INFORMED BY COGNITIVE THEORIES OF LEARNING, THIS WORK EXAMINED HOW STUDENTS' SELF-REPORTED STUDY PATTERNS (SPACING VS. CRAMMING) CORRESPONDED TO THEIR ENGAGEMENT WITH THE LEARNING MANAGEMENT SYSTEM (LMS) ACROSS TWO YEARS IN A LARGE BIOLOGY COURSE. WE SPECIFICALLY FOCUSED ON HOW STUDENTS ACCESSED NON-MANDATORY RESOURCES (LECTURE VIDEOS, LECTURE SLIDES) AND CONSIDERED WHETHER THIS PATTERN DIFFERED BY UNDERREPRESENTED MINORITY (URM) STATUS. OVERALL, STUDENTS WHO SELF-REPORTED UTILIZING SPACING STRATEGIES THROUGHOUT THE COURSE HAD HIGHER GRADES THAN STUDENTS WHO REPORTED CRAMMING THROUGHOUT THE COURSE. WHEN EXAMINING LMS ENGAGEMENT, ONLY A SMALL PERCENTAGE OF STUDENTS ACCESSED THE LECTURE VIDEOS AND LECTURE SLIDES. APPLYING A NEGATIVE BINOMIAL REGRESSION MODEL TO DAILY COUNTS OF CLICK ACTIVITIES, WE ALSO FOUND THAT STUDENTS WHO UTILIZED SPACING STRATEGIES ACCESSED LMS RESOURCES MORE OFTEN BUT NOT EARLIER BEFORE MAJOR DEADLINES. MOREOVER, THIS FINDING WAS NOT DIFFERENT FOR UNDERREPRESENTED STUDENTS. OUR RESULTS PROVIDE SOME INITIAL EVIDENCE SHOWING HOW SPACING BEHAVIORS CORRESPOND TO ACCESSING LEARNING RESOURCES. HOWEVER, GIVEN THE LACK OF GENERAL ENGAGEMENT WITH LMS RESOURCES, OUR RESULTS UNDERSCORE THE VALUE OF ENCOURAGING STUDENTS TO UTILIZE THESE RESOURCES WHEN STUDYING COURSE MATERIAL.
LEARNING ANALYTICS ARE USED TO TRACK LEARNERS' PROGRESS AND EMPOWER EDUCATORS AND LEARNERS TO MAKE WELL-INFORMED DATA-DRIVEN DECISIONS. HOWEVER, DUE TO THE DISTRIBUTED NATURE OF THE LEARNING PROCESS, ANALYTICS NEED TO BE COMBINED TO OFFER BROADER INSIGHTS INTO LEARNER'S BEHAVIOR AND EXPERIENCES. CONSEQUENTLY, THIS PAPER PRESENTS AN ARCHITECTURE OF A LEARNING ECOSYSTEM, THAT INTEGRATES AND UTILIZES CROSS-PLATFORM ANALYTICS. THE PROPOSED CROSS-PLATFORM ARCHITECTURE HAS BEEN PUT INTO PRACTICE VIA A JAVA PROGRAMMING COURSE. AFTER A SERIES OF STUDIES, A PROOF OF CONCEPT WAS DERIVED THAT SHOWS HOW CROSS-PLATFORM ANALYTICS AMPLIFY THE RELEVANT ANALYTICS FOR THE LEARNING PROCESS. SUCH ANALYTICS COULD IMPROVE EDUCATORS' AND LEARNERS' UNDERSTANDING OF THEIR OWN ACTIONS AND THE ENVIRONMENTS IN WHICH LEARNING OCCURS.
THIS PAPER PRESENTS FINDINGS FROM A STUDY OF AUSTRALIAN AND NEW ZEALAND ACADEMICS (N = 276) WHO TEACH TERTIARY EDUCATION STUDENTS. THE STUDY AIMED TO EXPLORE PARTICIPANTS' EARLY EXPERIENCES OF LEARNING ANALYTICS IN A HIGHER EDUCATION MILIEU WHERE DATA ANALYTICS IS GAINING INCREASING PROMINENCE. BROADLY SPEAKING PARTICIPANTS WERE ASKED ABOUT: (1) THEIR TEACHING CONTEXT; (2) THEIR CURRENT STUDENT RETENTION ACTIVITIES; (3) THEIR INVOLVEMENT IN, AND ASPIRATIONS FOR, LEARNING ANALYTICS USE; AND (4) THEIR RELATIONSHIP WITH THEIR INSTITUTION AROUND LEARNING ANALYTICS. THE SAMPLED TEACHING STAFF BROADLY INDICATED A HIGH LEVEL OF INTEREST BUT LIMITED LEVEL OF SUBSTANTIVE INVOLVEMENT IN LEARNING ANALYTICS PROJECTS AND CAPACITY BUILDING ACTIVITIES. OVERALL, THE INTENTION IS TO PRESENT A CRITICAL SET OF VOICES THAT ASSIST IN IDENTIFYING AND UNDERSTANDING KEY ISSUES AND DRAW CONNECTIONS TO THE BROADER WORK BEING DONE IN THE FIELD.
ALTHOUGH STUDENTS ARE INCREASINGLY INVOLVED IN EXTRA LEARNING ACTIVITIES AIMED AT ENRICHING THE ATTRIBUTES AND THE CONTENTS OF CONVENTIONAL EDUCATIONAL PROGRAMMES, STILL LITTLE IS KNOWN ON THE MAIN IMPLICATIONS OF THESE INITIATIVES. EMBRACING A LEARNING ANALYTICS (LA) PERSPECTIVE, THE ARTICLE SHEDS LIGHT ON THE EFFECTS TRIGGERED BY STUDENTS' INVOLVEMENT IN INNOVATIVE EDUCATIONAL ACTIVITIES AND LEARNING PROCESSES CO-FINANCED BY THE CALL NO. 10862/2016 ISSUED BY THE NATIONAL OPERATIVE PROGRAMME (PON) 2014/2020. WE IMPLEMENTED A THREE-STEP STUDY DESIGN, WHICH CONSISTED OF: 1) A DESCRIPTIVE ANALYSIS; 2) A PRINCIPAL COMPONENT ANALYSIS; AND 3) A DISCRETE CHOICE REGRESSION ANALYSIS. OUR FINDINGS POINTED OUT THAT EDUCATIONAL ACTIVITIES AND LEARNING PROCESSES WERE ESPECIALLY EFFECTIVE IN IMPROVING SOCIAL RELATIONSHIPS AT SCHOOL; MOREOVER, THEY CONTRIBUTED IN INCREASING THE STUDENTS' WILLINGNESS TO EXPAND THEIR HORIZONS.
THE CURRENT PANDEMIC HAS SIGNIFICANTLY IMPACTED EDUCATIONAL PRACTICES, MODIFYING MANY ASPECTS OF HOW AND WHEN WE LEARN. IN PARTICULAR, REMOTE LEARNING AND THE USE OF DIGITAL PLATFORMS HAVE GREATLY INCREASED IN IMPORTANCE. ONLINE TEACHING AND E-LEARNING PROVIDE MANY BENEFITS FOR INFORMATION RETENTION AND SCHEDULE FLEXIBILITY IN OUR ON-DEMAND WORLD WHILE BREAKING DOWN BARRIERS CAUSED BY GEOGRAPHIC LOCATION, PHYSICAL FACILITIES, TRANSPORTATION ISSUES, OR PHYSICAL IMPEDIMENTS. HOWEVER, EDUCATORS AND RESEARCHERS HAVE NOTICED THAT STUDENTS FACE A LEARNING AND PERFORMANCE DECLINE AS A RESULT OF THIS SUDDEN SHIFT TO ONLINE TEACHING AND E-LEARNING FROM CLASSROOMS AROUND THE WORLD. IN THIS PAPER, WE FOCUS ON REVIEWING EYE-TRACKING TECHNIQUES AND SYSTEMS, DATA COLLECTION AND MANAGEMENT METHODS, DATASETS, AND MULTI-MODAL LEARNING DATA ANALYTICS FOR PROMOTING PERVASIVE AND PROACTIVE LEARNING IN EDUCATIONAL ENVIRONMENTS. WE THEN DESCRIBE AND DISCUSS THE CRUCIAL CHALLENGES AND OPEN ISSUES OF CURRENT LEARNING ENVIRONMENTS AND DATA LEARNING METHODS. THE REVIEW AND DISCUSSION SHOW THE POTENTIAL OF TRANSFORMING TRADITIONAL WAYS OF TEACHING AND LEARNING IN THE CLASSROOM, AND THE FEASIBILITY OF ADAPTIVELY DRIVING LEARNING PROCESSES USING EYE-TRACKING, DATA SCIENCE, MULTIMODAL LEARNING ANALYTICS, AND ARTIFICIAL INTELLIGENCE. THESE FINDINGS CALL FOR FURTHER ATTENTION AND RESEARCH ON COLLABORATIVE AND INTELLIGENT LEARNING SYSTEMS, PLUG-AND-PLAY DEVICES AND SOFTWARE MODULES, DATA SCIENCE, AND LEARNING ANALYTICS METHODS FOR PROMOTING THE EVOLUTION OF FACE-TO-FACE LEARNING AND E-LEARNING ENVIRONMENTS AND ENHANCING STUDENT COLLABORATION, ENGAGEMENT, AND SUCCESS.
THE MASSIVE ADOPTION OF TECHNOLOGY IN LEARNING PROCESSES COMES WITH AN EQUALLY LARGE CAPACITY TO TRACK LEARNERS. LEARNING ANALYTICS AIMS AT USING THE COLLECTED INFORMATION TO UNDERSTAND AND IMPROVE THE QUALITY OF A LEARNING EXPERIENCE. THE PRIVACY AND ETHICAL ISSUES THAT EMERGE IN THIS CONTEXT ARE TIGHTLY INTERCONNECTED WITH OTHER ASPECTS SUCH AS TRUST, ACCOUNTABILITY AND TRANSPARENCY. IN THIS PAPER, A SET OF PRINCIPLES IS IDENTIFIED TO NARROW THE SCOPE OF THE DISCUSSION AND POINT TO PRAGMATIC APPROACHES TO HELP DESIGN AND RESEARCH LEARNING EXPERIENCES WHERE IMPORTANT ETHICAL AND PRIVACY ISSUES ARE CONSIDERED.
MASSIVE OPEN ONLINE COURSES (MOOCS) ARE REMOTE COURSES THAT EXCEL IN THEIR STUDENTS' HETEROGENEITY AND QUANTITY. DUE TO THE PECULIARITY OF BEING MASSIVENESS, THE LARGE DATASETS GENERATED BY MOOC PLATFORMS REQUIRE ADVANCED TOOLS AND TECHNIQUES TO REVEAL HIDDEN PATTERNS FOR PURPOSES OF ENHANCING LEARNING AND EDUCATIONAL BEHAVIORS. THIS PUBLICATION OFFERS A RESEARCH STUDY ON USING CLUSTERING AS ONE OF THESE TECHNIQUES TO PORTRAY LEARNERS' ENGAGEMENT IN MOOCS. IT UTILIZES LEARNING ANALYTICS TECHNIQUES TO INVESTIGATE AN OFFERED MOOC BY GRAZ UNIVERSITY OF TECHNOLOGY HELD FOR UNDERGRADUATES. THE AUTHORS MAINLY SEEK TO CLASSIFY STUDENTS INTO APPROPRIATE CATEGORIES BASED ON THEIR LEVEL OF ENGAGEMENT. CLUSTERS AFTERWARD ARE COMPARED WITH ANOTHER CLASSICAL SCHEME (CRYER'S SCHEME OF ELTON) FOR FURTHER EXAMINATION AND COMPARISON. THE FINAL RESULTS OF THIS STUDY SHOW THAT EXTRINSIC FACTORS ARE NOT ENOUGH TO MAKE STUDENTS HIGHLY COMMITTED TO THE MOOC, YET, ADDING INTRINSIC FACTORS ARE RECOMMENDED TO IMPROVE FUTURE MOOCS.
THERE ARE GOOD REASONS FOR HIGHER EDUCATION INSTITUTIONS TO USE LEARNING ANALYTICS TO RISK-SCREEN STUDENTS. INSTITUTIONS CAN USE LEARNING ANALYTICS TO BETTER PREDICT WHICH STUDENTS ARE AT GREATER RISK OF DROPPING OUT OR FAILING, AND USE THE STATISTICS TO TREAT 'RISKY' STUDENTS DIFFERENTLY. THIS PAPER ANALYSES THIS PRACTICE USING NORMATIVE THEORIES OF DISCRIMINATION. THE ANALYSIS SUGGESTS THE PRINCIPAL ETHICAL CONCERN WITH THE DIFFERING TREATMENT IS THE FAILURE TO RECOGNIZE STUDENTS AS INDIVIDUALS, WHICH MAY IMPACT ON STUDENTS AS AGENTS. THIS CONCERN IS CROSS-EXAMINED DRAWING ON A PHILOSOPHICAL ARGUMENT THAT SUGGESTS THERE IS LITTLE OR NO DISTINCTIVE DIFFERENCE BETWEEN ASSESSING INDIVIDUALS ON GROUP RISK STATISTICS AND USING MORE 'INDIVIDUALIZED' EVIDENCE. THIS PAPER APPLIES THIS ARGUMENT TO THE USE OF LEARNING ANALYTICS TO RISK-SCREEN STUDENTS IN HIGHER EDUCATION. THE PAPER OFFERS REASONS TO CONCLUDE THAT JUDGMENT BASED ON GROUP RISK STATISTICS DOES INVOLVE A DISTINCTIVE FAILURE IN TERMS OF ASSESSING PERSONS AS INDIVIDUALS. HOWEVER, INSTRUCTIONAL DESIGN OFFERS WAYS TO MITIGATE THIS ETHICAL CONCERN WITH RESPECT TO LEARNING ANALYTICS. THESE INCLUDE DESIGNING FEATURES INTO COURSES THAT PROMOTE GREATER USE OF EFFORT-BASED FACTORS AND DYNAMIC RATHER THAN STATIC RISK FACTORS, AND GREATER USE OF SETS OF STATISTICS SPECIFIC TO INDIVIDUALS.
THE PREVAILING RHETORIC CONCERNING LEARNING ANALYTICS IS THAT ITS USE WILL SUPPORT THE EDUCATIONAL ENDEAVOR AND MAKE SIGNIFICANT IMPROVEMENTS TO TEACHING AND LEARNING. FOR ACADEMIC LIBRARIES, LEARNING ANALYTICS PRESENTS THE POSSIBILITY OF USING LIBRARY DATA TO COORDINATE, INTEGRATE, AND ALIGN WITH THE GOALS OF THE INSTITUTIONS IN WHICH THEY ARE EMBEDDED. WHILE LIBRARIES HAVE A LONG HISTORY OF COLLECTING DATA TO SUPPORT VARIOUS SERVICE AND LEARNING OBJECTIVES, THOSE DATA HAVE TYPICALLY BEEN SILOED, DE-IDENTIFIED, PRIVATE, AND CONFIDENTIAL. ALTHOUGH THERE ARE POSITIVE CONTRIBUTIONS THAT LEARNING ANALYTICS CAN MAKE TO THE LEARNING PROCESS, THERE ARE CONCERNS ASSOCIATED WITH ITS USE, PARTICULARLY THE TENSIONS BETWEEN THE OBJECTIVES OF LEARNING ANALYTICS CONTRASTED WITH DIFFERENT CONCEPTUALIZATIONS OF LEARNERS AND THE VALUES OF EDUCATION AND LIBRARIANSHIP. INSTITUTIONS OF HIGHER EDUCATION USE LEARNING ANALYTICS TO ACHIEVE INSTITUTIONALLY DEFINED GOALS AND OUTCOMES FOR STUDENTS, WHICH CREATES TENSIONS WITH THE ENSHRINED VALUES OF THE AMERICAN LIBRARY ASSOCIATION'S CODE OF ETHICS, LIBRARY BILL OF RIGHTS, AND CORE COMPETENCIES OF LIBRARIANSHIP. THE TRANSCENDENT BENEFITS TO SOCIETY THAT ARE INHERENT IN EDUCATION AND ACADEMIC LIBRARIANSHIP, SUCH AS THE RIGHTS AND RESPONSIBILITIES OF CITIZENSHIP, ARE NOT MEASURABLE THROUGH LEARNING ANALYTICS.
EMPIRICAL MEASUREMENT, MONITORING, ANALYSIS, AND REPORTING OF LEARNING OUTCOMES IN HIGHER INSTITUTIONS OF DEVELOPING COUNTRIES MAY LEAD TO SUSTAINABLE EDUCATION IN THE REGION. IN THIS DATA ARTICLE, DATA ABOUT THE ACADEMIC PERFORMANCES OF UNDERGRADUATES THAT STUDIED ENGINEERING PROGRAMS AT COVENANT UNIVERSITY, NIGERIA ARE PRESENTED AND ANALYZED. A TOTAL POPULATION SAMPLE OF 1841 UNDERGRADUATES THAT STUDIED CHEMICAL ENGINEERING (CHE), CIVIL ENGINEERING (CVE), COMPUTER ENGINEERING (CEN), ELECTRICAL AND ELECTRONICS ENGINEERING (EEE), INFORMATION AND COMMUNICATION ENGINEERING (ICE), MECHANICAL ENGINEERING (MEE), AND PETROLEUM ENGINEERING (PET) WITHIN THE YEAR RANGE OF 2002-2014 ARE RANDOMLY SELECTED. FOR THE FIVE-YEAR STUDY PERIOD OF ENGINEERING PROGRAM, GRADE POINT AVERAGE (GPA) AND ITS CUMULATIVE VALUE OF EACH OF THE SAMPLE WERE OBTAINED FROM THE DEPARTMENT OF STUDENT RECORDS AND ACADEMIC AFFAIRS. IN ORDER TO ENCOURAGE EVIDENCE-BASED RESEARCH IN LEARNING ANALYTICS, DETAILED DATASETS ARE MADE PUBLICLY AVAILABLE IN A MICROSOFT EXCEL SPREADSHEET FILE ATTACHED TO THIS ARTICLE. DESCRIPTIVE STATISTICS AND FREQUENCY DISTRIBUTIONS OF THE ACADEMIC PERFORMANCE DATA ARE PRESENTED IN TABLES AND GRAPHS FOR EASY DATA INTERPRETATIONS. IN ADDITION, ONE-WAY ANALYSIS OF VARIANCE (ANOVA) AND MULTIPLE COMPARISON POSTHOC TESTS ARE PERFORMED TO DETERMINE WHETHER THE VARIATIONS IN THE ACADEMIC PERFORMANCES ARE SIGNIFICANT ACROSS THE SEVEN ENGINEERING PROGRAMS. THE DATA PROVIDED IN THIS ARTICLE WILL ASSIST THE GLOBAL EDUCATIONAL RESEARCH COMMUNITY AND REGIONAL POLICY MAKERS TO UNDERSTAND AND OPTIMIZE THE LEARNING ENVIRONMENT TOWARDS THE REALIZATION OF SMART CAMPUSES AND SUSTAINABLE EDUCATION. (C) 2018 THE AUTHORS. PUBLISHED BY ELSEVIER INC.
UNIVERSITIES ARE PURSUING LEARNING ANALYTICS PRACTICES TO IMPROVE RETURNS FROM THEIR INVESTMENTS, DEVELOP BEHAVIORAL AND ACADEMIC INTERVENTIONS TO IMPROVE STUDENT SUCCESS, AND ADDRESS POLITICAL AND FINANCIAL PRESSURES. ACADEMIC LIBRARIES ARE ADDITIONALLY UNDERTAKING LEARNING ANALYTICS TO DEMONSTRATE VALUE TO STAKEHOLDERS, ASSESS LEARNING GAINS FROM INSTRUCTION, AND ANALYZE STUDENT-LIBRARY USAGE, ET CETERA. THE ADOPTION OF THESE TECHNIQUES LEADS TO MANY PROFESSIONAL ETHICS ISSUES AND PRACTICAL CONCERNS RELATED TO PRIVACY. IN THIS NARRATIVE LITERATURE REVIEW, WE PROVIDE A FOUNDATIONAL BACKGROUND IN THE FIELD OF LEARNING ANALYTICS, LIBRARY ADOPTION OF THESE PRACTICES, AND IDENTIFY ETHICAL AND PRACTICAL PRIVACY ISSUES.
STUDENT ENGAGEMENT IS A KNOWN CONTRIBUTOR TO ACADEMIC SUCCESS IN ONLINE LEARNING, AND PARTICULARLY IN THE HIGHLY DEMANDED AND CHALLENGING SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) EDUCATION. HOWEVER MUCH OF THE ENGAGEMENT DATA FROM THE THREE MAIN TYPES OF ENGAGEMENT; BEHAVIOURAL, COGNITIVE AND EMOTIONAL REMAIN UNCAPTURED IN A TIMELY FASHION AND IN-CONTEXT. IN TERMS OF MEASURING SOFTER QUALITIES OF STUDENT ENGAGEMENT WHICH ARE HARDER TO INFER SOLELY BASED ON ONLINE BEHAVIOURS, SELF-DISCLOSED STUDENT DATA HAS A HIGH VALUE. OUR PROPOSAL IS THEREFORE TOWARDS A MICROLEVEL STUDENT ENGAGEMENT (MSE) DATA CAPTURE FROM ONLINE STUDENTS IN ORDER TO GAIN TIMELY DATA THAT COULD PROVIDE BETTER INSIGHTS INTO STUDENT LEARNING AND COMPARE THE FINDINGS WITH THE MORE TRADITIONAL APPROACH OF MEASURING STUDENT ENGAGEMENT AT THE END OF A SEMESTER. AT UNIVERSITAT OBERTA DE CATALUNYA (UOC), SPAIN, WE HAVE CONDUCTED THE INITIAL TESTS OF ENGAGEMENT DATA CAPTURE IN VIRTUAL CLASSROOMS OF A PROGRAMMING COURSE, USING AN ENGAGEMENT DATA CAPTURE MODULE WHERE STUDENTS REPORT THEIR ENGAGEMENT WITH COMPONENTS IN THEIR ONLINE LEARNING ENVIRONMENT. BY COMPLEMENTING THIS DATA WITH THE DATA FROM A SET OF SHORT QUESTIONNAIRES PLACED DURING COURSE ELEMENTS SUCH AS PRACTICALS AND CONTINUOUS ASSESSMENTS AND THE SYSTEM-LEVEL LOG DATA CAPTURED AT UOC, WE HAVE GENERATED ACTIONABLE KNOWLEDGE SUCH AS STUDENT INTEREST LEVELS IN THE ONGOING LESSONS, MOST ENGAGED TYPES OF RESOURCES (VIDEOS, IMAGES, ALGORITHMS ETC.) AND LESSONS, COGNITIVE AND EMOTIONAL ENGAGEMENT LEVELS REQUIRED BY THESE DIFFERENT TYPES OF RESOURCES AND LESSONS. OUR GOAL IS TO FURTHER UNDERSTAND ENGAGEMENT LEVELS AND BEHAVIOURS THAT HELP ACHIEVE ACADEMIC SUCCESS AND THEREBY ENCOURAGE RICHER AND TIMELY FEEDBACK AND INFORMED DECISION MAKING BY THE TEACHERS AND THE INSTITUTIONS AND ALSO PROVIDE STUDENTS WITH AN UNDERSTANDING OF THEIR OWN ENGAGEMENT TRAJECTORIES.
THIS PAPER PRESENTS A CASE FOR THE USE OF TRANSACTION-LEVEL DATA WHEN ANALYZING AUTOMATED ONLINE ASSESSMENT RESULTS TO IDENTIFY KNOWLEDGE GAPS AND MISCONCEPTIONS FOR INDIVIDUAL STUDENTS. TRANSACTION-LEVEL DATA, WHICH RECORDS ALL OF THE STEPS A STUDENT USES TO COMPLETE AN ASSESSMENT ITEM, ARE PREFERRED OVER TRADITIONAL ASSESSMENT FORMATS THAT SUBMIT ONLY THE FINAL ANSWER, AS THE SYSTEM CAN DETECT PERSISTENT MISCONCEPTIONS. IN THIS STUDY WE COLLECTED TRANSACTION-LEVEL DATA FROM 996 STUDENTS ENROLLED IN AN ONLINE INTRODUCTORY SPREADSHEET CLASS. EACH STUDENT'S FINAL ANSWER AND STEP-BY-STEP ATTEMPTS WERE CODED FOR MISCONCEPTIONS OR KNOWLEDGE GAPS REGARDING THE USE OF ABSOLUTE REFERENCES OVER FOUR ASSESSMENT OCCASIONS. OVERALL, THE LEVEL OF ERROR REVEALED WAS SIGNIFICANTLY HIGHER IN THE STEP-BY-STEP PROCESSES COMPARED TO THE FINAL SUBMITTED ANSWERS. FURTHER ANALYSIS SUGGESTS THAT STUDENTS MOST OFTEN HAVE MISCONCEPTIONS REGARDING NON-CRITICAL ERRORS. DATA ANALYSIS ALSO SUGGESTS THAT MISCONCEPTIONS IDENTIFIED AT THE TRANSACTION LEVEL PERSIST OVER TIME.
PURPOSE - THIS PAPER AIMS TO ANSWER THE FOLLOWING QUESTIONS: WHAT IS THE PROCESS AND POLICY JOURNEY THAT A UNIVERSITY MUST GO THROUGH IN IMPLEMENTING AN EFFECTIVE LEARNING ANALYTICS PROGRAMME? WHAT ARE THE DRIVERS FOR UNDERTAKING SUCH AN ACTIVITY, AND WHO ARE THE STAKEHOLDERS? DESIGN/METHODOLOGY/APPROACH - THE JOURNEY OF THE INFORMATION, THE PROCESS OR THE STAKEHOLDER IS PLACED AT THE HEART OF THE PROGRAMME, SO A PRIMARILY ETHNOGRAPHIC METHODOLOGY IS APPROPRIATE, ALTHOUGH ULTIMATELY THE OUTCOMES OF ANALYTICS ARE USUALLY QUANTITATIVE. FINDINGS - IN UNDERTAKING THE JOURNEY TO A FULL IMPLEMENTATION OF AN ANALYTICS PROGRAMME, A UNIVERSITY MUST CLEARLY KNOW THE STRATEGIC DRIVER FOR STARTING, AND THE CLEAR OUTCOMES, IF A PURPOSEFUL PATH IS TO TRAVELLED. ORIGINALITY/VALUE - AS A CURRENTLY GROWING AREA OF INTEREST FOR LOTS OF HIGHER AND FURTHER EDUCATION INSTITUTIONS IN THE UK AND WORLDWIDE, LEARNING ANALYTICS SHOULD BE HIGH ON EVERYONE'S AGENDA. THIS VIEWPOINT IS INTENDED TO PROVIDE A SHORT OVERVIEW TO THE PROCESS AND THE IMPORTANT STRATEGIC DRIVERS.
BASED ON THE ACHIEVEMENT GOAL THEORY, THIS EXPERIMENTAL STUDY EXPLORED THE INFLUENCE OF PREDICTIVE AND DESCRIPTIVE LEARNING ANALYTICS DASHBOARDS ON GRADUATE STUDENTS' MOTIVATION AND STATISTICS ANXIETY IN AN ONLINE GRADUATE-LEVEL STATISTICS COURSE. PARTICIPANTS WERE RANDOMLY ASSIGNED INTO ONE OF THREE GROUPS: (A) PREDICTIVE DASHBOARD, (B) DESCRIPTIVE DASHBOARD, OR (C) CONTROL (I.E., NO DASHBOARD). MEASURES OF MOTIVATION AND STATISTICAL ANXIETY WERE COLLECTED IN THE BEGINNING AND THE END OF THE SEMESTER VIA THE MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE AND STATISTICAL ANXIETY RATING SCALE. INDIVIDUAL SEMI-STRUCTURED INTERVIEWS WERE USED TO UNDERSTAND LEARNERS' PERCEPTIONS OF THE COURSE AND WHETHER THE USE OF THE DASHBOARDS INFLUENCED THE MEANING OF THEIR LEARNING EXPERIENCES. RESULTS INDICATE THAT, COMPARED TO THE CONTROL GROUP, THE PREDICTIVE DASHBOARD SIGNIFICANTLY REDUCED LEARNERS' INTERPRETATION ANXIETY AND HAD AN EFFECT ON INTRINSIC GOAL ORIENTATION THAT DEPENDED ON LEARNERS' LOWER OR HIGHER INITIAL LEVELS OF INTRINSIC GOAL ORIENTATION. IN COMPARISON TO THE CONTROL GROUP, BOTH PREDICTIVE AND DESCRIPTIVE DASHBOARDS REDUCED WORTH OF ANXIETY (NEGATIVE ATTITUDES TOWARDS STATISTICS) FOR LEARNERS WHO STARTED THE COURSE WITH HIGHER LEVELS OF WORTH ANXIETY. THEMATIC ANALYSIS REVEALED THAT LEARNERS WHO ADOPTED A MORE PERFORMANCE-AVOIDANCE GOAL ORIENTATION APPROACH DEMONSTRATED HIGHER LEVELS OF ANXIETY REGARDLESS OF THE DASHBOARD USED.
PURPOSE THE PURPOSE OF THIS PAPER IS TO PRESENT THE METAL PROJECT, A FRENCH OPEN LEARNING ANALYTICS (LA) PROJECT FOR SECONDARY SCHOOL, THAT AIMS AT IMPROVING THE QUALITY OF TEACHING. THE ORIGINALITY OF METAL IS THAT IT RELIES ON RESEARCH THROUGH EXPLORATORY ACTIVITIES AND FOCUSES ON ALL THE ASPECTS OF A LEARNING ANALYTICS ENVIRONMENT. DESIGN/METHODOLOGY/APPROACH THIS WORK INTRODUCES THE DIFFERENT CONCERNS OF THE PROJECT: COLLECTION AND STORAGE OF MULTI-SOURCE DATA OWNED BY A VARIETY OF STAKEHOLDERS, SELECTION AND PROMOTION OF STANDARDS, DESIGN OF AN OPEN-SOURCE LRS, CONCEPTION OF DASHBOARDS WITH THEIR FINAL USERS, TRUST, USABILITY, DESIGN OF EXPLAINABLE MULTI-SOURCE DATA-MINING ALGORITHMS. FINDINGS ALL THE DIMENSIONS OF METAL ARE PRESENTED, AS WELL AS THE WAY THEY ARE APPROACHED: DATA SOURCES, DATA STORAGE, THROUGH THE IMPLEMENTATION OF AN LRS, DESIGN OF DASHBOARDS FOR SECONDARY SCHOOL, BASED ON CO-DESIGN SESSIONS DATA MINING ALGORITHMS AND EXPERIMENTS, IN LINE WITH PRIVACY AND ETHICS CONCERNS. ORIGINALITY/VALUE THE ISSUE OF A GLOBAL DISSEMINATION OF LA AT AN INSTITUTION LEVEL OR AT A BROADER LEVEL SUCH AS A TERRITORY OR A STUDY LEVEL IS STILL A HOT TOPIC IN THE LITERATURE, AND IS ONE OF THE FOCUS AND ORIGINALITY OF THIS PAPER, ASSOCIATED WITH THE LARGE SPECTRUM OF DIFFERENT CONCERNS.
DATA SCIENCE APPLICATIONS IN EDUCATION ARE QUICKLY PROLIFERATING, PARTIALLY DUE TO THE USE OF LMSS AND MOOCS. HOWEVER, THE APPLICATION OF DATA SCIENCE TECHNIQUES IN THE VALIDATION AND DEPLOYMENT OF SERIOUS GAMES IS STILL SCARCE. AMONG OTHER REASONS, OBTAINING AND COMMUNICATING USEFUL INFORMATION FROM THE VARIED INTERACTION DATA CAPTURED FROM SERIOUS GAMES REQUIRES SPECIFIC DATA ANALYSIS AND VISUALIZATION TECHNIQUES THAT ARE OUT OF REACH OF MOST NON-EXPERTS. TO MITIGATE THIS LACK OF APPLICATION OF DATA SCIENCE TECHNIQUES IN THE FIELD OF SERIOUS GAMES, WE PRESENT T-MON, A MONITOR OF TRACES FOR THE XAPI-SG STANDARD. T-MON OFFERS A DEFAULT SET OF ANALYSIS AND VISUALIZATIONS FOR SERIOUS GAME INTERACTION DATA THAT FOLLOWS THIS STANDARD, WITH NO OTHER CONFIGURATION REQUIRED. THE INFORMATION REPORTED BY T-MON PROVIDES AN OVERVIEW OF THE GAME INTERACTION DATA COLLECTED, BRINGING ANALYSIS AND VISUALIZATIONS CLOSER TO NON-EXPERTS AND SIMPLIFYING THE APPLICATION OF SERIOUS GAMES.
SOFTWARE ENGINEERING IS THE APPLICATION OF PRINCIPLES USED IN ENGINEERING DESIGN, DEVELOPMENT, TESTING, DEPLOYMENT, AND MANAGEMENT OF SOFTWARE SYSTEMS. ONE OF THE SOFTWARE ENGINEERING'S APPROACHES, HIGHLY USED IN NEW INDUSTRIES, IS AGILE DEVELOPMENT. USER STORIES ARE A COMMONLY USED NOTATION TO CAPTURE USER REQUIREMENTS IN AGILE DEVELOPMENT. NEVERTHELESS, FOR THE ELABORATION OF USER STORIES, A HIGH LEVEL OF COLLABORATION WITH THE CLIENT IS NECESSARY. THIS PROFESSIONAL SKILL IS RARELY MEASURED OR EVALUATED IN EDUCATIONAL CONTEXTS. THE PRESENT WORK APPROACHES COLLABORATION IN SOFTWARE ENGINEERING STUDENTS THROUGH MULTIMODAL LEARNING ANALYTICS, MODELING, AND EVALUATING STUDENTS' COLLABORATION WHILE THEY ARE WRITING USER STORIES. FOR THAT, WE USED MULTIDIRECTIONAL MICROPHONES IN ORDER TO DERIVE SOCIAL NETWORK ANALYSIS METRICS RELATED TO COLLABORATION (PERMANENCE AND PROMPTING) TOGETHER WITH HUMAN-ANNOTATED INFORMATION (QUALITY OF THE STORIES AND PRODUCTIVITY). RESULTS SHOW THAT GROUPS WITH A LOWER PRODUCTIVITY IN WRITING USER STORIES AND LESS PROFESSIONAL EXPERIENCE IN MANAGING SOFTWARE REQUIREMENTS PRESENT A NON-COLLABORATIVE BEHAVIOR MORE FREQUENTLY, AND THAT TEAMS WITH A FEWER NUMBER OF INTERVENTIONS ARE MORE LIKELY TO PRODUCE A GREATER NUMBER OF USER STORIES. MOREOVER, ALTHOUGH LOW EXPERIENCE SUBJECTS PRODUCED MORE USER STORIES, A GREATER PRODUCTIVITY OF THE MOST EXPERIENCED SUBJECTS WAS NOT STATISTICALLY VERIFIED. WE BELIEVE THAT THESE TYPES OF INITIATIVES WILL ALLOW THE MEASUREMENT AND EARLY DEVELOPMENT OF SUCH SKILLS IN UNIVERSITY STUDENTS.
IN THIS PAPER WE ANALYZE THE PROCESS OF DESIGNING AND DEVELOPING A SERIOUS GAME INTENDED TO TRAIN PEOPLE WITH INTELLECTUAL DISABILITIES IN MOVING AROUND A CITY USING THE PUBLIC TRANSPORTATION SYSTEM. THE FIRST STEP IN OUR INVESTIGATION IS TO UNDERSTAND THE COGNITIVE, PSYCHOLOGICAL AND MOTOR ABILITIES OF OUR USERS AND THEIR SPECIFIC NEEDS. SECONDLY, WE TRANSLATED THE CHARACTERISTICS OF THE PLAYERS INTO USER REQUIREMENTS, WITH ADAPTED MECHANICS TO IMPROVE THE UNDERSTANDING AND TO INCREASE THE PROBABILITY FOR THE USER TO BE ABLE TO CARRY OUT THE TASKS TO PERFORM IN THE VIDEO GAME. FINALLY, DUE TO THE SPECIFIC CHARACTERISTICS OF OUR FINAL USERS A LEARNING ANALYTICS MODULE HAS BEEN INCLUDED IN THE GAME TO COLLECT RELEVANT INFORMATION ABOUT HOW USERS ARE ACTUALLY PLAYING AND TO INFER HOW THE LEARNING PROCESS OF EVERY USER IS OCCURRING. WE ALSO DISCUSS THE NEXT STEPS IN OUR RESEARCH AND THE FUTURE WORK RELATED WITH IT: DESIGN A RANGE OF EXPERIMENTAL TESTS TO VERIFY THE ADEQUACY OF THE VIDEO GAME AS A LEARNING TOOL FOR THIS TYPE OF USERS.
GAMES HAVE BECOME ONE OF THE MOST POPULAR ACTIVITIES ACROSS CULTURES AND AGES. THERE IS AMPLE EVIDENCE THAT SUPPORTS THE BENEFITS OF USING GAMES FOR LEARNING AND ASSESSMENT. HOWEVER, INCORPORATING GAME ACTIVITIES AS PART OF THE CURRICULUM IN SCHOOLS REMAINS LIMITED. SOME OF THE BARRIERS FOR BROADER ADOPTION IN CLASSROOMS IS THE LACK OF ACTIONABLE ASSESSMENT DATA, THE FACT THAT TEACHERS OFTEN DO NOT HAVE A CLEAR SENSE OF HOW STUDENTS ARE INTERACTING WITH THE GAME, AND IT IS UNCLEAR IF THE GAMEPLAY IS LEADING TO PRODUCTIVE LEARNING. TO ADDRESS THIS GAP, WE SEEK TO PROVIDE SEQUENCE AND PROCESS MINING METRICS TO TEACHERS THAT ARE EASILY INTERPRETABLE AND ACTIONABLE. MORE SPECIFICALLY, WE BUILD OUR WORK ON TOP OF SHADOWSPECT, A THREE-DIMENSIONAL GEOMETRY GAME THAT HAS BEEN DEVELOPED TO MEASURE GEOMETRY SKILLS AS WELL OTHER COGNITIVE AND NONCOGNITIVE SKILLS. WE USE DATA FROM ITS IMPLEMENTATION ACROSS SCHOOLS IN THE U.S. TO IMPLEMENT TWO SEQUENCE AND PROCESS MINING METRICS IN AN INTERACTIVE DASHBOARD FOR TEACHERS. THE FINAL OBJECTIVE IS TO FACILITATE THAT TEACHERS CAN UNDERSTAND THE SEQUENCE OF ACTIONS AND COMMON ERRORS OF STUDENTS USING SHADOWSPECT SO THEY CAN BETTER UNDERSTAND THE PROCESS, MAKE PROPER ASSESSMENT, AND CONDUCT PERSONALIZED INTERVENTIONS WHEN APPROPRIATE.
WITH THE RECOGNITION OF THE IMPORTANCE OF SELF-REGULATED LEARNING (SRL) IN ASYNCHRONOUS ONLINE COURSES, RECENT RESEARCH HAS EXPLORED HOW SRL STRATEGIES IMPACT STUDENT LEARNING IN THESE LEARNING ENVIRONMENTS. HOWEVER, LITTLE HAS BEEN DONE TO EXAMINE DIFFERENT PATTERNS OF STUDENTS WITH DIFFERENT SRI, PROFILES OVER TIME, WHICH PRECLUDES PROVIDING OPTIMAL ON-GOING INSTRUCTIONAL SUPPORT FOR INDIVIDUAL STUDENTS. TO ADDRESS THE GAP IN RESEARCH, WE APPLIED LEARNING ANALYTICS TO ANALYZE LOG DATA FROM 284 UNDERGRADUATE STUDENTS ENROLLED IN AN ASYNCHRONOUS ONLINE STATISTICS COURSE. SPECIFICALLY, WE IDENTIFIED STUDENT SRI, PROFILES, AND EXAMINED THE ACTUAL STUDENT SRI LEARNING PATTERNS. THE K-MEDOIDS CLUSTERING IDENTIFIED THREE SELF-REGULATED LEARNING PROFILES: SELF-REGULATION, PARTIAL SELF-REGULATION, AND NON-SELF-REGULATION. SELF-REGULATED STUDENTS SHOWED MORE STUDY REGULARITY AND HELP-SEEKING, THAN DID THE OTHER TWO GROUPS OF STUDENTS. THE PARTIALLY SELF-REGULATED STUDENTS SHOWED HIGH STUDY REGULARITY BUT INACTIVE HELP-SEEKING, WHILE THE NON-SELF-REGULATED STUDENTS EXHIBITED LESS STUDY REGULARITY AND LESS FREQUENT HELP-SEEKING THAN THE OTHER TWO GROUPS; THEIR SELF-REPORTED TIME MANAGEMENT SCORES WERE SIGNIFICANTLY LOWER. THE ANALYSIS OF EACH WEEK'S LOG VARIABLES USING THE RANDOM FOREST ALGORITHM REVEALED THAT SELF-REGULATED STUDENTS STUDIED COURSE CONTENT EARLY BEFORE EXAMS AND SOUGHT HELP DURING THE GENERAL EXAM PERIOD, WHEREAS NON SELF-REGULATED STUDENTS STUDIED THE COURSE CONTENT DURING THE GENERAL EXAM PERIOD. BASED ON OUR FINDINGS, WE PROVIDE INSTRUCTIONAL STRATEGIES THAT CAN BE USED TO SUPPORT STUDENT SRL. WE ALSO DISCUSS IMPLICATIONS OF THIS STUDY FOR ADVANCED LEARNING ANALYTICS RESEARCH, AND THE DESIGN OF EFFECTIVE ASYNCHRONOUS ONLINE COURSES.
IN COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) CLASSROOMS IT MAY BE CHALLENGING FOR TEACHERS TO KEEP AWARENESS OF CERTAIN ASPECTS OF THE LEARNING PROCESS OF EACH SMALL GROUP OR ASSESS WHETHER THE ENACTMENT OF THE CLASS SCRIPT DEVIATES FROM THE ORIGINAL PLAN. ORCHESTRATION TOOLS, AIMED AT SUPPORTING THE MANAGEMENT OF THE INCREASING UNCERTAINTY AND COMPLEXITY OF CSCL CLASSROOMS, HAVE BEEN EMERGING IN RESPONSE. SIMILARLY, LEARNING ANALYTICS INNOVATIONS HOLD THE PROMISE OF EMPOWERING TEACHERS BY MAKING CERTAIN ASPECTS OF THE CLASSROOM VISIBLE AND BY PROVIDING INFORMATION THAT CAN PROMPT ACTIONABLE RESPONSES. HOWEVER, THE ACTIVE ROLE THAT DATA MAY PLAY IN TEACHERS' DECISION-MAKING AND ORCHESTRATION PROCESSES IS STILL NOT WELL UNDERSTOOD. THIS PAPER INVESTIGATES THE PERSPECTIVES OF TEACHERS WHO USED A REAL-TIME ANALYTICS TOOL TO SUPPORT THE ORCHESTRATION OF A CSCL CLASSROOM. A LONGITUDINAL STUDY WAS CONDUCTED WITH A HANDHELD DASHBOARD DEPLOYED IN A MULTI-DISPLAY COLLABORATIVE CLASSROOM DURING ONE FULL ACADEMIC TERM. THE DASHBOARD SHOWED REAL-TIME INFORMATION ABOUT GROUP PARTICIPATION AND TASK PROGRESS; THE CURRENT STATE OF THE CSCL SCRIPT; AND A SET OF TEXT NOTIFICATIONS INFORMING TEACHERS OF POTENTIAL STUDENTS' MISCONCEPTIONS AUTOMATICALLY DETECTED. THE STUDY INVOLVED FOUR TEACHERS CONDUCTING 72 CLASSROOM SESSIONS DURING 10 WEEKS WITH A TOTAL OF 150 STUDENTS. THE TEACHERS' PERSPECTIVES DISCUSSED IN THIS PAPER PORTRAY THE PROMISES AND CHALLENGES OF INTRODUCING NEW TECHNOLOGIES AIMED AT ENHANCING ORCHESTRATION AND AWARENESS IN A CSCL CLASSROOM.
IN THIS WORK WE PRESENT A FORMAL DESCRIPTION OF A LEARNING ENVIRONMENT FRAMEWORK THAT GIVES SUPPORT TO LEARNING ANALYTICS. THE FRAMEWORK IS BASED ON TECHNIQUES THAT EDUCATIONAL DATA MINING AND SOCIAL NETWORK ANALYSIS PROVIDE. THE PURPOSE IS TO STUDY OR DISCOVER COLLABORATIVE RELATIONSHIPS THAT STUDENTS GENERATE DURING THEIR LEARNING PROCESS AND MAKE PREDICTIONS OR ASSESSMENTS ABOUT STUDENT LEARNING. WE ALSO PRESENT A NEW MODEL IN THE WAY THE LEARNING PROCESS IS EVALUATED. THIS MODEL IS MORE PERSONAL AND CONTRIBUTES TO NEW POSSIBILITIES THAT THE DIGITAL TECHNOLOGIES OFFER IN TECHNOLOGY-ENHANCED ASSESSMENT.
LEARNING ANALYTICS IS TARGETED TO BETTER UNDERSTAND EACH LEARNER'S INTERESTS AND UNIQUE CHARACTERISTICS IN LEARNING SO AS TO BUILD A PERSONALIZED LEARNING ENVIRONMENT. HOWEVER, MANY LEARNING ANALYTICS TECHNIQUES MAY REQUIRE RELATIVELY INTENSIVE COMPUTATION, THUS INAPPROPRIATE FOR ANY MOBILE APPLICATION. IN THIS PAPER, WE PROPOSE AN INTERACTIVE AND PERSONALIZED E-LEARNING SYSTEM NAMED THE COMPAD+ SIMULATOR FACILITATED BY AN INTELLIGENT LEARNING ANALYTICS ALGORITHM RUNNING ON THE CLOUD SERVER TO QUICKLY ESTIMATE THE LEARNER'S AREAS OF INTERESTS ON THE SIMULATION RESULTS AS BASED ON HIS/HER INITIAL INPUTS AND THEN FLEXIBLY GENERATE THE SIMULATION DETAILS AS APPROPRIATE. TO PROTECT THE DATA PRIVACY OF EACH INDIVIDUAL LEARNER, THE PERSONAL DATA IS STORED IN THE LEARNER PROFILE UNDER EACH PASSWORD-PROTECTED ACCOUNT ON THE CLOUD SERVER WITH ALL THE INTERMEDIATE SIMULATION DATA TO BE ERASED AFTER EACH LEARNING TASK. UP TO OUR UNDERSTANDING, THIS WORK REPRESENTS THE FIRST ATTEMPT TO SUCCESSFULLY DEVELOP A FLEXIBLE, INTEREST-BASED, AND PLATFORM-INDEPENDENT SIMULATOR DIRECTLY RUN ON ANY MOBILE DEVICE FACILITATED BY AN EFFICIENT LEARNING ANALYTICS ALGORITHM WORKING ON THE CLOUD SERVER. TO DEMONSTRATE ITS FEASIBILITY, A PROTOTYPE OF OUR CLOUD-BASED COMPAD+ E-LEARNING SYSTEM IS BUILT AND CAREFULLY EVALUATED ON VARIOUS MOBILE DEVICES. CLEARLY, THERE ARE MANY PROMISING DIRECTIONS IN TERMS OF BOTH PEDAGOGICAL AND TECHNOLOGICAL IMPACTS TO EXTEND AND ENHANCE OUR INTEREST-BASED COMPAD+ SIMULATION PLATFORM FOR FUTURE E-LEARNING SYSTEMS.
FOLLOWING TRENDS IN HIGHER EDUCATION THAT EMPHASIZE QUANTITATIVE ANALYTICAL APPROACHES TO ASSESS EDUCATIONAL OUTCOMES, ACADEMIC LIBRARIES ARE INCREASINGLY ATTEMPTING TO QUANTIFY THEIR IMPACTS ON STUDENT LEARNING AND DEMONSTRATE THEIR VALUE TO THE UNIVERSITY'S EDUCATIONAL MISSION. BY APPLYING LEARNING ANALYTICS TECHNIQUES TO LIBRARY USE AND INSTRUCTIONAL DATA, LIBRARIES HAVE ESPECIALLY FOCUSED ON ATTEMPTING TO MEASURE THE IMPACT OF THE LIBRARY ON STUDENT GPA, RETENTION, AND ATTAINMENT MEASURES. BECAUSE LEARNING ANALYTICS STUDIES TYPICALLY REQUIRE LARGE DATASETS OF PERSONALLY IDENTIFIABLE INFORMATION (PII) , THEY PRESENT INHERENT RISKS TO THE PRIVACY, CONFIDENTIALITY, AND AUTONOMY OF RESEARCH SUBJECTS, WHO OFTEN ARE UNAWARE AND UNINFORMED OF THE DATA COLLECTED. THIS PAPER PRESENTS THE RESULTS OF A META-ANALYSIS OF LEARNING ANALYTICS STUDIES IN LIBRARIES THAT EXAMINE THE EFFECTS OF LIBRARY USE ON MEASURES OF STUDENT SUCCESS. BASED ON THE AGGREGATE RESULTS, WE ARGUE THAT OUTCOMES OF THESE STUDIES HAVE NOT PRODUCED FINDINGS THAT JUSTIFY THE LOSS OF PRIVACY AND RISK BORNE BY STUDENTS. MOREOVER, WE ARGUE THAT BASING HIGH-IMPACT DECISIONS ON STUDIES WITH NO, OR LOW, EFFECT SIZES, AND WEAK CORRELATION OR REGRESSION VALUES, HAS THE POTENTIAL TO HARM STUDENTS, PARTICULARLY THOSE IN ALREADY VULNERABLE POPULATIONS. FINALLY, WE BELIEVE THAT THESE STUDIES ALSO HAVE THE POTENTIAL TO HARM INSTITUTIONS THAT RELY ON THESE PARTICULAR ANALYTICAL APPROACHES TO MAKE CRUCIAL BUSINESS AND EDUCATIONAL DECISIONS.
THIS REVIEW AIMS TO PROVIDE A CONCISE OVERVIEW OF FOUR DISTINCT RESEARCH FIELDS: ARTIFICIAL INTELLIGENCE AND EDUCATION (AIED), COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL), EDUCATIONAL DATA MINING (EDM), AND LEARNING ANALYTICS (LA). WHILE ALL FOUR FIELDS ARE FOCUSED ON UNDERSTANDING LEARNING AND TEACHING USING TECHNOLOGY, EACH FIELD HAS A RELATIVELY UNIQUE OR COMMON PERSPECTIVE ON WHICH THEORETICAL FRAMEWORKS, METHODS, AND ONTOLOGIES MIGHT BE APPROPRIATE. IN THIS REVIEW WE ARGUE THAT RESEARCHERS SHOULD BE ENCOURAGED TO CROSS THE BOUNDARIES OF THEIR RESPECTIVE FIELD AND WORK TOGETHER TO ADDRESS THE COMPLEX CHALLENGES IN EDUCATION.
BLENDED INQUIRY LEARNING RELYING ON ONLINE LABS TO ENHANCE CLASSROOM ACTIVITIES IS CONSIDERED AS A PROMISING APPROACH TO INCREASE THE SKILLS AND THE INTEREST OF STUDENTS IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS. IN SUCH A FRAMEWORK WHERE STUDENTS COMBINE FACE-TO-FACE INTERACTION WITH PEERS OR TEACHERS AND ONLINE INTERACTION WITH REMOTE OR VIRTUAL LABS, ADEQUATE GUIDANCE SHOULD BE PROVIDED. SUCH A TASK IS FACILITATED IF TEACHERS ARE MADE AWARE OF THE PROGRESSES AND THE DIFFICULTIES OF THEIR STUDENTS. LEARNING ANALYTICS BASED ON THE ANALYSIS OF THE STUDENT ONLINE INTERACTION TRACES AND THEIR VISUALIZATION IS AN ADEQUATE MEAN. A SET OF CONTEXTUAL LEARNING ANALYTICS APPS WHICH PROVIDE THE TEACHER WITH LEARNING-SPECIFIC INFORMATION IS INTRODUCED IN THIS PAPER. A REQUIREMENT ANALYSIS GATHERED AT A SUMMER SCHOOL WITH 32 TEACHERS, THE DESIGN AND IMPLEMENTATION OF THREE CONTEXTUAL LEARNING ANALYTICS APPS, THE MAIN OUTCOMES FROM A CASE STUDY, AS WELL AS AN OUTLOOK ON FUTURE RESEARCH AVENUES ARE DETAILED.
K-12 CLASSROOMS USE BLOCK-BASED PROGRAMMING ENVIRONMENTS (BBPES) FOR TEACHING COMPUTER SCIENCE AND COMPUTATIONAL THINKING (CT). TO SUPPORT ASSESSMENT OF STUDENT LEARNING IN BBPES, WE PROPOSE A LEARNING ANALYTICS FRAMEWORK THAT COMBINES HYPOTHESIS- AND DATA-DRIVEN APPROACHES TO DISCERN STUDENTS' PROGRAMMING STRATEGIES FROM BBPE LOG DATA. WE USE A PRINCIPLED APPROACH TO DESIGN ASSESSMENT TASKS TO ELICIT EVIDENCE OF SPECIFIC CT SKILLS. PILOTING THESE TASKS IN HIGH SCHOOL CLASSROOMS ENABLED US TO ANALYZE STUDENT PROGRAMS AND VIDEO RECORDINGS OF STUDENTS AS THEY BUILT THEIR PROGRAMS. WE DISCUSS A PRIORI PATTERNS DERIVED FROM THIS ANALYSIS TO SUPPORT DATA-DRIVEN ANALYSIS OF LOG DATA IN ORDER TO BETTER ASSESS UNDERSTANDING AND USE OF CT IN BBPES.
APPLYING GAMES IN EDUCATION PROVIDES MULTIPLE BENEFITS CLEARLY VISIBLE IN ENTERTAINMENT GAMES: THEIR ENGAGING, GOAL-ORIENTED NATURE ENCOURAGES STUDENTS TO IMPROVE WHILE THEY PLAY. EDUCATIONAL GAMES, ALSO KNOWN AS SERIOUS GAMES (SGS) ARE VIDEO GAMES DESIGNED WITH A MAIN PURPOSE OTHER THAN PURE ENTERTAINMENT; THEIR MAIN PURPOSE MAY BE TO TEACH, TO CHANGE AN ATTITUDE OR BEHAVIOR, OR TO CREATE AWARENESS OF A CERTAIN ISSUE. AS EDUCATORS AND GAME DEVELOPERS, THE VALIDITY AND EFFECTIVENESS OF THESE GAMES TOWARDS THEIR DEFINED EDUCATIONAL PURPOSES NEEDS TO BE BOTH MEASURABLE AND MEASURED. FORTUNATELY, THE HIGHLY INTERACTIVE NATURE OF GAMES MAKES THE APPLICATION OF LEARNING ANALYTICS (LA) PERFECT TO CAPTURE STUDENTS' INTERACTION DATA WITH THE PURPOSE OF BETTER UNDERSTANDING OR IMPROVING THE LEARNING PROCESS. HOWEVER, THERE IS A LACK OF WIDELY ADOPTED STANDARDS TO COMMUNICATE INFORMATION BETWEEN GAMES AND THEIR TRACKING MODULES. GAME LEARNING ANALYTICS (GLA) COMBINES THE EDUCATIONAL GOALS OF LA WITH TECHNOLOGIES THAT ARE COMMONPLACE IN GAME ANALYTICS (GA), AND ALSO SUFFERS FROM A LACK OF STANDARDS ADOPTION THAT WOULD FACILITATE ITS USE ACROSS DIFFERENT SGS. IN THIS PAPER, WE DESCRIBE TWO KEY STEPS TOWARDS THE SYSTEMATIZATION OF GLA: 1), THE USE OF A NEWLY-PROPOSED STANDARD TRACKING MODEL TO EXCHANGE INFORMATION BETWEEN THE SG AND THE ANALYTICS PLATFORM, ALLOWING REUSABLE TRACKER COMPONENTS TO BE DEVELOPED FOR EACH GAME ENGINE OR DEVELOPMENT PLATFORM; AND 2), THE USE OF STANDARDIZED ANALYSIS AND VISUALIZATION ASSETS TO PROVIDE GENERAL BUT USEFUL INFORMATION FOR ANY SG THAT SENDS ITS DATA IN THE AFOREMENTIONED FORMAT. THESE ANALYSIS AND VISUALIZATIONS CAN BE FURTHER CUSTOMIZED AND ADAPTED FOR PARTICULAR GAMES WHEN NEEDED. WE EXAMINE THE USE OF THIS COMPLETE STANDARD MODEL IN THE GLA SYSTEM CURRENTLY UNDER DEVELOPMENT FOR USE IN TWO EU H2020 SG PROJECTS.
RESEARCHERS AND DEVELOPERS OF LEARNING ANALYTICS (LA) SYSTEMS ARE INCREASINGLY ADOPTING HUMAN-CENTRED DESIGN (HCD) APPROACHES, WITH GROWING NEED TO UNDERSTAND HOW TO APPLY DESIGN PRACTICE IN DIFFERENT EDUCATIONAL SETTINGS. IN THIS PAPER, WE PRESENT A DESIGN NARRATIVE OF OUR EXPERIENCE DEVELOPING DASHBOARDS TO SUPPORT MIDDLE SCHOOL MATHEMATICS TEACHERS' PEDAGOGICAL PRACTICES, IN A MULTI-UNIVERSITY, MULTI-SCHOOL DISTRICT, IMPROVEMENT SCIENCE INITIATIVE IN THE UNITED STATES. THROUGH DOCUMENTATION OF OUR DESIGN EXPERIENCE, WE OFFER WAYS TO ADAPT COMMON HCD METHODS - CONTEXTUAL DESIGN AND DESIGN TENSIONS - WHEN DEVELOPING VISUAL ANALYTICS SYSTEMS FOR EDUCATORS. WE ALSO ILLUMINATE HOW ADOPTING THESE DESIGN METHODS WITHIN THE CONTEXT OF IMPROVEMENT SCIENCE AND RESEARCH-PRACTICE PARTNERSHIPS FUNDAMENTALLY INFLUENCES THE DESIGN CHOICES WE MAKE AND THE FOCAL QUESTIONS WE UNDERTAKE. THE RESULTS OF THIS DESIGN PROCESS FLOW NATURALLY FROM THE APPROPRIATION AND REPURPOSING OF TOOLS BY DISTRICT PARTNERS AND DIRECTLY INFORM IMPROVEMENT GOALS.
PROVISION OF ADAPTIVE AND PERSONALIZED COMPUTER BASED ASSESSMENT (CBA) SERVICES TO LEARNERS IS A MULTIDIMENSIONAL RESEARCH FIELD. IN THIS PAPER WE INVESTIGATE THE EFFECT OF EXTRAVERSION AND CONSCIENTIOUSNESS WITH TEMPORAL LEARNING ANALYTICS ON STUDENTS' PERFORMANCE DURING COMPUTER BASED TESTING. FOR THIS PURPOSE, WE USED THE LAERS ASSESSMENT ENVIRONMENT TO TRACK THE TEMPORAL ACTIVITY - TIME-SPENT BEHAVIOR - OF 96 STUDENTS AND THE BIG FIVE INSTRUMENT (BFI) QUESTIONNAIRE TO RECORD THEIR PERSONALITY TRAITS. PARTIAL LEAST SQUARES (PLS) WAS USED TO FIND FUNDAMENTAL RELATIONSHIPS BETWEEN THE COLLECTED DATA. PRELIMINARY RESULTS INDICATE A POSITIVE EFFECT OF CONSCIENTIOUSNESS ON (UN-)CERTAINTY AND A POSITIVE EFFECT OF EXTRAVERSION ON GOAL EXPECTANCY. FURTHER IMPLICATIONS OF THESE RESULTS ARE ALSO DISCUSSED.
TEACHERS HAVE EMPLOYED ONLINE VIDEO AS AN ELEMENT OF THEIR INSTRUCTIONAL MEDIA PORTFOLIO, ALONGSIDE WITH BOOKS, SLIDES, NOTES, ETC. IN COMPARISON TO OTHER INSTRUCTIONAL MEDIA, ONLINE VIDEO AFFORDS MORE OPPORTUNITIES FOR RECORDING OF STUDENT NAVIGATION ON A VIDEO LECTURE. VIDEO ANALYTICS MIGHT PROVIDE INSIGHTS INTO STUDENT LEARNING PERFORMANCE AND INFORM THE IMPROVEMENT OF TEACHING TACTICS. NEVERTHELESS, THOSE ANALYTICS ARE NOT ACCESSIBLE TO LEARNING STAKEHOLDERS, SUCH AS RESEARCHERS AND EDUCATORS, MAINLY BECAUSE ONLINE VIDEO PLATFORMS DO NOT SHARE BROADLY THE INTERACTIONS OF THE USERS WITH THEIR SYSTEMS. AS A REMEDY, WE HAVE DESIGNED AN OPEN-ACCESS VIDEO ANALYTICS SYSTEM AND EMPLOYED IT IN A VIDEO-ASSISTED COURSE. IN THIS PAPER, WE PRESENT A LONGITUDINAL STUDY, WHICH PROVIDES VALUABLE INSIGHTS THROUGH THE LENS OF THE COLLECTED VIDEO ANALYTICS. IN PARTICULAR, WE COLLECTED AND ANALYZED STUDENTS' VIDEO NAVIGATION, LEARNING PERFORMANCE, AND ATTITUDES, AND WE PROVIDE THE LESSONS LEARNED FOR FURTHER DEVELOPMENT AND REFINEMENT OF VIDEO-ASSISTED COURSES AND PRACTICES.
IN RECENT YEARS, EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) METHODS HAVE MATURED INTO APPROACHES THAT CAN BE USED TO ANALYZE AND ENHANCE LEARNING AT BOTH THE K-12 AND POST-SECONDARY LEVELS. IN THIS WHITE PAPER, WE REVIEW A PAIR OF "PROOF CASES," EXAMPLES OF THE POTENTIAL OF EDM METHODS TO IMPROVE EDUCATION, WHICH DEMONSTRATE THE VALUE OF EDM IN A CONCRETE FASHION. IN THE FIRST PROOF CASE, WE DISCUSS WORK TO AUTOMATICALLY ASSESS STUDENT MASTERY AND THE STRUCTURE OF STUDENT KNOWLEDGE IN A DOMAIN, TOWARDS PROVIDING STUDENTS WITH THE RIGHT LEARNING MATERIALS AT THE RIGHT TIME DURING ONLINE LEARNING. IN THE SECOND PROOF CASE, WE DISCUSS WORK TO AUTOMATICALLY ASSESS STUDENT ENGAGEMENT AND AFFECT, CITING EXAMPLES THAT INDICATE THAT ENGAGEMENT AND AFFECT DURING ONLINE LEARNING ARE ASSOCIATED WITH SUBSTANTIAL DIFFERENCES IN LONG-TERM OUTCOMES (INCLUDING COLLEGE READINESS AND ATTENDANCE), AND THAT AUTOMATED MODELS CAN BE INTEGRATED INTO ONLINE LEARNING SOFTWARE TO PROVIDE INFORMATION TO TEACHERS IN REAL-TIME. THESE EXAMPLES SHOW THAT EDM/LA CAN BE USED AT SCALE TO IMPROVE THE DEGREE TO WHICH ONLINE LEARNING IS PERSONALIZED TO LEARNERS' INDIVIDUAL DIFFERENCES.
THIS STUDY ANALYZES THE IMPACT OF AN INSTRUCTOR ON THE STUDENTS' ENGAGEMENT USING LEARNING ANALYTICS APPROACH BY TRACKING ONE UNIVERSITY'S LOG DATA OF TEACHING AND LEARNING ACTIVITIES IN A WEB-BASED LEARNING PLATFORM. BASED ON THE TRACKING DATA AND THEORETICAL ANALYSIS, THIS STUDY BUILDS A TEACHING AND LEARNING INTERACTION ACTIVITY MODEL TO SHOW HOW THE INSTRUCTOR'S COURSE PREPARATION AND ASSISTANCE ACTIVITIES AFFECT DIFFERENT DIMENSIONS OF STUDENT ENGAGEMENT ACTIVITIES AND THE RELATIONSHIP BETWEEN THESE ACTIVITIES. THE RESULTS REVEAL THAT AN INSTRUCTOR'S COURSE PREPARATION IS SIGNIFICANTLY POSITIVELY RELATED TO THE STUDENTS' VIEWING ACTIVITIES, WHILE INSTRUCTOR'S GUIDANCE AND ASSISTANCE HAS A SIGNIFICANT IMPACT ON THE STUDENTS' COMPLETING LEARNING TASKS. THE STUDY ALSO INDICATES THAT THE STUDENTS' VIEWING ACTIVITIES HAVE A DIRECT POSITIVE INFLUENCE ON THEIR COMPLETING LEARNING TASKS ACTIVITIES. STUDENTS' COMPLETING LEARNING TASKS EXERT DIRECT POSITIVE INFLUENCE ON THEIR INTERACTION FOR LEARNING, WHILE THEIR VIEWING ACTIVITIES HAVE AN INDIRECT IMPACT ON THEIR INTERACTION ACTIVITIES. (C) 2014 ELSEVIER INC ALL RIGHTS RESERVED.
INTRODUCTION THIS STUDY EXAMINED RELATIONSHIPS OF STUDENTS' ACCESS OF E-LEARNING (LEARNING ANALYTICS) FOR A HEALTHCARE PSYCHOMOTOR SKILLS COURSE, APPROACHES TO LEARNING (R-SPQ-2F PSYCHOMETRIC) AND ACADEMIC ACHIEVEMENT (SUMMATIVE EXAMINATION RESULTS). AN UNDERSTANDING OF THE RELATIONSHIPS MAY HELP IN SUPPORTING STUDENTS LEARNING. METHODS TWO CONSECUTIVE COHORTS OF 5TH YEAR DENTAL STUDENTS WERE ASKED TO COMPLETE THE R-SPQ-2F QUESTIONNAIRE AND PERMISSION TO ACCESS DATA OF ONLINE VIDEOS AND ASSOCIATED QUIZZES AND TWO SUMMATIVE WRITTEN EXAMINATION RESULTS WERE EXAMINED. THE SUMMATIVE ASSESSMENTS WERE AN OSCA AND A WRITTEN-PAPER QUESTION BOTH IN PROSTHODONTICS. MULTIPLE LINEAR REGRESSION AND CORRELATION ANALYSIS WERE PERFORMED. RESULTS STUDENTS (N=98) PERFORMED A TOTAL OF 10470 VIDEO ACCESS EVENTS AND 7714 ATTEMPTS IN ONLINE QUIZZES. DEEP LEARNING APPROACH WAS THE STRONGEST PREDICTOR VARIABLE (BETA=0.270; P=.004) ON WRITTEN-PAPER QUESTION RESULT. WHILE VIDEO AND QUIZ ACCESS WERE MODERATELY CORRELATED (R=0.600; P<.001) TO EACH OTHER, VIDEO ACCESS WAS NOT A SIGNIFICANT PREDICTOR TO EITHER OF THE EXAMINATION RESULTS. QUIZ ACCESS WAS NEGATIVELY ASSOCIATED WITH ACADEMIC ACHIEVEMENT FOR THE WRITTEN-PAPER QUESTION RESULTS (BETA=-0.349; P<.001). CONCLUSIONS ONLY DEEP APPROACH TO LEARNING APPEARED TO BE RELEVANT FOR THE WRITTEN-PAPER QUESTION EXAMINATION GRADES. CONVERSELY, THE NUMBER OF VIDEO AND QUIZ ACCESS DID NOT RELATE TO EITHER EXAMINATION RESULTS. THIS SUGGESTS E-LEARNING ACCESS AND EXAMINATIONS DO NOT APPEAR TO RELATE UNDER THE CONDITIONS EXPLORED. OTHER ATTRIBUTES OF LEARNING MANAGEMENT ACCESS MAY NEED TO BE EXPLORED TO DETERMINE IF ACCESS TO LEARNING MANAGEMENT SYSTEMS MAY BE USEFUL IN OFFERING REMEDIAL SUPPORT TO STUDENTS.
MULTIMODAL LEARNING ANALYTICS (MMLA) SYSTEMS, UNDERSTOOD AS THOSE THAT EXPLOIT MULTIMODAL EVIDENCE OF LEARNING TO BETTER MODEL A LEARNING SITUATION, HAVE NOT YET SPREAD WIDELY IN EDUCATIONAL PRACTICE. THEIR INHERENT TECHNICAL COMPLEXITY, AND THE LACK OF EDUCATIONAL STAKEHOLDER INVOLVEMENT IN THEIR DESIGN, ARE AMONG THE HYPOTHESIZED REASONS FOR THE SLOW UPTAKE OF THIS EMERGENT FIELD. TO AID IN THE PROCESS OF STAKEHOLDER COMMUNICATION AND SYSTEMATIZATION LEADING TO THE SPECIFICATION OF MMLA SYSTEMS, THIS PAPER PROPOSES A MULTIMODAL DATA VALUE CHAIN (M-DVC). THIS CONCEPTUAL TOOL, DERIVED FROM BOTH THE FIELD OF BIG DATA AND THE NEEDS OF MMLA SCENARIOS, HAS BEEN EVALUATED IN TERMS OF ITS USEFULNESS FOR STAKEHOLDERS, IN THREE AUTHENTIC CASE STUDIES OF MMLA SYSTEMS CURRENTLY UNDER DEVELOPMENT. THE RESULTS OF OUR MIXED-METHODS EVALUATION HIGHLIGHT THE USEFULNESS OF THE M-DVC TO ELICIT UNSPOKEN ASSUMPTIONS OR UNCLEAR DATA PROCESSING STEPS IN THE INITIAL STAGES OF DEVELOPMENT. THE EVALUATION ALSO REVEALED LIMITATIONS OF THE M-DVC IN TERMS OF THE TECHNICAL TERMINOLOGY EMPLOYED, AND THE NEED FOR MORE DETAILED CONTEXTUAL INFORMATION TO BE INCLUDED. THESE LIMITATIONS ALSO PROMPT POTENTIAL IMPROVEMENTS FOR THE M-DVC, ON THE PATH TOWARDS CLEARER SPECIFICATION AND COMMUNICATION WITHIN THE MULTI-DISCIPLINARY TEAMS NEEDED TO BUILD EDUCATIONALLY-MEANINGFUL MMLA SOLUTIONS.
BY COLLABORATIVELY SOLVING A TASK, STUDENTS ARE CHALLENGED TO SHARE IDEAS, EXPRESS THEIR THOUGHTS, AND ENGAGE IN DISCUSSION. COLLABORATING GROUPS OF STUDENTS MAY ENCOUNTER PROBLEMS CONCERNING COGNITIVE ACTIVITIES (SUCH AS A MISUNDERSTANDING OF THE TASK MATERIAL). IF THESE PROBLEMS ARE NOT ADDRESSED AND RESOLVED IN TIME, THE COLLABORATIVE PROCESS IS HINDERED. THE TEACHER PLAYS AN IMPORTANT ROLE IN MONITORING AND SOLVING THE OCCURRENCE OF PROBLEMS. TO PROVIDE ADAPTIVE SUPPORT, TEACHERS CONTINUOUSLY HAVE TO BE AWARE OF STUDENTS' ACTIVITIES IN ORDER TO IDENTIFY RELEVANT EVENTS, INCLUDING THOSE THAT REQUIRE INTERVENTION. BECAUSE THE AMOUNT OF AVAILABLE INFORMATION IS HIGH, TEACHERS MAY BE SUPPORTED BY LEARNING ANALYTICS. THE PRESENT EXPERIMENTAL STUDY (N = 40) EXPLORED THE EFFECT OF TWO LEARNING ANALYTICS TOOLS (THE CONCEPT TRAIL AND PROGRESS STATISTICS) THAT GIVE INFORMATION ABOUT STUDENTS' COGNITIVE ACTIVITIES. THE RESULTS SHOWED THAT WHEN TEACHERS HAD ACCESS TO LEARNING ANALYTICS, THEY WERE NOT BETTER AT DETECTING PROBLEMATIC GROUPS, BUT THEY DID OFFER MORE SUPPORT IN GENERAL, AND MORE SPECIFICALLY TARGETED GROUPS THAT EXPERIENCED PROBLEMS. THIS COULD INDICATE THAT LEARNING ANALYTICS INCREASE TEACHERS' CONFIDENCE TO ACT, WHICH IN TURN MEANS STUDENTS COULD BENEFIT MORE FROM THE TEACHER'S PRESENCE. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
MANY HAVE SUGGESTED THAT TINKERING PLAYS A CRITICAL ROLE IN NOVICES LEARNING TO PROGRAM, AND RECENT WORK IN LEARNING ANALYTICS (BAKER & YACEF, 2009 BLIKSTEIN, 2011) ALLOWS US TO DESCRIBE NEW RELATIONSHIPS IN THE PROCESS. USING LEARNING ANALYTICS, WE EXPLORE HOW STUDENTS PROGRESS FROM EXPLORATION, THROUGH TINKERING, TO REFINEMENT, A PATHWAY THAT WE TERM EXTIRE. THE WORK CONTRIBUTES TO LEARNING SCIENCES BY: SHOWING EMPIRICAL SUPPORT FOR PREVIOUSLY THEORIZED PROCESSES; IDENTIFYING A ROLE OF TINKERING IN NOVICES' LEARNING; AND PRESENTING A DATA-DRIVEN APPROACH TO CREATING PROCESS DESCRIPTIONS. FURTHERMORE, OUR FINDINGS ILLUMINATE HOW TINKERING CAN BE A VALUABLE APPROACH FOR NOVICES.
MOOCS (MASSIVE OPEN ONLINE COURSES) HAVE REVOLUTIONIZED THE WAY WE LEARN. BUT A MAJOR CHALLENGE FACED BY MOOCS IS THE HIGH DROPOUT RATES AMONG STUDENTS AT VARIOUS STAGES OF THE COURSE. IT IS IMPORTANT TO IDENTIFY THE STUDENTS AT RISK IN TERMS OF THOSE WHO ARE LIKELY TO DROP OUT AT SOME STAGE OF THE COURSE; AND THOSE WHO MIGHT NOT MEET THE REQUIRED PASSING CRITERIA. IN THIS PAPER, A MODEL IS PROPOSED TO IDENTIFY LEARNERS WHO ARE AT RISK IN AN ONLINE COURSE USING ASSOCIATION ANALYSIS WHICH IS DONE WITH DATASET OF STUDENTS WHO HAVE TAKEN THE COURSE PREVIOUSLY. IDENTIFYING SUCH LEARNERS WHO ARE AT RISK AND PROVIDING THEM REAL TIME FEEDBACK WOULD HELP THE STUDENTS TO BE MORE AWARE OF THEIR CURRENT PERFORMANCE AND TO IMPROVE UPON IT. SEVERAL EXPERIMENTS WERE CONDUCTED TO TEST THE PERFORMANCE OF THE PROPOSED WORK AND THE RESULTS SHOW THAT THE PROPOSED MODEL IDENTIFIES 90% OF THE LEARNERS WHO ARE AT RISK. OUT OF 4,74,977 STUDENTS, 4,32,502 WERE IDENTIFIED AS AT RISK STUDENTS BASED ON THE EXPERIMENTAL ANALYSIS.
INTERACTIVE STRATIFIED ATTRIBUTE TRACKING DIAGRAM (ISAT) IS A DATA VISUALIZATION AND TOOL TO ASSIST INTERACTIVE VISUAL ANALYTICS OF MULTI-ATTRIBUTE LEARNING DATASET. THE PRESENT WORK REPORTS THE EVOLUTION OF THIS DIAGRAM THROUGH A DESIGN BASED RESEARCH METHODOLOGY FOLLOWING ITS THREE DESIGN ITERATIONS. THERE ARE TWO OUTPUT AT THE CURRENT STAGE I) ISAT AND ITS WEB-BASED INTERACTION. II) A LEARNING ANALYTICS METHOD SUITABLE FOR BOTH RESEARCHERS AND PRACTITIONERS TO TRACE STUDENT ATTRIBUTE VALUE. WE RECEIVED SATISFACTORY USER TESTING SCORE FOR PRE-TEST AND POST-TEST MARKS VISUALIZATION THROUGH A TWO-PHASE ISAT.
THIS PAPER PRESENTS THE IMPLEMENTATION AND PROTOTYPING OF AN INNOVATIVE WEB-BASED ELEARNING PLATFORM, FEATURING LEARNING ANALYTICS AND GAMIFICATION CALLED ICT-FLAG. A PREVIOUS CONTRIBUTION PRESENTED THE ANALYSIS AND DESIGN OF THE PLATFORM. FOLLOWING THE DESIGN, THIS PAPER IMPLEMENTS THE PLATFORM AND REPORTS ON THE FIRST EXPERIENCES OF CONNECTING AND INTEGRATING THE PLATFORM WITH A REAL ELEARNING TOOL THROUGH AN API. THE PURPOSE OF THIS CONNECTION IS TO PROVIDE THE TOOL WITH LEARNING ANALYTICS AND GAMIFICATION-BASED SERVICES. 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.
CONTEMPORARY EDUCATION IS A VAST FIELD THAT IS CONCERNED WITH THE PERFORMANCE OF EDUCATION SYSTEMS. IN A FORMAL E-LEARNING CONTEXT, STUDENT DROPOUT IS CONSIDERED ONE OF THE MAIN PROBLEMS AND HAS RECEIVED MUCH ATTENTION FROM THE LEARNING ANALYTICS RESEARCH COMMUNITY, WHICH HAS REPORTED SEVERAL APPROACHES TO THE DEVELOPMENT OF MODELS FOR THE EARLY PREDICTION OF AT-RISK STUDENTS. HOWEVER, MAXIMIZING THE RESULTS OBTAINED BY PREDICTIONS IS A CONSIDERABLE CHALLENGE. IN THIS WORK, WE DEVELOPED A SOLUTION USING ONLY STUDENTS' INTERACTIONS WITH THE VIRTUAL LEARNING ENVIRONMENT AND ITS DERIVATIVE FEATURES FOR EARLY PREDICT AT-RISK STUDENTS IN A BRAZILIAN DISTANCE TECHNICAL HIGH SCHOOL COURSE THAT IS 103 WEEKS IN DURATION. TO MAXIMIZE RESULTS, WE DEVELOPED AN ELITIST GENETIC ALGORITHM BASED ON DARWIN'S THEORY OF NATURAL SELECTION FOR HYPERPARAMETER TUNING. WITH THE APPLICATION OF THE PROPOSED TECHNIQUE, WE PREDICTED THE STUDENT AT RISK WITH AN AREA UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE (AUROC) ABOVE 0.75 IN THE INITIAL WEEKS OF A COURSE. THE RESULTS DEMONSTRATE THE VIABILITY OF APPLYING INTERACTION COUNT AND DERIVATIVE FEATURES TO GENERATE PREDICTION MODELS IN CONTEXTS WHERE ACCESS TO DEMOGRAPHIC DATA IS RESTRICTED. THE APPLICATION OF A GENETIC ALGORITHM TO THE TUNING OF HYPERPARAMETERS CLASSIFIERS CAN INCREASE THEIR PERFORMANCE IN COMPARISON WITH OTHER TECHNIQUES.
POPULAR LEARNING MANAGEMENT SYSTEMS (LMS) OFTEN FEATURE DASHBOARDS DISPLAYING VARIOUS ANALYTICS. THIS DASHBOARD DISPLAY MIGHT BE SUBOPTIMAL FOR SOME LEARNING AND DEVELOPMENT MANAGERS (L&D). MOREOVER, THE ANALYTICS PRESENTED ARE OFTEN BASED ON STANDARDISED QUIZZES OR SEMESTERS, WHICH MIGHT BE UNSUITABLE (E. G., INFORMAL LEARNING, CORPORATE EDUCATION, ETC.). FINALLY, EACH LMS HAS ITS BESPOKE REPORTING SOLUTION, THUS MAKING IT DIFFICULT FOR L&D MANAGERS TO MONITOR THE SITUATION ACROSS VARIOUS LMSS. WE PROPOSE AN INTERACTIVE SYSTEM WHERE AN L&D MANAGER CAN CUSTOMISE THE DATA SOURCE, QUERIES, FILTERS, AND VISUALISATIONS OF THEIR LMSS, AND DISPLAY THEM INLINE. TO THIS END, WE HAVE BUILT EVADE, A SYSTEM THAT ALLOWS L&D MANAGERS TO CAPTURE DATA FROM VARIOUS LMSS, ANALYSE THEM, AND EMBED RELATED VISUALISATIONS IN EACH LMS. IN THIS INSTANCE, WE HAVE INTEGRATED EVADE WITH A MOODLE INSTANCE FOR CORPORATE EDUCATION, AND ALMANAC, A TABLET APPLICATION FOR INFORMAL LEARNING. IN THIS PAPER WE PRESENT EVADE AND DISCUSS HOW IT CAN IMPROVE THE L&D MANAGER-LMS INTERACTION.
THE LITERATURE REVEALS THAT THE EFFECTIVENESS OF LEARNING ANALYTICS (LA) TOOLS IS BEST EVALUATED WITH A MIXED-METHOD APPROACH AND NOT ONLY WITH SUMMATIVE MEASUREMENTS SUCH AS GRADES. A RECENT STUDY ON THE IMPACT OF AN LA TOOL OVER ACADEMIC PERFORMANCE DID NOT SUBSTANTIATE ITS EFFECT IN IMPROVING GRADES FOR CODING TASKS DESPITE QUALITATIVE COMMENTS OF EXPERIMENTAL GROUP PARTICIPANTS INDICATING THAT THE TOOL WAS EFFECTIVE IN IMPROVING CODING COMPETENCES IN JAVA. IN ANALYZING THIS VIEWPOINT, THIS PAPER DESCRIBES THE IMPACT OF LA FORMATIVE FEEDBACK ON THE GROWTH OF STUDENT? CODING COMPETENCES.
ONE OF THE CHARACTERISTICS OF MASSIVE OPEN ONLINE COURSES (MOOCS) IS THAT THE OVERALL NUMBER OF SOCIAL INTERACTIONS TEND TO BE HIGHER THAN IN TRADITIONAL COURSES, HINDERING THE ANALYSIS OF SOCIAL LEARNING. LEARNERS TYPICALLY ASK OR ANSWER QUESTIONS USING THE FORUM. THIS MAKES MESSAGES A RICH SOURCE OF INFORMATION, WHICH CAN BE USED TO INFER LEARNERS' BEHAVIOR AND OUTCOMES. IT IS NOT FEASIBLE FOR TEACHERS TO PROCESS ALL FORUM MESSAGES AND AUTOMATED TOOLS AND ANALYSIS ARE REQUIRED. ALTHOUGH THERE ARE SOME TOOLS FOR ANALYZING LEARNERS' INTERACTIONS, THERE IS A NEED FOR METHODOLOGIES AND INTEGRATED TOOLS THAT HELP TO INTERPRET THE LEARNING PROCESS BASED ON SOCIAL INTERACTIONS IN THE FORUM. THIS PAPER PRESENTS THE 3S (SOCIAL, SENTIMENTS, SKILLS) LEARNING ANALYTICS METHODOLOGY FOR ANALYZING FORUM INTERACTIONS IN MOOCS. THIS METHODOLOGY CONSIDERS A TEMPORAL ANALYSIS COMBINING THE SOCIAL, SENTIMENTS, AND SKILL DIMENSIONS THAT CAN BE EXTRACTED FROM FORUM DATA. WE ALSO INTRODUCE LATS, A LEARNING ANALYTICS TOOL FOR EDX/OPEN EDX RELATED TO THE THREE DIMENSIONS (3S), WHICH INCLUDES VISUALIZATIONS TO GUIDE THE PROPOSED METHODOLOGY. WE APPLY THE 3S METHODOLOGY AND THE TOOL TO AN MOOC ON JAVA PROGRAMMING. RESULTS SHOWED, AMONG OTHERS, THE ACTION-REACTION EFFECT PRODUCED WHEN LEARNERS INCREASE THEIR PARTICIPATION AFTER INSTRUCTORS EVENTS. MOREOVER, A DECREASE OF POSITIVE SENTIMENTS OVER TIME AND BEFORE DEADLINES OF OPEN-ENDED ASSIGNMENTS WAS ALSO OBSERVED AND THAT THERE WERE CERTAIN SKILLS, WHICH CAUSED MORE TROUBLES (E.G., ARRAYS AND LOOPS). THESE RESULTS ACKNOWLEDGE THE IMPORTANCE OF USING A LEARNING ANALYTICS METHODOLOGY TO DETECT PROBLEMS IN MOOCS.
ON A DAILY BASIS, A LARGE AMOUNT OF DATA IS GATHERED THROUGH THE PARTICIPATION OF STUDENTS IN E-LEARNING ENVIRONMENTS. THIS WEALTH OF DATA IS AN INVALUABLE ASSET TO RESEARCHERS AS THEY CAN UTILIZE IT IN ORDER TO GENERATE CONCLUSIONS AND IDENTIFY HIDDEN PATTERNS AND TRENDS BY USING BIG DATA ANALYTICS TECHNIQUES. THE PURPOSE OF THIS STUDY IS A THREEFOLD ANALYSIS OF THE DATA THAT ARE RELATED TO THE PARTICIPATION OF STUDENTS IN THE ONLINE FORUMS OF THEIR UNIVERSITY. IN ONE HAND THE CONTENT OF THE MESSAGES POSTED IN THESE FORA CAN BE EFFICIENTLY ANALYZED BY TEXT MINING TECHNIQUES. ON THE OTHER HAND, THE NETWORK OF STUDENTS INTERACTING THROUGH A FORUM CAN BE ADEQUATELY PROCESSED THROUGH SOCIAL NETWORK ANALYSIS TECHNIQUES. STILL, THE COMBINED KNOWLEDGE ATTAINED FROM BOTH OF THE AFOREMENTIONED TECHNIQUES, CAN PROVIDE EDUCATORS WITH PRACTICAL AND VALUABLE INFORMATION FOR THE EVALUATION OF THE LEARNING PROCESS, ESPECIALLY IN A DISTANCE LEARNING ENVIRONMENT. THE STUDY WAS CONDUCTED BY USING REAL DATA ORIGINATING FROM THE ONLINE FORUMS OF THE HELLENIC OPEN UNIVERSITY (HOU). THE ANALYSIS OF THE DATA HAS BEEN ACCOMPLISHED BY USING THE R AND THE WEKA TOOLS, IN ORDER TO ANALYZE THE STRUCTURE AND THE CONTENT OF THE EXCHANGED MESSAGES IN THESE FORA AS WELL AS TO MODEL THE INTERACTION OF THE STUDENTS IN THE DISCUSSION THREADS.
STUDENT DATA, WHETHER IN THE FORM OF ENGAGEMENT DATA, ASSIGNMENTS OR EXAMINATIONS, FORM THE FOUNDATION FOR ASSESSMENT AND EVALUATION IN HIGHER EDUCATION. AS HIGHER EDUCATION INSTITUTIONS PROGRESSIVELY MOVE TO BLENDED AND ONLINE ENVIRONMENTS, WE HAVE ACCESS TO, NOT ONLY MORE DATA THAN BEFORE, BUT ALSO A GREATER VARIETY OF DEMOGRAPHIC AND BEHAVIOURAL DATA. WHILE THE NOTION OF 'STUDENT-CENTRED' IS WELL-ESTABLISHED IN THE DISCOURSES AND PRACTICES SURROUNDING ASSESSMENT AND EVALUATION, THE CONCEPT OF STUDENT-CENTRED LEARNING ANALYTICS IS YET TO BE FULLY REALISED BY THE SECTOR. THIS ARTICLE EXPLORES AND EXTENDS THIS DEBATE BY INTRODUCING THE TEACHINGS OF FREIRE AS A FRAMEWORK TO EXAMINE THE POTENTIAL TO INCLUDE STUDENTS AS PARTNERS IN THE COLLECTION, ANALYSIS AND USE OF THEIR DATA. THE EXCLUSION OF STUDENTS IN MUCH OF CURRENT LEARNING ANALYTICS PRACTICES, AS WELL AS DEFINING CATEGORIES OF ANALYSIS AND MAKING SENSE OF (THEIR) LEARNING, NOT ONLY IMPOVERISHES OUR (AND THEIR) UNDERSTANDING OF THE COMPLEXITIES OF LEARNING AND ASSESSMENT, BUT MAY ACTUALLY INCREASE VULNERABILITIES AND PERPETUATE BIAS AND STEREOTYPES. IN ACKNOWLEDGING THE VOICE AND AGENCY OF STUDENTS, AND RECENTRING THEM AS DATA OWNERS, RATHER THAN DATA OBJECTS, LEARNING ANALYTICS CAN REALISE ITS TRANSFORMATIVE POTENTIAL - FOR STUDENTS AND INSTITUTIONS ALIKE.
THE LITERATURE UNTIL 2020 HAS FORECASTED A SIGNIFICANT UPTAKE OF LEARNING ANALYTICS (LA) TO SUPPORT LEARNING DESIGN IN HIGHER EDUCATION. HOWEVER, THERE REMAIN ONLY A FEW INVESTIGATIONS INTO TEACHERS' COURSE DESIGN PRACTICES AND THEIR PERSPECTIVES ON LA AS A TOOL TO SUPPORT THEIR DESIGN PRACTICES. THIS PAPER PRESENTS FINDINGS FROM AN EXAMINATION OF 16 UNIVERSITY TEACHERS' DESIGN PRACTICES AND PERSPECTIVES ON LA AT TWO NORWEGIAN UNIVERSITIES (THE UNIVERSITY OF OSLO AND OSLO METROPOLITAN UNIVERSITY). ON ONE HAND, FINDINGS IDENTIFIED SITUATIONAL FACTORS, FEEDBACK SOURCES AND TEACHERS' INTUITION AS KEY INFLUENCERS OF TEACHERS' COURSE DESIGN DECISIONS. ON THE OTHER HAND, GUIDED BY PRINCIPLES OF THE TECHNOLOGY ACCEPTANCE MODEL, THIS STUDY IDENTIFIED MIXED REACTIONS AMONGST TEACHERS REGARDING THE AWARENESS, UNDERSTANDING AND POTENTIAL USE OF LA TO SUPPORT COURSE DESIGN PRACTICES. IN PARTICULAR, MOST TEACHERS APPRECIATED THE FORMATIVE AND NORMATIVE VALUE OF LA TO PROVIDE MORE OBJECTIVE EVIDENCE ABOUT STUDENTS' LEARNING PATTERNS AND TO SHAPE LEARNING TRAJECTORIES, BUT SOME WERE SKEPTICAL ABOUT THE EVALUATIVE ROLE WHERE LA IS USED TO EVALUATE TEACHERS' AND STUDENTS' PERFORMANCE BASED ON UNNUANCED DATA (E.G. NO THEORY GUIDANCE) WITH LIMITED DEPTH IN OBSERVATION. THIS ARTICLE CONTRIBUTES TO THE UNDERSTANDING OF FACTORS FUNDAMENTAL TO LINKING LA TO TEACHERS' COURSE DESIGN PRACTICES BY SYNTHESIZING FINDINGS TO PROPOSE A 'BI-DIRECTIONAL LA-COURSE DESIGN' CONCEPTUAL FRAMEWORK THAT CLARIFIES KEY ELEMENTS THAT INFLUENCE TEACHERS' DESIGN PRACTICES AND HIGHLIGHTING THEIR IMPLICATIONS FOR LA INTEGRATION.
MASSIVE OPEN ONLINE MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BEEN TRANSITIONING SLOWLY FROM BEING COMPLETELY OPEN AND WITHOUT CLEAR RECOGNITION IN UNIVERSITIES OR INDUSTRY, TO PRIVATE SETTINGS THROUGH THE EMERGENCE OF SMALL AND MASSIVE PRIVATE ONLINE COURSES (SPOCS AND MPOCS). COURSES IN THESE NEW FORMATS ARE OFTEN FOR CREDIT AND HAVE CLEAR MARKET VALUE THROUGH THE ACQUISITION OF COMPETENCIES AND SKILLS. HOWEVER, THE ENDEMIC ISSUE OF ACADEMIC DISHONESTY REMAINS LINGERING AND GENERATING UNTRUSTWORTHINESS REGARDING WHAT STUDENTS DID TO COMPLETE THESE COURSES. IN THIS CASE STUDY, WE FOCUS ON SPOCS WITH ACADEMIC RECOGNITION DEVELOPED AT THE UNIVERSITY OF CAUCA IN COLOMBIA AND HOSTED IN THEIR OPEN EDX INSTANCE CALLED SELENE UNICAUCA. WE HAVE DEVELOPED A LEARNING ANALYTICS ALGORITHM TO DETECT DISHONEST STUDENTS BASED ON SUBMISSION TIME AND EXAM RESPONSES PROVIDING AS OUTPUT A NUMBER OF INDICATORS THAT CAN BE EASILY USED TO IDENTIFY STUDENTS. OUR RESULTS IN TWO SPOCS SUGGEST THAT 17% OF THE STUDENTS THAT INTERACTED ENOUGH WITH THE COURSES HAVE PERFORMED ACADEMIC DISHONEST ACTIONS, AND THAT 100% OF THE STUDENTS THAT WERE DISHONEST PASSED THE COURSES, COMPARED TO 62% FOR THE REST OF STUDENTS. CONTRARY TO WHAT OTHER STUDIES HAVE FOUND, IN THIS STUDY, DISHONEST STUDENTS WERE SIMILARLY OR EVEN MORE ACTIVE WITH THE COURSEWARE THAN THE REST, AND WE HYPOTHESIZE THAT THESE MIGHT BE WORKING GROUPS TAKING THE COURSE SERIOUSLY AND SOLVING EXAMS TOGETHER TO ACHIEVE A HIGHER GRADE. WITH MOOC-BASED DEGREES AND SPOCS FOR CREDIT BECOMING THE NORM IN DISTANCE LEARNING, WE BELIEVE THAT IF THIS ISSUE IS NOT TACKLED PROPERLY, IT MIGHT ENDANGER THE FUTURE OF THE RELIABILITY AND VALUE OF ONLINE LEARNING CREDENTIALS.
DESPITE THE RECENT SURGE OF INTEREST IN LEARNING ANALYTICS (LA), THEIR ADOPTION IN EVERYDAY CLASSROOM PRACTICE IS STILL SLOW. KNOWLEDGE GAPS AND LACK OF INTER-STAKEHOLDER COMMUNICATION (PARTICULARLY WITH EDUCATIONAL PRACTITIONERS) HAVE BEEN POSITED AS CRITICAL FACTORS FOR PREVIOUS LA ADOPTION FAILURES. YET, WHAT ISSUES SHOULD RESEARCHERS, PRACTITIONERS AND OTHER ACTORS COMMUNICATE ABOUT, WHEN CONSIDERING THE ADOPTION OF AN LA INNOVATION IN A PARTICULAR CONTEXT? WE REVIEWED AND SYNTHESISED EXISTING LITERATURE ON FOUR FOCUS AREAS RELATED TO LA, THEIR ADOPTION, IMPLICATIONS FOR PRACTICE, AND MORE GENERAL FACTORS THAT HAVE EMERGED AS CRUCIAL WHEN STUDYING EVERYDAY CLASSROOM ADOPTION OF TECHNOLOGIES (I.E., CLASSROOM ORCHESTRATION). THIS SYNTHESIS RESULTED IN TWO CONVERSATIONAL FRAMEWORKS AND AN INTER-STAKEHOLDER COMMUNICATION TOOL. THESE CAN BE USED TO GUIDE AND SUPPORT CONVERSATIONS AND DECISION-MAKING ABOUT THE ADOPTION OF LA INNOVATIONS. WE ILLUSTRATE THEIR USEFULNESS WITH EXAMPLES OF USE IN ONGOING LA ADOPTION PROCESSES IN AUSTRALIA, SPAIN AND ESTONIA.
IN THIS PAPER WE PRESENT THE SOFTLEARN ACTIVITY REPORTER (SLAR) SERVICE WHICH AUTOMATICALLY GENERATES TEXTUAL SHORT-TERM REPORTS ABOUT LEARNERS' BEHAVIOR IN VIRTUAL LEARNING ENVIRONMENTS. THROUGH THIS APPROACH, WE SHOW HOW TEXTUAL REPORTING IS A COHERENT WAY OF PROVIDING INFORMATION THAT CAN COMPLEMENT (AND EVEN ENHANCE) VISUAL STATISTICS AND HELP TEACHERS TO UNDERSTAND IN A COMPREHENSIBLE MANNER THE BEHAVIOR OF THEIR STUDENTS DURING THE COURSE. THIS SOLUTION EXTRACTS RELEVANT INFORMATION FROM THE STUDENTS' ACTIVITY AND ENCODES IT INTO INTERMEDIATE DESCRIPTIONS USING LINGUISTIC VARIABLES AND TEMPORAL REFERENCES, WHICH ARE SUBSEQUENTLY TRANSLATED INTO TEXTS IN NATURAL LANGUAGE. THE EXAMPLES OF APPLICATION ON REAL DATA FROM AN UNDERGRADUATE COURSE SUPPORTED BY THE SOFTLEARN PLATFORM SHOW THAT AUTOMATIC TEXTUAL REPORTING IS A VALUABLE COMPLEMENTARY TOOL FOR EXPLAINING TEACHERS AND LEARNERS THE INFORMATION COMPRISED IN A LEARNING ANALYTICS DASHBOARD.
THE CONTRIBUTION DESCRIBES AND PROBLEMATIZES THE USE OF LEARNING ANALYTICS WITHIN A BLENDED UNIVERSITY COURSE BASED ON A SOCIO-CONSTRUCTIVIST APPROACH AND AIMED AT CONSTRUCTING ARTEFACTS AND KNOWLEDGE. SPECIFICALLY, THE AUTHORS FOCUS ON THE ASSESSMENT SYSTEM ADOPTED IN THE COURSE, DELIBERATELY INSPIRED BY THE PRINCIPLES OF FORMATIVE ASSESSMENT: AN ONGOING ASSESSMENT IN THE FORM OF FEEDBACK SHARED WITH THE STUDENTS, AND WHICH INTEGRATES THE TEACHER'S ASSESSMENT WITH SELF-ASSESSMENT AND PEER-ASSESSMENT. THIS SYSTEM OBVIOUSLY REQUIRES THE INTEGRATION OF QUALITATIVE PROCEDURES - FROM TEACHERS AND TUTORS - AND QUANTITATIVE - MANAGED THROUGH THE REPORTING FUNCTIONS OF THE LMS AND ONLINE TOOLS USED FOR THE COURSE. THE CONTRIBUTION ENDS WITH A REFLECTION ON THE POSSIBILITIES OF TECHNOLOGICAL DEVELOPMENT OF LEARNING ANALYTICS WITHIN THE LEARNING ENVIRONMENT, SUCH AS TO BETTER SUPPORT CONSTRUCTIVIST TEACHING: LEARNING ANALYTICS THAT COMES CLOSEST TO SOCIAL LA TECHNIQUES PROVIDING THE TEACHER WITH A RICHER PICTURE OF THE STUDENT'S BEHAVIOUR AND LEARNING PROCESSES.
LEARNING ANALYTICS (LA) IS A FIELD THAT AIMS TO OPTIMIZE LEARNING THROUGH THE STUDY OF DYNAMICAL PROCESSES OCCURRING IN THE STUDENTS' CONTEXT. IT COVERS THE MEASUREMENT, COLLECTION, ANALYSIS AND REPORTING OF DATA ABOUT STUDENTS AND THEIR CONTEXTS. THIS STUDY AIMS AT SURVEYING EXISTING RESEARCH ON LA TO IDENTIFY APPROACHES, TOPICS, AND NEEDS FOR FUTURE RESEARCH. A SYSTEMATIC MAPPING STUDY IS LAUNCHED TO FIND AS MUCH LITERATURE AS POSSIBLE. THE 127 PAPERS FOUND (RESULTING IN 116 WORKS) ARE CLASSIFIED WITH RESPECT TO GOALS, DATA TYPES, TECHNIQUES, STAKEHOLDERS AND INTERVENTIONS. DESPITE THE INCREASING INTEREST IN FIELD, THERE ARE NO STUDIES RELATING IT TO THE MASSIVE OPEN ONLINE COURSES (MOOCS) CONTEXT. THE GOAL OF THIS PAPER IS TWOFOLD, FIRST WE PRESENT THE SYSTEMATIC MAPPING ON LA AND AFTER WE ANALYZE ITS FINDINGS IN THE MOOCS CONTEXT. AS RESULTS WE PROVIDE AN OVERVIEW OF LA AND IDENTIFY PERSPECTIVES AND CHALLENGES IN THE MOOCS CONTEXT.
RESEARCH ON LEARNER ENGAGEMENT HAS INCREASED IN RECENT YEARS ARGUING THAT IT FAVORS ACADEMIC SUCCESS. TEACHERS WANT THEIR LEARNERS TO ENGAGE IN MEANINGFUL LEARNING ACTIVITIES, LIKE MIND MAPPING, BUT THEY LACK CLUES FOR OBSERVING THEIR ENGAGEMENT ALONG THE ACTIVITY. IN THIS PAPER, WE PROPOSE INDICATORS OF BEHAVIOURAL AND COGNITIVE DIMENSIONS OF LEARNER ENGAGEMENT FOR MIND MAPPING ACTIVITIES BASED ON INTERACTION TRACES. OUR INDICATORS HAVE BEEN DEFINED FROM FINAL MIND MAPS AS WELL AS FROM THE MIND MAPPING PROCESSES. WE DISCUSS IMPLICATIONS FOR THE OBSERVATION OF LEARNER ENGAGEMENT IN LEARNING ACTIVITIES SIMILAR TO MIND MAPPING.
TRANSPORT COMPANIES ARE PROBABLY ONE OF THE GREATEST SOURCES OF POLLUTION NOWADAYS. PERHAPS BECAUSE THESE COMPANIES WOULD LIKE TO IMPROVE THIS SITUATION, OR PERHAPS BECAUSE THEY SIMPLY WOULD LIKE TO REDUCE THE PETROL THEY CONSUME, THEY ARE MORE THAN EVER DEPLOYING PLANS IN ORDER TO INCREASE THE EFFICIENCY OF THEIR FLEETS. ONE OF THE EASIEST AND CHEAPEST WAYS TO ACHIEVE THIS IS TO TEACH THEIR DRIVERS HOW TO BE MORE EFFICIENT. NEVERTHELESS, TRADITIONAL LEARNING APPROACHES WERE ONLY SUCCESSFUL IN THE SHORT TERM, ACCORDING TO THE PREVIOUS WORK. IN ORDER TO ACHIEVE LONG-TERM RESULTS, NEW LEARNING PARADIGMS MUST BE TAKEN INTO ACCOUNT. FURTHERMORE, IF WE COMBINE THESE PARADIGMS WITH A LEARNING ANALYTICS SYSTEM, OPTIMAL RESULTS MAY BE REACHED FOR BOTH THE COMPANY AND THE DRIVERS. IN THIS PAPER, WE PRESENT A LEARNING ANALYTICS SYSTEM APPLIED TO THE EFFICIENT DRIVING CONTEXT. THIS LEARNING ANALYTICS SYSTEM IS USED AS A FUNDAMENTAL PIECE IN THE DEPLOYMENT OF THE BLENDED LEARNING METHODOLOGY FOR EFFICIENT PROFESSIONAL DRIVING DESIGNED BY OUR RESEARCH GROUP. WE DESCRIBE THE DESIGN AND THE INTEGRATION OF THIS SYSTEM WITH A REAL PRODUCT CURRENTLY BEING USED IN MANY TRANSPORT FLEETS. WITH A TECHNICAL APPROACH, WE ALSO DESCRIBE THE MAIN PROBLEMS FOUND DURING THE DEPLOYMENT OF THIS SYSTEM AND THE SOLUTIONS DESIGNED TO COPE WITH THESE PROBLEMS.
THE UNDERSTANDING OF ONLINE CLASSROOM TALK IS A CHALLENGE EVEN WITH CURRENT TECHNOLOGICAL ADVANCEMENTS. TO DETERMINE THE QUALITY OF IDEAS IN CLASSROOM TALK FOR INDIVIDUAL AND GROUPS OF STUDENTS, A NEW APPROACH SUCH AS PRECISION EDUCATION WILL BE NEEDED TO INTEGRATE LEARNING ANALYTICS AND MACHINE LEARNING TECHNIQUES TO IMPROVE THE QUALITY OF TEACHING AND CATER INTERVENTIVE PRACTICES FOR INDIVIDUALS BASED ON BEST AVAILABLE EVIDENCE. THIS PAPER PRESENTS A STUDY OF 20 SECONDARY SCHOOL STUDENTS ENGAGED IN ASYNCHRONOUS ONLINE DISCOURSE OVER A PERIOD OF TWO WEEKS. THE ONLINE DISCOURSE WAS RECORDED AND CLASSROOM TALK WAS CODED BEFORE UNDERGOING SOCIAL NETWORK ANALYSIS AND K-MEANS CLUSTERING TO IDENTIFY THREE TYPES OF IDEAS (PROMISING, POTENTIAL, AND TRIVIAL). THE QUALITY AND DISTRIBUTION OF IDEAS WERE THEN MAPPED TO THE DIFFERENT KINDS OF TALK THAT WERE CODED FROM THE ONLINE DISCOURSE. IDEA PROGRESS REPORTS WERE DESIGNED AND TRIALED TO PRESENT COLLECTIVE AND INDIVIDUAL STUDENT'S IDEA TRAJECTORIES DURING DISCOURSE. FINDINGS SHOW THAT THE MAJORITY OF IDEAS IN EXPLORATORY TALK ARE PROMISING TO THE STUDENTS, WHILE IDEAS IN CUMULATIVE AND DISPUTATIONAL TALKS ARE LESS PROMISING OR TRIVIAL. FEEDBACK ON THE DESIGN OF THE IDEA PROGRESS REPORTS WAS COLLECTED WITH SUGGESTIONS FOR IT TO BE MORE INFORMATIVE AND INSIGHTFUL FOR INDIVIDUAL STUDENT. OVERALL, THIS RESEARCH HAS SHOWN THAT CLASSROOM TALK CAN BE ASSOCIATED WITH THE QUALITY OF IDEAS USING A QUANTITATIVE APPROACH AND TEACHERS CAN BE ADEQUATELY INFORMED ABOUT COLLECTIVE AND INDIVIDUAL IDEAS IN CLASSROOM TALKS TO PROVIDE TIMELY INTERVENTIONS.
THE GROWING POPULARITY OF SOCIAL NETWORKING SITES (SNS) THAT ARE EMBEDDED WITH NETWORKED INFRASTRUCTURES SERVES AS AN IDEAL PLATFORM FOR DEVELOPING A NETWORKED LEARNING ENVIRONMENT CONNECTING GEOGRAPHICALLY DISPERSED LEARNERS. UNLIKE THE TRADITIONAL LEARNING SYSTEMS WHICH PROVIDE ONLY LIMITED SOURCES OF DATA, THE LEARNERS ENGAGED IN VIRTUAL NETWORKED SOCIAL ENVIRONMENTS TEND TO PRODUCE HUGE VOLUMES OF DIGITAL FOOTPRINTS THAT CANNOT BE ANALYZED USING CONVENTIONAL ANALYTICAL TECHNIQUES. TWO NEW BRANCHES OF ANALYTICAL SCIENCES - EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) - ARE BEING EMPLOYED FOR PROCESSING DIGITAL DATA DERIVED FROM ONLINE EDUCATIONAL PLATFORMS IN ORDER TO OBTAIN MEANINGFUL INFERENCES AND DATA-DRIVEN INSIGHTS. HENCE, THE PRESENT EXPERIMENTAL STUDY INVOLVING A SMALL GROUP OF GEOGRAPHICALLY DISPERSED LEARNERS INTEND TO EXAMINE THE ENGAGEMENT LEVEL AND INTERACTION PATTERNS THAT OCCUR WITHIN A FEMINIST NETWORKED LEARNING ENVIRONMENT CREATED IN FACEBOOK USING POPULAR EDM AND LEARNING ANALYTICAL TECHNIQUES SUCH AS K-MEANS CLUSTERING, SOCIAL NETWORK ANALYSIS AND CORRELATION MINING. UPON ANALYSIS, IT WAS FOUND THAT THE PEER NETWORK INFLUENCE PLAYED A VITAL ROLE IN ACTIVATING PASSIVE LEARNERS, EVENTUALLY LEADING TO THE DEVELOPMENT OF A CLOSELY BOUND NETWORKED LEARNING COMMUNITY OVER TIME.
QUANTITATIVE METHODS IN EDUCATION RESEARCH HAVE LONG BEEN LIMITED BY THE ABILITY TO COLLECT DETAILED LEARNER DATA IN A CONSISTENT, SCALABLE WAY. AS EDUCATION CONTINUES TO MOVE ONLINE WE ARE PRESENTED WITH AN UNPRECEDENTED OPPORTUNITY TO STUDY LEARNER INTERACTIONS WITHIN LEARNING SYSTEMS. HOWEVER, DOING SO REQUIRES INFRASTRUCTURE TO COLLECT AND STORE MASSIVE INTERACTION DATA FROM WHICH WE CAN LEARN. IN THIS PAPER WE PRESENT DATA DEPOSITORY, A FLEXIBLE, PLUGGABLE, DATA HUB FOR TRACKING INTERACTION DATA FROM ANY BROWSER-BASED APPLICATION, AIDING THE MEASUREMENT OF USAGE AND EFFECTIVENESS.
IN THIS ESSAY, WE EXPLORE THE TIMESCAPES OF LIBRARY LEARNING ANALYTICS. WE CONTEND THAT JUST-IN-TIME STRATEGIES, A FEATURE OF LATE CAPITAL MODES OF PRODUCTION, NEW PUBLIC MANAGEMENT, AND FUTURE-ORIENTED RISK-MANAGEMENT STRATEGIES INFORM THE ADOPTION OF LEARNING ANALYTICS. LEARNING ANALYTICS FUNCTION AS A FORM OF TEMPORAL GOVERNMENTALITY: CURRENT PERFORMANCE IS SCRUTINIZED IN ORDER TO ANTICIPATE FUTURE PERFORMANCE AND PRESCRIBE JUST-IN-TIME INTERVENTIONS TO MITIGATE RISK-NOT ONLY FOR THE STUDENT BUT ALSO FOR THE INSTITUTION. ULTIMATELY, WE ARGUE THAT USING TIME AS A LENS TO EXAMINE DISCOURSES SURROUNDING LIBRARY LEARNING ANALYTICS REVEALS THE TEMPORALITIES REPRODUCED IN THIS DISCOURSE, WHICH OBSCURES QUESTIONS OF POWER, POLITICS, AND HISTORY. IN DESCRIBING WHAT THE FUTURE IS, RATHER THAN WHAT IT COULD OR SHOULD BE, THIS DISCOURSE ERASES OUR ABILITY TO SHAPE OUR FUTURES, AND OUR RESPONSIBILITY FOR SO DOING.
LEARNING ANALYTICS IS RECEIVING A GROWING ATTENTION FROM BOTH MACHINE LEARNING AND EDUCATION COMMUNITIES, WHERE SUPPORT VECTOR MACHINES (SVM) ARE GAINING POPULARITY OVER EXISTING DATA MINING TECHNIQUES. IN THE SCOPE OF THIS WORK, WE EMPLOY SVM TO PREDICT STUDENT SUCCESS IN MATHEMATICS COURSE IN PORTUGAL UNDER TWO COMMON NONLINEAR KERNEL FUNCTIONS: POLYNOMIAL AND RADIAL BASIS FUNCTION KERNEL. IN ADDITION, WE EMPLOY THE K-NEAREST-NEIGHBOR (KNN) ALGORITHM AS A REFERENCE MODEL SINCE IT IS KNOWN TO BE FAST AND EFFECTIVE IN VARIOUS CLASSIFICATION PROBLEMS. FURTHERMORE, WE ADOPT THE BAYESIAN OPTIMIZATION (BO) TECHNIQUE IN A CROSS-VALIDATION FRAMEWORK TO OPTIMIZE SVM KEY PARAMETERS; NAMELY, THE SLACK PARAMETER AND PENALTY COEFFICIENT. THE OBTAINED EXPERIMENTAL RESULTS SHOW THAT THE SVM OUTPERFORM K-NEAREST-NEIGHBOR ALGORITHM UNDER BOTH NONLINEAR KERNEL FUNCTIONS. ADDITIONALLY, PROCESSING TIME ASSOCIATED WITH SVM OPTIMIZATION PROCESS INCREASES WITH POLYNOMIAL ORDER. FURTHERMORE, THE SVM TRAINED WITH THIRD-ORDER POLYNOMIAL KERNEL PERFORMS THE BEST. FINALLY, K-NEAREST-NEIGHBOR ALGORITHM IS FOUND TO BE FASTER COMPARED TO ALL SVM CLASSIFIERS.
THIS ARTICLE UNDERTAKES A LITERATURE REVIEW TO EXAMINE LEARNING ANALYTICS THROUGH THE LENS OF ATTENTION ENGINEERING. INFORMED BY A CRITICAL LITERATURE SYNTHESIS FROM THE FIELDS OF COGNITIVE SCIENCE, HISTORY, PHILOSOPHY, EDUCATION, TECHNOLOGY, ETHICS, AND LIBRARY SCIENCE, THIS ANALYSIS SITUATES LEARNING ANALYTICS IN THE CONTEXT OF COMMUNICATION AND EDUCATION TECHNOLOGIES AS TOOLS IN THE MANIPULATION OF ATTENTION. THE ARTICLE BEGINS BY DEFINING ATTENTION AS BOTH A COGNITIVE ACTIVITY AND A METAPHYSICAL STATE INTRINSIC TO INTELLECTUAL FREEDOM. THE PROGRESSIVE ERA CONCEPT OF ATTENTION ENGINEERING IS THEN INTRODUCED AND REINTERPRETED IN THE CONTEXT OF ATTENTION SCARCITY AND ACADEMIC CAPITALISM IN THE KNOWLEDGE ERA. THE AFFORDANCES OF INFORMATION AND COMMUNICATIONS TECHNOLOGY REPLICATED IN EDUCATIONAL TECHNOLOGY TO FACILITATE DATA CAPTURE, ANALYSIS, AND INTERVENTION IN THE FORM OF "NUDGE" LEARNING ANALYTICS ARE OUTLINED AS EVIDENCE OF CONTEMPORARY ATTENTION ENGINEERING IN EDUCATION. ATTENTION ENGINEERING IN EDUCATION IS CRITIQUED AS ANTITHETICAL TO STUDENTS' INTELLECTUAL FREEDOM AND DEVELOPMENT AS SELF-SUFFICIENT LEARNERS AND INDEPENDENT THINKERS. THE ACADEMIC LIBRARY'S ROLE IN TEACHING AND PROMOTING ATTENTIONAL LITERACY AND ATTENTIONAL AUTONOMY IS EXPLORED AS A RESPONSE TO THE INTELLECTUAL FREEDOM CHALLENGES POSED BY LEARNING ANALYTICS AS A FORM OF ATTENTION ENGINEERING.
WEB DATA MINING FOR EXTRACTING MEANINGFUL INFORMATION FROM LARGE AMOUNT OF WEB DATA HAS BEEN EXPLORED OVER A DECADE. THE CONCEPTS AND TECHNIQUES HAVE BEEN BORROWED INTO THE EDUCATION SECTOR AND THE NEW RESEARCH DISCIPLINE OF LEARNING ANALYTICS HAS EMERGED. WITH THE DEVELOPMENT OF WEB TECHNOLOGIES, IT HAS BEEN A COMMON PRACTICE TO DESIGN ONLINE COLLABORATIVE LEARNING ACTIVITIES TO ENHANCE LEARNING. TO APPLY LEARNING ANALYTICS TECHNIQUES TO MONITOR THE ONLINE COLLABORATIVE PROCESS ENABLES A LECTURER TO MAKE INSTANT AND INFORMED PEDAGOGICAL DECISIONS. HOWEVER, IT IS STILL A CHALLENGE TO BUILD STRONG CONNECTION BETWEEN LEARNING ANALYTICS AND LEARNING SCIENCE FOR UNDERSTANDING COGNITIVE PROGRESSION IN LEARNING. IN THIS CONNECTION, THIS CHAPTER REPORTS A STUDY TO APPLY LEARNING ANALYTICS TECHNIQUES IN THE ASPECT OF WEB USAGE MINING AND CLUSTERING ANALYSIS WITH UNDERPINNING BLOOM'S TAXONOMY TO ANALYZE STUDENTS' PERFORMANCE IN THE ONLINE COLLABORATIVE LEARNING PROCESS. THE IMPACTS OF INTERMEDIATE INTERVENTIONS ARE ALSO ELABORATED.
BUILDING A STUDENT PERFORMANCE PREDICTION MODEL THAT IS BOTH PRACTICAL AND UNDERSTANDABLE FOR USERS IS A CHALLENGING TASK FRAUGHT WITH CONFOUNDING FACTORS TO COLLECT AND MEASURE. MOST CURRENT PREDICTION MODELS ARE DIFFICULT FOR TEACHERS TO INTERPRET. THIS POSES SIGNIFICANT PROBLEMS FOR MODEL USE (E.G. PERSONALIZING EDUCATION AND INTERVENTION) AS WELL AS MODEL EVALUATION. IN THIS PAPER, WE SYNTHESIZE LEARNING ANALYTICS APPROACHES, EDUCATIONAL DATA MINING (EDM) AND HCI THEORY TO EXPLORE THE DEVELOPMENT OF MORE USABLE PREDICTION MODELS AND PREDICTION MODEL REPRESENTATIONS USING DATA FROM A COLLABORATIVE GEOMETRY PROBLEM SOLVING ENVIRONMENT: VIRTUAL MATH TEAMS WITH GEOGEBRA (VMTWG). FIRST, BASED ON THEORY PROPOSED BY HRASTINSKI (2009) ESTABLISHING ONLINE LEARNING AS ONLINE PARTICIPATION, WE OPERATIONALIZED ACTIVITY THEORY TO HOLISTICALLY QUANTIFY STUDENTS' PARTICIPATION IN THE CSCL (COMPUTER-SUPPORTED COLLABORATIVE LEARNING) COURSE. AS A RESULT, 6 VARIABLES, SUBJECT, RULES, TOOLS, DIVISION OF LABOR, COMMUNITY, AND OBJECT, ARE CONSTRUCTED. THIS ANALYSIS OF VARIABLES PRIOR TO THE APPLICATION OF A MODEL DISTINGUISHES OUR APPROACH FROM PRIOR APPROACHES (FEATURE SELECTION, AD-HOC GUESSWORK ETC.). THE APPROACH DESCRIBED DIMINISHES DATA DIMENSIONALITY AND SYSTEMATICALLY CONTEXTUALIZES DATA IN A SEMANTIC BACKGROUND. SECONDLY, AN ADVANCED MODELING TECHNIQUE, GENETIC PROGRAMMING (GP), UNDERLIES THE DEVELOPED PREDICTION MODEL. WE DEMONSTRATE HOW CONNECTING THE STRUCTURE OF VMTWG TRACE DATA TO A THEORETICAL FRAMEWORK AND PROCESSING THAT DATA USING THE GP ALGORITHMIC APPROACH OUTPERFORMS TRADITIONAL MODELS IN PREDICTION RATE AND INTERPRETABILITY. THEORETICAL AND PRACTICAL IMPLICATIONS ARE THEN DISCUSSED. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
STUDENT MONITORING, THE MOST COMMON PRACTICE IN LEARNING ANALYTICS (LA), HAS BECOME EASIER AND MORE EFFICIENT THANKS TO THE USE OF TRACKING APPROACH THAT CONSISTS OF COLLECTING DATA OF USERS AND OF THEIR INTERACTIONS THROUGHOUT LEARNING PLATFORMS. WHILE LA GIVES CONSIDERABLE ASSISTANCE TO THE TUTORS IN THE TASKS OF MONITORING ONLINE LEARNING, IT ALSO CREATES MAJOR DRAWBACKS FOR THE LEARNERS. FOR INSTANCE, TRACKING APPROACH IN LA RAISES MANY PRIVACY QUESTIONS. AS FOR THE LEARNERS, KNOWING THAT THEIR PERSONAL DATA ARE BEING USED, EVEN FOR EDUCATIONAL PURPOSES, THEY COULD RADICALLY CHANGE THEIR PERCEPTION ON E-LEARNING TECHNOLOGIES. NOT TO MENTION THAT THESE CONCERNS WOULD HAVE A STRONG IMPACT, SOMETIMES VERY NEGATIVELY, ON NOT ONLY THEIR BEHAVIORS BUT ALSO THEIR LEARNING OUTCOMES. TO BETTER UNDERSTAND THE SIDE EFFECT OF LA, MORE PARTICULARLY THE PRIVACY ISSUES IN E-LEARNING, THE RESEARCH EFFORT PRESENTED IN THIS PAPER COVERS TWO MAIN ASPECTS. FIRST, IT OUTLINES VARIOUS TRACKING APPROACHES IN E-LEARNING. SECOND, IT ANALYZES HOW THE LEARNERS PERCEIVE THE USE OF THEIR PERSONAL DATA AND THE RELATED PRIVACY ISSUES. TO DO SO, AN EXPERIMENT HAS BEEN CARRIED OUT WITH THE PARTICIPATION OF STUDENTS FROM THREE DIFFERENT UNIVERSITIES IN FRANCE AND ONE UNIVERSITY IN GERMANY. THE MAJOR CONTRIBUTION OF THIS PAPER IS THE AWARENESS-RAISING OF PRIVACY CONCERNS IN EXPLOITING TRACKING DATA IN E-LEARNING, WHICH ARE OFTEN OVERLOOKED BY RESEARCHERS AND LEARNING CONTENT PROVIDERS.
IN THE CONTEXT OF MOOCS, "WITH-ME-NESS" REFERS TO THE EXTENT TO WINCH THE LEARNER SUCCEEDS IN FOLLOWING THE TEACHER, SPECIFICALLY IN TERMS OF LOOKING AT THE AREA IN THE VIDEO THAT THE TEACHER IS EXPLAINING. IN OUR PREVIOUS WORKS, WE EMPLOYED EYE-TRACKING METHODS TO QUANTIFY LEARNERS' WITH-ME-NESS AND SHOWED THAT IT IS POSITIVELY CORRELATED WITH THEIR LEARNING GAINS. IN THIS CONTRIBUTION, WE DESCRIBE A TOOL THAT IS DESIGNED TO IMPROVE WITH-RUE-NESS BY PROVIDING A VISUAL AID SUPERIMPOSED ON THE VIDEO. THE POSITION OF THE VISUAL AID IS SUGGESTED BY THE TEACHERS' DIALOGUE AND DEIXIS, AND IT IS DISPLAYED WHEN THE LEARNER'S WITH-ME-NESS IS UNDER THE AVERAGE VALUE, WHICH IS COMPUTED FROM THE OTHER STUDENTS' GAZE BEHAVIOR. WE REPORT ON A USER-STUDY THAT EXAMINES THE EFFECTIVENESS OF THE PROPOSED TOOL. THE RESULTS SHOW THAT IT SIGNIFICANTLY IMPROVES THE LEARNING GAIN AND IT SIGNIFICANTLY INCREASES THE EXTENT TO WHICH THE STUDENTS FOLLOW THE TEACHER. FINALLY, WE DEMONSTRATE HOW WITH-ME-CRESS CAN CREATE A COMPLETE THEORETICAL FRAMEWORK FOR CONDUCTILIG GAZE BASED LEARNING ANALYTICS IN THE CONTEXT OF MOOCS.
THE RAPID EXPANSION OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN RELATION TO BIG DATA ANALYSIS GENERATES THE VITAL DEMAND TO GAIN THE MAXIMUM BENEFIT OF THE 'BIG DATA'. BUSINESS INTELLIGENCE AND LEARNING ANALYTICS ARE RECENTLY DRAWING CONSIDERABLE ATTENTION IN MARKET AND SOCIETY BEING ONE OF THE MOST COMPETITIVE AND PROFITABLE AREAS OF INTEREST TO BUSINESSES AND ORGANIZATIONS, INCLUDING HIGHER EDUCATION INSTITUTIONS. BUSINESS INTELLIGENCE SYSTEMS HAVE ALREADY DEMONSTRATED SIGNIFICANT IMPACT ON STRATEGIC DECISION MAKING AND TRENDS FORECASTING. LEARNING ANALYTICS IS GOING TO EXTEND THE VALUE OF BUSINESS INTELLIGENCE IN THE FIELD OF EDUCATION AND TRAINING IN PROPORTION TO RETENTION, STUDENTS' SUCCESS AND SATISFACTION HENCE, THE OVERALL ORGANIZATION'S PERFORMANCE AND RANKING -IN A WIDER SCOPE-WILL BE TRANSFORMED TOWARD INFORMED, TIMELIER, HIGH-VALUED, EVIDENCED AND HIGH-QUALITY DECISIONS BENEFITING THE STUDENTS, AND ACCORDINGLY, ACHIEVING SUSTAINABLE ORGANIZATION'S PERFORMANCE IN A MIDDLE TERM, AND SOCIETY ADVANCEMENT IN A LONGER TERM. THIS PAPER FOCUSES ON INVESTIGATING THE POTENTIAL OF BUSINESS INTELLIGENCE APPROACHES TOWARD A MEANINGFUL ANALYSIS OF THE ORGANIZATION'S AND STUDENT'S EXPERIENCE BY MAKING USE OF BUSINESS INTELLIGENCE ENHANCED LEARNING ANALYTICS. IT PROVIDES VALUABLE INSIGHTS INTO THE INTEGRATION AND USE OF BUSINESS INTELLIGENCE IN THE LEARNING ANALYTICS APPLICATION AREA BY HIGHLIGHTING RECENT RESEARCH AND DEVELOPMENTS. FURTHERMORE, IT SUGGESTS PATHS TO RESOLVING CURRENT ISSUES IN THE INTERRELATIONSHIP BETWEEN HIGHER EDUCATION INSTITUTIONS' AND LEARNING PERFORMANCE BY DESIGNING A NOVEL CLOSED-LOOP FEEDBACK FRAMEWORK THAT AIMS AT IMPROVING THE LEARNING OUTCOMES FOR THE STUDENTS, AND THE ORGANIZATION, AND PREPARES FOR SMART EDUCATIONAL MANAGEMENT.
IN THIS STUDY, AN INTERVENTION ENGINE BASED ON LEARNING ANALYTICS WAS DESIGNED AND DEVELOPED. THE INTERVENTION ENGINE IS NAMED THE INTELLIGENT INTERVENTION SYSTEM ((INS)-S-2). WITHIN THE SCOPE OF THIS RESEARCH; (INS)-S-2 SYSTEM AND ITS COMPONENTS HAVE BEEN INTRODUCED, AND THE SYSTEM IS EVALUATED BASED ON LEARNERS' VIEWS. (INS)-S-2 INCLUDES THREE TYPES OF INTERVENTION THAT ARE INSTRUCTIONAL, SUPPORTIVE, AND MOTIVATIONAL INTERVENTION. THE INSTRUCTIONAL INTERVENTION WAS STRUCTURED BASED ON ASSESSMENT TASKS. THE SUPPORTIVE AND MOTIVATIONAL INTERVENTIONS WERE STRUCTURED BASED ON THE LEARNING EXPERIENCES OF THE LEARNERS. SIGNAL LIGHTS (RED, YELLOW, AND GREEN) ARE PRESENTED TO THE LEARNERS FOR EACH ASSESSMENT TASK AS AN INSTRUCTIONAL INTERVENTION. SUPPORTIVE INTERVENTION IS PRESENTED TO THE LEARNERS VIA THE DASHBOARD. IN THE CONTEXT OF MOTIVATIONAL INTERVENTION, ELEMENTS OF GAMIFICATION AS A LEADER BOARD, BADGES, AND NOTIFICATIONS HAVE BEEN USED. IN ORDER TO OBTAIN THE LEARNER'S VIEWS ABOUT THE (INS)-S-2, SEMI-STRUCTURED INTERVIEWS WERE CONDUCTED WITH THE LEARNERS WHO HAD A PREVIOUS LEARNING EXPERIENCE WITH THE SYSTEM. THE LEARNING ENVIRONMENT WAS EVALUATED BASED ON THEIR VIEWS. LEARNERS HAD A NINE-WEEK LEARNING EXPERIENCE IN THE E-LEARNING ENVIRONMENT. THEN, EIGHT STUDENTS WHO USED THE SYSTEM MOST ACTIVELY AND EIGHT STUDENTS WHO USED THE SYSTEM MOST PASSIVELY WERE SELECTED FOR FOCUS GROUP INTERVIEWS.. ACCORDING TO THE FINDINGS, IT WAS SEEN THAT THE LEARNERS WHO USE THE INTERVENTION ENGINE INDICATED THAT THE SYSTEM IS USEFUL AND WANT TO USE IT IN THE CONTEXT OF OTHER COURSES.
POSITION STATEMENT ON THE ONGOING DEVELOPMENT OF AN INSTRUMENT TO CAPTURE SNAPSHOTS OF APP INVENTOR PROJECTS AS STUDENTS ARE DEVELOPING, ALLOWING EXAMINATION OF THE STUDENTS' PROCESSES. DATA MINING TECHNIQUES WILL BE USED TO PROCESS THE CODE SNAPSHOTS, IDENTIFYING PATTERNS OF BEHAVIOR. FUTURE TOOLS MAY INCLUDE REAL-TIME ASSESSMENT, AND IMPROVED TAXONOMY OF PROGRAMMING PROCESS.
EDUCATIONAL GAMES ARE INCREASINGLY POPULAR IN TEACHING AS THEY HAVE PROVEN TO BE EFFECTIVE LEARNING TOOLS. EDUCATIONAL VIDEOGAMES ARE BENEFICIAL FOR ALL KIND OF STUDENTS BUT WE THINK THEY ARE ESPECIALLY SUITED FOR USERS WITH INTELLECTUAL DISABILITIES DUE TO THE OPPORTUNITY OF TAILORING THE CONTENT TO THEIR IN-GAME PERFORMANCE. ADAPTING THE GAME EXPERIENCE TO THE COGNITIVE AND LEARNING ABILITIES TO THIS TYPE OF STUDENTS ALSO MAKE VIDEOGAMES A POWERFUL SOURCE OF LEARNING DATA. IN THIS PAPER WE INTRODUCE THE GLAID (GAME LEARNING ANALYTICS FOR INTELLECTUAL DISABILITIES) MODEL, A THEORETICAL ADAPTATION OF A MORE GENERAL ANALYTICS FRAMEWORK. IT DESCRIBES HOW TO COLLECT, PROCESS AND ANALYZE VIDEOGAME INTERACTION DATA IN ORDER TO PROVIDE AN OVERVIEW OF THE USER LEARNING EXPERIENCE, FROM AN INDIVIDUALIZED ASSESSMENT TO A COLLECTIVE PERSPECTIVE. BUT TO OBTAIN THESE GOALS IT IS NECESSARY TO TAKE INTO ACCOUNT THE RESTRICTIONS AND SPECIAL NEEDS OF USERS WITH INTELLECTUAL DISABILITIES BOTH IN THE LEARNING DESIGN AND IN TRANSLATING THEM INTO GAME MECHANICS AND THE CORRESPONDING OBSERVABLES THAT WILL BE COLLECTED FOR THE SUBSEQUENT DATA ANALYSIS. WE CONCLUDE WITH A DISCUSSION AND CONSIDERATIONS ABOUT THE MODEL AND FUTURE STEPS TO FOLLOW IN OUR INVESTIGATION.
DATA WITH HIGH VOLUME, VELOCITY, VARIETY AND VERACITY BRINGS THE NEW EXPERIENCE CURVE OF ANALYTICS. BIG DATA IN HIGHER EDUCATION COMES FROM DIFFERENT SOURCES THAT INCLUDE BLOGS, SOCIAL NETWORKS, STUDENT INFORMATION SYSTEMS, LEARNING MANAGEMENT SYSTEMS, RESEARCH, AND OTHER MACHINE-GENERATED DATA. ONCE THE DATA IS ANALYSED IT PROMISES BETTER STUDENT PLACEMENT PROCESSES; MORE ACCURATE ENROLMENT FORECASTS, AND EARLY WARNING SYSTEMS THAT IDENTIFY AND ASSIST STUDENTS AT-RISK OF FAILING OR DROPPING OUT. BIG DATA IS BECOMING A KEY TO CREATING COMPETITIVE ADVANTAGES IN HIGHER EDUCATION. LIKE WITH ANY ORGANIZATION, TRADITIONAL DATA PROCESSING AND ANALYSIS OF STRUCTURED AND UNSTRUCTURED DATA USING RDBMS AND DATA WAREHOUSING NO LONGER SATISFY BIG DATA CHALLENGES. THE LACK OF ADEQUATE CONCEPTUAL ARCHITECTURES FOR BIG DATA TAILORED FOR INSTITUTIONS OF HIGHER EDUCATION HAS LED TO MANY FAILURES TO PRODUCE MEANINGFUL, ACCESSIBLE, AND TIMELY INFORMATION FOR DECISION MAKING. THEREFORE, THIS CALLS FOR THE DEVELOPMENT OF CONCEPTUAL ARCHITECTURES FOR BIG DATA IN HIGHER EDUCATION. THIS PAPER PRESENTS AN ARCHITECTURE FOR BIG DATA ANALYTICS IN HIGHER EDUCATION.
THE GROWTH OF LEARNING ANALYTICS (LA) AS A RESEARCH FIELD HAS BEEN EXTENSIVELY DOCUMENTED SINCE ITS BEGINNINGS. THIS PAPER PROVIDES A BROAD OVERVIEW OF THE PUBLICATIONS THAT LATIN AMERICAN AUTHORS HAVE PUBLISHED IN THE LAST YEARS BY PERFORMING A QUANTITATIVE REVIEW OF THE LITERATURE (FROM 2011 TO 2019). A TOTAL OF 282 PAPERS WERE COLLECTED AND CATEGORIZED ACCORDING TO A MULTIDIMENSIONAL MODEL COMPOSED OF SEVEN DIMENSIONS (DATA COLLECTED, STAKEHOLDERS TARGETED, GOALS OF THE PAPER, TECHNIQUES EMPLOYED, MATURITY OF THE TOOL, RESEARCH APPROACHES AND ETHICAL ISSUES). THE PAPER DESCRIBES HOW THE FIELD HAS EVOLVED OVER THE YEARS IN LATIN AMERICA, PRESENTING THE MAIN CHARACTERISTICS OF THE PAPERS FROM DIFFERENT PERSPECTIVES (YEAR, TYPE OF VENUE, LANGUAGE) TOGETHER WITH THE DESCRIPTIVES OF THE CATEGORIZED DIMENSIONS AND THE RELATIONS BETWEEN THEM. AT LAST, PAPERS ARE PRESENTED IN NUMBER BY COUNTRIES AND INSTITUTIONS, AND WE PERFORM AN AUTHORSHIP AND COAUTHORSHIP ANALYSIS WITH INFORMATION EXTRACTED FROM THE SELECTED PAPERS.
THE CONCEPT OF LEARNING ANALYTICS, AS WE UNDERSTAND IT TODAY, IS RELATIVELY NEW BUT THE PRACTICE OF EVALUATING USER BEHAVIOR IS NOT INNOVATIVE. FOR YEARS, TECHNOLOGICAL DEVELOPMENT, ALONG WITH OTHER EDUCATIONAL ASPECTS, HAVE ENCOURAGED, DEVELOPED AND FACILITATED THIS PRACTICE AS A WAY OF PROVIDING A PERSONALIZED QUALITY EXPERIENCE TO STUDENTS. THE MAIN GOAL OF THIS STUDY, CARRIED OUT IN THE PRIMARY EDUCATION DEGREE OF THE UNIVERSITY OF MURCIA, WAS TO RESEARCH, FROM THE PERSPECTIVE OF SOCIAL LEARNING ANALYTICS, HOW STUDENTS LEARN AND COLLABORATE IN ONLINE ENVIRONMENTS, SPECIFICALLY THROUGH THEIR USE OF SOCIAL MEDIA. WITH THE IDEA OF IMPROVING AND OPTIMIZING FUTURE TEACHING EXPERIENCES, A PILOT STUDY WAS CONDUCTED USING WEBLOG, TWITTER AND FACEBOOK TO WORK WITH DIFFERENT TOPICS ON THE SUBJECT. THE METHOD USED IN THIS RESEARCH WAS A PARTICIPANT OBSERVATION AND THE ANALYSIS PERFORMED WAS BOTH QUANTITATIVE, BASED MAINLY ON THE DATA GATHERED FROM THE LEARNING ANALYTICS, AND QUALITATIVE (ANALYZING STUDENTS' CONTENT FROM COMMENTS). RESULTS SHOW THAT THERE WAS GREATER INTERACTION ON FACEBOOK THAN WEBLOGS, WHERE STUDENTS INTERACTED TO DEAL WITH ASPECTS RELATED TO THE LEARNING PROCESS AND THE TOPIC OF THE SUBJECT. THIS EXCHANGE OF INFORMATION GREW DURING THE DEVELOPMENT OF THE EXPERIENCE. IN ADDITION, LEARNING ANALYTICS SHOWS THAT THERE IS A RELATIONSHIP BETWEEN GROUP MEMBERS AND THEIR INTERACTION AND BEHAVIOR IN NETWORKS.
THIS RESEARCH AIMS TO INVESTIGATE THE RELATIONSHIP BETWEEN SELF-REGULATED LEARNING AWARENESS, LEARNING BEHAVIORS, AND LEARNING PERFORMANCE IN UBIQUITOUS LEARNING ENVIRONMENTS. IN ORDER TO DO SO, PSYCHOMETRIC DATA ABOUT SELF-REGULATED LEARNING AND LOG DATA SUCH AS MARKER, ANNOTATION, ACCESSING DEVICE TYPES THAT STORED THE LEARNING MANAGEMENT SYSTEM WERE COLLECTED AND ANALYZED USING MULTIPLE REGRESSION ANALYSIS WITH STEPWISE METHOD. THE RESULTS INDICATED THAT SELF-EFFICACY, INTERNAL VALUE, AND THE NUMBER OF READ SLIDES HAD A SIGNIFICANT INFLUENCE ON THE FINAL SCORE, AND THE AWARENESS OF COGNITIVE LEARNING STRATEGY USE HAS SLIGHTLY SIGNIFICANT POWER TO PREDICT THE FINAL SCORE.
COMPUTER-ASSISTED LANGUAGE LEARNING (CALL) IS OFTEN USED AS AN APPROACH TO FOREIGN LANGUAGE TEACHING AND LEARNING IN HIGHER EDUCATION. THE CALL COURSE IS OFFERED AT A NATIONAL UNIVERSITY IN JAPAN TO ALLOW FRESHMAN STUDENTS TO PERFORM SELF-REGULATED LEARNING WITH E-LEARNING MATERIALS FOR THE PURPOSE OF DEVELOPING LANGUAGE SKILLS. HOWEVER, AS NOVICE SELF-REGULATED LEARNERS, FRESHMAN STUDENTS HAVE LOW SELF-REGULATION SKILLS AND THEY ARE MORE LIKELY TO OBTAIN LOWER ACHIEVEMENT. IN ADDITION, IT IS DIFFICULT FOR INSTRUCTORS TO GRASP STUDENTS' LEARNING SITUATION DUE TO THE LARGE AMOUNT OF EVALUATION WORK. THEREFORE, IN THIS RESEARCH, A TOTAL OF 7,413,397 LEARNING LOGS WERE ANALYZED, WHICH WERE COLLECTED FROM 2,499 STUDENTS' LEARNING INTERACTIONS IN THE CALL COURSE. AFTER THAT, A LEARNING SUPPORT SYSTEM FOR FRESHMAN STUDENTS IS PROPOSED. THE SYSTEM IS PROVIDED FOR STUDENTS AND INSTRUCTORS THROUGH THE LEARNING DASHBOARD. ON THE ONE HAND, STUDENTS CAN CONDUCT SELF-MONITORING AND REFLECT THEIR BEHAVIORS IN A VISUAL WAY. ON THE OTHER HAND, INSTRUCTORS CAN IDENTIFY LEARNING BEHAVIORAL PATTERNS AND GRASP INDIVIDUAL LEARNING SITUATION TO PROVIDE ONE-ON-ONE INSTRUCTIONS.
LECTURE VIDEOS ARE AMONGST THE MOST WIDELY USED INSTRUCTIONAL METHODS WITHIN PRESENT MASSIVE OPEN ONLINE COURSES (MOOCS) AND OTHER DIGITAL EDUCATIONAL PLATFORMS. AS THE MAIN FORM OF INSTRUCTION, STUDENT ENGAGEMENT BEHAVIOUR, INCLUDING INTERACTION WITH VIDEOS, DIRECTLY IMPACTS THE STUDENT SUCCESS OR FAILURE AND ACCORDINGLY, IN-VIDEO DROPOUTS POSITIVELY CORRELATE WITH DROPOUT FROM MOOCS. THE PRIMARY FOCUS OF PREVIOUS VIDEO LEARNING ANALYTICS STUDIES IS ON ANALYZING VIDEO INTERACTION BEHAVIOUR USING EXPLICIT FACTORS (I.E., VIEWS). LIMITED RESEARCH STUDIES FOCUS ON IMPLICIT VIDEO LEARNING ANALYTICS (E.G., PAUSE, SEEK, CONTENT TYPE) AND THEIR IMPACT ON STUDENT SUCCESS, WITH EXISTING STUDIES ADDRESSING VIDEO INTERACTIONS AND THEIR RELATIONSHIP WITH VISUAL TRANSITIONS. WE AIM TO EXPLORE THE ASSOCIATION BETWEEN VIDEO INTERACTIONS AND NON-VISUAL (I.E., VERBAL) CONTENT. THIS RESEARCH FOCUSES ON LANGUAGE AND DISCOURSE FEATURES OF LECTURE VIDEO CONTENTS. WE CONDUCT A FINE-GRAINED ANALYSIS OF 3.4 MILLION VIDEO INTERACTIONS ACROSS TWO ADELAIDEX MOOCS - PROGRAMMING (CODE101X) AND CYBERWAR, SURVEILLANCE AND SECURITY (CYBER101X). ACCORDING TO OUR RESULTS, A NUMBER OF DISCOURSE FEATURES (E.G., LEXICAL DIVERSITY AND CAUSAL CONNECTIVES) DEMONSTRATE A STATISTICALLY SIGNIFICANT CORRELATION WITH VIDEO INTERACTIONS. WE PRESENT INSIGHTS REGARDING EDUCATIONAL VIDEO DESIGN IMPLICATIONS BASED ON DISCOURSE PROCESSING THEORIES.
IN THIS PAPER, WE DESCRIBE AN EXPLORATORY STUDY WHERE WE INVESTIGATE THE POSSIBILITIES OF MOTION CAPTURE SYSTEM AS AN INSTRUMENT TO CONSIDER IN MULTIMODAL ANALYSES OF FACE-TO-FACE COLLABORATIVE LEARNING SCENARIOS. THE GOAL IS TO UNDERSTAND TO WHAT EXTENT MOTION CAPTURE CAN FACILITATE CERTAIN MEASUREMENTS LEADING TO COLLABORATIVE LEARNING INDICATORS THAT ARE CURRENTLY TIME-CONSUMING TO ACHIEVE WITH OTHER INSTRUMENTS. WE FOCUS ON THE SIMULTANEOUS MEASUREMENT OF KNOWN PHYSICAL COLLABORATION INDICATORS SUCH AS GAZE DIRECTION, THE DISTANCE BETWEEN LEARNERS AND THE SPEED OF MOVEMENT/REACTIONS. THE STUDY CONSIDERS A LAB SETTING SIMULATING A CLASSROOM SCENARIO BASED ON THE JIGSAW COLLABORATIVE LEARNING FLOW PATTERN, WHICH PROPOSES A SEQUENCE OF ACTIVITIES WITH CHANGES IN GROUP SIZE AND FORMATION. PRELIMINARY RESULTS INDICATE A HIGH DEGREE OF APPLICABILITY OF THE SYSTEM IN MEASURING THESE INDICATORS, WITH CERTAIN LIMITATIONS FOR GAZE DIRECTION MEASUREMENTS. WITH APPROPRIATE MARKER POSITION ON THE PARTICIPANTS, THE SYSTEM IS ABLE TO AUTOMATICALLY PROVIDE DESIRED MEASUREMENTS WITH SATISFACTORY PRECISION. ADDITIONALLY, WITH A SMALL NUMBER OF ADDITIONAL MARKERS, WE WERE ABLE TO DETERMINE THE WAY STUDENTS USED WORKING SURFACES (SHARED DESKS).
THIS PAPER PROPOSES A SYSTEMATIC FRAMEWORK TO INTEGRATE LEARNING ANALYTICS INTO SERIOUS GAMES FOR PEOPLE WITH INTELLECTUAL DISABILITIES. SERIOUS GAMES FOR AN INCLUSIVE LEARNING ENVIRONMENT NEED TO BE PRUDENTLY DESIGNED WITH ADAPTIVE AND MEASURABLE COMPETENCIES TO MEET THE NEEDS OF THE TARGET USERS. THE RECENT EMERGENCE OF LEARNING ANALYTICS PROVIDES A CAPABILITY TO CAPTURE IMPORTANT DATA IN REAL TIME FROM WITHIN THE HIGHLY INTERACTIVE NATURE OF SERIOUS GAMES TO BETTER UNDERSTAND AND ENHANCE THE LEARNING PROCESS. ALTHOUGH PREVIOUS RESEARCH HAS ADDRESSED DIFFERENT APPLICATIONS OF LEARNING ANALYTICS IN SERIOUS GAMES, FEW STUDIES HAVE INVESTIGATED THE NEEDS OF INDIVIDUALS WITH DISABILITIES. THIS PAPER PROPOSES A FRAMEWORK FOR SERIOUS GAMES ANALYTICS SPECIALISED FOR PEOPLE WITH INTELLECTUAL DISABILITIES (SGAPID) FOR THE PURPOSE OF SUPPORTING THE INTEGRATION OF LEARNING ANALYTICS WITHIN SERIOUS GAMES TO CREATE AN INCLUSIVE LEARNING ENVIRONMENT. WE PROVIDE A FRAMEWORK BASED ON PREVIOUS STUDIES IN BOTH LEARNING ANALYTICS, SERIOUS GAMES AND EDUCATIONAL TECHNOLOGIES FOR PEOPLE WITH INTELLECTUAL DISABILITIES. THE FRAMEWORK CONSISTS OF THREE CENTRAL COMPONENTS, NAMELY LEARNER PROFILING, LEARNING ADAPTATION AND LEARNING EVALUATION. IT PROVIDES NEEDED GUIDANCE FOR EDUCATIONAL APPLICATION DEVELOPERS AND REFLECTS THE CONTEMPORARY TRENDS IN EDUCATIONAL TECHNOLOGIES. THE SGAPID FRAMEWORK WILL ALSO BE VALUABLE FOR THE DESIGN, IMPLEMENTATION, EVALUATION AND ADAPTATION OF SERIOUS GAMES FOR INCLUSIVE LEARNING AND TEACHING AT THE GROUP OR INDIVIDUAL LEVEL.
DATA USED IN LEARNING ANALYTICS RARELY PROVIDE STRONG AND CLEAR SIGNALS ABOUT HOW LEARNERS PROCESS CONTENT. AS A RESULT, LEARNING AS A PROCESS IS NOT CLEARLY DESCRIBED FOR LEARNERS OR FOR LEARNING SCIENTISTS. GASEVIC, DAWSON, AND SIEMENS (2015) URGED DATA BE SOUGHT THAT MORE STRAIGHTFORWARDLY DESCRIBE PROCESSES IN TERMS OF EVENTS WITHIN LEARNING EPISODES. THEY RECOMMENDED BUILDING ON WINNE'S (1982) CHARACTERIZATION OF TRACES - AMBIENT DATA GATHERED AS LEARNERS STUDY THAT MORE CLEARLY REPRESENT WHICH OPERATIONS LEARNERS APPLY TO WHICH INFORMATION - AND HIS COPES MODEL OF A LEARNING EVENT - CONDITIONS, OPERATIONS, PRODUCTS, EVALUATIONS, STANDARDS (WINNE, 1997). WE DESIGNED AND DESCRIBE AN OPEN SOURCE, OPEN ACCESS, SCALABLE SOFTWARE SYSTEM CALLED NSTUDY THAT RESPONDS TO THEIR CHALLENGE. NSTUDY GATHERS DATA THAT TRACE COGNITION, METACOGNITION, AND MOTIVATION AS PROCESSES THAT ARE OPERATIONALLY CAPTURED AS LEARNERS OPERATE ON INFORMATION USING NSTUDY'S TOOLS. NSTUDY CAN BE CONFIGURED TO SUPPORT LEARNERS' EVOLVING SELF-REGULATED LEARNING, A PROCESS AKIN TO PERSONALLY FOCUSED, SELF-DIRECTED LEARNING SCIENCE.
LEARNING ANALYTICS (LA) HAS EMERGED AS A FIELD THAT OFFERS PROMISING NEW WAYS TO PREVENT DROP-OUT AND AID RETENTION. HOWEVER, OTHER RESEARCH SUGGESTS THAT LARGE DATASETS OF LEARNER ACTIVITY CAN BE USED TO UNDERSTAND ONLINE LEARNING BEHAVIOUR AND IMPROVE PEDAGOGY. WHILE THE USE OF LA IN LANGUAGE LEARNING HAS RECEIVED LITTLE ATTENTION TO DATE, AVAILABLE RESEARCH SUGGESTS THAT LA COULD PROVIDE VALUABLE INSIGHTS INTO TASK DESIGN FOR INSTRUCTORS AND MATERIALS DESIGNERS, AS WELL AS HELP STUDENTS WITH EFFECTIVE LEARNING STRATEGIES AND PERSONALISED LEARNING PATHWAYS. THIS PAPER FIRST DISCUSSES PREVIOUS CALL RESEARCH BASED ON LEARNER TRACKING AND SPECIFIC AFFORDANCES OF LA FOR CALL, AS WELL AS ITS INHERENT LIMITATIONS AND CHALLENGES. THE SECOND PART OF THE PAPER ANALYSES DATA ARISING FROM THE VITAL PROJECT THAT IMPLEMENTED LA IN DIFFERENT BLENDED OR DISTANCE LEARNING SETTINGS. STATISTICAL AND PROCESS-MINING TECHNIQUES WERE APPLIED TO DATA FROM 285 UNDERGRADUATE STUDENTS ON A BUSINESS FRENCH COURSE. RESULTS SUGGESTED THAT MOST STUDENTS PLANNED THEIR SELF-STUDY SESSIONS IN ACCORDANCE WITH THE FLIPPED CLASSROOM DESIGN. OTHER METRICS MEASURING ACTIVE ONLINE ENGAGEMENTINDICATED SIGNIFICANT DIFFERENCES BETWEEN SUCCESSFUL AND NON-SUCCESSFUL STUDENTS' LEARNER PATTERNS. THE RESEARCH IMPLIED THAT VALUABLE INSIGHTS CAN BE ACQUIRED THROUGH LA AND THE USE OF VISUALISATION AND PROCESS-MINING TOOLS.
LEARNING ANALYTICS (LA) LEVERAGES LEARNER-RELATED DATA TO GENERATE RELIABLE AND FACTUAL INFORMATION FOR THE PURPOSE OF ENHANCING DECISION MAKING IN HIGHER EDUCATION, WORKPLACE AND SCHOOLS. THEREFORE, WE HAVE ENVISIONED THE NEED FOR A LEARNING ANALYTICS SYSTEM FOR A CONSORTIUM OF NIGERIAN UNIVERSITIES THAT COULD SERVE AS A TOOL FOR ACADEMIC ADVISING. THE SYSTEM SHOULD BE ABLE TO OFFER ADVICE AT SEVERAL LAYERS OF GRANULARITY SUCH AS INDIVIDUAL, DEPARTMENT, UNIVERSITY AND NATION WITH THE CARDINAL OBJECTIVE OF FACILITATING STUDENT RETENTION, STUDENT PROGRESSION AND COST SAVING. THIS PAPER REPORTS THE OPERATIONAL FRAMEWORK OF THE PROPOSED LA SYSTEM IN THE FORM OF AN N-TIER LAYERED ARCHITECTURE, WHICH WAS DESIGNED AFTER COMPREHENSIVE REQUIREMENTS ANALYSIS. THE DESIGN OF THE SYSTEM ENCOMPASSES MECHANISMS THAT ADDRESS OPEN ISSUES CONFRONTING LA SYSTEMS SUCH AS INTEROPERABILITY, SECURITY, SCALABILITY, AMONGST OTHERS. BESIDES ADDRESSING OPEN PROBLEMS, THE STUDY OFFERS PRACTICAL STEPS FOR THE APPLICATION OF LA IN AN AFRICAN CLIME, WHICH SO FAR, IS NOT COMMON IN THE LITERATURE. THE PROPOSED LA SYSTEM FOR ACADEMIC ADVISING IS PRESENTED AS FEASIBLE AND RELEVANT FOR THE ADVANCEMENT OF HIGHER EDUCATION IN NIGERIA. KEYWORDS-ACADEMIC ADVISING; LEARNER-RELATED DATA; HIGHER EDUCATION; LAYERS OF GRANULARITY; LEARNING ANALYTICS; USER REQUIREMENTS; MAPREDUCE FRAMEWORK
THIS PAPER PRESENTS THE ANALYSIS AND DESIGN OF AN INNOVATIVE WEB-BASED ELEARNING PLATFORM FEATURING LEARNING ANALYTICS AND GAMIFICATION CALLED ICT-FLAG. A PREVIOUS CONTRIBUTION PRESENTED THE SOFTWARE DEVELOPMENT FRAMEWORK THAT WILL GUIDE AND SUPPORT THE EFFECTIVE CONSTRUCTION OF THE PLATFORM. FOLLOWING THE SELECTED FRAMEWORK, THIS PAPER DEFINES THE REQUIREMENTS OF THIS PLATFORM AND PROPOSE A SOLUTION TO SATISFY THOSE REQUIREMENTS IN TERMS OF SOFTWARE ARTIFACTS. THE DESIGN INCLUDES CHALLENGING NON-FUNCTIONAL REQUIREMENTS, SUCH AS SCALABILITY, SECURITY AND PERFORMANCE. THE RESEARCH REPORTED IN THIS PAPER IS CURRENTLY UNDERTAKEN WITHIN THE PROJECT "ENHANCING ICT EDUCATION THROUGH FORMATIVE ASSESSMENT, LEARNING ANALYTICS AND GAMIFICATION" (ICT-FLAG) FUNDED BY THE SPANISH GOVERNMENT.
THE CHALLENGE OF DELIVERING PERSONALIZED LEARNING EXPERIENCES IS OFTEN INCREASED BY THE SIZE OF CLASSROOMS AND ONLINE LEARNING COMMUNITIES. SERIOUS GAMES (SGS) ARE INCREASINGLY RECOGNIZED FOR THEIR POTENTIAL TO IMPROVE EDUCATION. HOWEVER, THE ISSUES RELATED TO THEIR DEVELOPMENT AND THEIR LEVEL OF EFFECTIVENESS CAN BE SERIOUSLY AFFECTED WHEN BROUGHT TOO RAPIDLY INTO GROWING ONLINE LEARNING COMMUNITIES. DEEPER INSIGHTS INTO HOW THE STUDENTS ARE PLAYING IS NEEDED TO DELIVER A COMPREHENSIVE AND INTELLIGENT LEARNING FRAMEWORK THAT FACILITATES BETTER UNDERSTANDING OF LEARNERS' KNOWLEDGE, EFFECTIVE ASSESSMENT OF THEIR PROGRESS AND CONTINUOUS EVALUATION AND OPTIMIZATION OF THE ENVIRONMENTS IN WHICH THEY LEARN. THIS PAPER DISCUSSES CURRENT SOTA AND AIMS TO EXPLORE THE POTENTIAL IN THE USE OF GAMES AND LEARNING ANALYTICS TOWARDS SCAFFOLDING AND SUPPORTING TEACHING AND LEARNING EXPERIENCE. THE CONCEPTUAL MODEL (ECOSYSTEM AND ARCHITECTURE) DISCUSSED IN THIS PAPER AIMS TO HIGHLIGHT THE KEY CONSIDERATIONS THAT MAY ADVANCE THE CURRENT STATE OF LEARNING ANALYTICS, ADAPTIVE LEARNING AND SGS, BY LEVERAGING SGS AS AN SUITABLE MEDIUM FOR GATHERING DATA AND PERFORMING ADAPTATIONS.
AUTOMATIC ASSESSMENT AND EVALUATION OF TEAM PERFORMANCE DURING COLLABORATIVE TASKS IS KEY TO THE RESEARCH ON LEARNING ANALYTICS AND COMPUTER-SUPPORTED COOPERATIVE WORK. THERE IS GROWING INTEREST IN THE USE OF GAZE-ORIENTED CUES FOR EVALUATING THE COLLABORATION AND COOPERATIVENESS OF TEAMS. HOWEVER, COLLECTING GAZE DATA USING EYE-TRACKERS IS NOT ALWAYS FEASIBLE DUE TO TIME AND COST CONSTRAINTS. IN THIS PAPER, WE INTRODUCE AN AUTOMATED TEAM ASSESSMENT TOOL BASED ON GAZE POINTS AND JOINT VISUAL ATTENTION (JVA) INFORMATION DRAWN FROM COMPUTER VISION SOLUTIONS. WE EVALUATED TEAM COLLABORATIONS IN AN UNDERGRADUATE ANATOMY LEARNING ACTIVITY (N = 60, 30 TEAMS) AS A TEST USER STUDY. THE RESULTS INDICATE THAT HIGHER JVA WAS POSITIVELY ASSOCIATED WITH STUDENT LEARNING OUTCOMES (R((30)) = 0.50, P < 0.005). MOREOVER, TEAMS WHO PARTICIPATED IN TWO EXPERIMENTAL GROUPS AND USED INTERACTIVE 3D ANATOMY MODELS, HAD HIGHER JVA (F-(1,F-28) = 6.65, P < 0.05) AND BETTER KNOWLEDGE RETENTION (F-(1,F-28) = 7.56, P < 0.05) THAN THOSE IN THE CONTROL GROUP. ALSO, NO SIGNIFICANT DIFFERENCE WAS OBSERVED BASED ON JVA FOR DIFFERENT GENDER COMPOSITIONS OF TEAMS. THE FINDINGS FROM THIS WORK HAVE IMPLICATIONS IN LEARNING SCIENCES AND COLLABORATIVE COMPUTING BY PROVIDING A NOVEL JOINT ATTENTION-BASED MEASURE TO OBJECTIVELY EVALUATE TEAM COLLABORATION DYNAMICS. IMPLICATIONS FOR PRACTICE OR POLICY: STUDENT LEARNING OUTCOMES CAN BE IMPROVED BY RECEIVING CONSTRUCTIVE FEEDBACK ABOUT TEAM PERFORMANCES USING OUR GAZE-BASED COLLABORATIVE LEARNING METHOD. UNDERREPRESENTED AND UNDERSERVED MINORITIES OF SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS DISCIPLINES CAN BE ENGAGED IN MORE COLLABORATIVE PROBLEM-SOLVING AND TEAM-BASED LEARNING ACTIVITIES SINCE OUR METHOD OFFERS A BROADER REACH BY AUTOMATING COLLABORATION ASSESSMENT PROCESS. COURSE LEADERS CAN ASSESS THE QUALITY OF ATTENTION AND ENGAGEMENT AMONG STUDENTS AND CAN MONITOR OR ASSIST LARGER NUMBERS OF STUDENTS SIMULTANEOUSLY.
THE COLLECTION AND SELECTION OF THE DATA USED IN LEARNING ANALYTICS APPLICATIONS DESERVE MORE ATTENTION. OPTIMALLY, SELECTION OF DATA SHOULD BE GUIDED BY PEDAGOGICAL PURPOSES INSTEAD OF DATA AVAILABILITY. USING DESIGN SCIENCE RESEARCH METHODOLOGY, WE DESIGNED AN ARTIFACT TO COLLECT TIME-SERIES DATA ON STUDENTS' SELF-REGULATED LEARNING AND CONCEPTUAL THINKING. OUR ARTIFACT COMBINES CURRICULUM DATA, CONCEPT MAPPING, AND STRUCTURED LEARNING DIARIES. WE EVALUATED THE ARTIFACT IN A CASE STUDY, VERIFYING THAT IT PROVIDES RELEVANT DATA, REQUIRES A LIMITED AMOUNT OF EFFORT FROM STUDENTS, AND WORKS IN DIFFERENT EDUCATIONAL CONTEXTS. COMBINED WITH LEARNING ANALYTICS APPLICATIONS AND INTERVENTIONS, OUR ARTIFACT PROVIDES POSSIBILITIES TO ADD VALUE FOR STUDENTS, TEACHERS, AND ACADEMIC LEADERS.
GERMAN ORTHOGRAPHY IS KNOWN TO BE QUITE DIFFICULT TO MASTER, ESPECIALLY FOR PRIMARY-SCHOOL PUPILS IN WRITING TEXTS [CF. 1]. IN ORDER TO SUPPORT CHILDREN WITH THE ACQUISITION OF GERMAN ORTHOGRAPHY, WE ARE DEVELOPING A WEB-BASED PLATFORM FOR GERMAN-SPEAKING USERS BASED ON LEARNING ANALYTICS TECHNIQUES. OUR GOAL IS TO MOTIVATE PUPILS AGE 8 TO 12 TO IMPROVE THEIR SPELLING ABILITIES BY WRITING TEXTS AND BY THE POSSIBILITY TO PUBLISH THEM. CONCERNING SPELLING IN COMBINATION WITH LEARNING ANALYTICS THE SYSTEM PROVIDES - IN CASE OF AN ORTHOGRAPHIC MISTAKE - A SPECIFIC FEEDBACK THAT ENCOURAGES PUPILS TO THINK ABOUT THE SPELLING AND TO CORRECT IT. BASED ON OCCURRED MISTAKES THE TEACHERS AND THE STUDENTS ARE PROVIDED WITH A QUALITATIVE ANALYSIS OF THE MISTAKES. THIS ANALYSIS SHOWS THE PROBLEMATIC ORTHOGRAPHIC AREAS AND GIVES SUGGESTIONS FOR ONLINE AND OFFLINE EXERCISES AS WELL AS ONLINE COURSES THAT ARE EXPLAINING THE ORTHOGRAPHIC PHENOMENA. THE AIM OF THIS ARTICLE IS TO DESCRIBE THE ARCHITECTURE OF THE WEB-BASED SYSTEM AND A PROOF OF CONCEPT BY EVALUATING 60 ESSAYS. FURTHERMORE, RELEVANT BACKGROUND INFORMATION IS GIVEN IN ORDER TO GAIN A BETTER UNDERSTANDING IN THE COMPLEX INTERDISCIPLINARY DEVELOPMENT.
THE PRACTICE AND APPLICATION OF EDUCATION DATA MINING AND LEARNING ANALYTICS HAS BECOME THE FOCUS OF EDUCATIONAL RESEARCHERS. HOWEVER, IT IS STILL A DIFFICULT TASK TO EXPLORE THE LAW OF GROUP LEARNING AND THE CHARACTERISTICS OF INDIVIDUAL LEARNING. IN THIS STUDY, THE ONLINE LEARNING LOGS OF 1,088 STUDENTS FROM 22 CLASSES WERE ANALYZED FROM THE ASPECTS OF THEIR LOGIN BEHAVIORS, RESOURCE UTILIZATION, QUIZZES, INTERACTIVE BEHAVIORS, AND ACADEMIC ACHIEVEMENT. TO ADDRESS THESE ISSUES, MULTIPLE METHODS, INCLUDING STATISTICAL ANALYSIS, VISUALIZATION SOCIAL NETWORK ANALYSIS AND CORRELATION ANALYSIS, WERE USED TO ANALYZE THE PROCESS AND RESULTS OF ONLINE LEARNING. THE RESULTS REVEAL THE CHARACTERISTICS OF GROUP BEHAVIOR OF ONLINE LEARNERS AND HIGHLIGHT THE KEY FACTORS THAT INFLUENCE THE LEARNING PROCESS AND OUTCOMES OF INDIVIDUAL LEARNERS. FROM THE VIEW OF STUDENTS, THESE FACTORS INCLUDE THE LENGTH AND ALLOCATION OF ONLINE TIME, THE EFFECTIVE UTILIZATION OF RESOURCES, SOCIAL INTERACTION, ONLINE LEARNING SUPPORT AND SERVICES, ETC. FROM THE PERSPECTIVE OF TEACHERS, THE FACTORS INCLUDE THE MANAGEMENT OF ONLINE TEACHING, THE APPROPRIATENESS OF LEARNING RESOURCES, THE EFFECTIVENESS OF ONLINE INTERVENTION STRATEGIES, THE ACCURATE FEEDBACK FOR ONLINE LEARNERS, ETC. THEREFORE, LEARNING ANALYSIS TECHNOLOGY CAN NOT ONLY STANDARDIZE THE ASSESSMENT OF LEARNING OUTCOMES, BUT CAN ALSO FOCUS MORE ATTENTION ON THE STANDARDIZATION OF LEARNING PROCESS ASSESSMENT. IT ALSO IDENTIFIES THE MAIN FACTORS THAT AFFECT THE ONLINE LEARNING OUTCOMES AND THE GROUP CHARACTERISTICS OF ONLINE LEARNERS. AT THE SAME TIME, IT PROVIDES THE LEARNERS WITH PERSONALIZED LEARNING DIAGNOSIS REPORTS WHICH CAN BOTH HELP LEARNERS UNDERSTAND THEIR OWN LEARNING STATUS AND PROMOTE INSTRUCTORS' ACCURATE TEACHING AND REASONABLE EVALUATION.
TECHNIQUE PROGRESS IS DRIVING THE ADOPTION OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS IN EDUCATION RESEARCH AND EDUCATION ENVIRONMENT. A BIG CHALLENGE WHEN IMPLEMENTING THE METHODS OF THEM IS LACK OF EFFECTIVE KNOWLEDGE REPRESENTATION AND PROCESSING. IN THIS PAPER, WE DISCUSS THE FEATURES OF EDUCATION DATA USED IN EDUCATION RESEARCH AND DEMONSTRATES THE POSSIBILITY OF USING FUZZY SET AND ITS EXTENSION AS A TOOL TO GET RICHER INFORMATION AND INSIGHT FROM THE DATA.
ASSESSING THE QUALITY OF MOOCS IS AN IMPORTANT ISSUE FOR LEARNERS SINCE LEARNERS ARE PAYING FEES FOR ACCESSING THE CONTENT (E.G. GRADED ASSIGNMENTS), CERTIFICATES OF COMPLETION AND FOR COURSE CREDIT. ONE OF THE UNIQUE ADVANTAGES OF ONLINE COURSES IS THAT ALL THE CONTENT CAN BE ASSESSED AND ANALYZED EVEN BEFORE THE COURSES ARC RELEASED USING VARIOUS LEARNING ANALYTICAL AND NATURAL LANGUAGE PROCESSING TOOLS. HOWEVER, TO DATE THERE ARE FEW STUDIES IN THE LITERATURE PUBLISHED ON THE ANALYSIS OF MOOC CONTENT. FURTHERMORE, MOOC PROVIDERS EXPECT THE COURSE DEVELOPERS TO PERIODICALLY REVISE THEIR MOOCS. VARIOUS TYPES OF ANALYSIS THAT CAN BE DONE ON THE COURSE TEXT, VIDEO TRANSCRIPTS AND ASSESSMENTS SUCH AS READABILITY, LISTENABILITY, VIDEOLYTICS, AND TEXT ANALYSIS. BY ANALYZING THE COURSE CONTENT BEFORE ITS RELEASE, THE CONTENT CAN BE ADJUSTED TO TARGET VARIOUS LEARNERS. SUBSEQUENTLY, THE SAME TECHNIQUES CAN BE USED TO ANALYZE THE DISCUSSION BOARD POSTS AND POST-COURSE SURVEY TO IDENTIFY AREAS IN A COURSE THAT NEED TO BE MODIFIED IN TO ORDER TO IMPROVE THE COURSE QUALITY FOR SUBSEQUENT RELEASE. IN THIS PAPER NATURAL LANGUAGE PROCESSING AND MOOC ANALYTICS WERE APPLIED TO SEVERAL MOOCS TO IDENTIFY AREAS FOR REVISION TO ENHANCE THEIR QUALITY.
EVALUATING USER EXPERIENCE IS A CHALLENGING TASK, PARTICULARLY IN E-LEARNING. EXISTING E-LEARNING SYSTEMS ARE LIMITED IN THEIR ABILITY OF BEING EVALUATED BASED ON THE USER INTERFACES BECAUSE CURRENT EVALUATION APPROACHES ARE USUALLY EXPENSIVE IN TIME AND ORGANIZATION AND REQUIRE ACTIVE USERS' PARTICIPATION. MOREOVER, A USABILITY ASSESSMENT IS NEEDED WHENEVER A NEW VERSION OF THE "USER INTERFACE TAILORED FOR A GIVEN TYPE OF DEVICE (E.G. LAPTOP, TABLET, SMARTPHONE) IS DEVELOPED". IN THIS PAPER, WE GET AROUND THE PROBLEM LEVERAGING ON THE INCREASING ADOPTION OF ANALYTICS TOOLS IN E-LEARNING AND ON LOGS TRANSPARENTLY TRACKED BY E-LEARNING PLATFORMS. WE INTRODUCE AN AUTOMATED ANALYTICS APPROACH AIMING AT ASSESSING THE USABILITY OF BOTH DESKTOP AND MOBILE USER INTERFACES OF A LEARNING MANAGEMENT SYSTEM THROUGH SPECIFIC NATIVE INDICATORS (E.G. EFFICIENCY AND SATISFACTION). THEY ARE DEFINED AS COMPARABLE SCORES AND CALCULATED AUTOMATICALLY BASED ON THE TRACKED LOG FILES. IN ORDER TO PUT THE PROPOSED APPROACH INTO PRACTICE, WE IMPLEMENTED IT ON THE MOODLE LMS. OUR CONTRIBUTION PROMISES TO REDUCE BOTH TIME AND COST FOR USABILITY ASSESSMENT OF USER INTERFACES IN E-LEARNING, WHILE ENSURING ADAPTABILITY TO DIFFERENT DEVICES AND SYSTEMS.
TECHNOLOGICAL ADVANCEMENTS HAVE GENERATED A STRONG INTEREST IN EXPLORING LEARNER BEHAVIOR DATA THROUGH LEARNING ANALYTICS TO PROVIDE BOTH LEARNER AND INSTRUCTOR WITH PROCESS-ORIENTED FEEDBACK IN THE FORM OF DASHBOARDS. HOWEVER, LITTLE IS KNOWN ABOUT THE TYPOLOGY OF DASHBOARD FEEDBACK RELEVANT FOR DIFFERENT LEARNING GOALS, LEARNERS AND TEACHERS. WHILE MOST DASHBOARDS AND THE FEEDBACK THAT THEY GIVE ARE BASED ONLY ON LEARNER PERFORMANCE INDICATORS, RESEARCH SHOWS THAT EFFECTIVE FEEDBACK NEEDS ALSO TO BE GROUNDED IN THE REGULATORY MECHANISMS UNDERLYING LEARNING PROCESSES AND AN AWARENESS OF THE LEARNER'S LEARNING GOALS. THE DESIGN ARTEFACT PRESENTED IN THIS ARTICLE USES A CONCEPTUAL MODEL THAT VISUALIZES THE RELATIONSHIPS BETWEEN DASHBOARD DESIGN AND THE LEARNING SCIENCES TO PROVIDE COGNITIVE AND BEHAVIORAL PROCESS ORIENTED FEEDBACK TO LEARNERS AND TEACHERS TO SUPPORT REGULATION OF LEARNING. A PRACTICAL CASE EXAMPLE IS GIVEN THAT DEMONSTRATES HOW THE IDEAS PRESENTED IN THE PAPER CAN BE DEPLOYED IN THE CONTEXT OF A LEARNING DASHBOARD. THE CASE EXAMPLE USES SEVERAL ANALYTICS/VISUALIZATION TECHNIQUES BASED ON EMPIRICAL EVIDENCE FROM EARLIER RESEARCH THAT SUCCESSFULLY TESTED THESE TECHNIQUES IN VARIOUS LEARNING CONTEXTS. (C) 2018 ELSEVIER LTD. ALL RIGHTS RESERVED.
LEARNING ANALYTICS INVOLVE THE PROCESS OF GATHERING DATA ABOUT STUDENTS AND USING THE INFORMATION TO INTERVENE IN THEIR LIVES TO IMPROVE LEARNING AND INSTITUTIONAL OUTCOMES. MANY ACADEMIC LIBRARIES NOW PARTICIPATE IN LEARNING ANALYTICS. HOWEVER, SUCH PRACTICES RAISE PRIVACY AND INTELLECTUAL FREEDOM ISSUES DUE TO SENSITIVE DATA PRACTICES. BUT, FEW RESEARCH STUDIES ADDRESS HOW LIBRARY PRACTITIONERS PERCEIVE THE ETHICAL ISSUES. THIS ARTICLE DOES SO BY ANALYZING INTERVIEWS WITH LIBRARY PRACTITIONERS. THE FINDINGS SUGGEST THAT LIBRARY PROFESSIONALS SEEK ETHICAL "BRIGHT LINES"-THAT IS, CLEARLY DEFINED STANDARDS-WHERE FEW EXIST AND THAT ETHICAL GUIDANCE IS LIMITED. THOUGH LIBRARY PRACTITIONERS RECOGNIZE THAT DATA PRACTICES SHOULD BE SCOPED AND JUSTIFIED, THEIR EFFORTS HAVE COME UNDER SEVERE SCRUTINY-AND SOMETIMES HARASSMENT-FROM THEIR PROFESSIONAL PEERS. THE ARTICLE HIGHLIGHTS WHY ETHICAL DISSONANCE HAS EMERGED IN THE PROFESSION REGARDING LEARNING ANALYTICS AND HOW LIBRARY PRACTICES MIGHT BETTER ACCOUNT FOR THE HARMS AND BENEFITS OF LEARNING ANALYTICS.
THE FLIPPED CLASSROOM MODEL IS A FORM OF BLENDED LEARNING IN WHICH DELIVERY OF CONTENT OCCURS WITH ONLINE MATERIALS, AND FACE-TO-FACE MEETINGS ARE USED FOR TEACHER-GUIDED PRACTICE. IT IS IMPORTANT THAT TEACHERS STAY UP TO DATE WITH THE ACTIVITIES STUDENTS ENGAGE IN, WHICH MAY BE ACCOMPLISHED WITH THE HELP OF LEARNING ANALYTICS (LA). THIS STUDY INVESTIGATED UNIVERSITY TEACHERS' PERCEPTIONS OF WHETHER WEEKLY LA REPORTS THAT SUMMARIZED STUDENT ACTIVITIES SUPPORTED THEIR TEACHING ACTIVITIES (N = 7). THE TEACHERS REPORTED USING THE LA REPORTS FOR DIAGNOSING AND INTERVENING DURING STUDENT ACTIVITIES, AND THAT THE REPORTS ENCOURAGED THEM TO START INTERACTION WITH STUDENTS. TEACHERS DID SOMETIMES FIND IT DIFFICULT TO CONNECT THE INFORMATION FROM THE LA REPORTS TO CONCRETE INTERVENTIONS, WHICH WAS PARTLY DEPENDENT ON THE LEVEL OF THE TEACHER'S EXPERIENCE. LA REPORTS MIGHT SUPPORT TEACHERS FURTHER BY NOT ONLY OFFERING INFORMATION, BUT ALSO BY SUGGESTING INTERVENTIONS.
SEAMLESS LEARNING HAS BEEN RECOGNIZED AS AN EFFECTIVE LEARNING APPROACH ACROSS VARIOUS DIMENSIONS INCLUDING FORMAL AND INFORMAL LEARNING CONTEXTS, INDIVIDUAL AND SOCIAL LEARNING, AND PHYSICAL WORLD AND CYBERSPACE. WITH THE EMERGENCE OF SEAMLESS LEARNING, MAJORITY OF THE CURRENT RESEARCH FOCUS ON REALIZING A SEAMLESS LEARNING ENVIRONMENT AT SCHOOL OR UNIVERSITY. HOWEVER, THE UTILIZATION OF THE COLLECTED LEARNING LOGS STILL REMAINS A CHALLENGE YET TO BE EXPLORED. IN THIS STUDY, AN E-BOOK WITH UBIQUITOUS LEARNING SYSTEM CALLED SCROLL IS DEVELOPED TO COLLECT AND ANALYZE LEARNING LOGS IN THE SEAMLESS LEARNING ENVIRONMENT. MOREOVER, THIS PAPER PRESENTS OUR ANALYTICS IN CONTRIBUTION TO BRIDGING THE LEARNING BETWEEN EBOOK LEARNING AND REAL-LIFE LEARNING. AN EXPERIMENT WAS CONDUCTED TO EVALUATE (1) WHETHER VASCORLL 2.0 (VISUALIZATION AND ANALYSIS SYSTEM FOR CONNECTING RELATIONSHIPS OF LEARNING LOGS) IS EFFECTIVE IN CONNECTING THE WORDS LEARNED THROUGH EBOOK TO THOSE LEARNED FROM REAL-LIFE, AND (2) WHICH SOCIAL NETWORK CENTRALITY IS THE MOST 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 2.0. FURTHERMORE, THE BETWEENNESS CENTRALITY WAS FOUND USEFUL IN FINDING CENTRAL WORDS THAT BRIDGE EBOOK AND REAL-LIFE LEARNING.
THE MAJORITY OF APPLICATIONS AND PRODUCTS THAT USE LEARNING ANALYTICS TO UNDERSTAND AND IMPROVE LEARNING EXPERIENCES ASSUME THE CREATION OF ACTIONABLE ITEMS THAT WILL AFFECT STUDENTS THROUGH AN INTERMEDIARY. MUCH LESS FOCUS IS DEVOTED TO EXPLORING HOW TO PROVIDE INSIGHT DIRECTLY TO STUDENTS. FURTHERMORE, STUDENT ENGAGEMENT HAS ALWAYS BEEN A RELEVANT ASPECT TO INCREASE THE QUALITY OF A LEARNING EXPERIENCE. LEARNING ANALYTICS TECHNIQUES CAN BE USED TO PROVIDE REAL-TIME INSIGHT TIGHTLY INTEGRATED WITH THE LEARNING OUTCOMES DIRECTLY TO THE STUDENTS. THIS PAPER DESCRIBES A CASE STUDY DEPLOYED IN A FIRST YEAR ENGINEERING COURSE USING A FLIPPED LEARNING STRATEGY TO EXPLORE THE BEHAVIOR OF STUDENTS INTERACTING WITH A DASHBOARD UPDATED IN REAL TIME PROVIDING INDICATORS OF THEIR ENGAGEMENT WITH THE COURSE ACTIVITIES. THE RESULTS SHOW DIFFERENT PATTERNS OF USE AND THEIR EVOLUTION THROUGHOUT THE EXPERIENCE AND SHED SOME LIGHT ON HOW STUDENTS PERCEIVED THIS RESOURCE.
UNTIL NOW, ETWINNING SUPPORTS 160,000 EUROPEAN TEACHERS IS THEIR CONTINUOUS PROFESSIONAL DEVELOPMENT (CPD). ONLY COMPUTATIONAL TOOLS OF LEARNING ANALYTICS AT LARGE LIKE SOCIAL NETWORK ANALYSIS AND PERFORMANCE MEASUREMENTS ARE OFFERING DEEP INSIGHT INTO COMPETENCE DEVELOPMENT IN THE ANONYMOUS DATA SETS FROM THE ETWINNING DATABASE. TO THIS END, WE HAVE DEVELOPED CAFE, A LEARNING ANALYTICS TOOL FOR ETWINNING DATA. AFTER GETTING AN IDENTIFICATION NUMBER, TEACHERS CAN MONITOR THEIR PERFORMANCE IN ETWINNING WITH CAFE. TEACHERS MAY ALSO DRAW CONCLUSIONS AND GAIN INSIGHT INTO THEIR COMPETENCES AND THEIR CONTINUOUS PROFESSIONAL DEVELOPMENT. WE EVALUATED CAFE WITH TEACHERS IN ETWINNING WORKSHOPS IN GERMANY.
THE ARTICLE EXAMINES THE RESULTS OF THE STUDY ABOUT THE IMPLEMENTATION OF LEARNING ANALYTICS WITHIN EIGHT DIVISIONS OF THE OPEN UNIVERSITY AND DISTANCE EDUCATION SYSTEM OF THE UNAM, AS A WAY TO ANALYZE DIGITAL CULTURE AS DIGITAL PRAXIS. FOR THIS PURPOSE, AN INSTRUMENT WAS DESIGNED WITH A FOCUS ON COMPLEX SYSTEMS (INTERACTIONS, CONNECTIONS AND, AGENTS INTEGRATION) THAT WOULD ALLOW THE ASSESSMENT OF THE STATE OF THE ANALYTICS FROM A SCALE OF FIVE LEVELS BASED ON THE FIELDS OF KNOWLEDGE PROPOSED BY CHATTI, DYCKHOFF, SCHROEDER AND THUS, H. (2012). IT PROPOSED INITIALLY THAT UNIVERSITIES HAVE TRADITIONALLY COVERED THE BASIC LEVEL THAT IS THE MANAGEMENT OF DATA FOR INTERNAL INFORMATIONAL PURPOSES; HOWEVER, EDUCATIONAL STAKEHOLDERS HAVE BEGUN TO ASSIGN A BROADER AND MORE COMPREHENSIVE SENSE TO THE USE OF DATA IN ORDER TO KNOW THEIR POPULATION AND SOLVE MORE COMPLEX PROBLEMS. THE CONCLUSION OF THE TEXT FOCUSES ON SHOWING THAT THE DIVISIONS THAT WERE ANALYZED ARE IN THE POSSIBILITY OF IMPROVING DATA EXTRACTION METHODOLOGIES GIVEN THEIR EXPERIENCE AND THAT IN SOME CASES THERE COULD BE TRIALS TO GENERATE RECOMMENDATION SYSTEMS BASED ON PROFILES AND PATTERNS DETECTED.
THE CURRENT PAPER PRESENTS A CASE STUDY FOR EVALUATING AN INTERACTION ANALYSIS (IA) TOOLKIT FOR BLOGS, CALLED GIANT (GRAPHICAL INTERACTION ANALYSIS TOOL). THE PAPER FOCUSES ON THE PERSPECTIVE OF THE TEACHERS AND INVESTIGATES THE HYPOTHESIS THAT IA TECHNIQUES COULD BE IMPLEMENTED IN EDUCATIONAL GROUP-BLOGGING SYSTEMS, AS A TOOL FOR SUPPORTING TEACHERS. THE MAIN OBJECTIVE OF THE STUDY IS TO PROVIDE AN INDICATION THAT TEACHERS COULD BE FACILITATED, IN THE PROCESS OF EVALUATING AND ASSESSING STUDENTS' INTERACTION AND PARTICIPATION, THROUGH THE REAL-TIME GENERATED GRAPHS FROM THE GIANT TOOLKIT. THIS PAPER ALSO PROVIDES FURTHER EVALUATION EVIDENCE FOR THE GIANT TOOLKIT, FROM A TEACHERS' POINT OF VIEW, IN TERMS OF OVERALL TEACHER SATISFACTION, PERCEIVED USEFULNESS AND PERCEIVED EASE OF LEARNING. PARTICULARLY, 32 SECONDARY EDUCATION TEACHERS' USED AND EVALUATED GIANT DURING A COLLABORATIVE BLOG-BASED ACTIVITY WITH STUDENTS FROM A LITERATURE COURSE. OVERALL, TEACHERS WERE TO A LARGE EXTENT SATISFIED WITH THEIR FACILITATION FROM THE GIANT TOOLKIT, DURING THEIR BLOGGING EXPERIENCE, RATING THE AUTOMATICALLY GENERATED IA GRAPHS AS USEFUL, EASY TO UNDERSTAND AND DECODE.
THIS ARTICLE IS A COMPREHENSIVE LITERATURE REVIEW OF STUDENT-FACING LEARNING ANALYTICS REPORTING SYSTEMS THAT TRACK LEARNING ANALYTICS DATA AND REPORT IT DIRECTLY TO STUDENTS. THIS LITERATURE REVIEW BUILDS ON FOUR PREVIOUSLY CONDUCTED LITERATURE REVIEWS IN SIMILAR DOMAINS. OUT OF THE 945 ARTICLES RETRIEVED FROM DATABASES AND JOURNALS, 93 ARTICLES WERE INCLUDED IN THE ANALYSIS. ARTICLES WERE CODED BASED ON THE FOLLOWING FIVE CATEGORIES: FUNCTIONALITY, DATA SOURCES, DESIGN ANALYSIS, STUDENT PERCEPTIONS, AND MEASURED EFFECTS. BASED ON THIS REVIEW, WE NEED RESEARCH ON LEARNING ANALYTICS REPORTING SYSTEMS THAT TARGETS THE DESIGN AND DEVELOPMENT PROCESS OF REPORTING SYSTEMS, NOT ONLY THE FINAL PRODUCTS. THIS DESIGN AND DEVELOPMENT PROCESS INCLUDES NEEDS ANALYSES, VISUAL DESIGN ANALYSES, INFORMATION SELECTION JUSTIFICATIONS, AND STUDENT PERCEPTION SURVEYS. IN ADDITION, EXPERIMENTS TO DETERMINE THE EFFECT OF THESE SYSTEMS ON STUDENT BEHAVIOR, ACHIEVEMENT, AND SKILLS ARE NEEDED TO ADD TO THE SMALL EXISTING BODY OF EVIDENCE. FURTHERMORE, EXPERIMENTAL STUDIES SHOULD INCLUDE USABILITY TESTS AND METHODOLOGIES TO EXAMINE STUDENT USE OF THESE SYSTEMS, AS THESE FACTORS MAY AFFECT EXPERIMENTAL FINDINGS. FINALLY, OBSERVATIONAL STUDY METHODS, SUCH AS PROPENSITY SCORE MATCHING, SHOULD BE USED TO INCREASE STUDENT ACCESS TO THESE SYSTEMS BUT STILL RIGOROUSLY MEASURE EXPERIMENTAL EFFECTS.
RECENT DEVELOPMENTS IN EDUCATIONAL TECHNOLOGIES HAVE PROVIDED A VIABLE SOLUTION TO THE CHALLENGES ASSOCIATED WITH SCALING PERSONALISED FEEDBACK TO STUDENTS. HOWEVER, THERE IS CURRENTLY LITTLE EMPIRICAL EVIDENCE ABOUT THE IMPACT SUCH SCALED FEEDBACK HAS ON STUDENT LEARNING PROGRESS AND STUDY BEHAVIOUR. THIS PAPER PRESENTS THE FINDINGS OF A STUDY THAT LOOKED AT THE IMPACT OF A LEARNING ANALYTICS (LA)-BASED FEEDBACK SYSTEM ON STUDENTS' SELFREGULATED LEARNING AND ACADEMIC ACHIEVEMENT IN A LARGE, FIRST-YEAR UNDERGRADUATE COURSE. USING THE COPES MODEL OF SELF-REGULATED LEARNING (SRL), WE ANALYSED THE LEARNING OPERATIONS OF STUDENTS, BY WAY OF LOG DATA FROM THE LEARNING MANAGEMENT SYSTEM AND E-BOOK, AS WELL AS THE PRODUCTS OF SRL, NAMELY, PERFORMANCE ON COURSE ASSESSMENTS, FROM THREE YEARS OF COURSE OFFERINGS. THE LATEST COURSE OFFERING INVOLVED AN INTERVENTION CONDITION THAT MADE USE OF AN EDUCATIONAL TECHNOLOGY TO PROVIDE LA-BASED PROCESS FEEDBACK. PROPENSITY SCORE MATCHING WAS EMPLOYED TO MATCH A CONTROL GROUP TO THE STUDENT COHORT ENROLLED IN THE LATEST COURSE OFFERING, CREATING TWO EQUAL-SIZED GROUPS OF STUDENTS WHO RECEIVED THE FEEDBACK (THE EXPERIMENTAL GROUP) AND THOSE WHO DID NOT (THE CONTROL GROUP). GROWTH MIXTURE MODELLING AND MIXED BETWEEN-WITHIN ANOVA WERE ALSO EMPLOYED TO IDENTIFY DIFFERENCES IN THE PATTERNS OF ONLINE SELF-REGULATED LEARNING OPERATIONS OVER THE COURSE OF THE SEMESTER. THE RESULTS SHOWED THAT THE EXPERIMENTAL GROUP SHOWED SIGNIFICANTLY DIFFERENT PATTERNS IN THEIR LEARNING OPERATIONS AND PERFORMED BETTER IN TERMS OF FINAL GRADES. MOREOVER, THERE WAS NO DIFFERENCE IN THE EFFECT OF FEEDBACK ON FINAL GRADES AMONG STUDENTS WITH DIFFERENT PRIOR ACADEMIC ACHIEVEMENT SCORES, INDICATING THAT THE LA-BASED FEEDBACK DEPLOYED IN THIS COURSE IS ABLE TO SUPPORT STUDENTS' LEARNING, REGARDLESS OF PRIOR ACADEMIC STANDING.
DUE TO THE FUNDAMENTAL DIFFERENCES BETWEEN TRADITIONAL EDUCATION AND MASSIVE OPEN ONLINE COURSES (MOOCS), AND BECAUSE OF THE EVER-INCREASING POPULARITY OF THE LATTER, MORE RESEARCH IS NEEDED TO UNDERSTAND CURRENT AND FUTURE TRENDS IN MOOCS. ALTHOUGH RESEARCH IN THE FIELD HAS GROWN RAPIDLY IN RECENT YEARS, ONE OF THE MAIN CHALLENGES FACING RESEARCHERS REMAINS THE COMPLEXITY AND MESSINESS OF THE DATA. THEREFORE, IT IS IMPERATIVE TO PROVIDE TOOLS THAT PAVE THE WAY FOR MORE RESEARCH ON THIS NEW SUBJECT. THIS PAPER INTRODUCES A PACKAGE CALLED CRSRA BASED ON THE STATISTICAL SOFTWARE R TO HELP TIDY AND PERFORM PRELIMINARY ANALYSIS ON MASSIVE LOADS OF DATA PROVIDED BY COURSERA. THE ADVANTAGES OF THE PACKAGE ARE AS FOLLOWS: (A) FASTER LOADING AND ORGANIZING OF DATA FOR ANALYSIS, (B) AN EFFICIENT METHOD FOR COMBINING DATA FROM MULTIPLE COURSES AND EVEN FROM ACROSS INSTITUTIONS, AND (C) PROVISION OF A SET OF FUNCTIONS FOR ANALYZING STUDENT BEHAVIOURS.
IN THIS PAPER, WE DEVELOP PREDICTIVE LEARNING ANALYTICS (PLA) METHODOLOGY FOR LEARNER VIDEO-WATCHING BEHAVIOR IN MASSIVE OPEN ONLINE COURSES (MOOCS). AFTER DEFINING FEATURES TO SUMMARIZE SUCH BEHAVIOR FROM CLICKSTREAM MEASUREMENTS, WE PERFORM A STATISTICAL ANALYSIS OF A REAL-WORLD MOOC DATASET AND UNCOVER SEVERAL INTERESTING RELATIONSHIPS BETWEEN THE DIFFERENT FEATURES. MOTIVATED BY THIS ANALYSIS, WE PROPOSE THREE ALGORITHMS FOR PREDICTING FUTURE VIDEO-WATCHING BEHAVIOR, WHICH INCORPORATE BIASES FOR LEARNERS AND VIDEOS, COLLABORATIVE FILTERING ACROSS VIDEOS, AND REGULARIZATION TO REDUCE OVERFITTING. THROUGH EVALUATION ON OUR DATASET, WE FIND THAT THE PREDICTORS OBTAIN LOW RMSE OVERALL, AND THAT AUGMENTING THE BIAS PREDICTOR WITH EITHER COLLABORATIVE FILTERING OR REGULARIZATION IMPROVES PREDICTION QUALITY IN EIGHT OUT OF NINE CASES.
IN THIS LEARNING ANALYTICS STUDY, WE ATTEMPT TO UNDERSTAND THE ROLE OF TEMPORALITY MEASURES FOR THE PREDICTION OF ACADEMIC PERFORMANCE. THE STUDY INCLUDED FOUR ONLINE COURSES OVER A FULL-YEAR DURATION. TEMPORALITY WAS STUDIED ON DAILY, WEEKLY, COURSE-WISE AND YEAR-WISE. VISUALISING THE ACTIVITIES HAS HIGHLIGHTED CERTAIN PATTERNS. ON THE WEEK LEVEL, EARLY PARTICIPATION WAS A CONSISTENT PREDICTOR OF HIGH ACHIEVEMENT. THIS FINDING WAS CONSISTENT FROM COURSE TO COURSE AND DURING MOST PERIODS OF THE YEAR. ON COURSE LEVEL, HIGH ACHIEVERS WERE ALSO LIKELY TO PARTICIPATE EARLY AND CONSISTENTLY. WITH A FOCUS ON TEMPORAL MEASURES, WE WERE ABLE TO PREDICT HIGH ACHIEVERS WITH REASONABLE ACCURACY IN EACH COURSE. THESE FINDINGS HIGHLIGHT THE IDEA THAT TEMPORALITY DIMENSION IS A SIGNIFICANT SOURCE OF INFORMATION ABOUT LEARNING PATTERNS AND HAS THE POTENTIAL TO INFORM EDUCATORS ABOUT STUDENTS' ACTIVITIES AND TO IMPROVE THE ACCURACY AND REPRODUCIBILITY OF PREDICTING STUDENTS' PERFORMANCE.
GAMES HAVE BEEN USED BY TEACHERS AS A SUPPORT TOOL TO ENGAGE STUDENTS IN LEARNING TASKS. AS THEY OFTEN RECORD STUDENT'S PERFORMANCE AS LEARNING PROGRESSES, IT INTERESTING AND USEFUL TO DISCUSS HOW THAT INFORMATION CAN BE USED TO ASSESS LEARNING AND TO IMPROVE THE LEARNING EXPERIENCE. FOR INSTANCE, TEACHERS CAN USE THAT INFORMATION TO GIVE PERSONALIZED ATTENTION IN CLASSES. IN COMPUTER PROGRAMMING LEARNING, GAMES CAN PROVIDE AN ALTERNATIVE WAY TO INTRODUCE CONCEPTS AND, MAINLY, TO PRACTICE THEM. THIS PAPER PROPOSES A MODEL TO IDENTIFY THE STUDENTS' PROGRESS CONSIDERING THEIR PERFORMANCE IN PROGRAMMING TASKS. THE MODEL IS DEMONSTRATED BY AN IMPLEMENTATION IN A CASUAL COMPUTER PROGRAMMING SERIOUS GAME.
PLACING EDUCATIONAL DATA IN THE HANDS OF FACULTY AND ALLOWING THEM TO EXPLORE THESE DATA HAS BEEN OF INTEREST FOR MANY STAKEHOLDERS IN THE PAST FEW YEARS, WITH THE HOPES THAT FACULTY WILL USE THESE DATA TO IMPROVE THE EDUCATIONAL EXPERIENCE FOR STUDENTS. ALTHOUGH THESE DATA HAVE THE POTENTIAL TO BE POWERFUL FOR IMPROVING STEM EDUCATION, DATA ARE ONLY AS GOOD AS THEIR ANALYSES AND APPLICATION. USING 14 INTERVIEWS CONDUCTED AT ONE INSTITUTION, WE STUDIED WHAT COMPELS FACULTY TO SEEK OUT UNIVERSITY-COLLECTED EDUCATIONAL DATA, WHAT CHALLENGES THEY FACE, AND SUGGESTIONS THEY HAVE FOR IMPROVING DATA USAGE. OUR SAMPLE CONSISTED PRIMARILY OF THOSE ALREADY INVOLVED WITH EDUCATION REFORM AND WERE USING LEARNING ANALYTICS DATA TO INFORM THOSE PROJECTS. CHALLENGES INCLUDED UNFAMILIARITY WITH QUANTITATIVE EDUCATION DATA AND UNAWARENESS OF THE POSSIBILITIES FOR LEARNING ANALYTICS. WE RECOMMEND THAT STRATEGIC, THOUGHTFUL ADVERTISING AND ALREADY-EXISTING RESOURCES BE USED TO HELP MITIGATE CHALLENGES.
THIS PAPER PRESENTS LISSA ("LEARNING DASHBOARD FOR INSIGHTS AND SUPPORT DURING STUDY ADVICE" ), A LEARNING ANALYTICS DASHBOARD DESIGNED, DEVELOPED, AND EVALUATED IN COLLABORATION WITH STUDY ADVISERS. THE OVERALL OBJECTIVE IS TO FACILITATE COMMUNICATION BETWEEN STUDY ADVISERS AND STUDENTS BY VISUALIZING GRADE DATA THAT IS COMMONLY AVAILABLE IN ANY INSTITUTION. MORE SPECIFICALLY, THE DASHBOARD ATTEMPTS TO SUPPORT THE DIALOGUE BETWEEN ADVISER AND STUDENT THROUGH AN OVERVIEW OF STUDY PROGRESS, PEER COMPARISON, AND BY TRIGGERING INSIGHTS BASED ON FACTS AS A STARTING POINT FOR DISCUSSION AND ARGUMENTATION. WE REPORT ON THE ITERATIVE DESIGN PROCESS AND EVALUATION RESULTS OF A DEPLOYMENT IN 97 ADVISING SESSIONS. WE HAVE FOUND THAT THE DASHBOARD SUPPORTS THE CURRENT ADVISER-STUDENT DIALOGUE, HELPS THEM MOTIVATE STUDENTS, TRIGGERS CONVERSATION, AND PROVIDES TOOLS TO ADD PERSONALIZATION, DEPTH, AND NUANCE TO THE ADVISING SESSION. IT PROVIDES INSIGHTS AT A FACTUAL, INTERPRETATIVE, AND REFLECTIVE LEVEL AND ALLOWS BOTH ADVISER AND STUDENT TO TAKE AN ACTIVE ROLE DURING THE SESSION.
LEARNING ANALYTICS (LA) OFFER BENEFITS AND CHALLENGES FOR ONLINE LEARNING, BUT PRIOR TO COLLECTING DATA ON HIGH-STAKES SUMMATIVE ASSESSMENTS AS PROOF OF STUDENT LEARNING, LA RESEARCHERS SHOULD ENGAGE INSTRUCTORS AS PARTNERS TO ENSURE THE QUALITY OF COURSE MATERIALS THROUGH THE FORMATIVE EVALUATION OF INDIVIDUAL ITEMS (BIENKOWSKI ET AL., 2012; DYCKHOFF ET AL., 2013; MANTRA, 2019; VAN LEEUWEN, 2015). THIS EXPLORATORY STUDY DESCRIBES A VISUALIZATION TOOL THAT PROVIDES ACTIONABLE DATA FOR EARLY INTERVENTION WITH STUDENTS, AND ACTIONABLE DATA HIGHLIGHTING ODD PATTERNS IN STUDENT RESPONSES (CHATTI ET AL., 2012; GIBSON & DE FREITAS, 2016; MORGENTHALER, 2009; PEI ET AL., 2017), THUS ALLOWING INSTRUCTORS TO MAKE FULL USE OF THEIR TEACHING SKILLSET IN THE ONLINE ENVIRONMENT AS THEY WOULD IN A TRADITIONAL CLASSROOM (DAVIS & VARMA, 2008; DUNBAR, ET AL., 2014; GROSSMAN & THOMPSON, 2008; LOCKYER ET AL., 2013). TO ANSWER RESEARCH QUESTIONS RELATED TO THE VALUE OF LEARNING ANALYTICS AND THEIR USE IN MAKING INFORMED DECISIONS ABOUT STUDENT LEARNING, A VISUALIZATION TOOL WAS DEVELOPED FOR AND PILOTED IN AN ONLINE FRENCH COURSE. THE FINDINGS SUGGEST THAT USING THIS TOOL CAN LEAD NOT ONLY TO INTERVENTION WITH LOW- ACHIEVING STUDENTS BUT CAN ALSO DETERMINE IF STUDENTS STRUGGLE DUE TO POOR COURSE MATERIALS.
THE APPLICATION OF THE INFORMATION AND COMMUNICATION TECHNOLOGIES TO TEACHING AND LEARNING PROCESSES IS LINKED TO THE DEVELOPMENT OF NEW TOOLS AND SERVICES THAT CAN HELP STUDENTS AND TEACHERS. LEARNING PLATFORMS ARE A CLEAR EXAMPLE OF THIS. THEY ARE VERY POPULAR TOOLS IN ELEARNING CONTEXTS AND PROVIDE DIFFERENT TYPES OF LEARNING APPLICATIONS AND SERVICES. IN ADDITION, THESE ENVIRONMENTS ALSO REGISTER MOST OF THE INTERACTIONS BETWEEN THE LEARNING PROCESS STAKEHOLDERS AND THE SYSTEM. THIS INFORMATION COULD POTENTIALLY BE USED TO MAKE DECISIONS, BUT USUALLY IT IS STORED AS RAW DATA, WHICH IS VERY DIFFICULT TO UNDERSTAND. THIS PAPER PRESENTS A SYSTEM THAT EMPLOYS VISUAL LEARNING ANALYTIC TECHNIQUES TO FACILITATE THE EXPLOITATION OF THAT INFORMATION. THE SYSTEM PRESENTED INCLUDES SEVERAL TOOLS THAT MAKE POSSIBLE TO EXPLORE ISSUES, SUCH AS WHEN INTERACTION IS CARRIED OUT, WHICH CONTENTS ARE THE MOST IMPORTANT FOR USERS, AND HOW THEY INTERACT WITH OTHERS. THE SYSTEM WAS TESTED IN THE CONTEXT OF A SOFTWARE ENGINEERING SUBJECT, CONSIDERING THE STORED LOGS OF FIVE ACADEMIC YEARS. FROM THIS ANALYSIS, IT IS POSSIBLE TO SEE HOW VISUAL ANALYTICS CAN HELP DECISION-MAKING, AND IN THIS CONTEXT, HOW IT HELPS TO IMPROVE EDUCATIONAL PROCESSES.
EFFECTIVE COLLABORATIVE LEARNING IS RARELY A SPONTANEOUS PHENOMENON. IN FACT, IT REQUIRES THAT A SET OF CONDITIONS ARE MET. AMONG THESE CENTRAL CONDITIONS ARE GROUP FORMATION, SIZE AND INTERACTION DYNAMICS. WHILE PREVIOUS RESEARCH HAS DEMONSTRATED THAT SIZE MIGHT HAVE DETRIMENTAL EFFECTS ON COLLABORATIVE LEARNING, FEW HAVE EXAMINED HOW SOCIAL DYNAMICS DEVELOP DEPENDING ON GROUP SIZE. THIS LEARNING ANALYTICS PAPER REPORTS ON A STUDY THAT ASKS: HOW IS GROUP SIZE AFFECTING SOCIAL DYNAMICS AND PERFORMANCE OF COLLABORATING STUDENTS? IN CONTRAST TO PREVIOUS RESEARCH THAT WAS MAINLY QUALITATIVE AND ASSESSED A LIMITED SAMPLE SIZE, OUR STUDY INCLUDED 23,979 INTERACTIONS FROM 20 COURSES, 114 GROUPS AND 974 STUDENTS AND THE GROUP SIZE RANGED FROM 7 TO 15 IN THE CONTEXT OF ONLINE PROBLEM-BASED LEARNING. TO CAPTURE THE SOCIAL DYNAMICS, WE APPLIED SOCIAL NETWORK ANALYSIS FOR THE STUDY OF HOW GROUP SIZE AFFECTS COLLABORATIVE LEARNING. IN GENERAL, WE CONCLUDE THAT LARGER GROUPS ARE ASSOCIATED WITH DECREASED PERFORMANCE OF INDIVIDUAL STUDENTS, POORER AND LESS DIVERSE SOCIAL INTERACTIONS. A HIGH GROUP SIZE LED TO A LESS COHESIVE GROUP, WITH LESS EFFICIENT COMMUNICATION AND LESS INFORMATION EXCHANGE AMONG MEMBERS. LARGE GROUPS MAY FACILITATE ISOLATION AND INACTIVITY OF SOME STUDENTS, WHICH IS CONTRARY TO WHAT COLLABORATIVE LEARNING IS ABOUT.
FLIPPED CLASSROOM IS DESIGNED TO ENRICH STUDENTS' LEARNING EXPERIENCE THROUGH ACTIVE LEARNING ACTIVITIES IN THE CLASSROOM. TO PREPARE THE STUDENTS FOR THESE ACTIVE LEARNING ACTIVITIES, THE TEACHERS TYPICALLY PROVIDE PRE-RECORDED VIDEO LECTURES AND VARIOUS COMPUTER-MEDIATED LEARNING ACTIVITIES FOR THE STUDENTS TO GO THROUGH ONLINE BEFORE THE LESSONS. WHEN STUDENTS MEET WITH THE TEACHERS FACE-TO-FACE, THEY ARE ENGAGED WITH INTERACTIVE AND COLLABORATIVE LEARNING TASKS. THIS FLIPPED LEARNING IS FURTHER FACILITATED BY MOBILE TECHNOLOGY AS THE STUDENTS CAN ACCESS THESE LEARNING MATERIALS ANYTIME ANYWHERE ON THEIR MOBILE DEVICES WITHIN AND OUTSIDE THE CLASSROOM. THIS CHAPTER DESCRIBES A CONCEPTUAL MODEL AND AN INITIATIVE OF USING MOBILE LEARNING ANALYTICS TO UNDERSTAND THE LEARNERS' BEHAVIOURS INSIDE AND OUTSIDE THE CLASSROOM UNDER FLIPPED LEARNING APPROACH. EMPIRICAL DATA ON THE STUDENTS' PERCEPTIONS OF THIS INITIATIVE IS PRESENTED AS WELL TO SUPPLEMENT THE ANALYSIS. ISSUES AND IMPLICATIONS FOR DESIGNING FLIPPED LEARNING WITH MOBILE TECHNOLOGY AND LEARNING ANALYTICS ARE DISCUSSED.
THIS WORK AIMS TO EVALUATE THE USE OF LEARNING ANALITYCS (LA) IN HIGHER EDUCATION; STUDENTS, AND ATTRIBUTES SUCH AS: PROFILE, INTERACTIONS IN A VIRTUAL LEARNING ENVIRONMENT LEARNING - FOR THIS USE BUSINESS INTELLIGENCE PARADIGM IN ORDER TO EXPLORE AND EXPLOIT THE DATA FROM ONE OF THE ACTORS IN THE EDUCATIONAL PROCESS IS ANALYZED, TEST SCORES, AMONG OTHERS, WHICH WILL CONTRIBUTE TO THEIR EDUCATIONAL SUCCESS. IN PARTICULAR, THIS PAPER TRIES TO ANSWER THE FOLLOWING SPECIFIC OBJECTIVES: TO IDENTIFY FACTORS THAT INFLUENCE THE DECISION OF A COLLEGE STUDENT DISTANCE LEARNING TO ABANDON THEIR STUDIES AND GET THE PROFILE OF POTENTIALLY SUSCEPTIBLE STUDENTS FROM THEIR UNIVERSITY STUDIES. TO MEET THIS PURPOSE WE DEFINE TWO ANALYSIS TASKS LEARNING AND USE A BUSINESS INTELLIGENCE METHODOLOGY TO IMPLEMENT IT.
AUTOMATIC ANALYSIS OF TEACHER STUDENT INTERACTIONS IS AN INTERESTING RESEARCH PROBLEM IN SOCIAL COMPUTING. SUCH INTERACTIONS HAPPEN IN BOTH ONLINE AND CLASS ROOM SETTINGS. WHILE TEACHING EFFECTIVENESS IS THE GOAL IN BOTH SETTINGS, THE MECHANISM TO ACHIEVE THE SAME COULD DIFFER IN DIFFERENT SETTINGS. IN ORDER TO CHARACTERIZE THESE INTERACTIONS MULTIMODAL BEHAVIORAL SIGNALS AND LANGUAGE USE NEED TO BE MEASURED, AND A MODEL TO PREDICT EFFECTIVENESS NEEDS TO BE LEARNT. THESE WOULD HELP CHARACTERIZE THE TEACHING SKILL OF THE TEACHER AND LEVEL OF ENGAGEMENT OF STUDENTS. ALSO, THERE COULD BE MULTIPLE STYLES OF TEACHING WHICH CAN BE EFFECTIVE.
NOWADAYS, THE HIGHER EDUCATION INSTITUTIONS EXPERIENCE THE PROBLEM OF THE STUDENT DROP-OUT. IN RESPONSE TO THIS PROBLEM, UNIVERSITIES STARTED EMPLOYING ANALYTICAL DASHBOARDS AND EDUCATIONAL DATA MINING METHODS SUCH AS MACHINE LEARNING, TO DETECT STUDENTS AT RISK OF FAILING THEIR STUDIES. IN THIS PAPER, WE PRESENT INTERACTIVE WEB-BASED LEARNING ANALYTICS DASHBOARD - ANALYST, WHICH HAS BEEN SUCCESSFULLY DEPLOYED AT FACULTY OF MECHANICAL ENGINEERING (FME), CZECH TECHNICAL UNIVERSITY IN PRAGUE. THE DASHBOARD PROVIDES ACADEMIC TEACHING STAFF WITH THE OPPORTUNITY TO ANALYSE STUDENT-RELATED DATA FROM VARIOUS SOURCES IN MULTIPLE WAYS TO IDENTIFY THOSE, WHO MIGHT HAVE DIFFICULTIES TO COMPLETE THEIR DEGREE. FOR THIS PURPOSE, MULTIPLE ANALYTICAL DASHBOARD VIEWS HAVE BEEN IMPLEMENTED. IT INCLUDES SUMMARY STATISTIC, STUDY PROGRESSION GRAPH, AND CREDIT COMPLETION PROBABILITIES GRAPH. IN ADDITION, USERS HAVE THE OPTION TO EXPORT ALL ANALYSIS RELATED GRAPHS FOR THE FUTURE USE. BASED ON THE OUTCOMES PROVIDED BY THE ANALYST, THE UNIVERSITY SUCCESSFULLY RAN THE INTERVENTIONS ON THE SELECTED AT-RISK STUDENTS AND SIGNIFICANTLY INCREASED THE RETENTION RATE IN THE FIRST STUDY YEAR.
