LEARNING ANALYTICS (LA) IS A FIELD THAT EXAMINES DATA ABOUT LEARNERS AND THEIR CONTEXT, FOR UNDERSTANDING AND OPTIMIZING LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS. INTEGRATION OF MULTIPLE DATA SOURCES, AN IMPORTANT DIMENSION OF SCALABILITY, HAS THE POTENTIAL TO PROVIDE RICH INSIGHTS WITHIN LA. USING A COMMON STANDARD SUCH AS THE EXPERIENCE API (XAPI) TO DESCRIBE LEARNING ACTIVITY DATA ACROSS MULTIPLE SOURCES CAN ALLEVIATE OBSTACLES FOR DATA INTEGRATION. DESPITE THEIR POTENTIAL, HOWEVER, RESEARCH INDICATES THAT STANDARDS ARE SELDOM USED FOR INTEGRATION OF MULTIPLE SOURCES IN LA. OUR RESEARCH AIMS TO UNDERSTAND AND ADDRESS THE CHALLENGES OF USING CURRENT LEARNING ACTIVITY DATA STANDARDS FOR DESCRIBING LEARNING CONTEXT WITH REGARD TO INTEROPERABILITY AND DATA INTEGRATION. IN THIS PAPER, WE PRESENT THE RESULTS OF AN EXPLORATORY CASE STUDY INVOLVING IN-DEPTH INTERVIEWS WITH STAKEHOLDERS HAVING USED XAPI IN A REAL-WORLD PROJECT. BASED ON THE SUBSEQUENT THEMATIC ANALYSIS OF INTERVIEWS, AND EXAMINATION OF XAPI, WE IDENTIFIED CHALLENGES AND LIMITATIONS IN DESCRIBING LEARNING CONTEXT DATA, AND DEVELOPED RECOMMENDATIONS (PROVIDED IN THIS PAPER IN SUMMARIZED FORM) FOR ENRICHING CONTEXT DESCRIPTIONS AND ENHANCING THE EXPRESSIBILITY OF XAPI. BY SITUATING THE RESEARCH IN A REAL-WORLD SETTING, OUR RESEARCH ALSO CONTRIBUTES TO BRIDGE THE GAP BETWEEN THE ACADEMIC COMMUNITY AND PRACTITIONERS IN LEARNING ACTIVITY DATA STANDARDS AND SCALABILITY, FOCUSING ON DESCRIPTION OF LEARNING CONTEXT.
MONITORING AND GUIDING MULTIPLE GROUPS OF STUDENTS IN FACE-TO-FACE COLLABORATIVE WORK IS A DEMANDING TASK WHICH COULD POSSIBLY BE ALLEVIATED WITH THE USE OF A TECHNOLOGICAL ASSISTANT IN THE FORM OF LEARNING ANALYTICS. HOWEVER, IT IS STILL UNCLEAR WHETHER TEACHERS WOULD INDEED TRUST, UNDERSTAND, AND USE SUCH ANALYTICS IN THEIR CLASSROOM PRACTICE AND HOW THEY WOULD INTERACT WITH SUCH AN ASSISTANT. THE PRESENT RESEARCH AIMED TO FIND OUT WHAT THE PERCEPTION OF IN-SERVICE SECONDARY SCHOOL TEACHERS IS WHEN PROVIDED WITH A DASHBOARD BASED ON AUDIO AND DIGITAL TRACE DATA WHEN MONITORING A COLLABORATIVE LEARNING ACTIVITY. IN A VIGNETTE STUDY, WE PRESENTED TWENTY-ONE IN-SERVICE TEACHERS WITH VIDEOS FROM AN AUTHENTIC COLLABORATIVE ACTIVITY, TOGETHER WITH VISUALIZATIONS OF SIMPLE COLLABORATION ANALYTICS OF THOSE ACTIVITIES. THE TEACHERS PERCEIVED THE DASHBOARDS AS PROVIDERS OF USEFUL INFORMATION FOR THEIR EVERYDAY WORK. IN ADDITION TO ASSISTING IN MONITORING COLLABORATION, THE INVOLVED TEACHERS IMAGINED USING IT FOR PICKING OUT STUDENTS IN NEED, GETTING INFORMATION ABOUT THE INDIVIDUAL CONTRIBUTION OF EACH COLLABORATOR, OR EVEN AS A BASIS FOR ASSESSMENT. OUR RESULTS HIGHLIGHT THE NEED FOR GUIDING DASHBOARDS AS ONLY PROVIDING NEW INFORMATION TO TEACHERS DID NOT COMPEL THEM TO INTERVENE AND ADDITIONALLY, A GUIDING DASHBOARD COULD POSSIBLY HELP LESS EXPERIENCED TEACHERS WITH DATA-INFORMED ASSESSMENT.
THE AIM OF THIS PAPER IS TO EXPLORE STUDENTS' BEHAVIOR AND INTERACTION PATTERNS IN DIFFERENT TYPES OF ONLINE QUIZ-BASED ACTIVITIES WITHIN LEARNING MANAGEMENT SYSTEMS (LMS). ANALYZING STUDENTS' BEHAVIOR IN ONLINE LEARNING ACTIVITIES AND DETECTING SPECIFIC PATTERNS OF INTERACTION IN LMS IS A TOPIC OF GREAT INTEREST FOR THE EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) RESEARCH COMMUNITIES. PREVIOUS STUDIES HAVE FOCUSED PRIMARILY ON FREQUENCY ANALYSIS WITHOUT ADDRESSING THE TEMPORAL ASPECTS OF STUDENTS' LEARNING BEHAVIOR. THEREFORE, WE APPLY A PROCESS-ORIENTED APPROACH, INVESTIGATING PERSPECTIVES ON USING PROCESS MINING METHODS IN THE CONTEXT OF ONLINE LEARNING AND ASSESSMENT. TO EXPLORE A BROAD RANGE OF POSSIBLE STUDENT BEHAVIOR PATTERNS, WE ANALYZE STUDENTS' INTERACTIONS IN SEVERAL ONLINE QUIZZES FROM DIFFERENT COURSES AND WITH DIFFERENT SETTINGS. USING PROCESS MINING METHODS, WE IDENTIFY SPECIFIC TYPES OF INTERACTION SEQUENCES THAT SHED NEW LIGHT ON STUDENTS' QUIZ-TAKING STRATEGIES IN LMS. WE BELIEVE THAT THESE FINDINGS BRING IMPORTANT IMPLICATIONS FOR RESEARCHERS STUDYING STUDENT BEHAVIOR IN ONLINE ENVIRONMENTS AS WELL AS PRACTITIONERS USING ONLINE QUIZZES FOR LEARNING AND ASSESSMENT. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
NOWADAYS, WITH THE EMERGING OF RAPID REVOLUTION INDUSTRIES, THERE ARE VARIOUS EDUCATIONAL LEARNING STYLES EVOLVE IN SOLVING THE WAY OF TEACHING AND LEARNING AMONG THE TVET POLYTECHNIC STUDENTS. HOWEVER, THE SURROUNDING CHANGES OF ENORMOUS DATA IN LEARNING STYLES HAVE TANGLED THE TVET POLYTECHNIC STUDENTS TO GRAB THE OPPORTUNITY TO IMPROVE THE PERFORMANCE OF ACHIEVEMENT IN CLASS. THE STUDY WAS CONDUCTED VIA A QUANTITATIVE DATA SURVEY WITH A TOTAL OF 332 RESPONDENTS GATHERED FROM SEVERAL POLYTECHNIC MALAYSIA WITH ENGINEERING AND NON-ENGINEERING FIELD OF STUDIES. THE USE OF DATA ANALYTICS IS CONSIDERED AS A RELATIVELY NEW AREA IN TVET EDUCATION IN VISUALIZING THE PATTERN OF LEARNING STYLES THAT POLYTECHNIC STUDENTS' REQUIRED TO USE TO, SINCE TVET POLYTECHNIC STUDENTS HAVE A DIFFERENT BACKGROUND OF ACADEMIC LEVEL AND COURSES. THUS, THE FINDINGS OF THIS STUDY IS TO VISUALIZE THE PATTERN OF POTENTIAL LEARNING STYLES AMONG TVET POLYTECHNIC STUDENTS WITH DIFFERENT FIELD STUDIES. THIS RESULT AIMS TO GIVE TVET POLYTECHNIC LECTURERS' PROMISING DATA THAT LEAD TO AN IMPROVED STUDENT'S LEARNING ACHIEVEMENT OUTCOME, GIVE STUDENTS THE COMPETITIVE EDGE AND EMPOWER THE LECTURERS TEACHING STYLE IN RELATION TO THE INDUSTRIAL REVOLUTION 4.0 (IR4.0) LEARNING ANALYTICS.
PURPOSE TO INVESTIGATE THE EFFECTIVENESS OF USING NATIONAL, LONGITUDINAL MILESTONES DATA TO PROVIDE FORMATIVE ASSESSMENTS TO IDENTIFY RESIDENTS AT RISK OF NOT ACHIEVING RECOMMENDED COMPETENCY MILESTONE GOALS BY RESIDENCY COMPLETION. THE INVESTIGATORS HYPOTHESIZED THAT SPECIFIC, LOWER MILESTONE RATINGS AT EARLIER TIME POINTS IN RESIDENCY WOULD BE PREDICTIVE OF NOT ACHIEVING RECOMMENDED LEVEL (L) 4 MILESTONES BY GRADUATION. METHOD IN 2018, THE INVESTIGATORS CONDUCTED A LONGITUDINAL COHORT STUDY OF EMERGENCY MEDICINE (EM), FAMILY MEDICINE (FM), AND INTERNAL MEDICINE (IM) RESIDENTS WHO COMPLETED THEIR RESIDENCY PROGRAMS FROM 2015 TO 2018. THEY CALCULATED PREDICTIVE VALUES AND ODDS RATIOS, ADJUSTING FOR NESTING WITHIN PROGRAMS, FOR SPECIFIC MILESTONE RATING THRESHOLDS AT 6-MONTH INTERVALS FOR ALL SUBCOMPETENCIES WITHIN EACH SPECIALTY. THEY USED FINAL MILESTONES RATINGS (MAY-JUNE 2018) AS THE OUTCOME VARIABLES, SETTING L4 AS THE IDEAL EDUCATIONAL OUTCOME. RESULTS THE INVESTIGATORS INCLUDED 1,386 (98.9%) EM RESIDENTS, 3,276 (98.0%) FM RESIDENTS, AND 7,399 (98.0%) IM RESIDENTS IN THEIR ANALYSIS. THE PERCENTAGE OF RESIDENTS NOT REACHING L4 BY GRADUATION RANGED FROM 11% TO 31% IN EM, 16% TO 53% IN FM, AND 5% TO 15% IN IM. USING A MILESTONE RATING OF L2.5 OR LOWER AT THE END OF POST-GRADUATE YEAR 2, THE PREDICTIVE PROBABILITY OF NOT ATTAINING THE L4 MILESTONE GRADUATION GOAL RANGED FROM 32% TO 56% IN EM, 32% TO 67% IN FM, AND 15% TO 36% IN IM. CONCLUSIONS LONGITUDINAL MILESTONES RATINGS MAY PROVIDE EDUCATIONALLY USEFUL, PREDICTIVE INFORMATION TO HELP INDIVIDUAL RESIDENTS ADDRESS POTENTIAL COMPETENCY GAPS, BUT THE PREDICTIVE POWER OF THE MILESTONES RATINGS VARIES BY SPECIALTY AND SUBCOMPETENCY WITHIN THESE 3 ADULT CARE SPECIALTIES.
FOR DECADES, SELF-REPORT MEASURES BASED ON QUESTIONNAIRES HAVE BEEN WIDELY USED IN EDUCATIONAL RESEARCH TO STUDY IMPLICIT AND COMPLEX CONSTRUCTS SUCH AS MOTIVATION, EMOTION, COGNITIVE AND METACOGNITIVE LEARNING STRATEGIES. HOWEVER, THE EXISTENCE OF POTENTIAL BIASES IN SUCH SELF-REPORT INSTRUMENTS MIGHT CAST DOUBTS ON THE VALIDITY OF THE MEASURED CONSTRUCTS. THE EMERGENCE OF TRACE DATA FROM DIGITAL LEARNING ENVIRONMENTS HAS SPARKED A CONTROVERSIAL DEBATE ON HOW WE MEASURE LEARNING. ON THE ONE HAND, TRACE DATA MIGHT BE PERCEIVED AS "OBJECTIVE" MEASURES THAT ARE INDEPENDENT OF ANY BIASES. ON THE OTHER HAND, THERE IS MIXED EVIDENCE OF HOW TRACE DATA ARE COMPATIBLE WITH EXISTING LEARNING CONSTRUCTS, WHICH HAVE TRADITIONALLY BEEN MEASURED WITH SELF-REPORTS. THIS STUDY INVESTIGATES THE STRENGTHS AND WEAKNESSES OF DIFFERENT TYPES OF DATA WHEN DESIGNING PREDICTIVE MODELS OF ACADEMIC PERFORMANCE BASED ON COMPUTER-GENERATED TRACE DATA AND SURVEY DATA. WE INVESTIGATE TWO TYPES OF BIAS IN SELF-REPORT SURVEYS: RESPONSE STYLES (I.E., A TENDENCY TO USE THE RATING SCALE IN A CERTAIN SYSTEMATIC WAY THAT IS UNRELATED TO THE CONTENT OF THE ITEMS) AND OVERCONFIDENCE (I.E., THE DIFFERENCES IN PREDICTED PERFORMANCE BASED ON SURVEYS' RESPONSES AND A PRIOR KNOWLEDGE TEST). WE FOUND THAT THE RESPONSE STYLE BIAS ACCOUNTS FOR A MODEST TO A SUBSTANTIAL AMOUNT OF VARIATION IN THE OUTCOMES OF THE SEVERAL SELF-REPORT INSTRUMENTS, AS WELL AS IN THE COURSE PERFORMANCE DATA. IT IS ONLY THE TRACE DATA, NOTABLY THAT OF PROCESS TYPE, THAT STAND OUT IN BEING INDEPENDENT OF THESE RESPONSE STYLE PATTERNS. THE EFFECT OF OVERCONFIDENCE BIAS IS LIMITED. GIVEN THAT EMPIRICAL MODELS IN EDUCATION TYPICALLY AIM TO EXPLAIN THE OUTCOMES OF LEARNING PROCESSES OR THE RELATIONSHIPS BETWEEN ANTECEDENTS OF THESE LEARNING OUTCOMES, OUR ANALYSES SUGGEST THAT THE BIAS PRESENT IN SURVEYS ADDS PREDICTIVE POWER IN THE EXPLANATION OF PERFORMANCE DATA AND OTHER QUESTIONNAIRE DATA.
THIS PAPER FOREGROUNDS TEACHER PRACTITIONERS' IMPLEMENTATION EXPERIENCES AND REFLECTIONS OF A WEB-BASED COLLABORATIVE VIDEO ANNOTATION AND LEARNING ANALYTICS (COVAA) INTERVENTION AIMED AT ENHANCING VIDEO-BASED TEACHING AND LEARNING IN SCHOOLS, WITH A VIEW TO FOSTER SECONDARY STUDENTS' CONCEPTUAL UNDERSTANDING, SOCIAL KNOWLEDGE CONSTRUCTION, AND SELF-REGULATED LEARNING DISPOSITIONS. WE FIRST BRIEFLY OUTLINE THE KEY LEARNING PRINCIPLES THAT UNDERPIN THE DESIGN OF COVAA, NAMELY SOCIAL DIALOGIC LEARNING, ASSESSMENT FOR LEARNING, AND COMPUTER-SUPPORTED COLLABORATIVE LEARNING. NEXT, WE EXPLAIN ITS TWO KEY LEARNING AFFORDANCES: (I) TIMEPOINT-BASED COLLABORATIVE VIDEO ANNOTATION SUPPLEMENTED BY A LIVE INTERACTIVE CHATBOARD, AND (II) RAPID DIGITAL FORMATIVE FEEDBACK IN THE FORM OF TEACHER AND LEARNER DASHBOARDS. WE THEN ILLUSTRATE HOW TEACHERS IMPLEMENT THESE IN THEIR CLASSROOMS. TEACHERS' SENSE-MAKING OF THE LEARNING AND TEACHING GAINS, CHALLENGES AND PATHWAYS FORWARD FOR LEVERAGING ON THESE CONTEMPORARY DIGITAL SOCIAL LEARNING AFFORDANCES TO ENHANCE VIDEO-BASED CLASSROOM PRACTICES ARE PRESENTED AND DISCUSSED.
THE PAPER DEALS WITH ASSESSMENT OF THE INFLUENCE OF ADAPTIVE E-LEARNING AS A PART OF LEARNING ANALYTICS ON LEARNING EFFECTIVENESS OF PRIMARY SCHOOL PUPILS. E-LEARNING EXERCISES CONTAINING IMPLEMENTED ADAPTIVE ELEMENTS WERE CREATED IN ACCORDANCE WITH THE BLOOM'S TAXONOMY. WITHIN THE PILOT STUDY THE AUTHORS DETECTED HIGH PERCENTAGE SUCCESS RATE DURING E-LEARNING EXERCISE COMPLETION. THIS LEADS TO FORMULATION OF THE QUESTION "CAN ANY E-LEARNING EXERCISE OF LOWER COGNITIVE LEVELS OF BLOOM'S TAXONOMY BE SKIPPED WITHOUT AFFECTING THE COGNITIVE THINKING FOR SOLUTION OF THE E-LEARNING EXERCISES ON UPPER COGNITIVE LEVELS OF BLOOM'S TAXONOMY?" TO ANSWER THE QUESTION, THE ALGORITHM OF ADAPTIVE E-LEARNING WAS DEFINED AND HYPOTHESES WERE ESTABLISHED. THE RESEARCH WAS CARRIED OUT AS PEDAGOGICAL EXPERIMENT COMPARING THE RESULTS OF BOTH EXPERIMENTAL AND CONTROL GROUPS OF PUPILS. THE RESEARCH HYPOTHESES WERE CONFIRMED BY STATISTICAL ANALYSIS OF THE RESEARCH DATA. THE RESULTS CONFIRM THAT ADAPTIVE FEATURES OF E-LEARNING CAN BE IMPLEMENTED IN THE PRIMARY EDUCATION. THE RESEARCH RESULTS CONFIRM THE FACT THAT EDUCATIONAL OBJECTIVES CAN BE ACHIEVED WITH SOME PUPILS MORE EFFECTIVELY. CONSEQUENTLY, THE IMPLEMENTATION OF ADAPTIVE ELEMENTS INTO E-LEARNING AT THE PRIMARY SCHOOL SUPPORTS AN INDIVIDUAL APPROACH WHEN COMPLETING E-LEARNING EXERCISES ACCORDING TO THE PRINCIPLE OF COGNITIVE COMPUTING.
FROM THE CONCEPTUALIZATION TO THE EVALUATION OF COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) SCENARIOS, TEACHERS ADDRESS MULTIPLE TASKS, SOMETIMES BEING OVERWHELMED ON ACCOUNT OF THE REQUIRED TIME AND ASSOCIATED BURDEN. TO SUPPORT TEACHERS IN THIS ENDEAVOR, WE PROPOSE TO CONNECT THE PEDAGOGICAL DECISIONS MADE AT DESIGN TIME WITH THE ANALYSIS OF THE PARTICIPANTS' INTERACTIONS. THUS, TEACHERS WOULD BE PROVIDED WITH RELEVANT AND COARSE-GRAINED INFORMATION THAT COULD HELP THEM MANAGE THEIR CSCL SCENARIOS. THIS PAPER SYNTHESIZES THE MAIN CONTRIBUTIONS OBTAINED FROM A 3-YEAR DESIGN-BASED RESEARCH PROCESS, AND PRESENTS THE FINDINGS OBTAINED FROM THE EVALUATION OF THE CURRENT PROPOSAL IN TWO AUTHENTIC CSCL SCENARIOS. THE PARTICIPANT TEACHERS VALUED THE PROPOSAL POSITIVELY AND STATED THAT IT WAS HELPFUL FOR THEIR ORCHESTRATION OF CSCL SCENARIOS.
GROWING INTEREST IN EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) TO LEVERAGE BIG DATA AND TO BENEFIT EDUCATION AND THE SCIENCE OF LEARNING HAS MADE DATA OWNERSHIP AN IMPORTANT FOCUS POINT FOR INSTITUTIONS AND STUDENTS. WHILE EDM AND LA CAN PROVIDE IMPORTANT INFORMATION THAT HELP ENHANCE THE QUALITY OF TEACHING AND LEARNING, IT HAS BECOME CRITICAL TO ENSURE DATA PRIVACY AND STUDENT AGENCY OVER DATA. IN THIS PAPER, WE INTRODUCE KRATOS: AN IMMUTABLE AND PUBLICLY VERIFIABLE DATA MANAGEMENT SYSTEM THAT ENABLES EDM AND LA, WHILE MAINTAINING DATA PRIVACY AND EMPOWERING STUDENTS WITH A USER INTERFACE FOR DATA GOVERNANCE AND PARTICIPATION IN SCHOOL PROCESSES. THE SYSTEM AIMS TO ACHIEVE DATA INTEROPERABILITY, WHICH FACILITATES EDM AND LA AS INCENTIVES TO EDUCATIONAL STAKEHOLDERS (POLICY MAKERS, EDUCATORS, DEVELOPERS OF EDUCATION TECHNOLOGIES, ETC.), WHILE PRIORITIZING STUDENT AGENCY OVER THEIR DATA. OUR SYSTEM GIVES STUDENTS AND SCHOOLS AN IMMUTABLE LOG ALONG WITH COMPREHENSIVE ACCESS TO DATA THAT IS OTHERWISE SCATTERED ACROSS SYSTEMS AND VENDORS. THE UNDERLYING SET OF RULES OF THE SYSTEM ARE DEFINED IN A SET OF SMART CONTRACTS, CODIFIED FROM EXISTING NON VIRTUAL AGREEMENTS [1] BETWEEN SCHOOLS AND EDUCATION TECHNOLOGY (EDUTECH) VENDORS. WE PROPOSE THE SMART CONTRACTS TO BE DEPLOYED ON A PUBLIC BLOCKCHAIN (LIKE ETHEREUM OR BITCOIN), FOR NOTARIZING AND TIME-STAMPING VARIOUS INTERACTIONS WHICH USERS OF KRATOS MAY HAVE WITH DATA THIRD PARTIES REQUESTING ACCESS TO SCHOOL DATA HAVE A UNIQUE VIRTUAL TOKEN ASSIGNED TO THEM ON THE BLOCKCHAIN WHICH HELPS KEEP TRACK OF DATA MODIFICATIONS, ACCESS AND USE.
MONITORING LEARNERS' SATISFACTION (LS) IS A VITAL ACTION FOR COLLECTING PRECIOUS INFORMATION AND DESIGN VALUABLE ONLINE COLLABORATIVE LEARNING (CL) EXPERIENCES. TODAY'S CL PLATFORMS ALLOW STUDENTS FOR PERFORMING MANY ONLINE ACTIVITIES, THUS GENERATING A HUGE MASS OF DATA THAT CAN BE PROCESSED TO PROVIDE INSIGHTS ABOUT THE LEVEL OF SATISFACTION ON CONTENTS, SERVICES, COMMUNITY INTERACTIONS, AND EFFORT. BIG DATA IS A SUITABLE PARADIGM FOR REAL-TIME PROCESSING OF LARGE DATA SETS CONCERNING THE LS, IN THE FINAL AIM TO PROVIDE VALUABLE INFORMATION THAT MAY IMPROVE THE CL EXPERIENCE. BESIDES, THE ADOPTION OF BIG DATA OFFERS THE OPPORTUNITY TO IMPLEMENT A NON-INTRUSIVE AND IN-PROCESS EVALUATION STRATEGY OF ONLINE COURSES THAT COMPLEMENTS THE TRADITIONAL AND TIME-CONSUMING WAYS TO COLLECT FEEDBACK (E.G. QUESTIONNAIRES OR SURVEYS). ALTHOUGH THE APPLICATION OF BIG DATA IN THE CL DOMAIN IS A RECENT EXPLORED RESEARCH AREA WITH LIMITED APPLICATIONS, IT MAY HAVE AN IMPORTANT ROLE IN THE FUTURE OF ONLINE EDUCATION. BY ADOPTING THE DESIGN SCIENCE RESEARCH METHODOLOGY, THIS ARTICLE DESCRIBES A NOVEL METHOD AND APPROACH TO ANALYSE INDIVIDUAL STUDENTS' CONTRIBUTIONS IN ONLINE LEARNING ACTIVITIES AND ASSESS THE LEVEL OF THEIR SATISFACTION TOWARDS THE COURSE. A SOFTWARE ARTEFACT IS ALSO PRESENTED, WHICH LEVERAGES LEARNING ANALYTICS IN A BIG DATA CONTEXT, WITH THE GOAL TO PROVIDE IN REAL-TIME VALUABLE INSIGHTS THAT PEOPLE AND SYSTEMS CAN USE TO INTERVENE PROPERLY IN THE PROGRAM. THE CONTRIBUTION OF THIS PAPER CAN BE OF VALUE FOR BOTH RESEARCHERS AND PRACTITIONERS: THE FORMER CAN BE INTERESTED IN THE APPROACH AND METHOD USED FOR LS ASSESSMENT; THE LATTER CAN FIND OF INTEREST THE SYSTEM IMPLEMENTED AND HOW IT HAS BEEN TESTED IN A REAL ONLINE COURSE. (C) 2018 ELSEVIER LTD. ALL RIGHTS RESERVED.
MOST EDUCATIONAL SOFTWARE PROGRAMS USE AND GATHER PERSONAL INFORMATION AND METADATA FROM STUDENTS. ADDITIONALLY, MOST OF THE EDUCATIONAL SOFTWARE PROGRAMS ARE NO LONGER OPERATED BY THE LEARNING INSTITUTIONS BUT ARE RUN BY THIRD-PARTY AGENCIES. THIS MEANS THAT IN THE DECADE SINCE 2020, INFORMATION ABOUT STUDENTS IS STORED AND HANDLED OUTSIDE PREMISES AND CONTROL OF LEARNING INSTITUTIONS. THE PERSONAL INFORMATION ABOUT STUDENTS AND THEIR ACTIVITY WHILE THEY INTERACT WITH LEARNING MANAGEMENT SYSTEMS AND ONLINE LEARNING TOOLS IS INCREASINGLY IN CUSTODY OF CLOUD COMPUTING PLATFORMS, SOFTWARE-AS-A-SERVICE PROVIDERS, AND LEARNING TOOL VENDORS. THERE IS AN INCREASING WILL TO USE ALL THE DATA AND METADATA FROM THE ACTIVITY OF THE STUDENTS FOR RESEARCH, TO DEVELOP EDUCATION MANAGEMENT STRATEGIES, PEDAGOGY APPROACHES, AND DEVELOP BEHAVIOR CONTROL TOOLS OR LEARNING TOOLS INFORMED BY BEHAVIOR ANALYSIS FROM LEARNING ANALYTICS. MANY TIMES, THESE STUDIES LACK THE ETHICAL AND MORAL PERSPECTIVE. IN ADDITION, THERE IS AN INCREASING NUMBER OF CASES IN WHICH THIS INFORMATION HAS LEAKED OR HAS BEEN USED IN A SHADY WAY. ADDITIONALLY, THIS INFORMATION WILL BE AROUND FOR A LONG TIME, TIED TO THE FUTURE DIGITAL PROFILES OF THE STUDENTS WHOSE DATA HAS BEEN LEAKED. THIS PAPER HYPOTHESIZES THAT THERE HAS BEEN AN ONGOING PROCESS OF TECHNOLOGICAL EVOLUTION THAT LEADS TO A LOSS OF CONTROL OVER PERSONAL INFORMATION, WHICH MAKES IT EVEN MORE DIFFICULT TO PROTECT USER CONFIDENTIALITY AND ENSURING PRIVACY, THAT DATA SURVEILLANCE HAS ENTERED THE WORLD OF EDUCATION, AND THAT THE CURRENT LEGAL FRAMEWORKS ARE NOT ENOUGH TO REALLY PROTECT THE STUDENT'S PERSONAL INFORMATION. THE PAPER ANALYZES HOW THIS SITUATION CAME TO PASS, AND WHY THIS IS WRONG. WE CONCLUDE WITH SOME PROPOSALS TO ADDRESS IT FROM ITS DIFFERENT ROOT DIMENSIONS: TECHNICAL, CULTURAL, LEGAL, AND ORGANIZATIONAL.
STUDENTS, FACULTIES, AND OTHER MEMBERS OF THE HIGHER EDUCATION (HED) SYSTEM ARE INCREASINGLY RELIANT ON VARIOUS INFORMATION TECHNOLOGIES. SUCH A RELIANCE RESULTS IN A PLETHORA OF DATA THAT CAN BE EXPLORED TO OBTAIN RELEVANT STATISTICS OR INSIGHTS. ANOTHER REASON TO EXPLORE THE DATA IS TO ACQUIRE VALUABLE INSIGHT REGARDING THE NOVEL UNSTRUCTURED FORMS OF DATA THAT ARE DISCOVERED AND OFTEN FOUND TO HAVE A CONNECTION WITH ELEMENTS OF SOCIAL MEDIA SUCH AS PICTURES, VIDEOS, WEB PAGES, AUDIO FILES, ETC. MOREOVER, THE DATA CAN BRING ADDITIONAL VALUABLE BENEFITS WHEN PROCESSED IN THE CONTEXT OF HED. WHEN USED STRATEGICALLY, BIG DATA (BD) PROVIDES EDUCATIONAL INSTITUTIONS WITH THE CHANCE TO IMPROVE THE QUALITY OF EDUCATION FROM ALL THE PERSPECTIVES AND STEER STUDENTS OF HED TOWARD HIGHER RATES OF COMPLETION. FURTHER, THIS WILL IMPROVE STUDENT PERSISTENCE AND RESULTS, ALL OF WHICH ARE FACILITATED BY TECHNOLOGY. WITH THIS AIM, THE CURRENT RESEARCH PROPOSES A FRAMEWORK THAT ANALYZES THE DATA COLLECTED FROM HETEROGENEOUS SOURCES AND ANALYZES USING BD ANALYTICS TOOLS TO DO VARIOUS TYPES OF ANALYSIS THAT WILL BE BENEFICIAL FOR DIFFERENT LEARNERS, FACULTIES AND OTHER MEMBERS OF HED SYSTEM. MOREOVER, CURRENT RESEARCH ALSO FOCUSES ON THE CHALLENGES OF ACQUIRING BD FROM VARIOUS SOURCES.
EDUCATIONAL PRACTITIONERS AND POLICY MAKERS REQUIRE EVIDENCE SUPPORTING CLAIMS ABOUT EDUCATIONAL EFFICACY. EVIDENCE IS OFTEN FOUND USING CAUSAL RELATIONSHIPS BETWEEN EDUCATION INPUTS AND STUDENT LEARNING OUTCOMES. CAUSAL INFERENCE COVERS A WIDE RANGE OF TOPICS IN EDUCATION RESEARCH, INCLUDING EFFICACY STUDIES TO PROVE IF A NEW POLICY, SOFTWARE, CURRICULUM OR INTERVENTION IS EFFECTIVE IN IMPROVING STUDENT LEARNING OUTCOMES. RANDOMIZED CONTROLLED TRIALS (RCT) ARE CONSIDERED A GOLD STANDARD METHOD TO DEMONSTRATE CAUSALITY. HOWEVER, THESE STUDIES ARE EXPENSIVE, TIMELY AND COSTLY, AS WELL AS NOT BEING ETHICAL TO CONDUCT IN MANY EDUCATIONAL CONTEXTS. CAUSALITY CAN ALSO BE DEDUCTED PURELY FROM OBSERVATIONAL DATA. IN THIS TUTORIAL, WE WILL REVIEW METHODOLOGIES FOR ESTIMATING THE CAUSAL EFFECTS OF EDUCATION INPUTS ON STUDENT LEARNING OUTCOMES USING OBSERVATIONAL DATA. THIS IS AN INHERENTLY COMPLEX TASK DUE TO MANY HIDDEN VARIABLES AND THEIR INTERRELATIONSHIPS IN EDUCATIONAL RESEARCH. IN THIS TUTORIAL, WE DISCUSS CAUSAL INFERENCE IN THE CONTEXT OF EDUCATIONAL RESEARCH WITH BIG DATA. THIS IS THE FIRST TUTORIAL OF ITS KIND AT LEARNING ANALYTICS AND KNOWLEDGE CONFERENCE (LAK) THAT PROVIDES A HANDS-ON EXPERIENCE WITH PYTHON AND APACHE SPARK AS A PRACTICAL TOOL FOR EDUCATIONAL RESEARCHERS TO CONDUCT CAUSAL INFERENCE. AS A PREREQUISITE, ATTENDEES ARE REQUIRED TO HAVE FAMILIARITY WITH PYTHON.
INTRODUCTION THIS STUDY EXAMINED STUDENT ACCESS TO ONLINE RESOURCES OF A FACULTY'S LEARNING MANAGEMENT SYSTEM (LMS). ISSUES RELATING TO CURRENT E-LEARNING RESOURCES USAGE WERE IDENTIFIED AND FORMED THE BASIS FOR RECOMMENDATIONS TO HELP ASSIST STAKEHOLDERS IN TEACHING, LEARNING AND RESEARCH. METHODS LEARNING ANALYTICS FROM FOUR COHORTS OF UNDERGRADUATE DENTAL STUDENTS WERE EXTRACTED FROM THE DATABASE OF A LMS SPANNING BETWEEN 2012 AND 2016. INDIVIDUAL DATASETS WERE COMBINED INTO ONE MASTER FILE, RE-CATEGORISED, FILTERED AND ANALYSED BASED ON COHORT, YEAR OF STUDY, COURSE AND NATURE OF ONLINE RESOURCE. RESULTS A TOTAL OF 157,293 ACCESS EVENTS WERE DOCUMENTED. THE PROPORTION OF ADMINISTRATIVE TO LEARNING DATA VARIED ACROSS COHORTS, WITH OLDEST COHORT HAVING THE HIGHEST RATIO (82:18) IN THEIR FINAL YEAR AND MOST RECENT COHORT HAVING A RATIO OF 33:67 IN THEIR 4TH YEAR DEMONSTRATING A HIGHER PROPORTION TO LEARNING. SEVEN LEARNING DOMAINS WERE IDENTIFIED IN THE ACCESS DATA: ACCESS TO PROBLEM-BASED LEARNING RESOURCES WAS THE HIGHEST AND NEXT WAS FIXED PROSTHODONTICS VIDEOS. THE PROSTHODONTICS DISCIPLINE HAD THE HIGHEST ACCESS ACROSS THE CURRICULUM WHILE SOME OTHERS HAD VERY LIMITED OR EVEN NO LEARNING ACCESS EVENTS. CONCLUSION A NUMBER OF LIMITATIONS HAVE BEEN IDENTIFIED WITH THE ANALYTICS AND LEARNING RESOURCES IN THIS LMS AND ENGAGEMENT WITH LEARNING RESOURCE PROVISION. MORE DETAILED DATA CAPTURE OF ACCESS USE AND UNIQUE IDENTIFIERS TO RESOURCES AS WELL AS KEYWORD TAGGING OF THE RESOURCES ARE REQUIRED TO ALLOW ACCURATE MAPPING AND SUPPORT OF STUDENTS LEARNING. MOREOVER, MOTIVATION OR NUDGING OF STUDENTS BEHAVIOUR TO MORE ACTIVELY ENGAGE WITH LEARNING CONTENT NEEDS EXPLORATION.
SINCE THE TURN OF THE 21ST CENTURY, WE HAVE SEEN A SURGE OF STUDIES ON THE STATE OF U.S. EDUCATION ADDRESSING ISSUES SUCH AS COST, GRADUATION RATES, RETENTION, ACHIEVEMENT, ENGAGEMENT, AND CURRICULAR OUTCOMES. THERE IS AN EXPECTATION THAT GRADUATES SHOULD BE ABLE TO ENTER THE WORKPLACE EQUIPPED TO TAKE ON COMPLEX AND "MESSY" OR ILL-STRUCTURED PROBLEMS AS PART OF THEIR PROFESSIONAL AND EVERYDAY LIFE. IN THE CONTEXT OF ONLINE LEARNING, WE HAVE IDENTIFIED TWO KEY ISSUES THAT ARE ELUSIVE (HARD TO CAPTURE AND MAKE VISIBLE): LEARNING WITH ILL-STRUCTURED PROBLEMS AND THE INTERACTION OF SOCIAL AND INDIVIDUAL LEARNING. WE BELIEVE THAT THE INTERSECTION BETWEEN LEARNING AND ANALYTICS HAS THE POTENTIAL, IN THE LONG-TERM, TO MINIMIZE THE ELUSIVENESS OF DEEP LEARNING. A PROPOSED ANALYTICS MODEL IS DESCRIBED IN THIS ARTICLE THAT IS MEANT TO CAPTURE AND ALSO SUPPORT FURTHER DEVELOPMENT OF A LEARNER'S REFLECTIVE SENSEMAKING.
BACKGROUND FEEDBACK IS ESSENTIAL IN A SELF-REGULATED LEARNING ENVIRONMENT SUCH AS MEDICAL EDUCATION. WHEN FEEDBACK CHANNELS ARE WIDELY SPREAD, THE NEED ARISES FOR A SYSTEM OF INTEGRATING THIS INFORMATION IN A SINGLE PLATFORM. THIS ARTICLE REPORTS ON THE DESIGN AND INITIAL TESTING OF A FEEDBACK TOOL FOR MEDICAL STUDENTS AT CHARITE-UNIVERSITATSMEDIZIN, BERLIN, A LARGE TEACHING HOSPITAL. FOLLOWING A NEEDS ANALYSIS, WE DESIGNED AND PROGRAMMED A FEEDBACK TOOL IN A USER-CENTERED APPROACH. THE RESULTING INTERFACE WAS EVALUATED PRIOR TO RELEASE WITH USABILITY TESTING AND AGAIN POST RELEASE USING QUANTITATIVE/QUALITATIVE QUESTIONNAIRES. RESULTS THE TOOL WE CREATED IS A BROWSER APPLICATION FOR USE ON DESKTOP OR MOBILE DEVICES. STUDENTS LOG IN TO SEE A DASHBOARD OF "CARDS" FEATURING SUMMARIES OF ASSESSMENT RESULTS, A PORTAL FOR THE DOCUMENTATION OF ACQUIRED PRACTICAL SKILLS, AND AN OVERVIEW OF THEIR PROGRESS ALONG THEIR COURSE. USERS SEE THEIR COHORT'S AVERAGE FOR EACH FORMAT. LEARNING ANALYTICS RANK STUDENTS' STRENGTHS BY SUBJECT. THE INTERFACE IS CHARACTERIZED BY COLOURFUL AND SIMPLE GRAPHICS. IN ITS INITIAL FORM, THE TOOL HAS BEEN RATED POSITIVELY OVERALL BY STUDENTS. DURING TESTING, THE HIGH TASK COMPLETION RATE (78%) AND LOW OVERALL NUMBER OF NON-CRITICAL ERRORS INDICATED GOOD USABILITY, WHILE THE QUANTITATIVE DATA (SYSTEM USABILITY SCORING) ALSO INDICATES HIGH EASE OF USE. THE SOURCE CODE FOR THE TOOL IS OPEN-SOURCE AND CAN BE ADAPTED BY OTHER MEDICAL FACULTIES. CONCLUSIONS THE RESULTS SUGGEST THAT THE IMPLEMENTED TOOL LEVELUP IS WELL-ACCEPTED BY STUDENTS. IT THEREFORE HOLDS PROMISE FOR IMPROVED, DIGITALIZED INTEGRATED FEEDBACK ABOUT STUDENTS' LEARNING PROGRESS. OUR AIM IS THAT LEVELUP WILL HELP MEDICAL STUDENTS TO KEEP TRACK OF THEIR STUDY PROGRESS AND REFLECT ON THEIR SKILLS. FURTHER DEVELOPMENT WILL INTEGRATE USERS' RECOMMENDATIONS FOR ADDITIONAL FEATURES AS WELL AS OPTIMIZING DATA FLOW.
THIS PAPER PRESENTS AN APPROACH TO REBUILD THE BENEFITS LOST FROM MOVING FROM TRADITIONAL INSTRUCTOR-LED TRAINING TO WEB-BASED TRAINING BY USING COMPUTER-SUPPORTED COLLABORATIVE LEARNING AND SOCIAL NETWORKS. THE INNOVATIVE APPROACH OF USING LEARNING ANALYTICS AND EDUCATIONAL DATA MINING TECHNIQUES TO TRACK AND ANALYSE THE INTERACTIVE BEHAVIOR OF ON-LINE COLLABORATIVE LEARNING AND SOCIAL NETWORKS IS EXPLORED TO ENHANCE AND IMPROVE CORPORATE DISTANCE TRAINING. THE RESULTS OF THIS MULTIDISCIPLINARY PROPOSAL ADDRESSING PEDAGOGICAL, TECHNOLOGICAL AND BUSINESS ISSUES TO SUPPORT WEB-BASED TRAINING BECOMES DECISIVE FOR ENHANCING AND IMPROVING THE OVERALL DISTANCE TRAINING EXPERIENCE AND FOR FINDING NEW OPPORTUNITIES FOR COST-EFFECTIVE WAYS TO DELIVER TRAINING PROGRAMS. WE BELIEVE THE OUTCOMES OF THIS RESEARCH WILL BE CRUCIAL FOR GREATLY ENHANCE AND IMPROVE CORPORATE DISTANCE TRAINING.
PERSONALIZED LEARNING BASED ON LEARNING ANALYTICS HAS BECOME INCREASINGLY IMPORTANT FOR TEACHERS' DEVELOPMENT VIA PROVIDING ADAPTIVE CONTENTS AND STRATEGIES FOR TEACHERS BY IDENTIFYING THEIR QUESTIONS AND NEEDS. CURRENTLY, MOST STUDIES ON TEACHERS' PROFESSIONAL DEVELOPMENT FOCUS ON PRE-SERVICE TEACHERS, AND STUDIES ON TEACHERS' PERSONALIZED LEARNING FOCUS ON THE EXPERT GUIDANCE APPROACH. IN THIS PAPER, A PEER COACHING-BASED PERSONALIZED LEARNING APPROACH IS PROPOSED TO HELP IN-SERVICE TEACHERS IDENTIFY THEIR QUESTIONS AND NEEDS AND ADAPT THEIR TEACHING PLANS BASED ON PEER FEEDBACK AS A RESULT OF INTERACTING WITH THEIR PEERS AND REFLECTING ON THEIR WORK SO AS TO ENGAGE IN IN-DEPTH LEARNING AND TRANSFER OF KNOWLEDGE TO THEIR TEACHING PRACTICE. IN ORDER TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED APPROACH, A QUASI-EXPERIMENTAL DESIGN WAS EMPLOYED, INVOLVING 20 IN-SERVICE MANDARIN TEACHERS. THE EXPERIMENTAL GROUP TEACHERS LEARNED WITH THE PEER COACHING-BASED PERSONALIZED LEARNING APPROACH, WHILE THE CONTROL GROUP TEACHERS LEARNED WITH THE EXPERT GUIDANCE-BASED PERSONALIZED LEARNING APPROACH. THE STUDY WAS CONDUCTED USING A QUANTITATIVE APPROACH. THE INSTRUMENTS USED WERE A LEARNING PARTICIPATION RUBRIC AND PERFORMANCE ASSESSMENTS OF THE PARTICIPATING TEACHERS' LESSON PLANS AND TEACHING VIDEOS. THE FINDINGS INDICATED THAT THE POST-TEST SCORES OF THE EXPERIMENTAL GROUP WERE SIGNIFICANTLY HIGHER THAN THOSE OF THE CONTROL GROUP. THE PEER COACHING-BASED PERSONALIZED LEARNING APPROACH HAD A MUCH BETTER EFFECT THAN THE EXPERT GUIDANCE-BASED PERSONALIZED LEARNING APPROACH ON THE IN-SERVICE TEACHERS' LEARNING PARTICIPATION, LEARNING DESIGN SKILLS, AND IN-PRACTICE TEACHING ABILITIES.
IN ORDER TO OBTAIN A HOLISTIC PERSPECTIVE ON LEARNING, TECHNICAL INFRASTRUCTURE AT AN INSTITUTIONAL LEVEL CAN BE ADVANTAGEOUS FOR LEARNING ANALYTICS (LA). IF PERSONAL DATA IS COLLECTED AND PROCESSED IN SUCH INFRASTRUCTURE, LEGAL REQUIREMENTS ARE OF CRUCIAL IMPORTANCE. RECENT STUDIES HAVE EXAMINED VARIOUS ASPECTS OF LA INFRASTRUCTURE, SUCH AS ETHICAL TRADE-OFFS AND STAKEHOLDER NEEDS. HOWEVER, STRATEGIES FOR DESIGNING TECHNICAL LA INFRASTRUCTURE AT THE INSTITUTIONAL LEVEL AND STRATEGIES FOR DEALING WITH DATA PROTECTION REGULATIONS HAVE BEEN LACKING SO FAR, ALTHOUGH THEY ARE CRUCIAL FOR THE ADOPTION OF LA IN EUROPE. THE PURPOSE OF THIS PAPER IS TO EXAMINE THREE RESEARCH QUESTIONS: (RQ1) WHICH STRATEGIES ARE CURRENTLY USED TO DESIGN LA INFRASTRUCTURE; (RQ2) HOW DATA PROTECTION AND PRIVACY AFFECT THE DESIGN OF LA INFRASTRUCTURE; AND (RQ3) HOW COULD TECHNICAL MEASURES SUPPORT THE ADOPTION OF LA. THESE RESEARCH QUESTIONS WERE INVESTIGATED BY CONDUCTING ELEVEN INTERVIEWS WITH LA INFRASTRUCTURE DEVELOPERS REPRESENTING EIGHT DIFFERENT HIGHER EDUCATION INSTITUTIONS AND TEN DIFFERENT INFRASTRUCTURES. ACCORDING TO RQ1, THE PAPER FIRST EXAMINES THE DOMAIN SPECIFICITY OF LA INFRASTRUCTURE AND THE FOUR DESIGN STRATEGIES USED. THE PAPER ALSO EXAMINES, ACCORDING TO RQ2, THE INTERVIEWEES' AWARENESS OF DATA PROTECTION, THE CONFLICT WITH USERS' CONSENT, AND THE DATA SUBJECT RIGHTS. FINALLY, THE PAPER PRESENTS, IN LINE WITH RQ3, THE RESULTS OF THE STRATEGIES IN DEALING WITH TRUST, STAKEHOLDER EXPECTATIONS, AND ENGAGEMENT. RESEARCHERS AND INFRASTRUCTURE DEVELOPERS CAN USE AND ADOPT THESE FINDINGS TO IMPROVE THEIR STRATEGIES FOR DEVELOPING TECHNICAL LA INFRASTRUCTURE WITH REGARD TO DATA PROTECTION, PRIVACY, AND TRUST.
THE EFFECT OF CHOICE ON STUDENT ACHIEVEMENT AND ENGAGEMENT HAS BEEN AN EXTENSIVELY RESEARCHED AREA OF LEARNING ANALYTICS. CURRENT RESEARCH FINDINGS SUGGEST A POSITIVE RELATIONSHIP BETWEEN CHOICE AND VARIED OUTCOME MEASURES, BUT LITTLE HAS BEEN REPORTED TO INDICATE WHETHER THESE FINDINGS HOLD IN THE CONTEXT OF INTELLIGENT TUTORING SYSTEMS (ITS). IN THIS PAPER, WE REPORT THE RESULTS OF A RANDOMIZED CONTROLLED EXPERIMENT IN WHICH WE INVESTIGATE THE EFFECT OF STUDENT CHOICE ON ASSIGNMENT COMPLETION AND FUTURE ACHIEVEMENT IN AN ITS. THE EXPERIMENTAL DESIGN USES THREE CONDITIONS TO OBSERVE THE EFFECT OF CHOICE. IN THE FIRST CONDITION, STUDENTS ARE ABLE TO CHOOSE THE ORDER IN WHICH TO COMPLETE ASSIGNMENTS, WHILE IN THE SECOND CONDITION, STUDENTS ARE PRESCRIBED AN INTUITIVE ORDER IN WHICH TO COMPLETE ASSIGNMENTS. THOSE IN THE THIRD CONDITION WERE PRESCRIBED A COUNTER-INTUITIVE ORDER IN WHICH TO COMPLETE ASSIGNMENTS. RESULTS INDICATE THAT ALLOWING STUDENTS TO CHOOSE THE ORDER IN WHICH TO WORK ON ASSIGNMENTS LEADS TO HIGHER COMPLETION RATES AND BETTER ACHIEVEMENT AT POSTTEST. A POST -HOE ANALYSIS ALSO REVEALED THAT EVEN CONSIDERING STUDENTS WITH SIMILAR COMPLETION RATES, THOSE GIVEN CHOICE HAD HIGHER POSTTEST SCORES THAN THOSE OBSERVED IN ANY OTHER CONDITION. THESE RESULTS SEEM TO SUPPORT THE MANY THEORIES OF THE POSITIVE EFFECT OF CHOICE ON STUDENT ACHIEVEMENT.
THIS PAPER PROPOSES A NOVEL COMPUTATIONAL APPROACH BASED ON TIME SERIES ANALYSIS TO ASSESS ENGINEERING DESIGN PROCESSES USING A CAD TOOL. TO COLLECT RESEARCH DATA WITHOUT DISRUPTING A DESIGN LEARNING PROCESS, DESIGN ACTIONS AND ARTIFACTS ARE CONTINUOUSLY LOGGED AS TIME SERIES BY THE CAD TOOL BEHIND THE SCENES, WHILE STUDENTS ARE WORKING ON A DESIGN CHALLENGE. THESE FINE-GRAINED DATA CAN BE USED TO RECONSTRUCT AND ANALYZE THE ENTIRE DESIGN PROCESS OF A STUDENT WITH EXTREMELY HIGH RESOLUTION. RESULTS OF A PILOT STUDY IN A HIGH SCHOOL ENGINEERING CLASS, IN WHICH STUDENTS SOLVED A SOLAR URBAN DESIGN CHALLENGE, SUGGEST THAT THESE DATA CAN BE USED TO MEASURE THE LEVEL OF STUDENT ENGAGEMENT, REVEAL THE GENDER DIFFERENCES IN DESIGN BEHAVIORS, AND DISTINGUISH THE ITERATIVE AND NON-ITERATIVE CYCLES IN A DESIGN PROCESS. FROM THE PERSPECTIVE OF ENGINEERING EDUCATION, THIS PAPER CONTRIBUTES TO THE EMERGING FIELDS OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS THAT AIM TO EXPAND EVIDENCE APPROACHES FOR LEARNING IN A DIGITAL WORLD.
MULTIMODAL LEARNING ANALYTICS (MMLA) RESEARCHERS ARE PROGRESSIVELY EMPLOYING MACHINE LEARNING (ML) TECHNIQUES TO DEVELOP PREDICTIVE MODELS TO IMPROVE LEARNING AND TEACHING PRACTICES. THESE PREDICTIVE MODELS ARE OFTEN EVALUATED FOR THEIR GENERALIZABILITY USING METHODS FROM THE ML DOMAIN, WHICH DO NOT TAKE INTO ACCOUNT MMLA'S EDUCATIONAL NATURE. FURTHERMORE, THERE IS A LACK OF SYSTEMATIZATION IN MODEL EVALUATION IN MMLA, WHICH IS ALSO REFLECTED IN THE HETEROGENEOUS REPORTING OF THE EVALUATION RESULTS. TO OVERCOME THESE ISSUES, THIS PAPER PROPOSES AN EVALUATION FRAMEWORK TO ASSESS AND REPORT THE GENERALIZABILITY OF ML MODELS IN MMLA (EFAR-MMLA). TO ILLUSTRATE THE USEFULNESS OF EFAR-MMLA, WE PRESENT A CASE STUDY WITH TWO DATASETS, EACH WITH AUDIO AND LOG DATA COLLECTED FROM A CLASSROOM DURING A COLLABORATIVE LEARNING SESSION. IN THIS CASE STUDY, REGRESSION MODELS ARE DEVELOPED FOR COLLABORATION QUALITY AND ITS SUB-DIMENSIONS, AND THEIR GENERALIZABILITY IS EVALUATED AND REPORTED. THE FRAMEWORK HELPED US TO SYSTEMATICALLY DETECT AND REPORT THAT THE MODELS ACHIEVED BETTER PERFORMANCE WHEN EVALUATED USING HOLD-OUT OR CROSS-VALIDATION BUT QUICKLY DEGRADED WHEN EVALUATED ACROSS DIFFERENT STUDENT GROUPS AND LEARNING CONTEXTS. THE FRAMEWORK HELPS TO OPEN UP A "WICKED PROBLEM" IN MMLA RESEARCH THAT REMAINS FUZZY (I.E., THE GENERALIZABILITY OF ML MODELS), WHICH IS CRITICAL TO BOTH ACCUMULATING KNOWLEDGE IN THE RESEARCH COMMUNITY AND DEMONSTRATING THE PRACTICAL RELEVANCE OF THESE TECHNIQUES.
PURPOSE MANAGING EFFICIENTLY EDUCATIONAL BIG DATA, PRODUCED BY VIRTUAL LEARNING ENVIRONMENTS, IS BECOMING A COMPELLING NECESSITY, ESPECIALLY FOR THOSE UNIVERSITIES PROVIDING DISTANCE LEARNING. THIS PAPER AIMS TO PROPOSE A POSSIBLE FRAMEWORK TO COMPUTE EFFICIENTLY KEY PERFORMANCE INDICATORS, SUMMARIZING THE TRENDS OF STUDENTS' ACADEMIC CAREERS, BY USING EDUCATIONAL BIG DATA. DESIGN/METHODOLOGY/APPROACH THE FRAMEWORK IS DESIGNED AND IMPLEMENTED IN A DISTRIBUTED FASHION. THE PARALLEL COMPUTATION OF THE INDICATORS THROUGH MAP AND REDUCE NODES IS CAREFULLY DESCRIBED, TOGETHER WITH THE WORKFLOW OF DATA, FROM THE EDUCATIONAL SOURCES TO A NOSQL DATABASE AND TO THE LEARNING ANALYTICS ENGINE. FINDINGS THIS FRAMEWORK WAS TESTED AT ECAMPUS UNIVERSITY, AN ITALIAN DISTANCE LEARNING INSTITUTION, AND IT WAS ABLE TO SIGNIFICANTLY REDUCE THE AMOUNT OF TIME NEEDED TO COMPUTE KEY PERFORMANCE INDICATORS. MOREOVER, BY IMPLEMENTING A PROPER DATA REPRESENTATION DASHBOARD, IT RESULTED IN A USEFUL HELP AND SUPPORT FOR EDUCATIONAL DECISIONS AND PERFORMANCE ANALYSES AND FOR REVEALING POSSIBLE CRITICALITIES. ORIGINALITY/VALUE THE FRAMEWORK PROPOSED INTEGRATES FOR THE FIRST TIME, TO THE BEST OF THE AUTHORS' KNOWLEDGE, A SET OF MODULES, DESIGNED AND IMPLEMENTED IN A DISTRIBUTED FASHION, TO COMPUTE KEY PERFORMANCE INDICATORS FOR DISTANCE LEARNING INSTITUTIONS. IT CAN BE USED TO ANALYZE THE DROPOUTS AND THE OUTCOMES OF STUDENTS AND, THEREFORE, TO EVALUATE THE PERFORMANCES OF UNIVERSITIES, WHICH CAN, IN TURN, PROPOSE EFFECTIVE IMPROVEMENTS TOWARD ENHANCING THE OVERALL E-LEARNING SCENARIO.
THIS PAPER REPORTS ON THE EVALUATION OF AN AMBITIOUS ATTEMPT TO EMBED ACADEMIC LITERACY SUPPORT WITHIN A CORE CONTENT COURSE FOR FIRST-YEAR STUDENTS AT THE UNIVERSITY OF THE SOUTH PACIFIC. THE COURSE IS OFFERED IN BOTH BLENDED AND ONLINE MODES, CATERING FOR ON-CAMPUS AND OFF-CAMPUS STUDENTS, RESPECTIVELY, USING MOODLE AS THE VIRTUAL LEARNING ENVIRONMENT (VLE). WE BEGIN BY EXPLAINING HOW WE HAVE MADE THE MOST OF MOODLE AS A LEARNING PLATFORM, ENABLING US TO SUPPLEMENT THE CORE CONTENT RESOURCES WITH ADDITIONAL COMPONENTS THAT SUPPORT STUDENTS' ACADEMIC LITERACY DEVELOPMENT, RATHER THAN SACRIFICING CONTENT TO CREATE THIS SPACE. SINCE THE ADDITIONAL MATERIAL IS EMBEDDED INTO THE MAIN LEARNING DESIGN OF THE COURSE, STUDENTS ACQUIRE ACADEMIC LITERACY WITHIN THE DISCIPLINARY CONTEXT OF THEIR ACADEMIC PROGRAMME, RATHER THAN THROUGH STANDALONE 'STUDY SKILLS' PROVISION THAT IS DEVOID OF CONTENT. WE THEN DEVOTE THE MAJORITY OF THE PAPER TO DISCUSSING THE EVALUATION OF THIS COURSE DESIGN, AGAIN EXPLAINING HOW WE HAVE MADE THE MOST OF MOODLE TO DO SO. WE HAVE USED LEARNING ANALYTICS DATA FROM MOODLE TRACKING AND COMPLETION REPORTS TO CALCULATE ENGAGEMENT SCORES FOR EACH STUDENT, FOCUSING ON A COMPOSITE OF THEIR ACCESS TO RESOURCES, THEIR COMPLIANCE WITH SEQUENCING AND TIMING, THEIR INVESTMENT IN ACTIVITIES, AND THEIR OVERALL ACHIEVEMENT. WE HAVE THEN EXAMINED THE EXTENT TO WHICH THE DIFFERENT ELEMENTS OF ENGAGEMENT APPEAR TO IMPACT ACHIEVEMENT IN ASSIGNMENTS, DEMONSTRATING THAT THE STUDENTS WHO ACHIEVED THE HIGHEST GRADES WERE THOSE WHO ACCESSED MORE MATERIALS, KEPT UP WITH THE INTENDED SCHEDULE, AND INVESTED MORE THAN THEY NEEDED TO IN ACTIVITIES, DESPITE NO IMMEDIATE REWARDS FOR DOING SO.
THE AIM OF THIS RESEARCH PROJECT IS TO EVALUATE A NOVEL APPROACH TO PROVIDING ACADEMIC INTEGRITY THROUGH BEHAVIORAL PATTERN ANALYSIS FOR CONTINUOUS AND ON-DEMAND ASSESSMENTS. OUR OBJECTIVE IS TO EMPOWER INSTRUCTORS WITH EFFICIENT AND AUTOMATED TOOLS THAT PROMOTE ACCOUNTABILITY AND ACADEMIC INTEGRITY, WHILE PROVIDING STUDENTS WITH AN ACCESSIBLE, NON-INVASIVE, PRIVACY PRESERVING AND CONVENIENT VALIDATION OF THE STUDENT-GENERATED ACADEMIC CONTENT. THE CONTRIBUTIONS OF THE PROPOSED STUDY ARE THREEFOLD: (1) THE BRIDGED ANONYMITY GAP BETWEEN LEARNERS AND INSTRUCTORS, (2) AN OPEN SOURCE LEARNING TECHNOLOGY THAT ENHANCES ACADEMIC INTEGRITY, AND (3) UNDERSTANDING OF HOW THE BEHAVIORAL-BASED BIOMETRIC TECHNOLOGIES ARE PERCEIVED BY STUDENTS AND INSTRUCTORS.
BOTH EDUCATIONAL DATA MINING AND LEARNING ANALYTICS AIM TO UNDERSTAND LEARNERS AND OPTIMISE LEARNING PROCESSES OF EDUCATIONAL SETTINGS LIKE MOODLE, A LEARNING MANAGEMENT SYSTEM (LMS). ANALYTICS IN AN LMS COVERS MANY DIFFERENT ASPECTS: FINDING STUDENTS AT RISK OF ABANDONING A COURSE OR IDENTIFYING STUDENTS WITH DIFFICULTIES BEFORE THE ASSESSMENTS. THUS, THERE ARE MULTIPLE PREDICTION MODELS THAT CAN BE EXPLORED. THE PREDICTION MODELS CAN TARGET AT THE COURSE ALSO. FOR INSTANCE, WILL THIS ACTIVITY ASSESSMENT ENGAGE LEARNERS? TO EASE THE EVALUATION AND USAGE OF PREDICTION MODELS IN MOODLE, WE ABSTRACT OUT THE MOST RELEVANT ELEMENTS OF PREDICTION MODELS AND DEVELOP AN ANALYTICS FRAMEWORK FOR MOODLE. APART FROM THE SOFTWARE FRAMEWORK, WE ALSO PRESENT A CASE STUDY MODEL WHICH USES VARIABLES BASED ON ASSESSMENTS TO PREDICT STUDENTS AT RISK OF DROPPING OUT OF A MASSIVE OPEN ONLINE COURSE THAT HAS BEEN OFFERED EIGHT TIMES FROM 2013 TO 2018, INCLUDING A TOTAL OF 46,895 STUDENTS. A NEURAL NETWORK IS TRAINED WITH DATA FROM PAST COURSES AND THE FRAMEWORK GENERATES INSIGHTS ABOUT STUDENTS AT RISK IN ONGOING COURSES. PREDICTIONS ARE THEN GENERATED AFTER THE FIRST, THE SECOND, AND THE THIRD QUARTERS OF THE COURSE. THE AVERAGE ACCURACY THAT WE ACHIEVE IS 88.81% WITH A 0.9337 F1 SCORE AND A 73.12% OF THE AREA UNDER THE ROC CURVE.
TEACHING ANALYTICS IS AN EMERGING AND CHALLENGING FIELD OF RESEARCH THAT CAN HAVE A GREAT IMPACT ON EDUCATIONAL DESIGN METHODOLOGIES AS WELL AS ON DELIVERIES BY TEACHERS TO STUDENTS. IN THIS WORK, WE ANALYZE THE MOST RELEVANT LITERATURE ON THE SUBJECT. FOUR AXES FOR WORK WERE IDENTIFIED: FIRST, INTEGRATION OF LEARNING AND TEACHING ANALYTICS; SECOND, MULTIMODAL ANALYSIS OR ANALYSIS OF DATA COLLECTED BY DEVICES IN REAL TIME IN THE CLASSROOM; THIRD, DIGITAL LITERACY IN ANALYTICS FOR TEACHERS; AND FOURTH, PLANNING AND EVALUATION OF TEACHING ACTIVITIES. SEVERAL LIMITATIONS AND CHALLENGES WERE FOUND FOR THE ADVANCEMENT OF THE TEACHING ANALYTICS AREA, BUT THE MOST RELEVANT LIMITATION IS THE TEMPORARY SEPARATION BETWEEN THE DELIVERY AND THE ANALYSIS OF THEIR RESULTS, IN ADDITION TO NOT HAVING A STANDARD MEASURE OF STUDENT PERFORMANCE. THE MOST IMPORTANT CHALLENGE IS THE REGISTRATION AND MULTIMODAL ANALYSIS IN REAL TIME FOR THE TAKING OF ACTION OF THE TEACHER IN AN IMMEDIATE FORM.
RANDOMIZED EXPERIMENTS ENSURE ROBUST CAUSAL INFERENCE THAT IS CRITICAL TO EFFECTIVE LEARNING ANALYTICS RESEARCH AND PRACTICE. HOWEVER, TRADITIONAL RANDOMIZED EXPERIMENTS, LIKE A/B TESTS, ARE LIMITING IN LARGE SCALE DIGITAL LEARNING ENVIRONMENTS. WHILE TRADITIONAL EXPERIMENTS CAN ACCURATELY COMPARE TWO TREATMENT OPTIONS, THEY ARE LESS ABLE TO INFORM HOW TO ADAPT INTERVENTIONS TO CONTINUALLY MEET LEARNERS' DIVERSE NEEDS. IN THIS WORK, WE INTRODUCE A TRIAL DESIGN FOR DEVELOPING ADAPTIVE INTERVENTIONS IN SCALED DIGITAL LEARNING ENVIRONMENTS - THE SEQUENTIAL RANDOMIZED TRIAL (SRT). WITH THE GOAL OF IMPROVING LEARNER EXPERIENCE AND DEVELOPING INTERVENTIONS THAT BENEFIT ALL LEARNERS AT ALL TIMES, SRTS INFORM HOW TO SEQUENCE, TIME, AND PERSONALIZE INTERVENTIONS. IN THIS PAPER, WE PROVIDE AN OVERVIEW OF SRTS, AND WE ILLUSTRATE THE ADVANTAGES THEY HOLD COMPARED TO TRADITIONAL EXPERIMENTS. WE DESCRIBE A NOVEL SRT RUN IN A LARGE SCALE DATA SCIENCE MOOC. THE TRIAL RESULTS CONTEXTUALIZE HOW LEARNER ENGAGEMENT CAN BE ADDRESSED THROUGH CULTURALLY- TARGETED REMINDER EMAILS. WE ALSO PROVIDE PRACTICAL ADVICE FOR RESEARCHERS WHO AIM TO RUN THEIR OWN SRTS TO DEVELOP ADAPTIVE INTERVENTIONS IN SCALED DIGITAL LEARNING ENVIRONMENTS.
THIS PAPER PRESENTS A CUSTOMIZABLE LOCATION-BASED MOBILE LEARNING PROTOTYPE CALLED "RESUELVO EXPLORANDO". THE PROTOTYPE CONSIDERS THE CONCEPTS OF SCAFFOLDING, FORMATIVE FEEDBACK AND DATA COLLECTION IN ORDER TO PERFORM LEARNING ANALYTICS. THIS PAPER ALSO PROPOSES AN ADAPTATION OF AN EDUCATIONAL MODELLING APPROACH THAT IS IMPLEMENTED BY THE PROTOTYPE. "RESUELVO EXPLORANDO" IS CUSTOMIZED THROUGH TWO ELEMENTS: A JSON CONFIGURATION FILE AND A SET OF IMAGES TO ASSIST THE STUDENT DURING THEIR MOVEMENT IN A PHYSICAL SPACE OF USE. FINALLY, A CASE OF STUDY OF A PROTOTYPE'S CUSTOMIZATION IS PRESENTED BASED ON THE DESIGN OF AN EDUCATIONAL LOCATION-BASED EXPERIENCE, CARRIED OUT BY A MULTIDISCIPLINARY TEAM, IN ORDER TO OBTAIN A LOCATION-BASED MOBILE LEARNING APPLICATION.
CLOUD COMPUTING TECHNOLOGIES ARE BRINGING NEW SCALES OF COMPUTATIONAL PROCESSING POWER AND STORAGE CAPACITY TO MEET VERY DEMANDING REQUIREMENTS OF TODAY'S APPLICATIONS. ONE SUCH FAMILY OF APPLICATIONS IS THE ONE OF ANALYTICS BASED ON PROCESSING BIG DATA. MORE SPECIFICALLY, THERE IS A LARGE FAMILY OF ANALYTICS APPLICATIONS FROM PROCESSING LOG DATA FILES. INDEED, LOG DATA FILES ARE COMMONPLACE IN MANY INTERNET-BASED SYSTEMS AND APPLICATIONS, COMPRISING SYSTEM LOGS, SERVER LOGS, APPLICATION LOGS, DATABASES LOGS, USER ACTIVITY LOGS, ETC. THESE APPLICATIONS ARE ANALYTICS ORIENTED APPLICATIONS BASED ON PROCESSING THE VARIOUS TYPES OF LOG FILES. WHILE LOG DATA FILE PROCESSING HAS BEEN RECENTLY AN ISSUE OF INVESTIGATION BY MANY RESEARCHERS AND DEVELOPERS, THE NEW FEATURE IS THAT OF SCALE: CLOUD BASED SYSTEMS CAN ENABLE PROCESSING UNLIMITED AMOUNT OF DATA EITHER OFF-LINE OR ONLINE IN STREAMING MODE. IN THIS WORK WE EVALUATE THE PERFORMANCE OF A MAPREDUCE HADOOP-BASED IMPLEMENTATION FOR PROCESSING LARGE LOG DATA FILES OF A VIRTUAL CAMPUS. THE STUDY AIMS TO REVEAL THE POTENTIAL OF USING SUCH IMPLEMENTATIONS AS A BASIS FOR LEARNING ANALYTICS FOR USE BY A VARIETY OF USERS IN A VIRTUAL CAMPUS.
IN RECENT YEARS, AN INCREASING NUMBER OF ANALYSES IN LEARNING ANALYTICS AND EDUCATIONAL DATA MINING (EDM) HAVE ADOPTED A DISCOVERY WITH MODELS APPROACH, WHERE AN EXISTING MODEL IS USED AS A KEY COMPONENT IN A NEW EDM OR ANALYTICS ANALYSIS. THIS ARTICLE PRESENTS A THEORETICAL DISCUSSION ON THE EMERGENCE OF DISCOVERY WITH MODELS, ITS POTENTIAL TO ENHANCE RESEARCH ON LEARNING AND LEARNERS, AND KEY LESSONS LEARNED IN HOW DISCOVERY WITH MODELS CAN BE CONDUCTED VALIDLY AND EFFECTIVELY. WE ILLUSTRATE THESE ISSUES THROUGH DISCUSSION OF A CASE STUDY WHERE DISCOVERY WITH MODELS WAS USED TO INVESTIGATE A FORM OF DISENGAGED BEHAVIOR (I.E., CARELESSNESS) IN THE CONTEXT OF MIDDLE SCHOOL COMPUTER-BASED SCIENCE INQUIRY. THIS BEHAVIOR WAS ACKNOWLEDGED AS A PROBLEM IN EDUCATION AS EARLY AS THE 1920S. WITH THE INCREASING USE OF HIGH-STAKES TESTING, THE COST OF STUDENT CARELESSNESS CAN BE HIGHER. FOR INSTANCE, WITHIN COMPUTER-BASED LEARNING ENVIRONMENTS, CARELESS ERRORS CAN RESULT IN REDUCED EDUCATIONAL EFFECTIVENESS, WITH STUDENTS CONTINUING TO RECEIVE MATERIAL THEY HAVE ALREADY MASTERED. DESPITE THE IMPORTANCE OF THIS PROBLEM, IT HAS RECEIVED MINIMAL RESEARCH ATTENTION, IN PART BECAUSE OF DIFFICULTIES IN OPERATIONALIZING CARELESSNESS AS A CONSTRUCT. BUILDING FROM THEORY ON CARELESSNESS AND A BAYESIAN FRAMEWORK FOR KNOWLEDGE MODELING, WE USE MACHINE-LEARNED DETECTORS TO PREDICT CARELESSNESS WITHIN AUTHENTIC USE OF A COMPUTER-BASED LEARNING ENVIRONMENT. WE THEN USE A DISCOVERY WITH MODELS APPROACH TO LINK THESE VALIDATED CARELESSNESS MEASURES TO SURVEY DATA TO STUDY THE CORRELATIONS BETWEEN THE PREVALENCE OF CARELESSNESS AND STUDENT GOAL ORIENTATION.
WE PRESENT A DATASET OF 13446 SHELL COMMANDS FROM 175 PARTICIPANTS WHO ATTENDED CYBERSECURITY TRAINING AND SOLVED ASSIGNMENTS IN THE LINUX TERMINAL. EACH ACQUIRED DATA RECORD CONTAINS A COMMAND WITH ITS ARGUMENTS AND METADATA, SUCH AS A TIMESTAMP, WORKING DIRECTORY, AND HOST IDENTIFICATION IN THE EMULATED TRAINING INFRASTRUCTURE. THE COMMANDS WERE CAPTURED IN BASH, ZSH, AND METASPLOIT SHELLS. THE DATA ARE STORED AS JSON RECORDS, ENABLING VAST POSSIBILITIES FOR THEIR FURTHER USE IN RESEARCH AND DEVELOPMENT. THESE INCLUDE EDUCATIONAL DATA MINING, LEARNING ANALYTICS, STUDENT MODELING, AND EVALUATING MACHINE LEARNING MODELS FOR INTRUSION DETECTION. THE DATA WERE COLLECTED FROM 27 CYBERSECURITY TRAINING SESSIONS USING AN OPEN-SOURCE LOGGING TOOLSET AND TWO OPEN-SOURCE INTERACTIVE LEARNING ENVIRONMENTS. RESEARCHERS AND DEVELOPERS MAY USE THE DATASET OR DEPLOY THE LEARNING ENVIRONMENTS WITH THE LOGGING TOOLSET TO GENERATE THEIR OWN DATA IN THE SAME FORMAT. MOREOVER, WE PROVIDE A SET OF COMMON ANALYTICAL QUERIES TO FACILITATE THE EXPLORATORY ANALYSIS OF THE DATASET. (C) 2021 THE AUTHOR(S). PUBLISHED BY ELSEVIER INC.
THE GAP BETWEEN TECHNOLOGY AND LEARNING METHODS HAS TWO IMPORTANT IMPLICATIONS: ON THE ONE HAND, WE SHOULD NOT EXPECT THE INTEGRATION OF TECHNOLOGICAL ADVANCES INTO TEACHING TO BE AN EASY TASK; AND THERE IS A DANGER THAT MATURE EDUCATIONAL TECHNOLOGIES AND METHODS MIGHT NOT GIVE AN ADEQUATE ANSWER TO THE DEMANDS AND NEEDS OF SOCIETY, UNDERUSING THEIR TRANSFORMING POTENTIAL TO IMPROVE LEARNING PROCESSES. THIS STUDY DISCUSSES THE NEED FOR A NEW TECHNOLOGICAL ENVIRONMENT SUPPORTING LEARNING SERVICES, AND PROPOSES THE CONCEPT OF THE TECHNOLOGICAL LEARNING ECOSYSTEM AS A SOLUTION TO BOTH PROBLEMS. EDUCATIONAL ECOSYSTEMS SHOULD BE ABLE TO BREAK THE TECHNOLOGICAL CONSTRAINTS OF EXISTING LEARNING PLATFORMS AND ACHIEVE AN EFFECTIVE IMPROVEMENT IN LEARNING PROCESSES. OUR PROPOSED EDUCATIONAL ECOSYSTEMS PIVOT AROUND FIVE SPECIFIC LINES OF ACTION: 1) A FRAMEWORK ARCHITECTURE THAT SUPPORTS LEARNING SERVICE-BASED ECOSYSTEMS; 2) LEARNING ANALYTICS FOR EDUCATIONAL DECISION MAKING; 3) ADAPTIVE KNOWLEDGE SYSTEMS; 4) GAMIFICATION OF LEARNING PROCESSES; 5) SEMANTIC PORTFOLIOS TO COLLECT EVIDENCE OF LEARNING.
WITH THIS WORK WE INTEND TO DEVELOP COGNITIVE MODULES FOR LEARNING ANALYTICS SOLUTIONS USED IN INQUIRY LEARNING ENVIRONMENTS THAT CAN MONITOR AND ASSESS MENTAL ABILITIES INVOLVED IN SELF-DIRECTED LEARNING ACTIVITIES. WE REALIZE THIS IDEA BY DRAWING ON MODELS FROM MATHEMATICAL PSYCHOLOGY, WHICH SPECIFY ASSUMPTIONS ABOUT THE HUMAN MIND ALGORITHMICALLY AND THEREBY AUTOMATE A THEORY-DRIVEN DATA ANALYSIS. WE REPORT A STUDY TO EXEMPLIFY THIS APPROACH IN WHICH N=105 15-YEAR-OLD HIGH SCHOOL STUDENTS PERFORM A SELF-DETERMINED NAVIGATION IN A TAXONOMY OF DINOSAUR CONCEPTS. WE ANALYZE THEIR SEARCH AND LEARNING TRACES THROUGH THE LENS OF A CONNECTIONIST NETWORK MODEL OF WORKING MEMORY (WM). THE RESULTS ARE ENCOURAGING IN THREE WAYS. FIRST, THE MODEL PREDICTS STUDENTS' AVERAGE PROGRESS (AS WELL AS DIFFICULTIES) IN FORMING NEW CONCEPTS AT HIGH ACCURACY. SECOND, A SIMPLE (1-PARAMETER) EXTENSION, WHICH WE DERIVE FROM A META-COGNITIVE LEARNING FRAMEWORK, IS SUFFICIENT TO ALSO PREDICT AGGREGATED SEARCH PATTERNS. THIRD, OUR INITIAL ATTEMPT TO FIT THE MODEL TO INDIVIDUAL DATA OFFERS SOME PROMISING RESULTS: ESTIMATES OF A FREE PARAMETER CORRELATE SIGNIFICANTLY WITH A MEASURE OF WM CAPACITY. TOGETHER, WE BELIEVE THAT THESE RESULTS HELP DEMONSTRATE A NOVEL AND PROMISING WAY TOWARDS EXTENDING LEARNER MODELS BY COGNITIVE VARIABLES. WE ALSO DISCUSS CURRENT LIMITATIONS IN THE LIGHT OF OUR FUTURE WORK ON COGNITIVE-COMPUTATIONAL SCAFFOLDING TECHNIQUES IN INQUIRY LEARNING SCENARIOS.
TECHNOLOGICAL ADVANCEMENT IN RECENT DECADE ENABLED STUDENTS TO STUDY THROUGH VIRTUAL LEARNING AND PROPENSITY OF HIGHER ACADEMIC DEGREE THROUGH VIRTUAL LEARNING IS THE FOCUS OF THIS PRESENT PAPER. THE DISTANCE EDUCATION COUNCIL ALLOWS SOME UNIVERSITIES FOR SOME COURSES AND THE TENDENCY OF ADMISSION OF STUDENTS IN VIRTUAL EDUCATION TO COMPARE ITSELF, THE BEST NATIONAL OPEN UNIVERSITIES IN ORDER TO IMPROVE THE QUALITY OF THE VIRTUAL EDUCATION OFFERED. THE STUDENTS LEARNING THROUGH VIRTUAL EDUCATION SYSTEM MAY BE GROUPED INTO TWO: WHO ARE ALREADY DOING SERVICE, ANOTHER ARE FRESHER WHO ARE SEARCHING THE JOBS. IN ORDER TO VERIFY THE ADEQUACY OF DATA BASE WITH PROPOSED METHODOLOGY IT HAS BEEN FOUND THAT THE PEOPLE ARE INTERESTED IN DEFERENT WAYS, LIKE MBA DEGREE ARE LUCRATIVE TO SERVICE HOLDER STUDENT AND THE STUDENTS WHO ARE SEARCHING A JOB ARE INTERESTED FOR MCA DEGREE. WE THE AUTHORS AIM TO REVIEW THE SIMILARITIES AND DIFFERENCES BETWEEN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS.
NEW TECHNOLOGIES AND THE KNOWLEDGE ECONOMY ARE DESTABILISING GRADUATE PROFESSIONS, WITH ARTIFICIAL INTELLIGENCE AND THE ANALYSIS OF 'BIG DATA' MAKING SIGNIFICANT IMPACTS ON FORMERLY SECURE JOBS. BLOCKCHAIN TECHNOLOGY, OFFERING AUTOMATED SECURE CREDENTIALLING OF UNDERGRADUATE STUDENTS' ACTIVITIES AND ACHIEVEMENTS, MAY SIGNIFICANTLY ERODE EXISTING SYSTEMS OF ASSESSMENT. THE CHALLENGE FOR UNIVERSITIES WILL BE NOT ONLY TO MAINTAIN THE RELEVANCE OF THEIR CURRICULA BUT ALSO TO MANAGE EROSION OF THEIR CURRENT NEAR-MONOPOLY IN AWARDING DEGREES. THIS PAPER ENVISIONS A LANDSCAPE IN WHICH UNIVERSITIES MUST OUTSOURCE PARTS OF THEIR COURSE DELIVERY AND ASSESSMENT IN ORDER TO REMAIN COMPETITIVE. IT EXAMINES A POTENTIALLY SUSTAINABLE MISSION STRATEGY: TO MOVE AWAY FROM NARROW ACADEMIC DISCIPLINES TOWARDS AN AUTHENTIC LEARNING CURRICULUM FOCUSING ON THE DEVELOPMENT OF STUDENTS AS WHOLE PERSONS WITH ROUNDED EDUCATIONS. THIS PAPER EXAMINES IMPLICATIONS FOR THE ACADEMY OF THE CONVERGENCE OF ARTIFICIAL INTELLIGENCE, DATA ANALYTICS AND BLOCKCHAIN TECHNOLOGY.
A HUGE AMOUNT OF USERS' DATA ARE CREATED ON THE INTERNET DAY BY DAY. HOW TO SYNERGIZE THOSE USERS' DATA TO PROVIDE USEFUL INFORMATION WILL BE AN IMPORTANT ISSUE. BASED ON THE PHENOMENON MENTIONED ABOVE, THIS STUDY AIMS TO REFINE THE USERS' DATA ON AN EXPERIMENTAL SOCIAL NETWORK PLATFORM NAMED COCOING. INFO TO A REINTEGRATED CONCEPT MAP. THE COCOING. INFO PLATFORM HAS BEEN ONLINE FOR A HALF YEAR. DURING THE PERIOD, 646 USERS ENROLLED, AND 2,096 CONCEPT MAPS AND 4,569 USERS' RESPONSES WERE CREATED. TO SYNERGIZE THOSE USERS' DATA INTO A CONNECTED CONCEPT MAP FOR FURTHER VALUE-ADDED APPLICATIONS, TWO COMPUTER ALGORITHMS, TERM-GENERATION AND TERM-ASSOCIATION PROGRAMS, WERE DESIGNED TO FILTER THE USERS' DATA AND TO REINTEGRATE THE FILTERED DATA. BY THE TERM-GENERATION ALGORITHM, 46,490 TERMS WERE FILTERED. MOREOVER, THE TERM-ASSOCIATION ALGORITHM SYNERGIZED AS AN 18,011-NODE CONCEPT MAP BASED ON THE FILTERED TERMS. THE SYNERGIZED CONCEPT MAP CAN POTENTIALLY PROVIDE USERS TO IMPLEMENT ADAPTIVE VALUE-ADDED APPLICATIONS ON IT.
PROBLEM SOLVING, DOMAIN EXPERTISE, AND LEARNING ARE ANALYZED FOR THE MATH DATA CORPUS, WHICH INVOLVES MULTIMODAL DATA ON COLLABORATING STUDENT GROUPS AS THEY SOLVE MATH PROBLEMS TOGETHER ACROSS SESSIONS. COMPARED WITH NON-EXPERT STUDENTS, DOMAIN EXPERTS CONTRIBUTED MORE GROUP SOLUTIONS, SOLVED MORE PROBLEMS CORRECTLY AND TOOK LESS TIME. THESE DIFFERENCES BETWEEN EXPERTS AND NON-EXPERTS WERE ACCENTUATED ON HARDER PROBLEMS. A CUMULATIVE EXPERTISE METRIC VALIDATED THAT EXPERT AND NON-EXPERT STUDENTS REPRESENTED DISTINCT NON-OVERLAPPING POPULATIONS, A FINDING THAT REPLICATED ACROSS SESSIONS. GROUP PERFORMANCE ALSO IMPROVED 9.4% ACROSS SESSIONS, DUE MAINLY TO LEARNING BY EXPERT STUDENTS. THESE FINDINGS SATISFY GROUND-TRUTH CONDITIONS FOR DEVELOPING PREDICTION TECHNIQUES THAT AIM TO IDENTIFY EXPERTISE BASED ON MULTIMODAL COMMUNICATION AND BEHAVIOR PATTERNS. TOGETHER WITH THE MATH DATA CORPUS, THESE RESULTS CONTRIBUTE VALUABLE RESOURCES FOR SUPPORTING DATA-DRIVEN GRAND CHALLENGES ON MULTIMODAL LEARNING ANALYTICS, WHICH AIM TO DEVELOP NEW TECHNIQUES FOR PREDICTING EXPERTISE EARLY, RELIABLY, AND OBJECTIVELY. AS WELL AS LEARNING-ORIENTED PRECURSORS.
IN THIS ERA OF HIGH AND FAST-MOVING TECHNOLOGY, STUDENTS ARE MORE DEMANDING AND WILLING TO USE INNOVATIVE LEARNING METHODS, ALSO, THEY WILL BE SEEING FORWARD TO LIVING AND SURVIVING IN THE NEW ENVIRONMENT OF A SMART CLASSROOM. THIS SUBJECT GIVES A FRAMEWORK TO CLARIFY HOW IT CAN BE UTILIZED TO INSTITUTE A SMART AND INNOVATIVE UNIVERSITY CAMPUS LIFE TO IMPROVE THE DELIVERY AND EFFICIENCY OF EVERYDAY ACTIVITIES WITH CONSIDERATION OF THE SOCIAL AND ENVIRONMENTAL INTERACTIONS, AND HENCE PROVIDE NOT ONLY A SMART BUT LIKEWISE, A SUSTAINABLE CAMPUS. THE USE OF THE INTERNET OF THINGS (IOT) IS ABLE TO EXCHANGE AND MAKE USE OF INFORMATION FOR STUDENTS' PARTICIPATION AND INTERACTION WITH THE CLASS FELLOWS AND TEACHERS IN A VERY SUITABLE WAY. FOR THE EDUCATIONAL ASSESSMENT OF A STUDENT'S INTERACTION, THE MEASUREMENT OF STUDENT ATTENTIVENESS IS AN INDISPENSABLE COMPONENT OF IT. THE NEED FOR A HIGH METHOD FOR ASSESSMENT ALSO ARISES IN THE DEVELOPMENT OF NEW TECHNOLOGY IN THE MODE OF A NEW STYLE OF LEARNING. THE STUDENTS EXPERIENCE IN USING THE LMS MOODLE FOR E-LEARNING (ELECTRONIC LEARNING) SO THAT THE STUDENTS GET THE FULL ADVANTAGE BY INTERACTING OF THIS TECHNOLOGY AND THEIR LEARNING SKILLS INCREASE TO A HIGH-LEVEL BY USING LEARNING ANALYTICS AS DISCUSSED IN THIS REPORT. THE STORY EXPLORES THE APPLICABILITY OF THE RASPBERRY PI DEVELOPMENT BOARD OR SINGLE BOARD COMPUTER FOR TEACHING ENHANCEMENT, IOT TECHNOLOGIES AND ENVIRONMENTS. THE GOAL OF THIS RESEARCH IS TO IDENTIFY AND PROPOSE LOW-COST, EFFICIENT AND FLEXIBLE PLATFORM WHICH CAN ASSIST IN INTRODUCING THE IOT PARADIGM IN THE TEACHING PROCESS, AS CONSIDERABLY AS TO OFFER THE PROVISION TO FELLOW STUDENTS ABOUT SOCIAL BEHAVIOR AND FELLOW'S APPROACHABILITY BY USING LEARNING MANAGEMENT SYSTEM (LMS).
LEARNING TO PROGRAM IS A COGNITIVELY DEMANDING ACTIVITY. STUDENTS NEED TO COMBINE MENTAL MODELS OF VARIOUS CONCEPTS AND CONSTRUCTS TO SOLVE PROBLEMS. MANY STUDENTS NEW TO IT AND CS PROGRAMS HAVE LITTLE OR NO PRIOR EXPERIENCE WITH ABSTRACT REASONING AND PROBLEM-SOLVING. INSTRUCTORS ATTEMPT TO PRESENT THE CORE CONCEPTS EARLY TO ALLOW ADEQUATE TIME FOR STUDENTS TO COMPLETE THEIR PROGRAMMING ASSIGNMENTS. HOWEVER, MISCONCEPTIONS OF BASIC CONCEPTS FORMED IN THE EARLY STAGES OFTEN GET PROPAGATED BLOCKING ANY FURTHER PROGRESS. SUCH STUDENTS OFTEN BEGIN TO FORM POOR OPINIONS ABOUT THEIR CAPABILITY LEADING TO LOW SELF-ESTEEM AND PERFORMANCE. THIS PAPER PROPOSES A FRAMEWORK TO HELP INDIVIDUAL STUDENTS TO OVERCOME THEIR MISCONCEPTIONS THROUGH PERSONALIZED PRESCRIPTIVE QUIZZES. THESE QUIZZES ARE GENERATED BY COMBINING THE RICH META-DATA CAPTURED BY EACH QUIZ QUESTION WITH ANALYSIS OF PAST RESPONSES TO CLASS QUIZZES. THE PERSONALIZED PRESCRIPTIVE QUIZZES GENERATED HELPED TO IMPROVE STUDENT ENGAGEMENT AND PERFORMANCE SUBSTANTIALLY. OVER 91% OF THE STUDENTS SURVEYED INDICATED THAT PERSONALIZED QUIZZES HELPED THEM TO CLARIFY THEIR OWN MISCONCEPTIONS AND MADE THEM MORE CONFIDENT OF THEIR PROGRESS. STUDENTS USING THE PRESCRIPTIVE QUIZZES PERFORMED SIGNIFICANTLY BETTER THAN OTHERS IN SUBSEQUENT CLASS ASSESSMENTS AND THE FINAL EXAM.
IN THE LAST YEARS, THE DESIGN AND IMPLEMENTATION OF WEB-BASED EDUCATION SYSTEMS HAS GROWN EXPONENTIALLY, SPURRED BY THE FACT THAT NEITHER STUDENTS NOR TEACHERS ARE BOUND TO A SPECIFIC LOCATION AND THAT THIS FORM OF COMPUTER-BASED EDUCATION IS VIRTUALLY INDEPENDENT OF ANY SPECIFIC HARDWARE PLATFORM. THESE SYSTEMS ACCUMULATE A LARGE AMOUNT OF DATA: EDUCATIONAL DATA MINING AND LEARNING ANALYTICS ARE THE TWO MUCH RELATED FIELDS OF RESEARCH WITH THE AIM OF USING THESE EDUCATIONAL DATA TO IMPROVE THE LEARNING PROCESS. IN THIS CHAPTER, THE AUTHORS INVESTIGATE THE PEER ASSESSMENT SETTING IN COMMUNITIES OF LEARNERS. PEER ASSESSMENT IS AN EFFECTIVE DIDACTIC STRATEGY, USEFUL TO EVALUATE GROUPS OF STUDENTS IN EDUCATIONAL ENVIRONMENTS SUCH AS HIGH SCHOOLS AND UNIVERSITIES WHERE STUDENTS ARE REQUIRED TO ANSWER OPEN-ENDED QUESTIONS TO INCREASE THEIR PROBLEM-SOLVING SKILLS. FURTHERMORE, SUCH AN APPROACH COULD BECOME NECESSARY IN THE LEARNING CONTEXTS WHERE THE NUMBER OF STUDENTS TO EVALUATE COULD BE VERY LARGE AS, FOR EXAMPLE, IN MASSIVE OPEN ONLINE COURSES. HERE THE AUTHOR FOCUS ON THE AUTOMATED SUPPORT TO GRADING OPEN ANSWERS VIA A PEER EVALUATION-BASED APPROACH, WHICH IS MEDIATED BY THE (PARTIAL) GRADING WORK OF THE TEACHER, AND PRODUCES A (PARTIAL AS WELL) AUTOMATED GRADING. THE AUTHOR PROPOSE TO SUPPORT SUCH AUTOMATED GRADING BY MEANS OF TWO METHODS, COMING FROM THE DATA-MINING FIELD, SUCH AS BAYESIAN NETWORKS AND K-NEAREST NEIGHBOURS (K-NN), PRESENTING SOME EXPERIMENTAL RESULTS, WHICH SUPPORT OUR CHOICES.
5 SEVERAL DATA MINING TECHNIQUES ARE CURRENTLY APPLIED IN MANY AREAS WITH GREAT SUCCESS. VARIOUS EDUCATIONAL SYSTEMS COLLECT AND USE HUGE AMOUNT OF DATA ON STUDENTS, STAFF AND FACULTY. RESEARCHERS HAVE THE POTENTIAL TO EMPLOY SUCH DATASETS TO EXAMINE STUDENTS' PERFORMANCE OVER THEIR LEARNING TIME FROM ONE SEMESTER TO ANOTHER OR FROM ONE ACADEMIC YEAR TO ANOTHER. STUDENT'S KEY DEMOGRAPHIC CHARACTERISTICS, NUMBER OF EXAMINATION ATTEMPTS PER COURSE AND FINAL GRADE OF EACH COURSE CAN PROVIDE THE TRAINING DATA AND INFER A REGRESSION FUNCTION THAT ESTIMATES THE PERFORMANCE OF UPCOMING COURSES. WE HAVE CARRIED OUT SEVERAL EXPERIMENTS USING EIGHT COURSES MODELED FOR REGRESSION TASKS AND FAMILIAR DATA MINING MODELS (I.E. LINEAR REGRESSION, SUPPORT VECTOR MACHINES, DECISION TREES, MS RULES, AND K-NEAREST NEIGHBORS). THE RESULTS REPORTED THAT SATISFACTORY ACCURACY IS ACHIEVED, PROVIDED THAT THE FIRST SEMESTER GRADES ARE AVAILABLE, INDICATING THAT AN EARLY IDENTIFICATION OF STUDENTS AT RISK OF UNDERPERFORMING IN A COURSE CAN BE OBTAINED.
ADAPTIVE EDUCATIONAL TECHNOLOGIES HAVE THE CAPACITY TO MEET THE NEEDS OF INDIVIDUAL STUDENTS IN THEORY, BUT IN SOME CASES, THE DEGREE OF PERSONALIZATION MIGHT BE LESS THAN DESIRED, WHICH COULD LEAD TO INEQUITABLE OUTCOMES FOR STUDENTS. IN THIS PAPER, WE USE SIMULATIONS TO DEMONSTRATE THAT WHILE KNOWLEDGE TRACING ALGORITHMS ARE SUBSTANTIALLY MORE EQUITABLE THAN GIVING ALL STUDENTS THE SAME AMOUNT OF PRACTICE, SUCH ALGORITHMS CAN STILL BE INEQUITABLE WHEN THEY RELY ON INACCURATE MODELS. THIS CAN ARISE AS A RESULT OF TWO FACTORS: (1) USING STUDENT MODELS THAT ARE FIT TO AGGREGATE POPULATIONS OF STUDENTS, AND (2) USING STUDENT MODELS THAT MAKE INCORRECT ASSUMPTIONS ABOUT STUDENT LEARNING. IN PARTICULAR, WE DEMONSTRATE THAT BOTH THE BAYESIAN KNOWLEDGE TRACING ALGORITHM AND THE N-CONSECUTIVE CORRECT RESPONSES HEURISTIC ARE SUSCEPTIBLE TO THESE CONCERNS, BUT THAT KNOWLEDGE TRACING WITH THE ADDITIVE FACTOR MODEL MAY BE MORE EQUITABLE. THE BROADER MESSAGE OF THIS PAPER IS THAT WHEN DESIGNING LEARNING ANALYTICS ALGORITHMS, WE NEED TO EXPLICITLY CONSIDER WHETHER THE ALGORITHMS ACT FAIRLY WITH RESPECT TO DIFFERENT POPULATIONS OF STUDENTS, AND IF NOT, HOW WE CAN MAKE OUR ALGORITHMS MORE EQUITABLE.
HIGH LEVELS OF ATTRITION CHARACTERIZE UNDERGRADUATE SCIENCE COURSES IN THE USA. PREDICTIVE ANALYTICS RESEARCH SEEKS TO BUILD MODELS THAT IDENTIFY AT-RISK STUDENTS AND SUGGEST INTERVENTIONS THAT ENHANCE STUDENT SUCCESS. THIS STUDY EXAMINES WHETHER INCORPORATING A NOVEL ASSESSMENT TYPE (CONCEPT INVENTORIES [CI]) AND USING MACHINE LEARNING (ML) METHODS (1) IMPROVES PREDICTION QUALITY, (2) REDUCES THE TIME POINT OF SUCCESSFUL PREDICTION, AND (3) SUGGESTS MORE ACTIONABLE COURSE-LEVEL INTERVENTIONS. A CORPUS OF UNIVERSITY AND COURSE-LEVEL ASSESSMENT AND NON-ASSESSMENT VARIABLES (53 VARIABLES IN TOTAL) FROM 3225 STUDENTS (OVER SIX SEMESTERS) WAS GATHERED. FIVE ML METHODS WERE EMPLOYED (TWO INDIVIDUALS, THREE ENSEMBLES) AT THREE TIME POINTS (PRE-COURSE, WEEK 3, WEEK 6) TO QUANTIFY PREDICTIVE EFFICACY. INCLUSION OF COURSE-SPECIFIC CI DATA ALONG WITH UNIVERSITY-SPECIFIC CORPORA SIGNIFICANTLY IMPROVED PREDICTION PERFORMANCE. ENSEMBLE ML METHODS, IN PARTICULAR THE GENERALIZED LINEAR MODEL WITH ELASTIC NET (GLMNET), YIELDED SIGNIFICANTLY HIGHER AREA UNDER THE CURVE (AUC) VALUES COMPARED WITH NON-ENSEMBLE TECHNIQUES. LOGISTIC REGRESSION ACHIEVED THE POOREST PREDICTION PERFORMANCE AND CONSISTENTLY UNDERPERFORMED. SURPRISINGLY, INCREASING CORPUS SIZE (I.E., AMOUNT OF HISTORICAL DATA) DID NOT MEANINGFULLY IMPACT PREDICTION SUCCESS. WE DISCUSS THE ROLES THAT NOVEL ASSESSMENT TYPES AND ML TECHNIQUES MAY PLAY IN ADVANCING PREDICTIVE LEARNING ANALYTICS AND ADDRESSING ATTRITION IN UNDERGRADUATE SCIENCE EDUCATION.
WITH THE ADVANCEMENT OF DIGITAL TECHNOLOGIES, BIG DATA AND LEARNING ANALYTICS HAVE BECOME PREVALENT IN THE HIGHER EDUCATION. VARIOUS STUDENT-FACING SYSTEMS INCREASED THE AMOUNT OF DATA AVAILABLE TO STUDENTS, AND WHETHER STUDENTS CAN USE BIG DATA AND LEARNING ANALYTICS EFFECTIVELY WILL AFFECT THEIR ACADEMIC SUCCESS. MOST STUDIES, HOWEVER, HAVE FOCUSED ON HOW TEACHERS AND ADMINISTRATIVE PERSONNEL USE STUDENT DATA TO MAKE DATA-DRIVEN INSTRUCTION AND MANAGEMENT DECISIONS. AS A RESULT, LITTLE ATTENTION HAS BEEN GIVEN TO STUDENTS' USE OF RELEVANT DATA THAT GENERATED BY BIG DATA AND LEARNING ANALYTICS TO PROMOTE THEIR OWN LEARNING AND GROWTH. THIS STUDY EXPLORED USING SOCIAL COGNITIVE THEORY TO IDENTIFY POSSIBLE ENVIRONMENTAL, PERSONAL, AND BEHAVIOURAL FACTORS THAT AFFECT STUDENTS' DATA USE. WE USED AN ONLINE QUESTIONNAIRE THAT COLLECTED 242 COMPLETED SURVEYS FROM CHINESE UNIVERSITY STUDENTS. PARTIAL LEAST SQUARES (PLS) PATH MODELLING WAS USED TO ANALYSE THE DATA. THE INITIAL FINDINGS SUPPORT THE CONCLUSION THAT UNIVERSITY STUDENTS COULD BE ENCOURAGED TO EFFECTIVELY USE DATA IN THREE WAYS: (1) THROUGH THE PROMOTION OF UNIVERSITY-WIDE CULTURES OF DATA USE AND SUSTAINED IMPROVEMENTS IN DATA QUALITY, (2) THROUGH THE PROFESSIONAL DEVELOPMENT OF STUDENT DATA LITERACY, AND (3) THROUGH THE SUPPORT OF STUDENT DATA AUTONOMY, STUDENT DATA REFLECTIVENESS, AND STUDENTS' DIGITAL IDENTITIES.
THE MOTIVATION FOR THIS PAPER IS DERIVED FROM THE FACT THAT THERE HAS BEEN INCREASING INTEREST AMONG RESEARCHERS AND PRACTITIONERS IN DEVELOPING TECHNOLOGIES THAT CAPTURE, MODEL AND ANALYZE LEARNING AND TEACHING EXPERIENCES THAT TAKE PLACE BEYOND COMPUTER-BASED LEARNING ENVIRONMENTS. IN THIS PAPER, WE REVIEW CASE STUDIES OF TOOLS AND TECHNOLOGIES DEVELOPED TO COLLECT AND ANALYZE DATA IN EDUCATIONAL SETTINGS, QUANTIFY LEARNING AND TEACHING PROCESSES AND SUPPORT ASSESSMENT OF LEARNING AND TEACHING IN AN AUTOMATED FASHION. WE FOCUS ON PIPELINES THAT LEVERAGE INFORMATION AND DATA HARNESSED FROM PHYSICAL SPACES AND/OR INTEGRATES COLLECTED DATA ACROSS PHYSICAL AND DIGITAL SPACES. OUR REVIEW REVEALS A PROMISING FIELD OF PHYSICAL CLASSROOM ANALYSIS. WE DESCRIBE SOME TRENDS AND SUGGEST POTENTIAL FUTURE DIRECTIONS. SPECIFICALLY, MORE RESEARCH SHOULD BE GEARED TOWARDS A) DEPLOYABLE AND SUSTAINABLE DATA COLLECTION SET-UPS IN PHYSICAL LEARNING ENVIRONMENTS, B) TEACHER ASSESSMENT, C) DEVELOPING FEEDBACK AND VISUALIZATION SYSTEMS AND D) PROMOTING INCLUSIVITY AND GENERALIZABILITY OF MODELS ACROSS POPULATIONS.
INDUCTIVE REASONING IS AN IMPORTANT EDUCATIONAL PRACTICE BUT CAN BE DIFFICULT FOR TEACHERS TO SUPPORT IN THE CLASSROOM DUE TO THE HIGH LEVEL OF PREPARATION AND CLASSROOM TIME NEEDED TO CHOOSE THE TEACHING MATERIALS THAT CHALLENGE STUDENTS' CURRENT VIEWS. INTELLIGENT TUTORING SYSTEMS CAN POTENTIALLY FACILITATE THIS WORK FOR TEACHERS BY SUPPORTING THE AUTOMATIC ADAPTATION OF EXAMPLES BASED ON A STUDENT MODEL OF THE INDUCTION PROCESS. HOWEVER, CURRENT MODELS OF INDUCTIVE REASONING USUALLY LACK TWO MAIN CHARACTERISTICS HELPFUL TO ADAPTIVE LEARNING ENVIRONMENTS, INDIVIDUAL DIFFERENCES OF STUDENTS AND TRACING OF STUDENTS' LEARNING AS THEY RECEIVE FEEDBACK. IN THIS PAPER, WE DESCRIBE A MODEL TO PREDICT AND SIMULATE INDUCTIVE REASONING OF STUDENTS FOR A CATEGORIZATION TASK. OUR APPROACH USES A BAYESIAN MODEL FOR DESCRIBING THE REASONING PROCESSES OF STUDENTS. THIS MODEL ALLOWS US TO PREDICT STUDENTS' CHOICES IN CATEGORIZATION QUESTIONS BY ACCOUNTING FOR THEIR FEATURE BIASES. USING DATA GATHERED FROM 222 STUDENTS CATEGORIZING THREE TOPICS, WE FIND THAT OUR MODEL HAS A 75% ACCURACY, WHICH IS 10% GREATER THAN A BASELINE MODEL. OUR MODEL IS A CONTRIBUTION TO LEARNING ANALYTICS BY ENABLING US TO ASSIGN DIFFERENT BIAS PROFILES TO INDIVIDUAL STUDENTS AND TRACKING THESE PROFILE CHANGES OVER TIME THROUGH WHICH WE CAN GAIN A BETTER UNDERSTANDING OF STUDENTS' LEARNING PROCESSES. THIS MODEL MAY BE RELEVANT FOR SYSTEMATICALLY ANALYSING STUDENTS' DIFFERENCES AND EVOLUTION IN INDUCTIVE REASONING STRATEGIES WHILE SUPPORTING THE DESIGN OF ADAPTIVE INDUCTIVE LEARNING ENVIRONMENTS.
CONDUCTING EXPERIMENTS IS AN IMPORTANT PRACTICE FOR BOTH ENGINEERING DESIGN AND SCIENTIFIC INQUIRY. ENGINEERS ITERATIVELY CONDUCT EXPERIMENTS TO EVALUATE IDEAS, MAKE INFORMED DECISIONS, AND OPTIMIZE THEIR DESIGNS. HOWEVER, BOTH ENGINEERING DESIGN AND SCIENTIFIC EXPERIMENTATION ARE OPEN-ENDED TASKS THAT ARE NOT EASY TO ASSESS. RECENT STUDIES HAVE DEMONSTRATED HOW TECHNOLOGY-BASED ASSESSMENTS CAN HELP TO CAPTURE AND CHARACTERIZE THESE OPEN-ENDED TASKS USING UNOBTRUSIVE DATA LOGGING. THIS STUDY BUILDS UPON A MODEL TO CHARACTERIZE STUDENTS' EXPERIMENTATION STRATEGIES IN DESIGN (ESD). TEN UNDERGRADUATE STUDENTS WORKED ON A DESIGN CHALLENGE USING A COMPUTER-AIDED DESIGN (CAD) TOOL THAT CAPTURED ALL THEIR INTERACTIONS WITH THE SOFTWARE. THIS PROCESS DATA WAS COMPARED TO THINK-ALOUD DATA, WHICH INCLUDED STUDENTS' EXPLANATIONS OF THEIR RATIONALE FOR THE EXPERIMENTS. THE RESULTS SUGGEST THAT THE PROCESS DATA AND THE THINK-ALOUD DATA HAVE BOTH AFFORDANCES AND LIMITATIONS TOWARD THE GOAL OF ASSESSING STUDENTS' ESD. WHILE THE PROCESS DATA WAS AN EFFECTIVE APPROACH TO IDENTIFY RELEVANT SEQUENCES OF ACTIONS, THIS TYPE OF DATA FAILED TO ENSURE THAT STUDENTS CARRIED THEM OUT WITH A SPECIFIC PURPOSE. ON THE OTHER HAND, THE THINK-ALOUD DATA CAPTURED STUDENTS' RATIONALE FOR CONDUCTING EXPERIMENTS, BUT IT DEPENDED ON STUDENTS' ABILITY TO VERBALIZE THEIR ACTIONS. IN ADDITION, THE IMPLEMENTATION OF THINK-ALOUD PROCEDURES AND THEIR ANALYSIS ARE TIME CONSUMING TASKS, AND CAN ONLY BE DONE INDIVIDUALLY.
IN THIS WORK, WE PROPOSE A NEW METHODOLOGY TO PROFILE INDIVIDUAL STUDENTS OF COMPUTER SCIENCE BASED ON THEIR PROGRAMMING DESIGN USING A TECHNIQUE CALLED EMBEDDINGS. WE INVESTIGATE DIFFERENT APPROACHES TO ANALYZE USER SOURCE CODE SUBMISSIONS IN THE PYTHON LANGUAGE. WE COMPARE THE PERFORMANCES OF DIFFERENT SOURCE CODE VECTORIZATION TECHNIQUES TO PREDICT THE CORRECTNESS OF A CODE SUBMISSION. IN ADDITION, WE PROPOSE A NEW MECHANISM TO REPRESENT STUDENTS BASED ON THEIR CODE SUBMISSIONS FOR A GIVEN SET OF LABORATORY TASKS ON A PARTICULAR COURSE. THIS WAY, WE CAN MAKE DEEPER RECOMMENDATIONS FOR PROGRAMMING SOLUTIONS AND PATHWAYS TO SUPPORT STUDENT LEARNING AND PROGRESSION IN COMPUTER PROGRAMMING MODULES EFFECTIVELY AT A HIGHER EDUCATION INSTITUTION. RECENT WORK USING DEEP LEARNING TENDS TO WORK BETTER WHEN MORE AND MORE DATA IS PROVIDED. HOWEVER, IN LEARNING ANALYTICS, THE NUMBER OF STUDENTS IN A COURSE IS AN UNAVOIDABLE LIMIT. THUS WE CANNOT SIMPLY GENERATE MORE DATA AS IS DONE IN OTHER DOMAINS SUCH AS FINTECH OR SOCIAL NETWORK ANALYSIS. OUR FINDINGS INDICATE THERE IS A NEED TO LEARN AND DEVELOP BETTER MECHANISMS TO EXTRACT AND LEARN EFFECTIVE DATA FEATURES FROM STUDENTS SO AS TO ANALYZE THE STUDENTS' PROGRESSION AND PERFORMANCE EFFECTIVELY.
EARLY PREDICTION SYSTEMS HAVE ALREADY BEEN APPLIED SUCCESSFULLY IN VARIOUS EDUCATIONAL CONTEXTS. IN THIS STUDY, WE INVESTIGATED DEVELOPING AN EARLY PREDICTION SYSTEM IN THE CONTEXT OF EBOOK-BASED TEACHING-LEARNING AND USED STUDENTS' EBOOK READING DATA TO DEVELOP AN EARLY WARNING SYSTEM FOR STUDENTS AT-RISK OF ACADEMIC FAILURE -STUDENTS WHOSE ACADEMIC PERFORMANCE IS LOW. TO DETERMINE THE BEST PERFORMING MODEL AND OPTIMUM TIME FOR POSSIBLE INTERVENTIONS WE CREATED PREDICTION MODELS BY USING 13 PREDICTION ALGORITHMS WITH THE DATA FROM DIFFERENT WEEKS OF THE COURSE. WE ALSO TESTED EFFECTS OF DATA TRANSFORMATION ON PREDICTION MODELS. 10-FOLD CROSS-VALIDATION WAS USED FOR ALL PREDICTION MODELS. ACCURACY AND KAPPA METRICS WERE USED TO COMPARE THE PERFORMANCE OF THE MODELS. OUR RESULTS REVEALED THAT IN A SIXTEEN-WEEK LONG COURSE ALL MODELS REACHED THEIR HIGHEST PERFORMANCE WITH THE DATA FROM THE 15TH WEEK. ON THE OTHER HAND, STARTING FROM THE 3RD WEEK, THE MODELS CLASSIFIED LOW AND HIGH PERFORMING STUDENTS WITH AN ACCURACY OF OVER 79%. IN TERMS OF ALGORITHMS, RANDOM FOREST (RF) OUTPERFORMED OTHER ALGORITHMS WHEN RAW DATA WERE USED, HOWEVER, WITH THE TRANSFORMED DATA J48 ALGORITHM PERFORMED BETTER. WHEN CATEGORICAL DATA WERE USED, NAIVE BAYES (NB) OUTPERFORMED OTHER ALGORITHMS. RESULTS ALSO INDICATED THAT MODELS WITH TRANSFORMED DATA PERFORMED LOWER THAN THE MODELS CREATED USING CATEGORICAL DATA. HOWEVER, MODELS WITH CATEGORICAL DATA SHOWED SIMILAR PERFORMANCE WITH MODELS WITH RAW DATA. THE IMPLICATIONS OF THE RESULTS PRESENTED IN THIS RESEARCH WERE ALSO DISCUSSED WITH RESPECT TO THE FIELD OF LEARNING ANALYTICS.
ONLINE FORMATIVE ASSESSMENTS IN E-LEARNING SYSTEMS ARE INCREASINGLY OF INTEREST IN THE FIELD OF EDUCATION. WHILE SUBSTANTIAL RESEARCH INTO THE MODEL AND ITEM DESIGN ASPECTS OF FORMATIVE ASSESSMENT HAS BEEN CONDUCTED, FEW SOFTWARE SYSTEMS EMBODIED WITH A PSYCHOMETRIC MODEL HAVE BEEN PROPOSED TO ALLOW US TO ADAPTIVELY IMPLEMENT FORMATIVE ASSESSMENTS. THIS STUDY AIMED TO DEVELOP AN ADAPTIVE FORMATIVE ASSESSMENT SYSTEM, CALLED COMPUTERIZED FORMATIVE ADAPTIVE TESTING (CAFT) BY USING ARTIFICIAL INTELLIGENCE METHODS BASED ON COMPUTERIZED ADAPTIVE TESTING (CAT) AND BAYESIAN NETWORKS AS LEARNING ANALYTICS. CAFT CAN ADAPTIVELY ADMINISTER PERSONALIZED FORMATIVE ASSESSMENT TO A LEARNER BY DYNAMICALLY SELECTING APPROPRIATE ITEMS AND TESTS ALIGNED WITH THE LEARNER'S ABILITY. FORTY ITEMS IN AN ITEM BANK WERE EVALUATED BY 410 LEARNERS, MOREOVER, 1000 LEARNERS WERE RECRUITED FOR A SIMULATION STUDY AND 120 LEARNERS WERE ENROLLED TO EVALUATE THE EFFICIENCY, VALIDITY, AND RELIABILITY OF CAFT IN AN APPLICATION STUDY. THE RESULTS SHOWED THAT, THROUGH CAFT, LEARNERS CAN ADAPTIVELY TAKE ITEM S AND TESTS IN ORDER TO RECEIVE PERSONALIZED DIAGNOSTIC FEEDBACK ABOUT THEIR LEARNING PROGRESSION. CONSEQUENTLY, THIS STUDY HIGHLIGHTS THAT A LEARNING MANAGEMENT SYSTEM WHICH INTEGRATES CAT AS AN ARTIFICIALLY INTELLIGENT COMPONENT IS AN EFFICIENT EDUCATIONAL EVALUATION TOOL FOR A REMOTE PERSONALIZED LEARNING SERVICE.
THIS PAPER DESCRIBES THE TEACHING MODEL FOR EDUCATION 4.0, THAT USES ELEMENTS FROM DIFFERENT DISCIPLINES SUCH AS ELECTRONIC TECHNOLOGY, ARTIFICIAL INTELLIGENCE (AI), HEALTH, MULTIMODAL LEARNING ANALYTICS, EDUCATION, ETC. SMART BLENDED LEARNING PROCESS FOR EDUCATION 4.0, IMPLEMENTS IN EACH PHASE AI METHODS SUCH AS USER MODELING, ADAPTIVITY, MACHINE LEARNING, CHATBOTS, AND SEMANTIC TEXT RECOGNITION TOOLS TO INCREASE LEARNING SUCCESS AND TO REDUCE THE DROPOUT RATE IN EXAMINATIONS. ON THE ONE HAND, MULTIMODAL DATA, FROM ONLINE COURSE AND WEARABLES, ENABLES FURTHER STATEMENTS ABOUT THE CONCRETE LEARNING SITUATION, AND ON THE OTHER HAND, IT INCREASES THE STUDENTS' SUBJECTIVE EXPERIENCE OF EMOTIONAL STATES, THEIR STRESS PERCEPTION, WELL-BEING OR ENERGY LEVEL. THIS PAPER REPORTS ON EXPERIMENTS WHERE THE COMBINATION OF AI, INTERNET OF THINGS (IOT) AND BIOFEEDBACK IN A SMART BLENDED LEARNING PROCESS ATTEMPT TO CONSIDER THE CHARACTERISTICS OF LEARNERS BETTER.
INVOLVING STAKEHOLDERS THROUGHOUT THE CREATION OF NEW EDUCATIONAL TECHNOLOGIES CAN HELP ENSURE THEIR USEFULNESS AND USABILITY IN REAL-WORLD CONTEXTS. HOWEVER, GIVEN THE COMPLEXITY OF LEARNING ANALYTICS (LA) SYSTEMS, IT CAN BE CHALLENGING TO MEANINGFULLY INVOLVE NON-TECHNICAL STAKEHOLDERS THROUGHOUT THEIR DESIGN AND DEVELOPMENT. THIS ARTICLE PRESENTS A DETAILED CASE STUDY OF THE ITERATIVE CO-DESIGN OF LUMILO, A WEARABLE, REAL-TIME LEARNING ANALYTICS TOOL FOR TEACHERS WORKING IN AI-ENHANCED K-12 CLASSROOMS. IN THE PROCESS, WE ARGUE THAT THE CO-DESIGN OF LA SYSTEMS REQUIRES NEW KINDS OF PROTOTYPING METHODS. WE INTRODUCE ONE OF OUR OWN PROTOTYPING METHODS, RES, TO ADDRESS UNIQUE CHALLENGES OF CO-PROTOTYPING DATA-DRIVEN ALGORITHMIC SYSTEMS SUCH AS LA TOOLS. THIS WORK PRESENTS THE FIRST END-TO-END DEMONSTRATION IN THE LITERATURE OF HOW NON-TECHNICAL STAKEHOLDERS CAN PARTICIPATE THROUGHOUT THE WHOLE DESIGN PROCESS FOR A COMPLEX LA SYSTEM - FROM EARLY GENERATIVE PHASES TO THE SELECTION AND TUNING OF ANALYTICS TO EVALUATION IN REAL-WORLD CONTEXTS. WE CONCLUDE WITH A SUMMARY OF METHODOLOGICAL RECOMMENDATIONS FOR FUTURE LA CO-DESIGN EFFORTS.
THE PURPOSE OF THIS PAPER IS TO EXAMINE THE CROSS INSTITUTIONAL USE OF A QUANTIFIED-SELF APPLICATION CALLED PATTERN, WHICH IS DESIGNED TO PROMOTE SELF-REGULATION AND REFLECTIVE LEARNING IN LEARNERS. THIS PAPER PROVIDES A BRIEF LOOK INTO HOW LEARNERS REPORT SPENDING THEIR TIME AND REACT TO IN-APP RECOMMENDATIONS. INITIAL DATA IS ENCOURAGING; HOWEVER, THERE ARE LIMITATIONS OF PATTERN, AND ADDITIONAL RESEARCH AND DEVELOPMENT MUST BE UNDERTAKEN.
AS TECHNOLOGY ADVANCES, LEARNING ANALYTICS IS EXPANDING TO INCLUDE STUDENTS' COLLABORATION SETTINGS. DESPITE THEIR INCREASING APPLICATION IN PRACTICE, SOME TYPES OF ANALYTICS MIGHT NOT FULLY CAPTURE THE COMPREHENSIVE EDUCATIONAL CONTEXTS IN WHICH STUDENTS' COLLABORATION TAKES PLACE (E.G., WHEN DATA IS COLLECTED AND PROCESSED WITHOUT PREDEFINED MODELS, WHICH FORCES USERS TO MAKE CONCLUSIONS WITHOUT SUFFICIENT CONTEXTUAL INFORMATION). FURTHERMORE, EXISTING DEFINITIONS AND PERSPECTIVES ON COLLABORATION ANALYTICS ARE INCONGRUENT. IN LIGHT OF THESE CIRCUMSTANCES, THIS OPINION PAPER TAKES A COLLABORATIVE CLASSROOM SETTING AS CONTEXT AND EXPLORES RELEVANT COMPREHENSIVE MODELS FOR COLLABORATION ANALYTICS. SPECIFICALLY, THIS PAPER IS BASED ON PEI-LING TAN AND KOH'S ECOLOGICAL LENS (2017, SITUATING LEARNING ANALYTICS PEDAGOGICALLY: TOWARDS AN ECOLOGICAL LENS. LEARNING: RESEARCH AND PRACTICE, 3(1), 1-11. HTTPS://DOI.ORG/10.1080/23735082.2017.1305661), WHICH ILLUSTRATES THE CO-EMERGENCE OF THREE INTERACTIONS AMONG STUDENTS, TEACHERS, AND CONTENT INTERWOVEN WITH TIME. MOREOVER, THIS PAPER SUGGESTS SEVERAL FACTORS TO CONSIDER IN EACH INTERACTION WHEN EXECUTING COLLABORATION ANALYTICS. AGENDAS AND RECOMMENDATIONS FOR FUTURE RESEARCH ARE ALSO PRESENTED.
SMART LEARNING ENVIRONMENTS (SLE) PROVIDE STUDENTS WITH OPPORTUNITIES TO INTERACT WITH LEARNING RESOURCES AND ACTIVITIES IN WAYS THAT ARE CUSTOMISED TO THEIR PARTICULAR LEARNING GOALS AND APPROACHES. A CHALLENGE IN DEVELOPING SLES IS PROVIDING RESOURCES AND TASKS WITHIN A SINGLE SYSTEM THAT CAN SEAMLESSLY TAILOR LEARNING EXPERIENCE IN TERMS OF TIME, PLACE, PLATFORM, AND FORM. IN THIS PAPER WE INTRODUCE THE ICOLLAB PLATFORM, AN ADAPTIVE ENVIRONMENT WHERE LEARNING ACTIVITIES ARE MODERATED THROUGH CONVERSATION WITH AN INTELLIGENT AGENT WHO CAN OPERATE ACROSS MULTIPLE WEB-BASED PLATFORMS, INTEGRATING FORMAL AND INFORMAL LEARNING OPPORTUNITIES. FIFTY-EIGHT UNDERGRADUATE COMPUTER SCIENCE STUDENTS WERE RANDOMLY ASSIGNED TO EITHER AN INTERVENTION OR CONTROL GROUP FOR THE 12 WEEKS OF THE PILOT STUDY. LEARNING ANALYTICS WERE USED TO EXAMINE THEIR INTERACTIONS WITH ICOLLAB, WHILE THEIR COURSE PERFORMANCE INVESTIGATED THE IMPACT OF USING ICOLLAB ON LEARNING OUTCOMES. RESULTS FROM THE STUDY SHOWED A HIGH LEVEL OF INTERACTION WITH ICOLLAB, ESPECIALLY SOCIAL INTERACTION, INDICATING AN INTERWEAVING OF FORMAL LEARNING WITHIN THEIR INFORMAL NETWORK SPACES. THESE FINDINGS OPEN UP NEW POSSIBILITIES FOR WAYS THAT SLES CAN BE DESIGNED TO INCORPORATE DIFFERENT FACTORS, IMPROVING THE ABILITY OF THE SYSTEM TO PROVIDE ADAPTIVE AND PERSONALISED LEARNING EXPERIENCES IN RELATION TO CONTEXT AND TIME. IMPLICATIONS FOR PRACTICE OR POLICY: THIS SYSTEM ILLUSTRATES FEATURES THAT CAN BE IMPLEMENTED IN SMART LEARNING ENVIRONMENTS TO ENABLE ADAPTIVE, CONTEXT-BASED, AND PERSONALISED LEARNING.. SMART LEARNING ENVIRONMENTS CAN COMBINE FORMAL AND INFORMAL LEARNING CONTEXTS TO PROMOTE STUDENT ENGAGEMENT THROUGH THE PROVISION OF FLEXIBILITY OF THE PLATFORMS IN WHICH LEARNING OCCURS. CONVERSATIONAL INTELLIGENT TUTORING SYSTEMS CAN ADAPT THE FORM OF LEARNING RESOURCES/ACTIVITIES SO STUDENTS CAN INTERACT USING NATURAL LANGUAGE AS THEY WOULD WITH A TEACHER IN THE CLASSROOM.
PERSONALIZED LEARNING IS A LEARNING APPROACH THAT AIMS TO PERSONALIZE THE LEARNING EXPERIENCE ACCORDING TO THE UNIQUE NEEDS, GOALS, AND SKILLS OF INDIVIDUALS WHICH CAN BE ACHIEVED BY USING CURRENT INSTRUCTIONAL TECHNOLOGY THAT PROVIDES UNIQUE LEARNING EXPERIENCES IN DIFFERENT LEARNING ENVIRONMENTS. TECHNOLOGY IS THE MAIN COMPONENT THAT WILL ENABLE AND ENRICH PERSONALIZED LEARNING EXPERIENCE; HOWEVER, EVEN THOUGH TECHNOLOGY IS AVAILABLE TO PERSONALIZE THE LEARNING EXPERIENCE, THERE IS STILL A LACK OF UNIFIED AGREEMENT ON WHAT COMPONENTS NEED TO BE CONSIDERED FOR A DYNAMIC PERSONALIZED LEARNING APPROACH THAT IS TO BE ABLE TO PROVIDE A UNIQUE AND EFFECTIVE LEARNING EXPERIENCE TO EACH LEARNER. TO ADDRESS THIS NEED, THIS STUDY AIMS TO ANALYZE AND SYNTHESIZE DIFFERENT PERSONALIZED LEARNING APPROACHES THAT CONSIDER DIFFERENT LEARNING COMPONENTS, SO THAT WE HAVE AN EVOLVING AGREEMENT ON PERSONALIZED LEARNING MODELS AND APPROACHES. THE FINDINGS OF THIS RESEARCH IDENTIFIED THE FOLLOWING MAIN COMPONENTS: LEARNER PROFILES AND ATTITUDES, PREVIOUS KNOWLEDGE AND BELIEFS, PERSONALIZED ADAPTIVE LEARNING PATHS, AND FLEXIBLE SELF-PACED LEARNING ENVIRONMENTS THAT ARE GENERATED BY LEARNING ANALYTICS. THESE PROMINENT CHARACTERISTICS IMPLY THAT A PERSONALIZED LEARNING ENVIRONMENT (PLE) WOULD NEED TO BE DYNAMIC TO MAINTAIN A CURRENT RECORD OF LEARNER INTERESTS AND ATTITUDES, PAST EXPERIENCES AND PERFORMANCE, AND ACTIVITIES AND INTERACTIONS LIKELY TO MATCH A PARTICULAR LEARNER AND LEARNING GOAL.
THIS ARTICLE SURVEYS CURRENT AND EMERGING PRACTICE IN TECHNOLOGY-ENHANCED LEARNING IN HIGHER EDUCATION. THE ARTICLE USES 'DISRUPTIVE INNOVATION' AS A LENS TO EXAMINE TECHNOLOGY-ENHANCED LEARNING. DISRUPTIVE INNOVATION FOCUSES ON SIMPLE AND CONVENIENT TECHNOLOGIES WHICH DISRUPT MARKETS FOR EXISTING GOODS AND SERVICES. THE ARTICLE ARGUES THAT DISRUPTIVE TECHNOLOGIES ARE USED FREQUENTLY IN HIGHER EDUCATION, BUT ALSO THAT STUDENTS PRACTISE DEMARCATION, USING DIFFERENT TECHNOLOGIES TO SUPPORT THEIR LEARNING LIVES AND THEIR SOCIAL LIVES. THE ARTICLE FURTHER ARGUES THAT CURRENT PRACTICE IN TECHNOLOGY-ENHANCED LEARNING HAS IMPLICATIONS FOR THE DESIGN AND USE OF EMERGING TECHNOLOGIES (SUCH AS MASSIVE OPEN ONLINE COURSES) AND EMERGING PRACTICES (SUCH AS BRING YOUR OWN DEVICE, AND LEARNING ANALYTICS), AS WELL AS HAVING IMPLICATIONS FOR COST STRUCTURES, AND FOR THE FUTURE OF HIGHER EDUCATION AS A WHOLE.
ASYNCHRONOUS ONLINE DISCUSSIONS OFFER MANY ADVANTAGES IN AN EDUCATIONAL CONTEXT SUCH AS BUILDING A CLASS COMMUNITY, EMPOWERING STUDENTS TO EXPRESS THEMSELVES, FACILITATING EXPLORATORY LEARNING AND CONTRIBUTING TO THE DEVELOPMENT OF COGNITIVE, CRITICAL THINKING AND WRITING SKILLS. WHETHER INTEGRATED WITHIN A LEARNING MANAGEMENT SYSTEM OR AS AN EXTERNAL WEBSITE, ONE OF THE MOST COMMON PLATFORMS FOR HOSTING SUCH ASYNCHRONOUS ONLINE DISCUSSIONS IS A DISCUSSION BOARD. RECENT TECHNOLOGICAL ADVANCEMENTS, HOWEVER, OFFER A WIDE NUMBER OF ALTERNATIVE TOOLS. AMONG THEM, THE 'GROUPS' FEATURE FROM FACEBOOK, CURRENTLY THE LARGEST ONLINE SOCIAL NETWORK, HAS ATTRACTED A LOT OF ATTENTION BY THE ACADEMIC COMMUNITY AND AMPLE RESEARCH DEMONSTRATES THE BENEFITS OF THE SPECIFIC TOOL FOR EDUCATIONAL PURPOSES. COMPARING FACEBOOK GROUPS WITH A DISCUSSION BOARD IN TERMS OF SUPPORT FOR THE INSTRUCTOR TO APPRAISE STUDENT PARTICIPATION AND ENGAGEMENT REVEALS A DRAWBACK FOR FACEBOOK GROUPS. WHILE DISCUSSION BOARDS ARE SUPPORTED BY A NUMBER OF LEARNING ANALYTICS TOOLS, NO SUCH ACADEMIC SUPPORT SEEMS TO EXIST FOR A FACEBOOK GROUP. IN THIS PAPER WE INTRODUCE INGAUGE, A NOVEL EDUCATIONAL TOOL THAT ENABLES INSTRUCTORS TO GAUGE THE LEVEL OF STUDENT ENGAGEMENT AND PARTICIPATION WITHIN AN ACADEMIC FACEBOOK GROUP. FOUNDED ON EDUCATIONAL THEORIES FOR EVALUATING ONLINE ENGAGEMENT, INGAUGE CAN COLLECT AND ANALYSE ALL ACTIVITIES WITHIN THE GROUP AND GENERATE A NUMBER OF LEARNING ANALYTICS REPORTS. THE MOST IMPORTANT ACADEMIC FEATURE, HOWEVER, IS THAT INGAUGE OFFERS SUPPORT FOR CUSTOMIZING AN ASSESSMENT MODEL IN ORDER TO MEET THE STUDENT PARTICIPATION REQUIREMENTS OF ANY TYPE OF FACEBOOK GROUP THAT IS USED FOR EDUCATIONAL PURPOSES.
RECENT STUDIES HAVE PROVIDED EVIDENCE IN FAVOUR OF ADOPTING EARLY WARNING SYSTEMS AS A MEANS OF IDENTIFYING AT RISK STUDENTS. OUR STUDY EXAMINES EIGHT PREDICTION METHODS, AND INVESTIGATES THE OPTIMAL TIME IN A COURSE TO APPLY SUCH A SYSTEM. WE PRESENT FINDINGS FROM A STATISTICS UNIVERSITY COURSE WHICH HAS WEEKLY CONTINUOUS ASSESSMENT AND A LARGE PROPORTION OF RESOURCES ON THE LEARNING MANAGEMENT SYSTEM BLACKBOARD. WE IDENTIFY WEEKS 5-6 (HALF WAY THROUGH THE SEMESTER) AS AN OPTIMAL TIME TO IMPLEMENT AN EARLY WARNING SYSTEM, AS IT ALLOWS TIME FOR THE STUDENTS TO MAKE CHANGES TO THEIR STUDY PATTERNS WHILE RETAINING REASONABLE PREDICTION ACCURACY. USING DETAILED VARIABLES, CLUSTERING AND OUR FINAL PREDICTION METHOD OF BART (BAYESIAN ADDITIVE REGRESSIVE TREES) WE CAN PREDICT STUDENTS' FINAL MARK BY WEEK 6 BASED ON MEAN ABSOLUTE ERROR TO 6.5 PERCENTAGE POINTS. WE PROVIDE OUR R CODE FOR IMPLEMENTATION OF THE PREDICTION METHODS USED IN A GITHUB REPOSITORY(1).
THE USE OF ONLINE EDUCATION PLATFORMS HAS GROWN EXTENSIVELY AND MOST EDUCATION CENTERS AND COMPANIES USE THEM FOR THEIR LEARNING PROGRAMS. ALTHOUGH TECHNOLOGY HAS CHANGED THE LEARNING ENVIRONMENT, THE PEDAGOGICAL MODEL HAS MOSTLY REMAINED THE SAME AS IT WAS MANY YEARS AGO. THEREFORE, ANOTHER EDUCATION PARADIGM SHOULD ARRIVE TO ONLINE PLATFORMS IN A GENERALIZED WAY. IN THIS PAPER, NEUROK IS PRESENTED AS A NEW E-LEARNING PLATFORM LEVERAGING THE LATEST TECHNOLOGIES AND, MOREOVER, IMPLEMENTING NEW TOOLS THAT SUPPORT PEDAGOGICAL PRINCIPLES FROM NEUROSCIENCE. WHILE MOST OF TRADITIONAL PLATFORMS FOCUS ON CONTENT, CONTENT MANAGEMENT AND APPLYING TEACHER-CENTRED METHODOLOGIES (EVERYTHING GOES THROUGH THE TEACHER), NEUROK FOCUSES ON STUDENTS, AND USES COLLABORATIVE LEARNING, MOTIVATIONAL PROCESSES AND A LEARNING BY DOING" PERSPECTIVE TO ACHIEVE A LONG-TERM RELEVANT LEARNING. THE PROPOSED NEUROK FRAMEWORK DESCRIBES THE ALREADY IMPLEMENTED TOOLS AND THE NEW ONES TO BE INCLUDED IN NEXT VERSIONS. AN ACTIVE R&D PROCESS ALLOWS NEW METHODOLOGIES FROM THE FIELDS OF LEARNING ANALYTICS, DATA MINING AND SOCIAL LEARNING TO BE PROPOSED AND IMPLEMENTED. THE MAIN CONTRIBUTION OF THIS PLATFORM IS TO DEPLOY A SIGNIFICANT IMPROVEMENT IN THE E-LEARNING PROCESS BASED ON A NEURODIDACTICS APPROACH AND DATA ANALYSIS RESEARCH RESULTS.
SCRATCHJR IS A FREE PROGRAMMING APPLICATION FOR YOUNG CHILDREN AGES 5-7, AVAILABLE FOR MOST TABLET DEVICES. THIS PROGRAMMING ENVIRONMENT, DEVELOPED BY THE DEVTECH RESEARCH GROUP AT TUFTS UNIVERSITY, THE LIFELONG KINDERGARTEN GROUP AT MIT, AND THE PLAYFUL INVENTION COMPANY, WAS LAUNCHED IN JULY, 2014. DURING THE FIRST YEAR AFTER THE APP'S LAUNCH, NO INFORMATION WAS COLLECTED REGARDING USAGE OTHER THAN INFORMAL COMMUNICATION WITH LOCAL EDUCATORS AND PARENTS. STARTING IN JANUARY 2016, THE SCRATCHJR TEAM BEGAN TO USE THE TOOL GOOGLE ANALYTICS TO GAIN A DEEPER INSIGHT INTO USER BEHAVIOR, AND BEGAN TO INVESTIGATE THE LEARNING ANALYTICS DATA THAT COULD SHED LIGHT ON COMPUTATIONAL THINKING IN EARLY CHILDHOOD. THIS PAPER PRESENTS THE FIRST YEAR OF USER DATA COLLECTION OF SCRATCHJR.
LEARNING MANAGEMENT SYSTEM (LMS) PLATFORMS PROVIDE A WEALTH OF INFORMATION ON THE LEARNING PATTERNS OF STUDENTS. LEARNING ANALYTICS (LA) TECHNIQUES PERMIT THE ANALYSIS OF THE LOGS OR RECORDS OF THE ACTIVITIES OF BOTH STUDENTS AND TEACHERS ON THE ON-LINE PLATFORM. THE LEARNING PATTERNS DIFFER DEPENDING ON THE TYPE OF BLENDED LEARNING (B-LEARNING). IN THIS STUDY, WE ANALYSE: (1) WHETHER SIGNIFICANT DIFFERENCES EXIST BETWEEN THE LEARNING OUTCOMES OF STUDENTS AND THEIR LEARNING PATTERNS ON THE PLATFORM, DEPENDING ON THE TYPE OF B-LEARNING [REPLACEMENT BLEND (RB) VS. SUPPLEMENTAL BLEND (SB)]; (2) WHETHER A RELATION EXISTS BETWEEN THE METACOGNITIVE AND THE MOTIVATIONAL STRATEGIES (MS) OF STUDENTS, THEIR LEARNING OUTCOMES AND THEIR LEARNING PATTERNS ON THE PLATFORM. THE 87,065 LOG RECORDS OF 129 STUDENTS (69 IN RB AND 60 IN SB) IN THE MOODLE 3.1 PLATFORM WERE ANALYZED. THE RESULTS REVEALED DIFFERENT LEARNING PATTERNS BETWEEN STUDENTS DEPENDING ON THE TYPE OF B-LEARNING (RB VS. SB). WE HAVE FOUND THAT THE DEGREE OF BLEND, RB VS. SB, SEEMS TO CONDITION STUDENT BEHAVIOR ON THE PLATFORM. LEARNING PATTERNS IN RB ENVIRONMENTS CAN PREDICT STUDENT LEARNING OUTCOMES. ADDITIONALLY, IN RB ENVIRONMENTS THERE IS A RELATIONSHIP BETWEEN THE LEARNING PATTERNS AND THE METACOGNITIVE AND (MS) OF THE STUDENTS.
LIBRARIES REMAIN ONE OF THE LAST PLACES ON CAMPUS WHERE THE PURGING OF USAGE DATA IS ENCOURAGED AND "TRACKING" IS A DIRTY WORD. WHILE SOME LIBRARIES HAVE DEMONSTRATED THE USEFULNESS OF ANALYTICS, OPPONENTS BRING UP ISSUES OF PRIVACY AND DEBATE THE FEASIBILITY OF STUDENT-GENERATED LIBRARY DATA FOR PLANNING AND ASSESSMENT. USING A STUDY CONDUCTED AT THE UNIVERSITY LIBRARY CALIFORNIA STATE UNIVERSITY, LONG BEACH, THE AUTHORS OF THIS ARTICLE IDENTIFIED PRACTICAL KNOWLEDGE OF DATA RESEARCH THAT ACADEMIC LIBRARIANS WILL BENEFIT FROM UNDERSTANDING. READERS WILL LEARN ABOUT THE ROLE CAMPUS CULTURE PLAYS IN DATA GATHERING, BE EXPOSED TO THE COMPLEXITIES OF LEARNING ANALYTICS AND INSTITUTIONAL REVIEW BOARD (IRB) CLEARANCE, AND READ HOW THE AUTHORS WEIGHED THE ETHICAL USE OF BIG DATA ANALYSIS FOR ASSESSING STUDENTS.
EDUCATIONAL DATA PREPROCESSING FROM LOG FILES REPRESENTS A TIME-CONSUMING PHASE OF THE KNOWLEDGE DISCOVERY PROCESS. IT CONSISTS OF DATA CLEANING, USER IDENTIFICATION, SESSION IDENTIFICATION, AND PATH COMPLETION PHASE. THIS PAPER ATTEMPTS TO IDENTIFY PHASES, WHICH ARE NECESSARY IN THE CASE OF PREPROCESSING OF EDUCATIONAL DATA FOR FURTHER APPLICATION OF LEARNING ANALYTICS METHODS. SINCE THE SEQUENTIAL PATTERNS ANALYSIS IS CONSIDERED SUITABLE FOR ESTIMATING OF DISCOVERED KNOWLEDGE, THIS PAPER TRIES ANSWERING THE QUESTION, WHICH OF THESE PREPROCESSING PHASES HAS A SIGNIFICANT IMPACT ON DISCOVERED KNOWLEDGE IN GENERAL, AS WELL AS IN THE MEANING OF QUALITY AND QUANTITY OF FOUND SEQUENCE PATTERNS. THEREFORE, SEVERAL DATA PREPROCESSING TECHNIQUES FOR SESSION IDENTIFICATION AND PATH COMPLETION WERE APPLIED TO PREPARE LOGFILES WITH DIFFERENT LEVELS OF DATA PREPROCESSING. THE RESULTS SHOWED THAT THE SESSION IDENTIFICATION TECHNIQUE USING THE REFERENCE LENGTH, CALCULATED FROM THE SITEMAP, HAD A SIGNIFICANT IMPACT ON THE QUALITY OF EXTRACTED SEQUENCE RULES. THE PATH COMPLETION TECHNIQUE HAD A SIGNIFICANT IMPACT ONLY ON THE QUANTITY OF EXTRACTED SEQUENCE RULES. THE FOUND RESULTS TOGETHER WITH THE RESULTS OF THE PREVIOUS SYSTEMATIC RESEARCH IN EDUCATIONAL DATA PREPROCESSING CAN IMPROVE THE AUTOMATION OF THE EDUCATIONAL DATA PREPROCESSING PHASE AS WELL AS IT CAN CONTRIBUTE TO THE DEVELOPMENT OF LEARNING ANALYTICS TOOLS SUITABLE FOR DIFFERENT GROUPS OF STAKEHOLDERS ENGAGED IN THE EDUCATIONAL DATA MINING RESEARCH ACTIVITIES.
THE MISSION OF LEARNING ANALYTICS (LA) IS TO IMPROVE LEARNER EXPERIENCES USING THE INSIGHTS FROM DIGITALLY COLLECTED LEARNER DATA. WHILE SOME AREAS OF LA ARE MATURING, THIS IS NOT CONSISTENT ACROSS ALL LA SPECIALISATIONS. FOR INSTANCE, LA FOR SOCIAL LEARNING LACK VALIDATED APPROACHES TO ACCOUNT FOR THE EFFECTS OF CROSS-COURSE VARIABILITY IN LEARNER BEHAVIOR. ALTHOUGH THE ASSOCIATIONS BETWEEN NETWORK STRUCTURE AND LEARNING OUTCOMES HAVE BEEN EXAMINED IN THE CONTEXT OF ONLINE FORUMS, IT REMAINS UNCLEAR WHETHER SUCH ASSOCIATIONS REPRESENT BONA FIDE SOCIAL EFFECTS, OR MERELY REFLECT HETEROGENEITY IN INDIVIDUAL POSTING BEHAVIOR, LEADING TO SEEMINGLY COMPLEX BUT ARTEFACTUAL SOCIAL NETWORK STRUCTURES. WE ARGUE THAT TO START ADDRESSING THIS ISSUE, POSTING ACTIVITY SHOULD BE EXPLICITLY INCLUDED AND MODELLED IN FORUM NETWORK REPRESENTATIONS. TO GAIN INSIGHT TO WHAT EXTENT LEARNER DEGREE AND EDGE WEIGHT ARE MERELY DERIVATIVES OF LEARNER ACTIVITY, WE CONSTRUCT RANDOM MODELS THAT CONTROL FOR THE LEVEL OF POSTING AND POST PROPERTIES, SUCH AS POPULARITY AND THREAD HIERARCHY LEVEL. ANALYSIS OF FORUM NETWORKS IN TWENTY ONLINE COURSES PRESENTED IN THIS PAPER DEMONSTRATES THAT INDIVIDUAL POSTING BEHAVIOR IS HIGHLY PREDICTIVE OF BOTH THE BREADTH (DEGREE) AND FREQUENCY (STRENGTH) IN FORUM COMMUNICATION NETWORKS. THIS IMPLIES THAT, IN THE CONTEXT OF FORUM-BASED MODELLING, DEGREE AND FREQUENCY MAY NOT REFLECT THE SOCIAL DYNAMICS. HOWEVER, RESULTS SUGGEST THAT CLUSTERING OF THE NETWORK STRUCTURE IS NOT A DERIVATIVE OF INDIVIDUAL POSTING BEHAVIOUR. HENCE, WEIGHTED LOCAL CLUSTERING COEFFICIENT MAY BE A BETTER PROXY FOR SOCIAL RELATIONSHIPS. THE EMPIRICAL RESULTS ARE RELEVANT TO SCIENTISTS INTERESTED IN SOCIAL INTERACTIONS AND LEARNER NETWORKS IN DIGITAL LEARNING, AND MORE GENERALLY TO RESEARCHERS INTERESTED IN DERIVING INFORMATIVE SOCIAL NETWORK MODELS FROM ONLINE FORUMS.
RECENTLY, THE RISE OF MASSIVE OPEN ONLINE COURSES (MOOCS) AND SMALL PRIVATE ONLINE COURSES (SPOCS) HAVE RESHAPED THE SCOPE OF EDUCATION. IN THESE ONLINE LEARNING ENVIRONMENTS, LEARNERS HAVE CONTROL OVER THEIR LEARNING PROCESS AND ACTIVELY "PULL" INFORMATION THAT ARE HELPFUL TO THEM. THIS MAKES THE EDUCATIONAL PROCESS PRIMARILY LEARNER-DRIVEN. LEARNING ANALYTICS AND PEDAGOGIES HAVE BEEN PROPOSED TO ACTIVELY ENGAGE LEARNERS, TO PROVIDE SUITABLE LEARNING ENVIRONMENT AND TO EMPOWER LEARNERS TO LEARN. HOWEVER, EXISTING FRAMEWORKS ARE NOT HOLISTIC AND FAIL TO CAPTURE ALL LEARNER CONTACT POINTS WITHIN AND OUTSIDE THE COURSE. IN THIS MANUSCRIPT, A LEARNER DECISION JOURNEY FRAMEWORK IS PROPOSED FOR ANALYZING MOOC/SPOC DEVELOPMENT AND IN PARTICULAR, FOR UNDERSTANDING THE CIRCULAR LEARNING PROCESS IN MOOCS, TO GENERATE FURTHER INSIGHTS FOR COURSE IMPROVEMENTS. A CASE STUDY OF ANALYZING POST-LEARNING EXPERIENCES IN A MOOC IS USED TO ILLUSTRATE HOW THE PROPOSED FRAMEWORK CAN BE USED IN PRACTICE. THE PROPOSED FRAMEWORK CAN BE GENERALIZED TO STUDY ACTIVITIES IN ALL STAGES OF THE LEARNING DECISION JOURNEY TO PROVIDE A HOLISTIC ANALYSIS OF MOOC/SPOC DEVELOPMENT.
RESEARCH HAS REVEALED THE POSITIVE EFFECTS OF FLIPPED CLASSROOM APPROACHES ON STUDENTS' LEARNING ENGAGEMENT AND PERFORMANCE COMPARED WITH CONVENTIONAL LECTURE-BASED CLASSROOMS. HOWEVER, BECAUSE OF A LACK OF OUT-OF-CLASS LEARNING SUPPORT, MANY STUDENTS FAIL TO COMPREHENSIVELY PREPARE THE PROVIDED LECTURE MATERIALS BEFORE CLASS. ONE PROMISING SOLUTION TO THIS PROBLEM IS RECOMMENDATION SYSTEMS IN THE EDUCATIONAL AREA, WHICH HAVE BEEN INSTRUMENTAL IN HELPING LEARNERS IDENTIFY USEFUL AND RELEVANT LECTURE MATERIALS THAT SATISFY THEIR LEARNING NEEDS. THUS, IN THIS STUDY, WE PROPOSE A SUMMARIZED LECTURE MATERIAL RECOMMENDATION SYSTEM, WHICH IS INTEGRATED INTO AN E-BOOK READING SYSTEM AS AN ENHANCEMENT OF THE FLIPPED CLASSROOM APPROACH. THIS SYSTEM HELPS STUDENTS IDENTIFY PAGES THAT CONTAIN ESSENTIAL KNOWLEDGE THAT MUST BE THOROUGHLY STUDIED BEFORE CLASS. THE PROPOSED SYSTEM WAS CONSTRUCTED ON THE BASIS OF OUR PREVIOUS WORK. IN THIS STUDY, A QUASI-EXPERIMENT WAS CONDUCTED IN A GRADUATE COURSE THAT IMPLEMENTED THE FLIPPED CLASSROOM MODEL: EXPERIMENTAL GROUP STUDENTS LEARNED WITH THE PROPOSED SYSTEM, WHEREAS THE CONTROL GROUP STUDENTS HAD NO ACCESS TO THE ADDITIONAL FEATURES. THE FINDINGS OF THIS STUDY SUGGEST THAT STUDENTS WHO LEARN WITH THE PROPOSED RECOMMENDATION SYSTEM SIGNIFICANTLY OUTPERFORM THOSE WHO LEARN WITHOUT THE SYSTEM IN A FLIPPED CLASSROOM IN TERMS OF THEIR LEARNING OUTCOMES AND ENGAGEMENT IN PRECLASS PREPARATION.
IN COLLABORATIVE LEARNING ENVIRONMENTS, STUDENTS WORK TOGETHER ON ASSIGNMENTS IN VIRTUAL TEAMS AND DEPEND ON EACH OTHER'S CONTRIBUTION TO ACHIEVE THEIR LEARNING OBJECTIVES. THE ONLINE LEARNING ENVIRONMENT, HOWEVER, MAY NOT ONLY FACILITATE BUT ALSO HAMPER GROUP COMMUNICATION, COORDINATION, AND COLLABORATION. GROUP AWARENESS WIDGETS THAT VISUALIZE INFORMATION ABOUT THE DIFFERENT GROUP MEMBERS BASED ON INFORMATION COLLECTED FROM THE INDIVIDUALS CAN FOSTER AWARENESS AND REFLECTION PROCESSES WITHIN THE GROUP. IN THIS PAPER, WE PRESENT A FORMATIVE DATA STUDY ABOUT THE PREDICTIVE POWER OF SEVERAL INDICATORS OF AN AWARENESS WIDGET BASED ON AUTOMATICALLY LOGGED USER DATA FROM AN ONLINE LEARNING ENVIRONMENT. IN ORDER TO TEST WHETHER THE INFORMATION VISUALIZED BY THE WIDGET IS IN LINE WITH THE STUDY OUTCOMES, WE INSTANTIATED THE WIDGET INDICATORS WITH DATA FROM FOUR PREVIOUS RUNS OF THE EUROPEAN VIRTUAL SEMINAR ON SUSTAINABLE DEVELOPMENT (EVS). WE ANALYZED WHETHER THE TUTOR GRADINGS IN THESE PREVIOUS YEARS CORRELATED WITH THE STUDENTS' SCORES CALCULATED FOR THE WIDGET INDICATORS. FURTHERMORE, WE TESTED THE PREDICTIVE POWER OF THE WIDGET INDICATORS AT VARIOUS POINTS IN TIME WITH RESPECT TO THE FINAL GRADES OF THE STUDENTS. THE RESULTS OF OUR ANALYSIS SHOW THAT THE GRADES AND WIDGET INDICATOR SCORES ARE SIGNIFICANTLY AND POSITIVELY CORRELATED, WHICH PROVIDES A USEFUL EMPIRICAL BASIS FOR THE DEVELOPMENT OF GUIDELINES FOR STUDENTS AND TUTORS ON HOW TO INTERPRET THE WIDGET'S VISUALIZATIONS IN LIVE RUNS.
TO START MEDICAL OR DENTISTRY STUDIES IN FLANDERS, PROSPECTIVE STUDENTS NEED TO PASS A CENTRAL ADMISSION TEST. A BLENDED PROGRAM WITH FOUR SMALL PRIVATE ONLINE COURSES (SPOCS) WAS DESIGNED TO SUPPORT THOSE STUDENTS. THE LOGS FROM THE PLATFORM PROVIDE AN OPPORTUNITY TO DELVE INTO THE LEARNERS' INTERACTIONS AND TO DEVELOP PREDICTIVE MODELS TO FORECAST SUCCESS IN THE TEST. MOREOVER, THE USE OF DIFFERENT COURSES ALLOWS ANALYZING HOW MODELS CAN GENERALIZE ACROSS COURSES. THIS ARTICLE HAS THE FOLLOWING OBJECTIVES: (1) TO DEVELOP AND ANALYZE PREDICTIVE MODELS TO FORECAST WHO WILL PASS THE ADMISSION TEST, (2) TO DISCOVER WHICH VARIABLES HAVE MORE EFFECT ON SUCCESS IN DIFFERENT COURSES, (3) TO ANALYZE TO WHAT EXTENT MODELS CAN BE GENERALIZED TO OTHER COURSES AND SUBSEQUENT COHORTS, AND (4) TO DISCUSS THE CONDITIONS TO ACHIEVE GENERALIZABILITY. THE RESULTS SHOW THAT THE AVERAGE GRADE IN SPOC EXERCISES USING ONLY FIRST ATTEMPTS IS THE BEST PREDICTOR AND THAT IT IS POSSIBLE TO TRANSFER PREDICTIVE MODELS WITH ENOUGH RELIABILITY WHEN SOME CONTEXT-RELATED CONDITIONS ARE MET. THE BEST PERFORMANCE IS ACHIEVED WHEN TRANSFERRING WITHIN THE SAME COHORT TO OTHER SPOCS IN A SIMILAR CONTEXT. THE PERFORMANCE IS STILL ACCEPTABLE IN A CONSECUTIVE EDITION OF A COURSE. THESE FINDINGS SUPPORT THE SUSTAINABILITY OF PREDICTIVE MODELS.
DATA ANALYTICS IN HIGHER EDUCATION AIMS TO ADDRESS, AMONGST OTHER MATTERS, STUDENT SUCCESS AND THE FACTORS RELATED THERETO. FACED BY CONTINUOUS POOR STUDENT SUCCESS RATES, THE FACULTY OF ECONOMIC AND MANAGEMENT SCIENCES AT A SOUTH AFRICAN UNIVERSITY EMBARKED ON THE DEVELOPMENT AND IMPLEMENTATION OF A CONTEXTUALISED DATA ANALYTICS FRAMEWORK TO ADDRESS THIS PROBLEM. IMPLEMENTATION OF THE FRAMEWORK HIGHLIGHTED THE NEED FOR THE INCLUSION OF TEACHING ( TEACHER) ANALYTICS AS AN INTEGRAL PART OF THE FRAMEWORK. INCLUDING TEACHING ANALYTICS NOT ONLY ENSURED A MORE COMPREHENSIVE UNDERSTANDING OF THE TEACHING AND LEARNING PROCESS, BUT ALSO RESULTED IN UNEXPECTED EXTENSIONS OF THE FRAMEWORK FOR THE SCHOLARLY DEVELOPMENT OF TEACHERS. FEATURES OF THE ADAPTED DATA ANALYTICS FRAMEWORK, WITH A SPECIFIC FOCUS ON THE TEACHING (TEACHER) ANALYTICS COMPONENT, IS PRESENTED IN THIS ARTICLE.
STATWAY (R) IS ONE OF THE COMMUNITY COLLEGE PATHWAYS INITIATIVES DESIGNED TO PROMOTE STUDENTS' SUCCESS IN THEIR DEVELOPMENTAL MATH SEQUENCE AND REDUCE THE TIME REQUIRED TO EARN COLLEGE CREDIT. A RECENT CAUSAL ANALYSIS CONFIRMED THAT STATWAY DRAMATICALLY INCREASED STUDENTS' SUCCESS RATES IN HALF THE TIME ACROSS TWO DIFFERENT COHORTS. THESE IMPRESSIVE RESULTS WERE ALSO OBTAINED ACROSS GENDER AND RACE/ETHNICITY GROUPS. HOWEVER, THERE IS STILL ROOM FOR IMPROVEMENT. STUDENTS WHO DID NOT SUCCEED IN STATWAY OFTEN DID NOT COMPLETE THE FIRST OF THE TWO-COURSE SEQUENCE. THEREFORE, THE OBJECTIVE OF THIS STUDY IS TO FORMULATE A SERIES OF INDICATORS FROM SELF-REPORT AND ONLINE LEARNING SYSTEM DATA, ALERTING INSTRUCTORS TO STUDENTS' PROGRESS DURING THE FIRST WEEKS OF THE FIRST COURSE IN THE STATWAY SEQUENCE.
WE PROPOSE THREE EXTENSIONS FOR THE RELATIONAL EVENT FRAMEWORK TO MODEL THE CO-EVOLUTION OF MULTIPLE NETWORK EVENT STREAMS WHICH ARE INCREASINGLY AVAILABLE THANKS TO THE EXPLOSIVE GROWTH OF ONLINE APPLICATIONS. FIRSTLY, A FLEXIBLE STRATIFICATION APPROACH IS CONSIDERED TO ALLOW FOR MORE COMPLEX DATA STRUCTURES WITH MANY TYPES OF NODES AND EVENTS. SECONDLY, AN INFERENCE METHOD THAT COMBINES NESTED CASE-CONTROL SAMPLING WITH STRATIFICATION IS DISCUSSED TO SCALE THE APPROACH TO VERY LARGE DATA SETS. FINALLY, A SUITE OF NEW TEMPORAL AND NETWORK STATISTICS IS INTRODUCED TO REFLECT THE POTENTIALLY COMPLEX DEPENDENCIES AMONG EVENTS AND OBSERVED HETEROGENEITIES ON NODES AND EDGES. THE EMPIRICAL VALUE OF NEW EXTENSIONS IS DEMONSTRATED THROUGH AN ANALYSIS OF SOCIAL LEARNING IN MASSIVE OPEN ONLINE COURSES (MOOCS). IN PARTICULAR, THREE MODELING PROBLEMS ARE CONSIDERED FROM THE NETWORK PERSPECTIVE: (1) THE UTILITY OF SOCIAL FACTORS, PERFORMANCE INDICATORS, AND CLICKSTREAM BEHAVIORS IN THE PREDICTION OF COURSE DROPOUT, (2) THE SOCIAL AND TEMPORAL STRUCTURE OF LEARNER INTERACTIONS ACROSS DISCUSSION THREADS, AND (3) THE FORMS OF MUTUAL DEPENDENCE OF SOCIAL LEARNING INTERACTIONS ON PRIOR LEARNING SUCCESS, AND FUTURE LEARNING SUCCESS ON FORMS OF PRIOR SOCIAL LEARNING INTERACTION. (C) 2015 ELSEVIER B.V. ALL RIGHTS RESERVED.
THE STUDY PRESENTED HERE IS INTERESTED IN UNDERSTANDING THE WAYS IN WHICH SOCIAL INTERACTION IN TECHNOLOGY MEDIATED INSTITUTIONAL SETTINGS IS CONSTRAINED AND AFFORDED BY WHAT PENNYCOOK DEFINES AS "CRITICAL MOMENTS" IN THE EDUCATIONAL EXPERIENCE. DRAWING ON SOCIAL LEARNING ANALYTICS AND ON THE CONCEPTS OF HETEROGLOSSIA, CONTINGENCY AND CHAINING, THIS PAPER CRITICALLY DISCUSSES A METHODOLOGY THAT ALLOWS THE ANALYSTS, AND ULTIMATELY LEARNERS AND EDUCATORS, TO FOLLOW AND VISUALLY REPRESENT THE MOBILITY OF THE LEARNERS-IN-CONCERT-WITH-TOOLS ACROSS SPACE, TIME AND LANGUAGE VARIETIES AND MODALITIES IN TECHNOLOGY-MEDIATED COMMUNICATION. THE EMPIRICAL DATA FOCUSED ON HERE IS DRAWN FROM A LARGE PROJECT WHICH INCLUDES 40 HOURS OF NATURALLY OCCURRING INTERACTIONAL MATERIALS, GENERATED THROUGH SCREEN RECORDINGS OF ONLINE SESSIONS OF AN ITALIAN FOR BEGINNERS COURSE OFFERED BY A SWEDISH UNIVERSITY IN THE VIDEOCONFERENCING PLATFORM ADOBE CONNECT. PRELIMINARY FINDINGS SUGGEST THAT THE ENVIRONMENT, BOTH IN TERMS OF WHAT HAPPENS INSIDE AND OUTSIDE THE VIRTUAL LEARNING SITE, IS OF PRIMARY IMPORTANCE WHEN IT COMES TO THE ORGANIZATION OF THE INTERACTION AMONG INDIVIDUALS IN TERMS OF WHAT BECOMES THE PARTICIPANTS' FOCUS DURING THE ENCOUNTERS. A VISUAL REPRESENTATION THROUGH A MULTIVOCAL APPROACH OF THIS MOBILITY OF LEARNERS, TOPICS AND TOOLS WILL, IT IS SUGGESTED, SUPPORT LEARNERS, EDUCATORS AND DESIGNERS IN LOCATING WHERE AND WHEN IN THE INTERACTION "CRITICAL MOMENTS" HAVE OCCURRED, IN ORDER TO UNDERSTAND HOW SUCH SHIFTS IN FOCUS SUPPORT OR HINDER THE LEARNING EXPERIENCE.
THE PURPOSE OF THIS PAPER IS TO DEFINE A REFERENCE FRAMEWORK FOR INTRODUCING ELEARNING PRACTICES IN MAINLY FACE-TO-FACE HIGHER EDUCATION INSTITUTIONS. WE SUGGEST A SUITABLE ADOPTION AND MANAGEMENT OF ASSOCIATED INFRASTRUCTURES AND PROCESSES, IN ORDER TO GUARANTEE THE ETHICAL USE OF DATA IN THE RELATED ACADEMIC AND LEARNING ANALYTICS. A THEORETICAL FRAMEWORK IS PROPOSED AFTER YEARS OF PRACTICE AND EXPERIENCE IN THE INSTITUTIONAL GOVERNMENT OF IT PROCESSES RELATED TO LEARNING TECHNOLOGY. THE DIGITAL TRANSFORMATION OF TEACHING SHOULD IMPLY THE RIGHT TECHNOLOGICAL DECISIONS MADE BY PEOPLE AND FOR PEOPLE, IN ORDER TO ACHIEVE A MORE INCLUSIVE, PARTICIPATIVE, AND HUMAN UNIVERSITY SUPPORTED BY TECHNOLOGY. DIGITAL TRANSFORMATION IS A SOCIAL REQUIREMENT OF GOVERNMENTS, COMPANIES, AND INSTITUTIONS, AND IT SHOULD TAKE INTO ACCOUNT THE ASSOCIATED RISKS OF THE UNETHICAL USE OF TECHNOLOGY, WHICH LEADS TO THE DARK SIDE OF TRANSFORMATION PROCESSES. ELEARNING APPROACHES, ESPECIALLY WITH THE INFLUENCE OF THE COVID-19 OUTBREAKS, ARE INCREASING THE NEED FOR DIGITAL MECHANISMS IN UNIVERSITIES. FURTHER, THERE IS A NEED FOR STRATEGICAL SUPPORT AND REFERENCE MODELS IF WE ARE TO AVOID THESE UNDESIRED EFFECTS.
SEVERAL ENGINEERING SCHOOLS OFFER GAME BASED LEARNING EITHER AS A SUPPLEMENTARY TO LECTURERS OR AS AN INTEGRATED PART OF THEIR CURRICULUM. MOST OF THE GAMES ARE USED IN A WORKSHOP SETTING, AND MUCH OF THE LEARNING OUTCOME IS ACHIEVED THROUGH THE DEBRIEFING PART OF THE WORKSHOP, I.E. NOT AS AN INTEGRATED PART OF THE GAME. ONE REASON IS THAT MANY OF THE GAMES DO NOT OFFER STEALTH ASSESSMENT, AND THUS NEED INTERPRETATION OF A VERY WELL SKILLED FACILITATOR. IN ADDITIONS GAMES ONLY USED FOR ONE SPECIFIC COURSE ARE COSTLY TO DEVELOP, MAINTAIN AND IMPROVE AND IN ADDITION FACILITATED GAMES PUT LIMITATIONS ON THE CLASS SIZE AND MAKE IT COSTLY TO INTEGRATE. THUS, THIS ARTICLE LOOKS AT UNDER WHICH CIRCUMSTANCES AN ON-LINE GAME PRODUCED FOR A DIFFERENT COURSE COULD BE INTEGRATED IN A FACILITATED AND NOT-FACILITATED WAY.
THIS CHAPTER REVIEWS THE EMERGING FIELD WITHIN LEARNING ANALYTICS, DISCOURSE ANALYTICS. DISCOURSE ANALYTICS FOCUSES ON AUTOMATED DETECTION OF FEATURES OF DISCOURSE, AS WELL AS THE USE OF THIS TECHNOLOGY TO TRIGGER AUTOMATED INTERVENTIONS WITHIN THE CONTEXT OF COMPUTER -SUPPORTED COLLABORATIVE LEARNING. TO PROVIDE CONTEXT FOR OUR DISCUSSION OF DISCOURSE ANALYTICS TECHNOLOGIES, WE BRIEFLY REVIEW FOUR CASE STUDIES THAT ILLUSTRATE THE WAYS IN WHICH TEACHERS USE DISCOURSE TO SUPPORT ROBUST STUDENT LEARNING IN THE CLASSROOM. WE UTILIZE THESE CASE STUDIES TO DISCUSS A VISION FOR THE FUTURE OF DISCOURSE ANALYTICS GROUNDED IN THEORIES OF LEARNING.
NETWORK ANALYSIS HAS BECOME A MAJOR APPROACH FOR ANALYSING SOCIAL LEARNING, USED TO CAPTURE LEARNER POSITIONING IN ONLINE FORUM NETWORKS. LA RESEARCH INVESTIGATED THE ASSOCIATION BETWEEN POSITIONING IN FORUM NETWORKS WITH ACADEMIC PERFORMANCE AND DISCOURSE QUALITY, THE LATTER TWO SERVING AS PROXIES FOR LEARNING. HOWEVER, THE RESEARCH FINDINGS HAVE BEEN INCONSISTENT, IN PART DUE TO THE DISCREPANCIES IN THE ADOPTED APPROACHES TO NETWORK CONSTRUCTION. YET, IT IS STILL UNCLEAR HOW ONLINE FORUM NETWORKS SHOULD BE MODELLED TO ASSURE THAT THE LEARNERS' NETWORK POSITIONING IS PROPERLY CAPTURED. TO ADDRESS THIS GAP, THE CURRENT STUDY EXPLORED IF SOME EXISTING APPROACHES TO NETWORK CONSTRUCTION MAY COMPLEMENT EACH OTHER AND THUS OFFER RICHER INSIGHTS. IN PARTICULAR, WE HYPOTHESISED THAT THE POST-REPLY LEARNER NETWORK COULD REPRESENT INTERPERSONAL POSITIONING, WHEREAS THE NETWORK BASED ON CO-PARTICIPATION IN DISCUSSION THREADS COULD ENCAPSULATE INTERGROUP POSITIONING. THE STUDY USED LEARNER SOCIAL INTERACTION DATA FROM A LARGE EDX MOOC FORUM TO EXAMINE THE RELATIONSHIP BETWEEN THESE TWO KINDS OF NETWORK POSITIONING. THE RESULTS SUGGEST THAT INTERGROUP AND INTERPERSONAL POSITIONING MAY CAPTURE DIFFERENT ASPECTS OF SOCIAL LEARNING, POTENTIALLY RELATED TO DIFFERENT LEARNING OUTCOMES. WE FIND THAT ALTHOUGH INTERPERSONAL AND INTERGROUP POSITIONING INDICATORS COVARY, THESE MEASURES ARE NOT CONGRUENT FOR SOME 37% OF FORUM POSTERS. NETWORK COEVOLUTION ANALYSIS ALSO REVEALS AN INTERDEPENDENT RELATIONSHIP BETWEEN THE INTERGROUP AND INTERPERSONAL CENTRALITY IN A FORUM NETWORK. CO-OCCURRENCE OF LEARNERS IN A DISCUSSION THREAD PRIOR TO DIRECT EXCHANGES IS PREDICTIVE OF A DIRECT POST-REPLY INTERACTION AT A LATER STAGE OF THE COURSE, AND VICE-VERSA, SUGGESTING THAT INTERGROUP POSITIONING IS A PRECURSOR OF DIRECT COMMUNICATION. THE STUDY CONTRIBUTES TO THE DISCUSSION AROUND THE DEFINITION OF LEARNER FORUM POSITIONING IN LEARNING ANALYTICS, AND VALIDATED APPROACHES TOWARDS MEASURING IT.
VISUAL ANALYTICS APPROACHES BRING AN INNOVATIVE AND EFFECTIVE WAY HOW TO DELIVER THE KNOWLEDGE FROM A PARTICULAR DOMAIN TO AN INDIVIDUAL USER. WITH THE USE OF VISUAL ANALYTICS METHODS WE CAN EASILY DISCOVER THE UNEXPECTED RELATIONS AND INTERESTING PATTERNS, WHICH ARE HIDDEN IN THE HUGE DATA WAREHOUSES. IT BUILDS ON THE HUMAN MIND'S ABILITY TO UNDERSTAND THE COMPLEX VISUALIZATION OF INFORMATION. IN THIS PAPER WE INTRODUCE THE POTENTIAL USEFULNESS OF VISUAL ANALYTICS FOR RESEARCHERS WORKING IN THE FIELD OF ENVIRONMENTAL INFORMATICS. CURRENT CHALLENGES BEYOND THE SURVEY ARE DESCRIBED HERE, INCLUDING THE SUMMARY OF PARTICULAR WELL-PROVEN TOOLS AND SCENARIOS, WHICH CAN BE APPLIED IN MANY VARIOUS FIELDS OF ENVIRONMENTAL RESEARCH.
THE EFFECTIVENESS OF LEARNING IN MASSIVE OPEN ONLINE COURSES (MOOCS) CAN BE SIGNIFICANTLY ENHANCED BY INTRODUCING PERSONALIZED INTERVENTION SCHEMES WHICH RELY ON BUILDING PREDICTIVE MODELS OF STUDENT LEARNING BEHAVIORS SUCH AS SOME ENGAGEMENT OR PERFORMANCE INDICATORS. A MAJOR CHALLENGE THAT HAS TO BE ADDRESSED WHEN BUILDING SUCH MODELS IS TO DESIGN HANDCRAFTED FEATURES THAT ARE EFFECTIVE FOR THE PREDICTION TASK AT HAND. IN THIS PAPER, WE MAKE THE FIRST ATTEMPT TO SOLVE THE FEATURE LEARNING PROBLEM BY TAKING THE UNSUPERVISED LEARNING APPROACH TO LEARN A COMPACT REPRESENTATION OF THE RAW FEATURES WITH A LARGE DEGREE OF REDUNDANCY. SPECIFICALLY, IN ORDER TO CAPTURE THE UNDERLYING LEARNING PATTERNS IN THE CONTENT DOMAIN AND THE TEMPORAL NATURE OF THE CLICKSTREAM DATA, WE TRAIN A MODIFIED AUTO-ENCODER (AE) COMBINED WITH THE LONG SHORT-TERM MEMORY (LSTM) NETWORK TO OBTAIN A FIXED-LENGTH EMBEDDING FOR EACH INPUT SEQUENCE. WHEN COMPARED WITH THE ORIGINAL FEATURES, THE NEW FEATURES THAT CORRESPOND TO THE EMBEDDING OBTAINED BY THE MODIFIED LSTM-AE ARE NOT ONLY MORE PARSIMONIOUS BUT ALSO MORE DISCRIMINATIVE FOR OUR PREDICTION TASK. USING SIMPLE SUPERVISED LEARNING MODELS, THE LEARNED FEATURES CAN IMPROVE THE PREDICTION ACCURACY BY UP TO 17% COMPARED WITH THE SUPERVISED NEURAL NETWORKS AND REDUCE OVERFITTING TO THE DOMINANT LOW-PERFORMING GROUP OF STUDENTS, SPECIFICALLY IN THE TASK OF PREDICTING STUDENTS' PERFORMANCE. OUR APPROACH IS GENERIC IN THE SENSE THAT IT IS NOT RESTRICTED TO A SPECIFIC SUPERVISED LEARNING MODEL NOR A SPECIFIC PREDICTION TASK FOR MOOC LEARNING ANALYTICS.
HISTORICAL STUDENT DATA CAN HELP ELUCIDATE THE FACTORS THAT PROMOTE STUDENT SUCCESS IN MATHEMATICS COURSES. HEREIN WE USE BOTH MULTIPLE REGRESSION AND PRINCIPAL COMPONENT ANALYSES TO EXPLORE TEN YEARS OF HISTORICAL DATA FROM OVER 20,000 STUDENTS IN AN INTRODUCTORY COLLEGE-LEVEL ALGEBRA COURSE IN AN URBAN AMERICAN RESEARCH UNIVERSITY WITH A DIVERSE STUDENT POPULATION IN ORDER TO UNDERSTAND THE RELATIONSHIP BETWEEN COURSE SUCCESS AND STUDENT PERFORMANCE IN PREVIOUS COURSES, STUDENT DEMOGRAPHIC BACKGROUND, AND TIME SPENT ON COURSEWORK. WE FIND THAT INDICATORS OF STUDENTS' PAST PERFORMANCE AND EXPERIENCE, INCLUDING GRADE-POINT-AVERAGE AND THE NUMBER OF ACCUMULATED CREDIT HOURS, BEST PREDICT STUDENT SUCCESS IN THIS COURSE. WE ALSO FIND THAT OVERALL FINAL GRADES ARE REPRESENTATIVE OF THE ENTIRE COURSE AND ARE NOT UNDULY WEIGHTED BY ANY ONE TOPIC. FURTHERMORE, THE AMOUNT OF TIME SPENT WORKING ON ASSIGNMENTS LED TO IMPROVED GRADE OUTCOMES. WITH THESE BASELINE DATA, OUR TEAM PLANS TO DESIGN TARGETED INTERVENTIONS THAT CAN INCREASE RATES OF STUDENT SUCCESS IN FUTURE COURSES.
A COMMON USE OF TECHNOLOGY IN HIGHER EDUCATION IS THE PROVISION OF ONLINE COURSE MATERIALS, INVOKING AN INVESTIGATION OF THE WAYS IN WHICH STUDENTS ENGAGE WITH ONLINE COURSE CONTENT, AND HOW THEIR PARTICIPATION CHANGES OVER TIME. THIS IS PARTICULARLY NECESSARY IN THE CONTEXT OF HIGH ABSENTEEISM FROM LECTURES, WHERE ONLINE ACCESS MAY BE THE ONLY WAY IN WHICH PARTICULAR STUDENTS ARE ENGAGING WITH THE COURSE. IN THIS STUDY, WE EXAMINE LARGE-SCALE PATTERNS OF ATTENDANCE IN CLASS, AS WELL AS FOUR TYPES OF ACCESS TO ONLINE MATERIALS. WE DEFINE TWO ONLINE BEHAVIOURAL METRICS - RICHNESS AND EVENNESS - TO CAPTURE THE DISTRIBUTION OF ONLINE BEHAVIOURS WITHIN 255 COURSES, AND EXAMINE HOW THESE CHANGE OVER TIME. WE FIND THAT BOTH PHYSICAL AND ONLINE ATTENDANCE DECREASE THROUGHOUT THE SEMESTER, BUT THE FRACTION OF STUDENTS PRESENT ONLINE IS CONSIDERABLY HIGHER THAN THE FRACTION PRESENT IN LECTURES. STUDENTS ADAPT THEIR ONLINE BEHAVIOUR, AND RARE BEHAVIOURS DISAPPEAR OVER TIME. IT IS IMPORTANT TO CONSIDER HOW WE PROVIDE CONTENT, BOTH FACE-TO-FACE AND ONLINE, IN ORDER TO ENSURE THAT AS MANY STUDENTS AS POSSIBLE ARE ACCESSING THIS CONTENT IN WAYS THAT WE INTEND.
I ARGUE THAT THE RESEARCHERS AND DEVELOPERS LEVERAGING LEARNING ANALYTICS SHOULD FIND WAYS TO USE ANALYTICS INFORMATION TO INVOLVE LEARNERS AS ACTIVE PARTICIPANTS IN THEIR OWN LEARNING. INSCRUTABLE STUDENT MODELS ARE USED TO BUILD PREDICTIVE MODELS, TO DIRECT THE ACTIONS OF AUTOMATED SYSTEMS, TO ASSESS STUDENT LEARNING, OR TO RECOMMEND STUDENT INTERVENTIONS. KNOWING WHEN AND WHAT TO EXPOSE TO NURTURE SELF-DIRECTED AND SELF-REGULATED LEARNING ABILITIES INVOLVES, IN PART, HAVING STUDENT MODELS THAT ARE ACCURATE, UP TO DATE, AND RELEVANT. GOOD STUDENT MODELS CAN SUPPORT APPLICATIONS THAT SEEK TO INFLUENCE A RANGE OF INTRAPERSONAL ELEMENTS COGNITION, METACOGNITION, EMOTIONS, ENGAGEMENT, MOTIVATION, CREATIVITY AND THOSE INTERPERSONAL AS WELL: SOCIAL RELATIONSHIPS, IDENTITY, GROUP ROLES, AND COLLABORATION. THIS "SCRUTABLE" MODELING TASK IS A CENTRAL CHALLENGE FOR RESEARCH IN LEARNING ANALYTICS, BECAUSE DETAILED "BY CONCEPT" MODELS, AGENCY, EFFICACY, AND PSYCHOGRAPHIC FACTORS OF INDIVIDUALS SHOULD MERGE WITH MODELS OF SOCIAL ROLES AND RELATIONSHIPS TO PROVIDE ACTIONABLE INFORMATION TO STUDENTS, TEACHERS, AND ADMINISTRATORS. I DESCRIBE WHAT IS IMPORTANT TO MEASURE AND MODEL ABOUT THE LEARNER AND PRESENT METHODS THAT EXPERIMENTALISTS HAVE OF COLLECTING MULTIPLE FORMS OF DATA AND MAKING INFERENCES ABOUT LEARNERS. IN ONLINE LEARNING ENVIRONMENTS THAT SUPPORT THE COLLECTION OF SIMILAR DATA FROM MANY MORE STUDENTS THAN CAN BE SUPPORTED IN TRADITIONAL CLASSROOMS, COMPOSITE MODELS CAN BE CREATED THAT REFLECT "BEST GUESSES" OF WHAT TO DO NEXT FOR A GIVEN STUDENT. NUMEROUS CHALLENGES EXIST IN THE COMPLEXITY OF LEARNING AND THE LIMITATIONS OF MODELING FOR DRIVING ACTION, YET THE AREA HOLDS PROMISE FOR IMPROVEMENTS IN STUDENTS' OVERALL EXPERIENCES.
THE TOPIC OF PREDICTIVE ALGORITHMS IS OFTEN REGARDED AMONG THE MOST RELEVANT FIELDS OF STUDY WITHIN THE DATA ANALYTICS DISCIPLINE. THEY HAVE APPLICATIONS IN MULTIPLE CONTEXTS, EDUCATION BEING AN IMPORTANT ONE OF THEM. FOCUSING ON HIGHER EDUCATION SCENARIOS, MOST NOTABLY UNIVERSITIES, PREDICTIVE ANALYSIS TECHNIQUES ARE PRESENT IN STUDIES THAT ESTIMATE ACADEMIC OUTCOMES USING DIFFERENT KINDS OF STUDENT-RELATED DATA. FURTHERMORE, PREDICTIVE ALGORITHMS ARE THE BASIS OF TOOLS SUCH AS EARLY WARNING SYSTEMS (EWS): APPLICATIONS ABLE TO FORESEE FUTURE RISKS, SUCH AS THE LIKELIHOOD OF STUDENTS FAILING OR DROPPING OUT OF A COURSE, AND ALERT OF SUCH RISKS SO THAT CORRECTIVE MEASURES CAN BE TAKEN. THE PURPOSE OF THIS LITERATURE REVIEW IS TO PROVIDE AN OVERVIEW OF THE CURRENT STATE OF RESEARCH ACTIVITY REGARDING PREDICTIVE ANALYTICS IN HIGHER EDUCATION, HIGHLIGHTING THE MOST RELEVANT INSTANCES OF PREDICTORS AND EWS THAT HAVE BEEN USED IN PRACTICE. THE PRISMA GUIDELINES FOR SYSTEMATIC LITERATURE REVIEWS WERE FOLLOWED IN THIS STUDY. THE DOCUMENT SEARCH PROCESS YIELDED 1382 RESULTS, OUT OF WHICH 26 APPLICATIONS WERE SELECTED AS RELEVANT EXAMPLES OF PREDICTORS AND EWS, EACH OF THEM DEFINED BY THE CONTEXTS WHERE THEY WERE APPLIED AND THE DATA THAT THEY USED. HOWEVER, ONE COMMON SHORTCOMING IS THAT THEY ARE USUALLY APPLIED IN LIMITED SCENARIOS, SUCH AS A SINGLE COURSE, EVIDENCING THAT BUILDING A PREDICTIVE APPLICATION ABLE TO WORK WELL UNDER DIFFERENT TEACHING AND LEARNING METHODOLOGIES IS AN ARDUOUS TASK.
THIS ARTICLE FOCUSES ON EDUCATIONAL TECHNOLOGY AS APPLIED IN THE CONTEXT OF PROGRAMS AND INSTITUTIONS THAT OFFER COMPLETELY DISTANCE EDUCATION COURSES. ALL EDUCATION IN THE 21ST CENTURY IS DIGITAL EDUCATION IN THAT THE USE OF NETWORKING, TEXT AND IMAGE CREATION AND EDITING AND SEARCH AND RETRIEVAL OF INFORMATION PUNCTUATES THE LIFE OF ALMOST EVERY TEACHER AND STUDENT. HOWEVER, THE CONTEXT OF DISTANCE EDUCATION WHERE ALL THE INTERACTIONS ARE MEDIATED, CREATES A UNIQUE AND HEIGHTENED CONTEXT OF DIGITALIZATION. THIS PAPER FOCUSES ON TWO QUESTIONS: (1) WHAT ASPECTS HAVE NOT BEEN COMPLETELY SATISFACTORY IN THE TRANSIT AND TRANSFORMATION THAT EDUCATION HAS UNDERGONE, FROM ITS MORE TRADITIONAL, CAMPUS-BASED CONCEPTION, TOWARDS ITS NEW CONFIGURATION MARKED BY THE CONTINUOUS USE OF DIGITAL TECHNOLOGIES AND ENVIRONMENTS? (2) WHAT ARE THE FUTURE CHALLENGES THAT DISTANCE EDUCATION MUST DEAL WITH TO SUPPORT SUSTAINABILITY OF THIS TEACHING MODEL? FROM A THEORETICAL AND INTERPRETATIVE ANALYSIS, BASED ON THE REVIEW OF RELEVANT ARTICLES AND DOCUMENTS ON DISTANCE EDUCATION, SOME CRITICAL DIMENSIONS (LIMITATIONS, SHORTCOMINGS AND FUTURE CHALLENGES) THE USE OF DIGITAL TECHNOLOGIES IN DISTANCE EDUCATION IS IDENTIFIED AND SUBSEQUENTLY ANALYZED. THESE DIMENSIONS EVIDENCE HOW THE INITIAL (SOMETIMES EXCESSIVE) PROMISES OF DIGITAL TECHNOLOGIES IN DISTANCE EDUCATION HAS NOT (YET) BEEN FULLY REFLECTED IN REALITY.
THE INCREASE OF STUDENT'S DISENGAGEMENT IS A PROBLEM IN MANY DEVELOPED COUNTRIES' EDUCATIONAL SYSTEMS . THIS PAPER PRESENTS OUR APPROACH ON HOW THE USAGE OF VLES (VIRTUAL LEARNING ENVIRONMENTS) CAN BE TRACED AND USED TO ENHANCE THE TEACHING QUALITY SO AS TO IMPROVE ENGAGEMENT. WE FOCUS OUR WORK ON SECONDARY EDUCATION. THE QUESTION IS NOT TO PREDICT THE PERFORMANCE OF STUDENTS, FROM THE COLLECTED DATA WHICH IS ALWAYS CONTROVERSIAL: OR JUST DEFINE A SET OF PRE-ESTABLISHED METRICS, OBTAINED FROM THE DIGITAL DATA CONTAINED INTO THE VLE, AND MONITOR THEM, INDIRECTLY INDUCING AN ASSESSMENT REGIME. THE AIM IS TO LOOK BEYOND THE TRACES LEFT BY STUDENTS AND TO MEASURE THE MOTIVATION OF A STUDENT IN A PARTICULAR TASK, OF A GIVEN SUBJECT, AND IN A SPECIFIC DAY. FOR THIS PURPOSE, WE RELY ON THE PSYCHOMETRIC THEORY OF MEASUREMENT TO BUILD A COMPOSITE INDEX OF MOTIVATION AND WE EMBED IT INTO A LEARNING ANALYTICS SYSTEM. THE ULTIMATE GOAL OF THIS SYSTEM IS TO PROVIDE TEACHERS AND STAKEHOLDERS WITH OBJECTIVE AND ACCURATE (QUASI) REAL TIME INFORMATION ABOUT STUDENTS' MOTIVATION FOR LEARNING, AND HENCE ADEQUATELY ADAPT AND PERSONALIZE THE TEACHING METHODOLOGIES AND STRATEGIES IN CLASSROOMS. THE APPROACH IS GENERAL AND CAN ALSO BE USED IN TERTIARY EDUCATION FOR TRAINING STUDENTS AND RESEARCHERS IN HIGH PERFORMANCE AND DATA INTENSIVE COMPUTING. (C) 2017 ELSEVIER B.V. ALL RIGHTS RESERVED.
EMOTIONS HAVE A POWERFUL EFFECT ON LEARNING BUT RESULTS REGARDING THE NATURE OF THE IMPACT ARE INCONSISTENT AND LITTLE IS KNOWN ABOUT EFFECTS WITH YOUNG STUDENTS, AS PARTICIPANTS ARE USUALLY UNIVERSITY STUDENTS. THIS STUDY AIMS TO EXPLORE THE EMOTIONAL AND COGNITIVE DYNAMICS OF YOUNG STUDENTS IN BOTH ONLINE AND OFFLINE KNOWLEDGE BUILDING. CLASSROOM TRANSCRIPTS AND ONLINE DISCOURSE COLLECTED FOR 45 GRADE 1 AND 2 STUDENTS OVER SEVEN TO 8 WEEKS WERE ANALYZED. BASED ON THE TOTAL NUMBER OF SPOKEN AND WRITTEN WORDS, THE PARTICIPANTS WERE CLASSIFIED INTO HIGH- AND LOW-PARTICIPATION GROUPS. MULTIMODAL LEARNING ANALYTICS INCLUDING SPEECH EMOTION ANALYSIS, SENTIMENT ANALYSIS, AND IDEA IMPROVEMENT ANALYSIS WERE USED IN A MIXED METHOD RESEARCH DESIGN INCORPORATING CO-OCCURRENCE PATTERNS OF EMOTIONS AND IDEA IMPROVEMENT OF STUDENTS AT DIFFERENT PARTICIPATION LEVELS. HIGH-PARTICIPATION STUDENTS EXPRESSED SIGNIFICANTLY HIGHER FREQUENCIES OF EMOTIONS RECORDED AS NEUTRALITY, JOY, CURIOSITY, AND CONFIDENCE COMPARED TO LOW-PARTICIPATION STUDENTS. HIGH-PARTICIPATION STUDENTS WERE MORE LIKELY TO ELABORATE REASONS, DESCRIBE RELATIONSHIPS AND MECHANISMS SURROUNDING IDEAS THEY EXPLORED, AND TO INTRODUCE NEW IDEAS AND CONCEPTS INTO COMMUNITY RESOURCES. SURPRISE, CHALLENGE, AND NEUTRALITY CAN BE BENEFICIAL SINCE STUDENTS TENDED TO EXPRESS THESE EMOTIONS WHEN PRODUCING EXPLANATION-SEEKING QUESTIONS, NEW IDEAS, EXPLANATIONS, AND REGULATION. PERSONALIZED SUPPORT TO STUDENTS WITH DIFFERENT PARTICIPATION LEVELS IS PROPOSED, TO CREATE A MORE DISCURSIVELY CONNECTED COMMUNITY. FUTURE DIRECTIONS INCLUDE COLLECTING MORE DIVERSE DATA TO BETTER UNDERSTAND STUDENTS' EMOTIONS AND TO PROVIDE TEACHERS AND STUDENTS WITH REAL-TIME DATA TO SUPPORT KNOWLEDGE BUILDING AS IT PROCEEDS.
NEW NATIONAL SCIENCE STANDARDS HAVE ELEVATED ATTENTION TO STUDENT PERFORMANCE WITH A CORE SET OF SCIENCE AND ENGINEERING PRACTICES, YET GUIDANCE ABOUT HOW TO ASSESS THESE PRACTICES IS ONLY JUST EMERGING IN THE LITERATURE. THIS IS PARTICULARLY TRUE FOR THE SET OF ENGINEERING DESIGN-FOCUSED CONCEPTS AND PRACTICES ARTICULATED IN THE NEXT GENERATION SCIENCE STANDARDS' (NGSS) ENGINEERING, TECHNOLOGY, AND APPLICATION OF SCIENCE (ETS) STANDARDS. IN THIS WORK, WE PRESENT A MODEL OF STUDENT COGNITION FOR ASSESSING STUDENT FACILITY WITH THE ENGINEERING DESIGN PRACTICE OF OPTIMIZATION. WE OPERATIONALIZE THIS MODEL OF COGNITION WITHIN A SET OF ENGINEERING-FOCUSED UNITS FOR MIDDLE SCHOOL, FRAMED AS VIRTUAL ENGINEERING INTERNSHIPS (VEIS). TO OPERATIONALIZE THE ENGINEERING DESIGN PRACTICE OF OPTIMIZATION WITHIN OUR VEIS, WE FIRST BROKE OPTIMIZATION DOWN INTO TWO MORE SPECIFIC SUB-BEHAVIORS: EXPLORATION AND SYSTEMATICITY. WE THEN DESIGNED METRICS THAT PROVIDE EVIDENCE OF THOSE BEHAVIORS AND WOULD BE OBSERVABLE GIVEN STUDENT CLICKSTREAM DATA FROM A DIGITAL DESIGN TOOL. WE NORMALIZED THESE METRICS BASED ON THE OBTAINED DISTRIBUTIONS FROM A RESEARCH TRIAL. WE DISCUSS THE EXISTING CORRELATIONS BETWEEN THESE BEHAVIORS AND METRICS.
WE ILLUSTRATE A VERY RECENT RESEARCH STUDY THAT DEMONSTRATES THE VALUE OF MASSIVE OPEN ONLINE COURSES (MOOCS) AS VEHICLES FOR RESEARCH. WE DESCRIBE THE DEVELOPMENT OF THE INDIANA UNIVERSITY PLAGIARISM TUTORIALS AND TESTS (IPTAT). OUR NEW DESIGN HAS BEEN GUIDED BY FIRST PRINCIPLES OF INSTRUCTION: AUTHENTIC PROBLEMS, ACTIVATION, DEMONSTRATION, APPLICATION, AND INTEGRATION. WE FURTHER DISCUSS OUR DATA COLLECTION MECHANISMS AND EARLY USAGE OF THIS NEW MINI-MOOC. IN THE FIRST STUDY, WE INVESTIGATED A BUILT-IN ASSESSMENT FEATURE FOR STUDENTS TO EVALUATE INSTRUCTIONAL QUALITY AND USER EXPERIENCE. TO DO THIS, WE ADAPTED SCALES FROM THE TEACHING AND LEARNING QUALITY INSTRUMENT. AS A FOLLOW-UP STUDY, WE PLAN TO FURTHER INVESTIGATE PATTERNS OF USAGE OF THE IPTAT BY STUDENTS THROUGH CREATION OF INDIVIDUAL TEMPORAL MAPS. WE PLAN TO USE ANALYSIS OF PATTERNS IN TIME, A METHOD THAT PROVIDES LEARNING ANALYTICS.
SINCE INTRODUCTION OF THE EUROPEAN QUALIFICATIONS FRAMEWORK (EQF) AS ONE INSTRUMENT TO BRIDGE FROM LEARNING INSTITUTIONS TO COMPETENCE DRIVEN LIFELONG LEARNING, IT REMAINS A CHALLENGE FOR INSTRUCTORS AND TEACHERS IN HIGHER EDUCATION TO MAKE EFFICIENT USE OF THIS FRAMEWORK FOR DESIGNING, MONITORING, AND MANAGING THEIR LESSONS. THIS PAPER PRESENTS A SOFTWARE SUITE FOR ENABLING TEACHERS TO MAKE BETTER USE OF EQF IN THEIR TEACHING. THE SOFTWARE SUITE EXTENDS COURSE DESIGN BASED ON WELL-DEFINED LEARNING OUTCOMES, MONITORING PERFORMANCE AND COMPETENCE ACQUISITION ACCORDING TO THE EQF LEVELS, ASSESSMENT USING SCORING RUBRICS OF EQF LEVELS AND COMPETENCES IN A 360-DEGREE FEEDBACK, AS WELL AS VISUALIZATIONS OF LEARNING ANALYTICS AND OPEN STUDENT MODELS IN DASHBOARDS FOR DIFFERENT SOCIAL PERSPECTIVES IN SOCIAL PLANES. THIS PAPER INCLUDES A CASE STUDY WITH 20 TEACHERS WHO USED THE SOFTWARE SUITE IN ALL PHASES OF THE COURSE LIFECYCLE FOR THREE PROGRAMMING COURSES. THE RESULTS SHOW THAT INTEGRATED APPLICATIONS FOR ADOPTING THE EQF IN TEACHING PRACTICE ARE STRONGLY NEEDED. THESE RESULTS ALSO SHOW THAT THE SUITE CAN ASSIST TEACHERS IN CREATING CONTEXTUAL AWARENESS, KINDLING REFLECTION, UNDERSTANDING STUDENTS AND COURSE PROGRESS, AND INFERRING PATTERNS OF SUCCESS AND FAILURE IN COMPETENCES DEVELOPMENT.
THIS STUDY EXPLORED DATA ANALYTIC APPROACHES TO ASSESSING YOUNG CHILDREN'S ENGAGEMENT IN ROBOT-MEDIATED COLLABORATIVE INTERACTION. TO DEVELOP OUR ANALYTIC MODELS, WE TOOK A CASE-STUDY APPROACH AND LOOKED CLOSELY INTO FOUR CHILDREN'S BEHAVIORS DURING THREE CONVERSATIONAL SESSIONS. GROUNDED IN ENGAGEMENT THEORY, THREE SOURCES OF MULTIMODAL BEHAVIORAL DATA (UTTERANCES, KINESICS, AND VOCIE) WERE CODED THROUGH HUMAN ANNOTATION AND AUTOMATIC SPEECH RECOGNITION AND ANALYSIS. THEN, INFORMATION-THEORETIC METHODS WERE USED TO UNCOVER NONLINEAR DEPENDENCIES (CALLED MUTUAL INFORMATION) AMONG THE MULTIMODAL BEHAVIORS OF EACH CHILD. FROM THIS, WE DERIVED A MODEL TO COMPUTE A COMPOUND VARIABLE OF ENGAGEMENT. THIS COMPUTATION PRODUCED ENGAGEMENT TRENDS OF EACH CHILD, THE ENGAGEMENT RELATIONSHIP BETWEEN TWO CHILDREN IN A PAIR, AND THE ENGAGEMENT RELATIONSHIP WITH THE ROBOT OVER TIME. THE COMPUTED TRENDS CORRESPONDED WELL WITH THE DATA FROM HUMAN OBSERVATIONS. THIS APPROACH HAS IMPLICATIONS FOR QUANTIFYING ENGAGEMENT FROM RICH AND NATURAL MULTIMODAL BEHAVIORS.
THIS STUDY REVIEWS THE EDUCATIONAL THEORIES AND EXISTING LITERATURE FOR THE INTEGRATION OF ROBOTS TO SUPPORT TEACHING IN CLASSROOMS BY ASSESSING INTEGRATION METHODS AND THE EFFECTIVENESS OF EDUCATIONAL ROBOTICS ON STUDENT LEARNING. MOST OF THE RESEARCH ON THIS SUBJECT ARE ONLY FOCUSED ON CHILDREN FROM KINDERGARTEN TO MIDDLE SCHOOLS AND ANALYZED THE END RESULTS FROM QUESTIONNAIRES AND GRADES. WITHOUT ANALYSIS ON THE HUMAN-ROBOT INTERACTION ITSELF, THE EFFECTIVENESS OF ROBOTS IN CLASSROOMS IS QUESTIONABLE ESPECIALLY IN THE HIGHER EDUCATION CONTEXT. FUTURE RESEARCH SHOULD LEVERAGE MORE REFINED DATA ANALYSIS TECHNIQUES SUCH AS LEARNING ANALYTICS TO DIRECTLY FOCUS ON INTERACTION AND CONVERSATION DYNAMICS.
GAMIFICATION HAS RECEIVED INCREASED ATTENTION IN EDUCATION IN RECENT YEARS, AND IS SEEN AS A WAY TO IMPROVE STUDENT ENGAGEMENT, MOTIVATION, ATTENDANCE, AND ACADEMIC PERFORMANCE. WHILE EMPIRICAL STUDIES ON GAMIFICATION IN HIGHER EDUCATION ARE SHOWING MODEST GAINS IN SOME AREAS, THIS DATA CAN BE DIFFICULT TO INTERPRET BECAUSE OF THE MANY WAYS THAT GAMIFICATION CAN BE DESIGNED AND IMPLEMENTED. GAMIFICATION IS ALSO CONTROVERSIAL FOR APPEARING EXPLOITATIVE, SEEMING OVER-SIMPLIFIED, AND HAVING THE TENDENCY TO RELY ON EXTRINSIC MOTIVATION AND LEARNING ANALYTICS THAT MAY NOT TRANSLATE TO STUDENT LEARNING. THIS PAPER PROVIDES A BRIEF OVERVIEW OF GAMIFICATION IN HIGHER EDUCATION AND LOOKS AT FINDINGS FROM RECENT EMPIRICAL STUDIES. IT THEN EXAMINES ITS KEY CRITICISMS AS WELL AS ITS POTENTIAL CONTRIBUTIONS TO IMPROVING INSTRUCTIONAL DESIGN IN HIGHER EDUCATION. A PRACTICAL EXAMPLE AND A SET OF RECOMMENDATIONS ARE PROVIDED TO SHOW HOW INSTRUCTORS NEW TO GAMIFICATION AND INTERESTED IN IMPLEMENTING IT CAN ADAPT IT FOR THEIR COURSES.
THE LARGE UPFRONT INVESTMENTS REQUIRED FOR GAME DEVELOPMENT POSE A SEVERE BARRIER FOR THE WIDER UPTAKE OF SERIOUS GAMES IN EDUCATION AND TRAINING. ALSO, THERE IS A LACK OF WELL-ESTABLISHED METHODS AND TOOLS THAT SUPPORT GAME DEVELOPERS AT PRESERVING AND ENHANCING THE GAMES' PEDAGOGICAL EFFECTIVENESS. THE RAGE PROJECT, WHICH IS A HORIZON 2020 FUNDED RESEARCH PROJECT ON SERIOUS GAMES, ADDRESSES THESE ISSUES BY MAKING AVAILABLE REUSABLE SOFTWARE COMPONENTS THAT AIM TO SUPPORT THE PEDAGOGICAL QUALITIES OF SERIOUS GAMES. IN ORDER TO EASILY DEPLOY AND INTEGRATE THESE GAME COMPONENTS IN A MULTITUDE OF GAME ENGINES, PLATFORMS AND PROGRAMMING LANGUAGES, RAGE HAS DEVELOPED AND VALIDATED A HYBRID COMPONENT-BASED SOFTWARE ARCHITECTURE THAT PRESERVES COMPONENT PORTABILITY AND INTEROPERABILITY. WHILE A FIRST SET OF SOFTWARE COMPONENTS IS BEING DEVELOPED, THIS PAPER PRESENTS SELECTED EXAMPLES TO EXPLAIN THE OVERALL SYSTEM'S CONCEPT AND ITS PRACTICAL BENEFITS. FIRST, THE EMOTION DETECTION COMPONENT USES THE LEARNERS' WEBCAMS FOR CAPTURING THEIR EMOTIONAL STATES FROM FACIAL EXPRESSIONS. SECOND, THE PERFORMANCE STATISTICS COMPONENT IS AN ADD-ON FOR LEARNING ANALYTICS DATA PROCESSING, WHICH ALLOWS INSTRUCTORS TO TRACK AND INSPECT LEARNERS' PROGRESS WITHOUT BOTHERING ABOUT THE REQUIRED STATISTICS COMPUTATIONS. THIRD, A SET OF LANGUAGE PROCESSING COMPONENTS ACCOMMODATE THE ANALYSIS OF TEXTUAL INPUTS OF LEARNERS, FACILITATING COMPREHENSION ASSESSMENT AND PREDICTION. FOURTH, THE SHARED DATA STORAGE COMPONENT PROVIDES A TECHNICAL SOLUTION FOR DATA STORAGE - E.G. FOR PLAYER DATA OR GAME WORLD DATA - ACROSS MULTIPLE SOFTWARE COMPONENTS. THE PRESENTED COMPONENTS ARE EXEMPLARY FOR THE ANTICIPATED RAGE LIBRARY, WHICH WILL INCLUDE UP TO FORTY REUSABLE SOFTWARE COMPONENTS FOR SERIOUS GAMING, ADDRESSING DIVERSE PEDAGOGICAL DIMENSIONS.
THE NUMBER OF STUDENTS OPTING FOR ONLINE EDUCATIONAL PLATFORMS HAS BEEN ON THE RISE IN RECENT YEARS. DESPITE THE UPSURGE, STUDENT RETENTION IS STILL A CHALLENGING TASK, WITH SOME STUDENTS RECORDING LOW-PERFORMANCE MARGINS ON ONLINE COURSES. THIS PAPER AIMS TO PREDICT STUDENTS' PERFORMANCE AND BEHAVIOUR BASED ON THEIR ONLINE ACTIVITIES ON AN E-LEARNING PLATFORM. THE PAPER WILL FOCUS ON THE DATA LOGGING HISTORY AND UTILISE THE LEARNING MANAGEMENT SYSTEM (LMS) DATA SET THAT IS AVAILABLE ON THE SAKAI PLATFORM. THE DATA OBTAINED FROM THE LMS WILL BE CLASSIFIED BASED ON STUDENTS' LEARNING STYLES IN THE E-LEARNING ENVIRONMENT. THIS CLASSIFICATION WILL HELP STUDENTS, TEACHERS, AND OTHER STAKEHOLDERS TO ENGAGE EARLY WITH STUDENTS WHO ARE MORE LIKELY TO EXCEL IN SELECTED TOPICS. THEREFORE, CLUSTERING STUDENTS BASED ON THEIR COGNITIVE STYLES AND OVERALL PERFORMANCE WILL ENABLE BETTER ADAPTION OF THE LEARNING MATERIALS TO THEIR LEARNING STYLES. THE MODEL-BUILDING STEPS INCLUDE DATA PREPROCESSING, PARAMETER OPTIMISATION, AND ATTRIBUTE SELECTION PROCEDURES.
ENORMOUS AMOUNT OF EDUCATIONAL DATA HAS BEEN ACCUMULATED THROUGH MASSIVE OPEN ONLINE COURSES (MOOCS), AS WELL AS COMMERCIAL AND NON-COMMERCIAL LEARNING PLATFORMS. THIS IS IN ADDITION TO THE EDUCATIONAL DATA RELEASED BY US GOVERNMENT SINCE 2012 TO FACILITATE DISRUPTION IN EDUCATION BY MAKING DATA FREELY AVAILABLE. THE HIGH VOLUME, VARIETY AND VELOCITY OF COLLECTED DATA NECESSITATE USE OF BIG DATA TOOLS AND STORAGE SYSTEMS SUCH AS DISTRIBUTED DATABASES FOR STORAGE AND APACHE SPARK FOR ANALYSIS. THIS TUTORIAL WILL INTRODUCE RESEARCHERS AND FACULTY TO REAL WORLD APPLICATIONS INVOLVING DATA MINING AND PREDICTIVE ANALYTICS IN LEARNING SCIENCES. IN ADDITION, THE TUTORIAL WILL INTRODUCE STATISTICS REQUIRED TO VALIDATE AND ACCURATELY REPORT RESULTS. TOPICS WILL COVER HOW BIG DATA IS BEING USED TO TRANSFORM EDUCATION. SPECIFICALLY, WE WILL DEMONSTRATE HOW EXPLORATORY DATA ANALYSIS, DATA MINING, PREDICTIVE ANALYTICS, MACHINE LEARNING, AND VISUALIZATION TECHNIQUES ARE BEING APPLIED TO EDUCATIONAL BIG DATA TO IMPROVE LEARNING AND SCALE INSIGHTS DRIVEN FROM MILLIONS OF STUDENT'S RECORDS. THE TUTORIAL WILL BE HELD OVER A HALF DAY AND WILL BE HANDS ON WITH PRE-POSTED MATERIAL. DUE TO THE INTERDISCIPLINARY NATURE OF WORK, THE TUTORIAL APPEALS TO RESEARCHERS FROM A WIDE RANGE OF BACKGROUNDS INCLUDING BIG DATA, PREDICTIVE ANALYTICS, LEARNING SCIENCES, EDUCATIONAL DATA MINING, AND IN GENERAL, THOSE INTERESTED IN HOW BIG DATA ANALYTICS CAN TRANSFORM LEARNING. AS A PREREQUISITE, ATTENDEES ARE REQUIRED TO HAVE FAMILIARITY WITH AT LEAST ONE PROGRAMMING LANGUAGE.
PREDICTIVE MODELING IN ONLINE EDUCATION IS A POPULAR TOPIC IN LEARNING ANALYTICS RESEARCH AND PRACTICE. THIS STUDY PROPOSES A NOVEL PREDICTIVE MODELING METHOD TO IMPROVE MODEL TRANSFERABILITY OVER TIME WITHIN THE SAME COURSE AND ACROSS DIFFERENT COURSES. THE RESEARCH GAPS ADDRESSED ARE LIMITED EVIDENCE SHOWING WHETHER A PREDICTIVE MODEL BUILT ON HISTORICAL DATA RETROSPECTIVELY CAN BE DIRECTLY APPLIED TO A FUTURE OFFERING OF THE SAME COURSE OR TO ANOTHER DIFFERENT COURSE; LACKING INTERPRETABLE DATA MINING MODELS TO IMPROVE MODEL TRANSFERABILITY OVER TIME AND ACROSS COURSES. THREE DATASETS FROM TWO DISTINCT ONLINE COURSES WITH ONE COURSE OFFERED TWO TIMES OVER TWO YEARS WERE APPLIED USING DIRECT TRANSFERRING OF THE PREDICTIVE MODEL AND THE PROPOSED BAYESIAN UPDATING TECHNIQUE FOR MODEL TRANSFER. THE RESULTS SHOWED THAT THE DIRECT TRANSFERRING OF PREDICTIVE MODEL TO THE SUBSEQUENT OFFERING OF THE COURSE AND TO A TOTALLY DIFFERENT COURSE DID NOT WORK EFFECTIVELY. BY CONTRAST, THE PROPOSED BAYESIAN UPDATING PROVIDED A ROBUST AND INTERPRETABLE APPROACH WITH IMPROVED MODEL TRANSFERABILITY RESULTS FOR BOTH SITUATIONS. THIS BAYESIAN UPDATING MODEL CAN BE CONTINUOUSLY UPDATED WITH NEW COLLECTED DATA RATHER THAN BUILDING PREDICTION MODEL FROM SCRATCH EVERY TIME, WHICH CAN SERVE AS A NEW METHODOLOGICAL FRAMEWORK TO CARRY EXPERIENCE AND KNOWLEDGE FROM PAST AND OTHER COURSES FORWARD TO NEW COURSES.
IT IS QUITE COMPLEX TO ADAPT INSTRUCTION TO STUDENT NEEDS IN VIEW OF ONLINE EDUCATION OWING TO THE ENSUING COMMUNICATION DISCONNECTION IN SUCH LEARNING ENVIRONMENTS. DECISION SUPPORT SCHEMES OFFER ASSISTANCE BY AUTOMATICALLY GATHERING STUDENTS' DATA AND FORWARDING THEM TO THE TUTOR IN THE APPROPRIATE PERSPECTIVE, IN ORDER TO PREDICT THEIR BEHAVIOUR AND IMPLEMENT SOME ACTION BEFOREHAND TO AVERT OR PROMOTE THE FINAL UPSHOT. THIS STUDY SHOWS OF A DECISION SUPPORT SCHEME KNOWN AS U-TUTOR THAT IS CENTRED ON THE SIMILARITY COMPUTATION BETWEEN LEARNERS IN THE PAST, AND HOW IT WAS USED IN A REAL-CASE SCENARIO. FOR THIS CASE STUDY, THIS TOOL HAS BEEN UTILIZED BY TWO REAL COURSES COMPRISING OF 392 LEARNERS ALONGSIDE ACADEMIC FACULTY, AS OF 2015 TO 2019. THE ANALYSIS OFFERED FOCUSES ON 3 RESEARCH AREAS: (1) PERCEIVED USEFULNESS, (2) USABILITY OF THE TOOL AND (3) SUCCESS RATE OF CLASSIFICATION. FROM THE ACQUIRED DATA, IT CAN BE SEEN THAT THE TEACHING GROUP MANAGED TO OFFER EXCELLENT APPROXIMATIONS FOR THOSE LEARNERS WHO EVENTUALLY MANAGED TO PASS THE COURSE, WHEREAS U-TUTOR SEEMED TO BE AN EARLY WARNING FOR LEARNERS AT RISK, INDICATING ITS CAPACITY AS A TUTORS' SUPPORTIVE TOOL.
MORE RESEARCH IS NEEDED ON HOW TO BEST USE ANALYTICS TO SUPPORT EDUCATIONAL DECISIONS AND DESIGN EFFECTIVE LEARNING ENVIRONMENTS. THIS STUDY WAS TO EXPLORE AND MINE THE DATA CAPTURED BY A DIGITAL EDUCATIONAL GAME DESIGNED FOR MIDDLE SCHOOL SCIENCE TO UNDERSTAND LEARNERS' BEHAVIORAL PATTERNS IN USING THE GAME, AND TO USE EVIDENCE-BASED FINDINGS TO INFORM THE DESIGN AND IMPLEMENTATION OF THE GAME'S NEXT ITERATION. THE DATA WAS FROM 3,963 SIXTH-GRADE STUDENTS FROM 31 TEACHERS ACROSS NINE U.S. STATES WHO USED THE GAME. THE DATASET CONSISTED OF 3,063,267 LINES OF LOG DATA. THE FINDINGS SHOWED SIGNIFICANT POSITIVE RELATIONSHIPS BETWEEN STUDENTS' PERFORMANCE SCORES AND THEIR TOOL USAGE IN TERMS OF BOTH FREQUENCY AND DURATION OF TOOL USE. THIS WAS FURTHER CONFIRMED BY THE RELATIONSHIPS BETWEEN DIFFERENT LEVELS OF STUDENTS' PERFORMANCE AND TOOL USAGE: THE MORE FREQUENTLY AND LONGER THAT STUDENTS USED THE TOOLS, THE HIGHER THEIR PERFORMANCE SCORES, AND THERE WERE SIGNIFICANT DIFFERENCES BETWEEN STUDENTS WITH HIGH PERFORMANCE SCORES AND THOSE WITH LOW PERFORMANCE SCORES. COMPARING THREE DIFFERENT CASES OVER THE INDIVIDUAL DAYS OF GAME USE PROVIDED FURTHER EVIDENCE THAT IN ALL THREE COMPARISONS, THE STUDENTS WHO USED MORE ESSENTIAL TOOLS AND FOR LONGER TIME HAD HIGHER PERFORMANCE SCORES. IMPLICATIONS ARE DISCUSSED.
