS. THROUGH THIS TAXONOMY, WE HOPE TO OFFER POSSIBLE PATHWAYS FOR ACTIONABLE INSIGHT IN LA DESIGNS AND MAKE CLEARER THE ROLE OF THE TEACHER.
THE PURPOSES OF THE TWO STUDIES REPORTED IN THIS RESEARCH ARE TO ADAPT AND VALIDATE THE INSTRUMENT OF THE EVALUATION FRAMEWORK FOR LEARNING ANALYTICS (EFLA) FOR LEARNERS INTO THE TURKISH CONTEXT, AND TO EXAMINE HOW METACOGNITIVE AND BEHAVIORAL FACTORS PREDICT LEARNER PERFORMANCE. STUDY 1 WAS CONDUCTED WITH 83 ONLINE LEARNERS ENROLLED IN A 16-WEEK COURSE DELIVERED THROUGH THE MOODLE LEARNING MANAGEMENT SYSTEM. THE FINDINGS FROM THE CONFIRMATORY FACTOR ANALYSIS INDICATED THAT A THREE-FACTOR MODEL OF THE EFLA FOR LEARNERS PROVIDED THE BEST MODEL FIT FOR THE COLLECTED DATA. THE MODEL IS CONSISTENT WITH THE FACTORIAL STRUCTURE OF THE ORIGINAL INSTRUMENT DEVELOPED BASED ON THE DATA FROM THE EUROPEAN LEARNERS. STUDY 2 AIMED TO REVEAL HOW THE METACOGNITIVE AND BEHAVIORAL FACTORS PERTAINING TO THE LEARNING ANALYTICS DASHBOARD PREDICT LEARNERS' ACADEMIC PERFORMANCE. A TOTAL OF 63 ONLINE LEARNERS ENROLLED IN A 14-WEEK ONLINE COMPUTING COURSE PARTICIPATED IN THIS STUDY. THE RESULTS FROM THE LOGISTIC REGRESSION ANALYSIS INDICATED THAT ONLINE LEARNERS MORE FREQUENTLY INTERACTED WITH THE LEARNING ANALYTICS DASHBOARD DEMONSTRATED GREATER ACADEMIC PERFORMANCE. FURTHERMORE, THE DIMENSIONS OF THE EFLA, TOGETHER WITH THE INTERACTION WITH THE DASHBOARD, SIGNIFICANTLY PREDICTED LEARNERS' ACADEMIC PERFORMANCE. THIS MULTIPLE-STUDY INVESTIGATION CONTRIBUTES TO THE GENERALIZABILITY OF THE EFLA FOR LEARNERS AND HIGHLIGHTS THE IMPORTANCE OF METACOGNITIVE AND BEHAVIORAL FACTORS FOR THE IMPACT OF LEARNING ANALYTICS DASHBOARDS ON LEARNER PERFORMANCE.
MANY EDUCATIONAL INSTITUTIONS USE A LARGE NUMBER OF INFORMATION SYSTEMS TO AUTOMATE THEIR ACTIVITIES FOR DIFFERENT STAKEHOLDERS' GROUPS - LEARNING MANAGEMENT SYSTEMS, STUDENT DIARY, LIBRARY SYSTEM, DIGITAL REPOSITORIES, MANAGEMENT SYSTEM, ETC. THIS LEADS TO A SIGNIFICANT INCREASE IN THE VOLUME AND VARIETY OF DATA THAT CAN BE CAPTURED, STORED, AND HARNESSED TO IMPROVE STUDENT LEARNING AND SCHOOL EFFECTIVENESS. EDUCATIONAL INSTITUTIONS ARE REALISING THAT, WITH THE HELP OF TECHNOLOGY, THEY ARE COLLECTING DATA THAT COULD BE VERY VALUABLE WHEN PROPERLY ANALYSED, ALIGNED WITH LEARNING OUTCOMES, AND INTEGRATED INTO A TIGHTER FEEDBACK LOOP WITH STAKEHOLDERS. IN ADDITION, THE ANALYSIS OF DATA CAN HELP THEIR MANAGERS TO TAKE DATA-DRIVEN DECISION MAKING AT ALL LEVELS OF EDUCATIONAL INSTITUTIONS. THE PAPER PRESENTS A COMPREHENSIVE APPROACH TO LEARNING ANALYTICS IN THE FIELD OF SECONDARY EDUCATION FROM THE PERSPECTIVE OF ALL DIFFERENT STAKEHOLDERS, WHICH AIMS TO IMPROVE ITS METHODS OF APPROACHING AND ANALYSING LEARNING DATA. ON THE BASIS OF A LITERATURE REVIEW IN THE FIELD AND AN INVESTIGATION OF REQUIREMENTS FOR QUALITY EVALUATION OF LEARNING IN SCHOOL EDUCATION, THE CORRESPONDING STAKEHOLDER GROUPS ARE IDENTIFIED - STUDENTS, TEACHERS, CLASS TEACHERS, MANAGERS, PARENTS, INSPECTORS AND 6 MODELS (1 MODEL PER EACH STAKEHOLDER GROUP) FOR DATA COLLECTION AND PERSONALIZED AND MEANINGFUL ANALYSIS ARE PROPOSED FOR THE NEEDS OF LEARNING ANALYTICS. EACH MODEL CONSISTS OF MEASURABLE INDICATORS ALLOWING THE RELEVANT STAKEHOLDER TO TRACK DATA FOR STUDENTS' LEARNING OR TRAINING FOR DIFFERENT PURPOSES, E.G. MONITORING, ANALYSIS, FORECAST, INTERVENTION, RECOMMENDATIONS, ETC., BUT FINALLY TO IMPROVE THE QUALITY OF LEARNING AND TEACHING PROCESSES. THE PROPOSED MODELS ARE EVALUATED BY THE REPRESENTATIVES OF 4 STAKEHOLDER GROUPS - STUDENTS, TEACHERS, CLASS TEACHERS, PARENTS.
ANALYTICS IS ALL ABOUT INSIGHTS. LEARNING-ORIENTED INSIGHTS ARE THE TARGETS FOR LEARNING ANALYTICS RESEARCHERS. INSIGHTS COULD BE DETECTED, ANALYSED, OR CREATED IN THE CONTEXT OF VARIABLES SUCH AS THE QUALITY OF INTERACTIONS WITH THE CONTENT, STUDY HABITS, ENGAGEMENT, COMPETENCE GROWTH, SENTIMENTS, LEARNING EFFICIENCY, AND INSTRUCTIONAL EFFECTIVENESS. CLUSTERING TECHNIQUES OFFER AN EFFECTIVE SOLUTION FOR GROUPING LEARNERS USING OBSERVED PATTERNS. FOR INSTANCE, LEARNERS COULD BE CLUSTERED BASED ON THE EFFECTIVENESS OF LEARNERS' SELF-REGULATION INITIATIVES IN REACHING THE TARGET LEARNING OUTCOMES. EACH LEARNER COULD BELONG TO A NUMBER OF CLUSTERS THAT TARGET DIFFERENT TYPES OF INSIGHTS. ONE COULD ALSO ANALYSE THE DISTANCE BETWEEN CLUSTERS AS A MEANS TO GUIDE LEARNERS TOWARDS BETTER PERFORMANCE. FURTHER, ONE COULD ANALYSE THE EFFECTIVENESS OF COHESIVE PEER GROUPS WITHIN AND AMONG CLUSTERS. TRADITIONAL CLUSTERING TECHNIQUES ONLY COPE WITH NUMERICAL OR CATEGORICAL DATA AND ARE NOT READILY APPLICABLE IN OFFERING LEARNING ANALYTICS SOLUTIONS. IN ADDRESSING THIS GAP, THIS RESEARCH AIMS TO DESIGN A PARALLEL PARTICLE SWARM OPTIMIZATION (PPSO) ALGORITHM FOR THE PURPOSES OF LEARNING ANALYTICS, WHERE THE ARRIVAL OF DATA IS CONTINUOUS, THE TYPES OF DATA IS BOTH STRUCTURED AND UNSTRUCTURED, AND THE VOLUME OF DATA CAN BE SIGNIFICANTLY LARGE. THE RESEARCH WILL ALSO DESCRIBE THE APPLICATION OF THE PPSO ALGORITHM TO DETECT, ANALYSE, AND GENERATE LEARNING-ORIENTED INSIGHTS.
LEARNING TECHNOLOGIES ENABLE INTERVENTIONS IN THE LEARNING PROCESS AIMING TO IMPROVE LEARNING. LEARNING ANALYTICS PROVIDES SUCH INTERVENTIONS BASED ON ANALYSIS OF LEARNER DATA, WHICH ARE BELIEVED TO HAVE BENEFICIAL EFFECTS ON BOTH LEARNING AND THE LEARNING ENVIRONMENT. LITERATURE REPORTING ON THE EFFECTS OF LEARNING ANALYTICS INTERVENTIONS ON LEARNING ALLOWS US TO ASSESS IN WHAT WAY LEARNING ANALYTICS IMPROVES LEARNING. NO STANDARD SET OF OPERATIONAL DEFINITIONS FOR LEARNING AFFECTED BY LEARNING ANALYTICS INTERVENTIONS IS AVAILABLE. WE PERFORMED A SYSTEMATIC LITERATURE REVIEW OF 1932 SEARCH HITS, WHICH YIELDED 62 KEY STUDIES. WE ANALYZED HOW AFFECTED LEARNING WAS OPERATIONALIZED IN THESE KEY STUDIES AND CLASSIFIED OPERATIONAL DEFINITIONS INTO THREE CATEGORIES: 1) LEARNING ENVIRONMENT; 2) LEARNING PROCESS; AND 3) LEARNING OUTCOME. A DEEPENING ANALYSIS YIELDED A REFINED CLASSIFICATION SCHEME WITH 11 SUBCATEGORIES. MOST OF THE ANALYZED STUDIES RELATE TO EITHER LEARNING OUTCOME OR LEARNING PROCESS. ONLY NINE OF THE KEY STUDIES RELATE TO MORE THAN ONE CATEGORY. GIVEN THE COMPLEX NATURE OF APPLYING LEARNING ANALYTICS INTERVENTIONS IN PRACTICE, MEASURING THE EFFECTS ON A WIDER SPECTRUM OF ASPECTS CAN GIVE MORE INSIGHT INTO THE WORKINGS OF LEARNING ANALYTICS INTERVENTIONS ON THE DIFFERENT ACTORS, PROCESSES, AND OUTCOMES INVOLVED. BASED ON THE RESULTS OF OUR REVIEW, WE RECOMMEND MAKING DELIBERATE DECISIONS ON THE (MULTIPLE) ASPECTS OF LEARNING ONE TRIES TO IMPROVE BY APPLYING LEARNING ANALYTICS. OUR REFINED CLASSIFICATION WITH EXAMPLES OF OPERATIONAL DEFINITIONS MAY HELP BOTH ACADEMICS AND PRACTITIONERS DOING SO, AS IT ALLOWS FOR A MORE STRUCTURED, GROUNDED, AND COMPARABLE POSITIONING OF LEARNING ANALYTICS BENEFITS.
STRATEGIES THAT INCORPORATE LEARNING ANALYTICS ARE CONTINUING TO ADVANCE AS HIGHER EDUCATION INSTITUTIONS WORK TO PROMOTE STUDENT SUCCESS. ALTHOUGH MANY OF THE EARLY LEARNING ANALYTICS APPLICATIONS WERE INTENDED TO HELP TEACHING PROFESSIONALS TO IDENTIFY AT-RISK STUDENTS, SOME LEARNING ANALYTICS APPLICATIONS DISPLAY INFORMATION ON COURSE PROGRESS AND PERFORMANCE DIRECTLY TO STUDENTS. WHILE POSITIVE ASSOCIATIONS BETWEEN STUDENT USE OF LEARNING ANALYTICS AND ACHIEVEMENT HAVE BEEN REPORTED, SOME HAVE EXPRESSED CONCERN THAT FOR AT-RISK STUDENTS, LOW ESTIMATED GRADES MIGHT INDUCE NEGATIVE EMOTIONS, WHICH COULD LEAD TO DISENGAGEMENT OR EVEN WITHDRAWAL. HOWEVER, FEW STUDIES HAVE EXAMINED THE EFFECTS OF SUCH APPLICATIONS ON AT-RISK STUDENTS. ELEMENTS OF SUCCESS IS A LEARNING ANALYTICS PLATFORM THAT PROVIDES STUDENTS WITH WEEKLY PERFORMANCE FEEDBACK, INCLUDING A CURRENT ESTIMATED GRADE. THIS STUDY EXAMINED THE RELATIONSHIP BETWEEN STUDENT USE OF ELEMENTS OF SUCCESS AND ACADEMIC PERFORMANCE AMONG AT-RISK STUDENTS IN AN INTRODUCTORY CHEMISTRY COURSE. SPECIFICALLY, WE COMPARED FINAL GRADE OUTCOMES AND THE RISK OF WITHDRAWAL AMONG STUDENTS WHO RECEIVED A LOW ESTIMATED GRADE AFTER THE FIRST MIDTERM. RESULTS INDICATED THAT VIEWING PERFORMANCE FEEDBACK, INCLUDING A LOW ESTIMATED GRADE, WAS NOT ASSOCIATED WITH WITHDRAWAL FROM THE COURSE FOR AT-RISK STUDENTS. FURTHERMORE, AT-RISK STUDENTS WHO USED ELEMENTS OF SUCCESS WERE FOUND TO BE RESILIENT. AFTER CONTROLLING FOR PRIOR LEARNING OUTCOMES, DEMOGRAPHICS, AND SELF-REPORTED STUDY SKILLS, IT WAS FOUND THAT THEY WERE MORE LIKELY TO EARN A FINAL PASSING GRADE (C- OR ABOVE) THAN AT-RISK STUDENTS WHO NEVER USED IT. THE RESULTS AND LIMITATIONS ARE FURTHER DISCUSSED.
INTEREST IN LEARNING ANALYTICS (LA) HAS GROWN RAPIDLY AMONG HIGHER EDUCATION INSTITUTIONS (HEIS). HOWEVER, THE MATURITY LEVELS OF HEIS IN TERMS OF BEING 'STUDENT DATA-INFORMED' ARE ONLY AT EARLY STAGES. THERE OFTEN ARE BARRIERS THAT PREVENT DATA FROM BEING USED SYSTEMATICALLY AND EFFECTIVELY. TO ASSIST HIGHER EDUCATION INSTITUTIONS TO BECOME MORE MATURE USERS AND CUSTODIANS OF DIGITAL DATA COLLECTED FROM STUDENTS DURING THEIR ONLINE LEARNING ACTIVITIES, THE SHEILA FRAMEWORK, A POLICY DEVELOPMENT FRAMEWORK THAT SUPPORTS SYSTEMATIC, SUSTAINABLE AND RESPONSIBLE ADOPTION OF LA AT AN INSTITUTIONAL LEVEL, WAS RECENTLY BUILT. THIS PAPER PRESENTS A MIX-METHOD STUDY USING A GROUP CONCEPT MAPPING (GCM) APPROACH THAT WAS CONDUCTED WITH LA EXPERTS TO EXPLORE ESSENTIAL FEATURES OF LA POLICY IN HEI IN CONTRIBUTION THE DEVELOPMENT OF THE FRAMEWORK. THE STUDY IDENTIFIED SIX CLUSTERS OF FEATURES THAT AN LA POLICY SHOULD INCLUDE, PROVIDED RATINGS BASED ON EASE OF IMPLEMENTATION AND IMPORTANCE FOR EACH OF THE SIX THEMES, AND OFFERED SUGGESTIONS TO HEIS HOW THEY CAN PROCEED WITH THE DEVELOPMENT OF LA POLICIES.
LEARNING ANALYTICS (LA) IS A FAST-GROWING FIELD BUT ADOPTION BY TEACHERS REMAIN LIMITED. THIS PAPER PRESENTS THE RESULTS OF A REVIEW OF 18 LA FRAMEWORKS AND DISCUSSES HOW THEY HAVE TRIED TO ADDRESS PROMINENT CHALLENGES IN LA ADOPTION. THE RESULTS SHOW THAT RESEARCHERS HAVE MADE SIGNIFICANT ADVANCES IN DEVELOPING APPROPRIATE FRAMEWORKS TO CONCEPTUALISE LA ADOPTION AMONG TEACHERS, AND HAVE ADVANCED CONSIDERABLY IN CONNECTING LA AND LEARNING THEORY. HOWEVER, FEW FRAMEWORKS ARE CONCRETISED INTO TECHNOLOGICAL ARTEFACTS AND CONCRETE DATA STREAMS. MOREOVER, THERE IS A NEED TO EMPIRICALLY VALIDATE AND PUT INTO USE THE MOST PROMISING EXISTING FRAMEWORKS. WE HOPE THAT THIS REVIEW WILL BE INFORMATIVE FOR TEACHERS WHO HAVE LITTLE LA EXPERIENCE BUT ARE INTERESTED IN ADOPTING LA IN AUTHENTIC PRACTICE.
STUDY SUCCESS INCLUDES THE SUCCESSFUL COMPLETION OF A FIRST DEGREE IN HIGHER EDUCATION TO THE LARGEST EXTENT, AND THE SUCCESSFUL COMPLETION OF INDIVIDUAL LEARNING TASKS TO THE SMALLEST EXTENT. FACTORS AFFECTING STUDY SUCCESS RANGE FROM INDIVIDUAL DISPOSITIONS (E.G., MOTIVATION, PRIOR ACADEMIC PERFORMANCE) TO CHARACTERISTICS OF THE EDUCATIONAL ENVIRONMENT (E.G., ATTENDANCE, ACTIVE LEARNING, SOCIAL EMBEDDEDNESS). RECENT DEVELOPMENTS IN LEARNING ANALYTICS, WHICH ARE A SOCIO-TECHNICAL DATA MINING AND ANALYTIC PRACTICE IN EDUCATIONAL CONTEXTS, SHOW PROMISE IN ENHANCING STUDY SUCCESS IN HIGHER EDUCATION, THROUGH THE COLLECTION AND ANALYSIS OF DATA FROM LEARNERS, LEARNING PROCESSES, AND LEARNING ENVIRONMENTS IN ORDER TO PROVIDE MEANINGFUL FEEDBACK AND SCAFFOLDS WHEN NEEDED. THIS RESEARCH REPORTS A SYSTEMATIC REVIEW FOCUSING ON EMPIRICAL EVIDENCE, DEMONSTRATING HOW LEARNING ANALYTICS HAVE BEEN SUCCESSFUL IN FACILITATING STUDY SUCCESS IN CONTINUATION AND COMPLETION OF STUDENTS' UNIVERSITY COURSES. USING STANDARDISED STEPS OF CONDUCTING A SYSTEMATIC REVIEW, AN INITIAL SET OF 6220 ARTICLES WAS IDENTIFIED. THE FINAL SAMPLE INCLUDES 46 KEY PUBLICATIONS. THE FINDINGS OBTAINED IN THIS SYSTEMATIC REVIEW SUGGEST THAT THERE ARE A CONSIDERABLE NUMBER OF LEARNING ANALYTICS APPROACHES WHICH UTILISE EFFECTIVE TECHNIQUES IN SUPPORTING STUDY SUCCESS AND STUDENTS AT RISK OF DROPPING OUT. HOWEVER, RIGOROUS, LARGE-SCALE EVIDENCE OF THE EFFECTIVENESS OF LEARNING ANALYTICS IN SUPPORTING STUDY SUCCESS IS STILL LACKING. THE TESTED VARIABLES, ALGORITHMS, AND METHODS COLLECTED IN THIS SYSTEMATIC REVIEW CAN BE USED AS A GUIDE IN HELPING RESEARCHERS AND EDUCATORS TO FURTHER IMPROVE THE DESIGN AND IMPLEMENTATION OF LEARNING ANALYTICS SYSTEMS.
IN THIS PAPER, WE AIMED TO GUIDE ABOUT LATEST DEVELOPMENT AND STUDIES ABOUT STUDENTS' PERFORMANCE ANALYSIS AND LEARNING ANALYTICS IN MASSIVELY OPEN ONLINE COURSES ( MOOCS) FOR RESEARCHERS RELATED WITH THE TOPICS. FOR THIS PURPOSE SHORT REVIEW FOR USAGE OF PERFORMANCE PREDICTION AND LEARNING ANALYTICS IN MOOCS IS INVESTIGATED IN OUR STUDY, TO HELP READERS GET FAMILIAR WITH OUR TOPIC, FIRSTLY LITERATURE INFORMATION ABOUT BASIC CONCEPTS ARE EXPLAINED. THEN TO UNDERSTAND FEATURES' IMPORTANCE LEVEL AND THEIR RELATIONSHIPS MORE DETAILED, INFORMATION ABOUT SOME PAPERS WERE PROVIDED. AFTER THAT, FINDINGS ABOUT USAGE OF STUDENT PERFORMANCE PREDICTION AND LEARNING ANALYTICS IN MOOCS ARE SUMMARIZED.
PEER ASSESSMENT (PA) IS THE PROCESS OF STUDENTS GRADING AND GIVING FEEDBACK TO EACH OTHER'S WORK. LEARNING ANALYTICS IS A FIELD FOCUSED ON ANALYSING EDUCATIONAL DATA TO UNDERSTAND AND IMPROVE LEARNING PROCESSES. USING LEARNING ANALYTICS ON PA DATA HAS THE POTENTIAL TO GAIN NEW INSIGHTS INTO THE FEEDBACK GIVING/RECEIVING PROCESS. THIS EXPLORATORY STUDY FOCUSES ON BACKWARD EVALUATION, AN UNDER RESEARCHED ASPECT OF PEER ASSESSMENT, WHERE STUDENTS REACT TO THE FEEDBACK THAT THEY RECEIVED ON THEIR WORK. TWO ASPECTS ARE ANALYSED: 1) BACKWARD EVALUATION CHARACTERISTICS DEPENDING ON STUDENT PERCEPTION OF FEEDBACK THAT THEY RECEIVE ON THEIR WORK, AND 2) THE RELATIONSHIP BETWEEN RUBRIC CHARACTERISTICS AND BACKWARD EVALUATION. A BIG DATASET (N = 7,660 RECORDS) FROM AN ONLINE PLATFORM CALLED PEERGRADE WAS ANALYSED USING BOTH STATISTICAL METHODS AND EPISTEMIC NETWORK ANALYSIS. STUDENTS WHO FOUND FEEDBACK USEFUL TENDED TO BE MORE ACCEPTING BY ACKNOWLEDGING THEIR ERRORS, INTENDING TO REVISE THEIR TEXT, AND PRAISING ITS USEFULNESS, WHILE STUDENTS WHO FOUND THE FEEDBACK LESS USEFUL TENDED TO BE MORE DEFENSIVE BY EXPRESSING THAT THEY WERE CONFUSED ABOUT ITS MEANING, CRITICAL TOWARDS ITS FORM AND FOCUS, AND IN DISAGREEMENT WITH THE CLAIMS. MOREOVER, STUDENTS MOSTLY SUGGESTED FEEDBACK IMPROVEMENT IN TERMS OF FEEDBACK SPECIFICITY, JUSTIFICATION AND CONSTRUCTIVITY, RATHER THAN KINDNESS. THE PAPER CONCLUDES BY DISCUSSING THE POTENTIAL AND LIMITATIONS OF USING LA METHODS TO ANALYSE BIG PA DATASETS.
GLOBALLY, THE PARADIGM OF INCREASED SCHOOL AUTONOMY AND ACCOUNTABILITY IS GAINING MOMENTUM, INTRODUCING THE NEED FOR EFFECTIVE DECISION MAKING BASED ON EDUCATIONAL DATA. FOR THIS REASON, THE CAPACITY OF TEACHERS TO COLLECT, ANALYZE AND EXPLOIT EDUCATIONAL DATA TO REFLECT ON AND IMPROVE THEIR PRACTICE (A PROCESS KNOWN AS TEACHER INQUIRY) IS AN ESSENTIAL ELEMENT IN SCHOOL IMPROVEMENT AND SUSTAINABILITY. AS A RESPONSE TO THAT, DIGITAL EDUCATIONAL DATA ANALYTICS TECHNOLOGIES HAVE BEEN PROPOSED (I.E., TEACHING AND LEARNING ANALYTICS) AS THE MEANS TO SUPPORT TEACHERS DURING THE CYCLE OF COLLECTING, ANALYZING AND EXPLOITING EDUCATIONAL DATA, ENHANCE THEIR DATA LITERACY CAPACITY AND EFFECTIVELY IMPROVE DIFFERENT ASPECTS OF THE TEACHING AND LEARNING PROCESS. THIS TUTORIAL WILL PRESENT AN INTRODUCTION TO THE EMERGING FIELD OF TEACHING AND LEARNING ANALYTICS FROM THE PERSPECTIVE OF A CLASSROOM TEACHER AND DISCUSS HOW THEY CAN BE USED TO SUPPORT TEACHER INQUIRY AND IMPROVE CLASSROOM-BASED TEACHING AND LEARNING. IT IS BASED ON THE MATERIAL OF THE MASSIVE OPEN ONLINE COURSE (MOOC) ANALYTICS FOR THE CLASSROOM TEACHER DEVELOPED AND OFFER BY THE SCHOOL OF EDUCATION, CURTIN UNIVERSITY THROUGH THE EDX PLATFORM.
THE DESIGN OF EFFECTIVE LEARNING ANALYTICS (LA) EXTENDS BEYOND SOUND TECHNICAL AND PEDAGOGICAL PRINCIPLES. IF ANALYTICS ARE TO BE ADOPTED AND USED SUCCESSFULLY TO SUPPORT LEARNING AND TEACHING, THEIR DESIGN PROCESS NEEDS TO TAKE INTO ACCOUNT A RANGE OF HUMAN FACTORS, INCLUDING WHY AND HOW THEY WILL BE USED. IN THIS EDITORIAL, WE INTRODUCE PRINCIPLES OF HUMAN-CENTRED DESIGN DEVELOPED IN OTHER, RELATED FIELDS THAT CAN BE ADOPTED AND ADAPTED TO SUPPORT THE DEVELOPMENT OF HUMAN-CENTRED LEARNING ANALYTICS (HCLA). WE DRAW ON THE PAPERS IN THIS SPECIAL SECTION, TOGETHER WITH THE WIDER LITERATURE, TO DEFINE HUMAN-CENTRED DESIGN IN THE FIELD OF LA AND TO IDENTIFY THE BENEFITS AND CHALLENGES THAT THIS APPROACH OFFERS. WE CONCLUDE BY SUGGESTING THAT HCLA WILL ENABLE THE COMMUNITY TO ACHIEVE MORE IMPACT, MORE QUICKLY, WITH TOOLS THAT ARE FIT FOR THE PURPOSE AND A PLEASURE TO USE.
WE HAVE CREATED A VISUAL REPRESENTATION CALLED STRATIFIED ATTRIBUTE TRACKING (SAT) DIAGRAM TO EXPLICATE TRENDS THAT ARE OTHERWISE IMPLICIT IN LEARNING ANALYTICS DATA. SAT DIAGRAM IS A UNIFIED GRAPH THAT ENABLES TRACKING INDIVIDUAL ATTRIBUTE VALUES IN A DATASET AND STRATIFYING THEM ACCORDING TO CRITERIA SET BY THE RESEARCHER. SAT DIAGRAM REPRESENTS THE TRANSITION OF SAMPLES BETWEEN STRATA ACROSS ATTRIBUTES. IN THIS PAPER WE INTRODUCE THE SAT DIAGRAM AND ILLUSTRATE HOW TO GENERATE, INTERPRET AND ANALYZE THEM. WE BELIEVE THE PROCESS OF SAT DIAGRAM GENERATION WOULD ENABLE EXPLORING DEEPER RESEARCH QUESTIONS ON LEARNING DATA.
THE FIELD OF EDUCATION TECHNOLOGY IS EMBRACING A USE OF LEARNING ANALYTICS TO IMPROVE STUDENT EXPERIENCES OF LEARNING. ALONG WITH EXPONENTIAL GROWTH IN THIS AREA IS AN INCREASING CONCERN OF THE INTERPRETABILITY OF THE ANALYTICS FROM THE STUDENT EXPERIENCE AND WHAT THEY CAN TELL US ABOUT LEARNING. THIS STUDY OFFERS A WAY TO ADDRESS SOME OF THE CONCERNS OF COLLECTING AND INTERPRETING LEARNING ANALYTICS TO IMPROVE STUDENT LEARNING BY COMBINING OBSERVATIONAL AND SELF-REPORT DATA. THE RESULTS PRESENT TWO MODELS FOR PREDICTING STUDENT ACADEMIC PERFORMANCE WHICH SUGGEST THAT A COMBINATION OF BOTH OBSERVATIONAL AND SELF-REPORT DATA EXPLAINS A SIGNIFICANTLY HIGHER VARIATION IN STUDENT OUTCOMES. THE RESULTS OFFER A WAY INTO DISCUSSING THE QUALITY OF INTERPRETATIONS OF LEARNING ANALYTICS AND THEIR USEFULNESS FOR HELPING TO IMPROVE THE STUDENT EXPERIENCE OF LEARNING AND ALSO SUGGEST A PATHWAY FOR FUTURE RESEARCH INTO THIS AREA.
LEARNING ANALYTICS SEEK TO ENHANCE THE LEARNING PROCESSES THROUGH SYSTEMATIC MEASUREMENTS OF LEARNING RELATED DATA AND TO PROVIDE INFORMATIVE FEEDBACK TO LEARNERS AND TEACHERS. TRACK DATA FROM LEARNING MANAGEMENT SYSTEMS (LMS) CONSTITUTE A MAIN DATA SOURCE FOR LEARNING ANALYTICS. THIS EMPIRICAL CONTRIBUTION PROVIDES AN APPLICATION OF BUCKINGHAM SHUM AND DEAKIN CRICK'S THEORETICAL FRAMEWORK OF DISPOSITIONAL LEARNING ANALYTICS: AN INFRASTRUCTURE THAT COMBINES LEARNING DISPOSITIONS DATA WITH DATA EXTRACTED FROM COMPUTER-ASSISTED, FORMATIVE ASSESSMENTS AND LMSS. IN A LARGE INTRODUCTORY QUANTITATIVE METHODS MODULE, 922 STUDENTS WERE ENROLLED IN A MODULE BASED ON THE PRINCIPLES OF BLENDED LEARNING, COMBINING FACE-TO-FACE PROBLEM-BASED LEARNING SESSIONS WITH E-TUTORIALS. WE INVESTIGATED THE PREDICTIVE POWER OF LEARNING DISPOSITIONS, OUTCOMES OF CONTINUOUS FORMATIVE ASSESSMENTS AND OTHER SYSTEM GENERATED DATA IN MODELLING STUDENT PERFORMANCE OF AND THEIR POTENTIAL TO GENERATE INFORMATIVE FEEDBACK. USING A DYNAMIC, LONGITUDINAL PERSPECTIVE, COMPUTER-ASSISTED FORMATIVE ASSESSMENTS SEEM TO BE THE BEST PREDICTOR FOR DETECTING UNDERPERFORMING STUDENTS AND ACADEMIC PERFORMANCE, WHILE BASIC LMS DATA DID NOT SUBSTANTIALLY PREDICT LEARNING. IF TIMELY FEEDBACK IS CRUCIAL, BOTH USE-INTENSITY RELATED TRACK DATA FROM E-TUTORIAL SYSTEMS, AND LEARNING DISPOSITIONS, ARE VALUABLE SOURCES FOR FEEDBACK GENERATION. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
IN THIS PAPER, WE CONSIDER THE KEY DIMENSIONS OF LEARNING ANALYTICS APPLICATIONS IN ADAPTIVE LEARNING. WE THEN REVIEW RECENT PUBLICATIONS ON THE TOPIC AND MAP THEM TO THE DIMENSIONS OF THE REFERENCE MODEL. TWENTY ONE PEER-REVIEWED ARTICLES ARE IDENTIFIED AND ANALYZED. THE FINDINGS OF THE REVIEW SUGGEST THAT INTERESTING WORK HAS BEEN CARRIED OUT DURING THE LAST YEARS ON THE TOPIC. YET, THERE IS A CLEAR LACK OF STUDIES ON SCHOOL EDUCATION AND IN TOPICS OUTSIDE STEM. IN ADDITION, THERE IS A LACK OF STUDIES THAT DO NOT FOCUS SOLELY ON THE (SELF)REFLECTION OF STUDENTS OR TUTORS. FINALLY, THE MAJORITY OF THE STUDIES INCLUDED LOOK AT STUDENT PERFORMANCE, AS AN ADAPTATION PARAMETER. YET, A NEW TREND OF TAKING INTO ACCOUNT MORE COMPLEX STUDENT BEHAVIORS, LIKE COLLABORATION, IS EMERGING.
METACOGNITION PLAYS AN IMPORTANT ROLE IN THE LEARNING PROCESS AS IT ALLOWS LEARNERS TO BECOME BETTER AWARE OF THEIR LEVEL OF UNDERSTANDING AND COMPREHENSION. KNOWLEDGE OF LEARNERS' METACOGNITION LEVEL ALLOWS TEACHERS TO PROVIDE FEEDBACK TO LEARNERS TO ENABLE CORRECTION OF LEARNING STRATEGIES. HOWEVER, MEASURING METACOGNITION BASED UPON LEARNER ACTIVITIES OUTSIDE A COMPUTER-BASED LEARNING ENVIRONMENT IS A CHALLENGING TASK. THIS PAPER EXPLORES THIS CHALLENGE IN ADULT LEARNERS FOR THE PURPOSE OF DEVELOPING A LEARNING ANALYTICS MODEL THAT CAN BE USED TO INFLUENCE THE USE OF METACOGNITIVE STRATEGIES TO IMPROVE LEARNING AND COMPREHENSION. AS A SOLUTION, A NOVEL LEARNING ANALYTICS MODEL IS PRESENTED. THIS MODEL IS BASED UPON DATA CAPTURED FROM A TRADITIONAL PHYSICAL CLASSROOM ENVIRONMENT AND CAN BE USED TO BUILD A WORKING LEARNING ANALYTICS MODEL DESIGNED TO PROVIDE FEEDBACK TO THE LEARNER. SUCH FEEDBACK CAN PROVIDE INSIGHTS OF LEARNERS' USE OF METACOGNITIVE STRATEGIES WITH A VIEW TO IMPROVING THEIR COMPREHENSION OF A GIVEN TOPIC AND THUS TO IMPROVE THEIR UNDERSTANDING OF THAT TOPIC AND ULTIMATELY TO IMPROVE THEIR ACADEMIC SUCCESS.
WITH THE INCREASING INTEREST OF SOCIAL MEDIA USAGE AMONG THE STUDENTS, WE ARE MOTIVATED TO INTEGRATE THIS INFORMAL MODE OF SOCIALIZED LEARNING ENVIRONMENT INTO THE FORMAL LEARNING SYSTEM TO ENGAGE STUDENTS FOR THEIR LEARNING ACTIVITIES. THE EXISTING LEARNING ANALYTICS (LA) FOCUSED ONLY ON ANALYZING FORMAL DATA OBTAINED FROM CONTROLLED ONLINE LEARNING ENVIRONMENTS AND THE SOCIAL CONNECTIONS AND LEARNING EXPERIENCE OF STUDENTS ARE NOT ANALYZED. THE EXPECTED OUTPUT FOR THIS PROPOSED WORK IS A SNS-INTEGRATED VIRTUAL LEARNING ENVIRONMENT (VLE) NAMED SHELTER WHICH PROVIDES FORMAL AND INFORMAL LEARNING FOR ANY SUBJECT DOMAIN AREA. USER TESTING IS CONDUCTED AND BOTH INFORMAL AND FORMAL DATA ARE STORED AND ELICITED FROM SHELTER TO INVESTIGATE THE IMPACT OF THIS DATA COMBINATION FOR MORE INSIGHTFUL LA RESULTS.
DESPITE THE UBIQUITY OF LEARNING IN WORKPLACE AND PROFESSIONAL SETTINGS, THE LEARNING ANALYTICS (LA) COMMUNITY HAS PAID SIGNIFICANT ATTENTION TO SUCH SETTINGS ONLY RECENTLY. THIS MAY BE DUE TO THE FOCUS ON RESEARCHING FORMAL LEARNING, AS WORKPLACE LEARNING IS OFTEN INFORMAL, HARD TO GRASP, AND NOT UNEQUIVOCALLY DEFINED. THIS ARTICLE SUMMARIZES THE STATE OF THE ART OF WORKPLACE LEARNING ANALYTICS (WPLA), EXTRACTED FROM A TWO-ITERATION SYSTEMATIC LITERATURE REVIEW. OUR IN-DEPTH ANALYSIS OF 52 EXISTING PROPOSALS NOT ONLY PROVIDES A DESCRIPTIVE VIEW OF THE FIELD, BUT ALSO REFLECTS ON RESEARCHER CONCEPTIONS OF LEARNING AND THEIR INFLUENCE ON THE DESIGN, ANALYTICS, AND TECHNOLOGY CHOICES MADE IN THIS AREA. WE ALSO DISCUSS THE CHARACTERISTICS OF WORKPLACE LEARNING THAT MAKE WPLA PROPOSALS DIFFERENT FROM LA IN FORMAL EDUCATION CONTEXTS AND THE CHALLENGES RESULTING FROM THIS. WE FOUND THAT WPLA IS GAINING MOMENTUM, ESPECIALLY IN SOME FIELDS, LIKE HEALTHCARE AND EDUCATION. THE FOCUS ON THEORY IS GENERALLY A POSITIVE FEATURE IN WPLA, BUT WE ENCOURAGE A STRONGER FOCUS ON ASSESSING THE IMPACT OF WPLA IN REALISTIC SETTINGS.
THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW OF THE COORDINATED USE OF LEARNING ANALYTICS AND COMPUTATIONAL ONTOLOGIES TO SUPPORT EDUCATORS IN THE PROCESS OF ACADEMIC PERFORMANCE EVALUATION OF STUDENTS. THE AIM IS TO PROVIDE A GENERAL OVERVIEW FOR RESEARCHERS ABOUT THE CURRENT STATE OF THIS RELATIONSHIP BETWEEN LEARNING ANALYTICS AND ONTOLOGIES, AND HOW THEY HAVE BEEN APPLIED IN A COORDINATED WAY. WE SELECTED 31 OF A TOTAL OF 1230 STUDIES RELATED TO THE RESEARCH QUESTIONS. THE RETRIEVED STUDIES WERE ANALYZED FROM TWO PERSPECTIVES: FIRST, WE ANALYZED THE APPROACHES WHERE RESEARCHERS USED LEARNING ANALYTICS AND ONTOLOGIES IN A COORDINATED WAY TO DESCRIBE SOME TAXONOMY OF EDUCATIONAL OBJECTIVES; IN THE SECOND PERSPECTIVE, WE SEEK TO IDENTIFY WHICH MODELS OR METHODS HAVE BEEN USED AS AN ANALYTICAL TOOL FOR EDUCATIONAL DATA. THE RESULTS OF THIS REVIEW SUGGEST THAT: 1) FEW STUDIES CONSIDER THAT STUDENT INTERACTIONS IN THE LEARNING MANAGEMENT SYSTEM CAN REPRESENT STUDENTS' LEARNING EXPERIENCES; 2) MOST STUDIES USE ONTOLOGIES IN THE CONTEXT OF LEARNING OBJECT ASSESSMENT TO ENABLE LEARNING SEQUENCING; 3) WE DID NOT IDENTIFY METHODS OF EVALUATION OF ACADEMIC PERFORMANCE GUIDED BY TAXONOMIES OF EDUCATIONAL OBJECTIVES; AND 4) NO STUDIES WERE IDENTIFIED THAT REPORT THE COORDINATED USE OF LEARNING ANALYTICS AND COMPUTATIONAL ONTOLOGIES, IN THE CONTEXT OF ACADEMIC PERFORMANCE MONITORING. THUS, WE IDENTIFY FUTURE DIRECTIONS OF RESEARCH SUCH AS THE PROPOSAL OF A NEW MODEL OF EVALUATION OF ACADEMIC PERFORMANCE.
STUDENT ENGAGEMENT IS ONE OF THE MOST RELEVANT TOPICS WITHIN THE ACADEMIC AND RESEARCH COMMUNITY NOWADAYS. HIGHER EDUCATION CURRICULUM, TEACHING AND LEARNING INTEGRATE NEW TECHNOLOGY- SUPPORTED LEARNING SOLUTIONS. NEW METHODS AND TOOLS ENHANCE TEACHER AND LEARNER INTERACTIONS AND INFLUENCE LEARNER ENGAGEMENT POSITIVELY. THIS RESEARCH ADDRESSES THE NEED TO EXPLORE NEW WAYS OF IMPROVING TEACHING PRACTICES TO BETTER ENGAGE STUDENTS WITH THE HELP OF LEARNING ANALYTICS. THE PAPER INVESTIGATES HOW UNIVERSITY TEACHERS USE THE DATA FROM LEARNING ANALYTICS TO OBSERVE LEARNERS AND TO ENGAGE THEM IN ONLINE LEARNING. QUALITATIVE INQUIRY WAS CHOSEN TO APPROACH THE RESEARCH PROBLEM, AND SEMI-STRUCTURED INTERVIEWS WITH THE TEACHERS USING (BLENDED) ONLINE LEARNING WERE CONVEYED TO EXPLORE TEACHER PRACTICES IN STUDENTS' BEHAVIOUR AND ENGAGEMENT OBSERVATIONS ONLINE, DISCLOSING TEACHERS' ABILITIES TO UNDERSTAND THE CHALLENGING LEARNER ENGAGEMENT PROCESS BASED ON THE DATA FROM LEARNING ANALYTICS. THE NEW EVIDENCE PROVIDED BY THIS RESEARCH HIGHLIGHTS THE SUCCESSFUL PRACTICES IN THE USE OF LEARNING ANALYTICS DATA TO OBSERVE STUDENTS' BEHAVIOUR AND ENGAGEMENT AND TO INFORM TEACHERS ON THE PRESENCE NEEDED IN ORDER TO DEVELOP LEARNER-CENTRED ACTIVITIES AND TO MAKE CURRICULUM CHANGES. THE LIMITATION OF THIS STUDY LIES IN THE FACT THAT THE DIFFERENT ONLINE TEACHING EXPERIENCES THAT RESEARCH PARTICIPANTS HAD MIGHT HAVE RESTRICTED THEIR UNDERSTANDING OF THE USE OF LA DATA FOR CURRICULUM DEVELOPMENT AND LEARNERS' ENGAGEMENT.
TRADITIONAL LOG-BASED LEARNING ANALYTICS (LA) METHODS FAIL TO CAPTURE THE INTERACTIONS THAT OCCURRED DURING COLLABORATION IN COLLOCATED OR FACE-TO-FACE SETTINGS. MULTIMODAL LEARNING ANALYTICS (MMLA) HAS FACILITATED RESEARCHERS A WAY FOR ADDRESSING THIS ISSUE WITH THE HELP OF SENSORS FOR AUTOMATED CAPTURING OF LEARNING TRACES FROM REAL-WORLD SETTINGS, AND MACHINE LEARNING METHODS HELPED RESEARCHERS TO EXTRACT THE PATTERN FROM THE COLLECTED DATA TO IMPROVE LEARNING. MY PH.D. ADDRESSES THE PROBLEM OF AUTOMATICALLY ESTIMATING THE QUALITY OF COLLABORATION IN COLLOCATED SETTINGS USING MMLA TO SUPPORT TEACHERS WHEN MONITORING AND ASSESSING IT IN AUTHENTIC CLASSROOM SETTINGS. THIS PAPER DISCUSSES THE MOTIVATION, THE RESEARCH PROBLEM, AND THE RESEARCH METHODOLOGY OF MY PH.D. IT ALSO REPORTS THE INITIAL RESEARCH FINDINGS IN ADDITION TO THE FUTURE WORK DIRECTIONS.
THE LEARNING ANALYTICS AND KNOWLEDGE (LAK) DATASET REPRESENTS AN UNPRECEDENTED CORPUS WHICH EXPOSES A NEAR COMPLETE COLLECTION OF BIBLIOGRAPHIC RESOURCES FOR A SPECIFIC RESEARCH DISCIPLINE, NAMELY THE CONNECTED AREAS OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING. COVERING OVER FIVE YEARS OF SCIENTIFIC LITERATURE FROM THE MOST RELEVANT CONFERENCES AND JOURNALS, THE DATASET PROVIDES LINKED DATA ABOUT BIBLIOGRAPHIC METADATA AS WELL AS FULL TEXT OF THE PAPER BODY. THE LATTER WAS ENABLED THROUGH SPECIAL LICENSING AGREEMENTS WITH ACM FOR PUBLICATIONS NOT YET AVAILABLE THROUGH OPEN ACCESS. THE DATASET HAS BEEN DESIGNED FOLLOWING ESTABLISHED LINKED DATA PATTERN, REUSING ESTABLISHED VOCABULARIES AND PROVIDING LINKS TO ESTABLISHED SCHEMAS AND ENTITY COREFERENCES IN RELATED DATASETS. GIVEN THE TEMPORAL AND TOPIC COVERAGE OF THE DATASET, BEING A NEARCOMPLETE CORPUS OF RESEARCH PUBLICATIONS OF A PARTICULAR DISCIPLINE, IT FACILITATES SCIENTOMETRIC INVESTIGATIONS, FOR INSTANCE, ABOUT THE EVOLUTION OF A SCIENTIFIC FIELD OVER TIME, OR CORRELATIONS WITH OTHER DISCIPLINES, WHAT IS DOCUMENTED THROUGH ITS USAGE IN A WIDE RANGE OF SCIENTIFIC STUDIES AND APPLICATIONS.
ASSESSMENT OF COURSE LEARNING OUTCOMES IS A FUNDAMENTAL MEASURE OF STUDENT LEARNING SUCCESS IN A COURSE. THIS ASSESSMENT IS CORE MEASURE FOR EDUCATIONAL EFFECTIVENESS REQUIRED BY ANY SPECIALIZED ACCREDITATION BODY. LEARNING ANALYTICS IS A POWERFUL MEAN TO INFORM LEARNERS, INSTRUCTORS AND EDUCATIONAL INSTITUTIONS ABOUT THE STUDENT'S PERFORMANCE AND THE PROGRESS OF THE LEARNING PROCESS AND TO SUPPORT THE STUDENT SUCCESS. MANY USEFUL LA TOOLS ARE AVAILABLE IN DIFFERENT LMS PLATFORMS SUCH AS MOODLE. THEY ARE USED TO REPORT STUDENT PERFORMANCE IN SPECIFIC ACTIVITY. HOWEVER THERE IS A LACK OF INTEGRATED LA TOOL THAT CAN ASSESS COURSE LEARNING OUTCOMES AND PREDICT THE STUDENT PERFORMANCE AND ACHIEVEMENT AGAINST SPECIFIC OUTCOMES. IN THIS PAPER, WE PROPOSE A NEW FRAMEWORK FOR DEVELOPING LEARNING ANALYTICS TOOL TO ASSESS THESE OUTCOMES IN THE WELL-KNOWN OPEN SOURCE LMS MOODLE. OUR FRAMEWORK IS BASED ON DIRECT MEASURE OF THE COURSE LEARNING OUTCOMES THROUGH EMBEDDED EXAMINATION OF EACH OUTCOME AND ITS ASSOCIATED LMS ACTIVITIES, THEN TO ANALYZE THE RESULT IN ORDER TO EVALUATE THE ACHIEVEMENT OF PROGRAM OUTCOMES AND TO PROPOSE IMPROVEMENT FOR THE COMING SEMESTER WHERE THE STUDIED COURSE WILL BE OFFER.
DISTANCE LEARNING HAS ENABLED EDUCATIONAL PRACTICES BASED ON DIGITAL PLATFORMS, GENERATING MASSIVE AMOUNTS OF DATA. SEVERAL INITIATIVES USE THIS DATA TO IDENTIFY DROPOUT CONTEXTS, MAINLY PROVIDING TEACHER SUPPORT ABOUT STUDENT BEHAVIOR. APPROACHES SUCH AS ACTIVE METHODOLOGIES ARE KNOWN AS HAVING GOOD POTENTIAL TO INVOLVE AND MOTIVATE STUDENTS. THIS ARTICLE PRESENTS A SYSTEMATIC MAPPING AIMING TO IDENTIFY CURRENT EDUCATIONAL DATA MINING AND LEARNING ANALYTICS METHODS. BESIDES, WE IDENTIFY ACTIVE METHODOLOGIES' APPLICATION TO MITIGATE DROPOUT IN DISTANCE LEARNING. WE EVALUATED 668 PAPERS PUBLISHED FROM JANUARY 2015 TO MARCH 2020. THE RESULTS INDICATE A GROWING APPLICATION OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS TO IDENTIFY AND MITIGATE STUDENTS' ABANDONMENT IN DISTANCE LEARNING. HOWEVER, STUDIES WITH ACTIVE METHODOLOGIES TO MINIMIZE DROPOUT AND ENHANCE STUDENT PERMANENCE ARE SCARCE. SOME WORKS SUGGEST ACTIVE METHODS AS A POSSIBLE COMPLEMENT OF LEARNING ANALYTICS IN DROPOUT.
INSTRUCTIONAL DESIGN (ID), ESSENTIAL IN DISTANCE LEARNING, IS THE PLANNING AND SEQUENCING OF ACTIVITIES IN A COURSE. THE HIGH LEVEL ACTIVITIES NET (HLAN) IS A NET STRUCTURE FOR DESIGNING AND CONTROLLING THE LEARNING PROCESS. FROM THE NET FORMED IT IS POSSIBLE TO MONITOR STUDENTS IN RUN-TIME AND TO OBTAIN INFORMATION THAT HELPS BOTH THE IMPROVEMENT OF THE INSTRUCTIONAL DESIGN AND STUDENTS LEARNING BY MEANS OF LEARNING ANALYTICS. THIS PAPER BRIEFLY INTRODUCES THE HLAN AND PRESENTS AN INVESTIGATION WITH 17 CLASSES OF A DISTANCE COURSE, WITH A TOTAL OF 673 STUDENTS. BASED ON THE INFORMATION OBTAINED BY MEANS OF LEARNING ANALYTICS, WE OBSERVED THE RELATIONSHIP BETWEEN THE STUDENTS PERFORMANCE (WITH RESPECT TO HIS/HER SCORES), THE IDENTIFICATION OF MODULES OF THE COURSE THAT MUST BE ANALYZED AND ADAPTED REGARDING THE DEGREE OF QUALITY AND DIFFICULTY DEMANDED, AND ALSO A WEAK RELATIONSHIP BETWEEN THE NUMBER OF ACCESSES TO A COURSE AND THE STUDENT'S PERFORMANCE.
THE EDUCATIONAL LANDSCAPE CONTINUES TO TRANSFORM SIGNIFICANTLY IN A WAY THAT IT ENGAGES STUDENTS' INTEREST AND IMPROVES LEARNING PROCESSES. SUCH AN EXAMPLE IS THE GRADUAL USE OF EDUCATIONAL GAMES, WHICH SEEM TO BE AN EXCEPTIONAL MEANS TO MOTIVATE AND ENGAGE STUDENTS IN WAYS THAT WILL MAKE LEARNING MORE CHALLENGING AND INTERESTING FOR THEM. HOWEVER, ALTHOUGH EDUCATIONAL GAMES ARE A VERY INTERESTING TOOL THAT COULD BE USED IN EDUCATION, AN INTERESTING CHALLENGE THAT SHOULD BE ADDRESSED IS THE IDENTIFICATION OF ALL THE WAYS STUDENTS' PROGRESS AND RESULTS CAN BE MEASURED FOR EXAMINATION. THIS WAY, THERE IS A HIGH DEMAND FOR ACCOUNTABILITY REGARDING THE MEASUREMENT AND ANALYSIS OF ENRICHED GAME BASED LEARNING ACTIVITIES OR GAME-BASED LEARNING ANALYTICS (GBLA). THIS DEMAND IS ESPECIALLY HIGHLIGHTED IN ONE OF THE MOST DIFFICULT AREAS OF COMPUTER SCIENCE, WHICH IS COMPUTER PROGRAMMING EDUCATION. IN THIS PAPER, WE ARE PROPOSING A FRAMEWORK THAT WILL GUIDE THE INCORPORATION OF LEARNING ANALYTICS MECHANISMS IN COMPUTER PROGRAMMING EDUCATION. WE DEMONSTRATE THIS PROCESS THROUGH THE IMPLEMENTATION OF THE FRAMEWORK IN AN EDUCATIONAL MASSIVE MULTIPLAYER ONLINE ROLE-PLAYING GAME WE HAVE DEVELOPED FOR THE DOMAIN.
LEARNING TAKES PLACE IN A VARIETY OF DIFFERENT ENVIRONMENTS, BOTH IN-CLASS AS OF FORMAL LEARNING AND OUTSIDE AS INFORMAL LEARNING. SEAMLESS LEARNING OFFERS THE OPPORTUNITY TO LEARN IN DIFFERENT ENVIRONMENTS REGARDLESS OF LOCATION OR TIME. STUDENTS CAN BENEFIT FROM LEARNING IN MANY CONTEXTS, AND IT CAN ALSO PROVIDE INSIGHTS FOR TEACHERS INTO HOW LEARNING IS BEING CONDUCTED IN INFORMAL SITUATIONS OUTSIDE THE CLASSROOM. PREVIOUS WORK INTO THE ANALYSIS OF SEAMLESS LEARNING HAS MAINLY FOCUSED ON PURPOSE BUILD SPECIALIZED SYSTEMS THAT PROVIDE AN ENVIRONMENT FOR A SPECIFIC TASK. IN RECENT YEARS, THE FIELD OF LEARNING ANALYTICS HAS BEEN GROWING AND LEARNING ENVIRONMENTS ARE INCREASINGLY BEING DEVELOPED ACROSS DIFFERENT MODULAR SYSTEMS THAT ARE LINKED TOGETHER BY STANDARDS-BASED PROTOCOLS. IN ORDER TO ANALYZE SEAMLESS LEARNING ON A LARGER SCALE, THIS PAPER PROPOSES THE INTEGRATION OF AN INFORMAL LEARNING SYSTEM, SCROLL, INTO A WIDER MODULAR SYSTEM. THE PROPOSED SYSTEM ADDRESSES FUNDAMENTAL PROBLEMS, SUCH AS THE PROTECTION OF USER PRIVACY AND AUTHENTICATION WHILE INCREASING THE AVAILABILITY OF DATA FOR ANALYSIS FROM OTHER LEARNING SYSTEMS. WE ALSO EXAMINE THE ABILITY TO ANALYZE AND VISUALIZE LEARNING ACROSS NUMEROUS ENVIRONMENTS AND CONTEXTS.
LEARNING ANALYTICS IS A VAST CONCEPT AND A RAPIDLY GROWING FIELD IN HIGHER EDUCATION USED BY PROFESSORS TO MEASURE, COLLECT AND ANALYZE DIGITAL LEARNING RECORDS TO IMPROVE LEARNING, GENERATE NEW PEDAGOGIES, AND MAKE DECISIONS ABOUT TECHNOLOGY-DRIVEN LEARNING. THE FOLLOWING ARTICLE PRESENTS A MAPPING AND SYSTEMATIC LITERATURE REVIEW ON LEARNING ANALYTICS AND ITS LINK TO THE TEACHING SKILLS CARRIED OUT IN UNIVERSITY PRACTICE. THE RESEARCH PROCESS REVIEWED 7,886 ARTICLES DURING THE PERIOD FROM 2016 TO 2020. AFTER APPLYING THE INCLUSION AND EXCLUSION CRITERIA, 50 ARTICLES WERE ANALYZED IN-DEPTH UNDER THE DIMENSIONS OF (1) PURPOSES OF LEARNING ANALYTICS, (2) TEACHING COMPETENCIES, AND (3) TEACHING PRACTICE IN HIGHER EDUCATION. THIS WORK PROVIDES A BASIS FOR IDENTIFYING GAPS AND RESEARCH OPPORTUNITIES RELATED TO THE APPLICATION OF TEACHING COMPETENCIES IN THE FIELD OF LEARNING ANALYTICS AND INCORPORATING IT INTO TEACHING PRACTICE IN ONLINE TUTORING.
THERE IS AN INCREASING INTEREST IN MULTIMODAL LEARNING ANALYTICS (MMLA), WHICH INVOLVES COMPLEX TECHNICAL ISSUES IN GATHERING, MERGING AND ANALYZING DIFFERENT TYPES OF LEARNING DATA FROM HETEROGENEOUS DATA SOURCES. HOWEVER, THERE IS STILL NO COMMON REFERENCE ARCHITECTURE TO FACE THESE TECHNICAL CHALLENGES OF MMLA. THIS PAPER SUMMARIZES THE STATE OF THE ART OF MMLA SOFTWARE ARCHITECTURES THROUGH A SYSTEMATIC LITERATURE REVIEW. OUR ANALYSIS OF NINE ARCHITECTURE PROPOSALS HIGHLIGHTS THE UNEVEN SUPPORT PROVIDED BY EXISTING ARCHITECTURES TO THE DIFFERENT ACTIVITIES OF THE ANALYTICS DATA VALUE CHAIN (DVC). WE FIND OUT IN THOSE INFRASTRUCTURES THAT DATA ORGANIZATION AND DECISION-MAKING SUPPORT HAVE BEEN UNDER-EXPLORED SO FAR. BASED ON THE LESSONS LEARNT FROM THE REVIEW, WE ALSO IDENTIFY THAT DESIGN TENSIONS LIKE ARCHITECTURE DISTRIBUTION, FLEXIBILITY AND EXTENSIBILITY (AND AN INCREASED FOCUS ON DATA ORGANIZATION AND DECISION MAKING) ARE SOME OF THE MOST PROMISING ISSUES TO BE ADDRESSED BY THE MMLA COMMUNITY IN THE NEAR FUTURE.
ADVANCES IN LEARNING ANALYTICS ARE CONTRIBUTING NEW EMPIRICAL FINDINGS, THEORIES, METHODS, AND METRICS FOR UNDERSTANDING HOW STUDENTS LEARN. IT ALSO CONTRIBUTES TO IMPROVING PEDAGOGICAL SUPPORT FOR STUDENTS' LEARNING THROUGH ASSESSMENT OF NEW DIGITAL TOOLS, TEACHING STRATEGIES, AND CURRICULA. MULTIMODAL LEARNING ANALYTICS (MMLA)[1] IS AN EXTENSION OF LEARNING ANALYTICS AND EMPHASIZES THE ANALYSIS OF NATURAL RICH MODALITIES OF COMMUNICATION ACROSS A VARIETY OF LEARNING CONTEXTS. THIS MMLA GRAND CHALLENGE COMBINES EXPERTISE FROM THE LEARNING SCIENCES AND MACHINE LEARNING IN ORDER TO HIGHLIGHT THE RICH OPPORTUNITIES THAT EXIST AT THE INTERSECTION OF THESE DISCIPLINES. AS PART OF THE GRAND CHALLENGE, RESEARCHERS WERE ASKED TO PREDICT: (1) WHICH STUDENT IN A GROUP WAS THE DOMINANT DOMAIN EXPERT, AND (2) WHICH PROBLEMS THAT THE GROUP WORKED ON WOULD BE SOLVED CORRECTLY OR NOT. ANALYSES WERE BASED ON A COMBINATION OF SPEECH, DIGITAL PEN AND VIDEO DATA. THIS PAPER DESCRIBES THE MOTIVATION FOR THE GRAND CHALLENGE, THE PUBLICLY AVAILABLE DATA RESOURCES AND RESULTS REPORTED BY THE CHALLENGE PARTICIPANTS. THE RESULTS DEMONSTRATE THAT MULTIMODAL PREDICTION OF THE CHALLENGE GOALS: (1) IS SURPRISINGLY RELIABLE USING RICH MULTIMODAL DATA SOURCES, (2) CAN BE ACCOMPLISHED USING ANY OF THE THREE MODALITIES EXPLORED, AND (3) NEED NOT BE BASED ON CONTENT ANALYSIS.
LEARNING ANALYTICS IS AN EMERGING DISCIPLINE FOCUSED ON THE MEASUREMENT, COLLECTION, ANALYSIS AND REPORTING OF LEARNER INTERACTION DATA THROUGH THE E-LEARNING CONTENTS. SERIOUS GAME PROVIDES A POTENTIAL SOURCE FOR RELEVANT EDUCATIONAL USER DATA; IT CAN PROPOSE AN INTERACTIVE ENVIRONMENT FOR TRAINING AND OFFER AN EFFECTIVE LEARNING PROCESS. THIS PAPER PRESENTS METHODS AND APPROACHES OF EDUCATIONAL DATA MINING SUCH AS EM AND K-MEANS TO DISCUSS THE LEARNING ANALYTICS THROUGH SERIOUS GAMES, AND THEN WE PROVIDE AN ANALYSIS OF THE PLAYER EXPERIENCE DATA COLLECTED FROM THE EDUCATIONAL GAME "ELISA" USED TO TEACH STUDENTS OF BIOLOGY THE IMMUNOLOGICAL TECHNIQUE FOR DETERMINATION OF ANTI-HIV ANTIBODIES. FINALLY, WE PROPOSE CRITICALLY EVALUATION OF OUR RESULTS INCLUDING THE LIMITATIONS OF OUR STUDY AND MAKING SUGGESTIONS FOR FUTURE RESEARCH THAT LINKS LEARNING ANALYTICS AND SERIOUS GAMING.
THIS PAPER PRESENTS A LEARNING ANALYTICS FRAMEWORK FOR 3D EDUCATIONAL VIRTUAL WORLDS THAT FOCUS ON DISCOVERING LEARNING FLOWS AND CHECKING ITS CONFORMANCE THROUGH PROCESS MINING TECHNIQUES. THE CORE OF THIS FRAMEWORK IS AN OPENSIM-BASED VIRTUAL WORLD PLATFORM, KNOWN AS OPENET4VE, THAT IS COMPLIANT WITH THE IMS LEARNING DESIGN SPECIFICATION AND THAT HAS THE ABILITY OF MONITORING AND REGISTERING THE EVENTS GENERATED BY STUDENTS AND TEACHERS. BASED ON THESE EVENT LOGS, PROCESS MINING ALGORITHMS AUTOMATICALLY EXTRACT THE REAL LEARNING FLOW OF THE COURSE, ALLOWING TEACHERS TO INTRODUCE CHANGES IN THE LEARNING FLOW INITIALLY PROPOSED. (C) 2013 THE AUTHORS. PUBLISHED BY ELSEVIER B.V.
WE DEVELOP A NEW MODEL AND ALGORITHM FOR MACHINE LEARNING-BASED LEARNING ANALYTICS, WHICH ESTIMATE A LEARNER'S KNOWLEDGE OF THE CONCEPTS UNDERLYING A DOMAIN. OUR MODEL REPRESENTS THE PROBABILITY THAT A LEARNER PROVIDES THE CORRECT RESPONSE TO A QUESTION IN TERMS OF THREE FACTORS: THEIR UNDERSTANDING OF A SET OF UNDERLYING CONCEPTS, THE CONCEPTS INVOLVED IN EACH QUESTION, AND EACH QUESTION'S INTRINSIC DIFFICULTY. WE ESTIMATE THESE FACTORS GIVEN THE GRADED RESPONSES TO A SET OF QUESTIONS. WE DEVELOP A BI-CONVEX ALGORITHM TO SOLVE THE RESULTING SPARSE FACTOR ANALYSIS (SPARFA) PROBLEM. WE ALSO INCORPORATE USER-DEFINED TAGS ON QUESTIONS TO FACILITATE THE INTERPRETABILITY OF THE ESTIMATED FACTORS. EXPERIMENTS WITH SYNTHETIC AND REAL-WORLD DATA DEMONSTRATE THE EFFICACY OF OUR APPROACH.
"LEARNING DESIGN" BELONGS TO THAT INTERESTING CLASS OF CONCEPTS THAT APPEAR ON THE SURFACE TO BE SIMPLE AND SELF-EXPLANATORY, BUT WHICH ARE ACTUALLY DEFINITIONALLY VAGUE AND CONTESTED IN PRACTICE. LIKE "LEARNING ANALYTICS," THE FIELD OF LEARNING DESIGN ASPIRES TO IMPROVE TEACHING PRACTICE, THE LEARNING EXPERIENCE, AND LEARNING OUTCOMES. AND LIKE LEARNING ANALYTICS, THIS INTERDISCIPLINARY FIELD ALSO LACKS A SHARED LANGUAGE, COMMON VOCABULARY, OR AGREEMENT OVER ITS DEFINITION AND PURPOSE, RESULTING IN UNCERTAINTY EVEN ABOUT WHO ITS PRACTITIONERS ARE - EDUCATORS? DESIGNERS? RESEARCHERS? ALL OF THESE? (LAW, LI, FARIAS HERRERA, CHAN & PONG, 2017). ALMOST A DECADE AGO, HOWEVER, LEARNING ANALYTICS RESEARCHERS POINTED TO THE RICH POTENTIAL FOR SYNERGIES BETWEEN LEARNING ANALYTICS AND LEARNING DESIGN (LOCKYER & DAWSON, 2011). THESE AUTHORS (AND OTHERS SINCE, AS CITED BELOW) ARGUED THAT EFFECTIVE ALIGNMENT OF LEARNING ANALYTICS AND LEARNING DESIGN WOULD BENEFIT BOTH FIELDS, AND WOULD OFFER EDUCATORS AND INVESTIGATORS THE EVIDENCE THEY NEED THAT THEIR EFFORTS AND INNOVATIONS IN LEARNING DESIGN ARE "WORTH IT" IN TERMS OF IMPROVING TEACHING PRACTICE AND LEARNING: THE INTEGRATION OF RESEARCH RELATED TO BOTH LEARNING DESIGN AND LEARNING ANALYTICS PROVIDES THE NECESSARY CONTEXTUAL OVERLAY TO BETTER UNDERSTAND OBSERVED STUDENT BEHAVIOR AND PROVIDE THE NECESSARY PEDAGOGICAL RECOMMENDATIONS WHERE LEARNING BEHAVIOR DEVIATES FROM PEDAGOGICAL INTENTION (LOCKYER & DAWSON, 2011, P. 155).
WHILE VIRTUAL REALITY HAS ATTRACTED EDUCATORS' INTEREST BY PROVIDING NEW OPPORTUNITIES TO THE LEARNING PROCESS AND ASSESSMENT IN DIFFERENT SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) SUBJECTS, THE RESULTS FROM PREVIOUS STUDIES INDICATE THAT THERE IS STILL MUCH WORK TO BE DONE WHEN LARGE DATA COLLECTION AND ANALYSIS IS CONSIDERED. AT THE SAME TIME, LEARNING ANALYTICS EMERGED WITH THE PROMISE TO REVOLUTIONISE THE TRADITIONAL PRACTICES BY INTRODUCING NEW WAYS TO SYSTEMATICALLY ASSESS AND IMPROVE THE EFFECTIVENESS OF INSTRUCTION. HOWEVER, THE COLLECTION OF 'BIG' EDUCATIONAL DATA IS MOSTLY ASSOCIATED WITH WEB-BASED PLATFORMS (I.E., LEARNING MANAGEMENT SYSTEMS) AS THEY OFFER DIRECT ACCESS TO STUDENTS' DATA WITH MINIMAL EFFORT. THENCE, IN THE CONTEXT OF THIS WORK, WE PRESENT A FOUR-DIMENSIONAL THEORETICAL FRAMEWORK FOR VIRTUAL REALITY-SUPPORTED INSTRUCTION AND PROPOSE A SET OF STRUCTURAL ELEMENTS THAT CAN BE UTILISED IN CONJUNCTION WITH A LEARNING ANALYTICS PROTOTYPE SYSTEM. THE OUTCOMES OF THIS WORK ARE EXPECTED TO SUPPORT PRACTITIONERS ON HOW TO MAXIMISE THE POTENTIAL OF THEIR INTERVENTIONS AND PROVIDE FURTHER INSPIRATION FOR THE DEVELOPMENT OF NEW ONES.
UNDERSTANDING HOW AN EDUCATIONAL INTERVENTION IS IMPLEMENTED IS ESSENTIAL TO EVALUATING ITS EFFECTIVENESS. WITH THE INCREASED USE OF DIGITAL TOOLS IN CLASSROOMS, HOWEVER, TRADITIONAL METHODS OF MEASURING IMPLEMENTATION FALL SHORT. FORTUNATELY, THERE IS A WAY TO LEARN ABOUT THE INTERACTIONS THAT USERS HAVE WITH DIGITAL TOOLS THAT ARE EMBEDDED INTO THE TECHNOLOGIES THEMSELVES: USER DATA. THE PURPOSE OF THIS ARTICLE IS TO OUTLINE WAYS IN WHICH RESEARCHERS CAN HARNESS LEARNING ANALYTICS AND USER DATA TO GAIN A DEEPER UNDERSTANDING OF THE IMPLEMENTATION OF DIGITAL INNOVATIONS AND THEIR IMPACT ON TEACHING AND LEARNING. WE DISCUSS FOUR CONSIDERATIONS FOR THE INTEGRATION OF LEARNING ANALYTICS AND USER DATA IN IMPLEMENTATION RESEARCH, AND WE PROVIDE AN EXAMPLE OF INTEGRATION FROM AN EVALUATION OF A DIGITAL SCIENCE CURRICULUM, STEMSCOPES.
WITH THE INCREASED CAPABILITY OF LEARNING ANALYTICS IN HIGHER EDUCATION, MORE INSTITUTIONS ARE DEVELOPING OR IMPLEMENTING STUDENT DASHBOARDS. DESPITE THE EMERGENCE OF DASHBOARDS AS AN EASY WAY TO PRESENT DATA TO STUDENTS, STUDENTS HAVE HAD LIMITED INVOLVEMENT IN THE DASHBOARD DEVELOPMENT PROCESS. AS PART OF A LARGER PROGRAM OF RESEARCH EXAMINING STUDENT AND ACADEMIC PERCEPTIONS OF LEARNING ANALYTICS, WE REPORT HERE ON WORK IN PROGRESS EXPLORING STUDENT PERCEPTIONS OF DASHBOARDS AND STUDENT PREFERENCES FOR DASHBOARD FEATURES. FIRST, WE PRESENT FINDINGS ON HIGHER EDUCATION STUDENTS' ATTITUDES TOWARDS LEARNING ANALYTIC DASHBOARDS RESULTING FROM FOUR FOCUS GROUPS (N = 41). THEMATIC ANALYSIS OF THE FOCUS GROUP TRANSCRIPTS IDENTIFIED FIVE KEY THEMES RELATING TO DASHBOARDS: 'PROVIDE EVERYONE WITH THE SAME LEARNING OPPORTUNITIES', 'TO COMPARE OR NOT TO COMPARE', 'DASHBOARD PRIVACY', 'AUTOMATE ALERTS' AND 'MAKE IT MEANINGFUL-GIVE ME A CUSTOMIZABLE DASHBOARD'. NEXT WE PRESENT FINDINGS FROM A CONTENT ANALYSIS OF STUDENTS' DRAWINGS OF DASHBOARDS DEMONSTRATING THAT STUDENTS ARE INTERESTED IN FEATURES THAT SUPPORT LEARNING OPPORTUNITIES, PROVIDE COMPARISONS TO PEERS AND ARE MEANINGFUL TO THE STUDENT. FINALLY, WE PRESENT PRELIMINARY FINDINGS FROM A SURVEY OF HIGHER EDUCATION STUDENTS, REINFORCING STUDENTS' DESIRE TO CHOOSE WHETHER TO HAVE A DASHBOARD AND TO BE ABLE TO CUSTOMIZE THEIR DASHBOARDS. THESE FINDINGS HIGHLIGHT THE POTENTIAL FOR PROVIDING STUDENTS WITH SOME LEVEL OF CONTROL OVER LEARNING ANALYTICS AS A MEANS TO INCREASING SELF-REGULATED LEARNING AND ACADEMIC ACHIEVEMENT. FUTURE RESEARCH DIRECTIONS AIMED AT BETTER UNDERSTANDING STUDENTS EMOTIONAL AND BEHAVIORAL RESPONSES TO LEARNING ANALYTICS FEEDBACK ON DASHBOARDS AND ALERTS ARE OUTLINED.
THE WIDE RANGE OF DATA PRODUCED BY PARTICIPANTS IN THE LEARNING PROCESSES LED TO INCREASED INTEREST IN THE COLLECTION AND ANALYSIS OF DATA TO SUPPORT DATA-DRIVEN DECISION MAKING AT ALL LEVELS OF EDUCATIONAL INSTITUTIONS. THE PAPER IS DEVOTED TO THE EMERGING FIELD OF LEARNING ANALYTICS FROM THE TEACHER'S PERSPECTIVE. IT PROPOSES A SET OF INDICATORS THAT LEARNING ANALYTICS SHOULD PROVIDE TO HELP TEACHERS BE MORE REFLECTIVE IN THEIR TEACHING PRACTICE, TO IMPROVE THE QUALITY OF EDUCATION PROVIDED IN THEIR COURSES AND RELEVANCE OF THE LEARNING MATERIALS, TO DISCOVER MORE ABOUT STUDENTS' LEARNING, TO TRACK AND PREDICT STUDENTS' PERFORMANCE, TO IDENTIFY POTENTIAL ISSUES AND STUDENTS AT RISK. A MOBILE APPLICATION THAT ALLOWS TEACHERS TO TRACE OUT THE VALUES OF THESE INDICATORS ON THE BASIS OF DATA GATHERED FROM LEARNING MANAGEMENT SYSTEM IS DESIGNED AND IMPLEMENTED. THE APPLICATION CAPTURES, ANALYSES AND VISUALIZES DATA IN ORDER TO HELP TEACHERS IDENTIFY THE OPPORTUNITIES FOR IMPROVEMENTS OF THE QUALITY OF COURSES AND ENHANCING OF PERFORMANCE OF THEIR STUDENTS. USING THE APPLICATION, TEACHERS ARE ABLE TO KEEP TRACK ON ACTIVITY AND PROGRESS OF THEIR STUDENTS, ADHERENCE TO THE LEARNING SCHEDULE, AS WELL AS TO QUICKLY IDENTIFY STUDENTS WHO ARE AT RISK FOR FAILING OR DROPPING OUT AT AN EARLIER STAGE THAN IT OTHERWISE WOULD BE POSSIBLE.
DIGITALIZATION AND TECHNOLOGY IN SCHOOLS CONTINUES TO BE HOT TOPICS. IN THIS PAPER, WE SHARE OUR EXPERIENCES AND FINDINGS ON HOW TO BUILD AND DISTRIBUTE AN EDUCATIONAL SYSTEM THAT CAN BE USED AS A FRAMEWORK FOR LEARNING ANALYTICS. WE DISCUSS THE KEY FEATURES AND IDEOLOGY BEHIND THE SYSTEM BUT ALSO HOW TO TRAIN TEACHERS SUCCESSFULLY TO USE THE SYSTEM. FOR THIS, WE PRESENT THREE DIFFERENT TRAINING MODELS AND SHARE OUR EXPERIENCES AND TEACHERS' FEEDBACK ON THEM. THE NUMBER OF USER HAS INCREASED RAPIDLY AFTER IMPLEMENTING THE DIFFERENT TRAINING MODELS AND FEEDBACK FROM TEACHERS IS VERY POSITIVE.
STUDENTS' RETENTION AND DROPOUT FROM DEGREE COURSES BEFORE THEIR COMPLETION CONTINUE TO REPRESENT ISSUES WHICH CHALLENGE RESEARCHERS, TEACHERS AND HIGHER EDUCATION INSTITUTIONS TO SEEK SOLUTIONS. HIGHER EDUCATION INSTITUTIONS HAVE BEEN ATTEMPTING DIFFERENT STRATEGIES TO PROMOTE THEIR STUDENTS' SUCCESS, AMONG WHICH WE HIGHLIGHT THE IMPLEMENTATION AND USE OF VIRTUAL LEARNING ENVIRONMENTS (VLES). THROUGH THE USE OF LEARNING ANALYTICS IN THE ANALYSIS OF DATA REGARDING THE FREQUENCY OF ACCESS TO A VLE AS WELL AS THE LEARNING OUTCOMES OF A SAMPLE OF 2,636 UNDERGRADUATES, WE AIMED TO IDENTIFY INDICATORS WHICH CONTRIBUTE TO THE POSSIBLE PREDICTION OF STUDENT RETENTION AND DROPOUT SITUATIONS. THE METHODOLOGY FOLLOWED IS ESSENTIALLY QUANTITATIVE AND THE DESK REVIEW WAS THE MAIN DATA COLLECTION TOOL. THE DATA WAS TREATED BY USING THE STATISTIC SOFTWARE SPSS FOR THE ORGANISATION AND DESCRIPTIVE STATISTIC PRESENTATION, AS WELL AS INFERENTIAL STATISTICS THROUGH APPROPRIATE STATISTIC TESTS. THE OUTCOMES OF THIS STUDY RESULTED IN CONTRIBUTIONS WHICH ENABLE THE PREDICTION OF UNDERGRADUATES' RETENTION OR DROPOUT SINCE WE WERE ABLE TO PROVE THAT THE LOWER THE FREQUENCY OF ACCESS TO THE INSTITUTION'S VIRTUAL LEARNING ENVIRONMENT, THE LOWER THE ATTENDANCE TO ON-SITE LESSONS AND THE LOWER THE NUMBER OF COURSE UNITS IN WHICH STUDENTS OBTAIN A PASSING MARK. ABSENTEEISM AND THE LACK OF COURSE UNITS PASSED REPRESENT INDICATORS OF SCHOOL DROPOUT AND RETENTION. THIS RESEARCH WORK REVEALS TO BE OF GREAT IMPORTANCE SINCE, THROUGH A FRAMEWORK ASSOCIATED WITH LEARNING ANALYTICS, IT PROVIDES INDICATORS SUPPORTED BY A LARGE AMOUNT OF RECENT AND VALIDATED DATA, WHICH ENABLES US TO RETHINK THE CONNECTION BETWEEN UNDERGRADUATES' ACCESS TO A VIRTUAL LEARNING ENVIRONMENT AND ITS INFLUENCE ON EDUCATIONAL ATTAINMENT.
INCREASINGLY, MASSIVE OPEN ONLINE COURSES (MOOCS) ARE WIDELY USED AND HAVE BECOME A KEY INSTRUMENT IN TECHNOLOGY ENHANCED LEARNING (TEL) MODELS IN THE LAST FEW YEARS. HOWEVER, THE KEY CHALLENGE WITH THIS KIND OF LARGER SCALES PLATFORMS IS HOW TO PROVIDE COURSE PARTICIPANTS WITH A QUALITY LEARNING MATERIALS THAT PROMOTE EFFECTIVE LEARNING BASED ON THEIR NEEDS. INDEED, THIS REQUIRES CAREFUL PLANNING, MONITORING AND EVALUATION OF ALL LEARNING ACTIVITIES. RECENTLY, LEARNING ANALYTICS AND RECOMMENDER SYSTEMS ARE WIDELY USED IN MOOCS TO OVERCOME THIS CHALLENGE IN PROVIDING PERSONALIZATION AND ACCESSIBILITY LEARNING MATERIALS FOR COURSE PARTICIPANTS. THE PURPOSE OF THE CURRENT STUDY WAS TO DETERMINE THE USABILITY AND EFFECTIVENESS OF A PERSONALIZED LINKS RECOMMENDATION TOOL BASED ON LEARNING ANALYTICS IN MOOCS. THIS PERSONALIZED LINKS RECOMMENDATION TOOL WAS UNDERTAKEN THE POWER OF CROWD SOURCING TO PROVIDE COURSE PARTICIPANTS WITH AN HIGH QUALITY LEARNING MATERIAL FROM EXTERNALS RECOURSES, E.G., OPEN EDUCATION RESOURCES (OER). THE PRESENT STUDY MAKES SEVERAL NOTEWORTHY CONTRIBUTIONS SUCH AS RESEARCHING THE MAPPING OF LEARNING DATA, AN OPEN PERSONALIZED - LINKS RECOMMENDATION ARCHITECTURE, AND A USER-FRIENDLY AND DYNAMIC INTERFACE TO DELIVER THE RECOMMENDATIONS.
GAME-BASED LEARNING HAS BEEN CONSIDERED IN DIFFERENT STUDIES AS A SUPPLEMENTARY TOOL IF NOT AN ALTERNATIVE TOOL FOR TRADITIONAL CLASSROOM LEARNING. GAME-BASED LEARNING (GBL), TOGETHER WITH PERSONALIZED LEARNING ENVIRONMENT (PLE) AND LEARNING MANAGEMENT SYSTEMS (LMS) ARE PART OF THE DIGITAL LEARNING ENVIRONMENT WHERE THERE IS AN INCREASING DEMAND OF CONVERTING, TRANSLATING, INTERPRETING AND PRESENTING LEARNING-RELATED DATA FOR DIFFERENT KINDS OF ANALYSIS - LEARNING ANALYTICS. LEARNING ENVIRONMENTS SUCH AS PLES AND LMSS ARE THE COMMON SOURCE OF THESE DATA BUT ANOTHER RICH DATA SOURCE IS THE GBL ENVIRONMENT. DATA FROM GBL ENVIRONMENT ARE OFTEN UNDER COLLECTED IF NOT UNCOLLECTED THEREFORE NOT TRANSLATED AND ANALYZED PROPERLY. THIS RESEARCH PROPOSES A LEARNING ANALYTICS FRAMEWORK APPLIED TO A DIGITAL GBL ENVIRONMENT TO BE ABLE TO MATERIALIZE THEIR RELATIONSHIP. THE PROPOSED FRAMEWORK INCLUDES A WAY TO CAPTURE, TRANSLATE, PRESENT AND SHARE DATA FROM DIFFERENT GAME EVENTS THAT MAY CONTRIBUTE IN THE LEARNING PROCESS. THE RESEARCH ALSO INCLUDES A PROOF-OF-CONCEPT IMPLEMENTATION USING IOS AS THE GAME PLATFORM AND THE BUILT IN GAME CENTER OF IOS FOR PRESENTATION OF THE DATA COLLECTED. THE PROPOSED FRAMEWORK AND DATA PRESENTATION THROUGH THE GAME CENTER RECEIVED A POSITIVE FEEDBACK FROM THE STAKEHOLDERS - LEARNERS AND EDUCATORS.
THE LEARNING MANAGEMENT SYSTEMS (LMS) IN THE EDUCATION SECTOR IS WIDELY USED AND IT EVOLVED NOWADAYS. IT LED TO ENORMOUS AND SEVERAL OF DATA FROM THE STUDENTS' ACTIVITIES USING THE ONLINE CONTENT. IT GENERATES A LARGE AMOUNT OF WASTED AND UNUSED DATA AND THESE ISSUES CANNOT BE PROCESSED AND SUPPORTED BY THE TRADITIONAL LEARNING ANALYTICS. THE SOPHISTICATED TECHNOLOGY OF BIG DATA AND ALSO BECAME A RECENT TREND NOW IS VASTLY EVOLVED AND RELATES TO DATA-DRIVEN DECISION MAKING. IT IS KNOWN AS BIG DATA ANALYTICS AND SIGNIFICANCE TO APPLY FOR E-LEARNING IN HIGHER EDUCATION INSTITUTION. THIS PAPER REVIEWS AND EXPLORES THE USE OF BIG DATA IN E-LEARNING AS THE FUNDAMENTAL OF BIG DATA AND E-LEARNING AND ITS' IMPACT. BIG DATA ANALYTICS HAS BEEN PROVED AS AN EFFECTIVE APPROACH FOR EDUCATIONAL DATA MINING AND LEARNING ANALYTICS.
THIS CHAPTER PRESENTS A LEARNING ANALYTICS AND KNOWLEDGE REPRESENTATION FRAMEWORK TO SUPPORT ANALYSIS AND VISUALIZATION OF DATA EXTRACTED FROM MOOC ( MASSIVE OPEN ONLINE COURSE). DATA ANALYTICS PROVIDES METHODS AND TOOLS TO ANALYZE BIG DATA SETS. IN THE CONTEXT OF LEARNING, THESE METHODS HAVE HELPED ANALYZE MOOC DATA, MAINLY TO CHARACTERIZE GROUPS OF LEARNERS. ON THE OTHER HAND, SEMANTIC WEB AND ELEARNING RESEARCH GROUPS ARE WORKING ON ONTOLOGICAL REPRESENTATION OF LEARNING SCENARIO COMPONENTS. OUR PROJECT JOINS THOSE EFFORTS, BOTH TO DISCOVER RELATIONS THROUGH BOTTOM-UP ANALYTICS AND TO ORGANIZE AND FOCUS THE ANALYSIS WITH A SEMANTIC REPRESENTATION OF THE LEARNING SCENARIO. THE FIRST SECTION STATES THE PROBLEM AND SKETCHES THE SOLUTION. THE PROPOSED FRAMEWORK IS COMPOSED OF FOUR STAGES: DATA CLEANING AND FORMATTING, BOTTOM-UP DATA ANALYSIS, SEMANTIC ANALYSIS, AND FINALLY VISUALIZATION. THE SPECIFICATION AND IMPLEMENTATION OF EACH STEP IS DESCRIBED IN THE FOLLOWING SECTIONS. THE LAST SECTION PRESENTS THE EVALUATION OF THE PROJECT AND CONCLUSIONS.
DATA SHARING IS A COMMON CONTRIBUTION TO OPEN SCIENCE. THE CREATION OF OPEN DATASETS CAN SPEED UP RESEARCH ADVANCEMENTS BY ALLOWING RESEARCHERS TO FOCUS EFFORTS ON DEVELOPING AND VALIDATING ANALYTICAL TECHNIQUES, RATHER THAN ON OBTAINING DATA. OPEN DATASETS ALSO ALLOW RESEARCHERS TO BENCHMARK NEW ANALYTICAL APPROACHES AGAINST A KNOWN STANDARD, AND INCREASE THE REPRODUCIBILITY OF RESEARCH. THE FIELD OF HIGHER EDUCATION LEARNING ANALYTICS COULD BENEFIT FROM THE CREATION OF OPEN, SHARED DATASETS ON HIGHER EDUCATION STUDENTS AS THESE DATA DO NOT CURRENTLY EXIST IN OPEN AND ACCESSIBLE FORMATS. HERE, WE PROPOSE THE USE OF SYNTHETIC DATA GENERATORS TO CREATE OPEN ACCESS VERSIONS OF STUDENT DATA. SYNTHETIC DATASETS HAVE AN ADVANTAGE OVER REAL DATA, AS PRIVATE STUDENT DATA IS PROTECTED BY FEDERAL LAWS. WE COMPARE THE CHARACTERISTICS OF THE SYNTHETIC DATA TO THE ORIGINAL DATA AND ILLUSTRATE A MODEL FIX HOW THE SYNTHETIC DATA CAN HE LEVERAGED FOR DEVELOPING AND OPTIMIZING A COMMON LEARNING ANALYTICS ALGORITHM.
IN THE RECENT YEARS, BIG DATA AND LEARNING ANALYTICS HAVE BEEN EMERGING AS FAST-GROWING RESEARCH FIELDS. THE APPLICATION OF THESE EMERGING RESEARCH AREAS IS GRADUALLY ADDRESSING THE CONTEMPORARY CHALLENGES OF SCHOOL AND UNIVERSITY EDUCATION. TRACING OUT THE INFORMATION REGARDING STUDENTS' MISCONCEPTIONS AND DROPPING-OUT PROBABILITIES FROM THE COURSES AT THE RIGHT INSTANT OF TIME, DEVELOPMENT OF DETECTORS OF A RANGE OF EDUCATIONAL IMPORTANCE AND ACHIEVING THE HIGHEST LEVEL OF QUALITY IN THE HIGHER EDUCATION ARE BECOMING MORE CHALLENGING. MOREOVER, PROVIDING WELL TIMED AND THE BEST SUITABLE SOLUTIONS TO THE STUDENTS AT-RISK ARE EVEN MORE STRENUOUS. IN THIS CONCEPT PAPER, WE AIM TO ADDRESS THESE CONTEMPORARY CHALLENGES OF SCHOOL AND THE UNIVERSITY EDUCATION AND THEIR PROBABLE SOLUTIONS BY UTILIZING OUR RESEARCH EXPERIENCES OF AUTOMATED ASSESSMENT, IMMEDIATE FEEDBACK, LEARNING ANALYTICS AND THE IT TECHNOLOGIES. SOLVING SUCH PROBLEMS BY KNOWING THE HISTORY OF STUDENTS' ACTIVITIES, SUBMISSIONS, AND THE PERFORMANCES DATA IS POSSIBLE. THE IDENTIFICATION OF STUDENTS' MISCONCEPTIONS DURING THE LEARNING PROCESS, EXAMINING BEHAVIORAL PATTERNS AND SIGNIFICANT TRENDS BY EFFICIENTLY AGGREGATING AND CORRELATING THE MASSIVE DATA, IMPROVING THE STATE-OF-THE-ART SKILLS IN CREATIVE THINKING AND INNOVATION, AND DETECTING THE DROP-OUTS ON-TIME ARE HIGHLIGHTED IN THIS ARTICLE. WE ARE AIMING AT EXTRACTING SUCH KNOWLEDGE SO THAT ADAPTIVE AND PERSONALIZED LEARNING WILL BECOME A PART OF THE CURRENT EDUCATION SYSTEM. NOT ONLY THE AVAILABLE ALGORITHM OF SUPERVISED LEARNING METHODS SUCH AS SUPPORT VECTOR MACHINE, NEURAL NETWORK, DECISION TREES, DISCRIMINANT ANALYSIS, AND NEAREST NEIGHBORHOOD METHOD BUT ALSO NEW ENGINEERING AND DISTILLATION OF RELEVANT DATA FEATURES CAN BE CARRIED OUT TO SOLVE THESE EDUCATIONAL CHALLENGES.
MULTIMODALITY IN LEARNING ANALYTICS AND LEARNING SCIENCE IS UNDER THE SPOTLIGHT. THE LANDSCAPE OF SENSORS AND WEARABLE TRACKERS THAT CAN BE USED FOR LEARNING SUPPORT IS EVOLVING RAPIDLY, AS WELL AS DATA COLLECTION AND ANALYSIS METHODS. MULTIMODAL DATA CAN NOW BE COLLECTED AND PROCESSED IN REAL TIME AT AN UNPRECEDENTED SCALE. WITH SENSORS, IT IS POSSIBLE TO CAPTURE OBSERVABLE EVENTS OF THE LEARNING PROCESS SUCH AS LEARNER'S BEHAVIOUR AND THE LEARNING CONTEXT. THE LEARNING PROCESS, HOWEVER, CONSISTS ALSO OF LATENT ATTRIBUTES, SUCH AS THE LEARNER'S COGNITIONS OR EMOTIONS. THESE ATTRIBUTES ARE UNOBSERVABLE TO SENSORS AND NEED TO BE ELICITED BY HUMAN-DRIVEN INTERPRETATIONS. WE CONDUCTED A LITERATURE SURVEY OF EXPERIMENTS USING MULTIMODAL DATA TO FRAME THE YOUNG RESEARCH FIELD OF MULTIMODAL LEARNING ANALYTICS. THE SURVEY EXPLORED THE MULTIMODAL DATA USED IN RELATED STUDIES (THE INPUT SPACE) AND THE LEARNING THEORIES SELECTED (THE HYPOTHESIS SPACE). THE SURVEY LED TO THE FORMULATION OF THE MULTIMODAL LEARNING ANALYTICS MODEL WHOSE MAIN OBJECTIVES ARE OF (O1) MAPPING THE USE OF MULTIMODAL DATA TO ENHANCE THE FEEDBACK IN A LEARNING CONTEXT; (O2) SHOWING HOW TO COMBINE MACHINE LEARNING WITH MULTIMODAL DATA; AND (O3) ALIGNING THE TERMINOLOGY USED IN THE FIELD OF MACHINE LEARNING AND LEARNING SCIENCE.
THIS ARTICLE SUMMARIZES SOME EMERGING CONCERNS AS LEARNING ANALYTICS BECOME IMPLEMENTED THROUGHOUT EDUCATION. THE ARTICLE TAKES A SOCIOTECHNICAL PERSPECTIVE - POSITIONING LEARNING ANALYTICS AS SHAPED BY A RANGE OF SOCIAL, CULTURAL, POLITICAL, AND ECONOMIC FACTORS. IN THIS MANNER, VARIOUS CONCERNS ARE OUTLINED REGARDING THE PROPENSITY OF LEARNING ANALYTICS TO ENTRENCH AND DEEPEN THE STATUS QUO, DISEMPOWER AND DISENFRANCHISE VULNERABLE GROUPS, AND FURTHER SUBJUGATE PUBLIC EDUCATION TO THE PROFIT-LED MACHINATIONS OF THE BURGEONING "DATA ECONOMY." IN LIGHT OF THESE CHARGES, THE ARTICLE BRIEFLY CONSIDERS SOME POSSIBLE AREAS OF CHANGE. THESE INCLUDE THE DESIGN OF ANALYTICS APPLICATIONS THAT ARE MORE OPEN AND ACCESSIBLE, THAT OFFER GENUINE CONTROL AND OVERSIGHT TO USERS, AND THAT BETTER REFLECT STUDENTS' LIVED REALITY. THE ARTICLE ALSO CONSIDERS WAYS OF RETHINKING THE POLITICAL ECONOMY OF THE LEARNING ANALYTICS INDUSTRY. ABOVE ALL, LEARNING ANALYTICS RESEARCHERS NEED TO BEGIN TALKING MORE OPENLY ABOUT THE VALUES AND POLITICS OF DATA-DRIVEN ANALYTICS TECHNOLOGIES AS THEY ARE IMPLEMENTED ALONG MASS LINES THROUGHOUT SCHOOL AND UNIVERSITY CONTEXTS.
ONLINE DISCUSSION FORUMS ALLOW LEARNERS TO MAINTAIN DISCUSSIONS ABOUT THEIR LEARNING PROCESS AT ANY TIME AND ANY PLACE; WHERE THEY CAN EXPRESS THEIR CONCERNS AND IDEAS, AND ORGANIZE THEIR THOUGHTS. INDEED, ONLINE DISCUSSION FORUMS ARE USED AS AN INDICATOR OF LEARNER'S PERFORMANCE. IN THIS CONTEXT, THIS PAPER PRESENTS THE SOCIAL LEARNING ANALYTICS (SLA) PROCESS TO DESCRIBE THE INTERACTIONS BETWEEN LEARNERS IN AN ONLINE DISCUSSION FORUM IN MOODLE USING THE PAJEK VISUALIZATION TOOL.
THIS ARTICLE CONSIDERS IFENTHALER AND SCHUMACHER'S (IN EDUC TECHNOL RES DEV 64(5):923-938, 2016) PAPER ENTITLED "STUDENT PERCEPTIONS OF PRIVACY PRINCIPLES FOR LEARNING ANALYTICS" FROM A POLICY PERSPECTIVE. THE INCREASING SHIFT TO DIGITAL OCCURRING ACROSS THE EDUCATION LANDSCAPE HAS RESULTED IN GREATER ACCESS TO DATA THAT CAN BE USED TO ENHANCE THE STUDENT LEARNING EXPERIENCE. HOWEVER, IT IS ESSENTIAL THAT APPROPRIATE POLICY BE IN PLACE TO ENSURE THE ETHICAL USE OF SUCH DATA, AND EQUALLY IMPORTANT THAT THE STUDENT VOICE BE INCLUDED IN THE DEVELOPMENT OF SUCH POLICY. IFENTHALER AND SCHUMACHER'S STUDY INVESTIGATES STUDENTS' PREFERENCES FOR THE TYPES OF LEARNING ANALYTICS SYSTEMS THEY WOULD LIKE TO BE ABLE TO ACCESS AND WILLINGNESS TO SHARE THEIR DATA FOR USE IN THESE SYSTEMS. THEY FIND THAT STUDENTS WOULD LIKE ACCESS TO LEARNING ANALYTICS SYSTEMS THAT PROVIDE A BROAD RANGE OF INFORMATION THAT CAN PROMOTE METACOGNITIVE AWARENESS AND PROVIDE PERSONALISED FEEDBACK, BUT ARE NOT ENTIRELY COMFORTABLE WITH SHARING THE FULL RANGE OF DATA NECESSARY TO INFORM SUCH SYSTEMS. THESE FINDINGS ARE IMPORTANT IN ACKNOWLEDGING THE DIFFICULTIES IN BUILDING A SHARED UNDERSTANDING AND VOCABULARY WHEN SPEAKING TO STUDENTS ABOUT THE POSSIBILITIES AND ETHICAL IMPLICATIONS OF LEARNING ANALYTICS. CONSEQUENTLY, FURTHER CONSIDERATION IS NEEDED TO BUILD STUDENTS' UNDERSTANDING OF THE PURPOSES AND BENEFITS OF SHARING DATA AS WELL AS AWARENESS OF THE RANGE OF POLICIES THAT INFORM A CONSULTATIVE PROCESS AROUND SYSTEMS IMPLEMENTATION TO ENSURE TRANSPARENCY AND ACCOUNTABILITY SO THAT LEARNING ANALYTICS CAN PLAY AN EFFECTIVE, BUT SAFE, ROLE IN OUR INCREASINGLY DIGITAL EDUCATION ENVIRONMENTS.
COACH2 PROJECT INVESTIGATED USABILITY AND EFFECTIVENESS OF LEARNING ANALYTICS IN A GROUP OF BACHELOR COURSES IN THE AREA OF COMPUTER SCIENCE. AN ADVANCED ARCHITECTURE WAS DEVELOPED AND IMPLEMENTED, INCLUDING A STANDALONE LEARNING RECORD STORE FOR DATA STORAGE AND EASY ACCESS TO MISCELLANEOUS DATA, MACHINE LEARNING TECHNIQUES FOR DETERMINING RELEVANT PREDICTORS, AND A DASHBOARD FOR INFORMING LEARNERS. THE OVERALL APPROACH WAS BASED ON MIRRORING, THE IDEA THAT LEARNERS SEE THEMSELVES OPERATING IN THE CONTEXT OF THEIR PEERS. THE RESULTS WERE INFORMATIVE IN TERMS OF PRO'S AND CON'S REGARDING THE DESIGN AND APPROACH. THE TREATMENT SHOWED TENDENCIES, BUT FINDING STATISTICAL SIGNIFICANT RESULTS TURNED OUT DIFFICULT. THIS PAPER REPORTS ON THE COACH2 PROJECT.
IT IS NOT UNUSUAL FOR ACADEMIC LIBRARIES TO MODIFY THEIR SERVICES AROUND (LIBRARY-CREATED) DATA. DATA IS INFORMATION, ONE OF THE CRITICAL INPUTS AMONG OTHER VARIABLES THAT WE CAN MEASURE IN THE LIBRARIES. LIBRARIANS ARE FAMILIAR WITH THE STATISTICAL METHODS AND THEIR FULL POTENTIAL IN LIBRARY ANALYTICS FOR CREATING DATA-INFORMED OR DATA-DRIVEN DECISIONS REGARDING COLLECTION MANAGEMENT, LIBRARY CATALOGUE SEARCHES, LOGS TO ONLINE RESOURCES, ETC. LEARNING ANALYTICS, ON THE OTHER HAND, IS FOCUSED ON THE DATA ABOUT LEARNERS AND THEIR CONTEXT FOR PURPOSES OF UNDERSTANDING AND OPTIMIZING LEARNING AND THE ENVIRONMENT IN WHICH IT OCCURS. ACADEMIC LIBRARY AS A PART OF THE HIGHER EDUCATION ECOSYSTEM IS A NATURAL PARTNER WITH DATA SOURCES THAT CAN COMBINE WITH OTHER INSTITUTIONAL SYSTEMS AND GENERATE NEW INFORMATION ABOUT OUR LEARNERS, LEARNING AND INSTRUCTION. ALTHOUGH IT IS A NOVELTY FOR LIBRARIES, SOME OF THEM ARE USING LEARNING ANALYTICS TO EVALUATE THE IMPACT OF LIBRARIES SERVICES, COLLECTIONS AND STUDENT LEARNING. THE LITERATURE STATES THAT LIBRARIAN INVESTMENT IN LEARNING ASSESSMENT TO INVOLVEMENT IN LEARNING ANALYTICS IS A NATURAL ONE - BUT ARE WE READY FOR IT?
LEARNING ANALYTICS (LA) IS CONCERNED WITH THE USE OF DATA RESULTING FROM STUDENTS' INTERACTION WITH LEARNING MANAGEMENT SYSTEMS (LMS) TO CARRY OUT ANALYSES THAT CAN HELP STUDENTS AND TEACHERS TO UNDERSTAND AND EVALUATE THE LEARNING PROCESS. HOWEVER, SEVERAL WORKS ON THE SUBJECT ONLY EXPOSE FUTURE EXPECTATIONS OF WHAT LA APPLICATIONS CAN PERFORM. THUS, THIS PAPER AIMS AT FINDING OUT PRACTICAL RESULTS OF LA AND IF ITS USE HAS IMPROVED TEACHING AND LEARNING.
THE IMPLEMENTATION OF LEARNING ANALYTICS MAY EMPOWER DISTANCE LEARNING INSTITUTIONS TO PROVIDE REAL-TIME FEEDBACK TO STUDENTS AND TEACHERS. GIVEN THE LEADING ROLE OF THE OPEN UNIVERSITY UK (OU) IN RESEARCH AND APPLICATION OF LEARNING ANALYTICS, THIS STUDY AIMS TO SHARE THE LESSONS LEARNED FROM THE EXPERIENCES OF 42 PARTICIPANTS FROM A RANGE OF FACULTY, ACADEMIC AND PROFESSIONAL POSITIONS, AND EXPERTISE WITH LEARNING ANALYTICS. FURTHERMORE, WE EXPLORED WHERE DISTANCE LEARNING INSTITUTIONS SHOULD BE GOING NEXT IN TERMS OF LEARNING ANALYTICS ADOPTION. THE FINDINGS FROM THE LEARNING ANALYTICS USER STORIES (LAUS) WORKSHOP INDICATED FOUR KEY AREAS WHERE MORE WORK IS NEEDED: COMMUNICATION, PERSONALISATION, INTEGRATED DESIGN, AND DEVELOPMENT OF AN EVIDENCE-BASE. THE WORKSHOP OUTPUTS SIGNALLED THE ASPIRATION FOR AN INTEGRATED ANALYTICS SYSTEM TRANSCENDING THE ENTIRE STUDENT EXPERIENCE, FROM INITIAL STUDENT INQUIRY RIGHT THROUGH TO QUALIFICATION COMPLETION AND INTO LIFE-LONG LEARNING. WE HOPE THAT OUR STUDY WILL SPARK DISCUSSION ON WHETHER (OR NOT) DISTANCE LEARNING INSTITUTIONS SHOULD PURSUE THE DREAM OF AN INTEGRATED, PERSONALISED, AND EVIDENCE-BASED LEARNING ANALYTICS SYSTEM THAT CLEARLY COMMUNICATES USEFUL FEEDBACK TO STAFF AND STUDENTS, OR WHETHER THIS WILL BECOME AN ORWELLIAN NIGHTMARE.
LEARNING ANALYTICS IS A VERY PROMISING FIELD FOR IMPROVING EDUCATION, TEACHING AND LEARNING BY COLLECTING USER DATA IN SERIOUS GAMES. HOWEVER, THE RESEARCH ON THE USE OF PHYSIOLOGICAL TRACES IN LEARNING ANALYTICS IS SPARSE. THEREFORE, IN THIS EMPIRICAL EXPLORATIVE STUDY WE USED FUNCTIONAL NEAR-INFRARED SPECTROSCOPY TO EXAMINE IF NEUROPHYSIOLOGICAL MEASUREMENTS CAN HELP TO IDENTIFY IF A USER IS LEARNING DURING PLAYING A SIMPLE GAME. WE IDENTIFIED INCREASED BRAIN ACTIVATION IN FRONTO-PARIETAL AREAS WHILE USERS ACTIVELY LEARNED RULES OR APPLIED KNOWLEDGE DURING GAMING. IN CONTRAST, IN A RANDOM CONDITION, WHERE USERS ONLY REACTED TO THE GAME WITHOUT THE POSSIBILITY TO LEARN OR APPLY KNOWLEDGE, INCREASED BRAIN ACTIVATION WAS OBSERVED IN CENTRAL MOTOR AREAS OF THE BRAIN, REFLECTING THE MOTORIC INTERACTION WITH THE GAME PER SE. THIS PRELIMINARY NEUROPHYSIOLOGICAL EMPIRICAL STUDY SHOWED THAT IT IS POSSIBLE TO USE NEUROPHYSIOLOGICAL DATA TO GET INSIGHT ON USER LEARNING DURING PLAYING A VERY SIMPLE GAME. FUTURE STUDIES HAVE TO EXAMINE THIS EFFECT IN A MORE COMPLEX LEARNING SITUATION.
THE PURPOSE OF THIS PAPER IS TO MINE OR DETECT MEANINGFUL INFORMATION FOR IMPROVING LEARNING MATERIALS USING E-BOOK-BASED LOGS. THE ANALYSIS AND VISUALIZATION METHODS OF THIS STUDY USE ASSOCIATION ANALYSIS WITH APRIORI ALGORITHM. LOGS FOR E-BOOK-BASED LEARNING ANALYTICS WERE COLLECTED IN AN E-BOOK SYSTEM. IN ORDER TO ANALYZE AND VISUALIZE THE COLLECTED LOGS, THIS PAPER PROPOSES A SYSTEM CALLED SILM (SYSTEM FOR IMPROVING LEARNING MATERIALS). USING SILM, INSTRUCTORS OR TEACHERS CAN FIND THE LEARNING MATERIALS THAT SHOULD BE IMPROVED. WE BELIEVE THAT IMPROVING LEARNING MATERIALS WITH ANALYZING AND VISUALIZING EDUCATIONAL BIG DATA LEAD TO IMPROVING STUDENT RESULTS.
PURPOSE - THE PURPOSE OF THIS STUDY IS TO PROPOSE A REAL-TIME LECTURE SUPPORTING SYSTEM. THE TARGET OF THIS STUDY IS ON-SITE CLASSROOMS WHERE TEACHERS GIVE LECTURES AND A LOT OF STUDENTS LISTEN TO TEACHERS' EXPLANATIONS, CONDUCT EXERCISES, ETC. DESIGN/METHODOLOGY/APPROACH - THE PROPOSED SYSTEM USES AN E-LEARNING SYSTEM AND AN E-BOOK SYSTEM TO COLLECT TEACHING AND LEARNING ACTIVITIES FROM A TEACHER AND STUDENTS IN REAL TIME. THE COLLECTED DATA ARE IMMEDIATELY ANALYZED TO PROVIDE FEEDBACK TO THE TEACHER JUST BEFORE THE LECTURE STARTS AND DURING THE LECTURE. FOR EXAMPLE, THE TEACHER CAN CHECK WHICH PAGES WERE WELL PREVIEWED AND WHICH PAGES WERE NOT PREVIEWED BY STUDENTS USING THE PREVIEW ACHIEVEMENT GRAPH. DURING THE LECTURE, REAL-TIME ANALYTICS GRAPHS ARE SHOWN ON THE TEACHER'S PC. THE TEACHER CAN EASILY GRASP STUDENTS' STATUS AND WHETHER OR NOT STUDENTS ARE FOLLOWING THE TEACHER'S EXPLANATION. FINDINGS - THROUGH THE CASE STUDY, THE AUTHORS FIRST CONFIRMED THE EFFECTIVENESS OF EACH TOOL DEVELOPED IN THIS STUDY. THEN, THE AUTHORS CONDUCTED A LARGE-SCALE EXPERIMENT USING A REAL-TIME ANALYTICS GRAPH AND INVESTIGATED WHETHER THE PROPOSED SYSTEM COULD IMPROVE THE TEACHING AND LEARNING IN ON-SITE CLASSROOMS. THE RESULTS INDICATED THAT TEACHERS COULD ADJUST THE SPEED OF THEIR LECTURE BASED ON THE REAL-TIME FEEDBACK SYSTEM, WHICH ALSO RESULTED IN ENCOURAGING STUDENTS TO PUT BOOKMARKS AND HIGHLIGHTS ON KEYWORDS AND SENTENCES. ORIGINALITY/VALUE - REAL-TIME LEARNING ANALYTICS ENABLES TEACHERS AND STUDENTS TO ENHANCE THEIR TEACHING AND LEARNING DURING LECTURES. TEACHERS SHOULD START CONSIDERING THIS NEW STRATEGY TO IMPROVE THEIR LECTURES IMMEDIATELY.
LEARNING ANALYTICS (LA) HAS SHOWN A GREAT POTENTIAL IN IMPROVING LEARNING EXPERIENCE AND ENHANCING PEDAGOGICAL EFFECTIVENESS. HOWEVER, THE ADOPTION OF LA IN HIGHER EDUCATION INVOLVES VARIOUS SOCIAL, CULTURAL, AND TECHNICAL ISSUES THAT NEED TO BE ADDRESSED STRATEGICALLY. WE PRESENT A STUDY THAT AIMS TO ASSIST WITH THE DEVELOPMENT OF INSTITUTIONAL LA POLICIES TO ENSURE EFFECTIVE AND LEGITIMATE ADOPTION OF LA. THE STUDY TAKES AN ACTION RESEARCH APPROACH AND INVOLVES KEY STAKEHOLDERS DIRECTLY, SO AS TO INCORPORATE A WIDE RANGE OF PERSPECTIVES IN THE POLICY FORMATION. ETHICS AND PRIVACY ISSUES WERE CONSIDERED THE PRIORITY ELEMENT IN A LA POLICY AND THE TOP CONCERN FOR STUDENTS. A SENSE OF UNCERTAINTY ABOUT THE RETURNS IN INVESTMENT WAS OBSERVED AMONG SENIOR MANAGERS, WHEREAS TEACHING STAFF WERE MOSTLY WORRIED ABOUT TIME PRESSURE AND THE POTENTIAL OF LA TO BE USED FOR PERFORMANCE APPRAISAL. THIS POSTER PRESENTS A POLICY FRAMEWORK THAT CAN BE USED TO SUPPORT INSTITUTIONAL READINESS ASSESSMENT, STRATEGY FORMATION, AND POLICY DEVELOPMENT.
RECENT INDICATORS OF THE SUCCESS OF EVERY COLLEGE GRADUATE STILL REFER TO THE CUMULATIVE ACADEMIC ACHIEVEMENT INDEX. THE ERA OF DISRUPTION OF THE DIGITALIZATION OF EDUCATION HAS POSED DIFFERENT CHALLENGES AND PROBLEMS TO UNIVERSITIES TO IMPROVE STUDENT ACADEMIC PERFORMANCE. THE OBJECTIVE OF THIS STUDY WAS TO DETERMINE THE EXTENT OF INFLUENCES AND RELATIONSHIPS OF ANTECEDENT FACTORS, INCLUDING SOFT SKILLS AND LEARNING ANALYTICS, THE INTERVENING VARIABLES IN IMPROVING THIS PERFORMANCE. THIS STUDY ENGAGED 203 ACTIVE STUDENTS TAKING COURSES IN THE FIFTH AND SIXTH SEMESTERS OF UNIVERSITIES IN PONTIANAK, WEST KALIMANTAN, INDONESIA. THE RESEARCH WAS IN THE FORM OF SURVEYS. QUESTIONNAIRES WERE DISTRIBUTED ONLINE THROUGH GOOGLE FORM. SEM-PLS METHOD WAS CONDUCTED FOR THE ANALYSIS. RESULTS SHOW STRONG INFLUENCES AND RELATIONSHIPS BETWEEN ACADEMIC, EMOTIONAL LEARNING AND SOFT SKILLS THROUGH LEARNING ANALYTICS IN IMPROVING STUDENT ACADEMIC PERFORMANCE. FURTHERMORE, THE NEGATIVE INFLUENCES OF SOFT SKILLS HAVE INSIGNIFICANCE ON THIS PERFORMANCE. LASTLY, A SOCIO-ECONOMIC FACTOR IS IRRELEVANTLY LINKED TO LEARNING ANALYTICS AND STUDENT ACADEMIC PERFORMANCE. ALL OF THESE FINDINGS INDICATE THAT ACADEMIC, PERSONAL, SOCIO-ECONOMIC ANTECEDENTS ARE NO LONGER RELEVANT TO PREVIOUS RESEARCH.
WITH THE WIDESPREAD USE OF LEARNING ANALYTICS (LA) TOOLS, THERE IS A NEED TO EXPLORE HOW THESE TECHNOLOGIES CAN BE USED TO ENHANCE TEACHING AND LEARNING. LITTLE RESEARCH HAS BEEN CONDUCTED ON WHAT HUMAN PROCESSES ARE NECESSARY TO FACILITATE MEANINGFUL ADOPTION OF LA. THE RESEARCH PROBLEM IS THAT THERE IS A LACK OF EVIDENCE-BASED GUIDANCE ON HOW INSTRUCTORS CAN EFFECTIVELY IMPLEMENT LA TO SUPPORT STUDENTS. THE GOAL OF THE STUDY WAS TO DEVELOP AND VALIDATE A MODEL TO GUIDE INSTRUCTORS IN THE IMPLEMENTATION OF LA TOOLS. USING DESIGN AND DEVELOPMENT RESEARCH METHODS, AN IMPLEMENTATION MODEL WAS CONSTRUCTED AND VALIDATED INTERNALLY. THEMES EMERGED FALLING INTO THE CATEGORIES OF ADOPTION AND CAUTION WITH SIX THEMES FALLING UNDER ADOPTION INCLUDING: LA AS EVIDENCE, REACHING OUT, FREQUENCY, EARLY IDENTIFICATION/INTERVENTION, SELF-REFLECTION, AND ALIGN LA WITH PEDAGOGICAL INTENT. THREE THEMES EMERGED FALLING UNDER THE CATEGORY OF CAUTION INCLUDING: SKEPTICISM, FEAR OF OVERDEPENDENCE, AND QUESTION OF USEFULNESS. THE MODEL SHOULD ENHANCE INSTRUCTORS' USE OF LA BY ENABLING THEM TO BETTER TAKE ADVANTAGE OF AVAILABLE TECHNOLOGIES TO SUPPORT TEACHING AND LEARNING IN ONLINE AND BLENDED LEARNING ENVIRONMENTS. RESEARCHERS CAN FURTHER VALIDATE THE MODEL BY STUDYING ITS USABILITY (I. E., USEFULNESS, EFFECTIVENESS, EFFICIENCY, AND LEARNABILITY), AS WELL AS, HOW INSTRUCTORS' USE OF THIS MODEL TO IMPLEMENT LA IN THEIR COURSES AFFECTS RETENTION, PERSISTENCE, AND PERFORMANCE.
ONLINE EDUCATION IS GROWING BECAUSE OF ITS BENEFITS AND ADVANTAGES THAT STUDENTS ENJOY. EDUCATIONAL TECHNOLOGIES (E.G., LEARNING ANALYTICS, STUDENT MODELLING, AND INTELLIGENT TUTORING SYSTEMS) BRING GREAT POTENTIAL TO ONLINE EDUCATION. MANY ONLINE COURSES, PARTICULARLY IN SELF-PACED ONLINE LEARNING (SPOL), FACE SOME INHERENT BARRIERS SUCH AS LEARNING AWARENESS AND ACADEMIC INTERVENTION. THESE BARRIERS CAN AFFECT THE ACADEMIC PERFORMANCE OF ONLINE LEARNERS. RECENTLY, LEARNING ANALYTICS HAS BEEN SHOWN TO HAVE GREAT POTENTIAL IN REMOVING THESE BARRIERS. HOWEVER, IT IS CHALLENGING TO ACHIEVE THE FULL POTENTIAL OF LEARNING ANALYTICS WITH THE TRADITIONAL ONLINE COURSE LEARNING DESIGN MODEL. THUS, FOCUSING ON SPOL, THIS STUDY PROPOSES THAT LEARNING ANALYTICS SHOULD BE INCLUDED IN THE COURSE LEARNING DESIGN LOOP TO ENSURE DATA COLLECTION AND PEDAGOGICAL CONNECTION. WE PROPOSE A NOVEL LEARNING DESIGN-ANALYTICS MODEL IN WHICH COURSE LEARNING DESIGN AND LEARNING ANALYTICS CAN SUPPORT EACH OTHER TO INCREASE LEARNING SUCCESS. BASED ON THE PROPOSED MODEL, A SET OF ONLINE COURSE DESIGN STRATEGIES ARE RECOMMENDED FOR ONLINE EDUCATORS WHO WISH TO USE LEARNING ANALYTICS TO MITIGATE THE LEARNING BARRIERS IN SPOL. THESE STRATEGIES AND TECHNOLOGIES ARE INSPIRED BY JIM GREER'S WORK ON STUDENT MODELLING. BY FOLLOWING THESE RECOMMENDED DESIGN STRATEGIES, A COMPUTER SCIENCE COURSE IS USED AS AN EXAMPLE TO SHOW OUR INITIAL PRACTICES OF INCLUDING LEARNING ANALYTICS IN THE COURSE LEARNING DESIGN LOOP. FINALLY, FUTURE WORK ON HOW TO DEVELOP AND EVALUATE LEARNING ANALYTICS ENABLED LEARNING SYSTEMS IS OUTLINED.
LEARNING ANALYTICS (LA) IS A RELATIVELY NEW RESEARCH FIELD CONCERNED WITH ANALYSING DATA COLLECTED FROM VARIOUS SOURCES TO PROVIDE INSIGHTS INTO ENHANCING LEARNING AND TEACHING. A COMPLETE LA PROCESS TYPICALLY INVOLVES FIVE DISTINCT, YET INTERRELATED, STAGES - NAMELY CAPTURE, REPORT, PREDICT, ACT AND REFINE - WHICH FORM A SEQUENTIAL DECISION PROCESS. SO FAR, RESEARCH EFFORTS HAVE FOCUSED MOSTLY ON STUDYING INDEPENDENT RESEARCH QUESTIONS INVOLVED IN INDIVIDUAL STAGES. IT IS THEREFORE NECESSARY TO HAVE A FORMAL FRAMEWORK TO QUANTIFY AND GUIDE THE WHOLE LA PROCESS. IN THIS PAPER, WE DISCUSS HOW REINFORCEMENT LEARNING (RL), A SUBFIELD OF MACHINE LEARNING, CAN BE EMPLOYED TO ADDRESS THE SEQUENTIAL DECISION PROBLEM INVOLVED IN THE LA PROCESS. IN PARTICULAR, WE INTEGRATE THE LA STAGES WITH AN RL FRAMEWORK CONSISTING OF STATE SPACE, ACTION SPACE, TRANSITION FUNCTION AND REWARD FUNCTION AND ILLUSTRATE THIS WITH EXAMPLES OF HOW THE THREE MOST STUDIED OPTIMALITY CRITERIA IN RL - FINITE HORIZON, DISCOUNTED INFINITE HORIZON AND THE AVERAGE REWARD MODEL - CAN BE APPLIED TO THE LA PROCESS. THE UNDERLYING ASSUMPTIONS, ADVANTAGES AND ISSUES IN THE PROPOSED RL FRAMEWORK ARE ALSO DISCUSSED.
NOWADAYS, DIFFERENT WAYS OF COMMUNICATION AND INTERACTION AMONG MULTIPLE ACTORS ARE DOMINATING LEARNING PROCESSES. HOWEVER, THERE ARE CRITICAL OPINIONS THAT QUESTION THE CONTRIBUTION OF STUDENT INTERACTION TO REAL LEARNING. THIS STUDY APPLIES LEARNING ANALYTICS AND DATA MINING TECHNIQUES TO EXPLORE THE ONLINE DISCUSSION FORUMS OF 362 BUSINESS STUDENTS AT THE BACHELOR AND MASTER LEVELS, WHO PARTICIPATED IN BUSINESS SIMULATION GAMES BETWEEN 2011 AND 2016. THE FINDINGS REVEALED THAT THE MOST FREQUENT CONTENTS IN THE STUDENTS' ONLINE DISCUSSION FORUMS WERE RELATED, FIRSTLY, TO THE PARAMETERS AND FEATURES OF THE BUSINESS SIMULATION GAME, AND, SECONDLY, TO ELEMENTS THAT FOSTERED THE STUDENTS' LEARNING PROCESS, WHILE SMALL TALK OR REGULAR CONVERSATION DID NOT APPEAR TO BE RELEVANT. IN ADDITION, THE CONTENTS WITH PREDICTIVE POWER OVER LEARNING RESULTS WERE RELATED TO UNCERTAINTY, TIME, INTERACTION, COMMUNICATION AND COLLABORATION, ALTHOUGH NONE OF THESE ELEMENTS INFLUENCED TEACHER ASSESSMENT OF STUDENT LEARNING. THIS STUDY REVEALS THE USEFULNESS OF LEARNING ANALYTICS TOOLS TO GAIN A MORE WIDE AND HOLISTIC VIEW OF THE LEARNING PROCESS OF STUDENTS, DISCOVERING NEW ASPECTS THAT AFFECT STUDENTS' LEARNING RESULTS. (C) 2018 ELSEVIER LTD. ALL RIGHTS RESERVED.
ALTHOUGH DIALOGUE CAN AUGMENT THE IMPACT OF FEEDBACK ON STUDENT LEARNING, DIALOGIC FEEDBACK IS UNAFFORDABLE BY INSTRUCTORS TEACHING LARGE CLASSES. IN THIS REGARD, PEER FEEDBACK CAN OFFER A SCALABLE AND EFFECTIVE SOLUTION. HOWEVER, THE EXISTING PRACTICES OPTIMISTICALLY RELY ON STUDENTS' DISCUSSION ABOUT FEEDBACK AND LACK A SYSTEMATIC DESIGN APPROACH. IN THIS PAPER, WE PROPOSE A THEORETICAL FRAMEWORK OF COLLABORATIVE PEER FEEDBACK WHICH STRUCTURES FEEDBACK DIALOGUE INTO THREE DISTINCT PHASES AND OUTLINES THE LEARNING PROCESSES INVOLVED IN EACH OF THEM. THEN, WE PRESENT A WEB-BASED PLATFORM, CALLED SYNERGY, WHICH IS DESIGNED TO FACILITATE COLLABORATIVE PEER FEEDBACK AS CONCEPTUALISED IN THE THEORETICAL FRAMEWORK. TO ENABLE INSTRUCTOR SUPPORT AND FACILITATION DURING THE FEEDBACK PRACTICE, WE PROPOSE A LEARNING ANALYTICS SUPPORT INTEGRATED INTO SYNERGY. THE CONSOLIDATED MODEL OF LEARNING ANALYTICS, WHICH CONCERNS THREE CRITICAL PIECES FOR CREATING IMPACTFUL LEARNING ANALYTICS PRACTICES, THEORY, DESIGN AND DATA SCIENCE, WAS EMPLOYED TO BUILD THE ANALYTICS SUPPORT. THE LEARNING ANALYTICS SUPPORT AIMS TO GUIDE INSTRUCTORS' CLASS-WIDE ACTIONS TOWARD IMPROVING STUDENTS' LEARNING EXPERIENCES DURING THE THREE PHASES OF PEER FEEDBACK. THE ACTIONABLE INSIGHTS THAT THE LEARNING ANALYTICS SUPPORT OFFERS ARE DISCUSSED WITH EXAMPLES.
VIDEO ANALYSIS IS A STAPLE OF THE EDUCATION RESEARCH COMMUNITY. FOR MANY CONTEMPORARY EDUCATION RESEARCHERS, PARTICIPATION IN THE VIDEO CODING PROCESS SERVES AS A RITE OF PASSAGE. HOWEVER, RECENT DEVELOPMENTS IN MULTIMODAL LEARNING ANALYTICS MAY HELP TO ACCELERATE AND ENHANCE THIS PROCESS BY PROVIDING RESEARCHERS WITH A MORE NUANCED GLIMPSE INTO A SET OF LEARNING EXPERIENCES. AS AN EXAMPLE OF HOW TO USE MULTIMODAL LEARNING ANALYTICS TOWARDS THESE ENDS, THIS PAPER INCLUDES A PRELIMINARY ANALYSIS FROM 54 COLLEGE STUDENTS, WHO COMPLETED TWO ENGINEERING DESIGN TASKS IN PAIRS. GESTURE, SPEECH AND ELECTRO-DERMAL ACTIVATION DATA WERE COLLECTED AS STUDENTS COMPLETED THESE TASKS. THE GESTURE DATA WAS USED TO LEARN A SET OF CANONICAL CLUSTERS (N=4). A DECISION TREE WAS TRAINED BASED ON INDIVIDUAL STUDENTS' CLUSTER FREQUENCIES, AND PRE-POST LEARNING GAINS. THE NODES IN THE DECISION TREE WERE THEN USED TO IDENTIFY A SUBSET OF VIDEO SEGMENTS THAT WERE HUMAN CODED BASED ON PRIOR WORK IN LEARNING ANALYTICS AND ENGINEERING DESIGN. THE COMBINATION OF MACHINE LEARNING AND HUMAN INFERENCE HELPS ELUCIDATE THE PRACTICES THAT SEEM TO CORRELATE WITH STUDENT LEARNING. IN PARTICULAR, BOTH ENGAGEMENT AND DISENGAGEMENT SEEM TO CORRELATE WITH STUDENT LEARNING, ALBEIT IN A SOMEWHAT NUANCED FASHION.
NOWADAYS, SCHOOLS USE MANY INFORMATION SYSTEMS TO AUTOMATE THEIR ACTIVITIES FOR DIFFERENT STAKEHOLDERS' GROUPS - LEARNING MANAGEMENT SYSTEMS, STUDENT DIARY, LIBRARY SYSTEMS, DIGITAL REPOSITORIES, FINANCIAL MANAGEMENT AND ACCOUNTING SYSTEMS, DOCUMENT PROCESSING SYSTEMS, ETC. THE HUGE AMOUNT OF DATA GENERATED BY THE USERS OF THESE SYSTEMS, LED TO INCREASED INTEREST IN THE COLLECTION AND ANALYSIS OF DATA TO ENCOURAGE STUDENTS TO ACHIEVE HIGHER RESULTS, TEACHERS TO PROVIDE PERSONALIZED SUPPORT AND SCHOOL MANAGERS TO MAKE DATA-DRIVEN DECISIONS AT ALL LEVELS OF SCHOOL, AND STIMULATES RESEARCH INTO THE APPLICATION OF LEARNING ANALYTICS (LA) IN SCHOOLS. THE PAPER PRESENTS A LA MODEL AND A SOFTWARE PROTOTYPE OF THE LA TOOL DESIGNED FOR THE NEEDS OF BULGARIAN SCHOOL EDUCATION FROM THE PERSPECTIVE OF DIFFERENT STAKEHOLDER GROUPS (STUDENTS, TEACHERS, CLASS TEACHERS, PARENTS, SCHOOL MANAGERS, INSPECTORS FROM EVALUATION AGENCIES), AIMING TO IMPROVE SCHOOL METHODS OF APPROACHING AND ANALYZING LEARNING DATA. THE TOOL ALLOWS STAKEHOLDERS TO TRACK DATA FOR STUDENTS' LEARNING OR TRAINING FOR DIFFERENT PURPOSES, E.G. MONITORING, ANALYSIS, FORECAST, INTERVENTION, RECOMMENDATIONS, ETC., BUT FINALLY TO IMPROVE THE QUALITY OF LEARNING AND TEACHING PROCESSES. RESEARCH AND EXPERIMENTS WITH THE MODEL AND THE LA TOOL UNDER CONSIDERATION ARE CONDUCTED BASED ON THE INFORMATION INFRASTRUCTURE OF A TYPICAL BULGARIAN SCHOOL.
THE PRESENT TEXT IS THE RESULT OF A BIBLIOGRAPHICAL REVIEW ON THE TOPIC ANALYTICS OF LEARNING. LEARNING ANALYTICS ARE TECHNOLOGICAL DEVICES THAT ARE INCORPORATED INTO VIRTUAL ENVIRONMENTS OF INTERACTION, IN ORDER TO REGISTER THE FINGERPRINTS THAT LEAVE PARTICIPATING IN THEM, CREATING LARGE DATABASES. WE AIM TO IDENTIFY RELEVANT AUTHORS IN THE FIELD, TO EXPLAIN WHAT THE LEARNING ANALYZES ARE, TO RECOGNIZE THEIR APPLICATION IN CLOSED AND OPEN EDUCATIONAL ENVIRONMENTS (NETWORKS AND WEB APPLICATIONS) AND TO CONCLUDE WITH A REFLECTION THAT PRETENDS TO IMAGINE THE ANALYTICS IN THE SERVICE OF LEARNING.
THIS IS THE INTRODUCTION TO THE SPECIAL EDITION ON LEARNING ANALYTICS.
THIS PAPER DISCUSSES THE OPPORTUNITIES FOR GAMIFICATION AND DYNAMIC DIFFICULTY ADJUSTMENT BASED ON MULTIMODAL LEARNING ANALYTICS IN ASSIGNMENTS. ALTOGETHER THIS COVERS A BROADER TERM OF PERSONALIZED EDUCATION, WHICH IS GETTING MORE ATTENTION AMONG THE RESEARCHERS IN RECENT YEARS. THE DIFFERENCE OF THIS WORK FROM OTHER SIMILAR RESEARCHES IS THAT IT SUGGESTS COMBINING SEVERAL DOMAINS TO ACHIEVE BETTER RESULTS: GAMIFICATION (IN ORDER TO IMPROVE STUDENT'S MOTIVATION AND INVOLVEMENTS), AND DYNAMIC DIFFICULTY ADJUSTMENT. ALL THIS IS MADE POSSIBLE BY APPLYING MULTIMODAL LEARNING ANALYTICS AND CREATING USEFUL LEARNING DASHBOARDS FOR THE TEACHERS.
CLOUD PROVIDERS SUCH AS AMAZON WEB SERVICES (AWS) STAND OUT AS USEFUL PLATFORMS TO TEACH DISTRIBUTED COMPUTING CONCEPTS AS WELL AS THE DEVELOPMENT OF CLOUD-NATIVE SCALABLE APPLICATION ARCHITECTURES ON REAL-WORLD INFRASTRUCTURES. INSTRUCTORS CAN BENEFIT FROM HIGH-LEVEL TOOLS TO TRACK THE PROGRESS OF STUDENTS DURING THEIR LEARNING PATHS ON THE CLOUD, AND THIS INFORMATION CAN BE DISCLOSED VIA EDUCATIONAL DASHBOARDS FOR STUDENTS TO UNDERSTAND THEIR PROGRESS THROUGH THE PRACTICAL ACTIVITIES. TO THIS AIM, THIS PAPER INTRODUCES CLOUDTRAIL-TRACKER, AN OPEN-SOURCE PLATFORM TO OBTAIN ENHANCED USAGE ANALYTICS FROM A SHARED AWS ACCOUNT. THE TOOL PROVIDES THE INSTRUCTOR WITH A VISUAL DASHBOARD THAT DEPICTS THE AGGREGATED USAGE OF RESOURCES BY ALL THE STUDENTS DURING A CERTAIN TIME FRAME AND THE SPECIFIC USE OF AWS FOR A SPECIFIC STUDENT. TO FACILITATE SELF-REGULATION OF STUDENTS, THE DASHBOARD ALSO DEPICTS THE PERCENTAGE OF PROGRESS FOR EACH LAB SESSION AND THE PENDING ACTIONS BY THE STUDENT. THE DASHBOARD HAS BEEN INTEGRATED IN FOUR CLOUD SUBJECTS THAT USE DIFFERENT LEARNING METHODOLOGIES (FROM FACE-TO-FACE TO ONLINE LEARNING) AND THE STUDENTS POSITIVELY HIGHLIGHT THE USEFULNESS OF THE TOOL FOR CLOUD INSTRUCTION IN AWS. THIS AUTOMATED PROCUREMENT OF EVIDENCES OF STUDENT ACTIVITY ON THE CLOUD RESULTS IN CLOSE TO REAL-TIME LEARNING ANALYTICS USEFUL BOTH FOR SEMI-AUTOMATED ASSESSMENT AND STUDENT SELF-AWARENESS OF THEIR OWN TRAINING PROGRESS.
THIS ESSAY APPLIES THE CONCEPT OF SMART LEARNING BASED ON LEARNING ANALYTICS TECHNOLOGY. THE CHINESE GRAMMAR SELF-ADAPTIVE-ADJUSTING LEARNING SYSTEM WORKS BY CONSTRUCTING A USER DATA SET THAT RECORDS THE LEARNERS' LEARNING BEHAVIOR AND PROGRESS, THEN USES THE DATE TO COLLECT ON AN ADAPTIVE MECHANISM TO PROVIDE LEARNERS WITH ADVICES, SUITABLE EXERCISES, RELATED QUESTIONS BASED ON THE USER MODEL. BY MAKING DESIGN AND IMPLEMENTATION OF SMART LEARNING ENVIRONMENTS, THE SYSTEM CAN ALSO PRESENT A DETAILED INFORMATION OF THE LEARNER'S PROGRESS IN A VISUAL WAY.