ONE OF THE MOST VALUABLE SKILLS FOR TEACHERS IS THE ABILITY TO PRODUCE THEIR OWN DIGITAL SOLUTIONS, TRANSLATING TEACHING CONCEPTS INTO END-USER COMPUTER SYSTEMS. THIS OFTEN REQUIRES THE INVOLVEMENT OF COMPUTING SPECIALISTS. AS A RESULT, THE DEVELOPMENT OF EDUCATIONAL PROGRAMMING ENVIRONMENTS REMAINS A CHALLENGE. LEARNING EXPERIENCES BASED MULTIMODAL INTERACTION APPLICATIONS (GESTURE INTERACTION, VOICE RECOGNITION OR ARTIFICIAL VISION) ARE BECOMING COMMONPLACE IN EDUCATION BECAUSE THEY MOTIVATE AND INVOLVE STUDENTS. THIS CHAPTER ANALYSES THE STATE-OF-THE-ART IN LA TECHNIQUES AND USER-FRIENDLY AUTHORING TOOLS. IT PRESENTS A TOOL TO SUPPORT THE CREATION OF MULTIMODAL INTERACTIVE APPLICATIONS EQUIPPED WITH NON-INTRUSIVE MONITORING AND ANALYTICS CAPABILITIES. THIS TOOL ENABLES TEACHERS WITH NO PROGRAMMING SKILLS TO CREATE INTERACTIVE LA-ENRICHED LEARNING SCENARIOS. TO THIS END, SEVERAL COMPONENTS THAT MANAGE LA ACTIVITIES ARE INCLUDED IN THE TOOL, THEY RANGE FROM AUTOMATICALLY CAPTURING USERS' INTERACTION WITH MOBILE APPLICATIONS, TO QUERYING DATA AND RETRIEVING METRICS, TO VISUALISING TABLES AND CHARTS.
THIS CONVERSATIONS STARTER ARTICLE PRESENTS A SELECTED RESEARCH ABSTRACT FROM THE 2017 ASSOCIATION OF AMERICAN MEDICAL COLLEGES NORTHEASTERN REGION GROUP ON EDUCATIONAL AFFAIRS ANNUAL SPRING MEETING. THE ABSTRACT IS PAIRED WITH THE INTEGRATIVE COMMENTARY OF THREE EXPERTS WHO SHARED THEIR THOUGHTS STIMULATED BY THE STUDY. COMMENTATORS BRAINSTORMED WHAT'S NEXT WITH LEARNING ANALYTICS IN MEDICAL EDUCATION, INCLUDING ADVANCEMENTS IN INTERACTION METRICS AND THE USE OF INTERACTIVITY ANALYSIS TO DEEPEN UNDERSTANDING OF PERCEPTUAL, COGNITIVE, AND SOCIAL LEARNING AND TRANSFER PROCESSES.
THE OER MOVEMENT POSES CHALLENGES INHERENT TO DISCOVERING AND REUSE DIGITAL EDUCATIONAL MATERIALS FROM HIGHLY HETEROGENEOUS AND DISTRIBUTED DIGITAL REPOSITORIES. SEARCH ENGINES ON TODAY'S WEB OF DOCUMENTS ARE BASED ON KEYWORD QUERIES. SEARCH ENGINES DON'T PROVIDE A SUFFICIENTLY COMPREHENSIVE SOLUTION TO ANSWER A QUERY THAT PERMITS PERSONALIZATION OF OPEN EDUCATIONAL MATERIALS. TO FIND OER ON THE WEB TODAY, USERS MUST FIRST BE WELL INFORMED OF WHICH OER REPOSITORIES POTENTIALLY CONTAIN THE DATA THEY WANT AND WHAT DATA MODEL DESCRIBES THESE DATASETS, BEFORE USING THIS INFORMATION TO CREATE STRUCTURED QUERIES. LEARNING ANALYTICS REQUIRES NOT ONLY TO RETRIEVE THE USEFUL INFORMATION AND KNOWLEDGE ABOUT EDUCATIONAL RESOURCES, LEARNING PROCESSES AND RELATIONS AMONG LEARNING AGENTS, BUT ALSO TO TRANSFORM THE DATA GATHERED IN ACTIONABLE E INTEROPERABLE INFORMATION. LINKED DATA IS CONSIDERED AS ONE OF THE MOST EFFECTIVE ALTERNATIVES FOR CREATING GLOBAL SHARED INFORMATION SPACES, IT HAS BECOME AN INTERESTING APPROACH FOR DISCOVERING AND ENRICHING OPEN EDUCATIONAL RESOURCES DATA, AS WELL AS ACHIEVING SEMANTIC INTEROPERABILITY AND RE-USE BETWEEN MULTIPLE OER REPOSITORIES. IN THIS WORK, AN APPROACH BASED ON SEMANTIC WEB TECHNOLOGIES, THE LINKED DATA GUIDELINES, AND SOCIAL NETWORK ANALYSIS METHODS ARE PROPOSED AS A FUNDAMENTAL WAY TO DESCRIBING, ANALYZING AND VISUALIZING KNOWLEDGE SHARING ON OER INITIATIVES
NOWADAYS THE MAIN CHALLENGE IN LEARNING ANALYTICS IS TO SUGGEST EFFICIENT METHODS AND TECHNOLOGIES IN ORDER TO ACHIEVE BETTER LEARNER RESULTS. THIS PAPER PRESENTS A SOFTWARE ARCHITECTURE FOR ADAPTATION AND RECOMMENDATION OF COURSE CONTENT AND ACTIVITIES BASED ON LEARNING ANALYTICS. IT IS COMPRISED OF LAYERS FOR INGESTION LAYER, AGGREGATION LAYER, STORAGE LAYER AND BIG DATA PROCESSING AND ANALYSES LAYER. AN ALGORITHM FOR PREDICTION OF STUDENT LEARNING BASED ON MACHINE LEARNING FOR PROCESSING AND ANALYSIS OF DATA AND KNOWLEDGE DISCOVERY WITH RESPECT TO MAIN LEARNER AND TEACHER ACTIVITIES IS PRESENTED. THE PROPOSED ALGORITHM FOR STUDENT LEARNING CLASSIFICATION IS IMPLEMENTED BY USING AVERAGED PERCEPTRON METHOD. EXPERIMENTAL RESULTS ARE PRESENTED AND DISCUSSED. THE PURPOSE OF THE STUDY IS TO APPLY THE SOFTWARE ARCHITECTURE ON LEARNING ANALYTICS BY PRACTICAL EXPERIMENTS FOR SPECIFIC CASE STUDY IDENTIFYING EVENT ELEMENTS IN SEQUENCED LEARNERS' AND COURSES' ACTIVITIES LOGS, AND STUDENT LEARNING PREDICTION.
THIS ARTICLE OFFERS A CASE STUDY OF HOW PLATFORMS AND PREDICTIVE INFRASTRUCTURES ARE EMERGING IN HIGHER EDUCATION. IT EXAMINES A LEARNING ANALYTICS APPLICATION PROGRAMMING INTERFACE (API) FROM A POPULAR LEARNING MANAGEMENT SYSTEM. THE API IS TREATED FIRSTLY AS AN ARTEFACT BASED ON THE COMPUTATIONAL ABSTRACTION OF EDUCATIONAL PRINCIPLES, AND SECONDLY AS AN EMPIRICAL ENTRY POINT TO INVESTIGATE THE EMERGENCE OF A LEARNING ANALYTICS INFRASTRUCTURE IN A LARGE AUSTRALIAN UNIVERSITY. THROUGH IN-DEPTH ETHNOGRAPHIC INTERVIEWS AND THE INTERPRETATIVE ANALYSIS OF SOFTWARE DEVELOPMENT WORKFLOWS, THE PAPER DESCRIBES AN API-MEDIATED PLATFORMISATION PROCESS INVOLVING A RANGE OF ACTORS AND SYSTEMS: COMPUTATIONAL EXPERTS, ALGORITHMS, DATA-SAVVY ADMINISTRATIVE STAFF AND LARGE CORPORATE ACTORS INSERTING THEMSELVES THROUGH BACK-ENDS AND VARIOUS OTHER DEPENDENCIES. IN THE CONCLUSION, THE ARTICLE ARGUES THAT THE PLATFORMISATION OF HIGHER EDUCATION IS PART OF A BROADER PROJECT THAT MOBILISES PROGRAMMABILITY AND COMPUTATION TO RE-ENGINEER EDUCATIONAL INSTITUTIONS IN THE INTEREST OF EFFICIENCY AND PREDICTION. HOWEVER, THE SOCIAL-SCIENTIFIC STUDY OF THIS PROJECT CANNOT IGNORE THE PRACTICAL AND COMPROMISED DIMENSION WHERE HUMAN ACTORS AND TECHNICAL SYSTEMS INTERACT AND, IN THE PROCESS, GENERATE MEANING.
MANY LATIN-AMERICAN INSTITUTIONS RECOGNISE THE POTENTIAL OF LEARNING ANALYTICS (LA). HOWEVER, THE NUMBER OF ACTUAL LA IMPLEMENTATIONS AT SCALE REMAINS LIMITED, NOTWITHSTANDING CONSIDERABLE EFFORT MADE TO FORMULATE GUIDELINES AND FRAMEWORKS TO SUPPORT THE LA POLICY DEVELOPMENT. GUIDANCE ON HOW TO COORDINATE THE INTERACTION BETWEEN THE LA POLICYMAKING AND IMPLEMENTATION IS MOSTLY MISSING, LEAVING A DIFFICULT CHALLENGE UP TO PRACTITIONERS. IN THIS STUDY WE PROPOSE A COORDINATION MODEL TO SUPPORT FUTURE LA INITIATIVES AT SCALE. WE EXPLORE THE PROBLEM BY COMPARING TWO CASES IN BELGIUM AND ECUADOR. FOLLOWING UP WE USE THE LA IMPLEMENTATION TIMELINE AS A DRIVER FOR PLANNING THE INTERACTION BETWEEN THE POLICYMAKING AND IMPLEMENTATION. WE CONTINUE BY TESTING AN APPLICATION OF THE MODEL WITH LA EXPERTS PREDOMINANTLY FROM LATIN-AMERICAN INSTITUTIONS, ASKING THEM TO MAP LOW-LEVEL ITEMS OF THE SHEILA POLICY FRAMEWORK TO FOUR IMPLEMENTATION PHASES. THE RESULTS OF THIS MAPPING SUPPORT THAT LA POLICY BUILDING CAN BE SPREAD OVER TIME, THAT IT CAN COINCIDE WITH LA IMPLEMENTATION AT SCALE, AND THAT BOTH EFFORTS CAN BE COORDINATED. IT IS HOPED THAT THIS STUDY WILL PROVIDE ADDITIONAL GUIDANCE FOR FUTURE LATIN-AMERICAN AND OTHER LA INITIATIVES.
ONLINE COLLABORATIVE WRITING TOOLS PROVIDE AN EFFICIENT WAY TO COMPLETE A WRITING TASK. HOWEVER, EXISTING TOOLS ONLY FOCUS ON TECHNOLOGICAL AFFORDANCES AND IGNORE THE IMPORTANCE OF SOCIAL AFFORDANCES IN A COLLABORATIVE LEARNING ENVIRONMENT. THIS ARTICLE DESCRIBES A LEARNING ANALYTIC SYSTEM THAT ANALYZES WRITING BEHAVIORS, AND CREATES VISUALIZATIONS INCORPORATING INDIVIDUAL ENGAGEMENT AWARENESS AND GROUP RANKING AWARENESS (SOCIAL AFFORDANCE), AND REVIEW WRITING BEHAVIOUR HISTORY (TECHNOLOGICAL AFFORDANCE), TO SUPPORT STUDENT ENGAGEMENT. STUDIES EXAMINED THE PERFORMANCE OF THE SYSTEM USED BY UNIVERSITY STUDENTS IN TWO COLLABORATIVE WRITING ACTIVITIES: COLLABORATIVELY WRITING A PROJECT PROPOSAL (N = 41) AND WRITING TUTORIAL DISCUSSION ANSWERS (N = 25). RESULTS SHOW THAT STUDENTS AGREED WITH WHAT THE VISUALIZATION CONVEYS AND VISUALIZATIONS ENHANCE THEIR ENGAGEMENT IN A COLLABORATIVE WRITING ACTIVITY. IN ADDITION, STUDENTS STATED THAT THE VISUALIZATIONS WERE USEFUL TO HELP THEM REFLECT ON THE WRITING PROCESS AND SUPPORT THE ASSESSMENT OF INDIVIDUAL CONTRIBUTIONS.
THE LEARNING ANALYTICS IS AN EMERGING FIELD OF STUDY, WHICH IS FOCUSED ON THE STUDY OF STUDENTS' BEHAVIOR IN RELATION TO THE EDUCATIONAL CONTENT PRESENTED TO THEM. THE KEY IN THESE TECHNOLOGIES IS TO LOOK FOR A STANDARDIZATION OF THE OBTAINED DATA, BEING THIS TASK PARTICULARLY DIFFICULT, SINCE THE COLLECTION GENERATES DIVERSE RANGES OF DATA. THIS STUDY IS BASED ON REVIEWING THE TECHNOLOGIES ALREADY IMPLEMENTED BY VARIOUS AUTHORS, AND PRESENTING THE MOST IMPORTANT TOPICS ALREADY DEVELOPED IN THE AREA, BOTH IN PRIVATE AND EDUCATIONAL AREAS TO FIND SOLUTIONS TO THESE PROBLEMS. BOTH THEORETICAL METHODOLOGIES AND TOOLS IMPLEMENTED IN SOFTWARE THAT ARE AVAILABLE FOR REVIEW ARE REVIEWED.
RECENT ATTENTION ON THE POTENTIALITY OF COST-EFFECTIVE INFRASTRUCTURES FOR CAPTURING AND PROCESSING LARGE AMOUNTS OF DATA, KNOWN AS BIG DATA HAS RECEIVED MUCH ATTENTION FROM RESEARCHERS AND PRACTITIONERS ON THE FIELD OF ANALYTICS. IN THIS PAPER WE DISCUSS ON THE POSSIBLE BENEFITS THAT BIG DATA CAN BRING ON TEL BY USING THE CASE OF LARGE SCALE COMPARATIVE ASSESSMENTS AS AN EXAMPLE. LARGE SCALE COMPARATIVE ASSESSMENTS CAN POSE AS AN INTRINSIC MOTIVATIONAL TOOL FOR ENHANCING THE PERFORMANCE OF BOTH LEARNERS AND TEACHERS, AS WELL AS BECOMING A SUPPORT TOOL FOR POLICY MAKERS. WE ARGUE WHY DATA FROM LEARNING PROCESSES CAN BE CHARACTERIZED AS BIG DATA FROM THE VIEWPOINT OF DATA SOURCE HETEROGENEITY (VARIETY) AND DISCUSS SOME ARCHITECTURAL ISSUES THAT CAN BE TAKEN INTO ACCOUNT ON IMPLEMENTING SUCH AN INFRASTRUCTURE ON THE CASE OF COMPARATIVE ASSESSMENTS.
LECTURERS ARE OFTEN RELUCTANT TO SET EXAMINATIONS ONLINE BECAUSE OF THE POTENTIAL PROBLEMS OF FRAUDULENT BEHAVIOUR FROM THEIR STUDENTS. THIS CONCERN HAS INCREASED DURING THE CORONAVIRUS PANDEMIC BECAUSE COURSES THAT WERE PREVIOUSLY DESIGNED TO BE TAKEN FACE-TO-FACE HAVE TO BE CONDUCTED ONLINE. THE COURSES HAVE HAD TO BE REDESIGNED, INCLUDING SEMINARS, LABORATORY SESSIONS AND EVALUATION ACTIVITIES. THIS HAS BROUGHT LECTURERS AND STUDENTS INTO CONFLICT BECAUSE, ACCORDING TO THE STUDENTS, THE ACTIVITIES AND EXAMINATIONS THAT HAVE BEEN REDESIGNED TO AVOID CHEATING ARE ALSO HARDER. THE LECTURERS' CONCERN IS THAT STUDENTS CAN COLLABORATE IN TAKING EXAMINATIONS THAT MUST BE TAKEN INDIVIDUALLY WITHOUT THE LECTURERS BEING ABLE TO DO ANYTHING TO PREVENT IT, I.E. FRAUDULENT COLLABORATION. THIS RESEARCH PROPOSES A PROCESS MODEL TO OBTAIN EVIDENCE OF STUDENTS WHO ATTEMPT TO FRAUDULENTLY COLLABORATE, BASED ON THE INFORMATION IN THE LEARNING ENVIRONMENT LOGS. IT IS AUTOMATED IN A SOFTWARE TOOL THAT CHECKS HOW THE STUDENTS TOOK THE EXAMINATIONS AND THE GRADES THAT THEY OBTAINED. IT IS APPLIED IN A CASE STUDY WITH MORE THAN 100 UNDERGRADUATE STUDENTS. THE RESULTS ARE POSITIVE AND ITS USE ALLOWED LECTURERS TO DETECT EVIDENCE OF FRAUDULENT COLLABORATION BY SEVERAL CLUSTERS OF STUDENTS FROM THEIR SUBMISSION TIMESTAMPS AND THE GRADES OBTAINED.
THIS ARTICLE SHOWS THE ANALYSIS AND EVALUATION OF A CASE STUDY ABOUT THE IMPLEMENTATION OF THE CLOUDLABS CLASSROOM MANAGEMENT SOFTWARE, CONDUCTED IN AN EDUCATIONAL INSTITUTION IN BOGOTA, IN THE AREA OF NATURAL SCIENCES IN SIXTH TO ELEVENTH GRADES. THE OBJECTIVE WAS TO DEVELOP THE ANALYTICAL COMPETENCE IN THE TEACHERS THROUGH A MIXED LEARNING ENVIRONMENT, INNOVALACTLC, FOR THEIR APPROPRIATION OF KNOWLEDGE AND SKILLS IN THE PERSONALIZATION OF LEARNING ON THE BASIS OF THE DATA. FROM A QUALITATIVE METHODOLOGY, FOCUSED ON A RESEARCH-ACTION DESIGN, IT WAS POSSIBLE TO RECOGNIZE THE CONTRIBUTION OF THE LEARNING ANALYTICS TOOLS AND OF THE CLOUDLABS VIRTUAL LABORATORY IN THE PERSONALIZATION OF LEARNING AND IN THE FORMATIVE EVALUATION. MOREOVER, THE IMPLEMENTED LEARNING ENVIRONMENT ALLOWED TO DEFINE THE REQUIRED TEACHING COMPETENCES AND, IN GENERAL, THE DIFFICULTIES THAT MUST BE OVERCOME FOR THE USE OF DATA AS A TOOL FOR TRANSFORMATION IN CLASSROOMS.
THE PAPER REVIEWS A SPECIFIC APPROACH BUILT ON A TRANSDISCIPLINARY METHODOLOGY BASIS, WHICH INCLUDES A PUBLIC PARTICIPATION METHOD FOR BUILDING A MODEL OF SUSTAINABLE CAMPUS AT THIRUVALLUVAR UNIVERSITY. THE PURPOSE OF THIS CASE STUDY WAS TO EXPLORE POSTGRADUATE AND RESEARCH STUDENT'S EXPERIENCES OF COMPETITIVE AND TRANS-DISCIPLINARY LEARNING METHODS AND ITS ROLE TO ACHIEVE THE SUSTAINABLE DEVELOPMENTS GOALS (SDG). A FOCUS GROUP WAS CONDUCTED WITH 200 STUDENTS (MALE=70, FEMALE=130). THEIR MAIN POINTS INDICATED THAT COMPETITIVE AND TRANS DISCIPLINARY LEARNING WAS A KEY TO REACH THE SDG: NO POVERTY (SDG 1), GOOD HEALTH AND WELL-BEING (SDG 3), QUALITY EDUCATION (SDG 4), GENDER EQUALITY (SDG 5), DECENT WORK AND ECONOMIC GROWTH (SDG 8), RESPONSIBLE CONSUMPTION AND PRODUCTION (SDG 12) AND PEACE, JUSTICE AND STRONG INSTITUTIONS (SDG 16). THE GOALS WERE ACHIEVED THROUGH THE DESIGN OF ORGANIC FARM WITH MUSHROOM CULTIVATION AND HERBAL GARDEN THEREBY WE ATTAINED ALL THE SET OBJECTIVES WITH THE POSITIVE OUTCOME IN THE SUSTAINABLE MANNER WHICH HELPS IN THE OUTCOME BASED ATTAINMENT OF GAINFUL EMPLOYMENT OPPORTUNITY.
'TEACHING ANALYTICS' IS THE APPLICATION OF LEARNING ANALYTICS TECHNIQUES TO UNDERSTAND TEACHING AND LEARNING PROCESSES, AND EVENTUALLY ENABLE SUPPORTIVE INTERVENTIONS. HOWEVER, IN THE CASE OF (OFTEN, HALF-IMPROVISED) TEACHING IN FACE-TO-FACE CLASSROOMS, SUCH INTERVENTIONS WOULD REQUIRE FIRST AN UNDERSTANDING OF WHAT THE TEACHER ACTUALLY DID, AS THE STARTING POINT FOR TEACHER REFLECTION AND INQUIRY. CURRENTLY, SUCH TEACHER ENACTMENT CHARACTERIZATION REQUIRES COSTLY MANUAL CODING BY RESEARCHERS. THIS PAPER PRESENTS A CASE STUDY EXPLORING THE POTENTIAL OF MACHINE LEARNING TECHNIQUES TO AUTOMATICALLY EXTRACT TEACHING ACTIONS DURING CLASSROOM ENACTMENT, FROM FIVE DATA SOURCES COLLECTED USING WEARABLE SENSORS (EYE-TRACKING, EEG, ACCELEROMETER, AUDIO AND VIDEO). OUR RESULTS HIGHLIGHT THE FEASIBILITY OF THIS APPROACH, WITH HIGH LEVELS OF ACCURACY IN DETERMINING THE SOCIAL PLANE OF INTERACTION (90%, KAPPA = 0.8). THE RELIABLE DETECTION OF CONCRETE TEACHING ACTIVITY (E.G., EXPLANATION VS. QUESTIONING) ACCURATELY STILL REMAINS CHALLENGING (67%, KAPPA = 0.56), A FACT THAT WILL PROMPT FURTHER RESEARCH ON MULTIMODAL FEATURES AND MODELS FOR TEACHING ACTIVITY EXTRACTION, AS WELL AS THE COLLECTION OF A LARGER MULTIMODAL DATASET TO IMPROVE THE ACCURACY AND GENERALIZABILITY OF THESE METHODS.
THE MANAGEMENT OF MATERNITY HOSPITALS IS A KEY ELEMENT WHICH IS STUDIED AT MEDICAL UNIVERSITIES. THE PROPOSED ONLINE PLATFORM ANALYZES USING ELASTICSEARCH THE ACQUIRED FEEDBACK DATA FROM VARIOUS ROMANIAN MATERNITY HOSPITALS THAT IS STORED IN THE MONGODB NOSQL DATABASE. WHEN NEW DATA IS ADDED BY THE MEDICAL STUDENTS, A NEW DOCUMENT IS ADDED INSIDE THE MONGODB COLLECTION AND THE CHARACTERISTICS USED FOR MATCHING ARE INDEXED INSIDE ELASTICSEARCH. REGULAR EXPRESSIONS ARE UTILIZED FOR EXTRACTING THE RELEVANT INFORMATION. WHILE PERFORMING THE CRAWLING, THE FEATURES REGARDING THE MANAGEMENT CHARACTERISTICS LIKE LOCATION, NUMBER OF BEDS, INTENSIVE CARE UNITS, STAFF QUALIFICATIONS, EQUIPMENT AND MEDICINE AVAILABILITY ARE FOUND USING XPATH TO DETERMINE THE RELATIONSHIPS BETWEEN THE FEATURES. THE IMPORTANT FEATURES THAT ARE CLUSTERED ARE DETECTED BY CREATING A DOCUMENT TERM MATRIX BASED ON TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY. THE MATCHING IS DONE ACCORDING TO THE CHARACTERISTICS BY USING THE WEIGHTED TERMS ACCORDING TO THE MATRIX. AFTER THE MATCHING IS DONE, THE STUDENTS, AS WELL AS THE PREGNANT WOMEN HAVE ACCESS TO THE BEST CASE SITUATION WITH THE FINEST MANAGEMENT WHICH IS SUGGESTED BASED ON THE PREVIOUS ANALYZED CASES. THE MEDICAL STUDENTS COLLABORATE AND IMPROVE THE EXISTENT KNOWLEDGE BASE OF THE SYSTEM AFTER BEING APPROVED BY THEIR PROFESSORS, AS WELL AS THEY CAN LEARN HOW TO MANAGE BETTER MANAGE A MATERNITY HOSPITAL ACCORDING TO THE AUTOMATIC ASSIGNMENT WHICH IS DONE FOR DETERMINING THE BEST SUITED CHARACTERISTICS. AFTER A PERIOD OF TIME, FEEDBACK COMES FROM THE PATIENTS BASED ON THE IMPLEMENTED SOLUTION AND THE STUDENTS, AS WELL AS THE PROFESSOR CAN USE IT FOR LEARNING. THE SHIFT TO THE ONLINE PLATFORM HAS PROVIDED A CLOSE TO REALITY IMAGE OF TODAY'S MATERNITY HOSPITALS, WHILE THE AUTOMATIC MANAGEMENT RECOMMENDATION AIMS TO ENHANCE THE FACILITIES. THE PREGNANT WOMEN, THE MEDICAL STUDENTS AND THEIR PROFESSORS LEARN FROM THE PREVIOUS SUCCESSFULLY TESTED CASES.
ANALYTICS DISCOVERS MEANINGFUL INFERENCES FROM DATA. LEARNING ANALYTICS IS THE DISCOVERY OF ACTIONABLE INTELLIGENCE FROM EDUCATIONAL DATA. COURSE ANALYTICS IS ONE OF THE KEY RESEARCH AREA IN LEARNING ANALYTICS. IN THIS PAPER EXPLORATORY DATA ANALYSIS TECHNIQUES, VISUALIZATION AND SUMMARIZATION ARE APPLIED ON LEARNING CONTENT I.E. COURSES AND DRAWN VARIOUS VALID INFERENCES AND DISCUSSES COURSE RELATED PROBLEMS LIKE COURSE PREREQUISITES, COURSE INTERRELATION, COURSE SCHEDULING, COURSE DROPOUT ETC. THIS PAPER USES LORENZ CURVE, WHICH IS A VISUALIZATION METHOD TO KNOW THE DISPERSION AMONG THE COMPONENTS AND GINI INDEX WHICH SUMMARIZES THE LORENZ CURVE.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BECOME VERY POPULAR IN EDUCATION AND LEARNING ANALYTICS, AND CAN HELP STUDENTS UNDERSTAND THEIR LEARNING SITUATIONS AND ASSIST TEACHERS IN CLASS MANAGEMENT. HOWEVER, FACING DIFFERENT COURSE OBJECTIVES, MULTIPLE LEARNING ACTIVITIES AND STUDENT'S DIVERSITY IN MOTIVATION, LEARNING ANALYTICS OFTEN SUFFERS FROM HIGH COMPLEXITY AND INEFFICIENT DATA ANALYSIS. THIS RESULTS IN A LONG PROCESS FROM THE IMPLEMENTATION OF DATA ANALYSIS TO DECISION SUPPORT, AND AN INABILITY TO OFFER THE INSTANTANEITY REQUIRED BY TEACHERS. IN PARTICULAR, THIS PROBLEM IN MOOC COURSES IN SCHOOLS MAKES IT MORE DIFFICULT FOR TEACHERS TO UNDERSTAND STUDENTS' LEARNING SITUATIONS, PROVIDE TIMELY ASSISTANCE, AND IMPROVE COURSE PASS RATES. THEREFORE, HOW TO USE A GOOD ANALYTICS FRAMEWORK TO QUICKLY ESTABLISH VARIOUS ANALYSIS MODELS WITH CONVENIENCE AND FLEXIBILITY IS OF PARTICULAR IMPORTANCE IN MOOC DEVELOPMENT. CURRENT DATA ANALYTICS FRAMEWORKS ONLY FOCUS ON THE PROVISION OF DATA ANALYSIS STEPS, AND FAIL TO CONSIDER THE VARIABILITY OF DATA ANALYSIS AND THE REPEATABILITY OF ANALYSIS RESULTS IN TERMS OF SIMILAR PROBLEMS. THIS STUDY ATTEMPTS TO APPLY THE CONCEPT OF SOFTWARE PRODUCT LINES (SPL) IN SOFTWARE ENGINEERING TECHNOLOGY TO THE FRAMEWORK OF DATA ANALYTICS. SPL CAN GUIDE USERS AND MAKE THE DATA ANALYSIS PROCESS MORE REUSABLE, JUST LIKE THE DEVELOPMENT OF SOFTWARE PRODUCTS. TO VERIFY THE FEASIBILITY AND EFFECTIVENESS OF THE PROPOSED FRAMEWORK, THIS STUDY BUILT PRACTICAL MACHINE LEARNING MODELS ON THE FRAMEWORK TO PREDICT LEARNING PERFORMANCE THROUGH STUDENT LEARNING BEHAVIOR. THE RESULTS SHOW THAT THE SPL-BASED APPROACH CAN BE USED TO BUILD EFFECTIVE MOOC LEARNING ANALYTICS FRAMEWORKS.
THIS SURVEY IS AN UPDATED AND IMPROVED VERSION OF THE PREVIOUS ONE PUBLISHED IN 2013 IN THIS JOURNAL WITH THE TITLE "DATA MINING IN EDUCATION". IT REVIEWS IN A COMPREHENSIBLE AND VERY GENERAL WAY HOW EDUCATIONAL DATA MINING AND LEARNING ANALYTICS HAVE BEEN APPLIED OVER EDUCATIONAL DATA. IN THE LAST DECADE, THIS RESEARCH AREA HAS EVOLVED ENORMOUSLY AND A WIDE RANGE OF RELATED TERMS ARE NOW USED IN THE BIBLIOGRAPHY SUCH AS ACADEMIC ANALYTICS, INSTITUTIONAL ANALYTICS, TEACHING ANALYTICS, DATA-DRIVEN EDUCATION, DATA-DRIVEN DECISION-MAKING IN EDUCATION, BIG DATA IN EDUCATION, AND EDUCATIONAL DATA SCIENCE. THIS PAPER PROVIDES THE CURRENT STATE OF THE ART BY REVIEWING THE MAIN PUBLICATIONS, THE KEY MILESTONES, THE KNOWLEDGE DISCOVERY CYCLE, THE MAIN EDUCATIONAL ENVIRONMENTS, THE SPECIFIC TOOLS, THE FREE AVAILABLE DATASETS, THE MOST USED METHODS, THE MAIN OBJECTIVES, AND THE FUTURE TRENDS IN THIS RESEARCH AREA. THIS ARTICLE IS CATEGORIZED UNDER: APPLICATION AREAS > EDUCATION AND LEARNING
AMOEBA IS A UNIQUE TOOL TO SUPPORT TEACHERS' ORCHESTRATION OF COLLABORATION AMONG NOVICE PROGRAMMERS IN A NON-TRADITIONAL PROGRAMMING ENVIRONMENT. THE AMOEBA TOOL WAS DESIGNED AND UTILIZED TO FACILITATE COLLABORATION IN A CLASSROOM SETTING IN REAL TIME AMONG NOVICE MIDDLE SCHOOL AND HIGH SCHOOL PROGRAMMERS UTILIZING THE IPRO PROGRAMMING ENVIRONMENT. AMOEBA'S KEY AFFORDANCE IS SUPPORTING TEACHERS' PAIRING DECISIONS WITH REAL TIME ANALYSES OF STUDENTS' PROGRAMMING PROGRESSIONS. TEACHERS CAN TRACK WHICH STUDENTS ARE WORKING IN SIMILAR WAYS; THIS IS SUPPORTED BY REAL-TIME GRAPHICAL LOG ANALYSES OF STUDENT ACTIVITIES WITHIN THE PROGRAMMING ENVIRONMENT. PAIRING STUDENTS WITH SUPPORT FROM AMOEBA LED TO IMPROVEMENTS IN STUDENTS' PROGRAM COMPLEXITY AND DEPTH. ANALYSES OF THE DATA SUGGEST THAT THE DATA MINING TECHNIQUES UTILIZED IN AND THE METRICS PROVIDED BY AMOEBA CAN SUPPORT INSTRUCTORS IN ORCHESTRATING COOPERATION. THE PRIMARY CONTRIBUTIONS OF THIS PAPER ARE A SET OF DESIGN PRINCIPLES AROUND AND A WORKING TOOL FOR FOSTERING COLLABORATION IN COMPUTER SCIENCE CLASSES.
WITH DIGITISATION AND THE RISE OF E-LEARNING HAVE COME A RANGE OF COMPUTATIONAL TOOLS AND APPROACHES THAT HAVE ALLOWED EDUCATORS TO BETTER SUPPORT THE LEARNERS' EXPERIENCE IN SCHOOLS, COLLEGES AND UNIVERSITIES. THE MOVE AWAY FROM TRADITIONAL PAPER-BASED COURSE MATERIALS, REGISTRATION, ADMISSIONS AND SUPPORT SERVICES TO THE MOBILE, ALWAYS-ON AND ALWAYS ACCESSIBLE DATA HAS DRIVEN DEMAND FOR INFORMATION AND GENERATED NEW FORMS OF DATA OBSERVABLE THROUGH CONSUMPTION BEHAVIOURS. THESE CHANGES HAVE LED TO A PLETHORA OF DATA SETS THAT STORE LEARNING CONTENT AND TRACK USER BEHAVIOURS. MOST RECENTLY, NEW DATA ANALYTICS APPROACHES ARE CREATING NEW WAYS OF UNDERSTANDING TRENDS AND BEHAVIOURS IN STUDENTS THAT CAN BE USED TO IMPROVE LEARNING DESIGN, STRENGTHEN STUDENT RETENTION, PROVIDE EARLY WARNING SIGNALS CONCERNING INDIVIDUAL STUDENTS AND HELP TO PERSONALISE THE LEARNER'S EXPERIENCE. THIS PAPER PROPOSES A FOUNDATIONAL LEARNING ANALYTICS MODEL (LAM) FOR HIGHER EDUCATION THAT FOCUSES ON THE DYNAMIC INTERACTION OF STAKEHOLDERS WITH THEIR DATA SUPPORTED BY VISUAL ANALYTICS, SUCH AS SELF-ORGANISING MAPS, TO GENERATE CONVERSATIONS, SHARED INQUIRY AND SOLUTION-SEEKING. THE MODEL CAN BE APPLIED FOR OTHER EDUCATIONAL INSTITUTIONS INTERESTED IN USING LEARNING ANALYTICS PROCESSES TO SUPPORT PERSONALISED LEARNING AND SUPPORT SERVICES. FURTHER WORK IS TESTING ITS EFFICACY IN INCREASING STUDENT RETENTION RATES.
HOW CAN WE BEST ALIGN LEARNING ANALYTICS PRACTICES WITH DISCIPLINARY KNOWLEDGE PRACTICES IN ORDER TO SUPPORT STUDENT LEARNING? ALTHOUGH LEARNING ANALYTICS ITSELF IS AN INTERDISCIPLINARY FIELD, IT TENDS TO TAKE A 'ONE-SIZE-FITS-ALL' APPROACH TO THE COLLECTION, MEASUREMENT, AND REPORTING OF DATA, OVERLOOKING DISCIPLINARY KNOWLEDGE PRACTICES. IN LINE WITH A RECENT TREND IN HIGHER EDUCATION RESEARCH, THIS PAPER CONSIDERS THE CONTRIBUTION OF A REALIST SOCIOLOGY OF EDUCATION TO THE FIELD OF LEARNING ANALYTICS, DRAWING ON FINDINGS FROM RECENT STUDENT FOCUS GROUPS AT AN AUSTRALIAN UNIVERSITY. IT EXAMINES WHAT LEARNERS SAY ABOUT THEIR DATA NEEDS WITH REFERENCE TO ORGANIZING PRINCIPLES UNDERLYING KNOWLEDGE PRACTICES WITHIN THEIR DISCIPLINES. THE KEY CONTRIBUTION OF THIS PAPER IS A FRAMEWORK THAT COULD BE USED AS THE BASIS FOR ALIGNING THE PROVISION AND/OR USE OF DATA IN RELATION TO CURRICULUM, PEDAGOGY, AND ASSESSMENT WITH DISCIPLINARY KNOWLEDGE PRACTICES. THE FRAMEWORK EXTENDS RECENT RESEARCH IN LEGITIMATION CODE THEORY, WHICH UNDERSTANDS DISCIPLINARY DIFFERENCES IN TERMS OF THE PRINCIPLES THAT UNDERPIN KNOWLEDGE-BUILDING. THE PRELIMINARY ANALYSIS PRESENTED HERE BOTH PROVIDES A TOOL FOR ENSURING A FIT BETWEEN LEARNING ANALYTICS PRACTICES AND DISCIPLINARY PRACTICES AND STANDARDS FOR ACHIEVEMENT, AND SIGNALS DISCIPLINARITY AS AN IMPORTANT CONSIDERATION IN LEARNING ANALYTICS PRACTICES.
THE INTEGRATION OF QUALITY IN HIGHER EDUCATION IS MULTIDIMENSIONAL. HIGHER EDUCATION ADMINISTRATION, PROGRAMS, PROCEDURES AND EVALUATION PROVIDE THE CONTEXT FOR THE APPLICATION AND DIFFUSION OF QUALITY METRICS. OUR RESEARCH INTENDS TO PROVIDE A HOLISTIC DISCUSSION ON KEY PERFORMANCE INDICATORS (KPIS) RELATED TO QUALITY IN HIGHER EDUCATION. THE ANALYSIS OF RECENT LITERATURE RESULTED IN THE CONSTRUCTION OF TWO RESEARCH TOOLS. THE FIRST ONE IS RELATED TO A STRUCTURED AGENDA FOR A QUALITATIVE INTERVIEW TARGETED AT HIGHER EDUCATION ADMINISTRATORS. THE SECOND IS RELATED TO A QUANTITATIVE RESEARCH MODEL THAT ANALYSES THE RELATIONS OF VARIOUS QUALITY FACTORS. WE PROVIDE A MAPPING OF QUALITY PERCEPTIONS AS DISCUSSED IN PREVIOUS WORK AND WE CONSTRUCT A THEORETICAL MODEL FOR THE AFFORDANCES OF SCHOLARS TO THIS PERCEPTION. THE RESEARCH DESIGN INCLUDES INTERVIEWS WITH ACADEMIC ADMINISTRATORS AND TEACHING STAFF INVOLVED IN THE CREATION OF ACADEMIC PROGRAMMES AND COURSES. THE MAIN CONTRIBUTION IS AN ANALYTIC DISCUSSION OF THEIR PERCEPTIONS ABOUT QUALITY THAT UPDATES SIGNIFICANTLY CONTEMPORARY LITERATURE IN INTERESTING DIMENSIONS. THREE-DIMENSIONAL VALUE SPACE WITH TWENTY FACTORS IS PRESENTED. THE OUTCOMES OF THIS RESEARCH WORK ARE USED AS INPUT FOR OUR QUANTITATIVE STUDY. IN FACT, A LIST OF 20 QUALITY FACTORS IS EXPLOITED IN THREE MAIN DIMENSIONS OF LEARNING ANALYTICS NAMELY: CONTENT, PROCESS AND ENGAGEMENT. KEY PERFORMANCE INDICATORS ARE HIGHLIGHTED FOR FURTHER INVESTIGATION.
WHILST A SIGNIFICANT BODY OF LEARNING ANALYTICS RESEARCH TENDS TO FOCUS ON IMPACT FROM THE PERSPECTIVE OF USABILITY OR IMPROVED LEARNING OUTCOMES, THIS PAPER PROPOSES AN APPROACH BASED ON AFFORDANCE THEORY TO DESCRIBE AWARENESS AND INTENTION AS A BRIDGE BETWEEN USABILITY AND IMPACT. 10 EDUCATORS AT 3 EUROPEAN INSTITUTIONS PARTICIPATED IN DETAILED INTERVIEWS ON THE AFFORDANCES THEY PERCEIVE IN USING LEARNING ANALYTICS TO SUPPORT PRACTICE IN EDUCATION. EVIDENCE ILLUMINATES CONNECTIONS BETWEEN AN EDUCATOR'S EPISTEMIC BELIEFS ABOUT LEARNING AND THE PURPOSE OF EDUCATION, THEIR PERCEPTION OF THREATS OR RESOURCES IN DELIVERING A SUCCESSFUL LEARNING EXPERIENCE, AND THE TYPES OF DATA THEY WOULD CONSIDER AS EVIDENCE IN RECOGNIZING OR REGULATING LEARNING. THIS EVIDENCE CAN SUPPORT THE LEARNING ANALYTICS COMMUNITY IN CONSIDERING THE PROXIMITY TO THE STUDENT, THE ROLE OF THE EDUCATOR, AND THEIR PERSONAL BELIEF STRUCTURE IN DEVELOPING ROBUST ANALYTICS TOOLS THAT EDUCATORS MAY BE MORE LIKELY TO UTILIZE.
THE PRESENT WORK DESCRIBES THE MOODLE ANALYTICS DASHBOARD (MAD), A TOOL DEVELOPED TO ALLOW THE VISUALIZATION OF STUDENTS AND PROFESSORS LOGS IN MOODLE DISCIPLINES. MAD PROVIDES AN EASY WAY TO OBTAIN GRAPHICAL VISUALIZATION OF SEVERAL ASPECTS RELATED TO STUDENTS AND PROFESSORS ACCESSES IN VIRTUAL LEARNING DISCIPLINES, THUS HELPING PROFESSORS TO BETTER FOLLOW TEACHING AND LEARNING PROCESS, AS WELL AS TO VISUALLY IDENTIFY POTENTIAL AT-RISK STUDENTS, OR TO BETTER UNDERSTAND HOW THE DIFFERENT EDUCATIONAL RESOURCES ARE BEING USED. THE PAPER PRESENTS THE THEORETICAL FOUNDATIONS AND THE TECHNOLOGIES USED TO DEVELOP MAD, TOGETHER WITH THE MOST IMPORTANT FEATURES OF THE TOOL AND THE FIRST RESULTS OBTAINED DURING THE PRELIMINARY STAGES OF VALIDATION.
ONLINE EDUCATION IS RAPIDLY DEVELOPING IN RUSSIA AND IN THE WORLD OVER THE LAST DECADES. ONE OF THE MOST POPULAR AND AVAILABLE ONLINE LEARNING TECHNOLOGIES IS MASSIVE OPEN ONLINE COURSES (MOOC), WHICH ARE SUCCESSFULLY USED IN THE UNIVERSITY, SCHOOL, CONTINUING PROFESSIONAL EDUCATION AND INFORMAL LIFE-LONG LEARNING. NOWADAYS THE NUMBER OF MOOC LEARNERS IN THE WORLD IS ESTIMATED IN TENS OF MILLIONS OF PEOPLE. NEVERTHELESS, USING MOOC IN THE EDUCATIONAL PROCESS HAS BOTH ADVANTAGES AND DISADVANTAGES. THE LATTER INCLUDE PROBLEMS CONNECTED WITH INDIVIDUALIZATION OF TRAINING, ASSESSMENT OF PROGRESS AND SUPPORT FOR STUDENTS, AND ASSESSMENT OF THE QUALITY OF COURSES. ANALYZING AND PREDICTING THE SUCCESS OF STUDENTS IS AN IMPORTANT TASK AND TOOL FOR SOLVING THEM. THE PAPER IS DEVOTED TO DEVELOPING A MODEL OF STUDENTS' PROGRESS FORECASTING, WHICH IS BASED ON THE INFORMATION THEORY AND ALLOWS ONE TO ESTIMATE THE PROBABILITY OF STUDENTS' SUCCESS IN THE OF THE FINAL TEST WHEN OBSERVING THE CURRENT PERFORMANCE OF STUDENTS. THE PROBABILISTIC MODEL FOR GROUP FORECASTING OF THE FINAL PERFORMANCE, CONSIDERED IN THE PAPER, ALLOWS PREDICTING THE DISTRIBUTION OF STUDENTS' FINAL SCORES AND REDUCE THE LEVEL OF UNCERTAINTY IN ONLINE LEARNING USING MOOCS. IT CAN BE USEFUL FOR ASSESSING THE QUALITY OF THE COURSE AS A WHOLE AND DEVELOPING MEASURES TO IMPROVE IT. THE STATISTICAL MODEL FOR PERSONALIZED PERFORMANCE PREDICTION ALLOWS ONE TO MAKE A FORECAST OF THE FINAL PROGRESS FOR EACH STUDENT AND CAN BE USED TO IDENTIFY NEGATIVE TRENDS AND PROBLEMS OF STUDENTS IN LEARNING, PROVIDE THEM WITH RELEVANT FEEDBACK AND NECESSARY SUPPORT. THE APPLICATION OF THE MODELS IS CONSIDERED ON THE EXAMPLE OF AN ENGINEERING ONLINE COURSE CREATED BY THE URAL FEDERAL UNIVERSITY
IN THIS PAPER WE PRESENT OUR APPROACH TO INTRODUCE EDUCATIONAL VIDEOGAMES AS CLASS EXERCISES IN FACE-TO-FACE EDUCATION. THE MAIN OBJECTIVE IS TO SIMPLIFY TEACHERS' TASK WHEN USING GAMES BY PROVIDING REAL-TIME INFORMATION OF THE ACTUAL STUDENTS' USE OF THE GAMES WHILE IN THE CLASSROOM. THE APPROACH IS BASED ON DEFINING THE EDUCATIONAL GOALS FOR THE EXERCISE/GAME PRECISELY, DESIGNING A GAME THAT CAPTURES THESE GOALS, ESTABLISHING RELATIONS BETWEEN GAME INTERACTIONS AND EDUCATIONAL GOALS AND FINALLY, CREATE DATA CAPTURING AND VISUALIZATIONS OF THE RELEVANT INFORMATION TO SUPPORT THE TEACHER. WE APPLIED THIS APPROACH TO A REAL CASE STUDY, CREATING AN EDUCATIONAL VIDEOGAME ABOUT THE XML MARKUP LANGUAGE THAT SUBSTITUTED THE USUAL EXERCISES IN A WEB TECHNOLOGIES CLASS. THIS WAS TESTED WITH 34 COMPUTER SCIENCE STUDENTS WITH POSITIVE AND PROMISING RESULTS.
SERIOUS GAMES' EVALUATION AND PLAYERS' ASSESSMENT IS COMMONLY DONE WITH EXPERIMENTS WHERE THE USERS PLAY THE GAME AND FULFILL ONE OR MORE QUESTIONNAIRES. THE TOOL SIMVA WAS DESIGNED TO SIMPLIFY THESE COMPLEX EXPERIMENTS, WHICH COMMONLY INCLUDE THE COLLECTION OF GAME LEARNING ANALYTICS DATA TO PROVIDE FURTHER INSIGHT ABOUT PLAYERS' PROGRESS AND RESULTS. WE PRESENT THE LATEST UPDATES ON THE TOOL SIMVA AND THREE APPLICATIONS WHERE IT WAS USED TO VALIDATE SERIOUS GAMES USING PRE-POST EXPERIMENTS AND COLLECTING GAME LEARNING ANALYTICS DATA OF PLAYERS' IN-GAME INTERACTIONS.
DATA SCIENCE TECHNIQUES, NOWADAYS WIDESPREAD ACROSS ALL FIELDS, CAN ALSO BE APPLIED TO THE WEALTH OF INFORMATION DERIVED FROM STUDENT INTERACTIONS WITH SERIOUS GAMES. USE OF DATA SCIENCE TECHNIQUES CAN GREATLY IMPROVE THE EVALUATION OF GAMES, AND ALLOW BOTH TEACHERS AND INSTITUTIONS TO MAKE EVIDENCE-BASED DECISIONS. THIS CAN INCREASE BOTH TEACHER AND INSTITUTIONAL CONFIDENCE REGARDING THE USE OF SERIOUS GAMES IN FORMAL EDUCATION, GREATLY RAISING THEIR ATTRACTIVENESS. THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW ON HOW AUTHORS HAVE APPLIED DATA SCIENCE TECHNIQUES ON GAME ANALYTICS DATA AND LEARNING ANALYTICS DATA FROM SERIOUS GAMES TO DETERMINE: (1) THE PURPOSES FOR WHICH DATA SCIENCE HAS BEEN APPLIED TO GAME LEARNING ANALYTICS DATA, (2) WHICH ALGORITHMS OR ANALYSIS TECHNIQUES ARE COMMONLY USED, (3) WHICH STAKEHOLDERS HAVE BEEN CHOSEN TO BENEFIT FROM THIS INFORMATION AND (4) WHICH RESULTS AND CONCLUSIONS HAVE BEEN DRAWN FROM THESE APPLICATIONS. BASED ON THE CATEGORIES ESTABLISHED AFTER THE MAPPING AND THE FINDINGS OF THE REVIEW, WE DISCUSS THE LIMITATIONS OF THE STUDIES ANALYZED AND PROPOSE RECOMMENDATIONS FOR FUTURE RESEARCH IN THIS FIELD.
LEARNING ANALYTICS (LA) HAS DEMONSTRATED GREAT POTENTIAL IN IMPROVING TEACHING QUALITY, LEARNING EXPERIENCE AND ADMINISTRATIVE EFFICIENCY. HOWEVER, THE ADOPTION OF LA IN HIGHER EDUCATION IS OFTEN BESET BY CHALLENGES IN AREAS SUCH AS RESOURCES, STAKEHOLDER BUY-IN, ETHICS AND PRIVACY. ADDRESSING THESE CHALLENGES IN A COMPLEX SYSTEM REQUIRES AGILE LEADERSHIP THAT IS RESPONSIVE TO PRESSURES IN THE ENVIRONMENT AND CAPABLE OF MANAGING CONFLICTS. THIS PAPER EXAMINES LA ADOPTION PROCESSES AMONG 21 UK HIGHER EDUCATION INSTITUTIONS USING COMPLEXITY LEADERSHIP THEORY AS A FRAMEWORK. THE DATA WERE COLLECTED FROM 23 INTERVIEWS WITH INSTITUTIONAL LEADERS AND SUBSEQUENTLY ANALYSED USING A THEMATIC CODING SCHEME. THE RESULTS SHOWED A NUMBER OF PROMINENT CHALLENGES ASSOCIATED WITH LA DEPLOYMENT, WHICH LIE IN THE INHERENT TENSIONS BETWEEN INNOVATION AND OPERATION. THESE CHALLENGES REQUIRE A NEW FORM OF LEADERSHIP TO CREATE AND NURTURE AN ADAPTIVE SPACE IN WHICH INNOVATIONS ARE SUPPORTED AND ULTIMATELY TRANSFORMED INTO THE MAINSTREAM OPERATION OF AN INSTITUTION. THIS PAPER ARGUES THAT A COMPLEXITY LEADERSHIP MODEL ENABLES HIGHER EDUCATION TO SHIFT TOWARDS MORE FLUID AND DYNAMIC APPROACHES FOR LA ADOPTION, THUS ENSURING ITS SCALABILITY AND SUSTAINABILITY.
PURPOSE - THE PURPOSE OF THIS PAPER IS TO PRESENT AN INNOVATIVE WEB-BASED ELEARNING PLATFORM CALLED ICT-FLAG THAT PROVIDES E-ASSESSMENT TOOLS WITH GENERAL-PURPOSE FORMATIVE ASSESSMENT SERVICES FEATURING LEARNING ANALYTICS AND GAMIFICATION. DESIGN/METHODOLOGY/APPROACH - THE PAPER REPORTS ON THE TECHNICAL DEVELOPMENT OF THE PLATFORM DRIVEN BY THE REFERENCE MODEL FOR OPEN DISTRIBUTED PROCESSING SOFTWARE METHODOLOGY, WHICH GUIDES THE PLATFORM CONSTRUCTION, INCLUDING THE ANALYSIS AND DESIGN STEPS. FINDINGS - THE ICT-FLAG PLATFORM IS TECHNICALLY TESTED BY INTEGRATING IT INTO A REAL E-ASSESSMENT TOOL. RESULTS ARE POSITIVE IN TERMS OF FUNCTIONAL AND NON-FUNCTIONAL ASPECTS AS WELL AS USER'S SATISFACTION ON USABILITY, EMOTIONAL STATE, THUS VALIDATING THE PLATFORM AS A VALUABLE EDUCATIONAL TOOL. RESEARCH LIMITATIONS/IMPLICATIONS - BECAUSE OF THE CHOSEN TECHNICAL PAPER AS ARTICLE TYPE, VALIDATION OF THE IMPACT OF THE ICT-FLAG PLATFORM IN THE LEARNING PROCESS IS NOT PROVIDED. ONGOING RESEARCH WITH THIS PLATFORM IS TO MEASURE THE LEARNING OUTCOMES OF ITS USE IN A REAL CONTEXT OF ELEARNING. PRACTICAL IMPLICATIONS - THE PAPER SHOWS IMPLICATIONS OF THE MAIN TECHNICAL ISSUES AND CHALLENGES ENCOUNTERED DURING THE INTEGRATION OF THE ICT-FLAG PLATFORM WITH EXTERNAL ELEARNING TOOLS, INVOLVING RELEVANT ASPECTS OF INTEROPERABILITY, SECURITY, MODULARITY, SCALABILITY, PORTABILITY AND SO ON. ORIGINALITY/VALUE - THIS PLATFORM CAN FILL THE GAP OF MANY E-ASSESSMENT SYSTEMS, WHICH CURRENTLY DO NOT HAVE BUILT-IN ANALYTICAL AND GAMIFICATION TOOLS FOR LEARNING, THUS PROVIDING THEM WITH THE EXPERIENCE TO IMPROVE THE QUALITY OF EDUCATION AND LEARNING.
THIS STUDY INTRODUCES A DISTRIBUTED VIRTUAL LABORATORY FOR A MULTIAGENT PROGRAMMING COURSE WHICH HAS BEEN VERY SATISFACTORILY ADOPTED BY STUDENTS, WITH A SUCCESS RATE OF NEARLY 80%. IT ALSO AIMS AT CAPTURING THE DAILY ACTIVITY OF STUDENTS, PROVIDING THE BASIS FOR DATA-DRIVEN ASSESSMENT. FINALLY, IT ALSO ALLOWS FOR USING PROCESS MINING TECHNOLOGIES TO UNVEIL SUCCESSFUL AND FAILED BEHAVIORS OF STUDENTS ENABLING THE TEACHER FOR AN EARLY DETECTION AND INTERVENTION TO IMPROVE THEIR LEARNING EXPERIENCE(1).
PURPOSE THE PAPER AIMS TO PRESENT A NEW TWO STAGE LOCAL CAUSAL LEARNING ALGORITHM - HEISA. IN THE FIRST STAGE, THE ALGORITHM DISCOVERIES THE SUBSET OF FEATURES THAT BETTER EXPLAINS A TARGET VARIABLE. DURING THE SECOND STAGE, COMPUTES THE CAUSAL EFFECT, USING PARTIAL CORRELATION, OF EACH FEATURE OF THE SELECTED SUBSET. USING THIS NEW ALGORITHM, THE STUDY AIMS TO IDENTIFY THE ACTIONS THAT LEAD A STUDENT SUCCEED OR FAILURE IN A COURSE. DESIGN/METHODOLOGY/APPROACH THE PAPER PRESENTS A BRIEF REVIEW OF MAIN CONCEPTS USED IN THIS STUDY: CAUSAL LEARNING AND CAUSAL EFFECTS. THE PAPER ALSO DISCUSSES THE RESULTS OF APPLYING THE ALGORITHM IN EDUCATION DATA SET. DATA USED IN THIS STUDY WAS EXTRACTED FROM THE LOG OF ACTIONS OF A LEARNING MANAGEMENT SYSTEM, MOODLE. THESE ACTIONS REPRESENT THE BEHAVIOR OF 229 ENGINEERING STUDENTS THAT TAKE ALGORITHM AND DATA STRUCTURE COURSE OFFERED IN A BLENDED MODEL. FINDINGS THE ALGORITHM PROPOSED IN THE PAPER IDENTIFIES THAT FEATURES WITH WEAK RELEVANCE TO A TARGET MAY BECOME RELEVANT WHEN COMPUTING THE DIRECT EFFECT. RESEARCH LIMITATIONS/IMPLICATIONS THE ALGORITHM NEEDS TO BE IMPROVED TO AUTOMATICALLY DISCARD ATTRIBUTES THAT ARE UNDER A SPECIFIC THRESHOLD OF DIRECT EFFECT. RESEARCHERS ARE ALSO ENCOURAGED TO TEST THE PROPOSED PROPOSITIONS FURTHER. PRACTICAL IMPLICATIONS THE ALGORITHM PRESENTED IN THIS PAPER CAN BE USED TO IDENTIFY THE MOSTLY RELEVANT FEATURES GIVEN A CLASSIFICATION TASK. ORIGINALITY/VALUE THIS PAPER COMPUTES THE DIRECT EFFECT OF A SELECTED SUBSET OF FEATURES IN A TARGET VARIABLE TO EVALUATE IF A VARIABLE IN THIS SUBSET IS REALLY A CAUSE OF THE TARGET OR IF IT IS A SPURIOUS CORRELATION.
MANY STUDIES HAVE FOUND A RELATIONSHIP BETWEEN STUDENTS' SELF-REPORTED PROCRASTINATION AND THEIR GRADES. FEW STUDIES HAVE USED LEARNING ANALYTIC DATA AS A BEHAVIOURAL MEASURE OF PROCRASTINATION IN ORDER TO PREDICT PERFORMANCE, AND THERE IS NO SYSTEMATIC RESEARCH ON HOW THIS RELATIONSHIP MAY DIFFER ACROSS ASSESSMENTS OR DISCIPLINES. IN THIS STUDY WE ANALYSE NINE YEARS' WORTH OF INSTITUTIONAL ELECTRONIC SUBMISSION RECORDS, A TOTAL OF 73,608 ASSIGNMENT SUBMISSIONS, TO EXAMINE THE RELATIONSHIP BETWEEN SUBMISSION TIME AND GRADES ACROSS ASSIGNMENTS, STUDENTS, COURSES, AND DISCIPLINES IN HIGHER EDUCATION. A SIGNIFICANT NEGATIVE RELATIONSHIP WAS FOUND OVERALL, WITH STUDENTS WHO SUBMITTED CLOSER TO THE DEADLINE OBTAINING LOWER GRADES, HOWEVER THE SIZE OF THE RELATIONSHIP WAS NEGLIGIBLE, ACCOUNTING FOR LESS THAN 1% OF THE VARIANCE IN GRADES. THE RELATIONSHIP VARIED SIGNIFICANTLY DEPENDING ON STUDENT, ASSIGNMENT, COURSE AND DISCIPLINE.
THE INCREASED INTEREST IN MULTIMODAL DATA COLLECTION IN THE LEARNING SCIENCES DEMANDS FOR NEW AND POWERFUL METHODOLOGICAL AND ANALYTICAL TECHNIQUES AND TECHNOLOGIES. IT IS ESPECIALLY CHALLENGING FOR LEARNING SCIENTISTS TO HANDLE, ANALYSE, AND INTERPRET COMPLEX AND OFTEN INVISIBLE MULTIMODAL DATA WHEN INVESTIGATING REGULATION OF LEARNING IN COLLABORATIVE SETTINGS AS THIS DATA CAN BE COGNITIVE, SOCIAL AND/OR EMOTIONAL IN NATURE, MUCH OF WHICH IS COVERT IN NATURE. THE AIM OF THIS PAPER IS TO PRESENT WAYS TO SIMPLIFY THE ANALYSIS AND USE OF RICH MULTIMODAL DATA BY LEARNING SCIENTISTS. THIS IS DONE BY MAKING PRIMARILY INVISIBLE REGULATION PROCESSES AND THEIR ACCOMPANYING SOCIAL AND CONTEXTUAL REACTIONS VISIBLE, MEASURABLE, AND ULTIMATELY INTERPRETABLE. TO FACILITATE DATA VISUALISATION AND PROCESSING WITH RESPECT TO THE REGULATION OF LEARNING, A GRAPHICAL USER INTERFACE (GUI) KNOWN AS SLAM-KIT HAS BEEN DESIGNED. SLAM-KIT REVEALS PRINCIPAL FEATURES OF COMPLEX LEARNING ENVIRONMENTS BY ALLOWING USERS TO TRAVEL THROUGH THE LEARNERS' DATA AND ITS STATISTICAL CHARACTERISTICS. THIS KIT HAS PRACTICAL IMPLICATIONS AS IT SIMPLIFIES COMPLEX INFORMATION AND DATA WHILE MAKING THEM AVAILABLE THROUGH VISUALISATION AND ANALYSIS TO THE RESEARCHERS. OUR SHORT-TERM GOAL IS TO SIMPLIFY THIS TOOL FOR THE TEACHERS AND LEARNERS.
THE PURPOSE OF THIS PAPER IS TO SHOW THE EFFECT OF LEARNING ANALYTICS SYSTEM BASED TEACHING THAT INSTRUCTOR DEPENDS ON A SYSTEM WHICH MONITORS LEARNER'S PROGRAMMING ACTIVITIES AND GRASPS HIS/HER ACHIEVEMENT LEVEL IN REAL-TIME UNDER BLOCK-BASED PROGRAMMING ENVIRONMENT. BASED ON THE ANALYZED INFORMATION, TEACHERS MIGHT PROVIDE TIMELY HELP AND LEAD STUDENTS TO ENGAGE IN CODING EDUCATION. ALSO, WE FOUND THAT THERE EXITS STATISTICALLY SIGNIFICANT DIFFERENCE IN DECOMPOSITION, ABSTRACTION, AND ALGORITHM COMPONENTS AMONG SEVERAL COMPONENTS OF COMPUTATIONAL THINKING SKILLS.
THE ARTICLE IS FOCUSING ON ASPECTS RELATED TO TECHNOLOGY ACCEPTANCE OF LA IN THE CONTEXT OF SCHOOL EDUCATION. A SURVEY THAT WAS BASED ON THE TECHNOLOGY ACCEPTANCE MODEL WAS DISTRIBUTED TO 98 PARTICIPANTS WHO WORK AS SCHOOLTEACHERS AND/OR LOCAL EDUCATIONAL POLICYMAKERS AND ARE GEOGRAPHICALLY DISPERSED ACROSS TWO DIFFERENT EUROPEAN COUNTRIES. THE SURVEY COMPRISED OF QUESTIONS THAT SPAN ACROSS FOUR BROAD CATEGORIES: A) ACTUAL USAGE OF LA IN SCHOOLS, B) INTENTION TO USE LA, C) PERCEIVED USEFULNESS, AND D) ATTITUDES TOWARDS USE. REGARDING (A), MOST OF THE PARTICIPANTS WERE AWARE THAT THEIR EDUCATIONAL INSTITUTIONS RETAIN AND USE DIGITAL FORMATS OF STUDENT DATA AS WELL AS OTHER DATA RELEVANT TO THE LEARNING PROCESS. ALSO, THE PARTICIPANTS COULD VERIFY THE EXISTENCE OF A RESPECTIVE PRIVACY POLICY IN THEIR EDUCATIONAL INSTITUTIONS. WITH RESPECT TO THE PERCEIVED USEFULNESS ASPECT, THE PARTICIPANTS WERE ASKED TO OPINIONATE CONCERNING FOUR PROMISING TYPES OF LA USAGE, NAMELY 1) REAL-TIME FEEDBACK, 2) PREDICTION OF AT-RISK STUDENTS, 3) LEARNING ACTIVITIES RECOMMENDATION, AND 4) STUDENT GROUPS RECOMMENDATION. ALL FOUR TYPES WERE WELL-RECEIVED, BUT THE PERCEIVED USEFULNESS OF PREDICTION OF AT-RISK STUDENTS SCORED LOWER THAN THE OTHER THREE TYPES OF ACTIVITIES, WHILE THE LEARNING ACTIVITIES RECOMMENDATION SCORED HIGHER IN TERMS OF PERCEIVED USEFULNESS. THE PARTICIPANTS' INTENTION TO USE LA WAS EXAMINED IN CONJUNCTION WITH THEIR PROFESSIONALISM AND THAT PROSPECT WAS ALSO WELL-RECEIVED. THE RESULTS ARE PARTIALLY CONTROVERSIAL, SINCE THE PARTICIPANTS' ATTITUDES WERE NOT PARTICULARLY FAVORABLE: THEY ARE MODERATE WITH RESPECT TO HOW MUCH SCEPTIC, WILLING AND READY THEY FEEL ABOUT EMBARKING ON A LA ENDEAVOR.
WHEN PERFORMING ANALYTICS ON EDUCATIONAL DATASETS, THE BEST SCENARIO IS WHERE THE DATASET WAS DESIGNED TO BE ANALYZED. HOWEVER, THIS IS OFTEN NOT THE CASE AND THE DATA EXTRACTION BECOMES MORE COMPLICATED. THIS CONTRIBUTION IS FOCUSED ON EXTRACTING SOCIAL NETWORKS FROM A DATASET WHICH WAS NOT ADAPTED FOR THIS TYPE OF EXTRACTION AND WHERE THERE WAS NO RELATION AMONG STUDENTS: A SET OF REMOTE LABORATORIES WHERE STUDENTS INDIVIDUALLY TEST THEIR EXPERIMENTS BY SUBMITTING THEIR DATA TO A REAL REMOTE DEVICE. BY CHECKING WHICH FILES ARE SHARED AMONG STUDENTS AND SUBMITTED INDIVIDUALLY BY THEM, IT IS POSSIBLE TO KNOW WHO IS SHARING HOW MANY FILES WITH WHO, AUTOMATICALLY EXTRACTING WHAT STUDENTS ARE BIGGER SOURCES. WHILE IT IS IMPOSSIBLE TO EXTRACT THE FULL REAL SOCIAL NETWORK OF THESE STUDENTS, ALL THE EDGES FOUND ARE CLEARLY PART OF IT. THESE RELATIONS CAN INDEED BE USED AS A NEW INPUT FOR PERFORMING THE ANALYTICS ON THE DATASET.
WRITTEN COMMUNICATION IS AN IMPORTANT SKILL ACROSS ACADEMIA, THE WORKPLACE, AND CIVIC PARTICIPATION. EFFECTIVE WRITING INCORPORATES INSTANTIATIONS OF PARTICULAR TEXT STRUCTURES RHETORICAL MOVES - THAT COMMUNICATE INTENT TO THE READER. THESE RHETORICAL MOVES ARE IMPORTANT ACROSS A RANGE OF ACADEMIC STYLES OF WRITING, INCLUDING ESSAYS AND RESEARCH ABSTRACTS, AS WELL AS IN FORMS OF WRITING IN WHICH ONE REFLECTS ON LEARNING GAINED THROUGH EXPERIENCE. HOWEVER, LEARNING HOW TO EFFECTIVELY INSTANTIATE AND USE THESE RHETORICAL MOVES IS A CHALLENGE. MOREOVER, EDUCATORS OFTEN STRUGGLE TO PROVIDE FEEDBACK SUPPORTING THIS LEARNING, PARTICULARLY AT SCALE. WHERE EFFECTIVE SUPPORT IS PROVIDED, THE TECHNIQUES CAN BE HARD TO SHARE BEYOND SINGLE IMPLEMENTATION SITES. WE ADDRESS THESE CHALLENGES THROUGH THE OPEN-SOURCE ACAWRITER TOOL, WHICH PROVIDES FEEDBACK ON RHETORICAL MOVES, WITH A DESIGN THAT ALLOWS FEEDBACK CUSTOMIZATION FOR SPECIFIC CONTEXTS. WE INTRODUCE THREE EXAMPLE IMPLEMENTATIONS IN WHICH WE HAVE CUSTOMIZED THE TOOL AND EVALUATED IT WITH REGARD TO USER PERCEPTIONS, AND ITS IMPACT ON STUDENT WRITING. WE DISCUSS THE TOOL'S GENERAL THEORETICAL BACKGROUND AND PROVIDE A DETAILED TECHNICAL ACCOUNT. WE CONCLUDE WITH FOUR RECOMMENDATIONS THAT EMPHASIZE THE POTENTIAL OF COLLABORATIVE APPROACHES IN BUILDING, SHARING AND EVALUATING WRITING TOOLS IN RESEARCH AND PRACTICE.
A GROUP OF 22 MEDICAL EDUCATORS FROM DIFFERENT EUROPEAN COUNTRIES, GATHERED IN A MEETING IN UTRECHT IN JULY 2019, DISCUSSED THE TOPIC OF LEARNING ANALYTICS (LA) IN AN OPEN CONVERSATION AND ADDRESSED ITS DEFINITION, ITS PURPOSES AND POTENTIAL RISKS FOR LEARNERS AND TEACHERS. LA WAS SEEN AS A SIGNIFICANT ADVANCE WITH IMPORTANT POTENTIAL TO IMPROVE EDUCATION, BUT THE GROUP FELT THAT POTENTIAL DRAWBACKS OF USING LA MAY YET BE UNDER-EXPOSED IN THE LITERATURE. AFTER TRANSCRIPTION AND INTERPRETATION OF THE DISCUSSION'S CONCLUSIONS, A DOCUMENT WAS DRAFTED AND FED BACK TO THE GROUP IN TWO ROUNDS TO ARRIVE AT A SERIES OF 10 CAVEATS EDUCATORS SHOULD BE AWARE OF WHEN DEVELOPING AND USING LA, INCLUDING TOO MUCH STANDARDIZED LEARNING, WITH UNDUE CONSEQUENCES OF OVER-EFFICIENCY AND PRESSURE ON LEARNERS AND TEACHERS, AND A DECREASE OF THE VARIETY OF 'VALID' LEARNING RESOURCES. LEARNING ANALYTICS MAY MISALIGN WITH EVENTUAL CLINICAL PERFORMANCE AND CAN RUN THE RISK OF PRIVACY BREACHES AND INESCAPABILITY OF DOCUMENTED FAILURES. THESE CONSEQUENCES MAY NOT HAPPEN, BUT THE AUTHORS, ON BEHALF OF THE FULL GROUP OF EDUCATORS, FELT IT WORTH TO SIGNAL THESE CAVEATS FROM A CONSUMERS' PERSPECTIVE.
THE NEW METHODS OF TEACHING AND LEARNING, BASED ON VIRTUAL ENVIRONMENTS, WHICH ARE A N ASYNCHRONOUS EXTENSION OF THE CLASSROOMS, HAS DETERMINED THAT THE TEACHER HAS HAD TO ASSUME NEW ROLES, SOME OF WHICH GO THROUGH TO INCREASE THEIR OBSERVATION CAPACITY AND BECOME AN ANALYST OF THE LEARNING PROCESS THAT THE STUDENTS FOLLOW, ABOUT THE USES THEY MAKE OF DIGITAL PLATFORMS, HOW THEY INTERRELATE WITH THEM AND WITH OTHERS STUDENTS, AND HOW THEY ACQUIRE KNOWLEDGE AND DEVELOP SKILLS AND COMPETENCES. FROM THIS POINT OF VIEW, THE WORK DEALS WITH THE DESCRIPTION OF STATISTICAL USES AND REGISTERS, WHICH, AS A FINGERPRINT, LEAVE STUDENTS IN FACE-TO-FACE COURSES IN THE VIRTUAL PLATFORMS, AND WHICH CAN DEFINE TYPOLOGIES AND PATTERNS DIFFERENTIATED LEARNING, ASPECT THAT CAN LEAD TO A REFLECTION AND REORIENTATION OF THE FUTURE TEACHING-LEARNING PROCESS. SPECIFICALLY, THE CASE STUDY INCLUDES DESCRIPTIVE RESULTS OF THE INTERACTION OF STUDENTS' USE OF DIGITAL RESOURCES WITH CONTINUOUS ASSESSMENT AND GRADES IN THE COURSE OF ADVANCED MICROECONOMIC ANALYSIS, OF THE DEGREE IN ECONOMICS (GECO), AT THE UCM IN THE 2018-19 ACADEMIC YEAR, PROVIDING A PROFILE OF USE THAT IS STILL VERY TRADITIONAL AND POLARIZED, LINKED TO MINIMUM LAGS IN THE TIMETABLE OF THE PROPOSED ACTIVITIES BY THE TEACHER.
MANY STUDIES HAVE HIGHLIGHTED THE IMPORTANCE OF PERSONALIZED LEARNING, AND MOST CURRENT E-LEARNING SYSTEMS ARE ABLE TO PERSONALIZE MATERIALS, ACTIVITIES, ETC., BASED ON INDIVIDUALIZED LEARNER-FACTORS. HOWEVER, NONE OF THE EXTANT WORD-LEARNING SYSTEMS PROVIDES A PERSONALIZED LEARNING EXPERIENCE THAT IS GUIDED BY A COMPREHENSIVE WORD LEARNING THEORY. IN THIS STUDY, WE DEVELOP SUCH A SYSTEM BASED ON NATION AND WEBB'S CHECKLIST FOR TECHNIQUE FEATURE ANALYSIS - A THOROUGH SET OF FACTORS THAT PROMOTE EFFECTIVE WORD LEARNING. THIS SYSTEM RECOMMENDS PERSONALIZED WORD LEARNING TASKS BASED ON THE TECHNIQUE FEATURE ANALYSIS SCORES OF DIFFERENT TASKS AND USER MODELS. TO EXAMINE THE EFFECTIVENESS OF THE PROPOSED SYSTEM, WE CONDUCTED AN EXPERIMENT AMONG 105 ENGLISH LEARNERS, GROUPED THEM INTO THREE TEAMS RANDOMLY, AND ASKED THEM TO LEARN FORTY TARGET WORDS THROUGH THREE APPROACHES: A NON-PERSONALIZED APPROACH, A PERSONALIZED APPROACH GUIDED BY A PARTIAL VERSION OF THE TECHNIQUE FEATURE ANALYSIS, AND A PERSONALIZED APPROACH GUIDED BY THE FULL LIST OF THE TECHNIQUE FEATURE ANALYSIS. SIGNIFICANT DIFFERENCES WERE OBSERVED AMONG THE EFFECTIVENESS OF THE THREE APPROACHES IN PROMOTING WORD LEARNING, WITH THE PERSONALIZED APPROACH GUIDED BY THE COMPLETE CHECKLIST LEADING TO THE BEST LEARNING PERFORMANCE. IT IS THEREFORE SUGGESTED THAT E-LEARNING SYSTEMS SHOULD BE DESIGNED BASED ON COMPREHENSIVE LEARNING THEORIES.
ASSESSMENT FEEDBACK, WHICH IS AN IMPORTANT FACTOR IN STUDENT LEARNING DEVELOPMENT, HAS BEEN A SOURCE OF DISSATISFACTION FOR MANY YEARS. THE TECHNOLOGY USED IN UNIVERSITIES SUCH AS GRADEBOOK DOES NOT ALLOW THE TRACKING OF STUDENT ENGAGEMENT WITH THEIR FEEDBACK. IN GENERAL, ENGAGEMENT IS MAINLY REPORTED VIA OBSERVATION OR STUDENTS' SELF-REPORT. THIS PAPER PRESENTS RESULTS FROM USING A PROTOTYPE APPLICATION TO TRACK STUDENTS' ENGAGEMENT WITH THEIR FEEDBACK IN THREE STUDIES (N=218, N=70 AND N=148) INVOLVING SUMMATIVE AND FORMATIVE ASSESSMENT FEEDBACK. USING DIGITAL FOOTPRINTING DATA, IT WAS POSSIBLE TO GATHER DETAILED INFORMATION ON STUDENTS' ACCESS AND ENGAGEMENT WITH THEIR FEEDBACK.
COMPLEX COLLABORATIVE LEARNING ACTIVITIES REQUIRE THE LEARNERS TO SELF-REGULATE AND REFLECT ON THEIR LEARNING PROCESS. LEARNING ANALYTICS CAN BE A MEANS TO SUPPPORT THEM IN THESE TASKS. YET, CURRENT APPROACHES OF REPRESENTING ANALYTICS RESULTS ARE MAINLY ORIENTED TOWARDS RESEARCHERS OR INSTRUCTORS OF ONLINE COURSES. IN THIS PAPER WE WILL PRESENT OUR POSITION ON LEARNER-CENTERED VISUALISATIONS FOR ANALYTICS AND DESIGNS WE IMPLEMENTED IN THE METAFORA PROJECT TO SERVE THIS PURPOSE.
THIS PAPER PRESENTS A DIALOGICAL TOOL FOR THE ADVANCEMENT OF LEARNING ANALYTICS IMPLEMENTATION FOR STUDENT RETENTION IN HIGHER EDUCATION INSTITUTIONS. THE FRAMEWORK WAS DEVELOPED AS AN OUTCOME OF A PROJECT COMMISSIONED AND FUNDED BY THE AUSTRALIAN GOVERNMENT'S OFFICE FOR LEARNING AND TEACHING. THE PROJECT TOOK A MIXED-METHOD APPROACH INCLUDING A SURVEY AT THE INSTITUTIONAL LEVEL (N = 24), A SURVEY OF INDIVIDUAL TEACHING STAFF AND OTHER ACADEMICS WITH AN INTEREST IN STUDENT RETENTION (N = 353), AND A SERIES OF INTERVIEWS (N = 23). FOLLOWING THE COLLECTION AND ANALYSIS OF THESE DATA AN INITIAL VERSION OF THE FRAMEWORK WAS DEVELOPED AND PRESENTED AT A NATIONAL FORUM ATTENDED BY 148 COLLEAGUES FROM 43 DIFFERENT INSTITUTIONS. PARTICIPANTS AT THE FORUM WERE INVITED TO PROVIDE COMMENTARY ON THE USEFULNESS AND COMPOSITION OF THE FRAMEWORK WHICH WAS SUBSEQUENTLY UPDATED TO REFLECT THIS FEEDBACK. ULTIMATELY, IT IS ENVISAGED THAT SUCH A FRAMEWORK MIGHT OFFER INSTITUTIONS AN ACCESSIBLE AND CONCISE TOOL TO STRUCTURE AND SYSTEMATIZE DISCUSSION ABOUT HOW LEARNING ANALYTICS MIGHT BE IMPLEMENTED FOR STUDENT RETENTION IN THEIR OWN CONTEXT.
THIS PAPER PROPOSES AN ANALYSIS OF CURRENT RESEARCH IN LEARNING DESIGN (LD), A FIELD AIMING TO IMPROVE THE QUALITY OF EDUCATIONAL INTERVENTIONS BY SUPPORTING THEIR DESIGN AND FOSTERING THE SHARING AND REUSE OF INNOVATIVE PRACTICES AMONG EDUCATORS. THIS RESEARCH AREA, AT THE MOMENT, FOCUSES ON THREE MAIN STRANDS: THE REPRESENTATIONS THAT CAN BE USED AS A COMMON LANGUAGE TO COMMUNICATE ABOUT PEDAGOGICAL PLANS AND OTHER HALF-FABRICATES OF THE DESIGN PROCESS, THE METHODOLOGICAL APPROACHES TO LD AND THE TOOLS THAT SUPPORT THE LD PROCESS. FOR EACH OF THE THREE STRANDS, THE CURRENT LANDSCAPE IS DISCUSSED, POINTING AT OPEN ISSUES AND INDICATING FUTURE RESEARCH PERSPECTIVES, WITH PARTICULAR ATTENTION TO THE CONTRIBUTION THAT LEARNING ANALYTICS CAN MAKE TO TRANSFORM LD FROM A CRAFT, BASED ON EXPERIENCE, INTUITION AND TACIT KNOWLEDGE, INTO A MATURE RESEARCH AREA, GROUNDED ON DATA CONCERNING THE LEARNING PROCESS AND HENCE SUPPORTING ENQUIRY WHILE TEACHERS DESIGN, RUN AND EVALUATE THE LEARNING PROCESS.
COLLABORATION IS ONE OF THE IMPORTANT 21ST-CENTURY SKILLS. IT CAN TAKE PLACE IN REMOTE OR CO-LOCATED SETTINGS. CO-LOCATED COLLABORATION (CC) IS A VERY COMPLEX PROCESS THAT INVOLVES SUBTLE HUMAN INTERACTIONS THAT CAN BE DESCRIBED WITH INDICATORS LIKE EYE GAZE, SPEAKING TIME, PITCH, AND SOCIAL SKILLS FROM DIFFERENT MODALITIES. WITH THE ADVENT OF SENSORS, MULTIMODAL LEARNING ANALYTICS HAS GAINED MOMENTUM TO DETECT CC QUALITY. INDICATORS (OR LOW-LEVEL EVENTS) CAN BE USED TO DETECT CC QUALITY WITH THE HELP OF MEASURABLE MARKERS (I.E., INDEXES COMPOSED OF ONE OR MORE INDICATORS) WHICH GIVE THE HIGH-LEVEL COLLABORATION PROCESS DEFINITION. HOWEVER, THIS UNDERSTANDING IS INCOMPLETE WITHOUT CONSIDERING THE SCENARIOS (SUCH AS PROBLEM SOLVING OR MEETINGS) OF CC. THE SCENARIO OF CC AFFECTS THE SET OF INDICATORS CONSIDERED: FOR INSTANCE, IN COLLABORATIVE PROGRAMMING, GRABBING THE MOUSE FROM THE PARTNER IS AN INDICATOR OF COLLABORATION; WHEREAS IN COLLABORATIVE MEETINGS, EYE GAZE, AND AUDIO LEVEL ARE INDICATORS OF COLLABORATION. THIS CAN BE A RESULT OF THE DIFFERING GOALS AND FUNDAMENTAL PARAMETERS (SUCH AS GROUP BEHAVIOR, INTERACTION, OR COMPOSITION) IN EACH SCENARIO. IN THIS ARTICLE, WE PRESENT OUR WORK ON PROFILES OF INDICATORS ON THE BASIS OF A SCENARIO-DRIVEN PRIORITIZATION, THE PARAMETERS IN DIFFERENT CC SCENARIOS ARE MAPPED ONTO THE INDICATORS AND THE AVAILABLE INDEXES. THIS DEFINES THE CONCEPTUAL MODEL TO SUPPORT THE DESIGN OF A CC QUALITY DETECTION AND PREDICTION SYSTEM.
BASED ON THE ANALYSIS OF STUDENTS' LEARNING DATA, THIS STUDY DESIGNED TEACHING ACTIVITIES TO PROMOTE DEEP LEARNING IN AN EBAG LESSON PLANNING SYSTEM AND PRESENTED THESE ACTIVITIES THROUGH VISUAL TOOLS. THROUGH THIS ONLINE LESSON PLANNING SYSTEM, TEACHERS CAN PREPARE LESSONS ACCORDING TO STUDENTS' LEARNING ABILITIES TO ACHIEVE ACCURATE TEACHING AND PROMOTE THE OCCURRENCE OF DEEP LEARNING.
IN THE PAST DECADES, UBIQUITOUS LEARNING (U-LEARNING) HAS BEEN THE FOCUS OF ATTENTION IN EDUCATIONAL RESEARCH ACROSS THE WORLD. MAJORITY OF U-LEARNING SYSTEMS HAVE BEEN CONSTRUCTED USING UBIQUITOUS TECHNOLOGIES SUCH AS RFID TAGS AND CARDS, WIRELESS COMMUNICATION, MOBILE PHONES, AND WEARABLE COMPUTERS. THERE IS ALSO A GROWING RECOGNITION THAT IT CAN BE IMPROVED BY UTILIZING UBIQUITOUS LEARNING LOGS COLLECTED BY THE U-LEARNING SYSTEM TO ENHANCE AND INCREASE THE INTERACTIONS AMONG A LEARNER, CONTEXTS, AND CONTEXT-BASED KNOWLEDGE. ONE OF THE ISSUES OF ANALYTICS BASED ON U-LEARNING IS HOW TO DETECT OR MINE LEARNING LOGS COLLECTED BY U-LEARNING SYSTEMS. MOREOVER, IT IS NECESSARY TO EVALUATE WHETHER THE RECOMMENDATIONS DETECTED BY ANALYSIS ARE APPROPRIATE IN TERMS OF LEARNING LEVELS, CONTEXTS AND LEARNERS' PREFERENCE. TO TACKLE THE ISSUES, WE DEVELOPED A SYSTEM THAT COULD RECOMMEND USEFUL LEARNING LOGS AT THE RIGHT PLACE IN THE RIGHT TIME IN ACCORDANCE WITH PERSONALIZATION OF LEARNERS. AN EXPERIMENT WAS CONDUCTED TO EVALUATE THE SYSTEM'S PERFORMANCE AND THE RECOMMENDATIONS' USEFULNESS FOR LEARNING. IN THE EVALUATION EXPERIMENT, WE FOUND IMPORTANT CRITERIA FOR RECOMMENDING IN THE REAL-WORLD LANGUAGE LEARNING. IN ADDITION, THE PARTICIPANTS WERE ABLE TO INCREASE THEIR LEARNING OPPORTUNITIES BY OUR RECOMMENDATION METHOD.
THIS PAPER PRESENTS A FIRST STEP INTO THE CONSTRUCTION OF AN INNOVATIVE WEB-BASED ELEARNING PLATFORM FEATURING LEARNING ANALYTICS AND GAMIFICATION CALLED ICT-FLAG. THE MAIN GOAL AND KEY DECISION AT THIS INITIAL STAGE OF THE PLATFORM DEVELOPMENT IS THE SEARCH AND SELECTION PROCESSES OF THE MOST SUITABLE SOFTWARE DEVELOPMENT FRAMEWORK TO GUIDE AND SUPPORT THE EFFECTIVE CONSTRUCTION OF THE PLATFORM. DESPITE THE AVAILABILITY OF MANY ARCHITECTURE AND APPLICATION FRAMEWORKS FOR CREATING WEB-BASED SYSTEMS IN THE MARKET, ONLY A FEW ARE POTENTIALLY SUITABLE FOR THE SPECIFIC GOALS OF OUR ELEARNING PLATFORM. IN THIS PAPER, WE FIRST REVIEW EXISTING FRAMEWORKS IN TERMS OF PROS AND CONS CONSIDERING THE CHARACTERISTICS OF THE PROJECT. THEN, THE SEARCH'S OUTCOMES GO INTO A SELECTION METHODOLOGY WHOSE RESULTS ARE PRESENTED AND DISCUSSED, LEADING US TO MAKE A FINAL DECISION ON THE SOFTWARE FRAMEWORK THAT WILL EVENTUALLY SUPPORT AN EFFECTIVE DEVELOPMENT OF THE ICT-FLAG PLATFORM. THE RESEARCH REPORTED IN THIS PAPER IS CURRENTLY UNDERTAKEN WITHIN THE PROJECT "ENHANCING ICT EDUCATION THROUGH FORMATIVE ASSESSMENT, LEARNING ANALYTICS AND GAMIFICATION" (ICT-FLAG) FUNDED BY THE SPANISH GOVERNMENT.
THIS SPECIAL ISSUE DEALS WITH THREE AREAS. LEARNING DESIGN IS THE PRACTICE OF DEVISING EFFECTIVE LEARNING EXPERIENCES AIMED AT ACHIEVING DEFINED EDUCATIONAL OBJECTIVES IN A GIVEN CONTEXT. TEACHER INQUIRY IS AN APPROACH TO PROFESSIONAL DEVELOPMENT AND CAPACITY BUILDING IN EDUCATION IN WHICH TEACHERS STUDY THEIR OWN AND THEIR PEERS' PRACTICE. LEARNING ANALYTICS USE DATA ABOUT LEARNERS AND THEIR CONTEXTS TO UNDERSTAND AND OPTIMISE LEARNING AND THE ENVIRONMENTS IN WHICH IT TAKES PLACE. TYPICALLY, THESE THREEDESIGN, INQUIRY AND ANALYTICSARE SEEN AS SEPARATE AREAS OF PRACTICE AND RESEARCH. IN THIS ISSUE, WE SHOW THAT THE THREE CAN WORK TOGETHER TO FORM A VIRTUOUS CIRCLE. WITHIN THIS CIRCLE, LEARNING ANALYTICS OFFERS A POWERFUL SET OF TOOLS FOR TEACHER INQUIRY, FEEDING BACK INTO IMPROVED LEARNING DESIGN. LEARNING DESIGN PROVIDES A SEMANTIC STRUCTURE FOR ANALYTICS, WHEREAS TEACHER INQUIRY DEFINES MEANINGFUL QUESTIONS TO ANALYSE.
THE CURRENT KNOWLEDGE OF THE EFFECTS OF THE PHYSICAL ENVIRONMENT ON LEARNERS' BEHAVIOUR IN COLLABORATIVE PROBLEM-SOLVING TASKS IS UNDEREXPLORED. THIS PAPER AIMS TO CRITICALLY EXAMINE THE POTENTIAL OF MULTIMODAL LEARNING ANALYTICS, USING NEW DATA SETS, IN STUDYING HOW THE SHAPES OF SHARED TABLES AFFECT THE LEARNERS' BEHAVIOUR WHEN COLLABORATING IN TERMS OF PATTERNS OF PARTICIPATION AND INDICATORS RELATED TO PHYSICAL SOCIAL INTERACTIONS. THE RESEARCH PRESENTED IN THIS PAPER INVESTIGATES THIS QUESTION CONSIDERING THE POTENTIAL INTERPLAY WITH CONTEXTUAL ASPECTS (LEVEL OF EDUCATION) AND LEARNING DESIGN DECISIONS (GROUP SIZE). THREE DEPENDENT VARIABLES (DISTANCE BETWEEN STUDENTS, RANGE OF MOVEMENT AND LEVEL OF PARTICIPATION) ARE TESTED USING QUANTITATIVE AND QUALITATIVE ANALYSES OF DATA COLLECTED USING A MOTION CAPTURE SYSTEM AND VIDEO RECORDINGS. RESULTS SHOW THAT THE USE OF ROUND TABLES (VS RECTANGULAR TABLES) LEADS TO HIGHER LEVELS OF ON-TASK PARTICIPATION IN THE CASE OF ELEMENTARY SCHOOL STUDENTS. FOR UNIVERSITY STUDENTS, DIFFERENT TABLE SHAPES SEEM TO HAVE A LIMITED IMPACT ON THEIR LEVELS OF PARTICIPATION IN COLLABORATIVE PROBLEM SOLVING. THE ANALYSIS SHOWS SIGNIFICANT DIFFERENCES REGARDING THE RELATIONSHIP BETWEEN GROUP SIZE AND THE DISTANCE BETWEEN STUDENTS, BUT THERE IS NO SUBSTANTIAL EVIDENCE THAT GROUP SIZE AFFECTS THE LEVEL OF PARTICIPATION. THE FINDINGS SUPPORT PREVIOUS RESEARCH HIGHLIGHTING THE IMPORTANCE OF STUDYING THE ROLE OF THE PHYSICAL ENVIRONMENT AS AN ELEMENT OF LEARNING DESIGN AND THE POTENTIAL OF MULTIMODAL LEARNING ANALYTICS IN APPROACHING THESE STUDIES.
SEVERAL MAJOR ACCIDENTS IN THE OIL AND GAS INDUSTRY TRACED THEIR SOURCE TO DEFICIENT TRAINING RESULTING IN SERIOUS INJURIES AND EVEN CASUALTIES ALONG WITH EXTREMELY EXPENSIVE DAMAGE TO EQUIPMENT AND DECREASE IN PRODUCTIVITY. THIS PAPER PRESENTS A PROCEDURE EVALUATION/E-TRAINING TOOL CALLED PET TO TRACK THE KNOWLEDGE AND CONFIDENCE OF TRAINEES IN EMERGENCY OPERATING PROCEDURES. PET WAS TESTED WITH TWO EMERGENCY PROCEDURES IN AN OIL AND GAS COMPANY IN CANADA. A TEXT-BASED KNOWLEDGE TEST WAS IMPLEMENTED FOR EACH PROCEDURE. EACH TEST CONSISTED OF MULTIPLE-CHOICE QUESTIONS. ANSWERS WERE CLASSIFIED AS PERFECTLY CORRECT, INCOMPLETE BUT CORRECT, PARTIALLY CORRECT, MOSTLY INCORRECT, AND TOTALLY INCORRECT. THE PAPER ALSO DESCRIBES THE SIX-FACTOR CONFIDENCE MODEL UNDERLYING THE CONFIDENCE COMPUTATIONS IN PET: KNOWLEDGE, REACTION TIME, LINGERING, NUMBER OF VISITS (REVISION), NUMBER OF SELECTIONS, AND NUMBER OF SWITCHING ANSWERS. EACH CONFIDENCE FACTOR MEASURES A SPECIFIC ASPECT OF THE TARGETED BEHAVIOUR IN AN EMERGENCY. THE RESULTS OF TWO EXPERIMENTS CONDUCTED IN 2014 IN AN OIL AND GAS COMPANY ARE ALSO PRESENTED TO SHOW THE TYPES OF ANALYSIS THAT PET ENABLES. A PLAN TO MOVE PET INTO AN INTERACTIVE TRAINING ENVIRONMENT TO TRACK THE ACTIONS OF OPERATORS IN THEIR WORK ENVIRONMENT AND TRANSLATE THEIR INTERACTION INTO HIGHER-LEVEL COMPETENCES IS ALSO BRIEFLY INTRODUCED.
THIS PAPER INTRODUCES THE VISUAL INSPECTION TOOL (VIT) WHICH SUPPORTS RESEARCHERS IN THE ANNOTATION OF MULTIMODAL DATA AS WELL AS THE PROCESSING AND EXPLOITATION FOR LEARNING PURPOSES. WHILE MOST OF THE EXISTING MULTIMODAL LEARNING ANALYTICS (MMLA) SOLUTIONS ARE TAILOR-MADE FOR SPECIFIC LEARNING TASKS AND SENSORS, THE VIT ADDRESSES THE DATA ANNOTATION FOR DIFFERENT TYPES OF LEARNING TASKS THAT CAN BE CAPTURED WITH A CUSTOMISABLE SET OF SENSORS IN A FLEXIBLE WAY. THE VIT SUPPORTS MMLA RESEARCHERS IN 1) TRIANGULATING MULTIMODAL DATA WITH VIDEO RECORDINGS; 2) SEGMENTING THE MULTIMODAL DATA INTO TIME-INTERVALS AND ADDING ANNOTATIONS TO THE TIME-INTERVALS; 3) DOWNLOADING THE ANNOTATED DATASET AND USING IT FOR MULTIMODAL DATA ANALYSIS. THE VIT IS A CRUCIAL COMPONENT THAT WAS SO FAR MISSING IN THE AVAILABLE TOOLS FOR MMLA RESEARCH. BY FILLING THIS GAP WE ALSO IDENTIFIED AN INTEGRATED WORKFLOW THAT CHARACTERISES CURRENT MMLA RESEARCH. WE CALL THIS WORKFLOW THE MULTIMODAL LEARNING ANALYTICS PIPELINE, A TOOLKIT FOR ORCHESTRATION, THE USE AND APPLICATION OF VARIOUS MMLA TOOLS.
THIS PAPER EXPLORES THE POTENTIAL OF ANALYTICS FOR IMPROVING ACCESSIBILITY OF E-LEARNING AND SUPPORTING DISABLED LEARNERS IN THEIR STUDIES. A COMPARATIVE ANALYSIS OF COMPLETION RATES OF DISABLED AND NON-DISABLED STUDENTS IN A LARGE FIVE-YEAR DATASET IS PRESENTED AND A WIDE VARIATION IN COMPARATIVE RETENTION RATES IS CHARACTERIZED. LEARNING ANALYTICS ENABLE US TO IDENTIFY AND UNDERSTAND SUCH DISCREPANCIES AND, IN FUTURE, COULD BE USED TO FOCUS INTERVENTIONS TO IMPROVE RETENTION OF DISABLED STUDENTS. AN AGENDA FOR ONWARD RESEARCH, FOCUSED ON CRITICAL LEARNING PATHS, IS OUTLINED. THIS PAPER IS INTENDED TO STIMULATE A WIDER INTEREST IN THE POTENTIAL BENEFITS OF LEARNING ANALYTICS FOR INSTITUTIONS AS THEY TRY TO ASSURE THE ACCESSIBILITY OF THEIR E-LEARNING AND PROVISION OF SUPPORT FOR DISABLED STUDENTS.
ACTIVITY-CENTRIC DATA GATHER FEEDBACK ON STUDENTS' LEARNING TO ENHANCE LEARNING EFFECTIVENESS. THE HETEROGENEITY AND MULTIGRANULARITY OF SUCH DATA REQUIRE EXISTING DATA MODELS TO PERFORM COMPLEX ON-THE-FLY COMPUTATION WHEN RESPONDING TO QUERIES OF SPECIFIC GRANULARITY. THIS, IN TURN, RESULTS IN LATENCY. IN ADDITION, EXISTING DATA MODELS ARE INEFFICIENT IN STORING COMPUTED RESULTS, WHICH ARE OFTEN REQUIRED FOR FOLLOW-UP ANALYSIS. THESE FOLLOW-UP ANALYSES DEPEND LARGELY ON STAKEHOLDER OBJECTIVES, WHICH OFTEN IMPOSE CONSTRAINTS TO THE ANALYSIS PROCESS. IN THIS ARTICLE, WE PROPOSE A CONTEXT-BASED DATA MODEL THAT ADDRESSES TWO CHALLENGES ASSOCIATED WITH LEARNING ANALYTICS: THE INCREASED PROCESSING TIME DUE TO MULTIPLE DATA GRANULARITIES REQUIRED FROM VARIOUS STAKEHOLDER OBJECTIVES, AND THE LACK OF SUPPORT FOR ARCHIVING AND UPDATING OF NEW INFORMATION THAT EXIST DURING ITERATIVE ANALYSIS. WE DEMONSTRATE HOW THE PROPOSED MODEL CAN HELP SUPPORT ANALYSIS AND VISUALIZATION VIA THE XUETANGX AND ASSISTMENTS DATASETS. RESULTS SHOW THAT ALTHOUGH OUR PROPOSED DATA MODEL REQUIRES PREPROCESSING, IT REQUIRES LESS TIME TO PROCESS QUERIES ASSOCIATED WITH LEARNING-ACTIVITY ATTRIBUTES OF VARIOUS GRANULARITIES COMPARED TO EXISTING DATA MODELS.
THERE IS LITTLE DEBATE REGARDING THE IMPORTANCE OF STUDENT FEEDBACK FOR IMPROVING THE LEARNING PROCESS. HOWEVER, THERE REMAIN SIGNIFICANT WORKLOAD BARRIERS FOR INSTRUCTORS THAT IMPEDE THEIR CAPACITY TO PROVIDE TIMELY AND MEANINGFUL FEEDBACK. THE INCREASING ROLE TECHNOLOGY IS PLAYING IN THE EDUCATION SPACE MAY PROVIDE NOVEL SOLUTIONS TO THIS IMPEDIMENT. AS STUDENTS INTERACT WITH THE VARIOUS LEARNING TECHNOLOGIES IN THEIR COURSE OF STUDY, THEY CREATE DIGITAL TRACES THAT CAN BE CAPTURED AND ANALYSED. THESE DIGITAL TRACES FORM THE NEW KIND OF DATA THAT ARE FREQUENTLY USED IN LEARNING ANALYTICS TO DEVELOP ACTIONABLE RECOMMENDATIONS THAT CAN SUPPORT STUDENT LEARNING. THIS PAPER EXPLORES THE USE OF SUCH ANALYTICS TO ADDRESS THE CHALLENGES IMPEDING THE CAPACITY OF INSTRUCTORS TO PROVIDE PERSONALISED FEEDBACK AT SCALE. THE CASE STUDY REPORTED IN THE PAPER SHOWED HOW THE APPROACH WAS ASSOCIATED WITH A POSITIVE IMPACT ON STUDENT PERCEPTION OF FEEDBACK QUALITY AND ON ACADEMIC ACHIEVEMENT. THE STUDY WAS CONDUCTED WITH FIRST YEAR UNDERGRADUATE ENGINEERING STUDENTS ENROLLED IN A COMPUTER SYSTEMS COURSE WITH A BLENDED LEARNING DESIGN ACROSS THREE CONSECUTIVE YEARS (N-2013 = 290, N-2014 = 316 AND N-2015 = 415).
PREVIOUS RESEARCH NOTED THAT SOFTWARE REQUIREMENT ANALYSIS SHOULD MOVE BEYOND USABILITY TO UNDERSTAND AND DESIGN FOR MORE ENGAGING USER EXPERIENCES. THIS STUDY ANALYZES SYSTEM LOGS COLLECTED FROM THE MASSIVE OPEN ONLINE COURSES (MOOCS) TO CAPTURE USER ENGAGEMENT OVER TIME. WE MEASURED ENGAGEMENT THROUGH MAPPING EVENT LOGS WITH THREE COMPONENTS OF ENGAGEMENT. WE FURTHER ANALYZED THE HELPFULNESS OF ENGAGEMENT MEASUREMENT IN PREDICTING GRADES. THE RESULTS SHOWED THAT THERE WAS A SIGNIFICANT AND MODERATE POSITIVE CORRELATION BETWEEN THE BEHAVIORAL, COGNITIVE, AND EMOTIONAL ENGAGEMENT AND QUIZ SCORES. A MULTIPLE LINEAR REGRESSION ANALYSIS ALSO SHOWED THAT HIGHER BEHAVIORAL AND COGNITIVE ENGAGEMENT WERE RELATED TO HIGHER QUIZ SCORES. THUS, THIS STUDY PERFORMED THREE CLASSIFICATION METHODS AND FOUND THAT THE ANN METHOD GOT THE HIGHEST ACCURACY. THIS STUDY APPLIED SEQUENTIAL ANALYSIS TO DISCOVER THE DIFFERENCE BETWEEN VIDEO WATCHING BEHAVIOR OF THE HIGH AND LOW QUIZ SCORES STUDENTS. THE RESULTS INDICATE THAT IT IS IMPORTANT TO TAKE INTO ACCOUNT ALL OF THE BEHAVIORAL, COGNITIVE, AND EMOTIONAL ENGAGEMENT IN UNDERSTANDING USER ENGAGEMENT DURING THE SYSTEM DEVELOPMENT PROCESS.
THE APPLICATIONS OF COMPUTERIZED TOOLS FOR LEARNING MANAGEMENT SYSTEMS IN EDUCATION HAVE BEEN INCREASING IN THE LAST FEW YEARS. ONLINE CONTENTS BY MEANS OF MOBILE PHONES, PRIMARILY SMART PHONES ENABLE THE STUDENTS TO KEEP THE LEARNING PROCESSES AROUND THE CLOCK. THE ENORMOUS AMOUNT OF DATA CAPTURED FROM STUDENT'S ONLINE ACTIVITIES IS WASTED AS TRADITIONAL LEARNING ANALYTICS ARE NOT CAPABLE OF PROCESSING THEM. SO IN THIS PAPER IT IS PROPOSED TO USE BIG DATA TECHNOLOGIES AND TOOLS INTO EDUCATION FOR CAPTURING LEARNING PROCESS OF STUDENTS, STRATEGIC OPERATIONAL DECISIONS AT INSTITUTIONS. THE COMPUTING PLATFORM AND TEACHING METHODS TO ACCOMMODATE EMERGING TECHNOLOGIES FOR RELATED COURSES ARE ALSO INVESTIGATED IN THIS PAPER. IN ADDITION, VISUALIZATION-BASED DATA DISCOVERY TOOLS ARE FOCUSED ON THE FRONT END OF BIG DATA ON HELPING INSTITUTIONS EXPLORE THE DATA MORE EASILY AND UNDERSTAND IT MORE FULLY.
PURPOSE THE PURPOSE OF THIS PAPER IS TO REVEAL HOW INSTRUCTORS DISCUSS STUDENT DATA AND INFORMATION PRIVACY IN THEIR SYLLABI. DESIGN/METHODOLOGY/APPROACH THE AUTHORS COLLECTED A MIXTURE OF PUBLICLY ACCESSIBLE AND PRIVATELY DISCLOSED SYLLABI FROM 8,302 LIBRARY AND INFORMATION SCIENCE (LIS) COURSES TO EXTRACT PRIVACY LANGUAGE. USING PRIVACY CONCEPTS FROM THE LITERATURE AND EMERGENT THEMES, THE AUTHORS ANALYZED THE CORPUS. FINDINGS MOST SYLLABI DID NOT MENTION PRIVACY (98 PERCENT). PRIVACY TENDED TO BE MENTIONED IN THE CONTEXT OF DIGITAL TOOLS, COURSE COMMUNICATION, POLICIES AND ASSIGNMENTS. ORIGINALITY/VALUE IN AN AGE OF EDUCATIONAL DATA MINING AND ANALYTICS, THIS IS ONE OF THE FIRST STUDIES TO CONSIDER IF AND HOW INSTRUCTORS ARE ADDRESSING STUDENT DATA PRIVACY IN THEIR COURSES, AND THE STUDY INITIATES AN IMPORTANT CONVERSATION FOR REFLECTING ON PRIVACY VALUES AND PRACTICES.
PURPOSE ALTHOUGH BIG DATA ANALYTICS (BDA) HAVE GREAT BENEFITS FOR HIGHER EDUCATION INSTITUTIONS (HEIS), DUE TO LACK OF SUFFICIENT EVIDENCE ON HOW BDA INVESTMENT CAN PAY OFF, IT IS TOUGH FOR HEIS PRACTITIONERS TO REALIZE VALUE FROM SUCH ADOPTION. THE PURPOSE OF THIS PAPER IS TO PROPOSE A BIG DATA ACADEMIC AND LEARNING ANALYTICS ENABLED BUSINESS VALUE MODEL TO EXPLAIN BDA POTENTIAL BENEFITS AND BUSINESS VALUE WHICH CAN BE OBTAINED BY DEVELOPING SUCH ANALYTICS CAPABILITIES IN HEIS. DESIGN/METHODOLOGY/APPROACH THE STUDY EXAMINED 47 CASE DESCRIPTIONS FROM 26 HEIS TO INVESTIGATE THE CAUSAL ASSOCIATION BETWEEN THE BDA CURRENT AND POTENTIAL BENEFITS AND BUSINESS VALUE CREATION PATH FOR BIG DATA ACADEMIC AND LEARNING ANALYTICS SUCCESS IN HEIS. FINDINGS THE PRESSURE OF COMPLIANCE WITH ALL LEGAL AND REGULATORY REQUIREMENTS AND COMPETITION HAD PUSHED HEIS HARD TO ADOPT BDA TOOLS. HOWEVER, THE STUDY FOUND OUT THAT APPLICATION OF RISK AND SECURITY AND PREDICTIVE ANALYTICS TO HIGHER EDUCATION FIELDS IS STILL IN ITS INFANCY. USING THIS THEORETICAL MODEL, THE RESULTS PROVIDE NEW INSIGHTS TO HIGHER EDUCATION ADMINISTRATORS ON WAYS TO CREATE BDA CAPABILITIES FOR HEIS TRANSFORMATION AND SUGGEST AN EMPIRICAL FOUNDATION THAT CAN LEAD TO MORE THOROUGH ANALYSIS OF BDA IMPLEMENTATION. ORIGINALITY/VALUE A DISTINCTIVE THEORETICAL CONTRIBUTION OF THIS STUDY IS ITS CONCEPTUALIZATION OF UNDERSTANDING BUSINESS VALUE FROM BDA IN THE TYPICAL SETTING OF HIGHER EDUCATION. THE STUDY PROVIDES HEIS WITH AN ALL-INCLUSIVE UNDERSTANDING OF BDA AND GIVES INSIGHTS ON HOW IT HELPS TO TRANSFORM HEIS. THE NEW PERSPECTIVES ASSOCIATED WITH THE BIG DATA ACADEMIC AND LEARNING ANALYTICS ENABLED BUSINESS VALUE MODEL WILL CONTRIBUTE TO FUTURE RESEARCH IN THIS AREA.
THIS STUDY INVESTIGATED STUDENTS' MODELING PROGRESS AND STRATEGIES IN A PROBLEM-SOLVING SIMULATION GAME THROUGH CONTENT ANALYSIS, AND THROUGH SUPERVISED AND UNSUPERVISED LAG SEQUENTIAL ANALYSIS (LSA). MULTIPLE DATA SOURCES, INCLUDING SELF-REPORT MODELS AND ACTIVITY LOGS, WERE COLLECTED FROM 25 SENIOR HIGH SCHOOL STUDENTS. THE RESULTS OF THE CONTENT ANALYSIS FOUND THAT THE PROBLEM-SOLVING SIMULATION GAME HELPED MOST OF THE STUDENTS TO REFLECTIVELY PLAY WITH THE SCIENCE PROBLEM AND BUILD A WORKABLE MODEL TO SOLVE IT. BY USING THE SUPERVISED LSA, IT WAS FOUND THAT THE STUDENTS WHO SUCCESSFUL SOLVED THE GAME FREQUENTLY LINKED THE GAME CONTEXTS WITH THE PHYSICS TERMINOLOGIES, WHILE THOSE WHO DID NOT SOLVE THE PROBLEM SIMPLY RELIED ON THE INTUITIVE KNOWLEDGE PROVIDED IN THE REFERENCE MATERIALS. FURTHERMORE, THE UNSUPERVISED LSA IDENTIFIED FOUR ACTIVITY PATTERNS THAT WERE NOT NOTICED IN THE SUPERVISED LSA: THE FRAGMENTED, REFERENCE MATERIAL CENTERED, REFERENCE MATERIAL AIDED MODELING, AND MODELING CENTERED PATTERNS. EACH PATTERN HAS CERTAIN ASSOCIATIONS WITH CERTAIN PROBLEM-SOLVING OUTCOMES. THE RESULTS OF THIS STUDY ALSO SHED LIGHT ON THE USE OF DIFFERENT ANALYTICS TECHNIQUES. WHILE THE SUPERVISED LSA IS PARTICULARLY HELPFUL FOR DEPICTING A CONTRAST OF ACTIVITY PATTERNS BETWEEN TWO SPECIFIC STUDENT GROUPS, THE UNSUPERVISED LSA IS ABLE TO IDENTIFY HIDDEN SIGNIFICANT PATTERNS WHICH WERE NOT CLEARLY DISTINGUISHED IN THE PRE-DEFINED STUDENT GROUPS. RESEARCHERS MAY FIND THESE ANALYTICS TECHNIQUES USEFUL FOR ANALYZING STUDENTS' LEARNING PROCESSES.
THE WEB OF DATA IS AN EMERGING RESEARCH FIELD THAT CONTRIBUTES TO MAKE BETTER DECISIONS BECAUSE IT GATHERS, COMBINES AND ANALYSES DIFFERENT DATA SOURCES AVAILABLE WORLDWIDE. EDUCATIONAL DATA IS AN INTERESTING DOMAIN BECAUSE IT DEALS WITH THE QUALITY OF THE EDUCATION ITSELF AND EDUCATIONAL INSTITUTIONS WHICH ARE COMMON GOALS FOR EVERY COUNTRY. THIS PAPER IS DEVOTED TO PRESENT HOW THIS IDEA HAS BEEN USED TO IMPROVE A LEARNING ANALYTICS TOOL. BY MEANS OF THIS TOOL, TEACHERS CAN PERFORM TEAMWORK COMPETENCE ASSESSMENT OF A GROUP OF STUDENTS TAKING INTO ACCOUNT HOW THE INDIVIDUALS ACQUIRE THE ESSENTIAL COMPONENTS OF SUCH COMPETENCE. IN THIS SENSE, AUTHORS USE THE COMPREHENSIVE TRAINING MODEL OF THE TEAMWORK COMPETENCE IN ENGINEERING DOMAIN (CTMTC) METHOD TO GATHER COMPETENCE EVIDENCES AND IMPROVE THE SYSTEM WITH A LEARNING ANALYTICS TOOL TO SUPPORT THE PROCESS. THIS TOOL IS ABLE TO TRANSFORM COMPETENCE EVIDENCES AND STORES THEM IN A COMPETENCE ONTOLOGY BUILT UPON ISO 10018 CONCEPTS. THE FINAL RESULT IS THE PRODUCTION OF EDUCATIONAL RESULTS FOR THE WEB OF DATA. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
RECENT TECHNICAL AND INFRASTRUCTURAL DEVELOPMENTS POSIT FLIPPED CLASSROOM APPROACHES RIPE FOR EXPLORATION. FLIPPED CLASSROOM APPROACHES HAVE STUDENTS USE TECHNOLOGY TO ACCESS THE LECTURE AND OTHER INSTRUCTIONAL RESOURCES OUTSIDE THE CLASSROOM IN ORDER TO ENGAGE THEM IN ACTIVE LEARNING DURING IN-CLASS TIME. SCHOLARS AND EDUCATORS HAVE REPORTED A VARIETY OF POSITIVE OUTCOMES OF A FLIPPED (OR INVERTED) APPROACH TO INSTRUCTION. ALTHOUGH, FLIPPED CLASSROOM PRACTICES HAVE BEEN USED IN A NUMBER OF EDUCATION STUDIES, THE DETAILED FRAMEWORK AND DATA OBTAINED FROM STUDENTS' INTERACTION WITH THE TECHNOLOGY MATERIALS ARE TYPICALLY NOT DESCRIBED. IN THIS PAPER, WE PRESENT A FLIPPED CLASSROOM FRAMEWORK AND THE FIRST CAPTURED RESULTS OF SUCH DATA. THE FRAMEWORK INCORPORATES BASIC E-LEARNING TOOLS AND TRADITIONAL LEARNING PRACTICES, MAKING IT ACCESSIBLE TO ANYONE WANTING TO IMPLEMENT A FLIPPED CLASSROOM EXPERIENCE IN HIS/HER COURSE. THE FRAMEWORK IS STRUCTURED ON OPEN-SOURCE AND EASY-TO-USE TOOLS, ALLOWING FOR THE INCORPORATION OF ANY ADDITIONAL SPECIFICITIES OF A COURSE. THIS WORK-IN-PROGRESS CAN PROVIDE INSIGHTS FOR OTHER SCHOLARS AND PRACTITIONERS TO FURTHER VALIDATE, EXAMINE, AND EXTEND THE PROPOSED APPROACH. THIS APPROACH CAN BE USED FOR THOSE INTERESTED IN INCORPORATING FLIPPED CLASSROOM IN THEIR TEACHING, SINCE IT IS A FLEXIBLE PROCEDURE THAT MAY BE ADAPTED TO MEET THEIR NEEDS.
LEARNING ANALYTICS TECHNIQUES ARE TRADITIONALLY USED ON THE 'BIG DATA' COLLECTED AT THE COURSE OR UNIVERSITY LEVEL. THE APPLICATION OF SUCH TECHNIQUES TO THE DATA SETS GENERATED IN COMPLEX LEARNING ENVIRONMENTS CAN PROVIDE INSIGHTS INTO THE RELATIONSHIPS BETWEEN THE DESIGN OF LEARNING ENVIRONMENTS, THE PROCESSES OF LEARNING, AND LEARNING OUTCOMES. IN THIS PAPER, TWO OF THE CODES DESCRIBED AS PART OF THE COLLABORATIVE PROCESS ANALYSIS CODING SCHEME (CPACS) WERE EXTRACTED FROM THE MATH DATA CORPUS. THE CODES SELECTED WERE TENSE AND PRONOUNS, BOTH OF WHICH HAVE BEEN FOUND TO INDICATE PHASES OF GROUP WORK AND THE ACTION ASSOCIATED WITH COLLABORATION. RATHER THAN EXAMINE THESE MEASURES OF SOCIAL INTERACTIONS IN ISOLATION, A FRAMEWORK FOR THE ANALYSIS OF COMPLEX LEARNING ENVIRONMENTS WAS APPLIED. THIS FACILITATED AN ANALYSIS OF THE RELATIONSHIPS BETWEEN THE SOCIAL INTERACTIONS, THE TASK DESIGN AND LEARNING OUTCOMES, AS WELL AS TOOL USE. THE GENERATION OF A SUCCESSFUL PROBLEM SOLUTION OF ONE EXPERT AND ONE NON-EXPERT GROUP WAS ACCURATELY PREDICTED (75%-94%). THE EXAMINATION OF INTERACTIONS BETWEEN THE SOCIAL, EPISTEMIC AND TOOL ELEMENTS OF THE LEARNING ENVIRONMENT FOR ONE GROUP SHOWED THAT SUCCESSFUL ROLE DIFFERENTIATION AND PARTICIPATION WERE RELATED TO SUCCESSFUL PROBLEM SOLUTIONS IN THE FIRST MEETING. IN THE SECOND MEETING, THESE WERE LESS IMPORTANT. THE RELATIONSHIP BETWEEN THE DISCOURSE RELATED TO PROBLEM RESOLUTION THROUGH ACTION AND THE CORRECTNESS OF A PROBLEMS SOLUTION WAS FOUND TO BE LESS RELIABLE MEASURES, HOWEVER FURTHER ANALYSIS IS NEEDED AT A FINER GRAIN TO INVESTIGATE THIS FINDING. A RICH DESCRIPTION OF THE PROCESSES OF LEARNING (WITH REGARDS TO SOCIAL INTERACTION, GENERATION OF KNOWLEDGE, AND DISCOURSE RELATED TO ACTION) WAS GENERATED FOR ONE GROUP.
WASTE COLLECTION AND MANAGEMENT PRESENTS A MAJOR CHALLENGE FOR MUNICIPALITIES WANTING TO ACHIEVE CLEANER URBAN ENVIRONMENTS. SMART CITY INFRASTRUCTURE INCORPORATING THE INTERNET OF THINGS (IOT) PARADIGM OFFERS SUBSTANTIAL ADVANTAGES IN TERMS OF REAL-TIME WASTE MONITORING CAPABILITY. BASIC SENSORY MONITORING BY ITSELF, HOWEVER, FALLS SHORT OF ACHIEVING OPTIMAL WASTE MANAGEMENT WITHOUT COMPREHENSIVE DATA ANALYTICS. TO THIS END, THE PRESENT WORK PROPOSES AN OFF-THE-SHELF IOT-BASED WASTE MONITORING SOLUTION, COMBINED WITH BACK-END DATA ANALYTICS FOR EFFICIENT WASTE COLLECTION. THE WORK EMPLOYS RASPBERRY PI AND ULTRASONIC SENSORS, MOUNTED ON WASTE-BINS IN A SPECIFIC AREA OF A COOPERATING MUNICIPALITY FOR WASTE CAPACITY MONITORING. REAL-TIME BIN STATUS AND MACHINE LEARNING ANALYTICS ARE USED TO IDENTIFY PRESENT AS WELL AS PREDICT FUTURE WASTE COLLECTION SCHEDULING. DYNAMIC COLLECTION SERVICING ROUTES ARE ACCORDINGLY MAPPED FOR UTILIZATION BY WASTE COLLECTION VEHICLES. DURING A TEN-DAY TRIAL AND VALIDATION PERIOD, IT WAS OBSERVED THAT THE PROPOSED DESIGN INCREASES FUEL EFFICIENCY BY UP TO 46% AND A REDUCTION IN COLLECTION TIMES BY UP TO 18%. IN ADDITION TO THE NOTED QUANTITATIVE IMPROVEMENTS, THE PROPOSED SCHEME CAN ALSO AID IN OPTIMIZING LONG-TERM WASTE POLICIES IN SMART CITY ENVIRONMENTS USING THE RECORDED STATISTICS.
COMPARED TO OTHER PLATFORMS SUCH AS COURSERA AND EDX, FUTURELEARN IS A RELATIVELY NEW PLAYER IN THE MOOC ARENA AND RECEIVED LIMITED COVERAGE IN THE LEARNING ANALYTICS AND EDUCATIONAL DATA MINING RESEARCH. FOUNDED BY A PARTNERSHIP BETWEEN THE OPEN UNIVERSITY IN THE UK, THE BBC, THE BRITISH LIBRARY AND (ORIGINALLY) 12 UNIVERSITIES IN THE UK, FUTURELEARN HAS TWO DISTINCTIVE FEATURES RELEVANT TO THE WAY THEIR DATA IS DISPLAYED AND ANALYZED: 1) IT WAS DESIGNED WITH A SPECIFIC EDUCATIONAL PHILOSOPHY IN MIND WHICH FOCUSES ON THE SOCIAL DIMENSION OF LEARNING AND 2) EVERY LEARNING ACTIVITY PROVIDE OPPORTUNITIES FOR FORMAL DISCUSSION AND COMMENTING. THIS WORKSHOP PROVIDES AN OPPORTUNITY TO INVITE CONTRIBUTIONS AND CONNECT INDIVIDUAL AND GROUPS TO SHARE THEIR RESEARCH ACTIVITIES ON AN INTERNATIONAL STAGE. AS THE FIRST OF ITS KIND, THIS WORKSHOP WILL BRING IN A NUMBER OF SCHOLARS AND PRACTITIONERS, AS WELL AS DATA SCIENTISTS AND ANALYST INVOLVED IN THE REPORTING, RESEARCHING AND DEVELOPMENTS EMERGING FROM THE DATA OFFERED BY THE PLATFORM.
PERSONALIZATION AND ADAPTIVITY IN COMPUTER SIMULATIONS AND SERIOUS GAMES ARE BEING USED TO ACHIEVE POSITIVE LONG TERM EFFECTS ON THE USERS' ENGAGEMENT, MOTIVATION AND ULTIMATELY ON THE LEARNING OUTCOME. INTEROPERABILITY REGARDING THE COLLECTION OF USAGE DATA ALLOWS FOR AN EFFECTIVE ANALYSIS OF THE INTERACTION AND LEARNING PROGRESS DATA. THIS PAPER PRESENTS AN INTEROPERABLE ADAPTIVITY FRAMEWORK COMBINED WITH A WEB-BASED TUTORING INTERFACE WHICH GIVES LEARNING ANALYTICS INSIGHTS. THE DEVELOPED FRAMEWORK "E-LEARNING A.I." (ELAI) ACTS AS AN INTELLIGENT TUTORING AGENT FOR SIMULATIONS AND SERIOUS GAMES AND USES THE EXPERIENCE API (XAPI) PROTOCOL. THE APPLICATION OF THE ELAI HAS BEEN DEMONSTRATED IN AN ADAPTIVE MAP-BASED LEARNING GAME FOR AERIAL IMAGE INTERPRETATION. THE SCIENTIFIC RESEARCH QUESTIONS AFFECT THE POSSIBLE USAGES OF THE COLLECTED INTERACTION DATA, HOW TO MANIFEST ADAPTIVITY IN GAMES, HOW TO REALIZE INTEROPERABLE ADAPTIVITY MECHANISMS FOR SIMULATIONS AND SERIOUS GAMES, AND HOW TO MAKE USE OF COLLECTED USAGE DATA.
HIGHER EDUCATION INSTITUTIONS ARE MINING AND ANALYZING STUDENT DATA TO EFFECT EDUCATIONAL, POLITICAL, AND MANAGERIAL OUTCOMES. DONE UNDER THE BANNER OF "LEARNING ANALYTICS," THIS WORK CAN-AND OFTEN DOES-SURFACE SENSITIVE DATA AND INFORMATION ABOUT, INTER ALIA, A STUDENT'S DEMOGRAPHICS, ACADEMIC PERFORMANCE, OFFLINE AND ONLINE MOVEMENTS, PHYSICAL FITNESS, MENTAL WELLBEING, AND SOCIAL NETWORK. WITH THESE DATA, INSTITUTIONS AND THIRD PARTIES ARE ABLE TO DESCRIBE STUDENT LIFE, PREDICT FUTURE BEHAVIORS, AND INTERVENE TO ADDRESS ACADEMIC OR OTHER BARRIERS TO STUDENT SUCCESS (HOWEVER DEFINED). LEARNING ANALYTICS, CONSEQUENTLY, RAISE SERIOUS ISSUES CONCERNING STUDENT PRIVACY, AUTONOMY, AND THE APPROPRIATE FLOW OF STUDENT DATA. WE ARGUE THAT ISSUES AROUND PRIVACY LEAD TO VALID QUESTIONS ABOUT THE DEGREE TO WHICH STUDENTS SHOULD TRUST THEIR INSTITUTION TO USE LEARNING ANALYTICS DATA AND OTHER ARTIFACTS (ALGORITHMS, PREDICTIVE SCORES) WITH THEIR INTERESTS IN MIND. WE ARGUE THAT HIGHER EDUCATION INSTITUTIONS ARE PARADIGMS OF INFORMATION FIDUCIARIES. AS SUCH, COLLEGES AND UNIVERSITIES HAVE A SPECIAL RESPONSIBILITY TO THEIR STUDENTS. IN THIS ARTICLE, WE USE THE INFORMATION FIDUCIARY CONCEPT TO ANALYZE CASES WHEN LEARNING ANALYTICS VIOLATE AN INSTITUTION'S RESPONSIBILITY TO ITS STUDENTS.
LEARNER-CENTERED PEDAGOGY HIGHLIGHTS ACTIVE LEARNING AND FORMATIVE FEEDBACK. INSTRUCTORS OFTEN INCENTIVIZE LEARNERS TO ENGAGE IN SUCH FORMATIVE ASSESSMENT ACTIVITIES BY CREDITING THEIR COMPLETION AND SCORE IN THE FINAL GRADE, A PEDAGOGICAL PRACTICE THAT IS VERY RELEVANT TO MOOCS AS WELL. HOWEVER, PREVIOUS STUDIES HAVE SHOWN THAT TOO MANY MOOC LEARNERS EXPLOIT THE ANONYMITY TO ABUSE THE FORMATIVE FEEDBACK, WHICH IS CRITICAL IN THE LEARNING PROCESS, TO EARN POINTS WITHOUT EFFORT. UNFORTUNATELY, LIMITING FEEDBACK AND ACCESS TO DECREASE CHEATING IS COUNTER-PEDAGOGIC AND REDUCES THE OPENNESS OF MOOCS. WE AIMED TO IDENTIFY AND ANALYZE A MOOC ASSESSMENT STRATEGY THAT BALANCES THIS TENSION BETWEEN LEARNER-CENTERED PEDAGOGY, INCENTIVE DESIGN, AND RELIABILITY OF THE ASSESSMENT. IN THIS STUDY, WE EVALUATED AN ASSESSMENT MODEL THAT MITX BIOLOGY INTRODUCED IN A MOOC TO REDUCE CHEATING WITH RESPECT TO ITS EFFECT ON TWO ASPECTS OF LEARNER BEHAVIOR - THE AMOUNT OF CHEATING AND LEARNERS' ENGAGEMENT IN FORMATIVE COURSE ACTIVITIES. THE CONTRIBUTION OF THE PAPER IS TWOFOLD. FIRST, THIS WORK PROVIDES MOOC DESIGNERS WITH AN 'ANALYTICALLY-VERIFIED' MOOC ASSESSMENT MODEL TO REDUCE CHEATING WITHOUT COMPROMISING LEARNER ENGAGEMENT IN FORMATIVE ASSESSMENTS. SECOND, THIS STUDY PROVIDES A LEARNING ANALYTICS METHODOLOGY TO APPROXIMATE THE EFFECT OF SUCH AN INTERVENTION.
SYSTEMATIC ENDEAVORS TO TAKE COMPUTER SCIENCE (CS) AND COMPUTATIONAL THINKING (CT) TO SCALE IN MIDDLE AND HIGH SCHOOL CLASSROOMS ARE UNDERWAY WITH CURRICULA THAT EMPHASIZE THE ENACTMENT OF AUTHENTIC CT SKILLS, ESPECIALLY IN THE CONTEXT OF PROGRAMMING IN BLOCK-BASED PROGRAMMING ENVIRONMENTS. THERE IS, THEREFORE, A GROWING NEED TO MEASURE STUDENTS' LEARNING OF CT IN THE CONTEXT OF PROGRAMMING AND ALSO SUPPORT ALL LEARNERS THROUGH THIS PROCESS OF LEARNING COMPUTATIONAL PROBLEM SOLVING. THE GOAL OF THIS RESEARCH IS TO EXPLORE HYPOTHESIS-DRIVEN APPROACHES THAT CAN BE COMBINED WITH DATA-DRIVEN ONES TO BETTER INTERPRET STUDENT ACTIONS AND PROCESSES IN LOG DATA CAPTURED FROM BLOCK-BASED PROGRAMMING ENVIRONMENTS WITH THE GOAL OF MEASURING AND ASSESSING STUDENTS' CT SKILLS. INFORMED BY PAST LITERATURE AND BASED ON OUR EMPIRICAL WORK EXAMINING A DATASET FROM THE USE OF THE FAIRY ASSESSMENT IN THE ALICE PROGRAMMING ENVIRONMENT IN MIDDLE SCHOOLS, WE PRESENT A FRAMEWORK THAT FORMALIZES A PROCESS WHERE A HYPOTHESIS-DRIVEN APPROACH INFORMED BY EVIDENCE-CENTERED DESIGN EFFECTIVELY COMPLEMENTS DATA-DRIVEN LEARNING ANALYTICS IN INTERPRETING STUDENTS' PROGRAMMING PROCESS AND ASSESSING CT IN BLOCK-BASED PROGRAMMING ENVIRONMENTS. WE APPLY THE FRAMEWORK TO THE DESIGN OF ALICE TASKS FOR HIGH SCHOOL CS TO BE USED FOR MEASURING CT DURING PROGRAMMING.
A 2010-2015 READY TO LEARN GRANT FROM THE US DEPARTMENT OF EDUCATION SUPPORTED THE RESEARCH, DEVELOPMENT, AND DISSEMINATION OF PBS KIDS MATH AND LITERACY CONTENT FOR KIDS AGED 2-8, AND NEW WAYS TO MEASURE AND REPORT ON CHILDREN'S LEARNING. PBS KIDS, IN COLLABORATION WITH UCLA CRESST, DEVELOPED A PROTOTYPE LEARNING ANALYTICS PLATFORM (LAP) TO ANALYZE BACK-END DATA FROM CHILDREN'S INTERACTIONS WITH PBS KIDS TRANSMEDIA CONTENT, AND PROVIDE CUSTOM REPORTS TO PARENTS THAT ENABLE THEM TO BETTER IDENTIFY AND SUPPORT THEIR CHILD'S INDIVIDUAL LEARNING NEEDS. EVIDENCE SUGGESTS THAT THE PBS KIDS PROTOTYPE LAP DEMONSTRATES POTENTIAL FOR ASSESSMENT AND PREDICTION OF MATHEMATICS PERFORMANCE, INCLUDING ON THE TEMA-3 STANDARDIZED ASSESSMENT FOR EARLY MATHEMATICS. SIMILARLY, EARLY RESEARCH FINDINGS SUGGEST THAT THE REPORTING APPLICATIONS DEVELOPED TO PROVIDE LAP INFORMATION ARE VIEWED FAVORABLY BY PARENTS AND PROMOTE INTEREST IN LEARNING MORE ABOUT, MONITORING, AND ENGAGING WITH THEIR KIDS AROUND LEARNING. IMPLICATIONS FOR THE FUTURE ARE DISCUSSED.
IN SELF-REGULATED LEARNING, EVALUATION IS A COMPLEX TASK OF THE TEACHING PROCESS, BUT EVEN MORE IF STUDENTS HAVE SOCIAL MEDIA THAT ALLOW THEM TO BUILD THEIR PERSONAL LEARNING ENVIRONMENT IN DIFFERENT WAYS. IN THESE KIND OF VIRTUAL ENVIRONMENTS A LARGE AMOUNT OF DATA THAT NEEDS TO BE ASSESSED BY TEACHERS IS GENERATED, AND THEREFORE THEY REQUIRE TOOLS THAT FACILITATE THE ASSESSMENT TASK. IN THIS PAPER, WE PRESENT AN EXPERIMENT WITH A PROCESS MINING-BASED LEARNING ANALYTICS TOOL, CALLED SOFTLEARN, THAT HELPS TEACHERS TO ASSESS THE STUDENT'S ACTIVITY IN SELF-REGULATED LEARNING. THE SUBJECT OF THIS EXPERIMENT IS TAUGHT IN BLENDED LEARNING MODE WITH WEEKLY CLASSROOM SESSIONS, AND THE STUDENTS USE A SOCIAL NETWORK SOFTWARE, CALLED ELGG, AS AN E-PORTFOLIO IN WHICH THEY REFLECT THEIR INDIVIDUAL KNOWLEDGE PROCESS CONSTRUCTION. THE RESULTS SHOW THAT THE USE OF THIS TOOL REDUCES SIGNIFICANTLY THE ASSESSMENT TIME AND HELPS TEACHERS TO UNDERSTAND THE LEARNING PROCESS OF THE STUDENTS.
IN THE LAST DECADE, THERE HAS BEEN A GREAT DEAL OF INTEREST IN LANGUAGE MOOCS (LMOOCS) AND THEIR POTENTIAL TO OFFER LEARNING OPPORTUNITIES FOR LARGE AUDIENCES, INCLUDING THOSE IN DISADVANTAGED COMMUNITIES. HOWEVER, EXPERIENCES AND RESEARCH HAVE SHOWN MOOCS TO SUFFER FROM SEVERAL CHALLENGES. CHIEF AMONG THESE HAVE BEEN LOW PARTICIPATION AND COMPLETION RATES, WHICH ARE OFTEN ATTRIBUTED TO LIMITATIONS IN HOW OPPORTUNITIES FOR PERSONALISATION AND SOCIAL INTERACTION ARE IMPLEMENTED. FOR THE CURRENT STUDY, A DEDICATED LMOOC WAS DESIGNED AND IMPLEMENTED, CALLED THE "SOCIAL AND PERSONAL ONLINE LANGUAGE COURSE (SPOLC)." THIS LANGUAGE LEARNING ENVIRONMENT INCORPORATES A RECOMMENDATION SYSTEM AND EMPHASIZES PERSONALISATION AND SOCIAL INTERACTION. THE STUDY IDENTIFIED THE TYPES OF LEARNING BEHAVIOUR THAT WERE RELATED TO COURSE COMPLETION AND OBSERVED HOW 270 LEARNERS IN THE LMOOC USED THE VARIOUS COURSE FEATURES. THE DATA WERE COLLECTED USING LEARNING ANALYTICAL METHODS AND ANALYSED USING BINARY LOGISTIC REGRESSION AND FEATURE EXTRACTION PREDICTION MODEL. THE RESULTS DEMONSTRATED THAT WORKING IN GROUPS AND CREATING A LEARNING PLAN WERE IMPORTANT FACTORS ASSOCIATED WITH COURSE COMPLETION, WHILE INTERACTING WITH OTHER LEARNERS ONLINE WAS NOT. WE CONCLUDE WITH SEVERAL SUGGESTIONS AND IMPLICATIONS FOR FUTURE LMOOC DESIGN, IMPLEMENTATION, AND RESEARCH.
THIS PAPER AIMS TO IDENTIFY SELF-REGULATION STRATEGIES FROM STUDENTS' INTERACTIONS WITH THE LEARNING MANAGEMENT SYSTEM (LMS). WE USED LEARNING ANALYTICS TECHNIQUES TO IDENTIFY METACOGNITIVE AND COGNITIVE STRATEGIES IN THE DATA. WE DEFINE THREE RESEARCH QUESTIONS THAT GUIDE OUR STUDIES ANALYZING I) SELF-ASSESSMENTS OF MOTIVATION AND SELF REGULATION STRATEGIES USING STANDARD METHODS TO DRAW A BASELINE, II) INTERACTIONS WITH THE LMS TO FIND TRACES OF SELF REGULATION IN OBSERVABLE INDICATORS, AND III) SELF REGULATION BEHAVIOURS OVER THE COURSE DURATION. THE RESULTS SHOW THAT THE OBSERVABLE INDICATORS CAN BETTER EXPLAIN SELF-REGULATORY BEHAVIOUR AND ITS INFLUENCE IN PERFORMANCE THAN PRELIMINARY SUBJECTIVE ASSESSMENTS.
THIS PAPER DESCRIBES A METHOD TO REPRODUCE AND VISUALIZE STUDENT COURSE MATERIAL PAGE VIEWS CHRONOLOGICALLY AS A BASIS FOR IMPROVING LESSONS AND SUPPORTING LEARNING ANALYSIS. INTERACTIVE MINING WAS CONDUCTED ON MOODLE COURSE LOGS DOWNLOADED IN AN EXCEL FORMAT. THE METHOD USES A TIME-SERIES CROSS-SECTION (TSCS) ANALYSIS FRAMEWORK; IN THE RESULTING TSCS TABLE, THE PAGE VIEW STATUS OF STUDENTS CAN BE REPRESENTED NUMERICALLY ACROSS MULTIPLE TIME INTERVALS. THE TSCS TABLE, GENERATED BY AN EXCEL MACRO THAT THE AUTHOR CALLS TSCS MONITOR, MAKES IT POSSIBLE TO SWITCH FROM AN OVERALL, CLASS-WIDE VIEWPOINT TO MORE NARROWLY-FOCUSED PARTIAL VIEWPOINTS. USING NUMERICAL VALUES AND GRAPH, THE APPROACH ENABLES A TEACHER TO CAPTURE THE COURSE MATERIAL PAGE VIEW STATUS OF STUDENTS AND OBSERVE STUDENT RESPONSES TO THE TEACHER'S INSTRUCTIONS TO OPEN VARIOUS TEACHING MATERIALS. IT ALLOWS THE TEACHER TO IDENTIFY STUDENTS WHO FAIL TO OPEN PARTICULAR MATERIALS DURING THE LESSON OR WHO ARE LATE OPENING THEM.
ANALYSIS OF LEARNING BEHAVIOR OF MOOC ENTHUSIASTS HAS BECOME A POSED CHALLENGE IN THE LEARNING ANALYTICS FIELD, WHICH IS ESPECIALLY RELATED TO VIDEO LECTURE DATA, SINCE MOST LEARNERS WATCH THE SAME ONLINE LECTURE VIDEOS. IT HELPS TO CONDUCT A COMPREHENSIVE ANALYSIS OF SUCH BEHAVIORS AND EXPLORE VARIOUS LEARNING PATTERNS FOR LEARNERS AND PREDICT THEIR PERFORMANCE BY MOOC COURSES VIDEO. THIS PAPER EXPLOITS A TEMPORAL SEQUENTIAL CLASSIFICATION PROBLEM BY ANALYZING VIDEO CLICKSTREAM DATA AND PREDICT LEARNER PERFORMANCE, WHICH IS A VITAL DECISION-MAKING PROBLEM, BY ADDRESSING THEIR ISSUES AND IMPROVING THE EDUCATIONAL PROCESS. THIS PAPER EMPLOYS A DEEP NEURAL NETWORK (LSTM) ON A SET OF IMPLICIT FEATURES EXTRACTED FROM VIDEO CLICKSTREAMS DATA TO PREDICT LEARNERS' WEEKLY PERFORMANCE AND ENABLE INSTRUCTORS TO SET MEASURES FOR TIMELY INTERVENTION. RESULTS SHOW THAT ACCURACY RATE OF THE PROPOSED MODEL IS 82%-93% THROUGHOUT COURSE WEEKS. THE PROPOSED LSTM MODEL OUTPERFORMS BASELINE ANNS, SUPER VECTOR MACHINE (SVM) AND LOGISTIC REGRESSION BY AN ACCURACY OF 93% IN REAL USED COURSES' DATASETS.
THE SOFTLEARN ACTIVITY REPORTER IS A DATA-TO-TEXT SERVICE, WHICH AUTOMATICALLY GENERATES TEXTUAL REPORTS ABOUT THE ACTIVITY DEVELOPED BY STUDENTS WITHIN THE SOFTLEARN VIRTUAL LEARNING ENVIRONMENT. IN THIS PAPER, WE DESCRIBE THE CONCEPTION OF THE SERVICE, ITS ARCHITECTURE, AND ITS SUBSEQUENT EVALUATION BY AN EXPERT PEDAGOGUE, WHERE 20 FULL REPORTS GENERATED FROM REAL DATA FROM AN UNDERGRADUATE COURSE SUPPORTED BY THE SOFTLEARN PLATFORM WERE ASSESSED. RESULTS SHOW THAT THE AUTOMATICALLY GENERATED REPORTS ARE A VALUABLE COMPLEMENTARY TOOL FOR EXPLAINING TEACHERS AND STUDENTS THE INFORMATION COMPRISED IN A LEARNING ANALYTICS DASHBOARD. (C) 2016 WILEY PERIODICALS, INC.
ONE OF THE ULTIMATE GOALS OF SEVERAL LEARNING ANALYTICS (LA) INITIATIVES IS TO CLOSE THE LOOP AND SUPPORT STUDENTS' AND TEACHERS' REFLECTIVE PRACTICES. ALTHOUGH THERE HAS BEEN A PROLIFERATION OF END-USER INTERFACES (OFTEN IN THE FORM OF DASHBOARDS), VARIOUS LIMITATIONS HAVE ALREADY BEEN IDENTIFIED IN THE LITERATURE SUCH AS LITTLE ACCOUNT FOR SENSEMAKING NEEDS. THIS PAPER ADDRESSES THESE LIMITATIONS BY PROPOSING A QUESTION-DRIVEN LA DESIGN APPROACH TO ENSURE THAT END-USER LA INTERFACES EXPLICITLY ADDRESS TEACHERS' QUESTIONS. WE ILLUSTRATE THIS IN THE CONTEXT OF SYNCHRONOUS ONLINE ACTIVITIES ORCHESTRATED BY PAIRS OF TEACHERS USING AUDIO-VISUAL AND TEXT-BASED TOOLS (ZOOM AND GOOGLE DOCS). THIS LED TO THE DESIGN OF AN OPEN-SOURCE MONITORING TOOL TO BE USED IN REAL-TIME BY TEACHERS WHEN STUDENTS WORK COLLABORATIVELY IN BREAKOUT ROOMS, AND ACROSS LEARNING SPACES.
RECENTLY, E-LEARNING RESOURCES BECOME WIDESPREAD IN SCHOOL EDUCATION WORLDWIDE, AS THEY ARE A PREREQUISITE FOR AN EFFICIENT LEARNING PROCESS. THESE RESOURCES ARE SCATTERED ACROSS MANY WEBSITES AND THEIR SEARCH TAKES TOO LONG. DESPITE THEIR DIVERSITY, OFTEN IT IS DIFFICULT FOR TEACHERS TO FIND THE PROPER RESOURCES. AFTER ANALYZING THE FINDINGS OF AN ANONYMOUS ONLINE SURVEY OF BULGARIAN TEACHERS ON THE USE OF ICT AND E-LEARNING RESOURCES, RESEARCHERS IDENTIFIED THE NEED FOR EASY ACCESS TO VARIOUS E-RESOURCES FOR SCHOOL EDUCATION. THE PAPER OFFERS A CONCEPT FOR A CENTRALIZED ACCESS PORTAL WHERE USERS TO UPLOAD LINKS TO E-LEARNING RESOURCES THAT ARE SHORTLY DESCRIBED AND CLASSIFIED ACCORDING TO SEVERAL INDICATORS. THESE INCLUDE ACCESSIBILITY, TYPE, SUBJECT, SCHOOL GRADE, PURPOSE AND OTHER MEANINGFUL PARAMETERS. FREE REGISTRATION FOR TEACHERS AND STUDENTS WILL ALLOW DATA TO BE AGGREGATED BY USER GROUPS, TO CARRY OUT ANALYZES OF THE RESOURCES' USAGE, TO DRAW TRENDS, AND TO MAKE CONCLUSIONS FOR FUTURE POLICIES.
DURING FACE TO FACE CLASSROOM INTERACTIONS, LEARNING IS FACILITATED THROUGH THE DISSEMINATION OF COURSE MATERIALS BY SUBJECT SPECIALISTS, IN A PEDAGOGICAL MANNER, SUITING THE PACE OF THE LEARNERS. IT IS KNOWN THAT PROPER INTERACTIONS WITH CONTENTS, TUTORS AND PEERS ENCOURAGE LEARNING TO TAKE PLACE. HOWEVER, THE LEARNER-CONTENT PROCESS IS KNOWN TO BE CHALLENGING FOR ONLINE LEARNERS WHO STRUGGLE ALONE TO OVERCOME LEARNING DIFFICULTIES, AND WHICH MAY LEAD THEM TO DROP OUT FROM THE COURSE. IN ABSENCE OF FACE TO FACE INTERACTIONS, THE CONTENT, BEING THE PRIME SOURCE OF KNOWLEDGE FOR DISTANT LEARNERS, HAS TO BE UP TO DATE AND COMPREHENSIVE WHILE CATERING FOR DIFFERENT LEARNING STYLES OF STUDENTS. THEREFORE, THIS PAPER AIMS AT INVESTIGATING WHETHER LEARNERS MEET THE COGNITIVE LEVEL AS REQUIRED BY THE COURSE'S LEARNING OBJECTIVES. THROUGH A QUESTIONNAIRE, 18 ONLINE LEARNERS WERE REQUESTED TO IDENTIFY THEIR LEARNING ACTIVITIES DONE IN BOTH ASYNCHRONOUS AND SYNCHRONOUS MODES AS PART OF THEIR LEARNING PROCESS. FURTHERMORE, CONTENT ANALYSIS, USING BOTH ACTIVITY THEORY AND THE JAILLET & PANCHOO GRID, WAS DONE ON FOUR ONLINE TUTORIALS TO IDENTIFY IF LEARNERS STUDY THEIR COURSE MATERIALS AS DESIRED AND IF THEIR INTERACTIONS ARE OF COGNITIVE LEVEL. A TOTAL OF 761 LINES OF LOGGED TEXTUAL INTERACTIONS WAS CODED TWICE. RESULTS SHOW THAT LEARNERS GO THROUGH DIFFERENT LEARNING ACTIVITIES AS IMPOSED BY THE MODULE BUT LESS EMPHASIS IS PUT ON ENSURING THAT LEARNERS STUDY THE COURSE CONTENT BEFORE EMBARKING ON THE ASSIGNMENT. ACQUIRING THE RELEVANT KNOWLEDGE FROM THE COURSE MATERIALS PRIOR TO ONLINE DISCUSSIONS, WILL ENCOURAGE BETTER INTERACTIONS AND LEARNING TO TAKE PLACE IN THIS SOCIO-CONSTRUCTIVISM SET UP.
SUBSTANTIAL PROGRESS HAS BEEN MADE IN UNDERSTANDING HOW TEACHERS DESIGN FOR LEARNING. HOWEVER, THERE REMAINS A PAUCITY OF EVIDENCE OF THE ACTUAL STUDENTS' RESPONSE TOWARDS LEANING DESIGNS. LEARNING ANALYTICS HAS THE POWER TO PROVIDE JUST-IN-TIME SUPPORT, ESPECIALLY WHEN PREDICTIVE ANALYTICS IS MARRIED WITH THE WAY TEACHERS HAVE DESIGNED THEIR COURSE, OR SO-CALLED A LEARNING DESIGN. THIS STUDY INVESTIGATES HOW LEARNING DESIGNS ARE CONFIGURED OVER TIME AND THEIR IMPACT ON STUDENT ACTIVITIES BY ANALYZING LONGITUDINAL DATA OF 38 MODULES WITH A TOTAL OF 43,099 REGISTERED STUDENTS OVER 30 WEEKS AT THE OPEN UNIVERSITY UK, USING SOCIAL NETWORK ANALYSIS AND PANEL DATA ANALYSIS. OUR ANALYSIS UNPACKED DYNAMIC CONFIGURATIONS OF LEARNING DESIGNS BETWEEN MODULES OVER TIME, WHICH ALLOWS TEACHERS TO REFLECT ON THEIR PRACTICE IN ORDER TO ANTICIPATE PROBLEMS AND MAKE INFORMED INTERVENTIONS. FURTHERMORE, BY CONTROLLING FOR THE HETEROGENEITY BETWEEN MODULES, OUR RESULTS INDICATED THAT LEARNING DESIGNS WERE ABLE TO EXPLAIN UP TO 60% OF THE VARIABILITY IN STUDENT ONLINE ACTIVITIES, WHICH REINFORCED THE IMPORTANCE OF PEDAGOGICAL CONTEXT IN LEARNING ANALYTICS.
ONE-DIGIT MULTIPLICATION ERRORS ARE ONE OF THE MOST EXTENSIVELY ANALYSED MATHEMATICAL PROBLEMS. RESEARCH WORK PRIMARILY EMPHASISES THE USE OF STATISTICS WHEREAS LEARNING ANALYTICS CAN GO ONE STEP FURTHER AND USE MACHINE LEARNING TECHNIQUES TO MODEL SIMPLE LEARNING MISCONCEPTIONS. PROBABILISTIC PROGRAMMING TECHNIQUES EASE THE DEVELOPMENT OF PROBABILISTIC GRAPHICAL MODELS (BAYESIAN NETWORKS) AND THEIR USE FOR PREDICTION OF STUDENT BEHAVIOUR THAT CAN ULTIMATELY INFLUENCE LEARNING DECISION PROCESSES.
THE PROCESS OF USING ANALYTIC DATA TO INFORM INSTRUCTIONAL DECISION-MAKING IS ACKNOWLEDGED TO BE COMPLEX; HOWEVER, DETAILS OF HOW IT OCCURS IN AUTHENTIC TEACHING CONTEXTS HAVE NOT BEEN FULLY UNPACKED. THIS STUDY INVESTIGATED FIVE UNIVERSITY INSTRUCTORS' USE OF A LEARNING ANALYTICS DASHBOARD TO INFORM THEIR TEACHING. THE EXISTING LITERATURE WAS SYNTHESIZED TO CREATE A TEMPLATE FOR INQUIRY THAT GUIDED INTERVIEWS, AND INDUCTIVE QUALITATIVE ANALYSIS WAS USED TO IDENTIFY SALIENT EMERGENT THEMES IN HOW INSTRUCTORS 1) ASKED QUESTIONS, 2) INTERPRETED DATA, 3) TOOK ACTION, AND 4) CHECKED IMPACT. FINDINGS SHOWED THAT INSTRUCTORS DID NOT ALWAYS COME TO ANALYTICS USE WITH SPECIFIC QUESTIONS, BUT RATHER WITH GENERAL AREAS OF CURIOSITY. QUESTIONS ADDITIONALLY EMERGED AND WERE REFINED THROUGH INTERACTION WITH THE ANALYTICS. DATA INTERPRETATION INVOLVED TWO DISTINCT ACTIVITIES, OFTEN ALONG WITH AFFECTIVE REACTIONS TO DATA: READING DATA TO IDENTIFY NOTEWORTHY PATTERNS AND EXPLAINING THEIR IMPORTANCE IN THE COURSE USING CONTEXTUAL KNOWLEDGE. PEDAGOGICAL RESPONSES TO THE ANALYTICS INCLUDED WHOLE-CLASS SCAFFOLDING, TARGETED SCAFFOLDING, AND REVISING COURSE DESIGN, AS WELL TWO NEW NON-ACTION RESPONSES: ADOPTING A WAIT-AND-SEE POSTURE AND ENGAGING IN DEEP REFLECTION ON PEDAGOGY. FINDINGS WERE SYNTHESIZED INTO A MODEL OF INSTRUCTOR ANALYTICS USE THAT OFFERS USEFUL CATEGORIES OF ACTIVITIES FOR FUTURE STUDY AND SUPPORT.
PROJECT-BASED LEARNING HAS FOUND ITS WAY INTO A RANGE OF FORMAL AND INFORMAL LEARNING ENVIRONMENTS. HOWEVER, SYSTEMATICALLY ASSESSING THESE ENVIRONMENTS REMAINS A SIGNIFICANT CHALLENGE. TRADITIONAL ASSESSMENTS, WHICH FOCUS ON LEARNING OUTCOMES, SEEM INCONGRUENT WITH THE PROCESS-ORIENTED GOALS OF PROJECT-BASED LEARNING. MULTIMODAL INTERFACES AND MULTIMODAL LEARNING ANALYTICS HOLD SIGNIFICANT PROMISE FOR ASSESSING LEARNING IN OPEN-ENDED LEARNING ENVIRONMENTS. WITH ITS RICH INTEGRATION OF A MULTITUDE OF DATA STREAMS AND NATURALISTIC INTERFACES, THIS AREA OF RESEARCH MAY HELP USHER IN A NEW WAVE OF EDUCATION REFORM BY SUPPORTING ALTERNATIVE MODES OF LEARNING.
HIGHER EDUCATION INSTITUTES CONSIDER LOW COMPLETION RATE BY STUDENTS ENROLLED IN THEIR DEGREE PROGRAMME AS A POTENTIAL THREAT TO THE INSTITUTES LONG TERM FINANCIAL SECURITY. THEREFORE, THESE INSTITUTES SEEK WAYS THAT CAN SUPPORT STUDENT LEARNING AND ENCOURAGE SUCCESSFUL COMPLETIONS. HAVING SOME ANALYTICAL STRATEGY WHICH CAN ENABLE PREDICTIONS ON STUDENTS PERFORMANCES CAN HELP THE INSTITUTES MAKE TIMELY INTERVENTIONS FOR IMPROVING STUDENTS PERFORMANCE. HOWEVER, EFFECTIVE PREDICTION AND PROACTIVE INTERVENTION IS DEPENDENT ON USING DATA WHICH IS MEANINGFUL, RELIABLE AND ACCURATE. EXTENSIVE USE OF EDUCATION TOOLS LIKE LEARNING MANAGEMENT SYSTEMS (LMS) PROVIDE LARGE DATASETS WHICH HAS FACILITATED MUCH RESEARCH THAT IS AIMED ON IMPROVING STUDENTS ACADEMIC ACHIEVEMENTS. IN THIS STUDY, THE LMS DATA FROM A COURSE IS ANALYSED TO SEE HOW ACCURATELY WE CAN FORECAST STUDENT PERFORMANCES WHEN THEIR WEEKLY ENGAGEMENT DATA IS INTEGRATED WITH ASSIGNMENT SCORES. THIS STUDY HIGHLIGHTS THE IMPORTANCE OF LMS DATA; WHICH GIVES INSIGHTS ON STUDENTS BEHAVIOUR AND CAN LEAD TO DEVELOPING ACCURATE MODELS FOR PREDICTING STUDENTS FINAL OUTCOME IN COURSES THEY ARE ENROLLED.
AS A, UNIVERSITY DESKTOP", THE STUDENT LIFE CYCLE MANAGEMENT SYSTEM PROVIDES SELF SERVICES AND ADMINISTRATION APPLICATIONS REGARDING CURRICULUM DESIGN, COURSE DELIVERY AND EXAM RECORDS. BUILD UPON CURRICULUM DATA OF STUDENTS PROGRESS A WEB-BASED RECOMMENDATION APPLICATION WAS DEVELOPED TO SUPPORT INFORMATION AND SELECTION OF TEACHING MODULES IN STUDY PROGRAMS WITH PARTLY ELECTIVE CHARACTERISTICS. IT FEATURES DIFFERENT RECOMMENDATION ALGORITHMS THAT ARE BASED ON CONTENT-BASED, COLLABORATIVE AND CONVERSATIONAL RECOMMENDATION APPROACHES. FOR EXAMPLE, COLLABORATIVE FILTERING USES THE RECORDS OF SIMILAR STUDENTS IN ORDER TO RECOMMEND MODULES AND SUBJECTS TO STUDENTS WHILE CONTENT-BASED AND CONVERSATIONAL RECOMMENDATION TECHNIQUES ALLOW STUDENTS TO INTERACT WITH THE MODULE SELECTION. IN ADDITION, LECTURERS CAN ASSIGN APPROVED PREREQUISITE OPTIONS FOR RECOMMENDING NEW MODULES. THUS THE APPLICATION PROVIDES SEARCHING AND BROWSING FOR MODULES AND RANKS RESULTS BY DIFFERENT METRICS. FOR MATCHING INDIVIDUAL NEEDS AND INTERESTS, USERS ARE ABLE TO SELECT THE MOST INTERESTING METRICS AND TO APPLY SEVERAL FILTERS FOR DIFFERENT OUTCOMES. THIS ADVANTAGE COMES ALONG WITH ATTENDANCE FIGURES ON MODULES TYPED AS ELECTIVE. STUDENTS CAN SEE HOW MANY OTHER STUDENTS CHOOSE A SPECIFIC ELECTIVE FIELD AND LECTURERS GET INFORMATION ON ATTENDANCE OF THEIR COURSES. SUCH INFORMATION CAN BE ALSO USEFUL FOR ACADEMIC PLANNING OF COURSES AS WELL AS FURTHER DEVELOPMENT OF DEGREE PROGRAMS.
A LEARNER'S COGNITIVE LOAD IS HIGHLY ASSOCIATED WITH THEIR ACADEMIC ACHIEVEMENT WITHIN LEARNING SYSTEMS. DIAGNOSTIC INFORMATION ABOUT A LEARNER'S COGNITIVE LOAD IS USEFUL FOR ACHIEVING OPTIMAL LEARNING, BY ENABLING THE LEARNER TO MANAGE AND CONTROL THEIR COGNITIVE LOAD IN THE E-LEARNING ENVIRONMENT. HOWEVER, LITTLE EMPIRICAL RESEARCH HAS BEEN CONDUCTED TO OBTAIN DIAGNOSTIC INFORMATION ABOUT THE COGNITIVE LOAD IN E-LEARNING SYSTEMS. THE PURPOSE OF THIS STUDY WAS TO ANALYZE A PERSONALIZED DIAGNOSTIC EVALUATION FOR A LEARNER'S COGNITIVE LOAD IN AN E-LEARNING SYSTEM, USING THE BAYESIAN NETWORK (BN) AS A LEARNING ANALYTIC METHOD. DATA FROM 700 LEARNERS WERE COLLECTED FROM CYBER UNIVERSITY. A LEARNER'S COGNITIVE LOAD LEVEL WAS MEASURED IN TERMS OF THREE COMPONENTS: EXTRANEOUS COGNITIVE LOAD, INTRINSIC COGNITIVE LOAD, AND GERMANE COGNITIVE LOAD. THE BN WAS BUILT BY REPRESENTING THE RELATIONSHIP AMONG THE EXTRANEOUS COGNITIVE LOAD, INTRINSIC COGNITIVE LOAD, GERMANE COGNITIVE LOAD, AND ACADEMIC ACHIEVEMENT. THE CONDITIONAL AND MARGINAL PROBABILITIES IN THE BN WERE ESTIMATED. THIS STUDY FOUND THAT THE BN PROVIDED DIAGNOSTIC INFORMATION ABOUT A LEARNER'S LEVEL OF COGNITIVE LOAD IN THE E-LEARNING SYSTEM. IN ADDITION, THE BN PREDICTED THE LEARNER'S ACADEMIC ACHIEVEMENT IN TERMS OF THEIR DIFFERENT COGNITIVE LOAD PATTERNS. THIS STUDY'S RESULTS IMPLY THAT DIAGNOSTIC INFORMATION RELATED TO COGNITIVE LOAD HELPS LEARNERS TO IMPROVE ACADEMIC ACHIEVEMENT BY MANAGING AND CONTROLLING THEIR COGNITIVE LOADS IN THE E-LEARNING ENVIRONMENT. IN ADDITION, INSTRUCTIONAL DESIGNERS ARE ABLE TO OFFER MORE APPROPRIATELY CUSTOMIZED INSTRUCTIONAL METHODS BY CONSIDERING LEARNERS' COGNITIVE LOADS IN ONLINE LEARNING.
SEVERAL STUDIES HAVE BEEN CONDUCTED TO EVALUATE THE EXPERIENCE AND INVOLVEMENT OF ACADEMICS IN LEARNING ANALYTICS (LA) DUE TO ITS POTENTIAL FOR IMPROVING TEACHING AND LEARNING. HOWEVER, FINDINGS OFTEN REFLECT AN EDUCATIONAL CULTURE WHICH IS INDICATIVE OF THE INSTITUTIONAL OR NATIONAL CONTEXT WHERE THE STUDY HAS OCCURRED, RESULTING IN BIAS REGARDING LA PERSPECTIVES. THEREFORE, THIS STUDY SEEKS TO COMPARE AND CONTRAST THE EXPERIENCES OF LA AMONG ACADEMICS IN AUSTRALIA AND MALAYSIA, WITH INTENTIONS TO LEARN FROM EACH OTHER'S EXPERIENCE. AREAS OF COMPARISON WERE: (1) ACADEMICS' INVOLVEMENT IN LA ACTIVITIES; (2) ACADEMICS' RESPONSES TO THE INSTITUTIONAL CAPACITY IN SUPPORTING LA; AND 3) ACADEMICS' CONCERNS ABOUT THE ETHICAL ISSUES SURROUNDING LA. A SURVEY OF 353 AUSTRALIAN AND 224 MALAYSIAN ACADEMICS REVEALED SIMILARITIES AND DIFFERENCES. IT IS EVIDENT FROM THESE RESULTS THAT THE CONTEXT AND INFRASTRUCTURE FOR LA ARE AT DIFFERENT STAGES OF DEVELOPMENT IN BOTH COUNTRIES. NEVERTHELESS, THE RESULTS PROVIDE AN INTERESTING REFLECTION ON ACADEMICS' NEEDS, INSTITUTIONAL UNDERSTANDING, POLICIES, AND EDUCATIONAL CULTURAL BIASES IN APPLYING LA IN TEACHING AND LEARNING IN HIGHER EDUCATION INSTITUTIONS.
EPISTEMOLOGICAL BELIEFS ABOUT SCIENCE (EBAS) OR BELIEFS ABOUT THE NATURE OF SCIENCE KNOWLEDGE, AND HOW THAT KNOWLEDGE IS GENERATED DURING INQUIRY, ARE AN ESSENTIAL YET DIFFICULT TO ASSESS COMPONENT OF SCIENCE LITERACY. LEVERAGING LEARNING ANALYTICS TO CAPTURE AND ANALYZE STUDENT PRACTICES IN SIMULATED OR GAME-BASED AUTHENTIC SCIENCE ACTIVITIES IS A POTENTIAL AVENUE FOR ASSESSING EBAS. OUR PREVIOUS WORK CHARACTERIZED INQUIRY PRACTICES OF EXPERTS AND NOVICES ENGAGED IN SIMULATED AUTHENTIC SCIENCE INQUIRY AND SUGGESTED THAT PRACTICES MAY REFLECT EBAS. HERE, WE EXTEND OUR PRIOR QUALITATIVE WORK TO QUANTITATIVELY EXAMINE DIFFERENCES IN PRACTICES AND EBAS BETWEEN NON-SCIENCE MAJORS, BIOLOGY MAJORS, AND BIOLOGY GRADUATES. WE OBSERVED THAT INQUIRY PRACTICES OF NON-SCIENCE MAJORS AND BIOLOGY GRADUATES WERE SIMILAR TO THE NOVICE AND EXPERT PRACTICES, RESPECTIVELY, IN OUR PRIOR WORK. HOWEVER, BIOLOGY MAJORS SOMETIMES APPEARED TO ACT LIKE THEIR UNDERGRADUATE PEERS (E.G., PERFORMING FEWER PLANNING ACTIONS) BUT OTHER TIMES WERE MORE SIMILAR TO BIOLOGY GRADUATES (E.G., PERFORMING COMPLEX INVESTIGATIONS). WE NOTED THAT COGNITIVE CONSTRUCTS LIKE METACOGNITION WERE ALSO IMPORTANT FOR UNDERSTANDING WHICH PRACTICES WERE MOST LIKELY TO BE REFLECTIVE OF EBAS. THIS WORK ADVANCES HOW TO ASSESS EBAS USING LEARNING ANALYTICS AND RAISES QUESTIONS REGARDING THE DEVELOPMENT OF COGNITIVE PROCESSES LIKE EBAS AMONG ASPIRING BIOLOGISTS.
A VARIETY OF TOOLS ARE AVAILABLE TO COLLECT, PROCESS AND ANALYSE LEARNING DATA OBTAINED FROM THE CLICKSTREAM GENERATED BY STUDENTS WATCHING LEARNING RESOURCES IN VIDEO FORMAT. THERE IS ALSO SOME LITERATURE ON THE USES OF SUCH DATA IN ORDER TO BETTER UNDERSTAND AND IMPROVE THE TEACHING-LEARNING PROCESS. MOST OF THE LITERATURE FOCUSES ON LARGE SCALE LEARNING SCENARIOS, SUCH AS MOOCS, WHERE VIDEOS ARE WATCHED HUNDREDS OR THOUSANDS OF TIMES. WE HAVE DEVELOPED A SOLUTION TO COLLECT CLICKSTREAM ANALYTICS DATA APPLICABLE TO SMALLER SCENARIOS, MUCH MORE COMMON IN PRIMARY, SECONDARY AND HIGHER EDUCATION, WHERE VIDEOS ARE WATCHED TENS OR HUNDREDS OF TIMES, AND TO ANALYSE WHETHER THE SOLUTION IS USEFUL TO TEACHERS TO IMPROVE THE LEARNING PROCESS. WE HAVE DEPLOYED IT IN A REAL SCENARIO AND COLLECTED REAL DATA. FURTHERMORE, WE HAVE PROCESSED AND PRESENTED THE DATA VISUALLY TO TEACHERS FOR THOSE SCENARIOS AND HAVE COLLECTED AND ANALYSED THEIR PERCEPTION OF THEIR USEFULNESS. WE CONCLUDE THAT THE COLLECTED DATA ARE PERCEIVED AS USEFUL BY TEACHERS TO IMPROVE THE TEACHING AND LEARNING PROCESS.
SIGNIFICANT PROGRESS TO INTEGRATE AND ANALYSE MULTIMODAL DATA HAS BEEN CARRIED OUT IN THE LAST YEARS. YET, LITTLE RESEARCH HAS TACKLED THE CHALLENGE OF VISUALISING AND SUPPORTING THE SENSEMAKING OF MULTIMODAL DATA TO INFORM TEACHING AND LEARNING. IT IS NAIVE TO EXPECT THAT SIMPLY BY RENDERING MULTIPLE DATA STREAMS VISUALLY, A TEACHER OR LEARNER WILL BE ABLE TO MAKE SENSE OF THEM. THIS PAPER INTRODUCES AN APPROACH TO UNRAVEL THE COMPLEXITY OF MULTIMODAL DATA BY ORGANISING IT INTO MEANINGFUL LAYERS THAT EXPLAIN CRITICAL INSIGHTS TO TEACHERS AND STUDENTS. THE APPROACH IS ILLUSTRATED THROUGH THE DESIGN OF TWO DATA STORYTELLING PROTOTYPES IN THE CONTEXT OF NURSING SIMULATION. TWO AUTHENTIC STUDIES WITH EDUCATORS AND STUDENTS IDENTIFIED THE POTENTIAL OF THE APPROACH TO CREATE LEARNING ANALYTICS INTERFACES THAT COMMUNICATE INSIGHTS ON TEAM PERFORMANCE, AS WELL AS CONCERNS IN TERMS OF ACCOUNTABILITY AND AUTOMATED INSIGHTS DISCOVERY.
THE ASSURANCE OF LEARNING FOR GRADUATE EMPLOYABILITY FRAMEWORK IS A QUALITY ASSURANCE MODEL FOR CURRICULUM ENHANCEMENT FOR GRADUATE EMPLOYABILITY, ENABLING GRADUATES TO ACHIEVE "THE SKILLS, UNDERSTANDINGS AND PERSONAL ATTRIBUTES THAT MAKE [THEM] MORE LIKELY TO SECURE EMPLOYMENT AND BE SUCCESSFUL IN THEIR CHOSEN OCCUPATIONS TO THE BENEFIT OF THEMSELVES, THE WORKFORCE, THE COMMUNITY AND THE ECONOMY" (YORKE, 2006). OF PARTICULAR NOTE IS THE FRAMEWORK'S DEPENDENCE ON THREE FOUNDATIONS, INCLUDING EASY ACCESS TO INTEGRATED AND ACCESSIBLE TOOLS FOR STAFF AND STUDENT SELF-MANAGEMENT. IN OTHER WORDS, THIS APPROACH TO CURRICULUM QUALITY DEPENDS ON STAFF AND STUDENT ACCESS TO TOOLS THAT ENABLE THEM TO SELF-MANAGE THEIR LEARNING. THIS PAPER EXAMINES TWO ASPECTS WHICH INFORMED THE DESIGN OF A STUDENT E-PORTFOLIO SYSTEM, IPORTFOLIO, INTENDED FOR STUDENTS' SELF-MANAGEMENT OF THEIR LEARNING, PARTICULARLY RECORDING EVIDENCE OF THEIR ACHIEVEMENT OF CAPABILITIES. THE PAPER FOCUSES ON TWO PARTICULAR CONSIDERATIONS IN THE DESIGN OF THE IPORTFOLIO: ADOPTABILITY AND LEARNING ANALYTICS. ADOPTABILITY MEANS THE PHASE PRECEDING ADOPTION, WHETHER STUDENTS HAVE THE DEVICES, PLATFORMS AND TECHNOLOGY SKILLS TO BE ABLE TO USE SUCH AN INNOVATION. THE IPORTFOLIO ALSO FACILITATES LEARNING ANALYTICS: IT HAS THE CAPABILITY TO GATHER DATA RELATED TO LEARNING INDICATORS FOR COURSE QUALITY ASSURANCE PURPOSES. BOTH ADOPTABILITY AND ANALYTICS ARE VERY DYNAMIC FIELDS: NEW DEVICES, PLATFORMS AND APPLICATIONS CONSTANTLY SPARK CHANGES IN USER HABITS, AND POLICY CHANGES MEAN INSTITUTIONS NEED TO BE ABLE TO PROVIDE NEW DATA, OFTEN AT SHORT NOTICE. IN THE CONCLUSION, THE PAPER SUGGESTS HOW TOOLS SUCH AS THE IPORTFOLIO CAN BE DESIGNED FOR 'FUTURE PROOFING' AND SUSTAINABILITY.
IN THIS DECADE, THE USE OF LEARNING MANAGEMENT SYSTEMS (LMS) DOES NOT CEASE TO INCREASE, BECOMING ONE OF THE MOST POPULAR APPROACHES ADOPTED AND WIDELY USED IN THE LEARNING PROCESS. LEARNERS' ONLINE ACTIVITIES GENERATE A HUGE AMOUNT OF UNUSED DATA THAT IS WASTED BECAUSE TRADITIONAL LEARNING ANALYSES ARE NOT ABLE TO PROCESS THEM. IN THIS REGARD, A LARGE COLLECTION OF APPLICATIONS/TOOLS HAVE EMERGED TO CONDUCT RESEARCH IN EDUCATIONAL DATA MINING (EDM) AND / OR LEARNING ANALYSIS (LA). THIS STUDY LOOKS INTO THE RECENT APPLICATIONS/TOOLS OF BIG DATA TECHNOLOGIES IN EDUCATION AND PRESENTS SOME OF THE MOST WIDELY USED, ACCESSIBLE, AND POWERFUL TOOLS IN THIS FIELD OF RESEARCH. THE MAJORITY OF THESE TOOLS ARE FOR RESEARCHERS WITH THE PURPOSE OF CONDUCTING RESEARCH ON EDUCATIONAL DATA MINING AND LEARNING ANALYSIS.
NUMEROUS STUDIES HAVE ADDRESSED THE RELATIONSHIP BETWEEN PERFORMANCE IN MATHEMATICS PROBLEM-SOLVING AND READING COMPREHENSION IN STUDENTS OF ALL EDUCATIONAL LEVELS. THIS WORK PRESENTS A NEW PROPOSAL TO MEASURE THE COMPLEXITY OF ARITHMETIC WORD PROBLEMS THROUGH THE STUDENT READING COMPREHENSION OF THE PROBLEM STATEMENT AND THE USE OF LEARNING ANALYTICS. THE PROCEDURE TO QUANTIFY THIS READING COMPREHENSION COMPRISES TWO PHASES: (A) THE DIVISION OF THE STATEMENT INTO PROPOSITIONS AND (B) THE COMPUTATION OF THE TIME DEDICATED TO READ EACH PROPOSITION THROUGH A TECHNOLOGICAL ENVIRONMENT THAT RECORDS THE INTERACTIONS OF THE STUDENTS WHILE SOLVING THE PROBLEM. WE VALIDATED OUR APPROACH BY SELECTING A COLLECTION OF PROBLEMS CONTAINING MATHEMATICAL CONCEPTS RELATED TO FRACTIONS AND THEIR DIFFERENT MEANINGS, SUCH AS FRACTIONAL NUMBERS OVER A NATURAL NUMBER, BASIC MATHEMATICAL OPERATIONS WITH A NATURAL WHOLE OR FRACTIONAL WHOLE AND THE FRACTION AS AN OPERATOR. THE MAIN RESULTS INDICATE THAT A STUDENT'S READING TIME IS AN EXCELLENT PROXY TO DETERMINE THE COMPLEXITY OF BOTH PROPOSITIONS AND THE COMPLETE STATEMENT. FINALLY, WE USED THIS TIME TO BUILD A LOGISTIC REGRESSION MODEL THAT PREDICTS THE SUCCESS OF STUDENTS IN SOLVING ARITHMETIC WORD PROBLEMS.
IN THE INFORMATION AGE, ONE OF THE MOST INFLUENTIAL INSTITUTIONS IS EDUCATION. THE RECENT EMERGENCE OF MOOCS IS A SAMPLE OF THE NEW EXPECTATIONS THAT ARE OFFERED TO UNIVERSITY STUDENTS. BASING DECISIONS ON DATA AND EVIDENCE SEEMS OBVIOUS, AND INDEED, RESEARCH INDICATES THAT DATA-DRIVEN DECISION-MAKING IMPROVES ORGANIZATIONAL PRODUCTIVITY. THE MOST DRAMATIC FACTOR SHAPING THE FUTURE OF HIGHER EDUCATION IS BIG DATA AND ANALYTICS. BIG DATA EMPHASIZES THAT THE DATA ITSELF IS A PATH TO VALUE GENERATION IN ORGANIZATIONS AND IT IS, ALSO, A CRITICAL VALUE FOR HIGHER EDUCATION INSTITUTIONS. THE EMERGING PRACTICE OF ACADEMIC ANALYTICS IS LIKELY TO BECOME A NEW USEFUL TOOL FOR A NEW ERA. ANALYTICS AND BIG DATA HAVE A SIGNIFICANT ROLE TO PLAY IN THE FUTURE OF HIGHER EDUCATION. THIS PAPER ATTEMPTS AN ANALYTICAL PRACTICE ABOUT THE USE OF E-LEARNING TECHNOLOGICAL TOOLS TO GENERATE RELEVANT INFORMATION, FOR THE TEACHER AND THE STUDENTS WHO TRY TO OPTIMIZE THEIR LEARNING PROCESS. THIS COMBINATION OF DATA-PROCESSING AND ANALYTICAL LEARNING IS AN AID TO IMPROVE SIGNIFICANTLY HIGHER EDUCATION AND MARK THE PATH TO FOLLOW IN THE NEW EDUCATIONAL ERA.
E-LEARNING HAS BECOME A CRITICAL FACTOR IN THE ACADEMIC ENVIRONMENT DUE TO THE ENDLESS NUMBER OF POSSIBILITIES THAT IT OPENS FOR THE LEARNING CONTEXT. HOWEVER, THESE PLATFORMS OFTEN SUPPOSE TO INCREASE THE DIFFICULTIES FOR THE COMMUNICATION BETWEEN TEACHERS AND STUDENTS. WITHOUT HAVING REAL CONTACT BETWEEN TEACHERS AND STUDENTS, THE FORMER FINDS IT HARDER TO ADAPT THEIR METHODS AND CONTENT TO THEIR STUDENTS, WHILE THE STUDENTS ALSO FIND COMPLICATIONS FOR MAINTAINING THEIR FOCUS. THIS PAPER AIMS TO ADDRESS THIS CHALLENGE WITH THE USE OF EMOTION AND ENGAGEMENT RECOGNITION TECHNIQUES. WE PROPOSE AN EMOTION-AWARE E-LEARNING PLATFORM ARCHITECTURE THAT RECOGNIZES STUDENTS' EMOTIONS AND ATTENTION IN ORDER TO IMPROVE THEIR ACADEMIC PERFORMANCE. THE SYSTEM INTEGRATES A SEMANTIC TASK AUTOMATION SYSTEM THAT ALLOWS USERS TO EASILY CREATE AND CONFIGURE THEIR OWN AUTOMATION RULES TO ADAPT THE STUDY ENVIRONMENT. THE MAIN CONTRIBUTIONS OF THIS PAPER ARE: (1) THE DESIGN OF AN EMOTION-AWARE LEARNING ANALYTICS ARCHITECTURE; (2) THE INTEGRATION OF THIS ARCHITECTURE IN A SEMANTIC TASK AUTOMATION PLATFORM; AND (3) THE VALIDATION OF THE USE OF EMOTION RECOGNITION IN THE E-LEARNING PLATFORM USING PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING (PLS-SEM) METHODOLOGY.
COLLECTING AND ANALYZING LARGE AMOUNTS OF PROCESS DATA FOR THE PURPOSES OF EDUCATION CAN BE CONSIDERED A BIG DATA/LEARNING ANALYTICS (BD/ LA) APPROACH TO IMPROVING LEARNING. HOWEVER, IN THE EDUCATION OF HEALTH CARE PROFESSIONALS, THE APPLICATION OF BD/LA IS LIMITED TO DATE. THE AUTHORS DISCUSS THE POTENTIAL ADVANTAGES OF THE BD/LA APPROACH FOR THE PROCESS OF LEARNING VIA COGNITIVE SIMULATIONS. USING THE LENS OF A COGNITIVE MODEL OF RADIOGRAPH INTERPRETATION WITH FOUR PHASES (ORIENTATION, SEARCHING/SCANNING, FEATURE DETECTION, AND DECISION MAKING), THEY REANALYZED PROCESS DATA FROM A COGNITIVE SIMULATION OF PEDIATRIC ANKLE RADIOGRAPHY WHERE 46 PRACTITIONERS FROM THREE EXPERTISE LEVELS CLASSIFIED 234 CASES ONLINE. TO ILLUSTRATE THE BIG DATA COMPONENT, THEY HIGHLIGHT THE DATA AVAILABLE IN A DIGITAL ENVIRONMENT (TIME-STAMPED, CLICK-LEVEL PROCESS DATA). LEARNING ANALYTICS WERE ILLUSTRATED USING ALGORITHMIC COMPUTER-ENABLED APPROACHES TO PROCESS-LEVEL FEEDBACK. FOR EACH PHASE, THE AUTHORS WERE ABLE TO IDENTIFY EXAMPLES OF POTENTIALLY USEFUL BD/LA MEASURES. FOR ORIENTATION, THE TRACKABLE BEHAVIOR OF RE-REVIEWING THE CLINICAL HISTORY WAS ASSOCIATED WITH INCREASED DIAGNOSTIC ACCURACY. FOR SEARCHING/SCANNING, EVIDENCE OF SKIPPING VIEWS WAS ASSOCIATED WITH AN INCREASED FALSE-NEGATIVE RATE. FOR FEATURE DETECTION, HEAT MAPS OVERLAID ON THE RADIOGRAPH CAN PROVIDE A METACOGNITIVE VISUALIZATION OF COMMON NOVICE ERRORS. FOR DECISION MAKING, THE MEASURED INFLUENCE OF SEQUENCE EFFECTS CAN REFLECT SUSCEPTIBILITY TO BIAS, WHEREAS COMPUTER-GENERATED PATH MAPS CAN PROVIDE INSIGHTS INTO LEARNERS' DIAGNOSTIC STRATEGIES. IN CONCLUSION, THE AUGMENTED COLLECTION AND DYNAMIC ANALYSIS OF LEARNING PROCESS DATA WITHIN A COGNITIVE SIMULATION CAN IMPROVE FEEDBACK AND PROMPT MORE PRECISE REFLECTION ON A NOVICE CLINICIAN'S SKILL DEVELOPMENT.
LEARNING ANALYTICS HAS THE POTENTIAL TO DETECT AND EXPLAIN CHARACTERISTICS OF LEARNING STRATEGIES THROUGH ANALYSIS OF TRACE DATA AND COMMUNICATE THE FINDINGS VIA FEEDBACK. HOWEVER, THE ROLE OF LEARNING ANALYTICS-BASED FEEDBACK IN SELECTION AND REGULATION OF LEARNING STRATEGIES IS STILL INSUFFICIENTLY EXPLORED AND UNDERSTOOD. THIS RESEARCH AIMS TO EXAMINE THE SEQUENTIAL AND TEMPORAL CHARACTERISTICS OF LEARNING STRATEGIES AND INVESTIGATE THEIR ASSOCIATION WITH FEEDBACK. THREE YEARS OF TRACE DATA WERE COLLECTED FROM ONLINE PRE-CLASS ACTIVITIES OF A FLIPPED CLASSROOM, WHERE DIFFERENT TYPES OF FEEDBACK WERE EMPLOYED IN EACH YEAR. CLUSTERING, SEQUENCE MINING, AND PROCESS MINING WERE USED TO DETECT AND INTERPRET LEARNING TACTICS AND STRATEGIES. INFERENTIAL STATISTICS WERE USED TO EXAMINE THE ASSOCIATION OF FEEDBACK WITH THE LEARNING PERFORMANCE AND THE DETECTED LEARNING STRATEGIES. THE RESULTS SUGGEST A POSITIVE ASSOCIATION BETWEEN THE PERSONALISED FEEDBACK AND THE EFFECTIVE STRATEGIES.
WE PRESENT THE COLLECTED EXPERIENCES SINCE 2012 OF THE DUTCH SPECIAL INTEREST GROUP (SIG) FOR LEARNING ANALYTICS IN THE APPLICATION OF THE XAPI STANDARD. WE HAVE BEEN EXPERIMENTING AND EXCHANGING BEST PRACTICES AROUND THE APPLICATION OF XAPI IN VARIOUS CONTEXTS. THE PRACTICES INCLUDE DIFFERENT DESIGN PATTERNS CENTERED AROUND LEARNING RECORD STORES. WE PRESENT THREE PROJECTS THAT APPLY XAPI IN VERY DIFFERENT WAYS AND PUBLISH A CONSISTENT SET OF XAPI RECIPES.
THE ABILITY OF AUTOMATICALLY MODELING LEARNERS' PERSONALITIES IS AN IMPORTANT STEP IN BUILDING ADAPTIVE LEARNING ENVIRONMENTS. SEVERAL STUDIES SHOWED THAT KNOWING THE PERSONALITY OF EACH LEARNER CAN MAKE THE LEARNING INTERACTION WITH THE PROVIDED LEARNING CONTENTS AND ACTIVITIES WITHIN LEARNING SYSTEMS MORE EFFECTIVE. HOWEVER, THE TRADITIONAL METHOD OF MODELING PERSONALITY IS USING SELF-REPORTS, SUCH AS QUESTIONNAIRE, WHICH IS SUBJECTIVE AND WITH SEVERAL LIMITATIONS. THEREFORE, THIS STUDY PRESENTS A NEW UNOBTRUSIVE METHOD TO MODEL THE LEARNERS' PERSONALITIES IN AN INTELLIGENT MOODLE (IMOODLE) USING LEARNING ANALYTIC (LA) APPROACH WITH BAYESIAN NETWORK. TO EVALUATE THE ACCURACY OF THE PROPOSED APPROACH, AN EXPERIMENT WAS CONDUCTED WITH ONE HUNDRED THIRTY-NINE LEARNERS IN A PUBLIC UNIVERSITY. RESULTS SHOWED THAT RECALL, PRECISION, F-MEASURE AND ACCURACY VALUES ARE IN ACCEPTANCE RANGE FOR THREE PERSONALITY DIMENSIONS INCLUDING EXTRAVERSION, OPENNESS, AND NEUROTICISM. MOREOVER, THE RESULTS SHOWED THAT THE LA APPROACH HAS A FAIR AGREEMENT WITH THE BIG FIVE INVENTORY (BFI) IN MODELING THESE THREE PERSONALITY DIMENSIONS. FINALLY, THIS STUDY PROVIDES SEVERAL RECOMMENDATIONS WHICH CAN HELP RESEARCHERS AND PRACTITIONERS DEVELOP EFFECTIVE SMART LEARNING ENVIRONMENTS FOR BOTH LEARNING AND MODELING. FOR EXAMPLE, IT IS NEEDED TO HELP IDENTIFY MORE FEATURES OF THE HARDEST PERSONALITY TRAITS, SUCH AS AGREEABLENESS, USING GAMIFICATION COURSES.
FIREFIGHTER LEARNING IS HIGH STAKE. THEY NEED TO MAINTAIN CERTAIN COMPETENCE LEVELS RELATED TO PHYSICAL, MENTAL, AND FIREFIGHTING AND RESCUE SKILLS IN ORDER TO PROVIDE THE PUBLIC WITH A HIGH LEVEL OF EMERGENCY SERVICE. FIRE AND RESCUE SERVICES NEED TO MAINTAIN AN OVERVIEW OF THE CURRENT COMPETENCES OF THEIR PERSONNEL AND TO REACT WHEN THERE IS A COMPETENCE GAP. THIS POSTER PRESENTS OUR APPROACH TO USING COMPETENCE MODELLING, LEARNER MODELS, LEARNING ANALYTICS, AND VISUALISATIONS IN ORDER PROVIDE INSIGHT INTO COMPETENCE STATUS AND DEVELOPMENT ON THE INDIVIDUAL, TEAM, AND ORGANISATION LEVEL, AND TO PROVIDE EARLY-ALERTS AND AUTOMATED MESSAGES TO INSTRUCTORS RESPONSIBLE FOR PLANNING TRAINING ACTIVITIES, AS WELL AS TO TEAM LEADERS RESPONSIBLE FOR MAKING DECISIONS ABOUT TEAMS IN HIGH STAKES SITUATIONS.
DESPITE THEIR INCREASINGLY WIDESPREAD ADOPTION IN POST-SECONDARY EDUCATION, SCHOLARS AND PRACTITIONERS KNOW VERY LITTLE ABOUT THE IMPACT OF DIGITAL DATA DISPLAYS ON INSTRUCTORS' SENSE-MAKING AND ACADEMIC PLANNING. IN THIS MANUSCRIPT, I REPORT THE RESULTS OF COMPARATIVE CASE STUDIES OF FIVE DIFFERENT INTRODUCTORY PHYSICS INSTRUCTORS AT THREE INSTITUTIONS WHO USED DATA DASHBOARDS AS PART OF AN ACTIVE LEARNING APPROACH CALLED 'PEER INSTRUCTION'. INSTRUCTORS EXPRESSED FRUSTRATION WITH THE WAYS THAT DATA DISPLAYS UNDERMINED THEIR EXISTING PEDAGOGICAL STRATEGIES. THEY WERE STYMIED BY A LACK OF CLARITY ON HOW DATA IS ASSEMBLED AND IMBUED WITH MEANING, WHICH LIMITED THEIR OWN SENSEMAKING ABOUT THE DATA. THEY ALSO EXPRESSED CONCERNS ABOUT HOW THE DASHBOARDS FACILITATED DATA COLLECTION ABOUT THEIR INSTRUCTIONAL PLANNING AND DECISION-MAKING.
THIS STUDY AIMS TO APPLY A SEQUENTIAL ANALYSIS TO EXPLORE THE EFFECT OF LEARNING MOTIVATION ON ONLINE READING BEHAVIORAL PATTERNS. THE STUDY'S PARTICIPANTS CONSISTED OF 160 GRADUATE STUDENTS WHO WERE CLASSIFIED INTO THREE GROUP TYPES: LOW READING DURATION WITH LOW MOTIVATION, LOW READING DURATION WITH HIGH MOTIVATION, AND HIGH READING DURATION BASED ON A SECOND-ORDER CLUSTER ANALYSIS. AFTER PERFORMING A SEQUENTIAL ANALYSIS, THIS STUDY REVEALS THAT HIGHLY MOTIVATED STUDENTS EXHIBITED A RELATIVELY SERIOUS READING PATTERN IN A MULTI-TASKING LEARNING ENVIRONMENT, AND THAT ONLINE READING DURATION WAS A SIGNIFICANT INDICATOR OF MOTIVATION IN TAKING AN ONLINE COURSE. FINALLY, RECOMMENDATIONS WERE PROVIDED TO INSTRUCTORS AND RESEARCHERS BASED ON THE RESULTS OF THE STUDY.
CURRICULUM ANALYTICS (CA) EMERGED AS A SUB-FIELD OF LEARNING ANALYTICS, AIMING TO USE EVIDENCE TO DRIVE CURRICULUM DECISION-MAKING AND PROGRAM IMPROVEMENT. HOWEVER, ITS OVERALL IMPACT ON PROGRAM OUTCOMES REMAINS UNKNOWN. IN THIS CONTEXT, THIS PAPER PRESENTS WORK-IN-PROGRESS OF A LARGE RESEARCH PROJECT TO UNDERSTAND HOW CA COULD SUPPORT CONTINUOUS IMPROVEMENT PROCESSES AT A PROGRAM-LEVEL. WE FOLLOWED AN APPROACH BASED ON DESIGN-BASED RESEARCH TO DEVELOP A CA TOOL: THE INTEGRATIVE LEARNING DESIGN FRAMEWORK. THIS PAPER DESCRIBES THREE OUT OF FOUR PHASES OF THIS FRAMEWORK AND ITS MAIN RESULTS, INCLUDING THE EVALUATION OF THE LOCAL IMPACT OF THIS CA TOOL. THIS EVALUATION CONSISTED OF AN INSTRUMENTAL CASE STUDY TO EVALUATE ITS USE TO SUPPORT 124 TEACHING STAFF IN A 3-YEAR CONTINUOUS IMPROVEMENT PROCESS IN A LATIN AMERICAN UNIVERSITY. LESSONS LEARNED INDICATE THAT THE TOOL HELPED STAFF TO COLLECT INFORMATION FOR CURRICULUM DISCUSSIONS, FACILITATING THE AVAILABILITY OF EVIDENCE REGARDING STUDENT COMPETENCY ATTAINMENT. TO GENERALIZE THESE LESSONS, FUTURE WORK WILL CONSIST OF EVALUATING THE TOOL IN DIFFERENT UNIVERSITY SETTINGS.
COVID-19 HAS BEEN A TURNING POINT FOR ONLINE LEARNING ACROSS THE WORLD, BUT THE REMARKABLE STORY HAS BEEN THE METEORIC GROWTH OF SYNCHRONOUS OR "LIVE" ONLINE EDUCATION. AS INSTRUCTORS AROUND THE WORLD HAVE TURNED TO VIDEO CONFERENCING PLATFORMS TO TEACH, A LOT OF INSTRUCTOR FEEDBACK HAS RECENTLY EMERGED ON A VARIETY OF TOPICS. ONE OF THE KEY EMERGING ISSUES FOR INSTRUCTORS AND INSTITUTIONS HAS BEEN EXTRACTING AND CONSOLIDATING DATA ON STUDENT ENGAGEMENT AND LEARNING OUTCOMES. THIS PAPER ATTEMPTS TO IDENTIFY AND CLASSIFY THE TYPES OF LEARNING DATA REQUIRED TO UNDERSTAND STUDENT LEARNING BEHAVIOR IN SYNCHRONOUS (OR "LIVE") ONLINE CLASSES BASED ON A MULTITUDE OF FACTORS SUCH AS IMMEDIACY, CONSOLIDATION, AND AVAILABILITY TO DIFFERENT STAKEHOLDERS AT DIFFERENT POINTS IN TIME. FURTHER, AN ATTEMPT IS MADE TO ASSESS HOW POPULAR VIDEO CONFERENCING PLATFORMS ADDRESS THE ISSUES OF DATA GENERATION, PRESENTATION, AND COLLATION. LASTLY, THE PAPER ALSO PROPOSES AN ALTERNATIVE APPROACH THAT CAN POTENTIALLY HELP CREATE DATA IN AN EASIER, MORE EFFICIENT MANNER.
RESEARCH ON LEARNING ANALYTICS HAS PRIMARILY FOCUSED ON CERTAIN STAGES OF THE LEARNING ANALYTICS CYCLE, SUCH AS ANALYSIS TECHNIQUES AND DATA VISUALIZATION. HOWEVER, THERE IS STILL AN ALARMING SCARCITY OF AVAILABLE TOOLS FOR DATA COLLECTION AND PREPARATION. THIS STUDY PRESENTS A SOFTWARE APPLICATION THAT FACILITATES THE EXTRACTION AND SUBSEQUENT ANALYSIS OF PEER ASSESSMENT-RELATED DATA FROM MOODLE WORKSHOP ACTIVITIES: MOODLE WORKSHOP DATA EXTRACTOR (MWDEX). THE APPLICATION MAY BE RUN LOCALLY OR REMOTELY AND USES THE MOODLE WEB SERVICES LAYER TO ACCESS DATA IN A SECURE MANNER. THE STUDY PROVIDES AN OVERVIEW OF THE DESIGN AND MAIN COMPONENTS OF THE SYSTEM AND DESCRIBES HOW TO CONFIGURE AND RUN MWDEX WITH AN ILLUSTRATED EXAMPLE.
PURPOSE - THE PURPOSE OF THE STUDY WAS TO TEST THE USABILITY OF THE MYLA APP PROTOTYPE BY ITS POTENTIAL USERS. FURTHERMORE, THE WEB APP WILL BE INTRODUCED IN THE FRAMEWORK OF "MOBILE LEARNING ANALYTICS", A COOPERATION PROJECT BETWEEN THE COOPERATIVE STATE UNIVERSITY MANNHEIM AND UNIVERSITY OF MANNHEIM. THE PARTICIPATING UNIVERSITIES FOCUS ON THE SUPPORT OF PERSONALIZED AND SELF-REGULATED LEARNING. MYLA COLLECTS DATA SUCH AS LEARNING BEHAVIOR, AS WELL AS PERSONALITY TRAITS. LAST BUT NOT LEAST, THE PAPER WILL CONTRIBUTE TO THE TOPIC OF LEARNING ANALYTICS AND MOBILE LEARNING IN HIGHER EDUCATION. DESIGN/METHODOLOGY - FOR THE EMPIRICAL INVESTIGATION, A MIXED-METHOD DESIGN WAS CHOSEN. WHILE 105 PARTICIPANTS TOOK PART IN THE CONDUCTED ONLINE SURVEY, AFTER TESTING THE APP PROTOTYPE, SEVEN STUDENTS JOINED AN ADDITIONAL EYE TRACKING STUDY. FOR THE QUANTITATIVE PART, A SELECTED QUESTION POOL FROM HIMATT (HIGHLY INTEGRATED MODEL ASSESSMENT TECHNOLOGY AND TOOLS) INSTRUMENT WAS CHOSEN. THE EYE TRACKING INVESTIGATION CONSISTED OF THREE TASKS THE PARTICIPANTS HAD TO SOLVE. FINDINGS - THE FINDINGS SHOWED THAT THE STUDENTS ASSESSED THE IDEA OF THE APP, AS WELL AS THE NAVIGATION POSITIVELY. ONLY THE COLOR SCHEME OF THE PROTOTYPE WAS NOT VERY ATTRACTIVE TO A NOTICEABLE AMOUNT OF THE PARTICIPANTS. SO, IT REQUIRES SLIGHT MODIFICATIONS CONCERNING THE APP DESIGN. FOR THE EYE TRACKING STUDY, IT CAN BE STATED THAT THE STUDENTS VIEWED THE RELEVANT PARTS, AND THEY BASICALLY HAD NO DIFFICULTIES TO SOLVE THE TASKS. ORIGINALITY/VALUE - DUE TO THE EMPIRICAL TESTING OF THE APP PROTOTYPE, THE PROJECT TEAM WAS ABLE TO ADJUST THE APPLICATION AND TO ADD FURTHER FEATURES. FURTHERMORE, THE BACKEND WAS PROGRAMMED AND AN ADDITIONAL TOOL (MYLA DASHBOARD) WAS DEVELOPED FOR LECTURERS. A MUTUAL UNDERSTANDING OF THE TARGETS, PRIVACY ISSUE AND RELEVANT FEATURES ARE INDISPENSABLE FOR FURTHER DEVELOPMENT OF THE PROJECT.
PUPIL-TEACHER RATIO (PTR) IS ONE OF THE KEY MEASUREMENTS OF QUALITY EDUCATION. THIS ARTICLE PRESENTS THE PUPIL-TEACHER RATIO FOR 133 PUBLIC PRIMARY SCHOOLS IN A LOCAL GOVERNMENT AREA (LGA) OF OGUN STATE, NIGERIA. THE DATA WERE OBTAINED FROM A COMPLETE ENUMERATION OF THE RECORDS OF EACH SCHOOL. IT WAS DISCOVERED THAT THE AVERAGE PTR OBTAINED FROM THE DATA ANALYSIS IS HIGHER THAN THE NATIONAL AVERAGE. USING PTR EQUALS 35 AS A BENCHMARK OF NIGERIA, ONLY 25 (19%) SCHOOLS OUT OF THE 133 SCHOOLS CONSIDERED HAVE AN ACCEPTABLE PTR (BELOW 35), 52 (39%) SCHOOLS HAVE A MODERATE PTR (BETWEEN 35 AND 50) AND 56 (42%) SCHOOLS HAVE UNACCEPTABLE AND HIGH PTR (ABOVE 50). THIS ARTICLE SHOWS THAT THERE IS HIGH PUPIL-TEACHER RATIO IN THE COUNTRY'S PUBLIC PRIMARY SCHOOLS. THIS RESEARCH WILL BE HELPFUL IN THE FOLLOWING; EDUCATIONAL EVALUATION AND ASSESSMENT, AUDIT AND QUALITY ASSURANCE, DECISION MAKERS IN THE MINISTRY OF EDUCATION IN GAP ANALYSIS FOR RECRUITMENT PURPOSES, ASSESSING THE LEVEL OF IMPLEMENTATION OF POLICIES ON EDUCATION AND MONITORING OF THE PROGRESS MADE IN ATTAINING DEVELOPMENT SUSTAINABLE GOALS (SDG) AS IT RELATES TO ACCESS TO QUALITY EDUCATION.
CURRICULUM DESIGN, TEACHING METHODS, ASSESSMENTS AND RANGE OF ACADEMIC SUPPORT NEED TO BE INCLUSIVE IN OPEN ACCESS ENABLING COURSES. THE FINDINGS OF THIS STUDY CONFIRM A CORRELATION BETWEEN STUDENT ACCESS TO ONLINE LEARNING MATERIALS AND A POSITIVE IMPACT ON GRADES IN SCIENCE COURSES. MORE SPECIFICALLY, STUDENTS WHO FREQUENTLY USE THE ONLINE LEARNING SYSTEM TO ACCESS MATERIALS HAVE BETTER ASSESSMENT AND EXAM RESULTS.
LEARNING ANALYTICS (LA) IS AN EMERGING DISCIPLINE FOCUSED ON OBTAINING INFORMATION BY ANALYZING STUDENTS' INTERACTIONS WITH ON-LINE EDUCATIONAL CONTENTS. DATA IS USUALLY COLLECTED FROM ONLINE ACTIVITIES SUCH AS FORUMS OR VIRTUALIZED COURSES HOSTED ON LEARNING MANAGEMENT SYSTEMS (E.G. MOODLE). EDUCATIONAL GAMES ARE EMERGING AS A POPULAR TYPE OF E-LEARNING CONTENT AND THEIR HIGH INTERACTIVITY MAKES THEM POTENTIAL SOURCES FOR RELEVANT EDUCATIONAL USER DATA. HOWEVER, IT IS STILL UNCERTAIN HOW TO DEPLOY AND COMBINE THESE TWO INCIPIENT TECHNOLOGIES, AS MULTIPLE CHALLENGES REMAIN UNRESOLVED. THIS PAPER REPORTS ON OUR PRACTICAL EXPERIENCE USING LA TO IMPROVE ASSESSMENT OF EXPERIMENTAL RESEARCH ON EDUCATIONAL GAME-BASED INSTRUCTION. IN THE LAST YEAR, WE CONDUCTED FOUR EXPERIMENTS EVALUATING GAME-BASED INSTRUCTION UNDER DIFFERENT CONDITIONS (USING THREE ADVENTURE GAMES AND A PUZZLE GAME RESPECTIVELY) IN 13 EDUCATIONAL INSTITUTIONS INCLUDING SCHOOLS, UNIVERSITIES AND VOCATIONAL TRAINING ORGANIZATIONS. A LA SYSTEM WAS USED TO TRACK INTERACTION AROUND SIX HUNDRED STUDENTS. IN THESE EXPERIENCES, WE ENCOUNTERED SEVERAL PROBLEMS IN EACH OF THE STEPS OF THE PROCESS, FROM ISSUES RELATED TO THE DESIGN OF THE EXPERIMENT AND THE GAME TO DIFFERENT TECHNICAL AND PRACTICAL PROBLEMS, DERIVED FROM THE VERY DIVERSE CONDITIONS OF THE FACILITIES AND POLICIES OF EACH INSTITUTION (COMPUTER LABORATORIES, COMPUTERS HARDWARE, SOFTWARE INSTALLED, IRREGULAR INTERNET ACCESS), WHICH HINDERS THE DATA COLLECTING PROCESS (E.G. THE SYSTEM HAD TO DEAL WITH HIGH LATENCY INTERNET CONNECTIONS AND BACKUP PLANS HAD TO BE DEVISED FOR COLLECTING DATA WHEN NO INTERNET ACCESS WAS AVAILABLE). WE PRESENT THE LESSONS LEARNED AND PROPOSE GUIDELINES FROM A TECHNICAL AND PRACTICAL PERSPECTIVE FOR THE DESIGN OF EXPERIMENTAL RESEARCH USING BOTH LA SYSTEMS AND EDUCATIONAL GAMES.
LEARNING PULSE EXPLORES WHETHER USING A MACHINE LEARNING APPROACH ON MULTIMODAL DATA SUCH AS HEART RATE, STEP COUNT, WEATHER CONDITION AND LEARNING ACTIVITY CAN BE USED TO PREDICT LEARNING PERFORMANCE IN SELF-REGULATED LEARNING SETTINGS. AN EXPERIMENT WAS CARRIED OUT LASTING EIGHT WEEKS INVOLVING PHD STUDENTS AS PARTICIPANTS, EACH OF THEM WEARING A FITBIT HR WRISTBAND AND HAVING THEIR APPLICATION ON THEIR COMPUTER RECORDED DURING THEIR LEARNING AND WORKING ACTIVITIES THROUGHOUT THE DAY. A SOFTWARE INFRASTRUCTURE FOR COLLECTING MULTIMODAL LEARNING EXPERIENCES WAS IMPLEMENTED. AS PART OF THIS INFRASTRUCTURE A DATA PROCESSING APPLICATION WAS DEVELOPED TO PRE-PROCESS, ANALYSE AND GENERATE PREDICTIONS TO PROVIDE FEEDBACK TO THE USERS ABOUT THEIR LEARNING PERFORMANCE. DATA FROM DIFFERENT SOURCES WERE STORED USING THE XAPI STANDARD INTO A CLOUD-BASED LEARNING RECORD STORE. THE PARTICIPANTS OF THE EXPERIMENT WERE ASKED TO RATE THEIR LEARNING EXPERIENCE THROUGH AN ACTIVITY RATING TOOL INDICATING THEIR PERCEIVED LEVEL OF PRODUCTIVITY, STRESS, CHALLENGE AND ABILITIES. THESE SELF-REPORTED PERFORMANCE INDICATORS WERE USED AS MARKERS TO TRAIN A LINEAR MIXED EFFECT MODEL TO GENERATE LEARNER-SPECIFIC PREDICTIONS OF THE LEARNING PERFORMANCE. WE DISCUSS THE ADVANTAGES AND THE LIMITATIONS OF THE USED APPROACH, HIGHLIGHTING FURTHER DEVELOPMENT POINTS.
THIS PAPER DESCRIBES OPEN LEARNER MODELS AS VISUALISATIONS OF LEARNING FOR LEARNERS, WITH A PARTICULAR FOCUS ON HOW INFORMATION ABOUT THEIR LEARNING CAN BE USED TO HELP THEM REFLECT ON THEIR SKILLS, IDENTIFY GAPS IN THEIR SKILLS, AND PLAN THEIR FUTURE LEARNING. WE OFFER AN APPROACH THAT, IN ADDITION TO PROVIDING VISUALISATIONS OF THEIR LEARNING, ALLOWS LEARNERS TO PROPOSE CHANGES TO THEIR LEARNER MODEL. THIS AIMS TO HELP IMPROVE THE ACCURACY OF THE LEARNER MODEL BY TAKING INTO ACCOUNT STUDENT VIEWPOINTS ON THEIR LEARNING, WHILE ALSO PROMOTING LEARNER REFLECTION ON THEIR LEARNING AS PART OF A DISCUSSION OF THE CONTENT OF THEIR LEARNER MODEL. THIS ALIGNS WELL WITH RECENT CALLS FOR LEARNING ANALYTICS FOR LEARNERS. BUILDING ON PREVIOUS RESEARCH SHOWING THAT LEARNERS WILL USE OPEN LEARNER MODELS, WE HERE INVESTIGATE THEIR INITIAL REACTIONS TO OPEN LEARNER MODEL FEATURES TO IDENTIFY THE LIKELIHOOD OF UPTAKE IN CONTEXTS WHERE AN OPEN LEARNER MODEL IS OFFERED ON AN OPTIONAL BASIS. WE FOCUS ON UNIVERSITY STUDENTS' PERCEPTIONS OF A RANGE OF VISUALISATIONS AND THEIR STATED PREFERENCES FOR A FACILITY TO VIEW EVIDENCE FOR THE LEARNER MODEL DATA AND TO PROPOSE CHANGES TO THE VALUES.
ONLINE VIDEOS ARE EXTENSIVELY USED IN EDUCATION, AND A RECENT TREND IS THE INTEGRATION OF INTERACTIVE ELEMENTS AND WEB CONTENT INTO EDUCATIONAL VIDEOS. THIS PAPER DESCRIBES IN BRIEF HOW OPEN SOURCE TOOLS CAN BE USED FOR DEVELOPING LEARNING ENVIRONMENTS WHERE VIDEO CONTENT IS AGGREGATED WITH INTERACTIVE ELEMENTS, EDUCATOR CONTENT, AND CONTENT COMING FROM OPEN INTERNET RESOURCES. IT ALSO DESCRIBES HOW SUCH TOOLS CAN BE USED FOR CAPTURING AND STORING LEARNER ACTIVITY DATA WHICH CAN THEN BE USED FOR DATA ANALYSIS AND DATA MINING PURPOSES. FINALLY, THE PAPER PRESENTS INSIGHTS OBTAINED FROM ANALYZING LEARNER ACTIVITY DATA, GATHERED OVER A SEMESTER PERIOD.
ADDRESSING HETEROGENEITY IN THE CLASSROOM BY ADAPTING INSTRUCTION TO LEARNERS' NEEDS CHALLENGES TEACHERS IN THEIR DAILY WORK. TO PROVIDE ADAPTIVE INSTRUCTION IN THE MOST FLEXIBLE WAY, TEACHERS FACE THE PROBLEM OF ASSESSING STUDENTS' INDIVIDUAL CHARACTERISTICS (LEARNING PREREQUISITES AND LEARNING NEEDS) AND SITUATIONAL STATES (LEARNING EXPERIENCES AND LEARNING PROGRESS) ALONG WITH THE CHARACTERISTICS OF THE LEARNING ENVIRONMENT. TO SUPPORT TEACHERS IN GATHERING AND PROCESSING SUCH MULTIDIMENSIONAL DIAGNOSTIC INFORMATION IN CLASS, WE HAVE DEVELOPED A CLIENT-SERVER BASED SOFTWARE PROTOTYPE RUNNING ON MOBILE DEVICES: THE TEACHERS' DIAGNOSTIC SUPPORT SYSTEM. FOLLOWING THE GENERIC EDUCATIONAL DESIGN RESEARCH PROCESS, WE (1) DELINEATE THEORETICAL IMPLICATIONS FOR SYSTEM REQUIREMENTS DRAWN FROM A LITERATURE REVIEW, (2) DESCRIBE THE SYSTEMS' DESIGN AND TECHNICAL DEVELOPMENT AND (3) REPORT THE RESULTS OF A USABILITY STUDY. WE BROADEN OUR THEORETICAL UNDERSTANDING OF HETEROGENEITY WITHIN SCHOOL CLASSES AND ESTABLISH A BASIS FOR TECHNOLOGICAL INTERVENTIONS TO IMPROVE DIAGNOSTIC ACCURACY IN ADAPTIVE INSTRUCTIONAL STRATEGIES.
TOOLS FOR AUTOMATIC GRADING PROGRAMMING ASSIGNMENTS, ALSO KNOWN AS ONLINE JUDGES, HAVE BEEN WIDELY USED TO SUPPORT COMPUTER SCIENCE (CS) COURSES. NEVERTHELESS, FEW STUDIES HAVE USED THESE TOOLS TO ACQUIRE AND ANALYSE INTERACTION DATA TO BETTER UNDERSTAND THE STUDENTS' PERFORMANCE AND BEHAVIOURS, OFTEN DUE TO DATA AVAILABILITY OR INADEQUATE GRANULARITY. TO ADDRESS THIS PROBLEM, WE PROPOSE AN ONLINE JUDGE CALLED CODEBENCH, WHICH ALLOWS FOR FINE-GRAINED DATA COLLECTION OF STUDENT INTERACTIONS, AT THE LEVEL OF, EG, KEYSTROKES, NUMBER OF SUBMISSIONS, AND GRADES. WE DEPLOYED CODEBENCH FOR 3 YEARS (2016-18) AND COLLECTED DATA FROM 2058 STUDENTS FROM 16 INTRODUCTORY COMPUTER SCIENCE (CS1) COURSES, ON WHICH WE HAVE CARRIED OUT FINE-GRAINED LEARNING ANALYTICS, TOWARDS EARLY DETECTION OF EFFECTIVE/INEFFECTIVE BEHAVIOURS REGARDING LEARNING CS CONCEPTS. RESULTS EXTRACT CLEAR BEHAVIOURAL CLASSES OF CS1 STUDENTS, SIGNIFICANTLY DIFFERENTIATED BOTH SEMANTICALLY AND STATISTICALLY, ENABLING US TO BETTER EXPLAIN HOW STUDENT BEHAVIOURS DURING PROGRAMMING HAVE INFLUENCED LEARNING OUTCOMES. FINALLY, WE ALSO IDENTIFY BEHAVIOURS THAT CAN GUIDE NOVICE STUDENTS TO IMPROVE THEIR LEARNING PERFORMANCE, WHICH CAN BE USED FOR INTERVENTIONS. WE BELIEVE THIS WORK IS A STEP FORWARD TOWARDS ENHANCING ONLINE JUDGES AND HELPING TEACHERS AND STUDENTS IMPROVE THEIR CS1 TEACHING/LEARNING PRACTICES.