IN RECENT DECADE, LEARNING ANALYTICS HAS GAINED MORE ATTENTION AND SEVERAL ADVANCED DATA MINING MODELS ARE DEVELOPED FOR DERIVING THE HIDDEN SOURCES FROM EDUCATIONAL DATABASES. THE EXTRACTED DATA HELPS THE EDUCATIONAL INSTITUTIONS OR UNIVERSITIES TO ENHANCE THE TEACHING METHODOLOGIES OF FACULTIES AND STUDENT'S LEARNING PROCESS IN EFFICIENT MANNER. FOR IMPROVING THE STUDENT PERFORMANCE AND BETTER EDUCATIONAL RESULTS, THE STUDENT DATA EVALUATIONS BASED ON THEIR LEARNING MODES ARE SIGNIFICANT. WITH THAT NOTE, THE PROPOSED MODEL DEVELOPS A NEW MODEL CALLED ENSEMBLE BASED TWO-LEVEL STUDENT CLASSIFICATION MODEL (ESCM) FOR EFFECTIVELY ANALYSING AND CLASSIFYING THE STUDENT DATA. WITH THE STUDENT DATA PURSUING TECHNICAL HIGHER EDUCATION, THE ESCM IS PERFORMED WITH THREE TRADITIONAL CLASSIFICATION MODEL AND ENSEMBLE CLASSIFIER TECHNIQUES FOR ENHANCING THE CLASSIFICATION ACCURACY. THE MODEL UTILIZES SUPPORT VECTOR MACHINE, NAIVE BAYESIAN AND J48 CLASSIFIER THAT ARE COMBINED WITH ENSEMBLE CLASSIFICATION METHODS AS MODIFIED META CLASSIFIER SUCH AS BAGGING AND STACKING. HERE, THE TECHNICAL HIGHER EDUCATION STUDENT DATA COLLECTED FROM SRM STUDENT DATABASE BASED ON THE FEATURE SET CONTAINS THE STUDENT LEARNING FACTORS THAT SUPPORT PERFORMANCE ENHANCEMENT. THE RESULTS ARE EVALUATED WITH THE SRM STUDENT DATASETS AND COMPARED BASED ON THE CLASSIFICATION ACCURACY AND MODEL RELIABILITY. FURTHERMORE, THE OBTAINED RESULTS OUTPERFORM THE EXISTING MODELS. BASED ON THE ACCURATE PREDICTIONS, SPECIAL ATTENTIONS AND MEASURES ARE TAKEN TO IMPROVE THE STUDENT RESULTS AND INSTITUTIONAL REPUTATION.
IN THIS WORK, WE CONSIDER LEARNING ANALYTICS FOR PRIMARY AND SECONDARY SCHOOLS FROM THE PERSPECTIVE OF THE DESIGNER OF A LEARNING SYSTEM. WE PROVIDE AN OVERVIEW OF PRACTICALLY USEFUL ANALYTICS TECHNIQUES WITH DESCRIPTIONS OF THEIR APPLICATIONS AND SPECIFIC ILLUSTRATIONS. WE HIGHLIGHT DATA BIASES AND CAVEATS THAT COMPLICATE THE ANALYSIS AND ITS INTERPRETATION. ALTHOUGH WE INTENTIONALLY FOCUS ON TECHNIQUES FOR INTERNAL USE BY DESIGNERS, MANY OF THESE TECHNIQUES MAY INSPIRE THE DEVELOPMENT OF DASHBOARDS FOR TEACHERS OR STUDENTS. WE ALSO IDENTIFY THE CONSEQUENCES AND CHALLENGES FOR RESEARCH.
CURRENT WRITING SUPPORT TOOLS TEND TO FOCUS ON ASSESSING FINAL OR INTERMEDIATE PRODUCTS, RATHER THAN THE WRITING PROCESS. HOWEVER, SENSING TECHNOLOGIES, SUCH AS KEYSTROKE LOGGING, CAN ENABLE PROVISION OF AUTOMATED FEEDBACK DURING, AND ON ASPECTS OF, THE WRITING PROCESS. DESPITE THIS POTENTIAL, LITTLE IS KNOWN ABOUT THE CRITICAL INDICATORS THAT CAN BE USED TO EFFECTIVELY PROVIDE THIS FEEDBACK. THIS ARTICLE PROPOSES A PARTICIPATORY APPROACH TO IDENTIFY THE INDICATORS OF STUDENTS' WRITING PROCESSES THAT ARE MEANINGFUL FOR EDUCATIONAL STAKEHOLDERS AND THAT CAN BE CONSIDERED IN THE DESIGN OF FUTURE SYSTEMS TO PROVIDE AUTOMATED, PERSONALIZED FEEDBACK. THIS APPROACH IS ILLUSTRATED THROUGH A QUALITATIVE RESEARCH DESIGN THAT INCLUDED FIVE PARTICIPATORY SESSIONS WITH FIVE DISTINCT GROUPS OF STAKEHOLDERS: BACHELOR AND POSTGRADUATE STUDENTS, TEACHERS, WRITING SPECIALISTS, AND PROFESSIONAL DEVELOPMENT STAFF. RESULTS ILLUSTRATE THE VALUE OF THE PROPOSED APPROACH, SHOWING THAT STUDENTS WERE ESPECIALLY INTERESTED IN LOWER-LEVEL BEHAVIORAL INDICATORS, WHILE THE OTHER STAKEHOLDERS FOCUSED ON HIGHER-ORDER COGNITIVE AND PEDAGOGICAL CONSTRUCTS. THESE FINDINGS LAY THE GROUNDWORK FOR FUTURE WORK IN EXTRACTING THESE HIGHER-LEVEL INDICATORS FROM IN-DEPTH ANALYSIS OF WRITING PROCESSES. IN ADDITION, DIFFERENCES IN TERMINOLOGY AND THE LEVELS AT WHICH THE INDICATORS WERE DISCUSSED, HIGHLIGHT THE NEED FOR HUMAN-CENTERED, PARTICIPATORY APPROACHES TO DESIGN AND DEVELOP WRITING ANALYTICS TOOLS.
PREDICTING STUDENTS' LEARNING OUTCOMES IS ONE OF THE MAIN TOPICS OF INTEREST IN THE AREA OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS. TO THIS END, A PLETHORA OF MACHINE LEARNING METHODS HAS BEEN SUCCESSFULLY APPLIED FOR SOLVING A VARIETY OF PREDICTIVE PROBLEMS. HOWEVER, IT IS OF UTMOST IMPORTANCE FOR BOTH EDUCATORS AND DATA SCIENTISTS TO DEVELOP ACCURATE LEARNING MODELS AT LOW COST. FUZZY LOGIC CONSTITUTES AN APPROPRIATE APPROACH FOR BUILDING MODELS OF HIGH PERFORMANCE AND TRANSPARENCY. IN ADDITION, ACTIVE LEARNING REDUCES BOTH THE TIME AND COST OF LABELING EFFORT, BY EXPLOITING A SMALL SET OF LABELED DATA ALONG WITH A LARGE SET OF UNLABELED DATA IN THE MOST EFFICIENT WAY. IN ADDITION, CHOOSING THE PROPER METHOD FOR A GIVEN PROBLEM FORMULATION AND CONFIGURING THE OPTIMAL PARAMETER SETTING IS A DEMANDING TASK, CONSIDERING THE HIGH-DIMENSIONAL INPUT SPACE AND THE COMPLEXITY OF MACHINE LEARNING ALGORITHMS. AS SUCH, EXPLORING THE POTENTIAL OF AUTOMATED MACHINE LEARNING (AUTOML) STRATEGIES FROM THE PERSPECTIVE OF MACHINE LEARNING ADEPTNESS IS IMPORTANT. IN THIS CONTEXT, THE PRESENT STUDY INTRODUCES A FUZZY-BASED ACTIVE LEARNING METHOD FOR PREDICTING STUDENTS' ACADEMIC PERFORMANCE WHICH COMBINES, IN A MODULAR WAY, AUTOML PRACTICES. A LOT OF EXPERIMENTS WAS CARRIED OUT, REVEALING THE EFFICIENCY OF THE PROPOSED METHOD FOR THE ACCURATE PREDICTION OF STUDENTS AT RISK OF FAILURE. THESE INSIGHTS MAY HAVE THE POTENTIAL TO SUPPORT THE LEARNING EXPERIENCE AND BE USEFUL THE WIDER SCIENCE OF LEARNING.
STUDENTS ENROL INTO STEM PROGRAMS WITH VARYING DEGREES OF CONFIDENCE WITH CITING AND REFERENCING TEXTS IN THEIR WRITTEN WORK. STUDENTS OFTEN HAVE AN INCLINATION TO CHOOSE NUMBERS OVER WRITTEN LANGUAGE THROUGHOUT SCHOOLING WHICH MEANS LESS OPPORTUNITY TO PRACTICE REFERENCING AND CITATION. THIS IS COMPOUNDED BY LARGE NUMBERS OF STUDENTS FOR WHOM ENGLISH IS AN ADDITIONAL LANGUAGE OR WHO ARTICULATE FROM DIFFERENT CULTURAL WAYS-OF-DOING. THE SEARCH, EVALUATE, INTEGRATE, REFERENCE AND ACT ETHICALLY (SEIRA) MODULES WERE DEVELOPED TO PROVIDE DISCIPLINE-RELEVANCE TO A CONFOUNDING TASK. DATA ANALYSIS LOOKING AT THE STUDENT ENGAGEMENT WITH THE SEIRA SITE AND SUBSEQUENT STUDENT SUCCESS PROVIDES AN INDICATION OF THE VALUE OF THIS APPROACH TO DEVELOPING ACADEMIC LITERACY ACROSS THE STEM DISCIPLINES.
KNOWLEDGE TRACING (KT) IS TO TRACE THE KNOWLEDGE OF STUDENTS AS THEY SOLVE A SEQUENCE OF PROBLEMS REPRESENTED BY THEIR RELATED SKILLS. THIS INVOLVES ABSTRACT CONCEPTS OF STUDENTS' STATES OF KNOWLEDGE AND THE INTERACTIONS BETWEEN THOSE STATES AND SKILLS. THEREFORE, A KT MODEL IS DESIGNED TO PREDICT WHETHER STUDENTS WILL GIVE CORRECT ANSWERS AND TO DESCRIBE SUCH ABSTRACT CONCEPTS. HOWEVER, EXISTING METHODS EITHER GIVE RELATIVELY LOW PREDICTION ACCURACY OR FAIL TO EXPLAIN THOSE CONCEPTS INTUITIVELY. IN THIS PAPER, WE PROPOSE A NEW MODEL CALLED KNOWLEDGE QUERY NETWORK (KQN) TO SOLVE THESE PROBLEMS. KQN USES NEURAL NETWORKS TO ENCODE STUDENT LEARNING ACTIVITIES INTO KNOWLEDGE STATE AND SKILL VECTORS, AND MODELS THE INTERACTIONS BETWEEN THE TWO TYPES OF VECTORS WITH THE DOT PRODUCT. THROUGH THIS, WE INTRODUCE A NOVEL CONCEPT CALLED PROBABILISTIC SKILL SIMILARITY THAT RELATES THE PAIRWISE COSINE AND EUCLIDEAN DISTANCES BETWEEN SKILL VECTORS TO THE ODDS RATIOS OF THE CORRESPONDING SKILLS, WHICH MAKES KQN INTERPRETABLE AND INTUITIVE. ON FOUR PUBLIC DATASETS, WE HAVE CARRIED OUT EXPERIMENTS TO SHOW THE FOLLOWING: 1. KQN OUTPERFORMS ALL THE EXISTING KT MODELS BASED ON PREDICTION ACCURACY. 2. THE INTERACTION BETWEEN THE KNOWLEDGE STATE AND SKILLS CAN BE VISUALIZED FOR INTERPRETATION. 3. BASED ON PROBABILISTIC SKILL SIMILARITY, A SKILL DOMAIN CAN BE ANALYZED WITH CLUSTERING USING THE DISTANCES BETWEEN THE SKILL VECTORS OF KQN. 4. FOR DIFFERENT VALUES OF THE VECTOR SPACE DIMENSIONALITY, KQN CONSISTENTLY EXHIBITS HIGH PREDICTION ACCURACY AND A STRONG POSITIVE CORRELATION BETWEEN THE DISTANCE MATRICES OF THE SKILL VECTORS.
SERIOUS GAMES ARE HIGHLY INTERACTIVE SYSTEMS WHICH CAN THEREFORE CAPTURE LARGE AMOUNTS OF PLAYER INTERACTION DATA. THIS DATA CAN BE ANALYZED TO PROVIDE A DEEP INSIGHT INTO THE EFFECT OF THE GAME ON ITS PLAYERS. HOWEVER, TRADITIONAL TECHNIQUES TO ASSESS PLAYERS OF SERIOUS GAMES MAKE LITTLE USE OF INTERACTION DATA, RELYING INSTEAD ON COSTLY EXTERNAL QUESTIONNAIRES. WE PROPOSE AN EVIDENCE-BASED PROCESS TO IMPROVE THE ASSESSMENT OF PLAYERS BY USING THEIR INTERACTION DATA. THE PROCESS FIRST COMBINES PLAYER INTERACTION DATA AND TRADITIONAL QUESTIONNAIRES TO DERIVE AND REFINE GAME LEARNING ANALYTICS VARIABLES, WHICH CAN THEN BE USED TO PREDICT THE EFFECTS OF THE GAME ON ITS PLAYERS. ONCE THE GAME IS VALIDATED, AND SUITABLE PREDICTION MODELS HAVE BEEN BUILT, THE PREDICTION MODELS CAN BE USED IN LARGE-SCALE DEPLOYMENTS TO ASSESS PLAYERS SOLELY BASED ON THEIR INTERACTIONS, WITHOUT THE NEED FOR EXTERNAL QUESTIONNAIRES. WE BRIEFLY DESCRIBE TWO CASE STUDIES WHERE THIS COMBINATION OF TRADITIONAL QUESTIONNAIRES AND DATA MINING TECHNIQUES HAS BEEN SUCCESSFULLY APPLIED. THE EVIDENCE-BASED ASSESSMENT PROCESS PROPOSED RADICALLY SIMPLIFIES THE DEPLOYMENT AND APPLICATION OF SERIOUS GAMES IN REAL CLASS SETTINGS.
TEACHER DASHBOARDS PROVIDE REAL-TIME INFORMATION ABOUT STUDENTS' PERFORMANCE AND PROGRESS, WHICH HELP K-12 TEACHERS TO ADJUST FEEDBACK TO STUDENT' SPECIFIC NEEDS DURING LEARNING. PRIOR RESEARCH INDICATED TWO PROBLEMS WITH RESPECT TO HOW TEACHERS PROVIDE FEEDBACK: (I) TEACHERS DO NOT ALWAYS SELECT THE MOST EFFECTIVE FEEDBACK TO SUPPORT STUDENT' LEARNING AND (II) FEEDBACK IS NOT ALLOCATED EQUALLY TO STUDENTS WITH DIFFERENT ABILITIES. SPECIFICALLY, PROCESS FEEDBACK IS CONSIDERED MOST EFFECTIVE YET IS RELATIVELY SCARCE. IN ORDER TO UNDERSTAND HOW DASHBOARDS INFLUENCE THE TYPE AND ALLOCATION OF FEEDBACK, WE COMPARED CHARACTERISTICS OF FEEDBACK GIVEN AFTER DASHBOARD CONSULTATION (DASHBOARD-PROMPTED FEEDBACK) TO FEEDBACK TRIGGERED BY TEACHERS THEMSELVES OR IN RESPONSE TO STUDENTS' QUESTIONS (HUMAN-PROMPTED FEEDBACK) IN THIRTY-FIVE K-12 CLASSROOMS. RESULTS SHOWED THAT DASHBOARDS LED TO EQUAL AMOUNTS OF TASK AND PROCESS FEEDBACK, WHILE HUMAN-PROMPTS LED TO MUCH MORE TASK THAN PROCESS FEEDBACK AND THIS DIFFERENCE WAS ESPECIALLY LARGE FOR LOW-ABILITY STUDENTS. HENCE THE DIFFERENT TYPES OF DASHBOARD-PROMPTED FEEDBACK WERE MORE EQUALLY DISTRIBUTED AMONG STUDENTS OF DIFFERENT ABILITY LEVELS. THESE RESULTS INDICATE THAT DASHBOARDS CAN HAVE AN IMPORTANT EQUALIZING EFFECT ON TEACHER FEEDBACK PRACTICES.
SPEAKING AND PRESENTING IN PUBLIC ARE CRITICAL SKILLS FOR ACADEMIC AND PROFESSIONAL DEVELOPMENT. THESE SKILLS ARE DEMANDED ACROSS SOCIETY, AND THEIR DEVELOPMENT AND EVALUATION ARE A CHALLENGE FACED BY HIGHER EDUCATION INSTITUTIONS. THERE ARE SOME CHALLENGES TO EVALUATE OBJECTIVELY, AS WELL AS TO GENERATE VALUABLE INFORMATION TO PROFESSORS AND APPROPRIATE FEEDBACK TO STUDENTS. IN THIS PAPER, IN ORDER TO UNDERSTAND AND DETECT PATTERNS IN ORAL STUDENT PRESENTATIONS, WE COLLECTED DATA FROM 222 COMPUTER ENGINEERING (CE) FRESH STUDENTS AT THREE DIFFERENT TIMES, OVER TWO DIFFERENT YEARS (2017 AND 2018). FOR EACH PRESENTATION, USING A DEVELOPED SYSTEM AND MICROSOFT KINECT, WE HAVE DETECTED 12 FEATURES RELATED TO CORPORAL POSTURES AND ORAL SPEAKING. THESE FEATURES WERE USED AS INPUT FOR THE CLUSTERING AND STATISTICAL ANALYSIS THAT ALLOWED FOR IDENTIFYING THREE DIFFERENT CLUSTERS IN THE PRESENTATIONS OF BOTH YEARS, WITH STRONGER PATTERNS IN THE PRESENTATIONS OF THE YEAR 2017. A WILCOXON RANK-SUM TEST ALLOWED US TO EVALUATE THE EVOLUTION OF THE PRESENTATIONS ATTRIBUTES OVER EACH YEAR AND POINTED OUT A CONVERGENCE IN TERMS OF THE REDUCTION OF THE NUMBER OF FEATURES STATISTICALLY DIFFERENT BETWEEN PRESENTATIONS GIVEN AT THE SAME COURSE TIME. THE RESULTS CAN FURTHER HELP TO GIVE STUDENTS AUTOMATIC FEEDBACK IN TERMS OF THEIR POSTURES AND SPEECH THROUGHOUT THE PRESENTATIONS AND MAY SERVE AS BASELINE INFORMATION FOR FUTURE COMPARISONS WITH PRESENTATIONS FROM STUDENTS COMING FROM DIFFERENT UNDERGRADUATE COURSES.
SOCIETY HAS REACHED THE POINT WHERE THERE IS CONSIDERABLE AWARENESS ABOUT THE NEED OF TEACHING CODING SKILLS AT THE SCHOOLS. BOTH, POLITICAL LEADERS AND EDUCATIONAL SYSTEMS START TAKING POSITIONS IN THIS STRATEGIC NEW ARENA, BUT THEY LACK FROM MUCH OF THE EXPERTISE AND NECESSARY TOOLS ABOUT HOW TO TEACH THIS COMPLEX (BUT ALSO INTUITIVE TO CHILDREN AND YOUNGSTERS) FIELD OF KNOWLEDGE. IN THIS CONTEXT, TOOLBOX ACADEMY IS A PROGRAMMING PLATFORM WHICH SUBSCRIBES A SLIGHTLY NEW APPROACH TO ATTACK THE PROBLEM, IN FOUR DIFFERENTIAL ASPECTS: 1) WHILE MOST ATTENTION FOCUSES ON BLOCK-BASED APPROACHES (SCRATCH AND CODE.ORG BEING THE MOST POPULAR INITIATIVES), TOOLBOX EMBRACES CODING AS IT WORKS FOR DEVELOPERS: TEXT-BASED PROGRAMMING; 2) TOOLBOX EMBEDS MAINSTREAM PROGRAMMING LANGUAGES, WHICH ARE WIDELY USED, ARE SUPPORTED BY BIG COMMUNITIES, ARE WEAKLY TYPED, AND CLOSER TO HUMAN NATURAL LANGUAGE, LIKE IT IS JAVASCRIPT, FOR WEB APPLICATIONS, AND GNU OCTAVE, IN ENGINEERING AND SCIENTIFIC RESEARCH; 3) IT PROVIDES USER-ORIENTED PROBLEM-BASED LEARNING, DESIGNED UPON EDUCATIONAL RESEARCH RESULTS; AND 4) IT INTEGRATES CURRICULAR SUBJECTS IN LEARNING TO CODE. IN THIS PAPER, WE REVIEW THE DESIGN CONSTRAINTS TAKEN INTO ACCOUNT TO DEVELOP TOOLBOX ACADEMY, AND HOW IT HAS BEEN TESTED IN THE CLASSROOM. SOME FEATURES ARE PARTICULARLY STRESSED, LIKE THE USE OF OPEN FORMATS FOR CONTENT REPRESENTATION (DEFINITION OF TASKS OR PROBLEMS); THE DESIGN OF A SIMPLIFIED ENVIRONMENT TO REPRESENT MOST CS CONCEPTS, FROM BASIC LOOPS TO COMPLEX GRAPH TRAVERSAL STRATEGIES; AND THE USE OF LEARNING ANALYTICS BASED ON BIG DATA ALGORITHMS THAT CAN HELP EDUCATORS AND MANAGERS TO LEARN FROM THE INTERACTION OF THEIR STUDENTS WITH THIS ENVIRONMENT.
RESEARCH SHOWS THAT VARIOUS CONTEXTUAL FACTORS CAN HAVE AN IMPACT ON LEARNING. SOME OF THESE FACTORS CAN ORIGINATE FROM THE PHYSICAL LEARNING ENVIRONMENT (PLE) IN THIS REGARD. WHEN LEARNING FROM HOME, LEARNERS HAVE TO ORGANIZE THEIR PLE BY THEMSELVES. THIS PAPER IS CONCERNED WITH IDENTIFYING, MEASURING, AND COLLECTING FACTORS FROM THE PLE THAT MAY AFFECT LEARNING USING MOBILE SENSING. MORE SPECIFICALLY, THIS PAPER FIRST INVESTIGATES WHICH FACTORS FROM THE PLE CAN AFFECT DISTANCE LEARNING. THE RESULTS IDENTIFY NINE TYPES OF FACTORS FROM THE PLE ASSOCIATED WITH COGNITIVE, PHYSIOLOGICAL, AND AFFECTIVE EFFECTS ON LEARNING. SUBSEQUENTLY, THIS PAPER EXAMINES WHICH INSTRUMENTS CAN BE USED TO MEASURE THE INVESTIGATED FACTORS. THE RESULTS HIGHLIGHT SEVERAL METHODS INVOLVING SMART WEARABLES (SWS) TO MEASURE THESE FACTORS FROM PLES SUCCESSFULLY. THIRD, THIS PAPER EXPLORES HOW SOFTWARE INFRASTRUCTURE CAN BE DESIGNED TO MEASURE, COLLECT, AND PROCESS THE IDENTIFIED MULTIMODAL DATA FROM AND ABOUT THE PLE BY UTILIZING MOBILE SENSING. THE DESIGN AND IMPLEMENTATION OF THE EDUTEX SOFTWARE INFRASTRUCTURE DESCRIBED IN THIS PAPER WILL ENABLE LEARNING ANALYTICS STAKEHOLDERS TO USE DATA FROM AND ABOUT THE LEARNERS' PHYSICAL CONTEXTS. EDUTEX ACHIEVES THIS BY UTILIZING SENSOR DATA FROM SMARTPHONES AND SMARTWATCHES, IN ADDITION TO RESPONSE DATA FROM EXPERIENCE SAMPLES AND QUESTIONNAIRES FROM LEARNERS' SMARTWATCHES. FINALLY, THIS PAPER EVALUATES TO WHAT EXTENT THE DEVELOPED INFRASTRUCTURE CAN PROVIDE RELEVANT INFORMATION ABOUT THE LEARNING CONTEXT IN A FIELD STUDY WITH 10 PARTICIPANTS. THE EVALUATION DEMONSTRATES HOW THE SOFTWARE INFRASTRUCTURE CAN CONTEXTUALIZE MULTIMODAL SENSOR DATA, SUCH AS LIGHTING, AMBIENT NOISE, AND LOCATION, WITH USER RESPONSES IN A RELIABLE, EFFICIENT, AND PROTECTED MANNER.</P>
THIS IS A SYSTEMATIC REVIEW CONDUCTED OF PRIMARY RESEARCH LITERATURE PUBLISHED BETWEEN 2007 AND 2018 ON THE DEPLOYMENT AND EFFECTIVENESS OF DATA ANALYTICS IN HIGHER EDUCATION TO IMPROVE STUDENT OUTCOMES. WE TOOK A METHODOLOGICAL APPROACH TO SEARCHING DATABASES; APPRAISING AND SYNTHESISING RESULTS AGAINST PREDEFINED CRITERIA. WE REVIEWED RESEARCH ON THE EFFECTIVENESS OF THREE DIFFERENTIATED FORMS OF DATA ANALYTICS: LEARNING, ACADEMIC AND LEARNER ANALYTICS. STUDENT OUTCOMES ARE DEFINED AS RETENTION, ACADEMIC PERFORMANCE AND ENGAGEMENT. OUR RESULTS FIND THAT THREE QUARTERS OF STUDIES REPORT THE USE OF EDUCATIONAL DATA ANALYTICS TO BE EFFECTIVE IN IMPROVING STUDENT OUTCOMES BUT THEIR RELATIONSHIP WITH STUDENT OUTCOMES REQUIRES FURTHER AND MORE ROBUST INVESTIGATION AND ASSESSMENT. WE ARGUE THAT RESEARCH MUST INTERPRET AND COMMUNICATE EFFECTIVENESS QUALITATIVELY, AS WELL AS QUANTITATIVELY, BY INCLUDING THE STUDENT VOICE IN ASSESSMENTS OF IMPACT.
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. BESIDES ANALYZING THE RAW DATA FROM LOG FILES DIRECTLY, THERE IS AN OPTION TO USE MOODLE PLUGINS THAT PROVIDE LEARNING ANALYTICS AND ENABLE THE FASTER ANALYSIS OF STUDENTS' BEHAVIOR ON LMS. IN THIS PAPER, SOME OF THESE PLUGINS ARE PRESENTED. HOWEVER, THIS PAPER IS FOCUSED ON ANALYZING THE LOG FILES OF A COURSE IMPLEMENTED ON THE LMS MOODLE AT THE FACULTY OF ORGANIZATION AND INFORMATICS AT THE UNIVERSITY OF ZAGREB. THE RESULTS OF THE STUDENTS' BEHAVIOR, BASED ON LOGS IN MOODLE, WILL BE INTERPRETED IN TERMS OF STUDENT SUCCESS.
INTERDISCIPLINARY STUDIES FOSTER INTEGRATION OF IDEAS ACROSS DISCIPLINES. THE KNOWLEDGE BUILDING PEDAGOGY, WITH ITS 12 PRINCIPLES AND ASSOCIATED TECHNOLOGY, KNOWLEDGE FORUM (R), PROVIDES MULTIFACETED SUPPORT FOR LINKING IDEAS ACROSS DISCIPLINES AND COMMUNITIES. THIS EXPLORATORY STUDY AIMS TO ASSESS THE EXTENT TO WHICH ELEMENTARY-SCHOOL STUDENTS WITHIN KNOWLEDGE BUILDING COMMUNITIES WORK PRODUCTIVELY WITH IDEAS ACROSS CONTENT AREAS. TOWARD THAT END WE EXAMINE "CRISSCROSSING TOPICS" - STUDENT USE OF CONCEPTS FROM MULTIPLE CONTENT AREAS - TO EXPLORE THE EXTENT TO WHICH STUDENTS THINK AND THEORIZE ACROSS DISCIPLINARY BOUNDARIES, USE CONCEPTS FOUND IN CURRICULUM GUIDELINES AT AND BEYOND THEIR GRADE LEVEL, AND GENERATE CROSS-TOPIC NOTES THAT ADVANCE DISCOURSE. RESULTS SHOW THAT ELEMENTARY SCHOOL STUDENTS ENGAGED IN KNOWLEDGE BUILDING CAN EXTEND KNOWLEDGE BOUNDARIES AND BRING A CONSIDERABLE RANGE OF CONCEPTUAL CONTENT TO THEIR WORK, RESULTING IN PRODUCTIVE DISCOURSE THREADS THAT CONTRIBUTE TO COMMUNITY KNOWLEDGE.
THE LACK OF PHYSICAL CONTACT AND THE DEMANDING NEED FOR PERSONALIZED SERVICES HAS PROMPTED STAKEHOLDERS IN DISTANCE LEARNING TO BENEFIT FROM THE ENORMOUS VOLUME OF STUDENTS' ONLINE TRACES IN THE LEARNING MANAGEMENT SYSTEMS. DATA MINING METHODOLOGIES ARE WIDELY APPLIED TO ANALYZE DATA LOGS AND PREDICT TRENDS FOR EARLY AND EFFICIENT INTERVENTIONS. THUS, THE RETENTION OF STUDENTS IN THE EDUCATIONAL PROCESS CAN BE ACHIEVED WITH POSITIVE EFFECTS ON THE REPUTATION AND FINANCES OF THE INSTITUTIONS. THIS WORK DIVIDES THE MOODLE DATA SETS FROM SIX DIFFERENT SECTIONS OF AN ANNUAL POSTGRADUATE PROGRAM AT THE HELLENIC OPEN UNIVERSITY IN SIX PERIODS FOR EACH SECTION, DUE TO THE NUMBER OF WRITTEN ASSIGNMENTS. THEN IT IMPLEMENTS DATA MINING TECHNIQUES TO ANALYZE THE ACTIVITY, POLARITY AND EMOTIONS OF TUTORS AND STUDENTS IN ORDER TO PREDICT STUDENTS' GRADES. THE RESULTS INDICATE THE ALGORITHM WITH THE HIGHEST PRECISION IN EACH PREDICTION. IN ADDITION, THE RESEARCH CONCLUDES THAT POLARITY AND EMOTIONS AS INDEPENDENT VARIABLES PROVIDE BETTER PERFORMANCE IN COMPARATIVE MODELS. MOREOVER, TUTORS' VARIABLES ARE HIGHLIGHTED AS AN IMPORTANT FACTOR FOR MORE ACCURATE PREDICTIONS OF STUDENT GRADES. FINALLY, A COMPARISON OF ACTUAL AND PREDICTED GRADES INDICATES WHICH STUDENTS HAVE USED A THIRD PARTY TO FULFILL THEIR ASSIGNMENTS.
MOST OF THE CONTEMPORARY APPROACHES IN LEARNER BEHAVIOUR MODELLING EITHER QUANTIZE CONTINUOUS DATA INTO DISCRETE STATES/EVENTS (E.G., HMM), OR ASSUME THAT THE PATTERNS IN THE DATA ARE DISTRIBUTED HOMOGENEOUSLY IN TIME (E.G., AUTO-REGRESSION). THIS PAPER PROPOSES A NOVEL APPROACH THAT OVERCOMES THE ABOVE MENTIONED ISSUES AND MODELS LEARNER BEHAVIOUR USING GENERALIZED AUTO-REGRESSIVE CONDITIONAL HETEROSKEDASTICITY (GARCH). GARCH USES CONTINUOUS TIME-SERIES DATA, WITHOUT THE NEED FOR INFORMATION QUANTIZATION, AND IT CONSIDERS THE HETEROGENEITY OF EVENT DISTRIBUTION IN THE TIME-SERIES. A STUDY WAS CONDUCTED TO DEMONSTRATE HOW GARCH CAN BE CONFIGURED IN AN ADAPTIVE ASSESSMENT SETTING. SPECIFICALLY, GARCH WAS APPLIED ON SIX DIFFERENT CONSTRUCTS FROM EYE-TRACKING AND ELECTROENCEPHALOGRAM (EEG) DATA, AND COMPARED WITH EXISTING METHODS OF MODELLING TIME-SERIES DATA, SUCH AS MARKOV MODELS AND MODELS HAVING AUTO-REGRESSIVE COMPONENTS. THE COMPARISON SHOWS THAT THE MODELS HAVING A GARCH COMPONENT OUTPERFORM OTHER MODELS FOR MOST OF THE STUDENTS, FOR ALL THE VARIABLES CONSIDERED. THE RESULTS ARE ENCOURAGING TOWARDS BUILDING ACCURATE LEARNER BEHAVIOUR MODELS, ADEQUATE TO DRIVE THE DESIGN AND DEVELOPMENT OF ADAPTIVE FEEDBACK TOOLS (E.G., AN EARLY ALERT SYSTEM), BUT FURTHER INVESTIGATION IS REQUIRED.
THE MASSIVE QUANTITY OF LEARNERS GENERATING INFORMATION RELATED TO MASSIVE OPEN ONLINE COURSES (MOOCS) MAKE RESEARCHERS ANALYZE LARGE DATASETS ABOUT THE LEARNERS' PROFILE, INTERACTION, SATISFACTION, ETC. AND THESE DATASETS CAN COME FROM MULTIPLE DATA SOURCES RELATED TO THE SAME OR DIFFERENT COURSES. THIS PAPER DETAILS THE CONDUCTED EXPLORATORY ANALYSIS PERFORMED WITH DATA FROM TWO DIFFERENT MOOCS OF DIFFERENT KNOWLEDGE AREAS TO CHECK THE FACTORS THAT INFLUENCE THE LEARNERS' RETENTION AND CERTIFICATION. THE PRELIMINARY RESULTS WE OBTAINED ALLOW US TO PROVIDE SOME CONCLUSIONS WITH THE AIM TO KEEP RESEARCHING IN ORDER TO GENERALIZE OUR ACHIEVEMENTS.
GREER AND MARK'S (2016) PAPER SUGGESTED AND REVIEWED DIFFERENT METHODS FOR EVALUATING THE EFFECTIVENESS OF INTELLIGENT TUTORING SYSTEMS SUCH AS PROPENSITY SCORE MATCHING. THE CURRENT STUDY AIMED AT ASSESSING THE EFFECTIVENESS OF AUTOMATED PERSONALIZED FEEDBACK INTERVENTION IMPLEMENTED VIA THE STUDENT ADVICE RECOMMENDER AGENT (SARA) IN A FIRST-YEAR BIOLOGY CLASS BY MEANS OF STATISTICAL MATCHING AND BY REVIEWING AND COMPARING FOUR DIFFERENT STATISTICAL MATCHING METHODS (I.E., EXACT MATCHING, NEAREST NEIGHBOR MATCHING USING THE MAHALANOBIS DISTANCE, PROPENSITY SCORE MATCHING, AND COARSENED EXACT MATCHING). DATA FROM 1026 (73% FEMALE AND 27% MALE) STUDENTS WHO TOOK A FIRST-YEAR BIOLOGY COURSE AT A WESTERN CANADIAN UNIVERSITY WERE USED. TWO DIFFERENT MEASURES FOR BALANCE ASSESSMENT OF THE MATCHED DATA (I.E., % OF BALANCE IMPROVEMENT AND STANDARDIZED BIAS) WERE USED TO CHOOSE THE BEST PERFORMING STATISTICAL MATCHING METHOD. NEAREST NEIGHBOR MATCHING USING THE MAHALANOBIS DISTANCE WAS FOUND TO BE THE MOST APPROPRIATE METHOD FOR THIS STUDY AND RESULTS SHOWED A STATISTICALLY SIGNIFICANT BUT SMALL TREATMENT EFFECT FOR THE GROUP WHO RECEIVED PERSONALIZED FEEDBACK. RESEARCH AND PRACTICAL CONSIDERATIONS WERE DISCUSSED AND SUGGESTIONS FOR FUTURE RESEARCH ARE PROVIDED.
PERFORMANCE PREDICTION IS A LEADING TOPIC IN LEARNING ANALYTICS RESEARCH DUE TO ITS POTENTIAL TO IMPACT ALL TIERS OF EDUCATION. THIS STUDY PROPOSES A NOVEL PREDICTIVE MODELING METHOD TO ADDRESS THE RESEARCH GAPS IN EXISTING PERFORMANCE PREDICTION RESEARCH. THE GAPS ADDRESSED INCLUDE: THE LACK OF EXISTING RESEARCH FOCUS ON PERFORMANCE PREDICTION RATHER THAN IDENTIFYING KEY PERFORMANCE FACTORS; THE LACK OF COMMON PREDICTORS IDENTIFIED FOR BOTH K-12 AND HIGHER EDUCATION ENVIRONMENTS; AND THE MISPLACED FOCUS ON ABSOLUTE ENGAGEMENT LEVELS RATHER THAN RELATIVE ENGAGEMENT LEVELS. TWO DATASETS, ONE FROM HIGHER EDUCATION AND THE OTHER FROM A K-12 ONLINE SCHOOL WITH 13 368 STUDENTS IN MORE THAN 300 COURSES, WERE APPLIED USING THE PREDICTIVE MODELING TECHNIQUE. THE RESULTS SHOWED THE NEWLY SUGGESTED APPROACH HAD HIGHER OVERALL ACCURACY AND SENSITIVITY RATES THAN THE TRADITIONAL APPROACH. IN ADDITION, TWO GENERALIZABLE PREDICTORS WERE IDENTIFIED FROM INSTRUCTION-INTENSIVE AND DISCUSSION-INTENSIVE COURSES.
LEARNING ANALYTICS APPLIES DATA ANALYSIS TECHNIQUES TO LEARNING DATA IN ORDER TO SUPPORT STUDENTS' LEARNING PROCESSES AND TO IMPROVE THE QUALITY OF EDUCATION. DESPITE THE INCREASING ATTENTION TO LEARNING ANALYTICS FOR HIGHER EDUCATION, IT HAS NOT BEEN FULLY ADDRESSED IN PRIMARY AND PRESCHOOL EDUCATION. IN THIS RESEARCH, WE APPLY LEARNING ANALYTICS TO PRESCHOOL EDUCATION TO PREDICT THE CONTINUATION OF LEARNING OF PRESCHOOL CHILDREN. BASED ON OUR HYPOTHESIS THAT TEMPORAL PATTERNS IN THE ASSESSMENT SCORES OF DEVELOPMENT TESTS ARE EFFECTIVE FEATURES FOR PREDICTION, WE EXTRACT THE TEMPORAL PATTERNS USING TIME-SERIES CLUSTERING, AND USE THEM AS THE FEATURES OF PREDICTION MODELS. THE EXPERIMENTAL RESULTS USING A REAL PRESCHOOL EDUCATION DATASET SHOW THAT THE USE OF THE TEMPORAL PATTERNS IMPROVES THE PREDICTIVE ACCURACY OF FUTURE CONTINUATION OF STUDY.
INTEGRATING ENGINEERING DESIGN INTO K-12 CURRICULA IS INCREASINGLY IMPORTANT AS ENGINEERING HAS BEEN INCORPORATED INTO MANY STEM EDUCATION STANDARDS. HOWEVER, THE ILL-STRUCTURED AND OPEN-ENDED NATURE OF ENGINEERING DESIGN MAKES IT DIFFICULT FOR AN INSTRUCTOR TO KEEP TRACK OF THE DESIGN PROCESSES OF ALL STUDENTS SIMULTANEOUSLY AND PROVIDE PERSONALIZED FEEDBACK ON A TIMELY BASIS. THIS STUDY PROPOSES A BAYESIAN NETWORK MODEL TO DYNAMICALLY AND AUTOMATICALLY ASSESS STUDENTS' ENGAGEMENT WITH ENGINEERING DESIGN TASKS AND TO SUPPORT FORMATIVE FEEDBACK. SPECIFICALLY, WE APPLIED A BAYESIAN NETWORK TO 111 NINTH-GRADE STUDENTS' PROCESS DATA LOGGED BY A COMPUTER-AIDED DESIGN SOFTWARE PROGRAM THAT STUDENTS USED TO SOLVE AN ENGINEERING DESIGN CHALLENGE. EVIDENCE WAS EXTRACTED FROM THE LOG FILES AND FED INTO THE BAYESIAN NETWORK TO PERFORM INFERENTIAL REASONING AND PROVIDE A BAROMETER OF THEIR PERFORMANCE IN THE FORM OF POSTERIOR PROBABILITIES. RESULTS SHOWED THAT THE BAYESIAN NETWORK MODEL WAS COMPETENT AT PREDICTING A STUDENT'S TASK PERFORMANCE. IT PERFORMED WELL IN BOTH IDENTIFYING STUDENTS OF A PARTICULAR GROUP (RECALL) AND ENSURING IDENTIFIED STUDENTS WERE CORRECTLY LABELED (PRECISION). THIS STUDY ALSO SUGGESTS THAT BAYESIAN NETWORKS CAN BE USED TO PINPOINT A STUDENT'S STRENGTHS AND WEAKNESSES FOR APPLYING RELEVANT SCIENCE KNOWLEDGE TO ENGINEERING DESIGN TASKS. FUTURE WORK OF IMPLEMENTING THIS TOOL WITHIN THE COMPUTER-AIDED DESIGN SOFTWARE WILL PROVIDE INSTRUCTORS A POWERFUL TOOL TO FACILITATE ENGINEERING DESIGN THROUGH AUTOMATICALLY GENERATING PERSONALIZED FEEDBACK TO STUDENTS IN REAL TIME.
THOUGH RAPID COLLECTION OF BIG DATA IS UBIQUITOUS ACROSS DOMAINS, FROM INDUSTRY SETTINGS TO ACADEMIC CONTEXTS, THE ETHICS OF BIG DATA COLLECTION AND RESEARCH ARE CONTESTED. A NEXUS OF DATA ETHICS ISSUES IS THE CONCEPT OF CREEP, OR REPURPOSING OF DATA FOR OTHER APPLICATIONS OR RESEARCH BEYOND THE CONDITIONS OF ORIGINAL COLLECTION. DATA CREEP HAS PROVEN CONTROVERSIAL AND HAS PROMPTED CONCERNS ABOUT THE SCOPE OF ETHICAL OVERSIGHT. INSTITUTIONAL REVIEW BOARDS OFFER LITTLE GUIDANCE REGARDING BIG DATA, AND PROBLEMATIC RESEARCH CAN STILL MEET ETHICAL STANDARDS. WHILE ETHICS SEEM CONCRETE THROUGH INSTITUTIONAL DEPLOYMENT, I FRAME ETHICS AS PRODUCED. INFORMED BY MY ETHNOGRAPHIC RESEARCH AT A LARGE PUBLIC UNIVERSITY IN THE U.S., I EXPLORE ETHICS THROUGH TWO MODELS: ETHICS AS INSTITUTIONAL PROCEDURES AND ETHICS AS ACTS AND INTENTIONS. THE UNIVERSITY WHERE I CONDUCTED FIELDWORK IS THE DEVELOPMENT GROUNDS FOR A PREDICTIVE MODEL THAT USES STUDENT DATA TO ANTICIPATE ACADEMIC SUCCESS. WHILE STUDENTS CONSENT TO DATA COLLECTION, THE CIRCUMSTANCES OF CONSENT AND THE DEGREE TO WHICH THEY ARE INFORMED ARE NOT SO APPARENT, AS MANY DATA ARE A PRODUCT OF CREEP. DRAWING FROM INTERVIEWS AND PARTICIPANT OBSERVATION WITH ADMINISTRATORS, DATA SCIENTISTS, DEVELOPERS, AND STUDENTS, I EXAMINE DATA ETHICS, FROM A LARGER INSTITUTIONAL MODEL TO EVERYDAY ENACTMENTS RELATED TO DATA CREEP. AFTER DEMONSTRATING THE LIMITS OF SUCH MODELS, I PROPOSE A REMODELING OF ETHICS THAT DRAWS ON RECENT WORKS ON DATA, JUSTICE, AND REFUSAL TO POSE GENERATIVE QUESTIONS FOR RETHINKING ETHICS IN INSTITUTIONAL CONTEXTS.
THE AIM OF THE PAPER IS TO DESCRIBE THE DESIGN OF A WEB 3.0-BASED INTELLIGENT LEARNING SYSTEM (ILS) THAT ADDRESSING THE STUDENTS' NEEDS IN THE 21ST CENTURY. THE DESIGN IS BASED THEORETICALLY, ON THE PRINCIPLES OF THE CONNECTIVISM THEORY AND TECHNICALLY, IT IMPLEMENTS THE SEMANTIC WEB REPRESENTATIONS COMBINING WITH THE USE OF LEARNING ANALYTICS TECHNIQUES. THE WORK EMPHASISES THAT IMPLEMENTING A LEARNING ANALYTICS APPROACH THAT USES: TEXT CLASSIFICATION, SENTIMENT ANALYSIS, TOPICS EXTRACTION, AND TEXT CLUSTERING ON THE BASIS OF A SEMANTIC WEB AND ONTOLOGIES CAN SUPPORT THE CONNECTIVIST LEARNING. THE SEMANTIC LEARNING ANALYTICS PROCESS, REPRESENTS THE KEY ELEMENT OF THE PROPOSED INTELLIGENT LEARNING ANALYTICS SYSTEM TO INFER AND DEDUCE HIDDEN DATA IN THE MASSIVE LEARNING DATA THANKS TO SEMANTIC MODELS OF I-SOLEARN. THE AIM IS TO GUIDE STUDENTS TO UNDERSTAND THROUGH RECOMMENDATIONS, CHARTS AND VISUALISATIONS THEIR LEARNING BEHAVIOUR AND TO GIVE TEACHERS FEEDBACKS, ENABLING THEM TO EXAMINE BOTH STUDENTS' LEARNING AND ACTIVITIES. AN EXPERIMENTAL STUDY USING I-SOLEARN (AN INTELLIGENT SOCIAL LEARNING ENVIRONMENT), INDICATES THAT DESIGNING AN ILS BASED ON WEB 3.0 TECHNIQUES IS EFFECTIVE AND EXPECTED TO SHOW A GREAT ADVANTAGE IN ENHANCING THE CONNECTIVIST LEARNING OF STUDENTS IN THE DIGITAL AGE.
IN THIS PAPER WE PRESENT THE FUNDAMENTALS OF SOFTWARE DESIGNS CAPABLE OF MONITORING AND RECORDING DATA RELATED TO THE MANAGEMENT OF MATHEMATICAL PROBLEM SOLVING PROCESSES. THE SOFTWARE ALLOWS A SYSTEMATIC COLLECTION OF THE VARIABLES OF THE RESOLUTION PROCESS FROM WHICH IT IS POSSIBLE TO OBTAIN DIFFERENT STUDENT-LEARNING ANALYTICS. BY NOT FOCUSING EXCLUSIVELY ON THE FINAL OUTCOME OF THE RESOLUTION PROCESS, IT IS POSSIBLE TO IDENTIFY THE COGNITIVE TENDENCIES OF THE STUDENTS. IN THIS MANUSCRIPT, WE DESCRIBE THE CHARACTERISTICS OF THIS TECHNOLOGICAL ENVIRONMENT TOGETHER WITH THE DIDACTIC BASIS THAT JUSTIFIES THE INTEREST IN RESEARCH AT SCHOOL LEVELS IN WHICH POOR KNOWLEDGE LIMITS THE FORMALIZATION OF PROBLEM SOLVING SITUATIONS. THE INITIAL RESULTS OF A PILOT STUDY THAT HAS ALLOWED FOR THE EVALUATION OF THE STRENGTHS AND WEAKNESSES OF THE DEVELOPING VERSION ARE ATTACHED. FINALLY, FUTURE DIRECTIONS FOR THIS TECHNOLOGICAL ENVIRONMENT ARE DISCUSSED AS ANOTHER STEP FURTHER IN BOTH INNOVATION AND RESEARCH IN MATHEMATICS EDUCATION.
COVID-19 PANDEMIC ACCELERATED THE DIGITALIZATION AND THE IMPLEMENTATION OF TECHNOLOGICAL TOOLS TO DISTRIBUTE KNOWLEDGE AND CONTENT TO CERTIFY THE INSTRUCTIONAL PROCESS'S STEADINESS DESPITE THE RESTRICTIONS POSED IN MANY NATIONS WORLDWIDE. HOWEVER, MULTI-MODELS OF DEVELOPMENT AND INTEGRATIONS BASED ON MULTITUDES OF THEORETICAL AND CONCEPTUAL FRAMEWORKS MADE IT DIFFICULT FOR DECIDERS DURING THE YEAR - ESPECIALLY IN DEVELOPING COUNTRIES - TO FOLLOW A CLEAR PATH BASED ON THEIR CONTEXTUAL NEEDS. BASED ON A LITERATURE REVIEW AND HISTORICAL DATA, LEARNING ANALYTICS RESEARCH, AND ITS EMPIRICAL RESULTS, THIS ARTICLE PROPOSES A DATA-ANALYTICS MODEL FOR GROWTH TRAINING/EDUCATIONAL TECHNOLOGIES HELP STAKEHOLDERS USE DATA AS INTELLIGENCE SOURCES TO IMPLEMENT TECHNOLOGIES THAT WILL IMPROVE TRADITIONAL LEARNING PROCEDURES WITHOUT CONSTRAINING PRACTICES. AS A RESULT, THE PAPER ALSO SUGGESTS, ACCORDING TO THE PRAGMATIC RESULTS, A 4-YEAR PLAN APPLICABLE IN MULTIDIMENSIONAL CONTEXTS TO ENHANCE THE ORGANIZATION'S LEARNING CAPABILITIES AS A WHOLE UNIT TO FACE FUTURE TRIALS. THE RETURN ON EXPERIENCE IN THE LAST YEAR OF THE PANDEMIC CONTRACTS THE METHODOLOGICAL BASIS OF RESULTS. FURTHERMORE, THIS MANUSCRIPT'S AIM IS DEFINED BY THE URGENT NECESSITY OF POST-PANDEMIC SOLUTIONS TO POSITIVELY SAFEGUARD THE FUTURE OF KEEPING THE WHEEL OF KNOWLEDGE RUNNING FOR THE LEARNERS AND WARRANT UPCOMING TRANSITION INTO USING DATA AS A SOURCE OF DEVELOPING NEW LEARNING TECHNOLOGIES.
PREDICTING ACADEMIC PERFORMANCE IS A KEY ELEMENT IN EDUCATION, ALLOWING TEACHERS TO DESIGN PREVENTIVE DIDACTIC ACTIONS. VARIOUS COMPUTATIONAL DISCIPLINES ARE INVOLVED IN THIS PREDICTIVE PROCESS, WITH LEARNING ANALYTICS, MACHINE LEARNING, EDUCATIONAL DATA MINING, ARTIFICIAL NEURAL NETWORKS, AND FUZZY THEORIES BEING THE MOST FREQUENTLY USED. A SYSTEMATIC REVIEW OF THE SCIENTIFIC LITERATURE (2010-MARCH 2020) INDEXED IN SCOPUS, IEEE XPLORE, ACM DIGITAL LIBRARY AND SPRINGER IS PRESENTED, WITH THE AIM OF EVALUATING HOW ACADEMIC PERFORMANCE PREDICTION HAS BEHAVED IN TWO SCENARIOS: (1) STUDIES ONLINE (ONLINE) AND BLENDED; AND (2) TECHNOLOGICAL SUPPORT FOR THE FACE-TO-FACE MODALITY. THE ARTICLE CONCLUDES WITH THE DETERMINATION OF THE TRENDS BETWEEN THE DISCIPLINES OF EDUCATIONAL TECHNOLOGIES AND THE VARIABLES OF ACADEMIC PERFORMANCE.
ACTIVE LEARNING IN COMPUTER SCIENCE CLASSROOMS OFTEN INVOLVES HAVING STUDENTS SOLVE PROGRAMMING PROBLEMS IN CLASS USING A WEB-BASED INTERPRETER. SPINOZA IS ONE SUCH SYSTEM WHICH CAPTURES ALL OF THE STUDENT ATTEMPTS AND USES IT TO PROVIDE ACTIONABLE LEARNING ANALYTICS FOR THE INSTRUCTOR. IN THIS PAPER WE PRESENT SEVERAL NEW PEDAGOGICAL APPLICATIONS OF THE LOG DATA FROM SPINOZA, INCLUDING TWO APPROACHES TO TEAM FORMATION AND AN IN DEPTH ANALYSIS OF THE SOLVE-THEN-DEBUG DEBUGGING PEDAGOGY IN SPINOZA. WE PROVIDE SOME INITIAL EVIDENCE THAT THE TEAM FORMATION STRATEGIES MAY BE EFFECTIVE METHODS FOR FORMING EITHER DIVERSE OR HOMOGENEOUS TEAMS. THE SECOND APPLICATION WE EXAMINE IN DEPTH IS THE SOLVE-THEN-DEBUG PEDAGOGY IN WHICH STUDENTS WHO CORRECTLY SOLVE A SPINOZA PROGRAMMING PROBLEM ARE THEN ASKED TO ANALYZE AND DEBUG THE MOST COMMON ERRORS THAT THE CLASS HAS MADE SO FAR ON THAT PROBLEM. THIS IS A SOCIAL DEBUGGING PROCESS AND IN THIS PAPER WE PROVIDE A DETAILED EXPLANATION OF THE LEARNING GOALS FOR EACH STEP OF THIS PEDAGOGY. WE ALSO GIVE AN EXAMPLE OF HOW STUDENTS ENGAGED WITH ONE PARTICULAR SOLVE-THEN-DEBUG PROBLEM. THIS PROVIDES INITIAL EVIDENCE THAT THE SOLVE-THEN-DEBUG PEDAGOGY ENGAGES STUDENTS IN EFFECTIVE PROGRAM BUG ANALYSIS ACTIVITY.
AALTO UNIVERSITY, BORN IN 2010 FROM THE MERGER OF HELSINKI UNIVERSITY OF TECHNOLOGY, HELSINKI SCHOOL IF BUSINESS, AND HELSINKI UNIVERSITY OF ARTS AND DESIGN, IS A MULTIDISCIPLINARY UNIVERSITY WITH 15 000 UNDERGRADUATE AND DOCTORAL STUDENTS, AND APPROXIMATELY 4 000 STAFF MEMBERS. SINCE THE LAUNCH OF THE NEW UNIVERSITY, AALTO HAS PUT A LOT OF EFFORT IN DEVELOPING STUDENT AND TEACHER CENTRIC SERVICES. THE WORK HAS BEEN CARRIED OUT IN PROJECTS, STRATEGIC INITIATIVES, AND IN NATIONAL INITIATIVES LIKE ANALYTICS INTELLIGENCE PROJECT (2018-) INVOLVING MANY UNIVERSITIES TO WORK TOGETHER, TO CO-CREATE NEW INNOVATIVE SOLUTIONS AND APPROACHES TO PROMOTE EXCELLENCE IN TEACHING AND LEARNING. THE AIM OF THE PAPER IS TO PRESENT HOW WE IMPLEMENTED SERVICE DESIGN APPROACH IN TWO SEPARATE CASES TO DEVELOP NEW SERVICES FOR TEACHERS AND STUDENTS IN UNIVERSITY CONTEXT. THE PAPER COVERS TWO DEVELOPMENT EXPERIMENTS, WHICH WERE CONDUCTED IN AALTO UNIVERSITY TEACHER SERVICES (HTTPS://WWW.AALTO.FI/SERVICES/TEACHER-SERVICES-TEAM) DURING ACADEMIC YEAR 2018-2019 USING SERVICE DESIGN TOOLS AND METHODS TO BUILD SERVICES TO THE ACADEMICS AND STUDENTS. THE PAPER PRESENTS DESCRIPTIONS OF THE DESIGN EXPERIMENTS, FOLLOWED BY CONCLUSIONS OF APPLYING SERVICE DESIGN APPROACH.
ANALYSES OF LEARNING OFTEN RELY ON CODED DATA. ONE IMPORTANT ASPECT OF CODING IS ESTABLISHING RELIABILITY. PREVIOUS RESEARCH HAS SHOWN THAT THE COMMON APPROACH FOR ESTABLISHING CODING RELIABILITY IS SERIOUSLY FLAWED IN THAT IT PRODUCES UNACCEPTABLY HIGH TYPE I ERROR RATES. THIS PAPER FOCUSES ON TESTING WHETHER OR NOT THESE ERROR RATES CORRESPOND TO SPECIFIC RELIABILITY METRICS OR A LARGER METHODOLOGICAL PROBLEM. OUR RESULTS SHOW THAT THE METHOD FOR ESTABLISHING RELIABILITY IS NOT METRIC SPECIFIC, AND WE SUGGEST THE ADOPTION OF NEW PRACTICES TO CONTROL TYPE I ERROR RATES ASSOCIATED WITH ESTABLISHING CODING RELIABILITY.
IN THIS PAPER, WE EXAMINE HOW MULTIPLE SOCIAL MEDIA PLATFORMS ARE BEING USED FOR FORMAL AND INFORMAL LEARNING BY EXAMINING DATA FROM TWO CONNECTIVIST MOOCS (OR CMOOCS). OUR OVERARCHING GOAL IS TO DEVELOP AND EVALUATE METHODS FOR LEARNING ANALYTICS TO DETECT AND STUDY COLLABORATIVE LEARNING PROCESSES. FOR THIS PAPER, WE FOCUS ON HOW TO LINK MULTIPLE ONLINE IDENTITIES OF LEARNERS AND THEIR CONTRIBUTIONS ACROSS SEVERAL SOCIAL MEDIA PLATFORMS IN ORDER TO STUDY THEIR LEARNING BEHAVIOURS IN OPEN ONLINE ENVIRONMENTS. MANY CHALLENGES WERE FOUND IN COLLECTION, PROCESSING, AND ANALYZING THE DATA; RESULTS ARE PRESENTED HERE TO PROVIDE OTHERS WITH INSIGHT INTO SUCH ISSUES FOR EXAMINING DATA ACROSS MULTIPLE OPEN MEDIA PLATFORMS.
MOTIVATING STUDENTS TO ACTIVELY ENGAGE IN THEIR STUDYING EFFORTS IS AN ONGOING CHALLENGE, BECAUSE MOTIVATION IS A KEY FACTOR IN STUDY SUCCESS. IN THE WORK PRESENTED HERE, WE INVESTIGATE WHETHER THE USE OF A MOBILE APP WITH A TEACHER-LIKE AVATAR (STUDYGOTCHI), BASED ON THE SUCCESSFUL DIGITAL PET TAM-AGOTCHI, CAN BE DEPLOYED TO MOTIVATE AND ENGAGE COMPUTER SCIENCE UNIVERSITY STUDENTS IN THEIR BLENDED LEARNING PROGRAMMING COURSE. A RANDOMIZED CONTROLLED STUDY WAS PERFORMED WHICH SHOWED MIXED RESULTS. LESSONS LEARNED INCLUDE (I) BETTER UNDERSTANDING OF HOW TO EFFECTIVELY IMPLEMENT THE GAMEMECHANICS, AND (II) WAYS TO CIRCUMVENT TECHNICAL LIMITATIONS IN USAGE.
EFFECTIVE WRITTEN COMMUNICATION IS AN ESSENTIAL SKILL WHICH PROMOTES EDUCATIONAL SUCCESS FOR UNDERGRADUATES. HOWEVER, UNDERGRADUATE STUDENTS, ESPECIALLY THOSE IN THEIR FIRST YEAR AT UNIVERSITY, ARE UNUSED TO THIS FORM OF WRITING. AFTER THEIR LONG EXPERIENCE WITH THE SCHOOLROOM ESSAY, FOR MOST UNDERGRADUATES ACADEMIC WRITING DEVELOPMENT IS PAINSTAKINGLY SLOW. THUS, ESPECIALLY THOSE WITH POOR WRITING ABILITIES, SHOULD WRITE MORE TO BE BETTER WRITERS. YET, THE BIGGEST IMPEDIMENT TO MORE WRITING IS THAT OVERBURDENED TUTORS WOULD ASK LIMITED NUMBER OF DRAFTS FROM THEIR STUDENTS. TODAY, THERE EXIST POWERFUL COMPUTATIONAL LANGUAGE TECHNOLOGIES THAT COULD EVALUATE STUDENT WRITING, SAVING TIME AND PROVIDING TIMELY, SPEEDY, RELIABLE FEEDBACK WHICH CAN SUPPORT EDUCATORS MARKING PROCESS. THIS PAPER MOTIVATES AN UPDATED VISUAL ANALYTICS DASHBOARD, XIPIT, TO INTRODUCE A SET OF VISUAL AND WRITING ANALYTICS FEATURES EMBEDDED IN A MARKING ENVIRONMENT BUILT ON XIP OUTPUT.
THIS STUDY AIMED TO DEVELOP A PRACTICAL MODEL FOR PREDICTING STUDENTS AT RISK OF PERFORMING POORLY IN BLENDED LEARNING COURSES. PREVIOUS RESEARCH SUGGESTS THAT ANALYZING USAGE DATA STORED IN THE LOG FILES OF MODERN LEARNING MANAGEMENT SYSTEMS (LMSS) WOULD ALLOW TEACHERS TO DEVELOP TIMELY, EVIDENCE-BASED INTERVENTIONS TO SUPPORT AT RISK OR STRUGGLING STUDENTS. THE ANALYSIS OF STUDENTS' TRACKING DATA FROM A MOODLE LMS-SUPPORTED BLENDED LEARNING COURSE WAS THE FOCUS OF THIS RESEARCH IN AN EFFORT TO IDENTIFY SIGNIFICANT CORRELATIONS BETWEEN DIFFERENT ONLINE ACTIVITIES AND COURSE GRADE. OUT OF 29 LMS USAGE VARIABLES, 14 WERE FOUND TO BE SIGNIFICANT AND WERE INPUT IN A STEPWISE MULTIVARIATE REGRESSION WHICH REVEALED THAT ONLY FOUR VARIABLES - READING AND POSTING MESSAGES, CONTENT CREATION CONTRIBUTION, QUIZ EFFORTS AND NUMBER OF FILES VIEWED - PREDICTED 52% OF THE VARIANCE IN THE FINAL STUDENT GRADE. (C) 2015 ELSEVIER INC. ALL RIGHTS RESERVED.
HAVING IDENTIFIED LOW LEVELS OF COMPLETION OF ASSIGNED READING MATERIALS, THE AUTHOR EMPLOYED GAMIFICATION TO IMPROVE MOTIVATION. THE TREATMENT CONSISTS OF A BOUNDED TASK DURING THE READING TO SUPPORT A VARIATION ON "BUZZWORD BINGO" - A GAME PLAYED IN BUSINESS CONTEXTS TO MOCK THE REPETITIVE CORPORATE SPEECH PATTERNS. SOME OF ITS SUBVERSIVE NATURE ARE CARRIED OVER INTO THE CLASSROOM, LEADING TO STUDENTS TRYING TO GAME THE SYSTEM AND RESULTING IN TENSION FOR THE INSTRUCTOR WHO WELCOMED THESE CHALLENGES TO HIS AUTHORITY INTO THE CLASSROOM. AS THE TREATMENT WAS IMPLEMENTED USING A CUSTOM DIGITAL PLATFORM, THE AUTHOR WAS SUBSEQUENTLY ABLE TO COLLECT AND ANALYZE PERFORMANCE DATA. THE AUTHOR USED THE RESULTING INFORMATION TO CUSTOMIZE LESSONS TO STUDENT MISUNDERSTANDINGS AND TO MAKE STUDENT PROGRESS VISIBLE. THE AUTHOR DISCUSSES STUDENT RECEPTION, LESSONS LEARNED, AND PLANS TO ENHANCE THE TREATMENT IN THE FUTURE.
THIS SPECIAL ISSUE BRINGS TOGETHER A RICH COLLECTION OF PAPERS IN COLLABORATION ANALYTICS. WITH TOPICS INCLUDING THEORY BUILDING, DATA COLLECTION, MODELLING, DESIGNING FRAMEWORKS, AND BUILDING MACHINE LEARNING MODELS, THIS ISSUE REPRESENTS SOME OF THE MOST ACTIVE AREAS OF RESEARCH IN THE FIELD. IN THIS EDITORIAL, WE SUMMARIZE THE PAPERS; DISCUSS THE NATURE OF COLLABORATION ANALYTICS BASED ON THIS BODY OF WORK; DESCRIBE THE ASSOCIATED OPPORTUNITIES, CHALLENGES, AND RISKS; AND DEPICT POTENTIAL FUTURES FOR THE FIELD. WE CONCLUDE BY DISCUSSING THE IMPLICATIONS OF THIS SPECIAL ISSUE FOR COLLABORATION ANALYTICS.
PURPOSE THE PURPOSE OF THIS PAPER IS TO ANSWER THE FOLLOWING QUESTIONS: ON WHICH EARLY ALERT SYSTEM SUGGESTIONS ARE STUDENTS MORE LIKELY TO ACT? WHAT FACTORS DRIVE STUDENTS' DECISIONS TO ACT ON EARLY ALERT SYSTEM RECOMMENDATIONS? DESIGN/METHODOLOGY/APPROACH THIS STUDY EXAMINED WHETHER STUDENTS' BEHAVIOUR CHANGED AFTER RECEIVING THE RESULTS OF AN EARLY ALERT SYSTEM (CDR). IN THE MIDDLE OF A SEMESTER, 423 STUDENTS WITH VARYING LEVELS OF ENGLISH PROFICIENCY WERE INVITED TO TRY THE CDR AND COMPLETE A QUESTIONNAIRE THAT ASKED ABOUT THEIR PERCEPTION OF THE TOOL AND WHETHER THEY PLANNED TO ACT ON THE RECOMMENDATIONS THEY RECEIVED. FINDINGS RESULTS SUGGESTED THAT STUDENTS MAINLY PLANNED TO TAKE THE ASSESSMENT-RELATED RECOMMENDATIONS PROVIDED THROUGH THE CDR TO IMPROVE THEIR ASSESSMENT PERFORMANCE. RESULTS ALSO SUGGESTED THAT STUDENT ANXIETY AND STUDENT ABILITY AFFECTED THE LIKELIHOOD THAT STUDENTS WOULD ACT ON THE RECOMMENDATIONS. PRACTICAL IMPLICATIONS - THESE FINDINGS PROVIDE USEFUL INSIGHTS FOR EARLY ALERT SYSTEM DESIGNERS TO ESTABLISH A SYSTEM THAT GENERATES USEFUL RECOMMENDATIONS FOR STUDENTS. ORIGINALITY/VALUE THE FINDINGS OF THIS STUDY CONTRIBUTE TO THE DEVELOPMENT OF EARLY ALERT SYSTEMS. DESIGNERS CAN NOW REALISE WHAT SUGGESTIONS CAN BE EFFECTIVELY OFFERED TO STUDENTS.
IN THIS PAPER, WE PRESENT OUR PRELIMINARY RESULTS OF IDENTIFYING SERENDIPITOUS FINDINGS FROM DISCUSSION FORUMS OF STUDENTS BY USING A TEXT-MINING ANALYTICAL TOOL TO PREDICT THEIR ACADEMIC PERFORMANCES. THE ANALYTICAL RESULTS WERE VISUALIZED BY CONSTRUCTING KEYGRAPHS SO THAT TEACHERS CAN ASSESS THE EFFECTIVENESS OF TEACHING AND INNOVATION OF LEARNING RESPECTIVELY THROUGH THE VISUALIZATION OF HIDDEN PATTERNS IN THE ONLINE LEARNING ENVIRONMENT. OUR RESULTS SHOW THAT THE SERENDIPITOUS FINDINGS HAVE SHOWN A TRACEABLE PATTERN, WHICH IS STATISTICALLY SIGNIFICANT TO PREDICT THE ACADEMIC PERFORMANCE OF STUDENTS. THE RESEARCH FINDINGS CAN LEAD TO ADAPTIVE PEDAGOGICAL DESIGNS FOR TEACHING AND LEARNING BY FINDING HIDDEN PATTERNS AND LINKAGES AMONG THE STUDENTS' SERENDIPITOUS LEARNING. THE IDENTIFIED RESULTS ARE EXPECTED TO SUPPORT BOTH TEACHERS AND STUDENTS ON HOW TO IMPROVE TEACHING AND LEARNING WITH FEEDBACKS FROM THIS NEW TOOL. ULTIMATELY, THIS CREATES A NEW APPROACH FOR TRANSFORMATIVE LEARNING AND TEACHING IN EDUCATION BY USING THE ADVANCED MINING TECHNOLOGY TO ASSESS THE STUDENTS' KNOWLEDGE DISCOVERY PROCESS.
COMPUTATIONAL THINKING (CT) AND CREATIVITY ARE CONSIDERED TWO VITAL SKILLS FOR THE 21ST CENTURY THAT SHOULD BE INCORPORATED INTO FUTURE CURRICULA AROUND THE WORLD. WE STUDIED THE RELATIONSHIP BETWEEN THESE TWO CONSTRUCTS WHILE FOCUSING ON LEARNERS' PERSONAL CHARACTERISTICS. TWO TYPES OF CREATIVITY WERE EXAMINED: CREATIVE THINKING AND COMPUTATIONAL CREATIVITY. THE RESEARCH WAS CONDUCTED AMONG 174 MIDDLE SCHOOL STUDENTS FROM SPAIN. DATA COLLECTED USING A STANDARDIZED CREATIVITY TEST (TORRANCE'S TTCT) WERE TRIANGULATED WITH DATA DRAWN FROM STUDENTS' LOG FILES THAT DOCUMENTED THEIR ACTIVITY IN A GAME-BASED LEARNING ENVIRONMENT FOR CT (KODETU). WE FOUND SOME INTERESTING ASSOCIATIONS BETWEEN CT AND THE TWO CONSTRUCTS OF CREATIVITY. THESE ASSOCIATIONS SHED LIGHT ON POSITIVE ASSOCIATIONS BETWEEN EACH OF THE TWO CREATIVITY CONSTRUCTS AND CT ACQUISITION, AS WELL AS BETWEEN THE TWO CREATIVITY CONSTRUCTS THEMSELVES. ADDITIONALLY, WE HIGHLIGHT DIFFERENCES BETWEEN BOYS AND GIRLS, AS GIRLS WERE FOUND TO BE MORE CREATIVE ON BOTH CREATIVITY MEASURES. OTHER DIFFERENCES ASSOCIATED WITH SCHOOL AFFILIATION, PRIOR CODING KNOWLEDGE, AND TECHNOLOGY AFFINITY ARE ALSO DISCUSSED.
DATA MODELS BUILT FOR ANALYZING STUDENT DATA OFTEN OBFUSCATE TEMPORAL RELATIONSHIPS FOR REASONS OF SIMPLICITY, OR TO AID IN GENERALIZATION. WE PRESENT A MODEL BASED ON TEMPORAL RELATIONSHIPS OF HETEROGENEOUS DATA AS THE BASIS FOR BUILDING PREDICTIVE MODELS. WE SHOW HOW WITHIN- AND BETWEEN-SEMESTER TEMPORAL PATTERNS CAN PROVIDE INSIGHT INTO THE STUDENT EXPERIENCE. FOR EXAMPLE, IN A WITHIN-SEMESTER MODEL, THE PREDICTION OF THE FINAL COURSE GRADE CAN BE BASED ON WEEKLY ACTIVITIES AND SUBMISSIONS RECORDED IN THE LMS. IN THE BETWEEN-SEMESTER MODEL, THE PREDICTION OF SUCCESS OR FAILURE IN A DEGREE PROGRAM CAN BE BASED ON SEQUENCE PATTERNS OF GRADES AND ACTIVITIES ACROSS MULTIPLE SEMESTERS. THE BENEFITS OF OUR SEQUENCE DATA MODEL INCLUDE TEMPORAL STRUCTURE, SEGMENTATION, CONTEXTUALIZATION, AND STORYTELLING. TO DEMONSTRATE THESE BENEFITS, WE HAVE COLLECTED AND ANALYZED 10 YEARS OF STUDENT DATA FROM THE COLLEGE OF COMPUTING AT UNC CHARLOTTE IN A BETWEEN-SEMESTER SEQUENCE MODEL, AND USED DATA IN AN INTRODUCTORY COURSE IN COMPUTER SCIENCE TO BUILD A WITHIN-SEMESTER SEQUENCE MODEL. OUR RESULTS FOR THE TWO SEQUENCE MODELS SHOW THAT ANALYTICS BASED ON THE SEQUENCE DATA MODEL CAN ACHIEVE HIGHER PREDICTIVE ACCURACY THAN NON-TEMPORAL MODELS WITH THE SAME DATA.
THE LEARNING ANALYTICS OFFERS HIGHER EDUCATION VALUABLE INSIGHTS THAT CAN INFORM STRATEGIC DECISION-MAKING REGARDING RESOURCE ALLOCATION FOR EDUCATIONAL EXCELLENCE. RESEARCH DEMONSTRATES THAT VIRTUAL CAMPUS LEARNING MANAGEMENT SYSTEMS (LMSS) CAN INCREASE STUDENT SENSE OF COMMUNITY, SUPPORT LEARNING COMMUNITIES AND ENHANCE STUDENT ENGAGEMENT AND SUCCESS, AND LMSS HAVE THEREFORE BECOME CORE ENTERPRISE COMPONENT IN MANY UNIVERSITIES. WE STARTED THE DEVELOPMENT OF OUR NEW UNIVERSITY VIRTUAL CAMPUS CVUPT IN 2008 AS A CONTINUING ACTIVITY BASED ON NEW TECHNOLOGICAL DEVELOPMENTS BUT ALSO ON THE NEW LEARNING ANALYTICS TOOLS INTEGRATED IN THE LMS. A OUR UNIVERSITY IS A RESEARCH-INTENSIVE UNIVERSITY, WE USED DATA TO INFORM AND GUIDE AN LMS REVIEW AND STRATEGIC DEVELOPMENT PROCESS. USING A NEW LEARNING ANALYTICS TOOLS WE PREPARED A CONTINUOUS PREVIEW BASED IN THE LAST 4 ACADEMIC YEARS. THIS PAPER PRESENTS THE DATA FROM THIS ANALYSIS AND COMMENTS ON WHAT IT REVEALS ABOUT THE COMPARATIVE EFFECTIVENESS OF THIS INSTITUTION'S LMS INTEGRATION IN THE SERVICE OF LEARNING AND TEACHING.
RECENTLY, THE HCI COMMUNITY HAS SEEN INCREASED INTEREST IN THE DESIGN OF TEACHING AUGMENTATION (TA): TOOLS THAT EXTEND AND COMPLEMENT TEACHERS' PEDAGOGICAL ABILITIES DURING ONGOING CLASSROOM ACTIVITIES. EXAMPLES OF TA SYSTEMS ARE EMERGING ACROSS MULTIPLE DISCIPLINES, TAKING VARIOUS FORMS: E.G., AMBIENT DISPLAYS, WEARABLES, OR LEARNING ANALYTICS DASHBOARDS. HOWEVER, THESE DIVERSE EXAMPLES HAVE NOT BEEN ANALYZED TOGETHER TO DERIVE MORE FUNDAMENTAL INSIGHTS INTO THE DESIGN OF TEACHING AUGMENTATION. ADDRESSING THIS OPPORTUNITY, WE BROADLY SYNTHESIZE EXISTING CASES TO PROPOSE THE TA FRAMEWORK. OUR FRAMEWORK SPECIFIES A RICH DESIGN SPACE IN FIVE DIMENSIONS, TO SUPPORT THE DESIGN AND ANALYSIS OF TEACHING AUGMENTATION. WE CONTEXTUALIZE THE FRAMEWORK USING EXISTING DESIGNS CASES, TO SURFACE UNDERLYING DESIGN TRADE-OFFS: FOR EXAMPLE, BALANCING ACTIONABILITY OF PRESENTED INFORMATION WITH TEACHERS' NEEDS FOR PROFESSIONAL AUTONOMY, OR BALANCING UNOBTRUSIVENESS WITH INFORMATIVENESS IN THE DESIGN OF TA SYSTEMS. APPLYING THE TA FRAMEWORK, WE IDENTIFY OPPORTUNITIES FOR FUTURE RESEARCH AND DESIGN.
THIS PAPER INTRODUCES A 'LEARNING IN THE WILD' CODING SCHEMA, AN APPROACH DEVELOPED TO SUPPORT LEARNING ANALYTICS RESEARCHERS INTERESTED IN UNDERSTANDING THE DIFFERENT TYPES OF DISCOURSE, EXPLORATORY TALK, AND CONVERSATIONAL DIALOGUE HAPPENING ON SOCIAL MEDIA. THE RESEARCH EXAMINES HOW LEARNER-PARTICIPANTS ('REDDITORS') ARE LEVERAGING SUBREDDIT COMMUNITIES TO FACILITATE SELF-DIRECTED INFORMAL LEARNING PRACTICES ON THE SOCIAL NETWORKING SITE. THE CODING SCHEMA IS TESTED AND APPLIED ACROSS FOUR 'ASK' SUBREDDIT COMMUNITIES ('ASKHISTORIANS', 'ASK_POLITICS', 'ASKSCIENCE', 'ASKACADEMIA'). THE RESEARCH BRINGS ATTENTION TO HOW KNOWLEDGE, IDEAS, AND RESOURCES ARE BEING SHARED AND SUPPORTED OUTSIDE THE CONFINES OF TRADITIONAL EDUCATION AND PROFESSIONAL ENVIRONMENTS.
THE USE OF ELECTRONIC MEDIA IN EDUCATION HAS A STRONG IMPACT OF ON STUDENT PERFORMANCE. PRIOR RESEARCH HAS INVESTIGATED THE IMPACT OF FORUM READING AND POSTING, AS WELL AS ACCESS TO READING RESOURCES, ON ACADEMIC PERFORMANCE, HOWEVER, LITTLE IS KNOWN ABOUT THE RELATIONSHIP BETWEEN THE TEMPORAL FREQUENCY OF THESE ACTIVITIES AND INDIVIDUAL STUDENT'S PERFORMANCE. IN ADDITION, SOME STUDIES HAVE PROPOSED DIFFERENT CATEGORIZATIONS OF STUDENT ACTIVITIES, AND PROVED THE USEFULNESS OF THESE CHARACTERIZATIONS FOR PREDICTIVE PURPOSES AND BETTER UNDERSTANDING OF THE LEARNING PROCESS. ONE OF THE BIGGEST CONCERNS WHEN TEACHING COURSES IN A VIRTUAL LEARNING ENVIRONMENT IS TO CREATE AND DEVELOP INSTRUCTION IN A WAY THAT IT IMPROVES THE OVERALL LEARNING EXPERIENCE AND RESULTS. THE PRESENT STUDY ADDRESSES THIS CONCERN BY DRAWING UPON FACTORS INFLUENCING ACADEMIC PERFORMANCE; MORE SPECIFICALLY, THE STUDY FOCUSES ON OBJECTIVE FACTORS RELATED TO THE INTERACTION BETWEEN THE STUDENT AND THE SYSTEM (NUMBER OF RESOURCES VISITED IN THE LEARNING PLATFORM, AND NUMBER OF FORUM POSTS AND VIEWS). ON TOP OF THAT, A VISUALIZATION TOOL WAS DESIGNED AND IMPLEMENTED TO ALLOW FOR FURTHER INVESTIGATION OF THE RELEVANCE OF THE STUDY VARIABLES. THE RESULTS FROM THE STUDY SHOW THAT THERE IS A RECURRENT PATTERN IN THE FREQUENCY OF BEHAVIORS AND PERFORMANCE ACROSS DIFFERENT COURSES. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
VIRTUAL LEARNING ENVIRONMENTS (VLE) OFFER A WIDE RANGE OF COURSES AND LEARNING SUPPORTS FOR STUDENTS. SUCH INNOVATIVE LEARNING PLATFORMS GENERATE DAILY A HUGE QUANTITY OF DATA, REGARDING THE INTERACTIONS AMONG THE STUDENTS AND THE VLE. TO ANALYZE THESE BIG EDUCATIONAL DATA A NEW RESEARCH BRANCH CALLED EDUCATIONAL DATA MINING (EDM) HAS EMERGED, THAT PUTS TOGETHER COMPUTER SCIENTISTS AND PEDAGOGUES RESEARCHERS' EXPERTISE. SO FAR, EDUCATIONAL DATA HAVE BEEN STUDIED AS STATIONARY DATA BY TRADITIONAL MACHINE LEARNING METHODS. RATHER, EDUCATIONAL DATA ARE NON-STATIONARY IN NATURE AND CAN BE BETTER ANALYZED AS DATA STREAMS. IN THIS PAPER WE INVESTIGATE THE USE OF AN ADAPTIVE FUZZY CLUSTERING ALGORITHM CALLED DISSFCM (DYNAMIC INCREMENTAL SEMI-SUPERVISED FCM) TO PROCESS EDUCATIONAL DATA AS DATA STREAMS AND PREDICT THE STUDENTS' OUTCOMES TO ONE EXAM MODULE. NUMERICAL EXPERIMENTS ON THE OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD) SHOW THE RELIABILITY OF DISSFCM IN CREATING GOOD CLASSIFICATION MODELS OF EDUCATIONAL DATA.
THIS STUDY INVESTIGATES HOW MULTIMODAL USER-GENERATED DATA CAN BE USED TO REINFORCE LEARNER REFLECTION, IMPROVE TEACHING PRACTICES, AND CLOSE THE LEARNING ANALYTICS LOOP. IN PARTICULAR, THE AIM OF THE STUDY IS TO UTILIZE USER GAZE AND ACTION-BASED DATA TO EXAMINE THE ROLE OF A MIRRORING TOOL (I.E., EXERCISE VIEW IN ECLIPSE) IN ORCHESTRATING BASIC BEHAVIOURAL REGULATION DURING DEBUGGING. THE RESULTS DEMONSTRATED THAT STUDENTS WHO PROCESSED THE INFORMATION PRESENTED IN THE EXERCISE VIEW AND ACTED UPON IT, IMPROVED THEIR PERFORMANCE AND ACHIEVED A HIGHER LEVEL OF SUCCESS THAN THOSE WHO FAILED TO DO SO. THE FINDINGS SHED LIGHT ON WHAT CONSTITUTES RELEVANT DATA WITHIN A PARTICULAR LEARNING CONTEXT IN PROGRAMMING USING GAZE PATTERNS. MOREOVER, THESE FINDINGS COULD GUIDE THE COLLECTION OF ESSENTIAL LEARNER-CENTRED ANALYTICS FOR DESIGNING USABLE, MODULAR LEARNING ENVIRONMENTS BASED ON DATA-DRIVEN APPROACHES.
THE ARTICLE IS DEVOTED TO LEARNING ANALYTICS PROBLEMS ASSOCIATED WITH THE DIGITAL CULTURE DEVELOPMENT IN THE UNIVERSITY EDUCATIONAL SPACE AND WITH THE STUDENT ACTIVITY CONTROL IN THE VOCATIONAL TRAINING PROCESS. THE EMPIRICAL BASIS OF THE STUDY WAS A SERIES OF SURVEYS CONDUCTED BY THE CENTER FOR SOCIOLOGICAL RESEARCH OF THE PETER THE GREAT POLYTECHNIC UNIVERSITY IN 2018-2020. TO SYSTEMATIZE THE INFORMATION ON THE TRACES OF STUDENTS' ACTIVITY IN THE DIGITAL SPACE, THE METHOD OF CONSTRUCTING A PERSONAL MENTAL MAP, REFLECTING THE COGNITIVE CHARACTERISTICS OF THE STUDENT'S INTERACTIVE ACTIONS IN THE NETWORK, WAS USED. BECAUSE OF THE ANALYSIS OF THE MENTAL MAPS, THE GENERAL STRUCTURE OF THE PERSONAL DIGITAL FOOTPRINT WAS IDENTIFIED, WHICH IS SIGNIFICANT FOR ANALYTICS OF THE STUDENT'S ACADEMIC HISTORY AND SELF-ASSESSMENT OF HIS ACTIVITIES IN PROFESSIONAL DEVELOPMENT. IN CONCLUSION, THE CONSTRUCTIVE ROLE OF DIGITAL TECHNOLOGY IN ASSESSING AND MODELING THE EDUCATIONAL PROCESS IS EMPHASIZED. THE STUDY OF STUDENTS' DIGITAL FOOTPRINTS ON THE UNIVERSITY PLATFORMS, SUPPLEMENTED BY THE STUDY OF THEIR ACTIVITY IN SOCIAL NETWORKS, ALLOWS THE DEVELOPMENT OF EDUCATIONAL MODELING AIMED AT CREATING A MORE ADEQUATE SET OF COMPETENCIES AND SOFT SKILLS OF THE GRADUATE.
PRACTICE-BASED EXPERIENTIAL LEARNING ANALYTICS RESEARCH AND SUPPORT (PELARS) IS A FP7 EU PROGRAM PROPOSING TO CONTRIBUTE TO INTRODUCE NEW EDUCATIONAL TECHNOLOGIES. UNIVERSITY OF CRAIOVA (UCV) WAS ONE OF THE PELARS PARTNERS. IN THIS PAPER IS PRESENTING HOW THE TEACHING AND TRAINING EXPERIENCE ACQUIRED BY AUTHORS DURING THEIR ACTIVITIES AT UCV AND AT THE ROMANIAN AGENCY FOR QUALITY ASSURANCE IN HIGHER EDUCATION (ARACIS) WAS USED TO SUPPORT THE ACTIVITIES AND PELARS LEVEL. CORRESPONDENTLY, THE PAPER DISCUSSES HOW THE RESULTS OBTAINED IN THE FRAME OF PELARS COULD BE USEFUL FOR THE DAY BY DAY ACTIVITIES AT UCV AND ARACIS, INCLUDING THE PROCESS OF EUROPEAN ACCREDITED ENGINEER (EUR ACE) LABEL PROVIDING. THE MAIN IDEA IS THAT THE CHANGING WORLD MUST BE FOLLOWED BE CHANGING UPDATED EDUCATION TECHNOLOGIES.
COLLABORATION IS CONSIDERED AS ONE OF THE MAIN DRIVERS OF LEARNING AND IT HAS BEEN BROADLY STUDIED ACROSS NUMEROUS CONTEXTS, INCLUDING MASSIVE OPEN ONLINE COURSES (MOOCS). THE RESEARCH ON MOOCS HAS RISEN EXPONENTIALLY DURING THE LAST YEARS AND THERE HAVE BEEN A NUMBER OF WORKS FOCUSED ON STUDYING COLLABORATION. HOWEVER, THESE PREVIOUS STUDIES HAVE BEEN RESTRICTED TO THE ANALYSIS OF COLLABORATION BASED ON THE FORUM AND SOCIAL INTERACTIONS, WITHOUT TAKING INTO ACCOUNT OTHER POSSIBILITIES SUCH AS THE SYNCHRONICITY IN THE INTERACTIONS WITH THE PLATFORM. THEREFORE, IN THIS WORK WE PERFORMED A CASE STUDY WITH THE GOAL OF IMPLEMENTING A DATA-DRIVEN APPROACH TO DETECT AND CHARACTERIZE COLLABORATION IN MOOCS. WE APPLIED AN ALGORITHM TO DETECT SYNCHRONICITY LINKS BASED ON THEIR SUBMISSION TIMES TO QUIZZES AS AN INDICATOR OF COLLABORATION, AND APPLIED IT TO DATA FROM TWO LARGE COURSERA MOOCS. WE FOUND THREE DIFFERENT PROFILES OF USER ACCOUNTS, THAT WERE GROUPED IN COUPLES AND LARGER COMMUNITIES EXHIBITING DIFFERENT TYPES OF ASSOCIATIONS BETWEEN USER ACCOUNTS. THE CHARACTERIZATION OF THESE USER ACCOUNTS SUGGESTED THAT SOME OF THEM MIGHT REPRESENT GENUINE ONLINE LEARNING COLLABORATIVE ASSOCIATIONS, BUT THAT IN OTHER CASES DISHONEST BEHAVIORS SUCH AS FREE-RIDING OR MULTIPLE ACCOUNT CHEATING MIGHT BE PRESENT. THESE FINDINGS CALL FOR ADDITIONAL RESEARCH ON THE STUDY OF THE KIND OF COLLABORATIONS THAT CAN EMERGE IN ONLINE SETTINGS. (C) 2021 THE AUTHOR(S). PUBLISHED BY ELSEVIER B.V.
THIS PAPER DESCRIBES AN INFRASTRUCTURE FOR SEAMLESS LEARNING ANALYTICS TO BRIDGE DIGITAL TEXTBOOK LEARNING AND PRACTICAL TRAINING SUCH AS PROGRAMMING AND CONCEPTUAL MODELING EDUCATION. TO REALIZE THE INFRASTRUCTURE FOR SEAMLESS LEARNING ANALYTICS, WE PROPOSE THE INTEGRATION OF A DIGITAL TEXTBOOK SYSTEM INTO A SOFTWARE LEARNING SUPPORT SYSTEM. BY USING OUR PROPOSED INFRASTRUCTURE, ALL LEARNING DATA WILL BE SENT BY XAPI AND COLLECTED IN AN INDEPENDENT LRS. WE BELIEVE THAT ANALYZING AND VISUALIZING THE RELATIONSHIPS BETWEEN THE LEARNING IN THE DIGITAL TEXTBOOK SYSTEM AND PRACTICAL TRAINING IN THE SOFTWARE LEARNING SUPPORT SYSTEM LEADS TO IMPROVING THE QUALITY OF LEARNING AND TEACHING.
THE CURRENT STUDY DEVELOPED AN INTELLIGENT LEARNING ENVIRONMENT FOR ONLINE EDUCATION OF RESEARCH ETHICS AND INVESTIGATED HOW ENCOURAGEMENT AND WARNING INTELLIGENT FEEDBACK INFLUENCED LEARNERS? ENGAGEMENT (BEHAVIOURAL, EMOTIONAL, AND COGNITIVE) AND COGNITIVE LOAD (MENTAL LOAD AND MENTAL EFFORT). PARTICIPANTS INCLUDED 191 GRADUATE STUDENTS IN TAIWAN WHO WERE RANDOMLY ASSIGNED INTO FOUR GROUPS: A NON-FEEDBACK GROUP, A WARNING FEEDBACK GROUP, AN ENCOURAGEMENT FEEDBACK GROUP, AND AN ENCOURAGEMENT PLUS WARNING FEEDBACK GROUP. THE INTELLIGENT FEEDBACK WAS ESTABLISHED ON THE BASIS OF LEARNING ANALYTICS FROM A DATABASE OF PRIOR LEARNERS? ONLINE LEARNING OF OFF- OR ON-TASK BEHAVIOURS. THE SYSTEM DETECTED STUDENTS? SEQUENTIAL BEHAVIOURAL PATTERNS AND PROVIDED INSTANT INTELLIGENT FEEDBACK. THE RESULTS SHOWED THAT ENCOURAGEMENT PLUS WARNING FEEDBACK CAUSED HIGHER EMOTIONAL AND COGNITIVE ENGAGEMENT, WHEREAS WARNING FEEDBACK LED TO HIGHER MENTAL LOAD. THE STUDY SUGGESTS THAT WHEN DESIGNING INTELLIGENT LEARNING ENVIRONMENTS, ENCOURAGEMENT, AND WARNING INTELLIGENT FEEDBACK SHOULD BE USED JOINTLY TO ENHANCE THE APPEAL OF THE LEARNING JOURNEY AND TO BOOST EMOTIONAL ENGAGEMENT, THEREBY INCREASING STUDENTS? WILLINGNESS TO LEARN. IT IS IMPORTANT TO AVOID USING ONLY WARNING FEEDBACK TO REDUCE INTERFERENCE IN LEARNING.
LEARNING ANALYTICS PROVIDED NEW OPPORTUNITIES TO INVESTIGATE STUDENTS' ONLINE LEARNING EXPERIENCE IN A MORE IN-DEPTH AND COMPREHENSIVE WAY. IN THIS STUDY, LEARNING ANALYTICS WERE UTILIZED TO INVESTIGATE INTERNATIONAL STUDENTS' LEARNING EXPERIENCE IN AN ENGLISH MOOC IN ADDITION TO SURVEY DATA AND TEST RESULTS. USING K-MEANS CLUSTER ANALYSIS AND STEPWISE LINEAR REGRESSION, THIS STUDY CLASSIFIED INTERNATIONAL MOOC STUDENTS INTO FOUR MAJOR CATEGORIES AND IDENTIFIED THREE TYPES OF LEARNING BEHAVIORS AS SIGNIFICANT PREDICTORS FOR MOOC LEARNING PERFORMANCE. THE RESEARCH FINDINGS PROVIDE SEVERAL INTERESTING PERSPECTIVES ON INTERNATIONAL STUDENTS' ONLINE LEARNING BEHAVIORS, AND CAN INFORM THE DESIGN AND EVALUATION OF ONLINE INSTRUCTION FOR GLOBAL AUDIENCES.
THIS PAPER DESCRIBES AN INNOVATIVE VISUALIZATION METHOD CALLED SEAMLESS LEARNING VISUALIZATION (SLV). AS FAR, SEVERAL RESEARCHERS HAVE INTRODUCED IN THE SEAMLESS LEARNING ENVIRONMENTS IN ORDER TO BRIDGE FORMAL LEARNING OVER INFORMAL LEARNING. HOWEVER, THEIR FOCUS WAS THE IMPLEMENTATION OF THE SEAMLESS LEARNING ENVIRONMENT. OUR PROPOSED SLV IS TO VISUALIZE AND ANALYZE LEARNING LOGS COLLECTED IN THE SEAMLESS LEARNING ENVIRONMENT. THIS PAPER DESCRIBES HOW OUR VISUALIZATION METHOD COULD CONTRIBUTE TO BRIDGING THE GAP BETWEEN FORMAL AND INFORMAL LEARNING. AN EXPERIMENT WAS CONDUCTED TO EVALUATE (1) WHETHER SLV WOULD BE BENEFICIAL IN TERMS OF USABILITY IN FINDING WORDS IN THE SEAMLESS LEARNING ENVIRONMENT AND (2) WHICH VISUALIZATION LAYOUT (RANDOM LAYOUT, FORCE-DIRECTED LAYOUT, YIFAN-MULTILEVEL LAYOUT AND SLV) IS EFFECTIVE IN SUPPORTING LEARNING IN THE SEAMLESS LEARNING ENVIRONMENT. FIFTEEN INTERNATIONAL STUDENTS PARTICIPATED IN THE EVALUATION EXPERIMENT IN ORDER TO EVALUATE (1) AND (2). IN THE EXPERIMENT, IT WAS FOUND THAT SLV WAS VERY BENEFICIAL IN TERMS OF USABILITY AND EFFECTIVENESS FOR LEARNING THAN PREVIOUS VISUALIZATION METHODS.
THIS PAPER IS IN RESPONSE TO THE MANUSCRIPT ENTITLED "ETHICAL OVERSIGHT OF STUDENT DATA IN LEARNING ANALYTICS: A TYPOLOGY DERIVED FROM A CROSS-CONTINENTAL, CROSS-INSTITUTIONAL PERSPECTIVE" (WILLIS ET AL., EDUC TECHNOL RES DEV 64(5):881-901, 2016) FROM AN ETHICAL PERSPECTIVE. THE IMPACT OF THE ARTICLE IS THAT IT OFFERED A WORKING TYPOLOGY OF ETHICAL APPROACHES AND AIMS TO DETERMINE THE ETHICAL INTERSECTION OF INTERNAL STUDENT DATA USAGE AND APPLICATION. THEIR REVIEW OF ETHICAL APPROACHES INCLUDED RESEARCH INSTITUTIONS FROM THREE CONTINENTS. FINDINGS FROM THIS REVIEW HAVE IMPLICATIONS FOR APPLIED RESEARCH WITH STUDENT DATA. THIS IS PARTICULARLY INTERESTING GIVEN THE DIFFERENCES IN ETHICAL APPROACHES IN THE UNITED STATES (MORE UTILITARIAN) AND THE EUROPEAN UNION'S RATHER STRICT DEONTOLOGICAL APPROACH. WHILE LEARNING ANALYTICS OFFER RICH STUDENT DATA FOR PERSONALIZING EDUCATIONAL TECHNOLOGIES, THERE IS ASSOCIATED POTENTIAL FOR THREATS TO AUTONOMY AND PRIVACY. A LIMITATION OF THE TOPOLOGY IS THAT IT DID NOT INCLUDE OTHER PEOPLE GROUPS (E.G., ASIA, SOUTH AMERICA, AUSTRALIA). FUTURE LEARNING ANALYTICS, DESIGN, APPLICATION, AND RESEARCH WILL NEED TO CONSIDER BOTH WHERE THE TECHNOLOGIES WERE DEVELOPED AND WHERE EDUCATIONAL TECHNOLOGIES ARE BEING APPLIED.
THE PRESENCE OF MOOCS HAS INCREASED EXPONENTIALLY IN THE CONTEXT OF DISTANCE EDUCATION. HOWEVER, MANY STUDENTS WHO ENROLL IN THIS TYPE OF COURSES DROP OUT BEFORE COMPLETION. SEVERAL CIRCUMSTANCES MAY CAUSE DROPOUT AT ANY STAGE OF THE COURSE AND, IN ANY CASE, BEFORE GETTING THE CERTIFICATE. SOME STUDENTS ARE NOT ABLE TO FOLLOW THE COURSE, OTHERS FAIL IN THE EXAMS AND LEAVE, OTHERS ONLY AIM AT GETTING A GENERAL OVERVIEW OF THE COURSE CONTENTS, OTHERS TAKE ALL THE ACTIVITIES BUT THE FINAL TEST, BECAUSE OF BEING INTERESTED IN GAINING KNOWLEDGE BUT NOT IN OBTAINING THE CERTIFICATE, ETC. FOR THIS REASON, IT IS INTERESTING TO ANALYZE AND UNDERSTAND THE BEHAVIOR OF EACH STUDENT WHILE INTERACTING WITH THE COURSE. IN THIS DIRECTION, THE GOAL OF THIS WORK IS TO PREDICT WHETHER A STUDENT WILL ABANDON A MOOC BEFORE COMPLETING IT, SO THAT IT IS POSSIBLE TO INTERVENE ACCORDINGLY, BY WARNING THE TEACHER ABOUT THE DROPOUT RISK, NOTIFYING THE STUDENT ABOUT THIS RISK, ETC. DIFFERENT MACHINE LEARNING TECHNIQUES HAVE BEEN TESTED WITH REAL DATA OF A MOOC SUPPORTED BY EDX AT UAM. IN THIS ARTICLE, THE RESULTS OF THE WORK CARRIED OUT IN THIS DIRECTION ARE PRESENTED.
GAMEPLAY METRICS CAN HELP PROVIDE INSIGHTS INTO PLAYER BEHAVIOUR AND PERFORMANCE, SUPPORTING GAME DESIGNERS AND PLAYERS WISHING TO BETTER UNDERSTAND THEIR OWN GAME, YET SUCH DATA IS OFTEN NOT EASILY AVAILABLE TO THE WIDER PUBLIC. WE TAKE OUR INSPIRATION FROM THE FIELD OF LEARNING ANALYTICS, WHICH ATTEMPTS TO CREATE AWARENESS AND INSIGHTS FOR LEARNERS BY LEVERAGING THE GATHERED ACTIVITY DATA AND FEEDING THIS BACK TO THE STUDENTS IN AN OPEN WAY. THIS PAPER SUGGESTS AN OPEN STANDARD FOR GATHERING AND STORING GAMEPLAY METRICS DATA BASED ON PROVEN STANDARDS IN LEARNING ANALYTICS, FACILITATING EASIER DEVELOPMENT OF ANALYTICAL TOOLS ACROSS DIFFERENT VIDEO GAMES AND GENRES TO THE BENEFIT OF RESEARCHERS, DEVELOPERS, AND PLAYERS.
PREDICTION MODELS THAT UNDERLIE "EARLY WARNING SYSTEMS" NEED IMPROVEMENT. SOME PREDICT OUTCOMES USING ENTRENCHED, UNCHANGEABLE CHARACTERISTICS (E.G., SOCIOECONOMIC STATUS) AND OTHERS RELY ON PERFORMANCE ON EARLY ASSIGNMENTS TO PREDICT THE FINAL GRADES TO WHICH THEY CONTRIBUTE. BEHAVIORAL PREDICTORS OF LEARNING OUTCOMES OFTEN ACCRUE SLOWLY, TO THE POINT THAT TIME NEEDED TO PRODUCE ACCURATE PREDICTIONS LEAVES LITTLE TIME FOR INTERVENTION. WE AIMED TO IMPROVE ON THESE METHODS BY TESTING WHETHER WE COULD PREDICT PERFORMANCE IN A LARGE LECTURE COURSE USING ONLY STUDENTS' DIGITAL BEHAVIORS IN WEEKS PRIOR TO THE FIRST EXAM. EARLY PREDICTION BASED ONLY ON MALLEABLE BEHAVIORS PROVIDES TIME AND OPPORTUNITY TO ADVISE STUDENTS ON WAYS TO ALTER STUDY AND IMPROVE PERFORMANCE. THEREAFTER, WE TOOK THE NOT-YET-COMMON STEP OF APPLYING THIS MODEL AND TESTING WHETHER PROVIDING DIGITAL LEARNING SUPPORT TO THOSE PREDICTED TO PERFORM POORLY CAN IMPROVE THEIR ACHIEVEMENT. USING LEARNING MANAGEMENT SYSTEM LOG DATA, WE TESTED MODELS COMPOSED OF THEORY-ALIGNED BEHAVIORS USING MULTIPLE ALGORITHMS AND OBTAINED A MODEL THAT ACCURATELY PREDICTED POOR GRADES. OUR ALGORITHM CORRECTLY IDENTIFIED 75% OF STUDENTS WHO FAILED TO EARN THE GRADE OF B OR BETTER NEEDED TO ADVANCE TO THE NEXT COURSE. WE APPLIED THIS MODEL THE NEXT SEMESTER TO PREDICT ACHIEVEMENT LEVELS AND PROVIDED A DIGITAL LEARNING STRATEGY INTERVENTION TO STUDENTS PREDICTED TO PERFORM POORLY. THOSE WHO ACCESSED ADVICE OUTPERFORMED CLASSMATES ON SUBSEQUENT EXAMS, AND MORE STUDENTS WHO ACCESSED THE ADVICE ACHIEVED THE B NEEDED TO MOVE FORWARD IN THEIR MAJOR THAN THOSE WHO DID NOT ACCESS ADVICE.
THIS PAPER REPORTS ON OUR ONGOING RESEARCH AROUND LEARNING ANALYTICS. WE FOCUS ON HOW LEARNING ANALYTICS CAN BE USED TO INCREASE STUDENT MOTIVATION AND THE USE OF BADGES AS A WAY TO AGGREGATE LEARNING ACTIVITY BEING A REPRESENTATION OF THEIR GOALS AND PROGRESS ALONG THE COURSE. THE CONTEXT OF THIS WORK IS AN OPEN LEARNING ENVIRONMENT, BASED ON WIKIS, BLOGS, TWITTER, AN ACTIVITY STREAM MASH-UP AND AN OPEN BADGES SYSTEM. OUR EVALUATION ANALYSES PERCEIVED USEFULNESS AND USABILITY OF THE SYSTEM, AS WELL AS THE IMPACT ON STUDENT MOTIVATION. OUR RESULTS INDICATE THAT BADGES ARE USEFUL TO MOTIVATE STUDENTS WHILE ACTIVITY STREAMS HAVE THE POTENTIAL TO ACTIVATE STUDENTS.
THE LARGE-SCALE AND GRANULAR INTERACTION DATA COLLECTED IN ONLINE LEARNING PLATFORMS SUCH AS MASSIVE OPEN ONLINE COURSES (MOOCS) PROVIDE UNIQUE OPPORTUNITIES TO BETTER UNDERSTAND INDIVIDUALS' LEARNING PROCESSES AND COULD FACILITATE THE DESIGN OF PERSONALIZED AND MORE EFFECTIVE SUPPORT MECHANISMS FOR LEARNERS. IN THIS PAPER, WE PRESENT TWO DIFFERENT METHODS OF EXTRACTING STUDY PATTERNS FROM ACTIVITY SEQUENCES. UNLIKE MOST OF THE PREVIOUS WORKS, WITH POST HOC ANALYSIS OF ACTIVITY PATTERNS, OUR PROPOSED METHODS COULD BE DEPLOYED DURING THE COURSE AND ENABLE THE LEARNERS TO RECEIVE REAL-TIME SUPPORT AND FEEDBACK. IN THE FIRST METHOD, FOLLOWING A HYPOTHESIS-DRIVEN APPROACH, WE EXTRACT PREDEFINED PATTERNS FROM LEARNERS' INTERACTIONS WITH THE COURSE MATERIALS. WE THEN IDENTIFY AND ANALYZE DIFFERENT LONGITUDINAL PROFILES AMONG LEARNERS BY CLUSTERING THEIR STUDY PATTERN SEQUENCES DURING THE COURSE. OUR SECOND METHOD IS A DATA-DRIVEN APPROACH TO DISCOVER LATENT STUDY PATTERNS AND TRACK THEM OVER TIME IN A COMPLETELY UNSUPERVISED MANNER. WE PROPOSE A CLUSTERING PIPELINE TO MODEL AND CLUSTER ACTIVITY SEQUENCES AT EACH TIME STEP AND THEN SEARCH FOR MATCHING CLUSTERS IN PREVIOUS STEPS TO ENABLE TRACKING OVER TIME. THE PROPOSED PIPELINE IS GENERAL AND ALLOWS FOR ANALYSIS AT DIFFERENT LEVELS OF ACTION GRANULARITY AND TIME RESOLUTION IN VARIOUS ONLINE LEARNING ENVIRONMENTS. EXPERIMENTS WITH SYNTHETIC DATA SHOW THAT OUR PROPOSED METHOD CAN ACCURATELY DETECT LATENT STUDY PATTERNS AND TRACK CHANGES IN LEARNING BEHAVIOURS. WE DEMONSTRATE THE APPLICATION OF BOTH METHODS ON A MOOC DATASET AND STUDY THE TEMPORAL DYNAMICS OF LEARNERS' BEHAVIOUR IN THIS CONTEXT.
STUDENT ENGAGEMENT IS AN IMPORTANT FACTOR FOR LEARNING OUTCOMES IN HIGHER EDUCATION. ENGAGEMENT WITH LEARNING AT CAMPUS-BASED HIGHER EDUCATION INSTITUTIONS IS DIFFICULT TO QUANTIFY DUE TO THE VARIETY OF FORMS THAT ENGAGEMENT MIGHT TAKE (E.G. LECTURE ATTENDANCE, SELFSTUDY, USAGE OF ONLINE/DIGITAL SYSTEMS). MEANWHILE, THERE ARE INCREASING CONCERNS ABOUT STUDENT WELLBEING WITHIN HIGHER EDUCATION, BUT THE RELATIONSHIP BETWEEN ENGAGEMENT AND WELLBEING IS NOT WELL UNDERSTOOD. HERE WE ANALYSE RESULTS FROM A LONGITUDINAL SURVEY OF UNDERGRADUATE STUDENTS AT A CAMPUS-BASED UNIVERSITY IN THE UK, AIMING TO UNDERSTAND HOW ENGAGEMENT AND WELLBEING VARY DYNAMICALLY DURING AN ACADEMIC TERM. THE SURVEY INCLUDED MULTIPLE DIMENSIONS OF STUDENT ENGAGEMENT AND WELLBEING, WITH A DELIBERATE FOCUS ON SELFREPORT MEASURES TO CAPTURE STUDENTS' SUBJECTIVE EXPERIENCE. THE RESULTS SHOW A WIDE RANGE OF ENGAGEMENT WITH DIFFERENT SYSTEMS AND STUDY ACTIVITIES, GIVING A BROAD VIEW OF STUDENT LEARNING BEHAVIOUR OVER TIME. ENGAGEMENT AND WELLBEING VARY DURING THE TERM, WITH CLEAR BEHAVIOURAL CHANGES CAUSED BY ASSESSMENTS. RESULTS INDICATE A POSITIVE INTERACTION BETWEEN ENGAGEMENT AND HAPPINESS, WITH AN UNEXPECTED NEGATIVE RELATIONSHIP BETWEEN ENGAGEMENT AND ACADEMIC OUTCOMES. THIS STUDY PROVIDES IMPORTANT INSIGHTS INTO SUBJECTIVE ASPECTS OF THE STUDENT EXPERIENCE AND PROVIDES A CONTRAST TO THE INCREASING FOCUS ON ANALYSING EDUCATIONAL PROCESSES USING DIGITAL RECORDS.
TRAC IS A ONE-SHOT VISUALISATION APPLICATION DESIGNED TO HELP PROGRAM DIRECTORS TO CREATE AN INFORMED OPINION OF THE ACADEMIC SITUATION OF STUDENTS. TRAC OVERLAYS ACADEMIC DATA ON TOP OF THE (FIXED) CURRICULAR STRUCTURE THAT THE PROGRAMS IN OUR UNIVERSITY HAVE, AND INCLUDES FEATURES TO TRACK THE EVOLUTION OF THE ACADEMIC SITUATION TERM BY TERM. THE DESIGN OF TRAC ACTIVELY INVOLVED PROGRAM DIRECTORS, AND THE SOFTWARE ARCHITECTURE WAS DESIGNED TO FACILITATE DEPLOYMENT IN A COMPLEX INSTITUTION. AN ONGOING PILOT STUDY WITH DIRECTORS OF DIVERSE PROGRAMS SHOWS HOW TRAC EFFECTIVELY HELPS NOT ONLY TO INSPECT THE ACADEMIC SITUATION OF STUDENTS, BUT ALSO ALLOWS TO DISCOVER ISSUES OF THE STRUCTURE OF THE PROGRAMS, AND SPOT DIFFICULT COURSES AND SPECIAL SITUATIONS.
ALTHOUGH THERE HAS BEEN MUCH RESEARCH IN THE AREA OF DATA ANALYTICS IN RECENT YEARS (E.G. SHUM & FERGUSON 2012), THERE ARE QUESTIONS REGARDING WHICH ANALYTIC METHODOLOGIES CAN BE MOST EFFECTIVE IN INFORMING HIGHER EDUCATION TEACHING AND LEARNING PRACTICES (GIBSON & DE FREITAS 2016). THIS PROJECT FOCUSES ON ONE MODULE WITHIN THE SCHOOL OF COMPUTING AND COMMUNICATIONS IN THE STEM FACULTY OF THE OPEN UNIVERSITY, UK, TO GAIN A CLEARER UNDERSTANDING ON WHY STUDENTS MIGHT, OR MIGHT NOT, ENGAGE WITH COMPUTER AIDED LEARNING AND TEACHING (CALT) RESOURCES. WE EXPLORE THE USE OF SPECIFIC CALT RESOURCES ON THE MODULE 'COMMUNICATIONS TECHNOLOGY', A PRINT-BASED MODULE WITH A RANGE OF ONLINE RESOURCES DESIGNED TO SUPPLEMENT THE TEXT. IN PARTICULAR WE EXPLORE THE POSSIBLE CORRELATION BETWEEN USE OF CALT RESOURCES AND STUDENT EXAMINATION PERFORMANCE. THE RESEARCH QUESTIONS COVER TWO KEY AREAS; THE EFFECTIVENESS OF THE ANALYTICS TOOLS AND STUDENTS' PERCEPTION OF THE CALT RESOURCES. DATA ANALYTICS WERE USED TO DETERMINE WHEN STUDENTS ENGAGED WITH THE CALT RESOURCES AND WHETHER THIS WAS AT PREDICTED TIMES DURING THE MODULE. STUDENT FEEDBACK VIA INTERVIEW WAS USED TO EXPLORE WHAT MOTIVATES STUDENTS TO ENGAGE WITH CALT RESOURCES, WHETHER STUDENTS UNDERSTAND A TOPIC MORE DEEPLY AS A RESULT OF USING CALT RESOURCES, AND IF STUDENTS ARE DETERRED IF THE RESOURCES ARE TOO COMPLICATED OR TIME CONSUMING. OUR CONCLUSION FROM THIS CASE STUDY IS THAT LEARNING ANALYTICS ARE USEFUL FOR TRACKING STUDENT ENGAGEMENT. THE ANALYTICS WERE VERY USEFUL TO REVIEW DURING MODULE PRESENTATION, SPECIFICALLY FOR ANALYSING STUDENTS' ONLINE BEHAVIOUR. THE SUPPLEMENTARY INTERVIEWS HELPED TO SHED LIGHT ON THE POTENTIAL SIGNIFICANCE OF THE DATA GLEANED.
IN COLLABORATIVE LEARNING TOOLS SUPPORTED BY MOBILE DEVICES, USERS EXCHANGE MESSAGES IN ORDER TO SUPPORT THE COLLECTIVE CONSTRUCTION OF KNOWLEDGE BY THE MEMBERS OF A GROUP. ACTIVITY ENTRIES IN THE GROUP PROVIDE EVIDENCE OF HOW THE LEARNING PROGRESS OCCURS. COORDINATION MECHANISMS TOGETHER WITH LEARNING ANALYTIC TECHNIQUES CAN BE USED IN TEACHING ENVIRONMENTS, IN ORDER TO SUPPORT THE TEACHER IN THE COORDINATION, ALLOWING HIM/HER TO ADAPT THE PEDAGOGICAL PROPOSALS. THIS PAPER AIMS TO IDENTIFY THE COORDINATION MECHANISMS FOR MOBILE TEACHING-LEARNING COLLABORATIVE ENVIRONMENTS, AS WELL AS TO APPLY THEM TO MODEL A SYSTEM (EZCLASS) WITH EMPHASIS ON TEACHER COORDINATION IN A VIRTUAL LEARNING ENVIRONMENT. THE MECHANISMS ARE REPRESENTED BY FUNCTIONAL REQUIREMENTS AND THEY ARE USED IN THE DEFINITION OF THE EZCLASS SYSTEM. THE EZCLASS WAS DESIGNED TO ASSIST THE TEACHER IN COORDINATING ACTIVITIES ON A VIRTUAL LEARNING ENVIRONMENT, SO THAT THE TEACHER CAN MAKE DECISIONS BASED ON THE PRESENTED INDICATORS, ALLOWING TO ADAPT THE PEDAGOGICAL PROPOSAL TO BETTER SUPPLY THE STUDENTS' DEMANDS.
IN THIS ARTICLE, WE INTRODUCE THE RESULTS OF A STUDY CONCERNING THE USEFULNESS OF FIVE EDUCATIONAL DATA VISUALIZATIONS IMPLEMENTED IN AN ONLINE COURSE. INFORMATION ABOUT THE TEACHERS' AND STUDENTS' OPINIONS ON THE USEFULNESS OF THE VISUALIZATIONS WAS COLLECTED BY USING A WEB SURVEY AND THE FOCUS GROUP METHOD. TO ENSURE THAT THE VISUALIZATIONS ARE IN USED CORRECTLY DURING THE COURSE, THEIR USE WAS ALSO TRACKED BY USING A WEB ANALYTICS PLATFORM. WELL-DESIGNED AND USABLE VISUAL ANALYTIC TOOLS AND VISUALIZATIONS CAN HELP TEACHERS MAKE DECISIONS REGARDING POTENTIAL PEDAGOGICAL STRATEGIES, INSTRUCTIONAL GUIDANCE, ACTIONS, AND INTERVENTIONS THAT MAY BE USED TO PROMOTE AND SUPPORT STUDENTS' PARTICIPATION AND ACTIVITY. ACCORDING TO THE RESULTS OF THIS RESEARCH, MOST OF THE STUDENTS SEEMED TO PREFER VISUALIZATIONS THAT OFFER CONCRETE AND PRACTICAL SUPPORT FOR THEIR STUDIES RATHER THAN VISUALIZATIONS THAT SUPPORT THEM IN MONITORING AND EVALUATING THEIR OWN LEARNING PERFORMANCE.
IN THE METAFORA PROJECT WE DESIGNED A PLATFORM FOR COLLABORATIVE LEARNING AND PLANNING OF LEARNING CHALLENGES THAT MAKE USE OF HETEROGENEOUS AND MULTIPLE LEARNING TOOLS. THESE COMPLEX SCENARIOS OF UP TO 20 HOURS OF LEARNING REQUIRE ADVANCED AWARENESS SUPPORT FUNCTIONALITY TO ALLOW EFFECTIVE COLLABORATION. TO EVALUATE THE EFFECTIVENESS OF OUR AWARENESS SUPPORT FEATURES IN METAFORA WE CONDUCTED AN EYETRACKING STUDY. IN THIS STUDY WE APPLIED SYNCHRONOUS EYETRACKING ON DYADS OF LEARNERS IN A CONTROLLED LAB SITUATION WHERE AWARENESS MESSAGES ARE SENT TO THE DYAD. WE REPORT ABOUT OUR RESULTS AND DISCUSS THE METHOD AND ITS IMPLICATIONS FOR FUTURE WORK.
INSTRUCTORS ARE OFTEN MULTITASKING IN THE CLASSROOM. THIS MAKES IT INCREASINGLY DIFFICULT FOR THEM TO PAY ATTENTION TO EACH INDIVIDUAL'S ENGAGEMENT ESPECIALLY DURING ACTIVITIES WHERE STUDENTS ARE WORKING IN GROUPS. IN THIS PAPER, WE DESCRIBE A SYSTEM THAT AIDS INSTRUCTORS IN SUPPORTING GROUP COLLABORATION BY UTILIZING A CENTRALIZED, EASY-TO-NAVIGATE DASHBOARD CONNECTED TO MULTIPLE PODS DISPERSED AMONG GROUPS OF STUDENTS IN A CLASSROOM OR LABORATORY. THIS ALLOWS INSTRUCTORS TO CHECK MULTIPLE QUALITIES OF THE DISCUSSION SUCH AS: THE USAGE OF INSTRUCTOR SPECIFIED KEYWORDS, RELATIVE PARTICIPATION OF EACH INDIVIDUAL, THE SPEECH ACTS STUDENTS ARE USING AND DIFFERENT EMOTIONAL CHARACTERISTICS OF GROUP LANGUAGE.
IN THIS PAPER WE PROPOSE A NEW METHOD FOR IDENTIFYING CREATIVITY THAT IS BASED ON ANALYZING A CORPUS OF CHAT CONVERSATIONS ON THE SAME TOPIC AND EXTRACTING THE NEW IDEAS EXPRESSED BY PARTICIPANTS. THE APPLICATION IS A FIRST STEP IN SUPPORTING CREATIVITY IN ONLINE GROUP DISCUSSIONS BY HIGHLIGHTING THE NOVEL CONCEPTS PRESENT IN CONVERSATIONS (NEW IDEAS) AND ALSO BY IDENTIFYING TOPICS THAT COULD HAVE BECOME IMPORTANT, IF NOT FORGOTTEN DURING THE DEBATES (LOST IDEAS).
WE CATEGORIZE MOOC RESOURCES WITH A LEARNING DESIGN LENS IN ORDER TO INVESTIGATE THE RELATION BETWEEN LEARNING DESIGN AND LEARNER BEHAVIOR IN A COMPUTATIONAL THINKING MOOC, MITX6.00.1X INTRODUCTION TO COMPUTER SCIENCE AND PROGRAMMING USING PYTHON. VIDEOS ARE CATEGORIZED BY KNOWLEDGE TYPE (PROCEDURAL, CONCEPTUAL AND FACTUAL), PRACTICE EXERCISES ARE CATEGORIZED BY TEACHING INTENT, AND PROBLEM SETS AND PRACTICE EXERCISES ARE SCORED BY DIFFICULTY. WE CONSIDER VIDEO VIEWING AND COMPLETE WATCHING BEHAVIOR, RECAP AND REVIEW BEHAVIORAL TRAJECTORIES, AND PROBLEM ATTEMPTS. THE ANALYSIS IS CROSS-SECTIONED BY LEARNER PRIOR LEVEL OF EXPERIENCE. OUR FINE GRAINED ANALYSES, SHOWING VARIATION IN LEARNER BEHAVIOR AT A DETAILED LEVEL, PROVIDE INSTRUCTORS WITH A CLEARER PICTURE OF HOW SPECIFIC LEARNERS INTERACT WITH PARTICULAR LEARNING RESOURCES AND ACHIEVE LEARNING OUTCOMES.
THE HIGH FAILURE RATES OF MANY PROGRAMMING COURSES MEANS THERE IS A NEED TO IDENTIFY STRUGGLING STUDENTS AS EARLY AS POSSIBLE. PRIOR RESEARCH HAS FOCUSED UPON USING A SET OF TESTS TO ASSESS THE USE OF A STUDENT'S DEMOGRAPHIC, PSYCHOLOGICAL AND COGNITIVE TRAITS AS PREDICTORS OF PERFORMANCE. BUT THESE TRAITS ARE STATIC IN NATURE, AND THEREFORE FAIL TO ENCAPSULATE CHANGES IN A STUDENT'S LEARNING PROGRESS OVER THE DURATION OF A COURSE. IN THIS PAPER WE PRESENT A NEW APPROACH FOR PREDICTING A STUDENT'S PERFORMANCE IN A PROGRAMMING COURSE, BASED UPON ANALYZING DIRECTLY LOGGED DATA, DESCRIBING VARIOUS ASPECTS OF THEIR ORDINARY PROGRAMMING BEHAVIOR. AN EVALUATION USING DATA LOGGED FROM A SAMPLE OF 45 PROGRAMMING STUDENTS AT OUR UNIVERSITY, SHOWED THAT OUR APPROACH WAS AN EXCELLENT EARLY PREDICTOR OF PERFORMANCE, EXPLAINING 42.49% OF THE VARIANCE IN COURSEWORK MARKS - DOUBLE THE EXPLANATORY POWER WHEN COMPARED TO THE CLOSEST RELATED TECHNIQUE IN THE LITERATURE.
SINCE 2008, WHEN THE FIRST EXPERIMENT WITH MOOCS TOOK PLACE, MUCH HAS BEEN SAID, WRITTEN AND EXPLORED. HOWEVER, ALMOST TEN YEARS LATER WE ARE UNABLE TO SAY WHETHER MOOCS ARE REALLY A DESIRABLE LEARNING EXPERIENCE AND, MOREOVER, WHAT ARE THE FACTORS FOR SUCCESS IN THE MOOC ENVIRONMENT. LITERATURE IN THE FIELD SEEMS TO CLEARLY ENDORSE LEARNER ENGAGEMENT AND PARTICIPATION AS ACTIVITIES THAT ENSURE A HIGHER COMPLETION RATE AND A SATISFYING LEARNING EXPERIENCE, YET A HIGH DEGREE OF DROPOUT CAN BE ATTRIBUTED TO A REQUEST FOR PARTICIPATION WHICH LEARNERS FIND UNSUSTAINABLE. ON MANY MOOC PROJECTS, THE DATA OPENS OPPORTUNITIES FOR DISCUSSION BUT PROVIDES FEW ANSWERS, AS SO MUCH DEPENDS ON INDIVIDUAL VARIABLES OF THE SPECIFIC COURSE. FAR FROM BEING A LIMIT OF THE RESEARCH, THIS UNCERTAINTY IS THE ONLY WAY TO PRESERVE LEARNING FROM BECOMING A HOSTAGE OF ALGORITHMS, THUS LEAVING TEACHERS AND LEARNERS THE FREEDOM TO PLAN, DECIDE, AND EXPERIENCE, AND TO EVALUATE THEIR TEACHING AND LEARNING.
THERE IS LITTLE CONSENSUS ABOUT WHAT VARIABLES EXTRACTED FROM LEARNER DATA ARE THE MOST RELIABLE INDICATORS OF LEARNING PERFORMANCE. THE AIM OF THIS STUDY IS TO DETERMINE SUCH INDICATORS BY TAKING A WIDE RANGE OF VARIABLES INTO CONSIDERATION CONCERNING OVERALL LEARNING ACTIVITY AND CONTENT PROCESSING. A GENETIC ALGORITHM IS USED FOR THE SELECTION PROCESS AND VARIABLES ARE EVALUATED BASED ON THEIR PREDICTIVE POWER IN A CLASSIFICATION TASK. VARIABLES EXTRACTED FROM EXERCISE ACTIVITIES TURN OUT TO BE MOST INFORMATIVE. EXERCISES DESIGNED TO TRAIN STUDENTS IN UNDERSTANDING AND APPLYING MATERIAL ARE FOUND TO BE ESPECIALLY INFORMATIVE.
TEACHERS OFTEN WISH TO INTEGRATE ACTIVITIES FROM DISPARATE SOURCES INTO THEIR COURSES. FOR EXAMPLE, GAMIFIED ACTIVITIES, MEDIATED THROUGH TECHNOLOGY, CAN PROMOTE THE TYPE OF ACTIVE LEARNING REQUIRED TO DEVELOP HIGHER-LEVEL ENGAGEMENT BY STUDENTS. HOWEVER, UNLESS THE ACTIVITIES HAVE BEEN DESIGNED TO FACILITATE IT, INTEGRATING THEIR ANALYTICS INTO A SINGLE DASHBOARD CAN REQUIRE SIGNIFICANT DEVELOPMENT EFFORT. A GENERAL SOLUTION TO SUCH HETEROGENEOUS ANALYTICS INTEGRATION CAN BE OF GREAT VALUE, BY PRESENTING A SINGLE VIEW OF STUDENT ACTIONS THROUGHOUT THE DIFFERENT PARTS OF A COURSE. WE DESCRIBE THE PROBLEMS PRESENTED WHEN INTEGRATING THE ANALYTICS OF THREE HETEROGENEOUS STANDALONE ACTIVITIES, IN THE CONTEXT OF A EU PROJECT TO IMPROVE SOFTWARE ENGINEERING TEACHING. THE IDEA IS TO INCREASE STUDENT ENGAGEMENT VIA GAMIFICATION, AND EXPLORE THE DESIGN SPACE OF POSSIBLE SOLUTIONS FOR PROVIDING INTEGRATED ANALYTICS OVER THE HETEROGENEOUS ACTIVITIES. WE THEN DESCRIBE THE DESIGN OF A PROOF-OF-CONCEPT IMPLEMENTATION, BASED ON THE USE OF BOTH XAPI TRACKERS AND SIMPLE CSV FILES FOR INFORMATION EXCHANGE, SINGLE SIGN-ON, A MINIMAL CLASS MANAGEMENT WEB APPLICATION, AND UPDATES TO THE ANALYTICS PLATFORM TO ALLOW DYNAMIC CHANGES IN THE MULTI-LEVEL ANALYSIS. THE RESULTING APPROACH CAN BE READILY APPLIED TO SIMILAR HETEROGENEOUS SCENARIOS.
HOW DOES AI&EDAIED TODAY COMPARE TO 25 YEARS AGO? THIS PAPER ADDRESSES THIS EVOLUTION BY IDENTIFYING SIX TRENDS. THE TRENDS ARE ONGOING AND WILL INFLUENCE LEARNING TECHNOLOGIES GOING FORWARD. FIRST, THE PHYSICALITY OF INTERACTIONS AND THE PHYSICAL SPACE OF THE LEARNER BECAME GENUINE COMPONENTS OF DIGITAL EDUCATION. THE FRONTIER BETWEEN THE DIGITAL AND THE PHYSICAL HAS FADED OUT. SIMILARLY, THE OPPOSITION BETWEEN INDIVIDUAL AND SOCIAL VIEWS ON COGNITION HAS BEEN SUBSUMED BY INTEGRATED LEARNING SCENARIOS, WHICH MEANS THAT AIED PAYS MORE ATTENTION TODAY TO SOCIAL INTERACTIONS THAN IT DID AT ITS OUTSET. ANOTHER TREND IS THE PROCESSING OF LEARNERS' BEHAVIOURAL PARTICLES, WHICH DO NOT CARRY VERY MANY SEMANTICS WHEN CONSIDERED INDIVIDUALLY, BUT ARE PREDICTIVE OF KNOWLEDGE STATES WHEN LARGE DATA SETS ARE PROCESSED WITH MACHINE LEARNING METHODS. THE DEVELOPMENT OF PROBABILISTIC MODELS AND THE INTEGRATION OF CROWDSOURCING METHODS HAS PRODUCED ANOTHER TREND: THE DESIGN OF LEARNING ENVIRONMENTS HAS BECOME LESS DETERMINISTIC THAN BEFORE. THE NOTION OF LEARNING ENVIRONMENT EVOLVED FROM A RATHER CLOSED BOX TO AN OPEN ECOSYSTEM IN WHICH MULTIPLE COMPONENTS ARE DISTRIBUTED OVER MULTIPLE PLATFORMS AND WHERE MULTIPLE STAKEHOLDERS INTERACT. AMONG THESE STAKEHOLDERS, IT IS IMPORTANT TO NOTICE THAT TEACHERS PLAY A MORE IMPORTANT ROLE THAN BEFORE: THEY INTERACT NOT ONLY AT THE DESIGN PHASE (AUTHORING) BUT ALSO IN THE RUNTIME PHASE (ORCHESTRATION). THESE TRENDS ARE NOT SPECIFIC TO AIED; THEY DEPICT THE EVOLUTION OF LEARNING TECHNOLOGIES AS A WHOLE.
THE MAIN GOAL OF THIS DOCUMENT IS TO DESCRIBE THE IMPLEMENTATION OF EACH COMPONENT OF THE COLMOOC PLATFORM AND THE INTEGRATION STAGE OF THE OPERATIONAL PLATFORM AS A WHOLE. ADDITIONALLY, A FIRST SIMPLE OPERATIONAL SCENARIO THAT HAS BEEN IMPLEMENTED IS ALSO BEING DESCRIBED, IN ORDER TO DEMONSTRATE THE INTEROPERABILITY OF THE PLATFORM. FINALLY, THIS PAPER PRESENTS A POSITIVE TECHNICAL ASSESSMENT OF THE 1ST VERSION OF COLMOOC PLATFORM THROUGH PERFORMANCE BENCHMARKING EXPERIMENTS PERFORMED ON SYSTEM'S COMPONENTS IN ORDER TO VERIFY ITS CORRECT OPERATION AND INTEROPERABILITY OF THE DIFFERENT INTEGRATED MODULES TO MEET THE REQUIREMENTS OF THE USE CASES FOR THE END USERS.
HIGHER EDUCATION INSTITUTIONS ARE INCREASINGLY ABANDONING TRADITIONAL TEACHING METHODS BASED ON TRANSMISSIVE PEDAGOGY AND EMBRACING A MODEL BASED ON THE CONSTRUCTIVIST THEORY OF LEARNING. THIS IS A NECESSARY SHIFT TO FACE CURRENT CHALLENGES IN THEIR DOMAIN SUCH AS SHRINKING RESOURCES AND NEED TO REACH STUDENTS WITH DIFFERENT LEVEL OF MOTIVATION AND LEARNING STYLES. CONSTRUCTIVE ALIGNMENT (CA) COMBINES THE NEW PEDAGOGICAL APPROACH WITH ALIGNED DESIGN FOR OUTCOME-BASED TEACHING EDUCATION AND IT IS THE LEADING APPROACH UNDER-PINNING CURRENT REQUIREMENTS FOR PROGRAM SPECIFICATION AND SINGLE COURSE DESCRIPTION. ANOTHER IMPORTANT TREND EMERGING FROM DIGITALIZATION OF HIGHER EDUCATION IS THE COLLECTION AND USE OF EDUCATIONAL DATA TO IMPROVE ALL THE ASPECTS OF THE LEARNING PROCESS. THIS RESEARCH COMMUNITY HAS PRODUCED A LARGE BODY OF VALUABLE TOOLS AND METHODS, BUT THERE IS NO SPECIFIC CONTRIBUTION TARGETING THE ANALYSIS OF CONSTRUCTIVELY ALIGNED EDUCATION. THE CONALI ONTOLOGY IS AN ONGOING MODELLING EFFORT THAT CURRENTLY PROVIDES A FRAMEWORK TO DESCRIBE THE CA BODY OF KNOWLEDGE INCLUDING ALL THE RELEVANT SEMANTIC RELATIONSHIPS. THIS PAPER PRESENTS A NEW VERSION OF SUCH ONTOLOGY THAT SUPPORTS COLLECTION AND ANALYSIS OF EDUCATIONAL DATA FROM CONSTRUCTIVELY ALIGNED EDUCATIONAL UNITS AS A FIRST STEP TO FILL THE GAP DESCRIBED ABOVE. THE SPECIFIC REQUIREMENTS FOR THIS ITERATIVE UPGRADE HAVE BEEN INFERRED FROM A LITERATURE REVIEW OF THE TYPICAL ANALYSIS CONDUCTED IN THE DOMAIN OF EDUCATIONAL BIG DATA.
THIS STUDY EXAMINES THE RELATIONSHIP BETWEEN LOG DATA OF STUDENT ACTIVITY IN LEARNING MANAGEMENT SYSTEMS AND SELF-REPORTED STUDENT ENGAGEMENT SURVEY SCORES. LOG DATA HAS THE POTENTIAL TO SERVE AS A MEANINGFUL PROXY FOR SURVEY SCORES. SHOULD THIS BE THE CASE, LOG DATA COULD BE USED AS A MINIMALLY DISRUPTIVE AND SCALABLE APPROACH TO QUICKLY IDENTIFY WHO NEEDS HELP, EVALUATE DESIGN, AND PERSONALIZE INSTRUCTION. WE CORRELATED LMS LOG DATA VARIABLES TO STUDENT ENGAGEMENT SURVEY SCORES TO STUDY THE RELATIONSHIP BETWEEN THESE TWO SOURCES OF DATA. OVERALL, LOG DATA WAS NOT A STATISTICALLY SIGNIFICANT PROXY MEASURE OF STUDENTS' SELF-REPORTED COGNITIVE AND EMOTIONAL ENGAGEMENT. OUR RESULTS UNDERSCORE THE COMPLEXITY OF LEARNING AND THE RELATIONSHIP BETWEEN OBSERVED AND REPORTED COGNITIVE AND EMOTIONAL STATES. FUTURE EDUCATIONAL RESEARCH USING LOG DATA WILL NEED TO ACCOUNT FOR OTHER FACTORS THAT HELP EXPLAIN TRENDS IN STUDENT ENGAGEMENT. EXPLORING THE POTENTIAL OF LMS LOG DATA AS A PROXY MEASURE OF STUDENT ENGAGEMENT.
IN THIS RESEARCH WE PROPOSE A COMPREHENSIVE SET OF KNOWLEDGE INDICATORS AIMED TO ENHANCE LEARNERS' SELF-REFLECTION AND AWARENESS IN THE LEARNING AND TESTING PROCESS. SINCE EXAMINED INTELLIGENT TUTORING SYSTEMS DO NOT INCLUDE ADDITIONAL MESSAGING FEATURES, THE INTRODUCTION OF COMMON SET OF KNOWLEDGE INDICATORS DIFFERENTIATES OUR APPROACH FROM THE PREVIOUS STUDIES. IN ORDER TO INVESTIGATE THE RELATION BETWEEN PROPOSED KNOWLEDGE INDICATORS AND LEARNER PERFORMANCE, THE CORRELATION AND REGRESSION ANALYSIS WERE PERFORMED FOR 3 DIFFERENT COURSES AND EACH EXAMINED INTELLIGENT TUTORING SYSTEM. THE RESULTS OF CORRELATION AND REGRESSION ANALYSIS, AS WELL AS LEARNERS' FEEDBACK, GUIDED US IN DISCUSSION ABOUT THE INTRODUCTION OF KNOWLEDGE INDICATORS IN DASHBOARD-LIKE VISUALIZATIONS OF INTEGRATED INTELLIGENT TUTORING SYSTEM.
THE FOCUS ON QUALITY IMPROVEMENTS BY INSTITUTIONS FOR BETTER ONLINE AND BLENDED TEACHING CAN BE DELIVERED IN DIFFERENT WAYS. THIS ARTICLE REPORTS ON THE IMPLEMENTATION OF THIS PROCESS AND THE APPROACHES TAKEN FIRST, IN TERMS OF THE DESIGN OF NEW LEARNING MATERIALS, AND SECOND, WHEN REVIEWING THE EXISTING CURRICULUM. THE STUDY AIMS TO ASCERTAIN WHETHER THE COMBINATION OF A COLLABORATIVE, NETWORKED APPROACH AT THE INITIAL DESIGN STAGE, AUGMENTED WITH VISUALISATIONS, HAS CHANGED THE WAY EDUCATORS DESIGN THEIR COURSES AT THE OPEN UNIVERSITY, UK. ANALYSIS OF 148 LEARNING DESIGNS SHOWS BOTH BEFORE AND AFTER THE INTRODUCTION OF LEARNING DESIGN, WHICH OF THE PEDAGOGIC DECISIONS THAT EDUCATORS MADE SUBSTANTIALLY CHANGED. COURSES THAT WERE DESIGNED AFTER THE INTRODUCTION OF LEARNING DESIGN WERE MORE FOCUSED ON THE DEVELOPMENT OF A RANGE OF SKILLS AND INCLUDED FEWER ASSIMILATIVE ACTIVITIES (READING, WATCHING VIDEOS AND LISTENING TO AUDIO). OUR FINDINGS SUGGEST THAT BY VISUALISING THE DESIGN UPFRONT, EDUCATORS FOCUSED LESS ON TRADITIONAL TEACHING PATTERNS, SUCH AS THE 'TEACH, PRACTICE, APPLY' MODEL. REMARKABLY, JUST BY VISUALISING INITIAL DECISIONS AND WORKING IN COLLABORATIVE WORKSHOPS, EDUCATORS CREATED MORE STUDENT-CENTRED AND CREATIVE DESIGNS AIMED TO DEVELOP A RANGE OF SKILLS WHICH SUPPORT STUDENTS IN BOTH THEIR STUDIES AND THE WORKPLACE. FURTHER STUDIES ARE NEEDED TO ESTABLISH WHETHER THESE PEDAGOGIC DECISIONS HAVE AN IMPACT ON STUDENT OUTCOMES AND WHETHER THESE FINDINGS CAN BE REPLICATED IN DIFFERENT INSTITUTIONS.
MASSIVE OPEN ONLINE COURSES (MOOCS) ARE AMONG THE MOST POPULAR ONLINE LEARNING SYSTEMS, WITH COURSES CHARACTERIZED BY A VERY LARGE NUMBER OF ATTENDEES. MONITORING THE STUDENTS' LEARNING PROCESS IN A MOOC CAN BE HARD FOR THE TEACHER, DUE TO SHEER NUMBERS. MOREOVER, MOOC PLATFORMS PRODUCE LARGE AMOUNTS OF DATA RELATED TO THE DYNAMICS OF THE STUDENTS COMMUNITY, WHICH, IF WELL USED, CAN CONTRIBUTE TO IMPROVING THE EDUCATIONAL OFFER. HERE WE PRESENT A WEB-BASED SYSTEM, ALLOWING THE TEACHER TO SIMULATE A MOOC CLASS OF STUDENTS, AND EXPERIMENT WITH IT, BY APPLYING A PEDAGOGIC STRATEGY BASED ON PEER ASSESSMENT. THE SIMULATED STUDENTS (PEERS) ARE SUPPOSED TO PRODUCE ANSWERS TO A QUESTION, AND ASSESSMENTS OF SOME OTHER PEERS' ANSWERS, ACCORDING TO THE MODELS USED TO DEFINE THE SIMULATED CLASS. USING OUR SYSTEM THE TEACHER CAN OBSERVE THE DYNAMICS OF THE SIMULATED MOOC, BASED ON A MODIFIED VERSION OF THE K-NN ALGORITHM, IN LINE WITH THE TEACHING ANALYTICS DISCIPLINE. A FIRST TRIAL OF THE SYSTEM PRODUCED PROMISING RESULTS, SHOWING ITS USEFULNESS TO SUPPORT TEACHING ANALYTICS.
THE RECENT FOCUS ON LEARNING ANALYTICS (LA) TO ANALYZE TEMPORAL DIMENSIONS OF LEARNING HOLDS THE PROMISE OF PROVIDING INSIGHTS INTO LATENT CONSTRUCTS, SUCH AS LEARNING STRATEGY, SELF-REGULATED LEARNING (SRL), AND METACOGNITION. THESE METHODS SEEK TO PROVIDE AN ENRICHED VIEW OF LEARNER BEHAVIORS BEYOND THE SCOPE OF COMMONLY USED CORRELATIONAL OR CROSS-SECTIONAL METHODS. IN THIS ARTICLE, WE PRESENT A METHODOLOGICAL SEQUENCE OF TECHNIQUES THAT COMPRISES: 1) THE STRATEGIC CLUSTERING OF LEARNER TYPES; 2) THE USE OF MICROLEVEL PROCESSING TO TRANSFORM RAW TRACE DATA INTO SRL PROCESSES; AND 3) THE USE OF A NOVEL PROCESS MINING ALGORITHM TO EXPLORE THE GENERATED SRL PROCESSES. WE CALL THIS THE "TRACE-SRL" FRAMEWORK. THROUGH THIS FRAMEWORK, WE EXPLORED THE USE OF MICROLEVEL PROCESS ANALYSIS AND PROCESS MINING (PM) TECHNIQUES TO IDENTIFY OPTIMAL AND SUBOPTIMAL TRAITS OF SRL. WE ANALYZED TRACE DATA COLLECTED FROM ONLINE ACTIVITIES OF A SAMPLE OF NEARLY 300 COMPUTER ENGINEERING UNDERGRADUATE STUDENTS ENROLLED ON A COURSE THAT FOLLOWED A FLIPPED CLASS-ROOM PEDAGOGY. WE FOUND THAT USING A THEORY-DRIVEN APPROACH TO PM, A DETAILED ACCOUNT OF SRL PROCESSES EMERGED, WHICH COULD NOT BE OBTAINED FROM FREQUENCY MEASURES ALONE. PM, AS A MEANS OF LEARNER PATTERN DISCOVERY, PROMISES A MORE TEMPORALLY NUANCED ANALYSIS OF SRL. MOREOVER, THE RESULTS SHOWED THAT MORE SUCCESSFUL STUDENTS REGULARLY ENGAGE IN A HIGHER NUMBER OF SRL BEHAVIORS THAN THEIR LESS SUCCESSFUL COUNTERPARTS. THIS SUGGESTS THAT NOT ALL STUDENTS ARE SUFFICIENTLY ABLE TO REGULATE THEIR LEARNING, WHICH IS AN IMPORTANT FINDING FOR BOTH THEORY AND LA, AND FUTURE TECHNOLOGIES THAT SUPPORT SRL.
THE LEARNING ANALYTICS COMMUNITIES, AS WELL AS MOST LEARNING ANALYTICS RESEARCH, HAVE NOT FREQUENTLY FOCUSED ON TIME-BASED TRENDS IN THE SAME VIRTUAL LEARNING ENVIRONMENT OVER DIFFERENT YEARS OF DEPLOYMENT, OR ON TEMPORAL TRENDS IN THE SELECTION OF DIFFERENT ACTIVITY TYPES OVER A TYPICAL DAY. THIS PAPER CONTRIBUTES TO THIS DEBATE AND PROVIDES A NOVEL APPROACH TO LEARNING ANALYTICS USING A MULTINOMIAL LOGIT MODEL FOR MODELING THE PROBABILITIES OF STUDENTS' CHOICE OF LEARNING ACTIVITIES DURING THE HOURS OF THE DAY OVER SEVERAL ACADEMIC YEARS. AN ABSTRACTION CALLED ACTIVITY IS INTRODUCED, WHICH CATEGORIZES INDIVIDUAL STUDENT'S LOG ACCESSES TO THE VIRTUAL LEARNING ENVIRONMENT INTO MORE SEMANTICALLY MEANINGFUL CATEGORIES. CONSEQUENTLY, THE ACTIVITY REPRESENTS A SEQUENCE OF SEMANTICALLY MEANINGFUL WEB ACCESSES RELATED TO A PARTICULAR ACTIVITY OR TASK THAT A STUDENT OF THE VIRTUAL LEARNING ENVIRONMENT PERFORMS. THIS PAPER INCLUDES A COMPREHENSIVE EXPLANATION OF THE MODEL AND AN EVALUATION OF THE MODEL. THIS PAPER INTRODUCES A CASE STUDY, WHICH SHOWS THAT THE MULTINOMIAL LOGIT MODEL CAN GIVE USEFUL INSIGHT INTO THE COURSE SCHEDULE, AS IT SHOWS WHAT THE PEAK TIMES ARE FOR DIFFERENT TYPES OF ACTIVITIES. THIS PAPER ALSO DISCUSSES THE POSSIBLE IMPLICATIONS OF THE RESULTS IN THE CONTEXT OF VIRTUAL LEARNING ENVIRONMENT MANAGEMENT AND CONTENT IMPROVEMENT AT THE INSTITUTIONAL LEVEL.
THIS LARGE-SCALE STUDY WAS CONDUCTED FOR THE PURPOSES OF DETERMINING HOW REPRESENTATIVE LIBRARY USERS ARE COMPARED TO THE WHOLE STUDENT POPULATION, TO EXPLORE HOW LIBRARY SERVICES CONTRIBUTE TO STUDENT SUCCESS, AND TO POSITION THE LIBRARY TO BE INCLUDED IN THE INSTITUTION'S LEARNING ANALYTICS LANDSCAPE. TO THAT END, DATA WERE COLLECTED AS STUDENTS AT UNIVERSITY OF CENTRAL FLORIDA (N = 25,336) INTERACTED WITH FIVE LIBRARY SERVICE POINTS OVER FOUR SEMESTERS. ANALYSIS REVEALED A POSITIVE ASSOCIATION BETWEEN STUDENTS WHO USED ONE OR MORE LIBRARY SERVICES AND HIGHER END-OF-SEMESTER GPAS. THE ARTICLE EMPHASIZES HOW RESULTS WERE DISSEMINATED AND ONGOING WORK TO BUILD AN INTERACTIVE LEARNING ANALYTICS LIBRARY DASHBOARD THAT COMPLEMENTS EXISTING INSTITUTIONAL DASHBOARDS.
INTRODUCTIONRELATIONSHIPS BETWEEN BRAIN ATROPHY PATTERNS OF TYPICAL AGING AND ALZHEIMER'S DISEASE (AD), WHITE MATTER DISEASE, COGNITION, AND AD NEUROPATHOLOGY WERE INVESTIGATED VIA MACHINE LEARNING IN A LARGE HARMONIZED MAGNETIC RESONANCE IMAGING DATABASE (11 STUDIES; 10,216 SUBJECTS). METHODS: THREE BRAIN SIGNATURES WERE CALCULATED: BRAIN-AGE, AD-LIKE NEURODEGENERATION, AND WHITE MATTER HYPERINTENSITIES (WMHS). BRAIN CHARTS MEASURED AND DISPLAYED THE RELATIONSHIPS OF THESE SIGNATURES TO COGNITION AND MOLECULAR BIOMARKERS OF AD. RESULTS: WMHS WERE ASSOCIATED WITH ADVANCED BRAIN AGING, AD-LIKE ATROPHY, POORER COGNITION, AND AD NEUROPATHOLOGY IN MILD COGNITIVE IMPAIRMENT (MCI)/AD AND COGNITIVELY NORMAL (CN) SUBJECTS. HIGH WMH VOLUME WAS ASSOCIATED WITH BRAIN AGING AND COGNITIVE DECLINE OCCURRING IN AN APPROXIMATE TO 10-YEAR PERIOD IN CN SUBJECTS. WMHS WERE ASSOCIATED WITH DOUBLING THE LIKELIHOOD OF AMYLOID BETA (A BETA) POSITIVITY AFTER AGE 65. BRAIN AGING, AD-LIKE ATROPHY, AND WMHS WERE BETTER PREDICTORS OF COGNITION THAN CHRONOLOGICAL AGE IN MCI/AD. DISCUSSION: A BRAIN CHART QUANTIFYING BRAIN-AGING TRAJECTORIES WAS ESTABLISHED, ENABLING THE SYSTEMATIC EVALUATION OF INDIVIDUALS' BRAIN-AGING PATTERNS RELATIVE TO THIS LARGE CONSORTIUM.
THE ABILITY TO PREDICT STUDENT PERFORMANCE IN A COURSE OR PROGRAM CREATES OPPORTUNITIES TO IMPROVE EDUCATIONAL OUTCOMES. WITH EFFECTIVE PERFORMANCE PREDICTION APPROACHES, INSTRUCTORS CAN ALLOCATE RESOURCES AND INSTRUCTION MORE ACCURATELY. RESEARCH IN THIS AREA SEEKS TO IDENTIFY FEATURES THAT CAN BE USED TO MAKE PREDICTIONS, TO IDENTIFY ALGORITHMS THAT CAN IMPROVE PREDICTIONS, AND TO QUANTIFY ASPECTS OF STUDENT PERFORMANCE. MOREOVER, RESEARCH IN PREDICTING STUDENT PERFORMANCE SEEKS TO DETERMINE INTERRELATED FEATURES AND TO IDENTIFY THE UNDERLYING REASONS WHY CERTAIN FEATURES WORK BETTER THAN OTHERS. THIS WORKING GROUP REPORT PRESENTS A SYSTEMATIC LITERATURE REVIEW OF WORK IN THE AREA OF PREDICTING STUDENT PERFORMANCE. OUR ANALYSIS SHOWS A CLEARLY INCREASING AMOUNT OF RESEARCH IN THIS AREA, AS WELL AS AN INCREASING VARIETY OF TECHNIQUES USED. AT THE SAME TIME, THE REVIEW UNCOVERED A NUMBER OF ISSUES WITH RESEARCH QUALITY THAT DRIVES A NEED FOR THE COMMUNITY TO PROVIDE MORE DETAILED REPORTING OF METHODS AND RESULTS AND TO INCREASE EFFORTS TO VALIDATE AND REPLICATE WORK.
DATA IN THE EDUCATIONAL CONTEXT ARE BECOMING INCREASINGLY IMPORTANT IN DECISION-MAKING AND TEACHING-LEARNING PROCESSES. SIMILAR TO THE INDUSTRIAL CONTEXT, EDUCATIONAL INSTITUTIONS ARE ADOPTING DATA-PROCESSING TECHNOLOGIES AT ALL LEVELS. TO ACHIEVE REPRESENTATIVE RESULTS, THE PROCESSES OF EXTRACTION, TRANSFORMATION AND UPLOADING OF EDUCATIONAL DATA SHOULD BE UBIQUITOUS BECAUSE, WITHOUT USEFUL DATA, EITHER INTERNAL OR EXTERNAL, IT IS DIFFICULT TO PERFORM A PROPER ANALYSIS AND TO OBTAIN UNBIASED EDUCATIONAL RESULTS. IT SHOULD BE NOTED THAT THE SOURCE AND TYPE OF DATA ARE HETEROGENEOUS AND THAT THE ANALYTICAL PROCESSES CAN BE SO DIVERSE THAT IT OPENS UP A PRACTICAL PROBLEM OF MANAGEMENT AND ACCESS TO THE DATA GENERATED. AT THE SAME TIME, ENSURING THE PRIVACY, IDENTITY, CONFIDENTIALITY AND SECURITY OF STUDENTS AND THEIR DATA IS A "SINE QUA NON" CONDITION FOR COMPLYING WITH THE LEGAL ISSUES INVOLVED WHILE ACHIEVING THE REQUIRED ETHICAL PREMISES. THIS WORK PROPOSES A MODULAR AND SCALABLE DATA SYSTEM ARCHITECTURE THAT SOLVES THE COMPLEXITY OF DATA MANAGEMENT AND ACCESS. ON THE ONE HAND, IT ALLOWS EDUCATIONAL INSTITUTIONS TO COLLECT ANY DATA GENERATED IN BOTH THE TEACHING-LEARNING AND MANAGEMENT PROCESSES. ON THE OTHER HAND, IT WILL ENABLE EXTERNAL ACCESS TO THIS DATA UNDER APPROPRIATE PRIVACY AND SECURITY CONDITIONS.
THE NEED TO ANALYZE STUDENT INTERACTIONS IN VIRTUAL LEARNING ENVIRONMENTS (VLE) AND THE IMPROVEMENTS THIS GENERATES IS AN INCREASINGLY EMERGING REALITY IN ORDER TO MAKE TIMELY PREDICTIONS AND OPTIMIZE STUDENT LEARNING. THIS RESEARCH AIMS TO IMPLEMENT A PROPOSAL OF STANDARDIZED LEARNING BEHAVIOR INDICATORS IN VIRTUAL LEARNING ENVIRONMENTS (VLE) TO DESIGN AND IMPLEMENT EFFICIENT AND TIMELY LEARNING ANALYTICS (LA) PROCESSES. THE METHODOLOGY CONSISTED OF A DATA MANAGEMENT ANALYSIS THAT WAS CARRIED OUT IN THE MOODLE PLATFORM OF THE FACULTY OF EDUCATION SCIENCES OF THE NATIONAL UNIVERSITY OF SAN AGUSTIN OF AREQUIPA, WITH THE PARTICIPATION OF 20 TEACHERS, WHERE QUALITATIVE ONLINE QUESTIONNAIRES WERE USED TO COLLECT THE PARTICIPANTS' PERCEPTIONS. THE RESULTS PROPOSE A STANDARD IN TERMS OF INDICATORS OF BEHAVIOR IN THE TEACHING-LEARNING PROCESS IN EVA AS THEY ARE: PREPARATION FOR LEARNING, PROGRESS IN THE PROGRESS OF THE COURSE, RESOURCES FOR LEARNING, INTERACTION IN THE FORUMS AND EVALUATION OF RESOURCES. THESE WERE EVALUATED THROUGH LEARNING ANALYTICS AND SHOW THE EFFICIENCY OF THE PROPOSED INDICATORS. THE CONCLUSIONS HIGHLIGHT THE IMPORTANCE OF IMPLEMENTING STANDARDIZED BEHAVIOR INDICATORS THAT ALLOW US TO EFFICIENTLY DEVELOP LEARNING ANALYTICS PROCESSES IN VLE IN ORDER TO OBTAIN BETTER PREDICTIONS TO MAKE TIMELY DECISIONS AND OPTIMIZE THE TEACHING-LEARNING PROCESSES.
PAIR PROGRAMMING IS A COLLABORATIVE LEARNING MODE TO FOSTER NOVICE LEARNERS' COMPUTER PROGRAMMING. PREVIOUS EMPIRICAL RESEARCH HAS REPORTED CONTRASTING CONCLUSIONS ABOUT THE EFFECT OF PAIR PROGRAMMING ON STUDENT LEARNING. TO FURTHER UNDERSTAND STUDENTS' PAIR PROGRAMMING, THIS STUDY USES A MIXED METHOD TO ANALYZE THREE CONTRASTING PAIRS' COLLABORATIVE BEHAVIORS, DISCOURSES, AND PERCEPTIONS FROM A MULTI-DIMENSIONAL PERSPECTIVE. THE ANALYSIS RESULTS SHOW THAT THE HIGH-RANKED STUDENT PAIR IS CHARACTERIZED AS THE INTERACTIVE, SOCIALLY-SUPPORTIVE, AND GOAL-ORIENTED PAIR; THE MIDDLE-RANKED STUDENT PAIR IS CHARACTERIZED AS THE HIGHLY-INTERACTIVE, SOCIALLY-SUPPORTIVE, AND PROCESS-ORIENTED PAIR; AND THE LOW-RANKED STUDENT PAIR IS CHARACTERIZED AS THE LOWLY-INTERACTIVE, SOCIALLY-UNSUPPORTIVE, AND PROGRAMMING-DISTRACTED PAIR. THE RESEARCH REVEALS COMPLEX RELATIONS BETWEEN COLLABORATIVE BEHAVIORS, DISCOURSES, AND PERFORMANCES, WHICH HAVE CRITICAL INFLUENCES ON STUDENTS' PAIR PROGRAMMING QUALITY. BASED ON THE RESULTS, THIS RESEARCH PROPOSES PEDAGOGICAL, ANALYTICAL, AND THEORETICAL IMPLICATIONS FOR FUTURE INSTRUCTIONAL DESIGN, LEARNING ANALYTICS, AND EMPIRICAL RESEARCH OF COLLABORATIVE PROGRAMMING.
PREDICTIVE ANALYTICS IN HIGHER EDUCATION HAS BECOME INCREASINGLY POPULAR IN RECENT YEARS WITH THE GROWING AVAILABILITY OF EDUCATIONAL BIG DATA. PARTICULARLY, A WEALTH OF STUDENT ACTIVITY DATA IS AVAILABLE FROM LEARNING MANAGEMENT SYSTEMS (LMSS) IN MOST ACADEMIC INSTITUTIONS. HOWEVER, PREVIOUS INVESTIGATIONS INTO PREDICTIVE ANALYTICS IN HIGHER EDUCATION USING LMS ACTIVITY DATA DID NOT ADEQUATELYACCOMMODATE STUDENT BEHAVIOURS IN THE FORM OF TIME SERIES. IN THIS STUDY, WE HAVE APPLIED A DEEP LEARNING APPROACH - LONG SHORT-TERM MEMORY (LSTM) NETWORKS - TO ANALYZE STUDENT ONLINE TEMPORAL BEHAVIOURS USING THEIR LMS DATA FOR THE EARLY PREDICTION OF COURSE PERFORMANCE. TO REVEAL THE POTENTIAL OF THE DEEP LEARNING APPROACH IN PREDICTIVE ANALYTICS, WE COMPARED LSTM NETWORKS WITH EIGHT CONVENTIONAL MACHINE-LEARNING CLASSIFIERS IN TERMS OF THE PREDICTION PERFORMANCE AS MEASURED BY THE AREA UNDER THE ROC (RECEIVER OPERATING CHARACTERISTIC) CURVE (AUC) SCORES. RESULTS INDICATE THAT USING THE DEEP LEARNING APPROACH, TIME SERIES INFORMATION ABOUT CLICK FREQUENCIES SUCCESSFULLY PROVIDED EARLY DETECTION OF AT-RISK STUDENTS WITH MODERATE PREDICTION ACCURACY. IN ADDITION, THE DEEP LEARNING APPROACH SHOWED HIGHER PREDICTION PERFORMANCE AND STRONGER GENERALIZABILITY THAN THE MACHINE LEARNING CLASSIFIERS.
WITH THE ADVENT OF COMPUTERS IN EDUCATION, AND THE AMPLE AVAILABILITY OF ONLINE LEARNING AND PRACTICE ENVIRONMENTS, ENORMOUS AMOUNTS OF DATA ON LEARNING BECOME AVAILABLE. THE PURPOSE OF THIS PAPER IS TO PRESENT A DECADE OF EXPERIENCE WITH ANALYZING AND IMPROVING AN ONLINE PRACTICE ENVIRONMENT FOR MATH, WHICH HAS THUS FAR RECORDED OVER A BILLION RESPONSES. WE PRESENT THE METHODS WE USE TO BOTH STEER AND ANALYZE THIS SYSTEM IN REAL-TIME, USING SCORING RULES ON ACCURACY AND RESPONSE TIMES, A TAILORED RATING SYSTEM TO PROVIDE BOTH LEARNERS AND ITEMS WITH CURRENT ABILITY AND DIFFICULTY RATINGS, AND AN ADAPTIVE ENGINE THAT MATCHES LEARNERS TO ITEMS. MOREOVER, WE EXPLORE THE QUALITY OF FIT BY MEANS OF PREDICTION ACCURACY AND PARALLEL ITEM RELIABILITY. LIMITATIONS AND PITFALLS ARE DISCUSSED BY DIAGNOSING SOURCES OF MISFIT, LIKE VIOLATIONS OF UNIDIMENSIONALITY AND UNFORESEEN DYNAMICS. FINALLY, DIRECTIONS FOR DEVELOPMENT ARE DISCUSSED, INCLUDING EMBEDDED LEARNING ANALYTICS AND A FOCUS ON ONLINE EXPERIMENTATION TO EVALUATE BOTH THE SYSTEM ITSELF AND THE USERS' LEARNING GAINS. THOUGH MANY CHALLENGES REMAIN OPEN, WE BELIEVE THAT LARGE STEPS HAVE BEEN MADE IN PROVIDING METHODS TO EFFICIENTLY MANAGE AND RESEARCH EDUCATIONAL BIG DATA FROM A MASSIVE ONLINE LEARNING SYSTEM.
IN THIS ERA OF 'BIG DATA', 'MACHINE LEARNING' AND 'LEARNING ANALYTICS' WE EXPECT DATA TO BE JOINED WITH CONTEXTUAL INFORMATION AND UNDERLYING ONTOLOGIES SO THAT DIFFERENT DATA SETS CAN BE MAPPED UNTO EACH OTHER.
LEARNING DESIGN HAS TRADITIONALLY BEEN THOUGHT OF AS AN ACTIVITY OCCURRING PRIOR TO THE PRESENTATION OF A LEARNING EXPERIENCE OR A DESCRIPTION OF THAT ACTIVITY. WITH THE ADVENT OF NEAR REAL-TIME DATA AND NEW OPPORTUNITIES OF REPRESENTING THE DECISIONS AND ACTIONS OF LEARNERS IN DIGITAL LEARNING ENVIRONMENTS, LEARNING DESIGNERS CAN NOW APPLY DYNAMIC LEARNING ANALYTICS INFORMATION ON THE FLY IN ORDER TO EVALUATE LEARNER CHARACTERISTICS, EXAMINE LEARNING DESIGNS, ANALYSE THE EFFECTIVENESS OF LEARNING MATERIALS AND TASKS, ADJUST DIFFICULTY LEVELS, AND MEASURE THE IMPACT OF INTERVENTIONS AND FEEDBACK. IN A CASE STUDY WITH 3550 USERS, THE NAVIGATION SEQUENCE AND NETWORK GRAPH ANALYSIS DEMONSTRATE A POTENTIAL APPLICATION OF LEARNING ANALYTICS DESIGN. IMPLICATIONS BASED ON THE CASE STUDY SHOW THAT INTEGRATION OF ANALYTICS DATA INTO THE DESIGN OF LEARNING ENVIRONMENTS IS A PROMISING APPROACH.
WRITING ANALYTICS HAS EMERGED AS A SUB-FIELD OF LEARNING ANALYTICS, WITH APPLICATIONS INCLUDING THE PROVISION OF FORMATIVE FEEDBACK TO STUDENTS IN DEVELOPING THEIR WRITING CAPACITIES. RHETORICAL MARKERS IN WRITING HAVE BECOME A KEY FEATURE IN THIS FEEDBACK, WITH A NUMBER OF TOOLS BEING DEVELOPED ACROSS RESEARCH AND TEACHING CONTEXTS. HOWEVER, THERE IS NO SHARED CORPUS OF TEXTS ANNOTATED BY THESE TOOLS, NOR IS IT CLEAR HOW THE TOOL ANNOTATIONS COMPARE. THUS, RESOURCES ARE SCARCE FOR COMPARING TOOLS FOR BOTH TOOL DEVELOPMENT AND PEDAGOGIC PURPOSES. IN THIS PAPER, WE CONDUCT SUCH A COMPARISON AND INTRODUCE A SAMPLE CORPUS OF TEXTS REPRESENTATIVE OF THE PARTICULAR GENRES, A SUBSET OF WHICH HAS BEEN ANNOTATED USING THREE RHETORICAL ANALYSIS TOOLS (ONE OF WHICH HAS TWO VERSIONS). THIS PAPER AIMS TO PROVIDE BOTH A DESCRIPTION OF THE TOOLS AND A SHARED DATASET IN ORDER TO SUPPORT EXTENSIONS OF EXISTING ANALYSES AND TOOL DESIGN IN SUPPORT OF WRITING SKILL DEVELOPMENT. WE INTEND THE DESCRIPTION OF THESE TOOLS, WHICH SHARE A FOCUS ON RHETORICAL STRUCTURES, ALONGSIDE THE CORPUS, TO BE A PRELIMINARY STEP TO ENABLE FURTHER RESEARCH, WITH REGARD TO BOTH TOOL DEVELOPMENT AND TOOL INTERACTION.
TEMPORAL ANALYSES ARE CRITICAL TO UNDERSTANDING LEARNING PROCESSES, YET UNDERSTUDIED IN EDUCATION RESEARCH. DATA FROM DIFFERENT SOURCES ARE OFTEN COLLECTED AT DIFFERENT GRAIN SIZES, WHICH ARE DIFFICULT TO INTEGRATE. MAKING SENSE OF DATA AT MANY LEVELS OF ANALYSIS, INCLUDING THE MOST DETAILED LEVELS, IS HIGHLY TIME-CONSUMING. IN THIS PAPER, WE DESCRIBE A GENERALIZABLE APPROACH FOR MORE EFFICIENT YET RICH SENSEMAKING OF TEMPORAL DATA DURING STUDENT USE OF INTELLIGENT TUTORING SYSTEMS. THIS MULTI-STEP APPROACH INVOLVES USING COARSE-GRAIN TEMPORALITY - LEARNING TRAJECTORIES ACROSS KNOWLEDGE COMPONENTS - TO IDENTIFY AND FURTHER EXPLORE "FOCAL" MOMENTS WORTHY OF MORE FINE-GRAIN, CONTEXT-RICH ANALYSIS. WE DISCUSS THE APPLICATION OF THIS APPROACH TO DATA COLLECTED FROM A CLASSROOM STUDY IN WHICH STUDENTS ENGAGED IN A CHEMISTRY VIRTUAL LAB TUTORING SYSTEM. WE SHOW THAT THE APPLICATION OF THIS MULTI-STEP APPROACH EFFICIENTLY LED TO INTERPRETABLE AND ACTIONABLE INSIGHTS WHILE MAKING USE OF THE RICHNESS OF THE AVAILABLE DATA. THIS METHOD IS GENERALIZABLE TO MANY TYPES OF DATASETS AND CAN HELP HANDLE LARGE VOLUMES OF RICH DATA AT MULTIPLE LEVELS OF GRANULARITY. WE ARGUE THAT IT CAN BE A VALUABLE APPROACH TO TACKLING SOME OF THE MOST PROHIBITIVE METHODOLOGICAL CHALLENGES INVOLVED IN TEMPORAL LEARNING ANALYTICS.
THE FIELD OF LEARNING DESIGN STUDIES HOW TO SUPPORT TEACHERS IN DEVISING SUITABLE ACTIVITIES FOR THEIR STUDENTS TO LEARN. THE FIELD OF LEARNING ANALYTICS EXPLORES HOW DATA ABOUT STUDENTS' INTERACTIONS CAN BE USED TO INCREASE THE UNDERSTANDING OF LEARNING EXPERIENCES. DESPITE ITS CLEAR SYNERGY, THERE IS ONLY LIMITED AND FRAGMENTED WORK EXPLORING THE ACTIVE ROLE THAT DATA ANALYTICS CAN PLAY IN SUPPORTING DESIGN FOR LEARNING. THIS PAPER BUILDS ON PREVIOUS RESEARCH TO PROPOSE A FRAMEWORK (ANALYTICS LAYERS FOR LEARNING DESIGN) THAT ARTICULATES THREE LAYERS OF DATA ANALYTICS-LEARNING ANALYTICS, DESIGN ANALYTICS AND COMMUNITY ANALYTICS-TO SUPPORT INFORMED DECISION-MAKING IN LEARNING DESIGN. ADDITIONALLY, A SET OF TOOLS AND EXPERIENCES ARE DESCRIBED TO ILLUSTRATE HOW THE DIFFERENT DATA ANALYTICS PERSPECTIVES PROPOSED BY THE FRAMEWORK CAN SUPPORT LEARNING DESIGN PROCESSES.
THIS CHAPTER ADDRESSES THE QUESTION OF HOW ANALYTICS CAN BE USED TO SUPPORT THE IMPROVEMENT OF ACADEMIC PERSISTENCE, DEFINED AS THE FOCUSED PURSUIT OF BOTH SHORT- AND LONG-TERM ACADEMIC GOALS WHILE AVOIDING SEDUCTIVE DISTRACTIONS. TO IMPROVE MOTIVATION TO PERSIST, MEASURES, ANALYTICS, AND INTERVENTIONS CAN BE ORGANIZED TO FOCUS ON THE THREE FACTORS THAT OPERATIONALIZE MOTIVATION AT THE TASK LEVEL: STARTING, PERSISTING, AND INVESTING ADEQUATE MENTAL EFFORT (PINTRICH & SCHUNK, 2002, SCHUNK, PINTRICH & MEECE, 2008). USING THIS THREEPART FRAMEWORK HELPS BRING SOME COHERENCE TO THE DAUNTING ARRAY OF BOTH FORMAL RESEARCH AND INFORMAL CONSTRUCTS SURROUNDING PERSISTENCE AND MOTIVATION, AND ALSO PROVIDES A TARGET FOR ANALYTIC POSSIBILITIES. WE PRESENT EXAMPLES OF VALIDATED MEASURES AND RESEARCH-TESTED INTERVENTIONS FOR PERSISTENCE MOTIVATION FOR STUDENTS WHO NEED THEM MOST. ONLINE INSTRUCTIONAL TECHNOLOGIES HAVE OPENED MANY NEW OPPORTUNITIES TO CAPTURE INFORMATION ABOUT STUDENTS' PERSISTENCE MOTIVATION AND LEARNING AS THEY WORK, AND TO PROVIDE PERSONALIZED SUPPORT TO INDIVIDUAL STUDENTS. NEW ANALYTIC APPROACHES WILL NEED TO BE VALIDATED, HOWEVER, TO SHOW THAT THEY ARE ACTUALLY PROVIDING ACCURATE AND RELIABLE INFO THAT CAN BE USED TO IMPROVE ACADEMIC PERSISTENCE. IN ADDITION, LEARNING ANALYTICS MAY DISCOVER NEW INSIGHTS ABOUT STUDENT DIFFERENCES AND ENVIRONMENTAL FACTORS THAT IMPACT PERSISTENCE AND ADD TO THE INTERVENTIONS CURRENTLY AVAILABLE. THE GROWTH OF ONLINE INSTRUCTION ACROSS THE WORLD ALSO MAKES IT POSSIBLE TO CONDUCT RAPID EXPERIMENTATION ON NEW ANALYTIC STRATEGIES AND THE MEASURES AND INTERVENTIONS LINKED TO THEM.
TO IDENTIFY THE WAYS TEACHERS AND EDUCATIONAL SYSTEMS CAN IMPROVE LEARNING, RESEARCHERS NEED TO MAKE CAUSAL INFERENCES. ANALYSES OF EXISTING DATASETS PLAY AN IMPORTANT ROLE IN DETECTING CAUSAL PATTERNS, BUT CONDUCTING EXPERIMENTS ALSO PLAYS AN INDISPENSABLE ROLE IN THIS RESEARCH. IN THIS ARTICLE, WE ADVOCATE FOR EXPERIMENTS TO BE EMBEDDED IN REAL EDUCATIONAL CONTEXTS, ALLOWING RESEARCHERS TO TEST WHETHER INTERVENTIONS SUCH AS A LEARNING ACTIVITY, NEW TECHNOLOGY, OR ADVISING STRATEGY ELICIT RELIABLE IMPROVEMENTS IN AUTHENTIC STUDENT BEHAVIOURS AND EDUCATIONAL OUTCOMES. EMBEDDED EXPERIMENTS, WHEREIN THEORETICALLY RELEVANT VARIABLES ARE SYSTEMATICALLY MANIPULATED IN REAL LEARNING CONTEXTS, CARRY STRONG BENEFITS FOR MAKING CAUSAL INFERENCES, PARTICULARLY WHEN ALLIED WITH THE DATA-RICH RESOURCES OF CONTEMPORARY E-LEARNING ENVIRONMENTS. TOWARD THIS GOAL, WE OFFER A FIELD GUIDE TO EMBEDDED EXPERIMENTATION, REVIEWING EXPERIMENTAL DESIGN CHOICES, ADDRESSING ETHICAL CONCERNS, DISCUSSING THE IMPORTANCE OF INVOLVING TEACHERS, AND REVIEWING HOW INTERVENTIONS CAN BE DEPLOYED IN A VARIETY OF CONTEXTS, AT A RANGE OF SCALES. CAUSAL INFERENCE IS A CRITICAL COMPONENT OF A FIELD THAT AIMS TO IMPROVE STUDENT LEARNING; INCLUDING EXPERIMENTATION ALONGSIDE ANALYSES OF EXISTING DATA IN LEARNING ANALYTICS IS THE MOST COMPELLING WAY TO TEST CAUSAL CLAIMS.
LEARNING ANALYTICS IS TARGETED TO BETTER UNDERSTAND AND OPTIMIZE THE PROCESS OF LEARNING AND ITS ENVIRONMENTS THROUGH THE MEASUREMENT, COLLECTION AND ANALYSIS OF LEARNERS' DATA AND CONTEXTS. TO ADVISE PEOPLE'S LEARNING IN A SPECIFIC SUBJECT, MOST INTELLIGENT E-LEARNING SYSTEMS WOULD REQUIRE COURSE INSTRUCTORS TO EXPLICITLY INPUT SOME PRIOR KNOWLEDGE ABOUT THE SUBJECT SUCH AS ALL THE PRE-REQUISITE REQUIREMENTS BETWEEN COURSE MODULES. YET HUMAN EXPERTS MAY SOMETIMES HAVE CONFLICTING VIEWS LEADING TO LESS DESIRABLE LEARNING OUTCOMES. IN A PREVIOUS STUDY, WE PROPOSED A COMPLETE SYSTEM FRAMEWORK OF LEARNING ANALYTICS TO PERFORM AN EXPLICIT SEMANTIC ANALYSIS ON THE COURSE MATERIALS, FOLLOWED BY A HEURISTIC-BASED CONCEPT CLUSTERING ALGORITHM TO GROUP RELEVANT CONCEPTS BEFORE FINDING THEIR RELATIONSHIP MEASURES, AND LASTLY EMPLOYING A SIMPLE YET EFFICIENT EVOLUTIONARY APPROACH TO RETURN THE OPTIMAL LEARNING SEQUENCE. IN THIS PAPER, WE CAREFULLY CONSIDER TO ENHANCE THE ORIGINAL EVOLUTIONARY OPTIMIZER WITH THE HILL-CLIMBING HEURISTIC, AND ALSO CRITICALLY EVALUATE THE IMPACTS OF VARIOUS EXPERTS' RECOMMENDED LEARNING SEQUENCES POSSIBLY WITH CONFLICTING VIEWS TO OPTIMIZE THE LEARNING PATHS FOR THE NEXT-GENERATION E-LEARNING SYSTEMS. MORE IMPORTANTLY, THE INTEGRATION OF HEURISTICS CAN MAKE OUR PROPOSED FRAMEWORK MORE SELF-ADAPTIVE TO LESS STRUCTURED KNOWLEDGE DOMAINS WITH CONFLICTING VIEWS. TO DEMONSTRATE THE FEASIBILITY OF OUR PROTOTYPE, WE IMPLEMENTED A PROTOTYPE OF THE PROPOSED E-LEARNING SYSTEM FRAMEWORK FOR LEARNING ANALYTICS. OUR EMPIRICAL EVALUATION CLEARLY REVEALED MANY POSSIBLE ADVANTAGES OF OUR PROPOSAL WITH INTERESTING DIRECTIONS FOR FUTURE INVESTIGATION.
REFLECTIVE WRITING IS WIDELY ACKNOWLEDGED TO BE ONE OF THE MOST EFFECTIVE LEARNING ACTIVITIES FOR PROMOTING STUDENTS' SELF-REFLECTION AND CRITICAL THINKING HOWEVER, MANUALLY ASSESSING AND GIVING FEEDBACK ON REFLECTIVE WRITING IS TIME CONSUMING, AND KNOWN TO BE CHALLENGING FOR EDUCATORS. THERE IS LITTLE WORK INVESTIGATING THE POTENTIAL OF AUTOMATED ANALYSIS OF REFLECTIVE WRITING, AND EVEN LESS ON MACHINE LEARNING APPROACHES WHICH OFFER POTENTIAL ADVANTAGES OVER RULE-BASED APPROACHES. THIS STUDY REPORTS PROGRESS IN DEVELOPING A MACHINE LEARNING APPROACH FOR THE BINARY CLASSIFICATION OF PHARMACY STUDENTS' REFLECTIVE STATEMENTS ABOUT THEIR WORK PLACEMENTS. FOUR COMMON STATISTICAL CLASSIFIERS WERE TRAINED ON A CORPUS OF 301 STATEMENTS, USING EMOTIONAL, COGNITIVE AND LINGUISTIC FEATURES FROM THE LINGUISTIC INQUIRY AND WORD COUNT (LIWC) ANALYSIS, IN COMBINATION WITH AFFECTIVE AND RHETORICAL FEATURES FROM THE ACADEMIC WRITING ANALYTICS (AWA) PLATFORM. THE RESULTS SHOWED THAT THE RANDOM-FOREST ALGORITHM PERFORMED WELL (F-SCORE = 0.799) AND THAT AWA FEATURES, SUCH AS EMOTIONAL AND REFLECTIVE RHETORICAL MOVES, IMPROVED PERFORMANCE.
THE USE OF BIG DATA IN HIGHER EDUCATION HAS EVOLVED RAPIDLY WITH A FOCUS ON THE PRACTICAL APPLICATION OF NEW TOOLS AND METHODS FOR SUPPORTING LEARNING. IN THIS PAPER, WE DEPART FROM THE CORE EMPHASIS ON APPLICATION AND DELVE INTO A MOSTLY NEGLECTED ASPECT OF THE BIG DATA CONVERSATION IN HIGHER EDUCATION. DRAWING ON DEVELOPMENTS IN COGNATE DISCIPLINES, WE ANALYSE THE INHERENT DIFFICULTIES IN INFERRING THE COMPLEX PHENOMENON THAT IS LEARNING FROM BIG DATASETS. THIS FORMS THE BASIS OF A DISCUSSION ABOUT THE POSSIBILITIES FOR SYSTEMATIC COLLABORATION ACROSS DIFFERENT PARADIGMS AND DISCIPLINARY BACKGROUNDS IN INTERPRETING BIG DATA FOR ENHANCING LEARNING. THE AIM OF THIS PAPER IS TO PROVIDE THE FOUNDATION FOR A RESEARCH AGENDA, WHERE DIFFERING CONCEPTUALISATIONS OF LEARNING BECOME A STRENGTH IN INTERPRETING PATTERNS IN BIG DATASETS, RATHER THAN A POINT OF CONTENTION.
THIS PAPER PRESENTS A NOVEL APPROACH TO UNDERSTAND SPECIFIC STUDENT BEHAVIOR IN MOOCS. INSTRUCTORS CURRENTLY PERCEIVE PARTICIPANTS ONLY AS ONE HOMOGENEOUS GROUP. IN ORDER TO IMPROVE LEARNING OUTCOMES, THEY ENCOURAGE STUDENTS TO GET ACTIVE IN THE DISCUSSION FORUM AND REMIND THEM OF APPROACHING DEADLINES. WHILE THESE ACTIONS ARE MOST LIKELY HELPFUL, THEIR ACTUAL IMPACT IS OFTEN NOT MEASURED. ADDITIONALLY, IT IS UNCERTAIN WHETHER SUCH GENERIC APPROACHES SOMETIMES CAUSE THE OPPOSITE EFFECT, AS SOME PARTICIPANTS ARE BOTHERED WITH IRRELEVANT INFORMATION. ON THE BASIS OF FINE GRANULAR EVENTS EMITTED BY OUR LEARNING PLATFORM, WE DERIVE METRICS AND ENABLE TEACHERS TO EMPLOY CLUSTERING, IN ORDER TO DIVIDE THE VAST FIELD OF PARTICIPANTS INTO MEANINGFUL SUBGROUPS TO BE ADDRESSED INDIVIDUALLY.
MASSIVE OPEN ONLINE COURSES, UNIVERSALLY LABELLED AS MOOCS, BECOME MORE AND MORE RELEVANT IN THE ERA OF DIGITALIZATION OF HIGHER EDUCATION. THE AVAILABILITY OF FREE EDUCATION RESOURCES WITHOUT ACCESS RESTRICTIONS FOR A PLENTY OF POTENTIAL USERS HAS CHANGED THE LEARNING MARKET IN A WAY UNTHINKABLE ONLY FEW DECADES AGO. THIS FORM OF WEB-BASED EDUCATION ALLOWS TO TRACK ALL THE ACTIONS OF THE STUDENTS, THUS PROVIDING AN INFORMATION BASE TO UNDERSTAND HOW STUDENTS' BEHAVIOUR CAN INFLUENCE THEIR PERFORMANCE. THE PAPER PROPOSES A STRUCTURAL EQUATION MODEL IN THE FRAMEWORK OF THE COMPONENT-BASED APPROACH TO MEASURE WHICH ARE THE MAIN FACTORS AFFECTING STUDENTS' PERFORMANCE (PARTIAL LEAST SQUARES PATH MODELLING). THE NOVELTY OF THE APPROACH IS THE SIMULTANEOUS ANALYSIS OF MORE THAN ONE FACTOR THAT EXERTS AN IMPACT ON THE PERFORMANCE. THE ANALYSIS IS CARRIED OUT ON THE LOG DATA OF A COURSE AVAILABLE ON THE EDX MOOCS PLATFORM NAMED FEDERICAX.
RESEARCH IN THE FIELD OF COLLABORATION SHOWS THAT STUDENTS DO NOT SPONTANEOUSLY COLLABORATE WITH EACH OTHER. A SYSTEM THAT CAN MEASURE COLLABORATION IN REAL TIME COULD BE USEFUL BY, FOR EXAMPLE, HELPING THE TEACHER LOCATE A GROUP REQUIRING GUIDANCE. TO ADDRESS THIS CHALLENGE, MY RESEARCH FOCUSES ON BUILDING AND COMPARING COLLABORATION DETECTORS FOR DIFFERENT TYPES OF CLASSROOM PROBLEM SOLVING ACTIVITIES, SUCH AS CARD SORTING AND HAND WRITING. I AM ALSO STUDYING TRANSFER: HOW COLLABORATION DETECTORS FOR ONE TASK CAN BE USED WITH A NEW TASK. FINALLY, WE ATTEMPT TO BUILD A TEACHERS DASHBOARD THAT CAN DESCRIBE REASONING BEHIND THE TRIGGERED ALERTS THEREBY HELPING THE TEACHERS WITH INSIGHTS TO AID THE COLLABORATIVE ACTIVITY. DATA FOR BUILDING SUCH DETECTORS WERE COLLECTED IN THE FORM OF VERBAL INTERACTION AND USER ACTION LOGS FROM STUDENTS' TABLETS. THREE QUALITATIVE LEVELS OF INTERACTIVITY WAS DISTINGUISHED: COLLABORATION, COOPERATION AND ASYMMETRIC CONTRIBUTION. MACHINE LEARNING WAS USED TO INDUCE A CLASSIFIER THAT CAN ASSIGN A CODE FOR EVERY EPISODE BASED ON THE SET OF FEATURES. OUR PRELIMINARY RESULTS INDICATE THAT MACHINE LEARNED CLASSIFIERS WERE RELIABLE.
WHEN INTERACTING WITH A MOOC, STUDENTS CAN PERFORM DIFFERENT KINDS OF ACTIONS SUCH AS WATCHING VIDEOS, ANSWERING EXERCISES, PARTICIPATING IN THE COURSE FORUM, SUBMITTING A PROJECT OR REVIEWING A DOCUMENT. THESE ACTIONS REPRESENT THE DYNAMISM OF STUDENT LEARNING PATHS, AND THEIR PREFERENCES WHEN LEARNING IN AN AUTONOMOUS MODE. IN THIS PAPER WE PROPOSE TO ANALYSE THESE LEARNING PATHS WITH TWO GOALS IN MIND. THE FIRST ONE IS TO TRY TO DISCOVER THE DIFFERENT ROLES THAT STUDENTS MAY ADOPT WHEN INTERACTING WITH AN ONLINE COURSE. BY APPLYING K-MEANS, SIX OF THESE ROLES ARE DISCOVERED AND WE GIVE A QUALITATIVE INTERPRETATION OF THEM BASED ON STUDENT INFORMATION ASSOCIATED TO EACH CLUSTER. THE OTHER GOAL IS TO PREDICT ACADEMIC PERFORMANCE. IN THIS SENSE, WE PRESENT THE RESULTS OBTAINED WITH RANDOM FOREST AND NEURAL NETWORKS THAT ALLOW US TO PREDICT THE FINAL GRADE WITH AROUND 10% OF MEAN ABSOLUTE ERROR.
PURPOSE THIS RESEARCH STUDY AIMS TO EVALUATE THE CAPACITY AND SUSTAINABILITY OF AN ACCESSIBLE SOCIETY AS A SMART SOCIETY AND SERVICES WITH THE HELP OF MOOCS AND ASSISTIVE TECHNOLOGIES WITHIN THE LEARNING ANALYTICS FRAMEWORK. DESIGN/METHODOLOGY/APPROACH QUALITATIVE RESEARCH WAS EMPLOYED IN THIS RESEARCH THAT INTERVIEW FORMS WERE CONDUCTED TO GET DATA FROM 60 PARTICIPANTS. THEMATIC ANALYSIS WAS USED TO ANALYZE DATA. FINDINGS RESEARCH RESULTS REVEALED THAT MOOCS AND ASSERTIVE TECHNOLOGIES ARE CRUCIAL FOR SMART SOCIETY AND OPENS A MAP FOR OPEN PEDAGOGY. ACCESSIBLE MEDIA, SERVICES AND APPLICATIONS IN SMART SOCIETIES ARE KEY ELEMENTS FOR DISABLED PEOPLE LIVES. RESEARCH LIMITATIONS/IMPLICATIONS RESEARCH IS LIMITED TO NUMBERS OF RESEARCH PARTICIPANTS IN NORTHERN PART OF CYPRUS. PRACTICAL IMPLICATIONS ESTABLISHING STRATEGIES AND POLICIES FOR THE SMART AND ACCESSIBLE SOCIETY AND SERVICES ARE INTENSIFIED NEED FOR THE DISABLED PEOPLE WITHIN THE FRAMEWORK OF LEARNING ANALYTICS. SOCIAL IMPLICATIONS ASSISTIVE TECHNOLOGIES BECOME MEDIUM OF FACILITATING ACCESSIBLE AND SMART SOCIETY AND SERVICES FOR EVERYONE. ORIGINALITY/VALUE EDUCATION PLAYS A GREAT ROLE TO ENRICH SERVICES OF SOCIETIES IN ORDER TO CREATE INCLUSIVE EFFORTS TO THE LIFE OF DISABLED PEOPLE. UNITED NATIONS EDUCATIONAL SCIENTIFIC AND CULTURAL ORGANIZATIONS (UNESCO) UNDERLINE THE MAIN THEME OF MAKING INCLUSIVE AND EQUITABLE QUALITY EDUCATION AND PROMOTING LIFELONG LEARNING OPPORTUNITIES FOR ALL. IN THIS RESPECT, ACCESSIBILITY, INCLUSIVENESS, EQUITY, EQUALITY, QUALITY FOR LIFELONG LEARNING ARE MAIN COMPONENTS TO FOSTER ACCESSIBLE AND SMART SOCIETY FOR EVERYONE. INTEGRATING THE IMPORTANCE OF LEARNING ANALYTICS CREATES A VALUE FOR UNDERSTANDING OF BEING SMART SOCIETY.
A SIGNIFICANT AMOUNT OF RESEARCH EFFORT HAS BEEN PUT INTO FINDING VARIABLES THAT CAN IDENTIFY STUDENTS AT RISK BASED ON ACTIVITY RECORDS AVAILABLE IN LEARNING MANAGEMENT SYSTEMS (LMS). THESE VARIABLES OFTEN DEPEND ON THE CONTEXT, FOR EXAMPLE, THE COURSE STRUCTURE, HOW THE ACTIVITIES ARE ASSESSED OR WHETHER THE COURSE IS ENTIRELY ONLINE OR A BLENDED COURSE. TO THE BEST OF OUR KNOWLEDGE, A PREDICTIVE MODEL THAT CAN GENERALIZE WELL TO MANY DIFFERENT TYPES OF COURSES USING DATA AVAILABLE IN THE LMS DOES NOT CURRENTLY EXIST IN THE LEARNING ANALYTICS LITERATURE. IN THIS STUDY, EARLY PREDICTION OF STUDENTS AT RISK IS TACKLED BY TRAINING A NUMBER OF NEURAL NETWORKS TO PREDICT WHICH STUDENTS WOULD LIKELY SUBMIT THEIR ASSIGNMENTS ON TIME BASED ON THEIR ACTIVITY UP TO TWO DAYS BEFORE ASSIGNMENTS' DUE DATES. FIVE DIFFERENT DATASETS THAT COVER A TOTAL OF 78 722 STUDENT ENROLMENTS IN 5487 COURSES HAVE BEEN USED IN THIS STUDY. IN ORDER TO IMPROVE HOW WELL THE NEURAL NETWORKS GENERALIZE, OUR NETWORKS CAN PERFORM DIFFERENT FORMS OF FEATURE ENGINEERING USING COURSE PEERS DATA. THE DIFFERENT ARCHITECTURES OF THESE NETWORKS HAVE BEEN COMPARED TO FIND THE ONE WITH MORE PREDICTIVE POWER. TO VALIDATE THE MODELS TRAINED FROM THE NETWORKS, BOTH NEW DATASETS AND UNSEEN EXAMPLES EXTRACTED FROM THE SAME DATASETS HAVE BEEN USED FOR TRAINING. OUR RESEARCH SHOW THAT ADDING CONTEXTUAL INFORMATION RESULTS IN BETTER PREDICTION ACCURACIES AND F1 SCORES. OUR NETWORKS ARE ABLE TO GIVE PREDICTIONS WITH ACCURACIES IN THE 67.46-81.63% RANGE AND F1 SCORES IN THE 71.30-83.09% RANGE.
IN THE CONTEXT OF THE HYPE, PROMISE AND PERILS OF BIG DATA AND THE CURRENTLY DOMINANT PARADIGM OF DATA-DRIVEN DECISION-MAKING, IT IS IMPORTANT TO CRITICALLY ENGAGE WITH THE POTENTIAL OF BIG DATA FOR HIGHER EDUCATION. WE DO NOT QUESTION THE POTENTIAL OF BIG DATA, BUT WE DO RAISE A NUMBER OF ISSUES, AND PRESENT A NUMBER OF THESES TO BE SERIOUSLY CONSIDERED IN REALISING THIS POTENTIAL. THE UNIVERSITY OF SOUTH AFRICA (UNISA) IS ONE OF THE MEGA ODL INSTITUTIONS IN THE WORLD WITH MORE THAN 360,000 STUDENTS AND A RANGE OF COURSES AND PROGRAMMES. UNISA ALREADY HAS ACCESS TO A STAGGERING AMOUNT OF STUDENT DATA, HOSTED IN DISPARATE SOURCES, AND GOVERNED BY DIFFERENT PROCESSES. AS THE UNIVERSITY MOVES TO MAINSTREAMING ONLINE LEARNING, THE AMOUNT OF AND NEED FOR ANALYSES OF DATA ARE INCREASING, RAISING IMPORTANT QUESTIONS REGARDING OUR ASSUMPTIONS, UNDERSTANDING, DATA SOURCES, SYSTEMS AND PROCESSES. THIS ARTICLE PRESENTS A DESCRIPTIVE CASE STUDY OF THE CURRENT STATE OF STUDENT DATA AT UNISA, AS WELL AS EXPLORES THE IMPACT OF EXISTING DATA SOURCES AND ANALYTIC APPROACHES. FROM THE ANALYSIS IT IS CLEAR THAT IN ORDER FOR BIG(GER) DATA TO BE BETTER DATA, A NUMBER OF ISSUES NEED TO BE ADDRESSED. THE ARTICLE CONCLUDES BY PRESENTING A NUMBER OF THESES THAT SHOULD FORM THE BASIS FOR THE IMPERATIVE TO OPTIMISE THE HARVESTING, ANALYSIS AND USE OF STUDENT DATA.
CONTINUING EDUCATION ONLINE HAS BEEN STEADILY INCREASING WORLDWIDE AND ITS STUDENTS' PROFILES TEND TO BE DIFFERENT FROM THOSE IN OTHER FORMAL MODALITIES. HENCE, IT IS RELEVANT TO UNDERSTAND THE BEHAVIOR OF THESE STUDENTS ALONG THEIR COURSE TRAJECTORIES. IN THIS WORK, WE INVESTIGATE A SPECIALIZATION ONLINE COURSE INVOLVING HEARING HEALTHCARE PROFESSIONALS (N=96), TO DISCOVER DIFFERENT NAVIGATION STRATEGIES AND THE FACTORS WHICH MAY INFLUENCE IT. TO THAT END, WE APPLIED SEQUENCE ANALYSIS TECHNIQUES TO INTERPRET DIFFERENT NAVIGATION PATTERNS AND SELECTED DEMOGRAPHIC COVARIABLES WHICH MAY INFLUENCE THESE BEHAVIORAL TRAJECTORIES. THREE CLUSTERS WERE FOUND, WITH SUBTLE DIFFERENCES AMONG THEM AND THREE VARIABLES WITH GREATER PREDICTIVE POWER: AGE, PROFESSIONAL EXPERIENCE AND REGION.
THIS PAPER DESCRIBES THE CONTRIBUTIONS OF THE SOFTLEARN PLATFORM TO KEY ISSUES IN LEARNING ANALYTICS, AS I) DISCOVERY OF THE LEARNING PATH THAT STUDENTS FOLLOW IN A COURSE AND II) PROVIDE INTERPRETABILITY OF GRAPHS IN DASHBOARDS
COLLOCATED, FACE-TO-FACE TEAMWORK REMAINS A PERVASIVE MODE OF WORKING, WHICH IS HARD TO REPLICATE ONLINE. TEAM MEMBERS' EMBODIED, MULTIMODAL INTERACTION WITH EACH OTHER AND ARTEFACTS HAS BEEN STUDIED BY RESEARCHERS, BUT DUE TO ITS COMPLEXITY, HAS REMAINED OPAQUE TO AUTOMATED ANALYSIS. HOWEVER, THE READY AVAILABILITY OF SENSORS MAKES IT INCREASINGLY AFFORDABLE TO INSTRUMENT WORK SPACES TO STUDY TEAMWORK AND GROUPWORK. THE POSSIBILITY OF VISUALISING KEY ASPECTS OF A COLLABORATION HAS HUGE POTENTIAL FOR BOTH ACADEMIC AND PROFESSIONAL LEARNING, BUT A FRONTLINE CHALLENGE IS THE ENRICHMENT OF QUANTITATIVE DATA STREAMS WITH THE QUALITATIVE INSIGHTS NEEDED TO MAKE SENSE OF THEM. IN RESPONSE, WE INTRODUCE THE CONCEPT OF COLLABORATION TRANSLUCENCE, AN APPROACH TO MAKE VISIBLE SELECTED FEATURES OF GROUP ACTIVITY. THIS IS GROUNDED BOTH THEORETICALLY (IN THE PHYSICAL, EPISTEMIC, SOCIAL AND AFFECTIVE DIMENSIONS OF GROUP ACTIVITY), AND CONTEXTUALLY (USING DOMAIN-SPECIFIC CONCEPTS). WE ILLUSTRATE THE APPROACH FROM THE AUTOMATED ANALYSIS OF HEALTHCARE SIMULATIONS TO TRAIN NURSES, GENERATING FOUR VISUAL PROXIES THAT FUSE MULTIMODAL DATA INTO HIGHER ORDER PATTERNS.
MANY EDUCATIONAL INSTITUTIONS HAVE BEEN USING ONLINE JUDGES IN PROGRAMMING CLASSES, AMONGST OTHERS, TO PROVIDE FASTER FEEDBACK FOR STUDENTS AND TO REDUCE THE TEACHER'S WORKLOAD. THERE IS SOME EVIDENCE THAT ONLINE JUDGES ALSO HELP IN REDUCING DROPOUT. NEVERTHELESS, THERE IS STILL A HIGH LEVEL OF DROPOUT NOTICEABLE IN INTRODUCTORY PROGRAMMING CLASSES. IN THIS SENSE, THE OBJECTIVE OF THIS WORK IS TO DEVELOP AND VALIDATE A METHOD FOR PREDICTING STUDENT DROPOUT USING DATA FROM THE FIRST TWO WEEKS OF STUDY, TO ALLOW FOR EARLY INTERVENTION. INSTEAD OF THE CLASSICAL QUESTIONNAIRE-BASED METHOD, WE OPTED FOR A NON-SUBJECTIVE, DATA-DRIVEN APPROACH. HOWEVER, SUCH APPROACHES ARE KNOWN TO SUFFER FROM A POTENTIAL OVERLOAD OF FACTORS, WHICH MAY NOT ALL BE RELEVANT TO THE PREDICTION TASK. AS A RESULT, WE REACHED A VERY PROMISING 80% OF ACCURACY, AND PERFORMED EXPLICIT EXTRACTION OF THE MAIN FACTORS LEADING TO STUDENT DROPOUT.
SEVERAL STUDIES HAVE SHOWN THAT COMPLEX NONLINEAR LEARNING ANALYTICS (LA) TECHNIQUES OUTPERFORM THE TRADITIONAL ONES. HOWEVER, THE ACTUAL INTEGRATION OF THESE TECHNIQUES IN AUTOMATIC LA SYSTEMS REMAINS RARE BECAUSE THEY ARE GENERALLY PRESUMED TO BE OPAQUE. AT THE SAME TIME, THE CURRENT REVIEWS ON LA IN HIGHER EDUCATION POINT OUT THAT LA SHOULD BE MORE GROUNDED TO THE LEARNING SCIENCE WITH ACTUAL LINKAGE TO TEACHERS AND PEDAGOGICAL PLANNING. IN THIS STUDY, WE AIM TO ADDRESS THESE TWO CHALLENGES. FIRST, WE DISCUSS DIFFERENT TECHNIQUES THAT OPEN UP THE DECISION-MAKING PROCESS OF COMPLEX TECHNIQUES AND HOW THEY CAN BE INTEGRATED IN LA TOOLS. MORE PRECISELY, WE PRESENT VARIOUS GLOBAL AND LOCAL EXPLAINABLE TECHNIQUES WITH AN EXAMPLE OF AN AUTOMATIC LA PROCESS THAT PROVIDES INFORMATION ABOUT DIFFERENT RESOURCES THAT CAN SUPPORT STUDENT AGENCY IN HIGHER EDUCATION INSTITUTES. SECOND, WE EXEMPLIFY THESE TECHNIQUES AND THE LA PROCESS THROUGH RECENTLY COLLECTED STUDENT AGENCY DATA IN FOUR COURSES OF THE SAME CONTENT TAUGHT BY FOUR DIFFERENT TEACHERS. ALTOGETHER, WE DEMONSTRATE HOW THIS PROCESS-WHICH WE CALL EXPLAINABLE STUDENT AGENCY ANALYTICS-CAN CONTRIBUTE TO TEACHERS' PEDAGOGICAL PLANNING THROUGH THE LA CYCLE.
STUDENTS DON'T ASK ENOUGH QUESTIONS IN CLASSROOMS, IN PERSON OR ONLINE. ASKING QUESTIONS HAS BEEN FOUND TO BE A CRITICAL SKILL TOWARD DEVELOPING CRITICAL THINKING ABILITIES, IMPROVE LEARNING PERFORMANCE, AS WELL AS CAREER DEVELOPMENT. HOWEVER, 'HOW TO ASK PRODUCTIVE QUESTIONS' AS A KEY SKILL, IS NOT WELL STUDIED. THEREFORE, THE PRESENT PAPER INTRODUCES THE QUESTION PRODUCTIVITY INDEX (QPI) AND EXPLORES WAYS TOWARD QUANTITATIVELY AND RELIABLY MEASURE STUDENT-GENERATED QUESTIONS.
THIS WORK APPROACHES THE PREDICTION OF LEARNING GAINS IN AN ENVIRONMENT WITH INTENSIVE USE OF EXERCISES AND VIDEOS, SPECIFICALLY USING THE KHAN ACADEMY PLATFORM. WE PROPOSE A LINEAR REGRESSION MODEL WHICH CAN EXPLAIN 57.4% OF THE LEARNING GAINS VARIABILITY, WITH THE USE OF FOUR VARIABLES OBTAINED FROM THE LOW LEVEL DATA GENERATED BY THE STUDENTS. WE FOUND THAT TWO OF THESE VARIABLES ARE RELATED TO EXERCISES (THE PROFICIENT EXERCISES AND THE AVERAGE NUMBER OF ATTEMPTS IN EXERCISES), AND ONE IS RELATED TO BOTH VIDEOS AND EXERCISES (THE TOTAL TIME SPENT IN BOTH) RELATED TO EXERCISES, WHEREAS ONLY ONE IS RELATED TO VIDEOS.
THE FIELD OF ARTIFICIAL INTELLIGENCE WAS BORN IN THE MIDDLE OF THE 1950'S AND IT HAS BEEN APPLIED TO AN INCREASING NUMBER OF ASPECTS OF OUR LIFE SINCE THEN. THE PURPOSE OF ARTIFICIAL INTELLIGENCE IS TO BUILD INTELLIGENT MACHINES, OR AT LEAST MACHINES THAT BEHAVE AS IF THEY WERE INTELLIGENT. EARLY ON, A METHOD TO ESTABLISH THE INTELLIGENCE OF MACHINES WAS DEVISED: THE TURING TEST. IN THIS PAPER, SOME OF THE CONTRIBUTIONS OF ARTIFICIAL INTELLIGENCE TO THE FIELD OF EDUCATION ARE EXPLORED. AT THE BEGINNING, A DESCRIPTION OF THE ORIGINS OF ARTIFICIAL INTELLIGENCE IS GIVEN, TOGETHER WITH A DEFINITION OF THE FIELD. THEN, SOME OF THE MOST RELEVANT SUBFIELDS OF ARTIFICIAL INTELLIGENCE ARE BRIEFLY DESCRIBED, INCLUDING DEEP LEARNING, REINFORCEMENT LEARNING, ROBOTICS, COMPUTER VISION, NATURAL LANGUAGE PROCESSING AND HUMAN COMPUTATION. AFTER THAT SOME CONTRIBUTIONS OF ROBOTICS, INTELLIGENT TUTORING SYSTEMS AND LEARNING ANALYTICS TO THE FIELD OF EDUCATION ARE EXPLORED. THE USE OF ROBOTS AS A DIDACTIC TOOL IS ANALYZED, AND THREE RECENT EXAMPLES OF DIDACTIC ROBOTS THAT HAVE REACHED THE MARKET ARE GIVEN. INTELLIGENT TUTORING SYSTEMS ARE CAPABLE OF USING ARTIFICIAL INTELLIGENCE TECHNIQUES AND DETECTING DIFFERENT VARIABLES OF THE LEARNING PROCESS, BUT THEY HAVE NOT INFLUENCED EDUCATION PROCESSES AS MUCH AS IT WAS EXPECTED. THREE EXAMPLES OF INTELLIGENT TUTORING SYSTEMS ARE GIVEN, AND SOME OF THE REASONS FOR THEIR LACK OF ADOPTION ARE EXPLAINED. FINALLY, THE FIELD OF LEARNING ANALYTICS IS DESCRIBED AND SOME IMPORTANT CONDITIONS FOR A SUCCESSFUL PROJECT IN LEARNING ANALYTICS ARE MENTIONED.
COLLABORATION IS A 21ST CENTURY SKILL AS WELL AS AN EFFECTIVE METHOD FOR LEARNING, SO DETECTION OF COLLABORATION IS IMPORTANT FOR BOTH ASSESSMENT AND INSTRUCTION. SPEECH-BASED COLLABORATION DETECTION CAN BE QUITE ACCURATE BUT COLLECTING THE SPEECH OF STUDENTS IN CLASSROOMS CAN RAISE PRIVACY ISSUES. AN ALTERNATIVE IS TO SEND ONLY WHETHER OR NOT THE STUDENT IS SPEAKING. THAT IS, THE SPEECH SIGNAL IS PROCESSED AT THE MICROPHONE BY A VOICE ACTIVITY DETECTOR BEFORE BEING TRANSMITTED TO THE COLLABORATION DETECTOR. BECAUSE THE TRANSMITTED SIGNAL IS BINARY (1 = SPEAKING, 0 = SILENCE), THIS METHOD MITIGATES PRIVACY ISSUES. HOWEVER, IT MAY HARM THE ACCURACY OF COLLABORATION DETECTION. TO FIND OUT HOW MUCH HARM IS DONE, THIS STUDY COMPARED THE RELATIVE EFFECTIVENESS OF COLLABORATION DETECTORS BASED EITHER ON THE BINARY SIGNAL OR HIGH-QUALITY AUDIO. PAIRS OF STUDENTS WERE ASKED TO WORK TOGETHER ON SOLVING COMPLEX MATH PROBLEMS. THREE QUALITATIVE LEVELS OF INTERACTIVITY WAS DISTINGUISHED: INTERACTION, COOPERATION AND OTHER. HUMAN CODERS USED RICHER DATA (SEVERAL AUDIO AND VIDEO STREAMS) TO CHOOSE THE CODE FOR EACH EPISODE. MACHINE LEARNING WAS USED TO INDUCE A DETECTOR TO ASSIGN A CODE FOR EVERY EPISODE BASED ON THE FEATURES. THE BINARY-BASED COLLABORATION DETECTORS DELIVERED ONLY SLIGHTLY LESS ACCURACY THAN COLLABORATION DETECTORS BASED ON THE HIGH QUALITY AUDIO SIGNAL.
THIS PAPER PRESENTS A NEW APPROACH TO AUTOMATICALLY DETECTING LOWER-PERFORMING OR "AT-RISK" STUDENTS ON COMPUTER PROGRAMMING MODULES IN AN UNDERGRADUATE UNIVERSITY DEGREE COURSE. USING HISTORICAL DATA FROM PREVIOUS STUDENT COHORTS WE BUILT A PREDICTIVE MODEL USING LOGGED INTERACTIONS BETWEEN STUDENTS AND ONLINE RESOURCES, AS WELL AS STUDENTS' PROGRESS IN PROGRAMMING LABORATORY WORK. PREDICTIONS WERE CALCULATED EACH WEEK DURING A 12-WEEK SEMESTER. COURSE LECTURERS RECEIVED STUDENT LISTS RANKED BY THEIR PROBABILITY OF FAILING THE NEXT COMPUTER-BASED LABORATORY EXAM. AT-RISK STUDENTS WERE TARGETED AND OFFERED ASSISTANCE DURING LABORATORY SESSIONS BY THE LECTURER AND LABORATORY TUTORS. WHEN WE GROUP STUDENTS INTO TWO COHORTS DEPENDING ON WHETHER THEY FAILED OR PASSED THEIR FIRST LABORATORY EXAM, THE AVERAGE MARGIN OF IMPROVEMENT ON THE SECOND LABORATORY EXAM BETWEEN THE HIGHER AND LOWER-PERFORMING STUDENTS WAS FOUR TIMES HIGHER WHEN OUR PREDICTIONS WERE RUN AND SUBSEQUENT LABORATORY SUPPORT TARGETED AT THESE STUDENTS, COMPARED TO STUDENTS FROM THE YEAR OUR MODEL WAS TRAINED ON.
IN THE ERA OF BIG DATA, ANALYTICS PLAYS A CRUCIAL ROLE IN THE EDUCATIONAL DOMAIN. IN THIS PAPER, WE DESIGN A FRAMEWORK TO EXPLORE THE VALUE OF A DATA-DRIVEN DECISION MAKING PROCESS. CURRICULUM DESIGN AT THE DEPARTMENTAL LEVEL IS CHOSEN AS AN EXAMPLE TO ILLUSTRATE THE WHOLE SYSTEM AND PROCESS.
THIS ARTICLE PRESENTS RESULTS FROM AN INVESTIGATION OF THE ASSOCIATION BETWEEN STUDENT ACADEMIC PERFORMANCE AND SOCIAL TIES. BASED ON SOCIAL CAPITAL AND NETWORKED LEARNING RESEARCH, WE HYPOTHESIZED THAT (A) STUDENTS' SOCIAL CAPITAL ACCUMULATED THROUGH THEIR COURSE PROGRESSION IS POSITIVELY ASSOCIATED WITH THEIR ACADEMIC PERFORMANCE AND (B) STUDENTS WITH MORE SOCIAL CAPITAL HAVE SIGNIFICANTLY HIGHER ACADEMIC PERFORMANCE (OPERATIONALIZED AS GRADE POINT AVERAGE). BOTH HYPOTHESES WERE SUPPORTED BY RESULTS OF AN EMPIRICAL STUDY THAT ANALYZED 10 YEARS OF STUDENT COURSE ENROLMENT RECORDS (N = 505) IN A MASTER'S DEGREE PROGRAM OFFERED THROUGH DISTANCE EDUCATION AT A CANADIAN UNIVERSITY. THESE RESULTS ARE CONSISTENT WITH PREVIOUS STUDIES THAT LOOKED AT SOCIAL NETWORKS BUILT THROUGH STUDENT INTERACTION IN CLASSROOMS OR COMPUTER-MEDIATED COMMUNICATION ENVIRONMENTS. THE SIGNIFICANCE OF THIS RESEARCH LIES IN THE SIMPLICITY OF THE METHOD USED TO ESTABLISH STUDENT SOCIAL NETWORKS FROM EXISTING COURSE REGISTRATION RECORDS READILY AVAILABLE THROUGH AN INSTITUTION'S STUDENT INFORMATION SYSTEM. DIRECT IMPLICATIONS OF THIS RESEARCH ARE THAT (A) STUDY PLANS FOR STUDENTS SHOULD CONSIDER INVESTMENT IN BUILDING NEW SOCIAL TIES IN EACH COURSE DURING DEGREE PROGRAMS AND (B) READILY AVAILABLE DATA ABOUT CROSS-CLASS NETWORKS CAN BE USED IN SOFTWARE SYSTEMS SUPPORTING STUDY PLANNING.