EDUCATION FOR A PERSON PLAYS AN ANCHOR ROLE IN SHAPING AN INDIVIDUAL'S CAREER. IN ORDER TO ACHIEVE SUCCESS IN THE ACADEMIC PATH, CARE SHOULD BE TAKEN IN CHOOSING AN APPROPRIATE COURSE FOR THE LEARNERS. THIS RESEARCH WORK IS BASED ON THE FRAMEWORK TO DESIGN A COURSE ADVISORY SYSTEM IN AN EFFICIENT WAY. THE DESIGN APPROACH IS BASED ON OVERLAPPING OF LEARNING ANALYTICS, ACADEMIC ANALYTICS, AND PERSONALIZED SYSTEMS. THIS APPROACH PROVIDES AN EFFICIENT WAY TO BUILD COURSE ADVISORY SYSTEM. ALSO, MAPPING OF COURSE ADVISORY SYSTEMS INTO THE REFERENCE MODEL OF LEARNING ANALYTICS IS DISCUSSED IN THIS PAPER. COURSE ADVISORY SYSTEM IS CONSIDERED AS ENHANCED PERSONALIZED SYSTEM. THE CHALLENGES INVOLVED IN THE IMPLEMENTATION OF COURSE ADVISORY SYSTEM IS ALSO ELABORATED IN THIS PAPER.
THE EMERGENCE OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAS CAUSED A MAJOR IMPACT ON ONLINE EDUCATION. HOWEVER, LEARNING ANALYTICS SUPPORT FOR MOOCS STILL NEEDS TO IMPROVE TO FULFILL REQUIREMENTS OF INSTRUCTORS AND STUDENTS. IN ADDITION, MOOCS POSE CHALLENGES FOR LEARNING ANALYTICS TOOLS DUE TO THE NUMBER OF LEARNERS, SUCH AS SCALABILITY IN TERMS OF COMPUTING TIME AND VISUALIZATIONS. IN THIS WORK, WE PRESENT DIFFERENT VISUALIZATIONS OF OUR "ADD-ON OF THE LEARNING ANALYTICS SUPPORT FOR OPEN EDX" (ANALYSE), WHICH IS A LEARNING ANALYTICS TOOL THAT WE HAVE DESIGNED AND IMPLEMENTED FOR OPEN EDX, BASED ON MOOC FEATURES, TEACHER FEEDBACK, AND PEDAGOGICAL FOUNDATIONS. IN ADDITION, WE PROVIDE A TECHNICAL SOLUTION THAT ADDRESSES SCALABILITY AT TWO LEVELS: FIRST, IN TERMS OF PERFORMANCE SCALABILITY, WHERE WE PROPOSE AN ARCHITECTURE FOR HANDLING MASSIVE AMOUNTS OF DATA WITHIN EDUCATIONAL SETTINGS; AND, SECOND, REGARDING THE REPRESENTATION OF VISUALIZATIONS UNDER MASSIVENESS CONDITIONS, AS WELL AS ADVICE ON COLOR USAGE AND PLOT TYPES. FINALLY, WE PROVIDE SOME EXAMPLES ON HOW TO USE THESE VISUALIZATIONS TO EVALUATE STUDENT PERFORMANCE AND DETECT PROBLEMS IN RESOURCES.
PERVASIVE GAMING HAS EXPERIMENTED A HUGE COMMERCIAL GROWTH WITH LOCATION-BASED GAME SUCCESSES SUCH AS POKEMON GO OR INGRESS. THE SERIOUS GAME INDUSTRY HAS AN OPPORTUNITY TO TAKE ADVANTAGE OF LOCATION-BASED MECHANICS TO BETTER CONNECT GAMES WITH THE REAL WORLD, CREATING MORE AUTHENTIC IMMERSIVE LEARNING ENVIRONMENTS. GAMES SUCH AS HISTORICAL TOURS, STORY-BASED EXPLORATION, LABORATORIES, OR FLORA EXPLORATIONS CAN GREATLY BENEFIT FROM LOCATION-BASED MECHANICS. LOCATION-BASED GAMES USUALLY INCLUDE AN AUGMENTED MAP TO PROVIDE GAME CONTEXT, WHICH COMBINES BOTH TRADITIONAL GAME-DEPENDENT ELEMENTS SUCH AS AVATARS, AND LOCATION-BASED ELEMENTS SUCH AS AREAS OR POINTS OF INTEREST. FOR PLAYERS, INTERACTING WITH LOCATION-BASED ELEMENTS MAY INVOLVE ENTERING OR EXITING SPECIFIC AREAS; OR REACHING A CERTAIN LOCATION AND LOOKING IN A SPECIFIC DIRECTION. TO INCLUDE STANDARDS BASED LEARNING ANALYTICS FOR LOCATION-BASED SERIOUS GAMES (SGS), WE HAVE ADDED SUPPORT FOR PLAYER MOVEMENT AND LOCATION-BASED INTERACTIONS TO THE PRE-EXISTING XAPI SERIOUS GAME PROFILE. WE HAVE VALIDATED THIS APPROACH THROUGH A CASE-STUDY EXAMPLE THAT GUIDED PLAYERS THROUGH DIFFERENT SPORTS-RELATED FACILITIES WITHIN A LARGE OUTDOOR AREA. THIS WORK HAVE BEEN CARRIED OUT AND IT IS AVAILABLE AS PART OF THE ANALYTICS INFRASTRUCTURE USED IN EU H2020 RAGE AND BEACONING SERIOUS GAME PROJECTS.
LEARNING ANALYTICS DEALS WITH THE DATA THAT OCCURS FROM STUDENTS' INTERACTION WITH ICT: COLLECTING DATA, ANALYZING AND REPORTING CAN INFLUENCE LEARNING AND TEACHING. APPLICATION OF LEARNING ANALYTICS FOR ANALYZING ASSESSMENT HAS LAGGED BEHIND OTHER AREAS OF APPLICATION. WE ARGUE HERE FOR THE NEED FOR LEARNING ANALYTICS FOR ASSESSMENT WITH A SPECIAL FOCUS ON PEER AND SELF-ASSESSMENT. ALL FORMS OF ASSESSMENT SHOULD ENCOURAGE DEEP LEARNING. RELIABILITY AND VALIDITY OF PEER ASSESSMENT WILL BE DISCUSSED. IN THIS CONTEXT, A CASE STUDY WILL BE PRESENTED.
ONLINE LEARNING IS GAINING AN INCREASING ATTENTION BY RESEARCHERS AND EDUCATORS, SINCE IT MAKES STUDENTS LEARN WITHOUT BEING LIMITED IN TIME OR SPACE LIKE TRADITIONAL CLASSROOMS. HOWEVER, THIS TYPE OF LEARNING FACES SEVERAL CHALLENGES INCLUDE THE DIFFICULTIES FOR TEACHERS TO CONTROL THE LEARNING PROCESS AND KEEP TRACK OF THEIR STUDENTS' LEARNING PROGRESS. THEREFORE, THIS PAPER PRESENTS AN ONGOING PROJECT WHICH IS AN INTELLIGENT MOODLE (IMOODLE) THAT USES LEARNING ANALYTICS TO PROVIDE DASHBOARD FOR TEACHERS TO CONTROL THE LEARNING PROCESS AND MAKE DECISIONS. IT ALSO AIMS TO INCREASE THE STUDENTS' SUCCESS RATE WITH AN EARLY WARNING SYSTEM FOR IDENTIFYING AT-RISK STUDENTS AS WELL AS PROVIDING REAL TIME INTERVENTIONS OF SUPPORTIVE LEARNING CONTENT AS NOTIFICATIONS.
IN RECENT YEARS, LEARNING ANALYTICS SOLUTIONS HAVE HIGHLY APPEALED TO THE HIGHER EDUCATION COMMUNITY WHO MAINLY FOCUSES ON IMPROVING THE LEARNING PROCESS, SELF-REGULATED LEARNING SKILLS, AND LEARNERS' SUCCESS RATE. LEARNING ANALYTICS HAS TO DEAL WITH CONTINUOUS DATA, HOWEVER, CONVENTIONAL DATA MINING ALGORITHMS ARE NOT READILY APPLICABLE TO HANDLE THE CONTINUOUS INCOMING OF LEARNERS' DATA. IN ORDER TO COPE WITH THESE SCENARIOS, THE PROPOSED LEARNING ANALYTICS AIMED TO MANAGE THE CONTINUOUS DATA, PERFORM THE CLUSTERING PROCESS USING THE OPTIMIZATION APPROACH, DETECT THE 'AT-RISK' LEARNERS' WHO ARE IN A COURSE FAILURE SITUATION, AND GENERATE SIGNALS TO LEARNERS AND TEACHERS. BASED ON THE PREDICTED OUTCOME, THE PROPOSED SYSTEM IDENTIFIES AND ADAPTS THE LEARNING ACTIVITIES AND LEARNING CONTENTS TO HELP LEARNERS FIND THEIR WAY OUT OF THEIR LEARNING DIFFICULTIES AND COURSE FAILURE SITUATION. THE EXPERIMENTS WERE CONDUCTED TO ANALYZE THE PERFORMANCE OF THE PROPOSED WORK USING THE SIMULATED LEARNERS' DATA. THE EXPERIMENTAL RESULTS PROVIDE EMPIRICAL EVIDENCE THAT THE PROPOSED WORK REDUCES THE COURSE FAILURE RATE AND IMPROVES LEARNERS' SUCCESS RATE.
THIS PAPER PROPOSES A PROCESS BASED ON LEARNING ANALYTICS AND RECOMMENDER SYSTEMS TARGETED AT MAKING SUGGESTIONS TO STUDENTS ABOUT THEIR REMOTE LABORATORIES ACTIVITIES AND PROVIDING INSIGHTS TO ALL STAKEHOLDERS TAKING PART IN THE LEARNING PROCESS. TO APPLY THE PROCESS, A LOG WITH REQUESTS AND RESPONSES OF REMOTE EXPERIMENTS FROM THE VISIR PROJECT WERE ANALYZED. A REQUEST IS THE SETUP OF THE EXPERIMENT INCLUDING THE ASSEMBLED CIRCUITS AND THE CONFIGURATIONS OF THE MEASURING EQUIPMENT. IN TURN, A RESPONSE IS A MESSAGE PROVIDED BY THE MEASUREMENT SERVER INDICATING MEASURES OR AN ERROR WHEN IT IS NOT POSSIBLE TO EXECUTE THE EXPERIMENT. ALONG THE TWO PHASES OF ANALYSIS, THE LOG WAS ANALYZED AND SUMMARIZED IN ORDER TO PROVIDE INSIGHTS ABOUT STUDENTS' EXPERIMENTS. IN ADDITION, THERE IS A RECOMMENDATION SERVICE RESPONSIBLE FOR ANALYZING THE REQUESTS THUS RETURNING, IN CASE OF ERROR, PRECISE INFORMATION ABOUT THE ASSEMBLY OF CIRCUITS OR CONFIGURATIONS. THE EVALUATION OF THE PROCESS IS CONSISTENT IN WHAT REGARDS ITS ABILITY TO AFFORD RECOMMENDATIONS TO THE STUDENTS AS THEY CARRY OUT THE EXPERIMENTS. MOREOVER, THE SUMMARIZED INFORMATION INTENDS TO OFFER TEACHERS MEANS TO BETTER UNDERSTAND AND DEVELOP STRATEGIES TO SCAFFOLD STUDENTS' LEARNING.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BEEN TREMENDOUSLY SPREADING AMONG SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) ACADEMIC DISCIPLINES. THESE MOOCS HAVE SERVED AN AGGLOMERATION OF VARIOUS LEARNER GROUPS ACROSS THE WORLD. THE LEADING MOOCS PLATFORM IN AUSTRIA, THE IMOOX, OFFERS SUCH COURSES. THIS PAPER HIGHLIGHTS AUTHORS' EXPERIENCE OF APPLYING LEARNING ANALYTICS TO EXAMINE THE PARTICIPATION OF SECONDARY SCHOOL PUPILS IN ONE OF ITS COURSES CALLED "MECHANICS IN EVERYDAY LIFE". WE SIGHTED DIFFERENT PATTERNS AND OBSERVATIONS AND ON THE CONTRARY OF THE EXPECTED JUBILANT RESULTS OF ANY EDUCATIONAL MOOC, WE WILL SHOW, THAT PUPILS SEEMINGLY DECIDED TO CONSIDER IT NOT AS A REAL MOTIVATING LEARNING ROUTE, BUT RATHER AS AN OPTIONAL HOMEWORK.
OVER 2019, THE BREADTH OF ARTICLES PUBLISHED IN THE JOURNAL OF LEARNING ANALYTICS REFLECTS THE DIVERSITY OF CONTRIBUTIONS TO THE FIELD, INCLUDING QUANTITATIVE, QUALITATIVE, AND DESIGN-BASED STUDIES AND CONTRIBUTIONS THROUGH THE DIFFERENT SUBMISSION TYPES (RESEARCH, PRACTICAL, DATA AND TOOLS, AND BOOK REVIEW). THIS YEAR, WE HAVE ALSO PUBLISHED TWO SPECIAL SECTIONS. THE FIRST IS A SET OF EMPIRICAL PAPERS RELATED TO THE EMERGING AREA OF HUMAN-CENTRED LEARNING ANALYTICS (HCLA). IN THIS SECTION, FIVE SETS OF AUTHORS EACH ADDRESS THE CORE CHALLENGE OF HOW TO DESIGN AND IMPLEMENT LEARNING ANALYTICS IN WAYS THAT ARE PEOPLE-CENTRIC RATHER THAN TECHNOLOGY-CENTRIC TO ACHIEVE IMPACT IN THE FIELD. THE SECOND IS A NEW FORMAT FOR THE JOURNAL, AN INVITED DIALOGUE AROUND A CRITICAL COMMUNITY ISSUE. IN THIS SECTION, NEIL SELWYN ASKS THE INTENTIONALLY PROVOCATIVE QUESTION, "WHAT'S THE PROBLEM WITH LEARNING ANALYTICS?," WHICH IS ENGAGED WITH BY FOUR RESPONDENTS FROM THE COMMUNITY, REPRESENTING A VARIETY OF PERSPECTIVES. BOTH OF THESE SPECIAL SECTIONS TACKLE, IN THEIR OWN WAY, A CENTRAL ISSUE FOR THE LEARNING ANALYTICS COMMUNITY: HOW WE ENSURE THAT THE WORK WE DO HAS A POSITIVE IMPACT IN THE WORLD.
ITERATIVE DESIGN IS A POWERFUL METHOD FOR DEVELOPING DIGITAL CLASSROOM TOOLS AND CURRICULA. WE EXPLORE HOW INFUSING LEARNING ANALYTICS INTO THIS PROCESS HAS INFLUENCED OUR DEVELOPMENT OF ECOSURVEY, A DIGITAL MODELING TOOL FOR MAPPING THE ORGANISMS AND INTERACTIONS IN THE LOCAL ECOSYSTEM. WE HAVE FOUND THAT ANALYTIC TECHNIQUES CAN HELP US DISCOVER AREAS IN WHICH STUDENTS STRUGGLE TO ENGAGE WITH SCIENTIFIC MODELING, AND WE CAN ITERATIVELY USE LEARNING ANALYTICS TO DEMONSTRATE THE IMPACT OF DESIGN CHANGES.
IN THE LAST COUPLE OF YEARS, THE USE OF VARIOUS APPLICATIONS OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS HAS INCREASED. NOW A DAYS, STUDENTS HAVE STARTED USING INTERNET TO STUDY. DUE TO THE EASY AVAILABILITY OF ONLINE COURSES, STUDENT ONLINE ACTIVITIES HAVE CONSIDERABLY INCREASED WHICH IN TURN HAS GENERATED HUGE AMOUNT OF DATA. THESE DATA GETS WASTED AS CONVENTIONAL LEARNING METHODS ARE NOT EQUIPPED FOR PREPARING IT. NOW, DUE TO THIS, THE BIG DATA TECHNOLOGY HAS ENTERED IN THE EDUCATION FIELD, TO MINE THOSE HUGE CHUNKS OF DATASETS. THIS PAPER DISCUSSES THE LATEST TOOLS AND TECHNOLOGIES OF BIG DATA IN THE FIELD OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING.
PRECISION EDUCATION REQUIRES TWO EQUALLY IMPORTANT CONDITIONS: ACCURATE PREDICTIONS OF ACADEMIC PERFORMANCE BASED ON EARLY OBSERVATIONS OF THE LEARNING PROCESS AND THE AVAILABILITY OF RELEVANT EDUCATIONAL INTERVENTION OPTIONS. THE FIELD OF LEARNING ANALYTICS (LA) HAS MADE IMPORTANT CONTRIBUTIONS TO THE REALISATION OF THE FIRST CONDITION, ESPECIALLY IN THE CONTEXT OF BLENDED LEARNING AND ONLINE LEARNING. PREDICTION MODELS THAT USE DATA FROM INSTITUTIONAL INFORMATION SYSTEMS AND LOGS OF LEARNING MANAGEMENT SYSTEMS HAVE GAINED A GOOD REPUTATION IN PREDICTING UNDERPERFORMANCE AND DROPOUT RISK. HOWEVER, LESS PROGRESS IS MADE IN RESOLVING THE SECOND CONDITION: APPLYING LA GENERATED FEEDBACK TO DESIGN EDUCATIONAL INTERVENTIONS. IN OUR CONTRIBUTION, WE MAKE A PLEA FOR APPLYING DISPOSITIONAL LEARNING ANALYTICS (DLA) TO MAKE LA PRECISE AND ACTIONABLE. DLA COMBINES LEARNING DATA, AS IN LA, WITH LEARNERS' DISPOSITION DATA MEASURED THROUGH SELF-REPORT SURVEYS. THE ADVANTAGE OF DLA IS TWOFOLD: FIRST, IT IMPROVES THE ACCURACY OF PREDICTION, SPECIFICALLY EARLY IN THE MODULE, WHEN LIMITED LMS TRACE DATA ARE AVAILABLE. SECOND, THE MAIN BENEFIT OF DLA IS IN THE DESIGN OF EFFECTIVE INTERVENTIONS: INTERVENTIONS THAT FOCUS ON ADDRESSING INDIVIDUAL LEARNING DISPOSITIONS THAT ARE LESS DEVELOPED BUT IMPORTANT FOR BEING SUCCESSFUL IN THE MODULE. WE PROVIDE AN EMPIRICAL ANALYSIS OF DLA IN AN INTRODUCTORY MATHEMATICS MODULE, DEMONSTRATING THE IMPORTANT ROLE THAT A BROAD RANGE OF LEARNING DISPOSITIONS CAN PLAY IN REALISING PRECISION EDUCATION.
IN LEARNING ANALYTICS, WE USE EYE TRACKING TECHNOLOGY TO GET DEEPER INSIGHTS INTO THE LEARNING PROCESSES. IN THIS PAPER, WE PROPOSE TO CAPTURE EYE MOVEMENTS (FIXATION POINTS AND SACCADES) OF LEARNERS OF MATHEMATICS. THE STUDY AIMS AT USING EYE TRACKING TECHNOLOGY TO UNDERSTAND THE CORRELATION BETWEEN ATTENTION, GAZE PATTERNS AND PERFORMANCE. THE CAPTURED DATA WILL HELP US UNDERSTAND THE RELATIONSHIPS BETWEEN TEXTS, DIAGRAMS AND FORMULAS AS WELL AS IDENTIFY QUICK AND WEAK LEARNERS IN MATHEMATICS AND PROVIDE APPROPRIATE GUIDANCE.
THE USE OF SERIOUS GAMES TO IMPROVE COLLABORATIVE SKILL TRANSFER AND RETENTION HAS RECEIVED CONSIDERABLE ATTENTION FROM SCHOLARS, WEB MARKETING PRACTITIONERS AND BUSINESS CONSULTANTS. TEAM RANKINGS AND LEARNING PROGRESS IN GAME LEARNING ANALYTICS, HOWEVER, HAVE YET TO BE EMPIRICALLY EXAMINED. USING FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS TO STUDY THE PERFORMANCE OF COMPETING TEAMS IN A WEB MARKETING SERIOUS GAME (SIMBOUND), WE HIGHLIGHT A COMBINATION OF CAUSAL CONDITIONS (ENGAGEMENT, REACH AND PROFITABILITY) AFFECTING TEAM RANKINGS. THIS PAPER PROPOSES A CONCEPTUAL ARCHITECTURE OF THE FORCES THAT INFLUENCE LEARNING PROGRESS WITHIN A COLLABORATIVE LEARNING ENVIRONMENT. THIS LEARNING ENVIRONMENT IS STUDIED FOR WEB MARKETING BOOT CAMPS POWERED BY SIMBOUND AT THREE EUROPEAN UNIVERSITIES: GRENOBLE ALPES UNIVERSITY (FRANCE), UNIVERSITY OF MILANO-BICOCCA (ITALY) AND DUNAREA DE JOS UNIVERSITY OF GALATI (ROMANIA). GAINING KNOWLEDGE OF CASES THROUGH GAME LEARNING ANALYTICS IS VALUABLE FOR TWO REASONS: IT EMPHASISES THE INSTRUCTOR'S ROLE IN MOBILISING PLAYERS' ENGAGEMENT, AND IT TESTS VARIABILITY ACROSS CASES, OFFERING PRECURSORS OF TEAM PERFORMANCE RANKINGS. THIS APPROACH TO COLLECTIVE SKILL RETENTION HIGHLIGHTS THE MODERATING FACTORS OF TEAM PERFORMANCE RANKINGS, WHILST PURPOSELY CALIBRATING A GAMEABLE LEARNING ENVIRONMENT. THIS PAPER ENRICHES OUR KNOWLEDGE OF HOW ACTIVE EXPERIMENTATION IN LEARNING ANALYTICS METRICS CAN DEVELOP SKILLS FOR REAL BUSINESS COMPETITION.
THIS PAPER HIGHLIGHTS THE IMPORTANCE OF DEVELOPING DATA-CENTRIC TEACHING-RESEARCH SKILLS OF 21ST-CENTURY TEACHERS. IT CRITICALLY EXAMINES THE LITERATURE RELATED TO THE TEACHING-RESEARCH NEXUS AND LEARNING ANALYTICS, AND PRESENTS A MODEL OF THE INTEGRATION OF THESE CONCEPTS, AND THEIR RELEVANCE TO THE TEACHING AND LEARNING PROCESS. THIS PAPER ALSO PRESENTS THE DESIRABLE SKILLS SET OF LEARNING ANALYSTS AND IDENTIFIES THE ANALYTIC TOOLS THAT THEY CAN USE TO CARRY OUT THIS ROLE. FINALLY, THIS PAPER SUGGESTS THE FUTURE DIRECTION OF TEACHER TRAINING AND PROFESSIONAL DEVELOPMENT PROGRAMS THAT WILL EQUIP TEACHERS WITH THE RIGHT TEACHING-RESEARCH SKILLS AND TOOLS TO HELP THE MILLENNIAL LEARNERS GAIN ACADEMIC SUCCESS. THIS STUDY RECOMMENDS THAT TEACHERS NEED TO BE WELL INFORMED ABOUT THEIR STUDENTS BY HAVING ACCESS TO THEIR DATA AND ACQUIRING THE NECESSARY COMPETENCIES TO PERFORM APPROPRIATE DATA ANALYSIS. FURTHERMORE, THEY ARE EXPECTED TO ENGAGE WITH ANALYTICAL TOOLS AND EDUCATIONAL TECHNOLOGIES IN ORDER TO REALIZE A MORE EFFECTIVE TEACHING AND LEARNING EXPERIENCE, AND IMPROVE ACADEMIC OUTCOMES.
THIS PAPER IS BASED ON (A) A LITERATURE REVIEW FOCUSSING ON THE IMPACT OF LEARNING ANALYTICS ON SUPPORTING LEARNING AND TEACHING, (B) A DELPHI STUDY INVOLVING INTERNATIONAL EXPERT DISCUSSION ON CURRENT OPPORTUNITIES AND CHALLENGES OF LEARNING ANALYTICS AS WELL AS (C) OUTLINING A RESEARCH AGENDA FOR CLOSING IDENTIFIED RESEARCH GAPS. ISSUES AND CHALLENGES FACING EDUCATORS LINKED TO LEARNING ANALYTICS AND CURRENT RESEARCH GAPS WERE ORGANISED INTO FOUR THEMES, THE FURTHER DEVELOPMENT OF WHICH BY THE EXPERT PANEL, LED TO SIX STRATEGY AND ACTION AREAS. THE FOUR THEMES ARE 1. DEVELOPMENT OF DATA LITERACY IN ALL STAKEHOLDERS, 2. UPDATING OF GUIDING PRINCIPLES AND POLICIES OF EDUCATIONAL DATA, 3. STANDARDS NEEDED FOR ETHICAL PRACTICES WITH DATA QUALITY ASSURANCE, AND 4. FLEXIBLE USER-CENTRED DESIGN FOR A VARIETY OF USERS OF ANALYTICS, STARTING WITH LEARNERS AND ENSURING THAT LEARNERS AND LEARNING IS NOT HARMED. THE STRATEGIES AND ACTIONS ARE OUTCOMES OF THE EXPERT PANEL DISCUSSION AND ARE OFFERED AS PROVOCATIONS TO ORGANISE AND FOCUS THE RESEARCHER, POLICYMAKER AND PRACTITIONER DIALOGS NEEDED TO MAKE PROGRESS IN THE FIELD.
SERIOUS GAMES ARE STILL COMPLEX TO DEPLOY IN CLASSROOMS FOR AVERAGE TEACHERS. GAME LEARNING ANALYTICS CAN HELP TEACHERS TO APPLY SERIOUS GAMES, USING DATA FROM STUDENTS' IN-GAME INTERACTIONS TO PROVIDE LEARNING INFORMATION. MANY TEACHERS DO NOT SEE GAMES AS TOOLS TO IMPROVE THEIR CLASSES, PARTICULARLY DUE TO PERCEIVED LOSS OF CONTROL WHEN USING GAMES; SO IT IS ESSENTIAL TO RETAIN THEIR BENEFITS WHILE AVOIDING MOST OF THE DEPLOYMENT COMPLEXITY. IN THIS PAPER, WE DESCRIBE OUR EXPERIENCE USING GAME LEARNING ANALYTICS TO ENCOURAGE THE APPLICATION AND DEPLOYMENT OF SERIOUS GAMES IN CLASS AS LEARNING TOOLS.
IN THIS PAPER, WE PRESENT A BIG DATA SOFTWARE ARCHITECTURE THAT USES AN ONTOLOGY, BASED ON THE EXPERIENCE API SPECIFICATION, TO SEMANTICALLY REPRESENT THE DATA STREAMS GENERATED BY THE LEARNERS WHEN THEY UNDERTAKE THE LEARNING ACTIVITIES OF A COURSE, E.G., IN A COURSE. THESE DATA ARE STORED IN A RDF DATABASE TO PROVIDE A HIGH PERFORMANCE ACCESS SO LEARNING ANALYTICS SERVICES CAN PROCESS THE LARGE AMOUNT OF DATA GENERATED IN A VIRTUAL LEARNING ENVIRONMENT. THESE SERVICES PROVIDE VALUABLE INFORMATION TO TEACHERS AND INSTRUCTORS SUCH AS PREDICT THE LEARNER'S PERFORMANCE, DISCOVER THE REAL LEARNING PATHS, EXTRACT THE LEARNER'S BEHAVIOR PATTERNS AND SO ON. THE PROPOSED ARCHITECTURE HAS BEEN VALIDATED IN THE EDUCATIONAL TECHNOLOGY UNDERGRADUATE COURSE OF THE DEGREE IN PEDAGOGY AT THE FACULTY OF EDUCATION OF THE UNIVERSITY OF SANTIAGO DE COMPOSTELA.
WHILE LEARNING ANALYTICS (LA) IS MATURING FROM BEING A TREND TO BEING PART OF THE INSTITUTIONAL TOOLBOX, THE NEED FOR MORE EMPIRICAL EVIDENCES ABOUT THE EFFECTS FOR LA ON THE ACTUAL STAKEHOLDERS, I.E. LEARNERS AND TEACHERS, IS INCREASING. WITHIN THIS PAPER WE REPORT ABOUT A FURTHER EVALUATION ITERATION OF THE EVALUATION FRAMEWORK FOR LEARNING ANALYTICS (EFLA) THAT PROVIDES AN EFFICIENT AND EFFECTIVE MEASURE TO GET INSIGHTS INTO THE APPLICATION OF LA IN EDUCATIONAL INSTITUTES. FOR THIS EMPIRICAL STUDY WE HAVE THUS DEVELOPED AND IMPLEMENTED SEVERAL LA WIDGETS INTO A MOOC PLATFORM'S DASHBOARD AND EVALUATED THESE WIDGETS USING THE EFLA AS WELL AS THE FRAMEWORK ITSELF USING PRINCIPAL COMPONENT AND RELIABILITY ANALYSIS. THE RESULTS SHOW THAT THE EFLA IS ABLE TO MEASURE DIFFERENCES BETWEEN WIDGET VERSIONS. FURTHERMORE, THEY INDICATE THAT THE FRAMEWORK IS HIGHLY RELIABLE AFTER SLIGHTLY ADAPTING ITS DIMENSIONS.
LEARNING ANALYTICS CAN BE DEFINED AS A SERIES OF TECHNIQUES FOR COLLECTING, ANALYZING AND GRANTING PROCESSABLE DATA GENERATED BY STUDENTS. THEIR OBJECTIVE IS TO DEVELOP APPROPRIATE STRATEGIES TO IMPROVE THE LEARNING PROCESSES, THE PERFORMANCE OF THE STUDENTS OR THAT OF THE INSTITUTION ITSELF. THESE TYPES OF TECHNIQUES ARE ESPECIALLY USEFUL TO ESTABLISH PATTERNS OF ACTION THAT GUIDE AND GUIDE THE EDUCATIONAL PROCESS IN HIGHER EDUCATION. UNDER THESE PREMISES, THE PRESENT RESEARCH AIMS TO ANALYZE THE SCIENTIFIC PRODUCTION WITH THE GREATEST IMPACT ON THE USE OF LEARNING ANALYTICS IN HIGHER EDUCATION. FOR THIS PURPOSE, A QUANTITATIVE METHODOLOGY HAS BEEN FOLLOWED, BASED ON TEN VARIABLES: YEAR OF PUBLICATION, PERIODICALS, AUTHORS, INSTITUTIONS, COUNTRIES, TYPE OF DOCUMENT, PUBLICATION FORMAT, PUBLICATION AREA, LANGUAGE AND MOST CITED ARTICLES. THE RESULTS PROJECT A RESEARCH TREND THAT IS FULLY ON THE RISE, ESPECIALLY DUE TO THE GREATER SCIENTIFIC PRODUCTION THAT HAS OCCURRED IN RECENT YEARS (20152018), HIGHLIGHTING COUNTRIES SUCH AS AUSTRALIA, THE UNITED STATES AND THE UNITED KINGDOM. THE PUBLICATIONS, IN THEIR GREAT MAJORITY, COME FROM CONFERENCES AND ARE PUBLISHED IN ENGLISH. THE KNOWLEDGE AREAS OF COMPUTATIONAL SCIENCES AND SOCIAL SCIENCES STAND OUT.
THIS PAPER REPORTS ON THE INITIAL STAGE OF A DESIGN-BASED RESEARCH PROGRAM THAT AIMS TO DEVISE STUDENT-FACING SOCIAL LEARNING ANALYTICS IN A POSTSECONDARY SETTING. IN ORDER TO FOSTER STUDENT DISCUSSION IN ONLINE CLASSES, WE DEVISED AN ANALYTICS TOOLKIT THAT TURNS DISCUSSION FORUM DATA INTO INFORMATION FOR STUDENTS TO REFLECT UPON. THE FIRST PILOT STUDY CONDUCTED IN AN ONLINE COURSE SHOWED BOTH EARLY PROMISES AND CHALLENGES WITH THIS EFFORT. IN PARTICULAR, STUDENTS REPORTED USEFULNESS OF THE TOOLKIT FOR MONITORING POSTING BEHAVIORS AND SETTING PARTICIPATION GOALS. CONTENT ANALYSIS OF POST CONTENT LINKED TOOL USAGE WITH STUDENTS' INCREASED ATTEMPTS TO SOLICIT PEER RESPONSES TO THEIR POSTS AND INCREASED REFLECTION ON INDIVIDUAL PARTICIPATION. HOWEVER, THE TOOL WAS NOT FOUND USEFUL OR EASY TO USE BY SOME STUDENTS, AND IT DID NOT SHOW BENEFITS IN OTHER ASPECTS OF ONLINE DISCUSSION, SUCH AS CRAFTING STRONGER ARGUMENTATION OR REFLECTING ON GROUP PROCESSES. THESE FINDINGS HIGHLIGHT THE NEED OF SOCIAL LEARNING ANALYTICS TO STRIVE FOR TOOL SIMPLICITY, SCAFFOLD STUDENTS' DEVELOPMENT IN DATA LITERACY, AND BRING ANALYTICS AND PEDAGOGICAL DESIGNS INTO CLOSE COORDINATION.
AS IS THE CASE FOR MANY UNDERGRADUATE STEM DEGREE PROGRAMS, COMPUTING DEGREE PROGRAMS ARE PLAGUED BY HIGH ATTRITION RATES. THIS IS ESPECIALLY TRUE IN EARLY COMPUTING COURSES, IN WHICH FAILURE AND DROP-OUT RATES IN THE 35 TO 50 PERCENT RANGE ARE COMMON. BY COLLECTING LEARNING PROCESS DATA AS STUDENTS ENGAGE IN COMPUTER PROGRAMMING ASSIGNMENTS, COMPUTING EDUCATORS CAN PLACE THEMSELVES IN A POSITION NOT ONLY TO BETTER UNDERSTAND STUDENTS' STRUGGLES, BUT ALSO TO BETTER TAILOR INSTRUCTIONAL INTERVENTIONS TO STUDENTS' NEEDS. WE HAVE DEVELOPED OSBLE+, A LEARNING MANAGEMENT AND ANALYTICS ENVIRONMENT THAT INTERFACES WITH A COMPUTER PROGRAMMING ENVIRONMENT TO SUPPORT THE AUTOMATIC COLLECTION OF LEARNERS' PROGRAMMING PROCESS AND SOCIAL DATA AS THEY WORK ON PROGRAMMING ASSIGNMENTS, WHILE ALSO PROVIDING AN INTERACTIVE ENVIRONMENT FOR THE ANALYSIS AND VISUALIZATION OF THOSE DATA. IN ONGOING WORK, WE ARE USING OSBLE+ TO EXPLORE TWO POSSIBILITIES: (A) LEVERAGING LEARNING AND SOCIAL DATA TO STRATEGICALLY DELIVER AUTOMATED LEARNING INTERVENTIONS, AND (B) PRESENTING LEARNERS WITH VISUAL REPRESENTATIONS OF THEIR LEARNING DATA IN ORDER TO PROMPT THEM TO REFLECT ON AND DISCUSS THEIR LEARNING PROCESSES.
COMPETENCE-BASED LEARNING HAS BECOME MORE AND MORE POPULAR IN THE LAST FEW YEARS AND SOME RESEARCHERS HAVE CLAIMED THAT FORMATIVE ASSESSMENT SHOULD BE FOLLOWED TO IMPROVE THE LEARNING PROCESSES. IN ORDER TO ACHIEVE THE MASTERY OF COMPETENCIES, MANY LECTURERS HAVE BEEN REQUIRED TO ADOPT VARIOUS AND DIVERSE APPLICATIONS IN THEIR COURSES TO WORK ON THOSE COMPETENCES. THE USE OF SEVERAL APPLICATIONS BRINGS NEW CHALLENGES, AS THE INFORMATION OF THE LEARNING PROCESSES IS DISTRIBUTED AMONG THESE SYSTEMS AND GETTING A GLOBAL PICTURE OF THE EVOLUTION BECOMES MUCH HARDER. THEREFORE, SYSTEMS THAT ENABLE GATHERING AND UNIFYING FORMATIVE ASSESSMENT INFORMATION COMING FROM VARIOUS SOURCES IN ORDER TO EXTRACT SIGNIFICANT INFORMATION ABOUT THE GLOBAL LEARNING PROCESS ARE NEEDED. IN THIS PAPER, WE PRESENT A SYSTEM CALLED COBLE (COMPETENCE-BASED LEARNING ENVIRONMENT) THAT SUPPORTS COMPETENCE-BASED LEARNING AND COMBINES VISUAL LEARNING ANALYTICS AND RECOMMENDATION ASPECTS IN ORDER TO PROMOTE THE STUDENTS' AND LECTURERS' SELF-REFLECTION ABOUT THE LEARNING AND TEACHING PROCESSES. COBLE SUPPORTS DATA FROM DIFFERENT SOURCES TO BE INTEGRATED, PROVIDING THE USERS WITH THE COMPLETE REPORT OF WHAT IS GOING ON IN THEIR COURSES.
THIS ARTICLE REPORTS THE EXPERIENCE OF THE DESIGN, IMPLEMENTATION, AND USE OF A LEARNING ANALYTICS TOOL THAT PROVIDES NEAR-TO-REAL TIME VISUALISATIONS OF MOOC METRICS, CALLED SOUTHAMPTON MOOC DASHBOARD. THE DASHBOARD HAS BEEN DESIGNED AND DEVELOPED BY THE UNIVERSITY OF SOUTHAMPTON, IN COLLABORATION WITH THE UNIVERSITY AUTONOMA OF MADRID. THIS TOOL HAS BEEN USED TO ANALYSE THE DATA FROM UNIVERSITY OF SOUTHAMPTON MOOC, HOSTED IN THE FUTURELEARN PLATFORM. THE EVOLUTION OF TEACHING IN MASSIVE SETTINGS REQUIRES A CONSTANT AWARENESS OF LEARNERS PERFORMANCE AND ENGAGEMENT, SO AS TO DETERMINE THE EFFECTIVENESS OF THE PEDAGOGICAL DESIGN OF THESE COURSES. THE VISUALISATION THE DIGITAL FOOTPRINTS LEFT BY THE LEARNERS AFFORDS INTERVENTIONS TO IMPROVE THE QUALITY, BOTH IN THEIR DESIGN AND DELIVERY. THROUGH A DESCRIPTIVE AND EXPLORATORY APPROACH, THE FUTURELEARN AND UNIVERSITY OF SOUTHAMPTON MOOC DIGITAL MARKETING: CHALLENGES AND INSIGHTS HAS BEEN ANALYSED. THE RESULTS OF SUCH ANALYSIS ARE DISPLAYED THROUGH THE USE OF THE TOOL, SHOWING A SET OF LEARNER ENGAGEMENT AND PERFORMANCE METRICS.
THIS PAPER IS IN RESPONSE TO THE MANUSCRIPT TITLED "ETHICAL OVERSIGHT OF STUDENT DATA IN LEARNING ANALYTICS: A TYPOLOGY DERIVED FROM A CROSS-CONTINENTAL, CROSS-INSTITUTIONAL PERSPECTIVE" (WILLIS ET AL. IN EDUC TECHNOL RES DEV 66(4):1029-1049, 2016). THE RESPONSE IS FROM A K-12 EDUCATIONAL ENVIRONMENT PERSPECTIVE. WILLIS, SLADE, AND PRINSLOO'S TYPOLOGY OF DIFFERENT ETHICAL APPROACHES TO LEARNING ANALYTICS ADDS VALUE TO THE SPECIAL ISSUE TOPIC OF SHIFTING TO DIGITAL AS IT PROVIDES DIFFERENT WAYS TO VIEW LEARNING ANALYTICS AS WELL AS THE TYPE OF APPROVAL(S) POSSIBLY NEEDED FOR EACH VIEW. K-12 EDUCATIONAL INSTITUTIONS CAN UTILIZE THE MANUSCRIPT AS A STARTING PLACE FOR REVIEW OF THEIR ETHICAL OVERSIGHTS WHEN ANALYZING STUDENT DATA AS MORE SCHOOLS ARE SHIFTING TO DIGITAL. A LIMITATION OF WILLIS, SLADE, AND PRINSLOO'S MANUSCRIPT IN SUPPORTING THE SHIFT TO DIGITAL IS THAT THE MANUSCRIPT WAS PUBLISHED BEFORE THE OVERWHELMING SHIFT TO DIGITAL WAS MANDATED AS A RESULT OF COVID-19. FUTURE WORK RELATED TO THE MANUSCRIPT AND WITH A FOCUS ON THE K-12 EDUCATIONAL ENVIRONMENT COULD INCLUDE K-12 EDUCATION AGENCIES DERIVING A K-12 SPECIFIC TYPOLOGY FROM A REVIEW OF ETHICAL OVERSIGHT PROTOCOLS OR ANALYZING THE EFFECTS OF THE SHIFT TO DIGITAL IN K-12 ON THE ORIGINAL TYPOLOGY. DURING THE 2020 PANDEMIC, K-12 SCHOOLS IN THE UNITED STATES MOVED TO DIGITAL LEARNING QUICKLY AND IN LARGE NUMBERS. THIS MOVE HAS RESULTED IN A WEALTH OF DIGITAL LEARNING ANALYTICS AVAILABLE TO BE USED IN IMPROVING STUDENT LEARNING OUTCOMES. HOWEVER, LOCAL EDUCATION AGENCIES MUST FIRST MAKE SURE THEY HAVE A CLEAR FRAMEWORK FOR OVERSIGHT OF HOW DIGITAL LEARNING ANALYTICS WILL BE USED. K-12 STUDENTS ARE AT RISK OF HARM IF LOCAL EDUCATION AGENCIES DO NOT STOP AND CAREFULLY REFLECT ON THE POTENTIAL RISKS TO STUDENTS RESULTING FROM DECISIONS MADE AS A RESULT OF USING DIGITAL LEARNING ANALYTICS.
THE BURGEONING FIELD OF LEARNING ANALYTICS (LA) IS GAINING SIGNIFICANT TRACTION IN EDUCATION, BOLSTERED BY THE INCREASING AMOUNTS OF STUDENT DATA GENERATED THROUGH EDUCATIONAL SOFTWARE. HOWEVER, CRITICAL DISCUSSIONS OF LA ARE IN SHORT SUPPLY. DRAWING ON WORK IN THE CULTURAL STUDIES OF DATA AND CRITICAL ALGORITHM STUDIES, THIS PAPER BEGINS BY EXAMINING THREE CENTRAL ISSUES: THE DISTANCING AND BLACK BOXING OF LA DISCIPLINARY PRACTICES, THE MYTHOLOGIZING OF OBJECTIVE DATA, AND THE CONCERN FOR FUTURE PREDICTION. THE SECOND SECTION DESCRIBES THE DESIGN AND IMPLEMENTATION OF THE LEARNING ANALYTICS REPORT CARD (LARC), A PILOT PROJECT THAT SOUGHT TO DEVELOP EXPERIMENTAL APPROACHES TO LA. AS SUCH, RATHER THAN SEEKING TO SIMPLY PRODUCE ANALYTICS, THE LARC ATTEMPTED TO FOSTER CRITICAL AWARENESS OF COMPUTATIONAL DATA ANALYSIS AMONG TEACHERS AND LEARNERS.
THIS RESEARCH PROPOSAL IS ABOUT THE INTEGRATION BETWEEN LEARNING ANALYTICS AND COGNITIVE COMPUTING IN THE DEVELOPMENT OF RESOURCES TO SUPPORT THE PROCESS OF PERSONALIZED LEARNING IN HIGHER EDUCATION AND FROM THE USE OF THESE RESOURCES VERIFY THE IMPROVEMENT OF MOTIVATION AND ENGAGEMENT OF STUDENTS IN LEARNING PROCESSES. IT IS A WORK IN PROGRESS PROJECT IN WHICH IS OFFERED CUSTOMIZED PROCESSES TO STUDENTS, AS WELL AS PRESCRIPTIVE ANALYSIS OF DATA TO TEACHERS TO SUPPORT DECISION-MAKING AND MORE ASSERTIVE DESIGN OF LEARNING EXPERIENCES.
THE RAPID ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGIES, COUPLED WITH INCREASED ACCESS TO INFORMATION AND THE FORMATION OF GLOBAL COMMUNITIES, HAVE RESULTED IN INTEREST AMONG RESEARCHERS AND ACADEMICS TO REVISE EDUCATIONAL PRACTICE TO MOVE BEYOND TRADITIONAL 'LITERACY' SKILLS TOWARDS AN ENHANCED SET OF "MULTILITERACIES" OR "NEW MEDIA LITERACIES". MEASURING THE LITERACY OF A POPULATION, IN THE LIGHT OF ITS LINKAGE TO INDIVIDUAL AND COMMUNITY WEALTH AND WELLBEING, IS ESSENTIAL TO DETERMINING THE IMPACT OF COMPULSORY EDUCATION. THE OPPORTUNITY NOW IS TO DEVELOP TOOLS TO ASSESS INDIVIDUAL AND SOCIETAL ATTAINMENT OF THESE NEW LITERACIES. DRAWING ON THE WORK OF JENKINS AND COLLEAGUES (2006) AND NOTIONS OF A PARTICIPATORY CULTURE, THIS PAPER PROPOSES A CONCEPTUAL FRAMEWORK FOR HOW LEARNING ANALYTICS CAN ASSIST IN MEASURING INDIVIDUAL ACHIEVEMENT OF MULTILITERACIES AND HOW THIS EVALUATIVE PROCESS CAN BE SCALED TO PROVIDE AN INSTITUTIONAL PERSPECTIVE OF THE EDUCATIONAL PROGRESS IN FOSTERING THESE FUNDAMENTAL SKILLS.
ONE KEY FACTOR FOR THE SUCCESSFUL OUTCOME OF A LEARNING ANALYTICS (LA) INFRASTRUCTURE IS THE ABILITY TO DECIDE WHICH SOFTWARE ARCHITECTURE CONCEPT IS NECESSARY. BIG DATA CAN BE USED TO FACE THE CHALLENGES LA HOLDS. ADDITIONAL CHALLENGES ON PRIVACY RIGHTS ARE INTRODUCED TO THE EUROPEANS BY THE GENERAL DATA PROTECTION REGULATION (GDPR). BEYOND THAT, THE CHALLENGE OF HOW TO GAIN THE TRUST OF THE USERS REMAINS. WE FOUND DIVERSE ARCHITECTURAL CONCEPTS IN THE DOMAIN OF LA. SELECTING AN APPROPRIATE SOLUTION IS NOT STRAIGHTFORWARD. THEREFORE, WE CONDUCTED A STRUCTURED LITERATURE REVIEW TO ASSESS THE STATE-OF-THE-ART AND PROVIDE AN OVERVIEW OF BIG DATA ARCHITECTURES USED IN LA. BASED ON THE EXAMINATION OF THE RESULTS, WE IDENTIFY COMMON ARCHITECTURAL COMPONENTS AND TECHNOLOGIES AND PRESENT THEM IN THE FORM OF A MIND MAP. LINKING THE FINDINGS, WE ARE PROPOSING AN INITIAL APPROACH TOWARDS A TRUSTED AND INTEROPERABLE LEARNING ANALYTICS INFRASTRUCTURE (TIILA).
FROM A MEASUREMENT PERSPECTIVE, A VARIETY OF ANALYTIC APPROACHES ARE FAST EMERGING IN THE DATA MINING AND EXPLORATORY ANALYTICS BRANCHES OF THE FIELD OF DATA SCIENCES. IN PARTICULAR, FOR LEARNING ANALYTICS, MORE THEORY IS NEEDED SHOWING HOW THE ANALYTICAL APPROACHES ARE RELATED TO ONE ANOTHER AND TO THEIR RESPECTIVE PURPOSES WHEN MEASUREMENT IS INVOLVED. FOR EXAMPLE, MACHINE LEARNING ACTING ON PROCESS DATA CAN YIELD SETS OF SPECIFIC PATTERNS AS RESULTS, BUT THE CRITICAL QUESTION FROM A MEASUREMENT PERSPECTIVE IS: WHAT DO THESE RESULTS MEAN AND HOW CAN THEY BE USED SUCCESSFULLY IN LEARNING ANALYTICS? THAT IS, IF THE GOAL IS TO MAKE AN INFERENCE REGARDING SOME UNDERLYING VARIABLE OR SET OF ELEMENTS ABOUT A STUDENT (OR A TEACHER, SCHOOL, OR OTHER AGENT OR PROGRAM WITHIN AN EDUCATIONAL SETTING), WHAT CLAIMS ARE BEING MADE REGARDING THE EVIDENCE AND HOW CAN LEARNING ANALYTICS CONTRIBUTE? IN THIS PAPER WE INTRODUCE TECHNIQUES THAT MOVE TOWARD THEORY EXTENSIONS THAT NEED TO BE DEVELOPED AT THE INTERSECTION OF LEARNING ANALYTICS WITH MEASUREMENT TECHNOLOGY. FOR ELUCIDATING POTENTIAL THEORETICAL COMPONENTS FROM A MEASUREMENT PERSPECTIVE, WE DRAW ON A TYPE OF CASE STUDY RESEARCH IN THE COMPUTER SCIENCE DOMAIN, SPECIFICALLY EMPLOYING "USE CASES." A USE CASE IN COMPUTER SCIENCE DESCRIBES A SCENARIO OF USE FOR SOFTWARE. DIFFERENT USE CASES CAN DESCRIBE DIFFERENT SITUATIONS IN WHICH SOFTWARE MAY HAVE UTILITY. LIKE OTHER MULTI-CASE DESIGNS, USE CASES CAN OFFER A MEANS OF EXPLORING RELATIONSHIPS AND ADVANCING POTENTIAL THEORIES BY COMPARING SIMILARITIES AND DIFFERENCES AMONG THE CASES. HERE WE EXPLORE THREE LA USE CASE EXAMPLES THAT DIFFER PURPOSIVELY IN CRITICAL WAYS. EXAMINING THEIR SIMILARITIES AND DIFFERENCES HIGHLIGHTS POTENTIAL DIMENSIONS THAT DISTINGUISH AMONG EMERGING LA USE CASES AT THE INTERSECTION OF DATA SCIENCE AND MEASUREMENT TECHNOLOGY.
THIS PAPER PRESENTS A SUMMARY AND CRITICAL REFLECTION ON TEN MAJOR OPPORTUNITIES AND CHALLENGES FOR ADVANCING THE NEW FIELD OF MULTIMODAL LEARNING ANALYTICS (MLA). IT IDENTIFIES EMERGING TECHNOLOGY TRENDS LIKELY TO DISRUPT LEARNING ANALYTICS, CHALLENGES INVOLVED IN FORGING VIABLE PARTICIPATORY DESIGN PARTNERSHIPS, AND IMPENDING ISSUES ASSOCIATED WITH THE CONTROL OF DATA AND PRIVACY. TRENDS IN HEALTH CARE ANALYTICS PROVIDE ONE ATTRACTIVE MODEL FOR HOW NEW INFRASTRUCTURE CAN ENABLE THE COLLECTION OF LARGER-SCALE AND MORE DIVERSE DATASETS, AND HOW END-USER ANALYTICS CAN BE DESIGNED TO EMPOWER INDIVIDUALS AND EXPAND MARKET ADOPTION.
THIS PAPER PRESENTS MILA, A PROTOTYPE INTERACTIVE LEARNING ANALYTICS TOOL FOR THE MOODLE LEARNING MANAGEMENT SYSTEM THAT HAS BEEN DEVELOPED TO SUPPORT THE ANALYSIS AND IMPROVEMENT OF THE TEACHING AND LEARNING PROCESSES IN THE E-LEARNING ENVIRONMENT OF THE UNIVERSITY OF ROME UNITELMA SAPIENZA. MILA OFFERS A VARIETY OF INTERACTIVE AND REAL-TIME DATA VISUALIZATIONS THAT PROVIDE STATISTICS, TRENDS, AND INSIGHT INFORMATION BOTH ON THE MOODLE-BASED VIRTUAL LEARNING ENVIRONMENT AS A WHOLE, AND ON EACH COURSE INCLUDED IN IT. IN ADDITION TO MOODLE STANDARD LOGS, MILA IS ABLE TO ANALYZE TRACKING DATA GENERATED BY SCORM-COMPLIANT LEARNING OBJECTS AND TO MEASURE THE DURATION OF THE RELATED USER SESSIONS. ALTHOUGH MILA HAS BEEN DEVELOPED AS A MOODLE PLUGIN, ITS SOFTWARE ARCHITECTURE AND ANALYSIS MODELS CAN BE RE-USED AND ADAPTED TO DEVELOP A LEARNING ANALYTICS TOOL FOR ANY SCORM-COMPLIANT LEARNING MANAGEMENT SYSTEM.
LEARNING ANALYTICS CAN BE USED IN CONJUNCTION WITH LEARNER DISPOSITIONS TO IDENTIFY AT-RISK STUDENTS AND PROVIDE PERSONALIZED GUIDANCE ON HOW TO IMPROVE. PARTICIPANTS IN THE CURRENT STUDY WERE STUDENTS (N=192) STUDYING A FIRST YEAR ANATOMY AND PHYSIOLOGY COURSE. A TWO-STEP CLUSTER ANALYSIS WAS PERFORMED USING LEARNING ANALYTICS DATA FROM THE LEARNING MANAGEMENT SYSTEM AND SELF-REGULATED LEARNING BEHAVIOR FROM META-LEARNING ASSESSMENT TASKS. THREE CLUSTERS OF STUDENTS WERE IDENTIFIED HIGH, MEDIUM AND LOW SELF-REGULATED LEARNERS. HIGH SELF-REGULATED LEARNERS WERE ENGAGED WITH THE META LEARNING TASKS, REPORTED THE MOST SELF-REGULATED LEARNING STRATEGIES AND USED NEW STRATEGIES DURING SEMESTER. THEY ALSO HAD THE HIGHEST ACADEMIC ACHIEVEMENT. COMPARED TO LOW SELF-REGULATED LEANERS, MEDIUM SELF-REGULATED LEARNERS WERE MORE ENGAGED IN THE META-LEARNING TASKS AND USED MORE LEARNING STRATEGIES DURING SEMESTER, INCLUDING NEW STRATEGIES; HOWEVER, BOTH MEDIUM AND LOW SELF-REGULATED LEARNERS HAD SIMILAR LEVELS OF ACADEMIC ACHIEVEMENT. IT IS POSSIBLE THAT THE MEDIUM SELF-REGULATED LEARNERS REPRESENT STUDENTS WHO WERE ATTEMPTING TO IMPROVE THEIR LEARNING, BUT HAD NOT YET FOUND STRATEGIES THAT WERE RIGHT FOR THEM. FUTURE EVALUATION OF ACADEMIC PERFORMANCE MAY DETERMINE WHETHER THE ATTEMPTS TO IMPROVE LEARNING BY MEDIUM SELF-REGULATED LEARNERS DISTINGUISHES THEM FROM LOW SELF-REGULATED LEARNERS IN THE LATER YEARS OF THEIR STUDY.
THIS PAPER DRAWS UPON THE CONCEPT OF THE SCHOLARSHIP OF TEACHING AND LEARNING (SOTL) AND REFLECTIVE PRACTICE WIDELY KNOWN IN HIGHER EDUCATION AND HOW IT IS IN LINE WITH INCORPORATING LEARNING ANALYTICS IN THE CLASSROOM TO PROMOTE DATA-DRIVEN REFLECTION-ON-ACTION. IT BEGINS WITH A BRIEF INTRODUCTION TO SOTL, ITS HISTORY, AND HOW TO PRACTICE THIS APPROACH. IT THEN MOVES ON TO INTRODUCING REFLECTION-ON-ACTION AND REFLECTION-IN-ACTION IN CONNECTION TO SOTL. FOLLOWING THAT, A CASE STUDY IS DESCRIBED IN WHICH A BLENDED COURSE HAS BEEN DESIGNED, DEVELOPED, AND IMPLEMENTED TARGETING JAPANESE UNDERGRADUATE STUDENTS. IN THIS REPORT, LEARNING ANALYTICS DATA IN THE FORM OF COURSE REPORTS GENERATED BY THE LEARNING MANAGEMENT SYSTEM HAVE BEEN UTILIZED TO INVESTIGATE USERS' ACCESS TO AND CONTRIBUTION TO THE DISCUSSION BOARDS. THESE STATISTICS ARE USED AS AN INDICATOR OF THE LEVEL OF SOCIAL PRESENCE WITHIN THE COURSE. THE DATA HAS SHOWN THAT THE STUDENTS WERE NOT SO ENTHUSIASTIC ABOUT COMMUNICATING WITH THE INSTRUCTORS, TAS, AND OTHER PEERS ONLINE. THE FEW NUMBER OF POSTS ON THE DISCUSSION BOARDS, DESPITE THE LARGE CLASS SIZE, IS EVIDENCE FOR THIS OBSERVATION. SOME SUGGESTIONS HAVE BEEN MADE WITH THE AIM OF FOSTERING SOCIAL PRESENCE IN THE CURRENT COURSE AND OTHER SIMILAR FULLY ONLINE/BLENDED COURSES.
IN THIS PAPER, WE PRESENT AN EXTENSION OF A MIDDLEWARE FOR SMART EDUCATIONAL ENVIRONMENTS BASED IN AGENTS, USING THE PARADIGM OF CLOUD COMPUTING. IN THAT SENSE, WE DETAIL THE MIDDLEWARE COMPONENTS, WHICH ENABLE THE PROCESS OF MANAGEMENT OF THE CLOUD COMPUTING. WE ALSO PRESENT THE UTILIZATION OF THIS MIDDLEWARE TO PROVIDE SERVICES ON THE CLOUD ABOUT TASK OF LEARNING ANALYTICS THAT ALLOW PROCESSING OF DATA OF STUDENTS AND LEARNING ENVIRONMENTS, TO UNDERSTAND AND OPTIMIZE THE LEARNING PROCESSES.
FREQUENT SOCIAL INTERACTION AMONG INDIVIDUALS OF DIVERSE BACKGROUND AND KNOWLEDGE IS REGARDED AS A FACTOR TO PROMOTE COLLABORATIVE CREATIVITY. SOCIAL MEDIA IS KNOWN FOR ITS ABILITY TO FACILITATE SOCIAL INTERACTION. LEARNING ACTIVITY INCORPORATING SOCIAL MEDIA COULD CREATE A CASUAL SOCIAL SPACE THAT IS BENEFICIAL TO SPARK CREATIVITY AND PROMOTE LEARNER'S ENGAGEMENT. THIS PRELIMINARY WORK PROPOSED A MODULE-BASED LEARNING ANALYTICS SYSTEM FOR FACEBOOK-SUPPORTED COLLABORATIVE CREATIVITY LEARNING ACTIVITY. THE SYSTEM INCLUDES FIVE SUB-MODULES TO RETRIEVE LEARNERS' DIGITAL TRACES, ANALYZE THE LEARNING PROCESS AND FEEDBACK THE ANALYTICS RESULTS. THE PROPOSED SYSTEM CAN BE USED TO ANALYZE THE LEARNERS' BEHAVIORAL PATTERNS IN THE CREATIVE PROCESS AND THUS HELP INSTRUCTOR TO BETTER OVERSEE THE LEARNING ACTIVITY AND PROVIDE TIMELY INTERVENTION. MOREOVER, THROUGH LEARNING ANALYTICS, THE DRIVING FACTORS OF COLLABORATIVE CREATIVITY CAN BE BETTER DELINEATED.
FAILING TO UNDERSTAND THE PERSPECTIVES OF EDUCATORS, AND THE CONSTRAINTS UNDER WHICH THEY WORK, IS A HALLMARK OF MANY EDUCATIONAL TECHNOLOGY INNOVATIONS' FAILURE TO ACHIEVE USAGE IN AUTHENTIC CONTEXTS, AND SUSTAINED ADOPTION. LEARNING ANALYTICS (LA) IS NO EXCEPTION, AND THERE ARE INCREASINGLY RECOGNISED POLICY AND IMPLEMENTATION CHALLENGES IN HIGHER EDUCATION FOR EDUCATORS TO INTEGRATE LA INTO THEIR TEACHING. THIS PAPER CONTRIBUTES A DETAILED ANALYSIS OF INTERVIEWS WITH EDUCATORS WHO INTRODUCED AN AUTOMATED WRITING FEEDBACK TOOL IN THEIR CLASSROOMS (TRIANGULATED WITH STUDENT AND TUTOR SURVEY DATA), OVER THE COURSE OF A THREE-YEAR COLLABORATION WITH RESEARCHERS, SPANNING SIX SEMESTERS' TEACHING. IT EXPLAINS EDUCATORS' MOTIVATIONS, IMPLEMENTATION STRATEGIES, OUTCOMES, AND CHALLENGES WHEN USING LA IN AUTHENTIC PRACTICE. THE PAPER FOREGROUNDS THE VIEWS OF EDUCATORS TO SUPPORT CROSS-FERTILIZATION BETWEEN LA RESEARCH AND PRACTICE, AND DISCUSSES THE IMPORTANCE OF CULTIVATING EDUCATORS' AND STUDENTS' AGENCY WHEN INTRODUCING NOVEL, STUDENT-FACING LA TOOLS.
EXPLORATORY RESEARCH IN LEARNING ANALYTICS AND OTHER ALGORITHMIC APPROACHES TO IMPROVING LEARNING CAN LEAD TO POWERFUL FINDINGS THAT HAVE THE POTENTIAL TO IMPROVE STEM LEARNING AND TEACHING FOR A BROAD AUDIENCE OF STUDENTS, FACULTY, AND INSTITUTIONS. HOWEVER, THIS PROMISE OF LARGE-SCALE IMPACT IS RARELY ACHIEVED IN PRACTICE. THIS PANEL WILL DISCUSS CONSIDERATIONS FOR RESEARCHERS TO EFFECTIVELY SCALE UP EXPLORATORY RESEARCH FINDINGS INTO OPERATIONAL SETTINGS, INCLUDING DESIGN CONSIDERATIONS, DATA SAMPLING, ALGORITHM OPTIMISATION, AND POLICY/COMMERCIALISATION ISSUES.
AS EDUCATIONAL DATA ARE BEING CAPTURED AND MADE AVAILABLE, THERE IS A DEMAND FOR CONVERTING, TRANSLATING, INTERPRETING AND PRESENTING IT FOR ANALYSIS IN MULTIPLE WAYS. ASIDE FROM LEARNING MANAGEMENT SYSTEMS, ONE RICH SOURCE OF DATA, BUT OFTEN UNCOLLECTED, IS EDUCATIONAL GAMES. THIS PAPER PRESENTS A LEARNING ANALYTICS MODEL AS IT APPLIED IN GAME-BASED LEARNING ENVIRONMENT. THE PROPOSED MODEL PRESENTS A WAY TO CAPTURE DATA FROM GAME EVENTS, E.G. THE TIME SPENT IN EACH GAME PLAY AND THE NUMBER OF TIMES A HELP IS ACQUIRED. THE PAPER WILL ALSO PRESENT A PROOF-OF-CONCEPT IMPLEMENTATION USING GAME DEVELOPED ON IOS PLATFORM, USING THE BUILT IN GAME CENTER FEATURE TO PRESENT THE DATA COLLECTED.
MULTIMODAL LEARNING ANALYTICS (MMLA) PROVIDES OPPORTUNITIES FOR UNDERSTANDING AND SUPPORTING COLLABORATIVE PROBLEM-SOLVING. HOWEVER, THE IMPLEMENTATION OF MMLA SYSTEMS IS CHALLENGING DUE TO THE LACK OF SCALABLE TECHNOLOGIES AND LIMITED SOLUTIONS FOR COLLECTING DATA FROM GROUP WORK. THIS PAPER PROPOSES THE MULTIMODAL BOX (MBOX), AN IOT-BASED SYSTEM FOR MMLA, ALLOWING THE COLLECTION AND PROCESSING OF MULTIMODAL DATA FROM COLLABORATIVE LEARNING TASKS. MBOX INVESTIGATES THE DEVELOPMENT AND DESIGN FOR AN IOT FOCUSING ON SMALL GROUP WORK IN REAL-WORLD SETTINGS. MOREOVER, MBOX PROMOTES ADAPTATION TO DIFFERENT LEARNING ENVIRONMENTS AND ENABLES A BETTER SCALING OF COMPUTATIONAL RESOURCES USED WITHIN THE LEARNING CONTEXT.
RECENTLY, GAMES HAVE STARTED TO BE USED IN THE EDUCATIONAL FIELD TO TEACH DIFFERENT SUBJECTS. IN FACT, ACCORDING TO DIFFERENT RESEARCH WORKS, EDUCATIONAL GAMES HAVE MADE LEARNERS MORE MOTIVATED AND INTERESTED IN LEARNING. AT THE SAME TIME, LEARNING ANALYTICS IS GAINING AN INCREASING ATTENTION BECAUSE IT SUPPORTS EVALUATING THE LEARNER, THE GIVEN COURSES AND THE LEARNING ENVIRONMENT. THIS PAPER PRESENTS A NEWLY DESIGNED EDUCATIONAL GAME FOR TEACHING COMPUTER ARCHITECTURE SUBJECT. LEARNING ANALYTICS WAS USED TO EVALUATE THE GAME. THE OBTAINED RESULTS SHOWED THAT: (1) THE GAME IS EFFICIENT IN TEACHING COMPUTER ARCHITECTURE SUBJECT. (2) FURTHER SUPPORT FOR FEMALE LEARNERS IS NEEDED DURING THE LEARNING-PLAYING PROCESS. (3) SOME OF THE GAME LEARNING ACTIVITIES REQUIRE FURTHER DESIGN ENHANCEMENT TO BE NOTICEABLE BY LEARNERS DURING THE LEARNING-PLAYING PROCESS. (4) FURTHER MOTIVATIONAL GAME ELEMENTS ARE NEEDED DURING THE LEARNING-PLAYING PROCESS.
THIS PAPER FOCUS ON THE POTENTIAL CONTRIBUTES OF LEARNING ANALYTICS IN THE IMPROVEMENT STUDENTS LEARNING. THE ANALYSIS OF STUDENT'S DATA COLLECTED FROM VIRTUAL LEARNING ENVIRONMENTS IS IMPORTANT TO ASCERTAIN STUDENT'S ENGAGEMENT. THIS PAPER PRESENTS THE ANALYSIS OF DATA COLLECTED IN THE AMBIT OF MATACTIVA PROJECT. DATA WAS ANALYSED WITH GOOGLE ANALYTICS, COURSE DEDICATION AND MOODLE REPORTS. PROMISING RESULTS WERE OBTAINED.
IMPROVING EDUCATION THROUGH TECHNOLOGY HAS GROWN STEADILY. IT IS AN ONGOING CHALLENGE TO IMPLEMENT EDUCATION TECHNOLOGIES IN REMOTE ENVIRONMENTS. IT IS INCREASINGLY DIFFICULT TO GAIN FEEDBACK FROM SUCH REMOTE ENVIRONMENTS AS THESE TEND TO BE DISCONNECTED FROM THE INTERNET. IN THIS PAPER, WE EXAMINE A REMOTE EDUCATION ENVIRONMENT IMPLEMENTED THROUGH THE ONE LAPTOP PER CHILD (OLPC) PROJECT. WE DESIGN AN ARCHITECTURE AND DEVELOP A LEARNING ANALYTICS PLATFORM THAT ALLOWS VARIOUS STAKEHOLDERS TO GAIN FEEDBACK FROM EDUCATION ENVIRONMENTS ACROSS CONNECTED, INTERMITTENT, AND DISCONNECTED INTERNET INFRASTRUCTURES.
LEARNING ANALYTICS HAS BECOME A BUZZWORD, IN PRACTICE OFTEN ACCOMPANIED BY COMPREHENSIVE USE OF DIGITAL TECHNOLOGIES. THIS STUDY TAKES A QUALITATIVE APPROACH BY ANALYSING PERFORMANCE DATA WITHIN THE BOUNDS OF ONE MODULE. HIGH FAILURE RATES PROMPTED AN ACTION RESEARCH STUDY TO CHECK FOR PERFORMANCE PATTERNS AS SEEN AT THE EXAM. GENERALLY, STUDENTS WERE SUCCESSFUL AT CONDUCTING CALCULATIONS, BUT STRUGGLING WHEN BEING ASKED CONCEPTUAL AND THEORETICAL QUESTIONS. FURTHER ANALYSIS DEMONSTRATED A LACK OF ALIGNMENT BETWEEN TASKS POSED IN EXERCISES AND AT THE EXAM. PARTICULARLY IN THE SECOND HALF OF THE COURSE, STUDENTS CLAIMED THEY WERE ASKED THEORETICAL AND CONCEPTUAL QUESTIONS THAT HAD NOT BEEN PREVIOUSLY ADDRESSED IN THE EXERCISES. THE PROJECT SET OUT TO ALIGN CONCEPTUAL AND ALGORITHMIC TASKS AS FEATURED IN THE VARIOUS PARTS OF THE CURRICULUM. FURTHERMORE, A THEORETICAL MID-TERM TEST WAS INTRODUCED TO FOCUS EXCLUSIVELY ON CONCEPTUAL ISSUES, SOME OF WHICH WERE RE-ADDRESSED AT THE FINAL EXAM. DUE TO THE INTERVENTIONS, FAILURE RATES WERE GREATLY REDUCED AND WITH STUDENTS' FEEDBACK GENERALLY SUPPORTIVE.
PURPOSE IN HIGHER EDUCATION, THE AUTHORS SERVE MULTIPLE STAKEHOLDERS WITH VARYING PERSPECTIVES ON THE INSTITUTION'S LEARNING ANALYTIC SYSTEM. THE PURPOSE OF THIS PAPER IS TO HIGHLIGHT DUAL PERSPECTIVES WITHIN THE LEARNING ANALYTICS (LA) SYSTEM IN ONE INSTITUTION: THAT OF AN ADMINISTRATOR RESPONSIBLE FOR COLLEGE-WIDE IMPROVEMENT, AND A FACULTY MEMBER RESPONSIBLE FOR PROGRAMMATIC IMPROVEMENT. DESIGN/METHODOLOGY/APPROACH THIS MANUSCRIPT PROVIDES A CRITICAL PERSPECTIVE WITH DUAL SETS OF EXPERIENCES AND VIEWPOINTS. THIS APPROACH ALLOWS CLOSE EXAMINATION OF EACH PERSPECTIVE WITHIN THE CONTEXT OF THE EXISTING LITERATURE, AS THE AUTHORS EXAMINE TRANSITIONS IN THE USE OF LA. FINDINGS THESE VIEWPOINTS OFFER INSIGHT INTO THE INTERPRETATION OF LA THROUGH THE LENS OF VARIOUS ROLES. IN EXAMINING THESE VIEWPOINTS, THE AUTHORS OFFER THREE ACTIONABLE STEPS FOR OTHER INSTITUTIONS WHO SEEK TO IMPLEMENT. PRACTICAL IMPLICATIONS - THESE ACTIONABLE STEPS OFFER A STARTING POINT FOR OTHER INSTITUTIONS TO ENGAGE IN CONVERSATIONS RELATED TO THE ADOPTION OF LA FOR CONTINUOUS IMPROVEMENT ACROSS LEVELS AND ROLES. RELEVANT IMPLICATIONS FOR VARIOUS PARTIES ARE DISCUSSED, WITH AN EMPHASIS ON HOW ADMINISTRATORS WITHIN THE UNIVERSITY SYSTEM MAY SUPPORT FACULTY TO INCORPORATE LA AS PART OF THEIR SCHOLARLY AND TEACHING RESPONSIBILITIES.ORIGINALITY/VALUE FEW STUDIES HAVE EXAMINED THE PERSPECTIVES OF MULTIPLE STAKEHOLDERS WITHIN AN INSTITUTION. HERE THE AUTHORS HAVE PRESENTED THESE DUAL PERSPECTIVES IN ORDER TO FURTHER THE DIALOGUE BETWEEN UNIVERSITY ADMINISTRATION AND FACULTY AS ANALYTIC SYSTEMS BECOME MORE WIDESPREAD. THROUGH THIS DIALOGUE, THE AUTHORS SEE INCREASED OPPORTUNITIES FOR ALL STAKEHOLDERS TO BETTER UNDERSTAND THEIR ROLE IN LA.
THE FIELD OF LEARNING ANALYTICS IS BEING DISCUSSED IN MANY CIRCLES AS AN EMERGING CONCEPT IN EDUCATION. IN MANY DISTRICTS AND STATES, THE CORE PHILOSOPHY BEHIND LEARNING ANALYTICS IS NOT ENTIRELY NEW; FOR MORE THAN A DECADE, DISCUSSIONS OF DATA-DRIVEN DECISION MAKING AND THE USE OF DATA TO DRIVE INSTRUCTION HAVE BEEN COMMON. STILL, THE U.S. EDUCATION SYSTEM HAS NOT YET COME CLOSETO REACHING THE POTENTIAL OF LEARNING ANALYTICS. THE EFFECTIVE USE OF DATA AND LEARNING ANALYTICS ARE BOTH CRITICAL COMPONENTS OF A DIGITAL LEARNING STRATEGY TO PERSONALIZE INSTRUCTION FOR MANY MORE STUDENTS, ESPECIALLY TO INCREASE STUDENT ACHIEVEMENT IN THE HIGHEST-NEED SCHOOLS. THE LEARNING ANALYTICS INITIATIVES DESCRIBED IN THIS CHAPTER ARE HELPING STATES AND DISTRICTS MOVE FROM BEING DATA COLLECTORS TO BEING DATA ANALYZERS, ABLE TO USE THE VAST AMOUNT OF INFORMATION BEING COLLECTED IN A SECURE, PRACTICAL, CUSTOMIZED, AND PREDICTIVE SYSTEM. ULTIMATELY, MANY OF THE EXAMPLES PROVIDE A GLIMPSE INTO HOW DISTRICTS ARE PREPARING TO TAKE ADVANTAGE OF LEARNING ANALYTICS TO MEET THE NEEDS OF EACH STUDENT. THIS TRANSITION IS NOT JUST ABOUT IMPLEMENTING NEW OR BETTER DATA OR ASSESSMENT SYSTEMS, OR EVEN IMPROVING THE ANALYSIS OF DATA. EDUCATION SYSTEMS MUST CONSIDER CAPACITY IN INFRASTRUCTURE AND HUMAN CAPITAL, DATA USE CULTURE IN SCHOOLS AND COMMUNITIES, AND POLICIES THAT ENABLE THE MEANINGFUL USE OF DATA TO EFFECTIVELY APPLY AND USE LEARNING ANALYTICS.
BEFORE THE INCREASING EFFORTS FOR UNDERSTANDING, PREDICTING, AND ENHANCING STUDENTS' LEARNING IN EDUCATIONAL SETTINGS, LEARNING ANALYTICS (LA) EMERGES AS A CANDIDATE RESEARCH AREA TO TACKLE SUCH ISSUES. THUS, SEVERAL WORK LINES HAVE BEEN CONDUCTED, AS WELL AS DIVERSE CONCEPTUAL AND THEORETICAL PERSPECTIVES HAVE BEEN ARISEN. MOREOVER, QUITE INTERESTING AND USEFUL OUTCOMES HAVE BEEN PRODUCED DURING THE LA SHORT LIFETIME. HOWEVER, A CLEAR IDEA OF DIVERSE QUESTIONS IS STILL PENDING TO BE GIVEN. (E.G., WHAT DOES LEARNING ANALYTICS MEAN? WHAT ARE ITS BACKGROUNDS, RELATED DOMAINS, AND UNDERLYING ELEMENTS? WHICH ARE THE OBJECTS OF ITS APPLICATIONS? AND WHAT ABOUT THE TRENDS AND CHALLENGES TO BE CONSIDERED?) THIS IS THE REASON WHY THE CHAPTER AIMS AT RESPONDING THOSE CONCERNS BY A SKETCH OF A CONCEPTUAL SCENERY THAT EXPLAINS THE LA BACKGROUND, ITS UNDERLYING DOMAINS AND NATURE, INCLUDING A SURVEY OF RECENT AND RELEVANT APPROACHES, AND A RELATION OF RISKS AND OPPORTUNITIES.
LEARNING ANALYTICS IS A FAST GROWING AREA IN ACADEMIA; IT IS FOCUSED ON COLLECTING, ANALYSIS, CLEANING, PROCESSING, AND VISUALIZATION OF DATA FROM VARIOUS ACADEMIC SOURCES THAT ARE RELATED TO COURSE, STUDENT, FACULTY, PROGRAM DIRECTOR AND CHAIR/DEPARTMENT LEVELS. THIS PAPER PRESENTS THE FEATURES AND FUNCTIONS, ARCHITECTURAL MODELS, SOFTWARE ENGINEERING DIAGRAMS, MAIN COMPONENTS, INPUTS AND OUTPUTS, HIERARCHICAL LEVELS AND SMARTNESS FEATURES, MATHEMATICAL METHODS AND ALGORITHMS USED IN A DESIGN, DEVELOPMENT, TESTING AND IMPLEMENTATION OF THE INNOVATIVE INTERLABS SMART LEARNING ANALYTICS SYSTEM INTO THE COMPUTER SCIENCE AND INFORMATION SYSTEMS CURRICULUM AT BRADLEY UNIVERSITY (PEORIA, IL, USA). CONCEPTUALLY THE INTERLABS SYSTEM IS BASED ON 1) SMARTNESS FEATURES SUCH AS ADAPTIVITY, SENSING, INFERRING, ANTICIPATION, SELF-LEARNING, SELF-ORGANIZATION, AND 2) THE GARTNER'S ANALYTICS ASCENDANCY MODEL. THE INTERLABS SYSTEM IS FOCUSED ON DESCRIPTIVE, DIAGNOSTIC, PREDICTIVE AND PRESCRIPTIVE TYPES OF ANALYTICS OF STUDENT ACADEMIC PERFORMANCE.
THE UNTAMED BIG DATA ERA RAISES OPPORTUNITIES IN LEARNING ANALYTICS SECTOR FOR THE PROVISION OF ENHANCED EDUCATIONAL MATERIAL TO LEARNERS. NEVERTHELESS, BIG DATA ANALYTICS, BRINGS BIG TROUBLES IN EXPLORATION, VALIDATION AND PREDICTIVE MODEL DEVELOPMENT. IN THIS PAPER, THE AUTHORS PRESENT A DATA-DRIVEN METHODOLOGY FOR GREATER UTILIZATION OF LEARNING ANALYTICS DATASETS, WITH THE PURPOSE TO IMPROVE THE KNOWLEDGE OF INSTRUCTORS ABOUT LEARNERS PERFORMANCE AND PROVIDE BETTER PERSONALIZATION WITH OPTIMIZED INTELLIGENT TUTORING SYSTEMS. THE PROPOSED METHODOLOGY IS UNFOLDED IN THREE STAGES. FIRST, THE LEARNING ANALYTICS SUMMARIZATION FOR INITIAL EXPLORATORY PURPOSES OF LEARNERS EXPERIENCE AND THEIR BEHAVIOR IN E-LEARNING ENVIRONMENTS. SUBSEQUENTLY, THE EXPLORATION OF POSSIBLE INTERRELATIONSHIPS BETWEEN METRICS AND THE VALIDATION OF THE PROPOSED LEARNING ANALYTICS SCHEMAS, TAKES PLACE. LASTLY, THE DEVELOPMENT OF PREDICTIVE MODELS AND SIMULATION BOTH ON AN AGGREGATED AND MICRO-LEVEL PERSPECTIVE THROUGH AGENT-BASED MODELING IS RECOMMENDED, WITH THE PURPOSE TO REINFORCE THE FEEDBACK FOR INSTRUCTORS AND INTELLIGENT TUTORING SYSTEMS. THE STUDY CONTRIBUTES TO THE KNOWLEDGE EXPANSION BOTH FOR RESEARCHERS AND PRACTITIONERS WITH THE PURPOSE TO OPTIMIZE THE PROVIDED ONLINE LEARNING EXPERIENCE.
EDUCATIONAL INSTITUTIONS ARE DESIGNING, CREATING AND EVALUATING COURSES TO OPTIMIZE LEARNING OUTCOMES FOR HIGHLY DIVERSE STUDENT POPULATIONS. YET, MOST OF THE DELIVERY IS STILL MONITORED RETROSPECTIVELY WITH SUMMATIVE EVALUATION FORMS. THEREFORE, IMPROVEMENTS TO THE COURSE DESIGN ARE ONLY IMPLEMENTED AT THE VERY END OF A COURSE, THUS MISSING TO BENEFIT THE CURRENT COHORT. TEACHERS FIND IT DIFFICULT TO INTERPRET AND PLAN INTERVENTIONS JUST-IN-TIME. IN THIS CONTEXT, LEARNING ANALYTICS (LA) DATA STREAMS GATHERED FROM 'AUTHENTIC' STUDENT LEARNING ACTIVITIES, MAY PROVIDE NEW OPPORTUNITIES TO RECEIVE VALUABLE INFORMATION ON THE STUDENTS' LEARNING BEHAVIORS AND COULD BE UTILIZED TO ADJUST THE LEARNING DESIGN ALREADY "ON THE FLY" DURING RUNTIME. WE PRESUME THAT LEARNING ANALYTICS APPLIED WITHIN LEARNING DESIGN (LD) AND PRESENTED IN A LEARNING DASHBOARD PROVIDE OPPORTUNITIES THAT CAN LEAD TO MORE PERSONALIZED LEARNING EXPERIENCES, IF IMPLEMENTED THOUGHTFULLY. IN THIS PAPER, WE DESCRIBE OPPORTUNITIES AND CHALLENGES FOR USING LA IN LD. WE IDENTIFY THREE KEY OPPORTUNITIES FOR USING LA IN LD: (O1) USING ON DEMAND INDICATORS FOR EVIDENCE BASED DECISIONS ON LEARNING DESIGN; (O2) INTERVENING DURING THE RUN-TIME OF A COURSE; AND, (O3) INCREASING STUDENT LEARNING OUTCOMES AND SATISFACTION. IN ORDER TO BENEFIT FROM THESE OPPORTUNITIES, SEVERAL CHALLENGES HAVE TO BE OVERCOME. FOLLOWING A THOROUGH LITERATURE REVIEW, WE MAPPED THE IDENTIFIED OPPORTUNITIES AND CHALLENGES IN A CONCEPTUAL MODEL THAT CONSIDERS THE INTERACTION OF LA IN LD.
THIS PAPER EXAMINES THE CURRENT STATE OF LEARNING ANALYTICS (LA), ITS STAKEHOLDERS AND THE BENEFITS AND CHALLENGES THESE STAKEHOLDERS FACE. LA IS A FIELD OF RESEARCH THAT INVOLVES THE GATHERING, ANALYZING AND REPORTING OF DATA RELATED TO LEARNERS AND THEIR ENVIRONMENTS WITH THE PURPOSE OF OPTIMIZING THE LEARNING EXPERIENCE. STAKEHOLDERS IN LA ARE STUDENTS, EDUCATORS, RESEARCHERS, INSTITUTIONS, AND GOVERNMENT AGENCIES. THE WAY IN WHICH ANALYTICS INFORMATION FLOWS FROM STUDENTS TO OTHER STAKEHOLDERS INVOLVES A HIERARCHY, WHERE ALL STAKEHOLDERS ARE ABLE TO PROVIDE INPUT AND OFFER RECOMMENDATIONS TO ENRICH THE LEARNING PROCESS FOR THE STUDENT. CHALLENGES FACED BY STAKEHOLDERS INCLUDE THE MOVEMENT OF TRADITIONAL ANALYTICS TO LEARNER-CENTERED ANALYTICS, WORKING WITH DATASETS ACROSS VARIOUS SETTINGS, ADDRESSING ISSUES WITH TECHNOLOGY AND RESOLVING ETHICAL CONCERNS. DESPITE THESE CHALLENGES, RESEARCH POINTS TO SOLUTIONS THAT WILL ALLOW LA TO TRANSFORM TEACHING AND LEARNING.
THIS STUDY IS BASED ON A NOVEL CONCEPTUAL FRAMEWORK, PRECISION EDUCATION, AND TAKES A BLENDED PYTHON PROGRAMMING COURSE AS AN EXAMPLE TO EXPLORE HOW TO IMPLEMENT PRECISION EDUCATION THAT INCLUDES DIAGNOSIS, PREDICTION, PREVENTION AND TREATMENT. PRECISION EDUCATION FOLLOWS THE PRINCIPLES OF PERSONALIZED SERVICES FOR PRECISION MEDICINE. ITS PURPOSE IS TO STRENGTHEN THE LEARNING RISK PREDICTION AND EARLY INTERVENTION MENTIONED IN EMERGING LEARNING ANALYTICS THROUGH BIG DATA, ARTIFICIAL INTELLIGENCE AND OTHER EMERGING TECHNOLOGIES, THEREBY IMPROVING TEACHER TEACHING QUALITY AND STUDENT LEARNING EFFICIENCY. THIS STUDY IS BASED ON THE DESIGN OF THE E-BOOK LEARNING DASHBOARD, SO THAT TEACHERS CAN QUICKLY UNDERSTAND STUDENTS' LEARNING STATUS, AND IMPROVE THE E-BOOK THROUGH STUDENTS' FEEDBACK ON THE DASHBOARD TO ACHIEVE PRECISION DIAGNOSIS. NEXT, THIS STUDY USES MACHINE LEARNING ALGORITHMS TO PREDICT STUDENTS' LEARNING PERFORMANCE, AND THUS DETERMINE WHETHER STUDENTS ARE AT-RISK TO ACHIEVE PRECISION PREDICTION. FINALLY, THROUGH THE CORRESPONDENCE BETWEEN READING STRATEGY AND READING SEQUENCE, AND THEN CLEARLY DISTINGUISH THE TYPES OF STUDENTS BY GROUPING, AND USE IT AS A TREATMENT TARGET FOR PRECISION TREATMENT AND PREVENTION. IT IS HOPED THAT THIS EMPIRICAL STUDY CAN BE USED AS A CASE STUDY FOR IMPLEMENTING PRECISION EDUCATION.
TODAY, THE INFORMATION GATHERED FROM MASSIVE LEARNING PLATFORMS AND SOCIAL MEDIA SITES ALLOWS TO DERIVE A VERY COMPREHENSIVE SET OF LEARNING INFORMATION. TO THIS AIM, DATA MINING TECHNIQUES CAN SURELY HELP TO GAIN PROPER INSIGHTS, PERSONALISE LEARNING EXPERIENCES, FORMATIVE ASSESSMENTS, PERFORMANCE MEASUREMENTS, AS WELL AS TO DEVELOP NEW LEARNING AND INSTRUCTIONAL DESIGN MODELS. THEREFORE, A CORE REQUIREMENT IS TO CLASSIFY, MIX, FILTER AND PROCESS THE BIG DATA SOURCES INVOLVED BY MEANS OF PROPER LEARNING AND SOCIAL LEARNING ANALYTICS TOOLS. IN THIS PERSPECTIVE, THIS PAPER INVESTIGATES THE MOST PROMISING APPLICATIONS AND ISSUES OF BIG DATA FOR THE DESIGN OF THE NEXT-GENERATION OF MASSIVE LEARNING PLATFORMS AND SOCIAL MEDIA SITES. SPECIFICALLY, IT ADDRESSES THE METHODOLOGICAL TOOLS AND INSTRUMENTS FOR SOCIAL LEARNING ANALYTICS, PITFALLS ARISING FROM THE USAGE OF OPEN DATASETS, AND PRIVACY AND SECURITY ASPECTS. THE PAPER ALSO PROVIDES FUTURE RESEARCH DIRECTIONS.
BASED ON LEARNING ANALYTICS, INFORMATION RELATED TO LEXICAL AND SCRIPTURAL PERFORMANCE WAS COLLECTED IN 36 TEXTS PRODUCED BY STUDENTS BELONGING TO A HIGHER EDUCATION WRITING COURSE. THIS INFORMATION NOT ONLY ALLOWED TO MAKE PEDAGOGICAL DECISIONS IN THE SHORT TERM, BUT ALSO TO PLAN AN INVESTIGATION ON THE RELATIONSHIP BETWEEN BOTH COMPETENCES. THE ARTICLE PRESENTED HERE ADDRESSES THE SECOND CHALLENGE POSED, AND ITS OBJECTIVE IS TO IDENTIFY THE RELATIONSHIP THAT EXISTS BETWEEN THE ACADEMIC LEXICAL DOMAIN AND THE LEVEL OF THE TEXTS PRODUCED BY THIS GROUP OF STUDENTS BASED ON THE LEARNING ANALYTICS OBTAINED. TO DO THIS, A CASE STUDY WAS DEVELOPED, OF A PRE-EXPERIMENTAL AND CROSS-SECTIONAL TYPE, IN WHICH THE QUALITY OF THE TEXTS WRITTEN BY THE STUDENTS IN A WRITING TASK IS COMPARED WITH A LEXICOMETRIC STUDY CARRIED OUT ON THOSE SAME WRITINGS USING THE FREE SOFTWARE IRAMUTEQ. THE RESULTS INDICATE THAT THERE IS A CLOSE RELATIONSHIP BETWEEN ACADEMIC LEXICON AND CONTENT, BUT THAT LINK BECOMES LESS DECISIVE WHEN CONTRASTING ACADEMIC LEXICON WITH TEXTUAL QUALITY.
ALMOST ANY E-LEARNING SYSTEM PROVIDES VARIOUS FUNCTIONALITIES, RANGING FROM GENERAL ONES LIKE ACCESSING A COURSE OR COMMUNICATION WITH PEERS, TO MORE SPECIALIZED FUNCTIONS LIKE PROVIDING PERSONALIZED FEEDBACK OR LEARNING MATERIALS. AMONG THE FEATURES THAT MAY INCREASE THE QUALITY OF ON-LINE EDUCATION ENVIRONMENTS THERE IS FEEDING LEARNERS WITH PERSONALIZED SET OF QUIZZES DEPENDING THEIR CURRENT KNOWLEDGE LEVEL. IN THIS PAPER, WE PROPOSE A LEARNING ANALYTICS SYSTEM THAT BUILDS PERSONALIZED QUIZ SESSIONS USED TO PROVIDE A PERSONAL RANKING OF THE QUESTIONS FOR EACH STUDENT REGARDING BOTH HIS/HER EXPERIENCE AND THE EXPERIENCE OF OTHERS IN THE LEARNING PROCESS. THE SYSTEM USES THE DATA PROVIDED BY PREVIOUS EVALUATION OF STUDENTS USING FIVE TYPES OF QUIZZES AND THE COLLABORATIVE FILTERING ALGORITHM (SINGULAR VALUE DECOMPOSITION OR SVD) METHOD FOR EXTRACTING THE FEATURES OF THE STUDENTS AND QUIZZES FROM A STUDENT-QUIZ MATRIX. PRELIMINARY EXPERIMENTAL RESULTS SHOW THAT FOR STUDENTS THAT FIRST INTERACT WITH THE SYSTEM THE PROVIDED SET OF QUIZZES HAS AN AVERAGE DIFFICULTY LEVEL COMPARED TO ALREADY EXISTING ANSWERS AND AS THE STUDENT KEEPS USING THE SYSTEM THE DIFFICULTY OF THE TEST IS AUTOMATICALLY ADJUSTED ACCORDING WITH HIS PREVIOUS ANSWERS.
UK UNIVERSITIES ARE INCREASINGLY BEING 'ENCOURAGED' TO FOCUS ON STUDENT ENGAGEMENT, RETENTION AND PERFORMANCE, WITH LEARNING ANALYTICS BECOMING COMMONPLACE. BASED ON INTER-RELATED STUDENT-STAFF PARTNERSHIPS, THIS STUDY ADOPTED A HUMAN AND COMPASSIONATE APPROACH TO THE USE OF STUDENT DATA AND SUBSEQUENT INTERVENTIONS. ANALYSIS OF FOCUS GROUP AND INTERVIEW DATA FROM 86 STUDENT PARTICIPANTS EXPLORED KEY THEMES: PEER-MENTORING INCREASING ENGAGEMENT WITH THE COMMUNAL-HABITUS; INCREASED CONFIDENCE AND ENGAGEMENT; AND THE DEMYSTIFICATION AND HUMANISATION OF THE UNIVERSITY ENVIRONMENT. FINDINGS HIGHLIGHT THE IMPORTANCE OF EMPHASISING HUMAN AND COMPASSIONATE SUPPORT FOR STUDENTS WITHIN RAPIDLY DEVELOPING LEARNING ANALYTICS APPROACHES, WITH SUBJECT-SPECIFIC PEER-MENTORING FOUND HERE TO BE BENEFICIAL.
THIS PAPER INTRODUCES A RANDOMIZED STUDY CONDUCTED AMONG A GROUP OF 48 STUDENT JAVA PROGRAMMERS TO ASSESS THE IMPACT OF LEARNING ANALYTICS (LA) ON THEIR ACADEMIC PERFORMANCE. THE LA SYSTEM DESIGN INCORPORATED BOTH COGNITIVE AND METACOGNITIVE TOOLS TO HELP LEARNERS TAKE POSSESSION OF THEIR LEARNING PROCESSES. PARTICIPATION WAS VOLUNTARY AND DATA ABOUT POTENTIAL CONFOUNDING FACTORS WERE ALSO COLLECTED TO MINIMIZE BIAS BY BLOCKING ON TWO OR MORE FACTORS (FUTURE WORK). THIS PAPER SUMMARILY EXPLORES THE RELATIONSHIPS BETWEEN STUDENTS' PROGRAMMING EXPERTISE, CODING ASSIGNMENTS, USER EXPERIENCE AND SATISFACTION, AND ACADEMIC PERFORMANCE. THE RESULTS OF THIS PRELIMINARY EXPLORATION ARE INCONCLUSIVE AS TO WHETHER THE LA SYSTEM MADE A DIFFERENCE IN ACADEMIC PERFORMANCE. NEVERTHELESS, THEY SEEM TO INDICATE THAT LA WAS BENEFICIAL TO STUDENT PROGRAMMERS AND THAT SUMMATIVE MEASURES SUCH AS GRADES ARE NOT A PROPER METRIC TO MEASURE THE USEFULNESS OF LA SYSTEMS.
DISCUSSION AMONGST THE LEARNING ANALYTICS COMMUNITY IS FOR THE NEED TO LINK LEARNING ANALYTICS WITH LEARNING DESIGN. HOWEVER, LEARNING DESIGN IS A COMPLEX SPACE, AND NOT ALL APPROACHES TO DESIGN FOR LEARNING ARE THE SAME. WITH THIS PROPOSED INTEGRATED PRACTICE COMES UNIQUE CHALLENGES FOR THE CONTEMPORARY EDUCATOR. IN THIS PAPER, WE FOCUS ON THE UNDERLYING PROCESSES OF TEACHER INQUIRY WHEN CONNECTING DESIGN FOR LEARNING WITH LEARNING ANALYTICS. WE PROPOSE THAT VALUABLE CONNECTION BETWEEN LEARNING ANALYTICS AND DESIGN FOR LEARNING IS ONLY REALISED THROUGH THE MEDIATION OF EFFECTIVE TEACHER INQUIRY PROCESSES. HENCE, WE AIM TO BETTER UNDERSTAND THESE PROCESSES IN ORDER TO IDENTIFY TEACHERS' DEVELOPMENTAL NEEDS. TO THIS END, WE PROPOSE A WORKING MODEL AND EXAMINE EDUCATORS' (AS LEARNERS) INQUIRY PROCESS IN A PROFESSIONAL LEARNING WORKSHOP. WE CONCLUDE WITH IMPLICATIONS FOR THE PROPOSED MODEL AND PROFESSIONAL LEARNING.
THIS PAPER PRESENTS A PROCESS BASED ON LEARNING ANALYTICS AND RECOMMENDER SYSTEMS WITH THE OBJECTIVE OF ANALYZING STUDENT ASSESSMENT IN ORDER TO PROVIDE CLUES THAT CAN HELP TEACHERS IN SCAFFOLDING THE STUDENTS' PERFORMANCE. FOR THIS, A SET OF TESTS WAS USED TO EVALUATE STUDENTS' COMPETENCE IN DIRECT CURRENT CIRCUITS. THE TESTS HAD MULTIPLE VERSIONS AND TO SOLVE THEM EACH STUDENT HAD TO USE MULTIPLE APPROACHES. THE RESULTS INDICATE A BETTER PERFORMANCE IN CALCULUS AND SIMULATIONS APPROACHES WHEN COMPARED WITH HANDS-ON AND REMOTE LABORATORIES APPROACHES. THE ANALYSES ALSO PROVIDE SUPPORT FOR THE RECOMMENDATION STEP ALLOWING THE CONFIGURATION OF A KNOWLEDGE BASE. THE PROCESS AS A WHOLE IS CONSISTENT IN WHAT REGARDS ITS ABILITY TO MAKE SUGGESTIONS TO THE STUDENTS AS THEY COMPLETE A GIVEN TEST AND TO PROVIDE TEACHERS WITH INFORMATION THAT CAN HELP THEM FORMULATE STRATEGIES TO POSITIVELY IMPACT STUDENTS' LEARNING.
LEARNING ANALYTICS IS BASED ON DATA FROM THE DIGITAL TRACES LEFT BY LEARNING ACTIVITIES. IN THE CONTROLLED ENVIRONMENT OF A RESEARCH LAB COMBINING DATA FROM DIFFERENT SOURCES DOES NOT POSE MANY PROBLEMS. HOWEVER, WHEN SCALING UP LEARNING ANALYTICS FOR GENERAL USE IN SCHOOLS AND UNIVERSITIES DATA SHARING AND INTEROPERABILITY BECOME MAJOR CHALLENGES. THESE ISSUES ARE NOW BEING ADDRESSED IN STANDARDISATION SETTINGS, BOTH INTERNATIONALLY AND NATIONALLY. A CASE STUDY OF A NORWEGIAN STANDARDS PROJECT SHOWS THAT THERE ARE CONSIDERABLE CONCEPTUAL ISSUES EMERGING WHEN STAKEHOLDERS REPRESENTING DIFFERENT INTERESTS START WORKING TOWARDS CONSENSUS ON THESE ISSUES. BASED ON THE CASE STUDY THIS PAPER CONTRIBUTES WITH A NUMBER OF CONCEPTUAL CONSTRUCTS AND A PROCESS THAT WILL MAKE IT EASIER TO REACH CONSENSUS ABOUT DIFFERENT ASPECTS RELATED TO ACCESS TO AND EXCHANGE OF DATA FROM DIFFERENT SOURCES RELEVANT FOR ANALYSIS OF LEARNING AND THE CONTEXTS IN WHICH LEARNING OCCURS.
FOLLOWING ASYNCHRONOUS ONLINE DISCUSSION ACTIVITIES AS A COMPLEX COMMUNICATION PROCESS IS A DEMANDING TASK FOR TEACHERS. IN THIS PAPER, THE AUTHORS HAVE EXPLORED THE POTENTIAL IN SUPPORTING SUCH ACTIVITY THROUGH LEARNING ANALYTICS. FROM THE BEGINNING, THE AUTHORS ACKNOWLEDGED THE LIMITATIONS OF TECHNOLOGY TO SUPPORT THE COMPLEXITIES OF A PEDAGOGICAL ACTIVITY. THEREFORE, THE METHODOLOGY USED WAS PARTICIPATORY DESIGN-BASED RESEARCH (DBR) DIVIDED INTO TWO MAIN STAGES. THE FIRST DESIGN PHASE DEALT WITH THE ENGAGEMENT OF TEACHERS AND PEDAGOGICAL EXPERTS IN DEFINING THE DATA AND METRICS TO BE USED TO SUPPORT THE PEDAGOGICAL CONCEPTS. THE SECOND CONSISTED OF AN IMPLEMENTATION PHASE INCLUDING PILOTS WITH STUDENTS AND WITH CRUCIAL ENGAGEMENT OF TEACHERS IN COMMENTING THEIR UNDERSTANDING OVER STUDENTS' LEARNING PROCESSES AND THE FEEDBACK THE TEACHERS COULD OFFER TO THEM. OVERALL, THE STUDENTS SHOWN IMPROVEMENTS IN THEIR PERFORMANCE AS MONITORED THROUGH THE LEARNING ANALYTICS GROUP IN CONTRAST WITH CONTROL GROUPS. THE DISCUSSION OVER THE DESIGN AND ITS RESULTS COULD BE POTENTIALLY EXTRAPOLATED TO OTHER EDUCATIONAL CONTEXTS.
IN RECENT YEARS, LEARNING PROCESS DATA HAVE BECOME INCREASINGLY EASY TO COLLECT THROUGH COMPUTER-BASED LEARNING ENVIRONMENTS. THIS HAS LED TO INCREASED INTEREST IN THE FIELD OF LEARNING ANALYTICS, WHICH IS CONCERNED WITH LEVERAGING LEARNING PROCESS DATA IN ORDER TO BETTER UNDERSTAND, AND ULTIMATELY TO IMPROVE, TEACHING AND LEARNING. IN COMPUTING EDUCATION, THE LOGICAL PLACE TO COLLECT LEARNING PROCESS DATA IS THROUGH INTEGRATED DEVELOPMENT ENVIRONMENTS (IDES), WHERE COMPUTING STUDENTS TYPICALLY SPEND LARGE AMOUNTS OF TIME WORKING ON PROGRAMMING ASSIGNMENTS. WHILE THE PRIMARY PURPOSE OF IDES IS TO SUPPORT COMPUTER PROGRAMMING, THEY MIGHT ALSO BE USED AS A MECHANISM FOR DELIVERING LEARNING INTERVENTIONS DESIGNED TO ENHANCE STUDENT LEARNING. THE POSSIBILITY OF USING IDES BOTH TO COLLECT LEARNING PROCESS DATA, AND TO STRATEGICALLY INTERVENE IN THE LEARNING PROCESS, SUGGESTS AN EXCITING DESIGN SPACE FOR COMPUTING EDUCATION RESEARCH: THAT OF IDE-BASED LEARNING ANALYTICS. IN ORDER TO FACILITATE THE SYSTEMATIC EXPLORATION OF THIS DESIGN SPACE, WE PRESENT AN IDE-BASED DATA ANALYTICS PROCESS MODEL WITH FOUR PRIMARY ACTIVITIES: (1) COLLECT DATA, (2) ANALYZE DATA, (3) DESIGN INTERVENTION, AND (4) DELIVER INTERVENTION. FOR EACH ACTIVITY, WE IDENTIFY KEY DESIGN DIMENSIONS AND REVIEW RELEVANT COMPUTING EDUCATION LITERATURE. TO PROVIDE GUIDANCE ON DESIGNING EFFECTIVE INTERVENTIONS, WE DESCRIBE FOUR RELEVANT LEARNING THEORIES, AND CONSIDER THEIR IMPLICATIONS FOR DESIGN. BASED ON OUR REVIEW, WE PRESENT A CALL-TO-ACTION FOR FUTURE RESEARCH INTO IDE-BASED LEARNING ANALYTICS.
THE INCREASING PREVALENCE OF LEARNER-CENTRED FORMS OF LEARNING AS WELL AS AN INCREASE IN THE NUMBER OF LEARNERS ACTIVELY PARTICIPATING ON A WIDE RANGE OF DIGITAL PLATFORMS AND DEVICES GIVE RISE TO AN EVER-INCREASING STREAM OF LEARNING DATA. LEARNING ANALYTICS (LA) CAN ENABLE LEARNERS, TEACHERS, AND THEIR INSTITUTIONS TO BETTER UNDERSTAND AND PREDICT LEARNING AND PERFORMANCE. HOWEVER, THE PEDAGOGICAL PERSPECTIVE AND MATTERS OF LEARNING DESIGN HAVE BEEN UNDERREPRESENTED IN RESEARCH THUS FAR. IN OUR PAPER, WE PROPOSE A GENERAL DESIGN FRAMEWORK THAT INCLUDES CRITICAL DIMENSIONS OF LA AND ASSISTS IN CREATING LA SERVICES THAT SUPPORT EDUCATIONAL PRACTICE. ON THE BASIS OF A TWO-DIMENSIONAL FRAMEWORK (INDIVIDUAL VS. SOCIAL, REFLECTION VS. PREDICTION), WE THEN IDENTIFY FOUR GENERIC APPROACHES TO LA AIMED AT IMPROVING LEARNING PROCESS AND LEARNING OUTCOMES. TO DEMONSTRATE THE APPLICATION, FOUR USE CASES ARE OUTLINED THAT ARE BASED ON FOUR PREVIOUSLY ELABORATED GENERIC APPROACHES TO LA. FINALLY, WE DISCUSS THE VALIDATION OF THE MODEL AND CLOSE WITH AN OUTLOOK ON RELEVANT FUTURE RESEARCH.
THE USE OF DIGITAL GAMES IN EDUCATION IS ALREADY A REALITY, BUT THEIR USE IN THIS AREA HAS BEEN HAMPERED BY THE LACK OF CONSOLIDATED RESOURCES FOR EVALUATION OF GAME-BASED LEARNING. THIS PAPER REPORTS A COMPUTATIONAL ARCHITECTURE FOR LEARNING ANALYTICS IN GAME-BASED LEARNING THAT IS BASED ON RELATIONAL ANALYSIS AND DATA MINING OF DATA CONTAINING EVIDENCES OF LEARNING COLLECTED DURING THE GAME PLAY. IT ALSO USES COMPUTER VISION TECHNIQUES APPLIED ON IMAGES OF THE PLAYERS PLAYING TO OBTAIN INFORMATION ABOUT THEIR BEHAVIORS AND EMOTIONS DURING THE GAME.
THE USE OF THE FLIPPED CLASSROOM (FC) MODEL IN HIGHER EDUCATION IS BECOMING INCREASINGLY COMMON. ALTHOUGH THE FC MODEL HAS MANY BENEFITS, THERE ARE SOME LIMITATIONS USING THIS MODEL FOR LEARNERS WHO DO NOT HAVE SELF-DIRECTED LEARNING SKILLS AND DO NOT HAVE A DEVELOPED LEARNER AUTONOMY. ONE OF THESE LIMITATIONS IS THAT STUDENTS WITH LOW ACADEMIC SELF-EFFICACY (ASE) CANNOT IMPROVE THEIR PROBLEM-SOLVING SKILLS (PSS). IN THE FC MODEL, IT IS THOUGHT THAT THESE PROBLEMS CAN BE SOLVED AND SUPPORTED BY PROVIDING LEARNING ANALYTICS TO THE LEARNERS AND MAKING RECOMMENDATIONS AND GUIDANCE BASED ON THESE RESULTS. THE AIM OF THIS STUDY IS TO INVESTIGATE THE EFFECT OF RECOMMENDATIONS AND GUIDANCE BASED ON LEARNING ANALYTICS ON ASE AND PSS. THE RESEARCH WAS CONDUCTED ACCORDING TO EXPERIMENTAL DESIGN WITH PRETEST-POSTTEST CONTROL GROUP AND MIXED METHODS APPROACH (QUAN + QUAL) WAS USED. THIS STUDY WAS CARRIED OUT ON 44 UNIVERSITY STUDENTS RANDOMLY ASSIGNED TO EXPERIMENTAL GROUP (LA GROUP) AND CONTROL GROUP (NON-LA GROUPS). WHILE FEEDBACK MESSAGES SHOWING LEARNING ANALYTICS TO LA GROUPS STUDENTS WERE SENT VIA LEARNING MANAGEMENT SYSTEM (LMS) ON A WEEKLY BASIS, NON-LA GROUPS STUDENTS DID NOT RECEIVE ANY FEEDBACK MESSAGES. THE EXPERIMENTAL PROCESS OF THE STUDY CONTINUED FOR 7 WEEKS WITHIN THE FC MODEL. RESEARCH DATA WERE OBTAINED THROUGH ASE SCALE, PROBLEM-SOLVING INVENTORY AND STUDENT OPINION FORM. THE RESULTS OF THE RESEARCH SHOW THAT SENDING FEEDBACK MESSAGES SHOWING LEARNING ANALYTICS TO STUDENTS HAS A STATISTICALLY SIGNIFICANT EFFECT ON THEIR ASE AND PSS. THE QUALITATIVE FINDINGS OF THE STUDY CONFIRM THE RESULTS OBTAINED FROM THE QUANTITATIVE FINDINGS. BASED ON THE CURRENT IMPLEMENTATION, VARIOUS SUGGESTIONS WERE SUMMARIZED FOR INSTRUCTORS, INSTRUCTIONAL DESIGNERS, AND RESEARCHERS.
DIGITAL LEARNING MANAGEMENT SYSTEMS (LMS) ARE REVOLUTIONIZING LEARNING IN MANY AREAS, INCLUDING COMPUTER SCIENCE EDUCATION (CSE). THEY ARE CAPABLE OF TRACKING LEARNERS' CHARACTERISTICS, SUCH AS PRIOR KNOWLEDGE, AND OTHER LEARNING HABITS, AND MAY OFFER MORE PERSONALIZED LEARNING OR GUIDANCE ON USEFUL LEARNING PRACTICES. LMSS COLLECT LARGE AMOUNTS OF DATA. PROPER PROCESSING OF SUCH COLLECTED DATA CAN OFFER VALUABLE INSIGHTS ABOUT THE LEARNING PROCESS, SUPPORT FOR HIGHER QUALITY EDUCATION, INSIGHTS ON WHY SOME STUDENTS DROP OUT OF COURSES, AND SO ON. IN THIS PAPER, WE BRIEFLY REVIEW AND DISCUSS THE GLOBAL TRENDS IN DIGITAL LEARNING AND LEARNING LNALYTICS (LA), SPECIFICALLY FROM THE VIEWPOINT OF TWO LMS SYSTEMS AND RELATED LA RESEARCH, ONE IN FINLAND AND ONE IN SWEDEN. IN THIS PAPER, WE ADDRESS THE CONTEXT-, AND COURSE-SPECIFIC NATURE OF LA BY DEVELOPING THE IDEA OF CROSS-COUNTRY AND CROSS-SYSTEMS LEARNING ANALYTICS. SECOND, WE CONSIDER OUR RESEARCH ESPECIALLY FROM AN EDUCATIONAL PERSPECTIVE TO IDENTIFY THE MOST BENEFICIAL PRACTICES FOR TEACHERS AND STUDENTS. THIRD, WE DISCUSS, BASED ON FINDINGS FROM OUR PROJECTS, FUTURE AVENUES FOR RESEARCH.
TEACHERS DESIGN LEARNING ACTIVITIES PURPOSEFULLY TO IMPROVE STUDENT LEARNING. HOWEVER, THE IMPACT OF THIS IS USUALLY ONLY EVALUATED AFTER A COURSE HAS ENDED BY MAKING USE OF SELF-REPORTED DATA AND ASSESSMENT RESULTS. LEARNING ANALYTICS OFFERS THE OPPORTUNITY TO COLLECT, ANALYSE AND VISUALISE FEEDBACK ON ACTIVITIES USING AUTHENTIC DATA IN REAL-TIME. INCORPORATING LEARNING ANALYTICS INTO THE LEARNING DESIGN MAKES JUSTIN-TIME INTERVENTIONS ATTAINABLE. THIS PAPER PRESENTS THE FIRST STEPS OF THE DEVELOPMENT OF A LEARNING ANALYTICS FOR LEARNING DESIGN (LA4LD) TOOL THAT IS CO-CREATED WITH STUDENTS AND TEACHERS, USING A DESIGN-BASED RESEARCH METHODOLOGY. BOTH TEACHERS AND STUDENTS EXPRESS THE NEED TO PERSONALISE FEEDBACK ON LEARNING ACTIVITIES IN ORDER TO INCREASE THE QUALITY OF THE LEARNING PROCESS AND WANT THAT EMBEDDED IN THE TOOL.
DIGITALIZATION CATALYZES DRASTIC CHANGES TO A PARTICULAR SUBJECT OR AREA. DIGITALIZATION IS AN OPERATIONAL STRUCTURE TRANSFORMATION PROCESS, SUCH AS IN THE EDUCATIONAL DOMAIN. DIGITALIZATION IN THE ACADEMIC FIELD HAS BROUGHT THE CLASSROOM TO THE USERS' FINGERTIPS WITH THE PREVALENCE OF ELEARNING APPLICATIONS, LEARNING MANAGEMENT SYSTEMS, ETC. HOWEVER, WITH THE INCREASING NUMBER OF DIGITAL LEARNING PLATFORM USERS, EDUCATORS FIND IT HARD TO MONITOR THEIR STUDENTS' PROGRESS. ANALYTICS THAT ANALYZE DATA GENERATED FROM THE USAGE PATTERN OF THE USERS CONTRIBUTE TO GIVING THE EDUCATORS AN INSIGHT REGARDING THE PERFORMANCE OF THEIR STUDENTS. WITH THAT, THEY CAN APPLY EARLY INTERVENTION AND MODIFICATION OF THEIR DELIVERY METHOD TO SUIT THE STUDENTS' NEEDS AND, AT THE SAME TIME, INCREASE THE QUALITY OF THE CONTENT. THIS STUDY ILLUSTRATES THE DEVELOPMENT OF A LEARNING ANALYTICS DASHBOARD THAT CAN IMPROVE LEARNING OUTCOMES FOR EDUCATORS AND STUDENTS.
THIS STUDY INVESTIGATES LEARNER'S READING BEHAVIORS IN A CRITICAL READING TASK IN HUMANITIES COURSE USING LEARNING ANALYTICS TECHNIQUES. A CRITICAL ANALYSIS OF LITERATURE AND CINEMA COURSE WAS SELECTED AS A CONTEXT. THE COURSE ACTIVITIES EVOLVED OVER 10 YEARS, AND FOR THIS INSTANCE, SOME FACE-TO-FACE CLASSROOM CRITICAL READING ACTIVITIES WERE MIGRATED TO ONLINE MODE BY USING BOOKROLL, A LEARNING ANALYTICS ENHANCED EBOOK PLATFORM. STUDENTS (N=22 OUT OF THE 50 REGISTERED) ACCESSED HAYAVADANA, AN INDIAN PLAY UPLOADED ON BOOKROLL, AND ATTEMPTED TO IDENTIFY PERFORMATIVE ELEMENTS AND CULTURAL REFERENCES IN THE TEXT AND HIGHLIGHT THEM. IN THIS STUDY, WE ANALYZE LEARNER'S READING LOGS GATHERED IN THE LEARNING RECORD STORE LINKED TO BOOKROLL DURING THAT ACTIVITY. WE EXTEND OUR PREVIOUS WORK WHERE WE IDENTIFY FOUR ONLINE READING PROFILES: EFFORTFUL, STRATEGIC, WANDERERS, AND CHECK-OUT, BASED ON LEARNER'S CLICKSTREAM INTERACTIONS AND TIME SPENT WITH THE CONTENT. WE VALIDATE THE PROFILES WITH QUALITATIVE INTERVIEW DATA COLLECTED FROM THE LEARNERS AND ILLUSTRATE THE QUANTIFIED LEARNING BEHAVIORS OF EACH OF THOSE PROFILES BASED ON AN ENGAGEMENT METRIC. OUR WORK AIMS TO INITIATE FURTHER DISCUSSION RELATED TO THE APPLICATION OF LEARNING ANALYTICS IN HUMANITIES COURSES BOTH TO PROBE INTO THE LEARNING BEHAVIORS OF THE STUDENTS AND THEREBY ENHANCE THE EXPERIENCES WITH THE USE OF INTERACTIVE LEARNING ENVIRONMENTS AND DATA-DRIVEN SERVICES.
THE EMERGENCE OF BLENDED LEARNING HAS HUGE IMPACT ON TRADITIONAL LEARNING. BLENDED LEARNING HAS ITS OWN UNIQUE CHARACTERISTICS COMBINING THE ADVANTAGES OF TRADITIONAL LEARNING AND ONLINE LEARNING. HOWEVER, SOME PROBLEMS OF BLENDED LEARNING HAVE ALSO BEEN FOUND IN PRACTICE SUCH AS LOW STUDENT ENGAGEMENT AND LACK OF AUTONOMY IN LEARNING. THIS STUDY AIMS TO EXAMINE THE STUDENT ENGAGEMENT AND ACHIEVEMENT IN BLENDED LEARNING ENVIRONMENT AND TRY TO IMPROVE STUDENT ENGAGEMENT AND ACHIEVEMENT BY USING INTERVENTIONS BASED ON LEARNING ANALYTICS. THIS STUDY TAKES 31 UNDERGRADUATES OF NORTHEAST NORMAL UNIVERSITY AS THE RESEARCH OBJECT. QUANTITATIVE SELF-REPORT (STUDENT ENGAGEMENT QUESTIONNAIRE) AND QUANTITATIVE OBSERVATION MEASURES (NUMBER OF VIEWING RECORDS AND POSTS TO A DISCUSSION BOARD) WERE BEING USED TO MEASURE STUDENT ENGAGEMENT. CLASSROOM TEST SCORE WAS BEING USED TO MEASURE STUDENT ACHIEVEMENT. THE RESULTS OF THIS STUDY SHOW THAT: A) INTERVENTION BASED ON LEARNING ANALYTICS CAN IMPROVE STUDENT ENGAGEMENT IN BLENDED LEARNING MODE. B) IN THE BLENDED LEARNING MODE, WHETHER THE INTERVENTION WAS USED OR NOT, IT HAS NOT BEEN FOUND YET THAT STUDENTS' ACHIEVEMENT WAS RELATED TO STUDENTS' ENGAGEMENT. IN SUM, THIS STUDY INDICATES THAT EDUCATORS CAN USE SOME STRATEGIC METHODS TO PROMOTE STUDENT ENGAGEMENT AND ACHIEVEMENT WHEN DOING TEACHING PRACTICES IN BLENDED LEARNING ENVIRONMENT, SUCH AS INTERVENTION BASED ON LEARNING ANALYTICS.
PREVIOUS RESEARCH HAS ESTABLISHED THAT MOTIVATION HAS A POSITIVE IMPACT ON THE LEARNING PROCESSES AND BEHAVIORS [1][2]. LEARNING ANALYTICS (LA) CAN PLAY AN IMPORTANT ROLE IN ADDRESSING THE ISSUE OF COLLECTING LEARNING BEHAVIORS. IN THIS STUDY, WE OBSERVED THE TEACHING ACTIVITIES OF THREE CLASSES AND EXAMINED THE RELATIONSHIPS AMONG LEARNING MOTIVATION, LEARNING PERFORMANCE, AND LEARNING BEHAVIORS OF STUDENTS IN DIGITAL LEARNING MATERIAL READERS.
SO FAR, THE SYNERGISTIC RELATIONSHIP BETWEEN LEARNING DESIGN AND LEARNING ANALYTICS AND ITS IMPACT ON LEARNERS' EDUCATIONAL PRACTICES HAVE BEEN INVESTIGATED. HOWEVER, LITTLE WORK HAS BEEN DONE TO SHOW HOW INTEGRATED LEARNING DESIGN BASED ON CONSTRUCTIVISM LEARNING THEORY AND LEARNING ANALYTICS CAN AFFECT LEARNERS' ENGAGEMENT AND SELF-REGULATION. THUS, THIS STUDY AIMED TO INVESTIGATE THE EFFECT OF THE CONSTRUCTIVIST LEARNING DESIGN AND LEARNING ANALYTICS (CLDLA) MODEL ON LEARNERS' ENGAGEMENT AND SELF-REGULATION. IN THIS EXPERIMENTAL STUDY, 50 GRADUATE STUDENTS AT IRANIAN' ALLAMEH TABATABA'I UNIVERSITY WERE ASKED TO PARTICIPATE IN A MOODLE COURSE CALLED "TEACHING SKILLS" AND FILL OUT AGENTIC ENGAGEMENT SCALE AND SELF-REGULATION QUESTIONNAIRES IN TWO STEPS AS A PRE-TEST AND POST-TEST MEASUREMENTS. ANALYSIS OF THE DATA INDICATED THAT THE CLDLA MODEL HAS A POSITIVE IMPACT ON LEARNERS' ENGAGEMENT AND SELF-REGULATION. BASED ON THE RESULTS, RECOMMENDATIONS FOR FURTHER RESEARCH AND EDUCATIONAL PRACTICES ARE SUGGESTED.
SEVERAL LEARNING ANALYTICS APPLICATIONS ARE LIMITED BY THE COST OF GENERATING A COMPUTER UNDERSTANDABLE DESCRIPTION OF THE COURSE DOMAIN, WHAT IS CALLED AN INTELLIGENT CURRICULUM. THE FOLLOWING WORK CONTRIBUTES A NOVEL APPROACH TO (SEMI-)AUTOMATICALLY GENERATE INTELLIGENT CURRICULUM THROUGH ONTOLOGIES EXTRACTED FROM EXISTING LEARNING MATERIALS SUCH AS DIGITAL BOOKS OR WEB CONTENT. THROUGH A SERIES OF NATURAL LANGUAGE PROCESSING STEPS, THE SEMI-STRUCTURED INFORMATION PRESENT IN EXISTING CONTENT IS TRANSFORMED INTO A CONCEPT-GRAPH. THIS WORK ALSO EVALUATES THE PROPOSED METHODOLOGY BY APPLYING IT TO LEARNING CONTENT FOR TWO DIFFERENT COURSES AND MEASURING THE QUALITY OF THE EXTRACTED ONTOLOGIES AGAINST MANUALLY GENERATED ONES. THE RESULTS OBTAINED SUGGEST THAT THE TECHNIQUE CAN BE READILY USED TO PROVIDE DOMAIN INFORMATION TO OTHER LEARNING ANALYTICS TOOLS.
MOBILE MIXED REALITY HAS BEEN SHOWN TO INCREASE HIGHER ACHIEVEMENT AND LOWER COGNITIVE LOAD WITHIN SPATIAL DISCIPLINES. HOWEVER, TRADITIONAL METHODS OF ASSESSMENT RESTRICT EXAMINERS ABILITY TO HOLISTICALLY ASSESS SPATIAL UNDERSTANDING. MULTIMODAL LEARNING ANALYTICS SEEKS TO INVESTIGATE HOW COMBINATIONS OF DATA TYPES SUCH AS SPATIAL DATA AND TRADITIONAL ASSESSMENT CAN BE COMBINED TO BETTER UNDERSTAND BOTH THE LEARNER AND LEARNING ENVIRONMENT. THIS PAPER EXPLORES THE PEDAGOGICAL POSSIBILITIES OF A SMARTPHONE ENABLED MIXED REALITY MULTIMODAL LEARNING ANALYTICS CASE STUDY FOR HEALTH EDUCATION, FOCUSED ON LEARNING THE ANATOMY OF THE HEART. THE CONTEXT FOR THIS STUDY IS THE FIRST LOOP OF A DESIGN BASED RESEARCH STUDY EXPLORING THE ACQUISITION AND RETENTION OF KNOWLEDGE BY PILOTING THE PROPOSED SYSTEM WITH PRACTICING HEALTH EXPERTS. OUTCOMES FROM THE PILOT STUDY SHOWED ENGAGEMENT AND ENTHUSIASM OF THE METHOD AMONG THE EXPERTS, BUT ALSO DEMONSTRATED PROBLEMS TO OVERCOME IN THE PEDAGOGICAL METHOD BEFORE DEPLOYMENT WITH LEARNERS.
LEARNERS' ATTAINMENT OF ACADEMIC KNOWLEDGE IN POSTSECONDARY INSTITUTIONS IS PREDOMINANTLY EXPRESSED BY SUMMATIVE OR FORMATIVE ASSESSMENT APPROACHES. RECENT ADVANCES IN EDUCATIONAL TECHNOLOGY HAS HINTED AT A MEANS TO MEASURE LEARNING EFFICIENCY, IN TERMS OF PERSONALIZATION OF LEARNER COMPETENCY AND CAPACITY IN TERMS OF ADAPTABILITY OF OBSERVED PRACTICES, USING RAW DATA OBSERVED FROM STUDY EXPERIENCES OF LEARNERS AS INDIVIDUALS AND AS CONTRIBUTORS IN SOCIAL NETWORKS. WHILE ACCURATE COMPUTATIONAL MODELS THAT EMBODY LEARNING EFFICIENCY REMAIN A DISTANT AND ELUSIVE GOAL, BIG DATA LEARNING ANALYTICS APPROACHES THIS GOAL BY RECOGNIZING COMPETENCY GROWTH OF LEARNERS, AT VARIOUS LEVELS OF GRANULARITY, USING A COMBINATION OF CONTINUOUS, FORMATIVE AND SUMMATIVE ASSESSMENTS. THIS STUDY DISCUSSES A METHOD TO CONTINUOUSLY CAPTURE DATA FROM STUDENTS' LEARNING INTERACTIONS. THEN, IT ANALYZES AND CLUSTERS THE DATA BASED ON THEIR INDIVIDUAL PERFORMANCES IN TERMS OF ACCURACY, EFFICIENCY AND QUALITY BY EMPLOYING PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM.
IN DIGITAL EDUCATION, LEARNING ANALYTICS SHOULD SUPPORT ACTIVE MONITORING AND DYNAMIC DECISION-MAKING DURING LEARNING PROCESSES; THEY ARE MAINLY BASED ON DIGITAL ASSESSMENT, THROUGH WHICH IT IS POSSIBLE TO COLLECT AND ELABORATE DATA ABOUT STUDENTS' PROGRESSES. IN THIS PAPER WE START FROM BLACK AND WILIAM'S THEORETICAL FRAMEWORK ON FORMATIVE ASSESSMENT, WHICH IDENTIFIED 5 KEY STRATEGIES THAT 3 AGENTS (STUDENT, PEERS AND TEACHER) PURSUE WHEN ENACTING FORMATIVE PRACTICES IN A CONTEXT OF TRADITIONAL LEARNING, AND WE INTEGRATE IT IN A FRAMEWORK OF INNOVATIVE DIDACTICS. IN PARTICULAR, WE CONSIDER THE USE OF A DIGITAL LEARNING ENVIRONMENT INTEGRATED WITH AN AUTOMATIC ASSESSMENT SYSTEM BASED ON THE ENGINE OF AN ADVANCED COMPUTING ENVIRONMENT TO BUILD INTERACTIVE MATERIALS WITH AUTOMATIC ASSESSMENT ACCORDING TO A SPECIFIC MODEL OF FORMATIVE ASSESSMENT. IN THIS FRAMEWORK, ROOTED IN ACTIVITY THEORY, THE DIGITAL LEARNING ENVIRONMENT PLAYS THE ROLE OF MEDIATING ARTIFACT IN THE ACTIVITY OF ENACTING THE STRATEGIES OF FORMATIVE ASSESSMENT. THROUGH SEVERAL EXAMPLES OF APPLICATION OF AUTOMATIC FORMATIVE ASSESSMENT IN SEVERAL CONTEXTS AND MODALITIES, WE SHOW HOW IT IS POSSIBLE TO USE THE DATA GATHERED BY THE DIGITAL LEARNING ENVIRONMENT TO IMPROVE THE ENACTMENT OF BLACK AND WILIAM'S STRATEGIES OF FORMATIVE ASSESSMENT, STRENGTHEN AND EVALUATE THEIR ACTION.
RECENT PROGRESS IN TECHNOLOGY HAS ALTERED THE LEARNING BEHAVIORS OF STUDENTS; BESIDES GIVING A NEW IMPULSE THAT RESHAPES THE EDUCATION ITSELF. IT CAN EASILY BE SAID THAT THE IMPROVEMENTS IN TECHNOLOGIES EMPOWER STUDENTS TO LEARN MORE EFFICIENTLY, EFFECTIVELY, AND CONTENTEDLY. SMART LEARNING (SL), DESPITE NOT BEING A NEW CONCEPT DESCRIBING LEARNING METHODS IN THE DIGITAL AGE-HAS CAUGHT THE ATTENTION OF RESEARCHERS. SMART LEARNING ANALYTICS (SLA) PROVIDES STUDENTS OF ALL AGES WITH RESEARCH-PROVEN FRAMEWORKS, HELPING STUDENTS TO BENEFIT FROM ALL KINDS OF RESOURCES AND INTELLIGENT TOOLS. IT AIMS TO STIMULATE STUDENTS TO HAVE A DEEP COMPREHENSION OF THE CONTEXT AND LEADS TO HIGHER LEVELS OF ACHIEVEMENT. THE TRANSFORMATION OF EDUCATION TO SMART LEARNING WILL BE REALIZED BY REENGINEERING THE FUNDAMENTAL STRUCTURES AND OPERATIONS OF CONVENTIONAL EDUCATIONAL SYSTEMS. ACCORDINGLY, STUDENTS CAN LEARN THE PROPER INFORMATION YET TO SUPPORT TO LEARN REAL-WORLD CONTEXT, MORE AND MORE FACTORS ARE NEEDED TO BE TAKEN INTO ACCOUNT. LEARNING HAS SHIFTED FROM WEB-BASED DUMB MATERIALS TO CONTEXT-AWARE SMART UBIQUITOUS LEARNING. IN THE STUDY, SLA DATASET WAS EXPLORED, AND ADVANCED ENSEMBLE TECHNIQUES WERE APPLIED FOR THE CLASSIFICATION TASK. BAGGING TREE AND STACKING CLASSIFIERS HAVE OUTPERFORMED OTHER CLASSICAL TECHNIQUES WITH AN ACCURACY OF 79% AND 78% RESPECTIVELY.
LEARNING ANALYTICS (LA) AND LESSON OBSERVATIONS ARE TWO APPROACHES FREQUENTLY USED TO STUDY TEACHING AND LEARNING PROCESSES. IN BOTH CASES, IN ORDER TO EXTRACT MEANINGFUL DATA INTERPRETATIONS, THERE IS A NEED FOR CONTEXTUALIZATION. PREVIOUS WORKS PROPOSE TO ENRICH LA DATASETS WITH OBSERVATION DATA AND TO USE THE LEARNING DESIGN AS A FRAMEWORK TO GUIDE THE DATA GATHERING AND THE LATER ANALYSIS. HOWEVER, THE MAJORITY OF LESSON OBSERVATION TOOLS COLLECT DATA THAT IS NOT COMPLIANT WITH LA DATASETS. MOREOVER, THE CONNECTION BETWEEN THE LEARNING DESIGN AND THE DATA GATHERED IS NOT STRAIGHTFORWARD. THIS STUDY REFLECTS UPON OUR RESEARCH-BASED DESIGN TOWARDS AN LA MODEL FOR CONTEXT-AWARE SEMANTICALLY ANNOTATED LESSON OBSERVATIONS THAT MAY BE INTEGRATED IN MULTIMODAL LA DATASETS. SIX TEACHERS (OUT OF WHICH 2 WERE ALSO RESEARCHERS) WITH PREVIOUS EXPERIENCE IN LESSON OBSERVATION WERE ENGAGED IN A FOCUS GROUP INTERVIEW AND PARTICIPATORY DESIGN SESSION THAT HELPED US TO EVALUATE THE LA MODEL THROUGH THE CONCEPTUAL DESIGN OF OBSERVATA (A LESSON OBSERVATION TOOL THAT IMPLEMENTS OUR MODEL). THE FINDINGS SHOW THE FEASIBILITY AND USEFULNESS OF THE PROPOSAL AS WELL AS THE POTENTIAL LIMITATIONS IN TERMS OF ADOPTION.
LEARNING ANALYTICS (LA) PLAYS A CRUCIAL ROLE IN PROVIDING E-LEARNING PRACTITIONERS BOTH TECHNOLOGICAL AND METHODOLOGICAL ASSISTANCE IN TERMS OF LEARNER MONITORING AND EVALUATION. MOST RESEARCH EFFORTS IN LA STRONGLY PLACE EMPHASIS ON ANALYTIC PROCESSES AND TECHNOLOGIES THAT SUPPORT THE TASKS OF DATA ANALYSIS AND VISUALIZATION. CREATING CUTTING-EDGE AND EFFICIENT TECHNIQUES FOR THE LATTER APPEARS TO BE A PRIORITIZED RESEARCH CHALLENGE FOR THE LA RESEARCH COMMUNITY WHILE ETHICAL QUESTIONS ARE OFTEN NEGLECTED. YET, THESE QUESTIONS HAVE BEEN PROVEN WITH RESEARCH EVIDENCE TO BE COMPELLING FOR THE RESEARCHERS AND AT THE SAME TIME PREOCCUPIED FOR THE LEARNERS. THE RESEARCH EFFORT PRESENTED IN THIS PAPER TAKES A CLOSER LOOK AT A PARTICULAR ISSUE IN LA RELATED TO USER TRACKING AND HOW ETHICAL ISSUES ARE BEING HANDLED.
ON THE PROVISION OF EARLY CHILDHOOD EDUCATION, EDUCATION-ORIENTED SOFTWARE FOR ENABLING PARENT AND TEACHER COLLABORATION BECOMES POPULAR. HOWEVER, BY PROVIDING MERELY ON THE ASYNCHRONOUS COMMUNICATION SUPPORT, THIS SORT OF SOFTWARE OVERLOOKS A FEATURE ALLOWING PARENTS (OR TEACHERS) TO TRACE THEIR CHILD'S PHYSICAL DEVELOPMENT OR LEARNING PROGRESS. IN THIS PAPER, IT IS ARGUED THAT THE MISSING FEATURE IS INDEED NECESSARY, AND SHOULD BE REALIZED THROUGH THE USE OF LEARNING ANALYTICS TECHNOLOGY. IN THIS REGARD, A SERVICE-ORIENTED DESIGN FRAMEWORK FOR RAPID APPLICATION DEVELOPMENT IS SUGGESTED BY A MEANS OF WEB MASHUP OF OPEN SOURCE SOFTWARE. BASED ON THE MINIMUM VIABLE PRODUCT, IT IS CLEARLY THE GRAPH VISUALIZATION OF LEARNING ANALYTICS CAN POTENTIALLY EMPOWER PARENTS ON THEIR EARLY CHILDHOOD EDUCATION SO THAT ACTIONS CAN BE TAKEN IN TIME ACCORDINGLY IF NECESSARY.