MULTIMODAL LEARNING ANALYTICS, WHICH IS COLLECTION, ANALYSIS, AND REPORT OF DIVERSE LEARNING TRACES TO BETTER UNDERSTAND AND IMPROVE THE LEARNING PROCESS, HAS BEEN PRODUCING A SERIES OF INTERESTING PROTOTYPES TO ANALYZE LEARNING ACTIVITIES THAT WERE PREVIOUSLY HARD TO OBJECTIVELY EVALUATE. HOWEVER, NONE OF THESE PROTOTYPES HAVE BEEN TAKEN OUT OF THE LABORATORY AND INTEGRATED INTO REAL LEARNING SETTINGS. THIS ARTICLE IS THE FIRST TO PROPOSE, EXECUTE, AND EVALUATE A PROCESS TO SCALE AND DEPLOY ONE OF THESE APPLICATIONS, AN AUTOMATED ORAL PRESENTATION FEEDBACK SYSTEM, INTO AN INSTITUTION-WIDE SETTING. TECHNOLOGICAL, LOGISTICAL, AND PEDAGOGICAL CHALLENGES AND ADAPTATIONS ARE DISCUSSED. AN EVALUATION OF THE USE AND EFFECTIVENESS OF THE DEPLOYMENT SHOWS BOTH SUCCESSFUL ADOPTION AND MODERATE LEARNING GAINS, ESPECIALLY FOR LOW-PERFORMING STUDENTS. IN ADDITION, THE RECORDING AND SUMMARIZING OF THE PERCEPTION OF BOTH INSTRUCTORS AND STUDENTS POINT TO A GENERALLY POSITIVE EXPERIENCE IN SPITE OF THE COMMON PROBLEMS OF A FIRST-GENERATION DEPLOYMENT OF A COMPLEX LEARNING TECHNOLOGY.
WE DESCRIBE A METHOD OF IMPROVING THE ACCURACY OF A LEARNING ANALYTICS SYSTEM THROUGH THE APPLICATION OF A RECURRENT NEURAL NETWORK OVER ALL STUDENTS IN A UNIVERSITY, REGARDLESS OF COURSE. OUR TARGET IS TO DISCOVER HOW WELL A STUDENT WILL DO IN A CLASS GIVEN THEIR INTERACTION WITH A VIRTUAL LEARNING ENVIRONMENT. WE SHOW HOW THIS METHOD PERFORMS WELL WHEN WE WANT TO PREDICT HOW WELL STUDENTS WILL DO, EVEN IF WE DO NOT HAVE A MODEL TRAINED BASED ON THEIR SPECIFIC COURSE.
COMPUTER-BASED ASSESSMENTS CAN PROVIDE STUDENTS WITH FEEDBACK TO GUIDE THEIR LEARNING AS WELL AS INFORM TEACHERS WHO EXTRACT INFORMATION TO PREPARE THEIR TEACHING. FIVE SECONDARY SCHOOL TEACHERS WERE MONITORED DURING ONE SCHOOL YEAR TO ANSWER THE FOLLOWING RESEARCH QUESTIONS: (1) WHAT KIND OF STUDENT DATA DO TEACHERS USE FOR THEIR TEACHING PRACTICE? (2) HOW DO TEACHERS USE STUDENT DATA FOR THEIR INSTRUCTIONAL PRACTICE? AND (3) HOW ARE THESE CLASSROOM INSTRUCTIONS EVALUATED BY STUDENTS? TO USE DATA FOR THEIR TEACHING PRACTICE, TEACHERS PREFERRED DATA WITH DETAILED INFORMATION ABOUT EACH STUDENT, TASK AND RESPONSE. IN CLASS, TEACHERS FOCUSED ON PROVIDING LOW-PERFORMING STUDENTS WITH FEEDBACK, EITHER BY THEMSELVES OR BY A HIGH-PERFORMING PEER STUDENT. SUBSEQUENTLY, LOW-PERFORMING STUDENTS EVALUATED THIS FORMATIVE ASSESSMENT PRACTICE MORE POSITIVELY THAN HIGH-PERFORMING STUDENTS AS THEY VALUED THE CHANCE TO PRACTISE AND THE ADDITIONAL FEEDBACK AND INSTRUCTION THEY RECEIVED IN CLASS. IMPLICATIONS FOR TEACHER SUPPORT ARE DISCUSSED.
THE PERVASIVE COLLECTION OF DATA HAS OPENED THE POSSIBILITY FOR EDUCATIONAL INSTITUTIONS TO USE ANALYTICS METHODS TO IMPROVE THE QUALITY OF THE STUDENT EXPERIENCE. HOWEVER, THE ADOPTION OF THESE METHODS FACES MULTIPLE CHALLENGES PARTICULARLY AT THE COURSE LEVEL WHERE INSTRUCTORS AND STUDENTS WOULD DERIVE THE MOST BENEFIT FROM THE USE OF ANALYTICS AND PREDICTIVE MODELS. THE CHALLENGE LIES IN THE KNOWLEDGE GAP BETWEEN HOW THE DATA IS CAPTURED, PROCESSED AND USED TO DERIVE MODELS OF STUDENT BEHAVIOR, AND THE SUBSEQUENT INTERPRETATION AND THE DECISION TO DEPLOY PEDAGOGICAL ACTIONS AND INTERVENTIONS BY INSTRUCTORS. SIMPLY PUT, THE PROVISION OF LEARNING ANALYTICS ALONE HAS NOT NECESSARILY LED TO CHANGING TEACHING PRACTICES. IN ORDER TO SUPPORT PEDAGOGICAL CHANGE AND AID INTERPRETATION, THIS PAPER PROPOSES A MODEL THAT CAN ENABLE INSTRUCTORS TO READILY IDENTIFY SUBPOPULATIONS OF STUDENTS TO PROVIDE SPECIFIC SUPPORT ACTIONS. THE APPROACH WAS APPLIED TO A FIRST YEAR COURSE WITH A LARGE NUMBER OF STUDENTS. THE RESULTING MODEL CLASSIFIES STUDENTS ACCORDING TO THEIR PREDICTED EXAM SCORES, BASED ON INDICATORS DIRECTLY DERIVED FROM THE LEARNING DESIGN.
IN THE CURRENT STUDY INTERACTION DATA OF STUDENTS IN AN ONLINE LEARNING SETTING WAS USED TO RESEARCH WHETHER THE ACADEMIC PERFORMANCE OF STUDENTS AT THE END OF TERM COULD BE PREDICTED IN THE EARLIER WEEKS. THE STUDY WAS CARRIED OUT WITH 76 SECOND-YEAR UNIVERSITY STUDENTS REGISTERED IN A COMPUTER HARDWARE COURSE. THE STUDY AIMED TO ANSWER TWO PRINCIPLE QUESTIONS: WHICH ALGORITHMS AND FEATURES BEST PREDICT THE END OF TERM ACADEMIC PERFORMANCE OF STUDENTS BY COMPARING DIFFERENT CLASSIFICATION ALGORITHMS AND PRE-PROCESSING TECHNIQUES AND WHETHER OR NOT ACADEMIC PERFORMANCE CAN BE PREDICTED IN THE EARLIER WEEKS USING THESE FEATURES AND THE SELECTED ALGORITHM. THE RESULTS OF THE STUDY INDICATED THAT THE KNN ALGORITHM ACCURATELY PREDICTED UNSUCCESSFUL STUDENTS AT THE END OF TERM WITH A RATE OF 89%. WHEN FINDINGS WERE EXAMINED REGARDING THE ANALYSIS OF DATA OBTAINED IN WEEKS 3, 6, 9, 12, AND 14 TO PREDICT WHETHER THE END-OF-TERM ACADEMIC PERFORMANCE OF STUDENTS COULD BE PREDICTED IN THE EARLIER WEEKS, IT WAS OBSERVED THAT STUDENTS WHO WERE UNSUCCESSFUL AT THE END OF TERM COULD BE PREDICTED WITH A RATE OF 74% IN AS SHORT AS 3 WEEKS' TIME. THE FINDINGS OBTAINED FROM THIS STUDY ARE IMPORTANT FOR THE DETERMINATION OF FEATURES FOR EARLY WARNING SYSTEMS THAT CAN BE DEVELOPED FOR ONLINE LEARNING SYSTEMS AND AS INDICATORS OF STUDENT SUCCESS. AT THE SAME TIME, IT WILL AID RESEARCHERS IN THE SELECTION OF ALGORITHMS AND PRE-PROCESSING TECHNIQUES IN THE ANALYSIS OF EDUCATIONAL DATA.
THE PROGRAMME FOR INTERNATIONAL STUDENT ASSESSMENT (PISA) IS A WORLDWIDE STUDY THAT ASSESSES THE PROFICIENCIES OF 15-YEAR-OLD STUDENTS IN READING, MATHEMATICS, AND SCIENCE EVERY THREE YEARS. DESPITE THE HIGH QUALITY AND OPEN AVAILABILITY OF THE PISA DATA SETS, WHICH CALL FOR BIG DATA LEARNING ANALYTICS, ACADEMIC RESEARCH USING THIS RICH AND CAREFULLY COLLECTED DATA IS SURPRISINGLY SPARSE. OUR RESEARCH CONTRIBUTES TO REDUCING THIS DEFICIT BY DISCOVERING NOVEL KNOWLEDGE FROM THE PISA THROUGH THE DEVELOPMENT AND USE OF APPROPRIATE METHODS. SINCE FINLAND HAS BEEN THE COUNTRY OF MOST INTERNATIONAL INTEREST IN THE PISA ASSESSMENT, A RELEVANT REVIEW OF THE FINNISH EDUCATIONAL SYSTEM IS PROVIDED. THIS CHAPTER ALSO GIVES A BACKGROUND ON LEARNING ANALYTICS AND PRESENTS FINDINGS FROM A NOVEL CASE STUDY. SIMILAR TO THE EXISTING LITERATURE ON LEARNING ANALYTICS, THE EMPIRICAL PART IS BASED ON A STUDENT MODEL; HOWEVER, UNLIKE IN THE PREVIOUS LITERATURE, OUR MODEL REPRESENTS A PROFILE OF A NATIONAL STUDENT POPULATION. WE COMPARE FINLAND TO OTHER COUNTRIES BY HIERARCHICALLY CLUSTERING THESE STUDENT PROFILES FROM ALL THE COUNTRIES THAT PARTICIPATED IN THE LATEST ASSESSMENT AND VALIDATING THE RESULTS THROUGH STATISTICAL TESTING. FINALLY, AN EVALUATION AND INTERPRETATION OF THE VARIABLES THAT EXPLAIN THE DIFFERENCES BETWEEN THE STUDENTS IN FINLAND AND THOSE OF THE REMAINING PISA COUNTRIES IS PRESENTED. BASED ON OUR ANALYSIS, WE CONCLUDE THAT, IN GLOBAL TERMS, LEARNING TIME AND GOOD STUDENT-TEACHER RELATIONS ARE NOT AS IMPORTANT AS COLLABORATIVE SKILLS AND HUMILITY TO EXPLAIN STUDENTS' SUCCESS IN THE PISA TEST.
STUDENT RETENTION AND LEARNING IN STEM DISCIPLINES IS A GROWING PROBLEM. THE 2012 REPORT BY THE US PRESIDENT'S COUNCIL OF ADVISORS ON SCIENCE AND TECHNOLOGY (PCAST) PREDICTS A FUTURE DEFICIT IN SCIENCE, ENGINEERING, AND MATHEMATICS (STEM) IN THE FOLLOWING DECADE AND EMPHASIZES THE IMPORTANCE OF ADDRESSING THIS ISSUE. WITH THIS AS A MOTIVATING FACTOR, THE OSBLE+ SOCIAL PROGRAMMING ENVIRONMENT (SPE) WAS USED TO LEVERAGE SOCIAL AND PROGRAMMING DATA FOR THE BASIS OF AUTOMATICALLY GENERATED PROMPTS INSERTED INTO THE SPE. THESE PROMPTS WERE DESIGNED TO STIMULATE HELP-SEEKING, HELP-GIVING, AND SOCIAL INTERACTION IN THE LEARNING ENVIRONMENT. A SOCIAL NETWORK ANALYSIS WAS PERFORMED IN ORDER TO DETERMINE WHETHER EXPOSURE TO THE AUTOMATED INTERVENTIONS WOULD POSITIVELY AFFECT THE RELATIONSHIP AMONG STUDENTS OVER TIME. RESULTS OF THIS STUDY SUGGEST THAT STUDENTS IN THE EXPERIMENTAL TREATMENT WHO WERE PRESENTED WITH AUTOMATED PROMPTS DEVELOPED MORE CONNECTED AND SOCIAL NETWORKS THAN THOSE IN THE CONTROL TREATMENT.
INSTITUTIONS ARE APPLYING METHODS AND PRACTICES FROM DATA ANALYTICS UNDER THE UMBRELLA TERM OF LEARNING ANALYTICS TO INFORM INSTRUCTION, LIBRARY PRACTICES, AND INSTITUTIONAL RESEARCH, AMONG OTHER THINGS. THIS STUDY REPORTS FINDINGS FROM INTERVIEWS WITH PROFESSIONAL ADVISORS AT A PUBLIC HIGHER EDUCATION INSTITUTION. IT REPORTS THEIR PERSPECTIVE ON THEIR INSTITUTION'S RECENT ADOPTION OF EADVISING TECHNOLOGIES WITH PRESCRIPTIVE AND PREDICTIVE ADVISING AFFORDANCES. THE FINDINGS DETAIL WHY ADVISORS REJECTED THE TOOLS DUE TO USABILITY CONCERNS, MORAL DISCOMFORT, AND A BELIEF THAT USING PREDICTIVE MEASURES VIOLATED A PROFESSIONAL ETHICAL PRINCIPLE TO DEVELOP A COMPREHENSIVE UNDERSTANDING OF THEIR ADVISEES. THE DISCUSSION OF THESE FINDINGS CONTRIBUTES TO AN EMERGING BRANCH OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS RESEARCH FOCUSED ON SOCIAL AND ETHICAL IMPLICATIONS. SPECIFICALLY, IT HIGHLIGHTS THE CONSEQUENTIAL EFFECTS ON HIGHER EDUCATION PROFESSIONAL COMMUNITIES (OR MICRO CONTEXTS) DUE TO THE ASCENDANCY OF LEARNING ANALYTICS AND DATA-DRIVEN IDEOLOGIES.
MOBILE AND UBIQUITOUS LEARNING (M/U-LEARNING) ARE FINDING AN INCREASING ADOPTION IN EDUCATION. THEY ARE OFTEN DISTINGUISHED BY HYBRID LEARNING ENVIRONMENTS THAT ENCOMPASS ELEMENTS OF FORMAL AND INFORMAL LEARNING, IN ACTIVITIES THAT HAPPEN IN DISTRIBUTED SETTINGS (INDOORS AND OUTDOORS), ACROSS PHYSICAL AND VIRTUAL SPACES. DESPITE THEIR PURPORTED BENEFITS, THESE ENVIRONMENTS IMPLY ADDITIONAL COMPLEXITY IN THE DESIGN, MONITORING AND EVALUATION OF LEARNING ACTIVITIES. THE RESEARCH LITERATURE ON LEARNING DESIGN (LD) AND LEARNING ANALYTICS (LA) HAS STARTED TO DEAL WITH THESE ISSUES. THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW OF LD AND LA, IN M/U-LEARNING. APART FROM PROVIDING AN OVERVIEW OF THE CURRENT RESEARCH IN THE FIELD, THIS REVIEW ELICITS ELEMENTS OF COMMON GROUND BETWEEN BOTH COMMUNITIES, AS SHOWN BY THE SIMILAR LEARNING CONTEXTS AND COMPLEMENTARY RESEARCH CONTRIBUTIONS, AND BASED ON THE RESEARCH GAPS, PROPOSES TO: ADDRESS M/U-LEARNING BEYOND HIGHER EDUCATION SETTINGS, REINFORCE THE CONNECTION BETWEEN PHYSICAL AND VIRTUAL LEARNING SPACES, AND MORE SYSTEMATICALLY ALIGN LD AND LA PROCESSES.
LEARNING TO PROGRAM AT AN EARLY AGE HAS BEEN SHOWN TO BE A VEHICLE FOR THE DEVELOPMENT OF COMPUTATIONAL THINKING. GAME-BASED ENVIRONMENTS ARE OFTEN USED TO DEVELOP THESE SKILLS, BUT THEY LACK SUFFICIENT VOLUNTARINESS TO ASSESS ASPECTS RELATED TO INTRINSIC MOTIVATION, SUCH AS INTERESTS, SKILLS, PERSISTENCE IN SOLVING A PROBLEM AND BEHAVIOR IN RESPONSE TO REWARDS. THESE ASPECTS DIRECTLY AFFECT ACHIEVEMENT AND ACADEMIC PERFORMANCE, SO IT IS NECESSARY TO ANALYZE POSSIBLE AGE AND GENDER DIFFERENCES IN ORDER TO ADJUST COMPUTATIONAL THINKING CURRICULA. WITH THIS AIM, WE DEPLOYED A VOLUNTARY VIDEO GAME WHICH ADDRESSES BASIC COMPUTATIONAL CONCEPTS, BASED ON INTRINSIC MOTIVATION, AND AIMED AT EARLY AGES. DATA WERE COLLECTED AND ANALYZED USING GAME LEARNING ANALYTICS FOR 15 MONTHS, DURING WHICH 4124 USERS PLAYED MORE THAN 28187 GAMES. THE ANALYSIS SHOWS SIGNIFICANT AGE AND GENDER DIFFERENCES IN RELATION TO INTERESTS, SKILLS, ACHIEVEMENT, AND PROGRESSION THROUGH ATTEMPTS. IT WAS OBSERVED THAT THE CONCEPTS ADDRESSED WERE ACHIEVABLE BETWEEN THE AGES OF 3 AND 6 YEARS AND FULL MASTERY WAS POSSIBLE BY THE AGE OF 4 YEARS, REGARDLESS OF GENDER, AS CHILDREN PERSIST WITH THE CHALLENGE, INTRINSICALLY MOTIVATED, UNTIL IT IS OVERCOME. IN TERMS OF PERSISTENCE, SIGNIFICANTLY DIFFERENT BEHAVIORS WERE OBSERVED IN THE FACE OF THE CHALLENGE, WHICH CAN HELP US TO ADJUST THE DIFFERENT LEARNING METHODOLOGIES TO EACH AGE GROUP AND GENDER, ADAPTING THE WAY WE PROVIDE REINFORCEMENT AND REWARDS, ESPECIALLY FOR BOYS IN THE MORE COMPLEX CHALLENGES AND FOR GIRLS FROM THE AGE OF 5 YEARS ONWARDS.
PRACTISING SELF-REGULATED LEARNING (SRL) HAS BEEN PROPOSED TO DEVELOP LEARNING AUTONOMY. HOWEVER, THERE IS LACK OF EMPIRICAL EVIDENCE ON HOW SRL STRATEGIES AFFECT AUTONOMOUS LEARNING CAPACITY. THIS STUDY ATTEMPTS TO BRIDGE THAT GAP BY UTILIZING THE LEARNERS' TRACE DATA FOR MEASURING THE LEARNERS' AUTONOMOUS INTERACTIONS, AND INVESTIGATES THE EFFECTS OF FOUR SRL STRATEGIES ON LEARNERS' AUTONOMOUS CHOICES. THE GOAL IS TO EXPLAIN HOW THE EMPLOYED SRL STRATEGIES IMPACT AUTONOMOUS CONTROL (IN TERMS OF FREQUENCIES OF SELF-ENFORCED DECISIONS, AS WELL AS TIME-SPENT ON DECISION MAKING). THE RESULTS FROM AN EXPLORATORY STUDY WITH UNDERGRADUATE LEARNERS (N = 113) SHOWN THAT GOAL-SETTING AND TIME-MANAGEMENT HAVE STRONG POSITIVE EFFECTS ON AUTONOMOUS CONTROL, EFFORT-REGULATION MODERATELY POSITIVELY AFFECTS LEARNERS' AUTONOMY, WHILE HELP-SEEKING HAS A STRONG NEGATIVE EFFECT. THESE FINDINGS PROVIDE EMPIRICAL EVIDENCE AND CONTRIBUTE TO CLARIFYING THE ROLE OF EACH ONE OF THE SRL STRATEGIES IN THE DEVELOPMENT OF AUTONOMOUS LEARNING CAPACITY, FROM A LEARNING ANALYTICS PERSPECTIVE. LIMITATIONS AND POTENTIAL IMPLICATIONS FOR RESEARCH AND PRACTICE ARE ALSO DISCUSSED.
IN RECENT YEARS, THERE HAS BEEN AN INCREASING AMOUNT OF THEORETICAL AND APPLIED RESEARCH THAT HAS FOCUSED ON EDUCATIONAL DATA MINING. THE LEARNING ANALYTICS IS A DISCIPLINE THAT USES TECHNIQUES, METHODS, AND ALGORITHMS THAT ALLOW THE USER TO DISCOVER AND EXTRACT PATTERNS IN STORED EDUCATIONAL DATA, WITH THE PURPOSE OF IMPROVING THE TEACHING-LEARNING PROCESS. HOWEVER, THERE ARE MANY REQUIREMENTS RELATED TO THE USE OF NEW TECHNOLOGIES IN TEACHING-LEARNING PROCESSES THAT ARE PRACTICALLY UNADDRESSED FROM THE LEARNING ANALYTICS. IN AN ANALYSIS OF THE LITERATURE, THE EXISTENCE OF A SYSTEMATIC REVISION OF THE APPLICATION OF LEARNING ANALYTICS IN THE FIELD OF ENGINEERING EDUCATION IS NOT EVIDENT. THE STUDY DESCRIBED IN THIS ARTICLE PROVIDES RESEARCHERS WITH AN OVERVIEW OF THE PROGRESS MADE TO DATE AND IDENTIFIES AREAS IN WHICH RESEARCH IS MISSING. TO THIS END, A SYSTEMATIC MAPPING STUDY HAS BEEN CARRIED OUT, ORIENTED TOWARD THE CLASSIFICATION OF PUBLICATIONS THAT FOCUS ON THE TYPE OF RESEARCH AND THE TYPE OF CONTRIBUTION. THE RESULTS SHOW A TREND TOWARD CASE STUDY RESEARCH THAT IS MAINLY DIRECTED AT SOFTWARE AND COMPUTER SCIENCE ENGINEERING. FURTHERMORE, TRENDS IN THE APPLICATION OF LEARNING ANALYTICS ARE HIGHLIGHTED IN THE TOPICS, SUCH AS STUDENT RETENTION OR DROPOUT PREDICTION, ANALYSIS OF ACADEMIC STUDENT DATA, STUDENT LEARNING ASSESSMENT AND STUDENT BEHAVIOR ANALYSIS. ALTHOUGH THIS SYSTEMATIC MAPPING STUDY HAS FOCUSED ON THE APPLICATION OF LEARNING ANALYTICS IN ENGINEERING EDUCATION, SOME OF THE RESULTS CAN ALSO BE APPLIED TO OTHER EDUCATIONAL AREAS.
TODAY, THERE IS A GROWING INTEREST IN DATA AND ANALYTICS IN THE LEARNING ENVIRONMENT RESULTING IN A HIGHLY QUALIFIED RESEARCH CONCERNING MODELS, METHODS, TOOLS, TECHNOLOGIES AND ANALYTICS. THIS RESEARCH AREA IS REFERRED TO AS LEARNING ANALYTICS. METADATA BECOMES AN IMPORTANT ITEM IN AN E-LEARNING SYSTEM, MANY LEARNING ANALYTICS MODELS ARE CURRENTLY DEVELOPED. THEY USE METADATA TO TAG LEARNING MATERIALS, LEARNING RESOURCES AND LEARNING ACTIVITIES. IN THIS PAPER, WE FIRSTLY GIVE A DETAILED INJECTION OF THE EXISTING LEARNING ANALYTICS MODELS IN THE LITERATURE. WE PARTICULARLY OBSERVED THAT THERE IS A LACK OF MODELS DEDICATED TO CONCEIVE AND ANALYZE THE ASSESSMENT DATA. THAT IS WHY OUR OBJECTIVE IN THIS PAPER IS TO PROPOSE AN ASSESSMENT ANALYTICS MODEL INSPIRED BY THE EXPERIENCE API DATA MODEL. HENCE, AN ASSESSMENT ANALYTICS ONTOLOGY MODEL IS DEVELOPED SUPPORTING THE ANALYTICS OF ASSESSMENT DATA BY TRACKING THE ASSESSMENT ACTIVITIES, ASSESSMENT RESULT AND ASSESSMENT CONTEXT OF THE LEARNER.
INTERACTIVE FEATURES OF THE HYPER-VIDEO ENVIRONMENT, SUCH AS INDEXING, BOOKMARKS, LINKS TO LEARNING MATERIALS, MULTIPLE-CHOICE QUESTIONS, AND PERSONAL AND SHARED ANNOTATIONS, CAN ENHANCE LEARNING PROCESSES. THIS PAPER EXAMINES INTEGRATION OF THE ANNOTO HYPER-VIDEO PLATFORM IN THREE LARGE UNDERGRADUATE COURSES (A, B & C) AT A LARGE UNIVERSITY. THE STUDY COMBINES LEARNING ANALYTICS OF VIDEO-RECORDINGS OF SYNCHRONOUS LESSONS (9-15 SESSIONS PER COURSE, APPROXIMATELY TWO HOURS EACH), CONTENT ANALYSIS OF THE HYPER-VIDEO ANNOTATIONS WRITTEN BY STUDENTS AND LECTURERS, AND SEMI-STRUCTURED INTERVIEWS WITH THE LECTURERS AND WITH ACTIVELY-PARTICIPATING STUDENTS. THE LOG-ANALYSIS WAS CONDUCTED AT THE USER LEVEL (N = 880) AND AT THE VIDEO LEVEL (N = 37). CONTENT ANALYSIS WAS BASED ON THE COMMUNITY OF INQUIRY FRAMEWORK (GARRISON ET AL. IN INTERNET HIGH EDUC 2(2):87-105, 1999, INTERNET HIGH EDUC 13(1):5-9, 2010). THE FINDINGS REVEALED THAT WHEN HYPER-VIDEO IS INTEGRATED WITHOUT ACADEMIC CREDIT, SLIGHTLY OVER 10% OF UNDERGRADUATES CHOSE ACTIVE PARTICIPATION, BEYOND WATCHING VIDEOS AND READING OTHERS' ANNOTATIONS. THE MAJORITY OF ANNOTATIONS WERE SHARED POSTS AND REPLIES (73-96%), RATHER THAN PERSONAL NOTES. RELATIVE TO THE NUMBER OF STUDENTS, THE RATE OF READING ANNOTATIONS WAS SIGNIFICANTLY HIGHER IN COURSE C. ACCORDINGLY, CONTENT ANALYSIS REVEALED SIGNIFICANTLY MORE "COGNITIVE PRESENCE" AND "SOCIAL PRESENCE" CODES IN COURSE C, WHILE THE AMOUNT OF "TEACHING PRESENCE" WAS SIMILAR IN ALL COURSES. HOWEVER, THE THREE COURSES USED THE SAME INTERACTION PATTERN OF ANNOTATIONS: "STUDENT'S QUESTION-LECTURER'S ANSWER", WITHOUT PROMOTING PEER FEEDBACK. THE IMPLICATIONS FOR EDUCATIONAL THEORY AND THE PEDAGOGICAL DESIGN OF HYPER-VIDEO IN ACADEMIA ARE DISCUSSED.
THERE HAS BEEN A GROWING INTEREST IN HIGHER EDUCATION TO EXPLORE HOW LEARNING ANALYTICS (LA) COULD BE USED TO SUPPORT STUDENT ENGAGEMENT. PROVIDING ACTIONABLE FEEDBACK WITH LA FOR STUDENTS IS AN EMERGING AREA OF RESEARCH. PREVIOUS STUDIES HAVE COMMONLY FOCUSED ON COURSE-LEVEL ASPECTS OF SUPPORTING ENGAGEMENT WITH LA, BUT STUDENTS' PERSPECTIVES HAVE RECEIVED LIMITED ATTENTION. THIS STUDY ANALYZED PRE-SERVICE TEACHERS' NEEDS AND EXPECTATIONS FOR LA TO SUPPORT STUDENT ENGAGEMENT ON THE ACADEMIC PATH LEVEL, WHICH MEANS OBSERVING THE CONTINUUM OF STUDY PERIODS AND ACADEMIC YEARS. QUALITATIVE CONTENT ANALYSIS WAS CONDUCTED FOR VIDEO-RECORDED STUDENT SMALLGROUP CONVERSATIONS TO ANALYZE IN-DEPTH HOW PRE-SERVICE TEACHERS (N = 40) DESCRIBED THEIR NEEDS FOR SUPPORT STUDENT ENGAGEMENT AND EXPECTATIONS AND ETHICAL CONCERNS FOR USING LA TOOLS. STUDENTS SUGGESTED THAT LA TOOLS COULD SUPPORT THEIR ENGAGEMENT BY MEDIATING INFORMATION BETWEEN THE STUDENT AND INSTITUTION, FACILITATING EFFECTIVE STUDYING, INCREASING AWARENESS ABOUT THEMSELVES AS LEARNERS, PROVIDING HELP AND SUPPORT IN DIFFERENT CHALLENGING SITUATIONS, AND WORKING AS A FEEDBACK CHANNEL TO ADAPT THE LEARNING CONDITIONS ACCORDING TO THEIR INDIVIDUAL NEEDS. THE RESULTS OF THIS STUDY DEMONSTRATE HOW STUDENT NEEDS ARE SOMETIMES CONTRADICTORY, FOR EXAMPLE WHEN STUDENTS SUGGEST MORE POSSIBILITIES FOR AGENTIC CHOICES ON THEIR STUDIES, BUT SIMILARLY MORE INSTITUTIONAL MONITORING OR THEIR STUDY PROGRESSION. THIS OFFERS INSIGHTS TO CLARIFY SPECIFIC OBJECTIVES FOR SUPPORTING STUDENT ENGAGEMENT WITH LA.
LEARNING ANALYTICS (LA) RESEARCH HAS DEMONSTRATED THE POTENTIAL OF LA IN DETECTING AND MONITORING COGNITIVE-AFFECTIVE PARAMETERS AND IMPROVING STUDENT SUCCESS. BUT MOST OF IT HAS BEEN APPLIED TO ONLINE AND COMPUTERIZED LEARNING ENVIRONMENTS WHEREAS PHYSICAL CLASSROOMS HAVE LARGELY REMAINED OUTSIDE THE SCOPE OF SUCH RESEARCH. THIS PAPER ATTEMPTS TO BRIDGE THAT GAP BY PROPOSING A STUDENT FEEDBACK MODEL IN WHICH THEY REPORT ON THE DIFFICULT/EASY AND ENGAGING/BORING ASPECTS OF THEIR LECTURE. WE OUTLINE THE PEDAGOGICAL AFFORDANCES OF AN AGGREGATED TIME-SERIES OF SUCH DATA AND DISCUSS IT WITHIN THE CONTEXT OF LA RESEARCH.
THIS STUDY AIMS TO APPLY SEQUENTIAL ANALYSIS TO EXPLORE THE EFFECT OF LEARNING MOTIVATION ON ONLINE LEARNING BEHAVIORAL PATTERNS. THE STUDY PARTICIPANTS WERE 160 GRADUATE STUDENTS, WHO WERE CLASSIFIED INTO THREE GROUP TYPES: LOW READING DURATION WITH LOW MOTIVATION, LOW READING DURATION WITH HIGH MOTIVATION, AND HIGH READING DURATION BASED ON A SECOND-ORDER CLUSTER ANALYSIS. AFTER PERFORMING A SEQUENTIAL ANALYSIS, THIS STUDY REVEALED THAT HIGHLY MOTIVATED LEARNERS EXHIBITED A RELATIVELY SERIOUS LEARNING PATTERN IN A MULTI-TASKING LEARNING ENVIRONMENT, AND THAT ONLINE READING DURATION WAS A SIGNIFICANT INDICATOR OF SERIOUSNESS IN TAKING AN ONLINE COURSE. FINALLY, RELEVANT RECOMMENDATIONS WERE MADE TO INSTRUCTORS AND RESEARCHERS BASED ON THE RESULTS OF THE PRESENT STUDY.
OUR WORK FOCUSES ON A MULTI-INSTITUTIONAL IMPLEMENTATION AND EVALUATION OF A LEARNING ANALYTICS DASHBOARDS (LAD) AT SCALE, PROVIDING FEEDBACK TO N=337 ASPIRING STEM (SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS) STUDENTS PARTICIPATING IN A REGION-WIDE POSITIONING TEST BEFORE ENTERING THE STUDY PROGRAM. STUDY ADVISORS WERE CLOSELY INVOLVED IN THE DESIGN AND EVALUATION OF THE DASHBOARD. THE MULTI-INSTITUTIONAL CONTEXT OF OUR CASE STUDY REQUIRES CAREFUL CONSIDERATION OF EXTERNAL STAKEHOLDERS AND DATA OWNERSHIP AND PORTABILITY ISSUES, WHICH GIVES SHAPE TO THE TECHNICAL DESIGN OF THE LAD. OUR APPROACH CONFIRMS STUDENTS AS ACTIVE AGENTS WITH DATA OWNERSHIP, USING AN ANONYMOUS FEEDBACK CODE TO ACCESS THE LAD AND TO ENABLE STUDENTS TO SHARE THEIR DATA WITH INSTITUTIONS AT THEIR DISCRETION. OTHER DISTINGUISHING FEATURES OF THE LAD ARE THE SUPPORT FOR ACTIVE CONTENT CONTRIBUTION BY STUDY ADVISORS AND LATEX TYPE-SETTING OF QUESTION ITEM FEEDBACK TO ENHANCE VISUAL RECOGNIZABILITY. WE PRESENT OUR LESSONS LEARNT FROM A FIRST ITERATION IN PRODUCTION.
ONLINE DISCUSSION IS AN ESSENTIAL ELEMENT OF ONLINE INSTRUCTION AND IS LARGELY DEPENDENT UPON ITS DESIGNING AND FACILITATING STRATEGIES. TO INVESTIGATE THE EFFECTS OF GROUPING STRATEGIES ON ASYNCHRONOUS ONLINE DISCUSSION, THIS STUDY CONDUCTED A QUASI-EXPERIMENTAL RESEARCH DESIGN WITH 178 GRADUATE STUDENTS TO COMPARE THE DIFFERENCE IN STUDENT PARTICIPATION AND SOCIAL INTERACTIONS BETWEEN WHOLE-CLASS AND SMALL-GROUP DISCUSSION, AND AMONG GROUPS COMPOSED OF SELF-SELECTED ACQUAINTANCES, PARTIAL ACQUAINTANCES, AND RANDOMLY-ASSIGNED STRANGERS. BASED ON THE STATISTICAL RESULTS FROM LEARNING ANALYTICS AND SOCIAL NETWORK ANALYSIS, THE FINDINGS OF THIS STUDY SUPPORT THE SUPERIORITY OF SMALL DISCUSSION GROUPS SELF-FORMED BY ACQUAINTANCES, FOR INCREASED LEARNING PARTICIPATION AND SOCIAL INTERACTIONS.
THIS STUDY PRESENTS SEVERAL LATIN AMERICAN RESEARCH INITIATIVES IN THE FIELD OF LEARNING ANALYTICS (LA). THE STUDY'S PURPOSE IS TO ENHANCE AWARENESS AND UNDERSTANDING OF LA AMONG RESEARCHERS, PRACTITIONERS AND DECISION MAKERS, AND TO HIGHLIGHT THE IMPORTANCE OF SUPPORTING RESEARCH ON LA. WE ANALYZED CASE STUDIES OF LA RESEARCH CONDUCTED AT FOUR LEVELS OF THE EDUCATIONAL SYSTEM (THE NATIONAL, INSTITUTIONAL, CLASSROOM AND STUDENT LEVELS), WHICH WERE IMPLEMENTED IN FOUR COUNTRIES (BRAZIL, ECUADOR, MEXICO AND URUGUAY). DIVERSIFIED CASES WERE SELECTED TO DEMONSTRATE THE USE OF LA IN PRIMARY, SECONDARY AND HIGHER EDUCATION, AND TO ALLOW THE INCLUSION OF DIFFERENT TYPES OF DATASETS. THESE CASES ALSO SHOWED THE DEVELOPMENT OF LEGAL FRAMEWORKS FOR HANDLING ETHICAL ISSUES, AND THEY MET THE REQUIREMENTS FOR DATA PRIVACY PROTECTION IN LATIN AMERICA. THE STUDY CONCLUDES WITH A DISCUSSION OF THE FINDINGS AND THEIR IMPLICATIONS FOR FURTHER RESEARCH AND PRACTICE IN THE FIELD OF LA FOR TEACHING AND LEARNING.
BECAUSE OF VARIOUS REASONS: CUTS IN FINANCING, STUDENTS MASSIVE PARTICIPATION IN FULL-TIME WORK, RAPIDLY IMPROVING TECHNOLOGICAL POSSIBILITIES ETC. THE UNIVERSITY EDUCATION IS MASSIVELY MOVING ONLINE AND DIRECT CLASSROOM CONTACT OF TEACHERS WITH STUDENTS IS REPLACED WITH COMPUTER NETWORK MEDIATED COMMUNICATION, WHERE FEATURES OF EDUCATIONAL PROCESSES SHOULD BE EXTRACTED BY ANALYZING DATA CREATED IN THIS NETWORK-ASSISTED EDUCATION. IN THE FOLLOWING ARE PRESENTED SOME EXPERIENCES AND RESULTS FROM SUCH ANALYZE. BECAUSE OF RATHER SMALL SAMPLES WE DID NOT CALCULATE NUMERICAL STATISTICS, RESULTS ARE PRESENTED VISUALLY - THIS FORM OF PRESENTATION IS FOR HUMANS MUCH MORE UNDERSTANDABLE THAN TABLES WITH LOT OF NUMBERS.
DESPITE THE MOUNTING EVIDENCE SUPPORTING THE ROLE THAT GOAL SETTING HAS ON THE LEARNING PROCESS, THERE SEEMS TO BE ONLY A HANDFUL OF STUDIES THAT DIRECTLY INVESTIGATE GOAL SETTING IN THE CONTEXT OF LEARNING ANALYTICS (LA). ALTHOUGH INVESTIGATIONS HAVE INCORPORATED ELEMENTS OF GOAL SETTING, THE ATTENTION AFFORDED TO THEORY AND OPERATIONALIZATION HAVE BEEN MODEST. IN THIS WORKSHOP WE PLAN TO POSITION GOAL SETTING AT THE FOREFRONT OF LA RESEARCH. THE WORKSHOP WILL SERVE AS A VENUE TO BRING TOGETHER RESEARCHERS INTERESTED IN ADVANCING GOAL SETTING (GS) RESEARCH IN THE LA FIELD. TOPICS INCLUDE: (1) GS THEORY AND MEASUREMENT; (2) ANALYSIS AND VISUALIZATION OF GS DATA; (3) STRATEGIES FOR INTEGRATING GS IN THE LEARNING EXPERIENCE; AND (4) IMPLEMENTATION OF GS TECHNOLOGIES. PARTICIPANTS WHO NEED TOOLS TO EXECUTE THEIR GS IDEAS AND THOSE WHO ALREADY HAVE TOOLS AND ARE EXPLORING BETTER WAYS TO INTEGRATE A GOAL SETTING FEATURE CAN GAIN A LOT FROM THIS WORKSHOP. MOREOVER, PARTICIPANTS WILL HAVE THE OPPORTUNITY TO CONTRIBUTE TO THE CONCEPTUALIZATION AND STAGING OF GS IDEAS IN LA RESEARCH.
THIS PAPER PRESENTS AN EXPLORATORY ANALYSIS OF EXISTING LOG FILES OF THE VIBOA ENVIRONMENTAL POLICY GAMES AT UTRECHT UNIVERSITY. FOR REASONS OF STATISTICAL POWER WE HAVE COMBINED STUDENT COHORTS 2008, 2009, 2010, AND 2011, WHICH LED TO A SAMPLE SIZE OF 118 STUDENTS. THE VIBOA GAMES ARE INQUIRY-BASED GAMES, WHICH OFFER A LOT OF FREEDOM OF MOVEMENT. OUR PREMISE IS THAT THIS FREEDOM OF MOVEMENT IS ACCOMPANIED BY BEHAVIOURAL VARIABILITY ACROSS INDIVIDUALS, WHICH MAY INFLUENCE THE EFFICIENCY OF LEARNING. DESCRIPTIVE STATISTICS OF OUR SAMPLE REVEALED SUCH VARIABILITY OF DIVERSE GAME PARAMETERS. WE HAVE IDENTIFIED "SWITCHING BEHAVIOUR", DEFINED AS THE NUMBER OF GAME OBJECTS (VIDEOS, RESOURCES, LOCATIONS) ACCESSED PER UNIT TIME, AS A RELEVANT BEHAVIOURAL PATTERN. MULTIPLE REGRESSION ANALYSIS SHOWED THAT SWITCHING RATES OF VIDEOS AND LOCATIONS EXPLAIN 54 % OF THE VARIANCE OF LEARNING EFFICIENCY (DEFINED AS FINAL SCORE PER UNIT TIME). BOTH THE MODEL AND THE MODEL COEFFICIENTS WERE SIGNIFICANT BEYOND THE 0.001 LEVEL. THE SAME SWITCHING VARIABLES ALSO ACCOUNT FOR 45 % OF THE VARIANCE OF TOTAL TIME SPENT. PREDICTIVE MODELS OF FINAL SCORE WEREN'T FOUND. WE CONCLUDE THE PAPER BY CRITICALLY EVALUATING OUR FINDINGS, MAKING EXPLICIT THE LIMITATIONS OF OUR STUDY AND MAKING SUGGESTIONS FOR FUTURE RESEARCH THAT LINKS LEARNING ANALYTICS AND SERIOUS GAMING.
THIS STUDY IS A CONTINUATION OF THE FINDINGS FROM AN ONLINE HYBRID NON-CREDIT DISTANCE LEARNING RESEARCH COURSE DEVELOPED BY THE VIRTUAL ACADEMY (VIRTAC) [1] IN THE DEPARTMENT OF INFORMATION SCIENCES AND TECHNOLOGY (IST) [2] OF THE VOLGENAU SCHOOL OF ENGINEERING (VSE) [3] AT GEORGE MASON UNIVERSITY (GMU). THE DEVELOPMENT AND RESEARCH IS FUNDED BY A GRANT FROM CISCO SYSTEMS [4]. AN INITIAL STUDY [5] COMPARED THE EFFECTIVENESS OF TRADITIONAL AND SOCIAL MEDIA MARKETING STRATEGIES IN LAUNCHING THE FIRST ONLINE NON-CREDIT 24-WEEK CISCO CERTIFIED NETWORK ASSOCIATE (CCNA) COURSE. PRELIMINARY COST AND CONVERSION METRICS FROM THE FIRST COURSE SUPPORTED THE USE OF SOCIAL MEDIA TO MARKET THE COURSE. THE SECOND STUDY ANALYZED THE RESULTS OF THE MARKETING ACTIVITIES FOR THE SECOND OFFERING OF THE COURSE THAT IMPACTED RETENTION AND SATISFACTION. THIS THIRD STUDY COMPARES STUDENT PERFORMANCE ENROLLED IN THE SAME CCNA COURSE DELIVERED ONLINE AS A NON-CREDIT CERTIFICATION OFFERING COMPARED TO PERFORMANCE IN ONLINE AND FACE-TO-FACE FOR CREDIT COURSES OFFERED AT THE SAME UNIVERSITY. THE COMPARISONS OF HANDS-ON LABORATORY SCORES AND FINAL SCORES ACROSS FOUR MODULES FOR ONLINE AND FACE-TO-FACE CLASSES FOR CREDIT AND NON-CREDIT OFFERINGS WERE DONE. WE USED TWO SAMPLE WELCH T-TESTS TO COMPARE THE UNBALANCED GROUPS. RESULTS ARE MIXED WITH THE EXPERIMENTAL NON-CREDIT ONLINE COURSES SHOWING NO SIGNIFICANT DIFFERENCE IN PERFORMANCE. THE COMPARISON OF FACE-TO-FACE AND ONLINE FOR CREDIT COURSES SHOWED A SIGNIFICANT POSITIVE DIFFERENCE FOR THE ONLINE COURSE. THERE WERE NO SIGNIFICANT DIFFERENCES BETWEEN PERFORMANCE ON THE FINAL TEST IN THREE OUT OF FOUR MODULES WHEN COMPARING THE ONLINE NON-CREDIT COURSES WITH CREDIT COURSES. THE FINDINGS SUPPORT THE EFFECTIVENESS OF THE DELIVERY OF CORPORATE TECHNICAL TRAINING USING HYBRID ONLINE METHODS.
WHILE MUCH PROMISE HAS BEEN DEMONSTRATED IN THE LEARNING ANALYTICS FIELD WITH SENTIMENT ANALYSIS, THE ANALYSES ARE TYPICALLY POST HOC. THE UNIZIN SENTIMENT VISUALIZER DEMONSTRATES THAT THE APPLICATION OF SENTIMENT ANALYSIS IN REAL-TIME PROVIDES A POWERFUL NEW TOOL TO SUPPORT STUDENTS IN COMPLEX LEARNING ENVIRONMENTS.
IN THIS PAPER WE EXPLORE THE INTERRELATIONSHIP BETWEEN THE SOCIOTECHNICAL-PEDAGOGICAL CULTURE OF E-LEARNING, THE EMERGING DISCIPLINES OF WEB SCIENCE, SOCIAL SENSING AND THAT OF COGNITIVE COMPUTATION-AS AN EMERGING PARADIGM OF COMPUTATION. WE COMMENT IN PARTICULAR ON THE IMPORTANCE OF THIS RELATION FOR THE DEVELOPMENT OF LEARNING-ANALYTICS DISCOURSE. MOREOVER, WE PRESENT AN INITIAL RELATIONAL FRAMEWORK BETWEEN THESE DISCIPLINES AND SUGGEST HOW THESE RELATIONS CAN BE EXPLOITED TO SOLVE PROBLEMS IN EACH AREA. THIS PAPER ARGUES FOR (A) THE PARTICULAR IMPORTANCE OF THE ABSTRACT CLASS OF 'LEARNING MACHINES' FOR WEB SCIENCE, (B) UNDERSTANDING COGNITIVE COMPUTATION AS A NECESSARY PRACTICAL FRAMEWORK FOR THE INCREASINGLY DOMINATING, SITUATED INFORMAL LEARNING CONTEXT, AND (C) THE POTENTIAL BENEFIT OF WEB SCIENCE FRAMEWORKS FOR INVESTIGATING BOTH, CONTEMPORARY RESEARCH QUESTIONS IN E-LEARNING AND THE DEVELOPMENT OF THEORIES FOR INFORMAL UBIQUITOUS LEARNING. FINALLY, WE ARGUE THAT EXPLORING LINKS BETWEEN THESE DISCIPLINES IS NECESSARY FOR IMPROVING PRACTICAL RESEARCH, FOR THE PURPOSE OF DEVELOPING LEARNING-ANALYTICS METHODOLOGY FOR EVALUATING THE GROWING TYPES OF MODERN E-LEAMING CONTEXTS SUCH AS THE INFORMAL SITUATED LEARNING CONTEXT. (C) 2018 PUBLISHED BY ELSEVIER LTD.
DESPITE WIDESPREAD ENTHUSIASM, EVIDENCE OF THE EFFECTIVENESS OF LEARNING ANALYTICS REMAINS MIXED. ONE POSSIBLE EXPLANATION FOR THIS IS THAT INSUFFICIENT ATTENTION HAS BEEN PAID TO THE CONTEXTS IN WHICH IT IS INTRODUCED. WE REPORT HERE ON A SMALL-SCALE STUDY INTO THE PRIOR USE OF DATA AND COMMUNICATIONS TECHNOLOGIES BY TUTORS, WHO COMPRISE A KEY USER GROUP IN THE OPEN UNIVERSITY'S TUITION MODEL. TUTORS INTERVIEWED REPORTED USING A COMPLEX SET OF DATA SOURCES AND INFORMATION TOOLS, AND CREATING LOCAL/PERSONAL TOOLS AND METHODS FOR KEEPING TRACK OF STUDENTS AND THEIR INTERACTIONS WITH THEM.
THE RISE (RESOURCE INSPECTION, SELECTION, AND ENHANCEMENT) FRAMEWORK IS A FRAMEWORK SUPPORTING THE CONTINUOUS IMPROVEMENT OF OPEN EDUCATIONAL RESOURCES (OER). THE FRAMEWORK IS AN AUTOMATED PROCESS THAT IDENTIFIES LEARNING RESOURCES THAT SHOULD BE EVALUATED AND EITHER ELIMINATED OR IMPROVED. THIS IS PARTICULARLY USEFUL IN OER CONTEXTS WHERE THE COPYRIGHT PERMISSIONS OF RESOURCES ALLOW FOR REMIXING, EDITING, AND IMPROVING CONTENT. THE RISE FRAMEWORK PRESENTS A SCATTERPLOT WITH RESOURCE USAGE ON THE X-AXIS AND GRADE ON THE ASSESSMENTS ASSOCIATED WITH THAT RESOURCE ON THE Y-AXIS. THIS SCATTERPLOT IS BROKEN DOWN INTO FOUR DIFFERENT QUADRANTS (THE MEAN OF EACH VARIABLE BEING THE ORIGIN) TO FIND RESOURCES THAT ARE CANDIDATES FOR IMPROVEMENT. RESOURCES THAT RESIDE DEEP WITHIN THEIR RESPECTIVE QUADRANT (FARTHEST FROM THE ORIGIN) SHOULD BE FURTHER ANALYZED FOR CONTINUOUS COURSE IMPROVEMENT. WE PRESENT A CASE STUDY APPLYING OUR FRAMEWORK WITH AN INTRODUCTION TO BUSINESS COURSE. AGGREGATE RESOURCE USE DATA WAS COLLECTED FROM GOOGLE ANALYTICS AND AGGREGATE ASSESSMENT DATA WAS COLLECTED FROM AN ONLINE ASSESSMENT SYSTEM. USING THE RISE FRAMEWORK, WE SUCCESSFULLY IDENTIFIED RESOURCES, TIME PERIODS, AND MODULES IN THE COURSE THAT SHOULD BE FURTHER EVALUATED FOR IMPROVEMENT.
THIS PAPER SUMMARIZES INITIAL FIELD-TEST RESULTS FROM DATA ANALYTICS USED IN THE WORK OF THE ASSESSMENT AND TEACHING OF 21ST CENTURY SKILLS (ATC21S) PROJECT, ON THE "ICT LITERACY - LEARNING IN DIGITAL NETWORKS" LEARNING PROGRESSION. THIS PROJECT, SPONSORED BY CISCO, INTEL AND MICROSOFT, AIMS TO HELP EDUCATORS AROUND THE WORLD ENABLE STUDENTS WITH THE SKILLS TO SUCCEED IN FUTURE CAREER AND COLLEGE GOALS. THE PAPER BEGINS WITH DESCRIBING SOME EXPANSIONS TO A COMMON DEFINITION OF LEARNING ANALYTICS, THEN INCLUDES A REVIEW OF THE LITERATURE ON ICT LITERACY, INCLUDING THE SPECIFIC DEVELOPMENT THAT LED TO THE ATC21S EFFORT. THIS IS FOLLOWED BY A DESCRIPTION OF THE DEVELOPMENT OF A "LEARNING PROGRESSION" FOR THIS PROJECT, AS WELL AS THE LOGIC BEHIND THE INSTRUMENT CONSTRUCTION AND DATA ANALYTICS, ALONG WITH EXAMPLES OF EACH. DATA WERE COLLECTED IN A DEMONSTRATION DIGITAL ENVIRONMENT IN FOUR COUNTRIES: AUSTRALIA, FINLAND, SINGAPORE AND THE U.S. THE RESULTS INDICATE THAT THE NEW CONSTRUCTS DEVELOPED BY THE PROJECT, AND THE NOVEL ITEM FORMS AND ANALYTICS THAT WERE EMPLOYED, ARE INDEED CAPABLE OF BEING EMPLOYED IN A LARGE-SCALE DIGITAL ENVIRONMENT. THE PAPER CONCLUDES WITH A DISCUSSION OF THE NEXT STEPS FOR THIS EFFORT. ACKNOWLEDGEMENTS: WE THANK THE ATC21S PROJECT AND ITS FUNDERS FOR THEIR SUPPORT FOR THE WORK REPORTED IN THIS REPORT. WE ALSO ACKNOWLEDGE THE EXPERTISE AND CREATIVE INPUT OF THE ATC21S EXPERT PANEL IN ICT LITERACY: JOHN AINLEY (CHAIR), JULIAN FRAILLON, PETER PIROLLI, JEAN-PAUL REEFF, KATHLEEN SCALISE, AND MARK WILSON. OF COURSE, THE VIEWS AND OPINIONS EXPRESSED IN THIS PAPER ARE THOSE OF THE AUTHORS ALONE.
TO CAPTURE AND CODE AS MUCH AS POSSIBLE OF STUDENT BEHAVIOR AND ENVIRONMENT TO APPLY LEARNING ANALYTICS IN CONVENTIONAL CLASSROOMS, PATTERNS AMONG SUCCESSFUL INPUTS OF EXISTING ONLINE LEARNING / LEARNING MANAGEMENT SYSTEMS CAN BE IDENTIFIED, TO FIND EXISTING BUT UNCAPTURED CLASSROOM DATA. THE GOAL OF THIS REVIEW IS TO SUGGEST PROPOSALS ON EXPANDED USE OF LEARNING ANALYTICS IN TRADITIONAL CLASSROOMS. PREDICTORS FROM LEARNING MODELS USED IN ONLINE LEARNING CAN BE APPLIED TO THE TRADITIONAL CLASSROOM AND ANALOGUES MAY BE FOUND FOR UNAVAILABLE PREDICTORS. APPROACHES USED IN DEVELOPING THESE PREDICTORS CAN BE USED TO DEVELOP PREDICTORS FOR CONVENTIONAL CLASSROOMS. EXISTING DATA CAN BE USED, OR DATA THAT IS CONVENIENT MAY BE CAPTURED, WITH EMPHASIS ON APPROACHES THAT WORK ON SMALLER GROUPS, WHERE TRAINING OF INDIVIDUAL MODELS SHOULD BE ATTEMPTED. THE DATA COLLECTED SHOULD BE SIMPLE TO OBTAIN, SUPPORT THE USERS OTHERWISE, OR HAVE PROVABLE BENEFITS.
LEARNING ANALYTICS (LA) IS CURRENTLY THE MOST EFFECTIVE WAY OF ACHIEVING BETTER INFORMATION AND IN-DEPTH INSIGHTS OF THE LEARNING PROCESSES. SPECIFICATIONS LIKE EXPERIENCE API (XAPI) HAVE BEEN DEFINED AS PART OF LA INITIATIVES TO ADD INTEROPERABILITY AMONG LA-AWARE APPLICATIONS. TOOLS THAT VALIDATE THE CONFORMANCE OF DATA TO THESE SPECIFICATIONS ARE KEY COMPONENTS TO ASSURE THE INTEROPERABILITY AMONG APPLICATIONS. IN THIS PAPER, A COMPARATIVE EVALUATION OF RELEVANT VALIDATION TOOLS OF THE XAPI SPECIFICATION IS PRESENTED. THIS COMPARISON FOCUS SPECIALLY ON THE STRUCTURAL AND SEMANTIC FEATURES OF THE XAPI SPECIFICATION, REVEALING THAT MOST OF THE CURRENTLY AVAILABLE TOOLS DO NOT SUPPORT THE SEMANTIC CONSTRAINTS OF THE SPECIFICATION.
THIS PAPER PROPOSES AN APPROACH FOR DEVELOPING CURRICULUM-LEVEL OPEN STUDENT MODELS. THIS APPROACH ENTAILS EVALUATING STUDENT CORE COMPETENCIES USING THE CORRESPONDENCE BETWEEN COURSES AND CORE COMPETENCIES IN A COMPETENCY-BASED CURRICULUM AND STUDENTS' TAKEN COURSES AND GRADES. ON THE BASIS OF THIS APPROACH, A CURRICULUM-LEVEL, COMPETENCY-BASED VISUALIZED ANALYTIC SYSTEM, CALLED VISUALIZED ANALYTICS OF CORE COMPETENCIES (VACCS), WAS IMPLEMENTED. COURSE-COMPETENCY DIAGNOSTIC TOOLS, COURSE WORK PERFORMANCE RADAR CHARTS, AND PEER-BASED RANKING TABLES WERE DEVELOPED AS PART OF THE VACC ANALYTIC SYSTEM FOR STUDENT REFLECTION AND TO POSITION THEIR LEVELS OF CORE COMPETENCIES. THESE CURRICULUM-LEVEL OPEN STUDENT MODELS REVEALED MULTIPLE ASPECTS OF STUDENTS' CORE COMPETENCIES BY MONITORING THE QUANTITY AND QUALITY OF COURSES TAKEN BY STUDENTS, AND EVALUATING STUDENTS' RANKS REGARDING CORE COMPETENCIES COMPARED WITH THEIR CLASSMATES AND GRADUATES. VACC EVALUATION WAS CONDUCTED IN THIS PAPER. THE RESULTS SHOWED THAT MORE THAN 70% OF STUDENTS REPORTED THAT VACC HELPED IN REFLECTING ON THEIR CORE COMPETENCIES, ASSISTING THEM IN UNDERSTANDING THE CORRESPONDENCE BETWEEN THEIR TAKEN COURSES AND CORE COMPETENCIES, AND HELPING THEM TO SET GOALS REGARDING TAKING ADDITIONAL COURSES. IN ADDITION, THIS PAPER DISCUSSES POTENTIAL ANALYTICS AND APPLICATIONS OF OPEN STUDENT MODELS OF CORE COMPETENCIES.
THE IMPORTANCE OF LEARNING DESIGN IN EDUCATION IS WIDELY ACKNOWLEDGED IN THE LITERATURE. SHOULD LEARNERS MAKE EFFECTIVE USE OF OPPORTUNITIES PROVIDED IN A LEARNING DESIGN, ESPECIALLY IN ONLINE ENVIRONMENTS, PREVIOUS STUDIES HAVE SHOWN THAT THEY NEED TO HAVE STRONG SKILLS FOR SELF-REGULATED LEARNING (SRL). THE LITERATURE, WHICH REPORTS THE USE OF LEARNING ANALYTICS (LA), SHOWS THAT SRL SKILLS ARE BEST EXHIBITED IN CHOICES OF LEARNING TACTICS THAT ARE REFLECTIVE OF METACOGNITIVE CONTROL AND MONITORING. HOWEVER, IN SPITE OF HIGH SIGNIFICANCE FOR EVALUATION OF LEARNING EXPERIENCE, THE LINK BETWEEN LEARNING DESIGN AND LEARNING TACTICS HAS BEEN UNDER-EXPLORED. IN ORDER TO FILL THIS GAP, THIS PAPER PROPOSES A NOVEL LEARNING ANALYTIC METHOD THAT COMBINES THREE DATA ANALYTIC TECHNIQUES, INCLUDING A CLUSTER ANALYSIS, A PROCESS MINING TECHNIQUE, AND AN EPISTEMIC NETWORK ANALYSIS. THE PROPOSED METHOD WAS APPLIED TO A DATASET COLLECTED IN A MASSIVE OPEN ONLINE COURSE (MOOC) ON TEACHING IN FLIPPED CLASSROOMS WHICH WAS OFFERED ON A CHINESE MOOC PLATFORM TO PRE- AND IN-SERVICE TEACHERS. THE RESULTS SHOWED THAT THE APPLICATION OF THE APPROACH DETECTED FOUR LEARNING TACTICS (SEARCH ORIENTED, CONTENT AND ASSESSMENT ORIENTED, CONTENT ORIENTED AND ASSESSMENT ORIENTED) WHICH WERE USED BY MOOC LEARNERS. THE ANALYSIS OF TACTICS' USAGE ACROSS LEARNING SESSIONS REVEALED THAT LEARNERS FROM DIFFERENT PERFORMANCE GROUPS HAD DIFFERENT PRIORITIES. THE STUDY ALSO SHOWED THAT LEARNING TACTICS SHAPED BY INSTRUCTIONAL CUES WERE EMBEDDED IN DIFFERENT UNITS OF STUDY IN MOOC. THE LEARNERS FROM A HIGH-PERFORMANCE GROUP SHOWED A HIGH LEVEL OF REGULATION THROUGH STRONG ALIGNMENT OF THE CHOICES OF LEARNING TACTICS WITH TASKS PROVIDED IN THE LEARNING DESIGN. THE PAPER ALSO PROVIDES A DISCUSSION ABOUT IMPLICATIONS OF RESEARCH AND PRACTICE.
THE DOMINATION OF DIGITAL TECHNOLOGY HAS EXPANDED IN THE FIELD OF EDUCATION AND IT IS INEVITABLY LINKED TO E-LEARNING METHODS. LEARNING MANAGEMENT SYSTEMS, AS AN INTEGRAL PART OF DISTANCE LEARNING INFRASTRUCTURE, SUPPORT A GLOBAL HETEROGENEOUS STUDENT POPULATION TO INTERACT WITH TUTORS, TOOLS AND APPLICATIONS. EXPLOITATION AND ANALYSIS OF INTERACTION DATA ALLOW COLLEGES TO UNDERSTAND THE DIFFERENCES IN STUDENT LEARNING PROGRESS AND PROVIDE MORE PERSONALIZED INTERVENTION. IMPLEMENTATION OF LEARNING ANALYTICS AND MACHINE LEARNING TOOLS CAN ACCURATELY PREDICT STUDENTS' ACHIEVEMENTS AND FAILURES. EARLY PREDICTION COULD LEAD TO PROMPT TARGETED ACTION IN ORDER TO IMPROVE LEARNING OUTCOMES. THIS WORK PROPOSES A LEARNING ANALYTICS APPROACH USING DATA MINING AND MACHINE LEARNING TO PREDICT THE GRADES OF THE FOUR MAIN ASSIGNMENTS IN AN ANNUAL MODULE OF HELLENIC OPEN UNIVERSITY. BY DIVIDING THE ACADEMIC YEAR INTO FOUR PERIODS, A DATA ANALYSIS WORKFLOW IS DEVELOPED TO COMPARE SEVERAL REGRESSION ALGORITHMS TO ACCURATELY PREDICT STUDENTS' MARKS FOR THE ASSIGNMENTS OF EACH PERIOD. THE PAPER CONCLUDES IN THE ALGORITHM WITH THE HIGHEST DEGREE OF PRECISION THAT DETERMINES THE PREDICTABILITY OF THE MAIN WRITTEN ASSIGNMENTS. SUBSEQUENTLY, A STATISTICAL MEASURE IS APPLIED TO CLASSIFY THE INFLUENCE OF MODELS' VARIABLES. IN ADDITION, THE ANALYTICAL FRAMEWORK PROVIDES A COMPARISON OF THE ACTUAL AND PREDICTED VALUES IDENTIFYING STUDENTS WHO HAVE INCLUDED THIRD-PARTY SERVICES IN THEIR ASSIGNMENTS.
DIFFERENT SOURCES OF DATA ABOUT STUDENTS, RANGING FROM STATIC DEMOGRAPHICS TO DYNAMIC BEHAVIOR LOGS, CAN BE HARNESSED FROM A VARIETY SOURCES AT HIGHER EDUCATION INSTITUTIONS. COMBINING THESE ASSEMBLES A RICH DIGITAL FOOTPRINT FOR STUDENTS, WHICH CAN ENABLE INSTITUTIONS TO BETTER UNDERSTAND STUDENT BEHAVIOUR AND TO BETTER PREPARE FOR GUIDING STUDENTS TOWARDS REACHING THEIR ACADEMIC POTENTIAL. THIS PAPER PRESENTS A NEWRESEARCH METHODOLOGY TO AUTOMATICALLY DETECT STUDENTS "AT-RISK" OF FAILING AN ASSIGNMENT IN COMPUTER PROGRAMMING MODULES (COURSES) AND TO SIMULTANEOUSLY SUPPORT ADAPTIVE FEEDBACK. BY LEVERAGING HISTORICAL STUDENT DATA, WE BUILT PREDICTIVE MODELS USING STUDENTS' OFFLINE (STATIC) INFORMATION INCLUDING STUDENT CHARACTERISTICS AND DEMOGRAPHICS, AND ONLINE (DYNAMIC) RESOURCES USING PROGRAMMING AND BEHAVIOUR ACTIVITY LOGS. PREDICTIONS ARE GENERATED WEEKLY DURING SEMESTER. OVERALL, THE PREDICTIVE AND PERSONALISED FEEDBACK HELPED TO REDUCE THE GAP BETWEEN THE LOWER AND HIGHER-PERFORMING STUDENTS. FURTHERMORE, STUDENTS PRAISED THE PREDICTION AND THE PERSONALISED FEEDBACK, CONVEYING STRONG RECOMMENDATIONS FOR FUTURE STUDENTS TO USE THE SYSTEM. WE ALSO FOUND THAT STUDENTS WHO FOLLOWED THEIR PERSONALISED GUIDANCE AND RECOMMENDATIONS PERFORMED BETTER IN EXAMINATIONS.
ONE OF THE BIGGEST CHALLENGES FOR EFL (ENGLISH AS FOREIGN LANGUAGE) STUDENTS TO LEARN ENGLISH IS THE LACK OF PRACTICING ENVIRONMENTS. ALTHOUGH LANGUAGE RESEARCHERS HAVE ATTEMPTED TO CONDUCT FLIPPED CLASSROOMS TO INCREASE THE PRACTICING TIME IN CLASS, EFL STUDENTS GENERALLY HAVE DIFFICULTIES INTERACTING WITH PEERS AND TEACHERS IN ENGLISH IN CLASS. THE ADVANCEMENT OF ARTIFICIAL INTELLIGENCE ( AI) PROVIDES AN OPPORTUNITY TO ADDRESS THIS PROBLEM. WITH AI TECHNOLOGIES, COMPUTER SYSTEMS, IN PARTICULAR IN THE FORM OF AI CHATBOTS, ARE ABLE TO IDENTIFY THE MEANINGS OF USERS' STATEMENTS AND MAKE RESPONSES ACCORDINGLY. IN THE RESEARCH DESIGN, AI-BASED CHATBOTS WERE EMPLOYED IN THE IN-CLASS AND OUT-OF-CLASS ACTIVITIES FOR FACILITATING THE STUDENTS' SPEAKING PERFORMANCE AND INTERACTIONS DURING THE LEARNING PROCESS IN A UNIVERSITY FLIPPED ENGLISH SPEAKING CLASSROOM. THE EXPERIMENTAL RESULTS SHOW THAT THE MIND MAP-GUIDED AI CHATBOT APPROACH (MM-AI) PROMOTED THE STUDENTS' ENGLISH SPEAKING PERFORMANCES MORE THAN DID THE CONVENTIONAL AI CHATBOT APPROACH (C-AI). MOREOVER, THE MMAI ALSO PROMOTED THE STUDENTS' LEARNING PERFORMANCE AND ORGANIZED THE INTERACTION BETWEEN THE ROBOTS AND HUMANS MORE THAN THE C-AI DID. THE FINDINGS COULD BE A VALUABLE REFERENCE FOR LANGUAGE EDUCATORS AND RESEARCHERS WHO INTEND TO CONDUCT AI-SUPPORTED FLIPPED CLASSROOMS IN LANGUAGE LEARNING.
IN RECENT YEARS, THE CONNECTION BETWEEN LEARNING DESIGN (LD) AND LEARNING ANALYTICS (LA) HAS BEEN EMPHASIZED BY MANY SCHOLARS AS IT COULD ENHANCE OUR INTERPRETATION OF LA FINDINGS AND TRANSLATE THEM TO MEANINGFUL INTERVENTIONS. TOGETHER WITH NUMEROUS CONCEPTUAL STUDIES, A GRADUAL ACCUMULATION OF EMPIRICAL EVIDENCE HAS INDICATED A STRONG CONNECTION BETWEEN HOW INSTRUCTORS DESIGN FOR LEARNING AND STUDENT BEHAVIOUR. NONETHELESS, STUDENTS' TIMING OF ENGAGEMENT AND ITS RELATION TO LD AND ACADEMIC PERFORMANCE HAVE RECEIVED LIMITED ATTENTION. THEREFORE, THIS STUDY INVESTIGATES TO WHAT EXTENT STUDENTS' TIMING OF ENGAGEMENT ALIGNED WITH INSTRUCTOR LEARNING DESIGN, AND HOW ENGAGEMENT VARIED ACROSS DIFFERENT LEVELS OF PERFORMANCE. THE ANALYSIS WAS CONDUCTED OVER 28 WEEKS USING TRACE DATA, ON 387 STUDENTS, AND REPLICATED OVER TWO SEMESTERS IN 2015 AND 2016. OUR FINDINGS REVEALED A MISMATCH BETWEEN HOW INSTRUCTORS DESIGNED FOR LEARNING AND HOW STUDENTS STUDIED IN REALITY. IN MOST WEEKS, STUDENTS SPENT LESS TIME STUDYING THE ASSIGNED MATERIALS ON THE VLE COMPARED TO THE NUMBER OF HOURS RECOMMENDED BY INSTRUCTORS. THE TIMING OF ENGAGEMENT ALSO VARIED, FROM IN ADVANCE TO CATCHING UP PATTERNS. HIGH-PERFORMING STUDENTS SPENT MORE TIME STUDYING IN ADVANCE, WHILE LOW-PERFORMING STUDENTS SPENT A HIGHER PROPORTION OF THEIR TIME ON CATCHING-UP ACTIVITIES. THIS STUDY REINFORCED THE IMPORTANCE OF PEDAGOGICAL CONTEXT TO TRANSFORM ANALYTICS INTO ACTIONABLE INSIGHTS.
PROBLEM-BASED LEARNING (PBL) REFERS TO SMALL GROUP COLLABORATIVE LEARNING SITUATIONS WHERE STUDENTS SOLVE COMPLEX PROBLEMS WITH THE ASSISTANCE OF TEACHERS WHO SERVE AS FACILITATORS. SCALING PBL USING TECHNOLOGY REQUIRES SPECIFIC TOOLS SINCE ONLINE ASYNCHRONOUS PBL CAN INCREASE THE NUMBER OF SMALL GROUPS THAT ENGAGE IN THE CURRICULUM BUT POSES CHALLENGES TO PBL TEACHERS WHO MUST ATTEND TO MULTIPLE GROUPS. TO ADDRESS THE PROBLEM, WE HAVE BEEN RESEARCHING HOW TECHNOLOGY CAN BE USED TO DEVELOP SPECIFIC TOOLS TO EXTEND EXPERT TEACHERS' INSTRUCTIONAL CAPACITIES. BUILDING ON PREVIOUS WORK, WE PRESENT THE MOST RECENT DESIGN OF A PEDAGOGICAL DASHBOARD USED IN AN ONLINE ASYNCHRONOUS PBL ENVIRONMENT. WE ILLUSTRATE HOW THE NEW PEDAGOGICAL DASHBOARD VISUALIZATIONS CAN SUPPORT PBL INSTRUCTORS OBSERVING INDIVIDUAL STUDENT LEARNING ACTIVITIES, DIAGNOSING GROUP DYNAMICS AND INTERVENING WHEN NECESSARY.
LEARNING ANALYTICS HAS BEEN WIDELY USED IN THE FIELD OF EDUCATION. MOST STUDIES HAVE ADOPTED A LEARNING ANALYTICS DASHBOARD TO PRESENT DATA ON LEARNING PROCESSES OR LEARNING OUTCOMES. HOWEVER, ONLY PRESENTING LEARNING ANALYTICS RESULTS WAS NOT SUFFICIENT AND LACKED PERSONALISED FEEDBACK. IN RESPONSE TO THESE GAPS, THIS STUDY PROPOSED A LEARNING ANALYTICS-BASED PERSONALISED FEEDBACK APPROACH AND EXAMINED THE EFFECTS OF THE PROPOSED APPROACH ON COLLABORATIVE KNOWLEDGE BUILDING, EMOTIONAL STATUS, CO-REGULATED BEHAVIOURAL PATTERNS AND COGNITIVE LOAD. THE LEARNING ANALYTICS-BASED PERSONALISED FEEDBACK APPROACH ADOPTED A DEEP NEURAL NETWORK MODEL, NAMELY BERT (BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS), TO AUTOMATICALLY CLASSIFY DISCUSSION TRANSCRIPTS IN ONLINE COLLABORATIVE LEARNING. IN TOTAL, 60 UNDERGRADUATE STUDENTS PARTICIPATED IN THIS EXPLORATORY STUDY AND WERE RANDOMLY ASSIGNED INTO EXPERIMENTAL AND CONTROL GROUPS. THE STUDENTS IN THE EXPERIMENTAL GROUP LEARNED WITH THE LEARNING ANALYTICS-BASED PERSONALISED FEEDBACK APPROACH, AND THE STUDENTS IN THE CONTROL GROUP LEARNED WITH THE TRADITIONAL ONLINE COLLABORATIVE LEARNING APPROACH. THE LEARNING ANALYTICS-BASED APPROACH WAS FOUND TO HAVE SIGNIFICANT IMPACTS AND NO SIGNIFICANT DIFFERENCE IN COGNITIVE LOAD WAS NOTED BETWEEN THE EXPERIMENTAL AND CONTROL GROUPS.
BACKGROUND: ONLINE COLLABORATIVE WRITING (OCW) HAS BEEN REPORTED TO BE USEFUL TO IMPROVE WRITING QUALITY IN HIGHER EDUCATION. HOWEVER, CHALLENGES EMERGE DURING THE COLLABORATIVE PROCESSES, MAINLY CENTERING ON HOW TO SUSTAIN ACTIVE INTERACTIONS AND COMMUNICATIONS IN ORDER TO COMPLETE A HIGH QUALITY OF WRITING. OBJECTIVES: USING THE DESIGN-BASED RESEARCH, THIS RESEARCH DESIGNS AND IMPLEMENTS A STUDENT-FACING SLA TOOL THAT INTEGRATES MULTI-METHOD ANALYTICS TO PRESENT SOCIAL INTERACTIONS, WRITING TOPICS, AND TOPIC CONNECTIONS AND TO EMPIRICALLY INVESTIGATE THE EFFECTS OF THIS TOOL ON STUDENT ENGAGEMENT DURING OCW. METHODS: THIS RESEARCH USES BOTH QUANTITATIVE CONTENT ANALYSIS, STATISTICAL ANALYSIS, AND QUALITATIVE ANALYSIS TO EXAMINE THE EFFECTS OF THE SLA TOOL ON STUDENTS' SOCIAL AND COGNITIVE ENGAGEMENT AS WELL AS STUDENTS' PERCEPTIONS ABOUT THE USEFULNESS AND USABILITY OF THIS TOOL. RESULTS AND CONCLUSIONS: RESULTS SHOWED THAT THE SLA TOOL IMPROVED STUDENTS' SOCIAL AND COGNITIVE ENGAGEMENT, AND MOST STUDENTS PERCEIVED THAT THE SLA TOOL WAS USEFUL FOR THE OCW ACTIVITY. BASED ON THE FINDINGS, IMPLICATIONS OF TOOL DEVELOPMENT AND PEDAGOGICAL DESIGN ARE PROPOSED TO FOSTER STUDENTS' ONLINE COLLABORATIVE WRITING QUALITY.
CURRENTLY, THE IMPROVEMENT OF CORE SKILLS APPEARS AS ONE OF THE MOST SIGNIFICANT EDUCATIONAL CHALLENGES OF THIS CENTURY. HOWEVER, ASSESSING THE DEVELOPMENT OF SUCH SKILLS IS STILL A CHALLENGE IN REAL CLASSROOM ENVIRONMENTS. IN THIS CONTEXT, MULTIMODAL LEARNING ANALYSIS TECHNIQUES APPEAR AS AN ATTRACTIVE ALTERNATIVE TO COMPLEMENT THE DEVELOPMENT AND EVALUATION OF CORE SKILLS. THIS ARTICLE PRESENTS AN EXPLORATORY STUDY THAT ANALYZES THE COLLABORATION AND COMMUNICATION OF STUDENTS IN A SOFTWARE ENGINEERING COURSE, WHO PERFORM A LEARNING ACTIVITY SIMULATING SCRUM WITH LEGO (R) BRICKS. DATA FROM THE SCRUM PROCESS WAS CAPTURED, AND MULTIDIRECTIONAL MICROPHONES WERE USED IN THE RETROSPECTIVE CEREMONIES. SOCIAL NETWORK ANALYSIS TECHNIQUES WERE APPLIED, AND A CORRELATIONAL ANALYSIS WAS CARRIED OUT WITH ALL THE REGISTERED INFORMATION. THE RESULTS OBTAINED ALLOWED THE DETECTION OF IMPORTANT RELATIONSHIPS AND CHARACTERISTICS OF THE COLLABORATIVE AND NON-COLLABORATIVE GROUPS, WITH PRODUCTIVITY, EFFORT, AND PREDOMINANT PERSONALITY STYLES IN THE GROUPS. FROM ALL THE ABOVE, WE CAN CONCLUDE THAT THE MULTIMODAL LEARNING ANALYSIS TECHNIQUES OFFER CONSIDERABLE FEASIBILITIES TO SUPPORT THE PROCESS OF SKILLS DEVELOPMENT IN STUDENTS.
TECHNOLOGY HAS BECOME AN INTEGRAL PART OF OUR EVERYDAY LIFE, AND ITS USE IN EDUCATIONAL ENVIRONMENTS KEEPS GROWING. ADDITIONALLY, VIDEO GAMES ARE ONE OF THE MOST POPULAR MEDIUMS ACROSS CULTURES AND AGES. THERE IS AMPLE EVIDENCE THAT SUPPORTS THE BENEFITS OF USING GAMES FOR LEARNING AND ASSESSMENT, AND EDUCATORS ARE MAINLY SUPPORTIVE OF USING GAMES IN CLASSROOMS. HOWEVER, WE DO NOT USUALLY FIND EDUCATIONAL GAMES WITHIN THE CLASSROOM ACTIVITIES. ONE OF THE MAIN PROBLEMS IS THAT TEACHERS REPORT DIFFICULTIES TO ACTUALLY KNOW HOW THEIR STUDENTS ARE USING THE GAME SO THAT THEY CAN ANALYZE PROPERLY THE EFFECT OF THE ACTIVITY AND THE INTERACTION OF STUDENTS. TO SUPPORT TEACHERS, EDUCATIONAL GAMES SHOULD INCORPORATE LEARNING ANALYTICS TO TRANSFORM DATA GENERATED BY STUDENTS WHEN PLAYING USEFUL INFORMATION IN A FRIENDLY AND UNDERSTANDABLE WAY. FOR THIS WORK, WE BUILD UPON SHADOWSPECT, A 3D GEOMETRY PUZZLE GAME THAT HAS BEEN USED BY TEACHERS IN A GROUP OF SCHOOLS IN THE US. WE USE LEARNING ANALYTICS TECHNIQUES TO GENERATE A SET OF METRICS IMPLEMENTED IN A LIVE DASHBOARD THAT AIMS TO FACILITATE THAT TEACHERS CAN UNDERSTAND STUDENTS' INTERACTION WITH SHADOWSPECT. WE DEPICT THE MULTIDISCIPLINARY DESIGN PROCESS THAT WE HAVE FOLLOWED TO GENERATE THE METRICS AND THE DASHBOARD WITH GREAT DETAIL. FINALLY, WE ALSO PROVIDE USES CASES THAT EXEMPLIFY HOW TEACHERS CAN USE THE DASHBOARD TO UNDERSTAND THE GLOBAL PROGRESS OF THEIR CLASS AND EACH OF THEIR STUDENTS AT AN INDIVIDUAL LEVEL, IN ORDER TO INTERVENE, ADAPT THEIR CLASSES AND PROVIDE PERSONALIZE FEEDBACK WHEN APPROPRIATE.
HIGHER EDUCATION FOR THE 21ST CENTURY CONTINUES TO PROMOTE DISCOVERIES IN THE FIELD THROUGH LEARNING ANALYTICS (LA). THE PROBLEM IS THAT THE RAPID EMBRACE OF OF LA DIVERTS EDUCATORS' ATTENTION FROM CLEARLY IDENTIFYING REQUIREMENTS AND IMPLICATIONS OF USING LA IN HIGHER EDUCATION. LA IS A PROMISING EMERGING FIELD, YET HIGHER EDUCATION STAKEHOLDERS NEED TO BECOME FURTHER FAMILIAR WITH ISSUES RELATED TO THE USE OF LA IN HIGHER EDUCATION. FEW STUDIES HAVE SYNTHESIZED PREVIOUS STUDIES TO PROVIDE AN OVERVIEW OF LA ISSUES IN HIGHER EDUCATION. TO ADDRESS THE PROBLEM, A SYSTEMIC LITERATURE REVIEW WAS CONDUCTED TO PROVIDE AN OVERVIEW OF METHODS, BENEFITS, AND CHALLENGES OF USING LA IN HIGHER EDUCATION. THE LITERATURE REVIEW REVEALED THAT LA USES VARIOUS METHODS INCLUDING VISUAL DATA ANALYSIS TECHNIQUES, SOCIAL NETWORK ANALYSIS, SEMANTIC, AND EDUCATIONAL DATA MINING INCLUDING PREDICTION, CLUSTERING, RELATIONSHIP MINING, DISCOVERY WITH MODELS, AND SEPARATION OF DATA FOR HUMAN JUDGMENT TO ANALYZE DATA. THE BENEFITS INCLUDE TARGETED COURSE OFFERINGS, CURRICULUM DEVELOPMENT, STUDENT LEARNING OUTCOMES, BEHAVIOR AND PROCESS, PERSONALIZED LEARNING, IMPROVED INSTRUCTOR PERFORMANCE, POST-EDUCATIONAL EMPLOYMENT OPPORTUNITIES, AND ENHANCED RESEARCH IN THE FIELD OF EDUCATION. CHALLENGES INCLUDE ISSUES RELATED TO DATA TRACKING, COLLECTION, EVALUATION, ANALYSIS; LACK OF CONNECTION TO LEARNING SCIENCES; OPTIMIZING LEARNING ENVIRONMENTS, AND ETHICAL AND PRIVACY ISSUES. SUCH A COMPREHENSIVE OVERVIEW PROVIDES AN INTEGRATIVE REPORT FOR FACULTY, COURSE DEVELOPERS, AND ADMINISTRATORS ABOUT METHODS, BENEFITS, AND CHALLENGES OF LA SO THAT THEY MAY APPLY LA MORE EFFECTIVELY TO IMPROVE TEACHING AND LEARNING IN HIGHER EDUCATION.
THE STRUGGLE WITH FRACTION LEARNING IN KINDERGARTEN THROUGH GRADE 12 IN THE UNITED STATES IS A PERSISTENT PROBLEM AND ONE OF THE MAJOR STUMBLING BLOCKS TO SUCCEEDING IN HIGHER MATHEMATICS. RESEARCH INTO THIS PROBLEM HAS IDENTIFIED SEVERAL AREAS WHERE STUDENTS COMMONLY STRUGGLE WITH FRACTIONS. WHILE THERE ARE MANY THEORIES OF FRACTION LEARNING, NONE OF THE RESEARCH ON THESE THEORIES EMPLOYS SAMPLES LARGE ENOUGH TO TEST THEORIES AT SCALE OR NUANCED ENOUGH TO DEMONSTRATE HOW LEARNING UNFOLDS OVER TIME DURING INSTRUCTIONAL ACTIVITIES BASED ON THESE THEORIES. THE WORK REPORTED HERE USES LEARNING ANALYTICS METHODS WITH FINE-GRAINED LOG DATA FROM AN ONLINE FRACTION GAME TO UNPACK HOW SPLITTING (I.E. PARTITIONING A WHOLE INTO EQUAL-SIZED PARTS) IMPACTS LEARNING. STUDY 1 DEMONSTRATED THAT PLAYING THE GAME SIGNIFICANTLY IMPROVED STUDENTS' FRACTION UNDERSTANDING. IN ADDITION, A CLUSTER ANALYSIS SUGGESTED THAT EXPLORING SPLITTING WAS BENEFICIAL. STUDY 2 REPLICATED THE LEARNING RESULTS, AND A CLUSTER ANALYSIS SHOWED THAT COMPARED TO EARLY GAME PLAY, LATER GAME PLAY SHOWED MORE OPTIMAL SPLITTING STRATEGIES. IN ADDITION, IN LOOKING AT THE TYPES OF TRANSITIONS THAT WERE POSSIBLE BETWEEN A STUDENT'S EARLY CLUSTER CATEGORIZATION AND LATER CLUSTER CATEGORIZATION, WE FOUND THAT SOME TYPES OF TRANSITIONS WERE MORE BENEFICIAL FOR LEARNING THAN OTHERS.
GAME LEARNING ANALYTICS CAN BE USED TO CONDUCT EVIDENCE-BASED EVALUATIONS OF THE EFFECT THAT SERIOUS GAMES PRODUCE ON THEIR PLAYERS BY COMBINING IN-GAME USER INTERACTIONS AND TRADITIONAL EVALUATION METHODS. WE ILLUSTRATE THIS APPROACH WITH A CASE-STUDY WHERE WE CONDUCT AN EVIDENCE-BASED EVALUATION OF A SERIOUS GAME'S EFFECTIVENESS TO INCREASE AWARENESS OF BULLYING. IN THIS PAPER, WE DESCRIBE: (1) THE FULL PROCESS OF TRACKING IN-GAME INTERACTIONS, ANALYZING THE TRACES COLLECTED USING THE STANDARD XAPI-SG FORMAT, AND DERIVING GAME LEARNING ANALYTICS VARIABLES (TO BE USED ASEVIDENCES); AND (2) THE USE OF THOSE VARIABLES TO PREDICT THE INCREASE IN BULLYING AWARENESS. WE CONSIDER THAT THIS PROCESS CAN BE GENERALIZED AND REPLICATED TO SYSTEMATIZE, AND THEREFORE SIMPLIFY, EVIDENCE-BASED EVALUATIONS FOR OTHER SERIOUS GAMES BASED ON THE INTERACTION DATA OF THEIR PLAYERS.
ONLINE SOCIAL LEARNING IS A PREVALENT PEDAGOGICAL TOOL, ENABLING LEARNERS ACROSS ALL AGES AND CULTURES TO LEARN TOGETHER. EDUCATORS, POLICY-MAKERS, AND INTERNATIONAL ORGANIZATIONS SUCH AS THE ORGANIZATION FOR ECONOMIC COOPERATION AND DEVELOPMENT (OECD) STRESS THE NEED TO ASSESS COLLABORATIVE LEARNING SYSTEMATICALLY. HOWEVER, THE SYSTEMATIC ASSESSMENT OF LARGE ONLINE GROUPS' COLLABORATION IS STILL IN ITS INFANCY. IN THIS CHAPTER, WE SUGGEST PERCEIVING SOCIAL LEARNING THROUGH THE LENS OF INTERACTION NETWORKS BETWEEN LEARNERS AND CONTENT. BASED ON WELL-ACCEPTED LEARNING THEORIES, WE DEMONSTRATE THE HARNESSING OF DIGITAL TRACES OF ONLINE DISCUSSIONS TO THE ASSESSMENT OF SOCIAL LEARNING, AT BOTH THE INDIVIDUAL AND THE GROUP LEVELS. PRACTICALLY, OUR CONTRIBUTION IS TO SUGGEST A NETWORK ANALYSIS POINT OF VIEW FOR THE ASSESSMENT OF THE PERFORMANCE AND DESIGN OF LEARNING COMMUNITIES. OUR PROPOSED METHODOLOGY CAN BE USED BY INSTRUCTORS TO OPEN-UP THE BLACK BOX OF COLLABORATIVE LEARNING, TO BE ABLE TO EQUIP LEARNERS WITH TWENTY-FIRST-CENTURY SKILL-SET.
STUDYING ONLINE REQUIRES WELL-DEVELOPED SELF-REGULATED LEARNING SKILLS TO PROPERLY MANAGE ONE'S LEARNING STRATEGIES. LEARNING ANALYTICS RESEARCH HAS PROPOSED NOVEL METHODS FOR EXTRACTING THEORETICALLY MEANINGFUL LEARNING STRATEGIES FROM TRACE DATA ORIGINATING FROM FORMAL LEARNING SETTINGS (ONLINE, BLENDED, OR FLIPPED CLASSROOM). THUS IDENTIFIED STRATEGIES PROVED TO BE ASSOCIATED WITH ACADEMIC ACHIEVEMENT. HOWEVER, AUTOMATED EXTRACTION OF THEORETICALLY MEANINGFUL LEARNING STRATEGIES FROM TRACE DATA IN THE CONTEXT OF MASSIVE OPEN ONLINE COURSES (MOOCS) IS STILL UNDER-EXPLORED. MOREOVER, THERE IS A LACUNA IN RESEARCH ON THE RELATIONS BETWEEN AUTOMATICALLY DETECTED STRATEGIES AND THE ESTABLISHED PSYCHOLOGICAL CONSTRUCTS. THE PAPER REPORTS ON A STUDY THAT (A) APPLIED A STATE-OF-THE-ART ANALYTIC METHOD THAT COMBINES PROCESS AND SEQUENCE MINING TECHNIQUES TO DETECT LEARNING STRATEGIES FROM THE TRACE DATA COLLECTED IN A MOOC (N=1,397), AND (B) EXPLORED ASSOCIATIONS OF THE DETECTED STRATEGIES WITH ACADEMIC PERFORMANCE AND PERSONALITY TRAITS (BIG FIVE). FOUR LEARNING STRATEGIES DETECTED WITH THE ADOPTED ANALYTICS METHOD WERE SHOWN TO BE THEORETICALLY INTERPRETABLE AS THE WELLKNOWN APPROACHES TO LEARNING. THE RESULTS ALSO REVEALED THAT THE FOUR DETECTED LEARNING STRATEGIES WERE PREDICTED BY CONSCIENTIOUSNESS, EMOTIONAL INSTABILITY, AND AGREEABLENESS AND WERE ASSOCIATED WITH ACADEMIC PERFORMANCE. IMPLICATIONS FOR THEORETICAL VALIDITY AND PRACTICAL APPLICATION OF ANALYTICS-DETECTED LEARNING STRATEGIES ARE ALSO PROVIDED.
LEARNING ANALYTICS IS AN EMERGING FIELD FOCUSING ON TRACING, COLLECTING, AND ANALYSING DATA THROUGH LEARNERS' INTERACTIONS WITH EDUCATIONAL CONTENT. THE STANDARDISATION OF THE DATA COLLECTED TO SUPPORTING INTEROPERABILITY AND REUSE IS ONE OF THE KEY OPEN ISSUES IN THIS FIELD. ONE OF THE MOST PROMISING ROUTES TO DATA STANDARDISATION IS THROUGH THE XAPI: A FRAMEWORK FOR DEVELOPING STANDARD 'STATEMENTS' AS REPRESENTATIONS OF LEARNING ACTIVITY. THIS PAPER PRESENTS WORK CONDUCTED WITHIN THE CONTEXT OF THE INSTITUTE OF CODING (HTTPS:II INSTITUTEOFEODING.ORG/) . ADDITIONALLY, WE HAVE DEVELOPED A SYSTEM CALLED ADA FOR AUTOMATING THE LEARNING ANALYTICS DATA PROCESSING LIFE CYCLE. TO OUR KNOWLEDGE, ADA IS THE ONLY SYSTEM AIMING TO AUTOMATE THE TURNING DATA INTO XAPI STATEMENTS FOR STANDARDISATION, SENDING DATA TO AND EXTRACTING DATA FROM A LEARNING RECORD STORE OR MONGODB, AND PROVIDING LEARNING ANALYTICS. THE OPEN UNIVERSITY LEARNING ANALYTICS DATASET IS USED IN THE TEST CASE. THE TEST CASE STUDY HAS LED TO THE EXTENSION OF THE XAPI WITH FIVE NEW METHODS: (1) PERSONA ATTRIBUTES, (2) REGISTER, (3) UNREGISTER, (4) SUBMIT, AND (5) A NUMBER OF VIEWS INFORMATION.
RESEARCH ON LEARNING DASHBOARDS AIMS TO IDENTIFY WHAT DATA IS MEANINGFUL TO DIFFERENT STAKEHOLDERS IN EDUCATION, AND HOW DATA CAN BE PRESENTED TO SUPPORT SENSE-MAKING PROCESSES. THIS PAPER SUMMARIZES THE MAIN OUTCOMES OF A SYSTEMATIC LITERATURE REVIEW ON LEARNING DASHBOARDS, IN THE FIELDS OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING THE QUERY WAS RUN IN FIVE MAIN ACADEMIC DATABASES AND ENRICHED WITH PAPERS COMING FROM GSCHOLAR, RESULTING IN 346 PAPERS OUT OF WHICH 55 WERE INCLUDED IN THE FINAL ANALYSIS. OUR REVIEW DISTINGUISHES DIFFERENT KINDS OF RESEARCH STUDIES AS WELL AS DIFFERENT ASPECTS OF LEARNING DASHBOARDS AND THEIR MATURITY IN TERMS OF EVALUATION. AS THE RESEARCH FIELD IS STILL RELATIVELY YOUNG, MANY OF THE STUDIES ARE EXPLORATORY AND PROOF-OF-CONCEPT. AMONG THE MAIN OPEN ISSUES AND FUTURE LINES OF WORK IN THE AREA OF LEARNING DASHBOARDS, WE IDENTIFY THE NEED FOR LONGITUDINAL RESEARCH IN AUTHENTIC SETTINGS, AS WELL AS STUDIES THAT SYSTEMATICALLY COMPARE DIFFERENT DASHBOARD DESIGN OPTIONS.
MASSIVE OPEN ONLINE COURSES (MOOCS) APPEARED AS A PROPER WAY TO PROVIDE LIFELONG LEARNING FOR POTENTIAL LEARNERS OF BOTH PROFESSIONAL AND ACADEMIC SETTINGS. INDUSTRY LEADERS CAN BENEFIT FROM THESE COURSES BECAUSE THEY FOSTER THE PROFESSIONAL DEVELOPMENT OF THEIR EMPLOYEES IN THEIR INDUSTRY. DESPITE THESE BENEFITS, THESE ONLINE COURSES CONTINUE TO REGISTER A HIGH DROPOUT RATE AND A VAST NUMBER OF THEIR LEARNERS DO NOT ACQUIRE THE CERTIFICATE PROVIDED AT THE COMPLETION OF THE COURSE. THIS ARTICLE PROPOSES A PREDICTIVE MODELING TOOL WITH SEVERAL MACHINE LEARNING ALGORITHMS (FOR GENERATING PREDICTIVE MODELS) AND FEATURE ENGINEERING IN MOOCS DATA INTEGRATED TO CONTRIBUTE RESEARCH TO THIS SPECIFIC ISSUE. THE PROPOSED TOOL PREDICTS TWO SITUATIONS: WHICH LEARNERS ARE LIKELY TO LEAVE THE COURSE (DROPOUT) AND WHICH LEARNERS ARE EXPECTED TO PASS THE COURSE (CERTIFICATE ACQUISITION). THE TOOL WAS TESTED IN FIFTEEN DELIVERIES OF SEVEN MOOCS. INITIAL RESULTS PROVIDE INTERESTING INFORMATION, FOR INSTANCE, THAT THE ACCURACY OF PREDICTING CERTIFICATE ACQUISITION IS HIGHER THAN THE PRECISION OF PREDICTING DROPOUT FOR ALL ALGORITHMS.
THE ONGOING DATAFICATION OF OUR SOCIAL REALITY HAS RESULTED IN THE EMERGENCE OF NEW DATA-BASED BUSINESS MODELS. THIS DEVELOPMENT IS ALSO REFLECTED IN THE EDUCATION MARKET. AN INCREASING NUMBER OF EDUCATIONAL TECHNOLOGY (EDTECH) COMPANIES ARE ENTERING THE TRADITIONAL EDUCATION MARKET WITH DATA-BASED TEACHING AND LEARNING SOLUTIONS, AND THEY ARE PERMANENTLY TRANSFORMING THE MARKET. HOWEVER, DESPITE THE CURRENT MARKET DYNAMICS, THERE ARE HARDLY ANY BUSINESS MODELS THAT IMPLEMENT THE POSSIBILITIES OF LEARNING ANALYTICS (LA) AND ARTIFICIAL INTELLIGENCE (AI) TO CREATE ADAPTIVE TEACHING AND LEARNING PATHS. THIS PAPER FOCUSES ON EDTECH COMPANIES AND THE DRIVERS AND BARRIERS THAT CURRENTLY AFFECT DATA-BASED TEACHING AND LEARNING PATHS. THE RESULTS SHOW THAT LA ESPECIALLY ARE INTEGRATED INTO THE CURRENT BUSINESS MODELS OF EDTECH COMPANIES ON THREE LEVELS, WHICH ARE AS FOLLOWS: BASIC LEARNING ANALYTICS, LEARNING ANALYTICS AND ALGORITHMIC OR HUMAN-BASED RECOMMENDATIONS, AND LEARNING ANALYTICS AND ADAPTIVE TEACHING AND LEARNING (AI BASED). THE DISCOURSE ANALYSIS REVEALS A DIAMETRICAL RELATIONSHIP BETWEEN THE TRADITIONAL EDUCATIONAL IDEAL AND THE FUTURISTIC IDEA OF EDUCATION AND KNOWLEDGE TRANSFER. WHILE THE DESIRE FOR FLEXIBILITY AND INDIVIDUALIZATION DRIVES THE DEBATE ON AI-BASED LEARNING SYSTEMS, A LACK OF DATA SOVEREIGNTY, UNCERTAINTY AND A LACK OF UNDERSTANDING OF DATA ARE HOLDING BACK THE DEVELOPMENT AND IMPLEMENTATION OF APPROPRIATE SOLUTIONS AT THE SAME TIME.
THE IMPORTANCE OF TEMPORALITY IN LEARNING HAS BEEN LONG ESTABLISHED, BUT IT IS ONLY RECENTLY THAT SERIOUS ATTENTION HAS BEGUN TO BE PAID TO THE PRECISE IDENTIFICATION, MEASUREMENT, AND ANALYSIS OF THE TEMPORAL FEATURES OF LEARNING. FROM 2009 TO 2016, A SERIES OF TEMPORALITY WORKSHOPS EXPLORED TEMPORAL CONCEPTS AND DATA TYPES, ANALYSIS METHODS FOR EXPLOITING TEMPORAL DATA, TECHNIQUES FOR VISUALIZING TEMPORAL INFORMATION, AND PRACTICAL CONSIDERATIONS FOR THE USE OF TEMPORAL ANALYSES IN PARTICULAR CONTEXTS OF LEARNING. FOLLOWING FROM THESE EFFORTS, THIS TWO-PART SPECIAL SECTION SERVES TO CONSOLIDATE RESEARCH WORKING TO PROGRESS CONCEPTUAL, TECHNICAL AND PRACTICAL TOOLS FOR TEMPORAL ANALYSES OF LEARNING DATA. IN ADDITION, IN THIS SECOND AND FINAL EDITORIAL WE AIM TO MAKE FOUR CONTRIBUTIONS TO THE ONGOING DIALOUGE AROUND TEMPORAL LEARNING ANALYTICS TO HELP US MOVE TOWARDS A CLEARER MAPPING OF THE RESEARCH SPACE. FIRST, THE EDITORIAL PRESENTS AN OVERVIEW OF THE FIVE PAPERS IN PART 2 OF THE SPECIAL SECTION ON TEMPORAL ANALYSES, HIGHLIGHTING THE DIMENSIONS OF DATA TYPES, LEARNING CONSTRUCTS, ANALYSIS APPROACHES, AND POTENTIAL IMPACT. SECOND, IT DRAWS ON THE FLUID RELATIONSHIP BETWEEN 'ANALYZED TIME' AND 'EXPERIENCED TIME' TO HIGHLIGHT THE NEED FOR CAUTION AND CRITICALITY IN THE PURPOSES TEMPORAL ANALYSES ARE MOBILIZED TO SERVE. THIRD IT OFFERS A GUIDE FOR FUTURE WORK IN THIS AREA BY OUTLINING IMPORTANT QUESTIONS THAT ALL TEMPORAL ANALYSES SHOULD INTENTIONALLY ADDRESS. FINALLY, IT PROPOSES NEXT STEPS LEARNING ANALYTICS RESEARCHERS AND PRACTITIONERS CAN TAKE COLLECTIVELY TO ADVANCE WORK ON THE USE OF TEMPORAL ANALYSES TO SUPPORT LEARNING.
THIS STUDY AIMED TO IDENTIFY FACTORS INFLUENCING STUDENT ENGAGEMENT IN ONLINE AND BLENDED COURSES AT ONE AUSTRALIAN REGIONAL UNIVERSITY. IT APPLIED A DATA SCIENCE APPROACH TO LEARNING AND TEACHING DATA GATHERED FROM THE LEARNING MANAGEMENT SYSTEM USED AT THIS UNIVERSITY. DATA WERE COLLECTED AND ANALYSED FROM 23 SUBJECTS, SPANNING OVER 5500 STUDENT ENROLMENTS AND 406 LECTURER AND TUTOR ROLES, OVER A FIVE-YEAR PERIOD. BASED ON A THEORETICAL FRAMEWORK ADAPTED FROM COMMUNITY OF INQUIRY (COI) FRAMEWORK BY GARRISON ET AL. (2000), THE DATA WERE SEGREGATED INTO THREE GROUPS FOR ANALYSIS: STUDENT ENGAGEMENT, COURSE CONTENT AND TEACHER INPUT. THE DATA ANALYSIS REVEALED A POSITIVE CORRELATION BETWEEN STUDENT ENGAGEMENT AND TEACHER INPUT, AND INTERESTINGLY, A NEGATIVE CORRELATION BETWEEN STUDENT ENGAGEMENT AND COURSE CONTENT WHEN A CERTAIN THRESHOLD WAS EXCEEDED. THE FINDINGS OF THE STUDY OFFER USEFUL SUGGESTIONS FOR FUTURE COURSE DESIGN, AND PEDAGOGICAL APPROACHES TEACHERS CAN ADOPT TO FOSTER STUDENT ENGAGEMENT.</P>
MODELS AND PROCESSES DEVELOPED IN LEARNING ANALYTICS RESEARCH ARE INCREASING IN SOPHISTICATION AND PREDICTIVE POWER. HOWEVER, THE ABILITY TO TRANSLATE ANALYTIC FINDINGS TO PRACTICE REMAINS PROBLEMATIC. THIS STUDY AIMS TO ADDRESS THIS ISSUE BY ESTABLISHING A MODEL OF LEARNER BEHAVIOUR THAT IS BOTH PREDICTIVE OF STUDENT COURSE PERFORMANCE, AND EASILY INTERPRETED BY INSTRUCTORS. TO ACHIEVE THIS AIM, WE ANALYSED FINE GRAINED TRACE DATA (FROM 3 OFFERINGS OF AN UNDERGRADUATE ONLINE COURSE, N=1068) TO ESTABLISH A COMPREHENSIVE SET OF BEHAVIOUR INDICATORS ALIGNED WITH THE COURSE DESIGN. THE IDENTIFIED BEHAVIOUR PATTERNS, WHICH WE REFER TO AS OBSERVED STUDY STRATEGIES, PROVED TO BE ASSOCIATED WITH THE STUDENT COURSE PERFORMANCE. BY EXAMINING THE OBSERVED STRATEGIES OF HIGH AND LOW PERFORMERS THROUGHOUT THE COURSE, WE IDENTIFIED PROTOTYPICAL PATHWAYS ASSOCIATED WITH COURSE SUCCESS AND FAILURE. THE PROPOSED MODEL AND APPROACH OFFERS VALUABLE INSIGHTS FOR THE PROVISION OF PROCESS-ORIENTED FEEDBACK EARLY IN THE COURSE, AND THUS CAN AID LEARNERS IN DEVELOPING THEIR CAPACITY TO SUCCEED ONLINE.
BLENDED LEARNING (BL) IS A POPULAR E-LEARNING MODEL IN HIGHER EDUCATION THAT HAS THE POTENTIAL TO TAKE ADVANTAGE OF LEARNING ANALYTICS (LA) TO SUPPORT STUDENT LEARNING. THIS STUDY UTILIZED LA TO INVESTIGATE FOURTH-YEAR UNDERGRADUATES' (N = 157) USE OF SELF-REGULATED LEARNING (SRL) WITHIN THE ONLINE COMPONENTS OF A PREVIOUSLY UNEXAMINED BL DISCIPLINE, MUSIC TEACHER EDUCATION. SRL BEHAVIORS WERE CAPTURED UNOBTRUSIVELY IN REAL TIME THROUGH STUDENTS' INTERACTION WITH COURSE MATERIALS IN MOODLE. CATEGORIZED BY FUNCTION: (1) ACTIVATING-ONLINE ACCESS LOCATION, DAY-OF-THE-WEEK, TIME-OF-DAY; (2) SUSTAINING-ONLINE FREQUENCY; AND (3) STRUCTURING-ONLINE REGULARITY AND EXAM REVIEW PATTERNS, ALL SIX SRL BEHAVIORS WERE REVEALED TO HAVE WEAK TO MODERATE SIGNIFICANT RELATIONSHIPS WITH ACADEMIC ACHIEVEMENT. RESULTS INDICATED ACCESS DAY-OF-THE-WEEK AND ACCESS FREQUENCY AS THE STRONGEST PREDICTORS FOR STUDENT SUCCESS. FINDINGS REGARDING ACCESS REGULARITY WHEN VIEWED THROUGH RESULTS FROM PREVIOUS SRL-LA RESEARCH MAY SUGGEST THE IMPORTANCE OF THIS SRL BEHAVIOR FOR SUCCESSFUL STUDENTS WITHIN SEVERAL BL DISCIPLINE AREAS. IN ADDITION, THE ROLE OF LEARNING DESIGN (EG, FLIPPED INSTRUCTION) IN POTENTIALLY SCAFFOLDING STUDENTS' CHOICES TOWARD SPECIFIC SRL BEHAVIORS, WAS REVEALED AS AN IMPORTANT CONTEXT FOR FUTURE RESEARCHERS' CONSIDERATION.
THIS PAPER PRESENTS THREE MULTIMODAL LEARNING ANALYTIC APPROACHES FROM A HANDS-ON LEARNING ACTIVITY. WE USE VIDEO, AUDIO, GESTURE AND BIO-PHYSIOLOGY DATA FROM A TWO-CONDITION STUDY (N = 20), TO IDENTIFY CORRELATIONS BETWEEN THE MULTIMODAL DATA, EXPERIMENTAL CONDITION, AND TWO LEARNING OUTCOMES: DESIGN QUALITY AND LEARNING. THE THREE APPROACHES INCORPORATE: 1) HUMAN-ANNOTATED CODING OF VIDEO DATA, 2) AUTOMATED CODING OF GESTURE, AUDIO AND BIO-PHYSIOLOGICAL DATA AND, 3) CONCATENATED HUMAN-ANNOTATED AND AUTOMATICALLY ANNOTATED DATA. WITHIN EACH ANALYSIS WE EMPLOY THE SAME MACHINE LEARNING AND SEQUENCE MINING TECHNIQUES. ULTIMATELY WE FIND THAT EACH APPROACH PROVIDES DIFFERENT AFFORDANCES DEPENDING ON THE SIMILARITY METRIC AND THE DEPENDENT VARIABLE. FOR EXAMPLE, THE ANALYSIS BASED ON HUMAN-ANNOTATED DATA FOUND STRONG CORRELATIONS AMONG MULTIMODAL BEHAVIORS, EXPERIMENTAL CONDITION, SUCCESS AND LEARNING, WHEN WE RELAXED CONSTRAINTS ON TEMPORAL SIMILARITY. THE SECOND APPROACH PERFORMED WELL WHEN COMPARING STUDENTS' MULTIMODAL BEHAVIORS AS A TIME SERIES, BUT WAS LESS EFFECTIVE USING THE TEMPORALLY RELAXED SIMILARITY METRIC. THE TAKE-AWAY IS THAT THERE ARE SEVERAL STRATEGIES FOR DOING MULTIMODAL LEARNING ANALYTICS, AND THAT MANY OF THESE APPROACHES CAN PROVIDE A MEANINGFUL GLIMPSE INTO A COMPLEX DATA SET, GLIMPSES THAT MAY BE DIFFICULT TO IDENTIFY USING TRADITIONAL APPROACHES.
THE RESEARCH GOAL WAS SET TO DETERMINE TO WHAT EXTENT THE INFLUENCES OF LEARNING ANALYTICS AND ACADEMIC ANALYTICS, THE ANTECEDENT FACTORS IN PREDICTING STUDENT ACADEMIC ACHIEVEMENT THROUGH THE USE OF BIG DATA WERE. THERE HAS BEEN NO DISCUSSION ON PROGRESS, SUCCESS, RETENTION, OR DECLINE OF THIS ACHIEVEMENT. THEREFORE, THIS RESEARCH HAS SIGNIFICANCE FOR THE IMPROVEMENT OF HIGHER EDUCATION INSTITUTIONS. THE RESEARCH WAS IN THE FORM OF ONLINE SURVEYS INVOLVING 203 RESPONDENTS, I.E., LEADERS, STRUCTURAL STAFF, AND ACADEMIC ADVISORS FROM EACH OF THESE INSTITUTIONS IN PONTIANAK. TESTS OF EIGHT HYPOTHESES WERE CONDUCTED THROUGH SEM-PLS METHOD, AND TWO OF THEM HAD NO DIRECT INFLUENCES. THE RESULTS SHOW THAT THE TWO ANTECEDENT FACTORS, DIRECTLY AND INDIRECTLY, HAVE DIFFERENT INFLUENCES AND SIGNIFICANCE VALUES ON STUDENT ACADEMIC ACHIEVEMENT PREDICTION DESPITE THE CRITICAL ROLES OF BIG DATA. IN ADDITION, RESULTS OBTAINED THROUGH THE APPLICATION OF LEARNING ANALYTICS AND ACADEMIC ANALYTICS IN RELATION TO BIG DATA OF HIGHER EDUCATION INSTITUTIONS, ESPECIALLY FOR THE NEED TO PREDICT STUDENT ACADEMIC ACHIEVEMENT, ARE INFREQUENTLY SIMILAR.
PRACTITIONERS OF THE STATISTICS COURSE EMBEDDED IN A COMPUTER SCIENCE PROGRAMME AT A FULLY ONLINE UNIVERSITY WERE CONCERNED WITH THE HIGH DROPOUT RATE. IN THE ACADEMIC YEAR 2018-19, THEY DECIDED TO CARRY OUT A TWO-PHASE PROJECT IN ORDER TO ADDRESS THIS ISSUE. IN THE FIRST PHASE, AN EARLY CLASSIFIER TO IDENTIFY STUDENTS AT RISK OF DROPPING OUT OF THE 2018-19 STATISTICS COURSE WAS DETERMINED. THE SECOND PHASE WAS PLANNED TO DESIGN AND IMPLEMENT AN EARLY INTERVENTION BASED ON THE RESULTS OF THE FIRST PHASE. THIS ARTICLE PRESENTS THE ANALYSIS OF THIS INTERVENTION. IN THE 2019-20 ONLINE STATISTICS COURSE, 35 STUDENTS DID NOT SUBMIT THE FIRST FEW QUIZZES BEFORE THE RESPECTIVE DEADLINES. THEY WERE THE TARGET OF THIS INTERVENTION, WHICH GAVE THEM THE CHANCE TO CHANGE THEIR RECENT PAST BY SUBMITTING THE UNSUBMITTED QUIZZES. STUDENTS WERE ENCOURAGED TO CHANGE BY MEANS OF AN EMAIL MESSAGE. THE AIM OF THE INTERVENTION WAS TO REDUCE THE DROPOUT RATE AMONG THE TARGETED STUDENTS. OUR ANALYSIS ALSO INCLUDES THE STUDENTS' PERCEPTION OF THE INTERVENTION COLLECTED VIA A SET OF INTERVIEWS. THE RESULTS SHOW THAT THE INTERVENTION CLEARLY HAD A BENEFICIAL EFFECT FOR SOME STUDENTS.
COLLABORATIVE LEARNING IS CONSIDERED A CRITICAL 21ST CENTURY SKILL. MUCH IS KNOWN ABOUT ITS CONTRIBUTION TO LEARNING, BUT STILL INVESTIGATING A PROCESS OF COLLABORATION REMAINS A CHALLENGE. THIS PAPER APPROACHES THE INVESTIGATION ON COLLABORATIVE LEARNING FROM A PSYCHOPHYSIOLOGICAL PERSPECTIVE. AN EXPERIMENT WAS SET UP TO EXPLORE WHETHER BIOSENSORS CAN PLAY A ROLE IN ANALYSING COLLABORATIVE LEARNING. ON THE ONE HAND, WE IDENTIFIED FIVE PHYSIOLOGICAL COUPLING INDICES (PCIS) FOUND IN THE LITERATURE: 1) SIGNAL MATCHING (SM), 2) INSTANTANEOUS DERIVATIVE MATCHING (IDM), 3) DIRECTIONAL AGREEMENT (DA), 4) PEARSON'S CORRELATION COEFFICIENT (PCC) AND THE 5) FISHER'S Z-TRANSFORM (FZT) OF THE PCC. ON THE OTHER HAND, THREE COLLABORATIVE LEARNING MEASUREMENTS WERE USED: 1) COLLABORATIVE WILL (CW), 2) COLLABORATIVE LEARNING PRODUCT (CLP) AND 3) DUAL LEARNING GAIN (DLG). REGRESSION ANALYSES SHOWED THAT OUT OF THE FIVE PCIS, IDM RELATED THE MOST TO CW AND WAS THE BEST PREDICTOR OF THE CLP. MEANWHILE, DA PREDICTED DLG THE BEST. THESE RESULTS PLAY A ROLE IN DETERMINING INFORMATIVE COLLABORATION MEASURES FOR DESIGNING A LEARNING ANALYTICS, BIOFEEDBACK DASHBOARD.
RUBRICS ARE USEFUL SCORING TOOLS USED IN VARIOUS DISCIPLINES TO PROVIDE FEEDBACK TO STUDENTS AND PROMOTE LEARNING. TRADITIONALLY, STUDENTS WERE HANDED WITH A PAPER RUBRIC WHICH MIGHT SOMETIMES BE INACCESSIBLE TO ABSENT STUDENTS. WITH ADVANCES IN TECHNOLOGY, RUBRICS CAN BE EASILY EMBEDDED INTO A LMS SUCH AS MOODLE TO IMPROVE ACCESSIBILITY, FACILITATE ONLINE GRADING, AND REDUCE REDUNDANT PAPERWORK. IN THIS STUDY, A RUBRIC WAS INCORPORATED INTO A LEARNING MANAGEMENT SYSTEM (LMS). STUDENTS (N = 59) HAD ACCESS TO THE SCORING TOOL PRIOR TO COMPLETING THEIR WRITTEN WORK. AFTER SUBMISSION, THE LEARNING ANALYTICS ENRICHED RUBRIC-LAE-R TOOL WAS USED BY THE INSTRUCTOR TO PROVIDE FORMATIVE FEEDBACK ON STUDENTS' WRITING PERFORMANCE. DATA WERE COLLECTED FROM STUDENTS' SURVEYS, VIEWING RATES, AND ESSAY SCORES TO UNDERSTAND IF THERE WERE ANY BENEFITS FROM EMBEDDING THE RUBRIC AND WHETHER THE STUDENTS WERE SATISFIED WITH THE PAPERLESS RUBRIC AS A LEARNING AND A SCORING TOOL. THE RESULTS INDICATED THAT WHILE ENGAGING WITH THE RUBRIC DID NOT HELP MUCH IN PREDICTING STUDENTS' ASSIGNMENT SCORES, MOST OF THE STUDENTS WERE SATISFIED WITH THE RUBRIC AND RECOMMENDED FURTHER USE OF THE TOOL.
THIS QUALITATIVE STUDY FOCUSES ON CAPTURING STUDENTS' UNDERSTANDING TWO VISUALIZATIONS OFTEN UTILIZED BY LEARNING ANALYTICS-BASED EDUCATIONAL TECHNOLOGIES: BAR GRAPHS, AND LINE GRAPHS. IT IS FRAMED BY ACHIEVEMENT GOAL THEORY-A PROMINENT THEORY OF STUDENTS' ACADEMIC MOTIVATION-AND UTILIZES INTERVIEWS (N = 60) TO INVESTIGATE HOW STUDENTS AT RISK OF COLLEGE FAILURE INTERPRET VISUALIZATIONS OF THEIR POTENTIAL ACADEMIC ACHIEVEMENT. FINDINGS SUGGEST THAT VISUALIZATIONS ONLY CONTAINING INFORMATION ABOUT STUDENTS THEMSELVES (I.E., SELF-FOCUSED AFFORDANCES) EVOKED STATEMENTS CENTERED ON MASTERING MATERIAL. VISUALIZATIONS CONTAINING INFORMATION ABOUT STUDENTS AND A CLASS AVERAGE (I.E., COMPARATIVE INFORMATION), ON THE OTHER HAND, EVOKED RESPONSES THAT DISHEARTENED STUDENTS AND/OR MADE THEM FEEL ACCOUNTABLE TO DO BETTER. FINDINGS FROM THIS STUDY SUGGEST THE FOLLOWING GUIDELINES FOR DESIGNING VISUALIZATIONS FOR LEARNING ANALYTICS-BASED EDUCATIONAL TECHNOLOGIES: (1) NEVER ASSUME THAT MORE INFORMATION IS BETTER; (2) ANTICIPATE AND MITIGATE AGAINST POTENTIAL MISINTERPRETATIONS-OR HARMFUL ALTERNATIVE INTERPRETATIONS-OF VISUALIZATIONS; AND (3) ALWAYS SUGGEST A WAY FOR STUDENTS TO IMPROVE. THESE GUIDELINES HELP MITIGATE AGAINST POTENTIAL UNINTENDED CONSEQUENCES TO MOTIVATION INTRODUCED BY VISUALIZATIONS USED IN LEARNING ANALYTICS-BASED EDUCATIONAL TECHNOLOGIES.
THIS PAPER DISCUSSES THE USAGE OF LEARNING ANALYTICS AS A TOOL TO ENHANCE TEACHING METHODS. TO ACHIEVE THIS, WE WILL PROVIDE VISUAL REPRESENTATIONS ON THE DIFFERENT TEACHING METHODS USED AND THEIR RELATION TO THE PERFORMANCE OF THE STUDENTS. WE WILL ALSO SHOW POSSIBLE RELATIONSHIPS BETWEEN THE LEARNING ANALYTICS AND THE STUDENT BEHAVIOR AND PERFORMANCE IN THE COURSE, AND THE IMPORTANCE OF DATA ANALYTICS IN OPEN ONLINE COURSE IMPLEMENTATIONS. IN ADDITION, THE METHODS APPLIED ARE EXPLAINED. THE SYSTEM IS IMPLEMENTED AND RUN ON EXISTING DATA SETS FROM MITX AND HARVARDX [1].
NOWADAYS LARGE VOLUMES OF INDUSTRIAL DATA ARE BEING ACTIVELY GENERATED AND COLLECTED IN VARIOUS POWER SYSTEM APPLICATIONS. INDUSTRIAL ANALYTICS IN THE POWER SYSTEM FIELD REQUIRES MORE POWERFUL AND INTELLIGENT MACHINE LEARNING TOOLS, STRATEGIES, AND ENVIRONMENTS TO PROPERLY ANALYZE THE HISTORICAL DATA AND EXTRACT PREDICTIVE KNOWLEDGE. THIS PAPER DISCUSSES THE SITUATION AND LIMITATIONS OF CURRENT APPROACHES, ANALYTIC MODELS, AND TOOLS UTILIZED TO CONDUCT PREDICTIVE MACHINE LEARNING ANALYTICS FOR VERY LARGE VOLUMES OF DATA WHERE THE DATA PROCESSING CAUSES THE PROCESSOR TO RUN OUT OF MEMORY. TWO INDUSTRIAL ANALYTICS CASES IN THE POWER SYSTEMS FIELD ARE PRESENTED. OUR RESULTS INDICATED THE FEASIBILITY OF FORECASTING SUBSTATIONS FAULT EVENTS AND POWER LOAD USING MACHINE LEARNING ALGORITHM WRITTEN IN MAPREDUCE PARADIGM OR MACHINE LEARNING TOOLS SPECIFIC FOR BIG DATA.
ADVANCES IN MACHINE LEARNING AND BIG DATA RESEARCH OFFER GREAT POTENTIAL FOR INTERNATIONAL DEVELOPMENT AGENCIES TO LEVERAGE THE VAST INFORMATION GENERATED FROM ACCOUNTABILITY MECHANISMS TO GAIN NEW INSIGHTS, PROVIDING ANALYTICS THAT CAN IMPROVE DECISION-MAKING. FROM A KNOWLEDGE MANAGEMENT PERSPECTIVE, PROJECT OPERATIONAL REPORTS ARE CRUCIAL HISTORICAL RECORDS AS THEY TELL THE STORY OF A PROJECT, COMPILE DATA GENERATED THROUGHOUT THE PROJECT CYCLE, DISCUSS ACHIEVEMENTS OF INTENDED DEVELOPMENT OBJECTIVES, AND PROVIDE LEARNING THAT CAN INFORM FUTURE OPERATIONS. TAKING THE INTERNATIONAL FUND FOR AGRICULTURAL DEVELOPMENT (IFAD) AS A CASE STUDY, THIS PAPER EXPLORES HOW MACHINE LEARNING CAN HARNESS EXISTING PROJECT DATA TO UNCOVER LATENT INFORMATION ABOUT FOOD SYSTEMS DYNAMICS, WHICH IS ALREADY PRESENT IN DOCUMENTATION BUT HAS NOT YET BEEN INVESTIGATED. SPECIFICALLY, WE AIM TO PROVIDE EVIDENCE ON THE EVOLUTION OF FOOD SYSTEM DIMENSIONS WITHIN IFAD-FUNDED PROJECTS THROUGH THE APPLICATION OF SUPERVISED TEXT MINING, NETWORK ANALYSIS AND LASSO REGRESSION TO PROJECT DOCUMENTS COLLECTED FROM HUNDREDS OF PROJECTS SPANNING THE WHOLE OF IFAD'S INVESTMENT PORTFOLIO IN THE 1981-2019 INTERVAL. FINDINGS SHOW AN INCREASE IN REPORTING AGAINST FOOD SYSTEM DIMENSIONS AND CONSOLIDATE THE APPLICABILITY OF MACHINE LEARNING ANALYTICS TO UNCOVER HISTORICAL TRENDS ABOUT INTERNATIONAL AGENCIES' ACTIVITIES AND ACCELERATE KNOWLEDGE GENERATION AROUND STRATEGIC THEMES.
LEARNING ANALYTICS (LA) IS DATA SCIENCE APPLIED TO THE EDUCATIONAL FIELD. IT ENABLES THE MEASUREMENT, COLLECTION, AND ANALYSIS OF LEARNERS' DATA AND THEIR CONTEXT. IN THIS RESEARCH WE UTILIZED TWO ALGORITHMS FROM THE FIELD OF ARTIFICIAL INTELLIGENCE (AI): K-NEAREST NEIGHBOR AND RANDOM FOREST. THESE ALGORITHMS TRAINED A PREDICTIVE MODEL FOR THE ACADEMIC PERFORMANCE OF STUDENTS PURSUING AN ENGINEERING DEGREE. THIS RESEARCH FOUND THAT A GENERAL PICTURE OF THE PERFORMANCE OF THE GROUP IS ENOUGH TO IMPROVE, DESPITE THE FORECAST FOR EACH STUDENT NOT BEING ACCURATE. THIS ALLOWED THE INSTRUCTOR TO ADAPT THEIR TEACHING TECHNIQUE TO GET BETTER RESULTS. FINALLY, MOST STUDENTS AGREE TO TAKE ADVANTAGE OF LA AND THEY THINK THAT KNOWING THEIR PREDICTIVE RESULTS AT THE BEGINNING OF THE COURSE WILL HELP THEM DO BETTER IN CLASS.
THE AIM OF LEARNING ANALYTICS (LA) IS TO UNDERSTAND AND OPTIMIZE LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS. GIVEN THE COMPLEX NATURE OF LEARNING IT HAS BECOME NECESSARY TO USE TOOLS FROM VARIOUS FIELDS OF RESEARCH TO OBTAIN, DESCRIBE, ANALYZE AND INTERPRET DATA ABOUT STUDENTS, THEIR LEARNING PROCESSES AND CONTEXTS. RATHER THAN TAKING ISOLATED METHODS OR TECHNIQUES FOR THE STUDY OF THIS PROCESS, LEARNING ANALYTICS IS BEGINNING TO INTEGRATE THE PERSPECTIVES OF OTHER FIELDS TO ACHIEVE A RESEARCH OF LEARNING WITH A TRANSDISCIPLINARY VISION. ONE OF THESE FIELDS IS NEUROSCIENCES, SPECIFICALLY THOSE RELATED TO LEARNING OR EDUCATIONAL NEUROSCIENCES. THE OBJECTIVE OF THIS RESEARCH IS TO EXPLORE THE IMPLICATIONS AND CHALLENGES OF THE USE OF EEG TECHNOLOGIES (TRADITIONALLY USED IN NEUROLOGICAL STUDIES) IN CONJUNCTION WITH LEARNING ANALYTICS FOR THE STUDY OF STUDENTS LEARNING PROCESSES AND THEIR CONTEXT.
THE EXCEPTIONAL SITUATION GENERATED BY THE GLOBAL PANDEMIC COVID-19 HAS FORCED THE MASSIVE USE OF DIGITAL TOOLS AT ALL EDUCATIONAL LEVELS, EVIDENCING BOTH MISMATCHES IN THE TEACHING LOAD FOR TEACHERS AND STUDENTS IN THEIR ADAPTATION, AS WELL AS THE NEED TO OBSERVE IN MORE DETAIL THE LEARNING PROCESS THAT STUDENTS FOLLOW. THIS WORK MAKES A FIRST EXPLORATION OF THE CHANGES IN THE DIGITAL USES OF STUDENTS AS THEY MOVE FROM FACE-TO-FACE TEACHING WITH THE SUPPORT OF THE VIRTUAL CAMPUS TO THE ONLINE ONE, ACCORDING TO THE RECORDS OR TRACES THAT REMAIN WHEN ACCESSING VIRTUAL RESOURCES. THE CASE STUDY IS CONTEXTUALIZED IN THE SUBJECT INDUSTRIAL ORGANIZATION OF THE SECOND SEMESTER OF THE 2020 ACADEMIC YEAR, AND DESCRIBES TYPOLOGIES AND PATTERNS OF DIFFERENT USES BEFORE AND AFTER THE DECLARATION OF THE STATE OF NATIONAL ALARM, WITH A DIFFERENTIATED EFFECT, BEING THE STUDENTS IN QUARTILE Q1 OF THE DISTRIBUTION WHO MORE REACTIVATE THEIR DIGITAL ACTIVITY, THEY SEE AN OPPORTUNITY TO RE-ENGAGE IN ONLINE TEACHING, ELEMENTS THAT SERVE AS A GUIDE FOR A FUTURE EVENTUALITY.
TO DETERMINE THE IMPACT OF COGNITIVE STYLE ON CHANGE OF CONCEPT OF ENGAGEMENT IN THE FLIPPED CLASSROOM, A SEQUENTIAL ANALYSIS FROM THE PERSPECTIVE OF BLOOM'S TAXONOMY WAS CONDUCTED TO ESTABLISH IF SIGNIFICANT DIFFERENCES EXISTED BETWEEN THE LEARNING ACHIEVEMENTS AND ENGAGEMENT OF STUDENTS WITH DIFFERENT COGNITIVE STYLES. THE PARTICIPANTS WERE PRE-SERVICE TEACHERS WHO HAD REGISTERED FOR A SCHOOL-BASED CURRICULUM DEVELOPMENT METHODOLOGY COURSE IN CHINA, WITH A TOTAL OF 53 STUDENTS FROM TWO CLASSES, WHO PERFORMED A TOTAL OF 1,599 BEHAVIORS IN THE DISCUSSION SECTIONS. THE RESULTS SHOW THAT THE CONCEPTS OF "EVALUATE" (31.52%) AND "ANALYZE" (27.77%) WERE THE TWO MOST FREQUENT BEHAVIORS IN THE BLOOM'S TAXONOMY. THE "REMEMBER" AND "EVALUATE" WERE THE SIGNIFICANT STARTING BEHAVIORS FOR ALL OF THE STUDENTS; "REMEMBER" AND "EVALUATE" WERE THE SIGNIFICANT STARTING BEHAVIORS FOR THE INTUITIVE-STYLE STUDENTS, WHILE "UNDERSTAND" WAS A SIGNIFICANT STARTING BEHAVIOR FOR THE ANALYTICAL-STYLE STUDENTS. THIS CONFIRMS THAT THE STUDENTS WITH DIFFERENT COGNITIVE STYLES IMPLEMENTED MULTIPLE STRATEGIES FOR LEARNING. IT IS ALSO FOUND THAT THE FLIPPED LEARNING PROMOTED THE STUDENTS' ACHIEVEMENT. THE CONCLUSIONS SUGGEST THAT IT IS IMPORTANT TO MATCH THE COGNITIVE STYLES OF STUDENTS AS WELL AS THE INSTRUCTIONAL METHODS IN ORDER TO IMPROVE STUDENTS' LEARNING.
STATISTICS IS ONE OF THE MOST CHALLENGING COURSES FOR UNIVERSITY STUDENTS. THE PERSONAL LEARNING ENVIRONMENT (PLE) PEDAGOGICAL DESIGN WAS INTRODUCED TO ASSIST STUDENTS' STATISTICS LEARNING. WITH THE PLE PEDAGOGY, THIS STUDY EXAMINED LEARNERS' DEMOGRAPHIC BACKGROUNDS, MOTIVATIONAL MEASURES (I.E., HELP-SEEKING AND ACADEMIC PROCRASTINATION DUE TO THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES, ICT), AND ILL-STRUCTURED DATA (I.E., FACEBOOK POSTS AND COMMENTS) TO UNDERSTAND WHAT STUDENT DEMOGRAPHIC INFORMATION, HOW THEY FEEL, AND WHAT THEY DO CAN IMPACT THEIR STATISTICS LEARNING PERFORMANCE. SEVENTY-EIGHT PEOPLE JOINED FACEBOOK GROUPS TO FORM STATISTICS LEARNING COMMUNITIES. USING WEAKLY SUPERVISED MACHINE LEARNING (ML), WE CATEGORIZED STUDENTS' FACEBOOK MESSAGES INTO STATISTICS-RELEVANT AND STATISTICS-IRRELEVANT. RESULTS OF THE LEARNING ANALYTICS ON MULTIMODAL SOURCES OF STUDENT DATA SHOWED THAT HELP-SEEKING POSITIVELY PREDICTED STATISTICS ACHIEVEMENT. IN CONTRAST, ACADEMIC PROCRASTINATION WITH ICT NEGATIVELY PREDICTED STATISTICS ACHIEVEMENT, CONTROLLING FOR STUDENTS' DEMOGRAPHICS INFORMATION, INCLUDING AGE, GENDER, PRIOR KNOWLEDGE, AND INTERNET/SOCIAL MEDIA USE. MOREOVER, THE ENSEMBLE ML CLASSIFIED MESSAGES CONSTRUCTED BY TAKING THE SUM OF RELEVANCE CODING (0 OR 1) ACROSS THREE SELECTED ML ALGORITHMS WAS HIGHLY ALIGNED WITH THE HUMAN CODED MESSAGE IN TERMS OF THE DEGREE OF RELEVANCE TO STATISTICS. THE ENSEMBLE ML CLASSIFIED MESSAGES WERE CONCEPTUALIZED AS STUDENTS' COGNITIVE ENGAGEMENT IN STATISTICS LEARNING DUE TO THEIR HIGH CONSISTENCY WITH THE HUMAN-LABELED RELEVANCE CODING AND WERE POSITIVELY ASSOCIATED WITH STATISTICS ACHIEVEMENT WITH A LARGE EFFECT SIZE. THE STUDY CONTRIBUTED TO DEVELOPING AN INTEGRATED LEARNER-CENTERED LEARNING ANALYTICS FRAMEWORK WITH THE PLE PEDAGOGICAL DESIGN ENCOMPASSING LEARNER BACKGROUNDS AND UNSTRUCTURED LEARNER ARTIFACTS.
GLIOBLASTOMA MULTIFORME (GBM) IS THE MOST FREQUENTLY OCCURRING BRAIN MALIGNANCY. DUE TO ITS POOR PROGNOSIS WITH CURRENTLY AVAILABLE TREATMENTS, THERE IS A PRESSING NEED FOR EASILY ACCESSIBLE, NON-INVASIVE TECHNIQUES TO HELP INFORM PRE-TREATMENT PLANNING, PATIENT COUNSELING, AND IMPROVE OUTCOMES. IN THIS STUDY WE DETERMINED THE FEASIBILITY OF RESTING-STATE FUNCTIONAL CONNECTIVITY (RSFC) TO CLASSIFY GBM PATIENTS INTO SHORT-TERM AND LONG-TERM SURVIVAL GROUPS WITH RESPECT TO REPORTED MEDIAN SURVIVAL (14.6 MONTHS). WE USED A SUPPORT VECTOR MACHINE WITH RSFC BETWEEN REGIONS OF INTEREST AS PREDICTIVE FEATURES. WE EMPLOYED A NOVEL HYBRID FEATURE SELECTION METHOD WHEREBY FEATURES WERE FIRST FILTERED USING CORRELATIONS BETWEEN RSFC AND OS, AND THEN USING THE ESTABLISHED METHOD OF RECURSIVE FEATURE ELIMINATION (RFE) TO SELECT THE OPTIMAL FEATURE SUBSET. LEAVE-ONE-SUBJECT-OUT CROSS-VALIDATION EVALUATED THE PERFORMANCE OF MODELS. CLASSIFICATION BETWEEN SHORT- AND LONG-TERM SURVIVAL ACCURACY WAS 71.9%. SENSITIVITY AND SPECIFICITY WERE 77.1 AND 65.5%, RESPECTIVELY. THE AREA UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE WAS 0.752 (95% CI, 0.62-0.88). THESE FINDINGS SUGGEST THAT HIGHLY SPECIFIC FEATURES OF RSFC MAY PREDICT GBM SURVIVAL. TAKEN TOGETHER, THE FINDINGS OF THIS STUDY SUPPORT THAT RESTING-STATE FMRI AND MACHINE LEARNING ANALYTICS COULD ENABLE A RADIOMIC BIOMARKER FOR GBM, AUGMENTING CARE AND PLANNING FOR INDIVIDUAL PATIENTS.
WORKED-EXAMPLES HAVE BEEN ESTABLISHED AS AN EFFECTIVE INSTRUCTIONAL FORMAT IN PROBLEM-SOLVING PRACTICES. HOWEVER, LESS IS KNOWN ABOUT VARIATIONS IN THE USE OF WORKED EXAMPLES ACROSS INDIVIDUALS AT DIFFERENT STAGES IN THEIR LEARNING PROCESS IN STUDENT-CENTRED LEARNING CONTEXTS. THIS STUDY INVESTIGATES DIFFERENT PROFILES OF STUDENTS' LEARNING BEHAVIOURS BASED ON CLUSTERING LEARNING DISPOSITIONS, PRIOR KNOWLEDGE, AND THE CHOICE OF FEEDBACK STRATEGIES IN A NATURALISTIC SETTING. THE STUDY WAS CONDUCTED ON 1,072 STUDENTS OVER AN 8-WEEK LONG INTRODUCTORY MATHEMATICS COURSE IN A BLENDED INSTRUCTIONAL FORMAT. WHILE PRACTISING EXERCISES IN A DIGITAL LEARNING ENVIRONMENT, STUDENTS CAN OPT FOR TUTORED PROBLEM SOLVING, UNTUTORED PROBLEM SOLVING, OR CALL WORKED EXAMPLES. THE RESULTS INDICATED SIX DISTINCT PROFILES OF LEARNERS REGARDING THEIR FEEDBACK PREFERENCES IN DIFFERENT LEARNING PHASES. FINALLY, WE INVESTIGATED ANTECEDENTS AND CONSEQUENCES OF THESE PROFILES AND INVESTIGATED THE ADEQUACY OF USED FEEDBACK STRATEGIES CONCERNING 'HELP-ABUSE'. THIS RESEARCH INDICATES THAT THE USE OF INSTRUCTIONAL SCAFFOLDS AS WORKED-EXAMPLES OR HINTS AND THE EFFICIENCY OF THAT USE DIFFERS FROM STUDENT TO STUDENT, MAKING THE ATTEMPT TO FIND PATTERNS AT AN OVERALL LEVEL A HAZARDOUS ENDEAVOUR.
EXTENSIVE RESEARCH IN LEARNING SCIENCE HAS ESTABLISHED THE IMPORTANCE OF TIME MANAGEMENT IN ONLINE LEARNING. RECENTLY, LEARNING ANALYTICS (LA) HAS SHED FURTHER LIGHTS ON THE TEMPORAL CHARACTERISTICS OF LEARNING BY ALLOWING RESEARCHERS TO CAPTURE AUTHENTIC DIGITAL FOOTPRINTS OF STUDENT LEARNING BEHAVIOURS. NONETHELESS, STUDENTS' TIMING OF ENGAGEMENT AND ITS RELATION TO LEARNING DESIGN (LD) AND ACADEMIC PERFORMANCE HAVE RECEIVED LIMITED ATTENTION. THIS STUDY INVESTIGATES TO WHAT EXTENT STUDENTS' TIMING OF ENGAGEMENT ALIGNED WITH INSTRUCTOR LEARNING DESIGN, AND HOW ENGAGEMENT VARIED ACROSS DIFFERENT LEVELS OF PERFORMANCE. OUR FINDINGS REVEALED A MISMATCH BETWEEN HOW INSTRUCTORS DESIGNED FOR LEARNING AND HOW STUDENTS STUDY. IN MOST WEEKS, STUDENTS SPENT LESS TIME STUDYING THE ASSIGNED MATERIALS ON THE VIRTUAL LEARNING ENVIRONMENT (VLE) COMPARED TO THE NUMBER OF HOURS RECOMMENDED BY INSTRUCTORS. THE TIMING OF ENGAGEMENT ALSO VARIED, FROM IN ADVANCE TO CATCHING UP PATTERNS. HIGH-PERFORMING STUDENTS SPENT MORE TIME STUDYING IN ADVANCE, WHILE LOW-PERFORMING STUDENTS SPENT A HIGHER PROPORTION OF THEIR TIME ON CATCHING-UP ACTIVITIES. BY INCORPORATING THE PEDAGOGICAL CONTEXT INTO LEARNING ANALYTICS, NOT ONLY WE CAN UNDERSTAND WHAT, WHY, AND WHEN STUDENTS ENGAGE, BUT ALSO HOW THEIR BEHAVIOURS ARE INFLUENCED BY THE WAY INSTRUCTORS DESIGN FOR LEARNING.
THE AIM OF STUDENT-FACING DASHBOARDS IS TO SUPPORT LEARNING BY PROVIDING STUDENTS WITH ACTIONABLE INFORMATION AND PROMOTING SELF-REGULATED LEARNING. WE CREATED A NEW DASHBOARD DESIGN ALIGNED WITH SRL THEORY, CALLED MYLA, TO BETTER UNDERSTAND HOW STUDENTS USE A LEARNING ANALYTICS TOOL. WE CONDUCTED SEQUENCE ANALYSIS ON STUDENTS' INTERACTIONS WITH THREE DIFFERENT VISUALIZATIONS IN THE DASHBOARD, IMPLEMENTED IN A LMS, FOR A LARGE NUMBER OF STUDENTS (860) IN TEN COURSES REPRESENTING DIFFERENT DISCIPLINES. TO EVALUATE DIFFERENT STUDENTS' EXPERIENCES WITH THE DASHBOARD, WE COMPUTED CHI-SQUARED TESTS OF INDEPENDENCE ON DASHBOARD USERS (52%) TO FIND FREQUENT PATTERNS THAT DISCRIMINATE STUDENTS BY THEIR DIFFERENCES IN ACADEMIC ACHIEVEMENT AND SELF-REGULATED LEARNING BEHAVIORS. THE RESULTS REVEALED DISCRIMINATING PATTERNS IN DASHBOARD USE AMONG DIFFERENT LEVELS OF ACADEMIC ACHIEVEMENT AND SELF-REGULATED LEARNING, PARTICULARLY FOR LOW ACHIEVING STUDENTS AND HIGH SELF-REGULATED LEARNERS. OUR FINDINGS HIGHLIGHT THE IMPORTANCE OF DIFFERENCES IN STUDENTS' EXPERIENCE WITH A STUDENT-FACING DASHBOARD, AND EMPHASIZE THAT ONE SIZE DOES NOT FIT ALL IN THE DESIGN OF LEARNING ANALYTICS TOOLS.
IN THIS WORK, WE PRESENT AN INTELLIGENT CLASSROOM ORCHESTRATION TECHNOLOGY TO CAPTURE SEMANTIC LEARNING ANALYTICS FROM PAPER-BASED PROGRAMMING EXAMS. WE DESIGN AND STUDY AN INNOVATIVE VISUAL ANALYTICS SYSTEM, EDUANALYSIS, TO SUPPORT PROGRAMMING CONTENT SEMANTICS EXTRACTION AND ANALYSIS. EDUANALYSIS INDEXES EACH PROGRAMMING EXAM QUESTION TO A SET OF CONCEPTS BASED ON THE ONTOLOGY. IT UTILIZES AUTOMATIC INDEXING ALGORITHM AND INTERACTIVE VISUALIZATION INTERFACES TO ESTABLISH THE CONCEPTS AND QUESTIONS ASSOCIATIONS. WE COLLECT THE INDEXING GROUND TRUTHS OF THE TARGETED SET FROM TEACHERS AND EXPERTS FROM THE CROWD. WE FOUND THAT THE SYSTEM SIGNIFICANTLY EXTRACTED MORE AND DIVERSE CONCEPTS FROM EXAMS AND ACHIEVED HIGH COHERENCE WITHIN EXAM. WE ALSO DISCOVERED THAT INDEXING EFFECTIVENESS WAS ESPECIALLY PREVALENT FOR COMPLEX CONTENT. OVERALL, THE SEMANTIC ENRICHING APPROACH FOR PROGRAMMING PROBLEMS REVEALS SYSTEMATIC LEARNING ANALYTICS FROM THE PAPER EXAMS. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE CONCEPT OF AN EPORTFOLIO WAS INTRODUCED IN 1999 AS AN ELECTRONIC LEARNING RECORD AND FURTHER DEVELOPED TO OVERCOME THE LIMITATION OF LEARNING MANAGEMENT SYSTEMS LACKING PERSISTENT STORAGE FOR LEARNING ARTIFACTS. NOWADAYS, EDUCATIONAL PLATFORMS HAVE EVOLVED TOWARDS PERSONAL LEARNING ENVIRONMENTS AND SOCIAL MEDIA PLATFORMS, ENABLING THE CREATION, EXPLOITATION AND ARCHIVING OF ACTIVITY TRACES, LEARNING OUTCOMES, AND LEARNING ANALYTICS THANKS TO BUILT-IN EXPORT AND SHARING FEATURES. THIS PAPER PRESENTS THE DESIGN AND IMPLEMENTATION OF ARCHIVING AND SHARING SERVICES IN GRAASP, A GENERAL-PURPOSE LEARNING PLATFORM. THESE FEATURES ENABLE IMPLEMENTING GRAASP AS AN EPORTFOLIO PLATFORM, ALLOWING STUDENTS TO ARCHIVE LEARNING ARTIFACTS AS EVIDENCE OF COMPETENCES AND ACTIVITY TRACES FOR ANALYTICS-DRIVEN SELF-ASSESSMENT. ADDITIONALLY, WE DISCUSS USER REQUIREMENTS FOR DESIGNING SUCH GENERAL EPORTFOLIO SERVICES ELICITED FROM THE ANALYSIS OF FUNCTIONALITIES OF THE MAHARA POPULAR EPORTFOLIO PLATFORM AND FROM A PARTICIPATORY DESIGN SESSION WITH EXPERT USERS OF GRAASP.
THE TEMPORAL AND SEQUENTIAL NATURE OF LEARNING IS RECEIVING INCREASING FOCUS IN LEARNING ANALYTICS CIRCLES. THE DESIRE TO EMBED STUDIES IN RECOGNISED THEORIES OF SELF-REGULATED LEARNING (SRL) HAS LED RESEARCHERS TO CONCEPTUALISE LEARNING AS A PROCESS THAT UNFOLDS AND CHANGES OVER TIME. TO THAT END, A BODY OF RESEARCH KNOWLEDGE IS GROWING WHICH STATES THAT TRADITIONAL FREQUENCY-BASED CORRELATIONAL STUDIES ARE LIMITED IN NARRATIVE IMPACT. TO FURTHER EXPLORE THIS, WE ANALYSED TRACE DATA COLLECTED FROM ONLINE ACTIVITIES OF A SAMPLE OF 239 COMPUTER ENGINEERING UNDERGRADUATE STUDENTS ENROLLED ON A COURSE THAT FOLLOWED A FLIPPED CLASS-ROOM PEDAGOGY. WE EMPLOYED SRL CATEGORISATION OF MICRO-LEVEL PROCESSES BASED ON A RECOGNISED MODEL OF LEARNING, AND THEN ANALYSED THE DATA USING: 1) SIMPLE FREQUENCY MEASURES; 2) EPISTEMIC NETWORK ANALYSIS; 3) TEMPORAL PROCESS MINING; AND 4) STOCHASTIC PROCESS MINING. WE FOUND THAT A COMBINATION OF ANALYSES PROVIDED US WITH A RICHER INSIGHT INTO SRL BEHAVIOURS THAN ANY ONE SINGLE METHOD. WE FOUND THAT BETTER PERFORMING LEARNERS EMPLOYED MORE OPTIMAL BEHAVIOURS IN THEIR NAVIGATION THROUGH THE COURSE'S LEARNING MANAGEMENT SYSTEM.
LEARNING ANALYTICS IS A YOUNG FIELD OF EDUCATIONAL RESEARCH AND AIMS TO ANALYZE DATA FROM LEARNING MANAGEMENT SYSTEMS IN ORDER TO IMPROVE DIGITAL LEARNING SCENARIOS OR TO GENERATE FEEDBACK TO LEARNERS AND INSTRUCTORS. TO DATE, STUDIES IN THIS FIELD OF RESEARCH ARE LIMITED TO HIGHER EDUCATION AND THERE ARE ONLY FEW EXAMPLES OF THEORY-BASED CLASSIFICATIONS OF LEARNER-COMPUTER INTERACTIONS. THIS PAPER DESCRIBES THE DEVELOPMENT AND APPLICATION OF A THEORY-BASED CLASSIFICATION OF LOG-FILES AND COURSE STATISTICS FOR A COMPUTER-ASSISTED FORMATIVE ASSESSMENT OF BASIC GERMAN LANGUAGE KNOWLEDGE (FIVE SECONDARY CLASSROOMS, LOWER SECONDARY LEVEL, 129 STUDENTS). THE ANALYSIS OF INTERACTION FREQUENCIES YIELDS SEVERAL POSSIBILITIES FOR IMPROVING THE COURSE DESIGN. ONLY SOME INTERACTION CATEGORIES EXPLAIN LEARNING PROGRESSIONS WITHIN THE FORMATIVE ASSESSMENT COURSE. FINALLY, IMPLICATIONS FOR DEVELOPING LEARNING ANALYTICS METHODS FOR THE ANALYSIS AND IMPROVEMENT OF COMPUTER-ASSISTED FORMATIVE ASSESSMENT ARE DISCUSSED.
THIS PAPER REPORTS ON THE FINDINGS OF A STUDY THAT PROPOSED A NOVEL LEARNING ANALYTICS METHODOLOGY THAT COMBINES THREE COMPLIMENTARY TECHNIQUES - AGGLOMERATIVE HIERARCHICAL CLUSTERING, EPISTEMIC NETWORK ANALYSIS, AND PROCESS MINING. THE METHODOLOGY ALLOWS FOR IDENTIFICATION AND INTERPRETATION OF SELF-REGULATED LEARNING IN TERMS OF THE USE OF LEARNING STRATEGIES. THE MAIN ADVANTAGE OF THE NEW TECHNIQUE OVER THE EXISTING ONES IS THAT IT COMBINES THE TIME MANAGEMENT AND LEARNING TACTIC DIMENSIONS OF LEARNING STRATEGIES, WHICH ARE TYPICALLY STUDIED IN ISOLATION. THE NEW TECHNIQUE ALLOWS FOR NOVEL INSIGHTS INTO LEARNING STRATEGIES BY STUDYING THE FREQUENCY OF, STRENGTH OF CONNECTIONS BETWEEN, AND ORDERING AND TIME OF EXECUTION OF TIME MANAGEMENT AND LEARNING TACTICS. THE TECHNIQUE WAS VALIDATED IN A STUDY THAT WAS CONDUCTED ON THE TRACE DATA OF FIRST-YEAR UNDERGRADUATE STUDENTS WHO WERE ENROLLED INTO TWO CONSECUTIVE OFFERINGS (N-2017 = 250 AND N-2018 = 232) OF A COURSE AT AN AUSTRALIAN UNIVERSITY. THE APPLICATION OF THE PROPOSED TECHNIQUE IDENTIFIED FOUR STRATEGY GROUPS DERIVED FROM THREE DISTINCT TIME MANAGEMENT TACTICS AND FIVE LEARNING TACTICS. THE TACTICS AND STRATEGIES IDENTIFIED WITH THE TECHNIQUE WERE CORRELATED WITH ACADEMIC PERFORMANCE AND WERE INTERPRETED ACCORDING TO THE ESTABLISHED THEORIES AND PRACTICES OF SELF-REGULATED LEARNING.
ALTHOUGH COLLABORATION IS CONSIDERED A KEY 21ST-CENTURY SKILL, OFTENTIMES IT IS ONLY ASSESSED THROUGH THE OUTCOME MEASURES OF INDIVIDUAL LEARNERS. IN THIS PAPER, WE DRAW UPON COLLABORATIVE COGNITIVE LOAD THEORY (CCLT) TO EXPLAIN THE PROCESS OF COLLABORATION IN LEARNING FROM THE POINT OF VIEW OF THE COLLECTIVE, RATHER THAN AN INDIVIDUAL LEARNER. USING CCLT WE SUGGEST A NEW METHOD OF MEASURING THE PROCESS OF COLLABORATION REGARDLESS OF ITS OUTCOME MEASURES. OUR APPROACH - COLLABORATIVE LEARNING AS A PROCESS (CLAP) - USES SOCIAL NETWORK ANALYSIS TO EVALUATE THE BALANCE BETWEEN INTERACTIVITY GAINS AND COORDINATION COSTS OF LEARNER COMMUNITIES. HERE, WE DEMONSTRATE THE APPROACH USING REAL-WORLD DATA DERIVED FROM THE DIGITAL TRACKS OF TWO ONLINE DISCUSSION COMMUNITIES. WE ARGUE THAT OUR CONCEPTUAL APPROACH CAN ENABLE INSTRUCTORS AND LEARNERS TO UNLOCK THE BLACK BOX OF COLLABORATION IN LEARNING. IT HAS THE POTENTIAL TO SUPPORT THE DEVELOPMENT OF LEARNER SKILLS THAT GO BEYOND COGNITION. WE CONCLUDE THE PAPER WITH THE RESULTS OF OUR INVESTIGATION OF THE VALUE OF THE APPROACH TO THE ONLINE MODULE INSTRUCTOR.
DESPITE THE EXPLOSION OF INTEREST IN BIG DATA IN HIGHER EDUCATION AND THE ENSUING RUSH FOR CATCH-ALL PREDICTIVE ALGORITHMS, THERE HAS BEEN RELATIVELY LITTLE FOCUS ON THE PEDAGOGICAL AND PASTORAL CONTEXTS OF LEARNING. THE PROVISION OF PERSONALIZED FEEDBACK AND SUPPORT TO STUDENTS IS OFTEN GENERALIZED AND DECONTEXTUALIZED, AND EXAMPLES OF SYSTEMS THAT ENABLE CONTEXTUALIZED SUPPORT ARE NOTABLY ABSENT FROM THE LEARNING ANALYTICS LANDSCAPE. IN THIS CHAPTER WE DISCUSS THE DESIGN AND DEPLOYMENT OF THE STUDENT RELATIONSHIP ENGAGEMENT SYSTEM (SRES), A LEARNING ANALYTICS SYSTEM THAT IS GROUNDED PRIMARILY WITHIN THE UNIQUE CONTEXTS OF INDIVIDUAL COURSES. THE SRES, CURRENTLY IN USE BY TEACHERS FROM 19 DEPARTMENTS, TAKES A HOLISTIC AND MORE HUMAN-CENTRIC VIEW OF DATA-ONE THAT PUTS THE RELATIONSHIP BETWEEN TEACHER AND STUDENT AT THE CENTER. OUR APPROACH MEANS THAT TEACHERS' PEDAGOGICAL EXPERTISE IN RECOGNIZING MEANINGFUL DATA, IDENTIFYING SUBGROUPS OF STUDENTS FOR A RANGE OF SUPPORT ACTIONS, AND DESIGNING AND DEPLOYING THESE ACTIONS, IS FACILITATED BY A CUSTOMIZABLE TECHNOLOGY PLATFORM. WE DESCRIBE A CASE STUDY OF THE APPLICATION OF THIS HUMAN-CENTRIC APPROACH TO LEARNING ANALYTICS, INCLUDING ITS IMPACTS ON IMPROVING STUDENT ENGAGEMENT AND OUTCOMES, AND DEBATE THE CULTURAL, PEDAGOGICAL, AND TECHNICAL ASPECTS OF LEARNING ANALYTICS IMPLEMENTATION.
THIS PAPER CONTAINS A FRAMEWORK FOR THE DESIGN AND DEVELOPMENT OF ADAPTIVE VIRTUAL LEARNING ENVIRONMENTS (VLES) TO ASSIST EDUCATORS OF ALL DISCIPLINES WITH CONFIGURING AND CREATING ADAPTIVE VLES TAILORED TO THEIR NEEDS. THE PROPOSED WORK IS PERFORMED IN THREE STAGES: IN THE FIRST STAGE, DEVELOPMENT OF AN ADAPTIVE VLE THAT COLLECTS LEARNING ANALYTICS AND ENABLES THE EDUCATOR TO PARAMETRIZE (TURN ON/OFF) THEIR COLLECTION. THE OUTPUT OF THESE ANALYTICS GETS STORED FOR FURTHER PROCESSING. IN THE SECOND STAGE, DATA ANALYSIS AND PROCESSING HAS BEEN PERFORMED FOR THE COLLECTED INFORMATION. IN THE THIRD STAGE, THE RESULTS HAVE BEEN USED AS AN INPUT TO ADAPTIVE VLE TO ENABLE INFORMED PERSONALISATION OF THE STUDENT LEARNING PATH AND OTHER ADAPTATIONS. IN THIS PAPER, WE HAVE PROPOSED THE COMBINED USE OF TWO DIFFERENT ENVIRONMENTS FOR THE DIFFERENT STAGES TO ACHIEVE THE MOST FROM THEIR SPECIALISATION. FOR THE FIRST AND THE THIRD STAGE, THE MPS JETBRAINS ENVIRONMENT FOR THE DEVELOPMENT OF A DOMAIN-SPECIFIC LANGUAGE (DSL) FOR ADAPTIVE VLES WAS UTILISED. THIS DEVELOPMENT ENVIRONMENT ASSISTS THE CREATION OF A NEW DSL THAT WILL ENABLE EDUCATORS TO FOCUS ON THE DOMAIN ASPECTS AND CONFIGURE THEIR ADAPTIVE VLE IMPLEMENTATION TO THEIR NEEDS. FOR THE SECOND STAGE OF DATA ANALYSIS, THE WEKA LIBRARY WAS USED TO PROCESS THE DATA, APPLY A RANGE OF CLASSIFICATION ALGORITHMS AND PRODUCE/STORE RESULTS THAT CAN THEN BE USED AS AN INPUT TO THE ADAPTIVE VLE DSL. OVERALL, THE PROPOSED FRAMEWORK-SYSTEM IS ANTICIPATED TO ISOLATE THE DOMAIN PROBLEM FROM THE CORRESPONDING IMPLEMENTATION DETAILS OF WEB DEVELOPMENT AND DATA ANALYSIS AND GIVE THE ADAPTIVE VLE DEVELOPER A SEAMLESS ENVIRONMENT TO EXPERIMENT WITH A VERY QUICK TURN-AROUND TIME WITH IDEAS IN THEIR DOMAIN. MORE AUTOMATION AND INTEGRATION BETWEEN THE VLE AND THE DATA SCIENCE ALGORITHMS UTILISED FOR LEARNING ANALYTICS DATA ARE PART OF OUR FUTURE PLANS TOWARDS THE GREATER VISION OF MORE AUTONOMIC AND PERSONALISED VLES.
IN THIS PAPER, WE PROPOSE THE UTILIZATION OF THE "LEARNING ANALYTICS" PARADIGM IN A SMART CLASSROOM, A CLASSROOM THAT INTEGRATES ARTIFICIAL INTELLIGENCE TECHNOLOGY ON THE EDUCATIONAL PROCESS. LEARNING ANALYTICS CAN EXTRACT KNOWLEDGE FROM THE SMART CLASSROOM PLATFORM, TO BETTER UNDERSTAND STUDENTS AND HIS/HER LEARNING PROCESSES. IN THIS WAY, A SMART CLASSROOM CAN UNDERSTAND AND OPTIMIZE THE LEARNING PROCESS AND THE TEACHING ENVIRONMENTS PROPOSED. THE SMART CLASSROOM CAN ADAPT ITS COMPONENTS TO IMPROVE STUDENTS' PERFORMANCE, AMONG OTHER ASPECTS. PARTICULARLY, THIS PAPER PROPOSES A FRAMEWORK ABOUT HOW THE LEARNING ANALYTICS PARADIGM CAN BE USED IN A SMART CLASSROOM, IN ORDER TO PROVIDE KNOWLEDGE ABOUT THE ACTIVITIES TAKING PLACE WITHIN IT. THE FRAMEWORK IS DEFINED LIKE A CLOSED CYCLE OF LEARNING ANALYTICS TASKS, WHICH GENERATE METRICS USED LIKE FEEDBACK TO OPTIMIZE THE PEDAGOGICAL MODEL PROPOSED BY THE SMART CLASSROOM. THE METRICS EVALUATE THE LEARNING PROCESS AND PEDAGOGICAL PRACTICE PROVIDED BY THE SMART CLASSROOM. SO, OUR MAIN CONTRIBUTION IS ABOUT HOW THE LEARNING ANALYTICS PARADIGM CAN BE USED IN A SMART CLASSROOM IN ORDER TO IMPROVE THE STUDENTS' PERFORMANCE.
THIS PAPER CONTRIBUTES TO OUR UNDERSTANDING OF HOW TO DESIGN LEARNING ANALYTICS TO CAPTURE AND ANALYSE COLLABORATIVE PROBLEM-SOLVING (CPS) IN PRACTICE-BASED LEARNING ACTIVITIES. MOST RESEARCH IN LEARNING ANALYTICS FOCUSES ON STUDENT INTERACTION IN DIGITAL LEARNING ENVIRONMENTS, YET STILL MOST LEARNING AND TEACHING IN SCHOOLS OCCURS IN PHYSICAL ENVIRONMENTS. INVESTIGATION OF STUDENT INTERACTION IN PHYSICAL ENVIRONMENTS CAN BE USED TO GENERATE OBSERVABLE DIFFERENCES AMONG STUDENTS, WHICH CAN THEN BE USED IN THE DESIGN AND IMPLEMENTATION OF LEARNING ANALYTICS. HERE, WE PRESENT SEVERAL ORIGINAL METHODS FOR IDENTIFYING SUCH DIFFERENCES IN GROUPS CPS BEHAVIOURS. OUR DATA SET IS BASED ON HUMAN OBSERVATION, HAND POSITION (FIDUCIAL MARKER) AND HEADS DIRECTION (FACE RECOGNITION) DATA FROM EIGHTEEN STUDENTS WORKING IN SIX GROUPS OF THREE. THE RESULTS SHOW THAT THE HIGH COMPETENT CPS GROUPS SPEND AN EQUAL DISTRIBUTION OF TIME ON THEIR PROBLEM-SOLVING AND COLLABORATION STAGES. WHEREAS, THE LOW COMPETENT CPS GROUPS SPEND MOST OF THEIR TIME IN IDENTIFYING KNOWLEDGE AND SKILL DEFICIENCIES ONLY. MOREOVER, AS MACHINE OBSERVABLE DATA SHOWS, HIGH COMPETENT CPS GROUPS PRESENT SYMMETRICAL CONTRIBUTIONS TO THE PHYSICAL TASKS AND PRESENT HIGH SYNCHRONY AND INDIVIDUAL ACCOUNTABILITY VALUES. THE FINDINGS HAVE SIGNIFICANT IMPLICATIONS ON THE DESIGN AND IMPLEMENTATION OF FUTURE LEARNING ANALYTICS SYSTEMS.
EDUCATIONAL INSTITUTIONS ARE EVOLVING AWAY FROM THE ONE-APPLICATION-FITS- ALL LEARNING MANAGEMENT SYSTEM TO A LOOSELY CONNECTED DIGITAL LEARNING ECOSYSTEM COMPRISING DIVERSE SERVICES THAT INCREASINGLY LEVERAGE DATA ANALYTICS TO DRIVE PEDAGOGICAL INNOVATION. YET AN ECOSYSTEM RICH IN SERVICES BUT LACKING A COMMON APPROACH TO MEASURING LEARNING ACTIVITY WILL FIND DATA COLLECTION, AGGREGATION AND ANALYSIS TIME-CONSUMING AND COSTLY. THE IMS CALIPER ANALYTICS (TM) SPECIFICATION ADDRESSES THE NEED FOR DATA AND SEMANTIC INTEROPERABILITY BY PROVIDING AN EXTENSIBLE INFORMATION MODEL, CONTROLLED VOCABULARIES AND AN API FOR INSTRUMENTING LEARNING APPLICATIONS AND SYSTEMS THAT LOG LEARNING EVENTS. HOWEVER, MANY LEARNING ACTIVITIES HAVE YET TO BE MODELED BY THE CALIPER WORKING GROUP. ENGAGING THE SOLAR COMMUNITY DIRECTLY IN THIS EFFORT WILL HELP ENSURE THAT THE NEEDS OF RESEARCHERS AND OTHER CONSUMERS OF LEARNING ANALYTICS DATA WILL INFORM FUTURE VERSIONS OF THE SPECIFICATION. THE LAK16 CALIPER WORKSHOP IS BEING OFFERED WITH THIS GOAL IN MIND. THE HALF-DAY SESSION, FACILITATED BY MEMBERS OF TEAM CALIPER, WILL PROVIDE LAK16 PARTICIPANTS WITH AN OPPORTUNITY TO EXTEND THE CALIPER SPECIFICATION BY MODELING NEW LEARNING ACTIVITY PROFILES. NEW PROFILES, NEW CONNECTIONS AND NEW FRIENDSHIPS ARE EXPECTED OUTCOMES.
THIS EXPLORATORY STUDY FOCUSES ON THE DESIGN AND EVALUATION OF TEACHING ANALYTICS THAT RELATE SOCIAL LEARNING STRUCTURE WITH PERFORMANCE MEASURES IN A MASSIVE OPEN ONLINE COURSE (MOOC) PROTOTYPE ENVIRONMENT. USING REFLEXIVE ANALYSIS OF ONLINE LEARNING TRACE DATA AND QUALITATIVE PERFORMANCE MEASURES WE PRESENT AN EXPLORATORY EMPIRICAL STUDY THAT: (A) RIGOROUSLY EVALUATES A NOVEL, MULTI-DIMENSIONAL PERFORMANCE CONSTRUCT; (B) DESCRIBES DIFFERENCES IN SMALL GROUP DYNAMICS AND STRUCTURE; AND (C) DRAWS A CONNECTION BETWEEN LEARNING PERFORMANCE AND GROUP STRUCTURE. PERFORMANCE IS OPERATIONALIZED USING A COMBINATION OF KNOWLEDGE CONSTRUCTION MEASUREMENT FROM DISCUSSION BOARDS, RIGOROUS ANALYSIS OF STUDENT WORK PRODUCTS AND SEVERAL INDICATORS OF SMALL GROUP IDENTITY IN THE COURSE EXAMINED. INTERVIEW AND OBSERVATIONAL DATA ARE USED TO DEVELOP AN APPROACH FOR DERIVING AND VALIDATING A MODEL OF THE SOCIAL STRUCTURE OF STUDENTS IN THE COURSE USING TRACES OF INTERACTION DATA. THE CONNECTION BETWEEN PERFORMANCE AND STRUCTURE IS DEVELOPED AT THE SMALL GROUP UNIT OF ANALYSIS. IMPLICATIONS FOR MOOC DESIGN, SCALING MOOC ANALYTICS AND A VISION FOR DEVELOPING SOCIAL SENSORS IN MOOC ENVIRONMENTS ARE PRESENTED IN THE DISCUSSION AND CONCLUSION.
A UBIQUITOUS LEARNING LOG (ULL) IS DEFINED AS A DIGITAL RECORD OF WHAT A LEARNER HAS LEARNED IN DAILY LIFE USING UBIQUITOUS COMPUTING TECHNOLOGIES. IT ALLOWS LEARNERS TO LOG THEIR LEARNING EXPERIENCES WITH PHOTOS, AUDIOS, VIDEOS, LOCATIONS, RFID TAG AND SENSOR DATA, AND TO SHARE AND REUSE ULL WITH OTHERS. THE NUMBER OF ULLS WILL KEEP INCREASING AS THE LEARNERS KEEP LEARNING. THE SHEER VOLUME OF ULLS WILL BE ACCUMULATED IN THE UBIQUITOUS LEARNING SYSTEM CALLED SCROLL. IT CREATES A NECESSITY TO ANALYZE THE UBIQUITOUS LEARNING LOGS TO PROVIDE LEARNERS WITH APPROPRIATE LEARNING LOGS IN ACCORDANCE WITH THEIR LEARNING ABILITIES, CONTEXT, TIME AND LOCATION. HOWEVER, RESEARCHERS ON ANALYSIS AND VISUALIZATION ON UBIQUITOUS LEARNING IS VERY FEW, AND THERE ARE NOT YET PREVIOUS WORKS THAT VISUALIZE RELATIONSHIPS AMONG LEARNING LOGS ON SPATIAL AND TEMPORAL DIMENSIONS. THEREFORE, THIS PAPER INTRODUCES THE OVERVIEW OF SCROLL, AND THEN DESCRIBES AN INNOVATIVE VISUALIZATION SYSTEM WHICH INTEGRATES NETWORK VISUALIZATION TECHNOLOGIES AND TIME-MAP IN ORDER TO VISUALIZE THE UBIQUITOUS LEARNING LOGS ACCUMULATED IN THE SCROLL.
MULTIMODAL ANALYSIS HAS LONG BEEN AN INTEGRAL PART OF STUDYING LEARNING. HISTORICALLY MULTIMODAL ANALYSES OF LEARNING HAVE BEEN EXTREMELY LABORIOUS AND TIME INTENSIVE. HOWEVER, RESEARCHERS HAVE RECENTLY BEEN EXPLORING WAYS TO USE MULTIMODAL COMPUTATIONAL ANALYSIS IN THE SERVICE OF STUDYING HOW PEOPLE LEARN IN COMPLEX LEARNING ENVIRONMENTS. IN AN EFFORT TO ADVANCE THIS RESEARCH AGENDA, WE PRESENT A COMPARATIVE ANALYSIS OF FOUR DIFFERENT DATA SEGMENTATION TECHNIQUES. IN PARTICULAR, WE PROPOSE AFFECT-AND POSE-BASED DATA SEGMENTATION, AS ALTERNATIVES TO HUMAN-BASED SEGMENTATION, AND FIXED-WINDOW SEGMENTATION. IN A STUDY OF TEN DYADS WORKING ON AN OPEN-ENDED ENGINEERING DESIGN TASK, WE FIND THAT AFFECT-AND POSE-BASED SEGMENTATION ARE MORE EFFECTIVE, THAN TRADITIONAL APPROACHES, FOR DRAWING CORRELATIONS BETWEEN LEARNING-RELEVANT CONSTRUCTS, AND MULTIMODAL BEHAVIORS. WE ALSO FIND THAT POSE-BASED SEGMENTATION OUTPERFORMS THE TWO MORE TRADITIONAL SEGMENTATION STRATEGIES FOR PREDICTING STUDENT SUCCESS ON THE HANDS-ON TASK. IN THIS PAPER WE DISCUSS THE ALGORITHMS USED, OUR RESULTS, AND THE IMPLICATIONS THAT THIS WORK MAY HAVE IN NON-EDUCATION-RELATED CONTEXTS.
THE USE OF TRANSDERMAL ALCOHOL MONITORS HAS BURGEONED IN RECENT YEARS, NOW ENCOMPASSING HUNDREDS OF THOUSANDS OF INDIVIDUALS GLOBALLY. A NEW GENERATION OF SENSORS PROMISES TO EXPAND THE RANGE OF APPLICATIONS FOR TRANSDERMAL TECHNOLOGY EXPONENTIALLY, AND ADVANCES IN MACHINE-LEARNING MODELING APPROACHES OFFER NEW METHODS FOR TRANSLATING THE DATA PRODUCED BY TRANSDERMAL DEVICES. THIS ARTICLE PROVIDES (1) A REVIEW OF TRANSDERMAL SENSOR RESEARCH CONDUCTED TO DATE, INCLUDING AN ANALYSIS OF METHODOLOGICAL FEATURES OF PAST STUDIES POTENTIALLY KEY IN DRIVING REPORTED SENSOR PERFORMANCE; (2) UPDATES ON METHODOLOGICAL DEVELOPMENTS LIKELY TO BE TRANSFORMATIVE FOR THE FIELD OF TRANSDERMAL SENSING, INCLUDING THE DEVELOPMENT OF NEW-GENERATION SENSORS FEATURING SMARTPHONE INTEGRATION AND RAPID SAMPLING CAPABILITIES AS WELL AS DEVELOPMENTS IN MACHINE-LEARNING ANALYTICS SUITED TO DATA PRODUCED BY THESE NOVEL SENSORS AND; (3) AN ANALYSIS OF THE EXPANDED RANGE OF APPLICATIONS FOR THIS NEW GENERATION OF SENSOR, TOGETHER WITH CORRESPONDING REQUIREMENTS FOR SENSOR ACCURACY AND TEMPORAL SPECIFICITY. WE ALSO NOTE QUESTIONS AS YET UNANSWERED AND KEY DIRECTIONS FOR FUTURE RESEARCH.
LEARNING ANALYTICS SYSTEMS HAVE THE POTENTIAL TO BRING ENORMOUS VALUE TO ONLINE EDUCATION. UNFORTUNATELY, MANY INSTRUCTORS AND PLATFORMS DO NOT ADEQUATELY LEVERAGE LEARNING ANALYTICS IN THEIR COURSES TODAY. IN THIS PAPER, WE REPORT ON THE VALUE OF THESE SYSTEMS FROM THE PERSPECTIVE OF COURSE INSTRUCTORS. WE STUDY THESE IDEAS THROUGH OARS, A MODULAR AND REAL-TIME LEARNING ANALYTICS SYSTEM THAT WE DEPLOYED ACROSS MORE THAN TEN ONLINE COURSES WITH TENS OF THOUSANDS OF LEARNERS. WE LEVERAGE THIS SYSTEM AS A STARTING POINT FOR SEMI-STRUCTURED INTERVIEWS WITH A DIVERSE SET OF INSTRUCTORS. OUR STUDY SUGGESTS NEW DESIGN GOALS FOR LEARNING ANALYTICS SYSTEMS, THE IMPORTANCE OF REAL-TIME ANALYTICS TO MANY INSTRUCTORS, AND THE VALUE OF FLEXIBILITY IN DATA SELECTION AND AGGREGATION FOR AN INSTRUCTOR WHEN WORKING WITH AN ANALYTICS SYSTEM.
UNIVERSITIES INCREASINGLY IMPLEMENT ONLINE DELIVERY TO STRENGTHEN STUDENTS' ACCESS AND FLEXIBILITY. HOWEVER, THEY OFTEN DO SO WITH LIMITED UNDERSTANDING OF THE IMPACT OF ONLINE PEDAGOGY ON STUDENT ENGAGEMENT. TO EXPLORE THESE ISSUES, A RESEARCH PROJECT WAS CONDUCTED INVESTIGATING THE USE OF COURSE-SPECIFIC LEARNING ANALYTICS TO 'NUDGE' STUDENTS INTO ENGAGING MORE ACTIVELY IN THEIR COURSES. DRAWING ON PERSPECTIVES EMANATING FROM COMMUNICATION AND CRITICAL THEORIES, THE RESEARCH INVOLVED A STAGED INTERVENTION STRATEGY CONDUCTED ACROSS THREE COURSES (N=892) FOCUSSING ON A RANGE OF TIMELY, STRATEGIC COMMUNICATION INTERVENTIONS. RESEARCH FINDINGS REVEALED BENEFITS FOR STUDENTS WHO FELT SUPPORTED BY EXPLICIT EXPECTATION MANAGEMENT AND THE STRATEGIC USE OF EARLY NUDGING TO ENHANCE THEIR PRIORITISATION OF KEY COURSE-SPECIFIC RESOURCES. ACADEMICS BENEFITED BY MAKING USE OF NUDGING TEMPLATES/PRINCIPLES TO INCREASE STUDENT ENGAGEMENT IN THEIR COURSES. THE COURSE-SPECIFIC CONTEXT MEANT THAT ACADEMICS AND STUDENTS EXPLICITLY SHARED WAYS OF WORKING IN THE ONE PLACE WHERE LEARNERS ULTIMATELY SUCCEED - THE COURSE.
THIS CHAPTER EXPOSES MUSIX, AN ANALYTICS TOOL ABOUT TO BE DEVELOPED, THAT WILL ENRICH MUSIC LEARNING AND ENHANCE THE MUSIC TEACHING APPROACH. MUSIX WILL COLLECT DATA FROM MUSIC THEORY LESSONS, THE PLAYING OF INSTRUMENTAL PIECES, SIGHT-SINGING, AND VOCAL TRAINING, AND SUBSEQUENTLY TEACH AND HELP EACH STUDENT THROUGH COMPUTERIZED ANALYSIS TO BETTER THEMSELVES. THIS SOFTWARE WILL OFFER PRECISE INSTRUCTIONS, EXERCISES, GAMES, AND QUIZZES TO FILL IN GAPS AND BUILD A STRONG UNDERSTANDING USING SELF-REGULATION AND CO-REGULATION TECHNIQUES. COMPUTER SOFTWARE, AUDIO RECORDING, AND MIDI CONNECTION BETWEEN THE INSTRUMENT AND THE COMPUTER ARE DIFFERENT MEANS THAT WILL BE USED TO TRACK THE RESULTS THAT WILL BE ANALYZED AND THEN DISPLAYED IN A COMPELLING DASHBOARD.
COMPUTER-DRIVEN ANALYTICS ARE BECOMING INCREASINGLY ESSENTIAL TO VARIOUS DISCIPLINES. WRITTEN LANGUAGE IN PARTICULAR HAS RECEIVED A GREAT DEAL OF ATTENTION, AND MANY TOOLS NOW EXIST TO GATHER INFORMATION ABOUT COMPOSITIONS. THIS RESEARCH HAS RESULTED IN INTERNET SEARCH PROGRAMS, TRANSLATORS, AND COMPUTERIZED NARRATORS. HOWEVER, ANALYTICS HAVE YET TO BE RIGOROUSLY APPLIED TO THE FIELD OF ACADEMIC WRITING. WITHIN THIS SPECIALIZED AREA, THERE ARE MANY PROSPECTS FOR HOW ANALYTICS MAY BE APPLIED. THESE DETAILS, HOWEVER, FIRST REQUIRE DEFINITIONS OF ANALYTICS AND WRITING ANALYTICS IN PARTICULAR, CONSIDERATION ON HOW THEY MAY BE USED, AND THE TOOLS AVAILABLE FOR THEIR COLLECTION AND IMPLEMENTATION.
THE PRACTICAL POWER OF DATA VISUALIZATION IS CURRENTLY ATTRACTING MUCH ATTENTION IN THE E-LEARNING DOMAIN. A GROWING NUMBER OF STUDIES HAVE BEEN CONDUCTED IN RECENT YEARS TO HELP INSTRUCTORS BETTER ANALYZE LEARNER BEHAVIOR AND REFLECT ON THEIR TEACHING. HOWEVER, CURRENT E-LEARNING DASHBOARDS AND VISUALIZATION SYSTEMS USUALLY REQUIRE A LOT OF TIME AND EFFORT INTO THE EXPLORATION PROCESS. MOREOVER, THE LACK OF COMMUNICATION POWER OF EXISTING SYSTEMS CONSTRAINS USERS FROM ORGANIZING THE NARRATIVE OF INFORMATION PIECES INTO A COMPELLING DATA STORY. IN THIS PAPER, WE HAVE PROPOSED A NARRATIVE VISUALIZATION APPROACH WITH AN INTERACTIVE SLIDESHOW THAT HELPS INSTRUCTORS AND EDUCATION EXPERTS EXPLORE POTENTIAL LEARNING PATTERNS AND CONVEY DATA STORIES. THIS APPROACH CONTAINS THREE KEY COMPONENTS: GUIDED-TOUR CONCEPT, DRILL-DOWN PATH, AND DIG-IN EXPLORATION DIMENSION. THE USE CASES FURTHER DEMONSTRATE THE POTENTIAL OF EMPLOYING THIS VISUAL NARRATIVE APPROACH IN THE E-LEARNING CONTEXT.
THE USE OF ONLINE KNOWLEDGE COMMUNITIES (OKCS) AS INFORMAL LEARNING ENVIRONMENTS IN CONNECTION WITH FORMAL LEARNING ENCOUNTERS TWO MAIN ISSUES: THE LEARNING GUIDANCE ISSUE, AND THE NEWCOMER INTEGRATION ISSUE. WHILE THE FORMER CAN BE SOLVED BY INSTRUCTIONAL DESIGN, THE LATTER IS STILL OPEN AND MAY BE SOLVED BY SOCIAL LEARNING ANALYTICS (SLA). THIS PAPER PROPOSES B-LABS (BLOGS AND LEARNING ANALYTICS BASED SEMINAR), A HIGHER EDUCATION SEMINAR WITH A FORMAL COMPONENT AS A FACE-TO-FACE SEMINAR, AND AN INFORMAL COMPONENT COMPRISING LEARNING IN OKCS. B-LABS WAS CARRIED OUT WITH N = 65 UNDERGRADUATE STUDENTS. SLA TOOLS WERE EMPLOYED TO SELECT RESPONSIVE OKCS THAT WERE LIKELY TO INTEGRATE NEWCOMERS. THE STUDENTS SUCCESSFULLY FOLLOWED THE B-LABS SCRIPT; LEARNING OUTCOMES INCLUDED STUDENTS' INSIGHT IN MATHEMATICS DIDACTICS, AND KNOWLEDGE OF OKC SPECIFIC EDUCATIONAL APPROACHES. STUDENT ACCEPTANCE OF THE LEARNING ENVIRONMENT WAS PREDICTED BY PERFORMANCE EXPECTANCY AND PERCEIVED SOCIAL INFLUENCE. FUTURE DEVELOPMENT INCLUDES REFINING FUNCTIONS PREDICTING THE LIKELINESS OF OKC NEWCOMER INTEGRATION.
VARIOUS HINTS HAVE BEEN PROPOSED TO SCAFFOLD STUDENTS. SUCH HINTS CAN BE BROADLY DIVIDED INTO DIRECT HINTS AND INDIRECT HINTS. ON THE OTHER HAND, INDIVIDUAL DIFFERENCES EXISTED AMONG LEARNERS. IN PARTICULAR, COGNITIVE STYLES GREATLY AFFECT STUDENT LEARNING. HOWEVER, THERE IS A LACK OF STUDIES TO INVESTIGATE HOW COGNITIVE STYLES AFFECT STUDENTS' REACTIONS TO THE USE OF HINTS IN THE CONTEXT OF ACADEMIC ENGLISH. TO ADDRESS THIS ISSUE, THIS STUDY AIMED TO EXAMINE THE EFFECTS OF COGNITIVE STYLES ON THE USE OF HINTS IN SUCH A CONTEXT. TO OBTAIN DEEP UNDERSTANDINGS, A LEARNING-ANALYTICS APPROACH WAS APPLIED IN THIS STUDY, INCLUDING QUANTITATIVE MEASUREMENT, QUALITATIVE EVALUATION AND LAG SEQUENTIAL ANALYSES. THE RESULTS FROM THE QUANTITATIVE MEASUREMENT INDICATED THAT THE FREQUENCIES OF USING CHINESE HINTS AND SYNONYM HINTS WERE SIGNIFICANTLY ASSOCIATED WITH SERIALISTS' TASK SCORES WHILE SUCH SIGNIFICANT CORRELATIONS WERE NOT FOUND FOR HOLISTS. THE RESULTS FROM THE QUALITATIVE EVALUATION SHOWED THAT HOLISTS FAVORED TO USE SYNONYM HINTS WHILE SERIALISTS PREFERRED TO USE CHINESE HINTS. THE RESULTS FROM THE LAG SEQUENTIAL ANALYSES SUGGESTED THAT HOLISTS DEMONSTRATED ITERATIVE BEHAVIOR WHILE SERIALISTS SHOWED SEQUENTIAL BEHAVIOR. IN SUMMARY, COGNITIVE STYLES HAVE CONSIDERABLE INFLUENCES ON STUDENTS' LEARNING PATTERNS IN THE CONTEXT OF ACADEMIC ENGLISH.
FEATURED APPLICATION THIS STUDY PROVIDES RESEARCHERS AND EDUCATORS THE RESOURCES TO SET UP AND RUN A SOFTWARE TOOL FOR EXTRACTION, PROCESSING, VISUALIZATION AND ANALYSIS OF STUDENT SELF- AND PEER ASSESSMENT DATA IN MOODLE WORKSHOPS. ABSTRACT SITUATED IN THE INTERSECTION OF TWO EMERGING TRENDS, ONLINE SELF- AND PEER ASSESSMENT MODES AND LEARNING ANALYTICS, THIS STUDY EXPLORES THE CURRENT LANDSCAPE OF SOFTWARE APPLICATIONS TO SUPPORT PEER ASSESSMENT ACTIVITIES AND THEIR NECESSARY REQUIREMENTS TO COMPLETE THE LEARNING ANALYTICS CYCLE UPON THE INFORMATION COLLECTED FROM THOSE APPLICATIONS. MORE PARTICULARLY, THE STUDY FOCUSES ON THE SPECIFIC CASE OF MOODLE WORKSHOPS, AND PROPOSES THE DESIGN AND IMPLEMENTATION OF AN APPLICATION, THE MOODLE WORKSHOP DATA EXTRACTOR (MWDEX) TO OVERCOME THE DATA ANALYSIS AND VISUALIZATION SHORTCOMINGS OF THE MOODLE WORKSHOP MODULE. THIS RESEARCH PAPER DETAILS THE ARCHITECTURE DESIGN, CONFIGURATION, AND USE OF THE APPLICATION, AND PROPOSES AN INITIAL VALIDATION OF THE TOOL BASED ON THE CURRENT PEER ASSESSMENT PRACTICES OF A GROUP OF LEARNING ANALYTICS EXPERTS. THE RESULTS OF THE SMALL-SCALE SURVEY SUGGEST THAT THE USE OF SOFTWARE TOOLS TO SUPPORT PEER ASSESSMENT IS NOT SO EXTENDED AS IT WOULD INITIALLY SEEM, BUT ALSO HIGHLIGHT THE POTENTIAL OF MWDEX TO TAKE FULL ADVANTAGE OF MOODLE WORKSHOPS.
PRODUCTIVE AND EFFECTIVE COLLABORATIVE LEARNING IS RARELY A SPONTANEOUS PHENOMENON BUT RATHER THE RESULT OF MEETING A SET OF CONDITIONS, ORCHESTRATING AND SCAFFOLDING PRODUCTIVE INTERACTIONS. SEVERAL STUDIES HAVE DEMONSTRATED THAT CONFLICTS CAN HAVE DETRIMENTAL EFFECTS ON STUDENT COLLABORATION. THROUGH THE APPLICATION OF NETWORK SCIENCE, AND SOCIAL NETWORK ANALYSIS IN PARTICULAR, THIS LEARNING ANALYTICS STUDY INVESTIGATES THE CONCEPT OF GROUP ROBUSTNESS; THAT IS, THE CAPACITY OF COLLABORATIVE GROUPS TO REMAIN FUNCTIONAL DESPITE THE WITHDRAWAL OR ABSENCE OF GROUP MEMBERS, AND ITS RELATION TO GROUP PERFORMANCE IN THE FRAME OF COLLABORATIVE LEARNING. DATA ON ALL STUDENT AND TEACHER INTERACTIONS WERE COLLECTED FROM TWO PHASES OF A COURSE IN MEDICAL EDUCATION THAT EMPLOYED AN ONLINE LEARNING ENVIRONMENT. VISUAL AND MATHEMATICAL ANALYSIS WERE CONDUCTED, SIMULATING THE REMOVAL OF ACTORS AND ITS EFFECT ON THE GROUP'S ROBUSTNESS AND NETWORK STRUCTURE. IN ADDITION, THE EXTRACTED NETWORK PARAMETERS WERE USED AS FEATURES IN MACHINE LEARNING ALGORITHMS TO PREDICT STUDENT PERFORMANCE. THE STUDY CONTRIBUTES FINDINGS THAT DEMONSTRATE THE USE OF NETWORK SCIENCE TO SHED LIGHT ON ESSENTIAL ELEMENTS OF COLLABORATIVE LEARNING AND DEMONSTRATES HOW THE CONCEPT AND MEASURES OF GROUP ROBUSTNESS CAN INCREASE UNDERSTANDING OF THE CONDITIONS OF PRODUCTIVE COLLABORATIVE LEARNING. IT ALSO CONTRIBUTES TO UNDERSTANDING HOW CERTAIN INTERACTION PATTERNS CAN HELP TO PROMOTE THE SUSTAINABILITY OR ROBUSTNESS OF GROUPS, WHILE OTHER INTERACTION PATTERNS CAN MAKE THE GROUP MORE VULNERABLE TO WITHDRAWAL AND DYSFUNCTION. THE FINDING ALSO INDICATE THAT TEACHERS CAN BE A DRIVING FACTOR BEHIND THE FORMATION OF RICH CLUBS OF WELL-CONNECTED FEW AND LESS CONNECTED MANY IN SOME CASES AND CAN CONTRIBUTE TO A MORE COLLABORATIVE AND SUSTAINABLE PROCESS WHERE EVERY STUDENT IS INCLUDED.Y
IN LATIN AMERICAN UNIVERSITIES, LEARNING ANALYTICS (LA) HAS BEEN PERCEIVED AS A PROMISING OPPORTUNITY TO LEVERAGE DATA TO MEET THE NEEDS OF A DIVERSE STUDENT COHORT. ALTHOUGH UNIVERSITIES HAVE BEEN COLLECTING EDUCATIONAL DATA FOR YEARS, THE ADOPTION OF LA IN THIS REGION IS STILL LIMITED DUE TO THE LACK OF EXPERTISE AND POLICIES FOR PROCESSING AND USING EDUCATIONAL DATA. IN ORDER TO GET A BETTER PICTURE OF HOW EXISTING DATA-RELATED PRACTICES AND POLICIES MIGHT AFFECT THE INCORPORATION OF LA IN LATIN AMERICAN INSTITUTIONS, WE CONDUCTED A MIXED METHODS STUDY IN FOUR LATIN AMERICAN UNIVERSITIES (TWO CHILEAN AND TWO ECUADORIAN). IN THIS PAPER, THE QUALITATIVE DATA WERE BASED ON 37 INTERVIEWS WITH MANAGERS AND 16 FOCUS GROUPS WITH 51 TEACHING STAFF AND 45 STUDENTS; THE QUANTITATIVE DATA WERE COLLECTED THROUGH TWO SURVEYS ANSWERED BY 1884 STUDENTS AND 368 TEACHERS, RESPECTIVELY. THE FINDINGS REVEAL OPPORTUNITIES TO INCORPORATE LA SERVICES INTO EXISTING DATA PRACTICES IN THE FOUR CASE STUDIES. HOWEVER, THE LACK OF RELIABLE INFORMATION SYSTEMS AND POLICIES TO REGULATE THE USE OF DATA IMPOSES CHALLENGES THAT NEED TO BE OVERCOME FOR FUTURE LA ADOPTION.
PURPOSE - MANY HIGHER EDUCATION INSTITUTIONS ARE INVESTIGATING THE POSSIBILITY OF DEVELOPING PREDICTIVE STUDENT SUCCESS MODELS THAT USE DIFFERENT SOURCES OF DATA AVAILABLE TO IDENTIFY STUDENTS THAT MIGHT BE AT RISK OF FAILING A COURSE OR PROGRAM. THE PURPOSE OF THIS PAPER IS TO REVIEW THE METHODOLOGICAL COMPONENTS RELATED TO THE PREDICTIVE MODELS THAT HAVE BEEN DEVELOPED OR CURRENTLY IMPLEMENTED IN LEARNING ANALYTICS APPLICATIONS IN HIGHER EDUCATION. DESIGN/METHODOLOGY/APPROACH - LITERATURE REVIEW WAS COMPLETED IN THREE STAGES. FIRST, THE AUTHORS CONDUCTED SEARCHES AND COLLECTED RELATED FULL-TEXT DOCUMENTS USING VARIOUS SEARCH TERMS AND KEYWORDS. SECOND, THEY DEVELOPED INCLUSION AND EXCLUSION CRITERIA TO IDENTIFY THE MOST RELEVANT CITATIONS FOR THE PURPOSE OF THE CURRENT REVIEW. THIRD, THEY REVIEWED EACH DOCUMENT FROM THE FINAL COMPILED BIBLIOGRAPHY AND FOCUSED ON IDENTIFYING INFORMATION THAT WAS NEEDED TO ANSWER THE RESEARCH QUESTIONS FINDINGS - IN THIS REVIEW, THE AUTHORS IDENTIFY METHODOLOGICAL STRENGTHS AND WEAKNESSES OF CURRENT PREDICTIVE LEARNING ANALYTICS APPLICATIONS AND PROVIDE THE MOST UP-TO-DATE RECOMMENDATIONS ON PREDICTIVE MODEL DEVELOPMENT, USE AND EVALUATION. THE REVIEW RESULTS CAN INFORM IMPORTANT FUTURE AREAS OF RESEARCH THAT COULD STRENGTHEN THE DEVELOPMENT OF PREDICTIVE LEARNING ANALYTICS FOR THE PURPOSE OF GENERATING VALUABLE FEEDBACK TO STUDENTS TO HELP THEM SUCCEED IN HIGHER EDUCATION. ORIGINALITY/VALUE - THIS REVIEW PROVIDES AN OVERVIEW OF THE METHODOLOGICAL CONSIDERATIONS FOR RESEARCHERS AND PRACTITIONERS WHO ARE PLANNING TO DEVELOP OR CURRENTLY IN THE PROCESS OF DEVELOPING PREDICTIVE STUDENT SUCCESS MODELS IN THE CONTEXT OF HIGHER EDUCATION.
NOWADAYS, BLOCK-BASED PROGRAMMING ENVIRONMENTS ARE OFTEN USED TO OFFER A GENTLE INTRODUCTION TO LEARNING A PROGRAMMING LANGUAGE. HOWEVER, AN ASSESSMENT OF STUDENTS' PROGRAMMING SKILLS BASED ON THE RESULTS OF A PROGRAMMING TASK IS NOT SUFFICIENT TO DETERMINE ALL AREAS STUDENTS ARE STRUGGLING WITH. WE THEREFORE INTRODUCE A LEARNING ANALYTICS APPROACH OF MEASURING AND EVALUATING THE PROGRAMMING SEQUENCES OF STUDENTS THAT PROGRAM WITH SCRATCH 3. WITH OUR MEASUREMENT FRAMEWORK, IT IS POSSIBLE TO RECORD, STORE AND ANALYZE PROGRAMMING SEQUENCES DONE ON A PUBLICLY-AVAILABLE, INSTRUMENTED SCRATCH 3 ENVIRONMENT. CHANGES IN THE PROGRAMMING SEQUENCE ARE CATEGORIZED REGARDING THE USED BLOCK TYPES AND TYPES OF PROGRAM CHANGE. WE CONDUCTED AN EXPLORATORY PROGRAMMING TRIAL WITH LOWER AND UPPER SECONDARY SCHOOL STUDENTS TO INVESTIGATE SMALL-SCALE PROGRAMMING STRATEGIES IN THE RECORDED PROGRAMMING SEQUENCES. OUR GOALS ARE TO IDENTIFY STUDENTS IN NEED OF SUPPORT AND TO IDENTIFY RECURRING PATTERNS USED BY STUDENTS SUCCESSFUL IN THE TRIAL. CLUSTERING WITH K-MEANS MAKES IT POSSIBLE TO IDENTIFY STRUGGLING STUDENTS BASED ON BOTH INTERACTED BLOCK TYPES AND TYPES OF PROGRAM CHANGES. RECURRING PATTERNS IN THE PROGRAMMING SEQUENCES OF SUCCESSFUL STUDENTS SHOW THAT SMALL-SCALE PROGRAMMING STRATEGIES ARE VERY DIVERSE.
TWO RESEARCH TEAMS FROM THE UNIVERSITY OF MELBOURNE'S LEARNING ANALYTICS RESEARCH GROUP USED VALIDATION AS APPLIED IN EDUCATIONAL MEASUREMENT TO PROVIDE A FRAMEWORK FOR COLLABORATION. ONE TEAM WAS FOCUSSED ON DEFINING AND BUILDING MEASURES OF LEARNING CAPABILITY OF MOOCS PARTICIPANTS, AND THE OTHER ON USING TOPIC MODELLING TO DISCOVER TOPICS IN MOOC FORUMS. THE COLLABORATION EXPLORED THE SUITABILITY OF ITEMS DISCOVERED FROM MOOC FORUMS USING TOPIC MODELLING AS MEASURES OF LEARNING CAPABILITY OF PARTICIPANTS IN MOOCS.
IT IS DIFFICULT FOR TEACHERS TO ASSESS OVERALL CLASS PERFORMANCE IN LECTURE SITUATIONS WHERE THERE ARE MORE THAN 100 STUDENTS PER CLASS. IN SUCH SITUATIONS, IT IS DIFFICULT TO DECIDE WHEN TO TAKE APPROPRIATE REMEDIAL ACTION. IN AN ATTEMPT TO ASSESS THE OVERALL CLASS ATTITUDE AND DEGREE OF UNDERSTANDING BASED ON THE LEARNING BEHAVIOR SHOWN BY ALL THE STUDENTS IN A LARGE CLASS, INCLUDING INATTENTIVE OR INACTIVE STUDENTS, WE ANALYZED DATA LOGS GENERATED BY A FILL-IN WORKBOOK SYSTEM DEVELOPED BY THE AUTHORS. THIS TIME SERIES DATA MADE IT POSSIBLE TO IDENTIFY WHETHER STUDENTS WERE EFFECTIVELY FOLLOWING THE CLASS LEARNING SCHEDULE. ANALYSIS OF THE RESULTS CONFIRMED THAT THE DEGREE OF UNDERSTANDING SHOWN BY INDIVIDUAL STUDENTS COULD ALSO BE USEFULLY ASSESSED BY EMPLOYING THIS APPROACH.
THE LACK OF DATA INTEROPERABILITY AMONG DIFFERENT EDUCATIONAL SYSTEMS IMPOSES A CHALLENGE TO DATA ANALYTICS. TO FACE THESE PROBLEMS, WE HAVE DEVELOPED SCALA (SCALABLE COMPETENCY ASSESSMENT WEB PLATFORM THROUGH A LEARNING ANALYTICS APPROACH), AN INTEGRATED ANALYTICS SYSTEM THAT EMPLOYS LEARNING ANALYTICS TECHNIQUES TO VISUALIZE IN A SINGLE INTERFACE ENRICHED INDICATORS TO TEACHERS AND LEARNERS, GAINING INSIGHTS INTO THEIR HABITS AND THE IMPACT OF THEIR LEARNING ACTIVITIES.
INTERNET OF THINGS (IOT) TECHNOLOGY IS DEVELOPING RAPIDLY AND ATTRACT MORE AND MORE ATTENTIONS IN RECENT YEARS. WITH THE MATURITY OF THIS TECHNOLOGY, MANY COMPANIES HAVE ALREADY PROVIDED VARIOUS IOT DEVICES TO CONSUMERS, SUCH AS SMART SPEAKERS AND SMART METERS. THESE DEVICES ARE WILDLY USED NOT ONLY IN RESEARCHES BUT ALSO IN MANY PEOPLE'S DAILY LIVES. HOWEVER, WITH THE WIDELY USE OF THESE DEVICES, THE SECURITY PROBLEMS OF IOT DEVICES BECOME MORE AND MORE IMPORTANT. ALTHOUGH THE DEVICE MAKERS WILL PROVIDE THE SECURITY SOLUTIONS IN TECHNOLOGY LEVEL, USERS AND IOT PROFESSORS SHOULD HAVE ENOUGH SECURITY KNOWLEDGE AS WELL. IN THIS PAPER, WE INTRODUCE A NOVEL FRAMEWORK FOR GENERATING WEB-BASED 3D AND 360-DEGREE VR MATERIALS FOR IOT SECURITY EDUCATION. OUR FRAMEWORK ALSO SUPPORTS THE AUTOMATIC GENERATION OF QUIZZES OF IOT SECURITY, WHICH WILL BE USED FOR EVALUATING THE LEARNING EFFECTS. THE GENERATED E-LEARNING MATERIALS AND QUIZZES CAN COLLECT USERS' LOG DATA FOR FURTHER ANALYZATION. IN THIS PAPER,WE IMPORT TWO VISUALLY ANALYZING TOOLS, TIME TUNNEL AND CUBIC GANTT CHART, INTO OUR FRAMEWORK. BY USING THESE TWO TOOLS, WE CAN OBTAIN THE ACTIVITY PATTERNS OF THE LEARNERS IN DIFFERENT SCORE RANGES. BASED ON SUCH RESULT, WE CAN IMPROVE THE E-LEARNING MATERIALS FOR INCREASING THE EDUCATIONAL EFFECTS, OR PROVIDE ADVICES TO HELP LEARNERS EFFECTIVELY USE OUR E-LEARNING MATERIALS. (C) 2021 THE AUTHORS. PUBLISHED BY ELSEVIER B.V.
AIM: LEARNING ANALYTICS (LA) IS AN EMERGING DISCIPLINE THAT AIMS AT ANALYZING STUDENTS' ONLINE DATA IN ORDER TO IMPROVE THE LEARNING PROCESS AND OPTIMIZE LEARNING ENVIRONMENTS. IT HAS YET UN-EXPLORED POTENTIAL IN THE FIELD OF MEDICAL EDUCATION, WHICH CAN BE PARTICULARLY HELPFUL IN THE EARLY PREDICTION AND IDENTIFICATION OF UNDER-ACHIEVING STUDENTS. THE AIM OF THIS STUDY WAS TO IDENTIFY QUANTITATIVE MARKERS COLLECTED FROM STUDENTS' ONLINE ACTIVITIES THAT MAY CORRELATE WITH STUDENTS' FINAL PERFORMANCE AND TO INVESTIGATE THE POSSIBILITY OF PREDICTING THE POTENTIAL RISK OF A STUDENT FAILING OR DROPPING OUT OF A COURSE.METHODS: THIS STUDY INCLUDED 133 STUDENTS ENROLLED IN A BLENDED MEDICAL COURSE WHERE THEY WERE FREE TO USE THE LEARNING MANAGEMENT SYSTEM AT THEIR WILL. WE EXTRACTED THEIR ONLINE ACTIVITY DATA USING DATABASE QUERIES AND MOODLE PLUGINS. DATA INCLUDED LOGINS, VIEWS, FORUMS, TIME, FORMATIVE ASSESSMENT, AND COMMUNICATIONS AT DIFFERENT POINTS OF TIME. FIVE ENGAGEMENT INDICATORS WERE ALSO CALCULATED WHICH WOULD REFLECT SELF-REGULATION AND ENGAGEMENT. STUDENTS WHO SCORED BELOW 5% OVER THE PASSING MARK WERE CONSIDERED TO BE POTENTIALLY AT RISK OF UNDER-ACHIEVING.RESULTS: AT THE END OF THE COURSE, WE WERE ABLE TO PREDICT THE FINAL GRADE WITH 63.5% ACCURACY, AND IDENTIFY 53.9% OF AT-RISK STUDENTS. USING A BINARY LOGISTIC MODEL IMPROVED PREDICTION TO 80.8%. USING DATA RECORDED UNTIL THE MID-COURSE, PREDICTION ACCURACY WAS 42.3%. THE MOST IMPORTANT PREDICTORS WERE FACTORS REFLECTING ENGAGEMENT OF THE STUDENTS AND THE CONSISTENCY OF USING THE ONLINE RESOURCES.CONCLUSIONS: THE ANALYSIS OF STUDENTS' ONLINE ACTIVITIES IN A BLENDED MEDICAL EDUCATION COURSE BY MEANS OF LA TECHNIQUES CAN HELP EARLY PREDICT UNDERACHIEVING STUDENTS, AND CAN BE USED AS AN EARLY WARNING SIGN FOR TIMELY INTERVENTION.
UNIVERSITIES AND INSTITUTIONS BEGAN TO CREATE THEIR VIRTUAL CAMPUSES BY USING ELEARNING PLATFORMS WHETHER COMMERCIAL OR NOT. WITHIN THESE, IT HAS HIGHLIGHTED AMONG OTHER TOOLS INSTEAD OF THE DIDACTIC FORUMS WHERE STUDENTS AMONG THEMSELVES AND STUDENTS AND TEACHING TEAMS COULD DISCUSS AND IN GENERAL BUILD KNOWLEDGE WITHIN A COLLABORATIVE CONTEXT. THE DATA COMING FROM THESE FORUMS HAVE BEEN ANALYZED, WITH DIFFERENT TECHNIQUES, AND HAVE ALLOWED OBTAINING SOME VERY INTERESTING CONCLUSIONS. HOWEVER, IN RECENT ACADEMIC YEARS THERE IS A CLEAR TREND OF MIGRATION OF COLLABORATIVE WORK THAT PREVIOUSLY OCCURRED IN THE FORUMS TO SOCIAL NETWORKS AND MOBILES. THIS POSES A PROBLEM SINCE THESE TOOLS ARE NOT COORDINATED BY THE TEACHING TEAMS. THE PRESENT WORK ANALYZES THIS TREND AND POINTS OUT THE CONSEQUENCES TO WHICH IT MAY GIVE RISE.
MASSIVE OPEN ONLINE COURSES (MOOCS), DELIVERED ON PLATFORMS SUCH AS EDX AND COURSERA, HAVE LED TO A SURGE IN LARGE-SCALE LEARNING RESEARCH. MOOC PLATFORMS GATHER A CONTINUOUS STREAM OF LEARNER TRACES, WHICH CAN AMOUNT TO SEVERAL GIGABYTES PER MOOC, THAT LEARNING ANALYTICS RESEARCHERS USE TO CONDUCT EXPLORATORY ANALYSES AS WELL AS TO EVALUATE DEPLOYED INTERVENTIONS. EDX HAS PROVEN TO BE A POPULAR PLATFORM FOR SUCH EXPERIMENTS, AS THE DATA EACH MOOC GENERATES IS EASILY ACCESSIBLE TO THE INSTITUTION RUNNING THE MOOC. ONE OF THE ISSUES RESEARCHERS FACE IS THE PREPROCESSING, CLEANING AND FORMATTING OF THOSE LARGE-SCALE LEARNER TRACES. IT IS A TEDIOUS PROCESS THAT REQUIRES CONSIDERABLE COMPUTATIONAL SKILLS. TO REDUCE THIS BURDEN, A NUMBER OF TOOLS HAVE BEEN PROPOSED AND RELEASED WITH THE AIM OF SIMPLIFYING THIS PROCESS. THOSE TOOLS THOUGH STILL HAVE A SIGNIFICANT SETUP COST, ARE ALREADY OUT-OF-DATE OR REQUIRE ALREADY PREPROCESSED DATA AS A STARTING POINT. IN CONTRAST, IN THIS PAPER WE INTRODUCE ELAT, THE EDX LOG FILE ANALYSIS TOOL, WHICH IS BROWSER-BASED (I.E., NO SETUP COSTS), KEEPS THE DATA LOCAL (I.E., NO SERVER IS NECESSARY AND THE PRIVACY-SENSITIVE LEARNER DATA IS NOT SEND ANYWHERE) AND TAKES EDX DATA DUMPS AS INPUT. ELAT DOES NOT ONLY PROCESS THE RAW DATA, BUT ALSO GENERATES SEMANTICALLY MEANINGFUL UNITS (LEARNER SESSIONS INSTEAD OF JUST CLICK EVENTS) THAT ARE VISUALIZED IN VARIOUS WAYS (LEARNING PATHS, FORUM PARTICIPATION, VIDEO WATCHING SEQUENCES). WE REPORT ON TWO EVALUATIONS WE CONDUCTED: (I) A TECHNOLOGICAL EVALUATION AND A (II) USER STUDY WITH POTENTIAL END USERS OF ELAT. ELAT IS OPEN-SOURCE AND AVAILABLE AT HTTPS://MVALLET91.GITHUB.IO/ELAT/.
SOFTWARE ENGINEERING COURSES ARE NUCLEUS ELEMENTS OF THE COMPUTER SCIENCE SYLLABUS. WHILE THE MAIN ASPIRE OF SUCH COURSES IS TO GIVE STUDENTS PRACTICAL INDUSTRY-RELEVANT "SOFTWARE ENGINEERING IN THE LARGE" FAMILIARITY, FREQUENTLY SUCH COURSES PLUMMET SHORT OF THIS SIGNIFICANT INTENTION OWED TO BE SHORT OF INDUSTRIAL EXPERIENCE AND SUPPORT INFRASTRUCTURE, DEGENERATING THE COURSE INTO "ONE BIG CODING ASSIGNMENT". THEREFORE, IT IS OBLIGATORY TO DESIGN AND DEVELOP IMPROVED INFRASTRUCTURE SUPPORT FOR TEACHING OR TAKING SUCH COURSES. THIS WOULD BENEFIT INSTRUCTORS AND STUDENTS COMMUNITIES IN COMPUTER SCIENCE ATMOSPHERE. LEARNING ANALYTICS LEND A HAND TO THE LEARNERS TO ENHANCE THEIR LEARNING TRICKS. THIS WAS SCRUTINIZED AMONG THE SOFTWARE ENGINEERING STUDENTS HOW THE LEARNING STYLE AND LEARNING ENGAGEMENT SWAYS IN GATHERING KNOWLEDGE. THIS PAPER DISCUSSES THE IMPORTANCE OF LEARNING ANALYTICS IN SOFTWARE ENGINEERING EDUCATION ESPECIALLY THE LEARNING STYLE, LEARNING ENGAGEMENT AND ITS INFLUENCES IN THIS DOMAIN.
BLENDED LEARNING IS GAINING GROUND IN CONTEMPORARY EDUCATION. HOWEVER, STUDIES ON PREDICTIVE LEARNING ANALYTICS IN THE CONTEXT OF BLENDED LEARNING REMAIN RELATIVELY SCARCE COMPARED TO MASSIVE OPEN ONLINE COURSES (MOOCS), WHERE SUCH APPLICATIONS HAVE GAINED A STRONG FOOTHOLD. DATA SETS OBTAINED FROM BLENDED LEARNING ENVIRONMENTS SUFFER FROM A HIGH DIMENSIONALITY AND TYPICALLY EXPOSE A LIMITED NUMBER OF INSTANCES, WHICH MAKES PREDICTIVE ANALYSIS A CHALLENGING TASK. IN THIS WORK, WE EXPLORE THE LOG DATA OF A MASTER-LEVEL BLENDED COURSE TO PREDICT THE STUDENTS' GRADES BASED ENTIRELY ON THE DATA OBTAINED FROM AN ONLINE MODULE (A SMALL PRIVATE ONLINE COURSE), USING AND COMPARING LOGISTIC REGRESSION AND RANDOM FOREST-BASED PREDICTIVE MODELS. THE RESULTS OF THE ANALYSIS SHOW THAT, DESPITE THE LIMITED DATA, SUCCESS VS. FAIL PREDICTIONS CAN BE MADE AS EARLY AS IN THE MIDDLE OF THE COURSE. THIS COULD BE USED IN THE FUTURE FOR TIMELY INTERVENTIONS, BOTH FOR FAILURE PREVENTION AS WELL AS FOR REINFORCING POSITIVE LEARNING BEHAVIOURS OF STUDENTS.