WITH THE AIM OF SUPPORTING INSTRUCTIONAL DESIGNERS IN SETTING UP COLLABORATIVE LEARNING ACTIVITIES IN MOOCS, THIS PAPER DERIVES PREDICTION MODELS FOR STUDENT PARTICIPATION IN GROUP DISCUSSIONS. THE SALIENT FEATURE OF THESE MODELS IS THAT THEY ARE BUILT USING ONLY DATA PRIOR TO THE LEARNING ACTIVITY, AND CAN THUS PROVIDE ACTIONABLE PREDICTIONS, AS OPPOSED TO POST-HOC APPROACHES COMMON IN THE MOOC LITERATURE. SOME LEARNING DESIGN SCENARIOS THAT MAKE USE OF THIS ACTIONABLE INFORMATION ARE ILLUSTRATED.
THIS STUDY INVESTIGATES A LEARNING EXPERIENCE RELATED TO DIFFERENT E-MATERIALS OF 1ST YEAR ELECTRICAL ENGINEERING STUDENTS AT TAMPERE UNIVERSITY OF APPLIED SCIENCES. THE STUDY WAS CARRIED OUT AFTER STUDENTS HAD PARTICIPATED QUARTER OF FIRST YEAR'S STUDIES. AS SEVERAL FIRST QUARTER COURSES INCLUDED VAST VARIETY OF E-LEARNING MATERIALS, SUCH AS SHORT VIDEOS AND AUTOMATICALLY ASSESSED ONLINE EXERCISES, THE AUTHORS WANTED TO EXPLORE HOW DO STUDENTS EXPERIENCE THE UTILIZATION OF THEM. ESPECIALLY, IF IS THERE ALREADY TOO MUCH E-MATERIALS AND HOW HIGH COGNITIVE LOAD DO THE STUDENTS EXPERIENCE. LEARNING ITSELF REQUIRES COGNITIVE PROCESSING. LEARNING WITH DIVERSE E-MATERIALS HAS A POTENTIAL TO CAUSE COGNITIVE OVERLOAD. IN THIS STUDY, THE AUTHORS WANTED TO EXPLORE STUDENTS' OWN EXPERIENCES OF COGNITIVE LOAD RELATED TO E-MATERIALS. THUS, STUDENTS' LEARNING EXPERIENCES AND THEIR EXPERIENCES AND FEELINGS OF COGNITIVE LOAD WERE INVESTIGATED. A TOTAL OF SIXTY-NINE STUDENTS PARTICIPATED IN THIS STUDY. BASED ON THE ONLINE SURVEY RESULTS, STUDENTS FELT LEARNING WITH SHORT VIDEOS AND COMPUTER ASSESSED EXERCISES USEFUL AND THE COGNITIVE LOAD WAS NOT FELT TOO HIGH. IN FACT, THE AMOUNT OF E-MATERIALS COULD HAVE BEEN EVEN HIGHER. BASED ON THE RESULTS OF THIS STUDY, ALL TEACHERS SHOULD CONSIDER UTILIZING EDUCATIONAL TECHNOLOGY, SHORT VIDEOS AND AUTOMATICALLY ASSESSED ONLINE EXERCISES. ALSO, TO HELP THE STUDENTS TO SHARE THEIR WORKLOAD MORE EVENLY, REGULAR AND CONTINUOUS ASSESSMENT SHOULD BE CONSIDERED INSTEAD OF A MASSIVE FINAL EXAM.
THE AFFECTIVE STATE OF AN INDIVIDUAL CAN BE DETERMINED USING PHYSIOLOGICAL PARAMETERS; AN IMPORTANT METRIC THAT CAN THEN BE EXTRACTED IS ATTENTION. LOOKING MORE CLOSELY AT COMPACT EEGS, ALGORITHMS HAVE BEEN IMPLEMENTED IN SUCH DEVICES THAT CAN MEASURE THE ATTENTION AND OTHER AFFECTIVE STATES OF THE USER. NO INFORMATION ABOUT THESE ALGORITHMS IS AVAILABLE; ARE THESE FEATURE CLASSIFICATION ALGORITHMS ACCURATE? AN EXPERIMENT WAS CONDUCTED WITH 23 SUBJECTS WHO UTILIZED A PEDAGOGICAL AGENT TO LEARN THE SYNTAX OF THE PROGRAMMING LANGUAGE JAVA WHILE HAVING THEIR ATTENTION MEASURED BY THE NEUROSKY MINDWAVE MOBILE 2. USING A CONCURRENT VALIDITY APPROACH, THE ATTENTION VALUES MEASURED WERE COMPARED TO BAND POWERS, AS WELL AS MEASURES OF TASK PERFORMANCE. THE RESULTS OF THE EXPERIMENT WERE IN PART SUCCESSFUL AND SUPPORTIVE OF THE CLAIM THAT THE EEG DEVICE'S ATTENTION ALGORITHM DOES IN FACT REPRESENT A USER'S ATTENTION ACCURATELY. THE RESULTS OF THE ANALYSIS BASED ON RAW DATA CAPTURED FROM THE DEVICE WERE CONSISTENT WITH PREVIOUS LITERATURE. INCONCLUSIVE RESULTS WERE OBTAINED RELATING TO TASK PERFORMANCE AND ATTENTION.
DATA PRODUCED BY THE USE OF MOBILE DEVICES (SMARTPHONES AND WEARABLES) CAN BE USED TO OBTAIN PATTERNS AND INDICATORS OF USER BEHAVIOR. THIS PAPER FOCUSES ON OBTAINING SLEEP-RELATED INDICATORS TO APPLY THEM IN EDUCATIONAL SETTINGS. INITIALLY THE MOST RELEVANT INDICATORS DEFINED IN THE LITERATURE AND AVAILABLE IN EXISTING MOBILE PLATFORMS ARE STUDIED. BASED ON THEM, WE PROPOSE NEW INDICATORS THAT CAN BE CALCULATED AUTOMATICALLY AND TRANSPARENTLY ANALYZING THE DATA GENERATED BY MOBILE DEVICE SENSORS. THE ULTIMATE GOAL OF THESE INDICATORS IS TO FACILITATE THE CONSTRUCTION OF SOFTWARE SERVICES (RECOMMENDERS AND DETECTORS OF RISK SITUATIONS) TO IMPROVE THE LEARNING PROCESSES OF STUDENTS.
POPULARITY OF MASSIVE ONLINE OPEN COURSES (MOOCS) ALLOWED EDUCATIONAL RESEARCHERS TO ADDRESS PROBLEMS WHICH WERE NOT ACCESSIBLE FEW YEARS AGO. ALTHOUGH CLASSICAL STATISTICAL TECHNIQUES STILL APPLY, LARGE DATASETS ALLOW US TO DISCOVER DEEPER PATTERNS AND TO PROVIDE MORE ACCURATE PREDICTIONS OF STUDENT'S BEHAVIORS AND OUTCOMES. THE GOAL OF THIS TUTORIAL WAS TO DISSEMINATE KNOWLEDGE ON ELEMENTARY DATA ANALYSIS TOOLS AS WELL AS FACILITATE SIMPLE PRACTICAL DATA ANALYSIS ACTIVITIES WITH THE PURPOSE OF STIMULATING REFLECTION ON THE GREAT POTENTIAL OF LARGE DATASETS. IN PARTICULAR, DURING THE TUTORIAL WE INTRODUCE ELEMENTARY TOOLS FOR USING MACHINE LEARNING MODELS IN EDUCATION. ALTHOUGH THE METHODOLOGY PRESENTED HERE APPLIES IN ANY PROGRAMMING ENVIRONMENT, WE CHOOSE R AND CARET PACKAGE DUE TO SIMPLICITY AND ACCESS TO THE MOST RECENT MACHINE LEARNING METHODS.
APPLICATIONS THAT TRY TO ENHANCE LEARNERS' KNOWLEDGE CAN PROFIT BY THE CREATION AND ANALYSIS OF LEARNER PROFILES. THIS WORK DEALS WITH THE DERIVATION OF AN OPTIMAL SEQUENCE OF QUESTIONS BY COMPARING SIMILAR LEARNING BEHAVIOUR OF USERS OF A MATHEMATICS TRAINING APPLICATION. THE ADAPTATION OF THE LEARNERS' CLUSTERS TO THE ANSWERS OF THE REVISED OPTIMAL QUESTION SEQUENCE IMPROVES LEARNING.
WHILE UNIVERSITIES ROUTINELY USE STUDENT DATA TO MONITOR AND PREDICT STUDENT PERFORMANCE, THERE HAS BEEN LIMITED ENGAGEMENT WITH STUDENT AND STAFF VIEWS, SOCIAL AND ETHICAL ISSUES, POLICY DEVELOPMENT, AND ETHICAL GUIDANCE. WE REVIEWED PEER-REVIEWED AND GREY-LITERATURE ARTICLES OF 2007 TO 2018 DESCRIBING THE PERSPECTIVES OF STAFF AND STUDENTS IN TERTIARY EDUCATION ON THE USE OF STUDENT-GENERATED DATA IN DATA ANALYTICS, INCLUDING LEARNING ANALYTICS. WE USED AN ETHICS FRAMEWORK TO CATEGORIZE THE FINDINGS. THERE WAS CONSIDERABLE VARIATION BUT GENERALLY LOW AWARENESS AND UNDERSTANDING AMONGST STUDENTS AND STAFF ABOUT THE NATURE AND EXTENT OF DATA COLLECTION, DATA ANALYTICS, AND USE OF PREDICTIVE ANALYTICS. STAFF AND STUDENTS IDENTIFIED POTENTIAL BENEFITS BUT ALSO EXPRESSED CONCERNS ABOUT MISINTERPRETATION OF DATA, CONSTANT SURVEILLANCE, POOR TRANSPARENCY, INADEQUATE SUPPORT, AND POTENTIAL TO IMPEDE ACTIVE LEARNING. THIS REVIEW SUPPORTS THE CONTENTION THAT CONSIDERATION OF ETHICAL ISSUES HAS FAILED TO KEEP PACE WITH THE DEVELOPMENT OF PREDICTIVE ANALYTICS IN THE TERTIARY SECTOR.
TWO TYPES OF TEACHING COEXIST TODAY: TRADITIONAL FACE-TO-FACE METHODS AND TEACHING BASED ON SELF-LEARNING WHERE THE STUDENT HAS UBIQUITOUS ACCESS TO COURSE CONTENTS. THIS PAPER SHOWS HOW TESTS HAVE BEEN MADE OF A TYPE OF LEARNING THAT IS BECOMING MORE COMMON IN EDUCATIONAL ENVIRONMENTS. THIS HYBRID TEACHING METHOD COMBINES THE MASSIVELY OPEN ONLINE COURSE (MOOC) AND STUDENTS' FACE-TO-FACE WORK. ON THE ONE HAND, THE ADVANTAGES OF FACE-TO-FACE LEARNING ARE TAKEN ADVANTAGE OF: TEACHER IN THE CLASSROOM, SOCIAL BONDS AND DAILY WORK SESSIONS. ON THE OTHER HAND, THE ADVANTAGES OF DISTANCE LEARNING ARE INTEGRATED: FLEXIBILITY, ACCESSIBLE MATERIAL AND WIDE VARIETY. ALSO INCLUDED IN THE PAPER IS A DESCRIPTION OF THE EQUIPMENT AND THE WAY IN WHICH IT HAS BEEN USED WITH STUDENTS. FINALLY, THE PAPER DETAILS THE EDUCATIONAL EXPERIENCE CARRIED OUT WITH STUDENTS AND THE OUTCOMES OBTAINED. THESE PILOT TESTS ARE FRAMED WITHIN THE EDUCATION OF STEM TOPICS AND ROBOTICS.
WITH THE ONLINE LEARNING PLATFORM USED WIDELY, LEARNERS' BEHAVIOR IN ONLINE SYSTEM SHOULD BE AN IMPORTANT ELEMENT TO ASSESS THE ACHIEVEMENT AND REFLECT THE LEARNING PROCESS. THIS PAPER PROPOSES AN APPROACH TO ANALYZE THE STUDENTS' BEHAVIOR IN MOODLE'S ONLINE FORUM BY A TWO-DIMENSIONAL FRAMEWORK. ONE DIMENSION IS TOWARD THE INTERACTION ACTIVITIES AMONG PEERS' POSTS IN ONLINE FORUM. ANOTHER DIMENSION IS DESCRIBED BY WORD CLOUDS RELATED TO LEARNING CONTENTS OF THE POSTS. THE STUDENTS' LEARNING BEHAVIOR PATTERNS ARE ANALYZED AND DESCRIBED. IT FOUND THAT THERE IS HIGH CORRELATION BETWEEN THE PARTICIPATING IN ONLINE FORUM AND ACHIEVEMENT. THE FRAMEWORK IS HELPFUL TO DESIGN THE ARCHITECTURE OF AUTOMATICALLY RECOMMENDED SYSTEM AND ADAPTIVE LEARNING SYSTEM IN THE FUTURE.
WAVELET ANALYSIS IS A PATTERN RECOGNITION TECHNIQUE THAT HAS BEEN USED TO ANALYZE SIGNALS AND IMAGES SUCH AS MEDICAL SCANS AND HYPERSPECTRAL IMAGERY. WE HYPOTHESIZE THAT WAVELETS CAN ALSO BE USED TO ANALYZE EDUCATIONAL DATA. WE EXPLORE THE USE OF HAAR WAVELETS TO DIFFERENTIATE VARIOUS PATTERNS IN A STUDENT ATTENTION DATA SET. THE DATA CONSISTS OF A VERY LARGE SET OF BINARY ATTENTION DATA (ON-TASK/OFF- TASK) FOR INDIVIDUAL STUDENTS THAT WAS ELECTRONICALLY CAPTURED AT 20-SECOND INTERVALS. THE DATA SET CONTAINS INFORMATION FOR MULTIPLE LARGE LECTURES OF A FIRST YEAR ENGINEERING COURSE AND WAS CAPTURED OVER AN ENTIRE SEMESTER. FOR EACH LECTURE WITHIN A COURSE, THERE ARE APPROXIMATELY 200 STUDENT ATTENTION CHARTS WITH 150 ENTRIES PER STUDENT RECORD (3 PER MINUTE FOR 50 MINUTES). WE INVESTIGATE THE ACCURACY OF USING A HAAR WAVELET CLASSIFICATION SYSTEM TO DISTINGUISH STUDENT ATTENTION PATTERNS IN A GIVEN LECTURE. OUR RESULTS AND DISCUSSION DEMONSTRATE THE USEFULNESS OF APPLYING TRADITIONAL ENGINEERING ANALYSIS TECHNIQUES TO EDUCATIONAL DATA
CONCERNS ABOUT GLOBAL WARMING AND ENERGY COSTS HAVE INDUCED TRANSPORT COMPANIES TO TAKE MEASURES TO REDUCE FUEL CONSUMPTION. AMONG THE DIFFERENT OPTIONS AVAILABLE, EFFICIENT DRIVING IS WIDELY USED, ALLOWING A REDUCTION IN FUEL CONSUMPTION OF AROUND 10%. HOWEVER, CHANGING THE DRIVER'S BEHAVIOR IS NOT EXEMPT OF PROBLEMS. THE SUCCESS OF EFFICIENT DRIVING TECHNIQUES IN THE LONG TERM IS RELATED TO THE MOTIVATION OF THE DRIVER AND, FOR THAT REASON, AN ADAPTIVE TRAINING SYSTEM ACCORDING TO THE DRIVER'S NEEDS CAN PROVE MUCH MORE SUCCESSFUL THAN GIVING GENERAL INSTRUCTIONS THAT DO NOT SOLVE THEIR INEFFICIENCIES. THEREFORE, A PROPERLY DESCRIPTION OF THE DRIVER'S BEHAVIOR AND THE ADAPTATION OF THE EVALUATION ANALYSIS TO THE CONTEXT IS A KEY FACTOR FOR THE LEARNING PROCESS IN EFFICIENT DRIVING. IN THIS PAPER WE PROPOSE AN ADAPTIVE LEARNING SYSTEM FOR EFFICIENT DRIVING, WHICH ALLOWS THE EVALUATION OF PROFESSIONAL DRIVERS OF URBAN PUBLIC TRANSPORT IN THEIR WORK ENVIRONMENT. WITH THE PROPOSED SYSTEM, WE CAN IDENTIFY FAILURE POINTS RELATING TO THE CONTEXT, MAKING A FOCUSED EVALUATION. THE FIRST EVALUATION RESULTS SHOW THAT THE SET OF PATTERNS DESIGNED TO EVALUATE THE APPLICATION OF EFFICIENT DRIVING TECHNIQUES CAN IDENTIFY THE INCORRECT ACTIONS OF THE DRIVERS. BASED ON THE RESULTS, IT IS POSSIBLE TO MAKE PERSONALIZED RECOMMENDATIONS TO IMPROVE DRIVER PERFORMANCE.
WHEN TEACHING IN UNIVERSITY COURSES, ONE OF THE MAIN PROBLEMS IS THE STUDENTS' LACK OF MOTIVATION FOR EVERYDAY STUDY. LEARNING MANAGEMENT SYSTEMS (LMS) PROVIDE A SET OF TOOLS THAT STUDENTS MUST HANDLE TO FOLLOW THE COURSE, BUT USUALLY STUDENTS NEED ADDITIONAL AID TO EFFECTIVELY MASTERING THESE TOOLS. WE PRESENT HERE MERLIN-KNOW, A NEW MODULE FOR THE MOODIE LMS TO HELP STUDENTS DO EXTRA WORK AND REVIEWING ON-LINE. IT IS BASED ON A VIRTUAL TEACHER WHO GUIDES, MOTIVATES AND ENCOURAGES STUDENTS. WE ALSO SHOW TWO KINDS OF EVALUATION. FIRST, THE SYSTEM WAS USED FOR REVISING THE ACTIVITIES OF STUDENTS ALONG THE SEMESTER. WE CONCLUDE THAT MERLIN-KNOW HAS A STATISTICALLY POSITIVE INFLUENCE ON STUDENTS' LEARNING OUTCOMES, OBTAINING A POSITIVE CORRELATION FOR ALL DIFFICULTY LEVELS. SECOND, WE CONDUCTED A USABILITY EVALUATION TO ASSESS THE ACCEPTANCE BY STUDENTS' AND TO DETECT WAYS OF IMPROVEMENT. AVERAGE STUDENTS SAID THAT MERLIN-KNOW WAS USEFUL AND HELPED THEM TO BE MORE EFFECTIVE AND PRODUCTIVE, IT WAS EASY TO LEARN AND USE AND IT HAS A FRIENDLY INTERFACE, AND THEY EASILY REMEMBERED HOW TO USE IT.
THE PAPER TAKES A DEEPER LOOK AT PARTICIPATION RATES IN CMOOCS. TO GET A BETTER INSIGHT INTO THE BEHAVIOR OF LEARNERS IN MOOCS, STUDIUMDIGITALE HAS DEVELOPED A TOOL WHICH HELPS TO ANALYZE THE CONTRIBUTION OF PARTICIPANTS IN THE SO CALLED CMOOCS. THESE ARE MOOCS WHICH ARE FOSTERING THE ACTIVE PARTICIPATION OF LEARNERS IN VARIOUS TOOLS AND WHICH ARE BASED ON THE CONCEPT OF CONNECTIVISM [1]. AFTER AN APPROACH AT EACH PART OF THE DEFINITION OF MOOCS AND THE DISCUSSION OF THE DIFFERENT CATEGORIES OF THIS QUITE NEW PHENOMENA A DEEPER LOOK WILL BE TAKEN INTO THE ANALYSIS OF TWO CMOOCS, OPCO11 AND OPCO12 WHICH TOOK PLACE 2011 AND 2012 [2].
CURRENTLY, EDUCATION IS GOING THROUGH A CRITICAL MOMENT DUE TO THE 2019 CORONAVIRUS DISEASE THAT HAS BEEN DECLARED A PANDEMIC. THIS HAS FORCED MANY ORGANIZATIONS TO UNDERGO A SIGNIFICANT TRANSFORMATION, RETHINKING KEY ELEMENTS OF THEIR PROCESSES AND THE USE OF TECHNOLOGY TO MAINTAIN OPERATIONS. THE CONTINUITY OF EDUCATION HAS BECOME DEPENDENT ON TECHNOLOGICAL TOOLS, AS WELL AS ON THE ABILITY OF UNIVERSITIES TO COPE WITH A PRECIPITOUS TRANSITION TO A REMOTE EDUCATIONAL MODEL. THAT HAS GENERATED PROBLEMS THAT AFFECT STUDENT LEARNING. THIS WORK PROPOSES THE IMPLEMENTATION OF A BIG DATA FRAMEWORK TO IDENTIFY THE FACTORS THAT AFFECT STUDENT PERFORMANCE AND DECISION-MAKING TO IMPROVE LEARNING. SIMILAR WORKS COVER TWO MAIN RESEARCH TOPICS UNDER BIG DATA IN EDUCATION, THE MODELING AND STORAGE OF EDUCATIONAL DATA. HOWEVER, THEY DO NOT CONSIDER ISSUES SUCH AS STUDENT PERFORMANCE AND THE IMPROVEMENT OF THE EDUCATIONAL SYSTEM WITH THE INTEGRATION OF BIG DATA. IN ADDITION, THIS WORK PROVIDES A GUIDE FOR FUTURE STUDIES AND HIGHLIGHTS NEW INSIGHTS AND DIRECTIONS FOR THE SUCCESSFUL USE OF BIG DATA IN EDUCATION. REAL-WORLD DATA WERE COLLECTED FOR THE EVALUATION OF THE PROPOSED FRAMEWORK, THE COLLECTION OF THESE BEING THE EXISTING LIMITATION IN ALL RESEARCH DUE TO GENERALIZED REJECTION OF DATA CONSENT.
IN LARGE CLASS SETTINGS, INDIVIDUALIZED STUDENT-TEACHER INTERACTION IS DIFFICULT. HOWEVER, TEACHING INTERACTIONS (E.G., FORMATIVE FEEDBACK) ARE CENTRAL TO ENCOURAGING DEEP APPROACHES TO LEARNING. WHILE THERE HAS BEEN PROGRESS IN AUTOMATIC SHORT-ANSWER GRADING, ANALYSING STUDENT RESPONSES TO SUPPORT FORMATIVE FEEDBACK AT SCALE IS ARGUABLY SOME WAY FROM BEING WIDELY APPLIED IN PRACTICE. HOWEVER, ANALYSING STUDENT WRITTEN RESPONSES CAN PROVIDE INSIGHTS INTO STUDENT CONCEPTIONS, THUS DIRECTLY INFORMING TEACHER ACTIONS. INDEED, WE ARGUE THAT ANALYSING STUDENT RESPONSES TO PROVIDE FEEDBACK DIRECTLY TO TEACHERS IS AS WORTHY A GOAL AS PROVIDING INDIVIDUALIZED FEEDBACK TO STUDENTS AND IS ACHIEVABLE GIVEN THE CURRENT STATE-OF-THE-ART IN NATURAL LANGUAGE PROCESSING. IN THIS PAPER, WE ANALYSE STUDENT WRITTEN RESPONSES TO SHORT-ANSWER QUESTIONS POSED IN THE CONTEXT OF A LARGE FIRST YEAR HEALTH SCIENCES COURSE. EACH QUESTION WAS DESIGNED TO ELICIT DEEP RESPONSES. OUR QUALITATIVE ANALYSIS ILLUSTRATES THE VARIABILITY IN STUDENT RESPONSES AND REVEALS MULTIPLE RELATIONSHIPS BETWEEN THESE RESPONSES, COURSE MATERIALS AND THE QUESTIONS POSED. SUCH INFORMATION CAN BE INVALUABLE FOR TEACHER PRAXIS. WE CONCLUDE WITH A CONCEPTUAL DASHBOARD' THAT CATEGORIZES STUDENT RESPONSES AND REVEALS RELATIONSHIPS BETWEEN RESPONSES, COURSE RESOURCES AND THE QUESTIONS. SUCH A DASHBOARD COULD PROVIDE TIMELY, ACTIONABLE INSIGHTS FOR TEACHERS AND HELP FOSTER DEEP LEARNING APPROACHES FOR STUDENTS.
STUDENTS ACROSS THE WORLD FACE THE DIFFICULTIES ASSOCIATED WITH LEARNING PROGRAMMING LANGUAGES. SEVERAL STUDIES HAVE SHOWN THAT A PERMANENT MONITORING BY THE TEACHERS HIGHLY CONTRIBUTES FOR INCREASING THE STUDENTS' PERFORMANCE. WE PROPOSE THE USE OF A MONITORING TOOL CAPABLE OF CAPTURING AND DISPLAYING TO THE TEACHERS REAL-TIME INFORMATION ABOUT THE STUDENTS' PERFORMANCE BASED ON SNAPSHOTS OF THE CODE WHILE THEY ARE PROGRAMMING. THE TEACHERS CAN THEN USE THAT INFORMATION TO IDENTIFY PROBLEMS/DEVIATIONS AND ACT ACCORDINGLY. TO EVALUATE THE DEVELOPED SYSTEM, WE PRESENT SOME RESULTS OF A FIELD TEST INVOLVING STUDENTS FROM AN INTRODUCTORY COURSE ON PYTHON PROGRAMMING.
THIS PAPER DESCRIBES A VISUALIZATION SYSTEM TO DETECT THE LEARNING CONTENTS, ESPECIALLY THE WORDS THAT LEARNERS FEEL DIFFICULT IN DIGITAL TEXTBOOKS. WHEN LEARNERS ENCOUNTER UNKNOWN WORDS IN THE DIGITAL TEXTBOOKS, THEY SEARCH THEIR MEANINGS AND DEFINITIONS VIA INTERNET AND THEN USE THE HIGHLIGHT FUNCTION. GENERALLY, LOW-ACHIEVING LEARNERS TEND TO HIGHLIGHT MANY WORDS BECAUSE THEY ARE NOT FAMILIAR WITH MOST OF THEM. TO DETECT THE WORDS THAT THEY FEEL DIFFICULT, WE DEVELOPED A VISUALIZATION SYSTEM. AN EVALUATION EXPERIMENT WAS CONDUCTED TO VERIFY WHETHER TEACHERS CAN IDENTIFY THE WORDS THE LEARNERS FEEL DIFFICULT BY USING OUR VISUALIZATION SYSTEM. IN THE EXPERIMENT, PARTICIPANTS LEARNED JAPANESE WITH SIX DIGITAL TEXTBOOKS. WE COLLECTED DIGITAL TEXTBOOK LOGS AND VISUALIZED THEM. WE FOUND THAT WORDS THAT LEARNERS FEEL DIFFICULT CAN BE DETECTED BY USING OUR VISUALIZATION SYSTEM. WE BELIEVE THAT THE FINDINGS LEAD TO IMPROVE LEARNING MATERIALS AND TEACHERS' LECTURES.
THIS RESEARCH TO PRACTICE WORK IN PROGRESS PRESENTS AN ANALYTICS ADD-ON FOR QMSFC (QUESTION MANAGEMENT SYSTEM FOR FLIPPED CLASSROOM), AN ONLINE TOOL WITH THE PURPOSE OF INCREASING STUDENT ENGAGEMENT AND PARTICIPATION IN A FLIPPED CLASSROOM SETTING. THIS IS DONE BY PROVIDING AN INTERFACE THAT STUDENTS CAN USE TO POST QUESTIONS ABOUT EDUCATIONAL VIDEOS, WHICH WOULD THEN BE ANSWERED BY THE TEACHER IN THE CLASSROOM. THE DATA GENERATED BY QMSFC COULD BE COLLECTED AND USED TO EXPAND UPON THE FUNCTIONALITIES OF THE PLATFORM, SUCH AS ALERTING THE TEACHER ABOUT PARTICULARLY TOUGH TOPICS FOR STUDENTS. ADDITIONALLY, THESE DATA COULD HE ANALYZED TOGETHER WITH STUDENT GRADES IN ORDER TO FIND OUT THE CORRELATION BETWEEN THE VOLUME OF ACTIVITY ON THE PLATFORM AND EXAM PERFORMANCE. WORK REMAINING ON THIS PROJECT INVOLVES USING THE FULLY DEVELOPED TOOL IN A REAL LEARNING SITUATION AND ANALYZING THE RESULTS AND FEEDBACK OBTAINED.
STUDENT-CENTERED COURSES RELY ON THE ACTIVE PARTICIPATION OF THE STUDENTS IN FORUM ASSIGNMENTS. IN THIS WORK, WE INVESTIGATE A COURSE WHERE THE FORUM ASSIGNMENT DISCUSSES A CLINICAL CASE AMONG PROFESSIONAL STUDENTS (N = 94). WE PROPOSE A METHOD TO DISCOVER NAVIGATION PATTERNS RELATED TO PERFORMANCE GRADES, USING BEHAVIORAL ACTIONS IN AN LMS PLATFORM. WE SELECTED A SET OF SIGNIFICANT COURSE ACTIONS AND BUILT PER-USER SEQUENCES ALONG THE COURSE MODULE. THEN, WE APPLIED THE GSP ALGORITHM TO IDENTIFY ORDERED PATTERNS FROM THIS NAVIGATIONAL DATA. THE IDENTIFIED PATTERNS WERE THEN USED AS FEATURES FOR A LINEAR REGRESSION MODEL, TO PREDICT THE ASSIGNMENTS' PERFORMANCE, GRADED MANUALLY BY THE TEACHERS, AND CONTROLLING FOR FACTORS THAT MAY INFLUENCE IT. RESULTS SHOW SOME RULES CORRELATED TO THE STUDENTS' PERFORMANCES. THESE RESULTS CAN BE USED TO BETTER INFORM COURSE DESIGNERS ON HOW TO IMPROVE THE COURSEWARE AND INSTRUCTORS ON HOW TO BETTER GUIDE THEIR STUDENTS.
TEAM PROJECTS ALLOW STUDENTS TO APPLY THEIR TECHNICAL SKILLS TO A REAL-WORLD CONTEXT AND BUILD TWENTY-FIRST CENTURY COMPETENCIES, INCLUDING TEAMWORK, PROJECT MANAGEMENT AND COMMUNICATION SKILLS. HOWEVER, THE COMPLEX CHALLENGES THAT SUCH EXPERIENTIAL LEARNING PROJECTS PRESENT FOR STUDENTS AND FACULTY CAN LIMIT THE SCALE OF IMPLEMENTATION. THIS ARTICLE ARGUES THAT VIRTUAL BUSINESS PROJECTS (VBP), A MODEL OF TEAM-BASED EXPERIENTIAL LEARNING WHERE TEAMS OF STUDENTS COMPLETE A VIRTUAL BUSINESS PROJECT FOR AN INDUSTRY SPONSOR, CAN MITIGATE THESE PROBLEMS BY LEVERAGING INSTRUCTIONAL TECHNOLOGY AND LEARNING ANALYTICS. THE VBP MODEL IS DEPLOYED IN MULTIPLE UNIVERSITIES, WHICH HAVE PROVIDED MORE THAN 2500 VIRTUAL BUSINESS PROJECTS SINCE 2015. WE WILL DISCUSS HOW INNOVATIVE TECHNOLOGY, EMBEDDED IN THOUGHTFUL LEARNING DESIGN, SUPPORTS EXPERIENTIAL LEARNING BY TAKING ADVANTAGE OF FEATURES SUCH AS CUSTOMIZABLE SCAFFOLDING, AUTOMATED REFLECTION AND FEEDBACK LOOPS, AND LEARNING ANALYTICS.
THE LEARNING PROCESS USES DIFFERENT FORMS OF LEARNING (FACE-TO-FACE, E-LEARNING, WEBINAR, MOOC) TO MAXIMIZE THE STUDENTS' KNOWLEDGE, SKILLS, AND COMPETENCIES. TODAY, THE BENEFITS OF THE DIGITAL ENVIRONMENT, INCLUDING LEARNING PLATFORMS, NEED TO BE EXPLOITED IN A MEANINGFUL WAY. IN PARTICULAR, THE FUNCTIONALITY OF THE LEARNING PLATFORM IS USED WHEN ADDING TEACHING MATERIALS, TESTS, HOMEWORK SUBMISSIONS, DISCUSSION FORUMS, CALENDAR NOTIFICATIONS, WIKI, DICTIONARIES, SURVEYS, ETC. BY ANALYZING THE DATA ON LEARNING FROM THE LEARNING PLATFORM, WE CAN PROVIDE INFORMATION ON BOTH STUDENTS' KNOWLEDGE AND LEARNING HABITS AND STYLE. THE RESEARCH AIM IS TO FIND OUT HOW THE STUDY PROCESS CAN BE VISUALIZED IN MOODLE. QUALITATIVE RESEARCH OF LECTURER'S KNOW-HOW ON THE STUDY PROCESS VISUALIZATION IS CARRIED OUT. STUDENT'S HABITS ON THE ISSUE ARE ANALYZED. THE RESULTS SHOW THAT MOODLE IN SOME WAY PROVIDES THE STUDY PROCESS VISUALIZATION, BUT STUDENTS' HABIT ANALYSIS SUGGEST THAT THERE MISSING PERSONALISED KNOWLEDGE MANAGEMENT TOOL FOR THE STUDY PROCESS VISUALIZATION IN A WAY TO MOTIVATE LEARNERS, HELP THEM FOLLOW UP THEIR SUCCESS AND VISUALIZE THE STUDY PROCESS IN A MORE DYNAMIC WAY.
THIS PAPER DESCRIBES REDESIGN OF DATA COLLECTION IN DIGITAL TEXTBOOK SYSTEMS. PREVIOUS STUDIES IN THE DIGITAL TEXTBOOK SYSTEMS ALLOW US TO COLLECT THE DATA WHICH PAGES LEARNERS WERE BROWSING IN THE DIGITAL TEXTBOOKS. HOWEVER, IT IS DIFFICULT TO COLLECT THE DATA WHICH POSITIONS OF THE PAGES LEARNERS WERE BROWSING IN THE DIGITAL TEXTBOOKS UNLESS USING EYE-TRACKING TECHNOLOGIES. THIS STUDY DEVELOPED A DIGITAL TEXTBOOK SYSTEM CALLED SEA (SMART E-TEXTBOOK APPLICATION) TO COLLECT THE DATA WHICH POSITIONS OF THE PAGES THE LEARNERS WERE BROWSING IN THE DIGITAL TEXTBOOKS WITHOUT EYE-TRACKING TECHNOLOGIES. THE SYSTEM AUTOMATICALLY HIDES THE TEXTS IN THE DIGITAL TEXTBOOK SYSTEM WITH MASK PROCESSING. AN EVALUATION EXPERIMENT WAS CONDUCTED TO EVALUATE THE USER ACCEPTANCE REGARDING OUR DEVELOPED SYSTEM.
IN THIS PAPER, WE PROPOSE A METHOD FOR THE REAL TIME EVALUATION OF COLLABORATIVE ACTIVITIES. THE METHOD AIMS TO CLASSIFY COLLABORATIVE ACTIVITIES USING A MEMORY-BASED LEARNING MODEL AND TO EVALUATE THEM USING A REFERENCE SET OF PRE-EVALUATED ACTIVITIES. THE PROPOSED APPROACH USES TIME SERIES TO REPRESENT ACTIVITIES AND CLASSIFIES THEM REGARDING THEIR SIMILARITY. IN ORDER TO EVALUATE THE RESULTS, THE METHOD IS USED AS AN AUTOMATIC RATER OF COLLABORATION QUALITY AND THE RATINGS ARE COMPARED TO RATINGS OF HUMAN EXPERTS.
METACOGNITION IS A VALUABLE TOOL FOR LEARNING, SINCE IT IS CLOSELY RELATED TO SELF-REGULATION AND AWARENESS OF ONE'S OWN AFFECT. HOWEVER, METHODS FOR AUTOMATICALLY DETECTING AND STUDYING METACOGNITION ARE SCARCE. THUS, IN THIS PAPER WE DESCRIBE AN ALGORITHM FOR AUTOMATIC DETECTION OF METACOGNITIVE LANGUAGE IN WRITING. WE ANALYZED TEXT FROM THE FORUMS OF TWO ONLINE, UNIVERSITY-LEVEL SCIENCE COURSES, WHICH REVEALED COMMON PATTERNS OF PHRASES THAT WE USED FOR AUTOMATIC METACOGNITION DETECTION. THE ALGORITHM WE DEVELOPED EXHIBITED HIGH ACCURACY ON EXPERT-LABELED METACOGNITIVE PHRASES (SPEARMAN'S RHO = 0.878 AND COHEN'S KAPPA = 0.792), AND PROVIDES A RELIABLE, FAST METHOD FOR AUTOMATICALLY ANNOTATING TEXT CORPORA THAT ARE TOO LARGE FOR MANUAL ANNOTATION. WE APPLIED THIS ALGORITHM TO ANALYZE RELATIONSHIPS BETWEEN STUDENTS' METACOGNITIVE LANGUAGE AND THEIR ACADEMIC PERFORMANCE, FINDING SMALL CORRELATIONS WITH COURSE GRADE AND MEDIUM-SIZED DIFFERENCES IN METACOGNITION ACROSS COURSES. WE DISCUSS HOW OUR ALGORITHM CAN BE USED TO ADVANCE METACOGNITIVE STUDIES AND ONLINE EDUCATIONAL SYSTEMS.
THE RESEARCH STUDY AT HAND AIMS TO ANSWER THE QUESTION, WHETHER AN INNOVATIVE INFORMATION SYSTEM CAN BE IMPLEMENTED THAT WILL HELP TO ENHANCE READING LITERACY OF ELEMENTARY PUPILS. BASED ON PREDEFINED READING TESTS THIS WEB-BASED SYSTEM EVALUATES THE READING LITERACY OF PUPILS. IT CONSISTS OF TWO PRIMARY PARTS; THE SYSTEM THAT CREATES AND EVALUATES SUCH READING TESTS AND THE TEST PLATFORM ITSELF. IN ORDER TO ASSESS THE SYSTEM A FIELD TEST WAS CONDUCTED. THEREFORE IT WAS TESTED IN TWO SCHOOL CLASSES. IN THE COURSE OF THIS STUDY READING TESTS WERE CARRIED OUT AND RETRIEVED DATA AND RESULTS WERE EVALUATED. DESPITE SOME MINOR USABILITY PROBLEMS, THE SYSTEM PERFORMED VERY WELL. THE TEST SYSTEM DELIVERED GOOD ESTIMATIONS OF THE READING CAPABILITIES OF SINGLE PUPILS AND CLASSES. OF SPECIAL INTEREST IS THE SYSTEM'S ANALYSIS OF THE CREATED READING TESTS SINCE THE SYSTEM IS CAPABLE OF EVALUATING READING TEST ACCORDING TO THEIR DIFFICULTY.
THE PURPOSE OF ACADEMIC ADVISING IS TO HELP STUDENTS WITH DEVELOPING EDUCATIONAL PLANS THAT SUPPORT THEIR ACADEMIC CAREER AND PERSONAL GOALS, AND TO PROVIDE INFORMATION AND GUIDANCE ON STUDIES. PLANNING AND MANAGEMENT OF THE STUDENTS' STUDY PATH IS THE MAIN JOINT ACTIVITY IN ADVISING. BASED ON A STUDY LOG OF PASSED COURSES, WE PROPOSE TO USE ROBUST, PROTOTYPE-BASED CLUSTERING TO IDENTIFY A SET OF ACTUAL STUDY PATH PROFILES. SUCH PROFILES IDENTIFY GROUPS OF STUDENTS WITH SIMILAR PROGRESS OF STUDIES, WHOSE ANALYSIS AND INTERPRETATION CAN BE USED FOR BETTER INSTITUTIONAL AWARENESS AND TO SUPPORT EVIDENCE-BASED ACADEMIC ADVISING. A MODEL OF AUTOMATED ACADEMIC ADVISING SYSTEM UTILIZING THE POSSIBILITY TO DETERMINE THE STUDY PROFILES IS PROPOSED.
INSTITUTIONS IN HIGHER EDUCATION GENERATE TERABYTES OF DATA THAT HAS GREAT VALUE TO SHAPE FUTURE OF NATIONS. THIS BIG DATA IS IN HETEROGENEOUS FORMATS, VERY CURRENT, AND IN LARGE VOLUMES. WE PROPOSE A FRAMEWORK TO COLLECT, SCOPE AND VERIFY THIS LARGE AMOUNT OF DATA. ALTHOUGH THE FRAMEWORK IS EXPLAINED IN THE CONTEXT OF INSTITUTION ACCREDITATION IN HIGHER EDUCATION, THE FRAMEWORK CAN BE APPLIED IN THE FIELDS OF HEALTH-CARE, FINANCE, MARKETING ETC. OUR FRAMEWORK IS USED TO REDUCE HUMAN INVOLVEMENT IN THE COLLECTION AND ANALYSIS OF DATA, FOR THE PURPOSE OF ACCREDITATION. THE FRAMEWORK EXTENDS THE SCOPE OF DATA COLLECTED TO TARGET THE HETEROGENEOUS NATURE OF THE DATA. FINALLY, OUR FRAMEWORK HELPS TO VERIFY THE DATA AGAINST A STANDARD SET BY AN ACCREDITATION BODY.
THE CONCEPT OF MASSIVE OPEN ONLINE COURSE (MOOC) WITH FLIPPED CLASSROOM HAS BEEN EMERGED IN RECENT YEARS AS THE ARRANGEMENT HAS DEMONSTRATED THE POTENTIAL ENHANCEMENT OF STUDENT ENGAGEMENT AND ULTIMATELY THEIR ACADEMIC PERFORMANCE. IN THIS PAPER, WE ARE INTERESTED TO MODEL STUDENT ENGAGEMENT BASED ON STUDENTS' VIDEO-WATCHING BEHAVIORS AND THEIR FLIPPED CLASSROOM INVOLVEMENT. THE WORK IS BASED ON THE IMPLEMENTATION OF TWO ONLINE COURSES FROM TWO INSTITUTES RESPECTIVELY. IN OUR WORK, WE FIRST EXPLORED THE RELATIONSHIPS AMONG CONTENT FITNESS, EASE OF USE, STUDENTS' ENGAGEMENT AND LEARNING EFFECTIVENESS THROUGH STATISTICAL AND DATA MINING TECHNIQUES BY USING TRACKING LOGS, QUESTIONNAIRES AND ACADEMIC RESULTS OF STUDENTS. WE THEN FOUND OUT SOME INTERESTING ONLINE LEARNING PATTERNS OF VIDEO WATCHING TIME, PROCRASTINATORS, FLIPPED CLASSROOMS PERFORMANCE, AND HIGH/LOW ACHIEVERS AS WELL.
THE BENEFITS OF USING SPOC PLATFORMS INTO FACE-TO-FACE EDUCATION ARE YET TO BE COMPLETELY ANALYSED. IN THIS WORK WE PROPOSE TO USE SPOCS AND VIDEO CONTENTS TO FLIP THE CLASSROOM. THE OBJECTIVE IS TO IMPROVE THE INVOLVEMENT AND SATISFACTION OF STUDENTS WITH THE COURSE, TO REDUCE THE DROP-OUT RATES AND TO IMPROVE THE FACE-TO-FACE COURSE SUCCESS RATE. WE APPLY THESE IDEAS TO AN UNDERGRADUATE FIRST YEAR COURSE ON DATA STRUCTURES AND ALGORITHMS. THE STUDY IS VALIDATED BY COLLECTING DATA FROM TWO CONSECUTIVE EDITIONS OF THE COURSE, ONE IN WHICH THE FLIPPED CLASSROOM MODEL AND VIDEOS WERE USED AND OTHER IN WHICH THEY WERE NOT. THE GATHERED DATA INCLUDED ONLINE DATA ABOUT THE STUDENTS' INTERACTION WITH THE SPOC MATERIALS AND OFFLINE DATA COLLECTED DURING LECTURES AND EXAMS. IN THE EDITION WHERE THE SPOC MATERIALS WERE AVAILABLE, WE HAVE OBSERVED A CORRELATION BETWEEN THE STUDENTS' FINAL MARKS AND THEIR PERCENTAGE RATE OF VIDEO ACCESSES WITH RESPECT TO THE TOTAL NUMBER OF ACCESSES, WHICH INDICATES A BETTER ACADEMIC PERFORMANCE FOR STUDENTS WHO PREFER VIDEOS OVER DOCUMENTS.
ONLINE EDUCATION PLATFORMS PLAY AN INCREASINGLY IMPORTANT ROLE IN MEDIATING THE SUCCESS OF INDIVIDUALS' CAREERS. THEREFORE, WHILE BUILDING OVERLYING CONTENT RECOMMENDATION SERVICES, IT BECOMES ESSENTIAL TO GUARANTEE THAT LEARNERS ARE PROVIDED WITH EQUAL RECOMMENDED LEARNING OPPORTUNITIES, ACCORDING TO THE PLATFORM PRINCIPLES, CONTEXT, AND PEDAGOGY. THOUGH THE IMPORTANCE OF ENSURING EQUALITY OF LEARNING OPPORTUNITIES HAS BEEN WELL INVESTIGATED IN TRADITIONAL INSTITUTIONS, HOW THIS EQUALITY CAN BE OPERATIONALIZED IN ONLINE LEARNING ECOSYSTEMS THROUGH RECOMMENDER SYSTEMS IS STILL UNDER-EXPLORED. IN THIS PAPER, WE SHAPE A BLUEPRINT OF THE DECISIONS AND PROCESSES TO BE CONSIDERED IN THE CONTEXT OF EQUALITY OF RECOMMENDED LEARNING OPPORTUNITIES, BASED ON PRINCIPLES THAT NEED TO BE EMPIRICALLY-VALIDATED (NO EVALUATION WITH LIVE LEARNERS HAS BEEN PERFORMED). TO THIS END, WE FIRST PROVIDE A FORMALIZATION OF EDUCATIONAL PRINCIPLES THAT MODEL RECOMMENDATIONS' LEARNING PROPERTIES, AND A NOVEL FAIRNESS METRIC THAT COMBINES THEM TO MONITOR THE EQUALITY OF RECOMMENDED LEARNING OPPORTUNITIES AMONG LEARNERS. THEN, WE ENVISION A SCENARIO WHEREIN AN EDUCATIONAL PLATFORM SHOULD BE ARRANGED IN SUCH A WAY THAT THE GENERATED RECOMMENDATIONS MEET EACH PRINCIPLE TO A CERTAIN DEGREE FOR ALL LEARNERS, CONSTRAINED TO THEIR INDIVIDUAL PREFERENCES. UNDER THIS VIEW, WE EXPLORE THE LEARNING OPPORTUNITIES PROVIDED BY RECOMMENDER SYSTEMS IN A COURSE PLATFORM, UNCOVERING SYSTEMATIC INEQUALITIES. TO REDUCE THIS EFFECT, WE PROPOSE A NOVEL POST-PROCESSING APPROACH THAT BALANCES PERSONALIZATION AND EQUALITY OF RECOMMENDED OPPORTUNITIES. EXPERIMENTS SHOW THAT OUR APPROACH LEADS TO HIGHER EQUALITY, WITH A NEGLIGIBLE LOSS IN PERSONALIZATION. THIS PAPER PROVIDES A THEORETICAL FOUNDATION FOR FUTURE STUDIES OF LEARNERS' PREFERENCES AND LIMITS CONCERNING THE EQUALITY OF RECOMMENDED LEARNING OPPORTUNITIES.
INTELLIGENT TUTORING SYSTEMS (ITSS) USUALLY MAKE ADAPTATION DECISIONS BASED ON USER MODELS THAT RELY ON STUDENTS' KNOWLEDGE. HOWEVER, THERE ARE OTHER INTERESTING INDICATORS, WHICH COULD BE USED FOR ADAPTATION THAT NEED FURTHER EXPLORATION. STUDENTS' EFFICIENCY (DEFINED AS WHETHER THEY REQUIRE A LOT OF TIME TO ACHIEVE CORRECTNESS IN THEIR EXERCISES) AND CONSTANCY (DEFINED AS WHETHER THEY SPEND A SIMILAR TIME EACH DAY THEY TAKE EXERCISES IN THE ITS) ARE TWO OF THESE INDICATORS. THIS WORK AIMS TO ANALYZE 1) HOW THESE VARIABLES ARE DISTRIBUTED AMONG STUDENTS, 2) THEIR EVOLUTION OVER TIME, AND 3) HOW THEY ARE RELATED TO OTHER OUTCOMES. RESULTS SHOW THAT THERE ARE DIFFERENT PROFILES BASED ON THE EFFICIENCY; E.G., STUDENTS WITH LOW EFFICIENCY THAT NEED A LOT OF TIME TO SOLVE EXERCISES CORRECTLY, AND LOW REFLECTIVE STUDENTS, AMONG OTHERS. FURTHERMORE, EFFICIENCY AND CONSTANCY DO NOT VARY ON AVERAGE THROUGHOUT THE COURSE. IN ADDITION, STUDENTS ARE LESS CONSTANT IN THEIR DAILY TIME SPENT WHEN THEIR TOTAL TIME SPENT AND AVERAGE TIME PER EXERCISE IS HIGHER, AND MORE EFFICIENT STUDENTS TEND TO BE MORE CONSTANT. FINALLY, IT WAS FOUND THAT NEITHER EFFICIENCY NOR CONSTANCY CORRELATE WITH BETTER GRADES. THE EXISTENCE OF DIFFERENT PROFILES BASED ON THESE VARIABLES AND THAT THEY ADD A DIFFERENT DIMENSION FROM STUDENT KNOWLEDGE BASED ON ANSWER ON EXERCISES SUGGEST THAT ITSS CAN MAKE ADAPTATION BASED ON EFFICIENCY AND CONSTANCY.
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.
MULTIPLE REGISTRIES ARE CURRENTLY COLLECTING PATIENT-SPECIFIC DATA ON LUMBAR SPONDYLOLISTHESIS INCLUDING OUTCOMES DATA. THE COLLECTION OF IMAGING DIAGNOSTICS DATA ALONG WITH COMPARATIVE OUTCOMES DATA FOLLOWING DECOMPRESSION VERSUS DECOMPRESSION AND FUSION TREATMENTS FOR DEGENERATIVE SPONDYLOLISTHESIS REPRESENTS AN ENORMOUS OPPORTUNITY FOR MODERN MACHINE-LEARNING ANALYTICS RESEARCH.
LEARNING ANALYTICS CONSTITUTES A KEY TOOL FOR SUPPORTING LEARNING DESIGN AND TEACHER-LED INQUIRY INTO STUDENT LEARNING. IN THIS PAPER, WE DEMONSTRATE HOW A SOCIAL LEARNING ANALYTICS TOOLKIT CAN COMBINE SOCIAL NETWORK ANALYSIS AND CONTENT ANALYSIS FOR SUPPORTING A GLOBAL AND FORMAL TEACHER INQUIRY. THIS TOOLKIT NOT ONLY SUPPORTS TEACHERS IN IMPROVING THE ORGANISATION OF THE LEARNING PROCESS BUT ALSO GENERATES IMPORTANT INPUT TO IMPROVE THE STUDENTS' REFLECTION ON THEIR OWN LEARNING. OUR EXAMPLES SHOW HOW COMBINATIONS OF DIFFERENT LEVELS OF ANALYSIS CAN PROVIDE DEEP INSIGHT IN THE LEARNING PROCESS. WE REPORT A CASE STUDY THAT EXEMPLIFIES THE MAIN FEATURES OF OUR APPROACH AND THE KIND OF OUTCOMES THAT CAN BE OBTAINED. COMMENTING AND RATING PROCESSES ON VIDEOS ARE ANALYSED BASED ON USER TRACES FROM A SOCIAL LEARNING PLATFORM. FINALLY, WE POINT OUT IMPLICATIONS ON THE LEARNING DESIGN FOR NETWORKED LEARNING ENVIRONMENTS IN GENERAL.
RECENT YEARS HAVE SEEN A GROWING RECOGNITION OF THE VALUE OF POSITIONING TEACHING AS A DESIGN SCIENCE. IN THIS CONTEXT, WE SEE A GREAT POTENTIAL IN THE POSSIBLE SYNERGY BETWEEN EDUCATIONAL DESIGN, LEARNING ANALYTICS AND TEACHER INQUIRY. THIS SYNERGY IS DEMONSTRATED IN THE FORM OF COMPUTER SUPPORTED CURRICULUM ANALYSIS, DESIGN AND EVALUATION FOR SCIENCE EDUCATION IN AFRICA (CASCADE-SEA), A COMPUTER-BASED SUPPORT TOOL THAT ASSISTS TEACHERS IN CREATING EXEMPLARY LESSON MATERIALS FOR SECONDARY LEVEL SCIENCE AND MATHEMATICS IN SOUTHERN AFRICA. THE PURPOSE OF THE PRESENT STUDY IS TO ARTICULATE HOW TEACHERS MIGHT BE SUPPORTED IN THE SYNERGISTIC PROCESSES THAT ARE INTEGRAL TO EDUCATIONAL DESIGN. IT DOES SO BY REVISITING EXISTING DATA COLLECTED DURING THE DESIGN, DEVELOPMENT AND EVALUATION OF CASCADE-SEA, AND SHARING THE RESULTS OF A RETROSPECTIVE ANALYSIS IN LIGHT OF RECENT DEVELOPMENTS IN LEARNING ANALYTICS, LEARNING DESIGN AND TEACHER INQUIRY. THE RETROSPECTIVE ANALYSIS REVEALED INSIGHTS RELATED TO EACH OF THE AFOREMENTIONED THEMES. FOR LEARNING ANALYTICS, THOSE WERE SOCIAL STRUCTURES OF LEARNING AND TEACHING PRACTICES; PEDAGOGY-DRIVEN COLLECTION AND ANALYSIS OF DATA; AND THE USE OF DATA TO INFORM TEACHING AND LEARNING. FOR LEARNING DESIGN, THE FINDINGS PERTAINED TO THE LANGUAGE, PRACTICES AND TOOLS USED TO ACHIEVE PARTICULAR EDUCATIONAL AIMS. FOR TEACHER INQUIRY, THE FINDINGS RELATE TO THE EMPIRICAL PROCESS OF QUESTION FORMULATION, METHODS SELECTION, COLLECTION AND ANALYSIS OF DATA, AND SHARING/REFLECTING ON THE FINDINGS AS WELL AS OVERALL PROCESS. BASED ON THE RETROSPECTIVE ANALYSIS, DESIGN CONSIDERATIONS ARE OFFERED FOR A MODERN APPLICATION TO REPLACE THE PRACTICAL AND EFFECTIVE DESKTOP SOFTWARE THAT WAS DEVELOPED DURING THE 1990S.
COLLABORATION HAS GARNERED GLOBAL ATTENTION AS AN IMPORTANT SKILL FOR THE 21ST CENTURY. WHILE RESEARCHERS HAVE BEEN DOING WORK ON COLLABORATION FOR NEARLY A CENTURY, MANY OF THE QUESTIONS THAT THE FIELD IS INVESTIGATING OVERLOOK THE NEED FOR STUDENTS TO LEARN HOW TO READ AND RESPOND TO DIFFERENT COLLABORATIVE SETTINGS. EXISTING RESEARCH FOCUSES ON CHRONICLING THE VARIOUS FACTORS THAT PREDICT THE EFFECTIVENESS OF A COLLABORATIVE EXPERIENCE, OR ON CHANGING USER BEHAVIOUR IN THE MOMENT. THESE ARE WORTHWHILE RESEARCH ENDEAVOURS FOR DEVELOPING OUR THEORETICAL UNDERSTANDING OF COLLABORATION. HOWEVER, THERE IS ALSO A NEED TO CENTRE STUDENT PERCEPTIONS AND EXPERIENCES WITH COLLABORATION AS AN IMPORTANT AREA OF INQUIRY. BASED ON A SURVEY OF 131 UNIVERSITY STUDENTS, WE FIND THAT STUDENT COLLABORATION-RELATED CONCERNS CAN BE REPRESENTED ACROSS SEVEN DIFFERENT CATEGORIES OR DIMENSIONS: CLIMATE, COMPATIBILITY, COMMUNICATION, CONFLICT, CONTEXT, CONTRIBUTION, AND CONSTRUCTIVE. THESE CATEGORIES EXTEND PRIOR RESEARCH ON COLLABORATION AND CAN HELP THE FIELD ENSURE THAT FUTURE COLLABORATION ANALYTICS TOOLS ARE DESIGNED TO SUPPORT THE WAYS THAT STUDENTS THINK ABOUT AND UTILIZE COLLABORATION. FINALLY, WE DESCRIBE OUR INSTANTIATION OF MANY OF THESE DIMENSIONS IN OUR COLLABORATIVE ANALYTICS TOOL, BLINC, AND SUGGEST THAT THESE SEVEN DIMENSIONS CAN BE INSTRUCTIVE FOR RE-ORIENTING THE MULTIMODAL LEARNING ANALYTICS (MMLA) AND COLLABORATION ANALYTICS COMMUNITIES.
THE CURRENT EVOLUTION IN MULTIDISCIPLINARY LEARNING ANALYTICS RESEARCH POSES SIGNIFICANT CHALLENGES FOR THE EXPLOITATION OF BEHAVIOR ANALYSIS BY FUSING DATA STREAMS TOWARD ADVANCED DECISION-MAKING. THE IDENTIFICATION OF STUDENTS THAT ARE AT RISK OF WITHDRAWALS IN HIGHER EDUCATION IS CONNECTED TO NUMEROUS EDUCATIONAL POLICIES, TO ENHANCE THEIR COMPETENCIES AND SKILLS THROUGH TIMELY INTERVENTIONS BY ACADEMIA. PREDICTING STUDENT PERFORMANCE IS A VITAL DECISION-MAKING PROBLEM INCLUDING DATA FROM VARIOUS ENVIRONMENT MODULES THAT CAN BE FUSED INTO A HOMOGENOUS VECTOR TO ASCERTAIN DECISION-MAKING. THIS RESEARCH STUDY EXPLOITS A TEMPORAL SEQUENTIAL CLASSIFICATION PROBLEM TO PREDICT EARLY WITHDRAWAL OF STUDENTS, BY TAPPING THE POWER OF ACTIONABLE SMART DATA IN THE FORM OF STUDENTS' INTERACTIONAL ACTIVITIES WITH THE ONLINE EDUCATIONAL SYSTEM, USING THE FREELY AVAILABLE OPEN UNIVERSITY LEARNING ANALYTICS DATA SET BY EMPLOYING DEEP LONG SHORT-TERM MEMORY (LSTM) MODEL. THE DEPLOYED LSTM MODEL OUTPERFORMS BASELINE LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS BY 10.31% AND 6.48% RESPECTIVELY WITH 97.25% LEARNING ACCURACY, 92.79% PRECISION, AND 85.92% RECALL.
BACKGROUND: UNIVERSITY STUDENTS EXPECT TO USE TECHNOLOGY AS PART OF THEIR STUDIES, YET HEALTH PROFESSIONAL TEACHERS CAN STRUGGLE WITH THE CHANGE IN STUDENT LEARNING HABITS FUELLED BY TECHNOLOGY. OUR RESEARCH AIMED TO DOCUMENT THE LEARNING HABITS OF CONTEMPORARY MEDICAL STUDENTS DURING A CLINICAL ROTATION BY EXPLORING THE USE OF LOCALLY AND EXTERNALLY DEVELOPED DIGITAL AND PRINT SELF-DIRECTED LEARNING RESOURCES, AND STUDY GROUPS. METHODS: WE INVESTIGATED THE LEARNING HABITS OF FINAL-STAGE MEDICAL STUDENTS DURING THEIR CLINICAL PAEDIATRIC ROTATION USING MIXED METHODS, INVOLVING LEARNING ANALYTICS AND A STUDENT QUESTIONNAIRE. LEARNING ANALYTICS TRACKED AGGREGATE STUDENT USAGE STATISTICS OF LOCALLY PRODUCED E-LEARNING RESOURCES ON TWO LEARNING MANAGEMENT SYSTEMS AND MOBILE LEARNING RESOURCES. THE QUESTIONNAIRE RECORDED STUDENT-REPORTED USE OF DIGITAL AND PRINT LEARNING RESOURCES AND STUDY GROUPS. RESULTS: THE STUDENTS MADE EXTENSIVE USE OF DIGITAL SELF-DIRECTED LEARNING RESOURCES, ESPECIALLY IN THE 2 WEEKS BEFORE THE EXAMINATION, WHICH PEAKED THE DAY BEFORE THE WRITTEN EXAMINATION. ALL STUDENTS USED LOCALLY PRODUCED DIGITAL FORMATIVE ASSESSMENT, AND MOST (74/98; 76%) ALSO USED DIGITAL RESOURCES DEVELOPED BY OTHER INSTITUTIONS. MOST REPORTED FINDING LOCALLY PRODUCED E-LEARNING RESOURCES BENEFICIAL FOR LEARNING. IN TERMS OF TRADITIONAL FORMS OF SELF-DIRECTED LEARNING, ONE-THIRD (28/94; 30%) INDICATED THAT THEY NEVER READ THE COURSE TEXTBOOK, AND FEW STUDENTS USED FACE-TO-FACE 39/98 (40%) OR ONLINE 6/98 (6%) STUDY GROUPS. DISCUSSION: LEARNING ANALYTICS AND STUDENT QUESTIONNAIRE DATA CONFIRMED THE EXTENSIVE USE OF DIGITAL RESOURCES FOR SELF-DIRECTED LEARNING. THROUGH CLARIFICATION OF LEARNING HABITS AND EXPERIENCES, WE THINK TEACHERS CAN HELP STUDENTS TO OPTIMISE EFFECTIVE LEARNING STRATEGIES; HOWEVER, THE IMPACT OF CONTEMPORARY LEARNING HABITS ON LEARNING EFFICACY REQUIRES FURTHER EVALUATION.
PREDICTING THAT A STUDENT IS LIKELY TO FAIL IN A COURSE IS CRITICAL FOR PERFORMING EARLY INTERVENTIONS, PREVENT DROPOUT AND INCREASE PERFORMANCE ON DISTANCE LEARNING. THIS WORK INVESTIGATES THE MOST PROMISING MACHINE LEARNING MODEL TO PERFORM THIS TASK USING A SMALL (35 SAMPLES) DATASET THAT CONCERNS TWO CLASSES OF ONE UNDERGRADUATE COURSE SUBJECT. THE RESULTS BRING EVIDENCE THAT THE IMPLEMENTED ENSEMBLE CAN PERFORM A PREDICTION AT THE END OF THE FIRST WEEK OF THE COURSE, WITH A MEAN ACCURACY OF 78%, WHEN PRESENTED TO UNSEEN DATA. THIS PAPER ALSO INVESTIGATES THE INFLUENCE OF PAST DATA ON THE RESULTS OF THE CLASSIFIERS BY BUILDING DATASETS WITH DIFFERENT TIME WINDOW CONFIGURATIONS.
SINCE VARIOUS FEATURES HAVE DIFFERENT DEGREES OF ASSOCIATION WITH LEARNING OUTCOME, IT IS NECESSARY TO EVALUATE EACH FEATURE BY GIVING A REASONABLE WEIGHT. IN THIS PAPER, WE PROPOSE A DIFFERENT WEIGHTING OF THE FEATURES, WEIGHTING THE FEATURES IS DIFFERENT FROM OTHER RESEARCHES, WHEN WE CARRY OUT CORRELATION ANALYSIS BETWEEN THESE FEATURES AND GRADE ACHIEVED (IN THE END OF THE SEMESTER). BY USING THIS WEIGHTING OF THE FEATURES AND THE STUDENTS' GRADE ACHIEVED, WE GROUPED STUDENTS INTO FIVE CLUSTERS AND ANALYZED THEIR LEARNING BEHAVIORAL PATTERNS. WE FOUND SOME INTERESTING PATTERNS, SUCH AS THE STUDENTS WHO ALWAYS USE THE MEMO OR MAKERS FUNCTION, ALWAYS GET BETTER GRADE ACHIEVED.
THIS PAPER TAKES A BLENDED COURSE AS AN EXAMPLE IN MOODLE PLATFORM. AS A CASE STUDY, MULTIPLE METHODS INCLUDING STATISTICAL ANALYSIS, VISUALIZATION AND SOCIAL NETWORK ANALYSIS WERE USED TO ANALYZE THE PROCESS AND RESULTS OF ONLINE LEARNING. WITH THE CONCEPT OF BIG DATA, IT ANALYZES 22 CLASSES OF 1088 STUDENTS AND TEACHERS FROM A COMPREHENSIVE PERSPECTIVE. IT INCLUDES THE ACCESS BEHAVIOR, RESOURCE UTILIZATION, SELF-TEST EXERCISES, INTERACTIVE BEHAVIOR AND ACADEMIC POINT OF VIEW OF GROUP BEHAVIOR CHARACTERISTICS OF ONLINE LEARNING. IN ADDITION, IT OFFERS A SAMPLE OF THE SELF-DIAGNOSTIC STUDY REPORT, THEREBY PROVIDING A LEARNING DIAGNOSIS AND DECISION-MAKING REFERENCE FOR LEARNERS, TEACHERS AND ADMINISTRATORS.
MASSIVE OPEN ONLINE COURSES (MOOCS) ARE A NEW FORM OF LEARNING ENVIRONMENT AIMED TOWARDS ACCESSIBILITY AND OPENNESS USING CONTEMPORARY TECHNOLOGIES. ONE OF THE MOOC'S KEY FEATURES IS THE SOCIAL INTERACTION WHICH USUALLY TAKES PLACE IN DISCUSSION FORUMS. THIS ARTICLE FOCUSES ON REWORKING AND ANALYZING THE EXISTING IMOOX FORUM BY ENRICHING THE NEW DESIGN WITH GAMIFICATION ELEMENTS. THE INTENDED OBJECTIVES AIM AT REFRESHING THE CURRENT STYLE AND IMPROVING ITS HANDLING AND USABILITY. THIS ARTICLE PROVIDES OUR EXPERIENCE OF THIS IMPLEMENTATION AS WELL AS EXAMINING WHETHER OR NOT THE GAMIFICATION ELEMENTS CAN HELP INCREASE THE PARTICIPATION RATE AND THE TEACHER-TO-STUDENT AND STUDENT-TO-STUDENT COMMUNICATION EXCHANGE. RESULTS OF THE NEW DESIGN SHOWED A GOOD USER SATISFACTION AND A SLIGHT POSITIVE IMPACT.
IN GENERAL, RESEARCH CONFIRMS THAT LEARNING IS MORE EFFECTIVE WHEN STUDENTS OBTAIN FEEDBACK REGARDING THEIR LEARNING PROGRESS. CURRENTLY, NEW VERSIONS OF E-LEARNING PLATFORMS INCLUDE INDICATORS THAT PROVIDE SOME STATIC FEEDBACK MECHANISMS AND HELP BOTH LEARNERS AND EDUCATORS IN PLANNING THEIR LEARNING STRATEGIES. THIS PAPER EXPLAINS THE USAGE OF INDICATORS IN CURRENT E-LEARNING SYSTEMS, GENERATES A TAXONOMY FOR THEIR CLASSIFICATION, AND STUDIES THEIR INFLUENCE ON STUDENT PERFORMANCE. ALSO, IT PROVIDES A STUDY WHICH IS BASED ON THE COMBINATION OF A USER-BASED EVALUATION PROCESS THAT FACILITATES DATA COLLECTION AND DATA MINING ALGORITHMS TO INFER ASSOCIATION RULES BETWEEN LEARNING VARIABLES AND PERFORMANCE. THE RESULTS HIGHLIGHT HOW PROCRASTINATION INFLUENCES NEGATIVE LEARNING PERFORMANCE AND HOW TIME-RELATED INDICATORS ARE TIGHTLY COUPLED WITH STUDENTS' PERFORMANCE IN E-LEARNING PLATFORMS.
LITTLE RESEARCH EFFORT CONTRIBUTED TO HELP LEARNERS REFLECT ON THEIR CURRENT COGNITIVE SKILLS. A STOREHOUSE OF THIS VALUABLE INFORMATION COULD PROVIDE VALUABLE INDICATIONS TO EXPERIENCED MENTORS TO INTERVENE AND GUIDE LEARNERS IMPROVING THEIR LEARNING CAPABILITIES. LEARNING HOW TO LEARN USING THE PROPOSED COGNITIVE APPRENTICESHIP APPROACH IN THIS PAPER GOES BEYOND THE GIVEN CLASSROOM KNOWLEDGE TO REFLECT ON HOW TO MAXIMIZE LEARNING READINESS UNDER THE SUPERVISION OF EXPERIENCED MENTORS. IN THIS PAPER, WE PROMOTE AN AUTONOMOUS INQUIRY-BASED LEARNING CYCLE TO SUPPORT FORMAL EDUCATION PROCESSES, WHEREBY STUDENTS LEARN FROM INDIVIDUAL DISCOVERIES AND FROM PEERS. AT THE COMPLETION OF EACH INQUIRY-BASED LEARNING CYCLE, A COGNITIVE APPRENTICESHIP CYCLE IS TRIGGERED TO ASSESS THE IMPROVEMENTS IN LEARNING DISPOSITIONS AND GUIDE APPRENTICES IN DEVELOPING THEIR OWN COGNITIVE SKILLS. WE PROPOSE AN ONLINE INSTRUMENT FOR COLLECTING SELF-ASSESSMENT DATA AND COMMUNICATING WITH MENTORS, TO PRODUCE WILLING, CONFIDENT AND CREATIVE LIFELONG LEARNERS. THE PROVIDED INSTRUMENT PRESENTS A STOREHOUSE VIEW OF LEARNING DISPOSITION THROUGH AN INTEGRATED PORTAL WHICH CAPTURES SELF-STATED LEARNING EXPERIENCES AND CONVERTS THEM INTO ANALYTICAL RESULTS TO DIAGNOSE DEFICIENCIES AND PRESCRIBE IMPROVEMENT RECOMMENDATIONS. IN DOING SO, WE PROVIDE "LEARNING DISPOSITION" DIMENSIONS ALONG WHICH, WE ANALYTICALLY MEASURE LEARNING APTITUDES OF INDIVIDUAL LEARNERS. THE PROPOSED TOOL IN THIS PROJECT GENERATES AN INVENTORY OF LEARNING CAPABILITIES TO GUIDE LEARNERS REORGANIZING THEIR LEARNING AGENDA. COLLECTED DATA OUT OF THE PROPOSED TOOL REFLECTS LEARNERS' PERCEPTION AT A GIVEN POINT OF TIME. THE TOOL IS APPLIED AGAIN AT A LATER POINT OF TIME TO ASSESS THE CHANGES AND MOVE THE APPRENTICE TO THE NEXT LEVEL OF HIS COGNITIVE SKILLS.
IN THIS RESEARCH, WE AIM TO FIND OUT THE RELATIONSHIP BETWEEN THE HIGH-SCHOOL STUDENTS' SCORES AND THEIR ACTIVITY PATTERNS WHEN THEY ARE USING 3D E-LEARNING CONTENTS. FOR THIS PURPOSE, WE COLLABORATE WITH HIGH SCHOOLS IN FUKUOKA, JAPAN. WE PROVIDE THE E-LEARNING CONTENT TO THEIR STUDENTS AS THE ASSISTANT TEXTBOOK. SUCH E-LEARNING CONTENT IS DEVELOPED BY USING HTML, WEBGL AND THREEJS TECHNOLOGY. IT REPRESENTS ONE ARTICLE OF THE CHINESE ANCIENT LITERATURE. THIS ARTICLE IS A PART OF THE BOOK "SHIJI". IT IS VERY DIFFICULT FOR STUDENTS TO UNDERSTAND, BECAUSE BOTH THE KANJI AND THE GRAMMA OF THE ANCIENT CHINESE ARE DIFFERENT FROM THE CURRENT JAPANESE. THIS E-LEARNING CONTENT IS PROVIDED AS A WEB APPLICATION. IN THIS APPLICATION, WE USE 3D ANIMATIONS TO REPRESENT THE STORY, AND THE ORIGINAL ANCIENT ARTICLE, THE NEWLY WRITTEN ARTICLE, AND ITS TRANSLATIONS OF CURRENT JAPANESE ARE ALSO SHOWN. STUDENTS CAN WATCH THE ANIMATIONS STEP BY STEP. DURING THIS PROCESS, STUDENTS CAN CHANGE THEIR VIEWPOINTS FREELY, REPEAT SPECIFIC ANIMATION CLIPS, COMPARING THE DIFFERENT TYPES OF TEXT DESCRIPTIONS. THESE ACTIVITIES WILL BE RECORDED AS THE LOG DATA FOR FURTHER VISUAL ANALYZATION. USERS ALSO NEED TO FINISH A PRE- AND A POSTTEST AND A QUESTIONNAIRE. THE TEST RESULTS ARE USED TO EVALUATE IF THE STUDENTS UNDERSTAND THIS ARTICLE, AND THE QUESTIONNAIRE RESULT IS USED TO OBTAIN STUDENTS' MOTIVATIONS OF USING 3D APPLICATIONS AND THEIR CUSTOMIZATIONS ON USING 3D APPLICATIONS. WE WILL GROUP STUDENTS ACCORDING TO THE RESULTS OF THEIR TESTS AND QUESTIONNAIRES. THEN, WE WILL VISUALIZE THE STUDENTS' OPERATIONS TO FIND OUT IF THERE ARE ANY COMMON ACTIVITY PATTERNS IN DIFFERENT GROUPS. ACCORDING TO THE ANALYZATION RESULT, WE HOPE TO PREDICT NEW STUDENTS' SCORES AND TO PROVIDE OPERATING ADVICE TO THE STUDENTS WHOSE SCORES ARE NOT GOOD.
THE ROLE OF SOCIAL TOOLS IN MASSIVE OPEN ONLINE COURSES (MOOCS) IS ESSENTIAL AS THEY CONNECT PARTICIPANTS. OF ALL THE PARTICIPANTS IN AN MOOC, TOP CONTRIBUTORS ARE THE ONES WHO MORE ACTIVELY CONTRIBUTE VIA SOCIAL TOOLS. THIS ARTICLE ANALYSES AND REPORTS EMPIRICAL DATA FROM FIVE DIFFERENT SOCIAL TOOLS PERTAINING TO AN ACTUAL MOOC TO CHARACTERIZE TOP CONTRIBUTORS AND PROVIDE SOME INSIGHTS AIMED AT FACILITATING THEIR EARLY DETECTION. THE RESULTS OF THIS ANALYSIS SHOW THAT TOP CONTRIBUTORS HAVE BETTER FINAL SCORES THAN THE REST. IN ADDITION, THERE IS A MODERATE POSITIVE CORRELATION BETWEEN PARTICIPANTS' OVERALL PERFORMANCE (MEASURED IN TERMS OF FINAL SCORES) AND THE NUMBER OF POSTS SUBMITTED TO THE FIVE SOCIAL TOOLS. THIS ARTICLE ALSO STUDIES THE EFFECT OF PARTICIPANTS' GENDER AND SCORES AS FACTORS THAT CAN BE USED FOR THE EARLY DETECTION OF TOP CONTRIBUTORS. THE ANALYSIS SHOWS THAT GENDER IS NOT A GOOD PREDICTOR AND THAT TAKING THE SCORES OF THE FIRST ASSESSMENT ACTIVITIES OF EACH TYPE (TEST AND PEER ASSESSMENT IN THE CASE STUDY) RESULTS IN A PREDICTION THAT IS NOT SUBSTANTIALLY IMPROVED BY ADDING SUBSEQUENT ACTIVITIES. FINALLY, BETTER PREDICTIONS BASED ON SCORES ARE OBTAINED FOR AGGREGATE CONTRIBUTIONS IN THE FIVE SOCIAL TOOLS THAN FOR INDIVIDUAL CONTRIBUTIONS IN EACH SOCIAL TOOL.
FEATURED APPLICATION COMBINING DIFFERENT DATA SOURCES HAS HIGH POWER TO PREDICT STUDENTS AT-RISK OF FAILURE AND TO IDENTIFY BEHAVIOR PATTERNS TO DEVELOP INSTITUTIONAL POLICES BASED ON EVIDENCE. THIS PAPER DESCRIBES THE APPLICATION OF DATA SCIENCE AND EDUCATIONAL DATA MINING TECHNIQUES TO DATA FROM 4529 STUDENTS, SEEKING TO IDENTIFY BEHAVIOR PATTERNS AND GENERATE EARLY PREDICTIVE MODELS AT THE UNIVERSIDAD DE LA REPUBLICA DEL URUGUAY. THE PAPER DESCRIBES THE USE OF DATA FROM DIFFERENT SOURCES (A VIRTUAL LEARNING ENVIRONMENT, SURVEY, AND ACADEMIC SYSTEM) TO GENERATE PREDICTIVE MODELS AND DISCOVER THE MOST IMPACTFUL VARIABLES LINKED TO STUDENT SUCCESS. THE COMBINATION OF DIFFERENT DATA SOURCES DEMONSTRATED A HIGH PREDICTIVE POWER, ACHIEVING PREDICTION RATES WITH OUTSTANDING DISCRIMINATION AT THE FOURTH WEEK OF A COURSE. THE ANALYSIS SHOWED THAT STUDENTS WITH MORE INTERACTIONS INSIDE THE VIRTUAL LEARNING ENVIRONMENT TENDED TO HAVE MORE SUCCESS IN THEIR DISCIPLINES. THE RESULTS ALSO REVEALED SOME RELEVANT ATTRIBUTES THAT INFLUENCED THE STUDENTS' SUCCESS, SUCH AS THE NUMBER OF SUBJECTS THE STUDENT WAS ENROLLED IN, THE STUDENTS' MOTHER'S EDUCATION, AND THE STUDENTS' NEIGHBORHOOD. FROM THE RESULTS EMERGED SOME INSTITUTIONAL POLICIES, SUCH AS THE ALLOCATION OF COMPUTATIONAL RESOURCES FOR THE VIRTUAL LEARNING ENVIRONMENT INFRASTRUCTURE AND ITS WIDESPREAD USE, THE DEVELOPMENT OF TOOLS FOR FOLLOWING THE TRAJECTORY OF STUDENTS, AND THE DETECTION OF STUDENTS AT-RISK OF FAILURE. THE CONSTRUCTION OF AN INTERDISCIPLINARY EXCHANGE BRIDGE BETWEEN SOCIOLOGY, EDUCATION, AND DATA SCIENCE IS ALSO A SIGNIFICANT CONTRIBUTION TO THE ACADEMIC COMMUNITY THAT MAY HELP IN CONSTRUCTING UNIVERSITY EDUCATIONAL POLICIES.
FEEDBACK IS AN ESSENTIAL COMPONENT OF ANY LEARNING EXPERIENCE. IT ALLOWS STUDENTS TO IDENTIFY GAPS IN THEIR LEARNING AND IMPROVE THEIR SELF-REGULATION. HOWEVER, PROVIDING USEFUL FEEDBACK IS A CHALLENGING AND TIME-CONSUMING TASK. IN DIGITAL LEARNING ENVIRONMENTS, THIS CHALLENGE IS EVEN MORE SIGNIFICANT DUE TO A LARGE NUMBER OF STUDENTS. THUS, THIS PAPER REPORTS ON THE FINDINGS OF AN ANALYSIS OF THE QUALITY OF FEEDBACK PROVIDED BY INSTRUCTORS IN AN ONLINE COURSE. THE PAPER ALSO PROPOSES A SUPERVISED MACHINE LEARNING ALGORITHM THAT CAN IDENTIFY THE PRESENCE OF GOOD PRACTICES IN FEEDBACK MESSAGES SENT TO STUDENTS IN A DIGITAL LEARNING ENVIRONMENT. THE RESULTS REVEAL THE MOST COMMONLY USED KINDS OF FEEDBACK AND HOW TO IDENTIFY THEM AUTOMATICALLY. THE RESULTS OF THE STUDY COULD POTENTIALLY BE USED TO IMPROVE THE QUALITY OF THE FEEDBACK PROVIDED BY INSTRUCTORS IN ONLINE EDUCATION.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE RECENTLY TAKEN CENTER STAGE IN DISCUSSIONS SURROUNDING ONLINE EDUCATION, BOTH IN TERMS OF THEIR POTENTIAL AS WELL AS THEIR HIGH DROPOUT RATES. THE HIGH ATTRITION RATES ASSOCIATED WITH MOOCS HAVE OFTEN BEEN DESCRIBED IN TERMS OF A SCALE-EFFICACY TRADEOFF. BUILDING FROM THE LARGE NUMBERS ASSOCIATED WITH MOOCS AND THE ABILITY TO TRACK INDIVIDUAL STUDENT PERFORMANCE, THIS STUDY TAKES AN INITIAL STEP TOWARDS A MECHANISM FOR THE EARLY AND ACCURATE IDENTIFICATION OF STUDENTS AT RISK FOR DROPPING OUT. FOCUSING ON STRUGGLING STUDENTS WHO REMAIN ACTIVE IN COURSE DISCUSSION FORUMS AND WHO ARE ALREADY MORE LIKELY TO FINISH A COURSE, WE DESIGN A TEMPORAL MODELING APPROACH, ONE WHICH PRIORITIZES THE AT-RISK STUDENTS IN ORDER OF THEIR LIKELIHOOD TO DROP OUT OF A COURSE. IN IDENTIFYING ONLY A SMALL SUBSET OF AT-RISK STUDENTS, WE SEEK TO PROVIDE SYSTEMATIC INSIGHT FOR INSTRUCTORS SO THEY MAY BETTER PROVIDE TARGETED SUPPORT FOR THOSE STUDENTS MOST IN NEED OF INTERVENTION. MOREOVER, WE PROFFER APPENDING HISTORICAL FEATURES TO THE CURRENT WEEK OF FEATURES FOR MODEL BUILDING AND TO INTRODUCE PRINCIPLE COMPONENT ANALYSIS IN ORDER TO IDENTIFY THE BREAKPOINT FOR TURNING OFF THE FEATURES OF PREVIOUS WEEKS. THIS APPENDED MODELING METHOD IS SHOWN TO OUTPERFORM SIMPLER TEMPORAL MODELS WHICH SIMPLY SUM FEATURES. TO DEAL WITH THE KIND OF DATA VARIABILITY PRESENTED BY MOOCS, THIS STUDY ILLUSTRATES THE EFFECTIVENESS OF AN ENSEMBLE STACKING GENERALIZATION APPROACH TO BUILD MORE ROBUST AND ACCURATE PREDICTION MODELS THAN THE DIRECT APPLICATION OF BASE LEARNERS. PUBLISHED BY ELSEVIER LTD.
A PUBLIC CLOUD PLATFORM OFFERS ECONOMY OF SCALE, EASE OF MANAGEMENT, AND ELASTICITY TO SOLUTIONS. IN ADDITION, REGULATORY COMPLIANCE AND SECURITY MUST BE ASSURED FOR SOLUTIONS HANDLING SENSITIVE DATA, SUCH AS STUDENT AND HEALTHCARE DATA. WITH THE STEEP RISE IN DATA BREACHES AT LARGE ENTERPRISES, IT IS A REQUIREMENT TO EMPHASIZE THE SECURITY, PRIVACY, AND COMPLIANCE OF CLOUD-DELIVERED SOLUTIONS THAT HOLD PERSONALLY IDENTIFIABLE INFORMATION (PII). AN EXAMPLE OF A SOLUTION IN NEED OF SUCH ASSURANCES IS AN EDUCATION AND LEARNING-RELATED ANALYTICS SERVICE THAT HANDLES CONFIDENTIAL STUDENT DATA ON A PUBLIC CLOUD PLATFORM. IN THIS PAPER, WE PROPOSE AN APPROACH FOR MANAGING THE SECURITY AND PRIVACY OF AN EDUCATION AND LEARNING-ANALYTICS SOLUTION ON A PUBLIC CLOUD PLATFORM WHILE ASSURING COMPLIANCE WITH THE FAMILY EDUCATIONAL RIGHTS AND PRIVACY ACT (FERPA). WE ALSO PROPOSE A NEW AGILE DEPLOYMENT APPROACH THAT IS BOTH RAPID AND AUTOMATIC. A PROTOTYPE OF A LEARNING-ANALYTICS SOLUTION WAS IMPLEMENTED ON A SOFTLAYER PUBLIC CLOUD, AND THE NEW DEPLOYMENT METHOD WAS EVALUATED IN COMPARISON WITH EXISTING METHODS.
VERSION CONTROL SYSTEMS ARE COMMONLY USED BY INFORMATION AND COMMUNICATION TECHNOLOGY PROFESSIONALS. THEY ALLOW MONITORING THE SINGLE ACTIVITY OF DIFFERENT PEOPLE WORKING IN THE SAME PROJECT THROUGH ALL THE PROJECT'S LIFETIME. THUS, VERSION CONTROL SYSTEMS ARE ALSO USED BY EDUCATIONAL INSTITUTIONS. THE AIM OF THIS WORK IS TO DEMONSTRATE THAT THE ACADEMIC SUCCESS IN COMPUTER SCIENCES SUBJECTS MAY BE PREDICTED BY MONITORING STUDENTS' INTERACTION WITH A VERSION CONTROL SYSTEM. IN ORDER TO DO SO, WE HAVE BUILT A PREDICTION MODEL BY USING MODEL EVALUATOR, A TOOL WHICH ALLOWS EVALUATING SEVERAL MACHINE LEARNING MODELS IN ORDER TO SELECT THE MOST SUITABLE FOR A SPECIFIC PROBLEM. A SET OF EXPERIMENTS ARE BEING BEEN CARRIED OUT TO EVALUATE THE PREDICTION MODEL. AT THOSE EXPERIMENTS, STUDENTS OF DIFFERENT SUBJECTS OF THE DEGREE IN COMPUTER SCIENCE OF THE UNIVERSITY OF LEON WERE INVOLVED. ALL THE SELECTED SUBJECTS: OPERATING SYSTEMS EXTENSION, COMPUTER PROGRAMMING, AND COMPUTER ORGANIZATION, REQUIRE STUDENTS TO COMPLETE PRACTICAL ASSIGNMENTS WHICH IMPLY DEVELOPING SOFTWARE SOLUTIONS IN DIFFERENT PROGRAMMING LANGUAGES. A COMMON REQUISITE OF THOSE PRACTICAL ASSIGNMENTS IS THE USE OF A GIT REPOSITORY TO STORE SOURCE CODE AND DOCUMENTATION. TENTATIVE RESULTS SUGGEST THAT CONTINUOUS INTERACTION WITH THE GIT REPOSITORIES HAS A BIG IMPACT ON ACADEMIC SUCCESS.
THIS PAPER IS ABOUT ORCHESTRATING THE EMERGENCE OF CONCEPTUAL LEARNING IN A COLLABORATIVE SETTING. WE ELABORATE ON THE IDEA OF CRITICAL MOMENTS IN GROUP LEARNING, EVENTS WHICH MAY LEAD TO A PARTICULAR DEVELOPMENT AT THE EPISTEMIC LEVEL REGARDING THE SHARED OBJECT. WE CONJECTURE THAT TEACHERS' IDENTIFICATION OF CRITICAL MOMENTS MAY HELP THEM GUIDE STUDENTS TO THE EMERGENCE OF CONCEPTUAL LEARNING. THE COMPLEXITY OF SMALL GROUP SETTINGS IN CLASSROOMS PREVENTS TEACHERS FROM NOTICING THESE CRITICAL MOMENTS, THOUGH. HERE WE PRESENT AN ENVIRONMENT, SAGLET (SYSTEM FOR ADVANCING GROUP LEARNING IN EDUCATIONAL TECHNOLOGIES), BASED ON THE VMT (VIRTUAL MATH TEAMS) ENVIRONMENT (STAHL 2009), WHICH ALLOWS TEACHERS TO OBSERVE MULTIPLE GROUPS ENGAGING IN PROBLEM-SOLVING IN GEOMETRY. SAGLET CAPITALIZES ON MACHINE LEARNING TECHNIQUES TO INFORM TEACHERS ABOUT ON-LINE CRITICAL MOMENTS BY SENDING THEM ALERTS, SO THAT THEY CAN THEN DECIDE WHETHER (AND HOW) TO USE THE ALERTS IN GUIDING THEIR STUDENTS. ONE TEACHER IN AN ELEMENTARY SCHOOL USED SAGLET TO HELP MULTIPLE GROUPS OF STUDENTS SOLVE DIFFICULT PROBLEMS IN GEOMETRY. WE OBSERVED HOW THE TEACHER MEDIATED TWO COHORTS OF MULTIPLE GROUPS AT TWO DIFFERENT TIMES IN A MATHEMATICS CLASSROOM. WE SHOW THAT IN BOTH CASES THE TEACHER COULD DETECT THE NEEDS OF THE GROUPS (PARTLY THANKS TO THE ALERTS) AND COULD PROVIDE ADAPTIVE GUIDANCE FOR ALL THE GROUPS.
ANALYZING THE PROCESS DATA OF STUDENTS AS THEY COMPLETE PROGRAMMING ASSIGNMENTS HAS THE POTENTIAL TO PROVIDE COMPUTING EDUCATORS WITH INSIGHTS INTO BOTH THEIR STUDENTS AND THE PROCESSES BY WHICH THEY LEARN TO PROGRAM. IN PRIOR RESEARCH, WE EXPLORED THE RELATIONSHIP BETWEEN (A) STUDENTS' PROGRAMMING BEHAVIORS AND COURSE OUTCOMES, AND (B) STUDENTS' PARTICIPATION WITHIN AN ONLINE SOCIAL LEARNING ENVIRONMENT AND COURSE OUTCOMES. IN BOTH STUDIES, WE DEVELOPED STATISTICAL MEASURES DERIVED FROM OUR DATA THAT SIGNIFICANTLY CORRELATE WITH STUDENTS' COURSE GRADES. ENCOURAGED BOTH BY SOCIAL THEORIES OF LEARNING AND A DESIRE TO IMPROVE THE ACCURACY OF OUR STATISTICAL MODELS, WE EXPLORE HERE THE IMPACT OF INCORPORATING OUR PREDICTIVE MEASURE DERIVED FROM SOCIAL BEHAVIOR INTO THREE SEPARATE PREDICTIVE MEASURES DERIVED FROM PROGRAMMING BEHAVIORS. WE FIND THAT, IN COMBINING THE MEASURES, WE ARE ABLE TO IMPROVE THE OVERALL PREDICTIVE POWER OF EACH MEASURE. THIS FINDING AFFIRMS THE IMPORTANCE OF SOCIAL INTERACTION IN THE LEARNING PROCESS, AND PROVIDES EVIDENCE THAT PREDICTIVE MODELS DERIVED FROM MULTIPLE SOURCES OF LEARNING PROCESS DATA CAN PROVIDE SIGNIFICANTLY BETTER PREDICTIVE POWER BY ACCOUNTING FOR MULTIPLE FACTORS RESPONSIBLE FOR STUDENT SUCCESS.
THIS ARTICLE STEMS FROM THE THEORETICAL AND METHODOLOGICAL FRAMEWORK OF THE AUTHOR'S DOCTORAL THESIS WHICH RESEARCHES EDUCATIONAL DATA MINING (EDM) ONSITE IN THE CLASSROOM. THE VARIABLES STUDIED INCLUDE TEACHING STRATEGIES, STUDENT ENGAGEMENT AND EVALUATION OF STUDENT LEARNING. SCHOOL INFORMATION SYSTEMS PROCESS VAST QUANTITIES OF INFORMATION INCLUDING, INTERNAL AND EXTERNAL EVALUATIONS, STUDENT ATTENDANCE, SOCIOECONOMIC STATUS, MEAL PROGRAMS, TARDINESS, YEARLY PERFORMANCE, TO NAME A FEW, ALL WHICH SIGNIFICANTLY CONTRIBUTE TO INFORMED DECISION-MAKING AT SCHOOLS. SUCH INFORMATION IS OFTEN STOCKPILED AND STORED IN OBSCURE FILES SOMEWHERE WITHIN THE SCHOOL WITHOUT EVER MAKING ANY EFFECTIVE USE OF IT. EVEN WORSE, IMPORTANT INFORMATION TENDS TO BE UNDERRECORDED, THEREBY JEOPORDAZING ANY OPPORTUNITIES FOR ACADEMIC IMPROVEMENT. THE OBJECTIVE OF THIS ARTICLE IS TO INTRODUCE CONCEPTS ASSOCIATED WITH DATA ANALYSIS IN A SCHOOL CONTEXT AND ITS CONTRIBUTION TO EDUCATIONAL MANAGEMENT WHICH LEADS TO INFORMED DECISION-MAKING AND THE DEVELOPMENT OF ACTION PLANS.
LEARNING THEORY IN EDUCATION HAS PROGRESSED DRAMATICALLY WITH THE RAPID DEVELOPMENT OF MODERN TECHNOLOGIES. MODERN LEARNING THEORY HAS FOUND THAT LEARNING PERFORMANCE CAN BE IMPROVED EFFECTIVELY THROUGH COOPERATIVE/COLLABORATIVE LEARNING. HOW TO GROUP STUDENTS IN ONE CLASS BASED ON THEIR DIFFERENT LEARNING CAPABILITIES IS A KEY TO SUCCESS IN EDUCATION. TWO GROUPING TYPES OF HOMOGENEOUS AND HETEROGENEOUS HAVE BEEN APPLIED INTO CLASS GROUPING. RESEARCH PROVES THAT HOMOGENEOUS GROUPS ARE GOOD AT SPECIFIC GOAL AND HETEROGENEOUS GROUPS ARE BETTER IN EXPANDED TASKS. LEARNING EFFICIENCY IS AFFECTED IN BOTH GROUP LEVEL AND INDIVIDUAL LEVEL BY DIFFERENT GROUPING METHOD. HOWEVER GROUPING STUDENTS IN AN OPTIMAL WAY IS A DIFFICULT NP-HARD RESEARCH PROBLEM WITH HIGH COMPLEXITY; AND TO ACHIEVE THE BEST RESULTS, SUCH GROUPING CAN BE VERY TIME CONSUMING AND TEDIOUS FOR THE TEACHERS TO PERFORM MANUALLY, ESPECIALLY WHEN LOCAL CULTURAL CONTEXT (FOR INSTANCE, GENDER PARAMETER) HAS TO BE TAKEN INTO ACCOUNT, SUCH AS IN THE MIDDLE EAST REGION. SO THE ABILITY OF ALLOWING EDUCATORS TO DEFINE RULES DESCRIBING GROUPS IS VERY IMPORTANT FOR BOTH HOMOGENEOUS AND HETEROGENEOUS GROUPING CAPABILITIES. THIS PAPER THUS DESCRIBES A SMART GROUPING TOOL PORTAL WHICH IS EASY TO ACCESS AND USER FRIENDLY, AND AIMS TO ASSIST TEACHERS TO AUTOMATICALLY GROUP STUDENTS BASED ON DIFFERENT CRITERIA. THIS PORTAL HAS BEEN TRIALLED AT KHALIFA UNIVERSITY OF SCIENCE, TECHNOLOGY & RESEARCH (KUSTAR). A CASE STUDY OF THE TRIAL HAS ALSO BEEN ANALYZED TO ILLUSTRATE THE DERIVATIVE BENEFITS OF OUR SMART GROUPING TOOL PORTAL.
PREDICTING KNOWLEDGE LEVELS FROM USER'S IMPLICIT INTERACTIONS WITH AN ADAPTIVE SYSTEM IS A DIFFICULT TASK, PARTICULARLY IN LEARNING SYSTEMS THAT ARE USED IN THE CONTEXT OF DAILY WORK TASKS. WE HAVE COLLECTED INTERACTIONS OF SIX PERSONS WORKING WITH THE ADAPTIVE WORK-INTEGRATED LEARNING SYSTEM APOSDLE OVER A PERIOD OF TWO MONTHS TO FIND OUT WHETHER NATURALLY OCCURRING INTERACTIONS WITH THE SYSTEM CAN BE USED TO PREDICT THEIR LEVEL OF EXPERTISE. ONE SET OF INTERACTIONS IS BASED ON THE TASKS THEY PERFORMED, THE OTHER ON A NUMBER OF ADDITIONAL KNOWLEDGE INDICATING EVENTS (KIE). WE FIND THAT THE ADDITION OF KIE SIGNIFICANTLY IMPROVES THE PREDICTION AS COMPARED TO USING TASKS ONLY. BOTH APPROACHES ARE SUPERIOR TO A MODEL THAT USES ONLY THE FREQUENCIES OF EVENTS.
CROWDSOURCING HAS BECOME AN INCREASINGLY POPULAR MEANS TO ACQUIRE HUMAN INPUT ON DEMAND. MICROTASK CROWDSOURCING MARKETPLACES FACILITATE THE ACCESS TO MILLIONS OF PEOPLE (CALLED WORKERS) WHO ARE WILLING TO PARTICIPATE IN TASKS IN RETURN FOR MONETARY REWARDS OR OTHER FORMS OF COMPENSATION. THIS PARADIGM PRESENTS A UNIQUE LEARNING CONTEXT WHERE WORKERS HAVE TO LEARN TO COMPLETE TASKS ON-THE-FLY BY APPLYING THEIR LEARNING IMMEDIATELY THROUGH THE COURSE OF TASKS. HOWEVER, MOST WORKERS TYPICALLY DROPOUT EARLY IN LARGE BATCHES OF TASKS, DEPRIVING THEMSELVES OF THE OPPORTUNITY TO LEARN ON-THE-FLY THROUGH THE COURSE OF BATCH COMPLETION. BY DOING SO WORKERS SQUANDER A POTENTIAL CHANCE AT IMPROVING THEIR PERFORMANCE AND COMPLETING TASKS EFFECTIVELY. IN THIS PAPER, WE PROPOSE A NOVEL METHOD TO ENGAGE AND RETAIN WORKERS, TO IMPROVE THEIR LEARNING IN CROWDSOURCED INFORMATION FINDING TASKS BY USING ACHIEVEMENT PRIMING. THROUGH RIGOROUS EXPERIMENTAL FINDINGS, WE SHOW THAT IT IS POSSIBLE TO RETAIN WORKERS IN LONG BATCHES OF TASKS BY TRIGGERING THEIR INHERENT MOTIVATION TO ACHIEVE AND EXCEL. AS A CONSEQUENCE OF INCREASED WORKER RETENTION, WE FIND THAT WORKERS LEARN TO PERFORM MORE EFFECTIVELY, DEPICTING RELATIVELY MORE STABLE ACCURACY AND LOWER TASK COMPLETION TIMES IN COMPARISON TO WORKERS WHO DROP OUT EARLY.
VERSION CONTROL SYSTEMS ARE COMMONLY USED BY INFORMATION AND COMMUNICATION TECHNOLOGY PROFESSIONALS. THESE SYSTEMS ALLOW MONITORING PROGRAMMERS ACTIVITY WORKING IN A PROJECT. THUS, VERSION CONTROL SYSTEMS ARE ALSO USED BY EDUCATIONAL INSTITUTIONS. THE AIM OF THIS WORK IS TO EVALUATE IF THE ACADEMIC SUCCESS OF STUDENTS MAY BE PREDICTED BY MONITORING THEIR INTERACTION WITH A VERSION CONTROL SYSTEM. IN ORDER TO DO SO, WE HAVE BUILT A MACHINE LEARNING MODEL WHICH PREDICTS STUDENT RESULTS IN A SPECIFIC PRACTICAL ASSIGNMENT OF THE OPERATING SYSTEMS EXTENSION SUBJECT, FROM THE SECOND COURSE OF THE DEGREE IN COMPUTER SCIENCE OF THE UNIVERSITY OF LEON, THROUGH THEIR INTERACTION WITH A GIT REPOSITORY. TO BUILD THE MODEL, SEVERAL CLASSIFIERS AND PREDICTORS HAVE BEEN EVALUATED. IN ORDER TO DO SO, WE HAVE DEVELOPED MODEL EVALUATOR (MOEV), A TOOL TO EVALUATE MACHINE LEARNING MODELS IN ORDER TO GET THE MOST SUITABLE FOR A SPECIFIC PROBLEM. PRIOR TO THE MODEL DEVELOPMENT, A FEATURE SELECTION FROM INPUT DATA IS DONE. THE RESULTING MODEL HAS BEEN TRAINED USING RESULTS FROM 2016-2017 COURSE AND LATER VALIDATED USING RESULTS FROM 2017-2018 COURSE. RESULTS CONCLUDE THAT THE MODEL PREDICTS STUDENTS' SUCCESS WITH A SUCCESS HIGH PERCENTAGE.
INCREASING EDUCATIONAL ATTAINMENT FROM A BROADER AND MORE DIVERSE STUDENT POPULATION IS A POLICY GOAL FOR MANY GOVERNMENTS. YET INCREASED ENROLMENTS BRINGS MANY CHALLENGES FOR FACULTY MEMBERS TRYING TO TRACK AND PREDICT ACADEMIC PERFORMANCE. ONE POSSIBLE MECHANISM FOR PREDICTION IS TO USE IN-CLASS PARTICIPATION DATA TO DETERMINE WHETHER PARTICIPATION IS LINKED TO ACADEMIC PERFORMANCE. IN THIS STUDY, WE COMBINED IN-CLASS AND OUT-OF-CLASS (E.G., LEARNING MANAGEMENT SYSTEM) DATA WITH A RANGE OF QUALITATIVE AND QUANTITATIVE SELF-REPORT MEASURES. WE THEN USED A RANGE OF DATA MINING (DM) ALGORITHMS TO PREDICT FINAL COURSE OUTCOMES. WE FOUND THAT STUDENTS WHO PARTICIPATED MORE AND THOUGHT THAT THE TOOL HELPED THEM TO LEARN, ENGAGED AND INCREASED THEIR INTEREST IN THE COURSE MORE, AND EVENTUALLY ACHIEVED THE HIGHEST SCORES. THIS FINDING SUPPORTS THE VIEW THAT IN-CLASS PARTICIPATION IS CRITICAL TO LEARNING AND ACADEMIC SUCCESS.
THIS ARTICLE PRESENTS AN ONGOING INVESTIGATION THAT USES MINING TOOLS TO ANALYZE A LARGE VOLUME OF DATA FROM STUDENTS OF ONLINE COURSES IN A LMS PLATFORM TO DISCOVER ASSOCIATION RULES USED TO IDENTIFYING DROPOUT SITUATIONS. THESE ARS ARE USED ALONG WITH SEVERAL EXPLICIT (FORMALIZED) RULES ELICITED FROM COURSE OPERATORS AND ARE INTENDED TO BE USED BY A SOFTWARE AGENT TO DETECT INDIVIDUALS WITHIN A CERTAIN RISK REGION, TRIGGERING A COMMUNICATION PROCESS TO A STUDENT SUPPORT TEAM.
EVALUATION IS A KEY ELEMENT IN ANY INSTRUCTIONAL DESIGN. THE WAY IT IS DESIGNED AFFECTS THE WAY IN WHICH STUDENTS CONFRONT THEIR LEARNING PROCESS, AND IMPROVING IT POSITIVELY AFFECTS THE LEARNING OF THE STUDENTS. EVALUATION IN A HIGHER EDUCATION CONTEXT IMPLIES BOTH THE ASSESSMENT AND THE GRADING OF THE WORK STUDENTS HAVE DONE. HOWEVER, DEFINING A GOOD EVALUATION PROCESS IS A HARD TASK WHAT MOTIVATES THAT MANY TIMES THE FORMATIVE ASPECT OF THE EVALUATION IS DISREGARDED AND ONLY THE GRADING IS TAKEN INTO ACCOUNT. IN THIS PAPER, WE PRESENT A METHODOLOGY TO HELP TEACHERS DEFINE APPROPRIATE FORMATIVE EVALUATION PROCESSES AND A TOOL TO SUPPORT FOLLOWING THE METHODOLOGY. THE METHODOLOGY HAS BEEN USED DURING ONE ACADEMIC YEAR TO DEFINE THE EVALUATION OF FOUR COURSES. THE OBTAINED RESULTS HAVE BEEN VERY POSITIVE AND IT ENCOURAGES US TO CONTINUE APPLYING THE METHODOLOGY IN NEW COURSES AND WITH MORE TEACHERS.
CURRENTLY THERE IS A PUSH TOWARD OFFERING MORE LANGUAGE COURSES ONLINE BECAUSE THEY CAN PROVIDE STUDENTS WITH NEW FORMS OF SOCIAL AND LEARNING INTERACTION, WIDEN THEIR ACCESS TO EDUCATION, AND OFFER AN INDIVIDUALIZED LEARNING EXPERIENCE IN LARGE CLASSES. LITTLE RESEARCH EXISTS EXAMINING HOW STUDENTS TRANSITION BETWEEN ONLINE AND ON-CAMPUS LANGUAGE COURSES AND WHAT EFFECT THIS HAS ON STUDENTS' ACADEMIC SUCCESS. WE ANALYZED STUDENT DATA FROM LANGUAGE COURSES WITH ONLINE AND ON-CAMPUS COUNTERPARTS, TO BETTER UNDERSTAND THE EXTENT TO WHICH ONLINE LEARNING ENABLES STUDENTS TO MEET THEIR INTENDED LEARNING OUTCOMES. OUR GOALS WERE TO ESTABLISH HOW THE MEDIUM OF LEARNING (ONLINE VS. CLASSROOM) IMPACTS STUDENTS' ACADEMIC SUCCESS, TO IDENTIFY PATTERNS IN THE STUDENTS' TRANSITIONING BETWEEN ONLINE AND ON-CAMPUS COURSES, AND TO GATHER EVIDENCE-BASED INFORMATION ABOUT STUDENTS' COURSE CHOICES AND THEIR DECISIONS ABOUT ONLINE VS. ON-CAMPUS. IN ADDITION TO THE COMPREHENSIVE STATISTICAL ANALYSIS OF LEARNER DATA, WE CONDUCTED A QUALITATIVE ANALYSIS OF LANGUAGE BIOGRAPHY SURVEYS AND SEMI-STRUCTURED INTERVIEWS WITH STUDENTS CURRENTLY ENROLLED IN THE GERMAN PROGRAM. WHILE THE STATISTICAL DATA PROVIDED A BIRDS-EYE VIEW OF STUDENT TRAJECTORIES OVER 10YEARS, THE SURVEYS AND INTERVIEWS GAVE US IN-DEPTH INFORMATION ABOUT INDIVIDUAL LEARNING TRAJECTORIES AND STUDENTS' CURRICULAR DECISIONS.
AN IMPORTANT, YET LARGELY UNSTUDIED, PROBLEM IN STUDENT DATA ANALYSIS IS TO DETECT MISCONCEPTIONS FROM STUDENTS' RESPONSES TO OPEN-RESPONSE QUESTIONS. MISCONCEPTION DETECTION ENABLES INSTRUCTORS TO DELIVER MORE TARGETED FEEDBACK ON THE MISCONCEPTIONS EXHIBITED BY MANY STUDENTS IN THEIR CLASS, THUS IMPROVING THE QUALITY OF INSTRUCTION. IN THIS PAPER, WE PROPOSE A NEW NATURAL LANGUAGE PROCESSING (NLP) FRAMEWORK TO DETECT THE COMMON MISCONCEPTIONS AMONG STUDENTS' TEXTUAL RESPONSES TO OPEN-RESPONSE, SHORT-ANSWER QUESTIONS. WE INTRODUCE A PROBABILISTIC MODEL FOR STUDENTS' TEXTUAL RESPONSES INVOLVING MISCONCEPTIONS AND EXPERIMENTALLY VALIDATE IT ON A REAL-WORLD STUDENT-RESPONSE DATASET. PRELIMINARY EXPERIMENTAL RESULTS SHOW THAT OUR PROPOSED FRAMEWORK EXCELS AT CLASSIFYING WHETHER A RESPONSE EXHIBITS ONE OR MORE MISCONCEPTIONS. MORE IMPORTANTLY, IT CAN ALSO AUTOMATICALLY DETECT THE COMMON MISCONCEPTIONS EXHIBITED ACROSS RESPONSES FROM MULTIPLE STUDENTS TO MULTIPLE QUESTIONS; THIS IS ESPECIALLY IMPORTANT AT LARGE SCALE, SINCE INSTRUCTORS WILL NO LONGER NEED TO MANUALLY SPECIFY ALL POSSIBLE MISCONCEPTIONS THAT STUDENTS MIGHT EXHIBIT.
EDUCATIONAL GAMES (AKA SERIOUS GAMES, SG) ARE POWERFUL EDUCATIONAL CONTENTS. HOWEVER, THEY ARE COSTLY TO DEVELOP, AND ONCE DEVELOPED, SGS BECOME DEPENDENT ON SOFTWARE AND HARDWARE COMBINATIONS THAT MAY BECOME OBSOLETE, SUCH AS ADOBE FLASH OR JAVA APPLETS. ADDRESSING THESE PROBLEMS WOULD ALLOW A MUCH GREATER USE OF SGS IN EDUCATION. THE EADVENTURE AUTHORING TOOL, DEVELOPED BY THE E-UCM RESEARCH GROUP, ADDRESSED HIGH DEVELOPMENT COSTS, AND RESULTED IN THE CREATION OF MULTIPLE SGS IN COLLABORATION WITH DIFFERENT INSTITUTIONS. HOWEVER, EADVENTURE'S JAVA APPLETS HAVE BECOME INCREASINGLY DIFFICULT TO RUN DUE TO PLATFORM OBSOLESCENCE. TO MAINTAIN THE BENEFITS OF THE EADVENTURE PLATFORM AND USER BASE, WE HAVE CREATED NEW PLATFORM CALLED UADVENTURE: AN SG EDITOR BUILT ON TOP OF THE GAME ENGINE UNITY THAT ALLOWS FOR THE CREATION OF EDUCATIONAL ADVENTURE GAMES WITHOUT REQUIRING PROGRAMMING. SINCE UNITY IS SUPPORTED ON A MAJORITY OF PLATFORMS (INCLUDING MOBILE). BY DEVELOPING SGS WITH UADVENTURE, THE GAMES BECOME FUTURE-PROOF, AS THEY CAN BE UPDATED AND RETARGETED FOR NEW PLATFORMS AS REQUIRED. IN THIS SENSE, UADVENTURE IMPROVES THE LIFECYCLE OF SGS BY REDUCING BOTH AUTHORING AND MAINTENANCE COSTS.
GENERATING QUESTIONS TO ENGAGE AND MEASURE STUDENTS IS OFTEN CHALLENGING AND TIME-CONSUMING. FURTHERMORE, THESE QUESTIONS DO NOT ALWAYS TRANSFER WELL BETWEEN STUDENT POPULATIONS DUE TO DIFFERENCES IN BACKGROUND, COURSE EMPHASIS, OR AMBIGUITY IN THE QUESTIONS OR ANSWERS. WE INTRODUCE A CONTRIBUTING STUDENT PEDAGOGY ACTIVITY FACILITATED BY MACHINE LEARNING THAT CAN GENERATE QUESTIONS WITH ASSOCIATED ANSWER-REASONING SETS. WE CALL THIS PROCESS ADAPTIVE TOOL-DRIVEN CONCEPTION GENERATION. A TOOL IMPLEMENTING THIS PROCESS HAS BEEN DEPLOYED, AND IT EXPLICITLY OPTIMIZES THE PROCESS FOR QUESTIONS THAT DIVIDE STUDENT OPINION. IN A STUDY INVOLVING ARRAYS IN JAVA, THIS NOVEL PROCESS: GENERATES QUESTIONS SIMILAR TO EXPERT-DESIGNED QUESTIONS, PRODUCES NOVEL QUESTIONS THAT IDENTIFY POTENTIAL STUDENT MISCONCEPTIONS, AND PROVIDES STATISTICAL ESTIMATES OF THE PREVALENCE OF MISCONCEPTIONS. THIS PROCESS ALLOWS THE GENERATION OF QUIZ AND DISCUSSION QUESTIONS WITH LESS EXPERT EFFORT, FACILITATES A SUBPROCESS IN THE CREATION OF CONCEPT INVENTORIES, AND ALSO RAISES THE POSSIBILITY OF RUNNING REPRODUCTION STUDIES RELATIVELY CHEAPLY.
TEACHING ACTIVITIES HAVE NOWADAYS BEEN SUPPORTED BY A VARIETY OF ELECTRONIC DEVICES. FORMATIVE ASSESSMENT TOOLS ALLOW TEACHERS TO EVALUATE THE LEVEL OF UNDERSTANDING OF LEARNERS DURING FRONTAL LESSONS AND TO TAILOR THE NEXT TEACHING ACTIVITIES ACCORDINGLY. DESPITE PLENTY OF TEACHING MATERIALS ARE AVAILABLE IN THE TEXTUAL FORM, MANUALLY EXPLORING THESE VERY LARGE COLLECTIONS OF DOCUMENTS CAN BE EXTREMELY TIME-CONSUMING. THE ANALYSIS OF LEARNER-PRODUCED DATA (E.G., TEST OUTCOMES) CAN BE EXPLOITED TO RECOMMEND SHORT EXTRACTS OF TEACHING DOCUMENTS BASED ON THE ACTUAL LEARNER'S NEEDS. THIS PAPER PROPOSES A NEW METHODOLOGY TO RECOMMEND SUMMARIES OF POTENTIALLY LARGE TEACHING DOCUMENTS. SUMMARY RECOMMENDATIONS ARE CUSTOMIZED TO STUDENT'S NEEDS ACCORDING TO THE RESULTS OF COMPREHENSION TESTS PERFORMED AT THE END OF FRONTAL LECTURES. SPECIFICALLY, STUDENTS UNDERGO MULTIPLE-CHOICE TESTS THROUGH A MOBILE APPLICATION. IN PARALLEL, A SET OF TOPIC-SPECIFIC SUMMARIES OF THE TEACHING DOCUMENTS IS GENERATED. THEY CONSIST OF THE MOST SIGNIFICANT SENTENCES RELATED TO A SPECIFIC TOPIC. ACCORDING TO THE RESULTS OF THE TESTS, SUMMARIES ARE PERSONALLY RECOMMENDED TO STUDENTS. WE ASSESSED THE APPLICABILITY OF THE PROPOSED APPROACH IN REAL CONTEXT, I.E., A B.S. UNIVERSITY-LEVEL COURSE. THE RESULTS ACHIEVED IN THE EXPERIMENTAL EVALUATION CONFIRMED ITS USABILITY.
IN THIS WORK-IN-PROGRESS REPORT, WE OUTLINE THE FIRST RESULTS FROM OUR STUDY OF COLLABORATION NETWORKS IN A TWO-YEAR DATA SCIENCE MINOR FOR NON-STEM STUDENTS AT THE HIGHER SCHOOL OF ECONOMICS, ST. PETERSBURG, RUSSIA.
THE MONITORING PROCESS AND EVALUATION OF AN OFFICIAL HIGHER EDUCATION DEGREE OR MASTER IS PERFORMED BY AN ANNUAL REPORT BASED ON A SET OF REQUIREMENTS DEFINED BY THE QUALITY AGENCIES WHICH ARE RESPONSIBLE TO EVALUATE SUCH QUALITY. TAKING INTO ACCOUNT THAT THESE REPORTS ARE MADE UP BY A SET OF EVIDENCES THAT ARE GATHERED DURING THE WHOLE INSTRUCTIONAL PROCESS, WE PROPOSE THE USE OF A SUPPORT TOOL. THIS TOOL WILL ALLOW THE IMPROVEMENT OF THE EFFICIENCY OF THE PROCESS AND WILL FACILITATE THE QUALITY EVALUATION. THIS PAPER INTRODUCES THE COLLABORATIVE EVALUATION PROCESS APPLIED IN OUR FACULTY AND HOW THE SUPPORTIVE TOOL HELPS TO ENHANCE THE EVALUATION PROCESS.
WE PRESENT THE LEADING EXPERIMENT THAT HELPS DESIGNING THE RHYTHM NETWORK. THE MODEL DIVERGES FROM TYPICAL TIME GRAPHS AND IS NOT EXPLICITLY HELPFUL FOR LONGITUDINAL STUDIES. HOWEVER, THE OBJECTIVE IS TO DESIGN SEQUENCES OF EVENTS, INSERTED INTO A GRAPH DEPENDING ON THEIR PLACE ON A TIMELINE. THIS EXPERIMENT SHOWS HOW THE MODEL CAN HELP ENGINEERING COURSES IN AN E-LEARNING ENVIRONMENT. RHYTHM IS FUNDAMENTAL IN MANY CONTEXTS. IT SAYS A LOT ABOUT THE QUALITY OF TEACHING AND LEARNING. THE FEATURE MIGHT GIVE IMPORTANT INSIGHT ON HOW LEARNERS INTERACT WITH KNOWLEDGE.
IN THIS PAPER, WE ATTEMPTED TO ANALYZE LEARNERS' MOTIVATION BASED ON PEER ASSESSMENT PROCESS LOGS. IN MANY PREVIOUS STUDIES, THE RELIABILITY OR VALIDITY OF PEER ASSESSMENT WAS DISCUSSED BASED ON THE STUDENTS' PEER ASSESSMENT SCORE. IN CONTRAST, WE HAVE DISCUSSED THE NECESSITY OF ANALYZING EVALUATION- TIMING AND TEMPORAL BEHAVIOR IN THE ASSESSMENT PROCESSES. WE CONDUCTED AN EXPERIMENT AND THE FOLLOWING FEATURE VARIABLES, SUCH AS " EVALUATION TIME" OR " STANDARD DEVIATION OF THE SCORES OF THE EVALUATION ITEMS", WERE CALCULATED FROM THE COLLECTED LOG DATA. WE ANALYZED THE RELATIONSHIPS AMONG THESE FEATURE VARIABLES. AS A RESULT, WE FOUND SOME POSITIVE AND NEGATIVE CORRELATIONS AMONG THEM. FURTHERMORE, WHEN CONDUCTING CONTINUOUS EVALUATIONS DURING A SINGLE LESSON, THESE TRENDS WERE MORE NOTICEABLE IN THE EVALUATION AT THE END OF THE LESSON THAN AT THE BEGINNING OF THE LESSON. WE EXPECT THAT THE CHARACTERISTICS OF THE EVALUATION BEHAVIOR WOULD BE USEFUL AS INDICATORS OF THE LACK OF MOTIVATION IN TERMS OF SERIOUS EVALUATION IN PEER ASSESSMENT EVALUATIONS.
PROGRAMMING EXERCISES ARE TIME-CONSUMING ACTIVITIES FOR MANY STUDENTS. THEREFORE, MOST CLASSES PROVIDE METICULOUS SUPPORTS FOR STUDENTS BY EMPLOYING TEACHING ASSISTANTS (TAS). HOWEVER, THE PROGRAMMING BEHAVIORS OF A PARTICULAR STUDENT ARE QUITE DIFFERENT FROM OTHER STUDENTS' BEHAVIOR, EVEN THOUGH THEY ARE SOLVING THE SAME PROBLEM. IT IS HARD FOR TAS TO UNDERSTAND THE DETAILED FEATURES OF EACH STUDENT'S PROGRAMMING BEHAVIOR. WE HAVE PERFORMED DATA MINING OVER THE RECORDS OF STUDENTS' PROGRAMMING BEHAVIORS IN ORDER TO ELICIT THE DETAILED FEATURES OF EACH STUDENT'S PROGRAMMING BEHAVIOR. THE PURPOSE OF THIS STUDY IS TO PRESENT THE ELICITED SUCH FEATURES FOR TAS SO THAT THEY CAN PROVIDE EFFECTIVE ASSISTANCES. WE HAVE PERFORMED DATA MINING OVER THE CHRONOLOGICAL RECORDS OF THE COMPILATION AND EXECUTION OF INDIVIDUAL STUDENTS. AS A RESULT, WE HAVE FOUND THAT THERE IS A CORRELATION BETWEEN THE PROGRAMMING ACTIVITIES AND THE DURATION TIME FOR PROBLEM SOLVING. BASED ON THE DATA MINING, WE HAVE PROVIDED TAS SOME GUIDELINES FOR EACH PARTICULAR GROUP OF STUDENTS. WE HAVE CONFIRMED THAT OUR CLASSIFICATIONS AND GUIDELINES ARE REASONABLE THROUGH EXPERIMENTS OVER PROGRAMMING EXERCISES. WE HAVE OBSERVED STUDENTS WHO RECEIVED APPROPRIATE GUIDANCE BASED ON OUR DATA MINING IMPROVED THEIR PROGRAMMING PERFORMANCES.
THIS ARTICLE PROPOSES A TRANSFERABLE MODEL FOR ONLINE LEARNING, BASED ON THE PRINCIPLES OF SOCIAL CONSTRUCTIVISM. THE FINDINGS HAVE BEEN COLLECTED THROUGH 'END-OF-COURSE FEEDBACK SURVEYS', AND CONCERN A STUDENT-SAMPLE OF 170 NATIONALITIES, RESIDING IN 154 COUNTRIES. ALL STUDENTS WERE REGISTERED IN COHORTS OF THE SAME POSTGRADUATE PROGRAMME OVER A PERIOD OF FOUR YEARS. THE FINDINGS THROW LIGHT ON LEARNERS' DIFFICULTIES IN DIFFERENT PARTS OF THE WORLD; ALONG WITH PRACTICAL IMPLICATIONS FOR UNIVERSITY DECISION-MAKERS AND INSTRUCTORS. THE ARTICLE'S CONTRIBUTION IS ENCAPSULATED IN AN EIGHT-PILLAR LEARNING MODEL, AIMING TO FACILITATE EDUCATIONAL INCLUSION, QUALITY ENHANCEMENT, AND PROMOTE THE BENEFIT OF LEARNERS.
LITERATURE IN THE AREA OF PSYCHOLOGY AND EDUCATION PROVIDES DOMAIN KNOWLEDGE TO LEARNING APPLICATIONS. THIS WORK DETECTS THE DIFFICULTY LEVELS WITHIN A SET OF MULTIPLICATION PROBLEMS AND ANALYSES THE DATASET ON DIFFERENT ERROR TYPES AS DESCRIBED AND DETERMINED IN SEVERAL PEDAGOGICAL SURVEYS AND INVESTIGATIONS. OUR RESEARCH SHEDS LIGHT TO THE IMPACT OF EACH ERROR TYPE IN SIMPLE MULTIPLICATION PROBLEMS AND THE EVOLUTION OF ERROR RATES FOR DIFFERENT ERROR TYPES IN RELATION TO THE INCREASING PROBLEM-SIZE.
LEARNING DESIGN APPROACHES, SUCH AS THOSE ADOPTED BY THE OPEN UNIVERSITY, PROVIDE A SET OF TOOLS AND RESOURCES FOR PURPOSEFULLY-DESIGNING MODULES WITH A FOCUS ON STUDENT EXPERIENCES. HOWEVER, MANY OF THE CURRENT LEARNING DESIGN STRATEGIES HAVE BEEN SITUATED WITHIN SPECIFIC INSTITUTIONS IN EUROPE AND NORTH AMERICA. THIS MEANS THAT THERE ARE SEVERAL ISSUES WORTH CONSIDERING AROUND IF AND HOW ESTABLISHED LEARNING DESIGN APPROACHES MAKE SENSE IN DIVERSE INSTITUTIONAL AND CULTURAL CONTEXTS. TO CRITICALLY ASSESS THE RELEVANCE AND APPROPRIATENESS OF LEARNING DESIGN STRATEGIES IN NEW CONTEXTS, THIS ARTICLE DESCRIBES AN IN-DEPTH PARTICIPATORY WORKSHOP WITH 34 EDUCATION PROFESSIONALS FROM FIVE AFRICAN COUNTRIES. ALTOGETHER, 10 SUGGESTIONS FOR LEARNING DESIGN PRACTICES WERE DERIVED FROM THE CONSENSUS OF WORKSHOP PARTICIPANTS, WHICH PROVIDE A FOUNDATION FOR THE DEVELOPMENT OF LEARNING DESIGN PRACTICES MOVING FORWARD.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BECOME AN IMPORTANT ONLINE LEARNING TOOL FOR EDUCATORS AND LEARNERS, BUT ONE OF THE MAJOR ISSUES ARE THE HIGH DROP-OUT RATES. RECENT RESEARCH SUGGESTS NOT ONLY TO IDENTIFY AND SUPPORT LEARNERS AT-RISK TO DROP-OUT BUT ALSO TO DIFFERENTIATE BETWEEN THE GROUP OF HEALTHY ATTRITION (INTENTIONALLY LEAVING THE MOOC) AND UNHEALTHY ATTRITION (STRUGGLING TO COMPLETE THE MOOC). IN THIS PAPER, WE FOCUS ON TWO RESEARCH QUESTIONS: FIRSTLY, CAN WE ALREADY IDENTIFY LEARNERS AT-RISK TO DROP-OUT A MOOC IN AN EARLY STAGE? SECONDLY, CAN WE DIFFERENTIATE BETWEEN THE GROUP OF HEALTHY ATTRITION AND UNHEALTHY ATTRITION? EXPERIMENTATION WITH SUPPORT VECTOR MACHINES BASED ON LEARNERS LOGS FROM ELEVEN MOOCS ON THE TELESCOPE PLATFORM SHOW FIRST PROMISING RESULTS.
ONE-DIGIT MULTIPLICATION PROBLEMS IS ONE OF THE MAJOR FIELDS IN LEARNING MATHEMATICS AT THE LEVEL OF PRIMARY SCHOOL THAT HAS BEEN STUDIED OVER AND OVER. HOWEVER, THE MAJORITY OF RELATED WORK IS FOCUSING ON DESCRIPTIVE STATISTICS ON DATA FROM MULTIPLE SURVEYS. THE GOAL OF OUR RESEARCH IS TO GAIN INSIGHTS INTO MULTIPLICATION MISCONCEPTIONS BY APPLYING MACHINE LEARNING TECHNIQUES. TO REACH THIS GOAL, WE TRAINED A PROBABILISTIC GRAPHICAL MODEL OF THE STUDENTS' MISCONCEPTIONS FROM DATA OF AN APPLICATION FOR LEARNING MULTIPLICATION. THE USE OF THIS MODEL FACILITATES THE EXPLORATION OF INSIGHTS INTO HUMAN LEARNING COMPETENCE AND THE PERSONALIZATION OF TUTORING ACCORDING TO INDIVIDUAL LEARNER'S KNOWLEDGE STATES. THE DETECTION OF ALL RELEVANT CAUSAL FACTORS OF THE ERRONEOUS STUDENTS ANSWERS AS WELL AS THEIR CORRESPONDING RELATIVE WEIGHT IS A VALUABLE INSIGHT FOR TEACHERS. FURTHERMORE, THE SIMILARITY BETWEEN DIFFERENT MULTIPLICATION PROBLEMS - ACCORDING TO THE STUDENTS BEHAVIOR - IS QUANTIFIED AND USED FOR THEIR GROUPING INTO CLUSTERS. OVERALL, THE PROPOSED MODEL FACILITATES REAL-TIME LEARNING INSIGHTS THAT LEAD TO MORE INFORMED DECISIONS.
WITH THE INCREASE IN ENROLMENT FIGURES FROM SECOND LEVEL EDUCATION TO THIRD LEVEL EDUCATION OVER THE LAST NUMBER OF DECADES, NON-PROGRESSION RATES CONTINUE TO GIVE CAUSE FOR CONCERN IN CERTAIN LEVELS AND DISCIPLINES. IT HAS BEEN WIDELY ARGUED THAT IN ADDITION TO INCREASING ENROLMENT NUMBERS, HIGHER EDUCATION MUST ALSO BE CONCERNED WITH THE SUCCESS OF THESE STUDENTS. IN BOTH THE IRISH AND THE INTERNATIONAL SECTOR, THE NEGATIVE CONSEQUENCES OF NON-PROGRESSION HAS BEEN HIGHLIGHTED, NOT JUST ON A SOCIETAL LEVEL, BUT ALSO FOR THE STUDENTS THEMSELVES. IT IS CRUCIAL FOR FIRST-YEAR STUDENT EXPERIENCE TO HAVE A POSITIVE EXPERIENCE AND BE FULLY SUPPORTED IN ACHIEVING THE GOALS OF HIGHER EDUCATION. FROM RESEARCHING SEVERAL REPORTS IN THE AREA OF RETENTION AND IN PARTICULAR THE REPORTS PUBLISHED BY THE IRISH HIGHER EDUCATION AUTHORITY AND THE NATIONAL FORUM FOR THE ENHANCEMENT OF TEACHING AND LEARNING IN HIGHER EDUCATION IN THIS AREA, IT IS CLEAR THAT THERE IS A NEED TO ANALYSE THE DATA AVAILABLE AND PRESENT THE FINDINGS IN A CLEAR WAY TO THE KEY DECISION MAKERS TO ALLOW FOR EARLY INTERVENTION. THIS PAPER USES THE DIFFERENT PHASES OF THE CRISP-DM METHODOLOGY AND APPLIES DATA MINING TECHNIQUES AND MODELS TO A REAL STUDENT DATASET WITH THE AIM TO PREDICT THE STUDENTS THAT WILL PROGRESS.
ABERTAY IS A RELATIVELY SMALL, MODERN UNIVERSITY (UNDERGRADUATE POPULATION OF AROUND 4000) WITH A WIDE PORTFOLIO AND A DIVERSE STUDENT POPULATION. AROUND 35% OF OUR STUDENTS ARE DIRECT ENTRY FROM LOCAL PARTNER COLLEGES TO YEARS 2 AND 3 OF OUR PROGRAMMES AND A SIGNIFICANT NUMBER ARE FIRST GENERATION HIGHER EDUCATION WITHIN THEIR FAMILIES. AS SUCH, PARTNERSHIP WORKING WITH COLLEGES AND SUPPORT TO AID STUDENT TRANSITIONS ARE KEY ASPECTS OF ABERTAY'S PROVISION. SINCE 2013/14, THE UNIVERSITY HAS DEVELOPED AND IMPLEMENTED A NEW TEACHING AND LEARNING ENHANCEMENT STRATEGY THAT HAS CATALYSED WHOLESCALE TRANSFORMATION ACROSS THE INSTITUTION. THIS PAPER PROVIDES AN OVERVIEW OF TECHNOLOGY ENHANCED LEARNING AT THE UNIVERSITY WITH THE DRIVERS FOR CHANGE BEING TO THE QUALITY OF OUR STUDENTS' LEARNING EXPERIENCE, IMPROVE STUDENT RETENTION AND PROGRESSION AND ENHANCE LEARNERS' ENGAGEMENT.
TEACHERS FREQUENTLY MAKE ERRORS WHEN ASSESSING MUSIC STUDENTS. WE PROPOSE A MACHINE LEARNING APPLICATION THAT, GIVEN TWO PERFORMANCES OF A PIECE OF MUSIC, DETERMINES WHICH PERFORMANCE IS BETTER, PROVIDING AN OBJECTIVE AND ACCURATE ASSESSMENT OF PROGRESS. SEVERAL FEATURES ARE EXTRACTED FROM PERFORMANCES USING MUSIC ANALYSIS ALGORITHMS, CREATING A VECTOR OF FEATURES FOR EACH PERFORMANCE. THE VECTORS FROM TWO PERFORMANCES OF A PIECE OF MUSIC ARE SUBTRACTED FROM EACH OTHER, AND THIS VECTOR OF DIFFERENCES IS INPUT TO A MACHINE LEARNING CLASSIFIER WHICH MAPS THE VECTOR TO AN ASSESSMENT OF PROGRESS. THE IMPLEMENTATION DEMONSTRATES THAT SUCH A TOOL IS FEASIBLE.
TEXT MINING HAS BEEN USED FOR SCIENTIFIC TEXT ANALYSIS IN ORDER TO IDENTIFY ASPECTS AS THE MAIN SUBJECT OF A TEXT, CONCEPTS THAT APPEAR AND HOW THEY ARE USED, AS WELL AS THE DIFFERENT POSITIONS ON A SUBJECT, AMONG OTHERS. IN THIS WORK, IT WAS PROPOSED TO USE TEXT MINING TO DEVELOP A WEB SYSTEM THAT ALLOWS AUTOMATIC ANALYSIS OF THE ANSWERS TO A QUESTIONNAIRE THAT REQUIRED WRITTEN EXPLANATIONS. THE EVALUATION INSTRUMENT USED WAS A QUESTIONNAIRE DESIGNED TO IDENTIFY THE CONCEPTUAL UNDERSTANDING OF HIGH SCHOOL STUDENTS ABOUT MENDELIAN GENETICS. THE GENERATED WEB SYSTEM IDENTIFIES, IN THE STUDENTS' ANSWERS, THE FREQUENCY OF THE TERMS USED AND THE CONNECTIONS BETWEEN THEM TO DEFINE A CONCEPT OR PROCESS. DIFFERENT LINES OF INVESTIGATION OF COMPUTATIONAL LINGUISTICS WERE CONSIDERED FOR THE DEVELOPMENT OF THE SYSTEM, IN PARTICULAR FOR THE ANALYSIS OF WRITTEN TEXTS, AND THUS TO BE ABLE TO DETERMINE THE SPECIFIC PROCEDURE FOR THE ANALYSIS OF STUDENT ANSWERS. THE BUILT WEB APPLICATION WAS USEFUL, SINCE IT SUPPORTED RESEARCHERS WITH THE ANALYSIS OF CONCEPTS AND RELATIONSHIPS BETWEEN FREQUENT CONCEPTS, WHICH ALLOWS TO ASSIGN AN AUTOMATIC GRADE, WHICH IS RELATED TO THE LEVEL OF CONCEPTUAL UNDERSTANDING ACHIEVED BY THE STUDENTS.
USE OF LEARNING MANAGEMENT SYSTEM (LMS) IS VERY COMMON, WHICH PROVIDE SUPPORT TO TEACHING STAFF FOR COMMUNICATION, DELIVERY OF RESOURCES AND IN DESIGN OF LEARNING ACTIVITIES. THE WIDE SPREAD USE OF TECHNOLOGIES LIKE LMS, PROVIDE LARGE AMOUNT OF DATA. RESEARCH SHOWS THAT HIGHER EDUCATION INSTITUTES CAN MAKE USE OF THIS DATA TO EXTRACT DATA-DRIVEN INSIGHTS TO UNDERSTAND THE LEARNING PROCESS AND BENEFIT THE STUDENTS BY SUPPORTING THEM IN THEIR ACADEMICS. IN THIS STUDY WE USED SEVERAL MACHINE LEARNING ALGORITHMS TO PREDICT STUDENT'S OUTCOME IN A COURSE USING LMS TRACE DATA AND ASSESSMENT SCORES. SELECTION OF THE COURSES IS BASED ON THE EXTENT THE LMS IS USED AND IS DIVIDED INTO TWO CATEGORIES; DISTANCE AND INTERNAL. THIS STUDY CONFIRMS THE IMPORTANCE OF LMS DATA AND ASSESSMENT SCORES IN THE PREDICTION OF ACADEMIC PERFORMANCE. HOWEVER, FREQUENT USE OF LMS MAY INCREASE THE TRACE DATA BUT IT IS NOT NECESSARY IMPROVE THE PREDICTIVE ACCURACY. PREDICTIVE MODELS DEVELOPED FOR COURSES, WITHOUT CONSIDERING THE CONTEXT OF USE OF LMS DATA, MAY NOT GENERALIZE THE EFFECTS OF LMS TRACE DATA ON STUDENT'S OUTCOME IN THE COURSE.
IN RECENT YEARS, ALONG WITH THE PROLIFERATION OF THE LEARNING MANAGEMENT SYSTEM (LMS), A LARGE AMOUNT OF DATA REGARDING THE INTERACTION BETWEEN THE SYSTEM AND THE LEARNERS HAS BEEN ACCUMULATED. CORRESPONDINGLY, VARIOUS DATA MINING METHODS HAVE BEEN APPLIED TO THESE DATA. IN ORDER TO EMPLOY A SUITABLE COMPUTATIONAL MODEL THAT IS AT THE CORE OF THE DATA MINING METHOD AND IS NOT AUTOMATICALLY ACQUIRED BY THE MINING METHOD ITSELF, IT IS IMPORTANT TO MAKE OR FIND VARIOUS REASONABLE HYPOTHESES FOR TARGET VARIABLES. IN THIS PAPER, WE PROPOSE A METHOD FOR ANALYZING CLOSELY THE DEGREE TO WHICH THE EXPLANATORY VARIABLES REPRESENTED IN INTEGER VALUE CONTRIBUTES TO PREDICTING CATEGORICAL OBJECTIVE VARIABLES, SUCH AS A LEARNER'S ACADEMIC PERFORMANCE. SPECIFICALLY, WE DESCRIBE THAT A DECISION TREE COMBINING SUPPORT VECTOR MACHINES (SVM) ACHIEVES ACCURACY CONSISTENT WITH EXISTING RESEARCH, AND IT CONTRIBUTES FURTHER EXTRACTION OF PARTICULAR EXPLANATORY VALUES FROM THE INTEGER FEATURES. BEFORE MAKING A MODEL WITH SVM, OUR PROPOSAL METHOD EXPANDS ORIGINAL FEATURES REPRESENTED BY INTEGER VALUE TO CORRESPONDING BINARY FEATURES. WITH THIS EXPANSION OF ORIGINAL FEATURES, WE CAN IDENTIFY THE KEY VALUES THAT CLOSELY RELATE TO A LEARNER'S ACADEMIC PERFORMANCE FROM BEHAVIORAL FEATURES GATHERED FROM LMS. IDENTIFYING SUCH KEY VALUES IN SPECIFIC FEATURES PLAYS AN IMPORTANT ROLE IN DEVELOPING A HYPOTHESIS THAT EXPLAINS THE OBJECTIVE VARIABLES, USING THEM AS EXPLANATORY VARIABLES. WE BELIEVE THAT CLOSER ANALYSIS OF THESE KEY EXPLANATORY VALUES WILL FIND LATENT KNOWLEDGE THAT CAN IMPROVE LEARNERS' ACADEMIC ABILITIES.
OVER TWO ITERATIONS OF A MASSIVE OPEN ONLINE COURSE (MOOC) FOR SCHOOL LEADERS, LAUNCHING INNOVATION IN SCHOOLS, WE DEVELOPED AND TESTED DESIGN ELEMENTS TO SUPPORT THE TRANSFER OF ONLINE LEARNING INTO OFFLINE ACTION. EFFECTIVE PROFESSIONAL LEARNING IS JOB-EMBEDDED: LEARNERS SHOULD EMPLOY NEWS SKILLS AND KNOWLEDGE AT WORK AS PART OF THEIR LEARNING EXPERIENCE. THIS MOOC AIMED TO GET PARTICIPANTS TO PLAN AND ACTUALLY LAUNCH NEW CHANGE EFFORTS, AND A SUBSET OF OUR MOST ENGAGED PARTICIPANTS WERE ABLE AND WILLING TO DO SO DURING THE COURSE. REQUIRED ASSESSMENTS SPURRED STUDENT ACTIONS, ALONG WITH INSTRUCTOR CALLS TO ACTION AND MODELING AND EXEMPLARS PROVIDED BY COURSE ELEMENTS. WE FOUND THAT PARTICIPANTS LED CHANGE INITIATIVES, HELD STAKEHOLDER MEETINGS, COLLECTED NEW DATA ABOUT THEIR CONTEXTS, AND SHARED AND USED COURSE MATERIALS COLLABORATIVELY. COLLECTING DATA ABOUT PARTICIPANT LEARNING AND BEHAVIOR OUTSIDE THE MOOC ENVIRONMENT IS ESSENTIAL FOR RESEARCHERS AND DESIGNERS LOOKING TO CREATE EFFECTIVE ONLINE ENVIRONMENTS FOR PROFESSIONAL LEARNING.
DISCUSSION FORUMS ARE THE PRIMARY AND MOST WIDELY USED VENUE FOR SOCIAL LEARNING AND COMMUNICATION IN MOOC CONTEXT. THERE HAS BEEN AN INTEREST IN THE LAST FEW YEARS TO STUDY THE INTERACTIONS AND PARTICIPATION BEHAVIOR IN THIS ENVIRONMENT. THIS SYSTEMATIC REVIEW STUDIED EIGHTY-FOUR PAPERS PUBLISHED BETWEEN 2013-2017 TO PROVIDE A STATE OF THE ART, AND SUGGEST FUTURE DIRECTIONS IN THE LITERATURE FROM TWO PERSPECTIVES: DESCRIPTIVE ANALYSIS AND CONTENT ANALYSIS. THE DESCRIPTIVE ANALYSIS AGGREGATES THE REVIEWED PAPERS INTO FIVE DIMENSIONS: TIME OF THE PUBLICATION, STAKEHOLDERS INVOLVED IN THE ANALYSIS, OBJECTIVES, METHODS, AND DATA SOURCES USED IN THE STUDIES. THE CONTENT ANALYSIS AIMS TO HIGHLIGHT THE MAIN ISSUES ADDRESSED AND THE MAJOR CONTRIBUTIONS OF THE PAPERS COVERED BY THE LITERATURE REVIEW. THESE CONTRIBUTIONS CAN BE CATEGORIZED INTO THREE MAJOR AREAS, FIRST, EXPLORE THE RELATIONSHIP BETWEEN FORUMS PARTICIPATION AND RETENTION OR LEARNING OUTCOMES AND EVALUATE WAYS TO INCREASE THE PARTICIPATION ACTIVITY AND LEARNING OUTCOMES, SECOND, ANALYZE PARTICIPANTS' CONTRIBUTION IN THE FORUM AND HOW TO ORGANIZE THE CONTENT AND EFFICIENTLY MONITOR THEM, FINALLY, ANALYZE PARTICIPANTS' INTERACTIONS AND HOW IT INFLUENCES LEARNING. AT THE END, WE OFFER SOME SUGGESTIONS TO FURTHER ADVANCE THIS AREA OF RESEARCH.
THIS PAPER DESCRIBES AN APPLICATION OF CLUSTERING AND LABELING METHODS IN ACTUAL LEARNING RECORDS OF E-PORTFOLIOS IMPLEMENTED THROUGH PERSONAL BLOGS. IN THE CLUSTERING PROCESS, WE HAVE USED K-MEANS ALGORITHM WITH EUCLIDIAN DISTANCE AND RANDOM INITIALIZATION OF THE CENTROIDS, USING THE ELBOW METHOD TO DETERMINE THE BEST VALUE OF K; WE USED CENTROID LABELS FOR LABELING. ANALYSIS OF 4,241 INSTANCES OF THE MODEL (POSTS) HAVE SHOWN THAT ABSENCE OF FORMAL DESCRIPTORS WAS OVERCOME THROUGH REPRESENTATION OF THE TITLES GIVEN TO POSTS, SUGGESTING ITS USE FOR AUTOMATIC STRUCTURING OF CONTENTS OF THIS NATURE.
STUDENTS IN AN INTRODUCTORY SCIENCE COURSE OFFERED BOTH FACE-TO-FACE AND ON-LINE AT THE UNIVERSITY OF MICHIGAN USED A WEB APPLICATION THAT ALLOWED THEM TO RESPOND TO QUESTIONS, POSE QUESTIONS AND TAKE NOTES. THE DATA FROM THEIR PARTICIPATION WERE RECORDED, QUANTIFIED AND COMPARED WITH THEIR RESULTING GRADES ON EXAMS TO ASCERTAIN TO WHAT DEGREE MEASURES OF STUDENT PARTICIPATION WERE RELATED TO STUDENT LEARNING. THESE MEASURES OF STUDENT PARTICIPATION WERE AUGMENTED BY DATA FROM THE UNIVERSITY'S STUDENT INFORMATION SYSTEM, WHICH PROVIDED BACKGROUND INFORMATION. DATA WAS ALSO COLLECTED FROM A NUMBER OF SURVEYS ADMINISTERED OVER THE SEMESTER. ANALYSES OF THE DATA REVEALED SOME STRONG RELATIONSHIPS BETWEEN GRADES AND THE DEGREE AND QUALITY OF STUDENT PARTICIPATION. MOREOVER THE BEHAVIOR OF STUDENTS VARIED SIGNIFICANTLY WHEN STRATIFIED BY THEIR INCOMING GPA. THE EMPIRICAL RELATIONSHIPS REPORTED HERE PROVIDE EVIDENCE-BASED AFFIRMATION THAT STUDENT PARTICIPATION IS RELATED TO LEARNING AND SUGGESTS THAT THE STRENGTH OF GPA AS A PREDICTOR OF GRADES MAY BE DUE MORE TO A LACK THE STUDY SKILLS AND/OR MOTIVATION RATHER THAN COGNITIVE ABILITY.
THIS STUDY EXAMINES THE PROBLEM OF THE FRAGMENTATION OF ASYNCHRONOUS ONLINE DISCOURSE BY USING THE KNOWLEDGE CONNECTION ANALYZER (KCA) FRAMEWORK AND TOOLS AND EXPLORES HOW STUDENTS COULD USE THE KCA DATA IN CLASSROOM REFLECTIONS TO DEEPEN THEIR KNOWLEDGE BUILDING (KB) INQUIRY. WE APPLIED THE KCA TO NINE KNOWLEDGE FORUM(R) (KF) DATABASES TO EXAMINE THE FRAMEWORK, IDENTIFY ISSUES WITH ONLINE DISCOURSE THAT MAY INFORM FURTHER DEVELOPMENT, AND PROVIDE DATA ON HOW THE TOOLS WORK. OUR COMPARISONS OF THE KCA DATA SHOWED THAT THE DATABASES WITH MORE SOPHISTICATED TEACHER-RESEARCHER CO-DESIGN HAD HIGHER KCA INDICES THAN THOSE WITH REGULAR KF USE, VALIDATING THE FRAMEWORK. ANALYSIS OF KF DISCOURSE USING THE KCA HELPED IDENTIFY SEVERAL ISSUES INCLUDING LIMITED COLLABORATION AMONG PEERS, UNDERDEVELOPED PRACTICES OF SYNTHESIZING AND RISING ABOVE OF COLLECTIVE IDEAS, LESS ANALYSIS OF CONCEPTUAL DEVELOPMENT OF DISCUSSION THREADS, AND LIMITED COLLABORATIVE REFLECTION ON INDIVIDUAL CONTRIBUTION AND PROMISING INQUIRY DIRECTION. THESE ISSUES THAT OPEN OPPORTUNITIES FOR FURTHER DEVELOPMENT CANNOT BE IDENTIFIED BY OTHER PRESENT ANALYTICS TOOLS. THE EXPLORATORY USE OF THE KCA IN REAL CLASSROOM REVEALED THAT THE KCA CAN SUPPORT STUDENTS' PRODUCTIVE REFLECTIVE ASSESSMENT AND KB. THIS STUDY DISCUSSES THE IMPLICATIONS FOR EXAMINING AND SCAFFOLDING ONLINE DISCUSSIONS USING THE KCA ASSESSMENT FRAMEWORK, WITH A FOCUS ON COLLECTIVE PERSPECTIVES REGARDING COMMUNITY KNOWLEDGE, SYNTHESIS, IDEA IMPROVEMENT, AND CONTRIBUTION TO COMMUNITY UNDERSTANDING.
THIS PAPER DESCRIBES AN AUTOMATIC QUIZ GENERATION SYSTEM DESIGNED TO SUPPORT LANGUAGE LEARNING THAT UTILIZES DIGITAL TEXTBOOK LOGS. LEARNERS OFTEN MEMORIZE WORDS IN DIGITAL TEXTBOOKS WHILE PREPARING FOR AN EXAMINATION, AND THEY OFTEN USE THE HIGHLIGHT FUNCTION FOR THE WORDS. PREVIOUS STUDIES REGARDING ANNOTATIONS AND HIGHLIGHTS HAVE SHOWN THAT LEARNING ONLY BY USING THE HIGHLIGHT FUNCTION ON IMPORTANT CONTENT IN TEXTBOOKS DID NOT AFFECT LEARNING ACHIEVEMENTS. THEREFORE, IN THIS STUDY, WE DEVELOPED A SYSTEM THAT CAN SUPPORT THE REPEATED LEARNING BY ANALYZING DIGITAL TEXTBOOK LOGS AND PROVIDING APPROPRIATE QUIZZES. AN EVALUATION EXPERIMENT INVOLVING 31 INTERNATIONAL STUDENTS WAS CONDUCTED TO ASSESS WHETHER THE QUIZZES PROVIDED BY OUR PROPOSED SYSTEM ARE ABLE TO ENHANCE THE LEARNING ACHIEVEMENTS AS COMPARED TO TEACHER-CREATED QUIZZES. THE RESULTS SHOW THAT THE QUIZZES BY OUR PROPOSED SYSTEM AND THE TEACHER-CREATED QUIZZES WERE BOTH EQUALLY EFFECTIVE. A CORRELATION ANALYSIS WAS CONDUCTED TO IDENTIFY THE CORRELATION AMONG THE LEARNING ACHIEVEMENTS, THE NUMBER OF QUIZZES, AND EACH VARIABLE IN QUESTIONNAIRES. WE FOUND THAT THERE IS A POSITIVE CORRELATION BETWEEN THE NUMBER OF QUIZZES AND THE STUDENTS' LEARNING ACHIEVEMENTS.
WITH THE ADVENT OF THE INTERNET AND WIDE-SPREAD POPULARITY OF ONLINE TECHNOLOGY-ENHANCED LEARNING PLATFORMS, MANY PEDAGOGICAL ACTIVITIES TODAY INVOLVE LEARNERS IN ONLINE DISCUSSIONS SUCH AS SYNCHRONOUS CHAT. IN THIS STUDY, WE DESCRIBE A TEXT MINING METHOD USED FOR ANALYZING TEAMWORK FROM SUCH CHAT DIALOGUE OF STUDENTS. THE STEPS IN THE TEXT MINING METHOD SUCH AS PRE-PROCESSING AND CLASSIFICATION ARE DESCRIBED AND THE RESULTS OF OUR ANALYSIS ARE PRESENTED IN THIS PAPER.
THIS PAPER ENVISAGES EMERGING TRENDS AND METHODS IN LEARNING ANALYTICS FOR POST-LMS ERA, WHERE LEARNING INCREASINGLY TAKES PLACE IN DISTRIBUTED, USER-DEFINED DIGITAL LEARNING ECOSYSTEMS. INSPIRED BY THE RECENT DEVELOPMENTS ON UPTAKE FRAMEWORK AND EXPERIENCE API, WE PROPOSE LEARNING FLOW AS THE MAIN UNIT OF ANALYSIS WHILE STUDYING LEARNING-RELATED INTERACTIONS.
TEACHERS PLAY A MAJOR ROLE DURING CSCL BY MONITORING AND STIMULATING THE TYPES OF INTERACTIONS BETWEEN STUDENTS THAT ARE CONDUCIVE TO LEARNING. TEACHER DASHBOARDS ARE INCREASINGLY BEING DEVELOPED TO AID TEACHERS IN MONITORING STUDENTS' COLLABORATIVE ACTIVITIES, THEREBY CONSTITUTING A FORM OF INDIRECT SUPPORT FOR CSCL IN THE CLASSROOM. HOWEVER, THE PROCESS OF HOW TEACHERS FIND AND INTERPRET RELEVANT INFORMATION FROM DASHBOARDS, AND WHICH HELP THEY NEED DURING THIS PROCESS, REMAINS LARGELY UNEXAMINED. WE FIRST DESCRIBE HOW WE ARRIVED AT THE DESIGN OF A PROTOTYPE TEACHER DASHBOARD IN THE CONTEXT OF PRIMARY SCHOOL FRACTION ASSIGNMENTS, BASED ON TEACHER INTERVIEWS. WE THEN REPORT AN EXPERIMENTAL STUDY (N = 53) TO INVESTIGATE THE EFFECT OF THE TYPE OF SUPPORT A TEACHER DASHBOARD OFFERS (MIRRORING, ALERTING, OR ADVISING) ON TEACHERS' DETECTION AND INTERPRETATION OF POTENTIALLY PROBLEMATIC SITUATIONS WHERE INTERVENTION MIGHT BE NEEDED. THE RESULTS SHOWED THAT THE TYPE OF SUPPORT DID NOT INFLUENCE DETECTION OF EVENTS, BUT DID INFLUENCE TEACHERS' INTERPRETATION OF A CSCL SITUATION.
EUROPEAN SCHOOLS ARE INCREASINGLY RELYING ON VENDORS TO COLLECT, PROCESS, ANALYSE, AND EVEN MAKE DECISIONS BASED ON A CONSIDERABLE AMOUNT OF STUDENT DATA THROUGH BIG DATA TOOLS AND METHODS. CONSEQUENTLY, PORTIONS OF SCHOOL'S POWER ARE GRADUALLY SHIFTING FROM TRADITIONAL PUBLIC SCHOOLS TO THE HANDS OF FOR-PROFIT ORGANISATIONS. THIS ARTICLE DISCUSSES THE CURRENT AND FORTHCOMING EUROPEAN UNION (EU) DATA PROTECTION REGIME WITH RESPECT TO THE PROTECTION OF STUDENT RIGHTS FROM THE POTENTIAL RISK OF OUTSOURCING STUDENT DATA UTILISATION IN KINDERGARTEN-12TH GRADE (K-12) EDUCATIONAL SYSTEMS. THE ARTICLE IDENTIFIES WHAT LESSONS CAN BE DRAWN FROM RECENT DEVELOPMENTS IN THE UNITED STATES (US) "STUDENT DATA AFFAIR". THESE LESSONS CAN PROVIDE A NEW PERSPECTIVE FOR DESIGNING A BALANCED POLICY FOR REGULATING THE SHIFT IN SCHOOL'S POWER.
SELF-REGULATED LEARNING HAS ACHIEVED PROMINENCE IN RECENT YEARS. HELPING STUDENTS TO BECOME SELF-REGULATED LEARNERS IS BECOMING A FAVORITE TARGET OF RESEARCHERS AND EDUCATIONAL PRACTITIONERS. THIS STUDY PROPOSES A NOVEL FRAMEWORK THAT CAN AUTOMATICALLY ANALYZE ONLINE SELF-REGULATED LEARNING PROCESSES AND COMPETENCIES. IT PRESENTS AN ANALYTICS DASHBOARD THAT ENCOURAGES STUDENTS TO INITIATE ACTIVITIES OF SELF-REGULATION AND GUIDE THEM TO REFLECT ON THEIR COMPETENCIES. THE ARCHITECTURE OF THE FRAMEWORK AND AN APPLIED SCENARIO ARE PRESENTED AND DISCUSSED IN DETAIL.
VERSION CONTROL SYSTEMS ARE COMMONLY USED BY INFORMATION AND COMMUNICATION TECHNOLOGY PROFESSIONALS. THESE SYSTEMS ALLOW MONITORING PROGRAMMERS ACTIVITY WORKING IN A PROJECT. THUS, VERSION CONTROL SYSTEMS ARE ALSO USED BY EDUCATIONAL INSTITUTIONS. THE AIM OF THIS WORK IS TO DEMONSTRATE THAT THE ACADEMIC SUCCESS OF STUDENTS MAY BE PREDICTED BY MONITORING THEIR INTERACTION WITH A VERSION CONTROL SYSTEM. IN ORDER TO DO SO, WE HAVE BUILT A MACHINE LEARNING MODEL TO PREDICT STUDENT RESULTS IN A SPECIFIC PRACTICAL ASSIGNMENT OF THE AMPLIACION DE SISTEMAS OPERATIVOS SUBJECT, FROM THE SECOND COURSE OF THE DEGREE IN COMPUTER SCIENCE OF THE UNIVERSITY OF LEON, THROUGH THEIR INTERACTION WITH A GIT REPOSITORY. TO BUILD THE MODEL, SEVERAL CLASSIFIERS AND PREDICTORS HAVE BEEN EVALUATED. IN ORDER TO DO SO, WE HAVE DEVELOPED MODEL EVALUATOR (MOEV), A TOOL TO EVALUATE DIFFERENT MACHINE LEARNING MODELS IN ORDER TO GET THE MOST SUITABLE FOR A SPECIFIC PROBLEM. PRIOR TO THE MODEL DEVELOPMENT, A FEATURE SELECTION OF THE INPUT DATA IS DONE. THE RESULTING MODEL HAS BEEN TRAINED USING RESULTS FROM 2016-2017 COURSE AND LATER VALIDATED USING RESULTS FROM 2017-2018 COURSE. RESULTS CONCLUDE THAT THE MODEL PREDICT STUDENTS' SUCCESS WITH A SUCCESS HIGH PERCENTAGE.
IN THIS PAPER, WE PRESENT A NEW SET OF FEATURES INTRODUCED IN COURSE BUILDER THAT ALLOW INSTRUCTORS TO ADD SKILL MAPS TO THEIR COURSES. WE SHOW HOW SKILL MAPS CAN BE USED TO PROVIDE UP-TO-DATE AND ACTIONABLE INFORMATION ON STUDENTS' LEARNING BEHAVIOR AND PERFORMANCE.
THE PURPOSE OF THIS STUDY WAS TO EXPLORE HOW STUDENTS INTERACTED WITH GUIDANCE TO CONDUCT A SCIENTIFIC INQUIRY IN A PHYSICS SIMULATION BY USING THE EYE-TRACKING TECHNIQUES. THE PARTICIPANTS WERE 51 7TH GRADERS, AND AN EYE-TRACKING SYSTEM WAS USED TO RECORD THEIR VISUAL BEHAVIORS AND LOG DATA WHILE THEY WERE USING THE SIMULATION. AS FOR DATA ANALYSIS, WE FIRST CHECKED EACH PARTICIPANT'S LOG DATA TO EXAMINE WHETHER THEY COMPLETED THE REQUIREMENT OF THE GUIDANCE, AND THEN CHECKED THE CORRECTNESS OF HER/HIS ANSWER TO THE INQUIRY TASK. THE PARTICIPANTS WERE THUS DIVIDED INTO TWO GROUPS (CORRECT VS. WRONG), AND THE PATTERNS OF THEIR VISUAL BEHAVIORS WERE EXAMINED BY A SET OF EYE-MOVEMENT INDICES, NORMALIZED HEAT MAPS AND LAG SEQUENTIAL ANALYSES. THE RESULTS INDICATE THAT BOTH SPATIAL DISTRIBUTIONS AND TEMPORAL SEQUENCES OF THE PARTICIPANTS' VISUAL ATTENTION WERE ASSOCIATED WITH THEIR PERFORMANCES ON THE INQUIRY TASK. REGARDING THE SPATIAL DISTRIBUTION, THE CORRECT GROUP TENDED TO ALLOCATE MORE VISUAL ATTENTION TO THE REGIONS PRESENTING THE TARGET PHENOMENON THAN THE WRONG GROUP. CONCERNING THE TEMPORAL SEQUENCE, THE CORRECT GROUP TENDED TO MAKE MORE VISUAL TRANSITIONS AMONG THE CONTENT OF THE GUIDANCE, THE RELEVANT CONTROL PANELS AND THE TARGET PHENOMENA THAN THE WRONG GROUP.
EDUCATIONAL DATA MINING (EDM) AIMS TO PRODUCE NEW KNOWLEDGE FROM EDUCATIONAL SETTINGS TO SUPPORT EDUCATORS, LEARNERS AND OTHER STAKEHOLDERS. EDM AIMS TO FACILITATE THE UNDERSTANDING OF THE EDUCATIONAL CONTEXT BY UTILIZING DIFFERENT METHODS OF STATISTICS AND MACHINE LEARNING. LIKE WISE TO THE CURRENT TRENDS IN DATA MINING, ALSO EDM APPROACHES HAVE SHIFTED FROM BLACK BOX TOOLS AND ALGORITHMS TO MORE OPEN-ENDED TOOLS AND ALGORITHMS WHERE THE EDM END-USERS CAN ADJUST MULTIPLE PARAMETERS, VIEW VISUALIZATIONS, AND EVEN ADJUST THE PREDICTIVE MODELS. MULTIPLE STUDIES HAVE SHOWN THAT THE EDM END-USERS BENEFIT FROM THE WHITE BOX APPROACHES AND TOOLS. WE INTRODUCE THE CONCEPT OF AUGMENTED INTELLIGENCE (AUI) METHOD IN EDM. AUI METHOD IS APPLIED IN AN ITERATIVE PROCESS WHERE A WHITE BOX MACHINE LEARNING ALGORITHM GENERATES A PREDICTIVE MODEL, WHICH IS ADJUSTABLE BY THE EDM END-USER. THE ADJUSTABLE PREDICTIVE MODEL AFFECTS TO THE PERCEPTION OF THE END-USER AND THE ADJUSTING AFFECTS TO THE OUTPUT OF THE PREDICTIVE MODEL. WHEN APPLIED IN CYCLES, THE AUI METHOD GENERATES NEW KNOWLEDGE FROM THE EDUCATIONAL CONTEXT. TO STUDY AUI METHOD, A POTENTIAL EDM END-USER GENERATED MULTIPLE ADJUSTABLE DECISION TREE MODELS AND WE OBSERVED THE EVOLUTION OF THE MODELS. THE STUDY INDICATES THAT, OVER TIME, THE MODELS GENERALIZE BETTER AND THE AUI METHOD HELPS TO AVOID THE ISSUE OF OVERFITTING. MOREOVER, THE STUDY INDICATES THAT THE CYCLIC NATURE OF THE AUI METHOD FACILITATES DEEPER KNOWLEDGE GENERATION FROM THE DATASET, IF THE CONTEXT IS KNOWN BY THE END-USER.
MANY EDUCATIONAL INSTITUTIONS IMPLEMENT BLENDED LECTURES USING AN LMS, BUT FEW STUDIES EXIST THAT HAVE ANALYSED LEARNING EFFECTS AND HOW BEST TO CONDUCT LEARNING MODULES. IN THIS PAPER, WE ANALYSED STUDENTS' LEARNING ACTIVITIES AND LEARNING EFFECTS BY USING BASIC LOG DATA FROM AN LMS ON BLENDED INFORMATION LITERACY LECTURES TO EVALUATE THE LECTURES. IN ADDITION, WE DIVIDED STUDENTS INTO GROUPS AND ANALYSED THEIR DIFFERENCES BY THE NUMBER OF LEARNING EFFECTS AND LEARNING ACTIVITIES. THE RESULTS SHOW THAT SOME STUDENTS SHOWED NO ASSOCIATIONS BETWEEN THE NUMBER OF TIMES THEY USED TEACHING MATERIALS AND THEIR LEARNING EFFECTS. WE REPORT THE DETAILS OF THESE DIFFERENCES AND MAKE SUGGESTIONS FOR IMPROVEMENTS IN LECTURES. IN ADDITION, WE PROPOSE THREE LECTURE PATTERNS TO ENCOURAGE STUDENTS' ACTIVITIES. (C) 2019 THE AUTHORS. PUBLISHED BY ELSEVIER B.V. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY-NC-ND LICENSE (HTTP://CREATIVECOMMONS.ORG/LICENSES/BY-NC-ND/4.0/) PEER-REVIEW UNDER RESPONSIBILITY OF KES INTERNATIONAL.
WIKIS ARE APPROPRIATE TOOLS FOR DEPLOYING AUTHENTIC ASSESSMENT EXPERIENCES FOR LEARNING AND WORK SCENARIOS IN WHICH A GROUP OF USERS ARE ASKED TO DEVELOP A SHARED TASK. HOWEVER, WHEN THE NUMBER OF WIKI USERS INCREASES, THE NUMBER OF CONTRIBUTIONS CAN GROW AT A PACE WHEREBY ACCURATELY ASSESSING THEM BECOMES A COMPLEX AND NON-SCALABLE TASK. WHILE DIFFERENT QUANTITATIVE APPROACHES HAVE BEEN SHOWN TO BE SCALABLE, THEY ARE USUALLY COARSE-GRAINED AND PROVIDE LIMITED FEEDBACK ABOUT THE ASSESSMENT. THIS WORK PROPOSES A SCALABLE ASSESSMENT METHODOLOGY FOR WIKI-BASED TASKS, BASED ON QUALITATIVE SELF- AND PEER ASSESSMENT OF WIKI CONTRIBUTIONS. THE METHODOLOGY IS IMPLEMENTED USING A SOFTWARE TOOL AND IS APPLIED AS PART OF AN UNDERGRADUATE COURSE, COMPLEMENTING A QUANTITATIVE ASSESSMENT APPROACH. POSITIVE EVIDENCE ON THE SCALABILITY OF THE METHOD AND HOW IT IMPLEMENTS A MORE FINE-GRAINED QUALITATIVE ASSESSMENT THAN THE REGULAR QUANTITATIVE APPROACH IS FOUND, PROVIDING INDICATORS FOR ASSESSING BOTH INDIVIDUAL AND GROUP GENERIC SKILLS.
THE IMPORTANCE OF TEXTUAL PROGRAMMING LANGUAGES IS INCREASING WITH THE DEVELOPMENT OF PUBLIC AND PRIVATE SMART ENVIRONMENTS. ON THE WAY TO A MODEL OF OBJECT-ORIENTED PROGRAMMING COMPETENCE ENCODING AND DECODING PROCESSES OF FORMAL LANGUAGE TEXTS (FLT), CALLED PROGRAMS, ARE MOST IMPORTANT. HOWEVER THERE EXISTS A LONG RESEARCH HISTORY IN NATURAL LANGUAGE TEXTS (NLT), COMPREHENSIBILITY, THE LEVEL BETWEEN SURFACE STRUCTURES AND THE SEMANTICS BEHIND, IS STILL A CHALLENGE. SO IT IS IN FLT. IN ORDER TO HELP CLOSING THIS GAP, WE DEVELOPED A COMPUTER-BASED TEST WITH FIRST ITEMS ON THREE COMPLEXITY LEVELS. IN A STUDY WITH 42 PARTICIPANTS TENDENCIES OF KNOWLEDGE EFFECTS ON ENCODING AND DECODING PROCESSES CAN BE INTERPRETED AS WELL AS EFFECTS OF DIFFERENT FLT STRUCTURES ON THESE PROCESSES.
PREDICTIVE MODELS OF STUDENT SUCCESS IN MASSIVE OPEN ONLINE COURSES (MOOCS) ARE A CRITICAL COMPONENT OF EFFECTIVE CONTENT PERSONALIZATION AND ADAPTIVE INTERVENTIONS. IN THIS ARTICLE WE REVIEW THE STATE OF THE ART IN PREDICTIVE MODELS OF STUDENT SUCCESS IN MOOCS AND PRESENT A CATEGORIZATION OF MOOC RESEARCH ACCORDING TO THE PREDICTORS (FEATURES), PREDICTION (OUTCOMES), AND UNDERLYING THEORETICAL MODEL. WE CRITICALLY SURVEY WORK ACROSS EACH CATEGORY, PROVIDING DATA ON THE RAW DATA SOURCE, FEATURE ENGINEERING, STATISTICAL MODEL, EVALUATION METHOD, PREDICTION ARCHITECTURE, AND OTHER ASPECTS OF THESE EXPERIMENTS. SUCH A REVIEW IS PARTICULARLY USEFUL GIVEN THE RAPID EXPANSION OF PREDICTIVE MODELING RESEARCH IN MOOCS SINCE THE EMERGENCE OF MAJOR MOOC PLATFORMS IN 2012. THIS SURVEY REVEALS SEVERAL KEY METHODOLOGICAL GAPS, WHICH INCLUDE EXTENSIVE FILTERING OF EXPERIMENTAL SUBPOPULATIONS, INEFFECTIVE STUDENT MODEL EVALUATION, AND THE USE OF EXPERIMENTAL DATA WHICH WOULD BE UNAVAILABLE FOR REAL-WORLD STUDENT SUCCESS PREDICTION AND INTERVENTION, WHICH IS THE ULTIMATE GOAL OF SUCH MODELS. FINALLY, WE HIGHLIGHT OPPORTUNITIES FOR FUTURE RESEARCH, WHICH INCLUDE TEMPORAL MODELING, RESEARCH BRIDGING PREDICTIVE AND EXPLANATORY STUDENT MODELS, WORK WHICH CONTRIBUTES TO LEARNING THEORY, AND EVALUATING LONG-TERM LEARNER SUCCESS IN MOOCS.
ONLINE LEARNING PLATFORMS CAN PROVIDE AUTOMATIC INSTANT FEEDBACK ON STUDENTS' ACTIVITIES WITH FORMATIVE ASSESSMENT TASKS SUCH AS HOMEWORK ASSIGNMENTS. HOWEVER, THE VARIOUS WAYS THAT STUDENTS INTERACT WITH FEEDBACK FEATURES ADD TO THE UNCERTAINTY OF THE EFFECTIVENESS OF AUTOMATIC IMMEDIATE FEEDBACK ON STUDENT LEARNING. WE STUDY STUDENT BEHAVIORAL PATTERNS WITH AN INSTANT FEEDBACK FEATURE: "CHECKABLE ANSWERS," POWERED BY THE EDX LEARNING PLATFORM. WE SHOW HOW THESE BEHAVIORAL PATTERNS ARE RELATED TO STUDENT COURSE PERFORMANCE AND OTHER FACTORS SUCH AS SELF-EFFICACY AND STUDENT PERCEPTIONS OF THE UTILITY OF THE COURSE FOR THEIR FUTURE.
THIS ARTICLE ATTEMPTS TO EXPLAIN THE RESULTS OF AN INVESTIGATION INTO THE MODEL SURICATA, CONDUCIVE TO ANALYSIS AND VISUALIZATION OF RESULTS IN A FORMAL AND INFORMAL LEARNING ENVIRONMENT. FOR THE FORMAL SETTING A BASIC COMPUTER COURSE IS ANALYZED AT THE FACULTY OF TRANSLATORS AND INTERPRETERS. TWO GROUPS, GERMAN 2 AND FRENCH 2, ARE ANALYZED, THE SUBJECT BEING EXACTLY THE SAME FOR BOTH GROUPS. FOR THESE RESULTS A PLATFORM OF FORMAL EDUCATION, BASED ON MOODLE WAS EMPLOYED. WHERE TOOLS HAVE BEEN IMPLEMENTED SOCIAL NETWORK ANALYSIS WAS USED. THE RESIDENT PLUGIN ALLOWED FILES TO BE CONVERTED INTO PAJEK FORMAT. THE ANALYSIS OF THESE FILES IN PAJEK FORMAT PROVIDES THE RESULTS PRESENTED IN THIS ARTICLE. FOR THE INFORMAL ENVIRONMENT AN OPEN SOURCE SOFTWARE, CALLED ELGG, WAS USED.
PURPOSE THIS STUDY AIMS TO BETTER UNDERSTAND THE RELATIONSHIP BETWEEN PEER FEEDBACK IN THE CONTEXT OF ONLINE COLLABORATIVE NOTE-TAKING AND HOW COMMENTS IMPACTED STUDENT PERFORMANCE AND UNDERSTANDING. DESIGN/METHODOLOGY/APPROACH THIS ONE SAMPLE INVESTIGATION WAS OF GRADUATE STUDENTS PARTICIPATING IN AN ACADEMIC WRITING CLASS WORKING COLLABORATIVELY ONLINE. DATA WAS GATHERED ON STUDENT FEEDBACK DURING NOTE-TAKING ACTIVITY TO TEST FOR ITS EFFECTS ON STUDENT PERFORMANCE AND UNDERSTANDING. FINDINGS THE USE OF PEER COMMENTS IN ONLINE NOTE-TAKING WAS FOUND TO IMPACT STUDENT QUIZ SCORES AND ACADEMIC WRITING SKILLS POSITIVELY. HOWEVER, NO SIGNIFICANCE WAS FOUND BETWEEN COMMENTS AND THE COMPLETENESS OF THEIR NOTES TAKEN, SUGGESTING ITS LIMITS TO PROMOTE DEEPER UNDERSTANDING. RESEARCH LIMITATIONS/IMPLICATIONS THE LEVEL AND DETAIL ABOUT THE COMMENTS MADE AND HOW ACCURATELY THEY RECALL THE IMPORTANT DETAILS FROM THE VIDEO LECTURES IS NOT KNOWN. THE AVERAGE NUMBER OF COMMENTS MADE WEEKLY BY EACH GROUP WAS ALSO LOW. PRACTICAL IMPLICATIONS DESIGNERS AND TEACHERS USING ONLINE COLLABORATIVE ACTIVITIES COULD BENEFIT BY UNDERSTANDING THE NATURE IN WHICH PEER COMMENTS CAN ENHANCE STUDENT LEARNING, BEARING IN MIND THE NEED FOR EXPLICIT GUIDANCE IN HOW TO COMMENT AND AT WHAT LEVEL OF KNOWLEDGE THEIR COMMENTS SHOULD TARGET. SOCIAL IMPLICATIONS ONLINE COLLABORATION, PEER EDITING AND COMMENTING IS WIDELY USED BY EDUCATORS AND THE PUBLIC. A BETTER UNDERSTANDING OF HOW THESE ELEMENTS OPERATE MIGHT IMPROVE THE QUALITY OF KNOWLEDGE ARTEFACTS SUCH AS ACADEMIC WRITING AND RESEARCH NOTES. ORIGINALITY/VALUE EXISTING LITERATURE FOCUSES MAINLY ON PEER FEEDBACK ON WRITING OR OTHER ARTEFACTS; THIS PAPER SEEKS TO FIND OUT MORE ABOUT THE IMPACT OF COMMENTS IN PARTICULAR ON COLLABORATIVE NOTE-TAKING.
MIND-WANDERING OR LOSS OF FOCUS IS A FREQUENTLY OCCURRING EXPERIENCE FOR MANY LEARNERS AND NEGATIVELY IMPACTS LEARNING OUTCOMES. WHILE IN A CLASSROOM SETTING, A SKILLED TEACHER MAY BE ABLE TO REACT TO STUDENTS' LOSS OF FOCUS, IN MASSIVE OPEN ONLINE COURSES (MOOCS) NO SUCH INTERVENTION IS POSSIBLE (YET). PREVIOUS STUDIES SUGGEST A STRONG RELATIONSHIP BETWEEN LEARNERS' MIND-WANDERING AND THEIR GAZE, MAKING IT POSSIBLE TO DETECT MIND-WANDERING IN REAL-TIME USING EYE-TRACKING DEVICES. EXISTING RESEARCH IN THIS AREA THOUGH HAS MADE USE OF SPECIALIZED (AND EXPENSIVE) HARDWARE, AND THUS CANNOT BE EMPLOYED IN MOOC SCENARIOS DUE TO THE INABILITY TO SCALE BEYOND LAB SETTINGS. IN ORDER TO MAKE A STEP TOWARDS SCALABLE MIND-WANDERING DETECTION AMONG ONLINE LEARNERS, WE PROPOSE THE USE OF UBIQUITOUSLY AVAILABLE CONSUMER GRADE WEBCAMS. IN A CONTROLLED STUDY, WE COMPARE THE ACCURACY OF MIND-WANDERING DETECTION FROM GAZE DATA RECORDED THROUGH A STANDARD WEBCAM AND RECORDED THROUGH A SPECIALIZED AND HIGH-QUALITY EYE TRACKER. OUR RESULTS SUGGEST THAT A LARGE-SCALE APPLICATION OF WEBCAM-BASED MIND-WANDERING DETECTION IN MOOCS IS INDEED POSSIBLE.
THE GROWTH OF THE MAKER MOVEMENT HAS CREATED A DEMAND TO INCLUDE TOOLS FOR DIGITAL FABRICATION IN THE SCHOOL CURRICULUM TO FOSTER STEAM EDUCATION. YET, THE TOOLS USED BY THE MAKER MOVEMENT REMAIN SPARSE AND DO NOT EXIST INTEGRATED IN THE SAME ENVIRONMENT FOR EDUCATIONAL PURPOSES. IN THIS PAPER, WE INTRODUCE A SMART LEARNING ENVIRONMENT THAT COLLECTS THE TOOLS OF DESIGN, 3D-PRINTING, PROGRAMMING, SHARING AND DATA ANALYTICS INTO THE SINGLE FRAME WHERE K12 LEVEL STUDENTS AND THEIR EDUCATORS CAN MAKE MAKER MOVEMENT ARTEFACTS WHILE ENHANCING THEIR STEAM SKILLS. OUR DEVELOPED SMART LEARNING ENVIRONMENT GATHERS DATA FROM THE USERS' DIGITAL TRAILS AND ANALYZES THESE DATA WITH SEVERAL WHITE-BOX DATA MINING ALGORITHMS IN ORDER TO SUPPORT THE EDUCATORS' INTERVENTIONS IN THE MAKING ACTIVITIES CARRIED OUT IN THE CLASSROOM.
IN THIS PAPER, WE PRESENT A STUDY ABOUT STUDENTS' BEHAVIOR BASED ON ACTIVITY LOGS IN MOODLE (AN ONLINE LEARNING MANAGEMENT SYSTEM LMS) ANALYZING THREE CHARACTERISTICS: ONLINE TIME (SEPARATED BY ITS LOCATION), TASKS DELIVERED AND SUPPORT MATERIAL VIEWS. WE RELATE THESE THREE CHARACTERISTICS WITH THE STUDENTS' PERFORMANCE (I.E. SUCCESS, FAIL AND DROPOUT) AND PROVIDING A GENERALIZATION OF FOUR STUDENTS' GROUPS (BASED ON THEIR BEHAVIOR ON THE LMS). AFTER ANALYZING THESE CHARACTERISTICS, WE EVALUATE THE CORRELATION BETWEEN EACH CHARACTERISTIC AND THE INDIVIDUAL STUDENT PERFORMANCE, IDENTIFYING A PROMISING FEATURE TO ENRICH PREDICTIVE ALGORITHMS. FINALLY, WE GENERATED A NAIVE BAYES MODEL TO PREDICT IF THE STUDENT WILL SUCCEED, FAIL OR DROPOUT. TO EVALUATE THE PREDICTION, WE COMPARED THE MODELS GENERATED WITH ONLY THE PERFORMANCE DATA AND THE MODELS WITH THE ENRICHED DATA, ACCORDING WITH THE PREVIOUSLY ANALYZED FEATURES. THE RESULTS SHOWS THAT THE ENRICHED DATA MODEL ARE MORE ACCURATE AND MAY HELP THE TEACHER TO IDENTIFY "AT RISK" STUDENTS.
THE USE OF NEW TECHNOLOGIES SUCH AS LEARNING ANALYTICS BY TEACHERS IS CHALLENGING DUE TO THE CHANGES THEY BRING TO TEACHERS' PRACTICES AND THEIR PEDAGOGICAL INTERVENTIONS. A DESIGN APPROACH INTO TEACHING HAS BEEN PROPOSED FOR MAPPING PEDAGOGY WITH TECHNOLOGY TO EFFECTIVELY INTEGRATE THESE CHANGES. HOWEVER, LIMITED COLLECTIVE APPROACHES EXIST IN WHICH TEACHERS PARTICIPATE IN PROFESSIONAL COMMUNITIES TO BUILD KNOWLEDGE FOR THE DESIGN AND IMPLEMENTATION OF LEARNING SCENARIOS WITH LEARNING ANALYTICS. IN THIS PAPER, WE PROPOSE A FRAMEWORK (CIDA) FOR COLLECTIVE INQUIRY IN SUCH PROFESSIONAL COMMUNITIES OF TEACHERS. BASED ON OTHER COLLECTIVE INQUIRY FRAMEWORKS FOR KNOWLEDGE BUILDING, WE PROPOSE THREE INTERCONNECTED COMPONENTS: THE INQUIRY PROCESS, THE COLLECTIVE PROCESS, AND TECHNOLOGICAL SUPPORT TO FACILITATE AND STUDY TEACHERS' DESIGN PRACTICES IN TECHNOLOGICAL ENVIRONMENTS. WE EXPLAIN THE FRAMEWORK WITH EXAMPLES OF IMPLEMENTED TECHNOLOGIES AND PRESENT EMPIRICAL RESULTS OF THREE CASES; TWO HIGH SCHOOLS AND A MASSIVE OPEN ONLINE COURSE (MOOC). RESULTS INFORM FURTHER HOW OUR FRAMEWORK CAN BE IMPLEMENTED IN PRACTICE AND ITS REQUIRED SUPPORTS TO FACILITATE KNOWLEDGE BUILDING FOR INQUIRY COMMUNITIES OF TEACHERS.
MOOCS SOCIAL MEDIA HAS APPEALED TO MOST INSTRUCTORS AND LEARNERS LIKE STRONG MAGNETS BY USING UBIQUITOUS HANDHELD DEVICES TO SHARE AND DISCUSS FILMS, PICTURES, AND MESSAGES. THROUGH THE SOCIAL PLATFORM, USERS CAN SHARE, TRACK, AND SEARCH FOR THE INFORMATION OF THEIR SPECIFIC INTERESTS. THUS, THEY CAN MAKE INTERACTIVE DISCUSSIONS AS WELL AS SOCIAL LEARNING ON MULTIMEDIA TEXTS AND LEARNING MATERIALS. IT PROVIDES TEACHERS AND STUDENTS THE SOCIAL PLATFORM TO EXPRESS OPINIONS OR ASK QUESTIONS. WE DEVELOPED AN ASSISTANT SYSTEM TO HELP STUDENTS EVALUATE PROBLEMS AND SOLUTIONS IS AN EMERGING ISSUE, AND THE MOOCS SOCIAL SEARCHING SYSTEM IS A KEY SOLUTION. WE REPORT A CASE STUDY THAT FOCUSES ON MOOCS LEARNERS' SOCIAL SEARCHING RESULTS AND ITS INFLUENCE ON LEARNING BEHAVIORS AND LEARNING OUTCOMES. A CASE STUDY WAS CARRIED OUT WITH 36 HIGH SCHOOL STUDENTS IN NORTHERN TAIWAN. RESULTS SHOW THAT THE MECHANISM BENEFITS STUDENT INFORMATION GATHERING AND IMPROVES LEARNING PERFORMANCE.
VIRTUAL LEARNING ENVIRONMENTS (VLES) PROVIDE STUDENTTS WITH ACTIVITIES TO IMPROVE THEIR LEARNING (E.G., READING TEXTS, WATCHING VIDEOS OR SOLVING EXERCISES). BUT VLES USUALLY ALSO PROVIDE OPTIONAL ACTIVITIES (E.G., CHANGING AN AVATAR PROFILE OR SETTING GOALS). SOME OF THESE HAVE A CONNECTION WITH THE LEARNING PROCESS, BUT ARE NOT DIRECTLY DEVOTED TO LEARNING CONCEPTS (E.G., SETTING GOALS). FEW WORKS HAVE DEALT WITH THE USE OF OPTIONAL ACTIVITIES AND THE RELATIONSHIPS BETWEEN THESE ACTIVITIES AND OTHER METRICS IN VLES. THIS PAPER ANALYZES THE USE OF OPTIONAL ACTIVITIES AT DIFFERENT LEVELS IN A SPECIFIC CASE STUDY WITH 291 STUDENTS FROM THREE COURSES (PHYSICS, CHEMISTRY AND MATHEMATICS) USING THE KHAN ACADEMY PLATFORM. THE LEVEL OF USE OF THE DIFFERENT TYPES OF OPTIONAL ACTIVITIES IS ANALYZED AND COMPARED TO THAT OF LEARNING ACTIVITIES. IN ADDITION, THE RELATIONSHIP BETWEEN THE USAGE OF OPTIONAL ACTIVITIES AND DIFFERENT STUDENT BEHAVIORS AND LEARNING METRICS IS PRESENTED.
THIS WORK IS AIMING TO CONTRIBUTE TO THE FIELD OF DISTANCE LEARNING THROUGH LEARNING ANALYTICS. WE PROPOSE A METHODOLOGICAL FRAMEWORK BASED ON NETWORK ANALYSIS METRICS TO PROVIDE MULTIPLE INDICATORS FOR COURSE LEARNING ANALYTICS. SOCIAL NETWORK ANALYSIS IS PROPOSED FOR THIS PURPOSE DUE TO ITS CAPACITY TO PROVIDE AN INTEGRATED REPRESENTATION OF STUDENTS' INTERACTION, WHERE INDIVIDUAL BEHAVIOR IS EXPRESSED WITHIN THE CONTEXT OF A LEARNING COMMUNITY. WE PERFORM EXPERIMENTAL EVALUATION ON REAL-LIFE DATA FROM ANONYMIZED FORUM POSTS OF POSTGRADUATE STUDENTS AND THEIR TUTORS IN THE SCHOOL OF SCIENCE AND TECHNOLOGY AT THE HELLENIC OPEN UNIVERSITY. INITIALLY, WE CREATE AND EXAMINE TWO-MODE NETWORKS (PARTICIPANT-DISCUSSION) FOR TWO DIFFERENT MODULES. SUBSEQUENTLY, THESE NETWORKS ARE TRANSFORMED INTO ONE-MODE NETWORKS. KEY MEASURES ARE ESTIMATED AND COMPARED AND THE DIFFERENCES BETWEEN THEIR PEDAGOGICAL INTERPRETATIONS ARE HIGHLIGHTED. WE CONCLUDE THAT THE CHOICE BETWEEN WORKING WITH A BIMODAL NETWORK OR PROJECTING IT INTO A UNIMODAL ONE IS DETERMINED BY THE NATURE OF THE RESEARCH QUESTIONS BECAUSE OF THE DISTINCT FEATURES THAT EACH ONE OF THEM EXHIBITS.
IMPROVEMENT IN UNDERGRADUATE RETENTION AND PROGRESSION IS A PRIORITY AT MANY US POSTSECONDARY INSTITUTIONS. A NUMBER OF INSTITUTIONS ADDRESS THIS ISSUE BY IDENTIFYING GATEWAY COURSES (FOUNDATIONAL COURSES IN WHICH A LARGE NUMBER OF STUDENTS FAIL OR WITHDRAW) AND CONCENTRATING ON "FIXING" THEM. THIS PAPER ARGUES THAT MAY NOT BE THE BEST USE OF LIMITED RESOURCES. NO MATTER WHAT WE DO, THERE WILL ALWAYS BE COURSES WITH TOO MANY STUDENTS RECEIVING GRADES OF D, F AND W SIMPLY BECAUSE OF THE NATURE OF THEIR CONTENT AND THE PREPARATION OF THE STUDENTS WHO MUST TAKE THEM. OUR RESEARCH SUGGESTS THAT STUDENT TYPE AND ACADEMIC STAGE AFFECT STUDENT SUCCESS AND THAT GATEWAY COURSES (COURSES WHICH BLOCK STUDENT PROGRESSION) CAN BE FOUND AT ALL UNDERGRADUATE LEVELS. SPECIFICALLY, WE HAVE FOUND THAT ONE CAN USE BINARY LOGISTIC REGRESSION WITH STUDENT TYPE, ACADEMIC STAGE, CUMULATIVE GPA, AND PRIOR WITHDRAWALS AS PREDICTOR VARIABLES TO PREDICT SUCCESS IN UNDERGRADUATE COURSES AT OUR INSTITUTION. MOREOVER, RELATING PREDICTIONS TO OBSERVED DFW RATES CAN HIGHLIGHT COURSES EXCEEDING EXPECTATIONS, AS WELL AS THOSE WHICH FAIL TO MEET THEM, TO SUPPORT A MORE NUANCED UNDERSTANDING OF WHERE AND WHAT ATTENTION IS NEEDED. IN THIS PAPER, WE DESCRIBE OUR "GAP ANALYSIS" PROCEDURE AND ILLUSTRATE THE UTILITY OF SUCH APPROACH BY EXAMINING ISSUES SURROUNDING SUCCESS IN ONLINE COURSES AT OUR INSTITUTION.
IN THIS PAPER WE PRESENT A REFERENCE ARCHITECTURE FOR ETL STAGES OF EDM AND LA THAT WORKS WITH DIFFERENT DATA FORMATS AND DIFFERENT EXTRACTION SITES, ENSURING PRIVACY AND MAKING EASIER FOR NEW PARTICIPANTS TO ENTER INTO THE PROCESS WITHOUT DEMANDING MORE COMPUTING POWER. CONSIDERING SCENARIOS WITH A MULTITUDE OF VIRTUAL ENVIRONMENTS HOSTING EDUCATIONAL ACTIVITIES, ACCESSIBLE THROUGH A COMMON INFRASTRUCTURE, WE DEVISED A REFERENCE MODEL WHERE DATA GENERATED FROM INTERACTION BETWEEN USERS AND AMONG USERS AND THE ENVIRONMENT ITSELF, ARE SELECTED, ORGANIZED AND STORED IN LOCAL "BASKETS". LOCAL BASKETS ARE THEN COLLECTED AND GROUPED IN A GLOBAL BASKET. ORGANIZATION RESOURCES LIKE ITEM MODELING ARE USED IN BOTH LEVELS OF BASKET CONSTRUCTION. USING THIS REFERENCE UPON A CLIENT-SERVER ARCHITECTURAL STYLE, A REFERENCE ARCHITECTURE WAS DEVELOPED AND HAS BEEN USED TO CARRY OUT A PROJECT FOR AN OFFICIAL FOUNDATION LINKED TO BRAZILIAN MINISTRY OF EDUCATION, INVOLVING EDUCATIONAL DATA MINING AND SHARING OF 100+ HIGHER EDUCATION INSTITUTIONS AND THEIR RESPECTIVE VIRTUAL ENVIRONMENTS. IN THIS ARCHITECTURE, A CLIENT-COLLECTOR INSIDE EACH VIRTUAL ENVIRONMENT COLLECTS INFORMATION FROM DATABASE AND EVENT LOGS. THIS INFORMATION ALONG WITH DEFINITIONS OBTAINED FROM ITEM MODELS ARE USED TO BUILD LOCAL BASKETS. A SYNCHRONIZATION PROTOCOL KEEPS ALL ITEM MODELS SYNCED WITH CLIENT-COLLECTORS AND SERVER-COLLECTORS GENERATING GLOBAL BASKETS. THIS APPROACH HAS SHOWN IMPROVEMENTS ON ETL LIKE: PARALLEL PROCESSING OF ITEMS, ECONOMY ON STORAGE SPACE AND BANDWIDTH, PRIVACY ASSURANCE, BETTER TENACITY, AND GOOD SCALABILITY.
THE BENEFITS OF OPEN LEARNER MODEL FOR LEARNERS HAVE BEEN WIDELY DEMONSTRATED: SUPPORTING LEARNING AND METACOGNITION, FACILITATING SELF-MONITORING AND PLANNING, IMPROVING SELF-ASSESSMENT SKILLS... IN THIS PAPER, WE INVESTIGATE THE BENEFITS OF USING AN OLM FOR TEACHERS. 10 TEACHERS HAVE BEEN USING THE OLM IN ORDER TO MONITOR THEIR CLASS IN THE CONTEXT OF A 12 DAY INTENSIVE COURSE USING THE SPEED READING APPLICATION HIZLIGO AND INVOLVING 87 STUDENTS. THE OLM HAVE BEEN REGULARLY USED BY TEACHERS, USING DIFFERENT VISUALISATIONS, MAINLY IN THE AIM TO IDENTIFY THE STRENGTHS AND WEAKNESS OF BOTH THEIR CLASS AND THEIR INDIVIDUAL STUDENTS. TEACHERS FOUND THE OLM EASY TO USE AND TO UNDERSTAND AND HELPFUL FOR THEIR TEACHING.
MUSIC EDUCATORS ASSESS THE PROGRESS MADE BY THEIR STUDENTS BETWEEN LESSONS. THIS ASSESSMENT PROCESS IS ERROR PRONE, RELYING ON SKILLS AND MEMORY. AN OBJECTIVE EAR IS A TOOL THAT TAKES AS INPUT A PAIR OF PERFORMANCES OF A PIECE OF MUSIC AND RETURNS AN ACCURATE AND RELIABLE ASSESSMENT OF THE PROGRESS BETWEEN THE PERFORMANCES. THE TOOL EVALUATES PERFORMANCES USING DOMAIN KNOWLEDGE TO GENERATE A VECTOR OF METRICS. THE VECTORS FOR A PAIR OF PERFORMANCES ARE SUBTRACTED FROM EACH OTHER AND THE DIFFERENCES ARE USED AS INPUT TO A MACHINE-LEARNING CLASSIFIER WHICH MAPS THE DIFFERENCES TO AN ASSESSMENT. THE IMPLEMENTATION DEMONSTRATES THAT AN OBJECTIVE EAR TOOL IS A FEASIBLE AND PRACTICAL SOLUTION TO THE PROBLEM OF ASSESSMENT.
LEARNING ANALYTICS HAS BECOME MAINSTREAMS IN MANY RESEARCH FIELDS. ALTHOUGH MANY RESEARCHES HAVE BEEN DONE IN LA, THERE ARE STILL NUMEROUS ISSUES AND CHALLENGES SUCH AS HOW TO UTILIZE LA RESULTS TO SUPPORT LEARNING. IN THIS PAPER, WE PROPOSED A FEEDBACK SYSTEM BASED ON A DIGITAL BOOK SYSTEM. AND WE ALSO GET SOME FEEDBACK ABOUT USING THE FEEDBACK SYSTEM.
WE DESCRIBE THE DESIGN RATIONALE AND PRINCIPLES OF A WEB AUTHORING TOOL FOR BEGINNER WEB DEVELOPERS CALLED SNOWBALL. WE EXPLAIN HOW THIS CONSTRUCTIONIST TOOLKIT EXPOSES CONTENT CREATORS TO COMPUTATIONAL FEATURES OF WEB PAGES, ENABLING THEM TO CREATE MEANINGFUL ARTIFACTS WHILE THEY MOVE FROM CONTENT CREATION TO BASIC CODING. FINALLY, WE DISCUSS HOW WE ARE INSTRUMENTING LEARNING ANALYTICS IN SNOWBALL.
IN THIS PAPER WE PRESENT HUMAN WORK INTERACTION DESIGN CHALLENGES AND OPPORTUNITIES FOR THE VISION OF THE SMART UNIVERSITY AS A PLATFORM THAT PROVIDES FOUNDATIONAL CONTEXT DATA TO DELIVER THE UNIVERSITY OF THE FUTURE. WHILE LEARNING ANALYTICS HAVE ENABLED ACCESS TO DIGITAL FOOTPRINTS OF STUDENT ACTIVITIES AND PROGRESS IN TERMS OF DATA SUCH AS DEMOGRAPHICS, GRADES, RECRUITMENT AND PERFORMANCE, THEY CANNOT PROVIDE INFORMATION ABOUT ACTIVITIES AND INTERACTION IN THE PHYSICAL STUDY AND WORK SPACES IN A UNIVERSITY. THE SMART UNIVERSITY PROPOSES A NOVEL PLATFORM THAT WILL PROVIDE CONTEXT AWARE INFORMATION TO STUDENTS THROUGH THE INTEGRATION OF LEARNING ANALYTICS WITH DATA SENSED USING CYBER-PHYSICAL DEVICES IN ORDER TO PROVIDE A HOLISTIC VIEW OF THE ENVIRONMENTS THAT UNIVERSITIES OFFER TO STUDENTS. HOWEVER, DESIGNING THE INTERACTION OF STUDENTS AND STAFF IN THE SMART UNIVERSITY ECOLOGY OF INFORMATION AND SENSING DEVICES REQUIRES AN UNDERSTANDING OF HOW THEY WORK AS INDIVIDUALS, AS MEMBERS OF TEAMS AND COMMUNITIES. THROUGH TWO USE CASES WE ILLUSTRATE HOW INSIGHTS OBTAINED FROM SOCIAL COGNITIVE WORK ANALYSIS CAN BE USED FOR THE DESIGN GUIDELINES OF THE DIFFERENT INTERFACES PART OF THE SMART UNIVERSITY ECOLOGY.
THE CHALLENGE OF DELIVERING PERSONALIZED LEARNING EXPERIENCES IS AMPLIFIED BY THE SIZE OF CLASSROOMS AND OF ONLINE LEARNING COMMUNITIES. IN TURN, SERIOUS GAMES ARE INCREASINGLY RECOGNIZED FOR THEIR POTENTIAL TO IMPROVE EDUCATION, BUT A TYPICAL REQUIREMENT FROM INSTRUCTORS IS TO GAIN INSIGHT INTO HOW THE STUDENTS ARE PLAYING. WHEN WE BRING GAMES INTO THE RAPIDLY GROWING ONLINE LEARNING COMMUNITIES, THE CHALLENGES MULTIPLY AND HINDER THE POTENTIAL EFFECTIVENESS OF SERIOUS GAMES. THERE IS A NEED TO DELIVER A COMPREHENSIVE, FLEXIBLE 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 AIMS TO EXPLORE THE POTENTIAL IN THE USE OF GAMES AND LEARNING ANALYTICS TOWARDS SCAFFOLDING AND SUPPORTING TEACHING AND LEARNING EXPERIENCE. THE CONCEPTUAL MODEL DISCUSSED AIMS TO HIGHLIGHT KEY CONSIDERATIONS THAT MAY ADVANCE THE CURRENT STATE OF LEARNING ANALYTICS, ADAPTIVE LEARNING AND SERIOUS GAMES, BY LEVERAGING SERIOUS GAMES AS AN IDEAL MEDIUM FOR GATHERING DATA AND PERFORMING ADAPTATIONS. THIS OPPORTUNITY HAS THE POTENTIAL TO AFFECT THE DESIGN AND DEPLOYMENT OF EDUCATION AND TRAINING IN THE FUTURE.
COMPUTER SCIENCE (CS) ENROLLMENTS ARE AT AN ALL-TIME HIGH, [1] AND SUCCESSFUL UNDERGRADUATE CS GRADUATIONS ARE INDISPUTABLY IMPORTANT. WITH A STUDENT POPULATION OF APPROXIMATELY 51,000, GEORGIA STATE UNIVERSITY IS A USA BASED STATE UNIVERSITY WHICH IS DIVERSE AND FORMS A RICH BIG DATA FOOTPRINT AS STUDENTS NAVIGATE PATHWAYS TO GRADUATION. QUOTED IN A JULY 2017 ARTICLE FROM HIGHERED.COM, "GEORGIA STATE'S EXTENSIVE PREDICTIVE ANALYTICS EFFORTS ARE LEADING TO BETTER GRADES AND STUDENT RETENTION - AND MORE MINORITIES GRADUATING FROM STEM PROGRAMS." THIS DOCTORAL PROJECT BUILDS UPON CURRENT DATA MINING AND MODELING, MACHINE LEARNING APPLICATIONS, AND LEARNING ANALYTICS FOR PREDICTING STUDENT SUCCESS THAT IS BEYOND RETENTION. GAINING KNOWLEDGE OF CS STUDENT LEARNING, DEVELOPING BETTER ALERTING MODELS FOR SUCCESS, AND DISCOVERING BEHAVIORAL INDICATORS FROM LEARNING ANALYTICS REPORTING IS THE GOAL OF THIS RESEARCH. USING THIS KNOWLEDGE AS EVIDENCE BASED DATA FOR IMPROVING THE CS STUDENT EXPERIENCE WILL AID IN PERFORMANCE IMPROVEMENTS AND INCREASE PATHWAYS TO GRADUATION. MY SUPPORTING RESEARCH PROJECT IS BUILDING CS STUDENT DATASETS TO REPRESENT THE STUDENT AS DIRECTED GRAPHICAL MODELS, INVESTIGATING THEIR RELATIONSHIPS USING MACHINE LEARNING FRAMEWORKS, AND COMPLEX MATHEMATICAL COMPUTATIONS (TENSORS OR GRADIENT BOOSTING) ALONG WITH GRAPH DATA MINING TECHNIQUES.
IN THIS ERA OF RAPID DEVELOPMENT, THE INFORMATION TECHNOLOGY IS EXERTING SIGNIFICANT AND FAR-REACHING IMPACT ON OUR LIFE. SUCH NEW TECHNOLOGIES AS LEARNING ANALYTICS, TABLET COMPUTING, GAMES AND GAMIFICATION, WEARABLE TECHNOLOGY, 3D PRINTING AND SO ON ARE INTRODUCED IN THE NMC HORIZON REPORT. THIS PAPER MAINLY INTRODUCES THE PRINCIPLE OF 3D PRINTING TECHNOLOGY, ITS APPLICATION FIELD, ITS APPLICATION EXAMPLES AND ITS EFFECT ON SCHOOL EDUCATION.
IN THIS PAPER, WE PRESENT A NOVEL SYSTEM, INVIDEO, FOR AUTOMATICALLY INDEXING AND SEARCHING VIDEOS BASED ON THE KEYWORDS SPOKEN IN THE VIDEOS AND THE CONTENT OF THE VIDEO FRAMES. USING THE HIGHLY EFFICIENT VIDEO INDEXING ENGINE WE DEVELOPED, INVIDEO IS ABLE TO ANALYZE VIDEOS USING MACHINE LEARNING AND PATTERN RECOGNITION WITHOUT THE NEED FOR INITIAL VIEWING BY A HUMAN. THE TIME-STAMPED COMMENTING AND TAGGING FEATURES REFINE THE ACCURACY OF SEARCH RESULTS. THE CLOUD-BASED IMPLEMENTATION MAKES IT POSSIBLE TO CONDUCT ELASTIC SEARCH, AUGMENTED SEARCH, AND DATA ANALYTICS. OUR RESEARCH SHOWS THAT INVIDEO PRESENTS AN EFFICIENT TOOL IN PROCESSING AND ANALYZING VIDEOS AND INCREASING INTERACTIONS IN VIDEO-BASED ONLINE LEARNING ENVIRONMENT. DATA FROM A CYBERSECURITY PROGRAM WITH MORE THAN 500 STUDENTS SHOW THAT APPLYING INVIDEO TO CURRENT VIDEO MATERIAL, INTERACTIONS BETWEEN STUDENT-STUDENT AND STUDENT-FACULTY INCREASED SIGNIFICANTLY ACROSS 24 SECTIONS PROGRAM-WIDE.
INTRODUCTION. THE AIM OF THIS PAPER IS TO ADVANCE IN THE UNDERSTANDING OF PROBLEMS RELATED TO MATHEMATICAL LEARNING IN HIGHER EDUCATION. MORE SPECIFICALLY, IN A DEGREE THAT HAS ENJOYED ENORMOUS POPULARITY IN THE SPANISH UNIVERSITY CONTEXT FOR YEARS: THE DOUBLE DEGREE IN BUSINESS ADMINISTRATION AND LAW. METHOD. WORKING ON A SAMPLE OF 973 STUDENTS, A CLUSTER ANALYSIS WAS FIRST CARRIED OUT TO IDENTIFY DIFFERENT STUDENT PROFILES, USING AS VARIABLES THEIR GRADES IN THREE DIFFERENT TYPES OF SUBJECTS: QUANTITATIVE, LAW AND GENERAL ONES. SUBSEQUENTLY, AND ON EACH OF THE CLUSTERS IDENTIFIED, REGRESSION MODELS HAVE BEEN ADJUSTED IN ORDER TO DETERMINE THE RELATIONSHIPS BETWEEN THE DIFFERENT SUBJECTS. RESULTS. ABOUT 30% OF THE STUDENTS SHOWED A STRONG QUALITATIVE BIAS, WITH A VERY DIFFERENT PROFILE FROM OTHER GROUPS, AND WHICH WAS CHARACTERIZED BY A MEDIOCRE PERFORMANCE IN QUANTITATIVE SUBJECTS, AND, HOWEVER, A GOOD ACHIEVEMENT IN THE REST OF THE SUBJECTS. IN ADDITION, IN THIS GROUP OF STUDENTS THERE DOES NOT SEEM TO BE ANY RELATION IN THE PERFORMANCE IN DIFFERENT MATHEMATICS SUBJECTS. DISCUSSION. THIS RESULT SUGGESTS THAT THE APPROACH OF THIS GROUP OF STUDENTS TO A GIVEN MATHEMATICS SUBJECT IS INDEPENDENT OF WHAT WAS STUDIED PREVIOUSLY, AS IF IT HAD NO RELATION WITH THE PREVIOUS MATHEMATICS AS A SUBJECT. MOREOVER, THIS CHARACTERISTIC IS EXCLUSIVE TO THIS GROUP, SINCE IN ALL OTHER STUDENTS, AROUND 70%, THERE IS A STRONG RELATIONSHIP. THIS LEADS US TO CONSIDER THAT THESE STUDENTS MAY BE OPTING FOR A ROTE APPROACH TO QUANTITATIVE SUBJECTS.
METHODOLOGY ELEARNING IMPROVES EDUCATIONAL AND PROMOTES TO SHARE AND COLLABORATE. THIS WORK PRESENTS A WAY TO ANALYZE AND AUTOMATE LOG FILES FROM THE EDUCATIONAL FORUMS IN A COURSE OF VIRTUAL LEARNING SYSTEMS. WE HAVE DEVELOPED A PIECE OF SOFTWARE TO PROCESS THE LOGS FILES. TEXT REPORTS AND DATABASE FILES ARE PRODUCED. THEN IT IS POSSIBLE TO STUDY THEM AND TO OBTAIN CONCLUSIONS ABOUT THE WORK OF OUR STUDENTS IN THIS CONTEXT.
THE STUDY PRESENTED IN THIS PAPER DEALS WITH COPYING ANSWERS IN MOOCS. OUR FINDINGS SHOW THAT A SIGNIFICANT FRACTION OF THE CERTIFICATE EARNERS IN THE COURSE THAT WE STUDIED HAVE USED WHAT WE CALL HARVESTING ACCOUNTS TO FIND CORRECT ANSWERS THAT THEY LATER SUBMITTED IN THEIR MAIN ACCOUNT, THE ACCOUNT FOR WHICH THEY EARNED A CERTIFICATE. IN TOTAL, 2.5% OF THE USERS WHO EARNED A CERTIFICATE IN THE COURSE OBTAINED THE MAJORITY OF THEIR POINTS BY USING THIS METHOD, AND 10% OF THEM USED IT TO SOME EXTENT. THIS PAPER HAS TWO MAIN GOALS. THE FIRST IS TO DEFINE THE PHENOMENON AND DEMONSTRATE ITS SEVERITY. THE SECOND IS CHARACTERIZING KEY FACTORS WITHIN THE COURSE THAT AFFECT IT, AND SUGGESTING POSSIBLE REMEDIES THAT ARE LIKELY TO DECREASE THE AMOUNT OF CHEATING. THE IMMEDIATE IMPLICATION OF THIS STUDY IS TO MOOCS. HOWEVER, WE BELIEVE THAT THE RESULTS GENERALIZE BEYOND MOOCS, SINCE THIS STRATEGY CAN BE USED IN ANY LEARNING ENVIRONMENTS THAT DO NOT IDENTIFY ALL REGISTRANTS.
ONLINE SOCIAL NETWORKING (OSN) PROVIDES A NEW WAY FOR HEALTH PROFESSIONALS TO COMMUNICATE, COLLABORATE AND SHARE IDEAS WITH EACH OTHER FOR INFORMAL LEARNING ON A MASSIVE SCALE. IT HAS IMPORTANT IMPLICATIONS FOR ONGOING EFFORTS TO SUPPORT CONTINUING PROFESSIONAL DEVELOPMENT (CPD) IN THE HEALTH PROFESSIONS. HOWEVER, THE CHALLENGE OF ANALYSING THE DATA GENERATED IN OSNS MAKES IT DIFFICULT TO UNDERSTAND WHETHER AND HOW THEY ARE USEFUL FOR CPD. THIS PAPER PRESENTS A CONCEPTUAL MODEL FOR USING MIXED METHODS TO STUDY DATA FROM OSNS TO EXAMINE THE EFFICACY OF OSN IN SUPPORTING INFORMAL LEARNING OF HEALTH PROFESSIONALS. IT IS EXPECTED THAT USING THIS MODEL WITH THE DATASET GENERATED IN OSNS FOR INFORMAL LEARNING WILL PRODUCE NEW AND IMPORTANT INSIGHTS INTO HOW WELL THIS INNOVATION IN CPD IS SERVING PROFESSIONALS AND THE HEALTHCARE SYSTEM.
MANAGING INTEGRITY OF CONTINUOUS AND AUTHENTIC ASSESSMENTS IN AN OPEN AND DISTANCE ENVIRONMENTS IS A COMPLEX PROCESS. THE CORE CHALLENGE IS TO MAP STUDENT IDENTITIES WITH THEIR ACADEMIC WORK IN EFFECTIVE AND EFFICIENT MANNERS, WHILE PRESERVING PRIVACY, ENSURING MINIMAL DISRUPTION, MINIMIZING IMPACTS ON ACCESSIBILITY AND CONVENIENCE. TO ADDRESS THIS ISSUE, WE HAVE DEVELOPED A PROTOTYPE CLOUD-BASED APPLICATION ENTITLED OPENPROCTOR THAT EMPLOYS MACHINE-LEARNING TECHNIQUES TO ANALYZE PATTERNS IN THE LEARNER-PRODUCED TEXTUAL CONTENT IN ORDER TO PROVIDE IDENTITY AND AUTHORSHIP ASSURANCE. IN CONTRAST TO THE TRADITIONAL ACADEMIC INTEGRITY APPROACHES THAT SEEK TO CONTROL THE REMOTE LEARNING ENVIRONMENT, OUR METHOD TAKES ADVANTAGE OF THE READILY AVAILABLE LEARNER-GENERATED DATA TO ANALYZE HOW THE STUDENTS GO ABOUT DOING THEIR ACADEMIC WORK. WE PRESENT THE FRAMEWORK, THE ARCHITECTURE AND MAIN FUNCTIONS OF THE OPENPROCTOR SYSTEM AND DISCUSS THE FUTURE RESEARCH DIRECTIONS AND PLANS FOR THE SYSTEM'S FUTURE DEVELOPMENT.
