A SUDDEN LOCKDOWN WAS DECLARED ON 14 MARCH 2020 DUE TO COVID-19 CRISIS, LEADING TO AN IMMEDIATE CHANGE FROM FACE-TO-FACE TO ONLINE LEARNING IN ALL UNIVERSITIES WITHIN SPANISH JURISDICTION. AT LA SALLE SCHOOL OF ARCHITECTURE, THE CONCRETE AND STEEL STRUCTURES SUBJECT STARTED ONLINE CLASSES IMMEDIATELY AFTER THE LOCKDOWN LAW WAS PUBLISHED, USING A METHODOLOGY BASED ON THE FLIPPED CLASSROOM APPROACH AND ADAPTING THE MONITORING OF THE STUDENT TO THE VIRTUAL ENVIRONMENT. THIS ARTICLE PRESENTS A PILOT STUDY TO ANALYZE THE ADAPTATION OF THE MODEL TO THE ONLINE FORMAT USING A MIXED APPROACH IN WHICH QUALITATIVE AND QUANTITATIVE SURVEYS WERE CONDUCTED AT THE END OF THE COURSE WITH 48 PARTICIPANTS. RESPONSES FROM BOTH SURVEYS WERE ORGANIZED ACCORDING TO SIX CATEGORIES (TEACHERS, ASSESSMENT, METHODS, CLASS DEVELOPMENT, STUDENTS AND DOCUMENTS) AND 14 SUBCATEGORIES, AS DEVELOPED IN AN UNDERGOING RESEARCH PROJECT INVOLVING THE SUBJECT SINCE THE ACADEMIC YEAR 2017/2018. THUS, THE OPEN RESPONSES OF THE STUDENTS HAVE BEEN ANALYZED ALONGSIDE WITH THE QUANTITATIVE DATA. THE RESULTS DEMONSTRATE A PROPER ADAPTATION OF THE MODEL, AS WELL AS THE NEGATIVE PERCEPTION OF THE STUDENTS OF THE ONLINE FORMAT DUE TO THE LOSS OF FACE-TO-FACE BENEFITS OF THE FLIPPED CLASSROOM.
PROVIDING FEEDBACK IS ONE OF THE MOST EFFECTIVE METHODS TO ENHANCE STUDENT'S LEARNING. THE ABSENCE OF READILY AVAILABLE DATA ON PAPER-BASED ASSESSMENTS MAKES IT IMPOSSIBLE TO KNOW WHETHER STUDENTS RECEIVED FEEDBACK AND WHETHER THEY HAVE ACTED UPON IT OR NOT. IN OUR INSTITUTION, WE HAVE BEEN USING A HOMEGROWN EDUCATIONAL TECHNOLOGY TO SUPPORT BLENDED-INSTRUCTION CLASSES BY INTEGRATING PHYSICAL AND CYBERLEARNING ANALYTICS. USING THE COLLECTED DIGITAL FOOTPRINTS OF STUDENTS, WE WERE ABLE TO ANALYZE THEIR REVIEWING BEHAVIOR. A STUDY WAS CONDUCTED TO INVESTIGATE THE EFFECTS OF PERSONALIZING THE REVIEWING SEQUENCE OF PAPER-BASED ASSESSMENTS. EACH STUDENT IS PRESENTED WITH A PERSONALIZED SEQUENCE OF QUESTIONS TO REVIEW BASED ON THE IMPORTANCE OF THEIR MISTAKES. WE FOUND THAT THE STUDENTS WHO FOLLOWED THE SUGGESTED SEQUENCE IMPROVED SIGNIFICANTLY HIGHER IN THE SUCCEEDING EXAM THAN THOSE WHO REVIEWED THE ASSESSMENT ARBITRARILY OR DID NOT HAVE ANY REVIEWING STRATEGY. RESULTS SHOWED THAT PERSONALIZED GUIDANCE ON REVIEWING GRADED ASSESSMENTS EFFECTIVELY HELPED IMPROVE STUDENT PERFORMANCE.
THE DEVELOPMENT OF NEW EDUCATIONAL TECHNOLOGIES, IN THE AREA OF PRACTICAL ACTIVITIES IS THE MAIN AIM OF THE FP7 PELARS PROJECT. AS PART OF THE CONSTRUCTIVIST LEARNING SCENARIOS, ACCORDING TO THE PROJECT PROPOSAL, THE DEVELOPMENT AND EVALUATION OF TECHNOLOGY DESIGNS ARE ENVISAGED, FOR ANALYTIC DATA GENERATION FOR SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) SUBJECTS, SUCH AS: TECHNOLOGY SOLUTIONS, INFRASTRUCTURE, ACTIVITIES, ASSESSMENT, CURRICULA, AND CLASSROOM FURNITURE AND ENVIRONMENT DESIGNS. INSIDE FOUR EU NATIONAL SETTINGS, THREE SEPARATE LEARNING CONTEXTS ARE BEING DEALT WITH - FROM SECONDARY-LEVEL HIGH SCHOOL STEM LEARNING ENVIRONMENTS TO POST-SECONDARY LEVEL ENGINEERING CLASSES AND DESIGN STUDIOS. GIVEN THIS EXPERIENCE AND FRAMEWORK, THE PRESENT PAPER PROVIDES A PERSPECTIVE ON THE IMPORTANCE OF USING SUCH RESEARCH EXPERIENCE AND ITERATIVE PROTOTYPING IN REAL LEARNING ENVIRONMENTS FOR ENGINEERING STUDENTS.
BASED ON A DATASET OF MORE THAN 7.4 MILLION RECORDS OF LEARNERS' DOING ONLINE TESTS ACTIVITY IN AN ONLINE PLATFORM, AND THE CONCEPT OF ONLINE LEARNING ACTIVITY INDEX (OLAI), THIS PAPER ANALYSES THE TIMEOUT BEHAVIOURS IN ONLINE TESTS AND ATTEMPTS TO ANSWER THE FOLLOWING TWO QUESTIONS: WHAT ARE THE CHARACTERISTICS OF THOSE ONLINE LEARNERS WITH TIMEOUT BEHAVIOURS IN ONLINE TESTS? DOES THE AMOUNT OF QUESTIONS IN A TEST INFLUENCE LEARNERS' TIMEOUT BEHAVIOURS? DESCRIPTIVE STATISTICS, CORRELATION ANALYSIS AND REGRESSION ANALYSIS ARE CONDUCTED TO FIND THE GENERAL PATTERN UNDERNEATH THE CASES. AFTERWARDS, THIS PAPER INVESTIGATES HOW TO OPTIMISE THE AMOUNT OF QUESTIONS IN ONE TEST IN VIEW OF THE TIMEOUT BEHAVIOURS.
THE CONCEPT OF ONLINE LEARNING HAS WITNESSED AN INCREASE IN THE HIGHER EDUCATION SECTOR, WHERE ENROLMENT RATES IN ONLINE COURSES HAVE SIGNIFICANTLY GROWN IN RECENT YEARS. ACCORDING TO THE LITERATURE, ONE OF THE CRITICAL COMPONENTS OF QUALITY ONLINE EDUCATION IS TO ENSURE LEARNER ENGAGEMENT. IN SHORT, THE NEED TO EFFECTIVELY MEASURE LEARNER ENGAGEMENT IS IMPERATIVE TO DETERMINE THE PERFORMANCES AND THE SUCCESSFUL ACHIEVEMENTS OF LEARNERS. IN THIS RESEARCH, THE AIM WAS TO UNDERSTAND THE RELATIONSHIP BETWEEN STUDENTS' ENGAGEMENT IN AN ONLINE MODULE WITH THEIR OVERALL PERFORMANCES BY ANALYSING STUDENTS' LEARNING ACTIVITIES IN AN ONLINE MODULE. THREE MEASURABLE INDICATORS WERE IDENTIFIED AND CONSIDERED RELEVANT WITHIN THE CURRENT RESEARCH CONTEXT AND BASED ON THE AVAILABLE DATA, AND FROM RESEARCH LITERATURE FOR ASSESSING LEARNER ENGAGEMENT WITHIN THE MODULE. THESE WERE (I) THE NUMBER OF COMPLETED LEARNING ACTIVITIES; (II) IMPORTANCE LEVEL (AS PER COURSE OUTCOMES) OF COMPLETED LEARNING ACTIVITIES; AND (III) ACTIVITIES REQUIRING PLATFORM PRESENCE. IT WAS FOUND THAT THERE IS A SIGNIFICANT BUT WEAK POSITIVE CORRELATION BETWEEN THE ENGAGEMENT OF STUDENTS IN THE ONLINE MODULE AND THEIR PERFORMANCES IN THE FINAL LEARNING ACTIVITY. IT WAS FURTHER OBSERVED THAT WHEN CONTINUOUS LEARNING ACTIVITIES WERE CONSIDERED, THERE WAS A VERY STRONG POSITIVE CORRELATION BETWEEN ENGAGEMENT AND PERFORMANCES. IN GENERAL, THE AVERAGE ENGAGEMENT LEVEL OF STUDENTS WAS SIGNIFICANTLY HIGHER FOR GOOD PERFORMERS AS COMPARED TO LOW PERFORMERS. SIMILARLY THE MEAN PERFORMANCE OF HIGHLY ENGAGED STUDENTS WAS SIGNIFICANTLY BETTER THAN THOSE WITH LOW ENGAGEMENT LEVELS.
RESEARCH INTO THE TEACHING AND ASSESSMENT OF STUDENT WRITING SHOWS THAT MANY STUDENTS FIND ACADEMIC WRITING A CHALLENGE TO LEARN, WITH LEGAL WRITING NO EXCEPTION. IMPROVING THE AVAILABILITY AND QUALITY OF TIMELY FORMATIVE FEEDBACK IS AN IMPORTANT AIM. HOWEVER, THE TIME-CONSUMING NATURE OF ASSESSING WRITING MAKES IT IMPRACTICAL FOR INSTRUCTORS TO PROVIDE RAPID, DETAILED FEEDBACK ON HUNDREDS OF DRAFT TEXTS WHICH MIGHT BE IMPROVED PRIOR TO SUBMISSION. THIS PAPER DESCRIBES THE DESIGN OF A NATURAL LANGUAGE PROCESSING (NLP) TOOL TO PROVIDE SUCH SUPPORT. WE REPORT PROGRESS IN THE DEVELOPMENT OF A WEB APPLICATION CALLED AWA (ACADEMIC WRITING ANALYTICS), WHICH HAS BEEN PILOTED IN A CIVIL LAW DEGREE. WE DESCRIBE: THE UNDERLYING NLP PLATFORM AND THE PARTICIPATORY DESIGN PROCESS THROUGH WHICH THE LAW ACADEMIC AND ANALYTICS TEAM TESTED AND REFINED AN EXISTING RHETORICAL PARSER FOR THE DISCIPLINE; THE USER INTERFACE DESIGN AND EVALUATION PROCESS; AND FEEDBACK FROM STUDENTS, WHICH WAS BROADLY POSITIVE, BUT ALSO IDENTIFIES IMPORTANT ISSUES TO ADDRESS. WE DISCUSS HOW OUR APPROACH IS POSITIONED IN RELATION TO CONCERNS REGARDING AUTOMATED ESSAY GRADING, AND WAYS IN WHICH AWA MIGHT PROVIDE MORE ACTIONABLE FEEDBACK TO STUDENTS. WE CONCLUDE BY CONSIDERING HOW THIS DESIGN PROCESS ADDRESSES THE CHALLENGE OF MAKING EXPLICIT TO LEARNERS AND EDUCATORS THE UNDERLYING MODE OF ACTION IN ANALYTIC DEVICES SUCH AS OUR RHETORICAL PARSER, WHICH WE TERM ALGORITHMIC ACCOUNTABILITY.
RECENT FINDINGS HAVE PROVIDED STRONG EVIDENCE THAT RETRIEVAL-BASED LEARNING IS AN EFFECTIVE STRATEGY FOR ENHANCING KNOWLEDGE RETENTION AND LONG-TERM MEANINGFUL LEARNING, BUT IT IS NOT A PREFERRED LEARNING STRATEGY FOR THE MAJORITY OF STUDENTS. THE PRESENT RESEARCH ANALYZES THE APPLICATION OF LEARNING GAMIFICATION PRINCIPLES IN ONLINE, OPEN-BOOK, MULTIPLE-CHOICE TESTS IN ORDER TO MOTIVATE STUDENTS TO ENGAGE IN REPEATED RETRIEVAL-BASED LEARNING ACTIVITIES. THE RESULTS REVEAL A STRONG POSITIVE CORRELATION BETWEEN THE NUMBER OF SUCCESSFUL RETRIEVAL ATTEMPTS IN THESE TESTS THAT COVER CONTENT FROM THE COURSE TEXTBOOK, AND LONG-TERM KNOWLEDGE RETENTION AS DEMONSTRATED IN A LIVE, FINAL, CLOSED-BOOK, CUMULATIVE EXAM CONSISTING OF MULTIPLE-CHOICE, LABELING, DEFINITIONS, AND OPEN-ENDED QUESTIONS COVERING THE CONTENT OF BOTH TEXTBOOK READINGS AND LECTURES. THE PRESENTED RESULTS SUGGEST THAT ONLINE, OPEN-BOOK TESTS DESIGNED USING GAMIFICATION PRINCIPLES, EVEN WHEN COVERING PARTIAL COURSE CONTENT AND ONE TYPE OF QUESTIONS, ARE AN EFFECTIVE STRATEGY FOR USING EDUCATIONAL TECHNOLOGY TO MOTIVATE STUDENTS TO REPEATEDLY ENGAGE IN RETRIEVAL-BASED LEARNING ACTIVITIES AND IMPROVE LONG-TERM KNOWLEDGE RETENTION, REGARDLESS OF THE COURSE DELIVERY MODE.
CERTAIN AFFECTIVE STATES ARE LESS CONDUCIVE TO LEARNING THAN OTHERS. MOREOVER, RESULTS FROM STUDIES SUGGEST THAT A CLASSROOM'S SOCIAL-EMOTIONAL CLIMATE AFFECTS STUDENT MOTIVATION AND PERFORMANCE; AND THAT MOODS CAN BE AUTOMATICALLY TRANSFERRED AMONG INDIVIDUALS IN A GROUP. THE CLASSMOOD APP IS AN ONLINE CLASSROOM ORCHESTRATION TOOL FOR SOCIAL EMOTIONAL LEARNING THAT IDENTIFIES THE AGGREGATE MOOD OF A CLASS AND SUGGESTS CLASSROOM ACTIVITIES FOR EDUCATORS TO HELP SHIFT THE CLASS MOOD TO ONE THAT IS MORE CONDUCIVE TO LEARNING. SUGGESTED ACTIVITIES ARE CATEGORIZED BASED ON HOW THEY AIM TO IMPACT STUDENTS' INTERNAL STATE OF AROUSAL. THE APPLICATION AIMS TO FACILITATE LEARNER AND EDUCATOR DEVELOPMENT OF SELF-AWARENESS AND SELF-MANAGEMENT COMPETENCIES CONSISTENT WITH THE CASEL FRAMEWORK FOR SYSTEMIC SOCIAL AND EMOTIONAL LEARNING. PRELIMINARY RESULTS, CONDUCTED AS PART OF AN ITERATIVE DESIGNED-BASED RESEARCH PROCESS, SUGGEST THAT THE TOOL IS PERCEIVED AS BEING EASY-TO-USE FOR BOTH EDUCATORS AND UNDERGRADUATE STUDENTS.
ONE OF THE MOST VEXING ASPECTS OF TERTIARY EDUCATION IS THE LEARNING BEHAVIOUR OF MANY BEGINNERS: LATE DROP-OUTS AFTER MUCH TIME HAS ALREADY BEEN INVESTED IN ATTENDING A COURSE, INCOMPLETE HOMEWORK EVEN THOUGH COMPLETED HOMEWORK IS A SUFFICIENT CONDITION FOR SUCCESS AT EXAMINATIONS, AND MISCONCEPTIONS THAT ARE NOT OVERCOME EARLY ENOUGH. THIS ARTICLE PRESENTS THREE PREDICTORS RELATED TO THESE LEARNING-IMPAIRING BEHAVIOURS THAT HAVE BEEN BUILT FROM DATA COLLECTED WITH A LEARNING PLATFORM AND BY EXAMINING HOMEWORK ASSIGNMENTS, AND DEVELOPED AS HIDDEN MARKOV MODEL, BY RELYING ON COLLABORATIVE FILTERING, AND BY USING MULTIPLE LINEAR REGRESSION. THE SENSITIVITIES AND SPECIFICITIES OF THE FIRST TWO PREDICTORS ARE ABOVE 70% AND THE R-2-ERROR OF THE THIRD PREDICTOR IS ABOUT 20%. CONSIDERING THE LARGE NUMBERS OF UNKNOWN PARAMETERS LIKE COURSE-INDEPENDENT LEARNING, THIS QUALITY IS SATISFYING. THE PREDICTORS HAVE BEEN DEVELOPED FOR FOSTERING A BETTER LEARNING BY RAISING THE LEARNERS' CONSCIOUSNESS OF THE DEFICIENCIES OF THEIR LEARNING. IN OTHER WORDS, THE PREDICTORS AIM AT "GETTING IT WRONG". THE ARTICLE REPORTS ON THE PREDICTORS AND THEIR EVALUATION.
WE STUDY LEARNING OUTCOME PREDICTION FOR ONLINE COURSES. WHEREAS PRIOR WORK HAS FOCUSED ON SEMESTER-LONG COURSES WITH FREQUENT STUDENT ASSESSMENTS, WE FOCUS ON SHORT-COURSES THAT HAVE SINGLE OUTCOMES ASSIGNED BY INSTRUCTORS AT THE END. THE LACK OF PERFORMANCE DATA AND GENERALLY SMALL ENROLLMENTS MAKES THE BEHAVIOR OF LEARNERS, CAPTURED AS THEY INTERACT WITH COURSE CONTENT AND WITH ONE ANOTHER IN SOCIAL LEARNING NETWORKS (SLN), ESSENTIAL FOR PREDICTION. OUR METHOD DEFINES SEVERAL (MACHINE) LEARNING FEATURES BASED ON THE PROCESSING OF BEHAVIORS COLLECTED ON THE MODES OF (HUMAN) LEARNING IN A COURSE, AND USES THEM IN APPROPRIATE CLASSIFIERS. THROUGH EVALUATION ON DATA CAPTURED FROM THREE TWO-WEEK COURSES HOSTED THROUGH OUR DELIVERY PLATFORMS, WE MAKE THREE KEY OBSERVATIONS: (I) BEHAVIORAL DATA CONTAINS SIGNALS PREDICTIVE OF LEARNING OUTCOMES IN SHORT-COURSES (WITH CLASSIFIERS ACHIEVING AUCS >= 0.8 AFTER THE TWO WEEKS), (II) EARLY DETECTION IS POSSIBLE WITHIN THE FIRST WEEK (AUCS >= 0.7 WITH THE FIRST WEEK OF DATA), AND (III) THE CONTENT FEATURES HAVE AN "EARLIEST" DETECTION CAPABILITY (WITH HIGHER AUC IN THE FIRST FEW DAYS), WHILE THE SLN FEATURES BECOME THE MORE PREDICTIVE SET OVER TIME AS THE NETWORK MATURES. WE ALSO DISCUSS HOW OUR METHOD CAN GENERATE BEHAVIORAL ANALYTICS FOR INSTRUCTORS.
ARTIFICIAL INTELLIGENCE (AI) AND ROBOTICS ARE LIKELY TO HAVE A SIGNIFICANT LONG-TERM IMPACT ON HIGHER EDUCATION (HE). THE SCOPE OF THIS IMPACT IS HARD TO GRASP PARTLY BECAUSE THE LITERATURE IS SILOED, AS WELL AS THE CHANGING MEANING OF THE CONCEPTS THEMSELVES. BUT DEVELOPMENTS ARE SURROUNDED BY CONTROVERSIES IN TERMS OF WHAT IS TECHNICALLY POSSIBLE, WHAT IS PRACTICAL TO IMPLEMENT AND WHAT IS DESIRABLE, PEDAGOGICALLY OR FOR THE GOOD OF SOCIETY. DESIGN FICTIONS THAT VIVIDLY IMAGINE FUTURE SCENARIOS OF AI OR ROBOTICS IN USE OFFER A MEANS BOTH TO EXPLAIN AND QUERY THE TECHNOLOGICAL POSSIBILITIES. THE PAPER DESCRIBES THE USE OF A WIDE-RANGING NARRATIVE LITERATURE REVIEW TO DEVELOP EIGHT SUCH DESIGN FICTIONS THAT CAPTURE THE RANGE OF POTENTIAL USE OF AI AND ROBOTS IN LEARNING, ADMINISTRATION AND RESEARCH. THEY PROMPT WIDER DISCUSSION BY INSTANTIATING SUCH ISSUES AS HOW THEY MIGHT ENABLE TEACHING OF HIGH ORDER SKILLS OR CHANGE STAFF ROLES, AS WELL AS EXPLORING THE IMPACT ON HUMAN AGENCY AND THE NATURE OF DATAFICATION.
IN A CONTEXT OF AN ONLINE COLLABORATIVE COURSE, STUDENTS WORKED IN GROUPS IN ORDER TO PERFORM A SERIES OF ACTIVITIES. THE AIM OF THE COURSE IS TO ACHIEVE A CERTAIN LEVEL OF SKILLS IN GROUP WORK. PREDICTIVE MODELING IS AN IMPORTANT PART OF LEARNING ANALYTICS; WE USED IT IN THIS RESEARCH IN ORDER TO ESTIMATE THE STUDENT SKILLS' LEVEL AFTER ACCOMPLISHING THE WHOLE COURSE WITH ITS ACTIVITIES. DATA IS COLLECTED BY USING A COMMUNICATION ACTIVITY IN CLASS FOLLOWED BY AN ONLINE QUESTIONNAIRE. A TOTAL OF 119 STUDENTS DURING 4 CONSECUTIVE YEARS, FOLLOWING THE SAME COURSE, IS INCLUDED IN THE STUDY. WE USED RANDOM DECISION FORESTS MACHINE LEARNING ALGORITHM, AS A METHOD FOR CLASSIFICATION OF STUDENTS, WITH MICROSOFT AZURE MACHINE LEARNING AS A TOOL FOR DESIGNING THE MODEL AND EVALUATING ITS PERFORMANCE AND ACCURACY. PRELIMINARY RESULTS PRESENTED HIGH LEVEL OF ACCURACY OF THE PREDICTING MODEL.
THIS PAPER PROPOSES A MULTILAYERED METHODOLOGY FOR ANALYZING DISTANCE LEARNING STUDENTS' DATA TO GAIN INSIGHT INTO THE LEARNING PROGRESS OF THE STUDENT SUBJECTS BOTH IN AN INDIVIDUAL BASIS AND AS MEMBERS OF A LEARNING COMMUNITY DURING THE COURSE TAKING PROCESS. THE COMMUNICATION ASPECT IS OF HIGH IMPORTANCE IN EDUCATIONAL RESEARCH. ADDITIONALLY, IT IS DIFFICULT TO ASSESS AS IT INVOLVES MULTIPLE RELATIONSHIPS AND DIFFERENT LEVELS OF INTERACTION. SOCIAL NETWORK ANALYSIS (SNA) ALLOWS THE VISUALIZATION OF THIS COMPLEXITY AND PROVIDES QUANTIFIED MEASURES FOR EVALUATION. THUS, INITIALLY, SNA TECHNIQUES WERE APPLIED TO CREATE ONE-MODE, UNDIRECTED NETWORKS AND CAPTURE IMPORTANT METRICS ORIGINATING FROM STUDENTS' INTERACTIONS IN THE FORA OF THE COURSES OFFERED IN THE CONTEXT OF DISTANCE LEARNING PROGRAMS. PRINCIPAL COMPONENT ANALYSIS AND CLUSTERING WERE USED NEXT TO REVEAL LATENT STUDENTS' TRAITS AND COMMON PATTERNS IN THEIR SOCIAL INTERACTIONS WITH OTHER STUDENTS AND THEIR LEARNING BEHAVIOR. WE SELECTED TWO DIFFERENT COURSES TO TEST THIS METHODOLOGY AND TO HIGHLIGHT CONVERGENT AND DIVERGENT FEATURES BETWEEN THEM. THREE MAJOR FACTORS THAT EXPLAIN OVER 70% OF THE VARIANCE WERE IDENTIFIED AND FOUR GROUPS OF STUDENTS WERE FOUND, CHARACTERIZED BY COMMON ELEMENTS IN STUDENTS' LEARNING PROFILE. THE RESULTS HIGHLIGHT THE IMPORTANCE OF ACADEMIC PERFORMANCE, SOCIAL BEHAVIOR AND ONLINE PARTICIPATION AS THE MAIN CRITERIA FOR CLUSTERING THAT COULD BE HELPFUL FOR TUTORS IN DISTANCE LEARNING TO CLOSELY MONITOR THE LEARNING PROCESS AND PROMPTLY INTEREVENT WHEN NEEDED.
IN RECENT YEARS, LEARNING MANAGEMENT SYSTEMS (LMSS) HAVE PLAYED A FUNDAMENTAL ROLE IN HIGHER EDUCATION TEACHING MODELS. A NEW LINE OF RESEARCH HAS BEEN OPENED RELATING TO THE ANALYSIS OF STUDENT BEHAVIOR WITHIN AN LMS, IN THE SEARCH FOR PATTERNS THAT IMPROVE THE LEARNING PROCESS. CURRENT E-LEARNING PLATFORMS ALLOW FOR RECORDING STUDENT ACTIVITY, THEREBY ENABLING THE EXPLORATION OF EVENTS GENERATED IN THE USE OF LMS TOOLS. THIS PAPER PRESENTS A CASE STUDY CONDUCTED AT THE CATHOLIC UNIVERSITY OF MURCIA, WHERE STUDENT BEHAVIOR IN THE PAST FOUR ACADEMIC YEARS WAS ANALYZED ACCORDING TO LEARNING MODALITY (THAT IS, ON-CAMPUS, ONLINE, AND BLENDED), CONSIDERING THE NUMBER OF ACCESSES TO THE LMS, TOOLS EMPLOYED BY STUDENTS AND THEIR ASSOCIATED EVENTS. GIVEN THE DIFFICULTY OF MANAGING THE LARGE VOLUME OF DATA GENERATED BY USERS IN THE LMS (UP TO 70 GB IN THIS STUDY), STATISTICAL AND ASSOCIATION RULE TECHNIQUES WERE PERFORMED USING A BIG DATA FRAMEWORK, THUS SPEEDING UP THE STATISTICAL ANALYSIS OF THE DATA. THE OBTAINED RESULTS ARE DEMONSTRATED USING VISUAL ANALYTIC TECHNIQUES, AND EVALUATED IN ORDER TO DETECT TRENDS AND DEFICIENCIES IN THE USE OF THE LMS BY STUDENTS. (C) 2018 ELSEVIER B.V. ALL RIGHTS RESERVED.
THIS PAPER PRESENTS AN EMPIRICAL STUDY ON THE INTEGRATION OF AN INTELLIGENT TEACHING SYSTEM, MOSO TEACH (MT), INTO AN ENGLISH CURRICULUM IN A TOP-RANKED FOUR-YEAR UNIVERSITY IN CHINA. DATA SOURCES INCLUDE THE REPORTS AND ANALYSIS GENERATED BY THE LEARNING ANALYTICS TOOLS IN MT, A SURVEY OF STUDENTS, FINAL EXAM SCORES, AND THE INSTRUCTOR'S OBSERVATION OF CLASSROOM ACTIVITIES. FINDINGS SUGGEST THAT AN INTELLIGENT AND ADAPTIVE LEARNING PLATFORM COUPLED WITH WELL-DESIGNED TEAM-BASED ACTIVITIES CAN INDEED INCREASE STUDENTS' ENGAGEMENT IN LEARNING IN A WIDE VARIETY OF ACTIVITIES AND CONTEXTS, FROM PERSONALIZED LEARNING TO TEAM COLLABORATION, AND INCLUDING CLASSROOM PARTICIPATION AND ASYNCHRONOUS POSTINGS ON A DISCUSSION FORUM. THESE FINDINGS ARE SIGNIFICANT BECAUSE CHINESE UNIVERSITIES TEND TO HAVE LARGE-SIZE CLASSES WITH FEW OPPORTUNITIES FOR INTERACTION. MOST IMPORTANTLY, THIS STUDY REVEALS THE IMPACT OF COLLABORATIVE MOBILE LEARNING ON STEM STUDENTS IN BOTH LEARNING ENGAGEMENT AND LEARNING OUTCOMES.
VIRTUAL WORLDS (VWS) HAVE BEEN WIDELY USED TO SUPPORT LEARNING PROCESSES. ONE MAIN ADVANTAGE IS PROVIDING VALUABLE DATA ON STUDENT BEHAVIOUR AND INTERACTION. NONETHELESS, MOST PLATFORMS PROVIDE ONLY LIMITED ACCESS TO STUDENT LOGS. MOREOVER, ACCESSING LOGS USUALLY REQUIRES TECHNICAL SKILLS MOST TEACHERS DO NOT HAVE. IN THIS CONTEXT, THE AUTHORS PRESENT A DOMAIN SPECIFIC LANGUAGE (DSL) DESIGNED TO ALLOW TEACHERS TO GENERATE QUERIES FOR RETRIEVING VALUABLE LOG INFORMATION WITH A VIEW TO OBTAIN EVIDENCE ON LEARNER BEHAVIOUR AND INTERACTION; HENCE, TO AID IN THE ANALYSIS OF IN-WORLD BEHAVIOUR AND LEARNING PROCESSES. SINCE THIS DATA IS AUTOMATICALLY RETRIEVED, THE TEACHER CAN EASILY RUN NEW QUERIES TO REFINE INDICATORS OR CONTRAST HYPOTHESES. THE AUTHORS DESCRIBE A CASE STUDY CARRIED OUT WITH UNDERGRADUATE GERMAN LANGUAGE STUDENTS USING A VW-BASED VIDEO GAME. THE RESULTS PROVIDE A SET OF INDICATORS FOR ANALYSING INDIVIDUAL AND GROUP BEHAVIOUR MEASURING STUDENT COMPETENCE TO COMMUNICATE IN THE TARGET LANGUAGE.
THIS STUDY ATTEMPTS TO MAKE INFERENCES ABOUT THE MECHANISMS THAT DRIVE NETWORK CHANGE OVER TIME. IT ADOPTS SIMULATION INVESTIGATION FOR EMPIRICAL NETWORK ANALYSIS TO EXAMINE THE PATTERNS AND EVOLUTION OF RELATIONSHIPS FORMED IN THE CONTEXT OF A MASSIVE OPEN ONLINE COURSE (MOOC) DISCUSSION FORUM. FOUR NETWORK EFFECTSHOMOPHILY, RECIPROCITY, TRANSITIVITY, AND PREFERENTIAL ATTACHMENTWERE TESTED TO EXPLAIN THE DYNAMIC MECHANISMS OF INTERACTION IN THE MOOC FORUM. UNDERSTANDING THE NETWORK DYNAMICS OF RELATIONSHIPS WILL ALLOW US TO EXPLORE HOW TO DEVELOP A ROBUST PEER-SUPPORTED LEARNING ENVIRONMENT AND IN TURN IMPROVE THE ONLINE LEARNING EXPERIENCE IN MOOCS.
AS INFORMATION & COMMUNICATION TECHNOLOGY (ICT) IS RAPIDLY EVOLVED, EDUCATIONAL PARADIGMS HAVE BEEN CHANGING. THE ULTIMATE GOAL OF EDUCATION WITH THE AID OF ICT IS TO PROVIDE CUSTOMIZED TRAINING FOR LEARNERS TO IMPROVE THE EFFECTIVENESS OF THEIR LEARNING AT ANYTIME AND ANYWHERE. IN THE ONLINE LEARNING ENVIRONMENT WHERE THE INTERNET, MOBILE DEVICES, PEER-TO-PEER (P2P) AND THE CLOUD TECHNOLOGY ARE LEVERAGED, ALL THE INFORMATION IN LEARNING ACTIVITIES IS CONVERTED INTO DIGITAL DATA AND STORED IN THE COMPUTER SUPPORTED COLLABORATIVE LEARNING (CSCL) SYSTEM. THE DATA IN THE CSCL SYSTEM CONTAINS VARIOUS LEARNERS' INFORMATION INCLUDING THE LEARNING OBJECTIVES, LEARNING PREFERENCES, COMPETENCES AND ACHIEVEMENTS. THUS, BY ANALYZING THE ACTIVITY INFORMATION OF LEARNERS IN AN ONLINE CSCL SYSTEM, MEANINGFUL AND USEFUL INFORMATION CAN BE EXTRACTED AND PROVIDED FOR LEARNERS, TEACHERS AND ADMINISTRATORS AS FEEDBACK. IN THIS PAPER, WE PROPOSE A LEARNER ACTIVITY MODEL THAT REPRESENTS THE LEARNER'S ACTIVITY INFORMATION STORED IN A CSCL SYSTEM. AS FOR THE PROPOSED LEARNER ACTIVITY MODEL, WE CLASSIFIED THE LEARNING ACTIVITIES IN A CSCL SYSTEM INTO THREE CATEGORIES: VIVACITY, LEARNING AND RELATIONSHIP; THEN WE CREATED QUOTIENTS TO REPRESENT THEM ACCORDINGLY. IN ADDITION, WE DEVELOPED A CSCL SYSTEM, WHICH WE TERMED AS COLLA, APPLIED THE PROPOSED LEARNER ACTIVITY MODEL AND ANALYZED THE RESULTS.
INFORMATION SEEKING AND PROCESSING ARE KEY LITERACY PRACTICES. HOWEVER, THEY ARE ACTIVITIES THAT STUDENTS, ACROSS A RANGE OF AGES, STRUGGLE WITH. THESE INFORMATION SEEKING PROCESSES CAN BE VIEWED THROUGH THE LENS OF EPISTEMIC COGNITION: BELIEFS REGARDING THE SOURCE, JUSTIFICATION, COMPLEXITY, AND CERTAINTY OF KNOWLEDGE. IN THE RESEARCH REPORTED IN THIS ARTICLE WE BUILD ON ESTABLISHED RESEARCH IN THIS AREA, WHICH HAS TYPICALLY USED SELF-REPORT PSYCHOMETRIC AND BEHAVIOR DATA, AND INFORMATION SEEKING TASKS INVOLVING CLOSED-DOCUMENT SETS. WE TAKE A NOVEL APPROACH IN APPLYING ESTABLISHED SELF-REPORT MEASURES TO A LARGE-SCALE, NATURALISTIC, STUDY ENVIRONMENT, POINTING TO THE POTENTIAL OF ANALYSIS OF DIALOGUE, WEB-NAVIGATION INCLUDING SITES VISITED AND OTHER TRACE DATA, TO SUPPORT MORE TRADITIONAL SELF-REPORT MECHANISMS. OUR ANALYSIS SUGGESTS THAT PRIOR WORK DEMONSTRATING RELATIONSHIPS BETWEEN SELF-REPORT INDICATORS IS NOT PARALLELED IN INVESTIGATION OF THE HYPOTHESIZED RELATIONSHIPS BETWEEN SELF-REPORT AND TRACE-INDICATORS. HOWEVER, THERE ARE CLEAR EPISTEMIC FEATURES OF THIS TRACE DATA. THE ARTICLE THUS DEMONSTRATES THE POTENTIAL OF BEHAVIORAL LEARNING ANALYTIC DATA IN UNDERSTANDING HOW EPISTEMIC COGNITION IS BROUGHT TO BEAR IN RICH INFORMATION SEEKING AND PROCESSING TASKS. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE USE OF BLOCK-BASED VISUAL LANGUAGE IN INTRODUCTORY PROGRAMMING IS A POPULAR METHOD IN EDUCATION. HOWEVER, THERE IS LITTLE RESEARCH WHICH PROVIDES EVIDENCE SHOWING ADVANTAGES OF BLOCK-BASED LANGUAGE. THIS PAPER PRESENTS THE RESULTS OF LEARNING DATA ANALYSIS WITH FINE GRAIN LOGS RECORDED BY STUDENTS' DEVELOPMENT ENVIRONMENT WHERE THE STUDENTS CAN SELECT THEIR LANGUAGE IN BLOCK-BASED OR JAVA. A TOTAL OF 400+ STUDENTS' LOGS COLLECTED EACH OF FOUR YEARS WERE ANALYZED. THE RESULTS SHOW THAT MIGRATION FROM BLOCK TO JAVA CAN BE CONSISTENTLY SEEN EACH YEAR, ALTHOUGH THE WHOLE BLOCK-EDITING RATE WAS INFLUENCED BY THE METHOD OF THE INSTRUCTOR'S INTRODUCTION. THOUGH BLOCK-EDITING DID NOT AFFECT WORKING TIME AND LINES OF CODE (LOC), IT COULD REDUCE THE COMPILE ERROR CORRECTION TIME, WHEREAS USING JAVA REQUIRES APPROXIMATELY 20% OF COMPILE ERROR CORRECTION TIME FOR STUDENTS. WE CONCLUDED THAT BLOCK-BASED LANGUAGE WORKED TO ENCOURAGE STUDENTS TO FOCUS HIGH-LEVEL ALGORITHM CREATION, AS WELL AS IT PROVIDES AN ADVANTAGE TO UNDERSTANDING TEXT-BASED LANGUAGE.