THIS PAPER DESCRIBES A REAL-TIME LEARNING ANALYTICS TO FIND LEARNING CONTENTS OR KEYWORDS THAT STUDENTS DON'T UNDERSTAND IN DIGITAL TEXTBOOKS. WE DEVELOPED A DIGITAL TEXTBOOK VIEWER SYSTEM THAT CAN COLLECT STUDENTS' LEARNING LOGS. BY ANALYZING AND VISUALIZING THE COLLECTED LEARNING LOGS IN REAL TIME, TEACHERS CAN VISUALLY FIND THE KEYWORDS THAT STUDENTS DON'T UNDERSTAND DURING A CLASS. THIS PAPER DESCRIBES THE CONTRIBUTION OF REAL-TIME LEARNING ANALYTICS FOR SUPPORTING TEACHERS.
IN THE PAST DECADES, ONLINE LEARNING HAS TRANSFORMED THE EDUCATIONAL LANDSCAPE WITH THE EMERGENCE OF NEW WAYS TO LEARN. THIS FACT, TOGETHER WITH RECENT CHANGES IN EDUCATIONAL POLICY IN EUROPE AIMING TO FACILITATE THE INCORPORATION OF GRADUATE STUDENTS TO THE LABOR MARKET, HAS PROVOKED A SHIFT ON THE DELIVERY OF INSTRUCTION AND ON THE ROLE PLAYED BY TEACHERS AND STUDENTS, STRESSING THE NEED FOR DEVELOPMENT OF BOTH BASIC AND CROSS-CURRICULAR COMPETENCIES. IN PARALLEL, THE LAST YEARS HAVE WITNESSED THE EMERGENCE OF NEW EDUCATIONAL DISCIPLINES THAT CAN TAKE ADVANTAGE OF THE INFORMATION RETRIEVED BY TECHNOLOGY-BASED ONLINE EDUCATION IN ORDER TO IMPROVE INSTRUCTION, SUCH AS LEARNING ANALYTICS. THIS STUDY EXPLORES THE APPLICABILITY OF LEARNING ANALYTICS FOR PREDICTION OF DEVELOPMENT OF TWO CROSS-CURRICULAR COMPETENCIES TEAMWORK AND COMMITMENT BASED ON THE ANALYSIS OF MOODLE INTERACTION DATA LOGS IN A MASTER'S DEGREE PROGRAM AT UNIVERSIDAD A DISTANCIA DE MADRID (UDIMA) WHERE THE STUDENTS WERE EDUCATION PROFESSIONALS. THE RESULTS FROM THE STUDY QUESTION THE SUITABILITY OF A GENERAL INTERACTION-BASED APPROACH AND SHOW NO RELATION BETWEEN ONLINE ACTIVITY INDICATORS AND TEAMWORK AND COMMITMENT ACQUISITION. THE DISCUSSION OF RESULTS INCLUDES MULTIPLE RECOMMENDATIONS FOR FURTHER RESEARCH ON THIS TOPIC. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE AREAS OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS FOCUS ON THE EXTRACTION OF KNOWLEDGE AND ACTIONABLE ITEMS FROM DATA SETS CONTAINING DETAILED INFORMATION ABOUT STUDENTS. HOWEVER, THE POTENTIAL IMPACT FROM THESE TECHNIQUES IS INCREASED WHEN PROPERLY CONTEXTUALIZED WITHIN A LEARNING ENVIRONMENT. MORE STUDIES ARE NEEDED TO EXPLORE THE CONNECTION BETWEEN STUDENT INTERACTIONS, APPROACHES TO LEARNING, AND ACADEMIC PERFORMANCE. SELF-REGULATED LEARNING (SRL) IS DEFINED AS THE EXTENT TO WHICH A STUDENT IS ABLE TO MOTIVATIONALLY, METACOGNITIVELY, AND COGNITIVELY ENGAGE IN A LEARNING EXPERIENCE. SRL HAS BEEN THE FOCUS OF RESEARCH IN TRADITIONAL CLASSROOM LEARNING AND IS ALSO ARGUED TO PLAY A VITAL ROLE IN THE ONLINE OR BLENDED LEARNING CONTEXTS. IN THIS PAPER, WE STUDY HOW SRL AFFECTS STUDENTS' ONLINE INTERACTIONS WITH VARIOUS LEARNING ACTIVITIES AND ITS INFLUENCE IN ACADEMIC PERFORMANCE. THE RESULTS DERIVED FROM A NATURALISTIC EXPERIMENT AMONG A COHORT OF FIRST YEAR ENGINEERING STUDENTS SHOWED THAT POSITIVE SELF-REGULATED STRATEGIES (PSRS) AND NEGATIVE SELF-REGULATED STRATEGIES (NSRS) AFFECTED BOTH THE INTERACTION WITH ONLINE ACTIVITIES AND ACADEMIC PERFORMANCE. NSRS DIRECTLY PREDICTED ACADEMIC OUTCOMES, WHEREAS PSRS ONLY CONTRIBUTED INDIRECTLY TO ACADEMIC PERFORMANCE VIA THE INTERACTIONS WITH ONLINE ACTIVITIES. THESE RESULTS POINT TO CONCRETE AVENUES TO PROMOTE SELF-REGULATION AMONG STUDENTS IN THIS TYPE OF LEARNING CONTEXTS.
FOUNDED ON THE NEED TO HELP UNIVERSITY STUDENTS DEVELOP A GREATER ACADEMIC METACOGNITIVE CAPACITY, STUDENT-FACING LEARNING ANALYTICS ARE CONSIDERED USEFUL TOOLS FOR MAKING STUDENTS OVERTLY AWARE OF THEIR OWN LEARNING PROCESSES, HELPING STUDENTS TO DEVELOP CONTROL OVER THEIR LEARNING, AND SUBSEQUENTLY SUPPORTING MORE EFFECTIVE LEARNING. HOWEVER, EARLY RESEARCH ON THE EFFECTIVENESS OF STUDENT-FACING ANALYTICS IS GIVING MIXED RESULTS AND IS CASTING SOME DOUBT OVER THE USEFULNESS OF STUDENT-FACING LEARNING ANALYTICS. ONE FACTOR CONTRIBUTING TO DOUBT OVER THE VALUE OF STUDENT-FACING LEARNING ANALYTICS IS THAT THEIR DESIGN AND IMPLEMENTATION REMAINS FIRMLY ROOTED IN THE TECHNICAL DOMAIN, WITH VIRTUALLY NO GROUNDING IN THE KNOWLEDGE BASE OF LEARNING AND TEACHING. IF THE GROWING INVESTMENT OF RESOURCES INTO THE DEVELOPMENT OF STUDENT-FACING LEARNING ANALYTICS SYSTEMS IS TO BE FRUITFUL, THEN THERE IS AN OBVIOUS, URGENT NEED TO RE-POSITION STUDENT-FACING LEARNING ANALYTICS WITHIN LEARNING AND TEACHING FRAMEWORKS. WITH THIS IN MIND, WE USE SCHRAW & DENNISON'S MODEL OF METACOGNITION AND VYGOTSKY'S ZONE OF PROXIMAL DEVELOPMENT TO UNPACK THE 'LEARNING' IN STUDENT-FACING ANALYTICS AND WORK TOWARDS AN UNDERSTANDING OF STUDENT-FACING ANALYTICS THAT IS MORE CONDUCIVE TO SUPPORTING METACOGNITION AND EFFECTIVE LEARNING.
IN THIS ARTICLE, WE EXAMINE THE SOCIOPOLITICAL IMPLICATIONS OF AI TECHNOLOGIES AS THEY ARE INTEGRATED INTO WRITING INSTRUCTION AND ASSESSMENT. DRAWING FROM NEW MATERIALIST AND BLACK FEMINIST THOUGHT, WE CONSIDER HOW LEARNING ANALYTICS PLATFORMS FOR WRITING ARE ANIMATED BY AND THROUGH ENTANGLEMENTS OF ALGORITHMIC REASONING, STATE STANDARDS AND ASSESSMENTS, EMBODIED LITERACY PRACTICES, AND SOCIOPOLITICAL RELATIONS. WE DO A CLOSE READING OF RESEARCH AND DEVELOPMENT DOCUMENTS ASSOCIATED WITH ESSAY HELPER, A MACHINE LEARNING PLATFORM THAT PROVIDES FORMATIVE FEEDBACK ON STUDENT WRITING BASED ON STANDARDS-ALIGNED RUBRICS AND TRAINING DATA. IN PARTICULAR, WE CONSIDER THE PERFORMATIVE ACTS OF THE ALGORITHM IN THE ESSAY HELPER PLATFORM - BOTH IN THE WAYS THAT RECONSTITUTES MATERIAL-DISCURSIVE RELATIONS OF DIFFERENCE, AND ITS IMPLICATIONS FOR TRANSACTIONS OF TEACHING AND LEARNING. WE ARGUE THAT, THROUGH THESE PROCESSES, THE ALGORITHMS FUNCTION AS RACIALIZING ASSEMBLAGES, AND CONCLUDE BY SUGGESTING PATHWAYS TOWARD ALTERNATIVE FUTURES THAT RECONFIGURE THE SOCIOPOLITICAL RELATIONS THE PLATFORM INHERITS.
IN THE PAST DECADE, THE APPLICATIONS OF BIG DATA AND LEARNING ANALYTICS IN EDUCATION HAVE MADE SIGNIFICANT HEADWAYS RESULTING IN NEW OPPORTUNITIES FOR EDUCATIONAL RESEARCH. HOWEVER, BIG DATA ANALYTICS (BDA) HAS BROUGHT NEW CHALLENGES TO EDUCATIONAL ANALYTICS. THIS PAPER CONDUCTS A SYSTEMATIC DATA-DRIVEN LITERATURE REVIEW OF BDA IN EDUCATION. USING A TOPIC MODELING APPROACH, WE HAVE IDENTIFIED SIX TOPICS AND 19 SUBTOPICS AND PERFORMED A NETWORK ANALYSIS TO EXPLORE THE LINKS BETWEEN THE TOPICS. BASED ON THE RESULTS, WE INVESTIGATE THE CHALLENGES IN THE FIELD AND CONCLUDE A THREE-DIMENSIONAL MODEL FOR EDUCATIONAL BDA TO ADDRESS THESE CHALLENGES.
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 ONLINE LABORATORIES. THIS PAPER PRESENTS AN IMPLEMENTATION 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 ONLINE LABORATORIES XAPI PROFILE: A PROFILE DEVELOPED TO ALIGN WITH THE MOST COMMON USE CASES IN THE ONLINE LABORATORIES DOMAIN. THE PROFILE IS APPLIED TO A CASE STUDY (A WINDMILL LAB), WHICH EXPLORES THE TECHNICAL PRACTICALITIES OF STANDARDIZING DATA ACQUISITION. IN SUMMARY, THE PAPER PRESENTS A FRAMEWORK TO TRACK ONLINE LABORATORIES AND THEIR IMPLEMENTATION WITH THE XAPI SPECIFICATION.
ORCHESTRATING TECHNOLOGY-ENHANCED LEARNING IS A DIFFICULT TASK, ESPECIALLY IN DEMANDING PEDAGOGICAL APPROACHES LIKE INQUIRY-BASED LEARNING (IBL). TO FOSTER EFFECTIVE TEACHER ADOPTION, BOTH THE COMPLEXITY OF DESIGNING IBL ACTIVITIES AND THE UNCERTAINTY ABOUT THE STUDENT LEARNING PATH DURING ENACTMENT NEED TO BE ADDRESSED. PREVIOUS RESEARCH SUGGESTS THAT ALIGNING LEARNING DESIGN AND LEARNING ANALYTICS CAN BE AN EFFECTIVE WAY TO PROVIDE SUCH ORCHESTRATION SUPPORT. THIS PAPER REPORTS ON A DESIGN-BASED RESEARCH (DBR) PROJECT TO EXPLORE TEACHERS' ORCHESTRATION NEEDS IN GO-LAB (A TECHNOLOGICAL ECOSYSTEM FOR IBL USED BY THOUSANDS OF PRIMARY AND SECONDARY SCHOOL TEACHERS AROUND THE WORLD), AND ON HOW SOLUTIONS THAT ALIGN LEARNING DESIGN AND ANALYTICS CAN FULFILL SUCH NEEDS. THE ANALYSIS OF DATA FROM MULTIPLE EVENTS (INCLUDING SURVEYS, CASE STUDIES, WORKSHOPS WITH TEACHERS, AND PLATFORM USAGE ANALYSES) LED TO A CATALOGUE OF IBL ORCHESTRATION NEEDS THAT CAN BE TACKLED BY ALIGNING LEARNING DESIGN AND ANALYTICS, AS WELL AS A LIST OF GUIDELINES FOR TECHNOLOGY DEVELOPMENT AIMING TO SUPPORT IBL ORCHESTRATION. THESE TWO CONTRIBUTIONS CAN SUPPORT THE CREATION OF FUTURE LEARNING ANALYTICS-ENHANCED IBL ENVIRONMENTS THAT ARE BOTH PEDAGOGICALLY GROUNDED AND USABLE BY TEACHERS IN AUTHENTIC SETTINGS.
THIS PAPER PROBLEMATISES A PERVASIVE DISCOURSE AROUND LEARNING ANALYTICS AS A HEGEMONIC "REGIME OF THOUGHT" (FOUCAULT, 1980, P. 81), OFFERING A " LOCAL CHARACTER OF CRITICISM" (P. 81) TO DISRUPT THIS DISCOURSE. MY CRITICISM HAS THE FOLLOWING FOCI: OBSESSION WITH LEARNING MANAGEMENT DATA, LITTLE ANALOGOUS DATA BEING AVAILABLE FOR FACE-TO-FACE TEACHING, AND THE UNDERVALUING OF ACADEMIC ANALYTICS COMPARED TO LEARNING ANALYTICS. POLICY AND PRACTICE SHIFTS BASED ON THE " BIG DATA" DISAGGREGATED FROM LMS AND RELATED PLATFORMS-A POOR INDICATION OF TEACHING AND LEARNING ACTIVITY IN MOST UNIVERSITY COURSES, WHICH ARE TAUGHT FACE-TO-FACE-CREATES RATHER THAN MITIGATES RISK FOR UNIVERSITY INSTITUTES.
TO ADDRESS THE CHALLENGE OF OVERWHELMING DATA INHERENT IN CLASSROOM LESSON VIDEOS, THIS STUDY PROPOSED A VISUAL LEARNING ANALYTICS (VLA) APPROACH TO VIDEO-BASED TEACHER PROFESSIONAL DEVELOPMENT (TPD). USING A TWO-YEAR EXPERIMENTAL DESIGN, 46 SECONDARY MATHEMATICS TEACHERS WERE DIVIDED RANDOMLY INTO A TREATMENT GROUP (N = 24) AND A CONTROL GROUP (N = 22) TO LEARN ABOUT AND INTEGRATE ACADEMICALLY PRODUCTIVE TALK INTO THEIR TEACHING. THE TREATMENT TEACHERS PARTICIPATED IN A VLA-SUPPORTED TPD PROGRAM, WHILE THE CONTROL TEACHERS PARTICIPATED IN CONVENTIONAL KNOWLEDGE-BASED WORKSHOPS. RESULTS SHOW THAT TEACHERS IN THE TREATMENT GROUP HAD MORE POSITIVE BELIEFS AND HIGHER SELF-EFFICACY IN THE POST-TEST AND DELAYED-POST-TEST, WHILE THE CONTROL GROUP IMPROVED, BUT NOT SIGNIFICANTLY, IN THEIR BELIEFS ABOUT THE USEFULNESS OF CLASSROOM TALK. IN ADDITION, ALTHOUGH THE CONTROL GROUP MADE A SIGNIFICANT IMPROVEMENT IN THEIR SELF-EFFICACY IN GUIDING CLASSROOM TALK IN THE POST-TEST, THIS IMPROVEMENT WAS NOT SUSTAINED TO THE DELAYED POST-TEST. MOREOVER, THE CODING OF CLASSROOM TEACHING BEHAVIOUR REVEALED THAT TEACHERS IN THE TREATMENT GROUP RELATIVE TO THE CONTROL GROUP SIGNIFICANTLY INCREASED THEIR USE OF ACADEMICALLY PRODUCTIVE TALK IN THE POST-TEST LESSONS TO ENCOURAGE THE STUDENTS' ELABORATION, REASONING, AND THINKING WITH OTHERS IN THE CLASSROOM. THE RESULTS SUGGEST THAT, WHILE ATTENDING KNOWLEDGE-BASED WORKSHOPS HAD, TO SOME DEGREE, POSITIVE EFFECTS ON THE CONTROL TEACHERS' BELIEFS AND SELF-EFFICACY, THESE EFFECTS WERE NOT SUSTAINABLE OVER TIME. IN CONTRAST, THE USE OF VISUAL LEARNING ANALYTICS TO SUPPORT THE TREATMENT GROUP'S REFLECTION ON THE CLASSROOM DATA NOT ONLY HAD SIGNIFICANT AND SUSTAINED EFFECTS ON THE TEACHERS' BELIEFS AND SELF-EFFICACY BUT ALSO SIGNIFICANTLY INFLUENCED THEIR ACTUAL CLASSROOM TEACHING BEHAVIOUR. IMPLICATIONS FOR DESIGNING VLA TO SUPPORT TEACHER LEARNING AND PROFESSIONAL DEVELOPMENT ARE DISCUSSED.
BY INVESTING TIME AND RESOURCES IN LEARNING ANALYTICS, STUDENTS, FACULTY, AND ADMINISTRATORS CAN IMPROVE LEARNING AND COURSE OUTCOMES, AND DEVELOP MORE ENGAGED AND EFFECTIVE TEACHING AND LEARNING TECHNIQUES. LEARNING ANALYTICS PROVIDES FEEDBACK TO INDIVIDUAL STUDENTS AND FACULTY, BUT CAN ALSO SURFACE TRENDS ACROSS SCHOOLS OR OTHER SCOPES OF INTEREST. THIS PAPER IS DEDICATED TO LEARNING ANALYTICS. AUTHORS HAVE ANALYZED LOGS FROM UNIVERSITAT POLITECNICA DE VALENCIA VIDEO LECTURE RECORDING AND MANAGEMENT SYSTEM. UPV IS USING THE PAELLA PLAYER WHICH IS AN OPEN SOURCE JAVASCRIPT VIDEO PLAYER OF THE OPENCAST COMMUNITY. LOGS HAVE BEEN ANALYZED WITH TWO DIFFERENT DATA AND ANALYTICS REPORTING TOOLS - POWER BI AND TABLEAU FROM DIFFERENT POINT OF VIEWS. ALSO THE RESULTS FROM TWO DIFFERENT STUDY YEARS HAVE BEEN COMPARED. PAPER INCLUDES RESULTS FROM BOTH BUSINESS ANALYTICS TOOLS, CONCLUSIONS ABOUT THE TOOLS AND ALSO RESULTS THAT CAN BE USED FOR IMPROVE AND BENEFIT MULTIPLE AUDIENCES FROM UNIVERSITY.
MATURE ENGLISH LANGUAGE LEARNERS (ELLS) LEARNING TO WRITE IN INFORMAL ENVIRONMENTS HAVE LITTLE ACCESS TO INSTRUCTOR FEEDBACK AND MUST RELY ON OTHER SOURCES TO SUPPORT THEIR WRITING DEVELOPMENT. WHILE IT IS KNOWN THAT MATURE ELLS TRUST INSTRUCTOR FEEDBACK, THEIR PERCEPTIONS TOWARDS FEEDBACK FROM NON-EXPERT SOURCES MAY BE MIXED. WE REPORT ON MATURE ELLS' PERCEPTIONS AND INTERPRETATIONS OF PEER AND AUTOMATED FEEDBACK WHEN USING DASHBOARD VISUALIZATIONS OF THEIR WRITING SKILLS DERIVED FROM SEVERAL METRICS AND SOURCES OF FEEDBACK. THESE PERCEPTIONS AND INTERPRETATIONS WERE COLLECTED THROUGH A SHORT-TERM DEPLOYMENT OF THE DASHBOARD WITHIN A WRITING APP WITH 16 MATURE ELLS, FOLLOWED BY INTERVIEWS WITH THE LEARNERS. FROM ANALYSES OF THESE INTERVIEWS, WE SUGGEST THREE DESIGN GUIDELINES (DG) RELATED TO LEARNING ANALYTICS DASHBOARD DESIGN FOR MATURE ELLS IN INFORMAL LEARNING CONTEXTS. FIRST, ANALYTICS-BASED FEEDBACK SHOULD CONTEXTUALIZE ELLS' LEARNING PROGRESS BY PROVIDING TEMPORAL INFORMATION ABOUT LEARNER PERFORMANCE. SECOND, JUSTIFICATIONS SHOULD ACCOMPANY FEEDBACK TO AVOID CRITICISM ARISING FROM ELLS' PRIOR BELIEFS. THIRD, LEARNER AUTONOMY SHOULD BE FOSTERED BY OFFERING EXPLICIT MECHANISMS FOR REFLECTING ON FEEDBACK THAT IS INCONSISTENT WITH LEARNER BELIEFS SINCE LEARNERS ARE WILLING TO QUESTION AUTOMATED FEEDBACK. WE DISCUSS HOW THESE THREE GUIDELINES CAN BE USED TO BENEFIT LEARNERS WHEN AN INSTRUCTOR IS NOT PRESENT.
THE USE OF ANALYTICAL METHODS FROM LEARNING ANALYTICS (LA) RESEARCH COMBINED WITH VISUALIZATIONS OF LEARNING ACTIVITIES USING LEARNING DESIGN (LD) TOOLS AND FRAMEWORKS HAS PROVIDED IMPORTANT INSIGHT INTO HOW INSTRUCTORS DESIGN FOR LEARNING. NONETHELESS, THERE ARE MANY SUBTLE NUANCES IN INSTRUCTORS' DESIGN DECISIONS THAT MIGHT NOT EASILY BE CAPTURED USING LA TOOLS. THEREFORE, THIS STUDY SETS OUT TO EXPLORE HOW AND WHY INSTRUCTORS DESIGN FOR LEARNING IN AN ONLINE AND DISTANCE HIGHER EDUCATION SETTING BY EMPLOYING A MIXED-METHOD APPROACH, WHICH COMBINED SEMI-STRUCTURED INTERVIEWS OF 12 INSTRUCTORS WITH NETWORK ANALYSES OF THEIR LDS. OUR FINDINGS UNCOVERED SEVERAL UNDERLYING FACTORS THAT INFLUENCED HOW INSTRUCTORS DESIGNED THEIR MODULES AND HIGHLIGHTED SOME DISCREPANCIES BETWEEN INSTRUCTORS' PEDAGOGICAL BELIEFS AND THEIR ACTUAL LD AS CAPTURED BY THE OPEN UNIVERSITY LEARNING DESIGN INITIATIVE (OULDI). THIS STUDY SHOWCASES THE POTENTIAL OF COMBINING LA WITH QUALITATIVE INSIGHTS FOR A BETTER UNDERSTANDING OF THE COMPLEX DESIGN PROCESS IN ONLINE DISTANCE HIGHER EDUCATION.
THIS RESEARCH TO PRACTICE FULL PAPER PROVIDES PRELIMINARY EVIDENCE THAT INTEGRATING REFLECTIONS IS A SIGNIFICANT FEATURE TO IDENTIFY AT-RISK STUDENTS EARLY IN A SEMESTER AS VERIFIED AND VALIDATED BY A SEQUENCE-BASED LEARNING ANALYTICS MODEL. WE'VE DEVISED AN ACTIVE LEARNING CLASSROOM MODEL WHICH INCORPORATES REFLECTION AT MULTIPLE POINTS IN STUDENTS' LEARNING EXPERIENCE. THIS ACTIVE LEARNING MODEL ADOPTS KOLB'S LEARNING MODEL TO PROVIDE A COHERENT AND CONNECTED SET OF ACTIVITIES BEFORE, DURING, AND AFTER THE CLASS. UNLIKE PERIODIC ASSESSMENT THROUGH TESTING, REFLECTIONS CAN PROVIDE NEARLY-REAL-TIME INFORMATION ABOUT STUDENT'S EXPERIENCES IN CLASS. WE EXTRACT SENTIMENT FEATURE VECTORS TO CAPTURE STUDENTS' AFFECT FROM WRITTEN REFLECTIONS. THESE FEATURES TYPICALLY AREN'T ASSESSED ON TESTS OR DURING IN-CLASS ACTIVITIES. THESE FEATURES WERE EXTRACTED AUTOMATICALLY USING LIWC (LINGUISTIC INQUIRY AND WORD COUNT) IS A TOOL FOR APPLIED NATURAL LANGUAGE PROCESSING) WHICH IS LESS CUMBERSOME TO IMPLEMENT THAN MANUALLY READING THE WRITTEN REFLECTIONS. WE FIND THAT USING THESE SENTIMENT FEATURE VECTORS EXTRACTED FROM THE REFLECTIONS IN OUR LEARNING MODEL INCREASED ACCURACY WHILE DECREASING TIME-TO-DETECT AT-RISK STUDENTS SIGNIFICANTLY.
THE SUSTAINABLE COMPUTER-BASED EVALUATION SYSTEM (SCE) IS A SCENARIO-BASED FORMATIVE EVALUATION SYSTEM, IN WHICH STUDENTS ARE ASSIGNED A TASK DURING A COURSE. THE TASKS INCLUDE THE DIVERSITY CONDITIONS IN REAL-WORLD SCENARIOS. THE GOALS OF THIS SYSTEM ARE LEARNING TO THINK AS A PROFESSIONAL IN A CERTAIN DISCIPLINE. WHILE THE SUBSTANTIVE, PSYCHOLOGICAL, INSTRUCTIONAL, AND TASK DEVELOPMENTAL ASPECTS OF THE ASSESSMENT HAVE BEEN INVESTIGATED, FEW ANALYTIC METHODS HAVE BEEN PROPOSED THAT ALLOW US TO PROVIDE FEEDBACK TO LEARNERS IN A FORMATIVE WAY. THE PURPOSE OF THIS PAPER IS TO INTRODUCE A FRAMEWORK OF A LEARNING ANALYTIC METHOD INCLUDING (1) AN ASSESSMENT DESIGN THROUGH EVIDENCE-CENTERED DESIGN (ECD), (2) A DATA MINING METHOD USING SOCIAL NETWORK ANALYSIS, AND (3) AN ANALYTIC METHOD USING A BAYESIAN NETWORK. THIS ANALYTIC FRAMEWORK CAN ANALYZE THE LEARNERS' PERFORMANCES BASED ON A COMPUTATIONAL PSYCHOMETRIC FRAMEWORK. THE TASKS WERE DESIGNED TO MEASURE 21ST CENTURY LEARNING SKILLS. THE 250 SAMPLES OF DATA COLLECTED FROM THE SYSTEM WERE ANALYZED. THE RESULTS FROM THE SOCIAL NETWORK ANALYSIS PROVIDE THE LEARNING PATH DURING A COURSE. IN ADDITION, THE 21ST CENTURY LEARNING SKILLS OF EACH LEARNER WERE INFERRED FROM THE BAYESIAN NETWORK OVER MULTIPLE TIME POINTS. THEREFORE, THE LEARNING ANALYTICS PROPOSED IN THIS STUDY CAN OFFER THE STUDENT LEARNING PROGRESSION AS WELL AS EFFECTIVE FEEDBACK FOR LEARNING.
THE MAIN OBJECTIVE OF THIS PAPER IS TO ANALYSE THE EFFECT OF THE AFFORDANCES OF A VIRTUAL LEARNING ENVIRONMENT AND A PERSONAL LEARNING ENVIRONMENT (PLE) IN THE CONFIGURATION OF THE STUDENTS' PERSONAL NETWORKS IN A HIGHER EDUCATION CONTEXT. THE RESULTS ARE DISCUSSED IN LIGHT OF THE ADAPTATION OF THE STUDENTS TO THE LEARNING NETWORK MADE UP BY TWO UNDERGRADUATE, INTER-UNIVERSITY AND ONLINE COURSES. BESIDES, WE ALSO EXAMINE THE INFLUENCE OF THIS EFFECT IN THE LEARNING PROCESS. THE FINDINGS REFLECT THE EFFECTIVENESS OF A PLE FOR FACILITATING STUDENT PARTICIPATION AND FOR ASSISTING STUDENTS IN THE CREATION OF LARGER AND MORE BALANCED PERSONAL NETWORKS WITH RICHER SOCIAL CAPITAL. HOWEVER, THE FINDINGS DO NOT PROVIDE EVIDENCES ABOUT A DIFFERENCE IN THE LEARNING PERFORMANCE BETWEEN THE TWO ENVIRONMENTS. FROM A METHODOLOGICAL POINT OF VIEW, THIS PAPER SERVES AS AN ILLUSTRATION OF THE ANALYSIS OF PERSONAL NETWORKS ON DIGITAL DATA COLLECTED FROM TECHNOLOGY-ENHANCED LEARNING ENVIRONMENTS.
ANALYZING LEARNING TRACES IS PRESENTLY HIGHLY REQUIRED IN E-LEARNING ENVIRONMENT. SEVERAL COMMUNITIES HAVE BEEN DEVELOPED TO ADDRESS THIS NEED, SUCH AS THOSE OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING. THE MAIN STEP OF PERFORMING A LEARNING ANALYTICS PROCESS IS THE EDUCATIONAL DATA COLLECTION. ACTUALLY, LEARNING ENVIRONMENTS SUCH AS MASSIVE OPEN ONLINE COURSE (MOOC) GENERATE A BIG AMOUNT OF EDUCATIONAL DATA. THEY CAN BE DIVIDED INTO ASSESSMENT DATA, COLLABORATION DATA, COMMUNICATION DATA, AND SO ON. WHEN WE FOCUS ON ASSESSMENT, WE CAN LAUNCH A NEW SOURCE OF DATA THAT CAN BE ANALYZED AND HENCE CONTRIBUTE TO THE IMPROVEMENT OF LEARNING ANALYTICS FIELD. IN THIS PAPER, WE EXPLORE, INVESTIGATE AND COMPARE THE SET OF LEARNING ANALYTICS MODELS IN THE LITERATURE. THEN, WE STUDY THEM FROM ASSESSMENT POINT OF VIEW. THE ONLY CURRENT LEARNING ANALYTICS MODEL WHICH CAN SUPPORT TRACKING AND MODELING ASSESSMENT DATA IS THE XAPI DATA MODEL. FOR THIS REASON, WE STUDY AND INVESTIGATE THE XAPI SPECIFICATION FROM ASSESSMENT POINT OF VIEW. BASED ON IDENTIFIED WEAKNESSES OF XAPI SPECIFICATION, WE PROPOSE AN ENHANCEMENT OF ITS DATA MODEL. THIS IS TO SUPPORT THE ASSESSMENT ANALYTICS EFFECTIVELY. WE PRESENT AN ONTOLOGICAL MODEL FOR ASSESSMENT ANALYTICS INSPIRED FROM THE XAPI SPECIFICATION. TO VALIDATE OUR APPROACH, WE FOCUS ON MASSIVE LEARNING TRACES EXTRACTED FROM A REAL MOOC. THUS, WE DEFINE AND EXECUTE THE SET OF PROPOSED STEPS OF PREPROCESSING STAGE THAT EXTRACTS ASSESSMENT DATA FROM WHOLE LEARNING DATA. FURTHERMORE, WE DEVELOP A JAVA SEMANTIC WEB APPLICATION TO CONVERT ASSESSMENT DATA EXTRACTED TO OWL FILE ACCORDING TO OUR PROPOSED ONTOLOGICAL MODEL FOR ASSESSMENT ANALYTICS.
THE ADOPTION OF THE FLIPPED CLASSROOM METHODOLOGY IS A NEW TREND IN THE FIELD OF EDUCATION, WITH A GROWING NUMBER OF PUBLICATIONS SHOWING SIGNIFICANT IMPACTS ON STUDENT BEHAVIOR AND PERFORMANCE. THIS METHODOLOGY ENCOURAGES THE STUDENT TO STUDY THE CURRICULAR CONTENT BEFORE ATTENDING THE CLASSROOM, WHEREAS THE CLASSROOM BECOMES A PLACE FOR ACTIVE LEARNING THAT FOCUSES ON DISCUSSIONS, QUESTIONS AND PRACTICAL ACTIVITIES. IN THE FLIPPED CLASSROOM, THE STUDENT'S PERFORMANCE IS DEPENDENT, MAINLY, ON ACTIVITIES OUTSIDE THE CLASSROOM CONDUCTED AT A DISTANCE. THESE ACTIVITIES REQUIRE A MORE ACTIVE POSTURE OF THE STUDENT IN THE MANAGEMENT OF THEIR STUDIES AND, BECAUSE OF THIS REQUIREMENT, RESEARCH ON FLIPPED CLASSROOM POINTS TO THE NEED TO DEVELOP APPROACHES THAT PROMOTE SELF-REGULATED LEARNING. IN THIS SENSE, THIS STUDY AIMED TO EVALUATE THE USABILITY OF A LEARNING ANALYTICS RESOURCE DEDICATED TO SELF-REGULATION LEARNING IN FLIPPED CLASSROOM. THE ADOPTED METHOD CONTEMPLATED THE CONVERGENCE OF MULTIPLE SOURCES OF EVIDENCE, WITH THE TRIANGULATION OF DATA COLLECTED THROUGH THE SUS QUESTIONNAIRE AND OBSERVATION. ACCORDING TO THE OPINION OF THIRTY-NINE USERS WHO COLLABORATED WITH THE RESEARCH, EVEN WITH SOME SUGGESTIONS FOR IMPROVEMENT, THE USABILITY OF THE EVALUATED RESOURCE IS SATISFACTORY AND, IN ADDITION, THE ARTIFACT STIMULATES THE REFLECTION OF THE STUDENT, OFFERING INFORMATION THAT FAVORS THE SELF-REGULATING PROCESSES IN THE FLIPPED CLASSROM.
THIS PAPER PROVIDES AN EFFECTIVE METHOD OF LEARNING ANALYTICS TO DESIGN SUPERIOR INSTRUCTION FOR A PRACTICAL EXERCISE OR WORKSHOP IN CYBERSECURITY EDUCATION. FOR ANALYZING LEARNER'S BEHAVIORS, THIS RESEARCH FOCUSED ON LOG DATA OF LINUX COMMAND. BECAUSE THE LOG DATA MEANS HISTORY OF OPERATION TAKEN BY LEARNERS DURING THE EXERCISE, IT'S SIGNIFICANTLY USEFUL FOR ANALYZING THE LEARNER'S BEHAVIORS. IN THIS EXPERIMENT, THE DUMPED LOG DATA WERE COLLECTED AND THEN THE COLLECTED DATA WERE REPRESENTED AS A VISUALIZED DATA FOR EFFECTIVELY ANALYZING THE LEARNER'S BEHAVIORS. AS THE RESULT OF THE ANALYTICS, THE VISUALIZED DATA SHOWED SEVERAL INSTRUCTIONAL POINTS WHICH THE INSTRUCTOR SHOULD IMPROVE. THIS PAPER EXPLAINS THE METHOD OF LEARNING ANALYTICS.
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, THERE IS A LACK OF TOOLS THAT ALLOW THE VISUALIZATION OF MULTIMODAL DATA. IN THIS PAPER, WE PRESENT A VISUALIZER THAT ALLOWS ANALYZING THE DATA PROVIDED BY A MULTIMODAL LEARNING ANALYTICS SOFTWARE. THE MULTIMODAL DATA VISUALIZER, IN ADDITION TO ALLOWING TO VISUALIZE 10 BODY POSTURES, PERMITS APPLYING CLUSTERING TECHNIQUES, SUCH AS K-MEANS. AS VALIDATION, WE ANALYZE THE DATA PROVIDED OF 43 ENGINEERING STUDENT PRESENTATIONS.
THE PAST FEW YEARS HAVE SEEN A RAPID DEVELOPMENT OF MACHINE-LEARNING ALGORITHMS. WHILE SURELY AUGMENTING PERFORMANCE, THESE COMPLEX TOOLS ARE OFTEN TREATED AS BLACK-BOXES AND MAY IMPAIR OUR UNDERSTANDING OF THE PHYSICAL PROCESSES UNDER STUDY. THE AIM OF THIS PAPER IS TO MOVE A FIRST STEP INTO THE DIRECTION OF APPLYING EXPERT-KNOWLEDGE IN PARTICLE PHYSICS TO CALCULATE THE OPTIMAL DECISION FUNCTION AND TEST WHETHER IT IS ACHIEVED BY STANDARD TRAINING, THUS MAKING THE AFOREMENTIONED BLACK-BOX MORE TRANSPARENT. IN PARTICULAR, WE CONSIDER THE BINARY CLASSIFICATION PROBLEM OF DISCRIMINATING QUARK-INITIATED JETS FROM GLUON-INITIATED ONES. WE CONSTRUCT A NEW VERSION OF THE WIDELY USED N-SUBJETTINESS, WHICH FEATURES A SIMPLER THEORETICAL BEHAVIOUR THAN THE ORIGINAL ONE, WHILE MAINTAINING, IF NOT EXCEEDING, THE DISCRIMINATION POWER. WE INPUT THESE NEW OBSERVABLES TO THE SIMPLEST POSSIBLE NEURAL NETWORK, I.E. THE ONE MADE BY A SINGLE NEURON, OR PERCEPTRON, AND WE ANALYTICALLY STUDY THE NETWORK BEHAVIOUR AT LEADING LOGARITHMIC ACCURACY. WE ARE ABLE TO DETERMINE UNDER WHICH CIRCUMSTANCES THE PERCEPTRON ACHIEVES OPTIMAL PERFORMANCE. WE ALSO COMPARE OUR ANALYTIC FINDINGS TO AN ACTUAL IMPLEMENTATION OF A PERCEPTRON AND TO A MORE REALISTIC NEURAL NETWORK AND FIND VERY GOOD AGREEMENT.
THERE IS AN INCREASING TREND OF LEARNING ANALYTICS DASHBOARDS (LADS) BEING USED TO PROVIDE FEEDBACK TO LEARNERS. HOWEVER, THERE IS LITTLE EMPIRICAL EVIDENCE ABOUT THE INFLUENCE OF THEIR DESIGN FEATURES ON LEARNERS' COGNITIVE AND AFFECTIVE OUTCOMES, ESPECIALLY IN HIGH-ANXIETY COURSES SUCH AS STATISTICS. TO ADDRESS THIS GAP, THIS STUDY EMPLOYED A TWO-GROUP EXPERIMENTAL DESIGN APPLIED TO AN AUTHENTIC SETTING TO ASSESS THE INFLUENCE OF TASK-VALUE SCAFFOLDING IN A LAD ON LEARNERS' ANXIETY, MOTIVATION, AND LEARNING PERFORMANCE IN AN ONLINE STATISTICS COURSE. THIS SEMESTER-LONG EXPERIMENT WAS IMPLEMENTED IN TWO INSTANCES OF THE COURSE OFFERING (FALL/2019 AND SPRING/2020) AND INVOLVED A TOTAL OF 146 STUDENTS. THE RESULTS SHOWED THAT TASK-VALUE SCAFFOLDING HAD A NEGATIVE IMPACT ON LEARNERS' COMPUTATION ANXIETY AND ATTITUDES TOWARDS STATISTICS IN COMPARISON TO THE CONTROL GROUP. ON THE OTHER HAND, THE TREATMENT HAD NO SIGNIFICANT INFLUENCE ON OTHER ASPECTS OF STATISTICS ANXIETY, MOTIVATION, AND LEARNING OUTCOMES. TAKEN JOINTLY, THESE RESULTS SUGGEST THAT THE USE OF TASK-VALUE SCAFFOLDING EMBEDDED IN LADS CAN HAVE DETRIMENTAL EFFECTS ON LEARNERS. MORE EXPERIMENTAL STUDIES ARE NECESSARY TO UNDERSTAND THE POSITIVE AND NEGATIVE EFFECTS OF LADS WITH MOTIVATIONAL SCAFFOLDING.
LEARNING ANALYTICS SYSTEMS CAN ANALYZE AND MEASURE LEARNERS' DATA TO INFER COMPETENCE, META-COMPETENCE, AND CONFIDENCE MEASURES. WHILE CATERING TO THE NEEDS OF STUDENTS, THE LEARNING ANALYTICS SYSTEM ALSO MEASURES EFFECTIVENESS AND EFFICIENCY OF THE LEARNING ENVIRONMENT. THESE MEASURES ENABLE THE LEARNING ANALYTICS SYSTEM TO AUTO-CONFIGURE AND AUTO-CUSTOMISE ITSELF TO OFFER PERSONALIZED INSTRUCTION AND OPTIMAL LEARNING PATHWAYS TO STUDENTS. SUCH A LEARNING ANALYTICS SYSTEM CAN BE CLASSIFIED A SMART LEARNING ENVIRONMENT, WHERE LEARNER ENGAGEMENT INITIATIVES ARE AUTO-GENERATED BY THE SYSTEM ITSELF. THIS PAPER PROPOSES THE PARALLEL PARTICLE SWARM OPTIMIZATION (PPSO) CLUSTERING AS A MECHANISM TO TRIGGER LEARNING ENGAGEMENT INITIATIVES. USING PPSO, LEARNERS ARE CLUSTERED USING SIMILARITY MEASURES INFERRED FROM OBSERVED COMPETENCE, META-COMPETENCE, AND CONFIDENCE VALUES, IN ADDITION TO EFFECTIVENESS MEASURES OF INSTRUCTIONAL TOOLS. A SIMULATION STUDY SHOWS THAT THE PPSO-BASED CLUSTERING IS MORE OPTIMAL THAN PARALLEL K-MEANS CLUSTERING.
IN THIS PAPER WE DISCUSS THE USE OF ANALYTICS IN OII-WEB, AN ONLINE PROGRAMMING CONTEST TRAINING SYSTEM. WE FIRST PROVIDE AN OVERVIEW OF THE CHALLENGES IN TRAINING FOR PROGRAMMING CONTESTS. THEN WE DISCUSS THE DATA COLLECTED IN THESE YEARS USING OII-WEB, A PLATFORM DEVOTED TO THE TRAINING OF STUDENTS FOR THE ITALIAN OLYMPIADS IN INFORMATICS (OLIMPIADI ITALIANE DI INFORMATICA - II), AND ANALYZE THEM COMPARING TWO DISTINCT GROUPS OF USERS IN TWO DISTINCT PLATFORMS BUILT ON OII-WEB, ONE DEVOTED TO STUDENTS AND ONE TO THEIR TEACHERS. MOST NOTABLY, THE TWO GROUPS ARE MORE SIMILAR THAN ONE WOULD EXPECT WHEN DEALING WITH PROGRAMMING CONTEST TRAINING.
INDUSTRY 4.0 IS CURRENTLY TRANSFORMING INDUSTRIAL WORKPLACES INTO SENSOR-ASSISTED HIGH-TECH ENVIRONMENTS. WHILE IT IS OFTEN FEARED THAT JOBS WILL BE LOST THROUGH INCREASING AUTOMATION, WE ARE CONVINCED THAT THERE IS AN ENORMOUS POTENTIAL FOR VERSATILE AND COMPETENT WORKERS AT INNOVATIVE WORKPLACES. TRAINING AT THE WORKPLACE MAY CONTRIBUTE TO THIS. IN THIS CONTEXT, BOTH BODY-NEAR WEARABLES AND INDUSTRIAL DEVICES PRODUCE AN ENORMOUS AMOUNT OF DATA. HOWEVER, THE QUESTION IS HOW TO DEAL WITH THIS AMOUNT OF DATA. TO THIS END, VISUAL ANALYTICS IS A COMBINATION OF COMPUTATIONAL TECHNIQUES FROM DATA MINING AND MACHINE LEARNING WITH HUMAN PERCEPTIONAL METHODS FROM HUMAN-COMPUTER INTERACTION. IN THIS ARTICLE WE PRESENT A METHOD AND TOOL SUPPORT TO CREATE RICH AND INTERACTIVE VISUAL ANALYTICS CHARTS TO ANALYZE INNOVATIVE TRAINING SOLUTIONS IN HIGH-TECH WORKPLACE SETTINGS. THIS BRINGS TOGETHER THE COMPUTER'S CAPACITIES TO HANDLE LARGE DATA AND CALCULATION RESOURCES WITH THE HUMAN ABILITY TO QUICKLY GRASP RELATIONSHIPS. OUR TECHNICAL EVALUATION SHOWS, THAT THE APPROACH IS FEASIBLE FROM A COMPUTATIONAL PERSPECTIVE; USABILITY TESTS REVEALED THAT THE DEVELOPED PIPELINE METAPHOR REACHES ITS GOAL. OUR RESULTS MAY HELP IN DESIGNING FUTURE SYSTEMS THAT FULFILL THE NEEDS OF BOTH TRAINERS AND LEARNERS IN INDUSTRY 4.0 SETTINGS.
SOCIAL LEARNING ANALYTICS IS AN EMERGING DISCIPLINE THAT OFFERS NEW METHODS TO EXPLORE DATA FROM ONLINE EDUCATIONAL DEVICES IN ORDER TO OBTAIN A BETTER UNDERSTANDING OF STUDENT BEHAVIOR. AS LEARNING TAKES PLACE IN HETEROGENEOUS AND COMPLEX ONLINE ENVIRONMENTS, THE INCORPORATION OF CONTEXTUAL INFORMATION ABOUT THE STUDENT HAS ATTRACTED MAJOR INTEREST. FOR SOME TIME, MOST METHODS OF STUDENT MODELLING HAVE CONSIDERED INTERACTIONS AS A KEY DIMENSION OF THE STUDENT'S SOCIAL CONTEXT, BUT ONLY RECENTLY, AUTOMATIC EXTRACTION SOFTWARE AGENTS BEGIN TO TACKLE INTERACTIONS IN A NON -EXCLUSIVELY QUANTITATIVE WAY. IN THIS PAPER, WE PROPOSE THE DISCOVERY OF SENTIMENTS IN ONLINE SOCIAL INTERACTIONS AS AN ADDITIONAL PROPERTY FOR THE MODELING OF STUDENTS IN ORDER TO PRODUCE A CONTEXTUALIZED DIAGNOSIS WHEN PERFORMING LEARNING ANALYTICS. WE PROPOSE ANSWERS TO THE QUESTIONS OF "WHICH ARE THE SENTIMENTS IN STUDENTS CONTEXT MODELING?", "WHY ARE THEY IMPORTANT FOR SOCIAL LEARNING ANALYTICS?", "HOW CAN WE VISUALIZE THEM?".
SERIOUS GAMES (SGS), DEFINED AS A GAME IN WHICH EDUCATION (IN ITS VARIOUS FORMS) IS THE PRIMARY GOAL RATHER THAN ENTERTAINMENT, HAVE BEEN PROVEN AS AN EFFECTIVE EDUCATIONAL TOOL FOR ENGAGING AND MOTIVATING STUDENTS. HOWEVER, MORE RESEARCH IS NEEDED TO SUSTAIN THE SUITABILITY OF THESE GAMES TO TRAIN USERS WITH COGNITIVE IMPAIRMENTS. THIS EMPIRICAL STUDY ADDRESSES THE USE OF A SG FOR TRAINING STUDENTS WITH INTELLECTUAL DISABILITIES IN TRAVELING AROUND THE SUBWAY AS A COMPLEMENT TO TRADITIONAL TRAINING. FIFTY-ONE ADULT PEOPLE WITH DOWN SYNDROME, MILD COGNITIVE DISABILITY OR CERTAIN TYPES OF AUTISM SPECTRUM DISORDER, ALL CONDITIONS CLASSIFIED AS INTELLECTUAL DISABILITIES, PLAYED THE LEARNING GAME DOWNTOWN, A SUBWAY ADVENTURE WHICH WAS DESIGNED AD-HOC CONSIDERING THEIR NEEDS AND COGNITIVE SKILLS. WE USED STANDARDS-BASED GAME LEARNING ANALYTICS TECHNIQUES (IE, EXPERIENCE API -XAPI), TO COLLECT AND ANALYZE LEARNING DATA BOTH OFF-LINE AND IN NEAR REAL-TIME WHILE THE USERS WERE PLAYING THE VIDEOGAME. THIS ARTICLE ANALYZES AND ASSESSES THE EVIDENCE DATA COLLECTED USING ANALYTICS DURING THE GAME SESSIONS, LIKE TIME COMPLETING TASKS, INACTIVITY TIMES OR THE NUMBER OF CORRECT/INCORRECT STATIONS WHILE TRAVELING. BASED ON A MULTIPLE BASELINE DESIGN, THE RESULTS VALIDATED BOTH THE GAME DESIGN AND THE TASKS AND ACTIVITIES PROPOSED IN DOWNTOWN AS A SUPPLEMENTARY TOOL TO TRAIN SKILLS IN TRANSPORTATION. DIFFERENCES BETWEEN HIGH-FUNCTIONING AND MEDIUM-FUNCTIONING USERS WERE FOUND AND EXPLAINED IN THIS PAPER, BUT THE FACT THAT ALMOST ALL OF THE STUDENTS COMPLETED AT LEAST ONE ROUTE WITHOUT MISTAKES, THE GENERAL IMPROVEMENT TROUGH SESSIONS AND THE LOW-MISTAKE RATIO ARE GOOD INDICATORS ABOUT THE APPROPRIATENESS OF THE GAME DESIGN.
MULTIMODAL LEARNING ANALYTICS IS A SUBFIELD OF LEARNING ANALYTICS THAT USES DATA COMING FROM COMPLEX LEARNING ENVIRONMENTS AND COLLECTED THROUGH ALTERNATIVE DEVICES THAT ARE DIFFERENT FROM THOSE NORMALLY OBSERVED IN THE LEARNING ANALYTICS LITERATURE. THE PRESENT WORK USES DATA CAPTURED BY MICROSOFT KINECT AND ORGANIZED WITH LELIKELEN SYSTEM TO FIND PATTERNS IN STUDENTS ORAL PRESENTATIONS DURING A GIVEN DISCIPLINE. FOR THAT, A TOTAL OF 16 DIFFERENT FEATURES RELATED TO THE RECORDS OF 43 STUDENTS PRESENTATIONS (85 OBSERVATIONS) WERE USED TO GENERATE CLUSTERS OF STUDENTS WITH SIMILAR BEHAVIOR. INITIAL RESULTS INDICATE THREE MAIN DIFFERENT PROFILES OF STUDENTS ACCORDING TO THEIR PATTERNS IN ORAL PRESENTATIONS: ACTIVE, PASSIVE, AND SEMI-ACTIVE. SUCH FINDINGS CAN BE FURTHER IMPLEMENTED IN LELIKELEN SYSTEM IN ORDER TO ALLOW INSTANT FEEDBACK TO STUDENTS. FUTURE WORK WILL ALSO EVALUATE HOW STUDENTS ORAL PRESENTATIONS PATTERNS EVOLVE DURING THE SEMESTER, AND COMPARE PATTERNS OF STUDENTS PRESENTATIONS ACROSS AREAS TO EVALUATE WHETHER THERE ARE SIMILARITIES OR NOT.
THIS PAPER IS PART OF AN EXTENSIVE ACTION RESEARCH PROJECT ON LEARNING ANALYTICS AND FOCUSES ON THE ASPECTS FOR ANALYSING A VIRTUAL COLLABORATIVE LEARNING (VCL) SETTING THAT HAS BEEN APPLIED FOR OVER 15 YEARS NOW. THEREIN 40 STUDENTS (MOSTLY OF BUSINESS AND ECONOMIC FACULTY) WORK TOGETHER IN SMALL GROUPS OF 3 TO 6 MEMBERS. THEY COLLABORATE IN INTERNATIONAL TEAMS AND SOLVE ILL-STRUCTURED PROBLEMS USING SOCIAL SOFTWARE. DURING THE IN AVERAGE 6 WEEK WORKING PERIOD THEY ARE SUPPORTED BY LECTURERS AND ETUTORS AS VIRTUAL LEARNING FACILITATORS. THEREFOR WE ANALYSE HOW LEARNING FACILITATORS CAN INCREASE THEIR EFFICIENCY BY (SEMI-) AUTOMATED LEARNING ANALYTICS. FOR THE PURPOSE OF THE MAIN PROJECT'S RESEARCH OBJECTIVE TO ENSURE SUCCESSFUL LEARNING FACILITATING IN FORMAL ELEARNING SETTINGS THROUGH LEARNING ANALYTICS, THIS PAPER FULFILS THE SUBGOAL OF PREPARING AN ANALYSIS FRAMEWORK FOR ASSESSING ECOLLABORATION. WE USED A SYSTEMATIC LITERATURE REVIEW FOR ANALYSING 32 PUBLICATIONS OF 11 DATABASES OF EBSCOHOST REGARDING THEIR ASSESSMENT ASPECTS. AS THE OVERALL RESULTS WE CAN PRESENT THE DIFFERENTIATING DIMENSIONS FOR ECOLLABORATION AS WELL AS AN OVERVIEW OF THE APPROPRIATE ASSESSMENT FACETS FOR THE CONCRETE COURSE SETTING. THESE WILL SERVE AS A FRAMEWORK FOR APPLYING AN ASSESSMENT CATALOGUE.
TECHNOLOGY ENHANCED LEARNING (TEL) SUCH AS ONLINE LEARNING ENVIRONMENT WITH ADAPTIVE TECHNOLOGIES HAS GAINED GROWING INTEREST IN RECENT PAST IN THE FIELD OF TEACHING AND LEARNING. IN THIS CONTEXT, MOBILE LEARNING HAS GOT MUCH MOMENTUM AND IS EXEMPLIFIED BY DIVERSE CHARACTERISTICS ASSOCIATED WITH THE TECHNOLOGIES AND DEVICES USED, THE ENORMOUS SIZE OF DATA GENERATED THROUGHOUT A LEARNING SESSION, AND THE INTERACTIONS AMONG THE LEARNERS THAT OCCUR OUTSIDE THE CLASSROOM. CONSEQUENTLY, SOPHISTICATED DATA ANALYSIS TECHNIQUES ARE REQUIRED TO HANDLE THE INTRICACY OF MOBILE LEARNING AND ANALYZE THE VAST AMOUNT OF DATASETS TO ENHANCE THE LEARNING EXPERIENCES OF MOBILE LEARNERS. THIS HAS LED TO THE ADOPTION OF BIG DATA ANALYTICS FOR EFFICIENT PROCESSING OF BIG LEARNING DATA TO ADD VALUE TO THE MOBILE LEARNING ENVIRONMENTS. YET LIMITED PROCESSING CAPABILITY OF THE MOBILE DEVICES IS ANOTHER KEY CHALLENGE FACED BY SUCH BIG DATA ANALYTICS IN MOBILE LEARNING ENVIRONMENTS. TO OVERCOME THIS LIMITATION, CERTAIN HEAVY COMPUTATIONAL PARTS COULD BE OFFLOADED TO THE CLOUD WHICH CAN PROVIDE ENOUGH COMPUTATION AND STORAGE RESOURCES. TO THIS END, THIS PAPER PRESENTS A CLOUD BASED MOBILE LEARNING FRAMEWORK THAT UTILIZES BIG DATA ANALYTICS TECHNIQUE TO EXTRACT VALUES FROM HUGE VOLUME OF MOBILE LEARNERS' DATA. FINALLY, WE INVESTIGATE LEARNERS' READINESS AND DRIVING FACTORS OF MOBILE LEARNING ADOPTION IN HIGHER EDUCATION INSTITUTIONS. IN PARTICULAR, WE PROPOSE A HYPOTHESIZED MODEL FOR MOBILE LEARNING ADOPTION BUILT ON A LOCALLY EXTENDED TECHNOLOGY ACCEPTANCE MODEL (TAM).
THIS RESEARCH PAPER INTRODUCES A STATE OF THE ART FOR COUPLING SOFTWARE-DEFINED NETWORKING (SDN) CAPABILITY IN SMART LEARNING ENVIRONMENT (SLE). SMART COMPETENCE LEARNING ANALYTICS PLATFORM (SCALE) IS THE LEARNING ANALYTICS PLATFORM, WHICH HAS BEEN DEVELOPED TO ANALYZE AND MEASURE THE LEARNERS DATA (BIG DATA) GENERATED IN SLE FOR UNDERSTANDING THE VARIOUS LEARNERS COMPETENCE MEASURES. THE LEARNING ANALYTICS INVOLVES VARIOUS PROCESSES SUCH AS DISTRIBUTING, ANALYZING, AND MERGING THE RESULTS ACROSS THE CLUSTERS. IT REQUIRES A LARGE AMOUNT OF BANDWIDTH AND COMPUTATION CYCLES IN AN ON-DEMAND MANNER TO REDUCE THE TOTAL COMPUTATION TIME AND COMPUTATION COST. HENCE, IN THIS RESEARCH PAPER, WE INTRODUCE A SDN-BASED NETWORKING PRINCIPLE WITH SCALE, WHICH PROVISION THE NETWORK PATHS AND ALLOCATES THE BANDWIDTH IN AN ON-DEMAND MANNER. THE EXPERIMENT RESULTS WERE CONDUCTED TO ANALYZE THE PERFORMANCE AND IMPACT OF SDN IN SCALE PLATFORM.
LEARNING ANALYTICS IS AN EDUCATIONAL APPLICATION OF WEB ANALYTICS AND IT IS USED TO ANALYZE THE LEARNERS' ACTIVITIES ON EDUCATIONAL PLATFORMS, IN ORDER TO PREDICT THEIR BEHAVIOUR. LEARNING ANALYTICS REFLECTS A FIELD AT THE INTERSECTION OF NUMEROUS ACADEMIC DISCIPLINES AND IS USING DIFFERENT METHODS LIKE: STATISTICS, VISUALIZATIONS, DATA/WEB MINING, AND SOCIAL NETWORK ANALYSIS. SOCIAL NETWORK ANALYSIS AIM IS TO DISCOVER PATTERNS IN INDIVIDUAL INTERACTIONS. NETWORK ANALYSIS IS BASED ON THE INTUITIVE NOTION THAT THESE PATTERNS ARE IMPORTANT ATTRIBUTES OF THE INDIVIDUALS' LIVES. THE SOCIAL NETWORK ANALYSIS CAN BE MADE WITH DIFFERENT SOFTWARE APPLICATIONS AND IT'S VERY IMPORTANT TO SUPPORT NETWORKED LEARNING. LEARNING ANALYTICS TOOLS FOR SOCIAL NETWORK ANALYSIS ARE PROVIDING A FRAMEWORK FOR INTERPRETING AND DEVELOPING AN UNDERSTANDING OF THE OBSERVED PATTERNS OF EXCHANGES THAT OCCUR BETWEEN SOCIAL ACTORS. THE ON-LINE/BLENDED EDUCATIONAL PROGRAMS FROM THE LAST DECADES HAVE COLLECTED AN ENORMOUS QUANTITY OF DATA. THESE DATA INCLUDE INITIAL EVALUATION INFORMATION, DATA FROM THE TRAINING PROCESS AND RESULTS OF THE TRAINING PROGRAM. THESE DATA CAN BE USED TO VALIDATE THE LEARNING ANALYTICS THEORY. SINCE THE DEFINITION AND PURPOSE OF LEARNING ANALYTICS IS STILL DISPUTED, COMPARING THE PREDICTIONS RESULTS OBTAINED ON THE BASIS OF INITIAL DATA WITH THE FINAL RESULTS OBTAINED BY LEARNERS CAN CONTRIBUTE TO THE VALIDATION OF SUCH APPLICATIONS. THIS ARTICLE PRESENT THE FINDINGS AND CONCLUSIONS OBTAIN AFTER ANALYZING SERIES OF FORUM POSTS ON THE EDUCATIONAL PLATFORMS USED IN TEACHERS TRAINING PROGRAMS. (C) 2016 PUBLISHED BY FUTURE ACADEMY WWW.FUTUREACADEMY.ORG.UK
LEARNING ANALYTICS IS MEASUREMENT AND ANALYSIS OF LEARNING DATA COLLECTED FROM LEARNING ENVIRONMENTS, LEARNING ANALYTICS FOCUS ON LEARNERS TO PREDICT THEIR ACADEMIC PERFORMANCE IN THE CLASSROOM. THIS PAPER IS CONDUCTED TO PREDICT STUDENTS' ACADEMIC PERFORMANCE OF FOURTH-YEAR BACHELOR STUDENTS IN THE INFORMATION SECURITY COURSE, AND A CLASSIFICATION ALGORITHM IS USED TO PREDICT FINAL STUDENT GRADES. THE DATA SET USED IN THIS STUDY CONSISTS OF STUDENT'S CUMULATIVE GPA, GRADE SCORES IN PRE-REQUISITE COURSE, AND SCORES ON TWO MID-TERM EXAMS; THESE DATA WERE COLLECTED FROM 5-YEAR PERIOD INTAKES FROM 2014 UNTIL 2018. THE EXTRACTED KNOWLEDGE OUT OF THIS RESEARCH WILL BE USED TO IDENTIFY AND PREDICT THE STUDENT PERFORMANCE TO DETERMINE THE STUDENTS' LEVEL OF SUCCESS IN THE INFORMATION SECURITY COURSE WHICH CAN ASSIST AT-RISK STUDENTS IN MAKING APPROPRIATE INTERVENTION DECISIONS.
IN RECENT YEARS, LEARNING ANALYTICS HAS BEEN ATTRACTING ATTENTION. 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. GENERALLY, ITS INFORMATION ARE STORED IN LEARNING MANAGEMENT SYSTEM (LMS) AND COURSE MANAGEMENT SYSTEM (CMS). HOWEVER, LMS AND CMS DID NOT HAVE THE METHOD TO MEASURE THE ACHIEVEMENT OF LEARNING USING LECTURE CONTENTS. THIS PAPER PROPOSES THE METHODS TO MEASURE THE ACHIEVEMENT OF LEARNING USING LECTURE CONTENTS.
WITH INCREASING ABUNDANCE AND UBIQUITY OF MOBILE PHONES, DESKTOP PCS, AND TABLETS IN THE LAST DECADE, WE ARE SEEING STUDENTS INTERMIXING THESE MODALITIES TO LEARN AND REGULATE THEIR LEARNING. HOWEVER, THE ROLE OF THESE MODALITIES IN EDUCATIONAL SETTINGS IS STILL LARGELY UNDER-RESEARCHED. SIMILARLY, LITTLE ATTENTION HAS BEEN PAID TO THE RESEARCH ON THE EXTENSION OF LEARNING ANALYTICS TO ANALYZE THE LEARNING PROCESSES OF STUDENTS ADOPTING VARIOUS MODALITIES DURING A LEARNING ACTIVITY. TRADITIONALLY, RESEARCH ON HOW MODALITIES AFFECT THE WAY IN WHICH ACTIVITIES ARE COMPLETED HAS MAINLY RELIED UPON SELF-REPORTED DATA OR MERE COUNTS OF ACCESS FROM EACH MODALITY. WE EXPLORE THE USE OF TECHNOLOGICAL MODALITIES IN REGULATING LEARNING VIA LEARNING MANAGEMENT SYSTEMS (LMS) IN THE CONTEXT OF BLENDED COURSES. WE USED DATA MINING TECHNIQUES TO ANALYZE PATTERNS IN SEQUENCES OF ACTIONS PERFORMED BY LEARNERS (N = 120) ACROSS DIFFERENT MODALITIES IN ORDER TO IDENTIFY TECHNOLOGICAL MODALITY PROFILES OF SEQUENCES. THESE PROFILES WERE USED TO DETECT THE TECHNOLOGICAL MODALITY STRATEGIES ADOPTED BY STUDENTS. WE FOUND A MODERATE EFFECT SIZE (C(2) = 0.12) OF STUDENTS' ADOPTED STRATEGIES ON THE FINAL COURSE GRADE. FURTHERMORE, WHEN LOOKING SPECIFICALLY AT ONLINE DISCUSSION ENGAGEMENT AND PERFORMANCE, STUDENTS' ADOPTED TECHNOLOGICAL MODALITY STRATEGIES EXPLAINED A LARGE AMOUNT OF VARIANCE (ETA(2) = 0.68) IN THEIR ENGAGEMENT AND QUALITY OF CONTRIBUTIONS. THE RESULT IMPLICATIONS AND FURTHER RESEARCH ARE DISCUSSED.
IN TAIWAN, THE SOCIAL COMMUNITY, FACEBOOK HAS BEEN MORE AND MORE POWERFUL EVER SINCE BEING LAUNCHED, AND IT SERVES AS IF A STRONG MAGNET, ATTRACTING TEACHERS AND STUDENTS TO SHARE AND DISCUSS ON SUCH PLATFORM, AND ALSO TO SEARCH FOR FILMS, PICTURES, AND MESSAGES OF THEIR SPECIFIC INTERESTS. SOME RESEARCHERS HAVE PROBED INTO THE LEARNING INTERACTION AND EFFICACY OF A COMPUTER PROGRAMMING COURSE, AND THE RESULTS SHOW THAT NOT ONLY COULD THE LEARNING INTERACTIONS BETWEEN TEACHERS AND STUDENTS ENHANCE, BUT ALSO BETWEEN STUDENTS THEMSELVES UNDER THIS ENVIRONMENT SUPPORTED BY FACEBOOK. WE PROPOSE FOUR STEPS OF LEARNING ANALYTICS TO STUDY STUDENTS' BEHAVIORS AND LEARNING PERFORMANCES IN A SOCIAL COMMUNITY SUPPORTING COMPUTER PROGRAMMING COURSE. FURTHERMORE, OUR LEARNING ANALYTICS METHOD COULD DECREASE THE TIME AND ENERGY CONSUMING PROCESS, WHICH INCLUDES COLLECTING, CORRELATING AND ORGANIZING STUDENTS' PARTICIPATIVE PATTERNS. A REPORTED CASE, FOCUSES ON STUDENTS' ENGAGEMENT BEHAVIORS AND ITS INFLUENCE ON STUDENTS' LEARNING OUTCOMES, IS CARRIED OUT WITH 43 FRESHMEN AT A UNIVERSITY IN NORTHERN TAIWAN. THE RESULTS SHOW THAT OUR LEARNING ANALYTICS METHOD BENEFITS STUDENTS' PARTICIPATIVE BEHAVIORS, WHICH ARE RELATED TO STUDENTS' LEARNING ACHIEVEMENTS IN THE COMPUTER PROGRAMMING COURSE SUPPORTED BY SOCIAL COMMUNITY, SINCE STUDENTS COULD OBTAIN BETTER UNDERSTANDING UNDER SUCH LEARNING MODE.
THE CREATION OF INCLUSIVE AND ACCESSIBLE OPEN EDUCATIONAL RESOURCES (IA-OERS) IS A CHALLENGE FOR TEACHERS BECAUSE THEY HAVE TO INVEST TIME AND EFFORT TO CREATE LEARNING CONTENTS CONSIDERING STUDENTS' LEARNING NEEDS AND PREFERENCES. AN IA-OER IS CHARACTERIZED BY ITS ALIGNMENT WITH THE UNIVERSAL DESIGN LEARNING (UDL) PRINCIPLES, THE QUALITY ON ITS CONTENTS AND THE WEB ACCESSIBILITY AS A WAY TO ADDRESS THE DIVERSITY OF STUDENTS. CREATING AN IA-OER WITH THESE CHARACTERISTICS IS NOT A STRAIGHTFORWARD TASK, ESPECIALLY WHEN TEACHERS DO NOT HAVE ENOUGH INFORMATION/FEEDBACK TO MAKE DECISIONS ON HOW TO IMPROVE THE LEARNING CONTENTS. IN THIS PAPER WE INTRODUCE ATCE AN ANALYTICS TOOL TO TRACE THE CREATION AND EVALUATION OF IA-OERS. THIS TOOL FOCUSES IN PARTICULAR ON THE ACCESSIBILITY AND QUALITY OF THE IA-OERS. ATCE WAS DEVELOPED AS A MODULE WITHIN THE ATUTOR LEARNING MANAGEMENT SYSTEM (LMS). AN ANALYTICS DASHBOARD WITH VISUALIZATIONS RELATED TO THE TEACHERS' COMPETENCES IN THE CREATION AND EVALUATION OF IA-OERS WAS INCLUDED AS PART OF THE TOOL. THIS PAPER ALSO PRESENTS A USE CASE OF THE VISUALIZATIONS OBTAINED FROM THE CREATION AND EVALUATION OF ONE IA-OER AFTER USING OUR ANALYTICS TOOL.
IN THE LEARNING ANALYTICS FIELD, IT IS HIGHLY SIGNIFICANT TO TRACK AND COLLECT THE BIG EDUCATIONAL DATA TO IMPROVE THE LEARNING EXPERIENCE. IN FACT, ONE OF THE E-LEARNING STANDARDS FOR DATA INTEROPERABILITY WHICH HAD ATTRACTED A REMARKABLE AMOUNT OF ATTENTION IN THE LAST YEARS IS THE EXPERIENCE API (XAPI). IN THIS PAPER, WE EXPLORE THE USE OF XAPI IN THE LEARNING ANALYTICS FIELD. THEREFORE ASSESSMENT DATA REPRESENTS AN IMPORTANT PROPORTION OF THE EDUCATIONAL DATA GENERATED. WHEN WE FOCUS ON ASSESSMENT, WE CAN LAUNCH A NEW SOURCE OF DATA THAT CAN BE ANALYZED AND HENCE CONTRIBUTE TO THE IMPROVEMENT OF THE FIELD OF LEARNING ANALYTICS. IN FACT, WE DISCUSS THE SUITABILITY OF XAPI STANDARD TO TRACK THE ASSESSMENT DATA AND TRY TO ENHANCE ITS DATA MODEL TO SUPPORT EFFECTIVELY THE ASSESSMENT ANALYTICS. AN ONTOLOGICAL MODEL IS PROPOSED SUPPORTING ASSESSMENT ANALYTICS PURPOSE BASED ON THE WEAKNESSES OF THE XAPI DATA MODEL FROM ASSESSMENT POINT OF VIEW. SINCE OUR PROPOSED PATTERN IS AN ONTOLOGICAL MODEL, THIS GIVES US THE CHANCE TO REASON ABOUT THE ASSESSMENT DATA BY PERFORMING SOME LOGIC RULES EDITED WITH SWRL(SEMANTIC WEB RULE LANGUAGE) FOR SUPPORTING INFERENCE MECHANISMS RELATED TO THE LEANER LEVEL ACCORDING TO ITS ASSESSMENT PERFORMANCE. (C) 2018 THE AUTHORS. PUBLISHED BY ELSEVIER LTD.
THE LARGE DATASETS PRODUCED BY LEARNING AT SCALE, AND THE NEED FOR WAYS OF DEALING WITH HIGH LEARNER/EDUCATOR RATIOS, MEAN THAT MOOCS AND RELATED ENVIRONMENTS ARE FREQUENTLY USED FOR THE DEPLOYMENT AND DEVELOPMENT OF LEARNING ANALYTICS. DESPITE THE CURRENT PROLIFERATION OF ANALYTICS, THERE IS AS YET RELATIVELY LITTLE HARD EVIDENCE OF THEIR EFFECTIVENESS. THE EVIDENCE HUB DEVELOPED BY THE LEARNING ANALYTICS COMMUNITY EXCHANGE (LACE) PROVIDES A WAY OF COLLATING AND FILTERING THE AVAILABLE EVIDENCE IN ORDER TO SUPPORT THE USE OF ANALYTICS AND TO TARGET FUTURE STUDIES TO FILL THE GAPS IN OUR KNOWLEDGE.
PURPOSE THIS STUDY AIMS TO EXPLORE DISCIPLINARY DIFFERENCES IN COMPLETING BLENDED LEARNING TASKS IN AN ACADEMIC LITERACY COURSE AND THE FEASIBILITY OF ADOPTING A BLENDED LEARNING ANALYTICS APPROACH TO EXPLORE DISCIPLINARY DIFFERENCES. DESIGN/METHODOLOGY/APPROACH FOLLOWING A LEARNING ANALYTICS APPROACH, THIS STUDY BLENDS DATA FROM THE LEARNING MANAGEMENT SYSTEM AND TIMETABLING ARRANGEMENTS. FINDINGS RESULTS SUGGEST THAT ONLINE BEHAVIORS OF DESIGN STUDENTS AND ACCOUNTING STUDENTS ARE DIFFERENT IN TERMS OF STARTING DAY AND COMPLETION RATE. BLENDING DATA SOURCES ALSO PROVIDES A NEW PERSPECTIVE TO OUR LEARNING ANALYTICS STUDY. ORIGINALITY/VALUE THIS STUDY IS AN IMPORTANT CONTRIBUTION TO THE FIELD BECAUSE STUDIES ON LEARNING ANALYTICS WITH MULTIPLE DATA SOURCES ARE RARE, AND MOST DISCIPLINARY STUDIES RELY ON SURVEY DATA; STUDENTS' ACTUAL BEHAVIORS ARE UNDER-EXPLORED.
THIS STUDY INVESTIGATES HOW TEACHERS USE DASHBOARDS IN PRIMARY SCHOOL CLASSROOMS. WHILE LEARNERS PRACTICE ON A TABLET REAL-TIME DATA INDICATING LEARNER PROGRESS AND PERFORMANCE IS DISPLAYED ON TEACHER DASHBOARDS. THIS STUDY EXAMINES HOW TEACHERS USE THE DASHBOARDS, APPLYING VERBERTS' LEARNING ANALYTICS PROCESS MODEL. TEACHER DASHBOARD CONSULTATIONS AND RESULTING PEDAGOGICAL ACTIONS WERE OBSERVED IN 38 MATHEMATICS LESSONS. IN STIMULATED RECALL INTERVIEWS, THE 38 TEACHERS WERE ASKED TO ELABORATE ON HOW THEY REFLECT ON AND MAKE SENSE OF THE INFORMATION ON THE DASHBOARD. THE RESULTS SHOWED THAT TEACHERS CONSULTED THE DASHBOARD ON AVERAGE 8.3 TIMES PER LESSON. TEACHERS ACTIVATED EXISTING KNOWLEDGE ABOUT STUDENTS AND THE CLASS TO INTERPRET DASHBOARD INFORMATION. TASK AND PROCESS FEEDBACK WERE THE PEDAGOGICAL ACTIONS MOST OFTEN USED FOLLOWING DASHBOARD CONSULTATION. ADDITIONALLY, TEACHERS WHO CONSULTED THE DASHBOARD MORE OFTEN ACTIVATED MORE AND MORE DIVERSE PEDAGOGICAL KNOWLEDGE TO INTERPRET THE DATA AND, CONSEQUENTLY, GAVE MORE AND MORE DIVERSE FEEDBACK. THESE RESULTS INDICATED THAT TEACHER DASHBOARDS WERE INDEED INFLUENCING TEACHERS' PEDAGOGICAL ACTIONS IN THEIR DAILY CLASSROOM ACTIVITIES. THIS STUDY PROVIDED THE FIRST EVIDENCE THAT DASHBOARDS PROGRESSIVELY IMPACT TEACHING PRACTICE AND INITIATE MORE PROFOUND BEHAVIORAL CHANGES AS TEACHERS BECOME MORE PROFICIENT IN USING THEM.
THE PAPER DESCRIBES THE ARCHITECTURE, PROCESSES AND ISSUES RELATED TO THE CURRENT PHASE OF THE DEVELOPMENT AND PIVOTAL DEPLOYMENT OF THE MICROLEARNING-BASED WEB APPLICATION FOR IMPROVING PROGRAMMING SKILLS AND LEARNING OTHER SOFTWARE ENGINEERING TOPICS. THIS APPLICATION INTEGRATES THE ADVANTAGES OF MICROLEARNING APPROACH, GAMIFICATION AND LEARNING ANALYTICS INTO THE EXISTING LEARNING ECOSYSTEM AND IT INFRASTRUCTURE OF THE CONTEMPORARY EDUCATIONAL ORGANISATION. THE MAIN AIM OF THE PAPER IS TO REVIEW AND ANALYSE DIFFERENT PARTS OF THE ARCHITECTURE OF A MODERN EDUCATIONAL APPLICATION, DESCRIBE THEIR MAIN FEATURES AND UNIQUE ROLES. THE NECESSITY TO USE OPEN STANDARDS FOR STORING EDUCATIONAL DATA AND ITS FURTHER ANALYSIS USING LEARNING ANALYTICS METHODS IS EMPHASISED. SUBSEQUENTLY, THE CURRENT VERSION OF THE DEVELOPED MICRO-LEARNING APPLICATION FOR LEARNING PROGRAMMING LANGUAGES IS PRESENTED. FINALLY, THE FIRST EXPERIENCE WITH ITS PIVOTAL DEPLOYMENT AS WELL AS THE DIRECTION OF ITS FUTURE DEVELOPMENT IS DESCRIBED IN DETAIL.
THIS SHORT ARTICLE DESCRIBES AN EXPERIMENTAL RADIO-FREQUENCY IDENTIFICATION (RFID) SYSTEM DESIGNED TO PLAYFULLY EXPLORE THE POSSIBILITIES OF OBJECT AGENCY, IN THE FORM OF 'TWEETING BOOKS' THE USE OF WEB-ENABLED SENSORS IS DISCUSSED IN THE CONTEXT OF THE EMERGING FIELD OF LEARNING ANALYTICS. THE ANALYSIS OF THE 'TWEETING BOOKS PROTOTYPE CHALLENGES THE IDEA OF STRAIGHTFORWARD 'NON-HUMAN DATA AND THE ISOLATION OF SPECIFIC AND LOCALISED AGENCY. I WILL DRAW UPON SOCIOMATERIAL THEORY, WHICH ENCOMPASSES A BROAD RECONSIDERATION OF THE DIVISIONS BETWEEN CULTURE AND NATURE, THE HUMAN AND THE NON-HUMAN. AS SUCH, THE PRODUCTION OF DATA CAN BE THOUGHT OF AS THE ENTANGLEMENT OF HUMAN USER AND NON-HUMAN TECHNOLOGY, RATHER THAN THE PRIVILEGING OF HUMAN INTENTION AS THE EXCLUSIVE SOURCE OF AGENCY. THIS WILL BE SUGGESTED TO HAVE IMPORTANT IMPLICATIONS FOR LEARNING ANALYTICS, WHICH IS OFTEN PREMISED ON A COMMITMENT TO THE IDEA THAT DATA DIRECTLY REPRESENTS HUMAN BEHAVIOUR.
IN THIS SPECIAL ISSUE, WE HAVE COLLECTED THREE RESPONSES FROM SCHOLARS WHO ARE CURRENTLY DOING WORK IN LEARNING ANALYTICS TO REACT TO, BETTER EXPLAIN, AND EXPOUND UPON THE IFENTHALER AND SCHUMACHER'S FINDINGS IN THEIR OWN SETTINGS AS RESEARCHERS AND PRACTITIONERS.
MOBILE TECHNOLOGIES AND THEIR APPLICATIONS HAVE THE POTENTIAL TO BENEFIT VARIOUS LEARNING CONTEXTS. USERS' PERCEPTIONS OF MOBILE LEARNING (M-LEARNING) TECHNOLOGIES ARE OF GREAT IMPORTANCE AND PRECEDE THE SUCCESSFUL INTEGRATION OF THESE TECHNOLOGIES IN EDUCATION. M-LEARNING ADOPTION HAS BEEN INVESTIGATED IN THE LITERATURE WITH REFERENCE TO VARIOUS FACTORS AND LEARNING ANALYTICS, BUT LARGELY WITHOUT CONSIDERING THE ROLE OF DIFFERENT CONFIGURATIONS (I.E., SPECIFIC COMBINATIONS OF VARIABLES), AND HOW THESE CONFIGURATIONS MIGHT AFFECT THE ADOPTION OF VARIOUS USER GROUPS. FOR INSTANCE, USERS WITH DIFFERENT BACKGROUNDS, EXPERIENCES, LEARNING STYLES, AND SO ON MIGHT NOT BE REPRESENTED BY THE ONE-MODEL-FITS-ALL PRODUCED FROM THE COMMON REGRESSION APPROACHES. IN THIS STUDY, WE BRIEFLY REVIEW FACTORS THAT HAVE BEEN PROVEN IMPORTANT IN THE CONTEXT OF MOBILE LEARNING ADOPTION, AND BUILD ON COMPLEXITY THEORY AND CONFIGURATION THEORY IN ORDER TO EXPLORE THE CAUSAL PATTERNS OF FACTORS THAT STIMULATE THE USE OF MOBILE LEARNING. TO TEST ITS PROPOSITIONS, THE STUDY EMPLOYS FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS (FSQCA) ON A DATA SAMPLE FROM 180 EXPERIENCED M-LEARNING USERS. FINDINGS INDICATE EIGHT CONFIGURATIONS OF COGNITIVE AND AFFECTIVE CHARACTERISTICS, AND SOCIAL AND INDIVIDUAL FACTORS, THAT EXPLAIN M-LEARNING ADOPTION. THIS RESEARCH STUDY CONTRIBUTES TO THE LITERATURE BY (1) OFFERING NEW INSIGHTS ON HOW PREDICTORS OF M-LEARNING ADOPTION INTERRELATE: (2) EXTENDING EXISTING KNOWLEDGE ON HOW COGNITIVE AND AFFECTIVE CHARACTERISTICS, AND SOCIAL AND INDIVIDUAL FACTORS, COMBINE TO LEAD TO HIGH M-LEARNING ADOPTION: AND (3) PRESENTING A STEP-BY-STEP METHODOLOGICAL APPROACH FOR HOW TO APPLY FSQCA IN THE AREA OF LEARNING SYSTEMS AND LEARNING ANALYTICS. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE STUDY SEEKS TO INVESTIGATE THE STAKEHOLDERS' PERCEPTIONS OF ORGANIZATIONAL FORCES AND OPERATIONAL ISSUES THAT WEIGH UPON THE ACADEMIC LIBRARY'S DATA CAPABILITIES, WITH A SPECIAL FOCUS ON THE GREEK ACADEMIC LIBRARIANS' APPRECIATION OF THE PROSPECTS OF LIBRARY USE DATA INTEGRATION IN CAMPUS INFORMATION SYSTEMS. FINDINGS FROM ONLINE INTERVIEWS WITH INTERNATIONAL LEARNING ANALYTICS EXPERTS AND GREEK ACADEMIC LIBRARY DIRECTORS, THAT AIMED TO RECORD THEIR VIEWPOINTS AROUND DIFFICULTIES OF INTEGRATING LIBRARY DATA INTO LEARNING ANALYTICS SYSTEMS, BROUGHT TO LIGHT SEVERAL INTERPRETATIVE, ORGANIZATIONAL CULTURE, LIBRARIAN PROFESSIONAL DEVELOPMENT, AND USER BUY-IN CONCERNS. INTERVIEWEES MADE SEVERAL RECOMMENDATIONS RELATED TO COLLECTIVE ACTION, TRAINING, AND COMMUNICATIONS ALSO STRESSING THE IMPORTANCE OF INSTITUTIONAL SUPPORT AND ACTIVE INVOLVEMENT OF PROFESSIONAL ASSOCIATIONS. THE ANALYSIS OF THE PARTICIPANTS' PERSPECTIVE PROVIDES A SOLID CONCEPTUAL FOUNDATION FOR INTERPRETING LOCAL ISSUES ASSOCIATED WITH LIBRARY USE DATA CAPITALIZATION WITHIN AND BEYOND INTRA-INSTITUTIONAL INITIATIVES IN THE OVERARCHING GOAL TO CONTRIBUTE ACTIONABLE DATA TO THE DEVELOPMENT OF CONTEXT-SPECIFIC TAILOR-MADE SOLUTIONS.
PREDICTIVE MODELLING OF ACADEMIC SUCCESS AND RETENTION HAS BEEN A KEY RESEARCH THEME IN LEARNING ANALYTICS. WHILE THE INITIAL WORK ON PREDICTIVE MODELLING WAS FOCUSED ON THE DEVELOPMENT OF GENERAL PREDICTIVE MODELS, PORTABLE ACROSS DIFFERENT LEARNING SETTINGS, LATER STUDIES DEMONSTRATED THE DRAWBACKS OF NOT CONSIDERING THE SPECIFICITIES OF COURSE DESIGN AND DISCIPLINARY CONTEXT. THIS STUDY BUILDS ON THE METHODS AND FINDINGS OF RELATED EARLIER STUDIES TO FURTHER EXPLORE FACTORS PREDICTIVE OF LEARNERS' ACADEMIC SUCCESS IN BLENDED LEARNING. IN DOING SO, IT DIFFERENTIATES ITSELF BY (I) RELYING ON A LARGER AND HOMOGENEOUS COURSE SAMPLE (15 COURSES, 50 COURSE OFFERINGS IN TOTAL), AND (II) CONSIDERING BOTH INTERNAL AND EXTERNAL CONDITIONS AS FACTORS AFFECTING THE LEARNING PROCESS. WE APPLY MIXED EFFECT LINEAR REGRESSION MODELS, TO EXAMINE: I) TO WHAT EXTENT INDICATORS OF STUDENTS' ONLINE LEARNING BEHAVIOUR CAN EXPLAIN THE VARIABILITY IN THE FINAL GRADES, AND II) TO WHAT EXTENT THAT VARIABILITY IS ATTRIBUTABLE TO THE COURSE AND STUDENTS' INTERNAL CONDITIONS, NOT CAPTURED BY THE LOGGED DATA. HAVING EXAMINED DIFFERENT TYPES OF BEHAVIOUR INDICATORS (E.G., INDICATORS OF THE OVERALL ACTIVITY LEVEL, THOSE INDICATIVE OF REGULARITY OF STUDY, ETC), WE FOUND LITTLE DIFFERENCE, IF ANY, IN THEIR PREDICTIVE POWER. OUR RESULTS FURTHER INDICATE THAT A LOW PROPORTION OF VARIANCE IS EXPLAINED BY THE BEHAVIOUR-BASED INDICATORS, WHILE A SIGNIFICANT PORTION OF VARIABILITY STEMS FROM THE LEARNERS' INTERNAL CONDITIONS. HENCE, WHEN VARIABILITY IN EXTERNAL CONDITIONS IS LARGELY CONTROLLED FOR (THE SAME INSTITUTION, DISCIPLINE, AND NOMINAL PEDAGOGICAL MODEL), STUDENTS' INTERNAL STATE IS THE KEY PREDICTOR OF THEIR COURSE PERFORMANCE.
USER ACCEPTANCE IS KEY FOR THE ADOPTION OF A NEW TECHNOLOGY. IN THIS WORK WE EXPERIMENT WITH A NOVEL SERVICE FOR TUTORS IN WORKPLACE LEARNING SETTINGS. SENTIMENT ANALYSIS IS A WAY TO EXTRACT FEELINGS AND EMOTIONS FROM A TEXT. IN A LEARNING SETTING SUCH A SENTIMENT ANALYSIS CAN BE PART OF LEARNING ANALYTICS. IT HAS THE POTENTIAL TO FOSTER THE UNDERSTANDING OF EMOTIONS IN SHARED DISCUSSIONS IN LEARNING ENVIRONMENTS, DETECT GROUP DYNAMICS AS WELL AS THE IMPACT OF CERTAIN TOPICS ON LEARNERS' SENTIMENTS. HOWEVER, SENTIMENT ANALYSIS PRESENTS SOME CHALLENGES TOO, AS LIVED EXPERIENCES, EXPECTATIONS AND ULTIMATELY ACCEPTANCE OF THIS TECHNOLOGY VARIES GREATLY AND CAN BECOME BARRIERS TO ADOPTION. IN ORDER TO DESIGN A SYSTEM FOR LEARNING ANALYTICS ACCEPTED BY TUTORS WE EXPERIMENTED WITH PROOF-OF-CONCEPT PROTOTYPES AND RECEIVED VALUABLE FEEDBACK FROM TUTORS REGARDING THE USEFULNESS OF THE OVERALL SENTIMENT ANALYSIS AS WELL AS CERTAIN FEATURES. THE QUALITATIVE FEEDBACK CONFIRMS THE OVERALL INTEREST OF TUTORS IN SENTIMENT ANALYSIS AND GIVES IMPORTANT HINTS TOWARDS MORE DETAILED ANALYTICAL ELEMENTS.
OVER THE LAST DECADE, LEARNING ANALYTICS AND ITS RELATED FIELDS HAVE RAPIDLY DEVELOPED. IT HAS BECOME ALSO POPULAR TO APPLY THE CONCEPT OF LEARNING ANALYTICS TO INSTITUTIONAL RESEARCH OR STUDENT SUPPORT. IN THIS CONTEXT, BUILDING A MODEL OF LEARNING PROCESS BY BAYESIAN NETWORK FROM A LARGE AMOUNT OF EDUCATIONAL DATA HAS BEEN STUDIED. IN THIS STUDY, SUCH A BAYESIAN NETWORK MODEL ABOUT LEARNING PROCESS FROM LOG DATA ACCUMULATED IN THE LMS (LEARNING MANAGEMENT SYSTEM) IS PROPOSED AND INVESTIGATED ITS AVAILABILITY.
THE NEED TO ENSURE PRIVACY AND DATA PROTECTION IN EDUCATIONAL CONTEXTS IS DRIVING A SHIFT TOWARDS NEW WAYS OF SECURING AND MANAGING LEARNING RECORDS. ALTHOUGH THERE ARE PLATFORMS AVAILABLE TO STORE EDUCATIONAL ACTIVITY TRACES OUTSIDE OF A CENTRAL REPOSITORY, NO SOLUTION CURRENTLY GUARANTEES THAT THESE TRACES ARE AUTHENTIC WHEN THEY ARE RETRIEVED FOR REVIEW. THIS PAPER PRESENTS A BLUEPRINT FOR AN ARCHITECTURE THAT EMPLOYS BLOCKCHAIN TECHNOLOGY TO SIGN AND VALIDATE LEARNING TRACES, ALLOWING THEM TO BE STORED IN A DISTRIBUTED NETWORK OF REPOSITORIES WITHOUT DIMINISHING THEIR AUTHENTICITY. OUR PROPOSAL PUTS PARTICIPANTS IN ONLINE LEARNING ACTIVITIES AT THE CENTER OF THE DESIGN PROCESS, GRANTING THEM THE OPTION TO STORE LEARNING TRACES IN A LOCATION OF THEIR CHOICE. USING SMART CONTRACTS, STAKEHOLDERS CAN RETRIEVE THE DATA, SECURELY SHARE IT WITH THIRD PARTIES AND ENSURE IT HAS NOT BEEN TAMPERED WITH, PROVIDING A MORE TRANSPARENT AND RELIABLE SOURCE FOR LEARNING ANALYTICS. NONETHELESS, A PRELIMINARY EVALUATION FOUND THAT ONLY 56% OF TEACHERS SURVEYED CONSIDERED TAMPER-EVIDENT STORAGE A USEFUL FEATURE OF A LEARNING TRACE REPOSITORY. THESE RESULTS MOTIVATE FURTHER EXAMINATION WITH OTHER END USERS, SUCH AS LEARNING ANALYTICS RESEARCHERS, WHO MAY HAVE STRICTER EXPECTATIONS OF AUTHENTICITY FOR DATA USED IN THEIR PRACTICE.
THIS PAPER REPORTS ON AN INVESTIGATION INTO PERCEPTIONS OF INTERNATIONAL UNIVERSITY LECTURERS AND THEIR CONCERNS ABOUT STUDENTS' SOCIAL AND ACADEMIC MOTIVATION FOR LEARNING IN ONLINE LEARNING ENVIRONMENTS. THE STUDY RESULTS SUGGEST THAT PEDAGOGIC LURKING IS A WIDESPREAD PHENOMENON, WITH SOME PERCEIVING IT AS PROBLEMATIC AND OTHERS AS A STEP TOWARDS MORE ACTIVE PARTICIPATION. MOST INTERESTINGLY, THERE DOES SEEM TO BE A LACK OF APPETITE FOR LEARNING ANALYTICS SOFTWARE THAT WOULD ALLOW THE CLOSE MONITORING OF STUDENT LEARNING ENGAGEMENT AND PROGRESS. IMPLICATIONS OF THIS STUDY FOR ONLINE LEARNING AND TEACHING ARE DISCUSSED.
RESEARCHERS INVESTED IN K-12 EDUCATION STRUGGLE NOT JUST TO ENHANCE PEDAGOGY, CURRICULUM, AND STUDENT ENGAGEMENT, BUT ALSO TO HARNESS THE POWER OF TECHNOLOGY IN WAYS THAT WILL OPTIMIZE LEARNING. ONLINE LEARNING PLATFORMS OFFER A POWERFUL ENVIRONMENT FOR EDUCATIONAL RESEARCH AT SCALE. THE PRESENT WORK DETAILS THE CREATION OF AN AUTOMATED SYSTEM DESIGNED TO PROVIDE RESEARCHERS WITH INSIGHTS REGARDING DATA LOGGED FROM RANDOMIZED CONTROLLED EXPERIMENTS CONDUCTED WITHIN THE ASSISTMENTS TESTBED. THE ASSESSMENT OF LEARNING INFRASTRUCTURE (ALI) BUILDS UPON EXISTING TECHNOLOGIES TO FOSTER A SYMBIOTIC RELATIONSHIP BENEFICIAL TO STUDENTS, RESEARCHERS, THE PLATFORM AND ITS CONTENT, AND THE LEARNING ANALYTICS COMMUNITY. ALI IS A SOPHISTICATED AUTOMATED REPORTING SYSTEM THAT PROVIDES AN OVERVIEW OF SAMPLE DISTRIBUTIONS AND BASIC ANALYSES FOR RESEARCHERS TO CONSIDER WHEN ASSESSING THEIR DATA. ALI'S BENEFITS CAN ALSO BE FELT AT SCALE THROUGH ANALYSES THAT CROSSCUT MULTIPLE STUDIES TO DRIVE ITERATIVE PLATFORM IMPROVEMENTS WHILE PROMOTING PERSONALIZED LEARNING.
FROM A HUMAN-CENTRED COMPUTING PERSPECTIVE, SUPPORTING THE INTERPRETATION OF EDUCATIONAL DASHBOARDS AND VISUALIZATIONS BY THE PEOPLE INTENDED TO USE THEM EXPOSES CRITICAL DESIGN CHALLENGES THAT MAY OFTEN BE TRIVIALIZED. EMPIRICAL EVIDENCE ALREADY SHOWS THAT "USABLE" VISUALIZATIONS ARE NOT NECESSARILY EFFECTIVE FROM AN EDUCATIONAL PERSPECTIVE. SINCE AN EDUCATOR'S INTERPRETATION OF VISUALIZED DATA IS ESSENTIALLY THE CONSTRUCTION OF A NARRATIVE ABOUT STUDENT PROGRESS, IN THIS PAPER, WE PROPOSE THE CONCEPT OF "EDUCATIONAL DATA STORYTELLING" AS AN APPROACH FOR EXPLAINING STUDENT DATA BY ALIGNING EDUCATIONAL VISUALIZATIONS WITH THE INTENDED LEARNING DESIGN. WE PRESENT A PILOT STUDY THAT EXPLORES THE EFFECTIVENESS OF THESE DATA STORYTELLING ELEMENTS BASED ON EDUCATOR RESPONSES TO PROTOTYPES BY ANALYZING THE KINDS OF STORIES THEY ARTICULATE, THEIR EYE-TRACKING BEHAVIOUR, AND THEIR PREFERENCES AFTER INSPECTING A SERIES OF STUDENT DATA VISUALIZATIONS. THE DUAL PURPOSE IS TO UNDERSTAND THE CONTRIBUTION OF EACH VISUAL ELEMENT FOR DATA STORYTELLING, AS WELL AS THE EFFECTIVENESS OF THE ENHANCEMENTS WHEN COMBINED.
HIGHER EDUCATION INSTITUTIONS ARE CONTINUING TO DEVELOP THEIR CAPACITY FOR LEARNING ANALYTICS (LA), WHICH IS A SOCIOTECHNICAL DATA-MINING AND ANALYTIC PRACTICE. INSTITUTIONS RARELY INFORM THEIR STUDENTS ABOUT LA PRACTICES, AND THERE EXIST SIGNIFICANT PRIVACY CONCERNS. WITHOUT A CLEAR STUDENT VOICE IN THE DESIGN OF LA, INSTITUTIONS PUT THEMSELVES IN AN ETHICAL GRAY AREA. TO HELP FILL THIS GAP IN PRACTICE AND ADD TO THE GROWING LITERATURE ON STUDENTS' PRIVACY PERSPECTIVES, THIS STUDY REPORTS FINDINGS FROM OVER 100 INTERVIEWS WITH UNDERGRADUATE STUDENTS AT EIGHT U.S. HIGHER EDUCATION INSTITUTIONS. FINDINGS DEMONSTRATE THAT STUDENTS LACKED AWARENESS OF EDUCATIONAL DATA-MINING AND ANALYTIC PRACTICES, AS WELL AS THE DATA ON WHICH THEY RELY. STUDENTS SEE POTENTIAL IN LA, BUT THEY PRESENTED NUANCED ARGUMENTS ABOUT WHEN AND WITH WHOM DATA SHOULD BE SHARED; THEY ALSO EXPRESSED WHY INFORMED CONSENT WAS VALUABLE AND NECESSARY. THE STUDY UNCOVERED PERSPECTIVES ON INSTITUTIONAL TRUST THAT WERE HERETOFORE UNKNOWN, AS WELL AS WHAT ACTIONS MIGHT VIOLATE THAT TRUST. INSTITUTIONS MUST BALANCE THEIR DESIRE TO IMPLEMENT LA WITH THEIR OBLIGATION TO EDUCATE STUDENTS ABOUT THEIR ANALYTIC PRACTICES AND TREAT THEM AS PARTNERS IN THE DESIGN OF ANALYTIC STRATEGIES RELIANT ON STUDENT DATA IN ORDER TO PROTECT THEIR INTELLECTUAL PRIVACY.
LEARNING ANALYTICS (LA) AND TOOLS FOR INTELLIGENT ANALYSIS OF DATA ACCUMULATED IN THE INFORMATION SYSTEMS USED IN HIGHER EDUCATION INSTITUTIONS (HEIS) ALLOW QUALITY EXPERTS TO INCREASE THE EFFECTIVENESS OF PROCESSES FOR MONITORING, QUALITY ASSURANCE AND EVALUATION OF TRAINING. THE PAPER PRESENTS LA MODEL AND A CORRESPONDENT SOFTWARE TOOL DESIGNED FOR THE NEEDS OF QUALITY EXPERTS IN BULGARIAN HIGHER EDUCATION INSTITUTIONS. THE TOOL ALLOWS THEM TO MONITOR AND IMPROVE THE LEARNING PROCESS. HOWEVER, THE EXPERIMENTS PRESENTED HERE SHOW THAT THE TOOL CAN ALSO SIGNIFICANTLY ASSIST IN THE PREPARATION OF SELF-ASSESSMENT REPORTS FOR INTERNAL AND EXTERNAL QUALITY ASSESSMENT IN HE. RESEARCH AND EXPERIMENTS WITH THE MODEL 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".
THE LEARNING ANALYTICS (LA) FIELD SEEKS TO ANALYZE DATA ABOUT STUDENTS' INTERACTIONS, AND IT HAS BEEN APPLIED IN THE DEVELOPMENT OF TOOLS FOR SUPPORTING BOTH LEARNING AND TEACHING PROCESSES. RECENT RESEARCH HAS PAID ATTENTION ON HOW LA MAY BENEFIT TEACHERS IN THE CREATION OF EDUCATIONAL RESOURCES. HOWEVER, MOST OF THE RESEARCH ON LA SOLUTIONS IS CARRIED OUT TO SUPPORT TEACHERS IN ANALYZING STUDENTS' BEHAVIOR DATA COLLECTED AS THEY INTERACT WITH VIRTUAL LEARNING ENVIRONMENTS. WHILE SOLUTIONS TO SUPPORT TEACHERS IN DIFFERENT VIRTUAL LEARNING AND TEACHING CONTEXTS HAVE BECOME IMPORTANT, TO DATE LITTLE RESEARCH HAS BEEN DONE ON HOW LA SOLUTIONS CAN HELP TEACHERS TO CREATE AND EVALUATE OPEN EDUCATIONAL RESOURCES (OERS). THIS STUDY AIMS AT PRESENTING THE EVALUATION OF A LA TOOL FOR SUPPORTING TEACHERS IN THE CREATION AND EVALUATION OF ACCESSIBLE AND QUALITY OERS CONSIDERING THAT BOTH PROCESSES FALL WITHIN THE COMPETENCES THAT TEACHERS CAN ACQUIRE AND STRENGTHEN BY PARTICIPATING AS AUTHORS (CREATION) AND EVALUATORS (EVALUATION) OF OERS. THE STUDY WAS CONDUCTED WITH COLOMBIAN TEACHERS AND THE RESULTS OBTAINED HIGHLIGHT THE POSITIVE EFFECT THE TOOL HAD ON THE TEACHERS' ACQUISITION OF THE COMPETENCES AND THE POSITIVE ATTITUDE THEY HAD TOWARD USING THE TOOL.
E-LEARNING STUDENTS TEND TO GET JADED AND EASILY DROPOUT FROM ONLINE COURSES. ENHANCING THE LEARNERS' EXPERIENCE AND REDUCING DROPOUT RATES IN THESE E-LEARNING BASED SCENARIOS IS THE MAIN PURPOSE OF THIS STUDY. THIS PAPER PRESENTS THE RESULTS OBTAINED SO FAR AND PRELIMINARY CONCLUSIONS. IN A FIRST STAGE, THE OBJECTIVE WAS TO STUDY THE BACKGROUND AND STATE OF THE ART OF THESE EDUCATIONAL SCENARIOS. IN A SECOND PHASE, IDENTIFYING KEY REASONS FOR DROPOUTS, THROUGH A SURVEY AND INTERVIEWS, WAS THE AIM TO UNDERSTAND AND DETECT MOTIVES AND BEHAVIOR PATTERNS OF STUDENTS WITH DROPOUT THOUGHTS. FINALLY, DEVELOPING, TESTING AND VALIDATING A FUNCTIONAL PROTOTYPE OF AN INTELLIGENT TUTORING SYSTEM WILL ALLOW TO EVALUATE CONCEPTS, COLLECT STATISTICAL INFORMATION ON ITS EFFECTIVENESS, ANALYZE AND DISCOVER IF COURSE COMPLETION RATES ARE IMPROVED.
EDUCATIONAL INSTITUTIONS ARE TRANSFERRING ANALYTICS COMPUTING TO THE CLOUD TO REDUCE COSTS. ANY DATA TRANSFER AND STORAGE OUTSIDE INSTITUTIONS INVOLVE SERIOUS PRIVACY CONCERNS, SUCH AS STUDENT IDENTITY EXPOSURE, RISING UNTRUSTED AND UNNECESSARY THIRD-PARTY ACTORS, DATA MISUSE, AND DATA LEAKAGE. INSTITUTIONS THAT ADOPT A "LOCAL FIRST" APPROACH INSTEAD OF A "CLOUD COMPUTING FIRST" APPROACH CAN MINIMIZE THESE PROBLEMS. THE WORK AIMS TO FOSTER THE USE OF LOCAL ANALYTICS COMPUTING BY OFFERING ADEQUATE NONEXISTENT TOOLS. RESULTS ARE USEFUL FOR ANY EDUCATIONAL ROLE, EVEN INVESTIGATORS, TO CONDUCT DATA ANALYSIS LOCALLY. THE NOVELTY RESULTS ARE TWOFOLD: AN OPEN-SOURCE JAVASCRIPT LIBRARY TO ANALYZE LOCALLY ANY EDUCATIONAL LOG SCHEMA FROM ANY LMS; A FRONT-END TO ANALYZE MOODLE LOGS AS PROOF OF WORK OF THE LIBRARY WITH DIFFERENT EDUCATIONAL METRICS AND INDICATOR VISUALIZATIONS. NIELSEN HEURISTICS USER EXPERIENCE IS EXECUTED TO REDUCE POSSIBLE USERS' DATA LITERACY BARRIER. VISUALIZATIONS ARE VALIDATED BY SURVEYING TEACHERS WITH LIKERT AND OPEN-ENDED QUESTIONS, WHICH CONSIDER THEM TO BE OF INTEREST, BUT MORE DIFFERENT DATA SOURCES CAN BE ADDED TO IMPROVE INDICATORS. THE WORK REINFORCES THAT LOCAL EDUCATIONAL DATA ANALYSIS IS FEASIBLE, OPENS UP NEW WAYS OF ANALYZING DATA WITHOUT DATA TRANSFER TO THIRD PARTIES WHILE GENERATING DEBATE AROUND THE "LOCAL TECHNOLOGIES FIRST" APPROACH ADOPTION.
BACKGROUND: EARLY DETECTION OF HEART FAILURE IS THE BASIS FOR BETTER MEDICAL TREATMENT AND PROGNOSIS. OVER THE LAST DECADES, BOTH PREVALENCE AND INCIDENCE RATES OF HEART FAILURE HAVE INCREASED WORLDWIDE, RESULTING IN A SIGNIFICANT GLOBAL PUBLIC HEALTH ISSUE. HOWEVER, AN EARLY DIAGNOSIS IS NOT AN EASY TASK BECAUSE SYMPTOMS OF HEART FAILURE ARE USUALLY NON-SPECIFIC. THEREFORE, THIS STUDY AIMS TO DEVELOP A RISK PREDICTION MODEL FOR INCIDENT HEART FAILURE THROUGH A MACHINE LEARNING-BASED PREDICTIVE MODEL. ALTHOUGH AFRICAN AMERICANS HAVE A HIGHER RISK OF INCIDENT HEART FAILURE AMONG ALL POPULATIONS, FEW STUDIES HAVE DEVELOPED A HEART FAILURE RISK PREDICTION MODEL FOR AFRICAN AMERICANS. METHODS: THIS RESEARCH IMPLEMENTED THE LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) LOGISTIC REGRESSION, SUPPORT VECTOR MACHINE, RANDOM FOREST, AND EXTREME GRADIENT BOOSTING (XGBOOST) TO ESTABLISH THE JACKSON HEART STUDY'S PREDICTIVE MODEL. IN THE ANALYSIS OF REAL DATA, MISSING DATA ARE PROBLEMATIC WHEN BUILDING A PREDICTIVE MODEL. HERE, WE EVALUATE PREDICTORS' INCLUSION WITH VARIOUS MISSING RATES AND DIFFERENT MISSING IMPUTATION STRATEGIES TO DISCOVER THE OPTIMAL ANALYTICS. RESULTS: ACCORDING TO HUNDREDS OF MODELS THAT WE EXAMINED, THE BEST PREDICTIVE MODEL WAS THE XGBOOST THAT INCLUDED VARIABLES WITH A MISSING RATE OF LESS THAN 30 PERCENT, AND WE IMPUTED MISSING VALUES BY NON-PARAMETRIC RANDOM FOREST IMPUTATION. THE OPTIMAL XGBOOST MACHINE DEMONSTRATED AN AREA UNDER CURVE (AUC) OF 0.8409 TO PREDICT HEART FAILURE FOR THE JACKSON HEART STUDY. CONCLUSION: THIS RESEARCH IDENTIFIES VARIATIONS OF DIABETES MEDICATION AS THE MOST CRUCIAL RISK FACTOR FOR HEART FAILURE COMPARED TO THE COMPLETE CASES APPROACH THAT FAILED TO DISCOVER THIS PHENOMENON.
THE SHEILA FRAMEWORK PROVIDES A POLICY AND STRATEGY FRAMEWORK INFORMING THE STRATEGIC IMPLEMENTATION AND USE OF LEARNING ANALYTICS. HOWEVER, AS EVIDENCED IN SEVERAL `GROUND-UP' IMPLEMENTATIONS OF TOOLS, THE INSTITUTIONAL PREPAREDNESS AND THE GOVERNANCE AROUND USE OFTEN COMES SECONDARY TO THE POLICY. IN THIS PAPER WE DEPART FROM FAMILIAR APPROACHES AND EVALUATE ONE SUCH EXAMPLE OF A TOOL'S DEVELOPMENT (SRES - STUDENT RELATIONSHIP ENGAGEMENT SYSTEM). SRES' ADOPTION AND SCALING ACROSS TWO INSTITUTIONS ARE EVALUATED USING AN AUTO-ETHNOGRAPHIC APPROACH SCAFFOLDED THROUGH THE DIMENSIONS OF THE SHEILA FRAMEWORK, FOCUSING ON THE INDIVIDUAL PERSPECTIVES OF THE INSTITUTIONAL CHAMPIONS WHO HAVE BEEN CENTRAL TO THIS JOURNEY. THIS PRACTICAL APPROACH AND THE EMERGING INSIGHTS MAY ENABLE OTHER INSTITUTIONS TO IDENTIFY AREAS OF POTENTIAL IMPROVEMENT AND INFORM SENIOR ACADEMIC MANAGERS ABOUT THE STRATEGIC REQUIREMENTS TO SCALE THE APPROACH, ACCOUNTING FOR ASPECTS NOT CONSIDERED IN THE INITIAL 'ORGANIC GROWTH' OF THE IMPLEMENTATION.
THE GOAL OF AFEL IS TO DEVELOP, PILOT AND EVALUATE METHODS AND APPLICATIONS, WHICH ADVANCE INFORMAL/COLLECTIVE LEARNING AS IT SURFACES IMPLICITLY IN ONLINE SOCIAL ENVIRONMENTS. THE PROJECT IS FOLLOWING A MULTI-DISCIPLINARY, INDUSTRY-DRIVEN APPROACH TO THE ANALYSIS AND UNDERSTANDING OF LEARNER DATA IN ORDER TO PERSONALIZE, ACCELERATE AND IMPROVE INFORMAL LEARNING PROCESSES. LEARNING ANALYTICS AND EDUCATIONAL DATA MINING TRADITIONALLY RELATE TO THE ANALYSIS AND EXPLORATION OF DATA COMING FROM LEARNING ENVIRONMENTS, ESPECIALLY TO UNDERSTAND LEARNERS' BEHAVIOURS. HOWEVER, STUDIES HAVE FOR A LONG TIME DEMONSTRATED THAT LEARNING ACTIVITIES HAPPEN OUTSIDE OF FORMAL EDUCATIONAL PLATFORMS, ALSO. THIS INCLUDES INFORMAL AND COLLECTIVE LEARNING USUALLY ASSOCIATED, AS A SIDE EFFECT, WITH OTHER (SOCIAL) ENVIRONMENTS AND ACTIVITIES. RELYING ON REAL DATA FROM A COMMERCIALLY AVAILABLE PLATFORM, THE AIM OF AFEL IS TO PROVIDE AND VALIDATE THE TECHNOLOGICAL GROUNDING AND TOOLS FOR EXPLOITING LEARNING ANALYTICS ON SUCH LEARNING ACTIVITIES. THIS WILL BE ACHIEVED IN RELATION TO COGNITIVE MODELS OF LEARNING AND COLLABORATION, WHICH ARE NECESSARY TO THE UNDERSTANDING OF LOOSELY DEFINED LEARNING PROCESSES IN ONLINE SOCIAL ENVIRONMENTS. APPLYING THE SKILLS AVAILABLE IN THE CONSORTIUM TO A CONCRETE SET OF LIVE, INDUSTRIAL ONLINE SOCIAL ENVIRONMENTS, AFEL WILL TACKLE THE MAIN CHALLENGES OF INFORMAL LEARNING ANALYTICS THROUGH 1) DEVELOPING THE TOOLS AND TECHNIQUES NECESSARY TO CAPTURE INFORMATION ABOUT LEARNING ACTIVITIES FROM (NOT NECESSARILY EDUCATIONAL) ONLINE SOCIAL ENVIRONMENTS; 2) CREATING METHODS FOR THE ANALYSIS OF SUCH INFORMAL LEARNING DATA, BASED ON COMBINING FEATURE ENGINEERING AND VISUAL ANALYTICS WITH COGNITIVE MODELS OF LEARNING AND COLLABORATION; AND 3) DEMONSTRATING THE POTENTIAL OF THE APPROACH IN IMPROVING THE UNDERSTANDING OF INFORMAL LEARNING, AND THE WAY IT IS BETTER SUPPORTED; 4) EVALUATE ALL THE FORMER ITEMS IN REAL WORLD LARGE SCALE APPLICATIONS AND PLATFORMS.
MACHINE-LEARNING-BASED ANALYTICS ARE AN EFFECTIVE TOOL TO HELP ASSESS STUDENT TEAMWORK SKILLS AND PREDICT LEARNING OUTCOMES IN SOFTWARE ENGINEERING COURSES.
THE LEARNING ANALYTICS COMMUNITY HAS MATURED SIGNIFICANTLY OVER THE PAST FEW YEARS AS A MIDDLE SPACE WHERE TECHNOLOGY AND PEDAGOGY COMBINE TO SUPPORT LEARNING EXPERIENCES. TO CONTINUE TO GROW AND CONNECT THESE PERSPECTIVES, RESEARCH NEEDS TO MOVE BEYOND THE LEVEL OF BASIC SUPPORT ACTIONS. THIS MEANS EXPLORING THE USE OF DATA TO PROVE RICHER FORMS OF ACTIONS, SUCH AS PERSONALIZED FEEDBACK, OR HYBRID APPROACHES WHERE INSTRUCTORS INTERPRET THE OUTPUTS OF ALGORITHMS AND SELECT AN APPROPRIATE COURSE OF ACTION. THIS PAPER PROPOSES THE FOLLOWING THREE CONTRIBUTIONS TO CONNECT DATA EXTRACTED FROM THE LEARNING EXPERIENCE WITH SUCH PERSONALIZED STUDENT SUPPORT ACTIONS: 1) A STUDENT-INSTRUCTOR CENTRED CONCEPTUAL MODEL CONNECTING A REPRESENTATION OF THE STUDENT INFORMATION WITH A BASIC SET OF RULES CREATED BY INSTRUCTORS TO DEPLOY PERSONALIZED LEARNING SUPPORT ACTIONS (PLSAS); 2) A SOFTWARE ARCHITECTURE BASED ON THIS MODEL WITH SIX CATEGORIES OF FUNCTIONAL BLOCKS TO DEPLOY THE PLSAS; AND 3) A DESCRIPTION OF THE IMPLEMENTATION OF THIS ARCHITECTURE AS AN OPEN-SOURCE PLATFORM TO PROMOTE THE ADOPTION AND EXPLORATION OF THIS AREA.
MANY MEDICAL SCHOOLS THAT HAVE IMPLEMENTED TEAM-BASED LEARNING (TBL) HAVE ALSO INCORPORATED AN ELECTRONIC LEARNING ARCHITECTURE, COMMONLY REFERRED TO AS A LEARNING MANAGEMENT SYSTEM (LMS), TO SUPPORT THE INSTRUCTIONAL PROCESS. HOWEVER, ONE LMS FEATURE THAT IS OFTEN OVERLOOKED IS THE LMS'S ABILITY TO RECORD DATA THAT CAN BE USED FOR FURTHER ANALYSIS. IN THIS ARTICLE, THE AUTHORS PRESENT A CASE STUDY ILLUSTRATING HOW ONE MEDICAL SCHOOL USED DATA THAT ARE ROUTINELY COLLECTED VIA THE SCHOOL'S LMS TO MAKE INFORMED DECISIONS. THE CASE STUDY STARTED WITH ONE INSTRUCTOR'S OBSERVATION THAT SOME TEAMS IN ONE OF THE UNDERGRADUATE MEDICAL EDUCATION LEARNING MODULES APPEARED TO BE STRUGGLING DURING ONE OF THE TEAM ACTIVITIES; THAT IS, SOME TEAMS SEEMED UNABLE TO EXPLAIN OR JUSTIFY THEIR RESPONSES TO ITEMS ON THE TEAM READINESS ASSURANCE TEST (TRAT). FOLLOWING THIS OBSERVATION, THE AUTHORS CONDUCTED 4 ANALYSES. THEIR ANALYSES DEMONSTRATE HOW LMS-GENERATED AND RECORDED DATA CAN BE USED IN A SYSTEMATIC MANNER TO INVESTIGATE ISSUES IN THE REAL EDUCATIONAL ENVIRONMENT. THE FIRST ANALYSIS IDENTIFIED A TEAM THAT PERFORMED SIGNIFICANTLY POORER ON THE TRAT. A SUBSEQUENT ANALYSIS INVESTIGATED WHETHER THE WEAKER TEAM'S POORER PERFORMANCE WAS CONSISTENT OVER A WHOLE MODULE. FINDINGS REVEALED THAT THE WEAKER TEAM PERFORMED POORER ON THE MAJORITY OF THE TBL SESSIONS. FURTHER INVESTIGATION USING LMS DATA SHOWED THAT THE WEAKER PERFORMANCE WAS DUE TO THE LACK OF PREPARATION OF ONE INDIVIDUAL TEAM MEMBER (RATHER THAN A COLLECTIVE POOR TRAT PERFORMANCE). USING THE FINDINGS OBTAINED FROM THIS CASE STUDY, THE AUTHORS HOPE TO CONVEY HOW LMS DATA ARE POWERFUL AND MAY FORM THE BASIS OF EVIDENCE-BASED EDUCATIONAL DECISION MAKING.
IN THIS PAPER WE PRESENT AN AUTOMATIC ASSESSMENT MODEL FOR COMPETENCES DEVELOPED IN A PHYSICS COURSE USING VISIR REMOTE EXPERIMENT, BASED ON A RUBRIC AND USING LEARNING ANALYTICS TECHNIQUES TO PROCESS DATA AUTOMATICALLY COLLECTED FROM STUDENTS ACTIVITY USING WEBLAB-DEUSTO PLATFORM.
SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) EDUCATION IS RECOGNIZED AS A TOP SCHOOL EDUCATION PRIORITY WORLDWIDE AND INQUIRY-BASED TEACHING AND LEARNING IS IDENTIFIED AS A PROMISING APPROACH. TO EFFECTIVELY ENGAGE STUDENTS IN INQUIRY TASKS, APPROPRIATE GUIDANCE SHOULD BE PROVIDED, USUALLY BY COMBINING DIGITAL TOOLS SUCHONLINE LABS AND MODELING TOOLS. THIS IS A CUMBERSOME TASK FOR TEACHERS, SINCE IT INVOLVES MANUALLY ASSESSING THE TYPE/LEVEL OF TOOL-SUPPORTED GUIDANCE TO BE PROVIDED AND POTENTIALLY REFINING THESE TO MEET GUIDANCE NEEDS OF INDIVIDUAL STUDENTS. IN OUR RESEARCH WE TARGET TO INVESTIGATE HOW TO SUPPORT THIS SYSTEMATIC REFLECTION PROCESS WITH EDUCATIONAL DATA ANALYTICS METHODS AND TOOLS FROM BOTH THE DESIGN AND THE DELIVERY OF INQUIRY-BASED EDUCATIONAL DESIGNS (IED). THE CONTRIBUTION OF THIS PAPER IS TO PROPOSE A NOVEL "TEACHING AND LEARNING" ANALYTICS METHOD AND RESEARCH PROTOTYPE TOOL, EXTENDING THE SCOPE OF PURELY LEARNING ANALYTICS METHODS, TO ANALYZE IED IN TERMS OF THE TOOL-SUPPORTED GUIDANCE THEY OFFER AND RELATE THESE ANALYSES TO STUDENTS' EDUCATIONAL DATA THAT ARE ALREADY BEING COLLECTED BY EXISTING LEARNING ANALYTICS SYSTEMS, INCREASING TEACHERS' AWARENESS. A TWO LAYER EVALUATION METHODOLOGY POSITIVELY ASSESSED THE CAPACITY OF OUR METHOD TO ANALYZE IED AND PROVIDED INITIAL EVIDENCE THAT THE INSIGHTS GENERATED OFFER STATISTICALLY SIGNIFICANT INDICATORS THAT IMPACT STUDENTS' ACTIVITY DURING THE DELIVERY OF THESE IED. THE INSIGHTS OF THIS WORK AIM TO CONTRIBUTE IN THE FIELD OF COGNITIVE DATA ANALYTICS FOR TEACHING AND LEARNING, BY INVESTIGATING NEW WAYS TO COMBINE ANALYSES OF THE EDUCATIONAL DESIGN AND STUDENTS' ACTIVITY, AND INFORM TEACHERS' REFLECTION FROM A HOLISTIC PERSPECTIVE. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
IN THIS PAPER WE PRESENT AN AUTOMATIC ASSESSMENT MODEL FOR THE DEVELOPMENT OF COMPETENCIES IN A PHYSICS COURSE USING VISIR REMOTE EXPERIMENT, BASED ON A RUBRIC, AND USING LEARNING ANALYTICS TECHNIQUES TO PROCESS DATA AUTOMATICALLY COLLECTED FROM STUDENTS' ACTIVITY USING WEBLAB-DEUSTO PLATFORM.
WITH THE ARRIVAL OF FINE-GRAINED LOG-DATA AND THE EMERGENCE OF LEARNING ANALYTICS, THERE MAY BE NEW AVENUES TO EXPLORE HOW SELF-REGULATED LEARNING (SRL) CAN PROVIDE A LENS TO HOW STUDENTS LEARN IN BLENDED AND ONLINE ENVIRONMENTS. IN PARTICULAR, RECENT RESEARCH HAS FOUND THAT THE NOTION OF TIME MAY BE AN ESSENTIAL BUT COMPLEX CONCEPT THROUGH WHICH STUDENTS MAKE (UN)CONSCIOUS AND SELF-REGULATED DECISIONS AS TO WHEN, WHAT, AND HOW TO STUDY. THIS STUDY EXPLORES DISTINCT CLUSTERS OF BEHAVIOURAL ENGAGEMENT IN AN ONLINE E-TUTORIAL CALLED SOWISO AT DIFFERENT TIME POINTS (BEFORE TUTORIALS, BEFORE QUIZZES, BEFORE EXAMS), AND THEIR ASSOCIATIONS WITH ACADEMIC PERFORMANCE, SELF-REGULATED LEARNING STRATEGIES, EPISTEMIC LEARNING EMOTIONS, AND ACTIVITY LEARNING EMOTIONS. USING A CLUSTER ANALYSIS ON TRACE DATA OF 1035 STUDENTS PRACTICING 429 ONLINE EXERCISES IN SOWISO, WE IDENTIFIED FOUR DISTINCT CLUSTER OF STUDENTS (E.G. EARLY MASTERY, STRATEGIC, EXAM-DRIVEN, AND INACTIVE). FURTHER ANALYSES REVEALED SIGNIFICANT DIFFERENCES BETWEEN THE FOUR CLUSTERS IN THEIR ACADEMIC PERFORMANCE, STEP-WISE COGNITIVE PROCESSING STRATEGIES, EXTERNAL SELF-REGULATION STRATEGIES, EPISTEMIC LEARNING EMOTIONS AND ACTIVITY LEARNING EMOTIONS. OUR FINDINGS TOOK A STEP FORWARD TOWARDS PERSONALISED AND ACTIONABLE FEEDBACK IN LEARNING ANALYTICS BY RECOGNIZING THE COMPLEXITY OF HOW AND WHEN STUDENTS ENGAGE IN LEARNING ACTIVITIES OVER TIME, AND SUPPORTING EDUCATORS TO DESIGN EARLY AND THEORETICALLY INFORMED INTERVENTIONS BASED ON LEARNING DISPOSITIONS.
STUDIES IN LEARNING ANALYTICS PROVIDE CONCRETE EXAMPLES OF HOW THE ANALYSIS OF DIRECT INTERACTIONS WITH LEARNING MANAGEMENT SYSTEMS CAN BE USED TO OPTIMIZE AND UNDERSTAND THE LEARNING PROCESS. LEARNING, HOWEVER, DOES NOT NECESSARILY ONLY OCCUR WHEN THE LEARNER IS DIRECTLY INTERACTING WITH SUCH SYSTEMS. WITH THE USE OF SENSORS, IT IS POSSIBLE TO COLLECT DATA FROM LEARNERS AND THEIR ENVIRONMENT UBIQUITOUSLY, THEREFORE EXPANDING THE USE CASES OF LEARNING ANALYTICS. FOR THIS REASON, WE DEVELOPED THE MULTIMODAL LEARNING HUB (MLH), A SYSTEM DESIGNED TO ENHANCE LEARNING IN UBIQUITOUS LEARNING SCENARIOS, BY COLLECTING AND INTEGRATING MULTIMODAL DATA FROM CUSTOMIZABLE CONFIGURATIONS OF UBIQUITOUS DATA PROVIDERS. IN THIS PAPER, WE DESCRIBE THE MLH AND REPORT ON THE RESULTS OF TESTS WHERE WE EXPLORED ITS RELIABILITY TO INTEGRATE MULTIMODAL DATA.
VISUALIZATION OF DATA IN LEARNING MANAGEMENT SYSTEMS BECAME ESSENTIAL FOR EASIER ANALYSIS OF LEARNER'S BEHAVIOR AND INTERPRETATION OF RESULTS ON SUCH SYSTEM. IN THIS PAPER, WE PRESENTED DESIGN AND DEVELOPMENT PROCESS OF DASHBOARD FOR THE PROTOTYPE OF PROGRAM SUPPORT-CM TUTOR. AFTER ANALYSIS OF VARIOUS APPROACHES OF DESIGNING A LEARNING ANALYTICS DASHBOARD, WE SELECTED MOST IMPORTANT ITEMS FOR DISPLAY ON THE DASHBOARD, AND WE CREATED OUR OWN. WE HAVE DESIGNED AND BUILT A DASHBOARD FOR INTELLIGENT LEARNING MANAGEMENT SYSTEMS. THE DASHBOARD IS INTENDED FOR BOTH STUDENTS AND TEACHERS. THE TEACHER HAS ACCESS TO ALL THE FUNCTIONALITIES OF DASHBOARD, WHILE STUDENT APPROACH IS LIMITED.
ENGAGEMENT WITH SOCIAL NETWORKING SITES IS INFLUENCED BY THE COGNITIVE AND LEARNING PROCESSES WHICH IN TURN IS INFLUENCED BY CULTURE. THIS PAPER AIMS AT UNBUNDLING THE EFFECT OF CULTURE ON THE USE OF SOCIAL NETWORK SITES AND THUS CONTRIBUTES TO OUR UNDERSTANDING ON THE WAY COGNITIVE AND LEARNING PROCESSES INFLUENCE THE ENGAGEMENT WITH SOCIAL NETWORKS. THE STUDY OF OVER 600 SOCIAL NETWORKING USERS ADDRESSES HOW PROFESSIONAL AND LEISURE USE OF SOCIAL NETWORKS DIFFERS ACROSS CULTURES, GENDER AND OTHER DEMOGRAPHICS. FIRSTLY, BY UTILISING ANOVA TECHNIQUES WE ASSOCIATE USERS' BEHAVIOUR WITH NATIONALITY AND FURTHERMORE THROUGH THE USE OF AN ORDERED LOGISTICS REGRESSION WE DELINEATE CLUSTERS OF USERS BY THE PURPOSE OF SOCIAL NETWORKING ADOPTION AND THEIR CULTURAL CHARACTERISTICS. OUR STUDY HELPS BRIDGE THE GAP IN LITERATURE ON IDENTIFYING HOW CULTURAL TRAITS, NATIONALITY AND GENDER AFFECT BOTH BUSINESS AND LEISURE USE OF SOCIAL NETWORKING. THE IMPLICATIONS OF DIFFERENCES IN USER BEHAVIOUR DRIVEN BY NATIONALITY AND GENDER WARRANT FURTHER NEED FOR APPLYING LEARNING ANALYTICS IN SOCIAL PLATFORMS TO ENHANCE USER EXPERIENCE. FUTURE DIRECTIONS OF RESEARCH ON SOCIAL NETWORKING IN RELATION TO COGNITION AND CULTURE ARE OFFERED FOR DISCUSSION. (C) 2018 PUBLISHED BY ELSEVIER LTD.
THE RAPID RISE OF MOOCS (MASSIVE OPEN ONLINE COURSES) SIGNALS A SHIFT IN THE WAYS IN WHICH DIGITAL TEACHING AND LEARNING ARE ENGAGED IN AND UNDERSTOOD. DRAWING UPON A COMPREHENSIVE SEARCH OF NINE LEADING ACADEMIC DATABASES, THIS PAPER EXAMINES THE INITIAL PHASE OF MOOC SCHOLARSHIP (2009-2013), AND OFFERS AN ANALYSIS OF THESE EMPIRICAL STUDIES THAT CONCEPTUALIZES THEMES IN MOOC SCHOLARSHIP AND LOCATES THEM WITHIN A CHRONOLOGICAL FRAMEWORK. TWO KEY PHASES OF SCHOLARSHIP ABOUT MOOCS ARE IDENTIFIED, EACH WITH ASSOCIATED RESEARCH IMPERATIVES AND THEMES. PHASE ONE: CONNECTIVIST MOOCS, ENGAGEMENT AND CREATIVITY 2009-2011/2012. THEMES OF PHASE ONE INCLUDE: DEVELOPMENT OF CONNECTIVISM AS A LEARNING THEORY, AND TECHNOLOGICAL EXPERIMENTATION AND INNOVATION IN EARLY CMOOCS. PHASE TWO: XMOOCS, LEARNING ANALYTICS, ASSESSMENT, AND CRITICAL DISCOURSES ABOUT MOOCS 2012-2013. THEMES OF PHASE TWO INCLUDE: THE RISE OF XMOOCS, FURTHER DEVELOPMENT OF MOOC PEDAGOGY AND PLATFORMS, GROWTH OF LEARNING ANALYTICS AND ASSESSMENT, AND THE EMERGENCE OF A CRITICAL DISCOURSE ABOUT MOOCS.
MULTIMODAL LEARNING ANALYTICS IS A FIELD THAT STUDIES HOW TO PROCESS LEARNING DATA FROM DISSIMILAR SOURCES IN ORDER TO AUTOMATICALLY FIND USEFUL INFORMATION 10 GIVE FEEDBACK TO THE LEARNING PROCESS. THIS WORK PROCESSES VIDEO, AUDIO AND PEN STROKES INFORMATION INCLUDED IN THE MATH DATA CORPUS, A SET OF MULTIMODAL RESOURCES PROVIDED TO THE PARTICIPANTS OF THE SECOND INTERNATIONAL WORKSHOP ON MULTIMODAL LEARNING ANALYTICS. THE RESULT OF THIS PROCESSING IS A SET OF SIMPLE FEATURES THAT COULD DISCRIMINATE BETWEEN EXPERTS AND NON-EXPERTS IN GROUPS OF STUDENTS SOLVING MATHEMATICAL PROBLEMS. THE MAIN FINDING IS THAT SEVERAL OF THOSE SIMPLE FEATURES, NAMELY THE PERCENTAGE OF TIME THAT THE STUDENTS USE THE CALCULATOR, THE SPEED AT WHICH THE STUDENT, WRITES OR DRAWS AND THE PERCENTAGE OF TIME THAT THE STUDENT MENTIONS NUMBERS OR MATHEMATICAL TERMS, ARE GOOD DISCRIMINATORS BETWEEN EXPERTS AND NON-EXPERTS STUDENTS. PRECISION LEVELS OF 63% ARE OBTAINED FBR INDIVIDUAL PROBLEMS AND UP TO 80% WHEN FULL SESSIONS (AGGREGATION OF 16 PROBLEMS) ARE ANALYZED. WHILE THE RESULTS ARE SPECIFIC FOR THE RECORDED SETTINGS, THE METHODOLOGY USED TO OBTAIN AND ANALYZE THE FEATURES COULD BE USED TO CREATE DISCRIMINATIONS MODELS FOR OTHER CONTEXTS.
THIS ARTICLE PRESENTS A THESIS PROJECT THAT FALLS WITHIN THE FIELD OF THE INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) RELATED TO KNOWLEDGE DISCOVERY AND LEARNING ANALYTICS (LA). THE MAIN AIM IS TO LEVERAGE DATA GENERATED BY MOBILE DEVICES (SMARTPHONES, WEARABLES, AND SO FORTH) AND COMBINE THEM TOGETHER WITH ALREADY KNOWN LA VARIABLES, SO THAT RELEVANT FEATURES AND PATTERNS THAT DEFINE THE STUDENT BEHAVIOR CAN BE IDENTIFIED.
LANGUAGE EDUCATION HAS A RICH HISTORY OF RESEARCH AND SCHOLARSHIP FOCUSING ON THE EFFECTIVENESS OF LEARNING ACTIVITIES AND THE IMPACT THESE HAVE ON STUDENT BEHAVIOUR AND OUTCOMES. ONE OF THE BASIC ASSUMPTIONS IN FOREIGN LANGUAGE PEDAGOGY AND CALL IN PARTICULAR IS THAT LEARNERS WANT TO BE ABLE TO COMMUNICATE EFFECTIVELY WITH NATIVE SPEAKERS OF THEIR CHOSEN LANGUAGE. COMBINING PRINCIPLES OF LEARNING ANALYTICS AND BIG DATA WITH LEARNING DESIGN, THIS STUDY USED A STUDENT ACTIVITY BASED TAXONOMY ADOPTED BY THE OPEN UNIVERSITY UK TO INFORM MODULE DESIGN. THE LEARNING DESIGNS OF FOUR INTRODUCTORY AND INTERMEDIARY LANGUAGE EDUCATION MODULES AND ONLINE ENGAGEMENT OF 2111 LEARNERS WERE CONTRASTED USING WEEKLY LEARNING DESIGN DATA. IN THIS STUDY, WE AIMED TO EXPLORE HOW LEARNING DESIGN DECISIONS MADE BY LANGUAGE TEACHERS INFLUENCED STUDENTS' ENGAGEMENT IN THE VLE. USING FIXED EFFECT MODELS, OUR FINDINGS INDICATED THAT 55% OF VARIANCE OF WEEKLY ONLINE ENGAGEMENT IN THESE FOUR MODULES WAS EXPLAINED BY THE WAY LANGUAGE TEACHERS DESIGNED WEEKLY LEARNING DESIGN ACTIVITIES. OUR LEARNING ANALYTICS STUDY HIGHLIGHTS THE POTENTIAL AFFORDANCES FOR CALL RESEARCHERS TO USE THE POWER OF LEARNING DESIGN AND BIG DATA TO EXPLORE AND UNDERSTAND THE COMPLEXITIES AND DYNAMICS OF LANGUAGE LEARNING FOR STUDENTS AND TEACHERS.
PRIOR RESEARCH HAS DEMONSTRATED THAT THE QUALITY OF MODERATION AND MANAGEMENT IN ONLINE COMMUNITIES OF PRACTICE IS KEY TO THEIR SUCCESSFUL SUPPORT OF LEARNING. HOWEVER, AS COMMUNITIES GROW IN SIZE AND COMPLEXITY, IT BECOMES INCREASINGLY DIFFICULT FOR UNAIDED EXPERTS TO FULLY UNDERSTAND AND TAKE ACTION IN RESPONSE TO THE ACTIVITY OF PARTICIPANTS WITHIN THEM. LEARNING ANALYTICS HAS THE POTENTIAL TO PROVIDE THE SUPPORT THAT COMMUNITY OF PRACTICE LEADERS NEED TO IMPROVE THEIR PERFORMANCE. THE NATIONAL SCIENCE TEACHERS ASSOCIATION AND U. S. DEPARTMENT OF EDUCATION'S CONNECTED EDUCATORS PROJECT ARE EXPLORING THREE APPROACHES FOR MANAGING FORUMS TO MAKE THEM ACCESSIBLE AND TO SYNTHESIZE THE KNOWLEDGE THEY GENERATE: ARCHIVING, SUMMARIZING, AND REORGANIZING. THIS PAPER DESCRIBES MANUAL HEURISTICS FOR THE FIRST TWO OF THESE, AS WELL AS THE USE OF SOCIAL NETWORK ANALYSIS TO HELP DEVELOP ALGORITHMS TO AUTOMATE THE THIRD, COMMUNITY FORUM REORGANIZATION.
LEARNER-CENTERED COACHING AND FEEDBACK ARE RELEVANT TO VARIOUS EDUCATIONAL CONTEXTS. SPACED RETRIEVAL ENHANCES LONG-TERM KNOWLEDGE RETENTION. WE EXAMINED THE EFFICACY OF BLANK SLATE, A NOVEL SPACED RETRIEVAL SOFTWARE APPLICATION, TO PROMOTE LEARNING AND PREVENT FORGETTING, WHILE GATHERING AND ANALYZING DATA IN THE BACKGROUND ABOUT LEARNERS' PERFORMANCE. A TOTAL OF 93 STUDENTS FROM 6 UNIVERSITIES IN THE UNITED STATES WERE ASSIGNED RANDOMLY TO CONTROL, SEQUENTIAL OR ALGORITHM CONDITIONS. PARTICIPANTS WATCHED A VIDEO ON THE REPUBLIC OF GEORGIA BEFORE TAKING A 60 MULTIPLE-CHOICE-QUESTION ASSESSMENT. SEQUENTIAL (NON-SPACED RETRIEVAL) AND ALGORITHM (SPACED RETRIEVAL) GROUPS HAD ACCESS TO BLANK SLATE AND 60 DIGITAL CARDS. THE ALGORITHM GROUP REVIEWED SUBSETS OF CARDS DAILY BASED ON PREVIOUS INDIVIDUAL PERFORMANCE. THE SEQUENTIAL GROUP REVIEWED ALL 60 CARDS DAILY. ALL 93 PARTICIPANTS WERE RE-ASSESSED 4 WEEKS LATER. SEQUENTIAL AND ALGORITHM GROUPS WERE SIGNIFICANTLY DIFFERENT FROM THE CONTROL GROUP BUT NOT FROM EACH OTHER WITH REGARD TO AFTER AND DELTA SCORES. BLANK SLATE PREVENTED ANTICIPATED FORGETTING; AUTHENTIC LEARNING IMPROVEMENT AND RETENTION HAPPENED INSTEAD, WITH SPACED RETRIEVAL INCURRING ONE-THIRD OF THE TIME INVESTMENT EXPERIENCED BY NON-SPACED RETRIEVAL. EMBEDDED ANALYTICS ALLOWED FOR REAL-TIME MONITORING OF LEARNING PROGRESS THAT COULD FORM THE BASIS OF HELPFUL FEEDBACK TO LEARNERS FOR SELF-DIRECTED LEARNING AND EDUCATORS FOR COACHING.
THE PURPOSE OF LEARNING ANALYTICS IS TO OPTIMISE LEARNING AND THE ENVIRONMENT IN WHICH IT OCCURS. HOWEVER, WHILE MUCH RESEARCH HAS BEEN CONDUCTED INTO HOW TO MEASURE, COLLECT AND ANALYSE LEARNER DATA, LITTLE HAS BEEN CONDUCTED INTO HOW TO AESTHETICALLY DISPLAY INFORMATION TO LEARNERS IN DASHBOARDS AND EDUCATIONAL MATERIALS ONLINE TO MAXIMISE ITS APPEAL. THIS EXPERIMENT EXAMINED THE RELATIONSHIP BETWEEN CHILDREN'S AGE AND THEIR AESTHETIC PREFERENCES TOWARDS VISUAL COMPLEXITY IN E-LEARNING WEB PAGES DESIGNED FOR THEM. IT APPLIED BERLYNE'S THEORY OF AESTHETIC PREFERENCE: A THEORY THAT SUGGESTS THAT PEOPLE PREFER A MEDIUM LEVEL OF STIMULI TO A LOW OR HIGH LEVEL OF STIMULI. IT ASKED 180 TAIWANESE PRIMARY SCHOOL CHILDREN TO INDICATE THEIR AESTHETIC PREFERENCES TOWARDS WEB PAGES OF HIGH, MEDIUM AND LOW VISUAL COMPLEXITY. THE CHILDREN WERE DIVIDED INTO THREE GROUPS: 7-8 YEAR-OLDS, 9-10 YEAR-OLDS AND 11-12 YEAR-OLDS. WHEN THE RATINGS OF THE THREE AGE GROUPS WERE LOOKED AT TOGETHER, THEY REVEALED THAT THE CHILDREN PREFERRED E-LEARNING WEB PAGES THAT HAD A MEDIUM LEVEL OF VISUAL COMPLEXITY TO THOSE THAT HAD A HIGH OR LOW LEVEL OF VISUAL COMPLEXITY. AS SUCH, THE RESULTS SUPPORTED BERLYNE'S THEORY. HOWEVER, WHEN THE RATINGS OF THE AGE GROUPS WERE LOOKED AT SEPARATELY WITH RESPECT TO AESTHETIC FACTORS, DIFFERENCES WERE FOUND. IN PARTICULAR, IT WAS FOUND THAT THE 7-8 YEAR-OLDS PREFERRED E-LEARNING WEB PAGES THAT HAD A HIGH LEVEL OF COLOURFULNESS WHILE THE 9-12 YEAR-OLDS DID NOT.
DATA ABOUT LEARNING CAN SUPPORT TEACHERS IN THEIR DECISION-MAKING PROCESSES AS THEY DESIGN TASKS AIMED AT IMPROVING STUDENT EDUCATIONAL OUTCOMES. HOWEVER, TO ACHIEVE SYSTEMIC IMPACT, A DEEPER UNDERSTANDING OF TEACHERS' PERSPECTIVES ON, AND EXPECTATIONS FOR, DATA AS EVIDENCE IS REQUIRED. IT IS CRITICAL TO UNDERSTAND HOW TEACHERS' ACTIONS ALIGN WITH EMERGING LEARNING ANALYTICS TECHNOLOGIES, INCLUDING THE PRACTICES OF PRE-SERVICE TEACHERS WHO ARE DEVELOPING THEIR PERSPECTIVES ON DATA USE IN CLASSROOM IN THEIR INITIAL TEACHER EDUCATION PROGRAMME. THIS MAY LEAD TO AN INTEGRATION GAP IN WHICH TECHNOLOGY AND DATA LITERACY ALIGN POORLY WITH EXPECTATIONS OF THE ROLE OF DATA AND ENABLING TECHNOLOGIES. THIS PAPER DESCRIBES TWO PARTICIPATORY WORKSHOPS THAT PROVIDE EXAMPLES OF THE VALUE OF HUMAN-CENTRED APPROACHES TO UNDERSTAND TEACHERS' PERSPECTIVES ON, AND EXPECTATIONS FOR, DATA AS EVIDENCE. THESE WORKSHOPS FOCUS ON THE DESIGN OF PRE-SERVICE TEACHERS ENROLLED IN TEACHER EDUCATION PROGRAMMES (N = 21) AT TWO AUSTRALIAN UNIVERSITIES. THE APPROACH POINTS TO THE SIGNIFICANCE OF (A) PRE-SERVICE TEACHERS' INTENTIONS TO TRACK THEIR STUDENTS' DISPOSITIONS TO LEARNING AND THEIR ABILITY TO LEARN EFFECTIVELY, (B) THE MATERIALITY OF LEARNING ANALYTICS AS AN ENABLING TECHNOLOGY AND (C) THE ALIGNMENT OF LEARNING ANALYTICS WITH LEARNING DESIGN, INCLUDING THE HUMANCENTRED, ETHICAL AND INCLUSIVE USE OF EDUCATIONAL DATA IN THE TEACHING PRACTICE. IMPLICATIONS FOR PRACTICE OR POLICY: PRE-SERVICE TEACHERS OUGHT TO BE GIVEN OPPORTUNITIES TO ENGAGE AND UNDERSTAND MORE ABOUT LEARNING DESIGN, LEARNING ANALYTICS AND THE USE OF DATA IN CLASSROOMS. PROFESSIONAL EXPERIENCE PLACEMENTS FOR PRE-SERVICE TEACHERS SHOULD INCLUDE PARTICIPATORY DATA SESSIONS OR LEARNING DESIGN WORKSHOPS. TEACHER EDUCATION ACADEMICS IN UNIVERSITIES MUST BE PROVIDED WITH ONGOING PROFESSIONAL DEVELOPMENT TO SUPPORT THEIR PREPARATION WORK OF PRE-SERVICE TEACHERS' DATA LITERACY, LEARNING ANALYTICS AND THE INCREASING PRESENCE OF DATA.
THE RISE OF LEARNING ANALYTICS, THE APPLICATION OF COMPLEX METRICS DEVELOPED TO EXPLOIT THE PROLIFERATION OF BIG DATA' IN EDUCATIONAL WORK, RAISES IMPORTANT MORAL QUESTIONS ABOUT THE NATURE OF WHAT IS MEASURABLE IN EDUCATION. TEACHERS, SCHOOLS AND NATIONS ARE INCREASINGLY HELD TO ACCOUNT BASED ON METRICS, EXACERBATING THE TENDENCY FOR FINE-GRAINED MEASUREMENT OF LEARNING EXPERIENCES. IN THIS ARTICLE, THE ORIGINS OF LEARNING ANALYTICS ONTOLOGY ARE EXPLORED, DRAWING UPON CORE IDEAS IN THE PHILOSOPHY OF COMPUTING, SUCH AS THE GENERAL DEFINITION OF INFORMATION AND THE INFORMATION-THEORETIC ACCOUNT OF KNOWLEDGE. DRAWING UPON A READING OF DESCARTES MEDITATIO II, WHICH EXTENDS THE PHENOMENOLOGY OF JEAN-LUC MARION INTO A PEDAGOGY OF INTENTIONALITY, THE ARTICLE IDENTIFIES A FUNDAMENTAL INCOMPATIBILITY BETWEEN THE SUBJECTIVE EXPERIENCE OF LEARNING AND THE INFORMATION-THEORETIC ACCOUNT OF KNOWLEDGE. HUMAN SUBJECTS EXPERIENCE AND VALUE THEIR OWN INFORMATION INCOMMENSURABLY WITH THE WAYS IN WHICH COMPUTERS MEASURE AND QUANTIFY INFORMATION. THE CONSEQUENCES OF THIS FINDING FOR THE DESIGN OF ONLINE LEARNING ENVIRONMENTS, AND THE NECESSARY LIMITATIONS OF LEARNING ANALYTICS AND MEASUREMENT ARE EXPLORED.
LEARNING ANALYTICS IS AN INNOVATIVE DISCIPLINE THAT HAS AN ENORMOUS POTENTIAL TO IMPROVE THE QUALITY OF MEDICAL EDUCATION AND LEARNING ASSESSMENT. IT IS DEFINED AS: "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". IN RECENT DECADES, THE APPEARANCE OF LARGE VOLUMES OF DATA (BIG DATA), ACCOMPANIED BY A QUICK EVOLUTION OF EDUCATIONAL DATA MINING TECHNIQUES, THE EMERGENCE OF SOPHISTICATED TECHNOLOGIES TO ANALYZE AND VISUALIZE ANY TYPE OF DATA, AS WELL AS THE AVAILABILITY OF PERMANENTLY-CONNECTED MOBILE ELECTRONIC DEVICES, HIGHER PROCESSING SPEED AND CAPACITY OF INFORMATION RETRIEVAL, HAVE GENERATED A CONTEXT THAT FAVORS THE USE OF LEARNING ANALYTICS IN CLINICAL MEDICINE AND MEDICAL EDUCATION. IN THIS PAPER, THE RECENT HISTORY OF THE CONCEPT OF LEARNING ANALYTICS IS DESCRIBED, AS WELL AS ITS ADVANTAGES AND DISADVANTAGES IN HIGHER EDUCATION, AND ITS APPLICATIONS IN THE TEACHING OF HEALTH SCIENCES AND EDUCATIONAL ASSESSMENT. IT IS NECESSARY FOR THE COMMUNITY OF MEDICAL EDUCATORS TO BE ACQUAINTED WITH LEARNING ANALYTICS, IN ORDER TO BE ABLE TO INTEGRATE IT TO OUR CONTEXT IN AN EFFICACIOUS AND TIMELY MANNER.
THE PURPOSE OF THE LILE2019 WORKSHOP IS TO PROVIDE AN INTERDISCIPLINARY FORUM FOR RESEARCHERS AND PRACTITIONERS WHO MAKE INNOVATIVE USE OF WEB DATA FOR EDUCATIONAL PURPOSES, SPANNING AREAS SUCH AS LEARNING ANALYTICS, WEB MINING, DATA AND WEB SCIENCE, PSYCHOLOGY AND THE SOCIAL SCIENCES. THE PREVIOUS EDITIONS OF THE LILE WORKSHOP WERE SUCCESSFULLY HELD AT THE ESWC, WWW, ISWC AND WEBSCI CONFERENCES. LILE2019 CONSISTS OF KEYNOTES, PRESENTATIONS OF ACCEPTED PAPERS AND POSTERS AND DISCUSSION.
PERSONALIZED LEARNING IS CONSIDERED A KEY FACTOR IN INNOVATIVE LEARNING ENVIRONMENTS [1]. IT REQUIRES THAT INDIVIDUAL LEARNING PROCESSES ARE DOCUMENTED OR OBSERVED, ANALYZED AND ASSESSED, FEEDBACK IS PROVIDED, AND ADEQUATE MEASURES ARE TAKEN. TO SUPPORT LEARNERS WITH FORMATIVE ASSESSMENT [2], FEEDBACK TAILORED TO THE SPECIFIC NEEDS HAS TO BE FED BACK IMMEDIATELY. AT UNIVERSITY LEVEL, COMPUTERAIDED ASSESSMENT AND AUTOMATIC FEEDBACK ARE PROMISING APPROACHES TO IMPLEMENT SUCH FORMATIVE ASSESSMENT AT THE REQUIRED LARGE SCALE. IN THIS PAPER, WE INVESTIGATE HOW COMPUTER-AIDED FORMATIVE ASSESSMENT CAN BE SUPPLEMENTED BY LEARNING ANALYTICS TO IDENTIFY THE ADEQUATE TYPE OF FEEDBACK REQUIRED FOR PERSONALIZED LEARNING SUPPORT. WE DEVELOPED SEVERAL INTERACTIVE LEARNING APPLICATIONS IN MATH, DESIGNED AS TUTORING SYSTEMS WITH SCAFFOLDING, (SEMI-)AUTOMATIC ASSESSMENT, AND FEEDBACK [3]. INTEGRATED LEARNING ANALYTICS ALLOWS FOR RECORDING ALL INTERMEDIATE SOLUTIONS, AUTOMATIC ASSESSMENT RESULTS, AND SEMANTICALLY RELEVANT INTERACTIONS BETWEEN STUDENTS AND THE LEARNING APPLICATION. DATA COLLECTED IN INTRODUCTORY MATH COURSES AT UNIVERSITY ALLOWED US TO ANALYZE ASPECTS SUCH AS THE INTERDEPENDENCE BETWEEN THE NUMBER OF FEEDBACK REQUESTS IN VARIOUS SITUATIONS AND FINAL OUTCOMES (SUCCESS VS. NO SUCCESS), AND IDENTIFYING CRITICAL POINTS WHERE THE PROBABILITY FOR SOLVING AN EXERCISE SUCCESSFULLY DROPS BELOW A GIVEN THRESHOLD. THIS LEADS US TO INDICATORS FOR WHEN AUTOMATIC FEEDBACK IS NOT GOOD ENOUGH AND DIFFERENT LEVELS OF FEEDBACK ARE REQUIRED.
LEARNING MANAGEMENT SYSTEMS (LMSS) ARE UBIQUITOUS COMPONENTS OF THE ACADEMIC TECHNOLOGY EXPERIENCE FOR LEARNERS ACROSS A WIDE VARIETY OF INSTRUCTIONAL CONTEXTS. LEARNERS' INTERACTIONS WITHIN AN LMS ARE OFTEN CONTINGENT UPON HOW INSTRUCTORS ARCHITECT A MODULE, COURSE, OR PROGRAM OF STUDY. PATTERNS RELATED TO THESE LEARNER INTERACTIONS, OFTEN REFERRED TO AS LEARNING ANALYTICS IMPLEMENTATION (LAI), CAN BE REPRESENTED BY COMBINING SYSTEM-LEVEL LMS DATA WITH COURSE-LEVEL DESIGN DECISIONS TO INFORM MORE GRANULAR INSIGHTS INTO LEARNER BEHAVIOUR. THE PURPOSE OF THIS PAPER IS TO USE THE LAI FRAMEWORK, SPECIFICALLY THE PRINCIPLES OF COORDINATION AND COMPARISON (WISE & VYTASEK, 2017), TO EXAMINE HOW LEARNER INTERACTION PATTERNS ASSOCIATED WITH LMS-USE VARIABLES CORRESPOND TO DELIBERATE LEARNING DESIGN DECISIONS AND COURSE OUTCOMES FOR A GROUP OF COURSES IN THE SAME UNDERGRADUATE WRITING PROGRAM. VISUALIZATIONS OF LEARNER ACTIVITY EXHIBITED SIMILAR PATTERNS OF LEARNER ENGAGEMENT ACROSS COURSES, CORROBORATING THE OBSERVATION THAT DESIGN DECISIONS HEAVILY INFLUENCE LEARNER BEHAVIOUR. PREDICTIVE ANALYSES DEMONSTRATED STRONG INFLUENCE OF LMS USE ON FINAL GRADES WHILE ACCOUNTING FOR COURSE INSTRUCTOR. THAT IS, WHILE PAGE VIEWS WERE NOT RELATED TO FINAL GRADE, THE LENGTH OF DISCUSSION ENTRIES WAS OFTEN PREDICTIVE. THESE RESULTS SUGGEST THAT STUDENTS WHO PRACTISED WRITING MORE - THE MAIN LEARNING OBJECTIVE OF THIS COURSE - HAD HIGHER FINAL GRADES, REGARDLESS OF VARIATIONS IN INSTRUCTOR AND SEMESTER.
WITH THE POPULARITY OF WEARABLE DEVICES, SMART WATCHES CONTAINING VARIOUS SENSORS HAVE BEEN WIDELY ADOPTED FOR MANY HEALTHCARE APPLICATIONS. YET THERE IS RARELY ANY RESEARCH STUDY ON THE POSSIBLE USES OF SMART WATCHES FOR LEARNING ANALYTICS, PARTICULARLY FOR ANALYZING STUDENTS' LEARNING ACTIVITIES THROUGH THE PHYSIOLOGICAL AND/OR MOVEMENT DATA COLLECTED ON THEIR SMART WATCHES. THIS PAPER CONSIDERS A PIONEERING AND SOPHISTICATED LEARNING ANALYTICS PLATFORM USING FINE-TUNED DEEP LEARNING MODELS TO PREDICT STUDENTS' LEARNING ACTIVITIES BASED ON THE REAL-TIME DATA, INCLUDING THEIR HEART RATES, CALORIES, THREE-AXIS ACCELEROMETER AND GYROSCOPE DATA, CAPTURED ON WEARABLE DEVICES AND THEN UPLOADED ONTO A CLOUD SERVER 14 THOROUGH ANALYSES. TO VALIDATE ON THE ACTUAL ACTIVITIES CONDUCTED BY EACH STUDENT, AN INTELLIGENT MOBILE APPLICATION IS DEVELOPED TO PUSH INSTANT NOTIFICATIONS FOR STUDENTS TO REPORT THEIR OWN ACTIVITIES WHENEVER THE CHANGE OF HEART RATES ARE DEVIATED SIGNIFICANTLY FROM THEIR NORMAL VALUES. BASED ON STUDENTS' HEART RATES AND CALORIES, A LONG-SHORT TERM MEMORY (LSTM) MODEL IS BUILT TO CLASSIFY STUDENTS' LEARNING STATES AS ACTIVE OR NOT WITH AN IMPRESSIVE PREDICTION ACCURACY OF 95% WHEREAS ANOTHER HYBRID MODEL COMBINING BOTH THE LSTM AND CONVOLUTIONAL NEURAL NETWORKS ATTAINS THE HIGHEST PREDICTION ACCURACY OF 74% TO PREDICT STUDENTS' SPECIFIC LEARNING ACTIVITIES AS BASED ON THEIR PHYSIOLOGICAL AND MOVEMENT DATA. THE PROTOTYPE IMPLEMENTATION CLEARLY DEMONSTRATES THE FEASIBILITY OF THE PROPOSED FRAMEWORK FOR LEARNING ANALYTICS. MORE IMPORTANTLY, THIS WORK SHED LIGHTS ON VARIOUS DIRECTIONS INCLUDING THE INTEGRATION OF NOISE FILTERS TO PREPROCESS THE COLLECTED DATA 14 FURTHER INVESTIGATION.
THE LIFETIME OF SKILLS AND PARTICULAR KNOWLEDGE IS GETTING SHORTER. IN PARALLEL, THE NEW SKILLS ARE OBTAINED AS A PART OF THE LIFELONG LEARNING PROCESS. ONLY A PART OF THESE PROCESSES IS ACTUALLY RECOGNIZED AND EVEN FIXED IN A FORM OF DEGREES, CERTIFICATES AND REFERENCES. THE DIVERSITY OF BOTH PERSONAL LEARNING ENVIRONMENTS AND SITUATIONS WHEN YOU HAVE TO PRESENT YOURSELF IN A SPECIFIC WAY (E.G. TO THE POTENTIAL EMPLOYER OR GROUP OF FRIENDS) DEMANDS THE FLEXIBLE AND TRANSPARENT SYSTEM THAT ENABLES CONTINUOUS BUILDING OF E-PORTFOLIO OF REACHED ACHIEVEMENTS AND LEARNING GOALS. THE HUGE POTENTIAL IN THIS FIELD IS PRESENTED BY THE OPEN BADGE INFRASTRUCTURE CREATED BY THE MOZILLA FOUNDATION. THIS INFRASTRUCTURE SUPPORTS SO-CALLED "BADGES" GRANTED FROM ANY ISSUER TO ANYBODY IN THE FORM OF A DIGITAL PICTURE. SUCH A FILE IS THE INDEPENDENT DIGITAL OBJECT WITH ENCODED INFORMATION ABOUT PARAMETERS OF REACHED ACHIEVEMENT, ISSUER, EARNER ETC. INTEGRATION OF SUCH A TOOL INTO THE EXISTING CONTENT MANAGEMENT SYSTEMS (CMS), LEARNING MANAGEMENT SYSTEMS (LMS), E-PORTFOLIO SYSTEMS, (DIGITAL) SOCIAL NETWORKS, ONLINE SERVICE PROVIDERS INCLUDING GAMES AND ALSO INTRANETS COULD LEAD TO THE UNIQUE DISTRIBUTED PERSONAL LEARNING ANALYTICS DATA SOURCE. THIS PAPER DESCRIBES BRIEFLY THE CURRENT STATUS OF IMPLEMENTATION AND ALSO RISKS, THREATS AND WEAKNESSES OF SUCH AN INFRASTRUCTURE.
THE POTENTIAL OF ANALYTICS AND DATA MINING METHODOLOGIES, THAT EXTRACT USEFUL AND ACTIONABLE INFORMATION FROM LARGE DATA-SETS, HAS TRANSFORMED ONE FIELD OF SCIENTIFIC INQUIRY AFTER ANOTHER. ANALYTICS HAS BEEN WIDELY APPLIED IN BUSINESS ORGANIZATIONS AS BUSINESS ANALYTICS AND WHEN APPLIED TO EDUCATION, THESE METHODOLOGIES ARE REFERRED TO AS LEARNING ANALYTICS AND EDUCATIONAL DATA MINING. LEARNING ANALYTICS PROPOSES TO COLLECT, MEASURE AND ANALYZE DATA IN LEARNING ENVIRONMENTS TO IMPROVE TEACHING AND LEARNING PROCESS. EDUCATIONAL DATA MINING (EDM) THRIVES ON EXISTING DATA COLLECTED BY LEARNING MANAGEMENT SYSTEMS. THE APPLICABILITY OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING CAN BE EXTENDED TO TRADITIONAL LEARNING PROCESSES BY SUITABLY COMBINING DATA COLLECTED FROM TECHNOLOGY ENABLED PROCESSES SUCH AS ADMISSION AND ASSESSMENT WITH DATA GENERATED FROM ANALYSIS OF LEARNING INTERACTIONS. THE INTELLECTUAL PERFORMANCE OF THE STUDENTS CAN BE ANALYZED USING SOME WELL KNOWN LEARNING FRAMEWORKS. THIS PAPER DEMONSTRATES THE COMPLETE ANALYTICS PROCESS FROM DATA COLLECTION, MEASUREMENT TO ANALYSIS USING GUILFORD'S STRUCTURE OF INTELLECT MODEL. AN ANALYTIC DASHBOARD PROVIDES THE NECESSARY INFORMATION IN CONCISE AND VISUAL FORM AND IN AN INTERACTIVE MODE. THE ANALYTIC PROCESS PRESENTED ON TALENT EXAMINATION DATA CAN BE GENERALIZED TO SIMILAR EXAMINATIONS IN TRADITIONAL EDUCATIONAL SETUP.
THE PAPER ANALYZES THE POSSIBILITIES OF USING GOOGLE ANALYTICS AND SMARTLOOK, AS TWO TOOLS FOR WEB ANALYTICS IN THE EDUCATIONAL CONTEXT, TO POINT OUT THE POSSIBILITIES AND, ON THE CONTRARY, THE LIMITS OF SUCH AN APPROACH AND ITS RELATION TO LEARNING ANALYTICS AS SUCH. MEASUREMENT AND ANALYSIS OF WEB COURSES BRINGS A NUMBER OF INTERESTING OPTIONS, BOTH TOWARDS REDESIGN AND INNOVATION (DATA WITH INTERVENTION POTENTIAL), AS WELL AS PURELY RESEARCH CHARACTER. IN THE INTRODUCTION, WE FOCUS ON THE RELATIONSHIP BETWEEN WEB ANALYTICS AND LEARNING ANALYTICS. NEXT, WE'LL DESCRIBE GOOGLE ANALYTICS IN TERMS OF ITS EDUCATIONAL OPPORTUNITIES. THIS DESCRIPTION IS RELATED TO ALREADY EXISTING RESEARCH PAPERS THAT DEAL WITH THIS TOPIC. THE FOLLOWING IS A SMARTOLOOK TOOL THAT LETS YOU CREATE HEATMAPS AND CAPTURE THE CURSOR MOVEMENT ON A WEB PAGE, WHICH AGAIN RELATES TO THE ABILITY TO ANALYZE STUDENT INTERACTION WITH THE LEARNING MATERIAL. USING THE TOOLS DESCRIBED ABOVE, IT IS NOT POSSIBLE TO PERFORM THE LEARNING ANALYTICS AS AN ACTIVITY LEADING TO INTERVENTION, FOR EXAMPLE TO IDENTIFY PROBLEMATIC STUDENTS BUT RATHER TO FIND OUT WHAT USERS OF THE COURSE ARE STUDYING, THEIR TECHNICAL EQUIPMENT, TO UNCOVER THEIR LEARNING HABITS AND PATTERNS OF BEHAVIOR ONLINE ENVIRONMENT. THESE ANALYZES ARE ILLUSTRATED ON DATA FROM THE COURSE TAUGHT AT THE FACULTY OF ARTS OF THE MASARYK UNIVERSITY IN BRNO - CREATIVE WORK. WITH INFORMATION.
AS UNIVERSITIES OFFER MORE FLEXIBLE DELIVERY OPTIONS THERE ARE PARALLEL CONCERNS ABOUT INCREASING LEVELS OF ENGAGEMENT. WITHDRAWAL AND DISCONNECTION FROM STUDY IS MOST COMMON DURING THE FIRST YEAR OF UNIVERSITY AS STUDENTS EXPERIENCE SOCIAL, CULTURAL AND FINANCIAL ADJUSTMENTS AND ATTEMPT TO UNDERSTAND THE NUANCES OF ACADEMIC LEARNING. THIS ARTICLE REPORTS ON THE IMPACT OF A TECHNOLOGY-ENABLED SUPPORT SYSTEM DELIVERING TIMELY, PERSONALISED AND ACTIONABLE FEEDBACK ON ONLINE ACTIVITY AND SUPPORT EMAILS AT CRITICAL PERIODS IN TWO COURSES. LEARNING ANALYTICS DATA WAS USED TO IDENTIFY APPROPRIATE ENGAGEMENT METRICS FOR PERSONALISING FEEDBACK TO STUDENTS WITH RESULTS INDICATING AN IMPROVEMENT IN COURSE GRADES. WHILE THE LEARNING ANALYTICS APPROACH PROVIDES A TECHNOLOGY-MEDIATED MEANS FOR SCALING PERSONALISED FEEDBACK AND COMMUNICATING WITH LARGE COHORTS OF LEARNERS, THE QUALITATIVE RESULTS INDICATED THAT STUDENTS FELT THEY WERE NOTICED AS INDIVIDUAL LEARNERS, WERE MORE WILLING TO CONTACT EDUCATORS FOR SUPPORT, AND MORE MOTIVATED TO ENGAGE WITH THE ONLINE COURSE LEARNING MATERIALS.
OVER THE PAST CENTURY, PSYCHOMETRICIANS HAVE DEVELOPED AN ARMAMENTARIUM OF METHODS FOR REASONING ABOUT STUDENTS' CAPABILITIES FROM THEIR PERFORMANCES IN FAMILIAR FORMS OF ASSESSMENT. THIS CHAPTER DISCUSSES HOW KEY CONCEPTS UNDERLYING THESE TOOLS CAN BE RECONCEIVED AND EXTENDED TO PLAY A VALUABLE ROLE FOR REASONING FROM PERFORMANCES IN NEW FORMS OF ASSESSMENT WITH MORE COMPLEX DATA. IT DESCRIBES COMPLEMENTARY ROLES FOR LEARNING-ANALYTIC METHODS TO IDENTIFY EVIDENCE IN PERFORMANCES AND FOR MODULAR PROBABILITY-BASED LATENT-VARIABLE MODELS TO INTEGRATE EVIDENCE INTO A CROSS-TASK METRIC AND TO QUANTIFY ITS VALUE. IN ADDITION TO PRACTICAL BENEFITS, PROBABILITY MODELS WITH LATENT VARIABLES PROVIDE WAYS OF CONCEPTUALIZING AND CHARACTERIZING THE ASSESSMENT PRINCIPLES OF RELIABILITY, VALIDITY, COMPARABILITY, AND FAIRNESS. IDEAS ARE ILLUSTRATED WITH EXCERPTS FROM TWO ASSESSMENTS THAT ADDRESS SYSTEMS THINKING: A SCENARIO-BASED TASK USING MORE FAMILIAR DATA FORMS AND THE GAME-BASED ASSESSMENT SIMCITYEDU: POLLUTION CHALLENGE!
A GOOD ASSESSMENT PROCESS ENTAILS ANALYZING STUDENTS' RESULTS IN ORDER TO DETECT ANOMALIES AND BE ABLE TO PROVIDE ADEQUATE FEEDBACK TO SOLVE THEM. THE ANALYSIS OF THE RESULTS BECOMES HARDER WHEN THE AMOUNT OF INFORMATION AND ITS HETEROGENEITY INCREASES. CURRENT EDUCATION TENDENCIES (FOR EXAMPLE BLENDED LEARNING), FACE THIS KIND OF PROBLEM AS THEY HAVE TO DEAL WITH A LOT OF INFORMATION FROM MANY DIFFERENT SOURCES. IN THIS PAPER, WE PRESENT A SYSTEM THAT HELPS LECTURERS TO INTEGRATE INFORMATION FROM DIFFERENT SOURCES AND TO PERFORM AN ANALYSIS OF THE DATA THROUGH THE USE OF VISUAL LEARNING ANALYTICS TECHNIQUES. THE ACCEPTANCE OF THE SYSTEM HAS BEEN SATISFACTORILY EVALUATED.
OPEN, FLEXIBLE AND DISTANCE LEARNING HAS BECOME PART OF MAINSTREAM EDUCATION IN CHINA. USING A BLENDED LEARNING PROGRAM IN A CHINESE HIGH SCHOOL AS THE CASE, THIS STUDY ADOPTED DATA-MINING APPROACHES TO ESTABLISH PREDICTIVE MODELS USING PERSONALITY TRAITS. RESULTS SHOWED THAT, FOR STUDENTS WITH HIGH OE AND LOW EXTRAVERSION, AND STUDENTS WHO ARE LOW ON BOTH OF THESE CONSTRUCTS, THE NUMBER OF POSTINGS IN DIGEST (NPD) AND AVERAGE SCORE OF AFTER-CLASS TEST (SCT) WERE SIGNIFICANT PREDICTORS OF THEIR ACHIEVEMENT. FOR STUDENTS WITH LOW OE AND HIGH EXTRAVERSION, TIME SPENT ON VIEWING COURSE RESOURCES AND NUMBER OF ANSWERS PROVIDED IN THE FORMAT OF TEXT WERE SIGNIFICANT PREDICTORS. FOR THOSE WITH HIGH OE AND LOW EXTRAVERSION, TIME SPENT ON LEARNING ONLINE AND NUMBER OF QUESTIONS RAISED IN THE FORMAT OF HYPERMEDIA, NPD AND SCT WERE SIGNIFICANT. FURTHERMORE, DEEP BELIEF NETWORKS PERFORMED BEST IN IDENTIFYING AT-RISK STUDENTS AT EACH STAGE.
INVESTIGATING EFFECTIVENESS OF LEARNING ANALYTICS IS A MAJOR TOPIC OF RESEARCH, WITH A RECENT SYSTEMATIC REVIEW FINDING 689 PAPERS IN THIS FIELD (LARRABEE SONDERLUND ET AL., 2019). FEW OF THESE (11 OUT OF 689) HIGHLIGHT THE POTENTIAL OF INTERVENTIONS BASED ON LEARNING ANALYTICS. THE OPEN UNIVERSITY UK (OU) IS ONE OF FEW INSTITUTIONS TO SYSTEMATICALLY DEVELOP AND IMPLEMENT A LEARNING ANALYTICS FRAMEWORK AT SCALE. THIS PAPER REVIEWS THE IMPACT OF ONE PART OF THIS FRAMEWORK - THE ANALYTICS FOR ACTION (A4A) PROCESS, FOCUSING ON THE 2017-18 ACADEMIC YEAR AND REVIEWING BOTH FEEDBACK FROM MODULE TEAMS AND INTERVENTIONS COMING OUT OF THE PROCESS. THE A4A PROCESS INCLUDES HANDS-ON TRAINING FOR STAFF, FOLLOWED BY DATA SUPPORT MEETINGS WITH EDUCATORS WHEN THE COURSE IS LIVE TO STUDENTS. THE AIM BEING TO HELP EDUCATORS WITH MAKING INFORMED, EVIDENCE-BASED INTERVENTIONS TO AID STUDENT RETENTION AND ENGAGEMENT. FINDINGS FROM THIS STUDY INDICATE THAT PARTICIPANTS ARE SATISFIED WITH THE TRAINING AND THAT THE DATA SUPPORT MEETINGS ARE HELPING IN PROVIDING NEW PERSPECTIVES ON THE DATA. THE SCOPE AND NATURE OF ACTIONS TAKEN BY MODULE TEAMS VARIES WIDELY, RANGING FROM NO INTERVENTION AT ALL TO INTERVENTIONS SPANNING OVER MULTIPLE PRESENTATIONS. IN SOME CASES, MEASURING THE IMPACT OF THE ACTIONS TAKEN WILL REQUIRE DATA ANALYSIS FROM FURTHER PRESENTATIONS. THE PAPER ALSO PRESENTS FINDINGS INDICATING ROOM FOR IMPROVEMENT IN THE FOLLOW UP OF THE ACTIONS AGREED, SUPPORT GIVEN TO MODULE TEAMS TO IMPLEMENT SUCH ACTIONS AND FINAL EVALUATION OF IMPACT ON STUDENT OUTCOMES.
PRACTICE-BASED EXPERIENTIAL LEARNING ANALYTICS RESEARCH AND SUPPORT (PELARS) IS A PROJECT ABOUT LEARNING AND MAKING. THE PELARS PROJECT FINDS WAYS OF GENERATING "ANALYTICS" (DATA ABOUT THE LEARNING PROCESS AND ANALYSIS OF THIS DATA), WHICH HELPS LEARNERS AND TEACHERS BY PROVIDING FEEDBACK FROM HANDS-ON, PROJECT-BASED AND EXPERIENTIAL LEARNING SITUATIONS. IN THIS PAPER, WE PRESENT OUR PROPOSAL FOR IMPROVING ANALYTICS EDUCATION WITH HANDS-ON, PROJECT-BASED AND EXPERIMENTAL SCENARIOS FOR ENGINEERING STUDENTS. THIS IS DONE THROUGH TEACHER AND LEARNER ENGAGEMENT, USER STUDIES AND EVALUATED TRIALS, PERFORMED AT UCV (UNIVERSITY OF CRAIOVA, ROMANIA) AND DTU DIPLOM (TECHNICAL UNIVERSITY OF DENMARK, CAMPUS BALLERUP, DENMARK). THE PELARS PROJECT PROVIDES TECHNOLOGICAL TOOLS AND ICT-BASED METHODS FOR COLLECTING ACTIVITY DATA ( MOVING IMAGE-BASED AND EMBEDDED SENSING) FOR LEARNING ANALYTICS (DATA-MINING AND REASONING) OF PRACTICE-BASED AND EXPERIENTIAL STEM.
IN THIS PAPER, WE PRESENT WORK ON LEARNING ANALYTICS THAT AIMS TO SUPPORT LEARNERS AND TEACHERS THROUGH DASHBOARD APPLICATIONS, RANGING FROM SMALL MOBILE APPLICATIONS TO LEARNSCAPES ON LARGE PUBLIC DISPLAYS. DASHBOARDS TYPICALLY CAPTURE AND VISUALIZE TRACES OF LEARNING ACTIVITIES, IN ORDER TO PROMOTE AWARENESS, REFLECTION, AND SENSE-MAKING, AND TO ENABLE LEARNERS TO DEFINE GOALS AND TRACK PROGRESS TOWARD THESE GOALS. BASED ON AN ANALYSIS OF OUR OWN WORK AND A BROAD RANGE OF SIMILAR LEARNING DASHBOARDS, WE IDENTIFY HCI ISSUES FOR THIS EXCITING RESEARCH AREA.
IN THE LAST YEARS, EDUCATIONAL COMMUNITY IS ASKING FOR A CHANGE BY INTRODUCING SOCIAL TOOLS IN FORMAL LEARNING CONTEXTS. BESIDES, ALTHOUGH MEANINGFUL RESEARCH ON THE EFFECTS OF SOCIAL LEARNING CAN BE FOUND IN THE LITERATURE, ALL AUTHORS AGREE THAT FURTHER RESEARCH IS NEEDED. SOCIAL NETWORK ANALYSIS (SNA) HAS BEEN PROVEN TO BE A USEFUL TOOL FOR BOTH RESEARCHERS AND TEACHERS. HOWEVER, BOTH STANDARD SNA TOOLS AND THE MOST POPULAR SOCIAL NETWORK SITES ARE EXTERNAL TO THE LEARNING MANAGEMENT SYSTEMS (LMS) ANDA, THEREFORE, THEY ARE HARD TO INTEGRATE BY TEACHERS INTO THEIR EDUCATIONAL DESIGNS. THIS STUDY SHOWS MSOCIAL, A NOVEL TOOL INTEGRATED INTO THE MOODLE LMS, THAT ALLOWS STUDENTS INTERACT IN THEIR SOCIAL NETWORKS WITHOUT LOSING THEIR PRESENCE IN THE LMS. MSOCIAL MONITORS STUDENT ACTIVITY IN SOCIAL NETWORKS (BOTH INSIDE AND OUTSIDE OF MOODLE), CALCULATES SNA METRICS AND SHOWS THE RESULTS PROMPTLY IN THE MOODLE COURSE SITE. THEREFORE, TEACHERS CAN EASILY UNDERSTAND, VISUALIZE, AND ANALYZE THE SOCIAL PARTICIPATION AND INTERACTION OF STUDENTS AND INCORPORATE THESE RESULTS TO IMPROVE THE LEARNING PROCESS.
TIME MANAGEMENT STRATEGIES AND SELF-REGULATED LEARNING HAVE RECEIVED MUCH ATTENTION. NEVERTHELESS, THERE IS A LACK OF RESEARCH IN TERMS OF STUDENT SELF-AWARENESS RELATED TO THEIR OWN SELF-REGULATION AND AUTONOMY. THIS STUDY AIMS TO VALIDATE WHETHER STUDENTS ARE SELF-CONSCIOUS ABOUT THEIR WORKING PATTERNS AND THEIR TIME MANAGEMENT. IN ORDER TO DO SO, SEVERAL PARAMETERS LIKE WORK SESSIONS REGULARITY OR INVESTED TIME HAVE BEEN COMPUTED AND ANALYZED. THIS INVESTED TIME IS MEASURED THANKS TO A DATA-GATHERING TOOL THAT COLLECTS EVENTS GENERATED BY STUDENTS. RESULTS SHOW THAT STUDENTS ARE, IN GENERAL, SELF-AWARE OF THEIR OWN WORKING PATTERNS AND THAT THE REGULARITY OF THEIR WEEKLY WORK TIME IS CORRELATED WITH THEIR FINAL MARKS.
THIS METHODOLOGICAL-THEORETICAL SYNERGY PROVIDES AN INTEGRATIVE FRAMEWORK OF LEARNING ANALYTICS THROUGH THE DEVELOPMENT OF THE HUMAN-AND-MACHINE SYMBIOTIC REINFORCEMENT LEARNING. THE FRAMEWORK INTENDS TO ADDRESS THE CHALLENGES OF THE CURRENT LEARNING ANALYTICS MODEL, INCLUDING A LACK OF INTERNAL VALIDITY, GENERALIZABILITY, IMMEDIACY, TRANSFERABILITY, AND INTERPRETABILITY FOR PRECISION EDUCATION. THE PROPOSED FRAMEWORK CONSISTS OF A MASTER COMPONENT (THE BRAIN) AND ITS FOUR SUBSUMING COMPONENTS: SOCIAL NETWORKING, THE SMART CLASSROOM, THE INTELLIGENT AGENT, AND THE DASHBOARD. THE BRAIN COMPONENT TAKES IN AND ANALYZES MULTIMODAL STREAMS OF STUDENT DATA FROM THE OTHER COMPONENTS WITH THE MODEL-BASED REINFORCEMENT LEARNING, WHICH FORMS POLICIES OF ADEQUATE ACTIONS THAT MAXIMIZE THE LONG-TERM REWARDS FOR BOTH THE HUMAN AND MACHINE IN THE SEAMLESS LEARNING ENVIRONMENT. AN EXAMPLE CASE PLAN IN ADVANCED STATISTICS WAS DEMONSTRATED TO ILLUSTRATE THE COURSE DESCRIPTION, DATA COLLECTED IN EACH COMPONENT, AND HOW THE COMPONENTS MEET DIFFERENT FEATURES OF THE SMART LEARNING ENVIRONMENT TO DELIVER PRECISION EDUCATION. AN EMPIRICAL DEMONSTRATION WAS PROVIDED USING SOME SELECTED MULITMODAL DATA TO INFORM THE EFFECTIVENESS OF THE PROPOSED FRAMEWORK. THE HUMAN-AND-MACHINE SYMBIOTIC REINFORCEMENT LEARNING HAS THEORETICAL AND PRACTICAL IMPLICATIONS FOR THE NEXT-GENERATION LEARNING ANALYTICS MODELS AND RESEARCH.
ONE OF THE MAIN CHALLENGES IN TEACHING AND LEARNING ACTIVITIES IS THE ASSESSMENT: IT ALLOWS TEACHERS AND LEARNERS TO IMPROVE THE FUTURE ACTIVITIES ON THE BASIS OF THE PREVIOUS ONES. IT ALLOWS A DEEP ANALYSIS AND UNDERSTANDING OF THE WHOLE LEARNING PROCESS. THIS IS PARTICULARLY DIFFICULT IN VIRTUAL LEARNING ENVIRONMENTS WHERE A GENERAL OVERVIEW IS NOT ALWAYS AVAILABLE. IN THE LATEST YEARS, LEARNING ANALYTICS ARE BECOMING THE MOST POPULAR METHODS TO ANALYZE THE DATA COLLECTED IN THE LEARNING ENVIRONMENTS IN ORDER TO SUPPORT TEACHERS AND LEARNERS IN THE COMPLEX PROCESS OF LEARNING. IF THEY ARE PROPERLY INTEGRATED IN LEARNING ACTIVITIES, INDEED, THEY CAN SUPPLY USEFUL INFORMATION TO ADAPT THE ACTIVITIES ON THE BASIS OF STUDENT'S NEEDS. IN THIS CONTEXT, THE PAPER PRESENTS A SOLUTION FOR THE DIGITALLY ENHANCED ASSESSMENT. TWO DIFFERENT LEARNING DASHBOARDS HAVE BEEN DESIGNED IN ORDER TO REPRESENT THE MOST INTERESTING LEARNING ANALYTICS AIMING AT PROVIDING TEACHERS AND LEARNERS WITH EASY UNDERSTANDABLE VIEW OF LEARNING DATA IN VIRTUAL LEARNING ENVIRONMENTS. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
EFFECTIVE USE OF LEARNING ANALYTICS SYSTEMS HAS BEEN PURPORTED TO CONFER VARIOUS BENEFITS TO LEARNERS IN TERMS OF BOTH ATTAINMENT AND RETENTION. THERE IS, HOWEVER, LITTLE AGREEMENT ON WHICH DATA ARE MEANINGFUL OR USEFUL. WHILST MEASURES OF ENGAGEMENT MIGHT CORRELATE WITH OUTCOMES, THEREBY RETROSPECTIVELY 'PREDICTING' THEM, THERE ARE FEWER STUDIES WHICH ATTEMPT TO PREDICT USING 'LIVE' SYSTEM DATA IN A FACE-TO-FACE TEACHING ENVIRONMENT. THIS STUDY REPORTS AN ANALYSIS OF WEEK BY WEEK DATA FROM A LEARNING ANALYTICS SYSTEM WHICH MONITORED 1,602 FIRST YEAR UK UNDERGRADUATES. UNIQUELY, ALTHOUGH STUDENTS COULD VIEW THEIR OWN DATA, NO FORMAL INTERVENTIONS TOOK PLACE. RESULTS SHOWED THAT STUDENTS WHO OBTAINED THE HIGHEST END-OF-YEAR MARKS WERE MORE LIKELY TO BE IN A HIGHER ENGAGEMENT QUINTILE AS EARLY AS THE FIRST 3-4 WEEKS, AND THAT EARLY ENGAGEMENT WAS HIGHLY PREDICTIVE OF FUTURE ENGAGEMENT. STUDENTS WHO STARTED IN A HIGHER ENGAGEMENT QUINTILE, BUT THEIR ENGAGEMENT DECREASED, WERE MORE LIKELY TO HAVE HIGHER MARKS THAN THOSE THAT STARTED IN A LOWER QUINTILE AND THEN INCREASED THEIR ENGAGEMENT. EARLY MEASURES OF ENGAGEMENT ARE THEREFORE PREDICTIVE OF FUTURE BEHAVIOUR AND OF FUTURE OUTCOMES, A FINDING WHICH HAS IMPORTANT IMPLICATIONS FOR UNIVERSITIES WISHING TO IMPROVE STUDENT OUTCOMES.
AS ADDRESSED BY STEPHEN YANG IN HIS ICCE 2019 KEYNOTE SPEECH (YANG, 2019), PRECISION EDUCATION IS A NEW CHALLENGE WHEN APPLYING ARTIFICIAL INTELLIGENCE (AI), MACHINE LEARNING, AND LEARNING ANALYTICS TO IMPROVE TEACHING QUALITY AND LEARNING PERFORMANCE. THE GOAL OF PRECISION EDUCATION IS TO IDENTIFY AT-RISK STUDENTS AS EARLY AS POSSIBLE AND PROVIDE TIMELY INTERVENTION ON THE BASIS OF TEACHING AND LEARNING EXPERIENCES (LU ET AL., 2018). DRAWING FROM THIS MAIN THEME OF PRECISION EDUCATION, THIS SPECIAL ISSUE ADVOCATES AN IN-DEPTH DIALOGUE BETWEEN COLD TECHNOLOGY AND WARM HUMANITY, IN TURN OFFERING GREATER UNDERSTANDING OF PRECISION EDUCATION. FOR THIS SPECIAL ISSUE, THIRTEEN RESEARCH PAPERS THAT SPECIALIZE IN PRECISION EDUCATION, AI, MACHINE LEARNING, AND LEARNING ANALYTICS TO ENGAGE IN AN IN-DEPTH RESEARCH EXPERIENCES CONCERNING VARIOUS APPLICATIONS, METHODS, PEDAGOGICAL MODELS, AND ENVIRONMENTS WERE EXCHANGED TO ACHIEVE BETTER UNDERSTANDING OF THE APPLICATION OF AI IN EDUCATION.
ACADEMIC WRITING IS A KEY SKILL REQUIRED FOR HIGHER EDUCATION STUDENTS, WHICH IS OFTEN CHALLENGING TO LEARN. A PROMISING APPROACH TO HELP STUDENTS DEVELOP THIS SKILL IS THE USE OF AUTOMATED TOOLS THAT PROVIDE FORMATIVE FEEDBACK ON WRITING. HOWEVER, SUCH TOOLS ARE NOT WIDELY ADOPTED BY STUDENTS UNLESS USEFUL FOR THEIR DISCIPLINE-RELATED WRITING, AND EMBEDDED IN THE CURRICULUM. THIS RECOGNITION MOTIVATES AN INCREASED EMPHASIS IN THE FIELD ON ALIGNING LEARNING ANALYTICS APPLICATIONS WITH LEARNING DESIGN, SO THAT ANALYTICS-DRIVEN FEEDBACK IS CONGRUENT WITH THE PEDAGOGY AND ASSESSMENT REGIME. THIS PAPER DESCRIBES THE DESIGN, IMPLEMENTATION, AND EVALUATION OF A PEDAGOGIC INTERVENTION THAT WAS DEVELOPED FOR LAW STUDENTS TO MAKE USE OF AN AUTOMATED ACADEMIC WRITING ANALYTICS TOOL (AWA) FOR IMPROVING THEIR ACADEMIC WRITING. IN EXEMPLIFYING THIS PEDAGOGICALLY ALIGNED LEARNING ANALYTIC INTERVENTION, WE DESCRIBE THE DEVELOPMENT OF A LEARNING ANALYTICS PLATFORM TO SUPPORT THE PEDAGOGIC DESIGN, ILLUSTRATING ITS POTENTIAL THROUGH EXAMPLE ANALYSES OF DATA DERIVED FROM THE TASK.
TWO OF THE MOST IMPORTANT OUTCOMES OF LEARNING ANALYTICS ARE PREDICTING STUDENTS' LEARNING AND PROVIDING EFFECTIVE FEEDBACK. LEARNING MANAGEMENT SYSTEMS (LMS), WHICH ARE WIDELY USED TO SUPPORT ONLINE AND FACE-TO-FACE LEARNING, PROVIDE EXTENSIVE RESEARCH OPPORTUNITIES WITH DETAILED RECORDS OF BACKGROUND DATA REGARDING USERS' BEHAVIORS. THE PURPOSE OF THIS STUDY WAS TO INVESTIGATE THE EFFECTS OF UNDERGRADUATE STUDENTS' LMS LEARNING BEHAVIORS ON THEIR ACADEMIC ACHIEVEMENTS. IN LINE WITH THIS PURPOSE, THE PARTICIPATING STUDENTS' ONLINE LEARNING BEHAVIORS IN LMS WERE EXAMINED BY USING LEARNING ANALYTICS FOR 14 WEEKS, AND THE RELATIONSHIP BETWEEN STUDENTS' BEHAVIORS AND THEIR ACADEMIC ACHIEVEMENTS WAS ANALYZED, FOLLOWED BY AN ANALYSIS OF THEIR VIEWS ABOUT THE INFLUENCE OF LMS ON THEIR ACADEMIC ACHIEVEMENT. THE PRESENT STUDY, IN WHICH QUANTITATIVE AND QUALITATIVE DATA WERE COLLECTED, WAS CARRIED OUT WITH THE EXPLANATORY MIXED METHOD. A TOTAL OF 71 UNDERGRADUATE STUDENTS PARTICIPATED IN THE STUDY. THE RESULTS REVEALED THAT THE STUDENTS USED LMSS AS A SUPPORT TO FACE-TO-FACE EDUCATION MORE INTENSIVELY ON COURSE DAYS (AT THE BEGINNING OF THE RELATED LESSONS AND AT NIGHTS ON COURSE DAYS) AND THAT THEY ACTIVATED THE CONTENT ELEMENTS THE MOST. LASTLY, ALMOST ALL THE STUDENTS AGREED THAT LMSS HELPED INCREASE THEIR ACADEMIC ACHIEVEMENT ONLY WHEN LMSS INCLUDED SUCH FEATURES AS EFFECTIVENESS, INTERACTION, REINFORCEMENT, ATTRACTIVE DESIGN, SOCIAL MEDIA SUPPORT, AND ACCESSIBILITY.
EDUCATION IS ONE OF IMPORTANT APPLICATION FIELDS OF BIG DATA, AND WITH THE TECHNOLOGY DEVELOPING OF BIG DATA, EDUCATION DATA IN SCALE IS BECOMING LARGER AND LARGER, AND THE BREADTH AND DEPTH OF DATA ARE EXPANDING. THROUGH TECHNIQUES OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING, EDUCATORS CAN GET INFORMATION OF STUDENTS' LEARNING PERFORMANCES, SUCH AS LEARNING ATTITUDES, INTERESTS AND EFFECTS, PREDICT STUDENTS' LEARNING BEHAVIORS IN FUTURE, FIND THE POSSIBLE PROBLEMS HIDDEN IN THE PROCESS OF LEARNING, AND THEN TRY TO PROVIDE THE MOST SUITABLE TEACHING CONTENTS AND ACTIVITIES FOR THE ADAPTIVE TEACHING. THIS PAPER DISCUSSED THE INFLUENCES OF BIG DATA ON EDUCATION AND TEACHING, EXPOUNDED THE APPLICATIONS OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING IN THE ADAPTIVE TEACHING, AND LASTLY PRESENTED A FUNCTIONAL STRUCTURE OF A TYPICAL ADAPTIVE TEACHING SYSTEM.
LEARNING SPACES ARE GRADUALLY DEVELOPING FOR SUPPORTING SELF-REGULATED LEARNING. THE AVAILABILITY OF LEARNING AND MOVEMENT DATA IN CLASSROOM TEACHING, OUTDOOR LEARNING AND CAMPUS ENVIRONMENTS HAS BEEN ENHANCED, OWING TO THE EMERGENCE OF WEARABLE TECHNOLOGIES. CONSEQUENTLY, THE LEARNING ANALYTICS TECHNOLOGY FOR THE PHYSICAL SPACES IS EMERGING AT THE RIGHT MOMENT. LEARNING SPACES ARE FACING PROFOUND TRANSFORMATIONS UNDER THE EDUCATIONAL DEVELOPMENT OF "HUMAN-TECHNOLOGY" CO-EVOLUTION, THE LEARNING SUPPORT SERVICES IN PHYSICAL SPACES THUS NEED TO BE RESHAPED URGENTLY. IN RECENT YEARS, RESEARCHERS HAVE FOCUSED ON USING SENSOR TECHNOLOGY TO COLLECT LEARNERS' PHYSIOLOGICAL AND BEHAVIORAL DATA, COMBINING LEARNING ANALYTICS TO INFER THEIR EMOTIONAL STATES, AND ADOPTING APPROPRIATE INTERVENTION MECHANISMS TO IMPROVE THE POSITIVE EMOTIONAL AROUSAL AND ACADEMIC SUCCESS. FIRST, THIS PAPER INTRODUCES SOME CONCEPTS RELATED TO THE PERCEPTION OF ACADEMIC EMOTIONS IN PHYSICAL SPACES. THEN THE MAIN METHODS AND TYPICAL CASES ARE POINTED OUT. SPECIFICALLY, OUR AIM WAS TO REVEAL THE RESEARCH STATUS AND DEVELOPMENT TREND OF EMOTION PERCEPTION IN PHYSICAL SPACES UNDER THE LEARNING ANALYTICS FIELDS AND TO PROVIDE A NOVEL INSIGHT FOR THE FUTURE RESHAPING OF THE PHYSICAL LEARNING SPACES.
ONE OF THE MOST CHALLENGING ISSUES IN LEARNING ANALYTICS IS THE DEVELOPMENT OF TECHNIQUES AND TOOLS THAT FACILITATE THE EVALUATION OF THE LEARNING ACTIVITIES CARRIED OUT BY LEARNERS. IN THIS PAPER, WE FACED THIS ISSUE THROUGH A PROCESS MINING-BASED PLATFORM, CALLED SOFTLEARN, THAT IS ABLE TO DISCOVER COMPLETE, PRECISE AND SIMPLE LEARNING PATHS FROM EVENT LOGS. THIS PLATFORM HAS A GRAPHICAL INTERFACE THAT ALLOWS TEACHERS TO BETTER UNDERSTAND THE REAL LEARNING PATHS UNDERTAKEN BY LEARNERS.
IN THIS PAPER WE INTRODUCE THE 'LEARNING ACTIVITY PROVIDER' (LAP). WE COMBINE THE CONCEPTS OF A LEARNING OBJECT REPOSITORY INTO A METADATA PROVIDER SYSTEM AND EXTEND IT WITH AUTOMATIC EXTRACTION OF METADATA OUT OF RAW CONTENT AND DYNAMIC CREATION OF METADATA BASED ON TRACKED USAGE DATA. THE LAP IS INTENDED TO LEVERAGE THE CONCEPT OF A 'METADATA PROVIDER' TO A MORE POWERFUL SYSTEM THAT CAN BE USEFUL WHEN WORKING WITH XAPI DATA, SPECIFICALLY IN LEARNING ANALYTICS CONTEXTS.
E-LEARNING HAS BECOME A MORE FLEXIBLE LEARNING APPROACH THANKS TO THE EXTENSIVE EVOLUTION OF THE INFORMATION AND COMMUNICATION TECHNOLOGIES. A PERCEIVED FOCUS WAS INVESTIGATED FOR THE EXPLOITATION OF THE LEARNERS' INDIVIDUAL DIFFERENCES TO ENSURE A CONTINUOUS AND ADAPTED LEARNING PROCESS. NOWADAYS, RESEARCHERS HAVE BEEN ORIENTED TO USE LEARNING ANALYTICS FOR LEARNER MODELING IN ORDER TO ASSIST EDUCATIONAL INSTITUTIONS IN IMPROVING LEARNER SUCCESS AND INCREASING LEARNER RETENTION. IN THIS PAPER, WE DESCRIBE A NEW IMPLICIT APPROACH USING LEARNING ANALYTICS TO CONSTRUCT AN INTERPRETATIVE VIEWS OF THE LEARNERS' INTERACTIONS, EVEN THOSE MADE OUTSIDE THE E-LEARNING PLATFORM. WE AIM TO DEDUCE AUTOMATICALLY A LEARNERS' PROFILING RULES INDEPENDENTLY OF THE LEARNING STYLE MODELS PROPOSED IN THE LITERATURE. IN THIS WAY, WE PROVIDE AN INNOVATIVE PROCESS THAT MAY HELP THE TUTORS TO PROFILE LEARNERS AND EVALUATE THEIR PERFORMANCES, SUPPORT THE COURSES' DESIGNER IN THEIR AUTHORING TASKS AND ADAPT THE LEARNING OBJECTS TO THE LEARNERS' NEEDS.
TEACHERS ARE ALWAYS IN NEED OF NEW TOOLS TO SUPPORT THE LEARNING PROCESS. LEARNING ANALYTICS HAS EMERGED AS A SOLUTION TO PROVIDE FEEDBACK ABOUT THE LEARNING PROGRESS OF STUDENTS. THIS SOLUTION DOES NOT ONLY PROVIDE MEANINGFUL INFORMATION TO INSTRUCTORS TO ANALYZE AND IMPROVE THE LEARNING PROCESS, BUT ALSO TO MANAGERS AND OTHER STAKEHOLDERS OF THE LEARNING PROCESSES. IN THIS CHAPTER, WE EXTEND THE VISION OF LEARNING ANALYTICS TO PREDICTIVE ANALYTICS. CURRENTLY, WE ARE READY TO SEE FURTHER IN THE FUTURE AND PREDICT THE BEHAVIOR OF STUDENTS BASED ON THEIR ACTIONS, AND THIS IDEA OPENS A BROAD POTENTIAL FOR EDUCATIONAL SETTINGS. THIS CHAPTER DISCUSSES CHALLENGES, BENEFITS AND WEAKNESSES OF A PREDICTIVE SYSTEM FOR EDUCATION. ADDITIONALLY, THE DESIGN OF A GENERIC PREDICTIVE SYSTEM IS PROPOSED AND EXPERIMENTAL RESULTS IN A REAL SCENARIO ARE SHOWN TO VALIDATE ITS POTENTIAL.
THE PHENOMENON OF SOCIAL LEARNING ANALYTICS PRESENTS A SYNERGY BETWEEN VARIETY OF DISCIPLINES, SUCH AS BUSINESS INTELLIGENCE, EDUCATIONAL DATA MINING, CYBERLEARNING, AND CYBER INFRASTRUCTURE. THE MAIN CONTRIBUTION OF THIS RESEARCH IS TO COMBINE TWO TYPES OF SOCIAL LEARNING ANALYTICS, SOCIAL LEARNING NETWORK ANALYSIS AND SOCIAL LEARNING CONTENT ANALYSIS IN STUDYING THE IMPACT OF THE SOCIAL MULTIMEDIA SYSTEMS (SMSS) ON CYBERLEARNERS. THE RESEARCH STUDY PROVIDED IN THIS PAPER IS BASED ON THE SURVEY DATA COLLECTED IN SPRING 2011 AT WESTERN KENTUCKY UNIVERSITY. THE EVIDENCE OBTAINED FROM THE ANALYSIS SHOWS THAT SMS IMPACTS (A) THE DIGITAL COMMUNICATION BETWEEN FACULTY AND STUDENTS; (B) STUDENTS' SUCCESS AND GRADES; (C) THE AMOUNT OF MATERIALS COVERED AND LEARNED; (D) THE EFFECTIVENESS OF STUDYING; (E) THE DEPTH OF LEARNING; (F) THE ABILITY TO FOCUS ON THE MOST IMPORTANT LEARNING OBJECTIVES; (G) THE DEGREE OF COLLABORATION AMONG STUDENTS; AND (H) THE STUDENTS' MOTIVATION OF STUDYING. (C) 2012 ELSEVIER LTD. ALL RIGHTS RESERVED.
PREDICTING STUDENT PERFORMANCE IS A MAJOR TOOL IN LEARNING ANALYTICS. THIS STUDY AIMS TO IDENTIFY HOW DIFFERENT MEASURES OF MASSIVE OPEN ONLINE COURSE (MOOC) DATA CAN BE USED TO IDENTIFY POINTS OF IMPROVEMENT IN MOOCS. IN THE CONTEXT OF MOOCS, STUDENT PERFORMANCE IS OFTEN DEFINED AS COURSE COMPLETION. HOWEVER, STUDENTS COULD HAVE OTHER LEARNING OBJECTIVES THAN MOOC COMPLETION. THEREFORE, WE DEFINE STUDENT PERFORMANCE AS OBTAINING PERSONAL LEARNING OBJECTIVE(S). THIS STUDY EXAMINES A SUBSAMPLE OF STUDENTS IN A GRADUATE-LEVEL BLENDED MOOC WHO SHARED ON-CAMPUS COURSE COMPLETION AS A LEARNING OBJECTIVE. AGGREGATED ACTIVITY FREQUENCIES, SPECIFIC COURSE ITEM FREQUENCIES, AND ORDER OF ACTIVITIES WERE ANALYSED TO PREDICT STUDENT PERFORMANCE USING CORRELATIONS, MULTIPLE REGRESSIONS, AND PROCESS MINING. ALL AGGREGATED MOOC ACTIVITY FREQUENCIES RELATED POSITIVELY TO ON-CAMPUS EXAM GRADE. HOWEVER, THIS RELATION IS LESS CLEAR WHEN CONTROLLING FOR PAST PERFORMANCE. IN TOTAL, 65% OF THE SPECIFIC COURSE ITEMS SHOWED SIGNIFICANT CORRELATIONS WITH FINAL EXAM GRADE. STUDENTS WHO PASSED THE COURSE SPREAD THEIR LEARNING OVER MORE DAYS COMPARED WITH STUDENTS WHO FAILED. LITTLE DIFFERENCE WAS FOUND IN THE ORDER OF ACTIVITIES WITHIN THE MOOC BETWEEN STUDENTS WHO PASSED AND WHO FAILED. THE RESULTS ARE COMBINED WITH COURSE EVALUATIONS TO IDENTIFY POINTS OF IMPROVEMENT WITHIN THE MOOC.
INCREASED USAGE OF INFORMATION TECHNOLOGIES IN EDUCATIONAL TASKS RESULTED IN A HIGH VOLUME OF DATA THAT CAN BE EXPLOITED TO OFFER PRACTICAL INSIGHT IN THE LEARNING PROCESS. IN THIS PAPER, WE PROPOSED A SYSTEM ARCHITECTURE OF INTEGRATING LEARNING ANALYTICS TECHNIQUES INTO AN EDUCATIONAL PLATFORM. TO TEST THE APPROACH, THREE ANALYSIS SCENARIOS WERE IMPLEMENTED INTO A CUSTOM REPORT FOR OUR EDUCATIONAL PLATFORM BUILT ON MOODLE. THE IMPLEMENTATION STEPS WITH CHOSEN SOLUTIONS ARE DISCUSSED. THE SIGNIFICANCE OF THIS RESEARCH LIES IN THE POTENTIAL OF THIS APPROACH TO BUILD ANALYTICS SYSTEMS THAT CAN USE MULTIPLE DATA SETS AND ANALYTICS TECHNIQUES.
THIS PAPER PRESENTS THE MAJOR FINDINGS FROM A STUDY CONDUCTED WITH FOUR DIFFERENT UNIVERSITIES IN THE U.A.E REGARDING THEIR USE OF THE LEARNING ANALYTICS (LA) CAPABILITIES AVAILABLE WITHIN THEIR LEARNING MANAGEMENT SYSTEMS (LMS). DATA WAS COLLECTED FROM AN ONLINE SURVEY INSTRUMENT, IN-DEPTH INTERVIEWS WITH IT DIRECTORS AND ACADEMIC ADMINISTRATORS, AND A CASE STUDY IN ZAYED UNIVERSITY. THE STUDY REVEALS THAT WHILE THE EXAMINED UNIVERSITIES SEEM TO RECOGNIZE AND APPRECIATE THE NEW ANALYTICS FUNCTIONS AVAILABLE WITHIN LEARNING MANAGEMENT SYSTEMS, THEY SEEM TO STRUGGLE WITH FORMING A CONSISTENT AND EFFECTIVE APPROACH TO LINK THOSE FUNCTIONS TO IMPROVE THE DECISION-MAKING PROCESS. MOST POPULAR ANALYTICS FUNCTIONS INCLUDE ANALYTICS CONCERNED WITH CURRICULUM COVERAGE, GOAL PERFORMANCE, STUDENT PERFORMANCE, AND EARLY ALERT SYSTEMS AT THE COURSE AND DEGREE PROGRAM LEVELS.
INSTRUCTIONAL TECHNOLOGISTS AND FACULTY IN POST-SECONDARY INSTITUTIONS HAVE INCREASINGLY ADOPTED LEARNING ANALYTICS INTERVENTIONS SUCH AS DASHBOARDS THAT PROVIDE REAL-TIME FEEDBACK TO STUDENTS TO SUPPORT STUDENT' ABILITY TO REGULATE THEIR LEARNING. BUT ANALYSES OF THE EFFECTIVENESS OF SUCH INTERVENTIONS CAN BE CONFOUNDED BY MEASURES OF STUDENTS' PRIOR LEARNING AS WELL AS THEIR BASELINE LEVEL OF SELF-REGULATED LEARNING. FOR THIS RESEARCH STUDY, WE SOUGHT TO EXAMINE WHETHER THE FREQUENCY OF ACCESSING A DASHBOARD WAS ASSOCIATED WITH LEARNING OUTCOMES AFTER MATCHING SUBJECTS ON CONFOUNDING VARIABLES. AND BECAUSE PRIOR RESEARCH HAS SUGGESTED THAT MEASURES OF PRIOR LEARNING ARE ASSOCIATED WITH STUDENTS' LIKELIHOOD TO USE LEARNING ANALYTICS INTERVENTIONS, WE SOUGHT TO ADEQUATELY CONTROL FOR LEARNERS' LIKELIHOOD TO ACCESS THE FEEDBACK BY USING A PROPENSITY SCORE MATCHING WITH A NON-BINARY TREATMENT VARIABLE. WE ADMINISTERED THE MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE AND ALSO COLLECTED DEMOGRAPHIC INFORMATION FOR A PROPENSITY SCORE MATCHING PROCESS. USERS' FREQUENCY OF ACCESSING THE INTERVENTION WAS CATEGORIZED AS HIGH, MODERATE, OR LOW/NO USAGE. AFTER MATCHING USERS ON CHARACTERISTICS ASSOCIATED WITH DASHBOARD USAGE (GENDER, HIGH SCHOOL GPA, AND THE "TEST ANXIETY" AND "SELF EFFICACY" FACTORS) WE FOUND THAT BOTH THE "HIGH" AND "MODERATE" USERS ACHIEVED SIGNIFICANTLY HIGHER COURSE GRADES THAN THE "LOW/NO" USERS. THE RESULTS SUGGEST LEARNERS BENEFITED FROM REGULARLY ACCESSING THE FEEDBACK, BUT EXTREME AMOUNTS OF USAGE WERE NOT NECESSARY TO ACHIEVE A POSITIVE EFFECT. WE DISCUSS THE IMPLICATIONS FOR RECOMMENDING HOW STUDENTS USE LEARNING ANALYTICS INTERVENTIONS WITHOUT EXCESSIVELY ACCESSING FEEDBACK.
IN THIS PAPER, WE PROPOSE AN INTERACTIVE DRAWING BASED ACTIVITY IN BOOKROLL, AN E-BOOK-BASED TEACHING-LEARNING PLATFORM. PARTICIPANTS WHO WERE RESEARCHERS DREW AN OVERVIEW OF THEIR RESEARCH AS A HANDWRITTEN MEMO. WE DEMONSTRATE THE AFFORDANCES OF THE LEARNING ANALYTICS (LA) ENHANCED PLATFORM TO FACILITATE PRACTICE AND RESEARCH OF SUCH A DRAWING ACTIVITY FOCUSED ON A HIGHER-ORDER THINKING TASK TO ORGANIZE KNOWLEDGE (RESEARCH OVERVIEW IN THIS CASE). WE SHARE THE MULTI-MODAL DATA OF 4 PARTICIPANTS TO ILLUSTRATE THE OUTCOMES OF THE ACTIVITY AND THEIR PERCEPTION OF THE ACTIVITY. AN INITIAL ANALYSIS OF THE HANDWRITTEN MEMO IS VISUALISED. FURTHER REFLECTIONS SHOW HOW THE USE OF LEARNING ANALYTICS-BASED PLATFORMS CAN ENRICH THE LEARNING DESIGN OF A KNOWLEDGE ORGANISATION TASK.
THE ACT OF LEARNING IS BECOMING MORE AND MORE SOPHISTICATED, THANKS TO SEVERAL MODELS AND TOOLS AVAILABLE TODAY, LIKE MOOCS, PERSONALIZATION, SOCIAL NETWORKS, WEB 2.0, GAMIFICATION AND OTHERS. THIS VAST LANDSCAPE LEADS TO A HUGE AMOUNT OF DATASETS THUS THE BIG DATA PARADIGM IS ALSO BEING ADOPTED. JOINED WITH BIG DATA IS THE EMERGENCY OF EXTRACTING INFORMATION ABOUT STUDENTS' LEARNING INTERACTIONS AND RETURN THEM TO TEACHERS (LEARNING ANALYTICS). ACCORDING TO THIS SCENARIO, IN THIS PAPER WE INTRODUCE THE E-LEARNING PLATFORM CURRENTLY USED AT UNIVERSITY OF CATANIA. THE PLATFORM ALSO COLLECTS DATA ABOUT ALL THESE STUDENTS ACTIVITIES ACTING AS A BIG DATA BASE FOR FURTHER LEARNING ANALYTICS.
THE USE OF ONLINE VIDEO IN LEARNING ENVIRONMENTS IS PREVALENT SERVING IN A VARIETY OF ROLES, SUCH AS MULTIMEDIA TEXTBOOK, VIDEO LECTURE, OR MEDIUM FOR DOCUMENTING STUDENT COURSEWORK. FOR INSTRUCTORS WHO EMPLOY VIDEO AS A LEARNING MATERIAL, IT IS BECOMING INCREASINGLY IMPORTANT TO GAIN A DEEPER UNDERSTANDING OF THE FIT OF VIDEO CONTENT ACROSS STUDENT COHORTS, AS WELL AS THE IMPACT ON STUDENT ENGAGEMENT, LEARNING, AND SUCCESS. IN ORDER TO BETTER UNDERSTAND INSTRUCTOR INTERESTS AND NEEDS IN THIS REGARD, WE DEVELOPED A LEARNING ANALYTICS VISUALIZATION TOOL THAT LINKS DATA ON STUDENT VIEWING AND VIDEO INTERACTIONS WITH STUDENT DEMOGRAPHICS AND PERFORMANCE MEASURES. INSTRUCTORS CAN USE THE TOOL TO GENERATE AND COMPARE CORRELATED HEATMAP VISUALIZATIONS REPRESENTING STUDENT ENGAGEMENT ACROSS SEGMENTS OF THE VIDEO. THIS PAPER DESCRIBES AN INITIAL USER STUDY CONDUCTED WITH INSTRUCTORS IN AN HCI COURSE CONTEXT TO UNDERSTAND HOW THIS KIND OF FEEDBACK CAN INFORM THEIR USE OF VIDEO IN THE CLASSROOM. RESULTS SHOW THAT INSTRUCTORS FIND SUCH FEEDBACK VERY USEFUL ACROSS A VARIETY OF USE CASES.