WE ARE DEVELOPING A WEB-BASED PROGRAMMING EDUCATION SUPPORT TOOL PGTRACER AS A PLUG-IN OF WELL-KNOWN LEARNING MANAGEMENT SYSTEM MOODLE. PGTRACER PROVIDES FILL-IN-THE-BLANK QUESTIONS COMPOSED OF A C++ PROGRAM AND A TRACE TABLE TO STUDENTS. WHEN A STUDENT ANSWERS A QUESTION BY FILLING THE BLANKS, PGTRACER COLLECTS STUDENT'S ANSWERS, REQUIRED TIME, EVALUATION RESULT, ETC. AS STUDENT LOG. PGTRACER ALSO PROVIDES VARIOUS ANALYSIS FUNCTIONS OF THE COLLECTED LOG. A TEACHER CAN UTILIZE THE ANALYSIS FUNCTIONS IN ORDER TO ANALYZE THE STUDENT ACHIEVEMENT LEVEL AND THE DIFFICULTY LEVEL OF THE PROBLEMS. IN THIS PAPER, WE PROPOSE METHODS TO ANALYZE ACHIEVEMENT LEVEL OF THE STUDENTS UTILIZING THE ANALYSIS FUNCTIONS. WE PERFORM AN EVALUATION EXPERIMENT WITH 50 STUDENTS IN ORDER TO VALIDATE THOSE METHODS. AS A RESULT, DIFFERENCE WAS DISCOVERED AT THE ANSWER PROCESS CAUSED BY THE ACHIEVEMENT LEVEL OF THE STUDENT. THE PROPOSED METHOD IS USEFUL TO ANALYZE ACHIEVEMENT LEVEL OF THE STUDENTS IN DETAIL.
PRESENT MOOC AND SPOC PLATFORMS DO NOT PROVIDE TEACHERS WITH PRECISE METRICS THAT REPRESENT THE EFFECTIVENESS OF STUDENTS WITH EDUCATIONAL RESOURCES AND ACTIVITIES. THIS WORK PROPOSES AND ILLUSTRATES THE APPLICATION OF THE PRECISE EFFECTIVENESS STRATEGY (PES). PES IS A GENERIC METHODOLOGY FOR DEFINING PRECISE METRICS THAT ENABLE CALCULATION OF THE EFFECTIVENESS OF STUDENTS WHEN INTERACTING WITH EDUCATIONAL RESOURCES AND ACTIVITIES IN MOOCS AND SPOCS, TAKING INTO ACCOUNT THE PARTICULAR ASPECTS OF THE LEARNING CONTEXT. PES HAS BEEN APPLIED IN A CASE STUDY, CALCULATING THE EFFECTIVENESS OF STUDENTS WHEN WATCHING VIDEO LECTURES AND SOLVING PARAMETRIC EXERCISES IN FOUR SPOCS DEPLOYED IN THE KHAN ACADEMY PLATFORM. DIFFERENT VISUALIZATIONS WITHIN AND BETWEEN COURSES ARE PRESENTED COMBINING THE METRICS DEFINED FOLLOWING PES. WE SHOW HOW THESE VISUALIZATIONS CAN HELP TEACHERS MAKE QUICK AND INFORMED DECISIONS IN OUR CASE STUDY, ENABLING THE WHOLE COMPARISON OF A LARGE NUMBER OF STUDENTS AT A GLANCE, AND A QUICK COMPARISON OF THE FOUR SPOCS DIVIDED BY VIDEOS AND EXERCISES. ALSO, THE METRICS CAN HELP TEACHERS KNOW THE RELATIONSHIP OF EFFECTIVENESS WITH DIFFERENT BEHAVIORAL PATTERNS. RESULTS FROM USING PFS IN THE CASE STUDY REVEALED THAT THE EFFECTIVENESS METRICS PROPOSED HAD A MODERATE NEGATIVE CORRELATION WITH SOME BEHAVIORAL PATTERNS LIKE RECOMMENDATION LISTENER OR VIDEO AVOIDER. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
NOWADAYS, THERE ARE MORE AND MORE RESEARCHES FOCUSED ON PREDICTION OF LEARNING OUTCOME, AND MOST OF THEM APPLIED QUANTITATE TYPE OF ANALYSIS APPROACHES. THUS, WE WANT TO APPLY ANOTHER TYPE OF ANALYSIS APPROACH TO DO EARLY PREDICTION. IN THIS RESEARCH, WE APPLIED TEMPORAL FEATURES AND ANALYSIS APPROACH TO PREDICT STUDENTS' LEARNING OUTCOMES AND IDENTIFY AT-RISK STUDENTS. THE RESULT SHOWS THAT USING TEMPORAL FEATURES IS EFFECTIVE ON EARLY PREDICTION OF LEARNING OUTCOME AND THERE EXISTS DIFFERENCES OF LEARNING BEHAVIORS BETWEEN STUDENTS WHICH HAVE DIFFERENT LEARNING BACKGROUND.
THERE IS MUCH ENTHUSIASM IN HIGHER EDUCATION ABOUT THE BENEFITS OF ADAPTIVE LEARNING AND USING BIG DATA TO INVESTIGATE LEARNING PROCESSES TO MAKE DATA-INFORMED EDUCATIONAL DECISIONS. THE BENEFITS OF ADAPTIVE LEARNING TO ACHIEVE PERSONALIZED LEARNING ARE OBVIOUS. YET, THERE LACKS EVIDENCE-BASED RESEARCH TO UNDERSTAND HOW DATA SUCH AS USER BEHAVIOR PATTERNS CAN BE USED TO DESIGN EFFECTIVE ADAPTIVE LEARNING SYSTEMS. THE PURPOSE OF THIS STUDY, THEREFORE, IS TO INVESTIGATE WHAT BEHAVIOR PATTERNS LEARNERS WITH DIFFERENT CHARACTERISTICS DEMONSTRATE WHEN THEY INTERACT WITH AN ADAPTIVE LEARNING ENVIRONMENT. INCOMING 1ST-YEAR STUDENTS IN A PHARMACY PROFESSIONAL DEGREE PROGRAM ENGAGED IN AN ADAPTIVE LEARNING INTERVENTION THAT AIMED TO PROVIDE REMEDIAL INSTRUCTION TO BETTER PREPARE THESE PROFESSIONAL STUDENTS BEFORE THEY BEGAN THEIR FORMAL DEGREE PROGRAM. WE ANALYZED THE PARTICIPANTS' BEHAVIOR PATTERNS THROUGH THE USAGE DATA TO UNDERSTAND HOW THEY USED THE ADAPTIVE SYSTEM BASED UPON THEIR NEEDS AND INTERESTS. USING BOTH STATISTICAL ANALYSES AND DATA VISUALIZATION TECHNIQUES, THIS STUDY FOUND: (1) APART FROM LEARNERS' COGNITIVE ABILITY, IT IS IMPORTANT TO CONSIDER AFFECTIVE FACTORS SUCH AS MOTIVATION IN ADAPTIVE LEARNING, (2) LACK OF ALIGNMENT AMONG VARIOUS COMPONENTS IN AN ADAPTIVE SYSTEM CAN IMPACT HOW LEARNERS ACCESSED THE SYSTEM AND, MORE IMPORTANTLY, THEIR PERFORMANCE, AND (3) VISUALIZATIONS CAN REVEAL INTERESTING FINDINGS THAT CAN BE MISSED OTHERWISE. SUCH RESEARCH SHOULD PROVIDE MUCH NEEDED EMPIRICAL EVIDENCES AND USEFUL INSIGHTS ABOUT HOW THE ANALYTICS CAN INFORM THE EFFECTIVE DESIGNS OF ADAPTIVE LEARNING.
ACTIVITIES TAKEN BY STUDENTS WITHIN A VIRTUAL LEARNING ENVIRONMENT (VLE) CAN BE REPRESENTED BY USING BINARY STUDENT HISTORIES. VIRTUAL LEARNING ENVIRONMENTS ALLOW EDUCATORS TO TRACK MOST OF THE STUDENTS' INDIVIDUAL ACTIVITIES THAT CAN BE USED TO ELICIT THE STUDENTS SOCIAL COMMUNITIES. IN THIS WORK, WE ANALYSE THE IMPACT OF GRANULARITY IN THE SOCIAL COMMUNITY ELICITATION. GRANULARITY CAN BE SEEN AS THE RESOLUTION OF THE STUDENT HISTORY VECTORS WHERE EACH TIME SLOT IS DIRECTLY DEPENDENT FROM THIS VALUE. INDEED, THE HIGHER IS THE RESOLUTION OF THE STUDENTS HISTORIES THE MORE PRECISE IS THE REPRESENTATION OF THEIR ACTIONS WITHIN THE VLE. WHEN COMPARING THE HISTORIES USING VARIOUS SIMILARITY MEASURES TO ELICIT THE STUDENTS' GROUPS, WE FIND THE OPTIMAL GRANULARITY AND DEMONSTRATE THAT THERE IS A RESOLUTION LIMIT WHERE THE SIMILARITY MEASURES WILL NOT HELP TO DISTINGUISH THE SOCIAL COMMUNITIES.
THIS PAPER PRESENTS A GAME-BASED LEARNING ENVIRONMENT SUPPORTING THE ACQUISITION OF COMPUTATIONAL THINKING SKILLS. THE GAME ENVIRONMENT OFFERS TWO TYPES OF USAGE: A GUIDED PROGRESSION THROUGH LEVELS AND AN OPEN STAGE. SEVEN LEVELS OF GUIDED LEARNING WERE EVALUATED IN A STUDY WITH 40 STUDENTS TO MEASURE THEIR LEARNING PROGRESS. WE INTRODUCE FIVE FEATURES TO DESCRIBE THE STUDENTS' LEARNING BEHAVIOR AND MAP THE PERFORMANCE PARAMETERS TO COMPUTATIONAL THINKING COMPETENCES. IN OUR ANALYSIS, WE DISTINGUISH DIFFERENT BASIC ACTIONS AND COMPARE THE USAGE AND DISTRIBUTION OF THESE ACTIONS OVER TIME AND BETWEEN GROUPS OF LEARNERS WITH DIFFERENT ACHIEVEMENT LEVELS. THIS ALLOWS FOR IDENTIFYING SPECIFIC CHALLENGES RELATED THE INTERACTIVE ACQUISITION OF CT SKILLS.
ENHANCING STUDENT ENGAGEMENT AND ACHIEVEMENT IN LARGE UNDERGRADUATE UNIVERSITY COURSES IS A VIBRANT AREA OF RESEARCH. MANY EXISTING STUDIES HAVE DEMONSTRATED THE CAPACITY OF TECHNOLOGICALLY ENHANCED FORMAL AND INFORMAL LEARNING SPACES TO PROMOTE LEARNING. HOWEVER, THIS LITERATURE IS DOMINATED BY STUDIES THAT USE ASSESSMENT TO INCENTIVISE STUDENT PARTICIPATION, USUALLY IN THE FORM OF AWARDING OR DEDUCTING COURSEWORK MARKS. OUR STUDY PRESENTS AN ALTERNATIVE TO THIS "CARROT AND STICK" APPROACH. WE DESCRIBE THE DESIGN AND IMPLEMENTATION OF TWO OPTIONAL, NON-GRADE-BEARING LEARNING SUPPORT INITIATIVES IN A LARGE ENROLMENT, FIRST-YEAR HUMAN BIOLOGY COURSE: FIRST, AN ONLINE MOCK TEST THAT ALLOWS FOR TIMELY FEEDBACK; AND SECOND, PEER-TEACHING AND-LEARNING USING THE PIAZZA DISCUSSION PLATFORM. WE ANALYSED THE UPTAKE OF THESE INITIATIVES ACROSS THREE CONSECUTIVE COURSE OFFERINGS (2013-2015), AND CONVEYED TO STUDENTS THEIR POTENTIAL IMPACT AS A SERIES OF GRAPHS IN THE COURSE MATERIALS. REGRESSION ANALYSIS IDENTIFIED A POSITIVE CORRELATION BETWEEN PARTICIPATION IN THE MOCK TEST AND SUMMATIVE ACHIEVEMENT IN THE COURSE FOR GRADE POINT AVERAGE (GPA)-MATCHED STUDENT PAIRS, WITH THE BENEFIT BEING MOST PRONOUNCED FOR STUDENTS WITH LOW-TO-MIDDLE GPAS. MODELLING REVEALED FOUR DIFFERENT LEVELS OF STUDENT PARTICIPATION IN PIAZZA, WITH INCREASING PARTICIPATION CORRELATED WITH GREATER ACHIEVEMENT, PARTICULARLY FOR STUDENTS WITH LOW-TO-MIDDLE GPAS. THE BENEFITS OF THE TWO INITIATIVES WERE COMPLEMENTARY. INTERESTINGLY, FOR CERTAIN STUDENTS, 'PASSIVE' PARTICIPATION IN PIAZZA YIELDED SIMILAR LEARNING BENEFITS TO 'ACTIVE' PARTICIPATION, SUGGESTING THAT ACTIVE PARTICIPATION IS NOT AN ANTECEDENT FOR LEARNING GAINS. THIS FINDING HAS IMPORTANT IMPLICATIONS FOR INSTRUCTORS WHO CURRENTLY REQUIRE STUDENTS TO 'VISIBLY' ENGAGE TO EARN PARTICIPATION MARKS.
BLENDED LEARNING COMBINES AND GAINS THE ADVANTAGES OF FACE-TO-FACE AND COMPUTER-BASED DISTRIBUTED LEARNING. TEACHERS NEED TO ENCOURAGE STUDENTS TO ENGAGE MORE IN THE LEARNING PROCESS AND LEARNING ACTIVITIES. FORMATIVE FEEDBACK (FEEDBACK) FROM STAKEHOLDERS CAN ENCOURAGE STUDENTS TO PARTICIPATE MORE IN BOTH ONLINE AND CLASSROOM LEARNING. HOWEVER, THREE MAJOR CHALLENGES ARE BEING FACED BY RESEARCHERS. FIRSTLY, INCONSISTENT RESULTS HAVE BEEN FOUND FROM LINEAR MODELS ON THE AMOUNT OF FEEDBACK AND ACADEMIC PERFORMANCE. SECONDLY, FEEDBACK FROM DIFFERENT STAKEHOLDERS HAVE NOT BEEN ANALYSED IN AN INTEGRATED WAY. THIRDLY, THE CONNECTION BETWEEN FEEDBACK AND PERFORMANCE HAVE NOT BEEN IDENTIFIED. WE HAVE STARTED BY PROPOSING A LOGARITHM TRANSFORMATION ON THE AMOUNT OF FEEDBACK TO ADDRESS THE ISSUES FOUND IN THE LINEAR MODELS. THEN, WE HAVE SUGGESTED AN INITIAL CONCEPTUAL MODEL ON HOW ACADEMIC PERFORMANCE IS RELATED TO FEEDBACK FROM DIFFERENT STAKEHOLDERS. FINALLY, THE MODEL HAS BEEN VERIFIED AND IMPROVED THROUGHOUT THE THREE LONGITUDINAL STUDIES.
DESPITE STEM EDUCATION COMMUNITIES RECOGNIZING THE IMPORTANCE OF INTEGRATING COMPUTATIONAL THINKING (CT) INTO HIGH SCHOOL CURRICULA, COMPUTATION STILL REMAINS A SEPARATE AREA OF STUDY IN K-12 CONTEXTS. IN ADDITION, MUCH OF THE RESEARCH ON CT HAS FOCUSED ON CREATING GENERALLY AGREED-UPON DEFINITIONS AND CURRICULA, BUT FEW STUDIES HAVE EMPIRICALLY TESTED ASSESSMENTS OR USED CONTEMPORARY LEARNING SCIENCES METHODS TO DO SO. IN THIS PAPER, WE OUTLINE THE IMPLEMENTATION OF AN ASSESSMENT APPROACH FOR A 10-DAY HIGH SCHOOL BIOLOGY UNIT WITH COMPUTATIONAL THINKING ACTIVITIES THAT EXAMINES STUDENT PRE-POST RESPONSES AS WELL AS RESPONSES TO EMBEDDED ASSESSMENTS THROUGHOUT THE UNIT. USING PRE-POST SCORES, WE IDENTIFIED STUDENTS WITH BOTH POSITIVE AND NEGATIVE GAINS AND EXAMINED HOW EACH GROUP'S CT PRACTICES DEVELOPED AS THEY ENGAGED WITH THE CURRICULAR UNIT. OUR RESULTS SHOW THAT (1) STUDENTS EXHIBITED SCIENCE AND COMPUTATIONAL LEARNING GAINS AFTER ENGAGING WITH A SCIENCE UNIT WITH COMPUTATIONAL MODELS AND (2) THAT THE USE OF EMBEDDED ASSESSMENTS AND DISCOURSE ANALYTICS TOOLS REVEALS HOW STUDENTS THINK DIFFERENTLY WITH COMPUTATIONAL TOOLS THROUGHOUT THE UNIT.
ALL ESTONIA'S UPPER-SECONDARY SCHOOL STUDENTS HAVE THREE MANDATORY FINAL STATE EXAMINATIONS - IN MATHEMATICS, ESTONIAN LANGUAGE AND FOREIGN LANGUAGE. WE USED DATA OBTAINED FROM ONE SCHOOL IN ORDER TO PREDICT STUDENT'S FINAL EXAMINATION SCORES BASED ON PREVIOUS GRADES AND DEMOGRAPHIC DATA. OUR AIM WAS TO FIND MOST IMPORTANT FACTORS THAT CONTRIBUTE POSITIVELY TO FINAL RESULTS AND VICE VERSA. MACHINE LEARNING PACKAGE WEKA WAS USED TO BUILD PREDICTIVE MODELS AND WITH ALL TESTED ATTRIBUTE SETS WE GOT ACCURACY OVER 80% ON CLASSIFICATION. ON CONTINUOUS MODELS WE GOT MEAN ABSOLUTE ERROR CLOSE OR LESS TO 10, WHEN RANGE OF TEST RESULT WAS 0-100 POINTS. MOST OF OUR ATTRIBUTES WERE SUBJECT GRADES ON SCALE 1-5, AND THEREFORE, AS WE LIMITED OUR TESTING SAMPLE WITH ONE SCHOOL ONLY, IT GAVE INTERESTING INSIGHTS INTO HOW SOME SUBJECTS AND TEACHERS DO CONTRIBUTE TO FINAL TEST RESULTS. AND, SURPRISINGLY, IT TURNED OUT, THAT FOR SOME TEST RESULT, MOST SIGNIFICANT PREDICTOR WAS NEGATIVELY CORRELATED TO THE RESULT. IT MEANS THAT HAVING BAD GRADES IN SOME SUBJECTS WAS A GOOD PREDICTOR OF SUCCESS IN FINAL TEST. BESIDES BEING A USEFUL TOOL FOR STUDENTS WHO WANT TO ESTIMATE POSSIBLE TEST RESULTS BEFOREHAND, IT IS ALSO A USEFUL TOOL FOR MEASURING TEACHERS' CONTRIBUTIONS AND RELATIONSHIPS BETWEEN DIFFERENT SUBJECTS.
PRESENTATION HAS BEEN AN EFFECTIVE METHOD FOR DELIVERING INFORMATION TO AN AUDIENCE FOR MANY YEARS. OVER THE PAST FEW DECADES, TECHNOLOGICAL ADVANCEMENTS HAVE REVOLUTIONIZED THE WAY HUMANS DELIVER PRESENTATION. CONVENTIONALLY, THE QUALITY OF A PRESENTATION IS USUALLY EVALUATED THROUGH PAINSTAKING MANUAL ANALYSIS WITH EXPERTS. ALTHOUGH THE EXPERT FEEDBACK IS EFFECTIVE IN ASSISTING USERS TO IMPROVE THEIR PRESENTATION SKILLS, MANUAL EVALUATION SUFFERS FROM HIGH COST AND IS OFTEN NOT AVAILABLE TO MOST INDIVIDUALS. IN THIS WORK, WE PROPOSE A NOVEL MULTI-SENSOR SELF-QUANTIFICATION SYSTEM FOR PRESENTATIONS, WHICH IS DESIGNED BASED ON A NEW PROPOSED ASSESSMENT RUBRIC. WE PRESENT OUR ANALYTICS MODEL WITH CONVENTIONAL AMBIENT SENSORS (I.E., STATIC CAMERAS AND KINECT SENSOR) AND THE EMERGING WEARABLE EGOCENTRIC SENSORS (I.E., GOOGLE GLASS). IN ADDITION, WE PERFORMED A CROSS-CORRELATION ANALYSIS OF SPEAKER'S VOCAL BEHAVIOR AND BODY LANGUAGE. THE PROPOSED FRAMEWORK IS EVALUATED ON A NEW PRESENTATION DATASET, NAMELY, NUS MULTI-SENSOR PRESENTATION DATASET, WHICH CONSISTS OF 51 PRESENTATIONS COVERING A DIVERSE RANGE OF TOPICS. TO VALIDATE THE EFFICACY OF THE PROPOSED SYSTEM, WE HAVE CONDUCTED A SERIES OF USER STUDIES WITH THE SPEAKERS AND AN INTERVIEW WITH AN ENGLISH COMMUNICATION EXPERT, WHICH REVEALS POSITIVE AND PROMISING FEEDBACK.
THE LEARNING PROCESS IN A MOOC (MASSIVE OPEN ONLINE COURSE) CAN BE IMPROVED FROM KNOWING IN ADVANCE LEARNERS' GRADES ON DIFFERENT ASSIGNMENTS. THIS WOULD BE VERY USEFUL TO DETECT PROBLEMS WITH ENOUGH TIME TO TAKE CORRECTIVE MEASURES. IN THIS WORK, THE AIM IS TO ANALYSE HOW DIFFERENT COURSE SCORES CAN BE PREDICTED, WHAT ELEMENTS OR VARIABLES AFFECT THE PREDICTIONS AND HOW MUCH AND IN WHICH WAY IT IS POSSIBLE TO ANTICIPATE SCORES. TO DO THAT, DATA FROM A MOOC ABOUT JAVA PROGRAMMING HAVE BEEN USED. RESULTS SHOW THE IMPORTANCE OF INDICATORS OVER THE ALGORITHMS AND THAT FORUM-RELATED VARIABLES DO NOT ADD POWER TO PREDICT GRADES, UNLIKE PREVIOUS SCORES. FURTHERMORE, THE TYPE OF TASK CAN VARY THE RESULTS. REGARDING THE ANTICIPATION, IT WAS POSSIBLE TO USE DATA FROM PREVIOUS TOPICS BUT WITH WORSE PERFORMANCE, ALTHOUGH VALUES WERE BETTER THAN THOSE OBTAINED IN THE FIRST SEVEN DAYS OF THE CURRENT TOPIC.
WHILE THE TECHNOLOGY-RICH CLASSROOM MAKES IT COMPARATIVELY EASY TO GATHER, STORE AND ACCESS DATA ON STUDENTS' ACTIVITIES, TURNING THOSE INTO INFORMATION ON LEARNING THAT CAN INFORM PEDAGOGICAL DECISION-MAKING IS STILL HARD TO ACHIEVE. IN THE NEXT-TELL PROJECT, WE BUILD ON CONCEPTS FROM EDUCATIONAL ASSESSMENT DESIGN AND ON MODELING CONCEPTS FROM COMPUTER SCIENCE AS A BASIS FOR GENERATING QUALITY DATA ON STUDENTS' LEARNING. WE DESCRIBE A SET OF INTER-RELATED METHODS AND SOFTWARE COMPONENTS THAT CAN BE USED TO TURN ASSESSMENTS INTO SUPPORT MECHANISMS FOR LEARNING, AND TO MAKE USE AND SENSE OF DATA FOR IMPROVING TEACHING AND LEARNING.
THIS PAPER IS A NEXT STEP AFTER ESTABLISHING A NEW SYSTEM FOR REAL TIME CLASSROOM AND VIRTUAL LEARNING PROCESS EVALUATION. IN A PREVIOUS PAPER A THEORETICAL SYSTEM FROM THREE COLOUR CODES WAS ESTABLISHED: RED FOR "HAVE A PROBLEM", YELLOW FOR "WORK IN PROGRESS" AND GREEN FOR "JOB DONE". THE HYPOTHESIS IS THAT COLOUR CODE USAGE IMPROVES LEARNING PROCESS EVALUATION, TEACHER'S REACTION ABILITY AND LEARNING PACE. AN EXPERIMENT WAS CONDUCTED TO TEST THE HYPOTHESIS AND THE COLOUR CODE METHOD IN A REAL LEARNING ENVIRONMENT. IN THIS PAPER FIRST EXPERIMENTAL RESULTS IN A REAL CLASSROOM ARE ANALYSED AND DISCUSSED. EXPERIMENTS TOOK PART IN THE FIRST GRADE (7-8 YEARS OLD CHILDREN) AND IN THE FOURTH GRADE (10-11 YEARS OLD CHILDREN) IN LATVIA. THE RESULTS SUGGEST THAT THERE ARE LESS AND LOWER IMPLEMENTATION BARRIERS FOR THE CHILDREN IN THESE AGE GROUPS AS THOUGHT BEFORE, AND CHILDREN ARE EAGER TO USE THE NEW TOOLS AND MOSTLY AREN'T BOTHERED BY THE EXTRA EFFORT NEEDED TO REMEMBER TO USE THE SYSTEM TOOLS. THE BIGGEST CHALLENGE SEEMS TO BE IN THE TEACHERS' SIDE - IN ADJUSTING THE FLOW OF THE LEARNING PROCESS AND START USING THE SYSTEM IN ADVANCED MODE FOR DATA GATHERING. ANOTHER CONCLUSION FROM THIS IS THAT THE COLOUR CODE SYSTEM IS EASIER ADOPTED IN VOCATIONAL EDUCATION AND FOR TEACHERS AND TRAINERS THAT WORK WITH CHANGING LEARNER GROUPS. TEACHERS AND LEARNERS WOULD BENEFIT ALSO BECAUSE THE COLOUR CODE SYSTEM SERVES AS A GOOD TOOL FOR CHANGING TEACHERS' TACIT KNOWLEDGE TO EXPLICIT KNOWLEDGE WHICH CAN BENEFIT IN KNOWLEDGE SHARING AND OPERATIONALIZATION. ANOTHER INTERESTING CONCLUSION WAS THAT THE COLOURS IN THE COLOUR TOOL NEED TO BE COMPLEMENTED WITH GRAPHICAL OR OTHER COLOUR INDEPENDENT PATTERN TO USE WITH PEOPLE WITH COLOUR BLINDNESS. OVERALL IT IS NOW CLEAR THAT THE SYSTEM WORKS AND THE NEXT STEP CAN BE TAKEN TO TEST THE METHOD IN THE COMPUTER BASED LEARNING ENVIRONMENT.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE RECENTLY BEEN HIGHLY POPULAR AMONG WORLDWIDE LEARNERS, WHILE IT IS CHALLENGING TO MANAGE AND INTERPRET THE LARGE-SCALE DISCUSSION FORUM WHICH IS THE DOMINANT CHANNEL OF ONLINE COMMUNICATION. K-MEANS CLUSTERING, ONE OF THE FAMOUS UNSUPERVISED LEARNING ALGORITHMS, COULD HELP INSTRUCTORS IDENTIFY INFLUENTIAL USERS IN MOOC FORUM, TO BETTER UNDERSTAND AND IMPROVE ONLINE LEARNING EXPERIENCE. HOWEVER, TRADITIONAL K-MEANS SUFFERS FROM BIAS OF OUTLIERS AND RISK OF FALLING INTO LOCAL OPTIMUM. IN THIS PAPER, OP-DCI, AN OPTIMIZED K-MEANS ALGORITHM IS PROPOSED, USING OUTLIER POST-LABELING AND DISTANT CENTROID INITIALIZATION. OUTLIERS ARE NOT SOLELY FILTERED OUT BUT EXTRACTED AS DISTINCT OBJECTS FOR POST-LABELING, AND DISTANT CENTROID INITIALIZATION ELIMINATES THE RISK OF FALLING INTO LOCAL OPTIMUM. WITH OP-DCI, LEARNERS IN MOOC FORUM ARE CLUSTERED EFFICIENTLY WITH SATISFACTORY INTERPRETATION, AND INSTRUCTORS CAN SUBSEQUENTLY DESIGN PERSONALIZED LEARNING STRATEGIES FOR DIFFERENT CLUSTERS.
THE CONSTANT DEVELOPMENT OF NEW INTERNET PLATFORMS IS SHIFTING THE USERS' ROLE OF SUCH PLATFORMS, FROM VIEWERS TO MAIN ACTORS. IN THE FIELD OF EDUCATION, FACULTY CAN TAKE ADVANTAGE OF THESE NEW TECHNOLOGIES FOR THE DESIGN OF PEDAGOGICAL CONTENTS. THE FACE-TO-FACE OBSERVATION OF BEHAVIORAL PATTERNS ALLOWS FACULTY TO DETECT AND TRACK NEW PROBLEMS, AND TO APPLY POSSIBLE CORRECTIONS WHICH WOULD IMPROVE THE LEARNING/TEACHING PROCESS. HOWEVER, WITH A DISTANCE METHODOLOGY, THESE OBSERVATIONS ARE NOT POSSIBLE. WHEN FORUMS ARE CREATED THEY ARE INTENDED TO DISCUSS PARTICULAR TOPICS. IT IS RELEVANT TO MONITOR THAT THE TOPICS DISCUSSED ARE THE INTENDED ONES IN ORDER TO ACHIEVE COURSE OBJECTIVES. TO TACKLE THIS SHORTCOMING, OUR WORK STUDIES THE DYNAMICS OF RELEVANT TOPICS IN ON-LINE ASYNCHRONOUS DISCUSSION FORUMS, AND THIS IS DONE BY ANALYZING THE LARGE AMOUNT OF STUDENTS' INTERACTIONS GENERATED IN THE FORUMS OF OUR LEARNING MANAGEMENT SYSTEM (LMS). IN PARTICULAR, WE ANALYZE THE STUDENTS' BEHAVIOR PATTERNS IN THE FORUMS OF A DISTANCE SUBJECT, AND CHARACTERIZE THE RELEVANT TOPICS AND SUBTOPICS FROM THE FORUMS' MESSAGES BELONGING TO TWO ACADEMIC YEARS. FROM THE STATISTICAL AND GRAPHICAL RESULTS OBTAINED, A SET OF VALUABLE RECOMMENDATIONS ARE ALSO GIVEN. (C) 2013 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE AIMS OF THIS STUDY WERE TO CLUSTER STUDENTS INTO GROUPS ACCORDING TO THEIR LEARNING BEHAVIOR BY ANALYZING THE LEARNING LOG IN BLENDED LEARNING COURSES. IN OUR BLENDED COURSES, THE STUDENTS WERE REQUIRED TO PASS MORE THAN 2/3 PASSES OF ALL EXAMS ON LMS. WE ANALYZED LEARNING LOGS DATA ASSOCIATED WITH ONLINE EXAMS OF THE CLASSES. IN THE ANALYSIS, WE FOCUSED ON A PATICULAR WEEK, AND FIRSTLY DIVIDED THE EXAM DURATION INTO FOUR PERIODS. WE MADE CHARACTERISTIC VECTOR WITH 12 ELEMENTS INCLUDING A) THE ACCESS COUNT TO AN EXAM, B) MINIMUM AND MAXIMUM SCORE IN EACH PERIOD. THE STUDENTS WERE CLUSTERED ON THE TYPE OF THE FOUR LEARNING BEHAVIOR BY THE HIERARCHICAL CLUSTER ANALYSIS BY WARD METHOD. IT WAS SHOWED STUDENTS OF LEARNING HABIT TYPE HAD HIGHER PERFORMANCE THAN PROCRASTINATION TYPE. USING THIS ANALYZING METHOD, IT WILL BE ABLE TO PROVIDE APPROPRIATE FEEDBACK FOR STUDENTS EXCEPT FOR LEARNING HABIT TYPE IN THE FUTURE.
PEER ASSESSMENT (PA) IS A COMMONLY APPLIED EVALUATION STRATEGY IN THE EDUCATIONAL CONTEXT NOT ONLY DUE TO ITS EFFECTIVENESS TO REDUCE INSTRUCTOR'S EVALUATION LOADING, BUT ALSO ITS BENEFIT TOWARDS STUDENT DEVELOPMENT SUCH AS TEAMWORK, READING AND JUDGMENT SKILLS. PREVIOUSLY, THERE ARE A NUMBER OF RELATED STUDIES THAT HAVE EXPLORED MULTIPLE ISSUES OF PA AND HOW THESE FACTORS AFFECT THE PA QUALITY. NEVERTHELESS, THE EFFECT OF CONTENT KNOWLEDGE MASTERY AND EVALUATION EXPERIENCE TOWARDS PA QUALITY IS LESS EMPHASIZED. IN THIS STUDY, THE AUTHOR WISH TO QUANTITATIVELY EXAMINE HOW CONTENT KNOWLEDGE MASTERY AND EXPERIENCE OF EVALUATORS CAN AFFECT THE EVALUATION OUTCOME. FOR THIS PURPOSE, A CASE STUDY INVOLVING 29 STUDENT EVALUATORS WAS PERFORMED, WHERE EVALUATORS WERE REQUIRED TO RATE TWO WRITTEN ASSIGNMENTS OF A PRODUCTION ENGINEERING COURSE PROJECT. THE FINDINGS OF THIS STUDY IS DETAILED IN THE CASE STUDY SECTION. THE AUTHOR THEN CONCLUDE THE FINDINGS WITH FURTHER DISCUSSION AND FUTURE WORKS.
IN THIS PAPER, WE ILLUSTRATE THE SUCCESSFUL IMPLEMENTATION OF A SOCIAL ANNOTATION TOOL WITHIN A CONTENT AUTHORING PLATFORM THAT ALLOWS STUDENTS TO DISCUSS LEARNING MATERIAL WITH THEIR FELLOW CLASSMATES. THE TOOL ALSO ALLOWS STUDENTS TO SELF-REPORT ON THEIR COGNITIVE, METACOGNITIVE AND AFFECTIVE STATES BY SELF-CODING THEIR ANNOTATIONS AS THEY JOURNEY THROUGH THE LEARNING,MATERIAL. WE EXPLORE THE PREDICTIVE POTENTIAL OF SUCH SELF-REPORTS AGAINST THE STUDENTS COMPLETION RATE AND ASSESSMENT SCORES AND EXAMINE HOW THE VISUALISATION OF THESE ANNOTATION CLASSIFICATIONS CAN HELP INSTRUCTORS EASILY IDENTIFY ISSUES AND BETTER SUPPORT THEIR STUDENTS AND IMPROVE LEARNING MATERIAL.
THERE IS NO DOUBT THAT IN WHAT REFERS TO THE EDUCATIONAL AREA, TECHNOLOGY IS PRODUCING A SERIES OF CHANGES THAT WILL GREATLY AFFECT OUR NEAR FUTURE. THE INCREASE OF STUDENTS EXPERIENCES IN THE NEW EDUCATIONAL SYSTEMS IN DISTANCE LEARNING MAKES POSSIBLE TO HAVE INFORMATION RELATED TO THE STUDENTS 'ACTIVITIES AND HOW THESE CAN BE DEALT WITH AUTOMATIC PROCEDURES. THE IMPLEMENTATION OF THESE ANALYTICAL METHODS IS POSSIBLE THROUGH THE USE OF POWERFUL NEW TECHNOLOGIES SUCH AS DATA MINING OR BIG DATA. RELEVANT INFORMATION IS OBTAINED OF THE USE MADE BY THE STUDENTS OF THE TECHNOLOGICAL TOOLS IN A LEARNING MANAGEMENT SYSTEM, THUS, ALLOWING US TO INFER A PATTERN OF BEHAVIOR OF THE STUDENTS, TO BE USED IN THE FUTURE.
IN THIS ARTICLE, WE INTRODUCE THE RESULTS OF A STUDY CONCERNING USER DATA VISUALIZATIONS IN THE CONTEXT OF THE TUT CIRCLE, A SOCIAL MEDIA ENHANCED WEB SERVICE FOR LEARNING, NETWORKING, AND COMMUNICATION AT THE TAMPERE UNIVERSITY OF TECHNOLOGY (TUT). WELL-DESIGNED VISUALIZATIONS CAN PROVIDE VALUABLE INSIGHT INTO STUDENT ACTIVITY AND PARTICIPATION IN AN ONLINE COURSE, AND THEREFORE SUPPORT BOTH TEACHERS' AND STUDENTS' STRATEGIC DECISION MAKING. WE FOUND SOCIAL NETWORK ANALYTICS AND VISUALIZATIONS USEFUL IN PROVIDING INFORMATION TO IMPROVE THE PEDAGOGICAL PRACTICES OF ONLINE COURSES, AS WELL AS THE STUDENTS' OVERALL ENGAGEMENT IN THEIR STUDIES AND STUDY LIVES. HOWEVER, TO BE USEFUL, THE VISUALIZATIONS NEED TO HAVE ENOUGH HINTS SO THAT THE USERS CAN INTERPRET THE DIFFERENT ELEMENTS IN THEM IN THE RIGHT WAY.
WITH THE INTRODUCTION OF THE TEACHING EXCELLENCE FRAMEWORK A LOT OF ATTENTION IS FOCUSSED ON MEASURING LEARNING GAINS. A VAST BODY OF RESEARCH HAS FOUND THAT INDIVIDUAL STUDENT CHARACTERISTICS INFLUENCE ACADEMIC PROGRESSION OVER TIME. THIS CASE-STUDY AIMS TO EXPLORE HOW ADVANCED STATISTICAL TECHNIQUES IN COMBINATION WITH BIG DATA CAN BE USED TO PROVIDE POTENTIALLY NEW INSIGHTS INTO HOW STUDENTS ARE PROGRESSING OVER TIME, AND IN PARTICULAR HOW STUDENTS' SOCIO-DEMOGRAPHICS (I.E. GENDER, ETHNICITY, SOCIAL ECONOMIC STATUS, PRIOR EDUCATIONAL QUALIFICATIONS) INFLUENCE STUDENTS' LEARNING TRAJECTORIES. LONGITUDINAL ACADEMIC PERFORMANCE DATA WERE SAMPLED FROM 4222 FIRST-YEAR STEM STUDENTS ACROSS NINE MODULES AND ANALYSED USING MULTI-LEVEL GROWTH-CURVE MODELLING. THERE WERE SIGNIFICANT DIFFERENCES BETWEEN WHITE AND NON-WHITE STUDENTS, AND STUDENTS WITH DIFFERENT PRIOR EDUCATIONAL QUALIFICATIONS. HOWEVER, STUDENT-LEVEL CHARACTERISTICS ACCOUNTED ONLY FOR A SMALL PORTION OF VARIANCE. THE MAJORITY OF VARIANCE WAS EXPLAINED BY MODULE-LEVEL CHARACTERISTICS AND ASSESSMENT LEVEL CHARACTERISTICS.
THIS PAPER INTRODUCES SCALE, A SMART COMPETENCE ANALYTICS ENGINE ON LEARNING, AS A FRAMEWORK TO IMPLEMENT CONTENT ANALYSIS IN SEVERAL LEARNING DOMAINS AND PROVIDE MECHANISMS TO DEFINE PROFICIENCY AND CONFIDENCE METRICS. SCALE'S ONTOLOGICAL DESIGN PLAYS A CRUCIAL ROLE IN CENTRALIZING AND HOMOGENIZING DISPARATE DATA FROM DOMAIN-SPECIFIC PARSERS AND ULTIMATELY FROM SEVERAL LEARNING DOMAINS. THIS PAPER SHOWS HOW SCALE HAS BEEN APPLIED IN THE PROGRAMMING DOMAIN AND REVEALS SYSTEMATICALLY HOW THE WORK CONTENT OF A STUDENT CAN BE ANALYZED AND CONVERTED TO EVIDENCES TO ASSESS HIS/HER PROFICIENCY IN DOMAIN-SPECIFIC COMPETENCES AND HOW SCALE CAN ALSO ANALYZE THE STUDENT'S INTERACTION WITH A LEARNING ACTIVITY AND PROVIDE A CONFIDENCE METRIC TO ASSESS HIS/HER BEHAVIOR AS HE/SHE CULMINATES TOWARD GOAL ACHIEVEMENTS.
EXPOSING LEARNERS TO COGNITIVE COMPUTING CONCEPTS INVOLVES NEW LEARNING STRATEGIES. BECAUSE COGNITIVE COMPUTING IS PROBABILISTIC, USING MASSIVE SETS OF DATA IS FUNDAMENTAL IN UNDERSTANDING THESE CONCEPTS. ONE DATA SET IS THE RELEASE OF THE OFFSHORE PANAMA PAPERS LEAKS DATABASE (LEAKSDB) IN MAY 2016, IN WHICH RESEARCHERS WERE ABLE TO ACCESS THIS GRAPH DATABASE AS PART OF THE INTERNATIONAL CONSORTIUM OF INVESTIGATIVE JOURNALISTS (ICIJ) OFFSHORE LEAKS INVESTIGATION AND TO DRAW CONCLUSIONS ABOUT COMPANIES, TRUSTS, FOUNDATIONS, AND FUNDS INCORPORATED IN 21 TAX HAVENS. FOR THE PURPOSE OF THIS RESEARCH, GRAPHDB SERVER WAS INSTALLED AND CONFIGURED BY FACULTY MEMBERS FROM A MID-WESTERN UNIVERSITY. IN ADDITION, DBPEDIA AND GEONAMES REPOSITORIES WERE LINKED TO LEAKSDB, LEADING TO THE DISCOVERY OF INTERESTING PATTERNS ABOUT THESE GEO FACADE RELATIONSHIPS BETWEEN OFFICERS (PERSONS OR COMPANIES) AND COUNTRIES. (C) 2017 PUBLISHED BY ELSEVIER LTD.
I AM A LECTURER WITH PROFESSIONAL MARKETING EXPERIENCE, AND THIS STUDY WAS MOTIVATED BY MY DISMAY AT UNIVERSITY ABOUT WHAT I HAVE PERCEIVED AS OVERSIGHTS IN COLLEAGUES? TYPICAL USE OF MASS EMAIL (EMAILING A SINGLE MESSAGE TO A LARGE GROUP OF SUBSCRIBERS) FOR CONTENT DELIVERY, IN COMPARISON TO THE COMMUNICATION STRATEGIES OF MAJOR INDUSTRIES (FOR EXAMPLE, RETAIL AND PUBLIC ADMINISTRATION). I EXAMINE NON-ACADEMIC RESEARCH ABOUT MASS EMAIL BEST-PRACTISE, AS IMPLEMENTED IN INDUSTRY COMMUNICATIONS; THEN I PERFORM A SYSTEMATIC REVIEW OF ACADEMIC RESEARCH ON MASS EMAIL FOR CONTENT DELIVERY. MY REVIEW OF 19 STUDIES CONFIRMS ACADEMICS ARE BEHIND THE INDUSTRY WHEN IT COMES TO MASS EMAIL EVALUATION AND OPTIMISATION STRATEGIES. FINALLY, I OUTLINE TACTICS NECESSARY FOR MORE EFFECTIVE MASS EMAILS AT UNIVERSITY, THE MOST SALIENT BEING SYSTEMATICALLY ANALYSING A RANGE OF EMAIL METRICS, AND TESTING AND OPTIMISING EMAILS. ALSO, INCLUDED IN THIS STUDY IS MY IDENTIFICATION OF CURRENT OBSTACLES TO MORE EFFECTIVE MASS EMAIL AT UNIVERSITY, AND THESE INCLUDE THE TECHNOLOGICAL LIMITATIONS OF LEARNING MANAGEMENT SYSTEMS (LMS), AND THE LIMITED EXPERTISE OF TEACHING STAFF.
IN THE COLLABORATIVE LEARNING CONTEXT, TYPICAL CLASSROOM PRACTICES TEND TO CREATE THREE DISTINGUISHABLE LEVELS OF ACTIVITY: INDIVIDUAL-ACTIVITIES, SMALL-GROUP WORK, AND WHOLE-CLASS DISCUSSION. IT IS IMPORTANT TO CONNECT THE ANALYSIS OF THE LEVELS FOR TEACHERS TO UNDERSTAND AND IMPROVE THE DYNAMICS OF STUDENTS' UNDERSTANDING OF COLLABORATIVE LEARNING. THIS STUDY, WITH THE KIT-BUILD CONCEPT MAP METHOD, PROPOSES A METHOD TO ANALYZE THE THREE LEVELS OF ACTIVITIES IN THE CLASSROOM. KIT-BUILD CONCEPT MAP IS A TYPE OF CLOSE-ENDED CONCEPT MAP AND PROVIDES DECOMPOSED CONCEPTS AND LINKS FROM THE CONCEPT MAP MADE BY A TEACHER. THIS MECHANISM ENABLES TEACHERS TO CHECK STUDENTS' UNDERSTANDING AND TO FACILITATE HIS OR HER COORDINATION OF LEARNING IN A CLASSROOM. THIS PAPER ILLUSTRATES THE METHOD WITH THE RESULT OF A CASE STUDY IN A JUNIOR HIGH SCHOOL IN JAPAN.
THE OPEN UNIVERSITY OF JAPAN HAS OFFERED A FACE-TO-FACE DIGITAL LITERACY TRAINING SESSION (DLT) EVERY SEMESTER AT ALL 50 STUDY CENTERS SINCE 2010. EACH STUDENT ATTENDING DLT COMPLETES A CHECKLIST AT THE BEGINNING AND END OF THEIR SESSION. THE OBJECTIVE OF THIS STUDY WAS TO IDENTIFY HOW STUDENTS MIGRATED FROM ONE CLUSTER TO ANOTHER, REPRESENTING THEIR SELF-REPORTED PERCEIVED DIGITAL LITERACY SKILLS. WE ANALYZED RESPONSES FROM 1,417 STUDENTS WHO SUBMITTED CHECKLISTS BETWEEN SEMESTER 2, 2014 AND SEMESTER 2, 2016 AND COMPLETED ALL 20 ITEMS ON BOTH THE PRE- AND POST-SESSION CHECKLISTS. WE DEVELOPED CLUSTERS FOR PRE- AND POST-SESSION ABILITY, BASED ON EACH STUDENT'S REPORTED ICT SKILL LEVELS ON THE 20 CHECKLIST DIMENSIONS, USING K-MEANS WITH THE HARTIGAN-WONG ALGORITHM. WE ALSO ANALYZED HOW EACH OF THE PRE-SESSION CLUSTERS SHIFTED TO POST-SESSION CLUSTERS USING A SANKEY DIAGRAM. IN ADDITION, WE ANALYZED STUDENTS' POST-SESSION CHECKLIST COMMENTS ON DLT USING A CO-OCCURRENCE NETWORK METHOD.
THIS PAPER ANALYZES STUDENTS' DESIGN SOLUTIONS FOR AN NGSS-ALIGNED EARTH SCIENCES CURRICULUM, THE PLAYGROUND DESIGN CHALLENGE (PDC), FOR UPPER-ELEMENTARY SCHOOL (GRADE 5 AND 6) STUDENTS. WE PRESENT THE UNDERLYING COMPUTATIONAL MODEL AND THE USER INTERFACE FOR GENERATING DESIGN SOLUTIONS FOR A SCHOOL PLAYGROUND THAT HAS TO MEET COST, WATER RUNOFF, AND ACCESSIBILITY CONSTRAINTS. WE USE DATA FROM THE PRETEST AND POSTTEST ASSESSMENTS AND ACTIVITY LOGS COLLECTED FROM A PILOT STUDY RUN IN AN ELEMENTARY SCHOOL TO EVALUATE THE EFFECTIVENESS OF THE CURRICULUM AND INVESTIGATE THE RELATIONS BETWEEN STUDENTS' BEHAVIORS AND THEIR LEARNING PERFORMANCES. THE RESULTS SHOW THAT (1) THE STUDENTS' SCORES SIGNIFICANTLY INCREASED FROM PRETEST TO POSTTEST ON ENGINEERING DESIGN ASSESSMENTS, AND (2) STUDENTS' SOLUTION-GENERATION AND TESTING BEHAVIORS WERE INDICATIVE OF THE QUALITY OF THEIR DESIGN SOLUTIONS AS WELL AS THEIR PRE-POST LEARNING GAINS. IN THE FUTURE, TRACKING SUCH BEHAVIORS ONLINE WILL ALLOW US TO PROVIDE ADAPTIVE SCAFFOLDS THAT HELP STUDENTS IMPROVE ON THEIR ENGINEERING DESIGN SOLUTIONS.
MOOC IS AN EMERGING ONLINE EDUCATIONAL MODEL IN RECENT YEARS. WITH THE DEVELOPMENT OF BIG DATA TECHNOLOGY, A HUGE AMOUNT OF LEARNING BEHAVIOR DATA CAN BE MINED BY MOOC PLATFORMS. MINING LEARNERS' PAST CLICKSTREAM DATA TO PREDICT THEIR FUTURE LEARNING ACHIEVEMENT BY MACHINE LEARNING TECHNOLOGY HAS BECOME A HOT RESEARCH TOPIC RECENTLY. PREVIOUS METHODS ONLY CONSIDER THE STATIC COUNTING-BASED FEATURES AND IGNORE THE CORRELATIVE, TEMPORAL AND FRAGMENTED NATURE OF MOOC LEARNING BEHAVIOR, AND THUS HAVE THE LIMITATION IN INTERPRETABILITY AND PREDICTION ACCURACY. IN THIS PAPER, WE EXPLORE THE EFFECTIVENESS OF N-GRAM FEATURES IN CLICKSTREAM DATA AND MODEL THE MOOC LEARNING ACHIEVEMENT PREDICTION PROBLEM AS A MULTICLASS CLASSIFICATION TASK WHICH CLASSIFIES LEARNERS INTO FOUR ACHIEVEMENT LEVELS. WITH EXTENSIVE EXPERIMENTS ON FOUR REAL-WORLD MOOC DATASETS, WE EMPIRICALLY DEMONSTRATE THAT OUR METHODS OUTPERFORM THE STATE-OF-THE-ART METHODS SIGNIFICANTLY.
IN THIS PAPER WE PRESENT AN ANALYSIS OF XAPI FROM THE SELF-REGULATED LEARNING NEEDS POINT OF VIEW, FOCUSING ON HOW WE CAN RECORD ALL THE ACTIONS DERIVED FROM THE IMPLEMENTATION OF SELF-REGULATED LEARNING STRATEGIES. THERE ARE SEVERAL QUESTIONS UNANSWERED: WHICH INFORMATION COULD GENERATE EACH STRATEGY? HOW DO WE RECORD THIS INFORMATION USING XAPI? IS THE VOCABULARY AND STATEMENTS OF XAPI APPROPRIATE OR SHOULD BE EXTENDED? HERE WE TRY TO ANSWER THESE QUESTIONS, WHICH MAY LEAD US TOWARDS THE DEVELOPMENT OF A XAPI APPLICATION PROFILE FOR SRL.
INTERNATIONAL EDUCATIONAL PERFORMANCE MEASURES, LIKE THE PROGRAMME FOR INTERNATIONAL STUDENT ASSESSMENT (PISA), RANK COUNTRIES BASED ON THEIR EDUCATIONAL SYSTEM'S PERFORMANCE. HOWEVER, THESE ACTIVITIES REQUIRE YEARS OF WORK FOR THOUSANDS OF PEOPLE TO COLLECT, ANALYZE AND REPORT THE DATA. FURTHERMORE, THE RESULTS ARE ALWAYS TWO YEARS OLD WHEN PUBLISHED AND THERE ARE TENS OF MISSING COUNTRIES. IN THIS STUDY, A GLOBAL GAME-BASED ANALYTICS WAS INTRODUCED AND EVALUATED IN A REAL WORLD ENVIRONMENT. THE PAPER CONTINUES THE STUDY REPORTED IN ECGBL 2013. REAL-TIME GAME-BASED ASSESSMENTS DO HAVE SEVERAL ADVANTAGES COMPARED TO TRADITIONAL MEASURES, BUT THEY ARE NOT YET READY TO REPLACE PISA. FOR EXAMPLE, SAMPLE COLLECTION HAS TO BE RADICALLY IMPROVED IN ORDER TO MINIMIZE NOISE AND BRING OUT ONLY THE VALID CASES.
STUDENTS IN A LEARNING EXPERIENCE CAN BE SEEN AS A COMMUNITY WORKING SIMULTANEOUSLY (AND IN SOME CASES COLLABORATIVELY) IN A SET OF ACTIVITIES. DURING THESE WORKING SESSIONS, STUDENTS CARRY OUT NUMEROUS ACTIONS THAT AFFECT THEIR LEARNING. BUT THOSE ACTIONS HAPPENING OUTSIDE A CLASS OR THE LEARNING MANAGEMENT SYSTEM CANNOT BE EASILY OBSERVED. THIS PAPER PRESENTS A TECHNIQUE TO WIDEN THE OBSERVABILITY OF THESE ACTIONS. THE SET OF DOCUMENTS BROWSED BY THE STUDENTS IN A COURSE WAS RECORDED, DURING A PERIOD OF EIGHT WEEKS. THESE DOCUMENTS ARE THEN PROCESSED AND THE SET WITH HIGHEST SIMILARITY WITH THE COURSE NOTES ARE SELECTED AND RECOMMENDED BACK TO ALL THE STUDENTS. THE MAIN PROBLEM IS THAT THIS USER COMMUNITY VISITS THOUSANDS OF DOCUMENTS AND ONLY A SMALL PERCENT OF THEM ARE SUITABLE FOR RECOMMENDATION. USING A COMBINATION OF LEXICAN ANALYSIS AND INFORMATION RETRIEVAL TECHNIQUES, A FULLY AUTOMATIC PROCEDURE TO ANALYZE THESE DOCUMENTS, CLASSIFY THEM AND SELECT THE MOST RELEVANT ONES IS PRESENTED. THE APPROACH HAS BEEN VALIDATED WITH AN EMPIRICAL STUDY IN AN UNDERGRADUATE ENGINEERING COURSE WITH MORE THAN ONE HUNDRED STUDENTS. THE RECOMMENDED RESOURCES WERE RATED AS "RELEVANT TO THE COURSE" BY THE SEVEN INSTRUCTORS WITH TEACHING DUTIES IN THE COURSE.
CREATIVITY HAS LONG BEEN SUGGESTED AS AN IMPORTANT FACTOR IN LEARNING. IN THIS PAPER, WE PRESENT A PRELIMINARY STUDY OF CREATIVITY IN AN ONLINE PROGRAMMING LEARNING ENVIRONMENT. WE OPERATIONALIZE CREATIVITY USING AN EXISTING SCHEME FOR SCORING IT, AND THEN MEASURE IT AUTOMATICALLY BASED ON THE SYSTEM LOG FILES. WE ANALYZE THE DATA IN ORDER TO EXPLORE THE ASSOCIATIONS BETWEEN CREATIVITY AND PERSONAL/CONTEXTUAL VARIABLES. CREATIVITY IS ASSOCIATED WITH CONTEXTUAL VARIABLES AND IS NOT ASSOCIATED WITH PERSONAL VARIABLES. DIRECTIONS FOR CONTINUING THIS RESEARCH ARE DISCUSSED.
VIDEO IS BECOMING MORE AND MORE POPULAR AS A LEARNING MEDIUM IN A VARIETY OF EDUCATIONAL SETTINGS, RANGING FROM FLIPPED CLASSROOMS TO MOOCS TO INFORMAL LEARNING. THE PREVAILING EDUCATIONAL USAGE OF VIDEOS IS BASED ON WATCHING PREPARED VIDEOS, WHICH CALLS FOR ACCOMPANYING VIDEO USAGE WITH ACTIVITIES TO PROMOTE CONSTRUCTIVE LEARNING. IN THE ACTIVE VIDEO WATCHING (AVW) APPROACH, LEARNER ENGAGEMENT DURING VIDEO WATCHING IS INDUCED VIA INTERACTIVE NOTETAKING, SIMILAR TO VIDEO COMMENTING IN SOCIAL VIDEO-SHARING PLATFORMS. THIS COINCIDES WITH THE JUXTALEARN PRACTICE, IN WHICH STUDENT-CREATED VIDEOS WERE SHARED ON A SOCIAL NETWORKING PLATFORM AND COMMENTED BY OTHER STUDENTS. DRAWING ON THE EXPERIENCE OF BOTH AVW AND JUXTALEARN, WE COMBINE AND REFINE ANALYSIS TECHNIQUES TO CHARACTERISE LEARNER ENGAGEMENT. THE APPROACH DRAWS ON NETWORK-TEXT ANALYSIS OF LEARNER-GENERATED COMMENTS AS A BASIS. THIS ALLOWS FOR CAPTURING PEDAGOGICALLY RELEVANT ASPECTS OF DIVERGENCE, CONVERGENCE AND (DIS-) CONTINUITY IN TEXTUAL COMMENTING BEHAVIOUR RELATED TO DIFFERENT LEARNER TYPES. THE LEXICAL-SEMANTIC ANALYTICS APPROACH USING LEARNER-GENERATED ARTEFACTS PROVIDES DEEP INSIGHTS INTO LEARNER ENGAGEMENT. THIS HAS BROADER APPLICATION IN VIDEO-BASED LEARNING ENVIRONMENTS.
THE PAPER AIMS FOR A TECHNOLOGICAL CONTRIBUTION TO ONLINE LEARNING RESEARCH AND ESPECIALLY LEARNING ANALYTICS BY PROVIDING INSIGHTS ON THE DATA MARSHALLING AND DATA ORCHESTRATION APPROACH DEVELOPED IN THE CONTEXT OF THE NEXT-TELL PROJECT. THE PROJECT CONCENTRATES ON HOW CONCEPTUAL MODELLING CAN BE UTILIZED TO DESCRIBE ICT ENHANCED ASSESSMENT METHODS INDEPENDENTLY OF A CONCRETE LEARNING PLATFORM AND ON DEPLOYING THESE METHODS USING VARIOUS TECHNOLOGIES AND RUNTIME ENVIRONMENTS.
IN THIS ARTICLE, WE PRESENT MOODLEBOARD, A DYNAMIC AND INTERACTIVE DASHBOARD WHICH CAN BE USED AS A DECISION SUPPORT TOOL BY PEDAGOGICAL ENGINEERS AND MOODLE PLATFORMS ADMINISTRATORS. THESE USERS CAN EXPLORE VARIOUS INDICATORS IN AN INTERACTIVE WAY IN ORDER TO OBTAIN STATISTICS ABOUT ONLINE COURSES WITH DIFFERENT GRANULARITY LEVELS, BUT ALSO DETECT REMARKABLE COURSES AND THEIR TYPES, CLASSIFY COURSES, DETECT MISUSES OR INNOVATIONS IMPLEMENTED BY TEACHING OR ADMINISTRATIVE TEAMS IN THE ORGANIZATION. THIS WORK ALSO AIMS AT PROPOSING MODELS FOR DIGITAL COURSES IN MOODLE, IN ORDER TO FACILITATE THEIR ADAPTATION TO FULL-ONLINE DISTANCE LEARNING.
TAIWAN'S MINISTRY OF EDUCATION (MOE) STARTED PLANNING AND PROMOTING COMPREHENSIVE DIGITAL LEARNING INITIATIVE IN 2013. IT IS EXPECTED THAT TEACHERS AND STUDENTS CAN USE BROADBAND NETWORKS IN CAMPUS IN 2017. STUDENTS CAN DO ONLINE LEARNING WITH DIFFERENT KINDS OF DIGITAL DEVICES AT ANY TIME. BESIDES, STUDENTS CAN DO UBIQUITOUS LEARNING BY GETTING DIGITAL LEARNING RESOURCES ON CLOUD DEVICES. TEACHERS CAN GRASP INNOVATIVE TEACHING STRATEGIES WITH THE APPLICATION OF DIGITAL TECHNOLOGY AND DESIGN THE LEARNING ACTIVITIES THAT ARE MORE ADAPTIVE TO INDIVIDUAL STUDENTS. STUDENTS HAVE THE INFORMATION FORCE OF MOBILE LEARNING, THE ABILITIES TO USE INFORMATION TOOLS AND GRASP THE INFORMATION, AS WELL AS INTERNET LITERACY AND ETHICS. THE LEARNING MODEL OF EDUCATION AT ALL LEVELS WILL TRANSFORM INTO STUDENT-CENTERED TO FACILITATE JUSTICE, PUBLIC, AUTONOMY AND ADAPTIVE LEARNING OPPORTUNITIES AS WELL AS MAKE STUDENTS IN THE COUNTRY AND THOSE IN THE CITY HAVE EQUAL DIGITAL LEARNING OPPORTUNITIES. DIGITAL LEARNING INITIATIVE WILL HAVE PROFOUND INFLUENCE ON EDUCATION SYSTEM. EDUCATION CLOUD (EDUCLOUD) IS THE FLAGSHIP OF THE DIGITAL LEARNING INITIATIVE. THIS PAPER PRESENTS OUR RESEARCH AND EXPERIENCE OF APPLYING BIG DATA ANALYTICS TO THE EDUCLOUD. WE ADDRESS BIG DATA INFRASTRUCTURE AND ANALYTICS TO COVER THE ISSUES OF DATA COLLECTION, CLEANING, STORAGE, QUERY, ANALYTICS, AND VISUALIZATION IN ORDER TO MAKE EDUCLOUD MORE SUITABLE FOR TEACHERS AND STUDENTS IN TAIWAN.
A LARGE AMOUNT OF EDUCATIONAL CONTENT IS AVAILABLE AS LECTURE VIDEOS, WHICH RECORD TEACHERS AS THEY PROCEED THROUGH A COURSE. STUDENTS WATCH THESE VIDEOS IN DIFFERENT WAYS. THEY REWIND, SKIP FORWARD, WATCH SOME SCENES REPEATEDLY. THIS WORK INVESTIGATES WHAT CAN BE LEARNED BY ANALYZING SUCH VIEWING PATTERNS. WE SHOW HOW TO USE MACHINE LEARNING TECHNIQUES TO ANALYZE SUCH DATA, AND PRESENT THE OUTCOMES OF AN ANALYSIS OF DATA COLLECTED FROM THE INTERACTIONS OF 2992 STUDENTS IN 253 COURSES. THE VIEWING PATTERN WERE PUT INTO RELATION TO SEVEN DIFFERENT VARIABLES, SUCH AS THE FINAL SCORE OF THE STUDENT AND THE RATING TEACHERS RECEIVED FROM STUDENTS OUR ANALYSIS SHOWS THAT SOME VARIABLES, SUCH AS THE TEACHER RATING, WERE INDEED PREDICTABLE FROM THE VIEWING PATTERNS.
THE ADVANCEMENT OF THE EDUCATION INFORMATIONIZATION BRINGS THE MAJOR CHANGE OF THE TRADITIONAL CLASSROOM TEACHING. AS ONE OF THE HOTTEST COMPUTER SUPPORTED EDUCATION APPROACHES, ONLINE TEACHING PLATFORMS PROVIDES PLENTY LEARNING RESOURCES AND CONVENIENT COMMUNICATION BETWEEN TEACHERS AND STUDENTS, WHICH MAY GENERATE HUGE AMOUNT OF VALUABLE LEARNING BEHAVIOR DATA. IN THIS PAPER, BASED ON BLACKBOARD PLATFORM, TAKEN "DATABASE PRINCIPLE" COURSE AS AN EXAMPLE, STATISTICAL AND DATA MINING METHODS ARE IMPLEMENTED BY COMBINATION OF ONLINE TEACHING AND TRADITIONAL CLASSROOM TEACHING TO DISCOVER LEARNING BEHAVIOR OF REGISTERED STUDENTS, AND THE CORRELATIONS BETWEEN THE UTILIZATION OF THE ONLINE TEACHING PLATFORM AND THE FINAL SCORES OF REGISTERED STUDENTS ARE ALSO ANALYZED, WHICH DEMONSTRATED THE EFFECTIVENESS OF THE HYBRID TEACHING. THE AIM OF THE PROJECT IS TO IMPROVE STUDENTS' LEARNING EFFECT, ENFORCE AND ENLARGE COOPERATION ACTIVITIES AMONG A LOT OF STUDENTS, AND PROVIDE A REFERENCE TO FURTHER OPTIMIZE AND IMPROVE THE ONLINE TEACHING PLATFORMS.
IN THIS ARTICLE, WE INTRODUCE THE RESULTS OF A STUDY CONCERNING VISUALIZATIONS OF STUDENTS' PARTICIPATION AND ACTIVITY IN TUT CIRCLE, A SOCIAL MEDIA ENHANCED WEB SERVICE FOR LEARNING, NETWORKING, AND COMMUNICATION. WELL-DESIGNED VISUALIZATIONS CAN PROVIDE USEFUL INFORMATION FOR TEACHERS TO IMPROVE PEDAGOGICAL PRACTICES AND THE QUALITY OF ONLINE COURSES. VISUALIZATION ENABLES STUDENTS TO MONITOR THEIR OWN ACTIVITY LEVELS AND PERFORMANCE PROCESSES, AND MAKE STRATEGIC ADJUSTMENTS TO IMPROVE THEM.
THIS FULL PAPER IN THE RESEARCH CATEGORY PRESENTS A PRINCIPLED APPROACH TO INTEGRATE MACHINE LEARNING WITHIN QUALITATIVE EDUCATION RESEARCH. MORE SPECIFICALLY, WE SHOW HOW TO BUILD ON AN EXISTING THEORY OR CONCEPTUAL FRAMEWORK USING MACHINE LEARNING APPLIED TO QUALITATIVE DATA IN ORDER TO MAKE VALID CONCLUSIONS. OUR MODEL IS GUIDED BY THE ASSESSMENT TRIANGLE. ONE CASE STUDY IS PRESENTED. THE STUDY FOCUSES ON HABITS OF MIND AND THEIR RELATIONSHIP TO COURSE OUTCOMES. PATTERNS AMONG STUDENTS ARE IDENTIFIED USING THE N-TARP CLUSTERING METHOD AND VALIDATED STATISTICALLY. STUDENTS ARE REPRESENTED BY A PROFILE REPRESENTING THE PATTERNS THEY FOLLOW AND THEIR INDIVIDUAL COURSE OUTCOMES. WE SUBSEQUENTLY TEST FOR THE EXISTENCE OF A RELATIONSHIP BETWEEN THE PATTERNS OF HABITS OF MIND AND THE COURSE OUTCOMES USING A STATISTICAL APPROACH IN ORDER TO MEANINGFULLY INTERPRET THE PROFILES.
WE LIVE SURROUNDED BY HUGE AMOUNTS OF DATA. WE HAVE BEEN PRODUCING MORE INFORMATION IN THE LAST TWO DECADES THAN IN THOUSANDS OF PREVIOUS YEARS OF HUMANITY'S EXISTENCE. AND THE QUANTITIES MULTIPLY EXPONENTIALLY. THANKS TO BIG DATA TECHNOLOGIES COMPANIES WE CAN PREDICT OUR BUYING BEHAVIOR, ANALYZE OUR MOOD OR ESTABLISH NEW MODELS OF INTERACTION BETWEEN USERS. HOWEVER, WE HAVE NOT YET BEEN ABLE TO INTRODUCE ALL THIS TECHNOLOGY INTO THE EDUCATIONAL WORLD. IS THIS USEFUL AND VIABLE? WHAT DO TEACHERS THINK ABOUT IT? THE OBJECTIVE OF THIS STUDY IS TO KNOW THE OPINIONS AND ATTITUDES THAT THE UNIVERSITY FACULTY AND A GROUP OF TEACHERS FROM DIFFERENT EDUCATIONAL LEVELS HAVE TOWARDS THE USE OF BIG DATA TECHNOLOGIES IN THEIR CLASSROOMS.
MOOCS ARE CHARACTERIZED AS BEING COURSES TO WHICH A LARGE NUMBER OF STUDENTS ENROLL, BUT ONLY A SMALL FRACTION COMPLETES THEM. AN UNDERSTANDING OF STUDENTS' ENGAGEMENT CONSTRUCT IS ESSENTIAL TO MINIMIZE DROPOUT RATES. THIS RESEARCH IS OF A QUANTITATIVE DESIGN AND EXPLORATORY IN NATURE AND INVESTIGATES THE INTERACTION BETWEEN CONTEXTUAL FACTORS (DEMOGRAPHIC CHARACTERISTICS), STUDENT ENGAGEMENT TYPES (ACADEMIC, BEHAVIORAL, COGNITIVE AND AFFECTIVE), AND LEARNING OUTCOMES, WITH THE OBJECTIVE OF IDENTIFYING THE FACTORS THAT ARE ASSOCIATED WITH COMPLETION OF MASSIVE AND OPEN ONLINE COURSES. TWO LOGISTIC MODELS WERE ADJUSTED IN TWO SAMPLES, GENERAL AND SECONDARY, WITH THE BINARY DEPENDENT VARIABLE DEFINED AS COMPLETES THE COURSE YES/NO. THE RESULTS IN THE GENERAL SAMPLE (15% COMPLETION RATE) SHOWED THAT THE PROBABILITIES OF A PARTICIPANT COMPLETING THE COURSE ARE POSITIVELY AND SIGNIFICANTLY RELATED TO PARTICIPATION IN THE FORUM AND THE PARTICIPANT EDUCATIONAL LEVEL, AND NEGATIVELY RELATED TO GENDER (FEMALE) AND AGE. THE RESULTS IN THE SECONDARY SAMPLE (87% COMPLETION RATE) SHOWED THAT THE PROBABILITIES OF A PARTICIPANT COMPLETING THE COURSE ARE POSITIVELY AND SIGNIFICANTLY RELATED TO PARTICIPATION IN THE FORUM, GENDER (FEMALE), AND THE MOTIVATION AND SATISFACTION INDEXES, AND NEGATIVELY RELATED TO AGE, HAVING PREVIOUS EXPERIENCE IN OTHER MOOCS, AND SELF-EFFICACY AND TASK STRATEGIES INDEXES. THE RESULTS LEAD TO IDEAS ON HOW THESE VARIABLES CAN BE USED TO SUPPORT STUDENTS TO PERSIST IN THESE LEARNING ENVIRONMENTS.
MATHEMATICS IS VIEWED AS ESSENTIAL TO THE ECONOMIC COMPETITIVENESS OF THE UNITED STATES ON THE GLOBAL STAGE (US DEPARTMENT OF EDUCATION). AS A PART OF THE CORE SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) FIELDS, MATHEMATICS IS VITAL TO THE NATION'S ECONOMIC PROSPERITY, YET ENROLLMENT IN STEM FIELDS AT THE COLLEGE/UNIVERSITY LEVEL HAS REMAINED RELATIVELY STABLE, DESPITE MILLIONS OF DOLLARS EXPENDED TO IMPROVE STEM EDUCATION AND RETENTION. MOST EXTANT RESEARCH FOCUSES ON INNOVATIONS WITHIN CLASSROOM SETTINGS THAT CAN IMPROVE LEARNING OUTCOMES FOR STUDENTS PURSUING STEM MAJORS. ADDITIONAL RESEARCH IS NEEDED IN EXAMINING HOW THE SEQUENCING OF COURSES TAKEN BY STUDENTS CAN HELP OR HINDER THEIR PROGRESS TOWARD THE SUCCESSFUL COMPLETION OF A STEM DEGREE. IN THIS FIRST PHASE OF OUR RESEARCH, QUANTITATIVE ANALYTICS WAS USED TO IDENTIFY THE IMPACT OF SPECIFIC COURSES ON STUDENTS' RISK OF DROPPING OUT OF A MATH MAJOR. FINDINGS SUGGEST A GENERAL INVERSE CORRELATION BETWEEN ACADEMIC PERFORMANCE AND ATTRITION: GOOD PERFORMANCE PREDICTS LOWER ATTRITION RATES. SURVIVAL ANALYSIS INDICATED THAT THE FURTHER A STUDENT GOES WITHIN THE MATH MAJOR, THE GREATER THE RISK OF DROPPING OUT. STUDENTS IN A MATH MAJOR TEND TO BE MORE LIKELY TO DROP OUT AFTER THEIR 4TH YEAR COMPARED TO THEIR FIRST THREE YEARS. IN GENERAL, STUDENTS SHOW INCREASED INCLINATION TO SWITCH MAJORS FROM THEIR SECOND YEAR AND THOSE STUDENTS WHO HAVE STAYED IN THE PROGRAM FOR TOO LONG ARE VERY LIKELY TO SWITCH TO ANOTHER MAJOR. (C) 2017 PUBLISHED BY ELSEVIER LTD.
WEB ANALYTICS IN THE LEARNING ENVIRONMENT IS THE USE OF INTELLIGENT DATA, LEARNER-PRODUCED DATA, AND ANALYSIS MODELS TO DISCOVER INFORMATION AND SOCIAL CONNECTIONS. THIS PAPER DESCRIBES A WEB ANALYTICS AND VISUALIZATION TOOL WHICH COLLECTS, ANALYSES AND VISUALLY REPRESENTS THE DATA COLLECTED IN AN E-LEARNING ENVIRONMENT. THE GOAL OF THIS TOOL IS ALLOW THE TEACHER TO BETTER UNDERSTAND HIS STUDENTS' BEHAVIOR IN FRONT OF THE ENVIRONMENT AND SUPPORT THE DECISION MAKING RELATED TO THE PEDAGOGICAL CONTENT ADAPTED TO THE STUDENTS' NEED.
THE SPREAD OF NEW TECHNOLOGIES LIKE AUGMENTED REALITY AND RECENT TECHNOLOGICAL DEVELOPMENTS, PROVIDE INNOVATIVE TECHNIQUES AND TOOLS THAT SHOW INCREASING POTENTIAL IN EDUCATION. IN THIS PAPER WE WILL SHOWCASE THE WORK IMPLEMENTED WITHIN THE HORIZON 2020 EUROPEAN PROJECT ARETE (AUGMENTED REALITY INTERACTIVE EDUCATIONAL SYSTEM). ONE OF THE PILOTS OF THIS PROJECT AIMS TO INVESTIGATE FOR THE FIRST TIME THE INTRODUCTION OF AR TO SUPPORT A BEHAVIORAL LESSON IN SCHOOLS WHERE POSITIVE BEHAVIOUR INTERVENTION AND SUPPORT (PBIS) METHODOLOGY IS ADOPTED. IN PARTICULAR, WE PRESENT THE STUDY CONDUCTED TO TRACK USER INTERACTIONS WITH AUGMENTED REALITY OBJECTS THROUGH THE USE OF THE EXPERIENCE API STANDARD.
ENACT IS A FRAMEWORK FOR THE DESIGN AND ANALYSIS OF CRITICAL THINKING ENVIRONMENTS BASED ON 4E COGNITION PERSPECTIVES. IN THIS PAPER, WE DESCRIBE A WEB-BASED CRITICAL THINKING ENVIRONMENT DESIGNED TO IMPLEMENTOE THE ENACT FRAMEWORK. WHEN USERS PERFORM A CRITICAL THINKING TASK IN THE ENVIRONMENT THEIR INTERACTION LOGS ARE CAPTURED. WE REPORT ON A PILOT STUDY WITH UNDERGRADUATE PARTICIPANTS AND ANALYSE HOW PARTICIPANTS USED THE AFFORDANCES IN THE ENVIRONMENT AS THEY PERFORMED THE CRITICAL THINKING TASK. ONE CASE OF TASK-RELATED BEHAVIOURS (HIGH ACTIVITY) IS ELABORATED TO HIGHLIGHT THE CURRENT POSSIBILITIES OF THE SYSTEM AND DISCUSS IMPLICATIONS FOR REDESIGN.
MOOCS (MASSIVE OPEN ONLINE COURSES) HAVE A BUILT-IN FORUM WHERE LEARNERS CAN SHARE EXPERIENCES AS WELL AS ASK QUESTIONS AND GET ANSWERS. NEVERTHELESS, THE WORK OF THE LEARNERS IN THE MOOC FORUM IS USUALLY NOT TAKEN INTO ACCOUNT WHEN CALCULATING THEIR GRADE IN THE COURSE, DUE TO THE DIFFICULTY OF AUTOMATING THE CALCULATION OF THAT GRADE IN A CONTEXT WITH A VERY LARGE NUMBER OF LEARNERS. IN SOME SITUATIONS, DISCUSSION FORUMS MIGHT EVEN BE THE ONLY AVAILABLE EVIDENCE TO GRADE LEARNERS. IN OTHER SITUATIONS, FORUM INTERACTIONS COULD SERVE AS A COMPLEMENT FOR CALCULATING THE GRADE IN ADDITION TO TRADITIONAL SUMMATIVE ASSESSMENT ACTIVITIES. THIS PAPER PROPOSES AN ALGORITHM TO AUTOMATICALLY CALCULATE LEARNERS' GRADES IN THE MOOC FORUM, CONSIDERING BOTH THE QUANTITATIVE DIMENSION AND THE RELEVANCE IN THEIR CONTRIBUTIONS. IN ADDITION, THE ALGORITHM HAS BEEN IMPLEMENTED WITHIN A WEB APPLICATION, PROVIDING INSTRUCTORS WITH A VISUAL AND A NUMERICAL REPRESENTATION OF THE GRADE FOR EACH LEARNER. AN EXPLORATORY ANALYSIS IS CARRIED OUT TO ASSESS THE ALGORITHM AND THE TOOL WITH A MOOC ON PROGRAMMING, OBTAINING A MODERATE POSITIVE CORRELATION BETWEEN THE FORUM GRADES PROVIDED BY THE ALGORITHM AND THE GRADES OBTAINED THROUGH THE SUMMATIVE ASSESSMENT ACTIVITIES. NEVERTHELESS, THE COMPLEMENTARY ANALYSIS CONDUCTED INDICATES THAT THIS CORRELATION MAY NOT BE ENOUGH TO USE THE FORUM GRADES AS PREDICTORS OF THE GRADES OBTAINED THROUGH SUMMATIVE ASSESSMENT ACTIVITIES.
MANY EDUCATIONAL INSTITUTIONS ENFORCE ATTENDANCE POLICIES, WHERE STUDENTS ARE EXPECTED TO HAVE THEIR ABSENCES BELOW A CERTAIN PERCENTAGE IN EACH CLASS. ATTENDANCE RECORDS ARE COLLECTED TO ENFORCE SUCH POLICIES, BUT THEY ARE RARELY UTILIZED FOR ANYTHING ELSE. IN THIS PAPER, WE INVESTIGATE THE VALUE OF ANALYZING THE RECORDS PULLED FROM STUDENT ATTENDANCE SYSTEMS. WE APPLY A DATA MINING TECHNIQUE, THE MARKET BASKET ANALYSIS, ON STUDENT ATTENDANCE DATA. THE CONTRIBUTION OF THIS ANALYSIS IS THE IDENTIFICATION OF STUDENT GROUPS WHO SHARE HIGHLY SIMILAR ABSENCE RECORDS. SUCH SIMILARITY MAY INDICATE THAT THE STUDENTS ARE MISSING CLASSES DUE TO PEER PRESSURE, RATHER THAN VALID EXCUSES. THE PRESENTED METHOD HELPS INSTRUCTORS AND ADVISORS DISCOVER THIS BEHAVIOR, WHICH IS MORE EFFICIENT THAN RELYING ON INSTRUCTORS, WHO MAY TEACH MANY CLASSES. TO MINIMIZE THE NUMBER OF FALSE ALARMS, STUDENT GROUPS ARE RANKED BASED ON THEIR ABSENCE SIMILARITY. WE TESTED OUR METHOD BY ANALYZING STUDENT ATTENDANCE DATA FOR OVER TWO THOUSAND STUDENTS FOR ONE SEMESTER AT A PUBLIC HIGHER EDUCATION INSTITUTION. THE RESULTS WERE HELPFUL IN IDENTIFYING STUDENTS WITH MISS CLASSES DUE TO THEIR FRIENDS MISSING THE CLASSES.
GAMES ARE GAINING MOMENTUM IN THE FIELD OF ASSESSMENT FOR EVALUATING STUDENT PERFORMANCE AND OTHER COMPLEX 21ST CENTURY SKILLS. GAMING ENVIRONMENTS HAVE THE POTENTIAL TO PROVIDE LEARNING ACTIVITIES AND THE OPPORTUNITY TO COLLABORATE, PROBLEM SOLVE AND APPLY CRITICAL THINKING SKILLS. AS PART OF THE ASSESSMENT AND TEACHING OF 21ST CENTURY SKILLS (ATC21S(TM1)) PROJECT, AN ASSESSMENT SYSTEM FOR ASSESSING COGNITIVE AND NON-COGNITIVE SKILLS HAVE BEEN DEVELOPED. THE PROJECT FOCUSES ON ASSESSING AND TEACHING 21ST CENTURY SKILLS AND THE ASSESSMENT SYSTEM MEASURES THESE SKILLS IN AN ONLINE CAPACITY. THE DESIGN OF THE ASSESSMENT SYSTEM IS INNOVATIVE; USING DATA ANALYTIC TECHNIQUES, RASCH ANALYSIS AND AUTOMATED SCORING TO PRODUCE INSTANT FORMATIVE REPORTS FOR EDUCATORS. AS THE FIRST ASSESSMENT SYSTEM OF ITS KIND TO MEASURE 21ST CENTURY SKILLS A DISCUSSION HERE SURROUNDS THE REQUIREMENTS AND DEPLOYMENT TECHNIQUES FOR THE DESIGN OF SUCH COMPLEX ASSESSMENTS FROM DELIVERY TO REPORTING, MAKING IT MEANINGFUL AND USEFUL FOR TEACHERS' USE IN EVERYDAY CLASSROOM.
THIS PAPER PRESENTS ASPECTS OF BOTH ORAL AND WRITTEN COMMUNICATION DISFLUENCIES BASED ON SPEECH TRANSCRIPTS AND CHAT CONVERSATIONS. THE FOUND RESULTS COME FROM MANUAL ANALYSES OF BOTH AUDIO TRANSCRIPTIONS OF STUDENTS' VERBALIZATIONS AND XML CODIFICATION OF COMPUTER SUPPORTED COLLABORATIVE LEARNING CHAT SESSIONS. THE ASPECTS PRESENTED DURING THE ANALYSES ARE CONTINUITY, DISCONTINUITY, FLUENCY AND DISFLUENCY, THESE BEING SELECTED WITH THE PURPOSE OF BEING CORRELATED. THE ENTIRE ANALYSIS WAS BASED ON EMPHASIZING DISFLUENCY KEY WORDS AND CONCEPTS FROM DEDICATED TAXONOMIES.
VARIOUS FACTORS AFFECT LEARNING, SUCH AS THE QUALITY AND DIFFICULTY OF THE LEARNING CONTENTS AND THE LEARNER'S PROFICIENCY, AND IT MAY BE POSSIBLE TO DETECT THEIR EFFECTS BY USING THE LEARNER'S BROWSING AND EDIT HISTORY AS WELL AS BIOLOGICAL INFORMATION SUCH AS BRAIN WAVES AND EYE MOVEMENT. WE INVESTIGATED THE USE OF BRAIN WAVES TO ESTIMATE THE DEGREE OF DIFFICULTY OF A LEARNING TASK. THE FIRST EXPERIMENT USING A SIMPLE TYPING TEST CONFIRMED A PREVIOUS FINDING THAT THE BETA-WAVE TO ALPHA-WAVE RATIO INCREASES WITH TASK DIFFICULTY. FURTHERMORE, THE LOW-BETA-WAVE TO LOW-ALPHA-WAVE RATIO, WHERE "LOW" MEANS LOW FREQUENCY, OBSERVED UNDER VARIOUS LEARNING CONDITIONS WAS FOUND TO DEPEND ON THE FREQUENCIES OF THE WAVES, AND THE VALUE OF THE RATIO WAS SHOWN TO REPRESENT TASK DIFFICULTY. THE SECOND EXPERIMENT IN WHICH THE CHANGE IN BRAIN WAVES WAS MEASURED USING A SIMPLE ELECTROENCEPHALOGRAPH (EEG) AS THE EXAMINEES BECAME USED TO THE TASK SHOWED THAT THE VALUES FOR THE EXAMINEES WHO REPORTED THAT THE TASK WAS EASY FELL GRADUALLY, SUPPORTING OUR FINDING THAT THE VALUE OF THE RATIO REPRESENTS TASK DIFFICULTY. THIS INFORMATION COULD BE USED TO DYNAMICALLY ADJUST TASK DIFFICULTY AND THEREBY OPTIMIZE THE LEARNING EFFECT.
THE PAPER DETAILS THE PREMISES AND THE CHALLENGES ASSOCIATED TO DESIGNING AND DEVELOPING SERIOUS GAMES (SG). THE AUTHORS ADVANCE A HYBRID, PROACTIVE, COST-EFFICIENT APPROACH IN THE SG FIELD THAT BUILDS UPON THE POWER OF COMMERCIAL GAME ENGINES AND THAT INTEGRATES COMPLEX, SERIOUS GAMES-DRIVEN PEDAGOGICAL ANALYSIS. UNLIKE CURRENT DEVELOPMENTS THAT ARE EITHER COMMERCIAL, IMPLEMENTING THE LATEST TECHNOLOGICAL INNOVATIONS, OR EDUCATIONAL, FOCUSING ON COMPLEX PEDAGOGICAL CONSTRUCTS, THE AUTHORS EXPLORE AN INNOVATIVE GENERAL FRAMEWORK FOR SG DEVELOPMENT THAT WILL BRIDGE THE EXPERTISE FROM THE TWO AREAS. THE RESEARCHERS ADOPT A COMPONENT-BASED DEVELOPMENT APPROACH THAT INCREASES PRODUCTIVITY, REDUCES TIME TO IMPLEMENTATION, IT IS EASIER TO CUSTOMIZE AND IT REQUIRES LESS FINANCIAL RESOURCES. PACKAGE SOFTWARE IS MONOLITHIC, DIFFICULT TO INTEGRATE, AND EXPENSIVE TO CUSTOMIZE. CUSTOM DEVELOPMENT IS COSTLY AND TIME CONSUMING. SINCE ORGANISATIONS VARY IN SIZE, COMPETENCIES AND REQUIREMENTS, COMPONENT-BASED DEVELOPMENT ENABLES COMPANIES AND EDUCATIONAL ORGANISATIONS TO REUSE WHAT ARE ESSENTIALLY PART OF APPLICATIONS THAT CAN BE CUSTOMIZED AND TIED TOGETHER TO FORM A UNIQUE SOLUTION THAT SUITS THEIR NEEDS. COST SAVINGS ARE ACHIEVED THROUGH A MODULAR APPROACH THAT ENABLES THE REUSE OF COMPONENTS IN CUSTOMISED ARCHITECTURES. THE RESEARCH IS CARRIED OUT WITHIN THE "DEVELOPMENT ENVIRONMENT AND SYLLABUS FOR SERIOUS GAMES IMPLEMENTATION" PROJECT, FUNDED BY UEFISCDI.
TECHNOLOGY-ENHANCED LEARNING (TEL) CAN ENGAGE STUDENTS WITH LEARNING AND OFFER BENEFITS IN KNOWLEDGE ACQUISITION DUE TO THE FLEXIBILITY OF LEARNING IT PROVIDES. THERE IS DIFFICULTY ASCERTAINING BEST PRACTICE FOR THE IMPLEMENTATION OF TEL IN PHYSIOTHERAPY EDUCATION. THIS STUDY AIMS TO ADDRESS THIS THROUGH A CASE STUDY WITH PRE-REGISTRATION STUDENTS USING A VIRTUAL LEARNING ENVIRONMENT (VLE) TO SUPPLEMENT THEIR LEARNING. SEVENTY-NINE STUDENTS WERE ENROLLED ONTO A MOVEMENT & EXERCISE MODULE AND HAD ACCESS TO THE VLE RESOURCES. DATA WERE CAPTURED BY ONLINE SURVEY, STUDENT FOCUS GROUPS, LEARNING ANALYTICS DATA AND COMPARISON OF EXAMINATION RESULTS WITH A PREVIOUS COHORT WHO DID NOT HAVE ACCESS TO ALL THE RESOURCES. SURVEY DATA DEMONSTRATED THAT MOST STUDENTS WERE SATISFIED WITH THE RESOURCES AND THAT THEY FACILITATED KNOWLEDGE ACQUISITION. THEMATIC ANALYSIS FROM THE FOCUS GROUPS RESULTED IN FIVE HIGHER ORDER THEMES: (1) CONTENT QUALITY, (2) INTERACTION AND ACCESSIBILITY, (3) LEARNING GOAL ALIGNMENT, (4) SATISFACTION WITH RESOURCES AND (5) SUGGESTIONS FOR THE FUTURE. LEARNING ANALYTICS DATA REVEALED STUDENTS ACCESSED THE RESOURCES PREDOMINANTLY BEFORE EXAMINATION PERIODS. THERE WERE STATISTICALLY SIGNIFICANT IMPROVEMENTS IN MEAN EXAMINATION MARKS COMPARED TO THE PREVIOUS COHORT. IN CONCLUSION, SATISFACTION WITH THE TEL RESOURCES WAS HIGH, AND THERE MAY BE SOME POSITIVE EFFECT ON KNOWLEDGE ACQUISITION.
THIS IS A WORK IN PROGRESS RESEARCH TO PRACTICE CATEGORY PAPER. RESEARCH HAS SHOWN THAT NOVICE PROGRAMMERS STRUGGLE WITH LEARNING INTRODUCTORY CONCEPTS AND FIND IT DIFFICULT TO MONITOR THEIR OWN PROGRESS. TEACHERS OFTEN HAVE HUNDREDS OF STUDENTS AND MULTIPLE SECTIONS OF PROGRAMMING COURSES TO TEACH, MAKING IT INFEASIBLE TO PROVIDE THE AMOUNT OF INDEPENDENT FEEDBACK EACH STUDENT MAY NEED TO FLOURISH. WITH LIMITED INSTRUCTOR FEEDBACK, STUDENTS WHO CAN SELF-MONITOR AND SELF-ASSESS THEIR PROGRAMMING METACOGNITION HAVE A HIGHER CHANCE OF DEVELOPING A PROCESS FOR SOLVING PROGRAMMING CHALLENGES. IN THIS PAPER, WE EXPAND ON THE LITERATE PROGRAMMING PARADIGM BY USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING METHODS TO AUTOMATICALLY ANALYZE AND CLASSIFY STUDENT PROGRAMMING METACOGNITION LEVELS THROUGH THEIR SOURCE CODE COMMENTS. OUR INTENT IS TO ULTIMATELY INTEGRATE OUR CLASSIFICATION MODELS INTO AN INTERACTIVE DEVELOPER ENVIRONMENT TO PROVIDE REAL-TIME FEEDBACK TO STUDENTS ABOUT THEIR METACOGNITION WHILE LEARNING TO PROGRAM.
THE APPEARANCE OF MOOCS HAS BOOSTED THE USE OF EDUCATIONAL TECHNOLOGY IN ALL POSSIBLE CONTEXTS. UNIVERSITIES ARE TRYING TO UNDERSTAND THIS NEW PHENOMENON, WHILE CARRYING OUT THE FIRST TRIALS. BEST PRACTICES ARE STILL SCARCE AND WILL BE DEVELOPED IN THE COMING MONTHS. IN THIS PAPER, WE PRESENT FIRST EXPERIENCES CARRIED OUT AT UNIVERSIDAD CARLOS III DE MADRID, BOTH WITH MOOCS ( MASSIVE OPEN ONLINE COURSES) AND WITH SPOCS (SMALL PRIVATE ONLINE COURSES), WHICH ARE MOOC COUNTERPARTS FOR INTERNAL USE.
INTERESTS TOWARDS TEACHING PROGRAMMING SKILLS HAVE RISEN RECENTLY IN THE REALM OF COMPUTING EDUCATION. LEARNING HOW TO PROGRAM NOT ONLY ENABLES LEARNERS TO DEVELOP COMPUTING APPLICATIONS, BUT IT CAN ALSO ENHANCE LEARNERS' COMPUTATIONAL THINKING (CT) PRACTICE. CT REFERS TO LEARNERS' ABILITY TO APPROACH ILL-STRUCTURED TASKS SYSTEMATICALLY BASED ON ALGORITHMIC THINKING IN COMPUTING. ALONG WITH GROWING ACADEMIC INTERESTS TOWARDS CT IN RECENT STUDIES, RESEARCHERS HAVE EMPHASIZED THE ROLE OF TEACHING PROGRAMMING IN FACILITATING LEARNERS' PROBLEM-SOLVING SKILLS. EMERGING OERS HAVE EXPANDED LEARNERS' OPPORTUNITIES TO ENGAGE IN HANDS-ON PROGRAMMING EXERCISES; YET A CHALLENGE STILL REMAINS AS TO HOW LEARNERS' PROGRAMMING EXERCISES CAN BE TAILORED TO ACCOMMODATE INDIVIDUAL DIFFERENCES IN TERMS OF LEARNERS' DIGITAL LITERACY SKILLS. THERE IS STILL A LACK OF IN-DEPTH DISCUSSIONS ON HOW TO SUPPORT LEARNERS' PERSONALIZED LEARNING EXPERIENCES DURING PROGRAMMING EXERCISES ASSOCIATED WITH CT. THIS STUDY HENCE PROPOSES A CONCEPTUAL FRAMEWORK THAT SEEKS TO CONSIDER HOW TO PROMOTELEARNERS' PERSONALIZED LEARNING EXPERIENCES AND ENHANCE THEIR CT SKILLS IN OERS. THROUGH EXTENSIVE REVIEWING OF LITERATURE, THIS STUDY PROVIDES SEVERAL IMPLICATIONS FOR FURTHER RESEARCH.
THE ARTICLE REPORTS RESULTS FROM A QUALITATIVE STUDY OF ELEVBARO, A PROTOTYPE OF A DIGITAL TOOL FOR STUDENT FEEDBACK DEVELOPED IN CONNECTION WITH THE DEMONSTRATION SCHOOL PROJECT, INCLUSION, AND DIFFERENTIATED TEACHING IN DIGITAL LEARNING ENVIRONMENTS. AT THE SAME TIME THE STUDY REPRESENTS THE FIRST STEP OF VALIDATING ELEVBARO AS A SYSTEMATIC FEEDBACK TOOL. THERE IS GENERAL CONSENSUS THAT FEEDBACK IS CENTRAL TO THE QUALITY OF TEACHING, BUT THE FOCUS ON AND THE EXPLORATION OF SYSTEMATIC STUDENT FEEDBACK IS AN OVERLOOKED TOPIC. ESPECIALLY AS REGARDS DIFFERENTIATED TEACHING AND COMPLEX TEACHING PATTERNS, THERE IS A NEED FOR SUPPLEMENTARY AND DIGITAL MONITORING OF A GROUP OF STUDENTS AND OF INDIVIDUAL STUDENTS. THE ARTICLE EXAMINES HOW STUDENTS AND TEACHERS UNDERSTAND AND USE ELEVBARO, WHICH IS BUILT ON FREQUENT RATINGS OF FIVE SET STATEMENTS IN CONNECTION WITH TEACHING OVER A CERTAIN PERIOD OF TIME. THERE IS REASON TO ASSUME THAT THE USAGE OF ELEVBARO AFFECTS TEACHERS' PROFESSIONAL ASSESSMENT OF STUDENTS AS WELL AS STUDENTS' REFLECTIONS OF THEIR WORK AND ACQUISITION PROCESS. THIS REQUIRES AN ACTIVE APPROACH TO ELEVBARO AND THE INTEGRATION OF THE TOOL INTO THE TEACHER'S USUAL ASSESSMENT AND TEACHING PRACTICE. IN OUR VIEW, THERE IS DIDACTIC POTENTIAL FOR TEACHERS' DATA-INFORMED TEACHING DEVELOPMENT.
THE RECENT DIFFUSION OF ONLINE EDUCATION (BOTH MOOCS AND E-COURSES) HAS LED TO AN INCREASED ECONOMIC AND SCIENTIFIC INTEREST IN E-LEARNING ENVIRONMENTS. AS WIDELY DOCUMENTED, ONLINE STUDENTS HAVE A MUCH HIGHER CHANCE OF DROPPING OUT THAN THOSE ATTENDING CONVENTIONAL CLASSROOMS. IT IS OF PARAMOUNT INTEREST FOR INSTITUTIONS, STUDENTS, AND FACULTY MEMBERS TO FIND MORE EFFICIENT METHODOLOGIES TO MITIGATE WITHDRAWALS. FOLLOWING THE RISE OF ATTENTION ON THE STUDENT DROPOUT PREDICTION (SDP) PROBLEM, THE LITERATURE HAS WITNESSED A SIGNIFICANT INCREASE IN CONTRIBUTIONS TO THIS SUBJECT. IN THIS SURVEY, WE PRESENT AN IN-DEPTH ANALYSIS OF THE STATEOF-THE-ART LITERATURE IN THE FIELD OF SDP, UNDER THE CENTRAL PERSPECTIVE, BUT NOT EXCLUSIVE, OF MACHINE LEARNING PREDICTIVE ALGORITHMS. OUR MAIN CONTRIBUTIONS ARE THE FOLLOWING: (I) WE PROPOSE A COMPREHENSIVE HIERARCHICAL CLASSIFICATION OF EXISTING LITERATURE THAT FOLLOWS THE WORKFLOW OF DESIGN CHOICES IN THE SDP; (II) TO FACILITATE THE COMPARATIVE ANALYSIS, WE INTRODUCE A FORMAL NOTATION TO DESCRIBE IN A UNIFORM WAY THE ALTERNATIVE DROPOUT MODELS INVESTIGATED BY THE RESEARCHERS IN THE FIELD; (III) WE ANALYSE SOME OTHER RELEVANT ASPECTS TO WHICH THE LITERATURE HAS GIVEN LESS ATTENTION, SUCH AS EVALUATION METRICS, GATHERED DATA, AND PRIVACY CONCERNS; (IV) WE PAY SPECIFIC ATTENTION TO DEEP SEQUENTIAL MACHINE LEARNING METHODS-RECENTLY PROPOSED BY SOME CONTRIBUTORS- WHICH REPRESENT ONE OF THE MOST EFFECTIVE SOLUTIONS IN THIS AREA. OVERALL, OUR SURVEY PROVIDES NOVICE READERS WHO ADDRESS THESE TOPICS WITH PRACTICAL GUIDANCE ON DESIGN CHOICES, AS WELL AS DIRECTS RESEARCHERS TO THE MOST PROMISING APPROACHES, HIGHLIGHTING CURRENT LIMITATIONS AND OPEN CHALLENGES IN THE FIELD.
OVER THE LAST DECADE, INCREASED STUDENT NUMBERS, LIMITED RESOURCES ACCOMPANIED WITH THE QUICK EVOLUTION AND PENETRATION OF NEW LEARNING TECHNOLOGIES, HAVE LED TO AMPLIFIED USE OF MULTIPLE CHOICE QUESTIONS (MCQ). IN AN IDEAL CASE, NOT ONLY THE PROCESS OF QUESTION AND ITEM DEVELOPMENT IS DOCUMENTED, BUT RESPONSES ARE ALSO STUDIED TO ENSURE THAT THE BUILDING BLOCKS OF TESTS ARE SOUND. THE RELIABILITY AND VALIDITY OF TEST QUESTIONS AND ITEMS ARE IMPORTANT ASPECTS WHEN ASSESSING WHETHER A STUDENT UNDERSTANDS CONTENT AND IF NOT, WHERE AND IN WHAT WAY HE/SHE IS STRUGGLING. THIS PAPER AIMS AT IDENTIFYING THOSE CURRICULUM PARTS OF A MANAGEMENT INFORMATION SYSTEMS TRAINING PROGRAM THAT STUDENTS FIND CHALLENGING BASED ON THE ANALYSIS OF THEIR MCQ BASED TEST RESULTS. ALL IN ALL, MORE THAN 150000 TEST FILLS WERE ANALYSED COLLECTING DATA FROM TESTS OF GENERAL AND SPECIFIC MANAGEMENT INFORMATION SYSTEMS RELATED COURSES. BESIDE THE APPLICATION OF DESCRIPTIVE STATISTICAL METHODS, ITEM LEVEL ANALYSIS HAS ALSO BEEN RUN TO RANK RESPONSES IN A MULTIPLE-CHOICE QUESTION AND TO IDENTIFY OTHER FACTORS (LIKE THE EFFECT OF ITEM LENGTH) WHICH ENABLE EXAMINEES TO GAIN EXTRA MARKS. AS A CONCLUSION, THE PAPER DESCRIBES HOW FINDINGS MAY BE USED TO INFORM ON FUTURE IMPROVEMENTS CONCERNING TEACHING APPROACHES AND METHODS.
THE TRADITIONAL RECORDING OF STUDENT INTERACTION IN CLASSROOMS HAS RAISED PRIVACY CONCERNS IN BOTH STUDENTS AND ACADEMICS. HOWEVER, THE SAME STUDENTS ARE HAPPY TO SHARE THEIR DAILY LIVES THROUGH SOCIAL MEDIA. PERCEPTION OF DATA OWNERSHIP IS THE KEY FACTOR IN THIS PARADOX. THIS ARTICLE PROPOSES THE DESIGN OF A PERSONAL MULTIMODAL RECORDING DEVICE (MRD) THAT COULD CAPTURE THE ACTIONS OF ITS OWNER DURING LECTURES. THE MRD WOULD BE ABLE TO CAPTURE CLOSE-RANGE VIDEO, AUDIO, WRITING, AND OTHER ENVIRONMENTAL SIGNALS. DIFFERENTLY FROM TRADITIONAL CENTRALIZED RECORDING SYSTEMS, STUDENTS WOULD HAVE CONTROL OVER THEIR OWN RECORDED DATA. THEY COULD DECIDE TO SHARE THEIR INFORMATION IN EXCHANGE OF ACCESS TO THE RECORDINGS OF THE INSTRUCTOR, NOTES FORM THEIR CLASSMATES, AND ANALYSIS OF, FOR EXAMPLE, THEIR ATTENTION PERFORMANCE. BY SHARING THEIR DATA, STUDENTS PARTICIPATE IN THE CO-CREATION OF ENHANCED AND SYNCHRONIZED COURSE NOTES THAT WILL BENEFIT ALL THE PARTICIPATING STUDENTS. THIS WORK PRESENTS DETAILS ABOUT HOW SUCH A DEVICE COULD BE BUILD FROM AVAILABLE COMPONENTS. THIS WORK ALSO DISCUSSES AND EVALUATES THE DESIGN OF SUCH DEVICE, INCLUDING ITS FORESEEABLE COSTS, SCALABILITY, FLEXIBILITY, INTRUSIVENESS AND RECORDING QUALITY.
THIS PAPER PRESENTS THE RESULTS OF A DETAILED ANALYSIS USING E-LEARNING LOG DATA OF AN INTRODUCTORY COMPUTER SCIENCE COURSE LOCATED IN THE SECOND SEMESTER OF A RESPECTIVE PROGRAM DURING A CORONA INDUCED DISTANCE LEARNING PHASE. IT ALLOWS SOME INSIGHTS INTO HOW STUDENTS BEHAVED DURING A LOCKDOWN AND HOW MUCH EFFORT STUDENTS ACTUALLY INVESTED INTO THIS COURSE. IN CONTRAST TO THE FINDINGS OF OTHER STUDIES, IT SHOWS THAT TIME SPENT ON THE COURSE SIGNIFICANTLY DECREASED ONCE IT HAD TO BE SHIFTED FROM ONSITE TO REMOTE TEACHING. THIS SEEMS TO GO HAND IN HAND WITH AN INCREASED AMOUNT OF CHEATING AND PLAGIARISM.
RANDOMISED CONTROL TRIALS (RCTS) ARE AN EVIDENCE-BASED RESEARCH APPROACH WHICH HAS NOT YET BEEN ADOPTED AND WIDELY USED IN OPEN AND DISTANCE EDUCATION TO INFORM EDUCATIONAL POLICY AND PRACTICE. DESPITE THE CHALLENGES ENTAILED IN THEIR APPLICATION, RCTS HOLD THE POWER TO ROBUSTLY EVALUATE THE EFFECTS OF EDUCATIONAL INTERVENTIONS IN DISTANCE LEARNING AND CONCLUDE ON WHETHER (OR NOT) THESE INTERVENTIONS SHOULD BE ADOPTED AND USED EXTENSIVELY. THE AIM OF THIS PAPER IS TO SPARK DISCUSSIONS AROUND THE USE OF RCTS IN DISTANCE LEARNING BY ILLUSTRATING THEIR BENEFITS AND DRAWBACKS INCLUDING CHALLENGES IN ADOPTING RCTS IN EDUCATION. TO ACHIEVE THIS AIM, A RCT WAS IMPLEMENTED TO EXAMINE WHETHER A SMALL-SCALE INTERVENTION IN FOUR LANGUAGE MODULES COULD IMPROVE ATTENDANCE AT AN END-OF-MODULE SPEAKING ASSESSMENT, AND IN CONSEQUENCE, PERFORMANCE, COMPLETION AND PASS RATES. RESULTS RAISE THE NEED FOR FURTHER RESEARCH IN ORDER TO IDENTIFY WHAT TYPE OF INTERVENTIONS SHOULD BE DESIGNED AND PUT INTO PRACTICE TO ELICIT A POSITIVE IMPACT ON LEARNERS. THE PAPER CONCLUDES WITH A DISCUSSION ON WHY RCTS SHOULD BE BROUGHT TO THE FOREFRONT AS A VIABLE METHOD FOR THE EFFECTIVE EVALUATION OF THE IMPACT OF OPEN LEARNING ANALYTIC INTERVENTIONS.
THIS ARTICLE PRESENTS. RESEARCH DONE DURING A PILOT OF THE INQUIRY-BASED LEARNING MODEL AND BY THE MEANS OF EUROPEAN PROJECT WORKING ENVIRONMENT WITH SOCIAL AND PERSONAL OPEN TOOLS. THE FOCUS IS PUT ON APPROBATION OF AVAILABLE DATA ANALYTICS METHODS SUPPORTING YOUNG RESEARCHERS IN THEIR DESKTOP AND MOBILE ACTIVITIES. RESULTS AND ANALYSIS ARE DISCUSSED, RECOMMENDATIONS FOR THE SYSTEM IMPROVEMENT ARE GIVEN.
MASSIVE OPEN ONLINE COURSES (MOOCS) CAN BE ENHANCED WITH THE SO-CALLED LEARNING-BY-DOING, DESIGNING THE COURSES IN A WAY THAT THE LEARNERS ARE INVOLVED IN A MORE ACTIVE WAY IN THE LEARNING PROCESS. WITHIN THE OPTIONS FOR INCREASING LEARNERS' INTERACTION IN MOOCS, IT IS POSSIBLE TO INTEGRATE (THIRD-PARTY) EXTERNAL TOOLS AS PART OF THE INSTRUCTIONAL DESIGN OF THE COURSES. IN MOOCS ON COMPUTER SCIENCES, THERE ARE, FOR EXAMPLE, WEB-BASED INTEGRATED DEVELOPMENT ENVIRONMENTS (IDES) WHICH CAN BE INTEGRATED AND THAT ALLOW LEARNERS TO DO PROGRAMMING TASKS DIRECTLY IN THEIR BROWSERS WITHOUT INSTALLING DESKTOP SOFTWARE. THIS WORK FOCUSES ON ANALYZING THE EFFECT ON LEARNERS' ENGAGEMENT AND BEHAVIOR OF INTEGRATING A THIRD-PARTY WEB-BASED IDE, CODEBOARD, IN THREE MOOCS ON JAVA PROGRAMMING WITH THE PURPOSE OF PROMOTING LEARNING-BY-DOING (LEARNING BY CODING IN THIS CASE). IN ORDER TO MEASURE LEARNERS' LEVEL OF ENGAGEMENT AND BEHAVIOR, DATA WAS COLLECTED FROM CODEBOARD ON THE NUMBER OF COMPILATIONS, EXECUTIONS AND CODE GENERATED, AND COMPARED BETWEEN LEARNERS WHO REGISTERED IN CODEBOARD TO SAVE AND KEEP A RECORD OF THEIR PROJECTS (REGISTERED LEARNERS) AND LEARNERS WHO DID NOT REGISTER IN CODEBOARD AND DID NOT HAVE ACCESS TO THESE EXTRA FEATURES (ANONYMOUS LEARNERS). THE RESULTS SHOW THAT LEARNERS WHO REGISTERED IN CODEBOARD WERE MORE ENGAGED THAN LEARNERS WHO DID NOT REGISTER (IN TERMS OF NUMBER OF COMPILATIONS AND EXECUTIONS), SPENT MORE TIME CODING AND DID MORE CHANGES IN THE BASE CODE PROVIDED BY THE TEACHERS. THE MAIN IMPLICATION OF THIS STUDY SUGGESTS THE NEED FOR A TRADE-OFF BETWEEN DESIGNING MOOCS THAT ALLOW A VERY EASY AND ANONYMOUS ACCESS TO EXTERNAL TOOLS AIMED FOR A MORE ACTIVE LEARNING, AND FORCING LEARNERS TO GIVE A STEP FORWARD IN TERMS OF COMMITMENT IN EXCHANGE FOR BENEFITTING FROM ADDITIONAL FEATURES OF THE EXTERNAL TOOL USED.
PERSONALIZED AND ADAPTIVE LEARNING IS THE FASTEST GROWING FIELD IN E-LEARNING. ADAPTIVE E-LEARNING SYSTEMS ARE TYPICALLY WELL SUITED FOR REAL-WORLD HETEROGENEOUS USERS, WHICH EXHIBIT DIFFERENT LEVELS OF MOTIVATION AND KNOWLEDGE. FURTHERMORE, STUDENTS LEARN BEST WHEN THEY ARE IN FLOW, IE. WHEN THE LEVEL OF DIFFICULTY IS PERFECTLY ADJUSTED TO THEIR INDIVIDUAL ABILITIES. A PERSONALIZED, ADAPTIVE, AND INTELLIGENT LEARNING ENVIRONMENT CAN PROVIDE EACH STUDENT WITH THIS LEARNING EXPERIENCE. IN THIS PAPER, WE PRESENT A LARGE-SCALE EVALUATION OF LEARNING IN FLOW WITHIN AN ADAPTIVE AND PERSONALIZED SYSTEM, THE ADAPTEMY SYSTEM. THE PAPER PRESENTS THE RESULTS OF TWO STUDIES: AN OBJECTIVE STUDY WITH 7,614 IRISH SECONDARY SCHOOL STUDENTS IN MATH CLASSES ASSESSING THEIR LEARNING FLOW, AND A SUBJECTIVE STUDY WITH 80 STUDENTS ASSESSING THEIR PERCEIVED LEARNING EXPERIENCE. THE RESULTS FROM THE OBJECTIVE STUDY SHOW THAT 88% OF THE STUDENTS WORKED WITHIN THE FLOW CHANNEL. IN THE SUBJECTIVE STUDY, 70% OF STUDENTS REPORTED A PERCEIVED IMPROVEMENT IN THEIR MATH SKILLS AFTER THE EXERCISE STUDYING WITH THE ADAPTIVE AND INTELLIGENT LEARNING SYSTEM.
IN THE CONTEXT OF POPULAR PEER REVIEW EDUCATIONAL APPROACHES, TEACHERS WISH TO KNOW WHETHER THE STUDENTS ARE BENEFITING FROM PEER REVIEWS AND APPLYING THE CHANGES IN THEIR SECOND DRAFTS. THIS PAPER PRESENTS A TOOL FOR TEACHERS THAT COMPARES INFORMATION ABOUT STUDENTS FIRST AND SECOND DRAFTS OF PAPERS FOCUSING ON THE EXTENT AND TYPE OF CHANGES IN THE PAPERS.
THE INCREASING POPULARITY OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAS RAISED THE NEED FOR HIGHLY SCALABLE, CUSTOMIZABLE, OPEN LEARNING ENVIRONMENTS. AT THE SAME TIME, THERE IS A GROWING TREND TO OPEN THE SERVICES THAT THE COMPANIES OFFER ON THE WEB WITH OPEN APIS AND IN THE FORM OF REST SERVICES, FACILITATING THEIR INTEGRATION IN CUSTOMIZED APPLICATIONS. THE GOAL OF THIS WORK IS TO SHOW HOW SUCH OPEN SERVICES CAN BE USED FOR THE SUPPORT OF ON-LINE EDUCATIONAL SYSTEMS. THESE SERVICES WERE NOT CREATED FOR AN EDUCATION CONTEXT, SO IT IS NECESSARY TO COMPLEMENT IT WITH FUNCTIONALITIES FOR SUPPORTING ASPECTS SUCH AS EVALUATIONS, MONITORING OR COLLABORATION. THIS ARTICLE DISCUSSES ON THE STRATEGIES FOR INTEGRATING SERVICES FOR EDUCATION AND PRESENTS TWO CASES STUDIES: FIRST, SMLEARNING, A COLLABORATIVE LEARNING ENVIRONMENT SUPPORTED BY SOCIAL MEDIA PLATFORMS FACEBOOK AND YOUTUBE, AND SECOND, AN APPLICATION FOR PROJECT-BASED PROGRAMMING COURSES, CUSTOMIZED THROUGH A GENERATIVE ARCHITECTURE, MAKING HEAVY USE OF GOOGLE SERVICES.
WITH THE RAPID GROWTH OF TECHNOLOGY ENHANCED LEARNING, NEW MEDIUMS FOR LEARNING HAVE EMERGED. ONE OF THESE MEDIUMS IS COMPUTER BASED LEARNING WHERE THE MAIN CONCERN IS HOW TO DESIGN A COMPUTER BASED LEARNING SYSTEM WHICH TAKES INTO CONSIDERATION THE LEARNERS' DIFFERENCES. PERSONALITY IS CONSIDERED AS ONE OF THE MOST CRITICAL SOURCES OF INDIVIDUAL DIFFERENCES. THIS STUDY INVESTIGATES HOW PERSONALITY DIFFERENCES-WITHIN LEARNERS CAN AFFECT COMPUTER BASED LEARNING, THROUGH A COMPREHENSIVE REVIEW OF THE LITERATURE. THE HIGHLIGHTED RESULTS FROM THE OBTAINED NINETEEN STUDIES ARE: (A) THE MOST REFERRED TO PERSONALITY MODEL IN COMPUTER BASED LEARNING IS MBTI; (B) PERSONALITY TRAITS AFFECT HOW LEARNERS PREFER LEARNING CONTENT AND LEARNING APPROACH LIKE COLLECTING INFORMATION, COMMUNICATING WITH INSTRUCTOR AND PEER, STUDY BEHAVIOR, ACTING AND PERFORMING; (C) A NEW MODEL OF PERSONALITY VARIABLES SHOULD BE CONSIDERED IN COMPUTER BASED LEARNING BY TAKING ALL INTERESTED RESEARCHERS AND PRACTITIONERS INTO ACCOUNTS; AND (D) THE TRADITIONAL QUESTIONNAIRE APPROACH WHICH IS STILL THE PRE-DOMINANT METHOD TO IDENTIFY THE LEARNER'S PERSONALITY; AND THIS NEEDS TO BE CHANGED WITH NEW POTENTIAL OF BIG DATA AND LEARNING ANALYTICS. FURTHERMORE, THIS STUDY PRESENTS A NEW IMPLICIT APPROACH USING LEARNING ANALYTICS INSTEAD OF QUESTIONNAIRE-BASED APPROACH TO IDENTIFY THE LEARNER'S PERSONALITY. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
ONLINE DISTANCE LEARNING (ODL) CONTINUES TO EXPAND RAPIDLY, DESPITE PERSISTENT CONCERNS THAT STUDENT EXPERIENCE IS POORER AND RETENTION LOWER THAN FOR FACE-TO-FACE COURSES. VARIOUS FACTORS AFFECT ODL QUALITY, BUT THE IMPACT OF RECOMMENDED LEARNING ACTIVITIES, SUCH AS STUDENT INTERACTION ACTIVITIES AND THOSE INVOLVING FEEDBACK, HAVE PROVEN DIFFICULT TO ASSESS BECAUSE OF CHALLENGES IN DEFINITION AND MEASUREMENT. ALTHOUGH LEARNING DESIGN FRAMEWORKS AND LEARNING ANALYTICS HAVE BEEN USED TO EVALUATE LEARNING DESIGNS, THEIR USE IS HAMPERED BY THIS LACK OF AN AGREED TERMINOLOGY. THIS STUDY ADDRESSES THESE CHALLENGES BY INITIALLY IDENTIFYING KEY ODL ACTIVITIES THAT ARE ASSOCIATED WITH HIGHER QUALITY LEARNING DESIGNS. THE LEARNING ACTIVITY TERMINOLOGY WAS TESTED USING INDEPENDENT RATERS, WHO CATEGORISED THE LEARNING ACTIVITIES IN FOUR ODL COURSES AS 'INTERACTION', 'FEEDBACK' OR 'OTHER', WITH INTER-RATER RELIABILITY NEAR OR ABOVE RECOMMENDED LEVELS. WHILST CHALLENGES REMAIN FOR CONSISTENT CATEGORISATION, THE ANALYSIS SUGGESTS THAT INCREASED CLARITY IN THE LEARNING ACTIVITY WILL AID CATEGORISATION. AS A RESULT OF THIS ANALYSIS, THE E-DESIGN ASSESSMENT TOOL (EDAT) HAS BEEN DEVELOPED TO INCORPORATE THIS KEY TERMINOLOGY AND ENABLE IMPROVED QUANTIFICATION OF LEARNING DESIGNS. THIS CAN BE USED WITH LEARNING ANALYTICS, PARTICULARLY RETENTION AND ATTAINMENT DATA, THUS PROVIDING AN EFFECTIVE FEEDBACK LOOP ON THE LEARNING DESIGN.
MASSIVE OPEN ONLINE COURSES (MOOCS) AIM TO EDUCATE THE WORLD. MORE OFTEN THAN NOT, HOWEVER, MOOCS FALL SHORT OF THIS GOAL-A MAJORITY OF LEARNERS ARE ALREADY HIGHLY EDUCATED (WITH A BACHELOR'S DEGREE OR MORE) AND COME FROM SPECIFIC PARTS OF THE (DEVELOPED) WORLD. LEARNERS FROM DEVELOPING COUNTRIES WITHOUT A HIGHER DEGREE ARE UNDERREPRESENTED, THOUGH DESIRED, IN MOOCS. ONE REASON FOR THOSE LEARNERS TO DROP OUT OF A COURSE CAN BE FOUND IN THEIR FINANCIAL REALITIES AND THE SUBSEQUENT LIMITED AMOUNT OF TIME THEY CAN DEDICATE TO A COURSE BESIDES EARNING A LIVING. IF WE COULD PAY LEARNERS TO TAKE A MOOC, THIS HURDLE WOULD LARGELY DISAPPEAR. WITH MOOCS, THIS LEADS TO THE FOLLOWING FUNDAMENTAL CHALLENGE: HOW CAN LEARNERS BE PAID AT SCALE? ULTIMATELY, WE ENVISION A RECOMMENDATION ENGINE THAT RECOMMENDS TASKS FROM ONLINE MARKET PLACES SUCH AS UPWORK OR WITMART TO LEARNERS, THAT ARE RELEVANT TO THE COURSE CONTENT OF THE MOOC. IN THIS MANNER, THE LEARNERS LEARN AND EARN MONEY. TO INVESTIGATE THE FEASIBILITY OF THIS VISION, IN THIS PAPER, WE EXPLORED TO WHAT EXTENT (1) ONLINE MARKET PLACES CONTAIN TASKS RELEVANT TO A SPECIFIC MOOC, AND (2) LEARNERS ARE ABLE TO SOLVE REAL-WORLD TASKS CORRECTLY AND WITH SUFFICIENT QUALITY. FINALLY, BASED ON OUR EXPERIMENTAL DESIGN, WE WERE ALSO ABLE TO INVESTIGATE THE IMPACT OF REAL-WORLD BONUS TASKS IN A MOOC ON THE GENERAL LEARNER POPULATION.
THIS PAPER PRESENTS A DETAILED STUDY OF A FORM OF ACADEMIC DISHONESTY THAT INVOLVES THE USE OF MULTIPLE ACCOUNTS FOR HARVESTING SOLUTIONS IN A MASSIVE OPEN ONLINE COURSE (MOOC). IT IS TERMED CAMEO COPYING ANSWERS USING MULTIPLE EXISTENCE ONLINE. A PERSON USING CAMEO SETS UP ONE OR MORE HARVESTING ACCOUNTS FOR COLLECTING CORRECT ANSWERS; THESE ARE THEN SUBMITTED IN THE USER'S MASTER ACCOUNT FOR CREDIT. THE STUDY HAS THREE MAIN GOALS: DETERMINING THE PREVALENCE OF CAMEO, STUDYING ITS DETAILED CHARACTERISTICS, AND INFERRING THE MOTIVATION(S) FOR USING IT. FOR THE PHYSICS COURSE THAT WE STUDIED, ABOUT 10% OF THE CERTIFICATE EARNERS USED THIS METHOD TO OBTAIN MORE THAN 1% OF THEIR CORRECT ANSWERS, AND MORE THAN 3% OF THE CERTIFICATE EARNERS USED IT TO OBTAIN THE MAJORITY (> 50%) OF THEIR CORRECT ANSWERS. WE DISCUSS TWO OF THE LIKELY CONSEQUENCES OF CAMEO: JEOPARDIZING THE VALUE OF MOOC CERTIFICATES AS ACADEMIC CREDENTIALS, AND GENERATING MISLEADING CONCLUSIONS IN EDUCATIONAL RESEARCH. BASED ON OUR STUDY, WE SUGGEST METHODS FOR REDUCING CAMEO. ALTHOUGH THIS STUDY WAS CONDUCTED ON A MOOC, CAMEO CAN BE USED IN ANY LEARNING ENVIRONMENT THAT ENABLES STUDENTS TO HAVE MULTIPLE ACCOUNTS. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
WITHIN A LEARNING AND TEACHING SUPPORT PLATFORM, JANIS AND MANN'S (1977). "EMERGENCY DECISION MAKING: A THEORETICAL ANALYSIS OF RESPONSES TO DISASTER WARNINGS." JOURNAL OF HUMAN STRESS 3 (2): 35-48. HTTPS://DOI.ORG/10.1080/0097840X.1977.9936085) MODEL OF DECISION MAKING WAS USED TO EXAMINE RELATIONSHIPS BETWEEN PEOPLE'S DECISIONAL STYLES, OBJECTIVE MEASUREMENTS OF FILE USE AND THE SUBSEQUENT QUALITY OF THEIR WORK. A TOTAL OF 95 UNDERGRADUATE PSYCHOLOGY STUDENTS CONSENTED TO THE MONITORING OF THEIR ONLINE STUDY BEHAVIOURS, AND COMPLETED THE MELBOURNE DECISION MAKING QUESTIONNAIRE. BETTER ONLINE GRADES WERE LINKED TO THE GENERAL ABILITY AND BETTER DECISIONAL SELF-ESTEEM. GREATER AMOUNTS OF FILE ACCESS WERE ASSOCIATED WITH HYPERVIGILANCE AND MORE TIME SPENT ONLINE. THE NUMBER OF MISSED SUPPLEMENTARY ONLINE QUIZZES WAS RELATED TO POORER DECISIONAL SELF-ESTEEM, FEWER FILES ACCESSED ONLINE BUT LESS PROCRASTINATION. WHILE GOOD DECISIONAL STYLES WERE ASSOCIATED WITH BETTER QUALITY OUTCOMES, DEFECTIVE DECISIONAL STYLES CAN MANIFEST ONLINE AS GREATER FILE ACCESS OR FEWER TASKS COMPLETED. AN ABSENCE, INAPPROPRIATE OR AN EXCESS OF ONLINE ACTIVITY CAN INDICATE STUDENTS ARE DEFENSIVELY AVOIDANT OR PANICKING AND NEED ASSISTANCE.
HIGHER EDUCATION FACES THE CHALLENGE OF HIGH STUDENT ATTRITION, WHICH IS ESPECIALLY DISCONCERTING IF ASSOCIATED WITH LOW PARTICIPATION RATES, AS IS THE CASE IN SOUTH AFRICA. RECENTLY, THE USE OF LEARNING ANALYTICS HAS INCREASED, ENABLING INSTITUTIONS TO MAKE DATA-INFORMED DECISIONS TO IMPROVE TEACHING, LEARNING, AND STUDENT SUCCESS. MOST OF THE LITERATURE THUS FAR HAS FOCUSED ON "AT-RISK" STUDENTS. THE AIM OF THIS PAPER IS TWOFOLD: TO USE LEARNING ANALYTICS TO DEFINE A DIFFERENT GROUP OF STUDENTS, TERMED THE "MURKY MIDDLE" (MM), EARLY ENOUGH IN THE ACADEMIC YEAR TO PROVIDE SCOPE FOR TARGETED INTERVENTIONS; AND TO DESCRIBE THE LEARNING STRATEGIES OF SUCCESSFUL STUDENTS TO GUIDE THE DESIGN OF INTERVENTIONS AIMED AT IMPROVING THE PROSPECTS OF SUCCESS FOR ALL STUDENTS, ESPECIALLY THOSE OF THE MM. WE FOUND THAT IT WAS POSSIBLE TO IDENTIFY THE MM USING DEMOGRAPHIC DATA THAT ARE AVAILABLE AT THE START OF THE ACADEMIC YEAR. THE STUDENTS IN THE SUBGROUP WERE CLEANLY DEFINED BY THEIR GRADE 12 RESULTS FOR PHYSICAL SCIENCES. WE WERE ALSO ABLE TO DESCRIBE THE LEARNING STRATEGIES THAT ARE ASSOCIATED WITH SUCCESS IN FIRST-YEAR BIOLOGY. THIS INFORMATION IS USEFUL FOR CURRICULAR DESIGN, CLASSROOM PRACTICE, AND STUDENT ADVISING AND SHOULD BE INCORPORATED IN PROFESSIONAL DEVELOPMENT PROGRAMS FOR LECTURERS AND STUDENT ADVISORS.
BIG DATA-BASED METHODS OF LEARNING ANALYTICS ARE INCREASINGLY RELIED ON BY INSTITUTIONS OF HIGHER LEARNING IN ORDER TO INCREASE STUDENT RETENTION BY IDENTIFYING AT RISK STUDENTS WHO ARE IN NEED OF AN INTERVENTION TO ALLOW THEM TO CONTINUE ON IN THEIR EDUCATIONAL ENDEAVORS. IT IS WELL KNOWN THAT E-LEARNING STUDENTS ARE EVEN MORE AT RISK OF FAILING OUT OF UNIVERSITY THAN ARE TRADITIONAL STUDENTS, SO BIG DATA LEARNING ANALYTICS ARE EVEN MORE APPROPRIATE IN THIS CONTEXT. IN THIS PAPER, WE PRESENT OUR APPROACH TO THIS PROBLEM. WE WISH TO PLACE CONTROL OF A STUDENT'S LEARNING PROCESS IN HIS OWN HANDS, RATHER THAN THAT OF THE LEARNING INSTITUTION IN ORDER TO DECOUPLE THE STUDENT FROM THE INSTITUTION SINCE THE GOALS AND MOTIVATIONS OF THESE TWO MAY NOT BE COMPLETELY ALIGNED. IN THIS WAY, WE EMPOWER THE STUDENT BY GIVING HIM CONTROL OF THE PERSONAL LEARNING SYSTEM WHICH EMPLOYS BIG DATA TECHNIQUES TO GENERATE RECOMMENDATIONS ON HOW TO REACH A SET OF LEARNER-SPECIFIC LEARNING GOALS. WE PRESENT THE FORMALISM WHICH UNDERLIES OUR SYSTEM, THE ARCHITECTURE WHICH IMPLEMENTS THE SYSTEM, SCENARIOS FOR SYSTEM USE, A SURVEY OF RELATED WORKS AND THOUGHTS ON HOW THE SYSTEM WILL BE IMPLEMENTED IN A PROTOTYPE IN THE FUTURE.
MILLIONS OF PEOPLE HAVE ENROLLED AND ENROL (ESPECIALLY IN THE COVID-19 PANDEMIC WORLD) IN MOOCS. HOWEVER, THE RETENTION RATE OF LEARNERS IS NOTORIOUSLY LOW. THE MAJORITY OF THE RESEARCH WORK ON THIS ISSUE FOCUSES ON PREDICTING THE DROPOUT RATE, BUT VERY FEW USE EXPLAINABLE LEARNING PATTERNS AS PART OF THIS ANALYSIS. HOWEVER, VISUAL REPRESENTATION OF LEARNING PATTERNS COULD PROVIDE DEEPER INSIGHTS INTO LEARNERS' BEHAVIOUR ACROSS DIFFERENT COURSES, WHILST NUMERICAL ANALYSES CAN - AND ARGUABLY, SHOULD - BE USED TO CONFIRM THE LATTER. THUS, THIS PAPER PROPOSES AND COMPARES DIFFERENT GRANULARITY VISUALISATIONS FOR LEARNING PATTERNS (BASED ON CLICKSTREAM DATA) FOR BOTH COURSE COMPLETERS AND NON-COMPLETERS. IN THE LARGE-SCALE MOOCS WE ANALYSED, ACROSS VARIOUS DOMAINS, OUR FINE-GRAINED, FISH-EYE VISUALISATION APPROACH SHOWED THAT NONCOMPLETERS ARE MORE LIKELY TO JUMP FORWARD IN THEIR LEARNING SESSIONS, OFTEN ON A `CATCH-UP' PATH, WHILST COMPLETERS EXHIBIT LINEAR BEHAVIOUR. FOR COARSER, BIRDEYE GRANULARITY VISUALISATION, WE OBSERVED LEARNERS' TRANSITION BETWEEN TYPES OF LEARNING ACTIVITY, OBTAINING TYPED TRANSITION GRAPHS. THE RESULTS, BACKED UP BY STATISTICAL SIGNIFICANCE ANALYSIS AND MACHINE LEARNING, PROVIDE INSIGHTS FOR COURSE INSTRUCTORS TO MAINTAIN ENGAGEMENT OF LEARNERS BY ADAPTING THE COURSE DESIGN TO NOT JUST 'DRY' PREDICTED VALUES, BUT EXPLAINABLE, VISUALLY VIABLE PATHS EXTRACTED.
OVER THE COURSE OF THE LAST DECADE, TEACHERS HAVE BEEN INCREASINGLY PRESSURED TO USE DIGITAL ARTEFACTS IN THE CLASSROOM, AND TO SHIFT FROM GRADE-BASED ASSESSMENT TO SKILL-BASED ASSESSMENT. DASHBOARDS HAVE BEEN USED IN VARIOUS EDUCATIONAL SETTINGS; THEY COULD HELP BRIDGE THE GAP BETWEEN THESE TWO EXPECTATIONS. BASED ON ENGESTROM'S ACTIVITY THEORY, WE EXPLORE THE CONTRADICTIONS THAT COULD RISE IF EDUCATIONAL DASHBOARDS WERE TO BE USED IN AN EVALUATION CONTEXT. THIS ARTICLE IS BASED ON A CASE STUDY, THE DESIGN OF AN APPLICATION. COLLECTIVE SEMI-STRUCTURED INTERVIEWS BASED ON DASHBOARDS' MOCK-UPS WERE CONDUCTED WITH A PANEL OF TEACHERS FROM BOTH ELEMENTARY AND MIDDLE SCHOOL. EITHER THE ADDITIONAL TIME REQUIRED TO DEEPEN THE EVALUATION IS TOO HIGH TO ENABLE THE USE OF THE DASHBOARD, OR THE TEACHER WOULD RATHER SAVE TIME BY RELYING HEAVILY ON THE DIAGNOSTIC ABILITIES OF THE APPLICATION. GIVEN THAT THE ARTEFACT IS BLIND TO THE CHARACTERISTICS OF BOTH THE DIDACTIC SITUATION AND OF THE LEARNERS, THE DASHBOARD IS LIKELY TO PRODUCE INCOHERENT DIAGNOSTICS.
THIS PAPER ADDRESSES THE PROBLEM OF A FINITE SET OF ENTITIES WHICH ARE REQUIRED TO ACHIEVE A GOAL WITHIN A PREDEFINED DEADLINE. FOR EXAMPLE, A GROUP OF STUDENTS IS SUPPOSED TO SUBMIT A HOMEWORK BY A SPECIFIED CUTOFF. FURTHER, WE ARE INTERESTED IN PREDICTING WHICH ENTITIES WILL ACHIEVE THE GOAL WITHIN THE DEADLINE. THE PREDICTIVE MODELS ARE BUILT BASED ONLY ON THE DATA FROM THAT POPULATION. THE PREDICTIONS ARE COMPUTED AT VARIOUS TIME INSTANTS BY TAKING INTO ACCOUNT UPDATED DATA ABOUT THE ENTITIES. THE FIRST CONTRIBUTION OF THE PAPER IS A FORMAL DESCRIPTION OF THE PROBLEM. THE IMPORTANT CHARACTERISTIC OF THE PROPOSED METHOD FOR MODEL BUILDING IS THE USE OF THE PROPERTIES OF ENTITIES THAT HAVE ALREADY ACHIEVED THE GOAL. WE CALL SUCH AN APPROACH "SELF-LEARNING". SINCE TYPICALLY ONLY A FEW ENTITIES HAVE ACHIEVED THE GOAL AT THE BEGINNING AND THEIR NUMBER GRADUALLY GROWS, THE PROBLEM IS INHERENTLY IMBALANCED. TO MITIGATE THE CURSE OF IMBALANCE, WE IMPROVED THE SELF-LEARNING METHOD BY TACKLING INFORMATION LOSS AND BY SEVERAL SAMPLING TECHNIQUES. THE ORIGINAL SELF-LEARNING AND THE MODIFICATIONS HAVE BEEN EVALUATED IN A CASE STUDY FOR PREDICTING SUBMISSION OF THE FIRST ASSESSMENT IN DISTANCE HIGHER EDUCATION COURSES. THE RESULTS SHOW THAT THE PROPOSED IMPROVEMENTS OUTPERFORM THE SPECIFIED TWO BASE-LINE MODELS AND THE ORIGINAL SELF-LEARNER, AND ALSO THAT THE BEST RESULTS ARE ACHIEVED IF DOMAIN-DRIVEN TECHNIQUES ARE UTILISED TO TACKLE THE IMBALANCE PROBLEM. WE ALSO SHOWED THAT THESE IMPROVEMENTS ARE STATISTICALLY SIGNIFICANT USING WILCOXON SIGNED RANK TEST.
THIS STUDY PRESENTS A MODEL FOR THE EARLY IDENTIFICATION OF STUDENTS WHO ARE LIKELY TO FAIL IN AN ACADEMIC COURSE. TO ENHANCE PREDICTIVE ACCURACY, SENTIMENT ANALYSIS IS USED TO IDENTIFY AFFECTIVE INFORMATION FROM TEXT-BASED SELF-EVALUATED COMMENTS WRITTEN BY STUDENTS. EXPERIMENTAL RESULTS DEMONSTRATED THAT ADDING EXTRACTED SENTIMENT INFORMATION FROM STUDENT SELF-EVALUATIONS YIELDS A SIGNIFICANT IMPROVEMENT IN EARLY-STAGE PREDICTION QUALITY. THE RESULTS ALSO INDICATE THE LIMITED EARLY-STAGE PREDICTIVE VALUE OF STRUCTURED DATA, SUCH AS HOMEWORK COMPLETION, ATTENDANCE, AND EXAM GRADES, DUE TO DATA SPARSENESS AT THE BEGINNING OF THE COURSE. THUS, APPLYING SENTIMENT ANALYSIS TO UNSTRUCTURED DATA (E.G., SELF-EVALUATION COMMENTS) CAN PLAY AN IMPORTANT ROLE IN IMPROVING THE ACCURACY OF EARLY-STAGE PREDICTIONS. THE FINDINGS PRESENT EDUCATORS WITH AN OPPORTUNITY TO PROVIDE STUDENTS WITH REAL-TIME FEEDBACK AND SUPPORT TO HELP STUDENTS BECOME SELF-REGULATED LEARNERS. USING THE EXPLORING RESULTS FOR IMPROVEMENT IN TEACHING AND LEARNING INITIATIVES IS IMPORTANT TO MAINTAIN STUDENTS' PERFORMANCES AND THE EFFECTIVENESS OF THE LEARNING PROCESS.
PARTICIPANTS' ENGAGEMENT IN MASSIVE ONLINE OPEN COURSES (MOOCS) IS HIGHLY IRREGULAR AND SELF-DIRECTED. IT IS WELL KNOWN IN THE FIELD OF TELEVISION MEDIA THAT SUBSTANTIAL PARTS OF THE AUDIENCE TEND TO DROP OUT AT MAJOR EPISODIC, OR SEASONAL, CLOSURES, WHICH MAKES CREATING CLIFF-HANGERS A CRUCIAL STRATEGY TO RETAIN VIEWERS (BAKKER, 1993; CAZANI, 2016; THOMPSON, 2003). COULD THERE BE AN ANALOGOUS PATTERN IN MOOCS-WITH AN ELEVATED PROBABILITY OF DROPOUT AT MAJOR CHAPTER TRANSITIONS? APPLYING DISJOINT SURVIVAL ANALYSIS ON A SAMPLE OF 12,913 STUDENTS IN A POPULAR ASTRONOMY MOOC THAT BUILT PARTICIPANTS' CULTURAL CAPITAL (HOBBYIST PURSUITS), WE FOUND A SIGNIFICANT INCREASE IN DROPOUT RATES AT CHAPTER CLOSURES. MOREOVER, THE LATTER THE CHAPTER CLOSURE WAS POSITIONED IN THE COURSE SEQUENCE, THE HIGHER THE DROPOUT RATE BECAME. WE FOUND THIS PATTERN REPLICATED IN A SAMPLE OF 20,134 STUDENTS IN A POPULAR COMPUTER SCIENCE MOOC THAT INTRODUCED PARTICIPANTS TO PROGRAMMING.
THIS PAPER PRESENTS THE DESIGN AND DEVELOPMENT OF "STORYPLAY MULTIMODAL", A MOBILE MULTIMODAL ANALYTICS PLATFORM FOR THE EVALUATION OF SERIOUS GAMES. IT IS INTENDED TO SERVE RESEARCHERS, TEACHERS AND EDUCATIONAL GAME DEVELOPERS AS A MEANS TO ASSESS THEIR SERIOUS GAME DESIGN. THIS IS DONE BY CAPTURING, PRE-PROCESSING, SYNCHRONIZING AND VISUALIZING MULTIMODAL SERIOUS GAMES ANALYTICS AND MOBILE SENSOR DATA FROM PLAYTESTING SESSIONS. BY LINKING LOG DATA WITH MULTIMODAL DATA, IT IS POSSIBLE TO UNCOVER RELATIONS BETWEEN DESIGN ELEMENTS, GAMEPLAY INTERACTIONS, CONTEXT PARAMETERS AND AFFECTIVE AND COGNITIVE STATES. THIS IS CRUCIAL FOR GAINING FULL INSIGHT INTO A SESSION, EVEN IF NOT PRESENT WITH THE PLAYER AT THE SAME LOCATION. AFTER DISCUSSING DESIGN REQUIREMENTS, THE ARCHITECTURE OF THE SOFTWARE, THE DIFFERENT MODULES, ADDITIONAL FEATURES, IMPLEMENTATION CHALLENGES AND SOLUTIONS ARE PRESENTED. TESTING SETTINGS, PARTICIPANTS AND RESULTS ARE ALSO DISCUSSED TO DEMONSTRATE HOW AN EVALUATION PROCEDURE CAN BE APPLIED TO DELIVER VALUABLE OUTCOMES FOR SERIOUS GAMES RESEARCH.
WE ARE CONDUCTING A NATIONAL SURVEY OF JAPANESE UNIVERSITIES ON IT EDUCATION UNDER THE SUPPORT OF JAPANESE MISTRY OF EDUCATION. THIS PAPER DESCRIBES THE OVERVIEW OF THE SURVEY PROJECT AND PRELIMINARY ANALYSIS OF THE SURVEY RESULT. THE SURVEY IS COMPOSED OF FIVE DIFFERENT TYPES: (1) SURVEY OF IT EDUCATION AS A MAJOR FIELD OF STUDY, (2) SURVEY OF IT EDUCATION AS A PART OF A MAJOR FIELD OTHER THAN IT, (3) SURVEY OF GENERAL IT EDUCATION FOR COLLEGE STUDENTS BELONGING TO ALL FACULTIES, (4) SURVEY OF IT EDUCATION FOR THE STUDENTS WILLING TO HAVE A LICENCE TO BE A HIGH SCHOOL TEACHER ON IT, (5) SURVEY OF COMPUTING ENVIRONMENT FOR IT EDUCATION. THE SURVEY CONTAINS VARIOUS QUESTIONS ABOUT OUTLINE OF THE EDUCATIONAL PROGRAM, EDUCATIONAL CONTENTS AND ACHIEVEMENT LEVEL FOR INDEPENDENT TOPIC, STUDENTS, TEACHING STAFF, EDUCATIONAL ENVIRONMENT AND FUTURE PLAN. WE COLLECTED ABOUT 3,000 ANSWERS FROM 650 UNIVERSITIES USING A WEB-BASED SURVEY SYSTEM. THE SURVEY COVERS 85% OF THE JAPANESE UNIVERSITIES.
THE ADOPTION OF E-TEXTBOOK HAS OFFERED A NEW OPPORTUNITY TO GAIN INSIGHT INTO TEACHERS' USAGE OF ICT IN CLASS. USAGE OF TECHNOLOGY CAN VARY GREATLY FROM ONE GROUP OF TEACHERS FROM ANOTHER AND THEY HAVE DIFFERENT USING PATTERNS. IN THIS STUDY, WE DISCUSS THREE DIFFERENT USING PATTERNS WHICH WE DISCOVERED WITH FREQUENT CLOSED SEQUENCES MINING. WE FIND THAT DIFFERENT USING PATTERNS SHOW TEACHERS' USING HABITS, AND CAN PROBABLY AFFECT THE TEACHING EFFICIENCY DUE TO LEARNERS' DIFFERENT LEARNING EXPERIENCE IN CLASS.
RESEARCH FULL PAPER - VISUAL ANALYTICS IS AN EMERGING FIELD THAT ENABLES DETECTION OF THE EXPECTED AND DISCOVERY OF THE UNEXPECTED. THIS TECHNIQUE HAS BEEN USED IN NUMEROUS AREAS SUCH AS EDUCATION, WHERE THE MOTIVATION IS TO UNDERSTAND AND IMPROVE THE TEACHING AND LEARNING PROCESSES. THIS PAPER PRESENTS A SYSTEMATIC LITERATURE STUDY OF THE FIELD OF VISUAL ANALYSIS IN AN EDUCATIONAL CONTEXT IN ORDER TO PROVIDE MORE INSIGHTS INTO THIS RESEARCH AREA. THEREFORE, 128 PAPERS WERE RELATED TO THIS TOPIC. THE MAJORITY OF THEM WERE DEVELOPED IN THE UNITED STATES, ALTHOUGH SPAIN, CHINA, THE UNITED KINGDOM AND BRAZIL ARE IN THE RANKING OF COUNTRIES THAT PUBLISHED THE MOST. ACCORDING TO THIS LITERATURE STUDY, 72% OF THE FOUND PAPERS WERE PUBLISHED AFTER 2015, SHOWING THAT VISUAL LEARNING ANALYTICS IS AN EMERGING FIELD, ALSO, THERE ARE SEVERAL CONFERENCES THAT HAVE INCLUDED THIS TOPIC IN THEIR PUBLICATIONS. ADDITIONAL ANALYSES WERE MADE, FOCUSING ON THE DISCOVERY OF THE MOST USED ALGORITHMS, WHICH INCLUDE BAR CHARTS, LINE CHARTS, PIE CHARTS, HEATMAP, AND OTHERS; WHETHER DATA MINING IS COMMONLY COMBINED WITH VISUAL LEARNING ANALYTICS; WHICH EDUCATIONAL LEVEL IS MOST ANALYZED IN THE LITERATURE: HIGH SCHOOL, UNDERGRADUATE OR GRADUATE; AND WHETHER ANY SUBJECT HAS MORE APPROACHES AS WELL AS WHETHER THE INITIAL COMPUTING CLASSES HAVE BEEN ANALYZED.
ENGAGEMENT ANALYTICS IS A BRANCH OF LEARNING ANALYTICS (LA) THAT FOCUSES ON STUDENT ENGAGEMENT, WITH MOST STUDIES CONDUCTED BY COMPUTER SCIENTISTS. THUS, RATHER THAN FOCUSING ON LEARNING, RESEARCH IN THIS FIELD USUALLY TREATS EDUCATION AS A SCENARIO FOR ALGORITHMS OPTIMIZATION AND IT RARELY CONCLUDES WITH IMPLICATIONS FOR PRACTICE. WHILE LA AS A RESEARCH FIELD IS REACHING TEN YEARS, ITS CONTRIBUTION TO OUR UNDERSTANDING OF TEACHING AND LEARNING AND ITS IMPACT ON LEARNING ENHANCEMENT ARE STILL UNDERDEVELOPED. THIS PAPER ARGUES THAT DATA-DRIVEN MODELING OF ENGAGEMENT ANALYTICS IS HELPFUL TO ASSESS STUDENT ENGAGEMENT AND TO PROMOTE REFLECTIONS ON THE QUALITY OF TEACHING AND LEARNING. IN THIS ARTICLE, THE AUTHORS A) INTRODUCE FOUR KEY CONSTRUCTS (STUDENT ENGAGEMENT, LEARNING ANALYTICS, ENGAGEMENT ANALYTICS, MODELING AND DATA-DRIVEN MODELING); B) EXPLAIN WHY DATA-DRIVEN MODELING IS CHOSEN FOR ENGAGEMENT ANALYTICS AND THE LIMITATIONS OF USING A PREDEFINED FRAMEWORK; C) DISCUSS HOW TO USE ENGAGEMENT ANALYTICS TO PROMOTE PEDAGOGICAL REFLECTION USING A PILOT STUDY AS A DEMONSTRATION. AS A FINAL REMARK, THE AUTHORS SEE THE NEED OF INTERDISCIPLINARY COLLABORATION ON ENGAGEMENT ANALYTICS BETWEEN COMPUTER SCIENCE AND EDUCATIONAL SCIENCE. IN FACT, THIS COLLABORATION SHOULD ENHANCE THE USE OF MACHINE LEARNING AND DATA MINING METHODS TO EXPLORE BIG DATA IN EDUCATION TO PROVIDE EFFECTIVE INSIGHTS FOR QUALITY EDUCATIONAL PRACTICE.
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 USING COGNITIVE, SOCIAL AND BEHAVIORAL INDICATORS OF STUDENTS THROUGH FILTERS, NOTIFICATIONS AND INTERACTIVE GRAPHS GENERATED FROM MOODIE VLE DATA.
LEARNING ANALYTICS IS CONCERNED WITH COLLECTING, ANALYZING, AND UNDERSTANDING DATA OBTAINED FROM STUDENTS. IN THIS PAPER, WE INTRODUCE METHODS BASED ON DATA MINING AND SOCIAL NETWORK ANALYSIS TO PREDICT A STUDENT SUCCESS. WE ALSO FOCUS ON IDENTIFICATION OF DIFFERENT TYPES OF DATA THAT CAN BE OBTAINED FROM UNIVERSITY INFORMATION SYSTEMS. WE DISCUSS THEIR INFLUENCE ON THE PREDICTION ACCURACY. WE CONFIRM THAT DATA ABOUT STUDENT SOCIAL BEHAVIOUR IMPROVE ACCURACY SUCCESSFULLY BY 3% FOR ONE THIRD OF THE 62 INVESTIGATED COURSES.
IN PREVIOUS RESEARCH WE PROPOSED A VIRTUALIZATION PROCESS OF LIVE COLLABORATIVE SESSIONS FROM WEB DISCUSSION FORUMS AND CHATS WITH THE AIM TO PRODUCE INTERACTIVE AND ATTRACTIVE ONLINE LEARNING RESOURCES TO BE PLAYED BY LEARNERS, THUS HAVING A POSITIVE EFFECT IN LEARNER ENGAGEMENT. IN ORDER TO ENHANCE FURTHER LEARNING ENGAGEMENT, IN THIS PAPER WE ENDOW OUR VIRTUALIZATION PROCESS WITH A MULTIFOLD ASSESSMENT FRAMEWORK THAT PROVIDES EFFECTIVE AWARENESS AND CONSTRUCTIVE FEEDBACK TO LEARNERS FROM THE ORIGINAL COLLABORATIVE INTERACTIONS AMONGST GROUP MEMBERS. THE RESEARCH HERE PRESENTED FOCUSES ON E-ASSESSMENT OF COLLABORATIVE AND SOCIAL LEARNING AND EXTENDS IT WITH LEARNING ANALYTICS AND SOCIAL NETWORK ANALYSIS TECHNIQUES THAT ARE ABLE TO ANALYZE AND REPRESENT COGNITIVE AND SOCIAL INTERACTIONS UNDERLYING LIVE COLLABORATIVE SESSIONS. THE INTERACTION DATA EXTRACTED FROM COLLABORATIVE KNOWLEDGE AND SOCIAL NETWORKING IS INTEGRATED INTO THE VIRTUALIZED COLLABORATIVE LEARNING TO PRODUCE AN EFFICIENT AND PERSONALIZED AWARENESS AND FEEDBACK SYSTEM ABOUT THE COLLABORATIVE ACTIVITY AND THE SOCIAL BEHAVIOUR OF THE ORIGINAL PARTICIPANTS OF LIVE COLLABORATION. THIS PAPER DESCRIBES BOTH THE CONCEPTUAL AND METHODOLOGICAL RESEARCH TO BUILD OUR MULTI-FOLD E-ASSESSMENT FRAMEWORK. THE RESEARCH IS EVALUATED IN REAL CONTEXT OF E-LEARNING AND VALIDATED BY EMPIRICAL DATA AND INTERPRETATION.
IN THIS PAPER WE FOCUS ON THE ACHIEVEMENTS OF EMADRID INITIATIVE IN SOME FIELDS OF TECHNOLOGY-ENHANCED LEARNING, MAINLY INVOLVING THE IMPROVEMENT OF THE MECHANISMS FOR OPEN EDUCATIONAL CONTENT RETRIEVAL FROM INTERNET, CONSIDERING INTERNET RESOURCES AS POTENTIAL LEARNING OBJECTS. ALSO WE FACILITATE THE INTEGRATION OF REMOTE LABORATORIES AND EXTERNAL TOOLS IN VIRTUAL CAMPUSES ARCHITECTURES SUPPORTING ENRICHED CAPABILITIES AND DESCRIBE A WAY TO CLUSTER AND IDENTIFY LEARNER WEAKNESSES USING A LEARNING ANALYTICS APPROACH IN COMBINATION WITH THE ITEM RESPONSE THEORY.
THIS MIXED-METHOD STUDY FOCUSES ON ONLINE LEARNING ANALYTICS, A RESEARCH AREA OF IMPORTANCE. SEVERAL IMPORTANT STUDENT ATTRIBUTES AND THEIR ONLINE ACTIVITIES ARE EXAMINED TO IDENTIFY WHAT SEEMS TO WORK BEST TO PREDICT HIGHER GRADES. THE PURPOSE IS TO EXPLORE THE RELATIONSHIPS BETWEEN STUDENT GRADE AND KEY LEARNING ENGAGEMENT FACTORS USING A LARGE SAMPLE FROM AN ONLINE UNDERGRADUATE BUSINESS COURSE AT AN ACCREDITED AMERICAN UNIVERSITY (N = 228). RECENT STUDIES HAVE DISCOUNTED THE ABILITY TO PREDICT STUDENT LEARNING OUTCOMES FROM BIG DATA ANALYTICS BUT A FEW SIGNIFICANT INDICATORS HAVE BEEN FOUND BY SOME RESEARCHERS. CURRENT STUDIES TEND TO USE QUANTITATIVE FACTORS IN LEARNING ANALYTICS TO FORECAST OUTCOMES. THIS STUDY EXTENDS THAT WORK BY TESTING THE COMMON QUANTITATIVE PREDICTORS OF LEARNING OUTCOME, BUT QUALITATIVE DATA IS ALSO EXAMINED TO TRIANGULATE THE EVIDENCE. PRE AND POST TESTING OF INFORMATION TECHNOLOGY UNDERSTANDING IS DONE AT THE BEGINNING OF THE COURSE. FIRST QUANTITATIVE DATA IS COLLECTED, AND DEPENDING ON THE HYPOTHESIS TEST RESULTS, QUALITATIVE DATA IS COLLECTED AND ANALYZED WITH TEXT ANALYTICS TO UNCOVER PATTERNS. MOODLE ENGAGEMENT ANALYTICS INDICATORS ARE TESTED AS PREDICTORS IN THE MODEL. DATA IS ALSO TAKEN FROM THE MOODLE SYSTEM LOGS. QUALITATIVE DATA IS COLLECTED FROM STUDENT REFLECTION ESSAYS. THE RESULT WAS A SIGNIFICANT GENERAL LINEAR MODEL WITH FOUR ONLINE INTERACTION PREDICTORS THAT CAPTURED 77.5 % OF GRADE VARIANCE IN AN UNDERGRADUATE BUSINESS COURSE.
THE APPLICATION OF "BIG DATA" THROUGH LEARNING ANALYTICS TO CREATE "PERSONALIZED LEARNING" IS UNDERWAY IN K-12 EDUCATION. THIS CHAPTER OFFERS A CONTENT ANALYSIS OF NINE DOCUMENTS THAT REVEAL HOW BIG DATA IS CONTRIBUTING TO THE PRIVATIZATION OF PUBLIC EDUCATION. THE ANALYSIS SHOWS THAT THAT "PERSONALIZED LEARNING" FRAMEWORKS AND TECHNOLOGIES MINIMIZE THE ROLE OF THE TEACHER; IMPOSE PRIVATIZED FORMS OF DECISION-MAKING; NARROWLY DEFINE THE PURPOSE OF EDUCATION AS PREPARING STUDENTS FOR THE TWENTY-FIRST CENTURY WORKFORCE; AND DISREGARD STUDENT PRIVACY.
THE DEVELOPMENT OF MULTIMODAL SENSOR-BASED APPLICATIONS DESIGNED TO SUPPORT LEARNERS WITH THE IMPROVEMENT OF THEIR SKILLS IS EXPENSIVE SINCE MOST OF THESE APPLICATIONS ARE TAILOR-MADE AND BUILT FROM SCRATCH. IN THIS PAPER, WE SHOW HOW THE PRESENTATION TRAINER (PT), A MULTIMODAL SENSOR-BASED APPLICATION DESIGNED TO SUPPORT THE DEVELOPMENT OF PUBLIC SPEAKING SKILLS, CAN BE MODULARLY EXTENDED WITH A VIRTUAL REALITY REAL-TIME FEEDBACK MODULE (VR MODULE), WHICH MAKES USAGE OF THE PT MORE IMMERSIVE AND COMPREHENSIVE. THE DESCRIBED STUDY CONSISTS OF A FORMATIVE EVALUATION AND HAS TWO MAIN OBJECTIVES. FIRSTLY, A TECHNICAL OBJECTIVE IS CONCERNED WITH THE FEASIBILITY OF EXTENDING THE PT WITH AN IMMERSIVE VR MODULE. SECONDLY, A USER EXPERIENCE OBJECTIVE FOCUSES ON THE LEVEL OF SATISFACTION OF INTERACTING WITH THE VR EXTENDED PT. TO STUDY THESE OBJECTIVES, WE CONDUCTED USER TESTS WITH 20 PARTICIPANTS. RESULTS FROM OUR TEST SHOW THE FEASIBILITY OF MODULARLY EXTENDING EXISTING MULTIMODAL SENSOR-BASED APPLICATIONS, AND IN TERMS OF LEARNING AND USER EXPERIENCE, RESULTS INDICATE A POSITIVE ATTITUDE OF THE PARTICIPANTS TOWARDS USING THE APPLICATION (PT+VR MODULE).
THIS STUDY EXAMINED THE EFFECTS OF USING AN E-PORTFOLIO SYSTEM TO IDENTIFY CONCERNS RAISED DURING SESSIONS AT KANSAI UNIVERSITY WRITING CENTER. THE RATIO OF SESSIONS WHEREBY TUTORS ASKED STUDENTS ABOUT THEIR PROBLEMS AND PROGRESS WAS SIGNIFICANTLY HIGH DURING SESSIONS WHEN USERS DROPPED IN OR WERE FIRST-TIME REPEATERS. THE RESULTS INDICATE THAT ONE OF THE E-PORTFOLIO SYSTEMS USED BY KANSAI UNIVERSITY, TEC BOOK, GATHERS AND SHARES INFORMATION ABOUT STUDENTS AND THE HISTORICAL DATA OF INSTRUCTIONAL RESULTS, THUS PROMOTING THE MEETING AT THE BEGINNING OF SESSIONS. SINCE BOTH TUTORS AND STUDENTS COOPERATE IN THESE SESSIONS, IT IS IMPORTANT FOR TUTORS TO QUICKLY AND EASILY UNDERSTAND STUDENTS' CONCERNS. E-PORTFOLIO SYSTEMS SUCH AS TEC BOOK CAN BE ONE OF THE BEST KINDS OF SUPPORT FOR WRITING SESSIONS.
MOBILE AND UBIQUITOUS TECHNOLOGIES HAVE BEEN APPLIED TO A WIDE RANGE OF LEARNING FIELDS SUCH AS SCIENCE, SOCIAL SCIENCE, HISTORY AND LANGUAGE LEARNING. MANY RESEARCHERS HAVE BEEN INVESTIGATING THE DEVELOPMENT OF UBIQUITOUS LEARNING ENVIRONMENTS; NEVERTHELESS, TO DATE, THERE HAVE NOT BEEN ENOUGH RESEARCH WORKS RELATED TO THE REFLECTION, ANALYSIS AND TRACES OF LEARNERS' ACTIVITIES IN THE HISTORY OF UBIQUITOUS LEARNING ENVIRONMENT. THEREFORE THIS PAPER PRESENTS A RESEARCH ON THE DESIGN AND DEVELOPMENT OF A DASHBOARD FUNCTION WHICH PROPOSES NEW OPPORTUNITY FOR UBIQUITOUS LEARNING. THE DASHBOARD CAPTURES, ANALYZES AND VISUALIZES TRACES OF LEARNING ACTIVITIES IN ORDER TO PROMOTE AWARENESS AND ENABLES LEARNERS TO REFLECT ON THEIR OWN ACTIVITY AND HELPS TO RECALL WHAT THEY HAVE LEARNED. AN INITIAL EVALUATION HAS BEEN CONDUCTED WITH 14 INTERNATIONAL STUDENTS. RESULTS INDICATE THAT THE DASHBOARD IS A USEFUL TOOL FOR SELF-REFLECTION ON ACTIVITIES AND RECALL WHAT LEARNERS HAVE LEARNED BY REPEATED QUIZZES.
RECENT EDUCATION ROLES ENCOURAGE WILLINGNESS TO LEARN INDIVIDUALLY, SOLVE UNFAMILIAR PROBLEMS USING KNOWLEDGE ACQUIRED THROUGH INFORMATION AND COMMUNICATION TECHNOLOGIES (ICTS), AND COLLABORATE WITH OTHERS. VARIOUS LEARNING METHODS TO CULTIVATE THIS ABILITY HAVE BEEN INVENTED AND RESEARCHERS DISCUSSED LEARNING EFFECT ON SUCH METHODS WITH QUALITATIVE AND QUANTITATIVE ANALYSES. ONE OF THE AUTHORS INTRODUCED PAIR PROGRAMMING AS A METHOD OF PEER LEARNING AND FLIP TEACHING, WHICH CONSISTS OF PRELIMINARY LEARNING OF BASIC PROGRAMMING AND ADVANCED LEARNING PRACTICES BASED ON PEER ACTIVITY IN CLASSROOM LESSONS, INTO COMPUTER SIMULATION PRACTICES FOR UNDERGRADUATE STUDENTS. WITH INTRODUCING CLASS SCHEDULE DESIGN FOR FLIP TEACHING AND DEVELOPMENT OF PEER LEARNING PREPARATION SUPPORT SYSTEM FOR DETERMINING THE APPROPRIATE PAIR FORMATION AND SEAT ALLOCATION IN THE CLASSROOM UTILIZING A PROBABILISTIC COMBINATORIAL OPTIMIZATION ALGORITHM, THIS STUDY FOCUSES ON LEARNING BEHAVIOR OF WELL-PERFORMING STUDENTS, ANALYZING LEARNING RECORDS AND ACCESS LOGS ON A LEARNING MANAGEMENT SYSTEM AND ANSWERS TO A QUESTIONNAIRE ADMINISTERED AFTER THE PRACTICES. IN THIS ANALYSIS, WE ATTEMPTED TO DISCRIMINATE BEHAVIOR OF WELL-PERFORMING STUDENTS OBSERVED FROM THE LEARNING RECORDS, ACCESS LOGS, AND QUESTIONNAIRE TO DISCOVER BEST PRACTICES FOR IMPROVING THE PERFORMANCE OF MEDIUM TO BOTTOM-LINE STUDENTS. WELL-PERFORMING STUDENTS TENDED TO PREPARE FOR CLASSROOM LESSONS IN GOOD TIME, BUT THEIR PERFORMANCE DEPENDED ON THE LESSON CONTENT DIFFICULTY. A CORRELATION WAS OBSERVED BETWEEN THE FREQUENCY OF INTERACTION AMONG STUDENTS AND SKILL ACQUISITION. WE DISCUSS HOW TO IMPROVE LEARNING ENVIRONMENTS USING ICTS AND COLLABORATIVE LEARNING METHODS BASED ON THE ANALYSIS.
THIS WORK AIMS TO IMPLEMENT BUSINESS INTELLIGENCE STRATEGIES IN AN EDUCATIONAL INSTITUTION BASED ON THE DISTANCE EDUCATION, PARTICULARLY IN THE ONLINE TUTORING PROCESS. IN THIS PAPER WE PROPOSE TO USE THE BUSINESS INTELLIGENCE PARADIGM TO ANALYZE THE ONLINE TUTORING PROCESS, BASED ON THE DATA COLLECTED ON THE INTERACTIONS OF STUDENTS AND TEACHERS IN A VIRTUAL LEARNING ENVIRONMENT, AND THE RESULTS RECORDED IN THE INSTITUTIONAL ACADEMIC SYSTEM OF EVALUATIONS. THIS ANALYSIS SHOULD ANSWER THE FOLLOWING QUESTIONS: 1) CAN WE DEFINE A MODEL OF ONLINE TUTORING THAT CAN ADAPT TO EACH STUDENT PROFILE? 2) CAN WE PREDICT THE SUCCESS OF AN ONLINE TUTORING PROCESS FOR A COURSE AND A STUDENT GIVEN? TO THIS PURPOSE, THIS PAPER PRESENTS THREE ASPECTS: CHARACTERIZE AND DETERMINE THE KEY ELEMENTS IN AN ONLINE TUTORING PROCESS, BUILD A DESCRIPTIVE MODEL OF THE ONLINE TUTORING PROCESS, AND BUILD A PREDICTIVE MODEL OF THE SUCCESS OF THE ONLINE TUTORING PROCESS. THE MODELS TO BE DEFINED WILL BE BASED ON DATA MINING TECHNIQUES, AND WILL BE OBTAINED FROM THE CURRENT DATA STORED IN TRANSACTIONAL DATABASES OF THE UNIVERSITY. THIS DATA IS PREPROCESSED WITH ETL TECHNIQUES TO BUILD A MULTIDIMENSIONAL MODEL, AND THE KEY ELEMENTS ARE OBTAINED THROUGH OPERATIONS OLAP.
THE LEARNING ECONOMY IS A NEW LEARNING MODEL THAT TRADES THE LEARNER'S "LEARNING" WITH VIRTUAL CURRENCY USING BLOCKCHAIN TO GUARANTEE AND EVALUATE THE LEARNING BASED ON MARKET PRINCIPLES. IN THE LEARNING ECONOMY, NEITHER THE TEACHER NOR THE SCHOOL RECEIVES THE REWARD, ONLY THE LEARNER, WHICH IS SUBSTANTIALLY DIFFERENT TO THE EXISTING EDUCATION SYSTEM. THIS NEW SYSTEM ENABLES LEARNERS TO RECEIVE COMPENSATION FOR THEIR LEARNING IN VIRTUAL CURRENCY, AND THIS CAN PROVIDE A STRONG MOTIVATION FOR THEM TO GAIN KNOWLEDGE IN A PROACTIVE MANNER AND TO ACQUIRE AND UPDATE THEIR KNOWLEDGE AT A SPEED THAT COULD NOT BE ACHIEVED THROUGH THE TRADITIONAL SCHOOL SYSTEM. THIS PAPER PROPOSES THIS TYPE OF LEARNING ECONOMY AS A MODEL FOR LEARNING AND REPORTS ON THE RESULTS OF EXPERIMENTS USING A PROTOTYPE OF THE LEARNING ECONOMY MODEL.
HIGHER EDUCATION INSTITUTIONS ARE INCREASINGLY SEEKING TECHNOLOGICAL SOLUTIONS TO NOT ONLY ENHANCE THE LEARNING ENVIRONMENT BUT ALSO SUPPORT STUDENTS. IN THIS STUDY, WE EXPLORED THE CASE OF AN EARLY ALERT SYSTEM (EAS) AT A REGIONAL UNIVERSITY ENGAGED IN BOTH ON-CAMPUS AND ONLINE TEACHING. USING A TOTAL OF 16,142 OBSERVATIONS CAPTURED BETWEEN 2011 AND 2013, WE EXAMINED THE RELATIONSHIP BETWEEN EAS AND THE STUDENT RETENTION RATE. THE RESULTS INDICATE THAT WHEN CONTROLLING FOR DEMOGRAPHIC, INSTITUTION, STUDENT PERFORMANCE AND WORKLOAD VARIABLES, THE EAS IS ABLE TO IDENTIFY STUDENTS WHO HAVE A SIGNIFICANTLY HIGHER RISK OF DISCONTINUING FROM THEIR STUDIES. THIS IMPLIES THAT EARLY INTERVENTION STRATEGIES ARE EFFECTIVE IN ADDRESSING STUDENT RETENTION, AND THUS AN EAS IS ABLE TO PROVIDE ACTIONABLE INFORMATION TO THE STUDENT SUPPORT TEAM.
SELF-REGULATED LEARNING IS AN ACTIVE FIELD OF RESEARCH IN PEDAGOGY. SELF-REGULATION TECHNIQUES ARE BEING TESTED AT SEVERAL EDUCATIONAL LEVELS, INCLUDING UNIVERSITY DEGREES. ONE OF THE MAIN PROBLEMS FOUND BY THE EDUCATORS IS THAT THEY NEED TO MONITOR ALL THEIR STUDENTS CONSTANTLY AND THOROUGHLY. THIS CANNOT BE DONE PROPERLY IF THE NUMBER OF STUDENTS IS NOT SMALL, WHICH HAPPENS SPECIALLY AT UNIVERSITY LEVELS. THEY NEED TOOLS TO ADDRESS THIS PROBLEM AND TO BE ABLE TO MONITOR THEIR LEARNING PROGRESS. WE ARE DEVELOPING A TOOL FOR SELF-REGULATED LEARNERS, TO PROVIDE THEM SUPPORT FOR THEIR LEARNING PROCESS USING EMBEDDED LEARNING STRATEGIES AND TOOLS TO ENABLE PLANNING, MONITORING AND EVALUATION OF THEIR OWN LEARNING PROCESS. ITS FUNCTIONALITIES HAVE BEEN EXTENDED IN ORDER TO ENABLE BOTH THE EDUCATORS AND THE LEARNERS TO MONITOR IN DEPTH THEIR LEARNING PROGRESS IN ORDER TO IMPROVE THEIR SELF-REGULATION SKILLS. IN THIS PAPER WE PRESENT THE MONITORING SYSTEM DESIGNED FOR THIS TOOL.
CREATIVITY AND COMPUTATIONAL THINKING (CT) HAVE BEEN BOTH EXTENSIVELY RESEARCHED IN RECENT YEARS. HOWEVER, THE ASSOCIATIONS BETWEEN THEM ARE STILL NOT FULLY UNDERSTOOD DESPITE THEIR RECOGNITION AS ESSENTIAL COMPETENCIES FOR THE DIGITAL AGE. THIS STUDY LOOKS TO BRIDGE THIS GAP BY EXAMINING THE ASSOCIATION BETWEEN CT AND TWO TYPES OF CREATIVITY, I.E., CREATIVE THINKING AND COMPUTATIONAL CREATIVITY. THE RESEARCH WAS CONDUCTED AMONG 124 MIDDLE SCHOOL STUDENTS FROM SPAIN, WHO WERE DIVIDED INTO CONTROL AND EXPERIMENTAL GROUPS; THE INTERVENTION INCLUDED AN EXPLICIT ENCOURAGEMENT TO BE AS CREATIVE AS POSSIBLE (I.E., TO SUBMIT MULTIPLE CORRECT SOLUTIONS) IN A GIVEN LEARNING TASK. DATA WERE ANALYZED FROM A STANDARDIZED CREATIVITY TEST (TORRANCE'S TTCT) AND CROSS-REFERENCED WITH LOG FILES THAT DOCUMENTED THE STUDENTS' ACTIVITIES IN THE KODETU GAME-BASED LEARNING ENVIRONMENT. OUR RESEARCH FINDINGS INDICATE SOME INTERESTING ASSOCIATIONS BETWEEN CT AND CREATIVITY. FIRST, WE FOUND THAT CREATIVITY CONTRIBUTES TO CT. SECOND, WE FOUND THAT CT IS TRANSFERABLE ACROSS DIFFERENT DOMAINS. FINALLY, WE FOUND THAT COMPUTATIONAL CREATIVITY CAN DEVELOP AND IMPROVE OVER TIME.
VERSION CONTROL SYSTEMS' USAGE IS A HIGHLY DEMANDED SKILL IN INFORMATION AND COMMUNICATION TECHNOLOGY PROFESSIONALS. THUS, THEIR USAGE SHOULD BE ENCOURAGED BY EDUCATIONAL INSTITUTIONS. THIS WORK DEMONSTRATES THAT IT IS POSSIBLE TO ASSESS IF A STUDENT CAN PASS A COMPUTER SCIENCE-RELATED SUBJECT BY MONITORING ITS INTERACTION WITH A VERSION CONTROL SYSTEM. THIS PAPER PROPOSES A METHODOLOGY THAT COMPARES THE PERFORMANCE OF SEVERAL MACHINE LEARNING MODELS SO AS TO SELECT THE APPROPRIATE PREDICTING MODEL FOR THE ASSESSMENT OF THE STUDENTS' ACHIEVEMENTS. TO FIT PREDICTING MODELS, THREE SUBJECTS OF THE DEGREE IN COMPUTER SCIENCE AT THE UNIVERSITY OF LEON ARE CONSIDERED TO OBTAIN THE DATASET: COMPUTER ORGANIZATION, COMPUTER PROGRAMMING, AND OPERATING SYSTEMS EXTENSION. THE COMMON ASPECT OF THESE SUBJECTS IS THEIR ASSIGNMENTS, WHICH ARE BASED ON DEVELOPING ONE OR SEVERAL PROGRAMS WITH PROGRAMMING LANGUAGES SUCH AS C OR JAVA. TO MONITOR THE PRACTICAL ASSIGNMENTS AND INDIVIDUAL PERFORMANCE, A GIT REPOSITORY IS EMPLOYED ALLOWING STUDENTS TO STORE SOURCE CODE, DOCUMENTATION, AND SUPPORTING CONTROL VERSIONS. ACCORDING TO THE PRESENTED EXPERIENCE, THERE IS A HUGE CORRELATION BETWEEN THE LEVEL OF INTERACTION FOR EACH STUDENT AND THE ACHIEVED GRADES.
SELF-DIRECTED LEARNING AND REGULAR REVISION OF IN-CLASS LECTURES PLAYS A CRUCIAL PART IN THE PROCESS OF LEARNING AND UNDERSTANDING. WELL-DESIGNED E-LEARNING RESOURCES SERVE AS AN EFFECTIVE TOOL FOR THAT PURPOSE AND IS THEREFORE AN ESSENTIAL ELEMENT IN DEVISING A SUPPLEMENTAL BLENDED LEARNING PEDAGOGY. AN EMPIRICAL STUDY INVOLVING THE DESIGN, DEVELOPMENT AND ADOPTION OF E-LEARNING RESOURCES AND SUBSEQUENT ANALYSIS OF STUDENT ENGAGEMENT AND STUDENT ACHIEVEMENT FOR A SPECIFIC UNDERGRADUATE COURSE ON RADIO FREQUENCY ENGINEERING AND ELECTROMAGNETIC COMPATIBILITY IS PRESENTED. STUDENTS ENGAGEMENT BEHAVIOUR IN USING C-LEARNING RESOURCES IS ANALYZED THROUGH QUANTITATIVE ENGAGEMENT DATA OBTAINED RETRIEVED FROM ONLINE E-LEARNING RESOURCE HOSTING TOOL. THE EFFECTIVENESS OF SUCH LEARNING RESOURCES FOR SELF-DIRECTED LEARNING LEADING TO HIGHER STUDENT ENGAGEMENT THAT EVENTUALLY RESULTED IN MARGINALLY BETTER ACHIEVEMENT OF LEARNING OUTCOMES IS PRESENTED.
THE EFFECTIVENESS OF COMPUTER-SUPPORTED COLLABORATIVE INQUIRY LEARNING IN STEM EDUCATION IS WELL-DOCUMENTED IN THE LITERATURE. AT THE SAME TIME, RESEARCH INDICATES THAT SOME STUDENTS STRUGGLE TO ARTICULATE RELEVANT CONCEPTS, TO MAKE THEIR REASONING EXPLICIT, AND TO REGULATE THEIR LEARNING-ALL OF WHICH ARE NECESSARY FOR EFFECTIVE COLLABORATION. IN THIS STUDY, 106 COLLEGE STUDENTS COMPLETED TASKS RELATED TO OHM'S LAW IN A SIMULATION-BASED, COLLABORATIVE-INQUIRY LEARNING ENVIRONMENT. USING QUALITATIVE ANALYSIS, MULTILEVEL MODELLING, AND DATA-MINING TECHNIQUES, WE INVESTIGATED THE RELATIONSHIP BETWEEN STUDENT ENGAGEMENT IN TRANSFORMATIVE AND NON-TRANSFORMATIVE LEARNING PROCESSES AND LEARNING OUTCOMES. THE RESULTS REVEALED THAT BY USING THE APPROPRIATE FEATURE ENGINEERING AND ALGORITHMS, WE COULD BUILD ACCURATE MACHINE-LEARNING MODELS THAT COULD AUTOMATICALLY IDENTIFY TRANSFORMATIVE AND NON-TRANSFORMATIVE DISCUSSIONS ON A LARGE SCALE. ADDITIONAL QUALITATIVE AND QUANTITATIVE ANALYSES INDICATED THAT WHEN GROUPS ENGAGED IN ADDITIONAL INTERPRETATION AND SUSTAINED MUTUAL UNDERSTANDING, THEIR MEMBERS TENDED TO HAVE STATISTICALLY BETTER INDIVIDUAL-LEARNING OUTCOMES. THESE ANALYSES ALSO INDICATED THAT WHEN GROUPS ENGAGED IN ADDITIONAL ORIENTATION AND PROPOSITION GENERATION, THEIR LEARNING OUTCOMES WERE STATISTICALLY LOWER. APPROXIMATELY TWO-THIRDS OF THE STUDENTS CONSIDERED THEIR GROUP WORK HELPFUL IN COMPLETING INQUIRY TASKS. EXPLANATIONS OF THESE RESULTS AND RESEARCH RECOMMENDATIONS ARE PROVIDED.
INCREASED CLASS SIZES AND RAPID ADVANCEMENT OF INFORMATION TECHNOLOGY HAS PROMPTED INSTITUTIONS TO MOVE TOWARD BLENDED LEARNING. THE EFFECTIVENESS OF THE INSTRUCTIONAL DESIGN OF THE BLENDED LEARNING COURSES HAS NOT BEEN STUDIED EXTENSIVELY IN LARGE CLASSES. THIS STUDY AIMS TO INTERROGATE THE EFFECTIVENESS OF THE INSTRUCTIONAL DESIGN OF A LARGE FIRST YEAR BIOLOGY CLASS WITH THE AIM OF PROVIDING THE MOST EFFECTIVE BLEND FOR ALL STUDENTS BUT FOCUSING ON THE AT-RISK AND MURKY MIDDLE STUDENTS. THIS WAS DONE BY FIRSTLY INVESTIGATING WHICH OF THE DIFFERENT LEARNING OPPORTUNITIES CONTRIBUTED MOST TO THE SUCCESS OF THE STUDENTS AND SECONDLY BY INVESTIGATING STUDENT ENGAGEMENT WITH THE LEARNING OPPORTUNITIES PROVIDED TO THEM. THE RESULTS SHOW THAT SMALL, FACE-TO-FACE TUTORIAL CLASSES AND ONLINE FORMATIVE ASSESSMENTS CONTRIBUTED THE MOST TO STUDENT SUCCESS. THE RESULTS ALSO SHOW THAT AT-RISK AND MURKY MIDDLE STUDENTS TEND TO MAKE USE OF LEARNING OPPORTUNITIES LESS AFTER THE FIRST SUMMATIVE ASSESSMENT, POSSIBLY PUTTING THEM AT RISK OF FAILING.
FORMATIVE ASSESSMENT IS ONE OF THE MAIN CHALLENGES FACING MOOC RESEARCH AND PRACTICE. PROVIDING TIMELY AND PERSONALIZED FEEDBACK TO LARGE COHORTS OF LEARNERS POSES ISSUES IN TERMS OF SCALABILITY AND SUSTAINABILITY. THIS PAPER PUTS FORWARD A PROPOSAL FOR AUTOMATED FEEDBACK WELL SUITED FOR ASSESSING NON-DECLARATIVE KNOWLEDGE. THE PROPOSED FEEDBACK STRATEGY CONSISTS IN DISPLAYING A COMPARISON OF RESPONSES AND BEHAVIORS OF INDIVIDUAL PARTICIPANTS WITH DESCRIPTIVE STATISTICS REFLECTING THE SAME DATA FOR THE ENTIRE COHORT. TO INVESTIGATE THE USEFULNESS AND POTENTIAL OF THIS FEEDBACK STRATEGY, QUALI-QUANTITATIVE DATA WERE COLLECTED DURING A MOOC ON LEARNING DESIGN. SELF-REPORTED DATA ABOUT USEFULNESS (FOR BOTH RESPONSES AND BEHAVIORS) WERE STATISTICALLY ABOVE THE MID-POINT OF THE SCALE, WITH NO SIGNIFICANT DIFFERENCE BETWEEN THE TWO TYPES OF DATA. SUGGESTIONS ON HOW TO IMPROVE THIS FEEDBACK STRATEGY WERE ALSO DRAWN FROM INTERVIEWS WITH SUBJECTS.
THIS PAPER PROPOSES A NOVEL APPROACH TO AUTOMATIC ESTIMATION OF ATTENTION OF STUDENTS DURING LECTURES IN THE CLASSROOM. THE APPROACH USES 2D AND 3D DATA OBTAINED BY THE KINECT ONE SENSOR TO BUILD A FEATURE SET CHARACTERIZING BOTH FACIAL AND BODY PROPERTIES OF A STUDENT, INCLUDING GAZE POINT AND BODY POSTURE. MACHINE LEARNING ALGORITHMS ARE USED TO TRAIN CLASSIFIERS WHICH ESTIMATE TIME-VARYING ATTENTION LEVELS OF INDIVIDUAL STUDENTS. HUMAN OBSERVERS' ESTIMATION OF ATTENTION LEVEL IS USED AS A REFERENCE. THE COMPARISON OF ATTENTION PREDICTION ACCURACY OF SEVEN CLASSIFIERS IS DONE ON A DATA SET COMPRISING 18 SUBJECTS. OUR BEST PERSON-INDEPENDENT THREE-LEVEL ATTENTION CLASSIFIER ACHIEVED MODERATE ACCURACY OF 0.753, COMPARABLE TO RESULTS OF OTHER STUDIES IN THE FIELD OF STUDENT ENGAGEMENT. THE RESULTS INDICATE THAT KINECT-BASED ATTENTION MONITORING SYSTEM IS ABLE TO PREDICT BOTH STUDENTS' ATTENTION OVER TIME AS WELL AS AVERAGE ATTENTION LEVELS AND COULD BE APPLIED AS A TOOL FOR NON-INTRUSIVE AUTOMATED ANALYTICS OF THE LEARNING PROCESS.
SELF-REGULATED LEARNING IS AN ACTIVE FIELD OF RESEARCH IN PEDAGOGY, AND A KEY ELEMENT IN THE CURRENT EUROPEAN HIGHER EDUCATION. ONE OF THE MAIN PROBLEMS FOUND BY EDUCATORS IN THE PROGRAMS CENTERED IN THE PROMOTION OF SELF-REGULATION IS THAT THEY NEED TO MONITOR ALL THEIR STUDENTS CONSTANTLY AND THOROUGHLY. IF THE NUMBER OF STUDENTS IS LARGE, WHICH IS TYPICAL IN HIGHER EDUCATION, THIS BECOMES A PROBLEM. THEY NEED TOOLS TO ADDRESS IT AND BE ABLE TO MONITOR THEIR LEARNING PROGRESS. WE ARE DEVELOPING A TOOL FOR SELF-REGULATED LEARNERS, TO PROVIDE THEM WITH SUPPORT FOR THEIR LEARNING PROCESS USING EMBEDDED LEARNING STRATEGIES AND TOOLS TO ENABLE PLANNING, MONITORING, AND EVALUATING THEIR OWN LEARNING PROCESS. IN THIS PAPER, WE PRESENT THE MONITORING SYSTEM DESIGNED FOR THIS TOOL, WHICH HAS BEEN ENHANCED IN ORDER TO ENABLE IN-DEPTH MONITORING FOR BOTH EDUCATORS AND LEARNERS. THIS ENABLES PROCESS AND PROGRESS TRACKING AND ALSO EASES SELF-MONITORING AND SELF-EVALUATION FOR LEARNERS.
FEEDBACK CONSTITUTES A FUNDAMENTAL ASPECT OF EDUCATIONAL SYSTEMS THAT HAS A SUBSTANTIAL IMPACT ON STUDENTS LEARNING AND CAN SHAPE THEIR MENTAL MODELS. THE DELIVERY OF APPROPRIATE FEEDBACK IN TERMS OF TIME AND CONTENT IS CRUCIAL FOR FACILITATING STUDENTS' KNOWLEDGE CONSTRUCTION AND COMPREHENSION. IN THIS PAPER, WE EXAMINE THE COMPLEX NATURE AND THE EFFICIENCY OF FEEDBACK IN THE CONTEXT OF A VIRTUAL REALITY EDUCATIONAL ENVIRONMENT. MORE SPECIFICALLY, WE STUDY THE EFFECT THAT DIFFERENT TYPES OF FEEDBACK SUCH AS FEEDBACK WITH VISUALIZED ANIMATIONS OF PROCEDURES, CAN HAVE ON STUDENTS LEARNING AND KNOWLEDGE CONSTRUCTION IN A VIRTUAL REALITY EDUCATIONAL ENVIRONMENT FOR LEARNING BLIND AND HEURISTIC SEARCH ALGORITHMS. AN EXPERIMENTAL STUDY WAS DESIGNED WHERE PARTICIPATING STUDENTS WERE ENGAGED WITH LEARNING ACTIVITIES AND SOLVED EXERCISES IN DIFFERENT FEEDBACK CONDITIONS. RESULTS FROM THE STUDY INDICATE THAT VISUAL TYPES OF FEEDBACK CAN HAVE A SUBSTANTIAL IMPACT ON STUDENTS' LEARNING, ASSISTING THEM IN BETTER UNDERSTANDING THE FUNCTIONALITY OF THE PROCESS STUDIED WITH RESPECT TO PERFORMANCE AND MISTAKES.
CURRENTLY, LEARNING WITH E-LEARNING SYSTEM HAS BEEN ACTIVELY PERFORMED. AN E-LEARNING SYSTEM AFFORDS THE LEARNERS ARRANGE THEIR LEARNING SCHEDULE ON DEMAND AND PROVIDES SOME ADVANTAGES IF THE LEARNERS HAVE ENOUGH MOTIVATION. IN OTHER WORDS, SUCCESS OF USE OF SUCH SYSTEM DEPENDS ON MOTIVATION OF THE LEARNER. IN ORDER TO MAINTAIN THE LEARNERS' MOTIVATION IN E-LEARNING SYSTEM, AUTOMATIC DETECTION WHICH LEARNER LOST HIS/HER MOTIVATION TO CONTINUE LEARNING AT THE EARLIER STAGE AS POSSIBLE WILL HELP TO GIVE THE ADDITIONAL INSTRUCTION AND CHANGE THE MATERIALS INDIVIDUALLY. IN THIS STUDY, FROM THE LEARNING ACTIVITY INFORMATION OF LEARNERS OBTAINED FROM THE GENERAL E-LEARNING SYSTEM, TO CONSIDER HOW TO CAPTURE THE LEARNING MOTIVATION AND MOTIVATION OF THE LEARNER.
IN THIS STUDY, WE EXPLORE THE POTENTIAL OF (SEMI-) AUTOMATED ARTIFACT ANALYSIS TO IDENTIFY AND CHARACTERIZE STUDENTS' COMPREHENSION PROBLEMS AROUND KNOWLEDGE ARTIFACTS IN THE FORM OF VIDEOS AVAILABLE ON A WEB PLATFORM. USING THE COMMENTS ASSIGNED TO THESE LEARNING VIDEOS AS SOURCES, WE HAVE APPLIED AVAILABLE AND TAILOR-MADE METHODS OF TEXT ANALYSIS TO IDENTIFY POTENTIAL PROBLEMS OF UNDERSTANDING. IN PARTICULAR, WE HAVE USED SIGNAL WORDS AND THEIR RELATIONSHIP TO DOMAIN CONCEPTS TO INDICATE MISSING PRE-KNOWLEDGE OR FALSE BELIEFS. IN FUTURE WORK, THESE RESULTS WILL BE USED TO SUPPORT MONITORING PROCESSES FOR INCREASING CONCEPTUAL AWARENESS AND SUPPORTING CONCEPTUAL CHANGE.
SELF-REGULATION IN LEARNING INVOLVES PLANNING AND UTILIZING DIFFERENT SHARED RESOURCES. THIS STUDY INVESTIGATES LEARNING STRATEGIES OF DIFFERENT STUDENT GROUPS WHEN THEY ARE ACCESSING COURSE MATERIALS IN DIGITAL MEDIUM - ONE GROUP IS THE STUDENTS WITH HIGH ACADEMIC PERFORMANCE, THE OTHER IS THE STUDENTS WITH LOW ACADEMIC PERFORMANCE. WE ANALYZE DATA OF 116 STUDENTS FROM A MATHEMATICS COURSE IN A JUNIOR HIGH SCHOOL. USING THE DIFFERENTIAL PATTERN MINING TECHNIQUE, WE HIGHLIGHT UNDERLYING COURSE CONTENT ACCESSING PATTERNS FROM THE LEARNING LOG COLLECTED BY AN E-BOOK SYSTEM, BOOKROLL.
UPLIFT MODELING IS AN APPROACH FOR ESTIMATING THE INCREMENTAL EFFECT OF AN ACTION OR TREATMENT AT THE INDIVIDUAL LEVEL. IT HAS GAINED ATTENTION IN THE MARKETING AND ANALYTICS COMMUNITIES DUE TO ITS ABILITY TO ADEQUATELY MODEL THE EFFECT OF DIRECT MARKETING ACTIONS VIA PREDICTIVE ANALYTICS. THE MAIN CONTRIBUTION OF OUR STUDY IS THE IMPLEMENTATION OF THE UPLIFT MODELING FRAMEWORK TO MAXIMIZE THE EFFECTIVENESS OF RETENTION EFFORTS IN HIGHER EDUCATION INSTITUTIONS I.E., IMPROVEMENT OF ACADEMIC PERFORMANCE BY OFFERING TUTORIALS. THE OBJECTIVE IS TO IMPROVE THE DESIGN OF RETENTION PROGRAMS BY TAILORING THEM TO STUDENTS WHO ARE MORE LIKELY TO BE RETAINED IF TARGETED. DATA FROM THREE DIFFERENT BACHELOR PROGRAMS FROM A CHILEAN UNIVERSITY WERE COLLECTED. STUDENTS WHO PARTICIPATED IN THE TUTORIALS ARE CONSIDERED THE TREATMENT GROUP, OTHERWISE, THEY ARE ASSIGNED TO THE NONTREATMENT GROUP. OUR RESULTS DEMONSTRATE THE VIRTUES OF UPLIFT MODELING IN TAILORING RETENTION EFFORTS IN HIGHER EDUCATION OVER CONVENTIONAL PREDICTIVE MODELING APPROACHES.
IN THIS PAPER WE PROPOSE A RECOMMENDATION TOOL FRAMEWORK TO HELP A STUDENT PICK THE RIGHT PROGRAM, RIGHT COURSE AND RIGHT INSTRUCTOR THAT FIT HIS/HER SKILLS AND CHARACTERISTICS. WE APPLY COLLABORATIVE FILTERING (CK), USING A NON-NEGATIVE MATRIX FACTORIZATION (NMF) METHOD, TO EXTRACT LATENT FEATURES CORRESPONDING TO THE STUDENT ACADEMIC SKILLS AND TO THE PROGRAM REQUIRED SKILLS: WHEN AUGMENTED WITH A STUDENT'S COMPETENCE LEVEL, THE REQUIRED SKILLS OF PROGRAMS CAN BE EXPLOITED TO PRODUCE A PROGRAM RECOMMENDER SYSTEM. THEN USING A MULTI-OBJECTIVE OPTIMIZATION METHOD, WE PROPOSE A DEGREE PLAN RECOMMENDER SYSTEM THAT IS ABLE TO DESIGN A CUSTOMIZED/PERSONALIZED DEGREE PLAN THAT WILL BETTER FIT TO REAL LIFE SITUATIONS BY MOVING THE COURSES WITH RELATIVELY HIGHER CRUCIAL VALUES AND HIGHER PREDICTED LETTER GRADES TO CLOSEST POSSIBLE TERMS WHILE MEETING ALL THE CONSTRAINTS. WE EXTEND OUR FRAMEWORK TO INCLUDE INSTRUCTOR RECOMMENDER SYSTEM. THE INTUITION IS TO RECOMMEND AN INSTRUCTOR THAT JOINTLY FITS THE REQUIRED SKILLS OF THE COURSE AND THE ACADEMIC SKILLS OF THE STUDENT. THE EXPERIMENTAL RESULTS CONDUCTED USING REAL DATA FROM THE UNIVERSITY OF NEW MEXICO (UNM) SHOW THAT OUR PROPOSED FRAMEWORK CAN ACCURATELY EXTRACT FEATURES RELATED TO STUDENTS AND COURSES. THE RESULTS ARE VALIDATED USING K-MEANS CLUSTERING TECHNIQUE. ULTIMATELY, USING THIS FRAMEWORK, WE WILL BE ABLE IMPROVE ENROLLMENT, HELP STUDENTS GRADUATE IN A TIMELY FASHION AND INCENTIVE THEIR ABILITY TO PERSIST.
THE USE OF BIG DATA SYSTEMS IN THE FIELD OF EDUCATION ALLOWS TO ENVISAGE NEW APPROACHES AND NEW LEARNING CONTEXTS. INDEED, THE RAPID EMERGENCE OF THE NEW E-LEARNING PLATFORMS HAVE BEEN PRESENTED IN MANY INTERESTS. HOWEVER, THE QUALITY OF THE TEACHING SERVICE RENDERED DEPENDS ON THE CAPACITY OF THE LEARNING APPROACHES TO BE PROVIDED TO LEARNERS, CONTENT AND LEARNING PATH TAILORED TO THEIR NEEDS. IN THIS PAPER, WE WILL PRESENT HOW BIG DATA HELPS TO SOLVE EDUCATION ISSUES THROUGH REACHING THE OBJECTIVE OF LEARNING. THEN, WE WILL INTRODUCE SOME OPPORTUNITIES OF BIG DATA ANALYTICS TO DEVELOP THE EFFICIENCY AND EFFECTIVENESS OF STUDENTS LEARNING AND MAXIMIZE THEIR KNOWLEDGE RETENTION. FINALLY, OUR RESEARCH METHOD SHOW THAT STUDENTS CAN GENERATE PERSONALIZED ACTIVITIES AND OFFER ACADEMIC ADVISING. BIG DATA CAN EXPOSE THE CAPABILITIES OF LEARNERS, PREDICT THEIR FUTURE PERFORMANCES AND OFFER ASSISTANCE FOR EDUCATIONAL ORGANIZATIONS TO MAKE STRATEGIC DECISIONS.
LEARNING ANALYTIC (LA) SYSTEMS ARE AIMED AT SUPPORTING TEACHERS IN UNDERSTANDING THE LEARNING PROCESS BY ANALYZING THE INFORMATION AND THE INTERACTION OF STUDENTS WITH COMPUTER SYSTEMS. IN THE CASE OF A PROJECT-BASED LEARNING PROCESS THERE IS A NEED OF INTRODUCING MEASURE THE STUDENT' ACTIVITY AS ACQUIRED VIA MULTIPLE MODALITIES AND THEN PROCESSED. THE ACQUISITION AND PROCESSING NEEDS TO TAKE INTO ACCOUNT THE SPECIFICITIES OF THE LEARNING CONTEXT AND DEPLOYMENT AT SCHOOLS, IN PARTICULAR IN TERMS OF SYSTEM ARCHITECTURE. THE PAPER PROPOSES AN ARCHITECTURE FOR THE ACQUISITION AND PROCESSING OF DATA FOR PROJECT-BASED LA DESIGNED TO BE INTEROPERABLE AND SCALABLE. SYSTEM DESIGN, DETAILS OF THE SOLUTIONS AND BRIEF EXAMPLES OF ACQUIRED DATA ARE PRESENTED.
UNDERSTANDING STUDENTS' BEHAVIOR WHILE SOLVING TASKS AT VARIOUS LEVELS IS ESSENTIAL FOR THE SUPPORT EDUCATORS MAY PROVIDE TO STUDENTS. THE CURRENT STUDY REPORTS ON A LARGE-SCALE EXPLORATION OF STUDENTS' ACTIVITY IN AN ONLINE LEARNING ENVIRONMENT FOR MATHEMATICS, WHILE COMPARING BETWEEN LOWER-ORDER THINKING (LOT) AND HIGHER-ORDER THINKING (HOT) APPLETS, AND BETWEEN GRADE LEVELS. WE ANALYZED LOG FILES OF N = 32,581 5TH- AND 6TH-GRADE STUDENTS FROM ALL OVER ISRAEL (A FULL SAMPLE OF USERS IN THE STUDIED PLATFORM), SPECIFICALLY COMPARING SCORES, COMPLETION RATES, COMPLETION TIMES, AND REPETITION LEVELS IN LOT AND HOT APPLETS. USING WITHIN-SUBJECT AND BETWEEN-SUBJECT T TESTS, WE FOUND THAT STUDENTS' PERFORMANCE AND COMPLETION RATE ON THE LOT APPLETS WERE OVERALL HIGHER THAN THOSE OF THE HOT APPLETS, WHICH, COMBINED WITH OTHER FINDINGS, MAY POINT TO META-COGNITIVE OR MOTIVATIONAL PROCESSES INVOLVED. WE ALSO POINT OUT TO THE HIGH RATES OF STUDENTS' MANIPULATION OF THE SYSTEM IN A WAY THAT ALLOW THEM TO INCREASE THEIR SCORE. FINALLY, WE FOUND THAT THE VARIOUS MEASURES WE USED TO CHARACTERIZE STUDENTS' ONLINE ACTIVITY ARE NOT NECESSARILY STRONGLY CORRELATED WITH EACH OTHER. THESE FINDINGS WILL HELP TEACHERS TO TAKE INFORMED DECISIONS REGARDING THE INCORPORATION OF DIGITAL LEARNING ENVIRONMENTS IN THEIR CLASSROOMS.
MACHINE LEARNING IS GAINING INCREASING POPULARITY IN THE EDUCATION SECTOR DUE TO ITS POTENTIAL TO IMPROVE VARIOUS ASPECTS OF THE EDUCATION SYSTEM. THE PRESENT STUDY AIMS TO DEVELOP EIGHT TYPES OF PREDICTIVE MODELS TO FORECAST STUDENTS' ACADEMIC PERFORMANCE. THE FIRST FOUR PREDICTIVE MODELS WERE DEVELOPED BASED ON THE (I) DEMOGRAPHICAL FEATURES, (II) ACADEMIC BACKGROUND FEATURES, (III) PARENTS SUPPORT FEATURES, AND (IV) LEARNING BEHAVIOUR FEATURES AND THE OTHER FOUR PREDICTIVE MODELS WERE GENERATED BASED ON THE (I) DEMOGRAPHICAL FEATURES, (II) ACADEMIC BACKGROUND FEATURES, AND (III) PARENTS SUPPORT FEATURES. THE MACHINE LEARNING ALGORITHMS USED TO DEVELOP THE MODELS INCLUDE THE DECISION TREE, THE NAIVE BAYES, THE SUPPORT VECTOR MACHINE AND THE NEURAL NETWORK. FROM THE RESULT ANALYSIS OF THE DATASET, IT SHOWED THAT SUPPORT VECTOR MACHINE ALGORITHM PERFORMS HIGH PREDICTION ON STUDENTS' ACADEMIC PERFORMANCE. THE FINDINGS ALSO REVEALED THAT LEARNING BEHAVIOUR FEATURES ARE SIGNIFICANT AND HAVE A GREATER IMPACT ON STUDENTS' ACADEMIC PERFORMANCE.
THIS PAPER PRESENTS AN ANALYTICS PLATFORM THAT HAS BEEN DEVELOPED FOR DESIGNERS AND TEACHERS WHO BUILD AND USE INTERACTIVE E-BOOKS FOR LEARNING. THE ANALYTICS DASHBOARD AIMS TO INCREASE AWARENESS OF THE USE OF THE E-BOOKS SO THAT DESIGNERS (AND TEACHERS IN THEIR ROLE AS DESIGNERS) CAN MAKE INFORMED DECISIONS ON HOW TO REDESIGN AND IMPROVE THEM TAKING INTO ACCOUNT BOTH THE OVERALL LEARNING DESIGN AND THE DATA FROM THEIR USAGE. THIS PAPER PRESENTS ARCHITECTURAL AND DESIGN DECISIONS ON KEY FEATURES OF THE DASHBOARD, AND THE EVALUATION OF A HIGH-FIDELITY PROTOTYPE. WE DISCUSS FINDINGS RELATED TO USE OF THE DASHBOARD FOR EXPLORATORY DATA ANALYSIS AND INQUIRY AND HOW THESE GENERALISE AND CAN BE TAKEN INTO ACCOUNT BY OUR FUTURE WORK OR THAT OF OTHERS.
IN ORDER TO SUPPORT BROAD ADOPTION OF REMOTE LABS IN ELEMENTARY AND SECONDARY SCHOOLS, WE NEED TO PROVIDE POWERFUL, EASY-TO-USE INTERFACES, NOT JUST TO STUDENTS, BUT ALSO TO TEACHERS. WE ARE DEVELOPING TOOLS TO PROVIDE RICHER, TECHNOLOGY-SUPPORTED REPRESENTATIONS THAT IMPROVE TEACHERS' ABILITY TO VISUALIZE STUDENT REMOTE LAB ACTIVITIES AND DIAGNOSE MISCONCEPTIONS WITH THE GOAL OF MAXIMIZING REMOTE LAB'S EDUCATIONAL VALUE AND IMPACT.
MODELING IS AN IMPORTANT ASPECT OF SCIENTIFIC PROBLEM-SOLVING. HOWEVER, MODELING IS A DIFFICULT COGNITIVE PROCESS FOR NOVICE LEARNERS IN PART DUE TO THE HIGH DIMENSIONALITY OF THE PARAMETER SEARCH SPACE. THIS WORK INVESTIGATES 50 COLLEGE STUDENTS' PARAMETER SEARCH BEHAVIORS IN THE CONTEXT OF ECOLOGICAL MODELING. THE STUDY REVEALED IMPORTANT DIFFERENCES IN BEHAVIORS OF SUCCESSFUL AND UNSUCCESSFUL STUDENTS IN NAVIGATING THE PARAMETER SPACE. THESE DIFFERENCES SUGGEST OPPORTUNITIES FOR FUTURE DEVELOPMENT OF ADAPTIVE COGNITIVE SCAFFOLDS TO SUPPORT DIFFERENT CLASSES OF LEARNERS.
IN THIS RESEARCH, WE AGGREGATED STUDENTS LOG DATA SUCH AS CLASS TEST SCORE (CTS), ASSIGNMENT COMPLETED (ASC), CLASS LAB WORK (CLW) AND CLASS ATTENDANCE (CATT) FROM THE DEPARTMENT OF MATHEMATICS, COMPUTER SCIENCE UNIT, USMANU DANFODIYO UNIVERSITY, SOKOTO, NIGERIA. SIMILARLY, WE EMPLOYED DATA MINING TECHNIQUES SUCH AS ID3 & J48 DECISION TREE ALGORITHMS TO ANALYZE THE DATA. WE COMPARED THESE ALGORITHMS ON 239 CLASSIFICATION INSTANCES. THE EXPERIMENTAL RESULTS SHOW THAT THE J48 ALGORITHM HAS HIGHER ACCURACY IN THE CLASSIFICATION TASK COMPARED TO THE ID3 ALGORITHM. THE IMPORTANT FEATURE ATTRIBUTES SUCH AS INFORMATION GAIN AND GAIN RATIO FEATURE EVALUATORS WERE ALSO COMPARED. BOTH THE METHODS APPLIED WERE ABLE TO RANK SEARCH METHODS. THE EXPERIMENTAL RESULTS CONFIRMED THAT THE TWO METHODS DERIVED THE SAME SET OF ATTRIBUTES WITH A SLIGHT DEVIATION IN THE RANKING. FROM THE RESULTS ANALYZED, WE DISCOVERED THAT 67.36 PERCENT FAILED THE COURSE TITLED "INTRODUCTION TO COMPUTER PROGRAMMING", WHILE 32.64 PERCENT PASSED THE COURSE. SINCE THE CATT HAS THE HIGHEST GAIN VALUE FROM OUR ANALYSIS; WE CONCLUDED THAT IT IS LARGELY RESPONSIBLE FOR THE SUCCESS OR FAILURE OF THE STUDENTS. RECOMMENDATIONS WERE GIVEN ON HOW TO IMPROVE THE FAILURE RATES IN THE FUTURE.
VIDEO IS FREQUENTLY USED AS A LEARNING MEDIUM IN A VARIETY OF EDUCATIONAL SETTINGS, INCLUDING LARGE ONLINE COURSES AS WELL AS INFORMAL LEARNING SCENARIOS. TO FOSTER LEARNER ENGAGEMENT AROUND INSTRUCTIONAL VIDEOS, OUR LEARNING SCENARIO FACILITATES INTERACTIVE NOTE TAKING AND COMMENTING SIMILAR TO POPULAR SOCIAL VIDEO-SHARING PLATFORMS. THIS APPROACH HAS RECENTLY BEEN ENRICHED BY INTRODUCING NUDGING MECHANISMS, WHICH RAISES QUESTIONS ABOUT ENSUING LEARNING EFFECTS. TO BETTER UNDERSTAND THE NATURE OF THESE EFFECTS, WE TAKE A CLOSER LOOK AT THE CONTENT OF THE COMMENTS. OUR STUDY IS BASED ON AN EX POST ANALYSIS OF A LARGER DATA SET FROM A RECENT STUDY. AS A FIRST STEP OF ANALYSIS, VIDEO COMMENTS ARE CLUSTERED BASED ON A FEATURE SET THAT CAPTURES THE TEMPORAL AND SEMANTIC ALIGNMENT OF COMMENTS WITH THE VIDEOS. BASED ON THE ENSUING TYPOLOGY OF COMMENTS, LEARNERS ARE CHARACTERIZED THROUGH THE TYPES OF COMMENTS THAT THEY HAVE CONTRIBUTED. THE RESULTS WILL ALLOW FOR A BETTER TARGETING OF NUDGES TO IMPROVE VIDEO-BASED LEARNING.
ACADEMIC DESERTION WITHIN THE UNIVERSITY SYSTEM IS A CHALLENGE FOR HIGHER EDUCATION INSTITUTIONS INSIDE AND OUTSIDE THE COUNTRY, AS A CONSEQUENCE OF SOCIAL, INDIVIDUAL AND INSTITUTIONAL FACTORS CLOSELY RELATED TO EACH OTHER. CONSEQUENTLY, THE STUDY OF EARLY ABANDONMENT OR CESSATION HAS BEEN WIDELY RECOGNIZED AS A SERIOUS PROBLEM, ESPECIALLY AT THE UNIVERSITY LEVEL. THEREFORE, THE ABILITY TO PREDICT A STUDENT'S PERFORMANCE COULD BE USEFUL. WITH THIS IDEA, IT IS INTENDED TO USE DATA MINING FOR THE EARLY DETECTION OF STUDENTS AT RISK OF DROPPING OUT, ANALYZING INFORMATION FROM STUDENTS ABOUT: ACADEMIC TUTORIALS, GRADES, SOCIOECONOMIC DATA, AMONG OTHERS, STORED IN DATABASES, FOCUSING ON FINDING FACTORS OF THE BEHAVIOR OF DROP OUT. THE AIM OF THIS PAPER IS TO CREATE A DATA WAREHOUSE THAT ALLOWS STUDENTS TO CLEAN AND TRANSFORM THEIR DATA, WITH THE PURPOSE OF CREATING A DATABASE WITH INFORMATION ABOUT STUDENT BEHAVIOR FACTORS TO ANALYZE THEM. NEXT, A DATA MINING MODEL BASED ON THE DATA WAREHOUSE WAS CREATED TO IDENTIFY CHARACTERISTICS AND PATTERNS THAT CAUSE REPETITION AND DESERTION, AND THUS BETTER GUIDE STUDENTS WITH A HIGHER RISK OF FAILURE OR ABANDONMENT. FOR THIS TASK, THE CRISP-DM METHODOLOGY WAS APPLIED, WHICH PROVIDES A STANDARDIZED DESCRIPTION OF THE LIFE CYCLE OF A STANDARD PROJECT OF DATA ANALYSIS, THROUGH PHASES THAT LINK THE TASKS AND RELATIONSHIPS BETWEEN THEM, FOR THE ELABORATION OF MODELS. IN ADDITION, WE USED BUSINESS INTELLIGENCE TO FACILITATE DECISION MAKING, THROUGH THE USE OF DATA MINING TOOLS, TO ASSIST AUTHORITIES AND ADMINISTRATIVE PERSONNEL IN THE ACCREDITATION PROCESSES, RESOURCE MANAGEMENT, AND TEACHER RECRUITMENT. THIS ARTICLE PRESENTS AN OUTLINE OF THE PROCESS TO FOLLOW TO GET THE DATA MINING MODEL AND THUS CONTRIBUTE TO FUTURE RESEARCHES.
THE ORIENTATION OF THE SPANISH EDUCATIONAL SYSTEM TOWARDS TEACHING-LEARNING SCENARIOS THAT TRANSCEND THE WALLS OF PHYSICAL CLASSROOMS TOWARDS VIRTUAL PLATFORMS, OPENS ASYNCHRONOUS LEARNING CHANNELS, AND LEADS TO RETHINKING HOW STUDENTS LEARN, WHAT USES THEY MAKE OF VIRTUAL PLATFORMS, HOW THEY INTERRELATE WITH THEM AND WITH EACH OTHER, AND HOW THEY ACQUIRE KNOWLEDGE AND DEVELOP COMPETENCES. THE WORK ADDRESSES THE IMPLEMENTATION OF THE LEARNING BASED ON A TEACHING PROJECT (ABPD) METHODOLOGY THROUGH THE DESCRIPTIVE ANALYSIS OF THE USES AND STATISTICAL REGISTERS, WHICH AS A FOOTPRINT, LEAVE THE STUDENTS IN FACE-TO-FACE COURSES IN THE VIRTUAL CAMPUS, MODDLE PLATFORM. FOR THIS PURPOSE, MARKERS THAT IDENTIFY PROGRESS IN THE LEARNING PROCESS ARE ESTABLISHED, ACCORDING TO A PREFIXED RUBRIC, AND SUPPLEMENTED WITH INFORMATION FROM AD-HOC QUESTIONNAIRES, AS WELL AS THE GRADES OBTAINED AND THEIR CORRELATIONS. AS A CASE, DESCRIPTIVE RESULTS ARE PRESENTED OF AN ACCUMULATED GROUP OF STUDENTS OF THE MASTER'S DEGREE IN TEACHER TRAINING, IN THE SPECIALTY OF ECONOMICS AND BUSINESS ADMINISTRATION OF THE UCM, WHICH ALLOW TO DEFINE DIFFERENT TYPOLOGIES IN THEIR APPROACH TO MATERIALS, WITH DIFFERENTIATED PATTERNS OF LEARNING ACCORDING TO THE DIGITAL GAPS.
RESEARCH INTO VIRTUAL FIELD TRIPS (VFTS) STARTED IN THE 1990S BUT, ONLY RECENTLY, THE MATURING TECHNOLOGY OF DEVICES AND NETWORKS HAS MADE THEM VIABLE OPTIONS FOR EDUCATIONAL SETTINGS. BY CONSIDERING AN EXPERIMENT, THE LEARNING BENEFITS OF LOGGING THE MOVEMENT OF STUDENTS WITHIN A VFT ARE SHOWN. THE DATA ARE VISUALIZED BY TWO TECHNIQUES: "ANIMATED PATH MAPS" ARE DYNAMIC ANIMATIONS OF STUDENTS' MOVEMENT IN A VFT; "PAINT SPRAY MAPS" SHOW WHERE STUDENTS CONCENTRATED THEIR VISUAL ATTENTION AND ARE STATIC. A TECHNIQUE FOR PRODUCING THESE VISUALIZATIONS IS DESCRIBED AND THE EDUCATIONAL USE OF TRACKING DATA IN VFTS IS CRITICALLY DISCUSSED.