HIGHER EDUCATION SECTOR IS ALWAYS INCORPORATING CHANGES PERTAINING TO THE TECHNOLOGY TO PROVIDE BETTER TEACHING AND LEARNING ENVIRONMENT TO THE STAKEHOLDERS, WHICH INCLUDES STUDENTS AND FACULTY MEMBERS. A SMART CLASSROOM IS THE ONE, WHICH IS EQUIPPED WITH LATEST TOOLS AND TECHNOLOGIES THAT ARE BASED ON THE INTERNET. VARIOUS WEB-BASED TOOLS ARE ALSO BEING USED TO ENHANCE TEACHING AND LEARNING EXPERIENCE. TO PROVIDE APPROPRIATE CONTENT TO THE STUDENTS, BASED ON THEIR LEVEL OF UNDERSTANDING, LEARNING ANALYTICS COULD BE HELPFUL. STUDENTS WOULD BE PROVIDED WITH THE CONTENT, BASED ON ASSESSMENTS AND TESTS, THE RESULT OF WHICH COULD ANALYZE THE AREA OF IMPROVEMENT SUGGESTED FOR THEM. IN THIS WAY, FACULTY CAN ENSURE THAT LEARNING IS TAKING PLACE AT EACH LEVEL IN THE CLASSROOM. THIS RESEARCH PAPER HIGHLIGHTS SUCH ENVIRONMENT WHICH PROVIDES A STRONG BASE FOR LEARNING ANALYTICS FOR ENHANCED LEARNING ENVIRONMENT ESPECIALLY IN HIGHER EDUCATION.
LEARNING ANALYTICS IS AN EMERGING FIELD IN WHICH SOPHISTICATED ANALYTIC TOOLS ARE USED TO INFORM AND IMPROVE LEARNING AND TEACHING. RESEARCHERS WITHIN A REGIONAL UNIVERSITY IN AUSTRALIA IDENTIFIED AN ASSOCIATION BETWEEN INTERACTION AND STUDENT SUCCESS IN ONLINE COURSES AND SUBSEQUENTLY DEVELOPED A LEARNING ANALYTICS SYSTEM AIMED AT INFORMING LEARNING AND TEACHING PRACTICES. CONTEMPORARY LITERATURE DRAWS ATTENTION TO ETHICAL CONSIDERATIONS AND MORAL TENSIONS IN THE IMPLEMENTATION OF LEARNING ANALYTICS. THIS PAPER PRESENTS A CASE STUDY OF THE ETHICAL IMPLICATIONS OF A LEARNING ANALYTICS IMPLEMENTATION AT CQUNIVERSITY, A REGIONAL AUSTRALIAN UNIVERSITY. THERE WAS AN INSTITUTIONAL ASSUMPTION THAT STUDENT DATA, CONSENSUALLY GATHERED AT ENROLLMENT COULD BE ANALYZED BEYOND THE SCOPE OF THE ORIGINAL CONSENT. FURTHER, ACADEMICS WERE USING THE DATA IN A MANNER NOT INTENDED BY THE DESIGNERS OF THE LEARNING ANALYTIC SYSTEM, AND ACADEMICS INTERPRETED THE STUDENT'S INDIVIDUALIZED DATA TO LABEL STUDENTS BASED ON THEIR ESTIMATE OF SUCCESS. THE LEARNING ANALYTICS SYSTEM IS STILL BEING USED AND THE ETHICAL FINDINGS FROM THIS PAPER HAVE IMPLICATIONS FOR CQUNIVERSITY, ACADEMICS AND STUDENTS. IN ORDER TO RESOLVE THE ETHICAL DILEMMAS THE UNIVERSITY COULD INCREASE TRANSPARENCY OF THE PROCESS TO STUDENTS AND OBTAIN CONSENT AT MULTIPLE LEVELS THROUGHOUT THE STUDENT JOURNEY.
THE LEARNING ANALYTICS DASHBOARD (LAD) IS AN APPLICATION TO SHOW STUDENTS' ONLINE BEHAVIOR PATTERNS IN A VIRTUAL LEARNING ENVIRONMENT. THIS SUPPORTING TOOL WORKS BY TRACKING STUDENTS' LOG-FILES, MINING MASSIVE AMOUNTS OF DATA TO FIND MEANING, AND VISUALIZING THE RESULTS SO THEY CAN BE COMPREHENDED AT A GLANCE. THIS PAPER REVIEWS PREVIOUSLY DEVELOPED APPLICATIONS TO ANALYZE THEIR FEATURES. BASED ON THE IMPLICATIONS FROM THE REVIEW OF PREVIOUS STUDIES AS WELL AS A PRELIMINARY INVESTIGATION ON THE NEED FOR SUCH TOOLS, AN EARLY VERSION OF THE LAD WAS DESIGNED AND DEVELOPED. ALSO, IN ORDER TO IMPROVE THE LAD, A USABILITY TEST INCORPORATING A STIMULUS RECALL INTERVIEW WAS CONDUCTED WITH 38 COLLEGE STUDENTS IN TWO BLENDED LEARNING CLASSES. EVALUATION OF THIS TOOL WAS PERFORMED IN AN EXPERIMENTAL RESEARCH SETTING WITH A CONTROL GROUP AND ADDITIONAL SURVEYS WERE CONDUCTED ASKING STUDENTS' ABOUT PERCEIVED USEFULNESS, CONFORMITY, LEVEL OF UNDERSTANDING OF GRAPHS, AND THEIR BEHAVIORAL CHANGES. THE RESULTS INDICATED THAT THIS NEWLY DEVELOPED LEARNING ANALYTICS TOOL DID NOT SIGNIFICANTLY IMPACT ON THEIR LEARNING ACHIEVEMENT. HOWEVER, LESSONS LEARNED FROM THE USABILITY AND PILOT TESTS SUPPORT THAT VISUALIZED INFORMATION IMPACTS ON STUDENTS' UNDERSTANDING LEVEL; AND THE OVERALL SATISFACTION WITH DASHBOARD PLAYS AS A COVARIANT THAT IMPACTS ON BOTH THE DEGREE OF UNDERSTANDING AND STUDENTS' PERCEIVED CHANGE OF BEHAVIOR. TAKING IN THE RESULTS OF THE TESTS AND STUDENTS' OPENENDED RESPONSES, A SCAFFOLDING STRATEGY TO HELP THEM UNDERSTAND THE MEANING OF THE INFORMATION DISPLAYED WAS INCLUDED IN EACH SUB SECTION OF THE DASHBOARD. FINALLY, THIS PAPER DISCUSSES FUTURE DIRECTIONS IN REGARD TO IMPROVING LAD SO THAT IT BETTER SUPPORTS STUDENTS' LEARNING PERFORMANCE, WHICH MIGHT BE HELPFUL FOR THOSE WHO DEVELOP LEARNING ANALYTICS APPLICATIONS FOR STUDENTS.
THE CONTRIBUTION IS AIMED AT REPORTING AND COMMENTING ON SOME SIGNIFICANT LEARNING ANALYTICS COLLECTED FROM A MOOC ON LANGUAGE AWARENESS, ADDRESSED TO TEACHERS, TRAINERS AND EDUCATORS FROM ALL OVER THE WORLD, PROMOTED BY THE EUROPEAN COMMISSION THROUGH THE SCHOOL EDUCATION GATEWAY PLATFORM AND MODERATED BY THE AUTHORS. THE ROLE OF MOOCS FOR TEACHERS' CONTINUOUS QUALITY PROFESSIONAL DEVELOPMENT WILL REPRESENT THE STARTING POINT OF THE DISCUSSION, ACCORDING TO THE FOLLOWING RESEARCH QUESTION: "WHAT IMPACT CAN A MOOC ON LANGUAGE AWARENESS HAVE ON TEACHERS' PROFESSIONAL DEVELOPMENT?" AFTER A BRIEF OVERVIEW OF THE INSPIRATIONAL BACKGROUND AND OF THE MOOC SYLLABUS, DATA WILL BE HIGHLIGHTED AND COMMENTED ON WITH REFERENCE TO THE ATTENDEES' PARTICIPATION, MOTIVATION AND ONLINE SOCIAL INTERACTION, ACCORDING TO THE FOLLOWING CATEGORIES IDENTIFIED IN THE LITERATURE: PEDAGOGICAL ISSUES, LEARNER ISSUES, TECHNICAL ISSUES. AMONG THE DIFFERENT LEARNING ENVIRONMENTS AND MEDIA CHANNELS USED DURING THE COURSE, LEARNING ANALYTICS FROM THE FACEBOOK GROUP, THE FORUM AND THE TWITTER CHAT WILL BE DESCRIBED AND COMMENTED ON AS CRUCIAL DIMENSIONS OF THE LEARNING EXPERIENCE.
IN ORDER TO UNDERSTAND THE CURRENT SITUATION OF LEARNING ANALYTICS, IT TACKLED DOMESTIC RESEARCH OF LEARNING ANALYTICS IN CHINA DURING PAST FIVE YEARS FROM THE PERSPECTIVE OF VISUAL RESEARCH. MULTIPLE METHODS INCLUDING LITERATURE ANALYSIS, WORD FREQUENCY ANALYSIS, AND CO-WORD CLUSTERED ANALYSIS ARE USED IN THIS RESEARCH. IT ANALYZED THE RESEARCH STATUS FROM DIFFERENT ASPECTS, SUCH AS PROCESS MODEL, TECHNOLOGICAL TOOL, TYPICAL APPLICATIONS, & ETC. FINALLY IT SUMMED UP THE LIMITATIONS OF CURRENT RESEARCH AND PUT FORWARD THE OPPORTUNITIES AND CHALLENGES FACING THE DEVELOPMENT OF LEARNING ANALYTICS IN THE FUTURE.
ACCURATE AND TIMELY MEASUREMENT OF LEARNING ENGAGEMENT IS CRUCIAL FOR THE APPLICATION OF PRECISION EDUCATION. AT THE SAME TIME, IT IS STILL A CENTRAL RESEARCH THEME, BOTH IN THE LEARNING ANALYTICS COMMUNITY AS IN THE BROADER AREA OF EDUCATIONAL RESEARCH. 'ENGAGEMENT IS ONE OF THE HOTTEST RESEARCH TOPICS IN THE FIELD OF EDUCATIONAL PSYCHOLOGY' IS FOR A GOOD REASON THE OPENING SENTENCE OF A RECENT SPECIAL ISSUE. IN OUR CONTRIBUTION, WE PROPOSE A HOLISTIC APPROACH TO THE MEASUREMENT OF ENGAGEMENT BY INTEGRATING DATA OF BEHAVIORAL TYPE THROUGH TRACES OF LEARNING PROCESSES CAPTURED FROM LOG FILES INTO AFFECTIVE, BEHAVIORAL, AND COGNITIVE MEASURES OF ENGAGEMENT COLLECTED WITH SURVEYS AND COGNITIVE MEASURES FROM ASSESSMENTS FOR AND AS LEARNING. WE APPLY THIS HOLISTIC APPROACH IN AN EMPIRICAL ANALYSIS OF DISPOSITIONAL LEARNING ANALYTICS. STARTING FROM FOUR DIFFERENT ENGAGEMENT PROFILES CREATED BY TWO-STEP CLUSTERING, WE FIND THAT THESE PROFILES PRIMARILY DIFFER IN THEIR TIMING OF ENGAGEMENT WITH LEARNING. NEXT, WE DEVELOP REGRESSION-BASED PREDICTION MODELS THAT MAKE CLEAR THAT TRACE, SURVEY, AND ASSESSMENT DATA HAVE COMPLEMENTARY ROLES IN SIGNALING STUDENTS AT RISK FOR FAILURE AND ARE ALL THREE CRUCIAL CONSTITUENTS OF PREDICTION EQUATIONS THAT DIFFER IN THE TIMING OF LEARNING FEEDBACK.
THIS PAPER PRESENTS A NOVEL APPROACH AND A METHOD OF LEARNING ANALYTICS TO STUDY STUDENT AGENCY IN HIGHER EDUCATION. AGENCY IS A CONCEPT THAT HOLISTICALLY DEPICTS IMPORTANT CONSTITUENTS OF INTENTIONAL, PURPOSEFUL, AND MEANINGFUL LEARNING. WITHIN WORKPLACE LEARNING RESEARCH, AGENCY IS SEEN AT THE CORE OF EXPERTISE. HOWEVER, IN THE HIGHER EDUCATION FIELD, AGENCY IS AN EMPIRICALLY LESS STUDIED PHENOMENON WITH ALSO LACKING COHERENT CONCEPTUAL BASE. FURTHERMORE, TOOLS FOR STUDENTS AND TEACHERS NEED TO BE DEVELOPED TO SUPPORT LEARNERS IN THEIR AGENCY CONSTRUCTION. WE STUDY STUDENT AGENCY AS A MULTIDIMENSIONAL PHENOMENON CENTRING ON STUDENT-EXPERIENCED RESOURCES OF THEIR AGENCY. WE CALL THE ANALYTICS PROCESS DEVELOPED HERE STUDENT AGENCY ANALYTICS, REFERRING TO THE APPLICATION OF LEARNING ANALYTICS METHODS FOR DATA ON STUDENT AGENCY COLLECTED USING A VALIDATED INSTRUMENT. THE DATA ARE ANALYSED WITH UNSUPERVISED AND SUPERVISED METHODS. THE WHOLE ANALYTICS PROCESS WILL BE AUTOMATED USING MICROSERVICE ARCHITECTURE. WE PROVIDE EMPIRICAL CHARACTERISATIONS OF STUDENT-PERCEIVED AGENCY RESOURCES BY APPLYING THE ANALYTICS PROCESS IN TWO UNIVERSITY COURSES. FINALLY, WE DISCUSS THE POSSIBILITIES OF USING AGENCY ANALYTICS IN SUPPORTING STUDENTS TO RECOGNISE THEIR RESOURCES FOR AGENTIC LEARNING AND CONSIDER CONTRIBUTIONS OF AGENCY ANALYTICS TO IMPROVE ACADEMIC ADVISING AND TEACHERS' PEDAGOGICAL KNOWLEDGE.
MASSIVE OPEN ONLINE COURSES (MOOCS) COLLECT LARGE AMOUNTS OF RICH DATA. A PRIMARY OBJECTIVE OF LEARNING ANALYTICS (LA) RESEARCH IS STUDYING THESE DATA IN ORDER TO IMPROVE THE PEDAGOGY OF INTERACTIVE LEARNING ENVIRONMENTS. MOST STUDIES MAKE THE UNDERLYING ASSUMPTION THAT THE DATA REPRESENT TRUTHFUL AND HONEST LEARNING ACTIVITY. HOWEVER, PREVIOUS STUDIES SHOWED THAT MOOCS CAN HAVE LARGE COHORTS OF USERS THAT BREAK THIS ASSUMPTION AND ACHIEVE HIGH PERFORMANCE THROUGH BEHAVIORS SUCH AS CHEATING USING MULTIPLE ACCOUNTS OR UNAUTHORIZED COLLABORATION, AND WE THEREFORE DENOTE THEM FAKE LEARNERS. BECAUSE OF THEIR ABERRANT BEHAVIOR, FAKE LEARNERS CAN BIAS THE RESULTS OF LEARNING ANALYTICS (LA) MODELS. THE GOAL OF THIS STUDY IS TO EVALUATE THE ROBUSTNESS OF LA RESULTS WHEN THE DATA CONTAIN A CONSIDERABLE NUMBER OF FAKE LEARNERS. OUR METHODOLOGY FOLLOWS THE RATIONALE OF 'REPLICATION RESEARCH'. WE CHALLENGE THE RESULTS REPORTED IN A WELL-KNOWN, AND ONE OF THE FIRST LA/PEDAGOGIC-EFFICACY MOOC PAPERS, BY REPLICATING ITS RESULTS WITH AND WITHOUT THE FAKE LEARNERS (IDENTIFIED USING MACHINE LEARNING ALGORITHMS). THE RESULTS SHOW THAT FAKE LEARNERS EXHIBIT VERY DIFFERENT BEHAVIOR COMPARED TO TRUE LEARNERS. HOWEVER, EVEN THOUGH THEY ARE A SIGNIFICANT PORTION OF THE STUDENT POPULATION (SIMILAR TO 15%), THEIR EFFECT ON THE RESULTS IS NOT DRAMATIC (DOES NOT CHANGE TRENDS). WE CONCLUDE THAT THE LA STUDY THAT WE CHALLENGED WAS ROBUST AGAINST FAKE LEARNERS. WHILE THESE RESULTS CARRY AN OPTIMISTIC MESSAGE ON THE TRUSTWORTHINESS OF LA RESEARCH, THEY RELY ON DATA FROM ONE MOOC. WE BELIEVE THAT THIS ISSUE SHOULD RECEIVE MORE ATTENTION WITHIN THE LA RESEARCH COMMUNITY, AND CAN EXPLAIN SOME 'SURPRISING' RESEARCH RESULTS IN MOOCS.
THE ARTICLE DEALS WITH THE INTERPLAY BETWEEN LEARNING ANALYTICS AND MASSIVE OPEN ONLINE COURSES (MOOCS) AND PROVIDES A CONCEPTUAL FRAMEWORK TO SITUATE ONGOING RESEARCH IN THE MOOC AND LEARNING ANALYTICS INNOVATION CYCLE (MOLAC FRAMEWORK). THE MOLAC FRAMEWORK IS ORGANIZED ON THREE LEVELS: ON THE MICRO-LEVEL, THE DATA COLLECTION AND ANALYTICS ACTIVITIES ARE FOCUSED ON INDIVIDUAL REFLECTION AND INDIVIDUAL PREDICTION. ON THE MESO-LEVEL, DATA FROM SEVERAL OPEN COURSES ARE COMBINED TO SUPPORT BENCHMARKING AND TO CREATE INSIGHTS ABOUT BEHAVIOUR OF GROUPS OF LEARNERS RATHER THAN THE INDIVIDUAL. THESE INSIGHTS CAN INFORM THE INSTITUTION TO ADAPT THEIR EDUCATIONAL MODEL. ON THE MACRO-LEVEL OF THE FRAMEWORK, CROSS-INSTITUTIONAL LEARNING ANALYTICS ENABLES TO DEVELOP LEARNING AND TEACHING INTERVENTIONS THAT CAN BE TESTED IN A CLUSTER OF EDUCATIONAL ORGANIZATIONS TO ANALYSE THE IMPACT OF THESE INTERVENTIONS BEYOND CONTEXTUAL FACTORS. THE ARTICLE PROPOSES FOUR AREAS OF FUTURE ACTIVITIES THAT ARE NEEDED TO ENABLE THE MOLAC FRAMEWORK. THESE ARE THE STANDARDIZATION OF THE DESCRIPTION OF THE EDUCATIONAL DESIGN OF MOOCS, DATA SHARING FACILITIES ACROSS INSTITUTIONS, JOINT POLICYMAKING AND ETHICAL GUIDELINES AND LAST BUT NOT LEAST STANDARDIZED EVALUATION APPROACHES.
EARLY WARNING DASHBOARDS IN HIGHER EDUCATION ANALYZE STUDENT DATA TO ENABLE EARLY IDENTIFICATION OF UNDERPERFORMING STUDENTS, ALLOWING TIMELY INTERVENTIONS BY FACULTY AND STAFF. TO UNDERSTAND PERCEPTIONS REGARDING THE ETHICS AND IMPACT OF SUCH LEARNING ANALYTICS APPLICATIONS, WE CONDUCTED A MULTI-STAKEHOLDER ANALYSIS OF AN EARLY-WARNING DASHBOARD DEPLOYED AT THE UNIVERSITY OF MICHIGAN THROUGH SEMI-STRUCTURED INTERVIEWS WITH THE SYSTEM'S DEVELOPERS, ACADEMIC ADVISORS (THE PRIMARY USERS), AND STUDENTS. WE IDENTIFY MULTIPLE TENSIONS AMONG AND WITHIN THE STAKEHOLDER GROUPS, ESPECIALLY WITH REGARD TO AWARENESS, UNDERSTANDING, ACCESS AND USE OF THE SYSTEM. FURTHERMORE, AMBIGUITY IN DATA PROVENANCE AND DATA QUALITY RESULT IN DIFFERING LEVELS OF RELIANCE AND CONCERNS ABOUT THE SYSTEM AMONG ACADEMIC ADVISORS AND STUDENTS. WHILE STUDENTS SEE THE SYSTEM'S BENEFITS, THEY ARGUE FOR MORE INVOLVEMENT, CONTROL, AND INFORMED CONSENT REGARDING THE USE OF STUDENT DATA. WE DISCUSS OUR FINDINGS' IMPLICATIONS FOR THE ETHICAL DESIGN AND DEPLOYMENT OF LEARNING ANALYTICS APPLICATIONS IN HIGHER EDUCATION.
WHILE THE FIELD OF LEARNING ANALYTICS IS IN FULL DEVELOPMENT, POTENTIAL USERS ARE JUST DISCOVERING THE OPPORTUNITIES OFFERED BY THESE TOOLS. ONE OF THE MAJOR DIFFICULTIES CONSISTS IN PROPOSING A VISUAL DATA REPRESENTATION WHICH MAKES SENSE TO THE USERS. AFTER REFINING OUR METHOD TO IDENTIFY USER NEEDS IN TERMS OF VISUALIZATION, AND TO DEFINE THE MAIN DIMENSIONS OF A LEARNING DASHBOARD, WE PROPOSE A TOOL TO SUPPORT PARTICIPATORY DESIGN. THIS TOOL IS BASED ON A CANVAS AND CARDS TO HELP DASHBOARDS' CREATION USING LEARNING ANALYTICS. IT ALLOWS USERS TO SUPPORT CREATIVITY AROUND DECISION MAKING, CHARACTERIZE THEIR CONTEXT, AND DRAW A DASHBOARD'S MOCKUP USING EXISTING CONTENT.
VIRTUAL TRAINING CENTERS ARE HOSTED SOLUTIONS FOR THE IMPLEMENTATION OF TRAINING COURSES IN THE FORM OF E.G. WEBINARS. MANY EXISTING CENTERS NEGLECT THE INFORMAL AND SOCIAL DIMENSION OF VOCATIONAL TRAINING AS WELL AS THE LEGITIMATE BUSINESS INTERESTS OF TRAINING PROVIDERS AND COMPANIES SENDING THEIR EMPLOYEES. IN THIS PAPER, WE PRESENT THE VIRTUAL TRAINING CENTER PLATFORM V3C THAT BLENDS FORMAL, CERTIFIED VIRTUAL TRAINING COURSES WITH SELF-REGULATED AND SOCIAL LEARNING IN SYNCHRONOUS AND ASYNCHRONOUS LEARNING PHASES. WE HAVE DEVELOPED AN INTEGRATED LEARNING ANALYTICS APPROACH TO COLLECT, STORE, ANALYZE AND VISUALIZE DATA FOR DIFFERENT PURPOSES LIKE CERTIFICATION, INTERVENTIONS AND GRADUAL IMPROVEMENT OF THE PLATFORM. THE RESULTS GIVEN HERE DEMONSTRATE THE ABILITY OF THE PLATFORM TO DELIVER DATA FOR KEY PERFORMANCE INDICATORS LIKE LEARNING OUTCOMES AND DROP-OUT RATES AS WELL AS THE INTERPLAY BETWEEN SYNCHRONOUS AND ASYNCHRONOUS LEARNING PHASES ON A VERY LARGE SCALE. SINCE THE PLATFORM IMPLEMENTATION IS OPEN SOURCE, RESULTS CAN BE EASILY TRANSFERRED AND EXPLOITED IN MANY CONTEXTS.
LOCO-ANALYST IS A LEARNING ANALYTICS TOOL WE DEVELOPED TO PROVIDE EDUCATORS WITH FEEDBACK ON STUDENTS LEARNING ACTIVITIES AND PERFORMANCE. EVALUATION OF THE FIRST VERSION OF THE TOOL LED TO THE ENHANCEMENT OF THE TOOL'S DATA VISUALIZATION, USER INTERFACE, AND SUPPORTED FEEDBACK TYPES. THE SECOND EVALUATION OF THE IMPROVED TOOL ALLOWED US TO SEE HOW THE IMPROVEMENTS AFFECTED THE USERS' PERCEIVED VALUE OF THE TOOL. HERE, WE PRESENT THE QUALITATIVE RESULTS OF OUR TWO EVALUATIONS AND DISCUSS IMPORTANT LESSONS LEARNED STEMMING FROM THE COMPARISON OF THE TWO STUDIES. THE RESULTS SHOW THAT EDUCATORS FIND THE KINDS OF FEEDBACK IMPLEMENTED IN THE TOOL INFORMATIVE AND THEY VALUE THE MIX OF TEXTUAL AND GRAPHICAL REPRESENTATIONS OF DIFFERENT KINDS OF FEEDBACK PROVIDED BY THE TOOL. (C) 2011 ELSEVIER LTD. ALL RIGHTS RESERVED.
WITH THE ONGOING PENETRATION RATE OF SMART DEVICES, THEIR USE IS BECOMING POPULAR IN MANY AREAS, SUCH AS IN THE FIELD OF EDUCATION. RECENTLY, THERE HAS BEEN AN INCREASING DEMAND FOR DIGITAL LEARNING BASED ON MOBILE DEVICES, NAMELY M-LEARNING. THE DEVELOPMENT OF AN EFFECTIVE MOBILE AUTHORING TOOL WHICH PROVIDES THE ABILITY TO INSTRUCTORS TO MANAGE THE LEARNING CONTENT WITH THE INTENTION NOT ONLY TO FACILITATE THE CONTENT DELIVERY, BUT ALSO TO IMPROVE THE LEARNING PROCESS BY ADAPTING CONTENT TO STUDENTS' NEEDS, IS A CRITICAL TASK. TO THIS DIRECTION, THIS PAPER PRESENTS THE DEVELOPMENT OF A NOVEL MOBILE CONTENT AUTHORING TOOL WHICH INCORPORATED DYNAMIC REPORTS ON REFINING TEACHING STRATEGIES ACCORDING TO LEARNING ANALYTICS; I.E. THE ANALYSIS OF THE LARGE AMOUNT OF INFORMATION RESULTING FROM THE INTERACTION OF STUDENTS WITH THE M-LEARNING APP. THE GOAL OF THIS AUTHORING TOOL IS TO SUPPORT AN M-LEARNING SYSTEM AND HELP TEACHERS TO IMPROVE THEIR INSTRUCTION THROUGH PERSONALIZED SUGGESTIONS ON THE DESIGN AND REVISION OF THE PROVIDED LEARNING CONTENT. THE DEVELOPED AUTHORING TOOL WAS USED BY TEACHERS IN SECONDARY SCHOOLS AND WAS EVALUATED BY THEM. THE RESULTS OF THE EVALUATION SHOWED THAT THE INCORPORATION OF LEARNING ANALYTICS IS INDEED CONSIDERED AS USEFUL FOR THE TUTORS SUPPORTING THEM THROUGH EFFICIENT AUTHORING TOOLS IN M-LEARNING ENVIRONMENTS.
PREDICTIVE LEARNING ANALYTICS (PLA) IS AN EDUCATIONAL INNOVATION THAT HAS THE POTENTIAL TO ENHANCE THE TEACHING PRACTICE AND FACILITATE STUDENT LEARNING AND SUCCESS. YET, THE DEGREE OF PLA ADOPTION ACROSS EDUCATIONAL INSTITUTIONS REMAINS LIMITED, WHILE TEACHERS WHO MAKE USE OF PLA DO NOT ENGAGE WITH IT IN A SYSTEMATIC MANNER. INFORMED BY THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT), WE CONDUCTED ELEVEN IN-DEPTH INTERVIEWS WITH UNIVERSITY TEACHERS AND EXAMINED THEIR ENGAGEMENT PATTERNS WITH PLA FOR THE DURATION OF A 37-WEEK UNDERGRADUATE COURSE. WE AIMED TO IDENTIFY (A) FACTORS THAT EXPLAIN THE DEGREE OF USING PLA IN THE TEACHING PRACTICE AND (B) THE IMPACT OF AN INTERVENTION - SENDING EMAIL REMINDERS TO TEACHERS - ON FACILITATING SYSTEMATIC ENGAGEMENT WITH PLA. FINDINGS SUGGESTED THAT, AMONGST THE FACTORS FACILITATING ENGAGEMENT WITH PLA WERE PERFORMANCE EXPECTANCY, EFFORT EXPECTANCY, AND SOCIAL INFLUENCE. AMONGST THE FACTORS INHIBITING ENGAGEMENT WITH PLA WERE PERFORMANCE EXPECTANCY AND FACILITATED CONDITIONS THAT WERE RELATED TO TRAINING AND A LACK OF UNDERSTANDING OF PREDICTIVE DATA. IMPLICATIONS FOR THE ADOPTION AND USE OF PLA IN HIGHER EDUCATION ARE DISCUSSED.
ALTHOUGH TECHNOLOGICAL ADVANCES HAVE BROUGHT ABOUT NEW OPPORTUNITIES FOR SCALING FEEDBACK TO STUDENTS, THERE REMAIN CHALLENGES IN HOW SUCH FEEDBACK IS PRESENTED AND INTERPRETED. THERE IS A NEED TO BETTER UNDERSTAND HOW STUDENTS MAKE SENSE OF SUCH FEEDBACK TO ADAPT SELF-REGULATED LEARNING PROCESSES. THIS STUDY EXAMINED STUDENTS' SENSE-MAKING OF LEARNING ANALYTICS-BASED PERSONALISED FEEDBACK ACROSS FOUR COURSES. RESULTS FROM A COMBINATION OF THEMATIC ANALYSIS AND EPISTEMIC NETWORK ANALYSIS SHOW AN ASSOCIATION BETWEEN STUDENT PERCEPTIONS OF THEIR PERSONALISED FEEDBACK AND HOW THESE MAP TO SUBSEQUENT SELF-DESCRIBED SELF-REGULATED LEARNING PROCESSES. MOST NOTABLY, THE RESULTS INDICATE THAT PERSONALISED FEEDBACK, ELABORATED BY PERSONAL MESSAGES FROM COURSE INSTRUCTORS, HELPS STUDENTS REFINE OR STRENGTHEN IMPORTANT FORETHOUGHT PROCESSES OF GOAL-SETTING, AS WELL AS TO REDUCE PROCRASTINATION. THE RESULTS HIGHLIGHT THE NEED FOR INSTRUCTORS TO INCREASE THE DIALOGIC ELEMENT IN PERSONALISED FEEDBACK IN ORDER TO REDUCE DEFENSIVE REACTIONS FROM STUDENTS WHO HOLD TO THEIR OWN LEARNING STRATEGIES. THIS APPROACH MAY PROMPT REFLECTION ON THE SUITABILITY OF STUDENTS' CURRENT LEARNING STRATEGIES AND ACHIEVEMENT OF ASSOCIATED LEARNING GOALS. IMPLICATIONS FOR PRACTICE OR POLICY: PERSONALISED FEEDBACK BASED ON LEARNING ANALYTICS SHOULD BE INFORMED BY AN UNDERSTANDING OF STUDENTS' SELF-REGULATED LEARNING. INSTRUCTORS IMPLEMENTING PERSONALISED FEEDBACK SHOULD ALIGN THIS CLOSELY WITH THE COURSE CURRICULUM. INSTRUCTORS IMPLEMENTING PERSONALISED FEEDBACK IN THEIR COURSES SHOULD CONSIDER THE RELATIONAL ELEMENT OF FEEDBACK BY USING A POSITIVE TONE. PERSONALISED FEEDBACK CAN BE FURTHER ENHANCED BY INCREASING THE DIALOGIC ELEMENT AND BY INCLUDING MORE INFORMATION ABOUT LEARNING STRATEGIES.
LEARNING ANALYTICS (LA) IS AN EMERGING FIELD IN WHICH SOPHISTICATED ANALYTIC TOOLS ARE USED TO IMPROVE LEARNING AND EDUCATION. IT DRAWS FROM, AND IS CLOSELY TIED TO, A SERIES OF OTHER FIELDS OF STUDY LIKE BUSINESS INTELLIGENCE, WEB ANALYTICS, ACADEMIC ANALYTICS, EDUCATIONAL DATA MINING, AND ACTION ANALYTICS. THE MAIN OBJECTIVE OF THIS RESEARCH WORK IS TO FIND MEANINGFUL INDICATORS OR METRICS IN A LEARNING CONTEXT AND TO STUDY THE INTER-RELATIONSHIPS BETWEEN THESE METRICS USING THE CONCEPTS OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING, THEREBY, ANALYZING THE EFFECTS OF DIFFERENT FEATURES ON STUDENT'S PERFORMANCE USING DISPOSITION ANALYSIS. IN THIS PROJECT, K-MEANS CLUSTERING DATA MINING TECHNIQUE IS USED TO OBTAIN CLUSTERS WHICH ARE FURTHER MAPPED TO FIND THE IMPORTANT FEATURES OF A LEARNING CONTEXT. RELATIONSHIPS BETWEEN THESE FEATURES ARE IDENTIFIED TO ASSESS THE STUDENT'S PERFORMANCE.
E-BOOKS HAVE BEEN INTRODUCED TO EDUCATIONAL INSTITUTIONS IN MANY COUNTRIES. THE USE OF E-BOOKS IN TRADITIONAL CLASSROOMS ENABLES THE RECORDING OF LEARNING LOGS. RECENTLY, RESEARCHERS HAVE BEGUN TO CARRY OUT LEARNING ANALYTICS ON THE LEARNING LOGS OF E-BOOKS. HOWEVER, THERE HAS BEEN LIMITED ATTENTION DEVOTED TO UNDERSTANDING THE TYPES OF LEARNING STRATEGIES THAT STUDENTS EMPLOY WHEN THEY READ E-BOOKS. IN THIS PAPER, USING E-BOOK LEARNING LOGS, WE EXAMINE THE LEARNING STRATEGIES THAT STUDENTS EMPLOYED WHEN READING E-BOOKS. IN THIS PAPER, WE WILL INTRODUCE HOW TO IDENTIFY LEARNING STRATEGIES FROM E-BOOK LEARNING LOGS WITH TWO CASE STUDIES. ONE IS "IDENTIFYING LEARNING STRATEGIES USING CLUSTERING" AND THE OTHER IS "EXAMINING LEARNING STRATEGIES USING SEQUENTIAL ANALYSIS."
IN RECENT YEARS, VIRTUAL LEARNING ENVIRONMENTS, LAPTOPS, TABLETS AND MOBILE DEVICES HAVE BEEN INTRODUCED IN THE CLASSROOM. THESE TECHNOLOGIES START A SNOWBALL EFFECT: THE OLD TOOLS TEACHERS USED TO FATHOM THE STUDENTS' LEARNING PROGRESS, SINCE SO MUCH HAPPENS ONLINE, ARE NOT ENOUGH. THUS, THE NEED FOR NEW TOOLS TO ANALYSE THE STUDENTS' ACTIVITY ON THE ONLINE LEARNING ENVIRONMENTS ARISES. THE FIELD OF LEARNING ANALYTICS CAN PROVIDE SOME OF THESE TOOLS. IN THIS PAPER, WE INTRODUCE THE STUDENT'S PROGRESS SNAPSHOT (SPS), A LEARNING ANALYTICS DASHBOARD THAT ALLOWS TEACHERS TO ANALYSE THE ACTIVITY OF THEIR STUDENTS ON MOODLE COURSES. THE SPS RUNNING AS SOFTWARE AS A SERVICE, INCLUDES BOTH CHARTS AND AUTOMATICALLY GENERATED EXPLANATORY TEXTS OF THESE CHARTS. DURING THE ACADEMIC COURSE 2015-2016 A PILOT WAS CONDUCTED TO VALIDATE THE SPS.
ELECTRONIC PORTFOLIOS (E-PORTFOLIOS) ARE CRUCIAL MEANS FOR WORKPLACE-BASED ASSESSMENT AND FEEDBACK. ALTHOUGH E-PORTFOLIOS PROVIDE A USEFUL APPROACH TO VIEW EACH LEARNER'S PROGRESS, SO FAR OPTIONS FOR PERSONALIZED FEEDBACK AND POTENTIAL DATA ABOUT A LEARNER'S PERFORMANCES AT THE WORKPLACE OFTEN REMAIN UNEXPLOITED. THIS PAPER ADVOCATES THAT E-PORTFOLIOS ENHANCED WITH LEARNING ANALYTICS, MIGHT INCREASE THE QUALITY AND EFFICIENCY OF WORKPLACE-BASED FEEDBACK AND ASSESSMENT IN PROFESSIONAL EDUCATION. BASED ON A 5-PHASED ITERATIVE DESIGN APPROACH, AN EXISTING E-PORTFOLIO ENVIRONMENT WAS ENHANCED WITH LEARNING ANALYTICS IN PROFESSIONAL EDUCATION. FIRST, INFORMATION ABOUT CRUCIAL PROFESSIONAL ACTIVITIES FOR PROFESSIONAL DOMAINS AND SUITED ASSESSMENT INSTRUMENTS WERE COLLECTED (PHASE 1). THEREAFTER PROBABILISTIC STUDENT MODELS WERE DEFINED (PHASE 2). NEXT, PERSONALIZED FEEDBACK AND VISUALIZATION OF THE PERSONAL DEVELOPMENT OVER TIME WERE DEVELOPED (PHASE 3). THEN THE PROTOTYPE OF THE E-PORTFOLIO-INCLUDING THE STUDENT MODELS AND FEEDBACK AND VISUALIZATION MODULES-WERE IMPLEMENTED IN PROFESSIONAL TRAINING-PROGRAMS (PHASE 4). LAST, EVALUATION CYCLES TOOK PLACE AND 121 STUDENTS AND 30 SUPERVISORS FROM FIVE INSTITUTES FOR PROFESSIONAL EDUCATION EVALUATED THE PERCEIVED USEFULNESS OF THE DESIGN (PHASE 5). IT WAS CONCLUDED THAT E-PORTFOLIOS WITH LEARNING ANALYTICS WERE PERCEIVED TO ASSIST THE DEVELOPMENT OF STUDENTS' PROFESSIONAL COMPETENCIES AND THAT THE DESIGN IS ONLY SUCCESSFUL WHEN DEVELOPED AND IMPLEMENTED THROUGH THE EYES OF THE USERS. FEEDBACK AND ASSESSMENT METHODS BASED UPON LEARNING ANALYTICS CAN STIMULATE LEARNING AT THE WORKPLACE IN THE LONG RUN. PRACTICAL, TECHNOLOGICAL AND ETHICAL CHALLENGES ARE DISCUSSED.
AMOUNT OF EDUCATIONAL DATA HAS BEEN CONSTANTLY INCREASING FOR YEARS IN ALL DOMAINS AND KINDS OF EDUCATION (FORMAL OR INFORMAL) AND EDUCATIONAL ACTIVITIES (TEACHING, LEARNING, ASSESSMENT, USE OF SOCIAL MEDIA AND COLLABORATION AND SO ON). ACCORDINGLY, LEARNING ANALYTICS (LA) BECOME A POWERFUL MECHANISM FOR SUPPORTING LEARNERS, INSTRUCTORS, TEACHERS, LEARNING SYSTEM DESIGNERS AND DEVELOPERS TO BETTER UNDERSTAND EDUCATIONAL PROCESSES AND PREDICT LEARNERS' NEEDS AND PERFORMANCES. IN THIS PAPER, WE ANALYZE THE IMPORTANT DIMENSIONS AND OBJECTIVES OF LA, APPLICATION POSSIBILITIES AND SOME CHALLENGES TO THE BENEFICIAL EXPLOITATION OF EDUCATIONAL DATA. THE REQUIRED SKILLS AND CAPABILITIES THAT MAKE MEANINGFUL USE OF LA TECHNIQUES AND TECHNOLOGIES IN THIS DOMAIN ARE CONSIDERED AND IDENTIFIED. PRESENTED FINDINGS CAN ACT AS A VALUABLE GUIDE FOR SETTING UP LA SERVICES IN SUPPORT OF EDUCATIONAL PRACTICE. ALSO, THEY CAN BE USED AS LEARNER GUIDANCE, IN QUALITY ASSURANCE, CURRICULUM DEVELOPMENT, AND IN IMPROVING LEARNING PROCESS EFFECTIVENESS AND EFFICIENCY. FINALLY, THIS PAPER PROPOSES THE UNAVOIDABLE CONSTRAINTS THAT AFFECT LA TECHNOLOGIES IN EDUCATION.
LEARNING ANALYTICS IS THE TECHNOLOGY USING THE PREDICTION MODEL AND DATA GENERATED IN THE LEARNING PROCESS TO ANALYZE AND PREDICT LEARNERS' FUTURE PERFORMANCE, FIND POTENTIAL PROBLEMS AND IMPLEMENT INTERVENTION. THE INTERVENTION MODEL IS THE KEY OF USING LEARNING ANALYTICS TO IMPROVE LEARNERS' LEARNING PERFORMANCE. THIS PAPER CONSTRUCTS THE INTERVENTION MODEL WHICH IS A LOOP STRUCTURE ON THE BASIS OF EXISTING RESEARCHES. THE CORE OF THE INTERVENTION MODEL IS INTERVENTION ENGINE INCLUDING LEARNERS' LEARNING STATUS IDENTIFICATION, INTERVENTION STRATEGY MATCHING AND CALCULATION, INTERVENTION APPLICATION AND INTERVENTION EFFECTIVENESS ANALYSIS. IT DESIGNS AND SPECIFIES THE INTERVENTION STRATEGIES FROM THE CONTENTS AND MEANS OF THE INTERVENTION. THE RESEARCH PRESENTS TYPICAL INTERVENTION STRATEGIES INVOLVING MEANS OF INTERVENTION AND CONTENT OF INTERVENTION HAVING IMPORTANT ACADEMIC AND PRACTICAL SIGNIFICANCE ON PROMOTING LEARNING ANALYTICS RESEARCH IN THE FUTURE.
TEMPORAL ANALYSIS OF LEARNING DATA IS ATTRACTING THE INTEREST OF RESEARCHERS, AND A GROWING BODY OF LEARNING ANALYTICS (LA) RESEARCH APPLIES LAG SEQUENTIAL ANALYSIS. HOWEVER, LACK OF METHODOLOGICAL FRAMEWORKS THAT GUIDE THE DATA GATHERING AND ANALYSIS POSES MULTIPLE CONCEPTUAL, METHODOLOGICAL, ANALYTICAL AND TECHNICAL CHALLENGES. WHILE OBSERVATION AS A TECHNIQUE HAS BEEN ALREADY USED IN LA, SYSTEMATIC OBSERVATION METHODS AND DESIGNS HAVE NOT BEEN APPLIED SO FAR, AND PARAMETERS OFTEN USED IN THE OBSERVATIONAL DOMAIN (SUCH AS ORDER AND DURATION) ARE STILL UNDER-RESEARCHED. IN THIS PAPER WE PROPOSE A METHODOLOGICAL FRAMEWORK, AND ILLUSTRATE ITS POTENTIAL BY APPLYING IT IN THE ANALYSIS OF A KNOWLEDGE FORUM DATASET. RESULTS SHOW THE POTENTIAL OF THE PROPOSED METHOD TO UNCOVER BEHAVIORAL PATTERNS PROSPECTIVELY (LAG +1 TO LAG +5) OR RETROSPECTIVELY (LAG -1 TO LAG -5), AND TO REDUCE THIS INFORMATION THROUGH POLAR COORDINATE ANALYSIS. MOREOVER, AS ILLUSTRATED IN THIS PAPER, OBSERVATIONAL METHODS OFFER A RIGOROUS FRAMEWORK FOR LA DATASETS, ENABLING THE REPLICABILITY, VALIDITY AND RELIABILITY OF THE RESULTS.
VISUAL REPRESENTATIONS OF STUDENT-GENERATED TRACE DATA DURING LEARNING ACTIVITIES HELP BOTH STUDENTS AND INSTRUCTORS INTERPRET THEM INTUITIVELY AND PERCEIVE HIDDEN ASPECTS OF THESE DATA QUICKLY. IN THIS PAPER, WE ELABORATE ON THE VISUALIZATION OF TEMPORAL TRACE DATA DURING ASSESSMENT. THE GOALS OF THE STUDY WERE TWOFOLD: A) TO DEPICT STUDENTS' ENGAGEMENT IN THE ASSESSMENT PROCEDURE IN TERMS OF TIME SPENT AND TEMPORAL FACTORS ASSOCIATED WITH LEARNING-SPECIFIC CHARACTERISTICS, AND B) TO EXPLORE THE FACTORS THAT INFLUENCE THE TEACHERS' BEHAVIOURAL INTENTION TO USE THE PROPOSED SYSTEM AS AN INFORMATION SYSTEM AND THEIR PERCEPTIONS OF THE EFFECTIVENESS AND ACCEPTANCE OF OUR APPROACH. THE PROPOSED VISUALIZATIONS HAVE BEEN EXPLORED IN A STUDY WITH 32 SECONDARY EDUCATION TEACHERS. WE ADOPTED A DESIGN-BASED RESEARCH METHODOLOGY AND EMPLOYED A SURVEY INSTRUMENT - BASED ON THE LEARNING ANALYTICS ACCEPTANCE MODEL (LAM) - IN ORDER TO MEASURE THE EXPECTED IMPACT OF THE PROPOSED VISUALIZATIONS. THE ANALYSIS OF THE FINDINGS INDICATES THAT A) TEMPORAL FACTORS CAN BE USED FOR VISUALIZING STUDENTS' BEHAVIOUR DURING ASSESSMENT, AND B) THE VISUALIZATION OF THE TEMPORAL DIMENSION OF STUDENTS' BEHAVIOUR INCREASES TEACHERS' AWARENESS OF STUDENTS' PROGRESS, POSSIBLE MISCONCEPTIONS (E.G., GUESSING THE CORRECT ANSWER) AND TASK DIFFICULTY.
PURPOSE - THE PURPOSE OF THIS PAPER IS TO DESCRIBE THE NEED FOR ACADEMIC LIBRARIES TO DEMONSTRATE AND INCREASE THEIR IMPACT OF STUDENT LEARNING AND SUCCESS. IT HIGHLIGHTS THE DATA PROBLEMS PRESENT IN EXISTING LIBRARY VALUE CORRELATION RESEARCH AND SUGGESTS A PATHWAY TO SURMOUNTING EXISTING DATA OBSTACLES. THE PAPER ADVOCATES THE INTEGRATION OF LIBRARIES INTO INSTITUTIONAL LEARNING ANALYTICS SYSTEMS TO GAIN ACCESS TO MORE GRANULAR STUDENT LEARNING AND SUCCESS DATA. IT ALSO SUGGESTS USING LIBRARY-INFUSED LEARNING ANALYTICS DATA TO DISCOVER AND ACT UPON NEW LINKAGES THAT MAY REVEAL LIBRARY VALUE IN AN INSTITUTIONAL CONTEXT. DESIGN/METHODOLOGY/APPROACH - THE PAPER DESCRIBES A PATTERN PERVASIVE IN EXISTING ACADEMIC LIBRARY VALUE CORRELATION RESEARCH AND IDENTIFIES MAJOR DATA OBSTACLES TO FUTURE RESEARCH IN THIS VEIN. THE PAPER ADVOCATES LEARNING ANALYTICS AS ONE ROUTE TO ACCESS MORE USABLE AND REVEALING DATA. IT ALSO ACKNOWLEDGES SEVERAL CHALLENGES TO THE SUGGESTED APPROACH. FINDINGS - THIS PAPER DESCRIBES LEARNING ANALYTICS AS IT MAY APPLY TO AND SUPPORT CORRELATION RESEARCH ON ACADEMIC LIBRARY VALUE. WHILE THIS PAPER ADVOCATES EXPLORING THE INTEGRATION OF LIBRARY DATA AND INSTITUTIONAL DATA VIA LEARNING ANALYTICS INITIATIVES, IT ALSO DESCRIBES FOUR CHALLENGES TO THIS APPROACH INCLUDING LIBRARIAN CONCERNS RELATED TO THE USE OF INDIVIDUAL LEVEL DATA, THE TENSION BETWEEN CLAIMS OF CORRELATION AND CAUSATION IN LIBRARY VALUE RESEARCH, THE NEED TO DEVELOP INTEROPERABILITY STANDARDS FOR LIBRARY DATA AND ORGANIZATIONAL READINESS AND LEARNING ANALYTICS MATURITY ISSUES. ORIGINALITY/VALUE - THIS PAPER OUTLINES A PATH FORWARD FOR ACADEMIC LIBRARY VALUE RESEARCH THAT MAY OTHERWISE BE STYMIED BY EXISTING DATA DIFFICULTIES.
THE RAPIDLY EXPANDING, HIGHLY EVOLVING ONLINE COURSE MARKET (MASSIVE OPEN ONLINE COURSE OR MOOC) IS INFLUENTIAL IN ACADEMIA. MANY WELL-KNOWN MOOC PLATFORMS HAVE EMERGED OVER THE LAST DECADE. THERE IS A NEED TO COMPARE THE SUPPORT LEVEL OFFERED BY THESE PLATFORMS SO THAT EDUCATORS CAN DELIVER MATERIALS AND STUDENTS ARE ABLE TO LEARN BETTER. ADDITIONALLY, THERE IS A NEED TO EXPLORE WHAT KINDS OF DATA ARE GATHERED FROM THESE PLATFORMS TO EXPLOIT BIG DATA ANALYTICS OPPORTUNITIES. THIS PAPER EVALUATES THREE MAJOR ONLINE MOOC COURSE PROVIDERS NAMELY COURSERA, EDX, AND UDACITY. WE FIRST COMPARE THESE PLATFORMS IN TERMS OF PEDAGOGICAL SUPPORT AND IMPLEMENTED TECHNOLOGIES. WE THEN LOOK AT THE LITERATURE AND COMPARE LEARNING ANALYTICS SUPPORT AMONG THESE TOOLS THAT ARE CURRENTLY OFFERED. WE ALSO SUGGEST A SET OF ANALYTICS CONCEPTS THAT CAN AID INTERACTION DATA AND GENERATE GUIDANCE FOR EFFECTIVE TEACHING.
THE FOCUS OF THIS PAPER IS THE FIRST LOOK AND INTERPRETATION OF LEARNING ANALYTICS DATA FROM LEARNING MANAGEMENT SYSTEM (LMS) AT THE UNIVERSITY OF WEST BOHEMIA IN PILSEN (UWB). WE CLAIM THAT THERE ARE THREE TYPES OF GRANULARITY OF LMS DATA. THE FIRST TYPE IS TOP-LEVEL, WHICH DESCRIBES APPROACHES AND USAGE OF LMS AS A WHOLE. THE SECOND ONE IS COURSE-LEVEL, WHICH DEALS WITH THE BEHAVIOUR AND ACTIVITIES OF ALL USERS AS A WHOLE ON A SPECIFIC COURSE. AND THE LAST USER-TYPE, WHICH INTERPRETS THE ACTIVITIES OF USERS IN THE COURSE, AND LOOKS FOR COMMON PATTERNS OF BEHAVIOUR. THIS PAPER PRESENTS THE FIRST TWO TYPES OF GRANULARITY, BASED ON REAL DATA FROM THE UNIVERSITY LMS. WE ARE INSPIRED BY MANY PREVIOUS STUDIES FOCUSING ON LEARNING SYSTEMS OF THE LMS THAT OFTEN PAY ATTENTION ESPECIALLY TO ACADEMIC SUCCESS PREDICTION OR AT-RISK STUDENT IDENTIFICATION (E.G. SMITH ET AL. 2012, JAYAPRAKASH ET AL., 2014, BAKER ET AL., 2015). THESE FINDINGS FORM THE BASIS FOR FURTHER RESEARCH ON IDENTIFYING USER BEHAVIOUR ON THE COURSE AND IDENTIFYING STUDENTS AT RISK OF LEARNING FAILURE.
IN RECENT YEARS, LEARNING ANALYTICS (LA) IS FINDING MORE AND MORE PRACTICAL ADOPTION, ALONGSIDE OF CONTINUED RESEARCH INTEREST. HOWEVER, QUESTIONS ABOUT THE IMPACT OF LA APPLICATIONS AND THEIR UNDERPINNING IN EDUCATIONAL SCIENCE ARE STILL BEING RAISED, IMPEDING VIABILITY OF SOME LA PROJECTS AT LARGER SCALE. WITHIN THIS PAPER WE DESCRIBE TWO EXAMPLES USING STUDENT-FACING LA DASHBOARDS (LAD) DEPLOYED AT SCALE AT A RELATIVELY LOW COST. LEVERAGING DATA COLLECTED BY THE DASHBOARDS THEMSELVES, USAGE DATA (N = 4070 STUDENTS) AND IN-DASHBOARD MICROINTERACTIONS (N = 367 STUDENTS), WE TRY TO PUT THE IMPACT QUESTION IN PERSPECTIVE. WE SUGGEST THAT WHEN INVESTMENT IS KEPT LIMITED, A BUSINESS CASE WITH MODEST BUT REALISTIC EXPATIATIONS OF RETURNS MAY BE FEASIBLE.
THE FUTURE OF EDUCATIONAL TECHNOLOGY HAS BEEN ENVISIONED TO HAVE INCREASING FOCUS ON SIMULATIONS, GAME BASED LEARNING, VIRTUAL LEARNING ENVIRONMENTS AND VIRTUAL WORLDS. THE TECHNOLOGIES AIM TO PROVIDE AUTHENTIC LEARNING AND ENABLE DEEPER, MORE COMPLEX AND CONTEXTUAL UNDERSTANDING FOR STUDENTS. TO STUDY THE IMPACT OF VIRTUAL LEARNING ENVIRONMENTS FOR NATURAL SCIENCES AND ENGINEERING EDUCATION, WE HAVE DESIGNED AND IMPLEMENTED A VIRTUAL LABORATORY, LABLIFE3D, IN SECOND LIFE. TO DATE WE HAVE DESIGNED SIX VIRTUAL LABORATORY EXERCISES IN THE BIOLOGICAL SCIENCES AND CHEMISTRY AND ADDITIONALLY CREATED A SYSTEM TO GATHER BEHAVIORISTIC DATA DURING LABORATORY SIMULATIONS FOR THE PURPOSE OF LEARNING ANALYTICS. THIS PAPER PRESENTS THE DESIGN PROCESS OF LABORATORY EXERCISES AND DISCUSSES THE CONTENT-SPECIFIC LEARNING GOALS AND OUTCOMES. ADDITIONALLY, THIS PAPER DISCUSSES THE USE OF HEURISTIC USABILITY REVIEW USED TO IMPROVE THE VIRTUAL LEARNING ENVIRONMENT. LASTLY, THE RESULTS FROM STUDENT AND TEACHER INTERVIEWS ARE PRESENTED, TOGETHER WITH RESULTS OF THE LEARNING ANALYTICS STUDY. THE DISCUSSION ALSO INCLUDES STUDENT IDENTIFIED AFFORDANCES AND BARRIERS FOR LEARNING. WE CONCLUDE THAT AUTHENTIC AND DEEP LEARNING IS POSSIBLE WITHIN VIRTUAL WORLDS. FURTHERMORE, THE RESULTS OF THIS STUDY ARE NOT ONLY LIMITED TO VIRTUAL WORLDS, BUT COULD ALSO APPLY TO OTHER AREAS OF DIGITAL EDUCATIONAL TECHNOLOGY.
THE PROLIFERATION OF VARIED TYPES OF MULTI-USER INTERACTIVE SURFACES (SUCH AS DIGITAL WHITEBOARDS, TABLETOPS AND TANGIBLE INTERFACES) IS OPENING A NEW RANGE OF APPLICATIONS IN FACE-TO-FACE (F2F) CONTEXTS. THEY OFFER UNIQUE OPPORTUNITIES FOR LEARNING ANALYTICS (LA) BY FACILITATING MULTI-USER SENSEMAKING OF AUTOMATICALLY CAPTURED DIGITAL FOOTPRINTS OF STUDENTS' F2F INTERACTIONS. THIS PAPER PRESENTS AN ANALYSIS OF CURRENT RESEARCH EXPLORING LEARNING ANALYTICS ASSOCIATED WITH THE USE OF SURFACE DEVICES. WE USE A FRAMEWORK TO ANALYSE OUR FIRST-HAND EXPERIENCES, AND THE SMALL NUMBER OF RELATED DEPLOYMENTS ACCORDING TO FOUR DIMENSIONS: THE ORCHESTRATION ASPECTS INVOLVED; THE PHASES OF THE PEDAGOGICAL PRACTICE THAT ARE SUPPORTED; THE TARGET ACTORS; AND THE LEVELS OF ITERATION OF THE LA PROCESS. THE CONTRIBUTION OF THE PAPER IS TWOFOLD: 1) A SYNTHESIS OF CONCLUSIONS THAT IDENTIFY THE DEGREE OF MATURITY, CHALLENGES AND PEDAGOGICAL OPPORTUNITIES OF THE EXISTING APPLICATIONS OF LEARNING ANALYTICS AND INTERACTIVE SURFACES; AND 2) AN ANALYSIS FRAMEWORK THAT CAN BE USED TO CHARACTERISE THE DESIGN SPACE OF SIMILAR AREAS AND LA APPLICATIONS.
ONLINE DISCUSSION PLAYS AN INCREASINGLY SIGNIFICANT ROLE IN ASYNCHRONOUS ONLINE LEARNING ENVIRONMENTS. WHILE PREVIOUS ATTEMPTS HAVE BEEN MADE TO DEVELOP LEARNING ANALYTICS DASHBOARDS TO FACILITATE SUCH DISCUSSIONS, MOST OF THESE DASHBOARDS HAVE BEEN DESIGNED WITHOUT REFERENCE TO DATA OR VISUALIZATION TECHNIQUES THAT HAVE BEEN PROVEN TO MAKE ONLINE DISCUSSIONS MORE EFFECTIVE. THIS STUDY IDENTIFIED THE DIFFICULTIES AND INCONVENIENCES EXPERIENCED BY LEARNERS IN ONLINE DISCUSSION ACTIVITIES AND GENERATED A SET OF VISUAL DESIGN GUIDELINES FOR OVERCOMING THEM. APPLYING THESE GUIDELINES, A SET OF LEARNING ANALYTICS DASHBOARDS WERE DEVELOPED AND EVALUATED. THE STUDY WAS CONDUCTED ACCORDING TO PROTOTYPING METHODOLOGY, WHICH YIELDED FIVE PROTOTYPE DASHBOARDS THAT DISPLAY INFORMATION ON PARTICIPATION, INTERACTION, DISCUSSION CONTENT KEYWORDS, DISCUSSION MESSAGE TYPES, AND THE DISTRIBUTION OF DEBATE OPINIONS, RESPECTIVELY. THE DEVELOPED DASHBOARDS WERE THEN REVISED AND REFINED IN A THREE-STEP PROCESS: (1) EXPERT VALIDATION TO VERIFY THAT THE DASHBOARDS COMPLIED WITH THE VISUAL GUIDELINES AND PROVIDED LEARNERS WITH THE INFORMATION THEY NEEDED; (2) TESTS TO IDENTIFY USABILITY PROBLEMS, COLLECT QUALITATIVE AND QUANTITATIVE DATA, AND DETERMINE PARTICIPANT SATISFACTION; AND (3) USER EXPERIENCE EVALUATIONS TO DETERMINE HOW LEARNERS AND INSTRUCTORS PERCEIVED THEIR INTERACTIONS WITH THE DASHBOARDS. PRACTICAL AND EMPIRICAL DISCUSSIONS ARE PROVIDED BASED ON THE RESULTS, WHICH OFFER A VALUABLE BASE OF USER EXPERIENCE DATA THAT CAN BE USED IN FUTURE STUDIES.
IN THIS PAPER, WE PRESENT THE VISION OF AN OPEN SOURCE LEARNING ANALYTICS PLATFORM, ABLE TO HARVEST DATA FROM DIFFERENT SOURCES, INCLUDING E-LEARNING PLATFORMS AND ENVIRONMENTS, REGISTRAR'S INFORMATION SYSTEMS, ALUMNI SYSTEMS, ETC., SO AS TO PROVIDE ALL STAKEHOLDERS WITH THE NECESSARY FUNCTIONALITY TO MAKE DECISIONS ON THE LEARNING PROCESS. THE PLATFORM'S ARCHITECTURE IS MODULAR, ALLOWING THE INTRODUCTION OF NEW FUNCTIONALITY OR CONNECTION TO NEW SYSTEMS TO COLLECT NEEDED DATA. ALL DATA CAN BE ANALYZED AND PRESENTED THOUGH INTERACTIVE VISUALIZATIONS TO FIND CORRELATIONS BETWEEN METRICS, TO MAKE PREDICTIONS FOR STUDENTS OR STUDENT GROUPS, TO IDENTIFY BEST PRACTICES FOR INSTRUCTORS AND LET THEM EXPLORE 'WHAT-IF' SCENARIOS, TO OFFER STUDENTS PERSONALIZED RECOMMENDATIONS AND PERSONALIZED DETAILED FEEDBACK, ETC. OUR OBJECTIVE IS TO INFORM AND EMPOWER ALL STAKEHOLDERS TO IMPROVE THE LEARNING EXPERIENCE.
OVER A PERIOD OF 4.5 MONTHS, A HIGH SCHOOL CLASS PARTICIPATED IN INQUIRY PROJECTS TO SELF-DIRECTLY LEARN TOPICS FROM APPLIED BIOLOGY BY MAKING USE OF PLATFORMS AND TOOLS DEVELOPED IN THE COURSE OF THE EUROPEAN RESEARCH PROJECTS WESPOT (HTTP://WESPOT.NET/) AND LEA'S BOX (HTTP://LEAS-BOX.EU). OVERALL, 34 STUDENTS (24 FEMALE) AGING FROM 14 TO 17 YEARS (M = 15.06; SD = 0.78 YEARS) WERE ASKED TO SELFDIRECTLY ENGAGE WITH LEARNING RESOURCES. THE LEARNING RESOURCES HAVE BEEN EVALUATED BY THE STUDENTS ON HOW HELPFUL THEY WERE IN THEIR CURRENT LEARNING STATE AND BY THE TEACHER WITH REGARDS TO THE FOUR DIMENSIONS OF THE INDEX OF LEARNING STYLES QUESTIONNAIRE (ILS). THE STUDENTS FILLED OUT DECLARATIVE KNOWLEDGE PRE- AND POSTTESTS, THE ILS AND A LEARNING LOGBOOK FOR THE LEARNING RESOURCE EVALUATION AS WELL AS THE INTRINSIC MOTIVATION INVENTORY. HIGHLY SIGNIFICANT CORRELATIONS HAVE BEEN OBSERVED BETWEEN THE LEARNING ANALYTICS AND THE TEACHERS' EVALUATION OF THE EXERCISES CARRIED OUT BY THE STUDENTS, THE STUDENTS' SELF-ASSESSMENT OF THEIR EXERCISES AND THE NUMBER OF CONSUMED LEARNING RESOURCES. AN ANALYSIS OF VARIANCE INDICATE THAT FEMALE STUDENTS CONSUME MORE LEARNING RESOURCES AND SUBMIT MORE AND BETTER EVALUATED EXERCISES AND A POSITIVE IMPACT OF INTRINSIC MOTIVATION FOR THE LEARNING RESOURCE EVALUATION. THE QUANTITATIVE RESULTS ARE ENRICHED BY A QUALITATIVE INTERVIEW WITH THE TEACHER ON HIS EXPERIENCES.
THIS ARTICLE REPORTS ON A STUDY EXPLORING STUDENT PERSPECTIVES ON THE COLLECTION AND USE OF STUDENT DATA FOR LEARNING ANALYTICS. WITH DATA COLLECTED VIA A MIXED METHODS APPROACH FROM 2,051 STUDENTS ACROSS SIX AUSTRALIAN UNIVERSITIES, IT PROVIDES CRITICAL INSIGHTS FROM STUDENTS AS A KEY STAKEHOLDER GROUP. FINDINGS INDICATE THAT WHILE STUDENTS ARE GENERALLY COMFORTABLE WITH THE USE OF DATA TO SUPPORT THEIR LEARNING, THEY DO HAVE CONCERNS PARTICULARLY IN RELATION TO THE USE OF DEMOGRAPHIC DATA, LOCATION DATA AND DATA COLLECTED FROM WIRELESS NETWORKS, SOCIAL MEDIA AND MOBILE APPLICATIONS. TWO KEY THEMES EMERGED RELATED TO THE NEED FOR TRANSPARENCY TO SUPPORT INFORMED CONSENT AND PERSONAL-PROFESSIONAL BOUNDARY BEING CRITICAL. THIS SUPPORTS FINDINGS FROM OTHER RESEARCH, WHICH REFLECTS THE NEED FOR A NUANCED APPROACH WHEN PROVIDING INFORMATION TO STUDENTS ABOUT THE DATA WE COLLECT, INCLUDING WHAT WE ARE COLLECTING, WHY AND HOW THIS IS BEING USED. IMPLICATIONS FOR PRACTICE OR POLICY: WHEN IMPLEMENTING THE USE OF DASHBOARDS, INSTITUTIONS SHOULD IDEALLY INCLUDE OPPORTUNITIES FOR STUDENTS TO OPT IN AND OUT, RATHER THAN BEING SET SO THAT STUDENTS HAVE AGENCY OVER THEIR DATA AND LEARNING. WHEN UNDERTAKING WORK IN RELATION TO LEARNING ANALYTICS, STAFF NEED TO ENSURE THE FOCUS OF THEIR WORK RELATES TO STUDENT LEARNING RATHER THAN ACADEMIC RESEARCH. WHEN INSTITUTIONS AND ACADEMIC STAFF COLLECT AND USE STUDENT DATA (REGARDLESS OF THE PURPOSE FOR DOING SO), ALL ASPECTS OF THESE PROCESSES NEED TO BE TRANSPARENT TO STUDENTS.
PREDICTING STUDENT LEARNING OUTCOMES IS ONE OF THE PROMINENT THEMES IN LEARNING ANALYTICS RESEARCH. THESE STUDIES VARIED TO A SIGNIFICANT EXTENT IN TERMS OF THE TECHNIQUES BEING USED, THE CONTEXTS IN WHICH THEY WERE SITUATED, AND THE CONSEQUENT EFFECTIVENESS OF THE PREDICTION. THIS PAPER PRESENTED THE PRELIMINARY RESULTS OF A SYSTEMATIC REVIEW OF STUDIES IN PREDICTIVE LEARNING ANALYTICS. WITH THE GOAL TO FIND OUT WHAT METHODOLOGIES WORK FOR WHAT CIRCUMSTANCES, THIS STUDY WILL BE ABLE TO FACILITATE FUTURE RESEARCH IN THIS AREA, CONTRIBUTING TO RELEVANT SYSTEM DEVELOPMENTS THAT ARE OF PEDAGOGIC VALUES.
MULTIMODAL LEARNING ANALYTICS PROVIDES RESEARCHERS NEW TOOLS AND TECHNIQUES TO CAPTURE DIFFERENT TYPES OF DATA FROM COMPLEX LEARNING ACTIVITIES IN DYNAMIC LEARNING ENVIRONMENTS. THIS PAPER INVESTIGATES HIGH-FIDELITY SYNCHRONISED MULTIMODAL RECORDINGS OF SMALL GROUPS OF LEARNERS INTERACTING FROM DIVERSE SENSORS THAT INCLUDE COMPUTER VISION, USER GENERATED CONTENT, AND DATA FROM THE LEARNING OBJECTS (LIKE PHYSICAL COMPUTING COMPONENTS OR LABORATORY EQUIPMENT). WE PROCESSED AND EXTRACTED DIFFERENT ASPECTS OF THE STUDENTS' INTERACTIONS TO ANSWER THE FOLLOWING QUESTION: WHICH FEATURES OF STUDENT GROUP WORK ARE GOOD PREDICTORS OF TEAM SUCCESS IN OPEN-ENDED TASKS WITH PHYSICAL COMPUTING? THE ANSWER TO THE QUESTION PROVIDES WAYS TO AUTOMATICALLY IDENTIFY THE STUDENTS PERFORMANCE DURING THE LEARNING ACTIVITIES.
BIG DATA HAS CHANGED THE APPROACH IN DESIGNING AN E-LEARNING. WHEN STUDENTS INTERACT WITH E-LEARNING CONTENT, AUTOMATICALLY GENERATING DATA, WE CAN COLLECT AND TRACE THEIR LEARNING TRACKS. THE DATA IS PROCESSED AND ANALYZED, THEN USED TO UNDERSTAND THE BEHAVIORAL CHARACTERISTICS OF THE USER OR STUDENT TO ENABLE A PERSONALIZED LEARNING EXPERIENCE. IN THIS STUDY, LEARNING ANALYTICS DASHBOARD WAS USED TO IMPROVE LEARNING INTERACTION WHICH IMPACTS THE LEARNING SUCCESSFULNESS. TESTS WERE CONDUCTED ON 67 STUDENTS IN THE EDUCATIONAL TECHNOLOGY DEPARTMENT OF THE STATE UNIVERSITY OF MALANG AND DISTRIBUTED QUESTIONNAIRES AND THEN ANALYZED USING DESCRIPTIVE METHODS. THE RESULT IS THAT MOST OF THE STUDENTS WHO TAKE ONLINE LEARNING USING THE LEARNING ANALYTICS DASHBOARD FIND IT HELPFUL TO CARRY OUT SELF-EVALUATIONS OF THEIR INTERACTION HENCE THEY CAN MANAGE THEIR LEARNING. THE RESULTS OF THE STUDY SHOWED THAT MOST PARTICIPANTS AGREED THAT LAD COULD PROVIDE INFORMATION TO THEM REGARDING THEIR INTERACTIONS WITH LEARNING CONTENT (M = 4.15, SD = 0.557), LEARNING ENVIRONMENT (M = 4.13, SD = 0.457), AS WELL AS THE PARTICIPANTS COULD CONDUCT PROBLEM IDENTIFICATION DURING THEIR LEARNING PROCESS (M = 3.88, SD = 0.477).
IN THIS ARTICLE WE INVESTIGATE THE EFFECTIVENESS OF LEARNING ANALYTICS FOR IDENTIFYING AT-RISK STUDENTS IN HIGHER EDUCATION INSTITUTIONS USING DATA OUTPUT FROM AN IN-SITU LEARNING ANALYTICS PLATFORM. AMONGST OTHER THINGS, THE PLATFORM GENERATES 'NO-ENGAGEMENT' ALERTS IF STUDENTS HAVE NOT ENGAGED WITH ANY OF THE DATA SOURCES MEASURED FOR 14 CONSECUTIVE DAYS. WE TESTED THE RELATIONSHIP BETWEEN THESE ALERTS AND STUDENT OUTCOMES FOR TWO COHORTS OF FIRST-YEAR UNDERGRADUATE STUDENTS. WE ALSO COMPARED THE EFFICIENCY OF USING THESE ALERTS TO IDENTIFY STUDENTS AT RISK OF POORER OUTCOMES WITH THE EFFICIENCY OF USING DEMOGRAPHIC DATA, USING WIDENING PARTICIPATION STATUS AS A CASE STUDY EXAMPLE. THE NO-ENGAGEMENT ALERTS WERE FOUND TO BE MORE EFFICIENT AT SPOTTING STUDENTS NOT PROGRESSING AND NOT ATTAINING THAN DEMOGRAPHIC DATA. IN ORDER TO INVESTIGATE THE EFFICACY OF LEARNING ANALYTICS FOR ADDRESSING DIFFERENTIAL STUDENT OUTCOMES FOR DISADVANTAGED GROUPS, THE TEAM ALSO ANALYSED THE LIKELIHOOD OF STUDENTS WITH WIDENING PARTICIPATION STATUS GENERATING ALERTS COMPARED WITH THEIR NON-WIDENING PARTICIPATION COUNTERPARTS. THE ODDS OF STUDENTS WITH WIDENING PARTICIPATION STATUS GENERATING AN ALERT WERE ON AVERAGE 43% HIGHER, DEMONSTRATING THE POTENTIAL OF SUCH A SYSTEM TO PREFERENTIALLY TARGET SUPPORT AT DISADVANTAGED GROUPS WITHOUT NEEDING TO TARGET DIRECTLY BASED ON IMMUTABLE FACTORS SUCH AS THEIR SOCIO-ECONOMIC BACKGROUND.
LEARNING ANALYTICS (LA) HAS BEEN INCREASINGLY USING IN TEACHING AND LEARNING. HOWEVER, IN THE FIELD OF APPLIED LINGUISTICS, THE USE OF LA HAS ONLY BEGUN TO TOUCH THE SURFACE. THERE IS A NEED FOR UNDERSTANDING HOW LA AND LEARNING DESIGN (LD) INFLUENCE EACH OTHER AND PROVIDE USEFUL INFORMATION TO LANGUAGE TEACHERS IN THE CONTEXT OF SPECIFIC COURSES OR LEARNING ENVIRONMENTS. IN THIS STUDY, A RETROSPECTIVE ANALYSIS WAS CONDUCTED TO IDENTIFY THE FACTORS INFLUENCING LA FOR COLLABORATIVE LANGUAGE LEARNING IN CLASSROOMS, FROM A HOLISTIC PERSPECTIVE BY INTEGRATING LA, LD, AND TEACHER INQUIRY. THE FINDINGS SUGGESTED THAT (1) LA FOCUSED ON INTERACTIONS CAN INFORM PEDAGOGICAL REFINEMENT EFFECTIVELY WHEN LD IN LANGUAGE LEARNING IS PREMISED ON SOCIAL CONSTRUCTIVIST THEORIES; (2) LA SUPPORTED TEACHER INQUIRY AND LD ON CONDITION THAT THE TEACHER HOLDS INNOVATION-ORIENTED BELIEFS AND THE PARTICIPATORY CULTURE BETWEEN TEACHERS AND RESEARCHERS. THE STUDY PROVIDED INSIGHTS INTO THE USE OF LA IN COLLABORATIVE LANGUAGE LEARNING CLASSROOMS THAT FOCUS ON COLLECTING AND EVALUATING LEARNERS' INTERACTION PROCESS BEYOND GLEANING LINGUISTIC OR BEHAVIORAL FACTS. PROFESSIONAL DEVELOPMENT IMPLICATIONS AND FUTURE RESEARCH ARE ALSO ADDRESSED.
EDUCATORS CONTINUE TO FACE SIGNIFICANT CHALLENGES IN PROVIDING HIGH QUALITY, POST-SECONDARY INSTRUCTION IN LARGE CLASSES INCLUDING: MOTIVATING AND ENGAGING DIVERSE POPULATIONS (E.G., ACADEMIC ABILITY AND BACKGROUNDS, GENERATIONAL EXPECTATIONS); AND PROVIDING HELPFUL FEEDBACK AND GUIDANCE. RESEARCHERS INVESTIGATE SOLUTIONS TO THESE KINDS OF CHALLENGES FROM ALTERNATIVE PERSPECTIVES, INCLUDING LEARNING ANALYTICS (LA). HERE, LA TECHNIQUES ARE APPLIED TO EXPLORE THE DATA COLLECTED FOR A LARGE, FLIPPED INTRODUCTORY PROGRAMMING CLASS TO (1) IDENTIFY GROUPS OF STUDENTS WITH SIMILAR PATTERNS OF PERFORMANCE AND ENGAGEMENT; AND (2) PROVIDE THEM WITH MORE MEANINGFUL APPRAISALS THAT ARE TAILORED TO HELP THEM EFFECTIVELY MASTER THE LEARNING OBJECTIVES. TWO STUDIES ARE REPORTED, WHICH APPLY CLUSTERING TO ANALYZE THE CLASS POPULATION, FOLLOWED BY AN ANALYSIS OF A SUBPOPULATION WITH EXTREME BEHAVIOURS.
THIS PAPER PROVIDES DOCUMENTATION ON DATASET RESOURCES FOR ESTABLISHING A NEW RESEARCH AREA CALLED MULTIMODAL LEARNING ANALYTICS (MMLA). RESEARCH ON THIS TOPIC HAS THE POTENTIAL TO TRANSFORM THE FUTURE OF EDUCATIONAL PRACTICE AND TECHNOLOGY, AS WELL AS COMPUTATIONAL TECHNIQUES FOR ADVANCING DATA ANALYTICS. THE MATH DATA CORPUS INCLUDES HIGH-FIDELITY TIME-SYNCHRONIZED MULTIMODAL DATA RECORDINGS (SPEECH, DIGITAL PEN, IMAGES) ON COLLABORATING GROUPS OF STUDENTS AS THEY WORK TOGETHER TO SOLVE MATHEMATICS PROBLEMS THAT VARY IN DIFFICULTY LEVEL. THE MATH DATA CORPUS RESOURCES INCLUDE INITIAL CODING OF PROBLEM SEGMENTATION, PROBLEM-SOLVING CORRECTNESS, AND REPRESENTATIONAL CONTENT OF STUDENTS' WRITING. THESE RESOURCES ARE MADE AVAILABLE TO PARTICIPANTS IN THE DATA-DRIVEN GRAND CHALLENGE FOR THE SECOND INTERNATIONAL WORKSHOP ON MULTIMODAL LEARNING ANALYTICS. THE PRIMARY GOAL OF THIS EVENT IS TO ANALYZE COHERENT SIGNAL, ACTIVITY, AND LEXICAL PATTERNS THAT CAN IDENTIFY DOMAIN EXPERTISE AND CHANGE IN DOMAIN EXPERTISE EARLY, RELIABLY, AND OBJECTIVELY, AS WELL AS LEARNING-ORIENTED PRECURSORS. AN ADDITIONAL AIM IS TO BUILD AN INTERNATIONAL RESEARCH COMMUNITY IN THE EMERGING AREA OF MULTIMODAL LEARNING ANALYTICS BY ORGANIZING A SERIES OF WORKSHOPS THAT BRING TOGETHER MULTIDISCIPLINARY SCIENTISTS TO WORK ON MMLA TOPICS.
FROM THE PERSPECTIVE OF LEARNING AND EDUCATIONAL TECHNOLOGIES, ACADEMIC ADVISING HAS BEEN ONE OF THE MOST OVERLOOKED ASPECTS OF ACADEMIC SUPPORT SYSTEMS, DESPITE BEING CRITICAL FOR THE LEARNING PROCESS AND FINAL SUCCESS OF STUDENTS. THE MAJORITY OF HIGHER EDUCATION INSTITUTIONS PROVIDES SIMPLE TECHNICAL SUPPORT TO ACADEMIC ADVISERS WITH BASIC DESCRIPTIVE STATISTICS. THIS ARTICLE PRESENTS THE GENERAL DESIGN AND IMPLEMENTATION OF A LEARNING ANALYTICS DASHBOARD FOR ADVISERS (LADA), TO SUPPORT THE DECISION-MAKING PROCESS OF ACADEMIC ADVISERS THROUGH COMPARATIVE AND PREDICTIVE ANALYSIS. MOREOVER, THIS WORK EVALUATES THE USE OF THIS TOOL TO SUPPORT DECISION-MAKING OF ACTUAL ADVISERS IN TWO DIFFERENT HIGHER EDUCATION INSTITUTIONS (UNIVERSITY A, UNIVERSITY B), COMPARED WITH MORE TRADITIONAL PROCEDURES AND TOOLS. RESULTS INDICATE THAT LADA ENABLES EXPERT ADVISERS TO EVALUATE SIGNIFICANTLY MORE SCENARIOS (MEDIAN = 2), ESPECIALLY FOR HIGH ADVISING DIFFICULTY CASES WITH STUDENTS THAT FAILED MANY COURSES (MEDIAN(A) = 3, MEDIAN(B) = 2.5), IN A NOT-SIGNIFICANTLY DIFFERENT AMOUNT OF TIME. FOR INEXPERIENCED ADVISERS, LADA IS PERCEIVED AS A VALUABLE TOOL FOR MORE ACCURATE AND EFFICIENT DECISION-MAKING, AS THEY WERE ABLE TO MAKE INFORMED DECISIONS IN A SIMILAR AMOUNT OF TIME COMPARED TO THE EXPERTS. THESE RESULTS ARE ENCOURAGING FOR FURTHER DEVELOPMENTS IN THE FIELD.
STUDENTS' ASSESSMENT BASED ON SCORE IS NO LONGER RELEVANT TODAY, THIS IS AFTER THE MAJOR CHANGES OF STUDENTS' WAY OF LIFE AND HOW THEY RECEIVE KNOWLEDGE, ESPECIALLY AFTER BEING IMMERSED IN SOCIAL MEDIAS AND MODERN TECHNOLOGIES. IN LIGHT OF THESE SIGNIFICANT CHANGES, NEW METHODS OF STUDENTS' ASSESSMENT MUST BE ADOPTED IN ORDER TO RESPOND TO THE REQUIREMENTS OF STUDENTS OF THE 21ST CENTURY, AND TO PROVIDE A REAL EVALUATION THAT REFLECTS THEIR KNOWLEDGE, PERFORMANCE, AND SKILLS. THEREFORE, THE COMPETENCY-BASED ASSESSMENT MIGHT BE A GOOD CANDIDATE TO MEET THESE REQUIREMENTS. THE SCOPE OF THIS PAPER IS TO ADDRESS THE ISSUE OF COMPETENCY MODELLING IN TECHNOLOGY-ENHANCED LEARNING SYSTEMS IN ORDER TO DISCOVER IMPLICIT COMPETENCIES HIDDEN BEHIND STUDENTS' ACTIVITIES AND HOW TO TRANSLATE THEM INTO ACQUIRED COMPETENCIES. TO FACE THESE CHALLENGES, THE AUTHORS PROPOSED AN APPROACH OF SEMANTIC ANALYTICS OF STUDENTS' ACTIVITIES DATA. THEREFORE, THEY MODELLED ALL KNOWLEDGE ABOUT STUDENTS AND THEIR COMPETENCIES BY SEMANTIC WEB AND ONTOLOGICAL REPRESENTATION; THEN STUDENTS' MODELS HAVE BEEN SUBJECTED TO A SET OF LEARNING ANALYTICS APPROACHES IN ORDER TO ANALYZE AND EVALUATE THE GENERATED DATA ACCORDING TO THE ASSESSMENT MODEL. AN EXPERIMENTAL STUDY INDICATES THAT THIS APPROACH IS EFFICIENT AND EXPECTED TO SHOW GREAT ADVANTAGES IN EVALUATING STUDENTS' COMPETENCIES.
LEARNING ANALYTICS (LA) IS A PROMISING TOOL TO SUPPORT LEARNING AND RETENTION IN STUDIES; SO FAR, MANY APPLICATIONS HAVE BEEN DEVELOPED TO MAKE USE OF BIG DATA SAVED AUTOMATICALLY WHILE USING ONLINE LEARNING MANAGEMENT SYSTEMS. HOWEVER, IT IS NOT CLEAR HOW DIFFERENT APPLICATIONS SUPPORT THE CONTEMPORARY LEARNING APPROACH OR HOW THEY ARE VALUED BY BOTH TEACHERS AND STUDENTS. THEREFORE, A QUALITATIVE STUDY WAS CONDUCTED TO EXPLORE UNIVERSITY TEACHERS' AND STUDENTS' EXPECTATIONS ON LA. SEVERAL SCENARIOS WERE PROVIDED TO THEM IN ORDER TO FEED THEIR IDEAS ABOUT THE POTENTIAL OF LA. THE RESULTS WERE LINKED TO THE CHARACTERISTICS OF THE CONTEMPORARY LEARNING APPROACH. THIS SHOWED THAT WHILE SELF-REGULATION IS THE MOST OFTEN SUPPORTED OBJECTIVE ENHANCED BY LA, COLLABORATION SKILLS AND SUBJECTIVE WELL-BEING NEED MUCH MORE ATTENTION. IT WAS ALSO FOUND THAT STUDENTS WERE MORE POSITIVE THAN TEACHERS IN STARTING TO USE LA AND TEACHERS' OPINIONS VARIED GREATLY.
SERIOUS GAMES (SGS) ARE BECOMING POPULAR WORLDWIDE TO EDUCATE, TRAIN AND INFORM OTHER THAN ENTERTAINMENT BUT THE ABSENCE OF SGS IS CLEARLY NOTICEABLE IN BANGLADESH REGION. ALTHOUGH THERE EXIST A FEW EDUCATIONAL GAMES IN BANGLADESH, THOSE ARE NOT SGS BY THEIR CHARACTERISTICS. SGS ARE APPLICABLE IN MANY SECTORS SUCH AS HEALTHCARE, SECURITY, EDUCATION, POLITICS, AND MARKETING. IN THIS PAPER, WE DEMONSTRATE THE USE OF SGS COMBINED WITH LEARNING ANALYTICS (LA) IN THE PRODUCT MARKETING DOMAIN. LA IS A SYSTEM FOR MEASURING, COLLECTING, ANALYZING AND REPORTING DATA ABOUT PLAYERS. PLAYERS ENGAGE WITH SGS FOR ENTERTAINMENT, CREATE RAW DATA AND OUR OBJECTIVE IS TO COLLECT THOSE DATA, REPRESENT THOSE IN A MEANINGFUL WAY TO REDUCE TYPICAL MARKETING COST AND TIME WITH THE HELP OF LA TECHNIQUE. WE ALSO PRESENT A CASE STUDY WHICH USES LA TECHNIQUES WITH A SG TO SHOW HOW USERS' DATA CAN BE USED IN PRODUCT MARKETING FOR A CONSUMER PRODUCT IN BANGLADESH.
INTEROPERABILITY STANDARDS ARE KEY ENABLERS FOR WIDESPREAD ADOPTION OF LEARNING TECHNOLOGIES, E.G., NEW DATA-DRIVEN ANALYTICS OF LEARNING, AN APPLICATION DOMAIN EXPLORED IN A CASE STUDY PRESENTED IN THIS PAPER. STANDARDS-MAKING IS A DESIGN PRACTICE THAT RELIES ON INPUT FROM RESEARCH AND END-USERS, INVOLVING EXPERTS THAT REPRESENT DIVERSE STAKEHOLDERS SPREAD ALL OVER THE GLOBE. HOWEVER, THE STANDARDS-SETTING CULTURE AND FORMAL RULES ARE SOMETIMES AT ODDS WITH THE CULTURE AND PRACTICE OF RESEARCH. BASED ON PREVIOUS RESEARCH IDENTIFYING LACK OF OPENNESS AND TRANSPARENCY, AND A SUBOPTIMAL INTERACTION WITH ACADEMIC RESEARCH AS ISSUES THAT COULD EXPLAIN LACK OF SUCCESS IN A EUROPEAN SETTING, THIS PAPER STUDIES HOW AN ONGOING INTERNATIONAL STANDARDS PROJECT ON PRIVACY AND DATA PROTECTION POLICIES FOR LEARNING ANALYTICS HAS INTERACTED WITH AN INTERNATIONAL ACADEMIC RESEARCH COMMUNITY. THE RESULTS OF THIS STUDY SHOW THAT ESTABLISHING FEEDBACK LOOPS BETWEEN STANDARDISATION, RESEARCH, AND DEVELOPMENT IS ESSENTIAL IN ORDER TO PRODUCE RESULTS.
THE PROFESSIONAL DEVELOPMENT OF EXPERIENCED TEACHERS HAS RECEIVED CONSIDERABLY LESS ATTENTION THAN THAT OF NOVICE TEACHERS. THIS STUDY FOCUSES ON FOUR EXPERIENCED SECONDARY MATHEMATICS TEACHERS IN SHANGHAI, CHINA, WITH TWO PARTICIPATING IN A YEAR-LONG PROFESSIONAL DEVELOPMENT PROGRAM (TREATMENT TEACHERS) AND THE OTHER TWO RECEIVED CONVENTIONAL KNOWLEDGE-BASED PROFESSIONAL DEVELOPMENT (COMPARISON TEACHERS). THE PROGRAM INTRODUCED PRODUCTIVE CLASSROOM TALK SKILLS WHICH CAN FACILITATE TEACHERS' FORMATIVE ASSESSMENT OF STUDENT LEARNING DURING CLASS. TO ENCOURAGE TEACHERS TO REFLECT ON THEIR CLASSROOM DISCOURSE WHEN REVIEWING RECORDINGS OF THEIR TEACHING, WE USED VISUAL LEARNING ANALYTICS WITH THE TREATMENT TEACHERS AND THEORIZED THE USE OF THIS TECHNOLOGY WITH ACTIVITY THEORY. AFTER COMPLETING THE PROGRAM, THE TREATMENT TEACHERS WERE BETTER ABLE TO USE PRODUCTIVE TALK MOVES TO ELICIT STUDENT RESPONSES AND TO PROVIDE TIMELY FORMATIVE FEEDBACK ACCORDINGLY. SPECIFICALLY, THE PERCENTAGE OF WORD CONTRIBUTIONS IN LESSONS FROM STUDENTS AND THE LENGTH OF THEIR RESPONSES INCREASED NOTICEABLY. QUALITATIVE FINDINGS SUGGEST THAT THE USE OF VISUAL LEARNING ANALYTICS MEDIATED THE TREATMENT TEACHERS AND IMPROVED CLASSROOM DISCOURSE. BASED ON THESE FINDINGS AND ACTIVITY THEORY, WE PROVIDE RECOMMENDATIONS FOR FUTURE USE OF VISUAL LEARNING ANALYTICS TO IMPROVE TEACHERS' CLASSROOM TALK AND DESIGNING PROFESSIONAL DEVELOPMENT ACTIVITIES FOR EXPERIENCED TEACHERS.
THERE HAS BEEN A GROWING INTEREST IN HOW TEACHING MIGHT BE INFORMED BY LEARNING DESIGN (LD), WITH A PROMISING METHOD FOR INVESTIGATING LD BEING OFFERED BY THE EMERGING FIELD OF LEARNING ANALYTICS (LA). IN THIS STUDY, WE USED A NOVEL LA FOR LD METHODOLOGY TO INVESTIGATE THE IMPLEMENTATION OF LD IN AN ONLINE DISTANCE LEARNING CONTEXT. A KEY INNOVATION IS THE FOCUS ON PATTERNS OF LD. USING DATA FROM THE VIRTUAL LEARNING ENVIRONMENT, OUTCOMES DATA, AND SELF-REPORTS, FOR 47,784 STUDENTS, WE INVESTIGATED THE IMPACT OF THOSE PATTERNS ON STUDENT BEHAVIOUR, PASS RATES AND SATISFACTION. A SECOND INNOVATION INVOLVES SOCIAL NETWORK ANALYSIS. OUR STUDY REVEALED THAT DIFFERENT PATTERNS OF LD WERE ASSOCIATED WITH STATISTICALLY SIGNIFICANT DIFFERENCES IN BEHAVIOUR, BUT NOT IN PASS RATES OR SATISFACTION. NONETHELESS, THE STUDY HIGHLIGHTS THAT APPLYING LA TO LD MIGHT, IN A VIRTUOUS CIRCLE, CONTRIBUTE TO THE VALIDITY AND EFFECTIVENESS OF BOTH, AND TO THE ENHANCEMENT OF ONLINE DISTANCE LEARNING.
ADAPTIVE LEARNING GIVES LEARNERS CONTROL OF CONTEXT, PACE, AND SCOPE OF THEIR LEARNING EXPERIENCE. THIS STRATEGY CAN BE IMPLEMENTED IN ONLINE LEARNING BY USING THE "ADAPTIVE RELEASE'' FEATURE IN LEARNING MANAGEMENT SYSTEMS. THE PURPOSE OF THIS STUDY WAS TO USE LEARNING ANALYTICS RESEARCH METHODS TO EXPLORE THE EXTENT TO WHICH THE ADAPTIVE RELEASE FEATURE AFFECTED STUDENT BEHAVIOR IN THE ONLINE ENVIRONMENT AND COURSE PERFORMANCE. EXISTING DATA FROM TWO SECTIONS OF AN ONLINE PRE-SERVICE TEACHER EDUCATION COURSES FROM A SOUTHEASTERN UNIVERSITY WERE ANALYZED FOR THIS STUDY. BOTH COURSES WERE TAUGHT BY THE SAME INSTRUCTOR IN A 15 WEEKS TIME PERIOD. ONE SECTION WAS DESIGNED WITH THE ADAPTIVE RELEASE FEATURE FOR CONTENT RELEASE AND THE OTHER DID NOT HAVE THE ADAPTIVE RELEASE FEATURE. ALL OTHER ELEMENTS OF THE COURSE WERE THE SAME. DATA FROM FIVE INTERACTION MEASURES WAS ANALYZED (LOGINS, TOTAL TIME SPENT, AVERAGE TIME PER SESSION, CONTENT MODULES ACCESSED, TIME BETWEEN MODULE OPEN AND ACCESS) TO EXPLORE THE EFFECT OF THE ADAPTIVE RELEASE FEATURE. THE FINDINGS INDICATED THAT THERE WAS A SIGNIFICANT DIFFERENCE BETWEEN THE USE OF ADAPTIVE RELEASE AND AVERAGE LOGIN SESSION. CONSIDERED AS THE AVERAGE TIME OF MODULE ACCESS ACROSS THE ENTIRE COURSE, ADAPTIVE RELEASE DID NOT SYSTEMATICALLY CHANGE WHEN STUDENTS ACCESSED COURSE MATERIALS. THE FINDINGS ALSO INDICATED SIGNIFICANT DIFFERENCES BETWEEN THE EXPERIMENTAL AND CONTROL COURSES, ESPECIALLY FOR THE FIRST COURSE MODULE. THIS STUDY HAS IMPLICATIONS FOR INSTRUCTORS AND INSTRUCTIONAL DESIGNERS WHO DESIGN BLENDED AND ONLINE COURSES.
THE DESIGN OF SOCIAL LEARNING ANALYTICS (SLA) TOOLS HAS BECOME A PRACTICAL MEANS TO MAKE AVAILABLE LEARNING INFORMATION WITH A GOAL TO IMPROVE STUDENTS' REGULATION, REFLECTION, AND ENGAGEMENT IN ONLINE LEARNING. THIS DESIGN-BASED RESEARCH USES THE MULTI-METHOD ANALYTICS TO ITERATIVELY DESIGN, IMPLEMENT, AND MODIFY THE SLA TOOL THAT MAKES AVAILABLE SOCIAL, TOPIC, AND COGNITIVE INFORMATION TO STUDENTS IN ONLINE DISCUSSIONS. THIS TOOL DEMONSTRATES STUDENTS' ONLINE DISCUSSIONS FROM SOCIAL, SEMATIC, AND COGNITIVE ASPECTS. THIS RESEARCH FURTHER USES ONLINE QUESTIONNAIRE AND INTERVIEW METHODS TO INVESTIGATE STUDENTS' PERCEPTIONS ABOUT THE TOOL'S USEFULNESS AND USABILITY. AFTER EXPERIENCING THIS TOOL, STUDENTS PROPOSED CRITICAL FEEDBACK FOR TOOL IMPROVEMENT FROM THE NETWORK VISUALIZATION, PEER REFERENCE, AND EXPLANATION SUPPORT PERSPECTIVES. OVERALL, THE SLA TOOL IS A BENEFICIAL TOOL TO FOSTER STUDENTS' AWARENESS OF THEIR OWN LEARNING AND PEER LEARNING PROCESS TO IMPROVE STUDENTS' REGULATION AND REFLECTION OF LEARNING AND EVENTUALLY IMPROVE STUDENT ENGAGEMENT IN ONLINE DISCUSSIONS.
DIFFERENT SECTORS HAVE SOUGHT TO TAKE ADVANTAGE OF OPPORTUNITIES TO INVEST IN BIG DATA ANALYTICS AND NATURAL LANGUAGE PROCESSING, IN ORDER TO IMPROVE THEIR PRODUCTIVITY AND COMPETITIVENESS. CURRENT CHALLENGES FACING THE HIGHER EDUCATION SECTOR INCLUDE A RAPIDLY CHANGING AND EVOLVING ENVIRONMENT, WHICH NECESSITATES THE DEVELOPMENT OF NEW WAYS OF THINKING. INTEREST HAS THEREFORE INCREASED IN ANALYTICS AS PART OF THE SOLUTION TO MANY ISSUES IN HIGHER EDUCATION, INCLUDING RATE OF STUDENT ATTRITION AND LEARNER SUPPORT. THIS STUDY PROVIDES A COMPREHENSIVE DISCUSSION ON BIG DATA, LEARNING ANALYTICS AND USE OF NLP IN HIGHER EDUCATION. IN ADDITION, IT INTRODUCES AN INTEGRATED LEARNING ANALYTICS SOLUTION LEVERAGING A DISTRIBUTED TECHNOLOGY SYSTEM CAPABLE OF SUPPORTING ACADEMIC AUTHORITIES AND ADVISORS AT EDUCATIONAL INSTITUTIONS IN MAKING DECISIONS CONCERNING INDIVIDUAL STUDENTS.
A REAL-TIME LEARNING ANALYTICS SYSTEM IS PROPOSED FOR IN-CLASSROOM USE. WE USED AN C-LEARNING SYSTEM AND AN E-BOOK SYSTEM TO COLLECT REAL-TIME LEARNING ACTIVITIES DURING LECTURES. THE COLLECTED LOGS WERE ANALYZED AND PRESENTED VISUALLY ON A WEB BASED SYSTEM FOR THE TEACHER. THE TEACHER CAN MONITOR HOW MANY STUDENTS ARE VIEWING THE SAME PAGE AS THE TEACHER, WHETHER THEY ARE FOLLOWING THE EXPLANATION, OR IF THEY ARE READING PREVIOUS OR SUBSEQUENT PAGES. THROUGH A CASE STUDY, WE CONFIRMED THE EFFECTIVENESS OF THE REAL-TIME LEARNING ANALYTICS SYSTEM, IN TERMS OF HIGH SYNCHRONIZATION BETWEEN THE TEACHER AND THE STUDENTS, I.E., THAT THE MAJORITY OF STUDENTS FOLLOWED THE TEACHER'S EXPLANATION AND ADDED MORE BOOKMARKS, HIGHLIGHTS, OR NOTES ON THE E-BOOK, COMPARED WITH THE CONTROL GROUP WHERE THE TEACHER DID NOT USE OUR SYSTEM.
IN THE DIGITAL ERA, THE TEACHER ASSUMES VERY DIVERSE ROLES AMONG WHICH ARE TO BE AN ADVISER, A GENERATOR OF MULTIMEDIA CONTENT, AND MORE RECENTLY A DATA ANALYST. BIG DATA ANALYTICS MAY PLAY A MAJOR ROLE IN HIGHER EDUCATION FOR ALL THE AGENTS INVOLVED, THE TEACHERS AND EDUCATORS, THE STUDENTS THEMSELVES AND THE MANAGERS OR HEADS OF UNIVERSITY CENTERS. THIS PAPER APPLIES LEARNING ANALYTICS TO THE SUBJECT OF THEORY OF MACHINES AND STRENGTH OF MATERIALS OF THE BACHELOR'S DEGREE IN CHEMICAL ENGINEERING AT UNIVERSITAT POLITECNICA DE VALENCIA (SPAIN). THE AIM OF ANALYZING THE AVAILABLE INFORMATION IS TO IMPROVE TEACHERS' ACTIONS AND COMMUNICATION, TO ENHANCE RESOURCE EFFICIENCY, TO ASSESS CLASSROOM PROCEDURES, THE ACHIEVEMENT OF TRANSVERSAL COMPETENCES, THE STUDENT TYPOLOGY AND THEIR RESULTS, OR THE ATTITUDES AND COMMITMENT THEY ACQUIRE WITH THE SUBJECT TAUGHT. RESULTS SHOW THE EXISTENCE OF NICHES WITH COMPETITIVE ADVANTAGES, IMPROVEMENTS IN THE QUALITY AND PERFORMANCE OF THE TEACHING-LEARNING EXPERIENCE.
ONE WAY TO IMPROVE THE EDUCATION OF OUR STUDENTS IS TO PROMOTE THE CREATION OF MASS COLLABORATIVE PROJECTS. THESE PROJECTS, ALSO SEEN AS LEARNING PRODUCTS, WOULD ALSO HELP TO BETTER SCALE OUR LEARNING EXPERIENCES (MASSIVE OPEN ONLINE COURSES) AND GENERATE COLLECTIVE VALUE FROM THE HOURS AND COGNITIVE EFFORTS INVESTED DOING ACADEMIC WORK. HOWEVER, THE COMPLEXITIES TO ASSESS THOSE PROJECTS ARE CHALLENGING. THIS REQUIRES DEVELOPING NEW MONITORING AND FEEDBACK SYSTEMS FOR THIS KIND OF PROJECTS. THIS PAPER PRESENTS AN EXPLORATORY ANALYSIS FOR APPLYING LEARNING ANALYTICS METHODOLOGIES BASED ON SOCIAL NETWORKS ANALYSIS, FACTORIAL ANALYSIS, K-MEANS CLUSTERING, AND "NAIVE" BAYES ALGORITHMS.
LEARNING ANALYTICS IN MOOCS CAN BE USED TO PREDICT LEARNER PERFORMANCE, WHICH IS CRITICAL AS HIGHER EDUCATION IS MOVING TOWARDS ADAPTIVE LEARNING. INTERDISCIPLINARY METHODS USED IN THE ARTICLE ALLOW FOR INTERPRETING EMPIRICAL QUALITATIVE DATA ON PERFORMANCE IN SPECIFIC TYPES OF COURSE ASSIGNMENTS TO PREDICT LEARNER PERFORMANCE AND IMPROVE THE QUALITY OF MOOCS. LEARNING ANALYTICS RESULTS MAKE IT POSSIBLE TO TAKE THE MOST FROM THE DATA REGARDING THE WAYS LEARNERS ENGAGE WITH INFORMATION AND THEIR LEVEL OF SKILLS AT ENTRY. THE ARTICLE PRESENTS THE RESULTS OF APPLYING THE PROPOSED LEARNING ANALYTICS ALGORITHM TO ANALYZE LEARNER PERFORMANCE IN SPECIFIC MOOCS DEVELOPED BY URAL FEDERAL UNIVERSITY AND OFFERED THROUGH THE NATIONAL OPEN EDUCATION PLATFORM.
THE GROWING PRESENCE OF DIGITAL MEDIATION SYSTEMS IN MOST EDUCATIONAL SPACES -WHETHER FACE-TO-FACE OR NOT, FORMALIZED OR OPEN, AND AT BASIC OR LIFELONG LEARNING LEVELS- HAS ACCELERATED THE ADVANCE OF LEARNING ANALYTICS AND THE USE OF DATA IN EDUCATION AS A COMMON PRACTICE. USING DIGITAL EDUCATIONAL TOOLS FACILITATES THE INTERACTION BETWEEN STUDENTS, TEACHERS AND LEARNING RESOURCES IN THE DIGITAL WORLD, AND GENERATES A REMARKABLE VOLUME OF DATA THAT CAN BE ANALYZED BY APPLYING A VARIETY OF METHODOLOGIES. THUS, RESEARCH FOCUSED ON INFORMATION GENERATED BY STUDENT ACTIVITY IN DIGITAL SPACES HAS RISEN EXPONENTIALLY. BASED ON THIS EVIDENCE, THIS SPECIAL ISSUE SHOWS A SET OF STUDIES IN THE FIELD OF DATA-DRIVEN EDUCATIONAL RESEARCH AND THE FIELD OF DIGITAL LEARNING, WHICH ENRICHES KNOWLEDGE ABOUT LEARNING PROCESSES AND MANAGEMENT OF TEACHING IN DIGITALLY MEDIATED SPACES.
THIS STUDY ANALYZED STUDENTS' ONLINE ASSIGNMENT SUBMISSION BEHAVIORS FROM THE PERSPECTIVES OF TEMPORAL LEARNING ANALYTICS. THIS STUDY AIMED TO MODEL THE TIME-DEPENDENT CHANGES IN THE ASSIGNMENT SUBMISSION BEHAVIOR OF UNIVERSITY STUDENTS BY EMPLOYING VARIOUS MACHINE LEARNING METHODS. PRECISELY, CLUSTERING, MARKOV CHAINS, AND ASSOCIATION RULE MINING ANALYSIS WERE USED TO ANALYZE STUDENTS' ASSIGNMENT SUBMISSION BEHAVIORS IN AN ONLINE LEARNING ENVIRONMENT. THE RESULTS REVEALED THAT STUDENTS DISPLAYED SIMILAR PATTERNS IN TERMS OF ASSIGNMENT SUBMISSION BEHAVIOR. MOREOVER, IT WAS OBSERVED THAT STUDENTS' ASSIGNMENT SUBMISSION BEHAVIOR DID NOT CHANGE MUCH ACROSS THE SEMESTER. WHEN THESE RESULTS ARE ANALYZED TOGETHER WITH THE STUDENTS' ACADEMIC PERFORMANCE AT THE END OF THE SEMESTER, IT WAS OBSERVED THAT STUDENTS' END-OF-TERM ACADEMIC PERFORMANCE CAN BE PREDICTED FROM THEIR ASSIGNMENT SUBMISSION BEHAVIORS AT THE BEGINNING OF THE SEMESTER. OUR RESULTS, WITHIN THE SCOPE OF PRECISION EDUCATION, CAN BE USED TO DIAGNOSE AND PREDICT STUDENTS WHO ARE NOT GOING TO SUBMIT THE NEXT ASSIGNMENTS AS THE SEMESTER PROGRESSES AS WELL AS STUDENTS WHO ARE GOING TO FAIL AT THE END OF THE SEMESTER. THEREFORE, LEARNING ANALYTICS INTERVENTIONS CAN BE DESIGNED BASED ON THESE RESULTS TO PREVENT POSSIBLE ACADEMIC FAILURES. FURTHERMORE, THE FINDINGS OF THE STUDY ARE DISCUSSED CONSIDERING THE DEVELOPMENT OF EARLY-WARNING INTERVENTION SYSTEMS FOR AT-RISK STUDENTS AND PRECISION EDUCATION.
THE COLLABORATIVE LEARNING PROCESSES OF STUDENTS IN ONLINE LEARNING ENVIRONMENTS CAN BE SUPPORTED BY PROVIDING LEARNING ANALYTICS-BASED VISUALISATIONS THAT FOSTER AWARENESS AND REFLECTION ABOUT AN INDIVIDUAL'S AS WELL AS THE TEAM'S BEHAVIOUR AND THEIR LEARNING AND COLLABORATION PROCESSES. FOR THIS EMPIRICAL STUDY WE IMPLEMENTED AN ACTIVITY WIDGET INTO THE ONLINE LEARNING ENVIRONMENT OF A LIVE FIVE-MONTHS MASTER COURSE AND INVESTIGATED THE PREDICTIVE POWER OF THE WIDGET INDICATORS TOWARDS THE STUDENTS' GRADES AND COMPARED THE RESULTS TO THOSE FROM AN EXPLORATORY STUDY WITH DATA COLLECTED IN PREVIOUS RUNS OF THE SAME COURSE WHERE THE WIDGET HAD NOT BEEN IN USE. TOGETHER WITH INFORMATION GATHERED FROM A QUANTITATIVE AS WELL AS A QUALITATIVE EVALUATION OF THE ACTIVITY WIDGET DURING THE COURSE, THE FINDINGS OF THIS CURRENT STUDY SHOW THAT THERE ARE INDEED PREDICTIVE RELATIONS BETWEEN THE WIDGET INDICATORS AND THE GRADES, ESPECIALLY THOSE REGARDING RESPONSIVENESS, AND INDICATE THAT SOME OF THE OBSERVED DIFFERENCES IN THE LAST RUN COULD BE ATTRIBUTED TO THE IMPLEMENTED ACTIVITY WIDGET.
THE PRESENT ARTICLE IS AIMED AT ANALYZING THE EFFECTS OF LEARNING ANALYTICS ON STUDENTS' SELF-REGULATED LEARNING IN A FLIPPED CLASSROOM. AN EXPERIMENT WAS CONDUCTED WITH 96 ENGINEERING STUDENTS, ENROLLED IN A SUBJECT OFFERED IN THE FLIPPED CLASSROOM MODEL. THE STUDENTS WERE DIVIDED INTO TWO GROUPS: AN EXPERIMENTAL GROUP (N = 51) AND A CONTROL GROUP (N = 45). AFTER EACH LEARNING UNIT, WHILE THE CONTROL GROUP DID NOT HAVE ACCESS TO ANY LEARNING ANALYTICS RESOURCES, STUDENTS FROM THE EXPERIMENTAL GROUP RECEIVED A BULLETIN WITH FEEDBACK TO SUPPORT SELF-REGULATED LEARNING. THE LEVELS OF STUDENT SELF-REGULATION WERE MEASURED THROUGH QUESTIONNAIRES AT THE BEGINNING AND THE END OF THE COURSE. THE ANALYSIS OF THE COLLECTED DATA REVEALED THAT THE BULLETIN PROMOTED SIGNIFICANT EFFECTS IN SELF-REGULATED LEARNING IN THE EXPERIMENTAL GROUP, STIMULATING THE SELF-REFLECTION AND COLLEAGUE'S SUPPORT SEARCH FOR CLARIFYING DOUBTS. THESE RESULTS DEMONSTRATE THAT LEARNING ANALYTICS CAN BE USED TO PROMOTE SELF-REGULATED LEARNING IN FLIPPED CLASSROOMS, HELPING STUDENTS IDENTIFY STRATEGIES THAT CAN INCREASE THEIR ACADEMIC PERFORMANCE.
BECAUSE ACADEMIC AND LEARNING ANALYTICS TOOLS AIM TO INFORM AND IMPROVE THE TEACHING AND LEARNING PROCESS, USERS HAVE A FUNDAMENTAL ROLE IN THEIR CONCEPTION AND DESIGN. THE EARLY INVOLVEMENT OF END-USERS HELPS TO ENSURE THE DELIVERY OF A VALUABLE AND UNDERSTANDABLE TOOL. CONSEQUENTLY THIS EASES ADOPTION BY AN EDUCATIONAL INSTITUTION. IN THIS REGARD, THE DEVELOPMENT OF LEARNING ANALYTICS TOOLS HAS MANY REASONS TO BENEFIT FROM AGILE PRACTICES BUT PARADOXICALLY THEY ARE USUALLY INSERTED IN TRADITIONALLY RIGID ENVIRONMENTS SUCH AS HIGHER EDUCATION INSTITUTIONS. THIS INHERENT RIGIDITY POSES CHALLENGES IN CONFLICT WITH THE USUAL AGILE SOFTWARE DEVELOPMENT LIFECYCLE (SDLC) PRACTICES AND PRINCIPLES (EG. INCREASED DIS-COMFORT WITH LATE REQUIREMENT CHANGES). THIS WORK PRESENTS, THROUGH THE EXPERIENCE OF THE AUSTRAL UNIVERSITY OF CHILE WITH THE SDLC OF THE TRAC AND VERA TOOLS, HOW TO RECONCILE THE NECESSARY AGILE PRACTICES TO OVERCOME THESE CHALLENGES TO CREATE USEFUL ANALYTICS TOOLS AND INCORPORATE THEM INTO A HIGHER EDUCATION INSTITUTION. BOTH TOOLS ARE IN PILOT PHASE IN THE UNIVERSITY AND THE PARTIAL FINDINGS SHOW THAT IT IS POSSIBLE TO RECONCILE AGILE DEVELOPMENT IN A RIGID ENVIRONMENT WITH APPROPRIATE STRATEGIES.
IN THIS PAPER, WE PROPOSE THE SOCIAL SEMANTIC SERVER (SSS) AS A SERVICE-BASED INFRASTRUCTURE FOR WORKPLACE AND PROFESSIONAL LEARNING ANALYTICS (LA). THE DESIGN AND DEVELOPMENT OF THE SSS HAVE EVOLVED OVER EIGHT YEARS, STARTING WITH AN ANALYSIS OF WORKPLACE LEARNING INSPIRED BY KNOWLEDGE CREATION THEORIES AND THEIR APPLICATION IN DIFFERENT CONTEXTS. THE SSS COLLECTS DATA FROM WORKPLACE LEARNING TOOLS, INTEGRATES IT INTO A COMMON DATA MODEL BASED ON A SEMANTICALLY ENRICHED ARTIFACT-ACTOR NETWORK, AND OFFERS IT BACK FOR LA APPLICATIONS TO EXPLOIT THE DATA. FURTHER, THE SSS DESIGN'S FLEXIBILITY ENABLES IT TO BE ADAPTED TO DIFFERENT WORKPLACE LEARNING SITUATIONS. THIS PAPER CONTRIBUTES BY SYSTEMATICALLY DERIVING REQUIREMENTS FOR THE SSS ACCORDING TO KNOWLEDGE CREATION THEORIES, AND BY OFFERING SUPPORT ACROSS A NUMBER OF DIFFERENT LEARNING TOOLS AND LA APPLICATIONS INTEGRATED INTO THE SSS. WE ALSO SHOW EVIDENCE FOR THE USEFULNESS OF THE SSS EXTRACTED FROM 4 AUTHENTIC WORKPLACE LEARNING SITUATIONS INVOLVING 57 PARTICIPANTS. THE EVALUATION RESULTS INDICATE THAT THE SSS SATISFACTORILY SUPPORTS DECISION MAKING IN DIVERSE WORKPLACE LEARNING SITUATIONS AND ALLOW US TO REFLECT ON THE IMPORTANCE OF KNOWLEDGE CREATION THEORIES FOR THIS ANALYSIS.
LEARNERS' ATTAINMENT OF ACADEMIC KNOWLEDGE IN POSTSECONDARY INSTITUTIONS IS PREDOMINANTLY EXPRESSED BY SUMMATIVE APPROACHES. INSTEAD, RECENT ADVANCES IN EDUCATIONAL TECHNOLOGY HAVE HINTED AT A MEANS TO CONTINUOUSLY MEASURE LEARNING ATTAINMENT IN TERMS OF PERSONALIZED LEARNER COMPETENCY, CAPACITY, AND EFFECTIVENESS. SIMILARLY, EDUCATIONAL TECHNOLOGY ALSO OFFERS GUIDELINES TO CONTINUOUSLY MEASURE INSTRUCTIONAL ATTAINMENT IN TERMS OF INSTRUCTIONAL COMPETENCY, INSTRUCTIONAL CAPACITY, AND INSTRUCTIONAL EFFECTIVENESS. WHILE ACCURATE COMPUTATIONAL MODELS THAT EMBODY THESE ATTAINMENTS, EDUCATIONAL AND INSTRUCTIONAL, REMAIN A DISTANT AND ELUSIVE GOAL, BIG DATA LEARNING ANALYTICS APPROACHES THIS GOAL BY CONTINUOUSLY OBSERVING STUDY EXPERIENCES AND INSTRUCTIONAL PRACTICES AT VARIOUS LEVELS OF GRANULARITY, AND BY CONTINUALLY CONSTRUCTING AND USING MODELS FROM THESE OBSERVATIONS. THIS ARTICLE OFFERS A NEW PERSPECTIVE ON LEARNING AND INSTRUCTIONAL ATTAINMENTS WITH BIG DATA ANALYTICS AS THE UNDERLYING FRAMEWORK, DISCUSSES APPROACHES TO THIS FRAMEWORK WITH EVIDENCES FROM THE LITERATURE, AND OFFERS A CASE STUDY THAT ILLUSTRATES THE NEED TO PURSUE RESEARCH DIRECTIONS ARISING FROM THIS NEW PERSPECTIVE.
SOCIAL NETWORKS HAVE BECOME A NEW FORM OF COMMUNICATION THAT ALLOW STUDENTS TO SHARE AND COLLABORATE. IN THIS SENSE, THEY HAVE JOINED FORCES WITH SELF-REGULATED LEARNING (SRL) SKILLS. THIS PAPER PRESENTS AN EXPERIENCE AT THE UNIVERSITY OF SANTIAGO DE COMPOSTELA TO ANALYZE HOW SRL IS DEVELOPED IN A COURSE USING A SOCIAL NETWORK. THIS RESEARCH USED THE FOLLOWING: 1) MSLQ QUESTIONNAIRE BY PINTRICH AND 2) SOCIAL NETWORK ANALYSIS TECHNIQUES WITHIN THE LEARNING ANALYTICS FRAMEWORK. THE RESULTS SHOW THAT PEDAGOGY ENCOURAGED STUDENTS TO INTERACT AND CREATE A RICH ENVIRONMENT FOR DEVELOPING SRL SKILLS.
THERE IS INCREASING INTEREST IN LEARNING ANALYTICS, A CONCEPT THAT HAS EVOLVED IN THE LAST YEARS FROM THE STATUS OF BUZZWORD OR TREND TO THE GENERATION OF A DISCIPLINE WITHIN THE EDUCATIONAL FIELD. IN THIS PAPER, WE INTRODUCE THE TRACK ON LEARNING ANALYTICS WITHIN THE TECHNOLOGICAL ECOSYSTEMS FOR ENHANCING MULTICULTURALITY 2013 CONFERENCE. THE PAPER PROVIDES A GENERAL OVERVIEW OF THE MOTIVATIONS BEHIND THE PROPOSAL OF THIS TRACK, INCLUDING A REVIEW OF THE CURRENT STATE OF LEARNING ANALYTICS, FROM ITS ORIGINS AND A DEFINITION OF THE TERM TO SOME PENDING ISSUES FOR FUTURE RESEARCH, WHICH ARE ADDRESSED BY THE PAPERS PARTICIPATING IN THE TRACK AND WHICH HOPEFULLY WILL BE EXPANDED BY FUTURE STUDIES. THE PAPER ALSO INCLUDES AN INSIGHT ON THE SUBMISSION MANAGEMENT AND PARTICIPANTS' SELECTION PROCESS, WHICH IS FOLLOWED BY A DETAILED SUMMARY OF THE MANUSCRIPTS ACCEPTED FOR PARTICIPATION IN THE CONFERENCE AND HOW THEY ARE LINKED TO THE TRACK OBJECTIVES.
RESEARCH AND DEVELOPMENT IN LEARNING ANALYTICS HAS ESTABLISHED VIABLE SOLUTIONS FOR SCALING PERSONALISED FEEDBACK TO ALL STUDENTS. HOWEVER, QUESTIONS REMAIN REGARDING HOW SUCH FEEDBACK IS PERCEIVED, INTERPRETED AND ACTED UPON BY STAKEHOLDERS. THE PRESENT STUDY REPORTS ON THE ANALYSIS OF FOCUS GROUP DATA FROM FOUR COURSES TO UNDERSTAND STUDENTS' SENSEMAKING OF PERSONALISED, LEARNING ANALYTICS-BASED FEEDBACK. DEDUCTIVE ANALYSIS AND EPISTEMIC NETWORK ANALYSIS WERE CARRIED OUT TO IDENTIFY STUDENTS' PERCEPTIONS AND ASSOCIATED EMOTIONAL RESPONSES TO THE FEEDBACK. THE RESULTS DEMONSTRATE THAT STUDENTS MOSTLY HELD POSITIVE PERCEPTIONS OF THEIR FEEDBACK - ESPECIALLY IN REGARD TO QUALITY, TASK-FOCUS AND INSTRUCTOR CARE. WHILE EMOTIONS TO FEEDBACK WERE OFTEN NEGATIVE, STUDENTS ALSO NOTED THIS INCREASED THEIR MOTIVATION TO INVEST MORE EFFORT IN THE LEARNING TASKS. THE RESULTS OF THE STUDY PROVIDE FURTHER EVIDENCE OF THE INTERPERSONAL COMMUNICATION VARIABLES INFLUENCING STUDENTS' PROACTIVE RECIPIENCE OF FEEDBACK. IMPORTANTLY, THE FINDINGS SHOW THAT STUDENTS WERE MOSTLY ACTIVELY ENGAGED WITH THEIR PERSONALISED FEEDBACK, AND THAT THE INSTRUCTOR-MEDIATED APPROACH TO FEEDBACK BASED ON LEARNING ANALYTICS IS ALIGNED TO THE PRINCIPLES OF EFFECTIVE FEEDBACK.
IN THIS STUDY, WE DESCRIBE THE RESULTS OF OUR RESEARCH TO MODEL COLLABORATIVE PROBLEM-SOLVING (CPS) COMPETENCE BASED ON ANALYTICS GENERATED FROM VIDEO DATA. WE HAVE COLLECTED SIMILAR TO 500 MINS VIDEO DATA FROM 15 GROUPS OF 3 STUDENTS WORKING TO SOLVE DESIGN PROBLEMS COLLABORATIVELY. INITIALLY, WITH THE HELP OF OPENPOSE, WE AUTOMATICALLY GENERATED FREQUENCY METRICS SUCH AS THE NUMBER OF THE FACE-IN-THE-SCREEN; AND DISTANCE METRICS SUCH AS THE DISTANCE BETWEEN BODIES. BASED ON THESE METRICS, WE BUILT DECISION TREES TO PREDICT STUDENTS' LISTENING, WATCHING, MAKING, AND SPEAKING BEHAVIOURS AS WELL AS PREDICTING THE STUDENTS' CPS COMPETENCE. OUR RESULTS PROVIDE USEFUL DECISION RULES MINED FROM ANALYTICS OF VIDEO DATA WHICH CAN BE USED TO INFORM TEACHER DASHBOARDS. ALTHOUGH, THE ACCURACY AND RECALL VALUES OF THE MODELS BUILT ARE INFERIOR TO PREVIOUS MACHINE LEARNING WORK THAT UTILIZES MULTIMODAL DATA, THE TRANSPARENT NATURE OF THE DECISION TREES PROVIDES OPPORTUNITIES FOR EXPLAINABLE ANALYTICS FOR TEACHERS AND LEARNERS. THIS CAN LEAD TO MORE AGENCY OF TEACHERS AND LEARNERS, THEREFORE CAN LEAD TO EASIER ADOPTION. WE CONCLUDE THE PAPER WITH A DISCUSSION ON THE VALUE AND LIMITATIONS OF OUR APPROACH.
THE POTENTIAL INFLUENCE OF DATA MINING ANALYTICS ON THE STUDENTS' LEARNING PROCESSES AND OUTCOMES HAS BEEN REALIZED IN HIGHER EDUCATION. HENCE, A COMPREHENSIVE REVIEW OF EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) IN HIGHER EDUCATION WAS CONDUCTED. THIS REVIEW COVERED THE MOST RELEVANT STUDIES RELATED TO FOUR MAIN DIMENSIONS: COMPUTER-SUPPORTED LEARNING ANALYTICS (CSLA), COMPUTER-SUPPORTED PREDICTIVE ANALYTICS (CSPA), COMPUTER-SUPPORTED BEHAVIORAL ANALYTICS (CSBA), AND COMPUTER-SUPPORTED VISUALIZATION ANALYTICS (CSVA) FROM 2000 TILL 2017. THE RELEVANT EDM AND LA TECHNIQUES WERE IDENTIFIED AND COMPARED ACROSS THESE DIMENSIONS. BASED ON THE RESULTS OF 402 STUDIES, IT WAS FOUND THAT SPECIFIC EDM AND LA TECHNIQUES COULD OFFER THE BEST MEANS OF SOLVING CERTAIN LEARNING PROBLEMS. APPLYING EDM AND LA IN HIGHER EDUCATION CAN BE USEFUL IN DEVELOPING A STUDENT-FOCUSED STRATEGY AND PROVIDING THE REQUIRED TOOLS THAT INSTITUTIONS WILL BE ABLE TO USE FOR THE PURPOSES OF CONTINUOUS IMPROVEMENT.
THE COLLECTION OF STUDENTS' SENSIBLE DATA RAISES ADVERSE REACTIONS AGAINST LEARNING ANALYTICS THAT DECREASES THE CONFIDENCE IN ITS ADOPTION. THE LAWS AND POLICIES THAT SURROUND THE USE OF EDUCATIONAL DATA ARE NOT ENOUGH TO ENSURE PRIVACY, SECURITY, VALIDITY, INTEGRITY AND RELIABILITY OF STUDENTS' DATA. THIS PROBLEM HAS BEEN DETECTED THROUGH LITERATURE REVIEW AND CAN BE SOLVED IF A TECHNOLOGICAL LAYER OF AUTOMATED CHECKING RULES IS ADDED ABOVE THESE POLICIES. THE AIM OF THIS THESIS IS TO RESEARCH ABOUT AN EMERGING TECHNOLOGY SUCH AS BLOCKCHAIN TO PRESERVE THE IDENTITY OF STUDENTS AND SECURE THEIR DATA. IN A FIRST STAGE A SYSTEMATIC LITERATURE REVIEW WILL BE CONDUCTED IN ORDER TO SET THE CONTEXT OF THE RESEARCH. AFTERWARDS, AND THROUGH THE SCIENTIFIC METHOD, WE WILL DEVELOP A BLOCKCHAIN BASED SOLUTION TO AUTOMATE RULES AND CONSTRAINTS WITH THE AIM TO LET STUDENTS THE GOVERNANCE OF THEIR DATA AND TO ENSURE DATA PRIVACY AND SECURITY.
THIS STUDY AIMS TO IDENTIFY THE TYPES OF LEARNING LOGS THAT CAN MEASURE LEARNING SKILLS. BY USING THE LEARNING ANALYTICS APPROACH, THE LEARNING PROCESS CAN BE ANALYZED. IDENTIFYING LEARNING SKILLS FROM THE BEHAVIORAL LEVEL CAN BE LINKED TO STUDENTS LEARNING BEHAVIORS AND HOW THEY SUPPORT THEIR OWN LEARNING. HOWEVER, THE SKILLS IDENTIFIED FROM THOSE LOGS WERE NOT PARTICULARLY CLEAR. THEREFORE, WE REVIEWED PREVIOUS STUDIES FROM THE DATABASES OF EDUCATIONAL PAPERS "ERIC" AND "GOOGLE SCHOLAR", LINKED THE BEHAVIORS WITH THE SKILLS AND IDENTIFIED THE LOGS THAT CAN OBJECTIVELY REFLECT THE BEHAVIORS. IN THIS STUDY, THE LEARNING SKILLS THAT COULD HE ANALYZED THROUGH LEARNING LOGS WERE PRESENTED.
THE AIM OF THIS PAPER IS TO EXAMINE AND DISCUSS THE DISSONANCE BETWEEN EXPECTATIONS AND HOPES TOWARDS UTILISING LEARNING ANALYTICS IN FINNISH UNIVERSITIES. THE ANALYSIS IS BASED ON DATA COLLECTED AMONG FINNISH UNIVERSITY STUDENTS AND STAFF IN SPRING 2019. AS A KEY RESULT WE PRESENT, THAT THE UNIVERSITY STAFF FOUND IT IMPORTANT THAT ALL DATA AND INFORMATION SHOULD AND COULD BE USED FOR VARIOUS PLANNING, MANAGEMENT AND COUNSELLING PURPOSES. AT THE SAME TIME STUDENT FOUND IT UNNECESSARY OR EVEN HARMFUL TO ALLOW UNIVERSITY STAFF EXAMINE THEIR PERSONAL DATA. WE THEREFORE PROPOSE THAT UNIVERSITIES SHOULD DEVELOP AND IMPLEMENT SPECIFIC POLICY FOR USING OF ANALYTICAL DATA.
DURING COLLABORATIVE LEARNING (CL) TEACHERS HAVE LITTLE TO NO INFORMATION ABOUT WHAT IS HAPPENING IN THE GROUPS, HOWEVER, THEY ARE EXPECTED TO PLAN, MONITOR, SUPPORT, CONSOLIDATE AND REFLECT UPON STUDENT INTERACTIONS. HOW IS A TEACHER ABLE TO PROVIDE SUPPORT THE STUDENTS WHEN SHE/HE IS NOT FULLY AWARE OF THE SITUATION AND PROGRESS OF THE STUDENTS? MULTIMODAL LEARNING ANALYTICS (MMLA) COULD OFFER TEACHERS VALUABLE INSIGHTS INTO THE CL PROCESS, HOWEVER, NOT MANY MMLA OUTPUTS ARE USED IN REAL PRACTICE TODAY, AND DESIGNING SUCH MMLA DASHBOARDS REMAINS A CHALLENGE. THE AIM OF THE PRESENT DESIGN-SCIENCE RESEARCH IS TO FIND OUT IF MULTIMODAL LEARNING ANALYTICS IS ABLE TO HELP TEACHERS HELP SUPPORT STUDENTS IN CL. AFTER HAVING GONE THROUGH TWO CYCLES OF DESIGN BASED RESEARCH, IT IS APPARENT THAT THE INVOLVED TEACHERS ARE INTERESTED IN GETTING AN OVERVIEW OF THE ACTIVITIES WITHIN THE GROUPS AND HELP WITH ASSESSMENT. THE INVOLVED TEACHERS WERE READY TO USE COLLABORATION ANALYTICS IF IT CAN ASSURE A CERTAIN LEVEL OF ACCURACY, MORE RESEARCH IS NEEDED ON HOW TO USE MMLA TO SUPPORT TEACHERS IN CL ASSESSMENT.
DATA MINING IS INCREASING ITS POPULARITY IN THE RESEARCH OF TECHNOLOGY-ENHANCED LANGUAGE LEARNING AND APPLIED LINGUISTICS IN GENERAL. IT ENABLES A BETTER UNDERSTANDING OF PROGRESS, PERFORMANCE AND POSSIBLE PITFALLS, WHICH WOULD BE USEFUL FOR LANGUAGE LEARNERS, TEACHERS AND RESEARCHERS. UNTIL RECENTLY IT WAS AN UNEXPLORED FIELD, BUT IT IS EXPECTED TO GROW EXPONENTIALLY IN THE FOLLOWING YEARS. THIS ARTICLE ATTEMPTS TO BE A RELEVANT CONTRIBUTION AS AN INSTANCE OF EMPIRICAL RESEARCH, SHOWING THE APPLICATION OF LEARNING ANALYTICS TO THE LANGUAGE MOOC (LMOOC) HOW TO SUCCEED IN THE ENGLISH B1 LEVEL EXAM.' THE FOCUS OR THE RESEARCH WAS THREEFOLD, TRYING TO FIND OUT: (1) WHAT TYPES OF LEARNING OBJECTS STUDENTS ENGAGE WITH MOST, (2) WHAT ASPECTS OF ONLINE INTERACTION RELATE MORE STRONGLY TO COURSE COMPLETION AND SUCCESS, AND (3) WHICH ARE THE MOST PROMINENT STUDENT PROFILES IN AN LMOOC. RESULTS SHOW THAT SHORT VIDEO-PILLS ARE THE MOST POWERFUL LEARNING OBJECTS IN THIS TYPE OF ONLINE COURSES, THE REGULAR SUBMISSION OF AUTOMATED GRADING ACTIVITIES IS A ROBUST INDICATOR TOWARDS COURSE SUCCESS, AND THE MOST PROMINENT STUDENT PROFILE IN LMOOCS IS VIEWERS', THOSE WHO ACCESS THE LEARNING MATERIALS BUT DO NOT SUBMIT TASKS OR ENGAGE IN ONLINE INTERACTION ACTIVELY, WHICH WOULD EXPLAIN WHY THE COMPLETION RATE IN LMOOCS IS SO LOW. THIS NOVEL PERSPECTIVE INTO STUDENTS' LANGUAGE LEARNING, WHICH BIG DATA HAS ASSISTED US IN, SHOULD GUIDE COURSE CREATORS TO RE-DESIGN THE LMOOC FOR THE ENHANCEMENT OF THE AUDIO-VISUAL CONTENT. LMOOC INSTRUCTORS AND FACILITATORS SHOULD ALSO ENCOURAGE PARTICIPANTS TO INCREASE THE SUBMISSION OF ACTIVITIES -ACKNOWLEDGING THESE SMALL ACHIEVEMENTS THROUGH MICRO-CREDENTIALING AND BADGES-, AND SPECIAL ATTENTION OUGHT TO BE PAID TO THE MOST PROMINENT LMOOC PROFILE, THOSE VIEWERS' WHO SHOULD BE LURED INTO BECOMING SOLVERS' OR, EVEN BETTER, ALL-ROUNDERS'.