BIG DATA ARE REFERRED TO BULK DATA WHICH CANNOT BE COLLECTED, SAVED, AND ANALYZED WITH THE TRADITIONAL DATA ANALYSIS TOOLS. THE FIELD OF LEARNING ANALYSIS, WHICH HAS CONSISTENTLY APPEARED ON THE HORIZON REPORT FROM NEW MEDIA CONSORTIUM FOR THE RECENT YEARS, IS RECEIVING FRESH ATTENTION WITH THE PROLIFERATION OF BIG DATA. THE PURPOSE OF THIS STUDY IS TO EXAMINE THE ENVIRONMENT FOR THE LEARNING ANALYTICS, A BRANCH FIELD OF BIG DATA, TO BE APPLIED TO THE KOREAN EDUCATION CURRICULAR AND ITS POSSIBILITY AND FIND OUT HOW TO IMPROVE THEM. FIRST, AS AN APPLICATION OF A NEW TECHNOLOGY INVOLVES SIDE EFFECTS IN MOST CASES, IT IS DESIRABLE THAT POTENTIAL PROBLEMS BE CONSIDERED FROM THE BEGINNING AND NEGATIVE EFFECTS BE MINIMIZED. SECOND, THE LEARNING ANALYTICS BEGINS WITH SECURING SUFFICIENT DATA, AND A DATA EXCHANGE SYSTEM SHOULD BE ESTABLISHED FOR SETTING UP THE DATA ECOLOGY. THIRD, AS BIG DATA ARE APPLIED TO PUBLIC DATA AND THE CORPORATE BUSINESS AREAS AND FREQUENTLY MENTIONED IN THE MEDIA, THE EXPECTATION FOR THEIR POTENTIAL GROWTH IS REACHING ITS PEAK. (C) 2016 PUBLISHED BY ELSEVIER B.V. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY-NC-ND LICENSE.
ADVANCEMENTS IN LEARNING ANALYTICS ALLOW TEACHERS TO TRACK STUDENT LEARNING PROGRESS AND PROMOTE LEARNING BY PROVIDING NECESSARY INTERVENTION AND SUPPORT. MULTIPLE DATA SOURCES ARE INVOLVED IN LEARNING ANALYTICS, AND THE MAJOR ONES ARE SYSTEMS THAT STUDENTS USE IN SCHOOL. TO FULLY COMPREHEND THE PROGRESS OF STUDENT LEARNING, OUT-OF-SCHOOL LEARNING BEHAVIORS SHOULD BE CONSIDERED AN IMPORTANT PART OF THE ACADEMIC LIVES OF STUDENTS. IN THIS STUDY, OUT-OF-SCHOOL LEARNING BEHAVIORS OF STUDENTS, PARTICULARLY HOME COMPUTER USE, WERE MEASURED USING FOUR ONLINE BEHAVIOR INDICATORS OF STUDENTS, WHICH WERE TRACKED AND COLLECTED. THE LEARNING PERFORMANCE DATA OF THE STUDENTS WERE ANALYZED. RESULTS SUGGESTED THAT THE OUT-OF-SCHOOL COMPUTER USE BEHAVIORS OF STUDENTS, SUCH AS MUTUAL FOLLOW-UP AND THE SHARING OF LEARNING EXPERIENCES, WERE POSITIVELY RELATED TO THEIR ACADEMIC PERFORMANCE LEVEL, REGARDLESS OF THE AGE AND GENDER OF THE STUDENTS. THIS STUDY PROVIDES INSIGHT INTO WHAT MAY BE THE MISSING PIECE IN UNDERSTANDING STUDENT LEARNING, THAT IS, OUT-OF-SCHOOL COMPUTER USE. WITH SUCH INSIGHTS, LEARNING ANALYTICS MAY BE ENHANCED TO IMPROVE THE UNDERSTANDING OF LEARNING WITHOUT BEING RESTRICTED TO SCHOOLS.
PURPOSE THIS PAPER AIMS TO PRESENT AN INSIGHT INTO THE INTRODUCTION OF AN E-TEXTBOOK'S LEARNING PLATFORM FOR ONLINE STUDENTS RESIDING IN ASIA BY AN ENGLISH BUSINESS SCHOOL. THIS PLATFORM ALSO GENERATED A RANGE OF DATA ANALYTICS THAT PROVIDED TEACHERS WITH AN UNDERSTANDING OF HOW THEIR STUDENTS INTERACT WITH LEARNING MATERIALS. DESIGN/METHODOLOGY/APPROACH THIS PAPER INTERROGATED THE DATA GENERATED THROUGH STUDENTS' INTERACTION WITH THE E-BOOKS LEARNING PLATFORM, AS WELL AS STUDENTS' FEEDBACK VIA A QUESTIONNAIRE. THE DATA GENERATED ARE PRESENTED IN A SERIES OF DIAGRAMS. FINDINGS THIS PAPER MAKES LINKS BETWEEN SELF-REGULATED THEORY AND HOW E-TEXTBOOKS ARE USED BY STUDENTS. THE FINDINGS ALSO OFFER TENTATIVE SUPPORT FOR THE USE OF E-BOOKS PLATFORMS AS BOTH A LEARNING RESOURCE AND A MECHANISM TO OBTAIN LEARNING ANALYTICS. RESEARCH LIMITATIONS/ IMPLICATIONS - THIS PAPER CONTRIBUTES TO THE WIDER DISCOURSE OF THE USE OF LEARNING ANALYTICS AND HOW INFORMATION TECHNOLOGY MAY ALIGN WITH SELF- REGULATED LEARNING. ORIGINALITY/VALUE - THIS PAPER REPRESENTS A CONTRIBUTION TO THE DEVELOPING FIELD OF RESEARCH RELATED TO LEARNING ANALYTICS AND HOW STUDENTSMANAGE THEIR USE OF INFORMATION TECHNOLOGY.
LEARNING ANALYTICS IS SEEN AS A PROMISING WAY OF IMPROVING ONLINE EDUCATION, FOCUSING ON HOW STUDENTS LEARN AND HOW TEACHERS' CAN HELP THE STUDENTS' TO BE EFFECTIVE IN THAT PROCESS. MOST APPROACHES RELY ON THE IDEA OF "BIG DATA" TO BE THE FOUNDATION UPON WHICH TO DESIGN FOR ASSISTING THE STUDENTS' LEARNING PROCESS, THE TEACHERS' SCAFFOLDING EFFORTS OR ENHANCEMENT OF THE INTERACTION DESIGN OF THE SOFTWARE. IN THIS PAPER, WE DEPART FROM A CASE OF E-TRAINING OF CLERKS IN A PUBLIC ORGANISATION. IT IS ARGUED THAT LEARNING ANALYTICS AND EDUCATIONAL MINING BASED ON "SMALL DATA" (I.E. USE-PATTERN LOG DATA FROM A SMALL SET OF LEARNERS) ALSO HAVE A PROMISING POTENTIAL FOR EXTRACTING DESIGN GUIDELINES FOR AN E-TRAINING ENVIRONMENT AND NOT ONLY PROVIDE SUPPORT AFTER THE SYSTEM IS DEVELOPED AND IMPLEMENTED, BUT ALSO GUIDE THE DESIGN OF FUTURE E-TRAINING SYSTEMS.
SOCIETY DEMANDS NEW COMPETENCES FROM PROFESSIONALS, WHO REQUIRE HAVING SPECIFIC SKILLS AND ABILITIES. UNIVERSITIES, ACCORDINGLY, HAVE CHANGED FROM A CONTENT-BASED TOWARDS A COMPETENCY-BASED EDUCATIONAL MODEL. HOWEVER, THE ASSESSMENT OF THESE COMPETENCES IS NOT A SCALABLE TASK, HAS A SUBJECTIVE NATURE AND MUST CONSIDER DATA FROM MANY DIFFERENT SOURCES. THIS PAPER DESCRIBES THE MODEL, ARCHITECTURE AND OBJECTIVES OF AN ON-GOING RESEARCH PROJECT AIMED AT DEVELOPING A WEB PLATFORM CALLED LACAMOLC, TO PROVIDE TEACHERS AND STUDENTS A DASHBOARD WHICH GATHERS USAGE AND SOCIAL DATA FROM DIFFERENT KNOWLEDGE AND LEARNING TECHNOLOGIES SUCH AS MOODLE, GOOGLE APPS FOR EDUCATION AND MEDIAWIKI TO PROVIDE VISUAL AND LEARNING ANALYTICS VISUALIZATIONS TO SUPPORT LEARNING AND ASSESSMENT PROCESS. WE SELECT PENTAHO AS OUR ANALYTICS SPECIFIC TOOL, BASED ON ITS CHARACTERISTICS IN ORDER TO EFFECTIVELY SCALE LEARNING ANALYTICS SYSTEMS AND ACHIEVE LONG-TERM SUSTAINABILITY AND SCALABILITY, AND WE DESIGN AN EXPERIMENT TO CARRY OUT FOR TEAMWORK COMPETENCE.
PURPOSE DIGITAL LEARNING HAS BECOME A GLOBAL TREND. PARTLY OR FULLY AUTOMATIC LEARNING SYSTEMS ARE INTEGRATED INTO EDUCATION IN SCHOOLS AND UNIVERSITIES ON A PREVIOUSLY UNSEEN SCALE. LEARNING SYSTEMS HAVE A LOT OF POTENTIAL FOR RE-EDUCATION, LIFE-LONG LEARNING AND FOR INCREASING ACCESS TO EDUCATIONAL RESOURCES. LEARNING SYSTEMS CREATE MASSIVE AMOUNTS OF DATA ABOUT LEARNING BEHAVIOUR. ANALYSING THAT DATA FOR EDUCATIONAL DECISION MAKING HAS BECOME AN IMPORTANT TRACK OF RESEARCH. THE PURPOSE OF THIS PAPER IS TO ANALYSE DATA FROM AN INTERMEDIATE-LEVEL COMPUTER SCIENCE COURSE, WHICH WAS TAUGHT TO 141 STUDENTS IN SPRING 2018 AT UNIVERSITY OF TURKU, DEPARTMENT OF FUTURE TECHNOLOGIES, FINLAND. DESIGN/METHODOLOGY/APPROACH THE AVAILABLE VARIABLES INCLUDED NUMBER OF SUBMISSIONS, SUBMISSION TIMES, VARIABLES OF GROUPWORK AND FINAL GRADES. ASSOCIATIONS BETWEEN THESE VARIABLES WERE LOOKED AT TO REVEAL PATTERNS IN STUDENTS' LEARNING BEHAVIOUR. FINDINGS IT WAS FOUND THAT TIME USAGE DIFFERS PER DIFFERENT GRADES SO THAT STUDENTS WITH GRADE 4 OUT OF 5 USED MOST TIME. ALSO, IT WAS FOUND THAT STUDYING AT NIGHT IS CONNECTED TO WEAKER LEARNING OUTCOMES THAN STUDYING DURING DAYTIME. SEVERAL ISSUES IN RELATION TO GROUPWORK WERE REVEALED. FOR EXAMPLE, ASSOCIATIONS WERE FOUND BETWEEN PRIOR SKILLS, PREFERENCE FOR INDIVIDUAL VS GROUPWORK, AND COURSE LEARNING OUTCOMES. ORIGINALITY/VALUE THE PAPER POINTS A NUMBER OF IMPORTANT IDEAS FOR FUTURE RESEARCH. ONE IMPORTANT OBSERVATION IS THAT SOME RESEARCH QUESTIONS IN LEARNING ANALYTICS NEED QUALITATIVE APPROACHES, WHICH NEED TO BE ADDED TO THE TOOLBOX OF LEARNING ANALYTICS RESEARCH.
LEARNING ANALYTICS HAVE PROVED PROMISING CAPABILITIES AND OPPORTUNITIES TO MANY ASPECTS OF ACADEMIC RESEARCH AND HIGHER EDUCATION STUDIES. DATA-DRIVEN INSIGHTS CAN SIGNIFICANTLY CONTRIBUTE TO PROVIDE SOLUTIONS FOR CURBING COSTS AND IMPROVING EDUCATION QUALITY. THIS PAPER ADOPTS A TWO-PHASE MACHINE LEARNING APPROACH, WHICH UTILIZES BOTH UNSUPERVISED AND SUPERVISED LEARNING TECHNIQUES FOR PREDICTING OUTCOMES OF STUDENTS FOLLOWING HIGHER EDUCATION PROGRAMS OF STUDIES. THE APPROACH HAS BEEN APPLIED IN A CASE-STUDY WHICH HAS BEEN PERFORMED IN THE CONTEXT OF AN UNDERGRADUATE COMPUTER SCIENCE CURRICULUM OFFERED BY THE UNIVERSITY OF THESSALY IN GREECE. STUDENTS INVOLVED IN THE CASE STUDY WERE INITIALLY GROUPED BASED ON THE SIMILARITY OF SPECIFIC EDUCATION-RELATED FACTORS AND METRICS. USING THE K-MEANS ALGORITHM, OUR CLUSTERING EXPERIMENTS REVEALED THE PRESENCE OF THREE COHERENT CLUSTERS OF STUDENTS. SUBSEQUENTLY, THE DISCOVERED CLUSTERS WERE UTILIZED TO TRAIN PREDICTION MODELS FOR ADDRESSING EACH PARTICULAR CLUSTER OF STUDENTS INDIVIDUALLY. IN THIS REGARD, TWO MACHINE LEARNING MODELS WERE TRAINED FOR EVERY CLUSTER OF STUDENTS IN ORDER TO PREDICT THE TIME TO DEGREE COMPLETION AND STUDENT ENROLLMENT IN THE OFFERED EDUCATIONAL PROGRAMS. THE DEVELOPED MODELS ARE CLAIMED TO PRODUCE PREDICTIONS WITH RELATIVELY HIGH ACCURACY. FINALLY, THE PAPER DISCUSSES THE POTENTIAL USEFULNESS OF THE CLUSTERING-AIDED APPROACH FOR LEARNING ANALYTICS IN HIGHER EDUCATION.
THIS PRELIMINARY RESEARCH INVESTIGATES THE RELATIONSHIP BETWEEN PSYCHOMETRIC DATA AND LEARNING BEHAVIORS IN THE LEARNING ANALYTICS RESEARCH FIELD, SPECIFICALLY, THE RELATIONSHIP BETWEEN SELF-REGULATED LEARNING AND LEARNING BEHAVIOR. THE RESULTS OF THIS LIMITED RESEARCH SHOW THAT MARKER AND ANNOTATION USE HAVE A WEAK SIGNIFICANT RELATIONSHIP WITH SELF-EFFICACY AND THE INTRINSIC VALUE OF LEARNING MATERIALS.
EDUCATIONAL CONTEXTS EVOLVE WITH THE INCREASING IMPACT OF MULTI-TOUCH TECHNOLOGIES ON EVERYDAY LIFE. WHEN BRINGING MULTI-TOUCH TECHNOLOGIES INTO A CLASSROOM, HIGH-QUALITY CONTENT APPLICATIONS MUST BE DEVELOPED TO ADD VALUE TO STUDENTS' LEARNING. THIS CONTRIBUTION PRESENTS A FRAMEWORK FOR MULTI-TOUCH LEARNING GAMES WHICH INCORPORATES LEARNING ANALYTICS COMPONENTS AND TANGIBLES TO IMPROVE COLLABORATIVE LEARNING EXPERIENCES.
THIS STUDY DESCRIBES THE PROCESS OF CONSTRUCTING PROXY VARIABLES FROM RECORDED LOG DATA WITHIN A LEARNING MANAGEMENT SYSTEM (LMS), WHICH REPRESENTS ADULT LEARNERS' TIME MANAGEMENT STRATEGIES IN AN ONLINE COURSE. BASED ON PREVIOUS RESEARCH, THREE VARIABLES OF TOTAL LOGIN TIME, LOGIN FREQUENCY, AND REGULARITY OF LOGIN INTERVAL WERE SELECTED AS CANDIDATES FROM THE DATA SET, ALONG WITH A GUIDELINE FOR MANIPULATING THE LOG DATA. ACCORDING TO THE RESULTS OF MULTIPLE REGRESSION ANALYSIS, WHICH WAS CONDUCTED TO DETERMINE WHETHER THE SUGGESTED VARIABLES ACTUALLY PREDICT LEARNING PERFORMANCE, (IR) REGULARITY OF THE LOGIN INTERVAL WAS CORRELATIVE WITH AND PREDICTIVE OF LEARNING PERFORMANCE. AS INDICATED IN THE PREVIOUS RESEARCH, THE REGULARITY OF LEARNING IS A STRONG INDICATOR FOR EXPLAINING LEARNERS' CONSISTENT ENDEAVORS AND AWARENESS OF LEARNING. THIS STUDY, WHICH WAS PRIMARILY BASED ON THEORETICAL EVIDENCE, DEMONSTRATED THE POSSIBILITY OF USING LEARNING ANALYTICS TO ADDRESS A LEARNER'S SPECIFIC COMPETENCE IN AN ONLINE LEARNING ENVIRONMENT. IMPLICATIONS FOR THE LEARNING ANALYTICS FIELD SEEKING A PEDAGOGICAL THEORY-DRIVEN APPROACH ARE DISCUSSED.
AMONG ALL OF THE LEARNERS IN MASSIVE OPEN ONLINE COURSES (MOOCS) WHO INTEND TO COMPLETE A COURSE, THE MAJORITY FAIL TO DO SO. THIS INTENTION-ACTION GAP IS FOUND IN MANY DOMAINS OF HUMAN EXPERIENCE, AND RESEARCH IN SIMILAR GOAL PURSUIT DOMAINS SUGGESTS THAT PLAN-MAKING IS A CHEAP AND EFFECTIVE NUDGE TO ENCOURAGE FOLLOW-THROUGH. IN A NATURAL FIELD EXPERIMENT IN THREE HARVARDX COURSES, SOME STUDENTS RECEIVED OPEN-ENDED PLANNING PROMPTS AT THE BEGINNING OF A COURSE. THESE PROMPTS INCREASED COURSE COMPLETION BY 29%, AND PAYMENT FOR CERTIFICATES BY 40%. THIS EFFECT WAS LARGEST FOR STUDENTS ENROLLED IN TRADITIONAL SCHOOLS. FURTHERMORE, THE CONTENTS OF STUDENTS' PLANS COULD PREDICT WHICH STUDENTS WERE LEAST LIKELY TO SUCCEED - IN PARTICULAR, STUDENTS WHOSE PLANS FOCUSED ON SPECIFIC TIMES WERE UNLIKELY TO COMPLETE THE COURSE. OUR RESULTS SUGGEST THAT PLANNING PROMPTS CAN HELP LEARNERS ADOPTED PRODUCTIVE FRAMES OF MIND AT THE OUTSET OF A LEARNING GOAL THAT ENCOURAGE AND FORECAST STUDENT SUCCESS.
RESEARCH BASED ON EDUCATIONAL DATA MINING CONDUCTED AT ACADEMIC INSTITUTIONS IS OFTEN LIMITED BY THE INSTITUTIONAL POLICY WITH REGARD TO THE TYPE OF LEARNING MANAGEMENT SYSTEM AND THE DETAIL LEVEL OF ITS ACTIVITY REPORTS. OFTEN, RESEARCHERS DEAL WITH ONLY RAW DATA. SUCH DATA NORMALLY CONTAIN NUMEROUS FICTITIOUS USER ACTIVITIES THAT CAN CREATE A BIAS IN THE ACTIVITY TRENDS, CONSEQUENTLY LEADING TO INACCURATE CONCLUSIONS UNLESS CAREFUL STRATEGIES FOR DATA CLEANING, FILTERING, AND INDEXING ARE APPLIED. IN ADDITION, PRE-PROCESSING PHASES ARE NOT ALWAYS REPORTED IN DETAIL IN THE SCIENTIFIC LITERATURE. AS EDUCATIONAL DATA MINING AND LEARNING ANALYTICS METHODOLOGIES BECOME INCREASINGLY POPULAR IN EDUCATIONAL RESEARCH, IT IS IMPORTANT TO PROMOTE RESEARCHERS AND EDUCATIONAL POLICYMAKERS' AWARENESS OF THE PRE-PROCESSING PHASE, WHICH IS ESSENTIAL TO CREATE A RELIABLE DATABASE PRIOR TO ANY ANALYSIS. THIS PHASE CAN BE DIVIDED INTO FOUR CONSECUTIVE PRE-PROCESSING STAGES: DATA GATHERING, DATA INTERPRETATION, DATABASE CREATION, AND DATA ORGANIZATION. TAKEN TOGETHER, THESE STAGES STRESS THE TECHNICAL AND COOPERATIVE NATURE OF THIS TYPE OF RESEARCH, AND THE NEED FOR CAREFUL INTERPRETATION OF THE STUDIED PARAMETERS. TO ILLUSTRATE THESE ASPECTS, WE APPLIED THESE STAGES TO ONLINE EDUCATIONAL DATA COLLECTED FROM SEVERAL CHEMISTRY COURSES CONDUCTED AT TWO ACADEMIC INSTITUTIONS. OUR RESULTS SHOW THAT ADEQUATE PRE-PROCESSING OF THE DATA CAN PREVENT MAJOR INACCURACIES IN THE RESEARCH FINDINGS, AND SIGNIFICANTLY INCREASE THE AUTHENTICITY AND RELIABILITY OF THE CONCLUSIONS.
IN ORDER TO BE EFFECTIVE, A LEARNING PROCESS REQUIRES THE USE OF VALID AND SUITABLE EDUCATIONAL RESOURCES. HOWEVER, MEASURING THE QUALITY OF AN EDUCATIONAL RESOURCE IS NOT AN EASY TASK FOR A TEACHER. THE DATA OF THE PERFORMANCE OF THE STUDENTS CAN BE USED TO MEASURE HOW APPROPRIATE THE DIDACTIC RESOURCES ARE. BESIDES THIS DATA, ADEQUATE METRICS AND STATISTICS ARE ALSO NEEDED. IN THIS PAPER, TEA, A VISUAL LEARNING ANALYTICS TOOL FOR MEASURING THE QUALITY OF A PARTICULAR TYPE OF EDUCATIONAL RESOURCES, IN PARTICULAR TEST-BASED EXERCISES, IS PRESENTED. TEA IS A TEACHER-ORIENTED TOOL AIMED AT HELPING THEM TO IMPROVE THE QUALITY OF THE LEARNING MATERIAL THEY HAVE CREATED BY ANALYZING AND VISUALIZING THE PERFORMANCE OF THE STUDENTS. TEA EVALUATES NOT ONLY THE ADEQUACY OF INDIVIDUAL ITEMS BUT ALSO THE APPROPRIATENESS OF A WHOLE TEST. TEA PROVIDES THE RESULTS OF THE EVALUATION SO THAT THEY ARE EASILY INTERPRETABLE BY TEACHERS AND DEVELOPERS OF EDUCATIONAL MATERIAL. THE DEVELOPMENT OF TEA REQUIRED A THOROUGH ANALYSIS AND CLASSIFICATION OF METRICS AND STATISTICS TO IDENTIFY THOSE WHICH ARE USEFUL TO MEASURE THE QUALITY OF TEST-BASED EXERCISES USING THE DATA ABOUT THE PERFORMANCE OF THE STUDENTS. THE TOOL PROVIDES VISUAL REPRESENTATIONS OF THE PERFORMANCE OF THE STUDENTS TO ALLOW TEACHERS TO EVALUATE THE APPROPRIATENESS OF THE TEST-BASED EXERCISES THEY HAVE CREATED. THE EXPERIMENTATION CARRIED OUT WITH TEA AT HIGHER EDUCATION LEVEL IS ALSO PRESENTED.
PREDICTION OF STUDENTS' PERFORMANCE IS ONE OF THE MOST EXPLORED ISSUES IN EDUCATIONAL DATA MINING. TO PREDICT IF STUDENTS WILL ACHIEVE THE OUTCOMES OF THE SUBJECT BASED ON THE PREVIOUS RESULTS ENABLES TEACHERS TO ADAPT THE LEARNING DESIGN OF THE SUBJECT TO THE TEACHING-LEARNING PROCESS. HOWEVER, THIS ADAPTATION IS EVEN MORE RELEVANT IF WE COULD PREDICT THE FULFILLMENT OF THE EDUCATIONAL OBJECTIVES OF A SUBJECT, SINCE TEACHERS SHOULD FOCUS THE ADAPTATION ON THE LEARNING RESOURCES AND ACTIVITIES RELATED TO THOSE EDUCATIONAL OBJECTIVES. IN THIS PAPER, WE PRESENT AN EXPERIMENT WHERE A SUPPORT VECTOR MACHINE IS APPLIED AS A CLASSIFIER THAT PREDICTS IF THE DIFFERENT EDUCATIONAL OBJECTIVES OF A SUBJECT ARE ACHIEVED OR NOT. THE INPUTS OF THE PROBLEM ARE THE MARKS OBTAINED BY THE STUDENTS IN THE QUESTIONNAIRES RELATED TO THE LEARNING ACTIVITIES THAT STUDENTS MUST UNDERTAKE DURING THE COURSE. THE RESULTS ARE VERY GOOD, SINCE THE CLASSIFIERS PREDICT THE ACHIEVEMENT OF THE EDUCATIONAL OBJECTIVES WITH PRECISION OVER 80%.
ENGINEERING STUDENTS OFTEN HAVE TO DELIVER SMALL COMPUTER PROGRAMS IN MANY ENGINEERING COURSES. INSTRUCTORS HAVE TO EVALUATE THESE ASSIGNEMENTS ACCORDING TO THE LEARNING GOALS AND THEIR QUALITY, BUT ENSURE AS WELL THAT THERE IS NO PLAGIARISM. IN THIS PAPER, WE REPORT THE EXPERIENCE OF USING MINING SOFTWARE REPOSITORIES TECHNIQUES IN A MULTIMEDIA NETWORKS COURSE WHERE STUDENTS HAVE TO SUBMIT SEVERAL SOFTWARE PROGRAMS. WE SHOW HOW WE HAVE PROCEEDED, THE TOOLS THAT WE HAVE USED AND PROVIDE SOME USEFUL LINKS AND IDEAS THAT OTHER LECTURERS MAY USE.
THE UNPRECEDENTED CONTEXT OF UNCERTAINTY CAUSED BY THE COVID-19 DISEASE STRESSES THE EDUCATIONAL SYSTEM OF SECONDARY EDUCATION. ISOLATION AND SOCIAL DISTANCING FORCED THE CHANGE FROM FACE TO FACE EDUCATION TO REMOTE EMERGENCY TRAINING TO GUARANTEE PEDAGOGICAL CONTINUITY. FACED WITH THIS CHANGE IN MODALITY, LEARNING ANALYTICS AS A RESEARCH METHOD OF TECHNO-PEDAGOGICAL D-LEARNING MODELS ALLOW EVALUATING ITS EFFECTS ON THE DEVELOPMENT OF MEANINGFUL LEARNING. THE PRESENT INVESTIGATION WAS CARRIED OUT IN A GROUP OF 69 STUDENTS WITH AN AVERAGE AGE OF 16.06 YEARS. THE TECHNO-PEDAGOGICAL MODEL WAS BASED ON THE USE OF THE APPLICATIONS OF THE COLLABORATION TOOLS OF GOOGLE SUIT FOR EDUCATION. THE CONTENTS WERE DEVELOPED FOR THE TEACHING OF NEWTON'S LAWS OF MOTION FROM AN INTERDISCIPLINARY APPROACH (PHYSICS AND PHYSICAL EDUCATION) BASED ON THE SCIENCE, TECHNOLOGY AND SOCIETY PERSPECTIVE. THE DATA COLLECTION OF THE LEARNING PROCESS WAS CARRIED OUT OVER A PERIOD OF THREE MONTHS. FOR ITS ANALYSIS, FOUR CATEGORIES OF ANALYTICS WERE APPLIED: THE INSTRUCTIONAL CONTEXT, THE MOTIVATION, THE CONTENT AND THE DISCOURSE. THE RESULTS IDENTIFY THE DEVELOPMENT OF METACOGNITIVE SKILLS: META-LEARNING AND META-KNOWLEDGE. FROM THE DISCUSSION, THE IDEA OF A MEANINGFUL LEARNING ANALYTICS THAT ALLOWS TRANSFORMING INFORMATION AND COMMUNICATION TECHNOLOGIES INTO LEARNING TECHNOLOGIES AND METACOGNITIVE KNOWLEDGE IS ARRIVED AT.
BACKGROUND: THE VIDEO-BASED LECTURE (VBL), AN IMPORTANT COMPONENT OF THE FLIPPED CLASSROOM (FC) AND MASSIVE OPEN ONLINE COURSE (MOOC) APPROACHES TO MEDICAL EDUCATION, HAS PRIMARILY BEEN EVALUATED THROUGH DIRECT LEARNER FEEDBACK. EVALUATION MAY BE ENHANCED THROUGH LEARNER ANALYTICS (LA) - ANALYSIS OF QUANTITATIVE AUDIENCE USAGE DATA GENERATED BY VIDEO-SHARING PLATFORMS.METHODS AND RESULTS: WE APPLIED LA TO AN EXPERIMENTAL SERIES OF TEN VBLS ON ELECTROENCEPHALOGRAPHY (EEG) INTERPRETATION, UPLOADED TO YOUTUBE IN THE MODEL OF A PUBLICLY ACCESSIBLE MOOC. TRENDS IN VIEW COUNT; TOTAL PERCENTAGE OF VIDEO VIEWED AND AUDIENCE RETENTION (AR) (PERCENTAGE OF VIEWERS WATCHING AT A TIME POINT COMPARED TO THE INITIAL TOTAL) WERE EXAMINED. THE PATTERN OF AVERAGE AR DECLINE WAS CHARACTERIZED USING REGRESSION ANALYSIS, REVEALING A UNIFORM LINEAR DECLINE IN VIEWERSHIP FOR EACH VIDEO, WITH NO EVIDENCE OF AN OPTIMAL VBL LENGTH. SEGMENTS WITH TRANSIENT INCREASES IN AR CORRESPONDED TO THOSE FOCUSED ON CORE CONCEPTS, INDICATIVE OF CONTENT REQUIRING MORE DETAILED EVALUATION. WE PROPOSE A MODEL FOR APPLYING LA AT FOUR LEVELS: GLOBAL, SERIES, VIDEO, AND FEEDBACK.DISCUSSION AND CONCLUSIONS: LA MAY BE A USEFUL TOOL IN EVALUATING A VBL SERIES. OUR PROPOSED MODEL COMBINES ANALYTICS DATA AND LEARNER SELF-REPORT FOR COMPREHENSIVE EVALUATION.
WE PRESENT TWO DESIGN FICTIONS ABOUT FUTURE ONLINE LEARNING ENVIRONMENTS, INSPIRED BY FUTURE SCENARIOS PRESENTED IN THE LEARNING ANALYTICS LITERATURE. THE DESIGN FICTIONS EXPLORE SPECIFIC ASPECTS OF THIS FUTURE TECHNOLOGY, ITS ADOPTION AND SOCIAL CONSEQUENCES. WE USE SYMMETRY THEORY TO ANALYZE THE DESIGN FICTIONS IN TERMS OF THE RELATIONSHIP BETWEEN THE READERS OF THE FICTIONS ON THE ONE HAND, AND THE FICTIONAL CHARACTERS AND TECHNOLOGY, ON THE OTHER. WE ARGUE THAT THESE RELATIONSHIPS DETERMINE TO WHAT EXTENT A DESIGN FICTION CAN STIMULATE THE READER TO REFLECT ON THE FUTURE TECHNOLOGY. AS SUCH, WE PRESENT SYMMETRY THEORY AS A USEFUL TOOL TO ANALYZE HOW AN EXISTING DESIGN FICTION CAN STIMULATE REFLECTION, AND TO PLAN THE WRITING PROCESS OF DESIGN FICTION.
THE QUALITY OF DISTANCE LEARNING COURSES IS DETERMINED BY THE PROPER ORGANIZATION OF THE TEACHING MATERIAL AND METHODOLOGY. HOWEVER, IT SHOULD BE TAKEN INTO ACCOUNT, AMONG OTHER FACTORS, THE HISTORY OF SUCCESSES AND FAILURES OF PREVIOUS ACHIEVEMENTS OF A COURSE, AS WELL AS THE PROFILE OF THE NEW CLASSES OF STUDENTS. THIS WORK PROPOSES A DATA VISUALIZATION MODEL BASED ON THE LEARNING ANALYTICS OF A COURSE, THAT ALLOWS THE TEACHER TO INCORPORATE IMPROVEMENTS IN NEW EDITIONS OF THE COURSE BASED ON PREVIOUS EDITIONS OF THE SAME. THE MODEL ALSO APPLIES TO THE MONITORING THE PROGRESS OF THE STUDENTS AT RUNTIME OF A COURSE AND IDENTIFY POSSIBLE PROBLEMS IN THE INSTRUCTION DESIGN, WITCH CAN BE ADAPTED DYNAMICALLY.
THIS PAPER ADDRESSES LEARNING ANALYTICS FOR EVALUATION OF LEARNERS PERFORMANCE IN REMOTE LABORATORIES. THE OBJECTIVES WE IDENTIFIED TO PROVIDE SELF- AND SOCIAL AWARENESS WHILE LEARNERS ARE PRACTICING IN THEIR VIRTUAL LEARNING ENVIRONMENT ARE THREEFOLD: (1) THE DEFINITION OF A PERFORMANCE METRIC THAT REQUIRES NO ASSESSMENT TESTS, (2) THE TRACKING OF DATA TO INFER THAT METRIC IN REAL TIME, AND (3) THE VISUALIZATION OF THE PERFORMANCE METRIC WITHOUT IMPACTING LEARNERS' COGNITIVE LOAD. TO SUPPORT THESE NEEDS, WE PROPOSE (I) A METRIC RELATED TO THE TECHNICAL RIGHTNESS OF INSTRUCTIONS CARRIED OUT BY LEARNERS, (II) A GENERIC LEARNING ANALYTICS FRAMEWORK FEATURING AN ENRICHING ENGINE ABLE TO INFER INDICATORS, AND (III) TWO DISTINCT VISUALIZATION TOOLS. THESE PROPOSALS HAVE BEEN IMPLEMENTED IN LAB4CE, OUR REMOTE LABORATORY FOR COMPUTER EDUCATION, AND EXPERIMENTED IN AN AUTHENTIC LEARNING CONTEXT. THIS EXPERIMENTATION SHOWED THAT MOST STUDENTS HAVE SIGNIFICANTLY USED BOTH VISUALIZATION TOOLS, AND THAT THEIR USAGE DECREASED WHILE THE OVERALL LEARNERS PERFORMANCE INCREASED.
THIS EMERGING TECHNOLOGY REPORT REVIEWS A NEW DEVELOPMENT IN EDUCATIONAL TECHNOLOGY, STUDENT-FACING DASHBOARDS, WHICH PROVIDE COMPARATIVE PERFORMANCE FEEDBACK TO STUDENTS CALCULATED BY LEARNING ANALYTICS-BASED ALGORITHMS ON DATA GENERATED FROM UNIVERSITY STUDENTS' USE OF EDUCATIONAL TECHNOLOGY. INSTRUCTOR- AND ADVISOR-FACING DASHBOARDS EMERGED AS ONE OF THE FIRST DIRECT APPLICATIONS OF LEARNING ANALYTICS, BUT THE RESULTS FROM EARLY IMPLEMENTATIONS OF THESE DISPLAYS FOR STUDENTS PROVIDE MIXED RESULTS ABOUT THE EFFECTS OF THEIR USE. IN PARTICULAR, THE "ONE-SIZE-FITS-ALL" DESIGN OF MANY EXISTING SYSTEMS IS QUESTIONED BASED ON FINDINGS IN RELATED RESEARCH ON PERFORMANCE FEEDBACK AND STUDENT MOTIVATION WHICH HAS SHOWN THAT VARIOUS INTERNAL AND EXTERNAL STUDENT-LEVEL FACTORS AFFECT THE IMPACT OF FEEDBACK INTERVENTIONS, ESPECIALLY THOSE USING SOCIAL COMPARISONS. INTEGRATING DATA FROM STUDENT INFORMATION SYSTEMS INTO UNDERLYING ALGORITHMS TO PRODUCE PERSONALIZED DASHBOARDS MAY MEDIATE THE POSSIBLE NEGATIVE EFFECTS OF FEEDBACK, ESPECIALLY COMPARATIVE FEEDBACK, AND SUPPORT MORE CONSISTENT BENEFITS FROM THE USE OF SUCH SYSTEMS.
FEATURED APPLICATION IN THIS WORK, PROCESS MINING TECHNIQUES ARE USED WITH A CURRICULAR ANALYTICS APPROACH, TO MODEL THE EDUCATIONAL TRAJECTORIES OF ENGINEERING STUDENTS DURING THEIR FIRST COURSES. CURRICULAR ANALYTICS IS THE AREA OF LEARNING ANALYTICS THAT LOOKS FOR INSIGHTS AND EVIDENCE ON THE RELATIONSHIP BETWEEN CURRICULAR ELEMENTS AND THE DEGREE OF ACHIEVEMENT OF CURRICULAR OUTCOMES. FOR HIGHER EDUCATION INSTITUTIONS, CURRICULAR ANALYTICS CAN BE USEFUL FOR IDENTIFYING THE STRENGTHS AND WEAKNESSES OF THE CURRICULA AND FOR JUSTIFYING CHANGES IN LEARNING PATHWAYS FOR STUDENTS. THIS WORK PRESENTS THE STUDY OF CURRICULAR TRAJECTORIES AS PROCESSES (I.E., SEQUENCE OF EVENTS) USING PROCESS MINING TECHNIQUES. SPECIFICALLY, THE BACKPACK PROCESS MODEL (BPPM) IS DEFINED AS A NOVEL MODEL TO UNVEIL STUDENT TRAJECTORIES, NOT BY THE COURSES THAT THEY TAKE, BUT ACCORDING TO THE COURSES THAT THEY HAVE FAILED AND HAVE YET TO PASS. THE USEFULNESS OF THE PROPOSED MODEL IS VALIDATED THROUGH THE ANALYSIS OF THE CURRICULAR TRAJECTORIES OF N = 4466 ENGINEERING STUDENTS CONSIDERING THE FIRST COURSES IN THEIR PROGRAM. WE FOUND DIFFERENCES BETWEEN BACKPACK TRAJECTORIES THAT RESULTED IN RETENTION OR IN DROPOUT; SPECIFIC COURSES IN THE BACKPACK AND A LARGER INITIAL BACKPACK SIZES WERE ASSOCIATED WITH A HIGHER PROPORTION OF DROPOUT. BPPM CAN CONTRIBUTE TO UNDERSTANDING HOW STUDENTS HANDLE FAILED COURSES THEY MUST RETAKE, PROVIDING INFORMATION THAT COULD CONTRIBUTE TO DESIGNING AND IMPLEMENTING TIMELY INTERVENTIONS IN HIGHER EDUCATION INSTITUTIONS.
MASSIVE OPEN ONLINE COURSES (MOOCS) CONTINUE TO APPEAR ACROSS THE HIGHER EDUCATION LANDSCAPE, ORIGINATING FROM MANY INSTITUTIONS IN THE USA AND AROUND THE WORLD. MOOCS TYPICALLY HAVE LOW COMPLETION RATES, AT LEAST WHEN COMPARED WITH TRADITIONAL COURSES, AS THIS COURSE DELIVERY MODEL IS VERY DIFFERENT FROM TRADITIONAL, FEE-BASED MODELS, SUCH AS COLLEGE COURSES. THIS RESEARCH EXAMINED MOOC STUDENT DEMOGRAPHIC DATA, INTENDED BEHAVIOURS AND COURSE INTERACTIONS TO BETTER UNDERSTAND VARIABLES THAT ARE INDICATIVE OF MOOC COMPLETION. THE RESULTS LEAD TO IDEAS REGARDING HOW THESE VARIABLES CAN BE USED TO SUPPORT MOOC STUDENTS THROUGH THE APPLICATION OF LEARNING ANALYTICS TOOLS AND SYSTEMS.
THE EDUCATOR IS A "TRANSLATOR" IE MANUFACTURER OF ALGORITHMS FOR A TEACHING IN THE INFOSPHERE. THE TEACHER WHO TURNS INTO A ROBOTIC MIND PERHAPS OF THE TYPE R2D2 A RESEARCH DROID, WILL BE THE EMBLEM OF OUR FUTURE. THE WORK AIMS TO VALIDATE THE MOMENTS OF TRANSFORMATION THROUGH WHICH, OVER THE CENTURIES, THE MATHEMATICAL SCIENCES, WITH THE HELP OF PHILOSOPHY, HAVE ELEVATED THE LANGUAGES NATURAL LANGUAGE GENERATION (NLG) TO FORMAL MODELS. THE STARTING HYPOTHESIS IS TO CORROBORATE AN EPISTEMOLOGICAL STATUTE, WHICH ENTRUSTS MENTAL PROCESSES WITH LOGICAL-MATHEMATICAL REASONING FOLLOWING FOUR MODELS: CHOMSKY (1956), WHICH, WITH DESCRIPTIVE GRAMMAR, MARKS A NEW MODEL FOR THE REWRITING OF LANGUAGES; GROSS (1975), WHICH, WITH THE RELATIONSHIP BETWEEN LINGUISTICS, INFORMATICS AND MATHEMATICS, GENERATES A RELATION CONCERNING A STRONGLY TRANSDISCIPLINARY DOMAIN, IN WHICH LINGUISTICS REALIZES MODELS AND PROCEDURES OF THE INFORMATICS TYPE; SILBERZSTEIN'S NOOJ SYSTEM (2015) FOR THE ELABORATION, DESCRIPTION AND ANALYSIS OF FIXED INLG SENTENCES. THE FOCAL PART OF THE RESEARCH IS THE COMPARISON WORK THAT THE TEAM HAS CARRIED OUT TO VALIDATE THE PROCESSING OF LANGUAGES ACCORDING TO THE TRANSFORMATIONAL ANALYSIS OF DIRECT TRANSITIVE BY M. SILBERZSTEIN AND THE LEXICON-GRAMMAR; THE PROBABILISTIC CALCULATION, ACCORDING TO THE PROBABILISTIC LATENT SEMANTIC ANALYSIS (HOFFMANN, 1999) AND THE EMPIRICAL METHOD.
THE ACQUISITION OF KNOWLEDGE IS NO LONGER ENOUGH TO SUCCEED IN A SOCIETY CHARACTERIZED BY A CONSTANT CHANGE AND HIGH LEVELS OF UNCERTAINTY. ACCORDINGLY, UNIVERSITIES HAVE INCREASINGLY EMPHASIZED SKILLS AND COMPETENCIES AS CENTRAL ELEMENTS OF STUDENTS' DEVELOPMENT. HOWEVER, THE ASSESSMENT OF THESE COMPETENCIES IS NOT AN EASY TASK. THE AVAILABILITY OF DATA THAT LEARNERS GENERATE IN EDUCATIONAL APPLICATION OFFER GREAT POTENTIAL TO STUDY HOW LEARNING TAKES PLACE, AND THUS, TO GATHER EVIDENCES FOR COMPETENCY-ASSESSMENT. THE LACK OF INTEROPERABILITY AND THE DECENTRALIZATION OF THOSE EDUCATIONAL APPLICATIONS POSES A CHALLENGE TO EXPLOIT THOSE TRACE DATA. TO FACE THESE PROBLEMS WE HAVE DESIGNED AND DEVELOPED SCALA (SCALABLE COMPETENCE ASSESSMENT THROUGH A LEARNING ANALYTICS APPROACH), AN ANALYTICS SYSTEM THAT INTEGRATES USAGE (HOW THE USER INTERACTS WITH RESOURCES AND PLATFORMS) AND SOCIAL (HOW STUDENTS AND TEACHERS INTERACT AMONG THEM) TRACE DATA TO SUPPORT COMPETENCY ASSESSMENT. IN THIS PAPER, WE ARE GOING TO FOCUS IN HOW WE HAVE EXTRACTED, TRANSFORMED AND LOADED (ETL) THOSE HETEROGENEOUS DATA SOURCES IN A SINGLE DATA MODEL FOLLOWING THE CALIPER FRAMEWORK FOR ENSURING THE INTEGRITY AND INTEROPERABILITY. FINALLY, WE SHOW SOME INITIAL RESULTS OF THE GRAPHS AND LEARNING ANALYTICS TECHNIQUES TO SUPPORT THE COMPETENCY-BASED ASSESSMENT.
SCIENCE EDUCATION IS RECOGNIZED AS A TOP PRIORITY FOR SCHOOL EDUCATION REFORMS WORLDWIDE. INQUIRY-BASED TEACHING STRATEGIES ARE RECOGNIZED AS APPROPRIATE FOR SUPPORTING THE DEVELOPMENT OF THE COGNITIVE PROCESSES THAT CULTIVATE PROBLEM SOLVING (PS) COMPETENCE, A KEY COMPETENCE OF SCIENTIFIC LITERACY. A WIDELY USED FRAMEWORK FOR ASSESSING INDIVIDUAL STUDENTS' PROBLEM SOLVING COMPETENCE AT LARGE SCALE IS PISA 2012 PROBLEM SOLVING FRAMEWORK (PSF). NEVERTHELESS, PISA 2012 PS COMPETENCE ASSESSMENT IS PRIMARILY SUMMATIVE AND NOT CONNECTED TO THE DAILY SCHOOL SCIENCE TEACHING PRACTICE. ON THE OTHER HAND, SCHOOL ACCOUNTABILITY AND SELF-IMPROVEMENT REQUIRES EVIDENCE TO RELATE STUDENTS' PS COMPETENCE DEVELOPMENT TO SPECIFIC DESIGN CONSIDERATIONS OF LESSON PLANS AND THEIR CORRESPONDING TEACHING AND LEARNING ACTIVITIES USED IN DAY-TO-DAY SCHOOL SCIENCE INQUIRY-BASED TEACHING PRACTICE. WITHIN THIS CONTEXT, THE SCOPE OF THIS BOOK CHAPTER IS TO PRESENT AND DISCUSS A SET OF TOOLS WHICH AIM TO SUPPORT THE AUTHORING AND DELIVERY OF TECHNOLOGY-ENHANCED SCIENCE EDUCATION LESSONS, WHICH FOLLOW AN ADAPTATION OF THE 5E MODEL, WHILE INCORPORATING PISA 2012 PSF COMPATIBLE ASSESSMENT ACTIVITIES AT EACH INQUIRY PHASES. THESE TOOLS SUPPORT SCIENCE TEACHERS IN COLLECTING INQUIRY LEARNING DATA, NAMELY DATA FROM STUDENTS' ACTIVITIES AND STUDENTS' PROBLEM SOLVING COMPETENCE PERFORMANCE DATA AT EACH STAGE OF THE INQUIRY PROCESS. THE FURTHER ANALYSIS OF THOSE INQUIRY LEARNING DATA CAN BE USED FOR EVIDENCE-BASED REFLECTIVE LESSON PLANNING TARGETING TO BETTER SUPPORT STUDENTS' PROBLEM SOLVING COMPETENCE DEVELOPMENT THROUGH INQUIRY-BASED TEACHING.
THIS STUDY EVALUATES THE VALIDITY OF AN ALGORITHM FOR MEASURING THE EFFICIENCY OF SOCIAL LEARNING NETWORKS IN DISCUSSION FORUMS ACCOMPANYING MOOCS OF CONVENTIONAL FORMAT, WHICH CONSIST OF VIDEO LECTURES AND PROBLEM ASSIGNMENTS. THE ALGORITHM MODELS SOCIAL LEARNING NETWORKS AS A FUNCTION OF USERS KNOWLEDGE SEEKING AND KNOWLEDGE DISSEMINATING TENDENCIES ACROSS COURSE TOPICS AND OFFERS A MEANS TO OPTIMIZE SOCIAL LEARNING NETWORKS BY CONNECTING USERS SEEKING AND DISSEMINATING INFORMATION ON SPECIFIC TOPICS. WE USE THE ALGORITHM TO ANALYZE THE SOCIAL LEARNING NETWORK MANIFEST IN THE DISCUSSION FORMAT OF A MOOC FORUM INCORPORATING VIDEO LECTURES AND PROBLEM ASSIGNMENTS. AS A MEASURE OF THE DEGREE THAT KNOWLEDGE SEEKERS AND KNOWLEDGE DISSEMINATORS ARE CONNECTED IN THE NETWORK, WE OBSERVE A VERY SPARSE NETWORK WITH FEW DISCUSSION PARTICIPANTS AND A LIMITED RANGE OF TOPICS. HENCE, ONLY SMALL GAINS ARE AVAILABLE THROUGH OPTIMIZATION, SINCE FOR A VERY SPARSE NETWORK, FEW CONNECTIONS CAN BE MADE. THE DEVELOPMENT OF A METRIC FOR THE ANALYSIS OF SOCIAL LEARNING NETWORKS WOULD PROVIDE INSTRUCTORS AND RESEARCHERS WITH A MEANS TO OPTIMIZE ONLINE LEARNING ENVIRONMENTS FOR EMPOWERING SOCIAL LEARNING. FINALLY, WE DISCUSS OUR FINDINGS WITH RESPECT TO THE POTENTIAL OF SELF-OPTIMIZING DISCUSSION FORUMS FOR SUPPORTING SOCIAL LEARNING ONLINE.
AS THE NEW TREND OF INFORMATION TECHNOLOGY DEVELOPMENT, BIG DATA HAS BEEN INFILTRATED INTO ALL WALKS OF LIFE AND THUS BECOME AN IMPORTANT CAUSE OF FORMATION. AT THE BIG DATA ERA OF DATA ANALYSIS DRIVING EDUCATION AND REFORMING TEACHING, THE EDUCATION FIELD ALSO RESERVES MASSIVE DATA WITH EXTENSIVE APPLICATION VALUE. ON THE BASIS OF EDUCATION DATA MINING, LEARNING ANALYTICS TECHNOLOGY AND OTHER KEY TECHNOLOGY ANALYSIS ON BID EDUCATION DATA, COMBINED WITH THE CO-WORD ANALYSIS, EDUCATION BLOGS AND OTHER SOCIAL NETWORK EDUCATION DATA, BUILD THE RELEVANT LEARNING ANALYTICS AND DATA MINING MODEL IN EDUCATION FIELD, EXPLORE THE RELEVANT RELATIONS AMONG THE EDUCATION VARIABLES, PRACTICE THE EDUCATION APPLICATION OF BIG DATA AND PROVIDE SUFFICIENT DECISION-MAKING SUPPORT SERVICE FOR THE EDUCATION TEACHING, TO FACILITATE THE EDUCATION TEACHING REFORM AND INNOVATION.
TODAY, IT IS ALMOST IMPOSSIBLE TO IMPLEMENT TEACHING PROCESSES WITHOUT USING INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT), ESPECIALLY IN HIGHER EDUCATION. EDUCATION INSTITUTIONS OFTEN USE LEARNING MANAGEMENT SYSTEMS (LMS), SUCH AS MOODLE, EDMODO, CANVAS, SCHOOLOGY, BLACKBOARD LEARN, AND OTHERS. WHEN ACCESSING THESE SYSTEMS WITH THEIR PERSONAL ACCOUNT, EACH STUDENT'S ACTIVITY IS RECORDED IN A LOG FILE. MOODLE SYSTEM ALLOWS NOT ONLY INFORMATION SAVING. THE PLUGINS OF THIS LMS PROVIDE A FAST AND ACCURATE ANALYSIS OF TRAINING STATISTICS. WITHIN THE STUDY, THE CAPABILITIES OF SEVERAL MOODLE PLUGINS PROVIDING THE ASSESSMENT OF STUDENTS' ACTIVITY AND SUCCESS ARE REVIEWED. THE RESEARCH IS AIMED AT DISCOVERING POSSIBILITIES TO IMPROVE THE LEARNING PROCESS AND REDUCE THE NUMBER OF UNDERPERFORMING STUDENTS. THE ACTIVITY LOGS OF 124 PARTICIPANTS ARE ANALYZED TO IDENTIFY THE RELATIONS BETWEEN THE NUMBER OF LOGS DURING THE E-COURSE AND THE FINAL GRADES. IN THE STUDY, A CORRELATION ANALYSIS IS PERFORMED TO DETERMINE THE IMPACT OF STUDENTS' EDUCATIONAL ACTIVITY IN THE MOODLE SYSTEM ON THE FINAL ASSESSMENT. THE RESULTS REVEAL THAT GENDER AFFILIATION CORRELATES WITH THE OVERALL PERFORMANCE BUT DOES NOT AFFECT THE SELECTION OF TRAINING MATERIALS. FURTHERMORE, IT IS SHOWN THAT STUDENTS WHO GOT THE HIGHEST GRADES PERFORMED AT LEAST 210 LOGS DURING THE COURSE. IT IS NOTED THAT THE PREVAILING PART OF STUDENTS PREFERS TO COMPLETE THE TASKS BEFORE THE DEADLINE. THE STUDY CONCLUDES THAT LMSS CAN BE USED TO PREDICT STUDENTS' SUCCESS AND STIMULATE BETTER RESULTS DURING THE STUDY. THE FINDINGS ARE PROPOSED TO BE USED IN HIGHER EDUCATION INSTITUTIONS FOR EARLY DETECTION OF STUDENTS EXPERIENCING DIFFICULTIES IN A COURSE.
IN THIS PAPER, WE PROPOSE THAT THE DATA GENERATED BY EDUCATIONAL ROBOTS CAN BE BETTER USED BY APPLYING LEARNING ANALYTICS METHODS AND TECHNIQUES WHICH CAN LEAD TO A DEEPER UNDERSTANDING OF THE LEARNERS' APPREHENSION AND BEHAVIOR AS WELL AS REFINED GUIDELINES FOR ROBOTICISTS AND IMPROVED INTERVENTIONS BY THE TEACHERS. AS A STEP TOWARDS THIS, WE PUT FORWARD ANALYZING BEHAVIOR AND TASK PERFORMANCE AT TEAM AND/OR INDIVIDUAL LEVELS BY COUPLING ROBOT DATA WITH THE DATA FROM CONVENTIONAL METHODS OF ASSESSMENT THROUGH QUIZZES. CLASSIFYING LEARNERS/TEAMS IN THE BEHAVIORAL FEATURE SPACE WITH RESPECT TO THE TASK PERFORMANCE GIVES INSIGHT INTO THE BEHAVIOR PATTERNS RELEVANT FOR HIGH PERFORMANCE, WHICH COULD BE BACKED BY FEATURE RANKING. AS A USE CASE, WE PRESENT AN OPEN-ENDED LEARNING ACTIVITY USING TANGIBLE HAPTIC-ENABLED CELLULO ROBOTS IN A CLASSROOM-LEVEL SETTING. THE PILOT STUDY, SPANNING OVER APPROXIMATELY AN HOUR, IS CONDUCTED WITH 25 CHILDREN IN TEAMS OF TWO THAT ARE AGED BETWEEN 11-12. A LINEAR SEPARATION IS OBSERVED BETWEEN THE HIGH AND LOW PERFORMING TEAMS WHERE TWO OF THE BEHAVIORAL FEATURES, NAMELY NUMBER OF DISTINCT ATTEMPTS AND THE VISITS TO THE DESTINATION, ARE FOUND TO BE IMPORTANT. ALTHOUGH THE PILOT STUDY IN ITS CURRENT FORM HAS LIMITATIONS, E.G. ITS LOW SAMPLE SIZE, IT CONTRIBUTES TO HIGHLIGHTING THE POTENTIAL OF THE USE OF LEARNING ANALYTICS IN EDUCATIONAL ROBOTICS.
DESIGN COURSEWORK IS ITERATIVE AND CONTINUOUSLY-EVOLVING. SEPARATION OF DIGITAL TOOLS USED IN DESIGN COURSES DISAFFECTS INSTRUCTORS' AND STUDENTS' ITERATIVE PROCESS EXPERIENCES. WE PRESENT A SYSTEM THAT INTEGRATES SUPPORT FOR DESIGN IDEATION WITH A LEARNING ANALYTICS DASHBOARD. A PRELIMINARY STUDY DEPLOYED THE SYSTEM IN TWO COURSES, EACH WITH SIMILAR TO 15 STUDENTS AND 1 INSTRUCTOR, FOR THREE MONTHS. WE CONDUCTED SEMI-STRUCTURED INTERVIEWS TO UNDERSTAND USER EXPERIENCES. FINDINGS INDICATE BENEFITS WHEN SYSTEMS CONTEXTUALIZE CREATIVE WORK WITH ASSESSMENT BY INTEGRATING SUPPORT FOR IDEATION WITH A LEARNING ANALYTICS DASHBOARD. INSTRUCTORS ARE BETTER ABLE TO TRACK STUDENTS AND THEIR WORK. STUDENTS ARE SUPPORTED IN REFLECTING ON RELATIONSHIPS AMONG DELIVERABLES. WE DERIVE IMPLICATIONS FOR CONTEXTUALIZING DESIGN WITH FEEDBACK TO SUPPORT CREATIVITY, LEARNING, AND TEACHING.
IN THIS PAPER, CONSIDERATION IS GIVEN TO EXTENDING AN EXISTING ON-LINE TUTORIAL SYSTEM IN ORDER TO UNDERSTAND THE USER EXPERIENCE WITH THE TUTORIAL BY INCLUDING ANALYSIS OF THE TOOL USE. THE AREAS OF WEB ANALYTICS AND LEARNING ANALYTICS WILL BE CONSIDERED AND USED. REFERENCE TO HOW WEB ANALYTICS TOOLS ARE USED AND THE FEATURES THEY PROVIDE WILL BE MADE. FEATURES OF AVAILABLE ANALYTICS TOOLS WILL BE CONSIDERED AND IMPORTANT FEATURES FOR USE IN AN EDUCATION CONTEXT, AND IN PARTICULAR THIS TUTORIAL ON-LINE TUTORIAL SYSTEM, WILL THEN BE INTEGRATED INTO THE TUTORIAL FOR USE BY THE TUTORIAL ADMINISTRATOR AND DEVELOPER.
LEARNING ANALYSIS CONSISTS OF COLLECTING THE TRACES THAT LEARNERS LEAVE BEHIND AND USING THESE TRACES TO ENHANCE LEARNING [1]. THE LEARNING ANALYTICS USE TECHNIQUES FROM DIFFERENT FIELDS SUCH AS ARTIFICIAL INTELLIGENT, MACHINE LEARNING, DATA MINING, TEXT MINING AND WEB ANALYTIC, ETC. MOREOVER, INDIVIDUAL LEARNERS INTENDED METHODS TO STIMULATE REFLECTION ON THEIR ACHIEVEMENTS AND THEIR BEHAVIOR IN RELATION TO OTHERS AND FOR TEACHERS AND SUPPORT STAFF TO PLAN SUPPORTIVE INTERVENTIONS WITH INDIVIDUALS AND GROUPS. THIS ARTICLE FOCUSES ON THE EXPLORATION OF ANALYTICAL MECHANISMS THAT TAKE INTO CONSIDERATION LEARNERS' PERSONAL CHARACTERISTICS SPECIFICALLY THE LEVEL OF PREPARATION AND PREVIOUS KNOWLEDGE, PREFERENCES, CULTURAL FEATURES, DEMOGRAPHIC CHARACTERISTICS LIKE AGE, GENDER, ETC. IN THE FEEDBACK MODEL. (C) 2019 THE AUTHORS. PUBLISHED BY ELSEVIER B. V.
IN THIS PAPER, WE PROPOSE LEARNING ANALYTIC TASKS TO UNDERSTAND THE LEARNING PROCESS IN A SMART CLASSROOM. LEARNING ANALYTICS CAN EXTRACT KNOWLEDGE FROM A COURSE TO BETTER UNDERSTAND STUDENTS AND THEIR LEARNING PROCESSES. THE LEARNING ANALYTIC TASKS MUST EVALUATE DIFFERENT ASPECTS IN THE COURSE: THE TEACHING AND LEARNING PROCESS, THE STUDENT PERFORMANCE, AND THE PEDAGOGICAL PRACTICES, AMONG OTHER THINGS. IN THIS PAPER, WE SHOW THE POWER OF THE LEARNING ANALYTIC IN A SMART CLASSROOM IN AN EXAMPLE. SPECIFICALLY, WE DEFINE A SET OF LEARNING ANALYTIC TASKS TO ANALYZE THE EVALUATION PROCESS IN A SMART CLASSROOM, BASED ON THE SCORE OF THE STUDENTS, THE SKILLS DEVELOPED BY THE STUDENTS, THE BEHAVIOR IN THE ASSESSMENT QUESTIONS, ETC. WE HAVE USED DIFFERENT DATA MINING TECHNIQUES TO DEVELOP THE LA TASKS
THIS WORKSHOP WILL EXPLORE COMMUNITY BASED REPOSITORIES FOR EDUCATIONAL DATA AND ANALYTIC TOOLS THAT ARE USED TO CONNECT RESEARCHERS AND REDUCE THE BARRIERS TO DATA SHARING. LEADING INNOVATORS IN THE FIELD, AS WELL AS ATTENDEES, WILL IDENTIFY AND REPORT ON BOTTLENECKS THAT REMAIN TOWARD OUR GOAL OF A UNIFIED REPOSITORY. WE WILL DISCUSS THESE AS WELL AS POSSIBLE SOLUTIONS. WE WILL PRESENT LEARNSPHERE, AN NSF FUNDED SYSTEM THAT SUPPORTS COLLABORATING ON AND SHARING A WIDE VARIETY OF EDUCATIONAL DATA, LEARNING ANALYTICS METHODS, AND VISUALIZATIONS WHILE MAINTAINING CONFIDENTIALITY. WE WILL THEN HAVE HANDS-ON SESSIONS IN WHICH ATTENDEES HAVE THE OPPORTUNITY TO APPLY EXISTING LEARNING ANALYTICS WORKFLOWS TO THEIR CHOICE OF EDUCATIONAL DATASETS IN THE REPOSITORY (USING A SIMPLE DRAG-AND-DROP INTERFACE), ADD THEIR OWN LEARNING ANALYTICS WORKFLOWS (REQUIRES VERY BASIC CODING EXPERIENCE), OR BOTH. LEADERS AND ATTENDEES WILL THEN JOINTLY DISCUSS THE UNIQUE BENEFITS AS WELL AS THE LIMITATIONS OF THESE SOLUTIONS. OUR GOAL IS TO CREATE BUILDING BLOCKS TO ALLOW RESEARCHERS TO INTEGRATE THEIR DATA AND ANALYSIS METHODS WITH OTHERS, IN ORDER TO ADVANCE THE FUTURE OF LEARNING SCIENCE.
IN THIS PAPER, WE EVALUATE THE COMPLETE UNDERGRADUATE COENROLLMENT NETWORK OVER A DECADE OF EDUCATION AT A LARGE AMERICAN PUBLIC UNIVERSITY. WE PROVIDE DESCRIPTIVE PROPERTIES OF THE NETWORK, DEMONSTRATING THAT THE COENROLLMENT NETWORKS EVALUATED FOLLOW POWER-LAW DEGREE DISTRIBUTIONS SIMILAR TO MANY OTHER LARGE-SCALE NETWORKS; THAT THEY REVEAL STRONG PERFORMANCE-BASED ASSORTATIVITY; AND THAT NETWORK-BASED FEATURES CAN SIGNIFICANTLY IMPROVE GPA-BASED STUDENT PERFORMANCE PREDICTORS. WE THEN IMPLEMENT A NETWORK-BASED, MULTI-VIEW CLASSIFICATION MODEL TO PREDICT STUDENTS' FINAL COURSE GRADES. IN PARTICULAR, WE ADAPT A STRUCTURAL MODELING APPROACH FROM [19, 34], WHEREBY WE MODEL THE UNIVERSITY-WIDE UNDERGRADUATE COENROLLMENT NETWORK AS AN UNDIRECTED GRAPH. WE COMPARE THE PERFORMANCE OF OUR PREDICTOR TO TRADITIONAL METHODS USED FOR GRADE PREDICTION IN UNDERGRADUATE UNIVERSITY COURSES, AND DEMONSTRATE THAT A MULTI-VIEW ENSEMBLING APPROACH OUTPERFORMS BOTH PRIOR "FLAT" AND NETWORK-BASED MODELS FOR GRADE PREDICTION ACROSS SEVERAL CLASSIFICATION METRICS. THESE FINDINGS DEMONSTRATE THE USEFULNESS OF COMBINING DIVERSE APPROACHES IN MODELS OF STUDENT SUCCESS, AND DEMONSTRATE SPECIFIC NETWORK-BASED MODELING STRATEGIES WHICH ARE LIKELY TO BE MOST EFFECTIVE FOR GRADE PREDICTION.
CAPTURING AND STORING LEARNERS' DATA IS THE FIRST STEP TO IMPLEMENT A LEARNING ANALYTICS ARCHITECTURE. THE EXPERIENCE API (XAPI) SPECIFICATION IS A DE FACTO STANDARD THAT DESCRIBES (I) A REST-BASED API TO STORE AND RETRIEVE THE LEARNERS' ACTIVITY DATA, AND (II) AN RDF-BASED DATA MODEL WHERE THE RESTRICTIONS AMONG DATA ARE SPECIFIED IN NATURAL LANGUAGE. IN THIS PAPER, WE PRESENT AN ONTOLOGY THAT FORMALLY REPRESENTS BOTH THE DATA MODEL AND THE RESTRICTIONS OF THE XAPI SPECIFICATION, FACILITATING THE CONFORMANCE TESTING OF THE DATA COMING FROM THE LEARNING MANAGEMENT SYSTEM TO GUARANTEE THE COMPLIANCE WITH THE SPECIFICATION.
PROBLEM-BASED LEARNING HAS BEEN PROVED AS A GOOD METHOD TO TEACH SOFTWARE ENGINEERING, CONCRETELY WHEN PROJECT DEVELOPMENT SKILLS ARE INVOLVED. IN THOSE CASES, PROJECT WORK DEPARTS FROM SOME STAKEHOLDER REQUIREMENTS THAT ARE OFTEN ILL-DEFINED, AND THE TEAM PROGRESSES BY ANALYSIS AND DESIGN IDENTIFYING THE PARTS THAT THE TEAM CAN DEVELOP AND WHICH OF THEM REQUIRES FURTHER INVESTIGATION. FROM A TEACHING PERSPECTIVE, AGILE METHODS LIKE SCRUM PERFECTLY FIT THE PROBLEM-BASED LEARNING MODEL, SINCE SPRINT RESULTS CAN PROVIDE A GOOD INDICATOR OF GROUP AND INDIVIDUAL STUDENT PROGRESS. IN THIS PAPER, A CASE STUDY AIMED AT INTRODUCING LEARNING ANALYTICS IN AN INSTRUCTIONAL DESIGN FOR ADVANCED SOFTWARE ENGINEERING COURSES USING PROBLEM-BASED LEARNING APPROACH IS REPORTED. LEARNING ANALYTICS WERE CARRIED OUT BOTH FROM THE PROJECT WORK PROCESS, ADOPTING SOME TOOLS OF THE PLATFORM USED TO MANAGE TASK AND ACTIVITIES (JIRA SOFTWARE), AND FROM THE OBTAINED FINAL GRADE, USING JUPYTER NOTEBOOKS.
IMPROVING THE WRITTEN LITERACY OF NEWCOMERS TO ENGLISH-SPEAKING COUNTRIES CAN LEAD TO BETTER EDUCATION, EMPLOYMENT, OR SOCIAL INTEGRATION OPPORTUNITIES. HOWEVER, THIS REMAINS A CHALLENGE IN TRADITIONAL CLASSROOMS WHERE PROVIDING FREQUENT, TIMELY, AND PERSONALIZED FEEDBACK IS NOT ALWAYS POSSIBLE. ANALYTICS CAN SCAFFOLD THE WRITING DEVELOPMENT OF ENGLISH LANGUAGE LEARNERS (ELLS) BY PROVIDING SUCH FEEDBACK. TO DESIGN THESE ANALYTICS, WE CONDUCTED A FIELD STUDY ANALYZING ESSAY SAMPLES FROM IMMIGRANT ADULT ELLS (A GROUP OFTEN OVERLOOKED IN WRITING ANALYTICS RESEARCH) AND IDENTIFYING THEIR EPISTEMIC BELIEFS AND LEARNING MOTIVATIONS. WE IDENTIFIED COMMON THEMES ACROSS INDIVIDUAL LEARNER DIFFERENCES AND PATTERNS OF ERRORS IN THE WRITING SAMPLES. THE STUDY REVEALED STRONG ASSOCIATIONS BETWEEN EPISTEMIC WRITING BELIEFS AND LEARNING STRATEGIES. THE RESULTS ARE USED TO DEVELOP GUIDELINES FOR DESIGNING WRITING ANALYTICS FOR ADULT ELLS, AND TO PROPOSE IDEAS FOR ANALYTICS THAT SCAFFOLD WRITING DEVELOPMENT FOR THIS GROUP.
EDUCATIONAL MAPPING IS THE PROCESS OF ANALYZING AN EDUCATIONAL SYSTEM TO IDENTIFY ENTITIES, RELATIONSHIPS AND ATTRIBUTES. THIS PAPER PROPOSES A NETWORK MODELING APPROACH TO EDUCATIONAL MAPPING. CURRENT MAPPING PROCESSES IN EDUCATION TYPICALLY REPRESENT DATA IN FORMS THAT DO NOT SUPPORT SCALABLE LEARNING ANALYTICS. FOR EXAMPLE, A CURRICULUM MAP IS USUALLY A TABLE, WHERE RELATIONSHIPS AMONG CURRICULAR ELEMENTS ARE REPRESENTED IMPLICITLY IN THE ROWS OF THE TABLE. THE PROPOSED NETWORK MODELING APPROACH OVERCOMES THIS LIMITATION THROUGH EXPLICIT MODELING OF THESE RELATIONSHIPS IN A GRAPH STRUCTURE, WHICH IN TURN UNLOCKS THE ABILITY TO PERFORM SCALABLE ANALYSES ON THE DATASET. THE PAPER PRESENTS NETWORK MODELS FOR EDUCATIONAL USE CASES, WITH CONCRETE EXAMPLES IN CURRICULUM MAPPING, ACCREDITATION MAPPING AND CONCEPT MAPPING. ILLUSTRATIVE EXAMPLES DEMONSTRATE HOW THE FORMAL MODELING APPROACH ENABLES VISUALIZATION AND LEARNING ANALYTICS. THE ANALYSIS PROVIDES INSIGHT INTO LEARNING PATHWAYS, SUPPORTING DESIGN OF ADAPTIVE LEARNING SYSTEMS. IT ALSO PERMITS GAP ANALYSIS OF CURRICULUM COVERAGE, SUPPORTING STUDENT ADVISING, STUDENT DEGREE PLANNING AND CURRICULAR DESIGN AT SCALES RANGING FROM AN ENTIRE INSTITUTION TO AN INDIVIDUAL COURSE.
RECENTLY, THERE HAS BEEN AN INCREASING INTEREST IN LEARNER MODELING IN ORDER TO PROVIDE PERSONALIZED COMPUTER BASED LEARNING EXPERIENCES. GENERALLY, RESEARCHERS AND PRACTITIONERS USE QUESTIONNAIRES AS A METHOD TO MODEL LEARNERS, INCLUDING THEIR PERSONALITIES, WHICH CAN BE TIME CONSUMING AND NOT MOTIVATING. THEREFORE, THIS PAPER PRESENTS A NEW APPROACH FOR MODELING THE LEARNER'S PERSONALITY, SPECIFICALLY INTROVERT/EXTROVERT, USING AN ONLINE EDUCATIONAL GAME AND LEARNING ANALYTICS (LA) SYSTEM.
THE CONVERGENCE IN THE USE OF TECHNOLOGY IN CLASSROOMS AND THE DEVELOPMENT OF NEW METHODOLOGIES HAVE INVOLVED A REDEFINITION OF THE DIFFERENT EDUCATIONAL AGENTS' PERFORMANCE, FOR THE UPCOMING HORIZON REPORTS TO GENERATE A RADIOGRAPHY OF THE EMERGING TECHNOLOGICAL TRENDS THAT WILL HAVE AN IMPACT IN THE UPCOMING YEARS. AS A CONSEQUENCE, WE WILL FOCUS ON ADAPTATIVE LEARNING TECHNOLOGIES BASED ON THE PERSPECTIVES OF PROFOUND LEARNING, WHERE THE ACHIEVEMENT OF OBJECTIVES WILL BE REFLECTED THROUGH GENERATED LEARNING ANALYTICS, WHOSE ASSOCIATION MAY PRODUCE CONSISTENT VERIFIABLE BLOCKCHAINS. FOR THAT MATTER, THIS WORK PROPOSES A META-ANALYSIS OF 62 RESEARCH STUDIES INDEXED IN THE WOS AND SCOPUS DATABASES DURING 2013 AND 2018, IN THE AREA OF SOCIAL SCIENCES, TAKING AS DESCRIPTORS THE TECHNOLOGIES MENTIONED IN THOSE REPORTS. A SEARCH STRATEGY BASED ON FOUR DIFFERENT CRITERIA HAS BEEN USED: PUBLIC (TARGET), TOPIC, METHODOLOGICAL DESIGN AND MAIN CONCLUSIONS.
THESE DAYS' DATA MINING IS AN EMERGING TREND, WHICH IS PRESENTLY USED IN DIFFERENT AREAS ESPECIALLY IN STUDENT EDUCATIONAL AND LEARNING ANALYTICS. IT IS VERY HARD AND TIME CONSUMING TO ANALYZE DATA AND FINDING THE HIDDEN INFORMATION MANUALLY. TO IMPROVISE EDUCATIONAL DATA MINING, CLUSTERING WILL BE USED IN THE PAPER. AS WE NEED TO IMPROVISE PERFORMANCE AS WELL AS UNAMBIGUOUSNESS OF OBTAINED MODELS. WE HAVE USED 84 UNDER-GRADUATE STUDENT DATA AND GROUPED STUDENTS ACCORDING TO THEIR FINAL MARKS THEY ACHIEVED IN THE COURSE AND THIS WE HAVE DONE BY USING CLUSTERING APPROACH. THE RESULT WHICH WE GET SHOWS THAT THE CLARITY OF SPECIFIC MODEL IS MUCH BETTER THAN THE GENERAL MODEL AND THE UNAMBIGUOUSNESS OF THE MODEL IS ALSO INCREASE.
THE ARTICLE USES THE EXAMPLE OF THE DIGITAL SCHUL-CLOUD (SCHOOL-CLOUD) TO ANALYSE THE TRANSFORMATION OF KNOWLEDGE IN EDUCATIONAL MEDIA AND EDUCATION POLICY CONTROL AS WELL AS THE POSSIBLE EFFECTS ON SCHOOL LEARNING. TWO MAIN THESES ARE PUT FORWARD: FIRST, THAT THERE IS A FUSION OF LEARNING AND CONSUMPTION IN DIGITISED CLOUD-DRIVEN LEARNING PRACTICES AND, SECONDLY, THAT THE PROGRESSIVE CONTROL OF LEARNING PROCESSES IN THE CONTEXT OF LEARNING ANALYTICS, WHICH INVOLVES A COMPREHENSIVE PRODUCTION OF DATA, IMPLIES THE PROMISE OF PREDICTABILITY OF STUDENT PERFORMANCE.
THIS PAPER PRESENTS A TOOL THAT USES TECHNIQUES OF BUSINESS INTELLIGENCE (BI) AND LEARNING ANALYTICS (LA) TO ASSIST THE DISTANCE EDUCATION MANAGERS (EAD) IN MONITORING THE ACTIONS OF BOTH STUDENTS AND TEACHERS/TUTORS IN THE VIRTUAL LEARNING ENVIRONMENT (VLE) MOODLE. THE TOOL PROVIDES ANALYTICAL AND CONSOLIDATED REPORTS ON ALL THE RESPONSIBILITY ROOMS OF A CERTAIN TEACHER/TUTOR, AND DASHBOARDS, WITH THE VISION OF THE STUDENTS AND THE TEACHER/TUTOR, THUS ALLOWING A BETTER MANAGEMENT IN VIRTUAL LEARNING ENVIRONMENTS AS AVA MOODLE LEARNING ANALYTICS OFFERS SOME FEATURES, HOWEVER, THE VIEWS REFER TO ROOMS, RESOURCES OR SPECIFIC PARTICIPANTS, MAKING IT DIFFICULT TO INTERPRET, MAKING IT LABORIOUS PROCESS MANAGEMENT. THE USE OF THIS TOOL BY EAD MANAGERS ALLOWS FAULTS TO BE CORRECTED DURING THE PROCESS OF TEACHING AND LEARNING AND HELPS THE DECISIONS TO BE TAKEN CONCERNING THE PEOPLE RELATED TO THE PROCESS IN REFERENCE TO TEACHERS, TUTORS THAT SHOULD BE RELIABLE AND ASSERTIVE, AS THEY ARE AN INTEGRAL AND VERY IMPORTANT PART OF THIS PROCESS OF TEACHING AND LEARNING.
THE LEARNING ANALYTICS IS A RESEARCH AREA THAT SEEKS TO UNDERSTAND LEARNING PROCESSES BY USING THE VARIOUS COMPUTER SCIENCE TECHNIQUES. IN THIS PAPER, WE FOCUS ON THE ANALYSIS OF CERTAIN CLASSROOM SITUATIONS, SUCH AS LECTURINGS, PERFORMING EXERCISES, AND TESTING. THESE ANALYSES DO NOT DIRECTORY APPLY TO STUDENTS; HOWEVER, THEY ARE VERY USEFUL FOR ANALYZING AND INTERPRETING STUDENTS' BEHAVIORS IN THE CLASSROOM. THIS IS SIGNIFICANT AS STUDENTS' BEHAVIORS CAN AFFECT VERY REAL CHANGES IN CLASSROOM SITUATIONS. THIS PAPER EMPLOYS THE CONVOLUTIONAL NEURAL NETWORKS TO IDENTIFY VARIOUS CLASSROOM SITUATIONS FROM THE SPECTROGRAMS OF ENVIRONMENTAL SOUNDS IN THE CLASSROOM. EXPERIMENTAL RESULTS SHOW THE EFFECTIVENESS OF THE PROPOSED SYSTEMS.
MANY UNIVERSITIES ARE USING COMPUTER-SUPPORTED COLLABORATIVE-INQUIRY-LEARNING (CSCIL) ENVIRONMENTS TO DEVELOP THEIR STUDENTS' SKILLS IN COLLABORATION, PROBLEM SOLVING, AND CRITICAL THINKING. DIVERSE STATES OF DISCOURSE DURING CSCIL OCCUR IN SEQUENCES, BUT WE DO NOT YET FULLY UNDERSTAND WHICH PATTERNS ARE BENEFICIAL TO LEARNING AND WHEN EXACTLY TO FOSTER THEM. THIS STUDY USED TRANSITION-RATE ANALYSIS, ENTROPY-ANALYSIS, AND SEQUENTIAL PATTERN MINING TO ANALYZE THE CHAT MESSAGE OF 144 STUDENTS OF TWO-YEAR COLLEGES. THE PARTICIPANTS WORKED ON TASKS RELATED TO OHM'S LAW IN A SIMULATION-BASED COLLABORATIVE-INQUIRY-LEARNING ENVIRONMENT. THE RESULTS REVEALED THAT STUDENTS IN GROUPS WHO COMPLETED TASKS SUCCESSFULLY TENDED TO ENSURE THAT EVERYONE IN THEIR GROUP HAD A SHARED UNDERSTANDING OF THE RELATIONSHIP BETWEEN THE VARIABLES BEFORE THEY MOVED ON TO THE NEXT STEP. IN CONTRAST, STUDENTS IN GROUPS WHO DID NOT COMPLETE TASKS SUCCESSFULLY WERE MORE LIKELY TO REGULATE THE PROCESS WITHOUT REACHING A SHARED UNDERSTANDING. GAOXIA ZHU IS A PHD CANDIDATE AND RESEARCH ASSISTANT IN THE INSTITUTE FOR KNOWLEDGE INNOVATION & TECHNOLOGY, ONTARIO INSTITUTE FOR STUDIES IN EDUCATION (OISE), UNIVERSITY OF TORONTO WITH BACKGROUND IN EDUCATIONAL TECHNOLOGY AND CURRICULUM STUDIES. HER RESEARCH INTERESTS INCLUDE KNOWLEDGE BUILDING, LEARNING ANALYTICS, AND CSCL. WANLI XING IS AN ASSISTANT PROFESSOR IN INSTRUCTIONAL TECHNOLOGY AT TEXAS TECH UNIVERSITY, USA WITH BACKGROUND IN LEARNING SCIENCES, STATISTICS, COMPUTER SCIENCE AND MATHEMATICAL MODELLING. HIS RESEARCH INTERESTS ARE EDUCATIONAL DATA MINING, LEARNING ANALYTICS, AND CSCL. VITALIY POPOV IS A RESEARCH ASSOCIATE IN JACOBS INSTITUTE FOR INNOVATION IN EDUCATION AT UNIVERSITY OF SAN DIEGO. DR. POPOV AREAS OF INTEREST INCLUDE: COMPUTER SUPPORTED COLLABORATIVE LEARNING, MOBILE LEARNING, LEARNING SCIENCES, LEARNING ANALYTICS, AND TECHNOLOGY-ENHANCED LEARNING ACROSS CULTURES.
VARIOUS TYPES OF LEARNERS CAN BE OBSERVED IN TODAY'S E-LEARNING ENVIRONMENTS. USING SOME OF THE EXISTING MODELS FOR THE LEARNER TYPE CLASSIFICATION, LIKE FELDER-SILVERMAN LEARNING STYLES MODEL (FSLSM), WE CAN IDENTIFY LEARNERS' PREFERENCES AND NEEDS AND USE THEM TO ADJUST THE ENVIRONMENT. ON THE OTHER HAND, LEARNING ANALYTICS CAN OFFER INSIGHTS ON A STUDENT'S ENGAGEMENT AND BEHAVIOUR, BY TRACKING THEIR ACTIONS AND PERFORMANCE. WE CAN CORRELATE THIS DATA WITH THE DATA ON LEARNERS' TYPE, AND USE IT FOR DEEPER USER MODELLING. SUPPORTED BY VISUALIZATIONS ON LEARNING STYLES AND LEARNING BEHAVIOUR, STUDENTS AND INSTRUCTORS CAN REFLECT THE LEARNING PROCESS. BASED ON OUR PREVIOUSLY PUBLISHED CONCEPTUAL MODEL FOR LINKING LEARNING ANALYTICS AND LEARNING STYLES IN E-LEARNING ENVIRONMENTS, WE PRESENT AN INTEGRATED LEARNING STYLES ANALYTICS SYSTEM (ILSA), WHICH SUPPORTS IDENTIFICATION OF LEARNING STYLES AS WELL AS ANALYSIS AND VISUALIZATION OF ACTIVITY DATA IN THE MOODLE LMS. ILSA CONSISTS OF A QUESTIONNAIRE FOR LEARNING STYLE IDENTIFICATION DERIVED FROM FSLSM AND OFFERS IMMEDIATE RESULTS TO ITS PARTICIPANTS. NEXT, BY UTILIZING VARIOUS DATA SOURCES IN MOODLE, E.G. LOG DATA OR THE GRADE BOOK, WE ARE ABLE TO CORRELATE A USER'S ACTIVITY AND PERFORMANCE WITH THEIR LEARNING STYLES. THIS WORK OFFERS DETAILS ON THE FINALIZED CONCEPT AS WELL AS ITS IMPLEMENTATION. BY PROVIDING INSIGHTS ON DATA SOURCES IN MOODLE AND PRESENTING VARIOUS VISUALIZATIONS, THIS WORK ALLOWS TEACHERS TO REUSE THE SYSTEM IN THEIR E-LEARNING COURSES.
A COMMON RECOMMENDATION IN CRITIQUES OF DATAFICATION IN EDUCATION IS FOR GREATER CONVERSATION BETWEEN THE TWO SIDES OF THE (CRITICAL) DIVIDE - WHAT MIGHT BE CHARACTERISED AS SCEPTICAL SOCIAL SCIENTISTS AND (SUPPOSEDLY) MORE TECHNICALLY-MINDED AND ENTHUSIASTIC DATA SCIENTISTS. THIS ARTICLE TAKES THE FORM OF A DIALOGUE BETWEEN TWO ACADEMICS REPRESENTING THESE DIFFERENT PERSPECTIVES - DRAGAN GASEVIC (A DATA SCIENTIST WHO IS ONE OF THE FOUNDERS OF THE 'LEARNING ANALYTICS' FIELD) AND NEIL SELWYN (A SOCIAL SCIENTIST AND LONG-TIME CRITIC OF EDUCATIONAL TECHNOLOGY). OVER THE COURSE OF A SIX-MONTH EMAIL EXCHANGE, WE EXPLORE VARIOUS ARGUMENTS FOR AND AGAINST THE DATAFICATION OF HIGHER EDUCATION. THUS, WE ARE ABLE TO ADD A COMPUTATIONAL DIMENSION TO WELL-WORN SOCIAL CRITICISMS OF DATA REPRESENTATIVENESS, REDUCTIONISM AND INJUSTICE, AS WELL AS EXPLORING SOCIAL TENSIONS INHERENT IN TECHNICAL CLAIMS TO DATA-BASED PRECISION, CLARITY AND PREDICTABILITY. CRUCIALLY, THESE EXCHANGES ALLOW US TO EXPLORE WAYS IN WHICH OUR TWO VIEWPOINTS CONVERGE, AND THEREFORE HIGHLIGHT OPPORTUNITIES FOR PRODUCTIVE INTER-DISCIPLINARY EXCHANGE AND COLLABORATION.
IN RECENT TIMES, EDUCATIONAL DATA MINING AND LEARNING ANALYTICS HAVE BEEN ABUNDANTLY USED TO MODEL DECISION-MAKING TO IMPROVE TEACHING/LEARNING ECOSYSTEMS. HOWEVER, THE ADAPTATION OF STUDENT MODELS IN DIFFERENT DOMAINS/COURSES NEEDS A BALANCE BETWEEN THE GENERALIZATION AND CONTEXT SPECIFICITY TO REDUCE THE REDUNDANCY IN CREATING DOMAIN-SPECIFIC MODELS. THIS PAPER EXPLORES THE PREDICTIVE POWER AND GENERALIZATION OF A FEATURE - CONTEXT-BOUND COGNITIVE SKILL SCORE- IN ESTIMATING THE LIKELIHOOD OF SUCCESS OR FAILURE OF A STUDENT IN A TRADITIONAL HIGHER EDUCATION COURSE SO THAT THE APPROPRIATE INTERVENTION IS PROVIDED TO HELP THE STUDENTS. TO IDENTIFY THE STUDENTS AT RISK IN DIFFERENT COURSES, WE APPLIED CLASSIFICATION ALGORITHMS ON CONTEXT-BOUND COGNITIVE SKILL SCORES OF A STUDENT TO ESTIMATE THE CHANCES OF SUCCESS OR FAILURE, ESPECIALLY FAILURE. THE CONTEXT-BOUND COGNITIVE SKILL SCORES WERE AGGREGATED BASED ON THE LEARNING OBJECTIVE OF A COURSE TO GENERATE MEANINGFUL VISUAL FEEDBACK TO TEACHERS AND STUDENTS SO THAT THEY CAN UNDERSTAND WHY SOME STUDENTS ARE PREDICTED TO BE AT RISK. EVALUATION OF THE GENERATED MODEL SHOWS THAT THIS FEATURE IS APPLICABLE IN A RANGE OF COURSES, AND IT MITIGATES THE EFFORT IN ENGINEERING FEATURES/MODELS FOR EACH DOMAIN. WE SUBMIT THAT OVERALL, CONTEXT-BOUND COGNITIVE SKILL SCORES PROVE TO BE EFFECTIVE IN FLAGGING THE STUDENT PERFORMANCE WHEN THE ACCURATE METRICS RELATED TO LEARNING ACTIVITIES AND SOCIAL BEHAVIORS OF THE STUDENTS ARE UNAVAILABLE.
OVER THE PAST TEN YEARS, A LOT OF ADVANCES HAVE BEEN MADE IN BOTH THE LEARNING DESIGN AND LEARNING ANALYTICS RESEARCH COMMUNITIES IN TERMS OF RESEARCH OUTPUTS. ON THE OTHER HAND, THESE ADVANCES HAVE NOT MADE VISIBLE IMPACT ON THE EVERYDAY PRACTICES OF THE KEY STAKEHOLDERS THEY AIM TO SERVE: TEACHERS AND LEARNERS. THIS PAPER ARGUES THAT TO ADVANCE BOTH FIELDS AND TO CONNECT THEM INTO AN ITERATIVE CYCLE OF CONTINUOUS IMPROVEMENT, WE NEED A LEARNING DESIGN PATTERN LANGUAGE IN THE ALEXANDRIAN TRADITION TO GUIDE LEARNING DESIGN PRACTICE, AND TO CONNECT DESIGNERS' INTUITIVE FORMULATION OF QUESTIONS ABOUT LEARNING PROCESSES AND OUTCOMES WITH EVIDENCE-BASED QUESTIONS AND OPERATIONALIZABLE QUESTIONS. IT FURTHER DESCRIBES THE PATTERN LANGUAGE AND THE ONLINE TOOL, LEARNING DESIGN STUDIO THAT THE AUTHORS HAVE DEVELOPED TO SERVE AS AN ORGANICALLY EXTENSIBLE LANGUAGE AND A TECHNOLOGY PLATFORM TO SUPPORT LEARNING DESIGN, AND INTER-PROFESSIONAL COLLABORATION AMONG LEARNING DESIGNER AND LEARNING ANALYTICS COMMUNITIES.
THE PROLIFERATION OF WEB-BASED LEARNING OBJECTS MAKES FINDING AND EVALUATING ONLINE RESOURCES PROBLEMATIC. WHILE ESTABLISHED LEARNING ANALYTICS METHODS USE WEB INTERACTION TO EVALUATE LEARNER ENGAGEMENT, THERE IS UNCERTAINTY REGARDING THE APPROPRIATENESS OF THESE MEASURES. IN THIS PAPER WE PROPOSE A METHOD FOR EVALUATING PEDAGOGICAL ACTIVITY IN WEB-BASED COMMENTS USING A PEDAGOGICAL FRAMEWORK, AND PRESENT A PRELIMINARY STUDY THAT ASSIGNS A PEDAGOGICAL VALUE (PV) TO COMMENTS. THIS HAS VALUE AS IT CATEGORISES DISCUSSION IN TERMS OF PEDAGOGICAL ACTIVITY RATHER THAN WEB INTERACTION. RESULTS SHOW THAT PV IS DISTINCT FROM TYPICAL INTERACTIONAL MEASURES; THERE ARE NEGATIVE OR INSIGNIFICANT CORRELATIONS WITH ESTABLISHED LEARNING ANALYTICS METHODS, BUT STRONG CORRELATIONS WITH RELEVANT LINGUISTIC INDICATORS OF LEARNING, SUGGESTING THAT THE USE OF PEDAGOGICAL FRAMEWORKS MAY PRODUCE MORE ACCURATE INDICATORS THAN INTERACTION ANALYSIS, AND THAT LINGUISTIC RATHER THAN INTERACTION ANALYSIS HAS THE POTENTIAL TO AUTOMATICALLY IDENTIFY LEARNING BEHAVIOUR.
IN THE TRANSITION FROM SECONDARY TO HIGHER EDUCATION, STUDENTS ARE EXPECTED TO DEVELOP A SET OF LEARNING SKILLS. THIS PAPER REPORTS ON A DASHBOARD IMPLEMENTED AND DESIGNED TO SUPPORT THIS DEVELOPMENT, HEREBY BRIDGING THE GAP BETWEEN LEARNING ANALYTICS RESEARCH AND THE DAILY PRACTICE OF SUPPORTING STUDENTS. TO DEMONSTRATE THE SCALABILITY AND USEFULNESS OF THE DASHBOARD, THIS PAPER REPORTS ON AN INTERVENTION WITH 1406 FIRST-YEAR STUDENTS IN 12 DIFFERENT PROGRAMS. THE RESULTS SHOW THAT THE DASHBOARD IS PERCEIVED AS CLEAR AND USEFUL. WHILE STUDENTS NOT ACCESS-ING THE DASHBOARD HAVE LOWER LEARNING SKILLS, THEY MAKE MORE USE OF THE EXTRA REMEDIATION POSSIBILITIES IN THE DASHBOARD.
THIS STUDY AIMS TO INVESTIGATE STUDENTS' REACTIONS AND PERCEPTIONS TO THE LEARNING ANALYTICS DASHBOARD (LAD). LAD WAS DESIGNED AND DEVELOPED BY RESEARCHERS TO PRESENT STUDENTS' ONLINE LEARNING ACTIVITY IN A VISUALIZED DISPLAY. AN EYE TRACKING SYSTEM WAS INCORPORATED TO MEASURE STUDENTS' EYE-MOVEMENT, INCLUDING EYE FIXATION, SACCADE AND THEIR SUB DERIVATIVES ON LAD. THE RESULTS ARE DERIVED FROM THE DATA-MINING OF WHAT THE EYE-TRACKING SYSTEM GENERATES. THIS STUDY IS EXPECTED TO SUPPORT A SMART LEARNING ENVIRONMENT, WHERE STUDENTS CAN EFFECTIVELY MONITOR THEIR ONLINE BEHAVIOR PATTERNS IN REAL-TIME USING THEIR MOBILE DEVICES. STUDENTS CAN UTILIZE SUCH INFORMATION TO CHANGE THEIR LEARNING PATTERNS, AND IMPROVE PERFORMANCE.
FINDING A SOLUTION TO THE PROBLEM OF STUDENT RETENTION IS AN OFTEN-REQUIRED TASK ACROSS HIGHER EDUCATION. MOST OFTEN MANAGERS AND ACADEMICS ALIKE RELY ON INTUITION AND EXPERIENCE TO IDENTIFY THE POTENTIAL RISK STUDENTS AND FACTORS. THIS PAPER EXAMINES THE LITERATURE SURROUNDING CURRENT METHODS AND MEASURES IN USE IN LEARNING ANALYTICS. WE FIND THAT WHILE TOOLS ARE AVAILABLE, THEY DO NOT FOCUS ON EARLIEST POSSIBLE IDENTIFICATION OF STRUGGLING STUDENTS. OUR WORK DEFINES A NEW DESCRIPTIVE STATISTIC FOR STUDENT ATTENDANCE AND APPLIES MODEM MACHINE LEARNING TOOLS AND TECHNIQUES TO CREATE A PREDICTIVE MODEL. WE DEMONSTRATE HOW STUDENTS CAN BE IDENTIFIED AS EARLY AS WEEK 3 (OF THE FALL SEMESTER) WITH APPROXIMATELY 97% ACCURACY. WE, FURTHERMORE, SITUATE THIS RESULT WITHIN AN APPROPRIATE PEDAGOGICAL CONTEXT TO SUPPORT ITS USE AS PART OF A MORE COMPREHENSIVE STUDENT SUPPORT MECHANISM.
IN THE LAST FEW YEARS, OPEN TEXTBOOK DEVELOPMENT HAS PICKED UP DRAMATICALLY DUE BOTH TO THE EXPENSE OF COMMERCIALLY PUBLISHED TEXTBOOKS AND THE INCREASING AVAILABILITY OF HIGH-QUALITY OER ALTERNATIVES. WHILE THIS OFFERS A TREMENDOUS BENEFIT IN TERMS OF LOWERING STUDENT TEXTBOOK COSTS, THE QUESTION REMAINS, TO WHAT EXTENT (IF ANY) DO OPEN TEXTBOOKS INFLUENCE THE AMOUNT AND METHOD IN WHICH STUDENTS ENGAGE WITH THEIR OPEN TEXTBOOKS. THE ANSWERS TO THESE QUESTIONS SHOULD PROVIDE USEFUL NEW INSIGHTS FOR ALL INVOLVED IN OPEN TEXTBOOKS AND AID IN THEIR CONTINUED DEVELOPMENT AND PROPAGATION. DESPITE SEEMING DIFFICULTY IN GATHERING DATA TO ANSWER THESE QUESTIONS, THE DIGITAL NATURE OF OPEN TEXTBOOKS, MAKES IT POSSIBLE TO ENABLE A LEARNING ANALYTICS SYSTEM FOR OPEN TEXTBOOKS TO TRACK HOW EXACTLY STUDENTS ARE INTERACTING WITH THEIR OPEN TEXTBOOKS. TO DATE LEARNING ANALYTICS FOR OPEN TEXTBOOKS HAS RECEIVED RELATIVELY LITTLE ATTENTION. TO THIS END, THIS PAPER PROPOSES A FRAMEWORK THAT CAN ACT AS A USEFUL GUIDE FOR SETTING UP LEARNING ANALYTICS SYSTEM FOR OPEN TEXTBOOKS ALSO APPLICABLE TO OTHER DIGITAL TEXTBOOKS. THE PAPER CONCLUDES BY OUTLINING DIRECTIONS FOR FUTURE WORK.
THE KOREAN SOCIETY FOR EDUCATIONAL TECHNOLOGY (KSET) HOSTED ITS SECOND PANEL DISCUSSION PARTNERING WITH THE ASSOCIATION FOR EDUCATIONAL COMMUNICATIONS AND TECHNOLOGY (AECT) AT THE 2019 AECT CONVENTION IN LAS VEGAS, NEVADA. A TOTAL OF FOUR PANELISTS, TWO FROM KOREA AND TWO FROM THE U.S., PARTICIPATED IN THE DISCUSSION ON THE TRENDS OF EDUCATIONAL TECHNOLOGY IN KOREA AND IN THE U.S. FOR ONE HOUR. THE TOPICS COVERED WERE SMART SCHOOLS IN A SMART CITY OF KOREA, CHARACTERISTICS OF MOBILE LEARNING ENVIRONMENTS, LEARNING ANALYTICS FOR INSTRUCTIONAL DESIGN, AND ARTIFICIAL INTELLIGENCE FOR LEARNING SCIENCES RESEARCH.
THIS PANEL INTENDS TO CRITICALLY IDENTIFY SOME LIMITS IN THE APPLICABILITY OF A SELECTION OF EDUCATIONAL SOFTWARE TECHNOLOGIES. IN PARTICULAR, WE HAVE SELECTED FOUR TECHNOLOGIES; SOME OF THEM HAVE A HIGH DEGREE OF MATURITY (NAMELY, COLLABORATIVE SYSTEMS AND INTELLIGENT TUTORS), WHILE OTHERS HAVE EMERGED IN LAST YEARS (NAMELY, GAMIFICATION AND LEARNING ANALYTICS). THE GOAL OF THE PANEL IS TO CONTRIBUTE TO ACHIEVE DEEPER KNOWLEDGE OF THE ACTUAL CAPABILITIES OF SUCH TECHNOLOGIES.
IN THIS PAPER, WE PRESENT AN IN-DEPTH CASE STUDY OF A SINGLE STUDENT WHO FAILED AN ONLINE MODULE WHICH FORMED PART OF A MASTER'S PROGRAMME IN PROFESSIONAL EDUCATION AND LEADERSHIP. WE USE THIS CASE STUDY TO EXAMINE ASSESSMENT PRACTICES IN HIGHER EDUCATION IN THE ONLINE ENVIRONMENT. IN TAKING THIS APPROACH, WE GO AGAINST THE CURRENT PREDILECTION FOR BIG DATA WHICH HAS GIVEN RISE TO LEARNING ANALYTICS', A DATA-INTENSIVE APPROACH TO MONITORING LEARNING. IN PARTICULAR, WE DRAW ATTENTION TO THE MODEL OF THE LEARNER PRODUCED BY LEARNING ANALYTICS AND TO ISSUES OF DATAVEILLANCE' IN ONLINE LEARNING. WE ALSO USE THE CASE TO EXAMINE ASSESSMENT IN HIGHER EDUCATION MORE BROADLY, EXPLORING THE TENSIONS BETWEEN THE REQUIREMENTS FOR CERTIFICATION AND THE NEED FOR LEARNING. WE CONCLUDE THAT ASSESSMENT PRACTICES IN HIGHER EDUCATION MAY HAVE MORE TO DO WITH QUALITY ASSURANCE' AND REGULATORY FRAMEWORKS THAN WITH ENHANCING THE STUDENT EXPERIENCE' AND INCULCATING THE QUALITIES THAT MARK OUT HIGHER EDUCATION AS AN ETHICAL PROJECT.
ACCURATELY MODELING INDIVIDUAL STUDENTS' KNOWLEDGE GROWTH IS IMPORTANT IN MANY APPLICATIONS OF LEARNING ANALYTICS. A KEY STEP IS TO DECOMPOSE THE KNOWLEDGE TARGETED IN THE INSTRUCTION INTO DETAILED KNOWLEDGE COMPONENTS (KCS). WE SEARCH FOR AN ACCURATE KC MODEL FOR BASIC EQUATION SOLVING SKILLS, USING DATA FROM AN INTELLIGENT TUTORING SYSTEM (ITS), LYNNETTE. KEY CRITERIA ARE DATA FIT AND PREDICTIVE ACCURACY BASED ON A STANDARD LOGISTIC MODEL CALLED THE ADDITIVE FACTORS MODEL (AFM). WE FOCUS ON THREE DIFFICULTY FACTORS FOR EQUATION SOLVING: UNDERSTANDING OF VARIABLES, THE NEGATIVE SIGN, AND THE COMPLEXITY OF THE EQUATION. FINE-GRAINED KC MODELS WERE FOUND TO HAVE GREATER FIT AND PREDICTIVE ACCURACY THAN AN "IDEAL," MORE ABSTRACT MODEL, INDICATING THAT THERE IS SUBSTANTIAL UNDER-GENERALIZATION IN STUDENTS' EQUATION-SOLVING SKILL RELATED TO ALL THREE DIFFICULTY FACTORS. THE WORK ENHANCES SCIENTIFIC UNDERSTANDING OF THE CHALLENGES STUDENTS FACE IN LEARNING EQUATION SOLVING. IT ILLUSTRATES HOW LEARNING ANALYTICS COULD INFORM THE IMPROVEMENT OF TECHNOLOGY-ENHANCED LEARNING ENVIRONMENTS.
THIS ARTICLE IT IS A MODEST ATTEMPT TO EXPLORE THE ISSUES FACING CYBER SECURITY AWARENESS AND TRAINING PROGRAMS AND POTENTIAL BENEFITS OF USING LEARNING ANALYTICS, AN EMERGING FIELD IN DATA ANALYTICS, FOR COMBINING EXISTING DATA SOURCES TO PROVIDE ADDITIONAL VALUE TO THESE PROGRAMS. THIS ARTICLE WAS WRITTEN UNDER THE ASSUMPTION THAT AWARENESS AND TRAINING ARE VALID PREVENTIVE CONTROLS AND THEREFORE THE PROS AND CONS OF IMPLEMENTING SUCH PROGRAMS ARE NOT BEING DISCUSSED.
RESEARCH ON LEARNING ANALYTICS IN TECHNOLOGY-ENHANCED LEARNING HAS RECENTLY BEEN ON THE RISE WITH THE INTENT TO SUPPORT LEARNERS' ACHIEVEMENTS, DEVELOP PERSONALIZED LEARNING ENVIRONMENTS, AND IMPROVE LEARNING METHODS. LEARNING ANALYTICS IS A DATA ANALYSIS METHOD INTENDED TO HELP UNDERSTAND LEARNERS' TENDENCIES TOWARD ACTIVITIES AND THE SIGNIFICANT ASPECTS OF SUCH ACTIVITIES MANIFEST IN THEIR TEACHING-LEARNING PROCESS. IN LEARNING, AN ACTIVITY IS COMPRISED OF A SERIES OF ACTIONS AND THEN GIVES A COUPLE OF EXAMPLES FOR DIFFERENT ACTIONS. TO BETTER UNDERSTAND A TEACHING-LEARNING ACTIVITY, THE DATA FROM THE STREAM OF ACTIONS TAKING PLACE NEED TO BE ANALYZED. TO THAT END, THIS PAPER PROPOSES A MODEL FOR COLLECTING AND STRUCTURING THE TEACHING-LEARNING ACTION DATA BASED ON ACTIVITY THEORY. THE PROPOSED MODEL IS DESIGNED TO IDENTIFY ACTIVITIES BASED ON A SERIES OF LEARNER ACTIONS OVER TIME. AMONG THE COMPONENTS OF ACTIVITY THEORY, THE MODEL FOCUSES ON SUBJECTS, OBJECTS, AND TOOLS TO COLLECT DATA, WHICH ELUCIDATES THE USE OF TOOLS THAT SERVE AS THE MEDIA BETWEEN SUBJECTS (I.E., LEARNERS) AND LEARNING ACTIVITIES. THE MODEL OFFERS INSIGHT INTO THE CONTINUITY AND PERSISTENCE OF OBJECTS OR TEACHING-LEARNING ACTIVITY SYSTEMS TO BETTER UNDERSTAND TEACHING-LEARNING ACTIVITIES.
AS ASYNCHRONOUS ONLINE DISCUSSIONS GAIN A BROADER USAGE DUE TO THE COVID-19 EPIDEMIC, THE NEED FOR UNDERSTANDING OF STUDENTS' IDEAS FROM UNSTRUCTURED TEXTUAL DATA BECOMES MORE PRESSING. IN THIS EXPERIMENTAL STUDY, WE EXAMINE THE EFFECTS OF RECOMMENDATIONS ON MESSAGE QUALITY AND COMMUNITY FORMATION FROM VOLUMINOUS ONLINE DISCUSSIONS. DRAWING ON LITERATURE FROM GROUP COGNITION, KNOWLEDGE BUILDING DISCOURSE, AND LEARNING ANALYTICS, WE CALCULATE MESSAGE QUASI-QUALITY INDEX (QQI) SCORES BASED ON MESSAGE LEXICAL COMPLEXITY AND TOPIC-RELATED KEYWORD USAGE BY PARTICIPANTS IN EXPLAINING THEIR IDEAS. FURTHERMORE, WE EXAMINE THE EMPIRICAL EVIDENCE ON THE RELATIONSHIP BETWEEN QQI SCORES AND PARTICIPANTS' INTERACTIONS. FINALLY, WE VISUALIZE NETWORK STRUCTURES VIA SOCIOGRAMS AND HIERARCHICALLY CLUSTER PARTICIPANTS TO IDENTIFY SUBGROUPS. OUR ANALYSIS OF 281 MESSAGES FINDS THAT RECOMMENDATIONS HELPED PARTICIPANTS TO WRITE MORE MESSAGES THAT COMPARED ALTERNATIVE VIEWPOINTS AND REFINED PRELIMINARY IDEAS WITH HIGHER QQI SCORES. RESULTS ALSO SHOW THAT RECOMMENDATIONS CULTIVATED A SENSE OF COLLECTIVE AGENCY TO INCREASE THE OPPORTUNITY FOR CREATIVITY AND REDUCE THE LIKELIHOOD THAT PERIPHERAL PARTICIPANTS WILL BE DISSATISFIED AND FAIL TO IDENTIFY WITH A COMMUNITY. THEORETICAL CONTRIBUTIONS AND PRACTICAL IMPLICATIONS ARE DISCUSSED.
IN A MOOC ENVIRONMENT, EACH STUDENT'S INTERACTION WITH THE COURSE CONTENT IS A CRUCIAL CLUE FOR LEARNING ANALYTICS, WHICH OFFERS AN OPPORTUNITY TO RECORD LEARNER ACTIVITY OF UNPRECEDENTED SCALE. IN ONLINE LEARNING, THE EDUCATORS AND THE ADMINISTRATORS NEED TO GET INFORMED WITH STUDENTS' LEARNING STATES SINCE THE PERFORMANCE OF UNSUPERVISED LEARNING STYLE IS DIFFICULT TO CONTROL. LEARNING ANALYTICS CONSIDERED AS A KEY PROCESS IS TO PROVIDE STUDENTS AND EDUCATORS WITH EVIDENCE-BASED, ANALYTICAL AND CONTEXTUAL OUTCOMES IN A WAY OF MAKING SENSE OF THEIR LEARNING ENGAGEMENTS. IN THIS CONCEPTUAL FRAMEWORK, THIS MANUSCRIPT PER THE AUTHORS INTENDS TO ADOPT SEQUENTIAL ANALYSIS METHOD TO EXPLOIT STUDENTS' LEARNING BEHAVIOR PATTERNS IN CLOUD CLASSROOM (AN ONLINE COURSE PLATFORM BASED ON MOOC). MOREOVER, THIS RESEARCH ALSO COMPARES THE BEHAVIORAL PATTERNS OF FOUR GRADE LEVELS IN A UNIVERSITY, WITH THE PURPOSE OF FINDING THE MOST KEY BEHAVIORAL PATTERNS OF EACH GRADE GROUP.
STUDENT INTENTION AND MOTIVATION ARE AMONG THE STRONGEST PREDICTORS OF PERSISTENCE AND COMPLETION IN MASSIVE OPEN ONLINE COURSES (MOOCS), BUT THESE FACTORS ARE TYPICALLY MEASURED THROUGH FIXED-RESPONSE ITEMS THAT CONSTRAIN STUDENT EXPRESSION. WE USE NATURAL LANGUAGE PROCESSING TECHNIQUES TO EVALUATE WHETHER TEXT ANALYSIS OF OPEN RESPONSES QUESTIONS ABOUT MOTIVATION AND UTILITY VALUE CAN OFFER ADDITIONAL CAPACITY TO PREDICT PERSISTENCE AND COMPLETION OVER AND ABOVE INFORMATION OBTAINED FROM FIXED-RESPONSE ITEMS. COMPARED TO SIMPLE BENCHMARKS BASED ON DEMOGRAPHICS, WE FIND THAT A MACHINE LEARNING PREDICTION MODEL CAN LEARN FROM UNSTRUCTURED TEXT TO PREDICT WHICH STUDENTS WILL COMPLETE AN ONLINE COURSE. WE SHOW THAT THE MODEL PERFORMS WELL OUT-OF-SAMPLE, COMPARED TO A STANDARD ARRAY OF DEMOGRAPHICS. THESE RESULTS DEMONSTRATE THE POTENTIAL FOR NATURAL LANGUAGE PROCESSING TO CONTRIBUTE TO PREDICTING STUDENT SUCCESS IN MOOCS AND OTHER FORMS OF OPEN ONLINE LEARNING.
IT IS POSSIBLE TO OBSERVE AN ENORMOUS INCREASE ON THE NUMBER OF RESEARCHES FOCUSED ON AUTOMATICALLY FIND PATTERNS AND FACTORS THAT AFFECT STUDENTS BEHAVIOR AND PERFORMANCE DURING THEIR LEARNING PROCESS. THE FIELDS OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING ARE IN CONSTANT GROWING, DEVELOPING NEW AND INNOVATIVE TOOLS. FURTHERMORE, NEW METHODOLOGIES ARE BEING CREATED TO FOLLOW AND HELP STUDENTS AND PROFESSORS INSIDE THE MANY DIFFERENT TYPES OF EDUCATIONAL SETTINGS. AT THE SAME TIME, IT IS ALSO POSSIBLE TO SEE THAT THE MAJORITY OF THE EXISTING WORKS ARE STILL RESTRICTED TO SMALL AND CONTROLLED EXPERIMENTS, CONDUCTED ON SAMPLES OF STUDENTS DATA. THE PRESENT WORK DESCRIBES THE FIRST STEP OF AN INTERNATIONAL COLLABORATION FOCUSED ON IMPLEMENTING LEARNING ANALYTICS ON A NATIONAL SCALE. PRECISELY, THIS WORK DESCRIBES THE METHODOLOGY APPLIED TO FIND RULES THAT CAN BE USED TO FOLLOW STUDENTS' TRAJECTORIES IN SECONDARY SCHOOLS IN URUGUAY. THE RESULTS POINTS OUT FOR THE POSSIBILITY OF DELIVERING RULES BY ANALYZING PATTERNS OF STUDENTS CLUSTERS BASED ON THEIR SUCCESS (OR FAILURE) IN THE SCHOOL YEAR. AMONG OTHER FINDINGS, THIS WORK SHOWS A STRONG RELATIONSHIP BETWEEN STUDENTS GRADES AND THEIR NUMBER OF ABSENCES IN THE CLASSES.
THE RISE OF MULTIMODAL LEARNING ANALYTICS (MMLA) GIVES OPPORTUNITY TO LEARN ABOUT TEAMWORK AND COLLABORATION THROUGH DETAILED PHYSIOLOGICAL RESPONSES, WITH THE AID OF MULTIMODAL TOOLS. THE PRIMARY GOAL OF THIS STUDY IS TO DETERMINE IF UNIQUE IDEA CREATION, OR SECONDARY AGREEMENT TO UNIQUE IDEAS, IN GROUP COLLABORATION CAN BE DISTINGUISHED THROUGH ONE'S PHYSIOLOGICAL RESPONSES. IN THIS PILOT STUDY PARTICIPANTS WHO PRESENTED NEW IDEAS DEMONSTRATED HIGHER LEVELS OF GALVANIC SKIN RESPONSE, INDICATIVE OF ENGAGEMENT, EMOTIONAL AROUSAL OR COGNITIVE LOAD.
AS A HUMAN-CENTRED EDUCATIONAL PRACTICE AND FIELD OF RESEARCH, LEARNING ANALYTICS MUST ACCOUNT FOR KEY STAKEHOLDERS IN TEACHING AND LEARNING. THE FOCUS OF THIS PAPER IS ON THE ROLE OF INSTITUTIONS TO SUPPORT TEACHERS TO INCORPORATE LEARNING ANALYTICS INTO THEIR PRACTICE BY UNDERSTANDING THE CONFLUENCE OF INTERNAL AND EXTERNAL FACTORS THAT INFLUENCE WHAT THEY DO. IN THIS PAPER, WE RECONCEPTUALISE 'TEACHING AS DESIGN' FOR 'ANALYTICS-ENABLED TEACHING AS DESIGN' TO SHAPE THIS DISCUSSION TO ALLOW FOR THE CONSIDERATION OF EXTERNAL FACTORS, SUCH AS PROFESSIONAL LEARNING OR ETHICAL CONSIDERATIONS OF STUDENT DATA, AS WELL AS PERSONAL CONSIDERATIONS, SUCH AS DATA LITERACY AND TEACHER BELIEFS AND IDENTITIES. IN ORDER TO ADDRESS THE REAL-WORLD CHALLENGES OF PROGRESSING TEACHERS' EFFICACY AND CAPACITY TOWARD 'ANALYTICS-ENABLED TEACHING AS DESIGN, WE HAVE PLACED THE TEACHER - AS A COGNITIVE, SOCIAL, AND EMOTIONAL BEING - AT THE CENTER. IN SO DOING, WE DISCUSS POTENTIAL DIRECTIONS TOWARDS RESEARCH FOR PRACTICE IN ELUCIDATING UNDERPINNING FACTORS OF TEACHER INQUIRY IN THE PROCESS OF AUTHENTIC DESIGN.
NOWADAYS EDUCATION IS INCONCEIVABLE WITHOUT THE USE OF E-ENVIRONMENT AND ALL THE BENEFITS IT CAN ENSURE. MODERN LEARNING SYSTEMS ARE FEATURED WITH A WIDE RANGE OF RESOURCES FOR GATHERING RELEVANT USER TRACES FOR DATA ANALYSIS. THE MAIN GOAL OF THE RESEARCH DESCRIBED IN THIS PAPER IS TO EXPLORE HOW THE LEARNING ANALYTICS METHODS CAN BE USED TO IMPROVE THE RESULTS OF E-LEARNING. THE NOVEL METHOD FOR CROSS-SYSTEM DATA COLLECTION IS ACCOMPLISHED IN A NEWLY DESIGNED DATABASE/SYSTEM E-ANALYTICS. USING THE DATA COLLECTED, VARIOUS ANALYSES AND REPORTS ARE PRESENTED AND DISCUSSED, AIMING TO DISCLOSE IMPORTANT LEARNER'S BEHAVIORS AND REGULARITIES DURING THE EDUCATIONAL PROCESS. RECOMMENDATIONS ARE MADE FOR FURTHER IMPROVEMENTS OF THE TEACHING AND LEARNING PROCESS. THE PAPER CONCLUDES BY ENUMERATING SOME CHALLENGES AND FURTHER WORKS FOR CREATING EFFECTIVE LEARNING ANALYTICS TOOLS.
THE CONCEPT OF USING ACADEMIC ANALYTICS TO UNDERSTAND TRENDS IN A STUDENT POPULATION IS NOT A NEW IDEA. MUCH OF THE ANALYSIS OF THESE MASSIVE NUMBERS LIVE IN THE "MASSIVE OR BIG DATA" ANALYSIS OF INFORMATION FOR WHOLE PROGRAMS. THERE IS LITTLE EVIDENCE THAT INSTRUCTORS ARE USING SMALLER SAMPLINGS WEEKLY, OR EVEN DAILY ANALYTICS, TO IMPROVE PERFORMANCE AND ENGAGEMENT. FOR THE PURPOSES OF THIS PAPER, LET US CALL THIS DATA: POCKET DATA ANALYTICS. THIS PAPER WILL PROVIDE AN INTRODUCTION TO THE PRACTICAL APPLICATION OF ANALYTICS IN WHICH INSTRUCTORS CAN USE TO PROACTIVELY IMPROVE STUDENT ENGAGEMENT AND PERFORMANCE. THESE CHANGES WOULD OCCUR DURING AN ON-GOING SEMESTER. ACCORDING TO SWAN (2012), "ONLINE LEARNING SEEMS TO BE ON THE FRONTLINES OF THE LEARNING ANALYTICS MOVEMENT, AND SO ONLINE EDUCATORS NEED TO INFORM THEMSELVES ABOUT LEARNING ANALYTICS".
ONLINE EDUCATION IS RAPIDLY BECOMING A SIGNIFICANT METHOD OF COURSE DELIVERY IN HIGHER EDUCATION. CONSEQUENTLY, INSTRUCTORS ANALYZE STUDENT PERFORMANCE IN AN ATTEMPT TO BETTER SCAFFOLD STUDENT LEARNING. LEARNING ANALYTICS CAN PROVIDE INSIGHT INTO ONLINE STUDENTS' COURSE BEHAVIORS. ARCHIVAL DATA FROM 167 GRADUATE LEVEL EDUCATION STUDENTS ENROLLED IN 4 DIFFERENT PROGRAMS AND 9 DIFFERENT ONLINE COURSES WERE ANALYZED TO DETERMINE WHETHER A RELATIONSHIP EXISTED BETWEEN GRADES EARNED AND TIME SPENT IN SPECIFIC AREAS WITHIN THE COURSE: TOTAL COURSE TIME, COURSE MODULES, DOCUMENT REPOSITORY, AND SYNCHRONOUS ONLINE SESSIONS. TIME SPENT IN EACH COMPONENT DID NOT PREDICT A SPECIFIC LETTER GRADE, BUT DID PREDICT WHETHER OR NOT AN A WOULD BE ACHIEVED. THE SAMPLE WAS COMPOSED OF STUDENTS FROM FOUR DIFFERENT GRADUATE EDUCATION PROGRAMS: EDUCATIONAL LEADERSHIP, READING, INSTRUCTIONAL DESIGN, AND SPECIAL EDUCATION. VARIATIONS FOUND AMONG PROGRAMS DID NOT SIGNIFICANTLY PREDICT THE GRADE EARNED IN THE COURSE. A LOGISTIC REGRESSION REVEALED THAT OF ALL THE PREDICTOR VARIABLES, TIME SPENT IN SYNCHRONOUS ONLINE SESSIONS ALONE SHOWED AS A SIGNIFICANT PREDICTOR OF RECEIVING AN A IN THE COURSE. THIS IS IMPORTANT INFORMATION FOR INSTRUCTORS WHEN PROVIDING SCAFFOLDING FOR STUDENTS.
WE DISCUSS THE APPLICATION OF A HAND-DRAWN SELF-VISUALIZATION APPROACH TO LEARNER-DATA, TO DRAW ATTENTION TO THE SPACE OF REPRESENTATIONAL POSSIBILITIES, THE POWER OF REPRESENTATION INTERACTIONS, AND THE PERFORMATIVITY OF INFORMATION REPRESENTATION.
THE APPLICATION OF TECHNIQUES RELATED TO LEARNING ANALYTICS, INCLUDING THE RECOVERY AND PROCESSING OF DATA AND EVIDENCES RELATED WITH THE LEARNING TASKS, MIGHT RESULT CAPITAL TO IMPROVE EDUCATIONAL PROCESSES THAT OCCURS WITHIN THE VIRTUAL WORLDS. THIS PAPER AIMS TO SHOW A REAL EXAMPLE OF APPLICATION OF A LEARNING ANALYTICS FRAMEWORK WHICH SUPPORTS A REAL CASE OF PRACTICAL LEARNING IN A 3D VIRTUAL ENVIRONMENT, ANALYZING IN DEPTH THE PROBLEMS THAT ARISES THEREFROM. AS REMARKABLE OUTCOMES, THIS PAPER SHOWS A WHOLE SYSTEM THAT COMPRISES SEVERAL TOOLS, WHICH MAKE POSSIBLE THE AUTOMATIZATION OF A LEARNING PROCESS INSIDE A VIRTUAL WORLD WITH THE SUBJECT OF QUALITY ASSURANCE IN PHARMACY LABORATORIES.
THE GROWING NUMBER OF UNIVERSITIES ADOPTING SOME FORM OF E-LEARNING IN RECENT YEARS HAS RAISED SOME CONCERNS ABOUT HOW TO ENSURE STUDENTS' AUTHENTICATION, AND THE AUTHORSHIP OF THE ASSESSMENT ACTIVITIES THEY DELIVER. THERE ARE SEVERAL STRATEGIES AND MARKET TOOLS THAT CAN HELP TEACHERS IN THESE TASKS. WHILE THE USAGE OF PLAGIARISM DETECTION TOOLS FOR CHECKING AUTHORSHIP IS COMMON PRACTICE (ABOVE ALL IN FULLY ONLINE UNIVERSITIES), THE USE OF BIOMETRIC INSTRUMENTS FOR ENSURING STUDENTS' IDENTITY IS LESS EXTENDED. ALTHOUGH ALL THESE TOOLS COLLECT A LARGE AMOUNT AND VARIETY OF DATA, THERE IS A LACK OF SOFTWARE SYSTEMS THAT CAN INTEGRATE SUCH DATA, AND SHOW THE INFORMATION THAT MAY BE EXTRACTED FROM THESE DATA IN A VISUAL AND MEANINGFUL WAY THAT FITS THE TEACHERS' NEEDS. PRECISELY, THE OBJECTIVE OF THIS CHAPTER IS TO PRESENT A SET OF DASHBOARDS THAT INTEGRATE DATA COLLECTED BY DIFFERENT KINDS OF AUTHENTICATION AND AUTHORSHIP INSTRUMENTS, ORIENTED TO ASSIST THE DECISION-MAKING PROCESS OF TEACHERS, ABOVE ALL IN CASE OF SUSPICION OF STUDENTS' DISHONEST ACADEMIC BEHAVIOR. ALTHOUGH THESE DASHBOARDS HAVE BEEN DESIGNED AND IMPLEMENTED IN THE CONTEXT OF TESLA PROJECT, THE EXPERIENCE AND CONCLUSIONS PROVIDED HERE ARE OF INTEREST TO RESEARCHERS AND PRACTITIONERS AIMING TO DEVELOP DASHBOARDS WITH LEARNING ANALYTICS PURPOSES AT HIGHER EDUCATION. FOR THIS REASON, THIS CHAPTER ALSO PROVIDES A DISCUSSION AND REVIEW OF THE MOST PROMINENT ANALYTICAL EFFORTS IN UNIVERSITIES.
THE INCLUSION OF E-LEARNING PLATFORMS IN THE TRADITIONAL EDUCATION ALLOWS OBTAINING A LOT OF DATA ABOUT THE STUDENTS' BEHAVIOR. STUDYING THESE DATA USING DATA MINING AND LEARNING ANALYTICS TECHNIQUES ALLOWS DETECTING DIFFERENT BEHAVIOR PATTERNS AND PREDICTING FUTURE RESULTS. ONE OF THE BIG CHALLENGES IN EDUCATION IS BEING ABLE TO PREDICT THE STUDENTS' FAILURE BEFORE IT OCCURS, AND AVOID IT. BEING AWARE OF THIS CHALLENGE, AND INSIDE THE LEARNING ANALYTICS CONTEXT, THIS PAPER DESCRIBES A DETECTION RISK ALGORITHM THAT TRIES TO DETECT THE STUDENTS WHO ARE AT RISK OF FAILING THE COURSE BASED ON THEIR INTERACTION WITH AN E-LEARNING PLATFORM. THE ALGORITHM IS BASED ON TIME SERIES FORMED BY THE DAILY STUDENTS' INTERACTIONS WITH THE PLATFORM. MORE CONCRETELY, THE TREND COMPONENT OF THE TIME SERIES IS USED AS A PREDICTOR FOR THE STUDENTS' RESULTS. WE APPLY OUR ALGORITHM TO ONE COURSE OF THE 2ND-YEAR OF THE TELECOMMUNICATION ENGINEERING DEGREE AT THE UNIVERSITY OF VIGO, OBTAINING SOME ENCOURAGING RESULTS.
FRESHMEN IN HIGHER EDUCATION ARE REQUIRED TO EXHIBIT A STRONG INCLINATION TO TAKING OWNERSHIP OF THEIR OWN LEARNING. IT ENTAILS WELL-DEVELOPED SELF-REGULATED LEARNING COMPETENCES. THIS DEMAND IS FURTHER EXACERBATED IN PURELY ONLINE SETTINGS SUCH AS OPEN DISTANT LEARNING, MOOCS, OR DISRUPTIVE CIRCUMSTANCES LIKE THE COVID PANDEMIC. TIME MANAGEMENT SKILLS ARE AN ESSENTIAL COMPONENT IN THIS PROCESS AND THE TARGET OF THIS STUDY, WHEREIN 348 STUDENTS COVERED A COURSE THROUGH TWO CONDITIONS: THE CONTROL GROUP ATTENDED THE SEMESTER IN AN UNCHANGED WAY, WHILE STUDENTS IN THE EXPERIMENTAL GROUP WERE WEEKLY INVITED TO ESTIMATE AND LOG THEIR WORKLOAD AND TIME ALLOCATIONS, VIA "REFLECTION AMPLIFIERS" PROVIDED ON THEIR MOBILE DEVICES. WHILE NO MAJOR DIFFERENCE IN TIME MANAGEMENT AND LEARNING PERFORMANCE WAS OBSERVABLE, DATA REVEALS THAT PERCEIVED TIME ALLOCATION AND PRESCRIBED STUDY-TIME DIFFER SUBSTANTIALLY. THESE RESULTS RAISE QUESTIONS, ON THE STUDENTS' SIDE, ABOUT THE POTENTIAL OF QUALITATIVE (SELF-INPUTTED) LEARNING ANALYTICS TO RAISE AWARENESS ON WHERE TIME INVESTMENTS GO. ON THE TEACHERS' SIDE, THE RESULTS HIGHLIGHT THE NEED TO BETTER PLAN THE CURRICULA WORKLOAD SPECIFICALLY FOR FIRST-YEAR STUDENTS.
THE USE OF LEARNING MANAGEMENT SYSTEMS IS BECOMING FREQUENT AS A FORM OF LEARNING. THIS IS BECAUSE IT ALLOWS GREATER FLEXIBILITY OF TIME AND SPACE IF WE COMPARE TO FACE-TO-FACE LEARNING. THUS, THIS WORK AIMS TO PRESENT HOW THE FIELD OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS ARE CONTRIBUTING TO THE EXTRACTION OF KNOWLEDGE FROM THE SELF-REGULATION OF TIME MANAGEMENT IN E-LEARNING ENVIRONMENTS. FOR THIS, WE CONSIDERED THE CONCEPT OF TIME MANAGEMENT BY PINTRICH (2000) AND CARRIED OUT A SYSTEMATIC REVIEW OF THE LITERATURE. WITH THAT, IT WAS POSSIBLE TO NOTICE THAT MOST OF THE ANALYZED WORKS DO NOT STUDY ONLY TIME MANAGEMENT. WE ALSO REALIZED THAT THE DATA, WHICH REPRESENTS TIME MANAGEMENT, ARE AGGREGATED DATA, THAT IS, THE PHENOMENON IS NOT STUDIED OVER TIME. WITH THESE RESULTS, YOU CAN SEE AN OVERVIEW OF HOW LEARNING ANALYTICS AND EDUCATIONAL DATA MINING ARE SUPPORTING THE EXTRACTION OF KNOWLEDGE ABOUT SELF-REGULATION OF TIME MANAGEMENT IN ONLINE ENVIRONMENTS.
MASSIVE OPEN ONLINE COURSES (MOOCS) AIM TO EDUCATE THE WORLD, ESPECIALLY LEARNERS FROM DEVELOPING COUNTRIES. WHILE MOOCS ARE CERTAINLY AVAILABLE TO THE MASSES, THEY ARE NOT YET FULLY ACCESSIBLE. ALTHOUGH ALL COURSE CONTENT IS JUST CLICKS AWAY, DEEPLY ENGAGING WITH A MOOC REQUIRES A SUBSTANTIAL TIME COMMITMENT, WHICH FREQUENTLY BECOMES A BARRIER TO SUCCESS. TO MITIGATE THE TIME REQUIRED TO LEARN FROM A MOOC, WE HERE INTRODUCE A DESIGN THAT ENABLES LEARNERS TO EARN MONEY BY APPLYING WHAT THEY LEARN IN THE COURSE TO REAL-WORLD MARKETPLACE TASKS. WE PRESENT A PAID TASK RECOMMENDER SYSTEM (REC-$YS), WHICH AUTOMATICALLY RECOMMENDS COURSE-RELEVANT TASKS TO LEARNERS AS DRAWN FROM ONLINE FREELANCE PLATFORMS. REC-$YS HAS BEEN DEPLOYED INTO A DATA ANALYSIS MOOC AND IS CURRENTLY UNDER EVALUATION.
GIVEN THE DIFFICULTY OF IDENTIFYING STUDENTS AT DROPOUT RISK FROM THE NATIVE LOGS AND REPORTS OF THE VIRTUAL LEARNING ENVIRONMENTS (VLES), COMPUTATIONAL METHODS AND TOOLS BASED ON LEARNING ANALYTICS (LA) HAVE BEEN RESEARCHED AND DEVELOPED. THE PRIMARY OBJECTIVE OF THIS WORK WAS TO DEVELOP A PLUGIN-TYPE TOOL THAT PRESENTS STUDENTS AT DROPOUT RISK. THE PLUGIN USES COGNITIVE, SOCIAL, AND BEHAVIORAL INDICATORS OF STUDENTS THROUGH FILTERS, NOTIFICATIONS, AND INTERACTIVE GRAPHS GENERATED FROM MOODLE DATA. THE FOCUS GROUP METHOD WAS USED TO EVALUATE THE PLUGIN WITH TEACHERS, TUTORS, AND MANAGERS OF HIGHER EDUCATION ONLINE COURSES, FOLLOWED BY A QUALITATIVE ANALYSIS OF THE DATA COLLECTED. THE PLUGIN-TYPE TOOL ENHANCES THE TEACHERS' AWARENESS OF THE STUDENTS' DROPOUT RISK, MAKING IT EASIER AND MORE OBJECTIVE. IT ALSO ALLOWS THE GRAPHICAL VISUALIZATION OF STUDENTS' STRATEGIC COGNITIVE, SOCIAL, AND BEHAVIORAL DATA.
THE VOCATIONAL EDUCATION AND TRAINING (VET) CURRICULUM AIMS TO PROVIDE STUDENTS WITH THE NECESSARY SKILLS TO MAKE THEM EMPLOYABLE AND SUCCESSFUL IN THEIR SPECIALTIES. THEREFORE, AN INCREASING NUMBER OF VET CURRICULA ARE EMPLOYING VARIOUS RESOURCES TO EQUIP STUDENTS WITH 21ST CENTURY SKILLS SUCH AS CRITICAL THINKING, CREATIVITY, COMMUNICATION, COLLABORATION, AND INFORMATION LITERACY. IN THIS PAPER, WE EXPLORE THE KEY DIMENSIONS OF ACTIVE LEARNING FOR EMPOWERING AND ENRICHING COLLABORATION, INDIVIDUAL EMPOWERMENT, AND DEVELOPMENT OF THE APPRENTICES' LEARNING PROCESSES THROUGH TEACHING AND LEARNING ANALYTICS (TLA), IN A LEARNING MANAGEMENT SYSTEM (LMS) FOR VET APPRENTICESHIPS. WE DESCRIBE IN DETAIL "HOW TLA CAN "WORK" IN A VET ENVIRONMENT, AND WE PRESENT HOW WE ORGANIZE THE TLA METHODOLOGY INSIDE THE PROPOSED LMS.
BREAST CANCER CONTINUES TO BE ONE OF THE LEADING CAUSES OF CANCER DEATH AMONG WOMEN. MAMMOGRAM IS THE STANDARD OF CARE FOR SCREENING AND DIAGNOSIS OF BREAST CANCER. THE AMERICAN COLLEGE OF RADIOLOGY DEVELOPED THE BREAST IMAGING REPORTING AND DATA SYSTEM (BI-RADS) LEXICON TO STANDARDIZE MAMMOGRAPHIC REPORTING TO ASSESS CANCER RISK AND FACILITATE BIOPSY DECISION-MAKING. HOWEVER, BECAUSE SUBSTANTIAL INTER-OBSERVER VARIABILITY REMAINS IN THE APPLICATION OF THE BI-RADS LEXICON, INCLUDING INAPPROPRIATE TERM USAGE AND MISSING DATA, CURRENT BIOPSY DECISION-MAKING ACCURACY USING THE UNSTRUCTURED FREE TEXT OR SEMI-STRUCTURED REPORTS VARIES GREATLY. HENCE, INCORPORATING NOVEL AND ACCURATE TECHNIQUE INTO BREAST CANCER DECISION-MAKING DATA IS CRITICAL. HERE, WE COMBINED NATURAL LANGUAGE PROCESSING AND DEEP LEARNING METHODS TO DEVELOP AN ANALYTIC MODEL THAT TARGETS WELL-CHARACTERIZED AND DEFINED SPECIFIC BREAST SUSPICIOUS PATIENT SUBGROUPS RATHER THAN A BROAD HETEROGENEOUS GROUP FOR DIAGNOSTIC SUPPORT OF BREAST CANCER MANAGEMENT.
EDUCATIONAL DATA MINING AND LEARNING ANALYTICS HAVE BECOME A VERY IMPORTANT TOPIC IN THE FIELD OF EDUCATION TECHNOLOGY. MANY FRAMEWORKS HAVE BEEN PROPOSED FOR LEARNING ANALYTICS WHICH MAKE IT POSSIBLE TO IDENTIFY LEARNING BEHAVIOR PATTERNS OR STRATEGIES. HOWEVER, IT IS DIFFICULT TO UNDERSTAND THE REASON WHY BEHAVIOR PATTERNS OCCUR AND WHY CERTAIN STRATEGIES ARE USED. IN OTHER WORDS, ALL OF THE EXISTING FRAMEWORKS LACK AN IMPORTANT STEP, THAT IS, RESULT CONFIRMATION. IN THIS PAPER, WE PROPOSE A RESULT CONFIRMATION-BASED LEARNING BEHAVIOR ANALYSIS (RECOLBA) FRAMEWORK, WHICH ADDS A RESULT CONFIRMATION STEP FOR EXPLORING THE HIDDEN REASONS UNDERLYING THE LEARNING PATTERNS AND STRATEGIES. USING THIS RECOLBA FRAMEWORK, A CASE STUDY WAS CONDUCTED WHICH ANALYZED E-BOOK READING DATA. IN THE CASE STUDY, WE FOUND THAT THE STUDENTS HAD A TENDENCY TO DELETE MARKERS AFTER ADDING THEM. THROUGH AN INVESTIGATION, WE FOUND THAT THE STUDENTS DID THIS BECAUSE THEY COULD NOT GRASP THE LEARNING EMPHASIS. TO APPLY THIS FINDING, WE PROPOSED A LEARNING STRATEGY WHEREBY THE TEACHER HIGHLIGHTS THE LEARNING EMPHASIS BEFORE STUDENTS READ THE LEARNING MATERIALS. AN EXPERIMENT WAS CONDUCTED TO EXAMINE THE EFFECTIVENESS OF THIS STRATEGY, AND WE FOUND THAT IT COULD INDEED HELP STUDENTS ACHIEVE BETTER RESULTS, REDUCE REPETITIVE BEHAVIORS AND SAVE TIME. THE FRAMEWORK WAS THEREFORE SHOWN TO BE EFFECTIVE.
A CENTRAL TOPIC TO COMPUTER SCIENCE EDUCATION IS THE TRAINING OF NOVICE PROGRAMMERS. NOVICE PROGRAMMING SKILLS HAVE ALREADY BEEN MAPPED TO HIERARCHICAL LEVELS, AND EXPERT PROGRAMMING SKILLS HAVE BEEN MEASURED BASED ON TASK PERFORMANCE. BUT DIDACTICAL INSTRUCTIONS AND INDIVIDUAL SUPPORT TO ACQUIRE EXPERT PROGRAMMING SKILLS HAVE NOT BEEN PROVIDED IN DETAIL. I PROPOSE THE INVESTIGATION OF STRUCTURAL AND SEMANTIC PATTERNS IN PROGRAM CONSTRUCTION SEQUENCES IN AN IDE-BASED LEARNING ANALYTICS SETTING. I AIM TO PROVIDE A MORE FINE-GRAINED ASSESSMENT OF PROGRAMMING SKILLS, TO ENABLE SKILL ASSESSMENTS DURING PROGRAMMING TASKS, AND TO SUPPORT THE INDIVIDUAL ACQUISITION OF PROGRAMMING SKILLS.
WE PROPOSE A NEW CHANGE DETECTION METHOD USING CLICKSTREAM DATA COLLECTED THROUGH AN E-BOOK SYSTEM. MOST OF THE PRIOR WORK HAS FOCUSED ON THE BATCH PROCESSING OF CLICKSTREAM DATA. IN CONTRAST, THE PROPOSED METHOD IS DESIGNED FOR ONLINE PROCESSING, WITH THE MODEL PARAMETERS FOR CHANGE DETECTION UPDATED SEQUENTIALLY BASED ON OBSERVATIONS OF NEW CLICK EVENTS. MORE SPECIFICALLY, OUR METHOD GENERATES A MODEL FOR AN INDIVIDUAL STUDENT AND PERFORMS MINUTE-BY-MINUTE CHANGE DETECTION BASED ON CLICK EVENTS DURING A CLASSROOM LECTURE. WE COLLECTED CLICKSTREAM DATA FROM FOUR FACE-TO-FACE LECTURES, AND CONDUCTED EXPERIMENTS TO DEMONSTRATE HOW THE PROPOSED METHOD DISCOVERED CHANGE POINTS AND HOW SUCH CHANGE POINTS CORRELATED WITH THE STUDENTS' PERFORMANCES.
RESEARCH IN LEARNING ANALYTICS PROPOSED DIFFERENT COMPUTATIONAL TECHNIQUES TO DETECT LEARNING TACTICS AND STRATEGIES ADOPTED BY LEARNERS IN DIGITAL ENVIRONMENTS THROUGH THE ANALYSIS OF STUDENTS' TRACE DATA. WHILE MANY PROMISING INSIGHTS HAVE BEEN PRODUCED, THERE HAS BEEN MUCH LESS UNDERSTANDING ABOUT HOW AND TO WHAT EXTENT DIFFERENT DATA ANALYTIC APPROACHES INFLUENCE RESULTS. THIS PAPER PRESENTS A COMPARISON OF THREE ANALYTIC APPROACHES INCLUDING PROCESS, SEQUENCE, AND NETWORK APPROACHES FOR DETECTION OF LEARNING TACTICS AND STRATEGIES. THE ANALYSIS WAS PERFORMED ON A DATASET COLLECTED IN A MASSIVE OPEN ONLINE COURSE ON SOFTWARE PROGRAMMING. ALL THREE APPROACHES PRODUCED FOUR TACTICS AND THREE STRATEGY GROUPS. THE TACTICS DETECTED BY USING THE SEQUENCE ANALYSIS APPROACH DIFFERED FROM THOSE IDENTIFIED BY THE OTHER TWO METHODS. THE PROCESS AND NETWORK ANALYTIC APPROACHES HAD MORE THAN 66% OF SIMILARITY IN THE DETECTED TACTICS. LEARNING STRATEGIES DETECTED BY THE THREE APPROACHES PROVED TO BE HIGHLY SIMILAR.
WE DESCRIBE IN THIS WORK A SENSOR BASED LEARNING PLATFORM WHICH SUPPORTS BOTH THE TEACHER AND THE LEARNER DURING EXERCISES. WE USE A COMBINATION OF EYE TRACKER, SENSOR PEN AND EXERCISE TEXTS TO CAPTURE THE PROGRESS OF LEARNERS. THE EYE TRACKER RETRIEVES INFORMATION ABOUT THE GAZE, FOR EXAMPLE READING OR SCANNING FOR KEY WORDS; THE SENSOR PEN CAPTURES TRENDS LIKE NUMBER OF WORDS OR THE PRESSURE APPLIED TO THE PAPER. COMBINING THIS INFORMATION, THE PLATFORM SHOULD BE USED TO INDICATE PROBLEMS OF THE LEARNER TO THE TEACHER. RESIDES PRESENTING THE DATA INFORMATION TO THE TEACHER, WE WORK ON ADVANCING THE PLATFORM TO AN ADAPTIVE SYSTEM, WHICH COULD GIVE INDIVIDUAL FEEDBACK TO THE LEARNERS THEMSELVES ACCORDING TO THEIR INDIVIDUAL COGNITIVE AND AFFECTIVE REQUIREMENTS.
TO HAVE INSIGHT INTO COGNITIVE LOAD (CL) DURING ONLINE COMPLEX PROBLEM SOLVING, THIS STUDY AIMED AT MEASURING CL THROUGH PHYSIOLOGICAL DATA. THIS STUDY EXPERIMENTALLY MANIPULATED INTRINSIC AND EXTRANEOUS LOAD OF EXERCISES IN THE DOMAIN OF STATISTICS, RESULTING IN FOUR CONDITIONS: HIGH COMPLEX WITH HINTS, LOW COMPLEX WITH HINTS, HIGH COMPLEX WITHOUT HINTS AND LOW COMPLEX WITHOUT HINTS. THE STUDY HAD A WITHIN-SUBJECT-DESIGN IN WHICH 67 STUDENTS SOLVED THE EXERCISES IN A RANDOMIZED ORDER. SELF-REPORTED CL WAS COMBINED WITH PHYSIOLOGICAL DATA, NAMELY, GALVANIC SKIN RESPONSE (GSR), SKIN TEMPERATURE (ST), HEART RATE (HR) AND HEART RATE VARIABILITY (HRV). MULTIPLE IMPUTATION WAS USED FOR HANDLING MISSING DATA FROM RESP. 16 AND 19 STUDENTS FOR GSR/ST AND HR/HRV. FIRST, DIFFERENCES BETWEEN CONDITIONS IN VIEW OF PHYSIOLOGICAL DATA WERE EXAMINED. SECOND, WE INVESTIGATED HOW MUCH VARIANCE OF SELF-REPORTED CL AND TASK PERFORMANCE WAS EXPLAINED BY PHYSIOLOGICAL DATA. FINALLY, WE INVESTIGATED WHICH FEATURES CAN BE USED TO ASSESS (OBJECTIVE) CL. RESULTS REVEALED NO SIGNIFICANT DIFFERENCES BETWEEN THE MANIPULATED CONDITIONS IN TERMS OF PHYSIOLOGICAL DATA. NONETHELESS, HR AND ST WERE SIGNIFICANTLY RELATED TO SELF-REPORTED CL, WHEREAS ST TO TASK PERFORMANCE. ADDITIONALLY, THIS STUDY REVEALED THE POTENTIAL OF ST AND HR TO ASSESS HIGH CL.
EDUCATIONAL DATA MINING (EDM) PROMISES BETTER UNDERSTANDING OF STUDENT BEHAVIOR AND KNOWLEDGE, AS WELL AS NEW INFORMATION ON THE IMPLICIT FACTORS THAT CONTRIBUTE TO STUDENT ACTIONS. THIS KNOWLEDGE CAN BE USEFUL IN GUIDING AT-RISK STUDENTS, IDENTIFYING PRIORITY LEARNING NEEDS FOR DIFFERENT GROUPS OF STUDENTS, INCREASING GRADUATION RATES, MAKING INFORMED DECISIONS ABOUT PEDAGOGY, IDENTIFYING STUDENT BEHAVIOR AND LEARNING PATTERNS, AND OPTIMIZING STUDENT SUCCESS. THIS STUDY PROVIDES AN OVERVIEW OF THE BODY OF KNOWLEDGE REGARDING EDUCATIONAL DATA MINING AND ITS APPLICATION IN TEACHING AND LEARNING. IN THIS PAPER WE HIGHLIGHT SIGNIFICANT ASPECTS OF BIG DATA, INCLUDING THE BIG DATA TECHNOLOGIES AND THE RECENT APPLICATIONS OF BIG DATA IN EDUCATION. FINALLY, WE EXPLORE AND REPORT ON THE CHALLENGES AND DIFFICULTIES ENCOUNTERED IN EDM AND PRESENT A DISCUSSION OF FUTURE DIRECTIONS FOR THE EDUCATION AND RESEARCH COMMUNITY.
ALTHOUGH LEARNING THROUGH PERSONALIZED REPRESENTATIONS HAS BEEN SHOWN TO BE EFFECTIVE IN VARIOUS DOMAINS, LITTLE IS KNOWN ABOUT THE MECHANISMS THAT ARE EFFECTING IT. WE FOCUS ON A COI BASED VISUALISATION TOOL DESIGN, TO FEATURE COMMUNITY DYNAMICS EFFECT IN A LEARNING DESIGN CONTEXT WHICH PROMOTES INTERACTION WITH PEERS AND SELF REFLECTION THROUGH PERSONALIZATION.
IN TODAY'S WORLD OF COMPETITIVE EDUCATIONAL INSTITUTIONS, IT IS IMPERATIVE THAT THE FINAL PRODUCT, THE STUDENTS, ARE OF THE OPTIMAL QUALITY AS REQUIRED BY THE PROFESSIONAL INDUSTRY. CONVENTIONALLY, THE PROGRESS AND QUALITY OF THE STUDENTS WERE ONLY ASSESSED IN END-OF-TERM RESULTS, OR AT A FEW STANDPOINTS, WHICH NEGLECTED THE POSSIBILITY OF IMPROVING THE WEAK AREAS. THIS SHORTCOMING OF THE CONVENTIONAL EDUCATIONAL SYSTEM RESULTED IN A HIGH REJECTION/DROPOUT OF THE POTENTIALLY CAPABLE STUDENTS. IN THIS WORK, THE AUTHORS PROPOSE ADAPTATION OF A NOVEL CONTENT DELIVERY, FORMATIVE ASSESSMENT, SMART ANALYTICS AND INSTANT FEEDBACK MECHANISM PIPELINED INTO THE EDUCATIONAL PROCESS. THE PROPOSED MODEL CAN POTENTIALLY CIRCUMVENT THE PITFALLS AND SIGNIFICANTLY REDUCE THE ERRORS OF ASSESSMENT, GRADING, AND THE DELIVERY OF FEEDBACK. THE PROPOSED APPROACH CONCURRENTLY ASSURES THE QUALITY OF STUDENTS AT EACH FORMATIVE STEP WITHIN A SEMESTER'S TIME, THUS IMPROVING THE QUALITY OF INTAKE OF THE SUBSEQUENT STANDPOINT. THE APPROACH HAS BEEN EVALUATED ON ONE SUBJECT, FUNCTIONAL ENGLISH, WITHIN A FOUR YEAR COMPUTER SCIENCE BACCALAUREATE PROGRAM. THE RESULTS OF THE OUTCOMES CAN BE PLAUSIBLY EXTENDED AND APPLIED ONTO OTHER EDUCATIONAL CONTEXTS.
AS IN MANY FIELDS, MOST PAPERS IN THE LEARNING ANALYTICS LITERATURE REPORT SUCCESS OR, AT LEAST, READ AS IF THEY ARE REPORTING SUCCESS. THIS IS ALMOST CERTAINLY NOT BECAUSE LEARNING ANALYTICS RESEARCH AND ACTIVITY ARE ALWAYS SUCCESSFUL. GENERALLY, WE REPORT OUR SUCCESSES WIDELY, BUT KEEP OUR FAILURES TO OURSELVES. AS BISMARCK IS ALLEGED TO HAVE SAID: IT IS WISE TO LEARN FROM THE MISTAKES OF OTHERS. THIS WORKSHOP OFFERS AN OPPORTUNITY FOR RESEARCHERS AND PRACTITIONERS TO SHARE THEIR FAILURES IN A LOWER-STAKES ENVIRONMENT, TO HELP THEM LEARN FROM EACH OTHER'S MISTAKES.
THIS PAPER PROPOSES A FRAMEWORK WHICH AIMS TO LEVERAGE DATA FROM INFORMAL LEARNING ENVIRONMENTS TO PROVIDE INSIGHTS ABOUT YOUTH ENGAGEMENT FOR VARIOUS STAKEHOLDERS. TO EXPLORE THE FRAMEWORK, WE CREATED METRICS TO EXAMINE THE ENGAGEMENT OF 98 MIDDLE SCHOOL-AGED GIRLS DURING A 20-WEEK STEM PROGRAM USING ATTENDANCE RECORDS AND LOG DATA FROM AN ONLINE LEARNING PLATFORM CODED TO REFLECT 21ST CENTURY LEARNING ACTIVITIES. WE PRESENT PRELIMINARY ANALYSES USING THE METRICS, FOCUSING ON HOW THEY CAN HELP STAKEHOLDERS UNDERSTAND ENGAGEMENT AND EQUITY OF PARTICIPATION.
DEVELOPING COMMUNICATION SKILLS IN HIGHER EDUCATION STUDENTS COULD BE A CHALLENGE TO PROFESSORS DUE TO THE TIME NEEDED TO PROVIDE FORMATIVE FEEDBACK. THIS WORK PRESENTS RAP, A SCALABLE SYSTEM TO PROVIDE AUTOMATIC FEEDBACK TO ENTRY-LEVEL STUDENTS TO DEVELOP BASIC ORAL PRESENTATION SKILLS. THE SYSTEM IMPROVES THE STATE-OF-THE-ART BY ANALYZING POSTURE, GAZE, VOLUME, FILLED PAUSES AND THE SLIDES OF THE PRESENTERS THROUGH DATA CAPTURED BY VERY LOW-COST SENSORS. THE SYSTEM ALSO PROVIDES AN OFF-LINE FEEDBACK REPORT WITH MULTIMODAL RECORDINGS OF THEIR PERFORMANCE. AN INITIAL EVALUATION OF THE SYSTEM INDICATES THAT THE SYSTEM'S FEEDBACK HIGHLY AGREES WITH HUMAN FEEDBACK AND THAT STUDENTS CONSIDERED THAT FEEDBACK USEFUL TO DEVELOP THEIR ORAL PRESENTATION SKILLS.
LOW LEVELS OF ONLINE STUDENT ENGAGEMENT IMPACT NEGATIVELY ON STUDENT SUCCESS AND ADVERSELY AFFECT ATTRITION. COURSE LEARNING ANALYTICS DATA (CLAD), COMBINED WITH NUDGING INITIATIVES, HAVE EMERGED AS STRATEGIES FOR ENGAGING ONLINE STUDENTS. THIS ARTICLE PRESENTS A MIXED METHOD CASE STUDY INVOLVING A STAGED INTERVENTION STRATEGY FOCUSSING ON THE EMPLOYMENT OF TIMELY, STRATEGIC COMMUNICATION INTERVENTIONS CONDUCTED ACROSS 19 COURSES AND SIX DISCIPLINES. THE RESEARCH METHODOLOGY UTILISED CLAD, ONLINE SURVEYS, STUDENT INTERVIEWS AND STUDENT EVALUATIONS OF TEACHING. THE FINDINGS SUBSTANTIATE BENEFITS FOR BOTH ACADEMICS AND STUDENTS. ACADEMICS BENEFITTED FROM THE PROVISION OF A RELATIVELY SIMPLE, ACCESSIBLE AND PROACTIVE INTERVENTION FOR INCREASING STUDENTS' CAPACITIES TO BE MORE IN CONTROL AND ENGAGED IN THEIR LEARNING. STUDENTS BENEFITTED AS THE INTERVENTION ACCENTUATED CRITICAL RESOURCES TO ASSIST THEM TO BETTER ADDRESS ASSESSMENT REQUIREMENTS, ALIGN THEIR EXPECTATIONS MORE REALISTICALLY WITH THOSE OF THE COURSE, AND MORE READILY DEMONSTRATE THEIR LEARNING OBLIGATIONS AND RESPONSIBILITIES.
IN THE CONTEXT OF LEARNING ANALYTICS, MACHINE LEARNING TECHNIQUES HAVE BEEN COMMONLY USED IN ORDER TO SHED A LIGHT ON SOLVING EDUCATIONAL PROBLEMS. THE STUDIES CAN BE ASSOCIATED WITH THE CURRICULUM DESIGN, MOSTLY AT COURSE LEVEL, WHICH TARGET TO IMPROVE THE LEARNING AND TEACHING PROCESSES. IN THIS STUDY, THE RELATIONSHIP BETWEEN THE COURSES WAS ANALYZED VIA ARTIFICIAL NEURAL NETWORK (ANN) TO PROVIDE A SUPPORT FOR CURRICULUM DEVELOPMENT PROCESS AT A PROGRAM LEVEL. EXTRACTING THE DEPENDENCE AMONG COURSES WITHIN A PROGRAM PLAYS A KEY ROLE IN PLACING THEM COHERENTLY TO ENSURE THE SUCCESS OF CURRICULUM. FOR THIS PURPOSE, IT WAS INVESTIGATED IF THE PERFORMANCE OF STUDENTS IN A SUBSEQUENT COURSE IS INFLUENCED FROM THE PERFORMANCE IN SOME PREVIOUS BASIC COURSES. THE RESULTS DEMONSTRATED THAT THE PERFORMANCE RELATIONS COULD BE USED TO DESCRIBE INFORMATION FOR PRIORITIZATION AND SEQUENCING OF COURSES WITHIN A PROGRAM. IN ADDITION, ANN CAN SUCCESSFULLY PREDICT THE STUDENT PERFORMANCES LEADING TO FIND OUT THE RELATIONSHIPS BETWEEN THESE COURSES. THE APPLICATION OF THE MULTILAYER FEEDFORWARD NEURAL NETWORK RESULTED IN AN ACHIEVEMENT OF A PROSPERING PREDICTION PERFORMANCE BASED ON THE GRADES OF PREREQUISITE COURSES WITH 87% ACCURACY RATE WITHOUT SACRIFICING A SIGNIFICANT SENSITIVITY.
IN THIS PAPER DATA MINING AND MACHINE LEARNING APPLICATIONS FOR EDUCATIONAL BIG DATA IN THE UNIVERSITY ARE EXAMINED. WE PROPOSED PREDICTION MODELS IN LEARNING ANALYTICS USING THE STUDENT TEACHING DATA AND SHOW THE EFFECTIVENESS OF MACHINE LEARNING METHODS. SOME EXAMPLES OF PRACTICAL APPLICATIONS WILL BE SHOWN TO VERIFY PRACTICAL EFFECTIVENESS.
DIGITAL FOOTPRINTS (DF) OFFER RELEVANT INFORMATION ABOUT EDUCATIONAL ACTIVITIES AND PROCESSES RELATED TO STRATEGIES OF ACADEMIC ASSESSMENT, IDENTIFICATION OF SKILLS AND PSYCHOLOGICAL TRAITS OF STUDENTS, AND PERMANENCE AND DROPOUT TRENDS, ETC. THIS STUDY ANALYZES SCIENTIFIC EVIDENCE ON THE USE OF DF IN EDUCATION, AND SHOWS THE RESULTS OF A SYSTEMATIC MAPPING OF LITERATURE CARRIED OUT BASED ON THE ANALYSIS OF DOCUMENTS PUBLISHED IN THE LAST FIFTEEN YEARS (2005-2019) IN TWO DATABASES: SCOPUS AND WEB OF SCIENCE (WOS). FINDINGS REVEAL THAT EDUCATIONAL RESEARCH ON DF IS FOCUSED ON LEARNING ANALYTICS, THE STUDY OF DIGITAL PRESENCE AND PSYCHOMETRIC MODELING. LIKEWISE, THE ARTICLE REPORTS ON THE SCARCE INVESTIGATION OF DF IN MASSIVE OPEN ONLINE COURSES (MOOCS) ENVIRONMENTS AND HIGHLIGHTS THE MULTIPLE MEANINGS OF DF AS AN ACTION AND AS A SERVICE, BEYOND THE GENERALIZED CONCEPTION OF DATA. THESE FINDINGS SUGGEST THE IMPORTANCE OF PREPARING EDUCATIONAL INSTITUTIONS TO IMPLEMENT DF USE AND MANAGEMENT PRACTICES IN ORDER TO FAVOR PROCESSES THAT RANGE FROM DF CURATION, TO COGNITIVE EVALUATION, DIGITAL EQUITY, PREDICTION OF SCHOOL SUCCESS AND/OR FAILURE, ETC. HENCE, DF MEANINGS (DATA, ACTION AND SERVICE) COULD AID TO GENERATE PROPER ADMINISTRATION AND POLICY-MAKING ACTIONS, PLACED AT THE SERVICE OF ALL STUDENTS AND THEIR EDUCATIONAL COMMUNITIES.
THE ONLINE HOMEWORK SYSTEM WEBWORK HAS BEEN SUCCESSFULLY USED AT SEVERAL HUNDRED COLLEGES AND UNIVERSITIES. DESPITE ITS POPULARITY, THE WEBWORK SYSTEM DOES NOT PROVIDE DETAILED METRICS OF STUDENT PERFORMANCE TO INSTRUCTORS. IN THIS ARTICLE, WE ILLUSTRATE HOW AN ANALYSIS OF THE LOG FILES OF THE WEBWORK SYSTEM CAN PROVIDE INFORMATION SUCH AS THE AMOUNT OF TIME STUDENTS SPEND ON WEBWORK ASSIGNMENTS AND HOW LONG THEY PERSIST ON PROBLEMS. WE ESTIMATE THE TIME SPENT ON AN ASSIGNMENT BY COMBINING LOG FILE EVENTS INTO SESSIONS OF STUDENT ACTIVITY. THE VALIDITY OF THIS METHOD IS CONFIRMED BY CROSS REFERENCING WITH ANOTHER TIME ESTIMATE OBTAINED FROM A LEARNING MANAGEMENT SYSTEM. AS AN APPLICATION OF THESE PERFORMANCE METRICS, WE CONTRAST THE BEHAVIOUR OF STUDENTS WITH WEBWORK SCORES LESS THAN 50% WITH THE REMAINDER OF THE CLASS IN A FIRST YEAR CALCULUS COURSE. THIS REVEALS THAT ON AVERAGE, THE STUDENTS WHO PERFORM POORLY ON THEIR HOMEWORK START LATER, HAVE SHORTER ACTIVITY SESSIONS, AND ARE LESS PERSISTENT WHEN SOLVING PROBLEMS. WE CONCLUDE BY DISCUSSING THE IMPLICATIONS OF WEBWORK ANALYTICS FOR INSTRUCTIONAL PRACTICES AND FOR THE FUTURE OF LEARNING ANALYTICS IN UNDERGRADUATE MATHEMATICS EDUCATION.
BASED ON THE EDUCATIONAL DATA PUBLISHED UNDER CREATIVE COMMONS LICENSE, THIS STUDY DESCRIBES ABOUT THE PERFORMANCE PREDICTION EXPERIMENT APPLIED WITH FOUR TYPES OF MACHINE LEARNING ALGORITHMS, INCLUDING THE DEEP LEARNING ALGORITHM, AND EXAMINES HOW THE PREDICTION ACCURACY IS AFFECTED DEPENDING ON THE SELECTED FEATURE QUANTITIES. THE AIM OF THIS PAPER IS TO COMPARE METHOD SELECTION AND FEATURE SELECTION IN TERMS OF THEIR ABILITY TO IMPROVE THE PREDICTION RESULTS. IN DATA ANALYSIS BY MACHINE LEARNING OR DEEP LEARNING, THE DETERMINANT OF RESULT IS OFTEN UNCLEAR. IN THE FIELD OF LEARNING ANALYTICS, ANALYSIS CAN BE PERFORMED EVEN IF THE AMOUNT OF DATA IS SMALL COMPARED TO THE FIELD OF IMAGE RECOGNITION. THEREFORE, IT IS MEANINGFUL TO COMPARE ANALYSIS ACCURACY USING MACHINE LEARNING AND DEEP LEARNING AND TO EXAMINE WHICH METHOD IS MOST EFFECTIVE FOR PREDICTION ACADEMIC PERFORMANCE. IN THIS RESEARCH, WE REVEALED THAT DEEP LEARNING HAS THE BEST METHOD FOR LEARNING ANALYTICS. ALSO, THE RESULTS OF THIS STUDY INDICATE THAT FEATURE SELECTION IS MORE IMPORTANT FOR IMPROVEMENT TO PREDICTION RATHER THAN METHOD SELECTION.
THE PURPOSE OF THIS STUDY IS TO REVIEW JOURNAL PAPERS ON EDUCATIONAL BIG DATA RESEARCH PUBLISHED FROM 2010 TO 2018. A TOTAL OF 143 PAPERS WERE SELECTED. THE PAPERS WERE CHARACTERIZED BASED ON THREE DIMENSIONS: (A) EDUCATIONAL GOALS; (B) EDUCATIONAL PROBLEMS ADDRESSED; AND (C) BIG DATA ANALYTICAL TECHNIQUES USED. A QUALITATIVE CONTENT ANALYSIS APPROACH WAS CONDUCTED TO DEVELOP A CODING SCHEME FOR ANALYZING THE SELECTED PAPERS. THE RESULTS IDENTIFIED FOUR TYPES OF EDUCATIONAL GOALS, WITH A CLEAR PREDOMINANCE OF QUALITY ASSURANCE. THE IDENTIFICATION OF THE MOST MENTIONED EDUCATIONAL PROBLEMS RESULTED IN FOUR MAIN CONCERNS: THE LACK OF DETECTING STUDENT BEHAVIOR MODELING AND WASTE OF RESOURCES; INAPPROPRIATE CURRICULA AND TEACHING STRATEGIES; OVERSIGHTS OF QUALITY ASSURANCE; AND PRIVACY AND ETHICAL ISSUES. CONCERNING THE MOST MENTIONED BIG DATA ANALYTICAL TECHNIQUES, THE CODING SCHEME REVEALED THAT THE MAJORITY OF THE PAPERS FOCUSED ON THE EDUCATIONAL DATA MINING TECHNIQUE FOLLOWED BY THE LEARNING ANALYTICS TECHNIQUE. THE VISUAL ANALYTICS TECHNIQUE WAS MENTIONED IN A FEW PAPERS. THE RESULTS ALSO INDICATED THAT THE EDUCATIONAL DATA MINING TECHNIQUE IS THE MOST SUITABLE TECHNIQUE TO USE FOR QUALITY ASSURANCE AND TO PROVIDE POTENTIAL SOLUTIONS FOR THE LACK OF DETECTING STUDENT BEHAVIOR MODELING AND THE WASTE OF RESOURCES IN INSTITUTIONS.
SERIOUS GAMES VALIDATION IS A HIGHLY COMPLEX AND BURDENSOME PROCESS. TO ENSURE THAT GAMES MEET THEIR INTENDED EDUCATIONAL GOALS, IT IS NECESSARY TO HAVE A CLEAR EXPERIMENTAL DESIGN AND THE NECESSARY TOOLS TO MINIMIZE THE ERRORS THAT MAY APPEAR IN THE PROCESS. IN THIS ARTICLE, AFTER DESCRIBING THE MOST COMMON PROBLEMS THAT WE HAVE FOUND WHILE VALIDATING OUR OWN GAMES, WE PRESENT SIMVA, A TOOL DESIGNED TO SIMPLIFY THE PROCESS OF VALIDATING SERIOUS GAMES WITH FORMAL QUESTIONNAIRES AND RELATING THEM WITH LEARNING ANALYTICS DATA, REDUCING TIME, COST, AND ERROR RATES.
NOWADAYS, UNIVERSITIES (ON-SITE AND ONLINE) HAVE A LARGE COMPETITION IN ORDER TO ATTRACT MORE STUDENTS. IN THIS PANORAMA, LEARNING ANALYTICS CAN BE A VERY USEFUL TOOL SINCE IT ALLOWS INSTRUCTORS (AND UNIVERSITY MANAGERS) TO GET A MORE THOROUGH VIEW OF THEIR CONTEXT, TO BETTER UNDERSTAND THE ENVIRONMENT, AND TO IDENTIFY POTENTIAL IMPROVEMENTS. IN ORDER TO PERFORM ANALYTICS EFFICIENTLY, IT IS NECESSARY TO HAVE AS MUCH INFORMATION AS POSSIBLE ABOUT THE INSTRUCTIONAL CONTEXT. THE PAPER PROPOSES A NOVEL APPROACH TO GATHER INFORMATION FROM DIFFERENT ASPECTS WITHIN COURSES. IN PARTICULAR, THE APPROACH APPLIES NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES TO ANALYZE THE COURSE'S MATERIALS AND DISCOVER WHAT CONCEPTS ARE TAUGHT, THEIR RELEVANCY IN THE COURSE AND THEIR ALIGNMENT WITH THE LEARNING OUTCOMES OF THE COURSE. THE CONTRIBUTION OF THE PAPER IS A SEMIAUTOMATIC SYSTEM THAT ALLOWS OBTAINING A BETTER UNDERSTANDING OF COURSES. A VALIDATION EXPERIMENT ON A MASTER OF THE OPEN UNIVERSITY OF CATALONIA IS PRESENTED IN ORDER TO SHOW THE QUALITY OF THE RESULTS. THE SYSTEM CAN BE USED TO ANALYZE THE SUITABILITY OF COURSE'S MATERIALS AND TO ENRICH AND CONTEXTUALIZE OTHER ANALYTICAL PROCESSES.
TRACES OF LEARNING BEHAVIORS GENERALLY PROVIDE INSIGHTS INTO LEARNERS AND THE LEARNING PROCESSES THAT THEY EMPLOY. IN THIS ARTICLE, A LEARNING-ANALYTICS-BASED APPROACH IS PROPOSED FOR MANAGING COGNITIVE LOAD BY ADJUSTING THE INSTRUCTIONAL STRATEGIES USED IN ONLINE COURSES. THE TECHNOLOGY-BASED LEARNING ENVIRONMENT EXAMINED IN THIS STUDY INVOLVED A VIDEO CONFERENCING SYSTEM AND LEARNING MANAGEMENT SYSTEM (LMS) FOR HOSTING COURSE CONTENT AND DISCUSSION FORUMS. THE SOCIAL NETWORKING SOFTWARE LINE WAS USED TO ENHANCE THE SOCIAL PRESENCE OF LEARNERS. STUDENTS (N = 869) ENROLLED IN A SUMMER COURSE PARTICIPATED IN A 9-WEEK EXPERIMENT. THEIR LMS LOG DATA AND SOCIAL MEDIA FOOTPRINTS WERE RECORDED, AND CONTENT EXPERTS ASSESSED THE INTRINSIC COGNITIVE LOAD (ICL) OF EACH CONTENT MODULE THROUGH A CONSENSUS PROCESS. A LEARNING ANALYTICS METHOD WAS APPLIED TO IDENTIFY CANDIDATE PARAMETERS RELATING LEARNING BEHAVIORS TO COGNITIVE LOAD. THE INSTRUCTOR ASSESSED THE LEARNERS' COGNITIVE PROCESSES AND ADJUSTED THE INSTRUCTIONAL STRATEGIES ACCORDING TO THE RESULTS OF STATISTICAL, DISCOURSE, AND QUALITATIVE ANALYSES. PRACTICAL GUIDELINES RELATED TO VARIOUS COGNITIVE LOAD EFFECTS WERE DESIGNED TO ASSIST THE STUDENTS WITH MANAGING THEIR COGNITIVE LOAD BY USING LEARNING BEHAVIORS AND ANALYTICS DATA AS SIGNALS FOR MAKING A CHANGE IN LEARNING PROCESSES. TEACHERS OF ONLINE COURSES CAN USE THE PROPOSED APPROACH AS A SUPPORT TOOL TO IDENTIFY LEARNING PROBLEMS AND ASSIST LEARNERS WITH MAINTAINING A COGNITIVE LOAD THAT IS CONDUCIVE TO LEARNING.
AS THE FIELD OF LEARNING ANALYTICS CONTINUES TO MATURE, THERE IS A CORRESPONDING EVOLUTION AND SOPHISTICATION OF THE ASSOCIATED ANALYTICAL METHODS AND TECHNIQUES. IN THIS REGARD SOCIAL NETWORK ANALYSIS (SNA) HAS EMERGED AS ONE OF THE CORNERSTONES OF LEARNING ANALYTICS METHODOLOGIES. HOWEVER, DESPITE THE NOTED IMPORTANCE OF SOCIAL NETWORKS FOR FACILITATING THE LEARNING PROCESS, IT REMAINS UNCLEAR HOW AND TO WHAT EXTENT SUCH NETWORK MEASURES ARE ASSOCIATED WITH SPECIFIC LEARNING OUTCOMES. MOTIVATED BY SIMMEL'S THEORY OF SOCIAL INTERACTIONS AND BUILDING ON THE ARGUMENT THAT SOCIAL CENTRALITY DOES NOT ALWAYS IMPLY BENEFITS, THIS STUDY AIMED TO FURTHER CONTRIBUTE TO THE UNDERSTANDING OF THE ASSOCIATION BETWEEN STUDENTS' SOCIAL CENTRALITY AND THEIR ACADEMIC PERFORMANCE. THE STUDY REVEALS THAT LEARNING ANALYTICS RESEARCH DRAWING ON SNA SHOULD INCORPORATE BOTH - DESCRIPTIVE AND STATISTICAL METHODS TO PROVIDE A MORE COMPREHENSIVE AND HOLISTIC UNDERSTANDING OF A STUDENTS' NETWORK POSITION. IN SO DOING RESEARCHERS CAN UNDERTAKE MORE NUANCED AND CONTEXTUALLY SALIENT INFERENCES ABOUT LEARNING IN NETWORK SETTINGS. SPECIFICALLY, WE SHOW HOW DIFFERENCES IN THE FACTORS FRAMING STUDENTS' INTERACTIONS WITHIN TWO INSTANCES OF A MOOC AFFECT THE ASSOCIATION BETWEEN THE THREE SOCIAL NETWORK CENTRALITY MEASURES (I.E., DEGREE, CLOSENESS, AND BETWEENNESS) AND THE FINAL COURSE OUTCOME.
A CENTRAL CONCERN IN LEARNING ANALYTICS SPECIFICALLY AND EDUCATIONAL RESEARCH MORE GENERALLY IS THE ALIGNMENT OF ROBUST, COHERENT MEASURES TO WELL-DEVELOPED CONCEPTUAL AND THEORETICAL FRAMEWORKS. CAPTURING AND REPRESENTING PROCESSES OF LEARNING REMAINS AN ONGOING CHALLENGE IN ALL AREAS OF EDUCATIONAL INQUIRY AND PRESENTS SUBSTANTIVE CONSIDERATIONS ON THE NATURE OF LEARNING, KNOWLEDGE, AND ASSESSMENT & MEASUREMENT THAT HAVE BEEN CONTINUOUSLY REFINED IN VARIOUS AREAS OF EDUCATION AND PEDAGOGICAL PRACTICE. LEARNING ANALYTICS AS A STILL DEVELOPING METHOD OF INQUIRY HAS YET TO SUBSTANTIVELY NAVIGATE THE ALIGNMENT OF MEASUREMENT, CAPTURE, AND REPRESENTATION OF LEARNING TO THEORETICAL FRAMEWORKS DESPITE BEING USED TO IDENTIFY VARIOUS PRACTICAL CONCERNS SUCH AS AT RISK STUDENTS. THIS STUDY SEEKS TO ADDRESS THESE CONCERNS BY COMPARING BEHAVIORAL MEASUREMENTS FROM LEARNING MANAGEMENT SYSTEMS TO ESTABLISHED MEASUREMENTS OF COMPONENTS OF LEARNING AS UNDERSTOOD THROUGH SELF-REGULATED LEARNING FRAMEWORKS. USING SEVERAL PROMINENT AND ROBUSTLY SUPPORTED SELF-REPORTED SURVEY MEASURES DESIGNED TO IDENTIFY DIMENSIONS OF SELF-REGULATED LEARNING, AS WELL AS TYPICAL BEHAVIORAL FEATURES EXTRACTED FROM A LEARNING MANAGEMENT SYSTEM, WE CONDUCTED DESCRIPTIVE AND EXPLORATORY ANALYSES ON THE RELATIONAL STRUCTURES OF THESE DATA. WITH THE EXCEPTION OF LEARNERS' SELF-REPORTED TIME MANAGEMENT STRATEGIES AND LEVEL OF MOTIVATION, THE CURRENT RESULTS INDICATE THAT BEHAVIORAL MEASURES WERE NOT WELL CORRELATED WITH SURVEY MEASUREMENTS. POSSIBILITIES AND RECOMMENDATIONS FOR LEARNING ANALYTICS AS MEASUREMENTS FOR SELF-REGULATED LEARNING ARE DISCUSSED.
THE LEARNING ANALYTICS COMMUNITY HAS RECENTLY PAID PARTICULAR ATTENTION TO EARLY PREDICT LEARNERS' PERFORMANCE. AN ESTABLISHED APPROACH ENTAILS TRAINING CLASSIFICATION MODELS FROM PAST LEARNER-RELATED DATA IN ORDER TO PREDICT THE EXAM SUCCESS RATE OF A STUDENT WELL BEFORE THE END OF THE COURSE. EARLY PREDICTIONS ALLOW TEACHERS TO PUT IN PLACE TARGETED ACTIONS, E.G., SUPPORTING AT-RISK STUDENTS TO AVOID EXAM FAILURES OR COURSE DROPOUTS. ALTHOUGH SEVERAL MACHINE LEARNING AND DATA MINING SOLUTIONS HAVE BEEN PROPOSED TO LEARN ACCURATE PREDICTORS FROM PAST DATA, THE INTERPRETABILITY AND EXPLAINABILITY OF THE BEST PERFORMING MODELS IS OFTEN LIMITED. THEREFORE, IN MOST CASES, THE REASONS BEHIND CLASSIFIERS' DECISIONS REMAIN UNCLEAR. THIS PAPER PROPOSES AN EXPLAINABLE LEARNING ANALYTICS SOLUTION TO ANALYZE LEARNER-GENERATED DATA ACQUIRED BY OUR TECHNICAL UNIVERSITY, WHICH RELIES ON A BLENDED LEARNING MODEL. IT ADOPTS CLASSIFICATION TECHNIQUES TO EARLY PREDICT THE SUCCESS RATE OF ABOUT 5000 STUDENTS WHO WERE ENROLLED IN THE FIRST YEAR COURSES OF OUR UNIVERSITY. IT PROPOSES TO APPLY ASSOCIATIVE CLASSIFIERS AT DIFFERENT TIME POINTS AND TO EXPLORE THE CHARACTERISTICS OF THE MODELS THAT LED TO ASSIGN PASS OR FAIL SUCCESS RATES. THANKS TO THEIR INHERENT INTERPRETABILITY, ASSOCIATIVE MODELS CAN BE MANUALLY EXPLORED BY DOMAIN EXPERTS WITH THE TWOFOLD AIM AT VALIDATING CLASSIFIER OUTCOMES THROUGH LOCAL RULE-BASED EXPLANATIONS AND IDENTIFYING AT-RISK/SUCCESSFUL STUDENT PROFILES BY INTERPRETING THE GLOBAL RULE-BASED MODEL. THE RESULTS OF AN IN-DEPTH EMPIRICAL EVALUATION DEMONSTRATE THAT ASSOCIATIVE MODELS (I) PERFORM AS GOOD AS THE BEST PERFORMING CLASSIFICATION MODELS, AND (II) GIVE RELEVANT INSIGHTS INTO THE PER-STUDENT SUCCESS RATE ASSIGNMENTS.
DATA MINING TOGETHER WITH LEARNING ANALYTICS ARE EMERGING TOPICS BECAUSE OF THE HUGE AMOUNT OF EDUCATIONAL DATA COMING FROM LEARNING MANAGEMENT SYSTEMS. THIS PAPER PRESENTS A CASE STUDY ABOUT STUDENTS' GRADE PREDICTION BY USING DATA MINING METHODS. DATA OBTAINED FROM MOODLE LOG FILES ARE EXPLORED TO UNDERSTAND THE TRENDS AND EFFECTS OF STUDENTS' ACTIVITIES ON MOODLE LEARNING MANAGEMENT SYSTEM. CORRELATIONS OF SYSTEM ACTIVITIES WITH THE STUDENT SUCCESS ARE FOUND. DATA IS CLASSIFIED AND MODELED BY USING DECISION TREE, BAYESIAN NETWORK AND SUPPORT VECTOR MACHINE ALGORITHMS. AFTER TRAINING THE MODEL WITH A ONE-YEAR COURSE ACTIVITY DATA, NEXT YEARS' GRADES ARE PREDICTED. WE FOUND THAT DECISION TREE CLASSIFICATION GIVES THE BEST ACCURACY ON THE TEST DATA FOR THE PREDICTION.
WITH THE INCREASE IN STUDENT ENROLMENT AND DIVERSIFICATION, ENHANCING THE LEARNING EXPERIENCE OF STUDENTS HAS BECOME INCREASINGLY IMPORTANT. BLENDED COURSES, WHICH COMBINE ONLINE AND FACE-TO-FACE DELIVERY, CAN AFFECT LEARNERS' LEARNING EXPERIENCE AND OUTCOMES, AND ULTIMATELY THEIR ACADEMIC PERFORMANCE. THIS STUDY INVESTIGATES THE FACTORS ASSOCIATED WITH THE PERFORMANCE OF ADULT LEARNERS IN BLENDED COURSES VIS-A-VIS IN FACE-TO-FACE COURSES IN THE SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS, FORMERLY KNOWN AS SIM UNIVERSITY). IT ALSO ILLUSTRATES THE APPLICATION OF LEARNING ANALYTICS IN A LEARNING ENVIRONMENT CATERED TO WORKING ADULTS. RECOMMENDATIONS FOR DESIGN OF COURSES ARE MADE BASED ON THE RESEARCH FINDINGS, AND SUGGESTIONS FOR FUTURE RESEARCH ARE INCLUDED IN THE CONCLUDING SECTION OF THE PAPER.
THE COLLECTION AND MANAGEMENT OF LEARNING TRACES, METADATA ABOUT ACTIONS THAT STUDENTS PERFORM WHILE THEY LEARN, IS A CORE TOPIC IN THE DOMAIN OF LEARNING ANALYTICS. IN THIS PAPER, WE PRESENT A SIMPLE ARCHITECTURE FOR COLLECTING AND MANAGING LEARNING TRACES. WE DESCRIBE REQUIREMENTS, DIFFERENT COMPONENTS OF THE ARCHITECTURE, AND OUR EXPERIENCES WITH THE SUCCESSFUL DEPLOYMENT OF THE ARCHITECTURE IN TWO DIFFERENT CASE STUDIES: A BLENDED LEARNING UNIVERSITY COURSE AND AN ENQUIRY BASED LEARNING SECONDARY SCHOOL COURSE. THE ARCHITECTURE RELIES ON TRACKERS, COLLECTING AGENTS THAT FETCH DATA FROM EXTERNAL SERVICES, FOR FLEXIBILITY AND CONFIGURABILITY. IN ADDITION, WE DISCUSS HOW OUR ARCHITECTURE MEETS THE REQUIREMENTS OF DIFFERENT LEARNING ENVIRONMENTS, CRITICAL REFLECTIONS AND REMARKS ON FUTURE WORK.
THIS PAPER(1) DEMONSTRATES HOW THE APPLICATION OF ITEM RESPONSE THEORY YIELDS USEFUL ITEM CHARACTERISTICS, WHICH FURTHER CAN BE THE FOUNDATION OF ITEM POOLS AND THEREFORE ADAPTIVE EDUCATIONAL SOFTWARE TO COME.
THE INCREASING USE OF DIGITAL SYSTEMS TO SUPPORT LEARNING LEADS TO A GROWTH IN DATA REGARDING BOTH LEARNING PROCESSES AND RELATED CONTEXTS. LEARNING ANALYTICS OFFERS CRITICAL INSIGHTS FROM THESE DATA, THROUGH AN INNOVATIVE COMBINATION OF TOOLS AND TECHNIQUES. IN THIS PAPER, WE EXPLORE STUDENTS' ACTIVITIES IN A MOOC FROM THE PERSPECTIVE OF PERSONAL CONSTRUCTIVISM, WHICH WE OPERATIONALIZED AS A COMBINATION OF LEARNING BEHAVIOUR AND LEARNING PROGRESS. THIS STUDY CONSIDERS STUDENTS' DATA ANALYZED AS PER THE MOOC PROCESS MINING: DATA SCIENCE IN ACTION. WE EXPLORE THE RELATION BETWEEN LEARNING BEHAVIOUR AND LEARNING PROGRESS IN MOOCS, WITH THE PURPOSE TO GAIN INSIGHT INTO HOW PASSING AND FAILING STUDENTS DISTRIBUTE THEIR ACTIVITIES DIFFERENTLY ALONG THE COURSE WEEKS, RATHER THAN PREDICT STUDENTS' GRADES FROM THEIR ACTIVITIES. COMMONLY-STUDIED AGGREGATED COUNTS OF ACTIVITIES, SPECIFIC COURSE ITEM COUNTS, AND ORDER OF ACTIVITIES WERE EXAMINED WITH CLUSTER ANALYSES, MEANS ANALYSES, AND PROCESS MINING TECHNIQUES. WE FOUND FOUR MEANINGFUL CLUSTERS OF STUDENTS, EACH REPRESENTING SPECIFIC BEHAVIOUR RANGING FROM ONLY STARTING TO FULLY COMPLETING THE COURSE. PROCESS MINING TECHNIQUES SHOW THAT SUCCESSFUL STUDENTS EXHIBIT A MORE STEADY LEARNING BEHAVIOUR. HOWEVER, THIS BEHAVIOUR IS MUCH MORE RELATED TO ACTUALLY WATCHING VIDEOS THAN TO THE TIMING OF ACTIVITIES. THE RESULTS OFFER GUIDANCE FOR TEACHERS.
STUDENT ENGAGEMENT HAS A TRAJECTORY (A TIMELINE) THAT UNFOLDS OVER TIME AND CAN BE SHAPED BY DIFFERENT FACTORS INCLUDING LEARNERS' MOTIVATION, SCHOOL CONDITIONS, AND THE NATURE OF LEARNING TASKS. SUCH FACTORS MAY RESULT IN EITHER A STABLE, DECLINING OR FLUCTUATING ENGAGEMENT TRAJECTORY. WHILE RESEARCH ON ONLINE ENGAGEMENT IS ABUNDANT, MOST AUTHORS HAVE EXAMINED STUDENT ENGAGEMENT IN A SINGLE COURSE OR TWO. LITTLE RESEARCH HAS BEEN DEVOTED TO STUDYING ONLINE LONGITUDINAL ENGAGEMENT, I.E., THE EVOLUTION OF STUDENT ENGAGEMENT OVER A FULL EDUCATIONAL PROGRAM. THIS LEARNING ANALYTICS STUDY EXAMINES THE ENGAGEMENT STATES (SEQUENCES, SUCCESSIONS, STABILITY, AND TRANSITIONS) OF 106 STUDENTS IN 1396 COURSE ENROLLMENTS OVER A FULL PROGRAM. ALL DATA OF STUDENTS ENROLLED IN THE ACADEMIC YEAR 2014-2015, AND THEIR SUBSEQUENT DATA IN 2015-2016, 2016-2017, AND 2017-2018 (15 COURSES) WERE COLLECTED. THE ENGAGEMENT STATES WERE CLUSTERED USING HIDDEN MARKOV MODELS (HMM) TO UNCOVER THE HIDDEN ENGAGEMENT TRAJECTORIES WHICH RESULTED IN A MOSTLY-ENGAGED (33% OF STUDENTS), AN INTERMEDIATE (39.6%), AND A TROUBLED (27.4%) TRAJECTORY. THE MOSTLY-ENGAGED TRAJECTORY WAS STABLE WITH INFREQUENT CHANGES, SCORED THE HIGHEST, AND WAS LESS LIKELY TO DROP OUT. THE TROUBLED TRAJECTORY SHOWED EARLY DISENGAGEMENT, FREQUENT DROPOUTS AND SCORED THE LOWEST GRADES. THE RESULTS OF OUR STUDY SHOW HOW TO IDENTIFY EARLY PROGRAM DISENGAGEMENT (ACTIVITIES WITHIN THE THIRD DECILE) AND WHEN STUDENTS MAY DROP OUT (FIRST YEAR AND EARLY SECOND YEAR).
BIG DATA AND LEARNING ANALYTICS' PROMISE TO REVOLUTIONISE EDUCATIONAL INSTITUTIONS, ENDEAVOURS, AND ACTIONS THROUGH MORE AND BETTER DATA IS NOW COMPELLING. MULTIPLE, AND CONTINUALLY UPDATING, DATA SETS PRODUCE A NEW SENSE OF PERSONALISED LEARNING'. A CRUCIAL ATTRIBUTE OF THE DATAFICATION, AND SUBSEQUENT PROFILING, OF LEARNER BEHAVIOUR AND ENGAGEMENT IS THE CONTINUAL MODIFICATION OF THE LEARNING ENVIRONMENT TO INDUCE GREATER LEVELS OF INVESTMENT ON THE PARTS OF EACH LEARNER. THE ASSUMPTION IS THAT MORE AND BETTER DATA, GATHERED FASTER AND FED INTO EVER-UPDATING ALGORITHMS, PROVIDE MORE COMPLETE TOOLS TO UNDERSTAND, AND THEREFORE IMPROVE, LEARNING EXPERIENCES THROUGH ADAPTIVE PERSONALISATION. THE ARGUMENT IN THIS PAPER IS THAT LEARNING PERSONALISATION NAMES A NEW LOGISTICS OF INVESTMENT AS THE COMMON SENSE' OF THE SCHOOL, IN WHICH DISCIPLINARY EDUCATION IS BOTH DISAPPEARING AND GIVING WAY TO FRIGHTFUL CONTINUAL TRAINING, TO CONTINUAL MONITORING'.
MEANINGFUL LEARNING IS PROVEN TO OCCUR WHEN IT IS DONE IN A PRACTICAL, RELEVANT AND PARTICIPATED WAY. THUS, THERE IS AN URGENT NEED TO DEVELOP INNOVATIVE PEDAGOGICAL METHODS WHICH PROMOTE THIS TYPE OF LEARNING PROCESS. A GROWING BODY OF RESEARCH HAS BEEN PRESENTING AN INCREASED AWARENESS OF THE CRITICAL ROLE OF ACTIVE LEARNING METHODOLOGIES IN FOSTERING STUDENTS' INVOLVEMENT IN THEIR OWN LEARNING, BY PROMOTING CRITICAL THINKING, CREATIVITY, TEAM-WORK, I.E., BY CREATING AN ENVIRONMENT FULL OF MEANINGFUL LEARNING ACTIVITIES. THESE METHODOLOGIES SHIFT THE LEARNING RESPONSIBILITY TO THE STUDENT (STUDENT-CENTRED LEARNING) AND EMPHASIS THE TEACHER'S ROLE AS A FACILITATOR IN MODERATING THE TEACHING AND LEARNING PROCESS. IN THIS WORK, WE PROPOSE A NEW TEACHING FRAMEWORK AND WE USE LEARNING ANALYTICS TOOLS IN ORDER TO MEASURE THE IMPACT OF NEW TEACHING METHODOLOGIES APPLIED IN MATH COURSES IN BACHELOR ENGINEERING DEGREES. THE PROPOSED TEACHING FRAMEWORK CONSISTS OF THE APPLICATION OF ACTIVE LEARNING TECHNIQUES, SUCH AS JIGSAW AND EDUSCRUM, IN THE CURRICULAR UNIT OF LINEAR ALGEBRA AND ANALYTICAL GEOMETRY, IN THE FIRST SEMESTER OF THE ACADEMIC YEAR OF 2017/2018.
LEARNING ANALYTICS (LA) DEALS WITH THE DEVELOPMENT OF METHODS THAT HARNESS EDUCATIONAL DATA SETS TO SUPPORT THE LEARNING PROCESS. TO ACHIEVE PARTICULAR LEARNER-CENTERED LA OBJECTIVES SUCH AS INTELLIGENT FEEDBACK, ADAPTATION, PERSONALIZATION, OR RECOMMENDATION, LEARNER MODELING IS A CRUCIAL TASK. LEARNER MODELING ENABLES TO ACHIEVE ADAPTIVE AND PERSONALIZED LEARNING ENVIRONMENTS, WHICH ARE ABLE TO TAKE INTO ACCOUNT THE HETEROGENEOUS NEEDS OF LEARNERS AND PROVIDE THEM WITH TAILORED LEARNING EXPERIENCE SUITED FOR THEIR UNIQUE NEEDS. IN THIS PAPER, WE FOCUS ON LEARNER MODELING IN ACADEMIC NETWORKS. WE PRESENT THEORETICAL, DESIGN, IMPLEMENTATION, AND EVALUATION DETAILS OF PALM, A SERVICE FOR PERSONAL ACADEMIC LEARNER MODELING. THE PRIMARY AIM OF PALM IS TO HARNESS THE DISTRIBUTED PUBLICATION INFORMATION TO BUILD AN ACADEMIC LEARNER MODEL.
THE PURPOSE OF THIS STUDY IS TO DETECT AT-RISK STUDENTS IN ONLINE DISCUSSION FORUMS IN THE PLATFORM MOODLE. IN THIS CONTEXT, THIS PAPER PRESENTS A SIMULATION STUDY WITH A LARGE DATABASE CENTRALIZED ON 4000 LEARNERS AND 117988 INTERACTIONS TO EVALUATE STUDENTS. TO ACHIEVE OUR GOAL, WE ARE USED GEPHI AS A SOCIAL NETWORK LEARNING ANALYTICS TOOL TO VISUALIZE THE LEARNERS SOCIAL NETWORK GRAPHS AND TO IMPLEMENT THREE ALGORITHMS OF LAYOUT AND CLUSTERING TO IDENTIFY THE LEARNING COMMUNITY IN ORDER TO PREDICT THE STUDENTS' STATUS (AT-RISK OR SAFE). FINALLY, WE DISCUSSED THE RESULT OF EACH ALGORITHM TO IMPROVE THE BEST ONE.
LEARNING ANALYTICS AIMS AT SUPPORTING THE UNDERSTANDING OF LEARNING MECHANISMS AND THEIR EFFECTS BY MEANS OF DATA-DRIVEN STRATEGIES. LA APPROACHES COMMONLY FACE TWO BIG CHALLENGES: FIRST, DUE TO PRIVACY REASONS, MOST OF THE ANALYZED DATA ARE NOT IN THE PUBLIC DOMAIN. SECONDLY, THE OPEN DATA COLLECTIONS, WHICH COME FROM DIVERSE LEARNING CONTEXTS, ARE QUITE HETEROGENEOUS. THEREFORE, THE RESEARCH FINDINGS ARE NOT EASILY REPRODUCIBLE AND THE PUBLICLY AVAILABLE DATASETS ARE OFTEN TOO SMALL TO ENABLE FURTHER DATA ANALYTICS. TO OVERCOME THESE ISSUES, THERE IS AN INCREASING NEED FOR INTEGRATING OPEN LEARNING DATA INTO UNIFIED MODELS. THIS PAPER PROPOSES UNIFORM, AN OPEN RELATIONAL DATABASE INTEGRATING VARIOUS LEARNING DATA SOURCES. IT PRESENTS ALSO A MACHINE LEARNING SUPPORTED APPROACH TO AUTOMATICALLY EXTENDING THE INTEGRATED DATASET AS SOON AS NEW DATA SOURCES BECOME AVAILABLE. THE PROPOSED APPROACH EXPLOITS A CLASSIFIER TO PREDICT ATTRIBUTE ALIGNMENTS BASED ON THE CORRELATIONS AMONG THE CORRESPONDING TEXTUAL ATTRIBUTE DESCRIPTIONS. THE INTEGRATION PHASE HAS REACHED A PROMISING QUALITY LEVEL ON MOST OF THE ANALYZED BECHMARK DATASETS. FURTHERMORE, THE USABILITY OF THE UNIFORM DATA MODEL HAS BEEN DEMONSTRATED IN A REAL CASE STUDY, WHERE THE INTEGRATED DATA HAVE BEEN EXPLOITED TO SUPPORT LEARNERS' OUTCOME PREDICTION. THE FL-SCORE ACHIEVED ON THE INTEGRATED DATA IS APPROXIMATELY 30% HIGHER THAT THOSE OBTAINED ON THE ORIGINAL DATA.
MAJORITY OF LEARNING ANALYTICS SYSTEMS ARE DESIGNED TO MONITOR AND ANALYZE STUDENTS' ONLINE INTERACTIONS DURING COLLABORATIVE LEARNING. IN THE CASE OF CO-LOCATED COLLABORATIVE LEARNING, STUDENT INTERACTIONS TAKE PLACE IN THE PHYSICAL SPACE AS WELL AS ONLINE. WHILE EXISTING LEARNING MANAGEMENT SYSTEMS PROVIDE SPECIFIC LOGS AND SNAPSHOTS OF STUDENTS' ONLINE RESPONSES THAT ARE AUTOMATICALLY CAPTURED, THE POTENTIAL OF INSIGHTS THAT CAN BE DERIVED FROM STUDENTS' NON-DIGITAL FACE-TO-FACE INTERACTIONS DURING COLLABORATIVE DISCOURSE REMAINS UNTAPPED. IN THIS PAPER, WE PROPOSE AN ARCHITECTURE FOR DATA ACQUISITION AND PROCESSING FROM CO-LOCATED FACE-TO-FACE COLLABORATIVE LEARNING, DESIGNED TO BE SCALABLE BEYOND DYADIC AND TRIADIC COLLABORATIVE LEARNING AND ACROSS DIFFERENT CURRICULA. WE OUTLINE THE SYSTEM DESIGN, CURRENT EXPERIENCE OF DEPLOYMENT ACROSS 4 SESSIONS OF CO-LOCATED COLLABORATIVE LEARNING SESSIONS, AS WELL AS BRIEF EXAMPLES OF ACQUIRED DATA.
COLLABORATIVE PROBLEM SOLVING (CPS) IN VIRTUAL ENVIRONMENTS IS AN INCREASINGLY IMPORTANT CONTEXT OF 21ST CENTURY LEARNING. HOWEVER, OUR UNDERSTANDING OF THIS COMPLEX AND DYNAMIC PHENOMENON IS STILL LIMITED. HERE, WE EXAMINE UNIMODAL PRIMITIVES (ACTIVITY ON THE SCREEN, SPEECH, AND BODY MOVEMENTS), AND THEIR MULTIMODAL COMBINATIONS DURING REMOTE CPS. WE ANALYZE TWO DATASETS WHERE 116 TRIADS COLLABORATIVELY ENGAGED IN A CHALLENGING VISUAL PROGRAMMING TASK USING VIDEO CONFERENCING SOFTWARE. WE INVESTIGATE HOW UI-INTERACTIONS, BEHAVIORAL PRIMITIVES, AND MULTIMODAL PATTERNS WERE ASSOCIATED WITH TEAMS' SUBJECTIVE AND OBJECTIVE PERFORMANCE OUTCOMES. WE FOUND THAT IDLING WITH LIMITED SPEECH (I.E., SILENCE OR BACKCHANNEL FEEDBACK ONLY) AND WITHOUT MOVEMENT WAS NEGATIVELY CORRELATED WITH TASK PERFORMANCE AND WITH PARTICIPANTS' SUBJECTIVE PERCEPTIONS OF THE COLLABORATION. HOWEVER, BEING SILENT AND FOCUSED DURING SOLUTION EXECUTION WAS POSITIVELY CORRELATED WITH TASK PERFORMANCE. RESULTS ILLUSTRATE THAT IN SOME CASES, MULTIMODAL PATTERNS IMPROVED THE PREDICTIONS AND IMPROVED EXPLANATORY POWER OVER THE UNIMODAL PRIMITIVES. WE DISCUSS HOW THE FINDINGS CAN INFORM THE DESIGN OF REAL-TIME INTERVENTIONS FOR REMOTE CPS.
LARGE SWATHS OF DATA ARE READILY AVAILABLE IN VARIOUS FIELDS, AND EDUCATION IS NO EXCEPTION. IN TANDEM, THE IMPETUS TO DERIVE MEANINGFUL INSIGHTS FROM DATA GAINS URGENCY. RECENT ADVANCES IN DEEP LEARNING, PARTICULARLY IN THE AREA OF VOICE AND IMAGE RECOGNITION AND SO-CALLED COMPLETE KNOWLEDGE GAMES LIKE CHESS, GO, AND STARCRAFT, HAVE RESULTED IN A FLURRY OF RESEARCH. USING TWO EDUCATIONAL DATASETS, WE EXPLORE THE UTILITY AND APPLICABILITY OF DEEP LEARNING FOR EDUCATIONAL DATA MINING AND LEARNING ANALYTICS. WE COMPARE THE PREDICTIVE ACCURACY OF POPULAR DEEP LEARNING FRAMEWORKS/LIBRARIES, INCLUDING, KERAS, THEANO, TENSORFLOW, FAST.AI, AND PYTORCH. EXPERIMENTAL RESULTS REVEAL THAT PERFORMANCE, AS ASSESSED BY PREDICTIVE ACCURACY, VARIES DEPENDING ON THE OPTIMIZER USED. FURTHER, FINDINGS FROM ADDITIONAL EXPERIMENTS BY TUNING NETWORK PARAMETERS YIELD SIMILAR RESULTS. MOREOVER, WE FIND THAT DEEP LEARNING DISPLAYS COMPARABLE PERFORMANCE TO OTHER MACHINE LEARNING ALGORITHMS SUCH AS SUPPORT VECTOR MACHINES, K-NEAREST NEIGHBORS, NAIVE BAYES CLASSIFIER, AND LOGISTIC REGRESSION. WE ARGUE THAT STATISTICAL LEARNING TECHNIQUES SHOULD BE SELECTED TO MAXIMIZE INTERPRETABILITY AND SHOULD CONTRIBUTE TO OUR UNDERSTANDING OF EDUCATIONAL AND LEARNING PHENOMENA; HENCE, IN MOST CASES, EDUCATIONAL DATA MINING AND LEARNING ANALYTICS RESEARCHERS SHOULD AIM FOR EXPLANATION OVER PREDICTION.
INTERACTION IS CRITICAL FOR SUCCESSFUL TEACHING AND LEARNING IN A VIRTUAL LEARNING ENVIRONMENT (VLE). THIS PAPER PRESENTS A WEB-BASED INTERACTION-AWARE VLE - WEBINTERA-CLASSROOM - WHICH AIMS TO AUGMENT LEARNING INTERACTIONS BY INCREASING THE LEARNER-TO-CONTENT AND LEARNER-TO-INSTRUCTOR INTERACTIONS. WE DESIGN A UBIQUITOUS INTERACTIVE INTERFACE THAT INTEGRATES A PEN-AND-PAPER INTERFACE WITH A WEB-BASED WHITEBOARD TO PROMOTE EFFECTIVE LEARNER-TO-CONTENT INTERACTIONS. WE DEVELOP A LEARNING ANALYTICS TOOL THAT INSTANTLY SHOWS LEARNERS' LEARNING INTERACTIONS, WITH WHICH INSTRUCTORS CAN SUPERVISE LEARNER-TO-INSTRUCTOR INTERACTIONS. WE IMPLEMENT A HIGH-GRANULARITY LEARNING ANALYTICS ENGINE (HGLAE) TO BROADCAST, RECORD AND PLAY A LECTURE RECORDING. THE HGLAE IDENTIFIES HOTSPOTS IN A LECTURE RECORDING AND RAISES STUDENTS' AWARENESS OF THE HOTSPOTS WHEN THEY WATCH THE RECORDING. WEBINTERA-CLASSROOM HAS BEEN DEPLOYED IN 11 UNIVERSITIES IN CHINA. THE SYSTEM OBTAINED HIGH SATISFACTION IN THE QUESTIONNAIRES (N = 464) AND FACE-TO-FACE INTERVIEWS (N = 60) CONDUCTED WITH THE USERS FROM CHINA JILIANG UNIVERSITY. WE COMPARED THE STUDENTS' PERFORMANCE, RESPECTIVELY, ACHIEVED IN TRADITIONAL CLASSROOM AND IN WEBINTERA-CLASSROOM AT CHINA JILIANG UNIVERSITY DURING 2012-2013. THE RESULT DEMONSTRATES THAT THE STUDENTS COULD GAIN PERFORMANCE IMPROVEMENT BY USING WEBINTERA-CLASSROOM.
THIS PAPER PRESENTS AN EVALUATION OF A LEARNING DASHBOARD THAT SUPPORTS THE DIALOGUE BETWEEN A STUDENT AND A STUDY ADVISOR. THE DASHBOARD WAS DESIGNED, DEVELOPED, AND EVALUATED IN COLLABORATION WITH STUDY ADVISERS. TO ENSURE SCALABILITY TO OTHER CONTEXTS, THE DASHBOARD USES DATA THAT IS COMMONLY AVAILABLE AT ANY HIGHER EDUCATION INSTITUTE. IT VISUALIZES THE GRADES OF THE STUDENT, AN OVERVIEW OF THE PROGRESS THROUGH THE YEAR, HIS/HER POSITION IN COMPARISON WITH PEERS, SLIDERS TO PLAN THE NEXT YEARS AND A PREDICTION OF THE LENGTH OF THE BACHELOR PROGRAM FOR THIS STUDENT IN YEARS BASED ON HISTORIC DATA. THE DASHBOARD WAS DEPLOYED AT KU LEUVEN, BELGIUM AND USED IN SEPTEMBER 2017 TO SUPPORT 224 SESSIONS BETWEEN STUDENTS AND STUDY ADVISERS. WE OBSERVED TWENTY OF THESE CONVERSATIONS. WE ALSO COLLECTED FEEDBACK FROM 101 STUDENTS WITH QUESTIONNAIRES. RESULTS OF OUR OBSERVATIONS INDICATE THAT THE DASHBOARD PRIMARILY TRIGGERS INSIGHTS AT THE BEGINNING OF A CONVERSATION. THE NUMBER OF INSIGHTS AND THE LEVEL OF THESE INSIGHTS (FACTUAL, INTERPRETATIVE AND REFLECTIVE) DEPENDS ON THE CONTEXT OF THE CONVERSATION. MOST INSIGHTS WERE TRIGGERED IN CONVERSATIONS WITH STUDENTS DOUBTING TO CONTINUE THE PROGRAM, INDICATING THAT OUR DASHBOARD IS USEFUL TO SUPPORT DIFFICULT DECISION-MAKING PROCESSES.
THE INCREASING AVAILABILITY OF STATISTICAL DATA RAISES OPPORTUNITIES FOR BIG' DATA AND LEARNING ANALYTICS. HERE, WE REVIEW THE ACADEMIC LITERATURE AND RESEARCH RELATING TO THE USE OF BIG DATA ANALYTICS IN THE PUBLIC SECTOR, AND ITS CONTRIBUTION TO PUBLIC ORGANIZATIONS' PERFORMANCE AND EFFICIENCY. WE OUTLINE THE ADVANTAGES AS WELL AS THE LIMITATIONS OF USING BIG DATA IN PUBLIC SECTOR ORGANIZATIONS AND IDENTIFY RESEARCH GAPS IN RECENT STUDIES AND INTERESTING AREAS FOR FUTURE RESEARCH.
THIS WORK-IN-PROGRESS IN THE INNOVATIVE PRACTICE CATEGORY DESCRIBES THE USE OF MULTIMODAL DATA CAPTURE TO INFORM INSTRUCTORS' AWARENESS OF THEIR ACTIVITIES IN THE CLASSROOM. BROADLY CONSTRUED, LEARNING ANALYTICS IS THE COLLECTION AND ANALYSIS OF DATA IN AN EDUCATIONAL CONTEXT WITH THE AIM OF IMPROVING EDUCATIONAL OUTCOMES. TO CAPTURE A MORE WHOLISTIC CHARACTERIZATION OF AN EDUCATIONAL CONTEXT, THERE HAS BEEN INCREASED INTEREST IN MULTIMODAL DATA SUCH AUDIO, GESTURES, POSITIONING AND MOVEMENT. THESE DATA CAN CHARACTERIZE THE CONTENT DELIVERED AND TEACHING TECHNIQUES EMPLOYED BY THE INSTRUCTOR. INSTRUCTOR REFLECTION ON BOTH MAY LEAD TO IMPROVEMENTS IN INSTRUCTION. PRESENTED HERE IS IATRACER, A LIGHTWEIGHT SYSTEM FOR MULTI-MODAL INSTRUCTOR DATA CAPTURE CONSISTING OF A LAVALIER MICROPHONE PAIRED WITH A POSITIONING BADGE. THE MICROPHONE CAPTURES CLASSROOM AUDIO AND USING GOOGLE CLOUD'S SPEECH-TO-TEXT API WITH DIARIZATION, THE INSTRUCTOR'S SPEECH CAN BE ISOLATED AND TRANSCRIBED. ANALYSIS OF THIS TEXT CAN PROVIDE INSIGHTS INTO WHAT TOPICS WERE COVERED, FOR HOW LONG AND WHAT QUESTIONS WERE ASKED. ADDITIONAL ANALYSIS COULD PROVIDE THE INSTRUCTOR FEEDBACK ON THE DELIVERY (E.G., LONG MONOLOGUES) AND THE LEVEL OF STUDENT INTERACTION (E.G., DIALOGUE, QUESTIONS DIRECTED TOWARDS STUDENTS). NOVEL ASPECTS OF THIS WORK-IN-PROGRESS INCLUDE THE LIGHTWEIGHT, ECONOMICAL NATURE OF THE SYSTEM AND ITS USE OF GOOGLE CLOUD SERVICES. THE INSIGHTS GENERATED BY THE SYSTEM WILL ENABLE FACULTY TO REFLECT UPON THEIR EMPLOYED TEACHING TECHNIQUES AND THE CONTENT OF THEIR INTERACTION WITH STUDENTS. SUCH REFLECTION ENSURES ALIGNMENT OF EMPLOYED TECHNIQUE WITH INTENT.
BIG DATA HAS RAISED CHALLENGES AND OPPORTUNITIES FOR THE EDUCATION SECTOR. EDUCATIONAL ANALYTICS ENCOMPASS A VARIETY OF COMPUTATIONAL TECHNIQUES TO PROCESS EDUCATIONAL BIG DATA FOR EFFECTIVE TEACHING, LEARNING, RESEARCH, SERVICE, AND ADMINISTRATIVE DECISION MAKING. LEARNING ANALYTICS AND ACADEMIC ANALYTICS HAVE BEEN WIDELY DISCUSSED IN THE LITERATURE OF EDUCATION; HOWEVER, KNOWLEDGE ANALYTICS HAVE NOT BEEN DISCUSSED IN THE EDUCATIONAL ANALYTICS FIELD. KNOWLEDGE ANALYTICS ARE A RELATIVELY NEW SUBJECT IN THE KNOWLEDGE MANAGEMENT AREA. KNOWLEDGE ANALYTICS LIE OUTSIDE OF THE DEFINITIONS OF LEARNING ANALYTICS AND ACADEMIC ANALYTICS, AND ENCOMPASS ANALYTICAL ACTIVITIES FOR KNOWLEDGE MANAGEMENT AMONG EDUCATORS IN TEACHING, RESEARCH, AND SERVICES. THIS PAPER DISCUSSES POTENTIAL APPLICATIONS OF KNOWLEDGE ANALYTICS IN EDUCATIONAL INSTITUTIONS AND ISSUES RELATED TO IMPLEMENTATION OF KNOWLEDGE ANALYTICS IN THE EDUCATIONAL ENVIRONMENT.
DESPITE THE PROVEN BENEFITS OF SERIOUS GAMES WHEN COMPARED TO TRADITIONAL E-LEARNING, UPTAKE OF GAME-BASED LEARNING IN MAINSTREAM EDUCATION IS STILL VERY LOW. INCREASING UPTAKE REQUIRES MAKING SUITABLE GAMES EASIER TO DEPLOY AS AN ACTIVITY TYPE IN EXISTING E-LEARNING PLATFORMS, WITHOUT COMPROMISING GAME QUALITY OR INCREASE DEVELOPMENT COST; AND LEVERAGING DATA GENERATED BY PLAYERS TO IMPROVE THEIR LEARNING OUTCOMES. WE BRIEFLY DISCUSS AVAILABLE E-LEARNING STANDARDS TO ADDRESS THESE ISSUES AND DESCRIBE OUR CURRENT STANDARDS-BASED APPROACH TO BOTH GAME DEPLOYMENT AND EVALUATION (INCLUDING LEARNING ANALYTICS).