SOCIETY TODAY IS THE INFORMATION SOCIETY, WITH GLOBAL CONNECTIONS, TELEPRESENCE, OMNIPRESENCE, INSTANTANEITY, WIDE REACH, MULTI FORMATS, CONVERGENCE, AND MORE. PEOPLE DON'T LIVE WITHOUT TECHNOLOGY, THEN, WHAT THE TECHNOLOGY CAN DO FOR EDUCATION? DESPITE ALL TECHNOLOGY AVAILABLE, THE EDUCATION STILL SUFFERS FROM OLD PROBLEMS: DISTANCE BETWEEN TECHNOLOGIES AND TEACHING METHODS, THE STUDENTS HAVE DIFFICULTY LEARNING, THE SCHOOL DROPOUT, IS NECESSARY TO UNDERSTAND THE HABITS AND MODE OF ACTION OF THE NEW GENERATIONS. THIS WORK IS A PHD THESIS IN PROGRESS AND INQUIRES IF AND HOW IT'S POSSIBLE TO MEASURE EVIDENCE OF STUDENTS' LEARNING IN CLASS, AT LAB PRACTICE SPECIALLY, AND HOW IT MAY SUPPORT TEACHER'S DECISIONS ON STUDENT GUIDANCE.
RESIDENTIAL BUILDINGS HAVE ACCOUNTED FOR MORE THAN 20% OF TOTAL ENERGY USAGE IN THE UNITED STATES OVER THE LAST DECADE. REDUCING HOUSEHOLD ENERGY CONSUMPTION HAS ENVIRONMENTAL AND ECONOMIC IMPACTS. BUILDING SCIENTISTS AND CONSTRUCTION ENGINEERS HAVE ATTEMPTED TO OBTAIN ACCURATE ENERGY USE PREDICTION; HOWEVER, FEW HAVE FOCUSED ON THE RELATIONSHIP BETWEEN CONSTRUCTION COST AND ENERGY USE. THIS CHAPTER INVESTIGATES THE ASSOCIATIONS AMONG DETAILED CONSTRUCTION COST TAKEOFFS AND ACTUAL ENERGY USE IN MULTIFAMILY GREEN BUILDINGS. THE RESEARCHERS EMPLOY ADVANCED MACHINE-LEARNING ANALYTICS TO MODEL THE CORRELATIONS BETWEEN CONSTRUCTION COSTS AND ENERGY USE DATA COLLECTED FROM MULTIFAMILY RESIDENTIAL UNITS. THE FINDINGS IDENTIFY COST DIVISIONS IN THE CONSTRUCTION STAGE THAT SIGNIFICANTLY CORRELATE WITH ENERGY USE IN THE OPERATIONAL STAGE. THE MODEL ALLOWS DEVELOPERS TO PREDICT ENERGY CONSUMPTION BASED ON CONSTRUCTION COSTS AND ENABLES THEM TO ADJUST THEIR INVESTMENT STRATEGIES TO AMPLIFY THE ENERGY EFFICIENCY OF GREEN BUILDING TECHNOLOGIES.
TODAY, THE USE OF LEARNING ANALYTICS IS BECOMING MORE CRUCIAL IN THE LEARNING ENVIRONMENT FOR THE PURPOSE OF UNDERSTANDING AND OPTIMIZING STUDENTS' LEARNING SITUATIONS. THE PURPOSE OF THIS PAPER IS TO EXAMINE THE IMPACTS OF TEACHER INTERVENTIONS (TIS) ON STUDENTS' ATTITUDES AND ACHIEVEMENTS INVOLVED WITH THE LESSON BY ANALYZING THEIR FREESTYLE COMMENT DATA AFTER EVERY LESSON. THE CURRENT STUDY PROPOSES A NEW METHOD FOR BUILDING AN ACCESSIBLE PREDICTION MODEL, WHICH REPRESENTS STUDENTS' ACTIVITIES, SITUATIONS AND VIEWPOINTS; THE METHOD CLASSIFIES WORDS IN THE STUDENT COMMENTS INTO SIX ATTRIBUTE TYPES AND INDICATES THE MOST IMPORTANT TYPES THAT AFFECT THE PREDICTION RESULTS. FURTHER, THE PREDICTION RESULTS ARE COMPARED WITH THE TOPIC BASED STATISTICAL METHOD THAT USES LATENT DIRICHLET ALLOCATION AND SUPPORT VECTOR MACHINE MODELS. THE RESULTS PROVED THAT THERE WERE POSITIVE CORRELATIONS BETWEEN TIS AND THE QUALITY OF WRITING COMMENTS THAT AFFECT ON IMPROVING THE PREDICTION RESULTS.
THIS PAPER DESCRIBES THE DEVELOPMENT OF 'STUDENTSATRISK', A PROTOTYPE EARLY DETECTION SYSTEM. IT IS BASED ON ENGAGEMENT WITH COURSE MATERIALS AND CAN BE USED TO IDENTIFY STUDENTS WHO ARE FALLING BEHIND BY AUTOMATICALLY FLAGGING THEM. THE SYSTEM ALSO ALLOWS INSTRUCTORS TO FLAG THESE STUDENTS MANUALLY. ON THEIR DASHBOARD, STUDENTS CAN FLAG THEMSELVES, AS ENGAGEMENT WITH THE MATERIAL MIGHT NOT REVEAL ALL THOSE WHO STRUGGLE.
THIS PAPER CONTRIBUTES TO THE DEVELOPMENT OF LEARNING AND ACADEMIC ANALYTICS IN HIGHER EDUCATION (HE) BY RESEARCHING HOW FOUR GRAPHICAL VISUALISATION METHODS CAN BE USED TO PRESENT STUDENT ASSESSMENT AND FEEDBACK DATA TO FIVE STAKEHOLDER GROUPS, INCLUDING STUDENTS, EXTERNAL EXAMINERS AND INDUSTRIALISTS. THE VISUALISATIONS AND UNDERLYING DATA SETS ARE DESCRIBED, TOGETHER WITH THE RESULTS OF A QUESTIONNAIRE DESIGNED TO ELICIT THE PERSPECTIVES OF THE STAKEHOLDER GROUPS ON THE POTENTIAL VALUE OF THE VISUALISATIONS. KEY FINDINGS OF THIS STUDY ARE THAT EXTERNAL EXAMINERS AGREE THAT THE VISUALISATIONS HELP THEM TO CARRY OUT THEIR ROLE AND STUDENTS CONCUR THAT THEY CAN ASSIST WITH STUDY ORGANISATION, RELATIVE PERFORMANCE ASSESSMENT AGAINST THE WIDER COHORT AND EVEN MODULE CHOICE. ALL STAKEHOLDER GROUPS WERE POSITIVE ABOUT THE BENEFITS OF GRAPHICAL VISUALISATIONS IN THIS HE CONTEXT AND SUPPORTED AN INCREASED USE OF VISUALISATIONS TO ASSIST WITH DATA INTERPRETATION.
THIS PAPER AIMS TO DEVELOP AN IMAGE RECOMMENDATION SYSTEM TO SUPPORT INFORMAL VOCABULARY LEARNING FOR A CONTEXT-AWARE LEARNING ENVIRONMENT CALLED SCROLL (SYSTEM FOR CAPTURING AND REMINDING OF LEARNING LOG). THE IDEA IS BASED ON UBIQUITOUS LEARNING LOGS ANALYSIS, IMAGE (CONTEXTUAL AND NON-CONTEXTUAL) FEATURE ANALYSIS, AND INFORMATION VISUALIZATION TECHNIQUES. WE DESIGNED A MODEL BASED ON WHICH THE PROPOSED FEATURE-BASED CONTEXT-SPECIFIC APPROPRIATE IMAGE (FCAI) RECOMMENDATION SYSTEM WILL BE DEVELOPED. THE PURPOSE OF RECOMMENDING FCAI IMAGES IS TO ASSIST LANGUAGE LEARNERS IN INFORMAL LEARNING OF FOREIGN VOCABULARY. THOSE IMAGES ARE RECOMMENDED BY ANALYZING 25350 UBIQUITOUS LEARNING LOGS (I. E. A LEARNER'S ETHNOGRAPHIC INFORMATION, LEARNING LOCATION, LEARNING TIME, LEARNING CONTEXT, IMAGE INFORMATION ETC.) AT THE RIGHT TIME, RIGHT PLACE AND IN THE RIGHT LEARNING CONTEXT-BASIS. THIS PAPER ALSO ARTICULATES THE APPROACHES FOR UBIQUITOUS LEARNING LOGS ANALYSIS, WORD-BANK FORMATION, IMAGE ANALYSIS METHODS, AND ALGORITHM DEVELOPMENT.
RESEARCH ACTIVITIES ARE COMPOSED OF STACKS OF SEVERAL TASKS SUCH AS SURVEYS, CREATION OF NOVEL IDEAS, WRITING OF RESEARCH DOCUMENTS, AND THEIR ELABORATION. IT IS QUITE IMPORTANT TO SUCCESSFULLY ACCUMULATE THE RESULTS AND CIRCUMSTANCES OF THESE TASKS (I.E., CONTEXTS). IT IS FURTHER IMPORTANT BUT DIFFICULT TO UNDERSTAND THE CONTEXTS AND TO APPLY THEM TO LATER WORK. IN PARTICULAR, IT IS MUCH MORE DIFFICULT FOR RESEARCH BEGINNERS TO ENSURE NOTEWORTHY TARGETS WITHIN CONTEXTS THAT CONTINUOUSLY INCREASE AS THE WORK PROGRESSES. THE FINAL GOAL OF THIS RESEARCH IS TO DEVELOP METHODS FOR SUPPORTING THE ACCUMULATION OF CONTEXTS AND FACILITATING THEIR UTILIZATION BY VISUALLY PRESENTING THOSE THAT ARE NOTEWORTHY TO RESEARCH BEGINNERS. THIS PAPER MAINLY DESCRIBES A METHOD FOR ORGANIZING THE CONTEXTS AND EXTRACTING IMPORTANT PORTIONS FROM HUGE ORGANIZED CONTEXTS. THIS PAPER ALSO DESCRIBES AN EXPERIMENT THAT WAS CONDUCTED BY USING ACTUAL HISTORY DATA, AND DISCUSSES THE FEATURES OF THE PROPOSED METHODS ON THE BASIS OF THE RESULTS.
THIS PAPER COMBINES A THEORETICAL-BASED MODEL WITH A DATA-DRIVEN APPROACH TO DEVELOP AN EARLY WARNING SYSTEM THAT DETECTS STUDENTS WHO ARE MORE LIKELY TO DROPOUT. THE MODEL USES INNOVATIVE MULTILEVEL STATISTICAL AND MACHINE LEARNING METHODS. THE PAPER DEMONSTRATES THE VALIDITY OF THE APPROACH BY APPLYING IT TO ADMINISTRATIVE DATA FROM A LEADING ITALIAN UNIVERSITY.
IN THE PAST, UNRAVELING LEARNER INTERACTION DATA IN TELE WAS A CHALLENGE. HOWEVER, THE ADVENT OF LA HAS HELPED IN UNCOVERING LATENT INFORMATION IN LOG DATA TO SCAFFOLD LEARNING. THIS PAPER FOCUSES ON LEARNER INTERACTION IN VERISIM, A TELE, TO TEACH SOFTWARE DESIGN DIAGRAMS. THE LEARNERS' PERFORMANCE IN THE SYSTEM IS USED TO CATEGORIZE THEM INTO THREE GROUPS, NAMELY, "FULL SCORERS", "PARTIAL SCORERS", AND "GIVE UPPERS". OUR ANALYSIS FOUND THAT THE FULL SCORERS SPEND A SIGNIFICANTLY HIGHER DURATION PER ACTION THAN THE GIVE-UPPERS IN AN INTRODUCTORY CHALLENGE PRESENTED IN THE LEARNING ENVIRONMENT. FURTHER ANALYSIS UNRAVELS THE STRATEGIES USED BY CONSISTENT AND INCONSISTENT LEARNERS, AND IT WAS OBSERVED THAT THE LEARNER INTERACTION STRATEGIES EVOLVE WITH INCREASING DIFFICULTY LEVELS AS THEY NAVIGATE THROUGH THE CHALLENGES.
REFLECTION IS A KEY ACTIVITY IN SELF-REGULATED LEARNING (SRL) AND A CRITICAL PART OF HEALTH PROFESSIONS EDUCATION THAT SUPPORTS THE DEVELOPMENT OF EFFECTIVE LIFELONG-LEARNING HEALTH PROFESSIONALS. DESPITE WIDESPREAD USE AND PLENTIFUL THEORETICAL MODELS, EMPIRICAL UNDERSTANDING OF AND SUPPORT FOR REFLECTION IN HEALTH PROFESSIONS EDUCATION REMAINS LIMITED DUE TO SIMPLE MANUAL ASSESSMENT AND RARE FEEDBACK TO STUDENTS. RECENT MOVES TO DIGITAL REFLECTION PRACTICES OFFER OPPORTUNITIES TO COMPUTATIONALLY STUDY AND SUPPORT REFLECTION AS A PART OF SRL. THE CRITICAL TASK IN SUCH AN ENDEAVOR, AND THE GOAL OF THIS PAPER, IS TO ALIGN HIGH-LEVEL REFLECTION QUALITIES THAT ARE VALUED CONCEPTUALLY WITH LOW-LEVEL FEATURES IN THE DATA THAT ARE POSSIBLE TO EXTRACT COMPUTATIONALLY. THIS PAPER APPROACHES THIS GOAL BY (A) DEVELOPING A UNIFIED FRAMEWORK FOR CONCEPTUALIZING REFLECTION ANALYTICS IN HEALTH PROFESSIONS EDUCATION AND (B) EMPIRICALLY EXAMINING POTENTIAL DATA FEATURES THROUGH WHICH THESE ELEMENTS CAN BE ASSESSED. SYNTHESIZING THE PRIOR LITERATURE YIELDS A CONCEPTUAL FRAMEWORK FOR HEALTH PROFESSIONS REFLECTION COMPRISED OF SIX ELEMENTS: DESCRIPTION, ANALYSIS, FEELINGS, PERSPECTIVE, EVALUATION, AND OUTCOME. THESE ELEMENTS THEN SERVE AS THE CONCEPTUAL GROUNDING FOR THE COMPUTATIONAL ANALYSIS IN WHICH 27 DENTAL STUDENTS' REFLECTIONS (IN SIX REFLECTIVE STATEMENT TYPES) OVER THE COURSE OF 4 YEARS WERE EXAMINED USING SELECTED LIWC (LINGUISTIC INQUIRY AND WORD COUNT) INDICES. VARIATION IN ELEMENTS OF REFLECTION ACROSS STUDENTS, YEARS, AND REFLECTION-TYPES SUPPORTS USE OF THE MULTI-DIMENSIONAL ANALYSIS FRAMEWORK TO (A) INCREASE PRECISION OF RESEARCH CLAIMS; (B) EVALUATE WHETHER REFLECTION ACTIVITIES ARE ENGAGED IN AS INTENDED; AND (C) DIAGNOSE ASPECTS OF REFLECTION IN WHICH SPECIFIC STUDENTS NEED SUPPORT. IMPLICATIONS FOR THE DEVELOPMENT OF HEALTH PROFESSIONS REFLECTION ANALYTICS THAT CAN CONTRIBUTE TO SRL AND PROMISING AREAS FOR FUTURE RESEARCH ARE DISCUSSED.
AS AN OVERT EXPRESSION OF INTERNAL MENTAL PROCESSES, DISCOURSES HAVE BECOME ONE MAIN DATA SOURCE FOR THE RESEARCH OF INTERACTIVE LEARNING. TO DEEPLY EXPLORE BEHAVIORAL REGULARITIES AMONG INTERACTIONS, THIS ARTICLE FIRSTLY ADOPTS THE CONTENT ANALYSIS METHOD TO SUMMARIZE STUDENTS' ENGAGEMENT PATTERNS WITHIN A COURSE FORUM IN A SMALL PRIVATE ONLINE COURSE (SPOC) SYSTEM. SECONDLY, THROUGH SENTIMENT WORD MATCHING AND SENTIMENT DENSITY CALCULATION, THE AUTHORS CHARACTERIZE THE EVOLUTION TRENDS OF COLLECTIVE POSITIVE AND NEGATIVE SENTIMENTS, AND COMPARE SENTIMENT STRENGTHS OF DIFFERENT ACHIEVING STUDENTS. THE ANALYTICAL RESULT SHOWS THAT THERE IS A SIGNIFICANT CORRELATION BETWEEN MOST ENGAGEMENT PATTERNS AND ACADEMIC ACHIEVEMENTS, AND HIGH-ACHIEVING GROUP SEEMS MORE ACTIVE THAN LOW-ACHIEVING GROUP IN TERMS OF INTERACTIVE, REGISTER, QUESTION, VIEWPOINT AND THEMATIC POSTINGS. BESIDES, BOTH OF HIGH AND MIDDLE-ACHIEVING STUDENTS ARE SUPERIOR TO LOW-ACHIEVING STUDENTS ON POSITIVE SENTIMENT. BUT, THERE IS NO SIGNIFICANT DIFFERENCE AMONG HIGH-, MIDDLE- AND LOW-ACHIEVING STUDENTS ON NEGATIVE SENTIMENTS.
INDIVIDUALS WITH STRONG SELF-REGULATED LEARNING (SRL) SKILLS, CHARACTERIZED BY THE ABILITY TO PLAN, MANAGE AND CONTROL THEIR LEARNING PROCESS, CAN LEARN FASTER AND OUTPERFORM THOSE WITH WEAKER SRL SKILLS. SRL IS CRITICAL IN LEARNING ENVIRONMENTS THAT PROVIDE LOW LEVELS OF SUPPORT AND GUIDANCE, AS IS COMMONLY THE CASE IN MASSIVE OPEN ONLINE COURSES (MOOCS). LEARNERS CAN BE TRAINED TO ENGAGE IN SRL AND ACTIVELY SUPPORTED WITH PROMPTS AND ACTIVITIES. HOWEVER, EFFECTIVE IMPLEMENTATION OF LEARNER SUPPORT SYSTEMS IN MOOCS REQUIRES AN UNDERSTANDING OF WHICH SRL STRATEGIES ARE MOST EFFECTIVE AND HOW THESE STRATEGIES MANIFEST IN ONLINE BEHAVIOR. MOREOVER, IDENTIFYING LEARNER CHARACTERISTICS THAT ARE PREDICTIVE OF WEAKER SRL SKILLS CAN ADVANCE EFFORTS TO PROVIDE TARGETED SUPPORT WITHOUT OBTRUSIVE SURVEY INSTRUMENTS. WE INVESTIGATED SRL IN A SAMPLE OF 4,831 LEARNERS ACROSS SIX MOOCS BASED ON INDIVIDUAL RECORDS OF OVERALL COURSE ACHIEVEMENT, INTERACTIONS WITH COURSE CONTENT, AND SURVEY RESPONSES. WE FOUND THAT GOAL SETTING AND STRATEGIC PLANNING PREDICTED ATTAINMENT OF PERSONAL COURSE GOALS, WHILE HELP SEEKING WAS ASSOCIATED WITH LOWER GOAL ATTAINMENT. LEARNERS WITH STRONGER SRL SKILLS WERE MORE LIKELY TO REVISIT PREVIOUSLY STUDIED COURSE MATERIALS, ESPECIALLY COURSE ASSESSMENTS. SEVERAL LEARNER CHARACTERISTICS, INCLUDING DEMOGRAPHICS AND MOTIVATION, PREDICTED LEARNERS' SRL SKILLS. WE DISCUSS IMPLICATIONS FOR THEORY AND THE DEVELOPMENT OF LEARNING ENVIRONMENTS THAT PROVIDE ADAPTIVE SUPPORT. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
INTELLIGENT TUTORING SYSTEMS (ITS) HAVE BEEN A TOPIC OF GREAT INTEREST FOR ABOUT FIVE DECADES. OVER THE YEARS, ITS RESEARCH HAS LEVERAGED AI ADVANCEMENTS, AND HAS ALSO HELPED PUSH THE BOUNDARIES OF AI CAPABILITIES WITH GROUNDED USAGE SCENARIOS. USING ITSS ALONG WITH CLASSROOM INSTRUCTION TO AUGMENT TRADITIONAL TEACHING IS A CANONICAL EXAMPLE OF HOW HUMANS AND MACHINES CAN WORK TOGETHER TO SOLVE PROBLEMS THAT ARE OTHERWISE OVERWHELMING AND NON-SCALABLE INDIVIDUALLY. THE EXPERIENCES OF PERSONALIZED LEARNING CREATED BY (1) SEAMLESS ORCHESTRATION OF HUMAN DECISION-MAKING AT FEW CRITICAL POINTS WITH (2) SCALABILITY OF COGNITIVE CAPABILITIES USING AI SYSTEMS CAN DRIVE INCREASED STUDENT ENGAGEMENT LEADING TO IMPROVED LEARNING OUTCOMES. BY CONSIDERING TWO PARTICULAR USE-CASES OF EARLY CHILDHOOD LEARNING AND HIGHER EDUCATION, WE DISCUSS THE CHALLENGES INVOLVED IN DESIGNING THESE COMPLEX HUMAN-CENTRIC SYSTEMS. THESE SYSTEMS INTEGRATE TECHNOLOGIES INVOLVING INTERACTIVITY, DIALOG, AUTOMATED QUESTION GENERATION, AND LEARNING ANALYTICS.
COMMONLY, LOW COMPLETION RATES IN MASSIVE OPEN ONLINE COURSES HAVE CALLED INTO QUESTION THE QUALITY OF THEIR LEARNING MATERIALS AND INSTRUCTION. THIS PAPER ATTEMPTS TO IDENTIFY CRUCIAL FACTORS OF ENGAGEMENT AND RETENTION IN LANGUAGE MASSIVE OPEN ONLINE COURSES (LMOOCS) IN THE CONTEXT OF THE OPEN ONLINE PROGRAM OF ICELANDIC ONLINE, A SELF-GUIDED COURSE FOR SECOND LANGUAGE LEARNERS OF ICELANDIC. THE STUDY SEEKS TO EXPLORE THE IMPACT OF FACTORS ASSOCIATED WITH THE COURSE'S INSTRUCTIONAL DESIGN ON ENGAGEMENT AND RETENTION AND REVEAL CRUCIAL DETERMINANTS OF ATTRITION. THE STUDY DEPENDS ON SURVEY AND TRACKING DATA FROM 400 LEARNERS AND QUALITATIVE DATA FROM 62 INFORMANTS IN ONE COURSE. IT BUILDS ON PREVIOUS STUDIES ON STUDENT ENGAGEMENT AND RETENTION IN LMOOCS (FRIORIKSDOTTIR, 2018, 2019). THE PRESENT STUDY IDENTIFIED SIX CONTENT-SPECIFIC FACTORS THAT THE MAJORITY OF PARTICIPANTS CONSIDERED TO BE IMPORTANT FOR THEIR MOTIVATION. SOME FACTORS, SUCH AS GRADUAL AND SCAFFOLDED PRESENTATION OF INPUT, HAD A POSITIVE IMPACT ON RETENTION. FURTHERMORE, STATEMENTS FROM LEARNERS IN THE STUDY WHO DISENGAGED BEFORE COMPLETING SHOW THAT NON-COURSE-RELATED FACTORS, SUCH AS TIME CONSTRAINTS, AFFECT LMOOC RETENTION. THE STUDY PROVIDES A NEW FRAMEWORK FOR HOW TO PROMOTE STUDENT ENGAGEMENT AND SUGGESTS SPECIFIC STRATEGIES FOR OTHER LMOOC DEVELOPERS.
CURRENTLY, UNIVERSITIES ARE BEING FORCED TO CHANGE THE PARADIGMS OF EDUCATION, WHERE KNOWLEDGE IS MAINLY BASED ON THE EXPERIENCE OF THE TEACHER. THIS CHANGE INCLUDES THE DEVELOPMENT OF QUALITY EDUCATION FOCUSED ON STUDENTS' LEARNING. THESE FACTORS HAVE FORCED UNIVERSITIES TO LOOK FOR A SOLUTION THAT ALLOWS THEM TO EXTRACT DATA FROM DIFFERENT INFORMATION SYSTEMS AND CONVERT THEM INTO THE KNOWLEDGE NECESSARY TO MAKE DECISIONS THAT IMPROVE LEARNING OUTCOMES. THE INFORMATION SYSTEMS ADMINISTERED BY THE UNIVERSITIES STORE A LARGE VOLUME OF DATA ON THE SOCIOECONOMIC AND ACADEMIC VARIABLES OF THE STUDENTS. IN THE UNIVERSITY FIELD, THESE DATA ARE GENERALLY NOT USED TO GENERATE KNOWLEDGE ABOUT THEIR STUDENTS, UNLIKE IN THE BUSINESS FIELD, WHERE THE DATA ARE INTENSIVELY ANALYZED IN BUSINESS INTELLIGENCE TO GAIN A COMPETITIVE ADVANTAGE. THESE SUCCESS STORIES IN THE BUSINESS FIELD CAN BE REPLICATED BY UNIVERSITIES THROUGH AN ANALYSIS OF EDUCATIONAL DATA. THIS DOCUMENT PRESENTS A METHOD THAT COMBINES MODELS AND TECHNIQUES OF DATA MINING WITHIN AN ARCHITECTURE OF BUSINESS INTELLIGENCE TO MAKE DECISIONS ABOUT VARIABLES THAT CAN INFLUENCE THE DEVELOPMENT OF LEARNING. IN ORDER TO TEST THE PROPOSED METHOD, A CASE STUDY IS PRESENTED, IN WHICH STUDENTS ARE IDENTIFIED AND CLASSIFIED ACCORDING TO THE DATA THEY GENERATE IN THE DIFFERENT INFORMATION SYSTEMS OF A UNIVERSITY.
PROGRAMMING IS CONSIDERED A DEMANDING TASK THAT REQUIRES FOCUSING ON DETAIL AT CODE LEVEL. STUDENTS LEARNING TO PROGRAM NEED TO LEARN TO THINK LIKE A PROGRAMMER, WHICH INVOLVES COMING UP WITH PLANS NEEDED TO SOLVE PROBLEMS, AND THEY NEED TO LEARN TO WRITE THE CODE THAT CORRESPONDS TO THE PLANS THAT THEY HAVE THOUGHT OF. THE USE OF MULTIPLE FILES CREATES ADDITIONAL OVERHEAD TO THE PROCESS, AS PART OF THE CODE IS NOT VISIBLE TO THE STUDENT. IF A STUDENT DOES NOT REMEMBER THE CONTENTS OF A PARTICULAR FILE, SHE NEEDS TO CONSCIOUSLY MOVE FROM WRITING CODE IN ONE FILE TO READING CODE IN ANOTHER FILE. THIS CONSCIOUS TRANSITION OF ATTENTION FROM ONE LOCATION TO ANOTHER IS KNOWN AS COGNITIVE SHIFTING. USING KEY-LEVEL DATA COLLECTED FROM A PROGRAMMING EXAM, WE ANALYZE STUDENTS' MOVEMENTS WITHIN FILES AND BETWEEN FILES, AND RELATE THESE MOVEMENTS WITH STUDENTS' PERFORMANCE IN THE COURSE. OUR RESULTS INDICATE THAT FREQUENTLY MOVING FROM ONE FILE TO ANOTHER MAY LEAD TO WORSE PERFORMANCE THAN MORE FOCUSED ACTIONS, BUT NO SUCH EFFECT EXISTS WHEN ANALYZING MOVEMENTS WITHIN AN INDIVIDUAL FILE.
AS PART OF THE STUDY, THE PROBLEM OF DEVELOPING AND APPLYING A METRIC FOR ASSESSING THE DEGREE OF SIMILARITY OF TIME SERIES IS CONSIDERED, WHICH MAKES IT POSSIBLE TO CONSIDER THE ASSUMPTIONS ABOUT THE MODEL OF A SERIES WHEN COMPARING, AS WELL AS TO COMPARE THE VALUES OF THE CORRESPONDING CHARACTERISTICS OF THE SERIES. CHARACTERISTICS CAN BE VALUES THAT DESCRIBE THE STRUCTURE OF A SERIES, OR DIRECTLY THE VALUES OF THE APPROXIMATING FUNCTION, WHICH CAN BE OBTAINED USING NONPARAMETRIC STATISTICS METHODS. ONE OF THE DIRECTIONS IN WHICH THIS APPROACH CAN BE APPLIED TO ASSESSING THE SIMILARITY OF TIME SERIES IS THE STUDY OF VOCAL PERFORMANCES. IN PRACTICE, THE DEGREE OF SIMILARITY IN THE PERFORMANCE OF MELODIES BY SEVERAL SPEAKERS WAS ANALYZED. IT WAS DETERMINED THAT, USING THE SYNCHRONICITY METRIC, IT IS POSSIBLE TO IMPLEMENT AN EXERCISE IN WHICH STUDENTS NEED TO REPEAT THE MELODY AFTER THE TEACHER. IN ADDITION, THIS APPROACH WAS APPLIED IN THE SEGMENT IDENTIFICATION MODULE WITH AN ABRUPT CHANGE IN THE SOUNDING OF THE FUNDAMENTAL FREQUENCY. THIS WORK IS DEVOTED TO THE MODIFICATION OF THE PROGRAM COMPLEX FOR VOCAL RECOGNITION IN ORDER TO IDENTIFY NOTES WITH A SHARP CHANGE IN THE FUNDAMENTAL FREQUENCY. THE COMPLEX IS AIMED AT CARRYING OUT ADDITIONAL INDEPENDENT TRAINING IN TEACHING VOCALS. THE USE OF THE SOFTWARE PACKAGE WILL ALLOW, IN REAL TIME, PROVIDING FEEDBACK TO THE USER WITH AN ASSESSMENT OF THE QUALITY OF THEIR SINGING. THIS SHOULD ALLOW STUDENTS TO STUDY NOT ONLY UNDER THE SUPERVISION OF A TEACHER, BUT ALSO INDEPENDENTLY IN THE EARLY STAGES OF LEARNING. THE BASIC ALGORITHM OF THE PROGRAM RECOGNIZES NOTES WITHOUT SHARP CHANGES IN FREQUENCIES WITH HIGH ACCURACY, WHICH IS CONFIRMED BY EXPERIMENTS. IN ORDER TO RECOGNIZE BY THE ALGORITHMS OF THE PROGRAM NOTES SUNG VIBRATO AND GLISSANDO IN SINGING, A NEW ANALYSIS METHOD BASED ON THE METRIC OF TIME SERIES SYNCHRONICITY IS PROPOSED.
THE CONSTANT INNOVATION IN NEW TECHNOLOGIES AND THE INCREASE IN THE USE OF COMPUTING DEVICES IN DIFFERENT AREAS OF THE SOCIETY HAVE CONTRIBUTED TO A DIGITAL TRANSFORMATION IN ALMOST EVERY SECTOR. THIS DIGITAL TRANSFORMATION HAS ALSO REACHED THE WORLD OF EDUCATION, MAKING IT POSSIBLE FOR MEMBERS OF THE EDUCATIONAL COMMUNITY TO ADOPT LEARNING MANAGEMENT SYSTEMS (LMS), WHERE THE DIGITAL CONTENTS REPLACING THE TRADITIONAL TEXTBOOKS ARE EXPLOITED AND MANAGED. THIS ARTICLE AIMS TO STUDY THE RELATIONSHIP BETWEEN THE TYPE OF COMPUTING DEVICE FROM WHICH STUDENTS ACCESS THE LMS AND HOW AFFECTS THEIR PERFORMANCE. TO ACHIEVE THIS, THE LMS ACCESSES OF STUDENTS IN A SCHOOL COMPRISING FROM ELEMENTARY TO BACHELOR'S DEGREE STAGES HAVE BEEN MONITORED BY MEANS OF DIFFERENT COMPUTING DEVICES ACTING AS SENSORS TO GATHER DATA SUCH AS THE TYPE OF DEVICE AND OPERATING SYSTEM USED BY THE STUDENTS.THE MAIN CONCLUSION IS THAT STUDENTS WHO ACCESS THE LMS IMPROVE SIGNIFICANTLY THEIR PERFORMANCE AND THAT THE TYPE OF DEVICE AND THE OPERATING SYSTEM HAS AN INFLUENCE IN THE NUMBER OF PASSED SUBJECTS. MOREOVER, A PREDICTIVE MODEL HAS BEEN GENERATED TO PREDICT THE NUMBER OF PASSED SUBJECTS ACCORDING TO THESE FACTORS, SHOWING PROMISING RESULTS.
NOWADAYS, MASSIVE OPEN ONLINE COURSES (MOOC) HAS OBTAINED A RAPID DEVELOPMENT AND DRAWN MUCH ATTENTION FROM THE AREAS OF LEARNING ANALYTICS AND ARTIFICIAL INTELLIGENCE. THERE ARE LOTS OF UNSTRUCTURED DATA BEING GENERATED IN ONLINE REVIEWS AREA. THE LEARNING BEHAVIORAL DATA BECOME MORE AND MORE DIVERSE, AND THEY PROMPT THE EMERGENCE OF BIG DATA IN EDUCATION. TO MINE USEFUL INFORMATION FROM THESE DATA, WE NEED TO USE EDUCATIONAL DATA MINING AND LEARNING ANALYSIS TECHNIQUE TO STUDY THE LEARNING FEELINGS AND DISCUSSED TOPICS AMONG LEARNERS. THIS PAPER AIMS TO MINE AND ANALYZE TOPIC INFORMATION HIDDEN IN THE UNSTRUCTURED REVIEWS DATA IN MOOC, A NOVEL AUTHOR TOPIC MODEL BASED ON AN UNSUPERVISED LEARNING IDEA IS PROPOSED TO EXTRACT LEARNING TOPICS FOR THE EACH LEARNER. ACCORDING TO THE EXPERIMENTAL RESULTS, WE WILL ANALYZE AND FOCUSES OF INTERESTS OF LEARNERS, WHICH FACILITATES FURTHER PERSONALIZED COURSE RECOMMENDATION AND IMPROVE THE QUALITY OF ONLINE COURSES.
OVER THE LAST YEARS VARIOUS ATTEMPTS TO LEGITIMATE TECHNOLOGY IN EDUCATION HAVE BEEN OBSERVED. BEGINNING FROM THE EXAMPLES OF SIMPLE DIGITIZATION OF SCHOOLS BOOKS THROUGH, IPAD SCHOOL" INITIATIVE IN THE NETHERLANDS TILL DEDICATED CLOSED TABLET SOLUTIONS IN THE USA. AT THE END OF 2014 FIRST ADAPTIVE LEARNING SOLUTIONS WERE INTRODUCED. THE KNEWTON'S SYSTEM USING KNOWLEDGE GRAPH STARTED GIVING RECOMMENDATIONS FOR LEARNERS ON WHAT QUESTIONS SHOULD THEY WORK NEXT, PROVIDED LEARNING ANALYTICS AND INSTRUCTIONS PREDICTING FUTURE RESULTS. IN 2015 COGNITIVE COMPUTING AND ARTIFICIAL INTELLIGENCE SYSTEMS THAT USE NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING ENTERED THE MARKET. IBM STARTED WORKING ON A COMPREHENSIVE SOLUTION FOR PERSONALIZED LEARNING. THE PRACTICAL IMPLEMENTATIONS OF THOSE SYSTEM CAN NOT ONLY IMPROVE STUDENTS' OUTCOMES BUT ALSO HELP TO CLOSE SKILLS GAPS AND RISE EMPLOYABILITY. THIS ARTICLE PRESENTS SOLUTIONS THAT ARE BEING UNLOCKED RIGHT NOW SHOWING EXAMPLES OF NEWLY CREATED VALUE OF TECHNOLOGY IN EDUCATION.
PERMANENT EDUCATION IS A PROCESS OF SELF-CONSTRUCTION BASED ON THE PEDAGOGICAL PRINCIPLES OF EDUCABILITY, EDUCATIVITY AND PERSONAL AUTONOMY. THE PROCESS OF HUMAN CONSTRUCTION IMPLIES A PROCESS OF LIFELONG AND LIFEWIDE LEARNING AND TRAINING IN ORDER TO ACHIEVE INDIVIDUAL AND COMMUNITY "WELL-BEING". THIS CONCEPTION OF CONTINUING EDUCATION INCLUDES THE CONSIDERATION OF NEW LEARNING SPACES SUCH AS THAT OF THE COGNITIVE CITY AND NEW POSSIBILITIES SUCH AS THE OPEN COURSES. WITHIN THE APPROACH OF CAPABILITIES BUILDING, THE POSSIBILITIES AND RISKS OF SELF-REGULATED LEARNING ARE STUDIED AS AN EFFECTIVE STRATEGY IN THE CURRENT SOCIO-TECHNICAL AND TECHNOLOGICAL GLOBALIZATION CONTEXT. FINALLY, THE RISK POTENTIAL OF THE CONTRIBUTION OF LEARNING ANALYTICS AND ARTIFICIAL INTELLIGENCE (AL) TO EDUCATION IS EXAMINED; AND IT IS CONCLUDED THAT THE PROCESS OF PERMANENT EDUCATION IN A SOCIO-TECHNICAL CONTEXT MUST BE BASED ON A SOLID HUMAN FORMATION CONCEIVED AS BASIC EDUCATION.