THE AIM OF THE STUDY IS TO DETERMINE THE IMPACT OF ONLINE LEARNING ACTIVITIES ON LEARNING OUTCOMES OF STUDENTS WHO PARTICIPATED IN THE BLENDED LEARNING COURSE, FOCUSING SPECIFICALLY ON SKILL-BASED COURSES. THE LEARNING OUTCOMES OR RESULTS OF A LEARNER ARE USUALLY MEASURED BY SCORES, KNOWLEDGE OR SKILLS GAINED IN THE COURSE. IN BLENDED LEARNING COURSES, THE LEARNING OUTCOMES CAN BE ASSESSED ACCORDING TO MANY CRITERIA. IN THIS STUDY, INTERACTIVE ACTIVITIES SUCH AS TEACHER-STUDENT INTERACTION, STUDENT-STUDENT INTERACTION, STUDENT-CONTENT INTERACTION AND STUDENT-TECHNOLOGY INTERACTION ARE CONSIDERED. UNDERGRADUATE STUDENTS PARTICIPATED IN THE BLENDED LEARNING COURSE IN WHICH FORMATIVE ASSESSMENT WAS USED TO EVALUATE STUDENT LEARNING OUTCOMES BY THE COMBINATION OF DIFFERENT LEARNING ACTIVITIES THROUGH A LEARNING MANAGEMENT SYSTEM. THE QUANTITATIVE RESULTS OBTAINED BY USING REGRESSION ANALYSIS OF DATA FROM THE SYSTEM SHOWED THAT THE STUDENTS WHO EFFECTIVELY INTERACTED WITH LEARNING ACTIVITIES IN THE COURSE HAVE BETTER RESULTS. QUANTITATIVE ANALYTICAL RESULTS INDICATED THAT STUDENT-STUDENT INTERACTION HAS A GREATER IMPACT ON STUDENT LEARNING OUTCOMES. THESE LEARNING ACTIVITIES ARE USED FOR INTERACTIVE ACTIVITIES AS SUGGESTIONS FOR TEACHERS TO DESIGN AND IMPLEMENT LEARNING ACTIVITIES FOR BLENDED LEARNING COURSES.
EARLIER STUDIES HAVE SHOWN THE POWER OF ONLINE LEARNING SYSTEMS DATA TO DEVELOP PREDICTION METHODS THAT TRY TO IDENTIFY SUCCESSFUL STUDENTS PATTERNS OF ACCOMPLISHMENT AND ENGAGEMENT TO ALLOW TIMELY PEDAGOGICAL INTERVENTIONS. OUR LEARNING PLATFORM, SOCIALWIRE, COLLECTS A DETAILED RECORD OF THE STUDENTS' ACTIVITY SO, IN THIS PAPER, WE COMPARE AND COMBINE THE POWER OF DIFFERENT CLASSIFIERS FOR SUCCESS/FAILURE LEARNING PREDICTION, USING AS INPUTS SOME OF THE FEATURES THAT HAVE MEASURABLE CORRELATION WITH THE STUDENTS' PERFORMANCE. THE MAIN CONCLUSION IS THAT, FOR THE COURSES IN STUDY, IT IS NOT THE TYPE OF EVENT/ACTIVITY INITIATED BY THE STUDENT WHAT BEST PREDICTS HIS/HER FINAL GRADE, BUT THE PACE OF THE EVENTS HE/SHE WAS ENGAGED IN.
AS A RAPIDLY GROWING FIELD OF LEARNING ANALYSIS TECHNOLOGY, LEARNING PREDICTION HAS BEEN DEVELOPED BY MANY RESEARCHERS FROM DIFFERENT ANGLES AND EDUCATIONAL ENVIRONMENTS. DIFFERENT PREDICTION MODELS HAVE THEIR OWN CHARACTERISTICS AND HAVE PRODUCED DIFFERENT RESULTS. HOWEVER, EACH MODEL HAS THEIR LIMITATIONS IN ADAPTABILITY AND GENERALIZATION. IN THIS CONCEPT PAPER, WE PROPOSED A RADIAL BASIS FUNCTION NEURAL NETWORK PREDICTION MODEL BASED ON ASSOCIATION RULES. THE PREDICTORS OF THE MODEL ARE NOT PRE-DETERMINED. THEY ARE SELECTED BY MINING THE ITEMS WITH A STRONG CORRELATION BETWEEN THE PREDICTED RESULTS.
THIS PAPER AIMS TO EXPLORE TIME MANAGEMENT STRATEGIES FOLLOWED BY STUDENTS IN A FLIPPED CLASSROOM THROUGH THE ANALYSIS OF TRACE DATA. SPECIFICALLY, AN EXPLORATORY STUDY WAS CONDUCTED ON THE DATASET COLLECTED IN THREE CONSECUTIVE OFFERINGS OF AN UNDERGRADUATE COMPUTER ENGINEERING COURSE (N = 1,134). TRACE DATA ABOUT ACTIVITIES WERE INITIALLY CODED FOR THE TIMELINESS OF ACTIVITY COMPLETION. SUCH DATA WERE THEN ANALYSED USING AGGLOMERATIVE HIERARCHICAL CLUSTERING BASED ON WARD'S ALGORITHM, FIRST ORDER MARKOV CHAINS, AND INFERENTIAL STATISTICS TO (A) DETECT TIME MANAGEMENT TACTICS AND STRATEGIES FROM STUDENTS' LEARNING ACTIVITIES AND (B) ANALYSE THE EFFECTS OF PERSONALIZED ANALYTICS-BASED FEEDBACK ON TIME MANAGEMENT. THE RESULTS INDICATE THAT MEANINGFUL AND THEORETICALLY RELEVANT TIME MANAGEMENT PATTERNS CAN BE DETECTED FROM TRACE DATA AS MANIFESTATIONS OF STUDENTS' TACTICS AND STRATEGIES. THE STUDY ALSO SHOWED THAT TIME MANAGEMENT TACTICS HAD SIGNIFICANT ASSOCIATIONS WITH ACADEMIC PERFORMANCE AND WERE ASSOCIATED WITH DIFFERENT INTERVENTIONS IN PERSONALIZED ANALYTICS-BASED FEEDBACK.
LEARNING AT SCALE IS A FAST GROWING FIELD THAT AFFECTS FORMAL, INFORMAL, AND WORKPLACE EDUCATION. HIGHLY INTERDISCIPLINARY, IT BUILDS ON SOLID FOUNDATIONS IN THE LEARNING SCIENCES, COMPUTER SCIENCE, EDUCATION, AND THE SOCIAL SCIENCES. WE DEFINE LEARNING AT SCALE AS THE STUDY OF THE TECHNOLOGIES, PEDAGOGIES, ANALYSES, AND THEORIES OF LEARNING AND TEACHING THAT TAKE PLACE WITH A LARGE NUMBER OF LEARNERS AND A HIGH RATIO OF LEARNERS TO FACILITATORS. THE SCALE OF THESE ENVIRONMENTS OFTEN CHANGES THE VERY NATURE OF THE INTERACTION AND LEARNING EXPERIENCES. WE IDENTIFY THREE TYPES OF TECHNOLOGIES THAT SUPPORT SCALE IN EDUCATION: DEDICATED CONTENT-AGNOSTIC PLATFORMS, SUCH AS MOOCS; DEDICATED TOOLS, SUCH AS INTELLIGENT TUTORING SYSTEMS; AND REPURPOSED PLATFORMS, SUCH AS SOCIAL NETWORKS. WE FURTHER IDENTIFY FIVE AREAS THAT SCALE AFFECTS: LEARNERS, RESEARCH AND DATA, ADAPTATION, SPACE AND TIME, AND PEDAGOGY. INTRODUCING THE PAPERS IN THIS SPECIAL ISSUE ON THE TOPIC, WE DISCUSS THE CHARACTERISTICS, AFFORDANCES, AND PROMISE OF LEARNING AT SCALE.
MOOCS (MASSIVE OPEN ONLINE COURSES) ARE AN INNOVATION THAT HAVE ATTRACTED PUBLIC ACADEMIC ATTENTION IN RECENT TIMES, AND WHICH HAVE RESULTED IN THE EVOLUTION OF E-LEARNING (ELECTRONIC LEARNING) AND SO FAR HAVE NOT ACHIEVED THEIR EXPECTATIONS IN HIGHER EDUCATION. THIS IS DUE TO THE FACT THAT FROM THE OUTSET THESE COURSES HAVE FACED A NUMBER OF PROBLEMS AND QUESTIONS REGARDING THEIR ACCEPTANCE, CREDIBILITY, QUALITY, METHODS OF ASSESSMENT, LEARNING OUTCOMES AND MORE; WHICH HAS GENERATED CONTROVERSY AMONG THEIR OPPONENTS, FANS AND EVEN THE CREATORS OF MOOCS. THIS WORK FORMS PART OF A DOCTORAL INVESTIGATION; IN ESSENCE THESE ARE THE RESULTS OF THE PRELIMINARY AND EXPLORATORY STAGES IN THE RESEARCH PROCESS, IN WHICH PROJECTIONS ON THE SOLUTION OF SOME PROBLEMS WILL BE INCLUDED. MOOCS: THEIR EVOLUTION, PROBLEMS AND CHALLENGES UP TO 2015, WILL SURELY HELP TO MINIMIZE THE HIGH DROPOUT RATES OF STUDENTS IN MOOCS, SUPPORTED BY MOBILE TECHNOLOGY.
USING ONLINE KNOWLEDGE COMMUNITIES (OKCS) FROM THE INTERNET AS INFORMAL LEARNING ENVIRONMENTS POSES THE QUESTION HOW LIKELY THESE COMMUNITIES WILL BE TO INTEGRATE LEARNERS AS NEW MEMBERS. SUCH PREDICTION IS THE PURPOSE OF THE CURRENT STUDY. BASED ON THE APPROACHES OF VOICES INTERANIMATION AND POLYPHONY, A NATURAL LANGUAGE PROCESSING TOOL WAS EMPLOYED FOR DIALOG ANALYSIS IN INTEGRATIVE VERSUS NON-INTEGRATIVE BLOG-BASED OKCS. FOUR DIALOG DIMENSIONS WERE IDENTIFIED: PARTICIPANTS' LONG-TERM PERSISTENCE IN THE DISCOURSE, THE COMMUNITY RESPONSE TO THEIR PARTICIPATION, THEIR COMMUNICATIVE CENTRALITY, AND THEIR COMMUNICATIVE PERIPHERALITY. HIERARCHICAL CLUSTERS BUILT UPON THESE DIMENSIONS REFLECT SOCIO-COGNITIVE STRUCTURES INCLUDING CENTRAL, REGULAR, AND PERIPHERAL OKC MEMBERS. WHILE THE SOCIO-COGNITIVE STRUCTURES DID NOT MAKE A SIGNIFICANT DIFFERENCE, INTEGRATIVE OKCS DISPLAY SIGNIFICANTLY STRONGER PERIPHERALITY, COMMUNITY RESPONSE, AND CENTRALITY AS COMPARED TO NON-INTEGRATIVE OKCS.
OVER THE LAST DECADE, ONLINE LEARNING HAS EXPERIENCED YEAR-OVER-YEAR, DOUBLE-DIGIT GROWTH, WITH ONE THIRD OF US BASED REGISTRATIONS CURRENTLY BEING ONLINE. HOWEVER, EDUCATIONAL TECHNOLOGY HAS NOT KEPT PACE WITH TECHNOLOGY IN GENERAL. CURRENT LEARNING PLATFORMS DO NOT HAVE THE ROBUST MEDIA CAPABILITIES, MOBILE ACCESS OPTIONS, OR BACKGROUND ANALYTICS THAT ARE PREVALENT IN THE COMMERCIAL;RECTOR. WITH OVER 120,000 ONLINE STUDENTS, AMERICAN PUBLIC UNIVERSITY SYSTEM (APUS) BELIEVED THAT ENHANCING THE LEARNING PLATFORM, IN SUCH A MANNER THAT IT WAS COMMENSURATE WITH COMMERCIAL OFFERINGS, SHOULD BE PARAMOUNT. IN RESPONSE TO THIS CHALLENGE APUS INTEGRATED A COMMERCIAL, APACHE SLING BASED PLATFORM WITH THE INSTITUTIONAL ERP AND DATA EXCHANGE COMPONENTS OF SAKAI - THE CURRENT LMS. THEY THEN CONSTRUCTED HIGHLY ORDERED TAXONOMIES THAT LEVERAGE RICH META AND PARA DATA TO PROVIDE FOR OBJECT REUSABILITY. FINALLY, OMNITURE WEB ANALYTICS (SITE CATALYST AND TEST AND TARGET 1:1) WERE INTEGRATED INTO THE SYSTEM TO PROVIDE GRANULAR VIEWS INTO STUDENT INTERACTIONS AND PROVIDE PERSONALIZED LEARNING EXPERIENCES.
IN THIS PAPER, WE DEVELOP A CONTEXT-AWARE, TABLET-BASED LEARNING MODULE FOR ADULT EDUCATION. SPECIFICALLY, WE FOCUS ON ADULT EDUCATION IN HEALTHCARE-TEACHING LEARNERS TO PERFORM A MEDICAL SCREENING PROCEDURE. BASED UPON HOW LEARNERS NAVIGATE THROUGH THE LEARNING MODULE (E.G. SWIPE-SPEED AND CLICK DURATION, AMONG OTHERS), WE USE MACHINE LEARNING TO PREDICT WHAT COMPREHENSIVE TEST QUESTIONS A USER WILL ANSWER CORRECTLY OR INCORRECTLY. COMPARED WITH OTHER CONTEXT AWARE LEARNING APPLICATIONS, THIS IS THE FIRST TIME TABLET-BASED NAVIGATION GESTURES HAVE BEEN USED TO SUPPORT LEARNING ASSESSMENT. WE CONDUCTED A 21 PARTICIPANT USER STUDY, SHOWING THE SYSTEM CAN PREDICT HOW USERS RESPOND TO TEST QUESTIONS WITH 87.7% ACCURACY WITHOUT USER-SPECIFIC CALIBRATION DATA. THIS IS COMPARED TO ITEM RESPONSE THEORY METHODS WHICH ACHIEVE ABOUT 70% ACCURACY. WE ALSO INVESTIGATE WHICH ATTRIBUTES ARE MOST RESPONSIBLE FOR MAKING PREDICTIONS.
A GROWING BODY OF RESEARCH FOCUSES ON WHAT OUTCOMES TO ASSESS IN MAKERSPACES, AND APPROPRIATE FORMATS FOR CAPTURING THOSE OUTCOMES (E.G. REFLECTIONS, SURVEYS, AND PORTFOLIOS). LINGUISTIC ANALYSIS AS A DATA MINING TECHNIQUE HOLDS PROMISE FOR REVEALING DIFFERENT DIMENSIONS OF LEARNING EXHIBITED BY STUDENTS IN MAKERSPACES. IN THIS STUDY, STUDENT REFLECTIONS ON MAKERSPACE PROJECTS WERE GATHERED IN 2 FORMATS OVER 2 YEARS: PRIVATE WRITTEN ASSESSMENTS CAPTURED IN THE 3D GAMELAB GAMIFICATION PLATFORM, AND SEMI-PUBLIC VIDEO-RECORDED ASSESSMENTS POSTED IN THE MORE SOCIAL FLIPGRID PLATFORM. TRANSCRIPTS OF STUDENT ASSESSMENTS WERE ANALYZED USING LINGUISTIC INQUIRY WORD COUNT (LIWC) TO GENERATE 4 SUMMARY VARIABLES THOUGHT TO INFORM MAKERSPACE OUTCOMES OF INTEREST (I.E. ANALYTICAL THINKING, AUTHENTICITY, CLOUT, AND EMOTIONAL TONE). COMPARATIVE FINDINGS INDICATE THAT WRITTEN ASSESSMENTS MAY ELICIT MORE ANALYTICAL THINKING ABOUT MAKER PROJECTS COMPARED WITH LESS ANALYTICAL CONVERSATION IN VIDEOS, WHILE VIDEO ASSESSMENTS MAY ELICIT SOMEWHAT HIGHER CLOUT SCORES AS EVIDENCE OF SOCIAL SCAFFOLDING ALONG WITH A MUCH MORE POSITIVE EMOTIONAL TONE. RECOMMENDATIONS ARE PROVIDED FOR LAYERING ASSESSMENT APPROACHES TO MAXIMIZE THE POTENTIAL BENEFITS OF EACH FORMAT, INCLUDING REFLECTIVE WRITING FOR SOCIAL SPACES, IN SOCIAL GROUPS, AND ABOUT DESIGN PROCESSES AND PROCEDURES.
DANCING IS AN ACTIVITY THAT POSITIVELY ENHANCES THE MOOD OF PEOPLE THAT CONSISTS OF FEELING THE MUSIC AND EXPRESSING IT IN RHYTHMIC MOVEMENTS WITH THE BODY. LEARNING HOW TO DANCE CAN BE CHALLENGING BECAUSE IT REQUIRES PROPER COORDINATION AND UNDERSTANDING OF RHYTHM AND BEAT. IN THIS PAPER, WE PRESENT THE FIRST IMPLEMENTATION OF THE DANCING COACH (DC), A GENERIC SYSTEM DESIGNED TO SUPPORT THE PRACTICE OF DANCING STEPS, WHICH IN ITS CURRENT STATE SUPPORTS THE PRACTICE OF BASIC SALSA DANCING STEPS. HOWEVER, THE DC HAS BEEN DESIGNED TO ALLOW THE ADDITION OF MORE DANCE STYLES. WE ALSO PRESENT THE FIRST USER EVALUATION OF THE DC, WHICH CONSISTS OF USER TESTS WITH 25 PARTICIPANTS. RESULTS FROM THE USER TEST SHOW THAT PARTICIPANTS STATED THEY HAD LEARNED THE BASIC SALSA DANCING STEPS, TO MOVE TO THE BEAT AND BODY COORDINATION IN A FUN WAY. RESULTS ALSO POINT OUT SOME DIRECTION ON HOW TO IMPROVE THE FUTURE VERSIONS OF THE DC.
ALTHOUGH THE PROLIFERATION OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAS CREATED HIGHLY INTERACTIVE LEARNING ENVIRONMENT FOR HIGHER EDUCATION, THE LOW COMPLETION RATE (OR HIGH DROPOUT RATE) DETERIORATES THE DEVELOPMENT OF MOOC COURSES. THIS STUDY EXPLORES WHY LEARNERS ARE NOT HIGHLY ENGAGED IN MOOCS FROM THE PERSPECTIVE OF ACADEMIC HARDINESS AND LEARNING ENGAGEMENT. THE INTERPLAY OF ONLINE ACADEMIC HARDINESS AND ONLINE LEARNING ENGAGEMENT IS MAPPED THROUGH BOTH STRUCTURAL EQUATION MODEL AND PREDICTIVE MODEL. OUR EXPLANATORY AND PREDICTIVE ANALYSIS FOUND THAT COMMITMENT IS THE MOST IMPORTANT FACTOR OF ONLINE ACADEMIC HARDINESS, SIGNIFICANTLY INFLUENCING LEARNING ENGAGEMENT. MOREOVER, THE ROLE OF CHALLENGE CONTRIBUTES MUCH TO COGNITIVE AND EMOTIONAL ENGAGEMENT. OTHER INTERESTING FINDINGS AND INSTRUCTIONAL IMPLICATION WILL BE DISCUSSED IN THE PAPER.
THE EMOTIONS STUDENTS EXPERIENCE IN THE CLASSROOM ARE LIKELY TO INFLUENCE THEIR LEARNING IN SCHOOL. FOR PURPOSES OF HELPING STUDENT IMPROVE THEIR LEARNING, THIS STUDY SEEKS TO FURTHER THE DEVELOPMENT OF THE USE OF AUTOMATED ANALYSIS OF OBSERVED DATA TO UNDERSTAND STUDENTS' EMOTIONS AND DURING THEIR LEARNING ACTIVITIES. THIS STUDY LOOKS AT LINKING BEHAVIORAL DATA ABOUT EMOTIONS WITH SELF-REPORT DATA ABOUT EMOTIONS WITH THE INTENT TO DETERMINE IF OBSERVED BEHAVIORS ARE ABLE TO PREDICT STUDENT'S EMOTIONS. THE ICT-SUPPORTED LEARNING EMOTION SCALE WAS USED FOR 57 STUDENTS TO MEASURE THEIR LEARNING EMOTION WHILE LEARNING IN AN ICT ENVIRONMENT, MEANWHILE, CLASSROOM OBSERVATIONS WERE CONDUCTED FOR 57 STUDENTS FOR SIX WEEKS IN 4 CLASSROOMS, PAYING CLOSE ATTENTION TO STUDENT EMOTIONAL BEHAVIORS AND EMOTIONAL CHANGES. THE RESULTS FOUND THAT THREE KEY GROUPS OF BEHAVIORAL INDICATORS HAPPENED FREQUENTLY. ONE OF THE KEY FINDINGS WAS THAT STUDENTS WHO VOLUNTEERED TO ANSWER QUESTIONS IN CLASS WERE OBSERVED TO HAVE AN OBSERVABLE "ENJOY" EMOTION 84.79% OF THE TIME. THE DISCUSSION HIGHLIGHTED THAT THE BRIDGE FUNCTION OF STUDENTS' EMOTION ENGAGEMENT BETWEEN CLASSROOM OBSERVATION AND QUESTIONNAIRE DATA.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE GROWN UP TO THE POINT OF BECOMING A NEW LEARNING SCENARIO FOR THE SUPPORT OF LARGE AMOUNTS OF STUDENTS. AMONG CURRENT RESEARCH EFFORTS RELATED TO MOOCS, SOME ARE STUDYING THE APPLICATION OF WELL-KNOWN CHARACTERISTICS AND TECHNOLOGIES. AN EXAMPLE OF THESE CHARACTERISTICS IS ADAPTATION, IN ORDER TO PERSONALIZE THE MOOC EXPERIENCE TO THE LEARNER'S SKILLS, OBJECTIVES AND PROFILE. SEVERAL EDUCATIONAL ADAPTIVE SYSTEMS HAVE EMPHASIZED THE ADVANTAGES OF INCLUDING AFFECTIVE INFORMATION IN THE LEARNER PROFILE. OUR HYPOTHESIS, BASED ON THEORETICAL MODELS FOR THE APPRAISAL OF EMOTIONS, IS THAT WE CAN INFER THE LEARNER'S EMOTIONS BY ANALYSING THEIR ACTIONS WITH TOOLS IN THE MOOC PLATFORM. WE PROPOSE FOUR MODELS, EACH TO DETECT AN EMOTION KNOWN TO CORRELATE WITH LEARNING GAINS AND THEY HAVE BEEN IMPLEMENTED IN THE KHAN ACADEMY PLATFORM. THIS ARTICLE PRESENTS THE FOUR MODELS PROPOSED, THE PEDAGOGICAL THEORIES SUPPORTING THEM, THEIR IMPLEMENTATION AND THE RESULT OF A FIRST USER STUDY.
COLLABORATION IS AN IMPORTANT 21ST CENTURY SKILL; IT CAN TAKE PLACE IN A REMOTE OR CO-LOCATED SETTING. CO-LOCATED COLLABORATION (CC) IS A VERY COMPLEX PROCESS WHICH INVOLVES SUBTLE HUMAN INTERACTIONS THAT CAN BE DESCRIBED WITH MULTIMODAL INDICATORS (MI) LIKE GAZE, SPEECH AND SOCIAL SKILLS. IN THIS PAPER, WE FIRST GIVE AN OVERVIEW OF RELATED WORK THAT HAS IDENTIFIED INDICATORS DURING CC. THEN, WE LOOK INTO THE STATEOF-THE-ART STUDIES ON FEEDBACK DURING CC WHICH ALSO MAKE USE OF MI. FINALLY, WE DESCRIBE A WIZARD OF OZ (WOZ) STUDY WHERE WE DESIGN A PRIVACY-PRESERVING RESEARCH PROTOTYPE WITH THE AIM TO FACILITATE REAL-TIME COLLABORATION IN-THE-WILD DURING THREE CO-LOCATED GROUP PHD MEETINGS (OF 3-7 MEMBERS). HERE, HUMAN OBSERVERS STATIONED IN ANOTHER ROOM ACT AS A SUBSTITUTE FOR SENSORS TO TRACK DIFFERENT SPEECH-BASED CUES (LIKE SPEAKING TIME AND TURN TAKING); THIS DRIVES A REAL-TIME VISUALIZATION DASHBOARD ON A PUBLIC SHARED DISPLAY. WITH THIS RESEARCH PROTOTYPE, WE WANT TO PAVE WAY FOR DESIGN-BASED RESEARCH TO TRACK OTHER MULTIMODAL INDICATORS OF CC BY EXTENDING THIS PROTOTYPE DESIGN USING BOTH HUMANS AND SENSORS.
THE PURPOSE OF THE PRESENT PAPER IS TO DESCRIBE A SYSTEM WHICH AUTOMATICALLY ANALYSES PAUSES WITHIN THE COMMUNICATION BETWEEN THE PARTICIPANTS TO A COLLABORATIVE LEARNING INSTANT MESSENGER (CHAT) CONVERSATION. LEARNERS USE CHAT AS AN ASSIGNMENT, FOR EXAMPLE, TO HAVE A DEBATE IN WHICH EACH PARTICIPANT TAKES THE ROLE OF A REPRESENTATIVE FOR ONE OF THE GIVEN SUBJECTS: "CHAT", "FORUM", "BLOG", "WIKI". THE UTTERANCES CONSIDERED BY OUR AUTOMATIC ANALYSIS ARE QUESTION-ANSWER TYPE SPEECH ACTS, AND THE PAUSES IN COLLABORATIVE LEARNING CHATS ARE CLASSIFIED BASED ON THE USED UTTERANCE TYPES AND TIME-STAMPS. IN ADDITION, IT IS PRESENTED THE LINK BETWEEN THE UTTERANCES AND THE PAUSES, RELATED IN THIS WAY TO THE IMPLICATION OF EACH PARTICIPANT. THE LAST PART OF THE PAPER IDENTIFIES THE LINK BETWEEN THE UTTERANCE TYPE AND THE PAUSE TYPE USED BY EACH PARTICIPANT, BASED ON THE AUTOMATIC ANALYSES. AN IMPORTANT CHARACTERISTIC OF COLLABORATIVE LEARNING CHATS IS HIGHLIGHTED BY THE OVERLAP OF THE UTTERANCES IN THE SAME TIME. BY USING THIS CHARACTERISTIC WE CAN SAY THAT MORE PARTICIPANTS CAN ISSUE MORE UTTERANCES IN THE SAME TIME. THE PARTICIPANTS EXCHANGE SPEECH ACTS AND, DEPENDING ON EVERYONE'S INVOLVEMENT, THERE MAY BE ESTABLISHED TYPES OF INTERCHANGEABLE UTTERANCES AND TYPES OF PAUSES FOR EACH PARTICIPANT. THERE ARE ALSO IMPLICIT AND EXPLICIT (STATED BY THE PARTICIPANTS) REFERENCES IN THE CONVERSATION. THE PAPER WILL DETERMINE SEVERAL STATISTICS LIKE THE NUMBER OF WORDS FOR EACH PARTICIPANT, THE NUMBER OF UTTERANCES, THE NUMBER AND TYPES OF PAUSES, TIME INTERVALS BETWEEN THE UTTERANCES OF EACH PARTICIPANT AND BETWEEN PARTICIPANTS WHO HAVE THE SAME REFERENCE. AT THE END, WE WILL PRESENT THE RESULTS OBTAINED AUTOMATICALLY, A COMPARISON BETWEEN THEM AND THE RESULTS OBTAINED BY A MANUAL ANALYSIS.
INDIVIDUAL DIFFERENCES EXIST AMONG LEARNERS. AMONG VARIOUS INDIVIDUAL DIFFERENCES, COGNITIVE STYLES CAN STRONGLY PREDICT LEARNERS' LEARNING BEHAVIOR. THEREFORE, COGNITIVE STYLES ARE ESSENTIAL FOR THE DESIGN OF ONLINE LEARNING. THERE ARE A VARIETY OF COGNITIVE STYLE DIMENSIONS AND OVERLAPS EXIST AMONG THESE DIMENSIONS. IN PARTICULAR, WITKIN'S FIELD DEPENDENCE/INDEPENDENCE AND PASK'S HOLISM/SERIALISM SHARE SOME SIMILARITIES. TO THIS END, IT IS NECESSARY TO DEVELOP A FRAMEWORK TO SHOW OVERLAPPED BEHAVIOR BETWEEN THESE TWO COGNITIVE STYLE DIMENSIONS. TO ADDRESS THIS ISSUE, THIS STUDY USED THE LAG SEQUENTIAL ANALYSIS TO EXAMINE THE OVERLAPS BETWEEN THESE TWO COGNITIVE STYLE DIMENSIONS FROM THE ASPECT OF ONLINE LEARNING BEHAVIOR. THE RESULTS FROM THIS STUDY INDICATED THAT THE OVERLAPS MAINLY APPEAR IN COMPREHENSIVE/LOCAL AND DYNAMIC/FIXED APPROACHES. BASED ON THE FINDINGS OF THIS STUDY, WE DEVELOP A FRAMEWORK THAT CAN SUPPORT THE IMPROVEMENT OF INSTRUCTION DESIGN SO THAT THE NEEDS OF DIFFERENT COGNITIVE STYLE GROUPS CAN BE ACCOMMODATED. ACCORDINGLY, THIS STUDY IS AN INTERDISCIPLINARY WORK, WHICH MAKES SCIENTIFIC CONTRIBUTIONS TO THREE COMMUNITIES, I.E. HUMAN-COMPUTER INTERACTION, DIGITAL LEARNING AND LEARNING ANALYTIC.
ONLINE TEACHERS' PROFESSIONAL DEVELOPMENT (TPD) HAS BECOME AN IMPORTANT WAY TO ENHANCE THEIR PROFESSIONAL CAPACITY. HOWEVER, IT IS DIFFICULT TO EVALUATE TEACHERS' PERFORMANCES IN ONLINE TPD. DUE TO THE COMPLEXITY OF ONLINE ACTIVITIES FOR TEACHERS, THERE IS NO SINGLE STANDARD TO EVALUATE PERFORMANCE. HOWEVER, ONLINE PROFESSIONAL DEVELOPMENT PLATFORMS ACCUMULATE DETAILED DATA ABOUT TEACHERS' ONLINE BEHAVIOR, AND THIS PROVIDES AN IMPORTANT OPPORTUNITY TO FIND A SOLUTION TO THE PROBLEM OF EVALUATION. IN THIS PAPER, WE SUGGEST A MODEL-BUILDING PROCESS TO BUILD A DATA MODEL FOR ONLINE TPD. WE DESCRIBE THE STRUCTURE AND PROCESS OF BUILDING A TEACHER MODEL FOR OURTEACHERS, ONE OF THE LARGEST ONLINE TPD PLATFORMS IN CHINA. IMPORTANT ISSUES CRITICAL TO THE MODEL-BUILDING METHODOLOGY ARE ALSO DISCUSSED IN THIS PAPER.
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. A PRELIMINARY WORK ANALYZES THIS TREND AND POINTS OUT THE CONSEQUENCES TO WHICH IT MAY GIVE RISE. THE PRESENT WORK EXPOSES A METHODOLOGY TO AUTOMATE THE INFORMATION OBTAINED FROM FACEBOOK WORKING GROUP, WHICH HAVE BEEN DESIGNED BY THE STUDENTS THEMSELVES.
IN THE LAST DECADE, MANY RESEARCHERS HAVE STUDIED THE USE OF GAME ELEMENTS IN EDUCATION. THE TERM "GAMIFICATION" REFERS TO THE APPLICATION OF ELEMENTS USED IN THE DEVELOPMENT OF VIDEO GAMES, SUCH AS MECHANICS AND DYNAMICS IN OTHER CONTEXTS UNRELATED TO GAMES, TO GENERATE MORE ENJOYABLE AND POSITIVE ATTITUDES FROM THE STUDENTS. THE GAMIFICATION PROCESS INVOLVES USING SEVERAL ELEMENTS PRESENT IN VIDEO GAMES, LIKE: POINTS, LEVELS, RANKINGS, REWARDS (BADGES / ACHIEVEMENTS) AND MISSIONS. IN THIS STUDY, WE ASSESS WHETHER OR NOT, GAMIFICATION ELEMENTS CAN HELP AND MOTIVATE STUDENTS ENROLLED IN A GAMIFIED ONTOLOGY-BASED ADAPTIVE ONLINE LEARNING ENVIRONMENT CALLED MEUTUTOR. IN THIS CONTEXT, WE FOLLOWED THE PEDAGOGICAL RECOMMENDATION PROCESS TO DISCOVER WHICH GAMIFICATION ELEMENTS WERE RELEVANT TO PROMOTE LEARNING, IN ORDER TO RECOMMEND IMPROVEMENTS TO THE ENVIRONMENT. TO DO THAT, THIS STUDY SHOWS A QUANTITATIVE ANALYSIS (CORRELATION ANALYSIS) OF THE GAMIFICATION ELEMENTS FROM MEUTUTOR.
THE PURPOSE OF THIS PAPER IS TO EXPLORE WHETHER THE USE OF BIG DATA HAS THE POTENTIAL FOR APPLICATION IN THE HIGHER EDUCATION (HE) SECTOR IN OMAN IN ADDRESSING PART OF THEIR CURRENT CHALLENGES. HIGHER EDUCATION INSTITUTIONS IN OMAN ARE FACING CHALLENGES RELATE TO ENHANCING THE LEARNING EXPERIENCE, IMPROVING EDUCATOR EFFECTIVENESS, PROVIDING APPROPRIATE AND EFFECTIVE LEARNING METHODS TAILORED TO INDIVIDUAL LEARNERS, HARNESSING INSIGHTS FROM THE LEARNING EXPERIENCES, TRACKING STUDENT LEARNING PROCESS AND PROGRESS, AND ENSURING STUDENT RETENTION. BY EXPLORING THE CURRENT LANDSCAPE OF BIG DATA AND ITS APPLICATIONS IN OTHER SECTORS, THIS PAPER CONSIDERS WHETHER BIG DATA CAPABILITIES SUCH AS REAL TIME FEEDBACK AND RECOMMENDATIONS, PERSONALIZED LEARNING AND CONTINUOUS IMPROVEMENT, SENTIMENT AND BEHAVIOR ANALYTICS, AND IMPROVED STUDENT RETENTION, CAN BE CONSIDERED AS PART OF THE SOLUTION TO THE CHALLENGES FACING HE IN OMAN. THE PAPER CONCLUDES THAT BIG DATA HAS THE POTENTIAL TO PROVIDE STUDENT INSIGHT INFORMATION THAT WILL HELP IN ADDRESSING PART OF THE CHALLENGES CURRENTLY FACED BY HE INSTITUTIONS IN OMAN. THIS WORK IS ALSO EXPECTED TO HELP THE HE SYSTEM DECISION MAKERS IN OMAN IDENTIFY POTENTIAL OPPORTUNITIES AND BENEFITS WHICH THE BIG DATA APPROACH COULD PROVIDE.
THE PROLIFERATION OF INFORMATION TECHNOLOGY TOOLS IN HIGHER EDUCATION HAS RESULTED IN AN EXPLOSION OF DATA ABOUT STUDENTS AND THEIR CONTEXTS. YET, CURRENT POLICIES GOVERNING THESE DATA ARE LIMITED IN THEIR USEFULNESS FOR INFORMING STUDENTS, INSTRUCTORS, AND ADMINISTRATORS OF THEIR RIGHTS AND RESPONSIBILITIES RELATED TO DATA USE BECAUSE THEY ARE BASED ON ANTIQUATED CONCEPTIONS OF DATA AND DATA SYSTEMS. TO UNDERSTAND HOW DATA PRIVACY POLICIES CONCEPTUALIZE AND REPRESENT DATA, PRIVACY, STUDENT AGENCY, AND INSTITUTIONAL POWER, WE CONDUCTED A POLICY DISCOURSE ANALYSIS OF 151 UNIVERSITY POLICY STATEMENTS RELATED TO STUDENT INFORMATION PRIVACY AND THE RESPONSIBLE USE OF STUDENT DATA FROM 78 PUBLIC AND PRIVATE POST-SECONDARY INSTITUTIONS IN THE U.S. THREE COMMON DISCOURSES EMERGED: EDUCATIONAL RECORDS ARE STATIC ARTIFACTS, PRIVACY SOLUTIONS ARE PREDICATED UPON INSTITUTIONAL RESPONSIBILITY AND STUDENT AGENCY, AND LEGITIMATE EDUCATIONAL INTEREST IN DATA ARE INSTITUTIONALLY DEFINED AND BROADLY APPLIED. WE EXPLORE THE ASSUMPTIONS, BIASES, SILENCES, AND CONSEQUENCES OF THESE DISCOURSES AND OFFER COUNTER- DISCOURSES TO BEGIN A FOUNDATION FOR THE DEVELOPMENT OF PRIVACY POLICIES IN A NEW DATA AGE.
MANY LEARNING ANALYSES FOCUS ON ONLINE LEARNING COURSES, SUCH AS MASSIVE OPEN ONLINE COURSES (MOOCS). THE ANALYSIS OF LEARNING BEHAVIORS FROM ACCESS LOG DATA IS EXPECTED TO BE OF BENEFIT TO INSTRUCTORS AND LEARNERS. HOWEVER, THERE ARE FEW STUDIES THAT FOCUS ON THE READING LOGS OF DIGITAL TEXTBOOKS IN THE TRADITIONAL CLASSROOM. THIS STUDY ADOPTS A NEW APPROACH TO ANALYZING LEARNING BEHAVIOR PATTERNS THROUGH DIGITAL TEXTBOOK USE. STUDENTS WERE GROUPED INTO FOUR CLUSTERS USING K-MEANS CLUSTERING TO ANALYZE THEIR LEARNING BEHAVIOR PATTERNS.
STUDENT ENGAGEMENT IS INTRINSICALLY LINKED TO TWO IMPORTANT METRICS IN LEARNING: STUDENT SATISFACTION AND THE QUALITY OF THE STUDENT EXPERIENCE. ONE OF THE WAYS THAT ENGAGEMENT CAN BE INFLUENCED IS THROUGH CAREFUL CURRICULUM DESIGN. USING THE KNOWLEDGE THAT MANY STUDENTS ARE 'ASSESSMENT-DRIVEN', A LOW-STAKES CONTINUOUS WEEKLY SUMMATIVE E-ASSESSMENT WAS INTRODUCED TO A MODULE. THE IMPACT THIS HAD ON STUDENT ENGAGEMENT WAS MEASURED BY STUDYING STUDENT ACTIVITY WITHIN THE MODULE VIRTUAL LEARNING ENVIRONMENT. IT WAS FOUND THAT INTRODUCTION OF THE E-ASSESSMENTS LED TO A SIGNIFICANT INCREASE IN VIRTUAL LEARNING ENVIRONMENT ACTIVITY COMPARED TO THE VIRTUAL LEARNING ENVIRONMENT ACTIVITY IN THAT MODULE THE PREVIOUS YEAR, AND ALSO COMPARED TO THE VIRTUAL LEARNING ENVIRONMENT ACTIVITY OF TWO OTHER MODULES STUDIED BY THE SAME STUDENT COHORT. AS MANY INSTITUTIONS MOVE TOWARDS GREATER BLENDED OR ONLINE DELIVERIES, IT WILL BECOME MORE IMPORTANT TO ENSURE THAT VIRTUAL LEARNING ENVIRONMENTS ENCOURAGE HIGH LEVELS OF STUDENT ENGAGEMENT IN ORDER TO MAINTAIN OR ENHANCE THE STUDENT EXPERIENCE.
THE UNIVERSAL DESIGN FOR LEARNING (UDL) FRAMEWORK EMPHASIZES MULTIPLICITY OF REPRESENTATION, EXPRESSION AND ENGAGEMENT TO CATER FOR THE WIDEST POSSIBLE SET OF LEARNING STYLES AND ABILITIES. DIGITAL GAME BASED LEARNING (DGBL) CAN SLOT INTO A UNIVERSALLY DESIGNED APPROACH TO EDUCATION AS ONE OF SEVERAL ALTERNATIVE WAYS OF LEARNING THAT WILL SUIT SOME LEARNING PREFERENCES, SUCH AS THOSE WHO PREFER TO LEARN IN AN ACTIVE WAY. HOWEVER, DGBL CAN ITSELF ENCAPSULATE THE PRINCIPLES OF UDL IF THE GAME DESIGNER EMBRACES UDL AS A FUNDAMENTAL SET OF GAME DESIGN PRINCIPLES. THIS PAPER DISCUSSES, WITH EXAMPLES, THE WAYS IN WHICH A GAME DESIGNER CAN UNIVERSALLY DESIGN A DGBL SOLUTION WITH RESPECT TO GAME MECHANICS, REPRESENTATION AND PERSONALIZATION, WITH AN EMPHASIS ON THE USE OF GAMEPLAY DATA FOR FORMATIVE AND SUMMATIVE EVALUATION DURING THE DESIGN, BUILD AND RETROSPECTIVE PHASES, AS WELL AS FOR ADAPTIVE LEARNING AND FORMATIVE FEEDBACK DURING THE DELIVERY PHASE.
THE EMERGING TECHNOLOGIES AND DIGITAL MATERIALS HAVE GRADUALLY BEEN POPULAR IN PRIMARY SCHOOL CAMPUS. IN THIS PAPER, WE DEVELOP A DIGITAL PEN TEACHING SYSTEM, WHICH IS BASED ON THE TEACHING IDEAS OF PRIMARY SCHOOL TEACHERS. BY MATHEMATICS TEACHING APPROACH, WITH COMBINED WITH THE DIGITAL PEN SYSTEM, IT COULD PROVIDE PLENTIFUL APPLICATION SERVICE TO TRIGGER ELEMENTARY STUDENTS' INTERESTING, AND THUS, TEACHERS ARE ABLE TO OBTAIN STUDENTS' LEARNING CONDITIONS CONVENIENTLY, AND ALSO, TIMELY FEEDBACKS COULD BE GIVEN TO EVERY STUDENT. THE EXPERIMENT RESULTS SHOW THAT STUDENTS ON THE QUANTITY OF THE DIGITAL PEN TEACHING SYSTEM USED IS POSITIVELY CORRELATED WITH STUDENTS' LEARNING ACHIEVEMENTS.
THE EMERGENCE OF MOOCS (MASSIVE OPEN ONLINE COURSES) MAKES AVAILABLE BIG AMOUNTS OF DATA ABOUT STUDENTS' INTERACTION WITH ONLINE EDUCATIONAL PLATFORMS. THIS ALLOWS FOR THE POSSIBILITY OF MAKING PREDICTIONS ABOUT FUTURE LEARNING OUTCOMES OF STUDENTS BASED ON THESE INTERACTIONS. THE PREDICTION OF CERTIFICATE ACCOMPLISHMENT CAN ENABLE THE EARLY DETECTION OF STUDENTS AT RISK, IN ORDER TO PERFORM INTERVENTIONS BEFORE IT IS TOO LATE. THIS STUDY APPLIES DIFFERENT MACHINE LEARNING TECHNIQUES TO PREDICT WHICH STUDENTS ARE GOING TO GET A CERTIFICATE DURING DIFFERENT TIMEFRAMES. THE PURPOSE IS TO BE ABLE TO ANALYZE HOW THE QUALITY METRICS CHANGE WHEN THE MODELS HAVE MORE DATA AVAILABLE. FROM THE FOUR MACHINE LEARNING TECHNIQUES APPLIED FINALLY WE CHOOSE A BOOSTED TREES MODEL WHICH PROVIDES STABILITY IN THE PREDICTION OVER THE WEEKS WITH GOOD QUALITY METRICS. WE DETERMINE THE VARIABLES THAT ARE MOST IMPORTANT FOR THE PREDICTION AND HOW THEY CHANGE DURING THE WEEKS OF THE COURSE.
THE PURPOSE OF THIS STUDY IS TO EXAMINE DIFFERENT FORMATS OF COMMENT DATA TO PREDICT STUDENT PERFORMANCE. HAVING STUDENTS WRITE COMMENT DATA AFTER EVERY LESSON CAN REFLECT STUDENTS' LEARNING ATTITUDES, TENDENCIES AND LEARNING ACTIVITIES INVOLVED WITH THE LESSON. IN THIS RESEARCH, LATENT DIRICHLET ALLOCATION (LDA) AND PROBABILISTIC LATENT SEMANTIC ANALYSIS (PLSA) ARE EMPLOYED TO PREDICT STUDENT GRADES IN EACH LESSON. IN ORDER TO OBTAIN FURTHER IMPROVEMENT OF PREDICTION RESULTS, A MAJORITY VOTE METHOD IS APPLIED TO THE PREDICTED RESULTS OBTAINED IN CONSECUTIVE LESSONS. THE RESEARCH FINDINGS SHOW THAT OUR PROPOSED METHOD CONTINUOUSLY TRACKED STUDENT LEARNING SITUATIONS AND IMPROVED PREDICTION PERFORMANCE OF FINAL STUDENT GRADES.
IN RECENT YEARS, LEVERAGING BIG DATA TECHNOLOGY TO EDUCATION DOMAIN DRAWS ATTENTION. THE RETENTION RATE OF STUDENT ON CAMPUS MAY BE SIGNIFICANTLY IMPROVED IF AN EARLY ALERT SYSTEM BASED ON DATA ANALYSIS IS SETUP AND THE INTERVENTION IS APPROPRIATELY DEPLOYED. HOWEVER, TO THE BEST OF OUR KNOWLEDGE, THE EXISTING PROPOSED EARLY ALERT SYSTEMS TAKE THE WHOLE PARTICIPANTS IN A GROUP OR ONE SINGLE CLASS AS ANALYSIS TARGET. IN THIS PAPER, WE COMPARE DIFFERENT STRATEGIES AND METHODS THAT DEAL WITH THE WHOLE PARTICIPANTS AND GROUPS IN DIFFERENT CLASSES TO IDENTIFY THE KEY ATTRIBUTES TO IMPROVE THE ACCURACY OF THE PROPOSED EARLY ALERT SYSTEMS. OUR RESULTS IN THIS STUDY INDICATE THAT TEACHERS IN DIFFERENT CLASSES MAY MAKE USE OF DIFFERENT FUNCTIONALITY OF LMS. DIFFERENT DATA VOLUMES ARE COLLECTED IN DIFFERENT CLASSES. A ROBUST EARLY ALERT PREDICTIVE MODEL NEEDS TO TAKE THE ABOVE INTO CONSIDERATION.
THIS STUDY WAS DESIGNED TO IDENTIFY CORRELATIONS BETWEEN STUDENTS' AWARENESS OF COLLABORATIVE PROBLEM SOLVING (CPS) AND LEARNING PERFORMANCE AND LEARNING BEHAVIOR IN SCIENCE EDUCATION. THE TOPIC OF THE COURSE WAS GENETIC DISEASES WHICH WAS IMPLEMENTED IN A TWELFTH-GRADE CLASS. TO ASSESS THE EFFECTIVENESS OF THIS INSTRUCTIONAL DESIGN, AND TO FIND OUT POTENTIAL FACTORS THAT AFFECT THE USING OF CPS SKILLS, MULTIPLE DATA SOURCES INCLUDING LEARNING TEST SCORES, QUESTIONNAIRE FEEDBACK, AND LEARNING LOGS WERE ANALYZED. FIRST, RESULTS INDICATED SIGNIFICANT IMPROVEMENTS IN STUDENTS' KNOWLEDGE ACQUISITION AND AWARENESS OF PARTICIPATION AND LEARNING AND KNOWLEDGE BUILDING IN CPS DURING THE COURSE. FURTHERMORE, WHEN WE INVESTIGATED THE CORRELATIONS BETWEEN CPS AWARENESS AND LEARNING PERFORMANCE AND LEARNING BEHAVIOR, THE FINDINGS INDICATED SIGNIFICANT POSITIVE CORRELATIONS BETWEEN STUDENTS' LEARNING MOTIVATION AND THEIR AWARENESS OF CPS PROCESSES. HOWEVER, THERE WERE NEGATIVE CORRELATIONS FOUND BETWEEN CERTAIN LEARNING BEHAVIOR FACTORS WITH CPS AWARENESS AND LEARNING MOTIVATION FACTORS RESPECTIVELY.
IN THIS PAPER WE INTRODUCE AN APPROACH FOR SELECTING A LINEAR MODEL TO ESTIMATE, IN A PREDICTIVE WAY, THE COMPLETION RATE OF MASSIVE OPEN ONLINE COURSES (MOOCS). DATA ARE DERIVED FROM LMS ANALYTICS AND NOMINAL SURVEYS. THE SAMPLE COMPRISES 722 OBSERVATIONS (USERS) CARRIED OUT IN SEVEN COURSES ON EDUOPEN, THE ITALIAN MOOCS PLATFORM. WE USED 24 INDEPENDENT VARIABLES (PREDICTORS), CATEGORISED INTO FOUR GROUPS (USER PROFILE, USER ENGAGEMENT, USER BEHAVIOUR, COURSE PROFILE). AS RESPONSE VARIABLES WE EXAMINED BOTH THE COURSE COMPLETION STATUS AND THE COMPLETION RATE OF THE LEARNING ACTIVITIES. A FIRST ANALYSIS CONCERNED THE CORRELATION BETWEEN THE PREDICTORS WITHIN EACH GROUP AND BETWEEN THE DIFFERENT GROUPS, AS WELL AS THAT BETWEEN ALL THE DEPENDENT VARIABLES AND THE TWO RESPONSE VARIABLES. THE LINEAR REGRESSION ANALYSIS WAS CONDUCTED BY MEANS OF A STEPWISE APPROACH FOR MODEL SELECTION USING THE ASYMPTOTIC INFORMATION CRITERION (AIC). FOR EACH OF THE RESPONSE VARIABLES WE ESTIMATED PREDICTIVE USING THE DIFFERENT GROUPS OF PREDICTORS BOTH SEPARATELY AND IN COMBINATION. THE MODELS WERE VALIDATED USING THE USUAL STATISTICAL TESTS. THE MAIN RESULTS SUGGEST A HIGH DEGREE OF DEPENDENCE OF COURSE COMPLETION AND COMPLETION RATE ON VARIABLES MEASURING THE USER'S BEHAVIOURAL PROFILE IN THE COURSE AND A WEAK DEGREE OF DEPENDENCE ON THE USER'S PROFILE, MOTIVATION AND COURSE PATTERN. IN ADDITION, RESIDUAL ANALYSIS INDICATES THE POTENTIAL OCCURRENCE OF INTERACTION EFFECTS AMONG VARIABLES AND NON-LINEAR DYNAMICS.
WE DEVELOPED A REAL-TIME, MULTIMODAL STUDENT ENGAGEMENT ANALYTICS TECHNOLOGY SO THAT TEACHERS CAN PROVIDE JUST-IN-TIME PERSONALIZED SUPPORT TO STUDENTS WHO RISK DISENGAGEMENT. TO INVESTIGATE THE IMPACT OF THE TECHNOLOGY, WE RAN AN EXPLORATORY SEMESTER-LONG STUDY WITH A TEACHER IN TWO CLASSROOMS. WE USED A MULTI-METHOD APPROACH CONSISTING OF A QUASI-EXPERIMENTAL DESIGN TO EVALUATE THE IMPACT OF THE TECHNOLOGY AND A CASE STUDY DESIGN TO UNDERSTAND THE ENVIRONMENTAL AND SOCIAL FACTORS SURROUNDING THE CLASSROOM SETTING. THE RESULTS SHOW THAT THE TECHNOLOGY HAD A SIGNIFICANT IMPACT ON THE TEACHER'S CLASSROOM PRACTICES (I.E., INCREASED SCAFFOLDING TO THE STUDENTS) AND STUDENT ENGAGEMENT (I.E., LESS BOREDOM). THESE RESULTS SUGGEST THAT THE TECHNOLOGY HAS THE POTENTIAL TO SUPPORT TEACHERS' ROLE OF BEING A COACH IN TECHNOLOGY-MEDIATED LEARNING ENVIRONMENTS.
MOST OF LITERATURE ON MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE FOCUSED ON DESCRIBING AND PREDICTING LEARNER'S BEHAVIOR WITH COURSE TRACE DATA. HOWEVER, LITTLE IS KNOWN ON THE EXTERNAL RESOURCES BEYOND THE MOOC THEY USE TO SHAPE THEIR LEARNING EXPERIENCE, AND HOW THESE INTERACTIONS RELATE WITH THEIR SUCCESS IN THE COURSE. THIS PAPER PRESENTS THE RESULTS OF AN EXPLORATORY STUDY THAT ANALYZES DATA FROM 572 LEARNERS IN 4 MOOCS TO UNDERSTAND (1) WHAT THE LEARNERS' ACTIVITIES BEYOND THE MOOC ARE, AND (2) HOW THEY RELATE WITH THEIR COURSE PERFORMANCE. WE ANALYZED FREQUENCIES OF THE STUDENTS' INDIVIDUAL ACTIVITIES IN AND BEYOND THE MOOC, AND THE TRANSITIONS BETWEEN THESE ACTIVITIES. THEN, WE ANALYZED THE TIME SPENT ON OUTSIDE THE MOOC CONTENT AS WELL AS THE NATURE OF THIS CONTENT. FINALLY, WE PREDICT WHICH TRANSITIONS BETTER PREDICT FINAL LEARNERS' GRADES. THE RESULTS SHOW THAT WE CAN PREDICT ACCURATELY STUDENTS' GRADES OF THE COURSE USING ONLY INTERNAL-COURSE FINE-GRAINED DATA OF STUDENT'S INTERACTIONS WITH VIDEO-LECTURES AND EXAMS COMBINED WITH TRACE DATA OF INTERACTIONS WITH CONTENT OUTSIDE THE MOOCS. ALSO, DATA SHOWS THAT LEARNERS SPENT 75% OF THEIR TIME ON THE MOOC, BUT GO FREQUENTLY TO OTHER CONTENT, MAINLY SOCIAL NETWORKING SITES, MAIL BOXES AND SEARCH ENGINES.
THE CLASSROOM RESPONSE SYSTEM (CRS) WAS DEVELOPED AS A TOOL FOR ENHANCING INTERACTIVITY BETWEEN TEACHERS AND STUDENTS IN THE CLASSROOM. MANY STUDIES HAVE REVEALED THE EFFECTIVENESS OF USING CRS IN EDUCATIONAL ENVIRONMENTS. HOWEVER, THE POPULARITY OF CRS HAS NOT INCREASED SUFFICIENTLY. THIS STUDY AIMS TO IMPLEMENT AN AUTOMATIC LEARNING SUPPORT FUNCTION ON CRS TO IMPROVE CONVENTIONAL SYSTEMS. IN THIS REGARD, I CONSIDERED APPLYING THE CONCEPT OF AN AGENT-ORIENTED SYSTEM (AOS) TO REALIZE THE IMPLEMENTATION OF THIS SUPPORT FUNCTION ON WEB-BASED CRS. IN ADDITION, I EXAMINED THE FEASIBILITY OF APPLYING THE CONCEPT OF AN AUTOMATIC LEARNING SUPPORT FUNCTION BY EXAMINING THE PREDICTION OF STUDENT RESPONSES BY USING STUDENT ACTIVITY LOGS. THIS PAPER DESCRIBES THE MODEL ON WHICH THE APPLICATION OF AN AOS IS BASED AND THE RESULT OF THE PREDICTION EXAMINATION.
AS THE USE OF BLENDED LEARNING ENVIRONMENTS AND DIGITAL TECHNOLOGIES BECOME INTEGRATED INTO THE HIGHER EDUCATION SECTOR, RICH TECHNOLOGIES SUCH AS ANALYTICS HAVE SHOWN PROMISE IN FACILITATING TEACHING AND LEARNING. ONE POPULAR APPLICATION OF ANALYTICS IS AUTOMATED WRITING EVALUATION (AWE) SYSTEMS. SUCH SYSTEMS CAN BE USED IN A FORMATIVE WAY; FOR EXAMPLE, BY PROVIDING STUDENTS WITH FEEDBACK ON DIGITALLY SUBMITTED ASSIGNMENTS. THIS PAPER PRESENTS WORK ON THE DEVELOPMENT OF AN AWE SOFTWARE TOOL FOR AN AUSTRALIAN UNIVERSITY USING ADVANCED TEXT ANALYTICS TECHNIQUES. THE TOOL WAS DESIGNED TO PROVIDE STUDENTS WITH TIMELY FEEDBACK ON THEIR INITIAL ASSIGNMENT DRAFTS, FOR REVISION AND FURTHER IMPROVEMENT. MOREOVER, IT COULD ALSO ASSIST ACADEMICS IN BETTER UNDERSTANDING STUDENTS' ASSIGNMENT PERFORMANCE SO AS TO INFORM FUTURE TEACHING ACTIVITIES. THE PAPER PROVIDES DETAILS ON THE METHODOLOGY USED FOR DEVELOPMENT OF THE SOFTWARE, AND PRESENTS THE RESULTS OBTAINED FROM THE ANALYSIS OF TEXT-BASED ASSIGNMENTS SUBMITTED IN TWO SUBJECTS. THE RESULTS ARE DISCUSSED, HIGHLIGHTING HOW THE TOOL CAN PROVIDE PRACTICAL VALUE, FOLLOWED BY INSIGHTS INTO EXISTING CHALLENGES AND POSSIBLE FUTURE DIRECTIONS.
RARELY DO UNIVERSITY DEPARTMENTS OF MATHEMATICS REDESIGN THEIR BASIC MATHEMATICS COURSES. THROUGH DEVELOPING AN ONLINE VERSION OF OUR ASSOCIATE DEGREE IN ENGINEERING IN COLLABORATION WITH OPEN UNIVERSITIES AUSTRALIA, WE REDESIGNED THE FIRST IN A SEQUENCE OF FIVE ENGINEERING MATHEMATICS COURSES. THE ONLINE COHORT PROVED DIFFERENT TO OUR FACE-TO-FACE EXPERIENCE. WE EMBARKED ON A PROCESS OF REFINING THE UNIT USING EXPERIENTIAL LEARNING AND ACTION RESEARCH. THE 13 WEEK UNIT IS DELIVERED UP TO FOUR TIMES A YEAR AND THIS PAPER REVIEWS THE FIRST 10 CYCLES OF ENHANCEMENTS OVER 3 YEARS AND UNPACKS THE LAYERS OF HYPOTHESES UNDERLYING DEVELOPMENT DECISIONS. SEVERAL CATEGORY THEMES WERE IDENTIFIED WITH A FOCUS ON STUDENTS, TEACHERS AND LEARNING ACTIVITIES. INVESTMENT IN ONLINE DEVELOPMENTS FOR MATHEMATICS CAN HAVE MULTIPLE FLOW-ON IMPACTS FOR OTHER TEACHING MODES. GOOD CURRICULUM DESIGN, REGARDLESS OF ENVIRONMENT, WILL ALWAYS BE A CORNERSTONE OF EFFECTIVE COURSE DEVELOPMENT PROCESSES.
IN THIS PAPER, WE PROPOSE A METHOD TO DETECT FAILURE OF LEARNING OF STUDENTS USING QUIZ RESPONSES IN A COURSE MANAGEMENT SYSTEM MOODLE. FAILURE OF LEARNING IS DEFINED AS A SITUATION IN WHICH THE CORRECT ANSWER RATE OF A LEARNING TOPIC IS SIGNIFICANTLY LOWER THAN THE CORRECT ANSWER RATE OF OTHER TOPICS. IN THE RESEARCH, THE RESEARCHERS IDENTIFIED THE PRESENCE OR ABSENCE OF FAILURE OF LEARNING IN ACTUAL CLASSES TO EVALUATE THE USEFULNESS OF THE PROPOSED METHOD. THE RESULTS REVEALED THAT STUDENTS CHECKED THE QUIZ RESULTS SIGNIFICANTLY MORE IN THE EXPERIMENTAL GROUP THAN IN THE CONTROL GROUP, AND THAT MORE INSTRUCTION WAS GIVEN TO THE EXPERIMENTAL GROUP.
STUDENTS' ONLINE BEHAVIOUR IN TWO ACADEMIC WRITING SUBJECTS WAS EXAMINED TO DETERMINE IF THERE IS A RELATIONSHIP BETWEEN FIRST ATTEMPT AT TOPIC QUIZZES AND ACADEMIC PERFORMANCE. OF THE 3078 STUDENTS WHO COMPLETED THE TWO SUBJECTS IN THE 2015/16 ACADEMIC YEAR, OVER HALF OF THE ENROLLED STUDENTS COMPLETED QUIZZES 1, 2 AND 3 IN THE FIRST TWO WEEKS FOLLOWING RELEASE. HOWEVER, IT TOOK FOUR WEEKS TO ACHIEVE A COMPLETION RATE ABOVE 50% FOR OTHER QUIZZES. AT THE END OF SEMESTER, A SUDDEN JUMP WAS OBSERVED AND BY WEEK 13, THE COMPLETION RATE FOR QUIZZES 1 TO 9 REACHED AS HIGH AS 90% WHILE IT HOVERED AROUND 70% FOR QUIZZES 10 TO 13. STUDENTS WERE CLASSIFIED INTO THREE GROUPS BASED ON THEIR FINAL SUBJECT GRADE: "AT-RISK" STUDENTS, TYPICAL STUDENTS AND HIGH PERFORMING STUDENTS. MEMBERSHIP OF THESE CATEGORIES WAS USED AS THE OUTCOME VARIABLE IN DECISION TREE ANALYSIS. THE TREE WHERE THREE VARIABLES (WEEK OF FIRST ATTEMPT OF QUIZ 1, 3 AND 5) WERE ENTERED TO PREDICT THE STUDENTS' GRADE MEMBERSHIP IDENTIFIED THAT THE WEEK OF FIRST ATTEMPT FOR QUIZZES 1, 3 AND 5 WERE SIGNIFICANT PREDICTORS AND THE LIKELIHOOD OF BEING AN AT-RISK STUDENT WAS HIGHER IF QUIZ 1 OR 3 (TOGETHER WITH QUIZ 5) WAS COMPLETED LATE. THESE RESULTS HAVE IMPLICATIONS FOR MONITORING FUTURE STUDENTS' LEARNING PROGRESS BY OBSERVING QUIZ ATTEMPT BEHAVIOR AND FOLLOWING UP STUDENTS WHO DO NOT COMPLETE THE QUIZZES OR COMPLETE THEM LATE. THE RESULTS CAN ALSO INFORM SETTING OF DEADLINES TO BETTER ALIGN MONITORING AND INTERVENTION. THIS STUDY HAS SHOWN THAT SIMPLE INDICATORS LIKE TIME OF QUIZ COMPLETION CAN PREDICT ACADEMIC PERFORMANCE AS MEASURED BY STUDENTS' GRADES IN THE SUBJECT. FOR THIS SUBJECT, TIME OF QUIZ COMPLETION APPEARS TO BE AN INDICATOR THAT TEACHERS CAN MONITOR TO HELP IMPROVE OUTCOMES FOR STUDENTS.
ONE OF THE CHALLENGES OF INTELLIGENT SYSTEMS FOR EDUCATION IS TO USE LOW-LEVEL DATA COLLECTED IN COMPUTER ENVIRONMENTS IN THE FORM OF EVENTS OR INTERACTIONS TO INFER INFORMATION WITH HIGH-LEVEL SIGNIFICANCE USING ARTIFICIAL INTELLIGENCE TECHNIQUES, AND PRESENT IT THROUGH VISUALIZATIONS IN A MEANINGFUL AND EFFECTIVE WAY. AMONG THIS INFORMATION, EMOTIONAL DATA IS GAINING TRACK IN BY INSTRUCTORS IN THEIR EDUCATIONAL ACTIVITIES. MANY BENEFITS CAN BE OBTAINED IF AN INTELLIGENT SYSTEMS CAN BRING TEACHERS WITH KNOWLEDGE ABOUT THEIR LEARNER'S EMOTIONS, LEARNING CAUSES, AND LEARNING RELATIONSHIPS WITH EMOTIONS. IN THIS PAPER, WE PROPOSE AND JUSTIFY A SET OF VISUALIZATIONS FOR AN INTELLIGENT SYSTEM TO PROVIDE AWARENESS ABOUT THE EMOTIONS OF THE LEARNERS TO THE INSTRUCTOR BASED ON THE LEARNERS' INTERACTIONS IN THEIR COMPUTERS. WE APPLY THESE LEARNER'S AFFECTIVE VISUALIZATIONS IN A PROGRAMMING COURSE AT UNIVERSITY LEVEL WITH MORE THAN 300 STUDENTS, AND ANALYZE AND INTERPRET THE STUDENT'S EMOTIONAL RESULTS IN CONNECTION WITH THE LEARNING PROCESS. (C) 2013 ELSEVIER LTD. ALL RIGHTS RESERVED.
AUTOMATED TRACKING OF PHYSICAL FITNESS HAS SPARKED A HEALTH REVOLUTION BY ALLOWING INDIVIDUALS TO TRACK THEIR OWN PHYSICAL ACTIVITY AND HEALTH IN REAL TIME. THIS CONCEPT IS BEGINNING TO BE APPLIED TO TRACKING OF COGNITIVE LOAD. IT IS WELL KNOWN THAT ACTIVITY IN THE BRAIN CAN BE MEASURED THROUGH CHANGES IN THE BODY'S PHYSIOLOGY, BUT CURRENT REAL-TIME MEASURES TEND TO BE UNIMODAL AND INVASIVE. WE THEREFORE PROPOSE THE CONCEPT OF A WEARABLE EDUCATIONAL FITNESS (EDUFIT) TRACKER. WE USE MACHINE LEARNING WITH PHYSIOLOGICAL DATA TO UNDERSTAND HOW TO DEVELOP A WEARABLE DEVICE THAT TRACKS COGNITIVE LOAD ACCURATELY IN REAL TIME. IN AN INITIAL STUDY, WE FOUND THAT BODY TEMPERATURE, SKIN CONDUCTANCE, AND HEART RATE WERE ABLE TO DISTINGUISH BETWEEN (I) A PROBLEM SOLVING ACTIVITY (HIGH COGNITIVE LOAD), (II) A LEISURE ACTIVITY (MODERATE COGNITIVE LOAD), AND (III) DAYDREAMING (LOW COGNITIVE LOAD) WITH HIGH ACCURACY IN THE TEST DATASET. IN A SECOND STUDY, WE FOUND THAT THESE PHYSIOLOGICAL FEATURES CAN BE USED TO PREDICT ACCURATELY USER-REPORTED MENTAL FOCUS IN THE TEST DATASET, EVEN WHEN RELATIVELY SMALL NUMBERS OF TRAINING DATA WERE USED. WE EXPLAIN HOW THESE FINDINGS INFORM THE DEVELOPMENT AND IMPLEMENTATION OF A WEARABLE DEVICE FOR TEMPORAL TRACKING AND LOGGING A USER'S LEARNING ACTIVITIES AND COGNITIVE LOAD.
CONCEPTUAL MODELING IS AN IMPORTANT PART OF ENTERPRISE MODELING, WHICH IS A CHALLENGING FIELD FOR BOTH TEACHERS AND LEARNERS. CREATING CONCEPTUAL MODELS IS A SO-CALLED 'ILL-STRUCTURED' TASK, I.E. MULTIPLE GOOD SOLUTIONS ARE POSSIBLE, AND THUS STUDENTS CAN FOLLOW VERY DISTINCT MODELING PROCESSES TO ACHIEVE SUCCESSFUL LEARNING OUTCOMES. NEVERTHELESS, IT IS POSSIBLE THAT SOME PRINCIPLES OF MODELING BEHAVIOR ARE MORE TYPICAL FOR HIGH-PERFORMING RATHER THAN LOW-PERFORMING STUDENTS, AND VICE VERSA. IN THIS STUDY, WE AIMED TO DISCOVER THOSE PATTERNS BY ANALYZING LOGGED STUDENT MODELING BEHAVIOR WITH PROCESS MINING, A SET OF TOOLS FOR DEALING WITH EVENT-BASED DATA. WE ANALYZED DATA FROM TWO INDIVIDUAL CONCEPTUAL MODELING ASSIGNMENTS IN THE JMERMAID MODELING ENVIRONMENT BASED ON THE MERODE METHOD. THE STUDY IDENTIFIED THE PRESENCE OF BEHAVIORAL PATTERNS IN THE MODELING PROCESS THAT ARE INDICATIVE FOR BETTER/WORSE LEARNING OUTCOMES, AND SHOWED WHAT THESE PATTERNS ARE. ANOTHER IMPORTANT FINDING IS THAT STUDENTS' PERFORMANCE IN INTERMEDIATE ASSIGNMENTS IS AS WELL INDICATIVE OF THEIR PERFORMANCE IN THE WHOLE COURSE. THUS, PREDICTING THESE PROBLEMS AS EARLY AS POSSIBLE CAN HELP TEACHERS TO SUPPORT STUDENTS AND CHANGE THEIR FINAL OUTCOMES TO BETTER ONES.
FORUM MESSAGES IN MOOCS (MASSIVE OPEN ONLINE COURSES) ARE THE MOST IMPORTANT SOURCE OF INFORMATION ABOUT THE SOCIAL INTERACTIONS HAPPENING IN THESE COURSES. FORUM MESSAGES CAN BE ANALYZED TO DETECT PATTERNS AND LEARNERS' BEHAVIORS. PARTICULARLY, SENTIMENT ANALYSIS (E.G., CLASSIFICATION IN POSITIVE AND NEGATIVE MESSAGES) CAN BE USED AS A FIRST STEP FOR IDENTIFYING COMPLEX EMOTIONS, SUCH AS EXCITEMENT, FRUSTRATION OR BOREDOM. THE AIM OF THIS WORK IS TO COMPARE DIFFERENT MACHINE LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS, USING A REAL CASE STUDY TO CHECK HOW THE RESULTS CAN PROVIDE INFORMATION ABOUT LEARNERS' EMOTIONS OR PATTERNS IN THE MOOC. BOTH SUPERVISED AND UNSUPERVISED (LEXICON-BASED) ALGORITHMS WERE USED FOR THE SENTIMENT ANALYSIS. THE BEST APPROACHES FOUND WERE RANDOM FOREST AND ONE LEXICON BASED METHOD, WHICH USED DICTIONARIES OF WORDS. THE ANALYSIS OF THE CASE STUDY ALSO SHOWED AN EVOLUTION OF THE POSITIVITY OVER TIME WITH THE BEST MOMENT AT THE BEGINNING OF THE COURSE AND THE WORST NEAR THE DEADLINES OF PEER-REVIEW ASSESSMENTS.
ALTHOUGH MASSIVE OPEN ONLINECOURSE(MOOC) SYSTEMS HAVE BECOME MORE PREVALENT IN RECENT YEARS, ASSOCIATED STUDENT ATTRITION RATES ARE STILL A MAJOR DRAWBACK. IN THE PAST DECADE, MANY RESEARCHERS HAVE SOUGHT TO EXPLORE THE REASONS BEHIND LEARNER ATTRITION OR LACK OF INTEREST. A GROWING BODY OF LITERATURE RECOGNISES THE IMPORTANCE OF THE EARLY PREDICTION OF STUDENT ATTRITION FROM MOOCS, SINCE IT CAN LEAD TO TIMELY INTERVENTIONS. AMONG THEM, MOST ARE CONCERNED WITH IDENTIFYING THE BEST FEATURES FOR THE ENTIRE COURSE DROPOUT PREDICTION. THIS STUDY FOCUSES ON INNOVATIONS IN PREDICTING STUDENT DROPOUT RATES BY EXAMINING THEIR NEXT-WEEK-BASED LEARNING ACTIVITIES AND BEHAVIOURS. THE STUDY IS BASED ON MULTIPLE MOOC PLATFORMS INCLUDING 251,662 STUDENTS FROM 7 COURSES WITH 29 RUNS SPANNING IN 2013 TO 2018. THIS STUDY AIMS TO BUILD A GENERALISED EARLY PREDICTIVE MODEL FOR THE WEEKLY PREDICTION OF STUDENT COMPLETION USING MACHINE LEARNING ALGORITHMS. IN ADDITION, THIS STUDY IS THE FIRST TO USE A 'LEARNER'S JUMPING BEHAVIOUR' AS A FEATURE, TO OBTAIN A HIGH DROPOUT PREDICTION ACCURACY.
PUPILS ARE OFTEN FIRST EXPOSED TO PROGRAMMING IN BLOCK-BASED PROGRAMMING ENVIRONMENTS LIKE SCRATCH. IDENTIFYING AND MEASURING THE PREVIOUS EXPERIENCE OF STUDENTS LEARNING TO PROGRAM IS A KEY TO IMPROVE THE TEACHING OF PROGRAMMING. IN THIS CONTRIBUTION, WE OUTLINE AN APPROACH TO MEASURE AND EVALUATE PROGRAMMING INTERACTIONS WITH THE BLOCK-BASED PROGRAMMING ENVIRONMENT SCRATCH. FIRST RESULTS, OBTAINED WITH EIGHT UPPER SECONDARY SCHOOL STUDENTS, SHOW THAT PROGRAMMING SKILLS AND PATTERNS CAN BE QUANTIFIED WITH INTERACTION METRICS MEASURED DURING PROGRAM CONSTRUCTION. THE AIM IS A MORE FINE-GRAINED IDENTIFICATION AND ASSESSMENT OF PROGRAMMING SKILLS.
THE SIZE AND COMPLEXITY OF MOOC DATA PRESENT OVERWHELMING CHALLENGES TO MANY INSTITUTIONS. THIS PAPER DETAILS THE FUNCTIONALITY OF EDX2BIGQUERY - AN OPEN SOURCE PYTHON PACKAGE DEVELOPED BY HARVARD AND MIT TO INGEST AND REPORT ON HUNDREDS OF MITX AND HARVARDX COURSE DATASETS FROM EDX, MAKING USE OF GOOGLE BIGQUERY TO HANDLE MULTIPLE TERABYTES OF LEARNER DATA. FOR THIS APPLICATION, WE FIND THAT GOOGLE BIGQUERY PROVIDES EASE OF USE IN LOADING THE MULTI-FACETED MOOC DATASETS AND NEAR REAL-TIME INTERACTIVE QUERYING OF DATA, INCLUDING LARGE CLICKSTREAM DATASETS; MOREOVER, WE ARE ABLE TO PROVIDE FLEXIBLE RESEARCH AND REPORTING DASHBOARDS, VISUALIZING AND AGGREGATING DATA, BY INTERFACING SERVICES ASSOCIATED WITH BIGQUERY. THIS FRAMEWORK MAKES IT FEASIBLE FOR EDX2BIGQUERY TO BE OPEN SOURCE, FOLLOWING STANDARDS WHICH EMPHASIZE THE IMPORTANCE OF DATA PRODUCTS THAT TRANSCEND A PARTICULAR DATA SCIENCE PLATFORM AND ALLOW TEAMS WITH DIVERSE BACKGROUNDS TO INTERACT WITH DATA. EDX2BIGQUERY IS BEING ADOPTED BY OTHER INSTITUTIONS WITH AN AIM TOWARD FUTURE COLLABORATION.
IN THIS WORK, THE DYNAMICS OF INTEREST TOPICS IN WEB-BASED EDUCATIONAL ENVIRONMENTS ARE STUDIED BY ANALYSING THE STUDENTS' INTERACTIONS DURING THE TEACHING/LEARNING PROCESS. FORUMS ARE THE MAIN TOOLS TO OBSERVE THESE INTERACTIONS FOR DISTANCE LEARNING. SPECIFIC TECHNIQUES ARE NEEDED FOR ANALYSING THE LARGE AMOUNT OF DATA GENERATED IN THE COURSE, WHICH ALLOWS FACULTY MAKING INTERESTING CONCLUSIONS TO IMPROVE IT. IN PARTICULAR, WE PROPOSE A SET OF ALGORITHMS TO DISCOVER RELEVANT TOPICS (AND SUBTOPICS) APPEARED IN THE FORUMS' MESSAGES. THE RESULTS OBTAINED ARE ALSO DISCUSSED IN ORDER TO FIND OUT PATTERNS OF BEHAVIOUR.
HIGHER EDUCATION INSTITUTIONS HAVE ACCESS TO HIGHER VOLUMES AND A GREATER VARIETY AND GRANULARITY OF STUDENT DATA, OFTEN IN REAL-TIME, THAN EVER BEFORE. AS SUCH, THE COLLECTION, ANALYSIS AND USE OF STUDENT DATA ARE INCREASINGLY CRUCIAL IN OPERATIONAL AND STRATEGIC PLANNING, AND IN DELIVERING APPROPRIATE AND EFFECTIVE LEARNING EXPERIENCES TO STUDENTS. STUDENT DATA - NOT ONLY IN WHAT DATA IS (NOT) COLLECTED, BUT ALSO HOW THE DATA IS FRAMED AND USED - HAS MATERIAL AND DISCURSIVE EFFECTS, BOTH PERMANENT AND FLEETING. WE HAVE TO CRITICALLY ENGAGE CLAIMS THAT ARTIFICIAL INTELLIGENCE AND THE EVER EXPANSIVE/EXPANDING SYSTEMS OF ALGORITHMIC DECISION-MAKING PROVIDE SPEEDY, ACCESSIBLE, REVEALING, PANORAMIC, PROPHETIC AND SMART ANALYSES OF STUDENTS' RISKS, POTENTIAL AND LEARNING NEEDS. WE NEED TO PRY OPEN THE BLACK BOXES HIGHER EDUCATION INSTITUTIONS (AND INCREASINGLY VENTURE CAPITAL AND LEARNING MANAGEMENT SYSTEM PROVIDERS) USE TO ADMIT, STEER, PREDICT AND PRESCRIBE STUDENTS' LEARNING JOURNEYS.
AUTOMATICALLY ASSESSED EXERCISES WITH IMMEDIATE FEEDBACK CAN BE A POWERFUL TOOL FOR ENHANCING THE EFFECTIVENESS OF EDUCATION. IN THIS ARTICLE, WE DISCUSS THE DESIGN AND IMPLEMENTATION OF A COLLABORATIVE LEARNING TOOL CALLED VILLE. THE DESIGN IS BASED ON EXPERIENCES GATHERED FROM A PREVIOUSLY DEVELOPED AND THOROUGHLY RESEARCHED VISUALIZATION TOOL. BASED ON OUR EARLIER RESULTS AND CURRENT EDUCATIONAL THEORIES, WE DEVELOPED FOUR DESIGN PRINCIPLES UPON WHICH VILLE IS CONSTRUCTED. VILLE INCLUDES VARIOUS DIFFERENT EXERCISE TYPES WHICH WERE DESIGNED TO ASSIST IN THE LEARNING OF COMPUTER SCIENCE, MATHEMATICS AND OTHER SUBJECTS. IT ALSO SUPPORTS DIFFERENT LEARNING AND TEACHING METHODS, SUCH AS PAIR PROGRAMMING AND PEER REVIEW. TO JUSTIFY THE DEVELOPMENT OF A NEW ENVIRONMENT, WE PRESENT FOUR DIVERSE CASE STUDIES WHERE VILLE WAS UTILIZED SUCCESSFULLY: PROGRAMMING EDUCATION, HIGH SCHOOL MATRICULATION EXAM, ELEMENTARY SCHOOL MATHEMATICS AND STUDENT COUNSELING AT UNIVERSITY LEVEL. THE RESULTS OBTAINED FROM THE STUDIES SEEM TO CONFIRM THAT VILLE CAN BE USED EFFECTIVELY TO ENHANCE STUDENT MOTIVATION AND TO IMPROVE STUDENT PERFORMANCE IN VARIOUS HETEROGENEOUS EDUCATIONAL SETUPS.
WORD LEARNING IS THE ONE OF MOST FUNDAMENTAL STEPS FOR ENGLISH AS SECOND LANGUAGE (ESL) STUDENTS. HOWEVER, IT IS A CHALLENGE TO UNDERSTAND EACH INDIVIDUAL STUDENT TO PROVIDE THEM WITH PERSONALIZED LEARNING EXPERIENCE, SINCE IT IS DIFFICULT TO IDENTIFY VARIOUS VARIABLES LIKE LEARNING STYLE, LANGUAGE PROFICIENCY, KNOWLEDGE BACKGROUND FOR EACH LEARNER IN AN E-LEARNING SYSTEM. TO ADDRESS THIS ISSUE, WE PROPOSE AN INTEGRATED FRAMEWORK TO DEPICT THE INDIVIDUAL VOCABULARY PROFICIENCY BY USING THE IMPLICIT AND EXPLICIT PROFILES. THE EXPLICIT USER PROFILING GIVES THE CONTROL OF USERS TO SPECIFY AND MODIFY THEIR VOCABULARY PROFICIENCY SO THAT THE USERS WILL HAVE A SENSE OF OWNERSHIP OF THEIR PERSONALIZED DATA, WHEREAS THE IMPLICIT USER PROFILING IS A NON-INTRUSIVE METHOD WHICH CAN RECORD AND UPDATE THE PROFICIENCY OF LEARNERS WITHOUT EXPLICIT MANUALLY INPUT FROM USERS. IN THIS PAPER, WE ATTEMPT TO INTEGRATE THESE TWO KINDS OF PROFILES TO BUILD A POWERFUL LEARNER PROFILE FOR PERSONALIZED VOCABULARY LEARNING. FURTHERMORE, THE PRELIMINARY EMPIRICAL RESULTS VALIDATE THE EFFECTIVENESS OF THE INTEGRATED FRAMEWORK.
THE USE OF MOBILE DEVICES, INCLUDING MOBILE PHONES AND TABLETS, IS A GROWING TREND IN EDUCATION. WIDESPREAD ACCESS TO MOBILE DEVICES AND THE OPPORTUNITY TO LEARN REGARDLESS OF TIME AND PLACE MAKE MOBILE LEARNING AN IMPORTANT TOOL FOR LIFELONG LEARNING. NEVERTHELESS M-LEARNING IS STILL IN ITS INFANCY AND GREAT EFFORTS NEED TO BE DONE SO AS TO INVESTIGATE THE POTENTIALS OF AN EDUCATIONAL PARADIGM SHIFT FROM THE TRADITIONAL ONE-SIZE-FITS-ALL TEACHING APPROACHES TO ADAPTIVE AND PERSONALIZED LEARNING THAT CAN BE DELIVERED VIA MOBILE DEVICES. IN ORDER TO ACHIEVE THIS, AN EXTENSIVE REVIEW OF LITERATURE WILL NEED TO BE CONDUCTED TO DEFINE A FRAMEWORK THAT SUPPORTS BOTH DESIGN AND DELIVERY APPROACHES OF THE LEARNING DESIGN VIA THE MODEL OF FLIPPED CLASSROOM, USING CASE-BASED LEARNING. SOME OTHER FACTORS THAT NEED TO BE EXAMINED INCLUDE THE ADAPTATION AND MANAGEMENT OF THE CONTENT PRODUCED AND ACQUIRED, AS WELL AS THE LINK BETWEEN MOBILE DEVICE AFFORDANCES AND LEARNING METHODOLOGIES. IN THIS WORK WE DEFINE CERTAIN AREAS THAT CAN HELP DEFINE SUCH A FRAMEWORK AND PROPOSE AN ARCHITECTURE THAT CAN HELP INCORPORATE AND EVALUATE THEM.
MANY RESEARCHERS STUDY TO DETERMINE THE LEARNING BEHAVIOR REGARDING THE ACQUISITION OF KNOWLEDGE IN E-LEARNING FROM THE LEARNING LOG IN LMS. THIS STUDY FOCUSED ON PROCRASTINATION TYPE LEARNERS AND TRIED TO DISTINGUISH BETWEEN PASSIVE AND ACTIVE PROCRASTINATION TYPES FROM THE LEARNING LOGS. CHARACTERISTIC VECTORS WERE GENERATED FROM THE LEARNING LOG AND DIVIDED INTO TWO GROUPS VIA UNSUPERVISED CLUSTERING. THE GROUP THAT ACCESSED THE ONLINE TEXTBOOK THE HIGHEST NUMBER OF TIMES WITHIN THE TIME FRAME OF THE EXAM WERE DESIGNATED AS ACTIVE PROCRASTINATION TYPE. ACTIVE PROCRASTINATION TYPE STUDENTS HAD HIGHER AVERAGE EXAM SCORES THAN PASSIVE PROCRASTINATION TYPE STUDENTS. THE RESULTS SUGGEST THAT THE NUMBER OF TIMES THAT STUDENTS ACCESSED THE ONLINE TEXTBOOK AND EXAM DURING A PERIOD WHEN STUDENTS WERE TAKING THE EXAM WERE EFFECTIVE IN DISTINGUISHING BETWEEN PASSIVE AND ACTIVE PROCRASTINATION TYPES.
PREDICTION OF STUDENT'S PERFORMANCE BECAME AN URGENT DESIRE IN MOST OF EDUCATIONAL ENTITIES AND INSTITUTES. THAT IS ESSENTIAL IN ORDER TO HELP AT-RISK STUDENTS AND ASSURE THEIR RETENTION, PROVIDING THE EXCELLENT LEARNING RESOURCES AND EXPERIENCE, AND IMPROVING THE UNIVERSITY'S RANKING AND REPUTATION. HOWEVER, THAT MIGHT BE DIFFICULT TO BE ACHIEVED FOR STARTUP TO MID-SIZED UNIVERSITIES, ESPECIALLY THOSE WHICH ARE SPECIALIZED IN GRADUATE AND POST GRADUATE PROGRAMS, AND HAVE SMALL STUDENTS' RECORDS FOR ANALYSIS. SO, THE MAIN AIM OF THIS PROJECT IS TO PROVE THE POSSIBILITY OF TRAINING AND MODELING A SMALL DATASET SIZE AND THE FEASIBILITY OF CREATING A PREDICTION MODEL WITH CREDIBLE ACCURACY RATE. THIS RESEARCH EXPLORES AS WELL THE POSSIBILITY OF IDENTIFYING THE KEY INDICATORS IN THE SMALL DATASET, WHICH WILL BE UTILIZED IN CREATING THE PREDICTION MODEL, USING VISUALIZATION AND CLUSTERING ALGORITHMS. BEST INDICATORS WERE FED INTO MULTIPLE MACHINE LEARNING ALGORITHMS TO EVALUATE THEM FOR THE MOST ACCURATE MODEL. AMONG THE SELECTED ALGORITHMS, THE RESULTS PROVED THE ABILITY OF CLUSTERING ALGORITHM IN IDENTIFYING KEY INDICATORS IN SMALL DATASETS. THE MAIN OUTCOMES OF THIS STUDY HAVE PROVED THE EFFICIENCY OF SUPPORT VECTOR MACHINE AND LEARNING DISCRIMINANT ANALYSIS ALGORITHMS IN TRAINING SMALL DATASET SIZE AND IN PRODUCING AN ACCEPTABLE CLASSIFICATION'S ACCURACY AND RELIABILITY TEST RATES.
THE TRADITIONAL APPROACH FOLLOWED BY TUTORS TO ASSESS THE STUDENTS IS THROUGH A SET OF QUESTIONS. THE QUALITY OF A QUESTION BANK HAS AN IMPACT ON THE EFFECTIVENESS OF EVALUATION IN EDUCATIONAL INSTITUTIONS. DETERMINING THE COVERAGE OF THESE QUESTIONS WITH RESPECT TO A SET OF PRESCRIBED TEXT/REFERENCE BOOKS HELPS IN EVALUATING STUDENTS EFFICIENTLY. IN THIS PAPER, WE DESCRIBE A TUTOR ASSISTING E-FRAMEWORK (TAEF) THAT ENABLES THE TUTORS TO ANALYZE THE QUALITY OF A QUESTION BANK. INITIALLY, IT CLUSTERS ALL INDIVIDUAL TOPICS OF EACH OF THE INPUT TEXT/REFERENCE BOOKS ACCORDING TO THEIR DEPENDENCIES. LATER, THE QUESTIONS ARE CLASSIFIED INTO THESE TOPICS. THE RESULT IS A SET OF TOPICS, EACH CONTAINING THE TOPIC TITLE AND THE PROBABILITY BY WHICH THE QUESTION IS RELATED TO IT. LOWER THE ACCURACY OF THE PREDICTED TOPICS, HIGHER IS THE QUALITY OF THE QUESTION. IN OTHER WORDS, IF QUESTION CONTAINS THE TOPIC TITLE UNALTERED, IT HAS A HIGHER PROBABILITY OF BEING RELATED TO THE TOPIC; THIS DEGRADES THE QUALITY OF QUESTION. FURTHERMORE, THE CONGRUENCE RELATION BETWEEN THE QUESTIONS AND THE SET OF TOPICS IS FOUND. THIS GIVES THE QUESTION COVERAGE OF EACH TOPIC. FINALLY, WITH THIS RELATION, THE PERCENTAGE OF UNDERSTANDING THE STUDENTS HAVE DEVELOPED IN EACH OF THESE TOPICS IS COMPUTED. THE TUTOR ASSISTING E-FRAMEWORK (TAEF) HELPS TO IMPROVE THE QUALITY OF A QUESTION BANK, TO CHECK THE TOPICS COVERED BY EACH QUESTION AND THE KNOWLEDGE GAINED. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
THIS ARTICLE INVESTIGATES THE RELATIONSHIP BETWEEN THE USE OF LIBRARY RESOURCES AND LEARNING OUTCOMES IN A CHILEAN RESEARCH-CENTERED UNIVERSITY BY ANALYZING DATA FROM TWO CONSECUTIVES SEMESTERS OF STUDENT RECORDS, LIBRARY BORROWING, AND ACCESS TO ELECTRONIC RESOURCES THROUGH THE LIBRARY. RESULTS SHOW THAT THE ACCESS TO ELECTRONIC RESOURCES HAS A GREATER IMPACT UPON PERFORMANCE THAN THE NUMBER OF LIBRARY ITEMS BORROWED. THEY ALSO SHOW THAT AN INCREASE IN THE NUMBER OF SESSIONS DEDICATED TO ACCESSING ELECTRONIC RESOURCES WAS ACCOMPANIED BY A DECREASE IN THE NUMBER OF LIBRARY ITEMS BORROWED AS STUDENTS PROGRESS IN THEIR DEGREES. FURTHER ANALYSES SHOWED THAT STUDENTS' BEHAVIOR IS ATTRIBUTED MORE TO THE REQUIREMENTS OF ADVANCED COURSES (COMMONLY ENCOUNTERED IN LATER YEARS OF THEIR DEGREES) THAN TO PERSONAL PREFERENCES. THIS RELATIONSHIP BETWEEN STUDENT RECORDS AND LIBRARY SERVICES ENLIGHTENS THE IMPACT OF DIFFERENT LIBRARY RESOURCES ON STUDENT LEARNING AND OFFERS EVIDENCE TO RATIONALIZE LIBRARY INVESTMENTS ACCORDING TO THEIR NEEDS AND IMPACTS. (C) 2016 ELSEVIER INC. ALL RIGHTS RESERVED.
THIS STUDY EXAMINES THE EFFECT OF REGULATION FEEDBACK IN A COMPUTER-BASED FORMATIVE ASSESSMENT IN THE CONTEXT OF SEARCHING FOR INFORMATION ONLINE. FIFTY 13-YEAR-OLD STUDENTS COMPLETED TWO RANDOMLY SELECTED ASSESSMENT TASKS, RECEIVING AUTOMATED REGULATION FEEDBACK BETWEEN THEM. STUDENT PERFORMANCE WAS (SELF-)GRADED BY STUDENTS AND BY EXPERTS. EXPERT, AS WELL AS STUDENT (SELF)GRADES SHOWED A SIGNIFICANT INCREASE BETWEEN TASK 1 AND TASK 2. HOWEVER, FURTHER ANALYSIS OF THE EXPERT GRADES SHOWED SIGNIFICANT IMPROVEMENT IN PERFORMANCE FOR GIRLS ONLY. FURTHERMORE, THE FORMATIVE ASSESSMENT SYSTEM TRACED THE NUMBER OF SEARCHES AND THE NUMBER OF WEBSITES CONSULTED PER STUDENT TO COMPLETE THE TWO ASSIGNMENTS. ON AVERAGE, THE RESULTS SHOWED THAT STUDENTS CONSULTED SIGNIFICANTLY MORE WEBSITES FOR TASK 2, COMPARED TO TASK 1. THE AVERAGE NUMBER OF SEARCHES DID NOT DIFFER SIGNIFICANTLY BETWEEN TASKS 1 AND 2. ON THE OTHER HAND, SIGNIFICANT DIFFERENCES WERE FOUND FOR THOSE STUDENTS WHO, DURING THE EVALUATION OF THEIR PERFORMANCE ON TASK 1, EXPLICITLY STATED THAT THEY WOULD INCREASE THEIR SEARCHES. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE CONTRIBUTION FOCUSES ON THE ROLE OF THE TUTOR IN ONLINE COURSES ALSO IN RELATIONSHIP TO RECENT ITALIAN REGULATION MINISTERIAL DECREE N. 6/2019 ("AUTO-EVALUATION, EVALUATION, INITIAL AND PERIODIC ACCREDITATION OF THE VENUES AND COURSES OF STUDY"), THAT HAS INTRODUCED CONCRETE INDICATIONS ON THE PRESENCE OF TUTORS IN DISTANCE LEARNING COURSES. IN THE FIRST PART, THE STUDY EXAMINES THE EVOLUTION AND SKILLS OF THE TUTOR, WITH RELATION TO THE INTERNATIONAL DEBATE ON THE SPREADING OF DISTANCE LEARNING. THE SECOND PART CONCERNS AN EXPLORATORY SURVEY CONDUCTED WITH THE AIM OF COLLECT THE OPINIONS AND SATISFACTION LEVELS OF INSTRUCTORS AND TUTORS ON THE TOOLS USED TO MONITOR LEARNING AND SUPPORT STUDENTS IN ONLINE COURSES (MOOCS) ON EDUOPEN PORTAL (HTTPS://LEARN.EDUOPEN.ORGL). THE NEED TO STRENGTHEN AND RETHINK THE ROLE OF THE TUTOR (GREATER PROFESSIONAL RECOGNITION) HAS INCREASED, PARTICULARLY IN THE CONTEXT OF DISTANCE LEARNING; IN MANY CASES THE TUTOR IS THE MAIN INTERLOCUTOR OF THE STUDENTS AND AS A SUPPORT FIGURE FOR THE TEAM OF INSTRUCTORS IS AT THE CORE OF PROCESSES OF DIDACTIC INNOVATION.
USING ONLINE LEARNING COMMUNITIES (OLCS) FROM THE INTERNET AS INFORMAL LEARNING ENVIRONMENTS RAISES THE QUESTION HOW LIKELY THESE COMMUNITIES WILL INTEGRATE LEARNERS AS NEW MEMBERS, I.E., HOW INTEGRATIVE THESE OLCS WILL BE. SUCH PREDICTION IS THE PURPOSE OF THE CURRENT STUDY. TO ACHIEVE THIS, AN IDENTIFICATION METHOD OF THE CENTRAL, INTERMEDIATE, AND PERIPHERAL OLC LAYERS IS PROPOSED. BASED ON THE CSCL APPROACHES OF VOICES INTERANIMATION AND POLYPHONY, AN ADVANCED NATURAL LANGUAGE PROCESSING FRAMEWORK WAS EMPLOYED FOR DIALOG ANALYSIS IN N = 20 INTEGRATIVE VS. NON -INTEGRATIVE BLOG-BASED OLCS INVOLVING 2342 USERS OVER ONE YEAR. HIERARCHICAL CLUSTERS BUILT UPON COMMUNICATIVE CENTRALITY REFLECT SOCIO-COGNITIVE STRUCTURES INCLUDING CENTRAL, INTERMEDIATE, AND PERIPHERAL OLC MEMBERS. THE RESULTING CLUSTERS WERE ASSIGNED TO THE CENTRAL, INTERMEDIATE AND PERIPHERAL COMMUNITY LAYERS WITH 55-100% CONSISTENCY, WHEREAS MOST CONSISTENT IDENTIFICATION CRITERION OF THE SOCIO-COGNITIVE OLC STRUCTURES WAS THE OUTDEGREE CENTRALITY, FOLLOWED BY THE BLOG OWNER INCLUSION, THE NUMBERS OF PARTICIPANTS, ECCENTRICITY AND INDEGREE CENTRALITY. SUBSEQUENTLY, OLC INTEGRATIVITY WAS PREDICTED WITH UP TO 90% ACCURACY BASED ON TOPIC COMPLEXITY, SOCIO-COGNITIVE STRUCTURE, AND AUTOMATICALLY ASSESSED DIALOG CHARACTERISTICS. CONSEQUENCES FOR FURTHER RESEARCH AND EDUCATIONAL PRACTICE ARE DISCUSSED.
PRECISION EDUCATION IS A NEW CHALLENGE IN LEVERAGING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND LEARNING ANALYTICS TO ENHANCE TEACHING QUALITY AND LEARNING PERFORMANCE. TO FACILITATE PRECISION EDUCATION, TEXT MARKING SKILLS CAN BE USED TO DETERMINE STUDENTS' LEARNING PROCESS. TEXT MARKING IS AN ESSENTIAL LEARNING SKILL IN READING. IN THIS STUDY, WE PROPOSED A MODEL THAT LEVERAGES THE STATE-OF-THE-ART TEXT SUMMARIZATION TECHNIQUE, BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BERT), TO CALCULATE THE MARKING SCORE FOR 130 GRADUATE STUDENTS ENROLLED IN AN ACCOUNTING COURSE. THEN, WE APPLIED LEARNING ANALYTICS TO ANALYZE THE CORRELATION BETWEEN THEIR MARKING SCORES AND LEARNING PERFORMANCE. WE MEASURED STUDENTS' SELF-REGULATED LEARNING (SRL) AND CLUSTERED THEM INTO FOUR GROUPS BASED ON THEIR MARKING SCORES AND MARKING FREQUENCIES TO EXAMINE WHETHER DIFFERENCES IN READING SKILLS AND TEXT MARKING INFLUENCE STUDENTS' LEARNING PERFORMANCE AND AWARENESS OF SELF-REGULATION. CONSISTENT WITH PAST RESEARCH, OUR RESULTS DID NOT INDICATE A STRONG RELATIONSHIP BETWEEN MARKING SCORES AND LEARNING PERFORMANCE. HOWEVER, HIGH-SKILL READERS WHO USE MORE MARKING STRATEGIES PERFORM BETTER IN LEARNING PERFORMANCE, TASK STRATEGIES, AND TIME MANAGEMENT THAN HIGH-SKILL READERS WHO USE FEWER MARKING STRATEGIES. FURTHERMORE, HIGH-SKILL READERS WHO ACTIVELY EMPLOY MARKING STRATEGIES ALSO ACHIEVE SUPERIOR SCORES OF ENVIRONMENT STRUCTURE, AND TASK STRATEGIES IN SRL THAN LOW-SKILL READERS WHO ARE INACTIVE IN MARKING. THE FINDINGS OF THIS RESEARCH PROVIDE EVIDENCE SUPPORTING THE IMPORTANCE OF MONITORING AND TRAINING STUDENTS' TEXT MARKING SKILL AND FACILITATING PRECISION EDUCATION.
THIS DATA ARTICLE REPRESENTS ACADEMIC PERFORMANCES AND PERSONAL HABITS OF HIGHER SECONDARY SCHOOL STUDENTS IN SELECT HIGH SCHOOL EDUCATION INSTITUTIONS LOCATED AT GUNTUR, ANDHRA PRADESH, INDIA FOR THE ACADEMIC YEAR OF 2017-18. THE DATASET OF 1116 RECORDS, EACH WITH 9 PARAMETERS AND THE PARAMETERS ARE MOTHER AND FATHER EDUCATION, THE IMPACT OF ADVISOR, TIME SPENT ON STUDY AFTER SCHOOL, TIME SPENT ON SPORTS, TIME SPENT WITH MOBILE PER DAY, THE IMPACT OF HEALTH PROBLEMS, GOAL, TIME SPENT ON YOGA OR PHYSICAL EXERCISE. WE PRESENT DESCRIPTIVE STATISTICS SHOWING MEAN, MEDIAN, MODE, MAXIMUM, MINIMUM, RANGE, STANDARD DEVIATION AND VARIANCE IN THE PERFORMANCES OF THESE STUDENTS AND A BAR CHART REPRESENTATION OF THE TOTAL NUMBER OF FEMALE AND MALE STUDENTS' GRADE POINT AVERAGE (GPA) WITH 9 PARAMETERS INDIVIDUALLY. THE PRIMARY AIM OF THIS CLAUSE IS PROVIDING A COMPARATIVE ANALYSIS OF FEMALE AND MALE STUDENTS IN GPA SCORING BASED ON 9 PARAMETERS. (C) 2020 THE AUTHOR(S). PUBLISHED BY ELSEVIER INC.
EFFECTIVE COLLABORATION BETWEEN STUDENT PEERS IS NOT SPONTANEOUS. A SYSTEM THAT CAN MEASURE COLLABORATION IN REAL-TIME MAY BE USEFUL, AS IT COULD ALERT AN INSTRUCTOR TO PAIRS THAT NEED HELP IN COLLABORATING EFFECTIVELY. WE TESTED WHETHER SUPERFICIAL MEASURES OF SPEECH AND USER INTERFACE ACTIONS WOULD SUFFICE FOR MEASURING COLLABORATION. PAIRS OF STUDENTS SOLVED COMPLEX MATH PROBLEMS WHILE DATA WERE COLLECTED IN THE FORM OF VERBAL INTERACTION AND USER ACTION LOGS FROM THE STUDENTS' TABLETS. WE DISTINGUISHED FOUR CLASSIFICATIONS OF INTERACTIVITY: COLLABORATION, COOPERATION, HIGH ASYMMETRIC CONTRIBUTION AND LOW ASYMMETRIC CONTRIBUTION. HUMAN CODERS USED RICHER DATA (SEVERAL VIDEO STREAMS) TO CHOOSE ONE OF THESE CODES FOR EACH EPISODE. THOUSANDS OF FEATURES WERE EXTRACTED COMPUTATIONALLY FROM THE LOG AND AUDIO DATA. MACHINE LEARNING WAS USED TO INDUCE A DETECTOR THAT ALSO ASSIGNED A CODE TO EACH EPISODE AS A FUNCTION OF THESE FEATURES. DETECTORS FOR COMBINATIONS OF CODES WERE INDUCED AS WELL. THE BEST DETECTOR'S OVERALL ACCURACY WAS 96 PERCENT (KAPPA - 0.92) COMPARED TO HUMAN CODING. THIS HIGH LEVEL OF AGREEMENT SUGGESTS THAT SUPERFICIAL FEATURES OF SPEECH AND LOG DATA DO SUFFICE FOR MEASURING COLLABORATION. HOWEVER, THESE RESULTS SHOULD BE VIEWED AS PRELIMINARY BECAUSE THE PARTICULAR TASK MAY HAVE MADE IT RELATIVELY EASY TO DISTINGUISH COLLABORATION FROM COOPERATION.
LEARNER-COMPUTER INTERACTION (LCI) RESEARCH ADDRESSES THE DESIGN, DEVELOPMENT AND USE OF INTERACTIVE TECHNOLOGIES TO SUPPORT AND AMPLIFY HUMAN LEARNING. LCI IS BASED ON THE RATIONALE THAT LEARNING WHILE INTERACTING WITH TECHNOLOGY IS A COMPLEX, MULTI-LAYERED PHENOMENON, THUS, DESIGNING THE CONDITIONS FOR ENGAGING IN MEANINGFUL LEARNING IS VITAL IN THE 21ST CENTURY, YET, IT REMAINS A CHALLENGING PROCESS. LCI DEVELOPMENTS ARE EXPECTED TO CONTRIBUTE TOWARDS A COHERENT NEW, HIGH-IMPACT WAY OF UNDERSTANDING AND BUILDING LEARNER-CENTERED INTERACTION CONCEPTS TO SUPPORT THE DESIGN OF FUTURE LEARNING ENVIRONMENTS. LCI PROVIDES AN INTERDISCIPLINARY PLAYGROUND FOR RESEARCHERS AND PROFESSIONALS ACROSS ALL AREAS OF LEARNING TECHNOLOGIES, PSYCHOLOGY, LEARNING SCIENCE AND HUMAN COMPUTER INTERACTION (NCI), WITH AN ULTIMATE OBJECTIVE OF PROVIDING A FORUM AT THE INTERSECTION OF THESE TOPICAL AREAS. LCI AIMS TO DEVELOP A CRITICAL DISCUSSION, DEBATE AND CO-DEVELOPMENT OF IDEAS AND APPROACHES ABOUT THE NEXT GENERATION OF LEARNING ENVIRONMENTS AND THEIR INTERACTION DESIGN CAPACITIES, THE FORM OF THESE CAPACITIES AND THE WAY THEY CAN BE IDENTIFIED, UTILIZED AND ENHANCED TO HELP US IMPROVE THE CONTEMPORARY LEARNING TECHNOLOGIES AND USERS' LEARNING EXPERIENCE.
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 WORKSHOP 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.
DURING THE LAST YEARS AN INCREASING NUMBER OF UNIVERSITY-LEVEL AND POST-GRADUATION COURSES ON DATA SCIENCE HAVE BEEN OFFERED. PRACTICES AND ASSESSMENTS NEED SPECIFIC LEARNING ENVIRONMENTS WHERE LEARNERS COULD PLAY WITH DATA SAMPLES AND RUN MACHINE LEARNING AND DATA MINING ALGORITHMS. TO FOSTER LEARNER ENGAGEMENT MANY CLOSED- AND OPEN-SOURCE PLATFORMS SUPPORT THE DESIGN OF DATA SCIENCE COMPETITIONS. HOWEVER, THEY SHOW LIMITATIONS ON THE ABILITY TO HANDLE PRIVATE DATA, CUSTOMIZE THE ANALYTICS AND EVALUATION PROCESSES, AND VISUALIZE LEARNERS' ACTIVITIES AND OUTCOMES. THIS PAPER PRESENTS DATA SCIENCE LAB ENVIRONMENT (DSLE, IN SHORT), A NEW OPEN-SOURCE PLATFORM TO DESIGN AND MONITOR DATA SCIENCE COMPETITIONS. DSLE OFFERS A EASILY CONFIGURABLE INTERFACE TO SHARE TRAINING AND TEST DATA, DESIGN GROUP WORKS OR INDIVIDUAL SESSIONS, EVALUATE THE COMPETITION RUNS ACCORDING TO CUSTOMIZABLE METRICS, MANAGE PUBLIC AND PRIVATE LEADERBOARDS, MONITOR PARTICIPANTS' ACTIVITIES AND THEIR PROGRESS OVER TIME. THE PAPER DESCRIBES ALSO A REAL EXPERIENCE OF USAGE OF DSLE IN THE CONTEXT OF A 1ST-YEAR M.SC. COURSE, WHICH HAS INVOLVED AROUND 160 STUDENTS.
THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) IN THE EDUCATIONAL PROCESSES HAS BROUGHT ABOUT A SERIES OF CHANGES IN STUDENTS' BEHAVIOUR WHILE RAISING NEW CHALLENGES FOR TEACHING THE ANALYSIS OF THE DATA PROVIDED BY THE LEARNING MANAGEMENT SYSTEMS (LMS) IS ESSENTIAL TO KNOW THE BEHAVIOUR OF STUDENTS AND IMPROVE EDUCATIONAL TECHNIQUES. THIS BEHAVIOUR MAY VARY DEPENDING ON THE LEVEL OF STUDIES AND THE EDUCATIONAL METHODOLOGY USED, AND THEIR KNOW LEDGE ALLOWS DETERMINING VARIOUS PARAMETERS TO GUIDE OR IMPLEMENT IMPROVEMENTS IN THE LEARNING PROCESS. THE PURPOSE OF THIS STUDY IS TO KNOW THE BEHAVIOUR OF STUDENTS WHEN CONNECTING TO THE LMS IN ONLINE STUDIES THAT FOLLOW THE TASK-BASED METHODOLOGY AT THE MASTER LEVEL.
TO IDENTIFY ANY PATTERNS IN LEARNING BEHAVIORS, LEARNERS' LEARNING BEHAVIOR DATA ARE CAPTURED AND STORED IN MANY ONLINE LEARNING PLATFORMS. IT IS CRUCIAL TO DETERMINE WHICH BEHAVIORS AND WHICH FEATURES OF A BEHAVIOR ARE MOST RELATED TO THE SPECIFIC ANALYTICS TASK. TO DO THIS, FEATURE SELECTION METHOD HAS OFTEN BEEN APPLIED TO DETERMINE A GLOBAL REDUCED FEATURE SPACE. HOWEVER, LITTLE ATTENTION HAS BEEN PAID TO SELECT THE BEHAVIORS AND FEATURES WITHIN BEHAVIOR SIMULTANEOUSLY. IN THIS WORK, WE PROPOSE A TWO-LEVEL FEATURE SELECTION METHOD WHICH CAN DETERMINE THE IMPORTANCE OF BEHAVIORS AND FEATURES SIMULTANEOUSLY. THE PROPOSED METHOD IS EMBEDDED INTO THE CLASSICAL K-MEANS TO CLUSTER A FAMOUS E-LEARNING DATASET. OUR EXPERIMENTAL RESULTS SHOW THAT THE PROPOSED METHOD IS AN EFFECTIVE WAY TO IMPROVE THE CLUSTERING PERFORMANCE SIGNIFICANTLY.
INFORMATION DASHBOARDS ARE INCREASING THEIR SOPHISTICATION TO MATCH NEW NECESSITIES AND ADAPT TO THE HIGH QUANTITIES OF GENERATED DATA NOWADAYS. THESE TOOLS SUPPORT VISUAL ANALYSIS, KNOWLEDGE GENERATION, AND THUS, ARE CRUCIAL SYSTEMS TO ASSIST DECISION-MAKING PROCESSES. HOWEVER, THE DESIGN AND DEVELOPMENT PROCESSES ARE COMPLEX, BECAUSE SEVERAL PERSPECTIVES AND COMPONENTS CAN BE INVOLVED. TAILORING CAPABILITIES ARE FOCUSED ON PROVIDING INDIVIDUALIZED DASHBOARDS WITHOUT AFFECTING THE TIME-TO-MARKET THROUGH THE DECREASE OF THE DEVELOPMENT PROCESSES' TIME. AMONG THE METHODS USED TO CONFIGURE THESE TOOLS, THE SOFTWARE PRODUCT LINES PARADIGM AND MODEL-DRIVEN DEVELOPMENT CAN BE FOUND. THESE PARADIGMS BENEFIT FROM THE STUDY OF THE TARGET DOMAIN AND THE ABSTRACTION OF FEATURES, OBTAINING HIGH-LEVEL MODELS THAT CAN BE INSTANTIATED INTO CONCRETE MODELS. THIS PAPER PRESENTS A DASHBOARD META-MODEL THAT AIMS TO BE APPLICABLE TO ANY DASHBOARD. THROUGH DOMAIN ENGINEERING, DIFFERENT FEATURES OF THESE TOOLS ARE IDENTIFIED AND ARRANGED INTO ABSTRACT STRUCTURES AND RELATIONSHIPS TO GAIN A BETTER UNDERSTANDING OF THE DOMAIN. THE GOAL OF THE META-MODEL IS TO OBTAIN A FRAMEWORK FOR INSTANTIATING ANY DASHBOARD TO ADAPT THEM TO DIFFERENT CONTEXTS AND USER PROFILES. ONE OF THE CONTEXTS IN WHICH DASHBOARDS ARE GAINING RELEVANCE IS LEARNING ANALYTICS, AS LEARNING DASHBOARDS ARE POWERFUL TOOLS FOR ASSISTING TEACHERS AND STUDENTS IN THEIR LEARNING ACTIVITIES. TO ILLUSTRATE THE INSTANTIATION PROCESS OF THE PRESENTED META-MODEL, A SMALL EXAMPLE WITHIN THIS RELEVANT CONTEXT (LEARNING ANALYTICS) IS ALSO PROVIDED.
THE NEW WAVE OF HUGE DATA SOURCES, ALSO REFERRED TO AS BIG DATA, ENTAILS NEW FORMS OF INFORMATION AND CONTENT FOR COMPUTER ASSISTED LANGUAGE LEARNING (CALL). OPEN DATA IS THE LATEST APPEAL IN THE OPEN DATA PHILOSOPHY AND TRENDS. IN THIS ARTICLE, WE EXPLORE THE AFFORDANCES OF (LINKED) OPEN DATA FOR EDUCATION IN GENERAL AND FOR CALL IN PARTICULAR. LEARNING ANALYTICS, ADAPTIVE LEARNING ENVIRONMENTS, AND OPEN EDUCATIONAL RESOURCES ARE THE OBVIOUS BENEFICIARIES, BUT CONTEXTUALIZATION OF THE LEARNING PROCESS APPEARS TO BE A RELATIVELY UNEXPLORED AFFORDANCE. AND THIS WHILE AVAILABLE LITERATURE ALREADY TOUTS ITS MERITS.
THIS DEMO PRESENTS ATTENTIVELEARNER, A MOBILE LEARNING SYSTEM OPTIMIZED FOR CONSUMING LECTURE VIDEOS IN MASSIVE OPEN ONLINE COURSES (MOOCS) AND FLIPPED CLASSROOMS. ATTENTIVELEARNER USES ON-LENS FINGER GESTURES FOR VIDEO CONTROL AND CAPTURES LEARNERS' PHYSIOLOGICAL STATES THROUGH IMPLICIT HEART RATE TRACKING ON UNMODIFIED MOBILE PHONES. THROUGH THREE USER STUDIES TO DATE, WE FOUND ATTENTIVELEARNER EASY TO LEARN, AND INTUITIVE TO USE. THE HEART BEAT WAVEFORMS CAPTURED BY ATTENTIVELEARNER CAN BE USED TO INFER LEARNERS' COGNITIVE STATES AND ATTENTION. ATTENTIVELEARNER MAY SERVE AS A PROMISING SUPPLEMENTAL FEEDBACK CHANNEL ORTHOGONAL TO TODAY'S LEARNING ANALYTICS TECHNOLOGIES.
IN THE LAST FEW YEARS THERE HAS BEEN A GROWING INTEREST IN THE USE OF ARTIFICIAL INTELLIGENCE TO IMPROVE DIFFERENT AREAS OF EDUCATION SUCH AS STUDENT TEAM FORMATION, LEARNING ANALYTICS, INTELLIGENT TUTORING SYSTEMS, OR THE RECOMMENDATION OF LEARNING RESOURCES. THIS PAPER PRESENTS A GENETIC ALGORITHM THAT AIMS TO IMPROVE THE ALLOCATION OF STUDENTS TO SUPERVISORS WHILE TAKING BOTH THE STUDENTS' AND SUPERVISORS' PREFERENCES WITH REGARDS TO RESEARCH TOPICS, AND BY PROVIDING A BALANCED ALLOCATION FOR SUPERVISORS' WORKLOAD. A PARETO OPTIMAL GENETIC ALGORITHM HAS BEEN DESIGNED AND TESTED FOR THE RESOLUTION OF THIS PROBLEM.
THE 1993 PAPER IN IJAIED ON EVALUATION METHODS FOR INTELLIGENT TUTORING SYSTEMS (ITS) STILL HOLDS UP WELL TODAY. BASIC EVALUATION TECHNIQUES DESCRIBED IN THAT PAPER REMAIN IN USE. APPROACHES SUCH AS KAPPA SCORES, SIMULATED LEARNERS AND LEARNING CURVES ARE REFINEMENTS ON PAST EVALUATION TECHNIQUES. NEW APPROACHES HAVE ALSO ARISEN, IN PART BECAUSE OF INCREASES IN THE SPEED, STORAGE CAPACITY AND CONNECTIVITY OF COMPUTERS OVER THE PAST 20 YEARS. THIS HAS MADE POSSIBLE TECHNIQUES IN CROWD SOURCING, PROPENSITY-SCORE MATCHING, EDUCATIONAL DATA MINING AND LEARNING ANALYTICS. THIS SHORT PAPER OUTLINES SOME OF THESE APPROACHES.
COMPETENCY-BASED EDUCATION IS BECOMING INCREASINGLY ADOPTED BY HIGHER EDUCATION INSTITUTIONS ALL OVER THE WORLD. THIS PAPER PRESENTS A FRAMEWORK THAT ASSISTS INSTRUCTORS IN THIS PEDAGOGICAL PARADIGM AND ITS CORRESPONDING OPEN-SOURCE IMPLEMENTATION. THE FRAMEWORK SUPPORTS THE FORMAL DEFINITION OF COMPETENCY ASSESSMENT MODELS AND THE STUDENTS' EVALUATION UNDER THESE MODELS. IT ALSO PROVIDES DISTINCT LEARNING ANALYTICS FOR IDENTIFYING COURSE SHORTCOMINGS AND VALIDATING CORRECTIVE ACTIONS INSTRUCTORS HAVE INTRODUCED IN A COURSE. FINALLY, THIS PAPER REPORTS THE BENEFITS OF APPLYING OUR FRAMEWORK TO AN ENGINEERING COURSE AT THE PONTIFICAL CATHOLIC UNIVERSITY, VALPARAISO, CHILE FOR THREE YEARS.
THIS PAPER INTRODUCES A CONTEMPORARY PLATFORM, CALLED FGAWEB, THAT CAN BE UTILIZED IN ORDER TO ADMINISTRATE FLEXIBLE GAMES. FGAWEB PLATFORM IS FOCUSED ON DIFFERENTIATING THE CONTENT OF PUZZLE AND QUIZ GAMES IN ORDER TO MAKE THEM MORE SUITABLE FOR USE IN EDUCATIONAL SETTINGS. INSTRUCTORS CAN UTILIZE THIS PLATFORM IN ORDER TO CREATE AND EDIT GAME ACTIVITIES AS WELL AS, TO HAVE ACCESS TO GAME AND LEARNING ANALYTICS. FGAWEB PLATFORM OFFERS AN API THAT MAKES THE WHOLE SYSTEM ACCESSIBLE FROM ANY GAME ENGINE THAT SUPPORTS THE HTTP PROTOCOL FOR DATA TRANSFERS.
WHAT SHOULD A COURSE PROVIDER DO IF ALL COURSE CONTENT, WHICH IS STORED IN MOODLE, NEEDS TO BE MIGRATED TO A NEW LEARNING MANAGEMENT SYSTEM? HOW COULD A PROVIDER EASILY USE ADVANCED TECHNOLOGIES LIKE LEARNING ANALYTICS, LEARNING RECOMMENDER SYSTEMS OR VIRTUAL LEARNING TO CREATE A COMPELLING LEARNING EXPERIENCE? HOW CAN A PROVIDER INCORPORATE THE CONTENT OF ANOTHER PROVIDER INTO AN EXISTING COURSE? TO ADDRESS SUCH QUESTIONS, WE DEVELOPED THE COMMON LEARNING MIDDLEWARE IN A JOINT PROJECT WITH SEVERAL FRAUNHOFER INSTITUTES TRYING TO SOLVE THESE TYPICAL CHALLENGES FACING EDUCATIONAL INSTITUTIONS.
REMEDIAL COLLEGE COURSES GENERATE TUITION AND FIT NEATLY INTO EXISTING COURSE STRUCTURES. BUT THEY OFTEN FAIL TO DELIVER ACCESS AND SUCCESS IN FOUNDATIONAL "GATEWAY" UNDERGRADUATE COURSES. BECAUSE UNDER-REPRESENTED MINORITIES ARE OVER-REPRESENTED IN THESE COURSES, REMEDIAL COURSES OBSTRUCT EQUITABLE ACCESS AND SUCCESS. SCHOOLS ARE INCREASINGLY TURNING INSTEAD TO SUPPLEMENTAL INSTRUCTION AND INTERNET-BASED INTELLIGENT TUTORING SYSTEMS (ITSS). BUT THESE ALTERNATIVES DON'T GENERATE TUITION, ARE DIFFICULT TO EVALUATE, AND CAN REQUIRE SUBSTANTIAL INFRASTRUCTURE. THIS STUDY COMPARED 2561 STUDENTS WHO QUALIFIED FOR PRINCIPLES OF CHEMISTRY AND BIOCHEMISTRY BY PASSING DEVELOPMENTAL CHEMISTRY WITH 607 STUDENTS WHO QUALIFIED BY COMPLETING A COMMERCIAL INTERNET-BASED ITS. PROPENSITY-SCORE TECHNIQUES WERE USED TO CONTROL FOR BIASED SELECTION AND ASSIGNMENT. WE FOUND THAT SPENDING APPROXIMATELY 30 H ON A $30 ITS WAS EQUALLY AS EFFECTIVE AS SPENDING HUNDREDS OF HOURS AND THOUSANDS OF DOLLARS OF TUITION ON A THREE OR FIVE CREDIT REMEDIAL COURSE IN TERMS OF FINAL GRADES IN PRINCIPLES FOR THE ENTIRE POPULATION AS WELL AS FOR UNDER-REPRESENTED MINORITIES. THIS FINDING HAS IMPLICATIONS FOR ADVISING STUDENTS AND FOR DESIGNING AND EVALUATING PROGRAMS FOR UNDERPREPARED STUDENTS AND UNDER-REPRESENTED MINORITIES.
PURPOSE THE PURPOSE OF THIS PAPER IS TO UNDERTAKE A FORMATIVE EVALUATION OF GROWTH OVER TIME THAT WOULD DEMONSTRATE DIVERSE LIBRARY USERS' DEVELOPMENT AS THEY INTERACT WITH MOBILE DIGITAL LIBRARY SERVICES. DESIGN/METHODOLOGY/APPROACH THIS PAPER INCORPORATED A SERVER LOG ANALYSIS TO EVALUATE FIRST, THE LOCATION OF USERS. TO STUDY THE NATURE OF DIVERSE USER DEVELOPMENT, USERS FROM UNIQUE LOCATIONS WERE IDENTIFIED AND TRACKED OVER SEVERAL YEARS. THE TYPE OF GROWTH THAT THIS PAPER ANALYZES IS THE DEVELOPMENT OF A LIBRARY USER FROM THE BEGINNING STAGES OF USE INTO ONE WHO IS MORE EXPERIENCED. FOR THE PURPOSES OF THIS PAPER, THE AUTHORS DEFINE LIBRARY EXPERTS AS EXPERIENCED LIBRARY USERS. THESE ARE USERS WHO HAVE COME BACK TO THE LIBRARY OVER MULTIPLE SESSIONS OF LEARNING AND BRANCHED OUT INTO MULTIPLE AREAS OF LIBRARY FUNCTIONALITY AND SERVICES. FINDINGS THE FINDINGS OF MODULAR MOBILE USE OVER TIME SUGGEST THAT, WHILE OVER HALF OF USERS ONLY UTILIZED ONE MODULE, 39 PER CENT OF ALL USERS ACCESSED MORE THAN ONE MODULE. THIS FORMATIVE APPROACH TO ASSESSING STUDENT LIBRARY ENGAGEMENT SUGGESTS ALTERNATIVE METRICS FOR ASSESSING OUTREACH AND DISTANCE LEARNING. ORIGINALITY/VALUE THE UNDERLYING DEPARTURE POINT FOR THIS STUDY IS THAT FORMATIVE MODELS MAY INTRODUCE DESCRIPTIVE DATA VALUABLE TO THE LEARNING ANALYTICS TOOLKIT. THE LIBRARY RESEARCH LITERATURE ON LEARNING ANALYTICS, AND PERHAPS LIBRARY SERVICE OFFERINGS THAT SUPPORT LEARNING, MAY GAIN ADDITIONAL VALUE BY ATTENDING TO STUDENTS' FORMATIVE DEVELOPMENT AS THEY INTERACT WITH LIBRARY RESOURCES. DESCRIBING THE WAY IN WHICH MOBILE APP USERS DEVELOP CAN YIELD INSIGHTS ABOUT LEARNING OVER TIME, BOTH ON CAMPUS AND AT A DISTANCE.
IN THIS ARTICLE, WE PROPOSE THE CONCEPT OF AUTONOMIC CYCLE OF LEARNING ANALYSIS TASKS (ACOLAT), WHICH DEFINES A SET OF TASKS OF LEARNING ANALYSIS, WHOSE OBJECTIVE IS TO IMPROVE THE LEARNING PROCESS. THE DATA ANALYSIS HAS BECOME A FUNDAMENTAL AREA FOR THE KNOWLEDGE DISCOVERY FROM DATA EXTRACTED FROM DIFFERENT SOURCES. IN THE AUTONOMIC CYCLE, EACH LEARNING ANALYSIS TASK INTERACTS WITH EACH OTHER AND HAS DIFFERENT ROLES: SOME OF THEM MUST OBSERVE THE LEARNING PROCESS, OTHERS MUST ANALYZE AND INTERPRET WHAT HAPPENS IN IT, AND FINALLY, OTHERS MAKE DECISIONS IN ORDER TO IMPROVE THE LEARNING PROCESS. IN THIS ARTICLE, WE STUDY THE APPLICATION OF THE AUTONOMIC CYCLE IN A SMART CLASSROOM, WHICH IS COMPOSED OF A SET OF INTELLIGENT COMPONENTS OF HARDWARE (E.G., SMART BOARD) AND SOFTWARE (E.G., VIRTUAL LEARNING ENVIRONMENTS), WHICH MUST EXPLOIT THE KNOWLEDGE GENERATED BY THE ACOLAT TO IMPROVE THE LEARNING PROCESS IN THE SMART CLASSROOM. MOREOVER, WE PRESENT THE SET OF ACOLATS PRESENT IN A SMART CLASSROOM AND THE IMPLEMENTATION OF SOME OF THEM.
EVALUATING THE LEARNERS TO DETERMINE THEIR PROFICIENCY IN PROGRAMMING IS A CHALLENGING TASK. IN THIS PAPER, WE ATTEMPT TO AUTOMATE THIS TASK BY ANALYZING DYNAMIC DATA COLLECTED FROM PROGRAMMING PLATFORM THROUGH SENSORS. SINCE THE PERFORMANCE OF STUDENTS IN PROGRAMMING COURSE AND THEIR ASSESSMENT DEPEND ON MULTIPLE CRITERIA, WE MODEL THE PROBLEM OF FINDING THE PROFICIENCY OF STUDENT AS A MULTI CRITERIA DECISION MAKING (MCDM) PROBLEM. WE PRESENT A NOVEL SOLUTION USING ANALYTIC HIERARCHY PROCESS (AHP) WITH WHICH EACH STUDENT IS CLASSIFIED INTO ONE OF THE EXPERTISE LEVELS WHICH REPRESENT THEIR COMPETENCY IN A PARTICULAR PROGRAMMING LANGUAGE. THIS COMPETENCY BASED APPROACH WILL HELP COURSE INSTRUCTORS TO GRADE THE STUDENTS. THIS GRADING HELPS THE TEACHERS TO IDENTIFY STUDENTS WHO ARE FACING DIFFICULTIES IN LEARNING AND TO GUIDE THEM TO IMPROVE THEIR TECHNICAL SKILLS. WE VALIDATE OUR RESULTS BY COMPARING THEM WITH MARKS SCORED BY THE STUDENTS IN THE EXAMINATION OF THAT COURSE.
DUE TO THE RAPID GROWTH OF MULTIMEDIA DATA AND THE DIFFUSION OF REMOTE AND MIXED LEARNING, TEACHING SESSIONS ARE BECOMING MORE AND MORE MULTI-MODAL. TO DEEPEN THE KNOWLEDGE OF SPECIFIC TOPICS, LEARNERS CAN BE INTERESTED IN RETRIEVING EDUCATIONAL VIDEOS THAT COMPLEMENT THE TEXTUAL CONTENT OF TEACHING BOOKS. HOWEVER, RETRIEVING EDUCATIONAL VIDEOS CAN BE PARTICULARLY CHALLENGING WHEN THERE IS A LACK OF METADATA INFORMATION. TO TACKLE THE AFORESAID ISSUE, THIS PAPER EXPLORES THE JOINT USE OF DEEP LEARNING AND NATURAL LANGUAGE PROCESSING TECHNIQUES TO RETRIEVE CROSS-MEDIA EDUCATIONAL RESOURCES (I.E., FROM TEXT SNIPPETS TO VIDEOS AND VICE VERSA). IT APPLIES NLP TECHNIQUES TO BOTH THE AUDIO TRANSCRIPT OF THE VIDEOS AND TO THE TEXT SNIPPETS IN THE BOOKS IN ORDER TO QUANTIFY THE SEMANTIC RELATIONSHIPS BETWEEN PAIRS OF EDUCATIONAL RESOURCES OF DIFFERENT MEDIA TYPES. THEN, IT TRAINS A DEEP LEARNING MODEL ON TOP OF THE NLP-BASED FEATURES. THE PROBABILITIES RETURNED BY THE DEEP LEARNING MODEL ARE USED TO RANK THE CANDIDATE RESOURCES BASED ON THEIR RELEVANCE TO A GIVEN QUERY. THE RESULTS ACHIEVED ON A REAL COLLECTION OF EDUCATIONAL MULTIMODAL DATA SHOW THAT THE PROPOSED APPROACH PERFORMS BETTER THAN STATE-OF-THE-ART SOLUTIONS. FURTHERMORE, A PRELIMINARY ATTEMPT TO APPLY THE SAME APPROACH TO ADDRESS A SIMILAR RETRIEVAL TASK (I.E., FROM TEXT TO IMAGE AND VICE VERSA) HAS SHOWN PROMISING RESULTS.
THIS STUDY DEVELOPED A LEARNING SYSTEM THAT ALLOWS TEACHERS TO EDIT ASSIGNMENTS DESIGNED TO TEACH STUDENTS THE TEXT STRUCTURE STRATEGY THROUGH THE USE OF FOUR PHASES: INSTRUCTING, MODELING, PRACTICING, AND REFLECTING. A 7-WEEK INSTRUCTIONAL EXPERIMENT WAS CONDUCTED IN WHICH 84 12TH-GRADE STUDENTS LEARNED THE TEXT STRUCTURE STRATEGY USING THIS SYSTEM. THE RESULTS PRODUCED SEVERAL SIGNIFICANT FINDINGS. FIRST, THE STUDENTS DEMONSTRATED VERY DIFFERENT PATTERNS OF ACCESSING TIME WHEN USING THIS SYSTEM, MAKING IT POSSIBLE TO CLASSIFY THEM INTO 3 CATEGORIES OR CLUSTERS OF USERS: "LOW-PRACTICE-LOW-REFLECTION STUDENTS", WHO SPENT LITTLE TIME USING THE SYSTEM; "LOW-PRACTICE-HIGH-REFLECTION STUDENTS" WHO SPENT MOST OF THEIR TIME IN THE REFLECTING PHASE, DURING WHICH THEY CONSTRUCTED GRAPHIC ORGANIZERS AND WROTE SUMMARIES PRIMARILY BY REFERRING TO THE TEACHER'S EXAMPLES; AND "HIGH-PRACTICE-LOW-REFLECTION STUDENTS" WHO SPENT MOST OF THEIR TIME IN THE PRACTICING PHASE, DURING WHICH THEY CONSTRUCTED GRAPHIC ORGANIZERS AND WROTE SUMMARIES WITHOUT REFERRING TO THE TEACHER'S EXAMPLES. SECOND, THE STUDENTS' PATTERNS OF ACCESSING TIME WERE RELATED TO THEIR PRIOR KNOWLEDGE, WEB EXPERIENCE, AND LEARNING PERFORMANCE. IN PARTICULAR, THE "LOW-PRACTICE-LOW-REFLECTION STUDENTS" HAD THE LOWEST LEARNING PERFORMANCE. THE "LOW-PRACTICE-HIGH-REFLECTION STUDENTS" HAD A HIGHER FREQUENCY OF USING PCS TO ACCESS THE WEB, A LOWER LEVEL OF PRIOR KNOWLEDGE, AND HIGHER GAIN SCORES THAN THE "HIGH-PRACTICE-LOW-REFLECTION STUDENTS". THESE FINDINGS ARE DISCUSSED, AND SEVERAL SUGGESTIONS ARE PROPOSED FOR FUTURE RESEARCH WORK.
COURSE FORUMS OFFER AN INTERACTIVE CHANNEL FOR LEARNERS TO EXPRESS OPINIONS AND FEEDBACK, WHICH CONTAIN VALUABLE EMOTIONS AND TOPIC INFORMATION TOWARDS COURSES. IN THIS PAPER, WE PROPOSE AN EMOTION ORIENTED TOPIC PROBABILISTIC MODEL THAT CAN BE USED TO CALCULATE DISTRIBUTIONS OF EMOTION-TOPIC OVER WORDS TO DISCOVER WHAT STUDENTS ARE MOST CONCERNED ABOUT. AN EXPERIMENT ON REAL-LIFE DATA INDICATES THAT STUDENTS HAD A POSITIVE ATTITUDE FOR KNOWLEDGE APPLICATIONS, A NEGATIVE EXPERIENCE FOR THE LEARNING SYSTEM, AND EXPRESSED CONFUSION ABOUT THE FINAL EXAM. WE ALSO VISUALIZE THE TEMPORAL TRENDS OF EMOTIONS OF THE WHOLE GROUP AND THE GROUPS WITH DIFFERENT LEVELS OF ACHIEVEMENT. THE PROPOSED MODEL HAS A POTENTIAL IN DISCOVERING STUDENTS' EMOTIONS IN THEIR FEEDBACK, THUS IMPROVING THE ONLINE LEARNING EXPERIENCE, AND IDENTIFYING ATRISK STUDENTS TIMELY.
THIS STUDY EXPLORES THE TYPES OF LEARNING PROFILES THAT EVOLVE FROM STUDENT USE OF VIDEO ANNOTATION SOFTWARE FOR REFLECTIVE LEARNING. THE DATA TRACES FROM STUDENT USE OF THE SOFTWARE WERE ANALYSED ACROSS FOUR UNDERGRADUATE COURSES WITH DIFFERING INSTRUCTIONAL CONDITIONS. THAT IS, THE USE OF GRADED OR NON-GRADED SELF-REFLECTIVE ANNOTATIONS. USING HIERARCHICAL CLUSTER ANALYSIS, FOUR PROFILES OF STUDENTS EMERGED: MINIMALISTS, TASK-ORIENTED, DISENCHANTED, AND INTENSIVE USERS. STUDENTS ENROLLED IN ONE OF THE COURSES WHERE GRADING OF THE VIDEO ANNOTATION SOFTWARE WAS PRESENT, WERE EXPOSED TO EITHER ANOTHER GRADED COURSE (ANNOTATIONS GRADED) OR NON-GRADED COURSE (ANNOTATIONS NOT GRADED) IN THEIR FOLLOWING SEMESTER OF STUDY. FURTHER ANALYSIS REVEALED THAT IN THE PRESENCE OF EXTERNAL FACTORS (I.E., GRADING), MORE STUDENTS FELL WITHIN THE TASK-ORIENTED AND INTENSIVE CLUSTERS. HOWEVER, WHEN THE EXTERNAL FACTOR IS REMOVED, MOST STUDENTS EXHIBITED THE DISENCHANTED AND MINIMALIST LEARNING BEHAVIORS. THE FINDINGS PROVIDE INSIGHT INTO HOW STUDENTS ENGAGE WITH THE DIFFERENT FEATURES OF A VIDEO ANNOTATION TOOL WHEN THERE ARE GRADED OR NON-GRADED ANNOTATIONS AND, MOST IMPORTANTLY, THAT HAVING EXPERIENCE WITH ONE COURSE WHERE THERE ARE EXTERNAL FACTORS INFLUENCING STUDENTS' USE OF THE TOOL IS NOT SUFFICIENT TO SUSTAIN THEIR LEARNING BEHAVIOUR IN SUBSEQUENT COURSES WHERE THE EXTERNAL FACTOR IS REMOVED.
THE LITERATURE IS MIXED AS TO WHETHER THE ADDITION OF LECTURE CAPTURE TECHNOLOGIES PROVIDE FOR BETTER STUDENT SUCCESS. IN THIS WORK, WE CONSIDER NOT JUST THE BROAD EFFECT OF LECTURE CAPTURE TECHNOLOGY ON ACADEMIC ACHIEVEMENT BETWEEN COHORTS, BUT WHETHER THIS EFFECT IS RELATED TO PATTERNS OF VIEWERSHIP AMONG LEARNERS. AT THE CENTRE OF OUR INTEREST IS DETERMINING WHETHER THERE ARE STRATEGIES LEARNERS TAKE IN THEIR REVIEWING OF CONTENT WEEK-TO-WEEK THAT MAY RESULT IN BETTER ACHIEVEMENT. TO INVESTIGATE THIS, WE DESCRIBE A METHOD FOR MODELLING LEARNERS BASED ON THEIR INTERACTIONS WITH LECTURE CAPTURE SYSTEMS. UNLIKE INVESTIGATIONS DONE BY OTHERS, OUR MODELS EMERGE FROM THE ACTIVITIES OF THE LEARNERS THEMSELVES, AND ARE BASED ON THE RESULTS OF APPLYING UNSUPERVISED MACHINE LEARNING (CLUSTERING) TECHNIQUES TO STUDENT VIEWERSHIP DATA. THESE MODELS DESCRIBE FIVE DIFFERENT CLASSIFICATIONS OF LEARNER INTERACTIONS, AND WE SHOW THAT ONE OF THESE IS POSITIVELY CORRELATED WITH ACADEMIC ACHIEVEMENT. WE FURTHER VALIDATE OUR RESULTS THROUGH REPEATED EXPERIMENTATION, AND DESCRIBE HOW SUCH MODELS MIGHT BE USED BY EARLY-ALERT SYSTEMS. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
PURPOSE THIS STUDY AIMS TO INVESTIGATE THE ORGANISATIONAL STRUCTURE TO EXPLOIT DATA ANALYTICS IN THE EDUCATIONAL SECTOR. THE PAPER PROPOSES THREE DIFFERENT ORGANISATIONAL CONFIGURATIONS, WHICH DESCRIBE THE CONNECTIONS AMONG EDUCATIONAL ACTORS IN A NATIONAL SYSTEM. THE ULTIMATE GOAL IS TO PROVIDE INSIGHTS ABOUT ALTERNATIVE ORGANISATIONAL SETTINGS FOR THE ADOPTION OF DATA ANALYTICS IN EDUCATION. DESIGN/METHODOLOGY/APPROACH THE PAPER IS BASED ON A PARTICIPANT OBSERVATION APPROACH APPLIED IN THE ITALIAN EDUCATIONAL SYSTEM. THE STUDY IS BASED ON FOUR RESEARCH PROJECTS THAT INVOLVED TEACHERS, SCHOOL PRINCIPALS AND GOVERNMENTAL ORGANISATIONS OVER THE PERIOD 2017-2020. FINDINGS AS A RESULT, THE CENTRALISED, THE DECENTRALISED AND THE NETWORK-BASED CONFIGURATIONS ARE PRESENTED AND DISCUSSED ACCORDING TO THREE ORGANISATIONAL DIMENSIONS OF ANALYSIS (ORGANISATIONAL LAYERS, ROLES AND DATA MANAGEMENT). THE NETWORK-BASED CONFIGURATION SUGGESTS THE PRESENCE OF A NETWORK EDUCATIONAL DATA SCIENTIST THAT MAY REPRESENT A CONCRETE SOLUTION TO FOSTER MORE EFFICIENT AND EFFECTIVE USE OF EDUCATIONAL DATA ANALYTICS. ORIGINALITY/VALUE THE VALUE OF THIS STUDY RELIES ON ITS SYSTEMIC APPROACH TO EDUCATIONAL DATA ANALYTICS FROM AN ORGANISATIONAL PERSPECTIVE, WHICH UNFOLDS THE ROLES OF SCHOOLS AND CENTRAL ADMINISTRATION. THE ANALYSIS OF THE ALTERNATIVE ORGANISATIONAL CONFIGURATION ALLOWS MOVING A STEP FORWARD TOWARDS A STRUCTURED, EFFECTIVE AND EFFICIENT SYSTEM FOR THE USE OF DATA IN THE EDUCATIONAL SECTOR.
THIS ARTICLE ADDRESSES THE PROBLEM OF PERFORMANCE PREDICTION IN A FLIPPED LEARNING COURSE ON DIGITAL SYSTEMS. THE COURSE IS A PART OF A FIRST-YEAR ENGINEERING DEGREE OFFERED AT A PUBLIC UNIVERSITY. THE EXTENT TO WHICH FAILURE AT THE END OF THE COURSE CAN BE ANTICIPATED IS ANALYZED BY EXAMINING THE STUDENTS' BEHAVIOR RELATED TO ACADEMIC ACTIVITIES. THIS PREDICTION WILL BE MADE BY CONSIDERING WHETHER STUDENTS PERFORM DIFFERENT ACTIVITIES AS INSTRUCTED. THE TIMEFRAME IS ALSO LIMITED TO THE EARLY STAGES OF THE COURSE. ALTHOUGH ANTICIPATING RESULTS AT EARLY STAGES ON THE BASIS OF BEHAVIORAL ASPECTS MAKES IT POSSIBLE TO REDRESS THE BEHAVIOR OF AT-RISK STUDENTS, AT THE SAME TIME, IT ALSO INTRODUCES LIMITATIONS FOR POTENTIAL MODELS. AS THERE ARE NO ASSESSMENTS DURING THE EARLY PERIOD, NO COURSE ACTIVITY GRADES WILL BE AVAILABLE FOR MODELING PURPOSES. IN THIS STUDY, DIFFERENT DATA MINING TECHNIQUES WILL BE USED, WHICH WILL OFFER BETTER RESULTS. RESULTS WILL BE ANALYZED IN TERMS OF THE PERFORMANCE OF AT-RISK STUDENTS AND COMPARED WITH PREVIOUS RESEARCH STUDIES. MODELS WILL ALSO PROVIDE INFORMATION ABOUT FACTORS OF RELEVANCE FOR THE ACADEMIC SUCCESS OF THE COURSE.
MASSIVE OPEN ONLINE COURSES (MOOCS) GENERATE LEARNERS' PERFORMANCE DATA THAT CAN BE USED TO UNDERSTAND LEARNERS' PROFICIENCY AND TO IMPROVE THEIR EFFICIENCY. HOWEVER, THE APPROACHES CURRENTLY USED, SUCH AS ASSESSING THE PROPORTION OF CORRECT RESPONSES IN ASSESSMENTS, ARE OVERSIMPLIFIED AND MAY LEAD TO POOR CONCLUSIONS AND DECISIONS BECAUSE THEY DO NOT ACCOUNT FOR ADDITIONAL INFORMATION ON LEARNER, CONTENT, AND CONTEXT. THERE IS A NEED FOR THEORETICALLY GROUNDED DATA-DRIVEN EXPLAINABLE EDUCATIONAL MEASUREMENT APPROACHES FOR MOOCS. IN THIS CONCEPTUAL PAPER, WE TRY TO ESTABLISH A CONNECTION BETWEEN PSYCHOMETRICS, A SCIENTIFIC DISCIPLINE CONCERNED WITH TECHNIQUES FOR EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, AND MOOCS. FIRST, WE DESCRIBE GENERAL PRINCIPLES OF TRADITIONAL MEASUREMENT OF LEARNERS' PROFICIENCY IN EDUCATION. SECOND, WE DISCUSS QUALITIES OF MOOCS WHICH HAMPER DIRECT APPLICATION OF APPROACHES BASED ON THESE GENERAL PRINCIPLES. THIRD, WE DISCUSS RECENT DEVELOPMENTS IN MEASURING PROFICIENCY THAT MAY BE RELEVANT FOR ANALYZING MOOC DATA. FINALLY, WE DRAW DIRECTIONS IN PSYCHOMETRIC MODELING THAT MIGHT BE INTERESTING FOR FUTURE MOOC RESEARCH.
ONE OF THE MORE NOTICEABLE CHANGES TO TERTIARY TEACHING OVER THE PAST DECADE HAS BEEN THE WIDESPREAD ADOPTION OF DIGITAL TECHNOLOGIES, IN PARTICULAR ELEARNING PLATFORMS AND LECTURE CAPTURE TECHNOLOGY. HOWEVER, MUCH OF THE CURRENT KNOWLEDGE OF HOW STUDENTS UTILISE THESE NEW TECHNOLOGIES AND THEIR EFFECT ON TRADITIONAL LECTURE ATTENDANCE IS SIMPLY DERIVED FROM STUDENT SURVEYS RATHER THAN COMPREHENSIVE INDEPENDENT ANALYSES. IN THIS STUDY, WE USE CLUSTER ANALYSIS TO IDENTIFY COMMON LECTURE RESOURCE UTILISATION PATTERNS FOR STUDENTS IN FOUR LARGE FIRST-YEAR BUSINESS SUBJECTS. WHILE COMMON USAGE PATTERNS WITH RESPECT TO LECTURE ATTENDANCE, VIDEO LECTURE RECORDING ACCESS AND DOWNLOAD OF LECTURE NOTES ARE IDENTIFIED ACROSS OUR SUBJECTS, THE PROPORTION OF STUDENTS WITHIN EACH OF THE UTILISATION CLUSTERS VARIES WIDELY. BUSINESS STATISTICS STUDENTS ARE MUCH MORE LIKELY TO EITHER ATTEND LECTURES OR VIEW VIDEO RECORDINGS COMPARED TO ECONOMICS STUDENTS, MANY OF WHOM RELY SOLELY ON THE DOWNLOAD OF LECTURE NOTES. IN ORDER TO GAIN INSIGHT INTO HOW STUDENT CHARACTERISTICS MAY AFFECT THESE UTILISATION PATTERNS, WE DEVELOP A PREDICTIVE MODEL, QUANTIFYING THE INFLUENCES OF PRIOR ACADEMIC PERFORMANCE, GENDER, AGE, DISTANCE FROM CAMPUS AND INTERNATIONAL STUDENT STATUS USING STATISTICAL MODELLING. WE FIND A STRONG ROLE FOR STUDENTS' PREVIOUS ACADEMIC PERFORMANCE IN EXPLAINING LECTURE RESOURCE UTILISATION PATTERNS. STUDENTS' COMMUTING DISTANCE TO CAMPUS IS ALSO ESTABLISHED AS A FACTOR DISSUADING PHYSICAL LECTURE ATTENDANCE. CONTRARY TO INITIAL EXPECTATIONS, WE ALSO FOUND THAT FEMALES AND OLDER STUDENTS TEND TO RELY MORE HEAVILY ON DIGITAL RESOURCES RATHER THAN LECTURE ATTENDANCE. IT IS HOPED THAT THESE FINDINGS CAN HELP FIRST-YEAR INSTRUCTORS AND UNIVERSITY ADMINISTRATORS UNDERSTAND THE HETEROGENEITY OF STUDENT LECTURE ENGAGEMENT PATTERNS WITHIN THE FIRST-YEAR EXPERIENCE.
WE PRESENT PRELIMINARY RESULTS OF INVESTIGATING AN INTERVENTION SYSTEM TO INCREASE USER ENGAGEMENTS IN LEARNING ON EDUCATIONAL SOCIAL Q&A. THROUGH THE EXPERIMENTAL PROCEDURE WITH 600 BRAINLY (SOCIAL Q&A PLATFORM FOR LEARNING) USERS, THE STUDY SHOWS THAT INTERVENTIONS THROUGH A FORMATIVE MESSAGE HAVE A SIGNIFICANT EFFECT ON USER BEHAVIORS AND ENGAGEMENTS IN LEARNING ON EDUCATIONAL SOCIAL Q&A SERVICES.
IN FLIPPED CLASSROOM, STUDENTS ARE EXPECTED TO LEARN NEW CONTENTS IN ONLINE LEARNING SYSTEM BEFORE ATTENDING OFFLINE CLASSES TO REINFORCE THEIR KNOWLEDGE. THIS ONLINE AND OFFLINE BLENDED EDUCATION MODEL HAS BECOME MORE AND MORE POPULAR. HOWEVER, SPENDING MORE TIME TO ACTIVELY ENGAGE IN ONLINE LEARNING DOES NOT RESULT IN BETTER LEARNING PERFORMANCE, SO THAT HOW TO WISELY ARRANGE ONLINE LEARNING PLAN IS A BIG CHALLENGE. IN THIS PAPER, WE BUILD A LASSO MODEL TO ACCURATELY PREDICT STUDENTS' PERFORMANCE IN COURSE PROJECTS AND THEIR FINAL GRADE BY ONLINE LEARNING BEHAVIOUR DATA IN FLIPPED CLASSROOM. THE LASSO SELECTED FEATURES SHOW THAT LEARNING ONLINE BETWEEN FIRST AND SECOND FLIPPED CLASSES AFTER MIDNIGHT, AND DURING THE SECOND FLIPPED CLASS WOULD BENEFIT STUDENTS' PROJECT PERFORMANCE BUT STUDYING ONE DAY BEFORE THE EXAMINATION AND STUDYING AT NIGHT IS COUNTERPRODUCTIVE. OUR RESULTS PROVIDE NOVEL INSIGHT INTO GUIDING STUDENTS TO LEARN WISELY AND PERFORM BETTER IN FLIPPED CLASSROOM.
ENABLING TEACHERS TO DEFINE OR PORTRAY EFFICIENT TEACHING IDEAS FOR SHARING, REUSE OR ADAPTATION HAS ATTRACTED THE INTEREST OF LEARNING DESIGN RESEARCHERS AND HAS LED TO THE DEVELOPMENT OF A VARIETY OF LEARNING DESIGN TOOLS. IN THIS PAPER, WE INTRODUCE A MULTI-DIMENSIONAL FRAMEWORK FOR THE ANALYSIS OF LEARNING DESIGN TOOLS AND USE IT TO REVIEW TWENTY-NINE TOOLS CURRENTLY AVAILABLE TO RESEARCHERS AND PRACTITIONERS. LASTLY, WE CATEGORISE THESE TOOLS ACCORDING TO THE MAIN FUNCTIONALITY THAT THEY OFFER.
PREDICTING STUDENT FAILURE IS AN IMPORTANT TASK THAT CAN EMPOWER EDUCATORS TO COUNTERACT THE FACTORS THAT AFFECT STUDENT PERFORMANCE. IN THIS PAPER, A PART OF THE BIGGER PROBLEM OF PREDICTING STUDENT FAILURE IS ADDRESSED: PREDICTING THE STUDENTS THAT DO NOT COMPLETE THEIR ASSIGNMENT TASKS. FOR SOLVING THIS PROBLEM, REAL DATA COLLECTED BY OUR UNIVERSITY'S EDUCATIONAL PLATFORM WAS USED. BECAUSE THE PROBLEM CONSISTED OF PREDICTING ONE OF THREE POSSIBLE CLASSES (MULTI-CLASS CLASSIFICATION), THE APPROPRIATE ALGORITHMS AND METHODS WERE SELECTED. SEVERAL EXPERIMENTS WERE CARRIED OUT TO FIND THE BEST APPROACH FOR THIS PREDICTION PROBLEM AND THE USED DATA SET. AN APPROACH OF TIME SEGMENTATION IS PROPOSED IN ORDER TO FACILITATE THE PREDICTION FROM EARLY ON. METHODS THAT ADDRESS THE PROBLEMS OF HIGH DIMENSIONALITY AND IMBALANCED DATA WERE ALSO EVALUATED. THE OUTCOME OF EACH APPROACH IS SHOWN AND COMPARED IN ORDER TO SELECT THE BEST PERFORMING CLASSIFICATION ALGORITHM FOR THE PROBLEM AT HAND.
THE USE OF COMPUTER-MEDIATED COMMUNICATION (CMC) HAS BEEN UBIQUITOUS IN HIGHER EDUCATION. TO BETTER UNDERSTAND STUDENTS' BEHAVIORS AND FACILITATE STUDENTS' LEARNING THROUGH CMC, THIS STUDY AIMED TO CLASSIFY MESSAGES IN FACEBOOK LEARNING GROUP WHICH WAS CREATED AS AN ON-LINE DISCUSSION BOARD. DIFFERENT MACHINE LEARNING AND DEEP LEARNING CLASSIFICATION MODELS WERE PROPOSED, TRAINED AND TESTIFIED WITH CORPUSES FROM PTT, ONE OF THE FAMOUS ON-LINE FORUMS IN TAIWAN. FURTHERMORE, THE CLASSIFICATION OF FACEBOOK MESSAGES BY THESE WELL-TRAINED MODELS WERE COMPARED WITH HUMAN CODING. RESULTS REVEALED THAT RECURRENT NEURAL NETWORK (RNN) WITH WORD TO VECTOR (W2V) FOR FEATURE EXTRACTION DEMONSTRATED THE BEST PERFORMANCE IN ACCURACY. IN ADDITION, THE COMBINATION OF RNN AND TF-IDF WAS PROVED TO HAVE THE HIGHEST CORRELATION WITH HUMAN WORK. IMPLICATIONS FOR ARTIFICIAL INTELLIGENCE (AI) IN EDUCATION CONTEXT WAS DISCUSSED.
DESPITE SEVERAL STUDIES HIGHLIGHTED THE IMPORTANCE OF CONSIDERING GENDER IN ONLINE LEARNING, THE CURRENT LITERATURE ABOUT HOW MALE AND FEMALE STUDENTS WOULD BEHAVE IS STILL FRAGMENTED. ADDITIONALLY, LITTLE ATTENTION HAS BEEN PAID TO INVESTIGATING THE IMPACT OF GENDER ON ONLINE LEARNING BEHAVIORAL PATTERNS. THIS STUDY APPLIES LAG SEQUENTIAL ANALYSIS (LSA) TO INVESTIGATE GENDER-RELATED DIFFERENCE IN THE BEHAVIORAL PATTERNS OF 116 STUDENTS IN AN ONLINE COURSE FOR SIX WEEKS. THE OBTAINED RESULTS INDICATED THAT OVERALL THERE IS NO SIGNIFICANT DIFFERENCE IN THE FREQUENCY OF ONLINE LEARNING BEHAVIORS BETWEEN FEMALE AND MALE STUDENTS. HOWEVER, THE LSA SHOWED THAT MALES AND FEMALES DEMONSTRATED DIFFERENT TRANSITIONAL PATTERNS IN THEIR ONLINE LEARNING BEHAVIORS. FEMALE BEHAVIORS WERE MORE COHERENTLY LINKED TO EACH OTHER. IN CONTRAST, SOME OF THE MALE BEHAVIORS WERE RELATIVELY ISOLATED WITHOUT SIGNIFICANT ANTECEDENTS AND CONSEQUENCES, CALLING FOR LEARNING SUPPORTS. ALSO, FEMALES TENDED TO VIEW THEIR ACHIEVEMENT REPORTS BEFORE STARTING THE MAIN COURSE ACTIVITIES, SHOWING THAT FEMALE STUDENTS WERE MORE ACHIEVEMENT-ORIENTED. THE FINDINGS PROVIDE EXPLANATIONS ABOUT HOW GENDER AFFECTS ONLINE LEARNING AND IMPLICATIONS ON HOW TO DESIGN PERSONALIZED ONLINE LEARNING INTERVENTIONS BASED ON CONSIDERING GENDER-RELATED DIFFERENCES.
THE USE OF FLIPPED CLASSROOMS, BLENDED LEARNING ENVIRONMENTS AND THE APPLICATION OF TECHNOLOGY HAS BEEN EXTENSIVELY STUDIED AND REPORTED IN THE LITERATURE, BUT THE IMPACT OF LEARNING ACTIVITY DESIGN ON CULTIVATING BETTER LEARNING ATTITUDES HAS RARELY BEEN EXPLORED. A RAPID ADOPTION OF 'E-SCHOOLBAG' IN BASIC EDUCATION IN CHINA HAS BEEN OBSERVED IN RECENT YEARS. ITS IMPACT ON ACADEMIC PERFORMANCE VARIES FROM DIFFERENT WAYS OF E-SCHOOLBAG ADOPTION. WE CONDUCTED A TWO-YEAR LONGITUDINAL STUDY TO REVIEW THE RELATIONSHIP BETWEEN LEARNING BEHAVIOURS AND ACADEMIC PERFORMANCE IN A PRIMARY SCHOOL WITH A BLENDED LEARNING ENVIRONMENT, AND INVESTIGATED HOW INTERVENTIONS THROUGH LEARNING ACTIVITY REDESIGN COULD IMPROVE THE ACADEMIC PERFORMANCE BY CULTIVATING BETTER LEARNING ATTITUDES. THE RESULTS SHOW THAT LEARNING ATTITUDES ARE POSITIVELY RELATED TO ACADEMIC PERFORMANCE WITH PROPER LEARNING ACTIVITY DESIGN. IT IS THEREFORE SUGGESTED THAT TEACHERS' INTERVENTIONS THROUGH LEARNING ACTIVITY REDESIGN CAN CULTIVATE BETTER LEARNING ATTITUDES AND THUS IMPROVE ACADEMIC PERFORMANCE FOR LEARNERS.
THIS PAPER ADDRESSES THE CHALLENGES OF TEACHING RAPIDLY EVOLVING ACADEMIC DISCIPLINES AND ARGUES ABOUT THE NECESSITY OF INCORPORATING SELF-DIRECTED LEARNING IN AREAS WHERE KNOWLEDGE AND SKILLS CHANGE FAST. WE PROPOSE A TEACHING METHOD, WHICH AIMS AT INCREASING STUDENTS' ENGAGEMENT AND INVOLVEMENT IN THE LEARNING PROCESS. TO FOSTER SELF-DIRECTED LEARNING THE PROPOSED METHOD LEVERAGES MOTIVATIONAL MECHANISMS FOUND IN GAMES. ADDING GAME-INSPIRED ELEMENTS IN LEARNING IS AIMED AT ENVIRONMENTS WHERE LEARNERS NATURALLY WANT TO SUCCEED WHILE PERCEIVING WHAT THEY DO AS INTERESTING. IN ADDITION, THE PROPOSED APPROACH GIVES INSTRUCTORS ACCESS TO NEW TYPES OF ANALYTICS YIELDING DATA THAT SPEAKS TO THE LEARNING PROCESSES, IN CONTRAST TO SIMPLY REPORTING STUDENT OUTCOMES.
DATA COLLECTION AND ANALYSIS IS NOTHING NEW IN COMPUTER-ASSISTED LANGUAGE LEARNING, BUT WITH THE PHENOMENON OF MASSIVE SETS OF HUMAN LANGUAGE COLLECTED INTO CORPORA, AND ESPECIALLY INTEGRATED INTO SYSTEMS DRIVEN BY ARTIFICIAL INTELLIGENCE, NEW OPPORTUNITIES HAVE ARISEN FOR LANGUAGE TEACHING AND LEARNING. WE ARE NOW SEEING POWERFUL ARTIFICIAL NEURAL NETWORKS WITH IMPRESSIVE LANGUAGE CAPABILITIES. IN EDUCATION, DATA PROVIDES MEANS TO TRACK LEARNER PERFORMANCE AND IMPROVE LEARNING, ESPECIALLY THROUGH THE APPLICATION OF DATA MINING TO EXPOSE HIDDEN PATTERNS OF LEARNER BEHAVIOR. MASSIVE DATA COLLECTION ALSO RAISES ISSUES OF TRANSPARENCY AND FAIRNESS. HUMAN MONITORING IS ESSENTIAL IN APPLYING DATA ANALYSIS EQUITABLY. BIG DATA MAY HAVE AS POWERFUL AN IMPACT IN LANGUAGE LEARNING AS IT IS HAVING IN SOCIETY GENERALLY; IT IS AN IMPORTANT RESOURCE TO HAVE AVAILABLE, BUT ONE TO USE WITH CARE AND CAUTION.
THIS ARTICLE SUMMARIZES THE EXPERIENCE OF EVIMED IN MOOCS FOR HEALTHCARE PROFESSIONALS ACROSS LATIN AMERICA, WHERE ONE MILLION PHYSICIANS LIVE AND WORK. EACH COURSE WORKLOAD IS DELIVERED IN SPANISH AND/OR PORTUGUESE, AND AVERAGES 30 HOURS AVER EIGHT WEEKS. APPROXIMATELY 30 TO 50 EXPERTS FROM LATIN AMERICA, NORTH AMERICA AND EUROPE PARTICIPATE IN EACH OF THE COURSES IN DIFFERENT ROLES, SUCH AS COORDINATORS, SPEAKERS, CONTENT DEVELOPERS AND TUTORS. COURSES ARE DEVELOPED IN PARTNERSHIP WITH SEVERAL INTERNATIONAL ORGANIZATIONS, INCLUDING THE LATIN AMERICAN SOCIETY OF NEPHROLOGY, THE WORLD GASTROENTEROLOGY ORGANIZATION, THE BMJ GROUP, AND THE AMERICAN SOCIETY FOR MICROBIOLOGY. THE ENROLLMENT USUALLY RANGES BETWEEN 500 AND 2,000 PAID PARTICIPANTS, JOINING AS INDIVIDUAL PROFESSIONALS OR TEAMS. COMPLETION RATE IS AVER 50%. IN CONCLUSION, INTERNATIONAL MULTILINGUAL MASSIVE PROGRAMS FOR CONTINUING EDUCATION OF HEALTHCARE PROFESSIONALS AND TEAMS ARE FEASIBLE AND WELL RECEIVED BY THE TARGET AUDIENCES IN LATIN AMERICA. SEVERAL ELEMENTS OF THE EXPERIENCE SHOWED IN THIS MANUSCRIPT COULD BE IMPLEMENTED IN OTHER REGIONS AND INDUSTRIES, AFTER ANY REQUIRED ADAPTATIONS.
E-LEARNING PLATFORMS, SUCH US MOODLE, COULD BE USEFUL TO IMPROVE THE TEACHING AND LEARNING PROCESSES. FOR THIS, IT IS NECESSARY TO ANALYZE THE STUDENTS' ONLINE BEHAVIOR AND TAKE IMPROVEMENT ACTIONS ACCORDINGLY. MOODLE PROVIDES SOME METRICS AND LOG DATA ABOUT HOW STUDENTS ARE USING THE PLATFORM. HOWEVER, THERE ARE VERY FEW TEACHERS AND ACADEMIC PROFESSIONALS WHO KNOW HOW TO ANALYZE EFFECTIVELY THESE LOG DATA. THE AIM OF THIS PAPER IS TO FULFILL THIS GAP, PROVIDING A METHODOLOGY TO ANALYZE MOODLE LOG DATA AND OFFERING NEW MEASURES, WHICH HOPEFULLY COULD RESULT IN BETTER E-LEARNING EXPERIENCES. MOREOVER, THIS METHODOLOGY WILL ALLOW MONITORING THE MOODLE PLATFORM, ANALYZING STUDENTS' ACTIVITY AND ANALYZING IF THERE IS ANY CORRELATION WITH THE ACADEMIC RESULTS OBTAINED BY THE STUDENTS IN THE COURSES. THIS WORK IS PART OF A PROJECT, WHICH AIMS TO ANALYZE ACTIVITY LOGS WITHIN SOME UNIVERSITY COURSES, BASED ON THE ANALYSIS OF ACTION LOGS RECORDED BY MOODLE. THIS IS A BRIEF EXPLORATORY COMMUNICATION OF A WORK IN PROGRESS IN ORDER TO OPEN NEW PATHS OF RESEARCH WITH REGARD TO MOODLE PLATFORM MONITORING AND ULTIMATELY, TEACHING AND LEARNING PROCESSES IMPROVEMENT.
ALTHOUGH MANY ALGORITHM VISUALIZATION (AV) TOOLS ARE AVAILABLE ONLINE, MOST OF THEM ARE NOT EFFECTIVE IN HELPING STUDENTS TO LEARN ALGORITHM AND DATA STRUCTURES. THE REASONS FOR THIS MAY BE THE POOR DESIGN CONSIDERATION OF THE TOOLS TO FULFILL THE LEARNERS' NEEDS IN TERMS OF PEDAGOGY, USABILITY, AND ACCESSIBILITY. THIS PAPER REPORTS A WORK-IN-PROGRESS RESEARCH PROJECT AT ATHABASCA UNIVERSITY ON DEVELOPING AN EFFECTIVE ALGORITHM VISUALIZATION TOOL FOR ONLINE LEARNING. WE ANALYZE THE PEDAGOGY, USABILITY, AND ACCESSIBILITY GOALS AND THEIR RESPECTIVE FEATURES FOR THE ONLINE LEARNERS AND EXAMINES HOW THE VISUALIZATION AND USER INTERACTION PRINCIPALS CAN HELP TO ACHIEVE THESE GOALS IN THE DESIGN PROCESS OF AN EFFECTIVE AV TOOL. WE PRESENT A REVIEW OF THE RELATED LITERATURE TO HIGHLIGHT CURRENT AND PAST RESEARCHES AND ILLUSTRATE THE NEED FOR BETTER USER EXPERIENCE DESIGN IN AV TOOLS. WE THEN IDENTIFY THE ONLINE LEARNERS' NEEDS AND THE CONTEXT OF THEIR ACTIVITIES TO DESIGN A USABLE, USEFUL, EFFECTIVE, AND PLEASURABLE INTERACTIVE AV TOOL. SUCH A TOOL CAN FULFILL THE PEDAGOGY, USABILITY, AND ACCESSIBILITY GOALS FOR THE ONLINE LEARNERS.
SELF-REGULATED LEARNING (SRL) ENVIRONMENTS PROVIDE STUDENTS WITH ACTIVITIES TO IMPROVE THEIR LEARNING (E.G., BY SOLVING EXERCISES), BUT THEY MIGHT ALSO PROVIDE OPTIONAL ACTIVITIES (E.G., CHANGING AN AVATAR IMAGE OR SETTING GOALS) WHERE STUDENTS CAN DECIDE WHETHER THEY WOULD LIKE TO USE OR DO THEM AND HOW. FEW WORKS HAVE DEALT WITH THE USE OF OPTIONAL ACTIVITIES IN SRL ENVIRONMENTS. THIS PAPER THUS ANALYZES THE USE OF OPTIONAL ACTIVITIES IN TWO CASE STUDIES WITH A SRL APPROACH. WE FOUND THAT THE LEVEL OF USE OF OPTIONAL ACTIVITES WAS LOW WITH ONLY 23.1 PERCENT OF STUDENTS MAKING USE OF SOME FUNCTIONALITY, WHILE THE LEVEL OF USE OF LEARNING ACTIVITIES WAS HIGHER. OPTIONAL ACTIVITIES WHICH ARE NOT RELATED TO LEARNING ARE USED MORE. WE ALSO EXPLORED THE BEHAVIOR OF STUDENTS USING SOME OF THE OPTIONAL ACTIVITIES IN THE COURSES SUCH AS SETTING GOALS AND VOTING COMMENTS, FINDING THAT STUDENTS FINISHED THE GOALS THEY SET IN MORE THAN 50 PERCENT OF THE TIME AND THAT THEY VOTED THEIR PEERS' COMMENTS IN A POSITIVE WAY. WE ALSO FOUND THAT GENDER AND THE TYPE OF COURSE CAN INFLUENCE WHICH OPTIONAL ACTIVITIES ARE USED. MOREOVER, THE RELATIONS OF THE USE OF OPTIONAL ACTIVITIES WITH PROFICIENT EXERCISES AND LEARNING GAINS IS LOW WHEN TAKING OUT THIRD VARIABLES, BUT WE BELIEVE THAT OPTIONAL ACTIVITIES MIGHT MOTIVATE STUDENTS AND PRODUCE BETTER LEARNING IN AN INDIRECT WAY.
LEARNER PROFILING IS A METHODOLOGY THAT DRAWS A PARALLEL FROM USER PROFILING. IMPLICIT FEEDBACK IS OFTEN USED IN RECOMMENDER SYSTEMS TO CREATE AND ADAPT USER PROFILES. IN THIS WORK THE IMPLICIT FEEDBACK IS BASED ON THE LEARNER'S ANSWERING BEHAVIOUR IN THE ANDROID APPLICATION UNLOCKYOURBRAIN, WHICH POSES DIFFERENT BASIC MATHEMATICAL QUESTIONS TO THE LEARNERS. WE INTRODUCE AN ANALYTICAL APPROACH TO MODEL THE LEARNERS' PROFILE ACCORDING TO THE LEARNER'S ANSWERING BEHAVIOUR. FURTHERMORE, SIMILAR LEARNER'S PROFILES ARE GROUPED TOGETHER TO CONSTRUCT A LEARNING BEHAVIOUR CLUSTER. THE CHOICE OF HIERARCHICAL CLUSTERING AS A MEANS OF CLASSIFICATION OF LEARNERS' PROFILES DERIVES FROM THE OBSERVATIONS OF LEARNERS BEHAVIOUR. THIS IN TURN REFLECTS THE SIMILARITIES AND SUBTLE DIFFERENCES OF LEARNER BEHAVIOUR, WHICH ARE FURTHER ANALYSED IN MORE DETAIL. BUILDING AWARENESS ABOUT THE LEARNER'S BEHAVIOUR IS THE FIRST AND NECESSARY STEP FOR FUTURE LEARNING-AWARE APPLICATIONS.
TEACHING IS COMPLEX ACTIVITY WHICH REQUIRES PROFESSORS TO EMPLOY THE MOST EFFECTIVE AND EFFICIENT TEACHING STRATEGIES TO ENABLE STUDENTS TO MAKE PROGRESS. MAIN PROBLEM IN TEACHING PROFESSORS SHOULD CONSIDER DIFFERENT TEACHING APPROACHES AND LEARNING TECHNIQUES TO SUIT EVERY STUDENT. TODAY, IN COMPUTER AGE, ELECTRONIC LEARNING (E-LEARNING) IS WIDELY USED IN PRACTICE. DEVELOPMENT OF WORLD WIDE WEB, ESPECIALLY WEB2.0 HAS LED TO REVOLUTION IN EDUCATION. STUDENT INTERACTION WITH LEARNING MANAGEMENT SYSTEMS - LMS RESULT IN CREATING LARGE DATA SETS WHICH ARE INTERESTING FOR RESEARCH. LMS SYSTEMS ALSO PROVIDE TOOLS FOR FOLLOWING EVERY INDIVIDUAL STUDENT AND STATISTICAL VIEW FOR DEEPER ANALYZING RESULT OF STUDENT - SYSTEM INTERACTION. HOWEVER, THESE TOOLS DO NOT INCLUDE ARTIFICIAL INTELLIGENCE ALGORITHMS AS A SUPPORT MECHANISM FOR DECISION. IN THIS ARTICLE WE PROVIDE FRAMEWORK FOR STUDENT MODELING TRAINED ON LARGE SETS OF DATA USING HADOOP AND MAHOUT. THIS KIND OF SYSTEM WOULD PROVIDE INSIGHT INTO EACH INDIVIDUAL STUDENT'S ACTIVITY. BASED ON THAT INFORMATION, PROFESSORS COULD ADJUST COURSE MATERIALS ACCORDING TO STUDENT INTEREST AND KNOWLEDGE.
THE SHIFT TO E-LEARNING AND ONLINE EDUCATION REQUIRES NEW AND DIFFERENT APPROACHES FOR TRACKING STUDENT PERFORMANCE AND BEHAVIOR. MOODLE IS CURRENTLY THE LEADING VIRTUAL LEARNING ENVIRONMENT IN SPAIN, AND A LOT OF DIFFERENT PLUGINS HAVE APPEARED TO COMPLEMENT THE LACK OF NATIVE TOOLS TO TRACK THE LEARNING PROCESS IN ALL ITS ASPECTS. HOWEVER, NONE OF THESE TAKE THE TEMPORAL DIMENSION AS A POSSIBLE BEHAVIOR MEASUREMENT INTO CONSIDERATION. IN THIS CASE STUDY, A NEW TECHNICAL AND STATISTICAL APPROACH TO TIME TRACKING OF STUDENTS IN MOODLE WILL BE DISCUSSED, SO THAT TIME AS A BEHAVIORAL ASPECT CAN BE CONSIDERED IN FURTHER ASSESSMENTS. FURTHERMORE, THIS APPROACH COULD NOT ONLY BE APPLIED TO MOODLE, BUT TO ALL OTHER ONLINE VIRTUAL LEARNING ENVIRONMENTS.
THIS CASE STUDY PRESENTS A SERIES OF STEPS TO HELP THE TRADITIONAL UNIVERSITY DEVELOP FLEXIBLE FORMS OF LEARNING, AND AT THE SAME TIME TO INCREASE THE ACCESSIBILITY OF LEARNING AND THE VISIBILITY OF THE INSTITUTION AS A WHOLE. THE TRANSITION OF THE UNIVERSITY OF WEST BOHEMIA IN THE CZECH REPUBLIC INCLUDES SYSTEMATIC WORK ON STRATEGIC PLANNING, MENTORING OF EXTERNAL EXPERTS, PROFESSIONAL STAFF DEVELOPMENT, INCREASING OF METHODOLOGICAL AND TECHNICAL SUPPORT, USING LEARNING ANALYTICS, INVESTING IN E-LEARNING INFRASTRUCTURE, CO-OPERATING WITH OTHER INSTITUTIONS AND PARTNERS, AND DEVELOPING A QUALITY FRAMEWORK FOR FLEXIBLE LEARNING AND COULD INSPIRE OTHER HIGHER EDUCATION INSTITUTIONS.
ACTIVATING LEARNERS' DEEPER THINKING MECHANISMS AND REFLECTIVE JUDGEMENT (I.E., METACOGNITION) IMPROVES LEARNING PERFORMANCE. THIS STUDY EXPLOITS VISUAL ANALYTICS TO PROMOTE METACOGNITION AND DELIVERS TASK-RELATED VISUALIZATIONS TO PROVIDE ON-DEMAND FEEDBACK. THE GOAL IS TO BROADEN CURRENT KNOWLEDGE ON THE PATTERNS OF ON-DEMAND METACOGNITIVE FEEDBACK USAGE, WITH RESPECT TO LEARNERS' PERFORMANCE. THE RESULTS FROM A BETWEEN-GROUP AND WITHIN-GROUP STUDY (N = 174) REVEALED STATISTICALLY SIGNIFICANT DIFFERENCES ON THE FEEDBACK USAGE PATTERNS BETWEEN THE PERFORMANCE-BASED LEARNER CLUSTERS. FOREMOST, THE FINDINGS SHOWN THAT LEARNERS WHO CONSISTENTLY REQUEST TASK-RELATED METACOGNITIVE FEEDBACK AND ALLOCATE CONSIDERABLE AMOUNTS OF TIME ON PROCESSING IT, ARE MORE LIKELY TO HANDLE TASK-COMPLEXITY AND COPE WITH CONFLICTING TASKS, AS WELL AS TO ACHIEVE HIGH SCORES. THESE FINDINGS CONTRIBUTE TO CONSIDERING TASK-RELATED VISUAL ANALYTICS AS A METACOGNITIVE FEEDBACK FORMAT THAT FACILITATES LEARNERS' ON-TASK ENGAGEMENT AND DATA-DRIVEN SENSE-MAKING AND INCREASES THEIR AWARENESS OF THE TASKS' REQUIREMENTS. IMPLICATIONS OF THE APPROACH ARE ALSO DISCUSSED.
THE MAIN OBJECTIVE OF THE UP2U PROJECT IS TO CLOSE A GAP BETWEEN SECONDARY AND HIGHER EDUCATION. WE INTENT TO INNOVATE AND EXPLORE SOLUTIONS TO INTEGRATE FORMAL AND INFORMAL LEARNING SCENARIOS AND ADAPT THE EMERGING TECHNOLOGIES WITH TEACHING AND LEARNING METHODOLOGIES THAT SECONDARY STUDENTS WILL FACE WITH ONCE THEY ENTER UNIVERSITIES. WE ARE FOCUSING ON THE CONTEXT OF SECONDARY EDUCATION AND THE SCHOOLS THAT SUPPORT IT, OFTEN DEFINED AS "SECONDARY SCHOOLS", WHICH INVOLVES STUDENTS BETWEEN THE AGES OF 11 AND 19, DEPENDING ON THE COUNTRY, AFTER PRIMARY EDUCATION AND BEFORE HIGHER EDUCATION. THE LEARNING CONTEXT FROM THE STUDENTS' PERSPECTIVE IS AN INTERSECTION OF FORMAL AND INFORMAL SPACES WITH A DYNAMIC HYBRID LEARNING ENVIRONMENT WHERE ACTIVITIES ARE SYNCHRONIZED BETWEEN VIRTUA WE PROPOSE TO DEVELOP AN INNOVATIVE THE ECOSYSTEM CALLED "UP TO UNIVERSITY" (UP2U) - BASED ON PROVEN TECHNOLOGY-BASED EXPERIENCES FROM HIGHER EDUCATION AND GROUNDED IN LARGE RESEARCH - TO FACILITATE CO-DESIGN, CO-CREATION AND REUSE OF DIGITAL EDUCATIONAL CONTENT, TECHNOLOGICAL TOOLS AND SERVICES. WE ARE ADAPTING TOOLS TO THE CONTEXT OF SECONDARY SCHOOLS IN THE PILOT COUNTRIES ANTICIPATING WHAT STUDENT WILL FIND AT UNIVERSITIES IN ORDER TO BETTER PREPARE THEM TO THAT LEVEL OF EDUCATION. WE WILL ADDRESS PROJECT-BASED LEARNING AND END-TO-END LEARNING SCENARIOS. WE FIRMLY BELIEVE THAT ALL THE TOOLS AND SERVICES THAT UP2U WILL USE AND/OR MAKE AVAILABLE (I.E., INCORPORATE, DESIGN, DEVELOP, AND TEST) ARE SUSTAINABLE AFTER A PROJECT LIFETIME. THEREFORE, THE PROJECT WILL INCLUDE BUSINESS PLANS AND INVESTIGATE APPROPRIATE MODELS USING THE EXPERIENCE OF SMALL AND MEDIUM BUSINESS PARTNERS AND THE NATIONAL RESEARCH AND EDUCATION NETWORKS (NRENS) AND THEIR CONTACTS WITH THIRD-PARTY ACTORS. OUR PLAN IS TO MAKE POSSIBLE THAT SECONDARY SCHOOLS JOIN THE UP2U CONSORTIUM INFRASTRUCTURE AND USE THIS FEDERATED ECOSYSTEM FOR THAT LEARNING COMMUNITY WITH THE LEAST DISRUPTION.
SUPPLEMENTAL RESOURCES IN SCIENCE EDUCATION ARE MADE AVAILABLE TO STUDENTS BASED ON THE BELIEF THAT THEY WILL IMPROVE COURSEBASED STUDENT LEARNING. THIS BELIEF IS UBIQUITOUS, WITH SUPPLEMENTAL RESOURCES BEING A TRADITIONAL COMPONENT OF PHYSIOLOGY EDUCATION. IN ADDITION, THE RECENT LARGE-SCALE TRANSITION TO REMOTE LEARNING CAUSED BY THE COVID-19 PANDEMIC SUGGESTS AN INCREASED RELEVANCE AND NECESSITY OF DIGITAL VERSIONS OF SUPPLEMENTAL RESOURCES. HOWEVER, THE USE OF A SUPPLEMENTAL RESOURCE IS ENTIRELY DEPENDENT ON WHETHER STUDENTS VIEW IT AS BENEFICIAL. IF STUDENTS IN A SPECIFIC COURSE DO NOT PERCEIVE A SUPPLEMENTAL RESOURCE AS USEFUL, THERE IS LITTLE REASON TO BELIEVE THE RESOURCES WILL BE USED AND ARE WORTHY OF INVESTMENT. CONSEQUENTLY, MEASUREMENT OF STUDENT PERCEPTION REGARDING THE EFFECTIVENESS OF ANY DIGITAL LEARNING TOOL IS ESSENTIAL FOR EDUCATORS AND INSTITUTIONS IN ORDER TO PRIORITIZE RESOURCES AND MAKE MEANINGFUL RECOMMENDATIONS TO STUDENTS. IN THIS STUDY, A SURVEY WAS USED TO DETERMINE STUDENT PERCEPTIONS OF A DIGITAL, SUPPLEMENTAL RESOURCE. QUANTITATIVE METHODS, INCLUDING EXPLORATORY FACTOR ANALYSIS, WERE PERFORMED ON DATA COLLECTED FROM THE SURVEY TO EXAMINE THE DIMENSIONALITY AND FUNCTIONALITY OF THIS SURVEY. THE FINDINGS FROM THIS STUDY WERE USED TO DEVISE AN IMPROVED, STANDARDIZED (I.E., RELIABLE AND VALID) SURVEY THAT CAN BE USED AND ADAPTED BY PHYSI3OLOGY RESEARCHERS AND EDUCATORS TO DETERMINE STUDENT PERCEPTION OF A DIGITAL SUPPLEMENTAL RESOURCE. THE SURVEY, WITH KNOWN CONSTRUCT VALIDITY AND INTERNAL RELIABILITY, CAN PROVIDE USEFUL INFORMATION FOR ADMINISTRATORS, INSTRUCTORS, AND DESIGNERS OF DIGITAL SUPPLEMENTAL RESOURCES.
IN THIS PAPER, WE DISCUSS EFFORTS TO COLLECT AND ANALYSE DATA AT THE LOCAL LEVEL TO PROMPT REFLECTIVE TEACHING IN LARGE FOUNDATIONAL ENGINEERING COURSES. THE WORK IS IN DIRECT RESPONSE TO THE GROWING DEMANDS AND CONSTRAINTS OF TEACHING IN LARGE CLASSES THAT COMPROMISE THE EDUCATIONAL ENVIRONMENT. WE FEEL THAT MOTIVATED IMPROVEMENT-SEEKING INSTITUTIONS AND ACADEMICS MUST STRIVE TO INNOVATE WITHIN THE CONSTRAINTS. TO DO THIS, WE ARGUE THAT LOCALLY ROOTED DATA ANALYTICS OFFERS UNIQUE OPPORTUNITIES TO INFUSE DATA BACK INTO DECISION-MAKING PROCESSES. AS A DEMONSTRATION OF OUR CLAIMS, WE DISCUSS SUMMARIES OF OUR TEAM'S EFFORTS WITHIN LARGE FOUNDATIONAL ENGINEERING MECHANICS COURSES AT A PUBLIC RESEARCH INSTITUTION IN THE UNITED STATES. SPECIFICALLY, WE REPORT ON WORK TO EXAMINE (1) STUDENT ENGAGEMENT AND PERFORMANCE (ANALYSIS OF STUDENT SURVEYS AND COURSE GRADES); (2) PERFORMANCE PATTERNS NOT PERFORMANCE POINTS (ANALYSIS OF COURSE-LEVEL DATA); AND (3) UNDERSTANDING COURSES IN CONTEXT (ANALYSIS OF EXISTING INSTITUTIONAL DATA).
WHILE MASSIVE OPEN ONLINE COURSE (MOOCS) PLATFORMS PROVIDE KNOWLEDGE IN A NEW AND UNIQUE WAY, THE VERY HIGH NUMBER OF DROPOUTS IS A SIGNIFICANT DRAWBACK. SEVERAL FEATURES ARE CONSIDERED TO CONTRIBUTE TOWARDS LEARNER ATTRITION OR LACK OF INTEREST, WHICH MAY LEAD TO DISENGAGEMENT OR TOTAL DROPOUT. THE JURY IS STILL OUT ON WHICH FACTORS ARE THE MOST APPROPRIATE PREDICTORS. HOWEVER, THE LITERATURE AGREES THAT EARLY PREDICTION IS VITAL TO ALLOW FOR A TIMELY INTERVENTION. WHILST FEATURE-RICH PREDICTORS MAY HAVE THE BEST CHANCE FOR HIGH ACCURACY, THEY MAY BE UNWIELDY. THIS STUDY AIMS TO PREDICT LEARNER DROPOUT EARLY-ON, FROM THE FIRST WEEK, BY COMPARING SEVERAL MACHINE-LEARNING APPROACHES, INCLUDING RANDOM FOREST, ADAPTIVE BOOST, XGBOOST AND GRADIENT-BOOST CLASSIFIERS. THE RESULTS SHOW PROMISING ACCURACIES (82%-94%) USING AS LITTLE AS 2 FEATURES. WE SHOW THAT THE ACCURACIES OBTAINED OUTPERFORM STATE OF THE ART APPROACHES, EVEN WHEN THE LATTER DEPLOY SEVERAL FEATURES.
PURPOSE - THIS PAPER AIMS TO HIGHLIGHT THE VALUE OF A LIBRARY LED E-TEXTBOOK PROGRAMME AT A THE UK UNIVERSITY. DESIGN/METHODOLOGY/APPROACH - THE DATA FOR THE RESULTS WERE OBTAINED FROM TWO SURVEYS OF STUDENTS. FIRST, FROM AN ONLINE SURVEY OF 575 STUDENTS WHO RECEIVED AN INDIVIDUAL COPY OF AN E-TEXTBOOK FROM THE UNIVERSITY OF MANCHESTER LIBRARY. SECOND, FROM A FACE TO FACE SURVEY OF 146 STUDENTS, BASED ON A RANDOM SAMPLE OF STUDENTS ENTERING THE ALAN GILBERT LEARNING COMMONS AT THE UNIVERSITY. IN ADDITION, A SERIES OF ONE-TO-ONE INTERVIEWS WERE CONDUCTED WITH 40 MEMBERS OF ACADEMIC FACULTY, WHO WERE TEACHING THE COURSE MODULES, ON WHICH THE STUDENTS RECEIVED THEIR OWN E-TEXTBOOK. FINDINGS - THIS RESEARCH HIGHLIGHTS THE SIGNIFICANT BENEFITS A LIBRARY LED E-TEXTBOOK SERVICE CAN OFFER TO STUDENTS, ACADEMIC FACULTY, THE WIDER UNIVERSITY AND TO THE PROFILE OF THE LIBRARY. PROVISION OF THE E-TEXTBOOKS LEADS TO HIGHER ENGAGEMENT OF LEARNING FROM STUDENTS, THEIR INCREASED SATISFACTION WITH THE UNIVERSITY AND LIBRARY, PLUS ADDRESSES ISSUE OF REDUCING THEIR DIRECT COSTS. IT ALSO LEADS TO ENHANCED PEDAGOGY FROM THE PERSPECTIVE OF ACADEMIC FACULTY. PRACTICAL IMPLICATIONS - THE PAPER ADDRESSES ISSUES OF INEQUALITY OF PROVISION FOR INDIVIDUAL STUDENTS AND CONTRIBUTES TO ENHANCED LEARNING FOR ALL STUDENTS. ORIGINALITY/VALUE - LIBRARY LED INITIATIVES ARE VERY NEW IN THIS FIELD AND THIS PROJECT IS ONE OF THE FIRST TO BOTH UNDERTAKE THIS PROVISION AND TO UNDERTAKE EXTENSIVE RESEARCH TO ASSESS THE VALUE OF THE PROJECT.
THIS ARTICLE AIMS TO CONCEPTUALIZE SOCIO-TECHNICAL SYSTEMS AS SOCIAL PRACTICES. ARTEFACTS ARE FRAMED AS EPISTEMIC OBJECTS AND PRODUCTIVE THINGS WHICH ARE CONSTITUTIVELY ENTANGLED WITH HUMAN ACTORS IN SOCIAL PRACTICES. AS TECHNOLOGY DOES NOT DETERMINE USE, PRACTICES ARE PERFORMATIVE, UNPREDICTABLY OPEN AND GENERATE PHENOMENA AND QUESTIONS WHICH CANNOT BE DERIVED FROM PRIOR ANALYSES AND THEORY. BASED ON THIS PERSPECTIVE, THE ROLE OF DATA AND ALGORITHMS IN SOCIAL PRACTICES IS DISCUSSED USING THE EXAMPLE OF LEARNING ANALYTICS. FINALLY, THE ARTICLE SETS FORTH THE ARGUMENT OF MATERIALITY AS A CONCEPT IN EDUCATIONAL THEORY, AS IT IS A CO-CONSTITUTIVE ELEMENT IN EDUCATIONAL PRACTICES.
MOOC PLATFORMS TYPICALLY GATHER DATA ON LEARNER ACTIVITY AND DEMOGRAPHICS ON MULTIPLE DATASETS FOR THEIR ANALYSIS. CROSS-ANALYSIS OF LEARNER ACTIVITY PATTERN AGAINST DEMOGRAPHIC FEATURES SUCH AS AGE, PROFESSION, AND NATIONALITY CAN PROVIDE SOME INSIGHTS ON THE RELATIONSHIP BETWEEN THE DIVERSITY OF LEARNERS AND THEIR BEHAVIOUR. THESE ANALYSES ARE OFTEN PERFORMED ON INDIVIDUAL COURSES, AND THE RESULTS DO NOT OFTEN TRANSLATE WELL ACROSS THE COURSE OFFERING. MOREOVER, DESPITE THE MASSIVE NUMBERS OF LEARNERS, DEMOGRAPHIC DATA IS OFTEN DIFFICULT TO OBTAIN, WITH A SMALL PROPORTION OF THE LEARNING COMMUNITY BEING REPRESENTED. THE ANALYSIS OF AGGREGATE DATA CAN HELP TO ENHANCE THE SIGNIFICANCE GENERALISABILITY OF THE RESULTS WHEN COMBINING ACTIVITY AND DEMOGRAPHIC DATA. HOWEVER, AGGREGATING DATA ENTAILS ANALYTICAL CHALLENGES THAT CAN SOMETIMES LEAD TO MISLEADING RESULTS. IN THIS PAPER WE ANALYSE AGGREGATE DATA FROM A SET OF MOOCS IN THE FUTURELEARN PLATFORM WHICH HAVE BEEN PRODUCED BY A BRITISH UNIVERSITY. SOME OF THE RESULTS SUGGEST THAT CERTAIN DEMOGRAPHIC FEATURES SUCH AS EDUCATION LEVEL DO NOT HAVE A SIGNIFICANT INFLUENCE IN ENGAGEMENT MEASURES SUCH AS COMPLETION OF COURSES. HOWEVER, OTHER DEMOGRAPHIC FEATURES SUCH AS AGE DO HAVE AN INFLUENCE. THE PAPER THEN CONSIDERS SOME OF THE CAVEATS OF AGGREGATING DATA FROM DIFFERENT COURSES, AND PROPOSES SOLUTIONS TO OVERCOME THEM.
THIS PAPER DESCRIBES AN ELEMENTARY SCHOOL INTERVENTION TO TEACH A TEXT-BASED PROGRAMMING LANGUAGE TO 10-11 YEAR OLD STUDENTS. WE COMPARE STUDENTS WITH NO PREVIOUS PROGRAMMING EXPERIENCE WITH STUDENTS WITH 3 SEMESTERS OF EXPERIENCE WITH A BLOCK-BASED PROGRAMMING LANGUAGE. WE ANALYZE STUDENTS' PERFORMANCE AND LEARNING BASED ON DETAILED LOGS IN AN ONLINE PROGRAMMING PLATFORM AND ON MULTIPLE CHOICE TESTS. ALTHOUGH BOTH GROUPS HAVE A SIMILAR PERCENTAGE OF SYNTACTICAL ERRORS, THE EXPERIENCED GROUP SHOWED A BETTER PERFORMANCE ON EXAM SCORES AND A LOWER NUMBER OF TEST CASE ERRORS. THESE FINDINGS SUGGEST THAT, 10-11 YEAR OLD STUDENTS BENEFIT FROM BLOCK-BASED EXPERIENCE WHEN LEARNING A NEW TEXTBASED PROGRAMMING LANGUAGE.
THE RELATIONSHIP BETWEEN PRICING AND LEARNING BEHAVIOR IS AN INCREASINGLY IMPORTANT TOPIC IN MOOC (MASSIVE OPEN ONLINE COURSE) RESEARCH. WE REPORT ON TWO CASE STUDIES WHERE COHORTS OF LEARNERS WERE OFFERED COUPONS FOR FREE-CERTIFICATES TO EXPLORE PRICE REDUCTIONS MIGHT INFLUENCE USER BEHAVIOR IN MOOC-BASED ONLINE LEARNING SETTINGS. IN CASE STUDY #1, WE COMPARE PARTICIPATION AND CERTIFICATION RATES BETWEEN COURSES WITH AND WITHOUT COUPONS FOR FREE-CERTIFICATES. IN THE COURSES WITH A FREE-CERTIFICATE TRACK, PARTICIPANTS SIGNED UP FOR THE VERIFIED CERTIFICATE TRACK AT HIGHER RATES AND COMPLETION RATES AMONG VERIFIED STUDENTS WERE HIGHER THAN IN THE PAID-CERTIFICATE TRACK COURSES. IN CASE STUDY #2, WE COMPARE THE BEHAVIORS OF LEARNERS WITHIN THE SAME COURSES BASED ON WHETHER THEY RECEIVED ACCESS TO A FREE-CERTIFICATE TRACK. ACCESS TO FREE-CERTIFICATES WAS ASSOCIATED WITH SOMEWHAT LOWER CERTIFICATION RATES, BUT OVERALL CERTIFICATION RATES REMAINED HIGH PARTICULARLY AMONG THOSE WHO VIEWED THE COURSES. THESE FINDINGS SUGGESTS THAT SOME OTHER INCENTIVES, OTHER THAN SIMPLY THE SUNK-COST OF PAYING FOR A VERIFIED CERTIFICATE-TRACK, MAY MOTIVATE LEARNERS TO COMPLETE MOOC COURSES.
THIS ARTICLE SETS OUT TO EXPLORE A SHIFT IN THE SOURCES OF EVIDENCE-OF-LEARNING IN THE ERA OF NETWORKED COMPUTING. ONE OF THE KEY FEATURES OF RECENT DEVELOPMENTS HAS BEEN POPULARLY CHARACTERIZED AS 'BIG DATA'. WE BEGIN BY EXAMINING, IN GENERAL TERMS, THE FRAME OF REFERENCE OF CONTEMPORARY DEBATES ON MACHINE INTELLIGENCE AND THE ROLE OF MACHINES IN SUPPORTING AND EXTENDING HUMAN INTELLIGENCE. WE GO ON TO EXPLORE THREE KINDS OF APPLICATION OF COMPUTERS TO THE TASK OF PROVIDING EVIDENCE-OF-LEARNING TO STUDENTS AND TEACHERS: (1) THE MECHANIZATION OF TESTS-FOR INSTANCE, COMPUTER ADAPTIVE TESTING, AND AUTOMATED ESSAY GRADING; (2) DATA MINING OF UNSTRUCTURED DATA-FOR INSTANCE, THE TEXTS OF STUDENT INTERACTION WITH DIGITAL ARTIFACTS, TEXTUAL INTERACTIONS WITH EACH OTHER, AND BODY SENSORS; (3) THE DESIGN AND ANALYSIS OF MECHANISMS FOR THE COLLECTION AND ANALYSIS OF STRUCTURED DATA EMBEDDED WITHIN THE LEARNING PROCESS-FOR INSTANCE, IN LEARNING MANAGEMENT SYSTEMS, INTELLIGENT TUTORS, AND SIMULATIONS. A CONSEQUENCE OF EACH AND ALL OF THESE DEVELOPMENTS IS THE POTENTIAL TO RECORD AND ANALYZE THE 'BIG DATA' THAT IS GENERATED. THE ARTICLE PRESENTS BOTH AN OPTIMISTIC VIEW OF WHAT MAY BE POSSIBLE AS THESE TECHNOLOGIES AND PEDAGOGIES EVOLVE, WHILE OFFERING CAUTIONARY WARNINGS ABOUT ASSOCIATED DANGERS.
THIS PAPER PROPOSES A SPECIAL SESSION ON THE USE OF COMPUTATIONAL METHODS FOR ANALYZING EDUCATIONAL DATA. COMPUTATION HAS PERMEATED ALL DISCIPLINES BECAUSE IT PROVIDES UNIQUE OPPORTUNITIES TO REPRESENT KNOWLEDGE AND UNDERSTAND COMPLEX PHENOMENA. IN EDUCATION, DISCIPLINES SUCH AS LEARNING ANALYTICS AND EDUCATIONAL DATA MINING HAVE EMERGED TO BETTER UNDERSTAND EDUCATIONAL PHENOMENA. THIS SPECIAL SESSION WILL DISCUSS THREE DIFFERENT APPROACHES TO USE COMPUTATIONAL METHODS TO ANALYZE QUALITATIVE EDUCATIONAL DATA. AFTER THE DISCUSSION, THE PARTICIPANTS WILL BE ABLE TO IMPLEMENT THESE METHODS USING R PROGRAMMING, WHILE REFLECTING ON HOW THEY CAN USE THESE METHODS IN THEIR OWN CONTEXT.
THE ADOPTION OF LEARNING MANAGEMENT SYSTEMS IN EDUCATION HAS BEEN INCREASING IN THE LAST FEW YEARS. VARIOUS DATA MINING TECHNIQUES LIKE PREDICTION, CLUSTERING AND RELATIONSHIP MINING CAN BE APPLIED ON EDUCATIONAL DATA TO STUDY THE BEHAVIOR AND PERFORMANCE OF THE STUDENTS. THIS PAPER EXPLORES THE DIFFERENT DATA MINING APPROACHES AND TECHNIQUES WHICH CAN BE APPLIED ON EDUCATIONAL DATA TO BUILD UP A NEW ENVIRONMENT GIVE NEW PREDICTIONS ON THE DATA. THIS STUDY ALSO LOOKS INTO THE RECENT APPLICATIONS OF BIG DATA TECHNOLOGIES IN EDUCATION AND PRESENTS A LITERATURE REVIEW ON EDUCATIONAL DATA MINING AND LEARNING ANALYTICS.
IN RECENT YEARS, SCHOOLS AND UNIVERSITIES HAVE BECOME MORE FOCUSED ON HOW TO ALLOW LEARNERS TO LEARN SUCCESSFULLY, AND IT HAS BECOME AN EXPECTATION TO DESIGN INSTRUCTION IN A WAY THAT TAKES INTO ACCOUNT THE INDIVIDUAL DIFFERENCES OF LEARNERS. ACCORDINGLY, THE PURPOSE OF THIS STUDY IS TO PREDICT, AT AN EARLIER STAGE IN A COURSE, WHICH STUDENTS ARE LIKELY TO FAIL, SO THAT ADEQUATE SUPPORT CAN BE PROVIDED FOR THEM. WE PROPOSED A NEW APPROACH TO IDENTIFY SUCH STUDENTS USING FREE-RESPONSE SELF-REFLECTION SHEETS. THIS METHOD USES THE UNRESTRICTED COMMENTS FROM THE STUDENTS TO CREATE A COMMENT VECTOR THAT CAN BE USED TO PREDICT WHO ARE LIKELY TO FAIL THE COURSE. SUBSEQUENTLY, WE CONDUCTED EXPERIMENTS TO VERIFY THE EFFECTIVENESS OF THIS PREDICTION. IN COMPARISON TO METHODS USED IN EXISTING RESEARCH WHICH PREDICT POTENTIAL FAILURES USING QUIZ SCORES AND THE STUDENTS' SUBJECTIVE LEVEL OF UNDERSTANDING, OUR PROPOSED METHOD WAS ABLE TO IMPROVE THE PREDICTION PERFORMANCE. IN ADDITION, WHEN CUMULATIVE DATA AFTER SEVERAL SESSIONS WERE USED TO PREDICT WHICH STUDENTS WERE LIKELY TO FAIL, THE PREDICTIONS MADE BY THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM SHOWED A CONSISTENT PREDICTION PERFORMANCE, AND THE PREDICTION ACCURACY WAS HIGHER THAN THAT OF OTHER ALGORITHMS.
PROGRAMMING SKILLS AND COMPUTATIONAL THINKING ARE FUNDAMENTAL CONCERNS IN PROFESSIONAL TRAINING IN SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) DISCIPLINES. IN SPITE OF THIS, IN THE CHILEAN HIGHER EDUCATION SYSTEM, AS IN MOST PARTS OF THE WORLD, TEACHING PROGRAMMING IS A CHALLENGING ENDEAVOR, PARTLY BECAUSE THE MAJORITY OF FRESHMEN STUDENTS IN STEM UNDERGRADUATE PROGRAMS HAVE NOT BEEN INTRODUCED TO THE FUNDAMENTALS OF THE DISCIPLINE AND ITS APPLICATIONS IN K12 EDUCATION. THEREFORE, STUDENTS' ATTITUDES, ENGAGEMENT, AND AUTONOMOUS LEARNING CAN MAKE A DIFFERENCE IN HOW STUDENTS IN STEM DISCIPLINES IN HIGHER EDUCATION FACE THEIR FIRST PROGRAMMING COURSE. IN THE PRESENT STUDY, WE CARRIED OUT MEASUREMENT OF VARIABLES ASSOCIATED TO THESE CONSTRUCTS INVOLVING A SAMPLE OF 1694 FRESHMEN OF SEVENTEEN STEM PROGRAMS IN A CHILEAN RESEARCH UNIVERSITY. RELEVANT DIFFERENCES WERE FOUND AMONG DIFFERENT STEM PROGRAMS, AND BY STUDENT GENDER. IMPLICATIONS OF THE RESULTS OBTAINED AND FUTURE RESEARCH AVENUES ARE DISCUSSED.
PROGRAMMING IS AN IMPORTANT SKILL IN THE 21ST CENTURY, BUT IT IS DIFFICULT FOR NOVICES TO LEARN. TO HELP STUDENTS PRACTICE AND LEARN EFFICIENTLY, THE AUTHORS DEVELOPED A MOBILE PLATFORM CALLED DAILY QUIZ, WHICH INCORPORATED DISTRIBUTED PRACTICE THEORY. TO EVALUATE THE IMPACT OF DISTRIBUTED PRACTICE IN PROGRAMMING LEARNING FACILITATED BY DAILY QUIZ, THE AUTHORS CONDUCTED A BETWEEN-SUBJECT EXPERIMENT WITH 200 FRESHMEN DIVIDED INTO TWO GROUPS. BOTH GROUPS RECEIVED THE SAME NUMBER OF MULTIPLE-CHOICE QUESTIONS VIA DAILY QUIZ. HOWEVER, THE CONTROL GROUP WAS ENCOURAGED TO PRACTICE EVERY 7 DAYS, WHEREAS THE EXPERIMENTAL GROUP WAS ENCOURAGED TO PRACTICE EVERY 3 DAYS. THE RESULTS SHOWED THAT THIS SIMPLE MANIPULATION SIGNIFICANTLY IMPROVED THE EXPERIMENTAL GROUP'S PERFORMANCE ON FINAL EXAMS. FURTHER ANALYSIS REVEALED THAT THE EXPERIMENTAL GROUP OF STUDENTS ACHIEVED A HIGHER RATE OF FIRST-CHECK CORRECTNESS AND TENDED TO BE MORE ENGAGED IN ACADEMIC SOCIAL INTERACTION. FINALLY, A BEHAVIORAL SEQUENCE ANALYSIS WAS ADOPTED TO COMPARE THE BEHAVIORAL PATTERNS OF THE TWO GROUPS TO INVESTIGATE HOW DISTRIBUTED PRACTICE HELPED THE STUDENTS LEARN MORE EFFICIENTLY.
IN THIS PAPER, WE PROPOSE A SYSTEM FOR IMPROVING THE DISCUSSION SKILLS OF PARTICIPANTS IN A MEETING BY AUTOMATICALLY EVALUATING STATEMENTS IN THE MEETING AND EFFECTIVELY FEEDING BACK THE RESULTS OF THE EVALUATION TO THEM. TO EVALUATE THE SKILLS AUTOMATICALLY, THE SYSTEM USES BOTH ACOUSTIC FEATURES AND LINGUISTIC FEATURES OF STATEMENTS. IT EVALUATES THE WAY A PERSON SPEAKS, SUCH AS THEIR "VOICE SIZE," ON THE BASIS OF THE ACOUSTIC FEATURES, AND IT ALSO EVALUATES THE CONTENTS OF A STATEMENT, SUCH AS THE "CONSISTENCY OF CONTEXT," ON THE BASIS OF LINGUISTIC FEATURES. THESE FEATURES CAN BE OBTAINED FROM MEETING MINUTES. SINCE IT IS DIFFICULT TO EVALUATE THE SEMANTIC CONTENTS OF STATEMENTS SUCH AS THE "CONSISTENCY OF CONTEXT," WE BUILD A MACHINE LEARNING MODEL THAT USES THE FEATURES OF MINUTES SUCH AS SPEAKER ATTRIBUTES AND THE RELATIONSHIP OF STATEMENTS. IN ADDITION, WE ARGUE THAT PARTICIPANTS' HEART RATE (HR) DATA CAN BE USED TO EFFECTIVELY EVALUATE THEIR COGNITIVE PERFORMANCE, SPECIFICALLY THE PERFORMANCE IN A DISCUSSION THAT CONSISTS OF SEVERAL Q&A SEGMENTS (QUESTION-AND-ANSWER PAIRS). WE COLLECT HR DATA DURING A DISCUSSION IN REAL TIME AND GENERATE MACHINE-LEARNING MODELS FOR EVALUATION. WE CONFIRMED THAT THE PROPOSED METHOD IS EFFECTIVE FOR EVALUATING THE DISCUSSION SKILLS OF MEETING PARTICIPANTS.
STUDENT LEARNING AND ACADEMIC PERFORMANCE HINGE LARGELY ON FREQUENCY OF CLASS ATTENDANCE AND PARTICIPATION. THE FINGERPRINT RECOGNITION SYSTEM AIMS AT PROVIDING AN ACCURATE AND EFFICIENT ATTENDANCE MANAGEMENT SERVICE TO STAFF AND STUDENTS WITHIN AN EXISTING PORTAL SYSTEM. THE INTEGRATION OF A UNIQUE AND ACCURATE IDENTIFICATION SYSTEM INTO THE EXISTING PORTAL SYSTEM OFFERS AT LEAST, TWO ADVANTAGES: ACCURATE AND EFFICIENT ANALYSIS AND REPORTING OF STUDENT ATTENDANCE ON A CONTINUOUS BASIS; AND ALSO FACILITATING THE PROVISION OF PERSONALIZED SERVICES, ENHANCING USER EXPERIENCE ALTOGETHER. AN INTEGRATED PORTAL SYSTEM WAS DEVELOPED TO AUTOMATE ATTENDANCE MANAGEMENT AND TESTED FOR FIFTY STUDENTS IN FIVE ATTEMPTS. THE 98% ACCURACY ACHIEVED BY THE SYSTEM POINTS TO THE FEASIBILITY OF LARGE SCALE DEPLOYMENT AND INTEROPERABILITY OF MULTIPLE DEVICES USING EXISTING TECHNOLOGY INFRASTRUCTURE.
THE DATA IN HIGHER EDUCATIONAL INSTITUTIONS COME FROM THE INTERACTION OF STUDENTS WITH THE VARIOUS ONLINE SYSTEMS, SUCH AS LEARNING MANAGEMENT, REGISTRATION, ADVISING AND EMAIL. RESEARCH IN THE FIELD OF EDUCATIONAL DATA MINING IS CONCERNED WITH THE COLLECTION AND ANALYSIS OF SUCH DATA TO DISCOVER NEW INSIGHTS ABOUT STUDENT BEHAVIOR, LEARNING STYLE AND SUCCESS FACTORS. SINCE DIFFERENT DEPARTMENTS IN AN INSTITUTION MANAGE DIFFERENT IT SYSTEMS, COLLECTING DATA FROM ALL OF THESE DEPARTMENTS REQUIRES COLLABORATION. THE DATA HAS TO BE EXTRACTED FROM MANY SYSTEMS, WHICH USES DIFFERENT DATA FORMATS. THEREFORE, A TYPICAL RESEARCH WORK IN THIS FIELD ANALYZES DATA EXTRACTED FROM ONE SYSTEM. THIS PAPER, ON THE OTHER HAND, ANALYZES THE NETWORK TRAFFIC THAT STUDENTS GENERATE WHILE ON CAMPUS. THIS APPROACH PROVIDES US WITH A BETTER VIEW OF THE STUDENT INTERACTION WITH THE EDUCATIONAL SYSTEMS, COMPARED TO THE SINGLE VIEW ACHIEVED BY ANALYZING DATA FROM ONE SYSTEM. WE ANONYMIZE STUDENT PERSONALLY IDENTIFIABLE INFORMATION TO PROTECT STUDENT PRIVACY. FURTHER, WE PROPOSE THE USE OF FOG COMPUTING TO ENHANCE STUDENT PRIVACY AND REDUCE NETWORK LOAD.
WHILE MOOCS AND OTHER FORMS OF LARGE-SCALE LEARNING ARE OF GROWING IMPORTANCE, THE VAST MAJORITY OF TERTIARY STUDENTS STILL STUDY IN TRADITIONAL FACE-TO-FACE SETTINGS. THIS PAPER EXAMINES SOME OF THE CHALLENGES IN ATTEMPTING TO APPLY THE BENEFITS OF LARGE-SCALE LEARNING TO THESE SETTINGS, BUILDING ON A GROWING REPOSITORY OF CROSS-INSTITUTIONAL DATA.
IN TECHNOLOGY ENHANCED LANGUAGE LEARNING, MANY PEDAGOGICAL ACTIVITIES INVOLVE STUDENTS IN ONLINE DISCUSSION SUCH AS SYNCHRONOUS CHAT, IN ORDER TO HELP THEM PRACTICE THEIR LANGUAGE SKILLS. BESIDES DEVELOPING THE LANGUAGE COMPETENCY OF STUDENTS, IT IS ALSO CRUCIAL TO NURTURE THEIR TEAMWORK COMPETENCIES FOR TODAY'S GLOBAL AND COMPLEX ENVIRONMENT. LANGUAGE COMMUNICATION IS AN IMPORTANT GLUE OF TEAMWORK. IN ORDER TO ASSESS THE LANGUAGE OF CHAT FOR TEAMWORK DIMENSIONS, SEVERAL TEXT MINING METHODS ARE POSSIBLE. HOWEVER, DIFFICULTIES ARISE SUCH AS PRE-PROCESSING BEING A BLACK BOX AND CLASSIFICATION APPROACHES AND ALGORITHMS BEING DEPENDENT ON THE CONTEXT. TO ADDRESS THESE ISSUES, THE STUDY WILL EVALUATE AND EXPLAIN PREPROCESSING AND CLASSIFICATION METHODS USED TO ANALYZE TEAMWORK DIALOGUE FROM A DATASET OF CHAT DATA. ANALYTICS METHODS EVALUATED IN THIS STUDY PROVIDE A DIRECTION FOR ASSESSING THE LANGUAGE OF CHAT FOR TEAMWORK DIALOGUE AND CAN HELP EXTEND THE WORK OF TECHNOLOGY ENHANCED LANGUAGE LEARNING TO NOT ONLY FOCUS ON ACADEMIC COMPETENCY, BUT ON THE COMMUNICATION ASPECT TOO.