LEARNING ANALYTICS IN MOBILE LEARNING IS A CHALLENGING RESEARCH TOPIC, DUE TO THE DISTINGUISHING FEATURES OF MOBILE LEARNING. IN FACT, MOBILE LEARNING IS CHARACTERIZED BY THE LEARNERS' MOBILITY, THE POSSIBILITY OF HAVING LOCALIZED DATA AND INFORMATION, THE LARGE AMOUNT OF DATA THAT CAN BE COLLECTED DURING A LEARNING SESSION, THE AFFORDANCES PROVIDED BY THE TECHNOLOGIES AND THE SOCIAL DYNAMICS THAT CHARACTERIZE THE CONTEXT IN WHICH LEARNING TAKES PLACE. AS A CONSEQUENCE, LEARNING ANALYTICS IN MOBILE LEARNING REQUIRES ORIGINAL METHODOLOGICAL APPROACHES WHICH ENRICH TECHNIQUES ALREADY TESTED IN DIFFERENT LEARNING CONTEXTS (E.G., IN VIRTUAL LEARNING ENVIRONMENTS) WITH SPECIFIC STRATEGIES TO DEAL WITH THE COMPLEXITY OF MOBILE LEARNING AND MANAGE THE CORRESPONDING DATASETS. THIS PAPER PRESENTS A TASK-INTERACTION FRAMEWORK TO SUPPORT EDUCATIONAL DECISION-MAKING IN MOBILE LEARNING. THE FRAMEWORK IS BASED ON THE RELATIONSHIPS BETWEEN THE DIFFERENT TYPES OF INTERACTIONS OCCURRING IN A MOBILE LEARNING ACTIVITY AND THE TASKS WHICH ARE PEDAGOGICALLY RELEVANT FOR THE LEARNING ACTIVITY. A CASE STUDY HAS BEEN DESIGNED TO DEMONSTRATE THE APPLICATION OF THE TASK-INTERACTION FRAMEWORK TO LEARNING SCENARIOS BASED ON THE USE OF MOBILE DEVICES. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
THIS PAPER PRESENTS A NEW LEARNING ANALYTICS DASHBOARD WHICH INTEGRATES ALL THE INFORMATION GATHERED BY A VIRTUAL EVALUATION LABORATORY DEPLOYED AT OUR INSTITUTION AND, ALSO, THE COLLABORATIVE EVALUATION FORUMS USED BY STUDENTS IN OUR COURSES. AS AN EXAMPLE, A SUBJECT FOCUSED ON THE CONFIGURATION OF NETWORK SERVICES HAS BEEN CHOSEN TO IMPLEMENT OUR APPROACH. OUR PROPOSAL WILL BE ABLE TO GRAPHICALLY SHOW THE STUDENTS' PROGRESS BOTH IN AN EXPERIMENTAL AND COLLABORATIVE WAY AT THE SAME TIME. THEREFORE, LECTURERS CAN GUIDE EACH STUDENT THROUGH THE LEARNING PROCESS BASED ON HIS/HER PARTICULAR KNOWLEDGE-LEVEL AND GRADE HER/HIM AT THE END OF THE TERM. SOME SPECIFIC TECHNIQUES ARE NEEDED BY THE SYSTEM, IN OUR CASE LEARNING ANALYTICS TECHNIQUES ARE USED, IN ORDER TO OBSERVE THE STUDENTS' BEHAVIOR AND THEIR LEVEL OF PROFICIENCY. IN PARTICULAR, A SET OF EVALUATION EVENTS FOR EACH ACTIVITY, THE STUDENTS' SOCIAL NETWORK, THE STUDENTS' TIMELINE FOR THEIR ACTIVITIES AND SOME RELEVANT METRICS ASSOCIATED TO THEM ARE GIVEN.
THIS PAPER PRESENTS THE MAJOR FINDINGS FROM A STUDY CONDUCTED WITH SIX DIFFERENT UNIVERSITIES IN THE U.S. REGARDING THEIR USE OF THE LEARNING ANALYTICS (LA) CAPABILITIES AVAILABLE WITHIN THEIR LEARNING MANAGEMENT SYSTEMS (LMS). DATA WAS COLLECTED FROM AN ONLINE SURVEY INSTRUMENT, IN-DEPTH INTERVIEWS WITH IT DIRECTORS AND ACADEMIC ADMINISTRATORS, AND A CASE STUDY IN EMBRY-RIDDLE AERONAUTICAL UNIVERSITY. ONE OBSERVATION IS THAT UNIVERSITIES ARE ATTEMPTING TO MAKE BETTER USE OF NEW ANALYTICS FUNCTIONS AND THE DATA STORED IN THE UNIVERSITY LMS IN ORDER TO MAKE MORE INFORMED DECISIONS REGARDING SHORT-TERM AND LONG-TERM GOALS AND OBJECTIVES. THE NEW FUNCTIONS INCLUDE 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.
PARTICIPATION IN COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) CAN BE CHALLENGING FOR MANY STUDENTS. THEREFORE, TIMELY AND PROPER FACILITATION AND ASSESSMENT IS CRUCIAL. IN THE CONTEXT OF ONLINE COLLABORATIVE LEARNING, THE COURSE INSTRUCTOR MAY OFTEN NOT RECEIVE ANY VISUAL CUES AND HAVE TO RELY ON THE WRITTEN CONTRIBUTIONS LEFT BY THE LEARNERS IN THE DISCUSSION FORUM IN. THIS WAY, THE INSTRUCTOR GETS ACCESS TO CERTAIN PARTS OF THE LEARNING PROCESS, WHILE SOME IMPORTANT COMPONENTS MAY REMAIN INVISIBLE. LEARNING ANALYTICS AND LOG DATA HAVE AN IMMENSE POTENTIAL TO ASSIST EDUCATORS IN ASSESSING STUDENT PARTICIPATION AND THE LEVEL OF COGNITIVE ENGAGEMENT, CLOSING SOME OF THESE GAPS AND SHEDDING MORE LIGHT ON THE GROUP LEARNING PROCESSES. ON THE OTHER HAND, THESE DATA CAN BE MISLEADING AND IMPOSE A RISK OF MISINTERPRETATION. THE PAPER DISCUSSES FOUR SCENARIOS OF INVISIBLE STUDENT PARTICIPATION IN ONLINE COLLABORATIVE LEARNING BASED ON A CASE OF A ONE-YEAR ONLINE COLLABORATIVE LEARNING COURSE AND ILLUSTRATED BY DATA FROM A FOCUS GROUP INTERVIEW WITH 14 COURSE PARTICIPANTS. THUS, INVISIBLE PARTICIPATION CAN STEM FROM STUDENTS' LACK OF TECHNICAL EXPERIENCE, LACK OF ESTABLISHED GROUP NORMS, INTERNET ACCESS ISSUES, AND INVOLVEMENT IN INFORMAL LEARNING GROUPS AND ALTERNATIVE COMMUNICATION CHANNELS. THE PAPER THEN DISCUSSES HOW LEARNING ANALYTICS AND LOG DATA CAN ASSIST EDUCATORS IN SCAFFOLDING AND ASSESSING SUCH PARTICIPATION. THE PAPER ALSO OUTLINES SEVERAL CRITICAL ISSUES IN THE AREA OF LEARNING ANALYTICS AND LOG DATA APPLICATION.
MASSIVE ONLINE OPEN COURSES ARE IN THE PROCESS OF RADICALLY CHANGING THE USE OF TECHNOLOGY IN EDUCATION, AS MEASURED BOTH BY THE NUMBER OF ENROLLED STUDENTS AND THE LEVEL OF ACADEMIC EXCELLENCE OF THE INSTITUTIONS THAT ARE DRIVING THEIR ADOPTION. TYPICALLY, ONLINE LEARNING MANAGEMENT SYSTEMS HAVE RELIED ON SIMPLE QUESTIONNAIRES FOR FEEDBACK AND EVALUATION. ON THE OTHER HAND, MOOC PLATFORMS SUCH AS EDX HAVE, FROM THE ONSET, PROVIDED A GREAT FLEXIBILITY IN THIS REGARD, PROVIDING PEER GRADING AND SEVERAL EXAMPLES OF HIGHLY INTERACTIVE ACTIVITIES SUCH AS HANDS-ON SIMULATIONS. THIS PAPER EXPLORES THE INTEGRATION OF SERIOUS GAMES AS A NEW TYPE OF MOOC ACTIVITY, PROVIDING INCREASED ENGAGEMENT AND A VALUABLE SOURCE OF LEARNING ANALYTICS. THE INCLUSION OF SERIOUS GAMES HAS IMPLICATIONS FOR BOTH COURSES AND GAMES. DUE TO THE DIVERSITY OF EXISTING SERIOUS GAME AND MOOC PLATFORMS, WE FOCUS ON THE SPECIFIC CASE OF INTEGRATING EADVENTURE GAMES INTO EDX.
THIS STUDY AIMS AT INVESTIGATING WHICH CUES TEACHERS DETECT AND PROCESS FROM THEIR STUDENTS DURING INSTRUCTION. THIS INFORMATION CAPTURING PROCESS DEPENDS ON TEACHERS' SENSITIVITY, OR AWARENESS, TO STUDENTS' NEEDS, WHICH HAS BEEN RECOGNIZED AS CRUCIAL FOR CLASSROOM MANAGEMENT. WE RECORDED THE GAZE BEHAVIORS OF TWO PRE-SERVICE TEACHERS AND TWO EXPERIENCED TEACHERS DURING A WHOLE MATH LESSON IN PRIMARY CLASSROOMS. THANKS TO A SIMPLE LEARNING ANALYTICS INTERFACE, THE DATA ANALYSIS REPORTS, FIRSTLY, WHICH WERE THE MOST OFTEN TRACKED STUDENTS, IN RELATION WITH THEIR CLASSROOM BEHAVIOR AND PERFORMANCE; SECONDLY, WHICH RELATIONSHIPS EXIST BETWEEN TEACHERS' ATTENTIONAL FREQUENCY DISTRIBUTION AND LABILITY, AND THE OVERALL CLASSROOM CLIMATE THEY PROMOTE, MEASURED BY THE CLASSROOM ASSESSMENT SCORING SYSTEM. RESULTS SHOW THAT PARTICIPANTS' GAZE PATTERNS ARE MAINLY RELATED TO THEIR EXPERIENCE. LEARNING ANALYTICS USE CASES ARE EVENTUALLY PRESENTED, ENABLING RESEARCHERS OR TEACHER TRAINERS TO FURTHER EXPLORE THE EYE-TRACKING DATA.
IN THIS PAPER WE DESCRIBE THE PROPOSAL FOR IMPROVING ANALYTICS EDUCATION WITH HANDS-ON, PROJECT-BASED AND EXPERIMENTAL SCENARIOS. THIS IS THE OBJECTIVE OF THE PELARS PROJECT, WITH TWELVE EUROPEAN PARTNERS THAT HAVE ALL BEEN CHOSEN FOR THEIR SPECIFIC EXPERTISE AND SKILLS IN THE FIELDS OF THIS PROJECT. THE CONSORTIUM INCLUDES SEVEN UNIVERSITIES, THREE SMALL MEDIUM ENTERPRISES AND TWO NON-PROFIT ORGANIZATIONS. THE PARTNERS ARE FROM TEN DIFFERENT COUNTRIES (BELGIUM, DENMARK, GERMANY, IRELAND, ITALY, ROMANIA, SPAIN, SWEDEN, SWITZERLAND AND UNITED KINGDOM). THIS GEOGRAPHICAL DISTRIBUTION IS DUE TO THE DESIRE TO BUILD A CONSORTIUM MAINLY BASED ON A THEMATIC STRUCTURE, AND AIMED AT ACHIEVING A LARGE INTERDISCIPLINARY NETWORK OF EXCELLENCE. IN THIS PAPER WE DESCRIBE HOW WE IDENTIFY CURRICULUM AREAS WITHIN ENGINEERING EDUCATION AT TWO PARTICIPATING UNIVERSITIES: TECHNICAL UNIVERSITY OF DENMARK, DENMARK AND UNIVERSITY OF CRAIOVA, ROMANIA. THIS IS THE BASIS TO IDENTIFICATION OF SUITABLE PELARS TOOLKITS.
EDUCATION RESEARCH HAS EXPLORED THE ROLE OF STUDENTS' AFFECTIVE STATES IN LEARNING, BUT SOME EVIDENCE SUGGESTS THAT EXISTING MODELS MAY NOT FULLY CAPTURE THE MEANING OR FREQUENCY OF HOW STUDENTS TRANSITION BETWEEN DIFFERENT STATES. IN THIS STUDY WE EXAMINE THE PATTERNS OF EDUCATIONALLY-RELEVANT AFFECTIVE STATES WITHIN THE CONTEXT OF BETTY'S BRAIN, AN OPEN-ENDED, COMPUTER-BASED LEARNING SYSTEM USED TO TEACH COMPLEX SCIENTIFIC PROCESSES. WE EXAMINE THREE TYPES OF AFFECTIVE TRANSITIONS BASED ON SIMILARITY WITH THE THEORIZED D'MELLO AND GRAESSER MODEL, TRANSITION BETWEEN TWO AFFECTIVE STATES, AND THE SUSTAINED INSTANCES OF CERTAIN STATES. WE CORRELATE OF THE FREQUENCY OF THESE PATTERNS WITH LEARNING OUTCOMES AND OUR FINDINGS SUGGEST THAT BOREDOM IS A POWERFUL INDICATOR OF STUDENTS' KNOWLEDGE, BUT NOT NECESSARILY INDICATIVE OF LEARNING. WE DISCUSS OUR FINDINGS WITHIN THE CONTEXT OF BOTH RESEARCH AND THEORY ON AFFECT DYNAMICS AND THE IMPLICATIONS FOR PEDAGOGICAL AND SYSTEM DESIGN.
STUDENT RETENTION AND TIMELY GRADUATION ARE ENDURING CHALLENGES IN HIGHER EDUCATION. WITH THE RAPIDLY EXPANDING COLLECTION AND AVAILABILITY OF LEARNING DATA AND RELATED ANALYTICS, STUDENT PERFORMANCE CAN BE ACCURATELY MONITORED, AND POSSIBLY PREDICTED AHEAD OF TIME, THUS, ENABLING EARLY WARNING AND DEGREE PLANNING "EXPERT SYSTEMS" TO PROVIDE DISCIPLINED DECISION SUPPORT TO COUNSELORS, ADVISORS, AND EDUCATORS. PREVIOUS WORK IN EDUCATIONAL DATA MINING HAS EXPLORED MATRIX FACTORIZATION TECHNIQUES FOR GRADE PREDICTION, ALBEIT WITHOUT TAKING CONTEXTUAL INFORMATION INTO ACCOUNT. TEMPORAL INFORMATION SHOULD BE INFORMATIVE AS IT DISTINGUISHES BETWEEN THE DIFFERENT CLASS OFFERINGS AND INDIRECTLY CAPTURES STUDENT EXPERIENCE AS WELL. TO EXPLOIT TEMPORAL AND/OR OTHER KINDS OF CONTEXT, WE DEVELOP THREE APPROACHES UNDER THE FRAMEWORK OF COLLABORATIVE FILTERING (CF). TWO OF THE PROPOSED APPROACHES BUILD UPON COUPLED MATRIX FACTORIZATION WITH A SHARED LATENT MATRIX FACTOR. THE THIRD UTILIZES TENSOR FACTORIZATION TO MODEL GRADES AND THEIR CONTEXT, WITHOUT INTRODUCING A NEW MODE PER CONTEXT DIMENSION AS IS COMMON IN THE CF LITERATURE. THE LATENT FACTORS OBTAINED CAN BE USED TO PREDICT GRADES AND CONTEXT, IF DESIRED. WE EVALUATE THESE APPROACHES ON GRADE DATA OBTAINED FROM THE UNIVERSITY OF MINNESOTA. EXPERIMENTAL RESULTS SHOW THAT FAIRLY GOOD PREDICTION IS POSSIBLE EVEN WITH SIMPLE APPROACHES, BUT VERY ACCURATE PREDICTION IS HARD. THE MORE ADVANCED APPROACHES CAN INCREASE PREDICTION ACCURACY, BUT ONLY UP TO A POINT FOR THE PARTICULAR DATASET CONSIDERED.
THE 2015 REPORT SKILLS, CHALLENGES, AND TRENDS IN INSTRUCTIONAL DESIGN PUBLISHED BY THE ASSOCIATION FOR TALENT DEVELOPMENT ( ATD) AND ITS PARTNERS IDENTIFIED FOURTEEN "EMERGING TRENDS" IN INSTRUCTIONAL DESIGN. THESE TRENDS INCLUDE: ADAPTIVE LEARNING (E.G. PERSONALIZED LEARNING), BADGES, DESIGNING CONTENT FOR USE IN VARIOUS CULTURES OR REGIONS, GAME-BASED LEARNING; INFOGRAPHICS, LEARNING ANALYTICS, MOBILE LEARNING, MOOCS, QR CODES, SOCIAL MEDIA AND SOCIAL LEARNING, STORYTELLING, VIRTUAL SIMULATION, WEARABLE TECHNOLOGIES (E.G. GOOGLE GLASS), AND 3-D PRINTING.
THIS PAPER PROOFS THREE INITIAL HYPOTHESES IN RELATION WITH THE TEAMWORK COMPETENCE, FIRSTLY, THE NEW UNIVERSITY STUDENTS ACCESS TO THE UNIVERSITY WITHOUT ENOUGH KNOWLEDGE ABOUT TEAMWORK COMPETENCE BECAUSE THEY HAVE NOT BEEN TRAINED OR EVALUATED IN THIS COMPETENCE. THIS JUSTIFIES THE USE OF OUR COMPREHENSIVE TRAINING MODEL OF THE TEAMWORK COMPETENCE (CTMTC) METHOD, WHICH ALLOWS INDIVIDUALIZED TRAINING AND ASSESSMENT OF TEAMWORK COMPETENCE IN ALL THE STAGES, INCLUDING THE OUTCOME OF THIS TEAMWORK TOO. SECONDLY, THE REALISED FIELDWORK FOR OBTAINING INDICATORS THAT ALLOW EVALUATE THE REQUIRED EFFORT IN ORDER TO USE CTMTC, FOCUSING ON ASPECTS OF MONITORING (FORMATIVE EVALUATION) AND OUTCOMES (SUMMATIVE EVALUATION) AND ALSO THE ENORMOUS EFFORT IN TIME FOR THE TEACHERS TO CARRY IT OUT, ARE SHOWN. THIRDLY, THE FINAL EVALUATION GRADES OBTAINED BY THE TEAMS AND BY THEIR INDIVIDUAL MEMBERS, WITH THE CTMTC METHOD, JUSTIFY THE NEED OF APPLYING THAT PERSONALIZED EVALUATION METHOD AND THE SEARCH OF TECHNOLOGICAL TOOLS, SUCH AS LEARNING ANALYTICS, TO HELP ITS IMPLEMENTATION.
AIM: THIS ARTICLE EXPLORES THE DEVELOPMENT AND USER EXPERIENCES OF A SUPPLEMENTARY E-LEARNING RESOURCE (CLINICAL ANATOMY E-CASES) FOR MEDICAL STUDENTS, ACROSS A FIVE-YEAR TEACHING PERIOD. METHODS: A SERIES OF ONLINE SUPPLEMENTARY E-LEARNING RESOURCES (THE CLINICAL ANATOMY E-CASES) WERE DEVELOPED AND INTRODUCED TO THE REGIONAL AND CLINICAL ANATOMY MODULE OF THE MEDICINE COURSE. USAGE ANALYTICS WERE COLLECTED ONLINE FROM A COHORT OF THIRD-YEAR MEDICAL STUDENTS AND ANALYSED TO GAIN A BETTER UNDERSTANDING OF HOW STUDENTS UTILISED THESE RESOURCES. RESULTS: KEY RESULTS SHOWED THAT THE STUDENTS USED THE SUPPLEMENTARY LEARNING RESOURCE DURING AND OUTSIDE REGULAR TEACHING HOURS THAT INCLUDES A SIGNIFICANT ACCESS DURING HOLIDAYS. ANALYSIS ALSO SUGGESTED THAT THE RESOURCES WERE FREQUENTLY ACCESSED DURING EXAMINATION PERIODS AND DURING SUBSEQUENT CLINICAL STUDY YEARS (FOURTH OR FIFTH YEARS OF MEDICINE COURSE). INCREASING INTEREST AND POSITIVE FEEDBACK FROM STUDENTS HAS LED TO THE DEVELOPMENT OF A FURTHER SERIES OF E-CASES. CONCLUSION: TAILOR-MADE E-LEARNING RESOURCES PROMOTE CLINICAL ANATOMY LEARNING OUTSIDE CLASSROOM HOURS AND MAKE SUPPLEMENTARY LEARNING A 24/7 TASK.
IT IS A WELL-ESTABLISHED FACT THROUGHOUT THE EDUCATION LITERATURE THAT THERE EXISTS A GAP IN STUDENTS' KNOWLEDGE ON CONCEPTS IN MATHEMATICS. MOST OF THE DISCUSSION ABOUT THIS GAP IS SURROUNDED AROUND THE LACK OF STUDENTS' KNOWLEDGE IN THE AREA OF DEVELOPMENTAL MATHEMATICS. THE GOAL OF THIS PAPER IS TO PRESENT THREE THINGS: -A UNIQUE WAY OF INTEGRATING DATA ON STUDENT PERFORMANCE AND MAPPING IT TO THE CONCEPTS THAT ARE BEING TESTED ON THE ASSESSMENT OF STUDENT PERFORMANCE AT THE UNIVERSITY LEVEL. PRESENT A NEW DATA INTEGRATION AND CONCEPT MAPPING SYSTEM TO UNDERSTAND THE STUDENTS' SHORTCOMINGS IN THE BASIC AREAS OF MATHEMATICS. TO SHOWCASE THE WAYS IN WHICH PROFESSORS, INSTRUCTORS AND TEACHING ASSISTANTS CAN USE THE TOPIC LEVEL DATA TO ENHANCE THEIR INSTRUCTION TO ADDRESS THE KNOWLEDGE GAP. A MAP OF FUTURE WORK THAT INVOLVES PREDICTIVE ANALYTICS FOR RETENTION OF STUDENTS IN THE STEM DISCIPLINES IS PRESENTED.
THE FIELD OF LEARNING ANALYTICS IS PROGRESSING AT A RAPID RATE. NEW TOOLS, WITH EVER-INCREASING NUMBER OF FEATURES AND A PLETHORA OF DATASETS THAT ARE INCREASINGLY UTILIZED DEMONSTRATE THE EVOLUTION AND MULTIFACETED NATURE OF THE FIELD. IN PARTICULAR, THE DEPTH AND SCOPE OF INSIGHT THAT CAN BE GLEANED FROM ANALYSING RELATED DATASETS CAN HAVE A SIGNIFICANT, AND POSITIVE, EFFECT IN EDUCATIONAL PRACTICES. WE INTRODUCE THE CONCEPT OF DEGREE PICTURES, A SYMBOLIC OVERVIEW OF STUDENTS' ACHIEVEMENT. DEGREE PICTURES ARE SMALL VISUALIZATIONS THAT DEPICT GRAPHICALLY 16 CATEGORIES OF OVERALL STUDENT ACHIEVEMENT, OVER THE DURATION OF A HIGHER EDUCATION COURSE. THEY OFFER A QUICK SUMMARY OF STUDENTS' ACHIEVEMENT AND ARE INTENDED TO INITIATE APPROPRIATE RESPONSES, SUCH AS TEACHING AND PASTORAL INTERVENTIONS. THIS CAN ADDRESS THE SUBJECTIVE NATURE OF ASSESSMENT, BY PROVIDING A METHOD FOR EDUCATORS TO CALIBRATE THEIR OWN MARKING PRACTICES BY SHOWING AN OVERVIEW OF ANY COHORT. WE PRESENT A PROTOTYPE IMPLEMENTATION OF DEGREE PICTURES, WHICH WAS EVALUATED WITHIN OUR SCHOOL OF COMPUTER SCIENCE, WITH FAVOURABLE RESULTS.
THE AIM OF THIS STUDY WAS TO EXAMINE THE EFFECTS OF TEACHER SUPPORTING TOOLS THAT PRESENT SUMMARIES, VISUALIZATIONS, AND ANALYSES OF STUDENT PARTICIPATION AND DISCUSSION ON THE WAY TEACHERS GUIDE COLLABORATING GROUPS OF STUDENTS IN A DIGITAL LEARNING ENVIRONMENT. AN EXPERIMENTAL SET-UP WAS USED IN WHICH AUTHENTIC STUDENT DATA WAS CONVERTED TO SIMULATION VIGNETTES THAT PARTICIPANTS COULD INTERACT WITH, ENABLING THEM TO ACT AS THE TEACHER. THE VIGNETTES CONTAINED GROUPS THAT HAD A PROBLEM CONCERNING PARTICIPATION OR DISCUSSION. WHEN PRESENTED WITH THE SUPPORTING TOOLS, TEACHERS AND STUDENT TEACHERS WERE BETTER ABLE TO SPOT THE PROBLEMS, REGARDING PARTICIPATION, INTERVENED MORE OFTEN IN PROBLEMATIC GROUPS AS TIME PROGRESSED, AND DISPLAYED MORE SPECIFIC EXPLANATIONS OF THEIR ACTIONS. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
MASSIVE OPEN ONLINE COURSES DIFFER FROM TRADITIONAL FORMS OF LEARNING, BECAUSE OF THEIR FREE ACCESS, ABUNDANCE OF RESOURCES AND OF USER INTERACTION TOOLS AVAILABLE. NOTWITHSTANDING THESE FEATURES, SOME LEARNERS REACH THEIR ACHIEVEMENT WHILE OTHERS DO NOT. IN LEARNING ANALYTICS LITERATURE THERE ARE MANY CONTRIBUTING RELATING TO HOW MASSIVE OPEN ONLINE COURSES LEARNERS' BEHAVIOURS DETERMINE PERFORMANCE. THE PAPER PROPOSES A STUDY TO FACE THE CONCEPTUALISATION, OPERATIVISATION AND MEASUREMENT OF LEARNING AND ENGAGEMENT, TWO DETERMINISTS OF LEARNERS' PERFORMANCE. MOREOVER, CONSIDERING THE MULTIDIMENSIONAL AND COMPLEX NATURE OF MASSIVE OPEN ONLINE COURSES LEARNING ASSESSMENT, A PARTIAL LEAST SQUARES MODEL IS ALSO PROPOSED.
LEARNING MANAGEMENT SYSTEMS PROVIDE US QUIZZES FOR ASSESSMENT OF STUDENTS' KNOWLEDGE, AND ALSO A STATISTIC REPORT OF THOSE QUIZZES. OUR AIM WAS TO USE STATISTIC REPORTS AS A TOOL FOR QUALITY ASSESSMENT OF THE FIVE QUIZZES, DURING TWO ACADEMIC YEARS 2015/2016 AND 2016/2017. THE EXPERIMENT COVERED FIVE MOODLE QUIZZES, 80 QUESTIONS, AND 55 STUDENTS. WE EXAMINED THE RELIABILITY OF THE QUIZZES AS THEY ARE AN IMPORTANT PART OF LEARNING AND TEACHING PROCESS. ITEM ANALYSIS OF THE QUIZ IS OBSERVED AS THE ONE STEP IN LEARNING ANALYTICS CYCLE. WE ALSO DISCUSS THE CONNECTION BETWEEN QUESTION TYPE AND STUDENTS' RESULTS ON QUIZZES.
WITH THE ADVENT OF PHYSIOLOGICAL COMPUTING SYSTEMS, NEW AVENUES ARE EMERGING FOR THE FIELD OF LEARNING ANALYTICS RELATED TO THE POTENTIAL INTEGRATION OF PHYSIOLOGICAL DATA. TO THIS END, WE DEVELOPED A PHYSIOLOGICAL COMPUTING INFRASTRUCTURE TO COLLECT PHYSIOLOGICAL DATA, SURVEYS, AND BROWSING BEHAVIOR DATA TO CAPTURE STUDENTS' LEARNING JOURNEY IN REMOTE LEARNING. SPECIFICALLY, OUR SOLUTION IS BASED ON THE RASPBERRY PI MINICOMPUTER AND POLAR H10 CHEST BELT. IN THIS WORK-IN-PROGRESS PAPER, WE PRESENT PRELIMINARY RESULTS AND EXPERIENCES WE COLLECTED FROM A FIELD STUDY WITH MEDICAL STUDENTS USING OUR DEVELOPED INFRASTRUCTURE. OUR RESULTS DO NOT ONLY PROVIDE A NEW DIRECTION FOR MORE EFFECTIVELY CAPTURING DIFFERENT TYPES OF DATA IN REMOTE LEARNING BY ADDRESSING THE UNDERLYING CHALLENGES OF REMOTE SETUPS, BUT ALSO SERVE AS A FOUNDATION FOR FUTURE WORK ON DEVELOPING A LESS OBTRUSIVE, (NEAR) REAL-TIME MEASUREMENT METHOD BASED ON THE CLASSIFICATION OF COGNITIVE-AFFECTIVE STATES SUCH AS FLOW OR OTHER LEARNING-RELEVANT CONSTRUCTS WITH THE CAPTURED DATA USING SUPERVISED MACHINE LEARNING.
IN LEARNING SITUATIONS THAT DO NOT OCCUR EXCLUSIVELY ONLINE, THE ANALYSIS OF MULTIMODAL EVIDENCE CAN HELP MULTIPLE STAKEHOLDERS TO BETTER UNDERSTAND THE LEARNING PROCESS AND THE ENVIRONMENT WHERE IT OCCURS. HOWEVER, MULTIMODAL LEARNING ANALYTICS (MMLA) SOLUTIONS ARE OFTEN NOT DIRECTLY APPLICABLE OUTSIDE THE SPECIFIC DATA GATHERING SETUP AND CONDITIONS THEY WERE DEVELOPED FOR. THIS PAPER FOCUSES SPECIFICALLY ON AUTHENTIC SITUATIONS WHERE MMLA SOLUTIONS ARE USED BY MULTIPLE STAKEHOLDERS (E.G., TEACHERS AND RESEARCHERS). IN THIS PAPER, WE PROPOSE AN ARCHITECTURE TO PROCESS MULTIMODAL EVIDENCE OF LEARNING TAKING INTO ACCOUNT THE SITUATION'S CONTEXTUAL INFORMATION. OUR ADAPTER-BASED ARCHITECTURE SUPPORTS THE PREPARATION, ORGANISATION, AND FUSION OF MULTIMODAL EVIDENCE, AND IS DESIGNED TO BE REUSABLE IN DIFFERENT LEARNING SITUATIONS. MOREOVER, TO STRUCTURE AND ORGANISE SUCH CONTEXTUAL INFORMATION, A DATA MODEL IS PROPOSED. FINALLY, TO EVALUATE THE ARCHITECTURE AND THE DATA MODEL, WE APPLY THEM TO FOUR AUTHENTIC LEARNING SITUATIONS WHERE MULTIMODAL LEARNING DATA WAS COLLECTED COLLABORATIVELY BY TEACHERS AND RESEARCHERS.
ACCORDING TO THE NMC HORIZON REPORT (JOHNSON ET AL. IN HORIZON REPORT EUROPE: 2014 SCHOOLS EDITION, PUBLICATIONS OFFICE OF THE EUROPEAN UNION, THE NEW MEDIA CONSORTIUM, LUXEMBOURG, AUSTIN, 2014 [1]), DATA-DRIVEN LEARNING IN COMBINATION WITH EMERGING ACADEMIC AREAS SUCH AS LEARNING ANALYTICS HAS THE POTENTIAL TO TAILOR STUDENTS' EDUCATION TO THEIR NEEDS (JOHNSON ET AL. 2014 [1]). FOCUSING ON THIS AIM, THIS ARTICLE PRESENTS A WEB-BASED (TRAINING) PLATFORM FOR GERMAN-SPEAKING USERS AGED 8-12.OUR OBJECTIVE IS TO SUPPORT PRIMARY-SCHOOL PUPILS-ESPECIALLY THOSE WHO STRUGGLE WITH THE ACQUISITION OF THE GERMAN ORTHOGRAPHY-WITH AN INNOVATIVE TOOL TO IMPROVE THEIR WRITING AND SPELLING COMPETENCIES. ON THIS PLATFORM, WHICH IS FREE OF CHARGE, THEY CAN WRITE AND PUBLISH TEXTS SUPPORTED BY A SPECIAL FEATURE, CALLED THE INTELLIGENT DICTIONARY. IT GIVES AUTOMATIC FEEDBACK FOR CORRECTING MISTAKES THAT OCCURRED IN THE COURSE OF FULFILLING A MEANINGFUL WRITING TASK. CONSEQUENTLY, PUPILS CAN FOCUS ON WRITING TEXTS AND ARE ABLE TO CORRECT TEXTS ON THEIR OWN BEFORE PUBLISHING THEM. ADDITIONALLY, THEY GAIN DEEPER INSIGHTS IN GERMAN ORTHOGRAPHY. EXERCISES WILL BE RECOMMENDED FOR FURTHER TRAINING BASED ON THE SPELLING MISTAKES THAT OCCURRED. THIS ARTICLE COVERS THE BACKGROUND TO GERMAN ORTHOGRAPHY AND ITS TEACHING AND LEARNING AS WELL AS DETAILS CONCERNING THE REQUIREMENTS FOR THE PLATFORM AND THE USER INTERFACE DESIGN. FURTHER, COMBINED WITH LEARNING ANALYTICS WE EXPECT TO GAIN DEEPER INSIGHT INTO THE PROCESS OF SPELLING ACQUISITION WHICH WILL SUPPORT OPTIMIZING OUR EXERCISES AND PROVIDING BETTER MATERIALS IN THE LONG RUN.
THIS SHORT PAPER PRESENTS THE EXPERIENCES OF SEVERAL DUTCH PROJECTS IN THEIR APPLICATION OF THE XAPI STANDARD AND DIFFERENT DESIGN PATTERNS INCLUDING THE DEPLOYMENT OF LEARNING RECORD STORES. IN THIS PAPER WE SHARE INSIGHTS AND ARGUE FOR THE FORMATION OF AN INTERNATIONAL SPECIAL INTEREST GROUP ON INTEROPERABILITY ISSUES TO CONTRIBUTE TO THE OPEN ANALYTICS FRAMEWORK AS ENVISIONED BY SOLAR AND ENACTED BY THE APEREO LEARNING ANALYTICS INITIATIVE. THEREFORE, WE PROVIDE AN OVERVIEW OF THE ADVANTAGES AND DISADVANTAGES OF IMPLEMENTING THE CURRENT XAPI STANDARD BY PRESENTING PROJECTS THAT APPLIED XAPI IN VERY DIFFERENT WAYS FOLLOWED BY THE LESSONS LEARNED.
DEVELOPER-FRIENDLY PROFESSIONAL AUTHORING TOOLS HAVE GREATLY SIMPLIFIED ENTERTAINMENT VIDEOGAME DEVELOPMENT. HOWEVER, THIS SIMPLIFICATION HAD A LIMITED IMPACT ON SERIOUS GAMES, WHICH REQUIRE THE ACTIVE COLLABORATION OF DEVELOPERS WITH EDUCATORS AND OTHER STAKEHOLDERS. TO ADDRESS THIS ISSUE, WE PRESENT UADVENTURE, AN EASY-TO-USE GAME DEVELOPMENT ENVIRONMENT FOR NARRATIVE "POINT-AND-CLICK" GRAPHIC ADVENTURE GAMES. UADVENTURE STARTED AS A RE-IMPLEMENTATION OF THE PREVIOUSLY VALIDATED EADVENTURE AUTHORING TOOL, NOW BUILT ON TOP OF THE UNITY GAME PLATFORM. THE IDEA IS TO ENJOY THE ADVANTAGES OF THE UNITY PROFESSIONAL ENVIRONMENT WHILE REDUCING ITS COMPLEXITY TO ALLOW NON-DEVELOPERS TO AUTHOR SERIOUS GAMES. UADVENTURE IS DESIGNED TO SIMPLIFY THE CREATION OF SERIOUS GAMES THAT INCLUDE EDUCATIONAL-ORIENTED CAPABILITIES SUCH AS LEARNING ANALYTICS WITHOUT REQUIRING PROGRAMMING KNOWLEDGE AND HAS BEEN FORMATIVELY TESTED WITH DIFFERENT TYPES OF USERS AND IN DIFFERENT SETTINGS. INITIAL TESTING WAS CARRIED OUT WITH A GROUP OF HETEROGENEOUS USERS WITH DIFFERENT COMPUTER USAGE PROFILES IN A VOCATIONAL ENVIRONMENT. IN THE SECOND ROUND OF TESTING, IT WAS USED BY STUDENTS OF TWO DIFFERENT UNIVERSITY COURSES TO DEVELOP SERIOUS GAMES THAT INCLUDE EDUCATIONAL FEATURES SUCH AS LOCATION-BASED MECHANICS AND LEARNING ANALYTICS. THE RESULTS OF THESE FORMATIVE EVALUATIONS SHOW THAT UADVENTURE CAN BE USED AS A SERIOUS GAME TEACHING TOOL AND THAT IT SIMPLIFIES THE CREATION OF SERIOUS GAMES WITH EDUCATIONAL FEATURES BY NON-EXPERT AUTHORS.
STUDENTS' ON-TASK MENTAL EFFORT IS AN IMPORTANT FACTOR OF THEIR ACHIEVEMENT BEHAVIOR. MOST STUDIES IN LITERATURE ADOPT SELF-REPORTED METHODS FOR EFFORT ESTIMATION. IN THIS PAPER WE PRESENT AND EVALUATE A METHOD FOR ESTIMATING STUDENTS' ONTASK MENTAL EFFORT DURING TESTING, BASED ONTEMPORAL, USER-GENERATED TRACE DATA. OUR GOAL IS TO CONSTRUCT A METRIC THAT UNOBTRUSIVELY AND SEAMLESSLY MEASURES STUDENTS' ON-TASK MENTAL EFFORT. ADDITIONALLY, WE USE THIS METRIC IN ORDER TO INVESTIGATE THE EFFECT OF EFFORT EXPENDITURE ON STUDENTS' PERFORMANCE DURING LOW-STAKES COMPUTER BASED TESTING PROCEDURES. FOR THAT REASON, WE EXAMINED ON-TASK MENTAL EFFORT - AS DEFINED HERE - WITH TEMPORAL LEARNING ANALYTICS. WE PRESENT THE RESULTS FROM AN EVALUATION CASE STUDY WITH 259 UNDERGRADUATE PARTICIPANT STUDENTS FOR THE COMPUTERS II COURSE. THE RESULTS ARE ENCOURAGING, SINCE THE PROPOSED FACTOR SIGNIFICANTLY IMPROVES THE GENERAL PREDICTION OF STUDENTS' PERFORMANCE.
ANALYSIS OF USER-GENERATED DATA (FOR EXAMPLE PROCESS DATA FROM LOGFILES, LEARNING ANALYTICS, AND DATA MINING) IN COMPUTER-BASED ENVIRONMENTS HAS GAINED MUCH ATTENTION IN THE LAST DECADE AND IS CONSIDERED A PROMISING EVOLVING FIELD IN LEARNING SCIENCES. IN THE AREA OF EDUCATIONAL ASSESSMENT, THE BENEFITS OF SUCH DATA AND HOW TO EXPLOIT THEM ARE INCREASINGLY EMPHASISED. EVEN THOUGH THE USE OF PROCESS DATA IN ASSESSMENT HOLDS SIGNIFICANT PROMISE, THE ETHICAL AND REGULATORY IMPLICATIONS ASSOCIATED WITH IT HAVE NOT BEEN SUFFICIENTLY CONSIDERED. TO ADDRESS THIS ISSUE AND TO PROVIDE AN OVERVIEW OF HOW ETHICAL AND REGULATORY REQUIREMENTS INTERFACE WITH PROCESS DATA FROM ASSESSMENTS IN PRIMARY AND SECONDARY EDUCATION (K-12), WE CONDUCTED A SYSTEMATIC LITERATURE REVIEW. INITIAL RESULTS SHOWED THAT FEW STUDIES CONSIDERED ETHICAL, PRIVACY AND REGULATORY ISSUES IN K-12 ASSESSMENT, PROMPTING A WIDENING OF THE SEARCH CRITERIA TO INCLUDE RESEARCH IN HIGHER EDUCATION ALSO, WHICH IDENTIFIED 22 STUDIES. THE LITERATURE THAT WAS RELEVANT TO OUR RESEARCH QUESTIONS REPRESENTED AN APPROXIMATE BALANCE IN THE NUMBER OF THEORETICAL AND EMPIRICAL STUDIES. THE STUDIES IDENTIFIED AS RELEVANT INTERPRET ISSUES OF PRIVACY LARGELY IN TERMS OF INFORMED CONSENT AND THE RESEARCH PAYS LITTLE ATTENTION TO ETHICAL AND PRIVACY ISSUES IN THE USE OF PROCESS DATA IN ASSESSMENT. THE IMPLICATIONS FOR THE FIELD OF EDUCATIONAL ASSESSMENT AND THE USE OF PROCESS DATA ARE DISCUSSED. THIS INCLUDES THE NEED TO DEVELOP A SPECIFIC CODE OF ETHICS TO GOVERN THE USE OF PROCESS- AND LOGFILE DATA IN EDUCATIONAL ASSESSMENT.
EDUCATION, BIG DATA, AND STUDENT PRIVACY ARE A COMBUSTIBLE MIX. THE IMPROVEMENT OF EDUCATION AND THE PROTECTION OF STUDENT PRIVACY ARE KEY SOCIETAL VALUES. BIG DATA AND LEARNING ANALYTICS OFFER THE PROMISE OF UNLOCKING INSIGHTS TO IMPROVING EDUCATION THROUGH LARGE-SCALE EMPIRICAL ANALYSIS OF DATA GENERATED FROM STUDENT INFORMATION AND STUDENT INTERACTIONS WITH EDUCATIONAL TECHNOLOGY TOOLS. THIS ARTICLE EXPLORES HOW LEARNING TECHNOLOGIES ALSO CREATE ETHICAL TENSIONS BETWEEN PRIVACY AND THE USE OF BIG DATA FOR EDUCATIONAL IMPROVEMENT. WE ARGUE FOR THE NEED TO DEMONSTRATE THE EFFICACY OF LEARNING SYSTEMS WHILE RESPECTING PRIVACY AND HOW TO BUILD ACCOUNTABILITY AND OVERSIGHT INTO LEARNING TECHNOLOGIES. WE CONCLUDE WITH POLICY RECOMMENDATIONS TO ACHIEVE THESE GOALS.
THIS STUDY SEEKS TO EXPLORE THE POTENTIALS AND LIMITATIONS OF USING GOOGLE ANALYTICS TO ANALYZE AND VISUALLY DISPLAY LEARNING ANALYTICS IN THE CONTEXT OF ADVANCED DEGREE ONLINE PROGRAMS. USING A GRADUATE-LEVEL, OPEN-ACCESS ONLINE COURSE AS A CASE, THIS STUDY HAS REPORTED KEY STATISTICS REGARDING LEARNERS' DEMOGRAPHICS, ONLINE LEARNING BEHAVIORS, AND TECHNOLOGY USE OVER A PERIOD OF THREE AND HALF YEARS. THE FINDINGS ARE EXPECTED TO DEMONSTRATE USEFUL FEATURES OF GOOGLE ANALYTICS AND ITS VISUALIZATION TOOLS, EXTEND OUR UNDERSTANDING OF LEARNERS' ONLINE LEARNING BEHAVIORS AT THE INDIVIDUAL COURSE LEVEL, AND INFORM THE DESIGN AND DEVELOPMENT OF ONLINE EDUCATIONAL RESOURCES AND WEBSITES FOR COLLEGIATE CONTEXTS.
THE 2ND LAK FAILATHON WILL BUILD ON THE SUCCESSFUL EVENT IN 2016 AND EXTEND THE WORKSHOP BEYOND DISCUSSING INDIVIDUAL EXPERIENCES OF FAILURE TO EXPLORING HOW THE FIELD CAN IMPROVE, PARTICULARLY REGARDING THE CREATION AND USE OF EVIDENCE. FAILURE IN RESEARCH IS AN INCREASINGLY HOT TOPIC, WITH HIGH-PROFILE CRISES OF CONFIDENCE IN THE PUBLISHED RESEARCH LITERATURE IN MEDICINE AND PSYCHOLOGY. AMONG THE MAJOR FACTORS IN THIS RESEARCH CRISIS ARE THE MANY INCENTIVES TO REPORT AND PUBLISH ONLY POSITIVE FINDINGS. THESE INCENTIVES PREVENT THE FIELD IN GENERAL FROM LEARNING FROM NEGATIVE FINDINGS, AND ALMOST ENTIRELY PRECLUDE THE PUBLICATION OF MISTAKES AND ERRORS. THUS PROVIDING AN ALTERNATIVE FORUM FOR PRACTITIONERS AND RESEARCHERS TO LEARN FROM EACH OTHER'S FAILURES CAN BE VERY PRODUCTIVE. THE FIRST LAK FAILATHON, HELD IN 2016, PROVIDED JUST SUCH AN OPPORTUNITY FOR RESEARCHERS AND PRACTITIONERS TO SHARE THEIR FAILURES AND NEGATIVE FINDINGS IN A LOWER-STAKES ENVIRONMENT, TO HELP PARTICIPANTS LEARN FROM EACH OTHER'S MISTAKES. IT WAS VERY SUCCESSFUL, AND THERE WAS STRONG SUPPORT FOR RUNNING IT AS AN ANNUAL EVENT. THIS WORKSHOP WILL BUILD ON THAT SUCCESS, WITH TWIN OBJECTIVES TO PROVIDE AN ENVIRONMENT FOR INDIVIDUALS TO LEARN FROM EACH OTHER'S FAILURES, AND ALSO TO CO-DEVELOP PLANS FOR HOW WE AS A FIELD CAN BETTER BUILD AND DEPLOY OUR EVIDENCE BASE.
THIS STUDY INVESTIGATES THE IMPACT OF ASSIGNED LEADERSHIP ROLE ON LEARNERS' PARTICIPATION AND LEARNING EXPERIENCE IN ONLINE COLLABORATIVE LEARNING. A TOTAL OF 94 STUDENTS OF AN INSTRUCTIONAL DESIGN COURSE WERE RANDOMLY ASSIGNED INTO 24 SMALL GROUPS, WITH 24 GROUP LEADERS RANDOMLY SELECTED FROM EACH GROUP. DRAWING ON LEARNING ANALYTICS AND SELF-REPORTS, THIS STUDY COMPARED SIX PARTICIPATING BEHAVIORS AND PERCEIVED COLLABORATIVE LEARNING EXPERIENCES BETWEEN GROUP LEADERS AND GROUP MEMBERS DURING A 12-WEEK BLENDED COURSE. THE FINDINGS IN GENERAL SUPPORT THE EFFECT OF ASSIGNED LEADERSHIP ON LEARNING PARTICIPATION RATHER THAN LEARNING EXPERIENCE. THE GROUP LEADERS EXHIBITED INCREASED PARTICIPATING BEHAVIORS SUCH AS LOGIN, FORUM VIEW, CHAT VIEW, AND POSTING WHEN COMPLETING HIGHLY COLLABORATIVE LEARNING TASKS, BUT FAILED TO OUTPERFORM THEIR PEER MEMBERS IN LESS INTERDEPENDENT LEARNING TASKS.
SYSTEMATIC INVESTIGATION OF THE COLLABORATIVE PROBLEM SOLVING PROCESS IN OPEN-ENDED, HANDS-ON, PHYSICAL COMPUTING DESIGN TASKS REQUIRES A FRAMEWORK THAT HIGHLIGHTS THE MAIN PROCESS FEATURES, STAGES AND ACTIONS THAT THEN CAN BE USED TO PROVIDE 'MEANINGFUL' LEARNING ANALYTICS DATA. THIS PAPER PRESENTS AN ANALYSIS FRAMEWORK THAT CAN BE USED TO IDENTIFY CRUCIAL ASPECTS OF THE COLLABORATIVE PROBLEM SOLVING PROCESS IN PRACTICE-BASED LEARNING ACTIVITIES. WE DEPLOYED A MIXED-METHODS APPROACH THAT ALLOWED US TO GENERATE AN ANALYSIS FRAMEWORK THAT IS THEORETICALLY ROBUST, AND GENERALIZABLE. ADDITIONALLY, THE FRAMEWORK IS GROUNDED IN DATA AND HENCE APPLICABLE TO REAL-LIFE LEARNING CONTEXTS. THIS PAPER PRESENTS HOW OUR FRAMEWORK WAS DEVELOPED AND HOW IT CAN BE USED TO ANALYSE DATA. WE ARGUE FOR THE VALUE OF EFFECTIVE ANALYSIS FRAMEWORKS IN THE GENERATION AND PRESENTATION OF LEARNING ANALYTICS FOR PRACTICE-BASED LEARNING ACTIVITIES.
THIS ARTICLE PRESENTS THE PRELIMINARY RESULTS OF AN INVESTIGATION PROJECT ABOUT NEW CLASSROOM TECHNOLOGIES IN ONLINE LEARNING, WHOSE MAIN OBJECTIVE IS THE DESIGN OF A NEW TECHNOLOGICAL TOOL IN ENVIROMENT ONLINE, THROUGH PERSONALIZED TUTORING. THE MODEL HAS SIX ELEMENTS: ACCESS AND PERMANENCE, USABILITY, ACTIVITY AND PARTICIPATION, COMMUNICATION, INTERNAL TRAJECTORY AND PERSONAL INTERACTION DYNAMICS. IT IS BASED ON THE PRINCIPLE OF LEARNING ANALYTICS WITH THE SUPPORT OF BIG DATA, AND IS FOCUSED ON ELEMENTS OF LEARNING WITH FORMATIVE AND EVALUATIVE COMPONENTS. AS PART OF THE MODEL, A ACCEPTABILITY MATRIX WAS ELABORATED WITH STATISTICAL FUNCTIONS THAT SHOW SIGNALIZED INDICATIONS, ALERTING THE TUTOR ABOUT THE PERFORMANCE OF EACH STUDENT TYPIFIED AS SUPERIOR, HIGH, BASIC, LOW AND VERY LOW. WHEN APPLYING THE MATRIX IN PILOT TESTS, IT WAS CONCEIVED AS AN INSTRUMENT OF APPROACH BETWEEN TUTOR AND STUDENT THAT ALLOWS A PERSONALIZED FOLLOW-UP AND ITS RESULT IS REFLECTED IN AN INCREASE IN ACADEMIC PERFORMANCE.
IN THIS PAPER, WE PROPOSE A COUNTERFACTUAL EXPLANATIONS-BASED APPROACH TO PROVIDE AN AUTOMATIC AND INTELLIGENT RECOMMENDATION THAT SUPPORTS STUDENT'S SELF-REGULATION OF LEARNING IN A DATA-DRIVEN MANNER, AIMING TO IMPROVE THEIR PERFORMANCE IN COURSES. EXISTING WORK UNDER THE FIELDS OF LEARNING ANALYTICS AND AI IN EDUCATION PREDICT STUDENTS' PERFORMANCE AND USE THE PREDICTION OUTCOME AS FEEDBACK WITHOUT EXPLAINING THE REASONS BEHIND THE PREDICTION. OUR PROPOSED APPROACH DEVELOPED AN ALGORITHM THAT EXPLAINS THE ROOT CAUSES BEHIND STUDENT'S PERFORMANCE DECLINE AND GENERATES DATA-DRIVEN RECOMMENDATIONS FOR ACTION. THE EFFECTIVENESS OF THE PROPOSED PREDICTIVE MODEL THAT CONSTITUTES THE INTELLIGENT RECOMMENDATIONS IS EVALUATED, WITH RESULTS DEMONSTRATING HIGH ACCURACY.
READING, BE IT INTENSIVE OR EXTENSIVE, IS ONE OF THE KEY SKILLS REQUIRED TO MASTER ENGLISH AS A FOREIGN LANGUAGE (EFL) LEARNER. COMPUTERIZED E-BOOK SYSTEMS PROVIDE CONVENIENT ACCESS TO LEARNING MATERIALS INSIDE AND OUTSIDE CLASS. STUDENTS MAY REGULARLY CHECK THE MEANING OF A WORD OR EXPRESSION USING A SEPARATE TOOL TO PROGRESS ON THEIR READING, WHICH IS NOT ONLY DISRUPTIVE BUT CAN LEAD TO OTHER LEARNING PROBLEMS. AN EXAMPLE OF A PARTICULAR ISSUE FACED IN EFL IS WHEN A STUDENT LEARNS AN INAPPROPRIATE MEANING OF A POLYSEMOUS WORD FOR THE CONTEXT IN WHICH IT IS PRESENTED. THIS IS ALSO A PROBLEM FOR TEACHERS AS THEY OFTEN NEED TO INVESTIGATE THE CAUSE. IN THIS PAPER, WE PROPOSE A SMART DICTIONARY INTEGRATED INTO AN E-BOOK READING PLATFORM. IT ALLOWS THE LEARNER TO SEARCH AND NOTE WORD DEFINITIONS DIRECTLY WITH THE PURPOSE OF REDUCING CONTEXT SWITCHING AND IMPROVE VOCABULARY RETENTION. FINALLY, WE PROPOSE THAT LEARNER INTERACTIONS WITH THE SYSTEM CAN BE ANALYZED TO SUPPORT EFL TEACHERS IN IDENTIFYING POSSIBLE PROBLEMS THAT ARISE THROUGH DICTIONARY USE WHILE READING.
THIS POSTER WILL BE A CHANCE FOR A WIDER LAK AUDIENCE TO ENGAGE WITH THE 2ND LAK FAILATHON WORKSHOP. BOTH OF THESE WILL BUILD ON THE SUCCESSFUL FAILATHON EVENT IN 2016 AND EXTEND BEYOND DISCUSSING INDIVIDUAL EXPERIENCES OF FAILURE TO EXPLORING HOW THE FIELD CAN IMPROVE, PARTICULARLY REGARDING THE CREATION AND USE OF EVIDENCE. FAILURE IN RESEARCH IS AN INCREASINGLY HOT TOPIC, WITH HIGH-PROFILE CRISES OF CONFIDENCE IN THE PUBLISHED RESEARCH LITERATURE IN MEDICINE AND PSYCHOLOGY. AMONG THE MAJOR FACTORS IN THIS RESEARCH CRISIS ARE THE MANY INCENTIVES TO REPORT AND PUBLISH ONLY POSITIVE FINDINGS. THESE INCENTIVES PREVENT THE FIELD IN GENERAL FROM LEARNING FROM NEGATIVE FINDINGS, AND ALMOST ENTIRELY PRECLUDE THE PUBLICATION OF MISTAKES AND ERRORS. THUS PROVIDING AN ALTERNATIVE FORUM FOR PRACTITIONERS AND RESEARCHERS TO LEARN FROM EACH OTHER'S FAILURES CAN BE VERY PRODUCTIVE. THE FIRST LAK FAILATHON, HELD IN 2016, PROVIDED JUST SUCH AN OPPORTUNITY FOR RESEARCHERS AND PRACTITIONERS TO SHARE THEIR FAILURES AND NEGATIVE FINDINGS IN A LOWER-STAKES ENVIRONMENT, TO HELP PARTICIPANTS LEARN FROM EACH OTHER'S MISTAKES. IT WAS VERY SUCCESSFUL, AND THERE WAS STRONG SUPPORT FOR RUNNING IT AS AN ANNUAL EVENT. THE 2ND LAK FAILATHON WORKSHOP WILL BUILD ON THAT SUCCESS, WITH TWIN OBJECTIVES TO PROVIDE AN ENVIRONMENT FOR INDIVIDUALS TO LEARN FROM EACH OTHER'S FAILURES, AND ALSO TO CO-DEVELOP PLANS FOR HOW WE AS A FIELD CAN BETTER BUILD AND DEPLOY OUR EVIDENCE BASE. THIS POSTER IS AN OPPORTUNITY FOR WIDER FEEDBACK ON THE PLANS DEVELOPED IN THE WORKSHOP, WITH INTERACTIVE USE OF STICKY NOTES TO ADD NEW IDEAS AND COLOURED DOTS TO ILLUSTRATE PRIORITISATION. THIS BROADENS THE PARTICIPANT BASE IN THIS IMPORTANT WORK, WHICH SHOULD IMPROVE THE QUALITY OF THE PLANS AND THE COMMITMENT OF THE COMMUNITY TO DELIVERING THEM.
USING THE TRENDS OF ESTIMATED ABILITIES IN TERMS OF ITEM RESPONSE THEORY FOR ONLINE TESTING, WE CAN PREDICT THE SUCCESS/FAILURE STATUS FOR THE FINAL EXAMINATION TO EACH STUDENT AT EARLY STAGES IN COURSES. IN PREDICTION, WE APPLIED THE NEWLY DEVELOPED NEAREST NEIGHBOR METHOD FOR DETERMINING THE SIMILARITY IN THE TRENDS OF ESTIMATED ABILITIES. THIS PAPER SHOWS THAT THE USE OF THE LEARNING ANALYTICS INCORPORATING THE TRENDS FOR ABILITIES IS EFFECTIVE.
EXISTING TOOLS AIMTO DETECT UNIVERSITY STUDENTSWITH EARLY DIAGNOSIS OF DYSLEXIA OR READING DIFFICULTIES, BUT THERE ARE NOT DEVELOPED TOOLS THAT LET THOSE STUDENTS BETTER UNDERSTAND SOME ASPECTS OF THEIR DIFFICULTIES. IN THIS PAPER, A DASHBOARD FOR VISUALIZING AND INSPECTING EARLY DETECTED READING DIFFICULTIES AND THEIR CHARACTERISTICS, CALLED PADA (ACRONYMFOR THE SPANISH NAME PANEL DE ANALITICAS DE APRENDIZAJE DEDISLEXIA EN ADULTOS), IS PRESENTED. PADA IS A WEB-BASED TOOL DESIGNED TO FACILITATE THE CREATION OF DESCRIPTIVE VISUALIZATIONS REQUIRED FOR A BETTER UNDERSTANDING BY STUDENTS ABOUT THEIR LEARNERMODEL. THROUGH INFORMATION VISUALIZATION TECHNIQUES, PADA SHOWS STUDENTS THE KNOWLEDGE IN THEIR LEARNER MODELS IN ORDER TO HELP THEMTO INCREASE THEIR AWARENESS AND TO SUPPORT REFLECTION AND SELF-REGULATION ABOUT THEIR DIFFICULTIES IN READING. PADA PROVIDES DIFFERENT LEARNING ANALYTICS ON READING PERFORMANCE OF STUDENTS, SO THAT THEY CAN SELF-IDENTIFY THEIR STRENGTHS AND WEAKNESSES AND SELF-REGULATE THEIR LEARNING. THIS PAPER DESCRIBES EXAMPLES THAT COVER A VARIETY OF VISUALIZATIONS (BAR-CHARTS, LINE-CHARTS, AND PIE-CHARTS) TO SHOW USER MODEL FRAGMENTS AS PERSONAL DETAILS, READING PROFILES, LEARNING STYLES, AND COGNITIVE TRAITS OF THE STUDENTS. WE TESTED PADA WITH 26 STUDENTS (AGED 21-53 YEARS) OF DIFFERENT ACADEMIC PROGRAMS AND LEVELS, DYSLEXIC AND NON-DYSLEXIC. THE RESULTS SHOW THAT PADA CAN ASSIST STUDENTS IN CREATING AWARENESS, AND HELP THEMTO UNDERSTAND THEIR DIFFICULTIES ASSOCIATED WITH THE READING TASKS, ASWELL AS FACILITATE REFLECTION AND SELF-REGULATION IN THE LEARNING PROCESS. IMPLICATIONS FOR THE DESIGN OF LEARNING ANALYTICS ARE DISCUSSED AND DIRECTIONS FOR FUTURE WORK ARE OUTLINED.
FEEDBACK IS A CRUCIAL ELEMENT IN HELPING STUDENTS IDENTIFY GAPS AND ASSESS THEIR LEARNING PROGRESS. IN ONLINE COURSES, FEEDBACK BECOMES EVEN MORE CRITICAL AS IT IS ONE OF THE RESOURCES WHERE THE TEACHER INTERACTS DIRECTLY WITH THE STUDENT. HOWEVER, WITH THE GROWING NUMBER OF STUDENTS ENROLLED IN ONLINE LEARNING, IT BECOMES A CHALLENGE FOR INSTRUCTORS TO PROVIDE GOOD QUALITY FEEDBACK THAT HELPS THE STUDENT SELF-REGULATE. IN THIS CONTEXT, THIS PAPER PROPOSED A CONTENT ANALYSIS OF FEEDBACK TEXT PROVIDED BY INSTRUCTORS BASED ON DIFFERENT INDICATORS OF GOOD FEEDBACK. A RANDOM FOREST CLASSIFIER WAS TRAINED AND EVALUATED AT DIFFERENT FEEDBACK LEVELS. THE RESULTS ACHIEVED OUTCOMES UP TO 87% AND 0.39 OF ACCURACY AND COHEN'S KAPPA, RESPECTIVELY. THE PAPER ALSO PROVIDES INSIGHTS INTO THE MOST INFLUENTIAL TEXTUAL FEATURES OF FEEDBACK THAT PREDICT FEEDBACK QUALITY.
MANY QUALITY ASSURANCE SYSTEMS RELY ON HIGH-STAKES ASSESSMENT FOR COURSE CERTIFICATION. SUCH METHODS ARE NOT AS OBJECTIVE AS THEY MIGHT APPEAR; THEY CAN HAVE DETRIMENTAL EFFECTS ON STUDENT MOTIVATION AND MAY LACK RELEVANCE TO THE NEEDS OF DEGREE COURSES INCREASINGLY ORIENTED TO VOCATIONAL UTILITY. ALTERNATIVE ASSESSMENT METHODS CAN SHOW GREATER FORMATIVE AND MOTIVATIONAL VALUE FOR STUDENTS BUT ARE NOT WELL SUITED TO THE DEMANDS OF COURSE CERTIFICATION. THE WIDESPREAD USE OF VIRTUAL LEARNING ENVIRONMENTS AND ELECTRONIC PORTFOLIOS GENERATES SUBSTANTIAL LEARNER ACTIVITY DATA TO ENABLE NEW WAYS OF MONITORING AND ASSESSING STUDENTS THROUGH LEARNING ANALYTICS. THESE EMERGING PRACTICES HAVE THE POTENTIAL TO SQUARE THE CIRCLE BY GENERATING OBJECTIVE, SUMMATIVE REPORTS FOR COURSE CERTIFICATION WHILE AT THE SAME TIME PROVIDING FORMATIVE ASSESSMENT TO PERSONALISE THE STUDENT EXPERIENCE. THIS PAPER INTRODUCES CONCEPTUAL MODELS OF ASSESSMENT TO EXPLORE HOW TRADITIONAL RELIANCE ON NUMBERS AND GRADES MIGHT BE DISPLACED BY NEW FORMS OF EVIDENCE-INTENSIVE STUDENT PROFILING AND ENGAGEMENT.
IN THIS ARTICLE WE INTRODUCE LEARNING ANALYTICS VISUALIZATIONS BASED ON CONTENT ANALYSIS PRINCIPLES THAT ARE IMPLEMENTED IN TUT CIRCLE, A SOCIAL MEDIA-ENHANCED LEARNING ENVIRONMENT DEVELOPED BY THE INTELLIGENT INFORMATION SYSTEMS LABORATORY (IISLAB) AT TAMPERE UNIVERSITY OF TECHNOLOGY IN FINLAND. TUT CIRCLE CONTAINS ALL THE BASIC FUNCTIONALITIES OF A MODERN SOCIAL MEDIA SERVICE, SUPPORTING COMMUNICATION, COLLABORATION, NETWORKING, AND INFORMATION SHARING [1]. DATA ABOUT STUDENTS' ACTIONS RECORDED IN THE LOG DATA OF TUT CIRCLE IS ANALYZED AND VISUALIZED IN ORDER TO SUPPORT MONITORING AND TRACKING STUDENTS' ACTIVITY AND PARTICIPATION. TUT CIRCLE PROVIDES SEVERAL DIFFERENT KINDS OF INTERACTIVE VISUALIZATIONS FOR TEACHERS AND STUDENTS. ACCORDING TO AN EARLIER STUDY THE MOST RELEVANT AND MOTIVATING VISUALIZATIONS FOR STUDENTS IN TUT CIRCLE WERE BASED ON CONTENT ANALYSIS. THEY WERE MORE USEFUL IN LEARNING AND STUDYING ACCORDING TO STUDENTS THAN, FOR EXAMPLE, VISUALIZATIONS OF STUDENT ACTIVITY OR NETWORK VISUALIZATIONS ILLUSTRATING READING AND COMMENTING ON EACH OTHER'S CONTENTS [2].
THE PREDICTION AND CLASSIFICATION OF STUDENT PERFORMANCE HAS ALWAYS BEEN A CENTRAL CONCERN WITHIN HIGHER EDUCATION INSTITUTIONS. IT IS THEREFORE NATURAL FOR HIGHER EDUCATION INSTITUTIONS TO HARVEST AND ANALYSE STUDENT DATA TO INFORM DECISIONS ON EDUCATION PROVISION IN RESOURCE CONSTRAINED SOUTH AFRICAN ENVIRONMENTS. ONE OF THE DRIVERS FOR THE USE OF LEARNING AND ACADEMIC ANALYTICS IS THE PRESSURES FOR GREATER ACCOUNTABILITY IN THE AREAS OF IMPROVED LEARNING OUTCOMES AND STUDENT SUCCESS. ADDED TO THIS IS THE PRESSURE ON LOCAL GOVERNMENT TO PRODUCE WELL-EDUCATED POPULACE TO PARTICIPATE IN THE ECONOMY. THE COUNTER DISCOURSE IN THE FIELD OF BIG DATA CAUTIONS AGAINST ALGOCRACY WHERE ALGORITHMS TAKEOVER THE HUMAN PROCESS OF DEMOCRATIC DECISION MAKING. PROPONENTS OF THIS VIEW ARGUE THAT WE RUN THE RISK OF CREATING INSTITUTIONS THAT ARE BEHOLDEN TO ALGORITHMS PREDICTING STUDENT SUCCESS BUT ARE UNSURE OF HOW THEY WORK AND ARE TOO AFRAID TO IGNORE THEIR GUIDANCE. THIS PAPER AIMS TO DISCUSS THE ETHICS, VALUES, AND MORAL ARGUMENTS REVOLVING THE USE OF BIG DATA USING A PRACTICAL DEMONSTRATION OF LEARNING ANALYTICS APPLIED AT UNISA.
THE EDUCATION IN INFORMAL LEARNING CONTEXTS IS SPREADING OVER THE WORLD. IN THESE CONTEXTS, FROM A PEDAGOGICAL POINT OF VIEW, THE MOST USED APPROACHES IS THE SOCIAL LEARNING AND, IN PARTICULAR THE COMMUNITIES OF PRACTICE (COP). FROM A TECHNOLOGICAL POINT OF VIEW, THE SOCIAL NETWORK IS THE FRAMEWORK MOST USED TO ALLOW USERS TO INTERACT AND TO SHARE RESOURCES. SEVERAL ASPECTS HAVE BEEN IDENTIFIED AS KEY FACTORS FOR THE SUCCESS OF THE COMMUNITY, THE ENGAGEMENT IS ONE OF THE MOST IMPORTANT. THUS, IN ORDER TO GUARANTEE A SUCCESSFUL LEARNING PROCESS IN A COP IT IS NECESSARY TO DETECT AND MONITOR USER'S ENGAGEMENT IN A CONTINUOUS AND UNOBTRUSIVE WAY. THE RESEARCH PROPOSES A MODEL TO DETECT AND MEASURE THE ENGAGEMENT IN ONLINE COMMUNITIES BY MEANS SOCIAL LEARNING ANALYTICS FROM LOG FILES.
MASSIVE OPEN ONLINE COURSES (MOOCS) ARE ALLOWING EDUCATION TO BECOME ACCESSIBLE TO ALL LEARNERS. HONS EVER, COMPUTERS ARE CURRENTLY NOT ABLE TO PROVIDE THE SAME INDIVIDUAL EXPERTISE AND SUPPORT AS A HUMAN INSTRUCTOR, MAKING IT DIFFICULT TO TAILOR THE COURSE FOR EACH STUDENT. THIS WORK PROPOSES USING MACHINE LEARNING ANALYTICS TO PROVIDE USEFUL INDIVIDUALIZED FEEDBACK TO LEARNERS IN A CARDIOVASCULAR ENGINEERING INNOVATION-BASED LEARNING COURSE. THE SYSTEM WILL MONITOR STUDENTS' HABITS AS THEY FORM LEARNING OBJECTIVES AND PROGRESS THROUGH THE COURSE, ALLOWING THE SYSTEM TO LEARN TO MAKE RECOMMENDATIONS FOR THE STUDENT. THE ARCHITECTURE OF THE DEVELOPED SYSTEM IS PRESENTED, A TEST SET IS USED TO VERIFY, AND THE CONTEXT OF FUTURE PILOT TESTS IS INTRODUCED. ALTHOUGH THIS WORK IS PRESENTLY SPECIFIC TO ONE PARTICULAR COURSE, IT CAN BE ADAPTED TO ALLOW FOR PERSONALIZATION OF ANY MOOC THAT ALLOWS FOR DIFFERENTIATED, HIGHER LEVEL LEARNING.
CONFUSION HAS BEEN SHOWN TO BE PREVALENT DURING COMPLEX LEARNING AND HAS MIXED EFFECTS ON LEARNING. WHETHER CONFUSION FACILITATES OR HAMPERS LEARNING MAY DEPEND ON WHETHER IT IS RESOLVED OR NOT. CONFUSION RESOLUTION, BEHIND WHICH IS THE RESOLUTION OF COGNITIVE DISEQUILIBRIUM, REQUIRES LEARNERS TO POSSESS SOME SKILLS, BUT IT IS UNCLEAR WHAT THESE SKILLS ARE. ONE POSSIBILITY MAY BE METACOGNITIVE STRATEGIES (MS), STRATEGIES FOR REGULATING COGNITION. THIS STUDY EXAMINED THE RELATIONSHIP BETWEEN CONFUSION AND ACTIONS RELATED TO MS IN BETTY'S BRAIN, A COMPUTER-BASED LEARNING ENVIRONMENT. THE RESULTS REVEALED THAT MS BEHAVIOR DIFFERED DURING AND OUTSIDE CONFUSION. HOWEVER, CONFUSION RESOLUTION WAS NOT RELATED TO MS BEHAVIOR, AND MS DID NOT MODERATE THE EFFECT OF CONFUSION ON LEARNING.
BEYOND CLIPPED OR ISOLATED DATA, BIG DATA HIGHLIGHTS THE INTEGRATED AND ALL-AROUND MINING OF THE DATA RELATED TO SOME SUBJECT, WHICH PROVIDES AN INNOVATIVE PERSPECTIVE FOR LEARNING ASSESSMENT, AND BIG DATA BASED LEARNING ANALYTICS EXPLORES A NEW APPROACH TO LEARNING EVALUATION AND IMPROVEMENT. KEY POINTS IN TERMS OF BIG DATA BASED PROCESS TYPICALLY INVOLVE WHERE TO CAPTURE THE BIG DATA, WHAT DATA TO USE AND HOW TO HANDLE THE COLLECTED DATA. THIS PAPER PROPOSES A CONCEPTUAL DESIGN FRAMEWORK FOR BIG DATA SUPPORTED LEARNING ASSESSMENT. A THREE-DIMENSIONAL DATA RESOURCES MODEL, A LEARNING DATA CUBE, AND A FOUR-STAGE PROCESS MODEL ARE SEPARATELY SUGGESTED TO RESPOND TO THE MENTIONED WHERE, WHAT, AND HOW QUESTIONS.
THE COLLECTION AND ANALYSIS OF DATA IS A VERY IMPORTANT RESEARCH AREA, PARTICULARLY FOR COMPANIES. MARKETING, ADVERTISING AND SALES DEPEND TO A LARGE EXTENT ON CONSUMPTION STUDIES AND CUSTOMER TRENDS. UNTIL RECENTLY, THIS FIELD WAS RESTRICTED TO CONDUCTING SURVEYS, MARKETING STUDIES, SOCIAL STUDIES... WITH THE ADVENT OF THE INTERNET ERA, ADVERTISING HAS GREATLY EXPANDED ITS FORM AND THE POTENTIAL FOR ACTION. AND, WITH THAT, ALSO THE RESEARCH ON THE COLLECTION AND PROCESSING OF DATA. BUT THE ANALYSIS OF DATA HAS NOT BEEN RESTRICTED ONLY TO THE FIELD OF MARKETING, BUT HAS BEEN EXTENDED TO OTHER AREAS. AMONG THEM, EDUCATION. THE SO-CALLED LEARNING ANALYTICS RELATES THE COLLECTION AND ANALYSIS OF DATA WITH THE EDUCATIONAL FIELD. THE FUNDAMENTAL IDEA IS TO IMPROVE TEACHING THROUGH BEHAVIORAL STUDIES OF STUDENTS AND TEACHERS. THE PRESENT STUDY AIMS TO ESTABLISH THE ADVANTAGES AND DISADVANTAGES, THE EXPECTATIONS AND THE EXISTING REALITY IN THIS AREA OF KNOWLEDGE SO RECENT AND IN CONSTANT EXPANSION.
PURPOSE THE PURPOSE OF THIS PAPER IS TO TAKE A STUDENT-CENTRED PERSPECTIVE TO UNDERSTANDING THE RANGE OF WAYS THAT STUDENTS RESPOND TO RECEIVING INFORMATION ABOUT THEIR LEARNING BEHAVIOURS PRESENTED ON A DASHBOARD. IT IDENTIFIES FOUR PRINCIPLES TO INFORM THE DESIGN OF DASHBOARDS WHICH SUPPORT LEARNER AGENCY AND EMPOWERMENT, FEATURES WHICH PRINSLOO AND SLADE (2016) SUGGEST ARE CENTRAL TO ETHICAL ADOPTION OF LEARNING ANALYTICS. DESIGN/METHODOLOGY/APPROACH THE STUDY INVOLVED SEMI-STRUCTURED INTERVIEWS WITH 24 FINAL-YEAR UNDERGRADUATES TO EXPLORE THE STUDENTS' RESPONSE TO RECEIVING DASHBOARDS THAT SHOWED THE STUDENTS' ACHIEVEMENT AND OTHER LEARNING BEHAVIOURS. FINDINGS THE PAPER IDENTIFIES FOUR PRINCIPLES THAT SHOULD BE USED WHEN DESIGNING AND ADOPTING LEARNER DASHBOARDS TO SUPPORT STUDENT AGENCY AND EMPOWERMENT. RESEARCH LIMITATIONS/IMPLICATIONS - THE STUDY WAS BASED ON A SMALL SAMPLE OF UNDERGRADUATE STUDENTS FROM THE FINAL YEAR FROM ONE ACADEMIC SCHOOL. THE DATA ARE BASED ON STUDENTS' SELF-REPORTING. PRACTICAL IMPLICATIONS - THE PAPER SUGGESTS THAT THESE FOUR PRINCIPLES ARE GUIDING TENETS FOR THE DESIGN AND IMPLEMENTATION OF LEARNER DASHBOARDS IN HIGHER EDUCATION. THE FOUR PRINCIPLES ARE: DESIGNS THAT ARE CUSTOMISABLE BY STUDENTS; FOREGROUNDS STUDENTS SENSE MAKING; ENABLES STUDENTS TO IDENTIFY ACTIONABLE INSIGHTS; AND DASHBOARDS ARE EMBEDDED INTO EDUCATIONAL PROCESSES. ORIGINALITY/VALUE THE PAPER'S ORIGINALITY IS THAT IT ILLUMINATES STUDENT-CENTRED PRINCIPLES OF LEARNER DASHBOARD DESIGN AND ADOPTION.
FEEDBACK HAS BEEN IDENTIFIED AS ONE OF THE FACTORS WITH THE LARGEST POTENTIAL FOR A POSITIVE IMPACT IN A LEARNING EXPERIENCE. THERE IS A SIGNIFICANT BODY OF KNOWLEDGE STUDYING FEEDBACK AND PROVIDING GUIDELINES FOR ITS IMPLEMENTATION IN LEARNING ENVIRONMENTS. IN PARALLEL, THE AREAS OF LEARNING ANALYTICS OR EDUCATIONAL DATA MINING HAVE EMERGED TO EXPLORE HOW TO ANALYSE EXHAUSTIVE DIGITAL TRAILS PRODUCED BY TECHNOLOGY MEDIATION TO IMPROVE LEARNING EXPERIENCES. CURRENT CONCEPTUALISATIONS OF FEEDBACK DO NOT TAKE INTO ACCOUNT THE PRESENCE OF THESE TRAILS NOR THE PRESENCE OF KNOWLEDGE EXTRACTED THROUGH ANALYTICS TECHNIQUES. THIS PAPER PRESENTS A MODEL TO RECONCEPTUALISE FEEDBACK IN DATA-RICH LEARNING EXPERIENCES. IT ACKNOWLEDGES THE PRESENCE OF ALGORITHMS TO ANALYSE AND PREDICT LEARNER BEHAVIOUR AND PROPOSES AN INTEGRATED VIEW BETWEEN THESE ELEMENTS AS PART OF THE FEEDBACK PROCESS AND ASPECTS CONVENTIONALLY PRESENT IN PREVIOUS MODELS. THIS NEW CONCEPTUALISATION OFFERS INSTRUCTORS, DESIGNERS AND RESEARCHERS A FRAMEWORK TO FORMULATE FEEDBACK PROCESSES IN SCENARIOS WITH COMPREHENSIVE DATA CAPTURING, WHILE RETAINING A SOLID CONNECTION WITH WELL-ESTABLISHED EDUCATIONAL THEORIES.
THIS PAPER PRESENTS AN EXPLORATORY ANALYSIS OF THE EXPERIENCE OF EDUCATIONAL INNOVATION IN THE CONFIGURATION OF A SOCIAL LEARNING NETWORK IN A SUBJECT FOR THE EDUCATION DEGREE COURSE AT THE UNIVERSITY OF SANTIAGO DE COMPOSTELA (SPAIN). THIS INNOVATION IS BASED ON THE PREMISE OF STUDENT-CENTERED TEACHING (INDEPENDENT LEARNING, SELF-REGULATED, AUTHENTIC AND BREAKING BOUNDARIES BETWEEN FORMAL AND INFORMAL AREAS) ENRICHED WITH COLLABORATIVE ACTIVITIES. THE STUDY AIMS TO ANALYZE THE INTENSITY AND RELEVANCE OF THE STUDENT'S CONTRIBUTIONS IN THIS COLLABORATIVE FRAMEWORK. WE USED LEARNING ANALYTICS TOOLS WITH TWO TYPES OF TECHNIQUES: SOCIAL NETWORK ANALYSIS (SNA) AND INFORMATION EXTRACTION, TO MEASURE THE INTENSITY, CENTRALITY AND RELEVANCE OF COLLABORATION AMONG STUDENTS. THE RESULTS OBTAINED ALLOW US TO CONFIRM: 1) THE CONSISTENCY AND COHERENCE BETWEEN THE PEDAGOGICAL APPROACH AND THE OPTION OF USING A SOCIAL NETWORK IN UNIVERSITY EDUCATION; 2) A DENSE NETWORK WITH A HIGH LEVEL OF INTERACTION, A MODERATE DEGREE OF CENTRALITY AND A LOW CENTRALIZATION INDEX (STRUCTURE MOVES AWAY FROM STAR), WITH A GROUP WITH THE CAPACITY TO INFLUENCE THE REST (DEGREE OF BETWEENNESS); 3) HIGH LEVEL OF RELEVANCE TO THE CONTENT ANALYZED; 4) THE USEFULNESS OF LEARNING ANALYTICS TECHNIQUES TO GUIDE TEACHER DECISION-MAKING.
THE AVAILABILITY OF PERSONAL DIGITAL DEVICES IN SCHOOLS AND AT HOME ARE OFFERING NEW WAYS OF ENGAGING STUDENTS IN THE AREA OF LANGUAGE LEARNING. IN THIS PUBLICATION, WE PRESENT A NEW APPROACH ON WRITING AND BLOGGING FOR CHILDREN AGED 8 TO 12 YEARS, WHICH IS ESPECIALLY HELPFUL FOR THOSE WHO STRUGGLE WITH THE ACQUISITION OF GERMAN ORTHOGRAPHY. ON A WEB-BASED PLATFORM THE PUPILS CAN WRITE ESSAYS AND BLOG THEM LATER ON. COMBINED WITH LEARNING ANALYTICS METHODS WE OFFER INDIVIDUALIZED FEEDBACK DURING THE PROCESS OF WRITING AND A TRAINING DATABASE WITH APPROPRIATE EXERCISES TO SUPPORT THE STUDENTS' AUTONOMOUS LEARNING.
GAME ENGINES AND DEVELOPER-FRIENDLY AUTHORING TOOLS HAVE GREATLY SIMPLIFIED ENTERTAINMENT GAME DEVELOPMENT. HOWEVER, THIS DOES NOT EXTEND TO SERIOUS GAMES WHICH, IN PARTICULAR, REQUIRE INVOLVEMENT OF NON-DEVELOPER STAKEHOLDERS. WE PRESENT THE FIRST EVALUATION OF UADVENTURE, AN EASY-TO-USE GAME DEVELOPMENT ENVIRONMENT FOR NARRATIVE POINT-AND-CLICK GRAPHIC ADVENTURE GAMES. UADVENTURE IS A RE-IMPLEMENTATION OF THE PREVIOUSLY-VALIDATED EADVENTURE ENVIRONMENT ON TOP OF THE UNITY GAME ENGINE. THE IDEA IS TO GET MOST OF THE ADVANTAGES OF THE UNITY PROFESSIONAL ENVIRONMENT AT A FRACTION OF ITS COMPLEXITY, AND WITHOUT REQUIRING PROGRAMMING KNOWLEDGE TO USE IT. UADVENTURE INCLUDE EDUCATIONAL-ORIENTED AFFORDANCES, SUCH AS ASSESSMENT AND LEARNING ANALYTICS; AND HAS BEEN FORMATIVELY EVALUATED BY HETEROGENEOUS USERS WITH DIFFERENT DEGREES OF TECHNICAL KNOWLEDGE. THE RESULTS OF OUR EVALUATION SHOW MUCH SIMPLER STORY CREATION FOR PROFILES THAT HAD NO PREVIOUS KNOWLEDGE OF THE ENGINE, AND POSITIVE FEEDBACK FROM MORE TECHNICAL PROFILES WHICH WOULD USE THE TOOL AS A PROTOTYPING TOOL FOR COMPLEX PROJECTS.
DESIGNING A GOOD CURRICULUM OR AN APPROPRIATE LEARNING PATH FOR LEARNERS IS CHALLENGING BECAUSE IT REQUIRES A VERY GOOD AND CLEAR UNDERSTANDING OF THE SUBJECTS CONCERNED AS WELL AS MANY OTHER FACTORS. ONE COMMON OBJECTIVE OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS IS TO ASSIST LEARNERS TO ENHANCE THEIR LEARNING VIA THE DISCOVERY OF INTERESTING AND USEFUL PATTERNS FROM LEARNING DATA. WE HAVE RECENTLY DEVELOPED A TECHNIQUE CALLED SKILL2VEC, WHICH UTILIZES AN ARTIFICIAL NEURAL NETWORK TO AUTOMATICALLY IDENTIFY THE RELATIONSHIP BETWEEN SKILLS FROM LEARNING DATA. THE OUTCOME OF SKILL2VEC CAN HELP INSTRUCTORS, COURSE PLANNERS AND LEARNERS TO HAVE A MORE OBJECTIVE AND DATA-INFORMED DECISION MAKING. SKILL2VEC TRANSFORMS A SKILL TO A VECTOR IN A NEW VECTOR SPACE BY CONSIDERING THE CONTEXTUAL SKILLS. SUCH A TRANSFORMATION, CALLED EMBEDDING, ALLOWS THE DISCOVERY OF RELEVANT SKILLS THAT MAY BE IMPLICIT. WE CONDUCTED EXPERIMENTS ON TWO REAL-WORLD DATASETS COLLECTED FROM AN ONLINE INTELLIGENT TUTORING SYSTEM. THE RESULTS SHOW THAT THE OUTCOME OF SKILL2VEC IS CONSISTENT AND RELIABLE.
WRITING IN AN ACADEMIC CONTEXT OFTEN REQUIRES STUDENTS IN HIGHER EDUCATION TO ACQUIRE A NEW SET OF SKILLS WHILE FAMILIARISING THEMSELVES WITH THE GOALS, OBJECTIVES AND REQUIREMENTS OF THE NEW LEARNING ENVIRONMENT. STUDENTS' ABILITY TO CONTINUOUSLY SELF-REGULATE THEIR WRITING PROCESS, THEREFORE, IS SEEN AS A DETERMINING FACTOR IN THEIR LEARNING SUCCESS. IN ORDER TO STUDY STUDENTS' SELF-REGULATED LEARNING (SRL) BEHAVIOUR, RESEARCH HAS INCREASINGLY BEEN TAPPING INTO LEARNING ANALYTICS (LA) METHODS IN RECENT YEARS, MAKING USE OF MULTIMODAL TRACE DATA THAT CAN BE OBTAINED FROM STUDENTS WRITING AND WORKING ONLINE. NEVERTHELESS, LITTLE IS STILL KNOWN ABOUT THE WAYS STUDENTS APPLY AND GOVERN SRL PROCESSES FOR ACADEMIC WRITING ONLINE, AND ABOUT HOW THEIR SRL BEHAVIOUR MIGHT CHANGE OVER TIME. TO PROVIDE NEW PERSPECTIVES ON THE USE OF LA APPROACHES TO EXAMINE SRL, THIS STUDY APPLIED A RANGE OF METHODS TO INVESTIGATE WHAT THEY COULD TELL US ABOUT THE EVOLUTION OF SRL TACTICS AND STRATEGIES ON A RELATIONAL, CO-TEMPORAL, CONTEMPORANEOUS AND LONGITUDINAL LEVEL. THE DATA ORIGINATES FROM A CASE STUDY IN WHICH A PRIVATE FACEBOOK GROUP SERVED AS AN ONLINE COLLABORATION SPACE IN A FIRST-YEAR ACADEMIC WRITING COURSE FOR FOREIGN LANGUAGE MAJORS OF ENGLISH. THE FINDINGS SHOW THAT LEARNERS USE A RANGE OF SRL TACTICS TO MANAGE THEIR WRITING TASKS AND THAT DIFFERENT TACTIC CAN TAKE UP KEY POSITIONS IN THIS PROCESS OVER TIME. SEVERAL SHIFTS COULD BE OBSERVED IN STUDENTS' BEHAVIOUR, FROM MAINLY ADDRESSING CONTENT-SPECIFIC TOPICS TO MORE FORM-SPECIFIC AND SOCIAL ONES. OUR RESULTS HAVE ALSO DEMONSTRATED THAT DIFFERENT METHODS CAN BE USED TO STUDY THE RELATIONAL, CO-TEMPORAL, CONTEMPORANEOUS, AND LONGITUDINAL DYNAMICS OF SELF-REGULATION IN THIS REGARD, DEMONSTRATING THE WEALTH OF INSIGHTS LA METHODS CAN BRING TO THE TABLE.
AS ONE OF THE MOST IMPORTANT APPROACHES FOR LEARNING ANALYTICS AND EDUCATIONAL DATA MINING, VARIOUS CLUSTERING ALGORITHMS HAVE BEEN EXPLORED AND COMPARED IN THE ANALYSIS OF ONLINE LEARNERS BY THEIR BEHAVIOR. HOWEVER, CHOOSING WHICH FEATURES FOR CLUSTERING HAS A STRONG IMPACT ON THE QUALITY OF CLUSTERING, AND HAS NOT RECEIVED ENOUGH ATTENTION YET. BY USING AN ENTROPY-BASED FEATURE SELECTION METHOD, THIS RESEARCH BROADENS THE RANGE OF CANDIDATE INITIAL FEATURES AND PROVIDES EFFICIENT ALGORITHMS FOR EXTRACTING THE MOST IMPORTANT FEATURES. EXPERIMENT WITH REAL-LIFE DATA REVEALS THAT THIS METHOD NOT ONLY OVERCOMES THE DATA SPARSITY AND COMPLEXITY PROBLEMS FOR CLUSTERING IN HIGH-DIMENSIONAL FEATURE SPACE BUT ALSO SURPASSES DIMENSIONALITY REDUCTION METHODS LIKE PCA IN INTERPRETABILITY.
MOST DISTANCE LEARNING INSTITUTIONS COLLECT VAST AMOUNTS OF LEARNING DATA. MAKING SENSE OF THIS 'BIG DATA' CAN BE A CHALLENGE, IN PARTICULAR WHEN DATA ARE STORED AT DIFFERENT DATA WAREHOUSES AND REQUIRE ADVANCED STATISTICAL SKILLS TO INTERPRET COMPLEX PATTERNS OF DATA. AS A LEADING INSTITUTE ON LEARNING ANALYTICS, THE OPEN UNIVERSITY UK INSTIGATED IN 2012 A DATA WRANGLING INITIATIVE. THIS PROVIDED EVERY FACULTY WITH A DEDICATED ACADEMIC WITH EXPERTISE DATA ANALYSIS AND WHOSE TASK IS TO PROVIDE STRATEGIC, PEDAGOGICAL AND SENSE-MAKING ADVICE TO STAFF AND SENIOR MANAGEMENT. GIVEN SUBSTANTIAL CHANGES WITHIN THE OU (E.G. NEW FACULTY STRUCTURE, REAL-TIME DASHBOARDS, TWO LARGE-SCALE ADOPTIONS OF PREDICTIVE ANALYTICS APPROACHES, INCREASED RELIANCE ON ANALYTICS), THIS EMBEDDED CASE STUDY PROVIDES AN IN-DEPTH REVIEW OF LESSONS LEARNED OF FIVE YEARS OF DATA WRANGLING. WE WILL ELABORATE ON THE DESIGN OF THE NEW STRUCTURE, ITS STRENGTHS AND POTENTIAL WEAKNESSES, AND AFFORDANCES TO BE ADOPTED BY OTHER INSTITUTIONS.
ANALYZING THE LEARNING ANALYTICS FROM A COURSE PROVIDES INSIGHTS THAT CAN IMPACT INSTRUCTIONAL DESIGN DECISIONS. THIS STUDY USED EDUCATIONAL DATA MINING TECHNIQUES, SPECIFICALLY A LONGITUDINAL K-MEANS CLUSTER ANALYSIS, TO IDENTIFY THE STRATEGIES STUDENTS USED WHEN COMPLETING THE ONLINE PORTION OF AN ONLINE FLIPPED SPREADSHEET COURSE. AN ANALYSIS OF THESE RESULTS SHOWED THAT STUDENTS DID TEND TO FOLLOW A SPECIFIC LEARNING STRATEGY AS THEY COMPLETED THIS COURSE. HOWEVER, STUDENTS ALSO SELF-REGULATED TO SOME DEGREE, BASED ON THE TOPIC AND CONTEXT OF SPECIFIC LESSONS. THESE INSIGHTS NOT ONLY IMPROVE OUR UNDERSTANDING ABOUT THE STUDENTS TAKING THE COURSE, BUT THEY ALSO PROVIDE GUIDANCE FOR HOW THE INSTRUCTIONAL DESIGN OF THE COURSE MIGHT BE IMPROVED. OF NOTE IS THE PRACTICAL VALUE OF THIS PROOF-OF-CONCEPT STUDY IN USING EDUCATIONAL DATA MINING TO IMPROVE THE INSTRUCTIONAL DESIGN OF A COURSE.
APPLICATION OF SERIOUS GAMES IN DISTANCE LEARNING CAN RAISE QUALITY OF EDUCATION AND STUDENT SATISFACTION ON A HIGHER LEVEL. HOWEVER, WHEN STUDENT LEARNS THROUGH GAME, HIS FOCUS IS MOVED FROM LEARNING DOMAIN TO DIFFERENT CONTEXT OF THE GAME. THIS ACTUALLY ENABLES TO ACHIEVE FUN AND LEARN AT THE SAME TIME. BUT THIS APPROACH ALSO MAKES HARDER FOR EDUCATORS TO TRACK AND ANALYSE STUDENTS LEARNING PROGRESS DURING GAME SESSION, WHICH IS CRUCIAL IN ORDER TO PROVIDE IMMEDIATE FEEDBACK AND TO HELP STUDENTS REACH ESTABLISHED LEARNING GOALS. SUCH A SPECIFIC LEARNING ENVIRONMENT REQUIRES CONCRETE REAL-TIME ANALYTICAL TOOL THAT WILL ADEQUATELY MATCH THE DYNAMIC GAME ENVIRONMENT. THIS PAPER PROPOSES A NEW TOOL FOR VISUALISATION OF STUDENT LEARNING MODEL DURING GAMEPLAY SESSION. TOOL CAN BE USED BY EDUCATORS AND BY STUDENTS TO TRACK THE GAME PROGRESS. USING THIS TOOL EDUCATORS ARE PROVIDED WITH REAL-TIME TRACKING OF STUDENTS LEARNING AND IT ENABLES THEM TO REACT AND INFLUENCE THE OVERALL LEARNING PROCESS. EVALUATION OF THE PROPOSED APPROACH WAS DONE THROUGH AN EMPIRICAL STUDY, CONDUCTED ON EDUCATORS GROUP MONITORING AN EDUCATIONAL GAME SESSION, USING THE COMBINATION OF TRADITIONAL ANALYTIC TOOL AND THE NEWLY PROPOSED VISUALISATION APPROACH. INITIAL QUANTITATIVE RESULTS AND RECORDED OPINIONS OF THE PARTICIPANTS SPEAK IN FAVOUR OF THE PROPOSED APPROACH AND JUSTIFY FURTHER INVESTMENT IN DEVELOPMENT OF THIS SPECIFIC LEARNING ANALYTICS METHOD. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
ECO-CONNECTIVIST COMMUNITIES ARE GROUPS OF INDIVIDUALS WITH SIMILAR CHARACTERISTICS, WHICH EMERGE IN A CONNECTIVIST LEARNING PROCESS WITHIN A KNOWLEDGE ECOLOGY. ARMAGAECO-C IS A REFLEXIVE AND AUTONOMIC MIDDLEWARE FOR THE MANAGEMENT AND OPTIMIZATION OF ECO-CONNECTIVIST KNOWLEDGE ECOLOGIES USING DESCRIPTION, PREDICTION AND PRESCRIPTION MODELS. ADAPTIVE LEARNING OBJECTS ARE AUTONOMIC COMPONENTS THAT SEEK TO PERSONALIZE LEARNING OBJECTS ACCORDING TO CERTAIN CONTEXTUAL INFORMATION, SUCH AS LEARNING STYLES OF THE LEARNER'S, TECHNOLOGICAL RESTRICTIONS, AMONG OTHER ASPECTS. MALO IS A SYSTEM THAT ALLOWS THE MANAGEMENT OF ADAPTIVE LEARNING OBJECTS. ONE OF THE MAIN CHALLENGES OF THE CONNECTIVIST LEARNING PROCESS IS THE ADAPTATION OF THE EDUCATIONAL CONTEXT TO THE STUDENT NEEDS. ONE OF THEM IS THE LEARNING OBJECTS. FOR THIS REASON, THIS WORK HAS TWO OBJECTIVES, SPECIFYING A DATA ANALYTICS TASK TO DETERMINE THE LEARNING STYLE OF A STUDENT IN AN ECO-CONNECTIVIST COMMUNITY AND, ADAPTING INSTANCES OF ADAPTIVE LEARNING OBJECTS USING THE LEARNING STYLES OF THE STUDENTS IN THE COMMUNITIES. WE USE GRAPH THEORY TO IDENTIFY THE REFERENTIAL MEMBER OF EACH ECO-CONNECTIVIST COMMUNITY, AND A LEARNING PARADIGM DETECTION ALGORITHM TO IDENTIFY THE SET OF ACTIVITIES, STRATEGIES, AND TOOLS THAT ADAPTIVE LEARNING OBJECTS INSTANCES SHOULD HAVE, ACCORDING TO THE LEARNING STYLE OF THE REFERENTIAL MEMBER. TO TEST OUR APPROACH, A CASE STUDY IS PRESENTED, WHICH DEMONSTRATES THE VALIDITY OF OUR APPROACH.
AGAINST THE BACKDROP OF THE EVER-INCREASING INFLUX OF BIG DATA, THIS ARTICLE EXAMINES THE OPPORTUNITIES AND CONCERNS OVER BIG DATA IN EDUCATION. SPECIFICALLY, THIS ARTICLE FIRST INTRODUCES BIG DATA, FOLLOWED BY DELINEATING THE POTENTIAL OPPORTUNITIES OF USING BIG DATA IN EDUCATION IN TWO AREAS: LEARNING ANALYTICS AND EDUCATIONAL POLICY. THEN, THE CONCERNS OVER DATA SECURITY, PRIVACY PROTECTION, AND ETHICAL BOUNDARIES OF ACCESSING PERSONAL DIGITAL DATA ARE DISCUSSED. THE ARTICLE CONCLUDES WITH AN INVITATION TO EDUCATION PRACTITIONERS, POLICYMAKERS, AND RESEARCHERS TO ADVANCE OUR UNDERSTANDING OF BIG DATA AND BETTER SERVE STUDENTS IN THE DIGITAL ERA.
THE AUTOMATIC ANALYSIS OF STUDENTS' IN-CLASSROOM BEHAVIOR IS VALUABLE TO EVALUATE THE EFFECT OF TEACHING. RECENT STUDIES OF IN-CLASSROOM VIDEO ANALYSIS MAINLY FOCUS ON LECTURE CONTENT, POSITIONS AND IDENTITIES OF STUDENTS. IN THIS PAPER, WE PROPOSE TO ANALYZE THE STUDENTS' CONCENTRATION DEGREE TO THE TEACHER OR TEACHING CONTENT. SPECIFICALLY, WE DETECT STUDENTS' FACES, TRACK FACES, AND ANALYZE THE STUDENTS' BEHAVIOR, I.E. RAISING OR DOWNING FACES AND CORRESPONDING HEAD ORIENTATIONS TO THE TEACHER, TEACHING CONTENT OR NOT. BESIDES, TEXTS ARE OBTAINED FROM THE TEACHER'S SPEECH AND THE COURSE TOPICS TAUGHT IN THE CLASS ARE EXTRACTED. AUDIO FEATURES OF THE TEACHER'S SPEECH ARE EXTRACTED AND ANALYZED. FINALLY, THE CORRELATION OF THE STUDENTS' CONCENTRATION DEGREE WITH THE COURSE TOPICS, AUDIO FEATURES ARE ANALYZED. THIS ANALYSIS CAN HELP TEACHERS FIND THE EFFECTIVE TEACHING CHARACTERISTIC TO BETTER IMPROVE STUDENTS' CONCENTRATION DEGREE. (C) 2019 ELSEVIER B.V. ALL RIGHTS RESERVED.
INTERNET OF THINGS (IOT) APPLICATIONS FOR HEALTHCARE ARE ONE OF THE MOST STUDIED ASPECTS IN THE RESEARCH LANDSCAPE DUE TO THE PROMISE OF MORE EFFICIENT RESOURCE ALLOCATION FOR HOSPITALS AND AS A COMPANION TOOL FOR HEALTH PROFESSIONALS. YET, THE REQUIREMENTS IN TERMS OF LOW POWER, LATENCY AND KNOWLEDGE EXTRACTION FROM THE LARGE AMOUNT OF PHYSIOLOGICAL DATA GENERATED REPRESENT A CHALLENGE TO BE ADDRESSED BY THE RESEARCH COMMUNITY. IN THIS WORK, WE EXAMINE THE BALANCE BETWEEN POWER CONSUMPTION, PERFORMANCE AND LATENCY AMONG EDGE, GATEWAY, FOG AND CLOUD LAYERS IN AN IOT MEDICAL PLATFORM FEATURING INFERENCE BY DEEP LEARNING MODELS. WE SETUP AN IOT ARCHITECTURE TO ACQUIRE AND CLASSIFY MULTICHANNEL ELECTROCARDIOGRAM (ECG) SIGNALS INTO NORMAL OR ABNORMAL STATES WHICH COULD REPRESENT A CLINICALLY RELEVANT CONDITION BY COMBINING CUSTOM EMBEDDED DEVICES WITH CONTEMPORARY OPEN SOURCE MACHINE LEARNING PACKAGES SUCH AS TENSORFLOW. DIFFERENT HARDWARE PLATFORMS ARE TESTED IN ORDER TO FIND THE BEST COMPROMISE IN TERMS OF CONVENIENCE, LATENCY, POWER CONSUMPTION AND PERFORMANCE. OUR EXPERIMENTS INDICATE THAT THE REAL TIME REQUISITES ARE FULFILLED, HOWEVER THERE IS A NEED TO REDUCE ENERGY EXPENDITURE BY MEANS OF INCORPORATING LOW POWER SOCS WITH INTEGRATED NEUROMORPHIC BLOCKS.
THE CONTRIBUTION OF THIS PAPER IS TWOFOLD. ON THE ONE HAND, WE PRESENT AN EXPERIENCE OF A MASTERS' LEVEL COURSE BASED ON REAL-WORLD PROJECTS WITH INTERACTION WITH REAL DEVELOPERS FROM INDUSTRY. THIS REALITY CHECK CREATED A SPECIAL MOTIVATION FOR STUDENTS AS WELL AS PROVIDED A USEFUL EXPERIENCE FOR THEIR FUTURE WORK. ON THE OTHER HAND, WE DESCRIBE THE TECHNICAL DETAILS OF THE THREE PROJECTS CARRIED OUT BY THE STUDENTS. THESE WERE EXTENSIONS TO GOOGLE'S MASSIVE OPEN ONLINE COURSE PLATFORM COURSE BUILDER. THEY WERE ABOUT AN IMPROVEMENT IN THE MANAGEMENT OF QUIZZES, AN EXTENSION WITH LEARNING ANALYTICS TOOLS, AND AN INTEGRATION WITH MOZILLA OPEN BADGES.
FOR THE SPECIAL ISSUE OF THE INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION DEDICATED TO THE MEMORY OF JIM GREER, THIS PAPER HIGHLIGHTS SOME OF JIM'S EXTENSIVE AND ALWAYS-TIMELY CONTRIBUTIONS TO THE FIELD: FROM HIS EARLY AI-FOCUSSED RESEARCH ON INTELLIGENT TUTORING SYSTEMS, THROUGH A VARIETY OF APPLICATIONS DEPLOYED TO SUPPORT STUDENTS IN UNIVERSITY COURSES, TO LEARNING ANALYTICS TOOLS FOR INSTRUCTIONAL EXPERTS AND UNIVERSITY ADMINISTRATORS. A SUBSTANTIAL QUANTITY OF HIS WORK INCLUDED SOME ASPECT OF OPEN LEARNER MODELLING, AND/OR INVOLVED CORE ISSUES THAT ARE ALSO CENTRAL TO OPEN LEARNER MODELLING. ACCORDINGLY, THIS PAPER IDENTIFIES JIM'S PROFOUND INFLUENCE THROUGHOUT AN OPEN LEARNER MODEL RESEARCH PROGRAMME.
MASTERY DATA DERIVED FROM FORMATIVE ASSESSMENTS CONSTITUTE A RICH DATA SET IN THE DEVELOPMENT OF STUDENT PERFORMANCE PREDICTION MODELS. THE DOMINANCE OF FORMATIVE ASSESSMENT MASTERY DATA OVER USE INTENSITY DATA SUCH AS TIME ON TASK OR NUMBER OF CLICKS WAS THE OUTCOME OF PREVIOUS RESEARCH BY THE AUTHORS IN A DISPOSITIONAL LEARNING ANALYTICS CONTEXT [1-3]. PRACTICAL IMPLICATIONS OF THESE FINDINGS ARE FAR REACHING, CONTRADICTING CURRENT PRACTICES OF DEVELOPING (LEARNING ANALYTICS BASED) STUDENT PERFORMANCE PREDICTION MODELS BASED ON INTENSITY DATA AS CENTRAL PREDICTOR VARIABLES. IN THIS EMPIRICAL FOLLOW-UP STUDY USING DATA OF 2011 STUDENTS, WE SEARCH FOR AN EXPLANATION FOR TIME ON TASK DATA BEING DOMINATED BY MASTERY DATA. WE DO SO BY INVESTIGATING MORE GENERAL MODELS, ALLOWING FOR NONLINEAR, EVEN NON-MONOTONIC, RELATIONSHIPS BETWEEN TIME ON TASK AND PERFORMANCE MEASURES. CLUSTERING STUDENTS INTO SUBSAMPLES, WITH DIFFERENT TIME ON TASK CHARACTERISTICS, SUGGESTS HETEROGENEITY OF THE SAMPLE TO BE AN IMPORTANT CAUSE OF THE NONLINEAR RELATIONSHIPS WITH PERFORMANCE MEASURES. TIME ON TASK DATA APPEAR TO BE MORE SENSITIVE TO THE EFFECTS OF HETEROGENEITY THAN MASTERY DATA, PROVIDING A FURTHER ARGUMENT TO PRIORITIZE FORMATIVE ASSESSMENT MASTERY DATA AS PREDICTOR VARIABLES IN THE DESIGN OF PREDICTION MODELS DIRECTED AT THE GENERATION OF LEARNING FEEDBACK.
HOW TO CHOOSE THE MOST APPROPRIATE COURSES TO STUDY THROUGHOUT THE LEARNING PROCESS REMAINS A QUESTION INTERESTED IN BY MANY STUDENTS. STUDENTS OFTEN CHOOSE SUITABLE COURSES ACCORDING TO THEIR INTERESTS, NEEDS, AND ADVICE FROM SUPPORTING STAFF, ETC. THIS PAPER PRESENTS THE RESULTS IN DEVELOPING A COURSE RECOMMENDATION SYSTEM THAT WILL SELECT APPROPRIATE COURSES FOR EACH STUDENT STUDYING A MAJOR IN THE FOLLOWING SEMESTERS BASED ON HIS/HER CURRENT ACADEMIC PERFORMANCE. WE HAVE APPLIED SEVERAL TECHNIQUES BASED ON DATA MINING AND LEARNING ANALYTICS TO PREDICT STUDENTS' LEARNING OUTCOMES IN THE NEXT SEMESTER AND DEVELOPED A MODEL TO SELECT THE APPROPRIATE COURSES BASED ON SUCH A RECOMMENDATION SYSTEM. BESIDES, OUR STUDY HAS FOCUSED ON COMPARING THE EFFECTIVENESS OF PREDICTIVE LEARNING METHODS BASED ON COLLABORATIVE FILTERING. EXPERIMENTS ANALYZED THE LEARNING RESULTS OF 510 STUDENTS WHO ENROLLED IN THE COURSES FROM 2015 TO 2019 AND SHOWED THAT THE MATRIX FACTORIZATION METHOD IS THE MOST EFFECTIVE. ALSO, THE PAPER HAS PROPOSED PROCEDURES AND CONSTRAINTS APPLICABLE TO DIFFERENT TRAINING CURRICULA.
WITHIN THE LAST FEW YEARS MOBILE LEARNING HAS BECOME VERY POPULAR. MANY PROJECTS AND CASE STUDIES EXPLORE ITS USEFULNESS IN THE EDUCATIONAL SECTOR, I.E. AT SCHOOLS AND UNIVERSITIES. FEW, HOWEVER, DEAL WITH WORK PLACE LEARNING. LOGIASSIST IS AN EDUCATIONAL ASSISTANCE AND LEARNING PLATFORM FOR TRUCK DRIVERS. THIS PAPER FIRST EXPLORES HOW EAGER TRUCK DRIVERS ARE TO ENGAGE IN ONLINE LEARNING AND THE USAGE OF AN ASSISTANCE SYSTEM. THIS IS JUXTAPOSED WITH FIRST RESULTS OF THE ACTUAL USAGE OF LOGIASSIST. IN A SECOND STEP, WE EXPLORE HOW THE SYSTEM WAS USED AND HOW THE USAGE CAN BE INTERPRETED FROM A LEARNING ANALYTICS POINT OF VIEW.
ONE AIM OF MULTIMODAL LEARNING ANALYTICS IS TO ANALYZE RICH NATURAL COMMUNICATION MODALITIES TO IDENTIFY DOMAIN EXPERTISE AND LEARNING RAPIDLY AND RELIABLY. IN THIS RESEARCH, WRITTEN AND MULTIMODAL REPRESENTATIONS ARE ANALYZED FROM THE MATH DATA CORPUS, WHICH INVOLVES MULTIMODAL DATA (DIGITAL PEN, SPEECH, IMAGES) ON COLLABORATING STUDENTS AS THEY SOLVE MATH PROBLEMS. FINDINGS REVEAL THAT IN 96-97% OF CASES THE CORRECTNESS OF A GROUP'S SOLUTION WAS PREDICTABLE IN ADVANCE BASED ON STUDENTS' WRITTEN WORK CONTENT. IN ADDITION, A LINEAR REGRESSION REVEALED THAT 65% OF THE VARIANCE IN INDIVIDUAL STUDENTS' DOMAIN EXPERTISE RANKINGS COULD BE ACCOUNTED FOR BASED ON THEIR WRITTEN WORK CONTENT. A MULTIMODAL CONTENT ANALYSIS BASED ON BOTH WRITTEN AND SPOKEN INPUT CORRECTLY PREDICTED THE DOMINANT DOMAIN EXPERT IN A GROUP 100% OF THE TIME, EXCEEDING UNIMODAL PREDICTION RATES. FURTHER ANALYSIS REVEALED A REVERSAL BETWEEN EXPERTS AND NONEXPERTS IN THE PERCENTAGE OF TIME THAT A MATCH VERSUS MISMATCH WAS PRESENT BETWEEN THEIR ORAL AND WRITTEN ANSWER CONTRIBUTIONS, WITH NON-EXPERTS DEMONSTRATING HIGHER MISMATCHES. IMPLICATIONS ARE DISCUSSED FOR DEVELOPING RELIABLE MULTIMODAL LEARNING ANALYTICS SYSTEMS THAT INCORPORATE DIGITAL PEN INPUT TO AUTOMATICALLY TRACK CONSOLIDATION OF DOMAIN EXPERTISE.
IN THIS WORK, WE STUDY STUDENTS' BEHAVIOURS FROM A BLENDED PROGRAMMING LEARNING ENVIRONMENT THAT INTEGRATES LEARNING ANALYTICS ACROSS PHYSICAL AND DIGITAL LEARNING SPACES. WE DESIGNED A CLASSROOM STUDY AND COLLECTED DATA FROM A COMPUTER SCIENCE CLASS. STUDENTS' REVIEWING AND REFLECTING BEHAVIOURS, BASED ON THE USE OF A HOME-GROWN EDUCATIONAL TECHNOLOGY WERE TRACKED AND MODELLED. MOREOVER, STUDENTS' REVIEWING EFFORTS, IN TERMS OF FREQUENCY, TIMING, AND THE ASSOCIATIONS WITH THEIR ACADEMIC PERFORMANCES WERE ANALYSED. RESULTS SHOWED THAT HIGH- AND LOW-LEVEL STUDENTS HAD DISTINCT REVIEWING STRATEGIES. HIGH-LEVEL STUDENTS WERE MORE VIGILANT IN REVIEWING. WHEN THEY REVIEWED PERSISTENTLY, THEY ACHIEVED HIGHER ACADEMIC PERFORMANCES. OVERALL, STUDENTS PUT EXTRA EMPHASIS ON REVIEWING PRIOR TO THE EXAMS AND THE EFFORTS DEMONSTRATED THE DESIRE TO REVIEW FORMAL ASSESSMENTS REGARDLESS OF IF THEY WERE GRADED FOR ACADEMIC PERFORMANCE. IN ADDITION, ALL STUDENTS PAID MORE ATTENTION TO REVIEWING QUIZZES AND EXAMS TOWARD THE END OF THE SEMESTER.
THIS POSTER PRESENTS A CROSS-PLATFORM LEARNING ANALYTICS DASHBOARD ON MOODIE AND OPEN EDX FOR MONITORING OUTCOME-BASED LEARNING PROGRESS. THE DASHBOARD VISUALIZES STUDENTS' INTERACTIONS WITH THE PLATFORMS IN NEAR REAL-TIME, AIMING TO HELP TEACHERS AND STUDENTS MONITOR STUDENTS' LEARNING PROGRESS. THE DASHBOARD HAS BEEN USED IN FOUR LARGE-SIZE GENERAL EDUCATION COURSES IN A COMPREHENSIVE UNIVERSITY IN HONG KONG, UNDERGOING EVALUATION AND IMPROVEMENT.
RESEARCH ON ONLINE INTERACTIONS DURING A LEARNING SITUATION TO BETTER UNDERSTAND USERS' PRACTICES AND TO PROVIDE THEM WITH QUALITY-ORIENTED FEATURES, RESOURCES AND SERVICES IS ATTRACTING A LARGE COMMUNITY. AS A RESULT, THE INTEREST FOR SHARING EDUCATIONAL DATA SETS THAT TRANSLATE THE INTERACTIONS OF USERS WITH E-LEARNING SYSTEMS HAS BECOME A HOT TOPIC TODAY. HOWEVER, THE CURRENT SYSTEMS AGGREGATING SOCIAL AND USAGE DATA ABOUT THEIR USERS SUFFER FROM A SERIES OF WEAKNESSES. IN PARTICULAR, THEY LACK A COMMON INFORMATION MODEL THAT WOULD ALLOW FOR EXCHANGES OF INTERACTION DATA AT A LARGE SCALE. TO TACKLE THIS ISSUE, WE PROPOSE IN THIS PAPER A GENERIC MODEL ABLE TO FEDERATE HETEROGENEOUS CONTEXT METADATA AND TO FACILITATE THEIR SHARE AND REUSE. THIS FRAMEWORK HAS BEEN SUCCESSFULLY APPLIED TO SEVERAL DATA SETS PROVIDED BY THE RESEARCH COMMUNITY, AND THUS GIVES ACCESS TO A BIG DATA SET THAT COULD HELP RESEARCHERS TO INCREASE EFFICIENCY OF EXISTING LEARNING ANALYTICS TECHNICS, AND PROMOTE RESEARCH AND DEVELOPMENT OF NEW ALGORITHMS AND SERVICES ON TOP OF THESE DATA.
HIGHER EDUCATIONAL INSTITUTIONS CONSTANTLY LOOK FOR WAYS TO MEET STUDENTS' NEEDS AND SUPPORT THEM THROUGH GRADUATION. RECENT WORK IN THE FIELD OF LEARNING ANALYTICS HAVE DEVELOPED METHODS FOR GRADE PREDICTION AND COURSE RECOMMENDATIONS. ALTHOUGH THESE METHODS WORK WELL, THEY OFTEN FAIL TO DISCOVER CAUSAL RELATIONSHIPS BETWEEN COURSES, WHICH MAY NOT BE EVIDENT THROUGH CORRELATION-BASED METHODS. IN THIS WORK, WE AIM AT UNDERSTANDING THE CAUSAL RELATIONSHIPS BETWEEN COURSES TO AID UNIVERSITIES IN DESIGNING BETTER ACADEMIC PATHWAYS FOR STUDENTS AND TO HELP THEM MAKE BETTER CHOICES. OUR METHODOLOGY EMPLOYS METHODS OF CAUSAL INFERENCE TO STUDY THESE RELATIONSHIPS USING HISTORICAL STUDENT PERFORMANCE DATA. WE MAKE USE OF A DOUBLY-ROBUST METHOD OF MATCHING AND REGRESSION IN ORDER TO OBTAIN THE CASUAL RELATIONSHIP BETWEEN A PAIR OF COURSES. THE RESULTS WERE VALIDATED BY THE EXISTING PREREQUISITE STRUCTURE AND BY CROSS-VALIDATION OF THE REGRESSION MODEL. FURTHER, OUR APPROACH WAS ALSO TESTED FOR ROBUSTNESS AND SENSITIVITY TO CERTAIN HYPER PARAMETERS. THIS METHODOLOGY SHOWS PROMISING RESULTS AND IS A STEP FORWARD TOWARDS BUILDING BETTER ACADEMIC PATHWAYS FOR STUDENTS.
COMPUTER-BASED ASSESSMENT (CBA) MEASURES LEARNER'S PROGRESS IN AN AUTOMATED MANNER. A CHALLENGE IS TO ALIGN THE CBA OUTCOMES WITH PREDEFINED LEARNING GOALS. IN THIS PAPER WE PROPOSE AN ARCHITECTURE THAT FEEDS THE MACHINE-READABLE RESULTS OF LEARNING ANALYTICS PROCESSES INTO AN EDUCATIONAL RECOMMENDER SYSTEM IN ORDER TO PRODUCE PERSONALIZED APPLICABLE FEEDBACK TO GUIDE LEARNER DURING ASSESSMENT. WE DESCRIBE THE ARCHITECTURE, ITS INNER STRUCTURE, ITS SEPARATE MODULES, THE DATA-FLOW BETWEEN THEM AND THE BASIC MECHANISMS FOR DATA PREPARATION AND PROCESSING. OUR GOALS ARE THREEFOLD: A) TO MEANINGFULLY INTERPRET LEARNER'S INTERACTION-GENERATED DATA INTO GOAL-ORIENTED FEEDBACK, B) TO IMPLICITLY LINK TASK-LEVEL COGNITIVE FEEDBACK TO LEARNING GOALS AND PERFORMANCE, AND C) TO AUTOMATE THE DELIVERY OF PERSONALIZED ASSESSMENT TASKS TO EXAMINEE. FINALLY, WE CONCLUDE WITH OUR PLANNING ON FUTURE WORK.
THE INCORPORATION OF ESTABLISHED STUDENT-CENTRED LEARNING STRATEGIES SUCH AS PROBLEM BASED LEARNING (PBL) AND THE UTILIZATION OF NEW TECHNOLOGIES FOR FOSTERING HIGH LEVEL TRANSVERSAL COGNITIVE SKILLS OFTEN DO NOT SHOW THE EXPECTED BENEFITS. THE MAIN REASONS INCLUDE AD-HOC COURSE DESIGN WITH NO CONSIDERATION OF PRIOR EXPERIENCES AND POOR UTILIZATION OF THE LARGE AMOUNTS OF GENERATED DATA, WHICH COULD ENHANCE LEARNING FOR BOTH LEARNERS AND EDUCATORS IF EXPLOITED PROPERLY. IN THE CONTEXT OF THIS THESIS, WE AIM TO RECONFIGURE BLENDED PBL DESIGN, IMPLEMENTATION AND EVALUATION SO AS TO ALLOW A) LIFELONG LEARNING CAPABILITIES' DEVELOPMENT SUCH AS TRANSVERSAL SKILLS AND B) LEARNING ANALYTICS INTEGRATION FOR DATA GATHERING AND ANALYSIS INTO MEANINGFUL INFORMATION FOR ALL PHASES OF LEARNING.
IN THIS PAPER, WE STUDY THE ENGAGEMENT AND PERFORMANCE OF STUDENTS IN A CLASSROOM USING A SYSTEM THE COGNITIVE LEARNING COMPANION (CLC). CLC IS DESIGNED TO KEEP TRACK OF THE RELATIONSHIP BETWEEN THE STUDENT, CONTENT INTERACTION AND LEARNING PROGRESSION. IT ALSO PROVIDES EVIDENCE-BASED ENGAGEMENT-ORIENTED ACTIONABLE INSIGHTS TO TEACHERS BY ASSESSING INFORMATION FROM A SENSOR-RICH INSTRUMENTED LEARNING ENVIRONMENT IN ORDER TO INFER A LEARNER'S COGNITIVE AND AFFECTIVE STATES. DATA CAPTURED FROM THE INSTRUMENTED ENVIRONMENT IS AGGREGATED AND ANALYZED TO CREATE INTERLINKED INSIGHTS HELPING TEACHERS IDENTIFY HOW STUDENTS ENGAGE WITH LEARNING CONTENT AND VIEW THEIR PERFORMANCE RECORDS ON SELECTED ASSIGNMENTS. WE CONDUCTED A 1 MONTH PILOT WITH 27 LEARNERS IN A PRIMARY SCHOOL IN NAIROBI, KENYA DURING THEIR MATHS AND SCIENCE INSTRUCTIONAL PERIODS. WE PRESENT OUR PRIMARY ANALYSIS OF CONTENT-LEVEL INTERACTIONS AND ENGAGEMENT AT THE INDIVIDUAL STUDENT AND CLASSROOM LEVEL.
OVER THE LAST 5YEARS, MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE INCREASINGLY PROVIDED LEARNING OPPORTUNITIES ACROSS THE WORLD IN A VARIETY OF DOMAINS. AS WITH MANY EMERGING EDUCATIONAL TECHNOLOGIES, WHY AND HOW PEOPLE COME TO MOOCS NEEDS TO BE BETTER UNDERSTOOD AND IMPORTANTLY WHAT FACTORS CONTRIBUTE TO LEARNERS' MOOC PERFORMANCE. IT IS KNOWN THAT ONLINE LEARNING ENVIRONMENTS REQUIRE GREATER LEVELS OF SELF-REGULATION, AND THAT HIGH LEVELS OF MOTIVATION ARE CRUCIAL TO ACTIVATE THESE SKILLS. HOWEVER, MOTIVATION IS A COMPLEX CONSTRUCT AND RESEARCH ON HOW IT FUNCTIONS IN MOOCS IS STILL IN ITS EARLY STAGES. RESEARCH PRESENTED IN THIS ARTICLE INVESTIGATED HOW MOTIVATION AND PARTICIPATION INFLUENCE STUDENTS' PERFORMANCE IN A MOOC, MORE SPECIFICALLY THOSE STUDENTS WHO PERSIST TO THE END OF THE MOOC. FINDINGS INDICATED THAT THE STRONGEST PREDICTOR OF PERFORMANCE WAS PARTICIPATION, FOLLOWED BY MOTIVATION. MOTIVATION INFLUENCED AND WAS INFLUENCED BY STUDENTS' PARTICIPATION DURING THE COURSE. MOREOVER, SITUATIONAL INTEREST PLAYED A CRUCIAL ROLE IN MEDIATING THE IMPACT OF GENERAL INTRINSIC MOTIVATION AND PARTICIPATION ON PERFORMANCE. THE RESULTS ARE DISCUSSED IN RELATION TO HOW EDUCATORS AND DESIGNERS OF MOOCS CAN USE KNOWLEDGE EMERGING FROM MOTIVATIONAL ASSESSMENTS AND PARTICIPATION MEASURES GLEANED FROM LEARNING ANALYTICS TO TAILOR THE DESIGN AND DELIVERY OF COURSES. (C) 2016 JOHN WILEY & SONS LTD
HOW TO USE MEANINGFUL LEARNING FEATURES TO PREDICT LEARNING OUTCOMES IS A KEY ISSUE IN LEARNING ANALYTICS. WITH FEATURE ENGINEERING, THIS STUDY CONSTRUCTED A FEATURE SET, INCLUDING EIGHT EFFECTIVE LEARNING FEATURES ABOUT INTERACTIONS OF STUDENT-STUDENT, STUDENT-TEACHER, STUDENT-CONTENT, AND STUDENT-INTERFACE. THE TRACE DATA OF 108 MIDDLE SCHOOL STUDENTS ON THE E-CLOUDBAG LMS WAS COLLECTED AND ANALYZED, WHICH PROVED THAT THE FEATURE SET CAN EFFECTIVELY PREDICT THE STUDENTS' LEARNING OUTCOMES. THE RANDOM FOREST ALGORITHM ACHIEVED THE BEST PREDICTION EFFECT, AND THE PREDICTION ACCURACY RATE WAS 73.15%. AT THE SAME TIME, THIS STUDY USED A K-MEANS CLUSTERING ALGORITHM TO CLUSTER 108 STUDENTS INTO FOUR CATEGORIES, AND ANALYZED THE DIFFERENCES BETWEEN FOUR TYPES OF STUDENTS IN REGARD TO LEARNING PATTERNS AND LEARNING OUTCOMES. THE RESULTS SHOWED THAT LMS USAGE, SPEED OF COMPLETING ASSIGNMENTS, ACADEMIC PROCRASTINATION, PEER EMOTION AND SOCIAL NETWORK SIZE WERE IMPORTANT FACTORS INFLUENCING LEARNING OUTCOMES.
ONE OF THE MOST COMMON GAMIFICATION TECHNIQUES IN EDUCATION IS THE USE OF BADGES AS A REWARD FOR MAKING SPECIFIC STUDENT ACTIONS. WE PROPOSE TWO INDICATORS TO GAIN INSIGHT ABOUT STUDENTS' INTENTIONALITY TOWARDS EARNING BADGES AND USE THEM WITH DATA FROM 291 STUDENTS INTERACTING WITH KHAN ACADEMY COURSES. THE INTENTIONALITY TO EARN BADGES WAS GREATER FOR REPETITIVE BADGES, AND THIS CAN BE RELATED TO THE FACT THAT THESE ARE EASIER TO ACHIEVE. WE PROVIDE THE GENERAL DISTRIBUTION OF STUDENTS DEPENDING ON THESE BADGE INDICATORS, OBTAINING DIFFERENT PROFILES OF STUDENTS WHICH CAN BE USED FOR ADAPTATION PURPOSES.
THIS PAPER PRESENTS A CASE STUDY ON TEACHING NETWORK ENGINEERING IN CONJUNCTION WITH INTERACTIVE LEARNING RESOURCES. THIS CASE STUDY HAS BEEN DEVELOPED IN COLLABORATION WITH THE CISCO NETWORKING ACADEMY IN THE CONTEXT OF THE FORGE PROJECT, WHICH PROMOTES ONLINE LEARNING AND EXPERIMENTATION BY OFFERING ACCESS TO VIRTUAL AND REMOTE LABS. THE MAIN GOAL OF THIS WORK IS ALLOWING LEARNERS AND EDUCATORS TO PERFORM NETWORK SIMULATIONS WITHIN A WEB BROWSER OR AN INTERACTIVE EBOOK BY USING ANY TYPE OF MOBILE, TABLET OR DESKTOP DEVICE. LEARNING ANALYTICS ARE EMPLOYED IN ORDER TO MONITOR LEARNING BEHAVIOUR FOR FURTHER ANALYSIS OF THE LEARNING EXPERIENCE OFFERED TO STUDENTS.
THIS PAPER INTRODUCES LARAE (LEARNING ANALYTICS REFLECTION & AWARENESS ENVIRONMENT), A TEACHER-ORIENTED DASHBOARD THAT VISUALIZES LEARNING TRACES FROM STUDENTS, BADGES AND COURSE CONTENT. WE ALSO PRESENT AN EVALUATION OF THE DASHBOARD IN A COURSE ON HUMAN-COMPUTER INTERACTION. THE LARAE TEACHER DASHBOARD PROVIDES A DETAILED OVERVIEW OF GROUP AND INDIVIDUAL ACTIVITIES, ACHIEVEMENTS AND COURSE OUTCOMES. TO HELP VISUALIZE THE ABUNDANCE OF TRACES, BADGES ARE USED TO ABSTRACT ESSENTIAL ASPECTS OF THE COURSE SUCH AS COURSE GOALS AND SOCIAL ACTIVITY. THIS PAPER REPORTS OUR WORK ON LARAE, PRESENTS THE COURSE IN WHICH WE EVALUATED OUR APPROACH WITH STUDENTS AND TEACHERS, AND ANALYSES OUR FIRST RESULTS THAT INDICATE THAT SUCH AN ENVIRONMENT CAN HELP WITH TEACHER AWARENESS.
SCIENCE EPISTEMOLOGY, OR BELIEFS ABOUT WHAT IT MEANS TO DO SCIENCE AND HOW SCIENCE KNOWLEDGE IS GENERATED, IS AN INTEGRAL PART OF AUTHENTIC SCIENCE INQUIRY. ALTHOUGH THE DEVELOPMENT OF A SOPHISTICATED SCIENCE EPISTEMOLOGY IS CRITICAL FOR ATTAINING SCIENCE LITERACY, EPISTEMOLOGY REMAINS AN ELUSIVE CONSTRUCT TO PRECISELY AND QUANTITATIVELY EVALUATE. PREVIOUS WORK HAS SUGGESTED THAT ANALYSIS OF STUDENT PRACTICES IN SCIENCE INQUIRY, SUCH AS THEIR USE OF LANGUAGE, MAY BE REFLECTIVE OF THEIR UNDERLYING EPISTEMOLOGIES. HERE WE DESCRIBE THE USAGE OF A LEARNING ANALYTICS TOOL, TAALES, AND KEYNESS ANALYSIS TO ANALYZE THE CONCLUDING STATEMENTS MADE BY STUDENTS AT THE END OF A COMPUTER-BASED AUTHENTIC SCIENCE INQUIRY EXPERIENCE. PRELIMINARY RESULTS INDICATE THAT LINGUISTIC ANALYSIS REVEALS DIFFERENCES IN DOMAIN-GENERAL LEXICAL SOPHISTICATION AND IN DOMAIN-SPECIFIC VERB USAGE THAT ARE CONSISTENT WITH THE EXPERTISE LEVEL OF THE PARTICIPANT. FOR EXAMPLE, EXPERTS TEND TO USE MORE HEDGING LANGUAGE SUCH AS "MAY" AND "SUPPORT" DURING CONCLUSIONS WHEREAS NOVICES USE STRONGER LANGUAGE SUCH AS "CAUSE." USING THESE DIFFERENCES, A SIMPLE, RULE-BASED PREDICTION ALGORITHM WITH LOOCV ACHIEVED PREDICTION ACCURACIES OF GREATER THAN 80%. THESE DATA UNDERSCORE THE POTENTIAL FOR THE USE OF LEARNING ANALYTICS IN SIMULATED AUTHENTIC INQUIRY TO PROVIDE A NOVEL AND VALUABLE METHOD OF ASSESSING INQUIRY PRACTICES AND RELATED EPISTEMOLOGIES.
IN THIS PAPER WE REPORT ON CURRENT AND FUTURE DEVELOPMENTS IN THE REMOTE LABORATORY NETLAB. AFTER A SHORT REVIEW OF REAL, VIRTUAL AND REMOTE LABORATORIES, NETLAB - A REMOTE LABORATORY CREATED AND OPERATED AT THE UNIVERSITY OF SOUTH AUSTRALIA SINCE 2002, IS INTRODUCED. EXPERIMENTS CONDUCTED IN 2015 USING REAL LABORATORY AND NETLAB ARE PRESENTED AND STUDENT FEEDBACK ON THOSE REPORTED. INCREASINGLY, BOTH ACADEMICS AND STUDENTS ACCEPT THAT PROPERLY STRUCTURED, DOCUMENTED AND SUPPORTED REMOTE LABORATORIES CONSTITUTE A VALID COMPLEMENTATION TO AND/OR REPLACEMENT OF REAL LABORATORIES. TO INCREASE THE STUDENTS' LEARNING SUPPORT IN REMOTE LABORATORIES A DEVELOPMENT OF AN INTELLIGENT TUTORING SYSTEM BASED ON THE LEARNING ANALYTICS IS PROPOSED AND OUTLINED IN THIS PAPER.
IN THIS WORK WE INTRODUCE A NOVEL LEARNING ANALYTICS APPROACH TO IDENTIFY STUDENTS' COMMUNITIES. THE INTRODUCTION OF LEARNING MANAGEMENT SYSTEMS IN HIGHER EDUCATION REQUIRES THE EDUCATORS TO PLAN THEIR LEARNING DESIGN (LD) PROCESS WITH THE ONLINE SCENARIO IN MIND. WE EXAMINED THE BLENDED LEARNING ENVIRONMENT WHERE THIS PROCESS TAKES PLACE IN THE VIRTUAL LEARNING ENVIRONMENT. THIS ALLOWS THE EDUCATORS TO TRACK MOST OF THE STUDENTS' INDIVIDUAL ACTIVITIES, BUT THE COMMUNICATIONS MAY BE EXCLUDED FROM TRACKING SINCE THE STUDENTS CAN USE SIDE COMMUNICATIONS CHANNELS, SUCH AS FACE-TO-FACE COMMUNICATION, INSTANT MESSAGING AND SOCIAL NETWORK PLATFORMS. OUR APPROACH, USING THE STUDENT-SYSTEM INTERACTIONS HISTORIES, HELPS TO DISCOVER HIDDEN RELATIONSHIPS AMONG THE STUDENTS. THE ELICITED INFORMATION ABOUT STUDENTS' GROUPINGS AND SOCIAL INTERACTIONS' EVOLUTION OVER TIME CAN BE USED BY EDUCATORS TO ADAPT AND IMPROVE THEIR LD PROCESS, TO FIND ASSOCIATIONS BETWEEN STUDENTS' SOCIAL INTERACTIONS AND THEIR ACADEMIC PERFORMANCE, AS WELL AS TO PROMOTE TEAM-BASED LEARNING.
THIS REPORT SYNTHESIZED A ONE-HOUR PANEL DISCUSSION AT THE ASSOCIATION FOR EDUCATIONAL COMMUNICATIONS AND TECHNOLOGY (AECT) INTERNATIONAL CONVENTION 2018 IN KANSAS CITY, MISSOURI, SPONSORED BY THE KOREAN SOCIETY FOR EDUCATIONAL TECHNOLOGY (KSET). THE THEME OF THE PANEL DISCUSSION WAS "NEW TECHNOLOGY, DESIGN, AND RESEARCH APPROACHES FOR INNOVATIVE EDUCATION." FOUR DISTINGUISHED PANELISTS AND MORE THAN 20 AECT/KSET MEMBERS PARTICIPATED IN DISCUSSIONS ON VARIOUS TOPICS, INCLUDING RAPID PROTOTYPING, INSTRUCTIONAL SYSTEMS DESIGN, ONLINE SUPPORT SYSTEM DEVELOPMENT FOR CREATIVITY, MICROLEARNING, VIRTUAL REALITY/SIMULATION, LEARNING ANALYTICS, CODING EDUCATION AND MAKERSPACES IN K-12 EDUCATION.
THIS PAPER DESCRIBES A NEW HOLISTIC APPROACH TO INTEGRATE REMOTE AND VIRTUAL LABORATORIES IN THE EDUCATIONAL PROCESS. AIM OF THE INTEGRATION IS TO TEST STUDENTS' KNOWLEDGE NOT ONLY ON LOWER LEVELS AS USUAL MULTIPLE CHOICE TESTS AND THE LIKE ARE DOING, BUT ALSO ON HIGHER-ORDER THINKING SKILLS. BASED ON THESE TESTS THE STUDENTS SHOULD GET A DETAILED FEEDBACK ABOUT THEIR STRENGTHS, WEAKNESSES OR MIS-CONCEPTIONS. THEREFORE LEARNING ANALYTICS METHODS ARE APPLIED TO A DATABASE, STORING LEARNING ACTIVITIES OF THE STUDENTS DURING THEIR ONLINE WORK-PHASE IN THE EDUCATIONAL PROCESS. THE PAPER DISCUSSES THE ACTUAL APPROACH AS WELL AS THE ARCHITECTURE OF A PROTOTYPE IMPLEMENTATION.
THIS PAPER DESCRIBES A STUDY THAT INVESTIGATES THE RELATION BETWEEN CRITICAL THINKING AND THE METACOGNITIVE ELEMENT OF COGNITION MONITORING IN ASYNCHRONOUS DISCUSSIONS ADOPTING THE COMMUNITY OF INQUIRY FRAMEWORK. THE AIM OF THIS STUDY IS TO PROPOSE MEASUREMENTS REFLECTING THE COGNITIVE DEVELOPMENT OF THE COMMUNITY THROUGH AN ASYNCHRONOUS DISCUSSION. THESE MEASUREMENTS WILL BE INTEGRATED IN AN ADAPTABLE VISUALIZATION LEARNING ANALYTICS TOOL TO ENHANCE PERSONALIZED INTERACTION. THE RESULTS OF THE STUDY SHOW MODERATE POSITIVE SIGNIFICANT RELATION BETWEEN LEARNERS' CRITICAL THINKING AND LEARNERS' MONITORING OF THE INQUIRY PROCESS WHILST THE LINEAR REGRESSION ANALYSIS INDICATED CRITICAL THINKING AS A SIGNIFICANT PREDICTING MEASUREMENT FOR LEARNERS' MONITORING OF THE INQUIRY PROCESS.
BUILDING AND USING A PERSONAL LEARNING ENVIRONMENT IS CHALLENGING AND REQUIRES THE STUDENTS TO HAVE THEIR METACOGNITIVE SKILLS DEVELOPED. MANY STUDENTS NEED HELP TO DEVELOP THESE SKILLS, TO LEARN HOW TO MANAGE AND EVALUATE THEIR LEARNING PROCESS IN ORDER TO IMPROVE IT. WE DESIGNED AN ORGANIZER WITH METACOGNITIVE SUPPORT TO HELP USERS TO MEET THEIR LEARNING GOALS. HERE, WE PROPOSE: 1) EMBEDDING LEARNING STRATEGIES TO SUPPORT METACOGNITIVE LEARNING; 2) A LEARNING STRATEGIES RECOMMENDER; 3) OUR VISION OF A LEARNING ANALYTICS MODULE FOR LEARNERS TO SUPPORT LEARNING AWARENESS AND THE REFLECTION PROCESS; AND 4) OUR SOLUTION FOR RECOMMENDATIONS BASED ON TAG RELATIONSHIPS, TAG ENRICHMENT, AND FOLKSONOMIES.
LOGGED TRACKING DATA FOR ONLINE COURSES ARE GENERALLY NOT AVAILABLE TO INSTRUCTORS, STUDENTS, AND COURSE DESIGNERS AND DEVELOPERS, AND EVEN IF THESE DATA WERE AVAILABLE, MOST CONTENT-ORIENTED INSTRUCTORS DO NOT HAVE THE SKILL SET TO ANALYZE THEM. LEARNING ANALYTICS, MINED FROM LOGGED COURSE DATA AND USUALLY PRESENTED IN THE FORM OF LEARNING DASHBOARDS, CAN INFORM INSTRUCTORS AND STUDENTS ABOUT HOW STUDENTS ARE DOING IN A COURSE, FOR EXAMPLE, QUESTIONS ANSWERED CORRECTLY OR INCORRECTLY, PAGES VISITED, EXERCISES COMPLETED, AND ASSESSMENT SCORES. HOWEVER, THESE DASHBOARDS DO NOT PROVIDE DATA ON WHAT STUDENTS ARE DOING WHEN INTERACTING WITH COURSE MATERIALS. THIS DEARTH OF INFORMATION SEVERELY LIMITS THE AMOUNT OF FEEDBACK THAT ONLINE COURSE INSTRUCTORS CAN GIVE TO STUDENTS ON HOW TO BEST MAXIMIZE THEIR TIME ONLINE TO MEET THEIR LEARNING OUTCOMES. USING LOGGED DATA FROM AN ONLINE FRENCH COURSE, THE RESEARCH PRESENTED HERE OFFERS A SAMPLE OF PRELIMINARY STATISTICAL VISUALIZATIONS CREATED USING DATA FROM STUDENTS INTERACTING WITH A COURSE VIDEO AND ITS ATTENDANT QUESTIONS. BY DEVELOPING A METHODOLOGY THAT CAN BE APPLIED TO OTHER SIMILAR DATASETS, THESE TYPES OF VISUALIZATIONS COULD BE AUTOMATICALLY GENERATED FOR ALL STAKEHOLDERS TO OBTAIN A FULLER PICTURE OF HOW STUDENTS BEHAVE IN ONLINE COURSES.
WE PRESENT A METHODOLOGICAL IMPROVEMENT FOR CALCULATING GRADE POINT AVERAGES (GPAS). HETEROGENEITY IN GRADING BETWEEN COURSES SYSTEMATICALLY BIASES OBSERVED GPAS FOR INDIVIDUAL STUDENTS: THE GPA OBSERVED DEPENDS ON COURSE SELECTION. WE SHOW HOW A LOGISTIC MODEL CAN ACCOUNT FOR COURSE SELECTION BY SIMULATING HOW EVERY STUDENT IN A SAMPLE WOULD PERFORM IF THEY TOOK ALL AVAILABLE COURSES, GIVING A NEW "MODELED GPA." WE THEN USE 10 YEARS OF GRADE DATA FROM A LARGE UNIVERSITY TO DEMONSTRATE THAT THIS MODELED GPA IS A MORE ACCURATE PREDICTOR OF STUDENT PERFORMANCE IN INDIVIDUAL COURSES THAN THE OBSERVED GPA. USING COMPUTER SCIENCE (CS) AS AN EXAMPLE LEARNING ANALYTICS APPLICATION, IT IS FOUND THAT REQUIRED CS COURSES GIVE SIGNIFICANTLY LOWER GRADES THAN AVERAGE COURSES. THIS DEPRESSES THE RECORDED GPAS OF CS MAJORS: MODELED GPAS ARE 0.25 POINTS HIGHER THAN THOSE THAT ARE OBSERVED. THE MODELED GPA ALSO CORRELATES MUCH MORE CLOSELY WITH STANDARDIZED TEST SCORES THAN THE OBSERVED GPA: THE CORRELATION WITH MATH ACT IS 0.37 FOR THE MODELED GPA AND IS 0.20 FOR THE OBSERVED GPA. THIS IMPLIES THAT STANDARDIZED TEST SCORES ARE MUCH BETTER PREDICTORS OF STUDENT PERFORMANCE THAN MIGHT OTHERWISE BE ASSUMED.
COMPUTATIONAL THINKING (CT) HAS BEEN GIVEN RECENT ATTENTION SUGGESTING THAT IT BE DEVELOPED IN CHILDREN OF ALL AGES. WITH THE CREATION OF K-12 COMPUTER SCIENCE STANDARDS BY THE COMPUTER SCIENCE TEACHER ASSOCIATION, STATES SUCH AS MASSACHUSETTS AND WASHINGTON ARE LEADING THE NATION IN ADOPTING THESE STANDARDS INTO THEIR SCHOOL SYSTEMS. THIS SEEMS SOMEWHAT PREMATURE, WHEN THERE ARE SO FEW MEASURES OF COMPUTATIONAL THINKING OR COMPUTER PROGRAMMING SKILLS THAT CAN BE APPLIED EASILY IN A K-12 SETTING TO ASSESS OUTCOMES OF SUCH STATE- WIDE INITIATIVES. THROUGH FUNDING FROM THE NATIONAL SCIENCE FOUNDATION, WE DEVELOPED AN ANALYSIS TOOL TO EFFICIENTLY CAPTURE STUDENT LEARNING PROGRESSIONS AND PROBLEM-SOLVING ACTIVITIES WHILE CODING IN SCRATCH, A POPULAR VISUAL PROGRAMMING LANGUAGE DEVELOPED BY MIT MEDIA LAB. OUR ANALYSIS TOOL, THE FUNCTIONAL UNDERSTANDING NAVIGATOR! OR FUN! TOOL, ADDRESSES THE NEED TO AUTOMATE PROCESSES TO HELP RESEARCHERS EFFICIENTLY CLEAN, ANALYZE, AND PRESENT DATA. WE SHARE OUR EXPERIENCES USING THE TOOL WITH SCRATCH DATA COLLECTED FROM THREE DIFFERENT WEEK-LONG SUMMER SCRATCH CAMPS WITH STUDENTS IN GRADES 5 TO 8. BASED ON OUR PRELIMINARY ANALYSES, WE SHARE IMPORTANT CONSIDERATIONS FOR RESEARCHERS INTERESTED IN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS IN THE AREA OF ASSESSING COMPUTATIONAL THINKING. WE ALSO PROVIDE LINKS TO THE PUBLICALLY AVAILABLE FUN! TOOL AND ENCOURAGE OTHERS TO PARTICIPATE IN A COMMUNITY DEVELOPING NEW MEASURES OF COMPUTATIONAL THINKING AND COMPUTER PROGRAMMING.
FEATURED APPLICATION THIS STUDY ANALYZES STUDENTS' PERSISTENCE USING THEIR INTERACTIONS IN ONLINE PLATFORMS. THE ANALYSIS OF PERSISTENCE CAN BE USEFUL TO GATHER INFORMATION IN ORDER TO BETTER UNDERSTAND STUDENTS' ABILITIES AND THEIR POSSIBLE DIFFICULTIES. THIS INFORMATION CAN ALSO BE USEFUL TO PROVIDE PERSONALIZED AND ADAPTATIVE FEEDBACK AND CONTENTS TO IMPROVE LEARNING. ABSTRACT IN EDUCATION, SEVERAL STUDIES HAVE TRIED TO TRACK STUDENT PERSISTENCE (I.E., STUDENTS' ABILITY TO KEEP ON WORKING ON THE ASSIGNED TASKS) USING DIFFERENT DEFINITIONS AND SELF-REPORTED DATA. HOWEVER, SELF-REPORTED METRICS MAY BE LIMITED, AND CURRENTLY, ONLINE COURSES ALLOW COLLECTING MANY LOW-LEVEL EVENTS TO ANALYZE STUDENT BEHAVIORS BASED ON LOGS AND USING LEARNING ANALYTICS. THESE ANALYSES CAN BE USED TO PROVIDE PERSONALIZED AND ADAPTATIVE FEEDBACK IN SMART LEARNING ENVIRONMENTS. IN THIS LINE, THIS WORK PROPOSES THE ANALYSIS AND MEASUREMENT OF TWO TYPES OF PERSISTENCE BASED ON STUDENTS' INTERACTIONS IN ONLINE COURSES: (1) LOCAL PERSISTENCE (BASED ON THE ATTEMPTS USED TO SOLVE AN EXERCISE WHEN THE STUDENT ANSWERS IT INCORRECTLY), AND (2) GLOBAL PERSISTENCE (BASED ON OVERALL COURSE ACTIVITY/COMPLETION). RESULTS SHOW THAT THERE ARE DIFFERENT STUDENTS' PROFILES BASED ON LOCAL PERSISTENCE, ALTHOUGH MEDIUM LOCAL PERSISTENCE STANDS OUT. MOREOVER, LOCAL PERSISTENCE IS HIGHLY AFFECTED BY COURSE CONTEXT AND IT CAN VARY THROUGHOUT THE COURSE. FURTHERMORE, LOCAL PERSISTENCE DOES NOT NECESSARILY RELATE TO GLOBAL PERSISTENCE OR ENGAGEMENT WITH VIDEOS, ALTHOUGH IT IS RELATED TO STUDENTS' AVERAGE GRADE. FINALLY, PREDICTIVE ANALYSIS SHOWS THAT LOCAL PERSISTENCE IS NOT A STRONG PREDICTOR OF GLOBAL PERSISTENCE AND PERFORMANCE, ALTHOUGH IT CAN ADD SOME VALUE TO THE PREDICTIVE MODELS.
THIS STUDY ANALYZED STUDENTS' INTERACTION PATTERNS IN ASYNCHRONOUS ONLINE DISCUSSION FORUMS BY USING LOG FILES LEFT IN THE LMS. IN TAKING SOCIAL NETWORK ANALYSIS (SNA) AND LEARNING ANALYTICS (LA) APPROACH, THE CENTRALITY OF PARTICIPANTS, THEIR NETWORKING PATTERNS, CHARACTERISTICS OF NETWORKS AMONG MULTIPLE TOPICS, AND CHANGED PATTERNS BY TIME WERE REVIEWED WITHIN A CASE STUDY. ADDITIONALLY, THIS STUDY FOUND THAT THE INSTRUCTOR'S INITIATION, STUDENTS' AUTONOMY TO SELECT TOPICS, TOGETHER WITH THE USE OF SAMPLE ESSAYS INFLUENCED THE ONLINE DISCUSSION PATTERNS, WHICH IS EFFECTIVELY ILLUSTRATED BY SNA RESULTS. FINALLY, THIS STUDY DISCUSSED THAT NOT ONLY THE USE OF SNA AS AN ANALYTICS TOOL BUT ALSO THE DISPLAY OF THE SNA OUTPUTS AS A PRESENTATION TOOL CAN FACILITATE THEIR SMART AND EFFECTIVE DISCUSSION ACTIVITY.
ADVANCES IN TECHNOLOGY IS REVOLUTIONIZING THE WAY WE THINK AND BEHAVE. AS YOUNG DIGITAL NATIVES COME WITH A NEW WAY OF LEARNING, WE, AS EDUCATORS, NEED TO APPROACH EDUCATION FROM A NEW ANGLE. IN ORDER TO ASSIST TEACHERS WITH THE TRANSITION FROM TRADITIONAL TEACHING TO ENGAGING ONLINE TEACHING, TECHNOLOGY-ENRICHED LEARNING INITIATIVE (TELI) IN THE UNIVERSITY OF HONG KONG HAS DESIGNED A BLENDED-LEARNING PROFESSIONAL DEVELOPMENT COURSE TITLED, "INTERACTIVE ONLINE LEARNING". IN THIS PAPER, WE DESCRIBE HOW WE USED BLENDED LEARNING AND LEARNING ANALYTICS TO FOSTER ENGAGEMENT AND DEMONSTRATE POSSIBILITY AND PRACTICALITY OF DIY ONLINE TEACHING.
LEARNING IS A MARKED CHANGE IN THE CONCEPTUAL REPRESENTATION OF THE WORLD, IN NATURALLY INTELLIGENT ENTITIES, SUCH AS HUMANS, AS WELL AS IN ARTIFICIALLY INTELLIGENT ENTITIES. ANALYTICS AIMS AT THE GENERATION OF SITUATIONAL AWARENESS, SPECIFICALLY, MOMENTS OF INSIGHT THAT EFFECT SUCH A MARKED CHANGE AND THE ENABLERS OF THE CHANGE. IN THAT, LEARNING ANALYTICS IS THE STUDY OF DETECTION, ANALYSIS, AND GENERATION OF MOMENTS OF INSIGHTS ABOUT LEARNING EXPERIENCES OF NATURALLY OR ARTIFICIALLY INTELLIGENT ENTITIES. IT ENRICHES LEARNING EXPERIENCES AS A MEASURABLE CONSEQUENCE OF THESE MOMENTS OF INSIGHTS. IN INFUSING AUTHENTICITY TO LEARNING EXPERIENCES, THIS CHAPTER DISCUSSES ABSTRACTION-ORIENTED PEDAGOGY AT ONE END OF A CONTINUUM AND REALITY-ORIENTED PEDAGOGY AT THE OTHER END AND OFFERS A CHARACTERIZATION OF THIS CONTINUUM.
THIS PAPER DESCRIBES A NEW HOLISTIC APPROACH TO INTEGRATE REMOTE AND VIRTUAL LABORATORIES IN THE EDUCATIONAL PROCESS. AIM OF THE INTEGRATION IS TO TEST STUDENTS' KNOWLEDGE NOT ONLY ON LOWER LEVELS AS USUAL MULTIPLE CHOICE TESTS AND THE LIKE ARE DOING, BUT ALSO ON HIGHER-ORDER THINKING SKILLS. BASED ON THESE TESTS THE STUDENTS SHOULD GET A DETAILED FEEDBACK ABOUT THEIR STRENGTHS, WEAKNESSES OR MISCONCEPTIONS. THEREFORE LEARNING ANALYTICS METHODS ARE APPLIED TO A DATABASE, STORING LEARNING ACTIVITIES OF THE STUDENTS DURING THEIR ONLINE WORK-PHASE IN THE EDUCATIONAL PROCESS. THE PAPER DISCUSSES THE ACTUAL APPROACH AS WELL AS THE ARCHITECTURE OF A PROTOTYPE IMPLEMENTATION.
NEW HIGH-FREQUENCY, AUTOMATED DATA COLLECTION AND ANALYSIS ALGORITHMS COULD OFFER NEW INSIGHTS INTO COMPLEX LEARNING PROCESSES, ESPECIALLY FOR TASKS IN WHICH STUDENTS HAVE OPPORTUNITIES TO GENERATE UNIQUE OPEN-ENDED ARTIFACTS SUCH AS COMPUTER PROGRAMS. THESE APPROACHES SHOULD BE PARTICULARLY USEFUL BECAUSE THE NEED FOR SCALABLE PROJECT-BASED AND STUDENT-CENTERED LEARNING IS GROWING CONSIDERABLY. IN THIS ARTICLE, WE PRESENT STUDIES FOCUSED ON HOW STUDENTS LEARN COMPUTER PROGRAMMING, BASED ON DATA DRAWN FROM 154,000 CODE SNAPSHOTS OF COMPUTER PROGRAMS UNDER DEVELOPMENT BY APPROXIMATELY 370 STUDENTS ENROLLED IN AN INTRODUCTORY UNDERGRADUATE PROGRAMMING COURSE. WE USE METHODS FROM MACHINE LEARNING TO DISCOVER PATTERNS IN THE DATA AND TRY TO PREDICT FINAL EXAM GRADES. WE BEGIN WITH A SET OF EXPLORATORY EXPERIMENTS THAT USE FULLY AUTOMATED TECHNIQUES TO INVESTIGATE HOW MUCH STUDENTS CHANGE THEIR PROGRAMMING BEHAVIOR THROUGHOUT ALL ASSIGNMENTS IN THE COURSE. THE RESULTS SHOW THAT STUDENTS' CHANGE IN PROGRAMMING PATTERNS IS ONLY WEAKLY PREDICTIVE OF COURSE PERFORMANCE. WE SUBSEQUENTLY HONE IN ON 1 SINGLE ASSIGNMENT, TRYING TO MAP STUDENTS' LEARNING PROCESS AND TRAJECTORIES AND AUTOMATICALLY IDENTIFY PRODUCTIVE AND UNPRODUCTIVE (SINK) STATES WITHIN THESE TRAJECTORIES. RESULTS SHOW THAT OUR PROCESS-BASED METRIC HAS BETTER PREDICTIVE POWER FOR FINAL EXAMS THAN THE MIDTERM GRADES. WE CONCLUDE WITH RECOMMENDATIONS ABOUT THE USE OF SUCH METHODS FOR ASSESSMENT, REAL-TIME FEEDBACK, AND COURSE IMPROVEMENT.
BODY LANGUAGE PLAYS AN IMPORTANT ROLE IN LEARNING PROCESSES AND COMMUNICATION. FOR EXAMPLE, COMMUNICATION RESEARCH PRODUCED EVIDENCE THAT MATHEMATICAL KNOWLEDGE CAN BE EMBODIED IN GESTURES MADE BY TEACHERS AND STUDENTS. LIKEWISE, BODY POSTURES AND GESTURES ARE ALSO UTILIZED BY SPEAKERS IN ORAL PRESENTATIONS TO CONVEY IDEAS AND IMPORTANT MESSAGES. CONSEQUENTLY, CAPTURING AND ANALYZING NON-VERBAL BEHAVIORS IS AN IMPORTANT ASPECT IN MULTIMODAL LEARNING ANALYTICS (MLA) RESEARCH. WITH REGARD TO SENSING CAPABILITIES, THE INTRODUCTION OF DEPTH SENSORS SUCH AS THE MICROSOFT KINECT HAS GREATLY FACILITATED RESEARCH AND DEVELOPMENT IN THIS AREA. HOWEVER, THE RAPID ADVANCEMENT IN HARDWARE AND SOFTWARE CAPABILITIES IS NOT ALWAYS IN SYNC WITH THE EXPANDING SET OF FEATURES REPORTED IN THE LITERATURE. FOR EXAMPLE, THOUGH ANVIL [ 21] IS A WIDELY USED STATE-OF-THE-ART ANNOTATION AND VISUALIZATION TOOLKIT FOR MOTION TRACES, ITS MOTION RECORDING COMPONENT BASED ON OPENNI IS OUTDATED. AS PART OF OUR RESEARCH IN DEVELOPING MULTIMODAL EDUCATIONAL ASSESSMENTS, WE BEGAN AN EFFORT TO DEVELOP AND STANDARDIZE ALGORITHMS FOR PURPOSES OF MULTIMODAL FEATURE EXTRACTION AND CREATING AUTOMATED SCORING MODELS. THIS PAPER PROVIDES AN OVERVIEW OF RELEVANT WORK IN MULTIMODAL RESEARCH ON EDUCATIONAL TASKS, AND PROCEEDS TO SUMMARIZE OUR WORK USING MULTIMODAL SENSORS IN DEVELOPING ASSESSMENTS OF COMMUNICATION SKILLS, WITH ATTENTION ON THE USE OF DEPTH SENSORS. SPECIFICALLY, WE FOCUS ON THE TASK OF PUBLIC SPEAKING ASSESSMENT USING MICROSOFT KINECT. ADDITIONALLY, WE INTRODUCE AN OPEN-SOURCE PYTHON PACKAGE FOR COMPUTING EXPRESSIVE BODY LANGUAGE FEATURES FROM KINECT MOTION DATA, WHICH WE HOPE WILL BENE FIT THE MLA RESEARCH COMMUNITY.
THE AMOUNT OF DATA EXTRACTED FROM LEARNING EXPERIENCES HAS GROWN AT AN ASTONISHING PACE BOTH IN DEPTH DUE TO THE INCREASING VARIETY OF DATA SOURCES, AND IN BREATH WITH COURSES NOW BEING OFFERED TO MASSIVE STUDENT COHORTS. HOWEVER, IN THIS EMERGING SCENARIO INSTRUCTORS ARE NOW FACING THE CHALLENGE OF CONNECTING THE KNOWLEDGE EMERGING FROM DATA ANALYSIS WITH THE PROVISION OF MEANINGFUL SUPPORT ACTIONS TO STUDENTS WITHIN THE CONTEXT OF AN INSTRUCTIONAL DESIGN. THE OBJECTIVE OF THIS TUTORIAL IS TO GIVE ATTENDEES A SET OF HYPOTHETICAL SCENARIOS IN WHICH THE KNOWLEDGE EXTRACTED FROM A LEARNING EXPERIENCE NEEDS TO BE USED TO PROVIDE FREQUENT PERSONALIZED FEEDBACK TO STUDENTS.
LEARNING SCIENCES ARGUE THAT STUDENT ENGAGEMENT IS COMPOSED OF BEHAVIORAL, MOTIVATIONAL AND COGNITIVE DIMENSIONS. MANY PROPOSALS IN LEARNING ANALYTICS HAVE PROVIDED TEACHERS WITH QUANTITATIVE INDICATORS FOCUSING ONLY ON STUDENTS' BEHAVIORS, SUCH AS THE NUMBER AND THE DURATION OF THEIR ACTIONS WITH THE LEARNING ENVIRONMENT. IN THIS PAPER, WE PROPOSE VISUAL REPRESENTATIONS OF COGNITIVE INDICATORS TO ADD EXPLANATORY ELEMENTS TO BEHAVIORAL INDICATORS. WE DESCRIBE OUR GENERAL ARCHITECTURE FOR COLLECTING AND AGGREGATING DATA USED TO BUILD THE PROPOSED VISUALIZATIONS. WE ILLUSTRATE THE USE OF THESE INDICATORS IN VARIOUS PEDAGOGICAL SCENARIOS ORIENTED TOWARDS SUPPORTING TEACHERS IN STUDENTS' ACTIONS AND PERFORMANCES UNDERSTANDING.
AS BUSINESS ANALYTICS (BA) APPLICATIONS PERMEATE ACROSS VARIOUS INDUSTRY SECTORS, THE WORKFORCE NEEDS TO BE TRAINED AND UPSKILLED TO MEET THE CHALLENGES OF UNDERSTANDING AND IMPLEMENTING ANALYTICS METHODOLOGIES. TO ACHIEVE PAYOFFS FROM THE RESOURCE INVESTMENTS IN BA TRAINING, IT IS CRITICAL FOR ENTERPRISES TO UNDERSTAND AN INDIVIDUAL'S LEARNING BEHAVIOR ALONG WITH THE PROCESS AND OUTCOME-CENTRIC SATISFACTION ASSOCIATED WITH A COLLABORATIVE ANALYTICS TRAINING TASK. THIS STUDY FOCUSES ON IDENTIFYING THE FACTORS THAT INFLUENCE THE PROCESS OF LEARNING DURING BA TRAINING TO ENTRY-LEVEL BA USERS. DRAWING ON THE THEORIES OF SITUATED COGNITION, GOAL SETTING, AND FLOW, WE PROPOSE A MODEL THAT EXPLAINS HOW TRAINEES IN A GROUP LEARN THROUGH A PROCESS THAT IS INFLUENCED BY THE CHARACTERISTICS OF BA TRAINING CONTEXT THROUGH CONTEXT AUTHENTICITY, THE TRAITS OF TRAINEES THROUGH TASK MOTIVATION AND PREFERENCE TOWARDS TEAMWORK. USING AN EXPERIMENTAL DESIGN BUILT ON DATA COLLECTION AND A UNIQUE TASK OF REAL VISITS TO A HISTORIC CEMETERY, WE FOUND THAT CONTEXT AUTHENTICITY AND TASK MOTIVATION HAVE SIGNIFICANT IMPACT ON FOCUSED IMMERSION, WHICH IN TURN SIGNIFICANTLY IMPACTS PROCESS AND OUTCOME SATISFACTION FOR LEARNING AN ANALYTICS TASK. RESULTS OF THIS STUDY EXTEND AND VALIDATE THE THEORIES OF SITUATED COGNITION, GOAL SETTING, AND FLOW WITHIN THE CONTEXT OF BUSINESS ANALYTICS TRAINING. BASED ON THESE FINDINGS, WE PROVIDE RECOMMENDATIONS FOR PRACTITIONERS FOR DESIGNING EFFECTIVE ANALYTICS TASKS FOR BETTER TRAINING OUTCOMES.
USING A MULTI-CASE APPROACH, WE EXAMINED STUDENTS' BEHAVIOR PATTERNS IN INTERACTING WITH A SERIOUS GAME ENVIRONMENT USING THE EMERGING TECHNOLOGIES OF LEARNING ANALYTICS AND DATA VISUALIZATION IN ORDER TO UNDERSTAND HOW THE PATTERNS MAY VARY ACCORDING TO STUDENTS' LEARNING CHARACTERISTICS. THE RESULTS CONFIRMED SOME PRELIMINARY FINDINGS FROM OUR PREVIOUS RESEARCH, BUT ALSO REVEALED PATTERNS THAT WOULD NOT BE EASILY DETECTED WITHOUT DATA VISUALIZATIONS. SUCH FINDINGS PROVIDED INSIGHTS ABOUT DESIGNING EFFECTIVE LEARNING SCAFFOLDS TO SUPPORT THE DEVELOPMENT OF PROBLEM-SOLVING SKILLS IN YOUNG LEARNERS AND WILL GUIDE OUR NEXT-STEP RESEARCH.
STUDENTS' ACQUISITIONOF TEAMWORK COMPETENCE HAS BECOME A PRIORITY FOREDUCATIONAL INSTITUTIONS. THE DEVELOPMENT OF TEAMWORK COMPETENCE IN EDUCATION GENERALLY RELIES IN PROJECT-BASED LEARNING METHODOLOGIES AND CHALLENGES. THE ASSESSMENT OF TEAMWORK IN PROJECT-BASED LEARNING INVOLVES, AMONG OTHERS, ASSESSING STUDENTS' PARTICIPATION AND THE INTERACTIONS BETWEEN TEAM MEMBERS. PROJECT-BASED LEARNING CAN EASILY BE HANDLED IN SMALL-SIZE COURSES, BUT COURSE MANAGEMENT AND TEAMWORK ASSESSMENT BECOME A BURDENSOME TASK FOR INSTRUCTORS AS THE SIZE OF THE CLASS INCREASES. ADDITIONALLY, WHEN PROJECT-BASED LEARNING HAPPENS IN A VIRTUAL SPACE, SUCH AS ONLINE LEARNING, INTERACTIONS OCCUR IN A LESS NATURAL WAY. THIS STUDY EXPLORES THE USE OF INSTANT MESSAGING APPS (MORE PRECISELY, THE USE OF TELEGRAM) AS TEAM COMMUNICATION SPACE IN PROJECT-BASED LEARNING, USING A LEARNING ANALYTICS TOOL TO EXTRACT AND ANALYZE STUDENT INTERACTIONS. FURTHER, THE STUDY COMPARES STUDENT INTERACTIONS (E.G., NUMBER OF MESSAGES EXCHANGED) AND INDIVIDUAL TEAMWORK COMPETENCE ACQUISITION BETWEEN TRADITIONAL ASYNCHRONOUS (E.G., LMS MESSAGE BOARDS) AND SYNCHRONOUS INSTANT MESSAGING COMMUNICATION ENVIRONMENTS. THE RESULTS SHOW A PREFERENCE OF STUDENTS FOR IM TOOLS AND INCREASED PARTICIPATION IN THE COURSE. HOWEVER, THE ANALYSIS DOES NOT FIND SIGNIFICANT IMPROVEMENT IN THE ACQUISITION OF INDIVIDUAL TEAMWORK COMPETENCE.
ROBOTICS TECHNOLOGY HAS BECOME INCREASINGLY COMMON BOTH FOR BUSINESSES AND FOR PRIVATE CITIZENS. PRIMARY AND SECONDARY SCHOOLS, AS A MIRROR OF SOCIETAL EVOLUTION, HAVE INCREASINGLY INTEGRATED SCIENCE, TECHNOLOGY, ENGINEERING AND MATH CONCEPTS INTO THEIR CURRICULA. OUR RESEARCH QUESTIONS ARE: "IN TEACHING ROBOTICS TO PRIMARY AND SECONDARY SCHOOL STUDENTS, WHICH PEDAGOGICAL-METHODOLOGICAL INTERVENTIONS RESULT IN BETTER UNDERSTANDING AND KNOWLEDGE IN THE USE OF SENSORS IN EDUCATIONAL ROBOTICS?", AND "IN TEACHING ROBOTICS TO PRIMARY AND SECONDARY SCHOOL STUDENTS, WHICH ANALYTICAL METHODS RELATED TO LEARNING ANALYTICS PROCESSES ARE PROPOSED TO ANALYZE AND REFLECT ON STUDENTS' BEHAVIOR IN THEIR LEARNING OF CONCEPTS AND SKILLS OF SENSORS IN EDUCATIONAL ROBOTICS?". TO ANSWER THESE QUESTIONS, WE HAVE CARRIED OUT A SYSTEMATIC REVIEW OF THE LITERATURE IN THE WEB OF SCIENCE AND SCOPUS DATABASES REGARDING ROBOTICS SENSORS IN PRIMARY AND SECONDARY EDUCATION, AND LEARNING ANALYTICS PROCESSES. WE APPLIED PRISMA METHODOLOGY AND REVIEWED A TOTAL OF 24 ARTICLES. THE RESULTS SHOW A CONSENSUS ABOUT THE USE OF THE LEARNING BY DOING AND PROJECT-BASED LEARNING METHODOLOGIES, INCLUDING THEIR DIFFERENT VARIATIONS, AS THE MOST COMMON METHODOLOGY FOR ACHIEVING OPTIMAL ENGAGEMENT, MOTIVATION AND PERFORMANCE IN STUDENTS' LEARNING. FINALLY, FUTURE LINES OF RESEARCH ARE IDENTIFIED FROM THIS STUDY.
THE PRIMARY GOAL OF HIGHER EDUCATION INSTITUTIONS IS TO SUPPORT ALL STUDENTS IN THE PURSUIT OF ACADEMIC SUCCESS, WHICH REQUIRES TIMELY ASSISTANCE FOR 'AT RISK' STUDENTS. THE ADOPTION OF LEARNING MANAGEMENT SYSTEMS RESULTS IN A LARGE AMOUNT OF DATA THAT CAN BE COLLECTED, PROCESSED AND UTILISED TO IMPROVE THE STUDENTS' LEARNING EXPERIENCES. THIS RESEARCH EXAMINES THE POTENTIAL APPLICATIONS OF ANALYTICS TECHNIQUES FOR EXTRACTING INSIGHTS FROM STUDENT-GENERATED CONTENT IN AN ACADEMIC SETTING. IT SHOWCASES HOW DIFFERENT TEXT ANALYTICS TECHNIQUES, FROM DESCRIPTIVE CONTENT ANALYSIS, SEMANTIC NETWORK ANALYSIS, TO TOPIC MODELLING SUPPORT THE DISCOVERY OF NEW INSIGHTS FROM UNSTRUCTURED, USER-GENERATED DATA. WE LOOKED AT 968 LETTERS WRITTEN BY 'AT RISK' STUDENTS IN AN AUSTRALIAN-BASED UNIVERSITY IN SOUTHEAST ASIA TO EXAMINE THE DIFFICULTIES THE STUDENTS FACED, WHICH LED TO THEIR ACADEMIC FAILURE. THE RESULTS SHOW THAT TIME MANAGEMENT, FAMILY, LEARNING, ASSESSMENT, AND SUBJECTS WERE THE LEADING CAUSES OF POOR PERFORMANCE, BUT IN A MORE NUANCED WAY THAN WAS EXPECTED. STUDENTS OFTEN FACED MULTIPLE CHALLENGES, ONE LED TO ANOTHER, WHICH RESULTED IN THE FAILING GRADES. OUR STUDY CONTRIBUTES A SET OF EFFECTIVE TEXT ANALYTICS TECHNIQUES FOR EXTRACTING INSIGHTS FROM STUDENT DATA, PROVIDING THE PRELIMINARY GUIDELINES FOR AN INFORMATION SYSTEM TO DETECT EARLY AT RISK STUDENTS.
FOR THE 21ST CENTURY LEARNER, DEVELOPING SELF-DIRECTION SKILL IS CRUCIAL FOR BOTH ACADEMIC ACTIVITIES AND MAINTAINING ONE'S HEALTHY LIFESTYLE. WHILE THERE ARE TECHNOLOGY SUPPORTS FOR SPECIFIC SELF-REGULATED LEARNING TASKS AND HEALTH MONITORING. RESEARCH IS LIMITED ON HOW TO SUPPORT DEVELOPMENT OF META-SKILL OF SELF-DIRECTION PROCESS ITSELF. IN OUR WORK, WE FOCUS ON DESIGNING SEAMLESS TECHNOLOGY INFRASTRUCTURE TO FOSTER SELF-DIRECTEDNESS OF LEARNERS. WE CONSIDER LEARNING AND PHYSICAL ACTIVITIES DATA AS A CONTEXT AND DAPER (DATA COLLECTION-ANALYZE-PLAN-EXECUTION MONITORING-REFLECT), AS A DATA-DRIVEN SELF-DIRECTION SKILL EXECUTION AND ACQUISITION MODEL. WE BRIDGE LEARNING ANALYTICS AND QUANTIFIED-SELF APPROACHES TO DEVELOP THE GOAL (GOAL ORIENTED ACTIVE LEARNER) SYSTEM TO SUPPORT SYNCHRONIZE-VISUALIZE-ANALYZE MULTISOURCE DATA REGARDING LEARNERS' LEARNING AND PHYSICAL ACTIVITIES. THIS PAPER PROPOSES A MEASUREMENT RUBRIC AS A BASIS OF ADAPTIVE SCAFFOLDING FOR SKILL DEVELOPMENT DURING THE PROCESS.
EXPLICIT VISUAL REPRESENTATIONS OF DOMAIN KNOWLEDGE HAVE THE POTENTIAL TO SUPPORT STUDENTS ENGAGED IN SCIENTIFIC INQUIRY LEARNING ACTIVITIES ON AN EPISTEMIC LEVEL. THIS CAN BE FACILITATED USING COMPUTATIONAL METHODS FOR THE EXTRACTION OF CONCEPTS FROM STUDENT GENERATED KNOWLEDGE ARTEFACTS SUCH AS HYPOTHESES, CONCEPT MAPS, OR WILD ARTICLES. WE PROPOSE AN APPLICATION OF THIS APPROACH IN THE CONTEXT OF INQUIRY LEARNING WITH ONLINE SCIENCE LABORATORIES. AS A COGNITIVE AWARENESS TOOL, THE "CONCEPT CLOUD" PRESENTS DOMAIN CONCEPTS AND KEY PHRASES TO THE LEARNERS IN ORDER TO HELP THEM REFLECT ON THEIR OWN LEARNING AND KNOWLEDGE BUILDING. AS PART OF A LEARNING ANALYTICS TOOLSET, THE CONCEPT CLOUD ALSO SUPPORTS TEACHERS IN SUPERVISING THEIR STUDENTS' KNOWLEDGE BUILDING FROM AN EPISTEMIC PERSPECTIVE. THE APPROACH HAS BEEN TESTED IN A CLASSROOM SCENARIO WITH 84 SECONDARY HIGH SCHOOL STUDENTS.
MUSIC IS A POPULAR FORM OF ENTERTAINMENT AND HAS BECOME COMMON PRACTICE TO ADJUST COGNITION, AFFECT, AND MOTIVATION. REGARDING THE EFFECTS OF BACKGROUND MUSIC ON LEARNING TASKS, RESULTS ARE INCONCLUSIVE IN THE LITERATURE. RECENT ADVANCEMENT OF WEARABLE DEVICES AND COMPUTING ANALYTICS SUPPORTS AUTOMATED DETECTION OF MULTIMODAL PHYSIOLOGICAL SIGNALS, SUCH AS EYE MOVEMENTS, NEURAL RESPONSES, AND HEART RATES IN A REALTIME FASHION, WHICH CAN FACILITATE TRACKING LEARNERS' CHANGES OF AFFECT, ATTENTION, AND COGNITION WHILE THEY STUDY WITH THE ACCOMPANIMENT OF BACKGROUND MUSIC. HOWEVER, MOST EXISTING STUDIES FOCUS ONLY ON BEHAVIORAL LEVELS, AND FEW EMPLOYED SIGNALS AT PHYSIOLOGICAL LEVELS TO INVESTIGATE THE IMPACT OF BACKGROUND MUSIC ON LEARNING. TO FILL IN THE RESEARCH GAP, THIS DOCTORAL PROJECT DESIGNS TWO TYPES OF VISUAL COGNITIVE TASKS, THAT IS, READING COMPREHENSION TASK AND ART APPRECIATION TASK. IT AIMS TO INTEGRATE MULTIMODAL DATA (E.G., EYE MOVEMENTS, ELECTROENCEPHALOGRAM (EEG), AND PERIPHERAL PHYSIOLOGICAL SIGNALS) TO PROBE THE EFFECT OF BACKGROUND MUSIC ON THE TASKS. ITS FINDINGS WILL EXTEND OUR KNOWLEDGE ON THE INTERACTIONS AMONG LEARNERS' PERFORMANCE, EMOTION, AND ENGAGEMENT AT BOTH PHYSIOLOGICAL AND BEHAVIORAL LEVELS IN MULTI-CHANNEL LEARNING SETTINGS, AND CONTRIBUTE TO A GOAL OF RECOMMENDING SUITABLE BACKGROUND MUSIC FOR SELF-LEARNING.
THIS PAPER INTRODUCES TWO MEASURES FOR THE RECOMMENDATION OF STUDY MATERIALS BASED ON STUDENTS' PAST STUDY ACTIVITY. WE USE RECORDS FROM THE VIRTUAL LEARNING ENVIRONMENT (VLE) AND ANALYSE THE ACTIVITY OF PREVIOUS STUDENTS. WE ASSUME THAT THE ACTIVITY OF PAST STUDENTS REPRESENTS PATTERNS, WHICH CAN BE USED AS A BASIS FOR RECOMMENDATIONS TO CURRENT STUDENTS. THE MEASURES WE DEFINE ARE RELEVANCE, FOR DESCRIPTION OF A SUPPOSED VLE ACTIVITY DERIVED FROM PREVIOUS STUDENTS OF THE COURSE, AND EFFORT, THAT REPRESENTS THE ACTUAL EFFORT OF INDIVIDUAL CURRENT STUDENTS. BASED ON THESE MEASURES, WE PROPOSE A COMPOSITE MEASURE, WHICH WE CALL IMPORTANCE. WE USE DATA FROM THE PREVIOUS COURSE PRESENTATIONS TO EVALUATE OF THE CONSISTENCY OF STUDENTS' BEHAVIOUR. WE USE CORRELATION OF THE DEFINED MEASURES RELEVANCE AND AVERAGE EFFORT TO EVALUATE THE BEHAVIOUR OF TWO DIFFERENT STUDENT COHORTS AND THE ROOT MEAN SQUARE ERROR TO MEASURE THE DEVIATION OF AVERAGE EFFORT AND INDIVIDUAL STUDENT EFFORT.
EMBODIED LEARNING AND THE DESIGN OF EMBODIED LEARNING PLATFORMS HAVE GAINED POPULARITY IN RECENT YEARS DUE TO THE INCREASING AVAILABILITY OF SENSING TECHNOLOGIES. IN OUR STUDY, WE MADE USE OF THE MATHEMATICAL IMAGERY TRAINER FOR PROPORTION (MIT-P) THAT USES A TOUCHSCREEN TABLET TO HELP STUDENTS EXPLORE THE CONCEPT OF MATHEMATICAL PROPORTION. THE USE OF SENSING TECHNOLOGIES PROVIDES AN UNPRECEDENTED AMOUNT OF HIGH-FREQUENCY DATA ON STUDENTS' BEHAVIORS. WE INVESTIGATED A STATISTICAL MODEL CALLED MIXTURE REGIME-SWITCHING HIDDEN LOGISTIC TRANSITION PROCESS (MIXRHLP) AND FIT IT TO THE STUDENTS' HAND MOTION DATA. SIMULTANEOUSLY, THE MODEL FINDS CHARACTERISTIC REGIMES AND ASSIGNS STUDENTS TO CLUSTERS OF REGIME TRANSITIONS. TO UNDERSTAND THE NATURE OF THESE REGIMES AND CLUSTERS, WE EXPLORE SOME PROPERTIES IN STUDENTS' AND TUTOR'S VERBALIZATION ASSOCIATED WITH THESE DIFFERENT PHASES.
THIS STUDY AIMED TO IDENTIFY MISCONCEPTIONS IN MEDICAL STUDENT KNOWLEDGE BY MINING USER INTERACTIONS IN THE MEDU ONLINE LEARNING ENVIRONMENT. DATA FROM 13000 ATTEMPTS AT A SINGLE VIRTUAL PATIENT CASE WERE EXTRACTED FROM THE MEDU MYSQL DATABASE. A SUBGROUP DISCOVERY METHOD WAS APPLIED TO IDENTIFY PATTERNS IN LEARNER-GENERATED ANNOTATIONS AND RESPONSES TO MULTIPLE-CHOICE ITEMS ON THE DIAGNOSIS AND MANAGEMENT OF ACUTE MYOCARDIAL INFARCTION (I.E., HEART ATTACK). FIRST, THE ALGORITHM GENERATED RULES WHERE SINGLE TERMS FROM THE LEARNER ANNOTATIONS WERE USED TO PREDICT INCORRECT ANSWERS TO THE MULTIPLE-CHOICE ITEMS. SECOND, THE POSSIBLE COMBINATIONS OF TERMS AND THEIR RELEVANT SYNONYMS WERE USED TO DETERMINE WHETHER THEIR INCLUSION LED TO BETTER RATES OF PREDICTION. THE SECOND STEP WAS FOUND TO SIGNIFICANTLY INCREASE PREDICTION PRECISION AND WEIGHTED RELATIVE ACCURACY, UNCOVERING FOUR MISCONCEPTIONS AT A RATE GREATER THAN 70%. THESE FINDINGS SERVE TO INFORM THE DESIGN OF AN ADAPTIVE SYSTEM THAT TAILORS THE DELIVERY OF FORMATIVE FEEDBACK TO PROMOTE BETTER LEARNING OUTCOMES IN THE DOMAIN OF CLINICAL REASONING.
THE PROLIFERATION OF WEB-BASED LEARNING OBJECTS MAKES FINDING AND EVALUATING RESOURCES A CONSIDERABLE HURDLE FOR LEARNERS TO OVERCOME. WHILE ESTABLISHED LEARNING ANALYTICS METHODS PROVIDE FEEDBACK THAT CAN AID LEARNER EVALUATION OF LEARNING RESOURCES, THE ADEQUACY AND RELIABILITY OF THESE METHODS IS QUESTIONED. BECAUSE ENGAGEMENT WITH ONLINE LEARNING IS DIFFERENT FROM OTHER WEB ACTIVITY, IT IS IMPORTANT TO ESTABLISH PEDAGOGICALLY RELEVANT MEASURES THAT CAN AID THE DEVELOPMENT OF DISTINCT, AUTOMATED ANALYSIS SYSTEMS. CONTENT ANALYSIS IS OFTEN USED TO EXAMINE ONLINE DISCUSSION IN EDUCATIONAL SETTINGS, BUT THESE INSTRUMENTS ARE RARELY COMPARED WITH EACH OTHER WHICH LEADS TO UNCERTAINTY REGARDING THEIR VALIDITY AND RELIABILITY. IN THIS STUDY, PARTICIPATION IN MASSIVE OPEN ONLINE COURSE (MOOC) COMMENT FORUMS WAS EVALUATED USING FOUR DIFFERENT ANALYTICAL APPROACHES: THE DIGITAL ARTEFACTS FOR LEARNING ENGAGEMENT (DIAL-E) FRAMEWORK, BLOOM'S TAXONOMY, STRUCTURE OF OBSERVED LEARNING OUTCOMES (SOLO) AND COMMUNITY OF INQUIRY (COI). RESULTS FROM THIS STUDY INDICATE THAT DIFFERENT APPROACHES TO MEASURING COGNITIVE ACTIVITY ARE CLOSELY CORRELATED AND ARE DISTINCT FROM TYPICAL INTERACTION MEASURES. THIS SUGGESTS THAT COMPUTATIONAL APPROACHES TO PEDAGOGICAL ANALYSIS MAY PROVIDE USEFUL INSIGHTS INTO LEARNING PROCESSES.
MANY INTRODUCTORY PROGRAMMING ENVIRONMENTS GENERATE A LARGE AMOUNT OF LOG DATA, BUT MAKING INSIGHTS FROM THESE DATA ACCESSIBLE TO INSTRUCTORS REMAINS A CHALLENGE. THIS RESEARCH DEMONSTRATES THAT STUDENT OUTCOMES CAN BE ACCURATELY PREDICTED FROM STUDENT PROGRAM STATES AT VARIOUS TIME POINTS THROUGHOUT THE COURSE, AND INTEGRATES THE RESULTING PREDICTIVE MODELS INTO AN INSTRUCTOR DASHBOARD. THE EFFECTIVENESS OF THE DASHBOARD IS EVALUATED BY MEASURING HOW WELL THE DASHBOARD ANALYTICS CORRECTLY SUGGEST THAT THE INSTRUCTOR HELP STUDENTS CLASSIFIED AS MOST IN NEED. FINALLY, WE DESCRIBE A METHOD OF MATCHING LOW-PERFORMING STUDENTS WITH HIGH-PERFORMING PEER TUTORS, AND SHOW THAT THE INCLUSION OF PEER TUTORS NOT ONLY INCREASES THE AMOUNT OF HELP GIVEN, BUT THE CONSISTENCY OF HELP AVAILABILITY AS WELL.
THE METALOGUE PROJECT AIMS TO DEVELOP A MULTI-MODAL, MULTI-PARTY DIALOGUE SYSTEM WITH METACOGNITIVE ABILITIES THAT WILL ADVANCE OUR UNDERSTANDING OF NATURAL CONVERSATIONAL HUMAN-MACHINE INTERACTION AND DIALOGUE INTERFACES. THIS PAPER INTRODUCES THE VISION FOR THE SYSTEM AND DISCUSSES ITS APPLICATION IN THE CONTEXT OF DEBATE SKILLS TRAINING WHERE IT HAS THE POTENTIAL TO PROVIDE LEARNERS WITH A RICH, IMMERSIVE EXPERIENCE. IN PARTICULAR, IT CONSIDERS A POTENTIALLY POWERFUL LEARNING ANALYTICS TOOL IN THE FORM OF A PERFORMANCE REFLECTION DASHBOARD.
STUDENT-FACING LEARNING ANALYTICS (SFLA) EMPOWER STUDENTS TO TAKE RESPONSIBILITY FOR THEIR OWN LEARNING. INCREASINGLY, HIGHER EDUCATION INSTITUTIONS (HEIS) ARE IMPLEMENTING SFLA TO ENABLE STUDENTS GAIN INSIGHT INTO THEIR OWN LEARNING PROCESS. HOWEVER, SUCCESSFUL IMPLEMENTATION DEPENDS ON WHETHER STUDENTS EMBRACE THEM. HENCE, GAINING INSIGHT INTO WHAT WILL DRIVE STUDENTS' ACCEPTANCE OF SFLA IS IMPORTANT FOR BOTH DESIGNERS AND HEIS. THE STUDY INVESTIGATED THE EFFECT OF STUDENTS' TECHNOLOGY READINESS ON ACCEPTANCE OF SFLA BY INTEGRATING TECHNOLOGY READINESS (TR) AND TECHNOLOGY ACCEPTANCE MODEL. DATA COLLECTED FROM UNDERGRADUATE STUDENTS WAS EXAMINED USING STRUCTURAL EQUATION MODELING. OPTIMISM AND INNOVATIVENESS WERE FOUND TO HAVE A SIGNIFICANT POSITIVE INFLUENCE ON PERCEIVED USEFULNESS (PU), PERCEIVED EASE OF USE (PEOU) AND BEHAVIORAL INTENTIONS, WHILE INSECURITY AND DISCOMFORT HAD A NEGATIVE EFFECT ON PEOU BUT NOT ON PU AND BEHAVIORAL INTENTIONS. THEREFORE, OPTIMISM AND INNOVATIVENESS SHOULD BE REINFORCED WHILE MINIMIZING INSECURITY AND DISCOMFORT TO ENHANCE STUDENTS' INTENTIONS TO ADOPT SFLA.
WE PRESENT PERFORMANCEVIS, A VISUAL ANALYTICS TOOL FOR ANALYZING STUDENT ADMISSION AND COURSE PERFORMANCE DATA AND INVESTIGATING HOMEWORK AND EXAM QUESTION DESIGN. TARGETING A UNIVERSITY-WIDE INTRODUCTORY CHEMISTRY COURSE WITH NEARLY 1000 STUDENT ENROLLMENT, WE CONSIDER THE REQUIREMENTS AND NEEDS OF STUDENTS, INSTRUCTORS, AND ADMINISTRATORS IN THE DESIGN OF PERFORMANCEVIS. WE STUDY THE CORRELATION BETWEEN QUESTION ITEMS FROM ASSIGNMENTS AND EXAMS, EMPLOY MACHINE LEARNING TECHNIQUES FOR STUDENT GRADE PREDICTION, AND DEVELOP AN INTERFACE FOR INTERACTIVE EXPLORATION OF STUDENT COURSE PERFORMANCE DATA. PERFORMANCEVIS INCLUDES FOUR MAIN VIEWS (OVERALL EXAM GRADE PATHWAY, DETAILED EXAM GRADE PATHWAY, DETAILED EXAM ITEM ANALYSIS, AND OVERALL EXAM & HOMEWORK ANALYSIS) WHICH ARE DYNAMICALLY LINKED TOGETHER FOR USER INTERACTION AND EXPLORATION. WE DEMONSTRATE THE EFFECTIVENESS OF PERFORMANCEVIS THROUGH CASE STUDIES ALONG WITH AN AD-HOC EXPERT EVALUATION. FINALLY, WE CONCLUDE THIS WORK BY POINTING OUT FUTURE WORK IN THIS DIRECTION OF LEARNING ANALYTICS RESEARCH. (C) 2019 ZHEJIANG UNIVERSITY AND ZHEJIANG UNIVERSITY PRESS. PUBLISHED BY ELSEVIER B.V.
AS A KIND OF DATA THAT CAN REFLECT LEARNING STATUS, E-BOOK LOGS HAVE BEEN WIDELY USED IN LEARNING ANALYTICS, ESPECIALLY FOR THE PREDICTION OF ACADEMIC PERFORMANCE. HOWEVER, THE BEST PREDICTION MODEL CANNOT BE FOUND WITHOUT DETERMINING THE CONTRIBUTION OF EBOOK LOGS TO THE PREDICTION PERFORMANCE OF THE MODEL AND ITS CREATION PROCESS. TO THIS END, THIS STUDY USED THE SCIKIT-LEARN, A FREE SOFTWARE MACHINE LEARNING LIBRARY, TO ANALYZE LEARNING PERFORMANCE OF 234 PARTICIPANTS BY LEARNING BEHAVIOR LOGS, WHICH WERE COLLECTED BY AN E-BOOK SYSTEM. FINALLY, SIX PREDICTION MODELS CONTAINING DECISION TREE, RANDOM FORESTS, XGBOOST, LOGISTIC REGRESSION, SUPPORT VECTOR MACHINES, AND K-NEAREST NEIGHBORS WERE CREATED. ALSO, THE CONTRIBUTION OF E-BOOK LOGS ON THE ESTABLISHMENT OF DIFFERENT PREDICTION MODELS WAS OBTAINED BY THREE FEATURE IMPORTANCE CALCULATION METHODS, I.E., THE IMPURITY-BASED FEATURE IMPORTANCE, COEFFICIENTS FEATURE IMPORTANCE, AND PERMUTATION FEATURE IMPORTANCE. BASED ON STATISTICAL RESULTS, IT WAS CONCLUDED THAT THE DECISION TREE AND RANDOM FORESTS HAD THE BEST PREDICTION PERFORMANCE, WHICH WAS COMPARED TO THE OTHER FOUR MODELS, WITH PREDICTION PERFORMANCE SCORES RANGING FROM 0.7 TO 0.8. BESIDES, THE FOUR DATA FEATURES OF PREY, HIGHLIGHT, MAKER, AND NEXT WERE FOUND TO HAVE THE GREATEST IMPACT ON MODEL PREDICTION CREATION.
MASSIVE OPEN ONLINE COURSES (MOOCS) OFFER A WIDE RANGE OF OPPORTUNITIES FOR LEARNING. THEIR GROWING POPULARITY HAS RESULTED IN A LARGE AMOUNT OF DATA BEING AVAILABLE FOR LEARNING ANALYTICS PURPOSES. A MAJOR PROBLEM OF MOOCS IS THE OVERWHELMING NUMBER OF POSTS IN THEIR DISCUSSION FORUMS. THE FORUM IS A KEY PART OF THE LEARNING PROCESS WITHIN A MOOC, SO THIS INFORMATION OVERLOAD AFFECTS NEGATIVELY THE PARTICIPANTS' LEARNING EXPERIENCE. AUTOMATIC CLASSIFICATION OF THE POSTS CAN HELP SEARCHING OF RELEVANT INFORMATION FOR BOTH THE LEARNERS AND TEACHING ASSISTANTS. IN THIS STUDY, WE ADDRESS THIS PROBLEM BY BUILDING TWO MULTICLASS CLASSIFICATION MODELS, USING NATURAL LANGUAGE PROCESSING TECHNIQUES, THAT CLASSIFY THE POSTS ACCORDING TO A THREE-CATEGORY CODING SCHEME. EACH MODEL WAS CREATED WITH DATA DERIVED FROM A MOOC OF DIFFERENT SUBJECT MATTER. THE MAIN GOAL WAS TO EVALUATE EACH MODEL'S ACCURACY ALONG WITH ITS GENERALIZABILITY TO COURSES OF DIFFERENT SUBJECT MATTER. THIS STUDY CONTRIBUTES TO THE LINE OF RESEARCH FOR AUTOMATIC CLASSIFICATION OF FORUM DISCUSSIONS, ULTIMATELY AIMING AT THE DEVELOPMENT OF TOOLS THAT MAY ASSIST PARTICIPANTS WHILE SEARCHING IN THE FORUM. FURTHERMORE IT PROVIDES INSIGHTS ON THE MAIN ISSUES THAT INHIBIT GENERALIZATION OF CLASSIFIERS CREATED FOR A SPECIFIC SUBJECT MATTER AND INVESTIGATE HOW THEIR LINGUISTIC FEATURES RELATE TO THIS INHIBITION.
WITH TEACHING AND LEARNING TAKING PLACE IN AN INCREASINGLY NETWORKED ENVIRONMENT OVER E-LEARNING PLATFORMS, VOLUMINOUS DATA ARE LOGGED IN DATABASES. THESE CAN BE MINED AND PROCESSED TO SUPPORT TEACHING AND LEARNING PRACTICES. TO DETERMINE THE IMPACT OF THESE PRACTICES, MEANINGFUL MEASURES OF LEARNERS' ONLINE ENGAGEMENT ARE NEEDED. IN PARTICULAR, THIS RESEARCH FOCUSES ON DISCOVERING USEFUL AND MEANINGFUL DATA FEATURES OF ONLINE ENGAGEMENT IN LEARNING ACTIVITIES FROM TRACES OF ADULT LEARNERS' ONLINE BEHAVIOURAL DATA. WHILST VAST DATA FROM LEARNING ACTIVITIES MAY BE AVAILABLE OVER A TECHNOLOGY-ENHANCED LEARNING ENVIRONMENT, IT BECOMES SIGNIFICANTLY IMPORTANT TO IDENTIFY A METHODICAL APPROACH TO TRANSFORM DATA FEATURES IN A MODUS THAT IS USEFUL FOR ANALYSIS. HENCE, THE PURPOSE OF THIS STUDY IS TWOFOLD: (1) TO ADAPT AND RECONSTRUCT THE RFM MODEL (A MARKETING SEGMENTATION TECHNIQUE ON CUSTOMERS' RECENCY, FREQUENCY AND MONETARY PURCHASING BEHAVIOUR), AS A COMMON FRAMEWORK TO CODIFY AND QUANTIFY LEARNERS' ONLINE STUDY BEHAVIOUR, IN THE LEARNING ANALYTICS CONTEXT; AND (2) TO EXPLORE THE ONLINE ENGAGEMENT PATTERNS OF ADULT LEARNERS USING DATA-MINING TECHNIQUES. WE SHOW EXAMPLES OF ITS APPLICATIONS USING REAL-WORLD DATA FROM AN ONLINE COURSEBY MODELLING ADULT LEARNERS' ONLINE ENGAGEMENT PATTERNS AND DISCOVERING LEARNERS' SEGMENTS BASED ON IMMEDIACY, FREQUENCY AND DURATION.
TECHNOLOGIES USED BY TEACHERS AND STUDENTS GENERATE VAST AMOUNTS OF DATA THAT CAN BE ANALYZED TO PROVIDE INSIGHTS INTO IMPROVING TEACHING AND LEARNING. HOWEVER, PRACTITIONERS ARE LEFT OUT OF THE PROCESS. WE DESCRIBE THE DEVELOPMENT OF AN APPROACH BY WHICH RESEARCHERS AND PRACTITIONERS CAN WORK TOGETHER TO USE DATA INTENSIVE RESEARCH METHODS TO LAUNCH IMPROVEMENT EFFORTS WITHIN SCHOOLS. THIS PAPER DESCRIBES ELEMENTS OF THE FIRST YEAR OF A RESEARCHER-PRACTITIONER PARTNERSHIP, HIGHLIGHTING INITIAL FINDINGS, CHALLENGES, AND STRATEGIES FOR OVERCOMING THESE CHALLENGES.
HIGHER EDUCATION IS FACING DISRUPTIVE INNOVATION THAT REQUIRES, AMONG OTHER THINGS, PROVISION OF A MORE EFFECTIVE AND CUSTOMIZED EDUCATION SERVICE TO INDIVIDUAL STUDENTS. IN THE FIELD OF LEARNING ANALYTICS (LA), THERE HAS BEEN MUCH EFFORT, IN THE FORM OF THE COLLECTION AND THOROUGH ANALYSIS OF A VARIETY OF STUDENT-RELATED DATASETS, TO OPTIMIZE LEARNING PERFORMANCE AND ENVIRONMENTS BY MEANS OF PERSONALIZATION. THE DATASETS INCLUDE TRADITIONAL QUESTIONNAIRE SURVEYS, LEARNING MANAGEMENT SYSTEM (LMS) LOG DATA OF LEARNER ACTIVITIES, AND, MORE RECENTLY IN THE WAKE OF THE BIG-DATA-ANALYTICS TREND, UNSTRUCTURED DATASETS SUCH AS SNS ACTIVITIES, TEXT DATA, AND OTHER TRANSACTIONAL DATA. SPATIAL DATA, HOWEVER, IS RARELY CONSIDERED AS A KEY DATASET, DESPITE ITS HIGH POTENTIAL FOR CHARACTERIZATION OF STUDENTS AND PREDICTION OF THEIR PERFORMANCES AND CONDITIONS. IN THIS CONTEXT, THE AUTHORS PROPOSE A NEW, SPATIAL-DATA-DRIVEN STUDENT-CHARACTERIZATION RESEARCH FRAMEWORK. THIS VISION PAPER DESCRIBES SPATIAL COMPUTING IN ITS THREE, DESCRIPTIVE, PREDICTIVE, AND PRESCRIPTIVE MODELING STAGES AS WELL AS ITS THREE CHALLENGES: (1) TECHNICAL SPATIAL DATA ACQUISITION ISSUES; (2) LEGAL AND ADMINISTRATIVE ISSUES; (3) EXPANSION OF THE APPLICATION DOMAINS IN WHICH SPATIAL DATA CAN CONTRIBUTE TO IMPROVED MODELING QUALITY. WITH RESPECT TO EACH CHALLENGE, THE ON-GOING EFFORTS ARE BRIEFLY INTRODUCED IN ORDER TO SUBSTANTIATE THE FEASIBILITY OF THE PROPOSED RESEARCH FRAMEWORK.
MULTIMODAL LEARNING ANALYTICS (MMLA) HAS SPARKED RESEARCHER INTEREST IN INVESTIGATING LEARNING IN REAL-WORLD SETTINGS BY CAPTURING LEARNING TRACES FROM MULTIPLE SOURCES OF DATA. THOUGH MULTIMODAL DATA OFFERS A MORE HOLISTIC PICTURE OF LEARNING, ITS INHERENT COMPLEXITY MAKES IT DIFFICULT TO UNDERSTAND AND INTERPRET. THIS PAPER ILLUSTRATES THE USE OF DIMENSIONALITY REDUCTION (DR) TO FIND A SIMPLE REPRESENTATION OF MULTIMODAL LEARNING DATA COLLECTED FROM CO-LOCATED COLLABORATION IN AUTHENTIC SETTINGS. WE EMPLOYED MULTIPLE DR METHODS AND USED TRIANGULATION TO INTERPRET THEIR RESULT WHICH IN TURN PROVIDED A MORE SIMPLISTIC REPRESENTATION. ADDITIONALLY, WE ALSO SHOW HOW UNEXPECTED EVENTS IN AUTHENTIC SETTINGS (E.G., MISSING DATA) CAN AFFECT THE ANALYSIS RESULTS.
MOOCS OFFER WORLD-WIDELY ACCESSIBLE ONLINE CONTENT TYPICALLY INCLUDING VIDEOS, READINGS, QUIZZES ALONG WITH SOCIAL COMMUNICATION TOOLS ON A PLATFORM THAT ENABLES PARTICIPANTS TO LEARN AT THEIR OWN PACE. THE NUMBER OF LEARNERS WHO SIGN UP AND ATTEND THE COURSES ARE EXPONENTIALLY GROWING. CONSEQUENTLY, MOOC PLATFORMS GENERATE A LARGE AMOUNT OF DATA ABOUT THEIR LEARNERS. RESEARCHERS USE PARTICIPANTS' DIGITAL TRACES TO MAKE SENSE OF THEIR ENGAGEMENT IN A COURSE AND IDENTIFY THEIR NEEDS TO PREDICT FUTURE PATTERNS AND TO MAKE INTERVENTIONS BASED ON THESE PATTERNS. THE RESEARCH REPORTED HERE WAS CONDUCTED TO FURTHER UNDERSTAND LEARNERS SOCIAL ENGAGEMENT ON A MOOC PLATFORM AND THE IMPACT OF ENGAGEMENT ON COURSE COMPLETION. THE PATTERNS OF LEARNERS SOCIAL ENGAGEMENT WERE MODELLED BY USING LEARNING ANALYTICS TECHNIQUES. THE FINDINGS OF THIS RESEARCH SHOW THAT THE INTEGRATED SOCIAL FEATURES SUCH AS COMMONLY KNOWN FOLLOW FEATURES AND DEEPER PEER INTERACTIONS HAVE POTENTIAL VALUE IN TRACKING, ANALYSING, AND GENERATING INSIGHTFUL INFORMATION RELATED TO PARTICIPANTS' BEHAVIOURS.
CREATING QUALITY ONLINE CONTENT REQUIRES A GREAT DEAL OF EFFORT FROM TEACHERS. IN ADDITION TO ISSUES SPECIFIC TO THE DESIGN AND CREATION OF THE ELEMENTS OF A COURSE, TEACHERS MUST FACE TECHNICAL HURDLES SO AS TO PERFORM COMMON TASKS, SUCH AS DEPLOYING THE SAME CONTENT ON DIFFERENT E-LEARNING PLATFORMS AND INTEGRATING CONTENT INTO EXTERNAL TOOLS, OR ACQUIRING THE ABILITY TO ANALYZE TRACKING DATA GENERATED DURING LEARNER INTERACTIONS. THESE PROBLEMS PRINCIPALLY ARISE OWING TO THE VERY LIMITED LEVEL OF INTEROPERABILITY PROVIDED BY CONTENT CREATION TOOLS. IN ORDER TO FACILITATE THE CREATION OF INTEROPERABLE CONTENTS, THE DIGITAL CONTENT PRODUCTION CENTER AT THE POLYTECHNIC UNIVERSITY OF CARTAGENA (SPAIN) HAS DEVELOPED THE UPCTFORMA TOOL, WHOSE MAIN ARCHITECTURAL DRIVER HAS BEEN INTEROPERABILITY. MORE SPECIFICALLY, THE TOOL TAKES ADVANTAGE OF THE LEARNING TOOLS INTEROPERABILITY AND CALIPER INTEROPERABILITY SPECIFICATIONS TO PROVIDE SEVERAL TYPES OF QUALITY REGARDING THREE KEY ASPECTS OF CONTENT PRODUCTION: TOOL INTEROPERABILITY, LEARNING ANALYTICS, AND MOTIVATION. IN THIS PAPER, WE PROVIDE A DETAILED DESCRIPTION OF THE COMPONENT-BASED ARCHITECTURE PROPOSED AND PRESENT A VALIDATION OF THE REQUIREMENTS ELICITED THROUGH THE USE OF A UPCTFORMA GAMIFICATION ACTIVITY CREATED FOR A REAL PROJECT INVOLVING APPROXIMATELY 4000 STUDENTS. ONE OF THE NOVEL ASPECTS OF THIS ARCHITECTURE IS THE TRANSFORMATION OF TRACKING DATA INTO LEARNING ANALYSIS MODELS THAT REPRESENT THE INFORMATION IN THE TRACKED LEARNING ACTIVITIES AT A HIGHER LEVEL OF ABSTRACTION. THESE MODELS ARE USED TO PROVIDE ACTIVITY-SPECIFIC LEARNING ANALYTICS AND MOTIVATION. PLATFORM INDEPENDENCY WITH RESPECT TO DATA ANALYTICS TECHNOLOGIES, MESSAGING SYSTEMS, AND COMMUNICATION PROTOCOLS IS ACHIEVED BY USING ADAPTERS.
EDUCATION IS EXPERIENCING A PARADIGM SHIFT TOWARDS BLENDED LEARNING MODELS IN TECHNOLOGY-ENHANCED LEARNING (TEL). DESPITE THE POTENTIAL BENEFITS OF BLENDED LEARNING, IT ALSO ENTAILS ADDITIONAL COMPLEXITY IN TERMS OF MONITORING, AWARENESS AND REFLECTION, AS LEARNING HAPPENS ACROSS DIFFERENT SPACES AND MODALITIES. IN RECENT YEARS, LITERATURE ON LEARNING ANALYTICS (LA) AND EDUCATIONAL DATA MINING (EDM) HAS GAINED MOMENTUM AND STARTED TO ADDRESS THE ISSUE. TO PROVIDE A CLEAR PICTURE OF THE CURRENT STATE OF THE RESEARCH ON THE TOPIC AND TO OUTLINE OPEN RESEARCH GAPS, THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW OF THE STATE-OF-THE-ART OF RESEARCH IN LA AND EDM ON MONITORING, AWARENESS AND REFLECTION IN BLENDED TEL SCENARIOS. THE SEARCH INCLUDED SIX MAIN ACADEMIC DATABASES IN TEL THAT WERE ENRICHED WITH THE PROCEEDINGS OF THE WORKSHOP ON 'AWARENESS AND REFLECTION IN TEL' (ARTEL), RESULTING IN 1089 PAPERS OUT OF WHICH 40 PAPERS WERE INCLUDED IN THE FINAL ANALYSIS.
WHILE COLLEGES AND UNIVERSITIES SEEK TO UTILIZE BIG, COMPLEX AND AGGREGATED DATA TO GENERATE INSIGHTS TO SUPPORT STUDENTS, BIG DATA IS LIMITED IN ITS USE TO DESIGN INTERVENTIONS DUE TO LIMITS IT INHERENTLY HAS. SMALL DATA, OR ONE-DIMENSIONAL TEACHING AND LEARNING ANALYTICS, PROVIDE QUALITY FEEDBACK TO TEACHERS AND STUDENTS. IN THIS PAPER, WE WILL DISCUSS HOW ABILENE CHRISTIAN UNIVERSITY (ACU), A PRIVATE UNIVERSITY IN THE WESTERN REGION OF THE UNITED STATES, USES ALERT SYSTEM TO SERVE AT-RISK STUDENTS, IDEA EVALUATIONS TO GATHER FEEDBACK TO IMPROVE TEACHING, AND DATA FROM THE USE OF A LEARNING MANAGEMENT SYSTEM CANVAS TO IDENTIFY WAYS TO IMPROVE STUDENT LEARNING BY PROVIDING FORMATIVE EVALUATION FOR FACULTY TO IMPROVE THEIR PRACTICES. TOWARDS THE END OF THE PAPER WE WILL DISCUSS WHAT STUDENTS CAN DO TO MAKE INFORMED CHOICES ABOUT THEIR LEARNING.
OBSERVING STUDENT BODY GESTURE HAS BEEN WIDELY USED TO ASSESS TEACHING EFFECTIVENESS OVER THE PAST FEW DECADES. HOWEVER, MANUAL OBSERVATION IS NOT SUITABLE FOR THE AUTOMATIC DATA ANALYSIS IN THE FIELD OF LEARNING ANALYTICS. CONSEQUENTLY, A STUDENT BODY GESTURE RECOGNITION METHOD BASED ON FISHER BROAD LEARNING SYSTEM (FBLS) AND LOCAL LOG-EUCLIDEAN MULTIVARIATE GAUSSIAN (L(2)EMG) IS PROPOSED IN THIS PAPER. FBLS IS DESIGNED BY INTRODUCING THE DISCRIMINATIVE INFORMATION INTO THE HIDDEN LAYER OF BROAD LEARNING SYSTEM (BLS) AND REDUCING THE DIMENSIONALITY OF HIDDEN-LAYER REPRESENTATIONS. FBLS HAS SUPERIORITIES IN ACCURACY AND SPEED. IN ADDITION, L(2)EMG, WHICH IS A HIGHLY DISTINCTIVE DESCRIPTOR, CHARACTERIZES THE LOCAL IMAGE WITH A MULTIVARIATE GAUSSIAN DISTRIBUTION. SO L(2)EMG FEATURES ARE FED INTO THE FBLS FOR RECOGNITION IN THIS PAPER. EXTENSIVE EXPERIMENTAL RESULTS ON SELF-BUILT DATASET SHOW THAT THE PROPOSED STUDENT BODY GESTURE RECOGNITION METHOD OBTAINS BETTER RESULTS THAN OTHER BENCHMARKING METHODS.
THIS PAPER REPORTS ON AN EXPLORATORY TRIAL OF DEVELOPING PEDAGOGICAL VISUALIZATIONS OF 16 STUDENTS' WRITTEN ASSIGNMENTS ON LITERARY ANALYSIS USING TWO SETS OF KEYWORDS AND EPISTEMIC NETWORK ANALYSIS (ENA). THE VISUALIZATIONS ARE AIMED AT SUMMATIVE EVALUATION AS A TOOL FOR THE PROFESSOR TO SUPPORT ASSESSMENT AND UNDERSTANDING OF SUBJECT LEARNING. RESULTS SHOW THAT ENA CAN VISUALLY DISTINGUISH LOW, MIDDLE AND HIGH PERFORMING STUDENTS, BUT NOT STATISTICALLY SIGNIFICANTLY. THUS, OUR TRIAL PROVIDES A TOOL FOR THE PROFESSOR THAT SUPPORTS UNDERSTANDING OF SUBJECT LEARNING AND FORMATIVE ASSESSMENT.
MOBILE AND UBIQUITOUS LEARNING MODELS HAVE BEEN WIDELY ADOPTED IN TECHNOLOGY-ENHANCED LEARNING (TEL) PRACTICES. APART FROM POTENTIAL BENEFITS, THESE MODELS INTRODUCE ADDITIONAL COMPLEXITY IN DESIGNING, MONITORING AND EVALUATING LEARNING ACTIVITIES, AS LEARNING HAPPENS ACROSS DIFFERENT SPACES. IN RECENT YEARS, LITERATURE ON LEARNING DESIGN (LD) AND LEARNING ANALYTICS (LA) HAS STARTED TO ADDRESS THESE ISSUES. THIS PAPER PRESENTS A SYSTEMATIC REVIEW ON HOW LD AND LA COMMUNITIES UNDERSTAND MOBILE AND UBIQUITOUS LEARNING, AS WELL AS THEIR RESPECTIVE CONTRIBUTIONS IN THESE FIELDS. THE SEARCH INCLUDED SEVEN MAIN ACADEMIC DATABASES IN TEL, RESULTING IN 1722 PAPERS, FROM WHICH 54 PAPERS WERE INCLUDED IN THE FINAL ANALYSIS. RESULTS POINT OUT THE LACK OF COMMON DEFINITIONS FOR MOBILE AND UBIQUITOUS LEARNING, RAISES RESEARCH TRENDS AND (UNEXPLOITED) SYNERGIES BETWEEN LD AND LA COMMUNITIES, AND IDENTIFIES AREAS THAT REQUIRE FURTHER ATTENTION FROM THESE COMMUNITIES.