THIS WORK PRESENTS AN INNOVATIVE FRAMEWORK WITH THE AIM TO CREATE FULL ENGAGEMENT FOR THE LEARNERS ON MASSIVE OPEN ONLINE LEARNING ENVIRONMENTS THROUGH A CONNECTIVIST APPROACH AS AN EXAMPLE OF AN ADAPTIVE LEARNING EXPERIENCE. THE PROPOSED FRAMEWORK RELIES ON THE IMPORTANCE OF CREATING ENGAGING EXPERIENCES BEFORE, DURING AND AFTER THE FINISH OF A COURSE TO INCREASE LEARNERS' PARTICIPATION AND REDUCE DROP-OUT RATES WITH THE HELP OF LEARNING ANALYTICS. THIS WORK PRESENTS A COMPELLING IDEA IN THE UNIVERSE OF MOOCS: IT INTENDS TO EXPAND THE EFFORTS OF THE LEARNING DESIGN TEAM TO ACHIEVE PRE AND POST-COURSE ENGAGEMENT, WHERE ENGAGEMENT TAKE THE FORM OF AN ONGOING COMMUNITY OF LEARNERS. THIS RESEARCH PROVIDES RESULTS FROM THE FIRST SUCCESSFUL EXPERIENCES IN TWO MICROMASTERS "PROFESSIONAL ANDROID DEVELOPER", TAUGHT IN ENGLISH, AND ONE SPECIALIZATION TAUGHT IN SPANISH: "E-LEARNING FOR TEACHERS: CREATE INNOVATIVE ACTIVITIES AND CONTENT" AT THE EDX PLATFORM.
THIS STUDY AIMS TO INCREASE ETL PROCESS EFFICIENCY AND REDUCE PROCESSING TIME BY APPLYING THE METHOD OF CHANGE DATA CAPTURE (CDC) IN DISTRIBUTED SYSTEM USING HADOOP DISTRIBUTED FILE SYSTEM (HDFS) AND APACHE SPARK IN THE DATA WAREHOUSE OF LEARNING ANALYTICS SYSTEM OF UNIVERSITAS INDONESIA. USUALLY, INCREASES IN I LIE NUMBER OF RECORDS IN THE DATA SOURCE RESULT IN AN INCREASE IN ETL PROCESSING TIME FOR THE DATA WAREHOUSE SYSTEM. THIS CONDITION OCCURS AS A RESULT OF INEFFICIENT ETL PROCESS USING THE FULL LOAD METHOD. USING THE FULL LOAD METHOD, ETL HAS TO PROCESS THE SAME NUMBER OF RECORDS AS THE NUMBER OF RECORDS IN THE DATA SOURCES. THE PROPOSED ETL MODEL DESIGN WITH THE APPLICATION OF CDC METHOD USING HDFS AND APACHE SPARK CAN REDUCE THE AMOUNT OF DATA IN THE ETL PROCESS. CONSEQUENTLY, THE PROCESS BECOMES MORE EFFICIENT AND THE ETL PROCESSING TIME IS REDUCED APPROXIMATELY 53% IN AVERAGE.
THIS STUDY EXAMINED STUDENTS' ENGAGEMENT CHARACTERISTICS IN ONLINE COURSES AND THEIR IMPACT ON ACADEMIC ACHIEVEMENTS, TRYING TO DISTINGUISH BETWEEN COURSE COMPLETERS AND NONCOMPLETERS. MOREOVER, THIS RESEARCH IS INTENDED TO DIFFERENTIATE BETWEEN THOSE WHO PASS THE FINAL EXAM AND THOSE WHO DO NOT. FOUR ONLINE COURSES WERE EXAMINED WITH A SIMILAR PEDAGOGICAL MODEL (N-STUDENTS = 646) USING LEARNING ANALYTICS METHODS. THE RESULTS REVEALED SIGNIFICANT DIFFERENCES BETWEEN STUDENTS WHO COMPLETED THE COURSES AND STUDENTS WHO DID NOT, IN ALL 13 VARIABLES. COMPLETERS' LEARNING ACTIVITIES WERE MORE THAN TWICE AS HIGH, EXCEPT FOR WRITING IN THE FORUMS. COURSE SUBJECT AND ONGOING TASK AND ASSIGNMENT SUBMISSIONS PREDICTED COURSE COMPLETION, WHEREAS, IN ADDITION TO THESE VARIABLES, ENGAGEMENT WITH COURSE MATERIALS AND READING THE FORUMS PREDICTED FINAL EXAM SUCCESS, AS WELL. THUS, THE PREDICTION OF SUCCESS IN FINAL EXAM EMPHASIZED THE SIGNIFICANT IMPORTANCE OF ENGAGEMENT IN VARIOUS ACTIVITIES IN THE ONLINE COURSE.
AFFECT HAS EMERGED AS AN IMPORTANT PART OF THE INTERACTION BETWEEN LEARNERS AND COMPUTERS, WITH IMPORTANT IMPLICATIONS FOR LEARNING OUTCOMES. AS A RESULT, IT HAS EMERGED AS AN IMPORTANT AREA OF RESEARCH WITHIN LEARNING ANALYTICS. RELIABLE AND VALID DATA LABELING IS A KEY TENET FOR TRAINING MACHINE LEARNING MODELS PROVIDING SUCH ANALYTICS. IN THIS STUDY, USING HUMAN EXPERT LABELING PROCESS (HELP) AS A BASELINE LABELING PROTOCOL, WE INVESTIGATED AN OPTIMIZED METHOD THROUGH SEVERAL EXPERIMENTS FOR LABELING STUDENT AFFECT BASED ON CIRCUMPLEX MODEL OF EMOTION (VALENCE AROUSAL). USING THE OPTIMIZED METHOD, WE THEN HAD THE HUMAN EXPERTS LABEL A LARGER QUANTITY OF STUDENT DATA SO THAT WE COULD TEST AND VALIDATE THIS METHOD ON A RELATIVELY LARGER AND DIFFERENT DATASET. THE RESULTS SHOWED THAT USING THE OPTIMIZED METHOD, THE EXPERTS WERE ABLE TO ACHIEVE AN ACCEPTABLE CONSENSUS IN LABELING OUTCOMES AS ALIGNED WITH AFFECT LABELING LITERATURE.
IN RECENT YEARS, ESCAPE ROOMS HAVE BECOME ONE OF THE LEADING LEISURE ACTIVITIES WORLDWIDE. DERIVED FROM THESE ACTIVITIES, EDUCATIONAL ESCAPE ROOMS HAVE EMERGED AS A NEW TYPE OF TEACHING PRACTICE WITH THE PROMISE OF ENHANCING STUDENTS' LEARNING THROUGH HIGHLY ENGAGING EXPERIENCES. THE RAPID RISE OF EDUCATIONAL ESCAPE ROOMS HAS LED TO A MISALIGNMENT BETWEEN EDUCATORS' NEEDS FOR BEING ABLE TO IMPLEMENT THIS NOVEL TEACHING PRACTICE AND THE AVAILABILITY OF TOOLS TO EASE THE PROCESS. MOREOVER, THIS LACK OF SUPPORT IS PREVENTING TEACHERS AND STUDENTS FROM TAKING FULL ADVANTAGE OF THE POTENTIAL OF EDUCATIONAL ESCAPE ROOMS. THIS ARTICLE PROVIDES A ROAD MAP OF THE MOST URGENT ISSUES TO BE ADDRESSED TO BRIDGE THE AFOREMENTIONED GAP: EASING THE CREATION OF DIGITAL PUZZLES, AIDING IN THE LOGISTICAL ASPECTS OF CONDUCTING AN EDUCATIONAL ESCAPE ROOM, HARNESSING LEARNING ANALYTICS, FOSTERING REMOTE COLLABORATION, AND INTEGRATING ARTIFICIAL INTELLIGENCE TO ADAPT THE EXPERIENCE TO EACH TEAM.
COGNITIVE AGING IN HEALTHY ADULTS EXHIBITS SIGNIFICANT AND HETEROGENEOUS VARIABILITY. IN THIS STUDY, WE APPLY A ROBUST UNIFIED LEARNING FRAMEWORK TO CLUSTER SUBGROUPS USING NEUROIMAGING DATA (BRAIN VOLUME AND WHITE MATTER), TO IDENTIFY NEUROLOGICAL PHENOTYPES THAT CAN SORT OUT THE HETEROGENEITY IN COGNITIVE AGING AND HELP IDENTIFY POTENTIAL RISK FACTORS FOR SUBOPTIMAL BRAIN AGING. USING MACHINE LEARNING ANALYTICS, RESULTS REVEALED TWO UNIQUE SUBGROUPS IN HEALTHY OLDER ADULTS WITH DIFFERENT PATTERNS OF WHITE MATTER INTEGRITY AND BRAIN VOLUMETRIC MEASURES. THE CLASSIFICATION OF PHENOTYPICAL SUBGROUPS IN HEALTHY OLDER ADULTS MAY INFORM THE UNDERSTANDING OF THE COMPLEXITY OF BRAIN CHANGES BEFORE THE ONSET OF CLINICAL SYMPTOMS. THE IDENTIFIED NEUROIMAGING FEATURES THAT DEFINED GROUP CLASSIFICATION ARE RECOGNIZED AS IMPORTANT STRUCTURES THAT SUBSERVE COGNITIVE PERFORMANCE. FURTHER ANALYSIS OF THESE POTENTIAL BIOMARKERS THAT HELP PREDICT TRAJECTORY OF COGNITIVE DECLINE IN SYMPTOM FREE INDIVIDUALS COULD LEAD TO THE DETECTION OF EARLY STAGES OF NEURODEGENERATIVE DISEASES.
THIS SPECIAL ISSUE BRINGS TOGETHER A SET OF CONTRIBUTIONS THAT REPRESENT AND EXPLORE THE STATE OF THE ART REGARDING THE DEVELOPMENT AND APPLICATION OF DIGITAL TECHNOLOGY-ENHANCED RESEARCH APPROACHES IN EDUCATIONAL RESEARCH-E-RESEARCH FOR EDUCATION. OVER THE RECENT YEARS, DIGITAL TECHNOLOGIES HAVE ENHANCED VARIOUS ASPECTS OF SCHOLARLY PRACTICES, SUCH AS DATA COLLECTION AND ANALYSIS, RESEARCH COLLABORATION, AND PUBLISHING. NEW TECHNOLOGY-ENHANCED RESEARCH APPROACHES HAVE BEEN EMERGING IN A NUMBER OF EDUCATIONAL RESEARCH DOMAINS, SUCH AS LEARNING ANALYTICS, EDUCATIONAL DATA MINING, DIGITAL ETHNOGRAPHY AND EDUCATIONAL INFORMATICS. ALTHOUGH THE POTENTIAL OF DIGITAL APPROACHES HAS BEEN GENERALLY ACKNOWLEDGED, MANY OVERARCHING CONCEPTUAL AND METHODOLOGICAL QUESTIONS REMAIN UNEXPLORED AND UNDER-THEORISED. THIS SPECIAL ISSUE PROVIDES A LANDMARK COLLECTION THAT SYNTHESISES SOME IMPORTANT EMPIRICAL AND METHODOLOGICAL ADVANCES IN THIS RESEARCH FIELD, INCLUDING METHODOLOGICALLY INNOVATIVE CASE STUDIES AND CRITICAL ANALYSES OF OVERARCHING EPISTEMOLOGICAL, METHODOLOGICAL AND PRACTICAL ISSUES THAT INEVITABLY WILL SHAPE FUTURE DEVELOPMENTS OF THIS RESEARCH FIELD.
PREVIOUS STUDIES HAVE INVESTIGATED THE ASSOCIATION BETWEEN THE FREQUENCY OF STUDENT LEARNING MANAGEMENT SYSTEM (LMS) USE (LOGINS, DISCUSSION BOARD USE, RESOURCES USED, ETC.) AND ACADEMIC ACHIEVEMENT. THESE STUDIES INDICATE THAT LOW LMS USE BY STUDENTS IS LIKELY TO RESULT IN LESS ACADEMIC SUCCESS. HOWEVER, THESE MODELS FAIL TO TAKE INTO ACCOUNT SELF-BELIEFS THAT MAY ALSO INCREASE THE EXPLANATORY VALUE OF LEARNING ANALYTICS FROM THE LMS. THIS STUDY SURVEYED 310 STUDENTS (M = 22.10 YEARS, SD = 6.30 YEARS) UNDERTAKING A FIRST YEAR HEALTH PSYCHOLOGY SUBJECT. RESULTS SHOW THE CENTRAL ROLE OF SELF-EFFICACY IN PREDICTING STUDENT PERFORMANCE. ONLINE ACTIVITY WAS NOT PREDICTIVE OF PERFORMANCE, SUGGESTING THE PRIMACY OF PSYCHOLOGICAL FACTORS MORE SO THAN ONLINE ENGAGEMENT IN DETERMINING OUTCOME. OF THE MOTIVATIONAL FACTORS, AMOTIVATION WAS THE SINGLE SIGNIFICANT PREDICTOR OF ACADEMIC ACHIEVEMENT. PROPOSED FUTURE RESEARCH DIRECTIONS INCLUDE THE NEED TO EVALUATE WHETHER THESE RESULTS ARE SUSTAINED OVER TIME.
ONLINE LEARNING HAS MATURED AS A MECHANISM THAT CAN CHANGE HOW WE DELIVER EDUCATION. OPEN EDUCATION RESOURCES ARE PROLIFERATING, AND INSTITUTIONS ARE CREATING MASSIVE OPEN ONLINE COURSES (MOOCS). IN ADDITION, EFFORTS ARE UNDERWAY TO DEVELOP PLATFORMS THAT ALLOW INDIVIDUALS TO CREATE, LEAD, AND PARTICIPATE IN THEIR OWN COURSES. THIS BOTTOM-UP, PEER-TO-PEER MODEL OF EDUCATION ILLUMINATES NEW DILEMMAS. FOR EXAMPLE, OPEN LEARNING ENVIRONMENTS ARE HEAVILY DEPENDENT ON VOLUNTARY INDIVIDUAL PARTICIPATION, BUT SUCH PARTICIPATION IS DIFFICULT TO FOSTER. IN THIS PAPER, WE USE LOG DATA FROM THE PEER 2 PEER UNIVERSITY (P2PU) TO EXPLORE FACTORS RELATED TO ACTIVE PARTICIPATION IN A SERIES OF TEACHER, PROFESSIONAL DEVELOPMENT COURSES (THE P2PU SCHOOL OF EDUCATION). WE EMPLOY LEARNING ANALYTICS TO IDENTIFY HOW FEATURES SUCH AS COURSE PAGE DESIGN AND COURSE ORGANIZER ACTIVITY INTERACT WITH NEW AND RETURNING PARTICIPANTS TO FOSTER INCREASED PARTICIPATION IN OPEN LEARNING GROUPS.
THIS REFLECTION CONSIDERS ISSUES FOR RESEARCHERS WHO STUDY OPEN, FLEXIBLE, AND DISTANCE LEARNING (OFDL) SYSTEMS. IN PARTICULAR, HUMAN LEARNING IS REGARDED AS AN EMERGENT PHENOMENON OCCURRING IN EDUCATIONAL COMPLEX SYSTEMS, WHICH MAY BE NON-DIGITAL OR DIGITAL IN NATURE. IT IS SUGGESTED THE TERM LEARNING IN OFDL, SUCH AS LEARNING ANALYTICS AND MACHINE LEARNING, REFERS TO COMPUTATIONAL TECHNIQUES THAT HELP IDENTIFY PATTERNS IN LARGE DATASETS GENERATED IN OFDL SYSTEMS. IT IS ALSO PROPOSED THAT OFDL SYSTEMS ARE EXAMPLES OF COMPLEX SYSTEMS, AND AN OVERVIEW IS PROVIDED OF A SET OF RELEVANT COMPLEXITY CONCEPTUAL PRINCIPLES. ISSUES ARE CONSIDERED ABOUT TYPES OF LEARNING DATA IN OFDL SYSTEMS AND THEIR LACK OF SUITABILITY FOR ASSESSING HIGHER-LEVEL EXPLANATORY AND PROCEDURAL UNDERSTANDINGS. FINALLY, AN EXAMPLE IS PROVIDED TO ILLUSTRATE HOW COMPLEXITY IDEAS MAY BE USED WITH LA TECHNIQUES TO STUDY AN OFDL RESEARCH TOPIC. IMPLICATIONS OF COMPLEXITY THEORIZING FOR OFDL RESEARCH ARE ALSO DISCUSSED.
BEGINNER TABLE-TENNIS PLAYERS REQUIRE CONSTANT REAL-TIME FEEDBACK WHILE LEARNING THE FUNDAMENTAL TECHNIQUES. HOWEVER, DUE TO VARIOUS CONSTRAINTS SUCH AS THE MENTOR'S INABILITY TO BE AROUND ALL THE TIME, EXPENSIVE SENSORS AND EQUIPMENT FOR SPORTS TRAINING, BEGINNERS ARE UNABLE TO GET THE IMMEDIATE REAL-TIME FEEDBACK THEY NEED DURING TRAINING. SENSORS HAVE BEEN WIDELY USED TO TRAIN BEGINNERS AND NOVICES FOR VARIOUS SKILLS DEVELOPMENT, INCLUDING PSYCHOMOTOR SKILLS. SENSORS ENABLE THE COLLECTION OF MULTIMODAL DATA WHICH CAN BE UTILISED WITH MACHINE LEARNING TO CLASSIFY TRAINING MISTAKES, GIVE FEEDBACK, AND FURTHER IMPROVE THE LEARNING OUTCOMES. IN THIS PAPER, WE INTRODUCE THE TABLE TENNIS TUTOR (T3), A MULTI-SENSOR SYSTEM CONSISTING OF A SMARTPHONE DEVICE WITH ITS BUILT-IN SENSORS FOR COLLECTING MOTION DATA AND A MICROSOFT KINECT FOR TRACKING BODY POSITION. WE FOCUSED ON THE FOREHAND STROKE MISTAKE DETECTION. WE COLLECTED A DATASET RECORDING AN EXPERIENCED TABLE TENNIS PLAYER PERFORMING 260 SHORT FOREHAND STROKES (CORRECT) AND MIMICKING 250 LONG FOREHAND STROKES (MISTAKE). WE ANALYSED AND ANNOTATED THE MULTIMODAL DATA FOR TRAINING A RECURRENT NEURAL NETWORK THAT CLASSIFIES CORRECT AND INCORRECT STROKES. TO INVESTIGATE THE ACCURACY LEVEL OF THE AFOREMENTIONED SENSORS, THREE COMBINATIONS WERE VALIDATED IN THIS STUDY: SMARTPHONE SENSORS ONLY, THE KINECT ONLY, AND BOTH DEVICES COMBINED. THE RESULTS OF THE STUDY SHOW THAT SMARTPHONE SENSORS ALONE PERFORM SUB-PAR THAN THE KINECT, BUT SIMILAR WITH BETTER PRECISION TOGETHER WITH THE KINECT. TO FURTHER STRENGTHEN T3'S POTENTIAL FOR TRAINING, AN EXPERT INTERVIEW SESSION WAS HELD VIRTUALLY WITH A TABLE TENNIS COACH TO INVESTIGATE THE COACH'S PERCEPTION OF HAVING A REAL-TIME FEEDBACK SYSTEM TO ASSIST BEGINNERS DURING TRAINING SESSIONS. THE OUTCOME OF THE INTERVIEW SHOWS POSITIVE EXPECTATIONS AND PROVIDED MORE INPUTS THAT CAN BE BENEFICIAL FOR THE FUTURE IMPLEMENTATIONS OF THE T3.
IN THIS PAPER, WE ANALYZE STUDENTS' E-BOOK READING PATTERNS BY USING MARKOV CHAINS (MCS). WE USED CLICK-STREAM DATA OF 236 STUDENTS WHILE THEY READ 7 DIFFERENT CONTENTS SHARED BY THE INSTRUCTOR ACROSS DIFFERENT WEEKS OF THE COURSE. TO ANALYZE READING PATTERNS, WE FIRST CLUSTERED STUDENTS INDEPENDENTLY BASED ON THEIR INTERACTION WITH EACH CONTENT. WE GROUPED STUDENTS IN NONE, LOW, MEDIUM, AND HIGH CLUSTERS. THEN BY USING MCS, WE CALCULATED CLUSTER TRANSITION PROBABILITIES BETWEEN DIFFERENT CONTENTS. WE ALSO VISUALIZED THESE PATTERNS AND APPLIED A PREDICTION ALGORITHM TO PREDICT STUDENTS' READING PATTERNS. RESULTS REVEALED THAT STUDENTS ARE LIKELY TO FOLLOW THE SAME READING PATTERNS ACROSS THE SEMESTER. IN OTHER WORDS, IF A STUDENT READS LESS IN THE FIRST CONTENT, S/HE IS LIKELY TO READ LESS DURING THE REST OF THE SEMESTER. WE ALSO FOUND THAT TRANSITION DATA COULD BE USED TO PREDICT STUDENTS' FURTHER READING BEHAVIORS. THE DEVELOPED MODEL CAN BE USED TO PLAN AN INTERVENTION SYSTEM FOR AT-RISK STUDENTS. VISUALIZATION OF THESE TRANSITIONS MAY HELP A TEACHER TO UNDERSTAND HOW WELL STUDENTS USE CONTENTS.
THE RESEARCH TOPICS IN EDUCATIONAL DATA MINING AREA AT HOME AND ABROAD ARE TRANSFORMING FROM THE CONCEPT, PRINCIPLE LEVEL TO MODELING ANALYSIS AND APPLICATION LEVEL, AND AT THE SAME TIME TRANSFORMING FROM STATISTICAL MONITORING OF EDUCATIONAL QUALITY, EDUCATIONAL POLICY AND OTHER MACRO-LEVEL APPLICATIONS TO THE FURTHER DEVELOPMENT OF SCHOOL TEACHING, STUDENT LEARNING AND OTHER MICRO APPLICATIONS. ON THE CONCEPTUAL DISCUSSION OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS, THIS PAPER PROPOSES A DATA MODEL AND A SYSTEM ARCHITECTURE COMBINED WITH THE TEACHING PRACTICE, ELABORATES "FOUR MODELING AND THREE ANALYSIS" METHODOLOGY IN THE STUDY OF EDUCATION DATA, CONSTRUCTS THE "WHOLE CHESSBOARD" ANALYSIS OF DATA MINING IN SMART CLASSROOM, IN WHICH 13 SPECIFIC RESEARCH QUESTIONS ARE DERIVED. MOREOVER, IT PUTS FORWARD FOUR KINDS OF APPLICATION MODES OF DATA MINING IN SMART CLASSROOM AND FINALLY EXPLORES THE APPLICATION OF INTERACTION BETWEEN TEACHER AND STUDENTS OF SMART CLASSROOM BASED ON REAL DATA.
WE CONSIDER TWO COMPLEMENTARY ASPECTS OF MATHEMATICAL SKILLS, I.E. PROCEDURAL FLUENCY AND CONCEPTUAL UNDERSTANDING, FROM A POINT OF VIEW THAT IS RELATED TO MODERN E-LEARNING ENVIRONMENTS AND COMPUTER-BASED ASSESSMENT. PEDAGOGICAL BACKGROUND OF TEACHING MATHEMATICS IS DISCUSSED, AND IT IS PROPOSED THAT THE TRADITIONAL BOOK MEDIUM HAS DETERMINED MUCH OF ITS HISTORICAL DEVELOPMENT, INCLUDING THE CLASSICAL STYLE OF PRESENTING MATHEMATICAL KNOWLEDGE. INFORMATION TECHNOLOGY IS LIKELY TO BE AN EMERGING GAME CHANGER IN LEARNING AND TEACHING OF MATHEMATICS, AND WE ARGUE THAT THE POTENTIAL OF E-LEARNING PLATFORMS EXTENDS BEYOND SIMPLE DRILL EXERCISES TO COMPLEX PROBLEMS (HAVING SEVERAL `RIGHT' SOLUTIONS) THAT ARE EXPECTED TO IMPROVE CONCEPTUAL UNDERSTANDING. WE DISCUSS THESE IDEAS REFERRING TO EXPERIENCES AND OBSERVATIONS FROM A STACK-BASED AUTOMATIC ASSESSMENT SYSTEM AT AALTO UNIVERSITY SINCE 2006. THE ARTICLE IS CONCLUDED BY VISIONS FOR THE FUTURE DEVELOPMENT INVOLVING, E.G. LEARNING ANALYTICS BASED ON STUDENTS'ANSWER DATA.
3D VIRTUAL WORLDS ARE BEING USED FOR EDUCATION AND TRAINING PURPOSES IN A CROSS-DISCIPLINARY WAY. HOWEVER, ITS WIDESPREAD ADOPTION, PARTICULARLY IN FORMAL LEARNING CONTEXTS, IS FAR FROM BEING A REALITY DUE A BROAD RANGE OF TECHNOLOGICAL CHALLENGES. IN THIS REFLECTION PAPER, OUR MAIN GOAL IS TO ARGUE WHY AND HOW IDENTITY FEDERATION SHOULD BE DISCUSSED AND ADOPTED AS A SOLUTION TO SEVERAL BARRIERS THAT EDUCATORS AND INSTITUTIONS FACE WHEN USING VIRTUAL WORLDS. BY PRESENTING A CLEAR SET OF SCENARIOS WITHIN DIFFERENT DIMENSIONS OF THE EDUCATIONAL PROCESS, AS CLASSROOM MANAGEMENT, CONTENT REUSE, LEARNING ANALYTICS, ACCESSIBILITY, AND RESEARCH, WE CONSIDER IDENTITY, TRACEABILITY, PRIVACY, ACCOUNTABILITY, AND INTEROPERABILITY AS MAIN CONCERNS IN ORDER TO SUPPORT OUR ARGUMENT. FINALLY, WE CONCLUDE THE PAPER BY PRESENTING PATHS TO A PROPOSAL FOR A WORKABLE SOLUTION, THROUGH THE ANALYSIS AND REFLECTION OF DIFFERENT AND CURRENT EFFORTS THAT HAS BEEN MADE BY OTHER TEAMS, TOWARDS FUTURE TECHNOLOGICAL DEVELOPMENTS.
E-LEARNING HAS GREATLY DEVELOPED IN RECENT YEARS BECAUSE OF THE POPULARITY OF SMARTPHONES AND TABLETS. BECAUSE OF THE IMPROVEMENT OF THE DEVICE PERFORMANCES AND THE DATA TRANSMISSION CAPABILITY OF THE INTERNET, MORE AND MORE COMPLEX E-LEARNING MATERIALS ARE PROVIDED TO LEARNERS. QUIZ GAMES ARE A KIND OF E-LEARNING FORMAT WHICH CAN BOTH TEST AND TRAIN LEARNERS. HOW TO AUTOMATICALLY GENERATE 'GOOD' QUIZZES IS DISCUSSED BY MANY RESEARCHERS. IN THIS PAPER, WE PROPOSED A NEW FRAMEWORK WHICH CAN SUPPORT USERS TO CREATE QUIZ GAMES BASED ON LINKED DATA. LINKED DATA STORES BOTH THE VALUES AND THE LINKAGES AMONG VALUES. THE QUIZZES GENERATED BY OUR FRAMEWORK ARE EASY TO IMPROVE AND EXTEND, HAVE MORE CHANGES, AND SUPPORT THE LEARNING ANALYTICS OF USERS' ACTIVITIES. FOR OBTAINING BETTER EDUCATIONAL EFFECTS, WE FURTHER EXTEND OUR FRAMEWORK FOR SUPPORTING THE GENERATION OF QUIZZES IN 3D ENVIRONMENTS. ESPECIALLY, WE DISCUSS HOW TO APPLY OUR FRAMEWORK IN MEDICAL EDUCATION.
TECHNOLOGY-MEDIATED TEACHING AND LEARNING ENABLES ACCESS TO EDUCATIONAL OPPORTUNITIES, IRRESPECTIVE OF LOCALITY, RURUALITY OR REMOTENESS. THE DESIGN, DEVELOPMENT AND DELIVERY OF TECHNOLOGY ENHANCED LEARNING IN PRE-SERVICE TEACHER EDUCATION PROGRAMS IS THEREFORE GAINING MOMENTUM, BOTH IN AUSTRALIA AND INTERNATIONALLY. MUCH RESEARCH REGARDING ONLINE, OR BLENDED LEARNING, HAS FOCUSSED ON THEORETICALLY-FOUNDED LEARNING AREAS, WITH LESS ATTENTION DIRECTED TOWARD FUNDAMENTALLY PRACTICAL LEARNING AREAS, SUCH AS DESIGN AND TECHNOLOGY EDUCATION. SITUATED WITHIN THE BACHELOR OF EDUCATION (EARLY CHILDHOOD EDUCATION, PRIMARY, AND PRIMARY/MIDDLE) DEGREES AT THE UNIVERSITY OF SOUTH AUSTRALIA, AUSTRALIA, THIS STUDY CAPTURES THE DESIGN, DEVELOPMENT AND DELIVERY OF A BLENDED DESIGN AND TECHNOLOGY COURSE WITH FIRST AND THIRD YEAR PRE-SERVICE TEACHERS. DRAWING ON COURSE LEARNING ANALYTICS, PRE-SERVICE TEACHER RESPONSES, AND THE REFLECTIVE PRACTICE OF TEACHING ACADEMICS, THIS PAPER HIGHLIGHTS THE FACILITATORS AND CHALLENGES IN TRANSITIONING TO A BLENDED MODEL OF CURRICULUM DELIVERY THAT ADDRESSES THE CONTEXTS OF THE AUSTRALIAN CURRICULUM: TECHNOLOGIES.
THE PURPOSE OF THIS STUDY WAS TO IDENTIFY THE RELATIONSHIP BETWEEN THE PSYCHOLOGICAL VARIABLES AND ONLINE BEHAVIORAL PATTERNS OF STUDENTS, COLLECTED THROUGH A LEARNING MANAGEMENT SYSTEM (LMS). AS THE PSYCHOLOGICAL VARIABLE, TIME AND STUDY ENVIRONMENT MANAGEMENT (TSEM), ONE OF THE SUB-CONSTRUCTS OF MSLQ, WAS CHOSEN TO VERIFY A SET OF TIME-RELATED ONLINE LOG VARIABLES: LOGIN FREQUENCY, LOGIN REGULARITY, AND TOTAL LOGIN TIME. DATA WAS COLLECTED FROM 188 COLLEGE STUDENTS IN A KOREAN UNIVERSITY. EMPLOYING STRUCTURAL EQUATION MODELING, A HYPOTHESIZED MODEL WAS TESTED TO MEASURE THE MODEL FIT. THE RESULTS PRESENTED A CRITERION VALIDITY OF ONLINE LOG VARIABLES TO ESTIMATE THE STUDENTS' TIME MANAGEMENT. THE STRUCTURAL MODEL, INCLUDING THE ONLINE VARIABLE TSEM, AND FINAL SCORE, WITH A MODERATE FIT, INDICATED THAT LEARNERS' ONLINE BEHAVIOR RELATED TO TIME MEDIATES THEIR PSYCHOLOGICAL FUNCTIONS AND THEIR LEARNING OUTCOMES. BASED ON THE RESULTS, THE FINAL DISCUSSION INCLUDES RECOMMENDATIONS FOR FURTHER STUDY AND MEANINGFULNESS IN REGARDS TO EXTENDING THE LEARNING ANALYTICS FOR PERFORMANCE AND ACTION (LAPA) MODEL.
INTERACTING WITH BIOLOGICAL SYSTEMS VIA EXPERIMENTS IS IMPORTANT FOR ACADEMIA, INDUSTRY, AND EDUCATION, BUT ACCESS BARRIERS EXIST DUE TO TRAINING, COSTS, SAFETY, LOGISTICS, AND SPATIAL SEPARATION. HIGH-THROUGHPUT EQUIPMENT COMBINED WITH WEB STREAMING COULD ENABLE INTERACTIVE BIOLOGY EXPERIMENTS ONLINE, BUT NO SUCH PLATFORM CURRENTLY EXISTS. WE PRESENT A CLOUD EXPERIMENTATION ARCHITECTURE (PARALLELING CLOUD COMPUTATION), WHICH IS OPTIMIZED FOR A CLASS OF DOMAIN-SPECIFIC EQUIPMENTS (BIOTIC PROCESSING UNITS - BPU) TO SHARE AND EXECUTE MANY EXPERIMENTS IN PARALLEL REMOTELY AND INTERACTIVELY AT ALL TIME. WE IMPLEMENTED AN INSTANCE OF THIS ARCHITECTURE THAT ENABLES CHEMOTACTIC EXPERIMENTS WITH A SLIME MOLD PHYSARUM POLYCEPHELUM. A USER STUDY IN THE BLENDED TEACHING AND RESEARCH SETTING OF A GRADUATE-LEVEL BIOPHYSICS CLASS DEMONSTRATED THAT THIS PLATFORM LOWERS THE ACCESS BARRIER FOR NON-BIOLOGISTS, ENABLES DISCOVERY, AND FACILITATES LEARNING ANALYTICS. THIS ARCHITECTURE IS FLEXIBLE FOR INTEGRATION WITH VARIOUS BIOLOGICAL SPECIMENS AND EQUIPMENTS TO FACILITATE SCALABLE INTERACTIVE ONLINE EDUCATION, COLLABORATIONS, RESEARCH, AND CITIZEN SCIENCE.
WITH THE EXPLOSION OF LEARNING DATA IN ONLINE EDUCATIONAL PLATFORMS, MANY EDUCATORS AND RESEARCHERS HAVE DEVELOPED A KEEN INTEREST IN LEARNING ANALYTICS, IN WHICH SOCIAL NETWORK ANALYSIS (SNA) AND SENTIMENT ANALYSIS ARE THE TWO CRITICAL METHODS FOR EXPLORING COLLECTIVE LEARNING PROCESSES. IN THIS STUDY, WE EXTRACTED TEXTUAL DATA FROM THE DISCUSSION FORUM OF A "CONTRACT LAW" COURSE IN A UNIVERSITY LEARNING PLATFORM. WE EXAMINED THE DIFFERENCES IN DISTRIBUTIONS OF MALES AND FEMALES IN THE OVERALL SOCIOGRAM. AS WELL, THROUGH CALCULATING LEARNERS' SOCIAL CHARACTERISTICS AND SENTIMENT DENSITIES ACROSS A SEMESTER, WE EXPLORED THE RELATIONSHIP AMONG SOCIAL CHARACTERISTICS, SENTIMENTS AND LEARNING OUTCOMES. THE EXPERIMENTAL RESULTS SHOWED THAT FEMALES TENDED TO BE MORE ACTIVE THAN MALES IN FORUM INTERACTIONS, AND HIGH-PERFORMING LEARNERS PARTICIPATED MORE ACTIVELY IN INTERACTIONS THAN DID LOW-PERFORMING LEARNERS. MOREOVER, THERE WAS A STRONG POSITIVE CORRELATION BETWEEN CONFUSION AND LEARNING OUTCOMES. AS WELL, THE LEARNERS WHO WERE INVOLVED ACTIVELY IN DISCUSSIONS WERE MORE LIKELY TO EXPRESS THE CONFUSED SENTIMENT.
IT HAS HISTORICALLY BEEN DIFFICULT TO FIND RELIABLE, UP-TO-DATE INFORMATION ABOUT EDUCATIONAL TECHNOLOGY TRENDS, SUCH AS WHAT RESEARCHERS ARE STUDYING AND WHAT TOOLS PRACTITIONERS ARE USING, THEREBY MAKING IT DIFFICULT FOR RESEARCHERS AND PRACTITIONERS TO SYNERGIZE THEIR EFFORTS IN MEANINGFUL, SOCIALLY-RESPONSIVE WAYS. IN THIS EDITORIAL, I ANALYZE TITLES AND ABSTRACTS OF 7708 RESEARCH ARTICLES FROM PROMINENT JOURNALS OVER THE PAST 5 YEARS TO IDENTIFY COMMON TOPICS-SUCH AS "ONLINE," "MOBILE," AND "LEARNING ANALYTICS." I ALSO EXTRACT LINKS FROM 51,496 K-12 SCHOOL AND 1317 UNIVERSITY WEBSITES IN THE U.S. TO IDENTIFY COMMON TOOLS THAT THEY ARE LINKING TO-SUCH AS FACEBOOK, TWITTER, GOOGLE DOCS, AND YOUTUBE. I PROPOSE THAT THESE SORTS OF METRICS PROVIDE A BASELINE UNDERSTANDING FOR OTHER RESEARCHERS AND PRACTITIONERS TO DRAW UPON WHEN SITUATING THEIR WORK AND THAT THEY CAN ALSO GIVE US INSIGHTS INTO AREAS THAT MERIT GREATER ATTENTION FOR ADDRESSING REAL-WORLD PROBLEMS.