THIS PAPER PROPOSES A COMPETENCE ANALYTICS FRAMEWORK CALLED SCALE (SMART COMPETENCE ANALYTICS ON LEARNING) AND HIGHLIGHTS HOW IT CAN BE APPLIED TO TRACK THE FORMATION AND DEVELOPMENT OF LEARNERS' COMPETENCES IN THE ENGLISH WRITING DOMAIN. SCALE IS ESSENTIALLY A GENERIC COMPETENCE ANALYTICS SOLUTION DESIGNED FOR ALL LEARNING DOMAINS. THIS PAPER SHOWS IN DETAIL HOW SCALE COLLECTS IN A TIMELY MANNER EVIDENCES TO TRACK THE DEVELOPMENT OF ENGLISH WRITING SKILLS. AS NEW EVIDENCES COME IN, EVERY COMPETENCE WILL BE RE-ASSESSED QUANTITATIVELY SO THAT EVERY CHANGE IN COMPETENCE LEVEL CAN BE RECORDED OVER TIME. USING AN EXPERIMENTAL DATASET, THE PAPER CONCLUDES BY EXHIBITING A SUBSET OF INSIGHTS POSSIBLE WITH SCALE.
THE ADVENT AND RISE OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BROUGHT MANY ISSUES TO THE AREA OF EDUCATIONAL TECHNOLOGY. RESEARCHERS IN THE FIELD HAVE BEEN ADDRESSING THESE ISSUES SUCH AS PEDAGOGICAL QUALITY OF MOOCS, HIGH ATTRITION RATES, AND SUSTAINABILITY OF MOOCS. HOWEVER, MOOCS PERSONALISATION HAS NOT BEEN SUBJECT OF THE WIDE DISCUSSIONS AROUND MOOCS. THIS PAPER PRESENTS A CRITICAL LITERATURE SURVEY AND ANALYSIS OF THE AVAILABLE LITERATURE ON PERSONALISATION IN MOOCS TO IDENTIFY THE NEEDS, THE CURRENT STATES AND EFFORTS TO PERSONALISE LEARNING IN MOOCS. THE FINDINGS ILLUSTRATE THAT THERE IS A GROWING ATTENTION TO PERSONALISATION TO IMPROVE LEARNERS' INDIVIDUAL LEARNING EXPERIENCES IN MOOCS. IN ORDER TO IMPLEMENT PERSONALISED SERVICES, PERSONALISED LEARNING PATH, PERSONALISED ASSESSMENT AND FEEDBACK, PERSONALISED FORUM THREAD AND RECOMMENDATION SERVICE FOR RELATED LEARNING MATERIALS OR LEARNING TASKS ARE COMMONLY APPLIED.
THIS ARTICLE EXAMINES THE EMERGENCE OF DIGITAL GOVERNANCE' IN PUBLIC EDUCATION IN ENGLAND. DRAWING ON AND COMBINING CONCEPTS FROM SOFTWARE STUDIES, POLICY AND POLITICAL STUDIES, IT IDENTIFIES SOME SPECIFIC APPROACHES TO DIGITAL GOVERNANCE FACILITATED BY NETWORK-BASED COMMUNICATIONS AND DATABASE-DRIVEN INFORMATION PROCESSING SOFTWARE THAT ARE BEING DISCURSIVELY PROMOTED IN EDUCATION BY CROSS-SECTORAL INTERMEDIARY ORGANIZATIONS. SUCH INTERMEDIARIES, INCLUDING NATIONAL ENDOWMENT FOR SCIENCE, TECHNOLOGY AND THE ARTS, DEMOS, THE INNOVATION UNIT, THE EDUCATION FOUNDATION AND THE NOMINET TRUST, ARE INCREASINGLY SEEKING TO PARTICIPATE IN NEW DIGITALLY MEDIATED FORMS OF EDUCATIONAL GOVERNANCE. THROUGH THEIR PROMOTION OF NETWORK-BASED PEDAGOGIES AND DATABASE-DRIVEN ANALYTICS SOFTWARE, THESE ORGANIZATIONS ARE SEEKING TO DELEGATE EDUCATIONAL DECISION-MAKING TO SOCIO-ALGORITHMIC FORMS OF POWER THAT HAVE THE CAPACITY TO PREDICT, GOVERN AND ACTIVATE LEARNERS' CAPACITIES AND SUBJECTIVITIES.
THE ABILITY TO CAPTURE LARGE AMOUNTS OF DATA THAT DESCRIBE THE INTERACTIONS OF LEARNERS BECOMES USEFUL WHEN ONE HAS A FRAMEWORK IN WHICH TO MAKE SENSE OF THE PROCESSES OF LEARNING IN COMPLEX LEARNING ENVIRONMENTS. THROUGH THE ANALYSIS OF SUCH DATA, ONE IS ABLE TO UNDERSTAND WHAT IS HAPPENING IN THESE NETWORKS; HOWEVER, DECIDING WHICH ELEMENTS WILL BE OF MOST INTEREST IN A SPECIFIC LEARNING CONTEXT AND HOW TO PROCESS, VISUALIZE, AND ANALYZE LARGE AMOUNTS OF DATA REQUIRES THE USE OF ANALYTICAL TOOLS THAT ADEQUATELY SUPPORT THE PHASES OF THE RESEARCH PROCESS. IN THIS ARTICLE, WE DISCUSS THE SELECTION, PROCESSING, VISUALIZATION, AND ANALYSIS OF MULTIPLE ELEMENTS OF LEARNING AND LEARNING ENVIRONMENTS AND THE LINKS BETWEEN THEM. WE DISCUSS, USING THE CASES OF TWO LEARNING ENVIRONMENTS, HOW STRUCTURE AFFECTS THE BEHAVIOR OF LEARNERS AND, IN TURN, HOW THAT BEHAVIOR HAS THE POTENTIAL TO AFFECT LEARNING. THIS APPROACH WILL ALLOW US TO SUGGEST POSSIBLE WAYS OF IMPROVING FUTURE DESIGNS OF LEARNING ENVIRONMENTS.
NEW INTERNET BASED TEACHING & LEARNING PLATFORMS, WHICH DEEPLY INTEGRATE INFORMATION TECHNOLOGY WITH EDUCATION TECHNOLOGY, ARE RICH IN FUNCTIONS AND EASY TO USE, AND THUS ARE WELCOMED BY FRONT-LINE TEACHERS. DIFFERENCES IN THE RESPECTS OF REALIZATION OF TEACHING PHILOSOPHY, SERVICE OBJECT, FEATURE SETS, AND SO ON LEAD TO THE FACT THAT SINGLE TEACHING & LEARNING PLATFORM COULD HARDLY MEET ALL THE TEACHING NEEDS OF SOME SPECIFIC COURSE. TO SOLVE THIS PROBLEM, THE IDEA OF DESIGNING BLENDED TEACHING MODE WITH THE SUPPORT OF MULTIPLE TEACHING & LEARNING PLATFORMS IS PROPOSED, AND A CONCRETE IMPLEMENTATION OF THIS IDEA IS GIVEN WITH A PROGRAMMING COURSE AS THE RESEARCH CASE. IN THIS MODE, TRADITIONAL ON-SITE TEACHING PROCESS IS REFACTORED WITH SEVERAL EMBEDDED ONLINE LEARNING ACTIVITIES BASED ON MORE THAN ONE TEACHING & LEARNING PLATFORMS. THREE TYPES OF LEARNER ARE IDENTIFIED BY CLUSTERING ANALYSIS OF LEARNING DATA GENERATED FROM ONLINE LEARNING ACTIVITIES. FURTHER ANALYSIS REVEALS THE STYLE AND PERFORMANCE OF EACH TYPE OF LEARNER, AND THUS PROVIDES THE ORIENTATION OF CONTINUOUS IMPROVEMENT IN TEACHING.
THIS DATA ARTICLE PRESENTS DATA ON THE RESULTS IN NATIONAL ASSESSMENTS FOR SECONDARY AND UNIVERSITY EDUCATION IN ENGINEERING STUDENTS. THE DATA CONTAINS ACADEMIC, SOCIAL, ECONOMIC INFORMATION FOR 12,411 STUDENTS. THE DATA WERE OBTAINED BY ORDERLY CROSSING THE DATABASES OF THE COLOMBIAN INSTITUTE FOR THE EVALUATION OF EDUCATION (ICFES). THE STRUCTURE OF THE DATA ALLOWS US TO OBSERVE THE INFLUENCE OF SOCIAL VARIABLES AND THE EVOLUTION OF STUDENTS' LEARNING SKILLS. IN ADDITION TO SERVING AS INPUT TO DEVELOP ANALYSIS OF ACADEMIC EFFICIENCY, STUDENT RECOMMENDATION SYSTEMS AND EDUCATIONAL DATA MINING. THE DATA IS PRESENTED IN COMMA SEPARATED VALUE FORMAT. DATA CAN BE EASILY ACCESSED THROUGH THE MENDELEY DATA REPOSITORY (HTTPS://DATA.MENDELEY.COM/DATASETS/83TCX8PSXV/1). (C) 2020 THE AUTHOR(S). PUBLISHED BY ELSEVIER INC.
THIS PAPER PRESENTS A STUDY ON STUDENTS' ENGAGEMENT AND PERSONALIZED WEEKLY PERFORMANCE NOTIFICATIONS. STUDENTS WERE OFFERED TO VOLUNTARILY OPT-IN TO RECEIVE CUSTOMIZED NOTIFICATIONS REGARDING THEIR PREDICTED COURSE PERFORMANCES AND RECOMMENDED RESOURCES. IN ADDITION, THE PREDICTED AT-RISK STUDENTS WERE ALSO RECOMMENDED WITH CODE SOLUTIONS FROM HIGHER PERFORMERS IN THE CLASS. DATA WAS COLLECTED FROM COMPUTER SCIENCE PROGRAMMING COURSES. STUDENTS' ENGAGEMENT WITH THE NOTIFICATIONS AND RESOURCES WERE TRACKED AND HAVE BEEN FOUND TO BE AN INDICATOR OF THEIR DIFFERENTIAL IMPROVEMENT BETWEEN THEIR EXAMS.
IN THE LAST DECADE, THE DEVELOPMENT OF AUTHORING SYSTEMS FOR WEB-BASED TRAININGS (WBT) FOCUSED ON FEATURES ESPECIALLY FOR THE TRAINEES. IN THE CONTEXT OF THE FAST CHANGING WEB, THESE ARE FEATURES LIKE RESPONSIVE DESIGN AND SOCIAL INTERACTIONS. BUT THERE ARE NEEDS REGARDING THE AUTHOR, EXCEPT TO MAKE THE CONTENT PRODUCTION MORE EFFICIENT. HOW WOULD HE CHANGE THE CONTENT IF HE KNEW THAT ONE PAGE OF HIS WBT WAS NEVER VISITED BY AN USER OR THE RETENTION TIME OF A VIDEO DROPS IN MOBILE SCENARIOS? IS THERE A NEED TO GENERATE SPECIALIZED CONTENT BASED ON THE TARGET SYSTEM (DESKTOP OR SMARTPHONE), SUCH AS PROVIDING DIFFERENT MEDIA OR TEXT LENGTHS? THE MAIN IDEA OF THIS PAPER IS TO PROVIDE INFORMATION ABOUT THE CONTENT TO AN AUTHOR, RIGHT WHERE THE CONTENT WAS PLACED. THIS COULD BE USED TO PROVIDE INSIGHTS ON HOW THE AUTHOR COULD REVISE HIS CONTENT. WE PROPOSE A PIPELINE TO CREATE APPROPRIATE VISUALIZATIONS BASED ON THE CURRENT CONTENT TYPE. FURTHERMORE, WE PRESENT A META DATA SET THAT NEEDS TO BE COLLECTED DURING THE USAGE OF A WBT.
SINCE ITS INCEPTION GENETIC PROGRAMMING, AND LATER VARIATIONS SUCH AS GRAMMAR-BASED GENETIC PROGRAMMING AND GRAMMATICAL EVOLUTION, HAVE CONTRIBUTED TO VARIOUS DOMAINS SUCH AS CLASSIFICATION, IMAGE PROCESSING, SEARCH-BASED SOFTWARE ENGINEERING, AMONGST OTHERS. THIS PAPER EXAMINES THE ROLE THAT GENETIC PROGRAMMING HAS PLAYED IN EDUCATION. THE PAPER FIRSTLY PROVIDES AN OVERVIEW OF THE IMPACT THAT GENETIC PROGRAMMING HAS HAD IN TEACHING AND LEARNING. THE USE OF GENETIC PROGRAMMING IN INTELLIGENT TUTORING SYSTEMS, PREDICTING STUDENT PERFORMANCE AND DESIGNING LEARNING ENVIRONMENTS IS EXAMINED. A CRITICAL ANALYSIS OF GENETIC PROGRAMMING IN EDUCATION IS PROVIDED. THE PAPER THEN EXAMINES FUTURE DIRECTIONS OF RESEARCH AND CHALLENGES IN THE APPLICATION OF GENETIC PROGRAMMING IN EDUCATION.
LEARNING DESIGN, AS A FIELD OF RESEARCH, PROVIDES PRACTITIONERS WITH GUIDELINES TOWARDS MORE EFFECTIVE TEACHING AND LEARNING. IN PARALLEL, OBSERVATIONAL METHODS (MANUAL OR AUTOMATED) HAVE BEEN USED IN THE CLASSROOM TO REFLECT ON AND REFINE TEACHING AND LEARNING, OFTEN IN COMBINATION WITH OTHER DATA SOURCES (SUCH AS SURVEYS AND INTERVIEWS). DESPITE THE FACT THAT BOTH LEARNING DESIGN AND CLASSROOM OBSERVATION AIM TO SUPPORT TEACHING AND LEARNING PRACTICES (RESPECTIVELY A PRIORI OR A POSTERIORI), THEY ARE NOT OFTEN ALIGNED. TO BETTER UNDERSTAND THE POTENTIAL SYNERGIES BETWEEN THESE TWO STRATEGIES, THIS PAPER REPORTS ON A SYSTEMATIC LITERATURE REVIEW BASED ON 24 WORKS THAT CONNECT LEARNING DESIGN AND CLASSROOM OBSERVATIONS. THE REVIEW ANALYSES THE PURPOSES OF THE STUDIES, THE STAKEHOLDERS INVOLVED, THE METHODOLOGICAL ASPECTS OF THE STUDIES, AND HOW DESIGN AND OBSERVATIONS ARE CONNECTED. THIS REVIEW REVEALS THE NEED FOR COMPUTER-INTERPRETABLE DOCUMENTED DESIGNS; THE LACK OF REPORTED SYSTEMATIC APPROACHES AND TECHNOLOGICAL SUPPORT TO CONNECT THE (MULTIMODAL) OBSERVATIONS WITH THE CORRESPONDING LEARNING DESIGNS; AND, THE PREDOMINANCE OF HUMAN-MEDIATED OBSERVATIONS OF THE PHYSICAL SPACE, WHOSE APPLICABILITY AND SCALABILITY ARE LIMITED BY THE HUMAN RESOURCES AVAILABLE. THE ADOPTION OF ICT TOOLS TO SUPPORT THE DESIGN PROCESS WOULD CONTRIBUTE TO EXTRACTING THE CONTEXT OF THE OBSERVATIONS AND THE PEDAGOGICAL FRAMEWORK FOR THE ANALYSIS. MOREOVER, EXTENDING THE TRADITIONAL MANUAL OBSERVATIONS WITH MULTIMODAL LEARNING ANALYTIC TECHNIQUES, WOULD NOT ONLY REDUCE THE OBSERVATION BURDEN BUT ALSO SUPPORT THE SYSTEMATIC DATA COLLECTION, INTEGRATION, AND ANALYSIS, ESPECIALLY IN SEMI-STRUCTURED AND STRUCTURED STUDIES.
SUCCESSFUL TEACHING AND LEARNING IN HIGHER EDUCATION IS CURRENTLY ASSOCIATED WITH THE USE OF NEW INFORMATION AND COMMUNICATION TECHNOLOGIES. LEARNING/LEARNER-CENTERED PRACTICE AND RESEARCH HAVE PUT INCREASED EMPHASIS ON ACTIVE, SELF-REGULATED AND, LATELY, SOCIAL LEARNING. SOCIAL CONSTRUCTIVIST THEORETICIANS AND PRACTITIONERS HAVE INVESTIGATED HOW SUCCESSFUL LEARNING IS SUPPORTED AND STIMULATED BY DISCUSSING, SHARING, COLLABORATING AND CONTRIBUTING KNOWLEDGE IN WEB 2.0/SOCIAL MEDIA ENVIRONMENTS WHERE PEERS AND TEACHERS ARE JUST ONE CLICK AWAY. IN 2015, THE AUTHORS INVESTIGATED STUDENTS' PERCEPTIONS OF EXTENDING THE ACTIVITY ON FACEBOOK, REVEALING THAT STUDENTS ACTIVE ON FACEBOOK PERFORM BETTER ON THE LEARNING MANAGEMENT PLATFORM, HAVING SUPERIOR LEARNING OUTCOMES. CONSEQUENTLY, A FACEBOOK CLOSED GROUP WAS ATTACHED TO THE LANGUAGE LEARNING COURSE AS A SPACE OF COMMUNICATION AND INFORMAL LEARNING WHERE STUDENTS MAY ACTIVELY CONTRIBUTE AND INTERACT WITH THEIR PEERS AND TEACHER. THE PRESENT RESEARCH AIMS TO FIND OUT WHETHER THIS SPACE ALLOWS FOR ACTIVE AND INTERACTIVE LEARNING, WHILE TRIGGERING BETTER ENGAGEMENT WITH COURSE CONTENT. STUDENTS' REPORTED MOTIVATION, FREQUENCY AND EASE OF USE OF THEIR FACEBOOK ACTIVITY ARE COMPARED WITH THEIR REAL ACTIVITY IN THE GROUP. DATA ARE COLLECTED BY MEANS OF THE QUESTIONNAIRE VALIDATED IN THE PREVIOUS STUDY AND FACEBOOK DATA. THE RESULTS ARE EXPECTED TO CONTRIBUTE TO EXISTING EMPIRICAL DATA ON THE BENEFITS AND CHALLENGES OF USING SOCIAL MEDIA FOR EDUCATIONAL PURPOSES, FOR ACTIVE LEARNING BEYOND THE CLASSROOM AND SUGGEST POSSIBLE CHANGES IN PEDAGOGICAL DESIGN TO ENSURE HIGHER LEVELS OF ENGAGEMENT AND CORRELATION WITH ACADEMIC SUCCESS.
IN THIS FULL REVIEW PAPER, THE RECENT EMERGING TRENDS IN EDUCATIONAL DATA SCIENCE HAVE BEEN REVIEWED AND EXPLORED TO ADDRESS THE RECENT TOPICS AND CONTRIBUTIONS IN THE ERA OF SMART EDUCATION. THIS INCLUDES A SET OF RIGOROUSLY REVIEWED WORLD-CLASS MANUSCRIPTS ADDRESSING AND DETAILING STATE-OF-THE-ART, FRAMEWORKS AND TECHNIQUES RESEARCH PROJECTS IN THE AREA OF DATA SCIENCE APPLIED TO EDUCATION, USING DIFFERENT APPROACHES SUCH AS INFORMATION FUSION, SOFT COMPUTING, MACHINE LEARNING, AND INTERNET OF THINGS, AMONG OTHERS. BASED ON THIS SYSTEMATIC REVIEW, WE HAVE PUT SOME RECOMMENDATIONS AND SUGGESTIONS FOR RESEARCHERS, PRACTITIONERS AND SCHOLARS TO IMPROVE THEIR RESEARCH QUALITY IN THIS AREA.
DISCUSSION FORUMS PROVIDE STUDENTS WITH ACCESSIBLE PLATFORMS FOR GROUP DISCUSSIONS IN E-LEARNING ENVIRONMENTS. THEY ALSO HELP LECTURERS TO TRACK AND CHECK STUDENT DISCUSSIONS. TO IMPROVE STUDENT LEARNING, IT IS IMPORTANT FOR LECTURERS TO IDENTIFY STUDENTS' COGNITIVE ENGAGEMENT IN DISCUSSION FORUMS. THEREFORE, THIS STUDY AIMS TO INVESTIGATE STUDENTS' COGNITIVE ENGAGEMENT IN E-LEARNING THROUGH CONTENT ANALYSIS OF FORUM POSTS. A TOTAL OF 267 FORUM POSTS CREATED BY STUDENTS DURING ONE SEMESTER WERE COLLECTED FOR ANALYSIS. INFERENTIAL STATISTICS WERE APPLIED TO EXPLORE THE RELATIONSHIP BETWEEN STUDENTS' COGNITIVE ENGAGEMENT AND THEIR GENDER AND THE NUMBER OF POSTS IN FORUMS. THE RESULTS REVEALED THAT ABOUT HALF OF THE STUDENTS GAVE THEIR POSTS WITHOUT ANY EXPLANATIONS, WHICH REFLECTED A LOW LEVEL OF COGNITIVE ENGAGEMENT. A LARGE NUMBER OF POSTS MADE LITTLE CONTRIBUTION TO A HIGH LEVEL OF COGNITIVE ENGAGEMENT. THE RESULTS ALSO SHOWED NO RELATIONSHIP BETWEEN GENDER AND THE LEVEL OF COGNITIVE ENGAGEMENT. THE LIMITATIONS AND IMPLICATIONS OF THIS STUDY ARE ALSO DISCUSSED.
A LEARNER'S GUESSING BEHAVIOR WHILE PLAYING EDUCATIONAL GAMES CAN BE A KEY INDICATOR OF HER DISENGAGEMENT THAT IMPACTS LEARNING NEGATIVELY. TO DISTINGUISH A LEARNER'S GUESSING BEHAVIOR FROM SOLUTION BEHAVIOR, WE PRESENT AN EXPLORATIVE STUDY OF USING MOTION FEATURES, WHICH REPRESENT A LEARNER'S FINGER MOVEMENTS ON A TABLET SCREEN. OUR DATA WAS COLLECTED FROM THE MISSING NUMBER GAME OF KITKIT SCHOOL, A TABLET-BASED MATH GAME DESIGNED FOR CHILDREN FROM PRE-K TO GRADE 2 IN ELEMENTARY SCHOOL. A TOTAL OF 5,040 PROBLEM SOLVING LOGS, WHICH WERE COLLECTED FROM 168 STUDENTS, WERE ANALYZED. A TWO-SAMPLE T-TEST SHOWED A SIGNIFICANT DIFFERENCE BETWEEN GUESSING AND SOLUTION BEHAVIOR FOR FOUR GROUPS OF MOTION FEATURES THAT INDICATE DISTANCE, CURVEDNESS, COMPLEXITY, AND PAUSE (P<0.001). ADDITIONALLY, OUR EMPIRICAL RESULTS SHOWED THE POSSIBILITY OF USING MOTION FEATURES IN AUTOMATIC DETECTION OF GUESSING BEHAVIOR. OUR BEST MODEL YIELDED AN ACCURACY OF 0.778 AND AUC VALUE OF 0.851 BY USING THE RANDOM FOREST CLASSIFIER.
IN RECENT YEARS, INTERACTIVE TEXTBOOKS HAVE GAINED PROMINENCE IN AN EFFORT TO OVERCOME STUDENT RELUCTANCE TO ROUTINELY READ TEXTBOOKS, COMPLETE ASSIGNED HOMEWORKS, AND TO BETTER ENGAGE STUDENTS TO KEEP UP WITH LECTURE CONTENT. INTERACTIVE TEXTBOOKS ARE MORE STRUCTURED, CONTAIN SMALLER AMOUNTS OF TEXTUAL MATERIAL, AND INTEGRATE MEDIA AND ASSESSMENT CONTENT. WHILE THESE ARE AN ARGUABLE IMPROVEMENT OVER TRADITIONAL METHODS OF TEACHING, ISSUES OF ACADEMIC INTEGRITY AND ENGAGEMENT REMAIN. IN THIS WORK WE DEMONSTRATE PRELIMINARY WORK ON BUILDING INTERACTIVE TEACHING MODULES FOR DATA STRUCTURES AND ALGORITHMS COURSES WITH THE FOLLOWING CHARACTERISTICS, (1) THE MODULES ARE HIGHLY VISUAL AND INTERACTIVE, (2) TRAINING AND ASSESSMENT ARE TIGHTLY INTEGRATED WITHIN THE SAME MODULE, WITH SUFFICIENT VARIABILITY IN THE EXERCISES TO MAKE IT NEXT TO IMPOSSIBLE TO VIOLATE ACADEMIC INTEGRITY, (3) A DATA LOGGING AND ANALYTIC SYSTEM THAT PROVIDES INSTANTANEOUS STUDENT FEEDBACK AND ASSESSMENT, AND (4) AN INTERACTIVE VISUAL ANALYTIC SYSTEM FOR THE INSTRUCTOR TO SEE STUDENTS' PERFORMANCE AT THE INDIVIDUAL, SUB-GROUP OR CLASS LEVEL, ALLOWING TIMELY INTERVENTION AND SUPPORT FOR SELECTED STUDENTS. OUR MODULES ARE DESIGNED TO WORK WITHIN THE INFRASTRUCTURE OF THE OPENDSA SYSTEM, WHICH WILL PROMOTE RAPID DISSEMINATION TO AN EXISTING USER BASE OF CS EDUCATORS. WE DEMONSTRATE A PROTOTYPE SYSTEM USING AN EXAMPLE DATASET.
THIS PAPER PRESENTS THE RESULTS OF A NATURAL EXPERIMENT INVESTIGATING THE EFFECTS OF INSTRUCTIONAL CONDITIONS AND EXPERIENCE ON THE ADOPTION AND SUSTAINED USE OF A LEARNING TOOL. THE EXPERIMENT WAS CONDUCTED WITH UNDERGRADUATE STUDENTS, ENROLLED INTO FOUR PERFORMING ART COURSES (N = 77) AT A RESEARCH INTENSIVE UNIVERSITY IN CANADA. THE STUDENTS USED THE VIDEO ANNOTATION SOFTWARE CLAS FOR COURSE-BASED SELF-ASSESSMENT ON THEIR PERFORMANCES. ALTHOUGH EXISTING RESEARCH OFFERS INSIGHTS INTO THE FACTORS PREDICTING STUDENTS' INTENTIONS OF ACCEPTING A LEARNING TOOL, MUCH LESS IS KNOWN ABOUT FACTORS THAT AFFECT ACTUAL ADOPTION AND SUSTAINED TOOL USE. THE STUDY EXPLORED THE USE OF CLAS AMONGST UNDERGRADUATE STUDENTS IN FOUR COURSES ACROSS TWO CONSECUTIVE SEMESTERS. TRACE DATA OF STUDENTS' TOOL USE, GRAPH-BASED MEASURES OF METACOGNITIVE MONITORING, AND TEXT COHESION OF VIDEO ANNOTATIONS WERE USED TO ESTIMATE THE VOLUME OF TOOL USE AND THE QUALITY OF THE LEARNING STRATEGY AND LEARNING PRODUCTS CREATED. THE RESULTS CONFIRMED THAT SCAFFOLDING (E.G., GRADED ACTIVITY WITH INSTRUCTIONAL FEEDBACK) IS REQUIRED TO GUIDE STUDENTS' INITIAL TOOL USE, ALTHOUGH SCAFFOLDING DID NOT HAVE AN INDEPENDENT SIGNIFICANT EFFECT ON THE QUANTITY OF TOOL USE. THE FINDINGS DEMONSTRATED THAT THE USE OF THE TOOL IS STRONGLY INFLUENCED BY THE EXPERIENCE AN INDIVIDUAL STUDENT GAINS FROM SCAFFOLDED CONDITIONS. THAT IS, THE STUDENTS SUSTAINED THEIR USE OF THE LEARNING TOOL IN FUTURE COURSES EVEN WHEN THE TOOL USE WAS NOT GRADED NOR WAS INSTRUCTIONAL FEEDBACK PROVIDED. AN IMPORTANT IMPLICATION IS THAT STUDENTS' TOOL USE IS NOT SOLELY DRIVEN BY MOTIVATION RATHER, IT IS SHAPED BY INSTRUCTIONAL CONDITIONS AND EXPERIENCE WITH THE TOOL USE. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
ANALYZING THE PROCESS DATA OF STUDENTS AS THEY COMPLETE PROGRAMMING ASSIGNMENTS HAS THE POTENTIAL TO PROVIDE COMPUTING EDUCATORS WITH INSIGHTS INTO THEIR STUDENTS AND THE PROCESSES BY WHICH THEY LEARN TO PROGRAM. IN PRIOR WORK, WE DEVELOPED A STATISTICAL MODEL THAT ACCURATELY PREDICTS STUDENTS' HOMEWORK GRADES. IN THIS PAPER, WE INVESTIGATE THE RELATIONSHIP BETWEEN THE PATHS THAT STUDENTS TAKE THROUGH THE PROGRAMMING STATES ON WHICH OUR STATISTICAL MODEL IS BASED, AND THEIR OVERALL COURSE ACHIEVEMENT. EXAMINING THE FREQUENCY OF THE MOST COMMON TRANSITION PATHS REVEALED SIGNIFICANT DIFFERENCES BETWEEN STUDENTS WHO EARNED A'S, B'S, AND C'S IN A CS 2 COURSE. OUR RESULTS INDICATE THAT A) STUDENTS OF DIFFERING ACHIEVEMENT LEVELS APPROACH PROGRAMMING TASKS DIFFERENTLY, AND B) THESE DIFFERENCES CAN BE AUTOMATICALLY DETECTED, OPENING UP THE POSSIBILITY THAT THEY COULD BE LEVERAGED FOR PEDAGOGICAL GAIN.
WE HAVE ADDRESSED THE PROBLEMS OF INDEPENDENT E-LECTURE LEARNING WITH AN APPROACH INVOLVING COLLABORATIVE LEARNING WITH LECTURE RECORDINGS. IN ORDER TO MAKE THIS TYPE OF LEARNING POSSIBLE, WE HAVE PROTOTYPICALLY ENHANCED THE VIDEO PLAYER OF A LECTURE VIDEO PLATFORM WITH FUNCTIONALITY THAT ALLOWS SIMULTANEOUS VIEWING OF A LECTURE ON TWO OR MORE COMPUTERS. WHILE WATCHING THE VIDEO, SYNCHRONIZATION OF THE PLAYBACK AND EVERY CLICK EVENT, SUCH AS PLAY, PAUSE, SEEK, AND PLAYBACK SPEED ADJUSTMENT CAN BE CARRIED OUT. WE HAVE ALSO ADDED THE OPTION OF ANNOTATING SLIDES. WITH THIS APPROACH, IT IS POSSIBLE FOR LEARNERS TO WATCH A LECTURE TOGETHER, EVEN THOUGH THEY ARE IN DIFFERENT PLACES. IN THIS WAY, THE BENEFITS OF COLLABORATIVE LEARNING CAN ALSO BE USED WHEN LEARNING ONLINE. NOW, IT IS MORE LIKELY THAT LEARNERS STAY FOCUSED ON THE LECTURE FOR A LONGER TIME (AS THE COLLABORATION CREATES AN ADDITIONAL OBLIGATION NOT TO LEAVE EARLY AND DESERT A FRIEND). FURTHERMORE, THE LEARNING OUTCOME IS HIGHER BECAUSE LEARNERS CAN ASK THEIR FRIENDS QUESTIONS AND EXPLAIN THINGS TO EACH OTHER AS WELL AS MARK IMPORTANT POINTS IN THE LECTURE VIDEO.
PERSONALIZED OR PRECISION EDUCATION (PE) CONSIDERS THE INTEGRATION OF MULTIMODAL TECHNOLOGIES TO TAILOR INDIVIDUALS' LEARNING EXPERIENCES BASED ON THEIR PREFERENCES AND NEEDS. TO IDENTIFY THE IMPACT THAT EMERGING MULTIMODAL TECHNOLOGIES HAVE ON PERSONALIZED EDUCATION, WE REVIEWED RECENT IMPLEMENTATIONS AND APPLICATIONS OF SYSTEMS (E.G., MOOCS, SERIOUS GAMES, ARTIFICIAL INTELLIGENCE, LEARNING MANAGEMENT SYSTEMS, MOBILE APPLICATIONS, AUGMENTED/VIRTUAL REALITY, CLASSROOM TECHNOLOGIES) THAT INTEGRATE SUCH FEATURES. OUR FINDINGS REVEALED THAT PE TECHNIQUES COULD LEVERAGE THE INSTRUCTIONAL POTENTIAL OF EDUCATIONAL PLATFORMS AND TOOLS BY FACILITATING STUDENTS' KNOWLEDGE ACQUISITION AND SKILL DEVELOPMENT. THE ADDED VALUE OF PE IS ALSO EXTENDED BEYOND THE ONLINE DIGITAL LEARNING CONTEXT, AS POSITIVE OUTCOMES WERE ALSO IDENTIFIED IN BLENDED/FACE-TO-FACE LEARNING SCENARIOS, WITH MULTIPLE CONNECTIONS BEING DISCUSSED BETWEEN THE IMPACT OF PE ON STUDENT EFFICACY, ACHIEVEMENT, AND WELL-BEING. IN LINE WITH THE RECOMMENDATIONS AND SUGGESTIONS THAT SUPPORTERS OF PE MAKE, WE PROVIDE IMPLICATIONS FOR RESEARCH AND PRACTICE AS WELL AS GROUND FOR POLICY FORMULATION AND REFORMATION ON HOW MULTIMODAL TECHNOLOGIES CAN BE INTEGRATED INTO THE EDUCATIONAL CONTEXT.
TEACHERS' SPATIAL BEHAVIOURS IN THE CLASSROOM CAN STRONGLY INFLUENCE STUDENTS' ENGAGEMENT, MOTIVATION AND OTHER BEHAVIOURS THAT SHAPE THEIR LEARNING. HOWEVER, CLASSROOM TEACHING BEHAVIOUR IS EPHEMERAL, AND HAS LARGELY REMAINED OPAQUE TO COMPUTATIONAL ANALYSIS. INSPIRED BY THE NOTION OF SPATIAL PEDAGOGY, THIS PAPER PRESENTS A SYSTEM CALLED 'MOODOO' THAT AUTOMATICALLY TRACKS AND MODELS HOW TEACHERS MAKE USE OF THE CLASSROOM SPACE BY ANALYSING INDOOR POSITIONING TRACES. WE ILLUSTRATE THE POTENTIAL OF THE SYSTEM THROUGH AN AUTHENTIC STUDY WITH SEVEN TEACHERS ENACTING THREE DISTINCT LEARNING DESIGNS WITH MORE THAN 200 UNDERGRADUATE STUDENTS IN THE CONTEXT OF SCIENCE EDUCATION. THE SYSTEM AUTOMATICALLY EXTRACTS SPATIAL METRICS (E.G. TEACHER-STUDENT RATIOS, FREQUENCY OF VISITS TO STUDENTS' PERSONAL SPACES, PRESENCE IN CLASSROOM SPACES OF INTEREST, INDEX OF DISPERSION AND ENTROPY), MAPPING FROM THE TEACHERS' LOW-LEVEL POSITIONING DATA TO HIGHER-ORDER SPATIAL CONSTRUCTS. WE ILLUSTRATE HOW THESE SPATIAL METRICS CAN BE USED TO GENERATE A DEEPER UNDERSTANDING OF HOW THE PEDAGOGICAL COMMITMENTS EMBEDDED IN THE LEARNING DESIGN, AND PERSONAL TEACHING STRATEGIES, ARE REFLECTED IN THE WAYS TEACHERS USE THE LEARNING SPACE TO PROVIDE SUPPORT TO STUDENTS.
THE PAPER ANALYZES THE POSSIBILITIES OF USING GOOGLE ANALYTICS IN THE STUDY OF STUDENTS' LEARNING BEHAVIOUR AND FOR THE DESIGN OF ONLINE COURSES. SPECIFICALLY, IT ANALYZES DATA FROM AN INFORMATION LITERACY COURSE FOR HIGH SCHOOL STUDENTS. THE PAPER USE DATA FROM AN ONLINE COURSE FOR HIGH SCHOOL STUDENTS BETWEEN 1 FEBRUARY 2020 AND 16 SEPTEMBER 2020. THERE ARE A TOTAL OF 785 UNIQUE VISITORS WHOSE BEHAVIOR ON THE WEB WAS THE BASIS FOR OUR ANALYSIS. WE ALSO USE INFORMATION FROM 7 SEMI-STRUCTURED INTERVIEWS WITH TEACHERS AND FROM 2 FOCUS GROUPS WITH STUDENTS (14 STUDENTS) TO INTERPRET THE DATA.
THE RECENTLY FINISHED EU PROJECT JUXTALEARN AIMED AT SUPPORTING STUDENTS' LEARNING OF STEM SUBJECTS THROUGH THE CREATION, EXCHANGE AND DISCUSSION OF LEARNER-MADE VIDEOS. THE APPROACH IS BASED ON AN EIGHT-STAGE ACTIVITY CYCLE IN THE BEGINNING OF WHICH TEACHERS IDENTIFY SPECIFIC "STUMBLING BLOCKS" FOR A GIVEN THEME (OR "TRICKY TOPIC"). IN JUXTALEARN, VIDEO COMMENTS WERE ANALYZED TO EXTRACT INFORMATION ON THE LEARNERS' ACQUISITION AND UNDERSTANDING OF DOMAIN CONCEPTS, ESPECIALLY TO DETECT PROBLEMS AND MISCONCEPTIONS. THESE ANALYSES WERE BASED ON MAPPING TEXTS TO NETWORKS OF CONCEPTS ("NETWORK-TEXT ANALYSIS") AS A BASIS FOR FURTHER PROCESSING. IN THIS ARTICLE WE USE DATA COLLECTED FROM RECENT FIELD TRIALS TO SHED LIGHT ON WHAT IS ACTUALLY DISCUSSED WHEN STUDENTS SHARE THEIR OWN VIDEOS IN SCIENCE DOMAINS. WOULD THE ASPECT OF VIDEO-MAKING DOMINATE OVER ACTIVITIES RELATED TO A DEEPENING OF DOMAIN UNDERSTANDING? OUR FINDINGS INDICATE THAT THERE ARE DIFFERENT WAYS OF BALANCING BOTH ASPECTS AND INTERVENTIONS WILL BE NEEDED TO BRING FORTH THE DESIRED BLEND.
E-LEARNING SYSTEMS ARE BECOMING A FUNDAMENTAL MEAN OF EDUCATION DELIVERY. RECENTLY, DATA MINING TECHNIQUES HAVE BEEN UTILIZED BY TUTORS AND RESEARCHERS TO ANALYZE STUDENTS LEARNING WITH THE AIM TO GET DEEPER SIGHT OF IT AND IMPROVE THE QUALITY OF THE EDUCATIONAL PROCEDURES. IN THIS PAPER, WE PRESENT A METHODOLOGY TO ANALYZE STUDENTS LEARNING AND EXTRACT SEMANTIC RULES THAT CAN BE USED TO PREDICT STUDENT'S FINAL PERFORMANCE IN THE COURSE. SPECIFICALLY, THE STUDENTS' PERFORMANCE AT INTERIM TESTS DURING THE SEMESTER IS ANALYZED AND THE METHODOLOGY UTILIZES DECISION TREES AND EXTRACTS RULES TO MAKE PREDICTIONS REGARDING THE STUDENT'S FINAL PERFORMANCE IN THE COURSE. THE METHODOLOGY HAS BEEN INTEGRATED IN AN EDUCATIONAL SYSTEM USED TO ASSIST STUDENTS IN LEARNING THE ARTIFICIAL INTELLIGENCE (AI) COURSE IN OUR UNIVERSITY. THE EDUCATIONAL SYSTEM UTILIZES SEMANTIC WEB TECHNOLOGIES SUCH AS ONTOLOGIES AND SEMANTIC RULES TO ENHANCE THE QUALITY OF THE EDUCATIONAL CONTENT AND THE DELIVERED LEARNING ACTIVITIES TO EACH STUDENT. THE METHODOLOGY CAN ASSISTS THE SYSTEM AND THE TUTOR TO GET A DEEPER INSIGHT OF THE STUDENTS' PERFORMANCE, TRACE STUDENTS THAT ARE UNDERACHIEVING OR IN THE EDGE TO FAIL THE FINAL EXAMS AND ALSO OFFER PROPER RECOMMENDATIONS AND ADVISES TO EACH ONE AND DRIVE BROADER PEDAGOGICAL IMPROVEMENTS.
THIS PAPER PROPOSES AN APPROACH FOR MODELING AND MINING CURRICULUM BIG DATA FROM REAL WORLD EDUCATION DATASETS CRAWLED ONLINE FROM UNIVERSITY WEBSITES IN AUSTRALIA. IT ADDRESSES THE SCENARIO TO GIVE A STUDENT A STUDY PLAN TO COMPLETE A COURSE BY ACCUMULATING CREDITS ON TOP OF SUBJECTS HE OR SHE HAS COMPLETED. ONE CHALLENGE TO BE ADDRESSED IS THAT SUBJECTS WITH SIMILAR TITLES FROM DIFFERENT UNIVERSITIES MAY PUT BARRIERS FOR SETTING UP A REASONABLE, TIME-SAVING LEARNING PATH BECAUSE THE STUDENT MAY BE UNABLE TO DISTINGUISH THEM BEFORE AN INTENSIVE RESEARCH ON ALL SUBJECTS RELATED TO THE DEGREE FROM THE UNIVERSITIES. WE USED CONCEPT GRAPH-BASED LEARNING TECHNIQUES AND DISCUSS DATA REPRESENTATIONS AND TECHNIQUES WHICH ARE MORE SUITED FOR LARGE DATASETS. WE CREATED GROUND TRUTH OF SUBJECTS RELATIONS AND SUBJECT'S DESCRIPTION WITH BAG OF WORDS REPRESENTATIONS BASED ON NATURAL LANGUAGE PROCESSING. THE GENERATED GROUND TRUTH WAS USED TO TRAIN A MODEL, WHICH SUMMARIZES A SUBJECT NETWORK AND A CONCEPTS GRAPH, WHERE THE CONCEPTS ARE AUTOMATICALLY EXTRACTED FROM THE SUBJECT DESCRIPTIONS ACROSS ALL THE UNIVERSITIES. THE PRACTICAL CHALLENGES TO COLLECT AND EXTRACT THE DATA FROM THE UNIVERSITY WEBSITES ARE ALSO DISCUSSED IN THE PAPER. THE WORK WAS VALIDATED ON NINETEEN REAL-WORLD EDUCATION DATASETS CRAWLED ONLINE FROM UNIVERSITY WEBSITES IN AUSTRALIA AND SHOWED GOOD PERFORMANCE.
THE CURRENT STUDY INVESTIGATED PREDICTORS OF SHALLOW VERSUS DEEP LEARNING WITHIN A SERIOUS GAME KNOWN AS OPERATION ARA. THIS GAME USES A MYRIAD OF PEDAGOGICAL FEATURES INCLUDING MULTIPLE-CHOICE TESTS, ADAPTIVE NATURAL LANGUAGE TUTORIAL CONVERSATIONS, CASE-BASED REASONING, AND AN E-TEXT TO ENGAGE STUDENTS. THE GAME TEACHES 11 TOPICS IN RESEARCH METHODOLOGY ACROSS THREE DISTINCT MODULES THAT TARGET FACTUAL INFORMATION, APPLICATION OF REASONING TO SPECIFIC CASES, AND QUESTION GENERATION. THE GOAL OF THIS INVESTIGATION IS TO DISCOVER PREDICTORS OF DEEP AND SHALLOW LEARNING BY BLENDING EVIDENCE-CENTERED DESIGN (ECD) WITH EDUCATIONAL DATA MINING. IN LINE WITH ECD, TIME-HONORED COGNITIVE PROCESSES OR BEHAVIORS OF TIME-ON-TASK, DISCRIMINATION, GENERATION, AND SCAFFOLDING WERE SELECTED BECAUSE THERE IS A LARGE RESEARCH HISTORY SUPPORTING THEIR IMPORTANCE TO LEARNING. THE STUDY INCLUDED 192 COLLEGE STUDENTS WHO PARTICIPATED IN A PRETEST-INTERACTION-POSTTEST DESIGN. THESE DATA WERE USED TO DISCOVER THE BEST PREDICTORS OF LEARNING ACROSS THE TRAINING EXPERIENCES. RESULTS REVEALED DISTINCTLY DIFFERENT PATTERNS OF PREDICTORS OF DEEP VERSUS SHALLOW LEARNING FOR STUDENTS ACROSS THE TRAINING ENVIRONMENTS OF THE GAME. SPECIFICALLY, MORE INTERACTIVITY IS IMPORTANT FOR ENVIRONMENTS CONTRIBUTING TO SHALLOW LEARNING WHEREAS GENERATION AND DISCRIMINATION IS MORE IMPORTANT IN TRAINING ENVIRONMENTS SUPPORTING DEEPER LEARNING. HOWEVER, IN SOME TRAINING ENVIRONMENTS THE POSITIVE IMPACT OF GENERATION MAY BE AT THE PRICE OF DECREASED DISCRIMINATION.
IN HIGHER EDUCATION ENORMOUS DATASETS EXIST ABOUT LEARNERS, THEIR LEARNING AND THE BACKGROUND IN WHICH THEY STUDY. THE EDUCATIONAL KNOWLEDGE OF THE LEARNER'S IS IMPROVED, MAKING USE OF DIFFERENT APPROACHES. THE OBJECTIVE OF THIS PAPER IS TO EVALUATE THE PROBLEMS FACED BY THE LEARNER WITH THE TRADITIONAL TEACHING METHOD AND TO ENHANCE THE TEACHING METHOD AND LEARNING ENVIRONMENT THAT IMPROVE THE RESULTING RATIO FOR PROVIDING HEALTHIER EMPLOYMENT TO THE LEARNER. THE PROPOSED WORK USES A NOVEL TEACHING, OBSERVING THE COGNITIVE SKILLS OF THE LEARNER BY CHANGING THE CLASSROOM ENVIRONMENT. THE LEARNER'S LEARNING IS IMPROVED WITH A POSITIVE APPROACH IN TEACHING, ENRICHING THE THINKING ABILITY AND CREATING A STRESS-FREE ENVIRONMENT AS LEARNING SHOULD BE MADE AN ENJOYABLE MOMENT. A REAL-TIME DATASET IS FORMED WITH THE TEACHING AND LEARNING ENVIRONMENT AND ANALYSIS OF THE DATASET IS MADE TO THE NAIVE BAYES ALGORITHM. DIFFERENT METRICS ARE EVALUATED TO SHOW THE INCREASED PERFORMANCE OF THE LEARNER'S WITH A POSITIVE APPROACH IN THE LEARNING ENVIRONMENT.
SERIOUS GAMES HAVE PROVEN TO BE EDUCATIONAL TOOLS WITH NUMEROUS POSITIVE EFFECTS, CAPABLE OF PROMOTING LEARNING, CHANGING BEHAVIOR, OR IMPROVING TRAINING AND SKILLS DEVELOPMENT, AMONG OTHER EFFECTS. SERIOUS GAMES CAN ALSO BE APPLIED TO ADDRESS AND PREVENT SOCIAL PROBLEMS. THIS STUDY DESCRIBES THE EXPERIMENTS AND ANALYSES WE HAVE CARRIED OUT TO TEST THE EFFECTIVENESS OF CONECTADO, A SERIOUS GAME CREATED TO PREVENT BULLYING AND CYBERBULLYING IN SCHOOLS BY INCREASING AWARENESS OF THE PROBLEM IN PLAYERS. WE HAVE USED THE RESULTS FROM THESE EXPERIMENTS TO ANSWER DIFFERENT RESEARCH QUESTIONS REGARDING THE GAME'S ACCEPTANCE AND EFFECTIVENESS, VALIDATING SEVERAL CHARACTERISTICS OF THE GAME DESIGN. WE HAVE ALSO STUDIED THE INFLUENCE OF PLAYERS' CHARACTERISTICS ON THE EFFECT OF THE GAME, AS WELL AS THE RELATIONSHIP BETWEEN THE IN-GAME BEHAVIORS OF PLAYERS AND THEIR DEGREE OF AWARENESS. WE HAVE VERIFIED A POSITIVE EFFECT OF THE GAME IN TERMS OF AN INCREASE OF AWARENESS REGARDING BULLYING AND CYBERBULLYING FOR ALL TARGET USERS, AND HAVE PROVIDED FURTHER INSIGHT INTO HOW THIS INCREASE IS RELATED TO DIFFERENT PLAYERS' CHARACTERISTICS AND BEHAVIORS.
THIS IS THE FIRST PAPER THAT PROVIDES CORRELATIONAL EVIDENCE ABOUT HOW INTERACTING WITH AN ONLINE ALLOPHONIC TRANSCRIPTION TOOL HELPS LEARNERS OF ENGLISH AS A SECOND LANGUAGE (ESL) TO IMPROVE THEIR PHONOLOGICAL AWARENESS. THE STUDY INVESTIGATES 55 ADVANCED ESL LEARNERS AT A POLISH UNIVERSITY ENROLLED IN A COURSE IN ENGLISH PHONETICS AND PHONOLOGY. THE COURSE PLACED HEAVY EMPHASIS ON READING AND WRITING ALLOPHONIC TRANSCRIPTION BASED ON THE INTERNATIONAL PHONETIC ALPHABET. APART FROM OBLIGATORY PRACTICE WITH TRADITIONAL PEN AND PAPER WORKSHEETS, LEARNERS COULD ALSO PRACTISE WITH A SUPPLEMENTARY CUSTOM-DESIGNED WEB APPLICATION THAT ALLOWED THEM TO ENTER THE PHONEMIC TRANSCRIPTION OF ANY WORD IN ORDER TO RECEIVE ITS ALLOPHONIC TRANSCRIPTION. THE RESULTS SHOW THAT USING THIS TOOL AT LEAST ONCE DURING THE COURSE IS ASSOCIATED WITH AN EXPECTED INCREASE IN MIDTERM TEST SCORE OF 5.03 PERCENTAGE POINTS, 95% CI [-10.61, 20.67]. THE ESTIMATED BENEFIT IS HIGHER FOR LEARNERS WHO SPACE THEIR USAGE OF THE TOOL; EACH ADDITIONAL DISTINCT DAY OF USAGE IS ASSOCIATED WITH AN ADDITIONAL INCREASE IN THE EXPECTED MIDTERMTEST SCORE OF 2.62 PERCENTAGE POINTS, 95% CI [-3.25, 8.49]. ADDITIONALLY, SOME LEARNERS PRACTISED TRANSCRIPTION ON NON-WORDS, AND THESE LEARNERS WERE OBSERVED TO PERFORM BETTER ON PHONOTACTICS-RELATED ASSESSMENT.
MANY PREVIOUS STUDIES HAVE SHOWN THAT STUDENTS' LEARNING STRATEGIES AND ENGAGEMENT ARE BOTH IMPORTANT FACTORS AFFECTING STUDENTS' LEARNING OUTCOME. WE WANT TO KNOW WHETHER THE ENGAGEMENT OF STUDENTS WHO USE DIFFERENT LEARNING STRATEGIES IS DIFFERENT, SO THAT WE CAN PROXY MEASURE STUDENTS' ENGAGEMENT ACCORDING TO THE LEARNING STRATEGIES USED BY STUDENTS. DIFFERENT FROM THE PAST METHODS OF USING QUESTIONNAIRES OR INTERVIEWS, THIS RESEARCH USED EDUCATION DATA MINING TECHNOLOGY TO DIRECTLY ANALYZE THE LEARNING LOGS OF ONLINE LEARNING SYSTEM TO EXTRACT STUDENTS' LEARNING STRATEGIES. WE COLLECTED THE LEARNING LOGS OF 56 STUDENTS, AND EXTRACTED THE LEARNING STRATEGIES OF THE STUDENTS USING SEQUENCE CLUSTERING. WE CLASSIFIED FOUR LEARNING STRATEGIES AND PERFORMED PEARSON CORRELATION ANALYSIS WITH STUDENTS' LEARNING OUTCOME AND ENGAGEMENT, AND DISCUSSED THE KEY STRATEGIES THAT AFFECT STUDENTS' LEARNING OUTCOME. WE ALSO FOUND THAT STUDENTS WHO USE DIFFERENT LEARNING STRATEGIES HAVE DIFFERENT LEVELS OF ENGAGEMENT.
AN EDUCATIONAL PROGRAM THAT DOES NOT ACCEPT THE CHANGE OF DISRUPTIVE TECHNOLOGY WILL INEVITABLY RESULT IN FUTURE DESTRUCTION. THERE ARE TWO OBJECTIVES INCLUDING (1) TO CONSTRUCT REASONABLE STUDENTS' DROPOUT PREDICTION MODEL FOR BUSINESS COMPUTER DISCIPLINES, AND (2) TO EVALUATE THE MODEL PERFORMANCE. DATA COLLECTED CONSISTS OF 2,017 RECORDS FROM STUDENTS WHO ENROLLED IN THE BUSINESS COMPUTER PROGRAM AT THE SCHOOL OF INFORMATION AND COMMUNICATION TECHNOLOGY, THE UNIVERSITY OF PHAYAO. RESEARCH TOOLS ARE DIVIDED INTO TWO PARTS. (1) MODELLING; IT CONSISTED OF THE ARTIFICIAL NEURAL NETWORK ALGORITHM, DECISION TREE ALGORITHM, AND NAIVE BAYES ALGORITHM. (2) MODEL TESTING; IT CONSISTED OF THE CONFUSION MATRIX PERFORMANCE, ACCURACY, PRECISION, AND RECALL MEASUREMENT. IT IS A CLEAR INNOVATION IN THE RESEARCH THAT THE RESEARCHER COMBINES THE KNOWLEDGE OF DATA SCIENCE IN ANALYSIS TO IMPROVE THE ACADEMIC ACHIEVEMENT OF STUDENTS IN HIGHER EDUCATION IN THAILAND. FROM THE ANALYSIS RESULTS, ITS SHOW THAT THE MODEL DEVELOPED FROM USING ARTIFICIAL NEURAL NETWORK ALGORITHMS HAS THE HIGHEST ACCURACY IN THE FIRST THREE DATA SETS (89.04%, 92.70% AND 93.71%), AND THE LAST MODEL IS APPROPRIATE FOR NAIVE BAYES ALGORITHM (91.68%). FINALLY, IT IS NECESSARY TO CONDUCT ADDITIONAL RESEARCH AND PRESENT RESEARCH RESULTS TO RELEVANT PARTIES AND ORGANIZATIONS.
STUDENT PERFORMANCE IS THE MOST FACTOR THAT CAN BE BENEFICIAL FOR MANY PARTIES, INCLUDING STUDENTS, PARENTS, INSTRUCTORS, AND ADMINISTRATORS. EARLY PREDICTION IS NEEDED TO GIVE THE EARLY MONITOR BY THE RESPONSIBLE PERSON IN CHARGE OF DEVELOPING A BETTER PERSON FOR THE NATION. IN THIS PAPER, THE IMPROVEMENT OF BAGGED TREE TO PREDICT STUDENT PERFORMANCE BASED ON FOUR MAIN CLASSES, WHICH ARE DISTINCTION, PASS, FAIL, AND WITHDRAWN. THE ACCURACY IS USED AS AN EVALUATION PARAMETER FOR THIS PREDICTION TECHNIQUE. THE BAGGED TREE WITH THE ADDITION OF BAG, ADABOOST, RUSBOOST LEARNERS HELPS TO PREDICT THE STUDENT PERFORMANCE WITH THE MASSIVE DATASETS. THE USE OF THE RUSBOOST ALGORITHM PROVED THAT IT IS VERY SUITABLE FOR THE IMBALANCE DATASETS AS THE ACCURACY IS 98.6% AFTER IMPLEMENTING THE FEATURE SELECTION AND 99.1% WITHOUT FEATURE SELECTION COMPARED TO OTHER LEARNER TYPES EVEN THOUGH THE DATA IS MORE THAN 30,000 DATASETS.
IN THIS PAPER WE DESCRIBE THE USE OF METRICS FOR ANALYSING STUDENT INTERACTIONS WITH EDUCATIONAL SOFTWARE. WE APPLIED THIS METRIC-BASED APPROACH TO A CLASS OF 200 SECOND-YEAR UNDERGRADUATE STUDENTS USING AN EDUCATIONALLY-ORIENTED SOFTWARE SIMULATOR TO SOLVE SPECIFIC PROBLEMS IN MECHANICAL ENGINEERING DESIGN. OUR RESULTS SHOW THAT A METRIC CAN FACILITATE CATEGORIZATION OF STUDENT LEARNING PATTERNS. WE SUGGEST HOW SIMILAR METRICS COULD ALLOW AUTOMATED FEEDBACK ON LEARNING TO BOTH STUDENTS AND EDUCATORS. SINCE IT IS BASED ON NUMERICAL ANALYSIS AND MODELLING, OUR APPROACH IS PARTICULARLY WELL-SUITED TO SOFTWARE USED TO SUPPORT TEACHING AND LEARNING IN ENGINEERING AND OTHER MATHEMATICALLY-BASED DISCIPLINES.
THIS PAPER DESCRIBES VISUALIZATION AND ANALYSIS METHODS USING EDUCATIONAL BIG DATA COLLECTED BY RESEARCH PROJECT AT KYUSHU UNIVERSITY IN JAPAN. THE PROJECT USES AN E-BOOK SYSTEM CALLED BOOKLOOPER, MOODLE, AND MAHARA. LOGS FOR THIS ANALYTICS WERE COLLECTED FROM 99 FIRST-YEAR STUDENTS IN AN INFORMATION SCIENCE COURSE AT KYUSHU UNIVERSITY. THE NUMBER OF LOGS ARE COLLECTED APPROXIMATELY 330,000, AND THIS PAPER VISUALIZE AND ANALYZE THE COLLECTED LOGS. THE PURPOSE OF THIS STUDY IS TO PREDICT STUDENTS' FINAL GRADE AND TO PROFILE VISUALIZATION AND ANALYSIS RESULTS. THE PREDICTION OF THIS STUDY SHOWS THAT IT LEADS TO DISCOVERIES OF STUDENTS WHO FAIL TO MAKE THE GRADE.
WIDENING PARTICIPATION INITIATIVES IN HIGHER EDUCATION HAVE GROWN OVERALL STUDENT NUMBERS WHILE ALSO INCREASING THE DIVERSITY OF STUDENT COHORTS. CONSEQUENTLY, ENHANCING STUDENT EXPERIENCES AND OUTCOMES HAS BECOME INCREASINGLY CHALLENGING. THIS STUDY IMPLEMENTED PERSONALISED EMAILS IN TWO FIRST-YEAR MATHEMATICS COURSES AS A SCALABLE STRATEGY FOR SUPPORTING STUDENTS WITH DIVERSE NEEDS. IMPACT ON STUDENT EXPERIENCE AND OUTCOMES WAS ASSESSED THROUGH SURVEYING AND STATISTICAL COMPARISONS TO PREVIOUS COHORTS. IT WAS FOUND THAT STUDENTS PERCEIVED THE PERSONALISED EMAILS FAVOURABLY AND BELIEVED THE INTERVENTION WOULD CONTRIBUTE TO THEM ACHIEVING BETTER GRADES. THIS TRANSLATED TO A STATISTICALLY SIGNIFICANT IMPROVEMENT IN BOTH STUDENT EXPERIENCE AND ACADEMIC PERFORMANCE IN ONE OF THE COURSES. THE RESULTS IMPLY THAT PERSONALISED EMAILS ARE WELL-SUITED TO COURSES TAKEN IN STUDENTS' FIRST SEMESTERS OF UNIVERSITY STUDY, AIDING THOSE TRANSITIONING TO THE HIGHER EDUCATION ENVIRONMENT BY FOSTERING FEELINGS OF BELONGING, SUPPORTING EFFECTIVE ENGAGEMENT, AND EASING NAVIGATION OF UNIVERSITY SYSTEMS AND PROCESSES.
EXTENSIVE STUDIES HAVE BEEN CONDUCTED TO DIAGNOSE AND PREDICT STUDENTS' ACADEMIC PERFORMANCE BY ANALYZING A LARGE AMOUNT OF DATA RELATED TO THEIR LEARNING BEHAVIORS IN A BLENDED LEARNING ENVIRONMENT. BUT THERE IS A LACK OF RESEARCH EXAMINING HOW INDIVIDUALIZED LEARNING INTERVENTIONS COULD IMPROVE STUDENTS' ACADEMIC PERFORMANCE IN SUCH A LEARNING CONTEXT. IN THIS STUDY, A QUASI-EXPERIMENT WAS DESIGNED TO INVESTIGATE THE EFFECT OF AN INDIVIDUALIZED INTERVENTION APPROACH ON STUDENTS' COURSE PERFORMANCE AND LEARNING BEHAVIORS IN A BLENDED COURSE. FORTY-NINE CHINESE TERTIARY STUDENTS UNDERTAKING THE COURSE WERE RANDOMLY ASSIGNED INTO TWO GROUPS - THE EXPERIMENTAL AND CONTROL GROUPS. DURING THE COURSE, THE EXPERIMENTAL GROUP RECEIVED INDIVIDUALIZED INTERVENTIONS, WHILE THE CONTROL GROUP RECEIVED UNDIFFERENTIATED INTERVENTIONS. THE DATA ABOUT THESE PARTICIPANTS' LEARNING BEHAVIORS WERE COLLECTED OVER 15 WEEKS. THE RESULTS INDICATED THAT, COMPARED WITH THE CONTROL GROUP, THE EXPERIMENTAL GROUP SHOWED SIGNIFICANTLY BETTER ACADEMIC PERFORMANCE, A HIGHER LEVEL OF LEARNING MOTIVATION, ATTITUDE AND SELF-EFFICACY, MORE ACTIVE LEARNING BEHAVIORS, AND FEWER PASSIVE LEARNING BEHAVIORS. THE CONTROL GROUP REVEALED SIMILAR ONLINE LEARNING TIME, BUT SIGNIFICANTLY MORE RESOURCE UTILIZATION AND FORUM ACCESS. IT IS CONCLUDED THAT PERSONALIZED LEARNING INTERVENTION CAN EFFECTIVELY IMPROVE STUDENTS' LEARNING BEHAVIORS, ATTITUDE, MOTIVATION, SELF-EFFICACY, AND ACADEMIC PERFORMANCE IN A BLENDED LEARNING ENVIRONMENT.
SUPPLEMENTAL INSTRUCTION (SI) IS A VOLUNTARY, NON-REMEDIAL, PEER-FACILITATED, COURSE-SPECIFIC INTERVENTION THAT HAS BEEN WIDELY DEMONSTRATED TO INCREASE STUDENT SUCCESS, YET CONCERNS PERSIST REGARDING THE BIASING EFFECTS OF DISPROPORTIONATE PARTICIPATION BY ALREADY HIGHER-PERFORMING STUDENTS. WITH A FOCUS ON MAINTAINING ACCESS FOR ALL STUDENTS, A LARGE, PUBLIC UNIVERSITY IN THE WESTERN UNITED STATES USED STUDENT DEMOGRAPHIC, PERFORMANCE, AND SI PARTICIPATION DATA TO EVALUATE THE INTERVENTION'S EFFICACY WHILE REDUCING SELECTION BIAS. THIS ANALYSIS WAS CONDUCTED IN THE FIRST YEAR OF SI IMPLEMENTATION WITHIN A TRADITIONALLY HIGH-CHALLENGE INTRODUCTORY PSYCHOLOGY COURSE. FINDINGS INDICATE A STATISTICALLY SIGNIFICANT RELATIONSHIP BETWEEN STUDENT PARTICIPATION IN SI AND INCREASED ODDS OF SUCCESSFUL COURSE COMPLETION. FURTHERMORE, THE APPLICATION OF COARSENED EXACT MATCHING REDUCED CONCERNS THAT INCREASED COURSE PERFORMANCE WAS ATTRIBUTED TO AN OVER-REPRESENTATION OF HIGHER PERFORMING STUDENTS WHO ELECTED TO ATTEND SI SESSIONS.
IN THIS PAPER, WE PROPOSE A CROSS ANALYTICS METHODOLOGY OF STUDENT ACTIVITIES AND COURSE ACTIVITIES USING E-BOOK OPERATION LOGS COLLECTED IN 15 COURSES WITH FACE-TO-FACE LECTURE STYLE OVER 4 WEEKS. THESE COURSES COMMONLY USE THE SAME LECTURE MATERIALS, BUT ARE CONDUCTED BY DIFFERENT TEACHERS. THE NEW ASPECT OF OUR RESEARCH IS THAT WE PERFORM CROSS ANALYSIS OVER COURSES. MOST PAST RESEARCHES FOCUS ON STUDENTS' ACTIVITIES IN A SPECIFIC COURSE, AND GIVE DISCUSSIONS ABOUT HOW THE STUDENTS BEHAVED, HOW THE BEHAVIORS DIFFER FROM EACH OTHER. IN CONTRAST, OUR RESEARCH FOCUSES ON THE COURSE ACTIVITIES AND CONDUCTS A COMPARISON AMONG COURSES. FIRST, WE BEGIN WITH DATA ALIGNMENT FOR ROW DATA TO RECTIFY A STUDENT ACTIVITY EVERY 10 SECONDS. THROUGH OUR ANALYTICS, IT BECOMES CLEAR THAT WHETHER STUDENTS' ACTIVITIES VARIES WITH TEACHERS OR THEIR TEACHING STYLES. IN THE EXPERIMENTS, WE APPLIED THE PROPOSED ANALYTICS TO 1.1-MILLION OPERATION LOGS, AND FOUND OUT INTERESTING CHARACTERISTICS THROUGH THE COMPARISON ACROSS COURSES.
THIS STUDY PROPOSES A STANDARDISED OPEN FRAMEWORK TO AUTOMATICALLY GENERATE AND LABEL DISCUSSION TOPICS FROM MASSIVE OPEN ONLINE COURSES (MOOCS). THE PROPOSED FRAMEWORK EXPECTS TO OVERCOME THE ISSUES EXPERIENCED BY MOOC PARTICIPANTS AND TEACHING STAFF IN LOCATING AND NAVIGATING THEIR INFORMATION NEEDS EFFECTIVELY. WE ANALYSED TWO MOOCS - MACHINE LEARNING AND STATISTICS: MAKING SENSE OF DATA OFFERED DURING 2013 AND OBTAINED STATISTICALLY SIGNIFICANT RESULTS FOR AUTOMATED TOPIC LABELING. HOWEVER, MORE EXPERIMENTS WITH ADDITIONAL MOOCS FROM DIFFERENT MOOC PLATFORMS ARE NECESSARY TO GENERALISE OUR FINDINGS.
SERIOUS GAMES CAN BENEFIT FROM THE COMMERCIAL VIDEO GAMES INDUSTRY BY TAKING ADVANTAGE OF CURRENT DEVELOPMENT TOOLS. HOWEVER, THE ECONOMICS AND REQUIREMENTS OF SERIOUS GAMES AND COMMERCIAL GAMES ARE VERY DIFFERENT. IN THIS PAPER, WE DESCRIBE THE FACTORS THAT IMPACT THE TOTAL COST OF OWNERSHIP OF SERIOUS GAMES USED IN EDUCATIONAL SETTINGS, REVIEW THE SPECIFIC REQUIREMENTS OF GAMES USED AS LEARNING MATERIAL, AND ANALYZE THE DIFFERENT DEVELOPMENT TOOLS AVAILABLE IN THE INDUSTRY HIGHLIGHTING THEIR ADVANTAGES AND DISADVANTAGES THAT MUST BE TAKEN INTO ACCOUNT WHEN USING THEM TO DEVELOP A SERIOUS GAME.
UNDERSTANDING THE DESIGN PROCESS MAY REVEAL WHEN AND WHERE RESOURCES SHOULD BE FOCUSED AND HOW ENGINEERS CAN BETTER USE TOOLS, METHODS, AND TECHNIQUES TO ENHANCE THE QUALITY OF DESIGNS AND CREATIVE PERFORMANCE. THE LITERATURE SUGGESTS THE IMPORTANCE OF ITERATIVE EVALUATION AND REFORMULATION IN THE ENGINEERING DESIGN PROCESS. THE CURRENT STUDY COLLECTED 111 HIGH SCHOOL STUDENTS' LOGS OF DESIGNING AN ENERGY-SAVING HOUSE IN ENERGY3D, A COMPUTER-AIDED DESIGN ENVIRONMENT. USING A CROSS-LAG MODEL, WE INVESTIGATED THE RECIPROCAL RELATIONSHIP BETWEEN STUDENTS' EVALUATION AND REFORMULATION BEHAVIORS AND HOW THESE BEHAVIORS INFLUENCE STUDENTS' DESIGN PERFORMANCE AT THE EARLY, MIDDLE, AND FINAL DESIGN STAGES. THE RESULTS SUGGEST THAT THERE IS A POSITIVE PREDICTIVE RELATIONSHIP BETWEEN STUDENTS' EVALUATION AND REFORMULATION PROCESS; REFORMULATION POSITIVELY PREDICTS DESIGN PERFORMANCE AND MEDIATES THE RELATIONSHIP BETWEEN EVALUATION AND DESIGN PERFORMANCE ACROSS TIME. THESE RESULTS PROVIDE EMPIRICAL EVIDENCE OF THE IMPORTANCE OF ITERATIVE EVALUATION AND REFORMULATION IN THE DESIGN PROCESS AND IMPLICATIONS FOR TEACHERS AND SYSTEM DESIGNERS TO SUPPORT STUDENTS' DESIGN.
WITH THE BIRTH OF NEW TECHNOLOGIES WHICH CAN HARNESS DATA ASSOCIATED WITH EDUCATION, THE FIELD OF EDUCATIONAL DATA MINING (EDM) HAS BLOOMED. EDM IS A RESEARCH AREA WHICH USES DATA MINING TECHNIQUES, MACHINE LEARNING ALGORITHMS AND STATISTICAL TECHNIQUES TO UNDERSTAND HOW STUDENTS LEARN, PREDICT STUDENTS' ACADEMIC PERFORMANCE AND HOW A STUDENT'S LEARNING CAN BE IMPROVED. THIS PAPER CONDUCTS EXTENSIVE REVIEW OF THE LITERATURE ON THE USE OF EDM FOR ANALYZING PERFORMANCE OF STUDENT.
PROVIDING INDIVIDUAL AND IMMEDIATE FEEDBACK TO STUDENTS IS A CRITICAL FACTOR FOR IMPROVING KNOWLEDGE AND SKILLS ACQUISITION IN HIGHER EDUCATION. HOWEVER, A GROWING NUMBER OF STUDENTS WITH VERY DIFFERENT HETEROGENEOUS PROFILES AND UNEQUAL PROBLEM SOLVING SKILLS, AS WELL AS THE LACK OF TEACHING RESOURCES, MAKE IT VERY CHALLENGING AND SOMETIMES EVEN IMPOSSIBLE TO GIVE IMMEDIATE FEEDBACK AT INDIVIDUAL LEVEL. THIS PAPER PRESENTS A SYNTHESIS AND PROGRESS OF A LONG-TERM PROJECT THAT ADDRESSES THIS CHALLENGE IN THE CONTEXT OF CONCEPTUAL MODELLING BY DEVELOPING SAILE@COMO, A SMART AND ADAPTIVE LEARNING ENVIRONMENT. BY CRAFTING INNOVATIVE PROCESS ANALYTICS TECHNIQUES AND EXPERT KNOWLEDGE ON FEEDBACK AUTOMATION, SAILE@COMO CAN AUTOMATICALLY PROVIDE PERSONALIZED AND IMMEDIATE FEEDBACK TO LEANERS.
AN IMPORTANT FACTOR THAT CONTRIBUTES TOWARDS THE EFFECTIVENESS OF THE LEARNING-TEACHING PROCESS IS THE ABILITY OF INSTRUCTORS TO MONITOR AND MEASURE THE ACTIVITY PERFORMED BY THE LEARNERS AND CONSEQUENTLY, TO ACT ACCORDING TO THE OBSERVED FACTS AND METRICS. AS A RESULT, E-LEARNING TOOLS LACK THE INDICATORS THAT CAN BE CONFIGURED BY THE INSTRUCTORS IN ORDER TO MEASURE THE ACTIVITY PERFORMED BY THEIR LEARNERS, THIS WORK PROPOSES A NOVEL METHOD POSITIVELY EVALUATED BY THE INSTRUCTORS INVOLVED IN DISTANCE COURSES TAUGHT IN THE UNIVERSITY OF CANTABRIA. THIS METHOD IS BASED ON A CUSTOMISABLE FORMULA WHICH ALLOWS INSTRUCTORS TO SET UP PARTICULAR PARAMETERS ACCORDING TO THEIR GOALS AND RESOURCES USED FOR DEVELOPING THE LEARNING ACTIVITIES ON THE COURSE. THESE INDICATORS ENABLE INSTRUCTORS TO ASSESS THE ACTIVITY PERFORMED BY THEIR STUDENTS IN COMPARISON WITH THEIR PEERS AND TO DETECT STUDENTS AT THE RISK OF DROP-OUT AMONG OTHERS. FURTHERMORE, THEY COULD BE USED AS PREDICTORS OF PERFORMANCE.
UNDER THE NOTION OF "CSCL SCRIPTS", DIFFERENT PEDAGOGICAL MODELS FOR STRUCTURING AND SUPPORTING COLLABORATION IN THE CLASSROOM HAVE BEEN PROPOSED. WE REPORT ON A PRACTICAL EXPERIENCE WITH SCRIPTS BASED ON THE PYRAMID COLLABORATIVE LEARNING FLOW PATTERN SUPPORTED BY A SPECIFIC CLASSROOM TOOL AND A TEACHER-FACING DASHBOARD THAT IMPLEMENTS MIRRORING AND GUIDING SUPPORT. THE INPUT DATA OF OUR ANALYSIS STEMS FROM RECORDINGS OF CLASSROOM INTERACTIONS GUIDED BY SEVERAL TEACHERS USING THE PYRAMIDAPP WITH DIFFERENT LEVELS OF TEACHING SUPPORT. FOR THE ANALYSIS, WE INTRODUCE A SPECIFIC CODING SCHEME ENABLING A QUANTITATIVE COMPARISON AND DEEPER ANALYSIS USING EPISTEMIC NETWORK ANALYSIS. THE RESULTS SHOW THAT THE GUIDING SUPPORT ENABLED TEACHERS TO PERFORM MORE ORCHESTRATION ACTIONS, MORE TARGETED INTERACTIONS AND TO MAKE MORE ANNOUNCEMENTS TO THE CLASS (REGARDING TIME, PHASE TRANSITIONS, AND STUDENTS' ACTIVITY PARTICIPATION) WHEN COMPARED TO THE MIRRORING SUPPORT. TEACHERS' ACTIONABLE DIFFERENCES OBSERVED UNDER THE MIRRORING AND GUIDING SUPPORT DIRECTED US TO DECONSTRUCT THE NOTION OF ORCHESTRATION LOAD INTO DIFFERENT FACETS AND TO DISCUSS HOW DIFFERENT SUPPORT PROVISIONS CORRESPOND TO THE DIFFERENT FACETS OF ORCHESTRATION LOAD.
THE CURRENT STUDY DRAWS ON THE USE OF WEB 2.0 TOOLS TO TEACH ADVANCED COMPUTER SKILLS IN A ROMANIAN BLENDED UNDERGRADUATE ECONOMICS COURSE PROGRAM. A META-ATTRIBUTE STRUCTURE IS CREATED USING QUESTIONNAIRES AND DATA GENERATED BY WEB 2.0 TOOLS. THE MAIN OBJECTIVE IS TO PROVIDE INFORMATION ON STUDENTS' ENGAGEMENT BEHAVIOR ON A MOODLE PLATFORM. SEVERAL PATTERNS ARE OBTAINED BY MEANS OF APPLYING CLASSIFICATION ALGORITHM CHARACTERISTIC TO THE STUDIED CONTEXT. THE RECEIVER OPERATING CHARACTERISTIC (ROC) REGRESSION ANALYSIS IS USED TO VALIDATE THE RESULT, OUTLINING THE PROFILE OF AN ACTIVELY ENGAGED STUDENT. NEXT, A COMPARISON IS MADE WITH A SIMILAR ANALYSIS PERFORMED EARLIER BY AUTHORS ON DATA GATHERED FROM A LANGUAGE COURSE DELIVERED ON A WIKI PLATFORM. THE IMPLICATIONS OF THE STUDY ARE THAT WEB 2.0 TOOLS BENEFIT THE LEARNING PROCESS ON CONDITION THAT SOCIAL LEARNING DESIGN IS INCORPORATED AND MORE EMPHASIS IS PUT ON THE PEDAGOGICAL ASPECTS; MOREOVER, TEACHERS MAY DISTRIBUTE STUDENTS INTO GROUPS AND FOCUS ON THOSE WHO REQUIRE MORE ATTENTION TO DEVELOP THE NECESSARY ATTRIBUTES IN SUCH AN ENVIRONMENT.
LEARNING BY PROBLEM-POSING IS PROMISING ACTIVITY IN LEARNING MATHEMATICS AND CONSIDERED TO CONTRIBUTE THE UNDERSTANDING OF THE PROBLEM'S STRUCTURE. IF WE CAN CLARIFY HOW LEARNERS ARE THINKING IN STEPS OF PROBLEM-POSING, WE WILL SUPPORT LEARNING BY PROBLEM-POSING MORE. THIS STUDY INVESTIGATE WHAT LEARNERS THINK DURING PROBLEM-POSING AS SENTENCE-INTEGRATION IN TERMS OF INTERMEDIATE PRODUCTS AS WELL AS THE POSED PROBLEMS AS THE RESULTANT PRODUCT. PROBLEM-POSING AS SENTENCE-INTEGRATION DEFINES THAT ARITHMETIC WORD PROBLEMS HAVE A STRUCTURE AND PROBLEM-POSING IS A TASK TO SATISFY ALL THE CONSTRAINTS AND THE REQUIREMENTS TO BUILD A VALID STRUCTURE. A PREVIOUS STUDY SHOWS, IN PROBLEM-POSING OF ARITHMETIC WORD PROBLEMS AS SENTENCE-INTEGRATION, LEARNERS TRY TO SATISFY RELATIVELY LARGE NUMBER OF THE CONSTRAINTS IN THE POSED PROBLEMS. THIS STUDY FOCUSES ON THE VIOLATION OF CONSTRAINTS IN THE INTERMEDIATE PRODUCTS DURING PROBLEM-POSING AS SENTENCE-INTEGRATION. IF LEARNERS HAVE CONCERN WITH THE STRUCTURE, THEY WILL TRY TO AVOID RELATIVELY LARGER NUMBER OF THE VIOLATED CONSTRAINTS ON THE WAY TO THE RESULTANT POSED PROBLEMS. WE CONDUCTED PEARSON'S CORRELATION TEST BETWEEN THE OCCURRENCE FREQUENCY OF INTERMEDIATE PRODUCTS AND THE NUMBER OF VIOLATED CONSTRAINTS OF THEM. THE RESULT SHOWS THE NEGATIVE CORRELATION BETWEEN THEM. WE CONSIDER THAT LEARNERS HAD AN INCLINATION TO AVOID AS MANY VIOLATED CONSTRAINTS AS POSSIBLE THROUGHOUT PROBLEM-POSING ACTIVITY.
SELF-REGULATED LEARNING (SRL) IS BEING PROMOTED AND ADOPTED INCREASINGLY BECAUSE OF THE NEEDS OF CURRENT EDUCATION, BASED ON COMPETENCES AND STUDENT CENTERED. AT THE SAME TIME, THE USE OF TECHNOLOGY FOR LEARNING IS NOW USUAL AND ALSO INCREASING. NEVERTHELESS, CURRENT LEARNING SOFTWARE OFFERS POOR SUPPORT FOR SRL. LEARNERS USE DIFFERENT TOOLS TO ORGANIZE THEIR LEARNING, TO VISUALIZE CONTENT, TO SUBMIT ASSIGNMENTS, TO ANSWER QUESTIONNAIRES, ETC. THESE FUNCTIONALITIES ARE USEFUL, BUT THEY ARE NOT RELEVANT FROM THE SRL APPROACH. PARTICULARLY, MONITORING STUDENTS' DEVELOPMENT OF SRL STRATEGIES AND STUDENTS' SELF-MONITORING ARE NOT WELL SUPPORTED IN EXISTING SYSTEMS. THIS PAPER INTRODUCES A PROPOSAL TO IMPROVE MONITORING SUPPORT IN E-LEARNING SYSTEMS. THE PROPOSAL IS BASED ON TIN CAN API OR XAPI, A RECENT STANDARD TO RECORD DATA ABOUT USERS' ACTIVITY IN LEARNING SYSTEMS.
IN THIS STUDY, 57 PARTICIPANTS FROM AN ONLINE COURSE PARTICIPATED IN ONLINE LEARNING ACTIVITIES FACILITATED BY ASSIGNED MODERATORS. SOCIAL NETWORK ANALYSIS TECHNIQUES WERE UTILIZED TO EXAMINE THE INFLUENCE OF MODERATOR ROLE ASSIGNMENT ON SOCIAL NETWORKS OF ONLINE CLASSES. THE RESULTS INDICATED WHEN STUDENTS WERE ASSIGNED TO THE MODERATOR POSITION THEIR PARTICIPATION QUANTITY, DIVERSITY, AND INTERACTION ATTRACTIVENESS INCREASED SIGNIFICANTLY AND THEIR NON-POSTING PARTICIPATION SIGNIFICANTLY INFLUENCED THE GROUP INTERACTION. STUDENTS' PARTICIPATION QUANTITY AND DIVERSITY ALSO SIGNIFICANTLY INFLUENCED THEIR INTERACTION ATTRACTIVENESS. QUALITATIVE FINDINGS REVEALED MODERATION CHARACTERISTICS OF THE HIGHEST DENSITY AND LOWEST DENSITY GROUPS. (C) 2013 ELSEVIER INC. ALL RIGHTS RESERVED.
IN THE LAST FEW YEARS, SOCIAL LEARNING, I.E., LEARNING BASED ON THE ANALYSIS AND DISCUSSION OF TOPICS BY MEANS OF SOCIAL COLLABORATIVE SYSTEMS, MAINLY SOCIAL NETWORK SERVICES SUCH AS FACEBOOK OR TWITTER, HAS ACQUIRED A GREAT IMPORTANCE AND LED MANY INSTRUCTORS AND INSTITUTIONS TO DEPLOY COURSES THAT INCLUDE ACTIVITIES TO BE PERFORMED IN THEM. FOR EFFECTIVE LEARNING, BOTH TEACHERS AND LEARNERS ARE REQUIRED TO GAIN INSIGHT INTO HOW THE INTERACTION TAKES PLACE AND HOW THE LEARNING PROCESS EVOLVES OVER THE TIME. GIVEN THAT THE NATURE OF THIS KIND OF LEARNING IS INHERENTLY SOCIAL, THE SOCIAL NETWORK ANALYSIS (SNA) THEORY IS PERFECTLY SUITABLE FOR THIS PURPOSE. THEREFORE, THIS PAPER PROPOSES THE USE OF SOCIOGRAMS, SNA REPRESENTATIONS, TO ANSWER MANY OF THE QUESTIONS THAT BOTH LEARNERS AND TEACHERS NEED TO KNOW TO MAKE THE BEST DECISIONS AND ACT ACCORDINGLY. FURTHERMORE, SEVERAL NETWORK SETTINGS ARE SUGGESTED AND THE INTERPRETATION OF THE MOST RELEVANT CENTRALITY MEASURES WHEN APPLIED TO ONLINE SOCIAL LEARNING IS PROVIDED. FINALLY, THE USEFULNESS OF SOCIOGRAMS IS SHOWN BY MEANS OF THE ANALYSIS OF THE ACTIVITY PERFORMED IN A MOOC COURSE HOSTED IN OPENMOOC PLATFORM.
STUDENTS' ACQUISITION OF GENERIC SKILLS IS A KEY TO THEIR INCORPORATION INTO THE JOB WORLD. HOWEVER, TEACHERS ENCOUNTER SEVERAL DIFFICULTIES WHEN MEASURING THEIR STUDENTS' PERFORMANCE IN GENERIC SKILLS. THESE DIFFICULTIES INCREASE IN ONLINE COURSES BASED ON LEARNING MANAGEMENT SYSTEMS, WHERE THERE IS NO DIRECT CONTACT BETWEEN TEACHERS AND THEIR STUDENTS. TO ADDRESS THIS PROBLEM, THIS PAPER ADOPTS A SOLUTION BASED ON THE USE OF ACTIVITY RECORDS FROM LEARNING MANAGEMENT SYSTEMS AS INDICATORS OF THE STUDENTS' PERFORMANCE IN GENERIC SKILLS. AFTER CONDUCTING A SURVEY AMONG TEACHERS OF ONLINE COURSES ON THE SUITABILITY OF THE SOLUTION, AN ARTEFACT WAS DEVELOPED AND APPLIED WITHIN FOUR ONLINE COURSES. INTERVIEWS CONDUCTED WITH TEACHERS AND THE RESULTS OF A SURVEY FOR MAPPING SKILLS AND ACTIVITIES PROVIDE POSITIVE EVIDENCE OF THIS SOLUTION'S POTENTIAL.
PROGRAMMING EXERCISES ARE TIME-CONSUMING ACTIVITIES FOR MANY STUDENTS. THEREFORE, MANY CLASSES PROVIDE METICULOUS SUPPORT FOR STUDENTS THROUGH TEACHING ASSISTANTS (TAS). HOWEVER, INDIVIDUAL STUDENTS' PROGRAMMING BEHAVIORS ARE QUITE DIFFERENT FROM EACH OTHER'S, EVEN WHEN THEY ARE SOLVING THE SAME PROBLEM. IT CAN BE HARD FOR TAS TO UNDERSTAND THE UNIQUE FEATURES OF EACH STUDENT'S PROGRAMMING BEHAVIOR. WE HAVE USED DATA MINING TO ANALYZE STUDENTS' PROGRAMMING BEHAVIORS IN ORDER TO IDENTIFY THEIR VARIOUS FEATURES. THE PURPOSE OF THIS STUDY IS TO PRESENT SUCH BEHAVIORAL FEATURES TO TAS TO IMPROVE THE EFFECTIVENESS OF THE ASSISTANCE THEY CAN PROVIDE. IN ORDER TO GRASP THE TIMING OF GUIDANCE, WE ESTIMATED THE GRADES FROM THE HISTORY OF PROGRAMMING BEHAVIOR.
USABILITY HAS BEEN IDENTIFIED AS ONE OF THE TOP UNSOLVED CHALLENGES TO IMPROVE A VISUAL PRESENTATION OF ANY TYPE OF INFORMATION. THE DESIGN AND IMPLEMENTATION OF A VISUALIZATION TOOL SHOULD INCORPORATE USABILITY BY DESIGN, AND THE DEVELOPMENT METHODOLOGY SHOULD INCORPORATE MECHANISMS TO ENSURE THAT USERS ARE ABLE TO RECOGNIZE THE INTENDED PATTERNS. THIS PAPER PRESENTS THE DEVELOPMENT METHODOLOGY FOR AN INFORMATION VISUALIZATION TOOL AND ITS EVOLUTION AS A REACTION OF THE EARLY FEEDBACK CAPTURED AT THE ITERATIVE EVALUATION PROCESS. THE IMPLEMENTED TOOL, A4LEARNING, TAKES THE STUDENTS TRACKING INFORMATION TO MATCH SIMILARITY AMONG THEM, AND MAKES USE OF THIS INFORMATION TO ESTIMATE THE STUDENTS PERFORMANCE, AND TO ENCOURAGE SELF-REFLECTION.
EDUCATIONAL RESEARCH SUGGESTS THAT INTERACTIVITY IS ONE OF THE MOST IMPORTANT TOOLS FOR LEARNING. THIS PAPER ANALYSES THE LEARNING PROCESS IN ONLINE COMMUNITIES BY EXAMINING THREE TYPES OF INTERACTIONS AMONG LEARNERS: (1) INTERACTIONS INVOLVING THE ACTIVE CONTRIBUTION OF CONTENT ('DIGITALLY SPEAKING'); (2) INTERACTIONS INVOLVING THE CONSUMPTION OF CONTENT ('DIGITALLY LISTENING'); AND (3) INTERACTIONS INVOLVING THE ORGANIZATION OF DIGITAL CONTENT. WE PRESENT A NETWORK ANALYSIS FRAMEWORK FOR ONLINE DISCUSSIONS ENTAILING BOTH STATIC AND DYNAMIC ANALYSES OF THESE TYPES OF INTERACTIONS. WE BUILD THREE SUB-NETWORKS DECONSTRUCTED FROM ONLINE DISCUSSIONS IN FOUR DIFFERENT LEARNING COMMUNITIES. THE SUGGESTED FRAMEWORK FOR NETWORK ANALYSIS FOLLOWS THE PARADIGM OF COLLABORATIVE LEARNING, AND THUS, IT ANALYSES THE DYNAMICS OF THE COLLECTIVE LEARNING, RATHER THAN INDIVIDUAL LEARNING. WE USE TOPOLOGICAL INDICATORS, SUCH AS THE DISTANCE BETWEEN LEARNERS, RECIPROCITY, AND THE INDIVIDUAL'S INFLUENCE ON THE COLLECTIVE, TO EXPLORE THE NETWORKS BETWEEN LEARNERS AND TO INVESTIGATE THEIR EVOLUTION DURING ONE SEMESTER. INTERACTIONAL PATTERNS IN THE SUB-NETWORKS ARE DIFFERENT AND SOMETIMES CONTRADICTORY, AND THE RELATIONSHIPS FOUND AMONG SPEAKING, LISTENING AND ORGANIZING DIGITAL CONTENT MIGHT BE ROOTED IN DIFFERENT LEARNING DESIGNS. OUR FINDINGS SUGGEST THAT SOCIAL LEARNING ASSESSMENT SHOULD ADDRESS DIFFERENT INTERACTION TYPES SEPARATELY.
THIS PAPER REPORTS THE FINDINGS OF A STUDY THAT PROPOSED A NOVEL LEARNING ANALYTIC METHODOLOGY THAT COMBINES PROCESS MINING WITH CLUSTER ANALYSIS TO STUDY TIME MANAGEMENT IN THE CONTEXT OF BLENDED AND ONLINE LEARNING. THE STUDY WAS CONDUCTED WITH FIRST-YEAR STUDENTS (N = 241) WHO WERE ENROLLED IN BLENDED LEARNING OF A HEALTH SCIENCE COURSE. THE STUDY IDENTIFIED FOUR DISTINCT TIME MANAGEMENT TACTICS AND THREE STRATEGIES. THE TACTICS AND STRATEGIES WERE INTERPRETED ACCORDING TO THE ESTABLISHED THEORETICAL FRAMEWORK OF SELF-REGULATED LEARNING IN TERMS OF STUDENT DECISIONS ABOUT WHAT TO STUDY, HOW LONG TO STUDY, AND HOW TO STUDY. THE STUDY ALSO IDENTIFIED SIGNIFICANT DIFFERENCES IN ACADEMIC PERFORMANCE AMONG STUDENTS WHO FOLLOWED DIFFERENT TIME MANAGEMENT STRATEGIES.
THE PEDAGOGICAL MODELLING OF EVERYDAY CLASSROOM PRACTICE IS AN INTERESTING KIND OF EVIDENCE, BOTH FOR EDUCATIONAL RESEARCH AND TEACHERS' OWN PROFESSIONAL DEVELOPMENT. THIS PAPER EXPLORES THE USAGE OF WEARABLE SENSORS AND MACHINE LEARNING TECHNIQUES TO AUTOMATICALLY EXTRACT ORCHESTRATION GRAPHS (TEACHING ACTIVITIES AND THEIR SOCIAL PLANE OVER TIME) ON A DATASET OF 12 CLASSROOM SESSIONS ENACTED BY TWO DIFFERENT TEACHERS IN DIFFERENT CLASSROOM SETTINGS. THE DATASET INCLUDED MOBILE EYE-TRACKING AS WELL AS AUDIOVISUAL AND ACCELEROMETRY DATA FROM SENSORS WORN BY THE TEACHER. WE EVALUATED BOTH TIME-INDEPENDENT AND TIME-AWARE MODELS, ACHIEVING MEDIAN F1 SCORES OF ABOUT 0.7-0.8 ON LEAVE-ONE-SESSION-OUT K-FOLD CROSS-VALIDATION. ALTHOUGH THESE RESULTS SHOW THE FEASIBILITY OF THIS APPROACH, THEY ALSO HIGHLIGHT THE NEED FOR LARGER DATASETS, RECORDED IN A WIDER VARIETY OF CLASSROOM SETTINGS, TO PROVIDE AUTOMATED TAGGING OF CLASSROOM PRACTICE THAT CAN BE USED IN EVERYDAY PRACTICE ACROSS MULTIPLE TEACHERS.
THIS PAPER INVESTIGATES THE IMPACT OF THE LEARNING ACTIVITIES ON THE FINAL GRADES IN STUDENTS IN ENGINEERING EDUCATION. A PRINCIPAL COMPONENT ANALYSIS IS CARRIED OUT ON THE UNDERGRADUATE "STOCHASTIC MODELS" COURSE. WE HAVE DETERMINE THAT THE FIRST PRINCIPAL COMPONENT HAS A POSITIVE CORRELATION WITH THE SCORE OF THE FINAL WRITTEN CUMULATIVE EXAM. THIS COULD POSSIBLY MEAN THAT THE FINAL EXAM COULD BE ELIMINATED FROM THE ENGINEERING CURRICULA, BUT THE VARIABILITY IS SIGNIFICANT AS MEASURED BY THE CORRELATION R STATISTIC. BASED ON INFORMATION GATHERED ON A MUCH LARGER SAMPLE, WE FOUND THAT THE VARIABILITY HAS INCREASED, INDICATING CHANGES IN THE COURSE AND STUDENTS' EMPHASIS IN LEARNING ACTIVITIES. THEREFORE IT CAN BE CONCLUDED THAT THE EVIDENCE PRESENTED DOES NOT JUSTIFY ELIMINATING WRITTEN CUMULATIVE FINAL EXAMS.
REASONING ABOUT CAUSAL MECHANISMS IS CENTRAL TO SCIENTIFIC INQUIRY. IN SCIENCE EDUCATION, IT IS IMPORTANT FOR TEACHERS AND RESEARCHERS TO DETECT STUDENTS' MECHANISTIC EXPLANATIONS AS EVIDENCE OF THEIR LEARNING, ESPECIALLY RELATED TO CAUSAL MECHANISMS. IN THIS PAPER, WE INTRODUCE A SEMI-AUTOMATED METHOD THAT COMBINES ASSOCIATION RULE MINING WITH HUMAN RATER'S INSIGHT TO CHARACTERIZE STUDENTS' MECHANISTIC EXPLANATIONS FROM THEIR WRITTEN RESPONSES TO SCIENCE QUESTIONS. WE SHOW AN EXAMPLE OF APPLYING THIS METHOD TO STUDENTS' WRITTEN RESPONSES TO A QUESTION ABOUT CLIMATE CHANGE AND COMPARE MECHANISTIC REASONING BETWEEN HIGH- AND LOW-SCORING STUDENT GROUPS. SUCH ANALYSIS PROVIDES IMPORTANT INSIGHT INTO STUDENTS' CURRENT KNOWLEDGE STRUCTURE AND INFORMS TEACHERS AND RESEARCHERS ABOUT FUTURE DESIGN OF INSTRUCTIONAL INTERVENTIONS.
THIS STUDY AIMS TO CONTRIBUTE TO EMPIRICAL AND INTERDISCIPLINARY KNOWLEDGE ON HOW VISUAL LEARNING ANALYTICS TOOLS CAN SUPPORT STUDENTS' COGNITIVE ENGAGEMENT IN COMPLEX IN-CLASS SCENARIOS. TAKING A HOLISTIC APPROACH, INSTRUCTIONAL DESIGN, LEARNING ANALYTICS, AND STUDENTS' PERCEPTIONS WERE EXAMINED TOGETHER. THE TEACHING OF SYSTEMATIC VIEWING AND IMAGE INTERPRETATION IN RADIOLOGY EDUCATION WAS USED TO EXEMPLIFY A COMPLEX IN-CLASS SCENARIO, AND A SPECIFIC LEARNING DASHBOARD WAS DESIGNED AS A SUPPORT TOOL. THE DESIGN WAS BASED ON BOTH EDUCATIONAL AND VISUALIZATION THEORIES AND ALIGNED WITH A PEDAGOGICAL SCENARIO INTEGRATING INDIVIDUAL AND CLASS-WIDE ACTIVITIES. THE QUANTITY AND QUALITY OF THE COGNITIVE ENGAGEMENT OF A GROUP OF 26 STUDENTS WERE EXPLORED. A MIXED METHOD APPROACH WAS USED, INCLUDING COMPUTER LOG FILE ANALYSES OF INDIVIDUAL WORK, ANALYSIS OF VIDEO RECORDINGS OF IN-CLASS SMALL GROUP DISCUSSIONS, AND A FOCUS GROUP DISCUSSION WITH THE STUDENTS INVOLVED. THE IN-CLASS SCENARIO WITH THE LEARNING DASHBOARD RESULTED IN A GOOD BALANCE BETWEEN INDIVIDUAL TASKS AND GROUP DISCUSSIONS AND A HIGH DEGREE OF ACTIVE COGNITIVE ENGAGEMENT. CONSTRUCTIVE AND INTERACTIVE FORMS OF COGNITIVE ENGAGEMENT WERE, HOWEVER, LESS EVIDENT. IN ADDITION, THE PRODUCTS OF THESE CONSTRUCTIVE (DESCRIPTION OF FINDINGS) AND INTERACTIVE (TYPE OF DIALOGUE) COGNITIVE ENGAGEMENTS WERE OF MEDIOCRE QUALITY AND THEREFORE NOT OPTIMAL FOR KNOWLEDGE TRANSFER. THE STUDY ALSO SHOWED THAT THE WAY THE STUDENTS AND TEACHER UNDERSTOOD THEIR RESPECTIVE TASKS AND USED THE AVAILABLE INTERACTION TECHNIQUES OF THE LEARNING DASHBOARD HIGHLY INFLUENCED THE STUDENTS' COGNITIVE ENGAGEMENT. FINALLY, SEVERAL IDEAS EMERGED THAT COULD HELP TO OVERCOME THE DEFICITS FOUND IN THE TRAINING OF PARTICIPANTS AND TO IMPROVE THE TASKS SET AND THE LEARNING DASHBOARD ITSELF.
DUE TO COVID-19, TEACHERS QUICKLY CHANGED THEIR COURSES FROM TRADITIONAL FACE-TO-FACE MODALITY TO EMERGENCY REMOTE TEACHING (ERT), RELYING ON LEARNING MANAGEMENT SYSTEMS (LMS). IN THIS SIMPLE PROSPECTIVE DESIGN STUDY, WE ANALYZED THE RELATION OF THE LEVEL OF TEACHERS' TECHNOLOGICAL ACCEPTANCE AT THE BEGINNING OF ERT (MARCH 2020) CONSIDERING THREE VARIABLES: THE TIME SPENT BY TEACHERS IN THE LMS DURING THAT SEMESTER, THE PERCENTAGE OF LMS'S RESOURCES THEIR STUDENTS VIEWED DURING THE SEMESTER, AND THE FINAL ACADEMIC PERFORMANCE OF THE SAME STUDENTS AT THE END OF THAT SEMESTER (SEPTEMBER 2020). THIS STUDY INCLUDED 251 TEACHERS (57% MALE) AND 12,185 STUDENTS (45% MALE). WE MEASURED THE TEACHERS' LEVEL OF ACCEPTANCE WITH THE SPANISH VERSION OF THE QUESTIONNAIRE TECHNOLOGY ACCEPTANCE MODEL (TAM). WE FOUND THAT THE RELATION BETWEEN THE TEACHER'S ACCEPTANCE AND THEIR TIME SPENT ON THE LMS WAS SIGNIFICANT AND POSITIVE (RHO = 0.24, P < 0.001). IN ADDITION, TEACHERS' PERCEPTION OF LMS'S EASINESS IS RELATED TO THE PERCENTAGE OF EDUCATIONAL RESOURCES THEIR STUDENTS UTILIZED (RHO = 0.26, P < 0.001). FINALLY, WE FOUND A RELATION BETWEEN THE USEFULNESS DIMENSION OF THE TAM TO THE ACADEMIC PERFORMANCE OF THE STUDENTS AT THE END OF THAT SEMESTER (RHO = 0.18, P < 0.01). CONSIDERING THESE RESULTS, WE DISCUSS PRACTICES FOR IMPLEMENTING QUALITY EDUCATION.</P>
IN THIS PAPER A MODEL TO TRACK LEARNING EXPERIENCES FROM AN INTERACTIVE E-BOOK IS PRESENTED. FOR THIS A DIGITAL BOOK COHERENT WITH THE EPUB3 STANDARDS HAS BEEN MADE BY EXPORTING AN EXISTING COURSE IN A LEARNING MANAGEMENT SYSTEM AND REPLICATING ITS INTERACTIVE BEHAVIOR. DEFINING TRIGGER POINTS THROUGHOUT THE BOOK AND AMONGST THE INTERACTIONS MADE BY THE STUDENTS, THAT SEND DATA TO A LEARNING RECORD STORE, ENRICH THE E-BOOK AND PROVIDE THE POSSIBILITY TO ANALYZE AND PREVIEW STATISTICAL DATA FOR STUDENTS' COMMITMENT TO LEARNING THE MATERIAL AND THEIR ACTIONS OVER THE WHOLE COURSE. THE XAPI SPECIFICATION HELPS COLLECT EDUCATIONAL DATA AND STORE IT IN A DATABASE, WHICH CAN BE USED FOR CREATING A FURTHER SET OF REPORTS ANYTIME IN THE FUTURE.
PURPOSE THIS PAPER AIMS TO EXPLORE PREDICTING EMPLOYEE LEARNING ACTIVITY VIA EMPLOYEE CHARACTERISTICS AND USAGE FOR TWO ONLINE LEARNING TOOLS. DESIGN/METHODOLOGY/APPROACH STATISTICAL ANALYSIS FOCUSED ON OBSERVATIONAL DATA COLLECTED FROM USER LOGS. DATA ARE ANALYZED VIA REGRESSION MODELS. FINDINGS FINDINGS ARE PRESENTED FOR OVER 40,000 EMPLOYEES' LEARNING ACTIVITY FOR ONE YEAR IN A MULTINATIONAL TECHNOLOGY COMPANY. VARIABLES INCLUDING JOB LEVEL AND TOOL USE YIELDED A PREDICTIVE MODEL FOR OVERALL LEARNING BEHAVIORS. IN ADDITION, RELEVANT DIFFERENCES ARE FOUND FOR MANAGERS AND NONPROFESSIONAL LEARNING. RESEARCH LIMITATIONS/IMPLICATIONS IMPORTANTLY, HOW WELL EMPLOYEES LEARNED CONTENT WAS NOT MEASURED. THIS RESEARCH IS ALSO LIMITED TO OBSERVATIONAL RELATIONSHIPS: FOR EXAMPLE, THE ONLINE TOOLS WERE USED BY SELF-SELECTED USERS, INSTEAD OF RANDOMLY ASSIGNED. FUTURE RESEARCH WHICH RANDOMLY ASSIGNS TOOL USE TO EMPLOYEE SUBGROUPS COULD EXPLORE CAUSAL RELATIONSHIPS. PRACTICAL IMPLICATIONS THIS PAPER PRESENTS IMPLICATIONS FOR BUSINESS ANALYSTS AND EDUCATIONAL TECHNOLOGY: HOW PREDICTIVE ANALYTICS CAN LEVERAGE DATA TO PLAN PROGRAMS, THE SIGNIFICANT CHALLENGES FOR THE ADOPTION AND USAGE FOR ONLINE LEARNING TOOLS, AND THE DISTINCT NEEDS OF MANAGERS ENGAGING WITH THESE TOOLS. ORIGINALITY/VALUE GIVEN A GROWING EMPHASIS ON USING EMPLOYEE DATA, IT IS IMPORTANT TO EXPLORE HOW LEARNING BEHAVIORS CAN BE MADE VISIBLE IN PEOPLE ANALYTICS. WHILE PREVIOUS RESEARCH HAS SURVEYED EMPLOYEE CULTURES ON LEARNING OR EXPLORED THE SOCIO-PSYCHOLOGICAL FACTORS WHICH CONTRIBUTE TO THIS LEARNING, THIS PAPER PRESENTS NOVEL DATA ON EMPLOYEE PARTICIPATION IN LEARNING PROGRAMS WHICH ILLUMINATES BOTH HOW HR METRICS CAN PRODUCTIVELY USE THIS DATA TO REIFY LEARNING PATTERNS, AND HOW WORKPLACE TECHNOLOGY DESIGNERS CAN CONSIDER IMPORTANT FACTORS SUCH AS INTERNAL HIERARCHIES.
ONE OF THE MOST INVESTIGATED QUESTIONS IN EDUCATION IS TO KNOW WHICH FACTORS OR VARIABLES AFFECT LEARNING. THE PREDICTION OF LEARNING OUTCOMES CAN BE USED TO ACT ON STUDENTS IN ORDER TO IMPROVE THEIR LEARNING PROCESS. SEVERAL STUDIES HAVE ADDRESSED THE PREDICTION OF LEARNING OUTCOMES IN INTELLIGENT TUTORING SYSTEMS ENVIRONMENTS WITH INTENSIVE USE OF EXERCISES, BUT FEW OF THEM ADDRESSED THIS PREDICTION IN OTHER WEB-BASED ENVIRONMENTS WITH INTENSIVE USE NOT ONLY OF EXERCISES BUT ALSO, FOR EXAMPLE, OF VIDEOS. IN ADDITION, MOST WORKS ON PREDICTION OF LEARNING OUTCOMES ARE BASED ON LOW LEVEL INDICATORS SUCH AS NUMBER OF ACCESSES OR TIME SPENT IN RESOURCES. IN THIS PAPER, WE APPROACH THE PREDICTION OF LEARNING GAINS IN AN EDUCATIONAL EXPERIENCE USING A LOCAL INSTANCE OF KHAN ACADEMY PLATFORM WITH AN INTENSIVE USE OF EXERCISES AND TAKING INTO ACCOUNT NOT ONLY LOW LEVEL INDICATORS BUT ALSO HIGHER LEVEL INDICATORS SUCH AS STUDENTS' BEHAVIOURS. OUR PROPOSED REGRESSION MODEL IS ABLE TO PREDICT 68% OF THE LEARNING GAINS VARIABILITY WITH THE USE OF SIX VARIABLES RELATED TO THE LEARNING PROCESS. WE DISCUSS THESE RESULTS PROVIDING EXPLANATION OF THE INFLUENCE OF EACH VARIABLE IN THE MODEL AND COMPARING THESE RESULTS WITH OTHER PREDICTION MODELS FROM OTHER WORKS.
IN THIS PAPER WE PROPOSE A BUSINESSIFICATION APPROXIMATION TO MEASURE AND ANALYSE STUDENTS' ENGAGEMENT IN A GAMIFIED LEARNING CONTEXT. GAMIFICATION IN EDUCATION IS USED TO ENHANCE STUDENTS EXPERIENCE AND IMPROVE LEARNING OUTCOMES. ITS TECHNICS SUCH AS POINTS, LEADERBOARD, BADGES OR RANKING ARE ALSO USED IN LEARNING INSTRUCTIONS WITH THE AIM TO IMPROVE STUDENTS' ENGAGEMENT. THIS ENGAGEMENT CAN BE CONSIDERED AS THE METRIC TO MEASURE THE SUCCESS OF GAMIFIED INSTRUCTIONS. THE GAMIFICATION MODEL CAN ALSO BE USED IN AN ONLINE LEARNING ENVIRONMENT. IN THIS VIRTUAL CONTEXT TEACHERS HAVE TO HAVE SOME TOOLS TO SEE WHAT HAPPENS DURING THE LEARNING PROCESS. SUCH VIRTUAL CONTEXT IS USUALLY WEB BASED. IN THIS SPECIFIC CONTEXT THE RESOURCES USED SUCH AS IMAGES, VIDEOS OR AUDIOS ARE FUNDAMENTAL TO ENGAGE STUDENTS. IN ORDER TO HELP TEACHERS ENHANCE ENGAGEMENT WE PROPOSE THE USE OF WEB ANALYTICAL TOOLS IN SUCH WEB BASED GAMIFIED LEARNING CONTEXTS TO TRACK, ANALYSE AND FINALLY ENHANCE SUCH RESOURCES.
THE PROBLEM OF SMOOTH AND SYNCHRONOUS INTEGRATION OF VARIOUS TYPES OF E-LEARNING AND ONLINE COURSES, INTERNSHIPS AND EXTRA-CURRICULAR ACTIVITIES INTO BLENDED, SYSTEMATIC ADAPTIVE EDUCATIONAL ENVIRONMENT IS BECOMING A NEW PARADIGM IN INNOVATIONS IN EDUCATIONAL PROCESS OF HIGHER EDUCATION INSTITUTIONS (HEIS). BUT THERE ARE FEW STUDIES THAT CONSISTENTLY DISCUSS THE IMPACT OF THE SYSTEMS PROVIDING ANALYSIS OF EDUCATIONAL OUTCOMES (ACADEMIC ANALYTICS SYSTEMS) ON QUALITY OF EDUCATION AND PROVISION OF INTEGRATED EDUCATIONAL ENVIRONMENT. THIS PAPER INVESTIGATES ROLE, APPLICATION AND TAXONOMY OF ACADEMIC ANALYTICS SYSTEMS APPLIED TO THE PROCESS OF IMPLEMENTING INNOVATIVE STRATEGIES IN HIGHER EDUCATION. SUCH SYSTEMS ARE INTENDED TO EXTRACT MEANINGFUL INFORMATION FROM MULTIPLE DATA SOURCES (LEARNING MANAGEMENT AND CONTENT SYSTEMS (LMS AND LCS), ONLINE COURSES AND OTHER) TO PREDICT, CLUSTER, FIND RELATIONS, AND PREPARE DATA FOR DECISION MAKING PROCESS AIMED AT CREATING KNOWLEDGE CRUCIAL FOR IMPROVING THE QUALITY OF EDUCATIONAL PROCESS IN HEIS. ACADEMIC ANALYTICS INCORPORATE THREE MAIN TYPES OF SYSTEMS: LEARNING ANALYTICS (LA), EDUCATIONAL DATA MINING (EDM) AND EDUCATIONAL BIG DATA (EBD) SYSTEMS. AS ACADEMIC ANALYTICS RESEARCH FIELD IS RAPIDLY DEVELOPING EVEN THOUGH A CONSIDERABLE NUMBER OF PAPERS HAVE PROVIDED SUBSTANTIAL INSIGHT ON THE THEORETICAL BASIS OF LA, EDM AND EBD SYSTEMS, TAXONOMY, CLASSIFICATION AND DIFFERENTIATION OF TYPES OF THE SYSTEMS ARE STILL UNDEFINED AND OFTEN OVERLAP. THE MAIN RESEARCH QUESTION OF THE STUDY IS TO EXAMINE THE CLASSIFICATION OF ACADEMIC ANALYTICAL SYSTEMS IN HIGHER EDUCATION ACCORDING TO THE NEEDS AND GOALS OF UNIVERSITIES AND INVESTIGATE IMPACT OF ACADEMIC ANALYTICS IN BLENDING DIFFERENT TYPES OF EDUCATIONAL ACTIVITY AND CREATION OF INTEGRATED EDUCATIONAL ENVIRONMENT. OUR FINDINGS ARE BASED ON THE ANALYSIS OF PAPERS ON LEARNING ANALYTICS IN HIGHER EDUCATION PUBLISHED BETWEEN 2010 AND 2018.
REDUCING DIMENSIONAL DATA IS AN ESSENTIAL STEP BEFORE DATA ANALYSIS IN PREDICTIVE LEARNING ANALYTICS (PLA) FOR STUDENT DROPOUTS. IT WAS REDUCING DIMENSIONS IN THE STUDY USING THE CATPCA METHOD. CATPCA HAS ADVANTAGES IN REDUCING DATA DIMENSIONS ON MEASUREMENT VARIABLES OF VARIOUS LEVELS SUCH AS NOMINAL, ORDINAL, AND NUMERICAL, WHICH MAY NOT HAVE A LINEAR CORRELATION BETWEEN ONE VARIABLE AND ANOTHER, SUCH AS VARIABLES RELATED TO THE PLA DATA PROCESSING. THIS STUDY'S RESULTS ARE FIVE FACTORS THAT STORE IMPORTANT INFORMATION ABOUT THE INPUT VARIABLES, NAMELY SOCIAL AND ECONOMIC, ACADEMIC PROGRAM, INSTITUTIONAL, ACADEMIC PERFORMANCE, AND PERSONAL. THE CONCLUSIONS OF THIS STUDY WILL BE BENEFICIAL FOR FURTHER RESEARCH IN THE PLA PROCESS
IN THIS INTRODUCTION TO THE SPECIAL ISSUE ON SYSTEMATIC REVIEWS ON EMERGING LEARNING ENVIRONMENTS AND TECHNOLOGIES, WE INTRODUCE BEST PRACTICES FOR CONDUCTING SYSTEMATIC REVIEWS AND META-ANALYSIS AND DISCUSS THE NEED FOR A SYSTEMATIC REVIEW ON EMERGING LEARNING ENVIRONMENTS AND TECHNOLOGIES. WE SYNTHESIZE RESEARCH ON SEVEN PRIMARY AREAS OF EMERGING LEARNING ENVIRONMENTS AND TECHNOLOGIES THAT INCLUDE: (1) SOCIAL MEDIA, (2) MASSIVE OPEN ONLINE COURSES, (3) SPECIAL EDUCATION TECHNOLOGY, (4) MOBILE LEARNING, (5) GAME-BASED LEARNING AND GAMIFICATION, (6) ADAPTIVE LEARNING, AND (7) LEARNING ANALYTICS AND INTRODUCE THE THIRTEEN ARTICLES THAT WERE INCLUDED IN THIS SPECIAL ISSUE. THIS ARTICLE ALSO PROVIDES IMPLICATIONS FOR THE READERS ON USING AND CONDUCTING SYSTEMATIC REVIEWS.
HIGHER EDUCATION IS WITNESSING A MENTAL HEALTH CRISIS WITHOUT PRECEDENTS. LEARNING ANALYTICS OFFER POWERFUL TOOLS TO MONITOR STUDENTS ENGAGEMENT, BUT WOULD INSTRUCTORS IN VIRTUAL LEARNING ENVIRONMENTS (VLES) BENEFIT FROM BEING ABLE TO MONITOR MENTAL WELL-BEING AS WELL? AND HOW CAN THIS BE IMPLEMENTED IN A WAY THAT IS NOT DISRUPTIVE TO EDUCATION? WE HAVE DEVISED A CONCEPTUAL FRAMEWORK ADDRESSING THIS ISSUE, WHICH INCORPORATE THE ROLE OF THE ONLINE TUTOR INTO AN ENVIRONMENT "OBSERVING" STUDENT COMMUNICATIVE ACTS. THE CONCEPTUAL FRAMEWORK IS EVALUATED IN A SERIES OF EXPERIMENTS AND CASE STUDIES, AIMED AT ASSESSING THE FEASIBILITY AND APPRECIATE THE USEFULNESS OF SINGLE POTENTIAL COMPONENTS, AND ON THE BASIS OF EXPERT COMMENTARIES.
MULTIMODALITY IS AN INTEGRAL PART OF TEACHING AND LEARNING. OVER THE PAST FEW DECADES RESEARCHERS HAVE BEEN DESIGNING, CREATING AND ANALYZING NOVEL ENVIRONMENTS THAT ENABLE STUDENTS TO EXPERIENCE AND DEMONSTRATE LEARNING THROUGH A VARIETY OF MODALITIES. THE RECENT AVAILABILITY OF LOW COST MULTIMODAL SENSORS, ADVANCES IN ARTIFICIAL INTELLIGENCE AND IMPROVED TECHNIQUES FOR LARGE SCALE DATA ANALYSIS HAVE ENABLED RESEARCHERS AND PRACTITIONERS TO PUSH THE BOUNDARIES ON MULTIMODAL LEARNING AND MULTIMODAL LEARNING ANALYTICS. IN AN EFFORT TO CONTINUE THESE DEVELOPMENTS, THE 2015 MULTIMODAL LEARNING AND ANALYTICS GRAND CHALLENGE INCLUDES A COMBINED FOCUS ON NEW TECHNIQUES TO CAPTURE MULTIMODAL LEARNING DATA, AS WELL AS THE DEVELOPMENT OF RICH, MULTIMODAL LEARNING APPLICATIONS.
SOCIAL DIMENSION PLAYS A CRUCIAL ROLE IN THE LEARNING PROCESS. THE USE OF TECHNOLOGICAL RESOURCES TO STIMULATE AND MEDIATE THE INTERACTION BETWEEN STUDENTS IS KNOWN AS COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL). DESPITE BEING WIDELY USED IN PROGRAMMING EDUCATION, THE SUMMARIZATION OF THE ACADEMIC LITERATURE ABOUT THIS TOPIC IS SCARCE. TO CREATE THAT BODY OF KNOWLEDGE, A SYSTEMATIC LITERATURE REVIEW WAS PERFORMED. THE FINDINGS PROVIDE AN UNDERSTANDING OF HOW COLLABORATION IS EXPLORED IN INTRODUCTORY PROGRAMMING, RESOURCES USED TO STIMULATE THEM AND CHALLENGES IN THE PROCESS. OPPORTUNITIES FOR FUTURE RESEARCH ARE DISCUSSED, ESPECIALLY RELATED TO MOTIVATION, SELF-EFFICACY AND ENGAGEMENT IN CSCL, AND THE EXPLOITATION OF LEARNING ANALYTICS.
THE COMMON APPROACH TO EVALUATE INTELLIGENT TUTORING SYSTEMS (ITSS) IS TO CONDUCT RANDOMIZED EXPERIMENTS USING THE PRE/POST-TEST DESIGN AND MEASURE LEARNING. IN THIS PAPER, WE TAKE A WIDER LOOK, AND USE LEARNING ANALYTICS TO INVESTIGATE HOW MUCH STUDENTS ENGAGE IN VOLUNTARY PRACTICE WITH EER-TUTOR, AN ITS THAT TEACHES CONCEPTUAL DATABASE DESIGN. WE ALSO INVESTIGATE THE RELATIONSHIP OF VOLUNTARY PRACTICE IN EER-TUTOR WITH STUDENTS' LEARNING. OVER 75% OF STUDENTS USED EER-TUTOR FOR TUTORED PROBLEM SOLVING. STUDENTS WHO COMPLETED AT LEAST FIVE PROBLEMS ACHIEVED SIGNIFICANTLY HIGHER RESULTS ON THE ASSIGNMENT AND EXAM. GIVEN THE BENEFITS OF TUTORED PROBLEM SOLVING, WE PROPOSE SOME MODIFICATIONS TO THE COURSE.
THIS PAPER PRESENTS PETRA, A FRAMEWORK PROPOSED FOR REPRESENTING AND TREATING MULTI-SOURCE HETEROGENEOUS TRACES FROM SIMULATED LEARNING ENVIRONMENTS. WE TESTED OUR PROPOSITION ON TRACES FROM TELEOS, A SIMULATION-BASED ITS DEDICATED TO PERCUTANEOUS ORTHOPEDIC SURGERY. THIS ITS CAPTURES LEARNERS INTERACTIONS FROM THREE DIFFERENT AND INDEPENDENT SOURCES. THE CONDUCTED EXPERIMENT DEMONSTRATED THAT THE SEQUENCES GENERATED BY PETRA FOSTERED EFFICIENTLY: 1) THE LEARNING ANALYTICS TASK OF EVALUATING THE INFLUENCE OF VISUAL PERCEPTIONS ON LEARNERS' ERRORS; 2) THE EXTRACTION OF INTERESTING ASSOCIATION RULES POTENTIALLY REUSABLE FOR TUTORING SERVICES PRODUCTION. HOWEVER, ITS GENERICITY HAS NOT BEEN TESTED AND IT WILL NEED TO BE EVALUATED AT A LARGER SCALE.
THE ARTICLE DISCUSSES THE POSSIBILITIES OF FORMING AND EVALUATING SUBJECT AND META-SUBJECT OUTCOMES PROVIDED BY THE USE OF PERSONALIZED LEARNING AND A DIGITAL PLATFORM. IT DEMONSTRATES HOW THE CONCEPT AND STRUCTURE OF THE PLATFORM INFLUENCE THE CONTENT AND EFFECTIVENESS OF THE EDUCATIONAL COURSE. EXAMPLES DESCRIBE THE USE OF LEARNING ANALYTICS FOR ASSESSING EDUCATIONAL OUTCOMES BASED ON THE ANALYSIS OF DATA COLLECTED BY THE DIGITAL PLATFORM. THE PERSONALIZED LEARNING TOOLS AIMED AT FORMATION OF SUBJECT AND META-SUBJECT OUTCOMES. THE SUMMARY GIVES AN OVERVIEW OF THE RESULTS OF THE TWO YEARS OF EXPERIENCE IN TEACHING PHYSICS AT SCHOOL WITH THE USE OF THE DIGITAL PLATFORM.
THIS PAPER IS MOTIVATED BY TWO OBSERVATIONS ON COMPUTER-SUPPORTED EDUCATION: FIRST, THERE HAS BEEN GROWING AVAILABILITY, RAPID PROLIFERATION, AND INCREASED DIVERSITY OF LEARNER-SYSTEM EDUCATIONAL DATA. SECOND, ADVANCES IN LEARNING ANALYTICS AND DATA MINING HAVE FACILITATED AND SPAWNED A VARIETY OF NOVEL INVESTIGATIONS USING SUCH DATA. DRIVEN BY THESE COMPLEMENTARY TRENDS, THE PRESENT WORK IS GEARED TOWARDS EXPLORING KNOWLEDGE-BASED DISCOVERY APPROACHES IN UNDERSTANDING LEARNER-SYSTEM USAGE DATA. MORE SPECIFICALLY, WITH AN EYE TOWARD TRACING AND COMPREHENDING LEARNER BEHAVIORS IN A MEDICAL INTELLIGENT TUTORING SYSTEM, WE EXPLORE THE UTILITY OF PROCESS MINING, IN UNDERSTANDING THE PROBLEM SOLVING TRAJECTORIES OF STUDENTS IN A MEDICAL COMPUTER-BASED LEARNING ENVIRONMENT.
THE BEHAVIORAL PATTERNS OF TEACHER IN AUTHENTIC LEARNING CONTEXT ARE HEATED TOPICS IN EDUCATION. HOWEVER, THERE ARE FEW METHODS AND TOOLS FOR EFFECTIVELY MEASURING TEACHER'S BEHAVIORS IN THE AUTHENTIC CLASS, ESPECIALLY FOR TEACHING PATTERNS AND KNOWLEDGE STRUCTURE IN SMART LEARNING ENVIRONMENT. LEARNING ANALYTICS(LA) PROVIDES A NEW WAY FOR UNDERSTANDING BEHAVIORAL PATTERNS AND KNOWLEDGE OF TEACHER. THIS STUDY CONDUCTS A VALID METHOD: LAG SEQUENCE ANALYSIS(LAS) BASED ON LA. AFTER CODING TEACHING PROCESS, LSA IS USED TO SUMMARIZE SEVERAL BEHAVIORAL PATTERNS OF TEACHER IN SMART LEARNING ENVIRONMENT. THE RESULTS OF THIS STUDY ALSO PROVIDE NEW IDEAS AND PERSPECTIVES FOR FUTURE RESEARCH ON TEACHING BEHAVIORAL PATTERNS FOR OTHER SCHOLARS.
SERIOUS GAMES (SG) ARE INCREASINGLY USED BY EDUCATORS TO ASSIST THE TEACHING AND LEARNING PROCESS AND OFFER MANY ADVANTAGES OVER TRADITIONAL EDUCATION. THEY ARE HIGHLY ENGAGING, MOTIVATING AND THEY HAVE THE POTENTIAL TO ADAPT TO EACH STUDENT QUICKLY BECOMING AN IDEAL SUPPLEMENTARY TOOL FOR EDUCATION. HOWEVER, IF THE MAJORITY OF TEACHERS AGREE THAT USING SGS INCREASES THE MOTIVATION, LEARNING AND RETENTION OF THEIR STUDENTS, VERY FEW OF THEM ARE READY TO TRUST THEIR ASSESSMENT TO VERIFY THAT THE LEARNING GOALS HAVE BEEN MET. THEY WOULD RATHER ADOPT A MORE CONVENTIONAL METHOD SUCH AS A PAPER-BASED TEST. WE BELIEVE TWO MAIN REASONS EXPLAIN THIS ATTITUDE: A LACK OF OWNERSHIP OVER THE GAMES USED AND THE RIGIDNESS OF THE GAMES, MAKING THEM UNMODIFIABLE BY THE TEACHER. TO OVERCOME THESE ISSUES, WE HAVE DEVELOPED AN ASSESSMENT ENGINE TO BE USED BY BOTH SG DEVELOPERS AND EDUCATORS. THE ENGINE'S DESIGN RESULTS IN A SEPARATION OF A GAME AND ITS ASSESSMENT, AND THE RESULTING MODULARITY ALLOWS THE TEACHERS TO MODIFY THE ASSESSMENT OF A GAME EVEN AFTER DISTRIBUTION. THIS PAPER FOCUSES ON THE TEACHER INTERFACE OF THE ASSESSMENT ENGINE. AFTER REVIEWING THE LITERATURE ASSOCIATED WITH INGAME ASSESSMENT AND LEARNING ANALYTICS, THE PAPER WILL PROVIDE A SUMMARY OF THE ENGINE AND ITS FUNCTIONALITIES, PRESENT THE LEARNING ANALYTICS (LA) DASHBOARD. WE WILL THEN DESCRIBE THE VISUAL LANGUAGE THAT ALLOWS TEACHERS TO EDIT A GAME'S ASSESSMENT SYSTEM BASED ON THE LA REPORTS. THEREAFTER, THE AUTHORISATION AND VERSIONING MECHANICS OF THE ENGINE WILL BE DETAILED, SHOWING HOW THE SYSTEM REGULATES THE ACCESS TO THE GAMES AND STRESSING THE FACT THAT EVERY TEACHER WILL HAVE, AFTER MODIFICATION, A UNIQUE GAME CUSTOMISED TO THEIR STUDENTS' NEEDS. FINALLY, WE WILL PROVIDE CONCLUSIONS AND STATE THE REMAINING WORK TO BE UNDERTAKEN.
D'MELLO AND GRAESSER'S (2012) HIGHLY-CITED MODEL OF AFFECT DYNAMICS PROPOSES A SEQUENCE OF THEORETICALLY-GROUNDED TRANSITIONS BETWEEN AFFECTIVE STATES DURING LEARNING. HOWEVER, EMPIRICAL STUDIES IN A RANGE OF CONTEXTS HAVE NOT PRODUCED THE PREDICTED RESULTS. SEVERAL FACTORS MAY EXPLAIN THIS LACK OF REPLICATION, INCLUDING THE DEMOGRAPHICS OF THE POPULATIONS STUDIED, THE DEGREE OF AUTHENTICITY OF THE LEARNING SETTING (E.G. CLASSROOMS VERSUS LABORATORY STUDIES), THE GRAIN-SIZE OF OBSERVATION, AFFECT DATA COLLECTION PROCEDURE, DURATION OF RESEARCH SESSIONS, AND THE METHODOLOGICAL CHOICES USED TO ANALYZE TRANSITIONAL PATTERNS. IN PARTICULAR, WHEREAS D'MELLO AND GRAESSER (2012) EXCLUDE SELF-TRANSITIONS (WHEN A STUDENT REMAINS IN THE SAME AFFECTIVE STATE ACROSS TWO OBSERVATIONS) IN CALCULATIONS OF TRANSITION PROBABILITIES, MOST OTHER PUBLISHED WORKS IN THIS AREA, INCLUDING SOME OF THEIR EARLIER PUBLICATIONS, DO NOT. THIS PAPER INVESTIGATES THE IMPACT OF THIS SEEMINGLY MINOR METHODOLOGICAL CHOICE BY APPLYING BOTH ANALYSES TO PREVIOUSLY COLLECTED DATA FROM A STUDY OF THE PHYSICS PLAYGROUND SYSTEM. IN PARTICULAR, THIS PAPER INVESTIGATES WHETHER THIS DIFFERENCE IS SUFFICIENT TO PRODUCE (OR SUPPRESS) THE TRANSITIONS THEORIZED IN D'MELLO AND GRAESSER'S THEORETICAL MODEL.
MALAYSIA AND MANY OTHER DEVELOPING COUNTRIES PROGRESSIVELY ADOPTING MASSIVELY OPEN ONLINE COURSE (MOOC) IN THEIR NATIONAL HIGHER EDUCATION APPROACH. WE HAVE OBSERVED AN INCREASING NEED FOR FACILITATING MOOC MONITORING THAT IS ASSOCIATED WITH THE RISING ADOPTION OF MOOCS. OUR OBSERVATION SUGGESTS THAT RECENT ADOPTION CASES LED ANALYST AND INSTRUCTORS TO FOCUS ON MONITORING ENROLMENT AND LEARNING ACTIVITIES. VISUAL ANALYTICS IN MOOC SUPPORT EDUCATION ANALYSTS IN ANALYZING MOOC DATA VIA INTERACTIVE VISUALIZATION. EXISTING LITERATURE ON MOOC VISUALIZATION FOCUSES ON ENABLING VISUAL ANALYSIS ON MOOC DATA FROM FORUM AND COURSE MATERIAL. WE FOUND LIMITED STUDIES THAT INVESTIGATE AND CHARACTERIZE DOMAIN PROBLEMS OR DESIGN REQUIREMENTS OF VISUAL ANALYTICS FOR MOOC. THIS PAPER AIMS TO PRESENT THE EMPIRICAL PROBLEM CHARACTERIZATION AND ABSTRACTION FOR VISUAL ANALYTICS IN MOOC LEARNER'S SUPPORT MONITORING. DETAILED CHARACTERIZATION AND ABSTRACTION OF THE DOMAIN PROBLEM HELP VISUALIZATION DESIGNER TO DERIVE DESIGN REQUIREMENTS IN GENERATING APPROPRIATE VISUALIZATION SOLUTION. WE EXAMINED THE LITERATURE AND CONDUCTED A CASE STUDY TO ELICIT A PROBLEM ABSTRACTION BASED ON DATA, USERS, AND TASKS. WE INTERVIEWED FIVE MALAYSIAN MOOC EXPERTS FROM THREE HIGHER EDUCATION INSTITUTES USING SEMISTRUCTURED QUESTIONS. OUR CASE STUDY REVEALS THE PRIORITY OF ENABLING MOOC ANALYSIS ON LEARNER'S PROGRESSION AND COURSE COMPLETION. THERE IS AN ASSOCIATION BETWEEN DESIGN AND ANALYSIS PRIORITY WITH THE PEDAGOGICAL TYPE OF IMPLEMENTED MOOC AND USERS. THE CHARACTERIZED DOMAIN PROBLEMS AND REQUIREMENTS OFFER A DESIGN FOUNDATION FOR VISUAL ANALYTICS IN MOOC MONITORING ANALYSIS.
THIS STUDY INVESTIGATES THE POSSIBILITY OF UTILIZING ONLINE LEARNING DATA TO DESIGN FACE-TO-FACE ACTIVITIES IN A FLIPPED CLASSROOM. WE FOCUS ON HETEROGENEOUS GROUP FORMATION FOR EFFECTIVE COLLABORATIVE LEARNING. FIFTY-THREE UNDERGRADUATE STUDENTS (18 MALES, 35 FEMALES) PARTICIPATED IN THIS STUDY, AND 8 STUDENTS (3 MALES, 5 FEMALES) AMONG THEM JOINED POST-STUDY INTERVIEWS. FOR THIS STUDY, A TOTAL OF 6 STUDENT CHARACTERISTICS WERE USED: THREE DEMOGRAPHIC CHARACTERISTICS OBTAINED FROM A SIMPLE SURVEY AND THREE ACADEMIC CHARACTERISTICS CAPTURED FROM ONLINE LEARNING DATA. WE DEFINE THREE DEMOGRAPHIC GROUP HETEROGENEITY VARIABLES AND THREE ACADEMIC GROUP HETEROGENEITY VARIABLES, WHERE EACH VARIABLE IS CALCULATED USING THE CORRESPONDING STUDENT CHARACTERISTIC. IN THIS WAY, EACH HETEROGENEITY VARIABLES REPRESENTS A DEGREE OF DIVERSITY WITHIN THE GROUP. THEN, A TWO-STAGE HIERARCHICAL REGRESSION ANALYSIS WAS CONDUCTED TO IDENTIFY THE SIGNIFICANT GROUP HETEROGENEITY VARIABLES THAT INFLUENCE FACE-TO-FACE GROUP ACHIEVEMENT. THE RESULTS SHOW THAT THE ACADEMIC GROUP HETEROGENEITY VARIABLES, WHICH WERE DERIVED FROM THE ONLINE LEARNING DATA, ACCOUNTED FOR A SIGNIFICANT PROPORTION OF THE VARIANCE IN THE GROUP ACHIEVEMENT WHEN THE DEMOGRAPHIC GROUP HETEROGENEITY VARIABLES WERE CONTROLLED. THE INTERVIEWS ALSO REVEAL THAT THE ACADEMIC GROUP HETEROGENEITY INDEED AFFECTED GROUP INTERACTION AND LEARNING OUTCOME. THESE FINDINGS HIGHLIGHT THAT ONLINE LEARNING DATA CAN BE UTILIZED TO OBTAIN RELEVANT INFORMATION FOR EFFECTIVE FACE-TO-FACE ACTIVITY DESIGN IN A FLIPPED CLASSROOM. BASED ON THE RESULTS, WE DISCUSS THE ADVANTAGES OF THIS DATA UTILIZATION APPROACH AND OTHER IMPLICATIONS FOR FACE-TO-FACE ACTIVITY DESIGN.
THIS ARTICLE PRESENTS A NEW FRAMEWORK FOR UNOBTRUSIVE OBSERVATION ANALYTICS OF KNOWLEDGE AND SKILLS-IN-ACTION THROUGH CONTINUOUS COLLECTION OF DATA FROM INDIVIDUALS WHILE THEY INTERACT WITH DIGITAL ASSETS EITHER AS INDIVIDUALS OR ON PROBLEM-SOLVING TEAMS. THE FRAMEWORK INCLUDES MEASURES OF THE SKILL AND KNOWLEDGE AREAS OF COLLABORATION, CREATIVITY, PERSONAL LEARNING, PROBLEM SOLVING, AND GLOBAL SUSTAINABILITY, WHICH ARE OBSERVED DURING NATURAL PRODUCTION AND USE OF COMMUNICATIONS, INTENTIONAL ARTIFACTS, AND RESOURCES IN A DIGITAL LEARNING SPACE DESIGNED FOR SELF-DIRECTED AND TEAM-BASED LEARNING CHALLENGES. THE ARTICLE DESCRIBES THE DIGITAL CONTEXT FOR DATA COLLECTION AND SHOWS SOME EXAMPLE DATA AND ANALYSES.
CURRENT RESEARCH HAS DEMONSTRATED THE CRUCIAL ROLE OF SPATIAL VISUALIZATION SKILLS IN THE SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) RELATED FIELDS. GIVEN THE NATIONWIDE TREND OF THE INCREASING POPULATION OF UNIVERSITY ENGINEERING STUDENTS, MORE AND MORE EFFORT HAS BEEN INVESTED INTO HOW TO EFFECTIVELY TRAIN STUDENTS' SPATIAL VISUALIZATION SKILLS. IN THIS STUDY, WE DESIGNED AND DEVELOPED A SCALABLE ONLINE PLATFORM FOR TRAINING SPATIAL VISUALIZATION SKILLS. OUR ONLINE PLATFORM IS CAPABLE OF INTELLIGENT LEARNING FEATURES SUCH AS INDIVIDUALIZED LEARNING TRAJECTORY AND PERSONALIZED HINTS. IN THE CURRENT STAGE OF THE PROJECT, WE DEPLOYED THE ONLINE PLATFORM WITH OVER 600 UNIVERSITY STUDENTS. BY ANALYZING STUDENT'S DATA, WE IDENTIFIED A KEY QUESTION THAT HAS MOST PREDICTIVE POWER ON STUDENT'S SPATIAL VISUALIZATION SKILL. THE PROMISING RESULT SHOWS A BRIGHT FUTURE FOR AN INTELLIGENT LEARNING PLATFORM.
MUCH AS THE TERM ANALYTICS IS NOT NEW IN THE RESEARCH FIELD. HOWEVER, ANALYTICS AND BIG DATA ARE STILL NOT FULLY UNDERSTOOD, NOR YET BEEN THOROUGHLY AND EMPIRICALLY RESEARCHED BY INSTITUTIONS OF HIGHER EDUCATION (HE), MORE SO, IN THE SOUTH AFRICAN CONTEXT. MANY ORGANIZATIONS AND BUSINESSES ARE WELCOMING AND ADOPTING SUCH AN INFORMATION SYSTEM INNOVATION. HOWEVER, THERE HAS BEEN A LIMITED INTEREST IN THE ADOPTION BIG DATA AND ANALYTICS IN INSTITUTIONS OF HE. THE LACK OF INTEREST IN BIG DATA AND ANALYTICS FROM HE INSTITUTIONS HAS CAUSED INSTITUTIONS TO FUNCTION WITH SUBSTANTIAL DELAYS IN ANALYSING READILY EVIDENT DATA. FURTHERMORE, THE LACK OF ADEQUATE THEORETICAL FRAMEWORKS FOR IS ADOPTION IN HE INSTITUTIONS HAS STEERED THE MANY FAILURES TO PRODUCE MEANINGFUL, ACCESSIBLE AND TIMELY INFORMATION FOR DECISION MAKING. THEREFORE THIS CALLS FOR THE DEVELOPMENT OF A CONCEPTUALIZED FRAMEWORK FOR THE ADOPTION OF BIG DATA AND ANALYTICS IN INSTITUTIONS OF HE.
WITH THE AVERAGE SHELF LIFE OF AN EMPLOYEE'S SKILLS AT LESS THAN FIVE YEARS, IT IS IMPERATIVE THAT ORGANIZATIONS SUPPORT THEIR EMPLOYEES IN STAYING CURRENT IN NEW AND EMERGING SKILLS AND IN LEARNING HOW TO LEARN. LEARNING MANAGEMENT SYSTEMS, ONCE SEEN AS A ONE-SIZE-FITS ALL LEARNING SOLUTION, HAVE NOT EFFECTIVELY KEPT PACE WITH WIDER TECHNOLOGY DEVELOPMENT, AND THE NEEDS AND EXPECTATIONS OF WORKPLACE LEARNING. MOREOVER, ORGANIZATIONS TEND TO HAVE TOO NARROW A VIEW WHEN CONSIDERING THE ELEMENTS THAT AFFECT LEARNING AT THEIR ORGANIZATION. AN ECOLOGICAL AND HOLISTIC APPROACH IS NEEDED TO IMPROVE LEARNING ENVIRONMENTS AND TO FUTURE-PROOF THESE ENVIRONMENTS FOR NEW DEVELOPMENTS IN EDUCATION AND TECHNOLOGY. THIS PAPER EXPLORES THE EXISTING LITERATURE AND FRAMEWORKS FOR LEARNING ECOSYSTEMS AND PROPOSES A NEW LEARNING ECOSYSTEM FRAMEWORK THAT CONSISTS OF SEVEN KEY ELEMENTS: (1) TECHNOLOGY AND DATA ARCHITECTURE, (2) GOVERNANCE, (3) ANALYTICS, (4) SEMANTIC EPORTFOLIOS, (5) INTRINSIC AND EXTRINSIC MOTIVATORS, (6) SOCIAL LEARNING AND ENGAGEMENT, AND (7) PERSONALIZATION.
THIS STUDY INVESTIGATED THE INTERMEDIATE PRODUCT ON THE WAY TO POSE PROBLEMS AS WELL AS THE POSED PROBLEMS AS THE PRODUCT OF PROBLEM-POSING ACTIVITY. EVERY SINGLE ACTION OF LEARNERS DURING POSE THE PROBLEMS HAVE BEEN ANALYZED. PEARSONS' CORRELATION TEST BETWEEN THE NUMBER OF ACTIONS AND THE VALIDITY OF POSED PROBLEMS IS CONDUCTED. SIGNIFICANT DIFFERENCE IS FOUND, WHICH SHOWS THAT MANY ACTIONS PERFORMED BY LEARNERS TRIED TO SATISFY HIGH VALIDITY PRODUCT. LEARNERS TRY TO POSE PROBLEMS WITH HIGH VALIDITY PRODUCT AND THEY HAD A TENDENCY TO ENHANCE THE VALIDITY OF POSED PROBLEMS.
COMPUTATIONAL THINKING (CT) HAS BEEN ADVOCATED AS AN ESSENTIAL PROBLEM SOLVING SKILL STUDENTS NEED TO DEVELOP. EMPHASIZING ON CT APPLIED IN BOTH PROGRAMMING AND EVERYDAY CONTEXTS, WE DEVELOPED A HUMANOID ROBOTICS CURRICULUM AND A COMPUTERIZED ASSESSMENT INSTRUMENT. WE IMPLEMENTED THE CURRICULUM WITH SIX CLASSES OF 125 FIFTH GRADERS. QUANTITATIVE METHODS WERE USED TO COMPARE STUDENTS' PERFORMANCE FROM PRETEST TO POSTTEST. LEARNING ANALYTICS TECHNIQUES WERE APPLIED TO EXAMINE STUDENTS' PROBLEM SOLVING PROCESSES. THE RESULTS SHOWED THAT STUDENTS' CT PERFORMANCE IMPROVED IN BOTH PROGRAMMING AND EVERYDAY REASONING CONTEXTS AND THAT THE CURRICULUM BENEFITED STUDENTS WITH VARIED INITIAL PERFORMANCE. THE STUDY SHED LIGHT ON HOW TO CONNECT AND ASSESS CT IN EVERYDAY REASONING AND PROGRAMMING CONTEXTS.
THE LEARNERS OF THE CURRENT GENERATION HAVE GROWN UP WITH DIGITAL TECHNOLOGIES. HOWEVER, THESE NEW SETS AND ABILITY TO LEARN WHENEVER WHEREVER ARE HIGHLY APPRECIATED BY LEARNERS AND EDUCATORS. IN THE CONTEXT OF THE LEARNING ANALYTICS AND ADAPTATION, THIS EVOLUTION CREATES VARIOUS CHALLENGES, SUCH AS THE LEARNER'S DATA TRACKING AND INTEROPERABILITY BETWEEN LEARNING SYSTEMS. IN THIS PAPER WE SHORTLY DESCRIBE THE XAPI, COMPARE IT TO ITS PREDECESSOR OF LEARNER PROFILE STANDARDS INTEROPERABILITY AND PRESENT OUR PROPOSED ARCHITECTURE TO PROVIDE AN INTEROPERABLE EXCHANGED LEARNER'S PROFILE EXPLOITING THE FEATURES OFFERED BY XAPI. FURTHERMORE, THIS SOLUTION OFFER ADAPTED LEARNING CONTENT TO THE LEARNER ACCORDING TO HIS FORMAL AND INFORMAL ACTIVITIES, WHICH ARE MONITORED VIA XAPI.
DISCUSSION WHICH CONSISTS OF SEVERAL Q&A SEGMENTS (QUESTION AND ANSWER PAIRS) IS OFTEN CONSIDERED AS ONE OF THE MOST FAMILIAR TYPES OF INTELLECTUAL AND CREATIVE ACTIVITIES AT MEETINGS. EVALUATING STUDENTS' ANSWER-QUALITY OF Q&A SEGMENTS IN DISCUSSION AND GIVING THEM FEEDBACK CAN EFFECTIVELY HELP THEM IMPROVE THEIR DISCUSSION SKILLS. CONSIDERING THAT THE DISCUSSION PROCESS IS A TYPE OF COGNITIVE ACTIVITY, WHICH COULD RESULT IN CHANGES IN CERTAIN PHYSIOLOGICAL DATA, SUCH AS HEART RATE (HR) VARIABILITY (HRV). IN THIS STUDY, WE ARGUE THAT STUDENTS' HR DATA CAN BE USED TO EFFECTIVELY EVALUATE THE ANSWER-QUALITY OF THEIR Q&A SEGMENTS AND AS A METHOD OF AUTOMATIC EVALUATION OF STUDENTS' DISCUSSION-SKILL COMPARED WITH USING TRADITIONAL NLP SUCH AS SEMANTIC ANALYSIS. IN ORDER TO CONFIRM THIS, WE USED A NON-INVASIVE DEVICE, I.E., APPLE WATCH, TO COLLECT REALTIME UPDATED HR DATA OF STUDENTS DURING THEIR DISCUSSIONS IN OUR LAB-SEMINAR ENVIRONMENT, THEIR HR DATA WERE ANALYZED BASED ON Q&A SEGMENTS, AND THREE MACHINE-LEARNING MODELS WERE GENERATED FOR EVALUATION: LOGISTIC REGRESSION, SUPPORT VECTOR MACHINE, AND RANDOM FOREST. THE SIGNIFICANT HR AND HRV FEATURES (METRICS) WERE ALSO DISCUSSED USING A FEATURE SELECTION METHOD. COMPARATIVE EXPERIMENTS WERE CONDUCTED INVOLVING SEMANTIC DATA OF Q&A STATEMENTS ALONE AND A COMBINATION OF HR AND SEMANTIC DATA. WE ALSO GAVE AN EXPERIMENTAL INVESTIGATION ON HR AND HRV FEATURES ROBUSTNESS ON THE NEW DATA SET WE COLLECTED ADDITIONALLY.
IN ACADEMIC INSTITUTIONS IT IS NORMAL PRACTICE THAT AT THE END OF EACH TERM, STUDENTS ARE REQUIRED TO COMPLETE A QUESTIONNAIRE THAT IS DESIGNED TO GATHER STUDENTS' PERCEPTIONS OF THE INSTRUCTOR AND THEIR LEARNING EXPERIENCE IN THE COURSE. THIS QUESTIONNAIRE COMPRISES OF LIKERT-SCALE QUESTIONS AND QUALITATIVE QUESTIONS. ONE OF THE IMPORTANT GOALS OF THIS EXERCISE IS TO ENABLE THE INSTRUCTOR AND THE SENIOR MANAGEMENT TO EXAMINE THE FEEDBACK AND THEN ENHANCE STUDENTS' LEARNING EXPERIENCE. IN MOST UNIVERSITIES, INCLUDING OUR OWN, A LOT OF ATTENTION IS PAID TO THE QUANTITATIVE FEEDBACK, WHICH IS SUMMARIZED AND STATISTICAL COMPARISONS ARE COMPUTED, ANALYSED AND PRESENTED. HOWEVER, THE QUALITATIVE COMMENTS GIVEN BY THE STUDENTS ARE NOT FULLY TAPPED. CAPTURING AND ANALYSING THE QUALITATIVE FEEDBACK DATA, AT THE INDIVIDUAL COURSE, SCHOOL AND UNIVERSITY-LEVEL, CAN PROVIDE VALUABLE INSIGHTS ON TEACHING PRACTICES AND CURRICULUM. IN THIS PAPER, WE PROPOSE A CONCEPTUAL FRAMEWORK FOR STUDENT FEEDBACK ANALYSIS THAT PROVIDES THE NECESSARY STRUCTURE FOR IMPLEMENTING A PROTOTYPE TOOL FOR MINING STUDENT COMMENTS. WE THEN DISCUSS THE APPLICATION OF THE TOOL TO ANALYSE FEEDBACK FROM SELECTED COURSES.
LEARNING WITH ONLINE VIDEO IS PERVASIVE IN HIGHER EDUCATION. RECENT RESEARCH HAS EXPLORED THE IMPORTANCE OF STUDENT ENGAGEMENT WHEN LEARNING WITH VIDEO IN ONLINE AND BLENDED COURSES. HOWEVER, LITTLE IS KNOWN ABOUT STUDENTS' GOALS AND INTENTS WHEN ENGAGING WITH VIDEO. FURTHERMORE, THERE IS LIMITED EMPIRICAL EVIDENCE ON THE IMPACT OF LEARNING CONTEXT ON ENGAGEMENT WITH VIDEO, WHICH LIMITS OUR UNDERSTANDING OF HOW STUDENTS LEARN FROM VIDEO. TO ADDRESS THIS GAP, WE IDENTIFY A SET OF ENGAGEMENT GOALS FOR LEARNING WITH VIDEO, AND STUDY ASSOCIATED STUDENT ACTIVITY IN RELATION TO LEARNING CONTEXT (COURSE WEEK, EXAM, AND REWATCH). IN STUDY 1, WE CONDUCTED A SURVEY (N = 116) THAT MAPS STUDENTS' VIDEO VIEWING ACTIVITIES TO THEIR ENGAGEMENT GOALS AND INTENTS. WE IDENTIFIED A VARIETY OF ENGAGEMENT GOALS, SPECIFICALLY REFLECT, FLAG, REMEMBER, CLARIFY, SKIM, SEARCH, ORIENT, AND TAKE A BREAK. IN STUDY 2, WE ANALYZED CLICKSTREAM DATA GENERATED BY 387 STUDENTS ENROLLED IN THREE SEMESTER-LONG COURSES. WE EXAMINED THE IMPACT OF LEARNING CONTEXT ON STUDENTS' ENGAGEMENT WITH VIDEO. A MULTILEVEL MODEL SHOWED DIFFERENT PATTERNS FOR ONLINE AND BLENDED COURSES. STUDENTS IN THE ONLINE COURSE SHOWED MUCH MORE STRATEGIC AND ADAPTIVE USE OF VIDEO. AS THE SEMESTER PROGRESSED, STUDENTS IN THE ONLINE COURSES PERFORMED FEWER REFLECT AND SEARCH. DURING EXAM WEEKS AND WHEN REWATCHING VIDEOS, ONLINE STUDENTS PERFORMED MORE SEARCH WITHIN THE VIDEO. THE ONLY TREND THAT WAS FOUND FOR BLENDED LEARNING STUDENTS WAS AN INCREASE IN SKIM WITH COURSE WEEK. THESE FINDINGS HAVE IMPLICATIONS FOR VIDEO PLAYERS THAT ADAPT TO CONTEXT, SUCH AS HELPING STUDENTS EASILY LOCATE IMPORTANT IN-VIDEO INFORMATION DURING THE EXAM WEEK OR WHEN REWATCHING PREVIOUSLY WATCHED VIDEOS.
THIS PAPER DESCRIBES THE CONCEPTUALIZATION AND OPERATIONALIZATION OF THE SCIENTIFIC ARGUMENTATION IN A CSCL ENVIRONMENT. AN ONLINE PLATFORM IS DESIGNED TO SUPPORT STUDENTS' COLLABORATIVE ARGUMENTATION WITH DIAGRAM-BASED REPRESENTATIONS OF ARGUMENTATION BASED ON TOULMIN'S (1958) ARGUMENTATION FRAMEWORK. BASED ON EXISTING ANALYTIC FRAMEWORKS OF COLLABORATIVE ARGUMENTATION WHILE ACCOMMODATING THE SPECIFIC DEMANDS AND CHARACTERISTICS OF THE TARGET USERS AND THE ENVIRONMENT INVOLVED, A CONCEPTUAL FRAMEWORK AND A GROUP OF INDICATORS ARE DERIVED FOR OPERATIONALIZING THE MEASUREMENT CONSTRUCTS OF THE PROCESS AND OUTCOME OF STUDENTS' COLLABORATIVE ARGUMENTATION.
IN RECENT YEARS, SEVERAL STUDIES HAVE BEEN CARRIED OUT INTO THE REASONS WHY STUDENTS DROP OUT OF ONLINE HIGHER EDUCATION, FOLLOWING THE RISE IN THE RELATIVE WEIGHT OF THIS FORM OF EDUCATION. HOWEVER, MORE EFFORT HAS GONE INTO ANALYZING THE CAUSES OF THIS PHENOMENON THAN INTO TRYING TO CHARACTERIZE STUDENTS WHO DROP OUT, THAT IS DEFINING WHAT A DROPOUT STUDENT IS. BUT OBTAINING A PROPER DEFINITION OF DROPOUT IS JUST AS IMPORTANT AS DESCRIBING ITS CAUSES. IT ALSO APPEARS THAT THE DEFINITION OF DROPOUT IS VERY SENSITIVE TO CONTEXT. AS ONE OF THE MAIN FINDINGS OF THIS ARTICLE, WE REACH A PURE EMPIRICAL DEFINITION, AT A PROGRAMME LEVEL, OF STUDENTS WHO DROP OUT OF AN ONLINE HIGHER EDUCATION CONTEXT WITH NON-MANDATORY ENROLLMENT. THIS DEFINITION IS BASED ON THE PROBABILITY OF STUDENTS NOT CONTINUING A SPECIFIC ACADEMIC PROGRAMME FOLLOWING SEVERAL CONSECUTIVE SEMESTERS OF "THEORETICAL BREAK", AND IS HIGHLY ADAPTABLE TO INSTITUTIONS OFFERING DISTANCE EDUCATION WITH NO PERMANENCE REQUIREMENTS, THAT IS ONES OFFERING THE POSSIBILITY OF TAKING BREAKS. OUR FINDINGS SHOW THAT THERE ARE DIFFERENCES REGARDING THE NUMBER OF CONSECUTIVE SEMESTERS THAT DEFINE DROPOUT DEPENDING ON WHETHER THE PROGRAMME REQUIRES PREVIOUS EXPERIENCE OR NOT. ADDITIONALLY, WE OBSERVE SIGNIFICANT DIFFERENCES IN THE DROPOUT RATE BETWEEN SPECIFIC PROGRAMMES, AS WELL AS A HIGHER LEVEL OF DROPOUT IN THE FIRST SEMESTERS. ANALYZING THE REASONS BEHIND THESE FACTS SHOULD HELP HIGHER EDUCATION INSTITUTIONS TO MAKE MORE SOUND AND EFFICIENT DECISIONS.
THE DEEP AND SURFACE LEARNING APPROACHES ARE CLOSELY RELATED TO THE STUDENTS' INTERACTION WITH LEARNING CONTENT AND LEARNING OUTCOMES. WHILE STUDENTS WITH A SURFACE APPROACH HAVE A TENDENCY TO ACQUIRE KNOWLEDGE WITHOUT QUESTIONING AND TO TRY TO PASS COURSES WITH MINIMUM EFFORT, STUDENTS WITH A DEEP LEARNING APPROACH TEND TO USE MORE SKILLS SUCH AS PROBLEM-SOLVING, QUESTIONING, AND RESEARCH. STUDIES SHOW THAT LEARNING APPROACHES OF STUDENTS CAN CHANGE DEPENDING ON SUBJECT, TASK AND TIME. THEREFORE, IT IS IMPORTANT TO IDENTIFY STUDENTS WITH A SURFACE LEARNING APPROACH IN ONLINE LEARNING ENVIRONMENTS AND TO PLAN INTERVENTIONS THAT ENCOURAGE THEM TO USE DEEP LEARNING APPROACHES. IN THIS STUDY, VIDEO VIEWING BEHAVIORS OF STUDENTS WITH DEEP AND SURFACE LEARNING APPROACHES ARE ANALYZED USING VIDEO ANALYTICS. VIDEO VIEWING PATTERNS OF STUDENTS WITH DIFFERENT LEARNING APPROACHES ARE ALSO COMPARED. FOR THIS PURPOSE, STUDENTS (N=31) ARE ASKED TO STUDY A 10-MINUTES-LONG VIDEO MATERIAL RELATED TO COMPUTER HARDWARE COURSE. VIDEO INTERACTIONS IN THIS PROCESS WERE ALSO RECORDED USING VIDEO PLAYER DEVELOPED BY THE AUTHORS. AT THE END OF THE LAB SESSION, STUDENTS WERE ASKED TO FILL IN THE LEARNING APPROACH SCALE BY TAKING INTO ACCOUNT THEIR LEARNING APPROACHES TO THE COURSE. AS A RESULT OF THE STUDY, IT WAS OBSERVED THAT THE STUDENTS WITH SURFACE APPROACH MADE A STATISTICALLY SIGNIFICANT FORWARD SEEK OVER TO THE STUDENTS USED DEEP LEARNING APPROACH WHILE WATCHING THE VIDEO. MOREOVER, AN INVESTIGATION ON THE TIME SERIES GRAPHS OF TWO GROUPS REVEALED THAT SURFACE LEARNERS WATCHED THE VIDEO MORE LINEARLY AND HAD FEWER INTERACTIONS WITH IT. THESE INTERACTION DATA CAN BE MODELED WITH MACHINE LEARNING TECHNIQUES TO PREDICT STUDENTS WITH SURFACE APPROACH AND CAN BE USED TO IDENTIFY DESIGN PROBLEMS IN VIDEO MATERIALS.
A KEY CONCERN FOR MOST INSTITUTIONS AND INSTRUCTORS IS WHETHER STUDENTS ARE SATISFIED WITH THEIR LEARNING EXPERIENCE. HOWEVER, RELATIVELY FEW STUDIES HAVE UNPACKED WHAT THE KEY DRIVERS FOR LEARNER SATISFACTION ARE IN BLENDED AND ONLINE COURSES. USING LOGISTICAL REGRESSION MODELLING, LEARNER SATISFACTION DATA OF 62,986 LEARNERS IN 401 UNDERGRADUATE BLENDED AND ONLINE MODULES WAS ANALYZED. THE DATA INCLUDED OVER 200 POTENTIAL EXPLANATORY VARIABLES BASED ON LEARNER AND MODULE LEARNING DESIGN CHARACTERISTICS. FINDINGS INDICATE THAT LEARNING DESIGN HAS A STRONG AND SIGNIFICANT IMPACT ON OVERALL SATISFACTION FOR BOTH NEW AND CONTINUING LEARNERS. LEARNERS WHO ARE MORE SATISFIED WITH THE QUALITY OF TEACHING MATERIALS, ASSESSMENT STRATEGIES, AND WORKLOAD ARE MORE SATISFIED WITH THE OVERALL LEARNING EXPERIENCE. FURTHERMORE, LONG-TERM GOALS OF LEARNERS (I.E., QUALIFICATIONS AND RELEVANCE OF MODULES WITH LEARNERS' PROFESSIONAL CAREERS) ARE IMPORTANT PREDICTORS OF LEARNER SATISFACTION. INDIVIDUAL LEARNER CHARACTERISTICS ARE MOSTLY INSIGNIFICANT, INDICATING THAT DESPITE A WIDE DIVERSITY OF LEARNERS STUDYING AT THE OPEN UNIVERSITY, UK, THE UNDERLYING LEARNING EXPERIENCES ARE SIMILAR. FUTURE RESEARCH SHOULD FOCUS ON HOW LEARNING DESIGN CHANGES CAN ENHANCE THE LEARNING EXPERIENCES OF STUDENTS.
AS TWITTER USE AT ACADEMIC CONFERENCES BECOMES THE NORM, THIS DISCUSSION BACKCHANNEL PROVIDES ATTENDEES WITH THE OPPORTUNITY TO LEARN FROM OTHERS ENGAGED IN TWEETING INFORMATION IMMEDIATELY RELEVANT TO THE CONFERENCE. THIS STUDY USES SOCIAL CONSTRUCTIVIST AND CONNECTIVIST LEARNING THEORIES TO EXAMINE THE ROLE OF MORE KNOWLEDGEABLE OTHERS (MKOS) IN LEARNING NETWORKS, AND ASKS HOW THE POSITIONS THEY OCCUPY IN THE SOCIAL NETWORK ALLOW THEM TO SHARE KNOWLEDGE. TO EXAMINE THEIR ROLE, SOCIAL NETWORK ANALYSES WERE CONDUCTED ON TWITTER NETWORK DATA COLLECTED FROM THE LEARNING ANALYTICS AND KNOWLEDGE CONFERENCE 2014. FINDINGS INDICATE THAT MORE KNOWLEDGEABLE OTHERS OCCUPY HIGHLY CENTRAL POSITIONS IN THE NETWORK, AND THAT THESE POSITIONS ALLOW THEM TO EFFECTIVELY PROVIDE ATTENDEES WITH ACCESS TO THEIR EXPERTISE AND KNOWLEDGE.
APART FROM BEING ABLE TO SUPPORT THE BULK OF STUDENT ACTIVITY IN SUITABLE DISCIPLINES SUCH AS COMPUTER PROGRAMMING, WEB-BASED EDUCATIONAL SYSTEMS HAVE THE POTENTIAL TO YIELD VALUABLE INSIGHTS INTO STUDENT BEHAVIOR. THROUGH THE USE OF EDUCATIONAL ANALYTICS, WE CAN DISPENSE WITH PRECONCEPTIONS OF HOW STUDENTS CONSUME AND REUSE COURSE MATERIAL. IN THIS PAPER, WE EXAMINE THE SPEED AT WHICH STUDENTS EMPLOY CONCEPTS WHICH THEY ARE BEING TAUGHT DURING A SEMESTER. TO SHOW THE WIDER UTILITY OF THIS DATA, WE PRESENT A BASIC CLASSIFICATION SYSTEM FOR EARLY DETECTION OF POOR PERFORMERS AND SHOW HOW IT CAN BE IMPROVED BY INCLUDING DATA ON WHEN STUDENTS USE A CONCEPT FOR THE FIRST TIME. USING OUR IMPROVED CLASSIFIER, WE CAN ACHIEVE AN ACCURACY OF 85% IN PREDICTING POOR PERFORMERS PRIOR TO THE COMPLETION OF THE COURSE.
REFLECTION HAS BEEN WIDELY CONSIDERED AS AN IMPORTANT ELEMENT IN STUDENT LEARNING IN HIGHER EDUCATION. AMONG DIFFERENT FORMS OF REFLECTIVE WRITING, ONE-MINUTE PAPERS CAN QUICKLY AND EASILY GET STUDENTS TO REFLECT ON THEIR LEARNING. UNLIKE SHORT QUIZZES, THE RESPONSES TO ONE-MINUTE PAPERS COULD COVER A WIDE OPEN RANGE AND COULD REQUIRE MORE TIME TO REVIEW AND SUMMARIZE. WHEN ONE-MINUTE PAPERS ARE ADMINISTRATED ONLINE, THEIR RESPONSES ARE AVAILABLE IN ELECTRONIC FORM AND THIS FACILITATES A COMPUTATIONAL APPROACH FOR ANALYSIS. IN THIS PAPER, WE PROPOSE A MACHINE LEARNING APPROACH TO ANALYZING THE STUDENTS' RESPONSES TO ONE-MINUTE PAPERS. WE BUILD A TEXT CLASSIFIER TO IDENTIFY THE TOPICS DISCUSSED IN THE RESPONSES. OUR RESULTS OF A PRELIMINARY STUDY CONDUCTED IN A BLENDED LEARNING COURSE DEMONSTRATE THAT THE CLASSIFIER CAN EFFECTIVELY DETECT THE TOPICS AND THE PROPOSED METHOD CAN BE USED TO MONITOR STUDENT PROGRESS BASED ON THE DETECTED TOPICS.
ALMOST ALL HIGHER EDUCATIONAL INSTITUTIONS USE VIRTUAL LEARNING ENVIRONMENTS (VLE) FOR THE DELIVERY OF EDUCATIONAL CONTENT TO THE STUDENTS. THOSE SYSTEMS COLLECT INFORMATION ABOUT STUDENT BEHAVIOUR, AND UNIVERSITY CAN TAKE ADVANTAGE OF ANALYSING SUCH DATA TO MODEL AND PREDICT STUDENT OUTCOMES. OUR WORK AIMS AT DISCOVERING WHETHER THERE EXISTS A DIRECT CONNECTION BETWEEN THE INTENSITY OF VLE BEHAVIOUR REPRESENTED AS RECORDED STUDENT ACTIVITIES AND THEIR STUDY OUTCOMES AND ANALYSE HOW INTENSE THIS CONNECTION IS. FOR THAT PURPOSE, WE EMPLOYED THE CLUSTERING METHOD TO DIVIDE STUDENTS INTO SO-CALLED VLE INTENSITY GROUPS AND COMPARED FORMED GROUPS (CLUSTERS) WITH THE STUDENT OUTCOMES IN THE COURSE. OUR ANALYSIS HAS BEEN PERFORMED USING OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD).
WHILE GLOBAL MASSIVE OPEN ONLINE COURSE (MOOC) PROVIDERS SUCH AS EDX, COURSERA, AND FUTURELEARN HAVE GARNERED THE BULK OF ATTENTION FROM RESEARCHERS AND THE POPULAR PRESS, MOOCS ARE ALSO PROVISIONED BY A SERIES OF REGIONAL PROVIDERS, WHO ARE OFTEN USING THE OPEN EDX PLATFORM. WE LEVERAGE THE DATA INFRASTRUCTURE SHARED BY THE MAIN EDX INSTANCE AND ONE REGIONAL OPEN EDX PROVIDER, EDRAAK IN JORDAN, TO COMPARE THE EXPERIENCE OF LEARNERS FROM ARAB COUNTRIES ON BOTH PLATFORMS. COMPARING LEARNERS FROM ARAB COUNTRIES ON EDX TO THOSE ON EDRAAK, THE EDRAAK POPULATION HAS A MORE EVEN GENDER BALANCE, MORE LEARNERS WITH LOWER EDUCATION LEVELS, GREATER PARTICIPATION FROM MORE DEVELOPING COUNTRIES, HIGHER LEVELS OF PERSISTENCE AND COMPLETION, AND A LARGER TOTAL POPULATION OF LEARNERS. THIS "APPLES TO APPLES" COMPARISON OF MOOC LEARNERS IS FACILITATED BY AN APPROACH TO MULTIPLATFORM MOOC ANALYTICS, WHICH EMPLOYS PARALLEL RESEARCH PROCESSES TO CREATE JOINT AGGREGATE DATASETS WITHOUT SHARING IDENTIFIABLE DATA ACROSS INSTITUTIONS. OUR FINDINGS SUGGEST THAT GREATER RESEARCH ATTENTION SHOULD BE PAID TOWARDS REGIONAL MOOC PROVIDERS, AND REGIONAL PROVIDERS MAY HAVE AN IMPORTANT ROLE TO PLAY IN EXPANDING ACCESS TO HIGHER EDUCATION.
PREDICTING ACADEMIC PERFORMANCE HAS BEEN A TOPIC OF RESEARCH FOR YEARS, WITH DIFFERENT FACTORS HAVING BEEN USED TO PREDICT STUDENT GRADES. ONE OF THOSE FACTORS IS PERSONALITY, WITH LITTLE WORK HAVING FOCUSED ON THE EFFECT OF PERSONALITY ON ACADEMIC PERFORMANCE IN MASSIVE OPEN ONLINE COURSES (MOOCS). CONTRIBUTING TO OUR LACK OF UNDERSTANDING OF HOW PERSONALITY IS LINKED TO ACADEMIC PERFORMANCE IN MOOCS, STUDIES THAT PREDICT ACADEMIC PERFORMANCE BY COMBINING PERSONALITY WITH ATTRIBUTES OF ONLINE COURSE DESIGN TO HAVE YET TO BE REPORTED. IN THIS PAPER, WE TRY TO TACKLE THIS PROBLEM BY USING PERSONALITY AND LEVEL OF COLLABORATION (A COURSE ATTRIBUTE) TO PREDICT ACADEMIC PERFORMANCE. WE CHOSE LEVEL OF COLLABORATION AS ONE OF THE COURSE ATTRIBUTES IN OUR RESEARCH BECAUSE SOCIAL FACTORS, SUCH AS THE AMOUNT OF STUDENT INTERACTION, CAN IMPACT LEARNER ATTRITION IN MOOCS. WE APPLY MACHINE LEARNING ALGORITHMS TO TWO DIFFERENT FEATURE SETS. THE FIRST FEATURE SET ONLY USES PERSONALITY AS A PREDICTOR AND THE SECOND FEATURE SET USES PERSONALITY AND LEVEL OF COLLABORATION IN A COURSE AS PREDICTORS OF ACADEMIC PERFORMANCE. A COMPARISON OF THESE PREDICTIVE MODELS REVEALED THAT ADDING LEVEL OF COLLABORATION CAN INCREASE THEIR PERFORMANCE SIGNIFICANTLY. THESE RESULTS PROVIDE FURTHER EVIDENCE OF THE IMPORTANCE OF VALIDATING CLASSROOM-BASED RESEARCH IN ONLINE SETTINGS. MOREOVER, THE RESULTS OF THIS WORK CAN BE USEFUL IN SEVERAL WAYS. FOR EXAMPLE, WE MAY BE ABLE TO GIVE BETTER RECOMMENDATIONS TO USERS BASED ON THEIR PERSONALITY AND THE ATTRIBUTES OF COURSES. WE MAY ALSO BE ABLE TO ADAPT COURSE ATTRIBUTES TO MATCH THE PERSONALITY CHARACTERISTICS OF EACH STUDENT.
IN RECENT YEARS, THE USE OF DIGITAL TOOLS AND TECHNOLOGIES IN EDUCATIONAL INSTITUTIONS ARE CONTINUING TO GENERATE LARGE AMOUNTS OF DIGITAL TRACES OF STUDENT LEARNING BEHAVIOR. THIS STUDY PRESENTS A PROOF-OF-CONCEPT ANALYTICS SYSTEM THAT CAN DETECT AT-RISK STUDENTS ALONG THEIR LEARNING JOURNEY. EDUCATORS CAN BENEFIT FROM THE EARLY DETECTION OF AT-RISK STUDENTS BY UNDERSTANDING FACTORS WHICH MAY LEAD TO FAILURE OR DROP-OUT. FURTHER, EDUCATORS CAN DEVISE APPROPRIATE INTERVENTION MEASURES BEFORE THE STUDENTS DROP OUT OF THE COURSE. OUR SYSTEM WAS BUILT USING SAS (R) ENTERPRISE MINER (EM) AND SAS (R) JMP PRO.
THE STUDY DESCRIBES AN APPROACH TO E-LEARNING BASED ON THE MOODLE PLATFORM THAT IS USED TO VISUALIZE PARTICIPATION IN THE LEARNING COMMUNITY AND IS PROPOSED TO BE USED TO INFORM STUDENTS AND TEACHERS ABOUT THEIR INVOLVEMENT IN THE SOCIAL LEARNING ENVIRONMENT. THE EXPERIMENT INVOLVED 5 TEACHERS AND 3 EXPERTS WHO DETERMINED THE MOST SIGNIFICANT VISUALIZATION INDICATORS FOR THE VIRTUAL LEARNING ENVIRONMENT DASHBOARDS. THERE WERE 42 STUDENTS AGED 21 TO 23. THE VIRTUAL LEARNING ENVIRONMENT IS BASED ON THE MOODLE AND BLACKBOARD PLATFORMS THAT ARE COMMONLY USED IN UNIVERSITIES. SOCIALWALL ALLOWED PARTICIPANTS TO PERFORM ACTIONS IN THE SOCIAL ENVIRONMENT THAT ARE VISUALIZED IN GRAPHS UNDER THE SPECIFIED CRITERIA. A WIKI REPOSITORY PLUGIN WAS ALSO ADDED IN ORDER TO ACCUMULATE STUDENT KNOWLEDGE IN SHARED STRUCTURED DOCUMENTS STORED IN A SHARED REPOSITORY. THE RELATIONAL DATABASE MANAGEMENT SYSTEM MYSQL ALLOWS CREATION OF ADDITIONAL RELATIONS, DATABASE DESIGN AND ADMINISTRATION. THE VISUALIZATION ACTIVITIES DESCRIBED IN THE STUDY ARE BASED ON MODIFIED STATE TRANSITION NETWORKS TO ANALYZE AND VISUALIZE THE STUDENT LEARNING PATH. STUDENT TRAJECTORY NETWORKS SHOW THE INTERACTION OF INDIVIDUAL LEARNERS OR GROUPS WITH THE COURSE STRUCTURE AND MATERIAL.
IN THIS PAPER, WE PROPOSE A NOVEL APPROACH TO THE ANALYSIS OF COLLABORATIVE LEARNING. THE APPROACH POSITS THAT DIFFERENT DIMENSIONS OF COLLABORATIVE LEARNING EMERGING FROM SOCIAL TIES AND CONTENT ANALYSIS OF DISCOURSE CAN BE MODELED AS NETWORKS. AS SUCH, THE COMBINATION OF SOCIAL NETWORK ANALYSIS (SNA) AND EPISTEMIC NETWORK ANALYSIS (ENA) ANALYSIS CAN DETECT INFORMATION ABOUT A LEARNER'S ENACTMENT OF WHAT THE LITERATURE ON COLLABORATIVE LEARNING HAS DESCRIBED AS A ROLE: AN ENSEMBLE OF COGNITIVE AND SOCIAL DIMENSIONS THAT IS MARKED BY INTERACTING WITH THE APPROPRIATE PEOPLE ABOUT APPROPRIATE CONTENT. THE PROPOSED APPROACH IS NAMED SOCIAL EPISTEMIC NETWORK SIGNATURE (SENS) AND IS DEFINED AS A COMBINATION OF THESE TWO COMPLEMENTARY NETWORK ANALYTIC TECHNIQUES. THE PROPOSED SENS APPROACH IS EXAMINED ON DATA PRODUCED IN COLLABORATIVE ACTIVITIES PERFORMED IN A MASSIVE OPEN ONLINE COURSE (MOOC) DELIVERED VIA A MAJOR MOOC PLATFORM. THE RESULTS OF A STUDY CONDUCTED ON A DATA SET COLLECTED IN A MOOC SUGGEST SNA AND ENA PRODUCE COMPLEMENTARY RESULTS WHICH CAN I) EXPLAIN COLLABORATION PROCESSES THAT SHAPED THE CREATION OF SOCIAL TIES AND THAT WERE ASSOCIATED WITH DIFFERENT NETWORK ROLES; II) DESCRIBE DIFFERENCES BETWEEN LOW AND HIGH PERFORMING GROUPS OF LEARNERS; AND III) SHOW HOW COMBINED PROPERTIES DERIVED FROM SNA AND ENA PREDICT ACADEMIC PERFORMANCE.
LOW AROUSAL STATES (ESPECIALLY BOREDOM) HAVE BEEN SHOWN TO BE MORE DELETERIOUS TO LEARNING THAN HIGH AROUSAL STATES, THOUGH THE LATTER HAVE RECEIVED MUCH MORE ATTENTION (E.G., TEST ANXIETY, CONFUSION, AND FRUSTRATION). AIMING AT PROFILING AROUSAL IN THE CLASSROOM (HOW ACTIVE STUDENTS ARE) AND EXAMINING HOW ACTIVATION LEVELS RELATE TO ACHIEVEMENT, WE STUDIED SYMPATHETIC AROUSAL DURING TWO RUNS OF AN ELECTIVE ADVANCED PHYSICS COURSE IN A REAL CLASSROOM SETTING, INCLUDING THE COURSE EXAM. PARTICIPANTS WERE HIGH SCHOOL STUDENTS (N=24) WHO WERE RANDOMLY SELECTED FROM THE COURSE POPULATION. AROUSAL WAS INDEXED FROM ELECTRODERMAL ACTIVITY, MEASURED UNOBTRUSIVELY VIA THE EMPATICA E4 WRISTBAND. LOW AROUSAL WAS THE LEVEL WITH THE HIGHEST INCIDENCE (60% OF THE LESSON ON AVERAGE) AND LONGEST PERSISTENCE, LASTING ON AVERAGE THREE TIMES LONGER THAN MEDIUM AROUSAL AND TWO TIMES LONGER THAN HIGH AROUSAL LEVEL OCCURRENCES. DURING THE COURSE EXAM, AROUSAL WAS POSITIVELY AND HIGHLY CORRELATED (R=.66) WITH ACHIEVEMENT AS MEASURED BY THE STUDENTS' GRADES. IMPLICATIONS FOR A NEED TO FOCUS MORE ON ADDRESSING LOW AROUSAL STATES IN LEARNING ARE DISCUSSED, TOGETHER WITH POTENTIAL APPLICATIONS FOR BIOFEEDBACK, TEACHER INTERVENTION, AND INSTRUCTIONAL DESIGN.
DATA ANALYSES PROVIDE THE MEANS FOR MONITORING THE QUALITY OF ACADEMIC PROCESSES AND THE MEANS FOR ASSESSING THE FISCAL AND OPERATIONAL HEALTH OF AN ORGANIZATION. DATA-DRIVEN DECISION MAKING CAN HELP TO EMPOWER ACADEMIC LEADERS, FACULTY, AND STAFF WITH QUANTITATIVE INSIGHTS THAT GUIDE STRATEGIES PERTAINING TO ENROLLMENT AND RETENTION, STUDENT SUPPORT AND QUALITY ASSURANCE, COMMUNICATION, BULLYING INTERVENTION, ACADEMIC PROGRESS, AND ACADEMIC INTEGRITY. HOWEVER, THE INTEGRATION OF ANALYTICS INTO THE INSTITUTIONAL CONTEXT IS NOT A TRIVIAL PROCESS. MUCH OF THE ANALYTICS APPROACHES DISCUSSED IN THE LITERATURE TAKE A THEORETICAL STANCE OUTLINING MAIN CONSIDERATIONS BUT LACKING THE PRAGMATIC EDGE. OUR AIM IN THIS CHAPTER IS TO ASSIST ACADEMIC LEADERS IN UNDERTAKING ANALYTICS DESIGN AND IMPLEMENTATION. TO THIS END, WE SYNTHESIZE THE EXISTING RESEARCH AND PROPOSE A PROCEDURAL FRAMEWORK FOR INTEGRATING DATA ANALYSIS TECHNIQUES AND METHODS INTO A PROCESS THAT FACILITATES DATA-DRIVEN DECISION MAKING BY ALIGNING INSTITUTIONAL NEEDS WITH ACTIONABLE STRATEGIES.
THE STUDY OF CLIMATE CHANGE AND DEGRADED LAND REQUIRES THE STUDY OF CAUSAL RELATIONS BETWEEN HUMAN CAUSES AND LAND USE CHANGES. SPECIALIZED TRAINING THROUGH EUROPEAN MASTERS ON ENVIRONMENTAL MANAGEMENT AND ITS RISKS AND THREATS WILL DYNAMICALLY LINK PEOPLE FROM DIFFERENT DISCIPLINES AND FROM OUTSIDE ACADEMIA TO IMPROVE STUDENTS' EXPERIENCE WHICH WILL ENABLE THEM TO LEARN TO DEAL WITH MULTI DISCIPLINARY DECISION MAKING SOLUTION. THIS WILL ALLOW FOR STUDENTS NOT TO BE JUST INSERTED INTO ONE SYSTEM OR DISCIPLINE, BUT SIMULTANEOUSLY INTO A PROCESS OF LEARNING ABOUT THE RELATION BETWEEN EDUCATION, POLITICAL, SOCIAL, ECOLOGICAL AND ECONOMIC ENVIRONMENT. BUT THE MOST INNOVATIVE ASPECT OF THESE TEACHING STRATEGIES AND METHODOLOGIES IS THE LINK CREATED BETWEEN TEACHING INNOVATION AND ENVIRONMENTAL DISCIPLINES. THERE IS NOT SUCH AN OFFER THAT COMBINES AND LINKS THESE TWO MAJOR ASPECTS DIRECTLY RELATED AND FOCUSES THE EDUCATIONAL PROGRAM IN THIS COMBINATION. FROM THIS PERSPECTIVE, WHICH IS WIDELY RECOGNIZED BY EXPERTS, STUDENTS WILL HAVE AN INNOVATIVE AND USEFUL LEARNING EXPERIENCE ON THE ISSUE COVERED BY THE PROGRAM.
FROM A NETWORK PERSPECTIVE, SELF-REGULATED LEARNING (SRL) CAN BE CONCEPTUALIZED AS NETWORKS OF MUTUALLY INTERACTING SELF-REGULATORY LEARNING BEHAVIORS. NEVERTHELESS, THE RESEARCH ON HOW SRL BEHAVIORS DYNAMICALLY INTERACT OVER TIME IN A NETWORK ARCHITECTURE IS STILL IN ITS INFANCY, ESPECIALLY IN THE CONTEXT OF STEM (SCIENCES, TECHNOLOGY, ENGINEERING, AND MATH) LEARNING. IN THE PRESENT PAPER, WE USED A MULTILEVEL VECTOR AUTOREGRESSION (VAR) MODEL TO EXAMINE THE TEMPORAL DYNAMICS OF SRL BEHAVIORS AS 101 STUDENTS DESIGNED GREEN BUILDINGS IN ENERGY3D, A SIMULATION-BASED COMPUTER-AIDED DESIGN (CAD) ENVIRONMENT. WE EXAMINED HOW DIFFERENT PERFORMANCE GROUPS (I.E., UNSUCCESSFUL, SUCCESS-ORIENTED, AND MASTERY-ORIENTED GROUPS) DIFFERED IN SRL COMPETENCY, ACTUAL SRL BEHAVIORS, AND SRL NETWORKS. WE FOUND THAT THE THREE GROUPS HAD NO SIGNIFICANT DIFFERENCE IN THEIR PERCEIVED SRL COMPETENCY; HOWEVER, THEY DIFFERED IN SRL BEHAVIORS OF EVALUATION. BOTH THE MASTERY-ORIENTED AND SUCCESS-ORIENTED GROUPS PERFORMED MORE EVALUATION BEHAVIORS THAN THE UNSUCCESSFUL GROUP. MOREOVER, THE MASTERY-ORIENTED GROUP SHOWED STRONGER INTERACTION BETWEEN SRL BEHAVIORS THAN THE SUCCESS-ORIENTED GROUP AND THE UNSUCCESSFUL GROUP. THE SRL NETWORKS OF THE THREE GROUPS SHARED SOME SIMILARITIES, BUT THEY WERE DIFFERENT FROM EACH OTHER IN GENERAL. THIS STUDY HAS SIGNIFICANT THEORETICAL AND METHODOLOGICAL IMPLICATIONS FOR THE ADVANCEMENT OF RESEARCH IN SRL DYNAMICS.
PREDICTING STUDENT PERFORMANCE USING E-MAIL ASSESSMENTS CAN HELP IN EARLY INTERVENTIONS TO BETTER ASSIST STUDENTS SOONER, RATHER THAN LATER, IN STEM COURSES. IN THIS PAPER, WE PROPOSE CORC-NET, A TINY ARTIFICIAL NEURAL NETWORK (ANN) THAT OPERATES ON LIMITED DATA COMPRISED OF FEATURES SCORED FROM STUDENT ASSESSMENTS BASED ON WRITING E-MAILS. ANNS ARE TYPICALLY BUILT USING LARGE SCALE DATA SETS TO TRULY REALIZE THEIR FULL POTENTIAL; HOWEVER, TINY NEURAL NETWORKS OVERCOME THIS PROBLEM BY UTILIZING SMALLER BATCHES OF DATA MAKING THEM EASIER TO TRAIN. CORC-NET USES SCORED E-MAILS FOR CONTENT, ORGANIZATION, AND CLARITY AND CLASSIFIES HOW STUDENTS WILL PERFORM. FORMATIVE INSTRUCTOR FEEDBACK PROVIDED BETWEEN THE ASSESSMENTS IMPLIES THAT CORC-NET IS A MORE LOGICAL FIT TO SIMULATE THE "LEARNING" PROCESS WHEN HUMAN REACTION TO FEEDBACK AND CORRECTIVE ACTION IS INVOLVED. THIS IS TRUE ESPECIALLY IN SEQUENTIAL COURSE ASSESSMENT TASKS. IN THIS PAPER, WE SHOW THAT CORC-NET OUTPERFORMS OTHER MULTI-CLASS CLASSIFICATION ALGORITHMS LIKE DECISION TREES, SUPPORT VECTOR MACHINES, GAUSSIAN NAIVE BAYES, AND K-NEAREST NEIGHBORS. CORC-NET'S SUCCESS IN CLASSIFYING STUDENT PERFORMANCE SHOWS GREAT POTENTIAL IN COURSES WHERE LONG-TERM TEMPORAL ASSESSMENT DATA IS NOT AVAILABLE.
MATHEMATICS HAS BEEN WIDELY RECOGNIZED AS BEING CHALLENGING FOR MOST STUDENTS. IN THIS STUDY, AN ONLINE FORMATIVE PEER-TUTORING APPROACH WAS PROPOSED TO COPE WITH THIS PROBLEM, AND AN ONLINE LEARNING SYSTEM WAS DEVELOPED ACCORDINGLY. TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED APPROACH, AN EXPERIMENT WAS CONDUCTED TO EXPLORE ITS EFFECTS ON STUDENTS' LEARNING BEHAVIORS AS WELL AS THEIR LEARNING PERFORMANCE AND PERCEPTIONS OF THE MATHEMATICS COURSE. THE EXPERIMENTAL RESULTS SHOWED THAT BOTH THE FORMATIVE AND CONVENTIONAL PEER-TUTORING APPROACHES SIGNIFICANTLY IMPROVED THE STUDENTS' LEARNING ACHIEVEMENT AS WELL AS REDUCED THEIR COGNITIVE LOAD IN COMPARISON WITH CONVENTIONAL ONLINE COLLABORATIVE LEARNING APPROACHES. IN ADDITION, THE LEARNING BEHAVIOR ANALYSIS RESULTS SHOWED THAT THE STUDENTS PLAYING THE ROLE OF TUTOR IN THE FORMATIVE PEER-TUTORING GROUP REVEALED MORE APPROPRIATE BEHAVIORS OF ASSISTING THEIR TUTEES VIA DISCUSSING WITH THEM AND PROVIDING HINTS TO THEM AS WELL AS GUIDING THEM TO SOLVE THE MATHEMATICS PROBLEMS, WHILE THE TUTEES WERE MORE WILLING TO ASK QUESTIONS THAN THOSE LEARNING WITH THE CONVENTIONAL PEER-TUTORING APPROACH. THIS IMPLIES THAT THE PROPOSED APPROACH COULD MOTIVATE BOTH THE TUTORS AND TUTEES TO BECOME ACTIVE LEARNERS WITH GOOD LEARNING CONCEPTIONS.
IT IS VITAL TO DEVELOP AN UNDERSTANDING OF STUDENTS' SELF-REGULATORY PROCESSES IN THE DOMAINS OF STEM (SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS) FOR THE QUALITY DELIVERY OF STEM EDUCATION. HOWEVER, MOST STUDIES HAVE FOLLOWED A VARIABLE-CENTERED APPROACH, LEAVING OPEN THE QUESTION OF HOW SPECIFIC SRL (SELF-REGULATED LEARNING) BEHAVIORS GROUP WITHIN INDIVIDUAL LEARNERS. FURTHERMORE, LITTLE IS KNOWN ABOUT HOW STUDENTS' SRL PROFILES UNFOLD OVER TIME IN STEM EDUCATION, SPECIFICALLY IN THE CONTEXT OF ENGINEERING DESIGN. IN THIS STUDY, WE EXAMINED THE CHANGE OF STUDENTS' SRL PROFILES OVER TIME AS 108 MIDDLE SCHOOL STUDENTS DESIGNED GREEN BUILDINGS IN A SIMULATION-BASED COMPUTER-AIDED DESIGN (CAD) ENVIRONMENT. WE IDENTIFIED THREE DISTINCT SRL PROFILES USING A LONGITUDINAL CLUSTERING APPROACH: REFLECTIVE-ORIENTED, ADAPTIVE, AND MINIMALLY SELF-REGULATED LEARNERS. IN ADDITION, WE FOUND THAT STUDENTS WITH DIFFERENT PROFILE MEMBERSHIPS DIFFERED SIGNIFICANTLY IN THEIR DESIGN PERFORMANCE. SPECIFICALLY, ADAPTIVE SELF-REGULATED LEARNERS OUTPERFORMED MINIMALLY SELF-REGULATED LEARNERS ON DESIGN COMPLETENESS, WHEREAS REFLECTIVE-ORIENTED SELF-REGULATED LEARNERS DEMONSTRATED HIGHER DESIGN EFFICIENCY THAN MINIMALLY SELF-REGULATED LEARNERS. THIS STUDY PROVIDES RESEARCHERS WITH BOTH THEORETICAL AND METHODOLOGICAL INSIGHTS CONCERNING SRL DYNAMICS. FINDINGS FROM THIS RESEARCH ALSO INFORM PRACTITIONERS ABOUT THE DESIGN OF ADAPTIVE INTERVENTIONS.
THIS PAPER DESCRIBES EVALUATORS, A SYSTEM FOR THE DEVELOPMENT OF EDUCATIONAL SERIOUS GAMES ORIENTED TO INTRODUCTORY COMPUTER LANGUAGE IMPLEMENTATION COURSES SIMILAR TO THOSE INCLUDED IN COMPUTER SCIENCE TERTIARY CURRICULA. EVALUATORS LETS INSTRUCTORS GENERATE GAMES FROM COLLECTIONS OF EXERCISES ADDRESSING BASIC CONCEPTS ABOUT THE DESIGN AND IMPLEMENTATION OF COMPUTER LANGUAGES (IN PARTICULAR, THE PROCESSING OF ARTIFICIAL LANGUAGES ACCORDING TO THE MODEL OF ATTRIBUTE GRAMMARS). BY PLAYING THE GENERATED GAMES, STUDENTS INTERACTIVELY LEARN THE FUNDAMENTALS OF THE SEMANTIC EVALUATION PROCESS BEHIND ATTRIBUTE GRAMMARS. INDEED, THEY IMPLICITLY FIND SOLUTIONS TO THE EXERCISES PRESENTED, AND THEY RECEIVE IMMEDIATE FEEDBACK ABOUT SUCCESSFUL AND INCORRECT ACTIONS. IN ADDITION, THE GAMES LOG STUDENTS' ACTIONS, WHICH CAN SUBSEQUENTLY BE ANALYZED BY THE INSTRUCTORS USING A SPECIALIZED ANALYTIC TOOL THAT IS INCLUDED IN EVALUATORS. ASSESSMENT OF THE SYSTEM, WHICH WAS PERFORMED ACCORDING TO THREE DIFFERENT DIMENSIONS (THE INSTRUCTORS' PERSPECTIVE, THE STUDENTS' PERSPECTIVE AND EDUCATIONAL EFFECTIVENESS PERSPECTIVE), (A) INDICATES THAT THE EXERCISE-DRIVEN APPROACH OF EVALUATORS IS A COST-EFFECTIVE APPROACH AMENABLE TO EXTRAPOLATION TO OTHER AREAS OF COMPUTER SCIENCE TERTIARY EDUCATION, (B) SHOWS A POSITIVE ATTITUDE OF STUDENTS TOWARD THE SERIOUS GAMES BUILT WITH EVALUATORS, AND (C) EVIDENCES A POSITIVE EFFECT OF THE SYSTEM AND ITS PEDAGOGICAL STRATEGY ON LONG-TERM STUDENT PERFORMANCE. (C) 2013 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE PAPER INTRODUCES AN ARCHITECTURE OF THE SYSTEM FOR TEACHING AND LEARNING PROGRAMMING BASED ON INTERACTIVITY AND MODERN EDUCATIONAL APPROACHES LIKE GAMIFICATION, MICROLEARNING, AUTOMATED ASSESSMENT, COMMUNITY LEARNING, LEARNING-BY-DOING, LEARNING ANALYTICS, AND PERSONALIZATION AND PRESENTS DESIGN OF THE SOFTWARE ARCHITECTURE. FIRSTLY, THE REQUIRED STUDENTS' KNOWLEDGE AND SKILLS ARE DIVIDED INTO SEVERAL DOMAINS ACCORDING TO THEIR RELATION TO THE MORE COMPLEX PROGRAMMING TEACHING TOPICS. SUBSEQUENTLY, THE PAPER PRESENTS IDEAS ON WHICH THE SYSTEM ARCHITECTURE IS DESIGNED. THE MAIN PART OF THE PAPER DESCRIBES KEY MODULES AND PRINCIPLES USED IN A DESIGN OF THE PROPOSED SYSTEM. THE CONCLUSION DESCRIBES THE STRUCTURE OF EDUCATIONAL CONTENT DIVIDED INTO THE LEVELS, ACCORDING TO THE SYSTEM ARCHITECTURE.
AUGMENTED REALITY (AR) HAS BEEN SHOWN ITS PROMINENCE IN IMMERSIVE LEARNING ACROSS VARIOUS FIELDS OF EDUCATION, ESPECIALLY IN STEM CURRICULA. RESEARCHERS HAVE DEMONSTRATED THAT AR VISUALIZATION CAN IMPROVE LEARNING AND COGNITIVE OUTCOMES BY MULTIMODAL INTERACTION. HOWEVER, COMPARED TO PASSIVE MODES OF LEARNING LIKE READING AND VIEWING, THERE IS LITTLE RESEARCH FOCUSED ON THE EFFECTS OF AR ON A COLLABORATIVE PROBLEM-SOLVING TASK LIKE PAIR PROGRAMMING IN COMPUTER SCIENCE EDUCATION, WHICH IS DIFFICULT TO ANALYZE DUE TO THE INCREASED COMPLEXITY IN THE TASK AND USER-USER INTERACTION. WE DESIGNED AN AR APPLICATION, WHERE TWO PARTICIPANTS CAN WORK TOGETHER TO SOLVE A PROGRAMMING TASK, AND CONDUCTED A WITHIN-SUBJECT STUDY TO EVALUATE HOW THE PARTICIPANTS' CODE EDITING AND VOCAL COMMUNICATION ARE DIFFERENT IN THE AR AND NON-AR VISUALIZATIONS. THE RESULT SHOWED THAT PARTICIPANTS USING THE AR VISUALIZATION EXHIBITED STRATEGIC PROGRAMMING BEHAVIOR AND HAD MORE INTERACTIVE COMMUNICATION THAN USING THE NON-AR VISUALIZATION. OVERALL, THIS PRELIMINARY WORK CONTRIBUTES TO THE UNDERSTANDING OF HOW AR CAN SUPPORT COLLABORATIVE PROBLEM-SOLVING AND ENGAGE USERS IN VERBAL COMMUNICATION BY MULTIMODAL ANALYTICS.
THIS ARTICLE INTRODUCES THE INITIAL VERSION OF A LEARNING ANALYTIC BASED IN THE MODEL OF DISTRIBUTED EDUCATIONAL INFLUENCE (DEI). THE CENTRAL IDEA OF THE DEI MODEL IS THAT IN COLLABORATIVE LEARNING SITUATIONS ALL THE PARTICIPANTS ARE POTENTIAL SOURCES OF SUPPORT FOR OTHER PARTICIPANTS. ON THIS BASIS, A RESEARCH WAS CARRIED OUT WITH A TRIPLE OBJECTIVE: I) TO ANALYZE IF THE INFORMATION PROVIDED TO THE PARTICIPANTS HAS AN IMPACT ON THEIR COLLABORATIVE PROCESS, II) TO DETERMINE WHETHER THE PROVIDED INFORMATION HAS A DIFFERENTIAL EFFECT DEPENDING ON THE NATURE OF ITS CONTENT AND III) TO CONTRAST THE USEFULNESS OF THE ANALYTIC DEVELOPED TO ANALYZE THE PROCESSES OF ONLINE COLLABORATION. FOR THIS PURPOSE, FOUR GROUPS OF MASTER'S DEGREE STUDENTS PARTICIPATING IN A SERIES OF ONLINE FORUMS WERE STUDIED. IN GENERAL, THE RESULTS INDICATE THAT THE INFORMATION PROVIDED TO THE PARTICIPANTS ABOUT THEIR ACTIVITY HAS AN IMPACT ON THEIR COLLABORATIVE PROCESSES. HOWEVER, IT HAS NOT BEEN POSSIBLE TO DEMONSTRATE ANY DIFFERENTIAL IMPACT ON THEIR COLLABORATION PROCESSES DEPENDING ON THE NATURE OF THE PROVIDED INFORMATION. THE RESULTS ALSO POINT OUT SOME LIMITATIONS ON THE INDICATORS OF THE LEARNING ANALYTIC PROPOSED.
THIS STUDY SOUGHT TO IDENTIFY SIGNIFICANT BEHAVIORAL INDICATORS OF LEARNING USING LEARNING MANAGEMENT SYSTEM (LMS) DATA REGARDING ONLINE COURSE ACHIEVEMENT. BECAUSE SELF-REGULATED LEARNING IS CRITICAL TO SUCCESS IN ONLINE LEARNING, MEASURES REFLECTING SELF-REGULATED LEARNING WERE INCLUDED TO EXAMINE THE RELATIONSHIP BETWEEN LMS DATA MEASURES AND COURSE ACHIEVEMENT. DATA WERE COLLECTED FROM 530 COLLEGE STUDENTS WHO TOOK AN ONLINE COURSE. THE RESULTS DEMONSTRATED THAT STUDENTS' REGULAR STUDY, LATE SUBMISSIONS OF ASSIGNMENTS, NUMBER OF SESSIONS (THE FREQUENCY OF COURSE LOGINS), AND PROOF OF READING THE COURSE INFORMATION PACKETS SIGNIFICANTLY PREDICTED THEIR COURSE ACHIEVEMENT. THESE FINDINGS VERIFY THE IMPORTANCE OF SELF-REGULATED LEARNING AND REVEAL THE ADVANTAGES OF USING MEASURES RELATED TO MEANINGFUL LEARNING BEHAVIORS RATHER THAN SIMPLE FREQUENCY MEASURES. FURTHERMORE, THE MEASURES COLLECTED IN THE MIDDLE OF THE COURSE SIGNIFICANTLY PREDICTED COURSE ACHIEVEMENT, AND THE FINDINGS SUPPORT THE POTENTIAL FOR EARLY PREDICTION USING LEARNING PERFORMANCE DATA. SEVERAL IMPLICATIONS OF THESE FINDINGS ARE DISCUSSED. (C) 2015 ELSEVIER INC. ALL RIGHTS RESERVED.
CLOUD TECHNOLOGIES OFFER MANY PEDAGOGICAL POSSIBILITIES FOR COLLABORATION, UBIQUITOUS LEARNING, AND DOCUMENTING STUDENT ENGAGEMENT ACTIVITIES THROUGH LEARNING ANALYTICS AMONG OTHERS. HOWEVER, EFFECTIVE INTEGRATION OF CLOUD TECHNOLOGIES DEMANDS A COMPLEX SET OF TEACHERS' THEORETICAL AND CONCEPTUAL UNDERSTANDINGS AND DIGITAL COMPETENCIES. FURTHERMORE, THE TEACHERS ALSO NEED TO RE-EXAMINE THEIR CRITICAL INTERNAL BARRIERS SUCH AS THE LACK OF DIGITAL SELF-EFFICACY. A CLOSER LOOK REVEALS THAT THERE IS AN UNDERGIRDING NOTION - THE TEACHERS' DIGITAL IDENTITY - THAT ENABLES THEM TO DEVELOP THEIR CONCEPTUAL AND PRACTICAL DIGITAL COMPETENCIES AND SELF-EFFICACY. IN THIS CHAPTER, I CRITICALLY EXPLORE AND EXAMINE NEW PEDAGOGICAL POSSIBILITIES OF INTEGRATING CLOUD TECHNOLOGIES INTO CLASSROOMS WITHIN THE TRIADIC INTERPLAY OF DIGITAL IDENTITY, COMPETENCIES, AND SELF-EFFICACY (3DS).
THIS PAPER SYSTEMATIZES LEARNING AND TEACHING EXPERIENCE WITH ABOUT 700 STUDENTS IN DIFFERENT SUBJECT DEGREES FROM THE UNIVERSITY OF SANTIAGO DE COMPOSTELA. THE EXPERIENCE WORKS WITH E-PORTFOLIOS AS PART OF A SOCIAL NETWORK. IN DEVELOPING THE E-PORTFOLIO, REFLECTION, COMMUNICATION AND COLLABORATION ARE INCLUDED BY A FRAME-CONSTRUCTIVE LEARNING COGNITIVE PERSPECTIVE DEVELOPS ON SOCIAL BASES. THIS WORK ALLOWS STUDENTS TO DEVELOP SELF AND SOCIAL-REGULATION LEARNING SKILLS; IMPROVE TOLERANCE TOWARDS THEIR PEERS AND RECIPROCITY OF KNOWLEDGE AND LEARNING. THE EVALUATION IS DONE THROUGH A RUBRIC. ELGG VERSION 1.9.8 SOFTWARE TOOLS COMPLEMENTED WITH LEARNING ANALYTICS IS USED. ASSESSMENT EXPERIENCE SHOWS SIGNIFICANT GAINS IN STUDENT LEARNING AND SOME DIFFICULTIES ARE POINTED OUT.
IT IS CHALLENGING TO UTILIZE LEARNING ANALYTIC TECHNOLOGIES TO EXAMINE GAMEPLAY LOG DATA FOR GAME-EMBEDDED ASSESSMENT IN THE FIELD OF GAME-BASED LEARNING. ANALYTICAL APPROACHES BASED ON A NEW PERSPECTIVE FOCUSING ON COMPLICATED CONTEXTUAL DATA ARE IMPERATIVE IN THE CURRENT SCENARIO. A RELATIVELY NEW CONCEPT CALLED PRECISION EDUCATION, WHICH FOCUSES ON INDIVIDUAL LEARNING AND PROVIDES PERSONALIZED AND TIMELY INTERVENTION TO DIFFERENT LEARNERS, CAN BE REGARDED AS A NEW PERSPECTIVE FOR GAME-BASED LEARNING. ADDITIONALLY, THE ORDER OF KNOWLEDGE ACQUISITION IN THE LEARNING ENVIRONMENT IS A KIND OF LEARNING PATH EXTRACTED FROM THE CONTEXTUAL INFORMATION OF IN-GAME BEHAVIOR LOGS. THEREFORE, IN THIS STUDY, THE AUTHORS PROPOSE A NEW ANALYTICAL APPROACH TO IDENTIFY LEARNING PATH PATTERNS AND ELUCIDATE THE FEATURES OF THESE PATTERNS FOR AN EDUCATIONAL GAME THEY DEVELOPED. THE STATISTICAL ANALYSIS SHOWS THAT LEARNERS WITH DIVERSE LEARNING PATH PATTERNS HAVE DIFFERENT LEARNING EFFECTS, SUGGESTING THAT THE LEARNING PATH IS AN IMPORTANT FACTOR IN PRECISION EDUCATION. THE PRACTICE OF USING THE EXPLANATION METHOD TO EXAMINE THE PROPOSED APPROACH CAN HELP US UNDERSTAND LEARNERS' KNOWLEDGE ACQUISITION AND PROVIDE EVIDENCE FOR ENHANCING THE ACCURACY OF PRECISION EDUCATION AND IMPROVING THE QUALITY OF THE EDUCATIONAL GAME. THE FINDINGS ARE EXPECTED TO CONTRIBUTE TO BOTH GAME-BASED LEARNING AND PRECISION EDUCATION.
A VISUAL DOMAIN EXPLORATION TOOL IS INTRODUCED THAT OFFERS SEVERAL VIEWPOINTS AS PARTS OF A PERSONALIZED STUDENT ADVICE AND GUIDANCE SOLUTION: EXPLANATION OF THE PERSONAL PROGRESS OVER A REFERENCE BODY OF KNOWLEDGE AREAS, WITHIN THE FIELD OF STUDY; MAPPING OF THE ACTUAL COURSES IN RELATION TO THE FIELD OF STUDY WHERE THE TAUGHT TOPICS BELONG; GUIDANCE IN CHOOSING COURSES ALIGNED TOWARDS PERSONAL INTERESTS AND ABILITIES IN RESPECTIVE AREAS. THE STUDENT CAN VISUALIZE AREAS THAT ARE SUCCESSFULLY COVERED BY PAST COURSES, AREAS THAT ARE A PROBABLE POINT OF RISK, MANDATING GREATER FOCUS AND DETERMINATION, AND OUT-OF-TOPIC AREAS WHERE THE STUDENT IS PREDICTED TO PERFORM WITH GREATER SUCCESS. THE STUDENT CAN VIEW THE PAST TRENDS OF PERSONAL PROGRESS, AND EXPERIMENT WITH FUTURE TRACKS.
IN THIS PAPER, WE PRESENT AN ARCHITECTURE FOR TRACKING USER INTERACTIONS OVER NATIONAL DIGITAL LIBRARY (NDL) PORTAL IN THE FORM OF USER EXPERIENCE. WE HAVE PROPOSED XAPI STANDARD-BASED MECHANISM FOR CAPTURING INTERACTION DATA THROUGH TIN CAN STATEMENTS, AND STORING THEM IN A LEARNING RECORD STORE (LRS). THE STATEMENTS FROM THE LRS ARE IMPORTED TO THE ANALYTICS MODULE THAT PERFORMS DIFFERENT ANALYTIC TASKS OVER THE DATA. THE PRESENT STUDY HAS BEEN PERFORMED OVER INTERACTION STATEMENTS COLLECTED OVER A PERIOD OF SIX MONTHS. DUE TO SEVERAL POLICIES, THE USER BASE IN NDL IS RATHER LIMITED. HENCE, THE INTERACTION DATA IS QUITE RESTRICTED AND SPARSE. THIS PAPER PRESENTS SOME INSIGHTS OVER USER INTERACTIONS ON THE NDL PORTAL BASED ON THE ANALYTICS PERFORMED OVER THIS SPARSE AND LIMITED INTERACTION DATA.
THIS PAPER GIVES A BRIEF OVERVIEW OF THE CHANGES WITHIN HIGHER EDUCATION SYSTEM DURING THE LAST FEW DECADES. FURTHER, THE PAPER BRINGS CRITICAL ANALYSIS OF APPROACHES TO STRATEGIC DECISION-MAKING AND STRATEGIC PLANNING IN HIGHER EDUCATION AND MOST IMPORTANTLY, IT BRINGS EXAMPLES OF STRATEGIC PROBLEMS AND POSSIBLE STRATEGIC DECISIONS AS WELL AS STRATEGIC DECISION EVALUATION POSSIBILITIES WITHIN HIGHER EDUCATION AND THE CORRESPONDING IMPACT EVALUATION WITHIN CROATIAN HE INSTITUTIONS.
THE PRESENT STUDY AIMS TO INCORPORATE A GROUP LEADERSHIP PROMOTION APPROACH INTO COLLABORATIVE LEARNING TASKS IN THE HOPE OF DEVELOPING STUDENTS' CREATIVITY, PROBLEM-SOLVING, AND CRITICAL THINKING. IN ORDER TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED APPROACH, WE RECRUITED 59 CROSS-PROFESSIONAL STUDENTS FROM A UNIVERSITY IN NORTHERN TAIWAN, AND DIVIDED THEM INTO AN EXPERIMENTAL GROUP (6 SUB-GROUPS) AND A CONTROL GROUP (6 SUB-GROUPS). THE EXPERIMENTAL GROUP STUDENTS LEARNED WITH THE GROUP LEADERSHIP PROMOTION APPROACH, WHILE THE CONTROL GROUP LEARNED WITH CONVENTIONAL BLENDED LEARNING APPROACH. THE RESULTS SHOW THAT THE STUDENTS COULD BETTER DEVELOP THEIR CREATIVITY, PROBLEM-SOLVING, CRITICAL THINKING, AND LEARNING ACHIEVEMENT VIA THE GROUP LEADERSHIP PROMOTION APPROACH. LASTLY, SEQUENTIAL ANALYSIS OF THEIR BEHAVIORS INDICATED THAT STUDENTS' INTERACTION WITH GROUP LEADERSHIP IN ONLINE COLLABORATIVE LEARNING CAN PROMOTE MANY MORE CREATIVE IDEAS AND COMMENTS, ENHANCE REACHING A CONSENSUS AND MAKING CONCLUSIONS, AND FINALLY, FACILITATE COMPLETION OF THE GROUP LEARNING TASKS.
EDUCATION SYSTEM IS ONE OF THE SECTORS THAT EXPERIENCES CHANGES IN TERMS OF TECHNOLOGICAL INNOVATIONS. MANY INSTITUTIONS ADOPT THE USE OF E-LEARNING PLATFORMS AND OTHER WEB SERVICES FOR INTERACTIONS OF THE STUDENTS. NUMEROUS USERS KEEP LOGGING INTO THESE APPLICATIONS WHICH RESULTED INTO SUPPLYING HUGE AMOUNT OF DATA. HOWEVER, DESPITE THESE HUGE AMOUNT OF DATA, INSTITUTIONS ARE YET TO MAKE USE OF MOST OF THESE DATA. THEY TEND TO MANAGE THE DATA SUBMITTED BY THE USERS REGARDING THEIR REQUESTS ONLY WHILE IGNORING DATA RELATED TO WEB BEHAVIORAL INPUTS. THEREFORE, THIS PAPER AIMED AT PROVIDING A MODEL FOR ENHANCING TEACHING AND LEARNING IN EDUCATIONAL INSTITUTES USING BIG DATA TECHNOLOGY. THE PAPER HAS OBJECTIVES AS TO IDENTIFY BENEFITS OF BIG DATA TECHNOLOGY IN EDUCATION, TO DESCRIBE THE APPLICATIONS OF BIG DATA IN EDUCATION AND FINALLY TO PROVIDE BIG DATA MODELS FOR EDUCATIONAL SYSTEM. THE PAPER USES BOTH SURVEY AND MODELLING APPROACHES, THE SURVEY MAKES USE OF REVIEW PAPERS TO OUTLINE THE BENEFITS AND APPLICATIONS OF BIG DATA INTO EDUCATION, WHILE THE MODELLING APPROACH USES UNIFIED MODELLING LANGUAGE (ITML) TO PROVIDE DESCRIPTION ON SMALL DATA AND BIG DATA INTERACTIONS IN EDUCATION SYSTEM. THE SMALL DATA PROVIDE TRADITIONAL FEEDBACK WITH EMPHASIS ON STUDENTS' PERFORMANCE ONLY, WHILE THE BIG DATA MEASURES THE ENTIRE LEARNING PROCESS CONSISTING OF BOTH STUDENTS AND TEACHERS.