EDUCATORS FACE SIGNIFICANT CHALLENGES RELATED TO MONITORING ACADEMIC PERFORMANCE AND PROVIDING INSTRUCTION IN ONLINE COURSES. DUE TO SPATIAL, TEMPORAL AND INTERACTIVE DISTANCE, THIS TYPE OF EDUCATION REQUIRES THAT STUDENTS HAVE GREATER DEDICATION, RESPONSIBILITY, AND SELF-REGULATION OF LEARNING SO THAT EDUCATIONAL GOALS CAN BE MET. IN THIS SENSE, EDUCATORS SEEK INSTRUMENTS TO CONSISTENTLY MONITOR AND EVALUATE THE STUDENT'S ACADEMIC PERFORMANCE. THE PRESENT WORK AIMS AT SYSTEMATICALLY MAPPING STUDIES THAT USE TECHNIQUES OF LEARNING ANALYTICS AND COMPUTATIONAL ONTOLOGIES, ALLIED TO A TAXONOMY OF EDUCATIONAL OBJECTIVES, TO MONITOR THE STUDENT'S STATE OF KNOWLEDGE. THE EVIDENCE, METHODS, ONTOLOGIES AND TAXONOMIES FOUND WILL ASSIST IN THE DEVELOPMENT OF AN EDUCATIONAL SOFTWARE ARCHITECTURE THAT AIMS TO ASSIST EDUCATORS IN SUPERVISING ACADEMIC PERFORMANCE. THREE HUNDRED AND TWENTY STUDIES WERE RETRIEVED, OF WHICH WE IDENTIFIED 21 (6.56%) WORKS RELEVANT TO THIS RESEARCH. THIS MAPPING SHOWS THAT THERE IS A RESEARCH GAP REGARDING THE COORDINATED USE OF LEARNING ANALYTICS AND ONTOLOGIES IN THE FIELD OF LEARNING ASSESSMENT.
BIG DATA IS OFTEN PRESENTED AS AN ICEBERG MOST OF WHICH IS HIDDEN UNDERWATER. THE PURPOSE OF THIS ARTICLE IS TO SEARCH A WAY TO REVEAL AS MUCH AS POSSIBLE FROM THE HIDDEN PART THROUGH DATA VISUALIZATION. A REVIEW OF EXISTING LEARNING ANALYTICS METHODS FOR DATA ANALYSIS LIKE CLICK-STREAM ANALYTICS AND NATURAL LANGUAGE PROCESSING AND THEIR CORRESPONDING TOOLS FOR DATA PRESENTATION IS PROPOSED. VISUAL COMPONENT AND APPLICATIONS IN HELP TO UNDERSTAND AND OPTIMIZE LEARNING PROCESS AND ELEARNING ENVIRONMENT ARE DISCUSSED. DATA COLLECTION STANDARDS FOR INTEROPERABILITY BETWEEN SYSTEMS AND TOOLS ARE COMMENTED. AUTHORS' EXPERIENCE AND CHALLENGES IN USING SOME OF THE PRESENTED METHODS AND TOOLS ARE SHARED, RECOMMENDATIONS FOR PERFORMANCE TIPS ARE SUGGESTED.
THE AVAILABILITY OF LARGE AMOUNTS OF INFORMATION ASSOCIATED WITH THE TEACHING AND LEARNING PROCESS CHALLENGES RESEARCHERS TO EXPLORE THIS INFORMATION BY USING LEARNING ANALYTICS TO OBTAIN INDICATORS THAT CAN CONTRIBUTE TO IMPROVING THE TEACHING AND LEARNING PROCESS, PARTICULARLY IN TERMS OF LEARNING OUTCOMES AND THE RELATIONSHIP STUDENTS HAVE WITH THE EDUCATIONAL INSTITUTIONS THEY ATTEND. CONSIDERING THAT FAILURE AND DROPPING OUT OF SCHOOL HAVE VERY NEGATIVE CONSEQUENCES FOR A LARGE NUMBER OF YOUNG PEOPLE, WE HAVE SOUGHT TO OBTAIN INDICATORS ASSOCIATED WITH THESE PROBLEMS THROUGH THE USE OF QUANTITATIVE METHODOLOGY AND DOCUMENTAL ANALYSIS PROCEDURES, BY ANALYZING DATA FROM A SAMPLE OF 1588 STUDENTS WITH REGARD TO THEIR FREQUENCY OF ACCESS TO THE VIRTUAL ENVIRONMENT OF THE INSTITUTION, THE NUMBER OF PRESENCES IN ONSITE LESSONS, THE NUMBER OF COURSE UNITS THEY PASSED AND THE AVERAGE MARK IN THE COURSE UNITS THEY PASSED. FROM THE OBTAINED RESULTS, WE EMPHASIZE THAT THERE IS A DEGREE OF POSITIVE ASSOCIATION, WITH MODERATE CORRELATION, BETWEEN EACH PAIR OF THE MENTIONED VARIABLES. AFTER ORGANIZING THE SAMPLE IN FOUR DISTINCT GROUPS RESULTING FROM THE DIVISION OF THE DATA REGARDING THE NUMBER OF ACCESSES TO THE INSTITUTION VIRTUAL ENVIRONMENT INTO QUARTILES, IT WAS FOUND THAT THERE ARE SIGNIFICANT DIFFERENCES REGARDING THE NUMBER OF COURSE UNITS PASSED BETWEEN THE GROUPS WITH HIGHER NUMBERS OF ACCESSES TO THE VIRTUAL LEARNING ENVIRONMENT AND THE ONES WITH LOWER NUMBERS OF ACCESSES, TO THE BENEFIT OF THE GROUPS WITH THE GREATEST NUMBER OF ACCESSES.
A SIGNIFICANT BODY OF LITERATURE DOCUMENTS THE NUMEROUS BENEFITS THAT VIRTUAL (VR) AND AUGMENTED REALITY (AR) SUPPORTED INTERVENTIONS HAVE BROUGHT TO THE EDUCATIONAL SCENERY, ESPECIALLY WITH REGARDS TO THE ATTAINMENT OF THE UNDERPINNING OBJECTIVES. AT THE SAME TIME, THE VAST EVOLUTION OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) HAS LED TO THE EMERGENCE OF A NEWLY FORMED DISCIPLINE, THE SO-CALLED LEARNING ANALYTICS (LA), WHICH SUGGESTS THE COLLECTION OF 'BIG DATA' FOR THE ASSESSMENT AND THE EVALUATION OF THE EDUCATIONAL PRACTICES. HOWEVER, BY EXAMINING THE RELEVANT LITERATURE IT BECAME APPARENT THAT THE ATTEMPTS TO BLEND THESE TOPICS ARE LIMITED. MOTIVATED BY THIS SHORTCOMING, WE PROPOSE A THEORETICAL FRAMEWORK WHICH ACCOUNTS THE ELEMENTS THAT INFLUENCE THE EDUCATIONAL PROCESSES AND THE UNIQUE FEATURES THAT IMMERSIVE TECHNOLOGIES HAVE. ON THE GROUNDS OF THIS FRAMEWORK, AN EFFORT WILL BE MADE TO DESIGN AND DEVELOP A UNIVERSAL LA SYSTEM WHICH WILL SUPPORT THE CONDUCT AND EVALUATION OF SUCH INTERVENTIONS IN DIFFERENT EDUCATIONAL CONTEXTS AND SCIENTIFIC FIELDS.
TECHNOLOGICAL PROGRESS IN RECENT DECADES HAS ENABLED PEOPLE TO LEARN IN DIFFERENT WAYS. UNIVERSITIES NOW HAVE MORE EDUCATIONAL MODELS TO CHOOSE FROM, I.E., B-LEARNING AND E-LEARNING. DESPITE THE INCREASING OPPORTUNITIES FOR STUDENTS AND INSTRUCTORS, ONLINE LEARNING ALSO BRINGS CHALLENGES DUE TO THE ABSENCE OF DIRECT HUMAN CONTACT. ONLINE ENVIRONMENTS ALLOW THE GENERATION OF LARGE AMOUNTS OF DATA RELATED TO LEARNING/TEACHING PROCESSES, WHICH OFFERS THE POSSIBILITY OF EXTRACTING VALUABLE INFORMATION THAT MAY BE EMPLOYED TO IMPROVE STUDENTS' PERFORMANCE. IN THIS PAPER, WE AIM TO REVIEW THE SIMILARITIES AND DIFFERENCES BETWEEN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS, TWO RELATIVELY NEW AND INCREASINGLY POPULAR FIELDS OF RESEARCH CONCERNED WITH THE COLLECTION, ANALYSIS, AND INTERPRETATION OF EDUCATIONAL DATA. THEIR ORIGINS, GOALS, DIFFERENCES, SIMILARITIES, TIME EVOLUTION, AND CHALLENGES ARE ADDRESSED, AS ARE THEIR RELATIONSHIP WITH BIG DATA AND MOOCS.
OPEN DATA INITIATIVES WORLDWIDE ARE BOOSTING WITH AN AIM TO INCREASE TRANSPARENCY AND CONTRIBUTE TO ECONOMIC GROWTH. WITH A GLOBAL ANNUAL ECONOMIC POTENTIAL VALUE ESTIMATED TO $3 TRILLION, THIS BOOST SEEMS JUSTIFIED. CURRENT PROGRESS HOWEVER IS NOT SATISFACTORY. WE BELIEVE A MAIN REASON IS THE LACK OF RELEVANT SKILLS AND COMPETENCIES. CURRENT EDUCATION AND TRAINING ACTIVITIES ARE SCARCE AND DO NOT EXPLOIT PRACTICE-ORIENTED LEARNING METHODS SUCH AS PROBLEM BASED LEARNING (PBL) OR NOVEL ASSESSMENT OPPORTUNITIES SUCH AS LEARNING ANALYTICS (LA). AS A RESULT, STAKEHOLDERS ARE MISSING SKILLS RELATED TO PUBLISHING AND REUSING OPEN DATA. THE AIM OF THIS TUTORIAL IS TO INTRODUCE TO THE AUDIENCE OPEN DATA EDUCATION AND TRAINING BASED ON THE PBL STRATEGY AND BY EXPLOITING LA FOR PERFORMANCE ASSESSMENT. WE WILL PRESENT A CURRICULUM STRUCTURE AND LEARNING CONTENT ON OPEN DATA FOR ACADEMIA, BUSINESS AND THE PUBLIC SECTOR THAT CAN BE REUSED BY ALL INTERESTED STAKEHOLDERS. WE WILL ALSO DEMONSTRATE A UNIVERSITY AND A VET COURSE MODEL THAT HAVE BEEN DESIGNED IN ORDER TO FACILITATE INNOVATIVE AND DATA-DRIVEN OPEN DATA EDUCATION AND TRAINING. FINALLY, WE WILL SHARE LESSONS LEARNT FROM THE PRACTICAL APPLICATION OF THE ABOVE IN UNIVERSITIES AND BUSINESSES ACROSS MULTIPLE EUROPEAN COUNTRIES.
LEARNING ANALYTICS OFFERS THE OPPORTUNITY TO COLLECT, ANALYSE AND VISUALISE FEEDBACK ON LEARNING ACTIVITIES USING AUTHENTIC DATA IN REAL-TIME. THE REFLECTOR PROJECT WAS USED TO INVESTIGATE WHETHER THERE ARE CORRELATIONS BETWEEN STUDENTS LEARNING STRATEGIES, THEIR ONLINE ACTIVITY AND THEIR GRADES. INFORMATION ABOUT THE LEARNING STRATEGIES WAS OBTAINED USING THE MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE. THE GRADES AND THE ONLINE ACTIVITY OF STUDENTS FOR TWO PILOT COURSES WAS COLLECTED FROM THE LOG DATA OF THE LEARNING MANAGEMENT SYSTEM. ANALYSIS OF THE COLLECTED DATA SHOWED THAT THERE ARE MODERATE CORRELATIONS TO BE FOUND, FOR INSTANCE BETWEEN METACOGNITIVE SELF-REGULATION, DOCUMENTS THAT ARE RELATED TO PLANNING AND GRADES. THE PILOT SESSIONS TAUGHT US THAT THERE ARE PRACTICAL ISSUES WITH REGARDS TO DATA STORAGE LOCATION AS WELL AS DATA SECURITY THAT NEED TO BE TAKEN INTO ACCOUNT WHEN LEARNING ANALYTICS IS INTEGRATED INTO EXISTING LEARNING DESIGNS. OVERALL, THE PROJECT RESULTS SHOW THAT A CLOSE RELATIONSHIP BETWEEN LEARNING ANALYTICS AND THE LEARNING DESIGN OF COURSES IS URGENTLY NEEDED TO MAKE LEARNING ANALYTICS EFFECTIVE.
THE ISSUE OF SCHOOL DROPOUT INVOLVES FACTORS SUCH AS STUDENTS' ENGAGEMENT THAT CAN PREDICT HIS OR HER SUCCESS IN SCHOOL. IT HAS BEEN SHOWN THAT STUDENT ENGAGEMENT HAS THREE COMPONENTS: BEHAVIORAL, EMOTIONAL AND COGNITIVE. MOTIVATION AND ENGAGEMENT ARE STRONGLY RELATED SINCE THE FORMER IS A PRECURSOR OF ENGAGEMENT. THE AIM OF THIS STUDY WAS TO COMPARE THE EFFICIENCY OF LINEAR REGRESSION AGAINST TWO DATA MINING TECHNIQUES TO PREDICT THE STUDENTS' ACADEMIC PERFORMANCE IN HIGHER EDUCATION. A DESCRIPTIVE CROSSSECTIONAL STUDY WAS CARRIED OUT WITH 222 STUDENTS FROM A PUBLIC HIGHER EDUCATION INSTITUTION IN MEXICO CITY. AN ANALYSIS OF HIERERCHICAL LINEAR REGRESSION (LR) AND LEARNING ANALYTICS TECHNIQUES SUCH AS NEURAL NETWORKS (NN) AND SUPPORT VECTOR MACHINE (SVM) WAS CONDUCTED. TO ASSESS THE ACCURACY OF THE LEARNING ANALYTICS TECHNIQUES, AN ANALYSIS OF VARIANCE (ANOVA) WAS CARRIED OUT. THE TECHNIQUES WERE COMPARED USING CROSS VALIDATION. THE RESULTS SHOWED THAT BEHAVIORAL ENGAGEMENT AND SELF-EFFICACY HAD POSITIVE EFFECTS ON STUDENT ACHIEVEMENTS, WHILE PASSIVITY SHOWED A NEGATIVE EFFECT. LIKEWISE, THE LR AND SVM TECHNIQUES HAD THE SAME PERFORMANCE ON PREDICTING STUDENTS' ACHIEVEMENTS. THE LR HAS THE ADVANTAGE OF PRODUCING A SIMPLE AND EASY MODEL. ON THE CONTRARY, THE SVM TECHNIQUE GENERATES A MORE COMPLEX MODEL. ALTHOUGH, IF THE MODEL WERE AIMED TO FORECAST THE PERFORMANCE, THE SVM TECHNIQUE WOULD BE THE MOST APPROPRIATE, SINCE IT DOES NOT REQUIRE TO VERIFY ANY STATISTICAL ASSUMPTION.
A SMART LEARNING ANALYTICS SYSTEM IS PROPOSED HERE. IN THE FIELD OF RESEARCH, EDUCATION LEARNING ANALYTICS IS MANIPULATED AS AN OPTIMISTIC WAY TO RENDER LEARNING. IN THIS PROPOSED SYSTEM, WE PUT FORWARD AN IDEA REGARDING THE DESIGN OF LEARNING ANALYTICS BASED ON DOCUMENT- DRIVEN DECISION SUPPORT SYSTEM (DSS) AND BANGIA HANDWRITTEN WORDS RECOGNITION. THE PROPOSED LEARNING ANALYTICS SYSTEM AIMS AT CLASSIFYING THE DOCUMENTS VIA SYNTACTIC ANALYSIS AS WELL AS CATEGORIZING THEM BY SEMANTIC ANALYSIS OF THEIR CONTENTS. BUT THE DOCUMENTS MAY BE PRESENTED AS PRINTED FORMAT OR HAND WRITTEN FORMAT. TO CHOOSE THE OPTIMAL FEATURE VECTOR FOR HAND WRITTEN WORDS RECOGNITION (ESPECIALLY FOR BANGIA BASIC CHARACTERS) PROJECTION- BASED FEATURES ARE MOSTLY EFFICIENT. DECISION MAKERS USE DSS FOR DECISION MAKING WHICH IS ACTUALLY A COMPUTERIZED INFORMATION SYSTEM. WE PROPOSE A ROUTE THROUGH THE CLASSIFIED OBSERVATIONS FOR PROVIDING THE EXPLANATION WHICH MAY IMPROVE THE ACCEPTANCE OF THE DECISION MAKER WITH A BETTER CORRECTION RATE USING KN N ALGORITHM. THIS SYSTEM CAN CERTAINLY ASSIST RESEARCHERS FOR LEARNING AS WELL AS DECISION MAKING.
THIS PAPER AIMS TO DEMONSTRATE THE ONGOING WORK OF DEVELOPING A FRAMEWORK THAT WILL ALLOWS TO IMPROVE PERFORMANCE TO STUDENTS. THE FRAMEWORK COMBINES USE OF THE OPEN SOURCE VIRTUAL WORLDS, THE SLOODLE MODULE AND A LEARNING ANALYTICS TOOL, IN ORDER TO FACILITATE THE EXECUTION OF THE COLLABORATIVE LEARNING TECHNIQUES AND IMPROVED THE PERFORMANCE TO STUDENTS THROUGH OF ANALYTICS LEARNING TOOL MONITORIZATION. THIS FRAMEWORK IS STILL IN THE DESIGN PHASE AND WILL LATER BE TESTED IN A CLASSROOM CONTEXT. THE TARGET PUBLIC WILL BE STUDENTS OF THE FIFTH YEAR OF BASIC EDUCATION, WITH AIM OF IMPROVE THE LEARNING MATHEMATICS.
LEARNING ANALYTICS (LA) HAS THE POTENTIAL TO GENERATE NEW INSIGHTS INTO THE COMPLEXITIES OF LEARNING BEHAVIOURS IN LANGUAGE MASSIVE OPEN ONLINE COURSES (LMOOCS). IN LA, THE COLLECTIVE ATTENTION MODEL TAKES AN ECOLOGICAL SYSTEM VIEW OF THE DYNAMIC PROCESS OF UNEQUAL PARTICIPATION PATTERNS IN ONLINE AND FLEXIBLE LEARNING ENVIRONMENTS. IN THIS STUDY, THE 'ORAL COMMUNICATION FOR EFL LEARNERS (SPRING)' ON XUETANGX WAS SELECTED AS AN EXAMPLE WITH WHICH TO EXAMINE THE ALLOCATION OF LEARNER ATTENTION IN THE CONTEXT OF LMOOCS. THE OPEN-FLOW NETWORK OF COLLECTIVE ATTENTION WAS USED TO MODEL THE DYNAMICS OF LEARNING BEHAVIOURS TO UNDERSTAND HOW DIFFERENT COHORTS OF SECOND LANGUAGE (L2) LEARNERS ALLOCATED THEIR ATTENTION AT THE COLLECTIVE LEVEL. THE RESULTS SHOWED THAT WHAT DISTINGUISHED HIGH-PERFORMING L2 LEARNERS WAS RELATED LESS TO WHERE THEY STARTED WITH LMOOC RESOURCES OR HOW MUCH ATTENTION THEY ALLOCATED TO CERTAIN LEARNING UNITS AND MORE TO THE EXTENT TO WHICH THEIR ATTENTION COULD BE MAINTAINED AND CIRCULATED INTO OTHER LEARNING UNITS. IN ADDITION, LEARNERS' ATTENTION TYPICALLY FOLLOWED THE PRE-DESIGNED COURSE STRUCTURE EACH TIME THEY ENTERED THE ONLINE SPACE. NO LEARNING RESOURCES OFFERED IN THE SELECTED LMOOC WERE FOUND TO DOMINATE THE COLLECTIVE ATTENTION FLOW, WHICH SUGGESTED THAT L2 LEARNERS' ONLINE ENGAGEMENT FOLLOWED CLASSROOM LEARNING PATTERNS. THE USE OF LA TO UNDERSTAND THE ALLOCATION OF L2 ATTENTION AT THE COLLECTIVE LEVEL PROVIDES NEW PERSPECTIVES ON DIGITAL BEHAVIOUR IN LMOOCS, WHICH MAY FACILITATE THE DESIGN OF COST-EFFECTIVE L2 RESOURCES THAT PREVENT LEARNER OVERLOAD IN THE INFORMATION-RICH AGE.
EDUCATIONAL DATA MINING AND LEARNING ANALYTICS PROMISE BETTER UNDERSTANDING OF STUDENT BEHAVIOR AND KNOWLEDGE, AS WELL AS NEW INFORMATION ON THE TACIT FACTORS THAT CONTRIBUTE TO STUDENT ACTIONS. THIS KNOWLEDGE CAN BE USED TO INFORM DECISIONS RELATED TO COURSE AND TOOL DESIGN AND PEDAGOGY, AND TO FURTHER ENGAGE STUDENTS AND GUIDE THOSE AT RISK OF FAILURE. THIS WORKING GROUP REPORT PROVIDES AN OVERVIEW OF THE BODY OF KNOWLEDGE REGARDING THE USE OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS FOCUSED ON THE TEACHING AND LEARNING OF PROGRAMMING. IN A LITERATURE SURVEY ON MINING STUDENTS' PROGRAMMING PROCESSES FOR 2005-2015, WE OBSERVE A SIGNIFICANT INCREASE IN WORK RELATED TO THE FIELD. HOWEVER, THE MAJORITY OF THE STUDIES FOCUS ON SIMPLISTIC METRIC ANALYSIS AND ARE CONDUCTED WITHIN A SINGLE INSTITUTION AND A SINGLE COURSE. THIS INDICATES THE EXISTENCE OF FURTHER AVENUES OF RESEARCH AND A CRITICAL NEED FOR VALIDATION AND REPLICATION TO BETTER UNDERSTAND THE VARIOUS CONTRIBUTING FACTORS AND THE REASONS WHY CERTAIN RESULTS OCCUR. WE INTRODUCE A NOVEL TAXONOMY TO ANALYSE REPLICATING STUDIES AND DISCUSS THE IMPORTANCE OF REPLICATING AND REPRODUCING PREVIOUS WORK. WE DESCRIBE WHAT IS THE STATE OF THE ART IN COLLECTING AND SHARING PROGRAMMING DATA. TO BETTER UNDERSTAND THE CHALLENGES INVOLVED IN REPLICATING OR REPRODUCING EXISTING STUDIES, WE REPORT OUR EXPERIENCES FROM THREE CASE STUDIES USING PROGRAMMING DATA. FINALLY, WE PRESENT A DISCUSSION OF FUTURE DIRECTIONS FOR THE EDUCATION AND RESEARCH COMMUNITY.
A COMMON PROBLEM WHEN TRYING TO APPLY DATA MINING TECHNIQUES TO IMPROVE EDUCATIONAL SYSTEMS IS THE DISCONNECTION BETWEEN THOSE WHO HAVE THE EXPERTISE (E.G. UNIVERSITIES) AND THOSE WHO HAVE ACCESS TO THE DATA (E.G. SMALL COMPANIES). BRINGING EXPERTISE INTO EDUCATIONAL IN-PRODUCTION SYSTEMS IS COMPLICATED BECAUSE COMPANIES ARE RELUCTANT TO INVEST A LOT OF EFFORT INTO INTEGRATING NEW TECHNOLOGY THAT THEY DO NOT FULLY TRUST, WHILE THE TECHNOLOGY CANNOT PROVE ITS WORTH WITHOUT ACCESS TO REAL, VALID DATA. IN THIS PAPER WE EXPLORE THE REQUIREMENTS THAT MACHINE LEARNING SYSTEMS HAVE TO BE APPLIED TO SPECIFIC LEARNING PROBLEMS (SEQUENCING AND PERFORMANCE PREDICTION), AND THEN PROPOSE A MINIMALLY INVASIVE PROTOCOL FOR SEQUENCING (BASED ON WEB SERVICES) TO EASILY INTEGRATE LEARNING ANALYTICS SERVICES INTO E-LEARNING SYSTEMS.
THE DIVERSITY OF SUBSTANTIVE EXPERTISE AND ANALYTICAL TECHNIQUES SPANNED BY LEARNING ANALYTICS CAN BE A CHALLENGE FOR UNDERSTANDING AND INTERROGATING METHODOLOGICAL CHOICES. BUT ACTIVE PURSUIT OF METHODOLOGICAL CLARITY IS ESSENTIAL TO PRODUCTIVE MULTIVOCALITY. THIS WAS THE FOCUS OF THE LAK '17 METHODOLOGY IN LEARNING ANALYTICS WORKSHOP FROM WHICH THE IDEA FOR THIS SPECIAL SECTION AROSE. DISCUSSIONS WERE FRAMED USING A BUILDING METAPHOR OF DEVELOPING STRUCTURALLY SOUND BRIDGES BETWEEN DISCIPLINARY METHODS. STRONG ARGUMENTS ARE BUTTRESSED BY SUPPORTING EVIDENCE AND WARRANTS BUT ALSO ENGAGE WITH POTENTIAL ALTERNATIVE EXPLANATIONS. PAPERS IN THIS SPECIAL SECTION FURTHER THE DISCUSSION AND ENCOMPASS A RANGE OF RELEVANT APPROACHES INCLUDING SENSITIVITY ANALYSIS, MODEL EVALUATION, MODEL FIT, CAUSAL INFERENCE, AND VISUALIZATION AS A METHODOLOGY. ALL ADVANCE THE CASE OF STRUCTURAL INTEGRITY IN THIS BRIDGE DISCIPLINE AND HIGHLIGHT THE IMPORTANCE OF ADEQUATE DESCRIPTIONS OF METHODOLOGICAL CONSIDERATIONS TO AID OUR COMMON UNDERSTANDING.
THE RESEARCH AREA RELATED TO THE USE OF LEARNING ANALYTICS AND THE PREDICTION OF STUDENT PERFORMANCE IS MULTIDIMENSIONAL; THEREFORE, IT CAN BE EXPLORED AND ANALYZED THROUGH DIFFERENT PERSPECTIVES. THIS RESEARCH ADDRESSES THE RELATIONSHIP BETWEEN PEDAGOGICAL INTERVENTIONS BASED ON LEARNING ANALYTICS AND STUDENT LEARNING PERFORMANCE. THE RESEARCH PROBLEM OF PREDICTING STUDENT PERFORMANCE CAN BE ANALYZED FROM VARIOUS ANGLES. THIS STUDY PRESENTS AN ANALYSIS BASED ON THE TECHNIQUE OF PATH ANALYSIS (PA) AND PROPOSES A MODEL BASED ON THE FOLLOWING VARIABLES: MEDIATION, MOTIVATION, COMMUNICATION, LEARNING DESIGN, AND LEARNING PERFORMANCE. THE STUDY'S FINDINGS DEMONSTRATE THE IMPORTANCE OF THE ROLE OF VIRTUAL TUTORS IN CARRYING OUT PEDAGOGICAL INTERVENTIONS THANKS TO THE INFORMATION RETRIEVED FROM THE LEARNING ANALYTICS TOOLS AND ITS APPROPRIATE ANALYSIS.
ADVANCES IN TECHNOLOGY HAVE GIVEN THE LEARNING ANALYTICS (LA) AREA FURTHER POTENTIAL TO ENHANCE THE LEARNING PROCESS BY USING METHODS AND TECHNIQUES THAT HARNESS EDUCATIONAL DATA. HOWEVER, THE LACK OF GUIDELINES ON WHAT SHOULD BE TAKEN INTO CONSIDERATIONS DURING APPLICATION OF LA HINDERS ITS FULL ADOPTION. THEREFORE, THIS ARTICLE INVESTIGATES THE ISSUES THAT SHOULD BE CONSIDERED DURING THE DESIGN OF LA EXPERIENCE FROM THE DATA USE PERSPECTIVE. THE RESULTS OBTAINED PRESENT A VALIDATED LA FRAMEWORK WHICH IS COMPOSED OF EIGHTEEN VALIDATED KEY ISSUES THAT SHOULD BE CONSIDERED BY VARIOUS STAKEHOLDERS IN THEIR CONTEXTS TO ENHANCE DESIGNING LA EXPERIENCES. THIS FRAMEWORK CAN ALSO BE USED BY RESEARCHERS AND PRACTITIONERS TO LEARN MORE ABOUT LA AND ITS DESIGNING ISSUES.
IN RECENT YEARS THE USE OF E-PORTFOLIOS HAS INCREASED IN TEACHER EDUCATION. MOREOVER, ANOTHER RAPIDLY EVOLVING AREA IN TEACHER EDUCATION IS LEARNING ANALYTICS (LA). THIS PAPER REPORTS THE EXPERIENCES OF 13 STUDENT TEACHERS IN THE IMPLEMENTATION OF AN E-PORTFOLIO THAT IS ENRICHED WITH LA IN A TEACHER EDUCATION PROGRAMME. THIRTEEN STUDENT TEACHERS OF PRIMARY SCHOOL TEACHER CURRICULUM RECEIVED FEEDBACK AND WERE ASSESSED BY THEIR SUPERVISORS IN THEIR INTERNSHIP VIA AN E-PORTFOLIO WITH LA. QUESTIONNAIRE AND A FOCUS GROUP INTERVIEW WERE ADMINISTERED AMONG THE PARTICIPANTS TO IDENTIFY HOW THE STUDENT TEACHERS PERCEIVE THE JOB-FIT AND THE EFFORT EXPECTANCY OF THE E-PORTFOLIO WITH THE LA APPLICATION AND TO INDICATE THE CHALLENGES ENCOUNTERED. THE STUDY REVEALED SEVERAL POSITIVE EVIDENCES: THE STUDENT TEACHERS WERE ON AGREEMENT THAT E-PORTFOLIO WITH LA IS TIME-ECONOMISING AND EASY TO USE. THE STUDENT TEACHERS APPRECIATED THAT THEY RECEIVED A GOOD OVERVIEW ABOUT THEIR PROFESSIONAL DEVELOPMENT. AS A CHALLENGE, MANY STUDENT TEACHERS QUESTIONED WHETHER THE USE OF E-PORTFOLIO CAN INCREASE THE QUALITY OF THEIR PROFESSIONAL ACTIVITIES (E.G. PLANNING LESSONS, CARRYING OUT TEACHING ACTIVITIES AND EVALUATING PUPILS' LEARNING). FUTURE RESEARCH SHOULD FOCUS ON HOW TO SUPPORT THE STUDENT TEACHERS SO THAT THEY WOULD COMPREHEND THE BENEFITS OF THE E-PORTFOLIO WITH LA ON THEIR PROFESSIONAL ACTIVITIES AND HOW TO INTEGRATE HUMAN EXPERTISE IN A MORE DYNAMIC WAY.
THE FOCUS OF THIS CASE STUDY IS THE EXPLOITATION OF VISUAL LEARNING ANALYTICS COUPLED WITH THE PROVISION OF FEEDBACK AND SUPPORT PROVIDED TO THE STUDENTS AND THEIR IMPACT IN PROVOKING CHANGE AT STUDENT PROGRAMMING HABITS. TO THIS END, WE DISCUSS MECHANISMS OF CAPTURING AND ANALYSING THE DEBUGGING HABITS AND THE QUALITY OF THE DESIGN SOLUTIONS PROVIDED BY THE STUDENTS IN THE CONTEXT OF AN OBJECT-ORIENTED PROGRAMMING COURSE. WE INSTRUMENTED THE PROGRAMMING ENVIRONMENT USE BY THE STUDENTS IN ORDER TO TRACK THE STUDENT BEHAVIOR AND VISUALIZE METRICS ASSOCIATED WITH IT, WHILE THE STUDENTS DEVELOPED PROGRAMS IN JAVA.
THIS WORKSHOP ON LEARNING ANALYTICS FOR CURRICULUM AND PROGRAM QUALITY IMPROVEMENT INVESTIGATES HOW LAK CAN DRIVE IMPROVEMENTS IN TEACHING PRACTICES, INSTRUCTIONAL AND CURRICULAR DESIGN, AND ACADEMIC PROGRAM DELIVERY. THIS WORKSHOP BRINGS FORWARD RESEARCH AND EXAMPLES OF HOW LAK CAN HELP BUILD THE CASE FOR INSTRUCTIONAL, CURRICULAR, OR PROGRAMMATIC CHANGE AND FURTHER HOW LAK CAN BE USED TO FOSTER ACCEPTANCE OF CHANGE PROCESSES BY TEACHERS, ADMINISTRATORS, AND OTHER STAKEHOLDERS IN THE EDUCATIONAL ENTERPRISE.
IT HAS BEEN LONG ARGUED THAT LEARNING ANALYTICS HAS THE POTENTIAL TO ACT AS A "MIDDLE SPACE" BETWEEN THE LEARNING SCIENCES AND DATA ANALYTICS, CREATING TECHNICAL POSSIBILITIES FOR EXPLORING THE VAST AMOUNT OF DATA GENERATED IN ONLINE LEARNING ENVIRONMENTS. ONE COMMON LEARNING ANALYTICS INTERVENTION IS THE LEARNING DASHBOARD, A SUPPORT TOOL FOR TEACHERS AND LEARNERS ALIKE THAT ALLOWS THEM TO GAIN INSIGHT INTO THE LEARNING PROCESS. ALTHOUGH SEVERAL RELATED WORKS HAVE SCRUTINISED THE STATE-OF-THE-ART IN THE FIELD OF LEARNING DASHBOARDS, NONE HAVE ADDRESSED THE THEORETICAL FOUNDATION THAT SHOULD INFORM THE DESIGN OF SUCH INTERVENTIONS. IN THIS SYSTEMATIC LITERATURE REVIEW, WE ANALYSE THE EXTENT TO WHICH THEORIES AND MODELS FROM LEARNING SCIENCES HAVE BEEN INTEGRATED INTO THE DEVELOPMENT OF LEARNING DASHBOARDS AIMED AT LEARNERS. OUR CRITICAL EXAMINATION REVEALS THE MOST COMMON EDUCATIONAL CONCEPTS AND THE CONTEXT IN WHICH THEY HAVE BEEN APPLIED. WE FIND EVIDENCE THAT CURRENT DESIGNS FOSTER COMPETITION BETWEEN LEARNERS RATHER THAN KNOWLEDGE MASTERY, OFFERING MISGUIDED FRAMES OF REFERENCE FOR COMPARISON.
IN RECENT EARS, SEVERAL TOOLS TO SUPPORT LEARNING PROCESSES HAVE EMERGED FROM THE RESEARCH ON LEARNING ANALYTICS (LA). HOWEVER, LITTLE ATTENTION HAS BEEN GIVEN TO THE CONTEXTS WHERE LEARNING TAKES PLACE, AND TO THE STAKEHOLDERS INVOLVED IN IMPLEMENTING AND USING THESE TOOLS. FOLLOWING THE SHEILA FRAMEWORK, WE PRESENT THE RESULTS OF INTERVIEWS AND FOCUS GROUPS WITH STUDENTS FROM A BRAZILIAN PUBLIC UNIVERSITY ON THEIR PERCEPTIONS OF THE POTENTIAL AND RISKS OF USING LA IN HIGHER EDUCATION. FINDINGS INDICATE GREAT DIFFICULTIES TO USE THE LEARNING MANAGEMENT SYSTEM; LACK OF EFFECTIVE COMMUNICATION WITH TEACHING STAFF; LACK OF QUALITY AND TIMELY FEEDBACK; HIGH PERCEIVED VALUE OF PERSONALIZATION; REJECTION OF COMPETITION AND RANKINGS; ISSUES WITH SELF-CONFIDENCE; AND TRUSTWORTHINESS OF THE EDUCATIONAL INSTITUTION. FROM THE ANALYSIS OF STUDENTS' PERSPECTIVES, WE POINT TO OPPORTUNITIES TO USE LA IN THE CONTEXT OF A BRAZILIAN PUBLIC UNIVERSITY.
NOWADAYS, THERE ARE QUITE A FEW EXPERIENCES OF THE USE OF THE FLIPPED CLASSROOM (FC) IN ENGINEERING EDUCATION DUE TO THEIR ADVANTAGES IN COMPARISON WITH THE TRADITIONAL METHODOLOGY. THE POSSIBILITY OF PROMOTING STUDENTS' ACTIVE LEARNING UNDER THE FC CAN LEAD TO IMPROVEMENTS IN THE LEARNING OUTCOMES. NONETHELESS, THERE ARE SEVERAL ISSUES THAT ARISE WHEN USING THE FC (SUCH AS THE STUDENTS' NEED OF PREPARING THE FACE-TO-FACE LESSON) THAT MUST BE ADDRESSED. TO TAKE ADVANTAGE OF THE WHOLE POTENTIAL OF THE FC, WE PROPOSE THE APPLICATION OF LEARNING ANALYTICS (LA) TO IMPROVE THE FC. IN PARTICULAR, WE DEFINE A METHODOLOGY THAT PROPOSES A SET OF RECOMMENDED ACTIONS TO BE DONE TO ENHANCE A SPECIFIC TYPE OF FLIPPED CLASSROOM THROUGH LA. MOREOVER, WE PRESENT A LA TOOL DESIGNED AND IMPLEMENTED SPECIFICALLY FOR THIS METHODOLOGY. THIS TOOL ALLOWS TEACHERS TO OBTAIN RELEVANT INFORMATION NEEDED TO IMPROVE THEIR FC EXPERIENCES. THE INFORMATION IS PROVIDED USING SEVERAL VISUALIZATIONS THAT ARE ANALYZED AND EXPLAINED IN DETAIL. WE ILLUSTRATE THE USE OF THE TOOL WITH STUDENTS FROM ENGINEERING COURSES.
DEVELOPING DATA VISUALISATION TOOLS TO SUPPORT ACADEMICS IN THE CLASSROOM IS A CHALLENGING PROCESS DUE TO THE KEY REQUIREMENTS OF USEFULNESS AND SCALABILITY, AND THE CONSTRAINTS OF A UNIVERSITY ECOSYSTEM. HERE WE DESCRIBE THE EVOLUTION OF AN ENTERPRISE-LEVEL, TEACHER-FACING DASHBOARD, DESIGNED TO DISPLAY DATA ABOUT STUDENTS' ENROLMENTS AND USE OF THE LEARNING MANAGEMENT SYSTEM IN A MEANINGFUL WAY, AND SUMMARISE THE CHALLENGES AND LESSONS WE ENCOUNTERED ALONG THE WAY. THIS LARGE UNIVERSITY HAS A MATURING LEARNING ANALYTICS UNIT, A NEW, DATA-FRIENDLY LMS SYSTEM, AND DATA-SAVVY AND DATA-HUNGRY EXECUTIVE LEADERSHIP. YET THE EXPERIENCED PATHWAY AND EVOLUTIONARY STEPS EVIDENCE THE POINTS THAT NEED BE RESOLVED TO SUCCESSFULLY DELIVER AND TRANSITION TO LEARNING ANALYTICS SOLUTIONS THAT HAVE PREVIOUSLY BEEN CONCEPTUALLY PROPOSED OR TESTED AT SMALL SCALES IN OTHER STUDIES. THE KEY FINDINGS THROUGH THE PROCESS HIGHLIGHT THE LEVEL OF UPLIFT (IN TECH, CAPACITY AND CAPABILITY) THAT UNIVERSITIES NEED TO MEET CONTEMPORARY DEMANDS AND FUTURE POSSIBILITIES.
THE WAY OF TEACHING AND LEARNING IN TWENTY-FIRST CENTURY SOCIETY CONTINUES TO CHANGE. AT PRESENT, A HIGH PERCENTAGE OF TEACHING TAKES PLACE THROUGH LEARNING MANAGEMENT SYSTEMS THAT APPLY LEARNING ANALYTICS TECHNIQUES. THE USE OF THESE TOOLS, AMONG OTHER THINGS, FACILITATES KNOWLEDGE OF STUDENT LEARNING PATTERNS AND THE DETECTION OF AT-RISK STUDENTS. THE AIM OF THIS STUDY IS TO ESTABLISH THE MOST EFFECTIVE LEARNING PATTERNS OF THE STUDENTS ON THE PLATFORM IN A HIERARCHICAL ORDER OF IMPORTANCE. IT WAS CONDUCTED OVER TWO ACADEMIC YEARS WITH 122 STUDENTS OF HEALTH SCIENCES. THE INSTRUMENTS USED WERE THE MOODLE V.3.1 PLATFORM AND THE ANALYSIS OF LOGS WITH MACHINE LEARNING REGRESSION TECHNIQUES. THE RESULTS INDICATED THAT THE AUTOMATIC LINEAR PREDICTION MODEL DETECTED BY ORDER OF IMPORTANCE: AVERAGE VISITS PER DAY, STUDENT SELF-ASSESSMENT QUESTIONNAIRES, AND TEACHER FEEDBACK. THE PERCENTAGE VARIANCE OF THE FINAL RESULTS EXPLAINED BY THESE VARIABLES WAS 50.8%. LIKEWISE, THE EFFECTIVENESS OF THE BEHAVIORAL PATTERN EXPLAINED 64.1% OF THE VARIANCE IN THOSE RESULTS, FINDING THREE CLUSTERS OF EFFECTIVENESS IN THE BEHAVIORAL PATTERNS THAT WERE DETECTED.
AUTOMATIC TEXT CATEGORIZATION IS AN EFFECTIVE WAY TO ORGANIZE LARGE TEXT DATASETS IN DIGITAL LIBRARIES (DL). HOWEVER, MOST OF THE AVAILABLE MACHINE LEARNING TOOLS ARE COMPLEX AND GO BEYOND THE SCOPE OF WHAT A DIGITAL LIBRARY CURATOR NEED OR IS ABLE TO DO IN ORDER TO CLASSIFY THE OBJECTS OF A DL. DRAWING INSPIRATION FROM THE FIELD OF LEARNING ANALYTICS AND INTERACTIVE MACHINE LEARNING, WE DESIGN AND IMPLEMENT VISUAL INTERACTIVE CLASSIFIERS THAT ARE INTUITIVE TO TRAIN AND EASY TO USE. IN THIS POSTER, WE PRESENT AN INTERACTIVE WEB APPLICATION IN R THAT ALLOWS USERS TO USE TEXT CLASSIFIER IN AN INNOVATIVE WAY.
LEARNING ANALYTICS (LA) AND TOOLS FOR INTELLIGENT ANALYSIS OF DATA ACCUMULATED IN THE INFORMATION SYSTEMS USED IN HIGHER EDUCATION INSTITUTIONS (HEIS) PROVIDE AN OPPORTUNITY TO INCREASE THE EFFECTIVENESS OF MONITORING, MANAGEMENT, QUALITY ASSURANCE AND EVALUATION OF TRAINING DELIVERED TO ALL MANAGEMENT GROUPS WHICH MAKE DECISIONS IN HIGHER EDUCATION INSTITUTIONS - PROGRAMME MANAGERS, FACULTY AND UNIVERSITY MANAGERS. THE PAPER PRESENTS LA MODELS AND A CORRESPONDENT SOFTWARE TOOL DESIGNED FOR THE NEEDS OF DECISION MAKING BODIES IN BULGARIAN HEI (PROGRAMME MANAGERS, DEANS AND RECTOR). THE TOOL ALLOWS THEM TO MONITOR THE LEARNING PROCESS AND MAKE TIMELY DATA-DRIVEN DECISIONS TO IMPROVE INSTITUTIONAL PROCESSES IN MANY ASPECTS. RESEARCH AND EXPERIMENTS WITH THE MODELS AND THE LA TOOL UNDER CONSIDERATION ARE CONDUCTED ON THE BASIS OF THE INFORMATION INFRASTRUCTURE OF A TYPICAL BULGARIAN UNIVERSITY - UNIVERSITY OF PLOVDIV "PAISII HILENDARSKI".
CURRENTLY THE WORLD AROUND US "REBOOTS" EVERY MINUTE AND "STAYING AT THE FOREFRONT" SEEMS TO BE A VERY ARDUOUS TASK. THE CONTINUOUS AND "SPEEDED" PROGRESS OF SOCIETY REQUIRES, FROM ALL THE ACTORS, A DYNAMIC AND EFFICIENT ATTITUDE BOTH IN TERMS PROGRESS MONITORING AND MOVING ADAPTATION. WITH REGARD TO EDUCATION, NO MATTER HOW UPDATED WE ARE IN RELATION TO THE CONTENTS, THE DIDACTIC STRATEGIES AND TECHNOLOGICAL RESOURCES, WE ARE INEVITABLY COMPELLED TO ADAPT TO NEW PARADIGMS AND RETHINK THE TRADITIONAL TEACHING METHODS. IT IS IN THIS CONTEXT THAT THE CONTRIBUTION OF E-LEARNING PLATFORMS ARISES. HERE TEACHERS AND STUDENTS HAVE AT THEIR DISPOSAL NEW WAYS TO ENHANCE THE TEACHING AND LEARNING PROCESS, AND THESE PLATFORMS ARE SEEN, AT THE PRESENT TIME, AS SIGNIFICANT VIRTUAL TEACHING AND LEARNING SUPPORTING ENVIRONMENTS. THIS PAPER PRESENTS A PROJECT AND ATTEMPTS TO ILLUSTRATE THE POTENTIAL THAT NEW TECHNOLOGIES PRESENT AS A "BACKING" TOOL IN DIFFERENT STAGES OF TEACHING AND LEARNING AT DIFFERENT LEVELS AND AREAS OF KNOWLEDGE, PARTICULARLY IN MATHEMATICS. WE INTEND TO PROMOTE A CONSTRUCTIVE DISCUSSION MOMENT, EXPOSING OUR ACTUAL PERCEPTION - THAT THE USE OF THE LEARNING MANAGEMENT SYSTEM MOODLE, BY HIGHER EDUCATION TEACHERS, AS SUPPLEMENTARY TEACHING-LEARNING ENVIRONMENT FOR VIRTUAL CLASSROOM SESSIONS CAN CONTRIBUTE FOR GREATER EFFICIENCY AND EFFECTIVENESS OF TEACHING PRACTICE AND TO IMPROVE STUDENT ACHIEVEMENT. REGARDING THE LEARNING ANALYTICS EXPERIENCE WE WILL PRESENT A FEW RESULTS OBTAINED WITH SOME ASSESSMENT LEARNING ANALYTICS TOOLS, WHERE WE PROFOUNDLY FELT THAT THE ASSESSMENT OF STUDENTS' PERFORMANCE IN ONLINE LEARNING ENVIRONMENTS IS A CHALLENGING AND DEMANDING TASK.
THE NUMBER OF PUBLICATIONS IN THE FIELD OF MEDICAL EDUCATION IS STILL MARKEDLY LOW, DESPITE RECOGNITION OF THE VALUE OF THE DISCIPLINE IN THE MEDICAL EDUCATION LITERATURE, AND EXPONENTIAL GROWTH OF PUBLICATIONS IN OTHER FIELDS. THIS NECESSITATES RAISING AWARENESS OF THE RESEARCH METHODS AND POTENTIAL BENEFITS OF LEARNING ANALYTICS (LA). THE AIM OF THIS PAPER WAS TO OFFER A METHODOLOGICAL SYSTEMIC REVIEW OF EMPIRICAL LA RESEARCH IN THE FIELD OF MEDICAL EDUCATION AND A GENERAL OVERVIEW OF THE COMMON METHODS USED IN THE FIELD IN GENERAL. SEARCH WAS DONE IN MEDLINE DATABASE USING THE TERM "LA." INCLUSION CRITERIA INCLUDED EMPIRICAL ORIGINAL RESEARCH ARTICLES INVESTIGATING LA USING QUALITATIVE, QUANTITATIVE, OR MIXED METHODOLOGIES. ARTICLES WERE ALSO REQUIRED TO BE WRITTEN IN ENGLISH, PUBLISHED IN A SCHOLARLY PEER-REVIEWED JOURNAL AND HAVE A DEDICATED SECTION FOR METHODS AND RESULTS. A MEDLINE SEARCH RESULTED IN ONLY SIX ARTICLES FULFILLING THE INCLUSION CRITERIA FOR THIS REVIEW. MOST OF THE STUDIES COLLECTED DATA ABOUT LEARNERS FROM LEARNING MANAGEMENT SYSTEMS OR ONLINE LEARNING RESOURCES. ANALYSIS USED MOSTLY QUANTITATIVE METHODS INCLUDING DESCRIPTIVE STATISTICS, CORRELATION TESTS, AND REGRESSION MODELS IN TWO STUDIES. PATTERNS OF ONLINE BEHAVIOR AND USAGE OF THE DIGITAL RESOURCES AS WELL AS PREDICTING ACHIEVEMENT WAS THE OUTCOME MOST STUDIES INVESTIGATED. RESEARCH ABOUT LA IN THE FIELD OF MEDICAL EDUCATION IS STILL IN INFANCY, WITH MORE QUESTIONS THAN ANSWERS. THE EARLY STUDIES ARE ENCOURAGING AND SHOWED THAT PATTERNS OF ONLINE LEARNING CAN BE EASILY REVEALED AS WELL AS PREDICTING STUDENTS' PERFORMANCE.
WE ADVOCATE FOR AND INTRODUCE LEARNSENSE, A FRAMEWORK FOR LEARNING ANALYTICS USING COMMODITY WEARABLE DEVICES TO CAPTURE LEARNER'S PHYSICAL ACTIONS AND ACCORDINGLY INFER LEARNER CONTEXT (E.G., STUDENT ACTIVITIES AND ENGAGEMENT STATUS IN CLASS). OUR WORK IS MOTIVATED BY THE OBSERVATIONS THAT: (A) THE FINE-GRAINED INDIVIDUAL-SPECIFIC LEARNER ACTIONS ARE CRUCIAL TO UNDERSTAND LEARNERS AND THEIR CONTEXT INFORMATION; (B) SENSOR DATA AVAILABLE ON THE LATEST WEARABLE DEVICES (E G., WRIST-WORN AND EYE WEAR DEVICES) CAN EFFECTIVELY RECOGNIZE LEARNER ACTIONS AND HELP TO INFER LEARNER CONTEXT INFORMATION; (C) THE COMMODITY WEARABLE DEVICES THAT ARE WIDELY AVAILABLE ON THE MARKET CAN PROVIDE A HASSLE-FREE AND NON-INTRUSIVE SOLUTION. FOLLOWING THE ABOVE OBSERVATIONS AND UNDER THE PROPOSED FRAMEWORK, WE DESIGN AND IMPLEMENT A SENSOR-BASED LEARNER CONTEXT COLLECTOR RUNNING ON THE WEARABLE DEVICES. THE LATEST DATA MINING AND SENSOR DATA PROCESSING TECHNIQUES ARE EMPLOYED TO DETECT DIFFERENT TYPES OF LEARNER ACTIONS AND CONTEXT INFORMATION. FURTHERMORE, WE DETAIL ALL OF THE ABOVE EFFORTS BY OFFERING A NOVEL AND EXEMPLARY USE CASE: IT SUCCESSFULLY PROVIDES THE ACCURATE DETECTION OF STUDENT ACTIONS AND INFERS THE STUDENT ENGAGEMENT STATES IN CLASS. THE SPECIFICALLY DESIGNED LEARNER CONTEXT COLLECTOR HAS BEEN IMPLEMENTED ON THE COMMODITY WRIST-WORN DEVICE. BASED ON THE COLLECTED AND INFERRED LEARNER INFORMATION, THE NOVEL INTERVENTION AND INCENTIVIZING FEEDBACK ARE INTRODUCED INTO THE SYSTEM SERVICE. FINALLY, A COMPREHENSIVE EVALUATION WITH THE REAL-WORLD EXPERIMENTS, SURVEYS AND INTERVIEWS DEMONSTRATES THE EFFECTIVENESS AND IMPACT OF THE PROPOSED FRAMEWORK AND THIS USE CASE. THE F1 SCORE FOR THE STUDENT ACTION CLASSIFICATION TASKS ACHIEVE 0.9, AND THE SYSTEM CAN EFFECTIVELY DIFFERENTIATE THE DEFINED THREE LEARNER STATES. FINALLY, THE SURVEY RESULTS SHOW THAT THE LEARNERS ARE SATISFIED WITH THE USE OF OUR SYSTEM (MEAN SCORE OF 3.7 WITH A STANDARD DEVIATION OF 0.55).
RECENTLY, RESEARCH HAS HIGHLIGHTED THE POTENTIAL OF MASSIVE OPEN ONLINE COURSES (MOOCS) FOR EDUCATION, AS WELL AS THEIR DRAWBACKS, WHICH ARE WELL KNOWN. SEVERAL STUDIES STATE THAT THE MAIN LIMITATIONS OF THE MOOCS ARE LOW COMPLETION AND HIGH DROPOUT RATES OF PARTICIPANTS. HOWEVER, MOOCS SUFFER ALSO FROM THE LACK OF PARTICIPANT ENGAGEMENT, PERSONALIZATION, AND DESPITE THE FACT THAT SEVERAL FORMATS AND TYPES OF MOOCS ARE REPORTED IN THE LITERATURE, THE MAJORITY OF THEM CONTAIN A CONSIDERABLE AMOUNT OF CONTENT THAT IS MAINLY PRESENTED IN A VIDEO FORMAT. THIS IS IN CONTRAST TO THE RESULTS REPORTED IN OTHER EDUCATIONAL SETTINGS, WHERE ENGAGEMENT AND ACTIVE PARTICIPATION ARE IDENTIFIED AS SUCCESS FACTORS. WE PRESENT THE RESULTS OF A STUDY THAT INVOLVED EDUCATIONAL EXPERTS AND LEARNING SCIENTISTS GIVING NEW AND INTERESTING INSIGHTS TOWARDS THE CONCEPTUALIZATION OF A NEW DESIGN APPROACH, THE FLIPPED MOOC, APPLYING THE FLIPPED CLASSROOM APPROACH TO THE MOOCS' DESIGN AND MAKING USE OF GAMIFICATION AND LEARNING ANALYTICS. WE FOUND IMPORTANT INDICATIONS, APPLICABLE TO THE CONCEPT OF A FLIPPED MOOC, WHICH ENTAILS TURNING MOOCS FROM MAINLY CONTENT-ORIENTED DELIVERY MACHINES INTO PERSONALIZED, INTERACTIVE, AND ENGAGING LEARNING ENVIRONMENTS. OUR FINDINGS SUPPORT THE IDEA THAT MOOCS CAN BE ENRICHED BY THE ORCHESTRATION OF A FLIPPED CLASSROOM APPROACH IN COMBINATION WITH THE SUPPORT OF GAMIFICATION AND LEARNING ANALYTICS.
PURPOSE THE OBJECTIVE OF THIS PAPER IS TO PRESENT A COMPREHENSIVE REVIEW OF THE LITERATURE ON LEARNING ANALYTICS (LA) STAKEHOLDERS' EXPECTATIONS TO REVEAL THE STATUS OF ONGOING RESEARCH IN THIS AREA AND TO HIGHLIGHT GAPS IN RESEARCH. DESIGN/METHODOLOGY/APPROACH CONDUCTING A LITERATURE REVIEW IS A WELL-KNOWN METHOD TO ESTABLISH KNOWLEDGE AND INTERPRET THE DOMAIN UNDER EXAMINATION (SNYDER, 2019). IN THIS PAPER, A DESCRIPTIVE APPROACH WAS USED TO INVESTIGATE THE DIFFERENT RESEARCH METHODS USED TO TACKLE LA STAKEHOLDERS' EXPECTATIONS, WHICH DEPENDS ON SUMMARISING THE STUDIES' CONTENT AND MINIMISING THE REVIEWER'S INTERPRETATION. FINDINGS MOST OF THE STUDIES HAVE USED INTERVIEWS AND FOCUS GROUPS WITHOUT TESTING ISOLATED VARIABLES. MOREOVER, IT WAS CONCLUDED THAT LA AWARENESS AND PERCEPTION ARE AFFECTED BY A LACK OF RESOURCES, TECHNICAL SKILLS, TRUSTWORTHINESS, DATA AVAILABILITY, ACCESSIBILITY, ORGANISATIONAL CULTURE, INTEGRATION AND LACK OF TECHNOLOGY. 10;THIS PAPER SHEDS LIGHT ON RESEARCH ON LA STAKEHOLDERS' EXPECTATIONS AND HIGHLIGHTS THE NEED FOR INVESTIGATING DEVELOPING COUNTRIES' CONTEXT IN ORDER TO TEST THE PERCEPTION AND IMPACT OF LA. THE STUDY HAS FOCUSSED MAINLY ON PAPERS PUBLISHED IN JOURNALS, WHICH MAY HAVE LED TO MISSING SOME PREVIOUS RESEARCH PAPERS. ORIGINALITY/VALUE LITERATURE REVIEW STUDIES PROVIDE AN OVERVIEW OF THE EXISTING LA LITERATURE FROM GENERAL FOCUS TO ANALYTICAL METHODS. AS IT IS A BROAD TOPIC, THIS PAPER FOCUSES ON REVIEWING STUDIES ABOUT STAKEHOLDERS, WHICH WILL GIVE A FULLER UNDERSTANDING OF HOW THEIR EXPECTATIONS ARE BEING DISCUSSED BEYOND THE CURRENTLY ASSUMED BENEFITS OF LA.
IN THIS PAPER, WE DISCUSS THE DESIGN, DEVELOPMENT, AND IMPLEMENTATION OF A LEARNING ANALYTICS (LA) DASHBOARD IN THE AREA OF HIGHER EDUCATION (HE). THE DASHBOARD MEETS THE DEMANDS OF THE DIFFERENT STAKEHOLDERS, MAXIMIZES THE MAINSTREAMING POTENTIAL AND TRANSFERABILITY TO OTHER CONTEXTS, AND IS DEVELOPED IN THE PATH OF OPEN SOURCE. THE RESEARCH CONCENTRATES ON DEVELOPING AN APPROPRIATE CONCEPT TO FULFIL ITS OBJECTIVES AND FINDING A SUITABLE TECHNOLOGY STACK. THEREFORE, WE DETERMINE THE CAPABILITIES AND FUNCTIONALITIES OF THE DASHBOARD FOR THE DIFFERENT STAKEHOLDERS. THIS IS OF SIGNIFICANT IMPORTANCE AS IT IDENTIFIES WHICH DATA CAN BE COLLECTED, WHICH FEEDBACK CAN BE GIVEN, AND WHICH FUNCTIONALITIES ARE PROVIDED. A KEY APPROACH IN THE DEVELOPMENT OF THE DASHBOARD IS THE MODULARITY. THIS LEADS US TO A DESIGN WITH THREE MODULES: THE DATA COLLECTION, THE SEARCH AND INFORMATION PROCESSING, AND THE DATA PRESENTATION. BASED ON THESE MODULES, WE PRESENT THE STEPS OF FINDING A FITTING OPEN SOURCE TECHNOLOGY STACK FOR OUR CONCEPT AND DISCUSS PROS AND CONS TROUGH OUT THE PROCESS.
TO EXAMINE THE SIMILARITIES AND DIFFERENCES BETWEEN TWO CLOSELY RELATED YET DISTINCT FIELDS - EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) - THIS STUDY CONDUCTED A LITERATURE REVIEW OF THE EMPIRICAL STUDIES PUBLISHED IN BOTH FIELDS. WE SYNTHESIZED 492 LA AND 194 EDM ARTICLES PUBLISHED DURING 2015-2019. WE COMPARED THE SIMILARITIES AND DIFFERENCES IN RESEARCH ACROSS THE TWO FIELDS BY EXAMINING DATA ANALYSIS TOOLS, COMMON KEYWORDS, THEORIES, AND DEFINITIONS LISTED. WE FOUND THAT MOST STUDIES IN BOTH FIELDS DID NOT CLEARLY IDENTIFY A THEORETICAL FRAMEWORK. FOR BOTH FIELDS, THEORIES OF SELF-REGULATED LEARNING ARE MOST FREQUENTLY USED. WE FOUND, THROUGH KEYWORD ANALYSIS, THAT BOTH FIELDS ARE CLOSELY RELATED TO EACH OTHER AS "LEARNING ANALYTICS" IS MOST FREQUENTLY LISTED KEYWORD FOR EDM AND VICE VERSA FOR LA. HOWEVER, ONE NOTABLE DIFFERENCE RELATES TO HOW LA STUDIES LISTED SOCIAL-RELATED KEYWORDS WHEREAS EDM STUDIES LISTED KEYWORDS RELATED TO TECHNICAL METHODS. THE TOOLS USED FOR DATA ANALYSIS OVERLAP LARGELY BUT SOME OF THE LA STUDIES LISTED TOOLS FOR QUALITATIVE DATA ANALYSIS AND SOCIAL NETWORK ANALYSIS WHEREAS EDM STUDIES DID NOT. FINALLY, THE DISTINCTION OF THE TWO FIELDS IS DEFINED DIFFERENTLY BY AUTHORS AS SOME DEMARCATE THE DIFFERENCES WHEREAS SOME ADDRESS THEM INTERCHANGEABLY.
LEARNING PROCESSES SUPPORTED BY MULTIMODAL INTERACTION SYSTEMS DEMAND EFFECTIVE TOOLS TO MEASURE LEARNER PERFORMANCE AND PROVIDE MEANINGFUL FEEDBACK TO STAKEHOLDERS. THIS PAPER REVIEWS KEY FEATURES AND DISCUSSES THE IMPLEMENTATION OF A LEARNING ANALYTICS TOOL THAT IS PART OF AN E-LEARNING MULTIMODAL DIALOGUE SYSTEM, WHICH, IN TURN, IS PART OF AN IMMERSIVE ENVIRONMENT TO SUPPORT DIFFERENT LEARNING SCENARIOS.
THE PRACTICE OF LEARNING DESIGN (LD) AND LEARNING ANALYTICS (LA) IS ANALYSED USING TWO LENSES. FIRSTLY, IT IS ARGUED THAT BOTH LD AND LA INVOLVE THE MAKING OF MODELS. ALL MODELS INVOLVE IDEALISATION, I.E. THE SIMPLIFICATION OF SOMETHING COMPLICATED TO MAKE IT MORE TRACTABLE. VARIOUS EDUCATIONAL ACTORS GENERATE IDEALISATIONS OF THE SYSTEM THEY ARE WORKING IN, BUT ONLY A SUBSET OF THESE IDEALISATIONS IS EMBODIED IN LD AND LA IMPLEMENTATIONS. SECONDLY, LD AND LA BOTH CHANGE THE WAY THAT THE ACTORS IN EDUCATION HANDLE THE COMPLEXITY WHICH THEY ARE FACED WITH IN THEIR PRACTICE. THIS IS ANALYSED IN TERMS OF THE CYBERNETIC CONCEPTS OF VARIETY AND BLACK BOXES. IT IS THEN ARGUED THAT LD AND LA IMPLEMENTATIONS SHARE A TENDENCY TO SHIFT CONTROL TO HIGHER LEVELS IN THE HIERARCHY, AND THAT THIS HAS CONSEQUENCES FOR THE AUTONOMY OF TEACHERS AND LEARNERS, AND FOR THE ACCEPTABILITY TO USERS OF LD AND LA. FINALLY, SOME CONCLUSIONS ARE OFFERED WHICH CAN HELP LD AND LA TO BE IMPLEMENTED WHILE ADDRESSING THE COMMON PROBLEMS THAT HAVE BEEN IDENTIFIED.
IMPROVING QUALITY OF LEARNING IN ONLINE ENVIRONMENTS REQUIRES FORMATIVE AND SUMMATIVE ASSESSMENTS PARTICULAR TO ITS DISTINCTIVE ONLINE LEARNING NATURE AND DYNAMICS. FEEDBACK MECHANISM ABOUT LEARNING ENGAGEMENT COULD BE A SOURCE OF ASSESSMENT FOR THE EDUCATORS AND THE STUDENTS. IN ONLINE LEARNING, NOT ONLY THE EDUCATORS BUT ALSO THE STUDENTS NEED TO GET INFORMED ABOUT THEIR LEARNING ENGAGEMENTS AS CYBERSPACE IS NOT CONTROLLABLE FOR THE EDUCATORS AND STUDENTS COULD BE IN INTERACTION WITH ANY CONTENT OR HUMAN AGENCY WITHOUT TEACHER GUIDANCE AND IN THIS CONTEXT, FEEDBACK BASED ON ANALYTICAL ASSESSMENT IS ESSENTIAL FOR THE SELF-LEARNING ACTIVITIES. SOMETIMES, IN ONLINE GROUP DISCUSSIONS, THE TEACHERS MAY NOT DECIDE WHEN TO INTERRUPT THE ONGOING DISCUSSIONS AND IN PARALLEL WITH THIS, THE STUDENTS IN A LEARNING GROUP MAY BE IN A VICIOUS CIRCLE AND REPEATEDLY DISCUSSING THE SAME TOPIC WITHOUT ANY ADVANCEMENT WHEREAS CRITICAL THINKING SKILLS REQUIRE ADAPTATION OF SOME INSTRUCTIONAL STRATEGIES. IN THESE OCCASIONS, LEARNING ANALYTICS (LA) IS A KEY PROCESS TO PROVIDE EDUCATIONAL STAKEHOLDERS WITH EVIDENCE BASED, ANALYTICAL AND CONTEXTUAL OUTCOMES IN A WAY OF MAKING SENSE OF THEIR LEARNING ENGAGEMENTS. IN THIS CONCEPTUAL FRAMEWORK, OUR RESEARCH HAS ATTEMPTED TO EXAMINE SEQUENTIAL ANALYSIS AS A LIKELY METHOD TO GET ADAPTED BY LA RESEARCHERS WITH ITS PROSPECTS, OPPORTUNITIES AND CHALLENGES. TO THIS END, WE FOCUSED ON A PART OF A BLENDED LEARNING COURSE AND ANALYZED THE STUDENTS' ONLINE DISCUSSIONS TO EXAMINE THE METHOD OF SEQUENTIAL ANALYSIS WITH ITS POTENTIAL TO BE AN EFFICIENT TOOL TO CONDUCT LEARNING ANALYTICS. THE OUTCOMES OF THIS STUDY HAVE IMPLICATIONS FOR THE DESIGN OF AN ONLINE LEARNING ENVIRONMENT AS WELL AS LA AS AN EMERGING AREA.
NOWADAYS, MANY MOOC PLATFORMS HAVE ARISEN TO PROVIDE FREE KNOWLEDGE. THESE PLATFORMS HAVE A LARGE CATALOG OF COURSES FOR DIFFERENT SPECIALIZATIONS THAT PROGRESSIVELY DEMAND MORE SPECIFIC LEARNING RESOURCES AND ASSESSMENT METHODS TO EVALUATE THE PROGRESSION OF STUDENTS. CURRENT MOOC PLATFORMS ARE GRADUALLY GIVING SUPPORT TO THESE NEW REQUIREMENTS BUT WITH A LIMITED ASSISTANCE. THIS PAPER PRESENTS THE STATE OF ART OF THE ANALYTICAL SYSTEM FOR THREE RELEVANT MOOC PLATFORMS, ONE OF THE MAIN PILLARS FOR ANALYZING THE PROGRESSION OF COURSES. OTHER INITIATIVES ARE ALSO REVIEWED TO SHOW THAT CURRENT MOOC ANALYTICAL SYSTEMS ARE NOT READY TO SUPPORT CUSTOM MOOC-AWARE INTELLIGENT TUTORING SYSTEMS (ITSS). THUS, THE DESIGN OF A LEARNING ANALYTICS SYSTEM TO ASSIST THESE TOOLS FOR MOOC PLATFORMS IS PRESENTED.
MULTIMODAL LEARNING ANALYTICS PROVIDES RESEARCHERS NEW TOOLS AND TECHNIQUES TO CAPTURE DIFFERENT TYPES OF DATA FROM COMPLEX LEARNING ACTIVITIES IN DYNAMIC LEARNING ENVIRONMENTS. THIS PAPER INVESTIGATES THE USE OF DIVERSE SENSORS, INCLUDING COMPUTER VISION, USER-GENERATED CONTENT, AND DATA FROM THE LEARNING OBJECTS (PHYSICAL COMPUTING COMPONENTS), TO RECORD HIGH-FIDELITY SYNCHRONISED MULTIMODAL RECORDINGS OF SMALL GROUPS OF LEARNERS INTERACTING. WE PROCESSED AND EXTRACTED DIFFERENT ASPECTS OF THE STUDENTS' INTERACTIONS TO ANSWER THE FOLLOWING QUESTION: WHICH FEATURES OF STUDENT GROUP WORK ARE GOOD PREDICTORS OF TEAM SUCCESS IN OPEN-ENDED TASKS WITH PHYSICAL COMPUTING? TO ANSWER THIS QUESTION, WE HAVE EXPLORED DIFFERENT SUPERVISED MACHINE LEARNING APPROACHES (TRADITIONAL AND DEEP LEARNING TECHNIQUES) TO ANALYSE THE DATA COMING FROM MULTIPLE SOURCES. THE RESULTS ILLUSTRATE THAT STATE-OF-THE-ART COMPUTATIONAL TECHNIQUES CAN BE USED TO GENERATE INSIGHTS INTO THE "BLACK BOX" OF LEARNING IN STUDENTS' PROJECT-BASED ACTIVITIES. THE FEATURES IDENTIFIED FROM THE ANALYSIS SHOW THAT DISTANCE BETWEEN LEARNERS' HANDS AND FACES IS A STRONG PREDICTOR OF STUDENTS' ARTEFACT QUALITY, WHICH CAN INDICATE THE VALUE OF STUDENT COLLABORATION. OUR RESEARCH SHOWS THAT NEW AND PROMISING APPROACHES SUCH AS NEURAL NETWORKS, AND MORE TRADITIONAL REGRESSION APPROACHES CAN BOTH BE USED TO CLASSIFY MULTIMODAL LEARNING ANALYTICS DATA, AND BOTH HAVE ADVANTAGES AND DISADVANTAGES DEPENDING ON THE RESEARCH QUESTIONS AND CONTEXTS BEING INVESTIGATED. THE WORK PRESENTED HERE IS A SIGNIFICANT CONTRIBUTION TOWARDS DEVELOPING TECHNIQUES TO AUTOMATICALLY IDENTIFY THE KEY ASPECTS OF STUDENTS SUCCESS IN PROJECT-BASED LEARNING ENVIRONMENTS, AND TO ULTIMATELY HELP TEACHERS PROVIDE APPROPRIATE AND TIMELY SUPPORT TO STUDENTS IN THESE FUNDAMENTAL ASPECTS.
THE GROWTH OF THE DISTANCE EDUCATION MODALITY ALLOWS RESEARCHERS TO PRESENT VARIED STUDIES RELATED TO THE THEME. TOGETHER WITH THESE STUDIES EMERGE CONCEPTS SUCH AS LEARNING ANALYTICS (LA). THE LA IS AN AREA THAT WILL ANALYZE, MEASURE, COLLECT AND RELATE STUDENT DATA IN THEIR CONTEXTS. HOWEVER, THE USE OF THIS DATA BRINGS WITH IT A NEW CONCERN REGARDING THE PROTECTION, PRIVACY AND CORRECT USE OF THE DATA. THE EUROPEAN UNION ALREADY FINDS PERSONAL DATA PROTECTION LEGISLATION WITH A BROAD GENERAL DATA PROTECTION REGULATION (GDPR). THIS ARTICLE AIMS TO PRESENT THE RESULT OF A SYSTEMATIC LITERATURE REVIEW THAT SEARCH TO IDENTIFY ACADEMIC RESEARCH RELATED TO LEARNING ANALYTICS (LA) AND GENERAL DATA PROTECTION REGULATION (GDPR). AFTER APPLYING THE INCLUSION AND EXCLUSION REQUIREMENTS OF THE SELECTED ARTICLES TEN ARTICLES WERE SELECTED FOR ANALYSIS. WITH THE ANALYSIS IT IS POSSIBLE TO IDENTIFY AN ALIGNMENT BETWEEN THE LA AND GDPR SHOWING THAT LA SHOULD FOLLOW THE GUIDELINES OF GDPR.
THE AIM OF THE PAPER IS TO PRESENT METHODOLOGY TO PERSONALISE LEARNING USING LEARNING ANALYTICS AND TO MAKE FURTHER DECISIONS ON SUITABILITY, ACCEPTANCE AND USE OF PERSONALISED LEARNING UNITS. IN THE PAPER, FIRST OF ALL, RELATED RESEARCH REVIEW IS PRESENTED. FURTHER, AN ORIGINAL METHODOLOGY TO PERSONALISE LEARNING APPLYING LEARNING ANALYTICS IN VIRTUAL LEARNING ENVIRONMENTS AND EMPIRICAL RESEARCH RESULTS ARE PRESENTED. USING THIS LEARNING PERSONALISATION METHODOLOGY, DECISION-MAKING MODEL AND METHOD ARE PROPOSED TO EVALUATE SUITABILITY, ACCEPTANCE AND USE OF PERSONALISED LEARNING UNITS. PERSONALISED LEARNING UNITS EVALUATION METHODOLOGY PRESENTED IN THE PAPER IS BASED ON (1) WELL-KNOWN PRINCIPLES OF MULTIPLE CRITERIA DECISION ANALYSIS FOR IDENTIFYING EVALUATION CRITERIA; (2) EDUCATIONAL TECHNOLOGY ACCEPTANCE & SATISFACTION MODEL (ETAS-M) BASED ON WELL-KNOWN UNIFIED THEORY ON ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) MODEL, AND (3) PROBABILISTIC SUITABILITY INDEXES TO IDENTIFY LEARNING COMPONENTS' SUITABILITY TO PARTICULAR STUDENTS' NEEDS ACCORDING TO THEIR LEARNING STYLES. IN THE PAPER, THERE ARE ALSO EXAMPLES OF IMPLEMENTING THE METHODOLOGY USING DIFFERENT WEIGHTS OF EVALUATION CRITERIA. THIS METHODOLOGY IS APPLICABLE IN REAL LIFE SITUATIONS WHERE TEACHERS HAVE TO HELP STUDENTS TO CREATE AND APPLY LEARNING UNITS THAT ARE MOST SUITABLE FOR THEIR NEEDS AND THUS TO IMPROVE EDUCATION QUALITY AND EFFICIENCY.
CHARACTER DEVELOPMENT REQUIRES NOT ONLY HIGH-QUALITY CURRICULUMS, BUT ALSO EDUCATORS WHO ARE ABLE TO ADAPT PROGRAMS TO LEARNERS' NEEDS AND CONTEXT AND STAFF DEVELOPMENT STRATEGIES. BIG DATA AND LEARNING ANALYTICS STRATEGIES MAY IMPROVE YOUTH CHARACTER DEVELOPMENT ESPECIALLY IN DEVELOPING COUNTRIES FACILITATING EDUCATORS' DEVELOPMENT AND PRACTICAL WISDOM, AS WELL AS CURRICULUM IMPLEMENTATION'S EFFECTIVENESS IN COUNTRIES WITH LESS KNOWHOW IN THE ISSUE. THIS STUDY PRESENTS A SYSTEMATIC MAPPING LITERATURE REVIEW ON THE MODELS AND METHODS OF LEARNING ANALYTICS APPLIED IN THE IMPROVEMENT OF YOUTH CHARACTER EDUCATION. BASED ON THE LITERATURE REVIEW RESULTS, THE RESEARCH PROVIDES INSIGHTS FOR FUTURE RESEARCH AND IMPLEMENTATION OF CHARACTER EDUCATION PROGRAMS, AND PROPOSES A REVISED PARTICIPATORY KNOWLEDGE MANAGEMENT DATA-DRIVEN PROCEDURE THAT MAY FACILITATE EDUCATORS TO IDENTIFY AND UNDERTAKE THE BEST CHARACTER FORMATION ACTIONS IN SPECIFIC SITUATIONS.
LEARNING ANALYTICS (LA) COLLECTS, ANALYSES, AND REPORTS BIG DATA ABOUT LEARNERS TO OPTIMISE LEARNING. LA ETHICS IS AN INTERDISCIPLINARY FIELD OF STUDY THAT ADDRESSES MORAL, LEGAL, AND SOCIAL ISSUES; THEREFORE, INSTITUTIONS ARE RESPONSIBLE FOR IMPLEMENTING FRAMEWORKS THAT INTEGRATE THESE TOPICS. MANY OF THE ETHICAL ISSUES RAISED APPLY EQUALLY TO EDUCATIONAL DATA SETS OF ANY SIZE. HOWEVER, IN THIS STUDY, WE FOCUS ON BIG DATA THAT INCREASES THE SCALE AND GRANULARITY OF DATA GATHERED. THE PURPOSE OF THIS STUDY IS TWOFOLD: (A) TO CRITICALLY REVIEW THE PUBLISHED (2011-2018) SCIENTIFIC LITERATURE ON LA ETHICS ISSUES AND (B) TO IDENTIFY CURRENT TRENDS AND ANSWER RESEARCH QUESTIONS IN THE FIELD. THIS STUDY'S RESEARCH QUESTIONS ARE AS FOLLOWS: WHAT IS ESSENTIAL IN LA ETHICS FOR KEY EDUCATIONAL STAKEHOLDERS, AND WHAT SHOULD A PROPOSED CHECKLIST FOR LA ETHICS INCLUDE FOR SPECIFIC EDUCATIONAL STAKEHOLDERS? AFTER SYSTEMATICALLY SEARCHING ONLINE BIBLIOGRAPHIC DATABASES, JOURNALS, AND CONFERENCES, A LITERATURE REVIEW IDENTIFIED 53 ARTICLES FROM A SAMPLE OF 562. THE SELECTED ARTICLES, BASED ON CRITICAL AND QUALITATIVE CONTENT ANALYSIS, WERE EXHAUSTIVELY ANALYSED. THE FINDINGS DEMONSTRATE THE SHORTAGE OF EMPIRICAL EVIDENCE-BASED GUIDELINES ON LA ETHICS AND HIGHLIGHT THE NEED TO ESTABLISH CODES OF PRACTICES TO MONITOR AND EVALUATE LA ETHICS POLICIES. FINALLY, THIS WORK PROPOSES A USEFUL CHECKLIST AS AN INSTRUCTIONAL DESIGN MODEL FOR SCHOLARS, POLICYMAKERS, AND INSTRUCTIONAL DESIGNERS, SO THAT TRUSTED PARTNERS MAY USE LA RESPONSIBLY TO IMPROVE TEACHING AND LEARNING.
LEARNING ANALYTICS (LA) HAVE A SIGNIFICANT IMPACT IN LEARNING AND TEACHING PROCESSES. THESE CAN BE IMPROVED USING THE AVAILABLE DATA RETRIEVED FROM THE STUDENTS' ACTIVITY INSIDE THE VIRTUAL CLASSROOMS OF A LMS. THIS PROCESS REQUIRES THE DEVELOPMENT OF A TOOL THAT ALLOWS TO HANDLE THE RETRIEVED INFORMATION PROPERLY. THIS PAPER PRESENTS A SOLUTION TO THIS NEED, IN THE FORM OF A DEVELOPMENT MODEL AND ACTUAL IMPLEMENTATION OF A LA TOOL. FOUR PHASES ARE IMPLEMENTED (EXPLANATION, DIAGNOSIS, PREDICTION, PRESCRIPTION); THIS APP ALLOWS THE TEACHER FOR TRACKING THE STUDENTS' ACTIVITY IN A VIRTUAL CLASSROOM IMPLEMENTED IN THE SAKAI LMS. IT ALSO ALLOWS FOR THE IDENTIFICATION OF USERS WITH CHALLENGES IN THEIR ACADEMIC PROCESS AND THE LEARNING ITINERARY IN COMBINATION WITH A PERSONALIZED MENTORING BY THE TEACHER OR TUTOR. THE USE OF THE TOOL WAS TESTED WITH GROUPS OF STUDENTS OF THE ALGORITHM COURSE IN THE PERIODS 2017-1, 2017-2, 2018-1 AND 2018-2, WITH A TOTAL OF 90 STUDENTS, IN PARALLEL WITH THE CONTROL GROUPS IN THE SAME PERIODS, CONFORMED BY 95 STUDENTS, OBTAINING SUPERIOR AVERAGES IN THE TEST GROUPS VS THE CONTROL GROUPS, WHICH EVIDENCED THE FUNCTIONALITY AND UTILITY OF THE SOFTWARE.
NEW HIGH-FREQUENCY, MULTIMODAL DATA COLLECTION TECHNOLOGIES AND MACHINE LEARNING TECHNIQUES COULD OFFER NEW INSIGHTS INTO LEARNING, ESPECIALLY WHEN STUDENTS HAVE THE OPPORTUNITY TO GENERATE UNIQUE, PERSONALIZED ARTIFACTS, SUCH AS COMPUTER PROGRAMS, E-TEXTILES, AND SOLUTIONS TO ENGINEERING CHALLENGES. HISTORICALLY, MOST OF THE WORK ON LEARNING ANALYTICS AND EDUCATIONAL DATA MINING HAS FOCUSED ON ONLINE COURSES AND COGNITIVE TUTORS, BOTH OF WHICH TEND TO PROVIDE A HIGH DEGREE OF STRUCTURE TO THE TASKS. IN THIS PAPER, WE ARGUE THAT MULTIMODAL LEARNING ANALYTICS MAY ALLOW THE CREATIVE EXPRESSION OF STUDENTS TO EXPAND BEYOND THESE LIMITATIONS WHILE, AT THE SAME TIME, OFFERING NEW INSIGHT INTO STUDENT LEARNING TRAJECTORIES. WE PRESENT SEVERAL EXAMPLES OF THIS WORK AND ITS EDUCATIONAL APPLICATION.
AS THE FIELDS OF LEARNING ANALYTICS AND LEARNING DESIGN MATURE, THE CONVERGENCE AND SYNERGIES BETWEEN THE TWO ARE BECOMING AN IMPORTANT AREA FOR RESEARCH. THIS PAPER INTENDS TO SUMMARIZE THE MAIN OUTCOMES OF A SYSTEMATIC REVIEW OF EMPIRICAL EVIDENCE ON LEARNING ANALYTICS FOR LEARNING DESIGN. MOREOVER, THIS PAPER PRESENTS AN OVERVIEW OF WHAT AND HOW LEARNING ANALYTICS HAVE BEEN USED TO INFORM LEARNING DESIGN DECISIONS AND IN WHAT CONTEXTS. THE SEARCH WAS PERFORMED IN SEVEN ACADEMIC DATABASES, RESULTING IN 43 PAPERS INCLUDED IN THE MAIN ANALYSIS. THE RESULTS FROM THE REVIEW DEPICT THE ONGOING DESIGN PATTERNS AND LEARNING PHENOMENA THAT EMERGED FROM THE SYNERGY THAT LEARNING ANALYTICS AND LEARNING DESIGN IMPOSE ON THE CURRENT STATUS OF LEARNING TECHNOLOGIES. FINALLY, THIS REVIEW STRESSES THAT FUTURE RESEARCH SHOULD CONSIDER DEVELOPING A FRAMEWORK ON HOW TO CAPTURE AND SYSTEMATIZE LEARNING DESIGN DATA GROUNDED IN LEARNING ANALYTICS AND LEARNING THEORY, AND DOCUMENT WHAT LEARNING DESIGN CHOICES MADE BY EDUCATORS INFLUENCE SUBSEQUENT LEARNING ACTIVITIES AND PERFORMANCES OVER TIME.
THE PURPOSE OF THIS PAPER IS TO EXPLORE THE EXPECTATIONS OF ACADEMIC STAFF TO LEARNING ANALYTICS SERVICES FROM AN IDEAL AS WELL AS A REALISTIC PERSPECTIVE. THIS MIXED-METHOD STUDY FOCUSED ON A CROSS-CASE ANALYSIS OF STAFF FROM HIGHER EDUCATION INSTITUTIONS FROM FOUR EUROPEAN UNIVERSITIES (SPAIN, ESTONIA, NETHERLANDS, UK). WHILE THERE ARE SOME DIFFERENCES BETWEEN THE COUNTRIES AS WELL AS BETWEEN IDEAL AND PREDICTED EXPECTATIONS, THE OVERARCHING RESULTS INDICATE THAT ACADEMIC STAFF SEES LEARNING ANALYTICS AS A TOOL TO UNDERSTAND THE LEARNING ACTIVITIES AND POSSIBILITY TO PROVIDE FEEDBACK FOR THE STUDENTS AND ADAPT THE CURRICULUM TO MEET LEARNERS' NEEDS. HOWEVER, ONE OF THE FINDINGS FROM THE STUDY ACROSS CASES IS THE GENERALLY CONSISTENTLY LOW EXPECTATION AND DESIRE FOR ACADEMIC STAFF TO BE OBLIGATED TO ACT BASED ON DATA THAT SHOWS STUDENTS BEING AT RISK OF FAILING OR UNDER-PERFORMING.
IN THIS PAPER WE EXPLORE THE POTENTIAL ROLE, VALUE AND UTILITY OF A PERSONAL CODE OF ETHICS (COE) FOR LEARNING ANALYTICS PRACTITIONERS, AND IN PARTICULAR WE CONSIDER WHETHER SUCH A COE MIGHT USEFULLY MEDIATE INDIVIDUAL ACTIONS AND CHOICES IN RELATION TO A MORE ABSTRACT INSTITUTIONAL COE. WHILE SEVERAL INSTITUTIONAL COES NOW EXIST, LITTLE ATTENTION HAS BEEN PAID TO DETAILING THE ETHICAL RESPONSIBILITIES OF INDIVIDUAL PRACTITIONERS. TO INVESTIGATE THE PROBLEMS ASSOCIATED WITH DEVELOPING AND IMPLEMENTING A PERSONAL COE, WE DRAFTED AN LA PRACTITIONER COE BASED ON OTHER PROFESSIONAL CODES, AND INVITED FEEDBACK FROM A RANGE OF LEARNING ANALYTICS STAKEHOLDERS AND PRACTITIONERS: ETHICISTS, STUDENTS, RESEARCHERS AND TECHNOLOGY EXECUTIVES. THREE MAIN THEMES EMERGED FROM THEIR REFLECTIONS: 1. A NEED TO BALANCE REAL WORLD DEMANDS WITH ABSTRACT PRINCIPLES, 2. THE LIMITS TO INDIVIDUAL ACCOUNTABILITY WITHIN THE LEARNING ANALYTICS SPACE, AND 3. THE CONTINUING VALUE OF DEBATE AROUND AN ASPIRATIONAL CODE OF ETHICS WITHIN THE FIELD OF LEARNING ANALYTICS.
IN CURRENT ONLINE COURSES, MOST LEARNING ANALYTICS TECHNIQUES COLLECT, ANALYZE AND DISPLAY LOW-LEVEL DATA ABOUT THE INTERACTIONS OF STUDENTS WITH EDUCATIONAL ACTIVITIES AND RESOURCES. THESE DATA ARE USED TO DETECT STUDENTS WITH DIFFICULTIES IN THE COURSE, AS WELL AS EDUCATIONAL ACTIVITIES AND RESOURCES WHICH MIGHT BE PROBLEMATIC. THIS PAPER PRESENTS THE PRECISE EFFECTIVENESS STRATEGY (PES) WHICH ENABLES TO CALCULATE THE EFFECTIVENESS OF STUDENTS WITH EDUCATIONAL ACTIVITIES AND RESOURCES IN ONLINE COURSES FROM LOW-LEVEL EVENTS IN A QUANTITATIVE WAY, TAKING INTO ACCOUNT DIFFERENT ASPECTS OF THE LEARNING CONTEXT. THE PES IS PARTICULARIZED FOR SPECIFIC EDUCATIONAL ACTIVITIES AND RESOURCES, WHICH ARE VIDEO RESOURCES AND PARAMETRIC EXERCISES. FINALLY, WE PROPOSE SOME VISUALIZATIONS RELATED TO VIDEO AND EXERCISE EFFECTIVENESS IN A REAL COURSE IN PHYSICS.
LEARNING ANALYTICS ENABLE AUTOMATED FEEDBACK TO STUDENTS THROUGH DASHBOARDS, REPORTS AND ALERTS. THE UNDERLYING UNTESTED ASSUMPTION IS THAT PROVIDING ANALYTICS WILL BE SUFFICIENT TO IMPROVE SELF-REGULATED LEARNING. WORKING WITHIN A FEEDBACK RECIPIENCE FRAMEWORK, WE BEGIN TO TEST THIS ASSUMPTION BY EXAMINING THE IMPACT OF LEARNING ANALYTICS MESSAGES ON STUDENT AFFECT AND ACADEMIC RESILIENCE. THREE HUNDRED AND TWENTY UNDERGRADUATE STUDENTS COMPLETED AN ONLINE SURVEY AND WERE EXPOSED TO THREE RANDOMLY ASSIGNED LEARNING ANALYTICS ALERTS (HIGH DISTINCTION, PASS, AND FAIL GRADES). MULTIVARIATE ANALYSES OF VARIANCE INDICATED SIGNIFICANT DIFFERENCES BETWEEN GRADE LEVELS (LARGE EFFECTS), WITH HIGHER POSITIVE AFFECT AND LOWER RESILIENCE IN RESPONSE TO HIGH DISTINCTION ALERTS THAN PASS OR FAIL ALERTS. WITHIN EACH HYPOTHETICAL GRADE LEVEL, THERE WERE NO DIFFERENCES IN STUDENT AFFECT AND ACADEMIC RESILIENCE. BASED UPON SYSTEMATIC CHANGES IN FEEDBACK SENDER, MESSAGE STYLE OR WHETHER COMPARATIVE PEER ACHIEVEMENT WAS INCLUDED OR NOT. THESE FINDINGS INDICATE THAT GRADE LEVEL HAS THE LARGEST IMPACT ON BOTH AFFECT AND ACADEMIC RESILIENCE. THE FAILURE OF MESSAGE AND SENDER CHARACTERISTICS TO IMPACT ON ACTIVITIES THAT PROMOTE SELF-REGULATED LEARNING SUGGESTS WE NEED TO LOOK BEYOND THESE CHARACTERISTICS OF INDIVIDUAL MESSAGES TO IDENTIFY DRIVERS OF ENGAGING STUDENTS IN SELF-REGULATED LEARNING.
THROUGH THE PROCESSES SET OUT BY LEARNING ANALYTICS, THIS PAPER DESCRIBES A DECISION MAKING MODEL WHICH SUPPORTS THE MANAGERS OF HIGHER EDUCATION INSTITUTIONS AT THE MOMENT OF MAKING ACADEMIC DECISIONS. IN THE ANALYSIS AND REQUIREMENTS PHASE, THE IMPORTANCE OF CREATING AND CATEGORIZING INDICATORS IS PRESENTED IN DETAIL, THEREBY FACILITATING THE FINDING OF HIDDEN PATTERNS IN THE EDUCATIONAL INFORMATION. OUR WORK IN THIS FIELD INCLUDES THE MODELLING AND CONSTRUCTION OF SOFTWARE ARCHITECTURE IN WHICH THE WORK OF SAID INDICATORS IS INCLUDED. FINALLY, THE IMPLEMENTATION OF THIS NEW DECISION MAKING SUPPORT TOOL IS SHOWN AS THE RESULT DURING A PILOT TEST CARRIED OUT IN A PUBLIC UNIVERSITY IN LATIN AMERICA.
LEARNING ANALYTICS DASHBOARDS CONSTITUTE AN EFFECTIVE TOOL FOR MONITORING LEARNING ACTIVITIES THAT TAKE PLACE IN ONLINE LEARNING ENVIRONMENTS. THANKS TO DASHBOARDS, TEACHERS CAN PROMPTLY DETECT LOW LEVELS OF STUDENT ENGAGEMENT IN GIVEN TASKS, INCORRECT USAGE OF A SYSTEM, AND OTHER TYPES OF PEDAGOGICALLY RELEVANT INFORMATION, WHICH HELPS THEM TO BETTER SUPPORT STUDENTS IN ACHIEVING THEIR LEARNING OBJECTIVES. THIS STUDY DESCRIBES THE INTEGRATION OF A DASHBOARD IN AN ONLINE LEARNING SYSTEM. THE SYSTEM INCLUDES A TOOL THAT GUIDES STUDENTS IN THE CREATION OF HIGHLY INFORMATIVE BILINGUAL GLOSSARIES, A SERVICE THAT TRACES STUDENT SEARCHES ON THE WEB FOR REFERENCE MATERIAL, AND A SERVICE THAT TRACKS STUDENT INTERACTIONS WITH THE GLOSSARY. THE DATA THUS COLLECTED ARE SELECTIVELY DISPLAYED IN THE NEWLY DEVELOPED DASHBOARD. THE DASHBOARD WAS SPECIFICALLY DESIGNED TO ALLOW TEACHERS TO MONITOR THE STUDENTS' APPROACHES TO GLOSSARY BUILDING AND TO PROVIDE INDIVIDUAL REMEDIAL FEEDBACK, IF NECESSARY. IT WAS ALSO INTENDED TO SPUR STUDENTS TO KEEP ON A PAR WITH THE REST OF THE CLASS, BY SEEING THEIR STATUS COMPARED TO THE REST OF THE CLASS. THE SYSTEM WAS TESTED WITH TWO GROUPS OF UNIVERSITY STUDENTS SPECIALIZING IN INTERPRETING, AND TWO DIFFERENT TEACHERS. THE RESULTS OF THE EXPERIMENTS SUGGEST THAT THIS INTEGRATED SYSTEM MANAGES TO ACHIEVE ITS GOALS AND PROVIDES STUDENTS AND TEACHERS OF INTERPRETING WITH AN INNOVATIVE ONLINE TOOL THAT CONCRETELY FOSTERS AND SUPPORTS VOCABULARY BUILDING.
THIS PAPER PRESENTS AN INITIAL EVALUATION OF AN INNOVATIVE WEB-BASED ELEAMING PLATFORM, FEATURING LEARNING ANALYTICS AND GAMIFICATION CALLED ICT-FLAG. PREVIOUS CONTRIBUTIONS PRESENTED THE DEVELOPMENT AND PROTOTYPING STEPS OF THE PLATFORM, AND FOLLOWING THESE STEPS, THIS PAPER REPORTS THE EVALUATION ON THE FIRST REAL EXPERIENCE OF CONNECTING AND INTEGRATING THE PLATFORM WITH A ELEAMING TOOL TO SUPPORT AN ONLINE COURSE BY LEARNING ANALYTICS SERVICES. RESULTS OF THE EVALUATION SERVES AS A FIRST VALIDATION OF THE PLATFORM AND GUIDE THE NEXT ITERATIVE SOFTWARE DEVELOPMENT AND EVALUATION PROCESS. THE RESEARCH REPORTED IN THIS PAPER IS CURRENTLY UNDERTAKEN WITHIN THE RESEARCH PROJECT "ENHANCING ICT EDUCATION THROUGH FORMATIVE ASSESSMENT, LEARNING ANALYTICS AND GAMIFICATION" (ICT-FLAG) FUNDED BY THE SPANISH GOVERNMENT.
THE SMILI (SIC) (STUDENT MODELS THAT INVITE THE LEARNER IN) OPEN LEARNER MODEL FRAMEWORK WAS CREATED TO PROVIDE A COHERENT PICTURE OF THE MANY AND DIVERSE FORMS OF OPEN LEARNER MODELS (OLMS). THE AIM WAS FOR SMILI (SIC) TO PROVIDE RESEARCHERS WITH A SYSTEMATIC WAY TO DESCRIBE, COMPARE AND CRITIQUE OLMS. WE EXPECTED IT TO HIGHLIGHT THOSE AREAS WHERE THERE HAD BEEN CONSIDERABLE OLM WORK, AS WELL AS THOSE THAT HAD BEEN NEGLECTED. HOWEVER, WE OBSERVED THAT SMILI (SIC) WAS NOT USED IN THESE WAYS. WE NOW REFLECT ON THE REASONS FOR THIS, AND CONCLUDE THAT IT HAS ACTUALLY SERVED A BROADER ROLE IN DEFINING THE NOTION OF OLM AND INFORMING OLM DESIGN. SINCE THE INITIAL SMILI (SIC) PAPER, MUCH HAS CHANGED IN TECHNOLOGY-ENHANCED LEARNING. NOTABLY, LEARNING TECHNOLOGY HAS BECOME FAR MORE PERVASIVE, BOTH IN FORMAL AND LIFELONG LEARNING. THIS PROVIDES HUGE, AND STILL GROWING AMOUNTS OF LEARNING DATA. THE FIELDS OF LEARNING ANALYTICS (LA), LEARNING AT SCALE (L@S), EDUCATIONAL DATA MINING (EDM) AND QUANTIFIED SELF (QS) HAVE EMERGED. THIS PAPER ARGUES THAT THERE HAS ALSO BEEN AN IMPORTANT SHIFT IN THE NATURE AND ROLE OF LEARNER MODELS EVEN WITHIN ARTIFICIAL INTELLIGENCE IN EDUCATION AND INTELLIGENT TUTORING SYSTEMS RESEARCH. IN LIGHT OF THESE TRENDS, AND REFLECTING ON THE USE OF SMILI (SIC), THIS PAPER PRESENTS A REVISED AND SIMPLER VERSION OF SMILI (SIC) ALONGSIDE THE ORIGINAL VERSION. IN BOTH CASES THERE ARE ADDITIONAL CATEGORIES TO ENCOMPASS NEW TRENDS, WHICH CAN BE APPLIED, OMITTED OR SUBSTITUTED AS REQUIRED. WE NOW OFFER THIS AS A GUIDE FOR DESIGNERS OF INTERFACES FOR OLMS, LEARNING ANALYTICS AND RELATED FIELDS, AND WE HIGHLIGHT THE AREAS WHERE THERE IS NEED FOR MORE RESEARCH.
THE FLIPPED CLASSROOM (FC) IS AN INSTRUCTION METHOD, WHERE THE TRADITIONAL LECTURE AND HOMEWORK SESSIONS ARE INVERTED. ONLINE MATERIAL IS GIVEN TO STUDENTS IN ORDER TO GAIN NECESSARY KNOWLEDGE BEFORE CLASS, WHILE CLASS TIME IS DEVOTED TO APPLICATION OF THIS KNOWLEDGE AND REFLECTION. THE HYPOTHESIS IS THAT THERE COULD BE DEEP AND CREATIVE DISCUSSIONS WHEN TEACHER AND STUDENTS PHYSICALLY MEET, WHICH HAS KNOWN A SIGNIFICANT SURGE OF POPULARITY IN THE PAST DECADE. A MARKED RECENT TREND IN THE FC IS THE INCREASED USE OF LEARNING ANALYTICS (LA) TO SUPPORT THE DEVELOPMENT OF THE FC AND STUDENTS' REFLEXIVE LEARNING. THE AIM OF THIS PAPER IS TO INVESTIGATE THE LITERATURE ON APPLICATIONS OF LA IN FCS, AND TO DETERMINE THE BEST PRACTICES AND NEEDS FOR TECHNOLOGICAL DEVELOPMENT SUPPORTING LA IN THE FC BY MEANS OF A SCOPING REVIEW. THIS LITERATURE REVIEW REVEALED THAT THERE IS POTENTIAL IN USING LA IN THE FC, ESPECIALLY AS A MEANS TO PREDICT STUDENTS' LEARNING OUTCOME AND TO SUPPORT ADAPTIVE LEARNING AND IMPROVEMENT ON THE CURRICULUM. HOWEVER, FURTHER LONG-TERM STUDIES AND DEVELOPMENT IS NECESSARY TO ENCOURAGE SELF-DIRECTED LEARNING IN STUDENTS AND TO DEVELOP THE WHOLE OF THE FC FOR A MORE DIVERSE POPULATION OF STUDENTS. WE ANTICIPATE AN INCREASED AND EXPANDED USE OF LA TO COME, WITH FOCUS ON PREDICTIVE AND PRESCRIPTIVE ANALYTICS PROVIDING MORE ADAPTIVE LEARNING EXPERIENCE. WE ALSO ANTICIPATE THAT LA WILL EXPAND BEYOND DATA MINING TO CORRELATE STUDENT PERFORMANCE AND ONLINE ENGAGEMENT WITH THE AIM TO INCLUDE A WIDER RANGE OF POSSIBILITIES OF INTERVENTIONS AND ADAPTATION OF THE LEARNING EXPERIENCE.
THERE IS AN URGENT NEED TO DEVELOP AN EVIDENCE-BASED FRAMEWORK FOR LEARNING ANALYTICS WHEREBY STAKEHOLDERS CAN MANAGE, EVALUATE, AND MAKE DECISIONS ABOUT WHICH TYPES OF INTERVENTIONS WORK WELL AND UNDER WHICH CONDITIONS. IN THIS ARTICLE, WE WILL WORK TOWARDS DEVELOPING A FOUNDATION OF AN ANALYTICS4ACTION EVALUATION FRAMEWORK (A4AEF) THAT IS CURRENTLY BEING TESTED AND VALIDATED AT THE OPEN UNIVERSITY UK. BY WORKING WITH 18 INTRODUCTORY LARGE-SCALE MODULES FOR A PERIOD OF TWO YEARS ACROSS THE FIVE FACULTIES AND DISCIPLINES WITHIN THE OU, ANALYTICS4ACTION PROVIDES A BOTTOM-UP-APPROACH FOR WORKING TOGETHER WITH KEY STAKEHOLDERS WITHIN THEIR RESPECTIVE CONTEXTS. A HOLISTIC A4AEF HAS BEEN DEVELOPED TO UNPACK, UNDERSTAND AND MAP THE SIX KEY STEPS IN THE EVIDENCE-BASED INTERVENTION PROCESS. BY MEANS OF AN EXEMPLAR IN HEALTH AND SOCIAL SCIENCE, A PRACTICAL ILLUSTRATION OF A4AEF IS PROVIDED. IN THE NEXT 3-5 YEARS, WE HOPE THAT A RICH, ROBUST EVIDENCE-BASE WILL BE PRESENTED TO SHOW HOW LEARNING ANALYTICS CAN HELP TEACHERS TO MAKE INFORMED, TIMELY AND SUCCESSFUL INTERVENTIONS THAT WILL HELP EACH LEARNER TO ACHIEVE THE MODULE'S LEARNING OUTCOMES.
BEFORE THE EMERGENCE OF COMPUTER-BASED EDUCATIONAL SYSTEMS (CBES) WHOSE AIMS OF PROVIDING TEACHING AND LEARNING EXPERIENCES TO HUNDREDS EVEN THOUSANDS OF USERS, AN EXPLOSION OF INFORMATION (E.G., STUDENTS' LOG DATA) DEMANDS SOPHISTICATED METHODS TO GATHER, ANALYZE, AND INTERPRET LEARNERS' TRACES TO REGULATE AND ENHANCE EDUCATION. THUS, LEARNING ANALYTICS (LA) ARISES AS A KNOWLEDGE DISCOVERY PARADIGM THAT PROVIDES VALUABLE FINDINGS AND FACILITATES STAKEHOLDERS TO UNDERSTAND THE LEARNING PROCESS AND ITS IMPLICATIONS. THEREFORE, A LANDSCAPE OF THE LA NATURE, ITS UNDERLYING FACTORS, AND APPLICATIONS ACHIEVED IS OUTLINED IN THIS PAPER ACCORDING TO A SUGGESTED LA TAXONOMY THAT CLASSIFIES THE LA DUTY FROM A FUNCTIONAL PERSPECTIVE. THE AIM IS TO PROVIDE AN IDEA OF THE LA TOIL, ITS RESEARCH LINES, AND TRENDS TO INSPIRE THE DEVELOPMENT OF NOVEL APPROACHES FOR IMPROVING TEACHING AND LEARNING PRACTICES. FURTHERMORE, THE SCOPE OF THIS REVIEW COVERS RECENTLY PUBLISHED PAPERS IN PRESTIGIOUS JOURNALS AND CONFERENCES, WHERE THE WORKS DATED FROM 2016 ARE SUMMARIZED AND THOSE CORRESPONDING TO 2014-2015 ARE CITED ACCORDING TO THE PROPOSED LA TAXONOMY. A GLIMPSE IS SKETCHED OF LA, WHERE UNDERLYING ELEMENTS FRAME THE FIELD FOUNDATIONS TO GROUND THE APPROACHES. MOREOVER, LA STRENGTHS, WEAKNESSES, CHALLENGES, AND RISKS ARE HIGHLIGHTED TO ADVICE HOW THE LA ARENA COULD BE ENHANCED AND EMPOWERED. IN ADDITION, THIS REVIEW OFFERS AN INSIGHT OF THE RECENT LA LABOR, AS WELL AS MOTIVATES READERS TO ENRICH THE LA ACHIEVEMENTS. THIS WORK PROMOTES THE LA PRACTICE GIVING AN ACCOUNT OF THE JOB BEING ACHIEVED AND REPORTED IN LITERATURE, AS WELL AS A REFLECTION OF THE STATE-OF-THE-ART AND AN ACUMENS VISION TO INSPIRE FUTURE LABOR. THIS ARTICLE IS CATEGORIZED UNDER: APPLICATION AREAS > EDUCATION AND LEARNING APPLICATION AREAS > SCIENCE AND TECHNOLOGY FUNDAMENTAL CONCEPTS OF DATA AND KNOWLEDGE > HUMAN CENTRICITY AND USER INTERACTION
BUILDING A CLOUD-BASED E-LEARNING PLATFORM USING THE APPROACH OF THE RESEARCH FIELD OF LEARNING ANALYTICS IS THE OVERALL GOAL OF THIS RESEARCH STUDY. WITH THE STRENGTHS OF A CLOUD SERVICE IT IS POSSIBLE TO ADD VALUE TO THE LEARNING PROCESS FOR ALL STAKEHOLDERS. WITH SUCH A SOLUTION, THE PROCESS OF COLLECTING, ANALYZING AND INTERPRETING DATA BECOMES RATHER EASILY. THIS RESEARCH STUDY AIMS TO INTRODUCE A NOVEL PLATFORM TO GENERATE INTERACTIVE E-BOOKS. IN ORDER TO CREATE DIGITALIZED FORMS OF EXERCISE BOOKS FOR PRIMARY SCHOOL CHILDREN, ADDITIONAL FEATURES HAVE TO BE ADDED TO GAIN ACCEPTANCE BY SUCH AN IDEA. THE EVOLUTION OF NEW E-BOOK STANDARDS AS EPUB IN ITS VERSION 3.0 MAKES IT POSSIBLE TO GENERATE THOSE ADDITIONAL FEATURES. THE DEVELOPMENTS OF THE PLATFORM AS WELL AS EACH INTERACTIVE EXERCISE, WHICH CAN BE DONE ARE DESCRIBED IN DETAIL. FINALLY THE WEAKNESS AND THE STRENGTH OF THE APPROACH IS DISCUSSED. THE STUDY CONCLUDED THAT THE APPROACH OFFERS HUGE POTENTIAL FOR TOMORROW'S LEARNING BEHAVIORS
BLENDED LEARNING COMBINES ONLINE DIGITAL RESOURCES WITH TRADITIONAL CLASSROOM ACTIVITIES AND ENABLES STUDENTS TO ATTAIN HIGHER LEARNING PERFORMANCE THROUGH WELL-DEFINED INTERACTIVE STRATEGIES INVOLVING ONLINE AND TRADITIONAL LEARNING ACTIVITIES. LEARNING ANALYTICS IS A CONCEPTUAL FRAMEWORK AND AS A PART OF OUR PRECISION EDUCATION USED TO ANALYZE AND PREDICT STUDENTS' PERFORMANCE AND PROVIDE TIMELY INTERVENTIONS BASED ON STUDENT LEARNING PROFILES. THIS STUDY APPLIED LEARNING ANALYTICS AND EDUCATIONAL BIG DATA APPROACHES FOR THE EARLY PREDICTION OF STUDENTS' FINAL ACADEMIC PERFORMANCE IN A BLENDED CALCULUS COURSE. REAL DATA WITH 21 VARIABLES WERE COLLECTED FROM THE PROPOSED COURSE, CONSISTING OF VIDEO-VIEWING BEHAVIORS, OUT-OF-CLASS PRACTICE BEHAVIORS, HOMEWORK AND QUIZ SCORES, AND AFTER-SCHOOL TUTORING. THIS STUDY APPLIED PRINCIPAL COMPONENT REGRESSION TO PREDICT STUDENTS' FINAL ACADEMIC PERFORMANCE. THE EXPERIMENTAL RESULTS SHOW THAT STUDENTS' FINAL ACADEMIC PERFORMANCE COULD BE PREDICTED WHEN ONLY ONE-THIRD OF THE SEMESTER HAD ELAPSED. IN ADDITION, WE IDENTIFIED SEVEN CRITICAL FACTORS THAT AFFECT STUDENTS' ACADEMIC PERFORMANCE, CONSISTING OF FOUR ONLINE FACTORS AND THREE TRADITIONAL FACTORS. THE RESULTS SHOWED THAT THE BLENDED DATA SET COMBINING ONLINE AND TRADITIONAL CRITICAL FACTORS HAD THE HIGHEST PREDICTIVE PERFORMANCE.
IN THIS EMPIRICAL CONTRIBUTION, A FOLLOW-UP STUDY OF PREVIOUS RESEARCH [1], WE FOCUS ON THE ISSUES OF STABILITY AND SENSITIVITY OF LEARNING ANALYTICS BASED PREDICTION MODELS. DO PREDICTIONS MODELS STAY INTACT, WHEN THE INSTRUCTIONAL CONTEXT IS REPEATED IN A NEW COHORT OF STUDENTS, AND DO PREDICTIONS MODELS INDEED CHANGE, WHEN RELEVANT ASPECTS OF THE INSTRUCTIONAL CONTEXT ARE ADAPTED? APPLYING BUCKINGHAM SHUM AND DEAKIN CRICK'S THEORETICAL FRAMEWORK OF DISPOSITIONAL LEARNING ANALYTICS COMBINED WITH FORMATIVE ASSESSMENTS AND LEARNING MANAGEMENT SYSTEMS, WE COMPARE TWO COHORTS OF A LARGE MODULE INTRODUCING MATHEMATICS AND STATISTICS. BOTH MODULES WERE BASED ON PRINCIPLES OF BLENDED LEARNING, COMBINING FACE-TO-FACE PROBLEM-BASED LEARNING SESSIONS WITH E-TUTORIALS, AND HAVE SIMILAR INSTRUCTIONAL DESIGN, EXCEPT FOR AN INTERVENTION INTO THE DESIGN OF QUIZZES ADMINISTERED IN THE MODULE. WE ANALYSE BIVARIATE AND MULTIVARIATE RELATIONSHIPS OF MODULE PERFORMANCE AND TRACK AND DISPOSITION DATA TO PROVIDE EVIDENCE OF BOTH STABILITY AND SENSITIVITY OF PREDICTION MODELS.
LEARNING ANALYTICS IS THE ANALYSIS AND VISUALIZATION OF STUDENT DATA WITH THE PURPOSE OF IMPROVING EDUCATION. LITERATURE REPORTING ON MEASURES OF THE EFFECTS OF DATA-DRIVEN PEDAGOGICAL INTERVENTIONS ON LEARNING AND THE ENVIRONMENT IN WHICH THIS TAKES PLACE, ALLOWS US TO ASSESS IN WHAT WAY LEARNING ANALYTICS ACTUALLY IMPROVES LEARNING. WE CONDUCTED A SYSTEMATIC LITERATURE REVIEW AIMED AT IDENTIFYING SUCH MEASURES OF DATA-DRIVEN IMPROVEMENT. A REVIEW OF 1034 PAPERS YIELDED 38 KEY STUDIES, WHICH WERE THOROUGHLY ANALYZED ON ASPECTS LIKE OBJECTIVE, AFFECTED LEARNING AND THEIR OPERATIONALIZATION (MEASURES). BASED ON PREVALENT LEARNING THEORIES, WE SYNTHESIZED A CLASSIFICATION SCHEME COMPRISED OF FOUR CATEGORIES: LEARNING PROCESS, STUDENT PERFORMANCE, LEARNING ENVIRONMENT, AND DEPARTMENTAL PERFORMANCE. MOST OF THE ANALYZED STUDIES RELATE TO EITHER STUDENT PERFORMANCE OR LEARNING PROCESS. BASED ON THE RESULTS, WE RECOMMEND TO MAKE DELIBERATE DECISIONS ON THE (MULTIPLE) ASPECTS OF LEARNING ONE TRIES TO IMPROVE BY THE APPLICATION OF LEARNING ANALYTICS. OUR CLASSIFICATION SCHEME WITH EXAMPLES OF MEASURES MAY HELP BOTH ACADEMICS AND PRACTITIONERS DOING SO, AS IT ALLOWS FOR STRUCTURED POSITIONING OF LEARNING ANALYTICS BENEFITS.
THE ARRIVAL OF BIG DATA AND AI ERA PROMOTE EDUCATIONAL REFORMS, WHICH MAKE PERSONALIZED LEARNING BECOME NORMALIZATION. TEACHING INTERVENTION IS AN INDISPENSABLE BRIDGE BETWEEN BIG DATA AND STUDENTS' PERSONALIZED LEARNING. THIS STUDY PROPOSES AN INTERVENTION MODEL BASED ON LEARNING ANALYTICS FROM FOUR ITERATION MODULES: DATA COLLECTION, DATA PROCESSING, INTERVENTION IMPLEMENTATION AND EFFECT EVALUATION, AND APPLIES IT TO BLENDED LEARNING ENVIRONMENT. THROUGH ONE-GROUP PRETEST-POSTTEST EXPERIMENT DESIGN, THE EFFECT OF THE INTERVENTION MODEL WERE MEASURED BY LEARNING ENGAGEMENT AND LEARNING ACHIEVEMENT. THE RESULTS SHOW THAT THE INTERVENTION MODEL CAN EFFECTIVELY IMPROVE STUDENTS' BEHAVIORAL ENGAGEMENT AND COGNITIVE ENGAGEMENT AS WELL AS LEARNING ACHIEVEMENT, ESPECIALLY FOR RISKY STUDENTS.
THE ABILITY TO FOLLOW LEARNING PROGRESS AND STUDENTS' PERFORMANCE CAN HE BENEFICIAL TO TEACHERS AND RESEARCHERS IN MODERN EDUCATIONAL SYSTEMS ACTIONS, ESPECIALLY IN E-LEARNING ENVIRONMENTS. IN THIS PAPER, AN EFFORT IS MADE TO INVESTIGATE AND UTILIZE THE LEARNING FEATURES SETS FROM THE LEARNING OBJECTIVES AND STUDENTS' INTERACTIONS IN LEARNING MANAGEMENT SYSTEMS TO FOLLOW THE STUDENT'S PERFORMANCE. THIS RESEARCH AIMS TO IMPLEMENT A SOFTWARE ARCHITECTURE CALLED STUDENT ACADEMIC PERFORMANCE EVALUATION SYSTEM (SAPES) BASED ON LEARNING ANALYTICS AND LEARNING OBJECTIVES TO SUPPORT THE TEACHER IN THE EVALUATION PROCESS. OUTCOMES PROVIDED BY SAPES SHOULD HELP EDUCATORS IN THE PROMOTION AND IMPROVEMENT IN THE LEARNING PROCESS.
NEW STATISTICAL METHODS ALLOW DISCOVERY OF CAUSAL MODELS PURELY FROM OBSERVATIONAL DATA IN SOME CIRCUMSTANCES. EDUCATIONAL RESEARCH THAT DOES NOT EASILY LEND ITSELF TO EXPERIMENTAL INVESTIGATION CAN BENEFIT FROM SUCH DISCOVERY, PARTICULARLY WHEN THE PROCESS OF INQUIRY POTENTIALLY AFFECTS MEASUREMENT. WHETHER CONTROLLED OR AUTHENTIC, EDUCATIONAL INQUIRY IS SPRINKLED WITH HIDDEN VARIABLES THAT ONLY CHANGE OVER THE LONG TERM, MAKING THEM CHALLENGING AND EXPENSIVE TO INVESTIGATE EXPERIMENTALLY. BIG DATA LEARNING ANALYTICS OFFERS METHODS AND TECHNIQUES TO OBSERVE SUCH CHANGES OVER LONGER TERMS AT VARIOUS LEVELS OF GRANULARITY. LEARNING ANALYTICS ALSO ALLOWS CONSTRUCTION OF CANDIDATE MODELS THAT EXPOUND HIDDEN VARIABLES AS WELL AS THEIR RELATIONSHIPS WITH OTHER VARIABLES OF INTEREST IN THE RESEARCH. THIS ARTICLE DISCUSSES THE CORE IDEAS OF CAUSALITY AND MODELING OF CAUSALITY IN THE CONTEXT OF EDUCATIONAL RESEARCH WITH BIG DATA ANALYTICS AS THE UNDERLYING DATA SUPPLY MECHANISM. IT PROVIDES RESULTS FROM STUDIES THAT ILLUSTRATE THE NEED FOR CAUSAL MODELING AND HOW LEARNING ANALYTICS COULD ENHANCE THE ACCURACY OF CAUSAL MODELS.
WITH THE DEVELOPMENT OF DATA MINING AND MACHINE LEARNING TECHNIQUES, DATA-DRIVEN BASED TECHNOLOGY-ENHANCED LEARNING (TEL) HAS DRAWN WIDER ATTENTION. RESEARCHERS AIM TO USE ESTABLISHED OR NOVEL COMPUTATIONAL METHODS TO SOLVE EDUCATIONAL PROBLEMS IN THE 'BIG DATA' ERA. HOWEVER, THE READINESS OF DATA APPEARS TO BE THE BOTTLENECK OF THE TEL DEVELOPMENT AND VERY LITTLE RESEARCH FOCUSES ON INVESTIGATING THE DATA SCARCITY AND INAPPROPRIATENESS IN THE TEL RESEARCH. THIS PAPER IS INVESTIGATING AN EMERGING RESEARCH TOPIC IN THE TEL DOMAIN, NAMELY MICRO LEARNING. MICRO LEARNING CONSISTS OF VARIOUS TECHNICAL THEMES THAT HAVE BEEN WIDELY STUDIED IN THE TEL RESEARCH FIELD. IN THIS PAPER, WE FIRSTLY PROPOSE A MICRO LEARNING SYSTEM, WHICH INCLUDES RECOMMENDATION, SEGMENTATION, ANNOTATION, AND SEVERAL LEARNING-RELATED PREDICTION AND ANALYSIS MODULES. FOR EACH MODULE OF THE SYSTEM, THIS PAPER REVIEWS REPRESENTATIVE LITERATURE AND DISCUSSES THE DATA SOURCES USED IN THESE STUDIES TO PINPOINT THEIR CURRENT PROBLEMS AND SHORTCOMINGS, WHICH MIGHT BE DEBACLES FOR MORE EFFECTIVE RESEARCH OUTCOMES. ACCORDINGLY, THE DATA REQUIREMENTS AND CHALLENGES FOR LEARNING ANALYTICS IN MICRO LEARNING ARE ALSO INVESTIGATED. FROM A RESEARCH CONTRIBUTION PERSPECTIVE, THIS PAPER SERVES AS A BASIS TO DEPICT AND UNDERSTAND THE CURRENT STATUS OF THE READINESS OF DATA SOURCES FOR THE RESEARCH OF MICRO LEARNING.
AS INFORMATION TECHNOLOGY CONTINUES TO EVOLVE RAPIDLY, PROGRAMMING SKILLS BECOME INCREASINGLY CRUCIAL. TO BE ABLE TO CONSTRUCT SUPERB PROGRAMMING SKILLS, THE TRAINING MUST BEGIN BEFORE COLLEGE OR EVEN SENIOR HIGH SCHOOL. HOWEVER, WHEN DEVELOPING COMPREHENSIVE TRAINING PROGRAMMERS, THE LEARNING AND TEACHING PROCESSES MUST BE CONSIDERED. IN ORDER TO IMPROVE THE STUDENTS' LEARNING OUTCOME AND ENGAGEMENT IN PROGRAMMING COURSE, THIS STUDY APPLIED LEARNING ANALYTICS INTO THE PROPOSED MASSIVE ONLINE OPEN COURSES (MOOCS) ENABLED COLLABORATIVE PROGRAMMING COURSE. THROUGH THE PROPOSED LEARNING ACTIVITY, INSTRUCTORS RECEIVE A MONTHLY REPORT THAT EXPLAINS WHICH STUDENTS ARE AT RISK AND IN NEED OF TIMELY INTERVENTION. THIS STUDY CONDUCTED AN EXPERIMENT TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED LEARNING ACTIVITY. STUDENTS IN THE EXPERIMENTAL GROUP RECEIVED LEARNING INTERVENTIONS FROM AN INSTRUCTOR ACCORDING TO THE RESULT OF LEARNING ANALYTICS, AND STUDENTS IN THE CONTROL GROUP RECEIVED INTERVENTIONS ACCORDING TO THE INSTRUCTOR'S OBSERVATION. THE DATA FOR THIS STUDY WERE COLLECTED OVER 10 WEEKS AT A UNIVERSITY IN TAIWAN. THE RESULT INDICATED THAT THE PROPOSED PROGRAMMING COURSE WITH LEARNING ANALYTICS IMPROVED STUDENTS' LEARNING OUTCOMES AND LEVELS OF ENGAGEMENT.
IN THE LAST DECADE, THE USE OF LEARNING ANALYTICS AND THE MANAGEMENT OF LARGE VOLUMES OF DATA HAVE CONTRIBUTED SUBSTANTIALLY TO THE WAY HIGHER EDUCATION INSTITUTIONS TRACK, ANALYZE INFORMATION AND PREDICT STUDENT PERFORMANCE (CLOW, 2013). THE OBJECTIVE OF THIS WORK WAS TO IDENTIFY THE VARIABLES THAT INFLUENCE THE ACADEMIC PERFORMANCE OF GRADUATE STUDENTS, THROUGH THE APPLICATION OF LEARNING ANALYTICS TECHNIQUES (CHATTI, ET AL., 2012). THE ALGORITHMS SELECTION OF ATTRIBUTES AND DECISION TREES (WITTEN, FRANK, HALL, PAL, 2016) WERE APPLIED TO A SAMPLE OF DATA COLLECTED FROM 136 GRADUATE STUDENTS IN A SIMPLE RANDOM WAY. IT WAS IDENTIFIED THAT IN GENERAL THEY PREFER TO STUDY IN THE AFTERNOON AND THAT THEY INVEST 43.83% OF THEIR TIME IN THE REVIEW OF THE COURSE CONTENT WHILE THEY ARE ACTIVE IN THE PLATFORM; 10.92% OF THE TIME THEY PARTICIPATE IN FORUMS AND 31.10% OF THE TIME THEY CARRY OUT ACTIVITIES. THROUGH THE ALGORITHM OF ATTRIBUTE SELECTION, THE FOUR MOST IMPORTANT VARIABLES THAT INFLUENCE PERFORMANCE ARE DEFINED, NAMELY: TOTAL TIME INVESTED IN THE COURSE OF COURSE CONSULTATION, ELABORATION OF TASKS, PARTICIPATION IN FORUMS AND TEAMWORK. ALSO, APPLYING DECISION TREES, 6 PATTERNS ARE ESTABLISHED THAT DETERMINE SOME FINAL NOTE, WHOSE MOST IMPORTANT VARIABLE IS THE TOTAL TIME SPENT ON THE PLATFORM. FINALLY, IT IS DETERMINED THAT THE VARIABLES: TIME INVESTED IN THE PLATFORM IN THE CONSULTATION OF CONTENT, TEAMWORK, TASKS AND FORUM ACTIVITY, POSITIVELY INFLUENCE THE SATISFACTORY PERFORMANCE OF THE GRADUATE STUDENT AND THOSE VARIABLES RELATED TO THE CONSULTATIONS, TIME AND DAY OF STUDY DO NOT INTERVENE IN SUCH PERFORMANCE, THESE FINDINGS GIVE THE GUIDELINE TO FOCUS EFFORTS ON BUILDING MEANINGFUL CONTENT AND TASKS FOCUSED ON ACHIEVING THE DESIRED LEARNING SUPPORTED BY TEAM ACTIVITIES.
SOCIAL LEARNING ANALYTICS (SLA) SEEKS TO OBTAIN HIDDEN INFORMATION IN LARGE AMOUNTS OF DATA, USUALLY OF AN EDUCATIONAL NATURE. SLA FOCUSES MAINLY ON THE ANALYSIS OF SOCIAL NETWORKS (SOCIAL NETWORK ANALYSIS, SNA) AND THE WEB, TO DISCOVER PATTERNS OF INTERACTION AND BEHAVIOR OF EDUCATIONAL SOCIAL ACTORS. THIS PAPER INCORPORATES THE SLA IN A SMART CLASSROOM. SPECIFICALLY, THIS PAPER PROPOSES TO DETERMINE THE LEARNING STYLES OF THE STUDENTS IN A SMART CLASSROOM USING SLA. IN THIS PROPOSAL IS ANALYZED EXTERNAL DATA FROM THE WEB AND SOCIAL NETWORKS TO BUILD KNOWLEDGE MODELS ABOUT THE STUDENTS, IN ORDER TO IMPROVE THE LEARNING PROCESSES THAT OCCUR IN THE SMART CLASSROOMS. IN GENERAL, THESE SLA TASKS WILL BE ORGANIZED IN AUTONOMOUS CYCLES, IN ORDER TO INTEGRATE THEM WITH EACH OTHER. THE AUTONOMIC CYCLE WILL AUTOMATE THE EXECUTION OF THOSE TASKS AND THE GENERATION OF KNOWLEDGE MODELS, IN SUCH A WAY TO PERMANENTLY MONITOR THE LEARNING PROCESS, OBSERVING IT, ANALYZING IT AND DETERMINING THE STUDENT LEARNING STYLES. FOR THE DEVELOPMENT OF THE SLA TASKS, WE WILL USE CONCEPTS FROM THE SEMANTIC MINING, TEXT MINING, DATA MINING, AMONG OTHER DOMAINS. FINALLY, WE EXPERIMENT IN A TEST SCENARIO, WITH RESULTS VERY INTERESTING.
INSTRUCTORS AND STUDENTS HAVE PROBLEMS MONITORING THE LEARNING PROCESS FROM LOW LEVEL INTERACTIONS IN ON-LINE COURSES BECAUSE IT IS HARD TO MAKE SENSE OF RAW DATA. IN THIS PAPER WE PRESENT A DEMONSTRATION OF THE ADD-ON OF THE LEARNING ANALYTICS SUPPORT IN THE KHAN ACADEMY PLATFORM (ALAS-KA). OUR TOOL PROCESSES THE RAW DATA IN ORDER TO TRANSFORM IT INTO USEFUL INFORMATION THAT CAN BE USED BY THE STUDENTS AND INSTRUCTORS THROUGH VISUALIZATIONS. ALAS-KA IS AN INTERACTIVE TOOL THAT ALLOWS TEACHERS AND STUDENTS TO SELECT THE PROVIDED INFORMATION DIVIDED BY COURSES AND TYPE OF INFORMATION. THE DEMONSTRATION IS ILLUSTRATED WITH DIFFERENT EXAMPLES BASED ON REAL EXPERIMENTS DATA.
E-LEARNING ENVIRONMENTS ARE STILL CHARACTERIZED BY LACK OF ADVANCED TOOLS FOR INCREASING STUDENT ENGAGEMENT. CONCEPTS OF SUPPORTING ENGAGEMENT AND MOTIVATION OF STUDENTS IN E-LEARNING ENVIRONMENTS ARE STILL BEING DISCUSSED IN RESEARCH COMMUNITY. THE CONCEPT OF GAMIFICATION IS PROVIDING CERTAIN COMPONENTS (POINTS, RANKINGS, LEVELS, ETC.), HOWEVER, ITS EFFECTIVE EMBEDMENT WITHIN LEARNING MANAGEMENT SYSTEMS IS STILL REMAINING A CHALLENGE. THE FIRST STEP TOWARDS SUCH AUTOMATED SYSTEM COULD BE MODELLING TOOL, DELIVERING INSTANT FEEDBACK NOT ONLY TO THE STUDENT, BUT ALSO TO THE TEACHER AND THE ACADEMIA ABOUT THE ENGAGEMENT WITHIN A COURSE. THE ARTICLE WILL SHARE THOUGHTS RELATED TO THE APPROACH OF MODELLING STUDENT ENGAGEMENT IN AN E-LEARNING ENVIRONMENT (MOODLE). ANALYSIS OF DATA ABOUT LEARNERS AND THEIR ACTIVITY WITHIN E-LEARNING ENVIRONMENT (LEARNING ANALYTICS) WILL PROVIDE BASIS FOR THIS MODELLING. WE GATHERED DATA FROM A GAMIFIED COURSE (MORE THAN 50 000 LOG DATA, MORE THAN N = 79 USERS) AND SUGGEST USING OF A MODELLING APPROACH FOR MEASURING EFFECTIVENESS OF DIFFERENT GAMIFICATION COMPONENTS WITHIN AN E-LEARNING ENVIRONMENT. THE AIM OF THE ARTICLE IS TO PROVIDE DEEPER INSIGHT INTO SPECIFIC ASPECTS OF ENGAGEMENT WITHIN E-LEARNING ENVIRONMENTS.
EVIDENCE SUGGESTS THAT INDIVIDUALS ARE OFTEN WILLING TO EXCHANGE PERSONAL DATA FOR (REAL OR PERCEIVED) BENEFITS. SUCH AN EXCHANGE MAY BE IMPACTED BY THEIR TRUST IN A PARTICULAR CONTEXT AND THEIR (REAL OR PERCEIVED) CONTROL OVER THEIR DATA. STUDENTS REMAIN CONCERNED ABOUT THE SCOPE AND DETAIL OF SURVEILLANCE OF THEIR LEARNING BEHAVIOR, THEIR PRIVACY, THEIR CONTROL OVER WHAT DATA ARE COLLECTED, THE PURPOSE OF THE COLLECTION, AND THE IMPLICATIONS OF ANY ANALYSIS. QUESTIONS ARISE AS TO THE EXTENT TO WHICH STUDENTS ARE AWARE OF THE BENEFITS AND RISKS INHERENT IN THE EXCHANGE OF THEIR DATA, AND WHETHER THEY ARE WILLING TO EXCHANGE PERSONAL DATA FOR MORE EFFECTIVE AND SUPPORTED LEARNING EXPERIENCES. THIS STUDY REPORTS ON THE VIEWS OF ENTRY LEVEL STUDENTS AT THE OPEN UNIVERSITY (OU) IN 2018. THE PRIMARY AIM IS TO EXPLORE DIFFERENCES BETWEEN STATED ATTITUDES TO PRIVACY AND THEIR ONLINE BEHAVIORS, AND WHETHER THESE SAME ATTITUDES EXTEND TO THEIR UNIVERSITY'S USES OF THEIR (PERSONAL) DATA. THE ANALYSIS INDICATES, INTER ALIA, THAT THERE IS NO OBVIOUS RELATIONSHIP BETWEEN HOW OFTEN STUDENTS ARE ONLINE OR THEIR AWARENESS OF/CONCERNS ABOUT PRIVACY ISSUES IN ONLINE CONTEXTS AND WHAT THEY ACTUALLY DO TO PROTECT THEMSELVES. SIGNIFICANTLY THOUGH, THE FINDINGS INDICATE THAT STUDENTS OVERWHELMINGLY HAVE AN INHERENT TRUST IN THEIR UNIVERSITY TO USE THEIR DATA APPROPRIATELY AND ETHICALLY. BASED ON THE FINDINGS, WE OUTLINE A NUMBER OF ISSUES FOR CONSIDERATION BY HIGHER EDUCATION INSTITUTIONS, SUCH AS THE NEED FOR TRANSPARENCY (OF PURPOSE AND SCOPE), THE PROVISION OF SOME ELEMENT OF STUDENT CONTROL, AND AN ACKNOWLEDGMENT OF THE EXCHANGE VALUE OF INFORMATION IN THE NEXUS OF THE PRIVACY CALCULUS.
USING GAMES FOR EDUCATION CAN INCREASE THE MOTIVATION AND ENGAGEMENT OF STUDENTS AND PROVIDE A MORE AUTHENTIC LEARNING ENVIRONMENT WHERE STUDENTS CAN LEARN, TEST AND APPLY NEW KNOWLEDGE. HOWEVER, THE ACTUAL (SERIOUS) GAME APPLICATION IN SCHOOLS IS STILL LIMITED, PARTLY BECAUSE TEACHERS CONSIDER THEIR USE AS A COMPLEX PROCESS. TO INCREASE GAME ADOPTION, THE INTEGRATION OF GAME LEARNING ANALYTICS (GLA) CAN PROVIDE TEACHERS A THOROUGH INSIGHT INTO THE KNOWLEDGE ACQUIRED BY THEIR STUDENTS AND USUALLY PRESENTED THROUGH A VISUAL DASHBOARD. ALTHOUGH IT IS POSSIBLE TO PROVIDE A USEFUL GENERAL METRICS AND A PREFAB DASHBOARD, IT MAY NOT FULLY COVER TEACHERS' EXPECTATIONS. IN THIS PAPER, WE STUDY THE AD-HOC ADAPTATION OF GENERIC DASHBOARDS TO INCREASE THEIR EFFECTIVENESS THROUGH THREE CASE-STUDIES. IN THESE EXPERIENCES, WE ADAPT DASHBOARDS FOR TEACHERS TO INCLUDE DETAILED INFORMATION FOR MORE-FOCUSED ANALYSIS. WITH THE POSITIVE RESULTS OBTAINED FROM THESE SCENARIOS, WE HAVE IDENTIFIED A METHODOLOGICAL PROCESS TO CREATE AD-HOC GLA DASHBOARDS AND EXTRACTED SOME LESSONS LEARNED FOR DASHBOARD DEVELOPMENT: SIMPLE BUT USEFUL DASHBOARDS CAN PROVIDE A HIGHER ADDED VALUE FOR STAKEHOLDERS COMPARED WITH MORE COMPLEX DASHBOARDS; TEACHERS AND GAME DEVELOPERS SHOULD BE INVOLVED IN DASHBOARD DESIGN FOR BETTER RESULTS; AND, IF POSSIBLE, AD-HOC DEVELOPED DASHBOARDS SHOULD BE USED AS THEY HAVE PROVED TO BE MORE EFFECTIVE THAN GENERIC DASHBOARDS.