A NUMBER OF STUDIES IN THE RESEARCH FIELD OF COMPUTER SUPPORTED COLLABORATIVE LEARNING (CSCL) HAVE PROPOSED VARIOUS SYSTEMS IN ORDER TO FACILITATE LEARNING IN THE CONTEXT OF SOCIAL INTERACTIONS. IN A COLLABORATIVE LEARNING, PARTICIPANTS EXCHANGE NOT ONLY VERBAL BUT ALSO NONVERBAL CUES SUCH AS UTTERANCE, GAZE AND GESTURE FOR MAINTAINING THE RELATIONSHIPS AMONG ONE ANOTHER. NEVERTHELESS, VERY FEW ATTEMPTS HAVE BEEN MADE TO CONSTRUCT A CSCL SYSTEM THAT CAN UTILIZE SUCH MULTIMODAL (VERBAL AND NON-VERBAL) INFORMATION TO SUPPORT COMMUNICATION IN A COLLABORATIVE LEARNING. IN THIS PAPER, WE PROPOSE A NOVEL PLATFORM THAT ENABLES CSCL SYSTEM DEVELOPERS TO CONSTRUCT THEIR LEARNING SUPPORT TOOLS THAT HAVE ORIGINAL FUNCTIONS TO PROCESS SUCH MULTIMODAL INFORMATION. BY BUILDING LEARNING SUPPORT TOOLS ON OUR MULTIMODAL AWARE PLATFORM, WE CONFIRMED ITS USEFULNESS AND ALSO POTENTIAL TO PIONEER UNEXPLOITED FILED OF RESEARCH IN LEARNING ANALYTICS FOR CSCL AND METHODS TO INTERVENE IN COLLABORATIVE LEARNING PROCESSES USING VERBAL AND NON-VERBAL INFORMATION UNUTILIZED SO FAR.
WE REVIEW THE ROLE OF ASSESSMENT FROM THE PERSPECTIVE OF EXPERIENTIAL LEARNING AND PROPOSE A DEFAULT ASSESSMENT APPROACH FOR COMPUTING DEGREE COURSES. WE DISCUSS A PROPOSED FORMAT FOR ASSESSMENT FOR LEARNING FOR COMPUTING PROGRAMMES. OUR CENTRAL OBJECTIVE IS TO ANALYSE ASSESSMENTS AND IDENTIFY HOW ASSESSMENTS ENHANCE LEARNING. WE REVIEW DIFFERENT ASSESSMENT TYPES AND EVALUATE THEIR ROLE IN ACHIEVING NEW ZEALAND QUALIFICATIONS AUTHORITY (NZQA) ACADEMIC LEVEL REQUIREMENTS FOR ACADEMIC LEVELS 5 (FIRST YEAR OF DEGREE) TO 7 (THIRD YEAR) IN NEW ZEALAND POLYTECHNICS. THE KEY POINTS DISCUSSED INCLUDE SKILLS, KNOWLEDGE AND APPLICATION OF KNOWLEDGE AND HOW THESE ACADEMIC REQUIREMENTS ARE MET BY EACH ASSESSMENT TYPE. THE DISCUSSION OF ASSESSMENT FOR LEARNING ROLES INCLUDES PORTFOLIOS, GROUP PROJECTS, CASE STUDIES, ASSIGNMENTS, ESSAYS, SIMULATIONS, TESTS, EXAMS, POSTERS AND SCREENCASTS. WE ALSO DISCUSS THE IMPACT OF BLENDED LEARNING, ANALYTICS AND TECHNOLOGY ENHANCED LEARNING. WE CONCLUDE WITH A PROPOSED ASSESSMENT REGIME AND AN EXAMPLE OF HOW TO IMPLEMENT IT.
TEAMS OF STUDENTS CREATING DIGITAL ARTEFACTS USING CRAFTS, 3D PRINTING, ELECTRONICS, MICROCONTROLLERS, AND COMPUTER PROGRAMMING CAN RESULT IN SIGNIFICANT SCIENCE, TECHNOLOGY, ENGINEERING, ART, AND MATHEMATICS (STEAM) LEARNING. AN ECOSYSTEM OF CAREFULLY SELECTED TOOLS, DIVERSE PROJECT IDEAS, AND A WELL-DESIGNED PEDAGOGIC STRUCTURE CAN GREATLY FACILITATE THIS. THE WORKSHOP WILL BEGIN WITH A PRESENTATION BY THE ECRAFT2LEARN PROJECT FUNDED BY THE EUROPEAN UNION'S HORIZON 2020 FRAMEWORK. THIS INCLUDES A UNIFIED USER INTERFACE TO A LARGE SET OF TOOLS FOR IDEATION, PLANNING, CREATING, PROGRAMMING, AND SHARING. SUPPORT HAS BEEN DEVELOPED TO ENABLE CHILDREN TO CREATE AI PROGRAMS THAT RELY UPON CLOUD SERVICES [1]. LEARNING ANALYTICS PROVIDES GUIDANCE TO TEACHERS AND COACHES [2] [3]. AN AUGMENTED REALITY APPLICATION HAS BEEN DEVELOPED TO AID TEAM 3D DESIGN. RESULTS FROM PILOT STUDIES WILL BE PRESENTED. FOLLOWING THE ECRAFT2LEARN PRESENTATIONS RESEARCHERS FROM THE WORLD OVER THAT ARE WORKING ON INCORPORATING THE MAKER MOVEMENT INTO EDUCATION AND LEARNING WILL PRESENT AND DEMONSTRATE THEIR WORK. PARTICIPANTS WILL DETERMINE TOPICS FOR PANEL DISCUSSIONS.
NOWADAYS, PREDICTING STUDENTS' PERFORMANCE IS ONE OF THE MOST SPECIFIC TOPICS FOR LEARNING ENVIRONMENTS, SUCH AS UNIVERSITIES AND SCHOOLS, SINCE IT LEADS TO THE DEVELOPMENT OF EFFECTIVE MECHANISMS THAT CAN ENHANCE ACADEMIC OUTCOMES AND AVOID DESTRUCTION. IN EDUCATION 4.0, ARTIFICIAL INTELLIGENCE (AI) CAN PLAY A KEY ROLE IN IDENTIFYING NEW FACTORS IN STUDENTS' PERFORMANCE AND IMPLEMENTING PERSONALIZED LEARNING, ANSWERING ROUTINE STUDENT QUESTIONS, USING LEARNING ANALYTICS, AND PREDICTIVE MODELING. IT IS A NEW CHALLENGE TO REDEFINE EDUCATION 4.0 TO RECOGNIZE THE CREATIVE AND INNOVATIVE INTELLIGENT STUDENTS, AND IT IS DIFFICULT TO DETERMINE STUDENTS' OUTCOMES. HENCE, IN THIS PAPER, HYBRIDIZED DEEP NEURAL NETWORK (HDNN) TO PREDICT STUDENT PERFORMANCE IN EDUCATION 4.0. THE PROPOSED HDNN METHOD IS UTILIZED TO DETERMINE THE DYNAMICS THAT LIKELY INFLUENCE THE STUDENT'S PERFORMANCE. THE DEEP NEURAL NETWORK MONITORS PREDICT, AND EVALUATE STUDENTS' PERFORMANCE IN AN EDUCATION 4.0 ENVIRONMENT. THE FINDINGS SHOW THAT THE PROPOSED HDNN METHOD ACHIEVED BETTER PREDICTION ACCURACY WHEN COMPARED TO OTHER POPULAR METHODS.
GESTURAL RECOGNITION SYSTEMS ARE IMPORTANT TOOLS FOR LEVERAGING MOVEMENT-BASED INTERACTIONS IN MULTIMODAL LEARNING ENVIRONMENTS BUT PERSONALIZING THESE INTERACTIONS HAS PROVEN DIFFICULT. WE OFFER AN ADAPTABLE MODEL THAT USES MULTIMODAL ANALYTICS, ENABLING STUDENTS TO DEFINE THEIR PHYSICAL INTERACTIONS WITH COMPUTER-ASSISTED LEARNING ENVIRONMENTS. WE ARGUE THAT THESE INTERACTIONS ARE FOUNDATIONAL TO DEVELOPING STRONGER CONNECTIONS BETWEEN STUDENTS' PHYSICAL ACTIONS AND DIGITAL REPRESENTATIONS WITHIN A MULTIMODAL SPACE. OUR MODEL USES REAL TIME LEARNING ANALYTICS FOR GESTURE RECOGNITION, TRAINING A HIERARCHICAL HIDDEN-MARKOV MODEL WITH A ONE-SHOT CONSTRUCT, LEARNING FROM USER-DEFINED GESTURES, AND ACCESSING 3 DIFFERENT MODES OF DATA: SKELETON POSITIONS, KINEMATICS FEATURES, AND INTERNAL MODEL PARAMETERS. THROUGH AN EMPIRICAL COMPARISON WITH A PRETRAINED MODEL, WE SHOW THAT OUR MODEL CAN ACHIEVE A HIGHER RECOGNITION ACCURACY IN REPEATABILITY AND RECALL TASKS. THIS SUGGESTS THAT OUR APPROACH IS A PROMISING WAY TO CREATE PRODUCTIVE EXPERIENCES WITH GESTURE-BASED EDUCATIONAL SIMULATIONS, PROMOTING PERSONALIZED INTERFACES, AND ANALYTICS OF MULTIMODAL LEARNING SCENARIOS.
MOOCS HAVE ENGENDERED EXCITEMENT AROUND THEIR POTENTIAL TO DEMOCRATISE EDUCATION. THEY APPEAR TO ACT AS A LEVELLER AND OFFER EQUAL OPPORTUNITY TO MILLIONS OF LEARNERS WORLDWIDE. YET, THIS ALLURING PROMISE IS NOT WHOLLY ACHIEVED BY MOOCS. THE COURSES ARE DESIGNED TO BE USED BY PEOPLE WHO ARE ALREADY ABLE TO LEARN, THEREBY EXCLUDING LEARNERS WHO ARE UNABLE TO LEARN WITHOUT DIRECT TUTOR SUPPORT. THE SOLUTIONS TO THIS PROBLEM TEND TO FOCUS ON THE COURSE, AS 'LEARNING DESIGN' OR 'LEARNING ANALYTICS'. WE ARGUE THAT EFFORT NEEDS TO BE FOCUSED ON THE LEARNER DIRECTLY, SUPPORTING HIM OR HER TO BECOME AN AUTONOMOUS LEARNER. SUPPORTING MILLIONS OF PEOPLE TO BECOME AUTONOMOUS LEARNERS IS COMPLEX AND COSTLY. THIS IS A PROBLEM WHERE EDUCATION IS SHAPED PRINCIPALLY BY ECONOMIC AND NEOLIBERAL FORCES, RATHER THAN SOCIAL FACTORS. HOWEVER, 'AUTOMATED' SOLUTIONS MAY RESULT IN ATTEMPTS TO QUANTIFY LEARNERS' BEHAVIOURS TO FIT AN 'IDEAL'. THERE IS A DANGER THAT OVERLY SIMPLIFIED SOLUTIONS AGGRAVATE AND INTENSIFY INEQUALITIES OF PARTICIPATION.
THE LABORATORY IS AN ESSENTIAL ELEMENT IN THE TEACHING AND LEARNING OF CHEMISTRY, BUT IT IS CHALLENGING TO PROVIDE THIS WHEN DELIVERING COURSES AND PROGRAMS ONLINE OR AT A DISTANCE. STUDIES HAVE REPEATEDLY SHOWN THAT ALTERNATE LABORATORY MODES CAN LEAD TO EQUIVALENT STUDENT PERFORMANCE WHEN COMPARED WITH IN-PERSON EXPERIENCES. IN THIS LITERATURE REVIEW, WE WILL EXAMINE FIVE MODES OF LABORATORY DELIVERY (I.E., FACE-TO-FACE, VIRTUAL, REMOTE CONTROL, HOME-STUDY KITS AND, TO A LESSER EXTENT, SELF-GUIDED FIELD TRIPS) THAT MAY BE CONSIDERED IN PROVIDING QUALITY PRACTICAL LABORATORY ACTIVITIES TO SUPPORT ONLINE STUDIES. EACH MODE BRINGS ITS OWN PARTICULAR STRENGTHS AND WEAKNESSES AND CAN BE USED INDIVIDUALLY OR IN COMBINATION. THE SELECTION AND INTEGRATION OF THOSE MODES, WHICH IS DRIVEN BY LEARNING OUTCOMES AND OTHER FACTORS, WILL BE EXAMINED AS PART OF THE DESIGN PROCESS. FINALLY, FUTURE LABORATORY DESIGN WILL CERTAINLY INCLUDE NEW TECHNOLOGIES, BUT POTENTIALLY ALSO ELEMENTS LIKE OPEN EDUCATIONAL RESOURCES, LEARNING ANALYTICS, UNIVERSAL DESIGN, AND CITIZEN SCIENCE.
THE AVALANCHE OF DATA OF MANY TYPES GENERATED AT HIGH SPEED PROPOSES NOWADAYS A NEW SCENARIO NOMINATED BIG DATA. THESE DATA GENERATED BY MANY TYPES OF SYSTEMS, SUCH AS COMMERCIAL SYSTEMS, FINANCIAL SYSTEMS, AND EVEN SYSTEMS INVOLVING AREAS SUCH AS HEALTHCARE AND EDUCATION NEED TO BE ANALYZED TO FAVOR DECISION MAKING. IN THIS SENSE, SPECIFIC TECHNIQUES AND TECHNOLOGIES CAN BE USED TOGETHER TO ESTABLISH ANALYTICS, A SPECIAL TERM USED FOR DATA ANALYSIS. GENERALLY CALLED ANALYTICS, SPECIFICATIONS SUCH AS BUSINESS ANALYTICS, LEARNING ANALYTICS, OR ACADEMIC ANALYTICS CAN BE IDENTIFIED. IN THIS WAY, THIS RESEARCH ADDRESSES THE RELATIONSHIP BETWEEN THE CONCEPTS OF ACADEMIC ANALYTICS AND A SPECIFIC MODEL FOR THE MANAGEMENT AND OPERATION OF DIGITAL SOLUTIONS IN THE EDUCATIONAL AREA CALLED EQETIC. ANALYZING THE PRACTICES PROPOSED BY THE MODEL AND THE CONCEPTS, IT IS POSSIBLE TO IDENTIFY, IN A NON-QUANTITATIVE WAY, THAT THE MODEL FAVORS THE ACTIVITIES OF DATA MANAGEMENT AND DATA ANALYSIS; CONSEQUENTLY IT IS ALSO FAVOR THE IMPLEMENTATION OF ACADEMIC ANALYTICS.
THIS CASE STUDY EVALUATED THE IMPACT OF SYNCHRONOUS AND ASYNCHRONOUS E-LANGUAGE LEARNING ACTIVITIES (ELL-TIVITIES) IN AN E-LANGUAGE LEARNING ENVIRONMENT (ELLE) AT VIRTUAL UNIVERSITY OF PAKISTAN. THE PURPOSE OF THE STUDY WAS TO ASSESS E-LANGUAGE LEARNING ANALYTICS BASED ON THE CONSTRUCTIVIST APPROACH OF COLLABORATIVE CONSTRUCTION OF KNOWLEDGE. THE COURSES SELECTED FOR RANDOM SAMPLING WERE ENGLISH COMPREHENSION (ENG101), BUSINESS & TECHNICAL ENGLISH (ENG201) AND BUSINESS COMMUNICATION (ENG301). THREE METHODS WERE EMPLOYED TO COLLECT THE DATA: OBSERVATION OF THE COMMUNICATION AND PERFORMANCE ON GIVEN CHANNELS, STUDENTS' OPINIONS ON GRADED DISCUSSION BOARD (GDB), AND A SURVEY QUESTIONNAIRE. OUT OF A TOTAL POPULATION OF 9919, 1025 RESPONSES WERE RECEIVED FOR THE SURVEY QUESTIONNAIRE. THE FINDINGS REVEALED THAT ASYNCHRONOUS E-LANGUAGE LEARNING WAS QUITE BENEFICIAL FOR SECOND LANGUAGE (L2) LEARNERS, BUT WITH SOME LIMITATIONS WHICH COULD BE SCAFFOLDED BY SYNCHRONOUS SESSIONS. BASED ON THE FINDINGS, THE RESEARCHER SUGGESTED A BLEND OF BOTH SYNCHRONOUS AND ASYNCHRONOUS PARADIGMS TO CREATE AN IDEAL ENVIRONMENT FOR E-LANGUAGE LEARNING IN PAKISTAN.
SERIOUS GAMES (SG) ARE INCREASINGLY USED TO DELIVER EDUCATION AND TRAINING TO SUPPORT WELL-BEING, SOCIAL SKILLS, INDEPENDENT LIVING AND ONLINE SAFETY. THIS PRESENTS OPPORTUNITIES AND CHALLENGES FOR USERS, DEVELOPERS AND EDUCATIONAL PROVIDERS TO ENSURE THEIR BENEFITS ARE AVAILABLE TO ALL. PEOPLE WITH INTELLECTUAL DISABILITY (ID) EXPERIENCE HIGH LEVELS OF DIGITAL EXCLUSION. THIS ARTICLE REVIEWS AND SYNTHESISES THE LITERATURE ON THE USE OF SG WITH PEOPLE WITH ID. RECENT WORK INDICATES THE IMPORTANCE OF THE PSYCHO-SOCIAL PROFILE OF THE GAMEPLAYER, MECHANICS AND GAME CONTEXT. WE CONSIDER HOW THESE FACTORS INFLUENCE THE INCLUSIVITY OF SG AND HOW THEY COULD BE ADDRESSED. WE DEMONSTRATE HOW A PSYCHOLOGICAL PERSPECTIVE ILLUMINATES THE IMPACT OF THE USER'S PSYCHOLOGICAL PROFILE ON GAME USE AND EFFECTIVENESS, AND EXPLORE HOW CULTURE, PERSONALISATION AND LEARNING ANALYTICS CAN INCREASE INCLUSIVITY OF SG. WE CONCLUDE BY IDENTIFYING WIDER CHALLENGES SUCH AS THE NEED FOR MULTI-DISCIPLINARY WORKING AND A MORE INTEGRATED, CONCEPTUAL FRAMEWORK FOR GAMES DESIGN WHICH ADDRESSES DIVERSITY IN LEARNING NEEDS.
PEER GRADING IS AN APPROACH INCREASINGLY ADOPTED FOR ASSESSING STUDENTS IN MASSIVE ON-LINE COURSES, ESPECIALLY FOR COMPLEX ASSIGNMENTS WHERE AUTOMATIC ASSESSMENT IS IMPOSSIBLE AND THE ABILITY OF TUTORS TO EVALUATE AND PROVIDE FEEDBACK AT SCALE IS LIMITED. UNFORTUNATELY, AS STUDENTS MAY HAVE DIFFERENT EXPERTISE, PEER GRADING OFTEN DOES NOT DELIVER ACCURATE RESULTS COMPARED TO HUMAN TUTORS. IN THIS PAPER, WE DESCRIBE AND COMPARE DIFFERENT METHODS, BASED ON GRAPH MINING TECHNIQUES, AIMED AT MITIGATING THIS ISSUE BY COMBINING PEER GRADES ON THE BASIS OF THE DETECTED EXPERTISE OF THE ASSESSOR STUDENTS. THE POSSIBILITY TO IMPROVE THESE RESULTS THROUGH OPTIMIZED TECHNIQUES FOR ASSESSORS' ASSIGNMENT IS ALSO DISCUSSED. EXPERIMENTAL RESULTS WITH BOTH SYNTHETIC AND REAL DATA ARE PRESENTED AND SHOW BETTER PERFORMANCE OF OUR METHODS IN COMPARISON TO OTHER EXISTING APPROACHES.
IN THIS STUDY, WE ANALYSE THE RELATIONSHIP BETWEEN ENGAGEMENT IN A VIRTUAL LEARNING ENVIRONMENT (VLE) AND MODULE GRADES AT A 'BRICKS-AND-MORTAR' UNIVERSITY IN THE UNITED KINGDOM. WE MEASURE VLE ACTIVITY FOR STUDENTS ENROLLED IN 38 DIFFERENT CREDIT-BEARING MODULES, EACH OF WHICH ARE COMPULSORY COMPONENTS OF SIX DEGREE PROGRAMMES. OVERALL WE FIND THAT HIGH VLE ACTIVITY IS ASSOCIATED WITH HIGH GRADES, BUT LOW ACTIVITY DOES NOT NECESSARILY IMPLY LOW GRADES. ANALYSIS OF INDIVIDUAL MODULES SHOWS A WIDE RANGE OF RELATIONSHIPS BETWEEN THE TWO QUANTITIES. GROUPING MODULE-LEVEL RELATIONSHIPS BY PROGRAMME SUGGESTS THAT SCIENCE-BASED SUBJECTS HAVE A HIGHER DEPENDENCY ON VLE ACTIVITY. CONSIDERING LEARNING DESIGN (LD), WE FIND THAT VLE USAGE IS MORE IMPORTANT IN MODULES THAT ADOPT AN INSTRUCTION-BASED LEARNING STYLE. WE ALSO TEST THE PREDICTIVE POWER OF VLE USAGE IN DETERMINING GRADES, AGAIN FINDING VARIATION BETWEEN DEGREE PROGRAMMES AND POTENTIAL FOR PREDICTING A STUDENT'S FINAL GRADE WEEKS IN ADVANCE OF ASSESSMENT. OUR FINDINGS SUGGEST THAT STUDENT ENGAGEMENT WITH LEARNING AT A BRICKS-AND-MORTAR UNIVERSITY IS IN GENERAL HARD TO DETERMINE BY VLE USAGE ALONE, DUE TO THE PREDOMINANCE OF OTHER "OFFLINE" LEARNING ACTIVITIES, BUT THAT VLE USAGE CAN NONETHELESS HELP TO PREDICT PERFORMANCE FOR SOME DISCIPLINES.
UNIVERSAL DESIGN FOR LEARNING (UDL) AND BLENDED LEARNING (BL) FORMATS, ARE WIDELY ADOPTED ACROSS K-12 LEARNING ENVIRONMENTS. UPON GRADUATION, PRESERVICE TEACHERS MAY BE EXPECTED TO IMPLEMENT UDL AND BL PRACTICES. THE PRESENT STUDY WAS MOTIVATED BY THE NEED TO PROVIDE PRESERVICE TEACHERS WITH LIVE MODELING OF UDL AND BL CONCEPTS. LEARNING ANALYTICS DATA FROM 197 PRESERVICE TEACHERS WAS EXAMINED FOR ENGAGEMENT WITH UDL/BL ACCESS FEATURES (LOCATION, DAY-OF-THE-WEEK, TIME-OF-DAY, AND REGULARITY), CONTENT FEATURES (SCREENCASTS AND QUIZZES), AND TO DETERMINE IF THERE WAS A RELATIONSHIP BETWEEN ENGAGEMENT AND ACHIEVEMENT. EXAMINATION OF THE LEARNING MANAGEMENT SYSTEM LOGIN DATA REVEALED REGULAR ACCESS TO THE DIGITAL CONTENT ACROSS DIFFERING LOCATIONS, WEEK DAYS, AND TIME OF DAY. ASSOCIATIONS WERE SIGNIFICANT BETWEEN ACADEMIC PERFORMANCE AND ALL FEATURES. DESIGNING THE BL DIGITAL COURSE COMPONENTS FOLLOWING UDL PRINCIPLES APPEARS TO HAVE SERVED AS A SELF-REGULATION ENABLER FOR PRESERVICE TEACHERS THEMSELVES WHILE PROVIDING EXEMPLARS TO ADOPT IN THEIR FUTURE PRACTICE.
THE RELATIONSHIP AMONG BODILY MOVEMENTS, COGNITIVE ABILITIES, AND ACADEMIC ACHIEVEMENT IN CHILDREN IS RECEIVING CONSIDERABLE ATTENTION IN THE RESEARCH COMMUNITY. THE EMBODIED LEARNING APPROACH IS BASED ON THE IDEA OF AN INSEPARABLE LINK BETWEEN BODY AND MIND IN LEARNING, AIMING FOR TEACHING METHODS THAT PROMOTE CHILDREN'S ACTIVE ENGAGEMENT IN THE CLASSROOM. THIS STUDY IMPLEMENTS EMBODIED LEARNING AS A PART OF THE CLASSROOM CURRICULUM IN A REAL CLASSROOM ENVIRONMENT USING MOTION-BASED GAMES. A TOTAL OF 52 ELEMENTARY STUDENTS ENGAGED IN EMBODIED LEARNING IN-CLASS ACTIVITIES FOR FOUR MONTHS. THE DATA-SET INCLUDED STANDARDIZED PRE-POST TESTING FOR CHILDREN'S COGNITIVE AND ACADEMIC PERFORMANCE, GENERAL LEARNING ANALYTICS FROM GAMES' USAGE, INTERVIEWS, AND OBSERVATIONS FROM THE TEACHERS INVOLVED. FINDINGS SHOWED SIGNIFICANT EFFECTS BOTH ON CHILDREN'S COGNITIVE ABILITIES (I.E., SHORT-MEMORY SKILLS) AND ACADEMIC PERFORMANCE (I.E., EXPRESSIVE VOCABULARY). THIS ARTICLE CONTRIBUTES TO THE EDUCATIONAL TECHNOLOGY COMMUNITY BY PROVIDING AN EXAMPLE OF IMPLEMENTING EMBODIED LEARNING VIA USE OF MOTION-BASED TECHNOLOGIES IN A REAL CLASSROOM ENVIRONMENT.
COLLABORATIVE PROBLEM SOLVING (CPS), AS A TEACHING AND LEARNING APPROACH, IS CONSIDERED TO HAVE THE POTENTIAL TO IMPROVE SOME OF THE MOST IMPORTANT SKILLS TO PREPARE STUDENTS FOR THEIR FUTURE. CPS OFTEN DIFFERS IN ITS NATURE, PRACTICE, AND LEARNING OUTCOMES FROM OTHER KINDS OF PEER LEARNING APPROACHES, INCLUDING PEER TUTORING AND COOPERATION; AND IT IS IMPORTANT TO ESTABLISH WHAT IDENTIFIES COLLABORATION IN PROBLEM-SOLVING SITUATIONS. THE IDENTIFICATION OF INDICATORS OF COLLABORATION IS A CHALLENGING TASK. HOWEVER, STUDENTS PHYSICAL INTERACTIVITY CAN HOLD CLUES OF SUCH INDICATORS. IN THIS PAPER, WE INVESTIGATE TWO NON-VERBAL INDEXES OF STUDENT PHYSICAL INTERACTIVITY TO INTERPRET COLLABORATION IN PRACTICE-BASED LEARNING ENVIRONMENTS: EQUALITY AND INFRA-INDIVIDUAL VARIABILITY. OUR DATA WAS GENERATED FROM TWELVE GROUPS OF THREE ENGINEERING STUDENTS WORKING ON OPEN-ENDED TASKS USING A LEARNING ANALYTICS SYSTEM. THE RESULTS SHOW THAT HIGH COLLABORATION GROUPS HAVE MEMBER STUDENTS WHO PRESENT HIGH AND EQUAL AMOUNTS OF PHYSICAL INTERACTIVITY AND LOW AND EQUAL AMOUNTS OF INFRA-INDIVIDUAL VARIABILITY.
SELF-ASSESSMENT IS AN IMPORTANT TOOL TO IMPROVE STUDENT PERFORMANCE. A GOOD SERIOUS GAME (SG) WOULD BE A VERY STRONG SELF-ASSESSMENT TOOL BECAUSE OF THE ADDITIONAL MOTIVATIONAL ASPECTS. HOWEVER, SELF-ASSESSMENT TOOLS HAVE TO FIT THE LEARNING MATTERS OF A COURSE BY 100 %, WHAT GENERAL SG SELDOM DO. THE GOAL OF THIS WORK IS TO BUILD A SIMPLE SG EDITOR EACH TEACHER CAN ADJUST TO THE LEARNING GOALS OF HIS COURSE. HERE WE PRESENT THE DEVELOPMENT OF A GAME-EDITOR FOR TEACHERS FOR A PUZZLE-STYLE GAME. BY THIS EDITOR TEACHERS CAN EDIT THEIR OWN COURSE BASED GAME WITHOUT SOPHISTICATED COMPUTER KNOWLEDGE. THE GAME MECHANICS MEET THE REQUIREMENTS OF MOBILE-AND MICRO-LEARNING STRATEGIES. FURTHERMORE, THE GAME IMPLEMENTS LEARNING ANALYTICS FOR STUDENTS AS WELL AS FOR TEACHERS. THE GAME ENGINE AND THE EDITOR ARE BOTH BASED ON STANDARD WEB TECHNOLOGIES. THE SOURCE CODE IS MAINTAINED AS AN OPEN SOURCE PROJECT TO LOWER THE BARRIERS FOR FURTHER UPTAKE.
AUTOMATIC PROGRAMMING, THE AUTOMATIC GENERATION OF A COMPUTER PROGRAM GIVEN A HIGH-LEVEL STATEMENT OF THE PROGRAM'S DESIRED BEHAVIOUR, IS A STATED OBJECTIVE OF THE FIELD OF GENETIC PROGRAMMING. AS THE GENERAL SOLUTION TO A COMPUTATIONAL PROBLEM IS TO WRITE A COMPUTER PROGRAM, AND GIVEN THAT GENETIC PROGRAMMING CAN AUTOMATICALLY GENERATE A COMPUTER PROGRAM, RESEARCHERS IN THE FIELD OF GENETIC PROGRAMMING REFER TO ITS ABILITY TO AUTOMATICALLY SOLVE PROBLEMS. GENETIC PROGRAMMING HAS ALSO BEEN DESCRIBED AS AN "INVENTION MACHINE" THAT IS CAPABLE OF GENERATING HUMAN-COMPETITIVE SOLUTIONS. WE ARGUE THAT THE MAJORITY OF SUCCESS AND FOCUS OF OUR FIELD HAS NOT ACTUALLY BEEN AS A RESULT OF AUTOMATIC PROGRAMMING. WE SET OUT TO CHALLENGE THE GENETIC PROGRAMMING COMMUNITY TO REFOCUS OUR RESEARCH TOWARDS THE OBJECTIVE OF AUTOMATIC PROGRAMMING, AND TO DO SO IN A MANNER THAT EMBRACES A WIDER PERSPECTIVE ENCOMPASSING THE RELATED FIELDS OF, FOR EXAMPLE, ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, ANALYTICS, OPTIMISATION AND SOFTWARE ENGINEERING.
A BOTTLENECK IN GATHERING BIG DATA ABOUT LEARNING IS INSTRUMENTATION DESIGNED TO RECORD DATA ABOUT PROCESSES STUDENTS USE TO LEARN AND INFORMATION ON WHICH THOSE PROCESSES OPERATE. THE SOFTWARE SYSTEM NSTUDY FILLS THIS GAP. NSTUDY IS AN EXTENSION TO THE CHROME WEB BROWSER PLUS A SERVER SIDE DATABASE FOR LOGGED TRACE DATA PLUS PERIPHERAL MODULES THAT ANALYZE TRACE DATA AND ASSEMBLE WEB PAGES AS LEARNING ANALYTICS. STUDENTS CAN USE NSTUDY ANYWHERE THEY CONNECT TO THE INTERNET. EVERY EVENT RELATED TO CREATING, MODIFYING, REVIEWING, LINKING AND ORGANIZING INFORMATION ARTIFACTS IS LOGGED IN FINE GRAIN WITH A TIME STAMP. THESE DATA FULLY TRACE INFORMATION STUDENTS OPERATE ON AND HOW THEY OPERATE ON IT. AMBIENT BIG DATA ABOUT STUDYING GATHERED AU NATUREL CAN BE TAILORED BY CONFIGURING SEVERAL OF NSTUDY'S FEATURES. THUS THE SYSTEM CAN BE USED TO GATHER DATA ACROSS A WIDE RANGE LAB STUDIES AND FIELD TRIALS DESIGNED TO TEST A RANGE OF MODELS AND THEORIES.
IN TRYING TO UNDERSTAND THE BIG PICTURE OF HOW USERS LEARN TO PROGRAM IN APP INVENTOR, WE WANT TO BE ABLE TO REPRESENT PROJECTS IN A WAY SUITABLE FOR LARGE SCALE LEARNING ANALYTICS. HERE I PRESENT DIFFERENT REPRESENTATIONS OF PROJECTS THAT COULD POTENTIALLY BE USED TO IDENTIFY APP INVENTOR PROJECTS THAT HAVE STRUCTURAL SIMILARITIES TO EACH OTHER, E.G., PROJECTS CREATED BY USERS FOLLOWING TUTORIALS. I COMPARE THE DIFFERENT REPRESENTATIONS BASED SOLELY ON HOW ACCURATELY THEY PREDICT THE CORRECT TUTORIAL FROM A LABELED DATA SET. THE RESULTS SUGGEST THAT WE USE BOTH BLOCKS AND COMPONENTS FROM A PROJECT, APPLY TF-IDF TO THE COUNTS OF EACH FEATURE, AND MEASURE DISTANCE OR SIMILARITY IN TERMS OF A GENERALIZED JACCARD DISTANCE. THIS WORK LAYS THE FOUNDATION FOR BEING ABLE TO FIND CLUSTERS OF SIMILAR PROJECTS TO DISTINGUISH ORIGINAL FROM UNORIGINAL PROJECTS AND TO BE ABLE TO FILTER OUT SIMILAR PROJECTS WHEN TRYING TO DETERMINE A USER'S SKILL LEVEL.
WITH THE HELP OF SEVERAL EMERGING COMPUTING PARADIGMS, SUCH AS UBIQUITOUS COMPUTING, SOCIAL COMPUTING, AND MOBILE COMPUTING, OLP (OPEN LEARNING PROCESS) HAS BECOME AN IMPORTANT RESEARCH ISSUE IN THE ONLINE LEARNING ENVIRONMENT. IT BECOMES POSSIBLE TO SHARE THE LEARNING PROCESS RELATED INFORMATION IN AND CROSS SYSTEMS TO MEET THE NEEDS OF ALL STAKEHOLDERS, SUCH AS TEACHERS, LEARNERS AND MANAGERS. IN THIS STUDY, WE FOCUS ON THE OLP IN THE SOCIAL LEARNING ENVIRONMENT BASED ON THE PERSONAL AND SOCIAL ANALYTICS, WHICH CAN PROVIDE USERS WITH THE PROCESS-ORIENTED LEARNING SUPPORT. WE PRESENT A DATA PROCESSING FRAMEWORK TO COLLECT, ANALYZE, AND ORGANIZE THE LEARNING-RELATED DATA IN A USER-CENTRIC WAY. THREE IMPORTANT ISSUES REGARDING TO THE UNIFIED DATA MANAGEMENT, SOCIALIZED LEARNING ANALYTICS, AND SHARABLE OPEN LEARNING PROCESS ARE DISCUSSED TOWARDS THE PROCESS-ORIENTED LEARNING INNOVATION. BASED ON THESE, AN OPEN LEARNING PLATFORM IS PROPOSED AND DESIGNED TO PROVIDE USERS WITH THE INDIVIDUALIZED SUPPORT IN THE COLLABORATIVE LEARNING PROCESS.
ONE OF THE MOST IMPORTANT FEATURES WHICH E-LEARNING TOOLS AND ENVIRONMENTS MUST POSSESS WITHIN THE SCOPE OF LIFELONG LEARNING IS SELF-DIRECTED LEARNING, WHICH CAN BE CONSIDERED AS A FORM OF SELF-LEARNING. THE AIM OF THIS STUDY WAS TO DETERMINE, BASED ON THE VIEWS AND RECOMMENDATIONS OF EXPERTS, INTERFACE DESIGN PRINCIPLES FOR THE DEVELOPMENT OF EDUCATIONAL WEB INTERFACES THAT WILL SUPPORT THE SELF-DIRECTED LEARNING OF ADULTS. THIS DESCRIPTIVE STUDY WAS CONDUCTED WITH THE CONTRIBUTION OF 12 ACADEMICIANS SPECIALIZING IN INTERFACE DESIGN AND SELF-DIRECTED LEARNING. WITHIN THE SCOPE OF THE STUDY, NEW INTERFACES FEATURES WERE IDENTIFIED BASED ON AN EVALUATION OF THE LITERATURE ON INTERFACE DESIGNS FOR SELF-DIRECTED LEARNING, AND THE VIEWS OF SUBJECT EXPERTS. BASED ON THE STUDY RESULTS, IT WAS DETERMINED THAT INTERFACE DESIGNS SUPPORTING SELF-DIRECTED LEARNING MUST POSSESS FIVE BASIC FEATURES, WHICH INCLUDE BEING USER-DIRECTED, ENSURING VARIETY, BEING SUPPORTED BY LEARNING ANALYTICS, BEING MOTIVATIONAL, AND BEING SHARING-ORIENTED.