THE WIDESPREAD USE OF COMPUTING AND COMMUNICATIONS TECHNOLOGIES HAS ENABLED THE POPULARITY OF SOCIAL NETWORKS ORIENTED TO LEARN. IN THIS WORK, WE STUDY THE NATURE AND STRENGTH OF ASSOCIATIONS BETWEEN STUDENTS USING AN ONLINE SOCIAL NETWORK EMBEDDED IN A LEARNING MANAGEMENT SYSTEM. WITH DATASETS FROM THREE OFFERINGS OF THE SAME COURSE, WE MINED THE SEQUENCES OF QUESTIONS AND ANSWERS POSTED BY THE STUDENTS TO IDENTIFY STRUCTURAL PROPERTIES OF THE SOCIAL GRAPH, PATTERNS OF COLLABORATION AMONG STUDENTS AND FACTORS INFLUENCING THE FINAL ACADEMIC ACHIEVEMENTS. THE RESULTS SHOW SOME HINTS TO UNDERSTAND HOW USERS REACT TO INCENTIVES IN ONLINE SOCIAL LEARNING SYSTEMS, USEFUL FOR INSTANCE TO MEASURE THE EFFECTIVENESS OF THE LEARNING TASKS BASED ON THE PATTERNS OF STUDENTS' PARTICIPATION IN THE PLATFORM AND ADAPT THEIR DESIGN, OR AS EARLY PREDICTORS OF ACADEMIC PERFORMANCE.
AS COLLABORATIVE LEARNING IS ACTUALIZED THROUGH EVOLVING DIALOGUES, TEMPORALITY INEVITABLY MATTERS FOR THE ANALYSIS OF COLLABORATIVE LEARNING. THIS STUDY ATTEMPTS TO UNCOVER SEQUENTIAL PATTERNS THAT DISTINGUISH PRODUCTIVE THREADS OF KNOWLEDGE-BUILDING DISCOURSE. A DATABASE OF GRADE 1-6 KNOWLEDGE-BUILDING DISCOURSE WAS FIRST CODED FOR THE POSTS' CONTRIBUTION TYPES AND DISCUSSION THREADS' PRODUCTIVITY. TWO DISTINCTIVE TEMPORAL ANALYSIS TECHNIQUES - LAG-SEQUENTIAL ANALYSIS (LSA) AND FREQUENT SEQUENCE MINING (FSM) - WERE SUBSEQUENTLY APPLIED TO DETECTING SEQUENTIAL PATTERNS AMONG CONTRIBUTION TYPES THAT DISTINGUISH PRODUCTIVE THREADS. THE FINDINGS OF LSA INDICATED THAT THREADS THAT WERE CHARACTERIZED BY MERE OPINION-GIVING DID NOT ACHIEVE MUCH PROGRESS, WHILE THREADS HAVING MORE TRANSITIONS AMONG QUESTIONING, OBTAINING INFORMATION, WORKING WITH INFORMATION, AND THEORIZING WERE MORE PRODUCTIVE. FSM FURTHER UNCOVERED FROM PRODUCTIVE THREADS DISTINGUISHING FREQUENT SEQUENCES INVOLVING SUSTAINED THEORIZING, INTEGRATED USE OF EVIDENCE, AND PROBLEMATIZATION OF PROPOSED THEORIES. BASED ON THE SIGNIFICANCE OF STUDYING TEMPORALITY IN COLLABORATIVE LEARNING REVEALED IN THE STUDY, WE ADVOCATE FOR MORE ANALYTICS TAPPING INTO TEMPORALITY OF LEARNING.
RECENT TECHNOLOGICAL ADVANCES HAVE LED TO TREMENDOUS CAPACITIES FOR COLLECTING, STORING AND ANALYZING DATA BEING CREATED AT AN EVER-INCREASING SPEED FROM DIVERSE SOURCES. ACADEMIC INSTITUTIONS WHICH OFFER OPEN AND DISTANCE LEARNING PROGRAMS, SUCH AS THE HELLENIC OPEN UNIVERSITY, CAN BENEFIT FROM BIG DATA RELATING TO ITS STUDENTS' INFORMATION AND COMMUNICATION SYSTEMS AND THE USE OF MODERN TECHNIQUES AND TOOLS OF BIG DATA ANALYTICS PROVIDED THAT THE STUDENT'S RIGHT TO PRIVACY IS NOT COMPROMISED. THE BALANCE BETWEEN DATA MINING AND MAINTAINING PRIVACY CAN BE REACHED THROUGH ANONYMISATION METHODS BUT ON THE OTHER HAND THIS APPROACH RAISES TECHNICAL PROBLEMS SUCH AS THE LOSS OF A CERTAIN AMOUNT OF INFORMATION FOUND IN THE ORIGINAL DATA. CONSIDERING THE LEARNING PROCESS AS A FRAMEWORK OF INTERACTING ROLES AND FACTORS, THE DISCOVERY OF PATTERNS IN THAT SYSTEM CAN BE REALLY USEFUL AND BENEFICIAL FIRSTLY FOR THE LEARNERS AND FURTHERMORE, THE ABILITY TO PUBLISH AND SHARE THESE RESULTS WOULD BE VERY HELPFUL FOR THE WHOLE ACADEMIC INSTITUTION.
CONVERSATIONAL AGENTS ASSIST TRADITIONAL TEACHING-LEARNING INSTRUMENTS IN PROPOSING NEW DESIGNS FOR KNOWLEDGE CREATION AND LEARNING ANALYSIS, ACROSS ORGANIZATIONAL ENVIRONMENTS. MEANS OF BUILDING COMMON EDUCATIVE BACKGROUND IN BOTH INDUSTRY AND ACADEMIC FIELDS BECOME OF INTEREST FOR ENSURING EDUCATIONAL EFFECTIVENESS AND CONSISTENCY. SUCH A CONTEXT REQUIRES TRANSFERABLE PRACTICES AND BECOMES THE BASIS FOR THE AGILE ADOPTION INTO HIGHER EDUCATION, AT BOTH CURRICULUM AND OPERATIONAL LEVELS. THE CURRENT WORK PROPOSES A MODEL FOR DELIVERING AGILE SCRUM TRAINING THROUGH AN ASSISTIVE WEB-BASED CONVERSATIONAL SERVICE, WHERE ANALYTICS ARE COLLECTED TO PROVIDE AN OVERVIEW ON LEARNERS' KNOWLEDGE PATH. BESIDES ITS SPECIFIC APPLICABILITY INTO SOFTWARE ENGINEERING (SE) INDUSTRY, THE MODEL IS TO ASSIST THE ACADEMIC SE CURRICULUM. A USER-ACCEPTANCE TEST HAS BEEN CARRIED OUT AMONG 200 UNDERGRADUATE STUDENTS AND PATTERNS OF INTERACTION HAVE BEEN DEPICTED FOR 2 CONVERSATIONAL STRATEGIES.
IN THIS PAPER, WE DESCRIBE OUR ONGOING RESEARCH FOCUSED ON IDENTIFYING REQUIREMENTS FOR DESIGNING, DEVELOPING AND DEPLOYING AN ENVIRONMENT CAPABLE TO ENHANCE EDUCATIONAL ACTIVITIES RELYING ON VIDEOS CONTRIBUTED BY STUDENTS. WE PRESENT A JOINT EFFORT CONDUCTED BY RESEARCHERS FROM THREE COUNTRIES BRINGING THEIR RESULTS FROM PREVIOUS EFFORTS FOCUSED ON THE INTERPLAY BETWEEN LEARNING AND TECHNOLOGY FOR OPTIMIZING THE PEDAGOGICAL EXPLOITATION OF ONLINE VIDEOS. IN ORDER TO REACH THESE GOALS, WE USE DATA COLLECTED FROM 204 STUDENTS WHERE THEY SHARED THEIR EXPERIENCES AND INSIGHTS BASED ON THEIR EXPERIENCES WHILE USING VIDEOS FOR THEIR OWN LEARNING. THE PRESENTED RESEARCH ILLUSTRATES THE TRANSFORMATION OF THESE STUDENTS' INSIGHTS INTO ACTUAL SYSTEM REQUIREMENTS. THESE REQUIREMENTS ARE COLLECTED AND EXAMINED IN ORDER TO BE USED FOR THE DESIGN, DEVELOPMENT, EVALUATION AND REFINEMENT OF AN EXISTING SYSTEM WHILE AIMING TO DEPLOY A MATURE ENVIRONMENT ENABLING AN OPTIMIZED EXPERIENCE OF ON-LINE VIDEOS IN. EDUCATIONAL SETTINGS. LAST BUT NOT LEAST, WE DISCUSS THE BENEFITS OF OUR APPROACH IN THE LIGHT OF BENEFITS AND FUTURE CHALLENGES TO INCORPORATE VIDEOS IN EDUCATIONAL ACTIVITIES SUPPORTED BY A SOFTWARE ARCHITECTURE THAT RELIES ON WEB TECHNOLOGIES. FINALLY, WE SUMMARIZE AND DISCUSS OUR EFFORTS IN THE LIGHT OF FUTURE RESEARCH WHILE AIMING TO PRACTICE FUTURE DEPLOYMENT OF SUCH ARCHITECTURE. IN THESE FUTURE ITERATIONS, WE DISCUSS NEW STAKEHOLDERS POSSIBLY ASSISTING IN IDENTIFYING NEW REQUIREMENTS AND CONTRIBUTING WITH INNOVATIVE IDEAS FOR BEST OPTIMIZATION OF VIDEOS SUPPORTED BY TEL ENVIRONMENTS OFFERING MEANINGFUL AND APPEALING LEARNING.
PERSISTENCE IN LEARNING PROCESSES IS PERCEIVED AS A CENTRAL VALUE; THEREFORE, DROPOUTS FROM STUDIES ARE A PRIME CONCERN FOR EDUCATORS. THIS STUDY FOCUSES ON THE QUANTITATIVE ANALYSIS OF DATA ACCUMULATED ON 362 STUDENTS IN THREE ACADEMIC COURSE WEBSITE LOG FILES IN THE DISCIPLINES OF MATHEMATICS AND STATISTICS, IN ORDER TO EXAMINE WHETHER STUDENT ACTIVITY ON COURSE WEBSITES MAY ASSIST IN PROVIDING EARLY IDENTIFICATION OF LEARNER DROPOUT FROM SPECIFIC COURSES OR FROM DEGREE TRACK STUDIES IN GENERAL. IT WAS FOUND IN THIS STUDY THAT IDENTIFYING THE CHANGES IN STUDENT ACTIVITY DURING THE COURSE PERIOD COULD HELP IN DETECTING AT-RISK LEARNERS IN REAL TIME, BEFORE THEY ACTUALLY DROP OUT FROM THE COURSE. DATA EXAMINATION ON A MONTHLY BASIS THROUGHOUT THE SEMESTER CAN ENABLE EDUCATORS AND INSTITUTIONS TO FLAG STUDENTS THAT HAVE BEEN IDENTIFIED AS HAVING UNUSUAL BEHAVIOR, DEVIATING FROM THE COURSE AVERAGE. IT WAS FOUND THAT A LARGE PERCENTAGE OF STUDENTS (66%) WHO HAD BEEN MARKED AS AT-RISK ACTUALLY DID NOT FINISH THEIR COURSES AND/OR DEGREE. THE PRESENTED ANALYSIS ALLOWS INSTRUCTORS TO OBSERVE WEBSITE STUDENT USAGE DATA DURING A COURSE, AND TO LOCATE STUDENTS WHO ARE NOT USING THE SYSTEM AS EXPECTED. FURTHERMORE, IT COULD ENABLE UNIVERSITY DECISION MAKERS TO SEE THE INFORMATION ON A CAMPUS LEVEL FOR INITIATING INTERVENTION PROGRAMS.
ALTHOUGH SERIOUS GAMES ARE PROVEN TO SERVE AS EDUCATIONAL TOOLS IN MANY EDUCATIONAL DOMAINS, THERE IS A LACK OF RELIABLE, AUTOMATED AND REPEATABLE METHODOLOGIES THAT MEASURE THEIR EFFECTIVENESS: WHAT DO PLAYERS KNOW AFTER PLAYING SERIOUS GAMES? DO THEY LEARN FROM THEM? PREVIOUS RESEARCH SHOWS THAT THE VAST MAJORITY OF SERIOUS GAMES ARE ASSESSED BY USING QUESTIONNAIRES, WHICH IS IN STARK CONTRAST TO CURRENT TRENDS IN THE VIDEO GAME INDUSTRY. COMMERCIAL VIDEOGAME DEVELOPERS HAVE BEEN LEARNING FROM THEIR PLAYERS THROUGH GAME ANALYTICS FOR YEARS VIA NON-DISRUPTIVE GAME TRACKING. IN THIS PAPER, WE PROPOSE A METHODOLOGY FOR ASSESSING SERIOUS GAME EFFECTIVENESS BASED ON NON-DISRUPTIVE IN-GAME TRACKING. THE METHODOLOGY INVOLVES A DESIGN PATTERN THAT STRUCTURES THE DELIVERY OF EDUCATIONAL GOALS THROUGH A GAME. THIS STRUCTURE ALSO ALLOWS ONE TO INFER LEARNING OUTCOMES FOR EACH INDIVIDUAL PLAYER, WHICH, WHEN AGGREGATED, DETERMINE THE EFFECTIVENESS OF A SERIOUS GAME. WE TESTED THE METHODOLOGY BY HAVING 320 STUDENTS PLAY A SERIOUS GAME. THE PROPOSED METHODOLOGY ALLOWED US TO INFER PLAYERS' LEARNING OUTCOMES, TO ASSESS THE GAME EFFECTIVENESS LEVELS AND TO IDENTIFY ISSUES IN THE GAME DESIGN.
THIS PAPER PRESENTS THE INTEGRATION OF A REAL TIME EVALUATION METHOD OF COLLABORATION QUALITY IN A MONITORING APPLICATION THAT SUPPORTS TEACHERS IN CLASS ORCHESTRATION. THE METHOD IS IMPLEMENTED AS AN AUTOMATIC RATER OF COLLABORATION QUALITY AND STUDIED IN A REAL TIME SCENARIO OF USE. WE ARGUE THAT AUTOMATIC AND SEMI-AUTOMATIC METHODS WHICH PROVIDE ASSESSMENTS OF COLLABORATION QUALITY OR DETECT DISORDERS AND ANOMALIES IN COLLABORATIVE ACTIVITIES SHOULD BE FURTHER USED TO ASSIST CLASS MONITORING. TO THAT END, WE STUDY THE ROUTINE OF TEACHERS WHEN THEY MONITOR COMPUTER-SUPPORTED COLLABORATIVE ACTIVITIES WITH RESPECT TO THE SIZE OF THE CLASSROOM AND THE SUPPORT OF AN AUTOMATIC RATER. WE FOCUS ON THE WAY THE RATER AFFECTS THEIR TASK AND HOW IT CAN BE FURTHER USED TO EMPOWER THE PRACTICE OF TEACHERS.
THIS STUDY EXPLORES THE EFFECTS OF STUDENT BEHAVIOR PATTERNS ON STUDENTS' LEARNING PERFORMANCE IN A STUDENT-GENERATED QUESTION EXPERIMENT. THIRTY-THREE ENGINEERING STUDENTS FROM A C PROGRAMMING COURSE WERE RECRUITED AS PARTICIPANTS. FOR DATA COLLECTION, STUDENT-GENERATED QUESTIONS, EXERCISES, A FINAL EXAMINATION, AND SYSTEM LOGS RECORDED BY AN ONLINE STUDENT-GENERATED QUESTIONS SYSTEM WERE ANALYZED. THE RESULTS REVEALED SEVERAL SIGNIFICANT FINDINGS. FIRSTLY, WE FOUND THAT THE STUDENTS ACCESSED THE QUESTIONS GENERATED BY THE CLASSMATE MORE FREQUENTLY AND FOR LONGER THAN OTHER MATERIALS (I.E., EXERCISES AND LECTURE SLIDES). OUR RESULTS ALSO REVEAL GREAT DIVERSITY AMONG STUDENT VIEWING ACTIVITIES; WE THEREFORE PARTITIONED THEM INTO THREE BEHAVIOR CLUSTERS: "HIGHLY-ENGAGED STUDENTS" WHO DOMINATE OTHER CLUSTERS IN THE USE OF ALL KINDS OF MATERIALS, "MODERATELY-ENGAGED STUDENTS" WHO SPENT MORE TIME ON THE LECTURE SLIDES AND SGQ, AND "LESSENGAGED STUDENTS" WHO SELDOM USED LEARNING MATERIALS BUT ACCESSED EXERCISES MORE FREQUENTLY THAN THE "MODERATELY-ENGAGED STUDENTS" AND ALSO HAD THE HIGHEST TEST ANXIETY. WE ALSO OBSERVED THAT MOTIVATION AND LEARNING PERFORMANCE ARE STRONGLY ASSOCIATED WITH BEHAVIOR PATTERNS. WE DISCUSS POSSIBLE EXPLANATIONS OF OUR FINDINGS AND PROPOSE SUGGESTIONS FOR FUTURE RESEARCH.
THE NUMBER OF STUDENTS' ENROLLED IN HIGHER EDUCATION ONLINE COURSES IS INCREASING, AND AS A RESULT, MORE DATA ABOUT THEIR LEARNING PROCESS IS GENERATED. THE DATA PRODUCED CAN BE DISPLAYED ON A DASHBOARD AND HELP STUDENTS IN THEIR LEARNING PROCESS. HOWEVER, LECTURERS' PERSPECTIVE SHOULD BE TAKEN INTO ACCOUNT TO DEFINE THE INDICATORS OF THE DASHBOARD AS THE DESIGN OF THE COURSES COULD IMPACT ON THE ELEMENTS INCLUDED ON IT. AND THAT IS PRECISELY THE AIM OF THIS PAPER: TO DEFINE THE INDICATORS NEEDED TO DESIGN A STUDENT DASHBOARD IN ONLINE COURSES TAKING INTO ACCOUNT LECTURERS' PERSPECTIVES. THIS STUDY WAS CARRIED OUT WITH 10 LECTURERS FROM THE FACULTY OF HUMANITIES AND EDUCATION FROM MONDRAGON UNIBERTSITATEA. ONLINE QUALITATIVE QUESTIONNAIRES WERE USED TO GATHER PARTICIPANTS' PERCEPTIONS. RESULTS SHOW THAT MOST OF THE LECTURERS PARTICIPATING IN THE RESEARCH STUDY IDENTIFIED FOUR INDICATORS TO DESIGN A STUDENT DASHBOARD: NUMBER OF TIMES STUDENTS ACCESS THE COURSE FORUM, AMOUNT OF CONTRIBUTIONS IN THE FORUM, NUMBER OF TIMES STUDENTS CONSULT THE INFORMATION BOOKLET, AND NUMBER OF TIMES EACH STUDENT HAS ACCESSED THE COURSE IN A WEEK TIME. CONCLUSIONS DRAWN FROM THIS STUDY HIGHLIGHT THE IMPORTANCE OF TRAINING LECTURERS AND STUDENTS ON THE PEDAGOGICAL USE OF DATA VISUALIZATION.
THIS PAPER PRESENTS A FULL-SEMESTER ADOPTION OF FLIPPED CLASSROOM IN TEACHING INTERNET OF THINGS DEVELOPMENT. STUDENT-PACED VIDEO LECTURES HAD BEEN USED, ALLOWING STUDENTS TO HAVE MORE FLEXIBILITY IN LEARNING BY DELIVERING LECTURES ONLINE, THE COURSE INSTRUCTOR CAN SPEND MORE TIME IN CLASS TO CONDUCT ACTIVITIES WHICH ENCOURAGE IN-DEPTH, ACTIVE LEARNING AND STUDENT-TEACHER INTERACTIONS. THE PROCESS OF PREPARING, IMPLEMENTING AND EVALUATING THE COURSE WILL BE DESCRIBED IN THE PAPER.
DESIGNING AND IMPLEMENTING ONLINE OR DIGITAL LEARNING MATERIAL IS A DEMANDING TASK FOR TEACHERS. THIS IS EVEN MORE THE CASE WHEN THIS MATERIAL IS USED FOR MORE ENGAGED FORMS OF LEARNING, SUCH AS INQUIRY LEARNING. IN THIS ARTICLE, WE GIVE AN INFORMED ACCOUNT OF GO-LAB, AN ECOSYSTEM THAT SUPPORTS TEACHERS IN CREATING INQUIRY LEARNING SPACES (ILSS). THESE ILSS ARE BUILT AROUND STEM-RELATED ONLINE LABORATORIES. WITHIN THE GO-LAB ECOSYSTEM, TEACHERS CAN COMBINE THESE ONLINE LABORATORIES WITH MULTIMEDIA MATERIAL AND LEARNING APPS, WHICH ARE SMALL APPLICATIONS THAT SUPPORT LEARNERS IN THEIR INQUIRY LEARNING PROCESS. THE GO-LAB ECOSYSTEM OFFERS TEACHERS READY-MADE STRUCTURES, SUCH AS A STANDARD INQUIRY CYCLE, ALTERNATIVE SCENARIOS OR COMPLETE ILSS THAT CAN BE USED AS THEY ARE, BUT IT ALSO ALLOWS TEACHERS TO CONFIGURE THESE STRUCTURES TO CREATE PERSONALIZED ILSS. FOR THIS ARTICLE, WE ANALYZED DATA ON THE DESIGN PROCESS AND STRUCTURE OF 2414 ILSS THAT WERE (CO)CREATED BY TEACHERS AND THAT OUR USAGE DATA SUGGEST HAVE BEEN USED IN CLASSROOMS. OUR DATA SHOW THAT TEACHERS PREFER TO START THEIR DESIGN FROM EMPTY TEMPLATES INSTEAD OF MORE DOMAIN-RELATED ELEMENTS, THAT THE MAKEUP OF THE DESIGN TEAM (A SINGLE TEACHER, A GROUP OF COLLABORATING TEACHERS, OR A MIX OF TEACHERS AND PROJECT MEMBERS) INFLUENCES KEY DESIGN PROCESS CHARACTERISTICS SUCH AS TIME SPENT DESIGNING THE ILS AND NUMBER OF ACTIONS INVOLVED, THAT THE CHARACTERISTICS OF THE RESULTING ILSS ALSO DEPEND ON THE TYPE OF DESIGN TEAM AND THAT ILSS THAT ARE OPENLY SHARED (I.E., PUBLISHED IN A PUBLIC REPOSITORY) HAVE DIFFERENT CHARACTERISTICS THAN THOSE THAT ARE KEPT PRIVATE.
STUDIES ON MASSIVE OPEN ONLINE COURSES (MOOCS) USERS DISCUSS THE EXISTENCE OF TYPICAL PROFILES AND THEIR IMPACT ON THE LEARNING PROCESS OF THE STUDENTS. HOWEVER DEFINING THE TYPICAL BEHAVIORS AS WELL AS CLASSIFYING THE USERS ACCORDINGLY IS A DIFFICULT TASK. IN THIS PAPER WE SUGGEST TWO METHODS TO MODEL MOOC USERS BEHAVIOUR GIVEN THEIR LOG DATA. WE MOLD THEIR BEHAVIOR INTO A MARKOV DECISION PROCESS FRAMEWORK. WE ASSOCIATE THE USER'S INTENTIONS WITH THE MDP REWARD AND ARGUE THAT THIS ALLOWS US TO CLASSIFY THEM.
THE USE OF DATA MINING WITHIN THE HIGHER EDUCATION CONTEXT HAS, INCREASINGLY, BEEN GAINING TRACTION. A PARALLEL EXAMINATION OF THE ACCURACY, ROBUSTNESS AND UTILITY OF THE ALGORITHMS APPLIED TO DATA MINING IS ARGUED AS A NECESSARY STEP TOWARD ENTRENCHING THE USE OF EDM. THIS ARTICLE PROVIDES A COMPARATIVE ANALYSIS OF VARIOUS CLASSIFICATION ALGORITHMS WITHIN AN OPEN DISTANCE LEARNING INSTITUTION IN SOUTH AFRICA. THE STUDY COMPARES THE PERFORMANCE OF THE ZEROR, ONER, NAIVE BAYES, IBK, SIMPLE LOGISTIC REGRESSION AND THE J48 IN CLASSIFYING STUDENTS WITHIN A COHORT OVER AN EIGHTYEAR TIME SPAN. THE INITIAL RESULTS APPEAR TO SHOW THAT, GIVEN THE DATA QUALITY AND STRUCTURE OF THE INSTITUTION UNDER STUDY, THE J48 MOST CONSISTENTLY PERFORMED WITH THE HIGHEST LEVELS OF ACCURACY.
THIS STUDY PROPOSES AN ANALYTIC APPROACH WHICH COMBINES TWO PREDICTIVE MODELS (THE PREDICTIVE MODEL OF SUCCESSFUL STUDENTS AND THE PREDICTIVE MODEL OF AT-RISK STUDENTS) TO ENHANCE PREDICTION PERFORMANCE FOR USE UNDER THE CONSTRAINTS OF LIMITED DATA COLLECTION. A CASE STUDY WAS CONDUCTED TO EXAMINE THE EFFECTS OF THE MODEL COMBINATION APPROACH. EIGHT VARIABLES WERE COLLECTED FROM A DATA WAREHOUSE AND THE LEARNING MANAGEMENT SYSTEM. THE BEST MODEL WAS SELECTED BASED ON THE LOWEST MISCLASSIFICATION RATE IN THE VALIDATION DATASET. THE CONFUSION MATRIX COMPARES THE MODEL'S PERFORMANCE WITH THE FOLLOWING PARAMETERS: ACCURACY, MISCLASSIFICATION, AND SENSITIVITY. THE RESULTS SHOW THE NEW COMBINATION APPROACH CAN CAPTURE MORE AT-RISK STUDENTS THAN THE SINGULAR PREDICTIVE MODEL, AND IS ONLY SUITABLE FOR THE ENSEMBLE PREDICTIVE ALGORITHMS.
THIS STUDY INVESTIGATES WHETHER LEARNERS CONSIDER CONSTRAINTS WHILE POSING ARITHMETIC WORD PROBLEMS. THROUGH LOG DATA FROM AN INTERACTIVE LEARNING ENVIRONMENT, WE ANALYZED ACTIONS OF 39 FIRST GRADE ELEMENTARY SCHOOL STUDENTS AND CONDUCTED CORRELATION ANALYSIS BETWEEN THE FREQUENCY OF ACTIONS AND VALIDITY OF ACTIONS. THE RESULTS SHOW THAT THE LEARNERS CONSIDER CONSTRAINTS WHILE POSING ARITHMETIC WORD PROBLEMS.
THIS STUDY EXAMINES LEARNING AND USAGE OF THE PLANT SCIENCES E-LIBRARY (PASSEL, PASSEL.UNL.EDU), A LARGE INTERNATIONAL, OPEN-SOURCE MULTIDISCIPLINARY LEARNING OBJECT REPOSITORY (7793 USERS FROM 14 COUNTRIES). THE ANALYSES EMPLOY PREDICTIVE ANALYTICS TO ISOLATE USAGE VARIABLES WHICH PREDICT LEARNING FROM THE INSTRUCTIONAL MATERIAL. SPECIFICALLY, THE STUDY FOCUSES ON STUDENT ENGAGEMENT AS MEASURED BY TOTAL TIME ONLINE AND TIME SPENT WITH DIFFERENT CONTENT MODALITY MATERIAL. THIS PAPER DESCRIBES THE ANALYTIC PROCESS, REPORTS DATA ON USAGE OF LEARNING OBJECT MODULES AND MODULE ELEMENTS, IDENTIFIES SIGNIFICANT PREDICTORS OF LEARNING, AND DISCUSSES FUTURE RESEARCH DIRECTIONS.
WE DEVELOP A MODELING FRAMEWORK FOR JOINT FACTOR AND CLUSTER ANALYSIS OF DATASETS WHERE MULTIPLE CATEGORICAL RESPONSE ITEMS ARE COLLECTED ON A HETEROGENEOUS POPULATION OF INDIVIDUALS. WE INTRODUCE A LATENT FACTOR MULTINOMIAL PROBIT MODEL AND EMPLOY PRIOR CONSTRUCTIONS THAT ALLOW INFERENCE ON THE NUMBER OF FACTORS AS WELL AS CLUSTERING OF THE SUBJECTS INTO HOMOGENEOUS GROUPS ACCORDING TO THEIR RELEVANT FACTORS. CLUSTERING, IN PARTICULAR, ALLOWS US TO BORROW STRENGTH ACROSS SUBJECTS, THEREFORE HELPING IN THE ESTIMATION OF THE MODEL PARAMETERS, PARTICULARLY WHEN THE NUMBER OF OBSERVATIONS IS SMALL. WE EMPLOY MARKOV CHAIN MONTE CARLO TECHNIQUES AND OBTAIN TRACTABLE POSTERIOR INFERENCE FOR OUR OBJECTIVES, INCLUDING SAMPLING OF MISSING DATA. WE DEMONSTRATE THE EFFECTIVENESS OF OUR METHOD ON SIMULATED DATA. WE ALSO ANALYZE TWO REAL-WORLD EDUCATIONAL DATASETS AND SHOW THAT OUR METHOD OUTPERFORMS STATE-OF-THE-ART METHODS. IN THE ANALYSIS OF THE REAL-WORLD DATA, WE UNCOVER HIDDEN RELATIONSHIPS BETWEEN THE QUESTIONS AND THE UNDERLYING EDUCATIONAL CONCEPTS, WHILE SIMULTANEOUSLY PARTITIONING THE STUDENTS INTO GROUPS OF SIMILAR EDUCATIONAL MASTERY. PUBLISHED BY ELSEVIER B.V.
PROGRAMMING KNOWLEDGE IS INCREASINGLY IMPORTANT TO FACILITATE CODE REUSE. NEVERTHELESS, COMPREHENDING ANOTHER PROGRAMMING LANGUAGE IS NOT SIMPLE BECAUSE OF ITS COMPLEXITY AND CLARIFICATION NEEDS. PRIOR WORK FOCUSED ON DIFFERENT LEARNING STYLES TO AID PROGRAMMING, BUT IT WAS IMPORTANT TO IDENTIFY WHICH ONES WERE MORE EFFECTIVE. THIS RESEARCH HIGHLIGHTS FINDINGS IN ASSESSING THE DIFFERENT DOCUMENTATION STYLES, INCLUDING SEQUENTIAL AND GLOBAL DOCUMENTATION STYLES. ORGANIZING AN OBSERVATION OF 125 INTERMEDIATE UNDERGRADUATES PARTICIPATED IN CLOUD HOSTING COMPUTATION AND FILE CONTENT PROGRAMMING EXERCISES, THIS EMPIRICAL INVESTIGATION REVEALED THAT SEQUENTIAL DOCUMENTATION EXHIBITS A POSITIVE IMPACT IN OBTAINING PROGRAMMING KNOWLEDGE, SIGNIFICANTLY PERTAINING FASTER COMPLETION TIME, HIGHER MULTIPLE CHOICE COMPREHENSION, AND FEWER DIFFICULTIES. THIS CONCLUDES THAT SEQUENTIAL DOCUMENTATION SOLUTIONS CAN LEAD INTERMEDIATE UNDERGRADUATES WITH SEQUENTIAL LEARNING STYLES TO FASTER GROWTH IN GAINING PROGRAMMING KNOWLEDGE.
ONLINE LEARNING PLATFORMS, SUCH AS EDX, GENERATE USAGE STATISTICS DATA THAT CAN BE VALUABLE TO EDUCATORS. HOWEVER, HANDLING THIS RAW DATA CAN PROVE CHALLENGING AND TIME CONSUMING FOR INSTRUCTORS AND COURSE DESIGNERS. THE RAW DATA FOR THE MIT COURSES RUNNING ON THE EDX PLATFORM (MITX COURSES) ARE PRE-PROCESSED AND STORED IN A GOOGLE BIGQUERY DATABASE. WE DESIGNED A TOOL BASED ON PYTHON AND ADDITIONAL OPEN-SOURCE PYTHON PACKAGES SUCH AS JUPYTER NOTEBOOK, TO ENABLE INSTRUCTORS TO ANALYZE THEIR STUDENT DATA EASILY AND SECURELY. WE EXPECT THAT INSTRUCTORS WOULD BE ENCOURAGED TO ADOPT MORE EVIDENCE-BASED TEACHING PRACTICES BASED ON THEIR INTERACTION WITH THE DATA.
WE DESCRIBE THREE ITERATIONS OF A MASSIVE OPEN ONLINE COURSE (MOOC) DEVELOPED FROM ONLINE PREPARATION MATERIALS FOR A REFORMED INTRODUCTORY PHYSICS CLASSROOM AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY, IN WHICH THE TEACHING STAFF INTERACT WITH SMALL GROUPS OF STUDENTS DOING PROBLEMS USING AN EXPERT PROBLEM-SOLVING PEDAGOGY. THE MOOC CONTAINS AN E-TEXT, SIMPLE CHECKPOINT PROBLEMS AND HOMEWORK. WE SHOW HOW CERTAIN COURSE DESIGN ASPECTS AFFECT STUDENT BEHAVIOUR: (A) FREQUENT QUIZZES CORRELATED WITH STUDENTS READING A LARGE FRACTION OF THE E-TEXT, AND (B) WHEN HOMEWORK SETS ARE ARRANGED BY INCREASING (INSTRUCTOR-ESTIMATED) DIFFICULTY, WE FOUND STRONG CORRELATIONS BETWEEN DIFFICULTY AND TIME TO SOLUTION, BUT WEAK CORRELATIONS WITH PERCENT CORRECT. MODIFICATIONS TO THE SECOND OFFERING OF THE COURSE RESULTED IN HIGHER RETENTION. THESE MODIFICATIONS INCLUDED TARGETING PHYSICS TEACHERS AND POSTING MATERIALS WELL IN ADVANCE. WE DEFINE RETENTION AS CERTIFICATES EARNED RELATIVE TO PARTICIPANTS WHO MAKE A SIGNIFICANT EFFORT ON THE SECOND ASSIGNMENT. RETENTION MEASURED THIS WAY VARIED BETWEEN 44% AND 72%, BEING HIGHEST IN THE COURSE AIMED AT TEACHERS. WE SHOW THAT THERE IS SIGNIFICANT LEARNING AMONG MOOC PARTICIPANTS. APPLYING ITEM RESPONSE THEORY TO COMMON HOMEWORK PROBLEMS SHOWED THAT THE MOOC PARTICIPANTS HAD SIGNIFICANTLY HIGHER ABILITY THAN STUDENTS IN A MASSACHUSETTS INSTITUTE OF TECHNOLOGY COURSE AND THAT THEY MAINTAINED THIS ADVANTAGE OVER THE DURATION OF THE MOOC.
THE FLIPPED CLASSROOM PEDAGOGY HAS BEEN WIDELY USED RECENTLY. DESPITE MANY RESEARCHES HAVE PAID ATTENTION WITH THE LEARNING OUTCOME OF FLIPPED CLASSROOM, THERE HAS BEEN LIMITED ATTENTION IN REGARD TO INVESTIGATE THE RELATIONSHIP BETWEEN LEARNING BEHAVIOR AND LEARNING OUTCOMES IN A FLIPPED CLASSROOM. IN THIS PAPER, WE PROPOSED TO INVESTIGATE THE INFLUENCE OF ONLINE LEARNING BEHAVIORS ON SHORT-TERM AND LONG-TERM LEARNING OUTCOMES IN A FLIPPED CLASSROOM. THIS STUDY USED CALCULUS AND GRADE POINT AVERAGE (GPA) SCORES TO REPRESENT SHORT-TERM AND LONG-TERM LEARNING OUTCOMES, RESPECTIVELY. MULTIPLE LINEAR REGRESSION INDICATED THAT STUDENTS' ONLINE LEARNING BEHAVIOR DOES NOT HAVE A SIGNIFICANT EFFECT ON SHORT-TERM LEARNING OUTCOMES, BUT HAS A SIGNIFICANT EFFECT ON LONG-TERM LEARNING OUTCOMES. FOR APPLYING MULTIPLE CORRESPONDENCE ANALYSIS, STUDENTS WERE DIVIDED INTO GROUPS ACCORDING TO FIVE GRADE LEVELS BASED ON THEIR SCORES. ACCORDING TO GPA GRADE LEVEL, STUDENTS' ONLINE LEARNING BEHAVIORS HAD A SIGNIFICANT EFFECT ON LONG-TERM LEARNING OUTCOMES FOR THE FIVE GROUPS (GPAA, GPAB, GPAC, GPAD, GPAE). ACCORDING TO THEIR CALCULUS GRADE LEVEL, STUDENTS' ONLINE LEARNING BEHAVIORS HAD A SIGNIFICANT EFFECT ON SHORT-TERM LEARNING OUTCOMES FOR THREE GROUPS (CALA, CALD, AND CALE), BUT TWO GROUPS (CALB AND CALC) DID NOT DEMONSTRATE THIS TREND. FOR EXPLORING THE EFFECTS OF ONLINE LEARNING BEHAVIORS ON FUTURE LEARNING OUTCOMES, GPA CAN BE CONSIDERED REPRESENTATIVE BECAUSE THE GPA WAS CALCULATED FOR THE ENTIRE ACADEMIC YEAR 2015. STUDENTS IN THE CALA GROUP EXHIBITED THE HIGHEST FREQUENCY OF ONLINE LEARNING BEHAVIORS AND OBTAINED THE HIGHEST GPA GRADE LEVELS (GPAA AND GPAB). FOR THE CALB, CALC, CALD, AND CALE GROUPS, STUDENTS WITH A HIGHER FREQUENCY OF ONLINE LEARNING BEHAVIORS OBTAINED A HIGHER GPA GRADE LEVEL. THESE RESULTS INDICATE THAT STUDENTS' ONLINE LEARNING BEHAVIORS HAVE A POSITIVE EFFECT ON FUTURE LEARNING OUTCOMES.
MASSIVE OPEN ONLINE COURSES (MOOCS) REPRESENT IMPORTANT PEDAGOGICAL TRANSFORMATIONS THAT AIM AT UNLIMITED PARTICIPATION AND OPEN ACCESS TO HIGH-QUALITY EDUCATIONAL EXPERIENCES AND RESOURCES. MOOCS ARE QUITE DIFFERENT FROM CLASSICAL DISTANCE LEARNING SETTINGS IN TERMS OF THEIR OPEN-ACCESS FEATURES, SUCH AS OPEN LICENSING OF COURSE CONTENT, AND THEIR USE OF SPECIFIC INFRASTRUCTURES FOR COMMUNICATIONS AND LEARNING TOOLS. STUDIES OVER THE PAST 2 YEARS (2018, 2019) HAVE EXAMINED THE MULTITUDE OF EXPERIMENTAL BUSINESS MODELS FOR MOOCS. HOWEVER, MOOCS HAVE ALSO EXPERIMENTED WITH A WIDE VARIETY OF COURSE CONTENT, TEACHING AND LEARNING METHODS, AND ASSESSMENT STRATEGIES. VERY FEW ARTICLES HAVE SYSTEMATICALLY EXAMINED THESE SIGNIFICANT CHANGES. TO ADDRESS THIS GAP, WE CONDUCTED AN EXTENSIVE, QUALITATIVE META-ANALYSES OF OVER 200 PEER-REVIEWED ARTICLES DESCRIBING RESEARCH ON MOOCS. THIS ANALYSIS IS ORGANIZED AROUND THE FOLLOWING QUESTIONS: (A) HOW CAN WE CLASSIFY EXISTING MOOCS IN TERMS OF THEIR SUPPORT FOR LEARNERS AND LEARNING? (B) WHICH LEARNERS ARE BENEFITING FROM MOOCS, AND TO WHAT DEGREE HAVE MOOCS EXPLORED APPROACHES TO STUDENT-CENTERED LEARNING? (C) TO WHAT DEGREE ARE MOOCS MEETING THE NEEDS OF DIFFERENT LEARNER AUDIENCES? (D) HOW CAN MOOC PROVIDERS AND PARTICIPANTS COMMUNICATE EFFECTIVELY TO SUPPORT STUDENT LEARNING AND ENGAGEMENT? (E) HOW CAN STUDENT ACTIVITIES BE MONITORED TO ENSURE THAT ASSESSMENTS OF LEARNING ARE PERFORMED EFFECTIVELY? THE RESULTS OF THESE ANALYSES PROVIDE A REFLECTION ON THE LAST DECADE OF MOOC RESEARCH AND DISCUSSING THE MAIN ACHIEVEMENTS AND THE MAJOR RESEARCH DIRECTIONS PURSUED IN THOSE YEARS AND POINTS TOWARD AREAS IN NEED OF ADDITIONAL RESEARCH AND DEVELOPMENT.
COMPUTER SCIENCE HAS THE HIGHEST DROPOUT RATE AMONG UNDERGRADUATE STEM DEGREE PROGRAMS [5]. THIS IS ESPECIALLY CONCERNING, GIVEN THAT COMPUTER SCIENCE-RELATED JOBS ARE PROJECTED TO GROW 12% IN THE NEXT SIX YEARS [2]. ONE CONTRIBUTING FACTOR IS THAT MEDIA REPRESENTATIONS OF COMPUTER SCIENCE CAN LEAD UNDERREPRESENTED GROUPS TO PERCEIVE THEMSELVES AS UNFIT FOR THE DISCIPLINE, AND ULTIMATELY TO DROP OUT [4]. TO ADDRESS THIS CONCERN, I PROPOSE AN EMPATHETIC IDE MODEL THAT USES AFFECTIVE COMPUTING TECHNOLOGIES TO PROMOTE EMPATHY AMONG COMPUTER SCIENCE STUDENTS. A QUASI-EXPERIMENTAL RESEARCH DESIGN WILL BE USED TO EVALUATE THE MODEL'S EFFECTIVENESS IN FOSTERING A SUPPORTIVE COMMUNITY BETWEEN INSTRUCTORS AND STUDENTS. BY LEVERAGING EMOTIONAL LEARNING PROCESS DATA AS A FORM OF CONSTANT FEEDBACK TO BOTH INSTRUCTORS AND STUDENTS, THIS RESEARCH CAN GAIN NEW INSIGHTS INTO HOW TO IMPROVE LEARNING ENVIRONMENTS FOR COMPUTER SCIENCE STUDENTS WITH OR WITHOUT AFFECTIVE COMPUTING TECHNOLOGIES.
IN THIS PAPER WE DEAL WITH THE PROBLEM OF FINDING MORE EFFECTIVE WAYS OF ASKING QUESTIONS TO A USER GATHERING PERSONALISED INFORMATION WHICH WILL ALLOW FOR AN EFFECTIVE PROFILING REGARDING THE EDUCATIONAL VARIOUS ASPECTS OF ITS NAVIGATION. OUR AIM IS TO DISCOVER NEW WAYS OF FILLING QUESTIONNAIRES BY WEB USERS, USING INTELLIGENT INFORMATION GATHERING SYSTEM (IIGS) IN RELATION TO THE DISSEMINATION OF CULTURAL CONTENT. OUR APPROACH IS TWO FOLDED, ON ONE SET WE HAVE TO DEVELOP NEW "QUESTIONING TECHNIQUES", WHICH WILL INCREASE RESPONSE RATES, AND ON THE OTHER SIDE USE THE MOST APPROPRIATE ANALYTICAL METHODS IN MEASURING EFFECTIVENESS OF A QUESTIONING TECHNIQUE AND METHODS OF COMPARING VARIOUS TECHNIQUE.
THE LAST DECADE HAS SEEN AN INCREASING INTEREST IN THE USE OF 3D VIRTUAL ENVIRONMENTS FOR EDUCATIONAL APPLICATIONS. HOWEVER, VERY FEW STUDIES INVESTIGATED THE INFLUENCE OF THE LEARNING CONTEXT, SUCH AS CLASS TYPE AND LEARNING TYPE, ON LEARNERS' ACADEMIC PERFORMANCE. THIS PAPER STUDIED THE IMPACT OF CLASS TYPE (I.E. COMPREHENSIVE OR SELECTIVE) CLASSES, AS WELL AS LEARNING TYPE (I.E. GUIDED OR CHALLENGE AND GUIDED), ON STUDENTS' LEVEL OF USAGE OF A VIRTUAL LEARNING ENVIRONMENT (VLE) AS WELL AS ON THEIR ACADEMIC PERFORMANCE. THE RESULTS SHOWED THAT, UNLIKE CLASS TYPE, THERE IS A SIGNIFICANT DIFFERENCE BETWEEN LEARNERS' IN THEIR USAGE OF THE VLE. MOREOVER, THE RESULTS SHOWED THAT THE LEVELS OF USING A VLE SIGNIFICANTLY CORRELATED WITH LEARNERS' ACADEMIC PERFORMANCE.
MASSIVE OPEN ONLINE COURSES (MOOCS) PROVIDE NUMEROUS OPEN-ACCESS LEARNING RESOURCES AND ALLOW FOR SELF-DIRECTED LEARNING. THE APPLICATION OF BIG DATA AND ARTIFICIAL INTELLIGENCE (AI) IN MOOCS HELP COMPREHEND RAW EDUCATIONAL DATA AND ENRICH THE LEARNING PROCESS FOR STUDENTS AND INSTRUCTORS. THUS, WE CREATED TWO DEEP NEURAL NETWORK MODELS. THE FIRST MODEL PREDICTS LEARNING OUTCOMES ON THE BASIS OF LEARNING BEHAVIORS OBSERVED WHEN STUDENTS WATCH VIDEOS. THE SECOND IS A NOVEL EXERCISE-BASED MODEL THAT PREDICTS IF A STUDENT WILL CORRECTLY ANSWER EXAMINATION QUESTIONS ON RELEVANT CONCEPTS. THE STUDY DATA WERE COLLECTED FROM TWO COURSES CONDUCTED ON THE NATIONAL TSING HUA UNIVERSITY'S MOOCS PLATFORM. THE FIRST MODEL ACCURATELY EVALUATED STUDENT PERFORMANCE ON THE BASIS OF THEIR LEARNING BEHAVIORS, AND THE SECOND MODEL EFFICIENTLY PREDICTED STUDENT PERFORMANCE ACCORDING TO HOW THEY ANSWERED THE EXERCISE QUESTIONS. IN CONCLUSION, OUR AI SYSTEM REMEDIES THE PRESENT-DAY INABILITY OF MOOCS TO EVALUATE STUDENT PERFORMANCE. INSTRUCTORS CAN USE THE SYSTEMS TO IDENTIFY POOR-PERFORMING STUDENTS AND OFFER THEM MORE ASSISTANCE ON A TIMELY BASIS.
ATTRITION IS ONE OF THE MAIN CONCERNS IN DISTANCE LEARNING DUE TO THE IMPACT ON THE INCOMES AND INSTITUTIONS REPUTATION. TIMELY IDENTIFICATION OF STUDENTS AT RISK HAS HIGH PRACTICAL VALUE IN EFFECTIVE STUDENTS' RETENTION SERVICES. BIG DATA MINING AND MACHINE LEARNING METHODS ARE APPLIED TO MANIPULATE, ANALYZE AND PREDICT STUDENTS' FAILURE, SUPPORTING SELF-DIRECTED LEARNING. DESPITE THE EXTENSIVE APPLICATION OF DATA MINING TO EDUCATION, THE IMBALANCE PROBLEM IN MINORITY CLASSES OF STUDENTS' ATTRITION IS OFTEN OVERLOOKED IN CONVENTIONAL MODELS. THIS DOCUMENT PROPOSES A LARGE DATA FRAME USING THE HADOOP ECOSYSTEM AND THE APPLICATION OF MACHINE LEARNING TECHNIQUES TO DIFFERENT DATASETS OF AN ACADEMIC YEAR AT THE HELLENIC OPEN UNIVERSITY. DATASETS WERE DIVIDED INTO THIRTY-FIVE WEEKS. THIRTY-TWO CLASSIFIERS WERE CREATED, COMPARED AND STATISTICAL ANALYZED TO ADDRESS THE MINORITY CLASSES' IMBALANCE OF STUDENT'S FAILURE. THE ALGORITHMS METACOST-SMO, AND C4.5 PROVIDE THE MOST ACCURATE PERFORMANCE FOR EACH TARGET CLASS. EARLY PREDICTIONS OF TIMEFRAMES DETERMINE A REMARKABLE PERFORMANCE, WHILE THE IMPORTANCE OF WRITTEN ASSIGNMENTS AND SPECIFIC QUIZZES IS NOTICEABLE. THE MODELS' PERFORMANCE IN ANY WEEK IS EXPLOITED BY DEVELOPING A PREDICTION TOOL FOR STUDENT ATTRITION, CONTRIBUTING TO TIMELY AND PERSONALIZED INTERVENTION.
WHILE IN THE CONTEXT OF CURRENT INTERNATIONAL AND NATIONAL LARGE-SCALE ASSESSMENTS WE ATTEND TO A FAST TRANSITION FROM PAPER-BASED TO COMPUTER-BASED TESTS, SCHOOL'S TEACHERS RARELY USE DIGITAL TESTS TO AWARD HIGH-STAKES GRADES. THAT ADOPTION GAP RAISES CRITICAL QUESTIONS ABOUT HOW MUCH PREPARED ARE CURRENT STUDENTS TO DEAL WITH DIGITAL TESTS AND TO WHAT EXTENT THE TESTING MODE CAN MAKE DIFFERENTIAL EFFECTS ON SOME SUBGROUPS. WHILE MOST OF STUDIES ABOUT MODE EFFECTS ARE BASED ON DATA FROM STANDARDIZED TESTS, LITTLE ATTENTION HAS BEEN PAID TO ALTERNATIVE ANALYSIS BASED ON DEPLOYMENTS WITHIN REAL SCHOOL'S SETTINGS. IN THIS PAPER, WE USE END-OF-COURSE SCHOOL GRADES AWARDED TO TWO COHORTS OF SECONDARY STUDENTS WHICH WERE EXAMINED EITHER THROUGH COMPUTERS OR PAPER-AND-PENCIL TESTS IN SCIENCE DISCIPLINES. AFTER CONVERTING THEM INTO PERCENTILE SCORES, WE STUDIED THE EFFECTS OF THE ADMINISTRATION MODE IN STUDENT'S RANKS THROUGH TESTS OF STATISTICAL SIGNIFICANCE AND EQUIVALENCE. IT WAS FOUND THAT, AT OVERALL LEVEL, FEMALES EARNED HIGHER GRADES THAN MALES IN SCIENCE AND THESE DIFFERENCES WERE STATISTICALLY SIGNIFICANT. INTERESTINGLY, WE ALSO FOUND THAT FEMALES AND MALES PERFORMED SIMILARLY IN BOTH TESTING MODES SUGGESTING THAT THE GENDER GAP IN SCIENCE ACHIEVEMENT WOULD NOT BE AFFECTED BY THE ASSESSMENT MODE.
THIS RESEARCH WAS AIMED AT DEVELOPING METHODS FOR ESTIMATING ERROR FACTORS IN PROGRAMMING EXERCISES SO THAT PROFESSORS COULD EFFECTIVELY GUIDE STUDENTS BASED ON AN UNDERSTANDING OF STUMBLING BY NOVICE STUDENTS. WE FIRST DEVELOPED A METHOD FOR SIMULTANEOUSLY ESTIMATING COMPILATION AND LOGIC ERRORS BY USING CASE-BASES. MOREOVER, WE DEVELOPED A MECHANISM FOR DYNAMICALLY REVISING ERROR FACTOR CASE-BASES BY USING STUDENTS' FEEDBACK:-EVALUATIONS AGAINST THE ESTIMATED ERROR FACTORS AND THEIR REPORTS ON NEW ERROR FACTORS. WE THEN DESIGNED A SUPPORT SYSTEM OF PROGRAMMING LANGUAGE EXERCISES BY INTRODUCING OUR METHODS. THIS SYSTEM ENABLED MORE PRACTICAL INDICATIONS OF ERROR FACTORS THAN THE EXISTING SYSTEMS. FEATURES OF OUR METHODS ARE DISCUSSED BASED ON THE RESULTS OF EXPERIMENTS.
VALUE CO-CREATION IN EDUCATION SERVICE WITH AN ICT UTILIZED ENVIRONMENT IS ARGUED. SERVICE PROVIDER (TEACHER) DOES DATA COLLECTION AND ANALYSIS ON LEARNING BEHAVIOR OF EACH SERVICE RECEIVER (STUDENTS), PROPOSES THE IMPROVED SERVICE CONTENT SUCCESSIVELY BASED ON THE RESULT OF ANALYSIS, AND GRADUALLY IMPROVES HIS COMPETENCY. THIS PAPER DESCRIBES SOME METHODOLOGIES OF LEARNING BEHAVIOR DATA COLLECTION AND ANALYSIS.
THE COVID-19 PANDEMIC CAUSED LOCKDOWNS AND SUDDEN SCHOOL CLOSURES AROUND THE WORLD IN SPRING 2020, SIGNIFICANTLY IMPACTING THE EDUCATION OF STUDENTS. HERE, WE INVESTIGATE HOW THE SWITCH TO DISTANCE LEARNING AFFECTED STUDY ACTIVITY AND PERFORMANCE IN AN ONLINE RETRIEVAL PRACTICE TOOL USED FOR LANGUAGE LEARNING IN DUTCH SECONDARY EDUCATION. WE REPORT INSIGHTS FROM A RICH DATA SET CONSISTING OF OVER 115 MILLION RETRIEVAL PRACTICE TRIALS COMPLETED BY MORE THAN 133 THOUSAND STUDENTS OVER THE COURSE OF TWO CONSECUTIVE SCHOOL YEARS. OUR FINDINGS SHOW THAT USAGE OF THE TOOL INCREASED SUBSTANTIALLY AT THE START OF LOCKDOWN, WITH THE BULK OF STUDY ACTIVITY OCCURRING ON WEEKDAY MORNINGS. IN GENERAL, STUDENTS' PROGRESS THROUGH THE MATERIAL WAS LARGELY UNAFFECTED BY LOCKDOWN, ALTHOUGH STUDENTS FROM THE HIGHEST EDUCATIONAL TRACK WERE SOMEWHAT MORE LIKELY TO BE ON OR AHEAD OF SCHEDULE THAN STUDENTS FROM LOWER TRACKS, COMPARED TO THE PREVIOUS YEAR. PERFORMANCE ON INDIVIDUAL STUDY TRIALS WAS GENERALLY STABLE, BUT ACCURACY AND RESPONSE TIME ON OPEN ANSWER QUESTIONS WENT UP, PERHAPS AS A RESULT OF STUDENTS BEING MORE FOCUSED AT HOME. THESE ENCOURAGING FINDINGS CONTRIBUTE TO A GROWING LITERATURE ON THE EDUCATIONAL RAMIFICATIONS OF DISTANCE LEARNING DURING LOCKDOWN.
THIS PAPER AIMS TO INVESTIGATE STUDENTS' BEHAVIORAL ENGAGEMENT (ONTASK VS. OFF-TASK) IN AUTHENTIC CLASSROOMS. WE PROPOSE A TWO-PHASED APPROACH FOR AUTOMATIC ENGAGEMENT DETECTION: IN PHASE 1, CONTEXTUAL LOGS ARE UTILIZED TO ASSESS ACTIVE USAGE OF THE CONTENT PLATFORM. IF THERE IS ACTIVE USE, THE APPEARANCE INFORMATION IS UTILIZED IN PHASE 2 TO INFER BEHAVIORAL ENGAGEMENT. THROUGH AUTHENTIC CLASSROOM PILOTS, WE COLLECTED AROUND 170 HOURS OF IN-THE-WILD DATA FROM 28 STUDENTS IN TWO DIFFERENT CLASSROOMS USING TWO DIFFERENT CONTENT PLATFORMS (ONE FOR MATH AND ONE FOR ENGLISH AS A SECOND LANGUAGE (ESL)). OUR DATA COLLECTION APPLICATION CAPTURED APPEARANCE DATA FROM A 3D CAMERA AND CONTEXT DATA FROM UNIFORM RESOURCE LOCATOR (URL) LOGS. WE EXPERIMENTED WITH TWO TEST CASES: (1) CROSS-CLASSROOM, WHERE TRAINED MODELS WERE TESTED ON A DIFFERENT CLASSROOM'S DATA; (2) CROSS-PLATFORM, WHERE THE DATA COLLECTED IN DIFFERENT SUBJECT AREAS (MATH OR ESL) WERE UTILIZED IN TRAINING AND TESTING, RESPECTIVELY. FOR THE FIRST CASE, THE BEHAVIORAL ENGAGEMENT WAS DETECTED WITH AN F1-SCORE OF 77%, USING ONLY APPEARANCE. INCORPORATING THE CONTEXTUAL INFORMATION IMPROVED THE OVERALL PERFORMANCE TO 82%. FOR THE SECOND CASE, EVEN THOUGH THE SUBJECT AREAS AND CONTENT PLATFORMS CHANGED, THE PROPOSED APPEARANCE CLASSIFIER STILL ACHIEVED 72% ACCURACY (COMPARED TO 77%). OUR EXPERIMENTS PROVED THAT THE ACCURACY OF THE PROPOSED MODEL IS NOT ADVERSELY IMPACTED CONSIDERING DIFFERENT SET OF STUDENTS OR DIFFERENT SUBJECT AREAS.
IT IS NOT POSSIBLE TO DIRECTLY OBSERVE HOW STUDENTS WORK IN AN ONLINE PROGRAMMING COURSE. THIS MAKES IT HARDER FOR TEACHERS TO HELP STRUGGLING STUDENTS. BY USING AN ONLINE PROGRAMMING ENVIRONMENT, WE HAVE THE OPPORTUNITY TO RECORD WHAT THE STUDENTS ACTUALLY DO TO SOLVE AN ASSIGNMENT. THESE RECORDINGS CAN BE ANALYZED TO PROVIDE TEACHERS WITH VALUABLE INFORMATION. WE DEVELOPED SUCH AN ONLINE PROGRAMMING TOOL WITH FINE-GRAINED EVENT LOGGING AND USED IT TO OBSERVE HOW OUR STUDENTS SOLVE PROBLEMS. OUR TOOL PROVIDES DESCRIPTIVE STATISTICS AND ACCURATE REPLAYS OF A STUDENT'S PROGRAMMING SESSIONS, INCLUDING MOUSE MOVEMENTS. WE USED THE TOOL IN A COURSE AND COLLECTED 1028 DETAILED RECORDINGS. IN THIS ARTICLE, WE COMPARE FINE-GRAINED LOGGING TO EXISTING COARSE-GRAINED LOGGING SOLUTIONS TO ESTIMATE ASSIGNMENT-SOLVING TIME. WE FIND THAT TIME AGGREGATIONS ARE IMPROVED BY INCLUDING TIME FOR ACTIVE READING AND NAVIGATION, BOTH ENABLED BY THE INCREASED GRANULARITY. WE ALSO DIVIDE THE TIME USERS SPENT INTO EDITING (ON AVERAGE 14.8%), ACTIVE USE (ON AVERAGE 37.8%), PASSIVE USE (ON AVERAGE 29.0%), AND ESTIMATE TIME USED FOR BREAKS (ON AVERAGE 18.2%). THERE IS A CORRELATION BETWEEN ASSIGNMENT SOLVING TIME FOR STUDENTS WHO PASS ASSIGNMENTS EARLY AND STUDENTS THAT PASS LATER BUT ALSO A CASE WHERE THE TIMES DIFFER SIGNIFICANTLY. OUR TOOL CAN HELP IMPROVE COMPUTER ENGINEERING EDUCATION BY PROVIDING INSIGHTS INTO HOW STUDENTS SOLVE PROGRAMMING ASSIGNMENTS AND THUS ENABLE TEACHERS TO TARGET THEIR TEACHING AND/OR IMPROVE INSTRUCTIONS AND ASSIGNMENTS.
WE PROPOSE SPARFA-TRACE, A NEW MACHINE LEARNING-BASED FRAMEWORK FOR TIME-VARYING LEARNING AND CONTENT ANALYTICS FOR EDUCATIONAL APPLICATIONS. WE DEVELOP A NOVEL MESSAGE PASSING-BASED, BLIND, APPROXIMATE KALMAN FILTER FOR SPARSE FACTOR ANALYSIS (SPARFA) THAT JOINTLY TRACES LEARNER CONCEPT KNOWLEDGE OVER TIME, ANALYZES LEARNER CONCEPT KNOWLEDGE STATE TRANSITIONS (INDUCED BY INTERACTING WITH LEARNING RESOURCES, SUCH AS TEXTBOOK SECTIONS, LECTURE VIDEOS, ETC., OR THE FORGETTING EFFECT), AND ESTIMATES THE CONTENT ORGANIZATION AND DIFFICULTY OF THE QUESTIONS IN ASSESSMENTS. THESE QUANTITIES ARE ESTIMATED SOLELY FROM BINARY-VALUED (CORRECT/INCORRECT) GRADED LEARNER RESPONSE DATA AND THE SPECIFIC ACTIONS EACH LEARNER PERFORMS (E.G., ANSWERING A QUESTION OR STUDYING A LEARNING RESOURCE) AT EACH TIME INSTANT. EXPERIMENTAL RESULTS ON TWO ONLINE COURSE DATASETS DEMONSTRATE THAT SPARFA-TRACE IS CAPABLE OF TRACING EACH LEARNER'S CONCEPT KNOWLEDGE EVOLUTION OVER TIME, ANALYZING THE QUALITY AND CONTENT ORGANIZATION OF LEARNING RESOURCES, AND ESTIMATING THE QUESTION-CONCEPT ASSOCIATIONS AND THE QUESTION DIFFICULTIES. MOREOVER, WE SHOW THAT SPARFA-TRACE ACHIEVES COMPARABLE OR BETTER PERFORMANCE IN PREDICTING UNOBSERVED LEARNER RESPONSES COMPARED TO EXISTING COLLABORATIVE FILTERING AND KNOWLEDGE TRACING METHODS.
ATTRITION IS ONE OF THE MAIN CONCERNS IN DISTANCE LEARNING DUE TO THE IMPACT ON THE INCOMES AND INSTITUTIONS REPUTATION. TIMELY IDENTIFICATION OF STUDENTS AT RISK HAS HIGH PRACTICAL VALUE IN EFFECTIVE STUDENTS' RETENTION SERVICES. BIG DATA MINING AND MACHINE LEARNING METHODS ARE APPLIED TO MANIPULATE, ANALYZE, AND PREDICT STUDENTS' FAILURE, SUPPORTING SELF-DIRECTED LEARNING. DESPITE THE EXTENSIVE APPLICATION OF DATA MINING TO EDUCATION, THE IMBALANCE PROBLEM IN MINORITY CLASSES OF STUDENTS' ATTRITION IS OFTEN OVERLOOKED IN CONVENTIONAL MODELS. THIS DOCUMENT PROPOSES A LARGE DATA FRAME USING THE HADOOP ECOSYSTEM AND THE APPLICATION OF MACHINE LEARNING TECHNIQUES TO DIFFERENT DATASETS OF AN ACADEMIC YEAR AT THE HELLENIC OPEN UNIVERSITY. DATASETS WERE DIVIDED INTO 35 WEEKS; 32 CLASSIFIERS WERE CREATED, COMPARED AND STATISTICALLY ANALYZED TO ADDRESS THE MINORITY CLASSES' IMBALANCE OF STUDENT'S FAILURE. THE ALGORITHMS METACOST-SMO AND C4.5 PROVIDE THE MOST ACCURATE PERFORMANCE FOR EACH TARGET CLASS. EARLY PREDICTIONS OF TIMEFRAMES DETERMINE A REMARKABLE PERFORMANCE, WHILE THE IMPORTANCE OF WRITTEN ASSIGNMENTS AND SPECIFIC QUIZZES IS NOTICEABLE. THE MODELS' PERFORMANCE IN ANY WEEK IS EXPLOITED BY DEVELOPING A PREDICTION TOOL FOR STUDENT ATTRITION, CONTRIBUTING TO TIMELY AND PERSONALIZED INTERVENTION.
STUDENTS' PERSONAL QUALITIES OTHER THAN COGNITIVE ABILITY ARE KNOWN TO INFLUENCE PERSISTENCE AND ACHIEVEMENT IN FORMAL LEARNING ENVIRONMENTS, BUT THE EXTENT OF THEIR INFLUENCE IN DIGITAL LEARNING ENVIRONMENTS IS UNCLEAR. THIS RESEARCH INVESTIGATES NON-COGNITIVE FACTORS IN MOBILE LEARNING IN A RESOURCE-POOR CONTEXT. WE SURVEYED 1,000 KENYAN HIGH SCHOOL STUDENTS WHO USE A POPULAR SMS-BASED LEARNING PLATFORM THAT PROVIDES FORMATIVE ASSESSMENTS ALIGNED WITH THE NATIONAL CURRICULUM. COMBINING SURVEY RESPONSES WITH PLATFORM INTERACTION LOGS, WE FIND GROWTH MINDSET TO BE ONE OF THE STRONGEST PREDICTORS OF ASSESSMENT SCORES. WE INVESTIGATE THEORY-BASED BEHAVIORAL MECHANISMS TO EXPLAIN THIS RELATIONSHIP. ALTHOUGH STUDENTS WHO HOLD A GROWTH MINDSET ARE NOT MORE LIKELY TO PERSIST AFTER FACING ADVERSITY, THEY SPEND MORE TIME ON EACH ASSESSMENT, INCREASING THEIR LIKELIHOOD OF ANSWERING CORRECTLY. RESULTS SUGGEST THAT CULTIVATING A GROWTH MINDSET CAN MOTIVATE STUDENTS IN A RESOURCE-POOR CONTEXT TO EXCEL IN A MOBILE LEARNING ENVIRONMENT.
COLLABORATION IS AN IMPORTANT SKILL OF THE 21ST CENTURY. IT CAN TAKE PLACE IN AN ONLINE (OR REMOTE) SETTING OR IN A CO LOCATED (OR FACE-TO-FACE) SETTING. WITH THE LARGE SCALE ADOPTION OF SENSOR USE, STUDIES ON CO-LOCATED COLLABORATION (CC) HAS GAINED MOMENTUM. CC TAKES PLACE IN PHYSICAL SPACES WHERE THE GROUP MEMBERS SHARE EACH OTHER'S SOCIAL AND EPISTEMIC SPACE. THIS INVOLVES SUBTLE MULTIMODAL INTERACTIONS SUCH AS GAZE, GESTURES, SPEECH, DISCOURSE WHICH ARE COMPLEX IN NATURE. THE AIM OF THIS PHD IS TO DETECT THESE INTERACTIONS AND THEN USE THESE INSIGHTS TO BUILD AN AUTOMATED REAL-TIME FEEDBACK SYSTEM TO FACILITATE CO-LOCATED COLLABORATION.
THE INCREASED AVAILABILITY OF VAST AND HIGHLY VARIED AMOUNTS OF DATA FROM LEARNERS, TEACHERS, LEARNING ENVIRONMENTS, AND ADMINISTRATIVE SYSTEMS WITHIN EDUCATIONAL SETTINGS IS OVERWHELMING. THE FOCUS OF THIS CHAPTER IS ON HOW DATA WITH A LARGE NUMBER OF RECORDS, OF WIDELY DIFFERING DATATYPES, AND ARRIVING RAPIDLY FROM MULTIPLE SOURCES CAN BE HARNESSED FOR MEANINGFUL ASSESSMENTS AND SUPPORTING LEARNERS IN A WIDE VARIETY OF LEARNING SITUATIONS. DISTINCT FEATURES OF ANALYTICS-DRIVEN ASSESSMENTS MAY INCLUDE SELF-ASSESSMENTS, PEER ASSESSMENTS, AND SEMANTIC RICH AND PERSONALIZED FEEDBACK AS WELL AS ADAPTIVE PROMPTS FOR REFLECTION. THE CHAPTER CONCLUDES WITH FUTURE DIRECTIONS IN THE BROAD AREA OF ANALYTICS-DRIVEN ASSESSMENTS FOR TEACHERS AND EDUCATIONAL RESEARCHERS.
THE PURPOSE OF THIS PAPER IS TO EXPLORE THE CHARACTERISTICS OF TEACHING ASSISTANTS' INTERVENTIONS IN MASSIVE OPEN ONLINE COURSES (MOOCS). THE STUDY OF INTERACTION PATTERNS IN MOOCS CAN INFORM THE DESIGN AND DEVELOPMENT OF MECHANISMS FOR MORE EFFECTIVE LEARNING. WE PERFORMED AN ANALYSIS OF TEACHING ASSISTANTS' INTERACTIONS IN TWO DIFFERENT MOOCS IN DIFFERENT SUBJECT MATTERS (TECHNOLOGY AND HUMANITIES). THE STUDY REVEALED NOTABLE DIFFERENCES IN THE CHARACTERISTICS OF TEACHING ASSISTANTS' INTERVENTIONS WITHIN THE TWO COURSES, IN TERMS OF LANGUAGE USED, LENGTH OF MESSAGES, RESPONSE TIME AND LENGTH OF DISCOURSE. THE FINDINGS OF THIS STUDY PROVIDE US WITH USEFUL INSIGHTS ON CURRENT HUMAN INTERVENTIONS IN MOOC FORUMS, DRIVING OUR EFFORT IN DESIGNING FUTURE MODULES TO SUPPORT THE LARGE NUMBER OF STUDENTS OF THESE COURSES, WITH THE ULTIMATE OBJECTIVE TO IMPROVE THE LEARNING EXPERIENCE FOR EACH INDIVIDUAL STUDENT OF FUTURE MOOCS.
FEATURED APPLICATION THIS WORK HAS AN IMPORTANT DIRECT APPLICATION FOR TEACHERS OR EDUCATIONAL INSTITUTIONS WORKING WITH MOODLE, BECAUSE IT PROVIDES AN OPEN ACCESS SOFTWARE APPLICATION, UBUMONITOR, WHICH FACILITATES THE DETECTION OF STUDENTS AT RISK. IN THIS STUDY, WE USED A MODULE FOR MONITORING AND DETECTING STUDENTS AT RISK OF DROPPING OUT. WE WORKED WITH A SAMPLE OF 49 THIRD-YEAR STUDENTS IN A HEALTH SCIENCE DEGREE DURING A LOCKDOWN CAUSED BY COVID-19. THREE FOLLOW-UPS WERE CARRIED OUT OVER A SEMESTER: AN INITIAL ONE, AN INTERMEDIATE ONE AND A FINAL ONE WITH THE UBUMONITOR TOOL. THIS TOOL IS A DESKTOP APPLICATION EXECUTED ON THE CLIENT, IMPLEMENTED WITH JAVA, AND WITH A GRAPHIC INTERFACE DEVELOPED IN JAVAFX. THE APPLICATION CONNECTS TO THE SELECTED MOODLE SERVER, THROUGH THE WEB SERVICES AND THE REST API PROVIDED BY THE SERVER. UBUMONITOR INCLUDES, AMONG OTHERS, MODULES FOR LOG VISUALISATION, RISK OF DROPPING OUT, AND CLUSTERING. THE VISUALISATION TECHNIQUES OF BOXPLOTS AND HEAT MAPS AND THE CLUSTER ANALYSIS MODULE (K-MEANS ++, FUZZY K-MEANS AND DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE (DBSCAN) WERE USED TO MONITOR THE STUDENTS. A TEACHING METHODOLOGY BASED ON PROJECT-BASED LEARNING (PBL), SELF-REGULATED LEARNING (SRL) AND CONTINUOUS ASSESSMENT WAS ALSO USED. THE RESULTS INDICATE THAT THE USE OF THIS METHODOLOGY TOGETHER WITH EARLY DETECTION AND PERSONALISED INTERVENTION IN THE INITIAL FOLLOW-UP OF STUDENTS ACHIEVED A DROP-OUT RATE OF LESS THAN 7% AND AN OVERALL LEVEL OF STUDENT SATISFACTION WITH THE TEACHING AND LEARNING PROCESS OF 4.56 OUT OF 5.
THIS IS A DEMO OF A VIRTUAL ACADEMIC ADVISER THAT ENABLES STUDENT SELF-GUIDANCE AND COUNTERS THE UNAVAILABILITY OF TRUE ACADEMIC ADVISERS. THE ADVISER INTEGRATES FOUR VISUAL TOOLS: FLEXIBLE AND PERSONALIZED PLANNING OF FUTURE TERMS AND COURSES, BODY OF KNOWLEDGE ACQUISITION MAPPING TOOL FOR EASIER FINDING OF COURSES THAT ARE ALIGNED TO PERSONAL INTERESTS IN TERMS OF THE ACQUIRED KNOWLEDGE AND ABILITIES, AND STUDY PROGRAM SWITCH EXPLORATION TOOL TO EASE STUDENT MOBILITY.
SELF-REGULATED LEARNING (SRL) HAS BECOME ONE OF THE MOST SUITABLE APPROACHES TO LEARNING AND TEACHING FOR THE DEVELOPMENT OF COMPETENT AND AUTONOMOUS LIFELONG LEARNERS. A MAIN ISSUE IS TO SUPPORT LEARNERS SETTING UP THEIR OWN LEARNING GOALS, MANAGING THEIR OWN RESOURCES, INCLUDING SPACES AND TIME, AND MONITORING AND REFLECTING ABOUT THEIR PERFORMANCE. TIME MANAGEMENT PLAYS A MAIN ROLE IN THIS APPROACH. DURING THE LAST YEARS MANY WEB TOOLS AND MOBILE APPS HAVE BEEN DEVELOPED TO SUPPORT STUDENTS MANAGING THEIR AGENDAS, RECORDING THE TIME THEY DEVOTE TO DIFFERENT ACTIVITIES AND VISUALIZING IT IN ATTRACTIVE GRAPHICS. IN GENERAL THESE SYSTEMS PROVIDE A VIEW OF THE ACTIVITIES PERFORMED BY THE STUDENT IN A PARTICULAR SYSTEM AND THE TIME DEVOTED TO EACH ACTIVITY. IN THIS PAPER WE PROPOSE A CONTRIBUTION ON THIS SUBJECT FOCUSED ON TWO ISSUES: SELF-REGULATION AND INTEROPERABILITY. WE ARE WORKING TOWARDS THE RECORDING OF ONLINE AND OFFLINE STUDENT ACTIVITIES NOT JUST TAKING INTO ACCOUNT THE FUNCTIONALITIES OR RESOURCES USED, BUT ALSO THE GOALS, STRATEGIES, TASKS OR REWARDS CONSIDERED FROM A SRL POINT OF VIEW. FROM THE INTEROPERABILITY POINT OF VIEW, OUR GOAL IS TO SUPPORT THE TRACKING FROM DIFFERENT SYSTEMS USING A SPECIFIC SRL XAPI PROFILE.
ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD) IS A MENTAL HEALTH DISORDER. PEOPLE DIAGNOSED WITH ADHD ARE OFTEN INATTENTIVE (HAVE DIFFICULTY FOCUSING ON A TASK FOR A CONSIDERABLE PERIOD OF TIME), OVERLY IMPULSIVE (MAKE RASH DECISIONS), AND ARE HYPERACTIVE (MOVING EXCESSIVELY, OFTEN AT INAPPROPRIATE TIMES). ADHD IS OFTEN DIAGNOSED THROUGH PSYCHIATRIC ASSESSMENTS WITH ADDITIONAL INPUT FROM PHYSICAL/NEUROLOGICAL EVALUATIONS. CURRENT TOOLS DESIGNED FOR ADHD SCREENING COLLECT DATA MANUALLY AND DO NOT INTEROPERATE WITH EACH OTHER. THIS PAPER WILL FIRST REVIEW THE EFFECTIVENESS OF COMMON SCREENING TOOLS IN RELATION TO THE DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS (DSM) FOR ADHD CLASSIFIER. THIS PAPER WILL ALSO INTRODUCE THE CONCEPT OF USING WRITTEN PERFORMANCE DATA AS A METHOD OF SCREENING, SINCE PREVIOUS RESEARCH HAS LINKED WRITTEN LANGUAGE DISORDER (WLD) TO ADHD AS WELL. THE CURRENT PHASE OF THIS RESEARCH PROPOSES THAT AN INTEGRATED COMPUTATIONAL MODEL THAT COMBINES OUTCOMES FROM THESE SCREENING TOOLS WILL HAVE A MORE EFFECTIVE DIAGNOSIS OF ADHD IN ADULT STUDENTS THAN FROM THE DIAGNOSIS OF ANY INDIVIDUAL SCREENING TOOL. THE INTEGRATED COMPUTATIONAL MODEL, BASED ON NEURAL NETWORKS, WILL BE BUILT AND TESTED IN A FUTURE PHASE WITH EACH OF THE DATASETS (PHYSICAL, BEHAVIOR AND LEARNING PERFORMANCE) BEING COLLECTED FROM STUDENTS.
THE AIM OF THIS PAPER IS TO EXPLORE AMBIENT LEARNING AND EMERGENT LEARNING IN THE CONTEXT OF EMERGING AND NEXT GENERATION TECHNOLOGIES SUCH AS WIRELESS GRIDS. USING WEJAY, AN EARLY PRE-STANDARDS SOCIAL RADIO TOOL, RESEARCH WITH FACULTY AND STUDENTS PROVIDES INSIGHT INTO THE POTENTIAL OF WIRELESS GRIDS FOR LEARNING IN DISTRIBUTED TECHNOLOGY-PERVASIVE ENVIRONMENTS. FINDINGS ARE INTERPRETED IN TERMS OF THEIR IMPLICATIONS FOR EDUCATIONAL, RESEARCH, AND PRACTICE SETTINGS. EXPANDED NOTIONS OF WORKPLACE, EDUCATIONAL SPACES, AND PLACES OF RESEARCH ARE ADVANCED TOGETHER WITH THE VALUE AND IMPORTANCE OF LEVERAGING ASSOCIATED LEARNING AND PREDICTIVE ANALYTICS DATA. THIS PAPER CONTRIBUTES TO RESEARCH ON: AMBIENT AND EMERGENT LEARNING, PERVASIVE LEARNING SPACES (PLSS), AMBIENT INTELLIGENCE (AMI), AND WIRELESS GRIDS. RESPONDING TO EARLIER CALLS FOR A NEW RESEARCH AGENDA AROUND LEARNING IN TECHNOLOGYPERVASIVE ENVIRONMENTS FOR THE 21ST CENTURY, THIS PAPER ELUCIDATES A NEW LEARNING APPROACH BASED ON AMBIENT INTELLIGENCE (AMI) WITH WIRELESS GRIDS.
THE INTERACTIONS THAT STUDENTS HAVE WITH EACH OTHER, WITH THE INSTRUCTORS, AND WITH EDUCATIONAL RESOURCES ARE VALUABLE INDICATORS OF THE EFFECTIVENESS OF A LEARNING EXPERIENCE. THE INCREASING USE OF INFORMATION AND COMMUNICATION TECHNOLOGY ALLOWS THESE INTERACTIONS TO BE RECORDED SO THAT ANALYTIC OR MINING TECHNIQUES ARE USED TO GAIN A DEEPER UNDERSTANDING OF THE LEARNING PROCESS AND PROPOSE IMPROVEMENTS. BUT WITH THE INCREASING VARIETY OF TOOLS BEING USED, MONITORING STUDENT PROGRESS IS BECOMING A CHALLENGE. THE PAPER ANSWERS TWO QUESTIONS. THE FIRST ONE IS HOW FEASIBLE IS TO MONITOR THE LEARNING ACTIVITIES OCCURRING IN A STUDENT PERSONAL WORKSPACE. THE SECOND IS HOW TO USE THE RECORDED DATA FOR THE PREDICTION OF STUDENT ACHIEVEMENT IN A COURSE. TO ADDRESS THESE RESEARCH QUESTIONS, THE PAPER PRESENTS THE USE OF VIRTUAL APPLIANCES, A FULLY FUNCTIONAL COMPUTER SIMULATED OVER A REGULAR ONE AND CONFIGURED WITH ALL THE REQUIRED TOOLS NEEDED IN A LEARNING EXPERIENCE. STUDENTS CARRY OUT ACTIVITIES IN THIS ENVIRONMENT IN WHICH A MONITORING SCHEME HAS BEEN PREVIOUSLY CONFIGURED. A CASE STUDY IS PRESENTED IN WHICH A COMPREHENSIVE SET OF OBSERVATIONS WERE COLLECTED. THE DATA IS SHOWN TO HAVE SIGNIFICANT CORRELATION WITH STUDENT ACADEMIC ACHIEVEMENT THUS VALIDATING THE APPROACH TO BE USED AS A PREDICTION MECHANISM. FINALLY A PREDICTION MODEL IS PRESENTED BASED ON THOSE OBSERVATIONS WITH THE HIGHEST CORRELATION. (C) 2011 ELSEVIER LTD. ALL RIGHTS RESERVED.
FEEDBACK IS A VITAL ELEMENT FOR EFFECTIVE LEARNING AS IT HELPS STUDENTS TO UNDERSTAND THE SUBJECT BEING STUDIED AND GIVE THEM CLEAR INSTRUCTIONS ON HOW TO IMPROVE THEIR LEARNING. IT HAS ALSO BEEN STATED THAT FEEDBACK IS STRONGLY RELATED TO STUDENT ACHIEVEMENT AND IMPROVE THE SELF-AWARENESS, ENTHUSIASM AND MOTIVATION OF STUDENTS FOR LEARNING. AS A RESULT, IT IS A CHALLENGING PROBLEM FOR MODERN ONLINE LEARNING SYSTEMS TO PROVIDE SUITABLE FEEDBACK TO STUDENTS THAT IS TAILORED TO THEIR LEARNING NEEDS AND SUPPORT DIFFERENT ASPECTS OF THEIR LEARNING. IN PARTICULAR, IN THIS PAPER WE DESCRIBE HOW WE HAVE USED PERSONALISED FEEDBACK AND INTERVENTIONS, THAT ARE AUTOMATICALLY TRIGGERED BY THE LEARNING ENVIRONMENT AT DIFFERENT COURSE PHASES, IN ORDER TO LEVERAGE THE LEARNING BEHAVIOUR OF STUDENTS AND DRAW THEIR ATTENTION AND ENGAGEMENT WITH THE ONLINE COURSE.
OUR UNIVERSITY HAS OFFERED A MASSIVE EDUCATIONAL VIDEO SERVICE SINCE 2010, AS PART OF A BLENDED LEARNING MODEL THAT ALLOWS STUDENTS TO BALANCE ACTIVE PARTICIPATION IN THE CLASSROOM WITH REMOTE ACCESS TO VIDEO-RECORDED LECTURES. IN THESE YEARS, WE HAVE COLLECTED A HUGE AMOUNT OF VERY DETAILED DATA ABOUT THE STUDENTS' ACCESS TO THE SERVICE. TOGETHER WITH ADDITIONAL INFORMATION THAT CHARACTERIZE A UNIVERSITY SYSTEM (E.G. STUDENTS' PERFORMANCE OR COURSE POPULATION), THESE DATA REPRESENT A PRECIOUS GROUND SET TO ASSESS THE EDUCATIONAL MODEL. THE PAPER DESCRIBES AN EXPERIMENTAL SET TO PROFILE THE USE OF THE EDUCATIONAL VIDEO SERVICE, WHOSE RESULTS WILL CONTRIBUTE TO IMPROVE THE MODEL. SPECIFICALLY THE PAPER ANALYZES THE STUDENTS' SERVICE USE RELATIVELY TO DIFFERENT TRANSVERSAL COURSE CHARACTERISTICS, SUCH AS LEVEL, MAIN TOPIC, POPULATION, SUCCESS RATE. AS A RESULT, IT OUTLINES THE PROFILE OF THE "IDEAL" COURSES FOR WHICH STUDENTS HIGHLY APPRECIATE THE SERVICE. THIS INFORMATION WILL HELP EDUCATIONAL DESIGNERS TO SELECT THE FUTURE COURSES TO BE INCLUDED IN THE SERVICE, BUT IT WILL ALSO GIVE DIRECTIONS ON THE SECTORS WHERE IMPROVEMENTS ARE NECESSARY. FINALLY, THE PAPER EXPERIMENTALLY DEMONSTRATES A POSITIVE IMPACT OF THE EDUCATIONAL VIDEO SERVICE ON STUDENTS' PERFORMANCE, AND SPECIFICALLY ON THE EXAM SUCCESS RATE.
PEDAGOGICALLY INFORMED DESIGNS OF LEARNING ARE INCREASINGLY OF INTEREST TO RESEARCHERS IN BLENDED AND ONLINE LEARNING, AS LEARNING DESIGN IS SHOWN TO HAVE AN IMPACT ON STUDENT BEHAVIOUR AND OUTCOMES. ALTHOUGH LEARNING DESIGN IS WIDELY STUDIED, OFTEN THESE STUDIES ARE INDIVIDUAL COURSES OR PROGRAMMES AND FEW EMPIRICAL STUDIES HAVE CONNECTED LEARNING DESIGNS OF A SUBSTANTIAL NUMBER OF COURSES WITH LEARNING BEHAVIOUR. IN THIS STUDY WE LINKED 151 MODULES AND 111.256 STUDENTS WITH STUDENTS' BEHAVIOUR (<400 MILLION MINUTES OF ONLINE BEHAVIOUR), SATISFACTION AND PERFORMANCE AT THE OPEN UNIVERSITY UK USING MULTIPLE REGRESSION MODELS. OUR FINDINGS STRONGLY INDICATE THE IMPORTANCE OF LEARNING DESIGN IN PREDICTING AND UNDERSTANDING VIRTUAL LEARNING ENVIRONMENT BEHAVIOUR AND PERFORMANCE OF STUDENTS IN BLENDED AND ONLINE ENVIRONMENTS. IN LINE WITH PROPONENTS OF SOCIAL LEARNING THEORIES, OUR PRIMARY PREDICTOR FOR ACADEMIC RETENTION WAS THE TIME LEARNERS SPENT ON COMMUNICATION ACTIVITIES, CONTROLLING FOR VARIOUS INSTITUTIONAL AND DISCIPLINARY FACTORS. WHERE POSSIBLE, APPROPRIATE AND WELL DESIGNED COMMUNICATION TASKS THAT ALIGN WITH THE LEARNING OBJECTIVES OF THE COURSE MAY BE A WAY FORWARD TO ENHANCE ACADEMIC RETENTION. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE NUMBER OF STUDENTS' ENROLLED IN HIGHER EDUCATION ONLINE COURSES IS INCREASING, AND AS A RESULT, MORE DATA ABOUT THEIR LEARNING PROCESS IS GENERATED. THE DATA PRODUCED CAN BE DISPLAYED ON A DASHBOARD AND HELP STUDENTS IN THEIR LEARNING PROCESS. HOWEVER, TEACHERS' PERSPECTIVE SHOULD BE TAKEN INTO ACCOUNT TO DEFINE THE INDICATORS OF THE DASHBOARD AS THE DESIGN OF THE COURSES COULD IMPACT ON THE ELEMENTS INCLUDED ON IT. AND THAT IS PRECISELY THE AIM OF THIS PAPER: TO DEFINE THE INDICATORS NEEDED TO DESIGN A STUDENT DASHBOARD IN ONLINE COURSES TAKING INTO ACCOUNT TEACHERS' PERSPECTIVES. THIS STUDY WAS CARRIED OUT WITH 10 TEACHERS FROM THE FACULTY OF HUMANITIES AND EDUCATION FROM MONDRAGON UNIBERTSITATEA. ONLINE QUALITATIVE QUESTIONNAIRES WERE USED TO GATHER PARTICIPANTS' PERCEPTIONS. RESULTS SHOW THAT MOST OF THE TEACHERS PARTICIPATING IN THE RESEARCH STUDY IDENTIFIED FOUR INDICATORS TO DESIGN A STUDENT DASHBOARD: NUMBER OF TIMES STUDENTS ACCESS THE COURSE FORUM, AMOUNT OF CONTRIBUTIONS IN THE FORUM, NUMBER OF TIMES STUDENTS CONSULT THE INFORMATION BOOKLET, AND NUMBER OF TIMES EACH STUDENT HAS ACCESSED THE COURSE IN A WEEK TIME. CONCLUSIONS DRAWN FROM THIS STUDY HIGHLIGHT THE IMPORTANCE OF TRAINING TEACHERS AND STUDENTS ON THE PEDAGOGICAL USE OF DATA VISUALIZATION.
DATA COLLECTION OF STUDENTS' LEARNING PERFORMANCE, ATTENDANCE AND BEHAVIOUR SUPPORT TEACHER'S CLASSROOM MANAGEMENT, SCHOOL ADMINISTRATION AND GOVERNANCE. DATA COLLECTION SUPPORT COLLABORATION OF SCHOOL WITH PARENTS AND COLLABORATION OF TEACHERS WITHIN THE SCHOOL AS BETWEEN SCHOOLS IN THE CURRICULUM AT THE LEVEL OF INSTRUCTIONAL DESIGN AND LESSON DELIVERY. THE PURPOSE OF THIS STUDY IS TO EXAMINE HOW TEACHERS PERCEIVE THE INCREASINGLY DATA-DRIVEN SCHOOL ROUTINES AND HOW THEY USE DOCUMENTATION INFORMATION SYSTEMS (DIS) IN THEIR PEDAGOGICAL WORK. A SURVEY WAS CONDUCTED ON A REPRESENTATIVE SAMPLE OF TEACHERS FROM 220 ELEMENTARY SCHOOLS. THESE SCHOOLS WERE RANDOMLY SELECTED FROM A LIST OF ALL SLOVENE SCHOOLS, IN TOTAL 452. THE ORIGINAL INSTRUMENT WAS DESIGNED BY AUTHORS OF THIS PAPER. FINDINGS INDICATE THAT RESPONDENTS ARE MOST OFTEN USING DIS FOR THE MANAGEMENT OF E-DIARIES, E-SCHEDULES AND ANNUAL AND SEMI-ANNUAL PRINTOUTS OF VARIOUS DOCUMENTS. THERE IS THE DIFFERENCE BETWEEN TEACHERS REGARDING THE SCHOOL LEVEL, AND THE FREQUENCY OF ICT USE GENERALLY AFFECTS THE FREQUENCY OF ICT USE IN THE PEDAGOGICAL PROCESS.
THIS STUDY EXAMINED THE RELATIONSHIP BETWEEN DISCUSSION FORUM CONTRIBUTIONS AND COURSE ASSESSMENT RESULTS IN A STATISTICS MOOC. AN IMPORTANT FEATURE OF THE STUDY IS THAT IT DISTINGUISHED BETWEEN DISCUSSIONS THAT WERE RELATED TO THE LEARNING OF COURSE MATERIAL ("CONTENT-RELATED") AND THOSE WHICH WERE NOT ("NON-CONTENT"). ANOTHER CONTRIBUTION IS THAT THE STUDY EVALUATED THE ADDITIONAL USEFULNESS OF SOCIAL CENTRALITY MEASURES IN PREDICTING COURSE GRADE AFTER THE QUANTITY OF FORUM CONTRIBUTIONS HAS BEEN ACCOUNTED FOR. RESULTS SHOWED THAT, OVERALL, 15% OF COURSE LEARNERS CONTRIBUTED TO THE FORUMS AND THESE LEARNERS HAD A SIGNIFICANTLY HIGHER RATE OF SUCCESSFULLY PASSING THE COURSE THAN NON-CONTRIBUTORS (64% VS 32% PASSING). LEARNERS WHO MADE POSTS TO BOTH CONTENT- RELATED AND NON-CONTENT THREADS HAD A HIGHER PASSING RATE THAN THOSE WHO ONLY CONTRIBUTED TO ONE TYPE OR THE OTHER. AMONG LEARNERS WHO SUCCESSFULLY PASSED THE COURSE, THERE WERE NO DIFFERENCES IN COURSE GRADE WHEN COMPARING DISCUSSION CONTRIBUTORS AND NON-CONTRIBUTORS OVERALL; HOWEVER THOSE WHO CONTRIBUTED TO CONTENTRELATED THREADS PERFORMED SLIGHTLY BETTER THAN THOSE WHO DID NOT (COURSE GRADE OF 87% VS 85%). A PREDICTIVE MODEL BASED ON THE NUMBER OF POSTS MADE TO CONTENT-RELATED THREADS EXPLAINED A SMALL PROPORTION OF VARIANCE IN COURSE GRADES; ADDITION OF SOCIAL CENTRALITY MEASURES DID NOT SIGNIFICANTLY IMPROVE THE VARIANCE EXPLAINED BY THE MODEL.
MOST ACADEMIC COURSES IN INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) OR ENGINEERING DISCIPLINES ARE DESIGNED TO IMPROVE PRACTICAL SKILLS; HOWEVER, PRACTICAL SKILLS AND THEORETICAL KNOWLEDGE ARE EQUALLY IMPORTANT TO ACHIEVE HIGH ACADEMIC PERFORMANCE. THIS RESEARCH AIMS TO EXPLORE HOW PRACTICAL SKILLS ARE INFLUENTIAL IN IMPROVING STUDENTS' ACADEMIC PERFORMANCE BY COLLECTING REAL-WORLD DATA FROM A COMPUTER PROGRAMMING COURSE IN THE ICT DISCIPLINE. TODAY, COMPUTER PROGRAMMING HAS BECOME AN INDISPENSABLE SKILL FOR ITS WIDE RANGE OF APPLICATIONS AND SIGNIFICANCE ACROSS THE WORLD. IN THIS PAPER, A NOVEL FRAMEWORK TO EXTRACT HIDDEN FEATURES AND RELATED ASSOCIATION RULES USING A REAL-WORLD DATASET IS PROPOSED. AN UNSUPERVISED K-MEANS CLUSTERING ALGORITHM IS APPLIED FOR DATA CLUSTERING, AND THEN THE FREQUENT PATTERN-GROWTH ALGORITHM IS USED FOR ASSOCIATION RULE MINING. WE LEVERAGE STUDENTS' PROGRAMMING LOGS AND ACADEMIC SCORES AS AN EXPERIMENTAL DATASET. THE PROGRAMMING LOGS ARE COLLECTED FROM AN ONLINE JUDGE (OJ) SYSTEM, AS OJS PLAY A KEY ROLE IN CONDUCTING PROGRAMMING PRACTICES, COMPETITIONS, ASSIGNMENTS, AND TESTS. TO EXPLORE THE CORRELATION BETWEEN PRACTICAL (E.G., PROGRAMMING, LOGICAL IMPLEMENTATIONS, ETC.) SKILLS AND OVERALL ACADEMIC PERFORMANCE, THE STATISTICAL FEATURES OF STUDENTS ARE ANALYZED AND THE RELATED RESULTS ARE PRESENTED. A NUMBER OF USEFUL RECOMMENDATIONS ARE PROVIDED FOR STUDENTS IN EACH CLUSTER BASED ON THE IDENTIFIED HIDDEN FEATURES. IN ADDITION, THE ANALYTICAL RESULTS OF THIS PAPER CAN HELP TEACHERS PREPARE EFFECTIVE LESSON PLANS, EVALUATE PROGRAMS WITH SPECIAL ARRANGEMENTS, AND IDENTIFY THE ACADEMIC WEAKNESSES OF STUDENTS. MOREOVER, A PROTOTYPE OF THE PROPOSED APPROACH AND DATA-DRIVEN ANALYTICAL RESULTS CAN BE APPLIED TO OTHER PRACTICAL COURSES IN ICT OR ENGINEERING DISCIPLINES.
EDUCATIONAL ENVIRONMENTS CONTINUE TO RAPIDLY EVOLVE TO ADDRESS THE NEEDS OF DIVERSE, GROWING STUDENT POPULATIONS, WHILE EMBRACING ADVANCES IN PEDAGOGY AND TECHNOLOGY. IN THIS CHANGING LANDSCAPE ENSURING THE CONSISTENCY AMONG THE ASSESSMENTS FOR DIFFERENT OFFERINGS OF A COURSE (WITHIN OR ACROSS TERMS), PROVIDING MEANINGFUL FEEDBACK ABOUT STUDENTS' ACHIEVEMENTS, AND TRACKING STUDENTS' PROGRESSION OVER TIME ARE ALL CHALLENGING TASKS, PARTICULARLY AT SCALE. HERE, A COLLECTION OF VISUAL TOPIC DEPENDENCY MODELS (TDMS) IS PROPOSED TO HELP ADDRESS THESE CHALLENGES. IT VISUALISES THE REQUIRED TOPICS AND THEIR DEPENDENCIES AT A COURSE LEVEL (E.G., CS 100) AND ASSESSMENT ACHIEVEMENT DATA AT THE CLASSROOM LEVEL (E.G., STUDENTS IN CS 100 TERM 1 2016 SECTION 001) BOTH AT ONE POINT IN TIME (STATIC) AND OVER TIME (DYNAMIC). THE COLLECTION OF TDMS SHARE A COMMON, TWO-WEIGHTED GRAPH FOUNDATION. AN ALGORITHM IS PRESENTED TO CREATE A TDM (STATIC ACHIEVEMENT FOR A COHORT). AN OPEN-SOURCE, PROOF OF CONCEPT IMPLEMENTATION OF THE TDMS IS UNDER DEVELOPMENT; THE CURRENT VERSION IS DESCRIBED BRIEFLY IN TERMS OF ITS SUPPORT FOR VISUALISING EXISTING (HISTORICAL, TEST) AND SYNTHETIC DATA GENERATED ON DEMAND.
STUDENTS' SATISFACTION FROM EDUCATIONAL RESOURCES IS A SUBJECTIVE PERCEPTION OF HOW WELL THESE RESOURCES MEET STUDENTS' EXPECTATIONS FOR LEARNING. RECOMMENDING EDUCATIONAL RESOURCES TO GROUPS OF STUDENTS, TARGETING AT OPTIMIZING ALL STUDENTS' SATISFACTION, IS A COMPLICATED TASK DUE TO THE LACK OF JOINT GROUP PROFILES. INSTEAD OF MERGING INDIVIDUAL PROFILES OR FUSING INDIVIDUAL RECOMMENDATIONS, THIS PAPER FOLLOWS A GAME-THEORETIC PERSPECTIVE FOR SOLVING CONFLICT OF INTEREST AMONG STUDENTS AND RECOMMENDING RESOURCES TO GROUPS IN ONLINE COLLABORATIVE LEARNING CONTEXTS: THE GROUP MEMBERS ARE THE PLAYERS, THE RESOURCES COMPRISE THE SET OF POSSIBLE ACTIONS, AND MAXIMIZING EACH INDIVIDUAL MEMBER'S SATISFACTION FROM THE SELECTED RESOURCES IS A PROBLEM OF FINDING THE NASH EQUILIBRIUM. IN CASE THE NASH EQUILIBRIUM IS PARETO EFFICIENT, NONE OF THE PLAYERS CAN GET MORE PAYOFF (SATISFACTION) WITHOUT DECREASING THE PAYOFF OF ANY OTHER PLAYER, INDICATING AN OPTIMAL BENEFIT FOR THE GROUP AS A WHOLE. THE COMPARATIVE EVALUATION OF THE SUGGESTED APPROACH TO OTHER STATE-OF-THE-ART METHODS PROVIDED STATISTICALLY SIGNIFICANT RESULTS REGARDING THE ERROR IN PREDICTED GROUP SATISFACTION FROM THE RECOMMENDATION AND THE GOODNESS OF THE RANKED LIST OF RECOMMENDATIONS.
TEACHING FACE-TO-FACE IS STILL A MAJOR EDUCATION MODE IN MANY UNIVERSITIES, YET INSTITUTIONS ARE INCREASINGLY TASKED WITH IMPROVING EFFICIENT USE OF TEACHING SPACES. THIS NEED TO UNDERSTAND SPACE USE CAN BE COUPLED WITH LEARNING AND TEACHING DATA TO BETTER INFORM STUDENT ATTENDANCE AND SUBSEQUENTLY PARTICIPATION. HERE, WE ANALYSE THERMAL SENSOR DATA USED TO MONITOR TRAFFIC INTO CLASSROOMS; THESE DATA ARE ASSOCIATED WITH THE TIMETABLE TO PROVIDE KNOWLEDGE OF THE COURSE AND THE TEACHING MODE (SUCH AS LECTURE, TUTORIAL OR WORKSHOP). FURTHER, WE INTEGRATE THESE TRAFFIC DATA WITH STUDENT FEEDBACK DATA TO INVESTIGATE THE DRIVERS OF STUDENT ATTENDANCE PATTERNS, AND AIM TO ALSO INCLUDE ONLINE ACTIVITY AND BEHAVIOUR TO DEVELOP BROAD MODELS OF BOTH ROOM OCCUPANCY AND STUDENT ATTENDANCE. COMBINING SPACE UTILISATION DATA WITH INFORMATION ON TEACHING MODALITY AND IN-CLASS AND OUT-OF-CLASS PARTICIPATION CAN INFORM ON HOW TO BOTH IMPROVE LEARNING AND DESIGN EFFECTIVE AND EFFICIENT TEACHING SPACES.
VIDEO MATERIALS HAVE BECOME AN INTEGRAL PART OF UNIVERSITY LEARNING AND TEACHING PRACTICE. WHILE EMPIRICAL RESEARCH CONCERNING THE USE OF VIDEOS FOR EDUCATIONAL PURPOSES HAS INCREASED, THE LITERATURE LACKS AN OVERVIEW OF THE SPECIFIC EFFECTS OF VIDEOS ON DIVERSE LEARNING OUTCOMES. TO ADDRESS SUCH A GAP, THIS PAPER PRESENTS PRELIMINARY RESULTS OF A LARGE-SCALE SYSTEMATIC REVIEW OF PEER-REVIEWED EMPIRICAL STUDIES PUBLISHED FROM 2007-2017. THE STUDY SYNTHESIZES THE TRENDS OBSERVED THROUGH THE ANALYSIS OF 178 PAPERS SELECTED FROM THE SCREENING OF 2531 ABSTRACTS. THE FINDINGS SUMMARIZE THE EFFECTS OF MANIPULATING VIDEO PRESENTATION, CONTENT AND TASKS ON LEARNING OUTCOMES, SUCH AS RECALL, TRANSFER, ACADEMIC ACHIEVEMENT, AMONG OTHERS. THE STUDY POINTS OUT THE GAP BETWEEN LARGE-SCALE ANALYSIS OF FINE-GRAINED DATA ON VIDEO INTERACTION AND EXPERIMENTAL FINDINGS RELIANT ON ESTABLISHED PSYCHOLOGICAL INSTRUMENTS. NARROWING THIS GAP IS SUGGESTED AS THE FUTURE DIRECTION FOR THE RESEARCH ON VIDEO-BASED LEARNING.
VIDEOS ARE OFTEN THE CORE CONTENT IN OPEN ONLINE EDUCATION, SUCH AS IN MASSIVE OPEN ONLINE COURSES (MOOCS). STUDENTS SPEND MOST OF THEIR TIME IN A MOOC ON WATCHING EDUCATIONAL VIDEOS. HOWEVER, MERELY WATCHING A VIDEO IS A RELATIVELY PASSIVE LEARNING ACTIVITY. TO INCREASE THE EDUCATIONAL BENEFITS OF ONLINE VIDEOS, STUDENTS COULD BENEFIT FROM MORE ACTIVELY INTERACTING WITH THE TO-BE-LEARNED MATERIAL. IN THIS PAPER TWO STUDIES (N = 13K) ARE PRESENTED WHICH EXAMINED THE EDUCATIONAL BENEFITS OF TWO MORE ACTIVE LEARNING STRATEGIES: 1) RETRIEVAL PRACTICE TASKS WHICH ASKED STUDENTS TO SHORTLY SUMMARIZE THE CONTENT OF VIDEOS, AND 2) GIVEN SUMMARY TASKS IN WHICH THE STUDENTS WERE ASKED TO READ PRE-WRITTEN SUMMARIES OF VIDEOS. WRITING, AS WELL AS READING SUMMARIES OF VIDEOS WERE POSITIVELY RELATED TO QUIZ GRADES. BOTH INTERVENTIONS SEEMED TO HELP STUDENTS TO PERFORM BETTER, BUT THERE WAS NO APPARENT DIFFERENCE BETWEEN THE EFFICACY OF THESE INTERVENTIONS. THESE STUDIES SHOW HOW THE QUALITY OF ONLINE EDUCATION CAN BE IMPROVED BY ADAPTING COURSE DESIGN TO ESTABLISHED APPROACHES FROM THE LEARNING SCIENCES.
THIS WORK PRESENTS A STUDY THAT TRACKED DATA GENERATED FROM DISTANCE EDUCATION SYSTEMS, AND MADE USE OF THESE DATA, OFTEN NEGLECTED, TO PRODUCE INFORMATION AND KNOWLEDGE ABOUT THE SYSTEM ITSELF AS WELL AS THE INVOLVED PEOPLE. THE STRATEGY PRESENTED GO FROM THE BASIC DATA HANDLE ON THE DISTANCE EDUCATION SYSTEMS, USING STATISTICAL APPLICATIONS METHODS, APPLIED FOR DATA PROCESSING. THE PRESENTED ACTIVITIES AIM TO IMPROVE THE QUALITY OF THE SERVICE OFFERED ON ONLINE COURSES, AND ALSO HELP SOLVE SOME ISSUES, FOR INSTANCE THE STUDENT'S EVASION. IT IS ALSO PRESENTING A CASE STUDY WHERE STATISTICAL METHODS WERE APPLIED TO DEAL WITH THE STUDENT'S EVASION IN A REAL DISTANCE EDUCATION SYSTEM. THE MAIN GOAL WAS TO START THE USE OF LEARNING ANALYTICS CONCEPTS.
IN THE CURRENT ERA OF GENERATION Z, WHERE TECHNOLOGY IS OMNIPRESENT IN EVERYDAY LIFE, UNIVERSITY STUDENTS CHECK THEIR SOCIAL MEDIA APPLICATIONS (APPS) EVERY DAY. THE PERVASIVE USAGE OF APPS SUCH AS INSTAGRAM AND SNAPCHAT TAKES UP A SIGNIFICANT AMOUNT OF THE STUDENTS' TIME. STUDENTS OFTEN USE THEM DURING CLASSES, BECOMING DISTRACTED IN THE PROCESS. THIS CHALLENGE HAS THUS MOTIVATED US TO CONDUCT A PILOT PROJECT BY MAKING USE OF THESE TWO PLATFORMS FOR LABORATORY TEACHING PURPOSES. WE AIM TO INTEGRATE THE ELEMENT OF URGENCY INTO THE LEARNING PROCESS AND STRENGTHEN THE CONCEPTS LEARNED DURING THE CURRICULUM AT ANY TIME WHERE STUDENTS CAN ACCESS COURSE-RELATED CONTENT AT THEIR CONVENIENCE. WE WILL ALSO DISCUSS THE LEARNING ANALYTICS OF INSTAGRAM AND SNAPCHAT IN THIS CHAPTER.
THIS STUDY FOCUSES ON COMPILING STUDENTS' RESPONSE-TIME ALLOCATED TO ANSWER CORRECTLY OR WRONGLY, THEIR SELF-REGULATION, AS WELL AS THEIR SATISFACTION FROM CONTENT, IN ORDER TO EXPLAIN HIGH OR MEDIUM/LOW LEARNING PERFORMANCE. TO THIS END, IT PROPOSES A CONCEPTUAL MODEL IN CONJUNCTION WITH RESEARCH PROPOSITIONS. FOR THE EVALUATION OF THE APPROACH, AN EMPIRICAL STUDY WITH 452 STUDENTS WAS CONDUCTED. THE FUZZY SET QUALITATIVE COMPARATIVE ANALYSIS (FSQCA) REVEALED FIVE CONFIGURATIONS DRIVEN BY THE ADMITTED FACTORS THAT EXPLAIN STUDENTS' HIGH PERFORMANCE, AS WELL AS FIVE ADDITIONAL PATTERNS, INTERPRETING STUDENTS' MEDIUM/LOW PERFORMANCE. THESE FINDINGS ADVANCE OUR UNDERSTANDING OF THE RELATIONS BETWEEN ACTUAL USAGE AND LATENT BEHAVIORAL FACTORS, AS WELL AS THEIR COMBINED EFFECT ON STUDENTS' TEST SCORE. LIMITATIONS AND POTENTIAL IMPLICATIONS OF THESE FINDINGS ARE ALSO DISCUSSED.
THE EXTENT TO WHICH THE UNDERTAKING A LEARNING ACTIVITY INFLUENCES THE UNDERSTANDING OF A CONCEPT OR, IN GENERAL, THE DEVELOPMENT OF A COMPETENCY, IS ONE OF THE ISSUES THAT EDUCATORS HAVE IN MIND WHEN DEVELOPING THE WORK PLAN OF A SUBJECT MAKING APPROPRIATE DECISIONS WITH RESPECT TO THIS CAN HAVE A SIGNIFICANT INFLUENCE ON THE LEARNING OF THE GROUP OF STUDENTS, SOMETIMES DIRECTLY AND SOMETIMES BECAUSE OF MOTIVATION CREATED WHEN THE ACTIVITY IS SEEN AS RELEVANT BY STUDENTS FOLLOWING THE PRINCIPLES OF LEARNING ANALYTICS, WE HAVE EXTRACTED THE DATA ABOUT THE NUMBER OF TIMES THAT THE STUDENTS HAVE ACCESSED THE AVAILABLE RESOURCES ON THE LEARNING MANAGEMENT SYSTEM AND ANSWERED THE QUESTIONS GIVEN IN EACH ACTIVITY. DATA CORRESPONDING TO STUDENTS, GROUPED BY TEST PERFORMANCE, IS PRESENTED STATISTICALLY.
ASSURANCE OF LEARNING (AOL) REFERS TO THE OUTCOMES ASSESSMENT PROCESS WHICH INVOLVES THE SYSTEMATIC COLLECTION, REVIEW, AND USE OF INFORMATION ABOUT EDUCATIONAL PROGRAMS UNDERTAKEN FOR THE PURPOSE OF IMPROVING STUDENT LEARNING AND DEVELOPMENT [8]. WHILE EMERGING TRENDS SUCH AS OPEN EDUCATION, OPEN LEARNING, LEARNING ANALYTICS, ACADEMIC ANALYTICS, AND BIG DATA IN EDUCATION HAVE RECENTLY BECOME MAINSTREAM, STUDIES REGARDING THE DESIGN AND DEVELOPMENT OF OPEN SOURCE ANALYTICS APPLICATIONS FOR AOL ARE NON-EXISTENT. IN THIS PAPER, WE DESCRIBE AN APPLICATION CALLED AOL ANALYZER THAT WE DEVELOPED FOR OUR BUSINESS SCHOOL LAST YEAR TO ASSIST IN THE ANALYSIS OF AOL RESULTS REPORTED BY FACULTY. TO THE BEST OF OUR KNOWLEDGE, THIS IS A FIRST PAPER TO BRIDGE THE EXISTING GAP IN AOL ANALYTICS RESEARCH.
THIS CASE STUDY INVESTIGATED HOW STUDENT INTERACTION PATTERNS (MOORE, 1993) WITH DIFFERENT INSTRUCTIONAL MEDIA IN A GRADUATE LEVEL ONLINE ENVIRONMENTAL TOXICOLOGY COURSE IMPACTED STUDENT OUTCOMES, AS MEASURED BY GRADES AND STUDENT ENGAGEMENT LEVELS. STUDENT ENGAGEMENT LEVELS WERE MEASURED AS POSTS, ACCESS, TIME, AND HITS (PATHS) BY USING THE PATHS FRAMEWORK (ADEBOWALE, 2017) APPROACH TO QUANTIFY LEARNING ANALYTICS OBTAINED FROM A LEARNING MANAGEMENT SYSTEM. DATA WAS OBTAINED FROM 13 ONLINE STUDENTS DURING THE FALL 2018 SEMESTER. FINDINGS INDICATE THAT NARRATED POWERPOINT AND SHAKY HAND VIDEO LED TO HIGHER STUDENT OUTCOMES COMPARED TO THE OTHER FORMS OF INSTRUCTIONAL MEDIA. IMPLICATIONS FOR COURSE DESIGN, QUALITY ASSURANCE MECHANISMS, ASSESSMENTS, IMPROVEMENT OF STUDENT OUTCOMES, AND CRITERIA FOR SELECTING THE TYPE OF INSTRUCTIONAL MEDIA TO USE IN ONLINE COURSES ARE ALSO DISCUSSED.
EDUCATIONAL DATA MINING (EDM) CAN BE CONSIDERED AS INTERDISCIPLINARY RESEARCH FIELD THAT INHERITS PROPERTIES FROM THE FIELD LIKE LEARNING ANALYTICS, DATA MINING, ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, DATABASE MANAGEMENT SYSTEM AND PSYCHOMETRICS ETC. IN THIS PAPER, WE EXPLORE IN DETAIL THE DIFFERENT PHASES OF E-LEARNING DEVELOPMENT FROM AN ORGANIZATIONAL PERSPECTIVE AND ALSO EXPLORE THE VARIOUS PHASES WHERE DATA MINING CAN BE USED. THE POSSIBILITY OF APPLYING EDM IN LEARNING MANAGEMENT SYSTEMS (LMS) WITH THE HELP OF SCORM (SHARABLE CONTENT OBJECT REFERENCE MODEL) STANDARD IS ALSO EXPLORED. SCORM IS A WIDELY USED E-LEARNING STANDARD SUPPORTED BY MORE THAN 90% OF THE LMS VENDORS IN THE MARKET. THE SHORTFALLS OF SCORM AND THE NEXT GENERATION LMS TECHNOLOGY IS ALSO DISCUSSED IN THIS PAPER.
DATA SCIENCE IS NOW IMPACTING THE EDUCATION SECTOR, WITH A GROWING NUMBER OF COMMERCIAL PRODUCTS AND RESEARCH PROTOTYPES PROVIDING LEARNING DASHBOARDS. FROM A HUMAN-CENTRED COMPUTING PERSPECTIVE, THE END-USER'S INTERPRETATION OF THESE VISUALISATIONS IS A CRITICAL CHALLENGE TO DESIGN FOR, WITH EMPIRICAL EVIDENCE ALREADY SHOWING THAT 'USABLE' VISUALISATIONS ARE NOT NECESSARILY EFFECTIVE FROM A LEARNING PERSPECTIVE. SINCE AN EDUCATOR'S INTERPRETATION OF VISUALISED DATA IS ESSENTIALLY THE CONSTRUCTION OF A NARRATIVE ABOUT STUDENT PROGRESS, WE DRAW ON THE GROWING BODY OF WORK ON DATA STORYTELLING (DS) AS THE INSPIRATION FOR A SET OF ENHANCEMENTS THAT COULD BE APPLIED TO DATA VISUALISATIONS TO IMPROVE THEIR COMMUNICATIVE POWER. WE PRESENT A PILOT STUDY THAT EXPLORES THE EFFECTIVENESS OF THESE DS ELEMENTS BASED ON EDUCATORS' RESPONSES TO PAPER PROTOTYPES. THE DUAL PURPOSE IS UNDERSTANDING THE CONTRIBUTION OF EACH VISUAL ELEMENT FOR DATA STORYTELLING, AND THE EFFECTIVENESS OF THE ENHANCEMENTS WHEN COMBINED.
STUDENT SUCCESS, A MAJOR FOCUS IN HIGHER EDUCATION, IN PART, REQUIRES STUDENTS TO REMAIN ACTIVELY ENGAGED IN THE REQUIRED COURSEWORK. IDENTIFYING STUDENT DISENGAGEMENT, WHEN A STUDENT STOPS COMPLETING COURSEWORK, AT SCALE HAS BEEN A CONTINUING CHALLENGE FOR HIGHER EDUCATION DUE TO THE HETEROGENEITY OF TRADITIONAL COLLEGE COURSES. THIS RESEARCH USES DATA FROM CONNECT BY MCGRAW-HILL EDUCATION, A WIDELY USED ONLINE LEARNING TOOL, TO BUILD A CLASSIFIER TO IDENTIFY LEARNING TOOL DISENGAGEMENT AT SCALE. THIS CLASSIFIER WAS TRAINED AND TESTED ON FOUR YEARS OF HISTORICAL DATA, REPRESENTING 4.5 MILLION STUDENTS IN 175,000 COURSES, ACROSS 256 DISCIPLINES. RESULTS SHOW THAT THE CLASSIFIER IS EFFECTIVE IN IDENTIFYING DISENGAGEMENT WITHIN THE ONLINE LEARNING TOOL AGAINST BASELINES, ACROSS TIME, AND WITHIN AND ACROSS DISCIPLINES. THE CLASSIFIER WAS ALSO EFFECTIVE IN IDENTIFYING STUDENTS AT RISK OF DISENGAGING FROM CONNECT AND THEN EARNING UNSUCCESSFUL GRADES IN A PILOT COURSE FOR WHICH THE ASSIGNMENTS IN CONNECT WERE WORTH A RELATIVELY SMALL PORTION OF THE OVERALL COURSE GRADE. BECAUSE CONNECT IS WIDELY USED, THIS CLASSIFIER IS POSITIONED TO BE A GOOD TOOL FOR INSTRUCTORS AND INSTITUTIONS TO IDENTIFY STUDENTS AT RISK FOR DISENGAGEMENT FROM COURSEWORK. INSTRUCTORS AND INSTITUTIONS CAN USE THIS INFORMATION TO DESIGN AND IMPLEMENT INTERVENTIONS TO IMPROVE ENGAGEMENT AND IMPROVE STUDENT SUCCESS AT THE INSTITUTION IN KEY COURSES.
AUTOMATIC ANALYSIS OF LEARNERS' QUESTIONS CAN BE USED TO IMPROVE THEIR LEVEL AND HELP TEACHERS IN ADDRESSING THEM. WE INVESTIGATED QUESTIONS (N=6457) ASKED BEFORE THE CLASS BY 1ST YEAR MEDICINE/PHARMACY STUDENTS ON AN ONLINE PLATFORM, USED BY PROFESSORS TO PREPARE THEIR ON-SITE Q&A SESSION. OUR LONG-TERM OBJECTIVES ARE TO HELP PROFESSORS IN CATEGORIZING THOSE QUESTIONS, AND TO PROVIDE STUDENTS WITH FEEDBACK ON THE QUALITY OF THEIR QUESTIONS. TO DO SO, FIRST WE MANUALLY CATEGORIZED STUDENTS' QUESTIONS, WHICH LED TO A TAXONOMY THEN USED FOR AN AUTOMATIC ANNOTATION OF THE WHOLE CORPUS. WE IDENTIFIED STUDENTS' CHARACTERISTICS FROM THE TYPOLOGY OF QUESTIONS THEY ASKED USING K-MEANS ALGORITHM OVER FOUR COURSES. THE STUDENTS WERE CLUSTERED BY THE PROPORTION OF EACH QUESTION ASKED IN EACH DIMENSION OF THE TAXONOMY. THEN, WE CHARACTERIZED THE CLUSTERS BY ATTRIBUTES NOT USED FOR CLUSTERING SUCH AS THE STUDENTS' GRADE, THE ATTENDANCE, THE NUMBER AND POPULARITY OF QUESTIONS ASKED. TWO SIMILAR CLUSTERS ALWAYS APPEARED: A CLUSTER (A), MADE OF STUDENTS WITH GRADES LOWER THAN AVERAGE, ATTENDING LESS TO CLASSES, ASKING A LOW NUMBER OF QUESTIONS BUT WHICH ARE POPULAR; AND A CLUSTER (D), MADE OF STUDENTS WITH HIGHER GRADES, HIGH ATTENDANCE, ASKING MORE QUESTIONS WHICH ARE LESS POPULAR. THIS WORK DEMONSTRATES THE VALIDITY AND THE USEFULNESS OF OUR TAXONOMY, AND SHOWS THE RELEVANCE OF THIS CLASSIFICATION TO IDENTIFY DIFFERENT STUDENTS' PROFILES.
RETRIEVAL PRACTICE HAS BEEN ESTABLISHED IN THE LEARNING SCIENCES AS ONE OF THE MOST EFFECTIVE STRATEGIES TO FACILITATE ROBUST LEARNING IN TRADITIONAL CLASSROOM CONTEXTS. THE COGNITIVE THEORY UNDERPINNING THE "TESTING EFFECT" STATES THAT ACTIVELY RECALLING INFORMATION IS MORE EFFECTIVE THAN PASSIVELY REVISITING MATERIALS FOR STORING INFORMATION IN LONG-TERM MEMORY. WE DOCUMENT THE DESIGN, DEPLOYMENT, AND EVALUATION OF AN ADAPTIVE RETRIEVAL PRACTICE SYSTEM (ARPS) IN A MOOC. THIS PUSH-BASED SYSTEM LEVERAGES THE TESTING EFFECT TO PROMOTE LEARNER ENGAGEMENT AND ACHIEVEMENT BY INTELLIGENTLY DELIVERING QUIZ QUESTIONS FROM PRIOR COURSE UNITS TO LEARNERS THROUGHOUT THE COURSE. WE CONDUCTED AN EXPERIMENT IN WHICH LEARNERS WERE RANDOMIZED TO RECEIVE ARPS IN A MOOC TO TRACK THEIR PERFORMANCE AND BEHAVIOR COMPARED TO A CONTROL GROUP. IN CONTRAST TO PRIOR LITERATURE, WE FIND NO SIGNIFICANT EFFECT OF RETRIEVAL PRACTICE IN THIS MOOC ENVIRONMENT. IN THE TREATMENT CONDITION, PASSING LEARNERS ENGAGED MORE WITH ARPS BUT EXHIBITED SIMILAR LEVELS OF KNOWLEDGE RETENTION AS NON-PASSING LEARNERS.
PREVIOUS STUDIES HAVE INVESTIGATED THE RELATIONSHIP BETWEEN AFFECT, BEHAVIOR, AND LEARNING IN BLENDED LEARNING SYSTEMS. THESE ARTICLES HAVE FOUND THAT AFFECT AND BEHAVIOR ARE CLOSELY LINKED WITH LEARNING OUTCOMES. IN THIS PAPER, WE ATTEMPT TO REPLICATE PRIOR WORK ON HOW AFFECTIVE STATES AND BEHAVIORS RELATE TO MATHEMATICS ACHIEVEMENT, INVESTIGATING THESE ISSUES WITHIN THE CONTEXT OF 5TH-GRADE STUDENTS IN SOUTH TEXAS USING A MATHEMATICS BLENDED LEARNING SYSTEM, REASONING MIND. WE USE AUTOMATIC DETECTORS OF STUDENT BEHAVIOR AND AFFECT, AND CORRELATE INFERRED RATES OF EACH BEHAVIOR AND AFFECTIVE STATE WITH THE STUDENTS' END-OF-YEAR STANDARDIZED ASSESSMENT SCORE. A POSITIVE CORRELATION BETWEEN ENGAGED CONCENTRATION AND TEST SCORES REPLICATES PREVIOUS STUDIES, AS DOES A NEGATIVE CORRELATION BETWEEN BOREDOM AND TEST SCORES. HOWEVER, OUR FINDINGS DIFFER FROM PREVIOUS FINDINGS RELATING TO CONFUSION, FRUSTRATION, AND OFF-TASK BEHAVIOR, SUGGESTING THE IMPORTANCE OF CONTEXTUAL FACTORS FOR THE RELATIONSHIP BETWEEN BEHAVIOR, AFFECT, AND LEARNING. OUR STUDY REPRESENTS A STEP IN UNDERSTANDING HOW BROADLY FINDINGS ON THE RELATIONSHIPS BETWEEN AFFECT/BEHAVIOR AND LEARNING GENERALIZE ACROSS DIFFERENT LEARNING PLATFORMS.
THIS PAPER DISCUSSES THE DESIGN FOR AN INTELLIGENT AND ADAPTIVE TUTORING SYSTEM OFFERING PEDAGOGICAL SUPPORT THAT DEVIATES FROM THE TRADITIONAL CHALK AND TALK FORM OF TEACHING FOR ONLINE COURSES. THE DESIGN PROPOSED UTILIZES THE INFORMATION IN THE MOOC DATABASE WITH USER-SYSTEM INTERACTION LOGS TO CREATE A MODEL WHICH ENABLES THE SYSTEM TO ADAPT ACCORDING TO USER NEEDS. THERE ALSO EXISTS ROOM FOR MANUAL INTERVENTION BY THE COURSE ADMINISTRATORS IN CASE OF AT-RISK PARTICIPANTS. EDUCATIONAL DATA MINING AND LEARNING ANALYTICS TECHNIQUES HAVE BEEN APPLIED TO PINPOINT THE BEST INSTRUCTIONAL METHODS AND PEDAGOGICAL SUPPORT FOR EACH STUDENT OVER TIME. THE DESIGN PROPOSED ALLOWS THE SYSTEM TO PERSONALIZE THE LEARNING EXPERIENCE AND SUPPORTS THE INSTRUCTOR IN ADMINISTERING THE COURSE TO A HUGE NUMBER OF PARTICIPANTS.
PREDICTING STUDENTS' PERFORMANCE IS A POPULAR OBJECTIVE OF LEARNING ANALYTICS, AIMED AT IDENTIFYING INDICATORS FOR LEARNING SUCCESS. VARIOUS DATA MINING APPROACHES HAVE BEEN APPLIED FOR THIS PURPOSE ON STUDENT DATA COLLECTED FROM LEARNING MANAGEMENT SYSTEMS OR INTELLIGENT TUTORING SYSTEMS. HOWEVER, THE EMERGING SOCIAL MEDIA-BASED LEARNING ENVIRONMENTS HAVE BEEN LESS EXPLORED SO FAR. HENCE, IN THIS PAPER WE PRESENT AN APPROACH FOR PREDICTING STUDENTS' PERFORMANCE BASED ON THEIR CONTRIBUTIONS ON WIKI, BLOG AND MICROBLOGGING TOOL. AN INNOVATIVE ALGORITHM (LARGE MARGIN NEAREST NEIGHBOR REGRESSION) IS APPLIED, AND COMPARISONS WITH OTHER ALGORITHMS ARE CONDUCTED. VERY GOOD CORRELATION COEFFICIENTS ARE OBTAINED, OUTPERFORMING COMMONLY USED REGRESSION ALGORITHMS. OVERALL, RESULTS INDICATE THAT STUDENTS' ACTIVE PARTICIPATION ON SOCIAL MEDIA TOOLS IS A GOOD PREDICTOR OF LEARNING PERFORMANCE.
THIS PAPER DISCUSSES THE DESIGN FOR AN INTELLIGENT AND ADAPTIVE TUTORING SYSTEM OFFERING PEDAGOGICAL SUPPORT THAT DEVIATES FROM THE TRADITIONAL CHALK AND TALK FORM OF TEACHING FOR ONLINE COURSES. THE DESIGN PROPOSED UTILIZES THE SYSTEMS DATA TO CREATE MODELS WHICH RECOGNIZES WHEN A USER IS STRUGGLING OR FALLING BEHIND. A NOVEL METHOD OF ASSESSING EFFECTIVENESS OF PEDAGOGY USING SYSTEM INTERACTION LOGS AND MOOC DATA IS PUT FORWARD. THE SYSTEM ALSO PROVIDES ROOM FOR MANUAL INTERVENTION BY THE COURSE ADMINISTRATORS IN CASE OF AT-RISK PARTICIPANTS. EDUCATIONAL DATA MINING AND LEARNING ANALYTICS TECHNIQUES HAVE BEEN APPLIED TO UNDERSTAND USER BEHAVIOR AND COURSE EFFECTIVENESS. THE DESIGN PROPOSED ALLOWS THE SYSTEM TO PERSONALIZE THE LEARNING EXPERIENCE AND SUPPORTS THE INSTRUCTOR IN ADMINISTERING THE COURSE TO A HUGE NUMBER OF PARTICIPANTS.
MATHEMATICAL PROBLEM SOLVING IS A COMPLEX COGNITIVE ACTIVITY, WHICH POSES DIFFICULTIES FOR STUDENTS WITH AND WITHOUT DISABILITIES IN INCLUSIVE LEARNING ENVIRONMENTS. WITH A VARIETY OF FUNCTIONS, LEARNING MANAGEMENT SYSTEMS (LMSS) HAVE THE POTENTIAL TO ENHANCE PERSONALIZED LEARNING TO MEET THE DIVERSE NEEDS OF ALL STUDENTS. THIS PAPER PROVIDES TEACHERS GUIDANCE ON USING LMSS TO IMPLEMENT EVIDENCE-BASED PRACTICES FOR MATH PROBLEM SOLVING IN AN ONLINE LEARNING ENVIRONMENT. THIS PAPER INTRODUCED MULTIPLE FUNCTIONS COMMONLY AVAILABLE IN MOST LMSS, SUCH AS QUIZ, MULTIMEDIA CONTENT EDITOR, LEARNING TOOLS INTEROPERABILITY (LTI), AND LEARNING ANALYTICS. GUIDANCE IS PROVIDED TO TEACHERS TO LEVERAGE THESE FEATURES TO MAXIMIZE STUDENT LEARNING EXPERIENCES, SUCH AS ENGAGING IN MULTIMEDIA LEARNING ACTIVITIES, INTERACTING WITH THE TEACHER AND PEERS, AND RECEIVING TAILORED FEEDBACK.
HIGHER EDUCATION INSTITUTIONS IN THE UNITED STATES AND ACROSS THE WESTERN WORLD FACE A CRITICAL PROBLEM OF ATTRITION OF COLLEGE STUDENTS AND THIS PROBLEM IS PARTICULARLY ACUTE WITHIN THE SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) FIELDS. STUDENTS ARE ESPECIALLY VULNERABLE IN THE INITIAL YEARS OF THEIR ACADEMIC PROGRAMS; MORE THAN 60% OF THE DROPOUTS OCCUR IN THE FIRST TWO YEARS. THEREFORE, EARLY IDENTIFICATION OF AT-RISK STUDENTS IS CRUCIAL FOR A FOCUSED INTERVENTION IF INSTITUTIONS ARE TO SUPPORT STUDENTS TOWARDS COMPLETION. IN THIS PAPER WE DEVELOPED AND EVALUATED A SURVIVAL ANALYSIS FRAMEWORK FOR THE EARLY IDENTIFICATION OF STUDENTS AT THE RISK OF DROPPING OUT. WE COMPARED THE PERFORMANCE OF SURVIVAL ANALYSIS APPROACHES TO OTHER MACHINE LEARNING APPROACHES INCLUDING LOGISTIC REGRESSION, DECISION TREES AND BOOSTING. THE PROPOSED METHODS SHOW GOOD PERFORMANCE FOR EARLY PREDICTION OF AT-RISK STUDENTS AND ARE ALSO ABLE TO PREDICT WHEN A STUDENT WILL DROPOUT WITH HIGH ACCURACY. WE PERFORMED A COMPARATIVE ANALYSIS OF NINE DIFFERENT MAJORS WITH VARYING LEVELS OF ACADEMIC RIGOR, CHALLENGE AND STUDENT BODY. THIS STUDY ENABLES ADVISORS AND UNIVERSITY ADMINISTRATORS TO INTERVENE IN ADVANCE TO IMPROVE STUDENT RETENTION.
WE DEMONSTRATE THAT, BY USING A SMALL SET OF HAND-GRADED STUDENT WORK, WE CAN AUTOMATICALLY GENERATE RUBRIC CRITERIA WITH A HIGH DEGREE OF VALIDITY, AND THAT A PREDICTIVE MODEL INCORPORATING THESE RUBRIC CRITERIA IS MORE ACCURATE THAN A PREVIOUSLY REPORTED MODEL. WE PRESENT THIS METHOD AS ONE APPROACH TO ADDRESSING THE OFTEN CHALLENGING PROBLEM OF GRADING ASSIGNMENTS IN PROGRAMMING ENVIRONMENTS. A CLASSIC SOLUTION IS CREATING UNIT-TESTS THAT THE STUDENT-GENERATED PROGRAM MUST PASS, BUT THE RIGID, STRUCTURED NATURE OF UNIT-TESTS IS SUBOPTIMAL FOR ASSESSING THE MORE OPEN-ENDED ASSIGNMENTS STUDENTS ENCOUNTER IN INTRODUCTORY PROGRAMMING ENVIRONMENTS LIKE ALICE. FURTHERMORE, THE CREATION OF UNIT-TESTS REQUIRES PREDICTING THE VARIOUS WAYS A STUDENT MIGHT CORRECTLY SOLVE A PROBLEM - A CHALLENGING AND TIME-INTENSIVE PROCESS. THE CURRENT STUDY PROPOSES AN ALTERNATIVE, SEMI-AUTOMATED METHOD FOR GENERATING RUBRIC CRITERIA USING LOW-LEVEL DATA FROM THE ALICE PROGRAMMING ENVIRONMENT.
LARGER STUDENT INTAKES BY UNIVERSITIES AND THE RISE OF EDUCATION THROUGH MASSIVE OPEN ONLINE COURSES HAS LED TO LESS DIRECT CONTACT TIME WITH TEACHING STAFF FOR EACH STUDENT. ONE POTENTIAL WAY OF ADDRESSING THIS CONTACT DEFICIT IS TO INVITE LEARNERS TO ENGAGE IN PEER LEARNING AND PEER SUPPORT; HOWEVER, WITHOUT TECHNOLOGICAL SUPPORT THEY MAY BE UNABLE TO DISCOVER SUITABLE PEER CONNECTIONS THAT CAN ENHANCE THEIR LEARNING EXPERIENCE. TWO DIFFERENT RESEARCH SUBFIELDS WITH TIES TO RECOMMENDER SYSTEMS PROVIDE PARTIAL SOLUTIONS TO THIS PROBLEM. RECIPROCAL RECOMMENDER SYSTEMS PROVIDE SOPHISTICATED FILTERING TECHNIQUES THAT ENABLE USERS TO CONNECT WITH ONE ANOTHER. TO DATE, HOWEVER, THE MAIN FOCUS OF RECIPROCAL RECOMMENDER SYSTEMS HAS BEEN ON PROVIDING RECOMMENDATION IN ONLINE DATING SITES. RECOMMENDER SYSTEMS FOR TECHNOLOGY ENHANCED LEARNING HAVE EMPLOYED AND TAILORED EXEMPLARY RECOMMENDERS TOWARDS USE IN EDUCATION, WITH A FOCUS ON RECOMMENDING LEARNING CONTENT RATHER THAN OTHER USERS. IN THIS PAPER, WE FIRST DISCUSS THE IMPORTANCE OF SUPPORTING PEER LEARNING AND THE ROLE RECOMMENDING RECIPROCAL PEERS CAN PLAY IN EDUCATIONAL SETTINGS. WE THEN INTRODUCE OUR OPEN-SOURCE COURSE-LEVEL RECOMMENDATION PLATFORM CALLED RIPPLE THAT HAS THE CAPACITY TO PROVIDE RECIPROCAL PEER RECOMMENDATION. THE PROPOSED RECIPROCAL PEER RECOMMENDER ALGORITHM IS EVALUATED AGAINST KEY CRITERIA SUCH AS SCALABILITY, RECIPROCALITY, COVERAGE, AND QUALITY AND SHOWS IMPROVEMENT OVER A BASELINE RECOMMENDER. PRIMARY RESULTS INDICATE THAT THE SYSTEM CAN HELP LEARNERS CONNECT WITH PEERS BASED ON THEIR KNOWLEDGE GAPS AND RECIPROCAL PREFERENCES, WITH DESIGNED FLEXIBILITY TO ADDRESS KEY LIMITATIONS OF EXISTING ALGORITHMS IDENTIFIED IN THE LITERATURE.
IN THIS STUDY, WE DEVELOP AND TEST THREE MEASURES FOR CONCEPTUALIZING THE POTENTIAL IMPACT OF CO-ENROLLMENT IN DIFFERENT COURSES ON STUDENTS' CHANGING RISK FOR ACADEMIC DIFFICULTY IN A FOCAL COURSE. TWO OF THESE MEASURES, CONCURRENT ENROLLMENT IN AT LEAST ONE DIFFICULT COURSE AND ACADEMIC DIFFICULTY IN THE PRIOR WEEK IN COURSES OTHER THAN THE FOCAL COURSE, SIGNIFICANTLY INCREASE STUDENTS' ODDS OF ACADEMIC DIFFICULTY IN THE FOCAL COURSE IN OUR MODELS. OUR RESULTS HAVE IMPLICATIONS FOR THE DESIGNS OF EARLY WARNING SYSTEMS AND SUGGEST THAT ACADEMIC PLANNERS CONSIDER THE RELATIONSHIP BETWEEN COURSE CO-ENROLLMENT AND STUDENTS' ACADEMIC SUCCESS.
IN THIS PAPER, WE DISCUSS THE INTERACTIONS BETWEEN LECTURERS AND STUDENTS. FIRST, WE DETECT THEIR BEHAVIORS BY EXTRACTING THE FACE REGIONS OF LECTURERS AND STUDENTS FROM MOVING IMAGES. THEREAFTER, WE MODEL THE INTERACTION BETWEEN THE LECTURERS' WRITING AND EXPLANATION BEHAVIORS AND THE STUDENTS' NOTE TAKING AND LISTENING BEHAVIORS BY USING MULTILAYERED NEURAL NETWORKS. WE DISCUSS THE ABOVE INTERACTIONS BASED ON THE INTERNAL REPRESENTATIONS OF MULTILAYERED NEURAL NETWORKS.
COMPETENCY BASED EDUCATION (CBE) IS SEEN BY MANY AS A WAY TO OPTIMIZE LEARNING ON COST, EFFICIENCY AND FLEXIBILITY. HOWEVER, DEFINING THE REQUIRED COMPETENCIES, ASSIGNING THEM TO SPECIFIC COURSES AND BUILDING THE ASSESSMENTS EVALUATING STUDENT'S PROFICIENCY CAN BE TEDIOUS. MORE PRECISELY, MAKING SURE THAT THE ASSESSMENTS EVALUATE WHAT THEY ARE SUPPOSED TO EVALUATE REQUIRES A FAIR AMOUNT OF PSYCHOMETRICS KNOWLEDGE AND TIME THAT CAN BE DIFFICULT FOR TEACHERS TO ACQUIRE, MAINTAIN AND USE. ADDRESSING ASSESSMENT VALIDITY AND MORE SPECIFICALLY COMPETENCY FRAMEWORKS MAPPING ADEQUACY, WE PROPOSE A RULE-BASED TOOL TO EASE THE BUILDING AND THE REFINEMENT OF CBE COURSES AND CURRICULA. AFTER INTRODUCING THE CONTEXT AND BRIEFLY THE RELATED WORK, WE PRESENT OUR SET OF RULES BEFORE ILLUSTRATING THE CAPACITY OF THE PROPOSED DIAGNOSTIC TOOL ON AN ENGINEERING CURRICULUM. EXPERIMENTS SHOW THAT THIS TOOL CAN IMPROVE MAPPING ADEQUACY IN TERM OF PREDICTIVE ACCURACY AND WOULD REQUIRE MORE EFFORTS TOWARDS COMPETENCY PARAMETERS RELIABILITY MEASUREMENT.
RESEARCH ON LEARNER BEHAVIORS AND COURSE COMPLETION WITHIN MASSIVE OPEN ONLINE COURSES (MOOCS) HAS BEEN MOSTLY CONFINED TO SINGLE COURSES, MAKING THE FINDINGS DIFFICULT TO GENERALIZE ACROSS DIFFERENT DATA SETS AND TO ASSESS WHICH CONTEXTS AND TYPES OF COURSES THESE FINDINGS APPLY TO. THIS PAPER REPORTS ON THE DEVELOPMENT OF THE MOOC REPLICATION FRAMEWORK (MORF), A FRAMEWORK THAT FACILITATES THE REPLICATION OF PREVIOUSLY PUBLISHED FINDINGS ACROSS MULTIPLE DATA SETS AND THE SEAMLESS INTEGRATION OF NEW FINDINGS AS NEW RESEARCH IS CONDUCTED OR NEW HYPOTHESES ARE GENERATED. IN THE PROOF OF CONCEPT PRESENTED HERE, WE USE MORF TO ATTEMPT TO REPLICATE 15 PREVIOUSLY PUBLISHED FINDINGS ACROSS 29 ITERATIONS OF 17 MOOCS. THE FINDINGS INDICATE THAT 12 OF THE 15 FINDINGS REPLICATED SIGNIFICANTLY ACROSS THE DATA SETS, AND THAT TWO FINDINGS REPLICATED SIGNIFICANTLY IN THE OPPOSITE DIRECTION. MORF ENABLES LARGER-SCALE ANALYSIS OF MOOC RESEARCH QUESTIONS THAN PREVIOUSLY FEASIBLE, AND ENABLES RESEARCHERS AROUND THE WORLD TO CONDUCT ANALYSES ON HUGE MULTI-MOOC DATA SETS WITHOUT HAVING TO NEGOTIATE ACCESS TO DATA.
AS ACADEMIC LIBRARIES EXPLORE THEIR CONNECTION TO STUDENT SUCCESS AND HOW LIBRARY STAFF SPEND THEIR TIME, IT IS IMPORTANT TO UNDERSTAND HOW RESEARCH CONSULTATIONS INFLUENCE ACADEMIC ACHIEVEMENT. SEVERAL STUDIES HAVE FOUND CORRELATIONS BETWEEN VARIOUS MEASURES OF LIBRARY USE AND GRADE POINT AVERAGES (GPAS), BUT ONLY LIMITED RESEARCH HAS INVESTIGATED THE IMPACT ON STUDENT SUCCESS OF LENGTHY RESEARCH CONSULTATIONS WITH A LIBRARIAN. THIS EXPLORATORY STUDY ATTEMPTED TO COMPARE CONSULTATION USERS WITH THE OVERALL STUDENT BODY. DUE TO A SMALL SAMPLE SIZE, ONLY DESCRIPTIVE STATISTICS WERE OBTAINED. THUS, THIS STUDY ALSO FOCUSES ON THE METHODOLOGICAL CHALLENGES OF CONDUCTING STUDENT SUCCESS STUDIES IN ACADEMIC LIBRARIES, INCLUDING THE CONCERNS ASSOCIATED WITH LEARNING ANALYTICS, PARTICIPANT RECRUITMENT, SAMPLING, AND BUY-IN FROM LIBRARY FACULTY AND STAFF.
WE LEVERAGE A UNIQUE NATIONAL DATASET OF 41,359 COLLEGE APPLICATIONS TO PROSPECTIVELY PREDICT 4-YEAR BACHELOR'S GRADUATION IN A GENERALIZABLE MANNER. OUR FEATURES INCLUDE SOCIODEMOGRAPHICS, INSTITUTIONAL GRADUATION RATES, ACADEMIC ACHIEVEMENT, STANDARDIZED TEST SCORES, ENGAGEMENT IN EXTRACURRICULAR ACTIVITIES, WORK EXPERIENCES, AND RATINGS BY TEACHERS AND HIGH-SCHOOL GUIDANCE COUNSELORS. A RANDOM FOREST CLASSIFIER SUCCESSFULLY PREDICTED 4-YEAR GRADUATION FOR 71.4% OF THE STUDENTS (BASE RATE = 44%) USING ALL 166 OF THE AFOREMENTIONED FEATURES AND A SPLIT-HALF VALIDATION METHOD. A STOCHASTIC HILL-CLIMBING FEATURE SELECTION PROCEDURE EFFECTIVELY MAINTAINED THE SAME CLASSIFICATION ACCURACY, BUT WITH A MINIMAL SET OF 37 FEATURES, CONSISTING OF AN APPROXIMATELY EQUAL REPRESENTATION OF SOCIODEMOGRAPHICS, COGNITIVE, AND NONCOGNITIVE FACTORS. WE ADVOCATE AGAINST USING THESE RESULTS FOR ADMISSIONS DECISIONS, INSTEAD CONTEMPLATING HOW THEY MIGHT BE USED TO PROVIDE PARENTS AND EDUCATORS WITH ACTIONABLE INFORMATION TO GUIDE STUDENTS TOWARDS COLLEGE SUCCESS.
THE ASSESSMENT OF WRITING PROFICIENCY GENERALLY INCLUDES ANALYSES OF THE SPECIFIC LINGUISTIC AND RHETORICAL FEATURES CONTAINED IN THE SINGULAR ESSAYS PRODUCED BY STUDENTS. HOWEVER, RESEARCHERS HAVE RECENTLY PROPOSED THAT AN INDIVIDUAL'S ABILITY TO FLEXIBLY ADAPT THE LINGUISTIC PROPERTIES OF THEIR WRITING MIGHT MORE CLOSELY CAPTURE WRITING SKILL. HOWEVER, THE FEATURES OF THE TASK, LEARNER, AND EDUCATIONAL CONTEXT THAT INFLUENCE THIS FLEXIBILITY REMAIN LARGELY UNKNOWN. THE CURRENT STUDY EXTENDS THIS RESEARCH BY EXAMINING RELATIONS BETWEEN LINGUISTIC FLEXIBILITY, READING COMPREHENSION ABILITY, AND FEEDBACK IN THE CONTEXT OF AN AUTOMATED WRITING EVALUATION SYSTEM. STUDENTS (N = 131) WROTE AND REVISED SIX ESSAYS IN AN AUTOMATED WRITING EVALUATION SYSTEM AND WERE PROVIDED BOTH SUMMATIVE AND FORMATIVE FEEDBACK ON THEIR WRITING. ADDITIONALLY, HALF OF THE STUDENTS HAD ACCESS TO A SPELLING AND GRAMMAR CHECKER THAT PROVIDED LOWER-LEVEL FEEDBACK DURING THE WRITING PERIOD. THE RESULTS PROVIDE EVIDENCE FOR THE FACT THAT DEVELOPING WRITERS DEMONSTRATE LINGUISTIC FLEXIBILITY ACROSS THE ESSAYS THAT THEY PRODUCE. HOWEVER, ANALYSES ALSO INDICATE THAT LOWER-LEVEL FEEDBACK (I.E., SPELLING AND GRAMMAR FEEDBACK) HAVE LITTLE TO NO IMPACT ON THE PROPERTIES OF STUDENTS' ESSAYS NOR ON THEIR VARIABILITY ACROSS PROMPTS OR DRAFTS. OVERALL, THE CURRENT STUDY PROVIDES IMPORTANT INSIGHTS INTO THE ROLE OF FLEXIBILITY IN WRITING SKILL AND DEVELOPS A STRONG FOUNDATION ON WHICH TO CONDUCT FUTURE RESEARCH AND EDUCATIONAL INTERVENTIONS.
WHEN USED IN CLASSROOMS, PERSONALIZED LEARNING SOFTWARE ALLOWS STUDENTS TO WORK AT THEIR OWN PACE, WHILE FREEING UP THE TEACHER TO SPEND MORE TIME WORKING ONE-ON-ONE WITH STUDENTS. YET SUCH PERSONALIZED CLASSROOMS ALSO POSE UNIQUE CHALLENGES FOR TEACHERS, WHO ARE TASKED WITH MONITORING CLASSES WORKING ON DIVERGENT ACTIVITIES, AND PRIORITIZING HELP-GIVING IN THE FACE OF LIMITED TIME. THIS PAPER REPORTS ON THE CO-DESIGN, IMPLEMENTATION, AND EVALUATION OF A WEARABLE CLASSROOM ORCHESTRATION TOOL FOR K-12 TEACHERS: MIXED-REALITY SMART GLASSES THAT AUGMENT TEACHERS' REAL-TIME PERCEPTIONS OF THEIR STUDENTS' LEARNING, METACOGNITION, AND BEHAVIOR, WHILE STUDENTS WORK WITH PERSONALIZED LEARNING SOFTWARE. THE MAIN CONTRIBUTIONS ARE: (1) THE FIRST EXPLORATION OF THE USE OF SMART GLASSES TO SUPPORT ORCHESTRATION OF PERSONALIZED CLASSROOMS, YIELDING DESIGN FINDINGS THAT MAY INFORM FUTURE WORK ON REAL-TIME ORCHESTRATION TOOLS; (2) REPLAY ENACTMENTS: A NEW PROTOTYPING METHOD FOR REAL-TIME ORCHESTRATION TOOLS; AND (3) AN IN-LAB EVALUATION AND CLASSROOM PILOT USING A PROTOTYPE OF TEACHER SMART GLASSES (LUMILO), WITH EARLY FINDINGS SUGGESTING THAT LUMILO CAN DIRECT TEACHERS' TIME TO STUDENTS WHO MAY NEED IT MOST.
SELF-EXPLANATIONS ARE COMMONLY USED TO ASSESS ON-LINE READING COMPREHENSION PROCESSES. HOWEVER, TRADITIONAL METHODS OF ANALYSIS IGNORE IMPORTANT TEMPORAL VARIATIONS IN THESE EXPLANATIONS. THIS STUDY INVESTIGATED HOW DYNAMICAL SYSTEMS THEORY COULD BE USED TO REVEAL LINGUISTIC PATTERNS THAT ARE PREDICTIVE OF SELF-EXPLANATION QUALITY. HIGH SCHOOL STUDENTS (N = 232) GENERATED SELF-EXPLANATIONS WHILE THEY READ A SCIENCE TEXT. RECURRENCE PLOTS WERE GENERATED TO SHOW QUALITATIVE DIFFERENCES IN STUDENTS' LINGUISTIC SEQUENCES THAT WERE LATER QUANTIFIED BY INDICES DERIVED BY RECURRENCE QUANTIFICATION ANALYSIS (RQA). TO PREDICT SELF-EXPLANATION QUALITY, RQA INDICES, ALONG WITH SUMMATIVE MEASURES (I.E., NUMBER OF WORDS, MEAN WORD LENGTH, AND TYPE-TOKEN RATION) AND GENERAL READING ABILITY, SERVED AS PREDICTORS IN A SERIES OF REGRESSION MODELS. REGRESSION ANALYSES INDICATED THAT RECURRENCE IN STUDENTS' SELF-EXPLANATIONS SIGNIFICANTLY PREDICTED HUMAN RATED SELF-EXPLANATION QUALITY, EVEN AFTER CONTROLLING FOR SUMMATIVE MEASURES OF SELF-EXPLANATIONS, INDIVIDUAL DIFFERENCES, AND THE TEXT THAT WAS READ (R-2 = 0.68). THESE RESULTS DEMONSTRATE THE UTILITY OF RQA IN EXPOSING AND QUANTIFYING TEMPORAL STRUCTURE IN STUDENT'S SELF-EXPLANATIONS. FURTHER, THEY IMPLY THAT DYNAMICAL SYSTEMS METHODOLOGY CAN BE USED TO UNCOVER IMPORTANT PROCESSES THAT OCCUR DURING COMPREHENSION.
ACQUIRING SIMPLE ARITHMETIC SKILLS AT THE PRESCHOOL LEVEL REQUIRES REPETITIVE PRACTICES. ONE METHOD FOR ENCOURAGING STUDENTS TO SPEND LONGER TIME PRACTICING IS BY PRESENTING THE SKILLS IN AN ENGAGING GAME. AS STUDENT RETENTION ON THE GAME INCREASES, THE STUDENT WILL BE MORE LIKELY TO ACQUIRE THE PRACTICED SKILL SINCE SHE WILL HAVE SPENT MORE TIME PRACTICING. IN THIS PAPER, WE EXAMINE THE RELATIONSHIP BETWEEN INTERNAL LOCUS OF CONTROL AND RETENTION IN GAME-BASED LEARNING APPLICATIONS FOR YOUNG CHILDREN USING TODO MATH, A MOBILE-BASED MATH LEARNING APPLICATION FOR CHILDREN FROM PRE-K TO 2ND GRADE. WE EXAMINE 345,783 USERS' LOG DATA TO SHOW THAT WHEN CHILDREN PREFER "FREE" MODE, WHICH HAS HIGH INTERNAL LOCUS OF CONTROL, THEIR RETENTION ON TODO MATH IS HIGHER THAN CHILDREN WHO PREFER "DAILY" MODE, WHICH HAS HIGH EXTERNAL LOCUS OF CONTROL. WE PRESENT THREE ANALYSES THAT SUPPORT OUR FINDINGS USING SURVIVAL ANALYSIS, POST-HOC ANALYSIS, AND T-TEST.
IF WE WISH TO EMBED ASSESSMENT FOR ACCOUNTABILITY WITHIN INSTRUCTION, WE NEED TO BETTER UNDERSTAND THE RELATIVE CONTRIBUTION OF DIFFERENT TYPES OF LEARNER DATA TO STATISTICAL MODELS THAT PREDICT SCORES ON ASSESSMENTS USED FOR ACCOUNTABILITY PURPOSES. THE PRESENT WORK SCALES UP AND EXTENDS PREDICTIVE MODELS OF MATH TEST SCORES FROM EXISTING LITERATURE AND SPECIFIES SIX CATEGORIES OF MODELS THAT INCORPORATE INFORMATION ABOUT STUDENT PRIOR KNOWLEDGE, SOCIO-DEMOGRAPHICS, AND PERFORMANCE WITHIN THE MATHIA INTELLIGENT TUTORING SYSTEM. LINEAR REGRESSION AND RANDOM FOREST MODELS ARE LEARNED WITHIN EACH CATEGORY AND GENERALIZED OVER A SAMPLE OF 23,000+ LEARNERS IN GRADES 6, 7, AND 8 OVER THREE ACADEMIC YEARS IN MIAMI-DADE COUNTY PUBLIC SCHOOLS. AFTER BRIEFLY EXPLORING HIERARCHICAL MODELS OF THIS DATA, WE DISCUSS A VARIETY OF TECHNICAL AND PRACTICAL APPLICATIONS, LIMITATIONS, AND OPEN QUESTIONS RELATED TO THIS WORK, ESPECIALLY CONCERNING TO THE POTENTIAL USE OF INSTRUCTIONAL PLATFORMS LIKE MATHIA AS A REPLACEMENT FOR TIME-CONSUMING STANDARDIZED TESTS.
RESEARCH HAS REVEALED THAT THE DESIGN OF ONLINE LEARNING ENVIRONMENTS CAN INFLUENCE STUDENTS' USE AND PERFORMANCE. IN THIS STUDY, AN ONLINE LEARNING ENVIRONMENT FOR LEARNING FRENCH AS A FOREIGN LANGUAGE WAS DEVELOPED IN LINE WITH THE FOUR COMPONENT INSTRUCTIONAL DESIGN (4C/ID) MODEL. WHILE THE 4C/ID-MODEL IS A WELL-ESTABLISHED INSTRUCTIONAL DESIGN MODEL, LITTLE IS KNOWN ABOUT (1) FACTORS IMPACTING STUDENTS' USE OF THE FOUR COMPONENTS, NAMELY, LEARNING TASKS, PART-TASK PRACTICE, SUPPORTIVE AND PROCEDURAL INFORMATION DURING THEIR LEARNING PROCESS AS WELL AS ABOUT (2) THE WAY IN WHICH STUDENTS' DIFFERENCES IN USE OF THE 4C/ID-BASED ONLINE LEARNING ENVIRONMENT IMPACTS COURSE PERFORMANCE. THE AIM OF THIS STUDY IS, THEREFORE, TWOFOLD. FIRSTLY, IT INVESTIGATES THE INFLUENCE OF STUDENTS' PRIOR KNOWLEDGE, TASK VALUE AND SELF-EFFICACY ON STUDENTS' USE OF THE FOUR DIFFERENT COMPONENTS OF THE 4C/ID-MODEL. SECONDLY, IT EXAMINES THE INFLUENCE OF STUDENTS' USE OF THE COMPONENTS ON THEIR LEARNING GAIN, TAKING INTO ACCOUNT THEIR CHARACTERISTICS. THE SAMPLE CONSISTED OF 161 STUDENTS IN HIGHER EDUCATION. RESULTS, BASED ON STRUCTURAL EQUATION MODELLING (SEM), INDICATE THAT PRIOR KNOWLEDGE HAS A NEGATIVE INFLUENCE ON STUDENTS' USE OF LEARNING TASKS AND PART-TASK PRACTICE. TASK VALUE HAS A POSITIVE INFLUENCE ON USE OF LEARNING TASKS AND SUPPORTIVE INFORMATION. ADDITIONALLY, RESULTS INDICATE THAT USE OF USE OF LEARNING TASKS, PROCEDURAL INFORMATION, CONTROLLED FOR STUDENTS' PRIOR KNOWLEDGE SIGNIFICANTLY CONTRIBUTE TO STUDENTS' LEARNING GAIN. RESULTS SUGGEST THAT STUDENTS' USE OF THE FOUR COMPONENTS IS BASED ON THEIR COGNITIVE AND MOTIVATIONAL CHARACTERISTICS. FURTHERMORE, RESULTS REVEAL THE IMPACT OF STUDENTS' USE OF LEARNING TASKS AND PROCEDURAL INFORMATION ON STUDENTS' LEARNING GAIN.
THERE HAS BEEN A GROWING TREND IN HIGHER EDUCATION TOWARDS INCREASED USE AND ADOPTION OF MASSIVE OPEN ONLINE COURSES (MOOCS). DESPITE THIS INTEREST IN LEARNING AT SCALE, LIMITED WORK HAS COMPARED MOOC ACTIVITY ACROSS SUBSEQUENT COURSE OFFERINGS. IN THIS STUDY, WE EXPLORE FORUM ACTIVITY IN TEN ITERATIONS OF THE SAME MOOC. OUR RESULTS SUGGEST THAT PARTICIPATION IN MOOC FORUMS HAS CHANGED OVER THE PAST FOUR YEARS OF DELIVERY. FIRST, OVERALL PARTICIPATION IN MOOC FORUMS HAVE DECREASED. SECOND, IN LATER ITERATIONS COHORTS OF MORE COMMITTED FORUM USERS START TO RESEMBLE FORMAL ONLINE COURSES IN SIZE (67>N>36). HOWEVER, DESPITE THE SMALLER GROUPS OF LEARNERS THAT SHOULD FIND IT EASIER TO FORM CONNECTIONS WITH ONE ANOTHER, OUR ANALYSIS DID NOT REVEAL THE EXPECTED INCREASE IN THE QUALITY OF SOCIAL ACTIVITY. INSTEAD, MOOC FORUMS EVOLVED INTO SMALLER ON-TASK QUESTION AND ANSWER (Q&A) SPACES, NOT CAPITALIZING ON THE OPPORTUNITIES FOR SOCIAL LEARNING. WE DISCUSS PRACTICAL AND RESEARCH IMPLICATIONS OF SUCH CHANGES.
ACADEMIC RESULTS IN MATHEMATICS SUBJECTS IN ENGINEERING, ESPECIALLY THOSE TAUGHT ONLINE, ARE POOR. THE KEY CONTEXTUAL ELEMENTS OF AN INSUFFICIENT LEVEL OF PRIOR KNOWLEDGE, POOR MOTIVATION, AND THE INTRINSIC DIFFICULTY OF THE SUBJECT MATTER HAS LED TO THE REVIEW OF THE STUDY MATERIALS AND THE CREATION OF A METHODOLOGY BASED ON CONTINUOUS ASSESSMENT WITH AUTOMATIC FEEDBACK AT THE UNIVERSITAT OBERTA DE CATALUNYA. THE MAIN OBJECTIVE OF THIS ARTICLE IS TO PRESENT EVIDENCE OF IMPROVED LEARNING OUTCOMES WITH THE APPLICATION AND USE OF WIRIS QUIZZES AS AN AUTOMATIC ASSESSMENT TOOL IN CALCULUS SUBJECTS. TO THAT END, WE CARRIED OUT DESCRIPTIVE RESEARCH WITH AN EX-POST-FACTO DESIGN, BASED ON STATISTICAL ANALYSIS OF THE RESULTS FOR CONTINUOUS ASSESSMENT AND STUDENTS' GRADES IN 11 SEMESTERS. THE NEW STRATEGY HAS BEEN APPLIED IN THE LAST TWO ACADEMIC YEARS, BASED ON INTRODUCTORY MOODLE LESSONS AND ASSESSMENT WITH INTERACTIVE QUESTIONNAIRES. THE DATA OBTAINED ARE INCONCLUSIVE: IN THE FIRST TWO SEMESTERS THAT THE METHODOLOGY WAS IMPLEMENTED, THE RESULTS SHOW A NOTICEABLE IMPROVEMENT IN SEVERAL KEY ASPECTS, SUCH AS UNDERTAKING AND PASSING THE CONTINUOUS ASSESSMENT AND IMPROVED RESULTS ACROSS THE SPECTRUM OF GRADES. HOWEVER, IN THE LAST TWO SEMESTERS, THE RESULTS ARE GENERALLY SIMILAR TO THOSE OBTAINED WITH THE OLD METHODOLOGY.
EDUCATIONAL DATA MINING AND LEARNING ANALYTICS ARE TWO GROWING FIELDS OF STUDY, TRYING TO MAKE SENSE OF EDUCATION DATA AND TO IMPROVE TEACHING AND LEARNING EXPERIENCE. WE STUDY DROPOUT PREDICTION IN MASSIVELY OPEN ONLINE COURSES (MOOCS), WHERE THE GOAL IS GIVEN STUDENT'S LEARNING BEHAVIOR LOG DATA IN ONE MONTH, TO PREDICT WHETHER STUDENTS WOULD DROP OUT IN NEXT TEN DAYS. WE COLLECT 39 COURSES DATA FROM XUETANGX PLATFORM, WHICH IS BASED ON THE OPEN SOURCE EDX PLATFORM. WE DESCRIBE OUR COMPLETE APPROACH TO COPE WITH DROP OUT PREDICTION TASK, INCLUDING DATA EXTRACTION FROM EDX PLATFORM, DATA PREPROCESSING, FEATURE ENGINEERING AND PERFORMANCE TEST ON SEVERAL SUPERVISED CLASSIFICATION MODEL SUCH AS SVM, LOGISTICS REGRESSION, RANDOM FOREST AND GRADIENT BOOSTING DECISION TREE. WE ACHIEVE 88% ACCURACY IN DROPOUT PREDICTION TASK WITH GBDT MODEL.
DUE TO THE COVID19 PANDEMIC, MORE HIGHER-LEVEL EDUCATION PROGRAMMES HAVE MOVED TO ONLINE CHANNELS, RAISING ISSUES IN MONITORING STUDENTS' LEARNING PROGRESS. THANKS TO ADVANCES IN ONLINE LEARNING SYSTEMS, HOWEVER, STUDENT DATA CAN BE AUTOMATICALLY COLLECTED AND USED FOR THE INVESTIGATION AND PREDICTION OF THE STUDENTS' LEARNING PERFORMANCE. IN THIS ARTICLE, WE PRESENT A NOVEL APPROACH TO ANALYSE STUDENTS' LEARNING BEHAVIOUR, AS WELL AS THE RELATIONSHIP BETWEEN THESE BEHAVIOURS AND LEARNING ASSESSMENT RESULTS, IN THE CONTEXT OF PROGRAMMING EDUCATION. A BESPOKE METHOD HAS BEEN BUILT BASED ON A COMBINATION OF RANDOM MATRIX THEORY, A COMMUNITY DETECTION ALGORITHM AND STATISTICAL HYPOTHESIS TESTS. THE DATASETS CONTAIN FINE-GRAINED INFORMATION ABOUT STUDENTS' LEARNING BEHAVIOURS IN TWO PROGRAMMING COURSES OVER TWO ACADEMIC YEARS WITH ABOUT 400 FIRST-YEAR STUDENTS IN A MEDIUM-SIZED METROPOLITAN UNIVERSITY IN DUBLIN. THE PROPOSED METHOD IS A NOVAL APPROACH TO DATA PREPROCESSING WHICH CAN IMPROVE THE ANALYSIS AND PREDICTION BASED ON LEARNING BEHAVIOURAL DATASETS. THE PROPOSED APPROACH DEALS WITH THE ISSUES OF NOISE AND TREND EFFECT IN THE DATA AND HAS SHOWN ITS SUCCESS IN DETECTING GROUPS OF STUDENTS WHO HAVE SIMILAR LEARNING BEHAVIOURS AND OUTCOMES. THE HIGHER PERFORMING GROUPS HAVE BEEN FOUND TO BE MORE ACTIVE IN PRACTICAL-RELATED ACTIVITIES THROUGHOUT THE COURSE. CONVERSELY, WE FOUND THAT THE LOWER PERFORMING GROUPS ENGAGE MORE WITH LECTURE NOTES INSTEAD OF DOING PROGRAMMING TASKS. THE LEARNING BEHAVIOURS DATA CAN ALSO BE USED TO PREDICT STUDENTS' OUTCOMES (I.E. PASS OR FAIL THE TERMINAL EXAMS) AT THE EARLY STAGES OF THE STUDY, USING POPULAR MACHINE LEARNING CLASSIFICATION TECHNIQUES. (C) 2021 THE AUTHOR(S). PUBLISHED BY ELSEVIER B.V.
IN EDUCATIONAL HRI, IT IS GENERALLY BELIEVED THAT A ROBOTS BEHAVIOR HAS A DIRECT EFFECT ON THE ENGAGEMENT OF A USER WITH THE ROBOT, THE TASK AT HAND AND ALSO THEIR PARTNER IN CASE OF A COLLABORATIVE ACTIVITY. INCREASING THIS ENGAGEMENT IS THEN HELD RESPONSIBLE FOR INCREASED LEARNING AND PRODUCTIVITY. THE STATE OF THE ART USUALLY INVESTIGATES THE RELATIONSHIP BETWEEN THE BEHAVIORS OF THE ROBOT AND THE ENGAGEMENT STATE OF THE USER WHILE ASSUMING A LINEAR RELATIONSHIP BETWEEN ENGAGEMENT AND THE END GOAL: LEARNING. HOWEVER, IS IT CORRECT TO ASSUME THAT TO MAXIMISE LEARNING, ONE NEEDS TO MAXIMISE ENGAGEMENT? FURTHERMORE, CONVENTIONAL SUPERVISED MODELS OF ENGAGEMENT REQUIRE HUMAN ANNOTATORS TO GET LABELS. THIS IS NOT ONLY LABORIOUS BUT ALSO INTRODUCES FURTHER SUBJECTIVITY IN AN ALREADY SUBJECTIVE CONSTRUCT OF ENGAGEMENT. CAN WE HAVE MACHINE-LEARNING MODELS FOR ENGAGEMENT DETECTION WHERE ANNOTATIONS DO NOT RELY ON HUMAN ANNOTATORS? LOOKING DEEPER AT THE BEHAVIORAL PATTERNS AND THE LEARNING OUTCOMES AND A PERFORMANCE METRIC IN A MULTI-MODAL DATA SET COLLECTED IN AN EDUCATIONAL HUMAN-HUMAN-ROBOT SETUP WITH 68 STUDENTS, WE OBSERVE A HIDDEN LINK THAT WE TERM AS PRODUCTIVE ENGAGEMENT. WE THEORIZE A ROBOT INCORPORATING THIS KNOWLEDGE WILL (1) DISTINGUISH TEAMS BASED ON ENGAGEMENT THAT IS CONDUCIVE OF LEARNING; AND (2) ADOPT BEHAVIORS THAT EVENTUALLY LEAD THE USERS TO INCREASED LEARNING BY MEANS OF BEING PRODUCTIVELY ENGAGED. FURTHERMORE, THIS SEMINAL LINK PAVES WAY FOR MACHINE-LEARNING MODELS IN EDUCATIONAL HRI WITH AUTOMATIC LABELLING BASED ON THE DATA.
THIS PAPER STUDIES THE PATTERNS OF LEARNING BEHAVIOUR IN CONNECTION WITH EDUCATIONAL ACHIEVEMENT IN MULTI-YEAR UNDERGRADUATE DATA SCIENCE MINOR SPECIALISATION FOR NON-STEM STUDENTS. IN PARTICULAR, THIS WORK FOCUSES ON ANALYSING THE PREDICTORS OF ACADEMIC ACHIEVEMENT IN BLENDED-LEARNING SETTING FACTORS RELATED TO INITIAL MATHEMATICS KNOWLEDGE, SPECIFIC TRAITS OF EDUCATIONAL PROGRAMS, ONLINE AND OFFLINE LEARNING ENGAGEMENT, AND CONNECTIONS WITH PEERS. ROBUST LINEAR REGRESSION AND NON-PARAMETRIC STATISTICAL TESTS REVEAL A SIGNIFICANT GAP IN THE ACHIEVEMENT OF STUDENTS FROM DIFFERENT EDUCATIONAL PROGRAMS AND ON THE CONNECTION BETWEEN THEIR CLASS ATTENDANCE AND ACHIEVEMENT. THE RESULTS INDICATE THAT ACHIEVEMENT IS NOT RELATED TO THE COMMUNICATION ON THE Q&A FORUM WHILE PEERS DO AFFECT ACADEMIC SUCCESS.
PERSONAL INFORMATICS (PI) SYSTEMS HAVE EMERGED AS POWERFUL TOOLS FOR HELPING PEOPLE TO BE AWARE OF THEIR BEHAVIORS IN DIVERSE DOMAINS. NEVERTHELESS, LITTLE ATTENTION HAS BEEN DEVOTED TO EDUCATIONAL SCENARIOS. FOR THIS REASON, THIS PAPER PRESENTS AN EDUCATIONAL PI SYSTEM CALLED GLANCE AND, MOREOVER, EVALUATES ITS USEFULNESS IN TWO FULLY ONLINE UNDERGRADUATE COURSES.
WE HAVE DEVELOPED TABLET PC-BASED SOFTWARE FOR LEARNING BY POSING ARITHMETICAL WORD PROBLEMS NAMED MONSAKUN WHERE SEVERAL SENTENCES ARE INTEGRATED TO POSE A PROBLEM BY A LEARNER. WE CALL THIS TYPE OF PROBLEM-POSING "SENTENCE INTEGRATION". BASED ON COLLECTED DATA BY PAST EXPERIMENTAL USE, WE HAVE BEEN ANALYZING USERS' PROBLEM-POSING PROCESS AS THE SELECTION PROCESS OF THE SENTENCES. AS THE FIRST STEP OF THIS ANALYSIS, WE FOUND THAT THE FIRST SENTENCE SELECTED IN THE PROCESS WERE DIFFERENT IN (1) TYPE OF APPROACH, (2) TYPE OF STORY AND (3) EXERCISE EXPERIENCE. THESE RESULTS ARE IMPORTANT TO MAKE AN ELABORATE PROCESS MODEL OF THE PROBLEM-POSING AND ADAPTIVE SUPPORT OF THE PROCESS.
THIS STUDY EXPLORES THE VIEWING PATTERNS OF 17 INSTRUCTIONAL VIDEOS IN BOTH A TRADITIONAL AND FLIPPED CLASSROOM ENVIRONMENT BY 732 BUSINESS STUDENTS TAKING AN IT-RELATED MODULE. WHILE PREVIOUS WORK HAS CONCENTRATED MAINLY ON OUTPUTS (E.G. STUDENT SATISFACTION/RESULTS), THIS STUDY FOCUSES ON HOW THE NATURE OF STUDENT'S INTERACTIONS WITH VIDEOS CAN BE DETERMINED THROUGH A DEEP ANALYSIS OF ANALYTICS DATA. THE MAIN FINDINGS SHOW THAT THERE WERE LESS INTERACTIONS WITH THE INSTRUCTIONAL VIDEOS IN THE FLIPPED CLASSROOM ENVIRONMENT COMPARED TO THE TRADITIONAL ENVIRONMENT, AND THAT VIDEOS WERE USED MORE AS A REVISION AID PRIOR TO EXAMS (IN BOTH ENVIRONMENTS) THAN AS AN ONGOING SUPPORT TO DEVELOP SKILLS DURING TERM. IMPLICATIONS OF THIS STUDY INCLUDE THE NEED FOR REGULAR MONITORING OF HOW INSTRUCTIONAL VIDEOS ARE BEING USED DURING TERM AND THE IMPORTANCE OF UNDERTAKING A DEEPER ANALYSIS OF ANALYTICS DATA AS THE INITIAL SUMMARY DATA MAY BE MISLEADING.
FOCUSING ON THE MASSIVE OPEN ONLINE COURSE (MOOC) PLATFORM, THE PURPOSE OF THIS STUDY IS TO REALIZE PERSONALIZED ADAPTIVE LEARNING ACCORDING TO THE NEEDS AND ABILITIES OF EACH LEARNER. TO THIS END, THE AUTHOR CREATED A PERSONALIZED ADAPTIVE LEARNING BEHAVIOUR ANALYSIS MODEL, AND DESIGNED A PERSONALIZED MOOC PLATFORM BASED ON THE MODEL. THROUGH THE ANALYSIS OF LEARNING BEHAVIOURS ON THE MOOC PLATFORM, THE MODEL DIGS DEEP INTO THE PATTERN OF LEARNING BEHAVIOURS, AND LAYS THE BASIS FOR PERSONALIZED INTERVENTION IN THE LEARNING PROCESS. THE COMPARISON EXPERIMENTS SHOW THAT OUR PREDICTION METHOD IS MORE ACCURATE THAN THE OTHER PREDICTION ALGORITHMS. THIS RESEARCH SHEDS NEW LIGHT ON THE DESIGN OF LEARNER-SPECIFIC MOOC PLATFORM.
IN THIS STUDY, A COLLABORATIVE PROBLEM SOLVING (CPS) BASED SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) COURSE WAS DESIGNED, FOLLOWING CPS PROCESSES AND THE IMPORTANT FACTORS OF STEM EDUCATION. THE COURSE WAS DIVIDED INTO THREE LEARNING PHASES BASED ON THE CONTENTS AND TEACHING APPROACH. THE PURPOSE OF THE STUDY WAS TO IDENTIFY THE LEARNING BEHAVIORS IN DIFFERENT PHASES AND TO CLARIFY THE RELATIONSHIPS BETWEEN LEARNING BEHAVIORS WITH LEARNING STRATEGIES. THE RESULTS INDICATED THAT LEARNING BEHAVIORS RELATED TO REHEARSAL STRATEGY, COGNITIVE STRATEGIES SUCH AS ORGANIZATION AND ELABORATION, AND METACOGNITIVE STRATEGIES SUCH AS REFLECTION SHOULD BE CONSIDERED IN DIFFERENT LEARNING PHASES. FINALLY, A FRAMEWORK OF CPS-BASED STEM COURSE WAS PROPOSED BASED ON THE RESULTS AND DISCUSSION.
PROCESS MINING METHODOLOGIES ARE DESIGNED TO UNCOVER UNDERLYING BUSINESS PROCESSES, DEVIATIONS FROM THEM, AND IN GENERAL, USAGE PATTERNS. ONE OF THE KEY LIMITATIONS OF THESE METHODOLOGIES IS THAT THEY STRUGGLE IN CASES IN WHICH THERE IS NO STRUCTURED PROCESS, OR WHEN A PROCESS CAN BE PERFORMED IN MANY WAYS. LEARNING MANAGEMENT SYSTEMS ARE A CLASSIC CASE OF UNSTRUCTURED PROCESSES SINCE EACH LEARNER FOLLOWS A DIFFERENT LEARNING PROCESS. IN THIS PAPER, WE ADDRESS THIS LIMITATION BY PROPOSING AND VALIDATING THE USER BEHAVIOR PATTERN DETECTION (UBPD) METHODOLOGY WHICH IS BASED ON DETECTING VERY SHORT USER ACTIVITIES AND CLUSTERING THEM BASED ON SHARED VARIANCE TO CONSTRUCT A MORE MEANINGFUL BEHAVIOR. WE DEVELOP AND VALIDATE THIS METHODOLOGY BY USING TWO DATASETS OF UNSTRUCTURED PROCESSES FROM DIFFERENT IMPLEMENTATIONS OF A LEARNING MANAGEMENT SYSTEM. THE FIRST DATASET USES A GAMIFIED COURSE WHERE USERS HAVE THE FREEDOM TO CHOOSE HOW TO USE THE SYSTEM, AND THE SECOND DATASET USES DATA FROM A MASSIVE ONLINE OPEN COURSE, WHERE AGAIN, SYSTEM USAGE IS BASED ON PERSONAL LEARNING PREFERENCES. THE KEY CONTRIBUTION OF THE METHODOLOGY IS ITS ABILITY TO DISCOVER USER-SPECIFIC USAGE PATTERNS AND CLUSTER USERS BASED ON THEM, EVEN IN NOISY SYSTEMS WITH NO CLEAR PROCESS. IT PROVIDES GREAT VALUE TO COURSE DESIGNERS AND TEACHERS TRYING TO UNDERSTAND HOW LEARNER INTERACT WITH THEIR SYSTEM AND SETS THE FOUNDATION FOR ADDITIONAL RESEARCH IN THIS CLASS OF SYSTEMS.
IT IS A COMMON PRACTICE TO ISSUE A SUMMARY OF A LEARNER'S LEARNING ACHIEVEMENTS IN FORM OF A TRANSCRIPT OR CERTIFICATE. HOWEVER, DETAILED INFORMATION ON THE DEPTH OF LEARNING AND HOW LEARNING OR TEACHINGS WERE CONDUCTED IS NOT PRESENT IN THE TRANSCRIPT OF SCORES. THIS WORK PRESENTS THE FIRST PRACTICAL IMPLEMENTATION OF A NEW PLATFORM FOR KEEPING TRACK OF LEARNING ACHIEVEMENTS BEYOND TRANSCRIPTS AND CERTIFICATES. THIS IS ACHIEVED BY MAINTAINING DIGITAL HASHES OF LEARNING ACTIVITIES AND MANAGING ACCESS RIGHTS THROUGH THE USE OF SMART CONTRACTS ON THE BLOCKCHAIN. THE BLOCKCHAIN OF LEARNING LOGS (BOLL) IS A PLATFORM THAT ENABLE LEARNERS TO MOVE THEIR LEARNING RECORDS FROM ONE INSTITUTION TO ANOTHER IN A SECURE AND VERIFIABLE FORMAT. THIS PRIMARILY SOLVES THE COLD-START PROBLEM FACED BY LEARNING DATA ANALYTIC PLATFORMS WHEN TRYING TO OFFER PERSONALIZED EXPERIENCE TO NEW LEARNERS. BOLL ENABLES EXISTING LEARNING DATA ANALYTIC PLATFORMS TO ACCESS THE LEARNING LOGS FROM OTHER INSTITUTIONS WITH THE PERMISSION OF THE LEARNERS AND/OR INSTITUTION WHO ORIGINALLY HAVE OWNERSHIP OF THE LOGS. THE MAIN CONTRIBUTION OF THIS PAPER IS TO INVESTIGATE HOW LEARNING RECORDS COULD BE CONNECTED ACROSS INSTITUTIONS USING BOLL. WE PRESENT AN OVERVIEW OF HOW THE IMPLEMENTATION HAS BEEN CARRIED OUT, DISCUSS RESOURCE REQUIREMENTS, AND COMPARE THE ADVANTAGES BOLL HAS OVER OTHER SIMILAR TOOLS.
ONLINE LEARNING ENVIRONMENTS BECAME POPULAR IN RECENT YEARS. DUE TO HIGH ATTRITION RATES, THE PROBLEM OF STUDENT DROPOUTS BECAME OF IMMENSE IMPORTANCE FOR COURSE DESIGNERS, AND COURSE MAKERS. IN THIS PAPER, WE UTILIZED LASSO AND RIDGE LOGISTIC REGRESSION TO CREATE A PREDICTION MODEL FOR DROPOUT ON THE OPEN UNIVERSITY DATABASE. WE INVESTIGATED HOW EARLY DROPOUT CAN BE PREDICTED, AND WHY DROPOUTS OCCUR. TO ANSWER THE FIRST QUESTION, WE CREATED MODELS FOR EIGHT DIFFERENT TIME FRAMES, RANGING FROM THE BEGINNING OF THE COURSE TO THE MID-TERM. THERE ARE TWO RESULTS BASED ON TWO DEFINITIONS OF DROPOUT. RESULTS SHOW THAT AT THE BEGINNING AUC OF THE PREDICTION MODEL IS 0.549 AND 0.661 AND RISES TO 0.681 AND 0.869 AT MID-TERM. BY ANALYZING LOGISTIC REGRESSION COEFFICIENTS, WE SHOWED THAT AT THE BEGINNING OF THE COURSE DEMOGRAPHIC FEATURES OF THE STUDENT AND COURSE DESCRIPTION FEATURES ARE THE MOST IMPORTANT VARIABLES FOR DROPOUT PREDICTION, WHILE LATER STUDENT ACTIVITY GAINS MORE IMPORTANCE.
IN OUTCOME-BASED ACADEMIC PROGRAMS, PROGRAM EDUCATION OBJECTS (PEOS) ARE THE KEY PILLARS ON WHICH PROGRAM COMPONENTS ARE BUILT. THEY ARE ARTICULATED LINGUISTICALLY AS BROAD STATEMENTS OF GRADUATES' PROFESSIONAL AND CAREER ACCOMPLISHMENTS WITHIN A FEW YEARS OF GRADUATION. MOREOVER, PEOS ARE MAPPED INTO A SET OF SKILLS AND ATTRIBUTES KNOWN AS PROGRAM LEARNING OUTCOMES (PLOS). IT GOES WITHOUT SAYING THAT A PROFOUND UNDERSTANDING OF THE PEOS IS A KEY FACTOR IN THE SUCCESS OF AN ACADEMIC PROGRAM. FOR THIS SAKE, THIS PAPER PROPOSES A DATA ANALYTICS-BASED APPROACH TO EXAMINE THE CORRELATIONS AMONG PEOS. MORE SPECIFICALLY, IT APPLIES A DATA SIMILARITY-BASED APPROACH TO ANALYZE THE CORRELATIONS AMONG THE PEOS OF ENGINEERING PROGRAMS. TO THIS END, A DATASET OF PEOS-PLOS MAPPING OF A SET OF ENGINEERING PROGRAMS HAS BEEN EXTRACTED FROM THEIR SELF-STUDY REPORTS. THE COLLECTED DATASET HAS UNDERGONE PREPROCESSING STEPS TO TRANSFORM IT INTO A SUITABLE REPRESENTATION. THIS INVOLVES DATA CLEANING, DATA ANNOTATION USING A DEVELOPED SET OF PEOS LABELS, AND REMOVAL OF DATA INSTANCES WITH MULTIPLE PEO LABELS. EACH PEO IS THEN REPRESENTED AS A VECTOR SPACE MODEL WHOSE DIMENSIONS ARE THE PLOS, AND THEIR VALUES ARE THE RELATIVE FREQUENCIES OF PLOS COMPUTED FROM ALL DATA INSTANCES OF THAT PEO. AFTER THAT, THREE DATA SIMILARITY MEASURES, NAMELY EUCLIDEAN DISTANCE, COSINE MEASURE, AND MANHATTAN DISTANCE, ARE APPLIED TO MEASURE THE SIMILARITY BETWEEN PEOS VECTOR SPACE MODELS. THE RESULTANT SIMILARITY MATRICES ARE THEN ANALYZED AT THE LEVEL OF A SPECIFIC MEASURE, AN AGREEMENT BETWEEN MEASURES, AND AVERAGE SIMILARITY ACROSS ALL MEASURES. THE ANALYSIS RESULTS CONTRIBUTE TO A BETTER UNDERSTANDING OF THE PEOS CORRELATIONS AND PROVIDE VERY USEFUL ACTIONABLE INSIGHTS FOR EMPOWERING DECISION MAKING TOWARD SYSTEMIZATION AND OPTIMIZATION OF ACADEMIC PROGRAMS PROCESSES.
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
LEARNING MANAGEMENT SYSTEM (LMS) PLATFORMS HAVE LED TO A TRANSFORMATION IN UNIVERSITIES IN THE LAST DECADE. HELPING THEM TO ADAPT AND EXPAND THEIR SERVICES TO NEW TECHNOLOGICAL CHALLENGES. THESE PLATFORMS HAVE MADE POSSIBLE THE EXPANSION OF DISTANCE EDUCATION. A CURRENT TREND IN THIS AREA IS FOCUSED ON THE EVALUATION AND IMPROVEMENT OF THE STUDENTS' SATISFACTION. IN THIS WORK A NEW TOOL TO ASSESS STUDENT SATISFACTION USING EMOTICONS (SMILEYS) IS PROPOSED TO EVALUATE THE QUALITY OF THE LEARNING CONTENT AND THE METHODOLOGY AT UNIT LEVEL FOR ANY COURSE AND AT ANY TIME. THE RESULTS INDICATE THAT THE ASSESSMENT OF STUDENT SATISFACTION IS SENSITIVE TO THE PERIOD WHEN THE SURVEY IS PERFORMED AND TO THE STUDENT'S STUDY LEVEL. MOREOVER, THE RESULTS OF THIS NEW PROPOSAL ARE COMPARED TO THE SATISFACTION RESULTS USING TRADITIONAL SURVEYS, SHOWING DIFFERENT RESULTS DUE TO A MORE ACCURACY AND FLEXIBILITY WHEN USING THE TOOL PROPOSED IN THIS WORK.
IN THIS PAPER, WE PRESENT A NEW FUZZY METHODOLOGY FOR EARLY STUDENTS' FAILURE DETECTION. HIGH SCHOOL BACKGROUND, SUBJECTS STUDIED IN THE UNIVERSITY AND ACTIVITIES IN LEARNING MANAGEMENT SYSTEMS WERE DETERMINED TO BE THE FACTORS INFLUENCING STUDENTS' PERFORMANCE. AFTER SELECTION OF THE IMPACT FACTORS OF STUDENTS' ASSESSMENT, WE CONVERT LINGUISTIC EVALUATIONS INTO FUZZY NUMBERS AND EMPLOY MULTI-CRITERIA METHODS FOR EDUCATIONAL DATA PROCESSING. IN TWO PRACTICAL EXAMPLES, THE AGGREGATE STUDENTS' SCORES WERE CALCULATED BY USING FUZZY MULTI-CRITERIA ALGORITHMS. THE OBTAINED STUDENTS' RANKING HELPS INSTRUCTORS DURING THE SEMESTER TO DETECT STUDENTS WHO WILL DROP OUT THE COURSE AND TO PLAN ADDITIONAL LEARNING ACTIVITIES FOR THESE STUDENTS. IN THE FUTURE, WE PLAN ON ANALYSING STUDENTS' DATA FROM DIFFERENT UNIVERSITY'S COURSES AND MAJORS AND MINING SEVERAL ACADEMIC YEARS IN ORDER TO CREATE A RELIABLE ASSESSMENT INDEX FOR EARLY PREDICTION OF STUDENTS' FAILURE.
WITH A GROWING NUMBER OF SCRATCHJR USAGE, OVER 19 MILLION USERS WORLDWIDE, WE EXAMINED THE USE IN THE UNITED STATES OF THE FREE SCRATCHJR PROGRAMMING LANGUAGE, EXPLICITLY DESIGNED FOR YOUNG CHILDREN AGES 5-7, TO LEARN HOW TO CODE. OUR OBJECTIVE WAS TO EXPLORE CHILDREN'S USAGE OF THE SCRATCHJR TABLET APP AT HOME AND SCHOOL SETTINGS. WE ANALYZED USAGE DATA FROM GOOGLE ANALYTICS IN 1.5 YEARS, COMPARING SCRATCHSR USAGE IN THE TWO DIFFERENT SETTINGS. OUR DATASET COMPRISED A TOTAL OF 4,352,802 CODING SESSIONS, GENERATED BY A DAILY AVERAGE OF 2525 HOME USERS AND 9969 SCHOOL USERS. THE RESULTS SUGGESTED THAT, ALTHOUGH CHILDREN IN BOTH SETTINGS ON AVERAGE SPENT AN EQUAL DURATION WITH SCRATCHJR, CHILDREN IN HOME SETTINGS SPENT MORE TIME EXPLORING ADVANCED CODING BLOCKS AND THE PAINT EDITOR COMPARED TO CHILDREN AT SCHOOL. FURTHER, CHILDREN AT SCHOOL TENDED TO USE SIMILAR TYPES OF CODING BLOCKS ACROSS SEVERAL DAYS. IN CONTRAST, CHILDREN AT HOME WERE MORE LIKELY TO USE A DIVERSITY OF BLOCK CATEGORIES AND DIFFICULTY LEVELS. THE IMPLICATIONS OF THIS RESEARCH ARE, FIRST, THAT USAGE PATTERNS MAY HELP US UNDERSTAND HOW CHILDREN ACROSS SETTINGS LEARN TO PROGRAM DIFFERENTLY. SECOND, BASED ON THESE FINDINGS, IT MAY BE ESSENTIAL FOR PARENTS AT HOME AND EDUCATORS AT SCHOOL TO CONSIDER USING DIFFERENT APPROACHES AND STRATEGIES.
MASSIVE OPEN ONLINE COURSES REPRESENT A FERTILE GROUND FOR EXAMINING STUDENT BEHAVIOR. HOWEVER, DUE TO THEIR OPENNESS MOOC ATTRACT A DIVERSE BODY OF STUDENTS, FOR THE MOST PART, UNKNOWN TO THE COURSE INSTRUCTORS. HOWEVER, A CERTAIN NUMBER OF STUDENTS ENROLL IN THE SAME COURSE MULTIPLE TIMES, AND THERE ARE RECORDS OF THEIR PREVIOUS LEARNING ACTIVITIES WHICH MIGHT PROVIDE SOME USEFUL INFORMATION TO COURSE ORGANIZERS BEFORE THE START OF THE COURSE. IN THIS STUDY, WE EXAMINED HOW STUDENT BEHAVIOR CHANGES BETWEEN SUBSEQUENT COURSE OFFERINGS. WE IDENTIFIED PROFILES OF RETURNING STUDENTS AND ALSO INTERESTING CHANGES IN THEIR BEHAVIOR BETWEEN TWO ENROLLMENTS TO THE SAME COURSE. RESULTS AND THEIR IMPLICATIONS ARE FURTHER DISCUSSED.
EFFECTIVE ANALYSIS AND DEMONSTRATION OF THESE DATA FEATURES IS OF GREAT SIGNIFICANCE FOR THE OPTIMIZATION OF INTERACTIVE LEARNING ENVIRONMENT AND LEARNING BEHAVIOR. THEREFORE, WE TAKE THE BIG DATA SET OF LEARNING BEHAVIOR GENERATED BY AN ONLINE INTERACTIVE LEARNING ENVIRONMENT AS THE RESEARCH OBJECT, DEFINE THE FEATURES OF LEARNING BEHAVIOR, AND DEMONSTRATE THEIR RELATIONSHIPS. A KEY FEATURE SELECTION METHOD FOR SPARSE LEARNING BASED ON DATA SET IS DESIGNED. THE MODELS AND ALGORITHMS ARE FULLY TRAINED AND TESTED THROUGH A LARGE NUMBER OF EXPERIMENTS. SEVERAL APPROXIMATE OPTIMAL ALGORITHMS ARE SELECTED TO COMPARE THE PERFORMANCE INDICATORS. ON THIS BASIS, THE RULE AND RELATIONSHIPS ARE MINED AND PREDICTED FOR THE KEY FEATURES, AND THE MEASURES TO IMPROVE THE KEY FEATURES ARE PROPOSED. THE CONCLUSION IS THAT THE GUIDANCE AND CONSTRUCTION OF LEARNING BEHAVIOR BASED ON THE KEY FEATURES CAN HAVE A SIGNIFICANCE ON THE LEARNING EFFECT, THAT HAS ALSO BEEN PROVED IN PRACTICE. DRIVEN BY THE ACTUAL DATA, IT IS AN INEVITABLE TREND TO DESIGN THE SUITABLE METHODS APPLIED TO KEY FEATURES OF EDUCATION BIG DATA. THIS RESEARCH METHOD AND PRACTICE PROCESS CAN PROVIDE TECHNICAL REFERENCE AND THEORETICAL BASIS FOR THE SIMILAR TOPICS.
THE INCORPORATION OF MOBILE APPLICATIONS IN EDUCATIONAL ENVIRONMENTS GENERATES A LARGE AMOUNT OF INFORMATION RESULTING FROM THE INTERACTION OF STUDENTS WITH THESE APPLICATIONS. THE ANALYSIS OF THIS INFORMATION CAN BE OF SIGNIFICANT IMPORTANCE. THE TEACHER MAY FIND IT USEFUL, AS IT CAN HELP THEM TO MAKE DECISIONS OR TO ASSESS THE PROCESS OF TEACHING/LEARNING THAT IT IS APPLYING WITHIN ITS TEACHING ACTIVITIES, ALLOWING TO IMPROVE THE RESULTS OR TO DETECT CERTAIN PATTERNS. HOWEVER, THE DEVELOPMENT OF MOBILE APPLICATIONS THAT ARE CAPABLE OF INCORPORATING THESE FEATURES IS NOT TRIVIAL FOR A USER WHO DOES NOT HAVE PROPER PROGRAMMING KNOWLEDGE. THIS PAPER PRESENTS THE VECTORIALZ APPLICATION TO SUPPORT MATHEMATICS LEARNING. THIS APPLICATION HAS BEEN DEVELOPED USING VEDILS MOBILE APPLICATION AUTHORING ENVIRONMENT, WHICH IS BASED ON MIT APP INVENTOR 2. FINALLY, THIS APPLICATION HAS BEEN USED WITH STUDENTS TO EVALUATE ITS USEFULNESS AND TO OBTAIN RESULTS ABOUT THE STUDENTS' LEARNING.
PERFORMANCE PREDICTION SYSTEMS ALLOW KNOWING THE LEARNING STATUS OF STUDENTS DURING A TERM AND PRODUCE ESTIMATIONS ON FUTURE STATUS, WHAT IS INVALUABLE INFORMATION FOR TEACHERS. THE MAJORITY OF CURRENT SYSTEMS STATICALLY CLASSIFY STUDENTS ONCE IN TIME AND SHOW RESULTS IN SIMPLE VISUAL MODES. THIS PAPER PRESENTS AN INNOVATIVE SYSTEM WITH PROGRESSIVE, TIME-DEPENDENT AND PROBABILISTIC PERFORMANCE PREDICTIONS. THE SYSTEM PRODUCES BY-WEEKLY PROBABILISTIC CLASSIFICATIONS OF STUDENTS IN THREE GROUPS: HIGH, MEDIUM OR LOW PERFORMANCE. THE SYSTEM IS EMPIRICALLY TESTED AND DATA IS GATHERED, ANALYSED AND PRESENTED. PREDICTIONS ARE SHOWN AS POINT GRAPHS OVER TIME, ALONG WITH CALCULATED LEARNING TRENDS. SUMMARY BLOCKS ARE WITH LATEST PREDICTIONS AND TRENDS ARE ALSO PROVIDED FOR TEACHER EFFICIENCY. MOREOVER, SOME METHODS FOR SELECTING BEST MOMENTS FOR TEACHER INTERVENTION ARE DERIVED FROM PREDICTIONS. EVIDENCE GATHERED SHOWS POTENTIAL TO GIVE TEACHERS INSIGHTS ON STUDENTS' LEARNING TRENDS, EARLY DIAGNOSE LEARNING STATUS AND SELECTING BEST MOMENT FOR INTERVENTION.
EDUCATIONAL DATA SCIENCE HAS MEANT AN IMPORTANT ADVANCEMENT IN THE UNDERSTANDING AND IMPROVEMEN OF LEARNING MODELS IN RECENT YEARS. ONE OF THE MOST RELEVANT RESEARCH TOPICS IS STUDENT PERFORMANCE PREDICTION THROUGH CLICK-STREAM ACTIVITY IN VIRTUAL LEARNING ENVIRONMENTS, WHICH PROVIDE ABUNDANT INFORMATION ABOUT THEIR BEHAVIOUR DURING THE COURSE. THIS WORK EXPLORES THE POTENTIAL OF DEEP LEARNING AND META-LEARNING IN THIS FIELD, WHICH HAS THUS FAR BEEN EXPLORED VERY LITTLE, SO THAT IT CAN SERVE AS A BASIS FOR FUTURE STUDIES. WE IMPLEMENTED A PREDICTIVE MODEL WHICH IS ABLE TO AUTOMATICALLY OPTIMISE THE ARCHITECTURE AND HYPERPARAMETERS OF A DEEP NEURAL NETWORK, TAKING AS A USE CASE AN EDUCATIONAL DATASET THAT CONTAINS INFORMATION FROM MORE THAN 500 STUDENTS FROM AN ONLINE UNIVERSITY MASTER'S DEGREE. THE RESULTS SHOW THAT THE PERFORMANCE OF THE AUTONOMOUS MODEL WAS SIMILAR TO THE TRADITIONALLY DESIGNED ONE, WHICH OFFERS SIGNIFICANT BENEFITS IN TERMS OF EFFICIENCY AND SCALABILITY. THIS ALSO OPENS UP INTERESTING AREAS OF RESEARCH RELATED TO META-LEARNING APPLIED TO EDUCATIONAL BIG DATA.
THE PURPOSE OF THIS STUDY IS TO ANALYSE THE RELATIONSHIP BETWEEN ONLINE LEARNING BEHAVIOUR AND LEARNING ACHIEVEMENT, AND IMPROVE ACADEMIC PERFORMANCE OF LEARNERS IN MOOCS. THIS PAPER ANALYSES THE LEARNING BEHAVIOUR OF 1,388 UNDERGRADUATES IN THE ONLINE ADVANCED MATHEMATICS COURSE OF THE ONLINE PLATFORM NAMED 'ERYA' WITH STATISTICAL ANALYSIS AND CLUSTERING METHODS. THE RESULTS SHOW THAT: 1) THE LACK OF POSITIVE INTERACTION BETWEEN TEACHERS AND LEARNERS CAN AFFECT LEARNERS' ENTHUSIASM FOR LEARNING AND LEARNERS' LEARNING OUTCOMES; 2) THE ACADEMIC PERFORMANCE RELATED WITH THE ETHNIC, THE NUMBER OF ACCESS AND THE COMPLETION OF THE AFTER-SCHOOL TASKS, BUT THE CORRELATION WITH THE DISCUSSION IS SMALL. IN ADDITION, WE ALSO MADE SOME SUGGESTIONS BASED ON THE RESULTS OF THE LEARNING BEHAVIOUR ANALYSIS TO IMPROVE ACADEMIC PERFORMANCE IN THE MASSIVE OPEN ONLINE COURSES.
THE GOAL OF THE STUDY WAS TO DISCOVER WHICH BUILT-IN TOOLS IN BUSINESS ANALYTICS WERE SUITABLE FOR IMPLEMENTATION IN THE ECAMPUS LMS FROM THE PERSPECTIVE OF VARIOUS STAKEHOLDERS. A SECOND OBJECTIVE WAS TO PREPARE DATA AND BUILT-IN ANALYTICS, AND TO CARRY OUT AND EVALUATE A PILOT IMPLEMENTATION OF LMS WITH DATA ANALYSIS TOOLS. WHEN E-LEARNING COURSES ARE CONDUCTED IN THE CONTEXT OF COMPANIES, MANAGERS ARE PRIMARILY INTERESTED IN THE EFFECTS OF THE E-COURSE ON THE SKILL SETS OF THEIR EMPLOYEES. THEY NEED TO MEASURE THE RETURN ON INVESTMENT IN EMPLOYEE TRAINING COURSES. HOWEVER, DATA COLLECTED BEFORE, DURING, AND AFTER E-LEARNING COURSES MAY ALSO BE USEFUL FOR OTHER STAKEHOLDERS: LEARNERS, INSTRUCTORS, MANAGERS OF HUMAN RESOURCES DEPARTMENTS, AND PROFESSIONALS IN THE COMPANIES THAT PROVIDE E-LEARNING. EACH OF THE VARIOUS STAKEHOLDERS, DEPENDING ON THEIR SPECIFIC ROLE, REQUIRE DIFFERENT KINDS OF ANALYSES AND REPORTS IN ORDER TO IMPROVE PROCESSES AND QUALITY.
IN THIS PAPER, WE SHOW A METHOD TO PREDICT STUDENTS' FINAL GRADES USING A RECURRENT NEURAL NETWORK (RNN). AN RNN IS A VARIANT OF A NEURAL NETWORK THAT HANDLES TIME SERIES DATA. FOR THIS PURPOSE, THE LEARNING LOGS FROM 937 STUDENTS WHO ATTENDED ONE OF SIX COURSES BY TWO TEACHERS WERE COLLECTED. NINE KINDS OF LEARNING LOGS ARE SELECTED AS THE INPUT OF THE RNN. WE EXAMINE THE PREDICTION OF FINAL GRADES, WHERE THE TRAINING DATA AND TEST DATA ARE THE LOGS OF COURSES CONDUCTED IN 2015 AND IN 2016, RESPECTIVELY. WE ALSO SHOW A WAY TO IDENTIFY THE IMPORTANT LEARNING ACTIVITIES FOR OBTAINING A SPECIFIC FINAL GRADE BY OBSERVING THE VALUES OF WEIGHT OF THE TRAINED RNN.
THIS PAPER FOCUS ON THE IDEA OF PERFORMANCE PREDICTION AS A NEW FEATURE IN E-LEARNING. THE CONCEPT REFERS TO THE COLLECTION OF STUDENTS' DATA SOURCES THAT CAN EASILY AND QUICKLY BE IDENTIFIED BY ANALYZING THE DATA OF STUDENTS' BEHAVIOR PATTERNS IN E-LEARNING. SUCH WAY DONE IN ORDER TO PREDICT THEIR FUTURE PERFORMANCE. THIS KIND OF METHOD IS CONSIDERED AS A NEW PARADIGM AND THE BEST PRACTICE IN EDUCATIONAL TECHNOLOGY. IT PLAYS AN IMPORTANT ROLE IN LEARNING TECHNOLOGY DEVELOPMENT NOWADAYS AS IT GIVES CLEAR GENERAL IDEAS ON CURRENT SITUATIONS AND PROVIDES DIRECTIONS FOR FUTURE TRENDS TO MEASURE STUDENTS' SUCCESS. ACCORDINGLY, THE GOAL OF THIS PAPER IS TO PROPOSE PERFORMANCE PREDICTION AS A MASSIVE E-LEARNING SOFTWARE SYSTEM INTEGRATION WHICH IS SIMPLE, EASY, ACCURATE AND ROBUST IN PREDICTING STUDENTS' GRADES BASED ON STUDENTS' BEHAVIOR IN E-LEARNING. THE SAMPLE USED IN THIS STUDY WERE TAKEN FROM OPEN UNIVERSITY STUDENTS IN INDONESIA. IN ADDITION, THE PREDICTION TECHNIQUE USED TO PREDICT STUDENTS' PERFORMANCE, BASED ON FEATURES IN E-LEARNING, IS MULTIPLE REGRESSION.
USING PREDICTIVE MODELING METHODS, IT IS POSSIBLE TO IDENTIFY AT-RISK STUDENTS EARLY AND INFORM BOTH THE INSTRUCTORS AND THE STUDENTS. WHILE SOME UNIVERSITIES HAVE STARTED TO USE STANDARDS-BASED GRADING, WHICH HAS EDUCATIONAL ADVANTAGES OVER COMMON SCORE-BASED GRADING, AT RISK PREDICTION MODELS HAVE NOT BEEN ADAPTED TO REAP THE BENEFITS OF STANDARDS-BASED GRADING IN COURSES THAT UTILIZE THIS GRADING. IN THIS PAPER, WE COMPARE PREDICTIVE METHODS TO IDENTIFY AT-RISK STUDENTS IN A COURSE THAT USED STANDARDS-BASED GRADING. ONLY IN-SEMESTER PERFORMANCE DATA THAT WERE AVAILABLE TO THE COURSE INSTRUCTORS WERE USED IN THE PREDICTION METHODS. WHEN IDENTIFYING AT-RISK STUDENTS, IT IS IMPORTANT TO MINIMIZE FALSE NEGATIVE (I.E., TYPE II) ERROR WHILE NOT INCREASING FALSE POSITIVE (I.E., TYPE I) ERROR SIGNIFICANTLY. TO INCREASE THE GENERALIZABILITY OF THE MODELS AND ACCURACY OF THE PREDICTIONS, WE USED A FEATURE SELECTION METHOD TO REDUCE THE NUMBER OF VARIABLES USED IN EACH MODEL. THE NAIVE BAYES CLASSIFIER MODEL AND AN ENSEMBLE MODEL USING A SEQUENCE OF MODELS (I.E., SUPPORT VECTOR MACHINE, IC NEAREST NEIGHBORS, AND NAIVE BAYES CLASSIFIER) HAD THE BEST RESULTS AMONG THE SEVEN TESTED MODELING METHODS. (C) 2016 THE AUTHORS. PUBLISHED BY ELSEVIER LTD. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE.
INTELLIGENT TUTORING SYSTEMS (ITSS) ARE ONE OF A WIDE RANGE OF LEARNING ENVIRONMENTS, WHERE THE MAIN ACTIVITY IS PROBLEM SOLVING. ONE OF THE MOST SUCCESSFUL APPROACHES FOR IMPLEMENTING ITSS IS CONSTRAINT BASED MODELING (CBM). CONSTRAINT-BASED TUTORS HAVE BEEN SUCCESSFULLY USED AS DRILL-AND-PRACTICE ENVIRONMENTS FOR LEARNING. MORE RECENTLY CBM TUTORS HAVE BEEN COMPLEMENTED WITH A MODEL DERIVED FROM THE FIELD OF PSYCHOMETRICS. THE GOAL OF THIS SYNERGY IS TO PROVIDE CBM TUTORS WITH A DATA-DRIVEN AND SOUND MECHANISM OF ASSESSMENT, WHICH MAINLY CONSISTS IN APPLYING THE PRINCIPLES OF ITEM RESPONSE THEORY (IRT). THE RESULT OF THIS SYNERGY IS, THEREFORE, A FORMAL APPROACH THAT ALLOWS CARRYING OUT ASSESSMENTS OF PERFORMANCE ON PROBLEM SOLVING TASKS. SEVERAL PREVIOUS STUDIES WERE CONDUCTED PROVING THE VALIDITY AND UTILITY OF THIS COMBINED APPROACH WITH SMALL GROUPS OF STUDENTS, IN SHORT PERIODS OF TIME AND USING SYSTEMS DESIGNED SPECIFICALLY FOR ASSESSMENT PURPOSES. IN THIS PAPER, THE APPROACH HAS BEEN EXTENDED AND ADAPTED TO DEAL WITH A LARGE SET OF STUDENTS WHO USED AN ITS OVER A LONG PERIOD OF TIME. THE MAIN RESEARCH QUESTIONS ADDRESSED IN THIS PAPER ARE: (1) WHICH IRT MODELS ARE MORE SUITABLE TO BE USED IN A CONSTRAINED-BASED TUTOR? (2) CAN DATA COLLECTED FROM THE ITS BE USED AS A SOURCE FOR CALIBRATING THE CONSTRAINTS CHARACTERISTIC CURVES? (3) WHICH IS THE BEST STRATEGY TO ASSEMBLE DATA FOR CALIBRATION? TO ANSWER THESE QUESTIONS, WE HAVE ANALYZED THREE YEARS OF DATA FROM SQL-TUTOR. (C) 2016 ELSEVIER B.V. ALL RIGHTS RESERVED.
FEATURED APPLICATION IN THIS WORK, PROCESS MINING TECHNIQUES ARE USED, WITH A CURRICULAR ANALYTICS APPROACH, TO UNDERSTAND THE EDUCATIONAL TRAJECTORIES OF HIGHER EDUCATION STUDENTS. LATE DROPOUT IS ONE OF THE MOST PRESSING CHALLENGES CURRENTLY FACING HIGHER EDUCATION, AND THE PROCESS THAT EACH STUDENT FOLLOWS TO ARRIVE AT THAT DECISION USUALLY INVOLVES SEVERAL ACADEMIC PERIODS. THIS WORK PRESENTS A CURRICULAR ANALYTICS APPROACH AT THE PROGRAM LEVEL, TO ANALYZE HOW EDUCATIONAL TRAJECTORIES OF UNDERGRADUATE STUDENTS IN HIGH-FAILURE RATE COURSES HELP TO DESCRIBE THE PROCESS THAT LEADS TO LATE DROPOUT. EDUCATIONAL TRAJECTORIES (N = 10,969) OF HIGH-FAILURE RATE COURSES ARE CREATED USING PROCESS MINING TECHNIQUES, AND THE RESULTS ARE DISCUSSED BASED ON ESTABLISHED THEORETICAL FRAMEWORKS. LATE DROPOUT WAS MORE FREQUENT AMONG STUDENTS WHO TOOK A STOPOUT WHILE HAVING HIGH-FAILURE RATE COURSES THEY MUST RETAKE. FURTHERMORE, STUDENTS WHO ENDED IN LATE DROPOUT WITH HIGH-FAILURE RATE COURSES THEY MUST RETAKE HAD EDUCATIONAL TRAJECTORIES THAT WERE ON AVERAGE SHORTER AND LESS SATISFACTORY. ON THE OTHER HAND, THE EDUCATIONAL TRAJECTORIES OF STUDENTS WHO ENDED IN LATE DROPOUT WITHOUT HIGH-FAILURE RATE COURSES THEY MUST RETAKE WERE MORE SIMILAR TO THOSE OF STUDENTS WHO GRADUATED LATE. MOREOVER, SOME DIFFERENCES FOUND AMONG ISCED FIELDS ARE ALSO DESCRIBED. THE PROPOSED APPROACH CAN BE REPLICATED IN ANY OTHER UNIVERSITY TO UNDERSTAND THE EDUCATIONAL TRAJECTORIES OF LATE DROPOUT STUDENTS FROM A LONGITUDINAL PERSPECTIVE, GENERATING NEW KNOWLEDGE ABOUT THE DYNAMIC BEHAVIOR OF THE STUDENTS. THIS KNOWLEDGE CAN TRIGGER IMPROVEMENTS TO THE CURRICULUM AND IN THE FOLLOW-UP MECHANISMS USED TO INCREASE STUDENT RETENTION.