GAMES CAN BE RICH ENVIRONMENTS FOR LEARNING AND CAN ELICIT EVIDENCE OF STUDENTS' CONCEPTUAL UNDERSTANDING AND INQUIRY PROCESSES. ILLUMINATING STUDENTS' CONTENT-SPECIFIC GAMEPLAY DECISIONS, OR METHODS OF COMPLETING GAME TASKS RELATED TO A CERTAIN DOMAIN, REQUIRES A CONTEXT THAT IS OPEN-ENDED ENOUGH FOR STUDENTS TO MAKE CHOICES THAT DEMONSTRATE THEIR THINKING. DOING THIS ALSO REQUIRES RICH LOG DATA AND METHODS OF GAME LEARNING ANALYTICS (GLA) THAT ARE GRANULAR ENOUGH TO LOOK AT THE SPECIFIC CHOICES MOST RELEVANT TO THAT CONTEXT AND DOMAIN. THIS PAPER PRESENTS RESEARCH DONE ON STUDENT EXPLORATION OF HIGH SCHOOL LEVEL MENDELIAN GENETICS IN A MULTIPLAYER ONLINE GAME CALLED THE RADIX ENDEAVOR. THE STUDY USES THREE APPROACHES TO IDENTIFY CONTENT-SPECIFIC GAMEPLAY DECISIONS AND DISTINGUISH PLAYERS UTILIZING DIFFERENT METHODS, LOOKING AT ACTIONS AND TOOL USE, PLAY PATTERNS AND PLAYER TYPES, AND TOOL INPUT PATTERNS. IN THE CONTEXT OF THE SELECTED GAME QUEST, THE THREE APPROACHES WERE FOUND TO YIELD INSIGHTS INTO DIFFERENT WAYS THAT STUDENTS COMPLETE TASKS IN GENETICS, SUGGESTING THE POTENTIAL FOR A SET OF MORE GENERALIZED GUIDING QUESTIONS IN THE GLA FIELD THAT COULD BE ADOPTED BY LEARNING GAMES DESIGNERS AND DATA SCIENTISTS TO CONVEY INFORMATION ABOUT CONTENT-SPECIFIC GAMEPLAY DECISIONS IN LEARNING GAMES.
CLASSROOM TEACHING METHODOLOGIES ARE GRADUALLY CHANGING FROM MASTERCLASSES TO ACTIVE LEARNING PRACTICES, AND PEER COLLABORATION EMERGES AS AN ESSENTIAL SKILL TO BE DEVELOPED. HOWEVER, THERE ARE SEVERAL CHALLENGES IN EVALUATING COLLABORATIVE ACTIVITIES MORE OBJECTIVELY, AS WELL AS TO GENERATE VALUABLE INFORMATION TO TEACHERS AND APPROPRIATE FEEDBACK TO STUDENTS ABOUT THEIR LEARNING PROCESSES. IN THIS CONTEXT, MULTIMODAL LEARNING ANALYTICS FACILITATE THE EVALUATION OF COMPLEX SKILLS USING DATA FROM MULTIPLE DATA SOURCES. IN THIS WORK, WE PROPOSE THE USE OF BEACONS TO COLLECT GEOLOCATION DATA FROM STUDENTS WHO CARRY OUT COLLABORATIVE TASKS THAT INVOLVE MOVEMENT AND INTERACTIONS THROUGH SPACE. FURTHERMORE, WE SUGGEST NEW WAYS TO ANALYZE, VISUALIZE, AND INTERPRET THE DATA OBTAINED. AS A FIRST PRACTICAL APPROACH, WE CARRIED OUT AN EXPLORATORY, COLLABORATIVE ACTIVITY WITH SIXTEEN UNDERGRADUATE STUDENTS WORKING IN A LIBRARY, WITH BOOKSHELVES AND WORK TABLES MONITORED BY BEACONS. FROM THE ANALYSIS OF STUDENT MOVEMENT DYNAMICS, THREE TYPES OF WELL-DIFFERENTIATED STUDENT ROLES WERE IDENTIFIED: THE COLLECTORS, THOSE WHO GO OUT TO COLLECT DATA FROM THE BOOKSHELVES, AMBASSADORS, THOSE WHO COMMUNICATE WITH OTHER GROUPS, AND THE SECRETARIES, THOSE WHO STAY AT THEIR WORK DESK TO SHAPE THE REQUESTED ESSAY. WE BELIEVE THESE FINDINGS ARE VALUABLE FEEDBACK FOR THE ENHANCEMENT OF THE LEARNING ACTIVITY AND THE FIRST STEP TOWARDS MMLA-DRIVEN TEACHING PROCESS IMPROVEMENT METHOD.
WE PROVIDE A SUMMARY REPORT OF A TWO-AND-HALF-DAY SYMPOSIUM SPONSORED BY THE ASSOCIATION FOR EDUCATIONAL COMMUNICATIONS AND TECHNOLOGY (AECT) AND HOSTED BY THE TEXAS CENTER FOR EDUCATIONAL TECHNOLOGY AT THE UNIVERSITY OF NORTH TEXAS ON MAY 16-18, 2018. THE THEME OF THE SYMPOSIUM WAS "HUMAN INTELLIGENCE 0.2 VERSUS ARTIFICIAL INTELLIGENCE 2.0 PARALLEL TO THE HUMAN-TECHNOLOGY FRONTIER: UNDERSTANDING THE LEARNING OF NOW TO PREPARE FOR THE WORK OF THE FUTURE." OVER 15 DISTINGUISHED SPEAKERS AND 80 PARTICIPANTS ENGAGED IN DISCUSSIONS OF A RANGE OF TOPICS SUCH AS HUMAN AND HOLISTIC LEARNING AS INFORMED BY NEW RESEARCH IN NEUROSCIENCE, CREATIVITY, CRITICAL THINKING, SELF-DIRECTED LEARNING, ARTIFICIAL INTELLIGENCE, LEARNING ANALYTICS, AND MEASUREMENTS. WE DISCUSS OUTCOMES AND PLANS FOR THE NEXT STEPS.
GAMIFICATION OFFERS POTENTIAL FOR IMPROVING STUDENTS' ONLINE DISCUSSION QUANTITY AND THE DEPTH OF DISCUSSION CONTENT. HOWEVER, VERY FEW STUDIES HAVE EXAMINED THE SOCIAL NETWORK PATTERNS WHEN STUDENTS ARE POSTING AND PROVIDING PEER-FEEDBACK TO EACH OTHER IN GAMIFICATION-BASED ENVIRONMENTS. IN RESPONSE TO THOSE GAPS, THIS STUDY USED A LEARNING ANALYTICS APPROACH TO UNDERSTAND THE EFFECTS OF GAMIFICATION ON STUDENTS' ONLINE INTERACTIVE PATTERNS, POSTING QUANTITY, AND PEER-FEEDBACK QUALITY. THE SOCIAL NETWORK ANALYSIS RESULTS SHOWED THAT STUDENTS IN THE GAMIFICATION-BASED COURSE HAD DENSER NETWORKS THAN THOSE IN THE CONTROL GROUP. MORE STUDENTS WERE ACTIVE IN THE NETWORK COMPARED WITH THE CONTROL GROUP. CONTENT ANALYSIS REVEALED THAT STUDENTS PROVIDED HIGHER QUALITY PEER-FEEDBACK THAN STUDENTS IN THE CONTROL GROUP. THIS STUDY PROVIDES EMPIRICAL EVIDENCE FOR THE POSITIVE EFFECTS OF GAMIFICATION ON STUDENTS' ONLINE INTERACTION. FUTURE RESEARCH TRENDS AND THE LIMITATIONS OF THIS STUDY ARE ALSO DISCUSSED.
IN THIS STUDY, COURSE MATERIALS, MAINLY IN THE FORM OF PDF FILES, WERE UPLOADED TO THE MOODLE LEARNING MANAGEMENT SYSTEM AND FACE-TO-FACE LESSONS WERE CONDUCTED. STUDENT CLICKSTREAMS OF THE COURSE MATERIALS AND THE RESULTS OF QUIZZES WERE ACCUMULATED. THE QUIZZES, WHICH USED A 5-ALTERNATIVE FORMAT, WERE PREPARED FROM THE COURSE MATERIALS. DATA FROM THE COURSE MATERIAL CLICKSTREAMS AND THE RESULTS OF THE QUIZZES WERE ANALYZED FROM AN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS PERSPECTIVE. A SCATTER CHART AND HEAT MAP WERE CREATED TO CLASSIFY STUDENTS INTO FOUR GROUPS BASED ON DEVIATIONS FROM THE AVERAGE VALUE OF CLICKSTREAMS AND QUIZ SCORES. THE GROUPS WERE COMPOSED OF (1) THOSE WITH HIGH CLICKSTREAM AND HIGH QUIZ SCORES, (2) THOSE WITH LOW CLICKSTREAM BUT HIGH QUIZ SCORES, (3) THOSE WITH LOW CLICKSTREAM AND LOW QUIZ SCORES, AND (4) THOSE WITH HIGH CLICKSTREAM BUT LOW QUIZ SCORES. IT IS SHOWN THAT CREATING A HEAT MAP BASED ON AN ANALYSIS OF THE RESULTS OF 13 QUIZZES AND STUDENT CLICKSTREAMS CAN PROVIDE AN EARLY INDICATION OF STUDENT ENGAGEMENT AND CAN BE USED TO SIGNAL THE NEED FOR A CORRECTIVE INTERVENTION IN THE CASE OF DISENGAGED STUDENTS.
THE INTERACTIVE LEARNING IS A CONTINUOUS PROCESS, WHICH IS FULL OF A LARGE NUMBER OF LEARNING INTERACTION ACTIVITIES. THE DATA GENERATED BETWEEN LEARNERS AND LEARNING INTERACTION ACTIVITIES CAN REFLECT THE ONLINE LEARNING BEHAVIORS. THROUGH THE CORRELATION ANALYSIS AMONG LEARNING INTERACTION ACTIVITIES, THIS PAPER DISCUSSES THE POTENTIAL ASSOCIATION RULES, DEFINES THE DATA STRUCTURES, MINES THE FREQUENT ITEM SETS, AND DESIGNS APPROPRIATE ALGORITHMS, THEN RECOMMEND LEARNING DECISION MAKINGS BASED ON ASSOCIATION RULES. THE RESEARCH METHODS AND CONCLUSIONS CAN PROVIDE FEASIBLE EDUCATIONAL DECISION MAKINGS FOR THE REALIZATION OF PERSONALIZATION, PROBABILITY PREDICTION AND DECISION FEEDBACK, WHICH WILL IMPROVE THE INTERACTIVE LEARNING ENVIRONMENT, THE ALGORITHMS, METHODS AND MODES DESIGNED IN THIS PAPER ARE USEFUL SUPPLEMENTS FOR LEARNING ANALYTICS.
ONLINE LEARNING COMMUNITIES HAVE GAINED POPULARITY AMONGST ENGINEERING LEARNERS WHO SEEK TO BUILD KNOWLEDGE AND SHARE THEIR EXPERTISE WITH OTHERS; YET TO DATE, LIMITED RESEARCH HAS BEEN DEVOTED TO THE DEVELOPMENT OF ANALYTICS FOR ENGINEERING COMMUNITIES. THIS IS ADDRESSED THROUGH OUR STUDY OF AN ONLINE ENGINEERING COMMUNITY THAT SERVES 31,219 ENGINEERING LEARNERS WHO CONTRIBUTED 503,908 MESSAGES IN 65,209 TOPICS. THE GUIDING THEORETICAL FRAMEWORK IS THE KNOWLEDGE CREATION METAPHOR, WHICH CONCEPTUALIZES LEARNING AS A COLLABORATIVE PROCESS OF DEVELOPING SHARED KNOWLEDGE ARTIFACTS FOR THE COLLECTIVE BENEFIT OF A COMMUNITY OF LEARNERS. THE AIMS OF THIS STUDY ARE TWOFOLD: (1) TO ANALYZE THE STATE OF KNOWLEDGE CREATION IN THE COMMUNITY; AND (2) TO EVALUATE THE STRENGTH OF ASSOCIATION BETWEEN PROPOSED ANALYTICS AND VARIABLES INDICATIVE OF KNOWLEDGE CREATION IN ONLINE ENVIRONMENTS. FINDINGS SUGGEST THAT THE COMMUNITY IS VIBRANT AS A WHOLE BUT ALSO REVEAL DISPARITY IN PARTICIPATION AT THE INDIVIDUAL LEVEL. AT THE TOPIC-LEVEL, KNOWLEDGE CREATION ACTIVITIES ARE STRONGLY ASSOCIATED WITH TOPIC LENGTH AND MODERATELY ASSOCIATED WITH TOPIC DURATION. AT THE INDIVIDUAL-LEVEL, PARTICIPATION IN KNOWLEDGE CREATION ACTIVITIES IS STRONGLY ASSOCIATED WITH INDIVIDUAL TOTAL INTERACTIONS AND WEAKLY ASSOCIATED WITH INDIVIDUAL TOTAL MEMBERSHIP PERIOD. THE IMPLICATIONS OF THE FINDINGS ARE DISCUSSED AND MAY PROVIDE GUIDANCE FOR EDUCATORS SEEKING TO ADOPT LEARNING ANALYTICS IN ONLINE COMMUNITIES. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE GOAL OF THIS DEMONSTRATION SESSION IS TO INTRODUCE SYNERGY, A PLATFORM TO HELP DESIGN AND IMPLEMENT DIALOGIC FEEDBACK PRACTICES. SYNERGY IS GROUNDED IN A THEORETICAL FRAMEWORK OF DIALOGIC FEEDBACK, WHICH SUGGESTS AN ONGOING DIALOGUE AMONG THE PEERS (PROVIDING FEEDBACK) AND THE TARGET STUDENT (RECEIVING FEEDBACK). SYNERGY ALLOWS INSTRUCTORS TO CREATE MULTIPLE REVIEW SESSIONS WITH SPECIFIC TASKS DEPENDING ON THE ROLE AS FEEDBACK RECEIVER OR PROVIDER. PEER REVIEW ACTIVITIES ARE ORGANIZED AROUND THREE PHASES, IN ACCORDANCE WITH THEORETICAL FRAMEWORK. USING SYNERGY, PEERS IN THE FIRST PHASE ASSESS STUDENT WORK, DISCUSS TOGETHER TO ALIGN THEIR PERSPECTIVES TOWARD THE QUALITY OF THE WORK. THEN, THE PEERS CREATE FEEDBACK TASKS (TO IDENTIFY WHO GIVES WHICH FEEDBACK). IN THE SECOND PHASE, SYNERGY ENABLES PEERS TO PROVIDE THE INTENDED FEEDBACK (BASED ON THE FEEDBACK TASKS) AND TO BUILD DIALOGUE WITH THE TARGET STUDENT. DURING DIALOGUE, IN COLLABORATION WITH PEERS, SYNERGY ALLOWS STUDENTS TO IDENTIFY LEARNING ACTIONS TO TRANSLATE THE FEEDBACK RECEIVED INTO CONCRETE PROGRESS. IN THE LAST PHASE, WHEN STUDENTS PERFORM THE PLANNED ACTIONS, SYNERGY TRACKS STUDENT ENGAGEMENT AND PROGRESS PER EACH ACTION AND ALSO ALLOWS THE STUDENTS TO SET THEIR PROGRESS MANUALLY. SYNERGY IS ENHANCED WITH LEARNING ANALYTICS TOOLS TO SUPPORT THE FEEDBACK PROCESSES DURING THE DEMO, WE WILL SHOW INTERACTIVELY THE USE CASE OF HOW SYNERGY CAN HELP DESIGN AND FACILITATE DIALOGIC PEER FEEDBACK.
LEARNING ANALYTICS HAS ATTRACTED A REMARKABLE ATTENTION IN THE LAST YEARS DUE TO ITS POTENTIAL TO SUPPORT AND IMPROVE THE LEARNING EXPERIENCE. THE FIRST STEP OF THE LEARNING ANALYTICS PROCESS IS THE EDUCATIONAL DATA COLLECTION, THEREFORE IT IS HIGHLY SIGNIFICANT TO TRACK AND COLLECT THE BIG EDUCATIONAL DATA GENERATED (LEARNING DATA, ASSESSMENT DATA, COMMUNICATION DATA ETC). ASSESSMENT DATA REPRESENT AN IMPORTANT PROPORTION OF THE EDUCATIONAL DATA GENERATED. WHEN WE FOCUS ON ASSESSMENT, WE CAN LAUNCH A NEW SOURCE OF DATA THAT CAN BE ANALYZED AND HENCE CONTRIBUTE TO THE IMPROVEMENT OF THE FIELD OF LEARNING ANALYTICS. IN THIS PAPER, WE WILL FOCUS ON ASSESSMENT DATA BY PROPOSING AN ASSESSMENT ANALYTICS ARCHITECTURE. THIS ARCHITECTURE USES THE SEMANTIC WEB TECHNOLOGIES AND FOCUS ESSENTIALLY ON ANALYZING THE SET OF ASSESSMENT ACTIVITIES, THE ASSESSMENT RESULT AND THE ASSESSMENT CONTEXT. THESE ASSESSMENT DATA ARE CONCEIVED AND ANNOTATED VIA AN ONTOLOGICAL MODEL, INSPIRED FROM THE XAPI DATA MODEL, WHICH ENABLE PERFORMING LOGIC RULES FOR SUPPORTING INFERENCE MECHANISMS RELATED TO ASSESSMENT ANALYTICS AND ESPECIALLY CONCERNING THE CONCRETE LEVEL OF THE ASSESSMENT ACTOR AND THE LEVEL OF DIFFICULTY OF THE ASSESSMENT OBJECT.
VISUALIZATIONS PLAY AN IMPORTANT ROLE IN LEARNING ANALYTICS, SUPPORTING REFLECTION AND DECISION MAKING. NETWORK REPRESENTATIONS ARE COMMONLY USED FOR DEPICTING SOCIAL INTERACTIONS BETWEEN LEARNERS. WHILE THERE ARE MANY NETWORK VISUALIZATION PLATFORMS AVAILABLE, MOST OF THEM ARE AIMED AT RESEARCHERS, REQUIRING SOCIAL NETWORK ANALYSIS EXPERTISE. OUR GOAL IS TO PROVIDE A SIMPLE TOOL FOR VISUALIZING STUDENTS' COLLABORATION PATTERNS IN A SOCIAL LEARNING ENVIRONMENT, WHICH SHOULD BE EASY TO USE BY THE TEACHER. THE PAPER PRESENTS A DESCRIPTION OF THIS TOOL (CALLED STUDENTVIZ), SOME DESIGN AND IMPLEMENTATION DETAILS, AND AN ILLUSTRATION OF ITS FUNCTIONALITIES. IT FURTHER SHOWS THAT THE TOOL ADEQUATELY ADDRESSES THE VISUALIZATION NEEDS OF THE INSTRUCTORS, FOSTERING INSIGHT GAINING.
WE PRESENT A NOVEL METHOD FOR PREDICTING THE EVOLUTION OF A STUDENT'S GRADE IN MASSIVE OPEN ONLINE COURSES (MOOCS). PERFORMANCE PREDICTION IS PARTICULARLY CHALLENGING IN MOOC SETTINGS DUE TO PER-STUDENT ASSESSMENT RESPONSE SPARSITY AND THE NEED FOR PERSONALIZED MODELS. OUR METHOD OVERCOMES THESE CHALLENGES BY INCORPORATING ANOTHER, RICHER FORM OF DATA COLLECTED FROM EACH STUDENT-LECTURE VIDEO-WATCHING CLICKSTREAMS-INTO THE MACHINE LEARNING FEATURE SET, AND USING THAT TO TRAIN A TIME SERIES NEURAL NETWORK THAT LEARNS FROM BOTH PRIOR PERFORMANCE AND CLICKSTREAM DATA. THROUGH EVALUATION ON TWO MOOC DATASETS, WE FIND THAT OUR ALGORITHM OUTPERFORMS A BASELINE OF AVERAGE PAST PERFORMANCE BY MORE THAN 60% ON AVERAGE, AND A LASSO REGRESSION BASELINE BY MORE THAN 15%. MOREOVER, THE GAINS ARE HIGHER WHEN THE STUDENT HAS ANSWERED FEWER QUESTIONS, UNDERSCORING THEIR ABILITY TO PROVIDE INSTRUCTORS WITH EARLY DETECTION OF STRUGGLING AND/OR ADVANCED STUDENTS. WE ALSO SHOW THAT DESPITE THESE GAINS, WHEN TAKEN ALONE, NONE OF THE BEHAVIORAL FEATURES ARE PARTICULARLY CORRELATED WITH PERFORMANCE, EMPHASIZING THE NEED TO CONSIDER THEIR COMBINED EFFECT AND NONLINEAR PREDICTORS. FINALLY, WE DISCUSS HOW COURSE INSTRUCTORS CAN USE THESE PREDICTIVE LEARNING ANALYTICS TO STAGE STUDENT INTERVENTIONS.
IN REGARD TO DISTANCE LEARNING, BRAZIL CURRENTLY LIVES A VERY SPECIAL MOMENT, AS MANY COURSES ARE BEING CREATED WITHIN THAT TYPE OF EDUCATION. VIRTUAL LEARNING ENVIRONMENTS (VLES) HAVE CONTRIBUTED TO BOTH THE GROWTH AND THE POPULARIZATION OF DISTANCE LEARNING. THE LEVEL OF MANAGEMENT OF LEARNING PROCESSES PROVIDED BY VLES, WITH GREAT INTERACTION AND TRACKING QUALITY, MAY BE SINGLED OUT AS A KEY INDICATOR OF THE SUCCESS OF THIS PROCESS. HOWEVER, THESE ACTIVITIES AREN'T ALWAYS PERFORMED ADEQUATELY. FACING THIS SCENARIO, THIS PAPER HAS THE GOAL OF PROPOSING A MULTI-AGENT ARCHITECTURE FOR MONITORING TUTORING ACTIVITIES IN VLES, BASED ON INFORMATION EXTRACTED BY LEARNING ANALYTICS (LA) PROCESSES, IN ORDER TO PROMOTE THE EFFECTIVE PARTICIPATION OF STUDENTS. THE MULTI-AGENT ARCHITECTURE WILL BE VALIDATED IN A CASE STUDY TO BE CARRIED OUT IN THE SECRETARY OF DISTANCE EDUCATION AT THE FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE.
EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) FOCUS ON DATA ANALYSIS OF LEARNERS IN THE CONTEXT OF EDUCATIONAL SETTINGS LIKE MOODLE, A LEARNING MANAGEMENT SYSTEM (LMS). BOTH EDM AND LA AIM TO UNDERSTAND LEARNERS AND OPTIMISE LEARNING PROCESSES. PREDICTIVE MODELLING SERVE A KEY ROLE IN OPTIMISING LEARNING PROCESSES. LEARNING ANALYTICS IN AN LMS COVERS MANY DIFFERENT ASPECTS: FINDING STUDENTS AT RISK OF ABANDONING A COURSE, PREDICTING STUDENTS FAILING A QUIZ OR STUDENTS NOT REACHING THE END OF A LESSON IN LESS THAN 15 MINUTES. THUS, THERE ARE MULTIPLE PREDICTION MODELS THAT CAN BE EXPLORED. THE PREDICTION MODELS CAN TARGET AT THE COURSE ALSO. FOR INSTANCE, WILL THIS COURSE ENGAGE LEARNERS? WILL THIS FORUM BE USEFUL TO THE STUDENTS OF THIS COURSE? TO EASE THE EVALUATION AND USAGE OF SUPERVISED LEARNING PREDICTION MODELS IN LMS, WE ABSTRACT THE KEY ELEMENTS OF PREDICTION MODELS AND WE BUILD AN ANALYTICS FRAMEWORK FOR MOODLE, ONE OF THE MOST POPULAR LEARNING MANAGEMENT SYSTEMS AVAILABLE IN THE MARKET. OUR SOFTWARE FRAMEWORK MANAGES THE COMPLETE CYCLE THAT PREDICTIVE MODELS FOLLOW UNTIL THEY ARE USED IN PRODUCTION, WHICH INCLUDES CALCULATIONS OF FEATURES AND LABELS FROM THE LMS DATABASE RAW DATA, NORMALIZATION, FEATURE ENGINEERING, MODEL EVALUATION AND A PRODUCTION-READY MODE TO GENERATE INSIGHTS FOR USERS FROM PREDICTIONS. APART FROM THE SOFTWARE FRAMEWORK WE ALSO PRESENT A USE CASE THAT SERVES AS AN EXAMPLE: A PREDICTION MODEL WHICH IS ABLE TO IDENTIFY STUDENTS AT RISK OF ABANDONING A COURSE WITH A 92% IN ACCURACY USING PAST VERSIONS OF THE COURSE AS TRAINING DATA.
THE E-LEARNING TRAINING, BOTH WITHIN MOST TRADITIONAL FORMAL EDUCATION CONTEXTS AND IN THE LATEST VERSION OF MOOCS, IS GROWING. HOWEVER, THE LIMITS ARE ALSO HIGHLIGHTED: HIGH RISK OF DROP-OUT, HIGH VARIABILITY IN THE QUALITY OF EDUCATION AND LEARNING OUTCOMES. THE COMMITMENT TO FURTHER QUALIFY E-LEARNING TRAINING IMPLIES A REDEFINITION OF EDUCATIONAL AND EVALUATION MODELS AND TOOLS THAT CONSIDER ASPECTS OF COGNITIVE, METACOGNITIVE AND MOTIVATIONAL NATURE. SPECIFICALLY IT SEEMS INTERESTING TO STUDY THE SELF-REGULATION PROCESSES WHICH MANY HAVE HIGHLIGHTED AS ONE OF THE MAIN SUCCESS FACTORS IN THE STUDY AND WHICH WE MUST CONSIDER AS PARTICULARLY CRITICAL WHEN THE REGULATION OF LEARNING PROCESSES IS COMPLETELY OR ALMOST AT THE EXPENSE OF THE SUBJECT, AS TYPICALLY HAPPENS IN E-LEARNING CONTEXTS. IT IS THEREFORE INTERESTING TO DEVELOP TOOLS AND STRATEGIES THAT HELP STUDENTS TO REFLECT AND BECOME AWARE OF THOSE DIMENSIONS THAT ARE BASIS OF SELF-REGULATING BEHAVIOUR. IN THIS WAY ALSO SOCIAL LEARNING ANALYTICS PERSPECTIVE, THROUGH THE IMPLEMENTATION OF FEEDBACK TOOLS, OFFERS INTERESTING SOLUTIONS TO IMPROVE LEVELS OF AWARENESS AND TRUST OF STUDENTS IN E-LEARNING ENVIRONMENTS.
THIS PAPER FOCUSES ON THE PROBLEM OF IDENTIFYING STUDENTS, WHO ARE AT RISK OF FAILING THEIR COURSE. THE PRESENTED METHOD PROPOSES A SOLUTION IN THE ABSENCE OF DATA FROM PREVIOUS COURSES, WHICH ARE USUALLY USED FOR TRAINING MACHINE LEARNING MODELS. THIS SITUATION TYPICALLY OCCURS IN NEW COURSES. WE PRESENT THE CONCEPT OF A "SELF-LEARNER" THAT BUILDS THE MACHINE LEARNING MODELS FROM THE DATA GENERATED DURING THE CURRENT COURSE. THE APPROACH UTILISES INFORMATION ABOUT ALREADY SUBMITTED ASSESSMENTS, WHICH INTRODUCES THE PROBLEM OF IMBALANCED DATA FOR TRAINING AND TESTING THE CLASSIFICATION MODELS. THERE ARE THREE MAIN CONTRIBUTIONS OF THIS PAPER: (1) THE CONCEPT OF TRAINING THE MODELS FOR IDENTIFYING AT-RISK STUDENTS USING DATA FROM THE CURRENT COURSE, (2) SPECIFYING THE PROBLEM AS A CLASSIFICATION TASK, AND (3) TACKLING THE CHALLENGE OF IMBALANCED DATA, WHICH APPEARS BOTH IN TRAINING AND TESTING DATA. THE RESULTS SHOW THE COMPARISON WITH THE TRADITIONAL APPROACH OF LEARNING THE MODELS FROM THE LEGACY COURSE DATA, VALIDATING THE PROPOSED CONCEPT.
PREVIOUS WORK IN LEARNING ANALYTICS HAVE BEEN FRUITFUL IN SHEDDING LIGHTS ON COLLABORATIVE LEARNING ENVIRONMENTS, SUCH WORK HAS PROVIDED INSIGHTS AND RECOMMENDATIONS THAT HELPED IMPROVE THE COLLABORATIVE PROCESS IN COMPUTER-MEDIATED LEARNING ENVIRONMENTS. GIVEN THE IMPORTANCE OF SOCIAL INTERACTIONS AND THEIR INFLUENCE ON LEARNING (E.G., IN DETERMINING ACADEMIC GROWTH, PERSEVERANCE IN THE COURSE AND PERSISTENCE). IN THIS STUDY, WE LOOK AT BOTH LEARNING AND SOCIAL NETWORKS, WHAT FACTORS THEY SHARE, HOW THEY IMPACT OR INFLUENCE LEARNING, AND WHAT INFLUENCES THE FORMATION OF THESE NETWORKS. OUR RESULTS SHOW SIMILARITIES AND DIFFERENCES BETWEEN BOTH NETWORKS SUCH AS: INTERACTIONS IN THE SOCIAL NETWORK PREDICT THOSE IN THE LEARNING NETWORK, HOWEVER, ONLY CENTRALITY MEASURES IN THE LEARNING NETWORK CORRELATE WITH PERFORMANCE, PROBABLY DUE TO THE SELECTIVE NATURE OF REPLIES AND INTERACTIONS IN THE LEARNING NETWORK.
IN A LARGE-SCALE QUANTITATIVE STUDY THAT ADOPTED A LEARNING ANALYTICS APPROACH, WE SEARCHED FOR ASSOCIATIONS BETWEEN STUDENTS' ACTIVITY IN A GAME-BASED ONLINE MATHEMATICS LEARNING ENVIRONMENT AND THEIR MATHEMATICS ACHIEVEMENTS ON A NATIONAL STANDARDIZED TEST. STUDENTS WERE ACTIVE IN THE ENVIRONMENT THROUGHOUT THE SCHOOL YEAR, AND PARTICULARLY DURING AN ONLINE NATIONAL MATHEMATICS OLYMPIAD (N = 239 SCHOOLS). OVERALL, OUR FINDINGS POINT TO A POSITIVE ASSOCIATION BETWEEN ONLINE GAME-BASED ACTIVITY DURING THE OLYMPIAD AND MATHEMATICS TEST SCORES. THIS ASSOCIATION REMAINED EVEN AFTER CONTROLLING FOR SOCIOECONOMIC FACTORS, WHICH ARE KNOWN TO CONTRIBUTE TO ACADEMIC ACHIEVEMENTS. THE PAPER DISCUSSES THESE FINDINGS AND SUGGESTS EVIDENCE FOR CAUSALITY, IN PARTICULAR COGNITIVE, META-COGNITIVE, AND AFFECTIVE EFFECTS OF THE ONLINE ACTIVITY THAT MAY HAVE POSITIVELY AFFECTED TEST SCORES.
ONLINE LEARNING ENVIRONMENTS ARE NOW PERVASIVE IN HIGHER EDUCATION. WHILE NOT EXCLUSIVELY THE CASE, IN THESE ENVIRONMENTS, THERE IS OFTEN MODEST TEACHER PRESENCE, AND STUDENTS ARE PROVIDED WITH ACCESS TO A RANGE OF LEARNING, ASSESSMENT, AND SUPPORT MATERIALS. THIS PLACES PRESSURE ON THEIR STUDY SKILLS, INCLUDING SELF-REGULATION. IN THIS CONTEXT, STUDENTS MAY ACCESS ASSESSMENT MATERIAL WITHOUT BEING FULLY PREPARED. THIS MAY RESULT IN LIMITED SUCCESS AND, IN TURN, RAISE A SIGNIFICANT RISK OF DISENGAGEMENT. THEREFORE, IF THE PREDICTION OF STUDENTS' ASSESSMENT READINESS WAS POSSIBLE, IT COULD BE USED TO ASSIST EDUCATORS OR ONLINE LEARNING ENVIRONMENTS TO POSTPONE ASSESSMENT TASKS UNTIL STUDENTS WERE DEEMED "READY". IN THIS STUDY, WE EMPLOYED A RANGE OF MACHINE LEARNING TECHNIQUES WITH AGGREGATED AND SEQUENTIAL REPRESENTATIONS OF STUDENTS' BEHAVIOUR IN A MASSIVE OPEN ONLINE COURSE (MOOC), TO PREDICT THEIR READINESS FOR ASSESSMENT TASKS. BASED ON OUR RESULTS, IT WAS POSSIBLE TO SUCCESSFULLY PREDICT STUDENTS' READINESS FOR ASSESSMENT TASKS, PARTICULARLY IF THE SEQUENTIAL ASPECTS OF BEHAVIOUR WERE REPRESENTED IN THE MODEL. ADDITIONALLY, WE USED SEQUENTIAL PATTERN MINING TO INVESTIGATE WHICH SEQUENCES OF BEHAVIOUR DIFFERED BETWEEN HIGH OR LOW LEVEL OF PERFORMANCE IN ASSESSMENTS. WE FOUND THAT A HIGH LEVEL OF PERFORMANCE HAD THE MOST SEQUENCES RELATED TO VIEWING AND REVIEWING THE LECTURE MATERIALS, WHEREAS A LOW LEVEL OF PERFORMANCE HAD THE MOST SEQUENCES RELATED TO SUCCESSIVE FAILED SUBMISSIONS FOR AN ASSESSMENT. BASED ON THE FINDINGS, IMPLICATIONS FOR SUPPORTING SPECIFIC BEHAVIOURS TO IMPROVE LEARNING IN ONLINE ENVIRONMENTS ARE DISCUSSED.
FEEDBACK IS A MAJOR FACTOR OF STUDENT SUCCESS WITHIN HIGHER EDUCATION LEARNING. HOWEVER, RECENT CHANGES - SUCH AS INCREASED CLASS SIZES AND SOCIO-ECONOMIC DIVERSITY OF THE STUDENT POPULATION - CHALLENGED THE PROVISION OF EFFECTIVE STUDENT FEEDBACK. ALTHOUGH THE USE OF EDUCATIONAL TECHNOLOGY FOR PERSONALISED FEEDBACK TO DIVERSE STUDENTS HAS GAINED TRACTION, THE FEEDBACK GAP STILL EXISTS: EDUCATORS WONDER WHICH STUDENTS RESPOND TO FEEDBACK AND WHICH DO NOT. IN THIS STUDY, A SET OF TRACKABLE CALL TO ACTION (CTA) LINKS WAS EMBEDDED IN TWO SETS OF FEEDBACK MESSAGES FOCUSING ON STUDENTS' TIME MANAGEMENT, WITH THE GOAL OF (1) EXAMINING THE ASSOCIATION BETWEEN FEEDBACK ENGAGEMENT AND COURSE SUCCESS AND (2), TO PREDICT STUDENTS' REACTION TO PROVIDED FEEDBACK. WE ALSO CONDUCTED TWO FOCUS GROUPS TO FURTHER EXAMINE STUDENTS' PERCEPTION OF PROVIDED FEEDBACK MESSAGES. OUR RESULTS REVEALED THAT EARLY ENGAGEMENT WITH THE FEEDBACK WAS ASSOCIATED WITH HIGHER CHANCES OF SUCCEEDING IN THE COURSE. LIKEWISE, PREVIOUS ENGAGEMENT WITH FEEDBACK WAS HIGHLY PREDICTIVE OF STUDENTS' ENGAGEMENT IN THE FUTURE, AND ALSO THAT CERTAIN STUDENT SUB-POPULATIONS, (E.G., FEMALE STUDENTS), WERE MORE LIKELY TO ENGAGE THAN OTHERS. SUCH INSIGHT ENABLES INSTRUCTORS TO ASK "WHY" QUESTIONS, IMPROVE FEEDBACK PROCESSES AND NARROW THE FEEDBACK GAP. PRACTICAL IMPLICATIONS OF OUR FINDINGS ARE FURTHER DISCUSSED.
STUDENT ATTRITION FROM HIGHER EDUCATION IS A PROBLEM WORLDWIDE. IT SIGNIFIES A PERSONAL LOSS TO THE INDIVIDUAL; IT CARRIES A SOCIAL AND ECONOMIC COST TO THE COMMUNITY; AND IT RESULTS IN A SIGNIFICANT FINANCIAL COST TO EACH UNIVERSITY. WHILE DIFFERENCES EXIST IN THE WAY UNIVERSITIES HAVE APPROACHED THIS ISSUE, THE LITERATURE SHOWS THE NEED FOR A HOLISTIC STRATEGY THAT AIMS TO FACILITATE AND ENHANCE STUDENT ENGAGEMENT WITH THEIR INSTITUTIONS. FLINDERS UNIVERSITY DEVELOPED AND IMPLEMENTED A CENTRALLY RUN PROGRAM CALLED THE STUDENT SUCCESS PROGRAM (SSP) TO CONTACT STUDENTS WHO ARE LIKELY TO DROP OUT IN THE FIRST YEAR OF THEIR ENROLMENT. THE AIM OF THE SSP IS TO MAXIMISE RETENTION AND IMPROVE THE STUDENT EXPERIENCE AT FLINDERS UNIVERSITY. THE RELEVANT LITERATURE AND INSTITUTIONAL EXPERIENCES LED TO THE CONSIDERATION OF ALL DATA SOURCES, BOTH STATIC AND DYNAMIC, THAT ARE READILY AVAILABLE FOR ALL STUDENTS IN SCOPE. PREDICTIONS FROM MACHINE LEARNING MODELS, BUILT USING BIG DATA, WERE UTILISED TO TARGET SUCH STUDENTS. AT LEAST THREE CALL-OUT CAMPAIGNS PER SEMESTER, WHICH ARE CONSISTENT WITH THE TRANSITION TIMELINE OF COMMENCING STUDENTS, WERE CONDUCTED EACH SEMESTER WITH EACH RUNNING FOR AT LEAST FOUR WEEKS. ABOUT 14270 STUDENTS WERE CONTACTED BY THEIR PEERS OVER A PERIOD OF THREE YEARS TO ELICIT INFORMATION ABOUT THEIR LEVEL OF PREPAREDNESS FOR THEIR STUDIES. THE FINDINGS DEMONSTRATE REDUCED STUDENT ATTRITION, IMPROVED APPRECIATION AND EFFECTIVE UTILISATION OF STUDENT SUPPORT SERVICES. DATA ANALYSIS ALSO SHOWS THAT THE SSP HAS SAVED DIRECTLY A SIGNIFICANT NUMBER OF STUDENTS AND HAS CONTRIBUTED TO THE ENHANCEMENT OF THE UNIVERSITY EXPERIENCE FOR MANY MORE.
STUDENT ENGAGEMENT IS CONSISTENTLY IDENTIFIED AS A KEY PREDICTOR OF LEARNER OUTCOMES WITHIN THE ONLINE LEARNING ENVIRONMENT. HOWEVER, THERE IS LIMITED GUIDANCE ABOUT USING PROACTIVE STRATEGIES TO IMPROVE ENGAGEMENT FOR LOW AND NON-ENGAGED STUDENTS: FOR EXAMPLE BY SPECIFICALLY EMPLOYING COURSE LEARNING ANALYTICS (CLA) AND NUDGING STRATEGIES IN COURSES TO ASSIST THESE STUDENTS. TO EXPLORE HOW CLA AND NUDGING CAN BE USED MORE EFFECTIVELY TO ENGAGE STUDENTS, THE AUTHORS WERE INFORMED BY A 12-MONTH RESEARCH PROJECT, AS WELL AS BY THE THEORETICAL PERSPECTIVES PRESENTED BY COMMUNICATION AND CRITICAL LITERACIES. THESE PERSPECTIVES WERE APPLIED TO DEVELOP A CONCEPTUAL FRAMEWORK WHICH THE AUTHORS DESIGNED TO PRIORITISE EXPECTATION MANAGEMENT AND ENGAGEMENT PRINCIPLES FOR BOTH STUDENTS AND ACADEMICS. THE ARTICLE EXPLAINS THE DEVELOPMENT OF THE FRAMEWORK AS WELL AS THE ELEMENTS AND KEY COMMUNICATION STRATEGIES IT EMBODIES. THE FRAMEWORK CONTRIBUTES TO PRACTICE BY EXPLAINING AND JUSTIFYING THE ACCESSIBLE, TIME-EFFICIENT, STUDENT-FOCUSED APPROACHES THAT CAN BE INTEGRATED SIMPLY INTO EACH COURSE'S ONLINE LEARNING PEDAGOGY TO SUPPORT BOTH ACADEMICS' AND STUDENTS' ENGAGEMENT.
NOWADAYS, LEARNING VIA SMARTPHONES HAS BECOME ONE OF THE MOST POPULAR TEACHING TOOLS USED BY YOUNG PEOPLE, THANKS TO THE EASE OF USE OF SUCH DEVICES IN THE FIELD OF EDUCATION. THERE ARE NOW A LARGE NUMBER OF BOTH INSTRUCTIONAL APPLICATIONS AND MOBILE SERIOUS GAMES "MSGS" WHICH ARE AVAILABLE IN MOBILE APPLICATIONS STORES. THE DIVERSITY OF SUCH APPLICATIONS ESPECIALLY MSGS CAN GUARANTEE A PERSONALIZED LEARNING EXPERIENCE FOR EACH LEARNER. HOWEVER, IT IS DIFFICULT TO DECIDE IF A GIVEN MSG IS EFFICIENT OR NOT BECAUSE THIS DECISION DEPENDS ON SEVERAL FACTORS. ONE OF THOSE MAJOR FACTORS IS THEIR ABILITY TO TRANSMIT KNOWLEDGE EFFECTIVELY TO THE LEARNERS, IN ORDER TO TEACH THEM NEW SKILLS. THIS ABILITY CAN BE MEASURED AND THEN ANALYZED BY USING SEVERAL TECHNIQUES AND ALGORITHMS LIKE LEARNING ANALYTICS, EDUCATIONAL DATA MINING, INFERENCE KNOWLEDGE E.G. "BAYESIAN KNOWLEDGE TRACING", ETC. HENCE THE NEED FOR THE ESTABLISHMENT OF A USER-FRIENDLY PLATFORM BASED ON THESE ALGORITHMS, THE PROPOSED PLATFORM WILL BE ABLE TO EVALUATE EASILY THE LEARNING OUTCOMES OF THIS KIND OF VIDEO GAMES.
MANY FACTORS INFLUENCE LEARNERS' PERFORMANCE ON AN ACTIVITY BEYOND THE KNOWLEDGE REQUIRED. LEARNERS' ON-TASK EFFORT HAS BEEN ACKNOWLEDGED FOR STRONGLY RELATING TO THEIR EDUCATIONAL OUTCOMES, REFLECTING HOW ACTIVELY THEY ARE ENGAGED IN THAT ACTIVITY. HOWEVER, EFFORT IS NOT DIRECTLY OBSERVABLE. MULTIMODAL DATA CAN PROVIDE ADDITIONAL INSIGHTS INTO THE LEARNING PROCESSES AND MAY ALLOW FOR EFFORT ESTIMATION. THIS PAPER PRESENTS AN APPROACH FOR THE CLASSIFICATION OF EFFORT IN AN ADAPTIVE ASSESSMENT CONTEXT. SPECIFICALLY, THE BEHAVIOUR OF 32 STUDENTS WAS CAPTURED DURING AN ADAPTIVE SELF-ASSESSMENT ACTIVITY, USING LOGS AND PHYSIOLOGICAL DATA (I.E., EYE-TRACKING, EEG, WRISTBAND AND FACIAL EXPRESSIONS). WE APPLIED K-MEANS TO THE MULTIMODAL DATA TO CLUSTER STUDENTS' BEHAVIOURAL PATTERNS. NEXT, WE PREDICTED STUDENTS' EFFORT TO COMPLETE THE UPCOMING TASK, BASED ON THE DISCOVERED BEHAVIOURAL PATTERNS USING A COMBINATION OF HIDDEN MARKOV MODELS (HMMS) AND THE VITERBI ALGORITHM. WE ALSO COMPARED THE RESULTS WITH OTHER STATE-OF-THE-ART CLASSIFICATION ALGORITHMS (SVM, RANDOM FOREST). OUR FINDINGS PROVIDE EVIDENCE THAT HMMS CAN ENCODE THE RELATIONSHIP BETWEEN EFFORT AND BEHAVIOUR (CAPTURED BY THE MULTIMODAL DATA) IN A MORE EFFICIENT WAY THAN THE OTHER METHODS. FOREMOST, A PRACTICAL IMPLICATION OF THE APPROACH IS THAT THE DERIVED HMMS ALSO PINPOINT THE MOMENTS TO PROVIDE PREVENTIVE/PRESCRIPTIVE FEEDBACK TO THE LEARNERS IN REAL-TIME, BY BUILDING-UPON THE RELATIONSHIP BETWEEN BEHAVIOURAL PATTERNS AND THE EFFORT THE LEARNERS ARE PUTTING IN.
IN THIS ARTICLE, WE STUDY A SET OF FACTORS UNDERLYING STUDENT SUCCESS IN A BOTTLENECK BUSINESS COURSE USING STATISTICAL AND DATA MINING TECHNIQUES. FACTORS INCLUDED LEARNING STYLES, MOTIVATIONAL AND OTHER COGNITIVE FACTORS, PERSONALITY TRAITS, LEARNING ANALYTICS, ALONG WITH BACKGROUND DEMOGRAPHIC AND ACADEMIC ONES. OUR ANALYSIS YIELDED INTERESTING INSIGHTS THAT SHOW SOME OF THESE FACTORS PLAY SIGNIFICANT ROLES IN PREDICTING BOTH STUDENT PERFORMANCE AND THEIR PROPENSITY TO UTILIZE RESOURCES THAT HELP IMPROVE THEIR PERFORMANCE, SUCH AS ADDITIONAL SUPPORT SERVICES. THE PREDICTIVE ACCURACY OF BOTH OF OUR MODELS WERE OVER 95% (ERROR RATE <5%). MOREOVER, QUANTILE REGRESSION MODELS WERE USED TO DETERMINE FACTORS THAT SPECIFICALLY AFFECT THE PERFORMANCE OF LOW-PERFORMING STUDENTS SO THAT TARGETED INTERVENTION AND SUPPORT SERVICES CAN BE DEVELOPED SPECIFICALLY FOR THEM. IN CONCLUSION, DEEPER ANALYTICS VIA STATISTICAL MODELS ARE CRUCIAL FOR FORMING AN IN-DEPTH UNDERSTANDING OF HOW TO IMPROVE STUDENT PERFORMANCE IN A BOTTLENECK COURSE AND THIS HAS FAR-REACHING IMPLICATIONS FOR BOTH EDUCATORS AND ADMINISTRATORS IN HIGHER EDUCATION.
THE HACKATHON IS INTENDED TO BE A PRACTICAL HANDS-ON WORKSHOP INVOLVING PARTICIPANTS FROM ACADEMIA AND COMMERCIAL ORGANIZATIONS WITH BOTH TECHNICAL AND PRACTITIONER EXPERTISE. IT WILL CONSIDER THE OUTSTANDING CHALLENGE OF VISUALIZATIONS WHICH ARE EFFECTIVE FOR THE INTENDED AUDIENCE: INFORMING ACTION, NOT LIKELY TO BE MISINTERPRETED, AND EMBODYING CONTEXTUAL APPROPRIACY, ETC. IT WILL SURFACE PARTICULAR ISSUES AS WORKSHOP CHALLENGES AND EXPLORE RESPONSES TO THESE CHALLENGES AS VISUALIZATIONS RESTING UPON INTEROPERABILITY STANDARDS AND API ORIENTED OPEN ARCHITECTURES.
REFLECTIVE WRITING CAN PROVIDE A POWERFUL WAY FOR STUDENTS TO INTEGRATE PROFESSIONAL EXPERIENCE AND ACADEMIC LEARNING. HOWEVER, WRITING REFLECTIVELY REQUIRES HIGH QUALITY ACTIONABLE FEEDBACK, WHICH IS TIME-CONSUMING TO PROVIDE AT SCALE. THIS PAPER REPORTS PROGRESS ON THE DESIGN, IMPLEMENTATION, AND VALIDATION OF A REFLECTIVE WRITING ANALYTICS PLATFORM TO PROVIDE ACTIONABLE FEEDBACK WITHIN A TERTIARY AUTHENTIC ASSESSMENT CONTEXT. THE CONTRIBUTIONS ARE: (1) A NEW CONCEPTUAL FRAMEWORK FOR REFLECTIVE WRITING; (2) A COMPUTATIONAL APPROACH TO MODELLING REFLECTIVE WRITING, DERIVING ANALYTICS, AND PROVIDING FEEDBACK; (3) THE PEDAGOGICAL AND USER EXPERIENCE RATIONALE FOR PLATFORM DESIGN DECISIONS; AND (4) A PILOT IN A STUDENT LEARNING CONTEXT, WITH PRELIMINARY DATA ON EDUCATOR AND STUDENT ACCEPTANCE, AND THE EXTENT TO WHICH WE CAN EVIDENCE THAT THE SOFTWARE PROVIDED ACTIONABLE FEEDBACK FOR REFLECTIVE WRITING.
IN RESPONSE TO CONTEMPORARY SECURITY CHALLENGES FOR INTERNET OF THINGS SYSTEMS, THIS PAPER INTRODUCES AN ARCHITECTURAL FRAMEWORK FOR DATA DRIVEN SECURITY MONITORING AND AUTOMATION. THE ARCHITECTURE SUPPORTS ADVANCED DATA ANALYTICS FOR DETECTING ANOMALIES AT ALL LAYERS OF AN IOT SYSTEM, BASED ON A POWERFUL MECHANISM OF REUSABLE SECURITY TEMPLATES. ALSO, THE PAPER PROVIDES A CONCRETE EXAMPLE OF DATA-DRIVEN IOT SECURITY FOR SMART OBJECTS, BASED ON THE USE OF DEEP LEARNING ALGORITHMS AND THEIR IMPLEMENTATION OVER THE INTRODUCED ARCHITECTURE FRAMEWORK. THE ALGORITHMS ARE SUCCESSFULLY DEPLOYED AND USED FOR EFFECTIVE AND PREDICTIVE DETECTION OF ANOMALIES AND ABNORMALITIES AT THE NETWORK AND APPLICATION LAYERS OF THE RESPECTIVE IOT SYSTEMS. THEY MANIFEST HOW DEEP LEARNING AND AI TECHNIQUES CAN BE USED FOR EFFICIENT SECURITY IN CONJUNCTION WITH THE INTRODUCED FRAMEWORK.
THE SUCCESS OF TRADITIONAL LEARNING IS BASED ON THE INDIVIDUAL USE OF LEARNING TOOLS, DEPENDING ON THE LEARNERS' PERSONALITY. BUT MODERN ELEARNING IS DISREGARDING THIS MAJOR FACT. BY USING LEARNING ANALYTICS, THIS PAPER EXAMINES THE CORRELATION BETWEEN PERSONALITY TRAITS OF THE USER AND PREFERRED LEARNING TOOLS IN AN ELEARNING SCENARIO. THE INVESTIGATION IS BASED ON A FIELD STUDY WITH 992 PARTICIPANTS. THE PARTICIPANTS WORKED ON A COLLABORATIVE AND ONLINE-BASED SCENARIO IN WHICH THEY WERE ASSIGNED WITH A TASK IN GROUPS OF FIVE STUDENTS. THE RESULTS OF THIS RESEARCH SHOW THAT PERSONALITY TRAITS, PARTICULARLY IN CASE OF ELEARNING, HAVE A SIGNIFICANT INFLUENCE ON THE INDIVIDUAL PREFERENCE OF LEARNING TOOLS. THIS FINDING CAN BE USED TO OFFER LEARNERS INDIVIDUAL LEARNING ENVIRONMENTS.
A LEARNING ENVIRONMENT GENERATES MASSIVE KNOWLEDGE BY MEANS OF THE SERVICES PROVIDED IN MOOCS. SUCH KNOWLEDGE IS PRODUCED VIA LEARNING ACTOR INTERACTIONS. THIS RESULT IS A MOTIVATION FOR RESEARCHERS TO PUT FORWARD SOLUTIONS FOR BIG DATA USAGE, DEPENDING ON LEARNING ANALYTICS TECHNIQUES AS WELL AS THE BIG DATA TECHNIQUES RELATING TO THE EDUCATIONAL FIELD. IN THIS CONTEXT, THE PRESENT ARTICLE UNFOLDS A UNIFORM MODEL TO FACILITATE THE EXPLOITATION OF THE EXPERIENCES PRODUCED BY THE INTERACTIONS OF THE PEDAGOGICAL ACTORS. THE AIM OF PROPOSING THE SAID MODEL IS TO MAKE A UNIFIED ANALYSIS OF THE MASSIVE DATA GENERATED BY LEARNING ACTORS. THIS MODEL SUGGESTS MAKING AN INITIAL PRE-PROCESSING OF THE MASSIVE DATA PRODUCED IN AN E-LEARNING SYSTEM, AND IT'S SUBSEQUENTLY INTENDS TO PRODUCE MACHINE LEARNING, DEFINED BY RULES OF MEASURES OF ACTORS KNOWLEDGE RELEVANCE. ALL THE PROCESSING STAGES OF THIS MODEL WILL BE INTRODUCED IN AN ALGORITHM THAT RESULTS IN THE PRODUCTION OF LEARNING ACTOR KNOWLEDGE TREE.
THIS PAPER PRESENTS DATA ON THE ACADEMIC PERFORMANCE OF A PARTICULAR SET OF ACCOUNTING STUDENTS FROM THE YEAR OF INCEPTION INTO A NIGERIAN UNIVERSITY TO THE YEAR OF GRADUATION. DESCRIPTIVE ANALYSIS WAS PERFORMED ON THE DATASET AND A REGRESSION MODEL WHICH IS CAPABLE OF MAKING PREDICTIONS WAS FITTED TO THE DATASET. FROM THE DATASET, 24 OUT OF THE STUDENTS WHO STARTED WITH A FIRST CLASS RESULT (CGPA ABOVE 4.50) STILL MAINTAINED A FIRST CLASS RESULT AT GRADUATION. 4 OUT OF THE STUDENTS WHO STARTED WITH A FIRST CLASS RESULT DROPPED TO SECOND CLASS UPPER DIVISION BEFORE GRADUATION. 4 OUT OF THE STUDENTS WHO STARTED WITH A SECOND CLASS UPPER DIVISION RESULT MOVED TO FIRST CLASS RESULT BEFORE GRADUATION. 28 OUT OF 35 STUDENTS WHO STARTED WITH A SECOND CLASS UPPER DIVISION MAINTAINED A SECOND CLASS UPPER DIVISION RESULT AT GRADUATION. (C) 2018 THE AUTHORS. PUBLISHED BY ELSEVIER INC.
EVALUATION IS A KEY ELEMENT IN THE PEDAGOGICAL STRATEGY OF ALL LEARNING ENVIRONMENTS, ESPECIALLY MASSIVE OPEN ONLINE COURSES (MOOC) SINCE IT ALLOWS QUALITY IMPROVING OF EDUCATIONAL CONTENT AND TRAINING TOOLS. RECENTLY, MOOC HAVE PROVEN THE NEED FOR DIAGNOSTIC AND EVALUATION IN TERMS OF HUGE AMOUNT OF POTENTIAL DATA. IN THIS CONTEXT, THIS PRESENT PAPER DESCRIBES OUR LOGICAL STEPWISE ANALYTICAL APPROACH WHICH REPRESENTS THE PROCESS WE HAVE DEVELOPED TO EFFECTIVELY EVALUATE MOOC. THIS APPROACH IS BASED ON A COMBINATION OF LEARNING ANALYTICS AND TRACES.
THIS PAPER PRESENTS A STUDY OF TEMPORAL ANALYTICS AND DISCOURSE ANALYSIS OF AN ONLINE DISCUSSION, THROUGH INVESTIGATION OF A GROUP OF 13 IN-SERVICE TEACHERS AND 2 INSTRUCTORS. A DISCUSSION FORUM CONSISTING OF 281 POSTS ON AN ONLINE COLLABORATIVE LEARNING ENVIRONMENT WAS INVESTIGATED. A TEXT-MINING TOOL WAS USED TO DISCOVER KEYWORDS FROM THE DISCOURSE, AND THROUGH SOCIAL NETWORK ANALYSIS BASED ON THESE KEYWORDS, A SIGNIFICANT PRESENCE OF RELEVANT AND PROMISING IDEAS WITHIN DISCOURSE WAS REVEALED. HOWEVER, UNCOVERING THE KEY IDEAS ALONE IS INSUFFICIENT TO CLEARLY EXPLAIN STUDENTS' LEVEL OF UNDERSTANDING REGARDING THE DISCUSSED TOPICS. A MORE THOROUGH ANALYSIS WAS THUS PERFORMED BY USING TEMPORAL ANALYTICS WITH STEP-WISE DISCOURSE ANALYSIS TO TRACE THE IDEAS AND DETERMINE THEIR IMPACT ON COMMUNAL DISCOURSE. THE RESULTS INDICATED THAT MOST IDEAS WITHIN THE DISCOURSE COULD BE TRACED TO THE ORIGIN OF A SET OF IMPROVABLE IDEAS, WHICH IMPACTED AND ALSO INCREASED THE COMMUNITY'S LEVEL OF INTEREST IN SHARING AND DISCUSSING IDEAS THROUGH DISCOURSE.
THIS PAPER PRESENTS A SYSTEMATIC LITERATURE REVIEW OF THE STATE-OF-THE-ART OF RESEARCH ON LEARNING DASHBOARDS IN THE FIELDS OF LEARNING ANALYTICS AND EDUCATIONALDATAMINING. RESEARCH ON LEARNING DASHBOARDS AIMS TO IDENTIFY WHAT DATA ISMEANINGFUL TO DIFFERENT STAKEHOLDERS AND HOW DATA CAN BE PRESENTED TO SUPPORT SENSE-MAKING PROCESSES. LEARNING DASHBOARDS ARE BECOMING POPULAR DUE TO THE INCREASED USE OF EDUCATIONAL TECHNOLOGIES, SUCH AS LEARNINGMANAGEMENT SYSTEMS (LMS) ANDMASSIVE OPEN ONLINECOURSES (MOOCS). THE INITIAL SEARCH OF FIVEMAIN ACADEMIC DATABASES AND GSCHOLAR RESULTED IN 346 PAPERS OUT OFWHICH 55 PAPERS WERE INCLUDED IN THE FINAL ANALYSIS. OUR REVIEW DISTINGUISHES DIFFERENT KINDS OF RESEARCH STUDIES ASWELL AS VARIOUS ASPECTS OF LEARNING DASHBOARDS AND THEIRMATURITY REGARDING EVALUATION. AS THE RESEARCH FIELD IS STILL RELATIVELY YOUNG, MOST STUDIES ARE EXPLORATORY AND PROOF-OF-CONCEPT. THE REVIEW CONCLUDES BY OFFERING A DEFINITION FOR LEARNING DASHBOARDS AND BY OUTLINING OPEN ISSUES AND FUTURE LINES OFWORK IN THE AREA OF LEARNING DASHBOARDS. THERE IS A NEED FOR LONGITUDINAL RESEARCH IN AUTHENTIC SETTINGS AND STUDIES THAT SYSTEMATICALLY COMPARE DIFFERENT DASHBOARD DESIGNS.
THIS PAPER REPORTS A DESIGN-BASED RESEARCH PROJECT DEVELOPING A TOOL (THE PROCESS TAB) THAT SUPPORTS TEACHERS' INTERVENTIONS WITH STUDENTS IN VIRTUAL INTERNSHIPS. THE TOOL USES A NETWORKED APPROACH AND ALLOWS INSIGHTS INTO THE DISCOURSE OF GROUPS AND INDIVIDUALS BASED ON CONTRIBUTIONS IN CHAT FORA AND ASSIGNMENTS. IN THE PAPER, WE PRESENT THE TOOL AND REPORTS FROM INTERVIEWS WITH THREE TEACHERS WHO USED THE TOOL. THE INTERVIEWS PROVIDE INSIGHTS ABOUT THE TEACHERS' HOPES, ACTUAL USE, AND DIFFICULTIES WITH THE TOOL. THE MAIN INSIGHT IS THAT EVEN THOUGH THE TEACHERS GENUINELY LIKED THE IDEA AND SPECIFIC REPRESENTATIONS OF THE PROCESS TAB, THEIR LACK OF ABILITY TO TEACH AND LOOK AT THE TOOL AT THE SAME TIME HINDERED THEIR USE. IN THE FINAL PART OF THE PAPER, WE DISCUSS HOW TO ADDRESS THIS ISSUE.
BLENDED FORMS OF LEARNING HAVE BECOME INCREASINGLY POPULAR. HOWEVER, IT REMAINS UNCLEAR UNDER WHAT CIRCUMSTANCES BLENDED LEARNING ENVIRONMENTS ARE SUCCESSFUL. STUDIES SUGGEST THAT BLENDED LEARNING CHALLENGES LEARNERS' SELF-REGULATION. YET LITTLE IS KNOWN ABOUT WHAT SELF-REGULATORY BEHAVIOUR LEARNERS EXHIBIT IN SUCH ENVIRONMENTS. THIS LIMITED UNDERSTANDING IS PROBLEMATIC SINCE THIS INSIGHT IS NEEDED FOR EFFECTIVE DESIGNS. THEREFORE, THE AIM OF THIS STUDY WAS TO IDENTIFY LEARNERS' SELF-REGULATORY BEHAVIOUR PROFILES IN BLENDED LEARNING ENVIRONMENTS AND TO RELATE THEM TO DESIGNS OF BLENDED LEARNING ENVIRONMENTS. LEARNERS' (N = 120) SELF-REGULATORY BEHAVIOUR IN SIX ECOLOGICALLY VALID BLENDED LEARNING COURSES WAS CAPTURED. LOG FILES WERE ANALYSED IN A LEARNING ANALYTICS FASHION FOR FREQUENCY, DIVERSITY, AND SEQUENCE OF EVENTS. THREE MAIN USER PROFILES WERE IDENTIFIED. THE DESIGNS WERE DESCRIBED USING A DESCRIPTIVE FRAMEWORK CONTAINING ATTRIBUTES THAT SUPPORT SELF-REGULATION IN BLENDED LEARNING ENVIRONMENTS. RESULTS INDICATE FEWER MIS-REGULATORS WHEN MORE SELF-REGULATORY DESIGN FEATURES ARE INTEGRATED. THESE FINDING HIGHLIGHTS THE VALUE OF INTEGRATING FEATURES THAT SUPPORT SELF-REGULATION IN BLENDED LEARNING ENVIRONMENTS.
IN THIS PAPER, WE DESCRIBE THE DESIGN, DEVELOPMENT, DEPLOYMENT AND EVALUATION OF NOOBLAB, OUR NOVEL ON-LINE ENVIRONMENT FOR THE TEACHING AND LEARNING OF COMPUTER PROGRAMMING. ALTHOUGH ORIGINALLY DEVISED FOR THE TEACHING OF ELEMENTARY JAVASCRIPT, THE SYSTEM NOW ALSO SUPPORTS JAVA, PHP AND A PRESCRIPTIVE FORM OF PSEUDOCODE DESIGNED TO ASSIST IN THE TEACHING OF ELEMENTARY PROGRAMMING CONCEPTS. THE SYSTEM INCORPORATES A NUMBER OF INNOVATIVE FEATURES, SUCH AS AUTOMATED CHECKING OF PROGRAM CODE, DETECTION OF PLAGIARISM, "GAMIFICATION", AND AUTOMATICALLY LOGS ALL OF EACH USER'S INTERACTIONS WITH IT, FACILITATING PERFORMING LEARNING ANALYTICS RELATING TO STUDENT ENGAGEMENT AND PERFORMANCE. THE SYSTEM HAS ALREADY PROVED TO BE OF VALUE IN TEACHING PROGRAMMING, AND HELPING PEOPLE TO LEARN TO PROGRAM, NOT JUST TO SPECIALIST COMPUTER SCIENCE STUDENTS, BUT ALSO PEOPLE STUDYING ENGINEERING OR MATHEMATICS AS THEIR MAIN SUBJECT DISCIPLINE.
PHYSICAL LITERACY HAS GAINED MUCH POPULARITY IN EDUCATIONAL CIRCLES WHO ARE WORKING ON THE IMPROVEMENT OF CURRICULUM AND OVERALL STANDARD OF EDUCATION. IT INVOLVES A HOLISTIC LIFELONG COMPREHENSIVE LEARNING APPROACH THAT INCLUDES MOVEMENTS AND PHYSICAL ACTIVITIES. OVERALL, IT HAS POSITIVE EFFECTS ON PHYSICAL, PSYCHOLOGICAL, SOCIAL, AND COGNITIVE HEALTH OF INDIVIDUALS, SO PHYSICAL LITERACY EXEMPLIFIES THE DEDICATION TO RAISE A HEALTHIER, MORE ACTIVE GENERATION. NUMEROUS FACTORS INTERACTING BETWEEN HUMANITIES AND SOCIAL SCIENCES AFFECT THE PROMOTION OF PHYSICAL LITERACY, SO SUCH A STUDY WILL BE INTERDISCIPLINARY WHICH WILL CONSIDER ACROSS ALL SOCIAL AND INDIVIDUAL FACTORS. THE CURRENT RESEARCH PROPOSES A SYSTEM DYNAMIC "SD" MODEL TO PROMOTE STUDENTS' PHYSICAL LITERACY BY BUILDING A COMPLETE CAUSAL LOOP DIAGRAM OF THE MODEL TO ILLUSTRATE THE GENERAL SYSTEM. BASED ON THE CASUAL LOOP DIAGRAM, THE SYSTEM IS THEN PRESENTED AS FOUR SUBSYSTEMS. THE MODEL IS SIMULATED BY ALLOCATING 14 DIFFERENT CHANGES OF INDEXES IN THE PHYSICAL LITERACY PROMOTION SYSTEM TO FIND BETTER ALLOCATIONS FOR OPTIMAL EFFECTIVENESS IN PROMOTING PHYSICAL LITERACY. SIMULATIONS ARE CARRIED OUT BY USING THE APACHE SPARK ARCHITECTURE UTILIZING "BIG DATA" TOOLS FOR EFFECTIVE, SPEEDY, AND RELIABLE ANALYSIS AND RESULTS. THE STUDY PROPOSES THAT DIFFERENT PHYSICAL LITERACY INDEXES IN DIFFERENT GRADES REQUIRE ATTENTION; THE OPTIMAL PROMOTION OF PHYSICAL LITERACY CAN BE ACHIEVED BY INCREASING THE PHYSICAL KNOWLEDGE OF LOWER-GRADE STUDENTS AND INCREASING THE PHYSICAL ATTITUDE OF HIGHER-GRADE STUDENTS. THE MODEL CAN BE USED TO MAKE DECISIONS ABOUT EFFICIENT PHYSICAL LITERACY MANAGEMENT AND PHYSICAL LITERACY PROMOTION PLANNING.
INTRODUCTION OF DATA-DRIVEN TOOLS AND PRACTICES IN EDUCATION MAY IMPROVE LEARNING BUT ALSO CHANGE EDUCATION IN WAYS NOT YET FULLY UNDERSTOOD. GLOBALLY, MINISTRIES ARE NOW BEGINNING TO DEFINE BIG DATA POLICIES FOR SCHOOLS. HOW WILL THIS INFLUENCE THE WORK OF TEACHERS, AND HOW DO THEY WANT TO ENGAGE WITH THE NEW SOURCES OF INFORMATION? THIS STUDY COMPARES TWO CASES FROM NORWAY AND CHINA, TWO COUNTRIES WITH VERY DIFFERENT EDUCATIONAL SYSTEMS AND CULTURES, AND DIFFERENT IMPLEMENTATION POLICIES. IN A SURVEY, A HUNDRED PRIMARY AND SECONDARY SCHOOL TEACHERS FROM EACH COUNTRY WERE ASKED WHAT THEY WANTED TO KNOW ABOUT THEIR STUDENTS, AND WHAT DATA THEY WANTED EDUCATIONAL BIG DATA ANALYSIS TO BE BASED UPON. QUALITATIVE ANALYSIS OF LEARNING TECHNOLOGY INFRASTRUCTURE PROJECTS IN TONGZHOU DISTRICT IN BEIJING AND IN OSLO AND NATIONAL POLICIES PROVIDED A BACKDROP FOR INTERPRETING THE TEACHERS' ANSWERS. FOCUS GROUP INTERVIEWS WITH TEACHERS GAVE ADDITIONAL INSIGHTS INTO THE DIFFERENT APPROACHES TO EDUCATIONAL BIG DATA. THIS STUDY SHOWS THAT CHINESE TEACHERS HAVE A MUCH MORE OPEN MIND AND INTEREST FOR ALL INFORMATION DATA MAY PROVIDE, WHILE NORWEGIAN TEACHERS ARE MORE FOCUSSED ON KNOWLEDGE ACQUISITION AND WOULD LIKE TO LIMIT THE SCOPE TO LEARNING AND TEACHING THE CURRICULUM, BEING CONCERNED ABOUT QUESTIONS RELATED TO PRIVACY AND DATA SURVEILLANCE. SOME OF THESE DIFFERENCES COULD BE EXPLAINED BY THE DIFFERENCES IN CONTEXT OF INTRODUCING BIG DATA IN SCHOOLS. IN CHINA, ECONOMIC INNOVATION IS THE DRIVER, WHILE IN NORWAY THE PRETEXT FOR THE INTRODUCTION OF LEARNING ANALYTICS IS ADAPTED EDUCATION. THIS STUDY CONTRIBUTES TO THE KNOWLEDGE OF HOW DATA POLICIES WILL IMPACT ON DESIGN OF LEARNING ANALYTICS TOOLS AND SOLUTIONS. APPROACHES AND TECHNOLOGIES IN THIS FIELD HAVE A GLOBAL SCOPE. THEREFORE, UNDERSTANDING HOW PERSONAL DIGITAL DATA IS PERCEIVED IN DIFFERENT CULTURAL CONTEXTS IS VITAL FOR BOTH DEVELOPMENT AND IMPLEMENTATION.
NOWADAYS, FACEBOOK IS CONSIDERED LIKE AN ONLINE LEARNING TOOL. IT IS AN INTEGRAL PART OF USERS' DAILY ROUTINE, WHETHER TEACHERS OR LEARNERS. THE IDEA OF THIS ARTICLE IS TO EVALUATE LEARNERS BY COMMUNITIES IN FACEBOOK GROUPS BASED ON THEIR INTERACTIONS VIA COMMENTS AND LIKE. IN ORDER TO ACHIEVE OUR GOAL, WE WILL PROCEED IN TWO STEPS, THE FIRST ONE CONSISTS TO IMPLEMENT TWO COMMUNITY DETECTION ALGORITHMS (FORCEATLAS2 AND LOUVIAN BLONDEL), AND IN THE SECOND STEP WE WILL CALCULATE THREE MEASURES OF CENTRALITY (DEGREE, CLOSENESS, BETWEENNESS) OF THE SOCIAL LEARNING ANALYTICS PROCESS (SLA) TO IDENTIFY COMMUNITIES AT-RISK.
PURPOSE AN IDEAL LEARNING ANALYTICS TOOL FOR PROGRAMMING EXERCISES PERFORMS THE ROLE OF A LECTURER WHO MONITORS THE CODE DEVELOPMENT, PROVIDES CUSTOMIZED SUPPORT AND IDENTIFIES STUDENTS AT RISK TO DROP OUT. BUT A RELIABLE PREDICTION AND PREVENTION OF DROP-OUT IS DIFFICULT, DUE TO THE HUGE PROBLEM SPACE IN PROGRAMMING TASKS AND VARIETY OF SOLUTIONS AND PROGRAMMING STRATEGIES. THE PURPOSE OF THIS PAPER IS TO TACKLE THIS PROBLEM BY, FIRST, IDENTIFYING ACTIVITY PATTERNS THAT INDICATE STUDENTS AT RISK; AND, SECOND, FINDING REASONS BEHIND SPECIFIC ACTIVITY PATTERN, FOR IDENTIFICATION OF INSTRUCTIONAL INTERVENTIONS THAT PREVENT DROP-OUT. DESIGN/METHODOLOGY/APPROACH THE AUTHORS COMBINE TWO INVESTIGATION STRATEGIES: FIRST, LEARNING ANALYTIC TECHNIQUES (DECISION TREES) ARE APPLIED ON FEATURES GATHERED FROM STUDENTS, WHILE COMPLETING PROGRAMMING EXERCISES, IN ORDER TO CLASSIFY PREDICTORS FOR DROP-OUTS. SECOND, THE AUTHORS DETERMINE COGNITIVE, MOTIVATIONAL AND DEMOGRAPHIC LEARNER CHARACTERISTICS BASED ON A QUESTIONNAIRE. FINALLY, BOTH PARTS ARE RELATED WITH A CORRELATION ANALYSIS. FINDINGS IT WAS POSSIBLE TO IDENTIFY GENERIC VARIABLES THAT COULD PREDICT EARLY AND LATER DROP-OUTS. FOR STUDENTS WHO DROP OUT EARLY, THE MOST RELEVANT VARIABLE IS THE DELAY TIME BETWEEN AVAILABILITY OF THE ASSIGNMENT AND THE FIRST LOGIN. THE CORRELATION ANALYSIS INDICATES A RELATION WITH PRIOR PROGRAMMING EXPERIENCE IN YEARS AND JOB OCCUPATION PER WEEK. FOR STUDENTS WHO DROP OUT LATER IN THE COURSE, THE NUMBER OF ERRORS WITHIN THE FIRST ASSIGNMENT IS THE MOST RELEVANT PREDICTOR, WHICH CORRELATES WITH PRIOR PROGRAMMING SKILLS. ORIGINALITY/VALUE THE FINDINGS INDICATE A RELATION BETWEEN ACTIVITY PATTERNS AND LEARNER CHARACTERISTICS. BASED ON THE RESULTS, THE AUTHORS DEDUCE INSTRUCTIONAL INTERVENTIONS TO SUPPORT STUDENTS AND TO PREVENT DROP-OUTS.
PREDICTIVE MODELING HAS BEEN A CORE AREA OF LEARNING ANALYTICS RESEARCH OVER THE PAST DECADE, WITH SUCH MODELS CURRENTLY DEPLOYED IN A VARIETY OF EDUCATIONAL CONTEXTS FROM MOOCS TO K-12. HOWEVER, ANALYSES OF THE DIFFERENTIAL EFFECTIVENESS OF THESE MODELS ACROSS DEMOGRAPHIC, IDENTITY, OR OTHER GROUPS HAS BEEN SCARCE. IN THIS PAPER, WE PRESENT A METHOD FOR EVALUATING UNFAIRNESS IN PREDICTIVE STUDENT MODELS. WE DEFINE THIS IN TERMS OF DIFFERENTIAL ACCURACY BETWEEN SUBGROUPS, AND MEASURE IT USING A NEW METRIC WE TERM THE ABSOLUTE BETWEEN-ROC AREA (ABROCA). WE DEMONSTRATE THE PROPOSED METHOD THROUGH A GENDER-BASED LSLICING ANALYSISZ USING FIVE DIFFERENT MODELS REPLICATED FROM OTHER WORKS AND A DATASET OF 44 UNIQUE MOOCS AND OVER FOUR MILLION LEARNERS. OUR RESULTS DEMONSTRATE (1) SIGNIFICANT DIFFERENCES IN MODEL FAIRNESS ACCORDING TO (A) STATISTICAL ALGORITHM AND (B) FEATURE SET USED; (2) THAT THE GENDER IMBALANCE RATIO, CURRICULAR AREA, AND SPECIFIC COURSE USED FOR A MODEL ALL DISPLAY SIGNIFICANT ASSOCIATION WITH THE VALUE OF THE ABROCA STATISTIC; AND (3) THAT THERE IS NOT EVIDENCE OF A STRICT TRADEOFF BETWEEN PERFORMANCE AND FAIRNESS. THIS WORK PROVIDES A FRAMEWORK FOR QUANTIFYING AND UNDERSTANDING HOW PREDICTIVE MODELS MIGHT INADVERTENTLY PRIVILEGE, OR DISPARATELY IMPACT, DIFFERENT STUDENT SUBGROUPS. FURTHERMORE, OUR RESULTS SUGGEST THAT LEARNING ANALYTICS RESEARCHERS AND PRACTITIONERS CAN USE SLICING ANALYSIS TO IMPROVE MODEL FAIRNESS WITHOUT NECESSARILY SACRIFICING PERFORMANCE.(1)
THE RAPID DEVELOPMENT OF INFORMATION TECHNOLOGY HAS BROUGHT THE ERA OF BIG DATA, A BUZZWORD NOW FAMILIAR AMONG MOST PEOPLE, WHICH HAS GENERATED A HUGE INFLUENCE ON HUMAN SOCIAL LIVES INCLUDING POLITICS, ECONOMICS, CULTURE AND SO ON, WHICH HAS ALSO TRANSFORMED THE EDUCATION. WITH VARIOUS ONLINE COURSES EMERGING AT DIFFERENT KINDS OF WEBSITES, HUGE VOLUMES OF DATA OF LEARNERS HAS BEEN GENERATED, FROM ATTENDANCE RATES TO TEST SCORES, TO TRACK THE LEARNERS COMPREHENSIVELY. NOW, BY LEARNING ANALYTICS TECHNOLOGY, MANY EDUCATIONAL INSTITUTIONS USE THESE DATA TO ANALYZE STUDENTS' INTERESTS, PERFORMANCE AND BEHAVIORAL ISSUES AND THEN MAKE A PERSONALIZED PLAN ACCORDINGLY, PROMOTING THE DEVELOPMENT OF INDIVIDUAL EDUCATION. IN CHINA, LITTLE RESEARCH HAS BEEN DONE BETWEEN BIG DATA AND INDIVIDUAL EDUCATION, SO THIS PAPER AIMS TO SHOW THE OPPORTUNITIES AND CHALLENGES CREATED BY THE BIG DATA, PUT FORWARD SOME COPING STRATEGIES WHEN FACING THE CHALLENGES AND PROVIDE A REFERENCE FOR THE OTHER RESEARCHERS TO MAKE A FURTHER STUDY.
CONTEMPORARY LITERATURE ON ONLINE AND DISTANCE EDUCATION ALMOST UNEQUIVOCALLY ARGUES FOR THE IMPORTANCE OF INTERACTIONS IN ONLINE LEARNING SETTINGS. NEVERTHELESS, THE RELATIONSHIP BETWEEN DIFFERENT TYPES OF INTERACTIONS AND LEARNING OUTCOMES IS RATHER COMPLEX. ANALYZING 204 OFFERINGS OF 29 COURSES, OVER THE PERIOD OF SIX YEARS, THIS STUDY AIMED AT EXPANDING THE CURRENT UNDERSTANDING OF THE NATURE OF THIS RELATIONSHIP. SPECIFICALLY, WITH THE USE OF TRACE DATA ABOUT INTERACTIONS AND UTILIZING THE MULTILEVEL LINEAR MIXED MODELING TECHNIQUES, THE STUDY EXAMINED WHETHER FREQUENCY AND DURATION OF STUDENT STUDENT, STUDENT INSTRUCTOR, STUDENT SYSTEM, AND STUDENT CONTENT INTERACTIONS HAD AN EFFECT OF LEARNING OUTCOMES, MEASURED AS FINAL COURSE GRADES. THE FINDINGS SHOW THAT THE TIME SPENT ON STUDENT SYSTEM INTERACTIONS HAD A CONSISTENT AND POSITIVE EFFECT ON THE LEARNING OUTCOME, WHILE THE QUANTITY OF STUDENT CONTENT INTERACTIONS WAS NEGATIVELY ASSOCIATED WITH THE FINAL COURSE GRADES. THE STUDY ALSO SHOWED THE IMPORTANCE OF THE EDUCATIONAL LEVEL AND THE CONTEXT OF INDIVIDUAL COURSES FOR THE INTERACTION TYPES SUPPORTED. OUR FINDINGS FURTHER CONFIRMED THE POTENTIAL OF THE USE OF TRACE DATA AND LEARNING ANALYTICS FOR STUDYING LEARNING AND TEACHING IN ONLINE SETTINGS. HOWEVER, FURTHER RESEARCH SHOULD ACCOUNT FOR VARIOUS QUALITATIVE ASPECTS OF THE INTERACTIONS USED WHILE LEARNING, DIFFERENT PEDAGOGICAL/MEDIA FEATURES, AS WELL AS FOR THE COURSE DESIGN AND DELIVERY CONDITIONS IN ORDER TO BETTER EXPLAIN THE ASSOCIATION BETWEEN INTERACTION TYPES AND THE LEARNING ACHIEVEMENT. FINALLY, THE RESULTS MIGHT IMPLY THE NEED FOR THE DEVELOPMENT OF THE INSTITUTIONAL AND PROGRAM-LEVEL STRATEGIES FOR LEARNING AND TEACHING THAT WOULD PROMOTE EFFECTIVE PEDAGOGICAL APPROACHES TO DESIGNING AND GUIDING INTERACTIONS IN ONLINE AND DISTANCE LEARNING SETTINGS. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
WITH THE TECHNOLOGY AT THE CENTER OF MUCH UPROAR AND IMPROVISING THESE DAYS, THE APPLICATION OF BIG DATA AND BLENDED LEARNING IN THE CURRENT INDIAN EDUCATION SYSTEM IS THE NEED OF THE HOUR. IT HAS BEEN OBSERVED THAT THE DATA IS GROWING AT A FAST PACE, THEREFORE THERE MUST BE CERTAIN TECHNIQUES WHICH WILL HELP TO IMPROVE THE CONVENTIONAL TEACHING METHODS IN ORDER TO TACKLE WITH THE YOUNG MINDS CURIOSITIES. TO ADDRESS THE ISSUE, A NEW EDUCATIONAL MODEL CALLED 4-STEP-MODEL IS PROPOSED. IT CAN BE USED IN PARALLEL THAT SUPPORT BOTH THE ONLINE AND TRADITIONAL FACE-TO-FACE LEARNING. MODEL INCLUDES- PLANNING, MODELING, IMPLEMENTATION AND TESTING PHASE. IT IMPLEMENTS BIG DATA ANALYTICS AND BLENDED LEARNING METHODS TO ACCESS THE TECHNOLOGY USAGE. UTILIZES ADVANCED ANALYTICS METHOD COMBINING LEARNING ANALYTICS AND BLENDED LEARNING TECHNIQUES AIMING TO ENHANCE THE QUALITY OF EDUCATION. THE POTENTIAL OUTCOME OF THIS MODEL WILL BE IMPROVED RESULTS IN THE LEARNING BEHAVIOR OF A STUDENT SUCH AS BETTER PERFORMANCE, ENHANCED LEARNING EFFECT, AND ADVANCED GROWTH RATES. THIS UNDERLINES THE NEED AND SIGNIFICANCE OF 4SM AS AN EDUCATIONAL MODEL WITH THE TALENT ALREADY IN THE HANDS OF AN EDUCATOR AND LEARNER THAT CAN BE USED TO INCORPORATE TECHNOLOGY IN THE EDUCATIONAL BODIES.
BACKGROUND: THE COMMUNITY OF INQUIRY (COI) DESCRIBES SUCCESS FACTORS FOR ONLINE-BASED LEARNING. OBJECTIVES: TO DEVELOP APPROACHES FOR AUTOMATIC ANALYSIS OF COI TO BE VISUALIZED WITHIN STUDENT AND TEACHER DASHBOARDS. METHODS: EXTENDING INDICATORS FROM SOCIAL NETWORK ANALYSIS AND LINGUISTICS; EVALUATION WITHIN A CASE STUDY. RESULTS: THE PROJECT IS JUST STARTING. CONCLUSION: RESULTS WILL HELP TO BETTER UNDERSTAND AND IMPROVE COOPERATIVE ONLINE-BASED LEARNING IN HIGHER EDUCATION.
THE PRESENTED STUDY TRIES TO TACKLE AN INTRIGUING QUESTION OF HOW USER-GENERATED DATA FROM CURRENT TECHNOLOGIES CAN BE USED TO REINFORCE LEARNERS' REFLECTIONS, IMPROVE TEACHING PRACTICES, AND CLOSE THE LEARNING ANALYTICS LOOP. IN PARTICULAR, THE AIM OF THE STUDY IS TO UTILIZE USERS' GAZE TO EXAMINE THE ROLE OF A MIRRORING TOOL (I.E. EXERCISE VIEW IN ECLIPSE) IN ORCHESTRATING BASIC BEHAVIORAL REGULATION OF PARTICIPANTS ENGAGED IN A DEBUGGING TASK. THE RESULTS DEMONSTRATED THAT STUDENTS WHO PROCESSED THE INFORMATION PRESENTED IN THE EXERCISE VIEW AND ACTED UPON IT, IMPROVED THEIR PERFORMANCE AND ACHIEVED HIGHER LEVEL OF SUCCESS THAN THOSE WHO FAILED TO DO IT. THE FINDINGS SHED A LIGHT HOW TO CAPTURE WHAT CONSTITUTE RELEVANT DATA WITHIN A PARTICULAR CONTEXT USING GAZE PATTERNS, THAT COULD GUIDE COLLECTION OF ESSENTIAL LEARNER-CENTRED ANALYTICS FOR THE PURPOSE OF DESIGNING USABLE AND MODULAR LEARNING ENVIRONMENTS BASED ON DATA-DRIVEN APPROACHES.
PERSISTENCE HAS BEEN IDENTIFIED AS A CRUCIAL QUALITY OF LEARNING. HOWEVER, IT IS HARD TO ATTAIN IN ONLINE GAME-BASED ENVIRONMENTS AS THE DRIVE TO PROGRESS IN THE GAME MAY INFLUENCE THE ABILITY TO ACHIEVE THE LEARNING GOALS. THIS STUDY AIMED TO EXAMINE THE ASSOCIATIONS BETWEEN MICRO-PERSISTENCE, THAT IS, THE TENDENCY TO COMPLETE AN INDIVIDUAL TASK SUCCESSFULLY, AND TASK DIFFICULTY WHILE ACQUIRING COMPUTATIONAL THINKING (CT). WE FURTHER EXPLORED WHETHER CONTEXTUAL OR PERSONAL ATTRIBUTES BETTER EXPLAIN MICRO-PERSISTENCE. WE ANALYSED DATA OF 111 SCHOOL STUDENTS WHO USED THE CODEMONKEY PLATFORM. WE TOOK A LEARNING ANALYTICS APPROACH FOR ANALYSING THE PLATFORM'S LOG FILES. WE FOUND THAT MICRO-PERSISTENCE IS ASSOCIATED WITH TASK DIFFICULTY AND THAT STUDENTS WHO DEMONSTRATED AN APTITUDE TO LEARN NEW MATERIAL ARE MOTIVATED TO ACHIEVE THE BEST SOLUTION. WE ALSO FOUND THAT CONTEXTUAL VARIABLES BETTER-EXPLAINED MICRO-PERSISTENCE THAN PERSONAL ATTRIBUTES. ENCOURAGING MICRO-PERSISTENCE CAN IMPROVE CT ACQUISITION AND THE LEARNING PROCESSES INVOLVED.
SCHOLARS HAVE EXAMINED HOW STUDENTS FEEL, BEHAVE, AND THINK WHEN ENGAGED IN ASYNCHRONOUS ONLINE DISCUSSION (AOD), WHICH HAS BECOME A PREVALENT MEANS OF COLLABORATIVE LEARNING IN HIGHER EDUCATION. HOWEVER, FEW HAVE CONSIDERED THE IMPACT OF TIME ON THESE LEARNING-RELATED DIMENSIONS, PARTLY DUE TO RELIANCE ON SELF-REPORTED INSTRUMENTS THAT ARE NOT SUFFICIENT TO MEASURE THE TEMPORAL, CHANGEABLE, AND ITERATIVE RELATIONSHIPS AMONG LEARNING-RELATED DIMENSIONS DURING ONLINE CONVERSATION. THE PURPOSE OF THE CURRENT STUDY WAS TO USE LEARNING ANALYTICS TO INVESTIGATE LONGITUDINALLY THE RELATIONSHIPS AMONG LEARNING-RELATED EMOTIONS, LEARNER PARTICIPATION LEVEL, AND COGNITIVE EFFORT OF STUDENTS. ANALYSIS OF SYSTEM-STORED STUDENT DATA FROM 56 STUDENTS ENROLLED IN A WIKI-BASED, BLENDED UNDERGRADUATE WRITING COURSE REVEALED THAT POSITIVE OR NEGATIVE EXPRESSED EMOTIONS LED TO HIGHER PARTICIPATION IN ONGOING DISCUSSIONS AND THAT PARTICIPATION LEVEL PREDICTED THE RATE OF CHANGE IN TEXTUAL REVISION. THESE FINDINGS NOT ONLY CONFIRM THEORIES OF LEARNER ENGAGEMENT BUT ALSO DEMONSTRATE THE VIABILITY OF TRACE DATA AND THEIR POTENTIAL IN EDUCATIONAL RESEARCH AND PRACTICE.
INTEREST IN TEMPORAL ANALYTICS ANALYTICS THAT PROBE TEMPORAL ASPECTS OF LEARNING SO AS TO GAIN INSIGHTS INTO THE PROCESSES THROUGH WHICH LEARNING OCCURS CONTINUES TO GROW. THE RELATIONSHIPS OF TEMPORAL PATTERNS TO LEARNING OUTCOMES IS A CENTRAL AREA OF INTEREST. HOWEVER, WHILE THE LITERATURE ON TEMPORAL ANALYSES IS DEVELOPING, THERE HAS BEEN LESS CONSIDERATION OF THE METHODS BY WHICH TEMPORAL ANALYSES MIGHT BE TRANSLATED TO ACTIONABLE INSIGHTS AND THUS, PUT INTO USE IN EDUCATIONAL PRACTICE. EMERGING TEMPORAL ANALYSIS TECHNIQUES PRESENT BOTH THEORETICAL AND PRACTICAL CHALLENGES FOR PRODUCING AND INTERPRETING RESULTS. SYNERGETIC ACTIONS ARE NEEDED IN ORDER TO SUPPORT PRACTITIONERS.
PRACTICAL ENVIRONMENTS ARE KEY ELEMENTS WITHIN THE LEARNING PROCESS OF ANY ENGINEERING SUBJECT. THEY ARE USED SO THAT STUDENTS LEARN PRACTICAL COMPETENCES, AND TO SUPPORT THEIR DEVELOPMENT OF AN APPLIED CRITICAL THINKING. PARTICULARLY, WHEN IT COMES TO A DISTANCE METHODOLOGY, THE EMPLOYMENT OF THESE TOOLS IS VERY RELEVANT, SINCE STUDENTS ARE NOT FACE-TO-FACE IN CLASSROOM. IN THIS SENSE, THE USE OF LEARNING ANALYTICS (LA) TECHNIQUES BECOMES EVEN MORE ESSENTIAL FOR IMPROVING THE LEARNING/TEACHING PROCESS. THIS WORK FOCUSES ON THE PROPOSAL AND ANALYSIS OF A CONTAINER-BASED VIRTUAL LABORATORY FOR CYBERSECURITY ACTIVITIES, FROM THE POINT OF VIEW OF THE STUDENTS' BEHAVIOR AND THEIR OUTCOMES. IN ADDITION TO THIS, THE FACTORS THAT INFLUENCE THE STUDENTS' ACCEPTANCE IN THE SUBJECT ARE ANALYZED. THE STUDY HAS BEEN COMPARATIVELY PERFORMED WITH A PREVIOUS ACADEMIC YEAR.
THIS WORK IN PROGRESS PROPOSES A SMART SCHOOLHOUSE APPROACH TO STEM EDUCATION IN PRIMARY SCHOOLS THROUGH ENGAGING LEARNERS IN INQUIRY ON THEIR SURROUNDINGS IN A DATA-RICH PHYSICAL AND DIGITAL ENVIRONMENT. THE PAPER SUMMARIZES THE PROCESS AND OUTCOMES OF AN INTERVIEW WITH A GROUP OF EXPERTS ON REQUIREMENTS ASSESSMENT FOR SETTING UP DATA-RICH SCHOOL BUILDINGS. EXPERTS FROM VARIOUS FIELDS (BUSINESS INFORMATICS, COMPUTER SCIENCE, ELECTRONICS, LEARNING ANALYTICS AND STEM PEDAGOGY) WERE ENGAGED IN A FOCUS GROUP WITH A TWOFOLD PURPOSE: TO IDENTIFY THE SETBACKS OF USING DIFFERENT SENSORS FOR DATA COLLECTION IN THE PHYSICAL LEARNING ENVIRONMENT; AND TO CLASSIFY THE POSSIBLE TYPES OF DATA COLLECTION TECHNOLOGIES. A SMALL RURAL SCHOOL HAS BEEN CHOSEN FOR PILOTING THE PROPOSED TECHNOLOGICAL AND PEDAGOGICAL SETUP AS AN EXPERIMENTAL SCHOOL PROVIDING IN-DEPTH INQUIRY-BASED LEARNING OF STEM SUBJECTS, USING THE SCHOOLHOUSE ITSELF AS AN OBJECT AND AN INSTRUMENT OF STUDY.
WE HAVE BEEN THE WITNESSES OF THE EXPONENTIAL GROWTH OF DATA IN THE LAST FEW DECADES. IN THE EDUCATIONAL FIELD, THE SOURCE OF DATA IS THE LEARNING AND TEACHING PROCESSES REALIZING IN THE VIRTUAL LEARNING ENVIRONMENTS. THESE ENVIRONMENTS STORE THE DATA IN PERSISTENT DATABASE LAYERS. WE CAN ANALYSE THESE DATA USING CONVENTIONAL STATISTICAL METHODS OR UTILIZING THE ADVANCED DATA MINING METHODS. IN THIS PAPER, WE FOCUS ON DIFFERENT WAYS HOW TO ANALYSE THESE DATA. WE PRESENT PROS AND CONS OF COMMONLY USED APPROACHES AND ANALYTICAL METHODS. WE SUMMARISE THE POSSIBILITIES AND WEAKNESSES OF INTEGRATED TOOLS THAT CAN HELP US TO ANALYSE LEARNING ENVIRONMENTS STAKEHOLDERS' BASIC ACTIVITY, BEHAVIOUR OR PERFORMANCE. CONSIDERING THE DIRECT DATA ANALYSIS, WHICH IS AVAILABLE ON THE APPLICATION OR DATABASE LAYER OF THE VIRTUAL LEARNING ENVIRONMENTS, HAS STRONG LIMITATIONS, WE INTRODUCE RELATIVELY NEW, CLOSELY RELATED, CONTEMPORARY RESEARCH AREAS - EDUCATIONAL DATA MINING AND LEARNING ANALYTICS. WE TAKE A LOOK AT THEM FROM THE PERSPECTIVE OF THE UNIVERSITY ENVIRONMENT. WE CONCLUDE THAT BOTH AREAS ARE VERY PROMISING, BUT WE HAVE NOT BEEN ABLE TO GENERALIZE OUR FINDINGS YET, BECAUSE THERE HAS NOT BEEN REALISED A SYSTEMATIC RESEARCH ON THE APPLICATION AND IMPACT OF THESE RESEARCH DISCIPLINES.
ASSESSMENT IN HIGHER EDUCATION HAS FOCUSED ON THE PERFORMANCE OF INDIVIDUAL STUDENTS. THIS FOCUS HAS BEEN A PRACTICAL AS WELL AS AN EPISTEMIC ONE: METHODS OF ASSESSMENT ARE CONSTRAINED BY THE TECHNOLOGY OF THE DAY, AND IN THE PAST THEY REQUIRED THE COMPLETION BY INDIVIDUALS UNDER CONTROLLED CONDITIONS OF SET-PIECE ACADEMIC EXERCISES. RECENT ADVANCES IN LEARNING ANALYTICS, DRAWING UPON VAST SETS OF DIGITALLY STORED STUDENT ACTIVITY DATA, OPEN NEW PRACTICAL AND EPISTEMIC POSSIBILITIES FOR ASSESSMENT, AND CARRY THE POTENTIAL TO TRANSFORM HIGHER EDUCATION. IT IS BECOMING PRACTICABLE TO ASSESS THE INDIVIDUAL AND COLLECTIVE PERFORMANCE OF TEAM MEMBERS WORKING ON COMPLEX PROJECTS THAT CLOSELY SIMULATE THE PROFESSIONAL CONTEXTS THAT GRADUATES WILL ENCOUNTER. IN ADDITION TO ACADEMIC KNOWLEDGE, THIS AUTHENTIC ASSESSMENT CAN INCLUDE A DIVERSE RANGE OF PERSONAL QUALITIES AND DISPOSITIONS THAT ARE KEY TO THE COMPUTER-SUPPORTED COOPERATIVE WORKING OF PROFESSIONALS IN THE KNOWLEDGE ECONOMY. THIS PAPER EXPLORES THE IMPLICATIONS OF SUCH OPPORTUNITIES FOR THE PURPOSE AND PRACTICES OF ASSESSMENT IN HIGHER EDUCATION, AS UNIVERSITIES ADAPT THEIR INSTITUTIONAL MISSIONS TO ADDRESS TWENTY-FIRST CENTURY NEEDS. THE PAPER CONCLUDES WITH A STRONG RECOMMENDATION FOR UNIVERSITY LEADERS TO DEPLOY ANALYTICS TO SUPPORT AND EVALUATE THE COLLABORATIVE LEARNING OF STUDENTS WORKING IN REALISTIC CONTEXTS.
LEARNER MODELING SYSTEMS SO FAR FORMULATED MODEL LEARNING IN THREE MAIN WAYS: A LEARNER'S "POSITION" WITHIN A LATTICE OF DECLARATIVE AND PROCEDURAL KNOWLEDGE ABOUT HIGHLY STRUCTURED DISCIPLINES SUCH AS GEOMETRY OR PHYSICS, A LEARNER'S PATH THROUGH CURRICULAR TASKS COMPARED TO MILESTONES, OR PROFILES OF A LEARNER'S ACHIEVEMENTS ON A SET OF TASKS RELATIVE TO MASTERY CRITERIA OR A PEER GROUP. OPENING THESE MODELS TO LEARNERS IDENTIFIES FOR THEM FACTORS AND RELATIONS AMONG FACTORS. OPEN LEARNER MODELS TACITLY INVITE LEARNERS TO REGULATE LEARNING. HOWEVER, CONTEMPORARY LEARNER MODELS OMIT DATA ABOUT HOW LEARNERS HAVE AND SHOULD PROCESS INFORMATION TO LEARN, UNDERSTAND, CONSOLIDATE AND TRANSFER NEW KNOWLEDGE AND SKILLS. WHAT TO DO WITH INFORMATION OPENED TO LEARNERS IS OPAQUE. I PROPOSE INCORPORATING TRACE DATA ABOUT LEARNING PROCESSES IN LEARNER MODELS. TRACE DATA ALLOW GENERATING LEARNING ANALYTICS THAT INFORM SELF-REGULATING LEARNERS ABOUT POTENTIALLY PRODUCTIVE ADAPTATIONS TO PROCESSES THEY HAVE USED TO LEARN. IN A CONTEXT OF BIG DATA, SUCH ELABORATED LEARNER MODELS ARE POSITIONED TO COLLABORATE WITH SELF-REGULATING LEARNERS. TOGETHER, THEY CAN COORDINATE SYMBIOTICALLY, CREATING A PLATFORM FOR THE SYSTEM TO IMPROVE ITS MODELS OF LEARNERS AND FOR LEARNERS TO MORE PRODUCTIVELY SELF-REGULATE LEARNING.
DIGITAL GAMES FOR LEARNING ARE ONE OF THE MOST PROMINENT EXAMPLES OF THE USE OF TECHNOLOGIES IN THE CLASSROOM, WHERE NUMEROUS STUDIES HAVE PRESENTED PROMISING RESULTS AMONG CHILDREN AND ADOLESCENTS. HOWEVER, SCARCE EVIDENCE EXISTS REGARDING DIFFERENT WAYS OF IMPLEMENTING GAMES WITHIN THE CLASSROOM AND HOW THOSE AFFECT STUDENTS' LEARNING AND BEHAVIORS. IN THIS STUDY WE EXPLORE THE EFFECT THAT COLLABORATION CAN HAVE IN DIGITAL GAMEPLAY IN A K12 CONTEXT. MORE SPECIFICALLY, WE HAVE DESIGNED A 2 X 2 EXPERIMENTAL STUDY IN WHICH HIGH SCHOOL FIRST YEAR STUDENTS PARTICIPATED IN SOLO OR COLLABORATIVE GAMEPLAY IN PAIRS, SOLVING PUZZLES OF DIVERSE DIFFICULTY, USING SHADOWSPECT, A DIGITAL GAME ON GEOMETRY. OUR MAIN RESULTS, COMPUTED BY APPLYING LEARNING ANALYTICS ON THE TRACE DATA RESULTS, SUGGEST THAT STUDENTS PLAYING SOLO HAD HIGHER IN-GAME ENGAGEMENT AND SOLVED MORE PUZZLES, WHILE STUDENTS COLLABORATING WERE LESS LINEAR IN THEIR PATHWAYS, SKIPPING MORE TUTORIAL LEVELS AND WERE MORE EXPLORATORY WITH SHADOWSPECT FEATURES. THESE SIGNIFICANT DIFFERENCES THAT WE OBSERVE IN SOLO AND COLLABORATIVE GAMEPLAY CALL FOR MORE EXPERIMENTATION AROUND THE EFFECT OF HAVING K12 STUDENTS COLLABORATE ON DIGITAL TASKS, SO THAT TEACHERS CAN TAKE BETTER DECISIONS ABOUT HOW TO IMPLEMENT THESE PRACTICES IN THE CLASSROOMS OF THE FUTURE.
LEARNING IN MASSIVE OPEN ONLINE COURSES (MOOCS) REQUIRES LEARNERS TO SELF-REGULATE THEIR LEARNING PROCESS OR RECEIVE EFFECTIVE SELF-REGULATED LEARNING (SRL) INTERVENTIONS TO ACCOMPLISH PERSONAL GOALS. MUCH ATTENTION HAS THUS BEEN PAID TO HOW SRL INFLUENCES LEARNER PERFORMANCE IN MOOCS, BUT RESEARCH HAS OVERLOOKED A PERSON-CENTERED ANALYSIS OF HOW ONLINE LEARNERS PERFORM SRL IN THIS SETTING. WITHOUT UNDERSTANDING THIS INDIVIDUAL DIFFERENCE, EDUCATORS ARE UNLIKELY TO PROVIDE EFFECTIVE SRL INTERVENTIONS TAILORED TO EACH TYPE OF SELF-REGULATED LEARNER. IN ADDITION, IT REMAINS UNCERTAIN HOW CULTURE SHAPES THE DIFFERENCES IN SRL TRACES, ESPECIALLY GIVEN THAT MOST OF THE EXISTING UNDERSTANDINGS OF SRL ARE ROOTED IN WESTERN CULTURES. TO FILL THE GAPS, THIS RESEARCH APPLIED LEARNING ANALYTICS TO EXPLORE LEARNER PROFILES IN TERMS OF HOW THEY PERFORMED SRL IN MOOCS. USING K-MEANS CLUSTERING ANALYSIS, THIS RESEARCH REVEALED FOUR DIFFERENT SELF-REGULATED LEARNER PROFILES: ALL-AROUND SRL LEARNERS, DISILLUSIONED SRL LEARNERS, CONTROL-ORIENTED SRL LEARNERS, AND CONTROL-DOMINATED SRL LEARNERS. IN ADDITION, ALL-AROUND SRL LEARNERS OUTPERFORMED THE OTHER THREE CLUSTERS IN COURSE GRADES. THIS RESEARCH ALSO IDENTIFIED CULTURAL DIFFERENCES BETWEEN THOSE CLUSTERS. PRACTICAL IMPLICATIONS ON HOW TO DESIGN EFFECTIVE SRL INTERVENTIONS ARE PROVIDED.
IN RECENT YEARS, WORK WITH EDUCATIONAL TESTING DATA HAS CHANGED DUE TO THE AFFORDANCES PROVIDED BY TECHNOLOGY, THE AVAILABILITY OF LARGE DATA SETS, AND BY THE ADVANCES MADE IN DATA MINING AND MACHINE LEARNING. CONSEQUENTLY, DATA ANALYSIS HAS MOVED FROM TRADITIONAL PSYCHOMETRICS TO COMPUTATIONAL PSYCHOMETRICS. DESPITE ADVANCES IN THE METHODOLOGY AND THE AVAILABILITY OF THE LARGE DATA SETS COLLECTED AT EACH ADMINISTRATION, THE WAY ASSESSMENT DATA IS COLLECTED, STORED, AND ANALYZED BY TESTING ORGANIZATIONS IS NOT CONDUCIVE TO THESE REAL-TIME, DATA INTENSIVE COMPUTATIONAL METHODS THAT CAN REVEAL NEW PATTERNS AND INFORMATION ABOUT STUDENTS. IN THIS PAPER, WE PROPOSE A NEW WAY TO LABEL, COLLECT, AND STORE DATA FROM LARGE SCALE EDUCATIONAL LEARNING AND ASSESSMENT SYSTEMS (LAS) USING THE CONCEPT OF THE "DATA CUBE." THIS PARADIGM WILL MAKE THE APPLICATION OF MACHINE-LEARNING, LEARNING ANALYTICS, AND COMPLEX ANALYSES POSSIBLE. IT WILL ALSO ALLOW FOR STORING THE CONTENT FOR TESTS (ITEMS) AND INSTRUCTION (VIDEOS, SIMULATIONS, ITEMS WITH SCAFFOLDS) AS DATA, WHICH OPENS UP NEW AVENUES FOR PERSONALIZED LEARNING. THIS DATA PARADIGM WILL ALLOW US TO INNOVATE AT A SCALE FAR BEYOND THE HYPOTHESIS-DRIVEN, SMALL-SCALE RESEARCH THAT HAS CHARACTERIZED EDUCATIONAL RESEARCH IN THE PAST.
UNDERSTANDING WHY AND HOW STUDENTS INTERACT WITH EDUCATIONAL VIDEOS IS ESSENTIAL TO FURTHER IMPROVE THE QUALITY OF MASSIVE ONLINE OPEN COURSES (MOOCS). IN THIS PAPER, WE LOOK AT THE COMPLEXITY OF VIDEOS TO EXPLAIN TWO RELATED ASPECTS OF STUDENT BEHAVIOR: THE DWELLING TIME (HOW MUCH TIME STUDENTS SPEND WATCHING A VIDEO) AND THE DWELLING RATE (HOW MUCH OF THE VIDEO THEY ACTUALLY SEE). BUILDING ON A STRONG TRADITION OF PSYCHOLINGUISTICS, WE FORMALIZE A DEFINITION FOR INFORMATION COMPLEXITY IN VIDEOS. FURTHERMORE, BUILDING ON RECENT ADVANCEMENTS IN TIME-ON-TASK MEASURES WE FORMALIZE DWELLING TIME AND DWELLING RATE BASED ON CLICK-STREAM TRACE DATA. THE RESULTING COMPUTATIONAL MODEL OF VIDEO COMPLEXITY EXPLAINS 22.44% OF THE VARIANCE IN THE DWELLING RATE FOR STUDENTS THAT FINISH WATCHING A PARAGRAPH OF A VIDEO. VIDEO COMPLEXITY AND STUDENT DWELLING SHOW A POLYNOMIAL RELATIONSHIP, WHERE BOTH LOW AND HIGH COMPLEXITY INCREASES DWELLING. THESE RESULTS INDICATE WHY STUDENTS SPEND MORE TIME WATCHING (AND POSSIBLY CONTEMPLATING ABOUT) A VIDEO. FURTHERMORE, THEY SHOW THAT EVEN FAIRLY STRAIGHTFORWARD PROXIES OF STUDENT BEHAVIOR SUCH AS DWELLING CAN ALREADY HAVE MULTIPLE INTERPRETATIONS; ILLUSTRATING THE CHALLENGE OF SENSE-MAKING FROM LEARNING ANALYTICS.
THE SPEED AT WHICH NEW SCIENTIFIC PAPERS ARE PUBLISHED HAS INCREASED DRAMATICALLY, WHILE THE PROCESS OF TRACKING THE MOST RECENT PUBLICATIONS HAVING A HIGH IMPACT HAS BECOME MORE AND MORE CUMBERSOME. IN ORDER TO SUPPORT LEARNERS AND RESEARCHERS IN RETRIEVING RELEVANT ARTICLES AND IDENTIFYING THE MOST CENTRAL RESEARCHERS WITHIN A DOMAIN, WE PROPOSE A NOVEL 2-MODE MULTILAYERED GRAPH DERIVED FROM COHESION NETWORK ANALYSIS (CNA). THE RESULTING EXTENDED CNA GRAPH INTEGRATES BOTH AUTHORS AND PAPERS, AS WELL AS THREE PRINCIPAL LINK TYPES: CO-AUTHORSHIP, CO-CITATION, AND SEMANTIC SIMILARITY AMONG THE CONTENTS OF THE PAPERS. OUR RANKINGS DO NOT RELY ON THE NUMBER OF PUBLISHED DOCUMENTS, BUT ON THEIR GLOBAL IMPACT BASED ON LINKS BETWEEN AUTHORS, CITATIONS, AND SEMANTIC RELATEDNESS TO SIMILAR ARTICLES. AS A PRELIMINARY VALIDATION, WE HAVE BUILT A NETWORK BASED ON THE 2013 LAK DATASET IN ORDER TO REVEAL THE MOST CENTRAL AUTHORS WITHIN THE EMERGING LEARNING ANALYTICS DOMAIN.
THROUGH CLEVER USE OF DIGITAL TECHNOLOGIES GOVERNMENT AGENCIES MAINTAIN KNOWLEDGEABLE WELL SKILLED EMPLOYEES TO SUSTAIN THEIR COMPETITIVE ADVANTAGE. AN EXPERIMENTAL RESEARCH STUDY WAS DESIGNED TO INVESTIGATE THE INTERACTIVE EFFECT OF INSTRUCTIONAL STRATEGIES AUGMENTED WITH EITHER A DIGITAL INSTRUCTIONAL ASSISTANT OR A TRADITIONAL CLASS-ROOM TUTOR, AND PARTICIPANTS' PREFERENCE FOR TRAINING-MODE ON THE ACQUISITION OF INTRODUCTORY ETHICS KNOWLEDGE. THE OBJECT-SPATIAL IMAGERY AND VERBAL QUESTIONNAIRE (OSIVQ) WAS USED TO ESTABLISH THE PARTICIPANTS' COGNITIVE STYLE [1]. THE QUEST INTERACTIVE TEST ANALYSIS SYSTEM PROVIDED THE COGNITIVE PERFORMANCE MEASURING TOOL [2], TO DEFINE THE LEARNING ANALYTICS AND TO ENSURE THERE WERE NO MEASUREMENT ERRORS IN THE INTRODUCTORY ETHICS KNOWLEDGE TESTING INSTRUMENTS. THEREFORE, RELIABILITY OF THE TESTING TOOLS WAS SECURED THROUGH THE QUEST CALIBRATION TECHNIQUES, THEREBY SAFEGUARDING THE PREDICTABILITY OF THE RESEARCH DESIGN. THE METHODOLOGY EMBRACED BY THIS EXPERIMENTAL RESEARCH LINKS THE DISCIPLINES OF INSTRUCTIONAL SCIENCE, COGNITIVE PSYCHOLOGY AND OBJECTIVE MEASUREMENT TO AUTHENTICATE VALUABLE MECHANISMS FOR ADOPTION BY THE EDUCATION, TRAINING AND DEVELOPMENT SECTORS.
PRIOR RESEARCH OFFERS EVIDENCE THAT DIFFERING LEVELS OF STUDENT ENGAGEMENT ARE ASSOCIATED WITH DIFFERENT OUTCOMES IN TERMS OF PERFORMANCE. IN THIS STUDY, WE INVESTIGATING THE EFFICACY OF A MODEL OF BEHAVIOURAL AND AGENTIC ENGAGEMENT TO PREDICT STUDENT PERFORMANCE (LOW, MIDDLE, HIGH) AT FOUR TIMEPOINTS IN A SEMESTER. THE MODEL WAS SIGNIFICANT AT ALL FOUR TIMEPOINTS. MEASURES OF BEHAVIOURAL AND AGENTIC ENGAGEMENT PREDICTED MEMBERSHIP ACROSS THE THREE GROUPS DIFFERENTLY. WITH A FEW EXCEPTIONS, THESE DIFFERENCES WERE CONSISTENT ACROSS TIMEPOINTS. LOOKING AT VARIATIONS IN STUDENT ENGAGEMENT ACROSS TIME CAN BE USED TO TARGET INTERVENTIONS TO SUPPORT STUDENT SUCCESS AT THE UNDERGRADUATE LEVEL.
NOTIONS OF WHAT CONSTITUTES QUALITY IN DESIGN IN TRADITIONAL ON-CAMPUS OR ONLINE TEACHING AND LEARNING MAY NOT ALWAYS TRANSLATE INTO SCALED DIGITAL ENVIRONMENTS. THE DESIGNLAK17 WORKSHOP BUILDS ON THE DESIGNLAK16 WORKSHOP TO EXPLORE ONE ASPECT OF THIS THEME, NAMELY THE OPPORTUNITIES ARISING FROM THE USE OF ANALYTICS IN SCALED ASSESSMENT DESIGN. NEW PARADIGMS FOR LEARNING DESIGN ARE EXPLOITING THE DISTINCTIVE CHARACTERISTICS AND POTENTIALS OF ANALYTICS, TRACE DATA AND NEWER KINDS OF SENSORY DATA USABLE ON DIGITAL PLATFORMS TO TRANSFORM ASSESSMENT. BUT, CHARACTERISTICS OF QUALITY ASSESSMENT DESIGN NEED TO BE RECONSIDERED, AND NEW METRICS FOR CAPTURING QUALITY ARE REQUIRED. THIS SYMPOSIUM AND WORKSHOP FOCUSES ON WHAT MIGHT BE APPROPRIATE QUALITY METRICS AND INDICATORS FOR ASSESSMENT DESIGN IN SCALED LEARNING. IT AIMS TO BUILD A COMMUNITY OF INTEREST ROUND THE TOPIC, TO SHARE PERSPECTIVES, AND TO GENERATE DESIGN AND RESEARCH IDEAS.
IN THIS STUDY, WE PRESENT A CASE STUDY INVOLVING TWO SELF-SERVICE DASHBOARDS PROVIDING FEEDBACK ON LEARNING AND STUDY SKILLS AND ON ACADEMIC ACHIEVEMENT. THESE DASHBOARDS WERE OFFERED TO FIRST-YEAR UNIVERSITY STUDENTS IN SEVERAL STUDY PROGRAMMES IN FLANDERS, BELGIUM. DATA FOR THIS STUDY WERE COLLECTED USING USAGE TRACKING (N = 2875) AND A SURVEY TAKEN AT THE BEGINNING OF THE SECOND YEAR BEFORE (N = 484) AND AFTER (N = 538) THE INTRODUCTION OF THE DASHBOARDS. WE FOUND THAT EARLY DASHBOARD USAGE IS RELATED TO ACADEMIC ACHIEVEMENT LATER IN THE ACADEMIC YEAR AND THAT STUDENTS' REVIEW OF THE FEEDBACK RECEIVED IN THE FIRST YEAR IMPROVED. ALTHOUGH THESE RESULTS ARE MODEST IN COMPARISON TO HOW HIGH THE BAR IS SOMETIMES SET FOR LEARNING ANALYTICS APPLICATIONS, WE ARGUE THAT LOW-COST DEPLOYMENTS OF SELF-SERVICE DASHBOARDS ARE AN INTERESTING APPROACH TO START BUILDING EXPERIENCE WITH SIMILAR TOOLS AND TO START PAVING THE WAY FOR FUTURE DEVELOPMENTS.
LEARNING ANALYTICS FOCUSES ON EXTRACTING MEANING FROM LARGE AMOUNTS OF DATA. ONE OF THE LARGEST DATASETS IN EDUCATION COMES FROM MASSIVE OPEN ONLINE COURSES (MOOCS) THAT TYPICALLY FEATURE ENROLLMENTS IN THE TENS OF THOUSANDS. ANALYZING MOOC DISCUSSION FORUMS PRESENTS LOGISTICAL ISSUES, RESULTING CHIEFLY FROM THE SIZE OF THE DATASET, WHICH CAN CREATE CHALLENGES FOR UNDERSTANDING AND ADEQUATELY DESCRIBING STUDENT BEHAVIORS. UTILIZING AUTOMATIC TEXT ANALYSIS, THIS STUDY BUILT A HIERARCHICAL LINEAR MODEL THAT EXAMINES THE INFLUENCE OF THE PACING CONDITION OF A MASSIVE OPEN ONLINE COURSE (MOOC), WHETHER IT IS SELF-PACED OR INSTRUCTOR-PACED, ON THE DEMONSTRATION OF COGNITIVE PROCESSING IN A HARVARDX MOOC. THE ANALYSIS OF 2,423 DISCUSSION POSTS GENERATED BY 671 STUDENTS REVEALED THE NUMBER OF DICTIONARY WORDS USED WERE POSITIVELY ASSOCIATED WITH COGNITIVE PROCESSING WHILE ANALYTICAL THINKING AND CLOUT WAS NEGATIVELY ASSOCIATED. WE FOUND THAT NONE OF THE STUDENT BACKGROUND INFORMATION (GENDER, EDUCATION), STATUS OF THE COURSE ENGAGEMENT (EXPLORED OR COMPLETED), OR THE COURSE PACE (SELF-PACED VERSUS INSTRUCTOR PACED) SIGNIFICANTLY INFLUENCED THE COGNITIVE PROCESSING OF THE POSTINGS.
CAPTURING STUDENTS' BEHAVIORAL PATTERNS THROUGH ANALYSIS OF SEQUENTIAL INTERACTION LOGS IS AN IMPORTANT TASK IN EDUCATIONAL DATA MINING AND COULD ENABLE MORE EFFECTIVE AND PERSONALIZED SUPPORT DURING THE LEARNING PROCESSES. THIS STUDY AIMS AT DISCOVERY AND TEMPORAL ANALYSIS OF LEARNERS' STUDY PATTERNS IN MOOC ASSESSMENT PERIODS. WE PROPOSE TWO DIFFERENT METHODS TO ACHIEVE THIS GOAL. FIRST, FOLLOWING A HYPOTHESIS-DRIVEN APPROACH, WE IDENTIFY LEARNERS' STUDY PATTERNS BASED ON THEIR INTERACTION WITH LECTURES AND ASSIGNMENTS. THROUGH CLUSTERING OF STUDY PATTERN SEQUENCES, WE CAPTURE DIFFERENT LONGITUDINAL ACTIVITY PROFILES AMONG LEARNERS AND DESCRIBE THEIR PROPERTIES. SECOND, WE PROPOSE A TEMPORAL CLUSTERING PIPELINE FOR UNSUPERVISED DISCOVERY OF LATENT PATTERNS IN LEARNERS' INTERACTION DATA. WE MODEL AND CLUSTER ACTIVITY SEQUENCES AT EACH TIME STEP AND PERFORM CLUSTER MATCHING TO ENABLE TRACKING LEARNING BEHAVIOURS OVER TIME. OUR PROPOSED PIPELINE IS GENERAL AND APPLICABLE IN DIFFERENT LEARNING ENVIRONMENTS SUCH AS MOOC AND ITS. MOREOVER, IT ALLOWS FOR MODELING AND TEMPORAL ANALYSIS OF INTERACTION DATA AT DIFFERENT LEVELS OF ACTIONS GRANULARITY AND TIME RESOLUTION. WE DEMONSTRATE THE APPLICATION OF THIS METHOD FOR DETECTING LATENT STUDY PATTERNS IN A MOOC COURSE.
NO COURSE EXISTS IN ISOLATION, SO EXAMINING STUDENT PROGRESSION THROUGH COURSES WITHIN A BROADER PROGRAM CONTEXT IS AN IMPORTANT STEP IN INTEGRATING COURSE-LEVEL AND PROGRAM-LEVEL ANALYTICS. INTEGRATION IN THIS MANNER ALLOWS US TO SEE THE IMPACT OF COURSE-LEVEL CHANGES TO THE PROGRAM, AS WELL AS IDENTIFY POINTS IN THE PROGRAM STRUCTURE WHERE COURSE INTERVENTIONS ARE MOST IMPORTANT. HERE WE HIGHLIGHT THE SIGNIFICANCE OF PROGRAM-LEVEL LEARNING ANALYTICS, WHERE THE RELATIONSHIPS BETWEEN COURSES BECOME CLEAR, AND THE IMPACT OF EARLY-STAGE COURSES ON PROGRAM OUTCOMES SUCH AS GRADUATION OR DROP-OUT CAN BE UNDERSTOOD. WE PRESENT A MATRIX MODEL OF STUDENT PROGRESSION THROUGH A PROGRAM AS A TOOL TO GAIN VALUABLE INSIGHT INTO PROGRAM CONTINUITY AND DESIGN. WE DEMONSTRATE ITS USE IN A REAL PROGRAM AND EXAMINE THE IMPACT UPON PROGRESSION AND GRADUATION RATE IF COURSE-LEVEL CHANGES WERE MADE EARLY ON. WE ALSO EXTEND THE MODEL TO MORE COMPLEX SCENARIOS SUCH AS MULTIPLE PROGRAM PATHWAYS AND SIMULTANEOUS COURSES. IMPORTANTLY, THIS MODEL ALSO ALLOWS FOR INTEGRATION WITH COURSE-LEVEL MODELS OF STUDENT PERFORMANCE.
THE QUICK TECHNOLOGICAL EVOLUTION OF THE LAST DECADES HAS ALSO REACHED LEARNING ENVIRONMENTS, WHERE THE USE OF NETWORKED COMPUTING DEVICES SUCH AS LAPTOPS, SMARTPHONES, TABLETS, IOT DEVICES, SERVERS, ETC. IS CONTINUOUSLY GROWING. IN PARTICULAR, THOSE COMPUTERIZED LEARNING ENVIRONMENTS HAVE THE POTENTIAL TO TRACK THE ACTIVITY OF TEACHERS AND STUDENTS IN THEM, WHICH ENABLES THE DEVELOPMENT OF INNOVATIVE APPLICATIONS THAT ENRICH THE LEARNING PROCESS BY ANALYZING THE COLLECTED DATA. THE MAJORITY OF RELATED WORK IN THIS FIELD HAS BEEN CENTERED ON BATCH GATHERING AND ANALYSIS OF THE DATA. HOWEVER, IN ORDER TO INTEGRATE MORE REACTIVE APPLICATIONS, THERE IS A NEED FOR AN INFRASTRUCTURE THAT ENABLES THE REAL-TIME COLLECTION AND ANALYSIS OF DATA IN LEARNING ENVIRONMENTS. SUCH AN INFRASTRUCTURE SHOULD BE SCALABLE AND FLEXIBLE ENOUGH TO COPE WITH HETEROGENEOUS DATA COMING FROM DIFFERENT TYPES OF LEARNING SETTINGS. THIS PAPER PRESENTS LOSTREGO, A STREAM-BASED, MODULAR, SCALABLE AND FLEXIBLE DISTRIBUTED INFRASTRUCTURE THAT ALLOWS THE GATHERING AND ANALYSIS OF EDUCATIONAL DATA FROM HETEROGENEOUS DATA SOURCES IN A REAL-TIME FASHION. LOSTREGO APPLICATIONS ARE COMPOSED BY INTERCONNECTED SERVICES THAT CAN BE REUSED IN DIFFERENT COURSES. THE RESULTS OF THE EVALUATION OF LOSTREGO IN TWO EDITIONS OF A COMPUTER PROGRAMMING COURSE WITH 233 STUDENTS AND 384,702 GATHERED EVENTS ARE ALSO REPORTED.
ANALYSIS PROCESSES OF LEARNING TRACES, USED TO GAIN IMPORTANT PEDAGOGICAL INSIGHTS, ARE YET TO BE EASILY SHARED AND REUSED. THEY FACE WHAT IS COMMONLY CALLED A REPRODUCIBILITY CRISIS. FROM OUR OBSERVATIONS, WE IDENTIFY TWO IMPORTANT FACTORS THAT MAY BE THE CAUSE OF THIS CRISIS: TECHNICAL CONSTRAINTS DUE TO RUNNABLE NECESSITIES, AND CONTEXT DEPENDENCIES. MOREOVER, THE MEANING OF THE REPRODUCIBILITY ITSELF IS AMBIGUOUS AND A SOURCE OF MISUNDERSTANDING. IN THIS PAPER, WE PRESENT AN ONTOLOGICAL FRAMEWORK DEDICATED TO TAKING FULL ADVANTAGE OF ALREADY IMPLEMENTED EDUCATIONAL ANALYSES. THIS FRAMEWORK SHIFTS THE ACTUAL PARADIGM OF ANALYSIS PROCESSES BY REPRESENTING THEM FROM A NARRATIVE POINT OF VIEW, INSTEAD OF A TECHNICAL ONE. THIS ENABLES A FORMAL DESCRIPTION OF ANALYSIS PROCESSES WITH HIGH-LEVEL CONCEPTS. WE SHOW HOW THIS DESCRIPTION IS PERFORMED, AND HOW IT CAN HELP ANALYSTS. THE GOAL IS TO EMPOWER BOTH EXPERT AND NONEXPERT ANALYSIS STAKEHOLDERS WITH THE POSSIBILITY TO BE INVOLVED IN THE ELABORATION OF ANALYSIS PROCESSES AND THEIR REUSE IN DIFFERENT CONTEXTS, BY IMPROVING BOTH HUMAN AND MACHINE UNDERSTANDING OF THESE ANALYSES. THIS POSSIBILITY IS KNOWN AS THE CAPITALISATION OF ANALYSIS PROCESSES OF LEARNING TRACES.
SOFTWARE CONCEPTUAL DESIGN KNOWLEDGE AND SKILLS ARE ESSENTIAL FOR GRADUATING COMPUTER ENGINEERING STUDENTS. SEVERAL APPROACHES HAVE BEEN USED TO TEACH SOFTWARE CONCEPTUAL DESIGN THROUGH VARIOUS FRAMEWORKS AND LEARNING ENVIRONMENTS. OUR BRIEF LITERATURE SURVEY FOUND THAT NONE OF THESE SYSTEMS ANALYSED LEARNER ENGAGEMENT WITH DESIGN ELEMENTS USING LEARNING ANALYTICS. THIS STUDY AIMS TO ANALYSE THE LEARNER ENGAGEMENT WITH DESIGN ELEMENTS AS THEY CREATE SOFTWARE DESIGNS. WE ANALYSED LOG DATA OF N=37 STUDENTS IN THINK & LINK, A TECHNOLOGY-ENHANCED LEARNING ENVIRONMENT (TELE), BASED ON THE FBS DESIGN FRAMEWORK. THE DIFFERENCES IN STUDENT ENGAGEMENT ARE COMPARED FOR TWO CATEGORIES OF LEARNERS, HIGH AND LOW, BASED ON THE LEARNING GAIN IN THE PRE AND POST-TEST PERFORMANCE. DIFFERENCES AND SIMILARITIES IN LOGS OF ACTIONS WERE IDENTIFIED FOR HIGH AND LOW LEARNERS. PROCESS MODELS WERE USED TO IDENTIFY DIFFERENCES IN THE SEQUENCE OF ENGAGEMENT WITH VARIOUS DESIGN ELEMENTS. WE FIND HIGH LEARNERS TO IDENTIFY SIGNIFICANT DESIGN ELEMENTS AND CREATE LINKS AMONG THEM. THE ANALYSIS ALSO PROVIDED INSIGHTS ON POINTS FOR SCAFFOLDING TO HELP LOW LEARNERS CREATE MEANINGFUL DESIGNS.
IN THIS PAPER WE REPORT ON THE USE OF A PURPOSE BUILT COMPUTER SUPPORT COLLABORATIVE LEARNING ENVIRONMENT DESIGNED TO SUPPORT LAB-BASED CAD TEACHING THROUGH THE MONITORING OF STUDENT PARTICIPATION AND IDENTIFIED PREDICTORS OF SUCCESS. THIS WAS CARRIED OUT BY ANALYSING DATA FROM THE INTERACTIVE LEARNING SYSTEM AND CORRELATING STUDENT BEHAVIOUR WITH SUMMATIVE LEARNING OUTCOMES. A TOTAL OF 331 UNDERGRADUATE STUDENTS, FROM EIGHT INDEPENDENT GROUPS AT THE UNIVERSITY OF SURREY TOOK PART IN THIS STUDY. THE DATA COLLECTED INCLUDED: TIME SPENT ON TASK, CLASS ATTENDANCE; SEATING LOCATION; AND GROUP ASSOCIATION. THE APPLICATION OF ANOVA AND PEARSON CORRELATION TO QUANTIZED DATA DEMONSTRATED THAT CERTAIN STUDENT BEHAVIOURS ENHANCED THEIR LEARNING PERFORMANCE. THE RESULTS INDICATED THAT STUDENT ACHIEVEMENT WAS POSITIVELY CORRELATED WITH ATTENDANCE, SOCIAL STABILITY IN TERMS OF PEER GROUPING, AND TIME SPENT ON TASK. A NEGATIVE RELATIONSHIP WAS SHOWN IN STUDENT SEATING DISTANCE RELATIVE TO THE LECTURER POSITION. LINEAR REGRESSION WAS USED IN THE FINAL PART OF THIS STUDY TO EXPLORE THE POTENTIAL FOR EMBEDDING PREDICTIVE ANALYTICS WITHIN THE SYSTEM TO IDENTIFY STUDENTS AT-RISK OF FAILURE. THE RESULTS WERE ENCOURAGING. THEY SUGGEST THAT LEARNING ANALYTICS CAN BE USED TO PREDICT STUDENT OUTCOMES AND CAN ENSURE THAT TIMELY AND APPROPRIATE TEACHING INTERVENTIONS CAN BE INCORPORATED BY TUTORS TO IMPROVE CLASS PERFORMANCE.
CURRENT TECHNOLOGIES USED IN LEARNING PROCESSES IMPLY THE LOGGING OF ALL THE PERFORMED ACTIVITIES. THESE DATA CAN BE EXPLOITED TO GAIN INSIGHT INTO THE LEARNING PROCESS AND CAN BE USED FOR THE ASSESSMENT OF STUDENTS, PROFESSORS AND THE PROCESSES THEMSELVES. HOWEVER, ALTHOUGH THIS WEALTH OF DATA EXISTS, IT IS STILL DIFFICULT FOR THE TEACHERS (AND INTERESTED STAKEHOLDERS) TO VERIFY HYPOTHESIS, EXTRACT CONCLUSIONS, OR MAKE DECISIONS BASED ON DISCOVERED FACTS OR SITUATIONS. THIS PAPER INTRODUCES AN EDUCATIONAL DATA ANALYSIS MODEL BASED ON VISUAL ANALYTICS, LEARNING ANALYTICS AND ACADEMIC ANALYTICS, BY MEANS OF A SOFTWARE TOOL THAT ALLOWS PERFORMING CONFIRMATORY AND EXPLORATORY DATA ANALYSIS THROUGH THE INTERACTING WITH THE GATHERED INFORMATION FROM A TYPICAL LEARNING MANAGEMENT SYSTEM. THE MAIN GOAL THUS IS TO DEFINE A MODEL WHICH ENABLE THE DISCOVERY OF KNOWLEDGE ON THE SPECIFIC LEARNING PROCESS THAT, IN TURN, WILL PERMIT TO IMPROVE IT.
WE DESIGNED AND STUDIED AN INNOVATIVE SEMANTIC VISUAL LEARNING ANALYTICS FOR ORCHESTRATING TODAY'S PROGRAMMING CLASSES. THE VISUAL ANALYTICS INTEGRATES SOURCES OF LEARNING ACTIVITIES BY THEIR CONTENT SEMANTICS. IT AUTOMATICALLY PROCESSS PAPER-BASED EXAMS BY ASSOCIATING SETS OF CONCEPTS TO THE EXAM QUESTIONS. RESULTS INDICATED THE AUTOMATIC CONCEPT EXTRACTION FROM EXAMS WERE PROMISING AND COULD BE A POTENTIAL TECHNOLOGICAL SOLUTION TO ADDRESS A REAL WORLD ISSUE. WE ALSO DISCOVERED THAT INDEXING EFFECTIVENESS WAS ESPECIALLY PREVALENT FOR COMPLEX CONTENT BY COVERING MORE COMPREHENSIVE SEMANTICS. SUBJECTIVE EVALUATION REVEALED THAT THE DYNAMIC CONCEPT INDEXING PROVIDED TEACHERS WITH IMMEDIATE FEEDBACK ON PRODUCING MORE BALANCED EXAMS.
THIS ARTICLE LOOKS AT USEFUL APPLICATIONS OF GRADING ANALYTICS, I.E. THE ANALYSIS OF DATA IN A CONVENTIONAL STUDENT INFORMATION SYSTEM OF A UNIVERSITY. MUCH RESEARCH IN EDUCATIONAL DATA MINING FOCUSES ON LEARNING ANALYTICS, A FIELD THAT HEAVILY EXPLOITS DATA THAT COMES FROM DIGITAL FOOTPRINTS OF STUDENTS AS THEY USE ONLINE LEARNING TOOLS AND LEARNING MANAGEMENT SYSTEMS. IN CONTRAST, OUR WORK USES PRIMARILY GRADES AS RAW FEATURES FOR THE ANALYSIS OF THE LEARNING AND THE TEACHING PROCESS. BASED ON THE DATA OF A TYPICAL STUDENT RECORD IN A GERMAN UNIVERSITY, WE PRESENT A COMPARISON OF DIFFERENT METHODS FOR STUDENT SUCCESS PREDICTIONS. OUR RESULTS SUGGEST THAT DEEP NEURAL NETWORKS CAN EXTRACT MORE ACCURATE INFORMATION FROM AGGREGATIVE FEATURES THAN CONVENTIONAL METHODS. GIVEN THE POWERFUL TOOLS OF MODERN DATA ANALYTICS, WE LOOK AT APPLICATIONS OF GRADING ANALYTICS FOR THE IMPROVEMENT OF A UNIVERSITY'S OPERATIONS, IN PARTICULAR FOR THE CORE MISSION OF A UNIVERSITY: HELPING STUDENTS TO SUCCESSFULLY MASTER THEIR FIELD OF STUDY.
THIS PAPER PRESENTS FOR THE FIRST TIME A DETAILED ANALYSIS OF FINE-GRAINED NAVIGATION STYLE IDENTIFICATION IN MOOCS BACKED BY A LARGE NUMBER OF ACTIVE LEARNERS. THE RESULT SHOWS 1) WHILST THE SEQUENTIAL STYLE IS CLEARLY IN EVIDENCE, THE GLOBAL STYLE IS LESS PROMINENT; 2) THE MAJORITY OF THE LEARNERS DO NOT BELONG TO EITHER CATEGORY; 3) NAVIGATION STYLES ARE NOT AS STABLE AS BELIEVED IN THE LITERATURE; AND 4) LEARNERS CAN, AND DO, SWAP BETWEEN NAVIGATION STYLES WITH DETRIMENTAL EFFECTS. THE APPROACH IS PROMISING, AS IT PROVIDES INSIGHT INTO ONLINE LEARNERS' TEMPORAL ENGAGEMENT, AS WELL AS A TOOL TO IDENTIFY VULNERABLE LEARNERS, WHICH POTENTIALLY BENEFIT PERSONALISED INTERVENTIONS (FROM TEACHERS OR AUTOMATIC HELP) IN INTELLIGENT TUTORING SYSTEMS (ITS).
IN THIS PAPER, WE PROPOSE AND DISCUSS A CONCEPTUAL FRAMEWORK, BASED ON KNOWLEDGE MANAGEMENT AND SELECTIVE LITERATURE REVIEW, FOR ENHANCING THE CONTRIBUTION OF STUDENTS IN THE INSTITUTIONAL QUALITY ASSURANCE PROCESSES (IQA). THE FRAMEWORK IS RELATED TO THE DESIGN OF A MECHANISM FOR IQA, WHICH ENSURES THE IMPROVEMENT OF THE ASSESSMENT AND USES VARIOUS METHODS, INDICATORS, AND CRITERIA OF AUDITING. THE MECHANISM IS SUPPORTED BY AN INTEGRATED SYSTEM FOR THE EVALUATION OF THE LEARNING PRACTICE, AND IT ALSO INCLUDES A SUBSYSTEM FOR EVALUATION BASED ON SOCIAL NETWORKING, A SUBSYSTEM OF LEARNING ANALYTICS, AND A SUBSYSTEM FOR INCREASING THE COMMITMENT TO THE IQA.
THREE YEARS AGO WE INTRODUCED FLIPPED TEACHING STRATEGIES TO LARGE COHORTS OF FIRST-YEAR UNIVERSITY STUDENTS LEARNING ABOUT CONSTRUCTION. PARADOXICALLY, OUR AIM IN PROVIDING ONLINE CONTENT WAS TO IMPROVE AND EXPAND THE ON-CAMPUS EXPERIENCE. BY TRANSFERRING LECTURE CONTENT ONLINE WE WERE ABLE TO EXTEND OUR TWO-HOUR FACE-TO-FACE TUTORIALS TO THREE HOURS AND ALSO INCREASE THE INTERDISCIPLINARY CONTENT IN KEEPING WITH THE SPIRIT OF OUR NEW UNDERGRADUATE DEGREE. THE TRANSITION HAS NOT BEEN WITHOUT HURDLES. EACH SEMESTER WE HAVE REFINED CONTENT AND ADOPTED STRATEGIES AIMED AT CONSOLIDATING AND ACCELERATING LEARNING BUT WE CONTINUE TO BE SURPRISED BY WHAT STUDENTS EXCEL AT AND WHAT THEY STRUGGLE WITH. AS WE BEGIN OUR SIXTH EXPERIENCE OF THE FLIPPED CLASSROOM, WE ARE EXPLORING THE POTENTIAL OF LEARNING ANALYTICS TO PROMOTE A DEEPER UNDERSTANDING OF CORE ISSUES WHILE CONCURRENTLY STRUGGLING WITH THE ISSUE OF STUDENTS WHO COME UNPREPARED TO CLASSES. AT THE HEART OF THIS TEACHING AND LEARNING APPROACH IS THE AMBITION FOR STUDENTS TO WORK COLLABORATIVELY, BRINGING TOGETHER KNOWLEDGE ACROSS DISCIPLINE DOMAINS AND ACROSS LEARNING STYLES AND STRENGTHS. WE SEE BENEFIT IN ERIC MAZUR'S ITERATIVE APPROACHES TO PEER INSTRUCTION TO ENCOURAGE DEEPER UNDERSTANDING OF HOW STRUCTURES, CONSTRUCTION AND MATERIALS INTERCONNECT INTO A KNOWLEDGE SYSTEM.
UNIVERSITIES HAVE INCREASINGLY EMPHASIZED COMPETENCIES AS CENTRAL ELEMENTS OF STUDENTS' DEVELOPMENT. HOWEVER, THE ASSESSMENT OF THESE COMPETENCIES IS NOT AN EASY TASK. THE AVAILABILITY OF DATA THAT LEARNERS GENERATE IN COMPUTER MEDIATED LEARNING OFFERS GREAT POTENTIAL TO STUDY HOW LEARNING TAKES PLACE, AND THUS, TO GATHER EVIDENCES FOR COMPETENCY ASSESSMENT USING ENRICHED RUBRICS. THESE ARE THE SO-CALLED ELECTRONIC ASSESSMENT INSTRUMENTS. AMONG THEM, THE ENRICHED RUBRICS ARISES AS A TOOL TO IMPROVE THE ASSESSMENT PROCESS. HOWEVER, THE LACK OF DATA INTEROPERABILITY AND THE DECENTRALIZATION OF THOSE EDUCATIONAL APPLICATIONS SET OUT A CHALLENGE TO EXPLOIT TRACE DATA. TO FACE THESE PROBLEMS WE HAVE DESIGNED AND DEVELOPED SCALA (SUPPORTING COMPETENCY ASSESSMENT THROUGH A LEARNING ANALYTICS APPROACH), AN ANALYTICS SYSTEM THAT INTEGRATES USAGE-HOW THE USER INTERACTS WITH RESOURCES- AND SOCIAL-HOW STUDENTS AND TEACHERS INTERACT AMONG THEM- TRACE DATA TO SUPPORT COMPETENCY ASSESSMENT. AFTER PRESENTING THE COMPONENTS OF SCALA (PROCESS, MODEL AND PLATFORM), WE EVALUATE THEM PRESENTING SIX SCENARIOS TO KNOW WHETHER IT IS VIABLE IN TERMS OF TIME, SUSTAINABILITY AND QUALITY ASSURANCE TO NORMALIZE THE HETEROGENEOUS DATA PRESENT IN TECHNOLOGY-RICH LEARNING ENVIRONMENTS. THE RESULTS SHOW AND CONFIRM THE VIABILITY OF THE PROPOSED SOLUTION AND THE POSSIBILITY TO OFFER REAL-TIME FEEDBACK TO THE TEACHERS TO ASSESS STUDENTS'.
THE RESULTS OBTAINED THANKS TO THE FLIPPED CLASSROOM WITH 'JUST IIIN TIME' TEACHING METHOD ARE REVIEWED. THIS METHOD ALLOWED TO KNOW WHAT THE STUDENTS DID NOT UNDERSTAND AFTER TRYING TO STUDY THE INSTRUCTIVE MATERIALS ASSIGNED TO THEM. TO ACHIEVE THAT THE STUDENTS WORK BEFORE CLASS METHODS OF MARKETING AND GAMIFICATION WERE DEVELOPED. METHODS FOR THE ANALYSIS OF STUDENT RESPONSES WERE ALSO DEVELOPED. THE JITT METHOD ALLOWES THE TEACHER KNOW IN ADVANCE THOSE ASPECTS THAT ARE MOST INTERESTING OR MOST DIFICULT TO UNDERSTAND FOR THE STUDENTS AS WELL AS THEIR MOST URGENT DOUBTS. FINALLY, WE HAVE DEVELOPED METHODS TO USE THE URGENT DOUBTS OF THE STUDENTS TO GENERATE FORMATIVE FEEDBACK AND ACTIVITIES TO DO IN THE CLASSROOM. IN THE METHOD NAMED "FLIPPED LEARNING FORTE" THE TEACHER ANSWER THE URGENT DOUBTS OF HIS STUDENTS. IN THE METHOD NAMED 'FLIP IN COLOURS' THE TEACHER CLASSIFY THE DOUBTS BY THEIR POSIBLE UTILITY IN THE CLASSROOM USING A COLOR CODE. WITH THE COMBINED APPLICATION OF THESE METHODS IN UNIVERSITY COURSES THE FAILURE RATE OF THE STUDENTS HAS DECREASED AND THE MEAN GRADE IN THE EXAMS FOR THE ASSESSMENT OF LEARNING HAS INCREASED IN MORE THAN ONE STANDARD DEVIATION. THE RATE OF STUDENTS THAT ATTAIN THE LEVEL OF MASTERY HAS INCREASED, AS WELL AS THE STUDENT EVALUATIONS ABOUT THE TEACHERS OF THESE COURSES. FINALLY, THE REASONS UNDERLYING THE EFFICACY OF THE PROPOSED FLIPPED METHOD ARE DISCUSSED.
CURRENT TECHNOLOGIES USED IN E-LEARNING PROCESSES REQUIRE THE LOGGING OF ALL ACTIVITIES PERFORMED. THESE DATA CAN BE USED IN THE ASSESSMENT OF STUDENTS, INSTRUCTORS AND THE PROCESSES THEMSELVES. HOWEVER, ALTHOUGH THIS WEALTH OF DATA EXISTS, IT IS STILL DIFFICULT FOR THOSE WHO TEACH (AND INTERESTED STAKEHOLDERS) TO VERIFY A HYPOTHESIS, EXTRACT CONCLUSIONS, OR MAKE DECISIONS BASED ON THE FACTS OR SITUATIONS THAT ARE DISCOVERED. THIS PAPER INTRODUCES AN EDUCATIONAL DATA ANALYSIS MODEL BASED ON VISUAL ANALYTICS, LEARNING ANALYTICS AND ACADEMIC ANALYTICS AND USING A SOFTWARE TOOL THAT ALLOWS PERFORMING CONFIRMATORY AND EXPLORATORY DATA ANALYSIS THROUGH INTERACTION WITH THE INFORMATION GATHERED FROM A TYPICAL LEARNING MANAGEMENT SYSTEM. THE MAIN GOAL IS TO ENABLE THE DISCOVERY OF KNOWLEDGE ON THE SPECIFIC LEARNING PROCESS THAT, IN TURN, WILL PERMIT ITS IMPROVEMENT.
THE PAPER PRESENTS SMART BLENDED LEARNING FOR FOSTERING STUDENTS' PERFORMANCE. IT USES MODELS OF STUDENT ACTIVITY AND DETECTS CAUSES OF A POSSIBLE DROP OUT, USING LEARNING ANALYTICS AND REAL-TIME DATA. TAKING ADVANTAGE OF EMBEDDED SENSORS (NON-INVASIVE, LOW-COST, FLEXIBLE AND DISTRACTION FREE) BUILT INTO WEARABLE DEVICES REAL-TIME DATA CAN BE USED TO SUPPORT STUDENTS' SUCCESS, WELL-BEING AND HEALTH. IN OUR CONTRIBUTION TO THE RESEARCH TOPIC WE EVALUATE THE STUDENTS' EXPERIENCE WITH SMART BLENDED LEARNING USING BIOFEEDBACK METHODS, SENSOR DATA EXTRACTED FROM WEARABLES FOR FIVE SPECIFIC LECTURES, DURING STUDY PERIODS AND IN EXAMINATIONS. THE PRELIMINARY RESULTS INDICATE A CORRELATION BETWEEN THE PHYSIOLOGICAL SIGNALS AND THE GRADES IN EXAMINATION.
BUSINESS MANAGEMENT EDUCATION IN INDIA HAS SHOWN AN UPWARD GROWTH TREND IN THE LAST COUPLE OF DECADES. DUE TO THE DIVERSE NATURE OF THE COURSE, STUDENTS FROM DIVERSE ACADEMIC BACKGROUNDS ARE BEING ADMITTED TO THE COURSE. THEREFORE, DIFFERENCES IN STUDENTS' ABILITIES AND THEIR LEARNING STYLES HAVE A SIGNIFICANT EFFECT ON THEIR LEARNING OUTCOMES. MEANWHILE, WITH THE DEVELOPMENT OF LEARNING TECHNOLOGIES, LEARNERS CAN BE PROVIDED A MORE EFFECTIVE LEARNING ENVIRONMENT TO OPTIMISE THEIR LEARNING. THE PURPOSE OF THIS STUDY WAS TO DEVELOP A MODEL TO AUTOMATICALLY DETECT THE STUDENTS' LEARNING STYLES FROM THEIR PERSONAL, ACADEMIC AND SOCIAL MEDIA DATA AND MAKE RECOMMENDATIONS FOR STUDENTS, TEACHERS, EDUCATORS AND ADMINISTRATORS FOR OVERALL IMPROVEMENT OF LEARNING OUTCOMES. DATA ANALYSIS IN THIS RESEARCH WAS REPRESENTED USING DATA COLLECTED FROM POST-GRADUATE BUSINESS MANAGEMENT STUDENTS IN INDIA. A 10-FOLD CROSS-VALIDATION WAS USED TO CREATE AND TEST THE MODELS. THE DATA WERE ANALYSED BY R AND R-STUDIO. CLASSIFICATION ACCURACY, PRECISION, RECALL, KAPPA, ROC CURVE AND F MEASURE WERE OBSERVED. THE RESULTS SHOWED THAT THE ACCURACY OF CLASSIFICATION BY THE C4.5 TECHNIQUE HAD THE HIGHEST VALUE AT 95.7%, AND IT COULD BE APPLIED TO DEVELOP FELDER-SILVERMAN'S LEARNING STYLE WHILE TAKING INTO CONSIDERATION STUDENTS' ACADEMIC, PERSONAL INFORMATION AND SOCIAL MEDIA PREFERENCES.
SELF-REGULATED LEARNING (SRL) IS BEING PROMOTED AND ADOPTED INCREASINGLY DUE TO THE NEEDS OF CURRENT EDUCATION, STUDENT CENTERED AND FOCUSED ON COMPETENCE DEVELOPMENT. ONE OF THE MAIN COMPONENTS OF SRL IS LEARNERS' SELF-MONITORING, WHICH EVENTUALLY CONTRIBUTES TO A BETTER PERFORMANCE. MONITORING IS ALSO IMPORTANT FOR TEACHERS, AS IT ENABLES THEM TO KNOW TO WHAT EXTENT THEIR LEARNERS ARE DOING WELL AND PROGRESSING PROPERLY. AT THE SAME TIME, THE USE OF TECHNOLOGY FOR LEARNING IS NOW COMMON AND FACILITATES MONITORING. NEVERTHELESS, THE AVAILABLE SOFTWARE STILL OFFERS POOR SUPPORT FROM THE SRL POINT OF VIEW, ESPECIALLY, FOR SRL MONITORING. THIS CLASHES WITH THE GROWTH OF LEARNING ANALYTICS AND EDUCATIONAL DATA MINING. THE MAIN ISSUE IS THE WIDE VARIETY OF SRL ACTIONS THAT NEED TO BE CAPTURED, COMMONLY PERFORMED IN DIFFERENT TOOLS, AND THE NEED TO INTEGRATE THEM TO SUPPORT THE DEVELOPMENT OF ANALYTICS AND DATA MINING DEVELOPMENTS, MAKING IMPERATIVE THE SEARCH OF INTEROPERABLE SOLUTIONS. THIS PAPER FOCUSES ON THE STANDARDIZATION OF SRL TRACES TO ENABLE DATA COLLECTION FROM MULTIPLE SOURCES AND DATA ANALYSIS WITH THE GOAL OF EASING THE MONITORING PROCESS FOR TEACHERS AND LEARNERS. FIRST, THE PAPER ANALYZES CURRENT MONITORING SOFTWARE AND ITS LIMITATIONS FOR SRL. THEN, AFTER A BRIEF ANALYSIS OF AVAILABLE STANDARDS ON THIS AREA, AN APPLICATION PROFILE FOR THE EXPERIENCE API SPECIFICATION IS PROPOSED TO ENABLE THE INTEROPERABLE RECORDING OF THE SRL TRACES. THE PAPER DESCRIBES THE PROCESS FOLLOWED TO CREATE THE PROFILE, FROM THE ANALYSIS TO THE FINAL IMPLEMENTATION, INCLUDING THE SELECTION OF THE INTERACTIONS THAT REPRESENT RELEVANT SRL ACTIONS, THE SELECTION OF VOCABULARIES TO RECORD THEM AND A CASE STUDY.
DUE TO THE RAPID CHANGE IN TECHNOLOGIES, NEW DATA FORMS EXIST WHICH LEAD TO A HUGE DATA SIZE ON THE INTERNET. AS A RESULT, SOME LEARNING PLATFORMS SUCH AS E-LEARNING SYSTEMS MUST CHANGE THEIR METHODOLOGIES FOR DATA PROCESSING TO BE SMARTER. THIS PAPER PROPOSES A FRAMEWORK FOR SMOOTHLY ADAPT THE TRADITIONAL E-LEARNING SYSTEMS TO BE SUITABLE FOR SMART CITIES APPLICATIONS. LEARNING ANALYTICS (LA) HAS TURNED INTO A NOTICEABLE WORLDVIEW WITH REGARDS TO INSTRUCTION OF LATE WHICH EMBRACES THE CURRENT PROGRESSIONS OF INNOVATION, FOR EXAMPLE, CLOUD COMPUTING, BIG DATA PROCESSING, AND INTERNET OF THINGS. LA ADDITIONALLY REQUIRES A CONCENTRATED MEASURE OF PREPARING ASSETS TO CREATE APPLICABLE INVESTIGATIVE OUTCOMES. BE THAT AS IT MAY, THE CUSTOMARY METHODOLOGIES HAVE BEEN WASTEFUL AT HANDLING LA DIFFICULTIES.
THE ABILITY TO INTEGRATE MULTIPLE LEARNING APPLICATIONS FROM DIFFERENT ORGANIZATIONS ALLOWS SHARING RESOURCES AND REDUCING COSTS IN THE DEPLOYMENT OF LEARNING SYSTEMS. IN THIS SENSE, LEARNING TOOLS INTEROPERABILITY (LTI) IS THE MAIN CURRENT LEADING TECHNOLOGY FOR INTEGRATING LEARNING APPLICATIONS WITH PLATFORMS LIKE LEARNING MANAGEMENT SYSTEMS (LMS). ON THE OTHER HAND, THE INTEGRATION OF LEARNING APPLICATIONS ALSO BENEFITS FROM DATA COLLECTION, WHICH ALLOWS LEARNING SYSTEMS TO IMPLEMENT LEARNING ANALYTICS (LA) PROCESSES. TIN CAN API IS A SPECIFICATION FOR LEARNING TECHNOLOGY THAT MAKES THIS POSSIBLE. BOTH LEARNING TECHNOLOGIES, LTI AND TIN CAN API, ARE SUPPORTED BY NOWADAYS LMS, EITHER NATIVELY OR THROUGH PLUGINS. HOWEVER, THERE IS NO SEAMLESS INTEGRATION BETWEEN THESE TWO TECHNOLOGIES IN ORDER TO PROVIDE LEARNING SYSTEMS WITH EXPERIENCE DATA FROM REMOTELY HOSTED LEARNING APPLICATIONS. OUR PROPOSAL DEFINES A LEARNING SYSTEM ARCHITECTURE READY TO APPLY ADVANCED LA TECHNIQUES ON EXPERIENCE DATA COLLECTED FROM REMOTELY HOSTED LEARNING APPLICATIONS THROUGH A SEAMLESS INTEGRATION BETWEEN LTI AND TIN CAN API. IN ORDER TO VALIDATE OUR PROPOSAL, WE HAVE IMPLEMENTED A LRS PROXY PLUG-IN IN MOODLE THAT STORES LEARNING RECORDS IN A SCORM CLOUD LRS SERVICE, AND A BASIC ONLINE LAB BASED ON EASY JAVASCRIPT SIMULATION (EJSS). MOREOVER, WE HAVE TESTED OUR IMPLEMENTATION USING RESOURCES LOCATED IN THREE EUROPEAN UNIVERSITIES.
ONLINE KNOWLEDGE BUILDING COMMUNITIES (OKBCS) PROVE BENEFICIAL IN INFORMAL LEARNING SETTINGS. EXTENDING THEIR USE TO THE INSTRUCTIONAL DESIGN OF FORMAL LEARNING ENVIRONMENTS REQUIRES IDENTIFICATION METHODS OF THE CENTRAL, INTERMEDIATE AND PERIPHERAL COMMUNITY LAYERS. THIS STUDY PROPOSES SUCH A SOCIAL LEARNING ANALYTICS METHOD, I.E., AN AUTOMATED DISCOURSE ANALYSIS BASED ON NATURAL LANGUAGE PROCESSING. THE METHOD WAS APPLIED TO THE DIALOG PRODUCED BY N = 1,990 PARTICIPANTS IN 20 BLOGGER COMMUNITIES. CENTRALITY CRITERIA BASED ON COHESION NETWORK ANALYSIS WERE HIGHLY CONSISTENT AND COULD SUCCESSFULLY IDENTIFY THE SOCIO-COGNITIVE LAYERS OF THE ANALYZED OKBCS. ONGOING RESEARCH PROPOSES INSTRUCTIONAL DESIGN BASED ON FORMAL LEARNERS' INTERACTIONS WITH OKBCS.
USING PERSONALLY IDENTIFIABLE INFORMATION (PII) (PATRON DATA) TO MAKE INFORMED DECISIONS IN ACADEMIC LIBRARIES THROUGH LEARNING ANALYTICS PROGRAMS HAS INCREASINGLY BECOME MORE COMMONPLACE. IN THIS COLUMN, WE DISCUSS HOW LIBRARIES AROUND THE WORLD HAVE USED PII TO MAKE INFORMED DECISIONS ABOUT HOURS (GATE COUNT), ELECTRONIC RESOURCES (COLLECTION USAGE AND AUTHENTICATION), AND RESEARCH ASSISTANCE (VIRTUAL REFERENCE). IN ADDITION, WE DISCUSS THE USE OF LEARNING ANALYTICS IN LIBRARY ENVIRONMENTS INCLUDING THE BENEFITS AND CONCERNS ASSOCIATED WITH ITS USE. FINALLY, WE DISCUSS HOW WE HAVE USED PII AT OUR INSTITUTION'S LIBRARY AND THE DATA METRICS WE PLAN UTILIZE AT SOME POINT IN THE FUTURE.
LEARNER DISENGAGEMENT IS A PERSISTING ISSUE IN THE SCIENCE TECHNOLOGY ENGINEERING AND MATHEMATICS (STEM) SUBJECTS. STUDENT ENGAGEMENT IS DYNAMICALLY CONSTITUTED BY THE BEHAVIOURAL, COGNITIVE AND EMOTIONAL DIMENSIONS OF ENGAGEMENT IN A LEARNING ENVIRONMENT. ALTHOUGH STRONGLY LINKED WITH ACADEMIC ACHIEVEMENT, MUCH OF THE DETAILS OF ENGAGEMENT BECOMES LOST IN A RETROSPECTIVE MEASUREMENT. TIMELY AND MICROLEVEL DATA ON THE OTHER HAND HAS THE ABILITY TO ENRICH THE TRADITIONAL LEARNING ANALYTICS DATASET. FROM A PILOT STUDY CARRIED OUT AT UNIVERSITAT OBERTA DE CATALUNYA, WHERE WE HAVE DESIGNED A SELF-REPORTED DATA CAPTURE MODULE THAT COLLECTS MICROLEVEL ENGAGEMENT DATA, INITIAL RESULTS SUGGEST THE VALIDITY OF THE PROPOSED APPROACH AND DATA. IN THIS PAPER WE EMPHASIZE HOW OUR APPROACH ENABLES BETTER UNDERSTANDING OF THE STUDENT LEARNING PROCESS AND THEIR CHARACTERISTICS SUCH AS COGNITIVE PATTERNS, EMOTIONAL STATES AND BEHAVIOURS THAT LEADS TO ACADEMIC SUCCESS AND ALSO ENABLE RICHER FEEDBACK FROM TEACHERS AND INFORMED DECISION MAKING BY THE INSTITUTION.
IN THIS PAPER, WE PRESENT AN APPLICATION FRAMEWORK FOR ENABLING EDUCATION PRACTITIONERS AND RESEARCHERS TO DEVELOP INTERACTIVE, MULTI-MODAL APPLICATIONS. THESE APPLICATIONS CAN BE DESIGNED USING TYPICAL HTML PROGRAMMING, AND WILL ENABLE A LARGER AUDIENCE TO MAKE APPLICATIONS THAT INCORPORATE SPEECH RECOGNITION, GESTURE RECOGNITION AND ENGAGEMENT DETECTION. THE APPLICATION FRAMEWORK USES OPEN-SOURCE SOFTWARE AND INEXPENSIVE HARDWARE THAT SUPPORTS BOTH MULTI-TOUCH AND MULTI-USER CAPABILITIES.
THIS CHAPTER DESCRIBES A STUDY OF A BLENDED LANGUAGE COURSE THAT COMBINES FACE-TO-FACE (F2F) INSTRUCTIONAL TIME WITH AN ONLINE COMPONENT. THE PURPOSE OF THE STUDY IS TO UNDERSTAND HOW TEACHERS AND STUDENTS EXPLOIT THE COMPLEMENTARITY OF THE TWO DELIVERY FORMATS TO IMPROVE THE LEARNING EXPERIENCE. THE STUDY FIRST EXAMINES HOW THE PRESENCE OF AN ONLINE COMPONENT AFFECTS THE F2F PORTION OF THE COURSE BY COMPARING IT TO A CORRESPONDING SECTION OF THE SAME COURSE TAUGHT ENTIRELY F2F. THE FINDINGS OF THIS FIRST PART OF THE STUDY SHOW MAJOR DIFFERENCES BETWEEN THE TWO DELIVERY FORMATS REGARDING TEACHING STRATEGIES AND TEACHING PRESENCE. THE SECOND PART OF THE STUDY USES LMS DATA TO ANALYZE STUDENT PARTICIPATION IN THE ONLINE COMPONENT OF THE BLENDED COURSE. DATA WERE GATHERED TO MEASURE ACTIVE PARTICIPATION, PASSIVE PARTICIPATION AND CONTINUITY. THE RESULTS SHOW THAT ONLINE BEHAVIORS DISTINGUISH LESS SUCCESSFUL STUDENTS FROM MORE SUCCESSFUL ONES, AND THAT THERE IS A STRONG CORRELATION BETWEEN LOW LEVELS OF ONLINE PARTICIPATION AND LOW GRADES IN THE COURSE. CONTINUITY PROVED TO BE THE STRONGEST PREDICTOR VARIABLE FOR SUCCESS IN THE COURSE. THE RESULTS HAVE IMPORTANT IMPLICATIONS FOR COURSE DESIGN AND THE USE OF LEARNING ANALYTICS FOR THE EARLY IDENTIFICATION OF AT-RISK STUDENTS.
THIS PAPER REPORTS THE LEARNING ANALYTICS ON THE INITIAL STAGES OF A LARGE-SCALE, GOVERNMENT-FUNDED PROJECT WHICH INDUCTS UNIVERSITY STUDENTS IN HONG KONG INTO CONSIDERATION OF ACADEMIC INTEGRITY AND ETHICS THROUGH MOBILE AUGMENTED REALITY (AR) LEARNING TRAILS-TRAILS OF INTEGRITY AND ETHICS (TIES)-ACCESSED ON SMART DEVICES. THE TRAILS IMMERSE STUDENTS IN COLLABORATIVE PROBLEM SOLVING TASKS CENTRED ON ETHICAL DILEMMAS, ADDRESSED IN REAL, ACTUAL LOCATIONS WHERE SUCH DILEMMAS MIGHT ARISE, WITH CONTEXTUALLY APPROPRIATE DIGITAL ADVICE AND INFORMATION AVAILABLE ON HAND. STUDENTS PLAY OUT THE CONSEQUENCES OF THEIR DECISIONS WHICH HELP REINFORCE THE LINKS BETWEEN THE THEORETICAL CONCEPT OF ACADEMIC INTEGRITY AND ETHICS AND THE PRACTICAL APPLICATION IN EVERYDAY CONTEXTS. TO EVALUATE THE EFFECTIVENESS OF THE TIES, TRIANGULATION OF DIFFERENT SETS OF DATA IS ADOPTED AND THESE DATASETS INCLUDE USER EXPERIENCE SURVEYS, QUALITATIVE FEEDBACK, CLICKSTREAM DATA, AND TEXT MINING OF PRE-/POST-TRAIL DISCUSSION. THOUSANDS OF STUDENTS' RESPONSES AND RELATED DATA GATHERED ARE ANALYSED TO ASCERTAIN THE EFFECTIVENESS OF THESE MOBILE LEARNING TRAILS IN ENHANCING STUDENTS' AWARENESS OF AIE ISSUES. THE POSITIVE LEARNING OUTCOME OF THE TIES SUGGESTS THAT THIS APPROACH CAN BE ADOPTED AND APPLIED TO A WIDER SCOPE OF THE ACADEMIC CURRICULUM AND CO-CURRICULUM.
ENGINEERING DESIGN IS A COMPLEX PROCESS WHICH REQUIRES SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATIC (STEM) KNOWLEDGE. STUDENTS' SELF-REGULATION PLAYS A CRITICAL ROLE IN INTERDISCIPLINARY TASKS. HOWEVER, THERE IS LIMITED RESEARCH INVESTIGATING WHETHER AND HOW SELF-REGULATION LEADS TO DIFFERENT LEARNING OUTCOMES AMONG STUDENTS IN ENGINEERING DESIGN. THIS STUDY ANALYZES THE ENGINEERING DESIGN BEHAVIORS OF 108 NINTH-GRADE U.S. STUDENTS USING PRINCIPAL COMPONENT ANALYSIS AND CLUSTER ANALYSIS. IT CLASSIFIES THE STUDENTS INTO FOUR DISTINCT TYPES: COMPETENT, COGNITIVE-ORIENTED, REFLECTIVE-ORIENTED, AND MINIMALLY SELF-REGULATED LEARNERS. COMPETENT SELF-REGULATED LEARNERS PERCEIVED THEMSELVES AS THE MOST SELF-REGULATED LEARNERS AND HAD THE GREATEST LEARNING GAINS, ALTHOUGH THEY DID NOT PERFORM BEST IN THE TASK. COGNITIVE-ORIENTED SELF-REGULATED LEARNERS PERCEIVED THEMSELVES AS THE LEAST SELF-REGULATED LEARNERS ALTHOUGH THEY WERE THE SECOND BEST IN BOTH THE PERFORMANCE OF THE TASK AND LEARNING GAINS. IN CONTRAST, REFLECTIVE LEARNERS HAD THE BEST PERFORMANCE IN THE TASK. MINIMALLY SELF-REGULATED LEARNERS DID NOT PERFORM WELL IN THE TASK AND HAD THE LOWEST LEARNING GAINS. THE RESULTS REVEALED THAT THE COMPETENT SELF-REGULATED LEARNERS HAD AN APPROPRIATE ASSESSMENT OF THEMSELVES TO OBTAIN KNOWLEDGE, COGNITIVE-ORIENTED SELF-REGULATED LEARNERS UNDERESTIMATED THEMSELVES, REFLECTIVE LEARNERS FOCUSED ON THE RESULTS OF THE TASK, AND MINIMALLY SELF-REGULATED LEARNERS OVERESTIMATED THEMSELVES AND EXERTED THE LEAST EFFORT. THE RESULTS ALSO OFFER NEW INSIGHTS INTO STEM EDUCATION AND SELF-REGULATED LEARNING WITH EMERGING LEARNING ANALYTICS.
SOCIAL COMPARISON THEORY ASSERTS THAT WE ESTABLISH OUR SOCIAL AND PERSONAL WORTH BY COMPARING OURSELVES TO OTHERS. IN IN-PERSON LEARNING ENVIRONMENTS, SOCIAL COMPARISON OFFERS STUDENTS CRITICAL FEEDBACK ON HOW TO BEHAVE AND BE SUCCESSFUL. BY CONTRAST, ONLINE LEARNING ENVIRONMENTS AFFORD FEWER SOCIAL CUES TO FACILITATE SOCIAL COMPARISON. CAN INCREASED AVAILABILITY OF SUCH CUES PROMOTE EFFECTIVE SELF-REGULATORY BEHAVIOR AND ACHIEVEMENT IN MASSIVE OPEN ONLINE COURSES (MOOCS)? WE DEVELOPED A PERSONALIZED FEEDBACK SYSTEM THAT FACILITATES SOCIAL COMPARISON WITH PREVIOUSLY SUCCESSFUL LEARNERS BASED ON AN INTERACTIVE VISUALIZATION OF MULTIPLE BEHAVIORAL INDICATORS. ACROSS FOUR RANDOMIZED CONTROLLED TRIALS IN MOOCS (OVERALL N = 33, 726), WE FIND: (1) THE AVAILABILITY OF SOCIAL COMPARISON CUES SIGNIFICANTLY INCREASES COMPLETION RATES, (2) THIS TYPE OF FEEDBACK BENEFITS HIGHLY EDUCATED LEARNERS, AND (3) LEARNERS' CULTURAL CONTEXT PLAYS A SIGNIFICANT ROLE IN THEIR COURSE ENGAGEMENT AND ACHIEVEMENT.
THIS STUDY DESIGNED A SCIENCE COURSE FOLLOWING COLLABORATIVE PROBLEM SOLVING (CPS) PROCESSES, AND EXAMINED THE EFFECT ON STUDENTS' CPS AWARENESS. THE LIMNIC ERUPTION CPS COURSE WAS IMPLEMENTED USING A MOODLE SYSTEM IN A TENTH-GRADE CLASS. CONSIDERING THE COMPLEX AND COORDINATED NATURE OF CPS, IN ORDER TO IMPROVE CPS SKILLS, IT IS IMPORTANT TO IDENTIFY WHAT ARE RELATED WITH THE DEVELOPMENT OF ALL SUB-SKILLS OF CPS. THUS THIS STUDY AIMED TO DETERMINE POTENTIAL FACTORS THAT AFFECT THE USE OF CPS SKILLS IN STUDENTS' MOTIVATIONAL AND BEHAVIORAL DIMENSIONS. MULTIPLE DATA SOURCES INCLUDING LEARNING TESTS, QUESTIONNAIRE FEEDBACK, AND LEARNING LOGS WERE COLLECTED AND EXAMINED BY LEARNING ANALYTICS APPROACH. THE RELATIONSHIPS BETWEEN STUDENTS' CPS AWARENESS WITH THEIR LEARNING MOTIVATION AND LEARNING BEHAVIORS WERE EXPLORED. THE RESEARCH FINDINGS INDICATED A SIGNIFICANT POSITIVE CORRELATION BETWEEN CPS AWARENESS AND CERTAIN LEARNING MOTIVATION FACTORS AND LEARNING BEHAVIOR FACTORS. CONSIDERING THE STUDENTS' INDIVIDUAL DIFFERENCES IN LEARNING ABILITIES, WE ALSO COMPARED THE RESULTS OF HIGH AND LOW PERFORMANCE GROUPS. AS A RESULT, LOW PERFORMERS' LEARNING MOTIVATION AND LEARNING BEHAVIORS WERE CORRELATED WITH THE SOCIAL DOMAIN OF CPS AWARENESS, WHILE THOSE OF HIGH PERFORMERS WERE CORRELATED WITH THEIR COGNITIVE AWARENESS.
ALTHOUGH FACED WITH RESISTANCES, DISTANCE AND DIGITAL EDUCATION IS OVERTAKING FACE-TO-FACE FORMATS. WHEN PEDAGOGICAL DESIGNS ARE SUCCESSFUL, THERE IS EVIDENCE OF THE QUALITY OF DIGITAL LEARNING. THE EFFECTIVENESS OF THESE SYSTEMS IS AT LEAST SIMILAR TO THAT OF FACE-TO-FACE PROPOSALS. THIS PAPER INTRODUCES THE DEBATE ON INNOVATIONS AND DISRUPTIVE TECHNOLOGIES, ARGUING THAT DIGITAL LEARNING (AS A NEW VERSION OF DISTANCE EDUCATION) IS AN EDUCATIONAL DISRUPTION BOTH BECAUSE IT BRINGS ABOUT A DRASTIC CHANGE OF MEDIA AND METHODS AND BECAUSE IT IS CONTINUALLY GAINING GROUND FROM CONVENTIONAL FORMATS. FINALLY, IT IS CLAIMED THAT THIS TREND IS NOT EXHAUSTED, BUT CONTINUES TO PENETRATE AND DEEPEN THE BREAK WITH NEW TECHNOLOGIES, SUCH AS LEARNING ANALYTICS, ADAPTIVE LEARNING AND MOBILE LEARNING.
TEACHING HAS MOVED ONLINE AS THE WORLD HAS MOVED ONLINE AND LEARNING IS LOSING ITS SENSE OF PHYSICAL LOCATION WITH THE AVAILABILITY OF MANY DIFFERENT OPTIONS FROM MOBILE TO MOOC (MASSIVE OPEN ONLINE COURSE). THE IMPACT OF ONLINE LEARNING IS NOT CONFINED TO DISTANCE LEARNING; WHEN A STUDENT ATTENDS A CAMPUS UNIVERSITY THEY ARE NOW AS LIKELY TO MEET WITH THEIR FELLOW LEARNERS VIRTUALLY AS FACE TO FACE. THE EDUCATION SECTOR HAS YET TO FULLY ADAPT TO WHAT THIS MEANS, AND INDEED THERE STRONG SIGNS OF A BUILT IN RESILIENCE FROM PROVIDERS, EMPLOYERS AND STUDENTS THEMSELVES WHICH MAY MEAN AN APPARENT EVOLUTION IS MORE LIKELY THAN A REVOLUTION. AT THE SAME TIME, THERE ARE SOME QUIET CHANGES UNDERWAY THAT MEAN WE SHOULD BE PREPARING TO INNOVATE FOR THE REVOLUTION TO COME. SOME OF THOSE CHANGES ARE CONSIDERED IN WORK UNDERTAKEN AT THE OPEN UNIVERSITY THAT HAS BEEN DISSEMINATED IN A SERIES OF INNOVATING PEDAGOGY REPORTS. THESE REPORTS ALLOW THE ACADEMIC AUTHORS TO BE MORE SPECULATIVE THAN IS USUAL PRACTICE AND ENGAGE IN CONSIDERING THE FUTURE, WHILE REMAINING BASED ON A VIEW OF WHAT IS HAPPENING IN THE SECTOR. IN PARTICULAR THEY ADOPT A POSITION FOCUSED ON PEDAGOGY THAT BALANCES TECHNOLOGY-BASED FUTUROLOGY THAT CAN DOMINATE YET FAIL TO RESONATE WITH THOSE ACTUALLY INVOLVED IN THE TEACHING PROCESS. THE ANNUAL INNOVATING PEDAGOGY REPORTS COVER 10 TOPICS EACH, WITH SOME DELIBERATE OVERLAP FROM YEAR TO YEAR AND DEVELOPMENT OF THEMES THAT SHOW INNOVATIONS MOVING INTO TEACHING PRACTICE. THIS IS ILLUSTRATED BY TWO CASES, THE IMPACT OF MOOCS AND THE APPLICATION OF LEARNING DESIGN AND ANALYTICS. THE DEVELOPMENT OF MOOCS DEMONSTRATES THE VALUE OF REVIEWING PEDAGOGY THAT ALIGNS WITH TECHNOLOGY. WHILE THE USE OF LEARNING DESIGN AND LEARNING ANALYTICS DEMONSTRATES HOW IMPROVEMENTS IN THE WAY WE DESCRIBE OUR LEARNING PROCESSES AND THE WAY WE UNDERSTAND LEARNER BEHAVIOUR IS HELPING DETERMINE HOW CHOICES IN PEDAGOGY IMPACT ON STUDENT SATISFACTION, PROGRESSION AND SUCCESS.
LEARNING MANAGEMENT SYSTEMS ARE WEB-BASED SYSTEMS IN WHICH LEARNERS CAN INTERACT WITH CONTENT/LEARNING RESOURCES AND ASSESSMENTS, AS WELL AS OTHER LEARNERS AND INSTRUCTORS. LMSS HAVE BEEN WIDELY USED ESPECIALLY SINCE THE BEGINNING OF THE INFORMATION AGE. IN THE CONTEXT OF THIS STUDY, THE AIM WAS TO DETERMINE THE EXPECTATIONS AND NEEDS OF THE LEARNERS, WHO ARE CONSIDERED TO BE ONE OF THE MOST IMPORTANT STAKEHOLDERS OF THE LMSS. AN OPEN-ENDED QUESTIONNAIRE AND A SEMI-STRUCTURED INTERVIEW FORM PREPARED BY THE RESEARCHERS WERE USED AS DATA COLLECTION TOOLS. CONTENT ANALYSIS WAS PERFORMED TO ANALYZE OPEN-ENDED QUESTIONS AND INTERVIEW DATA. ACCORDING TO THE FINDINGS IT WAS SEEN THAT LEARNERS WANT MORE ENTERTAINING AND SELF-MONITORING ENVIRONMENTS, ESPECIALLY WITH THE ELEMENTS OF GAMIFICATION. IT WAS ALSO SEEN THAT THE LEARNING ENVIRONMENTS HAVE REPORTING AND PREDICTIVE CAPABILITY ON STUDENT ACHIEVEMENT. LEARNERS' NEEDS AND EXPECTATIONS MATCH WITH THIRD-GENERATION LEARNING MANAGEMENT SYSTEMS. THE THIRD-GENERATION LEARNING MANAGEMENT SYSTEMS CAN BE DEVELOPED THROUGH EDUCATIONAL DATA MINING AND LEARNING ANALYTICS. WITHIN THE SCOPE OF THIS RESEARCH, THE LEARNER EXPECTATIONS AND NEEDS WERE DISCUSSED IN THE CONTEXT OF THE THIRD GENERATION LEARNING MANAGEMENT SYSTEMS, INTERVENTION AND TYPES OF INTERVENTION.
RESEARCH INTO SELF-REGULATED LEARNING HAS TRADITIONALLY RELIED UPON SELF-REPORTED DATA. WHILE THERE IS A RICH BODY OF LITERATURE THAT HAS EXTRACTED INVALUABLE INFORMATION FROM SUCH SOURCES, IT SUFFERS FROM A NUMBER OF SHORTCOMINGS. FOR INSTANCE, IT HAS BEEN SHOWN THAT SURVEYS OFTEN PROVIDE INSIGHT INTO STUDENTS' PERCEPTIONS ABOUT LEARNING RATHER THAN HOW STUDENTS ACTUALLY EMPLOY STUDY TACTICS AND LEARNING STRATEGIES. ACCORDINGLY, RECENT RESEARCH HAS SOUGHT TO ASSESS STUDENTS' LEARNING STRATEGIES AND, BY EXTENSION, THEIR SELF-REGULATED LEARNING VIA TRACE DATA COLLECTED FROM DIGITAL LEARNING ENVIRONMENTS. A NUMBER OF STUDIES HAVE AMPLY DEMONSTRATED THE ABILITY OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS METHODS TO IDENTIFY PATTERNS INDICATIVE OF LEARNING STRATEGIES WITHIN TRACE LOG DATA. HOWEVER, MANY OF THESE METHODS ARE LIMITED IN THEIR ABILITY TO DESCRIBE AND INTERPRET DIFFERENCES BETWEEN EXTRACTED LATENT REPRESENTATIONS AT VARYING LEVELS OF GRANULARITY (FOR INSTANCE, IN TERMS OF THE UNDERLYING DATA OF STUDENT ACTIONS AND BEHAVIOR). TO ADDRESS THIS LIMITATION, THE PRESENT STUDY PROPOSES A NEW METHODOLOGY WHEREBY INTERPRETABLE REPRESENTATIONS OF STUDENT'S SELF-REGULATING BEHAVIOR ARE DERIVED AT TWO THEORETICALLY INSPIRED LEVELS: THAT OF LEARNING STRATEGIES, AND THE STUDY TACTICS THAT COMPOSE THEM.
FOR STUDENTS, IN PARTICULAR FRESHMEN, THE DEGREE PATHWAY FROM SEMESTER TO SEMESTER IS NOT THAT TRANSPARENT, ALTHOUGH STUDENTS HAVE A REASONABLE IDEA WHAT COURSES ARE EXPECTED TO BE TAKEN EACH SEMESTER. AN OFTEN-PONDERED QUESTION BY STUDENTS IS: "WHAT CAN I EXPECT IN THE NEXT SEMESTER?" MORE PRECISELY, GIVEN THE COMMITMENT AND ENGAGEMENT I PRESENTED IN THIS PARTICULAR COURSE AND THE RESPECTIVE PERFORMANCE I ACHIEVED, CAN I EXPECT A SIMILAR OUTCOME IN THE NEXT SEMESTER IN THE PARTICULAR COURSE I SELECTED? ARE THE DEMANDS AND EXPECTATIONS IN THIS COURSE MUCH HIGHER SO THAT I NEED TO ADJUST MY COMMITMENT AND ENGAGEMENT AND OVERALL WORKLOAD IF I EXPECT A SIMILAR OUTCOME? IS IT BETTER TO DROP A COURSE TO MANAGE EXPECTATIONS RATHER THAN TO (PREDICTABLY) FAIL, AND PERHAPS HAVE TO LEAVE THE DEGREE ALTOGETHER? DEGREE AND COURSE ADVISORS AND STUDENT SUPPORT UNITS FIND IT CHALLENGING TO PROVIDE EVIDENCE BASED ADVISE TO STUDENTS. THIS PAPER PRESENTS RESEARCH INTO EDUCATIONAL PROCESS MINING AND STUDENT DATA ANALYTICS IN A WHOLE UNIVERSITY SCALE APPROACH WITH THE AIM OF PROVIDING INSIGHT INTO THE DEGREE PATHWAY QUESTIONS RAISED ABOVE. THE BETA-VERSION OF OUR COURSE LEVEL DEGREE PATHWAY TOOL HAS BEEN USED TO SHED LIGHT FOR UNIVERSITY STAFF AND STUDENTS ALIKE INTO OUR UNIVERSITY'S 1,300 DEGREES AND ASSOCIATED 6 MILLION COURSE ENROLMENTS OVER THE PAST 20 YEARS.
RESEARCH SUGGESTS THAT SCAFFOLDING IS AN IMPORTANT ELEMENT WHEN GAINING PROBLEM-SOLVING SKILLS. HOWEVER, VERY LITTLE RESEARCH HAS INVESTIGATED THE ROLE OF FADING AND EXPLORED ITS IMPLICATIONS IN THE CONTEXT OF DEEP LEARNING. TO ADDRESS THIS GAP, WE INVESTIGATED THE EFFECTS OF FADING QUESTION PROMPTS ON ARGUMENTATION AS LEARNERS WERE GIVEN TWO FADING SCHEDULES, (1) SUSTAINED SCAFFOLD SCHEDULE, AND (2) FADED SCAFFOLD SCHEDULE. WE EMPLOYED THREE DIFFERENT MEASUREMENTS OF ARGUMENTATION QUALITY TO TRIANGULATE THE RESULTS. WHEN USING THE RUBRIC ASSESSMENT METHOD, WE FOUND NO SIGNIFICANT DIFFERENCES BETWEEN THE TWO CONDITIONS IN ARGUMENTATION QUALITY. HOWEVER, WHEN USING LEARNING ANALYTICS TO EXAMINE THE CONCEPTUAL LINKAGES OF STUDENTS' KNOWLEDGE STRUCTURE, WE FOUND THAT STUDENTS IN THE SUSTAINED SCAFFOLDING CONDITION PERFORMED SIGNIFICANTLY BETTER THAN THE STUDENTS IN THE FADED CONDITION. THE RESULTS INDICATED THAT (A) SCAFFOLDS SHOULD NOT BE FADED BEFORE STUDENTS HAVE ACQUIRED THE NECESSARY PROBLEM-SOLVING PROCESSES AND (B) THAT NOVICES MIGHT NEED A FULLER SET OF SCAFFOLDS FOR A LONGER PERIOD TO SUPPORT THEIR PROBLEM-SOLVING PERFORMANCE. IMPLICATIONS FOR THEORY AND METHODS ARE DISCUSSED. (C) 2018 ELSEVIER LTD. ALL RIGHTS RESERVED.
OBJECTIVE. TO COLLECT THE RICHLY TEXTURED VIEWPOINTS OF ACADEMIC LIBRARIANS AND STUDENTS ON OBSTACLES TO LIBRARY INTEGRATION IN INSTITUTIONAL INFORMATION SYSTEMS, NAMELY STUDENT SUCCESS TECHNOLOGIES AND LEARNING ANALYTICS. THE AIM IS TO CONTRIBUTE WITH AN INITIAL SET OF RECURRING THEMES TO BASELINE KNOWLEDGE ON SPANISH AND GREEK PUBLIC UNIVERSITY LIBRARIES' PROSPECTS OF ENGAGING IN THIS NEW TYPE OF INTERVENTION. METHODOLOGY. INTERVIEWS WERE CONDUCTED BETWEEN JUNE AND NOVEMBER 2016 IN TWO UNIVERSITIES IN SPAIN AND GREECE AND WERE CONTINUOUSLY CROSS-CHECKED AGAINST CURRENT LITERATURE ON THE ACADEMIC CLIMATE IN AN INTERDISCIPLINARY APPROACH. THIS PROCESS HELPED TO EFFECTIVELY OUTLINE A CONCEPTUAL FRAMEWORK FOR UNDERSTANDING ORGANISATIONAL FORCES AND OPERATIONAL ISSUES THAT COULD AFFECT THE PROCESS OF CONNECTING LIBRARY USE DATA TO WIDER STUDENT SUPPORT SYSTEMS. RESULTS. INTERVIEWEES' FEEDBACK REVEALED THAT THE PRACTICAL DIKCULTIES INVOLVED IN SYSTEMATICALLY TRACKING IN-LIBRARY STUDENT ACTIVITY WORKLOWS AND CONNECTING THEM WITH CAMPUS-WIDE OR EVEN INTERINSTITUTIONAL LEARNING ANALYTICS INITIATIVES ARE ONLY SOME OF THE OPERATIONALISATION CHALLENGES. INFRASTRUCTURAL CAPACITY-RELATED CONSIDERATIONS, THE PROFESSIONAL DEVELOPMENT OF LIBRARIANS, USER CONTROL AND STRONG TEACHER BIAS CONCERNS ARE ALL HIGH ON THE STAKEHOLDER LIST OF INHIBITORS THAT, IF THEY ARE NOT TACKLED, COULD EVENTUALLY JEOPARDIZE A CO-CREATION AND SERVICE INNOVATION OPPORTUNITY OF UNIQUE VALUE. EASINESS OF USE, VOLUNTARINESS AND FEEDBACK WERE REPORTED AMONG THE MAIN CRITERIA WITH WHICH LIBRARY DATA INTEGRATION IN LEARNING ANALYTICS SYSTEMS MUST COMPLY. A CHANGE IN DATA COLLECTION PRACTICES, THE ACTIVE PURSUIT OF STAKEHOLDER ENGAGEMENT AND THE RECONSIDERATION OF EXISTING RESOURCES, NAMELY INFRASTRUCTURES AND FUNDING, WERE ALSO GIVEN AS MAJOR STRATEGIC PRIORITIES FOR THE SUCCESSFUL IMPLEMENTATION OF THIS TYPE OF INTERVENTION.
THE USE OF MASSIVE OPEN ONLINE COURSES (MOOCS) IS INCREASING WORLDWIDE AND BRINGS A REVOLUTION IN EDUCATION. THE APPLICATION OF MOOCS HAS TECHNOLOGICAL BUT ALSO PEDAGOGICAL IMPLICATIONS. MOOCS ARE USUALLY DRIVEN BY SHORT VIDEO LESSONS, AUTOMATIC CORRECTION EXERCISES, AND THE TECHNOLOGICAL PLATFORMS CAN IMPLEMENT GAMIFICATION OR LEARNING ANALYTICS TECHNIQUES. HOWEVER, MUCH MORE ANALYSIS IS REQUIRED ABOUT THE SUCCESS OR FAILURE OF THESE INITIATIVES IN ORDER TO KNOW IF THIS NEW MOOCS PARADIGM IS APPROPRIATE FOR DIFFERENT LEARNING SITUATIONS. THIS WORK AIMS AT ANALYZING AND REPORTING WHETHER THE INTRODUCTION OF MOOCS TECHNOLOGY WAS GOOD OR NOT IN A CASE STUDY WITH THE KHAN ACADEMY PLATFORM AT OUR UNIVERSITY WITH STUDENTS IN A REMEDIAL PHYSICS COURSE IN ENGINEERING EDUCATION. RESULTS SHOW THAT STUDENTS IMPROVED THEIR GRADES SIGNIFICANTLY WHEN USING MOOCS TECHNOLOGY, STUDENT SATISFACTION WAS HIGH REGARDING THE EXPERIENCE AND FOR MOST OF THE DIFFERENT PROVIDED FEATURES, AND THERE WERE GOOD LEVELS OF INTERACTION WITH THE PLATFORM (E.G., NUMBER OF COMPLETED VIDEOS OR PROFICIENT EXERCISES), AND ALSO THE ACTIVITY DISTRIBUTION FOR THE DIFFERENT TOPICS AND TYPES OF ACTIVITIES WAS APPROPRIATE. (C) 2016 WILEY PERIODICALS, INC.
21ST CENTURY EDUCATION DEVELOPS PROFESSIONAL COMPETENCIES SUCH AS LIFELONG LEARNING, CRITICAL THINKING AND CREATIVITY. LEARNING ANALYTICS (LA) AND BIG DATA APPROACHES ARE EMERGING TO DISCOVER READINESS PATTERNS OF HIGHER EDUCATION LEARNERS TOWARDS EMBRACING THESE COMPETENCES. TO SUPPLEMENT FORMAL EDUCATION INTO BUILDING THESE CAREER-ORIENTED HABITS, A PORTAL APPLICATION IS PROPOSED BY FEEDING DATA STREAMS GATHERED FROM LEARNERS THROUGHOUT THEIR HIGHER-EDUCATION LIFETIME, INTO AN ANALYTICS ENGINE THAT REVEALS LEVELS OF PROFESSIONAL COMPETENCIES ACHIEVEMENT AND TELLS LEARNERS HOW TO INCREASE THEIR CAREER READINESS. BIG DATA TECHNIQUES ARE USED TO GATHER AND CLUSTER DATA STREAMS, WHEREAS LA TECHNIQUES EXAMINE THESE DATA TO DERIVE LEVELS OF CAREER READINESS, WHICH GUIDE HIGHER EDUCATION LEARNERS INTO A SUCCESSFUL CAREER PATH. THE PROPOSED METHODOLOGY STARTS BY DEFINING AND VALIDATING STANDARD CAREER DISPOSITION CRITERIA INSPIRED FROM EDUCATION PSYCHOLOGY LITERATURE. THESE INTANGIBLE DISPOSITION INDICATORS AND OTHER CAREER-ORIENTED DATA ARE STORED IN A DATA WAREHOUSE FOR FURTHER AGGREGATION AND ANALYSIS, TO EVALUATE SIMILARITIES AMONG CAREER PATTERNS AND TO CLUSTER INDIVIDUALS WHOSE CAREER PROSPECTS ARE DEEMED SIMILAR, INTO A COMMUNITY OF PRACTICE (COP). WE SYNTHESIZE THIS NOVEL ONLINE SOCIAL STRUCTURE AS A RESULT OF THE PROPOSED PORTAL PROCESSES TO AUGMENT FORMAL EDUCATION WITH A VIRTUAL LEARNING ENVIRONMENT. THIS INFORMAL CLASSROOM-LIKE STRUCTURE IS SUPERVISED BY A PROFESSIONAL MENTOR TO BRIDGE DIVERSE VIEWPOINTS AND INSTILL A JOINT EFFORT TO LEVERAGE FUTURE WORKFORCE DEVELOPMENTS. COP ALSO PROVIDES INDICATORS AND MEANS TO INTERVENE IN ORDER TO POSITIVELY AFFECT CAREER READINESS.
RECENT TECHNOLOGIES ARE AN OPPORTUNITY FOR CREATING ADVANCED DATA ANALYTICS TOOLS FOR VARIOUS DOMAINS. THERE ARE DIVERSE METHODS BASED ON SMART TECHNOLOGIES FOR IMPLEMENTING AND SUPPORTING DATA ANALYTICS IN EDUCATIONAL ENVIRONMENTS, E.G. DESCRIPTIVE METHODS (REPORTS), DIAGNOSTIC METHODS (AUTOMATED ANALYSIS), PREDICTIVE AND PRESCRIPTIVE METHODS (RECOMMENDATIONS, ALERTS). THE EDUCATIONAL DATA COME FROM ONLINE SURVEYS, BIOMETRIC SOURCES, BUT, MOSTLY, FROM ACTIVITIES IN LEARNING MANAGEMENT SYSTEMS (LMS). THE CURRENT PAPER PRESENTS A NEW LEARNING ANALYTICS INSTRUMENT - LEPROVE - UCLUSTER FOR MOODIE LMS, WHICH CREATES VIRTUAL PROFILES OF THE STUDENTS AND AGGREGATES THOSE PROFILES INTO CLUSTERS WHICH ARE FURTHER USED BY TEACHERS TO OPTIMIZE THE TEACHING PROCESS. THE TOOL IS SUITABLE FOR TODAY'S SOCIAL AND CULTURAL VALUES, AS THE STUDENTS' PERFORMANCES (REFLECTED IN GRADES, COMPLETED ASSIGNMENTS) ARE NOT ANALYSED ONLY BASED ON A STANDARD A PRIORI SETTLED BY THE TEACHER, BUT CONSIDERING THE COLLEAGUES' PERFORMANCES AND SOCIAL INTERACTIONS AS WELL. THUS, THE TEACHER CAN USE THE RESULTS OBTAINED BY USING LEPROVE - UCLUSTER TO ADJUST THE HOMEWORK/EXAMS DIFFICULTY TO THE CLASS CONTEXT AND CAN RECOMMEND FURTHER USEFUL RESOURCES TO INDIVIDUAL STUDENTS AND CLUSTERS OF STUDENTS. THE TECHNOLOGIES AND METHODS WHICH ARE USED TO IMPLEMENT OUR INSTRUMENT ARE MODERN: SCALA, SPARK - A GENERAL ENGINE FOR BIG DATA PROCESSING, ANGULAR JS, DATA MINING ALGORITHMS (K-MEANS) AND BIG DATA SAMPLING ALGORITHMS. TO VALIDATE THE FUNCTIONALITY OF THE PROPOSED TOOL, WE USED 2167 ANONYMISED STUDENTS' PROFILES WHICH WERE IMPORTED FROM A MOODIE DATA SET FREELY AVAILABLE FOR RESEARCH PURPOSES. POSSIBLE INTERPRETATIONS OF DATA AND DIRECTIONS TO BE FOLLOWED BY TEACHERS TO IMPROVE THEIR TEACHING SKILLS ARE ALSO OFFERED.
IN THIS PAPER, WE AIM TO EXPLORE STUDENTS' STUDY APPROACHES (E.G., DEEP, STRATEGIC, SURFACE) FROM THE LOGS COLLECTED BY AN ELECTRONIC TEXTBOOK (EBOOK) SYSTEM. DATA WAS COLLECTED FROM 89 STUDENTS RELATED TO THEIR READING ACTIVITIES BOTH IN AND OUT OF THE CLASS IN A FRESHMAN ENGLISH COURSE. STUDENTS ARE GIVEN A TASK TO STUDY READING MATERIALS THROUGH THE EBOOK SYSTEM, HIGHLIGHT THE TEXT THAT IS RELATED TO THE MAIN OR SUPPORTING IDEAS, AND ANSWER THE QUESTIONS PREPARED FOR MEASURING THEIR LEVEL OF COMPREHENSION. STUDENTS IN AND OUT OF CLASS READING TIMES AND THEIR USAGE OF THE MARKER FEATURE WERE USED AS A PROXY TO UNDERSTAND THEIR STUDY APPROACHES. WE USED THEORY-DRIVEN AND DATA-DRIVEN APPROACHES TOGETHER TO MODEL THE STUDY APPROACHES OF STUDENTS. OUR RESULTS SHOWED THAT THREE GROUPS OF STUDENTS WHO HAVE DIFFERENT STUDY APPROACHES COULD BE IDENTIFIED. RELATIONSHIPS BETWEEN STUDENTS' READING BEHAVIORS AND THEIR ACADEMIC PERFORMANCE IS ALSO INVESTIGATED BY USING ASSOCIATION RULE MINING ANALYSIS. OBTAINED RESULTS ARE DISCUSSED IN TERMS OF MONITORING, FEEDBACK, PREDICTING LEARNING OUTCOMES, AND IDENTIFYING PROBLEMS WITH THE CONTENT DESIGN.
MODEL EVALUATION - THE PROCESS OF MAKING INFERENCES ABOUT THE PERFORMANCE OF PREDICTIVE MODELS - IS A CRITICAL COMPONENT OF PREDICTIVE MODELLING RESEARCH IN LEARNING ANALYTICS. WE SURVEY THE STATE OF THE PRACTICE WITH RESPECT TO MODEL EVALUATION IN LEARNING ANALYTICS, WHICH OVERWHELMINGLY USES ONLY NAIVE METHODS FOR MODEL EVALUATION OR STATISTICAL TESTS THAT ARE NOT APPROPRIATE FOR PREDICTIVE MODEL EVALUATION. WE CONDUCT A CRITICAL COMPARISON OF BOTH NULL HYPOTHESIS SIGNIFICANCE TESTING (NHST) AND A PREFERRED BAYESIAN METHOD FOR MODEL EVALUATION. FINALLY, WE APPLY THREE METHODS - THE NAIVE AVERAGE COMMONLY USED IN LEARNING ANALYTICS, NHST, AND BAYESIAN - TO A PREDICTIVE MODELLING EXPERIMENT ON A LARGE SET OF MOOC DATA. WE COMPARE 96 DIFFERENT PREDICTIVE MODELS, INCLUDING DIFFERENT FEATURE SETS, STATISTICAL MODELLING ALGORITHMS, AND TUNING HYPERPARAMETERS FOR EACH, USING THIS CASE STUDY TO DEMONSTRATE THE DIFFERENT EXPERIMENTAL CONCLUSIONS THESE EVALUATION TECHNIQUES PROVIDE.
THE PROVISION OF AWARENESS IS A WELL-KNOWN METHOD FOR FOSTERING STUDENTS' SELFREFLECTION, A METACOGNITIVE SKILL OFTEN RELATED TO ACADEMIC SUCCESS AND CONSIDERED ONE OF THE KEY SKILLS OF THE 21ST CENTURY. ALTHOUGH THE INFORMATION DISCOVERED USING LEARNING ANALYTICS TECHNIQUES CAN BE USEFUL IN FOSTERING SELF-REFLECTION, ITS DELIVERY TO STUDENTS SHOULD BE DONE WITHOUT DISTRACTING THEM FROM THEIR LEARNING GOALS. THIS PAPER PRESENTS A VISUALIZATION TECHNIQUE BASED ON SIMILARITY MEASURES AND THEIR RELATIONSHIP WITH FINAL COURSE RESULTS, IN ORDER TO FOSTER STUDENTS' AWARENESS. THE APPROACH IS BASED ON THE IDEA THAT ` STUDENTS THAT BEHAVE SIMILARLY ARE GRADED SIMILARLY'. THIS IDEA IS VALIDATED WITH AN EMPIRICAL EVALUATION TO DETERMINE THE VISUALIZATION TECHNIQUE'S ACCURACY WHEN USED TO FIND A RELATIONSHIP BETWEEN SIMILARITY AND GRADE. THE STUDY USED A PREVIOUSLY COLLECTED DATASET AND SEVERAL VOLUNTEERS WERE ASKED TO ESTIMATE THE STUDENTS' SCORES WITH GRAPHICS PROVIDED AS THE ONLY SOURCE OF INFORMATION. THE OBTAINED RESULTS VALIDATE THE PROPOSAL AS A MEANS TO FOSTER EFFECTIVE SELF-REFLECTION.
PROVIDING HIGH-QUALITY COURSES IS OF UTMOST IMPORTANCE TO DRIVE SUCCESSFUL LEARNING. THIS COMPELS COURSE AUTHORS TO CONTINUOUSLY REVIEW THEIR CONTENTS TO MEET LEARNERS' NEEDS. HOWEVER, IT IS CHALLENGING FOR THEM TO DETECT THE READING BARRIERS THAT LEARNERS FACE WITH CONTENT, AND TO IDENTIFY HOW THEIR COURSES CAN BE IMPROVED ACCORDINGLY. IN THIS PAPER, WE PROPOSE A LEARNING ANALYTICS APPROACH FOR ASSISTING COURSE AUTHORS PERFORMING THESE TASKS. USING LOGS OF LEARNERS' ACTIVITY, A SET OF INDICATORS RELATED TO COURSE READING ACTIVITY ARE COMPUTED AND USED TO DETECT ISSUES AND TO SUGGEST CONTENT REVISIONS. THE RESULTS ARE PRESENTED TO AUTHORS THROUGH COREADA, A LEARNING DASHBOARD EMPOWERED WITH ASSISTIVE FEATURES. WE INSTANTIATE OUR PROPOSALS USING THE LOGS OF A MAJOR EUROPEAN E-LEARNING PLATFORM, AND VALIDATE THEM THROUGH A STUDY. STUDY RESULTS SHOW THE EFFECTIVENESS OF OUR APPROACH PROVIDING AUTHORS WITH MORE AWARENESS AND GUIDANCE IN IMPROVING THEIR COURSES, TO BETTER SUIT LEARNERS' REQUIREMENTS.
COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) IS A PEDAGOGICAL STRATEGY ASSOCIATED WITH HOW LEARNERS CONSTRUCT KNOWLEDGE WITH A GROUP BY COMPUTER-BASED LEARNING SYSTEM. IN RECENT YEARS, MOST OF THE COMPUTER-BASED LEARNING SYSTEMS RECORD THE INTERACTION LOG OF EACH LEARNER WHEN DEVELOPING COURSE ASSIGNMENTS. HOWEVER, THE RECORDED DATA IS FACING A CHALLENGE TO EXPOSE THE LEARNERS BEHAVIORS DURING THE COURSE AND TO DESIGN A COMPUTER-SUPPORTED COLLABORATIVE LEARNING ACTIVITY. TO ADDRESS THOSE CHALLENGES IN THIS PAPER, A NOVEL COLLABORATIVE PROGRAMMING TOOL CALLED SOFTWARE PROJECT DEVELOPMENT AND INSIGHT LEARNING ENVIRONMENT (SPDI LEARNING ENVIRONMENT) IS DESCRIBED. THE SPDI LEARNING ENVIRONMENT ALLOWS LEARNERS OF COMPUTER SCIENCE TO DEVELOP COURSE ASSIGNMENTS COLLABORATIVELY. BESIDES, IT ALLOWS INSTRUCTORS TO INVESTIGATE THE LEARNERS BEHAVIORS BY ASSOCIATING A WEB-BASED INTEGRATED DEVELOPMENT ENVIRONMENT (IDE) WITH BIG DATA ANALYSIS PIPELINE AND VISUALIZATION DASHBOARD. IN ADDITION TO COLLECT REAL DATA FROM COURSES, WE DESIGNED LEARNING ACTIVITIES TO HELP TEACHERS TO ENGAGE THE FIELD OF CSCL AND LEARNING ANALYTICS.
IN THIS PAPER, WE STUDY STUDENTS' LEARNING EFFECTIVENESS THROUGH THEIR USE OF A HOMEGROWN EDUCATIONAL TECHNOLOGY, WEB PROGRAMMING GRADING ASSISTANT (WPGA), WHICH FACILITATES GRADING AND FEEDBACK DELIVERY OF PAPER-BASED ASSESSMENTS. WE DESIGNED A CLASSROOM STUDY AND COLLECTED DATA FROM A LOWER-DIVISION BLENDED-INSTRUCTION COMPUTER SCIENCE CLASS. WE TRACKED AND MODELED STUDENTS' REVIEWING AND REFLECTING BEHAVIORS FROM WPGA. THE RESULTS SHOW THAT STUDENTS DEMONSTRATED AN EFFORT AND DESIRE TO REVIEW ASSESSMENTS REGARDLESS OF IF THEY WERE GRADED FOR ACADEMIC PERFORMANCE OR FOR ATTENDANCE. HARDWORKING 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, THIS WORK CONTRIBUTES TO THE AGGREGATION OF MULTIDIMENSIONAL LEARNING ANALYTICS ACROSS THE PHYSICAL AND CYBERSPHERE.
WHEN PEOPLE ENGAGE IN SOCIAL NETWORKING SITES, THEY INFLUENCE ONE ANOTHER THROUGH THEIR CONTRIBUTIONS. PRIOR RESEARCH SUGGESTS THAT THE INTERPLAY BETWEEN INDIVIDUAL DIFFERENCES AND ENVIRONMENTAL VARIABLES, SUCH AS A PERSON'S OPENNESS TO CONFLICTING INFORMATION, CAN GIVE RISE TO EITHER PUBLIC SPHERES OR ECHO CHAMBERS. IN THIS WORK, WE AIM TO UNRAVEL CRITICAL PROCESSES OF THIS INTERPLAY IN THE CONTEXT OF LEARNING. IN PARTICULAR, WE OBSERVE HIGH SCHOOL STUDENTS' INFORMATION BEHAVIOR (SEARCH AND EVALUATION OF WEB RESOURCES) TO BETTER UNDERSTAND A POTENTIAL COUPLING BETWEEN CONFIRMATORY SEARCH AND POLARIZATION AND, IN FURTHER CONSEQUENCE, IMPROVE LEARNING ANALYTICS AND INFORMATION SERVICES FOR INDIVIDUAL AND COLLECTIVE SEARCH IN LEARNING SCENARIOS. IN AN EMPIRICAL STUDY, WE HAD 91 HIGH SCHOOL STUDENTS PERFORMING AN INFORMATION SEARCH IN A SOCIAL BOOKMARKING ENVIRONMENT. GATHERED LOG DATA WAS USED TO COMPUTE INDICES OF CONFIRMATORY SEARCH AND POLARISATION AS WELL AS TO ANALYZE THE IMPACT OF SOCIAL STIMULATION. WE FIND CONFIRMATORY SEARCH AND POLARIZATION TO CORRELATE POSITIVELY AND SOCIAL STIMULATION TO MITIGATE, I.E., REDUCE THE TWO VARIABLES' RELATIONSHIP. FROM THESE FINDINGS, WE DERIVE PRACTICAL IMPLICATIONS FOR FUTURE WORK THAT AIMS TO REFINE OUR FORMALISM TO COMPUTE CONFIRMATORY SEARCH AND POLARISATION INDICES AND TO APPLY IT FOR DEPOLARIZING INFORMATION SERVICES.
THE MASSIVE ONLINE OPEN COURSE (MOOC) REVOLUTION OFFERS OPPORTUNITIES FOR LEARNING ANALYTICS REGARDING THE DIVERSITY IN LEARNING ACTIVITIES OF MOOCS. IN MOOCS, VIDEO LECTURES REPRESENT THE PRINCIPAL LEARNING RESOURCE, AND LEARNERS SPEND MOST OF THEIR TIME ENGAGED IN SELF-PACED, INDEPENDENT LEARNING. THEREFORE, MOOCS PRESENT AN INNOVATIVE FORM OF VIDEO-BASED LEARNING. HOW TO IMPROVE THIS LEARNING ENVIRONMENT BY ADDING ADDITIONAL FEATURES TO VIDEO LECTURES REPRESENTS A PATH FOR PROGRESS. FEW STUDIES HAVE INVESTIGATED THE INTERACTION BETWEEN LEARNERS AND THE VIDEO CONTENT; HOWEVER, IMPROVEMENT IN THIS DOMAIN IS NECESSARY. THIS PAPER PROPOSES A VIDEO-BASED LEARNING ANALYSIS APPROACH CALLED VIDEOMARK, WHICH ENABLES LEARNERS TO JUDICIOUSLY REVIEW, CONSOLIDATE, AND CLARIFY SPECIFIC CONCEPTS INTRODUCED IN MOOCS. THIS ANALYTICAL APPROACH CAN BE APPLIED TO MOOC-STYLE SHARECOURSE PROGRAMS IN TAIWAN. FINALLY, THIS ANALYTICAL APPROACH IS PRESENTED AS A VISUAL KEYWORD CLOUD FOR LEARNERS TO CLARIFY SPECIFIC LEARNING CONCEPTS IN THE MOOC LEARNING PROCESS.
SOCIAL LEARNING ANALYTICS (SLA) TOOLS ARE DESIGNED TO VISUALLY DEMONSTRATE ONLINE DISCUSSIONS WITH A GOAL TO FOSTER STUDENT ENGAGEMENT. HOWEVER, EMPIRICAL STUDIES INDICATE CONTROVERSIAL RESULTS OF THE EFFECT OF SLA TOOLS ON STUDENT ENGAGEMENT. THIS DESIGN-BASED RESEARCH DESIGNS A STUDENT-FACING SLA TOOL TO DEMONSTRATE DISCUSSIONS FROM THREE PERSPECTIVES AND FURTHER USES MIXED METHODS TO INVESTIGATE THE EFFECTS OF THE TOOL. RESULTS INDICATE THE TOOLS HAVE POSITIVE INFLUENCES ON INCREASING STUDENT SOCIAL-COGNITIVE ENGAGEMENT. THE SOCIAL NETWORK VISUALIZATION HAS POSITIVE INFLUENCES ON BOTH SOCIALLY ACTIVE AND INACTIVE STUDENTS; IT PARTICULARLY INCREASES PERIPHERAL STUDENTS' SOCIAL ENGAGEMENT. THE TOPIC NETWORK VISUALIZATION IMPROVES ALL STUDENTS' PERSPECTIVE EXPRESSIONS, INDICATING THAT DEMONSTRATING STUDENTS' INTERESTED TOPICS MAY INCREASE COGNITIVE ENGAGEMENT. THE COGNITIVE NETWORK VISUALIZATION TRIGGERS STUDENTS' INFORMATION SHARING, WHICH IS CONSIDERED AS THE BEGINNING OF ONLINE ENGAGEMENT DEVELOPMENT. BASED ON THE RESULTS, THIS RESEARCH PROPOSES INTEGRATED IMPLICATIONS BY CONSIDERING LEARNING THEORY, PEDAGOGICAL SUPPORTS AND TOOL DEVELOPMENT.
THIS POSTER SYNTHESIZES THE DESIGN FEATURES OF A VISUALIZATION LAYER APPLIED ON THE OPEN ACADEMIC ANALYTICS INITIATIVE (OAAI), AN OPEN SOURCE ACADEMIC EARLY ALERT SYSTEM BASED ON PREDICTIVE ANALYTICS. THE POSTER EXPLORES WAYS TO CONVEY THE PREDICTIVE MODEL OUTPUTS AND BENCHMARK STUDENT PERFORMANCES USING VISUALLY INTUITIVE RADAR PLOTS.
CONSIDERABLE LITERATURE EXISTS REGARDING MOOCS. EVALUATIONS OF MOOCS RANGE FROM RINGING ENDORSEMENTS TO ITS VILIFICATION AS A DELIVERY MODEL. MUCH EVALUATION FOCUSES ON COMPLETION RATES AND/OR PARTICIPANT SATISFACTION. OVERALL, MOOCS ARE ILL-DEFINED AND RESEARCHERS STRUGGLE WITH APPROPRIATE EVALUATION CRITERIA BEYOND ATTRITION RATES. IN THIS PAPER, WE PROVIDE A BRIEF HISTORY OF MOOCS, A SUMMARY OF SOME EVALUATION RESEARCH, AND WE PROPOSE A NEW MODEL FOR EVALUATION WITH AN EXAMPLE FROM A PREVIOUSLY-DELIVERED MOOC. MEASUREMENT OF THE MOOC SUCCESS FRAMEWORK THROUGH FOUR STUDENT SATISFACTION TYPES IS PROPOSED IN THIS PAPER WITH A MODEL FOR INFORMAL LEARNING SATISFACTION, ONE OF THE PROPOSED TYPES, THEORIZED AND TESTED. RESULTS INDICATED THEORETICAL UNDERPINNINGS, WHILE INTENDED TO IMPROVE INSTRUCTION, MIGHT NOT HAVE INFLUENCED THE SAME SATISFACTION CONSTRUCT. THEREFORE, FUTURE RESEARCH INTO ALTERNATIVE SATISFACTION FACTOR MODELS IS NEEDED.