OVER 26 MILLION PEOPLE WORLDWIDE SUFFER FROM HEART FAILURE ANNUALLY. WHEN THE CAUSE OF HEART FAILURE CANNOT BE IDENTIFIED, ENDOMYOCARDIAL BIOPSY (EMB) REPRESENTS THE GOLD-STANDARD FOR THE EVALUATION OF DISEASE. HOWEVER, MANUAL EMB INTERPRETATION HAS HIGH INTER-RATER VARIABILITY. DEEP CONVOLUTIONAL NEURAL NETWORKS (CNNS) HAVE BEEN SUCCESSFULLY APPLIED TO DETECT CANCER, DIABETIC RETINOPATHY, AND DERMATOLOGIC LESIONS FROM IMAGES. IN THIS STUDY, WE DEVELOP A CNN CLASSIFIER TO DETECT CLINICAL HEART FAILURE FROM H&E STAINED WHOLE-SLIDE IMAGES FROM A TOTAL OF 209 PATIENTS, 104 PATIENTS WERE USED FOR TRAINING AND THE REMAINING 105 PATIENTS FOR INDEPENDENT TESTING. THE CNN WAS ABLE TO IDENTIFY PATIENTS WITH HEART FAILURE OR SEVERE PATHOLOGY WITH A 99% SENSITIVITY AND 94% SPECIFICITY ON THE TEST SET, OUTPERFORMING CONVENTIONAL FEATURE-ENGINEERING APPROACHES. IMPORTANTLY, THE CNN OUTPERFORMED TWO EXPERT PATHOLOGISTS BY NEARLY 20%. OUR RESULTS SUGGEST THAT DEEP LEARNING ANALYTICS OF EMB CAN BE USED TO PREDICT CARDIAC OUTCOME.
COLLABORATIVE PROBLEM-SOLVING (CPS) IS A FUNDAMENTAL SKILL FOR SUCCESS IN MODERN SOCIETIES, AND PART OF MANY COMMON CONSTRUCTIVIST TEACHING APPROACHES. HOWEVER, ITS EFFECTIVE IMPLEMENTATION AND EVALUATION IN BOTH DIGITAL AND PHYSICAL LEARNING ENVIRONMENTS ARE CHALLENGING FOR EDUCATORS. THIS PAPER PRESENTS AN ORIGINAL METHOD FOR IDENTIFYING DIFFERENCES IN STUDENTS' CPS BEHAVIOURS WHEN THEY ARE TAKING PART IN FACE-TO-FACE PRACTICE-BASED LEARNING (PBL). THE DATASET IS BASED ON HIGH SCHOOL AND UNIVERSITY STUDENTS' HAND POSITION AND HEAD DIRECTION DATA, WHICH CAN BE AUTOMATED DEPLOYING EXISTING MULTIMODAL LEARNING ANALYTICS SYSTEMS. THE FRAMEWORK USES NONVERBAL INDEXES OF STUDENTS' PHYSICAL INTER-ACTIVITY (NISPI) TO INTERPRET THE KEY PARAMETERS OF STUDENTS' CPS COMPETENCE. THE RESULTS SHOW THAT THE NISPI FRAMEWORK CAN BE USED TO JUDGE STUDENTS' CPS COMPETENCE LEVELS ACCURATELY BASED ON THEIR NON-VERBAL BEHAVIOUR DATA. THE FINDINGS HAVE SIGNIFICANT IMPLICATIONS FOR DESIGN, RESEARCH AND DEVELOPMENT OF EDUCATIONAL TECHNOLOGY. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE INTEREST IN EVALUATING, MEASURING AND VERIFYING THINGS HAS BEEN PRESENT THROUGHOUT THE TIMES. GENERALLY, EVALUATION IS CARRIED OUT FOR SEVERAL PURPOSES, AND THE INCORPORATION OF TECHNOLOGIES IN EDUCATIONAL SYSTEMS HAS NOT BEEN ALIEN TO THIS INTEREST; ON THE CONTRARY, IT HAS BEEN A CATALYST FOR THE PROCESSES OF VALIDATION, VERIFICATION, CONTROL AND EVIDENCE OF ACADEMIC PERFORMANCE BOTH IN EVALUATION MODELS FOCUSED ON COMPETENCIES AND IN OTHER SCHEMES (OBJECTIVES AND INSTRUCTIONS, TO NAME A FEW). THIS CHAPTER AIMS TO PROVIDE A FRAME OF REFERENCE AROUND THE TRANSFORMATIONS THAT EVALUATION IN HIGHER EDUCATION HAS SUFFERED, FOCUSED ON VIRTUAL ENVIRONMENTS, THE CHALLENGES FACED AND THE CONCEPTIONS OF EVALUATION IN LATIN AMERICA, AS WELL AS ON THE PROSPECTIVE OF EVALUATION FROM THE CONCEPTS OF DIGITAL REPUTATION AND IDENTITY, USER EXPERIENCE, COMPLEX CONTEXTS, SIMULATORS, AND LEARNING ANALYTICS. FOR THIS, A DOCUMENTARY REVIEW OF THE LAST TWO DECADES, A CONTENT ANALYSIS, AND DISCUSSIONS AMONG AUTHORS ARE PRESENTED.
THIS STUDY ANALYZES STUDENTS' BEHAVIORS IN A REMOTE LABORATORY ENVIRONMENT IN ORDER TO IDENTIFY NEW FACTORS OF PREDICTION OF ACADEMIC SUCCESS. IT INVESTIGATES RELATIONS BETWEEN LEARNERS' ACTIVITIES DURING PRACTICAL SESSIONS, AND THEIR PERFORMANCE AT THE FINAL ASSESSMENT TEST. BASED ON LEARNING ANALYTICS APPLIED TO DATA COLLECTED FROM AN EXPERIMENTATION CONDUCTED WITH OUR REMOTE LAB DEDICATED TO COMPUTER EDUCATION, WE DISCOVER RECURRENT SEQUENTIAL PATTERNS OF ACTIONS THAT LEAD US TO THE DEFINITION OF LEARNING STRATEGIES AS INDICATORS OF HIGHER LEVEL OF ABSTRACTION. RESULTS SHOW THAT SOME OF THE STRATEGIES ARE CORRELATED TO LEARNERS' PERFORMANCE. FOR INSTANCE, THE CONSTRUCTION OF A COMPLEX ACTION STEP BY STEP, OR THE REFLECTION BEFORE SUBMITTING AN ACTION, ARE TWO STRATEGIES APPLIED MORE OFTEN BY LEARNERS OF A HIGHER LEVEL OF PERFORMANCE THAN BY OTHER STUDENTS. WHILE OUR PROPOSALS ARE DOMAIN-INDEPENDENT AND CAN THUS APPLY TO OTHER LEARNING CONTEXTS, THE RESULTS OF THIS STUDY LED US TO INSTRUMENT FOR BOTH STUDENTS AND INSTRUCTORS NEW VISUALIZATION AND GUIDING TOOLS IN OUR REMOTE LAB ENVIRONMENT.
ONE OF THE CHALLENGES OF CSUL (COMPUTER SUPPORTED UBIQUITOUS LEARNING) RESEARCH IS CAPTURING WHAT LEARNERS HAVE LEARNED WITH THE CONTEXTUAL DATA, AND REMINDING THE LEARNERS OF IT IN THE RIGHT PLACE AND THE RIGHT TIME. THIS PAPER PROPOSES A UBIQUITOUS LEARNING LOG SYSTEM CALLED SCROLL (SYSTEM FOR CAPTURING AND REMINDING OF LEARNING LOG). UBIQUITOUS LEARNING LOG (ULL) IS DEFINED AS A DIGITAL RECORD OF WHAT LEARNERS HAVE LEARNED IN THE DAILY LIFE USING UBIQUITOUS TECHNOLOGIES. IT ALLOWS THE LEARNERS TO LOG THEIR LEARNING EXPERIENCES WITH PHOTOS, AUDIOS, VIDEOS, LOCATION, QR -CODE, RFID TAG, AND SENSOR DATA, AND TO SHARE AND TO REUSE ULL WITH OTHERS. USING SCROLL, THEY CAN RECEIVE PERSONALIZED QUIZZES AND ANSWERS FOR THEIR QUESTIONS. ALSO, THEY CAN NAVIGATE AND BE AWARE OF THEIR PAST ULLS SUPPORTED BY AUGMENTED REALITY VIEW. THIS PAPER ALSO DESCRIBES HOW SCROLL CAN BE USED IN DIFFERENT CONTEXTS SUCH AS LEARNING ANALYTICS FOR JAPANESE LANGUAGE LEARNING, SEAMLESS LANGUAGE LEARNING, AND MUSEUM.
THE WIDESPREAD ADOPTION OF DIGITAL E-LEARNING ENVIRONMENTS AND OTHER LEARNING TECHNOLOGY HAS PROVIDED RESEARCHERS WITH READY ACCESS TO LARGE QUANTITIES OF DATA. MUCH OF THIS DATA COMES FROM DISCUSSION FORUMS AND HAS BEEN STUDIED WITH ANALYTICAL METHODS DRAWN FROM SOCIAL NETWORK ANALYSIS. HOWEVER, WITHIN THIS LARGE BODY OF RESEARCH THERE EXISTS CONSIDERABLE VARIATION IN THE DEFINITION OF WHAT CONSTITUTES A SOCIAL TIE, AND THE CONSEQUENCES OF THIS CHOICE ARE RARELY DESCRIBED OR EXAMINED. THIS PAPER PRESENTS FINDINGS FROM TWO DISTINCT LEARNING ENVIRONMENTS REGARDING DIFFERENT SOCIAL TIE EXTRACTION METHODS AND THEIR INFLUENCE ON THE STRUCTURAL AND STATISTICAL PROPERTIES OF THE INDUCED NETWORKS, AND THE ASSOCIATION BETWEEN MEASURES OF CENTRALITY AND ACADEMIC PERFORMANCE. OUR FINDINGS INDICATE THAT SOCIAL TIE DEFINITIONS PLAY AN IMPORTANT ROLE IN SHAPING THE RESULTS OF OUR ANALYSES. THE PRIMARY PURPOSE OF THIS PAPER IS TO RAISE AWARENESS OF THE CONSEQUENCES THAT SUCH METHODOLOGICAL CHOICES MAY HAVE, AND TO PROMOTE TRANSPARENCY IN FUTURE RESEARCH.
THIS IS A SYSTEMATIC REVIEW CONDUCTED TO EXPLORE THE PRIMARY RESEARCH LITERATURE ON STUDENT AGENCY IN HIGHER EDUCATION, AS WELL AS LINKS BETWEEN STUDENT AGENCY AND STUDENT LEARNING. OUT OF MORE THAN 2,300 RESULTS, 42 ARTICLES PUBLISHED BETWEEN 1980 AND 2021 WERE ASSESSED. OF THESE, 29 STUDIES WERE INCLUDED IN A NARRATIVE REVIEW BASED ON THEIR CENTRALITY OF STUDENT AGENCY AND METHODOLOGICAL QUALITY. WE FOUND THAT A STUDENT AGENCY PERSPECTIVE IS INTEGRATED INTO RESEARCH FOCUSSED ON ASSESSMENT AND FEEDBACK, GLOBALISATION AND INTERNATIONALISATION, KNOWLEDGE PRODUCTION, LEARNING ANALYTICS, LEARNING CONNECTIONS AND TRANSFER, REAL-LIFE LEARNING SITUATIONS, AND STUDENT-FOCUSSED TEACHING AND LEARNING. OUR STUDY REVEALS THAT RESEARCHERS REPORT A LINK BETWEEN SOURCES OF AGENCY AND STUDENT AGENCY, AS WELL AS BETWEEN AGENCY AND INDIVIDUAL OUTCOMES, BUT THE RELATIONSHIP BETWEEN AGENCY AND SOCIETAL OUTCOMES REQUIRES FURTHER INVESTIGATION. WE ARGUE THAT RESEARCH MUST INCLUDE HOLISTIC NOTIONS AND CRITICAL DISCUSSIONS OF AGENCY FOR MOVING HIGHER EDUCATION PRACTICES AND STUDENT AGENCY LITERATURE FORWARD.
PRE-EMPTION DESCRIBES A SYSTEM OF AUTOMATED KNOWLEDGE CREATION AND INTERVENTION THAT STEERS THE PRESENT TOWARDS A DESIRABLE FUTURE, BY BUILDING ON KNOWLEDGE DERIVED FROM THE PAST. FOLDING TOGETHER TEMPORALITIES MAKES IT IMPOSSIBLE TO DISPROVE PRE-EMPTION. IT IS INCREASINGLY FEATURED WITHIN EDTECH, INTRODUCING NEW FORMS OF AUTOMATED GOVERNANCE INTO EDUCATION. THIS PAPER EXAMINES HOW STUDENTS AND EDTECH COME TOGETHER TO MAKE PRE-EMPTION POSSIBLE, NOT AS A SINGLE EVENT BUT AS A NORMALISED GOVERNANCE INSTRUMENT. FOR THIS, WE INTRODUCE LUCY SUCHMAN'S IDEA OF CONFIGURATION TO EXAMINE PRE-EMPTIVE EDTECH. THE PAPER PRESENTS THREE OPENINGS INTO THE CONFIGURATION OF STUDENTS AND PRE-EMPTIVE EDTECH. THESE INCLUDE OBSERVATIONS FROM AN EDTECH TRADE SHOW; INTERVIEWS WITH INSIDERS OF TECHNOLOGY COMPANIES; AND ANALYSIS OF ACCEPTED PAPERS TO A LEARNING ANALYTICS CONFERENCE. WE CONCLUDE THE DATA USED AT THE HEART OF PRE-EMPTIVE EDTECH SEEKS TO EXCLUDE STUDENTS AND CONFIGURES THEM AS ABSENT. YET, ITS INTERVENTIONS HAVE MATERIAL CONSEQUENCES.
NOWADAYS SOFTWARE SYSTEMS ARE ORGANIZED AROUND SEVERAL AND HETEROGENEOUS COMPONENTS. FOR EXAMPLE, A MODERN APPLICATION CAN BE COMPOSED OF DIFFERENT MICROSERVICES, ALONG WITH DEDICATED COMPONENTS FOR MACHINE LEARNING ANALYTICS AND RECURRING BATCH PROCESSING JOBS. WHILE CONTAINERS OFFER A MEANS TO DEPLOY THE SYSTEM AND TACKLE HETEROGENEITY, THESE COMPONENTS HAVE DIFFERENT EXECUTION MODELS, CAN EXPLOIT DIFFERENT RESOURCE TYPES (E.G., CPUS AND GPUS) AND RESULT IN COMPLETELY DIFFERENT EXECUTION TIMES (MILLISECONDS VS HOURS). THIS COMPLEXITY CALLS FOR A NEW, SCALABLE ARCHITECTURE TO ALLOW THE SYSTEMS TO OPERATE EFFICIENTLY. THIS PAPER PRESENTS COCOS, AN ARCHITECTURE, BASED ON CONTAINERS AND CONTROL-THEORY, THAT IS ABLE TO MANAGE LARGE AND HETEROGENEOUS SOFTWARE SYSTEMS. THE ARCHITECTURE IS BASED ON A THREE-LEVEL HIERARCHY OF CONTROLLERS THAT COOPERATIVELY ENFORCE USER-DEFINED REQUIREMENTS ON EXECUTION TIMES AND CONSUMED RESOURCES. THE PAPER ALSO SHOWS A PROTOTYPE IMPLEMENTATION OF COCOS BASED ON KUBERNETES, A WELL-KNOWN CONTAINER ORCHESTRATOR. THE EVALUATION SHOWS THE EFFICIENCY OF COCOS WHEN DEALING WITH MICROSERVICES, SPARK JOBS AND MACHINE LEARNING APPLICATIONS.
THIS PAPER PRESENTS A COMPREHENSIVE STUDY OF THE LITERATURE RELATED TO PERSONALISED LEARNING IN THE PAST DECADE, SUMMARISING HOW, AND IN WHAT ASPECTS, LEARNING HAS BEEN PERSONALISED. THE STUDY COVERS A TOTAL OF 179 RELEVANT ARTICLES COLLECTED FROM THE WEB-OF-SCIENCE AND SCOPUS DATABASES. THE RESULTS SHOW THAT, BETWEEN 2009-13 AND 2014-18, THERE WAS A CLEAR UPWARD TREND-A 120% INCREASE IN THE NUMBER OF RELEVANT PUBLICATIONS OVER THE YEARS. THE RESEARCH AND PRACTICE HAVE BECOME MORE DIVERSIFIED IN TERMS OF THE TARGET LEARNERS, RESEARCH ISSUES, AND OBJECTIVES. THE MEANS TO ACHIEVE PERSONALISED LEARNING MAINLY INVOLVE EDUCATIONAL TECHNOLOGIES, WHERE EMERGING TECHNOLOGIES SUCH AS LEARNING ANALYTICS, MASSIVE OPEN ONLINE COURSES AND AUGMENTED/VIRTUAL REALITY HAVE BEEN INCREASINGLY USED. THE RESULTS REVEAL THE AREAS FOR FURTHER INVESTIGATION, SUCH AS THE CHALLENGES ARISING FROM THE ADOPTION OF EMERGING TECHNOLOGY, THE ASPECTS OF LEARNING RECEIVING LITTLE ATTENTION, AND THE CHANGING ROLE OF TEACHERS IN THE MOVE TO STUDENT-CENTRED PERSONALISED LEARNING.
KNOWLEDGE OF HUMAN ANATOMY IS ESSENTIAL IN FIRST-YEAR UNDERGRADUATE ALLIED HEALTH COURSES. TRADITIONALLY THIS DISCIPLINE HAS USED TIME AND RESOURCE INTENSIVE LABORATORY CLASSES, HOWEVER RECENT CHANGES IN RESOURCING AND STUDENT LEARNING PREFERENCES HAVE LED TO A COMBINATION OF PEDAGOGICAL APPROACHES BEING USED. THE AIM OF THIS STUDY WAS TO EXAMINE WHETHER THE USE OF SMART SPARROW ADAPTIVE AND INTERACTIVE ONLINE RESOURCES PROVIDED REAL FLEXIBILITY IN LEARNING AND INFLUENCED BOTH STUDENT ENGAGEMENT AND MOTIVATION TO LEARN FOR ALLIED HEALTH STUDENTS STUDYING FIRST YEAR ANATOMY. TWENTY ADAPTIVE ONLINE ANATOMY LESSONS WERE CREATED. THESE ADAPTIVE LESSONS WERE EVALUATED WITH (I) A STUDENT SURVEY AND (II) INBUILT LEARNING ANALYTICS (N = 157). OUR RESULTS SHOWED THAT A TOTAL OF 96% OF STUDENTS AGREED OR STRONGLY AGREED THAT THE RESOURCES ENHANCED OVERALL ENGAGEMENT IN THE SUBJECT AND 96% FOUND THE RESOURCE EASY TO ACCESS. OVERALL OUR FINDINGS REVEALED THAT STUDENTS OVERWHELMINGLY ENGAGED WITH THE LESSONS, AND SELF-REPORTED IMPROVEMENTS IN THEIR LEARNING THROUGH REPEATED USE OFFLEXIBLE AND ADAPTIVE LEARNING RESOURCES.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE THE POTENTIAL TO SCALE UNIVERSITY EDUCATION, ALLOWING FOR MANY THOUSANDS OF STUDENTS TO PARTICIPATE IN A SINGLE ONLINE COURSE. BUT EVEN SUCCESSFUL MOOC PLATFORMS LIKE COURSERA, EDX OR IVERSITY FACE THE PROBLEM OF VERY LOW COMPLETION RATES. ANALYZING LEARNING ACTIVITIES IN A MOOC, LEARNING ANALYTICS COULD HELP TO IDENTIFY NECESSARY INTERVENTIONS. IN IVERSITY MOOCS, A FORUM IS THE BASIC COMMUNICATION PLATFORM, BOTH FOR STUDENT-STUDENT AND STUDENT-INSTRUCTOR COMMUNICATION. IN THIS PAPER, FEATURES OF SUCCESSFUL STUDENTS ARE DERIVED FROM FORUM ACTIVITIES AND COMBINED TO A LEARNING PROFILE. FROM THIS LEARNING PROFILE, FEEDBACK COULD BE GENERATED FOR STUDENTS WHO ARE CLASSIFIED AS "RISK STUDENTS". AN ANALYTICS TOOL WAS DEVELOPED, BASED ON MACHINE LEARNING, WHICH CLASSIFIES STUDENTS IN IVERSITY MOOCS, USING FEATURES LIKE NUMBER OF ANSWERS IN A FORUM OR NUMBER OF UP-VOTES. THUS, FEATURES OF SUCCESSFUL STUDENTS CAN BE DETERMINED, AND VISUALIZED IN AN INTUITIVE WAY.
THE TEACHING EXCELLENCE FRAMEWORK (TEF) CAN BE SEEN AS BOTH A REACTION TO AND A SYMPTOM OF CURRENT CHANGES IN THE HIGHER EDUCATION SECTOR. THESE CHANGES INCLUDE THE EMERGENCE OF THE SO-CALLED GENERATION Z, FOR WHOM MOBILE TECHNOLOGY IS A HISTORICAL FACT AND ACTIVE PARTNERSHIP A GIVEN; THE INTRODUCTION OF FLIPPED AND BLENDED LEARNING; AND THE 'CONSUMERIZATION' OF THE MARKET AND SUBSEQUENT FOCUS ON VALUE FOR MONEY. WHAT ROLE DO ACADEMIC LIBRARIES HAVE TO PLAY IN BOTH THE DELIVERY OF EXCELLENT TEACHING AND THE TEF ITSELF? THIS ARTICLE CONSIDERS THE BACKGROUND TO THE TEF AND POSES THE QUESTION: DO THOSE RESPONSIBLE FOR THE TEF IN OUR UNIVERSITIES RECOGNIZE THE ROLE OF LIBRARIES IN ENSURING AN EXCELLENT STUDENT EXPERIENCE? IT SUGGESTS SOME KEY ACTIONS THAT ACADEMIC LIBRARIES SHOULD LEAD, INCLUDING THE USE OF LEARNING ANALYTICS, THE PROVISION OF LEARNING RESOURCES AND THE DEVELOPMENT OF APPROPRIATE STAFF SKILLS, AND IT CHALLENGES LIBRARY LEADERS TO ENGAGE MORE ACTIVELY IN THE STRATEGIC DISCOURSE OF THEIR INSTITUTIONS.
THE INCREASED VARIETY OF LEARNING RESOURCES, LIKE E-BOOKS WITH MODERN COLLABORATIVE TOOLS, VIDEO LECTURES OF DIFFERENT TEACHERS ACROSS THE WORLD, LIVELY DISCUSSION BOARDS ETC. HAVE SUBSTANTIALLY AFFECTED LEARNING STYLES OF STUDENTS. HAVING ACCEPTED SUCH FORMS OF LEARNING MATERIALS, CONVENTIONAL ACADEMIC SETUPS ARE IN DESPERATE NEED TO MAKE LEARNING ACTIVITIES EFFICIENT, EFFECTIVE AND MEANINGFUL. IN THIS PAPER A DIVERSE RESOURCES HOSTING SYSTEM CRETAL (COMPILER OF RESOURCES IN ENGINEERING &TECHNOLOGY TO AID LEARNING) WITH RICH SET OF UNIQUE ANNOTATION TOOLS ARE DESCRIBED TO ADDRESS THE CHALLENGES OF THE MODERN STUDENTS TO QUERY, ACCESS, EXTRACT, CONNECT, PROCESS AND SHARE THE IMPORTANT CONCEPTS FROM THE DIFFERENT FORM OF THE LEARNING MATERIALS CREATED AND ADAPTED BY THE TEACHER. IN ADDITION TO THE SYSTEM DESCRIPTION, WE ALSO PRESENT THE USABILITY STUDY ALONG WITH THE IMPACT ON STUDENTS LEARNING AND TEACHER'S DECISION WHEN CRETAL WAS ADAPTED IN FEW UNDERGRADUATE COURSE USING EDUCATIONAL DATA MINING AND LEARNING ANALYTICS.
ANATOMY SUBJECTS ARE CONTENT HEAVY AND GENERALLY HAVE A HIGH STAKES PRACTICAL EXAMINATION THAT IS DIFFICULT TO PREPARE FOR OUTSIDE OF THE LABORATORY. WITH RECENT ADVANCES IN TECHNOLOGY, INCREASED CLASS SIZES AND REDUCTIONS IN TEACHING TIME, ALTERNATIVE AND NOVEL TEACHING TECHNOLOGIES ARE EMERGING. AN ADAPTIVE ONLINE SIMULATED PRACTICAL EXAMINATION WAS DEVELOPED. THIS LESSON WAS EVALUATED USING A SURVEY, STUDENT INTERVIEWS AND LEARNING ANALYTICS. OF THE 55 STUDENTS WHO COMPLETED THE SURVEY, 92% INDICATED THAT THE SIMULATED PRACTICAL EXAMINATION ENHANCED THEIR LEARNING. NINETY TWO PERCENT OF STUDENTS REPORTED THAT THE LESSON MET THEIR NEEDS FOR FLEXIBILITY AND IMPORTANTLY, OUR STUDY ALSO CONFIRMED THAT THERE WAS A POSITIVE RELATIONSHIP BETWEEN PERFORMANCE IN THE SIMULATED PRACTICAL EXAM AND BOTH THE ACTUAL PRACTICAL EXAMINATION AND OVERALL SUBJECT SCORE. FINDINGS FROM THIS STUDY REVEALED THAT ADAPTIVE LEARNING TECHNOLOGIES SHOW SIGNIFICANT PROMISE AND ARE AN EFFECTIVE, FLEXIBLE TOOL FOR PREPARING STUDENTS FOR PRACTICAL EXAMS IN A CHALLENGING SUBJECT.
THIS WORK PRESENTS AN INNOVATIVE FRAMEWORK WITH THE AIM TO CREATE FULL ENGAGEMENT FOR THE LEARNERS ON MASSIVE OPEN ONLINE LEARNING ENVIRONMENTS THROUGH A CONNECTIVIST APPROACH. THE PROPOSED FRAMEWORK RELIES ON THE IMPORTANCE OF CREATING ENGAGING EXPERIENCES BEFORE, DURING AND AFTER THE FINISH OF A COURSE TO INCREASE LEARNERS' PARTICIPATION AND REDUCE DROP-OUT RATES WITH THE HELP OF LEARNING ANALYTICS. THIS WORK PRESENTS A COMPELLING IDEA IN THE UNIVERSE OF MOOCS: IT INTENDS TO EXPAND THE EFFORTS OF THE LEARNING DESIGN TEAM TO ACHIEVE PRE AND POST-COURSE ENGAGEMENT, WHERE ENGAGEMENT TAKES THE FORM OF AN ONGOING COMMUNITY OF LEARNERS. THIS RESEARCH PROVIDES RESULTS FROM THE FIRST SUCCESSFUL EXPERIENCES IN TWO MICROMASTERS "PROFESSIONAL ANDROID DEVELOPER", TAUGHT IN ENGLISH, AND ONE SPECIALIZATION TAUGHT IN SPANISH: "E-LEARNING FOR TEACHERS: CREATE INNOVATIVE ACTIVITIES AND CONTENT" AT THE EDX PLATFORM. THE MICROMASTERS SHOWS TO BE A GREAT PATH FOR CAREER ADVANCEMENT, ESPECIALLY FOR THE UNDER-EMPLOYED.
AS HIGHER EDUCATION INCREASINGLY RELIES ON E-LEARNING, THE NEED FOR TOOLS THAT WILL ALLOW TEACHERS THEMSELVES TO DEVELOP EFFECTIVE E-LEARNING OBJECTS AS SIMPLY AND QUICKLY AS POSSIBLE HAS ALSO BEEN INCREASINGLY RECOGNIZED. THIS ARTICLE DISCUSSES THE DESIGN AND DEVELOPMENT OF A NOVEL TOOL, ENOOK (EVOLUTIONARY NOTE BOOK), FOR CREATING ACTIVITY-BASED LEARNING OBJECTS (LOS). THIS TOOL HAS THREE GENERAL FOCI: 1) CREATION OF VARIOUS ACTIVITY-BASED OBJECTS FOR EXPERIMENTAL LEARNING OR LEARNING BY DOING WHILE SIMULTANEOUSLY OFFERING AS MANY LEARNING RESOURCES AS A REAL E-BOOK THAT SERVICES AS BOTH A TEXTBOOK AND A NOTEBOOK, DUAL FUNCTIONS THAT PAPERBACK BOOKS CAN NEVER PROVIDE; 2) MANAGEMENT OF ALL ASPECTS OF LIVE INSTRUCTOR-LED CLASSROOM TRAINING IN A NETWORK ENVIRONMENT, SUCH THE WEB AND OR A MOBILE NETWORK; 3) FACILITATED USE OF DATA ON STUDENTS' PROGRESS TO MONITOR THEIR UNDERSTANDING OF THE MATERIAL AND TO PROVIDE OTHER MEANINGFUL INFORMATION THROUGH LEARNING ANALYTICS.
THIS CHAPTER OUTLINES AND EXPANDS ON CONTENT PRESENTED AT THE BIENNIAL CONFERENCE ON CHEMICAL EDUCATION IN 2014. THE USE OF THE FLIPPED CLASSROOM MODEL TO DEVELOP UNDERSTANDING AND TO ENHANCE SUBSEQUENT IN-LECTURE LEARNING IS DISCUSSED, WITH A FOCUS ON SUPPORTING INCOMING STUDENTS IN ADAPTING TO THE DEMANDS OF STUDYING AT A RESEARCH-INTENSIVE UNIVERSITY IN THE UK. 'PARTIAL FLIPPING', WHEREBY PART OF A LECTURE IS MADE AVAILABLE IN VIDEO FORM PRIOR TO THE SCHEDULED SESSION, HAS BEEN EMPLOYED TO FREE-UP TIME IN CLASS FOR MORE EFFECTIVE LEARNING ACTIVITIES. TWO CASE STUDIES ARE DESCRIBED, INCLUDING EVIDENCE REGARDING STUDENT ENGAGEMENT AND THEIR RESPONSE TO THE INTERVENTIONS. THE SECOND STUDY INVOLVES INTERACTIVE PRE-LECTURE VIDEOS WHICH FEATURE MULTIPLE-CHOICE AND OPEN-ANSWER QUESTIONS. THE LATTER APPROACH PROVIDES VALUABLE LEARNING ANALYTICS TO SUPPORT THE INTEGRATION OF JUST-IN-TIME SUPPORT INTO THE FRAMEWORK OF A LECTURE IN ORDER TO MEET THE NEEDS OF STUDENTS EFFECTIVELY AND PROVIDE TARGETED, TIMELY FEEDBACK.
PERFORMANCE DURING THE FIRST YEAR OF COLLEGE AND IN INTRODUCTORY COURSES HAS BEEN WIDELY IDENTIFIED AS CRITICAL TO COLLEGE STUDENTS' RETENTION AND SUCCESS. ACCORDINGLY, INTERVENTIONS TO ASSIST BEGINNING COLLEGE STUDENTS IN GATEWAY COURSES HAVE GAINED INCREASED ATTENTION IN HIGHER EDUCATION. THIS STUDY TESTED SUCH AN INTERVENTION USING LEARNING ANALYTICS AND EARLY PERFORMANCE FEEDBACK IN LARGE INTRODUCTORY COURSES, WITH COMPARISON BETWEEN COURSE SECTIONS NOT EXPOSED TO THE INTERVENTION. DISTINCT FROM OTHER RESEARCH, THE PRESENT STUDY REPLICATED THE EFFICACY OF THE INTERVENTION ACROSS 2 YEARS, IN MULTIPLE COURSE SUBJECTS, AND WITH DIFFERENT INSTRUCTORS AT A 4-YEAR HISPANIC-SERVING INSTITUTION. IMPORTANTLY, FINDINGS ALSO SHOWED THAT HIGH-RISK COLLEGE STUDENTS ESPECIALLY BENEFITTED FROM THE INTERVENTION IN TERMS OF BOTH ACHIEVEMENT AND PERSISTENCE. BASED ON THESE FINDINGS, RECOMMENDATIONS ARE MADE TO HIGHER EDUCATION ADMINISTRATORS ABOUT THE POTENTIAL GAINS TO STUDENTS WHEN THEY ARE PROVIDED WITH EARLY, CONSISTENT, AND INDIVIDUALIZED FEEDBACK IN ALL COURSES, MADE FEASIBLE BY CURRENT TECHNOLOGY.
THIS PAPER PRESENTS DEVELOPMENTAL TRENDS IN TECHNOLOGY-BASED ASSESSMENT IN AN EDUCATIONAL CONTEXT AND HIGHLIGHTS HOW TECHNOLOGY-BASED ASSESSMENT HAS RESHAPED THE PURPOSE OF EDUCATIONAL ASSESSMENT AND THE WAY WE THINK ABOUT IT. DEVELOPMENTS IN TECHNOLOGY-BASED ASSESSMENT STRETCH BACK THREE DECADES. AROUND THE TURN OF THE MILLENNIUM, STUDIES CENTRED ON COMPUTER-BASED AND PAPER-AND-PENCIL TEST COMPARABILITY TO ASCERTAIN THE EFFECT OF DELIVERY MEDIUM ON STUDENTS' TEST ACHIEVEMENT. A SYSTEMATIC REVIEW OF MEDIA STUDIES WAS CONDUCTED TO DETECT THESE EFFECTS; THE RESULTS WERE VARIED. RECENT WORK HAS FOCUSED ON LOGFILE ANALYSIS, EDUCATIONAL DATA MINING AND LEARNING ANALYTICS. DEVELOPMENTS IN IT HAVE MADE IT POSSIBLE TO DESIGN DIFFERENT ASSESSMENTS, THUS BOOSTING THE NUMBER OF WAYS STUDENTS CAN DEMONSTRATE THEIR SKILLS AND ABILITIES. PARALLEL TO THESE ADVANCES, THE FOCUS OF TECHNOLOGY-BASED ASSESSMENT HAS SHIFTED FROM AN INDIVIDUAL AND SUMMATIVE APPROACH TO ONE WHICH IS COOPERATIVE, DIAGNOSTIC AND MORE LEARNING-CENTRED TO IMPLEMENT EFFICIENT TESTING FOR PERSONALISED LEARNING.
PEER ASSESSMENT (PA) IS A PROMISING EVALUATION STRATEGY IN THE EDUCATIONAL CONTEXT, NOT ONLY DUE TO ITS EFFECTIVENESS TO REDUCE INSTRUCTOR'S EVALUATION LOADING, BUT MAINLY DUE TO ITS BENEFIT TOWARDS STUDENT DEVELOPMENT E.G., TEAMWORK, IN-DEPTH THINKING. IN THIS EXPLORATORY STUDY WE SOUGHT TO EXPLORE HOW DO EDUCATIONAL DATA, AS GENERATED BY AN ONLINE PLATFORM (I.E., PEERGRADE) AND DISPLAYED IN TEACHER'S AND STUDENTS' LEARNING ANALYTICS DASHBOARD (LAD), CAN POTENTIALLY INFORM US OF THE PA PROCESS AND THE PEER INTERACTIONS, AS THEY TAKE PLACE. PARTICIPANTS IN THE STUDY WERE 21 UNDERGRADUATE TEACHER-STUDENTS WHO ATTENDED A SCIENCE COURSE (ELECTRICAL CIRCUITS TOPIC) FOLLOWING THE INQUIRY-BASED APPROACH. STUDENTS WERE ASKED TO RECIPROCALLY AND INDIVIDUALLY ASSESS THE RESPONSES OF A PEER IN A GIVEN TASK. THE FINDINGS OF THIS STUDY HAVE IMPLICATIONS TOWARDS THE ESTABLISHMENT OF NEW THEORETICAL FRAMEWORKS AND DEVELOPMENTS FOR BRIDGING EDUCATIONAL THEORY, DESIGN PROCESS AND DATA SCIENCE, IN THE FIELD OF ASSESSMENT.
IN THIS PAPER AN INNOVATIVE DYNAMIC DIMENSIONAL MORPHING METAPHOR IS INTRODUCED TO MONITOR STUDENTS' ENGAGEMENT AND COHORT DYNAMIC. TEACHERS USING ELEARNING MONITORING TOOLS FIND THEM USUALLY LACKING USABILITY AND INADEQUATE TO GIVE PRODUCTIVE FEEDBACK ABOUT THEIR LEARNING DESIGNS. LEARNING ANALYTICS TOOLS MOSTLY FOCUS ON AFTER COURSE ANALYSIS WITH THE ASSUMPTION THAT USER COMPETENCE IN DATA ANALYSIS IS HIGH. THE TOOL WE PROPOSE IS BASED ON VISUAL INTERFACE MORPHING: RESHAPING OF THE INTERFACE ELEMENTS, SUCH AS LEARNING OBJECTS' LINKS AND ICONS, IS PUT IN PLACE TO REFLECT SOME KEY PERFORMANCE INDICATORS OF LEARNERS' ACTIVITIES. QUANTITATIVE AND TEMPORAL ANALYTICS DATA, AGGREGATED USING VARIOUS FUNCTIONS, IS USED TO PRESENT ANIMATED ENHANCED INFORMATION TO TEACHERS. EXPERIMENTS FOR THE ASSESSMENT OF THE EFFECTIVENESS OF THE PROPOSED TOOL HAS BEEN CONDUCTED ON DATA FROM HIGHER EDUCATION COURSES. THROUGH LOGS' ANALYSIS AND TEACHERS' QUESTIONNAIRES THE USABILITY AND VALIDITY OF THE PROPOSED METAPHOR HAS BEEN ASSESSED. THE PROPOSED TOOL OUTPERFORMS TRADITIONAL MONITORING TECHNIQUES.