THE CURRENT EDUOPEN DASHBOARD IS NOT CAPABLE OF MONITORING PERFORMANCES AND TRENDS OVER THE MEDIUM TO LONG TERM BOTH FOR THE STUDENTS AS FOR THE INSTRUCTORS; SUMMARISING AND SYNTHESISING THE ADEQUATE INFORMATION; ALLOWING IMPLEMENTATION OF ANY SORT OF PREDICTIVE ACTIONS AND FUNCTIONS (LEARNING PREDICTION). THE ARTICLE AIMS TO EXPOSE THE PROCESS OF INNOVATION AND REDEFINITION OF A LEARNING ANALYTICS DASHBOARD IN THE EDUOPEN MOOC PLATFORM IN ORDER TO DEFINE A MODEL TO DESIGN IT ACCURATELY IN TERMS OF PRODUCTIVITY FOR ALL USERS (TEACHERS AND STUDENTS ABOVE ALL). FROM THE LITERATURE ANALYSIS, MAIN MOOC PLATFORM COMPARISONS AND THE INSIGHTS FROM THE ROUND TABLES A TIME SPENT VARIABLE IS IDENTIFIED AS AT THE BASIS OF THE ENTIRE USER EXPERIENCE IN ONLINE TRAINING PATHS. A CONCRETE EXPERIMENTATION, THROUGH THE DESIGN OF A LEARNING TIMELINE AND A CONSTRUCTIVE FEEDBACK SYSTEM OF AN UPCOMING COURSE IN THE EDUOPEN CATALOGUE, IS DESIGNED AND EXPLAINED RELAYING ON THE HYPOTHESIS OF THE EXISTENCE OF A CORRELATION BETWEEN THE "TIME SPENT" (TIME VALUE) AND THE FINAL PERFORMANCE OF THE STUDENT.
THE EDUCATIONAL LANDSCAPE IN THE EUROPEAN UNION AND AT A GLOBAL LEVEL IS CONSTANTLY CHANGING TO ADAPT TO THE NEEDS AND DEMANDS OF KNOWLEDGE-BASED SOCIETIES. AS A CONSEQUENCE, MOST HIGHER EDUCATION INSTITUTIONS (HEI'S) HAVE INSTALLED EVALUATION STRATEGIES AS PART OF THEIR QUALITY ASSURANCE PROGRAMS. MOST OF THESE EVALUATION APPROACHES ARE OFTEN VERY SPECIFIC TO THE INDIVIDUAL HEI AND CAN'T TRULY SERVE IN NATIONAL OR EVEN INTERNATIONAL SETTINGS. THIS PAPER PRESENTS THE EVALUATION STRATEGIES USED AT THE HOME INSTITUTIONS OF THE AUTHORS AND ANALYSES THEIR LIKELIHOOD TO BE USED IN AN INTERNATIONAL SETTING. THE FINDINGS WILL BE SUPPLEMENTED WITH THE DESCRIPTION OF EVALUATION APPROACHES DEVELOPED AND USED IN THE SCOPE OF THE ERASMUS+ PROJECTS "INTERCULTURAL DESIGN CAMP" AND "CREATIVE INDUSTRIES GLOBAL NETWORK". TO PROVIDE A FURTHER INTERNATIONAL PERSPECTIVE, THE PAPER DISCUSSES THE ADMINISTRATION OF, ANALYSIS OF AND USE OF THE RESULTS OF THE WIDELY-ADMINISTERED NATIONAL SURVEY OF STUDENT ENGAGEMENT, A SURVEY MECHANISM USED TO MEASURE THE LEVEL OF STUDENT PARTICIPATION AT UNIVERSITIES AND COLLEGES IN CANADA AND THE UNITED STATES AS IT RELATES TO LEARNING AND ENGAGEMENT. THESE CONSIDERATIONS WILL BE COMPLEMENTED BY THE DESCRIPTION OF THE PROJECT "LEARNING ANALYTICS FOR EXAMINATIONS AND STUDY SUCCESS" CONDUCTED AT STUTTGART MEDIA UNIVERSITY. THIS IS A DATABASE APPLICATION THAT ANALYSES STUDY PROGRESS DATA FROM FORMER STUDENTS AND COMPARES THESE WITH THE DATA FROM ACTIVE STUDENTS WITH THE AIM TO TAKE MEASURES TO INDIVIDUALLY SUPPORT THE ACADEMIC SUCCESS OF STUDENTS.
THIS PAPER PROPOSES THE DESIGN OF AN EXPLANATORY CASE STUDY USING SOCIAL LEARNING ANALYTICS TO ANALYZE AND UNDERSTAND STUDENTS' ONLINE LEARNING PROCESS IN A NING-BASED UNDERGRADUATE-LEVEL ONLINE COURSE IN A MIDWEST COMPREHENSIVE UNIVERSITY IN THE UNITED STATES. THIS PAPER FIRSTLY BRIEFLY INTRODUCED THE STRUCTURE OF THE ONLINE COURSE. THEN, BASED ON DIFFERENT RESEARCH QUESTIONS, THIS PAPER EXPLORES THE POTENTIALS OF APPLYING SOCIAL LEARNING ANALYTICS METHODS, PARTICULARLY NETWORK-FOCUSED, DISCOURSE & CONTENT-FOCUSED, AND CONTEXT-FOCUSED LEARNING ANALYTICS TO UNDERSTAND STUDENTS' LEARNING EXPERIENCES IN THE NING-BASED ONLINE LEARNING COURSE.
IN THE LAST YEARS, THE USE OF LEARNING ANALYTICS (LA) TOOLS HAS BEEN INCREASING TO PROCESS THE AVAILABLE INFORMATION ABOUT STUDENTS COLLECTED THROUGH DIFFERENT LEARNING MANAGEMENT SYSTEMS (LMS). IN THIS STUDY, WE HAVE ANALYZED THE INTERACTIONS IN THE EDUCATIONAL SCENARIO COLLECTED THROUGH MOODLE (MODULAR OBJECT ORIENTED DEVELOPMENTAL LEARNING ENVIRONMENT), A LEADING OPEN SOURCE LMS, IN ORDER TO PROVIDE A CLASSIFICATION OF STUDENTS BY USING CLUSTERING TECHNIQUES, TO IMPROVE THE LEARNING PROCESS. STUDENTS ONLINE ACTIVITIES GENERATE LARGE QUANTITIES OF DATA THAT BEFORE WERE WASTED SINCE NO LA TOOLS WERE AVAILABLE TO PROCESS THEM. WITH THE IRRUPTION OF THE BIG DATA TECHNIQUES IN THE EDUCATIONAL SECTOR, A LOT OF INFORMATION CAN BE EASILY TREATED TO EXTRACT BEHAVIORS OF THE STUDENTS AND CLASSIFY THEM ACCORDING TO THEIR PROFILES. THESE ANALYSES DEFINE MODELS OF ACTION THAT HELP BOTH THE IN-SERVICE TEACHER AND THE NEW TEACHER WHO JOINS THE DEPARTMENT. IN ADDITION, DETAILED ANALYSIS OF THIS INFORMATION MAY HELP US IN THE STUDY OF A POSSIBLE RELATIONSHIP BETWEEN DIFFERENT INDICATORS OF USE OF THESE PLATFORMS AND THE PERFORMANCE OF STUDENTS, BOTH GENERATED THROUGHOUT THE LEARNING PROCESS AS THOSE COMING FROM SUMMATIVE EVALUATION. IN THIS STUDY, WE PROVIDE THE CONSTRUCTION OF A DATA ANALYSIS MODEL FACILITATED BY THE MOODLE PLATFORM, FROM THE DIFFERENT INTERACTIONS BETWEEN THE TEACHER, THE STUDENT AND THE DEVELOPED SUBJECT; SO THAT, THIS INFORMATION CAN BE TRANSFORMED INTO KNOWLEDGE AND THEIR UNDERSTANDING CAN HELP TO THE IMPROVEMENT OF TEACHING PRACTICE. IN PARTICULAR, IT ADDRESSES THE APPLICATION OF THESE MODELS TO THE IMPROVEMENT OF THE STUDENTS' LEARNING STRATEGIES ACCORDING TO THEIR TYPOLOGY.
THIS PAPER ARGUES THAT METHODS USED FOR THE CLASSIFICATION AND MEASUREMENT OF ONLINE EDUCATION ARE NOT NEUTRAL AND OBJECTIVE, BUT INVOLVED IN THE CREATION OF THE EDUCATIONAL REALITIES THEY CLAIM TO MEASURE. IN PARTICULAR, THE PAPER DRAWS ON MATERIAL SEMIOTICS TO EXAMINE CLUSTER ANALYSIS AS A PERFORMATIVE DEVICE' THAT, TO A SIGNIFICANT EXTENT, CREATES THE EDUCATIONAL ENTITIES IT CLAIMS TO OBJECTIVELY REPRESENT THROUGH THE EMERGING BODY OF KNOWLEDGE OF LEARNING ANALYTICS (LA). IT ALSO OFFERS A MORE CRITICAL AND POLITICAL READING OF THE ALGORITHMIC ASSEMBLAGES OF LA, OF WHICH CLUSTER ANALYSIS IS A PART. OUR ARGUMENT IS THAT IF WE WANT TO UNDERSTAND HOW ALGORITHMIC PROCESSES AND TECHNIQUES LIKE CLUSTER ANALYSIS FUNCTION AS PERFORMATIVE DEVICES, THEN WE NEED METHODOLOGICAL SENSIBILITIES THAT CONSIDER CRITICALLY BOTH THEIR POLITICAL DIMENSIONS AND THEIR TECHNICAL-MATHEMATICAL MECHANISMS. THE IMPLICATIONS FOR CRITICAL RESEARCH IN EDUCATIONAL TECHNOLOGY ARE DISCUSSED.
THE USE OF LEARNING ANALYTICS (LA) HAS SHOWN SIGNIFICANT IMPROVEMENTS IN ONLINE LEARNING ENVIRONMENTS. THIS PAPER INTENDS TO DISCUSS THE CHALLENGES OF APPLYINGLA IN THE CONTEXT OF ELEMENTARY SCHOOLS OF INDIA. OUT OF MANY CHALLENGES, WE FOCUS ON SOLVING THE UNAVAILABILITY OF SCHOOL DATA IN DIGITAL FORMAT. MOST OF THE DATA IN SUCH SCHOOLS IS STORED ON PHYSICAL MEDIUMS LIKE NOTEBOOKS. THESE SCHOOLS ALSO LACK THE CRITICAL RESOURCES LIKE THE AVAILABILITY OF COMPUTING DEVICES, INTERNET AND DIGITALLY LITERATE MANPOWER. AS THE PRIMARY OUTCOME OF THIS PAPER, WE PROPOSE A DATA COLLECTION SYSTEM, NAMED AS OSLA, WHICH PROVIDES MULTIPLE WAYS OF DATA COLLECTION IN RESOURCE CONSTRAINED ENVIRONMENT. OSLA DISTRIBUTES THE NEED OF RESOURCES ACROSS DIFFERENT TOUCHPOINTS TO TOLERATE THE UNAVAILABILITY OF SPECIFIC COMPUTING DEVICE, INTERNET AND DIGITALLY LITERATE MANPOWER.
THIS PAPER CONSIDERS THE PRACTICAL APPLICATIONS OF THE ARTICLE,ETHICAL OVERSIGHT OF STUDENT DATA IN LEARNING ANALYTICS: A TYPOLOGY DERIVED FROM A CROSS-CONTINENTAL, CROSS-INSTITUTIONAL PERSPECTIVEBY WILLIS ET AL. (EDUC TECHNOL RES DEV 64: 881-901, 2016). STUDENTS ENGAGING IN ONLINE LEARNING LEAVE BEHIND VAST QUANTITIES OF DATA. IN 2020, THE RAPID SHIFT TO ONLINE LEARNING DURING THE GLOBAL PANDEMIC ALLOWED VIRTUAL DATA COLLECTION TO OUTPACE PROCEDURES AND POLICIES THAT PROMOTE ETHICAL ANALYSIS. THE MERE AVAILABILITY OF DATA DOES NOT CONFER ETHICAL COLLECTION OF DATA. FURTHER, ANALYSIS OF DATA UNDER THE ASSUMPTION OF LEARNING OUTCOMES DOES NOT NECESSARILY ENSURE JUSTICE OR LEARNING FOR STUDENTS. THIS ARTICLE OFFERS POSSIBLE APPLICATIONS OF THE HEURISTIC BY WILLIS ET AL. (EDUC TECHNOL RES DEV 64: 881-901, 2016) FOR ETHICAL LEARNING ANALYTICS IN ORDER TO MITIGATE HARM TO STUDENTS. IT EXTENDS THEIR WORK BY SUGGESTING EDUCATORS CONSIDER THE RACIALIZED ENCODING OF DATA THEMSELVES, AND ARGUES THAT EVERY ACT OF SURVEILLANCE DURING THE PANDEMIC CREATES NORMS FOR FUTURE SURVEILLANCE.
THE ADVANCEMENT OF LEARNING ANALYTICS HAS ENABLED THE DEVELOPMENT OF PREDICTIVE MODELS TO FORECAST LEARNERS' BEHAVIORS AND OUTCOMES (E.G., PERFORMANCE). HOWEVER, MANY OF THESE MODELS ARE ONLY APPLICABLE TO SPECIFIC LEARNING ENVIRONMENTS AND IT IS USUALLY DIFFICULT TO KNOW WHICH FACTORS INFLUENCE PREDICTION RESULTS, INCLUDING THE PREDICTOR VARIABLES AS WELL AS THE TYPE OF PREDICTION OUTCOME. KNOWING THESE FACTORS WOULD BE RELEVANT TO GENERALIZE TO OTHER CONTEXTS, COMPARE APPROACHES, IMPROVE THE PREDICTIVE MODELS AND ENHANCE THE POSSIBLE INTERVENTIONS. IN THIS DIRECTION, THIS WORK AIMS TO ANALYZE HOW SEVERAL FACTORS CAN MAKE AN INFLUENCE ON THE PREDICTION OF STUDENTS' PERFORMANCE. THESE FACTORS INCLUDE THE EFFECT OF PREVIOUS GRADES, FORUM VARIABLES, VARIABLES RELATED TO EXERCISES, CLICKSTREAM DATA, COURSE DURATION, TYPE OF ASSIGNMENTS, DATA COLLECTION PROCEDURE, QUESTION FORMAT IN AN EXAM, AND THE PREDICTION OUTCOME (CONSIDERING INTERMEDIATE ASSIGNMENT GRADES, INCLUDING THE FINAL EXAM, AND THE FINAL GRADE). RESULTS SHOW THAT VARIABLES RELATED TO EXERCISES ARE THE BEST PREDICTORS, UNLIKE VARIABLES ABOUT FORUM, WHICH ARE USELESS. CLICKSTREAM DATA CAN BE ACCEPTABLE PREDICTORS WHEN EXERCISES ARE NOT AVAILABLE, BUT THEY DO NOT ADD PREDICTION POWER IF VARIABLES RELATED TO EXERCISES ARE PRESENT. PREDICTIVE POWER WAS ALSO BETTER FOR CONCEPT-ORIENTED ASSIGNMENTS AND BEST MODELS USUALLY CONTAINED ONLY THE LAST INTERACTIONS. IN ADDITION, RESULTS SHOWED THAT MULTIPLE-CHOICE QUESTIONS WERE EASIER TO PREDICT THAN CODING QUESTIONS, AND THE FINAL EXAM GRADE (ACTUAL KNOWLEDGE AT A SPECIFIC MOMENT) WAS HARDER TO PREDICT THAN THE FINAL GRADE (AVERAGE KNOWLEDGE IN THE LONG TERM), BASED ON DIFFERENT ASSIGNMENTS DURING THE COURSE.
THE PURPOSE OF LEARNING ANALYTICS IS TO PROMOTE STUDENT SUCCESS IN THE CLASSROOM. TO IMPLEMENT THE FRAMEWORK OF LEARNING ANALYTICS, RESEARCHERS HAVE ADOPTED MACHINE LEARNING METHODOLOGIES TO IDENTIFY AT-RISK STUDENTS AT AN EARLY STAGE. IN THEORY, MACHINE LEARNING IS A MATHEMATICAL ALGORITHM THAT IMPROVES AUTOMATION THROUGH EXPERIENCE. THE EXPERIENCE IS THE DATA COLLECTED FROM ONLINE LEARNING PLATFORMS, AND IN GENERAL, THE DATA CONTAIN VARIOUS FEATURES SUCH AS THE NUMBER OF TIMES THAT A STUDENT ACCESSES THE LEARNING MATERIAL EACH WEEK. RELEVANT STUDIES HAVE DEMONSTRATED EXTREMELY HIGH ACCURACY IN IDENTIFYING AT-RISK STUDENTS USING IDENTIFICATION MODELS TRAINED BY MACHINE LEARNING. HOWEVER, NUMEROUS DETAILS AND DATA CHALLENGES HAVE BEEN OVERLOOKED IN PRIOR STUDIES, CALLING INTO QUESTION THE ACCURACY OF PAST CONTRIBUTIONS. IN THIS STUDY, WE FOCUSED ON ONE TYPE OF DATA CHALLENGE: DATA IMBALANCE. THE DATA IMBALANCE PROBLEMS IN EDUCATION ARE USUALLY THE RESULT OF TEACHERS' GRADING POLICY. TO HIGHLIGHT THE SERIOUSNESS OF THIS ISSUE, WE COLLECTED DATA FROM 12 BLENDED LEARNING COURSES AND SUMMARIZED 3 TYPES OF GRADING POLICIES: DISCRIMINATION, STRINGENCY, AND LENIENCY. WE THEN PROVIDED EVIDENCE THAT THE LENIENCY STRATEGY CAUSES THE ILLUSION OF HIGH ACCURACY OF AT-RISK STUDENT IDENTIFICATION. FINALLY, WE VERIFIED A ROBUST METHOD TO ADDRESS THE EFFECTIVENESS OF THE LENIENCY STRATEGY, AND USING THESE RESULTS, WE SUMMARIZED THE CHARACTERISTICS OF STUDENTS WHO TEND TO BE MISIDENTIFIED BY MACHINE LEARNING METHODOLOGY.
LEARNING ANALYTICS (LA) CONSTITUTES A KEY OPPORTUNITY TO SUPPORT LEARNING DESIGN (LD) IN BLENDED LEARNING ENVIRONMENTS. HOWEVER, DETAILS AS TO HOW LA SUPPORTS LD IN PRACTICE AND INFORMATION ON TEACHER EXPERIENCES WITH LA ARE LIMITED. THIS STUDY EXPLORES THE POTENTIAL OF LA TO INFORM LD BASED ON A ONE-SEMESTER UNDERGRADUATE BLENDED LEARNING COURSE AT A NORWEGIAN UNIVERSITY. OUR FINDINGS INDICATE THAT CREATING VALUABLE CONNECTIONS BETWEEN LA AND LD REQUIRES A DETAILED ANALYSIS OF STUDENT CHECKPOINTS (E.G., ONLINE LOGINS) AND PROCESS ANALYTICS (E.G., ONLINE CONTENT AND INTERACTION DYNAMICS) TO FIND MEANINGFUL LEARNING BEHAVIOUR PATTERNS THAT CAN BE FORWARDED TO TEACHERS IN RETROSPECT TO SUPPORT THE REDESIGN OF COURSES. MOREOVER, THE TEACHERS IN OUR STUDY FOUND THE LA VISUALIZATIONS TO BE VALUABLE FOR UNDERSTANDING STUDENT ONLINE LEARNING PROCESSES, BUT THEY ALSO REQUESTED THE TIMELY SHARING OF AGGREGATED LA VISUALIZATIONS IN A SIMPLE, EASY-TO-INTERPRET FORMAT, YET DETAILED ENOUGH TO BE INFORMATIVE AND ACTIONABLE. WE CONCLUDE THE PAPER BY ARGUING THAT THE POTENTIAL OF LA TO SUPPORT LD IS IMPROVED WHEN MULTIPLE LEVELS OF LA ARE CONSIDERED.
THE KHAN ACADEMY PLATFORM ENABLES POWERFUL ON-LINE COURSES IN WHICH STUDENTS CAN WATCH VIDEOS, SOLVE EXERCISES, OR EARN BADGES. THIS PLATFORM PROVIDES AN ADVANCED LEARNING ANALYTICS MODULE WITH USEFUL VISUALIZATIONS. NEVERTHELESS, IT CAN BE IMPROVED. IN THIS PAPER, WE DESCRIBE ALAS-MA, WHICH PROVIDES AN EXTENSION OF THE LEARNING ANALYTICS SUPPORT FOR THE KHAN ACADEMY PLATFORM. WE HEREIN PRESENT AN OVERVIEW OF THE ARCHITECTURE OF ALAS-KA. IN ADDITION, WE REPORT THE DIFFERENT TYPES OF VISUALIZATIONS AND INFORMATION PROVIDED BY ALAS-MA, WHICH HAVE NOT BEEN AVAILABLE PREVIOUSLY IN THE KHAN ACADEMY PLATFORM. ALAS-MA INCLUDES NEW VISUALIZATIONS FOR THE ENTIRE CLASS AND ALSO FOR INDIVIDUAL STUDENTS. INDIVIDUAL VISUALIZATIONS CAN BE USED TO CHECK ON THE LEARNING STYLES OF STUDENTS BASED ON ALL THE INDICATORS AVAILABLE. ALAS-MA VISUALIZATIONS HELP TEACHERS AND STUDENTS TO MAKE DECISIONS IN THE LEARNING PROCESS. THE PAPER PRESENTS SOME GUIDELINES AND EXAMPLES TO HELP TEACHERS MAKE THESE DECISIONS BASED ON DATA FROM UNDERGRADUATE COURSES, WHERE ALAS-MA WAS INSTALLED. THESE COURSES (PHYSICS, CHEMISTRY, AND MATHEMATICS) FOR FRESHMEN WERE DEVELOPED AT UNIVERSIDAD CARLOS HI DE MADRID (UC3M) AND WERE TAKEN BY MORE THAN 300 STUDENTS. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
VARIOUS RESEARCH WORK HAVE HIGHLIGHTED THE IMPORTANCE OF MODELING LEARNER'S PERSONALITY TO PROVIDE A PERSONALIZED COMPUTER BASED LEARNING. IN PARTICULAR, QUESTIONNAIRE IS THE MOST USED METHOD TO MODEL PERSONALITY WHICH CAN BE LONG AND NOT MOTIVATING. THIS MAKES LEARNERS UNWILLING TO TAKE IT. THEREFORE, THIS PAPER REFERS TO LEARNING ANALYTICS (LA) TO IMPLICITLY MODEL LEARNERS' PERSONALITIES BASED ON THEIR TRACES GENERATED DURING THE LEARNING -PLAYING PROCESS. IN THIS CONTEXT, AN LA SYSTEM AND AN EDUCATIONAL GAME WERE DEVELOPED. FORTY FIVE PARTICIPANTS (34 LEARNERS AND 11 TEACHERS) PARTICIPATED IN AN EXPERIMENT TO EVALUATE THE ACCURACY LEVEL OF THE LEARNERS' MODELING RESULTS AND THE TEACHERS' SATISFACTION DEGREE TOWARDS THIS LA SYSTEM. THE OBTAINED RESULTS HIGHLIGHTED THAT THE LA SYSTEM HAS A HIGH LEVEL OF ACCURACY AND A "GOOD" AGREEMENT DEGREE COMPARED TO THE QUESTIONNAIRE PAPER. BESIDES, THE TEACHERS FOUND THE LA SYSTEM EASY TO USE, USEFUL AND THEY WERE WILLING TO USE IT IN THE FUTURE.
IN SELF-REGULATED LEARNING (SLR), THE LACK OF A PREDEFINED, FORMAL LEARNING TRAJECTORY MAKES IT MORE CHALLENGING TO ASSESS STUDENTS' PROGRESS (E.G. BY COMPARING IT TO SPECIFIC BASELINES) AND TO OFFER RELEVANT FEEDBACK AND SCAFFOLDING WHEN APPROPRIATE. IN THIS PAPER WE DESCRIBE A VISUAL LEARNING ANALYTICS (VLA) SOLUTION FOR EXPLORING STUDENTS' DATASETS COLLECTED IN A WEB-BASED LEARNING ENVIRONMENT (WBLE). WE EMPLOY MINING TECHNIQUES FOR THE ANALYSIS OF MULTIDIMENSIONAL DATA, SUCH AS T-SNE AND CLUSTERING, IN AN EXPLORATORY STUDY FOR IDENTIFYING PATTERNS OF STUDENTS WITH SIMILAR STUDY BEHAVIOR AND INTERESTS. AN EXAMPLE USE CASE IS PRESENTED AS EVIDENCE OF THE EFFECTIVENESS OF OUR PROPOSED METHOD, WITH A DATASET OF LEARNING BEHAVIORS OF 6423 STUDENTS WHO USED AN ONLINE STUDY TOOL DURING 18 MONTHS.
PURPOSE - THE EXPLOSIVE GROWTH IN THE NUMBER OF DIGITAL TOOLS UTILIZED IN EVERYDAY LEARNING ACTIVITIES GENERATES DATA AT AN UNPRECEDENTED SCALE, PROVIDING EXCITING CHALLENGES THAT CROSS SCHOLARLY COMMUNITIES. THIS PAPER AIMS TO PROVIDE AN OVERVIEW OF LEARNING ANALYTICS (LA) WITH THE AIM OF HELPING MEMBERS OF THE INFORMATION AND LEARNING SCIENCES COMMUNITIES UNDERSTAND HOW EDUCATIONAL BIG DATA IS RELEVANT TO THEIR RESEARCH AGENDAS AND HOW THEY CAN CONTRIBUTE TO THIS GROWING NEW FIELD. DESIGN/METHODOLOGY/APPROACH - HIGHLIGHTING SHARED VALUES AND ISSUES ILLUSTRATES WHY LA IS THE PERFECT MEETING GROUND FOR INFORMATION AND THE LEARNING SCIENCES, AND SUGGESTS HOW BY WORKING TOGETHER EFFECTIVE LA TOOLS CAN BE DESIGNED TO INNOVATE EDUCATION. FINDINGS - ANALYTICS-DRIVEN PERFORMANCE DASHBOARDS ARE OFFERED AS A SPECIFIC EXAMPLE OF ONE RESEARCH AREA WHERE INFORMATION AND LEARNING SCIENTISTS CAN MAKE A SIGNIFICANT CONTRIBUTION TO LA RESEARCH. RECENT REVIEWS OF EXISTING DASHBOARD STUDIES POINT TO A DEARTH OF EVALUATION WITH REGARD TO EITHER THEORY OR OUTCOMES. HERE, THE RELEVANT EXPERTISE FROM RESEARCHERS IN BOTH THE LEARNING SCIENCES AND INFORMATION SCIENCE IS OFFERED AS AN IMPORTANT OPPORTUNITY TO IMPROVE THE DESIGN AND EVALUATION OF STUDENT-FACING DASHBOARDS. ORIGINALITY/VALUE - THIS PAPER OUTLINES IMPORTANT TIES BETWEEN THREE SCHOLARLY COMMUNITIES TO ILLUSTRATE HOW THEIR COMBINED RESEARCH EXPERTISE IS CRUCIAL TO ADVANCING HOW WE UNDERSTAND LEARNING AND FOR DEVELOPING LA-BASED INTERVENTIONS THAT MEET THE VALUES THAT WE ALL SHARE.
THE RECENT BREAKTHROUGHS IN DEEP LEARNING HAVE PROVIDED POWERFUL DATA ANALYTICS TOOLS FOR A WIDE RANGE OF DOMAINS RANGING FROM ADVERTISING AND ANALYZING USERS' BEHAVIOR TO LOAD AND FINANCIAL FORECASTING. DEPENDING ON THE NATURE OF THE AVAILABLE DATA AND THE TASK AT HAND DEEP LEARNING ANALYTICS TECHNIQUES CAN BE DIVIDED INTO TWO BROAD CATEGORIES: (A) UNSUPERVISED LEARNING TECHNIQUES AND (B) SUPERVISED LEARNING TECHNIQUES. IN THIS CHAPTER WE PROVIDE AN EXTENSIVE OVERVIEW OVER BOTH CATEGORIES. UNSUPERVISED LEARNING METHODS, SUCH AS AUTOENCODERS, ARE ABLE TO DISCOVER AND EXTRACT THE INFORMATION FROM THE DATA WITHOUT USING ANY GROUND TRUTH INFORMATION AND/OR SUPERVISION FROM DOMAIN EXPERTS. THUS, UNSUPERVISED TECHNIQUES CAN BE ESPECIALLY USEFUL FOR DATA EXPLORATION TASKS, ESPECIALLY WHEN COMBINED WITH ADVANCED VISUALIZATION TECHNIQUES. ON THE OTHER HAND, SUPERVISED LEARNING TECHNIQUES ARE USED WHEN GROUND TRUTH INFORMATION IS AVAILABLE AND WE WANT TO BUILD CLASSIFICATION AND/OR FORECASTING MODELS. SEVERAL DEEP LEARNING MODELS ARE EXAMINED RANGING FROM SIMPLE MULTILAYER PERCEPTRONS (MLPS) TO CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND RECURRENT NEURAL NETWORKS (RNNS). HOWEVER TRAINING DEEP LEARNING MODELS IS NOT ALWAYS A STRAIGHTFORWARD TASK REQUIRING BOTH A SOLID THEORETICAL BACKGROUND AS WELL AS INTUITION AND EXPERIENCE. TO THIS END, WE ALSO PRESENT RECENT TECHNIQUES THAT ALLOW FOR EFFICIENTLY TRAINING DEEP LEARNING MODELS, SUCH AS BATCH NORMALIZATION, RESIDUAL CONNECTIONS, ADVANCED OPTIMIZATION TECHNIQUES AND ACTIVATION FUNCTIONS, AS WELL AS A NUMBER OF USEFUL PRACTICAL SUGGESTIONS. FINALLY, WE PRESENT AN OVERVIEW OF THE AVAILABLE OPEN SOURCE DEEP LEARNING FRAMEWORKS THAT CAN BE USED TO IMPLEMENT DEEP LEARNING ANALYTICS TECHNIQUES AND ACCELERATE THE TRAINING PROCESS USING GRAPHICS PROCESSING UNITS (GPUS).
LEARNING ANALYTICS IS AN EMERGING TECHNIQUE OF ANALYSING STUDENT PARTICIPATION AND ENGAGEMENT. THE RECENT COVID-19 PANDEMIC HAS SIGNIFICANTLY INCREASED THE ROLE OF LEARNING MANAGEMENT SYSTEMS (LMSS). LMSS PREVIOUSLY ONLY COMPLEMENTED FACE-TO-FACE TEACHING, SOMETHING WHICH HAS NOT BEEN POSSIBLE BETWEEN 2019 TO 2020. TO DATE, THE EXISTING BODY OF LITERATURE ON LMSS HAS NOT ANALYSED LEARNING IN THE CONTEXT OF THE PANDEMIC, WHERE AN LMS SERVES AS THE ONLY INTERFACE BETWEEN STUDENTS AND INSTRUCTORS. CONSEQUENTLY, PRODUCTIVE RESULTS WILL REMAIN ELUSIVE IF THE KEY FACTORS THAT CONTRIBUTE TOWARDS ENGAGING STUDENTS IN LEARNING ARE NOT FIRST IDENTIFIED. THEREFORE, THIS STUDY AIMED TO PERFORM AN EXTENSIVE LITERATURE REVIEW WITH WHICH TO DESIGN AND DEVELOP A STUDENT ENGAGEMENT MODEL FOR HOLISTIC INVOLVEMENT IN AN LMS. THE REQUIRED DATA WAS COLLECTED FROM AN LMS THAT IS CURRENTLY UTILISED BY A LOCAL MALAYSIAN UNIVERSITY. THE MODEL WAS VALIDATED BY A PANEL OF EXPERTS AS WELL AS DISCUSSIONS WITH STUDENTS. IT IS OUR HOPE THAT THE RESULT OF THIS STUDY WILL HELP OTHER INSTITUTIONS OF HIGHER LEARNING DETERMINE FACTORS OF LOW ENGAGEMENT IN THEIR RESPECTIVE LMSS.
UNDERSTANDING HUMAN JUDGEMENT AND DECISION MAKING DURING VISUAL INSPECTION OF DATA IS OF BOTH PRACTICAL AND THEORETICAL INTEREST. WHILE VISUALIZING DATA IS A COMMONLY EMPLOYED MECHANISM TO SUPPORT COMPLEX COGNITIVE PROCESSES SUCH AS INFERENCE, JUDGEMENT, AND DECISION MAKING, THE PROCESS OF SUPPORTING AND SCAFFOLDING COGNITION THROUGH EFFECTIVE DESIGN IS LESS WELL UNDERSTOOD. APPLYING INSIGHTS FROM COGNITIVE PSYCHOLOGY AND VISUALIZATION SCIENCE, THIS PAPER CRITICALLY DISCUSSES THE ROLE OF HUMAN FACTORS - VISUAL ATTENTION, PERCEPTION, JUDGEMENT, AND DECISION MAKING - TOWARD INFORMING METHODOLOGICAL CHOICES WHEN VISUALIZING DATA. THE VALUE OF VISUALIZING DATA IS DISCUSSED IN TWO KEY DOMAINS: 1) VISUALIZING DATA AS A MEANS OF COMMUNICATION; AND 2) VISUALIZING DATA AS RESEARCH METHODOLOGY. THE FIRST APPLIES COGNITIVE SCIENCE PRINCIPLES AND RESEARCH EVIDENCE TO INFORM DATA VISUALIZATION DESIGN FOR COMMUNICATION. THE SECOND APPLIES DATA- AND COGNITIVE-SCIENCE TO DEEPEN OUR UNDERSTANDING OF DATA, OF ITS UNCERTAINTY, AND OF ANALYSIS WHEN MAKING INFERENCES. THE EVIDENCE FOR HUMAN CAPACITY LIMITATIONS - ATTENTION AND COGNITION - ARE DISCUSSED IN THE CONTEXT OF DATA VISUALIZATIONS TO SUPPORT INFERENCE-MAKING IN BOTH DOMAINS, AND ARE FOLLOWED BY RECOMMENDATIONS. FINALLY, HOW LEARNING ANALYTICS CAN FURTHER RESEARCH ON UNDERSTANDING THE ROLE DATA VISUALIZATIONS CAN PLAY IN SUPPORTING COMPLEX COGNITION IS PROPOSED.
THE MASTER THESIS IS THE LAST FORMAL STEP IN MOST UNIVERSITIES AROUND THE WORLD. HOWEVER, ALL STUDENTS DO NOT FINISH THEIR MASTER THESIS. THUS, IT IS REASONABLE TO ASSUME THAT THE NON-COMPLETION OF THE MASTER THESIS SHOULD BE VIEWED AS A SUBSTANTIAL PROBLEM THAT REQUIRES SERIOUS ATTENTION AND PROACTIVE PLANNING. THIS LEARNING ANALYTICS STUDY AIMS TO UNDERSTAND BETTER FACTORS THAT INFLUENCE COMPLETION AND NON-COMPLETION OF MASTER THESIS PROJECTS. MORE SPECIFICALLY, WE ASK: WHICH STUDENT AND SUPERVISOR FACTORS INFLUENCE COMPLETION AND NON-COMPLETION OF MASTER THESIS? CAN WE PREDICT COMPLETION AND NON-COMPLETION OF MASTER THESIS USING SUCH VARIABLES IN ORDER TO OPTIMISE THE MATCHING OF SUPERVISORS AND STUDENTS? TO ANSWER THE RESEARCH QUESTIONS, WE EXTRACTED DATA ABOUT SUPERVISORS AND STUDENTS FROM TWO THESIS MANAGEMENT SYSTEMS WHICH RECORD LARGE AMOUNTS OF DATA RELATED TO THE THESIS PROCESS. THE SAMPLE USED WAS 755 MASTER THESIS PROJECTS SUPERVISED BY 109 TEACHERS. BY APPLYING TRADITIONAL STATISTICAL METHODS (DESCRIPTIVE STATISTICS, CORRELATION TESTS AND INDEPENDENT SAMPLE T-TESTS), AS WELL AS MACHINE LEARNING ALGORITHMS, WE IDENTIFY FIVE CENTRAL FACTORS THAT CAN ACCURATELY PREDICT MASTER THESIS COMPLETION AND NON-COMPLETION. BESIDES THE IDENTIFIED PREDICTORS THAT EXPLAIN MASTER THESIS COMPLETION AND NON-COMPLETION, THIS STUDY CONTRIBUTES TO DEMONSTRATING HOW EDUCATIONAL DATA AND LEARNING ANALYTICS CAN PRODUCE ACTIONABLE DATA-DRIVEN INSIGHTS. IN THIS CASE, INSIGHTS THAT CAN BE UTILISED TO INFORM AND OPTIMISE HOW SUPERVISORS AND STUDENTS ARE MATCHED AND TO STIMULATE TARGETED TRAINING AND CAPACITY BUILDING OF SUPERVISORS.
THE COVID-19 PANDEMIC CREATED A UNIQUE TEACHING AND LEARNING SITUATION FOR SOUTH AFRICAN UNIVERSITIES WITH A SUDDEN MIGRATION TO THE ONLINE PLATFORM. THIS EXPLORATORY STUDY REPORTS ON BASIC ISIZULU, A COMPULSORY SECOND-LANGUAGE MODULE AT THE UNIVERSITY OF KWAZULU-NATAL, SOUTH AFRICA. THE FOCUS IS ON STUDENT SUMMATIVE PERFORMANCE. EXTRACTING DATA FROM THE MOODLE ACTIVITY LOGS THE STUDY INVESTIGATES THE IMPACT OF FOUR VARIABLES, ACADEMIC DISCIPLINE, AGE, GENDER AND YEAR OF STUDY ON FORMATIVE ENGAGEMENT AND SUMMATIVE PERFORMANCE. FINDINGS FROM THE LEARNING ANALYTICS DATA INDICATE THAT HIGHER FORMATIVE ASSESSMENT SCORES HAVE A SMALL BUT HIGHLY SIGNIFICANT IMPACT ON THE MODULE'S FINAL SUMMATIVE ASSESSMENT SCORES. CORRELATION TESTS INDICATE A WEAK RELATIONSHIP BETWEEN ACADEMIC DISCIPLINE, AGE AND YEAR OF STUDY WITH ENGAGEMENT IN FORMATIVE ACTIVITY, AND THE GENDER VARIABLE INDICATES THAT MALE STUDENTS DISPLAY LOWER LEVELS OF ENGAGEMENT WITH FORMATIVE ACTIVITIES.
THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) IN BLENDED EDUCATION HAS RAPIDLY INCREASED IN THE 21ST CENTURY. AS A RESULT, THERE IS A GROWING INTEREST IN UNDERSTANDING TO WHAT EXTENT INNOVATIVE AND INTERACTIVE EDUCATIONAL TOOLS AND TECHNOLOGIES CAN BE USED FOR DESIGNING LEARNING CONTENT TO SUPPORT BLENDED LEARNING. THESE TOOLS ARE USED IN DIFFERENT COURSES, HOWEVER, THERE IS NO POSSIBILITY OF TRACKING WHETHER STUDENTS ARE OBTAINING THE MAXIMUM BENEFIT. LEARNING ANALYTIC TOOLS CAN BE USED FOR TRACKING PURPOSES. HOWEVER, THE DATA OBTAINED FROM THESE TOOLS ALONE WILL NOT OFFER BENEFITS FOR IMPROVING LEARNING DESIGNS. THEREFORE, IT IS IMPORTANT TO BUILD A SYNERGY BETWEEN LEARNING ANALYTICS AND LEARNING DESIGN WHERE IT IS POSSIBLE TO IMPROVE LEARNER PERFORMANCE, ENGAGEMENT, INTERACTIONS, AND SATISFACTION. ACCUMULATED RESEARCH IN THESE COLLABORATIVE LEARNING TECHNOLOGIES HAS PROPOSED AND EVALUATED MULTIPLE MODELS, FRAMEWORKS, AND IMPLEMENTATION TOOLS THAT ENGAGE LEARNERS IN BLENDED LEARNING ENVIRONMENTS. HOWEVER, IT IS UNCLEAR TO WHAT EXTENT THE SYNERGY BETWEEN LEARNING ANALYTICS AND LEARNING DESIGN CAN SUPPORT BLENDED LEARNING. THIS PAPER CONTRIBUTES WITH A LITERATURE REVIEW THAT AIMS AT PROVIDING AN ANSWER TO THIS BY OFFERING INSIGHTS ABOUT THE CONTEXT OF USE, TECHNOLOGIES, TYPES OF ACTIVITIES, AND COLLABORATION MECHANISMS DEVELOPED THAT CAN BE USED TO IMPROVE BLENDED LEARNING IN HIGHER EDUCATION.
MULTIMODAL LEARNING ANALYTICS (MMLA), WHICH HAS BECOME INCREASINGLY POPULAR, CAN HELP PROVIDE AN ACCURATE UNDERSTANDING OF LEARNING PROCESSES. HOWEVER, IT IS STILL UNCLEAR HOW MULTIMODAL DATA IS INTEGRATED INTO MMLA. BY FOLLOWING THE PREFERRED REPORTING ITEMS FOR SYSTEMATIC REVIEWS AND META-ANALYSES (PRISMA) GUIDELINES, THIS PAPER SYSTEMATICALLY SURVEYS 346 ARTICLES ON MMLA PUBLISHED DURING THE PAST THREE YEARS. FOR THIS PURPOSE, WE FIRST PRESENT A CONCEPTUAL MODEL FOR REVIEWING THESE ARTICLES FROM THREE DIMENSIONS: DATA TYPES, LEARNING INDICATORS, AND DATA FUSION. BASED ON THIS MODEL, WE THEN ANSWER THE FOLLOWING QUESTIONS: 1. WHAT TYPES OF DATA AND LEARNING INDICATORS ARE USED IN MMLA, TOGETHER WITH THEIR RELATIONSHIPS; AND 2. WHAT ARE THE CLASSIFICATIONS OF THE DATA FUSION METHODS IN MMLA. FINALLY, WE POINT OUT THE KEY STAGES IN DATA FUSION AND THE FUTURE RESEARCH DIRECTION IN MMLA. OUR MAIN FINDINGS FROM THIS REVIEW ARE (A) THE DATA IN MMLA ARE CLASSIFIED INTO DIGITAL DATA, PHYSICAL DATA, PHYSIOLOGICAL DATA, PSYCHOMETRIC DATA, AND ENVIRONMENT DATA; (B) THE LEARNING INDICATORS ARE BEHAVIOR, COGNITION, EMOTION, COLLABORATION, AND ENGAGEMENT; (C) THE RELATIONSHIPS BETWEEN MULTIMODAL DATA AND LEARNING INDICATORS ARE ONE-TO-ONE, ONE-TO-ANY, AND MANY-TO-ONE. THE COMPLEX RELATIONSHIPS BETWEEN MULTIMODAL DATA AND LEARNING INDICATORS ARE THE KEY FOR DATA FUSION; (D) THE MAIN DATA FUSION METHODS IN MMLA ARE MANY-TO-ONE, MANY-TO-MANY AND MULTIPLE VALIDATIONS AMONG MULTIMODAL DATA; AND (E) MULTIMODAL DATA FUSION CAN BE CHARACTERIZED BY THE MULTIMODALITY OF DATA, MULTI-DIMENSION OF INDICATORS, AND DIVERSITY OF METHODS.
WITH THE INCREASE OF ONLINE EDUCATION PROGRAMS, LEARNING ANALYTICS (LA) TOOLS HAVE BECOME A POPULAR ADDITION TO MANY LEARNING MANAGEMENT SYSTEMS (LMS). AS A TOOL FOR SUPPORTING LEARNING IN AN EDUCATIONAL CONTEXT, LA HAS GENERATED SOME CONTROVERSY AMONG SCHOLARS. THEREFORE, IN THIS TEXT, WE AIM TO PROVIDE A THEORETICAL AND ANALYTICAL UNDERSTANDING OF THE APPROACH AND ITS IMPLICATIONS FOR TEACHING AND LEARNING. TO ACHIEVE THIS, WE APPLY MCLUHAN'S SEMIOTIC ANALYSIS OF MEDIA (1988). THE "LAWS OF MEDIA" QUESTIONS ARE ASKED ABOUT LA TOOLS: WHAT DO THEY ENHANCE, MAKE OBSOLETE, RETRIEVE, AND REVERSE INTO. BY ANSWERING THESE QUESTIONS, WE OUTLINE WHICH PRACTICES OF TEACHING AND LEARNING ARE MORE LIKELY TO BECOME COMMON WHEN LA TOOLS ARE TAKEN INTO USE MORE WIDELY AND WHICH OTHERS WILL BE RELEGATED. IN THE ANALYSIS, WE POINT OUT THAT LA TOOLS ENHANCE PREDICTION AND PERSONALIZATION OF LEARNING, WHILE THEY DISPLACE CERTAIN TEACHERS' SKILLS, PERSONAL INTERACTION BETWEEN TEACHERS AND STUDENTS, AND QUALITATIVE INTERPRETATION AND ASSESSMENT OF LEARNING. SIMULTANEOUSLY, LA RETRIEVES BEHAVIOURIST VIEWS OF LEARNING AND URGES DISCUSSION ABOUT DATA LITERACY. TAKEN TO THE LIMITS, LA REVERSES ITS EFFECTS AND BECOMES A TOOL FOR SUPPORTING AWARENESS AND REFLECTION IN TEACHING AND LEARNING. WE CONSIDER THESE CONTRIBUTIONS RELEVANT FOR UNDERSTANDING AND REFLECTING ON THE TYPE OF PEDAGOGIES THAT LA SUPPORTS, THE IMPLICIT VALUES IT HOLDS, AND THE CHANGES IT INTRODUCES INTO EDUCATIONAL PRACTICE.
THIS PAPER SHOWS HOW AN AMERICAN INTERNATIONAL SCHOOL IN VIETNAM HAS BEEN USING DATA AND LEARNING ANALYTICS TO FIND OUT ABOUT STUDENTS' LEARNING FROM THEIR ASSESSMENTS AND HOW THEY USE THESE FINDINGS TO IMPROVE, AMONG OTHER AREAS, THE READING SKILLS OF THEIR MOSTLY ENGLISH AS AN ADDITIONAL LANGUAGE (EAL) STUDENT POPULATION. THE SOURCE OF DATA COMES PRIMARILY FROM A COMPUTER ADAPTIVE TESTING PLATFORM, COMMONLY KNOWN AS THE MAP GROWTH TEST, WHICH PROVIDES INFORMATION ABOUT MATH AND READING SKILLS FOR EACH PARTICULAR STUDENT. THE DATA PROVIDED IS TRANSFORMED AND PRESENTED TO EDUCATIONAL STAKEHOLDERS THROUGH VISUALIZATIONS CREATED IN SPECIALIZED SOFTWARE IN ORDER TO DIG INTO THE DATA AND ANSWER THE PEDAGOGICAL QUESTIONS EMERGED FROM TEACHERS AND ADMINISTRATORS. THIS PROCESS INVOLVES A NEW FIELD KNOWN AS LEARNING ANALYTICS AND VISUAL DATA MINING IN ORDER TO FIND NEW INFORMATION NOT USUALLY EVIDENT IN SCHOOL DATASETS. THE RESULTS INDICATE THAT TEACHERS GET IMMERSED IN A REFLECTIVE PROCESS THAT IMPROVES STUDENT LEARNING THROUGH ACTION PLANS INFORMED BY LEARNING ANALYTICS (LA); WHICH COULD BE SEEN AS THE SCIENTIFIC DATA BEHIND THE OBSERVATIONS EDUCATORS HAVE TRADITIONALLY DONE.
IMPACT HOW TO EFFECTIVELY DEVELOP A FRAMEWORK OF LEARNING AND DEVELOPMENT IS A CENTRAL CHALLENGE FOR CONTEMPORARY ORGANIZATIONS. NEW TECHNOLOGIES AND EDUCATIONAL PARTNERSHIPS HAVE TRANSFORMED THE WAYS IN WHICH PROFESSIONALS LEARN. THIS ARTICLE EXPLAINS HOW LEARNING ANALYTICS CAN BE USED TO DESIGN BLENDED LEARNING PROGRAMMES AND SUPPORT LEARNERS IN AN EVIDENCE-INFORMED WAY, SO THAT LEARNING 'FAILURES' (WITHDRAWAL, UNDERPERFORMANCE AND COMPLAINTS) CAN BE AVOIDED. THE ARTICLE DRAWS ON SEVERAL CASE STUDIES, INCLUDING THE CURRENT LEARNING AND DEVELOPMENT CHALLENGES FACED BY POLICE ORGANIZATIONS IN ENGLAND AND WALES, THAT SUPPORT THE CASE FOR LEARNING ANALYTICS TO ACHIEVE SUCCESS IN WORKFORCE TRAINING.
TO ADDRESS THE GOAL OF INCREASING AND BROADENING PARTICIPATION OF YOUTH IN STEM FIELDS, A LEARNING ECOSYSTEM APPROACH IS A PROMISING STRATEGY. LEARNING ANALYTICS CAN PLAY AN IMPORTANT ROLE IN SUCH EFFORTS WHICH AIM TO BUILD LEARNING SUPPORTS ACROSS THE DIVERSE SPACES IN WHICH LEARNING AND DEVELOPMENT OCCURS, INCLUDING INFORMAL, FORMAL, AND ONLINE CONTEXTS. THIS PAPER INTRODUCES A CITY-LEVEL LEARNING ANALYTICS IMPLEMENTATION EFFORT IN A DEVELOPING STEM ECOSYSTEM IN ONE MID-SIZED CITY. WE DESCRIBE ASPECTS OF OUR DESIGN AND RESEARCH APPROACH AND CHALLENGES THAT EMERGE BY TAKING A LEARNING ECOSYSTEM PERSPECTIVE OF LEARNING AND DEVELOPMENT.
DESPITE THE GREAT POTENTIAL OF SOCIAL NETWORK ANALYSIS (SNA) METHODS AND VISUALIZATIONS FOR LEARNING ANALYTICS IN COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL), THESE APPROACHES HAVE NOT BEEN FULLY EXPLORED DUE TO TWO IMPORTANT BARRIERS: THE SCARCITY AND LIMITED FUNCTIONALITY OF BUILT-IN TOOLS IN LEARNING MANAGEMENT SYSTEMS (LMS), AND THE DIFFICULTY TO IMPORT EDUCATIONAL DATA FROM FORMAL VIRTUAL LEARNING ENVIRONMENTS INTO SOCIAL NETWORK ANALYSIS PROGRAMS. THIS STUDY AIMS TO COVER THAT GAP BY INTRODUCING GRAPHFES, AN APPLICATION AND WEB SERVICE FOR EXTRACTION OF INTERACTION DATA FROM MOODLE MESSAGE BOARDS AND GENERATION OF THE CORRESPONDING SOCIAL GRAPHS FOR LATER ANALYSIS USING GEPHI, A GENERAL PURPOSE SNA SOFTWARE. IN ADDITION, THIS PAPER BRIEFLY ILLUSTRATES THE POTENTIAL OF THE COMBINATION OF THE THREE SYSTEMS (MOODLE, GRAPHFES AND GEPHI) FOR SOCIAL LEARNING ANALYTICS USING REAL DATA FROM A COMPUTER-SUPPORTED COLLABORATIVE LEARNING COURSE WITH STRONG FOCUS ON TEAMWORK AND INTENSIVE USE OF FORUMS.
THIS RESEARCH FULL PAPER IMPLEMENTS A FRAMEWORK THAT HARNESS SOURCES OF PROGRAMMING LEARNING ANALYTICS ON THREE COMPUTER PROGRAMMING COURSES A HIGHER EDUCATION INSTITUTION. THE PLATFORM, CALLED PREDICTCS, AUTOMATICALLY DETECTS LOWER-PERFORMING OR "AT-RISK" STUDENTS IN PROGRAMMING COURSES AND AUTOMATICALLY AND ADAPTIVELY SENDS THEM FEEDBACK. THIS SYSTEM HAS BEEN PROGRESSIVELY ADOPTED AT THE CLASSROOM LEVEL TO IMPROVE PERSONALIZED LEARNING. A VISUAL ANALYTICS DASHBOARD IS DEVELOPED AND ACCESSIBLE TO FACULTY. THIS CONTAINS INFORMATION ABOUT THE MODELS DEPLOYED AND INSIGHTS EXTRACTED FROM STUDENT'S DATA. BY LEVERAGING HISTORICAL STUDENT DATA WE BUILT PREDICTIVE MODELS USING STUDENT CHARACTERISTICS, PRIOR ACADEMIC HISTORY, LOGGED INTERACTIONS BETWEEN STUDENTS AND ONLINE RESOURCES, AND STUDENTS' PROGRESS IN PROGRAMMING LABORATORY WORK. PREDICTIONS WERE GENERATED EVERY WEEK DURING THE SEMESTER'S CLASSES. IN ADDITION, DURING THE SECOND HALF OF THE SEMESTER, STUDENTS WHO OPTED-IN RECEIVED PSEUDO REAL-TIME PERSONALISED FEEDBACK. NOTIFICATIONS WERE PERSONALISED BASED ON STUDENTS' PREDICTED PERFORMANCE ON THE COURSE AND INCLUDED A PROGRAMMING SUGGESTION FROM A TOP-STUDENT IN THE CLASS IF ANY PROGRAMS SUBMITTED HAD FAILED TO MEET THE SPECIFIED CRITERIA. AS A RESULT, THIS HELPED STUDENTS WHO CORRECTED THEIR PROGRAMS TO LEARN MORE AND REDUCED THE GAP BETWEEN LOWER AND HIGHER-PERFORMING STUDENTS.
WITH THE RAPID GROWTH OF ONLINE EDUCATION IN RECENT YEARS, LEARNING ANALYTICS (LA) HAS GAINED INCREASING ATTENTION FROM RESEARCHERS AND EDUCATIONAL INSTITUTIONS AS AN AREA WHICH CAN IMPROVE THE OVERALL EFFECTIVENESS OF LEARNING EXPERIENCES. HOWEVER, THE LACK OF GUIDELINES ON WHAT SHOULD BE TAKEN INTO CONSIDERATION DURING APPLICATION OF LA HINDERS ITS FULL ADOPTION. THEREFORE, THIS ARTICLE INVESTIGATES THE ISSUES THAT SHOULD BE CONSIDERED WHEN APPROACHING THE DESIGN OF LA EXPERIENCES FROM THE DATA PREPARATION PERSPECTIVE. THE OBTAINED RESULTS HIGHLIGHT A VALIDATED LA FRAMEWORK OF TWENTY-TWO DESIGNING ISSUES THAT SHOULD BE CONSIDERED BY VARIOUS STAKEHOLDERS IN DIFFERENT CONTEXTS AS WELL AS A SET OF GUIDELINES WHICH CAN ENHANCE DESIGNING LA EXPERIENCES.
WE PROPOSE THE CREATION OF AN INTERDISCIPLINARY RESEARCH GROUP RELATED TO MULTIMODAL LEARNING ANALYTICS AND SOCIAL NETWORK ANALYSIS FOR THE STUDY OF COLLABORATIVE WORK GROUPS IN LATIN AMERICA. WE BELIEVE THAT THE ADVANCED WORK SO FAR JUSTIFIES THE VIABILITY OF THE PROPOSAL.
BEGINNING RESEARCHERS IN GENERAL FACE VARIOUS DIFFICULTIES WHEN INITIATING A PROCESS OF SCIENTIFIC RESEARCH DUE TO THE UNAVAILABILITY OF PROPER TUTORING OR THE MINIMUM KNOWLEDGE ABOUT RESEARCH METHODOLOGY AND, THIS IMPACTS THE RELIABILITY OF THE PROCESS, THE TIME AND THE RESULTS OF THE RESEARCH IN QUESTION. THE PURPOSE OF THIS WORK IS TO SUPPORT THE ACQUISITION OF SCIENTIFIC SKILLS BY OFFERING TO BEGINNING RESEARCHERS LEARNING ANALYTICS IN EACH AND EVERY ONE OF THE PHASES AND STAGES OF THE INVESTIGATIVE PROCESS BASED ON THE ACTIONS AND INTERACTIONS THAT TEACHERS/SUPERVISORS, EXPERTS AND RESEARCHERS MAKE DURING THIS INVESTIGATIVE PROCESS. THEREFOR, IT IS PRESENTED, AS A DETAILED CASE STUDY, THE SKILL OF FORMULATING RESEARCH QUESTIONS BY DEFINING THE PROCESS THAT WAS USED, INCLUDING THE ACTORS, THE MEASUREMENTS, AND THE INDICATORS, THE FORMATIVE PROCESS AND THE INTERACTIONS MANAGED WITH THE BINNPROJECT SOFTWARE. FINALLY THE K-MEANS ALGORITHM IS USED IN ANALYZING STUDENTS' BEHAVIOR AND CREATING CLUSTERS ACCORDING TO THEIR PERFORMANCE DURING THE PROCESS OF FORMULATING SCIENTIFIC QUESTIONS, THIS WAY SUPPORTING THE PROCESS OF DETERMINING STRATEGIES ABLE TO STRENGTHEN SCIENTIFIC COMPETENCES FOR BOTH STUDENTS AND THE TEACHING PRACTICE.
WITH THE ADVENT OF MASSIVE ONLINE OPEN COURSES (MOOCS), THE DATA SCALE OF STUDENT LEARNING BEHAVIOR AND KNOWLEDGE MASTERY HAS SIGNIFICANTLY INCREASED. IN ORDER TO EFFECTIVELY AND EFFICIENTLY ANALYZE THESE DATASETS AND PRESENT ON-THE-FLY INTELLIGENT TUTORING TO ONLINE LEARNERS, IT IS NECESSARY TO IMPROVE EXISTING LEARNING ANALYTICS TOOLS IN A PARALLEL AND AUTOMATIC WAY. ONE OF THE MOST COMMON TOOLS IS BAYESIAN KNOWLEDGE TRACING (BKT) THAT CAN MODEL TEMPORAL PROGRESS OF ONLINE LEARNERS AND EVALUATE THEIR MASTERY OF COURSE KNOWLEDGE. CURRENT IMPLEMENTATION OF BKT IS MOSTLY BASED ON SINGLE MACHINE, WHICH LEADS TO SLOW EXECUTION PERFORMANCE DURING ITS EXPECTATION MAXIMIZATION(EM) ALGORITHM FOR PARAMETER FITTING. ALTHOUGH THERE ARE A FEW PARALLEL IMPLEMENTATIONS FOR EM ALGORITHM, THEY DON'T SUPPORT AUTOMATIC INITIAL BKT PARAMETER TUNING TO ENSURE THE CORRECT CONVERGENCE OF THE EM ITERATION. THEREFORE, THIS PAPER PRESENTS A NEW PARALLEL BKT OPEN SOURCE TOOL BASED ON THE SPARK COMPUTATIONAL FRAMEWORK WITH THE METHOD OF AUTOMATIC TUNING OF INITIAL PARAMETERS. THIS TOOL IMPROVES TRADITIONAL KNOWLEDGE TRACING SYSTEMS USING A PARALLEL EM ALGORITHM WITH THE CAPABILTY OF AUTOMATICALLY CHOOSING INITIAL PARAMETERS. EXPERIMENTAL RESULT DEMONSTRATES THAT OUR TOOL CAN ACHIEVE FAST EXECUTION SPEED AND GREATLY IMPROVE THE ACCURACY OF TRAINING PARAMETERS ON BOTH DIFFERENT SIZES OF SIMULATED DATA AND REAL EDUCATIONAL DATA SETS.
MODELING HAS A STRONG FOCUS IN CURRENT SCIENCE LEARNING FRAMEWORKS AS A CRITICAL SKILL FOR STUDENTS TO LEARN. HOWEVER, UNDERSTANDING STUDENTS' SCIENTIFIC MODELS AND THEIR MODELING PRACTICES AT SCALE IS A DIFFICULT TASK THAT HAS NOT BEEN TAKEN UP BY THE RESEARCH LITERATURE. THE COMPLEX VARIABLES INVOLVED IN CLASSROOM LEARNING, SUCH AS TEACHER DIFFERENCES, INCREASE THE DIFFICULTY OF UNDERSTANDING THIS PROBLEM. THIS WORK BEGINS WITH AN EXPLORATION OF THE METHODS USED TO EXPLORE STUDENTS' SCIENTIFIC MODELING IN THE LEARNING SCIENCES SPACE AND THE FRAMEWORKS DEVELOPED TO CHARACTERIZE STUDENT MODELING PRACTICES. LEARNING ANALYTICS CAN BE USED TO LEVERAGE THESE FRAMEWORKS OF SCIENTIFIC MODELING PRACTICES TO EXPLORE QUESTIONS AROUND STUDENTS' SCIENTIFIC MODELS AND THEIR MODELING PRACTICES. THESE ANALYSES ARE FOCUSED AROUND THE USE OF ECOSURVEY, A COLLABORATIVE, DIGITAL TOOL USED IN HIGH-SCHOOL BIOLOGY CLASSROOMS TO MODEL THE LOCAL ECOSYSTEM. THIS TOOL WAS DEPLOYED IN TEN BIOLOGY CLASSROOMS AND USED WITH VARYING DEGREES OF SUCCESS. THERE ARE SIGNIFICANT TEACHER-LEVEL DIFFERENCES FOUND IN THE ACTIVITY SEQUENCES OF STUDENTS USING THE ECOSURVEY TOOL. THE THEORETICAL METRICS AROUND SCIENTIFIC MODELING PRACTICES AND AUTOMATICALLY EXTRACTED FEATURE SEQUENCES WERE ALSO USED IN A CLASSIFICATION TASK TO AUTOMATICALLY DETERMINE A PARTICULAR STUDENT'S TEACHER. THESE RESULTS UNDERLINE THE POWER OF LEARNING ANALYTICS METHODS TO GIVE INSIGHT INTO HOW MODELING PRACTICES ARE REALIZED IN THE CLASSROOM. THIS WORK ALSO INFORMS CHANGES TO MODELING TOOLS, ASSOCIATED CURRICULA, AND SUPPORTING PROFESSIONAL DEVELOPMENT AROUND SCIENTIFIC MODELING.
THIS PROPOSES HOW TO FACE UP, FROM A TECHNOLOGICAL POINT OF VIEW, THE COLLECTION AND STORAGE OF HETEROGENEOUS DATA COMING FROM HETEROGENEOUS ENVIRONMENTS RELATED TO THE LEARNING PROCESS IN ORDER TO TAKE ADVANTAGE IN TASKS RELATED TO LEARNING ANALYTICS. IT INTRODUCES THE PROBLEM WITH THE HETEROGENEOUS DATA OVER THE TIME AND DEALS HOW TO RESOLVE THIS ISSUE GIVING UP A THEORETICAL APPROACH AND A PROOF OF CONCEPT.
IN UBIQUITOUS LEARNING, AUTHENTIC EXPERIENCES ARE CAPTURED AND LATER REUSED AS THOSE ARE RICH RESOURCES FOR FOREIGN VOCABULARY DEVELOPMENT. THIS ARTICLE PRESENTS AN EXPERIENTIAL THEORY-ORIENTED APPROACH TO THE DESIGN OF LEARNING ANALYTICS SUPPORT FOR SHARING AND REUSING AUTHENTIC EXPERIENCES. IN THIS REGARD, FIRST, A CONCEPTUAL FRAMEWORK TO SUPPORT VOCABULARY LEARNING USING LEARNERS' AUTHENTIC EXPERIENCES IS PROPOSED. NEXT, LEARNING EXPERIENCES ARE CAPTURED USING A CONTEXT-AWARE UBIQUITOUS LEARNING SYSTEM. FINALLY, GROUNDED IN THE THEORETICAL FRAMEWORK, THE DEVELOPMENT OF A WEB-BASED TOOL CALLED LEARN FROM OTHERS (LFO) PANEL IS PRESENTED. THE LFO PANEL ANALYZES VARIOUS LEARNING LOGS (AUTHENTIC, PARTIALLY-AUTHENTIC, AND WORDS) USING THE PROFILING METHOD WHILE DETERMINING THE TOP-FIVE LEARNING PARTNERS INSIDE A SEAMLESS LEARNING ANALYTICS PLATFORM. THIS ARTICLE CONTRIBUTES TO THE RESEARCH IN THE AREA OF THEORY-ORIENTED DESIGN OF LEARNING ANALYTICS FOR VOCABULARY LEARNING THROUGH AUTHENTIC ACTIVITIES AND FOCUSES ON CLOSING THE LOOPS OF EXPERIENTIAL LEARNING USING LEARNING ANALYTICS CYCLES.
LEARNING ANALYTICS OFFERS HIGHER EDUCATION VALUABLE INSIGHTS THAT CAN INFORM STRATEGIC DECISION-MAKING REGARDING RESOURCE ALLOCATION FOR EDUCATIONAL EXCELLENCE. RESEARCH DEMONSTRATES THAT LEARNING MANAGEMENT SYSTEMS (LMSS) CAN INCREASE STUDENT SENSE OF COMMUNITY, SUPPORT LEARNING COMMUNITIES AND ENHANCE STUDENT ENGAGEMENT AND SUCCESS, AND LMSS HAVE THEREFORE BECOME CORE ENTERPRISE COMPONENT IN MANY UNIVERSITIES. WE WERE INVITED TO UNDERTAKE A CURRENT STATE ANALYSIS OF ENTERPRISE LMS USE IN A LARGE RESEARCH-INTENSIVE UNIVERSITY, TO PROVIDE DATA TO INFORM AND GUIDE AN LMS REVIEW AND STRATEGIC PLANNING PROCESS. USING A NEW E-LEARNING ANALYTICS PLATFORM, COMBINED WITH DATA VISUALIZATION AND PARTICIPANT OBSERVATION, WE PREPARED A DETAILED SNAPSHOT OF CURRENT LMS USE PATTERNS AND TRENDS AND THEIR RELATIONSHIP TO STUDENT LEARNING OUTCOMES. THIS PAPER PRESENTS SELECTED DATA FROM THIS "CURRENT STATE ANALYSIS" AND COMMENTS ON WHAT IT REVEALS ABOUT THE COMPARATIVE EFFECTIVENESS OF THIS INSTITUTION'S LMS INTEGRATION IN THE SERVICE OF LEARNING AND TEACHING. MORE CRITICALLY, IT DISCUSSES THE REALITY THAT THE INSTITUTIONAL PLANNING PROCESS WAS NONETHELESS DOMINATED BY TECHNICAL CONCERNS, AND MADE LITTLE USE OF THE INTELLIGENCE REVEALED BY THE ANALYTICS PROCESS. TO EXPLAIN THIS PHENOMENON WE CONSIDER THEORIES OF CHANGE MANAGEMENT AND RESISTANCE TO INNOVATION, AND ARGUE THAT TO HAVE MEANINGFUL IMPACT, LEARNING ANALYTICS PROPONENTS MUST ALSO DELVE INTO THE SOCIO-TECHNICAL SPHERE TO ENSURE THAT LEARNING ANALYTICS DATA ARE PRESENTED TO THOSE INVOLVED IN STRATEGIC INSTITUTIONAL PLANNING IN WAYS THAT HAVE THE POWER TO MOTIVATE ORGANIZATIONAL ADOPTION AND CULTURAL CHANGE.
DURING THE CORONAVIRUS PANDEMIC, E-LEARNING SYSTEMS HAVE PROVEN TO BE AN ESSENTIAL PILLAR FOR EDUCATION. THIS RAISES TO SURFACE WHAT MANY STUDIES HAVE ADDRESSED EARLIER; CREATING A PLATFORM THAT COMPLETES THE TRADITIONAL CLASSROOM WORK AND MAXIMIZES THE EFFECTIVENESS OF LEARNING OUTCOMES. STRIVING TO ACHIEVE SUCH PLATFORM, STUDIES HAVE CONSIDERED GAMIFYING AND PERSONALIZING THE EDUCATIONAL RESOURCES FOR THE ADAPTATION OF EDUCATIONAL SYSTEMS AS PER THE INTENDED LEARNERS THROUGH INTENSIVE LEARNING ANALYTICS. BUT WAS THE LEARNER REALLY A PART OF THE ADAPTATION PROCESS TAKING PLACE? LEARNING ANALYTICS ARE USUALLY DESIGNED TO THE COURSE'S ADAPTATION AND SOLELY FOR THE TEACHERS. THUS, LEARNING ANALYTICS IN GAMIFIED ADAPTIVE EDUCATIONAL SYSTEMS INVOLVING THE COURSE, TEACHERS AND LEARNERS TOGETHER ARE STILL UNDER INVESTIGATION. IN THIS STUDY, THE PERSONALIZED ADAPTIVE GAMIFIED E-LEARNING (PAGE) MODEL IS INTRODUCED TO EXTEND MOOCS BY PROVIDING NEW SATISFACTORY LEVELS OF LEARNING ANALYTICS AND VISUALIZATION IN THE RICH E-LEARNING PROCESS THAT SUPPORTS THE LEARNER'S INTERVENTION IN THE RESULTANT LEARNING ANALYTICS. THE PROPOSED LEARNING ANALYTICS HAVE BEEN DEVELOPED TO MAKE THE NECESSARY ADAPTATION TO THE COURSE AND LEARNER'S LEARNING FLOW, AS WELL AS VISUALIZING THE PROCESS AND ADAPTATION DECISIONS TO THE LEARNERS. RESULTS SHOW A POSITIVE POTENTIAL TOWARDS LEARNING ADAPTATION AND VISUALIZATION, AND A NECESSITY TO PROVIDE AN ADDITIONAL FOCUS FOR THE GAMIFICATION CONCEPT.
LEANING ON THEORIES OF GOVERNMENTALITY, THE ARTICLE EXPLORES WHICH NEW FORMATS OF SELF-GOVERNING AND BEING-GOVERNED ARE EMERGING AT UNIVERSITIES DUE TO THE IMPLEMENTATION OF LEARNING ANALYTICS ARCHITECTURES. WE CARVE OUT HOW DASHBOARDS FUNCTION AS INTERFACES WITH REGIMES OF DATAFICATION, IN WHICH THE DIGITAL TRACES LEFT BY THE LEARNING SUBJECTS ARE PROCESSED AND PRESENTED IN REAL TIME FOR THE PURPOSES OF PROFILING, FORECASTING AND COMPARISON. WE MAINTAIN THAT IN THE CONTEXT OF DIGITAL HIGHER EDUCATION, NEW INTERDEPENDENCIES DEVELOP BETWEEN PRACTICES OF EXTERNAL GOVERNMENT AND SELF-GOVERNMENT. THE SPECIFIC FORMS OF INTERLOCKING EXTERNAL CONTROL AND VOLUNTARY, SELF-NORMALISING, INTERNAL CONTROL FOUND HERE DIFFER FROM THE ENACTMENTS OF POWER IN ANALOGUE CONTEXTS.
PREDICTIVE LEARNING ANALYTICS (PLA) AIM TO IMPROVE LEARNING BY IDENTIFYING STUDENTS AT RISK OF FAILING THEIR STUDIES. YET, LITTLE IS KNOWN ABOUT HOW BEST TO INTEGRATE AND SCAFFOLD PLA INITIATIVES INTO HIGHER EDUCATION INSTITUTIONS. TOWARDS THIS END, IT BECOMES ESSENTIAL TO CAPTURE AND ANALYZE THE PERCEPTIONS OF RELEVANT EDUCATIONAL STAKEHOLDERS (I.E., MANAGERS, TEACHERS, STUDENTS) ABOUT PLA. THIS PAPER PRESENTS AN "AT SCALE" IMPLEMENTATION OF PLA AT A DISTANCE LEARNING HIGHER EDUCATION INSTITUTION AND DETAILS, IN PARTICULAR, THE PERSPECTIVES OF 20 EDUCATIONAL MANAGERS INVOLVED IN THE IMPLEMENTATION. IT CONCLUDES WITH A SET OF RECOMMENDATIONS ABOUT HOW BEST TO ADOPT AND APPLY LARGE-SCALE PLA INITIATIVES IN HIGHER EDUCATION.
UNDERSTANDING STUDENT SENTIMENT PLAYS A VITAL ROLE IN UNDERSTANDING THE CHANGES THAT COULD OR SHOULD BE MADE IN CURRICULUM DESIGN AT UNIVERSITY. LEARNING ANALYTICS (LA) HAS SHOWN POTENTIAL FOR IMPROVING STUDENT LEARNING EXPERIENCES AND SUPPORTING TEACHER INQUIRY. YET, THERE IS LIMITED RESEARCH THAT REPORTS ON THE ADOPTION AND ACTUAL USE OF LA TO SUPPORT TEACHER INQUIRY. THIS FOUR-YEAR LONGITUDINAL STUDY CAPTURES SENTIMENT OF POSTGRADUATE STUDENTS AT A UNIVERSITY IN IRELAND, BY INTEGRATING LA WITH THE STEPS OF TEACHER INQUIRY. THIS STUDY MAKES THREE IMPORTANT CONTRIBUTIONS TO TEACHING AND LEARNING LITERATURE. FIRST, IT REPORTS ON THE USE OF LA TO SUPPORT TEACHER INQUIRY OVER FOUR ONE-YEAR CYCLES OF A MASTER OF SCIENCE IN BUSINESS ANALYTICS PROGRAMME BETWEEN 2016 AND 2020. SECOND, IT PROVIDES EVIDENCE-BASED RECOMMENDATIONS ON HOW TO OPTIMISE LA TO SUPPORT TEACHER INQUIRY, WITH SPECIFIC ATTENTION AS TO HOW THESE CAN IMPROVE THE ASSIMILATION OF LA INTO THE CURRICULUM DESIGN AND DELIVERY. THIRD, THE PAPER CONCLUDES WITH A RESEARCH AGENDA TO HELP IMPROVE THE ADOPTION AND INTEGRATION OF LA IN THE FUTURE.
STUDENTS OF INITIAL LEVEL PROGRAMMING COURSES GENERALLY FACE DIFFICULTIES WHILE LEARNING THE PROGRAMMING CONCEPTS. THE LEARNING ANALYTICS STUDIES, IN THESE COURSES, ARE MOSTLY ANECDOTAL ON THE ASPECT OF ASSESSMENT AS LESS OR NO ATTENTION IS GIVEN TO ASSESS LEARNING AT VARIOUS COGNITIVE LEVELS OF SPECIFIC CONCEPTS. FURTHERMORE, THE EXISTING WORK REFLECTS DEFICIENCIES IN EXAMINING THE EFFECT OF LEARNERS' COGNITIVE PERFORMANCE ON SUBSEQUENT STAGES OF THE COURSE. THIS GAP NEEDS TO BE ADDRESSED BY INTRODUCING MORE GRANULAR AND METHODICAL APPROACHES OF COGNITIVE ANALYSIS FOR SUSTAINING THE PROGRAMMING COURSES EFFECTIVELY IN COMPUTER SCIENCE AND ASSOCIATED DISCIPLINES. IN THIS ARTICLE, A FRAMEWORK-BASED APPROACH IS PROPOSED FOR COGNITIVE LEARNING ANALYTICS ON THE CONCEPTS TAUGHT IN INITIAL LEVEL PROGRAMMING COURSES. THE FRAMEWORK SERVES AS A PLATFORM THAT PROVIDES STRUCTURE TO THE CONCEPT DATA USING THE TECHNIQUE OF CONCEPT MAPPING AND EXAMINES LEARNERS' COGNITIVE PROPAGATION ON RELATED CONCEPTS USING ASSESSMENT DATA. LEARNERS' PERFORMANCE PREDICTION HAS BEEN EXAMINED ON RELATIVELY HIGHER-LEVEL PROGRAMMING CONCEPTS THROUGH THE METRICS ESTABLISHED FROM THE COGNITIVE MAPS OF LEARNERS, ACQUIRED BY DEPLOYING THE RELATED LAYERS OF FRAMEWORK. OVERALL MAXIMUM PREDICTION ACCURACY RANGE OBTAINED WAS 64.81% TO 90.86%, WHICH WAS BETTER THAN THE PREDICTION ACCURACIES PRESENTED IN MOST OF THE RELATED STUDIES.
LEARNING ANALYTICS (LA) MINES, ANALYZES AND VISUALIZES THE DATA OF STUDENTS' LEARNING BEHAVIOURS ON LEARNING PLATFORMS SUCH AS LEARNING MANAGEMENT SYSTEMS (LMS), BUT FEW LA TOOLS BUILT ARE ADAPTABLE TO MULTIPLE PLATFORMS OR FOR GENERAL EDUCATION COURSES. THIS STUDY SETS OUT TO EVALUATE A LIGHTWEIGHT LA TOOL IMPLEMENTED ON MOODLE AND OPEN EDX FOR MONITORING STUDENTS' LEARNING PROGRESS. SURVEY DATA WERE COLLECTED FROM 156 STUDENTS, SUPPLEMENTED BY INTERVIEW RESPONSES FROM 25 STUDENTS AND THREE INSTRUCTORS. PRELIMINARY RESULTS SHOW THAT A CONSIDERABLE PORTION OF SURVEYED STUDENTS USED THE LA TOOL AND THEY HELD POSITIVE OPINIONS ON ITS EFFICACY IN MONITORING SELF-PROGRESS AND THE EFFECTIVENESS OF ITS VISUALIZATIONS FOR INFORMATION DELIVERY. NONETHELESS, LEARNERS WHO DID NOT USE THE LA TOOL RAISED CONCERNS ABOUT IT RELYING ONLY ON THEIR ONLINE BEHAVIOURS WITHOUT CONSIDERING THEIR OFFLINE LEARNING. COUPLED WITH INSTRUCTORS' EVALUATION RESULTS, DISCUSSION AND IMPLICATIONS ARE PRESENTED.
DISTANCE EDUCATION COURSES ARE PRESENT IN LARGE NUMBER OF EDUCATIONAL INSTITUTIONS. VIRTUAL LEARNING ENVIRONMENTS DEVELOPMENT CONTRIBUTES TO THIS WIDE ADOPTION OF DISTANCE EDUCATION MODALITY AND ALLOWS NEW PEDAGOGICAL METHODOLOGIES. HOWEVER, DROPOUT RATES OBSERVED IN THESE COURSES ARE VERY EXPRESSIVE, BOTH IN PUBLIC AND PRIVATE EDUCATIONAL INSTITUTIONS. THIS PAPER PRESENTS A LEARNING ANALYTICS SYSTEM DEVELOPED TO DEAL WITH DROPOUT PROBLEM IN DISTANCE EDUCATION COURSES ON UNIVERSITY-LEVEL EDUCATION. SEVERAL COMPLEMENTARY TOOLS, ALLOWING DATA VISUALIZATION, DROPOUT PREDICTIONS, SUPPORT TO PEDAGOGICAL ACTIONS AND TEXTUAL ANALYSIS, AMONG OTHERS, ARE AVAILABLE IN THE SYSTEM. THE IMPLEMENTATION OF THESE TOOLS IS FEASIBLE DUE TO THE ADOPTION OF AN APPROACH CALLED MULTITRAIL TO REPRESENT AND MANIPULATE DATA FROM SEVERAL SOURCES AND FORMATS. THE OBTAINED RESULTS FROM EXPERIMENTS CARRIED OUT WITH COURSES IN A BRAZILIAN UNIVERSITY SHOW THE DROPOUT PREDICTION WITH AN AVERAGE OF 87% PRECISION. A SET OF PEDAGOGICAL ACTIONS CONCERNING STUDENTS AMONG THE HIGHER PROBABILITIES OF DROPOUT WAS IMPLEMENTED AND WE OBSERVED AVERAGE REDUCTION OF 11% IN DROPOUT RATES.
DESPITE A NARRATIVE THAT SEES LEARNING ANALYTICS (LA) AS A FIELD THAT AIMS TO ENHANCE STUDENT LEARNING, FEW STUDENT-FACING SOLUTIONS HAVE EMERGED. THIS CAN MAKE IT DIFFICULT FOR EDUCATORS TO IMAGINE HOW DATA CAN BE USED IN THE CLASSROOM, AND IN TURN DIMINISHES THE PROMISE OF LA AS AN ENABLER FOR ENCOURAGING IMPORTANT SKILLS SUCH AS SENSE-MAKING, METACOGNITION, AND REFLECTION. WE PROPOSE TWO LEARNING DESIGN PATTERNS THAT WILL HELP EDUCATORS TO INCORPORATE LA INTO THEIR TEACHING PROTOCOLS: DO-ANALYSE-CHANGE-REFLECT, AND ACTIVE LEARNING SQUARED. WE DISCUSS THESE PATTERNS WITH REFERENCE TO A CASE STUDY UTILISING THE CONNECTED LEARNING ANALYTICS (CLA) TOOLKIT, IN THREE TRIALS RUN OVER A PERIOD OF 18 MONTHS. THE RESULTS DEMONSTRATE THAT STUDENT-FACING LEARNING ANALYTICS IS NOT JUST A FUTURE POSSIBILITY, BUT AN AREA THAT IS RIPE FOR FURTHER DEVELOPMENT.
THE PURPOSE OF THIS STUDY WAS TO EXPLORE THE FACTORS THAT MIGHT AFFECT LEARNING PERFORMANCE AND COLLABORATIVE PROBLEM SOLVING (CPS) AWARENESS IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) EDUCATION. WE COLLECTED AND ANALYZED DATA ON IMPORTANT FACTORS IN STEM EDUCATION, INCLUDING LEARNING STRATEGY AND LEARNING BEHAVIORS, AND EXAMINED THEIR INTERRELATIONSHIPS WITH LEARNING PERFORMANCE AND CPS AWARENESS, RESPECTIVELY. MULTIPLE DATA SOURCES, INCLUDING LEARNING TESTS, QUESTIONNAIRE FEEDBACK, AND LEARNING LOGS, WERE COLLECTED AND EXAMINED FOLLOWING A LEARNING ANALYTICS APPROACH. SIGNIFICANT POSITIVE CORRELATIONS WERE FOUND FOR THE LEARNING BEHAVIOR OF USING MARKERS WITH LEARNING PERFORMANCE AND CPS AWARENESS IN GROUP DISCUSSION, WHILE SIGNIFICANT NEGATIVE CORRELATIONS WERE FOUND FOR SOME FACTORS OF STEM LEARNING STRATEGY AND LEARNING BEHAVIORS IN PRE-LEARNING WITH SOME FACTORS OF CPS AWARENESS. THE RESULTS IMPLY THE IMPORTANCE OF AN EFFICIENT APPROACH TO USING LEARNING STRATEGIES AND FUNCTIONAL TOOLS IN STEM EDUCATION.
LEARNING ANALYTICS IS THE ART AND SCIENCE OF COLLECTING, ANALYZING AND REPORTING DATA ABOUT LEARNERS AND THEIR LEARNING ENVIRONMENTS IN ORDER TO BETTER UNDERSTAND AND OPTIMIZE THE LEARNING PROCESS AND ITS ENVIRONMENT. IT IS CLOSELY ASSOCIATED TO EDUCATIONAL DATA MINING. MACHINE LEARNING, DATA MINING AND HUMAN COMPUTER INTERACTION TECHNIQUES ARE USED TO THE DATA COLLECTED ABOUT LEARNERS AND THEIR INTERACTIONS. IT AIMS AT LEVERAGING THE LEARNING PROCESS AND LEARNERS' EXPERIENCE THROUGH THE USE OF UBIQUITOUS SENSOR BASED INFRASTRUCTURE, INCREASED COLLECTION OF LARGE AND DIVERSE SET OF DATA, AND SUBSEQUENTLY APPLYING MACHINE LEARNING AND DATA ANALYTICS COULD REALLY MAKE IT USEFUL. BIG DATA PRINCIPLES CAN BE ALSO VERY HELPFUL TO SOLVE THE MYSTERIES RELATED TO THE COLLECTION, STORAGE MANAGEMENT AND ANALYSIS OF LARGE AND HETEROGENEOUS SET OF DATA. SYSTEMATIC LITERATURE REVIEW (SLR) IS A MORE STRUCTURED LITERATURE REVIEW PROCESS. IT ALSO PROVIDES MORE THOROUGH COVERAGE OF THE LITERATURE THEREBY MINIMIZING THE RESEARCH BIAS. THIS PAPER AIMS AT DEVELOPING A SYSTEMATIC LITERATURE REVIEW PROTOCOL FOR THE LEARNING ANALYTICS TO HIGHLIGHT THE APPLICATIONS, ISSUES AND CHALLENGES, EXISTING SOLUTIONS AND FUTURE DIRECTIONS IN THE CONTEXT OF BIG DATA.
THE DEVELOPMENT OF THE GROUP WORK COMPETENCE IS SOMETHING BASIC IN UNIVERSITY TEACHING. IT SHOULD BE EVALUATED, BUT THIS MEANS TO ANALYZE DIFFERENT ISSUES ABOUT THE PARTICIPATION OF A HIGH NUMBER OF STUDENTS WHICH IS VERY COMPLEX AND IMPLIES A LOT OF EFFORT. IN ORDER TO FACILITATE THIS EVALUATION IT IS POSSIBLE TO ANALYZE THE LOGS OF STUDENTS' INTERACTION IN LEARNING MANAGEMENT SYSTEMS. THE PRESENT WORK DESCRIBES THE DEVELOPMENT OF A LEARNING ANALYTICS SYSTEM THAT ANALYZES THE INTERACTION OF EACH OF THE MEMBERS OF WORKING GROUP. THIS TOOL IS SUPPORTED BY A THEORETICAL MODEL, WHICH ALLOWS ESTABLISHING LINKS BETWEEN THE EMPIRICAL EVIDENCES OF EACH STUDENT AND THE INDICATORS OF THEIR ACTION IN WORKING FORUMS.
THE MAIN OBJECTIVE OF EDUCATIONAL INSTITUTIONS IS TO ACHIEVE THE INTEGRAL DEVELOPMENT OF THEIR STUDENTS IN THEIR LEARNING AND KNOWLEDGE CONSTRUCTION PROCESS. ONE WAY TO ACHIEVE THESE OBJECTIVES IS THE ACCOMPANIMENT AND CONTINUOUS MONITORING OF STUDENTS IN THIS PROCESS, ADAPTING THE METHODS TO THEIR TRAINING NEEDS. IN ONLINE AND MIXED TEACHING MODALITIES (ELEARNING METHODOLOGY), THIS MONITORING IS CARRIED OUT THROUGH THE DIGITAL PLATFORMS IN WHICH IT IS CARRIED OUT IN THE ACADEMIC ACTIVITY, SUCH AS THE LEARNING MANAGEMENT SYSTEM PLATFORMS. THESE VIRTUAL TEACHING AND LEARNING ENVIRONMENTS (EVA) ALLOW ACCESS TO LEARNERS' FINGERPRINTS, GENERATING A LARGE VOLUME OF DATA, THAT ANALYSIS ALLOWS A DEEP WAY OF THEIR BEHAVIOR IN THOSE POLICIES. THIS ARTICLE COLLECTS THE RESULTS OF THE EXPLORATION OF STUDENT ACTIVITY IN THE VIRTUAL CAMPUS (BLACKBOARD LEARN), WHICH IS IN THE FIRST PHASE OF LEARNING ANALYTICS PROJECT CARRIED OUT BY THE NEBRIJA UNIVERSITY AND DISCUSSES ITS IMPLICATIONS FOR EDUCATIONAL INSTITUTIONS. THE DATA EXTRACTED CORRESPOND TO THE 2016-2017 COURSE AND HAVE BEEN ANALYZED AROUND FOUR BLOCKS OF INFORMATION: USER BEHAVIOR, USER ACTIVITY, ACTIVITY IN THE CONTENT AREAS AND ACTIVITY IN THE FORUMS.
LEARNING ANALYTICS IS AN EMERGING APPROACH THAT IS EQUALLY POPULAR AMONG RESEARCHERS AND EDUCATORS-PRACTITIONERS. ALTHOUGH THE METHODS AND TOOLS FOR LA HAVE BEEN DEVELOPING FAST, THERE STILL EXIST SEVERAL UNSOLVED PROBLEMS: LA IS TOO MUCH DATA DRIVEN, WEAKLY CONNECTED TO THEORY AND IS ABLE TO ANALYSE ONLY THE ACTIVITIES DOCUMENTED IN AN ONLINE SETTING - IN LMS. WE PROPOSE A SOLUTION FOR THE LA UNIT OF ANALYSIS DRAWING UPON THE RESEARCH OF EXISTING PRACTICES AND TOOLS USED FOR OFFLINE CONTEXTS: THE DATA IS COMING FROM THE PHYSICAL LEARNING INTERACTIONS BASED ON THE OBSERVATIONS IN THE CLASSROOM SETTING AND CAPTURED WITH CLASSROOM OBSERVATION APPLICATION. WE ARGUE THAT IF THE UNIT OF ANALYSIS HAS A PARTICULAR LOGIC AND STRUCTURE, IT CAN UNLEASH THE POSSIBILITIES FOR "OFFLINE" ANALYTICS THAT CAN BE LATER INTEGRATED WITH ONLINE LA.
THE AUTOMATIC COLLECTION OF DATA CONCERNING THE INTERACTION BETWEEN STUDENTS AND TECHNOLOGY ENHANCED LEARNING (TEL) SYSTEMS HAS BECOME INCREASINGLY COMMON. SUCH DATA AVAILABILITY HAS LED TO APPLICATIONS OF LEARNING ANALYTICS (LA) TECHNIQUES, CHARACTERIZED BY THE CAPABILITY OF EXTRACTING NON-TRIVIAL PATTERNS FROM THEM. IN THIS FRAMEWORK, WE DESCRIBE THE METHODOLOGY WE ARE ADOPTING TO EXPLORE THE WAY STUDENTS LEARN THE CONCEPTS OF DIGITAL ELECTRONICS BY EXPLOITING LA. IN PARTICULAR, A SIMULATOR NAMED DEEDS (DIGITAL ELECTRONICS EDUCATION AND DESIGN SUITE) IS USED, AND WE AIM AT EXTRACTING NON-TRIVIAL KNOWLEDGE FROM DATA, GATHERED THROUGH A LOGGING APPLICATION, PROPERLY REALIZED FOR MONITORING DEEDS USAGE.(1)
THIS STUDY INVESTIGATES ENHANCING STUDENT LEARNING AND PERFORMANCE BY EXPLORING STUDENT MOTIVATION THROUGH THE USE OF LEARNING ANALYTICS. A MIXED-METHODS APPROACH WILL BE USED TO COLLECT DATA FROM COMPUTER SCIENCE STUDENTS WITHIN THE UK HIGHER EDUCATION SECTOR. THE COLLECTED DATA WILL BE ANALYZED USING THEMATIC ANALYSIS TO DEVELOP A THEORETICAL FRAMEWORK THAT WILL BE TESTED SUBSEQUENTLY USING STRUCTURAL EQUATION MODELING. THE IDENTIFICATION OF STUDENT MOTIVATION FACTORS HELPS TUTORS AND LEARNING ANALYSTS TO BETTER UNDERSTAND STUDENT LEARNING MOTIVATION AND ADAPT THEIR LEARNING PRACTICES ACCORDINGLY.
PRECISION EDUCATION IS NOW RECOGNIZED AS A NEW CHALLENGE OF APPLYING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND LEARNING ANALYTICS TO IMPROVE BOTH LEARNING PERFORMANCE AND TEACHING QUALITY. TO PROMOTE PRECISION EDUCATION, DIGITAL LEARNING PLATFORMS HAVE BEEN WIDELY USED TO COLLECT EDUCATIONAL RECORDS OF STUDENTS' BEHAVIOR, PERFORMANCE, AND OTHER TYPES OF INTERACTION. ON THE OTHER HAND, THE INCREASING VOLUME OF STUDENTS' LEARNING BEHAVIORAL DATA IN VIRTUAL LEARNING ENVIRONMENTS PROVIDES OPPORTUNITIES FOR MINING DATA ON THESE STUDENTS' LEARNING PATTERNS. ACCORDINGLY, IDENTIFYING STUDENTS' ONLINE LEARNING PATTERNS ON VARIOUS DIGITAL LEARNING PLATFORMS HAS DRAWN THE INTEREST OF THE LEARNING ANALYTICS AND EDUCATIONAL DATA MINING RESEARCH COMMUNITIES. IN THIS STUDY, THE AUTHORS APPLIED DATA ANALYTICS METHODS TO EXAMINE THE LEARNING PATTERNS OF STUDENTS USING AN EBOOK SYSTEM FOR ONE SEMESTER IN AN UNDERGRADUATE COURSE. THE AUTHORS USED A CLUSTERING APPROACH TO IDENTIFY SUBGROUPS OF STUDENTS WITH DIFFERENT LEARNING PATTERNS. SEVERAL SUBGROUPS WERE IDENTIFIED, AND THE STUDENTS' LEARNING PATTERNS IN EACH SUBGROUP WERE DETERMINED ACCORDINGLY. IN ADDITION, THE ASSOCIATION BETWEEN THESE STUDENTS' LEARNING PATTERNS AND THEIR LEARNING OUTCOMES FROM THE COURSE WAS INVESTIGATED. THE FINDINGS OF THIS STUDY PROVIDE EDUCATORS OPPORTUNITIES TO PREDICT STUDENTS' LEARNING OUTCOMES BY ANALYZING THEIR ONLINE LEARNING BEHAVIORS AND PROVIDING TIMELY INTERVENTION FOR IMPROVING THEIR LEARNING EXPERIENCE, WHICH ACHIEVES ONE OF THE GOALS OF LEARNING ANALYTICS AS PART OF PRECISION EDUCATION.
THE ABILITY TO PAY ATTENTION AND SELF-REGULATE IS A FUNDAMENTAL SKILL REQUIRED OF LEARNERS OF ALL AGES. LEARNING ANALYTICS RESEARCHERS HAVE TO DATE RELIED ON DATA GENERATED BY A COMPUTING SYSTEM (SUCH AS A LEARNING MANAGEMENT SYSTEM, CLICK STREAM OR LOG DATA) TO EXAMINE LEARNERS' SELF-REGULATORY ABILITIES. THE DEVELOPMENT OF WEARABLE COMPUTING THROUGH FITNESS TRACKERS, WATCHES, HEART RATE MONITORS, AND CLINICAL GRADE DEVICES SUCH AS EMPATICA'S E4 WRISTBAND NOW PROVIDES RESEARCHERS WITH ACCESS TO BIOMETRIC DATA AS STUDENTS INTERACT WITH LEARNING CONTENT OR SOFTWARE SYSTEMS. THIS LEVEL OF DATA COLLECTION PROMISES TO PROVIDE VALUABLE INSIGHT INTO COGNITIVE AND AFFECTIVE EXPERIENCES OF INDIVIDUALS, ESPECIALLY WHEN COMBINED WITH TRADITIONAL LEARNING ANALYTICS DATA SOURCES. OUR STUDY DETAILS THE USE OF WEARABLE TECHNOLOGIES TO ASSESS THE RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND THE SELF-REGULATORY ABILITIES OF AN INDIVIDUAL. THIS IS RELEVANT FOR THE FIELD OF LEARNING ANALYTICS AS METHODS BECOME MORE COMPLEX AND THE ASSESSMENT OF LEARNER PERFORMANCE BECOMES MORE NUANCED AND ATTENTIVE TO THE AFFECTIVE FACTORS THAT CONTRIBUTE TO LEARNER SUCCESS.
WITH THE LOW LEARNER COMPLETION RATE BEING ONE OF MOOCS' MAJOR CRITICISMS, SEVERAL RESEARCH HAVE FOUND THAT LEARNERS' ENGAGEMENT IS A KEY SUCCESS FACTOR IN MOOCS. THIS PAPER PROPOSES A LEARNING ANALYTICS PROCESS TO DISCOVER INTERESTING PATTERNS FOUND IN MOOCS AND THEIR LEARNERS DATA USING TWO IMPORTANT DIMENSIONS: I) COURSE STRUCTURE & DESIGN, AND II) LEARNERS' PERFORMANCE & ENGAGEMENT. THE RELATIONSHIPS OF BOTH DIMENSIONS ARE ALSO INVESTIGATED. MORE SPECIFICALLY, MACHINE LEARNING AND VISUALIZATION TECHNIQUES HAVE BEEN EMPLOYED TO DISCOVER AND ANALYZE THESE ASPECTS WITH THE DATASETS EXTRACTED FROM THAI MOOC NATIONAL PLATFORM. REGARDING THE COURSE STRUCTURE & DESIGN DIMENSION, IT HAS BEEN FOUND THAT THE TOTAL COURSE LENGTH AND THE REQUIRED LEARNING EFFORT CAN BE USED TO CLUSTER THAI MOOCS INTO SHORT, MEDIUM-LENGTH AND LONG COURSE CLUSTERS. IN ADDITION, BY ANALYZING THE DEFINED LEARNING OUTCOMES, CLASSIFIED BY MEANS OF BLOOM'S TAXONOMY OF COGNITIVE PROCESS, WE HAVE CLUSTERED THAI MOOCS INTO UNDERSTANDING-FOCUS, APPLYINGFOCUS AND MULTIPLE-SKILL-FOCUS COURSE CLUSTERS. CONSIDERING THE LEARNERS' PERFORMANCE & ENGAGEMENT DIMENSION, GRADES AND ATTEMPT RATES HAVE BEEN USED TO CLUSTER THAI MOOC LEARNERS INTO SAMPLING, TARGETING AND COMPREHENSIVE LEARNERS. STATISTICAL TESTS HAVE ALSO CONFIRMED THE SIGNIFICANT RELATIONSHIP BETWEEN COURSE STRUCTURE & DESIGN AND LEARNERS PERFORMANCE & ENGAGEMENT. HENCE, VALUABLE INSIGHTS AND RECOMMENDATIONS HAVE BEEN DRAWN TO BETTER DESIGN MORE ENGAGING MOOCS AND TO BETTER SUPPORT LEARNERS.
IT IS NOTICED THAT THERE IS A RELEVANT ADOPTION OF VIRTUAL LEARNING ENVIRONMENTS (VLE) IN HIGHER EDUCATION, ESPECIALLY IN THE DISTANCE MODALITY. THESE ENVIRONMENTS, WITH THE SUPPORT OF BIG DATA, CAN PROVIDE ACTIONS FOR TEACHING EVALUATION PRACTICE BASED ON TOOLS THAT MEET THE OBJECTIVES OF LEARNING ANALYTICS (LA), ACCORDING TO MODELS AND LIFE CYCLES ALREADY DEFINED IN THE LITERATURE. THE PURPOSE OF THIS STUDY WAS TO CONSOLIDATE TEN POSSIBLE TEACHING ACTIONS BASED ON THESE TOOLS, MODELS AND LIFE CYCLES, WITH THE OBJECTIVE OF CONFRONTING THESE ACTIONS WITH TEACHERS INVOLVED FROM HIGHER EDUCATION INSTITUTIONS THAT WORK IN DISTANCE EDUCATION EVIDENCING THEM THROUGH AN EXPERIMENT WHERE THEY USED SIX OF THESE TOOLS OF TEACHING PROFILE IN THEIR RESPECTIVE DISCIPLINES IN VLE MOODIE. FOR COLLECTION, TWO SEMI-STRUCTURED QUESTIONNAIRES (PRE AND POST-EXPERIMENT) WERE USED. IT WAS CHARACTERIZED AS RESULTS OF THIS STUDY THAT THE TOOLS THAT HAD THE MOST HIGHLIGHT FOR LA ACTIONS WERE: COMPLETION PROGRESS (18.5%), COURSE DEDICATION (18.1%) AND LEVEL UP! (17.8%), FOLLOWED BY "EVALUATION /FEEDBACK" (13.0%)AND "INTERVENTION" (12.0%) %), THUS BEING COMPATIBLE WITH THE PURPOSE OF REACHING THE EVALUATIVE PRACTICE WITHIN VLE FROM THESE TOOLS.
EVEN THOUGH THE RECENT INFLUX OF TABLETS IN PRIMARY EDUCATION GOES TOGETHER WITH THE VISION THAT EDUCATIONAL TECHNOLOGY EMPOWERED WITH LEARNING ANALYTICS WILL REVOLUTIONIZE EDUCATION, EMPIRICAL RESULTS SUPPORTING THIS CLAIM ARE SCARES. ADAPTIVE EDUCATIONAL TECHNOLOGY SNAPPET COMBINES EXTRACTED AND EMBEDDED LEARNING ANALYTICS DAILY IN CLASSROOMS. WHILE STUDENTS MAKE EXERCISES ON THE TABLET THIS TECHNOLOGY DISPLAYS REAL-TIME DATA OF LEARNER PERFORMANCE IN A TEACHER DASHBOARD (EXTRACTED ANALYTICS). AT THE SAME TIME, LEARNER PERFORMANCE IS USED TO ADAPTIVELY ADJUST EXERCISES TO STUDENTS' PROGRESS (EMBEDDED ANALYTICS). THIS QUASI-EXPERIMENTAL STUDY COMPARES THE DEVELOPMENT OF STUDENTS' ARITHMETIC SKILLS OVER ONE SCHOOLYEAR (GRADE 2 AND 4) IN A TRADITIONAL PAPER BASED SETTING TO LEARNING WITH THE ADAPTIVE EDUCATIONAL TECHNOLOGY SNAPPET. THE RESULTS INDICATE THAT STUDENTS IN THE SNAPPET CONDITION MAKE SIGNIFICANTLY MORE PROGRESS ON ARITHMETIC SKILLS IN GRADE 4. MOREOVER, IN THIS GRADE STUDENTS WITH A HIGH ABILITY LEVEL, BENEFIT THE MOST FROM WORKING WITH THIS ADAPTIVE EDUCATIONAL TECHNOLOGY. OVERALL THE DEVELOPMENT PATTERN OF STUDENTS WITH DIFFERENT ABILITIES WAS MORE DIVERGENT IN THE AET CONDITION COMPARED TO THE CONTROL CONDITION. THESE RESULTS INDICATE THAT ADAPTIVE EDUCATIONAL TECHNOLOGIES COMBINING EXTRACTED AND EMBEDDED LEARNING ANALYTICS ARE INDEED CREATING NEW EDUCATION SCENARIOS THAT CONTRIBUTE TO PERSONALIZED LEARNING IN PRIMARY EDUCATION.
THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW OF LEARNING ANALYTICS DASHBOARDS (LADS) RESEARCH THAT REPORTS EMPIRICAL FINDINGS TO ASSESS THE IMPACT ON LEARNING AND TEACHING. SEVERAL PREVIOUS LITERATURE REVIEWS IDENTIFIED SELF-REGULATED LEARNING AS A PRIMARY FOCUS OF LADS. HOWEVER, THERE HAS BEEN MUCH LESS UNDERSTANDING HOW LEARNING ANALYTICS ARE GROUNDED IN THE LITERATURE ON SELF-REGULATED LEARNING AND HOW SELF-REGULATED LEARNING IS SUPPORTED. TO ADDRESS THIS LIMITATION, THIS REVIEW ANALYZED THE EXISTING EMPIRICAL STUDIES ON LADS BASED ON THE WELL-KNOWN MODEL OF SELF-REGULATED LEARNING PROPOSED BY WINNE AND HADWIN. THE RESULTS SHOW THAT EXISTING LADS ARE RARELY GROUNDED IN LEARNING THEORY, CANNOT BE SUGGESTED TO SUPPORT METACOGNITION, DO NOT OFFER ANY INFORMATION ABOUT EFFECTIVE LEARNING TACTICS AND STRATEGIES, AND HAVE SIGNIFICANT LIMITATIONS IN HOW THEIR EVALUATION IS CONDUCTED AND REPORTED. BASED ON THE FINDINGS OF THE STUDY AND THROUGH THE SYNTHESIS OF THE LITERATURE, THE PAPER PROPOSES THAT FUTURE RESEARCH AND DEVELOPMENT SHOULD NOT MAKE ANY A PRIORI DESIGN DECISIONS ABOUT REPRESENTATION OF DATA AND ANALYTIC RESULTS IN LEARNING ANALYTICS SYSTEMS SUCH AS LADS. TO FORMALIZE THIS PROPOSAL, THE PAPER DEFINES THE MODEL FOR USER-CENTERED LEARNING ANALYTICS SYSTEMS (MULAS). THE MULAS CONSISTS OF THE FOUR DIMENSIONS THAT ARE CYCLICALLY AND RECURSIVELY INTERCONNECTED INCLUDING: THEORY, DESIGN, FEEDBACK, AND EVALUATION.
HOME GAME IS AN EDUCATIONAL GAME AIMING TO FAMILIARIZE COGNITIVE IMPAIRED CHILDREN WITH THE OVERALL HOUSE ENVIRONMENT, AS WELL AS WITH THE EVERYDAY ACTIVITIES TAKING PLACE IN ITS CONTEXT. FOR THE EDUCATORS TO BE ABLE TO DERIVE MEANINGFUL INFORMATION FROM THE DATA COLLECTED DURING EACH GAMING SESSION, A TOOL TO ANALYSE AND PRESENT SUCH DATA IN A SIMPLE, YET EFFECTIVE WAY IS NECESSARY. TO ADDRESS THIS NEED, WE PRESENT A WEB-ORIENTED SYSTEM, NAMED ARISTEA, THAT COLLECTS LEARNING ANALYTICS AND STATISTICS OF STUDENTS INTERACTING WITH THE HOME GAME. EDUCATORS CAN EASILY DETECT LEARNING DIFFICULTIES AND MONITOR THE PROGRESS OF THE STUDENTS, WHILE THEY ACTIVELY PARTICIPATE IN THE LEARNING PROCESS BY PROVIDING ASSISTANCE WHEN REQUIRED. THIS WORK PRESENTS THE REQUIREMENTS OF ARISTEA AND HOW THEY SHAPED THE MAIN USER INTERFACE COMPONENTS, AS WELL AS THE OVERALL DESIGN RATIONALE TOWARDS FACILITATING EDUCATORS IN ADJUSTING THE LEARNING PROCESS ACCORDING TO THE NEEDS OF EACH STUDENT. FINALLY, IT REPORTS THE FINDINGS OF AN EXPERT-BASED EVALUATION OF THE TOOL.
THE FLIPPED CLASSROOM HAS BEEN INCREASINGLY EMPLOYED AS A PEDAGOGICAL STRATEGY IN THE HIGHER EDUCATION CLASSROOM. THIS APPROACH COMMONLY INVOLVES PRE-CLASS LEARNING ACTIVITIES THAT ARE DELIVERED ONLINE THROUGH LEARNING MANAGEMENT SYSTEMS THAT COLLECT LEARNING ANALYTICS DATA ON STUDENT ACCESS PATTERNS. THIS STUDY SOUGHT TO UTILIZE LEARNING ANALYTICS DATA TO UNDERSTAND STUDENT LEARNING BEHAVIOR IN A FLIPPED CLASSROOM. THE DATA ANALYZED THREE KEY PARAMETERS; THE NUMBER OF ONLINE STUDY SESSIONS FOR EACH INDIVIDUAL STUDENT, THE SIZE OF THE SESSIONS (NUMBER OF TOPICS COVERED), AND THE FIRST TIME THEY ACCESSED THEIR MATERIALS RELATIVE TO THE RELEVANT CLASS DATE. THE RELATIONSHIP BETWEEN THESE PARAMETERS AND ACADEMIC PERFORMANCE WAS ALSO EXPLORED. THE STUDY REVEALED PATTERNS OF STUDENT ACCESS CHANGED THROUGHOUT THE COURSE PERIOD, AND MOST STUDENTS DID ACCESS THEIR STUDY MATERIALS BEFORE THE RELEVANT CLASSROOM SESSION. USING K-MEANS CLUSTERING AS THE ALGORITHM, CONSISTENT EARLY ACCESS TO LEARNING MATERIALS WAS ASSOCIATED WITH IMPROVED ACADEMIC PERFORMANCE IN THIS CONTEXT. INSIGHTS DERIVED FROM THIS STUDY INFORMED ITERATIVE IMPROVEMENTS TO THE LEARNING DESIGN OF THE COURSE. SIMILAR ANALYSES COULD BE APPLIED TO OTHER HIGHER EDUCATION LEARNING CONTEXTS AS A FEEDBACK TOOL FOR EDUCATORS SEEKING TO IMPROVE THE ONLINE LEARNING EXPERIENCE OF THEIR STUDENTS.
AS SMART TECHNOLOGIES PERVADE OUR EVERYDAY ENVIRONMENTS, THEY CHANGE WHAT PEOPLE SHOULD LEARN TO LIVE MEANINGFULLY AS VALUABLE PARTICIPANTS OF OUR SOCIETY. FOR INSTANCE, UBIQUITOUS AVAILABILITY OF SMART DEVICES AND COMMUNICATION NETWORKS MAY HAVE REDUCED THE BURDEN FOR PEOPLE TO REMEMBER FACTUAL INFORMATION. AT THE SAME TIME, THEY MAY HAVE INCREASED THE BENEFITS TO MASTER THE USES OF NEW DIGITAL TECHNOLOGIES. IN THE MIDST OF SUCH A SOCIAL AND TECHNOLOGICAL SHIFT, WE COULD DESIGN NOVEL INTEGRATED PLATFORMS THAT SUPPORT PEOPLE AT ALL AGES TO LEARN, WORK, COLLABORATE, AND CO-CREATE EASILY. IN THIS PAPER, WE DISCUSS OUR IDEAS AND FIRST STEPS TOWARDS BUILDING AN EXTENDED LEARNING ANALYTICS PLATFORM THAT ELDERLY PEOPLE AND UNSKILLED ADULTS CAN USE. BY UNDERSTANDING THE CHARACTERISTICS AND NEEDS OF ELDERLY LEARNERS AND ADDRESSING CRITICAL USER INTERFACE ISSUES, WE CAN BUILD PERVASIVE AND INCLUSIVE LEARNING ANALYTICS PLATFORMS THAT TRIGGER CONTEXTUAL REMINDERS TO SUPPORT PEOPLE AT ALL AGES TO LIVE AND LEARN ACTIVELY REGARDLESS OF AGE-RELATED DIFFERENCES OF COGNITIVE CAPABILITIES. WE DISCUSS THAT RESOLVING CRITICAL USABILITY PROBLEMS FOR ELDERLY PEOPLE COULD OPEN UP A PLETHORA OF OPPORTUNITIES FOR THEM TO SEARCH AND EXPLOIT VAST AMOUNT OF INFORMATION TO ACHIEVE VARIOUS GOALS.
THE AUTHORS HAVE DEVELOPED A MOBILE APPLICATION FOR WORD REORDERING PROBLEMS (WRPS) AND ITS LEARNING MANAGEMENT SYSTEM (LMS) BASED ON THE WRP WEB APPLICATION IN ORDER TO COLLECT LEARNERS' STUDY LOGS. VARIOUS PARAMETERS SUCH AS "TOTAL DISTANCE OF MOVEMENTS" AND "ANSWER TIME" ARE GENERATED BY LEARNERS' TRAJECTORIES TO DETECT THEIR HESITATIONS IN ANSWERING WRPS. THE MAIN OBJECTIVE OF THIS RESEARCH IS TO EXPLOIT THE PRACTICAL USE OF NEW DATA TYPES PROVIDED BY PHYSICAL SENSORS EMBEDDED IN MOBILE DEVICES, WITH THE BELIEF THAT SPECIFIC NEW DATA TYPES POSSIBLY BENEFIT LEARNING ANALYTICS. THE AUTHORS USED ACCELERATION DATA AND FORCE DATA TO DETECT ABNORMAL TRAJECTORIES A SERIES OF TRAJECTORIES CAUSED BY UNSTABLE ENVIRONMENTS RESULTING IN LOW ACCURACY OF DETECTING HESITATIONS. THE AUTHORS CONDUCTED AN EXPERIMENT TO DETERMINE THE MOST SUITABLE PAIR OF THRESHOLDS (ACCELERATION AND FORCE PARAMETER VALUES). BY SETTING THIS PAIR, F-MEASURE TO IDENTIFY ABNORMAL TRAJECTORIES HAS REACHED APPROXIMATELY 0.727. THIS MEANS THAT WE ARE ABLE TO DETECT ABNORMAL TRAJECTORIES AT A CERTAIN RATE AND CUT SOME OF THEM OFF TO GET MORE ACCURATE RESULTS TO DETECT HESITATIONS USING MOBILE DEVICES.
LEARNING ANALYTICS (LA) PROMISES UNDERSTANDING AND OPTIMIZATION OF LEARNING AND LEARNING ENVIRONMENTS. TO ENABLE RICHER INSIGHTS REGARDING QUESTIONS RELATED TO LEARNING AND EDUCATION, LA SOLUTIONS SHOULD BE ABLE TO INTEGRATE DATA COMING FROM MANY DIFFERENT DATA SOURCES, WHICH MAY BE STORED IN DIFFERENT FORMATS AND HAVE VARYING LEVELS OF STRUCTURE. DATA INTEGRATION ALSO PLAYS A ROLE FOR THE SCALABILITY OF LA, AN IMPORTANT CHALLENGE IN ITSELF. THE OBJECTIVE OF THIS REVIEW IS TO ASSESS THE CURRENT STATE OF LA IN TERMS OF DATA INTEGRATION IN THE CONTEXT OF HIGHER EDUCATION. THE INITIAL SEARCH OF SIX ACADEMIC DATABASES AND COMMON VENUES FOR PUBLISHING LA RESEARCH RESULTED IN 115 PUBLICATIONS, OUT OF WHICH 20 WERE INCLUDED IN THE FINAL ANALYSIS. THE RESULTS SHOW THAT A FEW DATA SOURCES (E.G., LMS) APPEAR REPEATEDLY IN THE RESEARCH STUDIES; THE NUMBER OF DATA SOURCES USED IN LA STUDIES IN HIGHER EDUCATION TENDS TO BE LIMITED; WHEN DATA ARE INTEGRATED, SIMILAR DATA FORMATS ARE OFTEN COMBINED (A LOW-HANGING FRUIT IN TERMS OF TECHNICAL CHALLENGES); THE RESEARCH LITERATURE TENDS TO LACK DETAILS ABOUT DATA INTEGRATION IN THE IMPLEMENTED SYSTEMS; AND, DESPITE BEING A GOOD STARTING POINT FOR DATA INTEGRATION, EDUCATIONAL DATA SPECIFICATIONS (E.G., XAPI) SEEM TO SELDOM BE USED. IN ADDITION, THE RESULTS INDICATE A LACK OF STAKEHOLDER (E.G., TEACHERS/INSTRUCTORS, TECHNOLOGY VENDORS) INVOLVEMENT IN THE RESEARCH STUDIES. THE REVIEW CONCLUDES BY OFFERING RECOMMENDATIONS TO ADDRESS LIMITATIONS AND GAPS IN THE RESEARCH REPORTED IN THE LITERATURE.
LEARNING ANALYTICS DASHBOARDS (LADS) ARE INTENDED TO GIVE RELEVANT INFORMATION TO STUDENTS AND OTHER STAKEHOLDERS TO INFORM POTENTIAL NEXT STEPS IN THE LEARNING PROCESS. THE CURRENT STUDY EXAMINES THE RELATIONSHIP BETWEEN INFORMATION INDIRECTLY PRESENTED THROUGH ACADEMIC ADVISORS' USE OF LADS, AND COLLEGE STUDENTS' ACADEMIC MOTIVATION, SELF-REGULATED LEARNING, AND ACADEMIC ACHIEVEMENT. WE MODELED HOW CHANGES IN STUDENT MOTIVATION AND SELF-REGULATED LEARNING (SRL) WERE RELATED TO WHAT OCCURRED DURING 1-ON-1 MEETINGS WITH ACADEMIC ADVISORS DURING WHICH STUDENTS HAD THE POTENTIAL TO VIEW REPRESENTATIONS OF THEIR ACHIEVEMENT EMBEDDED WITHIN AN EARLY WARNING SYSTEM (EWS) THAT VISUALLY REPRESENTED ASPECTS OF THEIR ACADEMIC PERFORMANCE REFERENCED WITH COURSE AVERAGES. CONSTRUCTS ASSOCIATED WITH SRL WERE MODERATED BY ADVISOR-ADVISEE MEETINGS. RESULTS INDICATED THAT ADVISORS' USE OF EWS WHILE THEY MET WITH STUDENTS WAS NEGATIVELY ASSOCIATED WITH THE RATE OF DECREASE OF STUDENTS' REPORTING OF USING MEMORIZING STRATEGIES BUT POSITIVELY RELATED WHEN STUDENTS' PERFORMANCE WAS COMPARED TO THAT OF THEIR PEERS. WE DISCUSS THE MODERATING EFFECTS OF STUDENTS' EXPOSURE TO VISUALIZATIONS OF ACADEMIC PERFORMANCE ON THEIR SRL STRATEGIES AND ACADEMIC MOTIVATION. THIS STUDY POINTS TO THE IMPORTANCE OF MONITORING THE EFFECTS OF INFORMATION PRESENTED VIA EWS ON MOTIVATION AND SRL.
OVER RECENT YEARS THE USE OF LECTURE CAPTURE TECHNOLOGY HAS BECOME WIDESPREAD IN HIGHER EDUCATION. HOWEVER, CLEAR EVIDENCE OF THE LEARNING BENEFITS OF THIS TECHNOLOGY IS LIMITED, WITH CONTRADICTORY FINDINGS REPORTED IN THE LITERATURE. THE REASONS FOR THIS LACK OF CONSISTENT EVIDENCE MAY INCLUDE METHODOLOGICAL ISSUES AND DIFFERENCES IN THE CONTEXT OF PREVIOUS STUDIES. THIS PAPER DESCRIBES A STUDY USING SERVER LOG DATA TO EXPLORE STUDENT USE OF VIDEO RECORDINGS QUANTITATIVELY IN THE CONTEXT OF SCIENCE COURSES AT IMPERIAL COLLEGE LONDON. THE STUDY HAD TWO AIMS: TO UNDERSTAND MORE ABOUT THE GENERAL PRINCIPLES THAT UNDERPIN A LEARNING ANALYTICS STUDY AND TO SEEK ANSWERS TO THE FOLLOWING SPECIFIC RESEARCH QUESTIONS: (1) HOW MUCH USE IS MADE OF VIDEO RECORDINGS? (2) HOW DOES THE USE OF RECORDINGS IN A MODULE VARY OVER TIME? (3) IS THE USE OF RECORDINGS DIFFERENT FOR DIFFERENT MODULES OR SUBJECTS? (4) IS THE USE OF RECORDINGS DIFFERENT FOR SUBGROUPS OF STUDENTS, FOR EXAMPLE, STUDENTS WITH SPECIFIC LEARNING DIFFERENCES OR ENGLISH AS A SECOND LANGUAGE, STUDENTS ATTAINING DIFFERENT GRADES? (5) IS THE USE OF RECORDINGS DIFFERENT FOR DIFFERENT TYPES OF CONTENT? USING LEARNING ANALYTICS ENABLED THE DISCOVERY OF CONTEXT-SPECIFIC ACTIONABLE INSIGHTS: RECOMMENDATIONS FOR BOTH STAFF AND STUDENTS AND IDEAS FOR FURTHER RESEARCH. GENERAL CONCLUSIONS WERE ALSO DRAWN ON HOW BEST TO UNDERTAKE LEARNING ANALYTICS STUDIES IN ORDER TO DELIVER EVIDENCE AND INSIGHTS TO IMPROVE LEARNING AND TEACHING.
ALTHOUGH BLENDED LEARNING HAS MANY OPPORTUNITIES FOR FLEXIBLE LEARNING, IT ALSO INCLUDES CHALLENGES. ONE OF THE MAJOR CHALLENGES IS TO KEEP STUDENTS MOTIVATED. AN OPPORTUNITY OFTEN OVERLOOKED BY EDUCATIONAL SCIENTISTS IS THE VAST AMOUNT OF DATA GENERATED BY LEARNING MANAGEMENT SYSTEMS, OR SUMMARISED IN ONE WORD: LEARNING ANALYTICS (LA). LA COULD BE USED TO PROMOTE STUDENTS' MOTIVATION BY PROVIDING TEACHERS INSIGHT INTO STUDENTS' LEARNING ACTIVITIES WITHIN A BLENDED LEARNING CONTEXT. HOWEVER, THIS RESEARCH FIELD IS STILL UNDERDEVELOPED. LITTLE IS KNOWN ABOUT STUDENTS' PERCEPTIONS OF LA, BUT THIS INFORMATION IS FUNDAMENTAL TO DESIGN SUPPORTIVE LA INTERVENTIONS. THEREFORE, IN A FIRST PHASE, THIS STUDY AIMS TO INVESTIGATE HOW MOTIVATED STUDENT TEACHERS ARE IN A BLENDED LEARNING CONTEXT. FOLLOWING SELF-DETERMINATION THEORY, THIS STUDY EXAMINES WHETHER THE STUDENT TEACHERS' BASIC NEEDS ARE FULFILLED. IN A SUBSEQUENT SECOND PHASE, THE STUDENT TEACHERS' PERCEPTIONS OF A HYPOTHETICAL USE OF LA ARE EXPLORED. IN DECEMBER 2016, A STUDY WAS CONDUCTED IN A BLENDED LEARNING SETTING IN TEACHER EDUCATION. THE RESULTS INDICATE THAT STUDENT TEACHERS' BASIC NEEDS ARE NOT FULFILLED. THE SECOND MAJOR FINDING SUGGESTS THAT STUDENT TEACHERS HAVE DIFFERENT OPINIONS ABOUT THE USE OF LA, AND OVERALL THAT THE MAJORITY OF THE STUDENT TEACHERS IS NOT CONVINCED ABOUT THE ADDED VALUE. YET, THESE PERCEPTIONS ARE BASED ON A HYPOTHETICAL USE OF LA AND NOT ON STUDENT TEACHERS' OWN EXPERIENCES. FUTURE RESEARCH SHOULD INVESTIGATE WHAT STUDENT TEACHERS' PERCEPTIONS ARE AFTER A LA INTERVENTION IS CONDUCTED IN THE BLENDED LEARNING SETTING, AND WHETHER THIS INTERVENTION CAN GIVE APPROPRIATE INPUT TO THE TEACHER ENABLING HIM OR HER TO BETTER ACCOMMODATE STUDENTS' BASIC NEEDS.
MANY ADULT SECOND AND FOREIGN LANGUAGE LEARNERS HAVE INSUFFICIENT OPPORTUNITIES TO ENGAGE IN LANGUAGE LEARNING. HOWEVER, THEIR SUCCESSFUL ACQUISITION OF A TARGET LANGUAGE IS CRITICAL FOR VARIOUS REASONS, INCLUDING THEIR FAST INTEGRATION IN A HOST COUNTRY AND THEIR SMOOTH ADAPTATION TO NEW WORK OR EDUCATIONAL SETTINGS. THIS SUGGESTS THAT THEY NEED ADDITIONAL SUPPORT TO SUCCEED IN THEIR SECOND LANGUAGE ACQUISITION. WE ARGUE THAT SUCH SUPPORT WOULD BENEFIT FROM RECENT ADVANCES IN THE FIELDS OF MOBILE-ASSISTED LANGUAGE LEARNING, SELF-REGULATED LANGUAGE LEARNING, AND LEARNING ANALYTICS. IN PARTICULAR, THIS PAPER OFFERS A CONCEPTUAL FRAMEWORK, MOBILE-ASSISTED LANGUAGE LEARNING THROUGH LEARNING ANALYTICS FOR SELF-REGULATED LEARNING (MALLAS), TO HELP LEARNING DESIGNERS SUPPORT SECOND LANGUAGE LEARNERS THROUGH THE USE OF LEARNING ANALYTICS TO ENABLE SELF-REGULATED LEARNING. ALTHOUGH THE MALLAS FRAMEWORK IS PRESENTED HERE AS AN ANALYTICAL TOOL THAT CAN BE USED TO OPERATIONALISE THE SUPPORT OF MOBILEASSISTED LANGUAGE LEARNING IN A SPECIFIC EXEMPLARY LEARNING CONTEXT, IT WOULD BE OF INTEREST TO RESEARCHERS WHO WISH TO BETTER UNDERSTAND AND SUPPORT SELF-REGULATED LANGUAGE LEARNING IN MOBILE CONTEXTS. IMPLICATIONS FOR PRACTICE AND POLICY: MALLAS IS A CONCEPTUAL FRAMEWORK THAT CAPTURES THE DIMENSIONS OF SELF-REGULATED LANGUAGE LEARNING AND LEARNING ANALYTICS THAT ARE REQUIRED TO SUPPORT MOBILE-ASSISTED LANGUAGE LEARNING. DESIGNERS OF MOBILE-ASSISTED LANGUAGE LEARNING SOLUTIONS USING MALLAS WILL HAVE A SOLUTION WITH SOUND THEORETICALLY UNDERPINNED SOLUTION. LEARNING DESIGNERS CAN USE MALLAS AS A GUIDE TO DIRECT THEIR DESIGN CHOICES REGARDING THE DEVELOPMENT OF MOBILE-ASSISTED LANGUAGE LEARNING APPS AND SERVICES.
ONE OF THE DIFFICULTIES EXPERIENCED BY ONLINE LEARNERS IS THE LACK OF REGULAR SUPERVISION AS WELL AS THE NEED TO PROVIDE INSTRUCTIONS TO SUPPORT THE LEARNING PROCESS MORE EFFECTIVELY. THE ANALYSIS OF THE LEARNING DATA IN THE ONLINE COURSES IS NOT ONLY BECOMING INCREASINGLY IMPORTANT IN FORECASTING LEARNING OUTCOMES BUT ALSO PROVIDING EFFECTIVE INSTRUCTIONAL STRATEGIES FOR LEARNERS TO HELP THEM GET THE BEST RESULTS. IN THIS PAPER, WE PROPOSE A FORECAST LEARNING OUTCOMES MODEL BASED ON LEARNERS' INTERACTION WITH ONLINE LEARNING SYSTEMS BY PROVIDING LEARNING ANALYTICS DASHBOARD FOR BOTH LEARNERS AND TEACHERS TO MONITOR AND ORIENT ONLINE LEARNERS. THIS APPROACH IS MAINLY BASED ON SOME MACHINE LEARNING AND DATA MINING TECHNIQUES. THIS RESEARCH AIMS TO ANSWER TWO RESEARCH QUESTIONS: (1) IS IT POSSIBLE TO ACCURATELY PREDICT LEARNERS' LEARNING OUTCOMES BASED ON THEIR INTERACTIVE ACTIVITIES? (2) HOW TO MONITOR AND GUIDE LEARNERS IN AN EFFECTIVE ONLINE LEARNING ENVIRONMENT? TO ANSWER THESE TWO QUESTIONS, OUR MODEL HAS BEEN DEVELOPED AND TESTED BY LEARNERS PARTICIPATING IN THE MOODLE LMS SYSTEM. THE RESULTS SHOW THAT 75% OF STUDENTS HAVE OUTCOMES CLOSE TO THE PREDICTED RESULTS WITH AN ACCURACY OF OVER 50%. THESE POSITIVE RESULTS, THOUGH DONE ON A SMALL SCALE, CAN ALSO BE CONSIDERED AS SUGGESTIONS FOR STUDIES OF USING LEARNING ANALYTICS IN PREDICTING LEARNING OUTCOMES OF LEARNERS THROUGH LEARNING ACTIVITIES.
MUCH RESEARCH HAS SOUGHT TO PROVIDE A FLOW EXPERIENCE FOR STUDENTS IN GAMIFIED EDUCATIONAL SYSTEMS TO INCREASE MOTIVATION AND ENGAGEMENT. HOWEVER, THERE IS STILL A LACK OF QUANTITATIVE RESEARCH FOR EVALUATING THE INFLUENCE OF THE FLOW STATE ON LEARNING OUTCOMES. ONE OF THE ISSUES RELATED TO FLOW EXPERIENCE IDENTIFICATION IS THAT USED TECHNIQUES ARE OFTEN INVASIVE OR NOT SUITABLE FOR MASSIVE APPLICATIONS. THE CURRENT PAPER SUGGESTS A WAY TO DEAL WITH THIS CHALLENGE. WE DESCRIBE A METHODOLOGY BASED ON MULTIMODAL LEARNING ANALYTICS, AIMED TO PROVIDE AUTOMATIC STUDENTS' FLOW EXPERIENCE IDENTIFICATION IN THE GAMIFIED ASSIGNMENTS AND MEASURING ITS INFLUENCE ON THE LEARNING OUTCOMES. THE APPLICATION OF THE DEVELOPED METHODOLOGY SHOWED THAT THERE ARE CORRELATIONS BETWEEN LEARNING OUTCOMES AND FLOW STATE, BUT THEY DEPEND ON THE INITIAL LEVEL OF THE USER. THIS FINDING SUGGESTS ADDING DYNAMIC DIFFICULTY ADJUSTMENT AND OTHER FLOW EXPERIENCE DIMENSION TO THE GAMIFIED ASSIGNMENT.
THE CURRENT PAPER PRESENTS THE RESULTS RELATED TO THE USE OF A LEARNING ANALYTICS MANAGER FOR THE MONITORING PROCESSES OF THE COLLABORATIVE LEARNING ACTIVITIES AND THE STUDENTS' MOTIVATION INTO THE LEARNING MANAGEMENT SYSTEM MOODLE. THE PROPOSED MANAGER WAS DEVELOPED AS A NEW FUNCTIONALITY INTEGRATED TO A SERVICE CALLED MOTIVATION BOOSTER. THIS SERVICE IS THE RESULT OF A PREVIOUS WORK THAT ALLOWED IT EMBEDDED INTO THE MOODLE SYSTEM. A CASE STUDY WAS PERFORMED WITH STUDENTS ENROLLED IN A SOFTWARE ENGINEERING COURSE FROM THE COMPUTER SCIENCE DEPARTMENT DURING THE FIRST SEMESTER IN THE 2016 ACADEMIC YEAR AT THE UNIVERSIDAD PONTIFICIA BOLIVARIANA SECCIONAL MONTERIA (COLOMBIA). THE OUTCOMES ASSOCIATED WITH COLLABORATIVE LEARNING VARIABLES AND THE MOTIVATIONAL MESSAGES RECEIVED BY THE STUDENTS WERE ANALYZED FROM THE FEEDBACK INFORMATION SHOWED THROUGH THE IMPLEMENTED LEARNING ANALYTICS MANAGER. THE USE OF THE PROPOSED MANAGER LET TO CORROBORATE THAT THE STUDENTS IMPROVED THE WAY TO COMPLETE LEARNING ACTIVITIES IN THE SYSTEM.
WE HAVE DESIGNED, DEVELOPED, AND IMPLEMENTED A STUDENT-FACING LEARNING ANALYTICS DASHBOARD IN ORDER TO SUPPORT STUDENTS AS THEY LEARN IN ONLINE ENVIRONMENTS. THERE ARE TWO SEPARATE DASHBOARDS IN OUR SYSTEM: A CONTENT RECOMMENDER DASHBOARD AND A SKILLS RECOMMENDER DASHBOARD. THE CONTENT RECOMMENDER HELPS STUDENTS IDENTIFY GAPS IN THEIR CONTENT KNOWLEDGE; THE SKILLS RECOMMENDER HELPS STUDENTS IMPROVE THEIR METACOGNITIVE STRATEGIES. WE DISCUSS THE TECHNICAL REQUIREMENTS NEEDED TO DEVELOP A REAL-TIME STUDENT DASHBOARD AS WELL AS REPORT OUR INQUIRY INTO THE FUNCTIONALITY STUDENTS WANT IN A DASHBOARD. THE DASHBOARDS WERE EVALUATED WITH FOCUS GROUPS AND A PERCEPTIONS SURVEY. STUDENTS WERE POSITIVE IN THEIR PERCEPTIONS OF THE DASHBOARDS AND 79% OF THE STUDENTS THAT USED THE DASHBOARDS FOUND THEM USER-FRIENDLY, ENGAGING, USEFUL, AND INFORMATIVE. ONE CHALLENGE ENCOUNTERED WAS LOW STUDENT USE OF THE DASHBOARD. ONLY 25% OF STUDENTS USED THE DASHBOARD MULTIPLE TIMES, DESPITE FAVORABLE STUDENT PERCEPTIONS OF THE DASHBOARD. ADDITIONAL RESEARCH SHOULD EXAMINE HOW TO MOTIVATE AND SUPPORT STUDENTS TO ENGAGE WITH DASHBOARD FEEDBACK IN ONLINE ENVIRONMENTS.