THE DEVELOPMENT OF MASSIVE OPEN ONLINE COURSES (MOOCS) HAS PROCEEDED THROUGH THREE GENERATIONS, AND IN ALL THREE, ONLINE DISCUSSIONS HAVE BEEN CONSIDERED A CRITICAL COMPONENT. ALTHOUGH DISCUSSIONS IN MOOCS HAVE THE POTENTIAL TO PROMOTE LEARNING, INSTRUCTORS HAVE FACED CHALLENGES FACILITATING LEARNERS' KNOWLEDGE INQUIRY, CONSTRUCTION, AND MANAGEMENT THROUGH SOCIAL INTERACTION. IN ADDITION, UNDERSTANDING VARIOUS ASPECTS OF LEARNING CALLS FOR MORE MIXED-METHOD STUDIES TO PROVIDE BOTH QUANTITATIVE, GENERALIZED ANALYSIS AND QUALITATIVE, DETAILED DESCRIPTIONS OF LEARNING. THIS STUDY FILLS THESE PRACTICE AND RESEARCH GAPS. WE DESIGNED A CHINESE MOOC WITH THE SUPPORT OF A PEDAGOGICAL STRATEGY, A LEARNING ANALYTIC TOOL, AND A SOCIAL LEARNING ENVIRONMENT IN ORDER TO FOSTER LEARNER ENGAGEMENT IN DISCUSSIONS. MIXED METHODS WERE USED TO EXPLORE LEARNERS' DISCUSSION PATTERNS, PERCEPTIONS, AND PREFERENCES. RESULTS INDICATED THAT LEARNERS DEMONSTRATED VARIED PATTERNS, PERCEPTIONS, AND PREFERENCES, WHICH IMPLIES A COMPLEX LEARNING PROCESS DUE TO THE INTERPLAY OF MULTIPLE FACTORS. BASED ON THE RESULTS, THIS RESEARCH PROVIDED THEORETICAL, PEDAGOGICAL, AND ANALYTICAL IMPLICATIONS FOR MOOC DESIGN, PRACTICE, AND RESEARCH.
WHILE ONLINE LEARNING IS ALREADY A PART OF UNIVERSITY EDUCATION AND DIDACTICS, NOT ALL STUDENTS HAVE THE NECESSARY SELF REGULATION COMPETENCY TO REALLY LEARN ON THEIR OWN EFFICIENTLY AND EFFECTIVELY. IN CLASSROOM A TEACHER CAN TAKE OVER A MODERATING PART, SET INTERMEDIATE GOALS AND GIVE FEEDBACK TO ONE'S PROGRESS, BUT PARTICIPANTS OF ONLINE LEARNING COURSES (E.G. IN BLENDED SCENARIOS OR MASSIVE OPEN ONLINE COURSES (MOOCS)) FACE A HIGHER DEMAND OF SELF-REGULATION COMPETENCY. THIS PAPER PRESENTS A COURSE AND CONTENT INDEPENDENT ASSISTANT, PEERLA, WHICH ASSISTS IN IMPROVING SELF-REGULATION COMPETENCY. PEERLA ALLOWS SETTING OF LONG-TERM GOALS, BREAKDOWN INTO INTERMEDIATE GOALS AND KEEPS TRACK OF KNOWLEDGE INCREASE OR TIME NEEDED. A GRAPHICAL FEEDBACK ALLOWS COMPARISON OF EXISTING AND AIMED LEVEL OF KNOWLEDGE OR TIME INVESTMENTS. PEERLA ADDS PEER COMPARISON TO THE VISUALIZATION CHARTS FOR SOCIAL FRAME OF REFERENCE. THIS COMPARISON IS COURSE-WIDE OR ONLY WITH SIMILAR LEARNERS (CLOSE IN GOALS AND KNOWLEDGE LEVELS). PEERLA IS IMPLEMENTED AS A LEARNING MANAGEMENT SYSTEM (LMS) PLUGIN TO SUPPORT LEARNING PROGRESS IN MIXED FORMAL AND INFORMAL LEARNING SCENARIOS. PEERLA WAS EVALUATED WITH 83 STUDENTS IN AN ONLINE MATHEMATICS PREPARATION COURSE OVER FOUR WEEKS. RESULTS INDICATE THE BENEFITS OF SUCH A SELF-REGULATION ASSISTANCE, ESPECIALLY FOR UNIVERSITY FRESHMEN.
THIS STUDY AIMED TO ANALYZE THE STUDENT'S BEHAVIOUR IN RELATION TO THEIR DEGREE OF COMMITMENT, PARTICIPATION, AND CONTRIBUTION IN A MOOC BASED ON A SOCIAL LEARNING APPROACH. INTERACTION DATA WAS COLLECTED ON THE LEARNING PLATFORM AND IN SOCIAL NETWORKS, BOTH OF WHICH WERE USED IN THE THIRD EDITION OF A SOCIAL MOOC COURSE. THIS DATA WAS THEN STUDIED VIA STATISTICAL METHODS AND ANALYSIS OF SOCIAL NETWORKS. THIS STUDY ASSUMES THAT SOCIAL COMMUNITIES WOULD ARISE AROUND THE COURSE, WOULD REMAIN OVER TIME, AND THAT PARTICIPANTS WOULD EVEN CONTRIBUTE WITH NEW PROPOSALS. THE FINDINGS INDICATED THAT SOCIAL LEARNING COMMUNITIES ARE BUILT AND CONTINUE ONLY WHILE THE COURSE IS OPEN AND WHILE THE TEACHERS ARE INVOLVED IN FOSTERING PARTICIPATION. ALTHOUGH THIS STUDY IS LIMITED, THE DESIGN CRITERIA OF THE COURSE, THE PEDAGOGICAL MODEL ON WHICH THIS IS SUPPORTED, AND THE METHODS APPLIED FOR THIS ANALYSIS PROVIDE OTHER RESEARCHERS AND EDUCATORS WITH CLUES FOR BETTER UNDERSTAND THE DYNAMIC PROCESS OF SOCIAL LEARNING IN SOCIAL MOOCS.
THE AIM OF THE STUDY WAS TO DISCOVER STUDENTS' BEHAVIOUR PATTERNS BASED ON THE DATA RECORDED IN A LEARNING MANAGEMENT SYSTEM AND THE LINKS TO THEIR RESULTS ACHIEVED IN A SPECIFIC E-LEARNING COURSE. IN ORDER TO IDENTIFY BEHAVIOURAL PATTERNS DURING FORMATIVE ONLINE ASSESSMENT TEST (MULTIPLE ACCESS ALLOWED), THE COLLECTED DATA ARE ANALYSED BOTH WITHIN AND BETWEEN TWO GENERATIONS OF STUDENTS WHO PARTICIPATED IN THE E-COURSES. THE STUDENTS IN 2016/17 ACCESSED THE E-COURSE MORE OFTEN, ACHIEVED BETTER RESULTS AND ACCESSED THE CONTENT OF THE E-COURSE TO A GREATER EXTENT. AT THE SAME TIME, FOR THE SAME GENERATION, FEWER TEST ATTEMPTS ARE NOTED. BASED ON THE INSIGHT INTO THE STRUCTURE OF THE CONTENT THAT STUDENTS HAVE ACCESSED, THE BEST RESULTS ARE PARTICULARLY POSITIVELY LINKED WITH ACCESS TO DIFFERENT E-COURSE CONTENT.
AUTOMATED LEARNING ANALYTICS IS BECOMING AN ESSENTIAL TOPIC IN THE EDUCATIONAL AREA, WHICH NEEDS EFFECTIVE SYSTEMS TO MONITOR THE LEARNING PROCESS AND PROVIDES FEEDBACK TO THE TEACHER. RECENT ADVANCES IN VISUAL SENSORS AND COMPUTER VISION METHODS ENABLE AUTOMATED MONITORING OF BEHAVIOR AND AFFECTIVE STATES OF LEARNERS AT DIFFERENT LEVELS, FROM UNIVERSITY TO PRE-SCHOOL. THE OBJECTIVE OF THIS RESEARCH WAS TO BUILD AN AUTOMATIC SYSTEM THAT ALLOWED THE FACULTIES TO CAPTURE AND MAKE A SUMMARY OF STUDENT BEHAVIORS IN THE CLASSROOM AS A PART OF DATA ACQUISITION FOR THE DECISION MAKING PROCESS. THE SYSTEM RECORDS THE ENTIRE SESSION AND IDENTIFIES WHEN THE STUDENTS PAY ATTENTION IN THE CLASSROOM, AND THEN REPORTS TO THE FACILITIES. OUR DESIGN AND EXPERIMENTS SHOW THAT OUR SYSTEM IS MORE FLEXIBLE AND MORE ACCURATE THAN PREVIOUSLY PUBLISHED WORK.
SIMULATIONS AND SERIOUS GAINES (SGS) ARE VERY PROMISING EDUCATIONAL TOOLS AS THEY INCLUDE NEW POWERFUL CHARACTERISTICS SUCH AS LEARNER ENGAGEMENT OR AUTHENTIC ENVIRONMENT FOR LEARNING. MOREOVER SGS ARE HIGHLY INTERACTIVE APPLICATIONS THAT CAN PROVIDE VALUABLE INFORMATION ABOUT THE IN-GAME STUDENTS' INTERACTION. EDUCATORS CAN TAKE ADVANTAGE OF THIS INFORMATION WITH DIFFERENT PURPOSES SUCH AS STUDENTS' ASSESSMENT, EVALUATION OF THE GAME DESIGN OR FOR LEARNING ANALYTICS PURPOSES. BUT THERE ARE SEVERAL OPEN ISSUES THAT SHOULD BE ADDRESSED RELATED TO THE FULLY INTEGRATION OF GAMES INTO E-LEARNING PLATFORMS. THIS PAPER PRESENTS OUR EXPERIENCE INTEGRATING SGS INTO EXISTING E-LEARNING PLATFORM USING THE SCORM STANDARD. THIS PAPER ALSO DISCUSSES HOW THE CURRENT E-LEARNING STANDARDS TRENDS WILL AFFECT SGS INTEGRATION ON THE EXISTING AND FUTURE EDUCATIONAL PLATFORMS BY EXPLORING THE POSSIBILITIES OF THE NEXT SCORM GENERATION.
WHILE EDUCATIONAL GAMES HAVE BEEN INCREASINGLY POPULAR IN EDUCATION, INSUFFICIENT STUDIES HAVE COMPREHENSIVELY REVIEWED THEIR EFFECTIVENESS. TO COMPLEMENT THIS MISSING LINK, THIS STUDY EXPLORED GAME-BASED LEARNING OUTCOMES INCLUDING ACADEMIC ACHIEVEMENTS, PROBLEM-SOLVING, AND CRITICAL THINKING ABILITIES, KNOWLEDGE, LEARNING EFFICIENCY, SKILLS, STUDENT ATTITUDES, AND BEHAVIORS. BOTH NEGATIVE AND POSITIVE EFFECTS OF EDUCATIONAL GAMES ON MOTIVATION WERE ALSO EXPLORED BASED ON COMPREHENSIVE LITERATURE ANALYSIS. THE ROLE OF ENGAGEMENT IN GAME-BASED LEARNING WAS STUDIED, COUPLED WITH THE WAYS TO ENHANCE STUDENT ENGAGEMENT. WE ALSO EXPLORED THE IMPORTANCE OF GAMIFIED COMPONENTS IN STUDENT SATISFACTION AND PROVIDED CONSTRUCTIVE SUGGESTIONS FOR DESIGNERS AND PRACTITIONERS. FUTURE RESEARCH SHOULD HIGHLIGHT LEARNING ANALYTICS AND DATA MINING TECHNIQUES OF EDUCATIONAL GAME-BASED LEARNING AND TRY TO FIND OUT SOLUTIONS TO ADDRESS VARIOUS PROBLEMS TO IMPROVE THE EFFECTIVENESS OF EDUCATIONAL GAMES.
IN THIS STUDY, WE USED THE APPLICATION SOFTWARE INSTALLED ON STUDENTS' SMART PHONES AS INSTANTLY INTERACTIVE TOOLS DURING CLASS TIME IN A FLIPPED LEARNING CONTEXT. ONE POPULAR DAILY-LIFE INSTANT INTERACTION APPLICATION, LINE, PROVIDED THE STUDENTS WITH TEXT OR MULTIMEDIA FEEDBACK IN TIME SEQUENCE; ONE NON-DAILY INSTANT INTERACTION APPLICATION, PAL+, PROVIDED THE STUDENTS WITH INSCRIBED FEEDBACK IN TIME SEQUENCE; AND ONE EDUCATIONAL INSTANT INTERACTION APPLICATION, SPEAKUP!, PROVIDED THE STUDENTS WITH ANONYMOUS FEEDBACK IN TIME SEQUENCE. WE COMPARED THE STUDENTS' BEHAVIORS OF USING THE DIFFERENT INTERACTIVE SYSTEMS FOR INTERACTIONS IN THE FLIPPED CLASSROOM CLASS TIME. THE STUDY APPLIED LEARNING ANALYTICS TECHNIQUES AND USED BEHAVIORAL ANALYSIS TO IDENTIFY THE ROLE OF THE DAILY-LIFE INSTANT RESPONSE APPLICATION IN FLIPPED LEARNING, AND FOUND HOW THE INSTANT FEEDBACK SYSTEMS ENHANCED DISCUSSION IN THE CLASSROOM.
IN THIS PAPER 16 DIFFERENT MOODLE MACHINE LEARNING MODELS FOR PREDICTING THE SUCCESS OF 57 FULL-TIME STUDENTS ENROLLED IN THE APPLIED STATISTICS COURSE AT THE UNIVERSITY OF DUNAUJVAROS IN HUNGARY HAVE BEEN DEVELOPED AND TESTED IN TERMS OF "GOODNESS". THE SUCCESS CAN BE AFFECTED BY SEVERAL FACTORS, BUT HERE ONLY STUDENTS' COGNITIVE ACTIVITIES ARE EXAMINED. THE PREDICTORS USED IN THE MODELS ARE BASED ON: NUMBER OF VIEW OF PDF LECTURE NOTES, NUMBER OF VIEWS OF VIDEO LECTURES, NUMBER OF VIEWS OF BOOKS OF SOLVED EXERCISES, NUMBER OF VIEWS OF MINITAB VIDEOS (VIDEOS FOR PROBLEM SOLVING WITH A STATISTICAL SOFTWARE), NUMBER OF ATTEMPTS OF QUIZZES AND BEST GRADES ACHIEVED BY STUDENTS ON QUIZZES. THE MODELS DIFFERED IN THE NUMBER AND IN THE TYPES OF PREDICTORS. BINARY LOGISTIC REGRESSION WAS USED FOR MODEL TRAINING AND EVALUATION. THE TARGET OF THE MODELS INDICATES WHETHER A STUDENT IS AT RISK OF NOT ACHIEVING THE MINIMUM GRADE TO PASS THE COURSE. THE IMPACT OF COGNITIVE PREDICTORS THAT ARE PART OF THE MOODLE CORE ANALYTICS API ON PREDICTIVE POWER WAS ALSO EXAMINED. HAVING EVALUATED THE GOODNESS OF THE DIFFERENT MODELS, IT WAS SHOWN THAT STUDENTS' SUCCESS CAN BE PREDICTED PURELY FROM COGNITIVE ACTIVITIES, BUT THEIR PREDICTIVE POWERS ARE VERY DIVERSE. THE PREDICTORS OF QUIZZES HAVE THE LARGEST IMPACT ON THE SUCCESS, HOWEVER, SUPPLEMENTING THE MODEL WITH OTHER EVEN LESS EFFECTIVE PREDICTORS MUCH BETTER MODEL CAN BE MADE. MODELS BUILT FROM PURELY MOODLE CORE COGNITIVE PREDICTORS GIVE MUCH LESS RELIABLE RESULTS.
TECHNOLOGY ENHANCED LEARNING IS SHIFTING FROM A CENTRALIZED PLATFORM ENVIRONMENT TO A VARIETY OF ELEMENTS TO SUPPORT AND ENRICH INTERACTIONS BETWEEN LEARNERS AND EDUCATIONAL MATERIAL. IN THIS CONTEXT, THE PAPER ADDRESSES THE CREATION OF EBOOKS IN EPUB FORMAT BASED ON THE EXISTING CONTENT IN A MOODLE ENVIRONMENT. THE MAIN PURPOSE IS TO CREATE ENRICHED EBOOKS CAPABLE OF SUPPORTING AND TRACKING STUDENT ACTIVITY USUALLY RESERVED TO EDUCATIONAL ENVIRONMENTS. THE WORK DESCRIBES A PLUG-IN DEVELOPED TO SUPPORT MOODLE COURSE TRANSLATION TO EPUB AND DETAILS POTENTIAL USER TRACKING FORMALIZATION USING XAPI.
FOR PRIMARY SCHOOL STUDENTS, MATHEMATICAL WORD PROBLEMS ARE OFTEN MORE DIFFICULT TO SOLVE THAN STRAIGHTFORWARD NUMBER PROBLEMS. WORD PROBLEMS REQUIRE READING AND ANALYSIS SKILLS, AND IN ORDER TO EXPLAIN THEIR SITUATIONAL CONTEXTS, THE PROPER MATHEMATICAL KNOWLEDGE AND NUMBER OPERATIONS HAVE TO BE SELECTED. TO IMPROVE STUDENTS' ABILITY IN SOLVING WORD PROBLEMS, THE PROBLEM SOLVING PROCESS COULD BE SUPPORTED BY PROCEDURAL AND CONTENT SPECIFIC GUIDANCE OR WITH ONLY PROCEDURAL SUPPORT.. THIS PAPER EVALUATES THE EFFECT OF TWO TYPES OF HINTS, PROCEDURAL ONLY AND CONTENT-PROCEDURAL, PROVIDED BY A COMPUTER PROGRAMME PRESENTED IN TWO VERSIONS. STUDENTS OF GRADE 6 WERE RANDOMLY ASSIGNED TO THESE TWO VERSIONS, WHICH OFFERED FIVE LESSON UNITS CONSISTING OF EIGHT WORD PROBLEMS EACH. THE RESULTS INDICATE THAT ON AVERAGE THE STUDENTS IN THE PROCEDURAL-CONTENT HINTS GROUP (N=54) FINISHED ABOUT JUST AS MANY PROBLEMS IN THE PROGRAMME AS THEIR COUNTERPARTS IN THE PROCEDURAL-ONLY CONDITION (N=51). HOWEVER, THE PARTICIPANTS IN THE FIRST GROUP SOLVED MORE PROBLEMS CORRECTLY AND IMPROVED THEIR PROBLEM-SOLVING SKILLS MORE AS INDICATED BY THE SCORES ON THE PROBLEM-SOLVING POST-TEST. APART FROM PRESENTING OUR ANALYSIS OF THE FINDINGS OF THIS STUDY, ALSO ITS LIMITATIONS AND ITS POSSIBLE IMPLICATIONS FOR FUTURE RESEARCH ARE DISCUSSED IN THIS PAPER.
UNIVERSITIES ARE INTERESTED IN ATTRACTING STUDENTS, BUT THEY DO NOT USUALLY HAVE DATA RELATING TO STUDENTS' INTENTIONS. FURTHERMORE, BOTH GENERAL AND SUBJECTIVE FACTORS ARE TAKEN INTO ACCOUNT BY STUDENTS WHEN CHOOSING A DEGREE AND A UNIVERSITY. THIS PAPER PRESENTS A PSYCHOMETRIC STUDY OF A QUESTIONNAIRE WHICH AIMS TO ASSESS VARIABLES RELATING THE ASPECTS INVOLVED IN THE SELECTION PROCESS AND ACCESSING THE SPANISH UNIVERSITY SYSTEM. THIS QUESTIONNAIRE CONSIDERS SIX FACTORS: CONSIDERATION OF THE UNIVERSITY; PERCEIVED USEFULNESS; SOCIAL IMPACT; VOCATIONAL ASPECTS; INFLUENCE OF SURROUNDINGS AND GEOGRAPHIC LOCATION. THE ITEM ANALYSIS (DISCRIMINATION INDEX) REDUCED THE QUESTIONNAIRE TO 25 ITEMS, AND THE RELIABILITY AND VALIDITY OF THIS FINAL QUESTIONNAIRE WAS ANALYZED USING A SAMPLE OF 1532 STUDENTS FROM EIGHT SPANISH UNIVERSITIES. THE RESULTS SHOWED THAT THE RELIABILITY AS INTERNAL CONSISTENCY OF THE SIX FACTORS WAS HIGH; THE CONSTRUCT VALIDITY SHOWED A STRUCTURE OF SIX FIRST ORDER FACTORS CLUSTERED IN TWO SECOND ORDER FACTORS AND FINALLY, THERE WAS HIGH DISCRIMINANT VALIDITY BETWEEN GROUPS DEFINED BY VARIABLES SUCH AS GENDER OR ACADEMIC AREA. APPLYING PREDICTIVE ANALYTICS TO THE RESULTS OF THIS QUESTIONNAIRE WILL THEN ENABLE SPECIFIC RECRUITING POLICIES TO BE APPLIED, WHICH TARGET THE TYPE OF STUDENTS THAT UNIVERSITIES ARE LOOKING FOR, WHILE REDUCING COSTS. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
COURSE REVIEWS, WHICH IS DESIGNED AS AN INTERACTIVE FEEDBACK CHANNEL IN MASSIVE OPEN ONLINE COURSES, HAS PROMOTED THE GENERATION OF LARGE-SCALE TEXT COMMENTS. THESE DATA, WHICH CONTAIN NOT ONLY LEARNERS' CONCERNS, OPINIONS AND FEELINGS TOWARD COURSES, INSTRUCTORS, AND PLATFORMS BUT ALSO LEARNERS' INTERACTIONS (E.G., POST, REPLY), ARE GENERALLY SUBJECTIVE AND EXTREMELY VALUABLE FOR ONLINE INSTRUCTION. THE PURPOSE OF THIS STUDY IS TO AUTOMATICALLY REVEAL THESE POTENTIAL INFORMATION FROM 50 ONLINE COURSES BY AN IMPROVED UNIFIED TOPIC MODEL BEHAVIOR-SENTIMENT TOPIC MIXTURE, WHICH IS VALIDATED AND EFFECTIVE FOR DETECTING FREQUENT TOPICS LEARNERS DISCUSS MOST, TOPICS-ORIENTED SENTIMENTAL TENDENCY AS WELL AS HOW LEARNERS INTERACT WITH THESE TOPICS. THE RESULTS SHOW THAT LEARNERS FOCUS MORE ON THE TOPICS ABOUT COURSE-RELATED CONTENT WITH POSITIVE SENTIMENT, AS WELL AS THE TOPICS ABOUT COURSE LOGISTICS AND VIDEO PRODUCTION WITH NEGATIVE SENTIMENT. MOREOVER, THE DISTRIBUTIONS OF BEHAVIORS ASSOCIATED WITH THESE TOPICS HAVE SOME DIFFERENCES.
THE OBJECTIVES OF THIS RESEARCH WERE 1) TO STUDY THE FACTORS OF THE RELATIONSHIP BETWEEN THE LEARNER CONTEXT AND THE CURRICULUM IN HIGHER EDUCATION, 2) TO CONSTRUCT OF THE MODEL RELATIONSHIP BETWEEN THE LEARNER CONTEXT AND THE CURRICULUM IN HIGHER EDUCATION, AND 3) TO TEST THE QUALITY AND ACCURACY AFTER HAVING THE MODEL AND PROTOTYPE APPLICATION OF THE RELATIONSHIP BETWEEN THE LEARNER CONTEXT AND THE CURRICULUM IN HIGHER EDUCATION. SEVEN INSTRUMENTS WERE USED IN THIS RESEARCH, INCLUDING MEAN, STANDARD DEVIATION, PERCENTAGES, DECISION TREE, TEXT MINING, CROSS VALIDATION, AND CONFUSION MATRIX. THE RESEARCH FINDINGS ARE AS FOLLOWS; 1) THE FACTORS THAT ARE IMPORTANT TO THE LEARNER'S CONTINUING STUDYING CONSIST OF TWO FACTORS: EDUCATION SYSTEM, AND INTEREST IN STUDYING. 2) THE RESULTS OF THE MODEL PERFORMANCE SHOWED THAT THE MODEL HAS A HIGH LEVEL OF ACCURACY (76.50%). 3) THE RESULT OF THE PROTOTYPE TEST APPLICATION BY THE USER IS ALSO ACCEPTABLE, WITH 68.98 PERCENT ACCURACY FROM 1,109 TESTERS. IN THE FUTURE, THE RESEARCHER HAS THE EXPECTATION TO DEVELOP MORE ACCURATE PREDICTIONS.
PRIOR TO THE CORONAVIRUS DISEASE 2019 (COVID-19) PANDEMIC, DUE TO THE RARITY OF PANDEMICS IN RECENT CENTURIES, SUITABLE CONDITIONS DID NOT EXIST IN EDUCATIONAL INSTITUTIONS FOR THE IMPLEMENTATION OF ASYNCHRONOUS DISTANCE TEACHING. NO EMPIRICAL STUDIES HAVE BEEN CONDUCTED ON WHETHER THE CONSIDERABLE ENVIRONMENTAL CHANGES CAUSED BY COVID-19 HAVE AFFECTED STUDENTS' ONLINE LEARNING BEHAVIORS. THEREFORE, THIS STUDY COLLECTED INFORMATION ON STUDENTS' ONLINE LEARNING BEHAVIORS DURING THE COVID-19 PANDEMIC AND OTHER PERIODS TO EXAMINE WHETHER PANDEMIC-CAUSED ENVIRONMENTAL CHANGES AFFECTED STUDENTS' ONLINE LEARNING BEHAVIORS. THIS STUDY FOCUSES ON THE 60-DAY TRANSMISSION AFTER THE BEGINNING OF THE SECOND SEMESTER OF THE 2019 ACADEMIC YEAR. THE DATA SOURCE WAS FROM A COMPARATIVE ASSESSMENT BETWEEN THE PANDEMIC GROUP (331 STUDENTS) AND THE CONTROL GROUP (101 STUDENTS). THE SPEARMAN RANK CORRELATION TEST AND THE WILCOXON SIGNED-RANK TEST WERE USED AS OUR STATISTICAL METHODS. THIS PAPER PRESENTS PRELIMINARY RESULTS ON HOW COVID-19 HAS AFFECTED STUDENTS' ONLINE LEARNING BEHAVIORS AND PROPOSES ASYNCHRONOUS ONLINE LEARNING AS A METHOD FOR MAINTAINING UNIVERSITY STUDENTS' LEARNING DURING THE COVID-19 PANDEMIC.
WE HAVE DEVELOPED COCOVIEWER (COMPILE ERROR COLLECTION VIEWER) FOR LEARNERS IN PROGRAMMING TO ENABLE THEM TO CONDUCT SELF-ASSESSMENT FOR COMPILING ERROR RECORDS. COCOVIEWER GENERATES CHARTS THAT SHOW A TRAJECTORY OF REDUCING THE CORRECTION TIME OF THE COMPILE ERROR THAT IS CALCULATED BY LOGS RECORDED IN STUDENTS' COMPUTERS DURING A PROGRAMMING COURSE. STUDENTS CAN SEE LISTS OF CHARTS FOR MANY KINDS OF COMPILE ERRORS, AS WELL AS A PARTICULAR DETAILED CIRCUMSTANCE OF ERROR THAT IS SELECTED BY A STUDENT. WE HYPOTHESIZED THAT THE SYSTEM PROMOTES CLEAR UNDERSTANDING REGARDING THEIR COMPILE ERROR LEARNING, WHICH LEADS TO ENCOURAGE MORE EXPERIENCES OF COMPILATION ERROR CORRECTION, AS WELL AS TO REDUCE UNARTICULATED ANXIETY FOR THE COMPILE ERROR. THE SYSTEM WAS TRIED IN A SELF-ASSESSMENT CONTEXT AT AN UNDERGRADUATE INTRODUCTORY PROGRAMMING COURSE FOR APPROXIMATELY 100 NON-CS STUDENTS. THE RESULTS IN THE QUESTIONNAIRE SHOWED THAT THE STUDENTS APPRECIATED THE SYSTEM FOR A REFLECTION OF THEIR PROCESS, AND WE SUCCEEDED IN REDUCING UNARTICULATED ANXIETY FOR STUDENTS. THE RESULTS INDICATE THAT SELF-ASSESSMENT WITH COCOVIEWER ENABLE TO BOOST STUDENTS' MOTIVATION IN PROGRAMMING EDUCATION, WHICH FORMS THE BASIS OF COMPUTER ENGINEERING EDUCATION.
THE DETECTION AND ANALYSIS OF STUDENTS' DOMAIN-SPECIFIC AND METACOGNITIVE STRATEGY USE IN OPEN-ENDED LEARNING ENVIRONMENTS (OELE) IS A NECESSARY STEP TO SUPPORT THEIR LEARNING AND PROBLEM SOLVING THROUGH CONTEXTUALIZED SCAFFOLDING. WE PRESENT AN ANALYSIS OF STUDENTS' PERFORMANCE FROM INFORMATION CAPTURED IN LOG FILES IN URBANSIM, A TURN-BASED SIMULATION ENVIRONMENT FOR COUNTERINSURGENCY TRAINING. WE ILLUSTRATE THE BENEFITS OF THIS APPROACH WITHIN A TASK-MODEL FRAMEWORK. OUR OVERALL GOALS ARE TO IMPLEMENT A GENERALIZABLE DETECTION AND ADAPTIVE SCAFFOLDING FRAMEWORK IN AN EXTENDED VERSION OF THE GIFT TUTORING SYSTEM DEVELOPED AT ARL.
ANALYZING DATA COMING FROM E-LEARNING ENVIRONMENTS CAN PRODUCE KNOWLEDGE AND POTENTIALLY IMPROVE PEDAGOGICAL EFFICIENCY. NEVERTHELESS, TEL COMMUNITY FACES HETEROGENEITY CONCERNING E-LEARNING TRACES, ANALYSIS PROCESSES AND TOOLS LEADING THESE ANALYSES. THEREFORE, ANALYSIS PROCESSES HAVE TO BE REDEFINED WHEN THEIR IMPLEMENTATION CONTEXT CHANGES: THEY CANNOT BE REUSED, SHARED NOR EASILY IMPROVED. THERE IS NO CAPITALIZATION AND WE CONSIDER THIS DRAWBACK AS AN OBSTACLE FOR THE WHOLE COMMUNITY. IN THIS PAPER, WE PROPOSE AN INDEPENDENT FORMALISM TO DESCRIBE ANALYSIS PROCESSES OF E-LEARNING INTERACTION TRACES, IN ORDER TO CAPITALIZE THEM AND AVOID THESE TECHNICAL DEPENDENCIES. WE DISCUSS BOTH THIS CAPITALIZATION AND ITS PLACE AND EFFECTS IN THE ITERATIVE LEARNING ANALYSIS PROCEDURE.
WEB DEVELOPMENT IS A LEARNING CONTEXT WITH THE POTENTIAL TO SUPPORT RICH COMPUTATIONAL THINKING. LARGE-SCALE ANALYSIS OF COMPILATION AND RUNTIME ERRORS HAVE BEEN USED IN INTRODUCTORY PROGRAMMING COURSES AND SIMILAR APPROACHES CAN BE USED TO UNDERSTAND LEARNING IN WEB DEVELOPMENT ENVIRONMENTS. WE INVESTIGATED ACTIVITY LOGS OF A NOVEL WEB CODING GAME TO UNCOVER LEARNING TRAJECTORIES AND WHAT PEOPLE STRUGGLE WITH WHEN LEARNING FLEXIBLE BOX (FLEXBOX), A COLLECTION OF NEW CSS LAYOUT FEATURES. WE DESIGNED A GAME CALLED FLEXBOX FROGGY, IN WHICH LEARNERS SOLVE CHALLENGES BY WRITING A FEW LINES OF CSS CODE, MOVING FROM SIMPLE LEVELS THAT REQUIRE KNOWLEDGE OF ONE FLEXBOX PROPERTY, TO COMPLEX LEVELS COMBINING MULTIPLE PROPERTIES. WE INVESTIGATE LEARNING CURVES BASED ON THE CHANGES IN SYNTACTIC AND SEMANTIC ERRORS LEARNERS MAKE AS THEY COMPLETE THE GAME. OUR FINDINGS SHOW THAT PEOPLE PERFORMED BETTER ENCOUNTERING A SINGLE NEW PROPERTY THAN COMBINED WITH PROPERTIES THEY HAD ALREADY PRACTICED. CLUSTERS OF LEARNERS AT DIFFERENT LEVELS DID NOT DEMONSTRATE EXPECTED ERROR RATES BASED ON LEARNING CURVE THEORY. ALSO UNEXPECTEDLY, ADVANCED GROUPS THAT MASTERED SYNTAX HAD HIGHER SEMANTIC ERROR RATES THAN THE BEGINNER GROUP, ESPECIALLY WHEN ATTEMPTING NEW PROPERTIES OR COMPLEX USE CASES. WE CONCLUDE WITH IMPLICATIONS FOR DESIGNING AND DEVELOPING INTRODUCTORY WEB PROGRAMMING GAMES AND OTHER INSTRUCTIONAL MATERIALS.
RESEARCHERS HAVE INDICATED THAT THE COLLABORATIVE PROBLEM-SOLVING SPACE AFFORDED BY THE COLLABORATIVE SYSTEMS SIGNIFICANTLY IMPACT THE PROBLEM-SOLVING PROCESS. HOWEVER, RECENT INVESTIGATIONS INTO COLLABORATIVE SIMULATIONS, WHICH ALLOW A GROUP OF STUDENTS TO JOINTLY MANIPULATE A PROBLEM IN A SHARED PROBLEM SPACE, HAVE YIELDED DIVERGENT RESULTS REGARDING THEIR EFFECTS ON COLLABORATIVE LEARNING. HENCE, THIS STUDY ANALYSED HOW STUDENTS SOLVED A PHYSICS PROBLEM USING INDIVIDUAL-BASED AND COLLABORATIVE SIMULATIONS TO UNDERSTAND THEIR EFFECTS ON SCIENCE LEARNING. MULTIPLE DATA SOURCES INCLUDING GROUP DISCOURSE, PROBLEM-SOLVING ACTIVITIES, LEARNING TEST SCORES, AND QUESTIONNAIRE FEEDBACK WERE ANALYSED. LAG SEQUENTIAL ANALYSIS ON THE DATA FOUND THAT STUDENTS USING THE TWO SIMULATIONS COLLABORATED WITH PEERS TO SOLVE THE PROBLEM IN SIGNIFICANTLY DIFFERENT PATTERNS. THE STUDENTS USING THE COLLABORATIVE SIMULATIONS DEMONSTRATED ACTIVE ENGAGEMENT IN THE COLLABORATIVE ACTIVITY; HOWEVER, THEY DID NOT TRANSFORM DISCUSSIONS INTO WORKABLE PROBLEM-SOLVING ACTIVITIES. THE STUDENTS USING THE INDIVIDUAL-BASED SIMULATION SHOWED A LOWER LEVEL OF COLLABORATION ENGAGEMENT, STARTING WITH INDIVIDUAL EXPLORATION OF THE PROBLEM WITH THE SIMULATION, FOLLOWED BY GROUP REFLECTION. THE TWO GROUPS ALSO SHOWED SIGNIFICANT DIFFERENCES IN THEIR LEARNING TEST SCORES. THE FINDINGS AND PEDAGOGICAL SUGGESTIONS ARE DISCUSSED IN THE HOPE OF ADDRESSING CRITICAL ACTIVITY DESIGN ISSUES IN USING COMPUTER SIMULATIONS FOR FACILITATING COLLABORATIVE LEARNING.
IN THIS PAPER, WE DESCRIBE A CLASSIFICATION METHOD THAT DOES NOT RELY ON HISTORIC DATA TO PREDICT CHANGES IN STUDENT ACADEMIC PERFORMANCE, AND THEREFORE PREDICT IF A STUDENT WILL FAIL A CLASS OR NOT. BY CLASSIFYING STUDENTS INTO GROUPS GIVEN THEIR GRADES, AND EXTRACTING THE COMMON FEATURES IN BETWEEN THEM, IT IS POSSIBLE TO USE THOSE COMMON FEATURES TO PREDICT IF OTHER STUDENTS THAT SHARE COMMON CHARACTERISTICS WILL FALL INTO THE SAME CLASSIFICATION GROUPS. AS WELL, THOSE SAME COMMON FEATURES CAN BE USED TO HELP STUDENTS IMPROVE THEIR ACADEMIC PERFORMANCE.
THE PRIMARY OBJECTIVE OF A FOUNDATION IT COURSE IS TO EQUIP STUDENTS WITH NECESSARY IT SKILLS TO FACILITATE PROBLEM SOLVING IN DAILY LIFE, ACADEMIC STUDIES AND THE FUTURE WORKPLACE. AS AN IT EDUCATOR, WE ALWAYS COME ACROSS A HIGH DEGREE OF DIGITAL DIVIDE AMONG STUDENTS, AS EXEMPLIFIED BY THE POST-COURSE STUDENT EVALUATION FEEDBACK: A GROUP OF STUDENTS REFLECTED THE FOUNDATION IT COURSE AS "TOO EASY" BUT ANOTHER GROUP EXPRESSED AS "TOO DIFFICULT". EDUCATIONAL TECHNOLOGY SOLUTIONS, E.G., LECTURE CAPTURING, HAVE BEEN ADOPTED TO ARCHIVE THE CLASSROOM DELIVERY FOR SUBSEQUENT REVIEW BY STUDENTS SO THAT USERS CAN ACCESS THE RELEVANT CONTENTS ON DEMAND ANYTIME AND ANYWHERE WITH A MOBILE DEVICE CONNECTED TO THE INTERNET. EDUCATORS ADVOCATE THAT THE USE OF LECTURE CAPTURING GENERALLY IMPROVES STUDENTS' ACADEMIC PERFORMANCE. THIS PAPER EXTENDS THE DISCUSSION WITH ANOTHER ANGLE: HOW DOES LECTURE CAPTURING IMPROVE LEARNING? SPECIFICALLY, OUR RESEARCH QUESTIONS ARE: 1) DO STUDENTS' FREQUENCY OF ACCESS AND DURATION OF WATCHING THE CAPTURED LECTURE DIRECTLY INFLUENCE THE ACADEMIC RESULTS? AND 2) IS LECTURE CAPTURING EFFECTIVE FOR ALL STUDENTS, OR ONLY TO SOME STUDENT GROUPS WITH DIFFERENT ACADEMIC PERFORMANCE? A DATA DRIVEN APPROACH WAS USED TO ANALYZE THE RELATIONSHIP BETWEEN STUDENTS' ACADEMIC PERFORMANCE, THE FREQUENCY AND DURATION OF ACCESSING THE CAPTURED LECTURE CONTENTS. OUR RESULTS SUGGEST THAT LECTURE CAPTURING IS MORE EFFECTIVE AMONG THE MID-RANGE ACHIEVERS, AND TEND TO HAVE MINIMAL EFFECTS ON THE TWO ENDS OF THE ACHIEVER SCALE (I.E., TWO EXTREMES: THE HIGH ACHIEVERS AND THE LOW ACHIEVERS).
THIS IS THE FIRST OCCASION SMART LEARNING TRACK IS INCLUDED IN TEEM CONFERENCE. OUR CURRENT DIGITAL SOCIETY FACES CHALLENGES AS PREPARING OUR STUDENTS FOR AN UNCERTAIN AND CHANGING ENVIRONMENT. LEARNING SYSTEMS BASED ON TECHNOLOGY, ADAPTED TO THE DIFFERENT LEARNING NEEDS, LEARNING STYLES AND LEARNING RHYTHMS, AND PERSONALIZED FOR OUR LEARNERS, COULD BE A SOLUTION TO THIS PROBLEM. IT IS WHAT IS CALLED SMART LEARNING. THE CONTRIBUTIONS TO THIS TRACK COVER ASPECTS SUCH AS THE ORCHESTRATION OF SMART LEARNING ENVIRONMENTS WITH THE SUPPORT OF LEARNING ANALYTICS AND THE INTERNET OF THINGS, THE ESTIMATION OF THE DIFFICULTY OF LEARNING ACTIVITIES, THE USE OF DECISION SUPPORT TOOLS TO MAKE SYSTEMATIC SEARCHES, OR ONE EXPERIENCE OF GAMIFICATION. THESE WORKS SHOW THE DIVERSITY OF THE DISCIPLINE AND THE EXTENT OF THE RESEARCH THAT REMAINS TO BE DONE.
THIS ARTICLE DEVELOPS A PERSPECTIVE ON BIG DATA IN EDUCATION, DRAWING ON A BROADLY LIBERAL CONCEPTION OF EDUCATION'S PRIMARY PURPOSE. WE FOCUS ESPECIALLY ON THE RISE OF SO-CALLED LEARNING ANALYTICS AND THE ASSOCIATED RISE OF DIGITIZATION, WHICH WE EVALUATE ACCORDING TO THE LIBERAL VIEW THAT EDUCATION SHOULD SEEK TO CULTIVATE INDIVIDUALITY AND PROCEED PARTLY BY WAY OF EXPERIMENTATION AND WITH AN EMPHASIS ON CIVIC EDUCATION. OUR ARGUMENT IS NOT THAT THE USE OF BIG DATA IS WHOLLY OUT OF PLACE IN EDUCATION. INDEED, IT MIGHT HAVE SIGNIFICANT VALUE IN PURSUIT OF CERTAIN EDUCATIONAL AIMS. NEVERTHELESS, THE LIBERAL CONCEPTION SHOWS HOW EDUCATION IS DISTINCT FROM OTHER DOMAINS IN WHICH BIG DATA ARE BEING APPLIED, IN WAYS THAT SUGGEST THAT CONSIDERABLE CAUTION MUST BE EXERCISED WHEN THEY ARE USED IN EDUCATIONAL CONTEXTS.
STUDENT PERFORMANCE OVER A COURSE OF AN ACADEMIC PROGRAM CAN BE SIGNIFICANTLY AFFECTED AND POSITIVELY INFLUENCED THROUGH A SERIES OF FEEDBACK PROCESSES BY PEERS AND TUTORS. IDEALLY, THIS FEEDBACK IS STRUCTURED AND INCREMENTAL, AND AS A CONSEQUENCE, DATA PRESENTS AT LARGE SCALE EVEN IN RELATIVELY SMALL CLASSES. IN THIS PAPER, WE INVESTIGATE THE EFFECT OF SUCH PROCESSES AS WE ANALYZE ASSESSMENT DATA COLLECTED FROM ONLINE COURSES. WE PLAN TO FULLY ANALYZE THE MASSIVE DATASET OF OVER THREE AND A HALF MILLION GRANULAR DATA POINTS GENERATED TO MAKE THE CASE FOR THE SCALABILITY OF THESE KINDS OF LEARNING ANALYTICS. THIS COULD SHED CRUCIAL LIGHT ON ASSESSMENT MECHANISM IN MOOCS, AS WE CONTINUE TO REFINE OUR PROCESSES IN AN EFFORT TO STRIKE A BALANCE OF EMPHASIS ON FORMATIVE IN ADDITION TO SUMMATIVE ASSESSMENT.
CURRENT STUDENT RECORD MANAGEMENT SYSTEMS IN UNIVERSITIES FOCUS ONLY ON ACADEMIC ACTIVITIES OF LEARNERS AND ARE REFLECTED IN A FORMAL TRANSCRIPT SHOWING ONLY ACADEMIC RESULTS. TO ASSIST LEARNERS IN WHOLE PERSON DEVELOPMENT, WE PROPOSED A NEW SYSTEM MODEL TO ALSO RECORD EXTRACURRICULAR ACTIVITIES AND THEIR ASSOCIATED ACHIEVEMENTS WITH LINKAGE TO THE CORRESPONDING GRADUATE OUTCOMES. IN THIS PAPER, WE PROPOSED A NEW FORM OF E-PORTFOLIO WHICH CAPTURES BOTH ACADEMIC RESULTS AND NON-ACADEMIC ACTIVITIES; DISCUSSED ITS CHALLENGES AND RESOLUTION; AND REVIEWED A PRACTICAL IMPLEMENTATION, NAMED AS CRESDA, IN THE CITY UNIVERSITY OF HONG KONG. BY EXTENDING DATA LANDSCAPE OF LEARNERS, IT PROVIDES A PLATFORM FOR LEARNING ANALYTICS IN FUTURE, ESPECIALLY IN ENHANCING THE PREDICTION CAPABILITY OF STUDENT SUCCESS. SUCH AN IMPLEMENTATION MAY PROVIDE A USEFUL REFERENCE TO ALL EDUCATIONAL STAKEHOLDERS.
IN RECENT YEARS, THE AMERICAN ASSOCIATION OF COLLEGES OF PHARMACY (AACP) HAS ENCOURAGED THE APPLICATION OF BIG DATA ANALYTIC TECHNIQUES TO PHARMACEUTICAL EDUCATION. INDEED, THE 2013-2014 ACADEMIC AFFAIRS COMMITTEE REPORT INCLUDED A "LEARNING ANALYTICS IN PHARMACY EDUCATION" SECTION THAT REVIEWED THE POTENTIAL BENEFITS OF ADOPTING BIG DATA TECHNIQUES.(1) LIKEWISE, THE 2014-2015 ARGUS COMMISSION REPORT DISCUSSED USES FOR BIG DATA ANALYTICS IN THE CLASSROOM, PRACTICE, AND ADMISSIONS.(2) WHILE BOTH OF THESE REPORTS WERE THOROUGH, NEITHER DISCUSSED SPECIFIC ANALYTIC TECHNIQUES. CONSEQUENTLY, THIS COMMENTARY WILL INTRODUCE CLASSIFICATION TREES, WITH A PARTICULAR EMPHASIS ON THEIR USE IN ADMISSION. WITH ELECTRONIC APPLICATIONS, PHARMACY SCHOOLS AND COLLEGES NOW HAVE ACCESS TO DETAILED APPLICANT RECORDS CONTAINING THOUSANDS OF OBSERVATIONS. WITH DECLINING APPLICATIONS NATIONWIDE, ADMISSIONS ANALYTICS MAY BE MORE IMPORTANT THAN EVER.(3)
CONSIDERABLE EFFORT HAS BEEN PUT IN PLACE IN RECENT YEARS TO BUILD REPOSITORIES OF LEARNING RESOURCES AND ASSESSMENT ACTIVITIES. THUS, RAISING THE DEMAND TO COLLECT AND CURATE MEANINGFUL METADATA RELATED TO THEIR USE. THIS IS ORDER, TO IMPROVE USER EXPERIENCE LEVERAGING ON EDUCATIONAL DATA MINING TECHNIQUES. THIS WORK WILL PROPOSE FIRSTLY A SET OF DATA THAT CAN BE COLLECTED AND A SET OF SIMILARITY METRICS USEFUL FOR QUERY RECOMMENDATIONS ON BASED ON THEIR CONTENT. SECONDLY, THE SET OF RESULTS USER RECOMMENDATIONS EITHER BASED ON THE QUERIES THEY PERFORM ON THE SYSTEM, OR ON THE BASIS OF THE ACTIVITIES THEY PERFORM ON THE REPOSITORY. THIRDLY,LEARNING RESOURCE RECOMMENDATION BASED ON OTHER USERS' INTERESTS AND RESULTS OF SIMILAR QUERIES, PATTERN OF USAGE OF RESOURCES AND FEEDBACK GIVEN BY SIMILAR USERS. AN ACTIVITY MATRIX COLLECTING DATA IN THREE DIMENSIONS: RESOURCES, USERS AND QUERIES IS PROPOSED; LEAVING IMPLEMENTATION DETAILS AND EXPERIENCE REPORT FOR FURTHER STUDIES.
ESTIMATING THE EFFICACY OF DIFFERENT INSTRUCTIONAL MODALITIES, TECHNIQUES, AND INTERVENTIONS IS CHALLENGING BECAUSE TEACHING STYLE COVARIES WITH INSTRUCTOR, AND THE TYPICAL STUDENT ONLY TAKES A COURSE ONCE. WE INTRODUCE THE INDIVIDUALIZED TREATMENT EFFECT (ITE) FROM ANALYSES OF PERSONALIZED MEDICINE AS A MEANS TO QUANTIFY INDIVIDUAL STUDENT PERFORMANCE UNDER DIFFERENT INSTRUCTIONAL MODALITIES OR INTERVENTION STRATEGIES, DESPITE THE FACT THAT EACH STUDENT MAY EXPERIENCE ONLY ONE "TREATMENT." THE ITE IS PRESENTED WITHIN AN ENSEMBLE MACHINE LEARNING APPROACH TO EVALUATE STUDENT PERFORMANCE, IDENTIFY FACTORS INDICATIVE OF STUDENT SUCCESS, AND ESTIMATE PERSISTENCE. A KEY ELEMENT IS THE USE OF A PRIORI STUDENT INFORMATION FROM INSTITUTIONAL RECORDS. THE METHODS ARE MOTIVATED AND ILLUSTRATED BY A COMPARISON OF ONLINE AND STANDARD FACE-TO-FACE OFFERINGS OF AN UPPER DIVISION APPLIED STATISTICS COURSE THAT IS A CURRICULUM BOTTLENECK AT SAN DIEGO STATE UNIVERSITY. THE ITE ALLOWS US TO CHARACTERIZE STUDENTS THAT BENEFIT FROM EITHER THE ONLINE OR THE TRADITIONAL OFFERINGS. WE FIND THAT STUDENTS IN THE ONLINE CLASS PERFORMED AT LEAST AS WELL AS THE TRADITIONAL LECTURE CLASS ON A COMMON FINAL EXAM. WE DISCUSS THE GENERAL IMPLICATIONS OF THIS ANALYTICS FRAMEWORK FOR ASSESSING PEDAGOGICAL INNOVATIONS AND INTERVENTION STRATEGIES, IDENTIFYING AND CHARACTERIZING AT-RISK STUDENTS, AND OPTIMIZING THE INDIVIDUALIZED STUDENT LEARNING ENVIRONMENT.
THIS PAPER PRESENTS TWO INTERACTIVE VISUALIZATION TOOLS FOR LEARNING MANAGEMENT SYSTEMS (LMS) IN ORDER TO IMPROVE LEARNING AND TEACHING IN ONLINE COURSES. THE FIRST TOOL WAS DEVELOPED AT THE INTELLIGENT INFORMATION SYSTEMS LABORATORY (IISLAB) AT THE TAMPERE UNIVERSITY OF TECHNOLOGY (TUT). THE TOOL IS USED TO ANALYSE STUDENTS' ACTIVITY FROM AUTOMATICALLY RECORDED USER LOG DATA AND TO BUILD INTERACTIVE VISUALIZATIONS. THEY PROVIDE VALUABLE INSIGHTS INTO THE LEARNING PROCESS AND PARTICIPATION OF STUDENTS IN A COURSE OFFERED TO TEACHERS AND STUDENTS. THE SECOND TOOL WAS DEVELOPED AT THE UNITELMA SAPIENZA UNIVERSITY. IT EXTENDS NAVIGATION AND SEARCH FUNCTIONALITIES IN THE DISCUSSION FORUM OF AN LMS WITH A TOPIC-DRIVEN PARADIGM. THE TOOL ANALYSES FORUM CONTENT AND AUTOMATICALLY IDENTIFIES DISCUSSION TOPICS. IT THEN ENHANCES THE ORIGINAL FORUM WITH A TOPIC-DRIVEN NAVIGATION STRUCTURE AND AN INTERACTIVE SEARCH GRAPH. BOTH TOOLS HAVE BEEN DEVELOPED AS PLUG-INS FOR THE MOODLE LMS, BUT THEIR ANALYSIS PROCESSES AND TECHNIQUES CAN BE ADOPTED INTO ANY LMS.
IN THIS PAPER A NOVEL APPROACH TO FACILITATE THE REUSE OF UNITS OF LEARNING (UOLS) IS PRESENTED. TYPICALLY, E-LEARNING PLATFORMS DO NOT PROVIDE THE MEANS TO RETRIEVE DESIGNED UOLS IN A STANDARDIZED FORMAT TO BE REUSED IN A DIFFERENT PLATFORM, BUT THEY HAVE IN COMMON THAT THE STUDENTS AND TEACHERS INTERACTION WITH THE SYSTEM IS LOGGED TO FILES. TAKING THIS INTO ACCOUNT, WE PROPOSE TO USE THESE LOGS AND APPLY A THREE STEPS RE-ENGINEERING APPROACH TO TRANSLATE THESE UOLS INTO AN ACCEPTED EDUCATIONAL MODELLING LANGUAGE, SPECIFICALLY IMS LD. IN THE FIRST STEP, THE SEQUENCE OF ACTIVITIES AND THEIR FUNCTIONAL DEPENDENCIES ARE LEARNED BY A PROCESS MINING ALGORITHM. IN THE SECOND STEP, ANOTHER ALGORITHM ANALYSES THE VARIABLES AND THEIR VALUE CHANGE IN ORDER TO LEARN THE ADAPTATION RULES THAT MAY HAVE BEEN DEFINED IN THE UOL. AND FINALLY, IN THE LAST STEP THE INFERRED PROCESS STRUCTURE AND RULES ARE MATCHED WITH THE TYPICAL STRUCTURE OF ACTIVITIES, ACTS, AND PLAYS DEFINED BY IMS LD.
RECENT RESEARCH HAS CONFIRMED THE IMPACT OF ELECTRONIC MEDIA USAGE ON STUDENT OUTCOMES. HOWEVER, LITTLE IS KNOWN ABOUT THE IMPACT OF MEASURABLE READING ACCESS TO A FORUM AND OTHER RESOURCES. WE HAVE DEVELOPED A TOOL TO STUDY THIS ASPECT THROUGH VISUAL ANALYSIS. RESULTS FROM THIS STUDY SHOWED THAT NOT ONLY PARTICIPATION ON ELECTRONIC DISCUSSION FORA, SUCH AS READING OR POSTING, BUT ALSO THE READING ACCESS TO THE RESOURCE, CAN ALERT ABOUT THE STUDENT'S PERFORMANCE. THIS CAME ABOUT AFTER PROVING THE EXISTENCE OF A STRONG INFLUENCE RELATIONSHIP BETWEEN THE FREQUENCY OF READING ACCESS TO DISCUSSION FORA AND THE FREQUENCY OF ACCESS TO POSTING FORA, AS WELL AS BETWEEN THE FREQUENCY OF READING ACCESS TO RESOURCES AND THE FREQUENCY OF ACCESS TO POSTING FORA IN THE OUTCOME OF THE STUDENTS. HOWEVER, WE HAVE FOUND THAT THE UNION OF THE VARIABLES ABOVE, I.E., FREQUENCY OF POSTS IN FORA, READING OF DISCUSSION FORA AND READING OF RESOURCES CAN BE VISUALLY ANALYZED, THUS OBTAINING A BETTER VISUALIZATION OF THE PATTERNS OF STUDENTS' PERFORMANCE.
THE E-UCM GROUP IS WORKING TO SIMPLIFY THE PROCESSES OF BOTH CREATION AND SCIENTIFIC VALIDATION OF EDUCATIONAL ADVENTURE GAMES, BY MAKING THE ENTIRE PROCESS OF GAME CREATION, DATA ACQUISITION, AND ANALYSIS MORE TRANSPARENT AND INTEGRATED. THIS PAPER PRESENTS THE INTEGRATION OF THE UADVENTURE AUTHORING TOOL WITH SIMVA, A TOOL TO SIMPLIFY VALIDATION EXPERIMENTS, THUS ENCOMPASSING THE FULL PROCESS OF SERIOUS GAMES AUTHORING AND VALIDATION.
COLLABORATION IS AN IMPORTANT 21ST CENTURY SKILL; IT CAN TAKE PLACE IN A REMOTE OR CO-LOCATED SETTING. CO-LOCATED COLLABORATION (CC) GIVES RISE TO SUBTLE HUMAN INTERACTIONS THAT CAN BE DESCRIBED WITH MULTIMODAL INDICATORS LIKE GAZE, SPEECH AND SOCIAL SKILLS. IN THIS DEMO PAPER, WE FIRST GIVE A BRIEF OVERVIEW OF RELATED WORK THAT HAS IDENTIFIED INDICATORS DURING CC. THEN, WE LOOK BRIEFLY AT THE FEEDBACK MECHANISMS THAT HAVE BEEN DESIGNED BASED ON THESE INDICATORS TO FACILITATE CC. USING THESE THEORETICAL INSIGHTS, WE DESIGN A PROTOTYPE TO GIVE AUTOMATED REAL-TIME FEEDBACK TO FACILITATE CC TAKING THE HELP OF THE MOST ABUNDANT MODALITY DURING CC I.E., AUDIO CUES.
BIG DATA AND ANALYTICS ARE SHAPING THE FUTURE OF HIGHER EDUCATION. THEIR ROLE IS SIGNIFICANT IN CREATING GUIDELINES FOR REFORM ACTIVITIES AND IN ASSISTING EDUCATORS, STUDENTS, AND ADMINISTRATORS IN IMPROVING TEACHING AND LEARNING. THIS PAPER TACKLES ONE ASPECT OF THIS PHENOMENON. THE AIM OF THIS STUDY IS TO EXAMINE WHETHER AND TO WHAT EXTENT STUDENTS ACTIVITY AT THE LEARNING MANAGEMENT SYSTEM (LMS) CAN BE A PREDICTOR OF ACADEMIC SUCCESS. LOG FILE ANALYSIS IS PERFORMED FROM A BLENDED COURSE IN THE FIELD OF INFORMATION COMMUNICATION TECHNOLOGY (ICT). RESULTS INDICATED NO SIGNIFICANT CORRELATION BETWEEN STUDENTS' BEHAVIOR AND GRADE ATTITUDES AND PARTICIPATION COULD BE IDENTIFIED. HOWEVER, DIFFERENCES BETWEEN MALE AND FEMALE STUDENTS ARE IDENTIFIED.
PREDICTING STUDENTS' GRADES IS AN INTERESTING TASK THAT MAY BE TACKLED USING DIFFERENT INFORMATION, LIKE THE STUDENTS' LEARNING BACKGROUND OR THEIR PREVIOUS AND CURRENT PERFORMANCE. HOWEVER, AND WITH THE WIDELY USE OF THE E-LEARNING PLATFORMS, THE INTERACTION OF THE STUDENTS WITH THE PLATFORMS CONSTITUTES A DYNAMIC, HUGE, OBJECTIVE AND RELIABLE DATA SOURCE. ASSUMING THESE INTERACTIONS REFLECT THE INTEREST, FEELING AND WAY OF STUDY OF EACH STUDENT, OUR PROPOSAL USES THIS INFORMATION FOR THE EARLY PREDICTION OF THEIR PERFORMANCE (GRADES). CONTRARILY TO OTHER APPROACHES, WE DO NOT USE THE SAME DATA ANALYSIS MODEL FOR ALL THE STUDENTS, NEITHER THEIR CENSUS OR LEARNING BACKGROUND DATA. WE PREVIOUSLY DEFINE A CLASSIFICATION SCHEME OF FIVE DIFFERENT PROFILES, WHICH REPRESENT FIVE DIFFERENT WAYS OF LEARNING AND FACE THE COURSE. AFTER THAT, A SPECIFICALLY CREATED DATA ANALYSIS MODEL IS DEFINED FOR EACH KIND OF STUDENT. THE PROPOSAL IS VALIDATED WITH UNDERGRADUATE STUDENTS OF A BLENDED-COURSE FOR 3 CONSECUTIVE ACADEMIC YEARS.
IN THIS WORK WE FOCUS ON A SPECIFIC APPLICATION NAMED "1X1 TRAINER" THAT HAS BEEN DESIGNED TO ASSIST CHILDREN IN PRIMARY SCHOOL TO LEARN ONE DIGIT MULTIPLICATIONS. WE INVESTIGATE THE DATABASE OF LEARNERS' ANSWERS TO THE ASKED QUESTIONS BY APPLYING MARKOV CHAIN AND CLASSIFICATION ALGORITHMS. THE ANALYSIS IDENTIFIES DIFFERENT CLUSTERS OF ONE DIGIT MULTIPLICATION PROBLEMS IN RESPECT TO THEIR DIFFICULTY FOR THE LEARNERS. NEXT WE PRESENT AND DISCUSS THE OUTCOMES OF OUR ANALYSIS CONSIDERING MARKOV CHAIN OF DIFFERENT ORDERS FOR EACH QUESTION. THE RESULTS OF THE ANALYSIS INFLUENCE THE LEARNING PATH FOR EVERY PUPIL AND OFFER A PERSONALIZED RECOMMENDATION PROPOSAL THAT OPTIMIZES THE WAY QUESTIONS ARE ASKED TO EACH PUPIL INDIVIDUALLY.
THE COVID-19 SITUATION HAS A SERIOUS GLOBAL IMPACT ON THE EDUCATION SYSTEM. THUS, THE RESEARCH PURPOSE IS AIMED TO CONSTRUCT THE MODELS OF ONLINE LEARNING STRATEGIES FOR THAILAND STUDENTS ON LEARNING MANAGEMENT IN THE CORONAVIRUS 2019 SCENARIO. THE RESEARCH METHODOLOGY WAS CONDUCTED ACCORDING TO THE PROCESS OF THE CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING, KNOWN AS THE CRISP-DM MODEL FOR DEVELOPING THE BEST RESEARCH. THE DATA COLLECTED 487 STUDENTS FROM THE UNIVERSITY OF PHAYAO (UP), AND RAJABHAT MAHA SARAKHAM UNIVERSITY (RMU) FROM THE 1ST SEMESTER IN ACADEMIC YEAR 2020. THE COLLECTED DATA HAS BEEN AGREED UPON IN ACCORDANCE WITH RESEARCH ETHICS. THE RESULTS OF THE STUDY REVEALED THAT THE FACTORS INFLUENCING THE MODEL CONSISTED OF 8 OUT OF 38 ATTRIBUTES, WITH A HIGH PREDICTIVE ACCURACY (85.14%). FINALLY, THE RESEARCHERS CAN PLAN FOR THE MANAGEMENT OF TEACHING AND LEARNING FOR STUDENTS AT THE UNIVERSITY OF PHAYAO TO SOLVE THE CORONAVIRUS 2019 SCENARIO IN THE ACADEMIC YEAR 2021 AND THE FUTURE.
THE COVID-19 PANDEMIC HAS HAD A DEVASTATING IMPACT ON THE THAI EDUCATION SYSTEM, AS MANY UNIVERSITIES HAD TO TRANSFER THEIR LEARNING MANAGEMENT TO E-LEARNING. THIS RESEARCH AIMS (1) TO STUDY AND COMPARE THE ACADEMIC PERFORMANCE OF HIGHER EDUCATION STUDENTS AFFECTED BY THE COVID-19 HEALTH CRISIS, AND (2) TO BUILD A MODEL OF ACADEMIC PERFORMANCE WITH INSTRUCTIONAL ENGINEERING TECHNOLOGY TO SUPPORT THE LEARNING MANAGEMENT PROCESS OF HIGHER EDUCATION INSTITUTIONS. THE RESEARCH APPROACHES WERE CARRIED OUT ACCORDING TO THE THEORY OF DATA MINING TECHNIQUES USING THE CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING (CRISP-DM) METHODOLOGY. THE DATA COLLECTION WAS DIVIDED INTO TWO PARTS ACCORDING TO THE EDUCATION SITUATION. THE FIRST PART WAS A NORMAL SITUATION WITH DATA COLLECTED FROM 173 STUDENTS DURING THE SECOND SEMESTER OF THE 2019 ACADEMIC YEAR. THE SECOND PART, ON THE OTHER HAND, WAS AN ABNORMAL SITUATION, WITH DATA COLLECTED FROM 167 STUDENTS DURING THE FIRST SEMESTER OF THE ACADEMIC YEAR 2020. IT WAS FOUND THAT TRADITIONAL AND ONLINE TEACHING AND LEARNING ENABLED STUDENTS TO ACHIEVE SIMILAR ACADEMIC RESULTS. THE DATA COLLECTED AND ANALYZED FROM BOTH SITUATIONS SHOWED THAT THE STUDENTS' LEARNING OUTCOMES WERE NOT DIFFERENT. IN ADDITION, THE APPROPRIATE GROUPING OF LEARNERS ALLOWS FOR THREE SUB-GROUPS TO BE CLASSIFIED ACCORDING TO THE RESEARCH FINDINGS. THEREFORE, CONSIDERATION SHOULD BE GIVEN TO GROUPING LEARNERS WITH SIMILAR CHARACTERISTICS IN A SMALL GROUP AND DESIGNING MORE EFFECTIVE LEARNING ACTIVITIES. THIS ISSUE WILL BE THE SUBJECT OF FUTURE STUDY.
IN RECENT YEARS THERE HAS BEEN CONSIDERABLE INTEREST IN HOW MANY LEARNERS COMPLETE MOOCS, AND WHAT FACTORS DURING USAGE CAN PREDICT COMPLETION. OTHERS, HOWEVER, HAVE ARGUED THAT MANY LEARNERS NEVER INTEND TO COMPLETE MOOCS, AND TAKE MOOCS FOR OTHER REASONS. THERE HAS BEEN QUALITATIVE RESEARCH INTO WHY LEARNERS TAKE MOOCS, BUT THE LINK BETWEEN LEARNER GOALS AND COMPLETION HAS NOT BEEN FULLY ESTABLISHED. IN THIS PAPER, WE STUDY THE RELATIONSHIP BETWEEN LEARNER INTENTION TO COMPLETE A MOOC AND THEIR ACTUAL COMPLETION STATUS. WE COMPARE THAT RELATIONSHIP TO THE DEGREE TO WHICH MOOC COMPLETION IS PREDICTED BY OTHER DOMAIN-GENERAL MOTIVATIONAL FACTORS SUCH AS GRIT, GOAL ORIENTATION, ACADEMIC EFFICACY, AND THE NEED FOR COGNITION. WE FIND THAT GRIT AND GOAL ORIENTATION ARE ASSOCIATED WITH COURSE COMPLETION, WITH GRIT PREDICTING COURSE COMPLETION INDEPENDENTLY FROM INTENTION TO COMPLETE, AND WITH COMPARABLE STRENGTH.
STUDENT VIDEO-WATCHING BEHAVIOR AND QUIZ PERFORMANCE ARE STUDIED IN TWO MASSIVE OPEN ONLINE COURSES (MOOCS). IN DOING SO, TWO FRAMEWORKS ARE PRESENTED BY WHICH VIDEO-WATCHING CLICKSTREAMS CAN BE REPRESENTED: ONE BASED ON THE SEQUENCE OF EVENTS CREATED, AND ANOTHER ON THE SEQUENCE OF POSITIONS VISITED. WITH THE EVENT-BASED FRAMEWORK, RECURRING SUBSEQUENCES OF STUDENT BEHAVIOR ARE EXTRACTED, WHICH CONTAIN FUNDAMENTAL CHARACTERISTICS SUCH AS REFLECTING (I.E., REPEATEDLY PLAYING AND PAUSING) AND REVISING (I.E., PLAYS AND SKIP BACKS). IT IS FOUND THAT SOME OF THESE BEHAVIORS ARE SIGNIFICANTLY CORRELATED WITH CHANGES IN THE LIKELIHOOD THAT A STUDENT WILL BE CORRECT ON FIRST ATTEMPT (CFA) OR NOT IN ANSWERING QUIZ QUESTIONS, AND IN WAYS THAT ARE NOT NECESSARILY INTUITIVE. THEN, WITH THE POSITION-BASED FRAMEWORK, MODELS OF QUIZ PERFORMANCE ARE DEVISED BASED ON POSITIONS VISITED IN A VIDEO. IN EVALUATING THESE MODELS THROUGH CFA PREDICTION, IT IS FOUND THAT THREE OF THEM CAN SUBSTANTIALLY IMPROVE PREDICTION QUALITY, WHICH UNDERLINES THE ABILITY TO RELATE THIS TYPE OF BEHAVIOR TO QUIZ SCORES. SINCE THIS PREDICTION CONSIDERS VIDEOS INDIVIDUALLY, THESE BENEFITS ALSO SUGGEST THAT THESE MODELS ARE USEFUL IN SITUATIONS WHERE THERE IS LIMITED TRAINING DATA, E.G., FOR EARLY DETECTION OR IN SHORT COURSES.
IN THIS PAPER, WE INTRODUCE A NEW WAY OF DETECTING SEMANTIC SIMILARITIES BETWEEN LEARNING OBJECTS BY ANALYSING THEIR USAGE IN WEB PORTALS. OUR APPROACH RELIES ON THE USAGE-BASED RELATIONS BETWEEN THE OBJECTS THEMSELVES RATHER THEN ON THE CONTENT OF THE LEARNING OBJECTS OR ON THE RELATIONS BETWEEN USERS AND LEARNING OBJECTS. WE THEN TAKE THIS NEW SIMILARITY MEASURE TO ENHANCE EXISTING RECOMMENDATION APPROACHES FOR THE USE IN TECHNOLOGY ENHANCED LEARNING.
WE ARE WITNESSING A LEARNING COMMUNITY WHO IS ALWAYS WILLING AND EAGER TO BE ASSOCIATED TO INNOVATIVE PRACTICES ONLINE. WITH THE MASSIVE OPEN ONLINE COURSES (MOOC), DEVELOPING COUNTRIES ARE GIVEN NEW OPPORTUNITIES TO BE PART OF THE GLOBAL COMMUNITY WITH ACCESS TO A WIDER RANGE OF RESOURCES AND EXPERTISE FROM LEADING UNIVERSITIES AROUND THE WORLD. BUT THESE RESOURCES CANNOT BE INTEGRATED IN PROGRAMMES WITHOUT INVESTIGATING ON THEIR PERTINENCE AND THEIR VALUE ADDED FACTOR. THEREFORE, THE AIM OF THIS PAPER IS TO ANALYZE THE ACTUAL DISTANCE EDUCATION SYSTEMS IN MAURITIUS AND TO OUTLINE STRATEGIES ON HOW MOOC CAN BE USED EFFECTIVELY IN ORDER TO REVITALIZE HIGHER EDUCATION AND LIFELONG LEARNING BY GIVING ACCESS TO QUALITY COMPLEMENTARY RESOURCES. AS METHODOLOGY, THE EVOLUTION OF DISTANCE EDUCATION IN MAURITIUS IS ANALYZED AS WELL AS THE GROWTH OF MOOC. FURTHERMORE A FRAMEWORK OF THE INTEGRATION OF MOOC IN A NEW FORM OF EDUCATION THAT TRANSCENDS BOUNDARIES IS PROPOSED FOR A DEVELOPING COUNTRY LIKE MAURITIUS. IT IS FOUND THAT, FOR INNOVATION TO BE EFFECTIVE, IT NEEDS TO FIND ITS WAY IN THE EXISTING VALUED SOLID FOUNDATIONS THAT WERE INBUILT.
MENTORING IS THE ACTIVITY WHEN AN EXPERIENCED PERSON (THE MENTOR) SUPPORTS A LESS KNOWLEDGEABLE PERSON (THE MENTEE), IN ORDER TO ACHIEVE THE LEARNING GOAL. IN A PERFECT WORLD, THE MENTOR WOULD BE ALWAYS AVAILABLE WHEN THE MENTEE NEEDS IT. HOWEVER, IN THE REAL WORLD HIGHER EDUCATION INSTITUTIONS WORK WITH LIMITED RESOURCES. FOR THIS, WE NEED TO CAREFULLY DESIGN SOCIO-TECHNICAL INFRASTRUCTURES FOR SCALING MENTORING PROCESSES WITH THE HELP OF DISTRIBUTED ARTIFICIAL INTELLIGENCE. OUR APPROACH ALLOWS UNIVERSITIES TO QUICKLY SET UP A NECESSARY DATA PROCESSING ENVIRONMENT TO SUPPORT BOTH MENTORS AND MENTEES. THE PRESENTED FRAMEWORK IS BASED ON OPEN SOURCE STANDARDS AND TECHNOLOGIES. THIS WILL HELP LEVERAGING THE APPROACH, DESPITE THE ORGANIZATIONAL AND PEDAGOGICAL CHALLENGES. THE DEPLOYED INFRASTRUCTURE IS ALREADY USED BY SEVERAL UNIVERSITIES.
PROGRAMMING BECAME ONE OF THE MOST DEMANDED PROFESSIONAL SKILLS. THIS REALITY IS DRIVING PRACTITIONERS TO SEARCH OUT BETTER APPROACHES FOR FIGURING OUT HOW TO CODE AND HOW TO SUPPORT LEARNING PROGRAMMING PROCESSES. PRIOR WORKS HAVE FOCUSED ON DISCOVERING, IDENTIFYING, AND CHARACTERIZING LEARNING PROGRAMMING PATTERNS THAT BETTER RELATE TO SUCCESS. RESEARCHERS PROPOSE QUALITATIVE AND SUPERVISED ANALYTIC METHODS BASED ON TRACE DATA FROM CODING TASKS. HOWEVER, THESE METHODS ARE LIMITED FOR AUTOMATICALLY IDENTIFYING STUDENTS IN DIFFICULTIES WITHOUT HUMAN-INTERVENTION SUPPORT. THE MAIN GOAL OF THIS PAPER IS TO INTRODUCE A THREE-PHASE PROCESS AND A CASE STUDY IN WHICH UNSUPERVISED CLUSTERING TECHNIQUES ARE USED FOR AUTOMATICALLY IDENTIFYING LEARNERS' PROGRAMMING BEHAVIOR. THE CASE STUDY TAKES PLACE IN A SHELL PROGRAMMING COURSE IN WHICH WE ANALYZED DATA FROM 100 STUDENTS TO EXTRACT LEARNERS' BEHAVIORAL TRAJECTORIES THAT POSITIVELY CORRELATE WITH SUCCESS. AS A RESULT, WE IDENTIFIED: (1) A LIST OF FEATURES THAT IMPROVE THE QUALITY OF THE AUTOMATIC LEARNERS' PROFILES IDENTIFICATION PROCESS, AND (2) SOME STUDENTS' BEHAVIORAL TRAJECTORIES CORRELATED WITH THEIR PERFORMANCE AT THE FINAL EXAM.
THE TECHNOLOGICAL ADVANCEMENTS AND EDUCATIONAL CHANGES IN THE NEW ERA HAVE MADE EDUCATIONAL TECHNOLOGY A POPULAR RESEARCH TOPIC. PARTICULARLY, THE TOPIC OF SMART LEARNING ENVIRONMENTS IS ATTRACTING THE ATTENTION OF EDUCATIONAL RESEARCHERS. WHILE THE FEASIBILITY AND EFFECTIVENESS OF SMART LEARNING ENVIRONMENTS HAVE BEEN POPULARLY EXPLORED, THERE IS A CRUCIAL NEED TO FURTHER GAIN IN-DEPTH UNDERSTANDINGS OF DESIGNING, IMPLEMENTING, AND EVALUATING SMART LEARNING ENVIRONMENTS IN THE 21ST CENTURY EDUCATIONAL CONTEXTS. SMART LEARNING ENVIRONMENTS - LECTURE NOTES IN EDUCATIONAL TECHNOLOGY IS A BOOK THAT COLLECTS RESEARCH EXAMPLES RELATED TO THE LEARNING, PEDAGOGICAL, AND TECHNOLOGICAL ASPECTS OF DIGITAL LEARNING APPLICATIONS. IT GATHERS A VARIETY OF EMPIRICAL EVIDENCE ON SMART LEARNING ENVIRONMENTS AND PRESENTS THE WAYS OF HOW TECHNOLOGICAL PRODUCTS ARE ADOPTED IN VARIOUS EDUCATIONAL CONTEXTS TO FACILITATE LEARNING. THE BOOK PROVIDES SIGNIFICANT INSIGHTS FOR BOTH THE EDUCATIONAL TECHNOLOGY DEVELOPMENT AND TECHNOLOGY-ASSISTED PEDAGOGICAL DESIGN IN MULTI-DISCIPLINARY DOMAINS. FURTHERMORE, IT HELPS READERS TO CRITICALLY REFLECT ON DIFFERENT SMART LEARNING APPROACHES. IN THIS ARTICLE, WE WILL REVIEW THE CONTENT OF THE BOOK AND DISCUSS THE INTERCONNECTION BETWEEN THE CHAPTERS THEREIN.
THIS ARTICLE CONSIDERS RECENT PUBLIC DEBATES ABOUT MASSIVE OPEN ONLINE COURSES (MOOCS) AND THEIR POTENTIAL TO TRANSFORM HIGHER EDUCATION. DRAWING ON REPORTS AND MEDIA COMMENTARY, IT PROBES THE CLAIMS AND COUNTERCLAIMS OF MOOC PROPONENTS AND MOOC SCEPTICS. IT CONSIDERS THE IMPLICATIONS FOR STUDENTS, GOVERNMENTS, INSTITUTIONS AND SCHOLARS THEMSELVES. THE PAPER CONCLUDES THAT MASS-SCALE ONLINE COURSES HAVE THE POTENTIAL TO RESHAPE THE SECTOR SIGNIFICANTLY OVER TIME.
PREDICTING STUDENT RETENTION IS A CRUCIAL TASK FOR ALL STAKEHOLDERS IN HIGHER EDUCATION. THIS PAPER SURVEYED THE EDUCATIONAL DATA MINING (EDM) LITERATURE TO EXPLORE THE MOST RECENT METHODS USED IN BUILDING PREDICTIVE MODELS TO PREDICT STUDENT'S RETENTION, AND TO FORESEE THE FUTURE TRENDS IN DIFFERENT CONTEXT OF HIGHER EDUCATION. WE REVIEW A DIVERSIFIED SET OF APPROACHES, MODELS, DATA SETS, TOOLS, TECHNIQUES, AND PERFORMANCE MEASURES. THE APPROACHES VARY AS THE EDUCATIONAL CONTEXT VARIES WHERE OPPORTUNITIES AND CHALLENGES ARE ASSOCIATED WITH EACH APPROACH. WE ALSO PRESENT A DISCUSSION AND A FORESIGHT OF FUTURE DIRECTIONS.
STUDENTS ARE COMFORTABLE SHARING DIGITAL CONTENT WITH OTHERS, YET THE EFFECT OF SHARING OF DIGITAL MEDIA FOR LEARNING REMAINS LARGELY UNEXPLORED. BUILDING ON RESEARCH IN SOCIAL NETWORK ANALYSIS AND LEARNING ANALYTICS, THIS RESEARCH EXPLORES THE USE AND SHARING OF DIGITAL MEDIA IN LEARNING ACTIVITIES, ANALYZING THE EFFECTS OF THE DESIGN OF THE LEARNING ACTIVITIES ON THE RESULTING NETWORKS OF STUDENTS AND THEIR CITED RESOURCES, AND EXPLORING RELATIONSHIPS BETWEEN ATTRIBUTES OF THESE CITATION NETWORKS AND STUDENTS' PERCEPTIONS OF THE LEARNING OUTCOMES. RESULTS SUGGEST THAT THE EXTENT TO WHICH AN ASSIGNMENT IS WELL-STRUCTURED AND CONVERGES TOWARDS A SINGLE SOLUTION POSITIVELY INFLUENCES THE DENSITY AND CLUSTERING COEFFICIENT OF THE RESULTING CITATION NETWORK, AND THAT THESE NETWORK MEASURES IN TURN HAVE A POSITIVE INFLUENCE ON STUDENTS' PERCEPTIONS OF LEARNING FROM THE ASSIGNMENT.
THE ANALYSIS AND USE OF DATA GENERATED BY STUDENTS' INTERACTIONS WITH LEARNING SYSTEMS OR PROGRAMS - LEARNING ANALYTICS - HAS RECENTLY GAINED WIDESPREAD ATTENTION IN THE EDUCATIONAL TECHNOLOGY COMMUNITY. PART OF THE REASON FOR THIS INTEREST IS BASED ON THE POTENTIAL OF LEARNING ANALYTIC TECHNIQUES SUCH AS DATA MINING TO FIND HIDDEN PATTERNS IN STUDENTS' ONLINE INTERACTIONS THAT CAN BE MEANINGFULLY INTERPRETED AND THEN FED BACK TO STUDENTS IN A WAY THAT SUPPORTS THEIR LEARNING. IN THIS PAPER WE PRESENT AN INVESTIGATION OF HOW THE DIGITAL DATA RECORDS OF STUDENTS' INTERACTIONS WITHIN AN IMMERSIVE 3D ENVIRONMENT CAN BE MINED, MODELED AND ANALYSED, TO PROVIDE REAL-TIME FORMATIVE FEEDBACK TO STUDENTS AS THEY COMPLETE SIMULATED SURGICAL TASKS. THE ISSUES THAT EMERGED IN THIS INVESTIGATION AS WELL AS AREAS FOR FURTHER RESEARCH AND DEVELOPMENT ARE DISCUSSED.
THIS ARTICLE DESCRIBES A SOFTWARE, THAT AUTOMATICALLY CONVERTS ALREADY CREATED MOODLE LEARNING RESOURCES OF TYPE "LESSONS" INTO INTERACTIVE E-BOOKS, STORED IN EPUB FORMAT. THE ADVANTAGES OF EPUB FORMAT TO PRESENT LEARNING CONTENT ARE DISCUSSED, AS WELL AS EXISTING EXAMPLES OF ITS USE IN EDUCATION. EMPHASIS IS GIVEN TO CREATING INTERACTIVE EPUB LESSONS THAT ENABLE TRACKING LEARNERS' ACTIVITIES AND SAVING THEIR CURRENT PROGRESS WITH THE HELP OF XAPI STATEMENTS AND STATEAPI IMPLEMENTATION.
THE ANALYSIS OF LEARNING BEHAVIORS FROM THE LOG DATA OF DIGITAL TEXTBOOKS IS BENEFICIAL FOR IMPROVING EDUCATION SYSTEMS. THE FOCUS OF DISCUSSION IN ANY ANALYSIS OF LEARNING BEHAVIORS IS OFTEN ON DISCOVERING THE RELATIONSHIPS BETWEEN LEARNING BEHAVIOR AND LEARNING PERFORMANCE. HOWEVER, LITTLE ATTENTION HAS BEEN PAID TO INVESTIGATING AND ANALYZING LEARNING PATTERNS OR RULES AMONG LEARNING STYLE OF INDEX (LSI), COGNITIVE STYLE OF INDEX (CSI), AND THE LOGS OF DIGITAL TEXTBOOKS. IN THIS STUDY, THE AUTHORS PROPOSED A METHOD TO ANALYZE LEARNING PATTERNS OR RULES OF READING DIGITAL TEXTBOOKS. THE ANALYSIS METHOD USED ASSOCIATION ANALYSIS WITH THE APRIORI ALGORITHM. THE ANALYSIS WAS CONDUCTED USING LOGS OF DIGITAL TEXTBOOKS AND QUESTIONNAIRES TO INVESTIGATE STUDENTS' LEARNING AND COGNITIVE STYLES. FROM THE DETECTED MEANINGFUL ASSOCIATION RULES, THIS STUDY FOUND THREE STUDENT TYPES: POORLY MOTIVATED, EFFICIENT, AND DILIGENT. THE AUTHORS BELIEVE THAT CONSIDERATION OF THESE STUDENT TYPES CAN CONTRIBUTE TO THE IMPROVEMENT OF LEARNING AND TEACHING
WE PROPOSE A MODEL, LEARNING ACTIVITY INDEX (LAI) TO DESCRIBE LEARNERS' LEARNING ACTIVITIES COMPRISING THREE COMPONENTS, I.E., SPEED, QUALITY AND QUANTITY. WE THEN CONCRETIZE THIS MODEL INTO ONLINE LEARNING AND PROPOSE THE ONLINE LEARNING ACTIVITY INDEX (OLAI). WITH THE EXAMPLE OF A WEB-BASED MATHEMATICS LEARNING SYSTEM, OLAI CAN BE CALCULATED BY THE SUMMARIZATION OF STANDARDIZED AND DIMENSIONLESS SPEED, QUALITY AND QUANTITY. SO OLAI IS ALSO A DIMENSIONLESS VALUE AND CAN COMPREHENSIVELY DESCRIBE THE LEARNER'S ONLINE LEARNING BEHAVIOR. WE USE OLAIMAA (OLAI MEAN OF ALL ACTIVITIES) TO MEASURE ONE LEARNER'S ONLINE LEARNING ACTIVITIES DURING A CERTAIN PERIOD ON AVERAGE AND OLAISAA (OLAI SUM OF ALL ACTIVITIES) TO MEASURE THOSE FROM A SUMMATIVE PERSPECTIVE. SIMILARLY, THE PERFORMANCE OF A GROUP, SUCH AS A CLASS OR A SCHOOL, CAN ALSO BE DESCRIBED BY THOSE INDEXES. FURTHERMORE, WE DESIGN THE PERSONALIZED INSTRUCTIONAL STRATEGIES ADAPTIVE TO THE LEARNER'S OLAI AND THE OLAI OF THE LEARNING GROUP. THE INSTRUCTIONAL STRATEGIES INCLUDE THE FEEDBACK CORRESPONDING TO NUMERICAL INTERVALS OF THREE COMPONENTS OF OLAI, BONUS BASED ON LONGITUDINAL COMPARISON WITH THE LEARNER'S OWN ACTIVITY PROFILE, BONUS BASED ON LOCALLY HORIZONTAL COMPARISON WITH LEARNER'S CLASSMATES OR PARTNERS IN THE SAME ONLINE GROUP, AND BONUS BASED ON GLOBALLY HORIZONTAL COMPARISON WITH ALL LEARNERS IN THE SAME SYSTEM. WE SUGGEST THAT OLAI SHOULD BE TAKEN INTO CONSIDERATION FOR THE DESIGN OF ONLINE LEARNING PLATFORMS OR COURSE MANAGEMENT SYSTEM, AND BE REFERRED AS A BASIC APPROACH TO INDIVIDUALIZED INSTRUCTION BY BOTH TEACHERS AND PARENTS.
LEARNING AT SCALE OPENS UP A NEW FRONTIER TO LEARN ABOUT LEARNING. MASSIVE ONLINE OPEN COURSES (MOOCS) AND SIMILAR LARGE-SCALE ONLINE LEARNING PLATFORMS GIVE AN UNPRECEDENTED VIEW OF LEARNERS' BEHAVIOR WHILST LEARNING. IN THIS PAPER, WE ARGUE THAT THE ABUNDANCE OF DATA THAT RESULTS FROM SUCH PLATFORMS NOT ONLY BRINGS NOVEL OPPORTUNITIES TO THE STUDY OF LEARNING, BUT ALSO BEARS NOVEL METHODOLOGICAL CHALLENGES. WE SHOW THAT THE RESULTING DATA COMES WITH VARIOUS CHALLENGES WITH RESPECT TO THE GRANULAR, OBSERVATIONAL, AND LARGE NATURE OF THESE DATA. ADDITIONALLY, WE DISCUSS A SERIES OF POTENTIAL SOLUTIONS, SUCH AS SHARING VALIDATED MODELS AND PERFORMING PRE-REGISTERED CONFIRMATORY RESEARCH. WITH THESE CONTRIBUTIONS, THIS PAPER AIMS TO INCREASE AWARENESS AND UNDERSTANDING OF BOTH THE STRENGTHS AND CHALLENGES OF RESEARCH ON LEARNING AT SCALE.
AS COMMERCIAL PEN-CENTRIC SYSTEMS PROLIFERATE, THEY CREATE A PARALLEL NEED FOR ANALYTIC TECHNIQUES BASED ON DYNAMIC WRITING. WITHIN EDUCATIONAL APPLICATIONS, RECENT EMPIRICAL RESEARCH HAS SHOWN THAT SIGNAL-LEVEL FEATURES OF STUDENTS' WRITING, SUCH AS STROKE DISTANCE, PRESSURE AND DURATION, ARE ADAPTED TO CONSERVE TOTAL ENERGY EXPENDITURE AS THEY CONSOLIDATE EXPERTISE IN A DOMAIN. THE PRESENT RESEARCH EXAMINED HOW ACCURATELY THREE DIFFERENT MACHINE-LEARNING ALGORITHMS COULD AUTOMATICALLY CLASSIFY USERS' DOMAIN EXPERTISE BASED ON SIGNAL FEATURES OF THEIR WRITING, WITHOUT ANY CONTENT ANALYSIS. COMPARED WITH AN UNGUIDED MACHINE-LEARNING CLASSIFICATION ACCURACY OF 71%, HYBRID METHODS USING EMPIRICAL-STATISTICAL GUIDANCE CORRECTLY CLASSIFIED 79-92% OF STUDENTS BY THEIR DOMAIN EXPERTISE LEVEL. IN ADDITION TO IMPROVED ACCURACY, THE HYBRID APPROACH CONTRIBUTED A CAUSAL UNDERSTANDING OF PREDICTION SUCCESS AND GENERALIZATION TO NEW DATA. THESE NOVEL FINDINGS OPEN UP OPPORTUNITIES TO DESIGN NEW AUTOMATED LEARNING ANALYTIC SYSTEMS AND STUDENT-ADAPTIVE EDUCATIONAL TECHNOLOGIES FOR THE RAPIDLY EXPANDING SECTOR OF COMMERCIAL PEN SYSTEMS.
SOCIAL MEDIA HOLD GREAT PROMISES IN TERTIARY MASS EDUCATION BECAUSE THEY FOSTER THE CREATION AND SHARING OF INFORMATION AMONG COMMUNITIES. WITH EDUCATIONAL SOCIAL MEDIA, STUDENTS CAN EXCHANGE ON COURSES' CONTENTS AND DISCUSS HOMEWORK SOLUTIONS WHILE BEING SUPERVISED BY TEACHERS. HOWEVER, ALL TOO OFTEN SOCIAL MEDIA ARE USED IN EDUCATION AS MERE ORGANISATIONAL TOOLS, DELIVERY TOOLS FOR COURSE MATERIAL, OR COLLECTION TOOLS FOR HOMEWORK ASSIGNMENTS. THIS ARTICLE REPORTS ON A VERSATILE SOCIAL MEDIUM WHICH, AMONG OTHERS, EXPLOITS COLLABORATIVE CORRECTION, A FORM OF HUMAN COMPUTATION, FOR TRACKING SYSTEMATIC ERRORS OF STEM STUDENTS. LEARNING ANALYTICS GENERATED FROM BOTH HUMAN-PRODUCED AND SYSTEM-COLLECTED DATA AND THE RESULTS OF A REAL-LIFE EVALUATION ARE PRESENTED. THE RESULTS POINT TO A POSITIVE RECEPTION BY STUDENTS AND TEACHERS ALIKE AND TO THE EFFECTIVENESS OF THE MEDIUM FOR HIGHER STEM EDUCATION.
BACKGROUND THERE HAS BEEN INCREASING INTEREST IN ONLINE PROFESSIONAL LEARNING NETWORKS IN A VARIETY OF SOCIAL MEDIA PLATFORMS, ESPECIALLY IN TWITTER. TWITTER OFFERS IMMEDIACY, PERSONALIZATION, AND SUPPORT OF NETWORKS TO INCREASE PROFESSIONAL KNOWLEDGE AND THE SENSE OF MEMBERSHIP. KNOWING THE TOPICS DISCUSSED IN TWITTER AND THE FACTORS THAT AFFECT THE DURATION OF A TOPIC WOULD HELP TO SUSTAIN AND RECONSTRUCT TWITTER-BASED PROFESSIONAL LEARNING ACTIVITIES. OBJECTIVES THE PURPOSE OF THIS STUDY IS TO ANALYSE THE TOPICS DISCUSSED AND WHAT FACTORS AFFECT THE DURATION OF A SPECIFIC TOPIC IN 6 YEARS WITHIN A VIRTUAL PROFESSIONAL LEARNING NETWORK (VPLN) USING #EDCHAT IN TWITTER, BASED ON MEDIA RICHNESS FEATURES. METHODS INTERNET-MEDIATED RESEARCH AND DIGITAL METHODS ARE USED FOR DATA COLLECTION AND ANALYSIS. VARIOUS TEXT, NATURAL LANGUAGE PROCESSING, AND MACHINE LEARNING ALGORITHMS WERE USED ALONG WITH THE QUANTITATIVE MULTILEVEL MODELS. THIS STUDY EXAMINED 504,998 TWEETS POSTED BY 72,342 UNIQUE USERS BY USING #EDCHAT. RESULTS THERE WERE 150 TOPICS DISCUSSED OVER THE 6 YEARS AND MULTILEVEL RANDOM INTERCEPT REGRESSION MODEL REVEALED THAT A SPECIFIC TOPIC DISCUSSED IN THE #EDCHAT VPLN IS DISCUSSED LONGER WHEN IT HAS MORE TWEETS, RATHER THAN RETWEETS, POSTED BY A HIGH NUMBER OF DIFFERENT USERS ALONG WITH MODERATE TEXT, HIGH OR MODERATE MENTIONS WITH MORE HASHTAGS. TAKEAWAYS THE STUDY DEVELOPED AN AUTOMATED SOCIAL MEDIA RICHNESS FEATURE EXTRACTION FRAMEWORK THAT CAN BE ADAPTED FOR OTHER THEORETICAL APPLICATIONS IN EDUCATIONAL CONTEXT. EMERGENT TOPICS DISCUSSED IN TWITTER AMONG #EDCHAT VPLN MEMBERS FOR PROFESSIONAL DEVELOPMENT WERE IDENTIFIED. IT EXTENDS THE SOCIAL MEDIA RICHNESS THEORY FOR EDUCATIONAL CONTEXT AND EXPLORE FACTORS THAT AFFECT AN ONLINE PROFESSIONAL LEARNING ACTIVITY IN TWITTER.
WHEN ENGAGING WITH A TEXTBOOK, STUDENTS ARE INCLINED TO HIGHLIGHT KEY CONTENT. ALTHOUGH STUDENTS BELIEVE THAT HIGHLIGHTING AND SUBSEQUENT REVIEW OF THE HIGHLIGHTS WILL FURTHER THEIR EDUCATIONAL GOALS, THE PSYCHOLOGICAL LITERATURE PROVIDES LITTLE EVIDENCE OF BENEFITS. NONETHELESS, A STUDENT'S CHOICE OF TEXT FOR HIGHLIGHTING MAY SERVE AS A WINDOW INTO HER MENTAL STATE-HER LEVEL OF COMPREHENSION, GRASP OF THE KEY IDEAS, READING GOALS, AND SO ON. WE EXPLORE THIS HYPOTHESIS VIA AN EXPERIMENT IN WHICH 400 PARTICIPANTS READ THREE SECTIONS FROM A COLLEGE-LEVEL BIOLOGY TEXT, BRIEFLY REVIEWED THE TEXT, AND THEN TOOK A QUIZ ON THE MATERIAL. DURING INITIAL READING, PARTICIPANTS WERE ABLE TO HIGHLIGHT WORDS, PHRASES, AND SENTENCES, AND THESE HIGHLIGHTS WERE DISPLAYED ALONG WITH THE COMPLETE TEXT DURING THE SUBSEQUENT REVIEW. CONSISTENT WITH PAST RESEARCH, THE AMOUNT OF HIGHLIGHTED MATERIAL IS UNRELATED TO QUIZ PERFORMANCE. NONETHELESS, HIGHLIGHTING PATTERNS MAY ALLOW US TO INFER READER COMPREHENSION AND INTERESTS. USING MULTIPLE REPRESENTATIONS OF THE HIGHLIGHTING PATTERNS, WE BUILT PROBABILISTIC MODELS TO PREDICT QUIZ PERFORMANCE AND MATRIX FACTORIZATION MODELS TO PREDICT WHAT CONTENT WOULD BE HIGHLIGHTED IN ONE PASSAGE FROM HIGHLIGHTS IN OTHER PASSAGES. WE FIND THAT QUIZ SCORE PREDICTION ACCURACY RELIABLY IMPROVES WITH THE INCLUSION OF HIGHLIGHTING DATA (BY ABOUT 1%-2%), BOTH FOR HELD-OUT STUDENTS AND FOR HELD-OUT STUDENT QUESTIONS (I.E., QUESTIONS SELECTED RANDOMLY FOR EACH STUDENT), BUT NOT FOR HELD-OUT QUESTIONS. FURTHERMORE, AN INDIVIDUAL'S HIGHLIGHTING PATTERN IS INFORMATIVE OF WHAT SHE HIGHLIGHTS ELSEWHERE. OUR LONG-TERM GOAL IS TO DESIGN DIGITAL TEXTBOOKS THAT SERVE NOT ONLY AS CONDUITS OF INFORMATION INTO THE READER'S MIND BUT ALSO ALLOW US TO DRAW INFERENCES ABOUT THE READER AT A POINT WHERE INTERVENTIONS MAY INCREASE THE EFFECTIVENESS OF THE MATERIAL.
THIS PAPER FOCUSES ON ANTICIPATING THE DROP-OUT AMONG MOOC LEARNERS AND HELPING IN THE IDENTIFICATION OF THE REASONS BEHIND THIS DROP-OUT. THE MAIN REASONS ARE THOSE RELATED TO COURSE DESIGN AND LEARNERS BEHAVIOR, ACCORDING TO THE REQUIREMENTS OF THE MOOC PROVIDER OPENCLASSROOMS. TWO CRITICAL BUSINESS NEEDS ARE IDENTIFIED IN THIS CONTEXT. FIRST, THE ACCURATE DETECTION OF AT-RISK DROPPERS, WHICH ALLOWS SENDING AUTOMATED MOTIVATIONAL FEEDBACK TO PREVENT LEARNERS DROP-OUT. SECOND, THE INVESTIGATION OF POSSIBLE DROP-OUT REASONS, WHICH ALLOWS MAKING THE NECESSARY PERSONALIZED INTERVENTIONS. TO MEET THESE NEEDS, WE PRESENT A SUPERVISED MACHINE LEARNING BASED DROP-OUT PREDICTION SYSTEM THAT USES PREDICTIVE ALGORITHMS (RANDOM FOREST AND GRADIENT BOOSTING) FOR AUTOMATED INTERVENTION SOLUTIONS, AND EXPLICATIVE ALGORITHMS (LOGISTIC REGRESSION, AND DECISION TREE) FOR PERSONALIZED INTERVENTION SOLUTIONS. THE PERFORMED EXPERIMENTATIONS COVER THREE MAIN AXES; (1) IMPLEMENTING AN ENHANCED RELIABLE DROPOUT-PREDICTION SYSTEM THAT DETECTS AT-RISK DROPPERS AT DIFFERENT SPECIFIED INSTANTS THROUGHOUT THE COURSE. (2) INTRODUCING AND TESTING THE EFFECT OF ADVANCED FEATURES RELATED TO THE TRAJECTORIES OF LEARNERS' ENGAGEMENT WITH THE COURSE (BACKWARD JUMPS, FREQUENT JUMPS, INACTIVITY TIME EVOLUTION). (3) OFFERING A PRELIMINARY INSIGHT ON HOW TO USE READABLE CLASSIFIERS TO HELP DETERMINE POSSIBLE REASONS FOR DROP-OUT. THE FINDINGS OF THE MENTIONED EXPERIMENTAL AXES PROVE THE VIABILITY OF REACHING THE EXPECTED INTERVENTION STRATEGIES.
IN THIS STUDY, LEARNING ANALYTICS WAS CONDUCTED BASED ON THE DATA OF 2582 STUDENTS' LEARNING ON AN ONLINE LEARNING PLATFORM. THROUGH CORRELATION AND REGRESSION ANALYSIS, THE EFFECTS OF 13 INDICATORS OF BEHAVIORAL ENGAGEMENT ON LEARNING PERFORMANCE WERE INVESTIGATED. THE RESULTS SHOWED THAT NINE OF THE 13 INDICATORS WERE SIGNIFICANTLY CORRELATED WITH THE PASS RATE OF LESSON QUIZZES. AMONG THEM, 8 WERE SIGNIFICANT CONTRIBUTORS, WHICH EXPLAINED 59.8% OF VARIABILITY IN THE PASS RATE OF LESSON QUIZZES. TEN INDICATORS WERE SIGNIFICANTLY CORRELATED WITH THE MEAN SCORE OF UNIT TESTS. AMONG THEM, 7 WERE SIGNIFICANT CONTRIBUTORS, WHICH EXPLAINED 65.4% OF THE VARIABILITY. IT ALSO SHOWED THAT LEARNING MOTIVATION WAS A CRUCIAL FACTOR OF THE QUALITY OF ONLINE LEARNING ENGAGEMENT. THE QUALITY OF ONLINE LEARNING ENGAGEMENT OF THE NON-DEGREE STUDENTS WHO HAD INTERNAL MOTIVATION WAS BETTER THAN THAT OF DEGREE STUDENTS.
IN THIS PAPER WE PRESENT A SOCIAL ANALYSIS OF THE INTERACTIONS AMONG THE STUDENTS INVOLVED IN A TRIAL OF THE ITALIAN PRIN PROJECT "DIGITAL INTERACTIVE STORYTELLING IN MATHEMATICS: A COMPETENCE-BASED SOCIAL APPROACH". THE INSTRUCTIONAL DESIGN IS BASED ON COLLABORATIVE SCRIPTS WITHIN A DIGITAL STORYTELLING FRAMEWORK WHERE THE STORY FOLLOWS THE INTERACTIONS AMONG THE CHARACTERS PLAYED BY THE STUDENTS AND AN EXPERT (TEACHER OR RESEARCHER). WE REPORT THE RESULTS OF A TRIAL THAT INVOLVED TEACHERS AND STUDENTS FROM THE UPPER SECONDARY SCHOOL, ANALYSING FROM A SOCIAL NETWORK ANALYSIS POINT OF VIEW THE INTERVENTIONS OF THE EXPERT, THE INVOLVEMENT/PARTICIPATION OF THE STUDENTS AND THE INTERACTIONS AMONG PEERS AND WITH THE EXPERT. WE ALSO BRIEFLY DISCUSS POTENTIALITIES AND LIMITATIONS OF THE CURRENTLY AVAILABLE TOOLS TO PERFORM THIS KIND OF ANALYSIS, IN VIEW OF THE BROADER PERSPECTIVE OFFERED BY THE LEARNING ANALYTICS APPROACH.
AN URGENT NECESSITY DURING YEAR 2020, IT BECAME A MUST THAT ALL UNIVERSITIES AROUND THE WORLD TO MOVE FROM TRADITIONAL CLASSROOMS, COVID-19 EPIDEMIC FORCED SCHOOLS AND UNIVERSITIES TO CHANGE THEIR PLANS BY E-LEARNING STRATEGY AND/OR HOSTING MASSIVE OPEN ONLINE COURSES (MOOCS). SINCE DROPOUTS AND FAILURE RATES OF MOOCS' STUDENTS IS A WELL NOTICED PROBLEM, THIS PAPER PROPOSES A NEW METHODOLOGY IN CLASSIFYING STUDENTS' RESULTS THROUGHOUT MOOCS MODULES. BY USING OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD) AND APPLYING MODERN MACHINE LEARNING TECHNIQUES, IT BECOMES MORE USEFUL TO MONITOR FACTORS AFFECTING STUDENT PERFORMANCE AND ACHIEVEMENT. THE PROPOSED METHODOLOGY CONTRIBUTED A NEW MODEL THAT USES VARIOUS FEATURE SELECTION ALGORITHMS AND VARIOUS CLASSIFICATION ALGORITHMS INCLUDING PROBABILISTIC NEURAL NETWORK (PNN) AND OTHER CLASSIFICATION ALGORITHMS. RESULTS SHOWED THAT USING CERTAIN FEATURE SELECTION ALGORITHMS IN COMBINATION WITH PNN RESULTED IN ENHANCING TREND EXPLORATION AND ACCURACY.
MOOCS (MASSIVE OPEN ONLINE COURSES) ARE AN ONLINE TEACHING PROPOSAL THAT, IN THEIR SHORT LIVES, HAVE ALREADY THROWN UP TWO VERY DIFFERENT POSSIBILITIES: CMOOCS AND XMOOCS. BOTH ARE ANALYSED IN THIS PAPER FROM THE PERSPECTIVE OF ASSESSING STUDENTS' LEARNING. WHILE ASSESSMENT IN XMOOCS IS USUALLY LIMITED TO MULTIPLE CHOICE TESTS AND SOMETIMES DELIVERING TASKS, IN CMOOCS THE AIM IS TO FOSTER INTERACTION FROM AN EDUCATIONAL STANDPOINT, USUALLY ON THE BASIS OF PEER ASSESSMENT. PEDAGOGICALLY, BOTH MODELS HAVE THEIR LIMITATIONS. MULTIPLE CHOICE TESTS ARE MAINLY CONTENT BOUND WHILE PEER ASSESSMENT HAS ITS OWN DIFFICULTIES AND DRAWBACKS, WHICH WE EXPLAIN HERE. WE WILL PUT FORWARD SOME USEFUL IDEAS TO GIVE MORE FLEXIBILITY TO ASSESSMENT IN MOOCS (GROUPS OF EXPERTS, SEMANTIC WEB, PORTFOLIO, LEARNING ANALYTICS), IN AN ATTEMPT TO ADDRESS EDUCATIONAL ASSESSMENT NOT AS AN END IN ITSELF BUT AS ANOTHER PART OF THE EDUCATIONAL PROCESS.
MEDICAL CARE CAN BE IMPROVED BY EFFICIENT ANALYSIS OF THE LARGE AMOUNTS OF DATA INVOLVED IN PATIENT CARE. TWO IMPORTANT CHALLENGES FOR PATIENT CARE BIG-DATA ANALYSIS ARE THE NEED TO COPE WITH UNSTRUCTURED IMAGES AND TEXT (AS WELL AS STRUCTURED DATA) AND THE NEED FOR EFFICIENT METHODS THAT SCALE FOR LARGE AMOUNTS OF DATA GENERATED BY THE MANY PATIENTS. THE FRAMEWORK DESCRIBED HERE IS DESIGNED TO COPE WITH THESE CHALLENGES. FIRSTLY IT IS COMPOSED OF GENERIC COMPONENTS WHERE THE INITIAL STAGE CAN TRANSFORM UNSTRUCTURED IMAGE OR TEXT DATA INTO A SET OF FEATURES FOR THE LATTER STAGES. SECONDLY THE MACHINE LEARNING ANALYTICS ENGINE INCORPORATES THREE OPTIMIZATIONS WHICH ALLOW THE FRAMEWORK TO SCALE AND DEAL WITH LARGE AMOUNTS OF UNSTRUCTURED DATA. THE FRAMEWORK IS EVALUATED USING BOTH IMAGE AND TEXT DATA, AND SHOWN TO PROVIDE AN EFFICIENT SCALABLE SOLUTION.
THIS PAPER PRESENTS AN EXPERIENCE REPORT ON THE PARTNERSHIP BETWEEN THE FEDERAL UNIVERSITY OF MATO GROSSO AND THE COURT OF AUDITORS OF MATO GROSSO, FOCUSING ON ASPECTS OF EDUCATIONAL AND TECHNOLOGICAL INNOVATION, IN THE CONTEXT OF A COURSE PROPOSAL CARRIED OUT IN THE MODALITY OF DISTANCE EDUCATION. THE METHODOLOGY IS ORIENTED BY A CASE STUDY WITH THE AIM OF ANALYZING RESULTS OF IMPROVEMENT OR INNOVATION IN THE FIELD OF EDUCATIONAL AND TECHNOLOGICAL MEDIATION IN THE COURSE OF CITIZENSHIP AND SOCIAL CONTROL. THE RESULTS INDICATE INNOVATIONS IN THE IMPROVEMENT OF EDUCATIONAL AND TECHNOLOGICAL MEDIATION, WITH EMPHASIS ON THE DEVELOPMENT AND IMPLEMENTATION OF AN ELECTRONIC REACTION EVALUATION FORM BASED ON CONCEPTS OF LEARNING ANALYTICS, PRODUCTION OF NEW SOFTWARE FEATURES ORIENTED TOWARDS EDUCATIONAL AND TECHNOLOGICAL MEDIATION, DIRECTLY REFLECTING ON COURSE EVALUATION INDICATORS BASED ON COLLABORATIVE INTELLIGENCE PRINCIPLES RESULTING FROM THE PARTNERSHIP BETWEEN PUBLIC INSTITUTIONS.
IN RECENT YEARS, A WIDE ARRAY OF TOOLS HAVE EMERGED FOR THE PURPOSES OF CONDUCTING EDUCATIONAL DATA MINING (EDM) AND/OR LEARNING ANALYTICS (LA) RESEARCH. IN THIS ARTICLE, WE HOPE TO HIGHLIGHT SOME OF THE MOST WIDELY USED, MOST ACCESSIBLE, AND MOST POWERFUL TOOLS AVAILABLE FOR THE RESEARCHER INTERESTED IN CONDUCTING EDM/LA RESEARCH. WE WILL HIGHLIGHT THE UTILITY THAT THESE TOOLS HAVE WITH RESPECT TO COMMON DATA PREPROCESSING AND ANALYSIS STEPS IN A TYPICAL RESEARCH PROJECT AS WELL AS MORE DESCRIPTIVE INFORMATION SUCH AS PRICE POINT AND USER-FRIENDLINESS. WE WILL ALSO HIGHLIGHT NICHE TOOLS IN THE FIELD, SUCH AS THOSE USED FOR BAYESIAN KNOWLEDGE TRACING (BKT), DATA VISUALIZATION, TEXT ANALYSIS, AND SOCIAL NETWORK ANALYSIS. FINALLY, WE WILL DISCUSS THE IMPORTANCE OF FAMILIARIZING ONESELF WITH MULTIPLE TOOLSA DATA ANALYSIS TOOLBOXFOR THE PRACTICE OF EDM/LA RESEARCH.
INTRODUCING PHYSICAL COMPUTING INTO REGULAR SCHOOL CLASSES IS CHALLENGED BY CONSTRAINTS OF SCHEDULES AND CURRICULA STRUCTURES, WHICH DO NOT ALLOW FOR TIME-CONSUMING ELECTRONICS PROTOTYPING. WE PRESENT A NOVEL APPROACH TO PROTOTYPING WITH PHYSICAL COMPUTING COMPONENTS WITH THE ARDUINO-BASED TALKOO KIT: IT COMPRISES HARDWARE MODULES, A VISUAL IDE AND PROTOTYPING MATERIAL. SENSOR AND ACTUATOR MODULES ARE PLUGGABLE AND DO NOT REQUIRE SOLDERING AND PRIOR KNOWLEDGE IN ELECTRONICS. THE COMPONENTS HAVE THE ABILITY TO "TALK" BACK TO THE VISUAL IDE AND TO A LEARNING ANALYTICS SYSTEM. A NEW PARADIGM FOR VISUAL PROGRAMMING MAPS PHYSICAL MODULES ONTO VIRTUAL REPRESENTATIONS ON SCREEN MAKING PROGRAMMING MORE INTUITIVE. THE TALKOO KIT EXPANDS THE FIELD OF APPLICATION OF PHYSICAL COMPUTING FOR CHILDREN IN REGULAR SCHOOL CONTEXTS. PRELIMINARY EVALUATION RESULTS SHOW THAT CHILDREN WERE ABLE TO BUILD ELABORATIVE PROTOTYPES WITHIN AN HOUR.
COMPLETION RATES IN MASSIVE OPEN ONLINE COURSES (MOOCS) ARE DISTURBINGLY LOW. EXISTING ANALYSIS HAS FOCUSED ON PATTERNS OF RESOURCE ACCESS AND PREDICTION OF DROP-OUT USING LEARNING ANALYTICS. IN CONTRAST, THE EFFECTIVENESS OF TEACHING PROGRAMS IN TRADITIONAL HIGHER EDUCATION (HE) SETTINGS INTERNATIONALLY IS INCREASINGLY ASSESSED BY SURVEYS MEASURING STUDENT ENGAGEMENT. THE CONCEPTUALISATION OF ENGAGEMENT USED IS MUCH RICHER AND MORE INFORMATIVE THAN THE WAY THE TERM IS CURRENTLY INTERPRETED IN THE CONTEXT OF MOOCS. THIS PAPER CONSIDERS MOOC PARTICIPATION, LEARNING AND DROP-OUT IN THE CONTEXT OF THIS RICHER CONCEPTUALISATION OF STUDENT ENGAGEMENT. MOOC PEDAGOGY AND PRACTICE ARE EXAMINED AND WE EVALUATE HOW FAR HE ENGAGEMENT MEASURES CAN BE SUCCESSFULLY USED IN THE MOOC CONTEXT. WE IDENTIFY THE NEED FOR A MOOC ENGAGEMENT MODEL AND SUGGEST RECOMMENDATIONS FOR BASIC, INITIAL STEPS WHICH MOOC DEVELOPERS CAN MAKE TOWARDS IMPROVING ENGAGEMENT.
MOTIVATION: AS WHOLE-GENOME TUMOR SEQUENCE AND BIOLOGICAL ANNOTATION DATASETS GROW IN SIZE, NUMBER AND CONTENT, THERE IS AN INCREASING BASIC SCIENCE AND CLINICAL NEED FOR EFFICIENT AND ACCURATE DATA MANAGEMENT AND ANALYSIS SOFTWARE. WITH THE EMERGENCE OF INCREASINGLY SOPHISTICATED DATA STORES, EXECUTION ENVIRONMENTS AND MACHINE LEARNING ALGORITHMS, THERE IS ALSO A NEED FOR THE INTEGRATION OF FUNCTIONALITY ACROSS FRAMEWORKS. RESULTS: WE PRESENT ORCHID, A PYTHON BASED SOFTWARE PACKAGE FOR THE MANAGEMENT, ANNOTATION AND MACHINE LEARNING OF CANCER MUTATIONS. BUILDING ON TECHNOLOGIES OF PARALLEL WORKFLOW EXECUTION, IN-MEMORY DATABASE STORAGE AND MACHINE LEARNING ANALYTICS, ORCHID EFFICIENTLY HANDLES MILLIONS OF MUTATIONS AND HUNDREDS OF FEATURES IN AN EASY-TO-USE MANNER. WE DESCRIBE THE IMPLEMENTATION OF ORCHID AND DEMONSTRATE ITS ABILITY TO DISTINGUISH TISSUE OF ORIGIN IN 12 TUMOR TYPES BASED ON 339 FEATURES USING A RANDOM FOREST CLASSIFIER.
EDUCATION TECHNOLOGY ADVANCES MANY ASPECTS OF LEARNING. MORE AND MORE LEARNING IS TAKING PLACE ONLINE. LEARNERS' LEARNING BEHAVIORS, STYLE, AND PERFORMANCE CAN BE EASILY PROFILED THROUGH LEARNING ANALYTICS BY COLLECTING LEARNERS' ONLINE FOOTAGE AND LEARNING INFORMATION. IT ENABLES AND ENCOURAGES EDUCATIONAL RESEARCH, LEARNING SOFTWARE DEVELOPMENT, AND ONLINE EDUCATION PRACTICES TOWARDS PERSONALIZED AND ADAPTIVE LEARNING. AS WE CONTINUE TO SEE PERSONALIZED AND ADAPTIVE LEARNING PROGRESS, WE MUST ALSO PAY ATTENTION TO THE NEGATIVE EFFECTS THAT FEED INTO OUR RESEARCH. IN THIS PAPER, WE WILL PRESENT OUR INTROSPECTION OF PERSONALIZED AND ADAPTIVE LEARNING AND ARGUE THAT IT IS THE SOCIAL AND MORAL RESPONSIBILITY OF EDUCATORS AND INSTITUTIONS TO APPLY PERSONALIZED AND ADAPTIVE LEARNING WISELY AND RESPONSIBLY IN THEIR EDUCATION PRACTICE. WE SUGGEST THAT THE STRATEGICALLY BALANCED PRACTICES AND INNOVATED LEARNING TECHNOLOGY ARE CRUCIAL TOWARDS AN OPTIMAL LEARNING EXPERIENCE FOR THE LEARNERS.
THERE ARE MANY MOBILE APPS SUPPORTING INFORMAL LEARNING TASKS LIKE SENSE-MAKING OUT OF MULTIMEDIA MATERIALS. HOWEVER, REGARDLESS HOW WELL THESE HAVE BEEN IMPLEMENTED, THEIR SCOPE AND SCALABILITY IS LIMITED EITHER BY OVER-SPECIALIZATION AND CONSEQUENTLY LIMITED TRANSFERABILITY OR BY OVER-GENERALIZATION - WITH A LACK OF INFORMAL LEARNING SUPPORT FOR MOST WEB 2.0 APPS. OUR CLOUD-BASED MOBILE WEB INFORMATION SYSTEM APPROACH IS THEORETICALLY FOUNDED AND DESIGNED FOR SCALABILITY IN INFORMAL LEARNING. AS A PROOF OF CONCEPT WE PRESENT A SEMANTIC VIDEO ANNOTATION SCENARIO FOR SUPPORTING INFORMAL LEARNING AT WORKPLACE. AS THE APPROACH HAS BEEN DESIGNED FOR COMMUNITY LEARNING ANALYTICS, WE CAN PRESENT RESULTS FROM A PRELIMINARY EVALUATION. WE CONDUCTED OUR RESEARCH WITH A STRONG COMMITMENT TOWARDS OPEN SOURCE SOFTWARE DEVELOPMENT, SO THAT OUR SOLUTIONS ARE ALREADY AVAILABLE AND CAN HAVE IMPACT BEYOND THE USUAL SCOPE OF A FUNDED PROJECT.
EASILY ACCESSIBLE AND INTERPRETABLE PERFORMANCE DATA CONSTITUTE CRITICAL FEEDBACK FOR LEARNERS THAT FACILITATE INFORMED SELF-ASSESSMENT AND LEARNING PLANNING. TO PROVIDE THIS FEEDBACK, THERE HAS BEEN A PROLIFERATION OF EDUCATIONAL DASHBOARDS IN RECENT YEARS. AN EDUCATIONAL (LEARNER) DASHBOARD SYSTEMATICALLY DELIVERS TIMELY AND CONTINUOUS FEEDBACK ON PERFORMANCE AND CAN PROVIDE EASILY VISUALIZED AND INTERPRETED PERFORMANCE DATA. IN THIS PAPER, WE PROVIDE PRACTICAL TIPS FOR DEVELOPING A FUNCTIONAL, USER-FRIENDLY INDIVIDUAL LEARNER PERFORMANCE DASHBOARD AND LITERATURE REVIEW OF DASHBOARD DEVELOPMENT, ASSESSMENT THEORY, AND USERS' PERSPECTIVES. CONSIDERING KEY DESIGN PRINCIPLES AND MAXIMIZING CURRENT TECHNOLOGICAL ADVANCES IN DATA VISUALIZATION TECHNIQUES CAN INCREASE DASHBOARD UTILITY AND ENHANCE THE USER EXPERIENCE. BY BRIDGING CURRENT TECHNOLOGY WITH ASSESSMENT STRATEGIES THAT SUPPORT LEARNING, EDUCATORS CAN CONTINUE TO IMPROVE THE FIELD OF LEARNING ANALYTICS AND DESIGN OF INFORMATION MANAGEMENT TOOLS SUCH AS DASHBOARDS IN SUPPORT OF IMPROVED LEARNING OUTCOMES.
THE CONTEXT OF THIS PAPER IS A "LARGE LEARNER DATA" PROJECT THAT SEEKS TO RESPOND TO EXISTING CHALLENGES BY INTRODUCING EDUCATIONAL DATA MINING AND LEARNING ANALYTICS INTO K-12 ENGINEERING DESIGN RESEARCH. TO PROBE DEEPLY INTO STUDENT LEARNING, WE ARE DEVELOPING AND REFINING COMPUTATIONAL TECHNIQUES TO ANALYZE LARGE PROCESS ANALYTICS DATASETS GENERATED THROUGH A CAD-BASED SOFTWARE, ENERGY3D, THAT LOGS DESIGN PROCESS DATA AS STUDENTS COMPLETE AN ASSIGNED DESIGN CHALLENGE, SUCH AS A NET-ZERO ENERGY EFFICIENT BUILDING. WE ARE COMBINING THESE PROCESS ANALYTICS WITH DEMOGRAPHIC DATA AND PRE/POST-TESTS OF SCIENCE AND DESIGN KNOWLEDGE. IN THIS PAPER, WE REVISIT THREE ILLUSTRATIVE RESEARCH CASES TO REFLECT ON OUR EXPERIENCES AND LESSONS LEARNED WITH NAVIGATING BIG DATA, GENERATING USEFUL DATA VISUALIZATIONS, AND INTEGRATING PROCESS ANALYTICS WITH TRADITIONAL PERFORMANCE ASSESSMENT METHODS TO RELATE DESIGN ACTIONS TO KNOWLEDGE AND LEARNING OUTCOMES.
THIS PAPER PRESENTS AN ANALYSIS OF FIRST YEAR PROGRAMMING STUDENT (NOVICE PROGRAMMER) MESSAGES POSTED ON A FACEBOOK PROGRAMMING GROUP CREATED TO SUPPORT THE LEARNING OF PROGRAMMING BY INCREASING NOVICE TO NOVICE INTERACTIONS ANYWHERE, ANYTIME. THE CAPTURED MESSAGES ARE ANONYMIZED AND ANALYSED WITH A VIEW TO EXPLORING THE EXTENT TO WHICH FACEBOOK GROUPS HAVE SUPPORTED THE LEARNING OF PROGRAMMING, AS WELL AS TO IDENTIFY STRUGGLES AND CHALLENGES FACED BY LEARNERS WHO USE THE ENVIRONMENTS. THE RESULTS OF OUR ANALYSIS SHOW THAT INTERACTIONS ON FACEBOOK GROUPS CAN LEAD TO SOCIAL LEARNING AND CAN ENCOURAGE ENGAGEMENT WITH LEARNING. THE FACEBOOK GROUPS ARE USED EFFECTIVELY BY NOVICES TO SOLVE SIGNIFICANT PROBLEMS THAT WOULD NOT HAVE BEEN DISCUSSED IN THEIR RESPECTIVE LEARNING MANAGEMENT SYSTEMS. OUR FINDINGS HIGHLIGHT SOME INTERESTING PROBLEMS FACED BY NOVICE PROGRAMMERS AND INCLUDE USEFUL LEARNING ANALYTICS OF NOVICE PROGRAMMER INTERACTIONS WITHIN THESE GROUPS.
THE APPLICATION OF GAMIFICATION TECHNIQUES IN HIGHER EDUCATION CAN BE CHALLENGING. AT TIMES IT IS EASY TO LOSE FOCUS AND WE CAN NO LONGER EXPLAIN THE CONNECTIONS BETWEEN PLAYERS' MOTIVATION AND THE SEVERAL ELEMENTS OF THE GAME. THIS STUDY FOCUSED ON THE APPLICATION OF THE MDA FRAMEWORK (MECHANICS, DYNAMICS AND AESTHETICS) TO THE PLATFORM LEARNING SCORECARD (LS), CURRENTLY USED BY SEVERAL STUDENTS AT ISCTE-INSTITUTO UNIVERSITARIO DE LISBOA. THE LS IS A PLATFORM FOR LEARNING ANALYTICS THAT HAS BEEN USED SINCE 2016, ON COURSES OF DATA WAREHOUSE AND BUSINESS INTELLIGENCE. LS HAS TWO VIEWS: THE STUDENT AND TEACHER VIEWS. IN THIS WORK WE WILL REPORT ON THE APPLICATION OF THE MDA FRAMEWORK TO THE STUDENT VIEW OF LS. THIS VIEW AIMS TO IMPROVE THE LEARNING EXPERIENCE OF STUDENTS INCREASING THEIR ENGAGEMENT AND MOTIVATION THROUGH THE USE OF GAMIFICATION.
DIGITAL LEARNING ENVIRONMENTS ARE BECOMING MORE COMMON FOR STUDENTS TO ENGAGE IN DURING AND OUTSIDE OF SCHOOL. WITH THE IMMENSE AMOUNT OF DATA NOW AVAILABLE FROM THESE ENVIRONMENTS, RESEARCHERS NEED TOOLS TO PROCESS, MANAGE, AND ANALYZE THE DATA. CURRENT METHODS USED BY MANY EDUCATION RESEARCHERS ARE INEFFICIENT; HOWEVER, WITHOUT DATA SCIENCE EXPERIENCE TOOLS USED IN OTHER PROFESSIONS ARE NOT ACCESSIBLE. IN THIS PAPER, WE SHARE ABOUT A TOOL WE CREATED CALLED THE FUNCTIONAL UNDERSTANDING NAVIGATOR! (FUN! TOOL). WE HAVE USED THIS TOOL FOR DIFFERENT RESEARCH PROJECTS WHICH HAS ALLOWED US THE OPPORTUNITY TO (1) ORGANIZE OUR WORKFLOW PROCESS FROM START TO FINISH, (2) RECORD LOG DATA OF ALL OF OUR ANALYSES, AND (3) PROVIDE A PLATFORM TO SHARE OUR ANALYSES WITH OTHERS THROUGH GITHUB. THIS PAPER EXTENDS AND IMPROVES EXISTING WORK IN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS.
TECHNOLOGY-ENHANCED LEARNING ENVIRONMENTS PROVIDE AMPLE OPPORTUNITIES FOR LEARNERS TO SELF-REGULATE THEIR LEARNING PROCESSES AND ACTIVITIES FOR ACHIEVING THE INTENDED LEARNING OUTCOMES IN VARIOUS DISCIPLINES FROM SOFT TO HARD SCIENCES AND FROM HUMANITIES TO THE NATURAL AND SOCIAL SCIENCES. THIS SPECIAL ISSUE DISCUSSES THE EMERGING TECHNOLOGICAL ADVANCEMENTS AND CUTTING-EDGE RESEARCH ON SELF-REGULATED LEARNING DEALING WITH DIFFERENT COGNITIVE, MOTIVATIONAL, EMOTIONAL, AND SOCIAL PROCESSES OF LEARNING BOTH AT THE INDIVIDUAL AND GROUP LEVELS. SPECIFICALLY, IT DISCUSSES HOW TO OPTIMALLY USE ADVANCED TECHNOLOGIES TO FACILITATE LEARNERS' SELF-REGULATED LEARNING FOR ACHIEVING THEIR OWN INDIVIDUAL LEARNING NEEDS AND GOALS. IN THIS SPECIAL ISSUE, SEVEN RESEARCHERS/RESEARCH TEAMS FROM THE FIELDS OF COLLABORATIVE LEARNING, COMPUTATIONAL THINKING, EDUCATIONAL PSYCHOLOGY, AND LEARNING ANALYTICS PRESENTED CONTRIBUTIONS TO SELF-REGULATED LEARNING WITH THE GOAL OF STIMULATING CROSS-BORDER DISCUSSION IN THE FIELD.
IN THIS PAPER, WE ARE GOING TO PRESENT THE LATEST DEVELOPMENT OF AN ONGOING LEARNING ANALYTICS PROJECT EXTENDED BASED ON [9] AND [12], WHICH SETS THE DIRECTIONS FOR THE NEXT STAGES OF OUR EXPERIMENT TO AIM FOR A BETTER EDUCATIONAL TECHNOLOGY APPLICATION IN HELPING TEACHER EVALUATE THE LEARNING PROCESS OF STUDENTS THROUGH PERFORMANCE ANALYTICS OF A GENERAL EDUCATION COURSE MODULE WITH AN ONLINE DISCUSSION FORUM. AS IT IS TIME-CONSUMING TO MANUALLY SPOT THE DISCUSSION FORUMS BY HUMANS TO KNOW THE CHANGES AND THEREFORE, BETTER TOOLS ARE NEEDED. IN THIS PROJECT, CONTENTS OF DISCUSSION FORUMS OF STUDENTS WERE EXTRACTED INTO FOR MINING PATTERNS. IN OUR LATEST EXPERIMENTS, WE DEPLOYED TOPIC DETECTION AND DATA VISUALIZATION TOOLS TO ANALYZE THE DISCUSSION FORUM DATA BETTER TO GENERATE INTELLIGENCE TO UNDERSTAND THE HOW THE STUDENTS ARE PERFORMING IN AND FEELING ABOUT THE COURSE MODULES THEY ARE TAKING.
IN THE CONTEXT OF LEARNING SYSTEMS, IDENTIFYING CAUSAL RELATIONSHIPS AMONG INFORMATION PRESENTED TO THE USER, THEIR BEHAVIOR AND COGNITIVE EFFORT REQUIRED/EXERTED TO UNDERSTAND AND PERFORM A TASK IS KEY TO BUILDING EFFECTIVE LEARNING EXPERIENCES, AND TO MAINTAIN ENGAGEMENT IN LEARNING PROCESSES. AN UNEXPLORED QUESTION IS WHETHER OUR INTERACTION WITH PRESENTED INFORMATION AFFECTS OUR COGNITIVE EFFORT (AND BEHAVIOUR), OR VICE-VERSA. WE INVESTIGATE CAUSAL RELATIONSHIP BETWEEN INFORMATION PRESENTED AND COGNITIVE EFFORT (AND BEHAVIOUR) IN THE CONTEXT OF TWO SEPARATE STUDIES (N = 40, N = 98), AND STUDY THE EFFECT OF INSTRUCTION (ACTIVE/PASSIVE TASK). WE UTILIZE SCREEN -RECORDINGS AND EYE-TRACKING DATA TO INVESTIGATE THE RELATIONSHIP AMONG THESE VARIABLES. TO INVESTIGATE THE CAUSAL RELATIONSHIPS AMONG THE DIFFERENT MEASUREMENTS, WE USE GRANGER'S CAUSALITY. FURTHER, WE PROPOSE A NEW METHOD TO COMBINE TWO TIME-SERIES FROM MULTIPLE PARTICIPANTS FOR DETECTING CAUSAL RELATIONSHIPS. OUR RESULTS INDICATE THAT INFORMATION PRESENTATION DRIVES USER FOCUS SIZE (BEHAVIOUR), AND THAT COGNITIVE LOAD (A MEASURE OF COGNITIVE EFFORT EXERTED) DRIVES INFORMATION PRESENTATION. THIS RELATIONSHIP IS ALSO MODERATED BY INSTRUCTION TYPE AND PERFORMANCE-LEVEL (HIGH/LOW). WE DRAW IMPLICATIONS FOR DESIGN OF EDUCATIONAL MATERIAL AND LEARNING TECHNOLOGIES.
THE PURPOSE OF THIS STUDY IS TO TRAIN AN ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING STUDENT FAILURE IN THE ACADEMIC LEVELING COURSE OF THE ESCUELA POLITECNICA NACIONAL OF ECUADOR, BASED ON ACADEMIC AND SOCIOECONOMIC INFORMATION. FOR THIS, 1308 HIGHER EDUCATION STUDENTS PARTICIPATED, 69.0% OF WHOM FAILED THE ACADEMIC LEVELING COURSE; BESIDES, 93.7% OF THE STUDENTS SELF-IDENTIFIED AS MESTIZO, 83.9% CAME FROM THE PROVINCE OF PICHINCHA, AND 92.4% BELONGED TO GENERAL POPULATION. AS A FIRST APPROXIMATION, A NEURAL NETWORK MODEL WAS TRAINED WITH TWELVE VARIABLES CONTAINING STUDENTS' ACADEMIC AND SOCIOECONOMIC INFORMATION. THEN, A DIMENSIONALITY REDUCTION PROCESS WAS PERFORMED FROM WHICH A NEW NEURAL NETWORK WAS MODELED. THIS DIMENSION REDUCED MODEL WAS TRAINED WITH THE VARIABLES APPLICATION SCORE, VULNERABILITY INDEX, REGIME, GENDER, AND POPULATION SEGMENT, WHICH WERE THE FIVE VARIABLES THAT EXPLAINED MORE THAN 80% OF THE FIRST MODEL. THE CLASSIFICATION ACCURACY OF THE DIMENSION REDUCED MODEL WAS 0.745, WHILE PRECISION AND RECALL WERE 0.883 AND 0.778, RESPECTIVELY. THE AREA UNDER ROC CURVE WAS 0.791. THIS MODEL COULD BE USED AS A GUIDE TO LEAD INTERVENTION POLICIES SO THAT THE FAILURE RATE IN THE ACADEMIC LEVELING COURSE WOULD DECREASE.
THE TERM CLASSROOM PROXEMICS REFERS TO HOW TEACHERS AND STUDENTS USE CLASSROOM SPACE, AND THE IMPACT OF THIS AND THE SPATIAL DESIGN ON LEARNING AND TEACHING. THIS STUDY ADDRESSES THE DIVIDE BETWEEN, ON THE ONE HAND, SUBSTANTIAL WORK ON PROXEMICS BASED ON CLASSROOM OBSERVATIONS AND, ON THE OTHER HAND, EMERGING WORK TO DESIGN AUTOMATED FEEDBACK THAT HELPS TEACHERS IDENTIFY SALIENT PATTERNS IN THEIR USE OF THE CLASSROOM SPACE. THIS STUDY DOCUMENTS HOW DIGITAL ANALYTICS WERE DESIGNED IN SERVICE OF A SENIOR TEACHER'S PRACTICE-BASED INQUIRY INTO CLASSROOM PROXEMICS. INDOOR POSITIONING DATA FROM FOUR TEACHERS WERE ANALYSED, VISUALIZED AND USED AS EVIDENCE TO COMPARE THREE DISTINCT LEARNING DESIGNS ENACTED IN A PHYSICS CLASSROOM. THIS STUDY DEMONSTRATES HOW TEACHERS CAN MAKE EFFECTIVE USE OF SUCH VISUALIZATIONS, TO GAIN INSIGHT INTO THEIR CLASSROOM PRACTICE. THIS IS EVIDENCED BY (A) DOCUMENTING TEACHERS' REFLECTIONS ON VISUALIZATIONS OF POSITIONING DATA, BOTH THEIR OWN AND THAT OF PEERS AND (B) IDENTIFYING THE TYPES OF INDICATOR (OPERATIONALIZED AS ANALYTICAL METRICS) THAT FOREGROUND THE MOST USEFUL INFORMATION FOR TEACHERS TO GAIN INSIGHT INTO THEIR PRACTICE.
A MIXED-METHODS STUDY EVALUATING THE MOTIVATION AND SATISFACTION OF ARCHITECTURE DEGREE STUDENTS USING INTERACTIVE VISUALIZATION METHODS IS PRESENTED IN THIS PAPER. NEW TECHNOLOGY IMPLEMENTATIONS IN THE TEACHING FIELD HAVE BEEN LARGELY EXTENDED AND DEVELOPED; HOWEVER, THESE INNOVATIONS REQUIRE APPROVAL VALIDATION AND EVALUATION BY THE FINAL USERS, IN OUR CASE THE STUDENTS. IN THIS PAPER, WE WILL DISCUSS THE ADVANTAGES AND DISADVANTAGES OF APPLYING MIXED EVALUATION TECHNOLOGY IN A CASE STUDY CENTERED IN THE USE OF INTERACTIVE AND COLLABORATIVE TOOLS FOR THE VISUALIZATION OF 3D ARCHITECTONICAL MODELS, THE EVALUATION OF THE MOTIVATION AND THE STUDENTS' DEGREE OF SATISFACTION USING FRIENDLY TECHNOLOGY AS MOBILE PHONES AND AUGMENTED REALITY.
MAINTAINING HIGH-QUALITY TEACHING AND LEARNING IN THE TIMES OF A PANDEMIC POSES A HUGE CHALLENGE TO EDUCATION SYSTEMS. TO SCAFFOLD ADEQUATE PRACTICES IN OUR COURSES DURING THE PANDEMIC, MORE ADVANCED, AND FINE-GRAINED "LEARNING DESIGN" IS NEEDED THAN PROVIDING THE LEARNING OBJECTIVES AND LEARNING MATERIALS OF THE COURSE AND DEFINING THE DELIVERABLES AND ASSIGNMENTS. IN THIS PAPER, WE LEVERAGE ON OUR EXPERIENCE WITH PUTTING INTO PRACTICE DIFFERENT LEARNING DESIGNS AND TECHNOLOGIES, IN VARIOUS INFORMATION TECHNOLOGY (IT) CONTEXTS AND DISCUSS HOW IT EDUCATORS CAN FURTHER REFLECT ON THE LEARNING DESIGN OF THEIR COURSES AND SCAFFOLD FULLY REMOTE OR BLENDED LEARNING APPROACHES TO ACCOMMODATE THEIR COURSES' NEEDS DURING THE PANDEMIC. WE PROVIDE THREE IMPORTANT OBJECTIVES AND INDICATIVE TECHNOLOGICAL SOLUTION THAT CAN SUPPORT THOSE OBJECTIVES, AND DISCUSS SOME LESSONS LEARNED FROM OUR EXPERIENCE.
THIS PAPER PRESENTS THE DESIGN OF AN ASSESSMENT PROCESS AND ITS OUTCOMES TO INVESTIGATE THE IMPACT OF EDUCATIONAL ROBOTICS ACTIVITIES ON STUDENTS' LEARNING. THROUGH DATA ANALYTICS TECHNIQUES, THE AUTHORS WILL EXPLORE THE ACTIVITIES' OUTPUT FROM A PEDAGOGICAL AND QUANTITATIVE POINT OF VIEW. SENSORS ARE UTILIZED IN THE CONTEXT OF AN EDUCATIONAL ROBOTICS ACTIVITY TO OBTAIN A MORE EFFECTIVE ROBOT-ENVIRONMENT INTERACTION. PUPILS WORK ON SPECIFIC EXERCISES TO MAKE THEIR ROBOT SMARTER AND TO CARRY OUT MORE COMPLEX AND INSPIRATIONAL PROJECTS: THE INTEGRATION OF SENSORS ON A ROBOTIC PROTOTYPE IS CRUCIAL, AND LEARNERS HAVE TO COMPREHEND HOW TO USE THEM. IN THE PRESENTED STUDY, THE POTENTIAL OF EDUCATIONAL DATA MINING IS USED TO INVESTIGATE HOW A GROUP OF PRIMARY AND SECONDARY SCHOOL STUDENTS, USING VISUAL PROGRAMMING (LEGO MINDSTORMS EV3 EDUCATION SOFTWARE), DESIGN PROGRAMMING SEQUENCES WHILE THEY ARE SOLVING AN EXERCISE RELATED TO AN ULTRASONIC SENSOR MOUNTED ON THEIR ROBOTIC ARTIFACT. FOR THIS PURPOSE, A TRACKING SYSTEM HAS BEEN DESIGNED SO THAT EVERY PROGRAMMING ATTEMPT PERFORMED BY STUDENTS' TEAMS IS REGISTERED ON A LOG FILE AND STORED IN AN SD CARD INSTALLED IN THE LEGO MINDSTORMS EV3 BRICK. THESE LOG FILES ARE THEN ANALYZED USING MACHINE LEARNING TECHNIQUES (K-MEANS CLUSTERING) IN ORDER TO EXTRACT DIFFERENT PATTERNS IN THE CREATION OF THE SEQUENCES AND EXTRACT VARIOUS PROBLEM-SOLVING PATHWAYS PERFORMED BY STUDENTS. THE DIFFERENCE BETWEEN PROBLEM-SOLVING PATHWAYS WITH RESPECT TO AN INDICATOR OF EARLY ACHIEVEMENT IS STUDIED.
DATA-DRIVEN DASHBOARDS HAVE BEEN INCREASINGLY INTEGRATED INTO VARIOUS CONTEXTS, PARTICULARLY IN EDUCATIONAL SETTINGS. THERE IS A GROWING NEED TO UNDERSTAND HOW TO DESIGN LEARNING DASHBOARDS TO HELP EDUCATORS SUPPORT LEARNING EXPERIENCES BY PROVIDING REAL-TIME FORMATIVE FEEDBACK. WE ARE STUDYING THE DESIGN OF A LEARNING DASHBOARD THAT CAN SUPPORT EDUCATIONAL FACILITATION TASKS IN A MUSEUM SETTING. IN OUR APPROACH, WE USE DISCRETE FACILITATION TASKS AS THE CORNERSTONE OF OUR DESIGN PROCESS. USING THIS TASK-BASED APPROACH, WE CONDUCTED PILOT STUDIES AND PARTICIPATORY DESIGN SESSIONS TO BETTER UNDERSTAND THE CONTEXT OF DESIGN. IN THIS PAPER, WE OFFER PRELIMINARY FINDINGS AND DESIGN CONSIDERATIONS FOR SUPPORTING AND DIGITALLY AUGMENTING FACILITATION TASKS IN A HIGHLY INTERACTIVE, OPEN-ENDED LEARNING ENVIRONMENT.
IN THIS PAPER, WE PRESENT A RESPONSE TO THE INTEREST-DRIVEN CREATOR (IDC) THEORY FROM A EUROPEAN PERSPECTIVE. SPECIFICALLY, WE RAISE SIX QUESTIONS INTENDED TO START A DIALOG WITH RESPECT TO IDC THEORY'S PLACEMENT IN EXISTING LEARNING THEORIES, ITS ADOPTION IN EDUCATIONAL SYSTEMS, AND HOW IT CAN BE INFLUENCED BY EMERGING LEARNING TECHNOLOGIES AND DIGITALIZATION, WHICH IS CURRENTLY A DRIVING FORCE IN THE ALPS REGION. BY REFERRING TO OUR OWN WORK IN VOCATIONAL EDUCATION AND CLASSROOM ORCHESTRATION, WE DEMONSTRATE HOW IDC CAN BEGIN TO PLAY A PART IN GUIDING INNOVATIONS AND ITS POTENTIAL IMPACT ON EDUCATION BOTH IN AND OUTSIDE OF ASIA. WITH RESPECT TO DIGITALIZATION, RATHER THAN ALLOWING TECHNOLOGICAL INNOVATIONS TO FULLY GUIDE EDUCATIONAL DECISIONS, WE CALL FOR IDC THEORY TO BE PART OF THE CONVERSATION TO HELP GUIDE FUTURE EDUCATIONAL DESIGNS.
THE INCREASING USE OF THE LEARNING MANAGEMENT SYSTEMS (LMSS) IS MAKING AVAILABLE AN EVER-GROWING, VOLUME OF DATA FROM INTERACTIONS BETWEEN TEACHERS AND STUDENTS. THIS STUDY AIMED TO DEVELOP A MODEL CAPABLE OF PREDICTING STUDENTS' ACADEMIC PERFORMANCE BASED ON INDICATORS OF THEIR SELF-REGULATED BEHAVIOR IN LMSS. TO ACCOMPLISH THIS GOAL, THE AUTHORS ANALYZED BEHAVIORAL DATA FROM AN LMS PLATFORM USED IN A PUBLIC UNIVERSITY FOR DISTANCE LEARNING COURSES, COLLECTED DURING A PERIOD OF SEVEN YEARS. WITH THIS DATA, THEY DEVELOPED, EVALUATED, AND COMPARED PREDICTIVE MODELS USING FOUR ALGORITHMS: DECISION TREE (CART), LOGISTIC REGRESSION, SVM, AND NAIVE BAYES. THE LOGISTIC REGRESSION MODEL YIELDED THE BEST RESULTS IN PREDICTING STUDENTS' ACADEMIC PERFORMANCE, BEING ABLE TO DO SO WITH AN ACCURACY RATE OF 0.893 AND AN AREA UNDER THE ROC CURVE OF 0.9574. FINALLY, THEY CONCEIVED AND IMPLEMENTED A DASHBOARD-LIKE INTERFACE INTENDED TO PRESENT THE PREDICTIONS IN A USER-FRIENDLY WAY TO TUTORS AND TEACHERS, SO THEY COULD USE IT AS A TOOL TO HELP MONITOR THEIR STUDENTS' LEARNING PROCESS.
THIS PAPER DESCRIBES A METHOD TO COLLECT DATA OF WHICH SECTION OF PAGES LEARNERS WERE BROWSING IN DIGITAL TEXTBOOKS WITHOUT EYE-TRACKING TECHNOLOGIES. IN PREVIOUS RESEARCHES ON DIGITAL TEXTBOOK SYSTEMS, IT WAS DIFFICULT TO COLLECT SUCH DATA WITHOUT USING EYE-TACKERS. HOWEVER, EYE-TRACKERS COST A MASSIVE BUDGET. OUR PROPOSED SYSTEM AUTOMATICALLY HIDES THE TEXTS IN THE DIGITAL TEXTBOOKS WITH MASK PROCESSING BEFORE THE LEARNERS BROWSE THE TEXTS IN THE DIGITAL TEXTBOOKS. IF THEY CLICK THE HIDDEN TEXTS, THE SYSTEM GETS RID OF THE MASKS AND THE TEXTS APPEAR LETTER BY LETTER. WE USED NMF TO DISCOVER LEARNERS' BROWSING PATTERNS FROM THE COLLECTED LOGS. EVALUATION EXPERIMENTS WERE CONDUCTED TO EXAMINE THE EFFECTIVENESS OF OUR SYSTEM IN TERMS OF FASCINATION, UNDERSTANDABLENESS AND ENHANCEMENT OF THINKING AND TO DISCOVER LEARNERS' BROWSING PATTERNS. IT WAS FOUND THAT OUR METHOD COULD ENHANCE THINKING SKILLS. A BROWSING PATTERN OF DILIGENT LEARNERS WITH HIGH LEARNING ACHIEVEMENTS WAS ALSO FOUND.
THERE IS STRONG EVIDENCE THAT EMOTIONS INFLUENCE THE LEARNING PROCESS. FOR THIS REASON, WE EXPLORE THE RELEVANCE OF INDIVIDUAL AND GENERAL MOUSE AND KEYBOARD INTERACTION PATTERNS IN REAL-WORLD SETTINGS WHILE LEARNERS PERFORM FREE TEXT TASKS. TO THIS END, WE HAVE MODELED USERS' MOUSE MOVEMENTS AND KEYSTROKE DYNAMICS WITH DATA MINING TECHNIQUES, BUILDING ON PREVIOUS RELATED RESEARCH AND EXTENDING IT IN TERMS OF SOME CRITICAL MODELING ISSUES THAT MAY HAVE AN IMPACT ON DETECTION RESULTS. INSPIRED BY PRACTICE IN AFFECTIVE COMPUTING WHERE PHYSIOLOGICAL SENSORS ARE USED, WE ARGUE FOR THE CREATION OF AN INTERACTION BASELINE MODEL, AS A REFERENCE POINT IN THE WAY HOW LEARNERS INTERACT WITH THE KEYBOARD AND MOUSE. TO MAKE THE PROPOSED AFFECTIVE MODEL FEASIBLE, WE HAVE ADOPTED A SIMPLIFIED 2-D SELF-LABELING APPROACH FOR LABELING THE USERS' AFFECTIVE STATE. OUR APPROACH TO AFFECT DETECTION IMPROVES RESULTS WHEN THERE IS A SMALL AMOUNT OF DATA INSTANCES AVAILABLE AND DOES NOT REQUIRE ADDITIONAL AFFECT-ORIENTED TASKS FROM THE LEARNERS. SPECIFICALLY, LEARNERS ARE ONLY ASKED TO SELF-REFLECT THEIR EMOTIONAL STATE AFTER FINISHING THE TASKS AND IMMEDIATELY SELECTING TWO VALUES IN THE AFFECT SCALE. THE APPROACH WE HAVE FOLLOWED AIMS TO DISTILL TWO TYPES OF INTERACTION PATTERNS: 1) WITHIN-SUBJECT PATTERNS (FROM A SINGLE PARTICIPANT) AND 2) BETWEEN-SUBJECT PATTERNS (ACROSS ALL PARTICIPANTS). DOING THIS, WE AIM TO COMBINE BOTH THE APPROACHES AS MODELING FACTORS, THUS TAKING ADVANTAGE OF INDIVIDUAL AND GENERAL INTERACTION PATTERNS TO PREDICT AFFECT.
VIRTUAL WORLDS (VWS) HAVE BECOME INCREASINGLY POPULAR TO SUPPORT STUDENTS' FOREIGN LANGUAGE LEARNING, ESPECIALLY BEYOND THE CLASSROOM. UNFORTUNATELY STUDENTS' INTERACTION IN VWS IS NOT ALWAYS AVAILABLE FOR THE SUPERVISOR AND THUS IS NOT EASY TO ANALYSE. NONETHELESS, IT PROVIDES INTERESTING INFORMATION NOT ONLY IN TERMS OF ASSESSMENT, BUT ALSO TO DETECT GENERAL LEARNER PROFILES AND TRENDS IN THEIR USE. THEREFORE WE PROPOSE A COMPUTER SYSTEM THAT ACCEPTS QUERIES IN A SIMPLE BUT SPECIFIC LANGUAGE FOR VWS, ALLOWING THE SUPERVISOR TO DESIGN ASSESSMENT FORMULAS, GENERATE REPORTS ON STUDENTS' INTERACTION IN VWS AND REFINE THE FORMULAS FROM THESE RESULTS UNTIL OBTAINING A VALID INDICATOR FOR ANALYSING AND ASSESSING THE RETRIEVED DATA. IN THIS PAPER, WE DESCRIBE A CASE STUDY WHICH HAS BEEN CARRIED OUT IN A GERMAN FOREIGN LANGUAGE COURSE, USING A VW IMPLEMENTED VIDEO-GAME AND ASSESSING STUDENTS' FOREIGN LANGUAGE SKILLS. THE STUDY HAS BEEN DEPLOYED IN A SCALABLE WAY THROUGH OUR COMPUTER SYSTEM. PRELIMINARY RESULTS PROVIDE A SET OF INDICATORS FOR INDIVIDUAL AND GROUP BEHAVIOUR WHICH CAN BE USED TO ASSESS STUDENT'S ABILITY TO COMMUNICATE IN THE TARGET LANGUAGE.
PROVIDING APPROPRIATE TOOL-SUPPORTED GUIDANCE TO STUDENTS IS AN ESSENTIAL ASPECT OF TECHNOLOGY-SUPPORTED INQUIRY BASED STEM EDUCATION, IN ORDER TO FACILITATE THEM IN ENGAGING IN DIVERSE INQUIRY TASKS. HOWEVER, ANALYZING EDUCATIONAL DESIGNS AND EVALUATING THE LEVEL OF TOOL-SUPPORTED GUIDANCE PROVIDED TOWARDS REFLECTIVE REMEDYING ACTIONS IS NOT A TRIVIAL TASK, ESPECIALLY FOR NOVICE STEM TEACHERS. IN THIS CONTEXT, THE PAPER PRESENTS THE DESIGN AND PRELIMINARY EVALUATION OF AN INQUIRY-BASED STEM TEACHING ANALYTICS TOOL. THIS TOOL AIMS TO VISUALLY ANALYZE AND EVALUATE EXISTING EDUCATIONAL DESIGNS IN TERMS OF THE LEVEL OF TECHNOLOGY-SUPPORTED GUIDANCE THEY OFFER AND SUPPORT TEACHERS' REFLECTIVE (RE)DESIGN. THE PRELIMINARY EVALUATION RESULTS ATTEST TO THE HIGH LEVELS OF ACCURACY OF THE PROPOSED TEACHING ANALYTICS TOOL AND PROVIDE EVIDENCE OF ITS CAPACITY TO INFORM FUTURE TEACHING AND LEARNING ANALYTICS TOOLS FOR FACILITATING STEM TEACHERS TO ENGAGE IN DATA-DRIVEN REFLECTIVE (RE)DESIGN OF THEIR PRACTICE.
THE TRANSITION TO DIGITAL SOCIETY IS CHARACTERISED BY THE DEVELOPMENT OF NEW METHODS AND TOOLS FOR BIG DATA PROCESSING. NEW TECHNOLOGIES HAVE A SUBSTANTIAL IMPACT ON THE EDUCATION SECTOR. THE ARTICLE REPRESENTS THE RESULTS OF APPLYING BIG DATA TO ANALYSE AND TRANSFORM THE LEARNING CONTENT OF MOSCOW'S SCHOOLS. THE ANALYSIS OF THE SCHOOL CURRICULUM COMPRISED THE FOLLOWING: (A) IDENTIFYING ONE-TOPIC LESSON SCRIPTS, (B) ANALYSING CROSS-DISCIPLINARY CONNECTIONS BETWEEN SUBJECTS, (C) VERIFYING THE COMPLIANCE OF THE LESSON SCRIPT DIGITAL CONTENT TO THE FEDERAL EDUCATIONAL STANDARDS. THE ANALYSED MATERIAL INCLUDED 36,644 LESSON SCRIPTS. THE ANALYSIS HAS BEEN CONDUCTED USING SPECIFICALLY DESIGNED DIGITAL TOOLS FEATURING DATA MINING ALGORITHMS. THE ARTICLE CONSIDERS THE ISSUE OF APPLYING DATA MINING ALGORITHMS TO ANALYSE SCHOOL CURRICULUM FOR THE IMPROVEMENT OF ITS QUALITY.
THE PRESENT STUDY ASSESSED THE EFFECTIVENESS OF EBOOK INTERACTION LOGS IN THE READING PERFORMANCE OF SAUDI EFL LEARNERS. IT EMPLOYED AN EXPERIMENTAL RESEARCH DESIGN TO A TOTAL OF 30 MALE STUDENTS AT QASSIM . UNIVERSITY, SAUDI ARABIA, SPANNING TWELVE WEEKS. WE USED THE BOOKROLL SYSTEM AND ASKED THE EFL TEACHERS TO UPLOAD TEXTBOOK MATERIALS, ALLOWING LEARNERS FREE ACCESS ANYTIME, ANYWHERE. APART FROM AVAILABILITY, WHAT SET THE SYSTEM APART FROM CLASSROOM TEACHING WAS THE KIND OF PRIVACY AND INDIVIDUAL LEARNING PACE THAT THE SYSTEM PROVIDES TO LEARNERS: THEY MANAGED TO REVISIT RELEVANT SECTIONS MARKED BY THE TEACHER, CHECKED OUT THE NOTES AND ENGAGED (EVEN REPEATEDLY) IN OTHER READING-RELATED ACTIVITIES THAT THE TEACHER INCORPORATED INTO THE EBOOK. THE STUDY ANALYSED THE SYSTEM LOGS TO DEVELOP PREVALENT READING PATTERNS AND PREDICT READING BEHAVIOURS IN THE PARTICIPANTS. IT SHED LIGHT ON THE STRATEGIES THAT LEARNERS EMPLOYED IN READING THE TEXTS. THE RESULT SHOWED THAT BOOKROLL COULD BE USED TO PREDICT LEARNERS' READING BEHAVIOURS WITH A REASONABLE DEGREE OF ACCURACY WHICH WOULD BE BENEFICIAL TO A LARGE NUMBER OF INTERESTED PARTIES INCLUDING, BUT NOT RESTRICTED TO, EDUCATIONAL ADVISORS KEEN TO INTEGRATE TECHNOLOGY INTO THE EFL CLASSROOM IN THE KINGDOM OF SAUDI ARABIA, THE TEACHING COMMUNITY WHICH, FOR THE MOST PART, IGNORES READING AS A SKILL BY RESORTING TO QUICK SUMMARIES IN STUDENTS' MOTHER TONGUE AT TIMES AND LEARNERS WHO NEED TO FIND THEIR INDIVIDUAL LEARNING PACE AND ENVIRONMENT.
THIS PAPER DESCRIBES A STATISTICAL ANALYSIS OF THE EFFECTIVENESS OF COMBINING A CAREER-ORIENTED QUESTIONNAIRE WITH A PERSONALITY QUESTIONNAIRE. THE CAREER ANCHORS (CA) AND BIG-FIVE PERSONALITY (BFP) SURVEYS WERE PERFORMED WITH UNIVERSITY STUDENTS WHO SPECIALIZED IN INFORMATION SCIENCES. THE NUMBERS OF SUBJECTS WHO ANSWERED THE CA AND BFP SURVEYS WERE 612 AND 341, RESPECTIVELY. THE USEFULNESS OF CA FOR THE STUDENTS WAS EXAMINED, AND ALL ITEM PARAMETERS WERE EVALUATED BY ITEM RESPONSE THEORY, THE MARKOV CHAIN MONTE CARLO METHOD AND BAYES' THEOREM. THE CHARACTERISTICS OF EACH CA ITEM FOR THE SUBJECT GROUP WERE CLARIFIED, AS WELL AS ITS STATISTICAL DISTRIBUTION. ADDITIONALLY, STATISTICAL DISTRIBUTION WAS ANALYZED FOR BFP DATA. SEVERAL CHARACTERISTIC CORRELATIONS WERE FOUND BETWEEN THE CA ITEMS AND THE BFP ITEMS. THE RESULTS SUGGEST THAT THE QUESTIONNAIRE, WHICH COMBINES THESE TWO TYPES OF QUESTIONS, IS APPLICABLE TO STUDENTS' CAREER DEVELOPMENT.
SOCIAL NETWORK ANALYSIS TECHNIQUES HAVE SHOWN A POTENTIAL FOR INFLUENCING GRADUATES TO MEET INDUSTRY NEEDS. IN THIS PAPER, WE PROPOSE A SOCIAL-WEB DRIVEN SOLUTIONS TO BRIDGE FORMAL EDUCATION AND INDUSTRY NEEDS. THE PROPOSED CAREER DEVELOPMENT FRAMEWORK UTILIZES SOCIAL NETWORK ANALYTICS, INFLUENCE DIFFUSION ALGORITHMS AND PERSUASIVE TECHNOLOGY MODELS ALONG THREE PHASES: (1) CAREER READINESS TO MEASURE AND VISUALIZE THE GENERAL COGNITIVE DISPOSITIONS REQUIRED FOR A SUCCESSFUL CAREER IN THE 21ST CENTURY, (2) CAREER PREDICTION TO PERSUADE FUTURE GRADUATES INTO A DESIRED CAREER PATH BY CLUSTERING LEARNERS WHOSE CAREER PROSPECTS ARE DEEMED SIMILAR, INTO A COMMUNITY OF PRACTICE; AND (3) CAREER DEVELOPMENT TO DRIVE GROWTH WITHIN A SOCIAL NETWORK STRUCTURE WHERE SOCIAL NETWORK ANALYTICS AND PERSUASIVE TECHNIQUES ARE APPLIED TO INCITE THE ADOPTION OF DESIRED CAREER BEHAVIORS. THE PROCESS STARTS BY DISCOVERING BEHAVIORAL FEATURES TO CREATE A COGNITIVE PROFILE AND DIAGNOSE INDIVIDUAL DEFICIENCIES. THEN, WE DEVELOP A FUZZY CLUSTERING ALGORITHM THAT PREDICTS SIMILAR PATTERNS WITH CONTROLLED CONSTRAINT-VIOLATIONS TO CONSTRUCT A SOCIAL STRUCTURE FOR COLLABORATIVE COGNITIVE ATTAINMENT. THIS SOCIAL FRAMEWORK FACILITATES THE DEPLOYMENT OF NOVEL INFLUENCE DIFFUSION APPROACHES, WHEREBY WE PROPOSE A RECIPROCAL-WEIGHTED SIMILARITY FUNCTION AND A TRIADIC CLOSURE APPROACH. IN DOING SO, WE INVESTIGATE CONTEMPORARY SOCIAL NETWORK ANALYTICS TO MAXIMIZE INFLUENCE DIFFUSION ACROSS A SYNTHESIZED SOCIAL NETWORK. THE OUTCOME OF THIS SOCIAL COMPUTING APPROACH LEADS TO A PERSUASIVE MODEL THAT SUPPORTS BEHAVIORAL CHANGES AND DEVELOPMENTS. THE PERFORMANCE RESULTS OBTAINED FROM BOTH ANALYTICAL AND EXPERIMENTAL EVALUATIONS VALIDATE OUR DATA-DRIVEN STRATEGY FOR PERSUASIVE BEHAVIORAL CHANGE.
INSTEAD OF MEASURING SUCCESS IN MASSIVE OPEN ONLINE COURSES (MOOCS) BASED ON CERTIFICATION AND COMPLETION-RATES, RESEARCHERS STARTED TO DEFINE SUCCESS WITH ALTERNATIVE METRICS RECENTLY, FOR EXAMPLE BY EVALUATING THE INTENTION-BEHAVIOR GAP AND GOAL ACHIEVEMENT. ESPECIALLY SELF-REGULATED AND GOAL-ORIENTED LEARNING HAVE BEEN IDENTIFIED AS CRITICAL SKILLS TO BE SUCCESSFUL IN ONLINE LEARNING ENVIRONMENTS WITH LOW GUIDANCE LIKE MOOCS, BUT TECHNICAL SUPPORT IS RARE. THEREFORE, THIS PAPER EXAMINES THE CURRENT TECHNICAL CAPABILITIES AND LIMITATIONS OF GOAL-ORIENTED LEARNING IN MOOCS. AN OBSERVATIONAL STUDY TO EXPLORE HOW WELL LEARNERS IN FIVE MOOCS ACHIEVED THEIR INITIAL LEARNING OBJECTIVES WAS CONDUCTED, AND THE RESULTS ARE COMPARED WITH SIMILAR STUDIES. AFTERWARDS, A CONCEPT WITH A FOCUS ON TECHNICAL FEASIBILITY AND AUTOMATION OUTLINES HOW PERSONALIZED LEARNING OBJECTIVES CAN BE SUPPORTED AND IMPLEMENTED ON A MOOC PLATFORM.
LANGUAGE LEARNING OCCURRING IN AUTHENTIC CONTEXTS HAS BEEN SHOWN TO BE MORE EFFECTIVE. VIRTUAL WORLDS PROVIDE SIMULATED CONTEXTS THAT HAVE THE NECESSARY ELEMENTS OF AUTHENTIC CONTEXTS FOR LANGUAGE LEARNING, AND AS A RESULT, MANY STUDIES HAVE ADOPTED VIRTUAL WORLDS AS A USEFUL PLATFORM FOR LANGUAGE LEARNING. HOWEVER, FEW STUDIES SO FAR HAVE EXAMINED THE RELATIONSHIP BETWEEN LEARNING OUTCOMES AND LEARNING PATHS AND STRATEGIES INSIDE A VIRTUAL WORLD. THIS STUDY WAS DESIGNED TO FILL THIS RESEARCH GAP. IN ORDER TO UNDERSTAND THE IMPACT OF DIFFERENT LEARNING STRATEGIES ON LEARNING OUTCOMES IN A VIRTUAL WORLD, A VISUALIZATION ANALYTIC METHOD WAS DEVELOPED TO EXAMINE THE RECORDED LEARNER PATHS WITHIN A VIRTUAL WORLD WHILE LEARNING OCCURRED. IN PARTICULAR, THE VISUALIZATION ANALYTIC METHOD ADOPTED IN THIS STUDY WAS BASED ON SOCIAL NETWORK ANALYSIS. THIS STUDY INCLUDED 14 PARTICIPANTS WHO WERE LEARNERS OF MANDARIN CHINESE AS A FOREIGN LANGUAGE. THE LEARNING OUTCOMES WERE BASED ON THEIR TEST SCORES FROM 7 LEARNING SESSIONS AND THE POST-TEST. THROUGH THE VISUALIZATION ANALYSIS, THE CURRENT STUDY REVEALED A LINK BETWEEN THE LEARNING PATHS AND STRATEGIES AND LEARNERS' OUTCOMES. THE STRATEGIES INCLUDE THE "NEAREST STRATEGY," THE "FOCUS STRATEGY," AND THE "CLUSTER STRATEGY." THE FINDINGS SHOW THAT HIGH-ACHIEVING AND LOW-ACHIEVING STUDENTS TEND TO USE DIFFERENT STRATEGIES IN LEARNING NEW WORDS. THE VISUALIZATION ANALYTICS THUS EFFECTIVELY DISPLAYS THE LEARNING STRATEGIES OF VOCABULARY ACQUISITION. OUR METHODS COULD BE APPLIED TO OTHER SECOND LANGUAGE LEARNING STUDIES, AND THE RESULTS CAN ALSO PROVIDE INSIGHTS INTO THE CONSTRUCTION OF FUTURE VIRTUAL WORLDS FOR LEARNING SECOND LANGUAGES.
TO IMPROVE STUDENTS' PERFORMANCE EFFECTIVELY, MANY CHINESE UNIVERSITIES ARE ESTABLISHING SYSTEMS TO PREDICT STUDENT'S ACADEMIC PERFORMANCE AND SENDING STUDENT'S ACADEMIC EARLY ALERTS. WITH STUDENT'S POPULATION OF OVER 600,000, CHINESE-FOREIGN COOPERATION IN RUNNING SCHOOLS (CFCRS) HAS BECOME ONE OF THE BOOMING HIGHER EDUCATION FORMS IN CHINA. COMPARED WITH STUDENTS IN THE NON-COOPERATIVELY RUNNING PROGRAMS, CFCRS STUDENTS' ACADEMIC PERFORMANCES ARE WEAKER ON AVERAGE. TO PREDICT THE ACADEMIC-AT-RISK STUDENTS AND PROVIDE EFFICIENT SUPPORTS TO THE STUDENTS, A PRECISE AND PROMPT ACADEMIC PREDICTION IS IN GREAT NEED. THEREFORE, THIS RESEARCH AIMS AT REPRESENTING A MORE EFFICIENT AND ACCURATE MODEL TO PREDICT ACADEMIC PERFORMANCE OF CFCRS STUDENTS WHICH WILL BE BASED ON GRAPH CONVOLUTIONAL NETWORK. IN THIS RESEARCH, STUDENT'S SIMILARITY IS MEASURED ON ACADEMIC PERFORMANCE BY PEARSON CORRELATION COEFFICIENT. AN UNDIRECTED GRAPH IN WHICH SIMILAR STUDENTS ARE CONNECTED IS CONDUCTED. FEATURE MATRIX IS COMPOSED OF STUDENTS' PREVIOUS GRADES. IN ADDITION, GRAPH CONVOLUTIONAL NETWORK IS TRAINED BASED ON THE UNDIRECTED GRAPH AND FEATURE MATRIX. IN THE EXPERIMENT, IT SHOWS THAT THIS MODEL PREDICTS CERTAIN STUDENT'S PERFORMANCE ON CERTAIN COURSE FROM THE FINAL EXAM RESULTS IN PREVIOUS SEMESTERS, WHICH MIGHT IMPROVE LEARNING EFFICIENCY AND TEACHING QUALITY. WITH AN AVERAGE ACCURACY OF 81.5%, GRAPH CONVOLUTIONAL NETWORK OUTPERFORMS SUPPORT VECTOR MACHINE AND RANDOM FOREST MODELS.
THIS DATA ARTICLE REPRESENTS ACADEMIC PERFORMANCES OF UNDERGRADUATE STUDENTS IN A SELECT NIGERIAN PRIVATE TERTIARY INSTITUTION FROM 2008 TO 2013. THE 2413 DATASET CATEGORIZES STUDENTS WITH RESPECT TO THEIR ORIGINS (ETHNICITY), PRE-UNIVERSITY ADMISSION SCORES AND CUMULATIVE GRADE POINT AVERAGES EARNED AT THE END OF THEIR STUDY AT THE UNIVERSITY. WE PRESENT A DESCRIPTIVE STATISTICS SHOWING MEAN, MEDIAN, MODE, MAXIMUM, MINIMUM, RANGE, STANDARD DEVIATION AND VARIANCE IN THE PERFORMANCES OF THESE STUDENTS AND A BOXPLOT REPRESENTATION OF THE PERFORMANCES OF THESE STUDENTS WITH RESPECT TO THEIR ORIGINS. (C) 2018 THE AUTHORS. PUBLISHED BY ELSEVIER INC.
THIS AERA OPEN SPECIAL TOPIC CONCERNS THE LARGE EMERGING RESEARCH AREA OF EDUCATION DATA SCIENCE (EDS). IN A NARROW SENSE, EDS APPLIES STATISTICS AND COMPUTATIONAL TECHNIQUES TO EDUCATIONAL PHENOMENA AND QUESTIONS. IN A BROADER SENSE, IT IS AN UMBRELLA FOR A FLEET OF NEW COMPUTATIONAL TECHNIQUES BEING USED TO IDENTIFY NEW FORMS OF DATA, MEASURES, DESCRIPTIVES, PREDICTIONS, AND EXPERIMENTS IN EDUCATION. NOT ONLY ARE OLD RESEARCH QUESTIONS BEING ANALYZED IN NEW WAYS BUT ALSO NEW QUESTIONS ARE EMERGING BASED ON NOVEL DATA AND DISCOVERIES FROM EDS TECHNIQUES. THIS OVERVIEW DEFINES THE EMERGING FIELD OF EDUCATION DATA SCIENCE AND DISCUSSES 12 ARTICLES THAT ILLUSTRATE AN AERA-ANGLE ON EDS. OUR OVERVIEW RELATES A VARIETY OF PROMISES EDS POSES FOR THE FIELD OF EDUCATION AS WELL AS THE AREAS WHERE EDS SCHOLARS COULD SUCCESSFULLY FOCUS GOING FORWARD.
SELF-DIRECTION SKILLS IN THE CONTEXT OF LEARNING CAN BE SUPPORTED WITH DATA IN THIS DIGITAL ERA. THIS STUDY ANALYZES THE BEHAVIORS OF LEARNERS DURING A SELF-DIRECTED READING AND SUMMARIZATION TASK. OUR WORK INVESTIGATES AN UNDERGRADUATE COURSE (N=72) WHERE STUDENTS WORKED ON A READING AND SUMMARIZING ASSIGNMENT WHILE PLANNING AND MONITORING THE TASK IN GOAL, A PLATFORM SYNTHESIZING LEARNER'S ACTIVITY DATA FROM LEARNING AND PHYSICAL ACTIVITY CONTEXTS. THIS STUDY FOCUSES ON THE INITIAL COHORT ANALYSIS OF THE STUDENTS' BEHAVIOR BASED ON THE FINE GRAIN INTERACTION DATA COLLECTED IN THE DIFFERENT SYSTEMS USING VISUAL ANALYTICS TECHNIQUES. SUCH A DATA-RICH NARRATIVE OF SELF-DIRECTED IN-SEMESTER ACTIVITIES IS NOT DISCUSSED YET IN THE LITERATURE TO OUR KNOWLEDGE. WE DISCUSS THE IMPLICATIONS OF THE TRENDS THAT IS FOUND IN OUR COLLECTED DATASET FOR DESIGNING AI-SUPPORT FOR SELF-DIRECTION SKILLS WITH THE GOAL PLATFORM AND THE SCOPE OF DEEPER ANALYSIS TO FURTHER UNDERSTAND THE PROCESS.
ONLINE ENVIRONMENTS ARE INCREASINGLY USED AS PLATFORMS TO SUPPORT AND ENHANCE LEARNING EXPERIENCES. IN HIGHER EDUCATION, STUDENTS ENROLL IN PROGRAMS THAT ARE USUALLY FORMED OF A SET OF COURSES AND MODULES. SUCH COURSES ARE DESIGNED TO COVER A SET OF CONCEPTS AND ACHIEVE SPECIFIC LEARNING OBJECTIVES THAT COUNT TOWARDS THE RELATED DEGREE. HOWEVER WE OBSERVE THAT CONNECTIONS AMONG COURSES AND THE WAY THEY CONCEPTUALLY INTERLINK ARE HARD TO EXPLOIT. THIS IS NORMAL AS COURSES ARE TRADITIONALLY DESCRIBED USING TEXT IN THE FORM OF DOCUMENTS SUCH AS SYLLABI AND COURSE CATALOGS. WE BELIEVE THAT LINKED DATA CAN BE USED TO CREATE A CONCEPTUAL LAYER AROUND HIGHER EDUCATION PROGRAMS TO INTERLINK COURSES IN A GRANULAR AND REUSABLE MANNER. WE PRESENT IN THIS PAPER OUR WORK ON CREATING A SEMANTIC LINKED DATA LAYER TO CONCEPTUALLY CONNECT COURSES TAUGHT IN A HIGHER EDUCATION PROGRAM. WE HIGHLIGHT THE LINKED DATA MODEL WE CREATED TO BE COLLABORATIVELY EXTENDED BY COURSE INSTRUCTORS AND STUDENTS USING A SEMANTIC MEDIAWIKI PLATFORM. WE ALSO PRESENT TWO APPLICATIONS THAT WE BUILT ON TOP OF THE DATA TO (1) SHOWCASE HOW LEARNING MATERIAL CAN NOW FLOAT AROUND COURSES THROUGH THEIR INTERLINKED CONCEPTS IN ELEARNING ENVIRONMENTS (WE USE MOODLE AS A PROOF OF CONCEPT); AND (2) TO SUPPORT THE PROCESS OF HIGHER EDUCATION PROGRAM REVIEWS.
KNOWLEDGE TRACING (KT) AIMS TO TRACE THE STUDENT'S STATE OF EVOLUTIONARY MASTERY FOR A PARTICULAR KNOWLEDGE OR CONCEPT BASED ON THE STUDENT'S HISTORICAL LEARNING INTERACTIONS WITH THE CORRESPONDING EXERCISES. TAKING THE "EXERCISE-TO-CONCEPT" RELATIONSHIPS AS INPUT, SEVERAL EXISTING METHODS HAVE BEEN DEVELOPED TO TRACE AND MODEL STUDENTS' MASTERY STATES. HOWEVER, THESE STUDIES FACE TWO MAJOR SHORTCOMINGS IN KT: 1) THEY ONLY CONSIDER "EXERCISE-TO-CONCEPT" RELATIONSHIPS; 2) THE MULTI-HOT EMBEDDINGS LACK INTERPRETABILITY. IN ORDER TO ADDRESS THE ABOVE ISSUES, WE PROPOSE A JOINT GRAPH CONVOLUTIONAL NETWORK BASED DEEP KNOWLEDGE TRACING (JKT) FRAMEWORK TO MODEL THE MULTI-DIMENSIONAL RELATIONSHIPS OF "EXERCISE-TO-EXERCISE", AND "CONCEPT-TO-CONCEPT" INTO GRAPH AND FUSE THEM WITH "EXERCISE-TO-CONCEPT" RELATIONSHIPS. IN JKT, IT IS NOT ONLY POSSIBLE TO ESTABLISH CONNECTIONS BETWEEN EXERCISES UNDER CROSS-CONCEPTS, BUT ALSO TO HELP CAPTURE HIGH-LEVEL SEMANTIC INFORMATION AND INCREASE THE MODEL'S INTERPRETABILITY. IN ADDITION, SUFFICIENT EXPERIMENTS CONDUCTED ON FOUR REAL-WORLD DATASETS HAVE DEMONSTRATED THAT JKT PERFORMS BETTER THAN THE OTHER BASELINE MODELS. WE FURTHER ILLUSTRATE A CASE STUDY TO DEMONSTRATE ITS INTERPRETABILITY FOR LEARNING ANALYSIS (C) 2021 ELSEVIER INC. ALL RIGHTS RESERVED.
LEARNING ACTIVITIES MAKE USE TODAY OF A VARIETY OF SOFTWARE TOOLS, LIKE LEARNING MANAGEMENT SYSTEMS (LMSS), ONLINE SOCIAL NETWORKS, AND FORUMS. THEY ENHANCE THE EXPERIENCE BY FACILITATING INTERACTIONS AMONG PARTICIPANTS AND WITH RESOURCES. THESE INTERACTIONS CAN BE USED AS A SOURCE OF INFORMATION ON HOW THE PROCESS IS WORKING FOR INDIVIDUAL STUDENTS OR GROUPS, AND THEREFORE TO PROVIDE THEM A TAILORED SUPPORT. HOWEVER, LECTURERS CAN ONLY DO THIS MONITORING IN VERY SIMPLE WAYS, AS THEIR TIME IS LIMITED, TOOLS GENERATE LARGE AMOUNTS OF DATA, AND STUDENTS HAVE VERY DIFFERENT PROFILES. IN ORDER TO IMPROVE THIS SITUATION, THIS WORK PROPOSES THE USE OF SEMI-AUTOMATED ASSISTANTS TO ANALYSE THESE DATA, ABLE TO LEARN FROM THE LECTURERS' EXPERTISE ON WHAT SITUATIONS ARE RELEVANT. THE ASSISTANT FOR LEARNING ACTIVITIES (ALA) IS AN ADAPTIVE MULTI-AGENT SYSTEM (AMAS) THAT RAISES ALERTS WHEN SOME EVENTS APPEAR IN DATA. ITS ARCHITECTURE INCLUDES TWO GROUPS OF AGENTS. THE CORE ARE THE AGENTS THAT LEARN AND CLASSIFY THE DATA TO GENERATE THE ALERTS. THERE CAN BE MULTIPLE GROUPS OF THESE AGENTS TO IDENTIFY DIFFERENT TYPES OF ALERT. A SECOND GROUP OF AGENTS WRAPS AND TRANSFORMS DATA FROM SOURCES TO FEED THE FIRST GROUP. THE ALA SETUP USES A PROTOTYPICAL TRAINING BASED ON INFORMATION FROM PREVIOUS COURSES. THEN, IT LEARNS AND ADJUSTS FOR A SPECIFIC COURSE USING THE LECTURERS' FEEDBACK ON THE ALERTS IT GENERATES. THE PAPER REPORTS THE CURRENTLY UNDERGOING VALIDATION OF THE ALA KNOWLEDGE BASED ON THE INFORMATION FROM THE INSTITUTIONAL LMS OF A SPANISH UNIVERSITY.
ONLINE VIDEO LECTURES HAVE BEEN CONSIDERED AN INSTRUCTIONAL MEDIA FOR VARIOUS PEDAGOGIC APPROACHES, SUCH AS THE FLIPPED CLASSROOM AND OPEN ONLINE COURSES. IN COMPARISON TO OTHER INSTRUCTIONAL MEDIA, ONLINE VIDEO AFFORDS THE OPPORTUNITY FOR RECORDING STUDENT CLICKSTREAM PATTERNS WITHIN A VIDEO LECTURE. VIDEO ANALYTICS WITHIN LECTURE VIDEOS MAY PROVIDE INSIGHTS INTO STUDENT LEARNING PERFORMANCE AND INFORM THE IMPROVEMENT OF VIDEO-ASSISTED TEACHING TACTICS. NEVERTHELESS, VIDEO ANALYTICS ARE NOT ACCESSIBLE TO LEARNING STAKEHOLDERS, SUCH AS RESEARCHERS AND EDUCATORS, MAINLY BECAUSE ONLINE VIDEO PLATFORMS DO NOT BROADLY SHARE THE INTERACTIONS OF THE USERS WITH THEIR SYSTEMS. FOR THIS PURPOSE, WE HAVE DESIGNED AN OPEN-ACCESS VIDEO ANALYTICS SYSTEM FOR USE 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 FOUND THAT THERE IS A RELATIONSHIP BETWEEN VIDEO NAVIGATION (REPEATED VIEWS) AND THE LEVEL OF COGNITION/THINKING REQUIRED FOR A SPECIFIC VIDEO SEGMENT. OUR RESULTS INDICATED THAT LEARNING PERFORMANCE PROGRESS WAS SLIGHTLY IMPROVED AND STABILIZED AFTER THE THIRD WEEK OF THE VIDEO-ASSISTED COURSE. WE ALSO FOUND THAT ATTITUDES REGARDING EASINESS, USABILITY, USEFULNESS, AND ACCEPTANCE OF THIS TYPE OF COURSE REMAINED AT THE SAME LEVELS THROUGHOUT THE COURSE. FINALLY, WE TRIANGULATE ANALYTICS FROM DIVERSE SOURCES, DISCUSS THEM, AND PROVIDE THE LESSONS LEARNED FOR FURTHER DEVELOPMENT AND REFINEMENT OF VIDEO-ASSISTED COURSES AND PRACTICES.
PREVIOUS STUDIES HAVE INVESTIGATED THE PROVISION OF STUDENTS' RELEVANT DATA, USUALLY AVAILABLE IN INTUITIVE DASHBOARDS, TO ASSIST TEACHERS IN PEDAGOGICAL DECISION-MAKING IN LEARNING SYSTEMS. RESEARCHERS ARE ALSO INTERESTED IN INVESTIGATING HOW STUDENTS' INTERACTION DATA WITH GAMIFICATION ELEMENTS IMPROVE TEACHERS' UNDERSTANDING OF STUDENTS' STATUS AND HELPS STUDENTS WITH ADEQUATE PEDAGOGICAL RECOMMENDATIONS. HOWEVER, THERE IS A LACK OF UNDERSTANDING OF HOW TEACHERS PERCEIVE DATA VISUALIZATIONS PROVIDED THROUGH DASHBOARDS RECENTLY EXPLORED IN GAMIFIED EDUCATIONAL SYSTEMS. IN THIS PAPER, THE AUTHORS INVESTIGATE TEACHERS' PERCEPTIONS ABOUT THREE DIFFERENT DASHBOARDS WITH VISUALIZATIONS ABOUT 1) STUDENTS' INTERACTION WITH LEARNING RESOURCES, 2) STUDENTS' INTERACTION WITH GAMIFICATION ELEMENTS, AND 3) STUDENTS' INTERACTION WITH LEARNING RESOURCES AND GAMIFICATION ELEMENTS. AS SUCH, THE RESEARCHERS CONDUCTED A MIXED-METHOD STUDY WITH 47 TEACHERS TO EVALUATE THEIR PERCEIVED UNDERSTANDING, PERCEIVED USEFULNESS, PERCEIVED BEHAVIORAL CHANGE, AND PERCEIVED DECISION-MAKING SUPPORT REGARDING THE THREE DASHBOARDS. THE RESULTS SUGGEST TEACHERS PERCEIVED THAT THE DASHBOARD WITH VISUALIZATIONS CONCERNING STUDENTS' INTERACTION WITH LEARNING RESOURCES AND GAMIFICATION ELEMENTS PROVIDE BETTER SUPPORT TO THEM IN THE DECISION-MAKING PROCESS. TEACHERS ALSO PERCEIVED THE THIRD DASHBOARD HAS A MORE SIGNIFICANT POTENTIAL TO IMPACT BEHAVIORAL CHANGES THAN THE OTHER TWO DASHBOARDS.
UNDERSTANDING THE PROCESSES IN EDUCATION, SUCH AS THE STUDENT LEARNING BEHAVIOR WITHIN A SPECIFIC COURSE, IS A KEY TO CONTINUOUS COURSE IMPROVEMENT. IN ONLINE LEARNING SYSTEMS, STUDENTS' LEARNING CAN BE TRACKED AND EXAMINED BASED ON DATA COLLECTED BY THE SYSTEMS THEMSELVES. HOWEVER, IT IS NON-TRIVIAL TO DECIDE HOW TO EXTRACT THE DESIRED STUDENTS' BEHAVIOR FROM THE LIMITED DATA IN TRADITIONAL CLASSROOM COURSES. SOFTWARE DEVELOPMENT COURSES ARE A DOMAIN WHERE STUDENT BEHAVIOR ANALYSIS WOULD BE ESPECIALLY USEFUL, AS CONTINUOUS TEACHING IMPROVEMENT IN THIS FAST PROGRESSING DOMAIN IS NECESSARY. IN THIS PAPER, WE PROPOSE TO USE PROCESS MINING FOR IMPROVEMENT-MOTIVATED PROCESS ANALYSIS OF A SOFTWARE DEVELOPMENT COURSE (WEB DEVELOPMENT IN PARTICULAR). TO THIS END, WE ANALYZE GIT LOGS OF STUDENTS' PROJECTS TO UNDERSTAND THEIR DEVELOPMENT PROCESSES. PROCESS MINING HAS BEEN CHOSEN AS IT CAN HELP US TO FIND A DESCRIPTIVE MODEL OF THIS PROCESS. THE MAIN CONTRIBUTION OF THIS PAPER IS THE DETAILED METHODOLOGY OF PROCESS MINING USAGE FOR STUDENTS' PROJECT DEVELOPMENT ANALYSIS, CONSIDERING VARIOUS COMMIT CHARACTERISTICS, WHICH ARE CRUCIAL IN UNDERSTANDING STUDENT CODING-BEHAVIOR PATTERNS. THE PROCESS MINING ANALYSIS PROVED TO BE VERY USEFUL, INDICATING MULTIPLE DIRECTIONS FOR THE COURSE IMPROVEMENT, WHICH WE ALSO INCLUDE IN THIS WORK AS A SECONDARY CONTRIBUTION. THE THIRD CONTRIBUTION OF THIS WORK IS THE SUMMARY AND DISCUSSION OF THE PROCESS MINING ADVANTAGES AND CURRENT GAPS IN PROCESS MINING RESEARCH FOR THIS TASK. THE DATA WE USED ARE MADE PUBLICLY AVAILABLE TO OTHER RESEARCHERS.
WITH REFERENCE ONLY TO OBSERVATIONS OF PEOPLE'S ACTIVITIES WITHIN A SYSTEM, WE INVESTIGATED WHETHER IT IS POSSIBLE TO DETERMINE THE EXISTENCE OF THEIR SOCIAL RELATIONSHIPS. AS STUDENTS INTERACTED, EITHER INDIVIDUALLY OR IN GROUPS, WE AIMED TO DISCOVER THEIR SOCIAL COMMUNITIES GIVEN THE TEMPORAL AND SPATIAL CO-OCCURRENCE OF THEIR ACTIVITIES AND THEIR IMPLICIT USER-SYSTEM INTERACTIONS. TO ELICIT THOSE HIDDEN COMMUNITIES, WE DEVELOPED TWO INNOVATIVE APPROACHES: HISTORY-BASED ANALYSIS, WHICH EXPLOITS THE SIMILARITY OF USERS' HISTORIES OF ENGAGING IN CERTAIN ACTIVITIES, AND SESSION-BASED ANALYSIS, WHICH USES A GRAPH-BASED REPRESENTATION OF CONCURRENT USERS' ACTIVITY SESSIONS. WE TESTED AND VALIDATED BOTH APPROACHES USING A REAL-WORLD DATASET REPRESENTING THE ACTIVITY LOGS OF STUDENTS USING A VIRTUAL LEARNING ENVIRONMENT PLATFORM. THE MAJOR RESULTS OF OUR WORK CONFIRM THAT THE CO-OCCURRENCE OF PEOPLE'S ACTIVITIES IS AN EMERGING EPIPHENOMENON OF HIDDEN, IMPLICIT EXCHANGES OF INFORMATION IN SIDE-CHANNEL COMMUNICATIONS. (C) 2019 ELSEVIER B.V. ALL RIGHTS RESERVED.
RECENT STUDIES SUGGEST THAT LEARNING "HOW TO LEARN" IS IMPORTANT BECAUSE LEARNERS MUST BE SELF-REGULATED TO TAKE MORE RESPONSIBILITY FOR THEIR OWN LEARNING PROCESSES, META-COGNITIVE CONTROL, AND OTHER GENERATIVE LEARNING THOUGHTS AND BEHAVIORS. THE MECHANISM THAT ENABLES A LEARNER TO SELF-REGULATE HIS/HER LEARNING STRATEGIES HAS BEEN ACTIVELY STUDIED IN CLASSROOM SETTINGS, BUT HAS SELDOM BEEN STUDIED IN THE AREA OF REAL-WORLD LEARNING IN OUT-OF-SCHOOL SETTINGS (E.G., ENVIRONMENTAL LEARNING IN NATURE). A FEATURE OF REAL-WORLD LEARNING IS THAT A LEARNER'S COGNITION OF THE WORLD IS UPDATED BY HIS/HER BEHAVIOR TO INVESTIGATE THE WORLD, AND VICE VERSA. THIS PAPER MODELS THE MECHANISM OF REAL-WORLD LEARNING FOR EXECUTING AND SELFREGULATING A LEARNER'S COGNITIVE AND BEHAVIORAL STRATEGIES TO SELF-ORGANIZE HIS/HER INTERNAL KNOWLEDGE SPACE. FURTHERMORE, THIS PAPER PROPOSES MULTIMODAL ANALYTICS TO INTEGRATE HETEROGENEOUS DATA RESOURCES OF THE COGNITIVE AND BEHAVIORAL FEATURES OF REAL-WORLD LEARNING, TO STRUCTURE AND ARCHIVE THE TIME SERIES OF STRATEGIES OCCURRING THROUGH LEARNER-ENVIRONMENT INTERACTIONS, AND TO ASSESS HOW LEARNING SHOULD BE SELF-REGULATED FOR BETTER UNDERSTANDING OF THE WORLD. OUR ANALYSIS SHOWED THAT (1) INTELLECTUAL ACHIEVEMENTS ARE BUILT BY SELF-REGULATING LEARNING TO CHAIN THE EXECUTION OF COGNITIVE AND BEHAVIORAL STRATEGIES, AND (2) A CLUE TO PREDICT LEARNING OUTCOMES IN THE WORLD IS ANALYZING THE QUANTITY AND FREQUENCY OF STRATEGIES THAT A LEARNER USES AND SELF-REGULATES. ASSESSMENT BASED ON THESE FINDINGS CAN ENCOURAGE A LEARNER TO REFLECT AND IMPROVE HIS/HER WAY OF LEARNING IN THE WORLD.
THE MASSIVE AND OPEN NATURE OF MOOCS CONTRIBUTE TO ATTRACTING A GREAT DIVERSITY OF LEARNERS. HOWEVER, THE LEARNERS WHO ENROLL IN THESE TYPES OF COURSES HAVE TROUBLE ACHIEVING THEIR COURSE OBJECTIVES. ONE REASON FOR THIS IS THAT THEY DO NOT ADEQUATELY SELF-REGULATE THEIR LEARNING. IN THIS CONTEXT, THERE ARE FEW TOOLS TO SUPPORT THESE STRATEGIES IN ONLINE LEARNING ENVIRONMENT. ALSO, THE LACK OF METRICS TO EVALUATE THE IMPACT OF THE PROPOSED TOOLS MAKES IT DIFFICULT TO IDENTIFY THE KEY FEATURES OF THIS TYPE OF TOOLS. IN THIS PAPER, WE PRESENT THE PROCESS FOR DESIGNING NOTEMYPROGRESS, A WEB APPLICATION THAT COMPLEMENTS A MOOC PLATFORM AND SUPPORTS SELF-REGULATED LEARNING STRATEGIES. FOR DESIGNING NOTEMYPROGRESS WE FOLLOWED THE DESIGN BASED RESEARCH METHODOLOGY. FOR THE EVALUATION OF THE TOOL, WE CONDUCTED TWO CASE STUDIES USING A BETA VERSION OF NOTEMYPROGRESS OVER THREE MOOCS OFFERED IN COURSERA. THE FINDINGS OF THESE TWO CASE STUDIES ARE PRESENTED AS A SET OF LESSONS LEARNED THAT INFORM ABOUT: (1) A LIST OF REQUIREMENTS TO INFORM THE DESIGN OF A SECOND VERSION OF THE TOOL; (2) A LIST OF REQUIREMENTS THAT COULD SERVE AS A REFERENCE FOR OTHER DEVELOPERS TO DESIGN NEW TOOLS THAT SUPPORT SELF-REGULATED LEARNING IN MOOCS.
EVEN THOUGH INTELLIGENT TUTORING SYSTEMS (ITS) HAVE BEEN SHOWN TO HELP STUDENTS LEARN, LITTLE RESEARCH HAS INVESTIGATED HOW A DASHBOARD COULD HELP TEACHERS HELP THEIR STUDENTS. IN THIS PAPER, WE EXPLORE HOW A DASHBOARD PROTOTYPE DESIGNED FOR AN ITS AFFECTS TEACHERS' KNOWLEDGE ABOUT THEIR STUDENTS, THEIR CLASSROOM LESSON PLANS AND CLASS SESSIONS. WE CONDUCTED A QUASI-EXPERIMENTAL CLASSROOM STUDY WITH 5 MIDDLE SCHOOL TEACHERS AND 8 CLASSES. WE FOUND THAT THE DASHBOARD INFLUENCES WHAT TEACHERS KNOW ABOUT THEIR STUDENTS, WHICH IN TURN INFLUENCES THE LESSON PLANS THEY PREPARE, WHICH THEN GUIDES WHAT TEACHERS COVER IN A CLASS SESSION. WE BELIEVE THIS IS THE FIRST STUDY THAT EXPLORES HOW A DASHBOARD FOR AN ITS AFFECTS TEACHER'S KNOWLEDGE, DECISION-MAKING AND ACTIONS IN THE CLASSROOM.
INTELLIGENT TUTORING SYSTEMS (ITSS) HELP STUDENTS LEARN BUT OFTEN ARE NOT DESIGNED TO SUPPORT TEACHERS AND THEIR PRACTICES. A DASHBOARD WITH ANALYTICS ABOUT STUDENTS' LEARNING PROCESSES MIGHT HELP IN THIS REGARD. HOWEVER, LITTLE RESEARCH HAS INVESTIGATED HOW DASHBOARDS INFLUENCE TEACHER PRACTICES IN THE CLASSROOM AND WHETHER THEY CAN HELP IMPROVE STUDENT LEARNING. IN THIS PAPER, WE EXPLORE HOW LUNA, A DASHBOARD PROTOTYPE DESIGNED FOR AN ITS AND USED WITH REAL DATA, AFFECTS TEACHERS AND STUDENTS. RESULTS FROM A QUASI-EXPERIMENTAL CLASSROOM STUDY WITH 5 MIDDLE SCHOOL TEACHERS AND 17 CLASSES SHOW THAT LUNA INFLUENCES WHAT TEACHERS KNOW ABOUT THEIR STUDENTS' LEARNING IN THE ITS AND THAT THE TEACHERS' UPDATED KNOWLEDGE AFFECTS THE LESSON PLAN THEY PREPARE, WHICH IN TURN GUIDES WHAT THEY COVER IN A CLASS SESSION. RESULTS DID NOT CONFIRM THAT LUNA INCREASED STUDENT LEARNING. IN SUMMARY, EVEN THOUGH TEACHERS GENERALLY KNOW THEIR CLASSES WELL, A DASHBOARD WITH ANALYTICS FROM AN ITS CAN STILL ENHANCE THEIR KNOWLEDGE ABOUT THEIR STUDENTS AND SUPPORT THEIR CLASSROOM PRACTICES. THE TEACHERS TENDED TO FOCUS PRIMARILY ON DASHBOARD INFORMATION ABOUT THE CHALLENGES THEIR STUDENTS WERE EXPERIENCING. TO THE BEST OF OUR KNOWLEDGE, THIS IS THE FIRST STUDY THAT DEMONSTRATES THAT A DASHBOARD FOR AN ITS CAN AFFECT TEACHER KNOWLEDGE, DECISION-MAKING AND ACTIONS IN THE CLASSROOM.
IN THIS PAPER, ENGINEERING EDUCATION AS A DISCIPLINE HAS BEEN ANALYZED BY TAKING IEEE TRANSACTIONS ON EDUCATION (IEEE T EDUC) AS A CASE; FOR EXAMINING THE VARIOUS TRENDS THAT HAVE BEEN EMERGING OVER TIME. BASED ON VARIOUS CRITERIA OF AUTHORSHIP AND CITATION, AN EFFORT IS MADE TO HIGHLIGHT THE MAIN CONTRIBUTORS OR TOP AUTHORS OF THIS ENGINEERING EDUCATION COMMUNITY. IT WAS FOUND THAT AUTHORSHIP TRENDS HAVE BEEN SHIFTING MORE TOWARDS COLLABORATION. IT WAS ALSO FOUND THAT THE AUTHORSHIP COMMUNITY IS GROWING, BOTH IN TERMS OF PUBLICATIONS AND PUBLISHING AUTHORS. STUDY OF CITATION PATTERNS DURING THE LAST DECADE, REVEALS A HIGH CITATION COUNT PER ARTICLE, WHICH INDICATES A HIGH READERSHIP OF THIS JOURNAL. LATER, THE STUDY OF AUTHORSHIP AND CITATION PATTERNS SHED LIGHT ON THE TREND THAT MULTI-AUTHOR ARTICLES ARE CITED MORE OFTEN THAN SINGLE-AUTHOR ARTICLES. THIS STUDY WAS COMPARED WITH EARLIER STUDIES IN THE FIELD OF ENGINEERING EDUCATION RESEARCH (EER) USING KEYWORD ANALYSIS AND TEMPORAL EVOLUTION AND DISTRIBUTION OF KEYWORDS. ADDITIONALLY KEY-PHRASE AND TOPIC MODELING WAS PERFORMED TO IDENTIFY LEADING AND EVOLVING RESEARCH AREAS WITHIN THE EER., ANALYSIS OF WORD CO-OCCURRENCE WAS PERFORMED TO DISCOVER THE MAIN CONTEXT IN WHICH THE KEYWORDS HAVE BEEN USED. LASTLY, TOPIC MODELING TECHNIQUES WERE APPLIED FOR PROBABILISTIC DISTRIBUTIONS OF IEEE TOPICS AND THE RESULTS WERE IN LINE WITH EARLIER STUDIES.
THE PAPER PRESENTS AN APPROACH AND SOFTWARE ARCHITECTURE FOR SEMANTIC RECOMMENDATION OF DIFFERENT TYPES OF LEARNING RESOURCES USING LEARNING AND GAMING ANALYTICS OF BIG DATA PRODUCED BY MODERN E-LEARNING PLATFORMS AND EDUCATIONAL GAMES. IN ORDER FOR THE SYSTEM TO PROVIDE SEMANTIC RECOMMENDATION, THE AVAILABLE INFORMATION FOR EACH LEARNER WOULD BE USED, AS WELL AS AGGREGATED STATISTICS ON THE BEHAVIOUR OF A GROUP OF LEARNERS. THE INFORMATION WILL BE EXTRACTED FROM THE DATABASE AND THROUGH MACHINE LEARNING TECHNIQUES, THE SYSTEM WILL ANALYSE AND GENERATE PERSONAL RECOMMENDATIONS. ON THE OTHER SIDE TO ACHIEVE, SEMANTIC SEARCH OF RESOURCES CONTAINING MATERIALS RELATED TO THE SEARCH TERM FROM THE SUBJECT AREA, IT WILL USE ONTOLOGY FOR THE CONCEPTS AND RELATIONSHIPS BETWEEN THEM IN THE SUBJECT AREA IN WHICH THE EDUCATIONAL PLATFORM IS SPECIALIZED.
PURPOSE - THIS EMPIRICAL STUDY AIMS TO DEMONSTRATE HOW THE COMBINATION OF TRACE DATA DERIVED FROM TECHNOLOGY-ENHANCED LEARNING ENVIRONMENTS AND SELF-RESPONSE SURVEY DATA CAN CONTRIBUTE TO THE INVESTIGATION OF SELF-REGULATED LEARNING PROCESSES. DESIGN/METHODOLOGY/APPROACH - USING A SHOWCASE BASED ON 1,027 STUDENTS' LEARNING IN A BLENDED INTRODUCTORY QUANTITATIVE COURSE, THE AUTHORS ANALYSED THE LEARNING REGULATION AND ESPECIALLY THE TIMING OF LEARNING BY TRACE DATA. NEXT, THE AUTHORS CONNECTED THESE LEARNING PATTERNS WITH SELF-REPORTS BASED ON MULTIPLE CONTEMPORARY SOCIAL-COGNITIVE THEORIES. FINDINGS - THE AUTHORS FOUND THAT SEVERAL BEHAVIOURAL FACETS OF MALADAPTIVE LEARNING ORIENTATIONS, SUCH AS LACK OF REGULATION, SELF-SABOTAGE OR DISENGAGEMENT NEGATIVELY IMPACTED THE AMOUNT OF PRACTISING, AS WELL AS TIMELY PRACTISING. ON THE ADAPTIVE SIDE OF LEARNING DISPOSITIONS, THE PICTURE WAS LESS CLEAR. WHERE SOME ADAPTIVE DISPOSITIONS, SUCH AS THE WILLINGNESS TO INVEST EFFORTS IN LEARNING AND SELF-PERCEIVED PLANNING SKILLS, POSITIVELY IMPACTED LEARNING REGULATION AND TIMING OF LEARNING, OTHER DISPOSITIONS SUCH AS VALUING SCHOOL OR ACADEMIC BUOYANCY LACKED THE EXPECTED POSITIVE EFFECTS. RESEARCH LIMITATIONS/IMPLICATIONS - DUE TO THE BLENDED DESIGN, THERE IS A STRONG ASYMMETRY BETWEEN WHAT ONE CAN OBSERVE ON LEARNING IN BOTH MODES. PRACTICAL IMPLICATIONS - THIS STUDY DEMONSTRATES THAT IN A BLENDED SETUP, ONE NEEDS TO DISTINGUISH THE GRAND EFFECT ON LEARNING FROM THE PARTIAL EFFECT ON LEARNING IN THE DIGITAL MODE: THE MOST ADAPTIVE STUDENTS MIGHT BE LESS DEPENDENT FOR THEIR LEARNING ON THE USE OF THE DIGITAL LEARNING MODE. ORIGINALITY/VALUE - THE PAPER PRESENTS AN APPLICATION OF EMBODIED MOTIVATION IN THE CONTEXT OF BLENDED LEARNING.
RESEARCH HAS DEMONSTRATED THAT SPATIAL VISUALIZATION SKILLS ARE CRUCIAL FOR SUCCESS IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) DISCIPLINES. WITH AN INCREASING NUMBER OF STUDENTS ENTERING STEM DISCIPLINES, THE QUESTION OF HOW TO EFFECTIVELY TRAIN STUDENTS' SPATIAL VISUALIZATION SKILLS HAS BECOME VERY IMPORTANT. WHILE A SCALABLE EXISTING SOLUTION IS TO IMPLEMENT ONLINE WORKSHOPS FOR STUDENTS, THE PROBLEM OF HOW TO MOTIVATE STUDENTS TO PARTICIPATE IN THESE ONLINE WORKSHOPS REMAINS UNSOLVED. IN THIS STUDY, WE STUDIED GAMIFICATION AS A WAY TO MOTIVATE FIRST YEAR ENGINEERING STUDENTS TO TAKE PART IN AN ONLINE WORKSHOP DESIGNED TO TRAIN THEIR SPATIAL VISUALIZATION SKILLS. OUR GAME CONTAINS EIGHT MODULES, EACH DESIGNED TO TRAIN A DIFFERENT COMPONENT OF SPATIAL VISUALIZATION. THE GAME RECORDS PLAYERS' IN-GAME BEHAVIOR WITH HIGH GRANULARITY, WHICH ALLOWS US TO PROVIDE AUTOMATED, SCALABLE FEEDBACK ON PLAYERS' PROBLEM-SOLVING STRATEGIES. TEN STUDENTS WITH DIFFERENT LEVELS OF SPATIAL ABILITY PLAYED OUR GAME AND EXPRESSED A STRONG INTEREST IN USING THE GAME TO TRAIN THEIR SPATIAL VISUALIZATION SKILLS IN THE FUTURE. IN ADDITION, OUR ANALYSIS OF PLAYERS' IN GAME BEHAVIORS SHOWS THE POTENTIAL BENEFITS OF IMPLEMENTING ADAPTIVE AND PERSONALIZED LEARNING GUIDANCE.
OUR MOOC ON ENGINEERING MECHANICS AIMS TO DEVELOP IN OUR LEARNERS 'ENGINEERS' EYES'. OVER 50 SMALL VIDEOS SUPPORT THREE INTERDEPENDENT STRANDS: EXPERIMENTS THAT LEARNERS CAN DO THEMSELVES; CLASSICAL ENGINEERING ANALYSIS; AND DESIGN APPLICATIONS. LEARNING ACTIVITIES SPAN THE TECHNOLOGY SPECTRUM FROM ON-LINE ADAPTIVE TUTORIALS TO PAPER AND PENCIL 'RETRO TUTORIALS'. IT ALL PRODUCES A FLOOD OF DATA ON STUDENT BACKGROUNDS, THEIR PROGRESSION, AND THEIR RESPONSE TO THE COURSE. TO AVOID BEING OVERWHELMED BY THIS INVALUABLE RESOURCE, WE HAVE AUTOMATED THE ANALYSIS. ATTRITION RATES ARE TYPICAL OF MOOCS (DESPITE CONTINUOUS IMPROVEMENT), BUT LEARNERS WHO STAY THE COURSE GIVE GLOWING REPORTS.
WE PRESENT MOBILE AUGMENTED-REALITY GAMES FOR INSTRUCTIONAL SUPPORT (MAGIS), A FRAMEWORK FOR THE DEVELOPMENT OF MOBILE AUGMENTED-REALITY (AR) GAMES FOR EDUCATION. THE FRAMEWORK SUPPORTS OFF-THE-SHELF, STATE-OF-THE-ART TECHNOLOGIES THAT ENABLE AR TRACKING AND RENDERING ON CONSUMER-LEVEL MOBILE DEVICES, AND INTEGRATES THESE TECHNOLOGIES WITH CONTENT-GENERATION TOOLS THAT SIMPLIFY THE DEVELOPMENT OF EDUCATIONAL AR GAMES, ESPECIALLY THOSE THAT EXTENSIVELY USE NARRATIVE-BASED GAME DESIGN AND PLAYER-LOCATION TRACKING SUCH AS LOCATION-BASED HISTORICAL OR MUSEUM ADVENTURE GAMES. WE USE IGPAW: INTRAMUROS, A PROOF-OF-CONCEPT GAME DEVELOPED USING MAGIS, TO HELP DESCRIBE THE CURRENT STATE OF THE FRAMEWORK AND TO SHOW ITS EFFICACY FOR IMPLEMENTING OUTDOOR LOCATION-BASED EDUCATIONAL GAMES, AND WE BRIEFLY OUTLINE FUTURE DEVELOPMENT PLANS TO IMPROVE MAGIS' AR SUPPORT (ESPECIALLY THOSE INVOLVING INDOOR SCENARIOS) AS WELL AS TO IMPROVE THE INSTRUCTIONAL DESIGN AND AUTHORING PHASES OF AR APPLICATIONS WRITTEN USING THIS FRAMEWORK.
THIS STUDY PROPOSES A REWARDING SYSTEM FOR BLENDED LEARNING COURSES DELIVERED THROUGH THE MOODLE PLATFORM. THE PROPOSED REWARDING SYSTEM CONSISTS OF TWO TYPES OF REWARDS, THOSE THAT ARE AUTOMATICALLY PROVIDED BY MOODLE AND THOSE THAT ARE PROVIDED MANUALLY BY THE TUTOR. THE SPECIFIC REWARDING SYSTEM FOLLOWS THE PRINCIPLES OF THE GAMIFIED E-LEARNING MODEL, FOCUSING ON THE WAY STUDENTS SHOULD BE REWARDED IN ORDER TO BECOME ENGAGED IN THE COURSE AND FORM A COMMUNITY OF INQUIRY. AN EMPIRICAL STUDY IN WHICH THE PROPOSED REWARDING SYSTEM WAS IMPLEMENTED IN A BLENDED LEARNING COURSE ON EDUCATIONAL TECHNOLOGY, IS DESCRIBED. THE REWARDS PROVIDED BY THE TEACHER ARE BASED ON ANALYSIS OF THE MOODLE LOG FILES AND DATA COLLECTED FOR EACH STUDENT USING A LEARNING ANALYTICS TOOL. THE RESULTS OF THE STUDY PROVIDE EVIDENCE ABOUT STUDENTS' APPRECIATION OF THE VARIOUS TYPES OF REWARDS REWARDING INDIVIDUAL AND COLLABORATIVE EFFORT.
WE DISCUSS DYNAMIC REAL-TIME ANALYSIS FROM MULTIMODAL DATA FUSION FOR CONTEXTUAL RISK IDENTIFICATION TO GENERATE "RISK MAPS "FOR THE WORKPLACE, RESULTING IN TIMELY IDENTIFICATION OF HAZARDS AND ASSOCIATED RISK MITIGATION. IT INCLUDES NEW MACHINE/DEEP LEARNING, ANALYTICS, METHODS, AND ITS APPLICATIONS THAT DEAL WITH THE UNCONVENTIONAL DATA COLLECTED FROM PICTURES, VIDEOS, DOCUMENTS, MOBILE APPS, SENSORS/INTERNET OF THINGS, OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION (OSHA) RULES, AND BUILDING INFORMATION MODEL (BIM) MODELS. SPECCALLY, WE DESCRIBE A NUMBER OF ADVANCES AND CHALLENGES IN THIS FIELD WITH APPLICATIONS OF COMPUTER VISION, NATURAL LANGUAGE PROCESSING, AND SENSOR DATA ANALYSIS. APPLICATIONS INCLUDE AUTOMATED CAUSE IDENTIFICATION, DAMAGE PREVENTION, AND DISASTER RECOVERY USING CURRENT AND HISTORICAL CLAIMS DATA AND OTHER PUBLIC DATA. THE METHODS DEVELOPED CAN BE APPLIED TO ANY GIVEN SITUATION WITH DIFFERENT GROUPS OF PEOPLE, INCLUDING FIRST RESPONDERS. FINALLY, WE DISCUSS SOME OF THE IMPORTANT NONTECHNICAL CHALLENGES RELATED TO BUSINESS PRACTICALITY, PRIVACY, AND INDUSTRY REGULATIONS.
ALTHOUGH THE IMPORTANCE OF BOUNDARY SPANNING IN BLENDED AND ONLINE LEARNING IS WIDELY ACKNOWLEDGED, MOST EDUCATIONAL RESEARCH HAS IGNORED WHETHER AND HOW STUDENTS LEARN FROM OTHERS OUTSIDE THEIR ASSIGNED GROUP. ONE POTENTIAL APPROACH FOR UNDERSTANDING CROSS-BOUNDARY KNOWLEDGE SHARING IS SOCIAL NETWORK ANALYSIS (SNA). IN THIS ARTICLE, WE APPLY FOUR NETWORK METRICS TO UNPACK HOW STUDENTS DEVELOPED INTRA- AND INTER-GROUP LEARNING LINKS, USING TWO EXEMPLARY BLENDED CASE STUDIES IN SPAIN AND THE UK. OUR RESULTS INDICATE THAT SNA BASED UPON QUESTIONNAIRES CAN PROVIDE RESEARCHERS SOME USEFUL INDICATORS FOR A MORE FINE-GRAINED ANALYSIS HOW STUDENTS DEVELOP THESE INTER- AND INTRA-GROUP LEARNING LINKS, AND WHICH CROSS-BOUNDARY LINKS ARE PARTICULARLY IMPORTANT FOR LEARNING PERFORMANCE. THE MIXED FINDINGS BETWEEN THE TWO CASE-STUDIES SUGGEST THE RELEVANCE OF PRE-EXISTING CONDITIONS AND LEARNING DESIGN. SNA METRICS CAN PROVIDE USEFUL INFORMATION FOR QUALITATIVE FOLLOW-UP METHODS, AND FUTURE INTERVENTIONS USING LEARNING ANALYTICS APPROACHES.