THE STATE-OF-THE ART OF THE COMPUTER SUPPORTED OR ENHANCED TEACHING AND LEARNING PROCESSES IS MOSTLY FOCUSED ON CONTENT ACQUISITION, PROCESSING, AND MANAGEMENT; VIRTUAL LEARNING ENVIRONMENTS; LEARNING ANALYTICS; EDUCATIONAL DATA MINING. THERE IS AN ABSENCE OF APPROACHES BASED ON KNOWLEDGE EXCHANGE BETWEEN MACHINES AND HUMANS IN KNOWLEDGE BASED PROCESSES. THIS PAPER DESCRIBES SUCH AN APPROACH. WITHIN AN EMPIRICAL PARTICIPATORY ACTION RESEARCH ON TECHNOLOGY ENHANCED LEARNING THE ISSUE OF KNOWLEDGE IS UNDERSTOOD AS A KEY ELEMENT OF ANY AUTOMATION OF HUMAN KNOWLEDGE PROCESSING. TEACHING IS THUS CONSIDERED AS A TYPICAL KNOWLEDGE BASED PROCESS. IN THIS CONTEXT, THE UTILITY MODEL IS ALSO USED, WHICH IMPLEMENTED "VIRTUAL KNOWLEDGE" AS A UNIVERSAL CONSTRUCT, WHICH "INTERDISCIPLINARILLY" BRIDGES THE MENTAL PROCESSES OF HUMANS AND THE PHYSICAL PROCESSES PERFORMED BY COMPUTERS. EXAMPLES OF SUCH SOLUTIONS ARE BRIEFLY MENTIONED. IT IS ALSO EMPHASIZED THAT A COMPLEX COMPUTER SUPPORTED COLLABORATIVE LEARNING REQUIRES, IN THE REAL PRACTICE, TO EXECUTE A COMBINED PEDAGOGIC (DIDACTIC) AND INFORMATICS RESEARCH.
GENERATING LARGE AMOUNTS OF SEMANTICALLY-RICH DATA FOR TESTING BIG DATA WORKFLOWS IS PARAMOUNT FOR SCALABLE PERFORMANCE BENCHMARKING AND QUALITY ASSURANCE IN MODERN MACHINE-LEARNING AND ANALYTICS WORKLOADS. THE MOST OBVIOUS USE CASE FOR SUCH A GENERATIVE ALGORITHM IS IN CONJUNCTION WITH A BIG DATA APPLICATION BLUEPRINT, WHICH CAN BE USED BY DEVELOPERS (TO TEST THEIR EMERGING BIG DATA SOLUTIONS) AS WELL AS END USERS (AS A STARTING POINT FOR VALIDATING INFRASTRUCTURE INSTALLATIONS, BUILDING NOVEL APPLICATIONS, AND LEARNING ANALYTICS METHODS). WE PRESENT A NEW DOMAIN-DRIVEN, GENERATIVE DATA MODEL FOR BIGPETSTORE, A BIG DATA APPLICATION BLUEPRINT FOR THE HADOOP ECOSYSTEM INCLUDED IN THE APACHE BIGTOP DISTRIBUTION. WE DESCRIBE THE MODEL AND DEMONSTRATE ITS ABILITY TO GENERATE SEMANTICALLY-RICH DATA AT VARIABLE SCALE RANGING FROM A SINGLE MACHINE TO A LARGE CLUSTER. WE VALIDATE THE MODEL BY USING THE GENERATED DATA TO ANSWER QUESTIONS ABOUT CUSTOMER LOCATIONS AND PURCHASING HABITS FOR A FICTIONAL TARGETED ADVERTISING CAMPAIGN, A COMMON BUSINESS USE CASE.
THIS PAPER DESCRIBES THE DESIGN, DEVELOPMENT AND FIRST RESULTS AND IMPRESSIONS OF A NOVEL MASSIVE OPEN ONLINE COURSES (MOOC) ON INDUSTRIAL ELECTRONICS PRACTICES. THIS WAS POSSIBLE BY IMPLEMENTING A STATE-OF-THE-ART REMOTE LABORATORY WITHIN THE MOOC TO ALLOW ONLINE DELIVERY OF SUCH PRACTICES-WHICH MAKES THIS MOOC A FIRST OF ITS KIND. THUS, THE CORE OF THE MOOC IS THE REMOTE LABORATORY VIRTUAL INSTRUMENT SYSTEMS IN REALITY (VISIR) AND THE EXPERIMENTATION CARRIED OUT ON IT. A PRELIMINARY DISCUSSION ON THE DESIGN AND STRUCTURE OF THE MOOC IS PRESENTED, BASED ON THE LEARNING ANALYTICS OF THE MOOC PLATFORM AND THE OPERATIONS RECORD STORED BY THE REMOTE LABORATORY. THE STUDENTS' PROFILE INTERESTED IN THIS KIND OF MOOCS AND THEIR NEEDS FOR GETTING ADVANTAGE OUT OF THE REMOTE LABORATORY ARE ALSO CONSIDERED IN THE DISCUSSION. THE CONCLUSIONS WILL RESULT IN MODIFICATIONS IN THIS MOOC FOR A RE-EDITION AND THE EVALUATION OF THE CONVENIENCE OF AN ADVANCED NEW MOOC.
RESIDENTIAL BUILDINGS ACCOUNT FOR MORE THAN 20 PERCENT OF TOTAL ENERGY USAGE IN THE U.S. IN THE PAST DECADE. REDUCTION OF HOUSEHOLD ENERGY CONSUMPTION HAS ENVIRONMENTAL AND ECONOMIC IMPACTS. AS THE FIRST STEP, BUILDING SCIENTISTS AND CONSTRUCTION ENGINEERS HAVE ENDEAVORED EFFORTS TO OBTAIN AN ACCURATE ENERGY USE PREDICTION; HOWEVER, FEW HAVE FOCUSED ON THE RELATIONSHIP BETWEEN CONSTRUCTION COST AND ENERGY USE. THIS PAPER INVESTIGATES THE ASSOCIATIONS BETWEEN DETAILED CONSTRUCTION COST TAKEOFFS AND ACTUAL ENERGY USES IN MULTIFAMILY GREEN BUILDINGS. THE RESEARCHERS EMPLOY ADVANCED MACHINE LEARNING ANALYTICS TO MODEL THE CORRELATIONS BETWEEN CONSTRUCTION COST AND ENERGY USE DATA WHICH WERE COLLECTED FROM MULTIFAMILY RESIDENTIAL UNITS. FINDINGS IDENTIFY THE COST DIVISIONS IN THE CONSTRUCTION STAGE THAT SIGNIFICANTLY CORRELATES THE ENERGY USE IN THE OPERATIONAL STAGE. THE MODEL ALLOWS DEVELOPERS TO PREDICT THE ENERGY CONSUMPTION BASED ON THE CONSTRUCTION COSTS, AND ENABLES THEM TO ADJUST THE INVESTMENT STRATEGIES TO AMPLIFY THE ENERGY EFFICIENCY OF GREEN BUILDING TECHNOLOGIES.
UNIVERSITY COURSES OFTEN EMPLOY "ONE-SIZE-FITS-ALL" APPROACHES, DISREGARDING THE HETEROGENEITY IN STUDENTS' COGNITIVE AND MOTIVATIONAL CHARACTERISTICS. THIS INTERVENTION STUDY REPORTS ON AN INDIVIDUALIZED LEARNING DESIGN FOR ONLINE TEACHING IN HIGHER EDUCATION. IN A RANDOMIZED FIELD EXPERIMENT WITH N = 438 UNIVERSITY STUDENTS (57% FEMALE, MEAN AGE M = 20.96 YEARS), WE INVESTIGATED THE EFFECTS OF THE LEARNING DESIGN ON STUDENTS' MOTIVATION (SELF-CONCEPT, SELF-EFFICACY, INTRINSIC AND UTILITY TASK VALUES), ON THEIR PERFORMANCE, AND, BECAUSE OUR SAMPLE CONSISTED OF TEACHER STUDENTS, ON THEIR PROFESSIONAL DEVELOPMENT WITH REGARD TO INCLUSIVE EDUCATION. EMPLOYING STRUCTURAL EQUATION MODELING, WE FOUND THAT THE INTERVENTION POSITIVELY AFFECTED THE SELF-CONCEPTS OF EFFORT AVOIDANT STUDENTS. THE INTERVENTION ALSO POSITIVELY IMPACTED STUDENTS' ATTITUDES AND SELF-EFFICACY TOWARDS INCLUSIVE EDUCATION, BUT HAD NO EFFECT ON COURSE PERFORMANCE, COURSE-RELATED SELF-EFFICACY AND TASK VALUES. MOREOVER, LEARNING ANALYTICS DATA REVEALED IN-DEPTH INFORMATION ON STUDENTS' LEARNING BEHAVIOR. RESULTS ARE DISCUSSED REGARDING POSSIBLE INTERVENTION STRATEGIES TO BE IMPLEMENTED IN FUTURE VERSIONS OF THE LEARNING DESIGN.
RESEARCH INDICATES THAT DATA-INFORMED PRACTICE HELPS TEACHERS CHANGE THEIR TEACHING AND PROMOTES TEACHER PROFESSIONAL DEVELOPMENT (TPD). ALTHOUGH EDUCATIONAL DATA ARE OFTEN COLLECTED FROM DIGITAL SPACES, IN-ACTION EVIDENCE FROM PHYSICAL SPACES IS SELDOM GATHERED, PROVIDING AN INCOMPLETE VIEW OF THE CLASSROOM REALITY. ALSO, MOST LEARNING ANALYTICS TOOLS FOCUS ON LEARNERS AND DO NOT EXPLICITLY COLLECT OR ANALYSE TEACHING DATA. TO SUPPORT TEACHER-LED INQUIRIES IN TPD, THE AUTHORS' DESIGN-BASED RESEARCH EXPLORES THE FEASIBILITY AND EFFECTS OF TEACHERS ACTIVELY COLLECTING, WITH THE HELP OF TECHNOLOGY, DATA ABOUT THEIR CLASSROOM PRACTICE AND THE POSSIBLE IMPACT OF SUCH DATA ON THEIR OWN TEACHING. BASED ON AN ONLINE SURVEY (N = 94), PRIOR RESEARCH LITERATURE AND FEEDBACK FROM TEACHERS (N = 11), THE AUTHORS DEMONSTRATE THE FEASIBILITY OF SUCH DATA COLLECTION AND SUGGEST DESIGN PRINCIPLES FOR CLASSROOM DATA-COLLECTION TOOLS AS, BESIDES USABILITY AND EASE OF USE, THEY ALSO DETECTED INTEREST IN CUSTOMISATION, TRIGGERING TEACHER INTEREST AND INCLUSION OF TEACHING DATA.
ADVANCES IN THE FIELD OF MULTIMODAL LEARNING ANALYTICS (MMLA) RESEARCH IS OFTEN ACCOMPLISHED BY ACTIVELY EXPLORING NEW TECHNOLOGIES AND TECHNIQUES RELATED TO THE COLLECTION AND ANALYSIS OF DATA. EXPLORATION OF ETHICAL PRINCIPLES AND PROCEDURES FOR GOVERNING THE USE OF NEW TECHNOLOGIES AND TECHNIQUES, HOWEVER, IS NOT AS READILY PURSUED. AS COLLECTED DATA GROW IN COMPLEXITY AND INVASIVENESS, POTENTIALLY, A GROWING NEED IS ARISING TO SCRUTINIZE ETHICAL ASPECTS OF MMLA RESEARCH. IN OUR STUDY, WE INTRODUCE AN INFORMED CONSENT COMPREHENSION TEST FOR EDUCATIONAL TECHNOLOGY RESEARCH AND ASSESS THE EFFECTS OF ENHANCING MMLA CONSENT FORMS ON COMPREHENSION OF INFORMED CONSENT AND ON RATES OF ENROLLMENT IN A MMLA STUDY. ONE FORM IS WRITTEN FROM A RESEARCHER PERSPECTIVE AND THE OTHER FROM A PARTICIPANT PERSPECTIVE. RESULTS OF THE STUDY INVOLVING FIRST-YEAR UNDERGRADUATE STUDENTS SUGGEST THAT THE OVERALL LEVEL OF COMPREHENSION DID NOT DIFFER BETWEEN CONDITIONS. YET, THE PARTICIPANT-ORIENTED CONSENT FORM RESULTED IN SIGNIFICANTLY LOWER RATES OF ENROLLMENT. IMPLICATIONS FOR MMLA RESEARCHERS ARE DISCUSSED.
GOOGLE CLASSROOM (GC) HAS PROVIDED AFFORDANCES FOR BLENDED LEARNING IN HIGHER EDUCATION. GIVEN THIS, MOST INSTITUTIONS, INCLUDING MALAYSIAN HIGHER EDUCATIONAL INSTITUTIONS, ARE ADOPTING THIS LEARNING MANAGEMENT SYSTEM (LMS) TECHNOLOGY FOR SUPPORTING OUT OF CLASSROOM PEDAGOGICAL. EVEN THOUGH QUANTITATIVE EVIDENCE EXISTS TO CONFIRM THE USEFULNESS OF GC USAGE, THERE IS THE NEED TO ALSO HAVE QUALITATIVE NARRATIVES FROM STUDENTS AND INSTRUCTORS TO OBTAIN DETAILS ON ACTUAL EXPERIENCES. IN VIEW OF THIS, THE STUDY ATTEMPTED TO FILL THIS GAP BY WAY OF A QUALITATIVE APPROACH WITH DATA COLLECTED FROM SEVENTEEN STUDENTS AND THREE INSTRUCTORS FROM A MALAYSIAN HIGHER EDUCATION INSTITUTION. THE STUDY REVEALED USEFULNESS AND EASINESS WERE THE MAIN FACTORS INFLUENCING THE USE OF GC FROM BOTH PERSPECTIVES. HOWEVER, CONCERNS RAISED BY STUDENTS WERE BASED ON PRIVACY ISSUES, PEER INTERACTION, AND INTERFACE DESIGN, WHEREAS INSTRUCTORS, WHILE SUPPORTED SOME OF THESE CONCERNS, ADDED CHALLENGES IN ACCESS TO GC LEARNING ANALYTICS AND CLOUD STORAGE. RECOMMENDATIONS FROM THIS STUDY MAY OFFER SOME IMPLICATIONS FOR THE ADAPTATION OF GC IN HIGHER EDUCATION.
IT IS INCREASINGLY IMPORTANT THAT HIGHER EDUCATION INSTITUTIONS BE ABLE TO AUDIT AND EVALUATE THE SCOPE AND EFFICACY OF THEIR DIGITAL LEARNING RESOURCES ACROSS VARIOUS SCALES. TO DATE THERE HAS BEEN LITTLE EFFORT TO ADDRESS THIS NEED FOR A VALIDATED, APPROPRIATE, AND SIMPLE-TO-EXECUTE METHOD THAT WILL FACILITATE SUCH AN AUDIT, WHETHER IT BE AT THE SCALE OF AN INDIVIDUAL PROGRAM, DEPARTMENT, FACULTY, OR INSTITUTION. THE DATA ARE OF INCREASING VALUE TO ENSURE INSTITUTIONS MAINTAIN PROGRESS AND EQUITY IN THE STUDENT EXPERIENCE AS WELL AS FOR DEPLOYMENT AND INTERPRETATION OF LEARNING ANALYTICS. THIS STUDY PRESENTS A GENERALIZABLE FRAMEWORK FOR AUDITING DIGITAL LEARNING PROVISION IN HIGHER EDUCATION CURRICULA. THE FRAMEWORK IS CONTEXTUALIZED USING A CASE STUDY IN WHICH THE AUDIT IS CONDUCTED ACROSS A SINGLE FACULTY IN A RESEARCH-INTENSIVE UK UNIVERSITY. THIS WORK PROVIDES ACADEMICS AND HIGHER EDUCATION ADMINISTRATORS WITH KEY PRINCIPLES AND CONSIDERATIONS AS WELL AS EXAMPLE AIMS AND OUTCOMES.
A DECREASE IN STUDENT ATTENDANCE AT LECTURES BOTH NATIONALLY AND INTERNATIONALLY, HAS PROMPTED EDUCATORS TO RE-EVALUATE THEIR TEACHING METHODS AND INVESTIGATE STRATEGIES WHICH PROMOTE STUDENT ENGAGEMENT. THE FLIPPED CLASSROOM MODEL, GROUNDED IN ACTIVE LEARNING PEDAGOGY, TRANSFORMS THE FACE-TO-FACE CLASSROOM. STUDENTS PREPARE FOR THE FLIPPED CLASSROOM IN THEIR OWN TIME BY WATCHING SHORT ONLINE VIDEOS AND COMPLETING READINGS. FACE-TO-FACE TIME IS USED TO APPLY LEARNING THROUGH PROBLEM-SOLVING WITH PEERS. TO IMPROVE THE ENGAGEMENT AND LEARNING OUTCOMES OF OUR SECOND YEAR COHORT, LECTURES WERE REPLACED WITH SHORT ONLINE VIDEOS AND FACE-TO-FACE TIME WAS SPENT IN A FLIPPED CLASSROOM. THE IMPACT OF THE FLIPPED CLASSROOM WAS ANALYSED THROUGH SURVEYS, ATTENDANCE RECORDS, LEARNING ANALYTICS AND EXAM DATA BEFORE AND AFTER THE IMPLEMENTATION OF THE FLIPPED CLASSROOM. RESULTS SUGGEST AN INCREASE IN STUDENT ENGAGEMENT AND A POSITIVE ATTITUDE TOWARDS THE LEARNING METHOD. HOWEVER, THERE WERE NO MEASURABLE INCREASES IN STUDENT LEARNING OUTCOMES.
Y THE TOPIC PRESENTED WILL SHOW HOW DIFFERENT KINDS OF SENSORS CAN HELP TO IMPROVE OUR SKILLS IN LEARNING ENVIRONMENTS. WHEN WE OPEN THE MIND AND LET IT TAKE THE CONTROL TO BE CREATIVE, WE CAN THINK HOW A MARTIAL ART WOULD BE IMPROVED WITH REGISTERED SENSORS, OR HOW A PERSON MAY DANCE WITH MACHINES TO IMPROVE THEIR TECHNIQUE, OR HOW YOU MAY IMPROVE YOUR SOCCER KICK FOR A PENALTIES ROUND. THE USE OF SENSORS SEEMS EASY TO IMAGINE IN THESE EXAMPLES, BUT THEIR USE IS NOT LIMITED TO THESE TYPES OF LEARNING ENVIRONMENTS. USING DEPTH CAMERAS TO DETECT PATTERNS IN ORAL PRESENTATIONS, OR IMPROVING THE ASSESSMENT OF AGILITY THROUGH LOW COST-SENSORS WITH MULTIMODAL LEARNING ANALYTICS, OR USING COMPUTING DEVICES AS SENSORS TO MEASURE THEIR IMPACT ON PRIMARY AND SECONDARY STUDENTS' PERFORMANCES ARE THE FOCUS OF THIS STUDY AS WELL. WE HOPE READERS WILL FIND ORIGINAL IDEAS THAT ALLOW THEM TO IMPROVE AND ADVANCE IN THEIR OWN RESEARCHES.
THIS PAPER PRESENTS A PROTOTYPE OF A SENSOR DEVICE INCLUDING HEART RATE, EDA AND ACCELEROMETER SENSORS TO INVESTIGATE LEARNERS' INTERNAL STATE. THROUGH EXPERIMENTS, SENSOR DATA WERE COLLECTED, VISUALIZED AND CORRELATED WITH INFORMATION ON LEARNER'S' EMOTIONAL STATE DERIVED FROM A SELF-REPORT QUESTIONNAIRE. THE RESULTS CAN BE USED TO IMPROVE SIGNAL PROCESSING, AND HELP FIND APPROPRIATE INDICATORS FROM PHYSIOLOGICAL DATA FOR LEARNING ENVIRONMENT DESIGN.
TEXTUAL DATA, AS A KEY CARRIER OF LEARNING FEEDBACK, IS CONTINUOUSLY PRODUCED BY MANY STUDENTS WITHIN COURSE FORUMS. THE TEMPORAL NATURE OF DISCUSSION REQUIRES STUDENTS' EMOTIONS AND CONCERNED ASPECTS (E.G. TEACHING STYLES, LEARNING ACTIVITIES, ETC.) TO BE DYNAMICALLY TRACKED FOR UNDERSTANDING LEARNING REQUIREMENTS. TO CHARACTERIZE DYNAMICS OF EMOTION-ASPECTS, THIS PAPER PRESENTS AN UNSUPERVISED MODEL, NAMELY TEMPORAL EMOTION-ASPECT MODEL (TEAM), MODELING TIME JOINTLY WITH EMOTIONS AND ASPECTS TO CAPTURE EMOTION-ASPECT EVOLUTIONS OVER TIME. ESPECIALLY, THE PRIMARY OUTPUT OF TEAM IS TWO-FOLD: EMOTION-SPECIFIC ASPECT PROBABILISTIC DISTRIBUTIONS AND THEIR EVOLUTIONS OVER TIME. WITH THE DISCUSSION DATA FROM TWO ONLINE COURSE FORUMS, WE VALIDATED THE PERFORMANCE OF TEAM AND UTILIZED THIS MODEL TO DISCOVER MOST CONCERNED EMOTION-ASPECTS AS WELL AS THEIR EVOLUTIONARY TRENDS FOR THE WHOLE LEARNING GROUP AND DIFFERENT ACHIEVEMENT LEVELS OF GROUPS, RESPECTIVELY. THE RESULTS INDICATED THAT, (1) CONTENT-RELATED ASPECTS WERE THE MAIN FOCUS WITH HIGHER PROBABILITIES TO NEGATIVE AND CONFUSED EMOTIONS; (2) THERE WERE HIGHER PROBABILITIES OF EMOTIONAL EXPRESSIONS IN THE INITIAL AND FINAL STAGES OF A SEMESTER; (3) COMPARED WITH HIGH- AND MEDIUM-ACHIEVING STUDENTS, LOW-ACHIEVING STUDENTS WERE LESS ACTIVE IN EMOTIONAL ENGAGEMENT ON THE WHOLE, AND TENDED TO EXPRESS MORE CONFUSION IN THE FINAL STAGE OF A SEMESTER.
WE DEVELOPED A PROGRAMMING EDUCATION SUPPORT TOOL PGTRACER. PGTRACER PROVIDES FILL-IN-THE-BLANK QUESTIONS TO THE STUDENTS AND COLLECTS STUDENT LOG TO ANALYZE STUDENT'S LEARNING PROCESS AND UNDERSTANDING LEVEL. IN THIS PAPER, WE REPORT OUR EXPERIENCE TO UTILIZE PGTRACER AT AN ACTUAL PROGRAMMING COURSE AS HOMEWORK ASSIGNMENT. WE DEVELOP FILL-IN-THE-BLANK QUESTIONS CORRESPONDING TO THE COURSE SYLLABUS AT EACH WEEK. STUDENT ACTIVITIES ON PGTRACER ARE ANALYZED TO DEVELOP QUESTIONS FOR THE SUCCEEDING WEEKS. WE ALSO PROVIDE DATA TO THE INSTRUCTOR ABOUT THE ACTIVITIES AND ACHIEVEMENT OF THE STUDENTS FOR BETTER COLLABORATION BETWEEN LECTURE AND HOMEWORK. WE RECEIVED POSITIVE FEEDBACKS FROM BOTH OF THE TEACHER INTERVIEW AND STUDENT SURVEY ABOUT THE USEFULNESS OF PGTRACER AS PROGRAMMING EDUCATION SUPPORT TOOL.
MICRO LEARNING THROUGH OPEN EDUCATIONAL RESOURCES (OERS) IS BECOMING INCREASINGLY POPULAR. HOWEVER, ADAPTIVE MICRO LEARNING SUPPORT REMAINS INADEQUATE BY CURRENT OER PLATFORMS. TO ADDRESS THIS, OUR SMART SYSTEM, MICRO LEARNING AS A SERVICE (MLAAS), AIMS TO DELIVER PERSONALIZED OER WITH MICRO LEARNING TO SATISFY THEIR REAL-TIME NEEDS. IN THIS PAPER, WE FOCUS ON CONSTRUCTING A KNOWLEDGE BASE TO SUPPORT THE DECISION-MAKING PROCESS OF MLAAS. MLAAS IS BUILT USING A TOP-DOWN APPROACH. A CONCEPTUAL GRAPH-BASED ONTOLOGY CONSTRUCTION IS FIRST DEVELOPED. AN EDUCATIONAL DATA MINING AND LEARNING ANALYTIC STRATEGY IS THEN PROPOSED FOR THE DATA LEVEL. THE LEARNING RESOURCE ADAPTATION STILL REQUIRES LEARNERS' HISTORICAL INFORMATION. TO COMPENSATE FOR THE ABSENCE OF THIS INFORMATION INITIALLY (AKA 'COLD START'), WE SET UP A PREDICTIVE ONTOLOGY-BASED MECHANISM. AS THE FIRST RESOURCE IS DELIVERED TO THE BEGINNING OF A LEARNER'S LEARNING JOURNEY, THE MICRO OER RECOMMENDATION IS ALSO OPTIMIZED USING A TAILORED HEURISTIC.
EDUCATIONAL ACTIVITY IS INCREASINGLY MOVING ONLINE AND COURSE CONTENTS ARE BECOMING AVAILABLE IN DIGITAL FORMAT. THIS ENABLES DATA COLLECTION AND THE USE OF DATA FOR ANALYZING LEARNING PROCESS. FOR THE 4TH REVOLUTION IN EDUCATION, AN ACTIVE AND INTERACTIVE PRESENCE OF STUDENTS CONTRIBUTES TO A HIGHER LEARNING QUALITY. MACHINE LEARNING TECHNIQUES RECENTLY HAVE SHOWN IMPRESSIVE DEVELOPMENT STEPS OF THE USE OF DATA ANALYSIS AND PREDICTIONS. HOWEVER, IT HAS BEEN FAR LESS USED FOR ASSESSING THE LEARNING QUALITY. FOR THIS PAPER WE CONDUCTED ANALYSIS BASED ON NEURAL NETWORKS, SUPPORT VECTOR MACHINE, DECISION TREES AND CLUSTER ANALYSIS TO ESTIMATE STUDENT'S PERFORMANCE AT EXAMINATION AND SHAPE THE NEXT GENERATION'S TALENT FOR INDUSTRY 4.0 SKILLS.
THE STUDY OF PATIENT BEHAVIOURS (VITAL SIGN, PHYSICAL ACTION AND EMOTION) IS CRUCIAL TO IMPROVE ONE'S QUALITY OF LIFE. THE ONLY SOLUTION FOR HANDLING AND MANAGING MILLIONS OF PEOPLE'S BEHAVIOURS AND HEALTH WOULD BE BIG DATA AND IOT TECHNOLOGY BECAUSE MOST OF THE COUNTRIES ARE LACK OF MEDICAL PROFESSIONALS. IN THIS PAPER, A BIG DATA AND IOT-BASED PATIENT BEHAVIOUR MONITORING SYSTEM HAVE PROPOSED. QUALITATIVE STUDIES ARE CARRIED OUT ON THE SELECTED BEHAVIOURS ANALYTICS, CARDIOVASCULAR DISEASE IDENTIFICATION AND FALL DETECTION. AT LAST, AUTHORS HAVE SUMMARISED THE GENERAL CHALLENGES LIKE TRUST, PRIVACY, SECURITY AND INTEROPERABILITY AS WELL AS SPECIAL CHALLENGES IN VARIOUS SECTORS: GOVERNMENT, LEGISLATORS, RESEARCH INSTITUTIONS, INFORMATION TECHNOLOGY COMPANIES AND PATIENTS.
THE PAPER PRESENTS A FORM OF MANAGEMENT AND SUPPORT FOR PARTICIPANTS IN SOCIO-EDUCATIONAL CO-CREATIVE PROJECTS. THE METHOD IS BASED ON THE USE OF THE SOCIAL NETWORK ANALYSIS. THE RESULTS OF THE SOCIAL NETWORK ANALYSIS, STATIC AND DYNAMIC SOCIOGRAMS ARE USED AS MATERIALS THAT AUGMENT THE NARRATIVES THAT WE SEND TO PARTICIPANTS.
IN RECENT YEARS, MANY SCHOOLS HAVE HAD 'BRING YOUR OWN DEVICE' AND 'BLENDED E-LEARNING' INITIATIVES THAT HAVE CREATED A NEED FOR KNOWLEDGE ABOUT THE AFFORDANCES OF DIGITAL TOOLS AND ONLINE PLATFORMS IN 1:1 ENVIRONMENTS, WHERE EACH STUDENT HAS ACCESS TO A DIGITAL DEVICE TO SUPPORT LEARNING. THIS PAPER EXAMINES THIS NEED AMONG TEACHERS AT UPPER SECONDARY EDUCATIONAL SCHOOLS. THE RESEARCH QUESTION IS: HOW DO INNOVATIVE TEACHERS MANAGE TO EXPLOIT TECHNOLOGICAL AFFORDANCES AND CREATE EDUCATIONAL ADDED VALUE? THE PAPER PROVIDE RESEARCH EVIDENCE SUGGESTING THAT THESE TEACHERS CREATE EDUCATIONAL ADDED VALUE IN 1:1 CLASSROOMS, SINCE THEY REGULARLY PROVIDE IMMEDIATE, TASK RELEVANT AND FORMATIVE FEEDBACK TO THE STUDENTS AND ADDRESS THEIR PERCEIVED SELF-EFFICACY.
IN THIS PAPER, WE EXPLORE HOW THE DEVELOPMENT AND AFFORDANCES OF PREDICTIVE ANALYTICS MAY IMPACT HOW TEACHERS AND OTHER EDUCATIONAL ACTORS THINK ABOUT AND TEACH STUDENTS AND, MORE BROADLY, HOW SOCIETY UNDERSTANDS EDUCATION. OUR PARTICULAR FOCUS IS ON THE DATA DASHBOARDS OF LEARNING SUPPORT SYSTEMS WHICH ARE BASED ON MACHINE LEARNING (ML). WHILE PREVIOUS RESEARCH HAS FOCUSED ON HOW THESE SYSTEMS PRODUCE CREDIBLE KNOWLEDGE, WE EXPLORE HERE HOW THEY ALSO PRODUCE COMPELLING, PERSUASIVE AND CONVINCING NARRATIVES. OUR MAIN ARGUMENT IS THAT PARTICULAR KINDS OF STORIES ARE WRITTEN BY PREDICTIVE ANALYTICS AND WRITTEN INTO THEIR DATA DASHBOARDS. BASED ON A CASE STUDY OF A LEADING PREDICTIVE ANALYTICS SYSTEM, WE EXPLORE HOW DATA DASHBOARDS IMPLY CAUSALITY BETWEEN THE 'FACTS' THEY ARE VISUALISING. TO DO SO, WE ANALYSE THE STORIES THEY TELL ACCORDING TO THEIR SPATIAL AND TEMPORAL DIMENSIONS, CHARACTERS AND EVENTS, SEQUENTIALITY AS WELL AS TELLABILITY. IN THE STORIES WE IDENTIFY, TEACHERS ARE MANAGERS, STUDENTS ARE AT GREATER OR LESSER RISK, AND STUDENTS' SOCIALITY IS REDUCED TO MACHINE-READABLE INTERACTIONS. OVERALL, ONLY DATA MARKED AS INDIVIDUAL BEHAVIOURS BECOMES RELEVANT TO THE SYSTEM, RENDERING STRUCTURAL INEQUALITIES INVISIBLE. REFLECTING ON THE IMPLICATIONS OF THESE SYSTEMS, WE SUGGEST WAYS IN WHICH THE UPTAKE OF THESE SYSTEMS CAN INTERRUPT SUCH STORIES AND RESHAPE THEM IN OTHER DIRECTIONS.
THE FOCUS ON ASSESSMENT OF LEARNING EXPERIENCES HAS SHIFTED FROM KNOWLEDGE TO COMPETENCES. UNFORTUNATELY, ASSESSING CERTAIN COMPETENCES IS MAINLY A SUBJECTIVE TASK, BEING PROBLEMATIC FOR BOTH THE EVALUATORS AND THE EVALUATED. ADDITIONALLY, WHEN THE LEARNING PROCESS IS COMPUTER-SUPPORTED AND THE NUMBER OF STUDENTS INCREASES, TRADITIONAL ASSESSMENT PROCEDURES SUFFER FROM SCALABILITY PROBLEMS. IN THIS PAPER WE PROPOSE A QUERY LANGUAGE THAT SUPPORTS GRADING LEARNING COMPETENCES ACCORDING TO STUDENTS' PERFORMANCE IN AN ONLINE COURSE. USING IT WE AUTOMATICALLY EXTRACT DIFFERENT OBJECTIVE INDICATORS ABOUT STUDENTS WORK IN A LEARNING MANAGEMENT SYSTEM (LMS). EVALUATORS CAN USE THIS COMPUTER PROGRAMMING-LIKE LANGUAGE TO EXPRESS A NUMBER OF REQUIRED INDICATORS. SUCH INDICATORS ARE AUTOMATICALLY OBTAINED FROM THE ACTIVITY LOGS GENERATED BY THE LMS.
THIS MIXED-METHOD STUDY EXPLORED THE TREATMENT INTEGRITY OF VIRTUAL REALITY (VR)-BASED SOCIAL SKILL TRAINING FOR CHILDREN WITH HIGH-FUNCTIONING AUTISM (HFA). IN THIS STUDY, WE DESIGNED A VR-BASED SOCIAL SKILLS TRAINING ENVIRONMENT USING OPENSIMULATOR. DATA WERE COLLECTED THROUGH SCREEN RECORDINGS AND BEHAVIORAL OBSERVATIONS OF HFA CHILDREN'S ACTIONS DURING TRAINING. A TOTAL OF 90 TRAINING SESSIONS FROM 15 CHILDREN WITH HFA WERE ANALYZED USING JACCARD INDEX, A PROPOSED INDICATOR OF TREATMENT INTEGRITY. TO CORROBORATE THE JACCARD INDEX SCORES, WE ALSO USED THEMATIC ANALYSIS TO INVESTIGATE THE TREATMENT INTEGRITY OF THE VR-BASED SOCIAL SKILLS TRAINING. OVERALL, THIS STUDY CONFIRMED THAT VR-BASED SOCIAL SKILL TRAINING YIELDED HIGH TREATMENT INTEGRITY. IN ADDITION, THE RESULTS SHOWED THAT TREATMENT INTEGRITY SCORES DIFFERED ACCORDING TO THE TYPES OF SOCIAL SCENARIOS IN LEARNING ACTIVITIES. LASTLY, THIS STUDY DESCRIBED DESIGN IMPLICATIONS THAT SHOULD BE CONSIDERED WHEN VR-BASED SOCIAL SKILL TRAINING IS IMPLEMENTED.
NOWADAYS, THE E-LEARNING MARKET IS RAPIDLY GROWING BOTH FINANCIALLY AND GEOGRAPHICALLY. MORE AND MORE OFTEN, E-LEARNING RESOURCES INVOLVE A MULTICULTURAL AUDIENCE AND ARE BECOMING AVAILABLE TO PEOPLE WITH DIFFERENT EDUCATIONAL BACKGROUNDS. HOWEVER, THERE ARE COGNITIVE SPECIFICITY AND DIFFERENT APPROACHES TO THE LEARNING PROCESS IN DIFFERENT CULTURES. THIS PAPER IS DEVOTED TO ILLUSTRATING A POSSIBLE SOLUTION FOR ADAPTATION OF CONTENT OF AN E-LEARNING RESOURCE TO A MULTICULTURAL AUDIENCE. THE SOLUTION DESCRIBED APPLIES THE ADAPTIVE CONTENT CONCEPT BASED ON INDIVIDUAL EDUCATIONAL TRAJECTORIES AND PREPARING CONTENT ACCORDING TO THE INDIVIDUAL CULTURAL CHARACTERISTICS OF LEARNER AND HIS OR HER COMPETENCIES, BOTH OBTAINED AND DESIRED. DURING THE RESEARCH, THE LEARNER-CENTRIC MODEL OF LEARNING PROCESSES WAS DEVELOPED. IN THE ARTICLE, BOTH HIGH-LEVEL AND DETAILED MODELS ARE PRESENTED. PRINCIPLES OF PLANNING THE INDIVIDUAL LEARNING TRAJECTORY BASED ON THE LEARNER'S OBTAINED AND DESIRED COMPETENCIES, AND STATISTICAL DATA ABOUT HIS OR HER LEARNING STYLE ARE ALSO DESCRIBED. AS AN EXAMPLE OF THE POSSIBILITY TO APPLY HISTORICAL DATA ON HOW LEARNING STYLE AFFECTS SUCCESSFUL PASSING THROUGH THE LEARNING COURSE, THE STATISTICAL ANALYSIS IS PROVIDED. THE ANALYSIS RELIES ON PERSON-COURSE DE-IDENTIFIED DATASET FROM SEVEN COURSES ON HARVARDX AND MITX PLATFORMS PROVIDED DURING THE 2013/14 ACADEMIC YEAR. THIS ANALYSIS DEMONSTRATED THE STATISTICAL SIGNIFICANCE OF SEVERAL PARAMETERS. A COMPARISON OF ALGORITHMS FOR ESTIMATING THE PROBABILITY OF SUCCESSFULLY PASSING THE COURSE DEPENDING ON THE LEARNING STYLE, IS ALSO PRESENTED.