TO PERFORM SUCCESSFULLY IN HIGHER EDUCATION LEARNERS ARE CONSIDERED TO ENGAGE IN SELF-REGULATION. PROMPTS IN DIGITAL LEARNING ENVIRONMENTS AIM AT ACTIVATING SELF-REGULATION STRATEGIES THAT LEARNERS KNOW BUT DO NOT SPONTANEOUSLY SHOW. TO INVESTIGATE SUCH INTERVENTIONS LEARNING ANALYTICS APPROACHES CAN BE APPLIED. THIS QUASI EXPERIMENTAL STUDY (N = 110) INVESTIGATES WHETHER DIFFERENT PROMPTS BASED ON THEORY OF SELF-REGULATED LEARNING (E.G., COGNITIVE, METACOGNITIVE, MOTIVATIONAL) IMPACT DECLARATIVE KNOWLEDGE AND TRANSFER, PERCEPTIONS AS WELL AS ONLINE LEARNING BEHAVIOR, AND WHETHER TRACE DATA CAN INFORM LEARNING PERFORMANCE. FINDINGS INDICATE SMALL EFFECTS OF PROMPTS SUPPORTING THE PERFORMANCE IN A DECLARATIVE KNOWLEDGE AND TRANSFER TEST. IN ADDITION, THE PROMPTED GROUPS SHOWED DIFFERENT ONLINE LEARNING BEHAVIOR THAN THE CONTROL GROUP. HOWEVER, TRACE DATA IN THIS STUDY WERE NOT CAPABLE OF SUFFICIENTLY EXPLAINING LEARNING PERFORMANCE IN A TRANSFER TEST. FUTURE RESEARCH IS REQUIRED TO INVESTIGATE ADAPTIVE PROMPTS USING TRACE DATA IN AUTHENTIC LEARNING SETTINGS AS WELL AS FOCUSING ON LEARNERS? REACTIONS TO DISTINCT PROMPTS.
THE FIELD OF LEARNING ANALYTICS (LA) HAS SEEN A GRADUAL SHIFT FROM PURELY DATA-DRIVEN APPROACHES TO MORE HOLISTIC VIEWS OF IMPROVING STUDENT LEARNING OUTCOMES THROUGH DATA-INFORMED LEARNING DESIGN (LD). DESPITE THE GROWING POTENTIAL OF LA IN HIGHER EDUCATION (HE), THE BENEFITS ARE NOT YET CONVINCING TO THE PRACTITIONER, IN PARTICULAR ASPECTS OF ALIGNING LA DATA WITH LD TOWARD DESIRED LEARNING OUTCOMES. THIS REVIEW PRESENTS A SYSTEMATIC EVALUATION OF EFFECT SIZES REPORTED IN 38 KEY STUDIES IN PURSUIT OF EFFECTIVE LA APPROACHES TO MEASURING STUDENT LEARNING GAIN FOR THE ENHANCEMENT OF HE PEDAGOGY AND DELIVERY. LARGE POSITIVE EFFECTS ON STUDENT OUTCOMES WERE FOUND IN LDS THAT FOSTERED SOCIO-COLLABORATIVE AND INDEPENDENT LEARNING SKILLS. RECENT TRENDS IN PERSONALIZATION OF LEARNER FEEDBACK IDENTIFIED A NEED FOR THE INTEGRATION OF STUDENT-IDIOSYNCRATIC FACTORS TO IMPROVE THE STUDENT EXPERIENCE AND ACADEMIC OUTCOMES. FINALLY, KEY FINDINGS ARE DEVELOPED INTO A NEW THREE-LEVEL FRAMEWORK, THE LA LEARNING GAIN DESIGN (LALGD) MODEL, TO ALIGN MEANINGFUL DATA CAPTURE WITH PEDAGOGICAL INTENTIONS AND THEIR LEARNING OUTCOMES. SUITABLE FOR VARIOUS SETTINGS - FACE TO FACE, BLENDED, OR FULLY ONLINE - THE MODEL CONTRIBUTES TO DATA-INFORMED LEARNING AND TEACHING PEDAGOGIES IN HE.
LEARNING ANALYTICS (LA) AS A RESEARCH FIELD HAS GROWN RAPIDLY OVER THE LAST DECADE. HOWEVER, ADOPTION OF LA IS MOSTLY FOUND TO BE SMALL IN SCALE AND ISOLATED AT THE INSTRUCTOR LEVEL. THIS PAPER PRESENTS AN EXPLORATORY STUDY ON INSTITUTIONAL APPROACHES TO LA IN EUROPEAN HIGHER EDUCATION AND DISCUSSES PROMINENT CHALLENGES THAT IMPEDE LA FROM REACHING ITS POTENTIAL. BASED ON A SERIES OF CONSULTATIONS WITH SENIOR MANAGERS FROM 83 DIFFERENT HIGHER EDUCATION INSTITUTIONS IN 24 EUROPEAN COUNTRIES, WE OBSERVE THAT LA IS PRIMARILY PERCEIVED AS A TOOL TO ENHANCE TEACHING AND INSTITUTIONAL MANAGEMENT. AS A RESULT, TEACHING AND SUPPORT STAFF ARE FOUND TO BE THE MAIN USERS OF LA AND THE TARGET AUDIENCE OF TRAINING SUPPORT. IN CONTRAST, THERE IS LITTLE EVIDENCE OF ACTIVE ENGAGEMENT WITH STUDENTS OR USING LA TO DEVELOP SELF-REGULATED LEARNING SKILLS. WE HIGHLIGHT THE IMPORTANCE OF GROUNDING LA IN LEARNING SCIENCES AND INCLUDING STUDENTS AS A KEY STAKEHOLDER IN THE DESIGN AND IMPLEMENTATION OF LA. THIS PAPER CONTRIBUTES TO OUR UNDERSTANDING OF THE DEVELOPMENT OF LA IN EUROPEAN HIGHER EDUCATION AND HIGHLIGHTS AREAS TO ADDRESS IN BOTH PRACTICE AND RESEARCH.
IN THIS WORK, WE STUDY STUDENTS' LEARNING EFFECTIVENESS THROUGH THEIR USE OF A HOMEGROWN INNOVATIVE EDUCATIONAL TECHNOLOGY, WEB PROGRAMMING GRADING ASSISTANT (WPGA), WHICH FACILITATES GRADING AND FEEDBACK DELIVERY OF PAPER-BASED ASSESSMENTS. WE DESIGNED A LAB STUDY AND A CLASSROOM STUDY FROM A LOWER-DIVISION BLENDED-INSTRUCTION COMPUTER SCIENCE COURSE. WE EVALUATED A PARTIAL CREDIT ASSIGNMENT ALGORITHM. WE TRACKED AND MODELED STUDENTS' LEARNING BEHAVIORS THROUGH THEIR USE OF WPGA. RESULTS SHOWED THAT STUDENTS DEMONSTRATED AN EFFORT AND DESIRE TO REVIEW ASSESSMENTS REGARDLESS OF IF THEY WERE GRADED FOR ACADEMIC PERFORMANCE OR FOR ATTENDANCE. DILIGENT STUDENTS ACHIEVED HIGHER EXAM SCORES ON AVERAGE AND WERE FOUND TO REVIEW THEIR EXAMS AND THE CORRECT QUESTIONS FREQUENTLY. ADDITIONALLY, STUDENT COHORTS EXHIBITED SIMILAR INITIAL REVIEWING PATTERNS, BUT DIFFERENT IN-DEPTH REVIEWING AND REFLECTING STRATEGIES. ULTIMATELY, THE WORK CONTRIBUTES TO MULTIDIMENSIONAL LEARNING ANALYTICS AGGREGATION ACROSS THE PHYSICAL AND CYBERSPHERE.
THIS STUDY PROPOSED A THEORETICAL MODEL EXPLAINING CAUSAL RELATIONSHIPS BETWEEN STUDENT PARTICIPATION AND ACADEMIC ACHIEVEMENT THROUGH THEIR LEARNING ANALYTICS IN A WEB-BASED DISTANCE EDUCATION COURSE, TESTING IT USING STRUCTURAL EQUATION MODELING (SEM) WITH AN EMPIRICAL DATASET. THE SAMPLE WAS COMPOSED OF 167 UNIVERSITY STUDENTS ENROLLED IN THE DEPARTMENT OF COMPUTER PROGRAMMING IN THE DISTANCE EDUCATION VOCATIONAL SCHOOL AT A MAJOR STATE UNIVERSITY IN TURKEY. STUDENT PARTICIPATION WAS OPERATIONALIZED THROUGH LEARNING ANALYTICS OF THE NUMBER OF SUBMISSIONS TO DISCUSSION FORUMS AND ATTENDANCE TO ONLINE LECTURES WHEREAS ACADEMIC ACHIEVEMENT WAS REPRESENTED BY STUDENTS' PERFORMANCE ON THEIR PROJECT ASSIGNMENTS AND FINAL EXAM. THE RESULTS OF THE SEM ANALYSIS INDICATED THAT THE MODIFIED VERSION OF THE MODEL HAD A GOOD FIT WITH THE DATA (X(2)=2.52, DF=1, P>.05, X(2)/DF=2.52, GFI=.99, CFI=.99, RMSEA=.09, SRMR=.03). DISCUSSION FORUM SUBMISSION AND ONLINE LECTURE ATTENDANCE WERE FOUND TO BE POSITIVELY ASSOCIATED WITH EACH OTHER. THEY HAD A POSITIVE DIRECT EFFECT ON STUDENTS' PROJECT SCORES AND A POSITIVE INDIRECT EFFECT ON STUDENTS' FINAL EXAM SCORES VIA THEIR PROJECT SCORES. MOREOVER, DISCUSSION FORUM SUBMISSION WAS FOUND TO HAVE A DIRECT POSITIVE EFFECT ON STUDENTS' FINAL EXAM SCORES. PRACTICAL IMPLICATIONS AND SUGGESTIONS FOR FURTHER RESEARCH ARE DISCUSSED WITHIN THE CONTEXT OF ONLINE LEARNING.
SERIOUS GAMES HAVE PROVEN TO BE A POWERFUL TOOL IN EDUCATION TO ENGAGE, MOTIVATE, AND HELP STUDENTS LEARN. HOWEVER, THE CHANGE IN STUDENT KNOWLEDGE AFTER PLAYING GAMES IS USUALLY MEASURED WITH TRADITIONAL (PAPER) PREQUESTIONNAIRES-POSTQUESTIONNAIRES. WE PROPOSE A COMBINATION OF GAME LEARNING ANALYTICS AND DATA MINING TECHNIQUES TO PREDICT KNOWLEDGE CHANGE BASED ON IN-GAME STUDENT INTERACTIONS. WE HAVE TESTED THIS APPROACH IN A CASE STUDY FOR WHICH WE HAVE CONDUCTED PREEXPERIMENTS-POSTEXPERIMENTS WITH 227 STUDENTS PLAYING A PREVIOUSLY VALIDATED SERIOUS GAME ON FIRST AID TECHNIQUES. WE COLLECTED STUDENT INTERACTION DATA WHILE STUDENTS PLAYED, USING A GAME LEARNING ANALYTICS INFRASTRUCTURE AND THE STANDARD DATA FORMAT EXPERIENCE API FOR SERIOUS GAMES. AFTER DATA COLLECTION, WE DEVELOPED AND TESTED PREDICTION MODELS TO DETERMINE WHETHER KNOWLEDGE, GIVEN AS POSTTEST RESULTS, CAN BE ACCURATELY PREDICTED. ADDITIONALLY, WE COMPARED MODELS BOTH WITH AND WITHOUT PRETEST INFORMATION TO DETERMINE THE IMPORTANCE OF PREVIOUS KNOWLEDGE WHEN PREDICTING POSTGAME KNOWLEDGE. THE HIGH ACCURACY OF THE OBTAINED PREDICTION MODELS SUGGESTS THAT SERIOUS GAMES CAN BE USED NOT ONLY TO TEACH BUT ALSO TO MEASURE KNOWLEDGE ACQUISITION AFTER PLAYING. THIS WILL SIMPLIFY SERIOUS GAMES APPLICATION FOR EDUCATIONAL SETTINGS AND ESPECIALLY IN THE CLASSROOM EASING TEACHERS' EVALUATION TASKS.
THIS STUDY INTEGRATED THE MULTIMODAL FRAMEWORK OF LEARNING ANALYTICS (IMFLA) WITH THE CONCEPT MAPPING (CMAP) APPROACH TO IMPROVE STUDENTS' VOCABULARY AND READING ABILITIES. A TOTAL OF 70 PARTICIPANTS WERE DIVIDED INTO 2 CLASSES, CLASS 1 (EXPERIMENTAL) AND CLASS 2 (CONTROL), FOR A 1-YEAR PERIOD. VOCABULARY AND READING TESTS WERE IMPLEMENTED 3 TIMES. REPEATED MEASURES WERE CONDUCTED TO TEST THE EFFECT OF THE PROGRAM. THE RESULTS INDICATED THAT TIME HAD A SIGNIFICANT EFFECT ON ENHANCING BOTH OUTCOME MEASURES (P < .01 FOR VOCABULARY; P < .001 FOR READING). MOREOVER, SIGNIFICANT INTERACTION EFFECTS BETWEEN TIME AND THE PROGRAM ON BOTH VOCABULARY (P < .05) AND READING (P < .001) FURTHER SUGGEST THAT A LONGER PERIOD OF TIME SPENT ON THE PROGRAM WOULD RESULT IN A SIGNIFICANT EFFECT ON BOTH THE VOCABULARY AND READING OUTCOME MEASURES. THAT IS, A SIGNIFICANT INTERACTION EFFECT OCCURRED BETWEEN IMFLA AND THE TIME FACTOR. SUCH INTERACTION EFFECTS RESULTED IN BETTER VOCABULARY AND READING ABILITIES WHEN STUDENTS OF CLASS 1 SPENT MORE TIME ON THE IMFLA PROCEDURE. THE MULTIMODAL AND LEARNING ANALYSES OF STUDENTS' WEEKLY LOGS CONFIRMED THIS IMPROVEMENT. WE THEREFORE SUGGEST THAT INSTRUCTORS USE DIGITALIZED WORDLISTS AND CMAPS IN LANGUAGE INSTRUCTION TO ENHANCE STUDENTS' VOCABULARY AND READING ABILITIES.
DESPITE THE POTENTIAL OF PREDICTIVE LEARNING ANALYTICS (PLAS) TO IDENTIFY STUDENTS AT RISK OF FAILING THEIR STUDIES, RESEARCH DEMONSTRATING EFFECTIVE APPLICATION OF PLAS TO HIGHER EDUCATION IS RELATIVELY LIMITED. THE AIMS OF THIS STUDY ARE 1) TO IDENTIFY WHETHER AND HOW PLAS CAN INFORM THE DESIGN OF MOTIVATIONAL INTERVENTIONS AND 2) TO CAPTURE THE IMPACT OF THOSE INTERVENTIONS ON STUDENT RETENTION AT THE OPEN UNIVERSITY UK. A PREDICTIVE MODEL - THE STUDENT PROBABILITIES MODEL (SPM) - WAS USED TO PREDICT THE LIKELIHOOD OF A STUDENT REMAINING IN A COURSE AT THE NEXT MILESTONE AND EVENTUALLY COMPLETING IT. UNDERGRADUATE STUDENTS (N=630) WITH A LOW PROBABILITY OF COMPLETING THEIR STUDIES WERE RANDOMLY ALLOCATED INTO THE CONTROL (N=312) AND INTERVENTION GROUPS (N=318), AND CONTACTED BY THE UNIVERSITY STUDENT SUPPORT TEAMS (SSTS) USING A SET OF MOTIVATIONAL INTERVENTIONS SUCH AS TEXT, PHONE, AND EMAIL. THE RESULTS OF THE RANDOMIZED CONTROL TRIAL SHOWED STATISTICALLY SIGNIFICANT BETTER STUDENT RETENTION OUTCOMES FOR THE INTERVENTION GROUP, WITH THE PROPOSED INTERVENTION DEEMED EFFECTIVE IN FACILITATING COURSE COMPLETION. THE INTERVENTION ALSO IMPROVED THE ADMINISTRATION OF STUDENT SUPPORT AT SCALE AND LOW COST.
LEARNING ANALYTICS (LA) APPROACHES HAVE PROVED TO BE ABLE TO ENHANCE LEARNING PROCESS AND LEARNING PERFORMANCE. HOWEVER, LITTLE IS KNOWN ABOUT APPLYING THESE APPROACHES FOR SECOND LANGUAGE ACQUISITION USING EDUCATIONAL GAMES. THEREFORE, THIS STUDY APPLIED LA APPROACHES TO DESIGN A SMART COLLABORATIVE EDUCATIONAL GAME, TO ENHANCE PRIMARY SCHOOL CHILDREN LEARNING ENGLISH VOCABULARIES. SPECIFICALLY, THE GAME PROVIDED DASHBOARDS TO THE TEACHERS ABOUT THEIR STUDENTS IN A REAL-TIME MANNER. A PILOT EXPERIMENT WAS CONDUCTED IN A PUBLIC PRIMARY SCHOOL WHERE THE STUDENTS' DATA FROM EXPERIMENTAL AND CONTROL GROUPS, NAMELY LEARNING AND MOTIVATION TEST SCORES, INTERVIEW AND OBSERVATION, WERE COLLECTED AND ANALYZED. THE OBTAINED RESULTS SHOWED THAT THE EXPERIMENTAL GROUP (WHO USED THE SMART GAME WITH LA) HAD SIGNIFICANTLY HIGHER MOTIVATION AND PERFORMANCE FOR LEARNING ENGLISH VOCABULARIES THAN THE CONTROL GROUP (WHO USED THE SMART GAME WITHOUT LA). THE FINDINGS OF THIS STUDY CAN HELP RESEARCHERS AND PRACTITIONERS INCORPORATE LA IN THEIR EDUCATIONAL GAMES TO HELP STUDENTS ENHANCE LANGUAGE ACQUISITION.
EVEN IF WE WANT OR NOT, WHATEVER THE CONSEQUENCES ARE, WE ARE IN BOLOGNA AND WE HAVE TO CARRY OUT CERTAIN TASKS, AMONG THEM THE DEVELOPMENT OF COMPETENCIES AND THE COHERENT AND CONTINUOUS EVALUATION. WITHIN COMPETENCIES WE CAN FIND THE INSTRUMENTAL ONES, WHERE REMOTE LABORATORIES MAY PLAY A RELEVANT ROLE COMPLEMENTING THEORY AND PRACTICE. ASSESSMENT CAN BE CARRIED OUT USING THE SAME TOOL, AND INTEGRATING LEARNING ANALYTICS TECHNIQUES TO SUPPORT THE EVALUATION AND MAKE IT AUTOMATIC.
FIVE THOUSAND SIX HUNDRED TEN SCIENTIFIC ARTICLES RELATED TO LEARNING ANALYTICS (LA) ARE STORED IN SCOPUS AND WEB OF SCIENCE FROM 2014 UNTIL 2019, AS EVIDENCE OF THE IMPORTANCE AND INCREASING INTEREST IN THIS NEW LINE OF RESEARCH. LA IS THE MEASUREMENT, COLLECTION, ANALYSIS AND REPORTING OF DATA ABOUT LEARNERS AND THEIR CONTEXTS, FOR THE PURPOSES OF UNDERSTANDING AND OPTIMIZING LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS. LA IS WIDELY USED IN UNIVERSITIES; ECUADOR HAS THREE HUNDRED FORTY-FIVE HIGHER EDUCATIONAL INSTITUTIONS. ONE OF THE REASONS WHY RESEARCH IN THE AREA OF LA DOES NOT INCREASE IS THE LACK OF A BASELINE (CUTTING EDGE INFORMATION THAT PROVIDES STARTING POINTS FOR RESEARCHING AND PUBLISHING). THIS RESEARCH DISCOVERS AND ANALYSES SCIENTIFIC DOCUMENTS IN LA AND ANSWERS THE CENTRAL QUESTION: WHAT IS THE BASELINE OF SCIENTIFIC DOCUMENTS ON LA IN ECUADOR? THE METHODOLOGY USED WAS A SYSTEMATIC REVIEW (SR) TO ANSWER EIGHT RESEARCH QUESTIONS ABOUT SCIENTIFIC ARTICLES AND THESES IN LA BY ECUADORIAN AUTHORS. THE STUDY PRESENTED IN THIS PAPER WAS CARRIED OUT FROM 2014 TO JUNE 2019; NINETY-NINE SCIENTIFIC DOCUMENTS ABOUT LA WERE FOUND IN SCOPUS, WOS, IEEE, RRAAE AND SENESCYT. SIXTY-ONE SCIENTIFIC DOCUMENTS WERE DOWNLOADED, ARRANGED AND ANALYZED USING STATISTICAL IMPLICATIVE ANALYSIS (SIA) AFTER REMOVING DUPLICATES, APPLYING INCLUSION, EXCLUSION AND QUALITY CRITERIA. SIA WAS PROPOSED BY REGIS GRAS AND DISCOVERS RULES BETWEEN VARIABLES AND REPRESENTS THEM THROUGH DENDROGRAMS. SR TOGETHER WITH SIA WAS USED FOR THE FIRST TIME IN THIS PAPER. THE BUILT BASELINE WILL ALLOW LEARNING MORE ABOUT LA IN ECUADOR AND INCREASING RESEARCH AND PUBLICATIONS.
THIS PAPER AIMS TO EXPLORE THE USE OF COMMON LEARNING ANALYTICS METHODS, SUCH AS ACTIVITY METRICS AND NETWORK ANALYTICS, IN ORDER TO STUDY AND ANALYSE THE ACTIVITY OF USERS AND THE COMMUNICATION FLOW IN DISCUSSION FORUMS THAT SERVE MASSIVE OPEN ONLINE COURSES (MOOCS). WE PARTICULARLY SEEK TO IDENTIFY TRENDS AND PATTERNS THAT MAY POTENTIALLY BE USED TO SUPPORT THE COMMUNICATION AND INFORMATION EXCHANGE BETWEEN MOOCS PARTICIPANTS. TO THAT END, WE APPLIED EXISTING METRICS AND METHODS ON THE LOG FILES OF A DISCUSSION FORUM THAT SUPPORTED PARTICIPANTS' COMMUNICATION FOR A COURSERA MOOC. WE PRESENT THE METHODOLOGY OF THE STUDY AS WELL AS THE RESULTS AND FINDINGS WITH RESPECT TO KNOWLEDGE EXCHANGE AND INFORMATION FLOW IN THE CASE OF A MASSIVE ONLINE COURSE.
IN THIS ARTICLE, WE ARGUE THAT THE CONTRIBUTIONS OF DOCUMENTATION STUDIES CAN PROVIDE A USEFUL FRAMEWORK FOR ANALYZING THE DATAFICATION OF STUDENTS DUE TO EMERGING LEARNING ANALYTICS (LA) PRACTICES. SPECIFICALLY, THE CONCEPTS OF INDIVIDUALS BEING 'MADE INTO' DATA AND HOW THAT DATA IS 'CONSIDERED AS' CAN HELP TO FRAME VITAL QUESTIONS CONCERNING THE USE OF STUDENT DATA IN LA. MORE SPECIFICALLY, APPROACHES INFORMED BY DOCUMENTATION STUDIES WILL ENABLE RESEARCHERS TO ADDRESS THE SOCIOTECHNICAL PROCESSES UNDERLYING HOW STUDENTS ARE CONSTRUCTED INTO DATA, AND WAYS DATA ABOUT STUDENTS ARE CONSIDERED AND UNDERSTOOD. WE DRAW ON THESE CONCEPTS TO IDENTIFY AND DESCRIBE THREE AREAS FOR FUTURE RESEARCH IN LA. WITH THE DESCRIPTION OF EACH AREA, WE PROVIDE A BRIEF ANALYSIS OF CURRENT PRACTICES IN AMERICAN HIGHER EDUCATION, HIGHLIGHTING HOW DOCUMENTATION STUDIES ENABLES DEEPER ANALYTICAL DIGGING.
THIS PAPER EXAMINES SOME ISSUES AND QUESTIONS REGARDING THE USE OF LEARNING ANALYTICS (LA) IN THE MEASUREMENT, COLLECTION, ANALYSIS, AND REPORTING OF DATA RELATED TO SOFTWARE ENGINEERING (SE) COMPETENCES OBTAINED BY MASTER STUDENTS AT A UNIVERSITY. THE PAPER'S AUTHORS REVIEW SOME RESULTS OF ISECRET PROJECT WITH THE GOAL OF INTRODUCING DATA MINING APPROACH FOR BETTER CONTROL OF THE TEACHING PROCESS BASED ON REMOTE COMPETENCES EVALUATION. THIS SHIFT GAVE THE OPPORTUNITY TO APPLY 'BUSINESS INTELLIGENCE' TECHNIQUES TO EDUCATIONAL DATA. THE RESEARCH FOCUSES ON PROGRESS AND PROFESSIONAL STANDARDS FOR ACCOUNTABILITY SYSTEMS USED IN SOFTWARE ENGINEERING MASTER PROGRAM WITH AN EMPHASIS ON PROFESSIONAL COMPETENCES DEFINED BY E-CF FRAMEWORK. THE AUTHORS PAY PARTICULAR ATTENTION TO THE INFORMATION INFRASTRUCTURE AND MATHEMATICAL BACKGROUND NEEDED TO CONDUCT EFFECTIVE LEARNING ANALYTICS AT SE MASTER PROGRAM. ACCORDINGLY, THE PAPER ATTEMPTS TO DEVELOP AN ORIGINAL MATHEMATICAL MODEL WAS DONE. THE APPROACH IS BASED ON THE PILOT REMOTE MEASUREMENT OF THE COMPONENTS OF COMPETENCES USING SECEIP PORTAL. THE DEVELOPED TECHNIQUE OF USING LEARNING ANALYTICS FOR MEASURING THE COMPETENCES AT SE MASTER PROGRAM MAY BE USED FOR REENGINEERING OF LEGACY UNIVERSITY MANAGEMENT INFORMATION SYSTEMS AND UNIVERSITY EXECUTIVE STAFF TRAINING.
THIS LONGITUDINAL STUDY EXPLORES THE EFFECTS OF TRACKING AND MONITORING TIME DEVOTED TO LEARN WITH A MOBILE TOOL, ON SELF-REGULATED LEARNING. GRADUATE STUDENTS (N = 36) FROM THREE DIFFERENT ONLINE COURSES USED THEIR OWN MOBILE DEVICES TO TRACK HOW MUCH TIME THEY DEVOTED TO LEARN OVER A PERIOD OF FOUR MONTHS. REPEATED MEASURES OF THE ONLINE SELF-REGULATED LEARNING QUESTIONNAIRE AND VALIDITY AND RELIABILITY OF TIME MANAGEMENT QUESTIONNAIRE WERE TAKEN ALONG THE COURSE. OUR FINDINGS REVEAL POSITIVE EFFECTS OF TRACKING TIME ON TIME MANAGEMENT SKILLS. VARIATIONS IN THE CHANNEL, CONTENT AND TIMING OF THE MOBILE NOTIFICATIONS TO FOSTER REFLECTIVE PRACTICE ARE INVESTIGATED, AND TIME-LOGGING PATTERNS ARE DESCRIBED. THESE RESULTS NOT ONLY PROVIDE EVIDENCE OF THE BENEFITS OF RECORDING LEARNING TIME, BUT ALSO SUGGEST RELEVANT CUES ON HOW MOBILE NOTIFICATIONS SHOULD BE DESIGNED AND PROMPTED TOWARDS SELF-REGULATED LEARNING OF STUDENTS IN ONLINE COURSES. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
WITH THE RAPID DEVELOPMENT OF ONLINE LEARNING, USERS HAVE MORE AND MORE DIVERSE WAYS TO LEARNING. THE ADVANTAGE OF ONLINE LEARNING IS THAT THE E-LEARNERS' BEHAVIORS CAN BE COMPLETELY RECORDED IN THE LOG EVENTS. SO LEARNING ANALYTICS CAN BE MADE ON THESE DATA. MORE THAN 500 FRESHMEN USED MOODLE TO STUDY OUR "C PROGRAM" COURSE EACH YEAR. SO A LARGE NUMBER OF LEARNING DATA HAVE BEEN COLLECTED. HOW TO MAKE ANALYSIS OF THE DATA IS VERY MEANINGFUL. IN THIS PAPER, WE FOCUSED ON ANALYZING CHARACTERISTICS BOTH THE LEARNING BEHAVIORS AND THE NONINTELLECTUAL FACTOR OF E-LEARNERS, SUCH AS EMOTION. WE AIMED TO FIND THE MAIN FACTORS WHICH INFLUENCE LEARNER'S ACHIEVEMENTS. THESE FACTORS MIGHT BE SUBMIT TIME OF PROGRAMS, LOGIN TIMES, THE FREQUENCY OF ASSIGNMENT VIEW, ETC. BASED ON THE ANALYSIS, WE ADOPTED THE POSITIVE AND NEGATIVE AFFECT SCALE (PANAS) AND FELDER & SOLOMON LEARNING STYLES AND STRATEGIES SURVEYS TO OBTAIN E-LEARNER'S EMOTION AND LEARNING STYLE IN A CERTAIN PERIOD OF TIME OR CURRENTLY. THEN WE MADE THE CORRELATION ANALYSIS BETWEEN E-LEARNER'S EMOTION, LEARNING STYLE AND THEIR ACHIEVEMENTS. THE EXPERIMENTAL RESULTS MADE A VERIFICATION THAT POSITIVE EMOTION HAS A GREAT INFLUENCE ON THE USER. AND ALSO THE LEARNING ENTHUSIASM OF VISUAL LEARNERS IS OBVIOUSLY HIGHER THAN THAT OF VERBAL LEARNERS.
MANY PEDAGOGICAL MODELS IN THE FIELD OF LEARNING ANALYTICS ARE IMPLICIT AND DO NOT OVERTLY DIRECT LEARNER BEHAVIOR. WHILE THIS ALLOWS FLEXIBILITY OF USE, THIS COULD ALSO RESULT IN MISALIGNED PRACTICE, AND THERE ARE CALLS FOR MORE EXPLICIT PEDAGOGICAL MODELS IN LEARNING ANALYTICS. THIS PAPER PRESENTS AN EXPLICIT PEDAGOGICAL MODEL, THE TEAM AND SELF DIAGNOSTIC LEARNING (TSDL) FRAMEWORK, IN THE CONTEXT OF COLLABORATIVE INQUIRY TASKS. KEY INFORMING THEORIES INCLUDE EXPERIENTIAL LEARNING, COLLABORATIVE LEARNING, AND THE LEARNING ANALYTICS PROCESS MODEL. THE FRAMEWORK WAS TRIALED THROUGH A TEAMWORK COMPETENCY AWARENESS PROGRAM FOR 14 YEAR OLD STUDENTS. A TOTAL OF 272 STUDENTS PARTICIPATED IN THE PROGRAM. THIS PAPER FOREGROUNDS STUDENTS' AND TEACHERS' EVALUATIVE ACCOUNTS OF THE PROGRAM. FINDINGS REVEAL POSITIVE PERCEPTIONS OF THE STAGES OF THE TSDL FRAMEWORK, DESPITE IDENTIFIED CHALLENGES, WHICH POINTS TO ITS POTENTIAL USEFULNESS FOR TEACHING AND LEARNING. THE TSDL FRAMEWORK AIMS TO PROVIDE THEORETICAL CLARITY OF THE LEARNING PROCESS, AND FOSTER ALIGNMENT BETWEEN LEARNING ANALYTICS AND THE LEARNING DESIGN. THE CURRENT WORK PROVIDES TRIAL OUTCOMES OF A TEAMWORK COMPETENCY AWARENESS PROGRAM THAT USED DISPOSITIONAL ANALYTICS, AND FURTHER EFFORTS ARE UNDERWAY TO DEVELOP THE DISCOURSE LAYER OF THE ANALYTIC ENGINE. FUTURE WORK WILL ALSO BE DEDICATED TO APPLICATION AND REFINEMENT OF THE FRAMEWORK FOR OTHER CONTEXTS AND PARTICIPANTS, BOTH LEARNERS AND TEACHERS ALIKE.
ASSESSMENT IS ESSENTIAL TO ESTABLISH THE FAILURE OR SUCCESS OF ANY EDUCATIONAL ACTIVITY. TO MEASURE THE ACQUISITION OF THE KNOWLEDGE COVERED BY THE ACTIVITY AND ALSO TO DETERMINE THE EFFECTIVENESS OF THE ACTIVITY ITSELF. THE INCREASING ADOPTION OF NEW TECHNOLOGIES IS PROMOTING THE USE OF NEW TYPES OF ACTIVITIES IN SCHOOLS, LIKE EDUCATIONAL VIDEO GAMES THAT IN SOME CASES ARE DEVELOPED BY THE TEACHERS THEMSELVES. IN THIS KIND OF ACTIVITY, INTERACTIVITY INCREASES COMPARED TO TRADITIONAL ACTIVITIES (E. G. READING A DOCUMENT), WHICH CAN BE A POWERFUL SOURCE OF DATA TO FEED LEARNING ANALYTICS SYSTEMS THAT INFER KNOWLEDGE ABOUT THE EFFECTIVENESS OF THE EDUCATIONAL PROCESS. IN THIS PAPER, WE DISCUSS HOW A PART OF THE STUDENTS' ASSESSMENT CAN BE ACHIEVED SEMI-AUTOMATICALLY BY LOGGING THE INTERACTION WITH EDUCATIONAL VIDEO GAMES. WE CONCLUDE THAT EVEN THE APPLICATION OF RATHER SIMPLE TRACKING TECHNIQUES MEANS AN ADVANTAGE COMPARED TO OTHER SYSTEMS THAT ARE FED WITH LESS QUALITY DATA. (C) 2012 THE AUTHORS. PUBLISHED BY ELSEVIER B. V. SELECTION AND/OR PEER-REVIEW UNDER RESPONSIBILITY OF THE SCIENTIFIC PROGRAMME COMMITTEE OF VS-GAMES 2012
SOCIAL MEDIA HAS BEEN DISRUPTING TRADITIONAL TECHNOLOGY MEDIATED LEARNING, PROVIDING STUDENTS AND EDUCATORS WITH UNSUPERVISED AND INFORMAL TOOLS AND SPACES WHERE AUTHENTIC LEARNING OCCURS. STILL, THE TRADITIONAL LMS PERSISTS AS THE CORE ELEMENT IN THIS CONTEXT, WHILE LACKING ADDITIONAL MANAGEMENT, MONITORING AND ANALYSIS TOOLS TO HANDLE INFORMAL LEARNING AND CONTENT. IN THIS PAPER, WE PRESENT AN INTEGRATED METHODOLOGY THAT COMBINES SOCIAL NETWORK ANALYTICS, SENTIMENT ANALYSIS AND TOPIC CATEGORIZATION TO PERFORM SOCIAL CONTENT VISUALIZATIONS AND ANALYSIS AIMED AT INTEGRATED LEARNING ENVIRONMENTS. RESULTS PROVIDE INSIGHTS ON NETWORKED CONTENT DIMENSION, TYPE OF STRUCTURE, DEGREE OF POPULARITY AND DEGREE OF CONTROVERSY, AS WELL AS ON THEIR EDUCATIONAL AND FUNCTIONAL POTENTIAL IN THE FIELD OF LEARNING ANALYTICS.
THE ENGAGING AND GOAL-ORIENTED NATURE OF SERIOUS GAMES HAS BEEN PROVEN TO INCREASE STUDENT MOTIVATION. GAMES ALSO ALLOW LEARNING ASSESSMENT IN A NON-INTRUSIVE FASHION. TO INCREASE ADOPTION OF SERIOUS GAMES, THEIR FULL LIFE-CYCLE, INCLUDING DESIGN, DEVELOPMENT, VALIDATION, DEPLOYMENT AND ITERATIVE REFINEMENT MUST BE MADE AS SIMPLE AND TRANSPARENT AS POSSIBLE. CURRENTLY SERIOUS GAMES IMPACT ANALYSIS AND VALIDATION IS DONE ON A CASE-BY-CASE BASIS. IN THIS PAPER, WE DESCRIBE A GENERIC ARCHITECTURE THAT INTEGRATES A GAME AUTHORING TOOL, UADVENTURE, WITH A STANDARDS-BASED GAME LEARNING ANALYTICS FRAMEWORK, PROVIDING A HOLISTIC APPROACH TO BRING TOGETHER DEVELOPMENT, VALIDATION, AND ANALYTICS, THAT ALLOWS A SYSTEMATIC ANALYSIS AND VALIDATION OF SERIOUS GAMES IMPACT. THIS ARCHITECTURE ALLOWS GAME DEVELOPERS, TEACHERS AND STUDENTS ACCESS TO DIFFERENT ANALYSES WITH MINIMAL SETUP; AND IMPROVES GAME DEVELOPMENT AND EVALUATION BY SUPPORTING AN EVIDENCE-BASED APPROACH TO ASSESS BOTH GAMES AND LEARNING. THIS SYSTEM IS CURRENTLY BEING EXTENDED AND USED IN TWO EU H2020 SERIOUS GAMES PROJECTS.
WITH THE HELP OF LEARNING ANALYTICS, MOOCS HAVE WIDER POTENTIAL TO SUCCEED IN LEARNING THROUGH PROMOTING SELF-REGULATED LEARNING (SRL). THE CURRENT STUDY AIMS TO APPLY AND VALIDATE VISUALIZATION DESIGN GUIDELINES FOR A MOOC DASHBOARD TO ENHANCE SUCH SRL CAPABILITIES BASED ON LEARNING ANALYTICS. TO ACHIEVE THE RESEARCH OBJECTIVE, A MOOC DASHBOARD PROTOTYPE, LM-DASHBOARD, WAS DESIGNED AND DEVELOPED, REFLECTING THE VISUALIZATION DESIGN GUIDELINES TO PROMOTE SRL. THEN, BOTH EXPERT AND LEARNER PARTICIPANTS EVALUATED LM-DASHBOARD THROUGH ITERATIONS TO VALIDATE THE VISUALIZATION DESIGN GUIDELINES AND PERCEIVED SRL EFFECTIVENESS. THE RESULTS OF EXPERT AND LEARNER EVALUATIONS INDICATED THAT MOST OF THE VISUALIZATION DESIGN GUIDELINES ON LM-DASHBOARD WERE VALID AND SOME PERCEIVED SRL ASPECTS SUCH AS MONITORING A STUDENT'S LEARNING PROGRESS AND ASSESSING THEIR ACHIEVEMENTS WITH TIME MANAGEMENT WERE BENEFICIAL. HOWEVER, SOME FEATURES ON LM-DASHBOARD SHOULD BE IMPROVED TO ENHANCE SRL ASPECTS RELATED TO ACHIEVING THEIR LEARNING GOALS WITH PERSISTENCE. THE FINDINGS SUGGEST THAT IT IS NECESSARY TO OFFER APPROPRIATE FEEDBACK OR TIPS AS WELL AS TO VISUALIZE LEARNER BEHAVIORS AND ACTIVITIES IN AN INTUITIVE AND EFFICIENT WAY FOR THE SUCCESSFUL CYCLE OF SRL. CONSEQUENTLY, THIS STUDY CONTRIBUTES TO ESTABLISHING A BASIS FOR THE VISUAL DESIGN OF A MOOC DASHBOARD FOR OPTIMIZING EACH LEARNER'S SRL.
IN THIS PAPER, WE DESCRIBE A LARGE-SCALE STUDY ABOUT THE USE OF PREDICTIVE LEARNING ANALYTICS DATA WITH 240 TEACHERS IN 10 MODULES AT A DISTANCE LEARNING HIGHER EDUCATION INSTITUTION. THE AIM OF THE STUDY WAS TO ILLUMINATE TEACHERS' USES AND PRACTICES OF PREDICTIVE DATA, IN PARTICULAR IDENTIFY HOW PREDICTIVE DATA WAS USED TO SUPPORT STUDENTS AT RISK OF NOT COMPLETING OR FAILING A MODULE. DATA WERE COLLECTED FROM STATISTICAL ANALYSIS OF 17,033 STUDENTS' PERFORMANCE BY THE END OF THE INTERVENTION, TEACHER USAGE STATISTICS, AND FIVE INDIVIDUAL SEMI-STRUCTURED INTERVIEWS WITH TEACHERS. FINDINGS REVEALED THAT TEACHERS ENDORSE THE USE OF PREDICTIVE DATA TO SUPPORT THEIR PRACTICE YET IN DIVERSE WAYS AND RAISED THE NEED FOR DEVISING APPROPRIATE INTERVENTION STRATEGIES TO SUPPORT STUDENTS AT RISK.
KEYSTROKE LOGGING AND CLICKSTREAM DATA, BOTH EMERGENT AREAS OF STUDY IN THE FIELD OF LEARNING ANALYTICS, PRESENT PROMISING ALTERNATIVE METHODS OF DETECTING AND PREVENTING CONTRACT CHEATING. THE CURRENT STUDY EXAMINES WHETHER ANALYSIS OF KEYSTROKE AND CLICKSTREAM DATA CAN DETECT WHEN A STUDENT IS CREATING THEIR OWN AUTHENTIC WRITING OR TRANSCRIBING FROM ANOTHER SOURCE. PARTICIPANTS WERE 62 UNIVERSITY STUDENTS (47 WOMEN, 15 MEN) WHO COMPLETED THREE WRITING TASKS UNDER EXPERIMENTAL CONDITIONS: FREE WRITING, GENERAL TRANSCRIPTION, AND SELF-TRANSCRIPTION. ANALYSES REVEALED THAT WHILE COMPLETING THE FREE-WRITING TASK, PARTICIPANTS TYPED IN SHORTER BURSTS WITH LONGER PAUSES AND TYPED MORE SLOWLY WITH MORE REVISIONS COMPARED TO THE TWO TRANSCRIPTION TASKS. MODEL-BASED CLUSTERING WAS ABLE TO ACCURATELY DISTINGUISH THE FREE-WRITING TASK FROM THE TWO TRANSCRIPTION TASKS BASED ON PATTERNS OF BURSTS AND WRITING SPEED. OVERALL, THESE RESULTS SUGGEST THAT KEYSTROKE AND CLICKSTREAM ANALYSIS MAY BE ABLE TO DISTINGUISH BETWEEN A STUDENT WRITING AN AUTHENTIC PIECE OF WORK AND ONE TRANSCRIBING A COMPLETED WORK. THESE FINDINGS SIGNAL SIGNIFICANT IMPLICATIONS FOR THE DETECTION OF CONTRACT CHEATING.
THE DEPLOYMENT OF AUGMENTED REALITY (AR) HAS ATTRACTED EDUCATORS' INTEREST AND INTRODUCED NEW OPPORTUNITIES IN EDUCATION. ADDITIONALLY, THE ADVANCEMENT OF ARTIFICIAL INTELLIGENCE HAS ENABLED EDUCATIONAL RESEARCHERS TO APPLY INNOVATIVE METHODS AND TECHNIQUES FOR THE MONITORING AND EVALUATION OF THE TEACHING AND LEARNING PROCESS. THE SO-CALLED LEARNING ANALYTICS (LA) DISCIPLINE EMERGED WITH THE PROMISE TO REVOLUTIONIZE TRADITIONAL INSTRUCTIONAL PRACTICES BY INTRODUCING SYSTEMATIC AND MULTIDIMENSIONAL WAYS TO IMPROVE THE EFFECTIVENESS OF THE INSTRUCTIONAL PROCESS. HOWEVER, THE IMPLEMENTATION OF LA METHODS IS USUALLY ASSOCIATED WITH WEB-BASED PLATFORMS, WHICH OFFER DIRECT ACCESS TO LEARNERS' DATA WITH MINIMAL EFFORT OR ADJUSTMENTS. ON THE OTHER HAND, THE COMPLEX NATURE OF IMMERSIVE TECHNOLOGIES AND THE DIVERSE INSTRUCTIONAL APPROACHES WHICH ARE UTILIZED IN DIFFERENT SCIENTIFIC DOMAINS HAVE LIMITED THE OPPORTUNITIES FOR RESEARCH AND DEVELOPMENT IN THIS DIRECTION. WITHIN THESE RESEARCH CONTEXTS, WE PRESENT A CONCEPTUAL FRAMEWORK THAT DESCRIBES THE ELEMENTS OF AN LA PROCESS TAILORED TO THE INFORMATION THAT CAN BE GATHERED FROM THE USE OF EDUCATIONAL APPLICATIONS, AND FURTHER PROVIDE AN INDICATIVE CASE STUDY FOR AR-SUPPORTED EDUCATIONAL INTERVENTIONS. THE CURRENT WORK CONTRIBUTES BY ELUCIDATING AND CONCRETIZING THE DESIGN ELEMENTS OF AR-SUPPORTED APPLICATIONS AND PROVIDES RESEARCHERS AND DESIGNERS WITH GUIDELINES ON HOW TO APPLY INSTRUCTIONAL STRATEGIES IN (AUGMENTED) REAL-WORLD PROJECTS.
THE DESIGN AND EVALUATION OF PERSONAL LEARNING ENVIRONMENTS (PLES) IS A COMPLEX ISSUE WHERE HIGHER EDUCATION INSTITUTIONS AND LEARNING ANALYTICS CAN PLAY AN IMPORTANT ROLE. AS AN EXAMPLE OF THIS, THIS PAPER PRESENTS A CASE STUDY FOCUSED ON THE EFFECT THAT SERVICE MULTIPLEXITY OF INSTITUTIONALLY-POWERED PLES HAS ON THE COMPOSITION AND STRUCTURE OF STUDENTS' PERSONAL NETWORKS. THE EVALUATION OF THE CASE WAS CARRIED OUT USING SOCIAL NETWORK ANALYSIS AS THE PARTICULAR APPROACH TO LEARNING ANALYTICS.
MULTIMODAL LEARNING ANALYTICS (MMLA) AIMS TO SUPPORT EVIDENCE-BASED EDUCATIONAL PRACTICES BY COLLECTING, PROCESSING, ANALYZING AND SENSE-MAKING OF MULTIMODAL EVIDENCE OF LEARNING. MMLA IS NOT WIDESPREAD YET AND THERE ARE FEW TAILORED MMLA SOLUTIONS TO MEET THE REQUIREMENTS OF A SPECIFIC LEARNING SCENARIO. THIS PHD PROJECT INVESTIGATES THE MAIN CHALLENGES BEHIND THE LIMITED DEVELOPMENT OF MMLA SOLUTIONS AND PROPOSES AN MMLA INFRASTRUCTURE AS THE MAIN CONTRIBUTION. THE PROPOSED INFRASTRUCTURE INCLUDES THREE COMPONENTS - A DATA VALUE CHAIN, A DATA MODEL AND A SOFTWARE ARCHITECTURE. THE OVERALL PROJECT FOLLOWS THE DESIGN-BASED RESEARCH METHODOLOGY WHERE MULTIPLE ITERATIONS ARE INVOLVED TO REFINE THE CONTRIBUTIONS.
ONLINE HIGHER EDUCATION IS GROWING BUT CARRIES SOME INHERENT DIFFICULTIES FOR STUDENTS. AS MANY STUDENTS DO NOT ACTIVELY SEEK HELP WHEN STUCK IN LEARNING, INSTITUTIONS SHOULD CONSIDER PROVIDING ACADEMIC HELP IN A PROACTIVE WAY. SINCE STUDENTS ARE DIFFERENT AND LEARN DIFFERENTLY, PERSONALIZED HELP IS EXPECTED FOR THE EFFECTIVENESS. LEANING ANALYTICS (LA) IS THE TECHNOLOGY THAT COULD BE USED TO IDENTIFY WHO ARE EXPERIENCING ACADEMIC DIFFICULTIES AND WHAT ACADEMIC HELP IS NEEDED. CURRENTLY MOST LA SYSTEMS ARE NOT FUNCTIONAL TO RECOMMEND DETAILED INSTRUCTIVE FEEDBACK ON HOW TO IMPROVE A STUDENT'S LEARNING, AND THEY ALSO REMAIN A CHALLENGE ON DATA COLLECTING. A RESEARCH QUESTION IS PROPOSED FROM THE COURSE DESIGN PERSPECTIVE: HOW ONLINE COURSES CAN BE DESIGNED IN A WAY THAT SUPPORTS LEARNING ANALYTICS IN FACILITATING PERSONALIZED ACADEMIC HELP. SOME PRELIMINARY RESEARCH HAS BEEN CONDUCTED TO ANSWER THIS QUESTION.
THE INCREASE OF INTERACTIVE LEARNING ENVIRONMENTS, SUCH AS LEARNING MANAGEMENT SYSTEMS, PERSONAL LEARNING ENVIRONMENTS AND INTELLIGENT TUTORING SYSTEMS, GENERATES LARGE AMOUNTS OF DATA. NEVERTHELESS, IN THE MAJORITY OF CASES, THE DATA STORED IN THESE ENVIRONMENTS, EITHER IN FILES OR DATABASES, ARE UNDERUSED BY TEACHERS, STUDENTS AND INSTITUTIONS. THE ANALYSIS OF THESE DATA GENERATES A SPACE FOR THE INCREASE OF RESEARCH IN TOPICS RELATED TO EVALUATION OF LEARNING THEORIES, EARLY WARNING SYSTEMS, AND THE DEVELOPMENT OF FUTURE LEARNING APPLICATIONS. SEVERAL RESEARCHES HAVE SHOWN THE USE OF LEARNING ANALYTICS, MACHINE LEARNING, DATA MINING TECHNIQUES AND CLASSIFIERS TO PREDICT ON THE ISSUE OF LEARNING OUTCOMES IN STUDENTS. NEVERTHELESS, IT IS NECESSARY TO GUIDE THE STUDY OF METHODS AND TECHNIQUES OF DATA MINING AND LEARNING ANALYTICS CLASSIFYING THEM ACCORDING TO THE ACTORS AND USE OBJECTIVES. THIS PAPER PRESENTS A SUMMARY OF THE MOST RELEVANT APPROACHES OF THE USE OF TECHNIQUES AND TOOLS OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS, USING LEARNING MANAGEMENT SYSTEMS PLATFORM'S IN HIGHER EDUCATION.
WE INTRODUCE AN INDEPENDENT OPEN LEARNER MODEL FOR FORMATIVE ASSESSMENT AND LEARNING ANALYTICS BASED ON DEVELOPMENTS IN TECHNOLOGY USE IN LEARNING, ALSO MAINTAINING MORE TRADITIONAL NUMERICAL AND QUALITATIVE FEEDBACK OPTIONS.
THIS STUDY PROPOSES A PROCESS-ORIENTED, AUTOMATIC, FORMATIVE ASSESSMENT MODEL FOR SMALL GROUP LEARNING BASED ON COMPLEX SYSTEMS THEORY USING A SMALL DATASET FROM A TECHNOLOGY-MEDIATED, SYNCHRONOUS MATHEMATICS LEARNING ENVIRONMENT. WE FIRST CONCEPTUALIZE SMALL GROUP LEARNING AS A COMPLEX SYSTEM AND EXPLAIN HOW GROUP DYNAMICS AND INTERACTION CAN BE MODELED VIA THEORETICALLY GROUNDED, SIMPLE RULES. THESE RULES ARE THEN OPERATIONALIZED TO BUILD TEMPORALLY-EMBODIED MEASURES, WHERE VARYING WEIGHTS ARE ASSIGNED TO THE SAME MEASURES ACCORDING TO THEIR SIGNIFICANCE DURING DIFFERENT TIME STAGES BASED ON THE GOLDEN RATIO CONCEPT. THIS THEORY-BASED MEASURE CONSTRUCTION METHOD IN COMBINATION WITH A CORRELATION-BASED FEATURE SUBSET SELECTION ALGORITHM REDUCES DATA DIMENSIONALITY, MAKING A COMPLEX SYSTEM MORE UNDERSTANDABLE FOR PEOPLE. FURTHER, BECAUSE THE DISCIPLINE OF EDUCATION OFTEN GENERATES SMALL DATASETS, A TREE-AUGMENTED NAIVE BAYES CLASSIFIER WAS CODED TO DEVELOP AN ASSESSMENT MODEL, WHICH ACHIEVES THE HIGHEST ACCURACY (95.8%) AS COMPARED TO BASELINE MODELS. FINALLY, WE DESCRIBE A WEB-BASED TOOL THAT VISUALIZES TIME-SERIES ACTIVITIES, ASSESSES SMALL GROUP LEARNING AUTOMATICALLY, AND ALSO OFFERS ACTIONABLE INTELLIGENCE FOR TEACHERS TO PROVIDE REAL-TIME SUPPORT AND INTERVENTION TO STUDENTS. THE FUNDAMENTAL CONTRIBUTION OF THIS PAPER IS THAT IT MAKES COMPLEX, SMALL GROUP BEHAVIOR VISIBLE TO TEACHERS IN A LEARNING CONTEXT QUICKLY. THEORETICAL AND METHODOLOGICAL IMPLICATIONS FOR TECHNOLOGY MEDIATED SMALL GROUP LEARNING AND LEARNING ANALYTICS AS A WHOLE ARE THEN DISCUSSED.
PROBLEM BASED LEARNING (PBL) SUPPORTS THE DEVELOPMENT OF TRANSVERSAL SKILLS AND COULD UNDERPIN THE TRAINING OF A WORKFORCE COMPETENT TO WITHSTAND THE CONSTANT GENERATION OF NEW INFORMATION. HOWEVER, THE APPLICATION OF PBL IS STILL FACING CHALLENGES, AS EDUCATORS ARE USUALLY UNSURE HOW TO STRUCTURE STUDENT-CENTRED COURSES, HOW TO MONITOR STUDENTS' PROGRESS AND WHEN TO PROVIDE GUIDANCE. RECENTLY, THE ANALYSIS OF EDUCATIONAL DATA, NAMELY LEARNING ANALYTICS (LA), HAS BROUGHT FORTH NEW PERSPECTIVES TOWARDS INFORMATIVE COURSE MONITORING AND DESIGN. HOWEVER, EXISTING RESEARCH SHOWS THAT LIMITED STUDIES HAVE COMBINED PBL WITH LA TO EXPLORE THEIR POTENTIAL IN OFFERING DATA-DRIVEN, STUDENT-CENTRED COURSES. THIS PAPER PRESENTS A FRAMEWORK, TERMED PBL_LA, THAT AIMS TO ADDRESS THIS GAP BY COMBINING PBL WITH LA. THE FRAMEWORK IS POPULATED FROM THE LITERATURE AND DISCUSSIONS WITH PBL AND LA EXPERTS. THE PAPER ALSO PRESENTS RESULTS FROM REDESIGNING, DELIVERING AND ASSESSING TEN COURSES IN DIFFERENT DISCIPLINES AND COUNTRIES USING THE PROPOSED FRAMEWORK. RESULTS SHOWED POSITIVE FEEDBACK ON ALL DIFFERENT TESTING SETTINGS, EXHIBITING RELIABILITY OF THE FRAMEWORK AND POTENTIAL ACROSS COUNTRIES, DISCIPLINES AND SECTORS.
STUDENT ENGAGEMENT IS OFTEN CONSIDERED AN OVERARCHING CONSTRUCT IN EDUCATIONAL RESEARCH AND PRACTICE. THOUGH FREQUENTLY EMPLOYED IN THE LEARNING ANALYTICS LITERATURE, ENGAGEMENT HAS BEEN SUBJECTED TO A VARIETY OF INTERPRETATIONS AND THERE IS LITTLE CONSENSUS REGARDING THE VERY DEFINITION OF THE CONSTRUCT. THIS RAISES GRAVE CONCERNS WITH REGARDS TO CONSTRUCT VALIDITY: NAMELY, DO THESE VARIED METRICS MEASURE THE SAME THING? TO ADDRESS SUCH CONCERNS, THIS PAPER PROPOSES, QUANTIFIES, AND VALIDATES A MODEL OF ENGAGEMENT WHICH IS BOTH GROUNDED IN THE THEORETICAL LITERATURE AND DESCRIBED BY COMMON METRICS DRAWN FROM THE FIELD OF LEARNING ANALYTICS. TO IDENTIFY A LATENT VARIABLE STRUCTURE IN OUR DATA WE USED EXPLORATORY FACTOR ANALYSIS AND VALIDATED THE DERIVED MODEL ON A SEPARATE SUBSAMPLE OF OUR DATA USING CONFIRMATORY FACTOR ANALYSIS. TO ANALYZE THE ASSOCIATIONS BETWEEN OUR LATENT VARIABLES AND STUDENT OUTCOMES, A STRUCTURAL EQUATION MODEL WAS FITTED, AND THE VALIDITY OF THIS MODEL ACROSS DIFFERENT COURSE SETTINGS WAS ASSESSED USING MIMIC MODELING. ACROSS DIFFERENT DOMAINS, THE BROAD CONSISTENCY OF OUR MODEL WITH THE THEORETICAL LITERATURE SUGGEST A MECHANISM THAT MAY BE USED TO INFORM BOTH INTERVENTIONS AND COURSE DESIGN.
STUDENT DROPOUT IN ENGINEERING EDUCATION IS AN IMPORTANT PROBLEM WHICH HAS BEEN STUDIED FROM DIFFERENT PERSPECTIVES, AS WELL AS USING DIFFERENT TECHNIQUES. THIS MANUSCRIPT DESCRIBES THE METHODOLOGY USED IN ORDER TO ADDRESS THIS QUESTION IN THE CONTEXT OF LEARNING ANALYTICS. BAYESIAN NETWORKS (BNS) HAVE BEEN USED AS THEY PROVIDE ADEQUATE METHODS FOR THE REPRESENTATION, INTERPRETATION AND CONTEXTUALISATION OF DATA. THE PROPOSED APPROACH IS ILLUSTRATED THROUGH A CASE STUDY ABOUT COMPUTER SCIENCE (CS) DROPOUT AT THE UNIVERSITY OF CASTILLA-LA MANCHA (SPAIN), WHICH IS CLOSE TO 40%. TO THAT END, SEVERAL BNS WERE OBTAINED FROM A DATABASE WHICH CONTAINED 383 RECORDS REPRESENTING BOTH ACADEMIC AND SOCIAL DATA OF THE STUDENTS ENROLLED IN THE CS DEGREE DURING FOUR COURSES. THEN, THESE PROBABILISTIC MODELS WERE INTERPRETED AND EVALUATED. THE RESULTS OBTAINED REVEALED THAT THE BEST MODEL THAT FITS THE DATA IS PROVIDED BY THE K2 ALGORITHM ALTHOUGH THE GREAT HETEROGENEITY OF THE DATA STUDIED DID NOT PERMIT THE ADJUSTMENT OF THE DROPOUT PROFILE OF THE STUDENT TOO ACCURATELY. NONETHELESS, THE METHODOLOGY DESCRIBED HERE CAN BE TAKEN AS A REFERENCE FOR FUTURE WORKS.
AS AN EMERGING FIELD OF RESEARCH, LEARNING ANALYTICS (LA) OFFERS PRACTITIONERS AND RESEARCHERS INFORMATION ABOUT EDUCATIONAL DATA THAT IS HELPFUL FOR SUPPORTING DECISIONS IN MANAGEMENT OF TEACHING AND LEARNING. WHILE OFTEN COMBINED WITH EDUCATIONAL DATA MINING (EDM), CRUCIAL DISTINCTIONS EXIST FOR LA THAT MANDATE A SEPARATE REVIEW. THIS STUDY AIMS TO CONDUCT A SYSTEMATIC META-REVIEW OF LA FOR MINING KEY INFORMATION THAT COULD ASSIST IN DESCRIBING NEW AND HELPFUL DIRECTIONS TO THIS FIELD OF INQUIRY. WITHIN 901 LA ARTICLES ANALYZED, EIGHT REVIEWS WERE IDENTIFIED AND SYNTHESISED TO IDENTIFY AND DETERMINE CONSISTENCIES AND GAPS. RESULTS SHOW THAT LA IS AT THE STAGE OF EARLY MAJORITY AND HAS ATTRACTED GREAT RESEARCH EFFORTS FROM OTHER FIELDS. THE MAJORITY OF LA PUBLICATIONS WERE FOCUSED ON PROPOSING LA CONCEPTS OR FRAMEWORKS AND CONDUCTING PROOF-OF-CONCEPT ANALYSIS RATHER THAN CONDUCTING ACTUAL DATA ANALYSIS. COLLECTING SMALL DATASETS FOR LA RESEARCH IS PREDOMINANT, ESPECIALLY IN K-12 FIELD. FINALLY, FOUR MAJOR LA RESEARCH TOPICS, INCLUDING PREDICTION OF PERFORMANCE, DECISION SUPPORT FOR TEACHERS AND LEARNERS, DETECTION OF BEHAVIOURAL PATTERNS & LEARNER MODELLING AND DROPOUT PREDICTION, WERE IDENTIFIED AND DISCUSSED DEEPLY. THE FUTURE RESEARCH OF LA IS ALSO OUTLINED FOR PURPOSE OF BETTER UNDERSTANDING AND OPTIMISING LEARNING AS WELL AS LEARNING CONTEXTS.
EDUCATIONAL GAMES AIM TO BALANCE LEARNING AND PLAYING. HOWEVER, FOR PEOPLE TO BENEFIT FROM AN EDUCATIONAL GAME, THEY MUST BE ENCOURAGED TO PLAY THE GAME OFTEN. PROVIDING PLAYERS WITH INFORMATION ABOUT HOW TO IMPROVE THEIR PERFORMANCE COULD HELP IN ACHIEVING THIS GOAL. THIS PAPER EXAMINES HOW A LEARNING ANALYTICS DASHBOARD CAN BE INCORPORATED INTO AN EDUCATIONAL GAME TO ENCOURAGE PLAYERS TO PLAY MORE OFTEN AND CONTINUOUSLY. THE PROPOSED DASHBOARD PROVIDES PLAYERS WITH A VARIETY OF INFORMATION SUCH AS HOW THEIR PERFORMANCE AND SKILLS CHANGE OVER TIME. SUCH INFORMATION ALLOWS PLAYERS TO SEE THEIR PERFORMANCE AND PLAY HABITS, AND FIND STRATEGIES ON HOW TO IMPROVE THEIR PERFORMANCE, AND THEREFORE THEIR LEARNING, IN THE GAME.
IT APPROACH HAS BEEN GRADUALLY APPLIED IN COLLABORATIVE LEARNING GROUPS IN HIGHER EDUCATION NOWADAYS. HOWEVER, IT IS STILL DIFFICULT TO MEASURE AND ASSESS EACH STUDENT'S ENGAGEMENT IN LEARNING GROUPS. MOREOVER, HOW TO PROMOTE STUDENTS' ENGAGEMENT IN LEARNING GROUPS IS ALSO AN IMPORTANT PROBLEM. LEARNING ANALYTICS IN IT ENVIRONMENT HELPS US UNDERSTAND THE WHOLE PROCESS OF STUDENTS' LEARNING IN GROUPS, ANALYZE THESE PROBLEMS IN COLLABORATIVE LEARNING AND FIND WAYS TO IMPROVE GROUP LEARNING EFFECT. WE EXAMINED THE ROLE OF SLACK IN STUDENTS' ENGAGEMENT IN COLLABORATIVE LEARNING. SPECIFICALLY, WHETHER THE OPENNESS AND VISIBILITY OF EACH GROUP MEMBER'S CONTRIBUTION ON SLACK CAN PROMOTE STUDENTS' ENGAGEMENT. BASED ON THE BENEFITS AND LOSS OF ENGAGEMENT, WE PROPOSED THE RESEARCH MODEL AND HYPOTHESES. SURVEYS (N = 181) WERE ISSUED ONLINE TO POSTGRADUATE STUDENTS AT DIFFERENT TIMES OF A COURSE IN CHINA. DATA WAS ANALYSED BY SMARTPLS. RESULTS SHOW THAT MUTUAL TRUST, SOCIAL INFLUENCE AND REWARD VALENCE AMONG STUDENTS CAN PROMOTE THEIR TEAMWORK ENGAGEMENT. FURTHERMORE, TEAMWORK ENGAGEMENT CAN PROMPT STUDENTS' LEARNING AND WORK SATISFACTION. HOWEVER, THE MODERATION ROLE OF TASK COMPLEXITY BETWEEN TEAMWORK ENGAGEMENT AND PERSONAL SUCCESS (I.E. LEARNING AND WORK SATISFACTION) IS NOT SUPPORTED. THEORETICAL CONTRIBUTION AND IMPLICATIONS FOR EDUCATIONAL PRACTICES ARE DISCUSSED. RESEARCH LIMITATIONS AND FUTURE RESEARCH DIRECTIONS ARE ALSO PRESENTED. (C) 2017 PUBLISHED BY ELSEVIER LTD.
THIS PAPER PRESENTS THE FINDINGS OF A MIXED-METHODS RESEARCH THAT EXPLORED THE POTENTIALS EMERGING FROM ALIGNING LEARNING DESIGN (LD) AND LEARNING ANALYTICS (LA) DURING THE DESIGN OF A PREDICTIVE ANALYTICS SOLUTION AND FROM INVOLVING THE INSTRUCTORS IN THE DESIGN PROCESS. THE CONTEXT WAS A PAST MASSIVE OPEN ONLINE COURSE, WHERE THE LEARNER DATA AND THE INSTRUCTORS WERE ACCESSIBLE FOR POSTERIOR ANALYSIS AND ADDITIONAL DATA COLLECTION. THROUGH A CLOSE COLLABORATION WITH THE INSTRUCTORS, THE DETAILS OF THE PREDICTION TASK WERE IDENTIFIED, SUCH AS THE TARGET VARIABLE TO PREDICT AND THE PRACTICAL CONSTRAINTS TO CONSIDER. TWO PREDICTIVE MODELS WERE BUILT: LD-SPECIFIC MODEL (WITH FEATURES BASED ON THE LD AND PEDAGOGICAL INTENTIONS), AND A GENERIC MODEL (WITH CUMULATIVE FEATURES, NOT INFORMED BY THE LD). ALTHOUGH THE LD-SPECIFIC PREDICTIVE MODEL DID NOT OUTPERFORM THE GENERIC ONE, SOME LD-DRIVEN FEATURES WERE POWERFUL. THE QUANTITY AND THE POWER OF SUCH FEATURES WERE ASSOCIATED WITH THE DEGREE TO WHICH THE STUDENTS ACTED AS GUIDED BY THE LD AND PEDAGOGICAL INTENTIONS. THE LEADING INSTRUCTOR'S OPINION ABOUT THE IMPORTANCE OF THE LEARNING ACTIVITIES IN THE LD WAS COMPARED WITH THE RESULTS OF THE FEATURE IMPORTANCE ANALYSIS. THIS COMPARISON HELPED IDENTIFY THE PROBLEMS IN THE LD. THE IMPLICATIONS FOR IMPROVING THE LD ARE DISCUSSED.
IN THIS PAPER LEARNING ANALYTICS ON THE STUDENTS AND THEIR CONTEXTS WILL BE USED LIKE SUPPORT TO THE TEACHING AND TO THE IMPROVEMENT OF THE PROCESSES OF EDUCATION-LEARNING FROM AN APPROACH NOT "MAXIMALISTA". THE EFFECTIVE PRACTICES OF THE SUBJECTS WILL BE TAKEN INTO ACCOUNT WITH THE AIM OF IDENTIFY THE PROFILES OF APPROPRIATION OF INFORMATION AND COMMUNICATION TECHNOLOGY (TIC) BY THE STUDENTS, FROM THEIR DIGITAL RECORDS IN THE VIRTUAL CLASSROOM, IN COMBINATION WITH QUANTITATIVE DATA OF THE ACADEMIC PERFORMANCE AND USERS OPINIONS. THE STUDY IS APPLIED TO STUDENTS OF CELL BIOLOGY AND MICROORGANISMS ASSIGNMENT THAT IS TAUGHT IN THE FIRST SEMESTER OF THE FIRST YEAR OF BACHELOR AND FACULTY CAREERS IN BIOLOGICAL SCIENCES IN THE FACULTY OF NATURAL SCIENCES AND IML, NATIONAL UNIVERSITY OF TUCUMAN IN THE ACADEMIC YEAR 2018.
NOWADAYS, COMPANIES AND ORGANIZATIONS REQUIRE HIGHLY COMPETITIVE PROFESSIONALS THAT HAVE THE NECESSARY SKILLS TO CONFRONT NEW CHALLENGES. HOWEVER, CURRENT EVALUATION TECHNIQUES DO NOT ALLOW DETECTION OF SKILLS THAT ARE VALUABLE IN THE WORK ENVIRONMENT, SUCH AS COLLABORATION, TEAMWORK, AND EFFECTIVE COMMUNICATION. MULTIMODAL LEARNING ANALYTICS IS A PROMINENT DISCIPLINE RELATED TO THE ANALYSIS OF SEVERAL MODALITIES OF NATURAL COMMUNICATION (E.G., SPEECH, WRITING, GESTURES, SIGHT) DURING EDUCATIONAL PROCESSES. THE MAIN AIM OF THIS WORK IS TO DEVELOP A COMPUTATIONAL ENVIRONMENT TO BOTH ANALYZE AND VISUALIZE STUDENT DISCUSSION GROUPS WORKING IN A COLLABORATIVE WAY TO ACCOMPLISH A TASK. RESPEAKER DEVICES WERE USED TO COLLECT SPEECH DATA FROM STUDENTS, AND THE COLLECTED DATA WERE MODELED BY USING INFLUENCE GRAPHS. THREE CENTRALITY MEASURES WERE DEFINED, NAMELY PERMANENCE, PERSISTENCE, AND PROMPTING, TO MEASURE THE ACTIVITY OF EACH STUDENT AND THE INFLUENCE EXERTED BETWEEN THEM. AS A PROOF OF CONCEPT, WE CARRIED OUT A CASE STUDY MADE UP OF 11 GROUPS OF UNDERGRADUATE STUDENTS THAT HAD TO SOLVE AN ENGINEERING PROBLEM WITH EVERYDAY MATERIALS. THUS, WE SHOW THAT OUR SYSTEM ALLOWS TO FIND AND VISUALIZE NONTRIVIAL INFORMATION REGARDING INTERRELATIONS BETWEEN SUBJECTS IN COLLABORATIVE WORKING GROUPS; MOREOVER, THIS INFORMATION CAN HELP TO SUPPORT COMPLEX DECISION-MAKING PROCESSES.
THIS ARTICLE ANSWERS THE FOLLOWING OBJECTIVES: TO ELABORATE A STATISTICAL MODEL OF TUTORS' ACTIONS IN LMS THAT INTERFERE IN STUDENTS' PERFORMANCE OF THE CHEMISTRY DEGREE COURSE, IN DISTANCE EDUCATION MODALITY, AT THE OPEN UNIVERSITY OF BRAZIL / CEARA STATE UNIVERSITY (UAB/UECE), BASED ON LEARNING ANALYTICS; AND TO KNOW ACTIONS THAT INTERFERE IN STUDENTS' PERFORMANCE FROM TUTORS' AND STUDENTS' PERSPECTIVE, COMPARING THE RESULT WITH STATISTICAL MODEL OBTAINED IN THE RESEARCH. FOR THIS, THE METHODOLOGY IS BASED ON PRAGMATIC PARADIGM AND MIXED APPROACH. IT ADOPTED THE STATISTICAL METHOD, USING BINARY LOGISTIC REGRESSION ANALYSIS, SUPPLEMENTED BY THE USE OF QUESTIONNAIRES WITH OPEN AND CLOSED ITEMS, APPLIED TO TUTORS AND STUDENTS. AT THE END, A PREDICTIVE MODEL WAS OBTAINED CONTAINING 11 ACTIONS DEVELOPED BY TUTORS, SEVEN OF WHICH INCREASE STUDENT APPROVAL PROBABILITY AND FOUR THAT SHOULD BE AVOIDED BY TUTORS OR WHOSE FREQUENCY SHOULD BE DECREASED BECAUSE THEY REDUCE APPROVAL PROBABILITY.
A DISTINCTIVE FEATURE OF GAME-BASED LEARNING ENVIRONMENTS IS THEIR CAPACITY TO CREATE LEARNING EXPERIENCES THAT ARE BOTH EFFECTIVE AND ENGAGING. RECENT ADVANCES IN SENSOR-BASED TECHNOLOGIES SUCH AS FACIAL EXPRESSION ANALYSIS AND GAZE TRACKING HAVE INTRODUCED THE OPPORTUNITY TO LEVERAGE MULTIMODAL DATA STREAMS FOR LEARNING ANALYTICS. LEARNING ANALYTICS INFORMED BY MULTIMODAL DATA CAPTURED DURING STUDENTS' INTERACTIONS WITH GAME-BASED LEARNING ENVIRONMENTS HOLD SIGNIFICANT PROMISE FOR DEVELOPING A DEEPER UNDERSTANDING OF GAME-BASED LEARNING, DESIGNING GAME-BASED LEARNING ENVIRONMENTS TO DETECT MALADAPTIVE BEHAVIORS AND INFORMING ADAPTIVE SCAFFOLDING TO SUPPORT INDIVIDUALIZED LEARNING. THIS PAPER INTRODUCES A MULTIMODAL LEARNING ANALYTICS APPROACH THAT INCORPORATES STUDENT GAMEPLAY, EYE TRACKING AND FACIAL EXPRESSION DATA TO PREDICT STUDENT POSTTEST PERFORMANCE AND INTEREST AFTER INTERACTING WITH A GAME-BASED LEARNING ENVIRONMENT,CRYSTAL ISLAND. WE INVESTIGATED THE DEGREE TO WHICH SEPARATE AND COMBINED MODALITIES (IE, GAMEPLAY, FACIAL EXPRESSIONS OF EMOTIONS AND EYE GAZE) CAPTURED FROM STUDENTS (N = 65) WERE PREDICTIVE OF STUDENT POSTTEST PERFORMANCE AND INTEREST AFTER INTERACTING WITHCRYSTAL ISLAND. RESULTS INDICATE THAT WHEN PREDICTING STUDENT POSTTEST PERFORMANCE AND INTEREST, MODELS UTILIZING MULTIMODAL DATA EITHER PERFORM EQUALLY WELL OR OUTPERFORM MODELS UTILIZING UNIMODAL DATA. WE DISCUSS THE SYNERGISTIC EFFECTS OF COMBINING MODALITIES FOR PREDICTING BOTH STUDENT INTEREST AND POSTTEST PERFORMANCE. THE FINDINGS SUGGEST THAT MULTIMODAL LEARNING ANALYTICS CAN ACCURATELY PREDICT STUDENTS' POSTTEST PERFORMANCE AND INTEREST DURING GAME-BASED LEARNING AND HOLD SIGNIFICANT POTENTIAL FOR GUIDING REAL-TIME ADAPTIVE SCAFFOLDING.
LEARNING ANALYTICS (LA) IN EDUCATIONAL GAMES IS CONSIDERED AN EMERGING PRACTICE DUE TO ITS POTENTIAL OF ENHANCING THE LEARNING PROCESS. GROWING RESEARCH ON FORMATIVE ASSESSMENT HAS SHED LIGHT ON THE WAYS IN WHICH STUDENTS' MEANINGFUL AND IN-SITU LEARNING EXPERIENCES CAN BE SUPPORTED THROUGH EDUCATIONAL GAMES. TO UNDERSTAND LEARNERS' PLAYFUL EXPERIENCES DURING GAMEPLAY, RESEARCHERS HAVE APPLIED LA, WHICH FOCUSES ON UNDERSTANDING STUDENTS' IN-GAME BEHAVIOUR TRAJECTORIES AND PERSONAL LEARNING NEEDS DURING PLAY. HOWEVER, THERE IS A LACK OF STUDIES EXPLORING HOW FURTHER RESEARCH ON LA IN EDUCATIONAL GAMES CAN BE CONDUCTED. ONLY A FEW ANALYSES HAVE DISCUSSED HOW LA HAS BEEN DESIGNED, INTEGRATED, AND IMPLEMENTED IN EDUCATIONAL GAMES. ACCORDINGLY, THIS SYSTEMATIC LITERATURE REVIEW EXAMINED HOW LA IN EDUCATIONAL GAMES HAS EVOLVED. THE STUDY FINDINGS SUGGEST THAT: (1) THERE IS AN INCREASING NEED TO CONSIDER FACTORS SUCH AS STUDENT MODELLING, ITERATIVE GAME DESIGN AND PERSONALISATION WHEN DESIGNING AND IMPLEMENTING LA THROUGH EDUCATIONAL GAMES; AND (2) THE USE OF LA CREATES SEVERAL CHALLENGES FROM TECHNICAL, DATA MANAGEMENT AND ETHICAL PERSPECTIVES. IN ADDITION TO OUTLINING THESE FINDINGS, THIS ARTICLE OFFERS IMPORTANT NOTES FOR PRACTITIONERS, AND DISCUSSES THE IMPLICATIONS OF THE STUDY'S RESULTS.
THE HETEROGENEITY OF EXTERNAL SYSTEMS THAT CAN BE CONNECTED IN AN E-LEARNING, ENVIRONMENT CAN IMPOSE INTEROPERABILITY AND PERFORMANCE REQUIREMENTS FOR RECORDING AND STORING THE LEARNING DATA. WEB-BASED PROTOCOLS HAVE BEEN DEVELOPED TO IMPROVE E-LEARNING SYSTEMS' INTEROPERABILITY AND CAPABILITY TO PERFORM MEANINGFUL ANALYTICS. THE PRESENT PAPER DESCRIBES A WEB-BASED LEARNING ENVIRONMENT AIMED AT TRAINING HOW TO COMMAND AND CONTROL UNMANNED AUTONOMOUS VEHICLES, PROVIDED WITH ANALYTIC CAPABILITIES. IT INTEGRATES AN EXTERNAL WEB CONTENT MANAGEMENT SYSTEM AND A SIMULATION ENGINE THAT PRESENT DIFFERENT PERFORMANCE REQUIREMENTS FOR RECORDING ALL SIGNIFICANT EVENTS THAT OCCUR DURING THE LEARNING PROCESS. ITS RECORD STORE CONSTRUCTION, BASED ON STANDARD INTEROPERABILITY PROTOCOLS, IS EXPLORED HERE FROM THE PERFORMANCE VIEWPOINT. THE TESTS THAT WERE CONDUCTED TO ASSESS REGULAR DATA STORES USED FOR LEARNING ANALYTICS SHOW THAT PERFORMANCE SHOULD NOT BE OVERLOOKED WHEN CONSTRUCTING AND DEPLOYING LEARNING ANALYTICS SYSTEMS. (C) 2016 ELSEVIER B.V. ALL RIGHTS RESERVED.
LEARNING VOCABULARY IS A CRUCIAL BUT CHALLENGING ACTIVITY FOR ENGLISH AS A FOREIGN LANGUAGE LEARNERS, AND COMPUTER-ASSISTED LANGUAGE LEARNING CAN FACILITATE THIS PROCESS. MOREOVER, ELEARNING IS ATTRACTING A GREAT DEAL OF ATTENTION AS A NEW TECHNOLOGY TO BRING EDUCATIONAL SUPPORT WHICH TRADITIONAL LEARNING SYSTEMS CANNOT PROVIDE. RECOMMENDER SYSTEMS AS ITS IMPLEMENTATION HAVE BEEN SUBJECT TO DISCUSSION. IN THIS STUDY, WE PROPOSE A COMPREHENSIVE LEARNING ANALYTICS-BASED PLATFORM FOR EFFICIENT VOCABULARY LEARNING, INCLUDING AN E-BOOK READER AND A BOOK/QUIZ RECOMMENDER. THE SYSTEM ON THIS PLATFORM ESTIMATES LEARNERS' KNOWLEDGE BASED ON THEIR ACTIVITIES AND BRINGS PERSONALIZED RECOMMENDATION AND ITS BASES TO THE LEARNERS. ALSO, THIS PLATFORM PROVIDES TEACHERS WITH VISUALIZED FEEDBACK REGARDING THE RECOMMENDATION AND STUDENTS' ENGAGEMENT IN LEARNING.
IN THIS PAPER LOG DATA ON E-BOOK USAGE IS USED AS PART OF A LEARNING ANALYTICS APPROACH TO GENERATE USER MODELS WHICH DESCRIBE UNIVERSITY STUDENTS' CHARACTERISTICS IN MULTIPLE DIMENSIONS. E-BOOK USAGE IS LOGGED AND ANALYSED TO EXTRACT INFORMATION ON HOW USERS USE E-BOOKS FOR ACADEMIC PURPOSES. TWO CASES CONTRIBUTING TO USER MODELLING ARE PRESENTED: GENERATING USER ENGAGEMENT AND USER READING STYLE VARIABLES DESCRIBING STUDENTS' E-BOOK USAGE. THE PAPER DISCUSSES THE APPROACH TO THE VARIABLES EXTRACTION FROM DATA LOGS, DISCUSSES THE RELATIONSHIP BETWEEN THE VARIABLES AND PROPOSES A USER MODELLING APPROACH SPECIFICALLY TARGETED AT BUILDING HIGH-LEVEL SERVICES RELATED TO EDUCATIONAL E-BOOKS. THE STUDY EXAMINES HOW CAN USER MODEL VARIABLES FROM DIFFERENT DIMENSIONS BE USED IN GETTING A HIGHER-LEVEL OVERVIEW OF USER PERFORMANCE AND SKILLS; AND PROPOSES SERVICES WHICH LEVERAGE THE VARIABLES OF LEARNER ENGAGEMENT AND READING STYLES.
THE SKILL TO TAKE PART IN AND TO CONTRIBUTE TO DEBATES IS IMPORTANT FOR INFORMAL AND FORMAL LEARNING. ESPECIALLY WHEN ADDRESSING HIGHLY COMPLEX ISSUES, IT CAN BE DIFFICULT TO SUPPORT LEARNERS PARTICIPATING IN EFFECTIVE GROUP DISCUSSION, AND TO STAY ABREAST OF ALL THE INFORMATION COLLECTIVELY GENERATED DURING THE DISCUSSION. TECHNOLOGY CAN HELP WITH THE ENGAGEMENT AND SENSEMAKING OF SUCH LARGE DEBATES, FOR EXAMPLE, IT CAN MONITOR HOW HEALTHY A DEBATE IS AND PROVIDE INDICATORS OF PARTICIPATION'S DISTRIBUTION. A SPECIAL FRAMEWORK THAT AIMS AT HARNESSING THE INTELLIGENCE OF - SMALL TO VERY LARGE - GROUPS WITH THE SUPPORT OF STRUCTURED DISCOURSE AND ARGUMENTATION TOOLS IS CONTESTED COLLECTIVE INTELLIGENCE (CCI). CCI TOOLS PROVIDE A RICH SOURCE OF SEMANTIC DATA THAT, IF APPROPRIATELY PROCESSED, CAN GENERATE POWERFUL ANALYTICS OF THE ONLINE DISCOURSE. THIS STUDY PRESENTS A VISUALISATION DASHBOARD WITH SEVERAL VISUAL ANALYTICS THAT SHOW IMPORTANT ASPECTS OF ONLINE DEBATES THAT HAVE BEEN FACILITATED BY CCI DISCUSSION TOOLS. THE DASHBOARD WAS DESIGNED TO IMPROVE SENSEMAKING AND PARTICIPATION IN ONLINE DEBATES AND HAS BEEN EVALUATED WITH TWO STUDIES, A LAB EXPERIMENT AND A FIELD STUDY IN THE CONTEXT OF TWO HIGHER EDUCATION INSTITUTES. THE PAPER REPORTS FINDINGS OF A USABILITY EVALUATION OF THE VISUALISATION DASHBOARD. THE DESCRIPTIVE FINDINGS SUGGEST THAT PARTICIPANTS WITH LITTLE EXPERIENCE IN USING ANALYTICS VISUALISATIONS WERE ABLE TO PERFORM WELL ON GIVEN TASKS. THIS CONSTITUTES A PROMISING RESULT FOR THE APPLICATION OF SUCH VISUALISATION TECHNOLOGIES AS DISCOURSE-CENTRIC LEARNING ANALYTICS INTERFACES CAN HELP TO SUPPORT LEARNERS' ENGAGEMENT AND SENSEMAKING OF COMPLEX ONLINE DEBATES.
KNOWLEDGE-BASED SYSTEMS ARE MAJOR CONCERNS IN THE FIELD OF ARTIFICIAL INTELLIGENCE FOR THE DEVELOPMENT OF CYBER-PHYSICAL SYSTEMS CAPABLE OF SELF-MANAGEMENT AND ADAPTATION TO THEIR CONTEXT. THE REPRESENTATION AND KNOWLEDGE MANAGEMENT OF THESE CYBER-PHYSICAL SYSTEMS INTEGRATING HETEROGENEOUS ACTORS MUST ENSURE THE EMPOWERMENT AND OPTIMIZATION OF THESE SYSTEMS, AS WELL AS THEIR ABILITY TO ADAPT TO DYNAMIC AND UNPREDICTABLE CHANGES IN THEIR ENVIRONMENT. IN THIS DOCUMENT WE SHOW HOW A KNOWLEDGE-BASED SYSTEM BASED ON SEMANTIC WEB TECHNOLOGIES AND IBM'S REFERENCE MODEL OF AUTONOMIC COMPUTING (AC) CAN OFFER INTELLIGENT COLLABORATION AND COORDINATION BETWEEN PEOPLE, DATA, SERVICES, ROBOTS AND CONNECTED OBJECTS IN THE IMPLEMENTATION OF SELF-MANAGEMENT PROCESSES IN CYBER-PHYSICAL SYSTEMS. OUR SOLUTION CONSISTS TO DESIGN A KNOWLEDGE BASE IN THE FIELD OF LEARNING ANALYTICS (LAS) INVOLVING A COMPLEX RANGE OF KNOWLEDGE AND HETEROGENEOUS COMPONENTS. THIS ONTOLOGICAL KNOWLEDGE BASE IS GUIDED BY A FUNCTIONAL DECOMPOSITION APPROACH BASED ON THE OPERATING PRINCIPLE OF THE MAPE-K (MONITOR-ANALYZE-PLAN-EXECUTE AND KNOWLEDGE) AUTONOMOUS CONTROL LOOP TO PROVIDE THE SYSTEM WITH SELF-MANAGEMENT CAPABILITIES.
EVALUATING THE SUCCESS OF LEARNING TECHNOLOGIES WITH RESPECT TO IMPROVEMENT IN THE LEARNERS' ABILITIES AND KNOWLEDGE IS NOT AN EASY TASK. THE PROBLEM IS FORMED BY THE EXISTENCE OF MANY DIFFERENT DEFINITIONS WITH DIFFERENT PERSPECTIVES LIKE GRADES ON THE ONE HAND AND WORKPLACE PERFORMANCE ON THE OTHER. THIS PAPER REVIEWS DEFINITIONS FROM THE LITERATURE WITH THE AIM TO FIND A SUITABLE DEFINITION FOR THE EVALUATION OF LEARNING SUCCESS IN SPACED REPETITION BASED MOBILE LEARNING FOR KNOWLEDGE IMPROVEMENT. IT ALSO BORROWS APPROACHES FROM LEARNING ANALYTICS TO TACKLE THE FACT THAT LEARNER GROUPS ARE HETEROGENEOUS WHICH LEADS TO THE NEED OF ANALYZING LEARNING SUCCESS DIFFERENTLY IN DIFFERENT GROUPS OF LEARNERS.
THE PAPER IS AIMED TO ANALYSE APPLICATION OF LEARNING ANALYTICS (LA) METHODS TO ENHANCE LEARNING QUALITY AND EFFECTIVENESS IN VIRTUAL LEARNING ENVIRONMENTS (VLES) BY THE MEANS OF LEARNING PERSONALISATION. SINCE STUDY AND LEARNING PROCESSES SIGNIFICANTLY MOVED TO VIRTUAL ENVIRONMENT, IT IS VERY IMPORTANT TO ANALYSE WHAT IS STATE-OF-THE-ART IN THE AREA. THEREFORE, FIRST OF ALL, SYSTEMATIC REVIEW HAS BEEN PERFORMED. THE DATA COLLECTED FROM VLES CAN BE AGGREGATED OVER LARGE NUMBERS OF STUDENTS AND CAN CONTAIN MANY VARIABLES THAT LA / EDUCATIONAL DATA MINING (EDM) ALGORITHMS CAN EXPLORE FOR MODEL BUILDING. RESEARCH PRESENTED IN THE PAPER HAS ALSO SHOWN THAT THESE LA METHODS AND TECHNIQUES COULD BE SUCCESSFULLY USED TO ENHANCE LEARNING QUALITY AND EFFECTIVENESS BY THE MEANS OF LEARNING PERSONALISATION IN VLES. IN THE PAPER, FIVE-STAGE METHODOLOGY IS PRESENTED AIMED AT ENHANCING STUDENTS' LEARNING QUALITY AND EFFECTIVENESS.
A SMART LEARNING ENVIRONMENT (SLE) IS CHARACTERIZED BY THE KEY PROVISION OF PERSONALIZED LEARNING EXPERIENCES. TO APPROACH DIFFERENT DEGREES OF PERSONALIZATION IN ONLINE LEARNING, THIS PAPER INTRODUCES A FRAMEWORK CALLED SCALE THAT TRACKS FINER LEVEL LEARNING EXPERIENCES AND TRANSLATES THEM INTO OPPORTUNITIES FOR CUSTOM FEEDBACK. A PROTOTYPE VERSION OF THE SCALE SYSTEM HAS BEEN USED IN A STUDY TO TRACK THE HABITS OF NOVICE PROGRAMMERS. GROWTH OF CODING COMPETENCIES OF FIRST YEAR ENGINEERING STUDENTS HAS BEEN CAPTURED IN A CONTINUOUS MANNER. STUDENTS HAVE BEEN PROVIDED WITH CUSTOMIZED FEEDBACK TO OPTIMIZE THEIR LEARNING PATH IN PROGRAMMING. THIS PAPER DESCRIBES KEY ASPECTS OF OUR RESEARCH WITH THE SCALE SYSTEM AND HIGHLIGHTS RESULTS OF THE STUDY.
THE PURPOSE OF FINS STUDY IS TO SUGGEST MORE MEANINGFUL COMPONENTS FOR LEARNING ANALYTICS IN ORDER TO HELP LEARNERS TO IMPROVE THEIR LEARNING ACHIEVEMENT CONTINUOUSLY THROUGH AN EDUCATIONAL TECHNOLOGY APPROACH. 41 UNDERGRADUATE STUDENTS IN A WOMEN'S UNIVERSITY IN SOUTH KOREA PARTICIPATED IN THIS STUDY. THE SEVEN-PREDICTOR MODEL WAS ABLE TO ACCOUNT FOR 99.3% OF THE VARIANCE IN THE FINAL GRADE, F-(8,F- (32)) = 547.424, P < .001, R-2 = .993. TOTAL LOGIN FREQUENCY IN LMS, (IR)REGULARITY OF LEARNING INTERVAL IN LMS, AND TOTAL ASSIGNMENTS AND ASSESSMENT COMPOSITES HAD A SIGNIFICANT (P < .05) CORRELATION WITH FINAL GRADES. HOWEVER, TOTAL STUDYING TIME IN LMS (SS=.038, T=.868, P > .05), INTERACTIONS WITH CONTENT (SS=-.004, T=-.240, P > .05), INTERACTIONS WITH PEERS (SS=.015, T=.766, P > .05), AND INTERACTIONS WITH INSTRUCTOR (SS=.009, T=.354, P > .05) DID NOT PREDICT FINAL GRADES. THE RESULTS PROVIDE A RATIONALE FOR THE TREATMENT FOR STUDENT TIME MANAGEMENT EFFORT.
THE LEARNING ANALYTICS DASHBOARD (LAD) IS A NEWLY DEVELOPED LEARNING SUPPORT TOOL FOR VIRTUAL CLASSROOMS THAT IS BELIEVED TO ALLOW STUDENTS TO REVIEW THEIR ONLINE LEARNING BEHAVIOR PATTERNS INTUITIVELY THROUGH THE PROVISION OF VISUAL INFORMATION. THE PURPOSE OF THIS STUDY WAS TO EMPIRICALLY VALIDATE THE EFFECTS OF LAD. AN EXPERIMENTAL STUDY WAS CONDUCTED WITH A DASHBOARD TREATMENT GROUP AND A CONTROL GROUP. THE RESEARCHERS DEVELOPED A LAD AND EVALUATED ITS EFFECTIVENESS ON THE SAMPLE OF 151 COLLEGE STUDENTS AT A PRIVATE UNIVERSITY LOCATED IN KOREA, WHO WERE TAKING THE ONLINE COURSE TITLED "MANAGEMENT STATISTICS" IN THE FIRST SEMESTER OF 2014. THE FOLLOWING RESULTS WERE OBTAINED. FIRST, THE STUDENTS WHO RECEIVED DASHBOARD TREATMENT PRESENTED HIGHER FINAL SCORE THAN THOSE WHO DID NOT. SECOND, THE DASHBOARD USAGE FREQUENCY, AS MEASURED BY THE NUMBER OF TIMES THE DASHBOARD WAS OPENED, DID NOT HAVE A SIGNIFICANT IMPACT ON LEARNING ACHIEVEMENT. HOWEVER, A SLIGHTLY POSITIVE CORRELATION BETWEEN SATISFACTION WITH LAD AND LEARNING ACHIEVEMENT WAS OBSERVED. FURTHER ANALYSIS INDICATED THAT LEARNERS WHO USED THE DASHBOARD ONLY A FEW TIMES SHOWED RELATIVELY HIGH SATISFACTION WITH LAD. ON THE OTHER HAND, HIGH ACADEMIC ACHIEVERS WHO OPENED LAD RELATIVELY FREQUENTLY SHOWED LOWER SATISFACTION WITH DASHBOARD. THE RESULTS GUIDE THAT LAD SHOULD BE REVISED IN A WAY TO MOTIVATE LEARNERS CONSISTENTLY AND SUPPORT LEARNERS WHO HAVE DIFFERENT ACADEMIC ACHIEVEMENT LEVELS. THE STUDY DISCUSSES THE FURTHER RESEARCH TASKS IN TERMS OF LAD DEVELOPMENT AS AN EFFECTIVE AND PERSONALIZED FEEDBACK TOOL TO IMPROVE LEARNERS' ACADEMIC ACHIEVEMENT.
ONLINE EDUCATION IS GROWING BUT FACING A PROBLEM OF HIGH ACADEMIC FAILURE RATES. IN SELF-PACED ONLINE LEARNING (SPOL), THE LACK OF ACADEMIC SUPPORT - SOCIAL INTERACTION, FORMATIVE FEEDBACK, LEARNING AWARENESS, AND ACADEMIC INTERVENTION - IS RECOGNIZED AS A CRITICAL FACTOR CAUSING THE ACADEMIC FAILURE PROBLEM. TO FACILITATE SUCH ACADEMIC SUPPORT, THIS STUDY HAS IDENTIFIED THREE RELEVANT TECHNICAL AND PEDAGOGICAL STRATEGIES (FORMATIVE ASSESSMENT, ADAPTIVE ASSESSMENT AND LEARNING ANALYTICS) THAT COULD WORK TOGETHER AS A POSSIBLE SOLUTION. DESIGN-BASED RESEARCH IS CONSIDERED FOR THIS STUDY TO INVESTIGATE THE EFFECTIVENESS OF THIS SOLUTION IN THE CONTEXT OF STEM DISCIPLINES OF FORMAL HIGHER ONLINE EDUCATION. A COMPUTING COURSE IS SELECTED FOR A CASE STUDY. THE DESIGN PRINCIPLES OF THE ADAPTIVE ASSESSMENT MODEL AND THE INTERVENTION LEARNING ANALYTICS MODEL ARE EXPLAINED. ALSO, THE EXPECTED CONTRIBUTIONS ARE SUMMARIZED AT THE END.
NOWADAYS LEARNING ANALYTICS HAS BEEN GROWING AS A SCIENCE, AND AT THE UNIVERSITY OF TURIN WE ARE INTERESTED IN ITS POTENTIAL TO ENHANCE BOTH THE TEACHING AND THE LEARNING EXPERIENCE. IN THE LAST FEW YEARS WE HAVE GATHERED DATA FROM TWO PROJECTS: ORIENT@MENTE, AND START@UNITO, WITH THE LATTER OFFERING OPEN ONLINE UNIVERSITY COURSES IN VARIOUS DISCIPLINES. IN ADDITION, WE HAVE ALSO STUDIED AND ANALYSED THE RESULTS OF THE TEACHER TRAINING EXPERIENCE CARRIED OUT FOR THE START@UNITO PROJECT, AS WELL AS THOSE OBTAINED FROM A SURVEY INVOLVING SECONDARY SCHOOL TEACHERS AND THE POSSIBLE EMPLOYMENT OF THE START@UNITO OERS IN THEIR EVERYDAY TEACHING. OUR SOURCES OF DATA ARE STUDENTS' ACTIVITY ONLINE, THE RESULTS OF FORMATIVE AUTOMATIC ASSESSMENT, AND THE QUESTIONNAIRES GIVEN TO THE LEARNERS; THE TYPES OF QUESTIONS RANGE FROM LIKERT SCALE EVALUATIONS TO MULTIPLE CHOICE, YES/NO AND A FEW OPEN QUESTIONS. IN THIS PAPER WE DISCUSS THE DIFFERENT TASKS WE COMPLETED IN OUR PROJECTS AND EVALUATE THEIR ADHERENCE WITH THE LEARNING ANALYTICS TECHNIQUES IN TERMS OF STRUCTURE, AVAILABILITY, STATISTICS, OUTCOMES, INTERVENTIONS AND, IN GENERAL, THEIR USEFULNESS AND EFFECTIVENESS. IN THIS WAY, THE INSIGHTS GAINED FROM BOTH USAGE TRACKING AND QUESTIONNAIRES CAN BE USED WHENEVER POSSIBLE TO MAKE INTERVENTIONS TO IMPROVE THE TEACHING AND THE LEARNING EXPERIENCE; AT THE SAME TIME, WHEN SUCH INTERVENTIONS WERE NOT POSSIBLE, WE REFLECTED ON WHY THIS HAPPENED AND HOW WE CAN CHANGE AND IMPROVE OUR APPROACH.
LEARNING ANALYTICS (LA) TOOLS PROMISE TO IMPROVE STUDENT LEARNING AND RETENTION. HOWEVER, ADOPTION AND USE OF LA TOOLS IN HIGHER EDUCATION IS OFTEN UNEVEN. IN THIS CASE STUDY, PART OF A LARGER EXPLORATORY RESEARCH PROJECT, WE INTERVIEWED AND OBSERVED 32 FACULTY AND ADVISORS AT A PUBLIC RESEARCH UNIVERSITY TO UNDERSTAND THE TECHNOLOGICAL INCENTIVES AND BARRIERS RELATED TO LA TOOL ADOPTION AND USE. FINDINGS INDICATE THAT LACK OF A TRUSTWORTHY TECHNOLOGICAL INFRASTRUCTURE, MISALIGNMENT BETWEEN LA TOOL CAPABILITIES AND USER NEEDS, AND THE EXISTENCE OF ETHICAL CONCERNS ABOUT THE DATA, VISUALIZATIONS, AND ALGORITHMS THAT UNDERLIE LA TOOLS CREATED BARRIERS TO ADOPTION. IMPROVING TOOL INTEGRATION, CLARITY, AND ACCURACY, SOLICITING THE TECHNOLOGICAL NEEDS AND PERSPECTIVES OF LA TOOL USERS, AND PROVIDING DATA CONTEXT MAY ENCOURAGE INCLUSION OF THESE TOOLS INTO TEACHING AND ADVISING PRACTICE.
STUDIES IN LEARNING ANALYTICS (LA) HAVE GARNERED POSITIVE FINDINGS ON LEARNING IMPROVEMENT AND ADVANTAGES FOR INFORMING COURSE DESIGN. HOWEVER, LITTLE IS KNOWN ABOUT INSTRUCTIONAL DESIGNERS' PERCEPTION AND THEIR CURRENT STATE OF LA-RELATED ADOPTION. THIS QUALITATIVE STUDY EXPLORES THE PERCEPTION OF INSTRUCTIONAL DESIGNERS IN HIGHER EDUCATION REGARDING FACTORS INFLUENCING THEIR INTENT AND ACTUAL PRACTICE OF LA APPROACH IN COURSE DESIGN PRACTICE, BASED ON ANALYSIS OF MULTIPLE STRATEGIES SUCH AS FOCUS GROUP, INDIVIDUAL, AND EMAIL INTERVIEWS. MOST INSTRUCTIONAL DESIGNERS ADMITTED LA HAD GREAT POTENTIAL, BUT ADOPTION WAS LIMITED. THEIR PERCEPTION, INTENTION, AND THE CURRENT STATE OF ADOPTION ARE AFFECTED BY INDIVIDUAL DIFFERENCES, SYSTEM CHARACTERISTICS, SOCIAL INFLUENCE, AND FACILITATING CONDITIONS. FINDINGS HAVE IMPERATIVE IMPLICATIONS FOR PROMOTING EFFECTIVE IMPLEMENTATION OF LA APPROACH IN HIGHER EDUCATION.
LEARNING ANALYTICS EMPOWERED EDUCATIONAL TECHNOLOGIES (LA-ET) IN PRIMARY CLASSROOMS ALLOW FOR BLENDED LEARNING SCENARIOS WITH TEACHER-LEAD INSTRUCTIONS, CLASS-PACED AND INDIVIDUALLY-PACED PRACTICE. THIS QUASI-EXPERIMENTAL STUDY INVESTIGATES THE EFFECTS OF A LA-ET ON THE DEVELOPMENT OF STUDENTS' ARITHMETIC SKILLS OVER ONE SCHOOLYEAR. CHILDREN LEARNING IN A TRADITIONAL PAPER & PENCIL CONDITION WERE COMPARED TO LEARNERS USING A LA-ET ON TABLET COMPUTERS IN GRADE 4. THE EDUCATIONAL TECHNOLOGY COMBINED TEACHER DASHBOARDS (EXTRACTED ANALYTICS) AND CLASS AND INDIVIDUALLY PACED ASSIGNMENTS (EMBEDDED ANALYTICS). THE RESULTS INDICATED THAT CHILDREN IN THE LA-ET CONDITION MADE SIGNIFICANTLY MORE PROGRESS ON ARITHMETIC SKILLS IN ONE SCHOOLYEAR COMPARED TO CHILDREN IN THE PAPER & PENCIL CONDITION.
WHILE THERE IS NOW EXTENSIVE RESEARCH ON FEEDBACK IN THE CONTEXT OF HIGHER EDUCATION, INCLUDING PRE-SERVICE TEACHER EDUCATION, LITTLE HAS BEEN REPORTED REGARDING THE USE OF FEEDBACK FROM TEACHERS TO OTHER TEACHERS. MOREOVER, LITERATURE ON THE POTENTIAL ADVANTAGES THAT THE USE OF TECHNOLOGY, FOR EXAMPLE ELECTRONIC PORTFOLIOS AND LEARNING ANALYTICS, HAS IN IMPROVING FEEDBACK IN THE IN-SERVICE WORKPLACE PRACTICES, IS ALSO SPARSE. THEREFORE, THE AIM OF THIS EXPLORATORY CASE STUDY WAS TO EXPLORE HOW IN-SERVICE TEACHERS PERCEIVED THE PEER FEEDBACK THEY RECEIVED AND PROVIDED THROUGH A WEB-BASED ELECTRONIC PORTFOLIO DURING A PROFESSIONAL DEVELOPMENT COURSE CARRIED OUT IN THEIR WORKPLACE. QUESTIONNAIRE AND INTERVIEW DATA WERE COLLECTED FROM 38 TEACHERS WHO RECEIVED FEEDBACK THROUGH A LEARNING ANALYTICS ENHANCED ELECTRONIC PORTFOLIO AND FROM 23 TEACHERS WHO RECEIVED FEEDBACK ONLY BY THE ELECTRONIC PORTFOLIO. ADDITIONALLY, ONE INDIVIDUAL AND FOUR FOCUS GROUP INTERVIEWS WERE CONDUCTED WITH 15 TEACHERS WHO WERE THE FEEDBACK PROVIDERS. SEVERAL COMMON TOPICS WERE IDENTIFIED IN THE INTERVIEWS WITH THE FEEDBACK RECEIVERS AND PROVIDERS, INVOLVING THE BENEFITS AND CHALLENGES OF HUMAN INTERACTION AND THE FLEXIBILITY OF THE FEEDBACK PROCESS THAT THE ELECTRONIC PORTFOLIO OFFERED. THE RESULTS ALSO REVEALED BETTER FEEDBACK EXPERIENCE WITHIN THE GROUP OF TEACHERS WHO RECEIVED EXTRA FEEDBACK BY MEANS OF LEARNING ANALYTICS. IT IS CONCLUDED THAT ALTHOUGH AN ELECTRONIC PORTFOLIO PROVIDES A USEFUL TOOL IN TERMS OF FLEXIBILITY IN THE PROVISION AND RECEIPT OF FEEDBACK, THE NEED FOR HUMAN INTERACTION WAS ACKNOWLEDGED.
MOOC AIMS AT DELIVERING ONLINE COURSES TO TENS OF THOUSANDS TO MILLIONS OF HETEROGENEOUS LEARNERS AT THE SAME TIME, WITH MINIMAL OR NO CHARGE. IT PROVIDES AN ALTERNATE WAY TO DISSEMINATE QUALITY EDUCATION TO THE SECTION OF PEOPLE WHO CANNOT REACH PREMIER INSTITUTIONS. IT HAS GREAT POTENTIAL TO OVERCOME THE BARRIERS OF TRADITIONAL LEARNING SYSTEMS. HOWEVER, THERE ARE SEVERAL CHALLENGES IN MOOCS SUCH AS HUGE DROP-OUT RATES, IMPROPER AUTOMATED ASSESSMENTS, VARIED STUDENT ENGAGEMENT, AND ATTENTION ETC. LEARNING ANALYTICS HELPS US TO CONTAIN SUCH ISSUES. LEARNING ANALYTICS, WITH THE HELP OF BIG DATA TECHNOLOGIES, HELPS US TO INTERPRET HUMONGOUS MOOCS DATA TO ASSESS PROGRESS, PREDICT PERFORMANCE AND IDENTIFY PROBLEMS. TO PERFORM ANALYTICS, WE DEVELOPED A WORKFLOW USING APACHE SPARK, A SCALABLE IN-MEMORY COMPUTING FRAMEWORK. THE DATA FROM EDX PLATFORM HAS BEEN USED FOR EXPERIMENTS. IT CONTAINS THE INFORMATION OF MORE THAN 2 LAKH STUDENTS FROM 39 COURSES. INITIALLY, DETAILED STATISTICAL ANALYSIS HAS BEEN CARRIED OUT TO UNDERSTAND THE LEARNING PATTERNS AND THE BEHAVIOR OF ONLINE LEARNERS. LATER, WE HAVE DEVELOPED DROP-OUT PREDICTION MODELS USING VARIOUS MACHINE LEARNING ALGORITHMS SUCH AS RANDOM FOREST, GRADIENT BOOST, AND LOGISTIC REGRESSION. A STACKED ENSEMBLE MODEL IS DEVELOPED AND PERFORMANCE COMPARISON WITH BASELINE MODELS IS CARRIED OUT. IT OUTPERFORMED ALL OTHER MODELS WITH AN ACCURACY OF 91.2%.
ENGINEERING DESIGN PLAYS AN IMPORTANT ROLE IN EDUCATION. HOWEVER, DUE TO ITS OPEN NATURE AND COMPLEXITY, PROVIDING TIMELY SUPPORT TO STUDENTS HAS BEEN CHALLENGING USING THE TRADITIONAL ASSESSMENT METHODS. THIS STUDY TAKES AN INITIAL STEP TO EMPLOY LEARNING ANALYTICS TO BUILD PERFORMANCE PREDICTION MODELS TO HELP STRUGGLING STUDENTS. IT ALLOWS INSTRUCTORS TO OFFER IN-TIME INTERVENTION AND SUPPORT FOR THESE AT-RISK STUDENTS. SPECIFICALLY, WE DEVELOP A TASK MODEL TO CHARACTERIZE THE ENGINEERING DESIGN PROCESS SO THAT THE DATA FEATURES CAN BE ASSOCIATED WITH THE ABSTRACT ENGINEERING DESIGN PHASES. A TWO-STAGE FEATURE SELECTION METHOD IS PROPOSED TO ADDRESS THE DATA SPARSITY AND HIGH DIMENSIONALITY PROBLEMS. THEN, INSTEAD OF RELYING ON PROBABILITY-BASED ALGORITHMS SUCH AS BAYESIAN NETWORKS TO REPRESENT THE TASK MODEL, THIS STUDY USED THE RADIAL BASIS FUNCTION BASED SUPPORT VECTOR MACHINES FOR PREDICTION TO IDENTIFY THE STRUGGLING STUDENTS. NEXT, WE EMPLOY AN EXTRA-TREE CLASSIFICATION METHOD TO RANK THE IMPORTANCE OF THESE FEATURES. TEACHERS CAN INTEGRATE THE FEATURE IMPORTANCE RANKING WITH THE ABSTRACT TASK MODEL TO DIAGNOSE STUDENTS' PROBLEMS FOR SCAFFOLDING DESIGN. THE RESULTS SHOW THAT THE PROPOSED APPROACH CAN OUTPERFORM THE BASELINE MODELS AS WELL AS PROVIDING ACTIONABLE INSIGHTS FOR TEACHERS TO PROVIDE PERSONALIZED AND TIMELY FEEDBACK TO STUDENTS. IMPLICATIONS OF THIS STUDY FOR RESEARCH AND PRACTICE ARE THEN DISCUSSED.
PERSONALIZED LEARNING IS ONE OF THE MAIN FOCUSES IN 21ST-CENTURY EDUCATION, AND LEARNING ANALYTICS (LA) HAS BEEN RECOGNIZED AS A SUPPORTIVE TOOL FOR ENHANCING PERSONALIZATION. MEANWHILE, THE GENERAL DATA PROTECTION REGULATIONS (GDPR), WHICH CONCERN THE PROTECTION OF PERSONAL DATA, CAME INTO EFFECT IN 2018. HOWEVER, CONTEMPORARY RESEARCH LACKS THE ESSENTIAL KNOWLEDGE OF HOW AND IN WHICH WAYS THE PRESENCE OF GDPR INFLUENCE LA RESEARCH AND PRACTICES. HENCE, THIS STUDY INTENDS TO EXAMINE THE REQUIREMENTS FOR SUSTAINING LA UNDER THE LIGHT OF GDPR. ACCORDING TO THE STUDY OUTCOMES, THE LEGAL OBLIGATIONS FOR LA COULD BE SIMPLIFIED TO DATA ANONYMIZATION WITH CONSEQUENCES OF LIMITATIONS TO PERSONALIZED INTERVENTIONS, ONE OF THE POWERS OF LA. EXPLICIT CONSENT FROM THE DATA SUBJECTS (STUDENTS) PRIOR TO ANY DATA PROCESSING IS MANDATORY UNDER GDPR. THE CONSENT AGREEMENTS MUST INCLUDE THE PURPOSE, TYPES OF DATA, AND HOW, WHEN AND WHERE THE DATA IS PROCESSED. MOREOVER, TRANSPARENCY OF THE COMPLETE PROCESS OF STORING, RETRIEVING, AND ANALYSING DATA AS WELL AS HOW THE RESULTS ARE USED SHOULD BE EXPLICITLY DOCUMENTED IN LA APPLICATIONS. THE NEED FOR ACADEMIC INSTITUTIONS TO HAVE SPECIFIC REGULATIONS FOR SUPPORTING LA IS EMPHASIZED. REGULATIONS FOR SHARING DATA WITH THIRD PARTIES IS LEFT AS A FURTHER EXTENSION OF THIS STUDY.</P>
THE GROWTH OF LEARNING ANALYTICS AS A MEANS TO IMPROVE STUDENT LEARNING OUTCOMES MEANS THAT STUDENT DATA IS BEING COLLECTED, ANALYZED, AND APPLIED IN PREVIOUSLY UNFORESEEN WAYS. AS THE USE OF THIS DATA CONTINUES TO SHAPE ACADEMIC AND SUPPORT INTERVENTIONS, THERE IS INCREASING NEED FOR ETHICAL REFLECTION ON OPERATIONAL APPROVALS FOR LEARNING ANALYTICS RESEARCH. THOUGH THERE ARE CLEAR PROCESSES FOR VETTING STUDIES RESULTING IN PUBLICATION OF STUDENT-GATHERED DATA, THERE IS LITTLE COMPARABLE OVERSIGHT OF INTERNALLY GENERATED STUDENT-FOCUSED RESEARCH. INCREASINGLY, ETHICAL CONCERNS ABOUT THE COLLECTION AND HARVESTING OF STUDENT DATA HAVE BEEN RAISED, BUT THERE IS NO CLEAR INDICATION HOW TO ADDRESS OR OVERSEE THESE ETHICAL CONCERNS. IN ADDITION, STAFF MEMBERS WHO ARE NOT TYPICAL RESEARCHERS MAY BE LESS FAMILIAR WITH APPROVALS PROCESSES AND THE NEED TO DEMONSTRATE POTENTIAL FOR HARM, ETC. IF CURRENT TRENDS POINT TO A RANGE OF INDIVIDUALS HARVESTING AND ANALYZING STUDENT DATA (MOSTLY WITHOUT STUDENTS' INFORMED CONSENT OR KNOWLEDGE), HOW CAN THE REAL DANGER OF UNETHICAL BEHAVIOR BE CURBED TO MITIGATE THE RISK OF UNINTENDED CONSEQUENCES? A SYSTEMATIC APPRAISAL OF THE POLICY FRAMEWORKS AND PROCESSES OF ETHICAL REVIEW AT THREE RESEARCH INSTITUTIONS (NAMELY, THE UNIVERSITY OF SOUTH AFRICA, THE OPEN UNIVERSITY IN THE UNITED KINGDOM, AND INDIANA UNIVERSITY IN THE UNITED STATES) PROVIDES AN OPPORTUNITY TO COMPARE PRACTICES, VALUES, AND PRIORITIES. FROM THIS CROSS-INSTITUTIONAL REVIEW, A WORKING TYPOLOGY OF ETHICAL APPROACHES IS SUGGESTED WITHIN THE SCOPE OF DETERMINING THE MORAL INTERSECTION OF INTERNAL STUDENT DATA USAGE AND APPLICATION.
COLLABORATIVE LEARNING FLOW PATTERNS (CLFPS) ENCODE SOLUTIONS TO RECURRENT PEDAGOGICAL PROBLEMS, WHICH HAVE BEEN SUCCESSFULLY APPLIED TO THE DESIGN OF LEARNING EXPERIENCES. HOWEVER, THE PEDAGOGICAL KNOWLEDGE ENCODED IN THESE PATTERNS HAS SELDOM BEEN EXPLOITED IN LEARNING ANALYTICS (LA). THIS PAPER ANALYZES FOUR OF THE MOST COMMON CLFPS TO EXTRACT THE INTRINSIC CONSTRAINTS THAT LEAD TO A SUCCESSFUL COLLABORATIVE LEARNING ACTIVITY, AND USE THEM TO ENHANCE EXISTING LA SOLUTIONS. TO UNDERSTAND THE ADDED VALUE OF APPLYING SUCH CODIFIED KNOWLEDGE IN LA, WE PRESENT EVIDENCE FROM FIVE AUTHENTIC CASE STUDIES IN WHICH SUCH CONSTRAINTS AIDED UNIVERSITY TEACHERS IN MONITORING COMPLEX COLLABORATIVE SCRIPTS. THE RESULTS NOT ONLY ILLUSTRATE QUANTITATIVELY SUCH ADDED VALUE BUT ALSO UNEARTH QUALITATIVE BENEFITS, SUCH AS RAISING PRACTITIONERS AWARENESS ABOUT HOW THE CURRENT STATE OF ACTIVITIES MAY AFFECT FUTURE PHASES OF THE SCRIPT.
SINCE COMPLEX PROBLEM SOLVING (CPS) SKILLS REPRESENT A KEY COMPETENCE FOR EDUCATIONAL SUCCESS, THEY ARE OF GREAT RELEVANCE FOR LEARNING ANALYTICS. MORE SPECIFICALLY, CPS SERVES AS A PERTINENT SHOWCASE FOR ADDRESSING A CRUCIAL EXISTING GAP CONTEMPORARY EDUCATION IS FACING, THE GAP BETWEEN STUDENTS' ABILITY TO ACQUIRE AND SUBSEQUENTLY APPLY KNOWLEDGE IN UNCERTAIN SITUATIONS, WHICH ARE INCREASINGLY IMPORTANT IN THE 21ST CENTURY. WHILE THE CPS PROCESS INCORPORATES BOTH THE ACQUISITION AND APPLICATION OF KNOWLEDGE, MANY EARLIER STUDIES HAVE FOCUSED ON IDENTIFYING THE FACTORS RELEVANT FOR SUCCESS IN KNOWLEDGE ACQUISITION. GIVEN THE DEARTH OF EXISTING RESEARCH ON FACTORS INFLUENCING A SUCCESSFUL TRANSITION BETWEEN BOTH CPS PHASES, WE INVESTIGATED THE RATES OF SUCCESSFUL AND UNSUCCESSFUL KNOWLEDGE TRANSITION OVER THE COURSE OF NINE CPS ITEMS IN A SAMPLE OF N = 1151 STUDENTS IN 9TH GRADE. RESULTS SHOWED THAT MANY PARTICIPANTS WERE UNABLE TO TRANSITION THEIR KNOWLEDGE FROM THE ACQUISITION TO THE APPLICATION PHASE, WHICH WAS PRESUMABLY DUE TO AN INEFFICIENT MENTAL MODEL TRANSFER. FURTHERMORE, THE LIKELIHOOD OF STUDENTS BEING 'LOST IN TRANSITION' WAS HIGHER IN MORE COMPLEX ITEMS. IMPLICATIONS ARE DISCUSSED IN LIGHT OF LEARNING ANALYTICS, AND PARTICULARLY WITH REGARD TO THE FACTORS TO BE TAKEN INTO ACCOUNT BY FUTURE CPS TRAINING PROGRAMS.
STUDENT DROPOUT IN ENGINEERING EDUCATION IS AN IMPORTANT PROBLEM WHICH HAS BEEN STUDIED FROM DIFFERENT PERSPECTIVES AND USING DIFFERENT TECHNIQUES. THIS MANUSCRIPT DESCRIBES THE METHODOLOGY USED TO ADDRESS THIS QUESTION IN THE CONTEXT OF LEARNING ANALYTICS, USING BAYESIAN NETWORKS BECAUSE THEY PROVIDE ADEQUATE METHODS FOR THE REPRESENTATION, INTERPRETATION AND CONTEXTUALIZATION OF DATA. THE PROPOSED APPROACH IS ILLUSTRATED THROUGH THE CASE STUDY OF THE ABANDONMENT OF COMPUTER SCIENCE (CS) STUDIES AT THE UNIVERSITY OF CASTILLA-LA MANCHA, WHICH IS CLOSE TO 40%. TO THAT END, SEVERAL BAYESIAN NETWORKS WERE OBTAINED FROM A DATABASE CONTAINING 363 RECORDS REPRESENTING BOTH ACADEMIC AND SOCIAL DATA OF THE STUDENTS ENROLLED IN THE CS DEGREE DURING FOUR COURSES. THEN, THESE PROBABILISTIC MODELS WERE INTERPRETED AND EVALUATED. THE RESULTS OBTAINED REVEALED THAT THE GREAT HETEROGENEITY OF THE DATA STUDIED DID NOT ALLOW TO ADJUST THE MODEL ACCURATELY. HOWEVER, THE METHODOLOGY DESCRIBED HERE CAN BE TAKEN AS A REFERENCE FOR OTHER WORKS WHERE A LESS HETEROGENEOUS DATABASE COULD BE OBTAINED, AIMED AT ANALYSING STUDENT CHARACTERISTICS FROM A DATABASE.
AN INSTRUMENTAL CASE STUDY WAS CONDUCTED AT A LARGE, PUBLIC RESEARCH UNIVERSITY TO UNDERSTAND THE ORGANIZATIONAL BARRIERS, INCENTIVES, AND OPPORTUNITIES RELATED TO ADOPTION OF LEARNING ANALYTICS TOOLS BY FACULTY MEMBERS AND PROFESSIONAL ADVISING STAFF. DATA WAS CULLED FROM FOCUS GROUPS WITH SIX FACULTY AND TWENTY-ONE ADVISORS AND FROM INTERVIEW AND OBSERVATION SESSIONS WITH TWO FACULTY AND THREE ADVISORS. PRELIMINARY RESULTS INDICATE THAT ORGANIZATIONAL CONTEXT AND COMMITMENT, INCLUDING STRUCTURES, POLICIES, PROCESSES, AND LEADERSHIP IMPACT INDIVIDUAL DECISIONS TO TRUST AND ADOPT LEARNING ANALYTICS TOOLS. FURTHER, RESULTS INDICATE THE IMPORTANCE OF A COMPREHENSIVE, INCLUSIVE, AND WELL-COMMUNICATED IMPLEMENTATION PLAN FOR MAXIMUM ADOPTION BY USERS.
THE RECENT TREND IN THE DEVELOPMENT OF EDUCATION ACROSS THE GLOBE IS THE USE OF THE NEW LEARNING ANALYTICS (LA) TOOLS AND TECHNOLOGIES IN TEACHING AND LEARNING. THE POTENTIAL BENEFITS OF LA NOTWITHSTANDING, POTENTIAL ETHICAL ISSUES HAVE TO BE CONSIDERED AND ADDRESSED IN ORDER TO AVOID ANY LEGAL ISSUES THAT MIGHT ARISE FROM ITS USE. AS A RESULT OF THIS, HIGHER EDUCATION INSTITUTIONS (HEIS) INVOLVED IN THE DEVELOPMENT OF LA TOOLS NEED TO PAY PARTICULAR ATTENTION TO EVERY ETHICAL CHALLENGES/CONSTRAINT THAT MIGHT ARISE. THIS PAPER AIMS TO IDENTIFY AND DISCUSS SEVERAL ETHICAL ISSUES CONNECTED WITH THE PRACTICE AND USE OF LA TOOLS AND TECHNOLOGIES IN ANALYSING AND PREDICTING THE PERFORMANCE OF STUDENTS IN A SHARED NETWORK ENVIRONMENT OF HEIS. THE STUDY DISCUSSES THE FOUR ETHICAL ISSUES OF INFORMATION AND COMMUNICATION TECHNOLOGY NAMELY PRIVACY, ACCURACY, PROPERTY AND ACCESSIBILITY (PAPA'S MODEL) AS WELL AS OTHER APPROACHES TO EXPLAIN THESE FUTURE CONCERNS. THE PAPER ALSO PRESENTS THE EMPIRICAL EVIDENCE OF THE VIEWS OF STUDENTS ON THE ANALYTICAL USE AND STORAGE OF THEIR DATA. THE RESULTS INDICATE THAT EVEN THOUGH STUDENTS HAVE HIGH TRUST IN THE PRIVACY AND SECURITY OF THEIR DATA BEING USED BY THEIR INSTITUTIONS, MORE THAN HALF OF THE STUDENTS HAVE ETHICAL CONCERNS WITH THE ACCESSIBILITY AND STORAGE OF THEIR DATA BEYOND A CERTAIN PERIOD. IN THE LIGHT OF THIS, GENERALISED STRATEGIES ON ETHICAL ISSUES OF THE USE OF LEARNERS' DATA IN AN HEI SHARED NETWORKED ENVIRONMENT ARE PROPOSED.
THE APPLICATION OF LEARNING ANALYTICS (LA) TO RESEARCH AND PRACTICE IN HIGHER EDUCATION IS EXPANDING. RESEARCHERS AND PRACTITIONERS ARE USING LA TO PROVIDE AN EVIDENTIARY BASIS ACROSS HIGHER EDUCATION TO INVESTIGATE STUDENT LEARNING, TO DRIVE INSTITUTIONAL QUALITY IMPROVEMENT STRATEGIES, TO DETERMINE AT-RISK BEHAVIOURS AND DEVELOP INTERVENTION STRATEGIES, TO MEASURE ATTRITION MORE EFFECTIVELY AND TO IMPROVE CURRICULUM DESIGN AND EVALUATION IN BOTH ON-CAMPUS AND E-LEARNING SETTINGS. THIS PAPER IS A CASE STUDY REPORT OF THE NOVEL APPLICATION OF LA TO PROGRAMME CURRICULUM REVIEW FROM A MAJOR CROSS-INSTITUTIONAL PROJECT IN HONG KONG. THE PAPER DESCRIBES THE RATIONALE FOR THE PROJECT, THE CONCEPTUAL MODEL THAT LED THE APPROACH AND THE DEVELOPMENT OF A SOFTWARE TOOL THAT ALLOWED THE AUTOMATION OF STATISTICAL ANALYSES SPECIFICALLY RELEVANT TO PROGRAMME REVIEW. IN ADDITION, THE PAPER ADDRESSES A MAJOR CHALLENGE THAT THE PROJECT FACED IN RELATION TO DATA GOVERNANCE. THE PAPER CONCLUDES BY PROPOSING THE POTENTIAL BENEFITS OF LA FOR PROGRAMME CURRICULUM REVIEW.
LINEMEN TRAINING IS MANDATORY, COMPLEX, AND HAZARDOUS. ELECTRONIC TECHNOLOGIES, SUCH AS VIRTUAL REALITY OR LEARNING MANAGEMENT SYSTEMS, HAVE BEEN USED TO IMPROVE SUCH TRAINING, HOWEVER THESE LACK OF INTEROPERABILITY, SCALABILITY, AND DO NOT EXPLOIT TRACE DATA GENERATED BY USERS IN THESE SYSTEMS. IN THIS PAPER WE PRESENT OUR ONGOING WORK ON DEVELOPING A LEARNING ECOSYSTEM FOR TRAINING LINEMEN IN MAINTENANCE MANEUVERS USING THE EXPERIENCE API STANDARD, BIG DATA COMPONENTS, AND LEARNING ANALYTICS. THE PAPER DESCRIBES THE ARCHITECTURE OF THE ECOSYSTEM, ELABORATES ON COLLECTING LEARNING EXPERIENCES AND EMOTIONAL STATES, AND APPLIES ANALYTICS FOR THE EXPLOITATION OF BOTH, LEGACY AND NEW DATA. IN THE FORMER, WE EXPLOIT LEGACY E-LEARNING DATA FOR BUILDING A DOMAIN MODEL USING TEXT MINING AND UNSUPERVISED CLUSTERING ALGORITHMS. IN THE LATTER WE EXPLORE SELF-REPORTS CAPABILITIES FOR GATHERING EDUCATIONAL SUPPORT CONTENT, AND ASSESSING STUDENTS EMOTIONAL STATES. RESULTS SHOW THAT, A SUITABLE DOMAIN MODEL FOR PERSONALIZING MANEUVERS LINEMEN TRAINING PATH CAN BE BUILT FROM LEGACY TEXT DATA STRAIGHTFORWARDLY. REGARDING SELF REPORTS, PROMISING RESULTS WERE OBTAINED FOR TRACKING EMOTIONAL STATES AND COLLECTING EDUCATIONAL SUPPORT MATERIAL, NEVERTHELESS, MORE WORK AROUND LINEMEN TRAINING IS REQUIRED.
THE TRADITIONAL FLIPPED CLASSROOM IS DEFINED BASED ON THE VIDEO INSTRUCTIONS OF FLIPPED CLASS AT HOME. IN ADDITION, STUDENTS HAVE TIME IN CLASS TO WORK WITH THEIR TEACHER ON KEY LEARNING ACTIVITIES. WITH INTRODUCTION OF FLIPPED CLASSROOM MODEL AND RAPID DEVELOPMENT OF DATA SCIENCES FOR TAKING THE ADVANTAGES OF THE LEARNERS' DATA TO OPTIMIZE LEARNING PROCESS THERE HAS BEEN AN INCREASING CONSIDERATION OF EXPLOITING EDUCATIONAL DATA MINING AND LEARNING ANALYSIS. LARGE AMOUNT OF DATA TAKEN FROM PERFORMED ACTIONS OF STUDENTS AND TEACHERS IN FC HAS MADE DATA-DRIVEN LEARNING AND SELF-PACED LEARNING POSSIBLE TO PERFORM DIFFERENT ALGORITHMS OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING. THE GOAL IN THIS PAPER IS TO DEVISE A SYSTEMATIC MAPPING STUDY, IN ORDER TO EXPLORE AND ANALYZE EXISTING RESEARCHES ABOUT IMPLEMENTATIONS OF LEARNING STRATEGIES AND LEARNING ALGORITHMS. WE SELECTED 122 PAPERS AND ASSORTED ACCORDING TO FIELD OF INTEREST, VIDEO FIELD OF INTERESTS AND TECHNOLOGY USED IN FC.
THE USE OF VIRTUAL WORLDS IN EDUCATION IS BECOMING AN INNOVATIVE ALTERNATIVE TO TRADITIONAL EDUCATION. HOWEVER, THESE SOLUTIONS ARE CONFRONTED TO SEVERAL ISSUES SUCH AS: LACK OF INDICATORS TO FOLLOW UP THE STUDENTS' PROGRESS, LACK OF WELL-DEFINED EVALUATION PARAMETERS, DIFFICULTIES FOR EVALUATING COLLECTIVE AND INDIVIDUAL CONTRIBUTIONS, DIFFICULTIES FOR KEEPING STUDENTS ENGAGED AND MOTIVATED, A VERY TIME-CONSUMING TEACHERS' SUPERVISION, AND THE ABSENCE OF TUTORS FOR GUIDING THE LEARNING PROCESS, AMONG OTHERS. IN THIS REVIEW, WE EXPLORE AND DESCRIBE ACADEMIC CONTRIBUTIONS FOCUSED ON THE APPLICATION OF LEARNING ANALYTICS TO IMPROVE VIRTUAL WORLDS IN EDUCATION FROM THREE PERSPECTIVES: PERSONALIZED LEARNING, ADAPTIVE LEARNING AND EDUCATIONAL INTERVENTION. OUR RESULTS HIGHLIGHT THAT MOST OF THE RESEARCH FOCUS ON SUPPORT DECISIONS WHOSE NATURE CONCERNS OPERATIONAL NON-REAL-TIME ISSUES. ADDITIONALLY, ALMOST ALL THE CONTRIBUTIONS FOCUS IN SOLVING ONLY A FEW ISSUES, BUT NONE OF THEM OFFER A HOLISTIC FRAMEWORK THAT COULD BE USED BY TEACHERS OR PEDAGOGICAL PERSONNEL FOR DECISION MAKING.
ALTHOUGH LEARNING ANALYTICS (LA) DASHBOARD VISUALIZATIONS ARE INCREASINGLY BEING USED TO PROVIDE FEEDBACK TO STUDENTS, LITERATURE ON THE EFFECTIVENESS OF LA DASHBOARDS HAS BEEN INCONCLUSIVE. TO ADDRESS THIS, A LA STUDENT DASHBOARD VISUALIZING STUDENTS' LATEST DATA AGAINST THEIR OWN DATA FROM PREVIOUS WEEKS (I.E., SELF-REFERENCED DATA) WAS DESIGNED INFORMED BY FREDRICKSON'S (2004) BROADEN-AND-BUILD THEORY, AS WELL AS STUDIES HIGHLIGHTING PERSONAL BEST GOALS (MARTIN & ELLIOT, 2016) AND THE NEGATIVE EFFECTS OF PEER COMPARISONS (CORRIN & DE BARBA, 2014). THE SELF-REFERENCED LA STUDENT DASHBOARD WAS IMPLEMENTED AND EVALUATED IN A SINGAPORE SECONDARY SCHOOL AS PART OF A LARGER STUDY, WIREAD. THIS PAPER REPORTS ON THE QUANTITATIVE IMPACT OF THE WIREAD SELF-REFERENCED LA DASHBOARD VISUALIZATIONS ON 15-YEAR-OLD STUDENTS' CRITICAL READING FLUENCY, COGNITIVE READING ENGAGEMENT, AND ENGLISH LANGUAGE (EL) SELF-EFFICACY, AS WELL AS STUDENTS' QUALITATIVE FEEDBACK ON THE USEFULNESS AND SHORTCOMINGS OF THE LA DASHBOARD.
LEARNING ANALYTICS (LA) IS PERCEIVED TO BE A PROMISING STRATEGY TO TACKLE PERSISTING EDUCATIONAL CHALLENGES IN LATIN AMERICA, SUCH AS QUALITY DISPARITIES AND HIGH DROPOUT RATES. HOWEVER, LATIN AMERICAN UNIVERSITIES HAVE FALLEN BEHIND IN LA ADOPTION COMPARED TO INSTITUTIONS IN OTHER REGIONS. TO UNDERSTAND STAKEHOLDERS' NEEDS FOR LA SERVICES, THIS STUDY USED MIXED METHODS TO COLLECT DATA IN FOUR LATIN AMERICAN UNIVERSITIES. QUALITATIVE DATA WAS OBTAINED FROM 37 INTERVIEWS WITH MANAGERS AND 16 FOCUS GROUPS WITH 51 TEACHING STAFF AND 45 STUDENTS, WHEREAS QUANTITATIVE DATA WAS OBTAINED FROM SURVEYS ANSWERED BY 1884 STUDENTS AND 368 TEACHING STAFF. ACCORDING TO THE TRIANGULATION OF BOTH TYPES OF EVIDENCE, WE FOUND THAT (1) STUDENTS NEED QUALITY FEEDBACK AND TIMELY SUPPORT, (2) TEACHING STAFF NEED TIMELY ALERTS AND MEANINGFUL PERFORMANCE EVALUATIONS, AND (3) MANAGERS NEED QUALITY INFORMATION TO IMPLEMENT SUPPORT INTERVENTIONS. THUS, LA OFFERS AN OPPORTUNITY TO INTEGRATE DATA-DRIVEN DECISION-MAKING IN EXISTING TASKS.