THE INTERNATIONAL WORKSHOP OF SMART ENVIRONMENTS AND ANALYTICS ON VIDEO-BASED LEARNING (SE@VBL) AIMS TO CONNECT RESEARCH EFFORTS ON VIDEO-BASED LEARNING WITH SMART ENVIRONMENTS AND ANALYTICS TO CREATE SYNERGIES BETWEEN THESE FIELDS. THE MAIN OBJECTIVE IS TO BUILD A RESEARCH COMMUNITY AROUND THE INTERSECTION OF THESE TOPICAL AREAS. IN PARTICULAR, SE@VBL AIMS TO DEVELOP A CRITICAL DISCUSSION ABOUT THE NEXT GENERATION OF VIDEO-BASED LEARNING ENVIRONMENTS AND THEIR ANALYTICS, THE FORM OF THESE ANALYTICS AND THE WAY THEY CAN BE ANALYZED IN ORDER TO HELP US TO BETTER UNDERSTAND AND IMPROVE THE VALUE OF EDUCATIONAL VIDEOS TO SUPPORT TEACHING AND LEARNING. SE@VBL IS BASED ON THE RATIONALE THAT COMBINING AND ANALYZING LEARNERS' INTERACTIONS WITH OTHER AVAILABLE DATA OBTAINED FROM LEARNERS, NEW AVENUES FOR RESEARCH ON VIDEO-BASED LEARNING HAVE EMERGED. THIS CAN HAVE A SIGNIFICANT IMPACT IN CURRENT EDUCATIONAL TRENDS SUCH AS MASSIVE OPEN ONLINE COURSES (MOOCS) AND FLIPPED CLASSROOM.
RECENT RESEARCH USING LEARNING ANALYTICS DATA TO EXPLORE STUDENT PERFORMANCE OVER THE COURSE OF A TERM SUGGESTS THAT A SUBSTANTIAL PERCENTAGE OF STUDENTS WHO ARE CLASSIFIED AS ACADEMICALLY STRUGGLING MANAGE TO RECOVER. IN THIS STUDY, WE REPORT THE RESULT OF A HAZARD ANALYSIS BASED ON STUDENTS' BEHAVIORAL ENGAGEMENT WITH DIFFERENT DIGITAL INSTRUCTIONAL TECHNOLOGIES OVER THE COURSE OF A SEMESTER. WE OBSERVE SUBSTANTIALLY DIFFERENT ADOPTION AND USE BEHAVIOR BETWEEN STUDENTS WHO DID AND DID NOT EXPERIENCE ACADEMIC DIFFICULTY IN THE COURSE. STUDENTS WHO EXPERIENCED MODERATE ACADEMIC DIFFICULTY BENEFITED THE MOST FROM USING TOOLS THAT HELPED THEM PLAN THEIR STUDY BEHAVIORS. STUDENTS WHO EXPERIENCED MORE SEVERE ACADEMIC DIFFICULTY BENEFITED FROM TOOLS THAT HELPED THEM PREPARE FOR EXAMS. WE OBSERVED THAT STUDENTS ADOPTED MOST TOOLS AND SYSTEM FEATURES BEFORE THEY EXPERIENCED ACADEMIC DIFFICULTY, AND STUDENTS WHO ADOPTED EARLY WERE MORE LIKELY TO RECOVER.
IN THIS PAPER WE DISCUSS THE RESULTS OF A STUDY OF STUDENTS' ACADEMIC PERFORMANCE IN FIRST YEAR GENERAL EDUCATION COURSES. USING DATA FROM 566 STUDENTS WHO RECEIVED INTENSIVE ACADEMIC ADVISING AS PART OF THEIR ENROLLMENT IN THE INSTITUTION'S PREMAJOR/GENERAL EDUCATION PROGRAM, WE INVESTIGATE INDIVIDUAL STUDENT, ORGANIZATIONAL, AND DISCIPLINARY FACTORS THAT MIGHT PREDICT A STUDENTS' POTENTIAL CLASSIFICATION IN AN EARLY WARNING SYSTEM AS WELL AS FACTORS THAT PREDICT IMPROVEMENT AND DECLINE IN THEIR ACADEMIC PERFORMANCE. DISCIPLINARY COURSE TYPE (BASED ON BIGLAN'S [7] TYPOLOGY) WAS SIGNIFICANTLY RELATED TO A STUDENT'S LIKELIHOOD TO ENTER BELOW AVERAGE PERFORMANCE CLASSIFICATIONS. STUDENTS WERE THE MOST LIKELY TO ENTER A CLASSIFICATION IN FIELDS LIKE THE NATURAL SCIENCE, MATHEMATICS, AND ENGINEERING IN COMPARISON TO HUMANITIES COURSES. WE ATTRIBUTE THESE DISPARITIES IN ACADEMIC PERFORMANCE TO DISCIPLINARY NORMS AROUND TEACHING AND ASSESSMENT. IN PARTICULAR, THE TIMING OF ASSESSMENTS PLAYED A MAJOR ROLE IN STUDENTS' ABILITY TO EXIT A CLASSIFICATION. IMPLICATIONS FOR THE DESIGN OF EARLY WARNING ANALYTICS SYSTEMS AS WELL AS ACADEMIC COURSE PLANNING IN HIGHER EDUCATION ARE OFFERED.
IN THIS PAPER WE INTRODUCE SIMPLIFY ITS, A NOVEL APPROACH TO AN ADAPTIVE AND INTELLIGENT TUTORING SYSTEM (ITS) AIMED TO SUPPORT TEACHERS IN ENHANCING THE STUDENT KNOW LEDGE AND SKILLS. UNLIKE OTHER APPROACHES, OUR PROPOSAL DOES NOT INTEND TO REPLACE THE TEACHERS' ROLE, BUT RATHER PROVIDES TEACHERS WITH ACTIONABLE INFORMATION TO MAKE DECISIONS ABOUT HOW TO ADAPT THE LEARNING EXPERIENCE TO THE STUDENT NEEDS. ALSO, IT ADDRESSES TWO OF THE MORE IMPORTANT CHALLENGES IN LEARNING ANALYTICS SOLUTIONS: BUILDING ROBUST CONNECTIONS TO THE LEARNING SCIENCES AND FOCUSING ON THE STUDENT. IN OUR APPROACH, A STUDENT'S SKILL PROFILE IS AUTOMATICALLY ESTIMATED FROM THE STUDENT'S ACTIONS USING COGNITIVE DIAGNOSIS MODELS. THEN, SIMPLIFY ITS USES THE INFORMATION CONTAINED IN STUDENT PROFILE TO RECOMMEND ACTIVITIES DURING THE LEARNING PROCESS BUT ALSO, IT PROVIDES REVIEWS AND PRACTICES OF THOSE CONCEPTS THAT HAVE BEEN LEARNED PREVIOUSLY.
WHEN USED EFFECTIVELY, REFLECTIVE WRITING TASKS CAN DEEPEN LEARNERS' UNDERSTANDING OF KEY CONCEPTS, HELP THEM CRITICALLY APPRAISE THEIR DEVELOPING PROFESSIONAL IDENTITY, AND BUILD QUALITIES FOR LIFELONG LEARNING. AS SUCH, REFLECTING WRITING IS ATTRACTING SUBSTANTIAL INTEREST FROM UNIVERSITIES CONCERNED WITH EXPERIENTIAL LEARNING, REFLECTIVE PRACTICE, AND DEVELOPING A HOLISTIC CONCEPTION OF THE LEARNER. HOWEVER, REFLECTIVE WRITING IS FOR MANY STUDENTS A NOVEL GENRE TO COMPOSE IN, AND TUTORS MAY BE INEXPERIENCED IN ITS ASSESSMENT. WHILE THESE CONDITIONS SET A CHALLENGING CONTEXT FOR AUTOMATED SOLUTIONS, NATURAL LANGUAGE PROCESSING MAY ALSO HELP ADDRESS THE CHALLENGE OF PROVIDING REAL TIME, FORMATIVE FEEDBACK ON DRAFT WRITING. THIS PAPER REPORTS PROGRESS IN DESIGNING A WRITING ANALYTICS APPLICATION, DETAILING THE METHODOLOGY BY WHICH INFORMALLY EXPRESSED RUBRICS ARE MODELLED AS FORMAL RHETORICAL PATTERNS, A CAPABILITY DELIVERED BY A NOVEL WEB APPLICATION. THIS HAS BEEN THROUGH ITERATIVE EVALUATION ON AN INDEPENDENTLY HUMAN-ANNOTATED CORPUS, SHOWING IMPROVEMENTS FROM THE FIRST TO SECOND VERSION. WE CONCLUDE BY DISCUSSING THE REASONS WHY CLASSIFYING REFLECTIVE WRITING HAS PROVEN COMPLEX, AND REFLECT ON THE DESIGN PROCESSES ENABLING WORK ACROSS DISCIPLINARY BOUNDARIES TO DEVELOP THE PROTOTYPE TO ITS CURRENT STATE.
TEACHERS ARE INCREASINGLY BEING ENCOURAGED TO EMBRACE EVIDENCE-BASED PRACTICES. LEARNING ANALYTICS (LA) OFFER GREAT PROMISE IN SUPPORTING THESE BY PROVIDING EVIDENCE FOR TEACHERS AND LEARNERS TO MAKE INFORMED DECISIONS AND TRANSFORM THE EDUCATIONAL EXPERIENCE. HOWEVER, LA LIMITATIONS AND THEIR UPTAKE BY EDUCATORS ARE COMING UNDER CRITICAL SCRUTINY. THIS IS IN PART DUE TO THE LACK OF INVOLVEMENT OF TEACHERS AND LEARNERS IN THE DESIGN OF LA TOOLS. IN THIS PAPER, WE PROPOSE A HUMAN-CENTRED APPROACH TO GENERATE UNDERSTANDING OF TEACHERS' DATA NEEDS THROUGH THE LENS OF THREE KEY PRINCIPLES OF TRANSLUCENCE: VISIBILITY, AWARENESS AND ACCOUNTABILITY. WE ILLUSTRATE OUR APPROACH THROUGH A PARTICIPATORY DESIGN SPRINT TO IDENTIFY HOW TEACHERS TALK ABOUT CLASSROOM DATA. WE DESCRIBE TEACHERS' PERSPECTIVES ON THE EVIDENCE THEY NEED FOR MAKING BETTER-INFORMED DECISIONS AND DISCUSS THE IMPLICATIONS OF OUR APPROACH FOR THE DESIGN OF HUMAN-CENTRED LA IN THE NEXT YEARS.
THE USE OF LEARNING MANAGEMENT SYSTEMS (LMSS) FOR LEARNING AND KNOWLEDGE SHARING HAS ACCELERATED QUICKLY BOTH IN EDUCATION AND CORPORATE WORLDS. DESPITE THE BENEFITS BROUGHT BY LMSS, THE CURRENT SYSTEMS STILL FACE SIGNIFICANT CHALLENGES, INCLUDING THE LACK OF AUTOMATION IN GENERATING QUIZ QUESTIONS AND MANAGING COURSES. OVER THE PAST DECADE, MORE ATTENTION HAS BEEN ACCORDED TO ANALYZING THE RICH LEARNING DATA CAPTURED BY THE SYSTEMS AND DEVELOPING TOOLS THAT SUPPORT CONTEMPORARY LEARNING MODES. THIS ARTICLE CONSIDERS A POPULAR LMS, MOODLE, AND SHOWCASES FOUR INNOVATIVE PROJECTS THAT AIM TO EXTEND THE SYSTEM'S CAPABILITIES AND ADDRESS THE ABOVEMENTIONED PROBLEMS: 1) THE QUIZ MAKING LANGUAGE MARKUP SYSTEM; 2) AN APPROACH TO LEARNING ANALYTICS USING AD-HOC REPORTS AND ARTIFICIAL INTELLIGENCE ENABLED VISUALIZATION; 3) AUTOMATING COURSE ADMINISTRATION TASKS; AND 4) A 4-IN-1 LEARNING APPLICATION FOR FLIPPED LEARNING. THESE PROJECTS TARGET ALL MAIN USERS OF THE SYSTEM, INCLUDING INSTRUCTORS, LEARNERS, AND ADMINISTRATORS. IT IS ILLUSTRATED FROM THIS STUDY THAT THE INNOVATIVE USE OF WEB TECHNOLOGIES AND LEARNING ANALYTICS HAS GREAT POTENTIALS IN IMPROVING LMS USER PRODUCTIVITY, AND SUPPORTING AND INFORMING LEARNING.
THIS ARTICLE WILL EXPLORE ADVANCES IN THE FIELD OF EDUCATIONAL DATA MODELING THAT HAVE IMPLICATIONS FOR MODELING HUMANISTIC DATA. TRADITIONAL HUMANISTIC INQUIRY, BOLSTERED BY MICRO-ANALYSES CONDUCTED BY THE SCHOLAR, HAS MADE WAY FOR MACHINE-ASSISTED METHODS THAT PARSE AND QUANTIFY LARGE AMOUNTS OF QUALITATIVE DATA TO REVEAL POSSIBLE TRENDS AND FOCUS MORE ANALOGUE APPROACHES. AT BEST, THIS PLAY BETWEEN HUMAN-AND MACHINE-DIRECTED APPROACHES CAN LEAD TO MORE PROFOUND EXPLORATIONS OF TEXTS. IN THIS EXPLORATION OF QUALITATIVE-QUANTITATIVE METHODOLOGIES THAT LEVERAGE HUMAN AGENCY AND MACHINE-DIRECTED TECHNIQUES, I SUGGEST A MIXED METHODS APPROACH FOR DEALING WITH THE HUMANITIES. SPECIFICALLY, THIS DISCUSSION WILL ANALYZE THE CURRENT METHODOLOGICAL TENSIONS RELATED TO EDUCATIONAL DATA MINING AND LEARNING ANALYTICS TO REVEAL BEST PRACTICES FOR MODELING HUMANISTIC DATA. PRINCIPLE QUESTIONS OF INTEREST IN THIS ESSAY INCLUDE: WHAT DEFINES LITERARY "BIG DATA? HOW CAN WE DEFINE DH MODELING AND WHERE DOES IT DEPART FROM TRADITIONAL DATA MODELING? WHAT ROLE DOES MACHINE-BASED MODELING HAVE IN THE CONTEXT OF THE SCHOLARLY CLOSE READ? WHAT CAN WE LEARN FROM EDUCATIONAL DATA MODELING PRACTICES THAT ARE IN THE MIDST OF RESOLVING TENSIONS BETWEEN HUMAN-MACHINE PATTERNING?
MANY FACTORS COULD AFFECT THE ACHIEVEMENT OF STUDENTS IN DISTANCE LEARNING SETTINGS. INTERNAL FACTORS SUCH AS AGE, GENDER, PREVIOUS EDUCATION LEVEL AND ENGAGEMENT IN ONLINE LEARNING ACTIVITIES CAN PLAY AN IMPORTANT ROLE IN OBTAINING SUCCESSFUL LEARNING OUTCOMES, AS WELL AS EXTERNAL FACTORS SUCH AS REGIONS WHERE THEY COME FROM AND THE LEARNING ENVIRONMENT THAT THEY CAN ACCESS. IDENTIFYING THE RELATIONSHIPS BETWEEN STUDENT CHARACTERISTICS AND DISTANCE LEARNING OUTCOMES IS A CENTRAL ISSUE IN LEARNING ANALYTICS. THIS PAPER PRESENTS A STUDY THAT APPLIES UNSUPERVISED LEARNING FOR IDENTIFYING HOW DEMOGRAPHIC CHARACTERISTICS OF STUDENTS AND THEIR ENGAGEMENT IN ONLINE LEARNING ACTIVITIES CAN AFFECT THEIR LEARNING ACHIEVEMENT. WE UTILISE THE K-PROTOTYPES CLUSTERING METHOD TO IDENTIFY GROUPS OF STUDENTS BASED ON DEMOGRAPHIC CHARACTERISTICS AND INTERACTIONS WITH ONLINE LEARNING ENVIRONMENTS, AND ALSO INVESTIGATE THE LEARNING ACHIEVEMENT OF EACH GROUP. KNOWING THESE GROUPS OF STUDENTS WHO HAVE SUCCESSFUL OR POOR LEARNING OUTCOMES CAN AID FACULTY FOR DESIGNING ONLINE COURSES THAT ADAPT TO DIFFERENT STUDENTS' NEEDS. IT CAN ALSO ASSIST STUDENTS IN SELECTING ONLINE COURSES THAT ARE APPROPRIATE TO THEM.
EDUCATION AND LEARNING STAND OUT AMONG MANY APPLICATION AREAS OF COGNITIVE COMPUTING DUE TO THEIR PRACTICAL APPEAL AS WELL AS THEIR RESEARCH CHALLENGE. WE CAN GAUGE THE LEVEL OF RESEARCH CHALLENGE ONCE WE RECOGNIZE THE BROAD SPECTRUM OF HUMAN LEARNING, THE COMPLEX AND NOT FULLY UNDERSTOOD HUMAN LEARNING PROCESS, AND VARIOUS INFLUENCING FACTORS OF LEARNING, SUCH AS PEDAGOGY, TECHNOLOGY, AND SOCIAL ELEMENTS. WE SURVEY SOME OF THE KEY RESEARCH PROBLEMS AND RECENT EFFORTS TO SOLVE THEM. IN PARTICULAR, WE EXAMINE THE IMPORTANT ROLES PLAYED BY THE EDUCATIONAL DATA MINING AND LEARNING ANALYTICS COMMUNITIES IN THESE RESEARCH EFFORTS. WE FIND EVIDENCE OF SIGNIFICANT PROGRESS OVER THE LAST FEW YEARS. WE ALSO FIND EVIDENCE OF CONCERTED EFFORTS BY ACADEMIA AND INDUSTRY IN BRINGING SOME OF THESE RESEARCH RESULTS TO THE PUBLIC IN THE FORM OF LEARNING AND EDUCATIONAL SOFTWARE.
ASSESSMENT IS CRUCIAL TO BOTH LEARNERS, TO RECEIVE FEEDBACK ON THEIR PROGRESS, AND EDUCATORS, TO EVALUATE THE EFFECTIVENESS OF THEIR TEACHING. SERIOUS GAMES (SGS) USUALLY OFFER AN ASSESSMENT SYSTEM. HOWEVER, IF THE MAJORITY OF EDUCATORS AGREE THAT USING AN SG INCREASES THE MOTIVATION, LEARNING AND RETENTION OF THEIR STUDENTS, MANY OF THEM WOULD NOT TRUST ITS ASSESSMENT TO VERIFY THAT THE LEARNING GOALS HAVE BEEN MET. TO DO SO, THEY WOULD USE A MORE CONVENTIONAL METHOD, SUCH AS A PAPER-BASED TEST. WE BELIEVE THE MAIN REASONS FOR THAT ARE: THE TEACHER'S LACK OF OWNERSHIP OF THE GAME AND THE RIGIDNESS OF THE SG ITSELF AS IT USUALLY CANNOT BE CHANGED BY THE TEACHER. IN THIS PAPER, WE PRESENT AN ASSESSMENT ENGINE CAPABLE OF OVERCOMING THESE ISSUES. THE ENGINE IS USED BOTH BY SG DEVELOPERS AND TEACHERS AND ACTS AS A LINK BETWEEN THE TWO. AN EXAMPLE WILL BE PRESENTED TO ILLUSTRATE ITS FEATURES AND DATA FROM A PILOT STUDY WITH SG DEVELOPERS WILL BE ANALYSED. THE ENGINE IS NOT ONLY USEFUL TO TEACHERS AND DEVELOPERS REGARDING ASSESSMENT BUT, DUE TO ITS CORE DESIGN, IT ALSO OFFERS THE POSSIBILITY OF LEARNING ANALYTICS.
THE MAIN CHALLENGES COMMONLY ASSOCIATED WITH ACQUIRING PRACTICAL NETWORK ENGINEERING SKILLS ARE THE REQUIREMENTS FOR ACCESS TO SPECIALISED AND UP-TO-DATE NETWORK EQUIPMENT, AS WELL AS THE HIGH COSTS ASSOCIATED WITH OBTAINING AND MAINTAINING THIS EQUIPMENT. THE PT ANYWHERE INITIATIVE ADDRESSES THESE CHALLENGES BY OFFERING A MOBILE ENVIRONMENT FOR ACQUIRING PRACTICAL KNOWLEDGE AND SKILLS OF NETWORK ENGINEERING. PT ANYWHERE FACILITATES LEARNER-CENTRED ANYWHERE/ANYTIME LEARNING AND OFFERS LEARNERS ACCESS TO HIGH-QUALITY INTERACTIVE LEARNING RESOURCES. ADDITIONALLY, PT ANYWHERE EMPLOYS LEARNING ANALYTICS IN ORDER TO HELP LEARNERS AND EDUCATORS IN UNDERSTANDING AND IMPROVING LEARNING. THIS PAPER PRESENTS THE LEARNING DESIGN AND SOFTWARE DESIGN PRINCIPLES DRIVING THE DEVELOPMENT OF PT ANYWHERE, AS WELL AS THE OUTCOMES OF AN EVALUATION SESSION CONDUCTED WITH MEMBERS OF THE CISCO NETWORKING ACADEMY. THE RESULTS OF THIS EVALUATION INDICATE THAT PT ANYWHERE HOLDS THE POTENTIAL TO ENGAGE LEARNERS IN DIFFERENT WAYS INSIDE AND OUTSIDE OF THE CLASSROOM, WITHOUT THE NEED FOR ACCESS TO SPECIALISED EQUIPMENT.
THIS PAPER DESCRIBES THE ANALYSIS OF TEMPORAL BEHAVIOR OF 11-15 YEAR OLD STUDENTS IN A HEAVILY INSTRUCTIONALLY DESIGNED ADAPTIVE E-LEARNING ENVIRONMENT. THE E-LEARNING SYSTEM IS DESIGNED TO SUPPORT STUDENT'S ACQUISITION OF HEALTH LITERACY. THE SYSTEM ADAPTS TEXT DIFFICULTY DEPENDING ON STUDENTS' READING COMPETENCE, GROUPING STUDENTS INTO FOUR COMPETENCE LEVELS. CONTENT FOR THE FOUR LEVELS OF READING COMPETENCE WAS CREATED BY CLINICAL PSYCHOLOGISTS, PEDAGOGUES AND MEDICINE STUDENTS. THE E-LEARNING SYSTEM CONSISTS OF AN INITIAL READING COMPETENCE ASSESSMENT, TEXTS ABOUT HEALTH ISSUES, AND LEARNING TASKS RELATED TO THESE TEXTS. THE RESEARCH QUESTION WE INVESTIGATE IN THIS WORK IS WHETHER TEMPORAL BEHAVIOR IS A DIFFERENTIATOR BETWEEN STUDENTS DESPITE THE SYSTEM'S ADAPTATION TO STUDENTS' READING COMPETENCE, AND DESPITE STUDENTS HAVING COMPARATIVELY LITTLE FREEDOM OF ACTION WITHIN THE SYSTEM. FURTHER, WE ALSO INVESTIGATED THE CORRELATION OF TEMPORAL BEHAVIOUR WITH PERFORMANCE. UNSUPERVISED CLUSTERING CLEARLY SEPARATES STUDENTS INTO SLOW AND FAST STUDENTS WITH RESPECT TO THE TIME THEY TAKE TO COMPLETE TASKS. FURTHERMORE, TOPIC COMPLETION TIME IS LINEARLY CORRELATED WITH PERFORMANCE IN THE TASKS. THIS MEANS THAT WE INTERPRET WORKING SLOWLY IN THIS CASE AS DILIGENCE, WHICH LEADS TO MORE CORRECT ANSWERS, EVEN THOUGH THE LEVEL OF TEXT DIFFICULTY MATCHES STUDENT'S READING COMPETENCE. THIS RESULT ALSO POINTS TO THE DESIGN OPPORTUNITY TO INTEGRATE ADVICE ON OVERARCHING LEARNING STRATEGIES, SUCH AS WORKING DILIGENTLY INSTEAD OF RUSHING THROUGH, INTO THE STUDENT'S OVERALL LEARNING ACTIVITY. THIS CAN BE DONE EITHER BY TEACHERS, OR VIA ADDITIONAL ADAPTIVE LEARNING GUIDANCE WITHIN THE SYSTEM.
I FIRST MET JIM GREER AT THE NATO ADVANCED STUDY INSTITUTE ON SYNTHESES OF INSTRUCTIONAL SCIENCES AND COMPUTING SCIENCE FOR EFFECTIVE INSTRUCTIONAL COMPUTING SYSTEMS IN 1990 IN CALGARY, CANADA. IT WAS DURING THIS MEETING THAT I CAME TO REALIZE THAT JIM WAS ONE OF THOSE RARE INDIVIDUALS THAT COULD HELP "TRANSLATE" COMPUTER SCIENCE PRINCIPLES TO NON-COMPUTER SCIENTISTS. THROUGH THIS TRANSLATION PROCESS NEW KNOWLEDGE COULD BE DEVELOPED THROUGH INTERDISCIPLINARY PARTNERSHIPS WITH PSYCHOLOGY AND EDUCATION. IN THIS PAPER, I DESCRIBE THE MANNER IN WHICH JIM INFLUENCED MY OWN JOURNEY IN THE FIELD OF ARTIFICIAL INTELLIGENCE AND EDUCATION. IN PARTICULAR, HE HAS INFLUENCED TWO DIRECTIONS IN MY RESEARCH, ONE DIRECTION IS THE MANNER IN WHICH TECHNOLOGY CAN INFLUENCE TEACHING AND LEARNING FOR INDIVIDUALS WORKING SOLO. THE SECOND DIRECTION IS HOW TECHNOLOGY CAN INFLUENCE TEACHING AND LEARNING THROUGH COLLABORATION. IN BOTH SITUATIONS I WILL DISCUSS JIM GREER'S INFLUENCE ON MY RESEARCH WITH RESPECT TO LEARNER MODELLING, EDUCATIONAL DATA MINING, AND VISUALIZATION. IN THE CONTEXT OF SOLO LEARNING, I WILL DISCUSS BIOWORLD, A SYSTEM THAT FOSTERS CLINICAL REASONING IN MEDICAL STUDENTS, EMPHASIZING THE ROLE OF STUDENT MODELING AND EDUCATIONAL DATA MINING FOR FOSTERING AND IDENTIFYING PERFORMANCE DIFFERENCES IN CLINICAL REASONING. IN THE CONTEXT OF COLLABORATIVE LEARNING, I WILL DISCUSS HOWARD, AN ONLINE PLATFORM FOR SUPPORTING SMALL GROUP PROBLEM-BASED LEARNING IN MEDICAL STUDENTS. IN PARTICULAR, I WILL DISCUSS THE ROLE OF LEARNING ANALYTICS USED IN A PEDAGOGICAL DASHBOARD TO FOSTER TEACHERS' INTERPRETATION OF GROUP LEARNING.
OPEN EDUCATIONAL RESOURCES HAVE EMERGED AS ONE OF THE CORNERSTONES OF OPEN EDUCATION. ONE OF THE MAIN BARRIERS HAMPERING THEIR USE AND ADOPTION IS THE LACK OF SUSTAINABLE AND EFFECTIVE QUALITY CONTROL MECHANISMS IN DIGITAL REPOSITORIES. EVALUATION STRATEGIES SUCH AS PEER REVIEW HAVE NOT BEEN SUFFICIENTLY SCALABLE TO KEEP UP WITH THE FAST PACE OF OPEN CONTENT CREATION BY THE USER COMMUNITY. THIS STUDY PRESENTS A NEW APPROACH GROUNDED ON LEARNING ANALYTICS IN ORDER TO ESTIMATE THE QUALITY OF LEARNING OBJECTS BASED ON THE INTERACTIONS THAT STUDENTS HAVE WITH THEM IN OPEN ENVIRONMENTS. FOR THE STUDY, 146291 SESSIONS OF STUDENT INTERACTIONS WITH 256 LEARNING OBJECTS DISTRIBUTED THROUGH AN OPEN REPOSITORY WERE ANALYZED. A TOTAL OF 11 STUDENT-LEARNING OBJECT INTERACTIONS WERE CONSIDERED IN THE STUDY. THE QUALITY OF THE RESOURCES WAS MEASURED USING THE STANDARD EVALUATION INSTRUMENT LORI (LEARNING OBJECT REVIEW INSTRUMENT). TO STUDY THE RELATIONSHIPS BETWEEN THE STUDENT INTERACTIONS WITH THE LEARNING OBJECTS AND THEIR QUALITY AS WELL AS TO BUILD A PREDICTIVE METRIC, LINEAR REGRESSION ANALYSES WERE USED. THE RESULTS SHOW THAT THERE IS A RELATIONSHIP BETWEEN INTERACTIONS AND QUALITY, AND THAT IT IS POSSIBLE TO ESTIMATE WITH A MODERATE ERROR THE QUALITY OF THE LEARNING OBJECTS BASED ON THE INTERACTIONS THAT STUDENTS HAVE WITH THEM. THE RESULTS OBTAINED POINT OUT THAT THE PROPOSED LEARNING ANALYTIC CAN BE USED IN OPEN LEARNING OBJECT REPOSITORIES TO AUTOMATICALLY DETECT CONFLICTING OR LOW QUALITY RESOURCES.
THE PATHWAYS AND LEARNING OUTCOMES OF UNIVERSITY STUDENTS ARE THE CULMINATION OF NUMEROUS EXPERIENCES INSIDE AND OUTSIDE OF THE CLASSROOM, WITH FACULTY AND WITH OTHER STUDENTS, IN BOTH FORMAL AND CASUAL SETTINGS. THESE INTERACTIONS ARE GUIDED BY THE GENERAL EDUCATION REQUIREMENTS OF THE UNIVERSITY AND BY THE LEARNING GOALS OF THE STUDENT. THE ONLY OFFICIAL RECORD AND REPRESENTATION OF EACH STUDENT'S EDUCATION IS CAPTURED BY THEIR ACADEMIC TRANSCRIPT: TYPICALLY A LIST OF COURSES DESCRIBED BY NAME AND NUMBER, GRADES RECORDED ON AN A-F SCALE AND SUMMARIZED BY CPA, DEGREES AWARDED, AND HONORS RECEIVED. THIS LIMITED APPROACH REFLECTS THE TECHNOLOGICAL AFFORDANCES OF A 20TH CENTURY INDUSTRIAL AGE. IN RECENT, YEARS, SCHOLARS HAVE BEGUN TO IMAGINE A TRANSCRIPT OF THE FUTURE, PERHAPS COMBINING A RICHER RECORD OF THE STUDENT EXPERIENCE ALONG WITH A PORTFOLIO OF AUTHENTIC PRODUCTS OF STUDENT WORK. IN THIS PAPER, WE CONCENTRATE ON FIRST., AND DEVELOP ANALYTIC METHODS FOR IMPROVING MEASURES OF BOTH CLASSROOM PERFORMANCE AND INTELLECTUAL BREADTH. IN EACH CASE, THIS IS DONE BY PLACING ELEMENTS OF INDIVIDUAL TRANSCRIPTS IN CONTEXT USING INFORMATION ABOUT THEIR PEERS. WE FRAME THE STUDY BY ADDRESSING BASIC QUESTIONS. WERE THE COURSES TAKEN BY THE STUDENT DIFFICULT ON AVERAGE? DID THE INDIVIDUAL STAND OUT FROM THEIR PEERS? WERE THE COURSES REPRESENTATIVE OF IT BROAD INTELLECTUAL EXPERIENCE, OR DID THE STUDENT DELVE INTO DETAIL IN THE CHOSEN FIELD OF STUDY? AND WITH WHOM DID THEY TAKE COURSES?
DATA MINING TECHNIQUES HAVE BEEN APPLIED TO VARIOUS SECTORS IN OTHER TO GET INFORMATION AND KNOWLEDGE TO SUPPORT MAJOR DECISION-MAKING. WITH THE EMERGENCE OF NEW TECHNOLOGIES THAT CAN HARNESS DATA ASSOCIATED WITH EDUCATION AND THE RAPID GROWTH IN EDUCATIONAL DATA, DATA MINING CAN BE APPLIED TO A WIDE VARIETY OF APPLICATIONS IN THE EDUCATIONAL SECTOR FOR THE PURPOSE OF IMPROVING THE PERFORMANCE OF STUDENTS AS WELL AS THE STATUS OF THE EDUCATIONAL INSTITUTIONS. EDUCATIONAL DATA MINING (EDM) IS A MAJOR RESEARCH FIELD THAT USES DATA MINING TECHNIQUES, MACHINE LEARNING ALGORITHMS AND STATISTICAL TECHNIQUES TO BASICALLY HELPS THE TUTORS MODIFY THEIR TEACHING STRATEGIES IF THE RESULTS WITH THE CURRENT TEACHING MODEL ARE NOT SATISFACTORY. THE POTENTIAL INFLUENCE OF DATA MINING ANALYTICS ON THE STUDENTS' LEARNING PROCESSES AND OUTCOMES HAS BEEN REALIZED HENCE, A COMPREHENSIVE REVIEW OF EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) IN EDUCATION WAS CONDUCTED. IN THIS PAPER, WE CONDUCTED AN IN-DEPTH LITERATURE REVIEW ON THE USE OF EDM FOR ANALYZING THE PERFORMANCE OF STUDENTS.
WE DESIGN A GENERATIVE ADVERSARIAL NETWORK (GAN)-BASED PENCIL DRAWING LEARNING SYSTEM FOR ART EDUCATION ON LARGE IMAGE DATASETS TO HELP STUDENTS STUDY HOW TO DRAW PENCIL DRAWINGS FOR IMAGES AND SCENES. THE SYSTEM GENERATES A PENCIL DRAWING RESULT FOR A NATURAL IMAGE BASED ON GAN. THE GAN NETWORK IS TRAINED ON PENCIL DRAWING BIG DATASETS CONTAINING IMAGE PAIRS OF NATURAL IMAGES AND THEIR CORRESPONDING PENCIL DRAWINGS. USING THE PENCIL DRAWING LEARNING SYSTEM, STUDENTS CAN PAINT PENCIL DRAWINGS WHENEVER THEY WANT AND FOR WHATEVER THEY LIKE BY UPLOADING AN IMAGE OF THE CONTENT THEY WANT TO DRAW AND GETTING A PENCIL DRAWING EXAMPLE OF THE UPLOADED IMAGE FROM THE SYSTEM. WITH THE RETURNED PENCIL DRAWING, STUDENTS WILL SEE THE PENCIL DRAWING EFFECT OF NATURAL SCENES CLEARLY AND REALIZE HOW TO DRAW THE PENCIL DRAWING FOR THE NATURAL SCENE. BESIDES, WITH STUDENTS USING THE PENCIL DRAWING LEARNING SYSTEM, IT WILL BE CONVENIENT FOR TEACHERS ASSIGNING HOMEWORK TO STUDENTS. TEACHERS CAN KNOW THE LEARNING DEMANDS OF STUDENTS BY EVALUATING THE HAND-IN HOMEWORK AND UPDATE THE CONTENT CORRESPONDINGLY. WE HAVE CONDUCTED TWO USER STUDIES FOR EVALUATING THE PRACTICALITY OF THE SYSTEM, AND THE RESULT OF THE TWO USER STUDIES DEMONSTRATED THE APPLICABILITY AND PRACTICALITY OF THE SYSTEM.
INDIVIDUAL LEARNING IS OUT OF SYNC WITH THE ELEMENTS OF A CURRICULA AND THE DAILY PROGRAM OF A TEACHER. AT A TIME WHEN MULTIDIGIT MULTIPLICATION METHODS ARE TAUGHT, MANY CHILDREN ARE NOT PERFECTLY PERFORMING THE BASIC MULTIPLICATION TABLE. TEACHERS ORGANIZE SETTINGS FOR LEARNING AND THEY USUALLY HAVE NO TIME TO GIVE AN INDIVIDUAL FEEDBACK TO EVERY SINGLE STUDENT. FURTHERMORE THEY HAVE NO PHYSIOLOGICAL CAPACITY TO PRODUCE STATISTICS AND TO INVESTIGATE THE PROBABLE CAUSES OF ERRORS. OUR GOAL IS TO GIVE TEACHERS A TOOL TO HELP THEM TO REVIEW AND TRAIN SOME TYPICAL REQUIREMENTS FOR WRITTEN MULTIPLYING. WE USE THE UBIQUITY OF THE INTERNET AND PROVIDE ON DIFFERENT DEVICES FREE APPLICATIONS WITH WRITTEN MULTIPLICATION PROBLEMS. ALL DATA ARE COLLECTED CENTRALLY AND ARE USED TO DETERMINE TYPICAL ERRORS. THESE DATA COULD ALSO BE USED TO ANSWER FURTHER NEW QUESTIONS AND TEST NEW HYPOTHESIS. THIS IS ONE ASPECT OF LEARNING ANALYTICS. THE GIVEN TASKS ARE AUTOMATICALLY ADAPTED TO THE SKILL LEVEL OF A STUDENT. TEACHERS CAN SEE EVERY DETAIL OF THE SOLUTION PROCESS OF EVERY TASK. BUT REALLY USEFUL IS THAT TEACHERS CAN SEE THE TYPICAL PROBLEMS OF EACH STUDENT AS WELL AS OF THE WHOLE CLASS EVERY TIME AT A GLANCE. THEY NEED JUST A CONNECTION TO THE DATABASE WITH A WEB BROWSER - ANYWHERE, ANYTIME.
THE RECENT FOCUS ON LEARNING ANALYTICS TO ANALYSE TEMPORAL DIMENSIONS OF LEARNING HOLDS A STRONG PROMISE TO PROVIDE INSIGHTS INTO LATENT CONSTRUCTS SUCH AS LEARNING STRATEGY, SELF-REGULATED LEARNING, AND METACOGNITION. THERE IS, HOWEVER, A LIMITED AMOUNT OF RESEARCH IN TEMPORALLY-FOCUSED PROCESS MINING IN EDUCATIONAL SETTINGS. BUILDING ON A GROWING BODY OF RESEARCH AROUND EVENT-BASED DATA ANALYSIS, WE EXPLORE THE USE OF PROCESS MINING TECHNIQUES TO IDENTIFY STRATEGIC AND TACTICAL LEARNER BEHAVIOURS. WE ANALYSE TRACE DATA COLLECTED IN ONLINE ACTIVITIES OF A SAMPLE OF NEARLY 300 COMPUTER ENGINEERING UNDERGRADUATE STUDENTS ENROLLED IN A COURSE THAT FOLLOWED A FLIPPED CLASSROOM PEDAGOGY. USING A PROCESS MINING APPROACH BASED ON FIRST ORDER MARKOV MODELS IN COMBINATION WITH UNSUPERVISED MACHINE LEARNING METHODS, WE PERFORMED INTRA- AND INTER-STRATEGY ANALYSIS. WE FOUND THAT CERTAIN TEMPORAL ACTIVITY TRAITS RELATE TO PERFORMANCE IN THE SUMMATIVE ASSESSMENTS ATTACHED TO THE COURSE, MEDIATED BY STRATEGY TYPE. RESULTS SHOW THAT MORE STRATEGICALLY MINDED ACTIVITY, EMBODYING LEARNER SELF-REGULATION, GENERALLY PROVES TO BE MORE SUCCESSFUL THAN LESS DISCIPLINED REACTIVE BEHAVIOURS.
IN RESPONSE TO THE EXTENSIVE USE OF COMPUTER SYSTEMS IN LEARNING, WE ARE NOW IN THE AGE THAT WE CAN DO THE LEARNING ANALYTICS USING THE HUGE-SIZE OF DATABASE. WE HAVE RECENTLY ESTABLISHED THE FOLLOW-UP PROGRAM SYSTEM AIMED AT HELPING STUDENTS WHO NEED BASIC LEARNING AND AIMED AT ASSISTING TEACHERS WHO HAVE TO ENGAGE IN TEACHING A VARIETY OF EDUCATIONAL STUDENTS. IN THE FOLLOW-UP PROGRAM SYSTEM, THE LEARNING CHECK TESTING IS INCLUDED. ALL THE FIRST-YEAR UNDERGRADUATE STUDENTS TAKE THE LEARNING CHECK TESTING AT EVERY LECTURE. ALTHOUGH THE NUMBER OF QUESTIONNAIRES IS SMALL AND THE TESTING TIME IS SHORT, INVESTIGATING THE TREND OF THE CORRECT ANSWER RATES AND THE FINAL EXAMINATION RESULTS TOGETHER, IT IS REVEALED THAT THOSE WHO FAILED IN THE FINAL EXAMINATION SHOW THE DECREASING TREND OF CORRECT ANSWER RATES IN THE LEARNING CHECK TESTING COMPARING TO THOSE WHO WERE SUCCESSFUL IN THE FINAL EXAMINATION. THEREFORE, WE AGAIN RECOGNIZED THE IMPORTANCE OF INITIAL LEARNING HABITUATION NOT ONLY BY INTUITION BUT ALSO BY STATISTICAL VIEWPOINTS.
THIS PAPER DEMONSTRATES THE MEANINGFUL APPLICATION OF LEARNING ANALYTICS FOR DETERMINING DROPOUT PREDICTORS IN THE CONTEXT OF OPEN AND DISTANCE LEARNING IN A LARGE DEVELOPING COUNTRY. THE STUDY WAS CONDUCTED AT THE DIRECTORATE OF DISTANCE EDUCATION AT THE UNIVERSITY OF NORTH BENGAL, WEST BENGAL, INDIA. THIS STUDY EMPLOYED A QUANTITATIVE RESEARCH DESIGN USING A DATA MINING APPROACH TO EXAMINE THE PREDICTIVE RELATIONSHIP BETWEEN PRE-ENTRY DEMOGRAPHIC VARIABLES OF LEARNERS WITH THEIR DROPOUT BEHAVIOUR. DEMOGRAPHIC AND ACADEMIC VARIABLES OF LEARNERS, SUCH AS GENDER, MARITAL AND EMPLOYMENT STATUS, SUBJECT CHOSEN, SOCIAL STATUS, AGE AND INCOME STATUS WERE TAKEN AS INDEPENDENT OR EXPLANATORY VARIABLES FOR PREDICTING THE RESPONSE VARIABLES. DATA ANALYSIS SHOWED THAT THE PATTERN OF LEARNER ATTRITION IS STRONGLY BIASED TOWARDS A RELATIVELY DISADVANTAGED CATEGORY OF LEARNERS, NAMELY MARRIED AND EMPLOYED LEARNERS AND THOSE BELONGING TO A HIGHER AGE GROUP. IT ALSO INDICATED THAT EMPLOYED MEN OR MARRIED WOMEN ARE MORE LIKELY TO LEAVE DUE TO FACTORS SUCH AS PREGNANCY OR RELOCATION, AND THAT REMOTENESS OF LOCATION OF RESIDENCE CONTRIBUTED TO A HIGH DROPOUT RATE. THE RESULTS OF THIS STUDY PROVIDE IMPORTANT INPUT FOR COUNSELLORS AND FACULTY MEMBERS TO ADVISE LEARNERS FOR BEST POSSIBLE COMPLETION OPTIONS.
FLIPPED CLASSROOM IS A STUDENT-CENTERED METHODOLOGY THAT CAN HELP ENGINEERING STUDENTS TO ACQUIRE THE CROSS-CURRICULAR SKILLS DEMANDED BY SOCIETY. HOWEVER, ITS EFFECTIVENESS RELIES ON THE COMMITMENT OF BOTH INSTRUCTORS AND STUDENTS. IN PARTICULAR, THIS STRATEGY REQUIRES STUDENTS TO WORK ON A NUMBER OF PROPOSED ACTIVITIES BEFORE FACE-TO-FACE CLASSES. THEN, IN ORDER TO FOLLOW THE MOST APPROPRIATE PATH IN THOSE CLASSES, INSTRUCTORS NEED A RELIABLE WAY TO KNOW AT WHICH DEGREE THEIR STUDENTS WORKED ON THOSE PROPOSED ACTIVITIES, WHAT ISSUES THEY ENCOUNTERED WHILE DOING THEM AND WHICH CONCEPTS NEED TO BE REINFORCED IN CLASS. THIS PAPER PRESENTS A CASE STUDY OF A FLIPPED-CLASSROOM UNDERGRADUATE ENGINEERING COURSE. BY USING DATA-DRIVEN LEARNING DESIGN AND LEARNING ANALYTICS TECHNIQUES WE SHOW THAT: (1) BY DELAYING THEIR WORK ON THE COURSE ACTIVITIES OUR STUDENTS ACTUALLY DROVE THE COURSE TOWARDS THE TRADITIONAL APPROACH; (2) DESPITE DIRECTLY ASKING STUDENTS AT THE BEGINNING OF A FACE-TO-FACE CLASS MIGHT SEEM TO BE AN APPROPRIATE WAY OF GETTING RELIABLE INFORMATION ABOUT THEIR PREVIOUS WORK, IT MAY LEAD INSTRUCTORS TO ERRONEOUS CONCLUSIONS; (3) OUR STUDENTS WERE STRONGLY MARK- AND DEADLINE-ORIENTED, BUT EVEN A SMALL GRADE ENCOURAGED THEM TO WORK ON THE ASSIGNMENTS; (4) THE GATHERING AND CHECKING OF STUDENTS' LEARNING DATA BEFORE THE CLASS CAN HELP INSTRUCTORS TO TAILOR THE LESSON DESIGN; AND (5) IF STUDENTS DID NOT WORK ON PRE-CLASS ACTIVITIES, DEDICATING A SMALL AMOUNT OF TIME OF THE IN-CLASS LESSON TO EXPLAIN THE MOST DIFFICULT CONCEPTS CAN HELP STUDENTS TO BE MORE EFFICIENT WITH THEIR WORK, AT THE COST OF LOSING SOME OF THE SPIRIT OF THE FLIPPED CLASSROOM.
IN THIS ARTICLE WE PRESENT A PROTOTYPE OF A MOBILE KNOWLEDGE MANAGEMENT SYSTEM AND ITS USE IN THE MAINTENANCE SECTOR OF THE SWISS NATIONAL RAILROAD SERVICE SBB. THE PROTOTYPE IS A COMBINED PROJECT OF THE RAILROAD COMPANY AND TWO RESEARCH PARTNERS. THE MAIN PART OF THE SYSTEM IS A MOBILE APP FOR TABLETS. THIS SOFTWARE HELPS SERVICE MECHANICS IF A PROBLEM OCCURS DURING THEIR MAINTENANCE ACTIVITIES WHICH THEY CANNOT SOLVE ON THEIR OWN. IN THIS APPLICATION THEY CAN SEARCH FOR SOLUTIONS DOCUMENTED ALREADY BY OTHER MECHANICS OR GET IN CONTACT WITH EXPERTS TO GENERATE AND DOCUMENT THEIR OWN APPROACH. WITH THE USE OF THIS APP, THE KNOWLEDGE DATA BASE IS FILLED WITH USER GENERATED CONTENT. THE USABILITY OF THE APP HAS A BIG INFLUENCE ON THE USE AND THE QUALITY OF THE CONTENT. SO, IN THE DEVELOPMENT OF THE APP, WE PUT A GREAT WEIGHT ON ITS USABILITY. THE USAGE OF THE PRODUCT DEPENDS, HOWEVER, ON INDIVIDUAL AND ORGANISATIONAL FACTORS. THESE FACTORS WERE CONSIDERED IN THE DEVELOPMENT OF THE SYSTEM AND ARE TAKEN INTO ACCOUNT AT ITS IMPLEMENTATION. THE APP'S DATABASE CAN BE ANALYSED FOR FREQUENT ERRORS OCCURRING IN THE TRAIN FLEET AND SHOWS THE NEEDS FOR AN ON THE JOB TRAINING TO PREPARE THE MAINTENANCE CREWS FOR COMMON DEFECTS OF THE ROLLING STOCK. THE PROTOTYPE IS CURRENTLY IN USE IN A FIELD TEST. IN THIS PAPER WE DISCUSS THE UNDERLYING THEORETICAL ASSUMPTIONS, DESCRIBE THE DEVELOPMENT PROCESS OF THE APP AS WELL AS FEATURES OF THE APPLICATION AND GIVE FIRST RESEARCH INSIGHTS FROM THE FIELD TEST. THE CASE SHOWS A NEW WAY OF COMBINING MOBILE LEARNING, COMMUNITY OF PRACTICE, KNOWLEDGE SHARING AND LEARNING ANALYTICS.
DESPITE THE TRACTION GAINED BY THE OPEN DATA MOVEMENT AND THE RISE OF BIG DATA AND LEARNING ANALYTICS IN EDUCATION, THERE IS LIMITED SUPPORT FOR RESEARCHERS IN EDUCATION TO GENERATE, ACCESS, AND SHARE EXPERIMENTAL DATA USING OPENLY-AVAILABLE DIGITAL EDUCATION PLATFORMS. TO EXPLORE HOW THIS GAP COULD HE ADDRESSED AND ELICIT REQUIREMENTS, WE CONDUCTED A SURVEY WITH 40 RESEARCHERS IN THE FIELD OF TECHNOLOGY-ENHANCED LEARNING, EXAMINING THEIR EXPERIENCE AND NEEDS HANDLING RESEARCH DATA. DRAWING ON THE RESULTS OF OUR SURVEY, WE DEVISED A SET OF FEATURES THAT EDUCATIONAL PLATFORMS SHOULD PROVIDE TO ADDRESS THE IDENTIFIED REQUIREMENTS, ENABLING RESEARCHERS IN EDUCATION TO RUN STUDIES WITHIN TYPICAL LEARNING ENVIRONMENTS, ADHERE TO LEGAL AND ETHICAL FRAMEWORKS CONCERNING PRIVACY, AND SHARE THEIR DATA CONFIDENTLY WITH A WIDER AUDIENCE. WE THEN CATEGORIZED THESE FEATURES INTO FIVE STAGES THAT REPRESENT THE USER FLOW, NAMELY (1) BOOTSTRAPPING RESEARCH STUDIES, (2) ENSURING CONSENT, (3) GATHERING DATA, (4) MANAGING DATA SETS, AND (5) SUPPORTING OPEN RESEARCH AND COLLABORATION. OUR AIM IS TO GUIDE FORTHCOMING RESEARCH AND DEVELOPMENTS TO RELIEVE RESEARCHERS OF THE BURDENS OF CONDUCTING DATA-SENSITIVE EXPERIMENTS, SUPPORT THE ADOPTION OF HEST PRACTICES, AND PAVE THE WAY FOR OPEN DATA POLICIES IN DIGITAL EDUCATION.
PURPOSE STUDENTS STUDYING EXCLUSIVELY ONLINE FACE THE CHALLENGE OF GAUGING THEIR PROGRESS IN RELATION TO THAT OF THEIR DISPARATE PEERS. THE PURPOSE OF THIS PAPER IS TO DESCRIBE THE CREATION OF A STUDENT PROGRESS "DASHBOARD" IN AN ONLINE MASTERS PROGRAMME, AND THE PERCEIVED EFFECTIVENESS OF THE TOOL FOR ENGAGING STUDENTS. DESIGN/METHODOLOGY/APPROACH TABLEAU (R) VISUALISATION SOFTWARE WAS USED TO CREATE A DASHBOARD DISPLAYING COHORT COMPARISON DATA COMPRISING METRICS RELATING TO THE CONTINUOUS ASSESSMENT COMPONENTS OF THE MASTERS PROGRAMME. AN ANONYMOUS QUESTIONNAIRE GAUGED STUDENTS' PERCEPTIONS OF THE DASHBOARD. FINDINGS FEEDBACK FROM STUDENTS (N=137) SUGGESTS THE DASHBOARD IMPROVED THEIR MOTIVATION, INCENTIVISING CHANGE IN STUDY BEHAVIOURS, AND SENSE OF BELONGING TO AN ONLINE COMMUNITY OF LEARNERS. IT ALSO ACTED AS A CONVERSATION CATALYST BETWEEN STAFF AND STUDENTS, WHEREBY STUDENTS MORE READILY ENGAGED IN DIALOGUE WITH THEIR PERSONAL TUTOR. PRACTICAL IMPLICATIONS - DISTANCE LEARNERS ARE MORE LIKELY TO FEEL ISOLATED AND CAN BECOME DEMOTIVATED, WHICH CONTRIBUTES TO TYPICALLY HIGHER LEVELS OF WITHDRAWAL FROM ONLINE PROGRAMMES VS THOSE DELIVERED ON-CAMPUS. TUTORS MAY CONSIDER COMMUNICATING PROGRESS DATA AS DASHBOARDS TO ENABLE ONLINE STUDENTS TO MONITOR THEIR ACADEMIC PROGRESS ALONGSIDE THAT OF THEIR PEERS, AS A MOTIVATIONAL TOOL IN AN OTHERWISE DISPARATE GROUP OF LEARNERS, AND TO REDUCE FEELINGS OF ISOLATION BY REMINDING DISTANCE LEARNERS THAT THEY ARE PART OF A LARGER ONLINE COMMUNITY. ORIGINALITY/VALUE THIS PAPER SHARES STUDENT AND TUTOR PERSPECTIVES ON THE USE OF DASHBOARDS TO INCREASE ONLINE STUDENTS' MOTIVATION, AND EXAMINES WHETHER THE BENEFITS OF A PEER-COMPARISON DASHBOARD ARE RESERVED FOR HIGH-ACHIEVING STUDENTS.
IN THIS STUDY, STUDENTS' BEHAVIORAL PATTERNS IN AN EDUCATIONAL GAME WERE INVESTIGATED BY CONDUCTING A DIGITAL GAME-BASED LEARNING ACTIVITY IN AN ELEMENTARY SCHOOL SCIENCE COURSE. MOREOVER, THE BEHAVIORAL PATTERNS OF THE HIGH ACHIEVEMENT AND LOW-ACHIEVEMENT STUDENTS WERE COMPARED TO EXPLORE THE POSSIBLE EDUCATIONAL GAMING BEHAVIORAL PATTERNS AFFECTING THE STUDENTS' LEARNING ACHIEVEMENTS. IT WAS FOUND THAT THE HIGH-ACHIEVEMENT STUDENTS SHOWED MORE DEEP THINKING AND REFLECTIVE BEHAVIORAL PATTERNS THAN THE LOW-ACHIEVEMENT ONES, IMPLYING THE NECESSITY OF INCORPORATING LEARNING STRATEGIES TO GUIDE STUDENTS TO HAVE DEEP THINKING AND REFLECTIVE BEHAVIORS DURING THE DIGITAL GAME-BASED LEARNING PROCESS. THE FINDING OF THIS STUDY PROVIDES A GOOD REFERENCE FOR RESEARCHERS AND SCHOOL TEACHERS WHO INTEND TO EMPLOY THE DIGITAL GAME-BASED LEARNING APPROACH.
FOR LONG, ONLINE LEARNING HAS RECEIVED CRITICISM ON NOT BEING ABLE TO PROVIDE HELPFUL INSIGHT ON STUDENTS' LEARNING, FOR A LACK OF NECESSARY AND APPROPRIATE INTERACTION BETWEEN INSTRUCTOR AND STUDENTS. DESPITE THE FACT THAT LEARNING ANALYTICS (LA) ARE ON THE RISE IN TERMS OF THEIR POPULARITY IN ACADEMIC INSTITUTIONS, THE USEFULNESS OF LA COME INTO QUESTION, ESPECIALLY THE ELEMENTS INTENDED TO PROVIDE EFFECTIVE FEEDBACKS ON STUDENTS' LEARNING PROCESS. THE MAIN PROBLEM FOR THIS SEEMS TO BE A LACK OF ANALYSIS OF THE CHARACTERISTICS OF INDIVIDUALS. THEREFORE, MORE NEEDS TO BE DONE TO ACTUALLY HELP STUDENTS' LEARNING PROCESS. THE STUDY AIMS TO PRODUCE A SIGNIFICANT BODY DATA BY ANALYZING THE CONTENT AVAILABLE IN ONLINE LEARNING ENVIRONMENTS USING BLOOM'S TAXONOMY. INITIAL RESULTS SHOW THAT MOST CONTENT FOUND IN ONLINE LEARNING ENVIRONMENTS CONSIST OF OFFLINE LEARNING MATERIAL THAT WAS TRANSFERRED TO ONLINE LEARNING PLATFORMS BY TAKING VERY LIMITED ADVANTAGE OF THE RESOURCES OF THE INTERNET. IT IS HYPOTHESIZED THAT IN THIS STUDY LEARNING CONTENT IN ONLINE ENVIRONMENTS DO NOT SUPPORT LEARNERS EFFICIENTLY TRANSFER TO HIGHER COGNITIVE LEVELS DESCRIBED IN BLOOM'S TAXONOMY.
MANY WORLDWIDE INITIATIVES CONSIDER BOTH CREATIVITY AND COMPUTATIONAL THINKING AS CRUCIAL SKILLS FOR FUTURE CITIZENS, MAKING THEM A PRIORITY FOR TODAY'S LEARNERS. WE STUDIED THE ASSOCIATIONS BETWEEN THESE TWO CONSTRUCTS AMONG MIDDLE SCHOOL STUDENTS (N = 57), CONSIDERING TWO TYPES OF CREATIVITY: A GENERAL CREATIVE THINKING, AND A SPECIFIC COMPUTATIONAL CREATIVITY. WE DID SO USING LEARNING ANALYTICS, SPECIFICALLY, BY OPERATIONALIZING A LOG-BASED ASSESSMENT OF COMPUTATIONAL CREATIVITY. WE FIND SOME EVIDENCE FOR AN ASSOCIATION BETWEEN COMPUTATIONAL CREATIVITY AND COMPUTATIONAL THINKING: DEMONSTRATING ORIGINALITY IN AN EARLY STAGE OF THE GAME IS ASSOCIATED WITH SUCCEEDING IN THIS STAGE RELATIVELY EASILY, HOWEVER NEGATIVELY ASSOCIATED WITH PROGRESSING FARTHER IN THE GAME. WE ALSO FIND THAT COMPUTATIONAL CREATIVITY IS BETTER EXPLAINED BY A STATE- RATHER THAN A TRAIT-MODEL. NO ASSOCIATIONS WERE FOUND BETWEEN CREATIVE THINKING AND COMPUTATIONAL THINKING. FURTHERMORE, WE FIND SOME STRIKING ASSOCIATIONS BETWEEN THE TWO MEASURES OF CREATIVITY.
ARTIFICIAL INTELLIGENCE (AI) WILL PLAY A KEY ROLE IN HIGHER EDUCATION. OUR CONTRIBUTION LEADS THE ROAD TO INNOVATION WITHIN IOT FOR EDUCATION 4.0, USING A SYSTEM COMPRISING SMARTWATCH DATA, HEALTH DATA, LEARNING ANALYTICS AND ARTIFICIAL INTELLIGENCE. BY USING EMBEDDED SENSORS FROM WEARABLE DEVICES, WE CAN ADD VALUE TO EDUCATION 4.0. SMARTWATCHES ARE IOT DEVICES, EQUIPPED WITH A MULTITUDE OF EMBEDDED SENSORS COLLECTING DISTRACTION-FREE HUGE AMOUNTS OF REAL-TIME DATA DURING STUDENTS' LEARNING. IN OUR PAPER WE HIGHLIGHT ADVANTAGES OF WRIST-BASED WEARABLES LIKE SMARTWATCHES FOR EDUCATION 4.0. WE DEVELOP A USER EXPERIENCE QUESTIONNAIRE FOR THE MEASUREMENT OF ACCEPTANCE OF ADVANCED ELECTRONIC TECHNOLOGY IN HIGHER EDUCATION. WE IDENTIFY AS EXPERIMENT'S RESULTS THE MOST IMPORTANT SENSORS AND PROTOCOLS FOR EDUCATION 4.0.
TIME IS A KEY FACTOR TO CONSIDER IN AUTISM SPECTRUM DISORDER. DETECTING THE CONDITION AS EARLY AS POSSIBLE IS CRUCIAL IN TERMS OF TREATMENT SUCCESS. DESPITE ADVANCES IN THE LITERATURE, IT IS STILL DIFFICULT TO IDENTIFY EARLY MARKERS ABLE TO EFFECTIVELY FORECAST THE MANIFESTATION OF SYMPTOMS. ARTIFICIAL INTELLIGENCE (AI) PROVIDES EFFECTIVE ALTERNATIVES FOR BEHAVIOR SCREENING. TO THIS END, WE INVESTIGATED FACIAL EXPRESSIONS IN 18 AUTISTIC AND 15 TYPICAL INFANTS DURING THEIR FIRST ECOLOGICAL INTERACTIONS, BETWEEN 6 AND 12 MONTHS OF AGE. WE EMPLOYED OPENFACE, AN AI-BASED SOFTWARE DESIGNED TO SYSTEMATICALLY ANALYZE FACIAL MICRO-MOVEMENTS IN IMAGES IN ORDER TO EXTRACT THE SUBTLE DYNAMICS OF SOCIAL SMILES IN UNCONSTRAINED HOME VIDEOS. REDUCED FREQUENCY AND ACTIVATION INTENSITY OF SOCIAL SMILES WAS COMPUTED FOR CHILDREN WITH AUTISM. MACHINE LEARNING MODELS ENABLED US TO MAP FACIAL BEHAVIOR CONSISTENTLY, EXPOSING EARLY DIFFERENCES HARDLY DETECTABLE BY NON-EXPERT NAKED EYE. THIS OUTCOME CONTRIBUTES TO ENHANCING THE POTENTIAL OF AI AS A SUPPORTIVE TOOL FOR THE CLINICAL FRAMEWORK.
DESPITE THE SUCCESS OF ACADEMIC ADVISING DASHBOARDS IN SEVERAL HIGHER EDUCATIONAL INSTITUTIONS (HEI), THESE DASHBOARDS ARE STILL UNDER-EXPLORED IN LATIN AMERICAN HEI'S. TO CLOSE THIS GAP, THREE DIFFERENT LATIN AMERICAN UNIVERSITIES ADAPTED AN EXISTING ADVISING DASHBOARD, ORIGINALLY DEPLOYED AT THE KU LEUVEN TO THEIR OWN CONTEXT. IN ALL THREE CASES, THE CONTEXT WAS THE MAIN RULING FACTOR TO THESE ADAPTATIONS. IN THIS PAPER, WE DESCRIBE THESE ADAPTIONS USING A FRAMEWORK THAT FOCUSES ON FOUR DIFFERENT ELEMENTS OF THE CONTEXT: OBJECTIVES, STAKEHOLDERS, KEY MOMENT AND INTERACTIONS. EVALUATION OF THE ADAPTED DASHBOARDS IN THE THREE DIFFERENT LATIN AMERICAN UNIVERSITIES IS CONDUCTED THROUGH PILOTS. THIS EVALUATION SHOWS THE VALUE OF THE DASHBOARD APPROACH IN DIFFERENT CONTEXTS IN TERMS OF SATISFACTION, USEFULNESS AND IMPACT IN ACADEMIC DECISION-MAKING AND ADVISING TASKS. THE MAIN CONTRIBUTION OF THIS PAPER IS THE SYSTEMATIC REPORTING OF THE ADAPTATIONS TO AN ACADEMIC ADVISING DASHBOARD AND SHOWING THE VALUE OF AN ACADEMIC ADVISING DASHBOARD ON ACADEMIC DECISION-MAKING AND ADVISING TASKS.
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. OUR EARLIER RESEARCH EMPLOYED THE CONVENTIONAL PARTICLE SWARM OPTIMIZATION (PSO) BASED CLUSTERING MECHANISM TO CLUSTER LARGE NUMBERS OF LEARNERS BASED ON THEIR OBSERVED STUDY HABITS AND THE CONSEQUENT GROWTH OF SUBJECT KNOWLEDGE COMPETENCIES. THIS PAPER DESCRIBES A PARALLEL PARTICLE SWARM OPTIMIZATION (PPSO) BASED CLUSTERING MECHANISM TO CLUSTER LEARNERS. USING A SIMULATION STUDY, PERFORMANCE MEASURES OF QUALITY OF CLUSTERS SUCH AS THE INTER CLUSTER DISTANCE, THE INTRA CLUSTER DISTANCE, THE PROCESSING TIME AND THE ACCELERATION VALUES ARE ESTIMATED AND COMPARED.
LEARNING ANALYTICS IS AN EMERGING FIELD IN WHICH EDUCATORS AND RESEARCHERS ARE USING DATA TO IMPROVE THEIR STUDENTS' EDUCATIONAL EXPERIENCES. ONE OF THE MOST COMMON COURSES OFFERED BY HIGHER ACADEMIC INSTITUTIONS IN THE US IS DATA SCIENCE. THIS PAPER EXAMINES THE DATA SCIENCE SYLLABI FOUND IN TODAY'S ACADEMIC SECTOR AND COMPARES THE RESULTS TO THOSE OF FRIEDMAN'S, TECHNOLOGY, PEDAGOGY AND EDUCATION, 26(5), 135-148 (2017) STUDY OF BIG DATA SYLLABI. FOR THE PRESENT STUDY, 40 DATA SCIENCE SYLLABI USED IN PRIVATE AND PUBLIC ACADEMIC INSTITUTIONS IN THE US WERE COLLECTED, AND PALMER ET AL.'S, TO IMPROVE THE ACADEMY, 33, 14-36 (2014) RUBRIC WAS USED AS A FRAMEWORK TO ANALYZE THEM. THE STUDY FOUND THAT THE MOST FREQUENTLY USED COMMUNICATION ENGAGEMENT TOOL, ACCORDING TO THE SYLLABI, WAS DISCUSSION FORUMS (USED IN 53% OF THE SYLLABI), AND INSTANT MESSAGE APPLICATIONS WERE SECOND (21%). USING PALMER ET AL.'S RUBRIC, THE STUDY FOUND 95% OF ALL DATA SCIENCE SYLLABI THE STUDY EXAMINED PROVIDED VERY DETAILED ARTICULATION AND SCORED IN THE TOP RANGE OUTLINE BY THE MODEL. IN COMPARING DATA SCIENCE SYLLABI TO BIG DATA SYLLABI, THE STUDY FOUND THAT DATA SCIENCE SYLLABI PROVIDED BETTER DESCRIPTIONS OF THE LEARNING GOALS THAN DID BIG DATA SYLLABI. FUTURE STUDIES COULD EXAMINE STUDENTS' PARTICIPATION AND APPRECIATION OF THESE COURSES USING A MACHINE ANALYTICS RUBRIC.
FOR THE 21ST CENTURY LEARNER, SELF-DIRECTION SKILL IS CRUCIAL FOR DEVELOPING BOTH INTELLECTUAL ABILITIES AND MAINTAINING ONE'S HEALTHY LIFESTYLE. WHILE THERE ARE TECHNOLOGY SUPPORTS FOR SPECIFIC SELF-REGULATED LEARNING TASKS AND HEALTH MONITORING, RESEARCH IS LIMITED ON HOW TO SUPPORT DEVELOPMENT OF THE META-SKILL OF SELF-DIRECTION ITSELF. OUR WORK BRIDGES LEARNING ANALYTICS (LA) AND QUANTIFIED-SELF (QS) RESEARCH TO ENABLE TECHNOLOGY SUPPORT FOR SELF-DIRECTED ACTIVITIES OF LEARNERS. WE PROPOSE DAPER (DATA-ANALYZE-PLAN-EXECUTION MONITORING-REFLECT), A DATA-DRIVEN SELF-DIRECTION SKILL EXECUTION AND ACQUISITION MODEL. TO SUPPORT SYNCHRONIZE-VISUALIZE-ANALYZE MULTISOURCE DATA REGARDING LEARNERS' LEARNING AND PHYSICAL ACTIVITIES, WE ARE DEVELOPING THE GOAL (GOAL ORIENTED ACTIVE LEARNER) SYSTEM. IN THIS PAPER WE PRESENT OUR DAPER MODEL, THE INITIAL IMPLEMENTATION OF THE GOAL SYSTEM BASED ON THAT MODEL AND PILOT STUDY REGARDING THE NEED AND CONTEXT ANALYSIS OF THIS RESEARCH.
THIS TUTORIAL WILL INTRODUCE THREE MATCHING TECHNIQUES (COARSENED EXACT MATCHING, MAHALANOBIS DISTANCE MATCHING, AND PROPENSITY SCORE MATCHING) AND THREE DATA IMBALANCE METRICS (L1 VECTOR NORM, AVERAGE MAHALANOBIS IMBALANCE, AND DIFFERENCE IN MEANS) TO ASSESS THE LEVEL OF DATA IMBALANCE WITHIN MATCHED SAMPLE DATASETS IN AN INTERACTIVE SETTING. IT EXPLAINS KEY TRAITS OF OBSERVATIONAL STUDIES THAT ARE RELEVANT FOR AIED, CONSIDERING COMPARABLE TRAITS OF FULLY RANDOMIZED EXPERIMENTS. USING RANDOMIZED AND NON-RANDOMIZED DATA, PARTICIPANTS WILL CONDUCT AN OBSERVATIONAL STUDY BY APPROXIMATING BLOCKED RANDOMIZED EXPERIMENTS. THE HANDS-ON SESSION SPECIFICALLY TARGETS SKILLS THAT WILL ENABLE PARTICIPANTS TO RUN OBSERVATIONAL STUDIES USING R PACKAGES SUCH AS MATCHINGFRONTIER, CEM, AND MATCHIT THROUGH AN INTERACTIVE SHINY WEB APPLICATION AND PROGRAMMATICALLY BY WRITING AN R SCRIPT. A DISCUSSION ON A MATCHING-BASED OBSERVATIONAL STUDY DESIGN FOR A LEARNING ANALYTICS APPLICATION THAT USES LARGE, FINE-GRAINED, AND SELF-SIMILAR DATASETS CONCLUDES THE TUTORIAL.
THIS PAPER INTRODUCES AN OPEN LEARNER MODEL APPROACH TO LEARNING ANALYTICS TO COMBINE THE VARIETY OF DATA AVAILABLE FROM THE RANGE OF APPLICATIONS AND TECHNOLOGIES IN LANGUAGE LEARNING, FOR VISUALISATION OF LANGUAGE LEARNING COMPETENCES TO LEARNERS AND TEACHERS IN THE EUROPEAN LANGUAGE CONTEXT. SPECIFIC EXAMPLES ARE PROVIDED AS ILLUSTRATIONS (FACEBOOK, SECOND LIFE AND MOBILE ASSISTED LANGUAGE LEARNING (MALL)), THOUGH THE APPROACH IS A GENERAL ONE. WE DESCRIBE THE NEXT-TELL OPEN LEARNER MODEL AS AN EXEMPLAR THAT CAN ENCOMPASS A RANGE OF DATA FROM A VARIETY OF TECHNOLOGIES AND ACTIVITIES, AND AS A COMPETENCE-FOCUSSED VISUAL ANALYTICS TOOL THAT CAN BE READILY USED INSIDE AND OUTSIDE THE CLASSROOM. THE NEXT-TELL OPEN LEARNER MODEL OFFERS SEVERAL VISUALISATIONS FOR LEARNERS AND TEACHERS TO EXPLORE THE LEARNER'S CURRENT COMPETENCES, WHICH CAN BE SELECTED ACCORDING TO USER PREFERENCES OR THE PURPOSE OF VIEWING THE LEARNING DATA. THE SELECTION OF VISUALISATIONS MEANS THAT THE OPEN LEARNER MODEL IS APPROPRIATE FOR SCHOOL, UNIVERSITY AND OTHER LEARNING CONTEXTS. VIEWING THIS DATA CAN HELP STUDENTS TO REFLECT ON AND MONITOR THEIR LEARNING, AND CAN SUPPORT TEACHERS' DECISION-MAKING DURING CLASSROOM ACTIVITIES OR LATER, IN THEIR PLANNING OF SUBSEQUENT SESSIONS. AS AN EXAMPLE, WE OUTLINE THE USE OF THE NEXT-TELL OPEN LEARNER MODEL IN A SCHOOL IN NORWAY.
THE AIM OF THIS RESEARCH WAS TO ANALYZE THE CONTRIBUTION THAT A SELF-OBSERVATION TOOL (THE DIARY) CAN OFFER IN A BLENDED UNIVERSITY COURSE WITH UNIVERSITY STUDENTS. A GROUP OF 67 UNIVERSITY STUDENTS OBSERVED THEIR LISTENING, READING AND STUDY BEHAVIORS BY RECORDING IT IN A LECTURE DIARY. THESE BEHAVIORS WERE ALSO TRACKED AND RECORDED AS LOG DATA BY THE LEARNING MANAGEMENT SYSTEM USED (MOODLE). THE RESEARCH QUESTIONS WE TRIED TO ANSWER WERE ABOUT: A) THE ABILITY OF STUDENTS TO JUDGE THE DIFFICULTY OF THE TEACHING MATERIAL AND TO REGULATE THEIR BEHAVIOR ON THIS JUDGMENT, ASSUMING THAT A GROWTH IN THE JUDGMENT OF DIFFICULTY (INDICATED THROUGH THE DIARY) SHOULD HAVE CORRESPONDED TO AN INCREASE IN THE NUMBER OF PERFORMED ACTIVITIES (RECORDED VIA THE LOGS); FURTHERMORE: B) THEY CONCERN THE POSSIBILITY OF RELATING EFFORT TO PERFORMANCE, ASSUMING THAT AN INCREASE OF THE NUMBER OF ACTIVITIES (RECORDED BY LOG AND DECLARED THROUGH THE DIARY) WOULD HAVE IMPROVED MEMORY AND LEARNING. THE MAIN FACTOR UNDERLYING THE RESEARCH CAN BE DEFINED AS THE CONSISTENCY BETWEEN STUDENT JUDGMENTS AND DATA OBTAINED FROM THE TRACKING OF LOGS. FINALLY: C) WE CONSIDERED THE RELATIONSHIP BETWEEN PROFIT AND PARTICIPATION IN PRESENCE OR AT A DISTANCE. CUES FROM SELF-OBSERVATION AND AUTOMATIC RECORDING OF BEHAVIORS WERE THEN COMPARED. THE HYPOTHESIS WAS ONLY PARTIALLY CONFIRMED BY THE RESULTS OBTAINED. THE ABILITY TO EVALUATE THE DIFFERENT EFFORT REQUIRED BY THE PROPOSED TEACHING MATERIAL WAS PROBLEMATIC. THE INFORMATION CONTRIBUTION THAT CAN BE OBTAINED FROM THE PROPOSED TOOL FOR SELF-OBSERVATION, THE DIARY, IS ALSO UNDER DISCUSSION, ALTHOUGH IT IS USEFUL IN ORDER TO INVOLVE THE END USER IN THE EVALUATION AND SELF-REGULATION PROCESS.
THIS ARTICLE EXAMINES IBM'S 'SMARTER EDUCATION' PROGRAM, PART OF ITS WIDER 'SMARTER CITIES' AGENDA, FOCUSING SPECIFICALLY ON ITS LEARNING ANALYTICS APPLICATIONS (BASED ON MACHINE LEARNING ALGORITHMS) AND COGNITIVE COMPUTING DEVELOPMENTS FOR EDUCATION (WHICH TAKE INSPIRATION FROM NEUROSCIENCE FOR THE DESIGN OF BRAIN-LIKE NEURAL NETWORKS ALGORITHMS AND NEUROCOMPUTATIONAL DEVICES). THE ARTICLE CONCEPTUALIZES THE RELATIONSHIP BETWEEN LEARNING ALGORITHMS, NEUROSCIENCE, AND THE NEW LEARNING SPACES OF THE CITY BY COMBINING THE NOTION OF PROGRAMMABLE 'CODE/SPACE' WITH IDEAS ABOUT THE 'SOCIAL LIFE OF THE BRAIN' TO SUGGEST THAT NEW KINDS OF 'BRAIN/CODE/SPACES' ARE BEING DEVELOPED WHERE THE ENVIRONMENT ITSELF IS IMAGINED TO POSSESS BRAIN-LIKE FUNCTIONS OF LEARNING AND 'HUMAN QUALITIES' OF COGNITION PERFORMED BY ALGORITHMIC PROCESSES. IBM'S AMBITIONS FOR EDUCATION CONSTITUTE A SOCIOTECHNICAL IMAGINARY OF A 'COGNITIVE CLASSROOM' WHERE THE PRACTICES ASSOCIATED WITH DATA ANALYTICS AND COGNITIVE COMPUTING IN THE SMART CITY ARE BEING TRANSLATED INTO THE NEUROPEDAGOGIC BRAIN/CODE/SPACES OF THE SCHOOL, WITH SIGNIFICANT CONSEQUENCES FOR HOW LEARNERS ARE TO BE ADDRESSED AND ACTED UPON. THE IBM IMAGINARY OF SMARTER EDUCATION IS ONE SIGNIFICANT INSTANTIATION OF EMERGING SMART CITIES THAT ARE TO BE GOVERNED BY NEUROCOMPUTATIONAL PROCESSES MODELLED ON NEUROSCIENTIFIC INSIGHTS INTO THE BRAIN'S PLASTICITY FOR LEARNING, AND PART OF A 'NEUROFUTURE' IN-THE-MAKING WHERE NONCONSCIOUS ALGORITHMIC 'COMPUTING BRAINS' EMBEDDED IN URBAN SPACE ARE INTENDED TO INTERACT WITH HUMAN COGNITION AND BRAIN FUNCTIONING.
OUR PREVIOUS WORK HAS DEMONSTRATED THAT PLAYERS WHO PERCEIVE A GAME AS MORE CHALLENGING ARE LIKELY TO PERCEIVE GREATER LEARNING FROM THAT GAME [8]. HOWEVER, THIS MAY NOT BE THE CASE FOR ALL SOURCES OF CHALLENGE. IN THIS STUDY OF A SCIENCE LEARNING GAME CALLED QUANTUM SPECTRE, WE FOUND THAT STUDENTS' PROGRESS THROUGH THE FIRST ZONE OF THE GAME SEEMED TO ENCOUNTER A "ROADBLOCK" DURING GAMEPLAY, DROPPING OUT WHEN THEY CANNOT (OR DO NOT WANT TO) PROGRESS FURTHER. PREVIOUSLY WE HAD IDENTIFIED TWO PRIMARY TYPES OF ERRORS IN THE LEARNING GAME, QUANTUM SPECTRE: SCIENCE ERRORS RELATED TO THE GAME'S CORE EDUCATIONAL CONTENT; AND PUZZLE ERRORS RELATED TO RULES OF THE GAME BUT NOT TO SCIENCE KNOWLEDGE. USING THIS PRIOR ANALYSIS, ALONGSIDE SURVIVAL ANALYSIS TECHNIQUES FOR ANALYZING TIME-SERIES DATA AND DROP-OUT RATES, WE EXPLORED PLAYERS' GAMEPLAY PATTERNS TO HELP US UNDERSTAND PLAYER DROPOUT IN QUANTUM SPECTRE. THESE RESULTS DEMONSTRATE THAT MODELING PLAYER BEHAVIOR CAN BE USEFUL FOR BOTH ASSESSING LEARNING AND FOR DESIGNING COMPLEX PROBLEM SOLVING CONTENT FOR LEARNING ENVIRONMENTS.
USE OF ONLINE LEARNING SYSTEMS, SUCH AS LEARNER DASHBOARDS, IS INCREASING IN UNIVERSITY CHEMISTRY COURSES. LEARNING ANALYTICS CAN SUPPORT STUDENT LEARNING BY PROVIDING FEEDBACK ON CONCEPT MASTERY. THIS PAPER INVESTIGATED HOW STUDENT USE OF A CHEMISTRY LEARNER DASHBOARD MIGHT BE INCREASED THROUGH CLASS STRUCTURE, INSTRUCTOR PRACTICE, AND DASHBOARD DESIGN CHANGES ACROSS THREE ITERATIONS IN A GENERAL CHEMISTRY COURSE. ONCE ACCEPTABLE LEVELS OF STUDENT DASHBOARD USE WERE ACHIEVED, FURTHER INVESTIGATION OF HOW STUDENTS INTERACTED WITH THE DASHBOARD, INCLUDING ACCESSING THE VARIETY OF AVAILABLE RESOURCES, WAS CONDUCTED. SURVEY DATA AND CLICKSTREAM DATA FROM THE DASHBOARD WERE COLLECTED AND ANALYZED IN A CONVERGENT MIXED METHODS DESIGN. THIS STUDY FOUND THAT CHANGES IN COURSE STRUCTURE, TEACHER PRACTICE, AND DASHBOARD DESIGN INCREASED USE OF THE DASHBOARD AND UTILIZATION OF THE VARIETY OF MATERIALS OFFERED IN IT. RECOMMENDATIONS FOR INSTRUCTORS TO EFFECTIVELY UTILIZE A LEARNER DASHBOARD AND INCREASE STUDENT TRUST IN THE SYSTEM ARE PROVIDED.
MASSIVE OPEN ONLINE COURSES (MOOCS) REQUIRE STUDENTS' MOTIVATION EITHER INTRINSICALLY OR EXTRINSICALLY TO COMPLETE ANY OF ITS COURSES. EVEN THOUGH MOOCS ENJOY GREAT POPULARITY AND BRING MANY BENEFITS TO THE EDUCATIONAL COMMUNITY, SOME CONCERNS ARISE WITH MOOC ADVANCEMENT. IN FACT, MOOCS ARE AFFECTED BY LOW COMPLETION RATE AND FACE ISSUES WITH RESPECT TO INTERACTIVITY AND STUDENT ENGAGEMENT ALONG MOOC DURATION, WHICH MAY CONVERT STUDENT EXCITEMENT TO BOREDOM AND THEN DROP OUT AT ANY STAGE. A KEY RESULT OF RESEARCH IN THE PAST COUPLE OF YEARS HAS PROVED THAT STUDENTS' ENGAGEMENT IN MOOCS IS STRONGLY RELATED TO THEIR ACTIVITIES ONLINE. THESE ACTIVITIES ARE RELATED TO THE INTERACTION BETWEEN STUDENT AND LOGGING IN THE MOOC, READING AND WRITING IN THE MOOC DISCUSSION FORUM, WATCHING VIDEOS AND DOING QUIZZES. IN THIS RESEARCH PAPER, WE PRESENT OUR RESEARCH IN DEPLOYING A GAMIFICATION MECHANIC IN MOOCS TO INCREASE STUDENT ENGAGEMENT. THE GAMIFICATION APPROACH RELIES ON WEEKLY FEEDBACK TO DRIVE STUDENT INTRINSIC AND EXTRINSIC MOTIVATION. FOLLOWING LEARNING ANALYTICS ON STUDENTS' DATA FROM A MOOC OFFERED IN 2014, 2015, AND 2016, THE OUTCOME OF THIS APPROACH SHOWED AN OBVIOUS INCREASE IN STUDENTS' ACTIVITY AND ENGAGEMENT IN DISCUSSION FORUMS, LOGIN FREQUENCY AND QUIZ TRIALS. THE ACTIVE STUDENTS' COHORT ALLOTMENT HAS INCREASED IN COMPARISON WITH PREVIOUS VERSIONS OF THE SAME MOOC AS WELL AS THE COMPLETION RATE HAS INCREMENTED UP TO 26% OF THE TOTAL NUMBER OF PARTICIPANTS.
TODAY, THE CRISIS HAS WORSENED THE PANORAMA FOR UNIVERSITIES, PLACING NEW CONSTRAINTS THAT REQUIRE BEING MORE SUSTAINABLE ECONOMICALLY. IN ADDITION, UNIVERSITIES WILL ALSO HAVE TO IMPROVE THEIR RESEARCH AND TEACHING IN ORDER TO OBTAIN MORE RESEARCH FUNDS AND ATTRACT MORE STUDENTS. IN THIS PANORAMA, ANALYTICS CAN BE A VERY USEFUL TOOL SINCE IT ALLOWS ACADEMICS (AND UNIVERSITY MANAGERS) TO GET A MORE THOROUGH VIEW OF THEIR CONTEXT, TO BETTER UNDERSTAND THE ENVIRONMENT, AND TO IDENTIFY POTENTIAL IMPROVEMENTS. SOME ANALYTICS HAVE BEEN DONE UNDER THE NAMES OF LEARNING ANALYTICS, ACADEMIC ANALYTICS, EDUCATIONAL DATA MINING AND ETCETERA. HOWEVER, THESE SYSTEMS, UNDER OUR HUMBLE OPINION, ONLY TAKE INTO ACCOUNT THE SMALL PART OF DATA RELATED TO THE PROBLEM, BUT NOT CONTEXTUAL DATA. IN ORDER TO PERFORM ANALYTICS EFFICIENTLY AND REPRODUCE THEIR RESULTS EASILY IN OTHER CONTEXTS, IT IS NECESSARY TO HAVE AS MUCH INFORMATION AS POSSIBLE ABOUT THE CONTEXT. FOR EXAMPLE, WHEN COMMUNICATION FORUMS ARE ANALYZED TO SEE THE CONCEPTS IN WHICH STUDENTS HAVE MORE DOUBTS, IT IS IMPORTANT TO ANALYZE ALSO WHAT CONCEPTS ARE TAUGHT IN THE SUBJECT. HAVING ACCESS TO BOTH SOURCES, MORE INFORMATION CAN BE OBTAINED AND MAY HELP TO DISCOVER NOT ONLY THE PROBLEM (A CONCEPT IS DIFFICULT FOR STUDENTS) BUT ALSO ITS CAUSE (MAYBE IT IS NOT EXPLAINED IN THE MATERIALS OF THE COURSE). THE WORK PRESENTED IN THE PAPER PROPOSES A NOVEL APPROACH TO WORK IN THAT DIRECTION: GATHERING INFORMATION FROM DIFFERENT ASPECTS WITHIN SUBJECTS. IN PARTICULAR, THE PAPER PRESENTS AN APPROACH THAT USES NATURAL LANGUAGE PROCESSING TECHNIQUES TO ANALYZE THE SUBJECT'S MATERIALS IN ORDER TO DISCOVER WHICH CONCEPTS ARE TAUGHT AND THEIR IMPORTANCE IN THE SUBJECT. THE CONTRIBUTION OF THE PAPER IS A SYSTEM THAT ALLOWS OBTAINING A BETTER UNDERSTANDING OF SUBJECTS. THE RESULTS CAN BE USED FOR ANALYZING THE SUITABILITY OF MATERIALS TO SUBJECTS AND TO ENRICH AND CONTEXTUALIZE OTHER ANALYTICAL PROCESSES.
THE INK-12: TEACHING AND LEARNING USING INTERACTIVE INK INSCRIPTIONS IN K-12 PROJECT HAS BEEN DEVELOPING AND INVESTIGATING THE USE OF PEN-BASED TECHNOLOGY IN ELEMENTARY MATH CLASSES. THIS PAPER REPORTS ON PROGRESS MADE ON MACHINE ANALYSIS OF STUDENTS' VISUAL REPRESENTATIONS CREATED USING DIGITAL TOOLS DEVELOPED TO SUPPORT LEARNING MULTIPLICATION AND DIVISION. THE GOAL OF THE ANALYSIS IS TO MAKE STUDENT THINKING VISIBLE IN ORDER TO (A) BETTER UNDERSTAND HOW STUDENTS LEARN MULTIPLICATION AND DIVISION, AND (B) PROVIDE FEEDBACK TO TEACHERS, E.G., ABOUT STRATEGIES STUDENTS USE TO SOLVE PROBLEMS. STUDENT WORK FROM A FIVE-WEEK TRIAL IN A THIRD GRADE CLASS PROVIDES A CORPUS FOR DEVELOPMENT AND EVALUATION OF THE MACHINE ANALYSIS ROUTINES. PRELIMINARY FINDINGS INDICATE THAT THE ROUTINES CAN REPRODUCE HUMAN ANALYSES.
VIRTUAL LEARNING ENVIRONMENTS CONTAIN VALUABLE DATA ABOUT STUDENTS THAT CAN BE CORRELATED AND ANALYZED TO OPTIMIZE LEARNING. MODERN LEARNING ENVIRONMENTS BASED ON DATA MASHUPS THAT COLLECT AND INTEGRATE DATA FROM MULTIPLE SOURCES ARE RELEVANT FOR LEARNING ANALYTICS SYSTEMS BECAUSE THEY PROVIDE INSIGHTS INTO STUDENTS' LEARNING. HOWEVER, DATA SETS INVOLVED IN MASHUPS MAY CONTAIN PERSONAL INFORMATION OF SENSITIVE NATURE THAT RAISES LEGITIMATE PRIVACY CONCERNS. AVERAGE PRIVACY PRESERVATION METHODS ARE BASED ON PREEMPTIVE APPROACHES THAT LIMIT THE PUBLISHED DATA IN A MASHUP BASED ON ACCESS CONTROL AND AUTHENTICATION SCHEMES. SUCH LIMITATIONS MAY REDUCE THE ANALYTICAL UTILITY OF THE DATA EXPOSED TO GAIN STUDENTS' LEARNING INSIGHTS. IN ORDER TO RECONCILE UTILITY AND PRIVACY PRESERVATION OF PUBLISHED DATA, THIS RESEARCH PROPOSES A NEW DATA MASHUP PROTOCOL CAPABLE OF MERGING AND K-ANONYMIZING DATA SETS IN CLOUD-BASED LEARNING ENVIRONMENTS WITHOUT JEOPARDIZING THE ANALYTICAL UTILITY OF THE INFORMATION. THE IMPLEMENTATION OF THE PROTOCOL IS BASED ON LINKED DATA SO THAT DATA SETS INVOLVED IN THE MASHUPS ARE SEMANTICALLY DESCRIBED, THEREBY ENABLING THEIR COMBINATION WITH RELEVANT EDUCATIONAL DATA SOURCES. THE K-ANONYMIZED DATA SETS RETURNED BY THE PROTOCOL STILL RETAIN ESSENTIAL INFORMATION FOR SUPPORTING GENERAL DATA EXPLORATION AND STATISTICAL ANALYSIS TASKS. THE ANALYTICAL AND EMPIRICAL EVALUATION SHOWS THAT THE PROPOSED PROTOCOL PREVENTS INDIVIDUALS' SENSITIVE INFORMATION FROM RE-IDENTIFYING.
THIS STUDY AIMS TO INVESTIGATE LEARNERS' INTERACTION WITH THE LEARNING DASHBOARDS AS A PREDICTOR OUTCOME OF AN ONLINE LEARNING EXPERIENCE AND, TO WHAT EXTENT THIS INTERACTION DATA COULD BE USED TO PREDICT AND/OR PROVIDE GUIDANCE THROUGH THEIR ACADEMIC PERFORMANCE. FOR THIS PURPOSE, A PRESCRIPTIVE LEARNING DASHBOARD INTEGRATED INTO AN E-LEARNING ENVIRONMENT WAS DEVELOPED AS A LEARNING ANALYTICS TOOL. THE PARTICIPANTS CONSISTED OF 126 HIGHER EDUCATION STUDENTS ENROLLED IN THE 12-WEEK COMPUTER NETWORKS AND COMMUNICATION COURSE. DATA GATHERED THROUGH LOGS AND ACADEMIC PERFORMANCES OF LEARNERS WERE ANALYSED WITH DATA MINING TECHNIQUES. THE RESULT OF CLUSTER ANALYSIS, BASED ON INTERACTION WITH THE PRESCRIPTIVE LEARNING DASHBOARD, SHOWED THAT LEARNERS WERE SEPARATED INTO FOUR GROUPS ACCORDING TO THEIR BEHAVIOURAL PATTERNS. A SIMILAR PATTERN APPEARS WHEN THE RELATED CLUSTERS ARE PROFILED BASED ON THE ACADEMIC PERFORMANCES. AT PREDICTIVE ANALYSIS, THE STUDY INDICATES THAT THE INTERACTION WITH PRESCRIPTIVE LEARNING DASHBOARD HAD CERTAIN EFFECTS ON ACADEMIC PERFORMANCE OF LEARNERS SIGNIFICANTLY AND ARTIFICIAL NEURAL NETWORKS ALGORITHM YIELDED THE BEST PERFORMANCE FOR PREDICTING ACADEMIC PERFORMANCE. THE RESULTS SUPPORT THAT THE USAGE PRESCRIPTIVE LEARNING DASHBOARDS CAN BE APPLIED IN ONLINE COURSES AS AN INSTRUCTIONAL AID TO IMPROVE PERFORMANCE OF LEARNERS AND LEARNING DESIGN IN E-LEARNING ENVIRONMENTS.
WHILE THE INTEGRATION OF VIRTUAL LEARNING ENVIRONMENTS HAS BEEN SUCCESSFUL IN SOME UNIVERSITIES, EFFECTIVENESS OF THE COURSES AND LEARNER PERFORMANCE ARE STILL DISPUTABLE. THIS STUDY CONTRIBUTES TO THIS BODY OF KNOWLEDGE BY APPLYING USER LOG DATA OF A VLE PLATFORM TO EXPLORE THE EFFECTIVENESS OF AN ONLINE TEACHER PROFESSIONAL DEVELOPMENT COURSE THAT WAS OFFERED AT UNIVERSITY OF RWANDA. A LEARNING ANALYTICS APPROACH IS USED TO APPRECIATE WHETHER SUCH APPROACH CAN BE USEFUL TO MEASURE THE EFFECTIVENESS OF THE DELIVERED COURSE. RESULTS INDICATE THAT ALTHOUGH HALF OF THE REGISTERED TEACHERS NEVER ACCESSED THE COURSE, THE MAJORITY OF THE GROUP WERE ACTIVELY ENGAGING IN THE VLE ACTIVITIES. HOWEVER, THERE WAS A SIGNIFICANT DIFFERENCE IN THE ACCESS PATTERNS OF THE TWO GROUPS OF PARTICIPANTS IN THE COURSE. FURTHER, THE OUTCOME OF THE STUDY REVEALED THAT THE VLE IN UNIVERSITY OF RWANDA WAS UP AND RUNNING THROUGHOUT THE DURATION OF THE TWO OFFERED COURSES, WHICH JUSTIFIES THE POTENTIAL USE OF VLES' AND ONLINE EDUCATION AT THIS INSTITUTION. THIS STUDY ALSO HAS SET THE FOUNDATION FOR MODELLING STUDENTS' BEHAVIOUR IN VLES' SO THAT STUDENTS GRADES COULD BE PREDICTED BY THEIR BEHAVIOUR IN THE VLE.
DETECTION OF AT-RISK STUDENTS IS A FUNDAMENTAL ISSUE IN ENHANCING LEARNING SUPPORTS, AND HAS BEEN PROPOSED BASED ON STUDENTS' LEARNING ACTIVITY IN LEARNING ANALYTICS. HOWEVER, IT IS NOT CLEAR WHICH ACTIVITY WE SHOULD FOCUS ON TO DETECT AT-RISK STUDENTS SUCH AS LOW PERFORMANCE STUDENTS. IN THIS STUDY, WE PROPOSED A CLUSTERING-BASED METHOD FOR AT-RISK STUDENT DETECTION BASED ON THREE MAIN CLUSTERS OF STUDENTS: INACTIVE, PASSIVE, ACTIVE STUDENTS. OUR METHOD FOCUSED ON READING BEHAVIORS AND ACTION BEHAVIORS IN AN E-BOOK SYSTEM. IN ADDITION, WE CONSIDER WHICH PERIOD OF LEARNING ACTIVITIES IS EFFECTIVE FOR DETECTING AT-RISK STUDENTS. THE LEARNING LOGS OF 289 STUDENTS OF CYBER-SECURITY COURSE WERE COLLECTED FOR OUR ANALYSIS. IN OUR COMPARISON AT DIFFERENT MOMENT DURING THE LECTURE, WE FOUND THAT THE CLUSTER OF INACTIVE STUDENTS DETECTED AFTER 35 MINUTES OF LECTURE GOT SIGNIFICANT LOWER GRADES THAN OTHER STUDENTS, WHEN THE LECTURE WAS NOT TOO SHORT NOR TOO EASY.
THIS PAPER PRESENTS AN APPROACH TO MANAGE MULTILEVEL INFORMATION CONNECTED TO WEB-BASED LEARNING PROCESSES. THE MODEL MAKES THE MANAGEMENT OF MOBILE LEARNING EASIER. IN ADDITION, IT IS APPLICABLE IN MANY OTHER CONTEXTS WHERE THERE IS NEED TO APPLY ICT SOLUTIONS IN LEARNING. THE PAPER DESCRIBES THE NEED AND PEDAGOGICAL BENEFITS OF SYSTEMATIZING THE STUDYING PROCESS MENTIONED ABOVE. AS A PRACTICAL SOLUTION IT PRESENTS A SIMPLE PROGRAMME FOR MANAGING A LARGE AMOUNT OF VARIOUS APPLICATIONS, NETWORK TOOLS, UTILIZED IN WEB-BASED LEARNING. WITH HELP OF THIS PROGRAMME A TEACHER, DEVELOPER OR STUDENT CAN VISUALIZE THE SET OF TOOLS HE OR SHE IS GATHERING INTO ONES OWN "TOOLBOX". IN A WAY, THIS ALSO SERVES LEARNING ANALYTICS PURPOSES. THE MODEL CAN ALSO BE INTERPRETED AS AN APPLICATION OF THE CONCEPT OF PERSONAL LEARNING ENVIRONMENT (PLE).
THE INCLUSION OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE AND MEDICAL EDUCATION IS A FACT NOWADAYS. FURTHERMORE NUMEROUS VIRTUAL LEARNING ENVIRONMENTS HAVE BEEN ESTABLISHED IN ORDER TO HOST BOTH EDUCATIONAL MATERIAL AND LEARNER'S ONLINE ACTIVITIES. ONLINE MODULES IN A VLE CAN BE DESIGNED IN VERY DIFFERENT WAYS BEING PART OF DIFFERENT TYPES OF COURSES, WHILE DIFFERENT MODELS CAN BE USED TO DESIGN THE COURSE BASED ON WHAT THE CREATOR AIMS TO ACHIEVE. THUS, THE TYPES AND THE IMPORTANCE OF THE DIFFERENT ELEMENTS OF THE ONLINE COURSE MAY VARY A LOT. AT THE SAME TIME THE NEED OF A GLOBAL APPROACH TO GATHER BIG EDUCATIONAL DATA IN ORDER TO PROVIDE VALID MEANING TO THE DATA THROUGH LEARNING ANALYTICS AND EDUCATIONAL DATA MINING IS URGENT. IN ORDER THIS TO BE ACHIEVABLE WE PROPOSE A LEARNER ACTIVITY TAXONOMY IN WHICH THE DIFFERENT ELEMENTS OF THE LEARNERS ACTIVITY DATA CAN BE CATEGORISED AND A LEARNER ENGAGEMENT FRAMEWORK IN WHICH THE IMPORTANCE OF THE DIFFERENT ELEMENTS IS VITAL IN ORDER FOR AN ANALYSIS OF THE BIG EDUCATIONAL DATA TO PROVIDE A MEANINGFUL RESULT. THE INITIAL APPLICATION TO PRACTICE OF THE TAXONOMY AND THE FRAMEWORK ARE PRESENTED BASED ON DATA FROM 3 MODULES AT 2 UNIVERSITIES, WHILE THE IMPACT OF THEM ALONG WITH ITS LIMITATIONS ARE DISCUSSED.
IN EDUCATION, LEARNERS NO LONGER PERFORM LEARNING ACTIVITIES IN A WELL-DEFINED AND STATIC ENVIRONMENT LIKE A PHYSICAL CLASSROOM. DIGITAL LEARNING ENVIRONMENTS PROMOTE LEARNERS ANYTIME, ANYWHERE AND ANYHOW LEARNING. AS SUCH, THE CONTEXT IN WHICH LEARNERS UNDERTAKE THESE LEARNING ACTIVITIES CAN BE VERY DIVERSE. TO OPTIMIZE LEARNING AND THE ENVIRONMENT IN WHICH IT OCCURS, LEARNING ANALYTICS MEASURE DATA ABOUT LEARNERS AND THEIR CONTEXT. UNFORTUNATELY, CURRENT STATE OF THE ART STANDARDS AND SYSTEMS ARE LIMITED IN CAPTURING THE CONTEXT OF THE LEARNER. IN THIS PAPER WE PRESENT A LEARNING RECORD STORE (LRS), COMPATIBLE WITH THE EXPERIENCE API, THAT IS ABLE TO CAPTURE THE LEARNERS' CONTEXT, MORE CONCRETELY HIS LOCATION AND USED DEVICE. WE USE ONTOLOGIES TO MODEL THE XAPI AND CONTEXT INFORMATION. THE DATA IS STORED IN A RDF TRIPLE STORE TO GIVE ACCESS TO DIFFERENT SERVICES. THE SERVICES WILL SHOW THE ADVANTAGES OF CAPTURING CONTEXT INFORMATION. WE TESTED OUR SYSTEM BY SENDING STATEMENTS FROM 100 LEARNERS COMPLETING 20 QUESTIONS TO THE LRS.
AS THE LARGEST DISTANCE LEARNING UNIVERSITY IN THE UK, THE OPEN UNIVERSITY HAS MORE THAN 250,000 STUDENTS ENROLLED, MAKING IT ALSO THE LARGEST ACADEMIC INSTITUTE IN THE UK. HOWEVER, MANY STUDENTS END UP FAILING OR WITHDRAWING FROM ONLINE COURSES, WHICH MAKES IT EXTREMELY CRUCIAL TO IDENTIFY THOSE "AT RISK" STUDENTS AND INJECT NECESSARY INTERVENTIONS TO PREVENT THEM FROM DROPPING OUT. THIS STUDY THUS AIMS AT EXPLORING AN EFFICIENT PREDICTIVE MODEL, USING BOTH BEHAVIOURAL AND DEMOGRAPHICAL DATA EXTRACTED FROM THE ANONYMISED OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD). THE PREDICTIVE MODEL WAS IMPLEMENTED THROUGH MACHINE LEARNING METHODS THAT INCLUDED BART. THE ANALYTICS INDICATES THAT THE PROPOSED MODEL COULD PREDICT THE FINAL RESULT OF THE COURSE AT A FINER GRANULARITY, I.E., CLASSIFYING THE STUDENTS INTO WITHDRAWN, FAIL, PASS, AND DISTINCTION, RATHER THAN ONLY COMPLETERS AND NON-COMPLETERS (TWO CATEGORIES) AS PROPOSED IN EXISTING STUDIES. OUR MODEL'S PREDICTION ACCURACY WAS AT 80% OR ABOVE FOR PREDICTING WHICH STUDENTS WOULD WITHDRAW, FAIL AND GET A DISTINCTION. THIS INFORMATION COULD BE USED TO PROVIDE MORE ACCURATE PERSONALISED INTERVENTIONS. IMPORTANTLY, UNLIKE EXISTING SIMILAR STUDIES, OUR MODEL PREDICTS THE FINAL RESULT AT THE VERY BEGINNING OF A COURSE, I.E., USING THE FIRST ASSIGNMENT MARK, AMONG OTHERS, WHICH COULD HELP REDUCE THE DROPOUT RATE BEFORE IT WAS TOO LATE.
THERE HAS BEEN LIMITED RESEARCH ON HOW PERCEPTIONS OF SOCIOECONOMIC STATUS (SES) AND OPINION DIFFERENCE COULD INFLUENCE PEER FEEDBACK IN MASSIVE OPEN ONLINE COURSES (MOOCS). USING SOCIAL COMPARISON THEORY [12], WE INVESTIGATED THE INFLUENCE OF ABILITY AND OPINION-RELATED FACTORS ON PEER FEEDBACK TEXT IN A DATA SCIENCE MOOC. PERCEIVED SES OF PEERS AND THE FORMALITY OF WRITTEN RESPONSES WERE USED AS THE ABILITY-RELATED FACTOR, WHILE AGREEMENT BETWEEN LEARNERS REPRESENTED THE OPINION-RELATED FACTOR. WE FOCUSED ON UNDERSTANDING THE BEHAVIORS OF THOSE LEARNERS WHO ARE MOST PREVALENT IN MOOCS; THOSE FROM HIGH SOCIOECONOMIC COUNTRIES. THROUGH TWO STUDIES, WE FOUND A STRONG AND REPEATED INFLUENCE OF AGREEMENT ON AFFECT AND FORMALITY IN FEEDBACK TO PEERS. WHILE A MEDIATION EFFECT OF PERCEIVED SES WAS FOUND, A SIGNIFICANT EFFECT OF FORMALITY WAS NOT. THIS WORK CONTRIBUTES TO AN UNDERSTANDING OF HOW SOCIAL COMPARISON THEORY CAN BE OPERATIONALIZED IN ONLINE PEER WRITING ENVIRONMENTS.
OPERATOR TRAINING IN THE CHEMICAL INDUSTRY IS IMPORTANT BECAUSE OF THE POTENTIALLY HAZARDOUS NATURE OF PROCEDURES AND THE WAY OPERATORS' MISTAKES CAN HAVE SERIOUS CONSEQUENCES ON PROCESS OPERATION AND SAFETY. CURRENTLY, OPERATOR TRAINING IS FACING SOME CHALLENGES, SUCH AS HIGH COSTS, SAFETY LIMITATIONS AND TIME CONSTRAINTS. ALSO, THERE HAVE BEEN SOME INDICATIONS OF A LACK OF ENGAGEMENT OF EMPLOYEES DURING MANDATORY TRAINING. IMMERSIVE TECHNOLOGIES CAN PROVIDE SOLUTIONS TO THESE CHALLENGES. SPECIFICALLY, VIRTUAL REALITY (VR) HAS THE POTENTIAL TO IMPROVE THE WAY CHEMICAL OPERATORS EXPERIENCE TRAINING SESSIONS, INCREASING MOTIVATION, VIRTUALLY EXPOSING OPERATORS TO UNSAFE SITUATIONS, AND REDUCING CLASSROOM TRAINING TIME. IN THIS PAPER, WE PRESENT RESEARCH BEING CONDUCTED TO DEVELOP A VIRTUAL REALITY TRAINING SOLUTION AS PART OF THE EU HORIZON 2020 CHARMING PROJECT, A PROJECT FOCUSING ON THE EDUCATION OF CURRENT AND FUTURE CHEMICAL INDUSTRY STAKEHOLDERS. THIS PAPER INCLUDES THE DESIGN PRINCIPLES FOR A VIRTUAL REALITY TRAINING ENVIRONMENT INCLUDING THE FEATURES THAT ENHANCE THE EFFECTIVENESS OF VIRTUAL REALITY TRAINING SUCH AS GAME-BASED LEARNING ELEMENTS, LEARNING ANALYTICS, AND ASSESSMENT METHODS. THIS WORK CAN ASSIST THOSE INTERESTED IN EXPLORING THE POTENTIAL OF VIRTUAL REALITY TRAINING ENVIRONMENTS IN THE CHEMICAL INDUSTRY FROM A MULTIDISCIPLINARY PERSPECTIVE. (C) 2021 INSTITUTION OF CHEMICAL ENGINEERS. PUBLISHED BY ELSEVIER B.V. ALL RIGHTS RESERVED.
MASSIVE OPEN ONLINE COURSE (MOOC) HAS BEEN DRAWN MUCH ATTENTION FROM LEARNERS AND TEACHERS THROUGH THE WORLD. MOOC OFFERS A VARIETY OF INTERACTIVE WAYS, IN WHICH THE COURSE COMMENT PANEL IS USED FOR EXPRESS STUDENTS' OPINIONS AND FEELINGS. THESE COMMENTS GENERALLY CONTAIN SOME LEARNING PROBLEMS, ATTITUDES TOWARDS THE COURSE OR THE PLATFORM SUPPORT, ETC. THE FEEDBACK INFORMATION IS BENEFICIAL FOR THE EXCHANGE OF IDEAS AMONG TEACHERS, LEARNERS AND EDUCATIONAL ADMINISTRATORS. HOWEVER, IT IS QUITE TIME-CONSUMING TO ANALYZE THESE IMPORTANT OPINIONS ENTIRELY BY ARTIFICIAL READING. IT IS IMPERATIVE THAT THE MOOC NEEDS THE MACHINE LEARNING METHODS TO DETECT THE EMOTIONS AND TOPICS IN TEXT DATA. IN THIS PAPER, WE PROPOSE AN APPLICATION FRAMEWORK AND DESIGN SCHEME OF INTELLIGENT SYSTEM FOR THE EMOTION RECOGNITION AND TOPIC MINING, AIMING AT CONDUCTING THE INTELLIGENT AND PERSONALIZED LEARNING ANALYTICS ON MOOC. THE PURPOSES OF THE INTELLIGENT COMMENT MINING SYSTEM INCLUDE (1) PREDICTING POPULARITY LEVEL OF EACH COURSE; (2) OBTAINING EMOTION-TOPIC FEEDBACKS ABOUT CONTENT OF COURSES FOR TEACHERS TO ANALYZE AND IMPROVE THEIR TEACHING STRATEGIES; (3) OBTAINING EMOTION-TOPIC FEEDBACKS ABOUT PLATFORM SUPPORT FOR ADMINISTRATORS TO IMPROVE USER EXPERIENCES IN PLATFORM.
MULTIMODAL DATA HAVE THE POTENTIAL TO EXPLORE EMERGING LEARNING PRACTICES THAT EXTEND HUMAN COGNITIVE CAPACITIES. A CRITICAL ISSUE STRETCHING IN MANY MULTIMODAL LEARNING ANALYTICS (MLA) SYSTEMS AND STUDIES IS THE CURRENT FOCUS AIMED AT SUPPORTING RESEARCHERS TO MODEL LEARNER BEHAVIOURS, RATHER THAN DIRECTLY SUPPORTING LEARNERS. MOREOVER, MANY MLA SYSTEMS ARE DESIGNED AND DEPLOYED WITHOUT LEARNERS' INVOLVEMENT. WE ARGUE THAT IN ORDER TO CREATE MLA INTERFACES THAT DIRECTLY SUPPORT LEARNING, WE NEED TO GAIN AN EXPANDED UNDERSTANDING OF HOW MULTIMODAL DATA CAN SUPPORT LEARNERS' AUTHENTIC NEEDS. WE PRESENT A QUALITATIVE STUDY IN WHICH 40 COMPUTER SCIENCE STUDENTS WERE TRACKED IN AN AUTHENTIC LEARNING ACTIVITY USING WEARABLE AND STATIC SENSORS. OUR FINDINGS OUTLINE LEARNERS' CURATED REPRESENTATIONS ABOUT MULTIMODAL DATA AND THE NON-TECHNICAL CHALLENGES IN USING THESE DATA IN THEIR LEARNING PRACTICE. THE PAPER DISCUSSES 10 DIMENSIONS THAT CAN SERVE AS GUIDELINES FOR RESEARCHERS AND DESIGNERS TO CREATE EFFECTIVE AND ETHICALLY AWARE STUDENT-FACING MLA INNOVATIONS.
THIS PAPER INTRODUCES CT-BLOCKS ANALYSER WHICH IS USED FOR ANALYSING COMPUTATIONAL THINKING CONCEPTS OF CT-BLOCKS PROJECT ACTIVITIES. THIS USES THE EMERGING TECHNIQUES OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS TO AUTOMATE THE INSTRUCTIONAL APPROACH FOR CT-BLOCKS PROJECTS. CT-BLOCKS AIMS TO INFUSE COMPUTATIONAL THINKING WITHIN K-12 CURRICULUM. THE BIG DATA TOOLKIT CALLED, CT-BLOCKS ANALYSER WHICH IS SPECIFICALLY DEVELOPED FOR CT-BLOCKS, A VISUALLY APPEALING PROGRAMMING ENVIRONMENT FOR BEGINNERS; A BLOCK BASED PROGRAMMABLE PLATFORM INTERCONNECTED WITH MORE FEATURES IN WHICH STUDENTS ENJOY THE LEARNING PROCESS AND IT TRIES TO MAKE THE USERS MASTER IN COMPUTATIONAL THINKING.
AS THE DEVELOPMENT OF ONLINE LEARNING IS GROWING, A LARGE AMOUNT OF LOG DATA ON STUDENT ACTIVITY IS AVAILABLE AND ACCUMULATED IN LEARNING MANAGEMENT SYSTEMS (LMS). THIS MASSIVE VOLUME OF DATA CAN BE ANALYSED IN ORDER TO DISCOVER HIDDEN INFORMATION ABOUT STUDENTS' LEARNING BEHAVIOURS AND, IN PARTICULAR, TO IDENTIFY AT-RISK STUDENTS AND TO ADVISE INSTRUCTORS TO PROVIDE ADDITIONAL ASSISTANCE AND INTERVENTIONS TO HELP THOSE WHO ARE AT RISK, TO INCREASE RETENTION RATES. AS A RESULT, LEARNING ANALYTICS (LA) HAS EMERGED, WHICH AIMS TO MEASURE, ANALYSE AND REPORT STUDENTS' LEARNING BEHAVIOURS USING DATA ANALYTICS TECHNIQUES. IN THIS REGARD, THIS PAPER AIMS TO PROVIDE AN OVERVIEW OF HOW AT-RISK STUDENTS ARE IDENTIFIED OR PREDICTED BY ANALYSING THEIR LEARNING BEHAVIOURS IN ONLINE LEARNING, INCLUDING THE TYPES OF DATA AND TOOLS OR ANALYTICS METHODS USED. THE FINDINGS INDICATE THAT MOST OF THE DATA ANALYTICS TECHNIQUES HAVE SUCCESSFULLY IDENTIFIED AND PREDICTED AT-RISK STUDENTS AND VARIOUS TYPES OF DATA ARE DETERMINED AS ATTRIBUTES IN PREDICTING AT-RISK STUDENTS. FINALLY, FURTHER RESEARCH SHOULD BE CONDUCTED TO IDENTIFY AND PREDICT AT-RISK STUDENTS, SINCE THERE ARE DIVERSE STUDENTS IN ONLINE LEARNING WHO HAVE DIFFERENT DEMANDS, AND INTERVENTIONS SHOULD BE DEVELOPED TO TACKLE THE PROBLEMS FACED BY AT-RISK STUDENTS IN ORDER TO ENHANCE THEIR LEARNING PERFORMANCES.
HIGHER EDUCATION LEARNERS ARE INCREASINGLY SEEKING EDUCATIONAL EXPERIENCES THAT ARE DIRECTLY RELEVANT TO THEIR PROFESSIONAL INTERESTS AND ASPIRATION, ESPECIALLY, WHILE AIMING AT CAREER SUCCESS AND EMPLOYABILITY. THIS CREATES THE NEED FOR PRACTICE-ORIENTED LEARNING APPROACHES THAT FOCUS ON WORKFORCE SKILLS. IN THIS PAPER, WE PROPOSE VIRTUAL SOCIAL SPACE STRUCTURES WHICH CLUSTER LEARNERS INTO COMMUNITIES OF PRACTICE (COPS) TO COMPLEMENT ACADEMIC PATHWAYS WITH CAREER-SPECIFIC COMPETENCIES' DEVELOPMENT. CLUSTERING EMPLOYS SOCIAL LEARNING ANALYTICS (SLA) TECHNIQUES TO PREDICT GROUP TOGETHER SIMILAR CAREER-ASPIRATION PATTERNS INTO COPS, AND TO SUBSEQUENTLY TRACK DOMAIN-RELATED PRACTICE ACQUISITIONS. OUR APPROACH CONSIDERS BOTH PERSONAL PREFERENCES AND CAREER PREDISPOSITIONS OF LEARNERS AS THEY JOIN COPS AND THRIVE THROUGHOUT COPS LIFE CYCLE. WE DISCUSS OUR THREE-MODULE MODEL: (1) CAREER READINESS TO ASSERT GENERAL PROFESSIONAL DISPOSITIONS, (2) CAREER PREDICTION TO IDENTIFY A TARGETED DOMAIN OF EMPLOYMENT, AND (3) A CAREER DEVELOPMENT PROCESS TO RAISE THE SKILLS THAT ARE RELEVANT TO THE PREDICTED DOMAIN OF EMPLOYMENT.
THIS STUDY IS A CONTINUATION OF THE FINDINGS FROM AN ONLINE HYBRID DISTANCE LEARNING RESEARCH COURSE DEVELOPED BY THE VIRTUAL ACADEMY (VIRTAC) [1] IN THE DEPARTMENT OF INFORMATION SCIENCES AND TECHNOLOGY (IST) [2] OF THE VOLGENAU SCHOOL OF ENGINEERING (VSE) [3] AT GEORGE MASON UNIVERSITY (GMU). THE DEVELOPMENT AND RESEARCH IS FUNDED BY A GRANT FROM CISCO SYSTEMS [4]. AN INITIAL STUDY [5] COMPARED THE EFFECTIVENESS OF TRADITIONAL AND SOCIAL MEDIA MARKETING STRATEGIES IN LAUNCHING THE FIRST ONLINE NON-CREDIT 24-WEEK CISCO CERTIFIED NETWORK ASSOCIATE (CCNA) COURSE. PRELIMINARY COST AND CONVERSION METRICS FROM THE FIRST COURSE SUPPORTED THE USE OF SOCIAL MEDIA TO MARKET THE COURSE. THIS SECOND STUDY ANALYZES THE RESULTS OF THE MARKETING ACTIVITIES FOR THE SECOND OFFERING OF THE COURSE. IT APPLIES ROBERT CIALDINI'S [6] SIX TOOLS OF INFLUENCE: RECIPROCATION, LIKING, COMMITMENT AND CONSISTENCY, AUTHENTICITY, SOCIAL PROOF, AND SCARCITY TO THE MARKETING ACTIVITIES INCLUDING WORD OF MOUTH, REGISTRATION AND RETENTION. THE SIX TOOLS OF INFLUENCE ARE APPLIED TO THE FINDINGS IN RETROSPECT AND ANALYZED FOR FUTURE APPLICATION. THIS STUDY ALSO ANALYZES THE GROWTH IN REGISTRATION AND RETENTION IN COMPARISON TO THE FIRST SESSION. USING THE DATA FROM RESPONSES TO SURVEYS ADMINISTERED TO STUDENTS AT THE BEGINNING AND END OF THE COURSE, THE STUDY DISCUSSES THE IMPLICATION OF WORD-OF-MOUTH AND THE APPLICATION OF LEARNING ANALYTICS IN RETAINING STUDENTS, GROWING THE COURSE'S ENROLLMENT AND STUDENT SATISFACTION.
THE SOCIAL SYSTEMS DEVELOPED IN THE CONTEXT OF GLOBALISATION ARE FURTHER MORE COMPLEX THAT THOSE ARISEN WITHIN THE RULE-OF-LAW OF THE NATION-STATES. THE LOCAL, NATIONAL AND INTERNATIONAL RELATIONS IMPOSE INTO THESE SOCIAL SYSTEMS DIFFERENT FORCE FIELDS DETERMINING THE SPACE OF POSSIBILITIES IN WHICH THEY EVOLVE. IN THIS SITUATION, THE DECISION-MAKING IS CORRESPONDINGLY FURTHER MORE COMPLEX AS TO DRIVE DEMOCRATIC PARTICIPATION FROM THE ROOT-LEVEL OF INDIVIDUAL MEMBERS AND STAKEHOLDERS, ALL THE WAY THROUGH UNTIL THE GLOBAL SYSTEM. EPARTICIPATION REPRESENTS A POSSIBILITY TO MAKE IT POSSIBLE DETERMINED BY THE MEMBER PERCEPTIONS OF PARTAKING IN RELEVANT DECISIONS. A PARADIGMATIC EXAMPLE OF THESE GLOBALISED SOCIAL STRUCTURES IS THE EUROPEAN HIGHER EDUCATION SYSTEM, IN WHICH VERY WELL DEFINED LOCAL AND NATIONAL STRUCTURES COEXIST WITH A NORMATIVE FIELD OF GLOBALISED RELATIONS. BETWEEN 2010 AND 2013 AN EPARTICIPATION SYSTEM WAS DEVELOPED UNDER EU SUPPORT INVOLVING A SIGNIFICANT NUMBER OF UNIVERSITIES FROM EUROPE AND ABROAD. A FLEXIBLE APPROACH WAS USED TO ADAPT THE SYSTEM TO THE DIFFERENT CONTEXTS, WHEREAS AN ANALYTICAL FRAMEWORK WAS SET UP TO EVALUATE THE EXPERIENCE IN ORDER TO FIND GUIDANCE FOR FUTURE EPARTICIPATION DEVELOPMENTS. THE SOCIO-TECHNICAL AND ANALYTICAL FRAMEWORKS AND THE CORRESPONDING RESULTS ARE DISCUSSED AIMING TO PROPOSE A NEW ARCHITECTURE FOR EPARTICIPATION. THIS SOLUTION TARGETS THE CHALLENGES OF THE 21ST CENTURY UNIVERSITY IN WHICH THE CROSSROADS OF LEARNING ANALYTICS, EADMINISTRATION AND EPARTICIPATION ARE DEEPLY RE-STRUCTURING THE ACADEMIC ENVIRONMENT. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
INFORMAL LEARNING IN ONLINE KNOWLEDGE BUILDING COMMUNITIES (OKBCS) OFTEN STARTS WITH ONLINE ACADEMIC HELP SEEKING, AND WITH VISITOR INQUIRIES ON SPECIFIC TOPICS. IN SUCH A CONTEXT, LEARNING PRESUPPOSES ADEQUATE OKBC RESPONSE. EMPLOYING A SOCIAL LEARNING ANALYTICS APPROACH BASED ON NATURAL LANGUAGE PROCESSING AND BAKHTIN'S THEORY OF DIALOGISM, THIS STUDY AIMS TO PREDICT BLOGGER OKBC RESPONSE. MANIPULATING THE BLOG TOPIC (WELL-DEFINED VS. ILL-DEFINED) AND THE VISITOR INQUIRY FORMAT (OFF-TOPIC VS. ON-TOPIC), A FIELD EXPERIMENT WITH A 2 X 2 FACTORIAL DESIGN WAS CONDUCTED ON A SAMPLE OF N = 68 BLOGGER COMMUNITIES WITH A TOTAL OF 25,303 MEMBERS. FOR THE ENTIRE SAMPLE, THE COMMUNITY RESPONSE WAS INFLUENCED ONLY BY THE INQUIRY FORMAT. IN A SEPARATE EXAMINATION OF THE EXPERIMENTAL GROUPS, HOWEVER, THIS REMAINED TRUE ONLY FOR THE WELL-DEFINED TOPIC, WHEREAS FOR THE ILL-DEFINED TOPIC THE COMMUNITY RESPONSE ONLY DEPENDED ON THE PREVIOUSLY ESTABLISHED DIALOG QUALITY. THE FINDINGS SUGGEST IDENTIFICATION CRITERIA FOR RESPONSIVE COMMUNITIES, WHICH CAN SUPPORT NEWCOMER INTEGRATION IN OKBCS AND, FROM A LARGER PERSPECTIVE, THE USE OF OKBCS AS COMPONENTS OF FORMAL LEARNING ENVIRONMENTS.
DESPITE THE LAST 40 YEARS OF RESEARCH SHOWING THAT COMPUTER-AIDED DIAGRAMMING TOOLS IMPROVE STUDENT LEARNING, VERY LITTLE RESEARCH REVEALS THE COGNITIVE PROCESSES THAT EXPLAIN WHY AND HOW DIAGRAMMING TOOLS AND SPECIFIC FEATURES OF THE TOOLS AFFECT LEARNING. THIS STUDY DEVELOPED A TOOL THAT GRADUATE STUDENTS USED TO DIAGRAM AND ANALYZE ARGUMENTS AS THE TOOL MINED EACH STUDENTS' ACTIONS. THE MINED DATA WAS USED TO DEVELOP ALGORITHMS TO OPERATIONALIZE AND MEASURE THE USE OF BACKWARD, FORWARD, BREADTH, DEPTH-FIRST REASONING. REGRESSION MODELS WERE THEN COMPARED TO IDENTIFY WHICH ALGORITHM PRODUCED MEASURES THAT BEST PREDICTED DIAGRAM SCORES, AND TO DETERMINE THE COMBINED AND RELATIVE IMPACT OF EACH REASONING PROCESS ON DIAGRAM SCORES. THE FINDINGS SHOW THAT OBSERVING THE PLACEMENT AND LOCATION OF THE FIRST FIVE NODES MOVED AND POSITIONED ON SCREEN IN RELATION TO THE LOCATION OF THE PREVIOUSLY MOVED NODE PROVIDES SUFFICIENT DATA TO GENERATE BACKWARD/FORWARD AND BREADTH/DEPTH-FIRST RATIO SCORES THAT PREDICT DIAGRAM SCORES, WHILE INDIVIDUAL FREQUENCY COUNTS OF EACH OF THE FOUR PROCESSES DO NOT PREDICT SCORES. THE BEST-FIT REGRESSION MODEL USING THE RATIO SCORES SHOW THAT STUDENTS USING MORE BACKWARD AND DEPTH-FIRST PROCESSING CONSTRUCT DIAGRAMS WITH HIGHER SCORES - SCORES BASED ON CRITERIA THAT GAUGE THE DEPTH OF ANALYSIS AND NOT JUST THE NUMBER OF CORRECT DIAGRAM LINKS. THIS STUDY PRESENTS NEW TOOLS, METHODS, AND NEW LINES OF INQUIRY TO ADVANCE RESEARCH ON WAYS TO INTEGRATE LEARNING ANALYTICS INTO COMPUTER-AIDED DIAGRAMMING TOOLS.
EDUCATIONAL SERVICES PROVIDED TO VARIOUS STAKEHOLDERS NEED TO BE ACTIVELY DEVELOPED TO ACCOMMODATE THE DIVERSITY OF LEARNING MODELS AND TO GET THE ADVANTAGES OF AVAILABLE RESOURCES (E.G. DATA) IN SMART CITIES GOVERNANCE. DESPITE THE SUBSTANTIAL LITERATURE ON SMART CITIES, FOR TECHNOLOGY-ENHANCED LEARNING (TEL) AND ITS RELATED DOMAINS SUCH AS LEARNING ANALYTICS AND BIG DATA, LITTLE EFFORT HAS BEEN GIVEN TO THE CREATION OF CONNECTIVITY TO SMART CITIES GOVERNANCE TO MEET STAKEHOLDERS' DEMANDS, EVEN THOUGH THIS CONNECTION MAY GENERATE VARIOUS CHALLENGES ARISING FROM CONFLICT OF INTERESTS BETWEEN STAKEHOLDERS AND ORGANISATIONS. THIS PAPER PROPOSES A STRUCTURAL FRAMEWORK FOR SUCCESSFUL APPLICATION OF SMART LEARNING ENVIRONMENTS IN THE CONTEXT OF SMART CITY GOVERNANCE. IT REFLECTS ON SELECTED CHALLENGES AND PROPOSES SOME FUTURE DIRECTIONS.
LEARNING ANALYTICS RESEARCH HAS USED BOTH KNOWLEDGE ENGINEERING AND MACHINE LEARNING METHODS TO MODEL STUDENT BEHAVIORS WITHIN THE CONTEXT OF DIGITAL LEARNING ENVIRONMENTS. IN THIS PAPER, WE COMPARE THESE TWO APPROACHES, AS WELL AS A HYBRID APPROACH COMBINING THE TWO TYPES OF METHODS. WE ILLUSTRATE THE STRENGTHS OF EACH APPROACH IN THE CONTEXT OF A CASE STUDY IN BUILDING MODELS ABLE TO DETECT WHEN STUDENTS "GAME THE SYSTEM", A BEHAVIOR IN WHICH LEARNERS ABUSE THE ENVIRONMENT'S SUPPORT FUNCTIONALITIES IN ORDER TO SUCCEED BY GUESSING OR COPYING ANSWERS. WE COMPARE THE PREDICTIVE PERFORMANCE, INTERPRETABILITY AND GENERALIZABILITY OF MODELS CREATED USING EACH APPROACH, DOING SO ACROSS MULTIPLE INTELLIGENT TUTORING SYSTEMS. IN OUR CASE STUDY, WE SHOW THAT THE MACHINE-LEARNED MODEL REQUIRED LESS RESOURCES TO DEVELOP, BUT WAS LESS INTERPRETABLE AND GENERAL. IN CONTRAST, THE KNOWLEDGE ENGINEERING APPROACH RESULTED IN THE MOST INTERPRETABLE AND GENERAL MODEL. COMBINING BOTH APPROACHES IN A HYBRID MODEL ALLOWED US TO CREATE A MODEL THAT PERFORMED BEST ACROSS THE THREE DIMENSIONS, BUT REQUIRING INCREASED RESOURCES TO DEVELOP.
USING DATA TO GENERATE A DEEPER UNDERSTANDING OF COLLABORATIVE LEARNING IS NOT NEW, BUT AUTOMATICALLY ANALYZING LOG DATA HAS ENABLED NEW MEANS OF IDENTIFYING KEY INDICATORS OF EFFECTIVE COLLABORATION AND TEAMWORK THAT CAN BE USED TO PREDICT OUTCOMES AND PERSONALIZE FEEDBACK. COLLABORATION ANALYTICS IS EMERGING AS A NEW TERM TO REFER TO COMPUTATIONAL METHODS FOR IDENTIFYING SALIENT ASPECTS OF COLLABORATION FROM MULTIPLE GROUP DATA SOURCES FOR LEARNERS, EDUCATORS, OR OTHER STAKEHOLDERS TO GAIN AND ACT UPON INSIGHTS. YET, IT REMAINS UNCLEAR HOW COLLABORATION ANALYTICS GO BEYOND PREVIOUS WORK FOCUSED ON MODELLING GROUP INTERACTIONS FOR THE PURPOSE OF ADAPTING INSTRUCTION. THIS PAPER PROVIDES A CONCEPTUAL MODEL OF COLLABORATION ANALYTICS TO HELP RESEARCHERS AND DESIGNERS IDENTIFY THE OPPORTUNITIES ENABLED BY SUCH INNOVATIONS TO ADVANCE KNOWLEDGE IN, AND PROVIDE ENHANCED SUPPORT FOR, COLLABORATIVE LEARNING AND TEAMWORK. WE ARGUE THAT MAPPING FROM LOW-LEVEL DATA TO HIGHER-ORDER CONSTRUCTS THAT ARE EDUCATIONALLY MEANINGFUL, AND THAT CAN BE UNDERSTOOD BY EDUCATORS AND LEARNERS, IS ESSENTIAL TO ASSESSING THE VALIDITY OF COLLABORATION ANALYTICS. THROUGH FOUR CASES, THE PAPER ILLUSTRATES THE CRITICAL ROLE OF THEORY, TASK DESIGN, AND HUMAN FACTORS IN THE DESIGN OF INTERFACES THAT INFORM ACTIONABLE INSIGHTS FOR IMPROVING COLLABORATION AND GROUP LEARNING.
THE FLIPPED CLASSROOM GIVES STUDENTS THE FLEXIBILITY TO ORGANIZE THEIR LEARNING, WHILE TEACHERS CAN MONITOR THEIR PROGRESS ANALYZING THEIR ONLINE ACTIVITY. IN MASSIVE COURSES WHERE THERE ARE A VARIETY OF ACTIVITIES, AUTOMATED ANALYSIS TECHNIQUES ARE REQUIRED IN ORDER TO PROCESS THE LARGE VOLUME OF INFORMATION THAT IS GENERATED, TO HELP TEACHERS TAKE TIMELY AND APPROPRIATE ACTIONS. IN THESE SCENARIOS, IT IS CONVENIENT TO CLASSIFY STUDENTS INTO A SMALL NUMBER OF GROUPS THAT CAN RECEIVE DEDICATED SUPPORT. USING ONLY ONLINE ACTIVITY TO GROUP STUDENTS HAS PROVEN TO BE INSUFFICIENT TO CHARACTERIZE RELEVANT GROUPS, BECAUSE OF THAT THIS STUDY PROPOSES TO UNDERSTAND DIFFERENCES IN ONLINE ACTIVITY USING DIFFERENCES IN COURSE STATUS AND LEARNING EXPERIENCE, USING DATA FROM A PROGRAMMING COURSE (N = 409). THE MODEL BUILT SHOWS THAT LEARNING EXPERIENCE CAN BE CATEGORIZED IN THREE GROUPS, EACH WITH DIFFERENT ACADEMIC PERFORMANCE AND DISTINCT ONLINE ACTIVITY. THE RELATIONSHIP BETWEEN GROUPS AND ONLINE ACTIVITY ALLOWED US TO BUILD CLASSIFIERS TO DETECT STUDENTS WHO ARE AT RISK OF FAILING THE COURSE (AUC = 0.84) OR NEED SPECIAL SUPPORT (AUC = 0.73), PROVIDING TEACHERS WITH A USEFUL MECHANISM FOR PREDICTING AND IMPROVING STUDENT OUTCOMES.
ORGANIZATIONS CONTINUOUSLY INVEST IN THE ANALYSIS OF ACCUMULATED DATA BY LINKING THEIR EXPLOITATION TO MORE EFFECTIVE DECISION MAKING. HIGHER EDUCATION AND ESPECIALLY DISTANCE LEARNING AS A LARGE DATA TANK, FOLLOWS THE TECHNOLOGICAL AND FINANCIAL NEEDS TO INVOLVE MORE FLEXIBLE DATA ANALYSIS ENVIRONMENTS AND BETTER DATA-INFORMED DECISION CAPABILITIES. SHRINKING PUBLIC SUBSIDIES DRIVES HIGHER EDUCATION TO FORM A MORE COMPETITIVE LEARNING ENVIRONMENT. SATISFACTORY USER EXPERIENCE AND PERSONALIZED SERVICES REQUIRE A QUANTITATIVE AND QUALITATIVE ANALYSIS OF STUDENTS' DAILY ACTION IN LEARNING ENVIRONMENTS. THE INCREASING ADOPTION OF LEARNING ANALYTICS (LA) AND EDUCATIONAL DATA MINING (EDM) PUSH THE DEVELOPMENT OF NOVEL APPROACHES AND ADVANCEMENTS IN EDUCATION SECTOR. AT THE SAME TIME, THE RAPID DISCLOSURE OF HIDDEN KNOWLEDGE AND THE IMMEDIATE PRESENTATION OF RESULTS TO OPTIMIZE PERSONALIZED DECISION MAKING IS A CHALLENGE FOR THE COMPETITIVENESS OF DISTANCE LEARNING. THE OBJECTIVE OF THIS STUDY IS THE ANALYSIS OF PROCESSES, TECHNOLOGIES AND RESOURCES USED IN AN ANNUAL MODULE AT THE HELLENIC OPEN UNIVERSITY TO PROVIDE STAKEHOLDERS WITH THE VISIBILITY OF INTERACTIONS AND HIDDEN VALUE IN STUDENTS' INTERACTIONS. LA TECHNOLOGIES ARE USED ON LARGE SET OF DATA THAT HAS BEEN COLLECTED BY A MOODLE PLATFORM AND CAREFULLY ANALYZED. STUDENTS' LOGINS, REPLIES AND QUIZZES ARE BLENDED WITH THE AVERAGE GRADE OF THE MAIN WRITTEN EXERCISES DURING THE ACADEMIC YEAR. THE CONTRIBUTION OF THIS WORK IS THE USEFUL OBSERVATIONS FROM THE STUDENTS' EDUCATIONAL ON-LINE ACTIVITIES AS PREDICTIVE FACTORS FOR THEIR ACADEMIC PERFORMANCE.
LEARNING ANALYTICS HAS BEEN DOMINATING THE EDUCATION ARENA FOR THESE LAST FEW YEARS AS THE EFFICACY AND APPLICABILITY OF BIG DATA IN GENERAL HAS BEEN RECOGNIZED, APPRECIATED AND FRUITFULLY EMPLOYED. SUCH ANALYTICS CAN ASSIST AND SUPPORT HIGHER EDUCATION LEARNERS TO OPTIMIZE THEIR INTERACTION WITH AVAILABLE RESOURCES, WHILE PROVIDING CRUCIAL INSIGHTS ON THEIR LEARNING BEHAVIOUR AND STUDY PROCESSES. IN THIS PAPER, WE PRESENT A CASE STUDY OF HOW WE EMPLOY AN ONLINE ENVIRONMENT WITH MATURE STUDENTS WITHIN AN AMBIENT INTELLIGENT CLASSROOM. THE SPECIFICALLY DESIGNED LEARNING SPACE MAKES USE OF A NUMBER OF STRATEGICALLY POSITIONED SENSORS THAT PROVIDE EXPLICIT DATA TO THE UNDERLYING INTELLIGENT SYSTEM THAT FORMS PART OF THE ASSOCIATED ONLINE PORTAL. PHYSICALLY PRESENT AND REMOTELY CONNECTED LEARNERS HAVE ACCESS TO THEIR PERSONALISED LEARNING ENVIRONMENT THAT DEPENDS ON THEIR ACADEMIC NEEDS, PROGRESS, POSITION IN CLASS, AND THE OTHER LEARNERS IN THEIR GROUP OR VICINITY. THE AMBIENT INTELLIGENT SYSTEM COORDINATES THE INTERACTION BETWEEN ALL THE STUDENTS AND TEACHER WHO IS PRESENT IN CLASS. A REAL-TIME MACHINE LEARNING APPLICATION TO DETECT LEARNERS' LEVELS OF ATTENTION AND PARTICIPATION MAKES USE OF SENSORY DATA GENERATED TOGETHER WITH OTHER LEARNING DATA COLLECTED FROM THE PORTAL THAT COLLECTIVELY ASSISTS IN THE LEARNING ANALYSIS PROCESS THAT IS LOOPED BACK TO THE LEARNERS' LEARNING ENVIRONMENT, AS WELL AS TO THE EDUCATOR, IN AN ATTEMPT TO IMPROVE THE DELIVERY AND THE ENTIRE EDUCATIONAL PROCESS. WE REPORT ON SEVERAL ISSUES ENCOUNTERED TOGETHER WITH ACCURACY AND RELIABILITY CONCERNS AS WE DRAW A NUMBER OF CONCLUSIONS AND OFFER RECOMMENDATIONS.
IN THIS WORK WE INVESTIGATE THE USE OF DEEP LEARNING FOR TEXT ANALYSIS TO MEASURE ELEMENTS OF STUDENT THINKING RELATED TO ISSUES OF PRIVILEGE, OPPRESSION, DIVERSITY AND SOCIAL JUSTICE. WE LEVERAGE HISTORICAL EXPERT ANNOTATIONS AS WELL AS A LARGE LEXICAL MODEL TO CREATE A MORE GENERALIZABLE VOCABULARY FOR IDENTIFYING THESE CHARACTERISTICS IN SHORT STUDENT WRITING. WE DEMONSTRATE THE FEASIBILITY OF THIS APPROACH, AND IDENTIFY FURTHER AREAS FOR RESEARCH.
ANALYSIS OF STUDENT WRITING, BOTH FOR ASSESSMENT AND FOR ENABLING FEEDBACK HAVE BEEN OF INTEREST TO THE FIELD OF LEARNING ANALYTICS. WHILE MUCH PROGRESS CAN BE MADE THROUGH DETECTION OF LOCAL CUES IN WRITING, STRUCTURED PREDICTION APPROACHES OFFER CAPABILITIES THAT ARE PARTICULARLY WELL TAILORED TO THE NEEDS OF MODELS AIMING TO OFFER SUBSTANTIVE FEEDBACK ON RHETORICAL STRUCTURE. WE THUS CAST THE ANALYSIS OF RHETORICAL STRUCTURE IN ACADEMIC WRITING AS A STRUCTURED PREDICTION TASK IN WHICH WE EMPLOY MODELS THAT LEVERAGE BOTH LOCAL AND GLOBAL CUES IN WRITING. IN PARTICULAR, THIS PAPER PRESENTS A HIERARCHICAL NEURAL ARCHITECTURE THAT PERFORMS THIS TASK. THE EVALUATION DEMONSTRATES THAT THE ARCHITECTURE ACHIEVES NEAR-HUMAN PERFORMANCE WHILE SIGNIFICANTLY SURPASSING STATE-OF-THE-ART BASELINES. A MULTIFACETED APPROACH TO MODEL INTERPRETATION OFFERS INSIGHTS INTO THE INNER WORKINGS OF THE MODEL.
THE USE OF LEARNING TRACE DATA TOGETHER WITH VARIOUS ANALYTICAL METHODS HAS PROVEN SUCCESSFUL IN DETECTING PATTERNS IN LEARNING BEHAVIOUR, IDENTIFYING STUDENT PROFILES, AND CLUSTERING LEARNING RESOURCES. HOWEVER, INTERPRETATION OF THE FINDINGS IS OFTEN DIFFICULT AND UNCERTAIN DUE TO A LACK OF CONTEXTUAL DATA (E.G., DATA ON STUDENT MOTIVATION, EMOTION OR CURRICULUM DESIGN). IN THIS STUDY WE EXPLORED THE INTEGRATION OF STUDENT SELFREPORTS ABOUT COGNITIVE LOAD AND SELF-EFFICACY INTO THE LEARNING PROCESS AND COLLECTION OF RELEVANT STUDENTS' PERCEPTIONS AS LEARNING TRACES. OUR OBJECTIVE WAS TO EXAMINE THE ASSOCIATION OF TRACED MEASURES OF RELEVANT LEARNING CONSTRUCTS (COGNITIVE LOAD AND SELF-EFFICACY) WITH I) INDICATORS OF THE STUDENTS' LEARNING BEHAVIOUR DERIVED FROM TRACE DATA, AND II) THE STUDENTS' ACADEMIC PERFORMANCE. THE RESULTS INDICATED THE PRESENCE OF ASSOCIATION BETWEEN SOME INDICATORS OF STUDENTS' ENGAGEMENT WITH LEARNING ACTIVITIES AND TRACED MEASURES OF COGNITIVE LOAD AND SELF-EFFICACY. CORRELATIONAL ANALYSIS DEMONSTRATED SIGNIFICANT POSITIVE CORRELATION BETWEEN THE STUDENTS' COURSE PERFORMANCE AND TRACED MEASURES OF COGNITIVE LOAD AND SELF-EFFICACY.
LEARNING ANALYTICS HAS EMERGED AS A PROMISING TOOL FOR OPTIMIZING THE LEARNING EXPERIENCE AND RESULTS, ESPECIALLY IN ONLINE EDUCATIONAL ENVIRONMENTS. AN IMPORTANT CHALLENGE IN THIS AREA IS IDENTIFYING THE MOST DIFFICULT TOPICS FOR STUDENTS IN A SUBJECT, WHICH IS OF GREAT USE TO IMPROVE THE QUALITY OF TEACHING BY DEVOTING MORE EFFORT TO THOSE TOPICS OF GREATER DIFFICULTY, ASSIGNING THEM MORE TIME, RESOURCES AND MATERIALS. WE HAVE APPROACHED THE PROBLEM BY MEANS OF NATURAL LANGUAGE PROCESSING TECHNIQUES. IN PARTICULAR, WE PROPOSE A SOLUTION BASED ON A DEEP LEARNING MODEL THAT AUTOMATICALLY EXTRACTS THE MAIN TOPICS THAT ARE COVERED IN EDUCATIONAL DOCUMENTS. THIS MODEL IS NEXT APPLIED TO THE PROBLEM OF IDENTIFYING THE MOST DIFFICULT TOPICS FOR STUDENTS IN A SUBJECT RELATED TO THE STUDY OF ALGORITHMS AND DATA STRUCTURES IN A COMPUTER SCIENCE DEGREE. OUR RESULTS SHOW THAT OUR TOPIC IDENTIFICATION MODEL PRESENTS VERY HIGH ACCURACY (AROUND 90 PERCENT) AND MAY BE EFFICIENTLY USED IN LEARNING ANALYTICS APPLICATIONS, SUCH AS THE IDENTIFICATION AND UNDERSTANDING OF WHAT MAKES THE LEARNING OF A SUBJECT DIFFICULT. AN EXHAUSTIVE ANALYSIS OF THE CASE STUDY HAS ALSO REVEALED THAT THERE ARE INDEED TOPICS THAT ARE CONSISTENTLY MORE DIFFICULT FOR MOST STUDENTS, AND ALSO THAT THE PERCEPTION OF DIFFICULTY IN STUDENTS AND TEACHERS DOES NOT ALWAYS COINCIDE WITH THE ACTUAL DIFFICULTY INDICATED BY THE DATA, PREVENTING TO PAY ADEQUATE ATTENTION TO THE MOST CHALLENGING TOPICS.
LEARNING ANALYTICS (LA) IS AN ANALYSIS TOOLSET THAT ENABLES COLLECTION OF STUDENTS' DATA AND CONTEXT DATA, FOR THE PURPOSE OF VISUALIZING INDICATORS OF PERFORMANCE THAT ALLOW FOR IMPROVEMENTS FOR LEARNING AND ACADEMIC SUCCESS. IN THIS STUDY, ARTIFICIAL INTELLIGENCE (AI) ALGORITHMS LIKE K-NEAREST NEIGHBOR AND RANDOM FOREST WERE USED. THESE ALGORITHMS TRAINED A MODEL THAT COULD PREDICT THE ACADEMIC SUCCESS OF COLLEGE-LEVEL ENGINEERING STUDENTS. UNDER AN EXPERIMENTAL MODEL WITH 182 STUDENTS, THREE INSTRUCTORS LEADING SIX GROUPS OF PHYSICS II FOR ENGINEERING MAJORS OF THE TECNOLOGICO DE MONTERREY (MEXICO) ADMINISTERED ADAPTIVE MEASURES FOR ONE GROUP EACH AND NOT FOR THE OTHER OF THEIR GROUPS (THE CONTROL GROUPS). THREE FORECASTS WERE CALCULATED CONSIDERING STRUCTURED ACADEMIC INFORMATION (NUMERICAL GRADES) AND UNSTRUCTURED ACADEMIC INFORMATION (STUDENT ID PICTURES). UNSTRUCTURED DATA FROM THE FACIAL PHOTOGRAPH AND NUMERIC ACADEMIC INFORMATION FROM THE FIRST EVALUATION PERIOD WERE CONSIDERED FOR THE STUDY. THE RESULTS SHOW A SIGNIFICANT DIFFERENCE BETWEEN THE CONTROL AND EXPERIMENTAL GROUPS OF ONLY ONE INSTRUCTOR, WHILE THE RESULTS OF THE OTHER TWO INSTRUCTORS' CONTROL AND EXPERIMENTAL GROUPS WERE CONSISTENT. ONE FINDING OF THIS STUDY IS THAT, DESPITE THE PREDICTION NOT BEING CORRECT FOR EACH STUDENT, A GENERAL PICTURE OF THE PERFORMANCE OF THE GROUP WAS GIVEN. IT SEEMS THAT THE ALGORITHM MUST BE TRAINED WITH MORE DATA FOR THE FORECAST TO BE MORE PRECISE IN THE FUTURE.
WHILE TECHNOLOGY HAS HELPED IMPROVE PROCESS EFFICIENCY IN SEVERAL DOMAINS, IT STILL HAS AN OUTSTANDING DEBT TO EDUCATION. IN THIS ARTICLE, WE INTRODUCE NAIRA, A MULTIMODAL LEARNING ANALYTICS PLATFORM THAT PROVIDES REAL-TIME FEEDBACK TO FOSTER COLLABORATIVE LEARNING ACTIVITIES' EFFICIENCY. NAIRA PROVIDES REAL-TIME VISUALIZATIONS FOR STUDENTS' VERBAL INTERACTIONS WHEN WORKING IN GROUPS, ALLOWING TEACHERS TO PERFORM PRECISE INTERVENTIONS TO ENSURE LEARNING ACTIVITIES' CORRECT EXECUTION. WE PRESENT A CASE STUDY WITH 24 UNDERGRADUATE SUBJECTS PERFORMING A REMOTE COLLABORATIVE LEARNING ACTIVITY BASED ON THE JIGSAW LEARNING TECHNIQUE WITHIN THE COVID-19 PANDEMIC CONTEXT. THE MAIN GOALS OF THE STUDY ARE (1) TO QUALITATIVELY DESCRIBE HOW THE TEACHER USED NAIRA'S VISUALIZATIONS TO PERFORM INTERVENTIONS AND (2) TO IDENTIFY QUANTITATIVE DIFFERENCES IN THE NUMBER AND TIME BETWEEN STUDENTS' SPOKEN INTERACTIONS AMONG TWO DIFFERENT STAGES OF THE ACTIVITY, ONE OF THEM SUPPORTED BY NAIRA'S VISUALIZATIONS. THE CASE STUDY SHOWED THAT NAIRA ALLOWED THE TEACHER TO MONITOR AND FACILITATE THE LEARNING ACTIVITY'S SUPERVISED STAGE EXECUTION, EVEN IN A REMOTE LEARNING CONTEXT, WITH STUDENTS WORKING IN SEPARATE VIRTUAL CLASSROOMS WITH THEIR VIDEO CAMERAS OFF. THE QUANTITATIVE COMPARISON OF SPOKEN INTERACTIONS SUGGESTS THE EXISTENCE OF DIFFERENCES IN THE DISTRIBUTION BETWEEN THE MONITORED AND UNMONITORED STAGES OF THE ACTIVITY, WITH A MORE HOMOGENEOUS SPEAKING TIME DISTRIBUTION IN THE NAIRA SUPPORTED STAGE.
ADVANCES IN THE LEARNING ANALYTICS COMMUNITY HAVE CREATED OPPORTUNITIES TO DELIVER EARLY WARNINGS THAT ALERT TEACHERS AND INSTRUCTORS WHEN A STUDENT IS AT RISK OF NOT MEETING ACADEMIC GOALS [6], [71]. ALERT SYSTEMS HAVE ALSO BEEN DEVELOPED FOR SCHOOL DISTRICT LEADERS [33] AND FOR ACADEMIC ADVISORS IN HIGHER EDUCATION [39], BUT OTHER PROFESSIONALS IN THE K-12 SYSTEM, NAMELY GUIDANCE COUNSELORS, HAVE NOT BEEN WIDELY SERVED BY THESE SYSTEMS. IN THIS STUDY, WE USE COLLEGE ENROLLMENT MODELS CREATED FOR THE ASSISTMENTS LEARNING SYSTEM [55] TO DEVELOP REPORTS THAT TARGET THE NEEDS OF THESE PROFESSIONALS, WHO OFTEN WORK DIRECTLY WITH STUDENTS, BUT USUALLY NOT IN CLASSROOM SETTINGS. THESE REPORTS ARE DESIGNED TO FACILITATE GUIDANCE COUNSELORS' EFFORTS TO HELP STUDENTS TO SET LONG TERM ACADEMIC AND CAREER GOALS. AS SUCH, THEY PROVIDE THE CALCULATED LIKELIHOOD THAT A STUDENT WILL ATTEND COLLEGE (THE ASSISTMENTS COLLEGE PREDICTION MODEL OR ACPM), ALONGSIDE STUDENT ENGAGEMENT AND LEARNING MEASURES. USING DESIGN PRINCIPLES FROM RISK COMMUNICATION RESEARCH AND STUDENT FEEDBACK THEORIES TO INFORM A CO-DESIGN PROCESS, WE DEVELOPED REPORTS THAT CAN INFORM GUIDANCE COUNSELOR EFFORTS TO SUPPORT STUDENT ACHIEVEMENT.
COMPUTERIZED ASSESSMENTS ARE AN INCREASINGLY POPULAR WAY TO EVALUATE STUDENTS. THEY NEED TO BE OPTIMIZED SO THAT STUDENTS CAN RECEIVE AN ACCURATE EVALUATION IN AS LITTLE TIME AS POSSIBLE. SUCH OPTIMIZATION IS POSSIBLE THROUGH LEARNING ANALYTICS AND COMPUTERIZED ADAPTIVE TESTS (CATS): THE NEXT QUESTION IS THEN CHOSEN ACCORDING TO THE PREVIOUS RESPONSES OF THE STUDENT, THEREBY MAKING ASSESSMENT MORE EFFICIENT. USING THE DATA COLLECTED FROM PREVIOUS STUDENTS IN NON-ADAPTIVE TESTS, IT IS THUS POSSIBLE TO PROVIDE FORMATIVE ADAPTIVE TESTS TO NEW STUDENTS BY TELLING THEM WHAT TO DO NEXT. THIS CHAPTER REVIEWS SEVERAL MODELS OF CATS FOUND IN VARIOUS FIELDS, TOGETHER WITH THEIR MAIN CHARACTERISTICS. WE THEN COMPARE THESE MODELS EMPIRICALLY ON REAL DATA. WE CONCLUDE WITH A DISCUSSION OF FUTURE RESEARCH DIRECTIONS FOR COMPUTERIZED ASSESSMENTS.
JOURNAL WRITING IS AN IMPORTANT AND COMMON REFLECTIVE PRACTICE IN EDUCATION. STUDENTS' REFLECTION JOURNALS ALSO OFFER A RICH SOURCE OF DATA FOR FORMATIVE ASSESSMENT. HOWEVER, THE ANALYSIS OF THE TEXTUAL REFLECTIONS IN CLASS OF LARGE SIZE PRESENTS CHALLENGES. AUTOMATIC ANALYSIS OF STUDENTS' REFLECTIVE WRITING HOLDS GREAT PROMISE FOR PROVIDING ADAPTIVE REAL TIME SUPPORT FOR STUDENTS. THIS PAPER PROPOSES A METHOD BASED ON TOPIC MODELING TECHNIQUES FOR THE TASK OF THEMES EXPLORATION AND REFLECTION GRADE PREDICTION. WE EVALUATED THIS METHOD ON A SAMPLE OF JOURNAL WRITINGS FROM PRE-SERVICE TEACHERS. THE TOPIC MODELING METHOD WAS ABLE TO DISCOVER THE IMPORTANT THEMES AND PATTERNS EMERGED IN STUDENTS' REFLECTION JOURNALS. WEEKLY TOPIC RELEVANCE AND WORD COUNT WERE IDENTIFIED AS IMPORTANT INDICATORS OF THEIR JOURNAL GRADES. BASED ON THE PATTERNS DISCOVERED BY TOPIC MODELING, PREDICTION MODELS WERE DEVELOPED TO AUTOMATE THE ASSESSING AND GRADING OF REFLECTION JOURNALS. THE FINDINGS INDICATE THE POTENTIAL OF TOPIC MODELING IN SERVING AS AN ANALYTIC TOOL FOR TEACHERS TO EXPLORE AND ASSESS STUDENTS' REFLECTIVE THOUGHTS IN WRITTEN JOURNALS.
DESIGN FOR LEARNING IN SCALED COURSES IS SHIFTING AWAY FROM REPLICATION OF TRADITIONAL ON-CAMPUS OR ONLINE TEACHING TOWARDS EXPLOITING THE DISTINCTIVE CHARACTERISTIC AND POTENTIALS OF SCALE TO TRANSFORM BOTH TEACHING AND LEARNING. SCALED LEARNING ENVIRONMENTS SUCH AS MOOCS MAY REPRESENT A NEW PARADIGM FOR TEACHING. THIS WORKSHOP INVOLVES CONSIDERATION OF THE HOW LEARNING OCCURS IN SCALED ENVIRONMENTS, AND HOW LEARNING DESIGNERS AND ANALYSTS CAN ASSIST. IT WILL EXPLORE QUESTIONS AT THE HEART OF EFFECTIVE LEARNING DESIGN, USING EXPERT PANELISTS AND COLLABORATIVE KNOWLEDGE-BUILDING TECHNIQUES TO ARRIVE AT A STOCKTAKE OF THINKING.
IN WEB 2.0 ERA, THE USE OF TECHNOLOGY TO LEARN HAS BENEFITED FROM ALL ITS INFLUENCE GIVING THEREFORE BIRTH OF THE E-LEARNING 2.0 CONCEPT. AS A RESULT, TEACHING AND LEARNING ARE EVENTUALLY TAKING PLACE IN SOCIAL AND INTERACTIVE ENVIRONMENTS GIVING A VIRTUAL PRESENCE OF THE ACTORS (TEACHER, STUDENT, ETC.) AS WELL AS THEIR INTERACTIONS. THESE LATTER ARE OFTEN REPRESENTED AS DATA, WHICH OPEN UP PROMISING OPPORTUNITIES FOR THEIR EXPLOITATION TO IMPROVE TEACHING AND LEARNING OPERATIONS. NOWADAYS, LEARNING MANAGEMENT SYSTEMS, MASSIVE OPEN ONLINE COURSES AND TUTORING SYSTEMS ARE THE MOST EXPLOITED ENVIRONMENT TO EXTRACT USEFUL DATA. SEVERAL RESEARCHES ARE CONDUCTED TO EXPLOIT DATA AND INTERACTIONS IN MEASURING LEARNER ENGAGEMENT, PREDICTING ANALYSIS, EARLY DETECTION AND MANY OTHER APPLICATIONS. IN ANOTHER CONTEXT AND EVERMORE IN E-LEARNING 2.0 FIELD, OUR TEAM HAS DEVELOPED A COLLABORATIVE SOCIAL WEB IDE (INTEGRATED DEVELOPMENT ENVIRONMENT) TO BE USED IN PRACTICAL COMPUTER SCIENCE TEACHING. THIS ENVIRONMENT VIRTUALIZES THE SEQUENCE OF PRACTICAL WORK. IT ALSO ENABLES THE COLLABORATION BETWEEN STUDENT AND TEACHER AND STUDENTS THEMSELVES. THE USE OF OUR IDE IN PRACTICAL PROGRAMMING WORK HAS DEMONSTRATED A SET OF ADVANTAGES. THE RESULTS OF ITS EVALUATION HAVE BEEN PUBLISHED IN A PRIOR WORK. IN THE PRESENCE OF A RICH INTERACTION ENVIRONMENT, WHERE INTERACTIONS ARE RELATED TO PROGRAMMING TEACHING IN HIGHER EDUCATION. WE AIM TO EXPAND THE USE OF OUR IDE IN ORDER TO IMPROVE THIS OPERATION BY HARNESSING THE DATA THAT WE CAN COLLECT IN IT. THE FIRST STEP, IS TO DEFINE THE LEARNING ANALYTICS SPECIFIC TO THIS OPERATION THAT WILL ALLOW US TO COLLECT USEFUL DATA. WE BELIEVE THAT AFTER PROCESSING THIS DATA WE WILL BE ABLE TO EXPLOIT IT TO IMPROVE THE OPERATION OF TEACHING AND LEARNING. THE PURPOSE OF THIS WORK IS TO DEFINE A ROAD MAP OF THE DATA THAT CAN BE COLLECTED FROM WEB IDES USED IN TEACHING AS WELL AS THE POSSIBILITIES OF EXPLOITING THEM.
A COMMON CHALLENGE WHEN ADOPTING THE BLENDED LEARNING APPROACH TO ACTIVE LEARNING IS TO PROVIDE ADEQUATE TRACKING OF LEARNERS' PROGRESS. THE MAIN PROBLEM ADDRESSED IN THIS PUBLICATION IS HOW TO COLLECT AND PROCESS IN REAL TIME DATA (LEARNING ANALYTICS) ABOUT STUDENTS' OFFLINE LEARNING ACTIVITIES AND PROGRESS, IN ORDER TO ENABLE DYNAMIC ADAPTATION AND GAMIFICATION OF THESE ACTIVITIES, AS WELL AS INFORMED SUPPORT ACTIONS BY THE INSTRUCTOR. THIS PROBLEM IS SOLVED IN THE CONTEXT OF ACTIVE, SITUATED, AND GAMIFIED GROUP LEARNING SETUP, USING NOVEL APPROACH COMBINING ELEMENTS OF EXTREME PROGRAMMING, FLIPPED CLASSROOM, AND STATIONS ROTATION MODELS. IN ORDER TO IMPLEMENT AND EVALUATE THE EFFECTIVENESS OF THE PROPOSED METHOD A CLIENT-SERVER APPLICATION CALLED CHALLENGE UNICORDERT WAS DEVELOPED. UNICORDER ALLOWS THE STUDENTS AND TEAMS TO LOG THEIR PROGRESS ALONG THE LEARNING PATH OF CHALLENGES, PROVIDED IN ADAPTIVE WAY AT EACH STATION. THE LOGGING IS DONE USING MULTIMODAL (GESTURE, TEXT, AUDIO, VIDEO) MOBILE USER INTERFACE.
THERE HAS BEEN SIGNIFICANT EFFORTS IN STUDYING COLLABORATIVE AND SOCIAL LEARNING USING AGGREGATE NETWORKS. SUCH EFFORTS HAVE DEMONSTRATED THE WORTH OF THE APPROACH BY PROVIDING INSIGHTS ABOUT THE INTERACTIONS, STUDENT AND TEACHER ROLES, AND PREDICTABILITY OF PERFORMANCE. HOWEVER, USING AN AGGREGATED NETWORK DISCOUNTS THE FINE RESOLUTION OF TEMPORAL INTERACTIONS. BY DOING SO, WE MIGHT OVERLOOK THE REGULARITIES/IRREGULARITIES OF STUDENTS' INTERACTIONS, THE PROCESS OF LEARNING REGULATION, AND HOW AND WHEN DIFFERENT ACTORS INFLUENCE EACH OTHER. THUS, COMPRESSING A COMPLEX TEMPORAL PROCESS SUCH AS LEARNING MAY BE OVERSIMPLIFYING AND REDUCTIONIST. THROUGH A TEMPORAL NETWORK ANALYSIS OF 54 STUDENTS INTERACTIONS (IN TOTAL 3134 INTERACTIONS) IN AN ONLINE MEDICAL EDUCATION COURSE, THIS STUDY CONTRIBUTES WITH A METHODOLOGICAL APPROACH TO BUILDING, VISUALIZING AND QUANTITATIVELY ANALYZING TEMPORAL NETWORKS, THAT COULD HELP EDUCATIONAL PRACTITIONERS UNDERSTAND IMPORTANT TEMPORAL ASPECTS OF COLLABORATIVE LEARNING THAT MIGHT NEED ATTENTION AND ACTION. FURTHERMORE, THE ANALYSIS CONDUCTED EMPHASIZE THE IMPORTANCE OF CONSIDERING THE TIME CHARACTERISTICS OF THE DATA THAT SHOULD BE USED WHEN ATTEMPTING TO, FOR INSTANCE, IMPLEMENT EARLY PREDICTIONS OF PERFORMANCE AND EARLY DETECTION OF STUDENTS AND GROUPS THAT NEED SUPPORT AND ATTENTION.
WITH THE AIM OF BETTER SCAFFOLDING DISCUSSION TO IMPROVE LEARNING IN A MOOC CONTEXT, THIS WORK INVESTIGATES WHAT KINDS OF DISCUSSION BEHAVIORS CONTRIBUTE TO LEARNING. WE EXPLORED WHETHER ENGAGING IN HIGHER-ORDER THINKING BEHAVIORS RESULTS IN MORE LEARNING THAN PAYING GENERAL OR FOCUSED ATTENTION TO COURSE MATERIALS. IN ORDER TO EVALUATE WHETHER TO ATTRIBUTE THE EFFECT TO ENGAGEMENT IN THE ASSOCIATED BEHAVIORS VERSUS PERSISTENT CHARACTERISTICS OF THE STUDENTS, WE ADOPTED TWO APPROACHES. FIRST, WE USED PROPENSITY SCORE MATCHING TO PAIR STUDENTS WHO EXHIBIT A SIMILAR LEVEL OF INVOLVEMENT IN OTHER COURSE ACTIVITIES. SECOND, WE EXPLORED INDIVIDUAL VARIATION IN ENGAGEMENT IN HIGHER-ORDER THINKING BEHAVIORS ACROSS WEEKS. THE RESULTS OF BOTH ANALYSES SUPPORT THE ATTRIBUTION OF THE EFFECT TO THE BEHAVIORAL INTERPRETATION. A FURTHER ANALYSIS USING LDA APPLIED TO COURSE MATERIALS SUGGESTS THAT MORE SOCIAL ORIENTED TOPICS TRIGGERED RICHER DISCUSSION THAN MORE BIOPSYCHOLOGY ORIENTED TOPICS.
WHILE IT IS FREQUENTLY ARGUED THAT ASSESSMENT SITS AT THE HEART OF THE LEARNING PROCESS, IN PRACTICE ASSESSMENT OFTEN REMAINS NARROWLY FOCUSED ON QUALIFICATIONS AND REPORTING ACHIEVEMENTS, DRIVEN BY INSTITUTIONAL AND SOCIETAL ASPIRATIONS AND TENSIONS SUCH AS ACCOUNTABILITY AND ECONOMIC WELL BEING. YET, THE NEED FOR ASSESSMENT TO ACCOUNT FOR THE KNOWLEDGE, SKILLS, DISPOSITIONS AND ATTITUDES NECESSARY TO EQUIP YOUNG PEOPLE FOR A CHANGING AND INCREASINGLY DIGITAL WORLD IS ALSO INCREASINGLY ACKNOWLEDGED. BASED ON OUR RECENT RESEARCH REVIEW, THIS ARTICLE CRITICALLY EXAMINES THE ROLE OF TECHNOLOGY ENHANCED ASSESSMENT (OR TEA). WE ARGUE THAT WHILE TECHNOLOGY OFFERS MANY POTENTIALLY CREATIVE OPPORTUNITIES FOR INNOVATION AND FOR RETHINKING ASSESSMENT PURPOSES, THERE ARE ALSO NUMEROUS RISKS AND CHALLENGES. IN PARTICULAR WE HIGHLIGHT ETHICAL CONCERNS OVER SOCIAL EXCLUSION AND NEW FORMS OF DIGITAL DIVIDEDNESS AND THE INCREASING RISKS ASSOCIATED WITH BIG DATA AND THE RISE OF LEARNING ANALYTICS. FINALLY, WE NOTE THAT MUCH RESEARCH AND INNOVATION HAPPENS IN SILOS, WHERE POLICY, RESEARCH AND PRACTICE ON ASSESSMENT, TECHNOLOGY ENHANCED ASSESSMENT AND ETHICAL AND POLITICAL CONCERNS ARE NOT LINKED UP. WE CONCLUDE THAT THERE NEEDS TO BE A MUCH MORE WIDE-RANGING, CRITICAL AND NUANCED DISCUSSION IN EDUCATIONAL AND POLICY CIRCLES SO THAT DEBATES ABOUT THE POTENTIAL OF TECHNOLOGY CAN BE LINKED TO IMPROVING ASSESSMENT IN THE LIGHT OF THE RANGE OF SOCIAL AND POLITICAL CHALLENGES THAT SUCH PROGRESS PRESENTS. WE END WITH SOME CRITICAL QUESTIONS FOR POLICY, PRACTICE AND RESEARCH COMMUNITIES, WHICH WE OFFER AS A STARTING POINT FOR FUTURE THINKING AND WAYS FORWARD.
DISTANCE LEARNERS ARE OFTEN REFERRED TO AS 'LONE WOLVES' WHO, AS A RESULT OF THE GEOGRAPHICAL DISTANCE AND OTHER SYSTEM-SPECIFIC CHARACTERISTICS OF DISTANCE LEARNING PROGRAMS, PLACE LITTLE EMPHASIS ON COMMUNICATING WITH THE UNIVERSITY AND, IN PARTICULAR, THEIR FELLOW STUDENTS. THIS ARTICLE, FOLLOWING A DEMONSTRATION OF THE DEVELOPMENTS IN THE FIELD OF DISTANCE LEARNING, FOCUSES ON THE COMMUNICATION PATTERNS OF DISTANCE LEARNERS. AS PART OF A QUANTITATIVE STUDY, THE STUDENTS OF A GERMAN DISTANCE UNIVERSITY ANSWERED A SURVEY ON THIS TOPIC. A SIGNIFICANT RESULT WAS THE DIVISION OF THE STUDENTS INTO THREE GROUPS ACCORDING TO THEIR TENDENCY TO COMMUNICATE: POWER COMMUNICATORS, REGULAR COMMUNICATORS, AND THE LONE WOLVES. MOREOVER, A SHIFT WITHIN THE STUDENT COHORTS CAN BE IDENTIFIED: STUDENTS IN LATER SEMESTERS PREFER SYNCHRONOUS COMMUNICATION AS OPPOSED TO THE ASYNCHRONOUS USE OF EMAILS. LEARNING ANALYTICS AND A SHIFT IN THE ROLE OF ACADEMIC SUPPORT TOWARDS MENTORING CAN SUPPORT AN INCREASE IN THE TENDENCY TO COMMUNICATE AND, ULTIMATELY, IN STUDENT MOTIVATION AND PERFORMANCE.
GENERALIZABILITY OF THE VALUE OF METHODS BASED ON LEARNING ANALYTICS REMAINS ONE OF THE BIG CHALLENGES IN THE FIELD OF LEARNING ANALYTICS. ONE APPROACH TO TESTING GENERALIZABILITY OF A METHOD IS TO APPLY IT CONSISTENTLY IN DIFFERENT LEARNING CONTEXTS. THIS STUDY EXTENDS A PREVIOUSLY PUBLISHED WORK BY EXAMINING THE GENERALIZABILITY OF A LEARNING ANALYTICS METHOD PROPOSED FOR DETECTING LEARNING TACTICS AND STRATEGIES FROM TRACE DATA. THE METHOD WAS APPLIED TO THE DATASETS COLLECTED IN THREE DIFFERENT COURSE DESIGNS AND DELIVERY MODALITIES, INCLUDING FLIPPED CLASSROOM, BLENDED LEARNING, AND MASSIVE OPEN ONLINE COURSE. THE PROPOSED METHOD COMBINES PROCESS MINING AND SEQUENCE ANALYSIS. THE DETECTED LEARNING STRATEGIES ARE EXPLORED IN TERMS OF THEIR ASSOCIATION WITH ACADEMIC PERFORMANCE. THE RESULTS INDICATE THE APPLICABILITY OF THE PROPOSED METHOD ACROSS DIFFERENT LEARNING CONTEXTS. MOREOVER, THE FINDINGS CONTRIBUTE TO THE UNDERSTANDING OF THE LEARNING TACTICS AND STRATEGIES IDENTIFIED IN THE TRACE DATA: LEARNING TACTICS PROVED TO BE RESPONSIVE TO THE COURSE DESIGN, WHEREAS LEARNING STRATEGIES WERE FOUND TO BE MORE SENSITIVE TO THE DELIVERY MODALITIES THAN TO THE COURSE DESIGN. THESE FINDINGS, WELL ALIGNED WITH SELF-REGULATED LEARNING THEORY, HIGHLIGHT THE ASSOCIATION OF LEARNING CONTEXTS WITH THE CHOICE OF LEARNING TACTICS AND STRATEGIES.
STRUCTURED TASKS AND PEER-MODERATED DISCUSSIONS ARE PEDAGOGICAL MODELS THAT HAVE SHOWN UNIQUE BENEFITS FOR ONLINE COLLABORATIVE LEARNING. STUDENTS APPOINTED WITH LEADERSHIP ROLES ARE ABLE TO POSITIVELY AFFECT THE DYNAMICS IN THEIR GROUPS BY ENGAGING WITH PARTICIPANTS, RAISING QUESTIONS, AND ADVANCING PROBLEM SOLVING. TO HELP MONITORING AND CONTROLLING THE LATENT SOCIAL DYNAMICS ASSOCIATED WITH LEADERSHIP BEHAVIOR, WE PROPOSE A METHODOLOGICAL APPROACH THAT MAKES USE OF COMPUTATIONAL TECHNIQUES TO MINE THE CONTENT OF ONLINE COMMUNICATIONS AND ANALYZE GROUP STRUCTURE TO IDENTIFY STUDENTS WHO BEHAVE AS LEADERS. THROUGH TEXT MINING AND SOCIAL NETWORK ANALYSIS, WE SYSTEMATICALLY PROCESS THE DISCUSSION POSTS MADE BY STUDENTS FROM FOUR SECTIONS OF AN ONLINE COURSE IN AN AMERICAN UNIVERSITY. THE RESULTS ALLOW US TO QUANTIFY EACH INDIVIDUAL'S CONTRIBUTION AND SUMMARIZE THEIR ENGAGEMENT IN THE FORM OF A LEADERSHIP INDEX. THE PROPOSED METHODOLOGY, WHEN COMPARED TO JUDGEMENTS MADE BY EXPERTS WHO MANUALLY CODED SAMPLES OF THE DATA, IS SHOWN TO HAVE COMPARABLE PERFORMANCES, BUT, BEING FULLY AUTOMATED, HAS THE POTENTIAL TO BE EASILY REPLICABLE. THE SUMMARY OFFERED BY THE LEADERSHIP INDEX IS INTENDED AS ACTIONABLE INFORMATION THAT CAN GUIDE JUST-IN-TIME INTERVENTIONS TOGETHER WITH OTHER TOOLS BASED ON LEARNING ANALYTICS.
THIS PAPER REPORTS ON A STUDY THAT EXAMINES THE LEARNING BEHAVIORS AND CHARACTERISTICS OF STUDENTS IN A MOBILE APPLICATIONS COMPUTER PROGRAMMING CLASS THAT ADOPTED A "FLIPPED" LEARNING STYLE. BY HARVESTING LEARNING ANALYTICS DATA FROM A LEARNING MANAGEMENT SYSTEM, WE CREATED VISUALIZATIONS OF WORK INTENSITY TO EXPLORE TEMPORAL PATTERNS OF STUDENTS' BEHAVIOR AND THEN CORRELATE THEM WITH THE STUDENTS' PERFORMANCE. FINDINGS INDICATE THAT LOW, MEDIUM, AND HIGH PERFORMING STUDENTS TEND TO ACCESS LEARNING MATERIALS LATE WITH WORK INTENSITY SPIKING ON THE LECTURE DAY, SPECIFICALLY DURING THE LECTURE SESSION. WHILE HIGH AND LOW PERFORMING STUDENTS SHOW NO DIFFERENCE IN TEMPORAL ACCESS TO MATERIAL, MEDIUM PERFORMING STUDENTS DEMONSTRATE THE GREATEST DEGREE OF VIBRANCY REGARDING COURSE CONTENT MATERIAL ACCESS. FURTHER A DISCUSSION OF IMPLICATIONS AND INSIGHTS ON PROCRASTINATION IN THE CONTEXT OF FLIPPED CLASSROOMS ARE INCLUDED.
IN RECENT YEARS, AN INCREASING NUMBER OF UNIVERSITIES AND EVEN JUNIOR AND SENIOR HIGH SCHOOLS HAVE BEGUN INTRODUCING E-PORTFOLIO SYSTEMS. HOWEVER, IN MANY CASES THE STUDENTS' MOTIVATION IS LIMITED TO SIMPLY ACCUMULATING THEIR E-PORTFOLIO. AS A RESULT, THE INDEPENDENCE OF STUDENTS IS LOST AND THE LEARNING IS CONSIDERED NOT TO HAVE WORKED. ALSO ATTRACTING ATTENTION RECENTLY IS THE USE OF LEARNING ANALYTICS TO ANALYZE AND UTILIZE LEARNING RECORD DATA. AS A TOOL FOR THE VISUALIZATION OF THE ANALYSIS RESULTS, THE DASHBOARD, WHICH MAKES IT EASY TO CHECK INFORMATION AT A GLANCE, HAS BEEN ATTRACTING ATTENTION. OUR PURPOSE WITH THIS STUDY IS TO PROMOTE E-PORTFOLIO-BASED LEARNING ON E-PORTFOLIO SYSTEMS. SPECIFICALLY, TO PROVIDE THE ANALYSIS RESULT OF THE LEARNING RECORD DATA ACCUMULATED IN AN E-PORTFOLIO SYSTEM, WE DEVELOPED A DASHBOARD THAT VISUALIZES THE STATUS OF STUDENT ENGAGEMENT AND COLLABORATION IN TERMS OF "ENGAGEMENT", "COLLABORATION", AND "COLLABORATIVE COMMUNITY". WE ALSO EVALUATED OUR DASHBOARD TO DETERMINE ITS EFFECT ON E-PORTFOLIO-BASED LEARNING.
THE FOCUS OF THE LEARNING ANALYTICS COMMUNITY BRIDGES THE GAP BETWEEN CONTROLLED EDUCATIONAL RESEARCH AND DATA MINING. ONLINE LEARNING PLATFORMS CAN BE USED TO CONDUCT RANDOMIZED CONTROLLED TRIALS TO ASSIST IN THE DEVELOPMENT OF INTERVENTIONS THAT INCREASE LEARNING GAINS; DATASETS FROM SUCH RESEARCH CAN ACT AS A TREASURE TROVE FOR INQUISITIVE DATA MINERS. THE PRESENT WORK EMPLOYS A DATA MINING APPROACH ON RANDOMIZED CONTROLLED TRIAL DATA FROM ASSISTMENTS, A POPULAR ONLINE LEARNING PLATFORM, TO ASSESS THE BENEFITS OF INCORPORATING ADDITIONAL STUDENT PERFORMANCE DATA WHEN ATTEMPTING TO DIFFERENTIATE BETWEEN TWO USER GROUPS. THROUGH A RESAMPLING TECHNIQUE, WE SHOW THAT PARTIAL CREDIT, DEFINED AS AN ALGORITHMIC COMBINATION OF BINARY CORRECTNESS, HINT USAGE, AND ATTEMPT COUNT, CAN BENEFIT ASSESSMENT AND GROUP DIFFERENTIATION. PARTIAL CREDIT REDUCES SAMPLE SIZES REQUIRED TO RELIABLY DIFFERENTIATE BETWEEN GROUPS THAT ARE KNOWN TO DIFFER BY 58%, AND REDUCES SAMPLE SIZES REQUIRED TO RELIABLY DIFFERENTIATE BETWEEN LESS DISTINCT GROUPS BY 9%.
LEARNING ANALYTICS (LA) AND EDUCATIONAL DATA MINING (EDM) HAVE EMERGED AS PROMISING TECHNOLOGYENHANCED LEARNING (TEL) RESEARCH AREAS IN RECENT YEARS. BOTH AREAS DEAL WITH THE DEVELOPMENT OF METHODS THAT HARNESS EDUCATIONAL DATA SETS TO SUPPORT THE LEARNING PROCESS. A KEY AREA OF APPLICATION FOR LA AND EDM IS LEARNER MODELLING. LEARNER MODELLING ENABLES TO ACHIEVE ADAPTIVE AND PERSONALIZED LEARNING ENVIRONMENTS, WHICH ARE ABLE TO TAKE INTO ACCOUNT THE HETEROGENEOUS NEEDS OF LEARNERS AND PROVIDE THEM WITH TAILORED LEARNING EXPERIENCE SUITED FOR THEIR UNIQUE NEEDS. AS LEARNING IS INCREASINGLY HAPPENING IN OPEN AND DISTRIBUTED ENVIRONMENTS BEYOND THE CLASSROOM AND ACCESS TO INFORMATION IN THESE ENVIRONMENTS IS MOSTLY INTEREST-DRIVEN, LEARNER INTERESTS NEED TO CONSTITUTE AN IMPORTANT LEARNER FEATURE TO BE MODELED. IN THIS PAPER, WE FOCUS ON THE INTEREST DIMENSION OF A LEARNER MODEL AND PRESENT WIKI-LDA AS A NOVEL METHOD TO EFFECTIVELY MINE USER'S INTERESTS IN TWITTER. WE APPLY A MIXED-METHOD APPROACH THAT COMBINES LATENT DIRICHLET ALLOCATION (LDA), TEXT MINING APIS, AND WIKIPEDIA CATEGORIES. WIKI-LDA HAS PROVEN EFFECTIVE AT THE TASK OF INTEREST MINING AND CLASSIFICATION ON TWITTER DATA, OUTPERFORMING STANDARD LDA.
DESIGNING A LESSON PLAN FOR SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) IS AN IMPORTANT SKILL IN STEM TEACHING. IT REQUIRES SYSTEMATIC TRAINING THAT BEGINS AT THE PRE-SERVICE STAGE. IT IS PARTICULARLY DIFFICULT TO HELP PRE-SERVICE TEACHERS (PSTS) DEVELOP THEIR ABILITY TO DESIGN AN INTEGRATED STEM LEARNING UNIT THAT FEATURES COMPETENCE-ORIENTED, INTERDISCIPLINARY, STUDENT-CENTERED, AUTHENTIC, AND COLLABORATIVE LEARNING. THROUGH THREE ROUNDS OF ITERATIVE DESIGN, THIS STUDY AIMED TO DEVELOP A TECHNOLOGY-ENHANCED STEM PST TRAINING SOLUTION. EACH ROUND OF DESIGN SOUGHT TO ADDRESS PRACTICAL ISSUES THAT HAD BEEN IDENTIFIED DURING THE IMPLEMENTATION OF THE TRAINING IN THE PREVIOUS STAGE. WHEN DEVELOPING THE PROGRAM, WE ARTICULATED FIVE PRINCIPLES THAT GUIDED OUR DESIGN, THE FINAL OUTCOME OF WHICH WAS AN ONLINE TEACHER TRAINING ENVIRONMENT INTEGRATED WITH A VISUAL LEARNING DESIGN TOOL AND A LEARNING ANALYTICS FUNCTION. WE DISCUSS THE IMPLICATIONS OF OUR RESEARCH IN TERMS OF RECOMMENDED DESIGN PRINCIPLES FOR STEM PST TRAINING, AND WE SUGGEST DIRECTIONS FOR FUTURE RESEARCH ON THE CULTIVATION OF STEM LEARNING DESIGN EXPERTISE.
THIS PAPER BRINGS TOGETHER PREVIOUS WORK FROM A NUMBER OF RESEARCH PROJECTS AND TEACHING INITIATIVES IN AN EFFORT TO INTRODUCE GOOD PRACTICE IN SETTING UP SUPPORTIVE ENVIRONMENTS FOR COLLABORATIVE LEARNING. THE PAPER DISCUSSES PRIOR USE OF SOCIAL MEDIA IN LEARNING SUPPORT, THE ROLE OF DASHBOARDS FOR LEARNING ANALYTICS IN GLOBAL SOFTWARE DEVELOPMENT TRAINING, THE USE OF OPTICAL HEAD-MOUNTED DISPLAYS FOR FEEDBACK AND THE USE OF NODEXL VISUALIZATION IN MANAGING DISTRIBUTED TEAMS. THE SCOPE OF THE PAPER IS TO PROVIDE A STRUCTURED APPROACH IN ORGANIZING THE CREATION OF SMARTER TEACHING AND TRAINING ENVIRONMENTS AND EXPLORE WAYS TO COORDINATE LEARNING SCENARIOS WITH THE USE OF VARIOUS TECHNIQUES. THE PAPER ALSO DISCUSSES CHALLENGES FROM INTEGRATING MULTIPLE INNOVATIVE FEATURES IN EDUCATIONAL CONTEXTS. FINALLY THE PAPER ATTEMPTS TO INVESTIGATE THE USE OF SMART LABORATORIES IN ESTABLISHING ADDITIONAL LEARNING SUPPORT AND GATHER PRIMARY DATA FROM BLENDED AND HYBRID LEARNING PILOT STUDIES.
PURPOSE NUMEROUS STUDIES DOCUMENT THAT STUDENTS STRUGGLE TO COMPREHEND COMPLEX DYNAMIC SYSTEMS (CDS). THE PURPOSE OF THIS PAPER IS TO DESCRIBE A DESIGN FRAMEWORK APPLIED TO THE CREATION OF A PERSONALIZED AND ADAPTIVE ONLINE INTERACTIVE LEARNING ENVIRONMENT (OILE) TO SUPPORT STUDENTS IN THEIR STUDY OF CDS. DESIGN/METHODOLOGY/APPROACH A HOLISTIC INSTRUCTIONAL DESIGN IS APPLIED IN FIVE STEPS TO CREATE THE OILE. THE OILE HAS THE FOLLOWING CHARACTERISTICS: FIRST, IT PRESENTS A COMPLEX, DYNAMIC PROBLEM THAT LEARNERS SHOULD ADDRESS IN ITS ENTIRETY. IT THEN ALLOWS LEARNERS TO PROGRESS THROUGH A SEQUENCE OF LEARNING TASKS FROM EASY TO COMPLEX. SECOND, AFTER COMPLETION OF EACH LEARNING TASK, THE OILE PROVIDES LEARNERS WITH SUPPORTIVE INFORMATION BASED ON THEIR INDIVIDUAL PERFORMANCE. THE SUPPORT FADES AWAY AS LEARNERS GAIN EXPERTISE. THIRD, THE OILE TRACKS AND COLLECTS INFORMATION ON LEARNERS' PROGRESS AND GENERATES LEARNING ANALYTICS. THE OILE WAS TESTED ON 57 SYSTEM DYNAMICS STUDENTS. FINDINGS THIS PAPER PROVIDES EVIDENCE THAT SUPPORTS THE THEORETICAL DESIGN FRAMEWORK FROM THE LITERATURE. IT ALSO PROVIDES A SAMPLE FROM STUDENTS' PROGRESS LOGS TO DEMONSTRATE HOW THE OILE PRACTICALLY FACILITATED STUDENTS' COGNITIVE DEVELOPMENT. IN ADDITION, IT PROVIDES EMPIRICAL EVIDENCE REGARDING STUDENTS' ATTITUDES TOWARD THE OILE THAT WAS OBTAINED FROM ADMINISTERING TWO QUESTIONNAIRES. ORIGINALITY/VALUE IN LIGHT OF SUPPORTIVE EVIDENCE FROM THE LITERATURE, STUDENTS' PROGRESS IN THE COGNITIVE DOMAIN, AND CONFIRMATIVE RESPONSE IN THE AFFECTIVE DOMAIN, THE USE OF PERSONALIZED AND ADAPTIVE OILE TO SUPPORT LEARNING ABOUT CDS IS CONSIDERED PROMISING.
WITH THE POPULARITIES OF MASSIVE OPEN ONLINE COURSES, A GREAT NUMBER OF ENROLLMENTS IN MOOCS GENERATE MUCH EDUCATIONAL BIG DATA IN TERMS OF ONLINE ACTIVITIES AND LOGS, WHICH MIGHT BE VALUABLE FOR ACADEMIA AND PRACTITIONERS. MORE PERSONALIZED AND INTELLIGENT ONLINE LEARNING ENVIRONMENT COULD BE POTENTIALLY CREATED THROUGH EDUCATIONAL DATA MINING AND LEARNING ANALYTICS TECHNIQUES. BASED ON ITEM RESPONSE THEORY (IRT), THE CURRENT STUDY BUILDS AN ITEM ANALYSIS SYSTEM TO IDENTIFY ALTERNATIVE CONCEPTS/MISCONCEPTIONS FROM LEANERS' RESPONSE IN EXAMS. BY CALCULATING DIFFICULTY PARAMETER AND DISCRIMINATION PARAMETER FROM MASSIVE LEARNERS, OUR SYSTEMS ARE BELIEVED TO BENEFIT BOTH TEACHING FACULTIES AND ONLINE LEARNERS. WITH THE AFFORDANCES OF THE SYSTEM, TEACHING FACULTIES COULD ASSESS LEANERS' LEARNING PERFORMANCE AND QUALITY OF TEST ITEMS WHILE ALTERNATIVE CONCEPTION OF LEANERS WOULD BE IDENTIFIED FOR STRATEGIC LEARNING. OTHER PRACTICAL AND TECHNICAL IMPLICATIONS WILL BE DISCUSSED IN THIS PAPER.
CREATION OF A TECHNOLOGY ENABLED SOCIAL LEARNING ECOSYSTEM WAS STARTED AS AN EXPERIMENT IN 2014 AT THE BMS COLLEGE OF ENGINEERING, IN PARTNERSHIP WITH WIKSATE. THIS PLATFORM AVAILABLE TO THE STUDENTS FOR USE ANYTIME, ANYWHERE, IN ANY SMART DEVICE, ENABLED THEM TO CONNECT AND SHARE WITH THEIR PEERS 24X7, AND LEARN AS THEY INTERACT EVEN WHILE ON THE MOVE. THE IMPLEMENTATION OF THE PLATFORM WAS EXPANDED GRADUALLY OVER THE YEARS TO SUPPORT ALL THE DEPARTMENTS AND COURSES. THIS PLATFORM IS NOW AVAILABLE TO THE ENTIRE COLLEGE. WITH OVER 3500 STUDENTS ACTIVELY ENGAGED IN SUPPLEMENTING THEIR FORMAL EDUCATION WITH SOCIAL LEARNING, THE BMS COLLEGE OF ENGINEERING IS NOW THE LARGEST SOCIAL LEARNING ECOSYSTEM IN INDIA. THE WIKSATE PLATFORM PROVIDES THE STUDENTS AT THE BMS COLLEGE OF ENGINEERING, A POWERFUL INTERACTION ENGINE TO CONNECT AND SHARE THEIR LEARNINGS WITH THEIR CO-LEARNERS IN A KNOWLEDGE NETWORK. FURTHER, THIS PLATFORM PROVIDES REAL-TIME DATA TO FACULTY WHICH GIVE INSIGHT INTO STUDENT PREFERENCES AND ENGAGEMENT AND HELP PLAN CONTINUOUS IMPROVEMENTS IN THE COURSE DELIVERY AND OUTCOME. IT ALSO HELPS PROMOTING A CULTURE OF OPENNESS AND MODERATED LEARNING PROVIDING A VITAL BRIDGE TO CLOSE THE GAP BETWEEN EDUCATORS AND STUDENTS. THE RESULTS FROM THE SOCIAL LEARNING ANALYTICS SHOW THAT STUDENTS HAVE A NATURAL MOTIVATION AND INQUISITIVENESS TO GET INVOLVED IN INTERACTIONS WHEN EMPOWERED BY A MOBILE CENTRIC UBIQUITOUS ACCESS TO KNOWLEDGE NETWORKS. THE RESULTS ALSO SHOW A STEADY GROWTH OF STUDENT INTERACTIONS AT ALL LEVELS AFTER THEIR INDUCTION INTO THE ECOSYSTEM.
TO SCAFFOLD STUDENTS' INVESTIGATIONS OF AN INQUIRY-BASED IMMERSIVE VIRTUAL WORLD FOR SCIENCE EDUCATION WITHOUT UNDERCUTTING THE AFFORDANCES AN OPEN-ENDED ACTIVITY PROVIDES, THIS STUDY EXPLORES WAYS TIME-STAMPED LOG FILES OF GROUPS' ACTIONS MAY ENABLE THE AUTOMATIC GENERATION OF FORMATIVE SUPPORTS. GROUPS' LOGGED ACTIONS IN THE VIRTUAL WORLD ARE FILTERED VIA PRINCIPAL COMPONENT ANALYSIS TO PROVIDE A TIME SERIES TRAJECTORY SHOWING THE RATE OF THEIR INVESTIGATIVE ACTIVITIES OVER TIME. THIS TECHNIQUE FUNCTIONS WELL IN OPEN-ENDED ENVIRONMENTS AND EXAMINES THE ENTIRE COURSE OF THEIR EXPERIENCE IN THE VIRTUAL WORLD INSTEAD OF SPECIFIC SUBSEQUENCES. GROUPS' TRAJECTORIES ARE GROUPED VIA K-MEANS CLUSTERING TO IDENTIFY DIFFERENT TYPICAL PATHWAYS TAKEN THROUGH THE IMMERSIVE VIRTUAL WORLD. THESE DIFFERENT APPROACHES ARE THEN CORRELATED WITH LEARNING GAINS ACROSS SEVERAL SURVEY CONSTRUCTS (AFFECTIVE DIMENSIONS, ECOSYSTEM SCIENCE CONTENT, UNDERSTANDING OF CAUSALITY, AND EXPERIMENTAL METHODS) TO SEE HOW VARIOUS TRENDS ARE ASSOCIATED WITH DIFFERENT OUTCOMES. DIFFERENCES BY TEACHER AND SCHOOL ARE EXPLORED TO SEE HOW BEST TO SUPPORT INCLUSION AND SUCCESS OF A DIVERSE ARRAY OF LEARNERS.
PREVAILING SOCIAL NORMS PROHIBIT INTERRUPTING ANOTHER PERSON WHEN THEY ARE SPEAKING. IN THIS RESEARCH, SIMULTANEOUS SPEECH WAS INVESTIGATED IN GROUPS OF STUDENTS AS THEY JOINTLY SOLVED MATH PROBLEMS AND PEER TUTORED ONE ANOTHER. ANALYSES WERE BASED ON THE MATH DATA CORPUS, WHICH INCLUDES GROUND-TRUTH PERFORMANCE CODING AND SPEECH TRANSCRIPTIONS. SIMULTANEOUS SPEECH WAS ELEVATED 120-143% DURING THE MOST PRODUCTIVE PHASE OF PROBLEM SOLVING, COMPARED WITH MATCHED INTERVALS. IT ALSO WAS ELEVATED 18-37% IN STUDENTS WHO WERE DOMAIN EXPERTS, COMPARED WITH NON-EXPERTS. QUALITATIVE ANALYSES REVEALED THAT EXPERTS DIFFERED FROM NON-EXPERTS IN THE FUNCTION OF THEIR INTERRUPTIONS. ANALYSIS OF THESE FUNCTIONAL ASYMMETRIES PRODUCED NINE KEY BEHAVIORS THAT WERE USED TO IDENTIFY THE DOMINANT MATH EXPERT IN A GROUP WITH 95-100% ACCURACY IN THREE MINUTES. THIS RESEARCH DEMONSTRATES THAT OVERLAPPED SPEECH IS A MARKER OF GROUP PROBLEM-SOLVING PROGRESS AND DOMAIN EXPERTISE. IT PROVIDES VALUABLE INFORMATION FOR THE EMERGING FIELD OF LEARNING ANALYTICS.
THERE IS AN INCREASING INTEREST IN THE ANALYSIS OF BOTH STUDENT'S WRITING AND THE TEMPORAL ASPECTS OF LEARNING DATA. THE ANALYSIS OF HIGHER-LEVEL LEARNING FEATURES IN WRITING CONTEXTS REQUIRES ANALYSES OF DATA THAT COULD BE CHARACTERISED IN TERMS OF THE SEQUENCES AND PROCESSES OF TEXTUAL FEATURES PRESENT. THIS PAPER (1) DISCUSSES THE EXTANT LITERATURE ON SEQUENTIAL AND PROCESS ANALYSES OF WRITING; AND, BASED ON THIS AND OUR OWN FIRST-HAND EXPERIENCE ON SEQUENTIAL ANALYSIS, (2) PROPOSES A NUMBER OF APPROACHES TO BOTH PRE-PROCESS AND ANALYSE SEQUENCES IN WHOLE-TEXTS. WE ILLUSTRATE HOW THE APPROACHES COULD BE APPLIED TO EXAMPLES DRAWN FROM OUR OWN DATASETS OF 'RHETORICAL MOVES' IN WRITTEN TEXTS, AND THE POTENTIAL EACH APPROACH HOLDS FOR PROVIDING INSIGHT INTO THAT DATA. WORK IS IN PROGRESS TO APPLY THIS MODEL TO PROVIDE EMPIRICAL INSIGHTS. ALTHOUGH, SIMILAR SEQUENCE OR PROCESS MINING TECHNIQUES HAVE NOT YET BEEN APPLIED TO STUDENT WRITING, TECHNIQUES APPLIED TO EVENT DATA COULD READILY BE OPERATIONALISED TO UNDERCOVER PATTERNS IN TEXTS.
THE KINGDOM OF SAUDI ARABIA IS UNDERGOING A MAJOR TRANSFORMATION IN RESPONSE TO A REVOLUTIONARY VISION OF 2030, GIVEN THAT HEALTHCARE REFORM IS ONE OF THE TOP PRIORITIES. WITH THE OBJECTIVE OF IMPROVING HEALTHCARE AND ALLIED PROFESSIONAL PERFORMANCE IN THE KINGDOM TO MEET THE INTERNATIONAL STANDARDS, THE SAUDI COMMISSION FOR HEALTH SPECIALTIES (SCFHS) HAS RECENTLY DEVELOPED A STRATEGIC PLAN THAT FOCUSES ON EXPANDING TRAINING PROGRAMS' CAPACITY TO ALIGN WITH THE INCREASING DEMAND FOR THE COUNTRY'S HEALTHCARE WORKFORCE, PROVIDING COMPREHENSIVE QUALITY ASSURANCE AND CONTROL TO ENSURE TRAINING PROGRAMS UPHOLD HIGH QUALITY STANDARDS, AND PROVIDING ADVANCED TRAINING PROGRAMS BENCHMARKED AGAINST INTERNATIONAL STANDARDS. IN THIS RESEARCH PAPER, WE DESCRIBE OUR ATTEMPT FOR DEVELOPING A GENERAL FRAMEWORK FOR KEY PERFORMANCE INDICATORS (KPIS) AND THE RELATED METRICS, WITH THE AIM OF CONTRIBUTING TO DEVELOPING NEW STRATEGIES FOR BETTER MEDICAL TRAINING COMPATIBLE WITH THE FUTURE. WE PRESENT THE RESULTS OF A SURVEY CONDUCTED IN THE KINGDOM OF SAUDI ARABIA (KSA), FOR THE ENHANCEMENT OF QUALITY OF POSTGRADUATE MEDICAL TRAINING. THE RECENT DEVELOPMENTS IN THE FIELD OF LEARNING ANALYTICS PRESENT AN OPPORTUNITY FOR UTILIZING BIG DATA AND ARTIFICIAL INTELLIGENCE IN THE DESIGN AND IMPLEMENTATION OF SOCIO-TECHNICAL SYSTEMS WITH SIGNIFICANT POTENTIAL SOCIAL IMPACT. WE SUMMARIZE THE KEY ASPECTS OF THE TRAINING QUALITY ASSURANCE INITIATIVE AND SUGGEST A NEW APPROACH FOR DESIGNING A NEW DATA AND SERVICES ECOSYSTEM FOR PERSONALIZED HEALTH PROFESSIONALS TRAINING IN THE KSA. THE STUDY ALSO CONTRIBUTES TO THE THEORETICAL KNOWLEDGE ON THE INTEGRATION OF SUSTAINABILITY AND MEDICAL TRAINING AND EDUCATION BY PROPOSING A FRAMEWORK THAT CAN ENHANCE FUTURE INITIATIVES FROM VARIOUS HEALTH ORGANIZATIONS.
EDUCATIONAL RECOMMENDER SYSTEMS (ERSS) AIM TO ADAPTIVELY RECOMMEND A BROAD RANGE OF PERSONALISED RESOURCES AND ACTIVITIES TO STUDENTS THAT WILL MOST MEET THEIR LEARNING NEEDS. COMMONLY, ERSS OPERATE AS A "BLACK BOX" AND GIVE STUDENTS NO INSIGHT INTO THE RATIONALE OF THEIR CHOICE. RECENT CONTRIBUTIONS FROM THE LEARNING ANALYTICS AND EDUCATIONAL DATA MINING COMMUNITIES HAVE EMPHASISED THE IMPORTANCE OF TRANSPARENT, UNDERSTANDABLE AND OPEN LEARNER MODELS (OLMS) THAT PROVIDE INSIGHT AND ENHANCE LEARNERS' UNDERSTANDING OF INTERACTIONS WITH LEARNING ENVIRONMENTS. IN THIS PAPER, WE AIM TO INVESTIGATE THE IMPACT OF COMPLEMENTING ERSS WITH TRANSPARENT AND UNDERSTANDABLE OLMS THAT PROVIDE JUSTIFICATION FOR THEIR RECOMMENDATIONS. WE CONDUCT A RANDOMISED CONTROL TRIAL EXPERIMENT USING AN ERS WITH TWO INTERFACES ("NON-COMPLEMENTED INTERFACE" AND "COMPLEMENTED INTERFACE") TO DETERMINE THE EFFECT OF OUR APPROACH ON STUDENT ENGAGEMENT AND THEIR PERCEPTION OF THE EFFECTIVENESS OF THE ERS. OVERALL, OUR RESULTS SUGGEST THAT COMPLEMENTING AN ERS WITH AN OLM CAN HAVE A POSITIVE EFFECT ON STUDENT ENGAGEMENT AND THEIR PERCEPTION ABOUT THE EFFECTIVENESS OF THE SYSTEM DESPITE POTENTIALLY MAKING THE SYSTEM HARDER TO NAVIGATE. IN SOME CASES, COMPLEMENTING AN ERS WITH AN OLM HAS THE NEGATIVE CONSEQUENCE OF DECREASING ENGAGEMENT, UNDERSTANDABILITY AND SENSE OF FAIRNESS.
THE ABUNDANCE OF ACCESSIBLE EDUCATIONAL DATA, SUPPORTED BY THE TECHNOLOGY-ENHANCED LEARNING PLATFORMS, PROVIDES OPPORTUNITIES TO MINE LEARNING BEHAVIOR OF STUDENTS, ADDRESSING THEIR ISSUES, OPTIMIZING THE EDUCATIONAL ENVIRONMENT, AND ENABLING DATA-DRIVEN DECISION MAKING. VIRTUAL LEARNING ENVIRONMENTS COMPLEMENT THE LEARNING ANALYTICS PARADIGM BY EFFECTIVELY PROVIDING DATASETS FOR ANALYSING AND REPORTING THE LEARNING PROCESS OF STUDENTS AND ITS REFLECTION AND CONTRIBUTION IN THEIR RESPECTIVE PERFORMANCES. THIS STUDY DEPLOYS A DEEP ARTIFICIAL NEURAL NETWORK ON A SET OF UNIQUE HANDCRAFTED FEATURES, EXTRACTED FROM THE VIRTUAL LEARNING ENVIRONMENTS CLICKSTREAM DATA, TO PREDICT AT-RISK STUDENTS PROVIDING MEASURES FOR EARLY INTERVENTION OF SUCH CASES. THE RESULTS SHOW THE PROPOSED MODEL TO ACHIEVE A CLASSIFICATION ACCURACY OF 84%-93%. WE SHOW THAT A DEEP ARTIFICIAL NEURAL NETWORK OUTPERFORMS THE BASELINE LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINE MODELS. WHILE LOGISTIC REGRESSION ACHIEVES AN ACCURACY OF 79.82%-85.60%, THE SUPPORT VECTOR MACHINE ACHIEVES 79.95%-89.14%. ALIGNED WITH THE EXISTING STUDIES - OUR FINDINGS DEMONSTRATE THE INCLUSION OF LEGACY DATA AND ASSESSMENT-RELATED DATA TO IMPACT THE MODEL SIGNIFICANTLY. STUDENTS INTERESTED IN ACCESSING THE CONTENT OF THE PREVIOUS LECTURES ARE OBSERVED TO DEMONSTRATE BETTER PERFORMANCE. THE STUDY INTENDS TO ASSIST INSTITUTES IN FORMULATING A NECESSARY FRAMEWORK FOR PEDAGOGICAL SUPPORT, FACILITATING HIGHER EDUCATION DECISION-MAKING PROCESS TOWARDS SUSTAINABLE EDUCATION.
THE AUTHORS OF THIS PAPER ACTIVATED AS MEMBERS OF TWO EU PROGRAMS. THE FIRST ONE WAS A LLP ERASMUS PROGRAM, SALEIE - "STRATEGIC ALIGNMENT OF ELECTRICAL AND INFORMATION ENGINEERING IN EUROPEAN HIGHER EDUCATION INSTITUTIONS". THE SECOND ONE WAS "PRACTICE-BASED EXPERIENTIAL LEARNING ANALYTICS RESEARCH AND SUPPORT (PELARS), A FP7 EU PROGRAM". AS FAR AS THE QUALITY ASSESSMENT IS CONCERNED, THE ROMANIAN AGENCY FOR QUALITY ASSURANCE IN HIGHER EDUCATION (ARACIS) IS MANAGING THIS PROCESS IN ROMANIA. THIS PAPER PRESENTS THE EXPERIENCES ACQUIRED DURING THESE TWO EU PROGRAMS AND THE REALITY OF THE ROMANIAN QUALITY ASSESSMENT PROCESS AS IT HAS BEEN IDENTIFIED BY A NATIONAL SURVEY COMPLETED IN 2017 BY ARACIS.
PREDICTIVE MODELING IS AN IMPORTANT PART OF LEARNING ANALYTICS, WHOSE MAIN OBJECTIVE IS TO ESTIMATE STUDENT SUCCESS, IN TERMS OF PERFORMANCE, KNOWLEDGE, SCORE, OR GRADE. THE DATA USED FOR THE PREDICTIVE MODEL CAN BE EITHER STATE-BASED DATA (E.G., DEMOGRAPHICS, PSYCHOLOGICAL TRAITS, AND PAST PERFORMANCE) OR EVENT-DRIVEN DATA (I.E., BASED ON STUDENT ACTIVITY). THE LATTER CAN BE DERIVED FROM STUDENTS' INTERACTIONS WITH EDUCATIONAL SYSTEMS AND RESOURCES; LEARNING MANAGEMENT SYSTEMS ARE A WIDELY ANALYZED DATA SOURCE, WHILE SOCIAL MEDIA-BASED LEARNING ENVIRONMENTS ARE SCARCELY EXPLORED. IN THIS PAPER, OUR OBJECTIVE IS TO PREDICT STUDENTS' PERFORMANCE BASED ON THEIR SOCIAL MEDIA TRACES. DATA IS COLLECTED FROM A WEB APPLICATIONS DESIGN COURSE, IN WHICH STUDENTS USE WILD, BLOG, AND MICROBLOGGING TOOLS, FOR COMMUNICATION AND COLLABORATION ACTIVITIES IN A PROJECT-BASED LEARNING SCENARIO. A TOTAL OF 343 STUDENTS, FROM SIX CONSECUTIVE COURSE INSTALLMENTS, ARE INCLUDED IN THE STUDY. IN ADDITION TO THE NOVEL SETTINGS AND PERFORMANCE INDICATORS, AN INNOVATIVE REGRESSION ALGORITHM IS USED FOR GRADE PREDICTION. VERY GOOD CORRELATION COEFFICIENTS ARE OBTAINED AND 85% OF PREDICTIONS ARE WITHIN ONE POINT OF THE ACTUAL GRADE, OUTPERFORMING CLASSIC REGRESSION ALGORITHMS. FROM A PEDAGOGICAL PERSPECTIVE, RESULTS INDICATE THAT, AS A GENERAL RULE, A HIGHER ENGAGEMENT WITH SOCIAL MEDIA TOOLS CORRELATES WITH A HIGHER FINAL GRADE.
EDUCATION WITH THE INCLUSION AND INTEGRATION OF INTERNET AND DIGITAL LEARNING EDUCATION 2. 0 BROUGHT TOOLS IN THE DIFFERENT CONTEXT OF EDUCATION. THE USE OF SOCIAL NETWORKING CONCEPTS SUCH AS CHAT ROOMS AND THE EVER-GROWING STUDENT DATA HAVE PLACED EDUCATION ON THE BRINK OF BECOMING ONE OF THE CRATERS AND USERS OF BIG DATA. AS SUCH, THIS PAPER EXPLORES EDUCATIONAL DATA MINING TECHNIQUES ALONGSIDE SOME OF THE EMERGING LEARNING ANALYTICS WITH THE OBJECTIVE OF GAINING INSIGHT INTO SOME OF THE COMMON LEARNING BEHAVIORS AMONG STUDENTS. THE TASK AT HAND EMBRACES PREDICTIVE ANALYTICS AND IT EMPLOYS DECISION TREES, NEURAL NETWORKS, AND NAIVE BAYES ALGORITHMS TO CLASSIFY AND CLUSTER STUDENT LEARNING PATTERNS THAT CAN EXPLAIN ACADEMIC PERFORMANCE. PREDICTIVE ANALYTICS HAS EMERGED AS ONE OF THE TOOLS FURTHERING ADAPTIVE LEARNING AMONG OTHER LIFE CHANGING NOVELTIES. NONETHELESS, INTEGRATION OF BIG DATA IN ACADEMIA IS IN ITS INFANCY ALTHOUGH THE WESTERN HEMISPHERE IS MAKING PROGRESS TOWARDS THE INTEGRATION. SUCH PROGRESS WILL INCREASE THE RELEVANCE OF DATA MINING IN EDUCATION AND THIS PAPER ENVISAGES TO BE AMONG THE FIRST ONES TO ADDRESS THE APPLICABILITY OF MACHINE LEARNING IN IMPROVING EDUCATION. HENCE, THE OBJECTIVE OF THIS PAPER IS TO DEVELOP PREDICTIVE MODELS BASED ON THE DECISION TREE, NEURAL NETWORK, AND NAIVE BAYES ALGORITHMS.
THE MAIN OBJECTIVE OF EXAMS CONSISTS IN PERFORMING AN ASSESSMENT OF STUDENTS' EXPERTISE ON A SPECIFIC SUBJECT. SUCH EXPERTISE, ALSO REFERRED TO AS SKILL OR KNOWLEDGE LEVEL, CAN THEN BE LEVERAGED IN DIFFERENT WAYS (E.G., TO ASSIGN A GRADE TO THE STUDENTS, TO UNDERSTAND WHETHER A STUDENT MIGHT NEED SOME SUPPORT, ETC.). SIMILARLY, THE QUESTIONS APPEARING IN THE EXAMS HAVE TO BE ASSESSED IN SOME WAY BEFORE BEING USED TO EVALUATE STUDENTS. STANDARD APPROACHES TO QUESTIONS' ASSESSMENT ARE EITHER SUBJECTIVE (E.G., ASSESSMENT BY HUMAN EXPERTS) OR INTRODUCE A LONG DELAY IN THE PROCESS OF QUESTION GENERATION (E.G., PRETESTING WITH REAL STUDENTS). IN THIS WORK WE INTRODUCE R2DE (WHICH IS A REGRESSOR FOR DIFFICULTY AND DISCRIMINATION ESTIMATION), A MODEL CAPABLE OF ASSESSING NEWLY GENERATED MULTIPLE-CHOICE QUESTIONS BY LOOKING AT THE TEXT OF THE QUESTION AND THE TEXT OF THE POSSIBLE CHOICES. IN PARTICULAR, IT CAN ESTIMATE THE DIFFICULTY AND THE DISCRIMINATION OF EACH QUESTION, AS THEY ARE DEFINED IN ITEM RESPONSE THEORY. WE ALSO PRESENT THE RESULTS OF EXTENSIVE EXPERIMENTS WE CARRIED OUT ON A REAL WORLD LARGE SCALE DATASET COMING FROM AN E-LEARNING PLATFORM, SHOWING THAT OUR MODEL CAN BE USED TO PERFORM AN INITIAL ASSESSMENT OF NEWLY CREATED QUESTIONS AND EASE SOME OF THE PROBLEMS THAT ARISE IN QUESTION GENERATION.