IN THIS PAPER, WE INTRODUCE A USAGE-BASED TECHNIQUE FOR CLUSTERING LEARNING RESOURCES ACCESSED IN ONLINE LEARNING PORTALS. THIS APPROACH SOLELY RELIES ON THE USAGE OF THE LEARNING RESOURCES AND DOES NOT CONSIDER THEIR CONTENT OR THE RELATIONS BETWEEN THE USERS AND THE RESOURCES. IN ORDER TO CLUSTER THE RESOURCES, WE CALCULATE HIGHER-ORDER CO-OCCURRENCES, A TECHNIQUE TAKEN FROM CORPUS-DRIVEN LEXICOLOGY WHERE IT IS USED TO CLUSTER WORDS BASED ON THEIR USAGE IN LANGUAGE. WE FIRST OUTLINE HOW WE ADAPT THE APPROACH TO THEN PRESENT AN EXTENSIVE EVALUATION THAT SHOWS THE EFFECTS OF THE CLUSTERING. FINALLY, WE SHOW HOW THE RESULTING CLUSTERS CAN BE USED TO ENHANCE RECOMMENDER SYSTEMS.
IN MASSIVE OPEN ONLINE COURSES (MOOCS), LEARNERS CAN INTERACT WITH EACH OTHER USING DISCUSSION BOARDS. AUTOMATICALLY INFERRING THE STATES OR NEEDS OF LEARNERS FROM THEIR POSTS IS OF INTEREST TO INSTRUCTORS, WHO ARE FACED WITH A HIGH ATTRITION IN MOOCS. MACHINE LEARNING HAS PREVIOUSLY BEEN SUCCESSFULLY USED TO IDENTIFY STATES SUCH AS CONFUSION OR POSTING QUESTIONS, BUT NO SOLUTION HAS YET BEEN PROVIDED SO THAT INSTRUCTORS CAN TRACK THE PROGRESS OF THE LEARNERS USING A VALIDATED FRAMEWORK FROM EDUCATION RESEARCH. IN THIS PAPER, WE DEVELOP A MODEL TO AUTOMATICALLY LABEL A POST BASED ON THE FIRST PHASE OF THE INTERACTION ANALYSIS MODEL (IAM). THIS ALLOWS INSTRUCTORS TO AUTOMATICALLY IDENTIFY WHETHER STUDENTS ARE STATING OPINIONS, CLARIFYING DETAILS, OR ENGAGING IN ACTIVITIES SUCH AS PROVIDING EXAMPLES TO PEERS. OUR MODEL IS TESTED ON A COURSERA MOOC DEVOTED TO CHEMISTRY, FOR WHICH WE ARE ABLE TO CORRECTLY CATEGORIZE THE IAM STATUS IN 4 OUT OF 5 POSTS. OUR APPROACH THUS PROVIDES INSTRUCTORS WITH AN INTELLIGENT SYSTEM THAT GENERATES ACTIONABLE LEARNING ASSESSMENT DATA AND CAN COPE WITH LARGE ENROLLMENT. USING THE SYSTEM, INSTRUCTORS CAN QUICKLY IDENTIFY AND REMEDY LEARNING ISSUES, THUS SUPPORTING LEARNERS IN ATTAINING THEIR INTENDED OUTCOMES.
IN THIS PAPER, WE ANALYZE LEARNING ACTIVITY OF STUDENTS AND ITS EFFECT WHEN WE UTILIZE THE PROGRAMMING EDUCATION SUPPORT TOOL PGTRACER TO PROVIDE HOMEWORK FOR THEIR PROGRAMMING COURSE. WE ASSIGNED HOMEWORK UTILIZING PGTRACER TO THE STUDENTS AND INCORPORATED ITS LEARNING ACHIEVEMENT TO THEIR EVALUATION OF THE COURSE. AS A RESULT, THIS METHOD IMPROVED THE LEARNING ACTIVITY OF THE STUDENTS. THEIR UNDERSTANDING OF THE TRACE TABLE WAS RELATED TO UNDERSTANDING OF THE PROGRAMMING. THEIR ANSWERING PROCESSES TO REACH TO A CORRECT ANSWER VARIED ACCORDING TO THEIR PROGRAMMING SKILL. AS FOR THEIR UNDERSTANDING OF THE PROGRAM, THOSE WHO CONTINUOUSLY DID THEIR HOMEWORK UNDERSTOOD A PROGRAM BETTER THAN THOSE WHO ONLY PREPARED FOR THE EXAM IN A SHORT TERM. THEREFORE, WE CAN CONCLUDE THAT PROVIDING HOMEWORK UTILIZING PGTRACER IS EFFECTIVE FOR STUDENTS' CENTERED LEARNING OF PROGRAMMING.
MANY RESEARCHERS WHO STUDY THE IMPACT OF COMPUTER-BASED ASSESSMENT (CBA) FOCUS ON THE AFFORDANCES OR COMPLEXITIES OF CBA APPROACHES IN COMPARISON TO TRADITIONAL ASSESSMENT METHODS. THIS STUDY EXAMINES HOW CBA APPROACHES WERE CONFIGURED WITHIN AND BETWEEN MODULES, AND THE IMPACT OF ASSESSMENT DESIGN ON STUDENTS' ENGAGEMENT, SATISFACTION, AND PASS RATES. THE ANALYSIS WAS CONDUCTED USING A COMBINATION OF LONGITUDINAL VISUALISATIONS, CORRELATIONAL ANALYSIS, AND FIXED-EFFECT MODELS ON 74 UNDERGRADUATE MODULES AND THEIR 72,377 STUDENTS. OUR FINDINGS INDICATE THAT EDUCATORS DESIGNED VERY DIFFERENT ASSESSMENT STRATEGIES, WHICH SIGNIFICANTLY INFLUENCED STUDENT ENGAGEMENT AS MEASURED BY TIME SPENT IN THE VIRTUAL LEARNING ENVIRONMENT (VLE). WEEKLY ANALYSES INDICATED THAT ASSESSMENT ACTIVITIES WERE BALANCED WITH OTHER LEARNING ACTIVITIES, WHICH SUGGESTS THAT EDUCATORS TENDED TO AIM FOR A CONSISTENT WORKLOAD WHEN DESIGNING ASSESSMENT STRATEGIES. SINCE MOST OF THE ASSESSMENTS WERE COMPUTER-BASED, STUDENTS SPENT MORE TIME ON THE VLE DURING ASSESSMENT WEEKS. BY CONTROLLING FOR HETEROGENEITY WITHIN AND BETWEEN MODULES, LEARNING DESIGN COULD EXPLAIN UP TO 69% OF THE VARIABILITY IN STUDENTS' TIME SPENT ON THE VLE. FURTHERMORE, ASSESSMENT ACTIVITIES WERE SIGNIFICANTLY RELATED TO PASS RATES, BUT NO CLEAR RELATION WITH SATISFACTION WAS FOUND. OUR FINDINGS HIGHLIGHT THE IMPORTANCE OF CBA AND LEARNING DESIGN TO HOW STUDENTS LEARN ONLINE. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE GROWING POPULARITY OF AUTOMATICALLY GRADED ONLINE TESTS, EITHER AS AN EVALUATION OR SELF-ASSESSMENT TOOL IN ONLINE OR BLENDED EDUCATION, DEMANDS A REVIEW OF HOW THESE QUESTIONS ARE DESIGNED AND DELIVERED TO THEIR INTENDED AUDIENCE. THIS PAPER ANALYZES THE RESULTS OF OVER 20,000 PRE- UNIVERSITY MOCK ONLINE QUIZZES DESIGNED TO TRAIN THE STUDENTS FOR THE SPANISH UNIVERSITY ADMISSION TEST (KNOWN AS "PRUEBAS DE ACCESO A LA UNIVERSIDAD" OR "SELECTIVIDAD") IN THE TECHNICAL DRAWING SUBJECT, CORRESPONDING TO THE JUNE AND SEPTEMBER INTAKES OF 2009 AND 2015. THE INFLUENCE OF TWO KEY ASPECTS ON THE QUESTIONS SUCCESS AND SKIP RATES IS ASSESSED: (A) THE PRESENCE OR ABSENCE OF ILLUSTRATION SUPPORT AND (B) THE LENGTH OF THE QUESTION AS A PROXY OF READING COMPREHENSION DIFFICULTY. THE RESULTS SUPPORT THAT THE PRESENCE OF AN ACCOMPANYING ILLUSTRATION IN THE QUESTIONS RESULT IN FEWER SKIPPED QUESTIONS AND MODE SUCCESSFUL ANSWERS, WHILE THE LENGTH OF THE QUESTION HAS THE OPPOSITE EFFECT. THE PERFORMANCE DIFFERENCE IN THE 6-YEAR SPAN IS ALSO DISCUSSED, SHOWING A SLIGHT DECLINE OVER TIME IN THE PASS RATES WHILE THE SKIP RATES REMAIN STABLE. WHEN COMPARING BOTH TWO INTAKES, CORRESPONDING TO DIFFERENT ACADEMIC PROFILES OF STUDENTS THAT PASSED THE JUNE EXAM AND THOSE WHO DID NOT, THE SUCCESS RATIO IS UNSURPRISINGLY LOWER FOR THE STUDENTS IN THE SECOND INTAKE. THESE FINDINGS SHOULD HELP IMPROVING THE DESIGN OF ONLINE QUIZZES, INCLUDING MORE VISUAL CONTENT AND/OR REPHRASING THE QUESTIONS TO BE MORE CONCISE, TO FIT THE REQUIREMENTS OF STUDENTS EDUCATED IN A MORE VISUAL ENVIRONMENT OF MULTIMEDIA TECHNOLOGIES.
PROFOUND KNOWLEDGE OF ENGINEERING MECHANICS IS ONE OF THE KEY INGREDIENTS OF EVERY ENGINEERING EDUCATION. HOWEVER, MANY STUDENTS PERCEIVE THE SUBJECT AS RATHER DIFFICULT AND THE SUCCESS RATE IS RELATIVELY LOW. THUS, THE AIM OF THIS CONTRIBUTION IS TO SHARE SOME EXPERIENCES WITH BLENDED LEARNING, FLIPPED CLASSROOM SCENARIOS IN ENGINEERING MECHANICS ON AN UNDERGRADUATE LEVEL AT A TECHNICAL UNIVERSITY, TO INVESTIGATE IF THE LEARNING EXPERIENCE CAN BE IMPROVED AND THE SUCCESS RATE INCREASED. THE COURSE IN QUESTION IS AN EXERCISE COURSE (GERMAN: UEBUNGEN) ON THE FUNDAMENTALS OF STATICS AND MECHANICS OF MATERIALS WORTH 2 ECTS CREDITS. THE LEARNING TARGET IS TO BE ABLE TO TRANSFER MECHANICAL CONCEPTS TO SPECIFIC PROBLEMS IN ORDER TO ACCURATELY SOLVE THEM. ULTIMATELY, THE STUDENTS SHOULD BE ABLE TO PROPERLY SIZE TRUSSES AND SHAFTS USING THE CONCEPTS LEARNED THROUGHOUT THE COURSE IN ORDER TO BE PREPARED FOR FURTHER COURSES BASED ON THIS KNOWLEDGE. SUCH A SETTING IS THE IDEAL PLATFORM FOR FLIPPED CLASSROOM SCENARIOS DUE TO THE HIGH EMPHASIS ON THE SOLUTION OF SPECIFIC PROBLEM SETS. THIS ALLOWS USING THE LIMITED F2F TIME TO DISCUSS SPECIFIC DIFFICULTIES STUDENTS CAME ACROSS WHEN SOLVING THE PROBLEM SETS AND TO FURTHER CONSOLIDATE THE ACQUIRED KNOWLEDGE BY RE-TEACHING CERTAIN MISUNDERSTOOD CONCEPTS OR EVEN SOLVING ADDITIONAL PROBLEMS. SINCE THE AIM IS TO RAISE STUDENTS' KNOWLEDGE OF ENGINEERING MECHANICS BY ENGAGING THEIR INTERACTION WITH THE SUBJECT, THIS CONTRIBUTION WILL ALSO COMPARE OUTCOMES OF COURSES USING THE METHODS DESCRIBED ABOVE WITH SIMILAR COURSES THAT DO NOT USE SUCH METHODS.
BUILDING ON THE PROMISE SHOWN IN GAME-BASED LEARNING RESEARCH, THIS PAPER EXPLORES METHODS FOR GAME BASED LEARNING ASSESSMENTS (GBLA) USING A VARIETY OF EDUCATIONAL DATA MINING TECHNIQUES (EDM). GBLA RESEARCH EXAMINES PATTERNS OF BEHAVIORS EVIDENT IN GAME DATA LOGS FOR THE MEASUREMENT OF IMPLICIT LEARNING-THE DEVELOPMENT OF UNARTICULATED KNOWLEDGE THAT IS NOT YET EXPRESSIBLE ON A TEST OR FORMAL ASSESSMENT. THIS PAPER REPORTS ON THE STUDY OF TWO DIGITAL GAMES SHOWING HOW THE COMBINATION OF HUMAN CODING WITH EDM HAS ENABLED RESEARCHERS TO MEASURE IMPLICIT LEARNING OF PHYSICS. IN THE GAME IMPULSE, RESEARCHERS COMBINED HUMAN CODING OF VIDEO WITH EDUCATIONAL DATA MINING TO CREATE A SET OF AUTOMATED DETECTORS OF STUDENTS' IMPLICIT UNDERSTANDING OF NEWTONIAN MECHANICS. FOR QUANTUM SPECTRE, AN OPTICS PUZZLE GAME, HUMAN CODING OF INTERACTION NETWORKS WAS USED TO IDENTIFY COMMON STUDENT ERRORS. FINDINGS SHOW THAT SEVERAL OF OUR MEASURES OF STUDENT IMPLICIT LEARNING WITHIN THESE GAMES WERE SIGNIFICANTLY CORRELATED WITH IMPROVEMENTS IN EXTERNAL POSTASSESSMENTS. METHODS AND DETAILED FINDINGS WERE DIFFERENT FOR EACH TYPE OF GAME. THESE RESULTS SUGGEST GBLA SHOWS PROMISE FOR FUTURE WORK SUCH AS ADAPTIVE GAMES AND IN-CLASS, DATA-DRIVEN FORMATIVE ASSESSMENTS, BUT DESIGN OF THE ASSESSMENT MECHANICS MUST BE CAREFULLY CRAFTED FOR EACH GAME. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
STEM (SCIENCE, TECHNOLOGY, ENGINEERING, AND MATH) EDUCATION IS CURRENTLY RECEIVING MUCH ATTENTION FROM GOVERNMENTS AND EDUCATIONAL INSTITUTIONS. OUR WORK IS BASED ON ACTIVE LEARNING AND VIDEO-BASED LEARNING APPROACHES TO SUPPORT STEM EDUCATION. HERE, WE AIMED TO INCREASE STUDENT'S ENGAGEMENT THROUGH REFLECTIVE PROCESSES THAT EMBRACE VIDEO FILM-MAKING, AND SUBSEQUENT ONLINE DISCUSSION AND EVALUATION OF THOSE VIDEOS. WE PROPOSE A GROUP ENGAGEMENT SCORE THAT TAKES INTO ACCOUNT BOTH INDIVIDUAL ACTIVITY AND SIMILARITY OF PARTICIPATION, THUS ALLOWING CORRECTIVE ACTIONS TO BE TAKEN WHEN UNENGAGED STUDENTS OR GROUPS ARE IDENTIFIED. WE TESTED THESE IDEAS USING OUR OWN SOCIAL LEARNING PLATFORM THAT COMBINES THE PRINCIPAL FEATURES OF SOCIAL NETWORKS WITH TOOLS THAT FACILITATE COLLABORATIVE LEARNING DESIGN. THIS PLATFORM STIMULATES STUDENTS' LEARNING BY MEANS OF TWO MAIN REFLECTIVE PROCESSES: PARTICIPATORY PRODUCTION AND PEER-REVIEW. WE EVALUATED THIS PLATFORM AND THE LEARNING APPROACH IT SUPPORTS IN AN OBJECT ORIENTED PROGRAMMING COURSE AND IDENTIFIED INTERESTING DIFFERENCES BETWEEN GROUP ENGAGEMENT AND VIDEO RATINGS. OUR PRINCIPAL CONCLUSION IS THAT GREATER TEACHER AND STUDENT AWARENESS OF THE ONGOING ACTIVITIES AND GROUP ENGAGEMENT ARE NEEDED.
ARTIFICIAL INTELLIGENCE IS AT THE HEART OF MODERN SOCIETY WITH COMPUTERS NOW CAPABLE OF MAKING PROCESS DECISIONS IN MANY SPHERES OF HUMAN ACTIVITY. IN EDUCATION, THERE HAS BEEN INTENSIVE GROWTH IN SYSTEMS THAT MAKE FORMAL AND INFORMAL LEARNING AN ANYTIME, ANYWHERE ACTIVITY FOR BILLIONS OF PEOPLE THROUGH ONLINE OPEN EDUCATIONAL RESOURCES AND MASSIVE ONLINE OPEN COURSES. MOREOVER, NEW DEVELOPMENTS IN ARTIFICIAL INTELLIGENCE-RELATED EDUCATIONAL ASSESSMENT ARE ATTRACTING INCREASING INTEREST AS MEANS OF IMPROVING ASSESSMENT EFFICACY AND VALIDITY, WITH MUCH ATTENTION FOCUSING ON THE ANALYSIS OF THE LARGE VOLUMES OF PROCESS DATA BEING CAPTURED FROM DIGITAL ASSESSMENT CONTEXTS. IN EVALUATING THE STATE OF PLAY OF ARTIFICIAL INTELLIGENCE IN FORMATIVE AND SUMMATIVE EDUCATIONAL ASSESSMENT, THIS PAPER OFFERS A CRITICAL PERSPECTIVE ON THE TWO CORE APPLICATIONS: AUTOMATED ESSAY SCORING SYSTEMS AND COMPUTERIZED ADAPTIVE TESTS, ALONG WITH THE BIG DATA ANALYSIS APPROACHES TO MACHINE LEARNING THAT UNDERPIN THEM.
DETECTION AND VISUALIZATIONS OF AFFECTIVE STATES OF STUDENTS IN COMPUTER BASED LEARNING ENVIRONMENTS HAVE BEEN PROPOSED TO SUPPORT STUDENT AWARENESS AND IMPROVE LEARNING. HOWEVER, THE EVALUATION OF SUCH VISUALIZATIONS WITH STUDENTS IN REAL LIFE SETTINGS IS AN OPEN ISSUE. THIS RESEARCH REPORTS ON OUR EXPERIENCES FROM THE USE OF FOUR DIFFERENT TYPES OF DASHBOARD VISUALIZATIONS IN TWO USER STUDIES (N= 115). STUDENTS WHO PARTICIPATED IN THE STUDIES WERE BACHELOR AND MASTER LEVEL STUDENTS FROM TWO DIFFERENT STUDY PROGRAMS AT TWO UNIVERSITIES. THE RESULTS INDICATE THAT USABILITY, MEASURED BY INTERPRETABILITY, PERCEIVED USEFULNESS AND INSIGHT, IS OVERALL ACCEPTABLE. HOWEVER, THE FINDINGS ALSO SUGGEST THAT INTERPRETABILITY OF SOME VISUALIZATIONS, IN TERMS OF THE CAPABILITY TO SUPPORT EMOTION AWARENESS, STILL NEEDS TO BE IMPROVED. THE LEVEL OF STUDENTS AWARENESS ABOUT THEIR EMOTIONS DURING LEARNING ACTIVITIES BASED ON THE VISUALIZATION INTERPRETATION VARIED DEPENDING ON PREVIOUS KNOWLEDGE ON VISUALIZATION TECHNIQUES. FURTHERMORE, SIMPLER VISUALIZATIONS RESULTED IN BETTER OUTCOMES THAN MORE COMPLEX TECHNIQUES.
IN TODAY'S WORLD, NEW TECHNOLOGIES ARE BEING USED FOR THE TEACHING-LEARNING PROCESS IN THE CLASSROOM. THEIR USE TO SUPPORT LEARNING CAN PROVIDE SIGNIFICANT ADVANTAGES FOR THE TEACHING-LEARNING PROCESS AND HAVE POTENTIAL BENEFITS FOR STUDENTS, AS MANY OF THESE TECHNOLOGIES ARE A PART OF THE WORK LIFE OF MANY CURRENT PROFESSIONS. THE AIM OF THIS STUDY IS TO ANALYSE THE USE OF INNOVATIVE TECHNOLOGIES FOR ENGINEERING AND SCIENCE EDUCATION AFTER EXAMINING THE DATA OBTAINED FROM STUDENTS IN THEIR LEARNING PROCESS AND EXPERIENCES. THE STUDY HAS BEEN FOCUSED ON COMPUTATIONAL GEOGRAPHIC INFORMATION SYSTEMS, WHICH ALLOW ACCESS TO AND MANAGEMENT OF LARGE VOLUMES OF INFORMATION AND DATA, AND ON THE ASSESSMENT OF THIS TOOL AS A BASIS FOR A SUITABLE METHODOLOGY TO ENHANCE THE TEACHING-LEARNING PROCESS, TAKING INTO ACCOUNT THE GREAT SOCIAL IMPACT OF BIG DATA. THE RESULTS ALLOW IDENTIFYING THE MAIN ADVANTAGES, OPPORTUNITIES, AND DRAWBACKS OF USING THESE TECHNOLOGICAL TOOLS FOR EDUCATIONAL PURPOSES. FINALLY, A SET OF INITIATIVES HAS BEEN PROPOSED TO COMPLEMENT THE TEACHING ACTIVITY AND TO IMPROVE USER EXPERIENCE IN THE EDUCATIONAL FIELD.
THIS PAPER INTRODUCES A NOVEL APPROACH TO IDENTIFY AT-RISK STUDENTS WITH A FOCUS ON OUTPUT INTERPRETABILITY THROUGH ANALYZING LEARNING ACTIVITIES AT A FINER GRANULARITY ON A WEEKLY BASIS. SPECIFICALLY, THIS APPROACH CONVERTS THE PREDICTED OUTPUT FROM THE FORMER WEEKS INTO MEANINGFUL PROBABILITIES TO INFER THE PREDICTIONS IN THE CURRENT WEEK FOR MAINTAINING THE CONSECUTIVENESS AMONG LEARNING ACTIVITIES. TO DEMONSTRATE THE EFFICACY OF OUR MODEL IN IDENTIFYING AT-RISK STUDENTS, WE COMPARE THE WEEKLY AUCS AND AVERAGED PERFORMANCE (I.E., ACCURACY, PRECISION, RECALL, AND F1-SCORE) OVER EACH COURSE WITH THE BASELINE MODELS (I.E., RANDOM FOREST, SUPPORT VECTOR MACHINE, AND DECISION TREE), RESPECTIVELY. FURTHERMORE, WE ADOPT A TOP-K METRIC TO EXAMINE THE NUMBER OF AT-RISK STUDENTS THAT THE MODEL IS ABLE TO IDENTIFY WITH HIGH PRECISION DURING EACH WEEK. FINALLY, THE MODEL OUTPUT IS INTERPRETED THROUGH A MODEL-AGNOSTIC INTERPRETATION APPROACH TO SUPPORT INSTRUCTORS TO MAKE INFORMED RECOMMENDATIONS FOR STUDENTS' LEARNING. THE EXPERIMENTAL RESULTS DEMONSTRATE THE CAPABILITY AND INTERPRETABILITY OF OUR MODEL IN IDENTIFYING AT-RISK STUDENTS IN ONLINE LEARNING SETTINGS. IN ADDITION TO THAT OUR WORK ALSO PROVIDES SIGNIFICANT IMPLICATIONS IN BUILDING ACCOUNTABLE MACHINE LEARNING PIPELINES THAT CAN BE USED TO AUTOMATICALLY GENERATED INDIVIDUALIZED LEARNING INTERVENTIONS WHILE CONSIDERING FAIRNESS BETWEEN DIFFERENT LEARNING GROUPS.
IN LARGE-SCALE ASSESSMENTS SUCH AS THE ONES ENCOUNTERED IN MOOCS, A LOT OF USAGE DATA IS AVAILABLE BECAUSE OF THE NUMBER OF LEARNERS INVOLVED. NEWCOMERS, THAT JUST ARRIVE ON A MOOC, HAVE VARIOUS BACKGROUNDS IN TERMS OF KNOWLEDGE, BUT THE PLATFORM HARDLY KNOWS ANYTHING ABOUT THEM. THEREFORE, IT IS CRUCIAL TO ELICIT THEIR KNOWLEDGE FAST, IN ORDER TO PERSONALIZE THEIR LEARNING EXPERIENCE. SUCH A PROBLEM HAS BEEN CALLED LEARNER COLD-START. WE PRESENT IN THIS ARTICLE AN ALGORITHM FOR SAMPLING A GROUP OF INITIAL, DIVERSE QUESTIONS FOR A NEWCOMER, BASED ON A METHOD RECENTLY USED IN MACHINE LEARNING: DETERMINANTAL POINT PROCESSES. WE SHOW, USING REAL DATA, THAT OUR METHOD OUTPERFORMS EXISTING TECHNIQUES SUCH AS UNCERTAINTY SAMPLING, AND CAN PROVIDE USEFUL FEEDBACK TO THE LEARNER OVER THEIR STRONG AND WEAK POINTS.
IN THIS PAPER THE REQUIREMENTS TO BE FULFILLED BY VIRTUAL TEACHING LEARNING ENVIRONMENT ARE ANALYZED, REGARDING ADAPTATION BASED ON LEARNING PREFERENCES OF STUDENTS IN THE LEARNING ACTIVITY. SEVERAL MODULES THAT MEET THESE REQUIREMENTS ARE PROPOSED, AND THE RESULTS ACHIEVED IN THIS DIRECTION IS PRESENTED IN THE CONTEXT OF A UNIVERSITY EDUCATION INNOVATION PROJECT WITH ICT, IN WHICH SEVERAL UNIVERSITIES ARE INVOLVED.
THIS PAPER PRESENTS A NEW EDUCATIONAL SYSTEM TO AUTOMATICALLY ADAPT THE EVALUATION ACTIVITIES TO THE STUDENTS' NEEDS IN THE CONTEXT OF HIGHER ENGINEERING EDUCATION. AS AN EXAMPLE, A SUBJECT FOCUSED ON THE CONFIGURATION OF NETWORK SERVICES HAS BEEN CHOSEN TO IMPLEMENT OUR PROPOSAL. THEREFORE, THE SYSTEM WILL BE ABLE TO GUIDE EACH STUDENT THROUGH THE LEARNING PROCESS BASED ON HIS/HER PARTICULAR KNOWLEDGE-LEVEL. IN ADDITION TO THIS, SPECIFIC TECHNIQUES ARE NEEDED TO DYNAMICALLY EVOLVE THE SYSTEM DEPENDING ON THE STUDENTS' PROGRESS. IN OUR CASE, THIS IS ANALYZED BY USING DATA MINING TECHNIQUES. FINALLY, WE SHOW SURVEY RESULTS, WHICH ILLUSTRATE THE EASE OF USE AND USEFULNESS OF THE SYSTEM.
WE HAVE STUDIED THE MAIN LEARNING MANAGEMENT SYSTEMS (LMSS) TO COMPREHEND HOW PERSONAL DATA IS PROCESSED AND STORED. WE FOUND THAT ALL THE USERS' PERSONAL INFORMATION, ACTIVITY, AND LOGS ARE STORED UNENCRYPTED ON THE SERVER FILESYSTEM AND DATABASES. A USER WITH ACCESS TO SUCH RESOURCES MAY HAVE FULL ACCESS TO ALL THE PERSONAL INFORMATION AND META INFORMATION STORED. THEREFORE, THE LMSS ARE VERY VULNERABLE TO INFORMATION LEAKS IN FRONT OF TARGETED HACKER ATTACKS DUE TO WEAK GDPR COMPLIANCE. IN THIS PAPER, WE ANALYZE THIS PROBLEM FROM A TECHNICAL AND OPERATIONAL PERSPECTIVE FOR THE OPEN-SOURCE MARKET LEADER LMS MOODLE, AND WE PROPOSE A SOLUTION AND A PROTOTYPE OF IMPLEMENTATION.
WE ARE DEVELOPING A PROGRAMMING EDUCATION SUPPORT TOOL PGTRACER. PGTRACER PROVIDES FILL-IN-THE-BLANK QUESTIONS TO THE STUDENTS AND COLLECTS STUDENT LOG TO ANALYZE STUDENT'S LEARNING PROCESS AND UNDERSTANDING LEVEL. IN THIS PAPER, WE UTILIZE PGTRACER AT AN ACTUAL PROGRAMMING COURSE AND ANALYZE THE COLLECTED LOG. THERE IS A NEGATIVE CORRELATION BETWEEN REQUIRED TIME AND RIGHT ANSWER RATIO OF A BLANK. ALTHOUGH THERE IS A CORRELATION BETWEEN THE MID-TERM EXAMINATION SCORE AND PGTRACER SCORE, PGTRACER SCORE IS USEFUL TO DISTINGUISH PROGRAMMING ABILITY OF THE STUDENTS. WE ALSO PERFORM SURVEY AND INTERVIEW TO THE STUDENTS ABOUT THE USEFULNESS OF PGTRACER AND FILL-IN-THE-BLANK QUESTIONS.
LEARNING OUTCOMES IN HIGHER EDUCATION ARE DEFINED AS COMPETENCIES, WHICH ARE EITHER SPECIFIC OR GENERIC. THE FORMER REFERS TO THOSE SKILLS SPECIFIC TO THE SUBJECT STUDIED. THE LATTER, ALSO KNOWN AS TRANSFERABLE SKILLS, REFER TO OTHER CAPABILITIES THAT A CAPABLE PROFESSIONAL HAS TO PUT INTO PRACTICE TO WORK IN REAL-WORLD. GENERIC COMPETENCIES ARE USUALLY SHARED AMONGST MOST SCIENCE FIELDS, AND INCLUDE TEAMWORK, LEADERSHIP AND SELF-CRITICISM. THESE SKILLS ARE INHERENTLY DIFFICULT TO ASSESS, AS THEY ARE NOT USUALLY CONSIDERED IN TRADITIONAL ENGINEERING ASSESSMENT PROCEDURES. AS A RESULT, LECTURES USUALLY HAVE TO ASSESS THEM USING SUBJECTIVE INFORMATION. THIS IS ESPECIALLY PROBLEMATIC WHEN THE NUMBER OF STUDENTS INCREASES. IN THIS PAPER, WE INTRODUCE SIMPLE ASSESSMENT-SPECIFIC QUERY LANGUAGE (SASQL), A DOMAIN SPECIFIC LANGUAGE (DSL) TO ALLEVIATE THIS ISSUE. IT IS A FORMAL LANGUAGE THAT CAN BE AUTOMATICALLY PROCESSED TO EXECUTE SASQL SENTENCES USING AN ASSESSMENT-SPECIFIC VOCABULARY. SASQL HAS A SIMPLE SYNTAX, ORIENTED TO LEARNING ASSESSMENTS. USING OUR PROPOSED WORKFLOW, A COURSE COORDINATOR CAN EXTRACT DIFFERENT OBJECTIVE INDICATORS TO ASSESS COMPETENCIES DEFINED IN THE SYLLABUS USING SIMPLE QUERIES. SUCH INDICATORS ARE AUTOMATICALLY EXTRACTED FROM THE ACTIVITY LOGS GENERATED BY THE LEARNING MANAGEMENT SYSTEM (LMS). TWO CASE STUDIES WITH MOODLE LMS-BASED COURSES ARE CARRIED OUT TO EXPLAIN HOW SUCH INDICATORS CAN BE OBTAINED AND HOW TO INTERPRET THE RESULTS OBTAINED FOR STUDENT ASSESSMENTS.
THIS STUDY PRESENTS AN ANALYSIS OF A MOOC ON INQUIRY AND TECHNOLOGY FOR IN-SERVICE TEACHERS, WHICH WAS DESIGNED TO SCAFFOLD MULTIPLE DISCIPLINARY KNOWLEDGE COMMUNITIES THROUGH COMMON WEEKLY THEMES, AND COURSE-LONG COLLABORATION SCRIPTS HAPPENING AT DIFFERENT SOCIAL PLANES. USING OUR COURSE DESIGN TO INFORM THE DESIGN OF THE ANALYSIS, WE EXAMINE HOW THE DISCOURSE IN EACH SEMANTICALLY MEANINGFUL COHORT (SPECIAL INTEREST GROUPS, SIGS) IS INDEXED TO THE WEEKLY THEMES, AND DEVELOPS THESE THEMES IN AREAS INFORMED BY THE DISCIPLINE, AND BY THE GROUP DYNAMICS. WE SHOW THAT SIG MEMBERSHIP INFLUENCES INDIVIDUAL CONTRIBUTIONS, AND THAT MORE COHESIVE DISCIPLINARY SIGS ARE CORRELATED WITH HIGHER QUALITY STUDENT WORK.
THIS PAPER REPORTS THE RESULTS OF A STUDY OF THE ASSOCIATION OF A SET OF TECHNOLOGICAL SCAFFOLDING INTERVENTIONS - IMPLEMENTED IN A LEARNING SOFTWARE CALLED LEARN-B - WITH MICRO-LEVEL PROCESSES OF SELF-REGULATED LEARNING IN THE WORKPLACE. THE STUDY WAS CONDUCTED IN THE CONTEXT OF TWO EUROPEAN ORGANIZATIONS, IN WHICH THE STUDY PARTICIPANTS (N = 53) USED THE LEARNING SOFTWARE LEARN-B FOR TWO MONTHS IN THEIR REGULAR ACTIVITIES. DATA ABOUT PERCEIVED USEFULNESS OF THE TECHNOLOGICAL SCAFFOLDING INTERVENTIONS AND TRACES ABOUT THE EVENTS OF THE ACTUAL USE OF THE TECHNOLOGICAL INTERVENTIONS AND MICRO-LEVEL PROCESSES OF SELF-REGULATED LEARNING WERE COLLECTED AND ANALYZED. THE ANALYSIS OF THE DATA SHOWED THAT WHEN DIRECTLY ASKED ABOUT THEIR OWN PERSPECTIVES, THE PARTICIPANTS TENDED TO RELY ON THEIR ORGANIZATIONAL CONTEXT IN THEIR PLANNING PHASE. THE RESULTS ALSO INDICATE THAT THE PARTICIPANTS DID CONSIDER THE SOCIAL CONTEXT OF THEIR ORGANIZATION WHEN PLANNING THEIR LEARNING GOALS, BUT IT WAS NOT FOUND AS THE MOST INFLUENTIAL FACTOR. THE ANALYSIS OF THE TRACE DATA REVEALED A MODERATE BALANCE BETWEEN RELIANCE ON BOTH SOCIAL AND ORGANIZATIONAL CONTEXTS. THE ANALYSIS OF THE THEORIZED CONTINGENCIES VIA TRACE DATA SHOWED THAT THE PARTICIPANTS' USEFULNESS PERCEPTIONS OF THE PROPOSED INTERVENTIONS FOR THEIR SELF-REGULATED PROCESSES, POORLY MATCHED WITH THEIR ACTUAL LEARNING ACTIONS. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
GAME-BASED LEARNING HAS PROVEN TO BE EFFECTIVE FOR ENHANCING STUDENT MOTIVATION AND LEARNING OUTCOMES. IN THIS STUDY, THE AUTHORS FIRST DESIGNED AND THEN TESTED A 3D VIRTUAL WORLD-BASED VIDEO GAME TO SUPPORT STUDENTS IN LEARNING A FOREIGN LANGUAGE. TWO DATA MINING CLUSTERING TECHNIQUES ARE USED TO ANALYSE THE IMPACT OF THE GAME ON LEARNING PROCESSES AND OUTCOMES. THE AIM IS TO CLASSIFY STUDENTS ACCORDING TO LEARNING OUTCOMES, BY COMPARING SPECIFIC LANGUAGE COMPETENCIES, SUCH AS THE GRAMMAR, VOCABULARY AND WRITING, BEFORE, DURING AND AFTER A CASE STUDY. THE CASE STUDY USED HERE INVOLVED 102 UNDERGRADUATE GERMAN LANGUAGE STUDENTS FROM THE A1.2 LEVEL (CEFR). THE RESULTS OBTAINED FROM THE APPLICATION OF TWO DIFFERENT CLUSTERING TECHNIQUES INDICATE THAT LEARNERS DO NOT ALL BENEFIT FROM GAME-BASED LEARNING IN THE SAME WAY; SOME STUDENTS MIGHT BETTER DEVELOP LANGUAGE COMPETENCIES THROUGH MORE CONVENTIONAL AND GUIDED LEARNING APPROACHES.