THE EFFORTS TOWARDS PROVIDING PERSONALISED E-LEARNING IS AN INCREASING TREND DUE TO THE FACT THAT CONTENT PROVISION IS USUALLY A ONE SIZE FITS ALL APPROACH. STUDENTS' HAVE DIFFERENT LEARNING STYLES, SKILLS AND NEEDS WHICH DICTATE THE WAY IN WHICH THEY LEARN. THIS PAPER PRESENTS AN ADAPTIVE EDUCATIONAL SYSTEM CALLED IPAL (INTEGRATED PERSONALISED ASSESSMENT IN LEARNING). IPAL IS DESIGNED TO BE A MORE EFFECTIVE LEARNING ENVIRONMENT TO SATISFY THE ONLINE DELIVERY OF THEORETICAL AND PRACTICAL SCIENCE, TECHNOLOGY, ENGINEERING AND MATHS (STEM) SUBJECTS, AS A SUPPLEMENTARY COURSE TOOL IN HIGHER EDUCATION. THE PRACTICAL APPROACH OF IPAL SPECIFICALLY ADDRESSES THE LACK OF GAME BASED LEARNING IN ADAPTIVE EDUCATIONAL SYSTEMS. THE FOCUS OF THIS PAPER IS BASED ON A TWO TIERED APPROACH USING THE IMPLICIT APPROACH OF LEARNING ANALYTICS WITH EDUCATIONAL DATA MINING AND THE EXPLICIT APPROACH OF LEARNING STYLES TO PERSONALISE CONTENT IN IPAL. THIS PAPER DISCUSSES HOW THESE APPROACHES ARE EFFECTIVE ENABLERS IN THE DELIVERY OF PERSONALISED CONTENT. A CASE STUDY DEMONSTRATING THE PRACTICAL APPLICATION OF THE IPAL SYSTEM IS PRESENTED COMPRISING OF OVER 100 UNDERGRADUATE COMPUTING STUDENTS'. THE KEY SIGNIFICANCE OF THIS RESEARCH IS THAT IT PROVIDES CLEAR EVIDENCE FOR THE SUCCESSFUL USE OF IPAL AS A SUPPLEMENTARY COURSE TOOL IN HIGHER EDUCATION.
THIS PAPER REPORTS THE FINDINGS OF A RESEARCH USING MARKS OF STUDENTS IN LEARNING ACTIVITIES OF AN ONLINE MODULE TO BUILD A PREDICTIVE MODEL OF PERFORMANCE FOR THE FINAL ASSESSMENT OF THE MODULE. THE OBJECTIVES WERE (1) TO COMPARE THE PERFORMANCES OF STUDENTS OF TWO COHORTS IN TERMS OF CONTINUOUS LEARNING ASSESSMENT MARKS AND FINAL LEARNING ACTIVITY MARKS AND (2) TO MODEL THEIR FINAL PERFORMANCES FROM THEIR LEARNING ACTIVITIES FORMING THE CONTINUOUS ASSESSMENT USING PREDICTIVE ANALYTICS AND REGRESSION ANALYSIS. THE FINDINGS OF THIS STUDY COMBINED WITH OTHER FINDINGS AS REPORTED IN THE LITERATURE DEMONSTRATE THAT THE LEARNING DESIGN IS AN IMPORTANT FACTOR TO CONSIDER WITH RESPECT TO APPLICATION OF LEARNING ANALYTICS TO IMPROVE TEACHING INTERVENTIONS AND STUDENTS' EXPERIENCES. FURTHERMORE, TO MAXIMISE THE EFFICIENCY OF LEARNING ANALYTICS IN ELEARNING ENVIRONMENTS, THERE IS A NEED TO REVIEW THE WAY OFFLINE ACTIVITIES ARE TO BE PEDAGOGICALLY CONCEIVED SO AS TO ENSURE THAT THE ENGAGEMENT OF THE LEARNER THROUGHOUT THE DURATION OF THE ACTIVITY IS EFFECTIVELY MONITORED.
THE PAST DECADE HAS SEEN THE RAPID ADOPTION AND USE OF VARIOUS LEARNING MANAGEMENT SYSTEMS (LMS) IN AFRICA, AND TANZANIA IN PARTICULAR. INSTITUTIONS HAVE BEEN SPENDING THOUSANDS OF DOLLARS TO IMPLEMENT THESE SYSTEMS IN A BID TO IMPROVE THE QUALITY OF EDUCATION AS WELL AS INCREASING STUDENTS' ENROLMENTS THROUGH DISTANCE AND BLENDED LEARNING. HOWEVER, THE IMPACT OF THESE SYSTEM ON IMPROVING STUDENTS' PERFORMANCE HAS BEEN A POPULAR SUBJECT OF RESEARCH IN RECENT YEARS. STUDIES HAVE BEEN RELYING ON DATA FROM USERS' OPINIONS AND SUBJECTIVE INTERPRETATION THROUGH SURVEYS TO DETERMINE THE EFFECTIVENESS OF LMS USAGE ON STUDENTS' LEARNING PERFORMANCE. THE USE OF SUCH DATA IS NORMALLY SUBJECT TO THE POSSIBILITY OF DISTORTION OR LOW RELIABILITY. THEREFORE, THIS STUDY DESIGNED AND DEVELOPED LEARNING ANALYTICS TOOL AND USED THE TOOL TO DETERMINE THE CAUSATION BETWEEN LMS USAGE AND STUDENTS' PERFORMANCE. DATA FROM LMS LOG OF TWO COURSES DELIVERED AT MBEYA UNIVERSITY OF SCIENCE AND TECHNOLOGY (MUST) WERE EXTRACTED USING DEVELOPED LEARNING ANALYTICS TOOL AND SUBJECTED INTO LINEAR REGRESSION ANALYSIS WITH STUDENTS' FINAL RESULTS. THE STUDY FOUND THAT DISCUSSION POSTS, PEER INTERACTION, AND EXERCISES WERE DETERMINED TO BE SIGNIFICANT FACTORS FOR STUDENTS' ACADEMIC ACHIEVEMENT IN BLENDED LEARNING AT MUST. NONETHELESS, TIME SPEND IN THE LMS, NUMBER OF DOWNLOADS, AND LOGIN FREQUENCY WERE FOUND TO HAVE NO SIGNIFICANT IMPACT ON STUDENTS' LEARNING PERFORMANCE. THE IMPLICATIONS OF THESE RESULTS ON IMPROVING STUDENTS' LEARNING ARE DISCUSSED.
IN THIS PAPER, WE ADDRESS ISSUES OF TRANSPARENCY, MODULARITY, AND PRIVACY WITH THE INTRODUCTION OF AN OPEN SOURCE, WEB-BASED DATA REPOSITORY AND ANALYSIS TOOL TAILORED TO THE MASSIVE OPEN ONLINE COURSE COMMUNITY. THE TOOL INTEGRATES DATA REQUEST/AUTHORIZATION AND DISTRIBUTION WORKFLOW FEATURES AS WELL AS PROVIDES A SIMPLE ANALYTICS MODULE UPLOAD FORMAT TO ENABLE REUSE AND REPLICATION OF ANALYTICS RESULTS AMONG INSTRUCTORS AND RESEARCHERS. WE SURVEY THE EVOLVING LANDSCAPE OF COMPETING ESTABLISHED AND EMERGING DATA MODELS, ALL OF WHICH ARE ACCOMMODATED IN THE PLATFORM. DATA MODEL DESCRIPTIONS ARE PROVIDED TO ANALYTICS AUTHORS WHO CHOOSE, MUCH LIKE WITH SMARTPHONE APP STORES, TO WRITE FOR ANY NUMBER OF DATA MODELS DEPENDING ON THEIR NEEDS AND THE PROLIFERATION OF THE PARTICULAR DATA MODEL. TWO CASE STUDY EXAMPLES OF ANALYTICS AND RESPONSIVE VISUALIZATIONS BASED ON DIFFERENT DATA MODELS ARE DESCRIBED IN THE PAPER. THE RESULT IS A SIMPLE BUT EFFECTIVE APPROACH TO LEARNING ANALYTICS IMMEDIATELY APPLICABLE TO X CONSORTIUM MOOCS AND BEYOND.
LEARNING ANALYTICS IS THE ANALYSIS OF STATIC AND DYNAMIC DATA EXTRACTED FROM VIRTUAL LEARNING ENVIRONMENTS, IN ORDER TO UNDERSTAND AND OPTIMIZE THE LEARNING PROCESS. GENERALLY, THIS DYNAMIC DATA IS GENERATED BY THE INTERACTIONS WHICH TAKE PLACE IN THE VIRTUAL LEARNING ENVIRONMENT. AT THE PRESENT TIME, MANY IMPLEMENTATIONS FOR GROUPING OF DATA HAVE BEEN PROPOSED, BUT THERE IS NO CONSENSUS YET ON WHICH INTERACTIONS AND GROUPS MUST BE MEASURED AND ANALYZED. THERE IS ALSO NO AGREEMENT ON WHAT IS THE INFLUENCE OF THESE INTERACTIONS, IF ANY, ON LEARNING OUTCOMES, ACADEMIC PERFORMANCE OR STUDENT SUCCESS. THIS STUDY PRESENTS THREE DIFFERENT EXTANT INTERACTION TYPOLOGIES IN E-LEARNING AND ANALYZES THE RELATION OF THEIR COMPONENTS WITH STUDENTS' ACADEMIC PERFORMANCE. THE THREE DIFFERENT CLASSIFICATIONS ARE BASED ON THE AGENTS INVOLVED IN THE LEARNING PROCESS, THE FREQUENCY OF USE AND THE PARTICIPATION MODE, RESPECTIVELY. THE MAIN FINDINGS FROM THE RESEARCH ARE: A) THAT AGENT-BASED CLASSIFICATIONS OFFER A BETTER EXPLANATION OF STUDENT ACADEMIC PERFORMANCE; B) THAT AT LEAST ONE COMPONENT IN EACH TYPOLOGY PREDICTS ACADEMIC PERFORMANCE; AND C) THAT STUDENT-TEACHER AND STUDENT-STUDENT, EVALUATING STUDENTS, AND ACTIVE INTERACTIONS, RESPECTIVELY, HAVE A SIGNIFICANT IMPACT ON ACADEMIC PERFORMANCE, WHILE THE OTHER INTERACTION TYPES ARE NOT SIGNIFICANTLY RELATED TO ACADEMIC PERFORMANCE.
THIS PAPER PRESENTS THE FINDINGS FROM A STUDY CONDUCTED WITH A NUMBER OF UNIVERSITIES REGARDING THEIR USE OF LEARNING ANALYTICS (LA) AVAILABLE WITHIN THEIR LEARNING MANAGEMENT SYSTEMS (LMS). DATA WAS COLLECTED FROM A NUMBER OF DATA COLLECTION INSTRUMENTS INCLUDING AN ONLINE SURVEY, IN-DEPTH INTERVIEWS WITH IT DIRECTORS AND ACADEMIC ADMINISTRATORS, AND A CASE STUDY IN EMBRY-RIDDLE AERONAUTICAL UNIVERSITY. IT IS EVIDENT FROM THE STUDY THAT UNIVERSITIES ARE ATTEMPTING TO MAKE BETTER USE OF THEIR LEARNING MANAGEMENT SYSTEMS TO MAKE MORE INFORMED DECISIONS REGARDING SHORT-TERM AND LONG-TERM GOALS. SOME OF THE MOST POPULAR USAGE INCLUDES ANALYTICS PERFORMED AT THE INSTITUTIONAL LEVEL, COLLEGE LEVEL, DEGREE-PROGRAM LEVEL, COURSE LEVEL, AND EVEN COURSE SECTION LEVEL. COURSES AND DEGREE PROGRAMS AS WELL AS LEARNING PERFORMANCE AND OBJECTIVES CAN BE MEASURED AND ANALYZED USING DIFFERENT GOALS, CRITERIA, AND ACCREDITATION REQUIREMENTS.
IN ORDER TO ENHANCE THE EXPERIENCE OF LEARNING, MANY EDUCATORS APPLIED LEARNING ANALYTICS IN A CLASSROOM, THE MAJOR PRINCIPLE OF LEARNING ANALYTICS IS TARGETING AT-RISK STUDENT AND GIVEN TIMELY INTERVENTION ACCORDING TO THE RESULTS OF STUDENT BEHAVIOR ANALYSIS. HOWEVER, WHEN RESEARCHERS APPLIED MACHINE LEARNING TO TRAIN A RISK IDENTIFYING MODEL, THE REASON WHICH AFFECTED THE PERFORMANCE OF THE MODEL WAS OVERLOOKED. THIS STUDY COLLECTED SEVEN DATASETS WITHIN THREE UNIVERSITIES LOCATED IN TAIWAN AND JAPAN AND LISTED PERFORMANCE METRICS OF RISK IDENTIFICATION MODEL AFTER FED DATA INTO EIGHT CLASSIFICATION METHODS. U1, U2, AND U3 WERE USED TO DENOTE THE THREE UNIVERSITIES, WHICH HAVE THREE, TWO, AND TWO CASES OF DATASETS (LEARNING LOGS), RESPECTIVELY. ACCORDING TO THE RESULTS OF THIS STUDY, THE FACTORS INFLUENCING THE PREDICTIVE PERFORMANCE OF CLASSIFICATION METHODS ARE THE NUMBER OF SIGNIFICANT FEATURES, THE NUMBER OF CATEGORIES OF SIGNIFICANT FEATURES, AND SPEARMAN CORRELATION COEFFICIENT VALUES. IN U1 DATASET CASE 1.3 AND U2 DATASET CASE 2.2, THE NUMBERS OF SIGNIFICANT FEATURES, NUMBERS OF CATEGORIES OF SIGNIFICANT FEATURES, AND SPEARMAN CORRELATION COEFFICIENT VALUES FOR SIGNIFICANT FEATURES WERE ALL RELATIVELY HIGH, WHICH IS THE MAIN REASON WHY THESE DATASETS WERE ABLE TO PERFORM CLASSIFICATION WITH HIGH PREDICTIVE ABILITY.
LEARNING ANALYTICS DASHBOARDS (LADS) CAN PROVIDE LEARNERS WITH INSIGHTS ABOUT THEIR STUDY PROGRESS THROUGH VISUALISATIONS OF THE LEARNER AND LEARNING DATA. DESPITE THEIR POTENTIAL USEFULNESS TO SUPPORT LEARNING, VERY FEW STUDIES ON LADS HAVE CONSIDERED LEARNERS' NEEDS AND HAVE ENGAGED LEARNERS IN THE PROCESS OF DESIGN AND EVALUATION. ALIGNING WITH THAT, THERE IS A LIMITED UNDERSTANDING OF WHAT SPECIFIC STUDENT COHORTS, IN PARTICULAR DISTANCE AND ONLINE LEARNERS, MAY SEEK FROM LADS TO EFFECTIVELY SUPPORT THEIR STUDIES. IN THIS STUDY, WE PRESENT FINDINGS FROM 21 INTERVIEWS WITH UNDERGRADUATE DISTANCE LEARNERS, MAINLY HIGH PERFORMERS, THAT AIMED TO CAPTURE STUDENT PERCEPTIONS ABOUT THE USEFULNESS OF SPECIFIC LAD FEATURES AND THE FACTORS THAT EXPLAIN THESE PERCEPTIONS. OUR FINDINGS REVEALED THAT AMONGST THE LAD FEATURES FAVOURED BY STUDENTS WAS THE POTENTIAL TO RECEIVE STUDY RECOMMENDATIONS, WHEREAS COMPARISON WITH PEERS WAS AMONGST THE LEAST FAVOURED ELEMENTS, UNLESS INFORMED BY QUALITATIVE INFORMATION. FACTORS INCLUDING INFORMATION TRUST, ATTITUDES, AGE, PERFORMANCE AND ACADEMIC SELF-CONFIDENCE WERE FOUND TO EXPLAIN THESE PERCEPTIONS.
WITH THE INCREASING USE OF BLENDED LEARNING APPROACHES IN CLASSROOM, VARIOUS KINDS OF TECHNOLOGIES ARE INCORPORATED TO PROVIDE DIGITAL TEACHING AND LEARNING RESOURCES TO SUPPORT STUDENTS. THESE RESOURCES ARE OFTEN CENTRALIZED IN LEARNING MANAGEMENT SYSTEMS (LMSS), WHICH ALSO STORE VALUABLE LEARNING DATA OF STUDENTS. THE DATA COULD ASSIST TEACHERS IN THEIR PEDAGOGICAL DECISION MAKING BUT THEY ARE OFTEN NOT WELL UTILIZED. THIS PAPER PROPOSES THE USE OF DATA MINING AND VISUALIZATION TECHNIQUES AS LEARNING ANALYTICS TO PROVIDE A MORE COMPREHENSIVE OVERVIEW OF STUDENTS' LEARNING ONLINE BASED ON LOG DATA FROM LMSS. THE FOCUS OF THIS STUDY IS THE DISCOVERY OF FREQUENT NAVIGATIONAL PATTERNS BY SEQUENTIAL PATTERN MINING TECHNIQUES AND THE DEMONSTRATION OF HOW PRESENTATION OF PATTERNS THROUGH HIERARCHICAL CLUSTERING AND SUNBURST VISUALIZATION COULD FACILITATE THE INTERPRETATION OF PATTERNS. THE DATA IN THIS PAPER WERE COLLECTED FROM A BLENDED STATISTICS COURSE FOR UNDERGRADUATE STUDENTS.
THE PAPER AIMS TO ANALYSE APPLICATION OF LEARNING ANALYTICS (LA) TO SUPPORT LEARNING PERSONALISATION IN VIRTUAL LEARNING ENVIRONMENTS, NAMELY MOODLE. IN THE PAPER, FIRST OF ALL, LITERATURE REVIEW WAS PERFORMED ON LA METHODS AND TECHNIQUES USED TO PERSONALISE STUDENTS' E-LEARNING ACTIVITIES. LITERATURE REVIEW HAS REVEALED THAT LA ARE KNOWN AS THE MEASUREMENT, COLLECTION, ANALYSIS, AND REPORTING OF DATA ABOUT LEARNERS AND THEIR CONTEXTS TO UNDERSTAND AND OPTIMISE LEARNING AND ENVIRONMENTS IN WHICH IT OCCURS. IN THE PAPER, AN ORIGINAL METHODOLOGY TO PERSONALISE LEARNING IS PRESENTED. SECOND, EXISTING MOODLE-BASED LEARNING ACTIVITIES AND TOOLS WERE INTERLINKED WITH STUDENTS' LEARNING STYLES ACCORDING TO FELDER-SILVERMAN LEARNING STYLES MODEL USING EXPERT EVALUATION METHOD. THIRD, A GROUP OF STUDENTS WAS ANALYSED TO IDENTIFY THEIR INDIVIDUAL LEARNER PROFILES, AND PROBABILISTIC SUITABILITY INDEXES WERE CALCULATED FOR EACH ANALYSED STUDENT AND EACH MOODLE-BASED LEARNING ACTIVITY TO IDENTIFY WHICH LEARNING ACTIVITIES OR TOOLS ARE THE MOST SUITABLE FOR PARTICULAR STUDENT. THE HIGHER IS SUITABILITY INDEX THE BETTER LEARNING ACTIVITY OR TOOL FITS PARTICULAR STUDENT'S NEEDS. FOURTH, USING APPROPRIATE LA METHODS AND TECHNIQUES, WE COULD ANALYSE WHAT PARTICULAR LEARNING ACTIVITIES OR TOOLS WERE PRACTICALLY USED BY THESE STUDENTS IN MOODLE, AND TO WHAT EXTENT. FIFTH, THE DATA ON PRACTICAL USE OF MOODLE-BASED LEARNING ACTIVITIES OR TOOLS SHOULD BE COMPARED WITH STUDENTS' SUITABILITY INDEXES. IN THE CASE OF ANY NOTICEABLE DISCREPANCIES, STUDENTS' PROFILES AND ACCOMPANIED SUITABILITY INDEXES SHOULD BE IDENTIFIED MORE PRECISELY, AND STUDENTS' PERSONAL LEANING PATHS IN MOODLE SHOULD BE CORRECTED ACCORDING TO NEW IDENTIFIED DATA. THUS, USING LA, WE COULD NOTICEABLY ENHANCE STUDENTS' LEARNING QUALITY AND EFFECTIVENESS.
BY COLLECTING LONGITUDINAL LEARNER AND LEARNING DATA FROM A RANGE OF RESOURCES, PREDICTIVE LEARNING ANALYTICS (PLA) ARE USED TO IDENTIFY LEARNERS WHO MAY NOT COMPLETE A COURSE, TYPICALLY DESCRIBED AS BEING AT RISK. MIXED EFFECTS ARE OBSERVED AS TO HOW TEACHERS PERCEIVE, USE, AND INTERPRET PLA DATA, NECESSITATING FURTHER RESEARCH IN THIS DIRECTION. THE AIM OF THIS STUDY IS TO EVALUATE WHETHER PROVIDING TEACHERS IN A DISTANCE LEARNING HIGHER EDUCATION INSTITUTION WITH PLA DATA PREDICTS STUDENTS' PERFORMANCE AND EMPOWERS TEACHERS TO IDENTIFY AND ASSIST STUDENTS AT RISK. USING PRINCIPLES OF TECHNOLOGY ACCEPTANCE AND ACADEMIC RESISTANCE MODELS, A UNIVERSITY-WIDE, MULTI-METHODS STUDY WITH 59 TEACHERS, NINE COURSES, AND 1325 STUDENTS REVEALED THAT TEACHERS CAN POSITIVELY AFFECT STUDENTS' PERFORMANCE WHEN ENGAGED WITH PLA. FOLLOW-UP SEMI-STRUCTURED INTERVIEWS ILLUMINATED TEACHERS' ACTUAL USES OF THE PREDICTIVE DATA AND REVEALED ITS IMPACT ON TEACHING PRACTICES AND INTERVENTION STRATEGIES TO SUPPORT STUDENTS AT RISK.
HIGHER EDUCATION MASSIVE OPEN ONLINE COURSES (MOOCS) INTRODUCE A WAY OF TRANSCENDING FORMAL HIGHER EDUCATION BY REALIZING TECHNOLOGY-ENHANCED FORMATS OF LEARNING AND INSTRUCTION AND BY GRANTING ACCESS TO AN AUDIENCE WAY BEYOND STUDENTS ENROLLED IN ANY ONE HIGHER EDUCATION INSTITUTION. HOWEVER, ALTHOUGH MOOCS HAVE BEEN REPORTED AS AN EFFICIENT AND IMPORTANT EDUCATIONAL TOOL, THERE IS A NUMBER OF ISSUES AND PROBLEMS RELATED TO THEIR EDUCATIONAL IMPACT. MORE SPECIFICALLY, THERE IS AN IMPORTANT NUMBER OF DROP OUTS DURING A COURSE, LITTLE PARTICIPATION, AND LACK OF STUDENTS' MOTIVATION AND ENGAGEMENT OVERALL. THIS MAY BE DUE TO ONE-SIZE-FITS-ALL INSTRUCTIONAL APPROACHES AND VERY LIMITED COMMITMENT TO STUDENT-STUDENT AND TEACHER-STUDENT COLLABORATION. THIS PAPER INTRODUCES THE DEVELOPMENT AGENDA OF A NEWLY STARTED EUROPEAN PROJECT CALLED "COLMOOC" THAT AIMS TO ENHANCE THE MOOCS EXPERIENCE BY INTEGRATING COLLABORATIVE SETTINGS BASED ON CONVERSATIONAL AGENTS AND SCREENING METHODS BASED ON LEARNING ANALYTICS, TO SUPPORT BOTH STUDENTS AND TEACHERS DURING A MOOC COURSE. CONVERSATIONAL PEDAGOGICAL AGENTS GUIDE AND SUPPORT STUDENT DIALOGUE USING NATURAL LANGUAGE BOTH IN INDIVIDUAL AND COLLABORATIVE SETTINGS. INTEGRATING THIS TYPE OF CONVERSATIONAL AGENTS INTO MOOCS TO TRIGGER PEER INTERACTION IN DISCUSSION GROUPS CAN CONSIDERABLY INCREASE THE ENGAGEMENT AND THE COMMITMENT OF ONLINE STUDENTS AND, CONSEQUENTLY, REDUCE MOOCS DROPOUT RATE. MOREOVER, LEARNING ANALYTICS TECHNIQUES CAN SUPPORT TEACHERS' ORCHESTRATION AND STUDENTS' LEARNING DURING MOOCS BY EVALUATING STUDENTS' INTERACTION AND PARTICIPATION. THE RESEARCH REPORTED IN THIS PAPER IS CURRENTLY UNDERTAKEN WITHIN THE RESEARCH PROJECT COLMOOC FUNDED BY THE EUROPEAN COMMISSION.
THERE IS A GROWING BODY OF LITERATURE THAT RECOGNIZES THE IMPORTANCE OF THE 3D MULTI-USER VIRTUAL ENVIRONMENTS (3DMUVES) WITH HIGH RELEVANCE FOR PROSPECTIVE DEVELOPMENTS AND IMPROVEMENTS IN BOTH DISTANT AND IN-CLASS EDUCATION. HOWEVER, TO BE ACCEPTED ON A LARGER SCALE BY EDUCATION PRACTITIONERS MORE ANALITICAL STUDIES ARE REQUIRED TO EVALUATE THESE ENVIRONMENTS AGAINST USABILITY AND PEDAGOGICAL EFFECTIVENESS CRITERIA. THE PRESENT PAPER PROPOSES AN AUTOMATIC IN-WORLD MONITORING SYSTEM CONSISTING IN SCRIPTED TRACKING OBJECTS AND LEARNING ANALYTICS (LA) TOOLS FOR AN EXPERIMENTAL 3D VIRTUAL CAMPUS. THE RESEARCH WAS CONDUCTED WITHIN A TRANSVERSAL STUDY USING FOCUS GROUPS FROM TWO UNIVERSITIES, AN ENGINEERING AND A VOCATIONAL ONE. THE RESULTS HIGHLIGHTED THE VALIDITY OF THE AUTOMATED SYSTEM AND LA TOOLS, WHICH CAN CONSTITUTE THE GROUND FOR MORE EFFICIENT TEACHING AND LEARNING IN COMPLEX ACTIVITY-BASED 3D EDUCATIONAL SETTINGS.
LEARNING ANALYTICS (LA) AIMS TO ANALYZE THE DATA GENERATED BY BOTH STUDENTS AND TEACHERS IN ONLINE ENVIRONMENTS IN ORDER TO PROMOTE ACTIONS TO IMPROVE TEACHING AND LEARNING PROCESSES. THE RESULTS OF SUCH ANALYZES CAN HELP TEACHERS TO KNOW THEIR STUDENTS' STUDY PROCESSES, AS WELL AS BEING ABLE TO ASSIST WITH THE VERIFICATION AND CORRECTION OF BOTH EDUCATIONAL ACTIVITIES AND PRACTICES. FOR STUDENTS, LA CAN HELP WITH REFLECTION AND SELF-REGULATION OF LEARNING. HOWEVER, DESPITE ITS BENEFITS, INSTITUTIONS HAVE DIFFICULTIES IN ADOPTING IT. IN THIS SENSE, AN INSTRUMENT THAT CAN SUPPORT THE USE OF LA IS THE MATURITY MODEL (MM), WHICH HAS BEEN USED IN DIFFERENT KNOWLEDGE AREAS IN ORDER TO INDICATE AN IMPROVEMENT ROADMAP FOR ORGANIZATIONS. HENCE, THIS PAPER AIMS TO PRESENT THE ASSESSMENT RESULTS OF A MM PROPOSED FOR THE ADOPTION OF LA IN HIGHER EDUCATION INSTITUTIONS, CALLED MMALA. THE EVALUATION FOCUSED ON THE MODEL COMPOSITION AND WAS CARRIED OUT THROUGH A QUESTIONNAIRE ADDRESSED TO LA RESEARCHERS AND PROFESSIONALS. AFTER CONDUCTING ANALYZES, BOTH QUALITATIVE AND QUANTITATIVE, SUGGESTIONS FOR IMPROVEMENT FOR THE PROPOSED MODEL WERE IDENTIFIED, AND THE MODEL WAS VALIDATED, SUPPORTING ITS FURTHER DEVELOPMENT.
THIS PAPER PROVIDES AN ANALYTICAL LEARNING SYSTEM BASED ON FUZZY LOGIC AGGREGATION (FLAG) OF CRISP DATA PARTITIONS TO IMPROVE THE TRIAGE PROCESS IN A HOSPITALITY EMERGENCY DEPARTMENT. THE METHOD COMPARES PATIENT RANKINGS MADE BY NURSES WITH THOSE MADE BY AN EXPERT TO DETECT POINTS FOR IMPROVEMENT. SPECIFICALLY, A NORMALIZED CONCORDANCE INDEX PER NURSE AND THE AVERAGE OF THEM ALLOW FOR THE EVALUATION OF THE ABILITY OF THE NURSES OF WELL AGGREGATING THE CASES IN TRIAGE PROCESS. THE PROPOSED FLAG SYSTEM IS TESTED THROUGH AN EMPIRICAL CASE STUDY BY SIMULATING THE PATIENTS ARRIVING AT TWO EMERGENCY DEPARTMENTS TRIAGE. THE MAIN CONTRIBUTION IS THE DEFINITION OF THE GLOBAL PERFORMANCE INDEX COMBINING BOTH THE NURSE'S PARTITIONING CONCORDANCE WITH RESPECT TO THE EXPERT'S ONE AND THE ACCURACY IN CLASS ASSIGNMENT. THE EMPIRICAL DISTRIBUTION FUNCTION OF THE GLOBAL CONCORDANCE INDEX IS DERIVED THROUGH PERMUTATION METHOD. IN THIS WAY, KOLMOGOROV-SMIRNOV TESTING PROVIDES THE COMPARISON OF THE PERFORMANCES OF THE TWO HEALTHCARE UNITS. THE PAY-OFF TABLE CONCORDANCE-ACCURACY ALLOWS TO ADDRESS IMPROVEMENT ACTIONS. ANOTHER TOOL OF THE SYSTEM IS THE CORRESPONDENCE ANALYSIS TO VISUALIZE THE ACCURACY OF DECISIONS ON THE CLASS PRIORITY AS WELL AS THE SHARING BEHAVIOURS THAT INFLUENCE THE NURSE'S JUDGEMENTS. ALL THIS BECOMES PART OF THE FLAG LEARNING ANALYTICS SYSTEM WHICH IS ABLE TO OUTLINE CRITICAL POINTS BY RED FLAGS. TO IMPROVE FURTHER ASSIGNMENTS AND THE OVERALL MANAGEMENT ORGANIZATION. FLAG SYSTEM CAN BE ADAPTED IN ALL OTHER SITUATIONS OF RISK WHERE COGNITIVE HEURISTICS FACE AN ACCURACY-EFFORT TRADE-OFF SUCH THAT THEIR SIMPLIFIED DECISION PROCESS LEADS TO REDUCED ACCURACY. (C) 2020 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE PRESENT WORK DESCRIBES THE CHALLENGES FACED DURING THE DEVELOPMENT OF A COUNTRYWIDE LEARNING ANALYTICS STUDY AND TOOL FOCUSED ON TRACKING AND UNDERSTANDING THE TRAJECTORIES OF URUGUAYAN STUDENTS DURING THEIR FIRST THREE YEARS OF SECONDARY EDUCATION. DUE TO THE LARGE SCALE OF THE PROJECT, WHICH COVERS AN ENTIRE NATIONAL EDUCATIONAL SYSTEM, SEVERAL CHALLENGES AND CONSTRAINTS WERE FACED DURING ITS CONCEPTION AND DEVELOPMENT. EXAMPLES OF KEY CHALLENGES WERE: TO UNDERSTAND THE DIFFERENT NUANCES OF THE EDUCATIONAL SYSTEM, TO SATISFY ETHICAL AND LEGAL REQUIREMENTS WITHOUT NARROWING DOWN THE SCOPE AND POTENTIAL NOVELTY OF THE INITIATIVE AND TO DEAL WITH INTEGRATION AND INCONSISTENCIES IN THE DATABASES. THIS PAPER PRESENTS THE DESIGN DECISIONS AND SOLUTIONS FOUND TO ADDRESS OR MITIGATE THE PROBLEMS FOUND AS A CONTRIBUTION TO FACILITATE SIMILAR LARGE-SCALE PROJECTS. THREE MAIN EXPERIMENTS USING DATA MINING WERE CONDUCTED AND THEIR RESULTS ARE ALSO DESCRIBED, POINTING OUT THE FEASIBILITY OF FINDING MEANINGFUL PATTERNS THAT CAN BE USED BY EDUCATIONAL AUTHORITIES TO IMPROVE THEIR DECISION MAKING AND FOSTER PUBLIC EDUCATIONAL POLICIES. FINALLY, THE PAPER DESCRIBES THE USE OF THE DATA AND THE FINDINGS OF THE STUDY TO CREATE AN INTERACTIVE VISUAL TOOL TO EXPLORE THE RELATIONSHIP BETWEEN STUDENT VARIABLES, PERFORMANCE AND PERSISTENCE IN THE SYSTEM.
TODAY, WEB ANALYTICS (WA) IS COMMONLY USED TO OBTAIN KEY INFORMATION ABOUT USERS AND THEIR BEHAVIOR ON WEBSITES. BESIDES, WITH THE RISE OF ONLINE LEARNING, LEARNING ANALYTICS (LA) EMERGED AS A SEPARATE RESEARCH FIELD FOR COLLECTING AND ANALYZING LEARNERS' INTERACTIONS ON ONLINE LEARNING PLATFORMS. ALTHOUGH THE FOUNDATION OF BOTH METHODS IS SIMILAR, WA HAS NOT BEEN PROFOUNDLY USED FOR LA PURPOSES. HOWEVER, ESPECIALLY LARGE-SCALE ONLINE LEARNING ENVIRONMENTS MAY BENEFIT FROM WA AS IT IS MORE SOPHISTICATED AND WELL-ESTABLISHED IN COMPARISON TO LA. THEREFORE, THIS PAPER AIMS TO EXAMINE TO WHAT EXTENT WA CAN BE UTILIZED IN THIS CONTEXT, WITHOUT COMPROMISING THE LEARNERS' DATA PRIVACY. FOR THIS PURPOSE, GOOGLE ANALYTICS WAS INTEGRATED INTO THE MASSIVE OPEN ONLINE COURSE PLATFORM OF THE HASSO PLATTNER INSTITUTE AS A PROOF OF CONCEPT. IT WAS TESTED WITH TWO DEPLOYMENTS OF THE PLATFORM: OPENHPI AND OPENSAP, WHERE THOUSANDS OF LEARNERS GAIN ACADEMIC AND INDUSTRY KNOWLEDGE ABOUT ENGINEERING EDUCATION. BESIDES CAPTURING BEHAVIORAL DATA, THE PLATFORMS' EXISTING LA DASHBOARDS WERE EXTENDED BY WA METRICS. THE EVALUATION OF THE INTEGRATION SHOWED THAT WA COVERS A LARGE PART OF THE RELEVANT METRICS AND IS PARTICULARLY SUITABLE FOR OBTAINING AN OVERVIEW OF THE PLATFORM'S GLOBAL ACTIVITY, BUT REACHES ITS LIMITATIONS WHEN IT COMES TO LEARNER-SPECIFIC METRICS.
VIDEO-BASED ONLINE LEARNING IS BECOMING COMMONPLACE IN HIGHER EDUCATION SETTINGS. PRIOR STUDIES HAVE SUGGESTED DESIGN PRINCIPLES AND INSTRUCTIONAL STRATEGIES TO BOOST VIDEO-BASED LEARNING. HOWEVER, LITTLE RESEARCH HAS BEEN DONE ON DIFFERENT LEARNER CHARACTERISTICS, SUCH AS HOW LEARNERS BEHAVE, WHAT BEHAVIORAL PATTERNS THEY EXHIBIT, AND HOW DIFFERENT THEY ARE FROM EACH OTHER. TO FILL THIS RESEARCH GAP IN STUDENT-VIDEO INTERACTION, WE EMPLOYED LEARNING ANALYTICS TO OBTAIN USEFUL INSIGHTS INTO STUDENTS' LEARNING IN THE CONTEXT OF VIDEO-BASED ONLINE LEARNING. FROM 11 LOG BEHAVIORS REPRESENTED BY LOG DATA FROM 72 COLLEGE STUDENTS, FOUR BEHAVIORAL PATTERNS WERE IDENTIFIED WHILE STUDENTS LEARNED FROM VIDEOS: BROWSING, SOCIAL INTERACTION, INFORMATION SEEKING, AND ENVIRONMENT CONFIGURATION. BASED ON THE BEHAVIORAL PATTERNS OBSERVED, PARTICIPANTS WERE CLASSIFIED INTO TWO CLUSTERS. PARTICIPANTS IN THE ACTIVE LEARNER CLUSTER EXHIBITED FREQUENT USE OF SOCIAL INTERACTION, INFORMATION SEEKING, AND ENVIRONMENT CONFIGURATION, WHILE PARTICIPANTS IN THE PASSIVE LEARNER CLUSTER EXHIBITED ONLY FREQUENT BROWSING. WE FOUND THAT ACTIVE LEARNERS EXHIBITED HIGHER LEARNING ACHIEVEMENT THAN PASSIVE LEARNERS.
THE FLIPPED CLASSROOM (FC) IS AN INSTRUCTION METHOD, WHERE "EVENTS THAT HAVE TRADITIONALLY TAKEN PLACE INSIDE THE CLASSROOM NOW TAKE PLACE OUTSIDE AND VICE VERSA", WHICH HAS KNOWN A SIGNIFICANT SURGE OF POPULARITY IN THE PAST DECADE. IN FCS, DIFFERENT TYPES OF ACTIVITIES MAY TAKE PLACE DEPENDING ON THE SESSION TYPE (PRE-CLASS, IN-CLASS, OR AFTER-CLASS), THE LEARNING OBJECTIVES TO FULFIL, THE TYPE AND SIZE OF THE CLASS, THE AVAILABLE INFRASTRUCTURE, THE TIME AVAILABLE ETC. IN ORDER TO SUPPORT THE ACTIVITIES TAKING PLACE IN FCS, INSTRUCTORS CAN USE VARIOUS TECHNOLOGICAL TOOLS AND ONLINE ENVIRONMENT, ESPECIALLY TO SUPPORT THE PREPARATION OF STUDENTS BEFORE CLASS. A MARKED RECENT TREND IN THE FC IS THE INCREASED USE OF LEARNING ANALYTICS (LA) TO SUPPORT THE DEVELOPMENT OF THE FC AND STUDENTS' REFLEXIVE LEARNING. HOWEVER, THERE HAS BEEN NO SYSTEMATIC INVESTIGATION INTO COMBINING LA AND THE FC, AND IT APPEARED THAT THERE WAS A LACK OF RESEARCH ON THE ISSUE. THE AIM OF THIS PAPER IS TO INVESTIGATE THE LITERATURE ON APPLICATIONS OF LA IN FCS, AND TO DETERMINE THE BEST PRACTICES AND NEEDS FOR TECHNOLOGICAL DEVELOPMENT SUPPORTING LA IN THE FC. IN ORDER TO PERFORM THIS STUDY, WE DID A SCOPING REVIEW OF LITERATURE IN THE SUBJECT TO DETERMINE RESEARCH TRENDS IN THE USE OF LA IN THE FC. WE CONCLUDE THAT THERE IS GREAT POTENTIAL TO USE LA IN THE FC, AND TRY TO PROJECT WHERE FURTHER RESEARCH IS HEADING.
MOVING TOWARDS THE NEXT GENERATION OF PERSONALIZED LEARNING ENVIRONMENTS REQUIRES INTELLIGENT APPROACHES POWERED BY ANALYTICS FOR ADVANCED LEARNING CONTEXTS WITH ENRICHED DIGITAL CONTENT. MICRO-LEARNING THROUGH MASSIVE OPEN ONLINE COURSES IS RIDING THE WAVE OF POPULARITY AS A NOVEL PARADIGM FOR DELIVERING SHORT EDUCATIONAL VIDEOS IN SMALL PRE-ORGANIZED CHUNKS OVER TIME, SO THAT LEARNERS CAN GET KNOWLEDGE IN A MANAGEABLE WAY. HOWEVER, WITH THE EVER-INCREASING NUMBER OF VIDEOS, IT HAS BECOME CHALLENGING TO ARRANGE AND SEARCH THEM ACCORDING TO SPECIFIC CATEGORIES. IN THIS PAPER, WE GET AROUND THE PROBLEM BY BRIDGING LEARNING ANALYTICS AND COGNITIVE COMPUTING TO ANALYZE THE CONTENT OF LARGE VIDEO COLLECTIONS, GOING OVER TRADITIONAL TERM-BASED METHODS. WE PROPOSE AN EFFICIENT AND EFFECTIVE APPROACH TO AUTOMATICALLY CLASSIFY A COLLECTION OF EDUCATIONAL VIDEOS ON PRE-EXISTING CATEGORIES WHICH USES (I) A SPEECH-TO-TEXT TOOL TO GET VIDEO TRANSCRIPTS, (II) NATURAL LANGUAGE PROCESSING AND COGNITIVE COMPUTING METHODS TO EXTRACT SEMANTIC CONCEPTS AND KEYWORDS FROM VIDEO TRANSCRIPTS FOR THEIR REPRESENTATION, AND (III) APACHE SPARK AS BIG DATA TECHNOLOGY FOR SCALABILITY. SEVERAL CLASSIFIERS ARE TRAINED ON THE FEATURE VECTORS EXTRACTED BY COGNITIVE COMPUTING TOOLS. THEN, WE COMPARED OUR APPROACH WITH OTHER COMBINATIONS OF STATE-OF-THE-ART FEATURE TYPES AND CLASSIFIERS OVER A LARGE-SCALE DATASET WE COLLECTED FROM COURSERA. CONSIDERING THE EXPERIMENTAL RESULTS, WE EXPECT OUR APPROACH CAN FACILITATE THE DEVELOPMENT OF LEARNING ANALYTICS TOOLS POWERED BY COGNITIVE COMPUTING TO SUPPORT CONTENT MANAGERS ON MICRO-LEARNING VIDEO MANAGEMENT WHILE IMPROVING HOW LEARNERS SEARCH VIDEOS. (C) 2018 ELSEVIER LTD. ALL RIGHTS RESERVED.
LIVE-LINE MAINTENANCE IS A HIGH RISK ACTIVITY. HENCE, LINEWORKERS REQUIRE EFFECTIVE AND SAFE TRAINING. VIRTUAL REALITY TRAINING SYSTEMS (VRTS) PROVIDE AN AFFORDABLE AND SAFE ALTERNATIVE FOR TRAINING IN SUCH HIGH RISK ENVIRONMENTS. HOWEVER, THEIR EFFECTIVENESS RELIES MAINLY ON HAVING MEANINGFUL ACTIVITIES FOR SUPPORTING LEARNING AND ON THEIR ABILITY TO DETECT UNTRAINED STUDENTS. THIS STUDY BUILDS A STUDENT MODEL BASED ON LEARNING ANALYTICS (LA), USING DATA COLLECTED FROM 1399 STUDENTS THAT USED A VRTS FOR THE MAINTENANCE TRAINING OF LINEWORKERS IN 329 COURSES CARRIED OUT FROM 2008 TO 2016. BY EMPLOYING SEVERAL CLASSIFIERS, THE MODEL ALLOWS DISCRIMINATING BETWEEN TRAINED AND UNTRAINED STUDENTS IN DIFFERENT MANEUVERS USING THREE MINIMUM EVALUATION PROFICIENCY SCORES. USING THE BEST CLASSIFIER, A FEATURE IMPORTANCE ANALYSIS IS CARRIED OUT TO UNDERSTAND THE IMPACT OF THE VARIABLES REGARDING THE TRAINEES' FINAL PERFORMANCES. THE MODEL ALSO INVOLVES THE EXPLORATION OF THE TRAINEES' TRACE DATA THROUGH A VISUALIZATION TOOL TO POSE NONOBSERVABLE BEHAVIORAL VARIABLES RELATED TO DISPLAYED ERRORS. THE RESULTS SHOW THAT THE MODEL CAN DISCRIMINATE BETWEEN TRAINED AND UNTRAINED STUDENTS, THE RANDOM FOREST ALGORITHM STANDING OUT. THE FEATURE IMPORTANCE ANALYSIS REVEALED THAT THE MOST RELEVANT FEATURES REGARDING THE TRAINEES' FINAL PERFORMANCE WERE PROFILE AND COURSE VARIABLES ALONG WITH SPECIFIC MANEUVER STEPS. FINALLY, USING THE VISUAL TOOL, AND WITH HUMAN EXPERT AID, SEVERAL ERROR PATTERNS IN TRACE DATA ASSOCIATED WITH MISCONCEPTIONS AND CONFUSION WERE IDENTIFIED. IN THE LIGHT OF THESE, LA ENABLES DISASSEMBLING THE DATA JIGSAW QUANDARY FROM VRTS TO ENHANCE THE HUMAN-IN-THE-LOOP EVALUATION.
LEARNING PROGRAMMING IS A COMPLEX AND CHALLENGING TASK FOR MANY STUDENTS. IT INVOLVES BOTH UNDERSTANDING THEORETICAL CONCEPTS AND ACQUIRING PRACTICAL SKILLS. HENCE, ANALYZING LEARNERS' DATA FROM ONLINE LEARNING ENVIRONMENTS ALONE FAILS TO CAPTURE THE FULL BREADTH OF STUDENTS' ACTIONS IF PART OF THEIR LEARNING PROCESS TAKES PLACE ELSEWHERE. MOREOVER, EXISTING STUDIES ON LEARNING ANALYTICS APPLIED TO PROGRAMMING EDUCATION HAVE MAINLY RELIED ON FREQUENCY ANALYSIS TO CLASSIFY STUDENTS ACCORDING TO THEIR APPROACH TO PROGRAMMING OR TO PREDICT ACADEMIC ACHIEVEMENT. HOWEVER, FREQUENCY ANALYSIS PROVIDES LIMITED INSIGHTS INTO THE INDIVIDUAL TIME-RELATED CHARACTERISTICS OF THE LEARNING PROCESS. THE CURRENT STUDY EXAMINES STUDENTS' STRATEGIES WHEN LEARNING PROGRAMMING, COMBINING DATA FROM THE LEARNING MANAGEMENT SYSTEM AND FROM AN AUTOMATED ASSESSMENT TOOL USED TO SUPPORT STUDENTS WHILE SOLVING THE PROGRAMMING ASSIGNMENTS. THE STUDY INCLUDED THE DATA OF 292 ENGINEERING STUDENTS (228 MEN AND 64 WOMEN, AGED 20-26) FROM THE TWO AFOREMENTIONED SOURCES. TO GAIN AN IN-DEPTH UNDERSTANDING OF STUDENTS' LEARNING PROCESS AS WELL AS OF THE TYPES OF LEARNERS, WE USED LEARNING ANALYTICS METHODS THAT ACCOUNT FOR THE TEMPORAL ORDER OF LEARNING ACTIONS. OUR RESULTS SHOW THAT STUDENTS HAVE SPECIAL PREFERENCES FOR SPECIFIC LEARNING RESOURCES WHEN LEARNING PROGRAMMING, NAMELY, SLIDES THAT SUPPORT SEARCH, AND COPY AND PASTE. WE ALSO FOUND THAT VIDEOS ARE RELATIVELY LESS CONSUMED BY STUDENTS, ESPECIALLY WHILE WORKING ON PROGRAMMING ASSIGNMENTS. LASTLY, STUDENTS RESORT TO COURSE FORUMS TO SEEK HELP ONLY WHEN THEY STRUGGLE.
THIS PAPER PRESENTS THE SMARTLET PROJECT, A COORDINATED RESEARCH PROJECT FUNDED BY THE SPANISH MINISTRY OF SCIENCE, INNOVATION AND UNIVERSITIES, WHICH JUST STARTED IN 2018. THE MAIN AIM OF THIS PROJECT IS TO PROVIDE SUPPORT FOR THE DESIGN AND ORCHESTRATION OF SMART LEARNING ENVIRONMENTS (SLES) WITH THE SUPPORT OF LEARNING ANALYTICS AND THE INTERNET OF THINGS. THIS PAPER GIVES AN OVERVIEW OF OUR CONCEPTION OF SLES BASED ON PREVIOUS WORKS, PROVIDES SOME IDEAS ABOUT THE CONNECTION OF LEARNING DESIGN AND ORCHESTRATION WITH SLES, AND ANALYSES DIFFERENT ETHICAL AND PRIVACY ISSUES FOR SLES. IN ADDITION, AN INITIAL HYPOTHESIS AND SOME SPECIFIC OBJECTIVES FOR A SUPPORT ENVIRONMENT FOR SLES ARE PROPOSED.
THE APPLICATION OF ONLINE LEARNING HAS INCREASED SIGNIFICANTLY, RECENTLY. ONE OF THE KEY SUCCESSES IN ONLINE LEARNING IS STUDENT INTERACTIONS. AN ACTIVE LEARNING STRATEGY WOULD ENGAGE THE STUDENTS TO INTERACT WITH THE COURSE OR TO GET INVOLVED IN THE LEARNING PROCESS. THE OBJECTIVE OF THIS RESEARCH WAS TO PREDICT WHICH ONE OF THE STUDENT ACTIVITIES THAT WOULD IMPROVE THE LEARNING OUTCOME OF THE STUDENTS? ALL THE ACTIVITIES ARE RELATED TO NON-HUMAN INTERACTION. ONE OF THE ACTIVITIES IS CONCEPT MAPPING. ALL THE STUDENTS' ACTIVITIES IN ONLINE LEARNING WERE STORED IN LMS AND THE DATA GENERATED AS A LEARNING ANALYTICS. A LINEAR REGRESSION METHOD WAS USED TO ANALYZE THE DATA. THIS RESEARCH CONFIRMED THAT WORKING ON EXERCISES BY USING CONCEPT MAPPING YIELDS SIGNIFICANT RESULTS IN IMPROVING THE LEARNING OUTCOME OF STUDENTS.
A CHALLENGING AND DEMANDING TASK FOR THE TEACHERS AND RESEARCHERS IN E-LEARNING ENVIRONMENTS IS THE ASSESSMENT OF STUDENTS' PERFORMANCE. THIS PAPER IS TO PRESENT A NEW LEARNING ANALYTICS SYSTEM FOR LEARNING MANAGEMENT SYSTEMS (LMS), THAT WILL AID AND SUPPORT TEACHERS AND RESEARCHERS TO UNDERSTAND AND ANALYZE INTERACTION PATTERNS AND KNOWLEDGE CONSTRUCTION OF THE PARTICIPANTS INVOLVED IN ONGOING ONLINE INTERACTIONS. IT IS SEAMLESSLY INTEGRATED INTO MOODLE. LEARNING MANAGEMENT SYSTEMS (LMS) DOES NOT INCLUDE ANALYTICS TOOL FOR COMPREHENSIVE AUDIT LOGS OF STUDENTS' ACTIVITIES AND LOG ANALYSIS CAPABILITIES INTERACTIONS, ALSO LACK OF GOOD EVALUATION OF PARTICIPATORY LEVEL AND SUPPORT FOR ASSESSMENT OF STUDENTS' PERFORMANCE QUALITY ON LMS. SEMANTIC SIMILARITY MEASURES OF TEXT PLAY AN INCREASINGLY IMPORTANT ROLE IN TEXT RELATED RESEARCH AND APPLICATIONS IN TASKS SUCH AS TEXT MINING, WEB PAGE RETRIEVAL, AND DIALOGUE SYSTEMS. EXISTING METHODS FOR COMPUTING SENTENCE SIMILARITY HAVE BEEN ADOPTED FROM APPROACHES USED FOR MESSAGES TEXTS IN LMS. THE SYSTEM ENABLES ONE TO MEASURE SEMANTIC SIMILARITY BETWEEN TEXTS EXCHANGED DURING COMMUNICATION SESSIONS, IN ORDER TO FIND OUT THE DEGREE OF COHERENCE IN A DISCUSSION TREAD. IT IS GIVEN AS A VALUE OF RELEVANCE IN NUMERICAL FORMAT.
LEARNING ANALYTICS ARE OFTEN FORMATTED AS VISUALISATIONS DEVELOPED FROM TRACED DATA COLLECTED AS STUDENTS STUDY IN ONLINE LEARNING ENVIRONMENTS. OPTIMAL ANALYTICS INFORM AND MOTIVATE STUDENTS' DECISIONS ABOUT ADAPTATIONS THAT IMPROVE THEIR LEARNING. WE OBSERVE THAT DESIGNS FOR LEARNING OFTEN NEGLECT THEORIES AND EMPIRICAL FINDINGS IN LEARNING SCIENCE THAT EXPLAIN HOW STUDENTS LEARN. WE PRESENT SIX LEARNING ANALYTICS THAT REFLECT WHAT IS KNOWN IN SIX AREAS (WE CALL THEM CASES) OF THEORY AND RESEARCH FINDINGS IN THE LEARNING SCIENCES: SETTING GOALS AND MONITORING PROGRESS, DISTRIBUTED PRACTICE, RETRIEVAL PRACTICE, PRIOR KNOWLEDGE FOR READING, COMPARATIVE EVALUATION OF WRITING, AND COLLABORATIVE LEARNING. OUR DESIGNS DEMONSTRATE LEARNING ANALYTICS CAN BE GROUNDED IN RESEARCH ON SELF-REGULATED LEARNING AND SELF-DETERMINATION. WE PROPOSE DESIGNS FOR LEARNING ANALYTICS IN GENERAL SHOULD GUIDE STUDENTS TOWARD MORE EFFECTIVE SELF-REGULATED LEARNING AND PROMOTE MOTIVATION THROUGH PERCEPTIONS OF AUTONOMY, COMPETENCE, AND RELATEDNESS.
ALTHOUGH THE ATTAINMENT GAP BETWEEN BLACK AND MINORITY ETHNIC (BME) STUDENTS AND WHITE STUDENTS HAS PERSISTED FOR DECADES, THE POTENTIAL CAUSES OF THESE DISPARITIES ARE HIGHLY DEBATED. THE EMERGENCE OF LEARNING ANALYTICS ALLOWS RESEARCHERS TO UNDERSTAND HOW STUDENTS ENGAGE IN LEARNING ACTIVITIES BASED ON THEIR DIGITAL TRACES IN A NATURALISTIC SETTING. THIS STUDY INVESTIGATES THE ATTAINMENT GAP BY ANALYSING THE DIFFERENCES IN BEHAVIOURAL ENGAGEMENT BETWEEN DIFFERENT ETHNIC GROUPS. USING MULTILEVEL MODELS OF ACADEMIC PERFORMANCE, DEMOGRAPHICS, AND ONLINE TRACES OF 149,672 STUDENTS ENROLLED IN 401 MODULES IN A DISTANCE LEARNING SETTING, WE CONFIRMED THE EXISTING ATTAINMENT GAP. AFTER CONTROLLING FOR OTHER DEMOGRAPHICS, MODULE CHARACTERISTICS AND ENGAGEMENT, BME STUDENTS WERE BETWEEN 19% AND 79% LESS LIKELY TO COMPLETE, PASS OR ACHIEVE AN EXCELLENT GRADE COMPARED TO WHITE STUDENTS. GIVEN THE SAME ACADEMIC PERFORMANCE, BME STUDENTS SPENT 4-12% MORE TIME ON STUDYING THAN WHITE STUDENTS. WHILE THE ATTAINMENT GAP REMAINED PERSISTENT AFTER CONTROLLING FOR ACADEMIC ENGAGEMENT, OUR STUDY FURTHER HIGHLIGHTED THE INEQUALITY OF ATTAINMENT BETWEEN BME AND WHITE STUDENTS.
THE LITERATURE PROVIDES SOME EVIDENCE FOR THE EFFECTIVENESS OF ONLINE EDUCATIONAL VIDEOS IN STUDENTS' LEARNING. ALSO, GENERAL FEATURES OF EDUCATIONAL VIDEOS SUCH AS PRODUCTION STYLE AND VIDEO LENGTH WERE INVESTIGATED TO SOME EXTENT. HOWEVER, THE ACTUAL COGNITIVE FEATURES OF EDUCATIONAL VIDEOS AVAILABLE ON THE INTERNET HAVE REMAINED ALMOST UNTREATED, SO FAR. THE MAIN GOAL OF THIS STUDY IS TO USE LEARNING ANALYTICS TO INVESTIGATE HOW FAR EDUCATIONAL VIDEOS ON YOUTUBE SUPPORT COGNITIVE FEATURES -AS FORMULATED IN THE COGNITIVE THEORY OF MULTIMEDIA LEARNING- AND HOW FAR THESE FEATURES SUPPORT STUDENTS' LEARNING. WE MEASURE THE VIDEO COGNITIVE VALUE (VCV) BY VIEWERS' RATING, PARTICULARLY USING THE NUMBER OF LIKES BASED ON A SURVEY THAT WE CONDUCTED TO UNDERSTAND THE SEMANTICS OF LIKES AND DISLIKES OF YOUTUBE'S EDUCATIONAL VIDEOS. THEN, A SAMPLE OF 105 VIDEOS WAS COLLECTED AND ANALYZED WITH RESPECT TO COGNITIVE FEATURES. A REGRESSION ANALYSIS SHOWED THAT ONLY FOUR OUT OF TEN INVESTIGATED FEATURES ARE SIGNIFICANT FOR VCV (PRETRAINING, MODALITY, SPATIAL CONTIGUITY, AND EMBODIMENT) AND THE REGRESSION MODEL COULD ONLY EXPLAIN 63% OF THE DATA VARIANCE. FURTHER TESTS WERE PERFORMED TO INCLUDE OTHER FACTORS SUCH AS THE VIDEO PRODUCTION STYLE, THE VIDEO LENGTH, THE TALKING SPEED, THE GENDER OF THE SPEAKER, AND WHETHER SHE/HE SPEAKS ENGLISH AS NATIVE LANGUAGE. THE EXPANDED MODEL SHOWED ONLY A SLIGHTLY IMPROVED ADJUSTED R-SQUARE VALUE (68%). THESE RESULTS SUGGEST THAT FURTHER RESEARCH IS REQUIRED TO IDENTIFY AND SPECIFY ADDITIONAL COGNITIVE FEATURES IN EDUCATIONAL VIDEOS TOWARDS DEEPER UNDERSTANDING OF THE VIDEO COGNITIVE VALUE. (C) 2018 ELSEVIER LTD. ALL RIGHTS RESERVED.
UNIVERSITIES ARE NOW COMPELLED TO ATTEND TO METRICS THAT (RE)SHAPE OUR CONCEPTUALISATION OF THE STUDENT EXPERIENCE. NEW TECHNOLOGIES SUCH AS LEARNING ANALYTICS (LA) PROMISE THE ABILITY TO TARGET PERSONALISED SUPPORT TO PROFILED 'AT RISK' STUDENTS THROUGH MAPPING LARGE-SCALE HISTORIC STUDENT ENGAGEMENT DATA SUCH AS ATTENDANCE, LIBRARY USE, AND VIRTUAL LEARNING ENVIRONMENT ACTIVITY AS WELL AS DEMOGRAPHIC INFORMATION AND TYPICAL STUDENT OUTCOMES. YET SERIOUS ETHICAL AND IMPLEMENTATION ISSUES REMAIN. DATA-DRIVEN LABELLING OF STUDENTS AS 'HIGH RISK', 'HARD TO REACH' OR 'VULNERABLE' CREATES CONFLICT BETWEEN PROMOTING PERSONAL GROWTH AND HUMAN FLOURISHING AND TREATING PEOPLE MERELY AS DATA POINTS. THIS ARTICLE ARGUES THAT UNIVERSITIES MUST RESIST THE ASSUMPTION THAT NUMBERS AND ALGORITHMS ALONE CAN SOLVE THE 'PROBLEM' OF STUDENT RETENTION AND PERFORMANCE; RATHER, LA WORK MUST BE UNDERPINNED BY A RECONNECTION WITH THE AGREED VALUES RELATING TO THE PURPOSE OF HIGHER EDUCATION, INCLUDING DEMOCRATIC ENGAGEMENT, RECOGNITION OF DIVERSE AND INDIVIDUAL EXPERIENCE, AND PROCESSES OF BECOMING. SUCH A RECONNECTION, THIS ARTICLE CONTENDS, IS POSSIBLE WHEN LA WORK IS DESIGNED AND IMPLEMENTED IN GENUINE COLLABORATION AND PARTNERSHIP WITH STUDENTS.
ASSESSMENT IS A CRUCIAL ASPECT OF ANY TEACHING AND LEARNING PROCESS. EDUCATIONAL GAMES OFFER PROMISING ADVANTAGES FOR ASSESSMENT; PERSONALISED FEEDBACK TO STUDENTS AND AUTOMATED ASSESSMENT PROCESS. HOWEVER, WHILE MANY TEACHERS AGREE THAT EDUCATIONAL GAMES INCREASE MOTIVATION, LEARNING AND RETENTION, FEW OF THEM ARE READY TO FULLY TRUST THEM AS AN ASSESSMENT TOOL. WE BELIEVE THERE ARE TWO MAIN REASONS FOR THIS LACK OF TRUST: EDUCATORS ARE NOT GIVEN SUFFICIENT INFORMATION ABOUT THE GAMEPLAYS, AND MANY EDUCATIONAL GAMES ARE DISTRIBUTED AS BLACK-BOXES, UNMODIFIABLE BY TEACHERS. THIS PAPER PRESENTS AN ASSESSMENT ENGINE DESIGNED TO SEPARATE A GAME AND ITS ASSESSMENT. IT ALLOWS TEACHERS TO MODIFY A GAME'S ASSESSMENT AFTER DISTRIBUTION AND VISUALISE GAMEPLAY DATA VIA A LEARNING ANALYTICS DASHBOARD. THE ENGINE WAS EVALUATED QUANTITATIVELY BY 31 EDUCATORS. FINDINGS WERE OVERALL VERY POSITIVE: BOTH THE ASSESSMENT EDITOR AND THE LEARNING ANALYTICS DASHBOARD WERE RATED USEFUL AND EASY TO USE. THE EVALUATION ALSO INDICATES THAT, HAVING ACCESS TO ENGAGE, EDUCATORS WOULD BE MORE LIKELY TO TRUST A GAME'S ASSESSMENT. THIS PAPER CONCLUDES THAT ENGAGE CAN BE USED BY EDUCATORS EFFECTIVELY TO MODIFY EDUCATIONAL GAMES' ASSESSMENT AND VISUALISE GAMEPLAY DATA, AND THAT IT CONTRIBUTES TO INCREASING THEIR TRUST IN EDUCATIONAL GAMES AS AN ASSESSMENT TOOL.
A VAST NUMBER OF STUDIES REPORTED EXCITING INNOVATIONS AND PRACTICES IN THE FIELD OF LEARNING ANALYTICS (LA). WHILST THEY PROVIDED SUBSTANTIAL INSIGHTS, MOST OF THESE STUDIES HAVE BEEN IMPLEMENTED IN SINGLE-COURSE OR SMALL-SCALE SETTINGS. THERE ARE ONLY A FEW STUDIES THAT ARE LARGE-SCALE AND INSTITUTIONAL-WIDE ADAPTATIONS OF LA AND HAVE EXPLORED THE STAKEHOLDERS' PERSPECTIVES (I.E., TEACHERS, STUDENTS, RESEARCHERS, MANAGEMENT) AND INVOLVEMENT WITH LA. THIS STUDY REPORTS ON ONE SUCH LARGE-SCALE AND LONG-TERM IMPLEMENTATION OF PREDICTIVE LEARNING ANALYTICS (PLA) SPANNING A PERIOD OF 4 YEARS AT A DISTANCE LEARNING UNIVERSITY. OU ANALYSE (OUA) IS THE PLA SYSTEM USED IN THIS STUDY, PROVIDING PREDICTIVE INSIGHTS TO TEACHERS ABOUT STUDENTS AND THEIR CHANCE OF PASSING A COURSE. OVER THE LAST 4 YEARS, OUA HAS BEEN ACCESSED BY 1159 UNIQUE TEACHERS AND REACHED 23,180 STUDENTS IN 231 UNDERGRADUATE ONLINE COURSES. THE AIM OF THIS STUDY IS TWOFOLD: (A) TO REFLECT ON THE MACRO-LEVEL OF ADOPTION BY DETAILING USAGE, CHALLENGES, AND FACTORS FACILITATING ADOPTION AT AN ORGANISATIONAL LEVEL, AND (B) TO DETAIL THE MICRO-LEVEL OF ADOPTION, THAT IS THE TEACHERS' PERSPECTIVES ABOUT OUA. AMONGST THE FACTORS SHOWN TO BE CRITICAL TO THE SCALABLE PLA IMPLEMENTATION WERE: FACULTY'S ENGAGEMENT WITH OUA, TEACHERS AS "CHAMPIONS", EVIDENCE GENERATION AND DISSEMINATION, DIGITAL LITERACY, AND CONCEPTIONS ABOUT TEACHING ONLINE.
NETWORKED LEARNING PROVIDES OPPORTUNITIES FOR LEARNERS TO DEVELOP THEIR CRITICAL THINKING, AN IMPORTANT 21ST CENTURY COMPETENCY, THROUGH DIALOGUE WITH FELLOW LEARNERS TO CONSIDER OTHER PERSPECTIVES AND NEGOTIATE AND CRITIQUE IDEAS AND ARGUMENTS. HOWEVER, MUCH EXTANT LITERATURE HAS NOT EXAMINED NETWORKED LEARNING ENVIRONMENTS AMONG YOUNGER LEARNERS NOR THE OPTIMAL CONDITIONS FOR ENHANCING CRITICAL THINKING. THEREFORE, A STUDY WAS CARRIED OUT TO INVESTIGATE THESE CONDITIONS. A LEARNING ANALYTICS NETWORKED LEARNING ENVIRONMENT WAS DESIGNED AND 264 SECONDARY THREE STUDENTS PARTICIPATED IN THE 10-WEEK LONG INTERVENTION AS PART OF THEIR ENGLISH CURRICULUM. INDIVIDUAL AND COLLECTIVE SOCIAL NETWORK METRICS, CRITICAL READING SCORES, AND SELF-REPORTED SURVEY DATA WERE USED TO QUANTITATIVELY EVALUATE STUDENTS' CRITICAL READING PERFORMANCE IN RELATION TO THEIR PARTICIPATION IN NETWORKED LEARNING. RESULTS HIGHLIGHT SEVERAL OPTIMAL CONDITIONS, NOTABLY THAT IT IS NOT JUST PARTICIPATION OF THE LEARNER THAT ENHANCES CRITICAL THINKING BUT THE LEARNERS' RECIPROCITY IN REPLYING AND THE DISTANCE OF THOSE POSTS IN THE NETWORK. DISCUSSIONS AND IMPLICATIONS OF THE FINDINGS FOLLOW TO PROVIDE INSIGHTFUL UNDERSTANDING OF HOW THE RICH AND COMPLEX SETTINGS OF NETWORKED LEARNING CAN ENHANCE CRITICAL THINKING CAPACITIES IN SECONDARY SCHOOLING.
LEARNING ANALYTICS IS THE INTELLIGENT USE OF DATA GENERATED FROM STUDENTS WITH THE OBJECTIVE OF UNDERSTANDING AND IMPROVING THE TEACHING AND LEARNING PROCESS. CURRENTLY, THERE IS A LACK OF TOOLS TO MEASURE THE DEVELOPMENT OF COMPLEX SKILLS IN REAL CLASSROOM ENVIRONMENTS THAT ARE FLEXIBLE ENOUGH TO ADD AND PROCESS DATA FROM DIFFERENT SENSORS AND ORIENTED TOWARDS A MASSIVE PUBLIC. BASED ON THIS FINDING, WE DEVELOPED A FREE SOFTWARE SYSTEM THAT PERMITS TO CAPTURE AND TO VISUALIZE A SET OF 10 BODY POSTURES USING THE MICROSOFT KINECT SENSOR, ALONG WITH THE ABILITY TO TRACK CUSTOM BODY POSTURES AND DATA FROM OTHER SENSORS. THE DEVELOPED TOOL WAS VALIDATED BY MEANS OF PRECISION AND USABILITY TESTS. FURTHERMORE, WITH THE GOAL OF DEMONSTRATING THE POTENTIAL OF INCORPORATING THIS TYPE OF SOFTWARE INTO THE CLASSROOM, THE SOFTWARE WAS USED AS A TOOL TO GIVE FEEDBACK TO THE TEACHER AND TO THE STUDENTS AT THE MOMENT OF GIVING AND EVALUATING ORAL PRESENTATIONS. ALSO, A CLUSTERING ANALYSIS OF DATA GATHERED FROM 45 STUDENT PRESENTATIONS MDICATE THAT PRESENTATIONS ON SIMILAR TOPICS WITH ALSO SIMILAR COMPLEXITY LEVELS CAN BE SUCCESSFULLY DISCRIMINATED.
ALTHOUGH ASYNCHRONOUS ONLINE DISCUSSION (AOD) IS INCREASINGLY USED AS A MAIN ACTIVITY FOR BLENDED LEARNING, MANY STUDENTS FIND IT DIFFICULT TO ENGAGE IN DISCUSSIONS AND REPORT LOW ACHIEVEMENT. EARLY PREDICTION AND TIMELY INTERVENTION CAN HELP POTENTIAL LOW ACHIEVERS GET BACK ON TRACK AS EARLY AS POSSIBLE. THIS STUDY PRESENTED A DATA MINING PROCESS TO CONSTRUCT PROXY VARIABLES THAT REFLECT THEORETICAL AND EMPIRICAL EVIDENCE AND MEASURED THE ACCURACY OF A PREDICTION MODEL THAT INCORPORATED ALL OF THE VARIABLES FOR VALIDATION. FOR THE EMPIRICAL STUDY, DATA WERE OBTAINED FROM 105 UNIVERSITY STUDENTS WHO WERE ENROLLED IN TWO BLENDED LEARNING COURSES THAT USED AOD AS THEIR MAIN ACTIVITY. THE RESULTS INDICATED THE HIGH ACCURACY OF THE PREDICTION MODEL AS WELL AS THE POSSIBILITY OF EARLY DETECTION AND TIMELY INTERVENTIONS. IN ADDITION, WE EXAMINED PARTICIPANTS' LEARNING BEHAVIORS IN THE TWO COURSES USING THE PROXY VARIABLES AND PROVIDED SUGGESTIONS FOR PRACTICE. THE IMPLICATIONS OF THIS STUDY FOR EDUCATION DATA MINING AND LEARNING ANALYTICS ARE DISCUSSED. (C) 2016 ELSEVIER INC. ALL RIGHTS RESERVED.
WITH THE DEVELOPMENT OF A TECHNOLOGY-SUPPORTED ENVIRONMENT, IT IS PLAUSIBLE TO PROVIDE RICH PROCESS-ORIENTED FEEDBACK IN A TIMELY MANNER. IN THIS PAPER, WE DEVELOPED A LEARNING ANALYTICS DASHBOARD (LAD) BASED ON PROCESS-ORIENTED FEEDBACK IN ITUTOR TO OFFER LEARNERS THEIR FINAL SCORES, SUB-SCALE REPORTS, AND CORRESPONDING SUGGESTIONS ON FURTHER LEARNING CONTENT. WE ADOPTED A QUASI-EXPERIMENTAL DESIGN TO INVESTIGATE THE EFFECTIVENESS OF THE REPORT ON STUDENTS' LEARNING. NINETY-FOUR FRESHMAN FROM TWO CLASSES PARTICIPATED IN THIS RESEARCH. THE TWO CLASSES WERE DIVIDED INTO THE LAD GROUP AND THE ORIGINAL ANALYTICS REPORT (OAR) BASED ON A PRODUCT-ORIENTED FEEDBACK GROUP. BEFORE THE EXPERIMENT, ALL THE STUDENTS TOOK THE PRIOR KNOWLEDGE ASSESSMENT. AFTER A SEMESTER'S INSTRUCTION, ALL THE STUDENTS TOOK THE POST-TEST OF THE SUMMATIVE ASSESSMENT. RESULTS INDICATED THAT STUDENTS IN THE LAD GROUP EXPERIENCED BETTER LEARNING EFFECTIVENESS THAN STUDENTS IN THE OAR GROUP. LAD BASED ON PROCESS-ORIENTED FEEDBACK WAS ALSO EFFECTIVE IN IMPROVING THE SKILL LEARNING EFFECTIVENESS OF THE STUDENTS WITH LOW-LEVEL PRIOR KNOWLEDGE.
ITEM RESPONSE THEORY (IRT) PROVIDES MORE ACCURATE AND FAIRER EVALUATIONS OF INDIVIDUAL ABILITIES THAN CLASSICAL TEST THEORY DOES, AND THUS THE IRT HAS GRADUALLY BEEN RECOGNIZED AS ONE OF THE PROPER EVALUATION METHODOLOGIES IN MANY TESTING FIELDS. TEACHING USING TEXTBOOKS WORKS IN UNIVERSITY EDUCATION AS WELL AS SELF-STUDYING USING ONLINE LEARNING SYSTEMS. HOWEVER, THEY HAVE NOT BEEN CONNECTED EACH OTHER. THUS, TO UTILIZE BOTH BENEFICIAL SIDES OF TEXTBOOKS AND THE INTERNET SYSTEM, WE PROPOSE A NEW LEARNING STYLE USING TEXTBOOKS AND ONLINE TESTING FOR EXERCISES USING THE ADAPTIVE ONLINE IRT TESTING SYSTEMS, CALLED "AI ARUTTE". IN THIS PAPER, WE INTRODUCE A NEW USE OF THE ADAPTIVE ONLINE IRT TESTING SYSTEM TO ASSIST SELF-LEARNING IN STUDYING UNDERGRADUATE SUBJECT, LINEAR ALGEBRA, AND WE SHOW ITS LEARNING ANALYTICS. THE COMBINATION OF A MATHEMATICAL TEXTBOOK AND THE ADAPTIVE ONLINE IRT SYSTEM WORKS WELL. "AI ARUTTE" CASE SHOWS THAT STUDENTS FEEL THIS KIND OF SELF-STUDYING IS FUN AND INTERESTING.
EXISTING STUDIES HAVE SHED LIGHT ON POLICIES AND STRATEGIES FOR LEARNING ANALYTICS (LA) ADOPTION, YET THERE IS LIMITED UNDERSTANDING OF ASSOCIATIONS AMONG FACTORS THAT INFLUENCE ADOPTION PROCESSES OR THE CHANGE IN PRIORITIES WHEN INSTITUTIONAL EXPERIENCE WITH LA INCREASES. THIS PAPER ADDRESSES THIS GAP BY PRESENTING A STUDY BASED ON INTERVIEWS WITH INSTITUTIONAL LEADERS FROM 27 EUROPEAN HIGHER EDUCATION INSTITUTIONS. RESULTS SHOWED THAT EXPERIENCED INSTITUTIONS DEMONSTRATED MORE INTEREST IN EXPLORING LEARNING BEHAVIOUR AND PEDAGOGICAL REFORMATION THAN SIMPLY MEASURING A PHENOMENON. EXPERIENCED INSTITUTIONS ALSO PAID MORE ATTENTION TO METHODOLOGICAL APPROACHES TO LA THAN DATA CONSTRAINTS, AND DEMONSTRATED A BROADER INVOLVEMENT OF TEACHERS AND STUDENTS. THIS PAPER ALSO IDENTIFIES INTER-RELATED CONNECTIONS BETWEEN PREVAILING CHALLENGES THAT IMPEDE THE SCALING OF LA. BASED ON THE RESULTS, WE SUGGEST REGULAR EVALUATIONS OF LA ADOPTION TO ENSURE THE ALIGNMENT OF STRATEGY AND DESIRED CHANGES. WE ALSO IDENTIFY THREE AREAS THAT REQUIRE PARTICULAR ATTENTION WHEN FORMING SHORT-TERM GOALS FOR LA AT DIFFERENT PHASES OF ADOPTION
THIS STUDY PRESENTS AN ADVANCED PREDICTIVE LEARNING ANALYTICS SYSTEM, OU ANALYSE (OUA), AND EVIDENCE FROM ITS EVALUATION WITH ONLINE TEACHERS AT A DISTANCE LEARNING UNIVERSITY. OUA IS A PREDICTIVE SYSTEM THAT USES MACHINE LEARNING METHODS FOR THE EARLY IDENTIFICATION OF STUDENTS AT RISK OF NOT SUBMITTING (OR FAILING) THEIR NEXT ASSIGNMENT. TEACHERS HAVE ACCESS, VIA INTERACTIVE DASHBOARDS, TO WEEKLY PREDICTIONS OF RISK OF FAILING FOR EACH OF THEIR STUDENTS. IN THIS STUDY, WE EXAMINED HOW THE DEGREE OF OUA USAGE BY 559 TEACHERS, OF WHICH 189 WERE GIVEN ACCESS TO OUA, RELATED TO STUDENT LEARNING OUTCOMES OF MORE THAN 14 000 STUDENTS IN 15 UNDERGRADUATE COURSES. TEACHERS WHO MADE "AVERAGE" USE OF OUA, THAT IS ACCESSED OUA THROUGHOUT THE LIFE CYCLE OF A COURSE PRESENTATION, AND IN PARTICULAR BETWEEN 10% AND 40% OF THE WEEKS A COURSE WAS RUNNING, AND INTERVENED WITH STUDENTS FLAGGED AS AT RISK WERE FOUND TO BENEFIT THEIR STUDENTS THE MOST; AFTER CONTROLLING FOR DIFFERENCES IN ACADEMIC PERFORMANCE, THESE STUDENTS WERE FOUND TO HAVE SIGNIFICANTLY BETTER PERFORMANCE THAN THEIR PEERS IN THE PREVIOUS YEAR'S COURSE PRESENTATION DURING WHICH THE SAME TEACHERS MADE NO USE OF PREDICTIVE LEARNING ANALYTICS. PREDICTIVE LEARNING ANALYTICS IS AN INNOVATIVE STUDENT'S SUPPORT APPROACH IN ONLINE PEDAGOGY THAT, AS SHOWN IN THIS STUDY, CAN EMPOWER ONLINE TEACHERS IN EFFECTIVELY MONITORING AND INTERVENING WITH THEIR STUDENTS, OVER AND ABOVE OTHER APPROACHES, AND RESULT IN IMPROVED LEARNING OUTCOMES.
PROJECT-BASED LEARNING (PJBL) IS A PROMISING APPROACH FOR SUPPORTING LEARNING OF COMPUTER PROGRAMMING BY ADDRESSING THE GAP BETWEEN THE ATTAINMENT OF ABSTRACT KNOWLEDGE AND THE APPLICATION OF THIS KNOWLEDGE TO AUTHENTIC PROGRAMMING TASKS. THE WORLD WIDE WEB HAS CONSIDERABLE POTENTIAL TO EXPAND AND IMPROVE PJBL ENVIRONMENTS. HOWEVER, MAKING IMPLICIT ASPECTS OF A PROGRAMMING TASK ACCESSIBLE TO LEARNERS AND INSTRUCTORS TO SUPPORT REFLECTION AND IMPROVEMENT CAN BE CHALLENGING. THIS PAPER DISCUSSES THE CHALLENGE OF PJBL IN PROGRAMMING EDUCATION AND OUTLINES A SET OF KEY ASPECTS AND AN ANALYSIS FRAMEWORK TO INFORM DESIGN AND ANALYSIS OF PJBL PROGRAMMING IN WEB-BASED ENVIRONMENTS, BY EXPLOITING THE EMERGING FIELD OF TEACHING AND LEARNING ANALYTICS. BASED ON THE PROPOSED ANALYSIS FRAMEWORK, THE DESIGN OF AN EMPIRICAL STUDY IS OUTLINED, CAPITALIZING ON A NOVEL WEB-BASED PJBL ENVIRONMENT THAT MAKES COMPLEX COGNITIVE PROCESSES ACCESSIBLE AND AFFORDS THE ANALYSIS OF ITS EFFECTS ON LEARNING PROGRAMMING IN MULTIPLE ASPECTS.
LEARNING ANALYTICS AND MULTIMODAL LEARNING ANALYTICS ARE CHANGING THE WAY OF ANALYSING THE LEARNING PROCESS WHILE STUDENTS INTERACT WITH AN EDUCATIONAL CONTENT. THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW AIMED AT DESCRIBING PRACTICES IN RECENT MULTIMODAL LEARNING ANALYTICS AND LEARNING ANALYTICS RESEARCH LITERATURE IN ORDER TO IDENTIFY TOOLS AND STRATEGIES USEFUL FOR THE ASSESSMENT OF THE PROGRESS AND BEHAVIOUR OF CHILDREN UNDER 6 YEARS OLD IN RESPECT OF THEIR LEARNING. THE PURPOSE IS TO PROVIDE GUIDANCE FOR MULTIMODAL LEARNING ANALYTICS RESEARCH WITH CHILDREN UNDER 6 YEARS OLD TO ASSESS THEIR ENGAGEMENT IN A TASK, THEIR EMOTIONS, ATTENTION, UNDERSTANDING AND ACHIEVEMENT OF A GOAL. THE CURRENT STATE OF KNOWLEDGE ON MULTIMODAL LEARNING ANALYTICS RESEARCH SUGGESTS HOW PERFORMANCE ANALYTICS, FACE AND SPEECH RECOGNITION SYSTEMS, EYE TRACKING, KINECT ANALYTICS AND WRISTBANDS COULD BE USED WITH CHILDREN. THE RESULTS SHOW THE COMPLEXITY OF COLLECTING DATA USING NON-INVASIVE METHODOLOGIES WITH CHILDREN UNDER 6 YEARS OLD. ETHICAL IMPLICATIONS RELATED TO MULTIMODAL DATA FROM AUDIO, VISUAL, BIOMETRIC AND QUANTITATIVE MEASURES OF CHILD BEHAVIOUR ARE DISCUSSED.
ADDING UNCERTAINTY INFORMATION TO VISUALIZATIONS IS BECOMING INCREASINGLY COMMON ACROSS DOMAINS SINCE ITS ADDITION HELPS ENSURE THAT INFORMED DECISIONS ARE MADE. THIS WORK HAS SHOWN THE DIFFICULTY THAT IS INHERENT TO REPRESENTING UNCERTAINTY. MOREOVER, THE REPRESENTATION OF UNCERTAINTY HAS YET TO BE THOROUGHLY EXPLORED IN EDUCATIONAL DOMAINS EVEN THOUGH VISUALIZATIONS ARE OFTEN USED IN EDUCATIONAL REPORTING. WE ANALYZED 50 UNCERTAINTY-AUGMENTED VISUALIZATIONS FROM VARIOUS DISCIPLINES TO MAP OUT HOW UNCERTAINTY HAS BEEN REPRESENTED. WE THEN ANALYZED 106 VISUALIZATIONS FROM EDUCATIONAL REPORTING SYSTEMS WHERE THE LEARNER CAN SEE THE VISUALIZATION; THESE VISUALIZATIONS PROVIDE LEARNERS WITH INFORMATION ABOUT SEVERAL FACTORS INCLUDING THEIR KNOWLEDGE, PERFORMANCE, AND ABILITIES. THIS ANALYSIS MAPPED THE DESIGN SPACE THAT HAS BEEN EMPLOYED TO COMMUNICATE A LEARNER'S ABILITIES, KNOWLEDGE, AND INTERESTS. IT ALSO REVEALED SEVERAL OPPORTUNITIES FOR THE INCLUSION OF UNCERTAINTY INFORMATION WITHIN VISUALIZATIONS OF EDUCATIONAL DATA. WE DESCRIBE HOW UNCERTAINTY INFORMATION CAN BE ADDED TO VISUALIZATIONS OF EDUCATIONAL DATA AND ILLUSTRATE THESE OPPORTUNITIES BY AUGMENTING SEVERAL OF THE TYPES OF VISUALIZATIONS THAT ARE FOUND IN EXISTING LEARNING ANALYTICS REPORTS. THE DEFINITION OF THIS DESIGN SPACE, BASED ON A SURVEY OF THE LITERATURE, WILL ENABLE THE SYSTEMATIC EXPLORATION OF HOW DIFFERENT DESIGN DECISIONS AFFECT LEARNER TRUST, UNDERSTANDING, AND DECISION MAKING.
THIS CASE STUDY EXPLORES HOW A SCIENCE TEACHER ADOPTED KNOWLEDGE BUILDING AND LEARNING ANALYTICS TO SUPPORT A CLASS OF PRIMARY FIVE STUDENTS TO COLLABORATIVELY INQUIRE AND LEARN ABOUT ELECTRICITY. SPECIFICALLY, WE AIM TO UNDERSTAND HOW THE TEACHER IMPLEMENTED A LESSON DESIGN GUIDED BY KNOWLEDGE BUILDING PRINCIPLES OF IDEA IMPROVEMENT AND COMMUNITY KNOWLEDGE AND HOW HE USED VISUALISATIONS FROM AN ANALYTICS TOOL TO FACILITATE STUDENTS IN COLLABORATIVE INQUIRY IN SCIENCE. WE COLLECTED STUDENT NOTES FROM THEIR ONLINE DISCOURSE IN KNOWLEDGE FORUM, VIDEO-RECORDED A TOTAL OF 11 LESSON VIDEOS AND CONDUCTED INTERVIEWS WITH THE TEACHER AND STUDENTS. WE FOUND THAT STUDENTS' ONLINE DISCUSSION REFLECTED EXPLANATION-SEEKING QUESTIONS TO SUSTAIN THE INQUIRY ON THE TOPIC AND EXPLANATIONS TO DEEPEN AND IMPROVE THEIR IDEAS ON CONCEPTS OF ELECTRICITY. WE ALSO FOUND THAT THE VISUALISATIONS FROM OUR ANALYTICS TOOL SUPPORTED (I) TEACHER-FACILITATED WHOLE-CLASS DISCUSSIONS ON CURRICULUM KEYWORDS AND STUDENT IDEAS TO DEVELOP CONCEPTUAL UNDERSTANDING AND IDEA-BUILDING, AND (II) STUDENTS IN EXPLORING SCIENCE IDEAS THEY WERE INTERESTED IN. THE FINDINGS FROM OUR STUDY CONTRIBUTE TO THE UNDERSTANDING OF TEACHERS' ENACTMENT OF INQUIRY-SUPPORTED PEDAGOGIES IN PRIMARY SCIENCE CLASSROOMS.
THIS PAPER AIMS TO PROVIDE THE READER WITH A COMPREHENSIVE BACKGROUND FOR UNDERSTANDING CURRENT KNOWLEDGE ON LEARNING ANALYTICS (LA) AND EDUCATIONAL DATA MINING (EDM) AND ITS IMPACT ON ADAPTIVE LEARNING. IT CONSTITUTES AN OVERVIEW OF EMPIRICAL EVIDENCE BEHIND KEY OBJECTIVES OF THE POTENTIAL ADOPTION OF LA/EDM IN GENERIC EDUCATIONAL STRATEGIC PLANNING. WE EXAMINED THE LITERATURE ON EXPERIMENTAL CASE STUDIES CONDUCTED IN THE DOMAIN DURING THE PAST SIX YEARS (2008-2013). SEARCH TERMS IDENTIFIED 209 MATURE PIECES OF RESEARCH WORK, BUT INCLUSION CRITERIA LIMITED THE KEY STUDIES TO 40. WE ANALYZED THE RESEARCH QUESTIONS, METHODOLOGY AND FINDINGS OF THESE PUBLISHED PAPERS AND CATEGORIZED THEM ACCORDINGLY. WE USED NON-STATISTICAL METHODS TO EVALUATE AND INTERPRET FINDINGS OF THE COLLECTED STUDIES. THE RESULTS HAVE HIGHLIGHTED FOUR DISTINCT MAJOR DIRECTIONS OF THE LA/EDM EMPIRICAL RESEARCH. WE DISCUSS ON THE EMERGED ADDED VALUE OF LA/EDM RESEARCH AND HIGHLIGHT THE SIGNIFICANCE OF FURTHER IMPLICATIONS. FINALLY, WE SET OUR THOUGHTS ON POSSIBLE UNCHARTED KEY QUESTIONS TO INVESTIGATE BOTH FROM PEDAGOGICAL AND TECHNICAL CONSIDERATIONS.
LEARNERS ARE EXPECTED TO STAY WAKEFUL AND FOCUSED WHILE INTERACTING WITH E-LEARNING PLATFORMS. ALTHOUGH WAKEFULNESS OF LEARNERS STRONGLY RELATES TO EDUCATIONAL OUTCOMES, DETECTING DROWSY LEARNING BEHAVIORS ONLY FROM LOG DATA IS NOT AN EASY TASK. IN THIS STUDY, WE DESCRIBE THE RESULTS OF OUR RESEARCH TO MODEL LEARNERS' WAKEFULNESS BASED ON MULTIMODAL DATA GENERATED FROM HEART RATE, SEAT PRESSURE, AND FACE RECOGNITION. WE COLLECTED MULTIMODAL DATA FROM LEARNERS IN A BLENDED COURSE OF INFORMATICS AND CONDUCTED TWO TYPES OF ANALYSIS ON THEM. FIRST, WE CLUSTERED FEATURES BASED ON LEARNERS' WAKEFULNESS LABELS AS GENERATED BY HUMAN RATERS AND RAN A STATISTICAL ANALYSIS. THIS ANALYSIS HELPED US GENERATE INSIGHTS FROM MULTIMODAL DATA THAT CAN BE USED TO INFORM LEARNER AND TEACHER FEEDBACK IN MULTIMODAL LEARNING ANALYTICS. SECOND, WE TRAINED MACHINE LEARNING MODELS WITH MULTICLASS-SUPPORT VECTOR MACHINE (SVM), RANDOM FOREST (RF) AND CATBOOST CLASSIFIER (CATBOOST) ALGORITHMS TO RECOGNIZE LEARNERS' WAKEFULNESS STATES AUTOMATICALLY. WE ACHIEVED AN AVERAGE MACRO-F1 SCORE OF 0.82 IN AUTOMATED USER-DEPENDENT MODELS WITH CATBOOST. WE ALSO SHOWED THAT COMPARED TO UNIMODAL DATA FROM EACH SENSOR, THE MULTIMODAL SENSOR DATA CAN IMPROVE THE ACCURACY OF MODELS PREDICTING THE WAKEFULNESS STATES OF LEARNERS WHILE THEY ARE INTERACTING WITH E-LEARNING PLATFORMS.
NOWADAYS THE USE OF THE INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) IS MORE AND MORE COMMON IN THE LEARNING AND TEACHING PROCESS. MODERN FORMS OF EDUCATION HAVE RISEN AND THESE REQUIRE THE IMPLEMENTATION OF NEW LEARNING PARADIGMS: SITUATED COGNITION, STUDENT-CENTRED LEARNING, DISTRIBUTED COGNITION, CONSTRUCTIVISM, AND COMMUNITIES OF PRACTICE, AMONG OTHERS. HOWEVER THERE IS AN IMPORTANT PROBLEM THAT CONCERNS EDUCATORS, THE LACK OF STUDENT MOTIVATION AND ENGAGEMENT IN EDUCATION, ESPECIALLY IN THE E-LEARNING ENVIRONMENT (ONLINE EDUCATION, ONLINE COURSES), WHERE THE MOTIVATION AND THE ACTIVE ROLE OF STUDENTS ARE DEFINITELY THE KEY. THEREFORE, THE INTEREST OF RESEARCHERS IN THE SUBJECT OF GAMIFICATION, WHICH MAIN PILLARS ARE MOTIVATION, PROGRESSIVENESS AND INSTANT FEEDBACK, HAS INCREASED. FURTHERMORE, INFLUENCE OF GAMIFICATION, DEPENDING ON UTILIZED COMPONENTS (CHALLENGE, CURIOSITY, COMPETION, RECOGNITION, ETC) MAY HAVE DIFFERENT EFFECTS ON STUDENTS. THUS, THERE IS A NEED OF DEEPER UNDERSTANDING OF DEPENDENCIES BETWEEN ENGAGEMENT AND IMPLEMENTED GAMIFICATION ELEMENTS. THE GOAL OF SUCH ANALYSIS COULD BE PERSONALIZATION OF AN E-LEARNING SYSTEM BASED ON A MODEL THAT ENABLES MANAGEMENT OF GAMIFICATION PROCESS INDIVIDUALLY FOR EVERY STUDENT. THE PURPOSE OF THIS PAPER IS TO INVESTIGATE HOW GAMIFICATION AFFECTS A MATHEMATICS ONLINE COURSE THAT IS USING THIS MODEL AS A PEDAGOGICAL STRATEGY. IT RELATES SUCH STRATEGY WITH ACTIVE LEARNING PRACTICES AND DISCUSSES ITS EFFECTIVENESS, INVESTIGATING HOW GAMIFICATION CAN MOTIVATE STUDENTS TO PARTICIPATE MORE DEEPLY AND EVEN TO CHANGE THEIR SELF-CONCEPT AS LEARNERS. MOREOVER, THE LEARNING ANALYTICS AND DATA ANALYSIS SHOWS THAT IMPLEMENTATION OF THE GAMIFICATION COMPONENTS CONTRIBUTES TO THE ENGAGEMENT OF STUDENTS IN AN E-LEARNING ENVIRONMENT, HAVING A POSITIVE IMPACT ON THE FINAL GRADE.
THE PRESENT WORK IS PART OF A BROADER RESEARCH RELATED TO THE IMPROVEMENT OF THE TEACHING METHODOLOGY, IN THE UNDERGRADUATES DEGREE OF ARCHITECTURE AND BUILDING ENGINEERING THROUGH THE APPLICATION OF NEW TECHNOLOGIES. THE AIM OF THE PROPOSAL IS TO CONFIRM THAT CHANGES IN THE TEACHING METHODOLOGY IMPROVE THE LEARNING EXPERIENCE, IN OUR CASE USING A LEARNING ANALYTICS APPROACH. IN THIS CASE STUDY, WE FOCUSED IN ONE FIRST YEAR SUBJECT: DESCRIPTIVE GEOMETRY, WHICH HAS A HIGH RATE OF REPEATING STUDENTS, AS WELL AS AN EARLY DROPOUT. WE HAVE IMPLEMENTED AN EDUCATIONAL DATA MINING MIXED APPROACH RELATED TO THE MID-SEMESTER EXAMS (MIDTERM), AND WE STABLISHED A RELATION WITH THE FINAL MARKS OF THE SUBJECT IN TWO PERIODS WITH DIFFERENTIATED METHODOLOGIES, PRE-BOLOGNA (2006-09), AND POST-BOLOGNA (2015-18). THE OBJECTIVE OF THIS ANALYSIS IS TO PREDICT WHAT STUDENTS ARE CLOSER TO LEAVE THE COURSE AFTER THE MIDTERM RESULTS BASED ON THE TOPICS EXAMINED, SO THAT WE CAN INFLUENCE AND IMPLEMENT NEW METHODOLOGIES, TECHNOLOGIES AND SYSTEMS TO IMPROVE THESE TOPICS.
INTERNET PLUS MODEL IS INCREASINGLY GETTING MORE POPULARITY. TEACHER'S AND STUDENT'S LONGINGS FOR NEW KNOWLEDGE AND EXPECTATIONS FOR MORE FLEXIBLE TEACHING MODEL IN THE OCEAN OF LEARNING RESOURCES OFFER POWERFUL IMPETUS AND HUGE MARKET. THROUGH THE PREDICTION AND INTERVENTION FROM THE PERSPECTIVE OF LEARNING ANALYTICS LEARNERS CAN BE GUIDED DYNAMICALLY AND SCIENTIFICALLY. UNDER CURRENT RESOURCE DISTRIBUTION, ORDINARY UNIVERSITIES IN SHAANXI PROVINCE ARE SUPPOSED TO OPTIMIZE THE ABUNDANT RESOURCES, ADOPT THE ADVANCED TEACHING PROCEDURES IN MOOCS BASED ON NEED-ORIENTED PLATFORM, SO AS TO CUSTOMIZE MOOC DEVELOPING MODELS WHICH FIT THE NEEDS OF TEACHING PRACTICE OF SHAANXI UNIVERSITIES.
THE "EVERYONE CONNECTED" PROJECT PLAYS A VERY IMPORTANT ROLE IN THE CONSTRUCTION OF THE TWELFTH FIVE-YEAR PLAN OF EDUCATIONAL INFORMATIZATION IN CHINA. THE PROJECT SERVES PERSONAL LEARNING LEVEL, AND EXPRESSES THE REAL IMPLEMENTATION OF THE NEEDS OF PERSONALIZED LEARNING AND QUALITY EDUCATION. BY USING LEARNING ANALYTICS THROUGH THE ORGANIC INTERACTIVE MECHANISM BETWEEN PERSONAL LEARNING SPACE VIA THE "EVERYONE CONNECTED" PROJECT AND "TWO PLATFORMS", PERSONALIZED PUSH OF RESOURCES AND SERVICES AND CUSTOMIZED REPORT OF LEARNING CAN BE EFFECTIVELY REALIZED.
IN THE CURRENT EDUCATION, THERE ARE SEVERAL ANALYTICS STRATEGIES TO SUPPORT EDUCATORS IN THE MONITORING AND ANALYSIS OF COURSE EVENTS. THE SYSTEMS USED TO EXPLORE THE RAW DATA ABOUT FACTS AND PROCESSES IN COURSES CONTEXTS INCLUDE HUMANS AND INFORMATION AND COMMUNICATION TECHNOLOGY. THESE SOCIOTECHNICAL SYSTEMS ARE ENVIRONMENTS THAT ENABLE COLLABORATIVE WORK AMONG HUMANS AND BETWEEN HUMANS AND COMPUTER AGENTS. THESE ENVIRONMENTS HAVE FEATURES THAT DARE TECHNOLOGICAL DETERMINISM IN ORDER TO DEVELOP INNOVATIVE SITUATION AWARENESS AND DECISION SUPPORT APPROACHES. THIS WORK IS ABOUT SYSTEM THINKING AND META-DESIGN TO SUPPORT SOCIOTECHNICAL ENVIRONMENTS IN WHICH THE MANAGEMENT PROCESS OF THE LEARNING ANALYSIS CONSIDERS HUMANS FACTORS, ADDING VALUE TO LEARNING PROCESSES.
THIS PAPER PRESENTS AN EDUCATIONAL DEVELOPMENT METHODOLOGY CURRENTLY DEPLOYED AT THE FACULTY OF SCIENCE AND TECHNOLOGY AT AARHUS UNIVERSITY FOR LINKING LEARNING DESIGN, LEARNING ANALYTICS, AND LMS. THE METHODOLOGY DESCRIBES (1) HOW A LEARNING DESIGN IS DEVELOPED; (2) HOW IT IS IMPLEMENTED IN THE LMS INCLUDING HOW DATA IS COLLECTED, ANALYSED, AND PRESENTED TO THE EDUCATOR THROUGHOUT THE MODULE; AND (3) HOW SUMMATIVE LEARNING ANALYTICS INFORMS THE FUTURE LEARNING DESIGN. THE PAPER ALSO PRESENTS OUR ALTERNATIVE "STEALTH" APPROACH TO LEARNING DESIGN, WHERE THE IDEAS OF LEARNING DESIGN AND LEARNING DESIGN MODELS ARE INFORMALLY AND/OR INDIRECTLY PRESENTED TO THE EDUCATORS.
WE KNOW THAT EMPLOYING COLLABORATIVE LEARNING STRATEGIES DOES NOT LEAD TO PRODUCTIVE COLLABORATIVE LEARNING PER SE. IN FACT, SOME GROUPS ARE DYSFUNCTIONAL AND MIGHT HAVE A DETRIMENTAL INFLUENCE ON GROUP MEMBERS. THIS ISSUE HAS METHODOLOGICALLY BEEN STUDIED THROUGH SELF-REPORTED SURVEYS, TRANSCRIPTS CODING, AND OBSERVATIONAL METHODS. ALTHOUGH THESE METHODS ARE INFORMATIVE, THEY ARE ALSO TIME INTENSIVE, EXHAUSTING AND NOT PRACTICAL TO BE APPLIED IN REAL PRACTICE. SOCIAL NETWORK ANALYSIS (SNA) AND LEARNING ANALYTICS, ON THE OTHER HAND, OPEN THE DOOR FOR USING AUTOMATIC AND NON-INTRUSIVE METHODS THAT CAN HELP US MONITOR GROUP INTERACTIONS. HERE WE STUDY IF SNA COMBINED WITH MACHINE LEARNING TECHNIQUES CAN BE EMPLOYED IN ORDER TO BETTER UNDERSTAND AND PREDICT HOW COLLABORATIVE INTERACTIONS IN ONLINE ENVIRONMENTS AFFECT INDIVIDUAL AND GROUP PERFORMANCE. MORE SPECIFICALLY, WE STUDY THE CORRELATION BETWEEN GROUP INTERACTION PARAMETERS AS MEASURED BY SNA AND THE PERFORMANCE OF GROUPS AND INDIVIDUALS. USING INTERACTION PARAMETERS AND MACHINE LEARNING METHODS, WE IDENTIFY THE INDICATORS THAT BEST PREDICT GROUPS THAT WILL PERFORM AND GAIN AND GROUPS THAT WILL NOT, AS WELL AS INDIVIDUALS WHO GAIN IN PERFORMANCE AND THOSE WHO DO NOT. THE ARTICLE PROVIDES SUPPORT FOR THE IDEA THAT LEARNING ANALYTICS CAN HELP AUTOMATICALLY MONITOR GROUP PERFORMANCE AND OFFER AN OPPORTUNITY FOR EDUCATORS AND LEARNERS TO SUPPORT PRODUCTIVE COLLABORATIVE LEARNING.
LEARNING ANALYTICS (LA) HAS BECOME A KEY AREA OF STUDY IN EDUCOLOGY, WHERE IT COULD ASSIST IN CUSTOMISING TEACHING AND LEARNING. ACCORDINGLY, IT IS PRECISELY THIS DATA ANALYSIS TECHNIQUE THAT IS USED IN A SENSOR-ANALYTIC-DESIGNED TO IDENTIFY STUDENTS WHO ARE AT RISK OF FAILING A COURSE, AND TO PROMPT SUBSEQUENT TUTORING. THIS INSTRUMENT PROVIDES THE TEACHER AND THE STUDENT WITH THE NECESSARY INFORMATION TO EVALUATE ACADEMIC PERFORMANCE BY USING A RISK ASSESSMENT MATRIX; THE TEACHER CAN THEN CUSTOMISE ANY TUTORING FOR A STUDENT HAVING PROBLEMS, AS WELL AS ADAPT THE COURSE CONTENTS. THE SENSOR WAS VALIDATED IN A STUDY INVOLVING 39 STUDENTS IN THE FIRST TERM OF THE ENVIRONMENTAL ENGINEERING PROGRAM AT THE COOPERATIVE UNIVERSITY OF COLOMBIA. PARTICIPANTS WERE ALL ENROLLED IN AN ALGORITHMS COURSE. OUR FINDINGS LED US TO ASSERT THAT IT IS VITAL TO IDENTIFY STRUGGLING STUDENTS SO THAT TEACHERS CAN TAKE CORRECTIVE MEASURES. THE SENSOR WAS INITIALLY CREATED BASED ON THE THEORETICAL STRUCTURE OF THE PROCESSES AND/OR PHASES OF LA. A VIRTUAL CLASSROOM WAS BUILT AFTER THESE PHASES WERE IDENTIFIED, AND THE TOOL FOR APPLYING THE PHASES WAS THEN DEVELOPED. AFTER THE TOOL WAS VALIDATED, IT WAS ESTABLISHED THAT STUDENTS' EDUCATIONAL EXPERIENCES ARE MORE DYNAMIC WHEN TEACHERS HAVE SUFFICIENT INFORMATION FOR DECISION-MAKING, AND THAT TUTORING AND CONTENT ADAPTATION BOOST THE STUDENTS' ACADEMIC PERFORMANCE.
LEARNING PERFORMANCE IS CRUCIAL IN STUDENTS' ACADEMIC LIVES BECAUSE IT OPENS OPPORTUNITIES FOR FUTURE PROFESSIONAL DEVELOPMENT. HOWEVER, CONVENTIONAL EDUCATIONAL PRACTICES DO NOT PROVIDE ALL THE NECESSARY SKILLS FOR UNIVERSITY INSTRUCTORS AND STUDENTS TO SUCCEED IN TODAY'S EDUCATIONAL CONTEXT. IN ADDITION, DUE TO POOR INFORMATION RESOURCES, INEFFECTIVE ICT TOOL UTILIZATION AND THE TEACHING METHODOLOGIES IN DEVELOPING COUNTRIES, PARTICULARLY AFGHANISTAN, A LARGE GAP EXISTS ACROSS CURRICULUM PLANS AND INSTRUCTOR PRACTICES. LEARNING ANALYTICS, AS A NEW EDUCATIONAL INSTRUMENT, HAS MADE IT POSSIBLE FOR HIGHER EDUCATION ACTORS TO RESHAPE THE EDUCATIONAL ENVIRONMENT TO BE MORE EFFECTIVE AND CONSISTENT. IN THIS STUDY, WE ANALYZED MULTIPLE RESEARCH APPROACHES AND THE RESULTS OF ANALYTICS OF VARIOUS LEARNER ASPECTS TO ADDRESS THE AFOREMENTIONED ISSUES. THE RESEARCH METHODS WERE PREDOMINANTLY QUANTITATIVE-CUM-QUALITATIVE. REAL (QUANTITATIVE) DATA WERE COLLECTED BASED ON LEARNERS' EXPLICIT ACTIONS, SUCH AS COMPLETING ASSIGNMENTS AND TAKING EXAMS, AND IMPLICIT ACTIONS, SUCH INTERACTING AND POSTING ON DISCUSSION FORUMS. MEANWHILE, SECONDARY (QUALITATIVE) DATA COLLECTION WAS CONDUCTED ON-SITE AT KABUL POLYTECHNIC UNIVERSITY (KPU); BOTH BLENDED AND TRADITIONAL CLASS SAMPLES WERE INCLUDED. THE RESULTS OF THIS STUDY OFFER INSIGHT INTO VARIOUS ASPECTS OF LEARNERS' BEHAVIORS THAT LEAD TO THEIR SUCCESS AND INDICATE THE BEST ANALYTICAL MODEL/S TO PROVIDE THE HIGHEST PREDICTION ACCURACY. FURTHERMORE, THE RESULTS OF THIS STUDY COULD HELP EDUCATIONAL ORGANIZATIONS ADOPT LEARNING ANALYTICS TO CONDUCT EARLY ASSESSMENTS TO EVALUATE THE QUALITY OF TEACHING AND LEARNING AND IMPROVE LEARNERS' PERFORMANCE.
OVER THE PAST DECADE, THE FIELD OF EDUCATION HAS SEEN STARK CHANGES IN THE WAY THAT DATA ARE COLLECTED AND LEVERAGED TO SUPPORT HIGH-STAKES DECISION-MAKING. UTILIZING BIG DATA AS A MEANINGFUL LENS TO INFORM TEACHING AND LEARNING CAN INCREASE ACADEMIC SUCCESS. DATA-DRIVEN RESEARCH HAS BEEN CONDUCTED TO UNDERSTAND STUDENT LEARNING PERFORMANCE, SUCH AS PREDICTING AT-RISK STUDENTS AT AN EARLY STAGE AND RECOMMENDING TAILORED INTERVENTIONS TO SUPPORT SERVICES. HOWEVER, FEW STUDIES IN VETERINARY EDUCATION HAVE ADOPTED LEARNING ANALYTICS. THIS ARTICLE EXAMINES THE ADOPTION OF LEARNING ANALYTICS BY USING THE RETROSPECTIVE DATA FROM THE FIRST-YEAR PROFESSIONAL DOCTOR OF VETERINARY MEDICINE PROGRAM. THE ARTICLE GIVES DETAILED EXAMPLES OF PREDICTING SIX COURSES FROM WEEK 0 (I.E., BEFORE THE CLASSES STARTED) TO WEEK 14 IN THE SEMESTER OF SPRING 2018. THE WEEKLY MODELS FOR EACH COURSE SHOWED THE CHANGE OF PREDICTION RESULTS AS WELL AS THE COMPARISON BETWEEN THE PREDICTION RESULTS AND STUDENTS' ACTUAL PERFORMANCE. FROM THE PREDICTION MODELS, AT-RISK STUDENTS WERE SUCCESSFULLY IDENTIFIED AT THE EARLY STAGE, WHICH WOULD HELP INFORM INSTRUCTORS TO PAY MORE ATTENTION TO THEM AT THIS POINT.
THE PAPER FOCUSES ON UTILISING LEARNING ANALYTICS TO ASSESS STUDENT PERFORMANCE IN AN ONLINE BUSINESS. THE VALUE OF THIS PAPER IS IN THE LITERATURE REVIEW AND THE PROCESS USED FOR ANALYSIS SINCE IT PROVIDES A WAY OF HOW RESEARCH IN MOODLE LEARNING ANALYTICS OR OTHER SIMILAR SYSTEMS COULD BE LEVERAGED TO TRACK STUDENT PROGRESS AND FOR RETENTION. THE ENGAGEMENT ANALYTICS MODULE IS A RELATIVELY NEW COMPONENT OF MOODLE BUT IT HAS NOT YET BEEN INVESTIGATED WITH RESPECT TO STUDENT PERFORMANCE OR IN ONLINE BUSINESS COURSES. THE PURPOSE OF THIS STUDY WAS TO EXAMINE THE PREDICTIVE RELATIONSHIPS BETWEEN STUDENT ACADEMIC PERFORMANCE AND MOODLE ENGAGEMENT ANALYTICS INDICATORS ALONG WITH STUDENT ONLINE ACTIVITY DATA FROM THE SYSTEM LOGS. UNFORTUNATELY NONE OF THE HYPOTHESISED LEARNING ANALYTICS FACTORS WERE POSITIVELY RELATED TO, NOR COULD THEY PREDICT, STUDENT ACADEMIC PERFORMANCE. HOWEVER, SEVERAL INTERESTING DEDUCTIONS FROM THE LEARNING ANALYTICS DATA GAVE RISE TO IDEAS FOR FURTHER RESEARCH. SENSE MAKING OF PUZZLING STATISTICS SUGGESTED A MEDIATING PATTERN OF STUDENTS' POOR SELF-REGULATION SKILLS BECAUSE MORE FOCUS WAS PUT ON THE ASSIGNMENT REQUIREMENTS BUT LESS ON INTERACTING WITH THE LESSON MATERIALS NEEDED TO COMPLETE THE ASSIGNMENT AND THEREBY RESULTING IN LOWER GRADES.
HOW CAN WE BEST FACILITATE STUDENTS MOST IN NEED OF LEARNING SUPPORT, ENTERING A CHALLENGING QUANTITATIVE METHODS MODULE AT THE START OF THEIR BACHELOR PROGRAMME? IN THIS EMPIRICAL STUDY INTO BLENDED LEARNING AND THE ROLE OF ASSESSMENT FOR AND AS LEARNING, WE INVESTIGATE LEARNING PROCESSES OF STUDENTS WITH DIFFERENT LEARNING PROFILES. SPECIFICALLY, WE CONTRAST LEARNING EPISODES OF TWO CLUSTER ANALYSIS-BASED PROFILES, ONE PROFILE MORE DIRECTED TO DEEP LEARNING AND SELF-REGULATION, THE OTHER PROFILE MORE DIRECTED TOWARD STEPWISE LEARNING AND EXTERNAL REGULATION. IN A PROGRAMME BASED ON PROBLEM-BASED LEARNING, WHERE STUDENTS ARE SUPPOSEDLY BEING PRIMARILY SELF-DIRECTED, THIS FIRST PROFILE IS REGARDED AS BEING OF AN ADAPTIVE TYPE, WITH THE SECOND PROFILE LESS ADAPTIVE. MAKING USE OF A BROAD SPECTRUM OF LEARNING AND LEARNER DATA, COLLECTED IN THE FRAMEWORK OF A DISPOSITIONAL LEARNING ANALYTICS APPLICATION, WE COMPARE THESE PROFILES ON LEARNING DISPOSITIONS, SUCH AS LEARNING EMOTIONS, MOTIVATION AND ENGAGEMENT, LEARNING PERFORMANCE AND TRACE VARIABLES COLLECTED FROM THE DIGITAL LEARNING ENVIRONMENTS. OUTCOMES SUGGEST THAT THE BLENDED DESIGN OF THE MODULE WITH THE DIGITAL ENVIRONMENTS OFFERING MANY OPPORTUNITIES FOR ASSESSMENT OF LEARNING, FOR LEARNING AND AS LEARNING TOGETHER WITH ACTIONABLE LEARNING FEEDBACK, IS USED MORE INTENSIVELY BY STUDENTS OF THE LESS ADAPTIVE PROFILE.
LEARNING ANALYTICS SEEK TO EVALUATE HOW PEOPLE LEARN AND THE ENVIRONMENTS IN WHICH LEARNING OCCURS. INSTITUTIONAL REVIEW BOARDS (IRBS) ARE REQUIRED BY THE CODE OF FEDERAL REGULATIONS AS OVERSEERS OF RESEARCH AT FEDERALLY FUNDED INSTITUTIONS THAT INVOLVE HUMAN SUBJECTS. IN FURTHERANCE OF THIS PURPOSE, IRBS EVALUATE AND APPROVE RESEARCH PROPOSALS BY USING, AT A MINIMUM, THE FOLLOWING THREE FACTORS: RISK BENEFIT ANALYSIS TO DETERMINE WHETHER RESEARCH PROPOSALS SUBJECT HUMAN TEST PARTICIPANTS TO PHYSICAL AND/OR PSYCHOLOGICAL HARM; WHETHER PARTICIPANTS ARE INFORMED AND CONSENT TO PARTICIPATE VOLUNTARILY; AND WHETHER PROCESSES ARE IN PLACE TO KEEP PARTICIPANTS SAFE AND SECURE. THE INTERSECTION OF THESE IRBS AND RESEARCH ON EDUCATION IS AN INTERESTING AND DEVELOPING AREA. ESPECIALLY WHEN EVALUATING PRIVACY AND INFORMATION SECURITY PROTECTIONS IN THE CONTEXT OF EMERGING TECHNOLOGICAL SOLUTIONS, IRBS FACE A CHANGING LANDSCAPE WITH WHICH THEY MAY NOT BE FAMILIAR. THIS CHAPTER WILL DELVE INTO THE CURRENT STATE OF IRBS AND THE CHALLENGES THAT THEY FACE WHEN FULFILLING THEIR ROLE IN TERMS OF THE THREE FACTORS LISTED ABOVE. IN ADDITION, THIS CHAPTER WILL DETAIL THE CHANGING CIRCUMSTANCES AND CHALLENGES THAT IRBS FACE. FINALLY, RECOMMENDATIONS ARE PRESENTED ON HOW TO BEST HANDLE AND PERFORM TO OVERCOME SAID CHALLENGES. THE RECOMMENDATIONS WILL FOCUS ON AFFECTING POLICY, IMPROVING THE PROCESS IRBS USE TO EVALUATE RESEARCH PROPOSALS, AND CREATING A DISCIPLINE WITHIN THE FIELD OF LEARNING ANALYTICS THAT BUILDS UPON ITSELF AND IS PRESERVED FOR FUTURE USE.
OVER THE PAST FIVE YEARS, ETHICS AND PRIVACY AROUND STUDENT DATA HAVE BECOME MAJOR TOPICS OF CONVERSATION IN THE LEARNING ANALYTICS FIELD. HOWEVER, THE MAJORITY OF THESE HAVE BEEN THEORETICAL IN NATURE. THE AUTHORS OF THIS PAPER POSIT THAT MORE DIRECT STUDENT ENGAGEMENT NEEDS TO BE UNDERTAKEN, AND INITIAL DATA FROM INSTITUTIONS BEGINNING THIS PROCESS IS SHARED. WE FIND THAT, WHILE THE MAJORITY OF RESPONDENTS ARE ACCEPTING OF THE USE OF THEIR DATA BY THEIR INSTITUTIONS, APPROVAL VARIES DEPENDING ON THE PROPOSED PURPOSE OF THE ANALYTICS. THERE ALSO APPEAR TO BE NOTABLE VARIATIONS BETWEEN STUDENTS ENROLLED AT UNITED KINGDOM AND AMERICAN INSTITUTIONS.
SEVERAL GOVERNMENTS ACROSS THE WORLD HAVE TEMPORARILY CLOSED EDUCATIONAL INSTITUTIONS DUE TO THE COVID-19 PANDEMIC. IN RESPONSE, NUMEROUS UNIVERSITIES HAVE SEEN A GROWING TREND TOWARDS ONLINE LEARNING SCENARIOS. THUS, LEARNING TAKES PLACE NOT JUST WITHIN A PERSON, BUT WITHIN AND ACROSS THE NETWORKS. HOWEVER, THE CURRENT IMPLEMENTATIONS OF OPEN LEARNING SCENARIOS ARE FACING MANY CHALLENGES, INCLUDING: A) THERE IS NO SYSTEMATIC FEATURE TO TRACK, EVALUATE AND REPORT THE ONLINE LEARNING ACTIVITIES OF STUDENTS; B) LACK OF IMMEDIACY AND PERSONAL FEEDBACK; AND C) LACK OF SELF-REFLECTION. CONSEQUENTLY, LEARNING ANALYTICS (LA) IS ONE OF THE MOST DISTINGUISHED SOLUTIONS TO ADDRESS THESE CHALLENGES. IN THIS STUDY, THE TWO KINDS OF LA DASHBOARD, BEHAVIORAL AND COGNITIVE, HAVE BEEN INVESTIGATED. THE METHODOLOGICAL APPROACH TAKEN IN THIS INVESTIGATION WAS ADOPTED FROM A DESIGN-BASED RESEARCH FRAMEWORK, WHICH BLENDS EMPIRICAL EDUCATIONAL RESEARCH WITH THE THEORY-DRIVEN DESIGN OF ONLINE LEARNING ENVIRONMENTS. THE INITIAL SAMPLE CONSISTED OF 320 UNDERGRADUATE NURSING STUDENTS. THE COHORT WAS DIVIDED INTO TWO GROUPS ACCORDING TO THE TWO TYPES OF LA DASHBOARD. THE FINDINGS HAVE ARGUED THAT BEHAVIORAL DASHBOARDS TOOK PRECEDENCE OVER DATA EVALUATION ITEMS, WHILE COGNITIVE DASHBOARDS HAD THE ADVANTAGE IN ACHIEVING THE GOALS OF LA - E.G., AWARENESS, SELF-REFLECTION, AND IMPACT ON LEARNING PROCESS.
GAMES CAN BE USED AS EFFECTIVE LEARNING TOOLS, PROVED TO ENHANCE PLAYERS' PERFORMANCE IN A WIDE VARIETY OF COGNITIVE TASKS. IN THIS CONTEXT, LEARNING ANALYTICS (LA) CAN BE USED TO IMPROVE GAME QUALITY AND TO SUPPORT THE ACHIEVEMENT OF LEARNING GOALS. IN THIS PAPER, WE INVESTIGATE THE USE OF LA IN DIGITAL PUZZLE GAMES, WHICH ARE COMMONLY USED FOR EDUCATIONAL PURPOSES. WE DESCRIBE OUR APPROACH TO EXPLORE THE WAY PLAYERS LEARN GAME SKILLS AND SOLVE PROBLEMS IN AN OPEN-SOURCE PUZZLE GAME CALLED LIX. WE PERFORMED AN INITIAL STUDY WITH 15 PARTICIPANTS, IN WHICH WE APPLIED PROCESS MINING AND CLUSTER ANALYSIS IN A THREE-STEP ANALYSIS APPROACH. THIS APPROACH CAN BE USED AS A BASIS FOR RECOMMENDING INTERVENTIONS SO AS TO FACILITATE THE PUZZLE-SOLVING PROCESS OF PLAYERS.
THIS PAPER ADDRESSES A RELATIVELY UNEXPLORED AREA IN THE FIELD OF LEARNING ANALYTICS: HOW ANALYTICS ARE TAKEN UP AND USED AS PART OF TEACHING AND LEARNING PROCESSES. INITIAL STEPS ARE TAKEN TOWARDS DEVELOPING DESIGN KNOWLEDGE FOR THIS "MIDDLE SPACE," WITH A FOCUS ON STUDENTS AS ANALYTICS USERS. FIRST, A CORE SET OF CHALLENGES FOR ANALYTICS USE IDENTIFIED IN THE LITERATURE ARE COMPILED. THEN, A PROCESS MODEL IS PRESENTED FOR CONCEPTUALIZING STUDENTS' LEARNING ANALYTICS USE AS PART OF A SELF-REGULATORY CYCLE OF GROUNDING, GOAL-SETTING, ACTION AND REFLECTION-THE STUDENT TUNING MODEL. FINALLY, THE ALIGN DESIGN FRAMEWORK IS PRESENTED WITH INITIAL VALIDATION AS A TOOL FOR PEDAGOGICAL DESIGN THAT ADDRESSES THE IDENTIFIED CHALLENGES AND SUPPORTS STUDENTS' USE OF ANALYTICS AS PART OF THE TUNING PROCESS. TOGETHER, THE FRAMEWORK'S FOUR INTERCONNECTED PRINCIPLES OF INTEGRATION, AGENCY, REFERENCE FRAME AND DIALOGUE / AUDIENCE PROVIDE A USEFUL STARTING POINT FOR FURTHER INQUIRY INTO WELL-DESIGNED LEARNING ANALYTICS IMPLEMENTATIONS.
THERE IS AN INCREASED RECOGNITION THAT LEARNING DESIGN DRIVES BOTH STUDENT LEARNING EXPERIENCE AND QUALITY ENHANCEMENTS OF TEACHING AND LEARNING. THE OPEN UNIVERSITY UK (OU) HAS BEEN ONE OF FEW INSTITUTIONS THAT HAVE EXPLICITLY AND SYSTEMATICALLY CAPTURED THE DESIGNS FOR LEARNING AT A LARGE SCALE. BY APPLYING ADVANCED ANALYTICAL TECHNIQUES ON LARGE AND FINE-GRAINED DATASETS, THE OU HAS BEEN UNPACKING THE COMPLEXITY OF INSTRUCTIONAL PRACTICES, AS WELL AS PROVIDING CONCEPTUAL AND EMPIRICAL EVIDENCE OF HOW LEARNING DESIGN INFLUENCES STUDENT BEHAVIOUR, SATISFACTION, AND PERFORMANCE. THIS STUDY DISCUSSES THE IMPLEMENTATION OF LEARNING DESIGN AT THE OU IN THE LAST TEN YEARS, AND CRITICALLY REVIEWS EMPIRICAL EVIDENCE FROM EIGHT RECENT LARGE-SCALE STUDIES THAT HAVE LINKED LEARNING DESIGN WITH LEARNING ANALYTICS. FOUR FUTURE RESEARCH THEMES ARE IDENTIFIED TO SUPPORT FUTURE ADOPTIONS OF LEARNING DESIGN APPROACHES.
WE USE A RANDOMISED EXPERIMENT TO STUDY THE EFFECT OF OFFERING HALF OF 556 FRESHMAN STUDENTS A LEARNING ANALYTICS DASHBOARD AND A WEEKLY EMAIL WITH A LINK TO THEIR DASHBOARD, ON STUDENT BEHAVIOUR IN THE ONLINE ENVIRONMENT AND FINAL EXAM PERFORMANCE. THE DASHBOARD SHOWS THEIR ONLINE PROGRESS IN THE LEARNING MANAGEMENT SYSTEMS, THEIR PREDICTED CHANCE OF PASSING, THEIR PREDICTED GRADE AND THEIR ONLINE INTERMEDIATE PERFORMANCE COMPARED WITH THE TOTAL COHORT. THE EMAIL WITH DASHBOARD ACCESS, AS WELL AS DASHBOARD USE, HAS POSITIVE EFFECTS ON STUDENT BEHAVIOUR IN THE ONLINE ENVIRONMENT, BUT NO EFFECTS ARE FOUND ON STUDENT PERFORMANCE IN THE FINAL EXAM OF THE PROGRAMMING COURSE. HOWEVER, WE DO FIND DIFFERENTIAL EFFECTS BY SPECIALISATION AND STUDENT CHARACTERISTICS.
MOST TEACHERS TO DATE HAVE ADOPTED SUMMATIVE ASSESSMENT ITEMS AS A BENCHMARK TO MEASURE STUDENTS' LEARNING AND FOR MAKING PEDAGOGICAL DECISIONS. HOWEVER, THESE MAY NOT NECESSARILY PROVIDE COMPREHENSIVE EVIDENCE FOR THE ACTUAL LEARNING PROCESS, PARTICULARLY IN ONLINE LEARNING ENVIRONMENTS DUE TO THEIR FAILURE TO MONITOR STUDENTS' ONLINE LEARNING PATTERNS OVER TIME. IN THIS PAPER, WE EXPLORE HOW SOCIAL LEARNING ANALYTICS (SLA) CAN BE USED AS A PROXY BY TEACHERS TO UNDERSTAND STUDENTS' LEARNING PROCESSES AND TO SUPPORT THEM IN MAKING INFORMED PEDAGOGICAL DECISIONS DURING THE RUN OF A COURSE. THIS STUDY WAS CONDUCTED IN A SEMESTER-LONG UNDERGRADUATE COURSE, AT A LARGE PUBLIC UNIVERSITY IN NORWAY, AND MADE USE OF DATA FROM 4 WEEKLY ONLINE DISCUSSIONS DELIVERED THROUGH THE UNIVERSITY LEARNING MANAGEMENT SYSTEM CANVAS. FIRST, WE USED NODEXL A SOCIAL NETWORK ANALYSIS TOOL TO ANALYZE AND VISUALIZE STUDENTS' ONLINE LEARNING PROCESSES, AND THEN WE USED COH-METRIX, A THEORETICALLY GROUNDED, COMPUTATIONAL LINGUISTIC TOOL TO ANALYZE THE DISCOURSE FEATURES OF STUDENTS' DISCUSSION POSTS. OUR FINDINGS REVEALED THAT SLA PROVIDES INSIGHT AND AN OVERVIEW OF THE STUDENTS' COGNITIVE AND SOCIAL LEARNING PROCESSES IN ONLINE LEARNING ENVIRONMENTS. THIS EXPLORATORY STUDY CONTRIBUTES TO AN IMPROVED CONCEPTUAL UNDERSTANDING OF SLA AND DETAILS SOME OF THE METHODOLOGICAL IMPLICATIONS OF AN SLA APPROACH TO ENHANCE TEACHING AND LEARNING IN ONLINE LEARNING ENVIRONMENTS.
INFORMAL LEARNING HAS BECOME AN IMPORTANT DRIVER FOR PROFESSIONAL DEVELOPMENT AND WORKPLACE LEARNING. YET, HOWEVER POWERFUL INFORMAL LEARNING MAY BE, THERE IS A PROBLEM WHEN IT COMES TO MAKING IT A REAL ASSET WITHIN ORGANIZATIONS: INFORMAL LEARNING ACTIVITIES ARE SPONTANEOUS AND MOSTLY INVISIBLE TO OTHERS. THE AIM OF THIS STUDY IS TO DEVELOP A METHOD THAT HELPS RAISE AWARENESS ABOUT THESE ACTIVITIES USING A LEARNING ANALYTICS APPROACH. THIS METHOD CONCENTRATES ON DETECTING AND VISUALIZING INFORMAL PROFESSIONAL SOCIAL NETWORKS AND FINDING WAYS IN WHICH THEIR PRESENCE AND ACCESSIBILITY CAN BE IMPROVED. THIS STUDY SHOWS THAT THE PRESENTED METHODOLOGY IS A PROMISING RESEARCH-DRIVEN INTERVENTION. WITH THIS METHODOLOGY WE CAN DETECT MULTIPLE (ISOLATED) NETWORKS IN ORGANIZATIONS, CONNECT IDEAS, AND FACILITATE VALUE CREATION. USING THIS APPROACH, ORGANIZATIONS CAN LINK WITH EXISTING INFORMAL NETWORKS OF PRACTICE AND UNLOCK THEIR POTENTIAL FOR ORGANIZATIONAL LEARNING BY GIVING THEM A VOICE AND MAKING THEIR RESULTS MORE EXPLICIT WITHIN THE ORGANIZATION.
THE LEARNING ANALYTICS DASHBOARD (LAD) HAS BEEN RECOGNISED AS A USEFUL TOOL TO FACILITATE TEACHERS' DIAGNOSIS AND INTERVENTION IN COLLABORATIVE LEARNING. HOWEVER, LITTLE EMPIRICAL EVIDENCE HAS BEEN REPORTED CONCERNING THE EFFECTS OF LAD ON TEACHERS IN COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL). THE PURPOSE OF THIS STUDY WAS TO EVALUATE THE EFFECTIVENESS OF A LAD SYSTEM CALLED THE KNOWLEDGE-BEHAVIOR-SOCIAL DASHBOARD (KBSD) FOR SUPPORTING TEACHERS' DIAGNOSIS AND INTERVENTION OF CSCL ACTIVITIES. THIRTY-FIVE PRE-SERVICE TEACHERS PARTICIPATED IN THE EXPERIMENT. THE RESULTS INDICATED THAT TEACHERS USING KBSD USED MORE 'CROSS-GROUP' STRATEGIES TO DIAGNOSE STUDENTS' LEARNING PROBLEMS. TEACHERS IN THE EXPERIMENTAL GROUP ALSO PERFORMED MORE INTERVENTIONS INVOLVING 'COGNITIVE GUIDANCE', 'SCAFFOLD INSTRUCTING', 'POSITIVE EVALUATION' AND 'INDIVIDUAL FACILITATING'. MOREOVER, THE ANALYSIS OF THE INTERVIEW REVEALED THAT THE KBSD TOOL HAS THE POTENTIAL TO ASSIST TEACHERS IN DETECTING LEARNING PROBLEMS IN A GROUP IN AN EASY AND CONVENIENT MANNER. PEDAGOGICAL IMPLICATIONS AND FUTURE RESEARCH ARE ALSO ADDRESSED.
IN THIS GLOBAL INFORMATION AGE, SCHOOLS THAT TEACH PUBLIC AFFAIRS AND ADMINISTRATION MUST MEET THE NEEDS OF STUDENTS. INCREASINGLY, THIS MEANS PROVIDING STUDENTS INFORMATION IN ONLINE CLASSROOMS TO HELP THEM REACH THEIR HIGHEST POTENTIAL. THE ACTS OF TEACHING AND LEARNING ONLINE GENERATE DATA, BUT TO DATE, THAT INFORMATION HAS REMAINED LARGELY UNTAPPED FOR ASSESSING STUDENT PERFORMANCE. USING DATA GENERATED BY STUDENTS IN AN ONLINE MASTER OF PUBLIC ADMINISTRATION PROGRAM, DRAWN FROM THE MARIST COLLEGE OPEN ACADEMIC ANALYTICS INITIATIVE, 1 WE IDENTIFY AND ANALYZE CHARACTERISTICS AND BEHAVIORS THAT BEST PROVIDE EARLY INDICATION OF A STUDENT BEING ACADEMICALLY AT RISK, PAYING PARTICULAR ATTENTION TO THE USAGE OF ONLINE TOOLS. WE FIND THAT FAIRLY SIMPLE LEARNING ANALYTICS MODELS ACHIEVE HIGH LEVELS OF SENSITIVITY (OVER 80% OF AT-RISK STUDENTS IDENTIFIED) WITH RELATIVELY LOW FALSE POSITIVE RATES (13.5%). RESULTS WILL BE USED TO TEST INTERVENTIONS FOR IMPROVING STUDENT PERFORMANCE IN REAL TIME.
ANALOGOUS TO KEYWORDS DESCRIBING THE IMPORTANT AND RELEVANT CONTENT OF A DOCUMENT WE EXTRACT KEY ACTIONS FROM LEARNERS' USAGE DATA ASSUMING THAT THEY REPRESENT IMPORTANT AND RELEVANT PARTS OF THEIR LEARNING BEHAVIOUR. THESE KEY ACTIONS ENABLE THE TEACHERS TO BETTER UNDERSTAND THE DYNAMICS IN THEIR CLASSES AND THE PROBLEMS THAT OCCUR WHILE LEARNING. BASED ON THESE INSIGHTS, TEACHERS CAN INTERVENE DIRECTLY AS WELL AS IMPROVE THE QUALITY OF THEIR LEARNING MATERIAL AND LEARNING DESIGN. WE TEST OUR APPROACH ON USAGE DATA COLLECTED IN A LARGE INTRODUCTORY C PROGRAMMING COURSE AT A UNIVERSITY AND DISCUSS THE RESULTS BASED ON THE FEEDBACK OF THE TEACHERS.
LEARNING ANALYTICS (LA) SYSTEMS CAN OFFER NEW INSIGHTS INTO LEARNERS' BEHAVIOURS THROUGH ANALYSIS OF MULTIPLE DATA STREAMS. THERE REMAINS HOWEVER A DEARTH OF RESEARCH ABOUT HOW LA INTERFACES CAN ENABLE EFFECTIVE COMMUNICATION OF EDUCATIONALLY MEANINGFUL INSIGHTS TO TEACHERS AND LEARNERS. THIS HIGHLIGHTS THE NEED FOR A PARTICIPATORY, HORIZONTAL CO-DESIGN PROCESS FOR LA SYSTEMS. INSPIRED BY THE NOTION OF TRANSLUCENCE, THIS PAPER PRESENTS LAT-EP (LEARNING ANALYTICS TRANSLUCENCE ELICITATION PROCESS), A FIVE-STEP PROCESS TO DESIGN FOR THE EFFECTIVE USE OF TRANSLUCENT LA SYSTEMS. LAT-EP WAS OPERATIONALISED IN AN AUTHENTIC MULTIMODAL LEARNING ANALYTICS (MMLA) STUDY IN THE CONTEXT OF TEAMWORK IN CLINICAL SIMULATION. RESULTS OF THIS PROCESS ARE ILLUSTRATED THROUGH A SERIES OF VISUAL PROXIES CO-DESIGNED WITH TEACHERS, EACH PRESENTING TRACES OF SOCIAL, PHYSICAL, AFFECTIVE AND EPISTEMIC EVIDENCE CAPTURED WHILE TEAMS OF STUDENT NURSES PRACTISED CLINICAL SKILLS IN A SIMULATED HOSPITAL SETTING.
CRITICAL SUCCESS FACTORS (CSFS) HAVE BEEN AROUND SINCE THE LATE 1970S AND HAVE BEEN USED EXTENSIVELY IN INFORMATION SYSTEMS IMPLEMENTATIONS. CSFS PROVIDE A COMPREHENSIVE UNDERSTANDING OF THE MULTIPLE LAYERS AND DIMENSIONS OF IMPLEMENTATION SUCCESS. IN THE SPECIFIC CONTEXT OF LEARNING ANALYTICS (LA), IDENTIFYING CSFS CAN MAXIMISE THE POSSIBILITIES OF AN EFFECTIVE IMPLEMENTATION AND HARNESS THE VALUE OF CONVERTING DATA INTO ACTIONABLE INFORMATION. THIS PAPER PROPOSES A FRAMEWORK THAT AIMS TO IDENTIFY AND EXPLORE THE CSFS FOR THE IMPLEMENTATION OF LA WITHIN THE HIGHER EDUCATION SECTOR BY EXAMINING THE VIEWPOINTS OF HIGHER EDUCATION PROFESSIONALS. TO OBTAIN A ROUNDED INSIGHT INTO STAKEHOLDERS' PERCEPTIONS, WE CONDUCTED A MIXED-METHOD INQUIRY WITH FACTOR ANALYSIS, PROFILE ANALYSIS AND THEMATIC ANALYSIS OF BOTH QUANTITATIVE AND QUALITATIVE DATA COLLECTED WITH AN ONLINE QUESTIONNAIRE FROM AN INTERNATIONAL SAMPLE. THE RESPONSES VALIDATE FIVE CSFS OF LA IMPLEMENTATION: STRATEGY AND POLICY AT ORGANISATIONAL LEVEL, INFORMATION TECHNOLOGICAL READINESS, PERFORMANCE AND IMPACT EVALUATION, PEOPLE'S SKILLS AND EXPERTISE AND DATA QUALITY. RESULTS ALSO DISCLOSE DIVERSE VIEWS ABOUT THE CSFS' PRIORITIES AND THE ASSOCIATED DIFFICULTIES AND ACHIEVEMENTS. IMPLICATIONS FOR PRACTICE OR POLICY: HIGHER EDUCATION PRACTITIONERS SHOULD CONSIDER CSFS FOR IMPLEMENTING AN LA INITIATIVE SUCCESSFULLY. THIS STUDY VALIDATES FIVE DIMENSIONS OF THE CSFS OF IMPLEMENTING LA IN HIGHER EDUCATION. THE VALIDATED FRAMEWORK ENUMERATES SEVERAL FACTORS IN EACH OF THE MAIN DIMENSIONS FOR ACHIEVING OPTIMUM RESULTS. STAKEHOLDERS HAVE DIVERSE OPINIONS ABOUT THE PRIORITIES OF CSFS, PARTICULARLY IN ORGANISATIONAL COMMITMENT, DATA QUALITY AND HUMAN CAPITAL.
IN THIS CHAPTER WE ADDRESS CORE CHALLENGES AND FUNDAMENTAL CONCEPTUAL LENSES FOR EDUCATIONAL RESEARCHERS AND PRACTITIONERS IN THE FIELD OF LEARNING ANALYTICS IN FOUR SECTIONS. FIRST WE ADDRESS THE EMERGING SOCIOTECHNICAL CONTEXT BROUGHT ON BY THE UBIQUITY OF DATA GENERATING COMPUTING DEVICES AND THE SHIFT TOWARD CONTINUOUS HUMAN-DIGITAL INTERACTION. THIS IS ADDRESSED IN TERMS OF THE SHIFT FROM DIGITAL DESERT TO DIGITAL OCEAN DESCRIBED BY DICERBO AND BEHRENS (2012, 2014). NEXT WE INTRODUCE CULTURAL HISTORICAL ACTIVITY THEORY (CHAT) AS A GENERAL TOOL TO UNDERSTAND ANALYTIC SOCIAL CONTEXTS. THIS IS FOLLOWED BY A TREATMENT OF THE FUNDAMENTAL LOGICS OF INFERENCE AS PRESENTED IN EVIDENCE CENTERED DESIGN (ECD). THE FOURTH CONCERN IS THE GENERAL APPROACH TO WORKING WITH QUANTITATIVE MODELS WHICH WE BASE ON THE WORK OF JOHN TUKEY AND THE TRADITIONS OF DATA ANALYSIS (DA) AND EXPLORATORY DATA ANALYSIS (EDA). WE AIM TO HAVE AN INTEGRATED DIALOG AMONGST THESE DIFFERENT TRADITIONS TO INFORM THE WORK OF LEARNING ANALYSTS AND EDUCATIONAL DATA SCIENTISTS, A NEW PROFESSION FOCUSED ON DEVELOPING THE CAPACITY FOR DATA-INFORMED EDUCATIONAL SYSTEMS (PIETY, BEHRENS, & PEA, 2013; PIETY, HICKEY, & BISHOP, 2014).
THIS WORK IS DEVOTED TO PRESENTING SOME STRATEGIES AIMED AT INCREMENTING THE STUDENTS' COMMITMENT WITH THEIR FORMATIVE PROCESS AND, AT THE SAME TIME, ACHIEVING A SATISFACTORY EVALUATIVE PROCEDURE. THE STRATEGIES DESCRIBED IN THIS WORK HAVE BEEN APPLIED IN THE SUBJECT "COMPUTER STRUCTURE AND TECHNOLOGY", WHICH CORRESPONDS TO THE BSC IN COMPUTER SCIENCE SYLLABUS AT THE UNIVERSITY OF ALMERIA. BROADLY SPEAKING, THE SUBJECT DELVES INTO INTERNAL ASPECTS OF COMPUTER OPERATION, SUCH AS SEVERAL DEVICES FOUND IN THE COMPUTER DATA PATH, MEMORY ORGANIZATION, AND THE CONTROL UNIT. THESE ISSUES ARE FREQUENTLY VERY UNPOPULAR AMONG STUDENTS BECAUSE THEY ARE NOT FAMILIAR WITH THE DEEPEST LEVEL OF THE OPERATIONS AND, THEREFORE, THEY USUALLY NEED TO DEDICATE A HUGE AMOUNT OF TIME AND EFFORT TO UNDERSTAND THEM. FOR THESE REASONS, A STUDENT'S LEVEL OF COMPETENCY AT THE ENDO OF THE COURSE IS NOT USUALLY VERY HIGH AND MANY STUDENTS EVEN DROP OUT OF THE SUBJECT IN ITS EARLY STAGES. TO OVERCOME THIS SITUATION, WE HAVE INTRODUCED TWO NOVELTIES IN THE LEARNING METHODOLOGY. ON THE ONE HAND, WE HAVE TAKEN ADVANTAGE OF POSSIBILITIES THAT THE LEARNING MANAGEMENT SYSTEM OF OUR UNIVERSITY (BLACKBOARD) OFFERS USING LEARNING ANALYTICS TO FIND OUT WHAT ACTIVITIES ARE MORE RELEVANT FROM AN ACADEMIC RESULT POINT OF VIEW. ON THE OTHER HAND, WE HAVE PROPOSED EVALUATIVE MENTORING TO PERSONALLY DEAL WITH THE STUDENTS' NECESSITIES. THUS, THE METHODOLOGY COMBINES THE ADVANTAGES OF BOTH, ONLINE AND ON-SITE ACTIVITIES. AS AN OBTAINED RESULT, WE HAVE STUDIED THE VALIDITY OF THE METHODOLOGICAL STRATEGIES, DETECTING THE ISSUES THAT HAVE MORE IMPACT ON THE ASSESSMENT RESULTS. THIS FACT IS VERY HELPFUL TO FOCUS OUR ATTENTION ON THESE ISSUES IN THE FOLLOWING ACADEMIC YEARS. MOREOVER, WE HAVE DEMONSTRATED A SIGNIFICATIVE IMPROVEMENT IN STUDENTS' COMPETENCY LEVELS WITH RESPECT TO PREVIOUS ACADEMIC YEARS IN THE SAME SUBJECT AND OTHER SUBJECTS OF THE SAME COURSE.
THROUGH SIDES 3.0, WE ARE DEVELOPING AN ONTOLOGY-BASED E-LEARNING PLATFORM IN MEDICINE TO MAKE LEARNING ANALYTICS TRANSPARENT AND EXPLAINABLE TO END-USERS (LEARNERS AND TEACHERS). IN THIS PROJECT, THE EDUCATIONAL CONTENT, THE TRACES OF STUDENTS' ACTIVITIES AND THE CORRECTION OF EXAMS ARE LINKED AND RELATED TO ITEMS OF AN OFFICIAL REFERENCE PROGRAM IN A UNIFIED DATA MODEL. AS A RESULT, AN INTEGRATED ACCESS TO USEFUL INFORMATION IS PROVIDED FOR STUDENT PROGRESS MONITORING AND EQUIPPED WITH A POWERFUL QUERY LANGUAGE ALLOWING USERS TO EXPRESS THEIR SPECIFIC NEEDS RELATING TO DATA EXPLORATION AND ANALYSIS.
ADVANCES IN MOBILE TECHNOLOGY ARE PROVIDING NEW OPPORTUNITIES FOR MOBILE LEARNING. AUGMENTED REALITY (AR) TECHNOLOGIES MAY BE USED ON MOBILE DEVICES AND HAVE THE POTENTIAL TO FACILITATE IMPROVEMENTS IN LEARNING PERFORMANCE. MEANWHILE, THE AMOUNT OF DATA AVAILABLE ABOUT LEARNERS AND THEIR LEARNING ACTIVITIES IS RAPIDLY INCREASING. USING LEARNING ANALYTICS ALLOWS THE COLLECTION OF LEARNING DATA TO EFFECTIVELY ANALYZE LEARNING BEHAVIORS AND PROCESSES. IN THIS STUDY, WE DESIGNED AND DEVELOPED AN AR LEARNING SYSTEM BASED ON THE TEMPORAL AND SPATIAL CONTINUITY PRINCIPLES OF THE MULTIMEDIA LEARNING THEORY TO AID JAPANESE COMPOUND VERB ACQUISITION. WE ALSO EMPLOYED THE LEARNER-TO-LEARNING MATERIALS INTERACTION TO ANALYZE THE RELATIONSHIP BETWEEN LEARNING BEHAVIORS AND LEARNING PERFORMANCE. THE RESULTS SHOWED THAT THE USE OF AN AR LEARNING SYSTEM ASSISTED LEARNERS IN IMPROVING THEIR LEARNING AND RETENTION OF COMPOUND VERBS, WHEREAS REPEATED TRIALS OF THE LEARNING BEHAVIOR ENHANCED THE LEARNING OF COMPOUND VERBS.
WITH EDUCATION BEING A CORE DRIVER IN THE MODERN INDIVIDUAL'S SELF-REALIZATION, THE QUALITY OF THIS EDUCATION MUST BE SYSTEMATICALLY MANAGED. THIS PAPER EXPLORES THE COMPLEXITIES OF MEASURING AND OPTIMIZING THE QUALITY OF LECTURES. IN THIS PAPER A CONCEPT FOR A LEARNING ANALYTIC SYSTEM (LAS) IS PRESENTED, WHICH HAS BEEN DEVELOPED THROUGH MULTIPLE ITERATIONS, CONSISTING OF TWO INTERNET OF THINGS (IOT) PROTOTYPES AND ONE HIGH-LEVEL CONCEPT. THE PROTOTYPES WERE USED TO EXPLORE THE FEASIBILITY OF COLLECTING CLASSROOM DATA AS WELL AS IMPLEMENTING A TEACHER FEEDBACK LOOP. THE CONCEPTUAL LAS WAS USED TO EXPLORE THE TEACHERS' OPINION AS WELL AS A POTENTIAL VALUE PROPOSITION FOR SUCH A SYSTEM. THE RESULTS CONTAIN A DESCRIPTION AND A VISUAL MOCKUP FOR THIS PROPOSED LAS, CALLED EDUMONDO, THAT COULD SYSTEMATICALLY IMPROVE THE QUALITY OF LECTURING. IN CONCLUSION, THIS PAPER PRESENTS SEVERAL OPPORTUNITIES, CHALLENGES, AND RECOMMENDATIONS FOR IMPLEMENTING TEACHER AND CLASSROOM ASSISTING TECHNOLOGIES. THIS PAPER SHOULD SERVE AS A PLATFORM FOR PUBLIC DISCUSSION ON HOW EDUCATION SHOULD AND SHOULD NOT CHANGE, IN THE FACE OF TECHNOLOGIES PUSHING TOWARDS A PARADIGM SHIFT IN EDUCATION.
PURPOSE WITH THE GROWTH OF DIGITAL EDUCATION, STUDENTS INCREASINGLY INTERACT IN A VARIETY OF WAYS. THE POTENTIAL EFFECTS OF THESE INTERACTIONS ON THEIR LEARNING PROCESS ARE NOT FULLY UNDERSTOOD AND THE OUTCOMES MAY DEPEND ON THE TOOL USED. THIS STUDY EXPLORES THE COMMUNICATION PATTERNS AND LEARNING EFFECTIVENESS DEVELOPED BY STUDENTS USING TWO BASIC SYNCHRONOUS AND ASYNCHRONOUS COMMUNICATION TOOLS IN E-LEARNING ENVIRONMENTS, SPECIFICALLY BUSINESS SIMULATION GAMES. DESIGN/METHODOLOGY/APPROACH THE AUTHORS CONDUCT A QUASI-EXPERIMENT RESEARCH WITH 478 ONLINE BUSINESS STUDENTS, 267 OF WHOM USED ONLINE DISCUSSION FORUMS AND 211 INTERACTED VIA AN INSTANT MESSAGING APP. THE APPLICATION OF LEARNING ANALYTICS AND TEXT MINING ON NATURAL LANGUAGE PROCESSING ALLOWS US TO EXPLORE THE STUDENT COMMUNICATION PATTERNS WITH EACH OF TOOLS AND THEIR EFFECTIVENESS IN TERMS OF LEARNING. FINDINGS THE RESULTS CONFIRM THE COMPLEMENTARITY OF THE COMMUNICATION TOOLS, ASYNCHRONOUS TOOLS BEING ESPECIALLY THE SUITABLE FOR TASK-RELATED COMMUNICATION AND SYNCHRONOUS ONES FOR SPEEDING UP AND FACILITATING STUDENT SOCIAL INTERACTIONS. ORIGINALITY/VALUE THE MAIN VALUE OF THIS RESEARCH LIES IN THE USE OF DATA ANALYTICS AND TEXT MINING TO ACCESS AND ANALYSE THE CONTENT OF STUDENT INTERACTIONS TO ASSESS THE LEARNING PROCESS IN GREATER DEPTH, COMPARING SYNCHRONOUS AND ASYNCHRONOUS LEARNING MODES, CONSIDERING THAT LITTLE IS KNOWN ABOUT THE IMPACT OF ONLINE SYNCHRONOUS INTERACTION OR INSTANT MESSAGING, AND EVEN LESS ABOUT THE DIFFERENT FEATURES, CONTENT AND PERFORMANCE THAT EMERGE WHEN THESE TWO LEARNER INTERACTION MODALITIES ARE COMPARED.
INCREASINGLY LEARNING ANALYTICS (LA) HAS BEGUN UTILISING STAFF- AND STUDENT-FACING DASHBOARDS CAPTURING VISUALISATIONS TO PRESENT DATA TO SUPPORT STUDENT SUCCESS AND IMPROVE LEARNING AND TEACHING. THE USE OF LA IS COMPLEX, MULTIFACETED AND RAISES MANY ISSUES FOR CONSIDERATION, INCLUDING ETHICAL AND LEGAL CHALLENGES, COMPETING STAKEHOLDER VIEWS AND IMPLEMENTATION DECISIONS. IT IS WIDELY ACKNOWLEDGED THAT LA DEVELOPMENT REQUIRES INPUT FROM VARIOUS STAKEHOLDERS. THIS CONCEPTUAL ARTICLE EXPLORES THE LA LITERATURE TO DETERMINE HOW STUDENT PERSPECTIVES ARE POSITIONED AS DASHBOARDS AND VISUALISATIONS ARE DEVELOPED. WHILE THE SECTOR ACKNOWLEDGES THE CENTRAL ROLE OF STUDENTS, AS DEMONSTRATED HERE, MUCH OF THE LITERATURE REFLECTS AN ACADEMIC, TEACHER-CENTRIC OR INSTITUTIONAL VIEW. THIS VIEW REFLECTS SOME OF THE KEY ETHICAL CONCERNS RELATED TO INFORMED CONSENT AND THE ROLE OF POWER TRANSLATING TO A SOMEWHAT PATERNALISTIC APPROACH TO STUDENTS. WE SUGGEST THAT AS STUDENTS ARE THE PRIMARY STAKEHOLDERS - THEY SHOULD BE CONSULTED IN THE DEVELOPMENT AND APPLICATION OF LA. AN ETHICAL APPROACH TO LA REQUIRES THAT WE ENGAGE WITH OUR STUDENTS IN THEIR LEARNING AND THE SYSTEMS AND INFORMATION THAT SUPPORT THAT PROCESS RATHER THAN ASSUMING WE KNOW WE KNOW WHAT STUDENTS WANT, WHAT THEIR CONCERNS ARE OR HOW THEY WOULD LIKE DATA PRESENTED.
THE ADAPTIVE E-LEARNING SYSTEM (AE-LS) RESEARCH HAS LONG FOCUSED ON THE LEARNER MODEL AND LEARNING ACTIVITIES TO PERSONALIZE THE LEARNER'S EXPERIENCE. HOWEVER, THERE ARE MANY UNRESOLVED ISSUES THAT MAKE IT DIFFICULT FOR TRAINEE TEACHERS TO OBTAIN APPROPRIATE INFORMATION ABOUT THE LEARNER'S BEHAVIOR. THE EVOLUTION OF THE LEARNING ANALYTICS (LA) OFFERS NEW POSSIBILITIES TO SOLVE PROBLEMS OF AE-LS. IN THIS PAPER, WE PROPOSED A BUSINESS INTELLIGENCE FRAMEWORK FOR AE-LS TO MONITOR AND MANAGE THE PERFORMANCE OF THE LEARNER MORE EFFECTIVELY. THE SUGGESTED ARCHITECTURE OF THE ALS PROPOSES A DATA WAREHOUSE MODEL THAT RESPONDS TO THESE PROBLEMS. IT DEFINES SPECIFICS MEASURES AND DIMENSIONS, WHICH HELPS TEACHERS AND EDUCATIONAL ADMINISTRATORS TO EVALUATE AND ANALYZE THE LEARNER'S ACTIVITIES. BY ANALYZING THESE INTERACTIONS, THE ADAPTIVE E-LEARNING ANALYTIC SYSTEM (AE-LAS) HAS THE POTENTIAL TO PROVIDE A PREDICTIVE VIEW OF UPCOMING CHALLENGES. THESE PREDICTIONS ARE USED TO EVALUATE THE ADAPTATION OF THE CONTENT PRESENTATION AND IMPROVE THE PERFORMANCE OF THE LEARNING PROCESS.
NOWADAYS, EDUCATORS ARE FACING VERY FAST TECHNOLOGICAL CHANGES, INCOMPARABLE PROFESSIONAL PRESSURE, TO WHICH ONE MUST ADD THE CHALLENGE OF WORKING WITH STUDENTS WITH DIFFERENT MINDSETS, EXPECTATIONS AND NEEDS. THE EDUCATIONAL ENVIRONMENT IS CONTINUOUSLY CHANGING TO ADJUST TO THE NEEDS AND DEMANDS OF OUR SOCIETY. THEREFORE, MOST HIGHER EDUCATION INSTITUTIONS (HEI) HAVE DEVELOPED EVALUATION APPROACHES AS PART OF THEIR QUALITY ASSURANCE PROGRAMS. THERE HAS BEEN GROWING CONCERN OVER THE USE OF LEARNING ANALYSIS IN HEI, WHERE MANY OF THE FOLLOWERS SUGGEST THAT DEVELOPMENTS IN THIS AREA MAY BE HELPFUL TO ADDRESS SEVERAL CHALLENGES THAT EDUCATORS ARE FACING. THE APPLICATION RANGE OF THE LEARNING ANALYTICS PRACTICE IS BROAD AND DIVERSE, INCLUDING THE GAIN OF BETTER INSIGHT INTO HOW LEARNING OCCURS, THE POSSIBLE IDENTIFICATION OF STUDENTS AT RISK OF DROPPING OUT, THE IMPROVEMENT OF PASSING RATES, AND THE POSSIBILITY OF PROVIDING PERSONALIZED LEARNING. THE PURPOSE OF THE CURRENT PAPER IS TO DESCRIBE AN EXPERIMENT TO IMPROVE STUDENT ENGAGEMENT, EDUCATIONAL ACHIEVEMENT AND THE LEARNING ENVIRONMENT THROUGH A SMALL PRIVATE ONLINE COURSE (SPOC). THE PAPER ALSO PRESENTS A DESCRIPTION OF THE COURSE STRUCTURE AND CONTENTS IN A LEARNING MANAGEMENT SYSTEM. THE SAMPLE OF THIS STUDY, FROM A SECOND-YEAR HIGHER EDUCATION COURSE ON FINANCIAL MATHEMATICS, CONSISTED OF 803 STUDENTS, ENROLLED EACH WINTER SEMESTER SINCE 2014/2015. TRADITIONAL LECTURES WERE COMPLEMENTED BY THE APPLICATION OF PEDAGOGICAL PRINCIPLES THAT SUPPORT THE INVERTED LEARNING AND ONLINE SUPPORT THROUGH A SPOC. THE EFFECTIVENESS OF THE METHODOLOGY USED IS REVEALED BY THE RESULTS OBTAINED.
THIS EDITORIAL INTRODUCES A SPECIAL SECTION OF THE JOURNAL OF LEARNING ANALYTICS, FOR WHICH NEIL SELWYN'S KEYNOTE ADDRESS TO LAK '18 HAS BEEN WRITTEN UP AS AN ARTICLE, "WHAT'S THE PROBLEM WITH LEARNING ANALYTICS?" HIS CLAIMS AND ARGUMENTS ARE ENGAGED IN COMMENTARIES FROM ALFRED ESSA, REBECCA FERGUSON, PAUL PRINSLOO, AND CAROLYN ROSE, WHO PROVIDE DIVERSE PERSPECTIVES ON SELWYN'S PROPOSALS AND ARGUMENTS, FROM APPLAUSE TO REFUTATION. REFLECTING ON THE DEBATE, I NOTE SOME OF THE TENSIONS TO BE RESOLVED FOR LEARNING ANALYTICS AND SOCIAL SCIENCE CRITIQUES TO ENGAGE PRODUCTIVELY, OBSERVING THAT CENTRAL TO THE DEBATE IS HOW WE UNDERSTAND THE ROLE OF ABSTRACTION IN THE ANALYSIS OF DATA ABOUT TEACHING AND LEARNING, AND HENCE THE OPPORTUNITIES AND RISKS THIS ENTAILS.
LEARNING ANALYTICS FOCUSES ON THE COLLECTION AND ANALYSIS OF LEARNERS' DATA TO IMPROVE THEIR LEARNING EXPERIENCE BY PROVIDING INFORMED GUIDANCE AND TO OPTIMISE LEARNING MATERIALS. TO SUPPORT THE RESEARCH IN THIS AREA WE HAVE DEVELOPED A DATASET, CONTAINING DATA FROM COURSES PRESENTED AT THE OPEN UNIVERSITY (OU). WHAT MAKES THE DATASET UNIQUE IS THE FACT THAT IT CONTAINS DEMOGRAPHIC DATA TOGETHER WITH AGGREGATED CLICKSTREAM DATA OF STUDENTS' INTERACTIONS IN THE VIRTUAL LEARNING ENVIRONMENT (VLE). THIS ENABLES THE ANALYSIS OF STUDENT BEHAVIOUR, REPRESENTED BY THEIR ACTIONS. THE DATASET CONTAINS THE INFORMATION ABOUT 22 COURSES, 32,593 STUDENTS, THEIR ASSESSMENT RESULTS, AND LOGS OF THEIR INTERACTIONS WITH THE VLE REPRESENTED BY DAILY SUMMARIES OF STUDENT CLICKS (10,655,280 ENTRIES). THE DATASET IS FREELY AVAILABLE AT HTTPS://ANALYSE.KMI.OPEN.AC.UK/OPEN_DATASET UNDER A CC-BY 4.0 LICENSE.
BY TRACKING, AGGREGATING, AND ANALYZING STUDENT PROFILES ALONG WITH STUDENTS' DIGITAL AND ANALOG BEHAVIORS CAPTURED IN INFORMATION SYSTEMS, UNIVERSITIES ARE BEGINNING TO OPEN THE BLACK BOX OF EDUCATION USING LEARNING ANALYTICS TECHNOLOGIES. HOWEVER, THE INCREASE IN AND USAGE OF SENSITIVE AND PERSONAL STUDENT DATA PRESENT UNIQUE PRIVACY CONCERNS. I ARGUE THAT PRIVACY-AS-CONTROL OF PERSONAL INFORMATION IS AUTONOMY PROMOTING, AND THAT STUDENTS SHOULD BE INFORMED ABOUT THESE INFORMATION FLOWS AND TO WHAT ENDS THEIR INSTITUTION IS USING THEM. INFORMED CONSENT IS ONE MECHANISM BY WHICH TO ACCOMPLISH THESE GOALS, BUT BIG DATA PRACTICES CHALLENGE THE EFFICACY OF THIS STRATEGY. TO ENSURE THE USEFULNESS OF INFORMED CONSENT, I ARGUE FOR THE DEVELOPMENT OF PLATFORM FOR PRIVACY PREFERENCES (P3P) TECHNOLOGY AND ASSERT THAT PRIVACY DASHBOARDS WILL ENABLE STUDENT CONTROL AND CONSENT MECHANISMS, WHILE PROVIDING AN OPPORTUNITY FOR INSTITUTIONS TO JUSTIFY THEIR PRACTICES ACCORDING TOEXISTING NORMS AND VALUES.
EVIDENCE-BASED EDUCATION HAS BECOME MORE RELEVANT IN THE CURRENT TECHNOLOGY-ENHANCED TEACHING-LEARNING ERA. THIS PAPER INTRODUCES HOW EDUCATIONAL BIG DATA HAS THE POTENTIAL TO GENERATE SUCH EVIDENCE. AS EVIDENCE-BASED EDUCATION TRADITIONALLY HOOKS ON THE META-ANALYSIS OF THE LITERATURE, SO THERE ARE EXISTING PLATFORMS THAT SUPPORT MANUAL INPUT OF EVIDENCE AS STRUCTURED INFORMATION. HOWEVER, SUCH PLATFORMS OFTEN FOCUS ON RESEARCHERS AS END-USERS AND ITS DESIGN IS NOT ALIGNED TO THE PRACTITIONERS' WORKFLOW. IN OUR WORK, WE PROPOSE A TECHNOLOGY-MEDIATED PROCESS OF CAPTURING TEACHING-LEARNING CASES (TLCS) USING A LEARNING ANALYTICS FRAMEWORK. EACH CASE IS PRIMARILY A SINGLE DATA POINT REGARDING THE RESULT OF AN INTERVENTION AND MULTIPLE SUCH CASES WOULD GENERATE AN EVIDENCE OF INTERVENTION EFFECTIVENESS. TO CAPTURE TLCS IN OUR CURRENT CONTEXT, OUR SYSTEM AUTOMATICALLY CONDUCTS STATISTICAL MODELLING OF LEARNING LOGS CAPTURED FROM LEARNING MANAGEMENT SYSTEMS (LMS) AND AN E-BOOK READER. INDICATORS FROM THOSE LEARNING LOGS ARE EVALUATED BY THE LINEAR MIXED EFFECTS MODEL TO COMPUTE WHETHER AN INTERVENTION HAD A POSITIVE LEARNING EFFECT. WE PRESENT TWO CASE STUDIES TO ILLUSTRATE OUR APPROACH OF EXTRACTING CASE EFFECTIVENESS FROM TWO DIFFERENT LEARNING CONTEXTS - ONE AT A JUNIOR-HIGH MATH CLASS WHERE EMAIL MESSAGES WERE SENT AS INTERVENTION AND ANOTHER IN A BLENDED LEARNING CONTEXT IN A HIGHER EDUCATION PHYSICS CLASS WHERE AN ACTIVE LEARNING STRATEGY WAS IMPLEMENTED. OUR NOVELTY LIES IN THE PROPOSED AUTOMATED APPROACH OF DATA AGGREGATION, ANALYSIS, AND CASE STORING USING A LEARNING ANALYTICS FRAMEWORK FOR SUPPORTING EVIDENCE-BASED PRACTICE MORE ACCESSIBLE FOR PRACTITIONERS.
LEARNING ANALYTICS (LA) AND ADAPTIVE LEARNING ARE INEXTRICABLY LINKED SINCE THEY BOTH FOSTER TECHNOLOGY-SUPPORTED LEARNER-CENTRED EDUCATION. THIS STUDY IDENTIFIES DEVELOPMENTS FOCUSING ON THEIR INTERPLAY AND EMPHASISES INSUFFICIENTLY INVESTIGATED DIRECTIONS WHICH DISPLAY A HIGHER INNOVATION POTENTIAL. TWENTY-ONE PEER-REVIEWED STUDIES ARE IDENTIFIED VIA A SYSTEMATIC REVIEW AND CLASSIFIED BASED ON THE DIMENSIONS OF A PROPOSED FRAMEWORK. THE FINDINGS OF THE REVIEW SUGGEST THAT INTERESTING WORK HAS BEEN CARRIED OUT DURING THE LAST YEARS ON THE TOPIC. WE CONCLUDE WITH A NEED FOR MORE RESEARCH ON THE TOPIC IN SPECIFIC LEARNING DOMAINS AND SETTINGS. RECOMMENDATIONS INCLUDE: A CLEAR STRATEGY FOR ADAPTATION AUGMENTED BY LA, THE COMBINATION OF ON-TASK WITH PRE-TASK MEASURES, AND THE COMBINATION OF SYSTEM-CONTROLLED ADAPTATION WITH USER-CONTROLLED ADAPTATION. FUTURE TRENDS INCLUDE THE EMERGENCE OF CONSTRUCTIVIST-COLLABORATIVE ENVIRONMENTS THAT PROVIDE INSIGHTFUL MODELS OF COMPLEX STUDENT BEHAVIOUR.
WHILE LEARNING ANALYTICS (LA) PRACTICES HAVE BEEN SHOWN TO BE PRACTICAL AND EFFECTIVE, MOST OF THEM REQUIRE A HUGE AMOUNT OF DATA AND EFFORT. THIS PAPER REPORTS A CASE STUDY WHICH DEMONSTRATES THE FEASIBILITY OF PRACTISING LA AT A LOW COST FOR INSTRUCTORS TO IDENTIFY AT-RISK STUDENTS IN AN UNDERGRADUATE BUSINESS QUANTITATIVE METHODS COURSE. INSTEAD OF USING TRACKING DATA FROM A LEARNING MANAGEMENT SYSTEM AS PREDICTIVE VARIABLES, THIS STUDY UTILISED CLICKER RESPONSES AS FORMATIVE ASSESSMENTS, TOGETHER WITH STUDENT DEMOGRAPHIC DATA AND SUMMATIVE ASSESSMENTS. THIS LA PRACTICE MAKES USE OF FREE CLOUD SERVICES, GOOGLE FORMS AND GOOGLE SHEETS IN PARTICULAR FOR COLLECTING AND ANALYSING CLICKER DATA. DESPITE A SMALL DATASET BEING USED, THE LA IMPLEMENTATION WAS EFFECTIVE IN IDENTIFYING AT-RISK STUDENTS AT AN EARLY STAGE. A SYSTEMATIC PROACTIVE ADVISING APPROACH IS PROPOSED AS AN INTERVENTION STRATEGY BASED ON STUDENTS' AT-RISK PROBABILITY ESTIMATED BY A PREDICTION MODEL. THE RESULT SHOWS THAT THE INTERVENTION SUCCESS RATE INCREASES CORRESPONDINGLY WITH THE NUMBER OF INTERVENTIONS AND THE INTERVENTION EFFECTS ON PEER GROUPS ARE FAR MORE SUCCESSFUL THAN ON INDIVIDUAL STUDENTS. OVERALL, THE STUDENTS' PASS RATE IN THE STUDY WAS 7% HIGHER THAN THAT FOR THE WHOLE COURSE. PRACTICAL RECOMMENDATIONS AND CONCERNS ABOUT USING LINEAR REGRESSION AND LOGISTIC REGRESSION FOR CLASSIFICATION ARE ALSO DISCUSSED.
THE SMART CLASSROOMS OF THE FUTURE WILL USE DIFFERENT SOFTWARE, DEVICES AND WEARABLES AS AN INTEGRAL PART OF THE LEARNING PROCESS. THESE EDUCATIONAL APPLICATIONS GENERATE A LARGE AMOUNT OF DATA FROM DIFFERENT SOURCES. THE AREA OF MULTIMODAL LEARNING ANALYTICS (MMLA) EXPLORES THE AFFORDANCES OF PROCESSING THESE HETEROGENEOUS DATA TO UNDERSTAND AND IMPROVE BOTH LEARNING AND THE CONTEXT WHERE IT OCCURS. HOWEVER, A REVIEW OF DIFFERENT MMLA STUDIES HIGHLIGHTED THAT AD-HOC AND RIGID ARCHITECTURES CANNOT BE SCALED UP TO REAL CONTEXTS. IN THIS WORK, WE PROPOSE A NOVEL MMLA ARCHITECTURE THAT BUILDS ON SOFTWARE-DEFINED NETWORKS AND NETWORK FUNCTION VIRTUALIZATION PRINCIPLES. WE EXEMPLIFY HOW THIS ARCHITECTURE CAN SOLVE SOME OF THE DETECTED CHALLENGES TO DEPLOY, DISMANTLE AND RECONFIGURE THE MMLA APPLICATIONS IN A SCALABLE WAY. ADDITIONALLY, THROUGH SOME EXPERIMENTS, WE DEMONSTRATE THE FEASIBILITY AND PERFORMANCE OF OUR ARCHITECTURE WHEN DIFFERENT CLASSROOM DEVICES ARE RECONFIGURED WITH DIVERSE LEARNING TOOLS. THESE FINDINGS AND THE PROPOSED ARCHITECTURE CAN BE USEFUL FOR OTHER RESEARCHERS IN THE AREA OF MMLA AND EDUCATIONAL TECHNOLOGIES ENVISIONING THE FUTURE OF SMART CLASSROOMS. FUTURE WORK SHOULD AIM TO DEPLOY THIS ARCHITECTURE IN REAL EDUCATIONAL SCENARIOS WITH MMLA APPLICATIONS.
STUDENTS ON INTRODUCTORY PROGRAMMING COURSES FIND THE ABSTRACT PROGRAMMING CONCEPTS DIFFICULT TO UNDERSTAND, AND OFTEN LACK THE INTRINSIC MOTIVATION. HENCE, THE DROPOUT RATES ARE USUALLY QUITE HIGH. THE TEACHERS HAVE DIFFICULTIES IN IDENTIFYING THE STUDENTS THAT ARE AT RISK OF DROPPING OUT - IN FACT, THE IDENTIFICATION OF THOSE STUDENTS TYPICALLY CANNOT BE DONE BEFORE THE END OF THE COURSE. THE IDENTIFICATION IS ESPECIALLY DIFFICULT ON LARGE COURSES WHERE THE TEACHERS DO NOT KNOW THE INDIVIDUAL STUDENTS. YET IT IS REASONABLE TO ASSUME THAT THE STUDENTS' STUDY HABITS AND GENERAL PERFORMANCE STAY RELATIVELY SIMILAR THROUGHOUT THE COURSE. IN THIS PAPER, WE SHOW THAT BY UTILIZING NOVEL METHODS OF LEARNING ANALYTICS TOGETHER WITH A LEARNING MANAGEMENT SYSTEM (LMS), IT IS POSSIBLE TO IDENTIFY THE STUDENTS-AT-RISK ALREADY THREE WEEKS BEFORE THE END OF A SEVEN-WEEK COURSE. COMMONLY, ALL OF THE COURSE EVENTS, SUCH AS EXERCISE SUBMISSIONS, LECTURE ATTENDANCES AND WEEKLY ASSIGNMENTS, ARE RECORDED BY THE LMS. THE ANALYSIS UTILIZES THIS AUTOMATICALLY COLLECTED DATA TO IDENTIFY THE STUDENTS-AT-RISK. WE DESCRIBE THE METHOD USED FOR ANALYSIS IN DETAIL, AND SHOW HOW THE STUDENT PERFORMANCE DATA ON THE COURSE CAN BE USED TO RELIABLY PREDICT THOSE WHO ARE AT RISK, ALREADY AT EARLY STAGES OF THE COURSE.
HIGHER ORDER THINKING SKILLS LIKE ANALYSIS, EVALUATION AND SYNTHESIS KNOWLEDGE ARE DIFFICULT TO ASSESS IN E-LEARNING SCENARIOS. SUCH SKILLS ARE USUALLY TESTED IN HANDSON EXPERIMENTS AND PROJECTS. IN THE PAPER WE WILL DESCRIBE, HOW WE CONNECT OUR ONLINE LAB WITH ANALYTIC TOOLS TO GET DATA FROM THE LEARNING PROCESS DURING THE INTERACTION OF THE STUDENTS WITH THE REMOTE LAB. USING THESE DATA WE WANT TO ANALYZE THE HIGHER ORDER THINKING SKILLS.
THIS WORK PROVIDES AN OVERVIEW OF AN EDUCATION AND TECHNOLOGY MONITORING SYSTEM DEVELOPED AT PLAN CEIBAL, A NATIONWIDE INITIATIVE CREATED TO ENABLE TECHNOLOGY ENHANCED LEARNING IN URUGUAY. PLAN CEIBAL CURRENTLY OFFERS ONE-TO-ONE ACCESS TO TECHNOLOGY AND CONNECTIVITY TO EVERY STUDENT AND TEACHER (FROM PRIMARY AND SECONDARY EDUCATION) AS WELL AS A COMPREHENSIVE SET OF EDUCATIONAL SOFTWARE PLATFORMS. ALL THESE RESOURCES GENERATE MASSIVE AMOUNTS OF DATA ABOUT THE PROGRESS AND STYLE OF STUDENTS LEARNING. THIS WORK INTRODUCES THE CONCEPTUAL FRAMEWORK, DESIGN AND PRELIMINARY RESULTS OF THE BIG DATA CENTER FOR LEARNING ANALYTICS CURRENTLY BEING DEVELOPED AT PLAN CEIBAL. THIS INITIATIVE IS FOCUSED ON EXPLOITING THESE DATASETS AND CONDUCTING ADVANCED ANALYTICS TO SUPPORT THE EDUCATIONAL SYSTEM. TO THIS AIM, A 360 DEGREES PROFILE WILL BE BUILT INCLUDING INFORMATION CHARACTERIZING THE USER'S ONLINE BEHAVIOR AS WELL AS A SET OF TECHNOLOGY ENHANCED LEARNING FACTORS. THESE PROFILES WILL BE STUDIED BOTH AT USER (E.G., STUDENT OR TEACHER) AND LARGER SCALE LEVELS (E.G., PER SCHOOL OR SCHOOL SYSTEM), ADDRESSING BOTH THE NEED OF UNDERSTANDING HOW TECHNOLOGY IS BEING USED FOR LEARNING AS WELL AS TO PROVIDE ACCURATE FEEDBACK TO SUPPORT EVIDENCE BASED EDUCATIONAL POLICIES.
THIS STUDY PROVIDES A CONCEPTUAL FRAMEWORK HOW ORGANIZATIONS MAY ADOPT EVIDENCE-BASED INTERVENTIONS AT SCALE, AND HOW INSTITUTIONS MAY EVALUATE THE COSTS AND BENEFITS OF SUCH INTERVENTIONS. BUILDING ON A NEW CONCEPTUAL MODEL DEVELOPED BY THE OPEN UNIVERSITY UK (OU), WE WILL ANALYSE THREE CASE-STUDIES OF EVIDENCE-BASED INTERVENTIONS. BY WORKING WITH 90+ LARGE-SCALE MODULES FOR A PERIOD OF TWO YEARS ACROSS THE FIVE FACULTIES AND DISCIPLINES WITHIN THE OU, ANALYTICS4ACTION PROVIDES A BOTTOM-UP-APPROACH FOR WORKING TOGETHER WITH KEY STAKEHOLDERS WITHIN THEIR RESPECTIVE CONTEXTS. USING PRINCIPLES OF EMBEDDED CASE-STUDY APPROACHES BY YIN [1], BY COMPARING THE LEARNING BEHAVIOR, SATISFACTION AND PERFORMANCE OF 11079 LEARNERS THE FINDINGS INDICATED THAT EACH OF THE THREE LEARNING DESIGNS LED TO SATISFIED STUDENTS AND AVERAGE TO GOOD STUDENT RETENTION. IN THE SECOND PART WE HIGHLIGHTED THAT THE THREE MODULE TEAMS MADE IN-PRESENTATION INTERVENTIONS BASED UPON REAL-TIME ANALYTICS, WHEREBY INITIAL USER DATA INDICATED VLE BEHAVIOUR IN LINE WITH EXPECTATIONS. IN 2-5 YEARS, WE HOPE THAT A RICH, ROBUST EVIDENCE-BASE WILL BE PRESENTED TO SHOW HOW LEARNING ANALYTICS CAN HELP TEACHERS TO MAKE INFORMED, TIMELY AND SUCCESSFUL INTERVENTIONS THAT WILL HELP LEARNERS TO ACHIEVE THEIR LEARNING OUTCOMES.
IN RECENT YEARS, RESEARCH USING LEARNING ANALYTICS TO PREDICT LEARNING OUTCOMES HAS BEGUN TO INCREASE. THIS EMERGING FIELD OF RESEARCH ADVOCATES THE USE OF READILY-AVAILABLE DATA TO INFORM TEACHING AND LEARNING. THE CURRENT CASE STUDY ADOPTS A LEARNING ANALYTICS APPROACH TO EVALUATE THE ONLINE LEARNING PACKAGE OF AN ACADEMIC ENGLISH COURSE IN A UNIVERSITY IN HONG KONG. THIS STUDY AIMS TO (1) EXPLORE THE COMPLETION PATTERN OF USE OF THE ONLINE LEARNING PACKAGE BY STUDENTS IN A GENERIC UNDERGRADUATE ACADEMIC SKILLS COURSE; AND (2) PREDICT STUDENT OUTCOMES BASED ON THEIR ONLINE BEHAVIOUR PATTERNS. OVER THREE ACADEMIC YEARS, THE STUDY EXAMINED USAGE LOGS FOR 7000+ STUDENTS THAT WERE AVAILABLE ON THE UNIVERSITY'S LEARNING MANAGEMENT SYSTEM. STUDENT ASSESSMENT COMPONENT SCORES, ONLINE ACTIVITY COMPLETION RATES, AND ONLINE BEHAVIOURAL PATTERNS WERE IDENTIFIED AND EXAMINED USING DESCRIPTIVE ANALYSIS, BIVARIATE CORRELATION ANALYSIS, AND MULTIPLE REGRESSION ANALYSIS. THE FINDINGS REVEAL INSIGHTS INTO DIFFERENT ONLINE LEARNING BEHAVIOURAL PATTERNS THAT WOULD BENEFIT BLENDED COURSE DESIGNERS. FOR INSTANCE, SOME STUDENTS STARTED USING THE ONLINE LEARNING PACKAGE EARLY IN THE SEMESTER BUT FULFILLED ONLY THE MINIMUM REQUIRED ONLINE WORK, WHEREAS OTHERS GREATLY EXCEEDED THE BASIC REQUIREMENT AND CONTINUED DOING ACTIVITIES IN THE ONLINE PACKAGE EVEN AFTER THE SEMESTER HAD FINISHED. THE RELATIONSHIP BETWEEN LEARNING ACTIVITIES IN THE ONLINE PACKAGE AND ASSESSMENT COMPONENT GRADES WAS FOUND TO BE WEAK BUT MEANINGFUL. A REGRESSION MODEL WAS DEVELOPED DRAWING ON THE COMPLETION RATES TO PREDICT OVERALL STUDENT SCORES, AND THIS MODEL SUCCESSFULLY IDENTIFIED SEVERAL SPECIFIC FACTORS, SUCH AS TOTAL NUMBER OF ATTEMPTS AND PERFORMANCE IN INDIVIDUAL ONLINE LEARNING ACTIVITIES, AS PREDICTORS OF THE FINAL COURSE GRADE.
PURPOSE TO HELP WORKERS MAKE THE RIGHT DECISION, OVER THE YEARS, TECHNOLOGICAL SOLUTIONS AND WORKPLACE LEARNING ANALYTICS SYSTEMS HAVE BEEN DESIGNED TO AID THIS PROCESS (RUIZ-CALLEJA ET AL., 2019). RECENT DEVELOPMENTS IN ARTIFICIAL INTELLIGENCE (AI) HAVE THE POTENTIAL TO FURTHER REVOLUTIONISE THE INTEGRATION OF HUMAN AND ARTIFICIAL LEARNING AND WILL IMPACT HUMAN AND MACHINE COLLABORATION DURING TEAM WORK (SEEBER ET AL., 2020). DESIGN/METHODOLOGY/APPROACH COMPLEX PROBLEM-SOLVING HAS BEEN IDENTIFIED AS ONE OF THE KEY SKILLS FOR THE FUTURE WORKFORCE (HAGER AND BECKETT, 2019). PROBLEMS FACED BY TODAY'S WORKFORCE EMERGE IN SITU AND EVERYDAY WORKPLACE LEARNING IS SEEN AS AN EFFECTIVE WAY TO DEVELOP THE SKILLS AND EXPERIENCE WORKERS NEED TO EMBRACE THESE PROBLEMS (CAMPBELL, 2005; JONASSEN ET AL., 2006). FINDINGS IN THIS COMMENTARY THE AUTHORS ARGUE THAT THE INCREASED DIGITIZATION OF WORK AND SOCIAL INTERACTION, COMBINED WITH RECENT RESEARCH ON WORKPLACE LEARNING ANALYTICS AND AI OPENS UP THE POSSIBILITY FOR DESIGNING AUTOMATED REAL-TIME FEEDBACK SYSTEMS CAPABLE OF JUST-IN-TIME, JUST-IN-PLACE SUPPORT DURING COMPLEX PROBLEM-SOLVING AT WORK. AS SUCH, THESE SYSTEMS CAN SUPPORT AUGMENTED LEARNING AND PROFESSIONAL DEVELOPMENT IN SITU. ORIGINALITY/VALUE THE COMMENTARY REFLECTS ON THE BENEFITS OF AUTOMATED REAL-TIME FEEDBACK SYSTEMS AND ARGUES FOR THE NEED OF SHARED RESEARCH AGENDA TO COHERE RESEARCH IN THE DIRECTION OF AI-ENABLED WORKPLACE ANALYTICS AND REAL-TIME FEEDBACK TO SUPPORT LEARNING AND DEVELOPMENT IN THE WORKPLACE.
LEARNING ANALYTICS (LA) HAS A SIGNIFICANT IMPACT IN LEARNING AND TEACHING PROCESSES. THESE PROCESSES CAN BE IMPROVED USING THE AVAILABLE DATA RETRIEVED FROM STUDENTS' ACTIVITY INSIDE THE VIRTUAL CLASSROOMS OF A LEARNING MANAGEMENT SYSTEM (LMS). THIS PROCESS REQUIRES THE DEVELOPMENT OF A TOOL THAT ALLOWS ONE TO HANDLE THE RETRIEVED INFORMATION PROPERLY. THIS PAPER PRESENTS A SOLUTION TO THIS NEED, IN THE FORM OF A DEVELOPMENT MODEL AND ACTUAL IMPLEMENTATION OF AN LA TOOL. FOUR PHASES (EXPLANATION, DIAGNOSIS, PREDICTION AND PRESCRIPTION) ARE IMPLEMENTED IN THE TOOL, ALLOWING A TEACHER TO TRACK STUDENTS' ACTIVITY IN A VIRTUAL CLASSROOM VIA THE SAKAI LMS. IT ALSO ALLOWS FOR THE IDENTIFICATION OF USERS WHO FACE CHALLENGES IN THEIR ACADEMIC PROCESS AND THE INITIATION OF PERSONALISED MENTORING BY THE TEACHER OR TUTOR. THE USE OF THE TOOL WAS TESTED ON GROUPS OF STUDENTS IN AN ALGORITHMS COURSE IN THE PERIODS 2017-1, 2017-2, 2018-1 AND 2018-2, WITH A TOTAL OF 90 STUDENTS - IN PARALLEL WITH THE CONTROL GROUPS IN THE SAME PERIODS THAT TOTALLED 95 STUDENTS - OBTAINING SUPERIOR AVERAGES IN THE TEST GROUPS VERSUS THE CONTROL GROUPS, WHICH EVIDENCED THE FUNCTIONALITY AND UTILITY OF THE SOFTWARE.
AN INCREASING NUMBER OF RESEARCHERS ARE TAKING LEARNING DESIGN INTO CONSIDERATION WHEN PREDICTING LEARNING BEHAVIOR AND OUTCOMES ACROSS DIFFERENT MODULES. THIS STUDY BUILDS ON PRELIMINARY LEARNING DESIGN WORK THAT WAS PRESENTED AT LAK2015 BY THE OPEN UNIVERSITY UK. IN THIS STUDY WE LINKED 151 MODULES AND 111.256 STUDENTS WITH STUDENTS' SATISFACTION AND PERFORMANCE USING MULTIPLE REGRESSION MODELS. OUR FINDINGS STRONGLY INDICATE THE IMPORTANCE OF LEARNING DESIGN IN PREDICTING AND UNDERSTANDING PERFORMANCE OF STUDENTS IN BLENDED AND ONLINE ENVIRONMENTS. IN LINE WITH PROPONENTS OF SOCIAL LEARNING ANALYTICS, OUR PRIMARY PREDICTOR FOR ACADEMIC RETENTION WAS THE AMOUNT OF COMMUNICATION ACTIVITIES, CONTROLLING FOR VARIOUS INSTITUTIONAL AND DISCIPLINARY FACTORS. WHERE POSSIBLE, APPROPRIATE COMMUNICATION TASKS THAT ALIGN WITH THE LEARNING OBJECTIVES OF THE COURSE MAY BE A WAY FORWARD TO ENHANCE ACADEMIC RETENTION.
THE UNIFIED MODELLING LANGUAGE (UML) IS CURRENTLY INCLUDED IN A LARGE NUMBER OF INFORMATION SYSTEMS TERTIARY CURRICULA AS PART OF SYSTEMS ANALYSIS AND DESIGN COURSES. HOWEVER, EXISTING LITERATURE SUGGESTS THAT LEARNING UML POSES SIGNIFICANT CHALLENGES PARTICULARLY FOR DISTANCE EDUCATION LEARNERS. THE AIM OF THIS PAPER IS TO INVESTIGATE THE USE OF LEARNING ANALYTICS IN LEARNING UML. FOR THIS PURPOSE, A LEARNING INTERVENTION HAS BEEN IMPLEMENTED AND DEPLOYED IN THE HELLENIC OPEN UNIVERSITY. THE RESULTS ENABLE US TO RELATE PERFORMANCE WITH STUDENTS' PARTICIPATION, ATTENDANCE AND ENGAGEMENT, TO IDENTIFY COMMON UML MISCONCEPTIONS AND TO EVALUATE THE EFFECTIVENESS OF DIFFERENT LEARNING OBJECTS FOR THE TASK AT HAND.
UNEQUAL STAKEHOLDER ENGAGEMENT IS A COMMON PITFALL OF ADOPTION APPROACHES OF LEARNING ANALYTICS IN HIGHER EDUCATION LEADING TO LOWER BUY-IN AND FLAWED TOOLS THAT FAIL TO MEET THE NEEDS OF THEIR TARGET GROUPS. WITH EACH DESIGN DECISION, WE MAKE ASSUMPTIONS ON HOW LEARNERS WILL MAKE SENSE OF THE VISUALISATIONS, BUT WE KNOW VERY LITTLE ABOUT HOW STUDENTS MAKE SENSE OF DASHBOARD AND WHICH ASPECTS INFLUENCE THEIR SENSE-MAKING. WE INVESTIGATED HOW LEARNER GOALS AND SELF-REGULATED LEARNING (SRL) SKILLS INFLUENCE DASHBOARD SENSE-MAKING FOLLOWING A MIXED-METHODS RESEARCH METHODOLOGY: A QUALITATIVE PRE-STUDY FOLLOWED-UP WITH AN EXTENSIVE QUANTITATIVE STUDY WITH 247 UNIVERSITY STUDENTS. WE UNCOVERED THREE LATENT VARIABLES FOR SENSE-MAKING: TRANSPARENCY OF DESIGN, REFERENCE FRAMES AND SUPPORT FOR ACTION. SRL SKILLS ARE PREDICTORS FOR HOW RELEVANT STUDENTS FIND THESE CONSTRUCTS. LEARNER GOALS HAVE A SIGNIFICANT EFFECT ONLY ON THE PERCEIVED RELEVANCE OF REFERENCE FRAMES. KNOWING WHICH FACTORS INFLUENCE STUDENTS' SENSE-MAKING WILL LEAD TO MORE INCLUSIVE AND FLEXIBLE DESIGNS THAT WILL CATER TO THE NEEDS OF BOTH NOVICE AND EXPERT LEARNERS.
THERE IS UNTAPPED POTENTIAL IN ACHIEVING THE FULL IMPACT OF LEARNING ANALYTICS THROUGH THE INTEGRATION OF TOOLS INTO PRACTICAL PEDAGOGIC CONTEXTS. TO MEET THIS POTENTIAL, MORE WORK MUST BE CONDUCTED TO SUPPORT EDUCATORS IN DEVELOPING LEARNING ANALYTICS LITERACY. THE PROPOSED WORKSHOP ADDRESSES THIS NEED BY BUILDING CAPACITY IN THE LEARNING ANALYTICS COMMUNITY AND DEVELOPING AN APPROACH TO RESOURCING FOR BUILDING 'WRITING ANALYTICS LITERACY'.
LEARNING OFFERINGS AND LEARNERS' ACTIVITIES ARE INCREASINGLY MOVING TO DECENTRALIZED LEARNING ENVIRONMENTS IN THE CLOUD. THE ADVENT OF MOOC PLATFORMS GIVES A GOOD EXAMPLE FOR THIS ONGOING EVOLUTION. HOWEVER, MOST LEARNING APPLICATIONS USE PROPRIETARY DATA MODELS. AS LEARNERS NOWADAYS NOT ONLY USE ONLY ONE E-LEARNING OFFERING, THE ANALYSIS OF LEARNER ACTIVITIES IS CHALLENGING IN SUCH DECENTRALIZED AND HETEROGENEOUS LEARNING ENVIRONMENTS. TO ADDRESS THIS PROBLEM, WE PRESENT THREE DIFFERENT LEARNING APPLICATIONS AND SHOW THE POTENTIAL OF EXPLOITING SEMANTIC WEB TECHNOLOGIES.
INCREASING SCHOOL DROPOUT RATES ARE A PROBLEM IN MANY EDUCATIONAL SYSTEMS, WITH STUDENT DISENGAGEMENT BEING ONE SIGNIFICANT FACTOR. LEARNING ANALYTICS IS A NEW FIELD WITH A KEY ROLE IN EDUCATIONAL INSTITUTIONS IN THE COMING YEARS. IT MAY HELP MAKE STRATEGIC DECISIONS TO REDUCE STUDENT DISENGAGEMENT. THE USE OF TECHNOLOGY IN EDUCATIONAL ENVIRONMENTS HAS GROWN SIGNIFICANTLY AND, WITH IT, AWARENESS OF THE IMPORTANCE OF STUDENT ENGAGEMENT. WE EXPLOIT TRACKING AND WEARABLE TECHNOLOGIES TO INCREASE USER ENGAGEMENT IN LEARNING PROCESSES, EXPLORING ALSO THE AREA OF MULTIMODAL LEARNING ANALYTICS (MMLA). WE USE WEARABLES AND INTERNET OF THINGS FOR EDUCATION, AN INTERACTIVE AND COLLABORATIVE SYSTEM DESIGNED TO IMPROVE MOTIVATION AND LEARNING. THIS ARTICLE PRESENTS THE RESULTS OBTAINED IN DIFFERENT EXPERIMENTS CONDUCTED IN A SECONDARY SCHOOL IN A LONG-TERM PARTICIPATORY LEARNING CONTEXT. THE CAPTURED DATA WERE ANALYZED AND USED TO IDENTIFY DIFFERENT STUDENTS' BEHAVIOR PATTERNS, SHOWING THEIR PROGRESS AND MOTIVATION. SUBSEQUENTLY, FROM THE CAPTURED DATA AND AIMING AT A DECISION-MAKING PHASE, WE USED MACHINE LEARNING TECHNIQUES AND MMLA METHODOLOGIES TO CONSTRUCT MODELS ABLE TO "EXPLAIN" WHEN STUDENT ENGAGEMENT IS PRESENT, SO THIS KNOWLEDGE CAN LATER BE EXPLOITED. IN PARTICULAR, WE CHOSE DECISION TREES AND RULE SYSTEMS BASED ON A SET OF VARIABLES WITH PROVEN RELEVANCE TO THE PROBLEM. THE EVALUATION OF THIS NOVEL ENGAGEMENT CLASSIFICATION SYSTEM CONFIRMS THE HIGH PERFORMANCE OF THESE VARIABLES. THE RULES OBTAINED, WHICH CAN BE EASILY INTERPRETED BY A NONEXPERT, HELP THE TEACHER TO OBSERVE, ANALYZE, AND MAKE DECISIONS WITH THE PURPOSE OF FOSTERING ENGAGEMENT.
IDENTIFYING EMOTIONS EXPERIENCED BY STUDENTS IN A LEARNING ENVIRONMENT CONTRIBUTES TO MEASURING THE IMPACT WHEN TECHNOLOGIES SUCH AS AUGMENTED REALITY (AR) ARE IMPLEMENTED IN THE EDUCATIONAL FIELD. THE MOST FREQUENT METHODS FOR COLLECTING EMOTIONAL METRICS ARE QUESTIONNAIRES, SURVEYS, AND OBSERVATIONS, BUT EACH OF THESE PROCESSES LACKS OBJECTIVITY AND VERACITY. FOR THIS REASON, THIS STUDY PROPOSES, DEVELOPS AND TESTS A LEARNING ANALYTICS SCHEME, BASED ON THE DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE ALGORITHM, AS A CLUSTERING TECHNIQUE WITH TIME SERIES ANALYSIS, USING THE BRAIN- COMPUTER INTERFACE DEVICE, EMOTIV EPOC, AS A WAY TO COLLECT EMOTIONAL METRICS. THE ABOVE, IN ORDER TO PERFORM EMOTIONAL BEHAVIOR CHARACTERIZATION BY USING AR IN A LEARNING ENVIRONMENT THROUGH AR-SANDBOX. THE PROPOSED METHOD SHOWS A CLEAR INCLINATION IN THE TENDENCY OF EACH EMOTION IN EACH CLUSTER, ALLOWING CLASSIFICATION OF CHILDREN DURING THEIR INTERACTION WITH THE IMMERSIVE ENVIRONMENT, AS WELL AS THE ABILITY TO DISTINGUISH EACH GROUP OF STUDENTS.
THE SELECTION OF CAREER PATHS AND MAKING OF ACADEMIC CHOICES IS A DIFFICULT AND OFTEN CONFUSING TASK FOR YOUNG PEOPLE. THE IMPACT ON THEIR LIVES, HOWEVER, IS ENORMOUS AS IT CAN DETERMINE ENTIRE FUTURE CAREER POSSIBILITIES. IN INDIA, A GENERAL REMEDY TO THIS STRESS IS THAT INSTEAD OF CHOOSING A FIELD OF STUDY TAILORED TO INDIVIDUAL PREFERENCES AND STRENGTHS, TOPICS ARE CHOSEN THAT ALIGN WITH THE CHOICES OF THE STUDENTS' FAMILIES OR THEIR FRIENDS. THIS CAN HAVE THE EFFECT OF ENTRENCHING PATTERNS OF INTERGENERATIONAL INEQUITY. THE AIM OF THIS RESEARCH IS TO GIVE STUDENTS GREATER ACCESS TO THE KNOWLEDGE CAPITAL WHICH WILL HELP THEM MAKE BETTER CHOICES. THIS IS ACHIEVED BY ENGAGING STUDENTS IN THE CAREER PLANNING PROCESS, IN ORDER TO CONVEY INFORMATION IN A LIKEABLE AND CREDIBLE WAY. THE COMPCAT (COMPETENCY AND CAREER ASSESSMENT TOOL) GAME ENGINE COMBINES THE USE OF LEARNING ANALYTICS AND REAL TIME, INTERACTIVE COMPUTER SIMULATIONS DESIGNED TO GAIN INSIGHTS INTO THE STUDENTS' ENGAGEMENT IN THE MAKING OF THESE COMPLEX DECISIONS. THIS PAPER PRESENTS THE CONCEPTUAL ARCHITECTURE OF THE GAME AND DEMONSTRATES ITS ROLE IN ENHANCING THE LEARNING EFFECTIVENESS OF THE STUDENTS.
THIS PAPER PRESENTS LEARNING ANALYTICS DATA FOR MEASURING THE IMPACT OF STUDY SATISFACTION ON STUDENTS' ACADEMIC SELF-EFFICACY AND PERFORMANCE. FOR THIS PURPOSE, A SPECIALLY DESIGNED QUESTIONNAIRE WAS DEVELOPED AND DISTRIBUTED ACROSS 124 UNDERGRADUATE STUDENTS. PRELIMINARY ANALYSIS USING DESCRIPTIVE STATISTICS FOR ITEMS AND CONFIRMATORY FACTOR ANALYSIS IS PROVIDED. THE ANALYSIS PROVIDES EVIDENCE FOR THE RELATION BETWEEN STUDENTS' SATISFACTION, SELF-EFFICACY, AND ACADEMIC PERFORMANCE, AND EVALUATES THE ROLE OF ACADEMIC INFORMATION RESOURCES IN FULFILLING STUDENTS' INFORMATION NEEDS. THESE DATA ARE OF IMPORTANCE FOR RESEARCHERS AND PRACTITIONERS INVOLVED WITH BUDGETARY DECISIONS IN ACADEMIC COLLECTIONS AS WELL AS THE INFLUENCE OF RESEARCH SPECIFIC (RATHER THAN TRAINING SPECIFIC) INFORMATION RESOURCES IN STUDENT SATISFACTION. (C) 2019 THE AUTHORS. PUBLISHED BY ELSEVIER INC.
FOR SEVERAL DECADES, MAJOR INDUSTRIES HAVE IMPLEMENTED ADVANCED ANALYTICS AND DECISION SUPPORT STRUCTURES TO ADVANCE AND SUPPORT THEIR GOALS. MORE RECENTLY, INSTITUTIONS OF HIGHER EDUCATION ARE STARTING TO ADAPT THESE METHODS TO TARGET FUND RAISING, INFORM ENROLLMENT DECISIONS, TARGET MARKETING EFFORTS, IMPROVE STUDENT SUPPORT PROCESSES, AND TO BETTER UNDERSTAND RETENTION/PERSISTENCE PATTERNS. SEPARATELY, REGIONAL, NATIONAL, AND SPECIALIZED ACCREDITORS, AS WELL AS THE FEDERAL GOVERNMENT, ARE RATCHETING UP EXPECTATIONS AROUND LEARNING OUTCOMES ASSESSMENT (E.G., ARTICULATION OF MEASURABLE LEARNING OUTCOMES, ASSESSMENT OF STUDENT ACHIEVEMENT OF THOSE OUTCOMES, AND THE USE OF RESULTING DATA). BOTH THREADS, WEAVING THEIR WAY THROUGH INSTITUTIONS OF HIGHER EDUCATION, ARE COMING TOGETHER IN THE AREA OF LEARNING ANALYTICS (OR, ACADEMIC ANALYTICS). THIS PAPER OUTLINES A CONCEPTUAL FRAMEWORK FOR THE DEVELOPMENT OF LEARNING ANALYTICS, HIGHLIGHTING LESSONS LEARNED FROM INDUSTRY, LIMITATIONS OF THE APPROACH, AND IMPORTANT ETHICAL ISSUES INVOLVED IN THE APPLICATION OF THESE METHODS TO EDUCATIONAL CONTEXTS.
E-LEARNING SYSTEMS HAVE CAUSED A RAPID INCREASE TO THE AMOUNT OF LEARNING CONTENT AVAILABLE ON THE WEB. IT HAS BECOME A TIME CONSUMING AND A DAUNTING TASK FOR E-LEARNERS TO FIND THE RELEVANT CONTENT THAT THEY SHOULD STUDY. EXISTING E-LEARNING TECHNOLOGY LACKS THE AUTOMATED CAPABILITY TO PROVIDE GUIDANCE FOR STUDENTS TO PRIORITIZE AND ENGAGE IN THE MOST VITAL COURSE CONTENT. THE STUDENTS WHO ARE UNABLE TO FIND OUT THE MOST SUITABLE RESOURCES, FOR THEIR STUDIES AND THE ASSIGNMENTS, MAY WASTE MOST OF THEIR TIME ON BROWSING AND SEARCHING. SOME OF THE "GOOD-STUDENTS" CAN INDIRECTLY ACT AS GOOD GUIDES TO OTHER STUDENTS. AVERAGE LEARNERS COULD FOLLOW THE CONTENT ADOPTED BY GOOD STUDENTS IN THE PROCESS OF LEARNING. IT IS POSSIBLE TO CAPTURE THE BEHAVIOUR OF "GOOD-STUDENTS" AND EXPOSE IT AS A FORM OF AUTOMATED GUIDING. FOR THIS TO WORK IT IS IMPORTANT TO BE ABLE TO PREDICT STUDENTS WHO ARE GOING TO BE SUCCESSFUL AT THE END OF THE COURSE BASED ON THEIR PERFORMANCE DURING THE EARLY PART OF THE COURSE. THIS WORK DEMONSTRATES THE USE OF DATA MINING TECHNIQUES ON E-LEARNING DATA TO ENABLE "GOOD-STUDENTS" TO INDIRECTLY GUIDE "AVERAGE-STUDENTS" TO FIND THE MOST RELEVANT CONTENT ON AN E-LEARNING ENVIRONMENT.
VIRTUAL REALITY (VR) IS AN EMERGING TECHNOLOGY WITH THE POTENTIAL TO EXTRACT SIGNIFICANTLY MORE DATA ABOUT LEARNERS AND THE LEARNING PROCESS. IN THIS ARTICLE, WE PRESENT AN ANALYSIS OF HOW VR EDUCATION TECHNOLOGY COMPANIES FRAME, USE AND ANALYSE THIS DATA. WE FOUND BOTH AN EXPANSION AND ACCELERATION OF WHAT DATA ARE BEING COLLECTED ABOUT LEARNERS AND HOW THESE DATA ARE BEING MOBILISED IN POTENTIALLY DISCRIMINATORY AND PROBLEMATIC WAYS. BEYOND PROVIDING EVIDENCE FOR HOW VR REPRESENTS AN INTENSIFICATION OF THE DATAFICATION OF EDUCATION, WE DISCUSS THREE INTERRELATED CRITICAL ISSUES THAT ARE SPECIFIC TO VR: THE FANTASY THAT VR DATA IS 'PERFECT', THE DATAFICATION OF SOFT-SKILLS TRAINING, AND THE COMMERCIALISATION AND COMMODIFICATION OF VR DATA. IN THE CONTEXT OF THE ISSUES IDENTIFIED, WE CAUTION THE UNREGULATED AND UNCRITICAL APPLICATION OF LEARNING ANALYTICS TO THE DATA THAT ARE COLLECTED FROM VR TRAINING.
BACKGROUND: ALTHOUGH BLENDED LEARNING (BL) HAS MULTIPLE EDUCATIONAL PROSPECTS, IT ALSO POSES CHALLENGES SUCH AS KEEPING STUDENTS MOTIVATED. OBJECTIVES: THIS STUDY INVESTIGATES STUDENTS' PERCEPTIONS OF HOW LEARNING ANALYTICS (LA) CAN BE USED TO SUPPORT THE DESIGN OF A BL ENVIRONMENT IN ORDER TO PROMOTE STUDENTS' BASIC NEED FOR RELATEDNESS, WHICH IS A DIMENSION OF MOTIVATION. HENCE, IT IS HYPOTHESIZED THAT SHARING LA TRENDS WITH STUDENTS AND ILLUSTRATING WHICH COURSE ADAPTATIONS WERE PERFORMED BASED ON THESE TRENDS, WILL RESULT IN POSITIVE STUDENT PERCEPTIONS AND CAN SUPPORT STUDENTS' BASIC NEED SATISFACTION FOR RELATEDNESS. METHODS: A QUASI-EXPERIMENTAL INTERVENTION STUDY WAS EXECUTED USING A MIXED-METHOD APPROACH (N = 257 STUDENTS) IN A BL COURSE IN UNIVERSITY-BASED TEACHER EDUCATION. THE INTERVENTION FOCUSES ON THREE TYPES OF LEARNING MANAGEMENT SYSTEM LA DATA (GENERAL, CONTENT, AND BACKGROUND) THAT ARE ACTIVELY USED BY THE INSTRUCTOR. GENERAL DATA CONSISTS OF GENERATED TIME ON TASK, CONTENT DATA DEALS WITH THE CONTENT OF A LEARNING PATH AND BACKGROUND DATA INCLUDES INFORMATION ABOUT STUDENTS' PREVIOUS EDUCATION. RESULTS AND CONCLUSIONS: THE RESULTS SHOW THAT STUDENTS' PERCEPTIONS REGARDING THESE LA ARE POSITIVE AND MOST IN FAVOUR OF CONTENT DATA. MOREOVER, THE QUALITATIVE DATA ILLUSTRATE THAT STUDENTS ACKNOWLEDGE THE POTENTIAL VALUE OF LA FOR STIMULATING RELATEDNESS.
THIS STUDY EXAMINED THE EXTENT TO WHICH INSTRUCTIONAL CONDITIONS INFLUENCE THE PREDICTION OF ACADEMIC SUCCESS IN NINE UNDERGRADUATE COURSES OFFERED IN A BLENDED LEARNING MODEL (N = 4134). THE STUDY ILLUSTRATES THE DIFFERENCES IN PREDICTIVE POWER AND SIGNIFICANT PREDICTORS BETWEEN COURSE-SPECIFIC MODELS AND GENERALIZED PREDICTIVE MODELS. THE RESULTS SUGGEST THAT IT IS IMPERATIVE FOR LEARNING ANALYTICS RESEARCH TO ACCOUNT FOR THE DIVERSE WAYS TECHNOLOGY IS ADOPTED AND APPLIED IN COURSE-SPECIFIC CONTEXTS. THE DIFFERENCES IN TECHNOLOGY USE, ESPECIALLY THOSE RELATED TO WHETHER AND HOW LEARNERS USE THE LEARNING MANAGEMENT SYSTEM, REQUIRE CONSIDERATION BEFORE THE LOG-DATA CAN BE MERGED TO CREATE A GENERALIZED MODEL FOR PREDICTING ACADEMIC SUCCESS. A LACK OF ATTENTION TO INSTRUCTIONAL CONDITIONS CAN LEAD TO AN OVER OR UNDER ESTIMATION OF THE EFFECTS OF LMS FEATURES ON STUDENTS' ACADEMIC SUCCESS. THESE FINDINGS HAVE BROADER IMPLICATIONS FOR INSTITUTIONS SEEKING GENERALIZED AND PORTABLE MODELS FOR IDENTIFYING STUDENTS AT RISK OF ACADEMIC FAILURE. (C) 2015 ELSEVIER INC. ALL RIGHTS RESERVED.
WITH THE WIDE USE OF THE INTERNET AND DIGITAL DATA SOURCES, THERE HAS BEEN A RECENT EMERGENCE OF EASY ACCESS TO STUDENT DATA WITHIN LEARNING MANAGEMENT SYSTEMS (LMS), GRADE DATA THROUGH STUDENT INFORMATION SYSTEMS (SIS) AND BROADER SECTOR DATA THROUGH BENCHMARKING METRICS AND STANDARDS. LEARNING ANALYTICS ON TOP OF THIS DATA HAS INTRODUCED GREATER CAPABILITIES FOR IMPROVING STUDENT PERFORMANCE THROUGH IMMEDIATE FEEDBACK. CURRENT LITERATURE CONSIDERS THE ROLE OF DASHBOARDS FOR STUDENT PERFORMANCE AND FEEDBACK, BUT FEW PAPERS CONSIDER THE EFFICACY OF FAST FEEDBACK TO STUDENTS OR OTHER WAYS THAT INFORMATION CAN BE FED BACK TO LEARNERS. IN THIS PAPER, WE CONSIDER THE WORK DONE BY THREE LEADING GROUPS ADDRESSING THE IMPACT OF GAMIFICATION IN UNIVERSITY EDUCATION, WITH A SPECIFIC FOCUS ON HOW DATA IS PRESENTED TO THE LEARNER, THAT IS USING ELEMENTS SUCH AS POINTS, LEVELLING UP, NARRATIVE AND PROGRESSION TO SCAFFOLD LEARNING. RESULTS INDICATE INCREASES IN STUDENT MOTIVATION, ENGAGEMENT, SATISFACTION, RETENTION AND PERFORMANCE ENHANCEMENTS.
LEARNING ANALYTICS (LA) IS PROVIDING NEW METHODOLOGIES THAT ARE BEING APPLIED TO THE DESIGN AND APPLICATION OF DASHBOARDS TO SUPPORT TEACHING AND LEARNING. HOWEVER, FEW STUDIES ATTEMPT TO UNDERSTAND HOW INSTRUCTORS INTERACT WITH AN LA DASHBOARD AND HOW SELF-REGULATED LEARNING (SRL) ACTIVITIES AND EMOTIONS OF INSTRUCTORS OCCUR AND CO-OCCUR IN THE INTERACTION. THE CURRENT STUDY INVESTIGATES TEN INSTRUCTORS' SRL ACTIVITIES AND EPISTEMIC EMOTIONS BY ANALYZING THE SCREEN CAPTURE VIDEOS AND THINK-ALOUD DATA WHILE THEY INTERACT WITH AN LA DASHBOARD DESIGNED TO SUPPORT THE ONLINE ASYNCHRONOUS COLLABORATION OF MULTIPLE GROUPS. THE RESULTS REVEAL THAT INSTRUCTORS DEMONSTRATED TWO WAYS OF NAVIGATING LA DASHBOARDS, AND THEY RELIED HEAVILY ON THE CONVERSATION EXPLORER FEATURE. INSTRUCTORS WERE MOSTLY ENGAGED IN ELABORATION, MONITORING, AND EVALUATION ACTIVITIES AND THEY FREQUENTLY EXPERIENCED CONFUSION AND ENJOYMENT. EXPERT INSTRUCTORS WERE MORE LIKELY TO REFER TO THEIR PERSONAL TEACHING EXPERIENCE AND DEMONSTRATED MORE EPISTEMIC EMOTIONS THAN NOVICE INSTRUCTORS. THIS STUDY CONTRIBUTES TO THE LITERATURE ON SRL AND TEACHER EMOTIONS BY REVEALING THE CRITICAL ROLE OF ELABORATION, MONITORING, EVALUATION, AND EPISTEMIC EMOTIONS WHEN INSTRUCTORS ATTEMPT TO UNDERSTAND A LA DASHBOARD BY THEMSELVES. THESE FINDINGS HIGHLIGHT THE IMPORTANCE OF PROVIDING PEDAGOGICAL ASSISTANCE TO TEACHERS WHO ARE TRYING TO NAVIGATE BETWEEN GROUP DYNAMICS AND VISUALIZATIONS VIEWED USING LA DASHBOARDS.
LEARNING ANALYTICS IS BECOMING A KEY TOOL FOR THE ANALYSIS AND IMPROVEMENT OF DIGITAL EDUCATION PROCESSES, AND ITS POTENTIAL BENEFIT GROWS WITH THE SIZE OF THE STUDENT COHORTS GENERATING DATA. IN THE CONTEXT OF OPEN EDUCATION, THE POTENTIALLY MASSIVE STUDENT COHORTS AND THE GLOBAL AUDIENCE REPRESENT A GREAT OPPORTUNITY FOR SIGNIFICANT ANALYSES AND BREAKTHROUGHS IN THE FIELD OF LEARNING ANALYTICS. HOWEVER, THESE POTENTIALLY HUGE DATASETS REQUIRE PROPER ANALYSIS TECHNIQUES, AND DIFFERENT ALGORITHMS, TOOLS AND APPROACHES MAY PERFORM BETTER IN THIS SPECIFIC CONTEXT. IN THIS WORK, WE COMPARE DIFFERENT CLUSTERING ALGORITHMS USING AN EDUCATIONAL DATASET. WE START BY IDENTIFYING THE MOST RELEVANT ALGORITHMS IN LEARNING ANALYTICS AND BENCHMARK THEM TO DETERMINE, ACCORDING TO INTERNAL VALIDATION AND STABILITY MEASUREMENTS, WHICH ALGORITHMS PERFORM BETTER. WE ANALYZED SEVEN ALGORITHMS, AND DETERMINED THAT K-MEANS AND PAM WERE THE BEST PERFORMERS AMONG PARTITION ALGORITHMS, AND DIANA WAS THE BEST PERFORMER AMONG HIERARCHICAL ALGORITHMS.
THE PRESENT STUDY INVESTIGATES SOUTH KOREAN UNIVERSITY STUDENTS' PERSONAL COMPUTER (PC) AND SMARTPHONE USAGE PATTERNS ON AN ONLINE TEST OF ENGLISH FOR INTERNATIONAL COMMUNICATION (TOEIC) WEBSITE USING LEARNING ANALYTICS. A TOTAL OF 107 STUDENTS TAKING A COLLEGE TOEIC COURSE PARTICIPATED DURING ONE ACADEMIC SEMESTER AND RECORDS OF THEIR ONLINE BEHAVIORS WERE ANALYZED. THE RESULTS REVEALED THAT NEARLY 30% OF THE PARTICIPANTS USED ONLY ONE DEVICE TO ACCESS THE WEBSITE. AROUND 70% OF THE PARTICIPANTS USED BOTH DEVICES AND ABOUT 71% OF THEM FAVORED SMARTPHONES OVER PCS. FOR TASK TYPES, STUDENTS PREFERRED A PARTICULAR DEVICE TO ITS COUNTERPART: (A) WHEN WATCHING VIDEOS, MORE STUDENTS CHOSE SMARTPHONES THAN PCS; (B) WHEN COMPLETING HOMEWORK, THEY ALSO PREFERRED SMARTPHONES TO PCS; (C) HOWEVER, PCS WERE CHOSEN MORE OFTEN FOR TEST-TAKING PRACTICE. SMARTPHONE USERS WERE QUITE ACTIVE DURING THE DAYTIME AND THEIR USAGE SHARPLY INCREASED BETWEEN 9:00 PM AND 11:00 PM, WHEREAS PC USERS WERE USUALLY ACTIVE BETWEEN 9:00 PM AND 11:00 PM. STUDENTS MOST OFTEN VISITED THE WEBSITE ON MONDAYS AND SUNDAYS. STUDENTS' CHOICES IN DEVICE TYPE DID NOT HAVE AN INFLUENCE ON THEIR LEARNING GAINS. IMPLICATIONS OFFER PRACTICAL INFORMATION ON ONLINE TEACHING AND PRE-SERVICE TEACHER TRAINING AS WELL AS ONLINE MATERIAL DEVELOPMENT.
EDUCATIONAL GAMES ARE HIGHLY ENGAGING, MOTIVATING AND THEY OFFER MANY ADVANTAGES AS A SUPPLEMENTARY TOOL FOR EDUCATION. HOWEVER, THE DEVELOPMENT OF EDUCATIONAL GAMES IS VERY COMPLEX, ESSENTIALLY BECAUSE OF ITS MULTIDISCIPLINARY ASPECT. FULLY INTEGRATING ASSESSMENT IS CHALLENGING AND THE GAMES CREATED ARE TOO OFTEN DISTRIBUTED AS BLACKBOXES; UNMODIFIABLE BY THE TEACHERS AND NOT PROVIDING MUCH INSIGHT ABOUT THE GAMEPLAYS. WE PROPOSE AN ASSESSMENT ENGINE, ENGAGE, TO OVERCOME THESE ISSUES. ENGAGE IS USED BY BOTH DEVELOPERS AND EDUCATORS. IT IS DESIGNED TO SEPARATE GAME AND ASSESSMENT. DEVELOPERS USE IT TO EASILY INTEGRATE ASSESSMENT INTO EDUCATIONAL GAMES AND TEACHERS CAN THEN MODIFY THE ASSESSMENT AND VISUALISE LEARNING ANALYTICS VIA AN ONLINE INTERFACE. THIS PAPER FOCUSES ON ENGAGE'S BENEFITS FOR GAMES DEVELOPERS. IT PRESENTS A QUANTITATIVE EVALUATION CARRIED OUT WITH 36 DEVELOPERS (7 EXPERIENCED AND 29 STUDENTS). FINDINGS WERE VERY POSITIVE: EVERY FEATURE OF THE ENGINE WAS RATED USEFUL AND ENGAGE RECEIVED A USABILITY SCORE OF 64 USING THE SYSTEM USABILITY SCALE. A MANN-WHITNEY U TEST SHOWED A SIGNIFICANT DIFFERENCE IN USABILITY (Z = -3.34, P < 0.002) BETWEEN NOVICE DEVELOPERS (MEAN = 56) AND EXPERIENCED DEVELOPERS (MEAN = 71) BUT NONE IN TERMS OF USEFULNESS. THIS PAPER CONCLUDES THAT DEVELOPERS CAN USE ENGAGE EFFECTIVELY TO INTEGRATE ASSESSMENT AND LEARNING ANALYTICS IN EDUCATIONAL GAMES.
IN FULL EXPANSION, A VITAL AREA SUCH AS EDUCATION COULD NOT REMAIN INDIFFERENT TO THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGY. OVER THE PAST TWO DECADES WE HAVE WITNESSED THE EMERGENCE AND DEVELOPMENT OF E-LEARNING SYSTEMS, THE PROLIFERATION OF MOOCS, AND GENERALLY THE RISE OF TECHNOLOGY ENHANCED EDUCATION. ALL OF THESE CONTRIBUTED TO GENERATION AND STORAGE OF UNPRECEDENTED VOLUMES OF DATA CONCERNING ALL AREAS OF LEARNING. AT THE SAME TIME, DOMAINS SUCH AS DATA MINING AND BIG DATA ANALYTICS HAVE EMERGED AND DEVELOPED. THEIR APPLICATIONS IN EDUCATION HAVE SPAWNED NEW AREAS OF RESEARCH SUCH AS EDUCATIONAL DATA MINING OR LEARNING ANALYTICS. AS AN INTERDISCIPLINARY RESEARCH AREA EDUCATIONAL DATA MINING (EDM) AIMS TO EXPLORE DATA FROM EDUCATIONAL ENVIRONMENT TO BUILD MODELS BASED ON WHICH STUDENTS' BEHAVIOR AND RESULTS ARE BETTER UNDERSTOOD. IN FACT, EDM IS A COMPLEX PROCESS THAT CONSISTS OF A FEW STEPS GROUPED IN THREE STAGES: DATA PREPROCESSING, MODELLING AND POSTPROCESSING. IT TRANSFORMS RAW DATA FROM EDUCATIONAL ENVIRONMENTS IN USEFUL INFORMATION THAT COULD INFLUENCE IN A POSITIVE WAY THE EDUCATIONAL PROCESS. ACCORDING TO SOCIETY FOR LEARNING ANALYTICS RESEARCH (SOLAR) WHICH TOOK OVER THE WORDING OF THE FIRST INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS AND KNOWLEDGE, LEARNING ANALYTICS IS "THE MEASUREMENT, COLLECTION, ANALYSIS AND REPORTING OF DATA ABOUT LEARNERS AND THEIR CONTEXTS FOR PURPOSES OF UNDERSTANDING AND OPTIMIZING LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS" (SIEMENS, 2011). THIS PAPER PROPOSES A COMPARATIVE STUDY OF THE TWO CONCEPTS: EDM AND LEARNING ANALYTICS. DUE TO CERTAIN VOICES IN THE SCIENTIFIC ENVIRONMENT THAT CLAIM THAT THE TWO TERMS REFER TO THE SAME THING, WE WANT TO EMPHASIZE THE SIMILARITIES AND DIFFERENCES BETWEEN THEM, AND HOW EACH ONE CAN SERVE TO RAISE THE QUALITY IN EDUCATIONAL PROCESSES.
IN THIS PAPER, THE AUTHORS ADDRESS LEARNING ANALYTICS AND THE WAYS ACADEMIC LIBRARIES ARE BEGINNING TO PARTICIPATE IN WIDER INSTITUTIONAL LEARNING ANALYTICS INITIATIVES. SINCE THERE ARE MORAL ISSUES ASSOCIATED WITH LEARNING ANALYTICS, THE AUTHORS CONSIDER HOW DATA MINING PRACTICES RUN COUNTER TO ETHICAL PRINCIPLES IN THE AMERICAN LIBRARY ASSOCIATION'S "CODE OF ETHICS." SPECIFICALLY, THE AUTHORS ADDRESS HOW LEARNING ANALYTICS IMPLICATES PROFESSIONAL COMMITMENTS TO PROMOTE INTELLECTUAL FREEDOM; PROTECT PATRON PRIVACY AND CONFIDENTIALITY; AND BALANCE INTELLECTUAL PROPERTY INTERESTS BETWEEN LIBRARY USERS, THEIR INSTITUTION, AND CONTENT CREATORS AND VENDORS. THE AUTHORS RECOMMEND THAT LIBRARIANS SHOULD EMBED THEIR ETHICAL POSITIONS IN TECHNOLOGICAL DESIGNS, PRACTICES, AND GOVERNANCE MECHANISMS.
RECENT STUDIES OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE FOCUSED ON GLOBAL PROVIDERS SUCH AS EDX, COURSERA, AND FUTURELEARN, WITH LESS ATTENTION TO LOCAL INITIATIVES THAT TARGET REGIONAL LEARNERS. IN THIS STUDY WE COMBINE DATA FROM THE MAIN EDX PLATFORM AND ONE REGIONAL MOOC PROVIDER, EDRAAK IN JORDAN, TO EXPLORE DIFFERENCES IN LEARNERS' BEHAVIOR AND PREFERENCES. WE FIND THAT REGIONAL PROVIDER EDRAAK ATTRACTS YOUNGER LEARNERS, MORE FEMALES AND THOSE WITH LOWER LEVELS OF EDUCATION COMPARED TO GLOBAL PROVIDERS. EDRAAK LEARNERS VALUE LOCAL COURSES BECAUSE THEY CATER TO THEIR INTERESTS AND LEARNING NEEDS. WE DOCUMENT OUR MULTI-PLATFORM LEARNING ANALYTICS PROCEDURE, WHERE WE ESTABLISH A COMMON DATA FORMAT AND SCRIPT THAT ENABLES AN "APPLES-TO-APPLES" COMPARISON WITHOUT EXCHANGING DATA - A COMMON PRIVACY AND DATA SECURITY CONCERN. THESE FINDINGS SUGGEST THE POTENTIAL OF THIS METHODOLOGICAL APPROACH TO STUDY AND LEARN FROM REGIONAL MOOC PROVIDERS, PARTICULARLY AROUND THE QUESTIONS OF EQUITY AND ACCESS IN THE GLOBAL MOOC ECOSYSTEM.
LEARNING ANALYTICS (LA) HAS BEEN WIDELY ADOPTED IN RESEARCH ON EDUCATION. HOWEVER, MOST STUDIES IN THE AREA HAVE CONDUCTED LA AFTER COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) ACTIVITIES RATHER THAN DURING CSCL. TO ADDRESS THIS PROBLEM, THIS STUDY PROPOSED A LA-BASED REAL-TIME FEEDBACK APPROACH BASED ON A DEEP NEURAL NETWORK MODEL TO IMPROVE CSCL PERFORMANCE. IN TOTAL, 72 UNIVERSITY STUDENTS PARTICIPATED IN THE STUDY AND WERE RANDOMLY ASSIGNED TO AN EXPERIMENTAL OR CONTROL GROUP. THE STUDENTS IN THE EXPERIMENTAL GROUP LEARNED WITH THE LA-BASED REAL-TIME FEEDBACK APPROACH, WHEREAS THE STUDENTS IN THE CONTROL GROUP LEARNED WITH THE CONVENTIONAL ONLINE COLLABORATIVE LEARNING APPROACH. TO ANALYSE THE DATA, BOTH QUANTITATIVE AND QUALITATIVE METHODS WERE ADOPTED. THE RESULTS INDICATED THAT THE LA-BASED REAL-TIME FEEDBACK APPROACH SIGNIFICANTLY PROMOTED KNOWLEDGE CONVERGENCE, KNOWLEDGE ELABORATION, INTERACTIVE RELATIONSHIPS AND GROUP PERFORMANCE. THE INTERVIEW RESULTS ALSO CONFIRMED THE EFFECTIVENESS OF THE PROPOSED APPROACH. PRACTITIONER NOTES WHAT IS ALREADY KNOWN REGARDING THIS TOPIC LEARNING ANALYTICS (LA) HAS BEEN WIDELY USED IN EDUCATION. MOST STUDIES IN THE AREA HAVE PRESENTED LA RESULTS ONLY AFTER COLLABORATIVE LEARNING AND HAVE LACKED REAL-TIME ANALYSIS AND FEEDBACK. WHAT THIS PAPER ADDS A LA-BASED REAL-TIME FEEDBACK APPROACH WAS PROPOSED AND VALIDATED IN THE COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) CONTEXT. THE EXPERIMENTAL RESULTS INDICATED THAT THE LA-BASED REAL-TIME FEEDBACK APPROACH SIGNIFICANTLY PROMOTED KNOWLEDGE ELABORATION, KNOWLEDGE CONVERGENCE, INTERACTIVE RELATIONSHIPS AND GROUP PERFORMANCE. IMPLICATIONS FOR PRACTICE AND/OR POLICY TO SHED LIGHT ON PROGRESS IN CSCL, REAL-TIME LA ARE RECOMMENDED. DEEP NEURAL NETWORK MODELS, SUCH AS BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS, CAN BE ADOPTED TO AUTOMATICALLY ANALYSE ONLINE DISCUSSION TRANSCRIPTS. REAL-TIME FEEDBACK BASED ON LA RESULTS CAN PROMOTE CSCL PERFORMANCE.
SINCE ITS FOUNDING IN 2011, KIDAPTIVE HAS BUILT CUSTOMIZED MODELS THAT PROVIDE ADAPTIVITY AND/OR PERSONALIZATION IN ONLINE LEARNING ENVIRONMENTS. WE HAVE SUPPORTED ADAPTIVE GAME-BASED LEARNING THROUGH RULE-BASED AND DYNAMIC BAYESIAN PSYCHOMETRIC MODELS, AND WE HAVE DEVELOPED BEHAVIORAL MODELS FOR ONLINE LEARNING AND ONLINE TEST PREPARATION ENVIRONMENTS BASED ON LEARNERS' TIME MANAGEMENT, ANSWER BEHAVIOR, AND TEST SCORES. OUR MODELS ARE DEPLOYED ON A SCALABLE DISTRIBUTED-COMPUTING PLATFORM THAT HAS SUPPORTED MILLIONS OF LEARNERS, BUT THE HUMAN EXPERTISE REQUIRED TO BUILD CUSTOM MODELS FOR EVERY LEARNING ENVIRONMENT IS NOT SCALABLE. TO ADDRESS THIS LIMITATION, WE HAVE RECENTLY BEEN WORKING TOWARD AN ABSTRACTED VERSION OF OUR PSYCHOMETRIC AND BEHAVIORAL MODELS, TO BE PROVIDED AS AN "OUT-OF-THE-BOX" PRODUCT OFFERING. THIS PAPER DESCRIBES INSIGHTS AND CHALLENGES ENCOUNTERED IN THIS PROCESS.
LEARNING ANALYTICS IS THE ANALYSIS OF ELECTRONIC LEARNING DATA WHICH ALLOWS TEACHERS, COURSE DESIGNERS AND ADMINISTRATORS OF VIRTUAL LEARNING ENVIRONMENTS TO SEARCH FOR UNOBSERVED PATTERNS AND UNDERLYING INFORMATION IN LEARNING PROCESSES. THE MAIN AIM OF LEARNING ANALYTICS IS TO IMPROVE LEARNING OUTCOMES AND THE OVERALL LEARNING PROCESS IN ELECTRONIC LEARNING VIRTUAL CLASSROOMS AND COMPUTER-SUPPORTED EDUCATION. THE MOST BASIC UNIT OF LEARNING DATA IN VIRTUAL LEARNING ENVIRONMENTS FOR LEARNING ANALYTICS IS THE INTERACTION, BUT THERE IS NO CONSENSUS YET ON WHICH INTERACTIONS ARE RELEVANT FOR EFFECTIVE LEARNING. DRAWING UPON EXTANT LITERATURE, THIS RESEARCH DEFINES THREE SYSTEM-INDEPENDENT CLASSIFICATIONS OF INTERACTIONS AND EVALUATES THE RELATION OF THEIR COMPONENTS WITH ACADEMIC PERFORMANCE ACROSS TWO DIFFERENT LEARNING MODALITIES: VIRTUAL LEARNING ENVIRONMENT (VLE) SUPPORTED FACE-TO-FACE (F2F) AND ONLINE LEARNING. IN ORDER TO DO SO, WE PERFORMED AN EMPIRICAL STUDY WITH DATA FROM SIX ONLINE AND TWO VLE-SUPPORTED F2F COURSES. DATA EXTRACTION AND ANALYSIS REQUIRED THE DEVELOPMENT OF AN AD HOC TOOL BASED ON THE PROPOSED INTERACTION CLASSIFICATION. THE MAIN FINDING FROM THIS RESEARCH IS THAT, FOR EACH CLASSIFICATION, THERE IS A RELATION BETWEEN SOME TYPE OF INTERACTIONS AND ACADEMIC PERFORMANCE IN ONLINE COURSES, WHEREAS THIS RELATION IS NON-SIGNIFICANT IN THE CASE OF VLE-SUPPORTED F2F COURSES. IMPLICATIONS FOR THEORY AND PRACTICE ARE DISCUSSED NEXT. (C) 2013 ELSEVIER LTD. ALL RIGHTS RESERVED.
PARTICIPATION IN VIRTUAL COMMUNITIES OF PRACTICE (VCOP) CAN BE INFLUENCED AT THE SAME TIME BY TECHNOLOGY ACCEPTANCE AND BY COMMUNITY FACTORS. TO OVERCOME METHODOLOGICAL ISSUES CONNECTED WITH THE ANALYSIS OF THESE INFLUENCES, LEARNING ANALYTICS WERE APPLIED. BASED ON A RECENT VCOP MODEL, THE COLLABORATIVE DIALOGUE COMPRISING 4040 INTERVENTIONS IN 1981 MESSAGES CREATED BY A VCOP LOCATED AT A US AMERICAN ONLINE UNIVERSITY WAS AUTOMATICALLY ANALYZED. THE TEXT-BASED ASYNCHRONOUS ONLINE DISCUSSIONS WERE SCORED USING A COHESION-BASED PARTICIPATION AND COLLABORATION ANALYSIS. ADDITIONALLY, A SAMPLE OF N = 133 VCOP PARTICIPANTS RESPONDED A TECHNOLOGY ACCEPTANCE SURVEY. THUS, A COMBINED RESEARCH MODEL INCLUDING THE VCOP MODEL AND AN ESTABLISHED TECHNOLOGY ACCEPTANCE MODEL WAS VERIFIED. THE RESULTS CONFIRMED THE VCOP MODEL ENTIRELY, AND THE ACCEPTANCE MODEL ONLY PARTIALLY. AS CONSEQUENCE FOR EDUCATIONAL RESEARCH, THE COP MODEL WAS CONFIRMED AND EXTENDED TO VCOP SETTINGS, WHILE THE ACCEPTANCE MODEL APPEARS TO NEED RECONSIDERATION. FOR ACADEMIC PRACTICE, THE STUDY INITIATES THE DEVELOPMENT OF ASSESSMENT TOOLS FOSTERING KNOWLEDGE SHARING THROUGH DIALOGUE IN VCOP. ALSO, IT SUGGESTS HOW VIRTUAL CLASSROOMS CAN BE EXTENDED TO OPEN SPACES WHERE VALUE CREATION TAKES PLACE THROUGH SOCIAL LEARNING. LEARNING ANALYTICS PROVED THUS SUCCESSFUL, PROVIDES INFORMATION THAT IMPACTS BOTH THEORY AND PRACTICE OF TECHNOLOGY-ENHANCED LEARNING. (C) 2013 ELSEVIER LTD. ALL RIGHTS RESERVED.
AS IN OTHER RESEARCH FIELDS, THE DEVELOPMENT OF LEARNING ANALYTICS IS INFLUENCED BY THE NETWORKS OF RESEARCHERS THAT CONTRIBUTE TO IT. THIS PAPER DESCRIBES ONE OF SUCH NETWORKS: THE SPANISH NETWORK OF LEARNING ANALYTICS (SNOLA). THE PAPER PRESENTS THE RESEARCH LINES OF THE MEMBERS OF SNOLA, AS WELL AS THE MAIN CHALLENGES THAT LEARNING ANALYTICS HAS TO ADDRESS IN THE NEXT FEW YEARS AS PERCEIVED BY THESE RESEARCHERS. THIS ANALYSIS IS BASED ON SNOLA'S ARCHIVAL DATA AND ON A SURVEY CARRIED OUT TO THE CURRENT MEMBERS OF THE NETWORK. ALTHOUGH THIS APPROACH DOES NOT COVER ALL THE ACTIVITY RELATED TO LEARNING ANALYTICS IN SPAIN, THE RESULTS PROVIDE A REPRESENTATIVE OVERVIEW OF THE CURRENT STATE OF RESEARCH RELATED TO LEARNING ANALYTICS IN THIS CONTEXT. THE PAPER DESCRIBES THESE TRENDS AND THE MAIN CHALLENGES, AMONG WHICH WE CAN POINT OUT THE NEED TO ADOPT AN ETHICAL COMMITMENT WITH DATA, TO DEVELOP SYSTEMS THAT RESPOND TO THE REQUIREMENTS OF THE END USERS, AND TO REACH A WIDER INSTITUTIONAL IMPACT.
BACKGROUND: WITH THE ADVENT OF DIGITAL MICROSCOPY, LEARNING ANALYTICS CAN BE LEVERAGED TO ADVANCE TEACHING OF DERMATOPATHOLOGY IN DERMATOLOGY RESIDENCY. OBJECTIVE: TO ANALYZE THE ACQUISITION AND DECAY OF DERMATOPATHOLOGY VISUAL RECOGNITION SKILLS AND AREAS OF DIAGNOSTIC CONFUSION AMONGST RESIDENTS USING LEARNING METRICS GENERATED BY A WEB-BASED LEARNING TOOL. METHODS: THIS WAS A PROSPECTIVE, LONGITUDINAL STUDY OF DERMATOLOGY RESIDENTS WHO STUDIED DIGITAL PHOTOMICROGRAPHS OF 18 ROUTINE DIAGNOSTIC ENTITIES USING AN ONLINE SOFTWARE PLATFORM. RESIDENTS AT DIFFERENT YEARS OF TRAINING WERE GIVEN 60 MINUTES TO COMPLETE ASSESSMENTS ON THREE OCCASIONS (INITIAL TEST WITH FOLLOW-UPS AT ONE AND THREE MONTHS). RESULTS: 4,938 RESPONSES WERE ANALYZED. ACCURACY AND TIME TO DIAGNOSIS IMPROVED WITHIN EACH ASSESSMENT AND BETWEEN THE FIRST AND SECOND ASSESSMENTS. FIRST YEAR RESIDENTS SHOWED KNOWLEDGE DECAY WHEN TESTED AT THREE MONTHS (67% VS 64%; P=0.002) WHILE THIRD YEAR RESIDENTS RETAINED KNOWLEDGE AND CONTINUED TO IMPROVE UPON THEIR ACCURACY AFTER THREE MONTHS (83% TO 91%, <0.001). LEARNING ANALYTICS HIGHLIGHTED DIAGNOSTICALLY CHALLENGING CASES FOR RESIDENTS THAT CONTRADICTED EXPERTS' PREDICTIONS (R=0.48). CONCLUSIONS: THE USE OF LEARNING ANALYTICS AND INTERACTIVE DIGITAL PLATFORMS ENHANCES DERMATOPATHOLOGY CURRICULUM DEVELOPMENT BY IDENTIFYING CHALLENGING DIAGNOSTIC ENTITIES, ASSESSING MASTERY OF SUBJECT MATERIAL, AND OPTIMIZING REVIEW SCHEDULES.
THIS RESEARCH AIMS TO INVESTIGATE INTO THE EFFECT OF USING LEARNING ANALYTICS (LA)-BASED PROCESS FEEDBACK ON STUDENTS' PERCEPTIONS OF COMMUNITY OF INQUIRY (TEACHING, SOCIAL AND COGNITIVE PRESENCE) AND THEIR REFLECTIVE THINKING SKILLS. BY USING A MIXED-METHOD RESEARCH APPROACH (QUAN + QUAL), THIS STUDY WAS CONDUCTED AS AN EXPERIMENTAL DESIGN WITH THE PRETEST-POSTTEST CONTROL GROUP. A TOTAL OF 104 UNIVERSITY STUDENTS WHO WERE RANDOMLY ASSIGNED TO THE EXPERIMENT GROUP (EG) AND CONTROL GROUP (CG) WERE RECRUITED IN THIS STUDY. THE PROCEDURE WAS CONDUCTED WITHIN THE SCOPE OF THE COMPUTING COURSE BASED ON THE FLIPPED CLASSROOM (FC) MODEL. WHILE THE PARTICIPANTS IN THE EG RECEIVED LA-BASED PROCESS FEEDBACK WHICH SHOWS THEIR LA RESULTS IN A WEEKLY MANNER, THOSE IN THE CG DID NOT GET ANY LA-BASED PROCESS FEEDBACK. THE DATA WERE COLLECTED THROUGH THE COMMUNITY OF INQUIRY SCALE, THE REFLECTIVE THINKING SCALE AND A SEMI-STRUCTURED STUDENT OPINION FORM. THE FINDINGS INDICATED THAT SENDING FEEDBACK INCLUDING THE STUDENTS' LA RESULTS HAD A STATISTICALLY SIGNIFICANT EFFECT ON THE STUDENTS' PERCEPTIONS OF COMMUNITY OF INQUIRY AND REFLECTIVE THINKING SKILLS. BASED ON THE FINDINGS OF THE STUDY, SEVERAL RECOMMENDATIONS FOR TEACHERS, INSTRUCTIONAL DESIGNERS AND RESEARCHERS HAVE BEEN MADE.
LEARNING ANALYTICS HAVE THE POTENTIAL TO IMPROVE TEACHING AND LEARNING IN K-12 EDUCATION, BUT AS STUDENT DATA IS INCREASINGLY BEING COLLECTED AND TRANSFERRED FOR THE PURPOSE OF ANALYSIS, IT IS IMPORTANT TO TAKE MEASURES THAT WILL PROTECT STUDENT PRIVACY. A COMMON APPROACH TO ACHIEVE THIS GOAL IS THE DE-IDENTIFICATION OF THE DATA, MEANING THE REMOVAL OF PERSONAL DETAILS THAT CAN REVEAL STUDENT IDENTITY. HOWEVER, AS WE DEMONSTRATE, DE-IDENTIFICATION ALONE IS NOT A COMPLETE SOLUTION. WE SHOW HOW WE CAN DISCOVER SENSITIVE INFORMATION ABOUT STUDENTS BY LINKING DE-IDENTIFIED DATASETS WITH PUBLICLY AVAILABLE SCHOOL DATA, USING UNSUPERVISED MACHINE LEARNING TECHNIQUES. THIS UNDERLINES THAT DE-IDENTIFICATION ALONE IS INSUFFICIENT IF WE WISH TO FURTHER LEARNING ANALYTICS IN K-12 WITHOUT COMPROMISING STUDENT PRIVACY.
DISTANCE LEARNING IS RAPIDLY GAINING GROUND GLOBALLY. IN THIS CASE STUDY, WE FOCUSED ON PROFESSIONAL (VOCATIONAL) TEACHER EDUCATION (PTE) STUDENT ONLINE ACTIVITY IN A BLENDED LEARNING CONTEXT. WE APPLIED LEARNING ANALYTICS (LA) TO IDENTIFY STUDENTS' (N = 19) ONLINE STUDY PATTERNS. OUR KEY INTEREST WAS IN DETERMINING WHEN AND WHAT KINDS OF ONLINE ACTIVITY AND BEHAVIOR PTE STUDENTS ENGAGE IN DURING THEIR STUDIES. WE APPLIED QUANTITATIVE CONTENT ANALYSIS TO ANALYZE THE STUDENTS' BEHAVIOR. MOODLE'S EVENT LOG ENABLED US TO IDENTIFY ACTIVE HOURS AND DAYS, VARIATION IN USE OF LEARNING MATERIALS, THE IMPACT OF INTERVENTIONS, AND STUMBLING BLOCKS TO STUDENT LEARNING IN THE STUDY UNIT. BASED ON OUR DATA, EDUCATOR AVAILABILITY IS AN ESSENTIAL FACTOR FOR GOOD STUDENT ENGAGEMENT IN DIGITAL LEARNING ENVIRONMENTS. INTERACTION FORUMS ARE IMPORTANT FOR PTE STUDENTS EFFECTIVE LEARNING. MONDAY AND TUESDAY AFTERNOONS ARE THE MOST EFFECTIVE TIMES FOR EDUCATORS TO BE AVAILABLE FOR PTE STUDENTS. THERE IS A CLEAR NEED FOR CONTACT LEARNING IN PROFESSIONAL TEACHER EDUCATION, EVEN WHEN OPERATING IN DIGITAL LEARNING ENVIRONMENTS. IT PLAYS AN ESSENTIAL ROLE IN KEEPING STUDENTS' ACTIVITY ALIVE. IT COULD BE BENEFICIAL TO PLAN A POST-PROCESS FOR STUDENTS WHO DO NOT GRADUATE AS PLANNED, INCLUDING REGULAR GROUP MEETINGS FOR SUPPORTING STUDIES, RECEIVING GUIDANCE, AND MEETING PEERS. PTE STUDENTS' BEHAVIOR IN A DISTANCE LEARNING ENVIRONMENT IN THE CONTEXT OF BLENDED LEARNING FOLLOWS ZIPF'S LAW, WHICH MODELS THE OCCURRENCE OF DISTINCT OBJECTS IN PARTICULAR SORTS OF COLLECTIONS.
LEARNING ANALYTICS DASHBOARDS (LAD) ARE BECOMING AN INCREASINGLY POPULAR WAY TO PROVIDE STUDENTS WITH PERSONALISED FEEDBACK. DESPITE THE NUMBER OF LADS BEING DEVELOPED, SIGNIFICANT RESEARCH GAPS EXIST AROUND THE STUDENT PERSPECTIVE, ESPECIALLY HOW STUDENTS MAKE SENSE OF GRAPHICS PROVIDED IN LADS, AND HOW THEY INTEND TO ACT ON THE FEEDBACK PROVIDED THEREIN. THIS STUDY EMPLOYED A RANDOMIZED-CONTROLLED TRIAL TO EXAMINE STUDENTS' SENSE-MAKING OF LADS SHOWING FOUR DIFFERENT FRAMES OF REFERENCE, AND TO WHAT EXTENT THE IMPACT OF LADS WAS MEDIATED BY BASELINE SELF-REGULATION. USING A MIX OF QUANTITATIVE AND QUALITATIVE DATA ANALYSIS, THE RESULTS REVEALED RATHER DISTINCT PATTERNS IN STUDENTS' SENSE-MAKING ACROSS THE FOUR LADS. THESE PATTERNS INVOLVED THE INTERSECTION OF VISUAL SALIENCE AND PLANNED LEARNING ACTIONS. HOWEVER, COLLECTIVELY, ACROSS ALL FOUR LADS A CONSISTENT THEME EMERGED AROUND STUDENTS PLANNED LEARNING ACTIONS. THIS THEME WAS CLASSIFIED AS TIME AND STUDY ENVIRONMENT MANAGEMENT. A KEY FINDING OF THE STUDY IS THAT THE USE OF LADS AS A PRIMARY FEEDBACK PROCESS SHOULD BE PERSONALIZED AND INCLUDE TRAINING AND SUPPORT TO AID STUDENT SENSEMAKING.
IN THIS RESEARCH, THE EFFECT OF THE USE OF LEARNING ANALYTICS (LA) BASED FEEDBACK AS A METACOGNITIVE TOOL ON THE LEARNERS' TRANSACTIONAL DISTANCE AND MOTIVATION WAS EXAMINED. THE RESEARCH WAS CARRIED OUT ACCORDING TO EXPERIMENTAL DESIGN AND WAS CARRIED OUT ON 81 UNIVERSITY STUDENTS. THE STUDENTS WERE RANDOMLY ASSIGNED TO THE EXPERIMENTAL AND CONTROL GROUPS. WHILE THE STUDENTS IN THE EXPERIMENTAL GROUP WERE PROVIDED FEEDBACK ON THE WEEKLY LA RESULTS, NO FEEDBACK WAS PROVIDED TO THE STUDENTS IN THE CONTROL GROUP ABOUT THE LA RESULTS. RESEARCH DATA WERE OBTAINED THROUGH TRANSACTIONAL DISTANCE SCALE AND MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE. THE RESULTS OF THE STUDY SHOWED THAT PROVIDING METACOGNITIVE FEEDBACK SUPPORT TO LEARNERS ABOUT LA RESULTS HAS AN EFFECT ON DECREASING TRANSACTIONAL DISTANCE AND INCREASING MOTIVATION. IN LINE WITH THE RESULTS OF THE RESEARCH, VARIOUS SUGGESTIONS WERE GIVEN FOR RESEARCHERS, TEACHERS AND INSTRUCTIONAL DESIGNERS.
ALTHOUGH TEMPORALITY IS EMBODIED IN INSTRUCTIONAL DESIGN, IMPLICITLY PRESENT IN SEVERAL LEARNING THEORIES AND CENTRAL TO THE SELF-REGULATION OF LEARNING AND AWARDING OF CREDITS, IT HAS NOT RECEIVED THE DUE ATTENTION IN THE FIELD EDUCATION. THIS LEARNING ANALYTICS STUDY INCLUDED FOUR HIGHER EDUCATION COURSES IN DENTAL EDUCATION OVER A FULL YEAR DURATION. TEMPORALITY IN TERMS OF WHEN STUDENTS ENGAGE IN LEARNING WAS STUDIED ON DAILY, WEEKLY, COURSE, AND YEAR BASIS. THE PATTERNS OF LOW AND HIGH ACHIEVER GROUPS IN EACH PERIOD WERE VISUALLY PLOTTED AND COMPARED. CORRELATION WITH THE PERFORMANCE WAS EVALUATED USING THE NON-PARAMETRIC SPEARMAN CORRELATION TEST USING RE-SAMPLING PERMUTATION TECHNIQUE. THE FINDINGS OF THIS STUDY HIGHLIGHT SOME IMPORTANT POINTS; TEMPORALITY IS A DEFINING FACTOR OF HOW STUDENTS REGULATE THEIR LEARNING AND SHOULD BE TAKEN INTO ACCOUNT WHEN DESIGNING A POSSIBLE MONITORING SYSTEM. HIGH ACHIEVERS WERE ALWAYS ACTIVE EARLY IN THE YEAR, IN THE COURSE, AND ON ASSIGNMENTS. LOW ACHIEVERS, ON THE OTHER HAND, TEND TO BE SIGNIFICANTLY MORE ACTIVE CLOSE TO EXAMINATION TIMES. USING ONLY TEMPORALITY PREDICTORS, WE WERE ABLE TO PREDICT HIGH ACHIEVERS WITH 100% PRECISION AND LOW ACHIEVERS WITH 82.3% TO 93.3% CLASS PRECISION. SINCE EARLY PARTICIPATION WAS THE PREDICTOR, IT MEANS THAT AN EARLY ALERT INDICATOR CAN BE ACHIEVED THAT ENABLES TIMELY INTERVENTION.
IN THIS PAPER, WE DESCRIBE A SCALABLE LEARNING ANALYTICS PLATFORM WHICH RUNS GENERALIZED ANALYTICS MODELS ON EDUCATIONAL DATA IN PARALLEL. AS A PROOF OF CONCEPT, WE USE THIS PLATFORM AS A BASE FOR AN END-TO-END AUTOMATED WRITING FEEDBACK SYSTEM. THE SYSTEM ALLOWS STUDENTS TO VIEW FEEDBACK ON THEIR WRITING IN NEAR REAL-TIME, EDIT THEIR WRITING BASED ON THE FEEDBACK PROVIDED, AND OBSERVE THE PROGRESSION OF THEIR PERFORMANCE OVER TIME. PROVIDING STUDENTS WITH DETAILED FEEDBACK IS AN IMPORTANT PART OF IMPROVING WRITING SKILLS AND AN ESSENTIAL COMPONENT TOWARDS SOLVING BLOOM'S "TWO SIGMA" PROBLEM IN EDUCATION. WE EVALUATE THE EFFECTIVENESS OF THE FEEDBACK FOR STUDENTS WITH AN ONGOING PILOT STUDY WITH 800 STUDENTS WHO ARE USING THE LEARNING ANALYTICS PLATFORM IN A COLLEGE ENGLISH COURSE.
A REMOTE LABORATORY IS A SOFTWARE AND HARDWARE TOOL WHICH ENABLES STUDENTS TO USE REAL EQUIPMENT -LOCATED IN AN EDUCATIONAL INSTITUTION-THROUGH THE INTERNET. THIS WAY, STUDENTS CAN EXPERIMENT AS IF THEY WERE USING THE LABORATORIES WITH THEIR OWN HANDS. AND, DEPENDING ON THE DESIGN, INSTRUCTORS CAN LATER SEE THE RESULTS OF THESE STUDENTS. DURING THE LAST DECADE, FEDERATION PROTOCOLS TO SHARE REMOTE LABORATORIES HAVE EMERGED. THE FOCUS OF THESE PROTOCOLS IS TO BE MAKE REMOTE LABORATORIES OF ONE INSTITUTION AVAILABLE IN OTHER IN AN AUTOMATED MANNER, THROUGH INSTITUTIONAL CONTRACTS. AND THESE FEDERATION PROTOCOLS USUALLY RELY ON EXISTING REMOTE LABORATORY MANAGEMENT SYSTEMS (RLMS), WHICH USUALLY PROVIDE APIS FOR TRACKING STUDENT USAGE. AT THE SAME TIME, THE INTEREST ON LEARNING ANALYTICS IS INCREASING. LEARNING ANALYTICS FOCUSES ON THE MEASUREMENT AND ANALYSIS OF DATA ABOUT LEARNERS IN THEIR CONTEXT. IN THE PARTICULAR CONTEXT OF FEDERATED REMOTE LABORATORIES, NEW CHALLENGES ARISE: ON THE ONE HAND, REMOTE LABORATORIES MUST BE PREPARED TO TRACK INSIGHTFUL INFORMATION FROM THE STUDENT SESSION SO AS TO EXTRACT PATTERNS, AND ON THE OTHER HAND, THE USAGE OF A FEDERATED ENVIRONMENT REQUIRES DIFFERENT DEGREES OF ANONYMITY. THIS CONTRIBUTION DESCRIBES THE NEW LEARNING ANALYTICS DASHBOARD OF WEBLAB-DEUSTO, DETAILING WHAT INFORMATION CAN BE EXTRACTED AND HOW THE USAGE OF A RLMS SIMPLIFIES THE DEVELOPMENT OF SUCH TOOLS IN A FEDERATED ENVIRONMENT.
INSTRUCTORS MAY DESIGN AND IMPLEMENT FORMATIVE ASSESSMENTS ON TECHNOLOGY-ENHANCED PLATFORMS (E.G., ONLINE QUIZZES) WITH THE INTENTION OF ENCOURAGING THE USE OF EFFECTIVE LEARNING STRATEGIES LIKE ACTIVE RETRIEVAL OF INFORMATION AND SPACED PRACTICE AMONG THEIR STUDENTS. HOWEVER, WHEN STUDENTS INTERACT WITH UNSUPERVISED TECHNOLOGY-ENHANCED LEARNING PLATFORMS, INSTRUCTORS ARE OFTEN UNAWARE OF STUDENTS' ACTUAL USE OF THE LEARNING TOOLS WITH RESPECT TO THE PEDAGOGICAL DESIGN. IN THIS STUDY, WE DESIGNED AND EXTRACTED FIVE VARIABLES FROM THE CANVAS QUIZ-LOG DATA, WHICH CAN PROVIDE INSIGHTS INTO STUDENTS' LEARNING BEHAVIORS. ANCHORING OUR CONCEPTUAL BASIS ON THE INFLUENTIAL CONVERSATIONAL FRAMEWORK, WE FIND THAT LEARNING ANALYTICS (LA) CAN PROVIDE INSTRUCTORS WITH CRITICAL INFORMATION RELATED TO STUDENTS' LEARNING BEHAVIORS, THEREBY SUPPORTING INSTRUCTORS' INQUIRY INTO STUDENT LEARNING IN UNSUPERVISED TECHNOLOGY-ENHANCED PLATFORMS. OUR FINDINGS SUGGEST THAT THE INFORMATION THAT LA PROVIDES MAY ENABLE INSTRUCTORS TO PROVIDE MEANINGFUL FEEDBACK TO LEARNERS AND IMPROVE THE EXISTING LEARNING DESIGNS.
PURPOSE ONE OF THE MISCONCEPTIONS OF TEACHING AND LEARNING FOR PRACTICAL-BASED PROGRAMMES, SUCH AS ENGINEERING, SCIENCES, ARCHITECTURE, DESIGN AND ARTS, IS THE NECESSITY TO DELIVER VIA FACE-TO-FACE PHYSICAL MODALITY. THIS PAPER REFUTES THIS CLAIM BY PROVIDING CASE STUDIES OF BEST PRACTICES IN DELIVERING SUCH COURSES AND THEIR HANDS-ON SKILLSETS USING COMPLETELY ONLINE VIRTUAL DELIVERY THAT UTILISES DIFFERENT FORMATS OF 2D AND 3D MEDIA AND TOOLS, SUPPORTED BY EVIDENCE OF EFFICIENCY USING LEARNING ANALYTICS. DESIGN/METHODOLOGY/APPROACH THE CASE STUDIES WERE DESIGNED USING PEDAGOGICAL PRINCIPLES OF CONSTRUCTIVISM AND DEEP LEARNING, CONDUCTED WITHIN A MIXTURE OF 2D AND 3D VIRTUAL LEARNING ENVIRONMENTS WITH FLEXIBLE INTERFACE AND TOOLS CAPABILITIES. STATE-OF-THE-ART CODING AND SCRIPTING TECHNIQUES WERE ALSO USED TO AUTOMATE DIFFERENT STUDENT TASKS AND INCREASE ENGAGEMENT. REGRESSION AND DESCRIPTIVE ANALYSIS METHODS WERE USED FOR LEARNING ANALYTICS. FINDINGS LEARNING ANALYTICS OF ALL CASE STUDIES DEMONSTRATED THE CAPABILITY TO ACHIEVE COURSE/PROJECT LEARNING OUTCOMES, WITH HIGH ENGAGEMENT FROM STUDENTS AMONGST PEERS AND WITH TUTORS. FURTHERMORE, THE DIVERSE VIRTUAL LEARNING TOOLS USED, ALLOWED STUDENTS TO DISPLAY CREATIVITY AND INNOVATION EFFICIENTLY ANALOGOUS TO PHYSICAL LEARNING. ORIGINALITY/VALUE THE SYNTHESIS OF UTILISED MEDIA AND TOOLS WITHIN THIS STUDY DISPLAYS INNOVATION AND ORIGINALITY IN COMBINING DIFFERENT TECHNOLOGY TECHNIQUES TO ACHIEVE AN EFFECTUAL LEARNING EXPERIENCE. THAT WOULD USUALLY NECESSITATE FACE-TO-FACE, HANDS-ON PHYSICAL CONTACT TO PERFORM PRACTICAL TASKS AND RECEIVE FEEDBACK ON THEM. FURTHERMORE, THIS PAPER PROVIDES SUGGESTIONS FOR FUTURE RESEARCH USING MORE ADVANCED TECHNOLOGIES.
IN RECENT YEARS, ONLINE CLASSES HAVE BEEN INCREASINGLY CONDUCTED IN VARIOUS SITUATIONS. HOWEVER, IN THESE CLASSES, ESPECIALLY NON-FACE-TO-FACE AND LARGE-SCALE ONES, IT IS MORE DIFFICULT FOR TEACHERS AND STUDENTS TO UNDERSTAND THE STATUS OF THE CLASS DURING A LECTURE. TO ADDRESS THIS ISSUE, WE PROPOSE A REAL-TIME LEARNING ANALYTICS DASHBOARD THAT PROVIDES SUMMARIZED INFORMATION ON TEACHERS' INSTRUCTION AND STUDENTS' LEARNING ACTIVITIES DURING LECTURES. IN THIS ARTICLE, WE INTRODUCE THE REAL-TIME LEARNING ANALYTICS DASHBOARD AND REPORT ITS EFFECTIVENESS THROUGH EXPERIMENTS IN AN ONLINE CLASS AT OUR UNIVERSITY.
TECHNOLOGY AND INNOVATION EMPOWER HIGHER EDUCATIONAL INSTITUTIONS (HEI) TO USE DIFFERENT TYPES OF LEARNING SYSTEMS-VIDEO LEARNING IS ONE SUCH SYSTEM. ANALYZING THE FOOTPRINTS LEFT BEHIND FROM THESE ONLINE INTERACTIONS IS USEFUL FOR UNDERSTANDING THE EFFECTIVENESS OF THIS KIND OF LEARNING. VIDEO-BASED LEARNING WITH FLIPPED TEACHING CAN HELP IMPROVE STUDENT'S ACADEMIC PERFORMANCE. THIS STUDY WAS CARRIED OUT WITH 772 EXAMPLES OF STUDENTS REGISTERED IN E-COMMERCE AND E-COMMERCE TECHNOLOGIES MODULES AT AN HEI. THE STUDY AIMED TO PREDICT STUDENT'S OVERALL PERFORMANCE AT THE END OF THE SEMESTER USING VIDEO LEARNING ANALYTICS AND DATA MINING TECHNIQUES. DATA FROM THE STUDENT INFORMATION SYSTEM, LEARNING MANAGEMENT SYSTEM AND MOBILE APPLICATIONS WERE ANALYZED USING EIGHT DIFFERENT CLASSIFICATION ALGORITHMS. FURTHERMORE, DATA TRANSFORMATION AND PREPROCESSING TECHNIQUES WERE CARRIED OUT TO REDUCE THE FEATURES. MOREOVER, GENETIC SEARCH AND PRINCIPLE COMPONENT ANALYSIS WERE CARRIED OUT TO FURTHER REDUCE THE FEATURES. ADDITIONALLY, THE CN2 RULE INDUCER AND MULTIVARIATE PROJECTION CAN BE USED TO ASSIST FACULTY IN INTERPRETING THE RULES TO GAIN INSIGHTS INTO STUDENT INTERACTIONS. THE RESULTS SHOWED THAT RANDOM FOREST ACCURATELY PREDICTED SUCCESSFUL STUDENTS AT THE END OF THE CLASS WITH AN ACCURACY OF 88.3% WITH AN EQUAL WIDTH AND INFORMATION GAIN RATIO.
FLIPPED CLASSROOMS HAVE BEEN WIDELY ADOPTED AND DISCUSSED BY SCHOOL TEACHERS AND RESEARCHERS IN THE PAST DECADE. HOWEVER, FEW STUDIES HAVE BEEN CONDUCTED TO FORMALLY EVALUATE THE EFFECTIVENESS OF FLIPPED CLASSROOMS IN TERMS OF IMPROVING EFL STUDENTS' ENGLISH ORAL PRESENTATION, NOT TO MENTION INVESTIGATING FACTORS AFFECTING THEIR FLIPPED LEARNING OUTCOMES. IN THIS STUDY, AN ONLINE COMMUNITY-BASED FLIPPED LEARNING APPROACH WAS PROPOSED FOR AN EFL ORAL PRESENTATION COURSE; MOREOVER, A LEARNING ANALYTICS APPROACH WAS USED TO ANALYZE FACTORS AFFECTING THE STUDENTS' ORAL PRESENTATION OUTCOMES. AN 18-WEEK RESEARCH DESIGN WAS IMPLEMENTED WITH THE ONLINE COMMUNITY-BASED FLIPPED CLASSROOM USING FACEBOOK AS THE PLATFORM FOR FACILITATING AND RECORDING PEER-TO-PEER INTERACTIONS DURING THE FLIPPED LEARNING PROCESS. IN ADDITION, THE STUDENTS' LEARNING PERFORMANCE AND PERCEPTIONS WERE COLLECTED IN 3 LEARNING STAGES DURING THE 18 WEEKS. THE EXPERIMENTAL RESULTS REVEAL POSITIVE EFFECTS OF THE ONLINE COMMUNITY-BASED FLIPPED INSTRUCTION OVER THE CONVENTIONAL VIDEO-BASED INSTRUCTION. THAT IS, FIRST, THE ONLINE COMMUNITY-BASED FLIPPED INSTRUCTION USING MOBILE DEVICES CAN ENHANCE STUDENTS' ENGLISH ORAL PERFORMANCE. MOREOVER, IT WAS FOUND THAT THE HIGH IMPROVERS HAD A SIGNIFICANTLY HIGHER FREQUENCY OF ONLINE PARTICIPATION, AS WELL AS MORE INTERACTIVE BEHAVIORS AND GREATER SATISFACTION WITH THE FLIPPED CLASSROOM THAN THE LOW IMPROVERS. THESE RESULTS IMPLY THAT THE ONLINE COMMUNITY FLIPPED CLASSROOM COULD NOT ONLY PROVIDE LEARNING MATERIALS AND OUT-OF-CLASS LEARNING FOR STUDENTS, BUT COULD ALSO HELP THEM BECOME MORE RESPONSIBLE AND AUTONOMOUS IN THEIR LEARNING AND COMMUNICATION. THESE FINDINGS COULD BE VALUABLE REFERENCES FOR THOSE WHO INTEND TO CONDUCT EFFECTIVE FLIPPED CLASSROOMS WITH AN ONLINE COMMUNITY TO FACILITATE STUDENTS' BEFORE-CLASS LEARNING PARTICIPATION AND TO IMPROVE THEIR IN-CLASS LEARNING PERFORMANCE.
TIME-ON-TASK ESTIMATION, MEASURED AS THE DURATION BETWEEN TWO CONSECUTIVE CLICKS USING STUDENT LOG-FILES DATA, HAS BEEN ONE OF THE MOST FREQUENTLY USED METRICS IN LEARNING ANALYTICS RESEARCH. HOWEVER, THE PROCESS OF HANDLING OUTLIERS (I.E., EXCESSIVELY LONG DURATIONS) IN TIME-ON-TASK ESTIMATION IS UNDER-EXPLORED AND OFTEN NOT EXPLICITLY REPORTED IN MANY STUDIES. ONE COMMON APPROACH TO HANDLE OUTLIERS IN TIME-TO-TASK ESTIMATION IS TO 'TRIM' ALL DURATIONS USING A CUT-OFF THRESHOLD, SUCH AS 60 OR 30 MINUTES. THIS PAPER CHALLENGES THIS EXISTING APPROACH BY DEMONSTRATING THAT THE TREATMENT OF OUTLIERS IN AN EDUCATIONAL CONTEXT SHOULD BE INDIVIDUAL-SPECIFIC, TIME-SPECIFIC, AND TASK-SPECIFIC. IN OTHER WORDS, WHAT CAN BE CONSIDERED AS OUTLIERS IN TIME-ON-TASK DEPENDS ON THE LEARNING PATTERN OF EACH STUDENT, THE STAGES DURING THE LEARNING PROCESS, AND THE NATURE OF THE TASK INVOLVED. THE ANALYSIS SHOWED THAT PREDICTIVE MODELS USING TIME-ON-TASK ESTIMATION ACCOUNTING FOR INDIVIDUAL, TIME, AND TASK DIFFERENCES COULD EXPLAIN 3-4% MORE VARIANCES IN ACADEMIC PERFORMANCE THAN MODELS USING AN OUTLIER TRIMMING APPROACH. AS AN IMPLICATION, THIS STUDY PROVIDES A THEORETICALLY GROUNDED AND REPLICABLE OUTLIER DETECTION APPROACH FOR FUTURE LEARNING ANALYTICS RESEARCH WHEN USING TIME-ON-TASK ESTIMATION.
EDUCATIONAL APPLICATIONS, IN GENERAL, TREAT DISPARATE STUDY THREADS AS A SINGULAR ENTITY, BUNDLE PEDAGOGICAL INTERVENTION AND OTHER STUDENT SUPPORT SERVICES AT A COARSER LEVEL, AND SUMMATIVELY ASSESS FINAL PRODUCTS OF ASSESSMENTS. IN THIS RESEARCH, WE PROPOSE AN ANALYTICS FRAMEWORK WHERE WE CLOSELY MONITOR INDIVIDUAL THREADS OF STUDY HABITS AND ASSESS STUDY THREADS IN AN INDIVIDUAL FASHION TO TRACE LEARNING PROCESSES LEADING INTO ASSESSMENT PRODUCTS. WE DEVELOPED CUSTOMIZED INTERVENTION TO TARGET SPECIFIC SKILLS AND NURTURE OPTIMAL STUDY HABITS. THE FRAMEWORK HAS BEEN IMPLEMENTED IN A SYSTEM CALLED SCALE (SMART CAUSAL ANALYTICS ON LEAMING). SCALE ENABLES THE TRACKING OF STUDENTS' INDIVIDUAL STUDY THREADS TOWARDS MULTIPLE FINAL STUDY PRODUCTS. THE LARGE VOLUME, MULTIPLE VARIETY, AND INCESSANT FLOW OF DATA CLASSIFIES OUR WORK IN THE REALMS OF BIG DATA ANALYTICS. WE CONDUCTED A PRELIMINARY STUDY USING SCALE. THE RESULTS SHOW THE ABILITY OF THE SYSTEM TO TRACK THE EVOLUTION OF COMPETENCIES. WE PROPOSE THAT EXPLICITLY SUPPORTING THE DEVELOPMENT OF A TARGETED SET OF COMPETENCIES IS ONE OF THE KEY TENETS OF SMART LEARNING ENVIRONMENTS.