THIS PAPER PRESENTS AN ARCHITECTURE DESIGNED FOR SIMULATION SERIOUS GAMES, WHICH AUTOMATICALLY GENERATES GAME-BASED SCENARIOS ADAPTED TO LEARNER'S LEARNING PROGRESSION. WE PRESENT THREE CENTRAL MODULES OF THE ARCHITECTURE: (1) THE LEARNER MODEL, (2) THE ADAPTATION MODULE AND (3) THE LOGS MODULE. THE LEARNER MODEL ESTIMATES THE PROGRESSION OF THE DEVELOPMENT OF SKILLS TARGETED IN THE GAME. THE ADAPTATION MODULE USES THIS ESTIMATION TO AUTOMATICALLY PLAN AN ADAPTED SEQUENCE OF IN-GAME SITUATIONS OPTIMIZING LEARNING. WE IMPLEMENTED OUR ARCHITECTURE IN GAME OF HOMES, A SIMULATION SERIOUS GAME, WHICH AIMS TO TRAIN ADULTS THE BASICS OF REAL ESTATE. WE BUILT A SCRIPTED-BASED VERSION OF GAME OF HOMES IN ORDER TO COMPARE THE IMPACT OF SCRIPTED-BASED SCENARIOS VERSUS GENERATED SCENARIOS ON LEARNING PROGRESSION. WE QUALITATIVELY ANALYZED LOGS FILES OF THIRTY-SIX ADULTS WHO PLAYED GAME OF HOMES FOR 90 MINUTES. THE MAIN RESULTS HIGHLIGHTED THE SPECIFICITY OF THE GENERATED PEDAGOGICAL SCENARIOS FOR EACH LEARNER AND, MORE SPECIFICALLY, THE OPTIMIZATION OF THE GUIDANCE PROVIDED AND OF THE PRESENTATION OF THE LEARNING CONTENT THROUGHOUT THE GAME.
THIS PAPER REPORTS ON THE FINDINGS OF AN EXPLORATORY STUDY IN WHICH THE EFFECTS OF TECHNOLOGICAL SCAFFOLDING INTERVENTIONS ON MICRO-LEVEL PROCESSES OF SELF-REGULATED LEARNING IN THE WORKPLACE WERE INVESTIGATED. EMPIRICAL RESEARCH IN THE WORKPLACE HAS BEEN MUCH LESS REPRESENTED THAN IN FORMAL EDUCATION. EVEN LESS RESEARCH IS AVAILABLE THAT AIMED TO IDENTIFY WHICH TECHNOLOGICAL SCAFFOLDING INTERVENTIONS, OUT OF THOSE AVAILABLE IN A LEARNING ENVIRONMENT, HAD THE HIGHEST INFLUENCE ON SPECIFIC MICRO-LEVEL PROCESS OF SELF-REGULATED LEARNING. THIS PAPER REPORTS ON THE FINDINGS OF A CASE STUDY CONDUCTED IN THE NATURALISTIC SETTINGS OF TWO ORGANIZATIONS IN EUROPE (N = 53) FOR THE PERIOD OF TWO MONTHS. TRACE DATA ABOUT THE EVENTS OF ENGAGEMENT WITH THE TECHNOLOGICAL SCAFFOLDING INTERVENTIONS AND MICRO-LEVEL PROCESSES OF SELF REGULATED LEARNING WERE COLLECTED. BOTH A TRANSITION GRAPH BASED ANALYSIS OF THE TEMPORAL DEPENDENCIES OF THE COLLECTED EVENTS AND MULTIPLE LINEAR REGRESSION ANALYSES SHOWED THAT AN INTERVENTION THAT PROMOTED SOCIAL AWARENESS HAD CONSISTENTLY THE HIGHEST EFFECT ON ALL THE MICRO-LEVEL PROCESSES USED IN THE STUDY. THIS INTERVENTION WAS FOLLOWED BY THE INTERVENTION THAT OFFERED SYSTEM-GENERATED RECOMMENDATIONS ABOUT LEARNING PATHS, LEARNING ACTIVITIES AND KNOWLEDGE ASSETS TO STIMULATE ENGAGEMENT INTO THE MICRO-LEVEL PROCESSES WITHIN THE FORETHOUGHT OR PREPARATORY PHASE OF SELF-REGULATED LEARNING. THESE FINDINGS SUGGEST THAT BOTH THE SOCIAL AND ORGANIZATIONAL CONTEXTS SHOULD BE TAKEN INTO ACCOUNT WHEN DEVELOPING INTERVENTIONS AIMED AT SUPPORTING THE FORETHOUGHT AND ENGAGEMENT PHASES. FURTHER DISCUSSION ABOUT RESEARCH, METHODOLOGICAL, AND LEARNING TECHNOLOGY DESIGN IMPLICATIONS IS PROVIDED. (C) 2016 ELSEVIER LTD. ALL RIGHTS RESERVED.
MOST WORK IN THE DESIGN OF LEARNING TECHNOLOGY USES CLICK-STREAMS AS THEIR PRIMARY DATA SOURCE FOR MODELLING & PREDICTING LEARNING BEHAVIOUR. IN THIS PAPER WE SET OUT TO QUANTIFY WHAT, IF ANY, ADVANTAGES DO PHYSIOLOGICAL SENSING TECHNIQUES PROVIDE FOR THE DESIGN OF LEARNING TECHNOLOGIES. WE CONDUCTED A LAB STUDY WITH 251 GAME SESSIONS AND 17 USERS FOCUSING ON SKILL DEVELOPMENT (I.E., USER'S ABILITY TO MASTER COMPLEX TASKS). WE COLLECTED CLICK-STREAM DATA, AS WELL AS EYE-TRACKING, ELECTROENCEPHALOGRAPHY (EEG), VIDEO, AND WRISTBAND DATA DURING THE EXPERIMENT. OUR ANALYSIS SHOWS THAT TRADITIONAL CLICK-STREAM MODELS ACHIEVE 39% ERROR RATE IN PREDICTING LEARNING PERFORMANCE (AND 18% WHEN WE PERFORM FEATURE SELECTION), WHILE FOR FUSED MULTIMODAL THE ERROR DROPS UP TO 6%. OUR WORK HIGHLIGHTS THE LIMITATIONS OF STANDALONE CLICK-STREAM MODELS, AND QUANTIFIES THE EXPECTED BENEFITS OF USING A VARIETY OF MULTIMODAL DATA COMING FROM PHYSIOLOGICAL SENSING. OUR FINDINGS HELP SHAPE THE FUTURE OF LEARNING TECHNOLOGY RESEARCH BY POINTING OUT THE SUBSTANTIAL BENEFITS OF PHYSIOLOGICAL SENSING.
UNDERSTANDING THE RELATIONSHIP BETWEEN A STUDENT'S BROADER WORK HABITS AND THEIR PERFORMANCE, PARTICULARLY ON OPEN-ENDED PROGRAMMING ASSIGNMENTS, IS KEY TOWARDS BEING ABLE TO GUIDE STUDENTS TOWARDS SUCCESS. IN SPITE OF THIS, MOST EVIDENCE OF STUDENT BEHAVIOR AND ITS RELATIONSHIP TO PERFORMANCE IS ANECDOTAL. THE ADVENT OF LARGE-SCALE COURSES WHICH USE ONLINE TOOLS FOR DELIVERING COURSE CONTENT, MONITORING THE PROGRAMMING ENVIRONMENT, AND PROVIDING AUTOMATIC FEEDBACK AS WELL AS GRADING NOW MAKES IT POSSIBLE TO DIVE INTO THE DATA AND DEVELOP DATA-DRIVEN METHODS FOR UNDERSTANDING HOW A STUDENT'S APPROACH TO AN ASSIGNMENT FROM THEIR FIRST EXPOSURE TO THE DESCRIPTION OF THE PROBLEM TO THEIR FINAL SUBMISSION OF THEIR COMPLETED ASSIGNMENT INFLUENCES THEIR FINAL PERFORMANCE. THIS STUDY IS A FIRST LOOK AT A SUBSET OF THE DATA COLLECTED FROM A PROJECT-BASED, ONLINE, UPPER-LEVEL COURSE ON CLOUD COMPUTING. WE EXAMINE THREE RAW DATA STREAMS WHICH INCLUDE INFORMATION ON THE TIME STUDENTS SPEND ON READING THE PROJECT WRITE-UP, THE TIMING, GRADES, AND NUMBER OF SUBMISSIONS THEY MAKE, AND THE CLOUD RESOURCES USED (BOTH TIME AND COST) IN SOLVING THE ASSIGNMENT. USING THESE AND SEVERAL SYNTHETIC METRICS WE WERE SURPRISED TO FIND THAT THERE ARE FEW BEHAVIORS THAT ARE HIGHLY CORRELATED TO STUDENT SUCCESS. INSTEAD, WE FIND THAT THERE IS A STRONG CORRELATION BETWEEN STUDENTS WHO CONTINUALLY APPLY CONSISTENT BEHAVIORS OVER THE COURSE OF THE SEMESTER TO GOOD FINAL PERFORMANCE IN THE COURSE. FROM THIS WE USE LASSO TO CREATE A PREDICTOR (ADJUSTED R-2 =.48) OF FINAL PERFORMANCE BASED ON TWO KINDS OF CONSISTENCY MEASURES ACROSS 15 METRICS.
THIS PAPER EXPLORES THE POTENTIAL OF USING NAIVE TOPIC MODELING TO SUPPORT INSTRUCTORS IN NAVIGATING MOOC DISCUSSION FORUMS. CATEGORIZING DISCUSSION THREADS INTO TOPICS CAN PROVIDE AN OVERVIEW OF THE DISCUSSION, IMPROVE NAVIGATION OF THE FORUM, AND SUPPORT REPLYING TO A REPRESENTATIVE SAMPLE OF CONTENT RELATED POSTS. WE INVESTIGATE FOUR DIFFERENT APPROACHES TO USING TOPIC MODELS TO ORGANIZE AND PRESENT DISCUSSION POSTS, HIGHLIGHTING THE STRENGTH AND WEAKNESSES OF EACH APPROACH TO SUPPORT INSTRUCTORS.
IT IS DIFFICULT FOR A STUDENT TO SUCCEED IN A COURSE WITHOUT ACCESS TO COURSE MATERIALS AND ASSIGNMENTS; AND YET, SOME STUDENTS DELAY UP TO A MONTH IN OBTAINING ACCESS TO THESE ESSENTIAL MATERIALS. STUDENTS DELAY BUYING MATERIAL REQUIRED FOR THEIR COURSE DUE TO MULTIPLE REASONS. OUT OF A CONCERN FOR STUDENTS WITH LIMITED FINANCIAL RESOURCES, SOME PUBLISHERS OFFER A PERIOD OF FREE COURTESY ACCESS. BUT THIS MAY LEAD TO STUDENTS HAVING ACCESS LATER IN THE COURSE BUT THEN HAVING A LAPSED PERIOD UNTIL THEY PAY FOR THE MATERIALS AFTER THE COURTESY ACCESS PERIOD ENDS. NOT HAVING KEY COURSE MATERIALS EARLY ON PROBABLY HURTS LEARNING, BUT HOW MUCH? IN THIS PAPER, WE INVESTIGATE THE QUESTION, "DOES LACK OF ACCESS TO INSTRUCTIONAL MATERIAL IMPACT STUDENT PERFORMANCE IN BLENDED LEARNING COURSES?" SPECIFICALLY, WE ANALYZE STUDENTS WHO PURCHASED AND OBTAINED ACCESS TO ONLINE CONTENT AT DIFFERENT POINTS IN THE COURSE. WE DETERMINE THAT BOTH TYPES OF FAILURE TO OBTAIN ACCESS TO COURSE MATERIALS (DELAYING IN SIGNING UP FOR THE PRODUCT, OR SIGNING UP FOR A FREE TRIAL AND LETTING THE TRIAL PERIOD LAPSE WITHOUT PURCHASING THE MATERIALS) ARE ASSOCIATED WITH SUBSTANTIALLY WORSE STUDENT OUTCOMES. STUDENTS WHO PURCHASED THE PRODUCT WITHIN THE FIRST FEW DAYS OF CLASS HAD THE BEST SCORES (MEDIAN 77). THOSE WHO WAITED TWO WEEKS BEFORE ACCESSING THE PRODUCT DID THE WORST (MEDIAN 56, EFFECT SIZE CLIFF'S DELTA=0.31(1)). WE CONCLUDE WITH A DISCUSSION OF POSSIBLE INTERVENTIONS AND ACTIONS THAT CAN BE TAKEN TO AMELIORATE THE SITUATION.
IN 2011, THE PHENOMENON OF MOOCS HAD SWEPT THE WORLD OF EDUCATION AND PUT ONLINE EDUCATION IN THE FOCUS OF THE PUBLIC DISCOURSE AROUND THE WORLD. ALTHOUGH RESEARCHERS WERE EXCITED WITH THE VAST AMOUNTS OF MOOC DATA BEING COLLECTED, THE BENEFITS OF THIS DATA DID NOT STAND TO THE EXPECTATIONS DUE TO SEVERAL CHALLENGES. THE ANALYSES OF MOOC DATA ARE VERY TIME-CONSUMING AND LABOR-INTENSIVE, AND REQUIRE AND REQUIRE A HIGHLY ADVANCED SET OF TECHNICAL SKILLS, OFTEN NOT AVAILABLE TO THE EDUCATION RESEARCHERS. BECAUSE OF THIS MOOC DATA ANALYSES ARE RARELY DONE BEFORE THE COURSES END, LIMITING THE POTENTIAL OF DATA TO IMPACT THE STUDENT LEARNING OUTCOMES AND EXPERIENCE. IN THIS PAPER WE INTRODUCE MOOCITO (MOOC INTERVENTION TOOL), A USER-FRIENDLY SOFTWARE PLATFORM FOR THE ANALYSIS OF MOOC DATA, THAT FOCUSES ON CONDUCTING DATA-INFORMED INSTRUCTIONAL INTERVENTIONS AND COURSE EXPERIMENTATIONS. WE COVER IMPORTANT DESIGN PRINCIPLES BEHIND MOOCITO AND PROVIDE AN OVERVIEW OF THE TRENDS IN MOOC RESEARCH LEADING TO ITS DEVELOPMENT. ALTHOUGH A WORK-IN PROGRESS, IN THIS PAPER, WE OUTLINE THE PROTOTYPE OF MOOCITO AND THE RESULTS OF A USER EVALUATION STUDY THAT FOCUSED ON SYSTEM'S PERCEIVED USABILITY AND EASE -OF-USE. THE RESULTS OF THE STUDY ARE DISCUSSED, AS WELL AS THEIR PRACTICAL IMPLICATIONS.
CURRENT LEARNING TECHNOLOGIES HAVE NO DIRECT WAY TO ASSESS STUDENTS' MENTAL EFFORT: ARE THEY IN DEEP THOUGHT, STRUGGLING TO OVERCOME AN IMPASSE, OR ARE THEY ZONED OUT? TO ADDRESS THIS CHALLENGE, WE PROPOSE THE USE OF EEG-BASED COGNITIVE LOAD DETECTORS DURING LEARNING. DESPITE ITS POTENTIAL, EEG HAS NOT YET BEEN UTILIZED AS A WAY TO OPTIMIZE INSTRUCTIONAL STRATEGIES. WE TAKE AN INITIAL STEP TOWARDS THIS GOAL BY ASSESSING HOW EXPERIMENTALLY MANIPULATED (EASY AND DIFFICULT) SECTIONS OF AN INTELLIGENT TUTORING SYSTEM (ITS) INFLUENCED EEG-BASED ESTIMATES OF STUDENTS' COGNITIVE LOAD. WE FOUND A MAIN EFFECT OF TASK DIFFICULTY ON EEG-BASED COGNITIVE LOAD ESTIMATES, WHICH WERE ALSO CORRELATED WITH LEARNING PERFORMANCE. OUR RESULTS SHOW THAT EEG CAN BE A VIABLE SOURCE OF DATA TO MODEL LEARNERS' MENTAL STATES ACROSS A 90-MINUTE SESSION.
WE INVESTIGATE AUTOMATIC DETECTION OF TEACHER QUESTIONS FROM AUDIO RECORDINGS COLLECTED IN LIVE CLASSROOMS WITH THE GOAL OF PROVIDING AUTOMATED FEEDBACK TO TEACHERS. USING A DATASET OF AUDIO RECORDINGS FROM 11 TEACHERS ACROSS 37 CLASS SESSIONS, WE AUTOMATICALLY SEGMENT THE AUDIO INTO INDIVIDUAL TEACHER UTTERANCES AND CODE EACH AS CONTAINING A QUESTION OR NOT. WE TRAIN SUPERVISED MACHINE LEARNING MODELS TO DETECT THE HUMAN-CODED QUESTIONS USING HIGHLEVEL LINGUISTIC FEATURES EXTRACTED FROM AUTOMATIC SPEECH RECOGNITION (ASR) TRANSCRIPTS, ACOUSTIC AND PROSODIC FEATURES FROM THE AUDIO RECORDINGS, AS WELL AS CONTEXT FEATURES, SUCH AS TIMING AND TURN-TAKING DYNAMICS. MODELS ARE TRAINED AND VALIDATED INDEPENDENTLY OF THE TEACHER TO ENSURE GENERALIZATION TO NEW TEACHERS. WE ARE ABLE TO DISTINGUISH QUESTIONS AND NON-QUESTIONS WITH A WEIGHTED FI SCORE OF 0.69. A COMPARISON OF THE THREE FEATURE SETS INDICATES THAT A MODEL USING LINGUISTIC FEATURES OUTPERFORMS THOSE USING ACOUSTIC-PROSODIC AND CONTEXT FEATURES FOR QUESTION DETECTION, BUT THE COMBINATION OF FEATURES YIELDS A 5% IMPROVEMENT IN OVERALL ACCURACY COMPARED TO LINGUISTIC FEATURES ALONE. WE DISCUSS APPLICATIONS FOR PEDAGOGICAL RESEARCH, TEACHER FORMATIVE ASSESSMENT, AND TEACHER PROFESSIONAL DEVELOPMENT.
IN THIS PAPER, WE DISCUSS COLORADO STATE UNIVERSITY ONLINE'S PROGRESS TOWARD DESIGNING AUTOMATED SURVEY REPORTS FOR STUDENT FEEDBACK DATA COLLECTED THROUGH OUR NEWLY DESIGNED LTI SURVEY TOOL. USING MULTIPLE R PACKAGES, INCLUDING 'RMARKDOWN' AND 'LIKED, THE REPORTING TOOL IMPORTS STUDENT SURVEY RESPONSE DATA AND GENERATES REPORTS FOR FACULTY AND INSTRUCTIONAL DESIGNERS. THESE REPORTS FOCUS ON STUDENT PERCEPTIONS OF COMMUNICATION, COURSE DESIGN, ACADEMIC CHALLENGE, GENERAL SATISFACTION, AND MORE. THESE REPORTS DISPLAY VISUAL REPRESENTATIONS OF THE LIKERT-TYPE RESPONSE FREQUENCIES, BASIC DESCRIPTIVE STATISTICS, AND FREE-RESPONSE COMMENTS. SURVEYS ARE ADMINISTERED JUST BEFORE HALF-WAY THROUGH THE SEMESTER TO PROVIDE FORMATIVE FEEDBACK AND JUST BEFORE THE END OF THE SEMESTER TO PROVIDE SUMMATIVE FEEDBACK. IN THIS WAY, FACULTY AND INSTRUCTIONAL DESIGNERS CAN OBTAIN A QUICK AND EASILY DIGESTIBLE REPORT TO MAKE CHANGES AND IMPROVEMENTS TO THEIR CLASSES WITH MINIMAL EFFORT IN THE BACK END PRODUCTION.
LEARNERS' EMOTIONAL STATE HAS PROVEN TO BE A KEY FACTOR FOR SUCCESSFUL LEARNING. VISUALIZING LEARNERS' EMOTIONS DURING SYNCHRONOUS ON-LINE LEARNING ACTIVITIES CAN HELP TUTORS IN CREATING AND MAINTAINING SOCIO-AFFECTIVE RELATIONSHIPS WITH THEIR LEARNERS. HOWEVER, FEW DASHBOARDS OFFER EMOTIONAL INFORMATION ON THE LEARNING ACTIVITY. THE CURRENT STUDY FOCUSES ON SYNCHRONOUS INTERACTIONS VIA A VIDEOCONFERENCING TOOL DEDICATED TO FOREIGN LANGUAGE TRAINING. WE COLLECTED DATA ON LEARNERS' EMOTIONS IN REAL CONDITIONS DURING TEN SESSIONS (FIVE SESSIONS FOR TWO LEARNERS). WE PROPOSE TO ADOPT AND COMBINE DIFFERENT MODELS OF EMOTIONS (DISCRETE AND DIMENSIONAL) AND TO USE HETEROGENEOUS APIS FOR MEASURING LEARNERS' EMOTIONS FROM DIFFERENT DATA SOURCES (AUDIO, VIDEO, SELF-REPORTING AND INTERACTION TRACES). BASED ON A THOROUGH DATA ANALYSIS, WE PROPOSE AN APPROACH TO COMBINE DIFFERENT CUES TO INFER INFORMATION ON LEARNERS' EMOTIONAL STATES. WE FINALLY PRESENT THE EMODA DASHBOARD, AN AFFECTIVE MULTIMODAL AND CONTEXTUAL VISUAL ANALYTICS DASHBOARD, WHICH ALLOWS THE TUTOR TO MONITOR LEARNERS' EMOTIONS AND BETTER UNDERSTAND THEIR EVOLUTION DURING THE SYNCHRONOUS LEARNING ACTIVITY.
AT STENDEN UNIVERSITY STUDENTS FROM ALL OVER THE WORLD STUDY TOGETHER; ALL THESE DIFFERENT NATIONALITIES AND CULTURES RESULT IN DIFFERENT IDEAS CONCERNING ACADEMIC SUCCESS. THE BASIS OF THIS PROJECT WAS TO DEVELOP A PERSONALIZED DASHBOARD FOR STUDENTS VIA MICROSOFT OFFICE 365 POWER BI IN WHICH STUDENTS CAN SET THEIR OWN PERSONAL KPI'S. THE RAW DATA FROM THE STUDENT INFORMATION SYSTEM (SIS) WAS TRANSFORMED INTO CLEAR VISUALIZATIONS THAT WILL HELP STUDENTS GAIN BETTER INSIGHT INTO THEIR ACADEMIC PERFORMANCE. THIS INFORMATION CAN BE USED EITHER INDEPENDENTLY OR IN CONSULTATION WITH THEIR STUDENT ADVISOR.
CUSTOMIZED CONTENT OF E-TEXTBOOK REQUIRE TEACHERS TO SPEND GREATER EFFORTS USING AUTHORING TOOLS AND PLANNING ACTIVITIES BEFORE CLASS, AND TEACHERS WITH VARIOUS CONTEXTS HAVE DIFFERENT DEMANDS ON E-TEXTBOOK. HOWEVER, SOME TEACHERS WHO LACK ICT SKILLS AND DISSATISFY WITH THE FEATURES TEND TO GIVE UP USING E-TEXTBOOK. THUS WE NEED TO KNOW THE STATUS OF TEACHERS' USAGE EARLIER BEFORE WE DECIDE TO GIVE THEM SOME TECHNICAL SUPPORTS. THIS PAPER DESCRIBES AN ANALYSIS METHOD FOR PREDICTING E-TEXTBOOK ADOPTION FROM USAGE RECORDS IN EARLY DAYS, AND AN EVENT SEGMENTATION METHOD OF TEACHERS' USAGE IS USED IN EFFORT TO PROVIDE FEATURES OF PREDICTIVE MODEL.
THE USE OF ONLINE SELF-ASSESSED QUESTIONNAIRESWIRIS-QUIZZES AS FORMATIVE ASSESSMENT TOOL IS AN INCREASINGLY WIDESPREAD PRACTICE IN A VARIETY OF SUBJECTS IN HIGHER EDUCATION. SOME PREVIOUS STUDIES HAVE EXAMINED THE IMPACT OF SUCH QUESTIONNAIRES ON LEARNING OUTCOMES IN CLASSROOM ENVIRONMENTS OR HYBRID TEACHING. THIS PAPER PRESENTS AN EXPLORATORY STUDY THAT PROVES THAT IN A COURSE OF MATHEMATICAL ANALYSIS IN A COMPLETELY ONLINE ENVIRONMENT, PRACTICES WITH WIRIS-QUIZZES IMPROVE LEARNING OUTCOMES. WE HAVE CONDUCTED AN EX-POST FACTO RESEARCH FROM THE RESULTS OF THE PRACTICE TESTS AND CONTINUOUS ASSESSMENT TESTS CARRYING OUT AN ANALYSIS USING THE TRIMMED MEANS YUEN'S TEST.
WHILE MASSIVE RESEARCH HAS BEEN CONDUCTED TO SEE HOW REMOTE LEARNING AND TEACHING IS CONDUCT DURING THE COVID-19 PANDEMIC, LESS FOCUS HAS BEEN PAID ON REMOTE SPECIAL EDUCATION FOR STUDENTS WITH DISABILITIES. THEREFORE, IT IS STILL NOT CLEAR HOW THOSE STUDENTS LEARNED AND WHAT TYPES OF CHALLENGES THEY FACED. TO FILL THIS GAP, THIS STUDY FIRST COLLECTED DATA FROM THE LITERATURE VIA A SYSTEMATIC LITERATURE REVIEW, AND FROM BOTH 51 TEACHERS AND 21 STUDENTS WITH DISABILITIES WHO WERE INVOLVED IN THIS REMOTE TEACHING AND LEARNING EXPERIENCES VIA SURVEYS. IT THEN CONDUCTED BIBLIOMETRIC, CONTENT AND THEMATIC ANALYSIS TO DRAW CONCLUSIONS. THE OBTAINED FINDINGS HIGHLIGHTED THAT ONLINE AND OFFLINE REMOTE TEACHING METHODS FROM HOME WERE APPLIED. ADDITIONALLY, DIFFERENT LEARNING ASSESSMENT METHODS, SUCH AS MINI-PROJECTS AND SIMPLE QUIZZES WERE ADOPTED BY TEACHERS TO ASSESS THE GAINED KNOWLEDGE OF STUDENTS REMOTELY, BUT NONE OF THESE METHODS RELIED ON EMERGING TECHNOLOGIES, SUCH AS BIG DATA AND LEARNING ANALYTICS. FINALLY, PARENTS WERE A CORE ACTOR TO MAINTAIN REMOTE LEARNING FROM HOME FOR STUDENTS WITH DISABILITIES.
A NUMBER OF STUDIES HAVE DEMONSTRATED LINKS BETWEEN LINGUISTIC KNOWLEDGE AND PERFORMANCE IN MATH. STUDIES EXAMINING THESE LINKS IN FIRST LANGUAGE SPEAKERS OF ENGLISH HAVE TRADITIONALLY RELIED ON CORRELATIONAL ANALYSES BETWEEN LINGUISTIC KNOWLEDGE TESTS AND STANDARDIZED MATH TESTS. FOR SECOND LANGUAGE (L2) SPEAKERS, THE MAJORITY OF STUDIES HAVE COMPARED MATH PERFORMANCE BETWEEN PROFICIENT AND NON-PROFICIENT SPEAKERS OF ENGLISH. IN THIS STUDY, WE TAKE A NOVEL APPROACH AND EXAMINE THE LINGUISTIC FEATURES OF STUDENT LANGUAGE WHILE THEY ARE ENGAGED IN COLLABORATIVE PROBLEM SOLVING WITHIN AN ON-LINE MATH TUTORING SYSTEM. WE TRANSCRIBE THE STUDENTS' SPEECH AND USE NATURAL LANGUAGE PROCESSING TOOLS TO EXTRACT LINGUISTIC INFORMATION RELATED TO TEXT COHESION, LEXICAL SOPHISTICATION, AND SENTIMENT. OUR CRITERION VARIABLES ARE INDIVIDUALS' PRETEST AND POSTTEST MATH PERFORMANCE SCORES. IN ADDITION TO EXAMINING RELATIONS BETWEEN LINGUISTIC FEATURES OF STUDENT LANGUAGE PRODUCTION AND MATH SCORES, WE ALSO CONTROL FOR A NUMBER OF NON-LINGUISTIC FACTORS INCLUDING GENDER, AGE, GRADE, SCHOOL, AND CONTENT FOCUS (PROCEDURAL VERSUS CONCEPTUAL). LINEAR MIXED EFFECT MODELING INDICATES THAT NON-LINGUISTIC FACTORS ARE NOT PREDICTIVE OF MATH SCORES. HOWEVER, LINGUISTIC FEATURES RELATED TO COHESION AFFECT AND LEXICAL PROFICIENCY EXPLAINED APPROXIMATELY 30% OF THE VARIANCE (R-2 = .303) IN THE MATH SCORES.
THE USE OF COMPUTER-BASED SYSTEMS IN CLASSROOMS HAS PROVIDED TEACHERS WITH NEW OPPORTUNITIES IN DELIVERING CONTENT TO STUDENTS, SUPPLEMENTING INSTRUCTION, AND ASSESSING STUDENT KNOWLEDGE AND COMPREHENSION. AMONG THE LARGEST BENEFITS OF THESE SYSTEMS IS THEIR ABILITY TO PROVIDE STUDENTS WITH FEEDBACK ON THEIR WORK AND ALSO REPORT STUDENT PERFORMANCE AND PROGRESS TO THEIR TEACHER. WHILE COMPUTER-BASED SYSTEMS CAN AUTOMATICALLY ASSESS STUDENT ANSWERS TO A RANGE OF QUESTION TYPES, A LIMITATION FACED BY MANY SYSTEMS IS IN REGARD TO OPEN-ENDED PROBLEMS. MANY SYSTEMS ARE EITHER UNABLE TO PROVIDE SUPPORT FOR OPEN-ENDED PROBLEMS, RELYING ON THE TEACHER TO GRADE THEM MANUALLY, OR AVOID SUCH QUESTION TYPES ENTIRELY. DUE TO RECENT ADVANCEMENTS IN NATURAL LANGUAGE PROCESSING METHODS, THE AUTOMATION OF ESSAY GRADING HAS MADE NOTABLE STRIDES. HOWEVER, MUCH OF THIS RESEARCH HAS PERTAINED TO DOMAINS OUTSIDE OF MATHEMATICS, WHERE THE USE OF OPEN-ENDED PROBLEMS CAN BE USED BY TEACHERS TO ASSESS STUDENTS' UNDERSTANDING OF MATHEMATICAL CONCEPTS BEYOND WHAT IS POSSIBLE ON OTHER TYPES OF PROBLEMS. THIS RESEARCH EXPLORES THE VIABILITY AND CHALLENGES OF DEVELOPING AUTOMATED GRADERS OF OPEN-ENDED STUDENT RESPONSES IN MATHEMATICS. WE FURTHER EXPLORE HOW THE SCALE OF AVAILABLE DATA IMPACTS MODEL PERFORMANCE. FOCUSING ON CONTENT DELIVERED THROUGH THE ASSISTMENTS ONLINE LEARNING PLATFORM, WE PRESENT A SET OF ANALYSES PERTAINING TO THE DEVELOPMENT AND EVALUATION OF MODELS TO PREDICT TEACHER-ASSIGNED GRADES FOR STUDENT OPEN RESPONSES.
WE PRESENT A DATA-DRIVEN ANALYSIS THAT PROVIDES GENERALIZED INSIGHTS OF HOW A CURRICULUM INTEGRATED EDUCATIONAL MATH GAME GETS USED AS A ROUTINIZED CLASSROOM ACTIVITY THROUGHOUT THE YEAR IN AUTHENTIC PRIMARY SCHOOL CLASSROOMS. OUR STUDY RELATES OBSERVATIONS FROM A FIELD STUDY ON SPATIAL TEMPORAL MATH (ST MATH) TO OUR FINDINGS MINED FROM ST MATH STUDENTS' SEQUENTIAL GAME PLAY DATA. WE IDENTIFIED FEATURES THAT VARY ACROSS GAME PLAY SESSIONS AND MODELED THEIR RELATIONSHIP WITH SESSION PERFORMANCE. WE ALSO DERIVED DATA-INFORMED SUGGESTIONS THAT MAY PROVIDE TEACHERS WITH INSIGHTS INTO HOW TO DESIGN CLASSROOM GAME PLAY SESSIONS TO FACILITATE MORE EFFECTIVE LEARNING.
MODELLING HEALTHCARE PROVIDERS' KNOWLEDGE WHILE THEY ARE GAINING NEW CONCEPTS IS AN IMPORTANT STEP TOWARDS SUPPORTING SELF-REGULATED PERSONALISED LEARNING AT SCALE. THIS IS ESPECIALLY IMPORTANT IF WE ARE TO ADDRESS HEALTH WORKFORCE SKILLS DEVELOPMENT AND ENHANCE THE SUBSEQUENT QUALITY OF CARE PATIENTS RECEIVE IN THE GLOBAL SOUTH, WHERE A HUGE SKILLS GAP EXISTS. RICH DATA ABOUT HEALTHCARE PROVIDERS' LEARNING CAN BE CAPTURED BY THEIR RESPONSES TO CLOSE-ENDED PROBLEMS WITHIN CONJUNCTIVE SOLUTION SPACE -SUCH AS CLINICAL TRAINING SCENARIOS FOR EMERGENCY CARE DELIVERY- ON SMARTPHONE-BASED LEARNING INTERVENTIONS WHICH ARE BEING PROPOSED AS A SOLUTION FOR REDUCING THE HEALTHCARE SKILLS GAP IN THIS CONTEXT. TOGETHER WITH SEQUENTIAL DATA DETAILING A LEARNER'S PROGRESS WHILE THEY ARE SOLVING A LEARNING TASK, THIS PROVIDES USEFUL INSIGHTS INTO THEIR LEARNING BEHAVIOUR. PREDICTING LEARNING OR FORGETTING CURVES FROM REPRESENTATIONS OF HEALTHCARE PROVIDERS KNOWLEDGE IS A DIFFICULT TASK, BUT RECENT PROMISING MACHINE LEARNING ADVANCES HAVE PRODUCED TECHNIQUES CAPABLE OF LEARNING KNOWLEDGE REPRESENTATIONS AND OVERCOMING THIS CHALLENGE. IN THIS STUDY, WE TRAIN A LONG SHORT-TERM MEMORY NEURAL NETWORK FOR PREDICTING LEARNERS' FUTURE PERFORMANCE AND FORGETTING CURVES BY FEEDING IT SEQUENCE EMBEDDINGS OF LEARNING TASK ATTEMPTS FROM HEALTHCARE PROVIDERS FROM GLOBAL SOUTH. FROM THIS TRAINING, THE MODEL CAPTURES NUANCED REPRESENTATIONS OF A HEALTHCARE PROVIDER'S CLINICAL KNOWLEDGE AND THEIR PATTERNS OF LEARNING BEHAVIOURS, PREDICTING THEIR FUTURE PERFORMANCE WITH HIGH ACCURACY. MORE SIGNIFICANTLY, BY DIFFERENTIATING REDUCED PERFORMANCE BASED ON SPACED LEARNING, THE MODEL CAN HELP PROVIDE TIMELY WARNING THAT HELPS SUPPORT HEALTHCARE PROVIDERS TO REINFORCE THEIR SELF-REGULATED LEARNING WHILE PROVIDING A BASIS FOR PERSONALISED INSTRUCTIONAL SUPPORT TO AID IMPROVED CLINICAL OUTCOMES FROM THEIR PROFESSIONAL PRACTICES.
HOW IDEAS DEVELOP AND EVOLVE IS A TOPIC OF INTEREST FOR EDUCATORS. BY UNDERSTANDING THIS PROCESS, DESIGNERS AND EDUCATORS ARE BETTER ABLE TO SUPPORT AND GUIDE COLLABORATIVE LEARNING ACTIVITIES. THIS PAPER PRESENTS AN APPLICATION OF OUR LIFESPAN OF AN IDEA FRAMEWORK TO MEASURE ENGAGEMENT PATTERNS AMONG INDIVIDUALS IN COMMUNAL SOCIO-TECHNICAL SPACES LIKE TWITTER. WE CORRELATED ENGAGEMENT WITH SOCIAL PARTICIPATION, ENABLING THE PROCESS OF IDEA EXPRESSION, SPREAD, AND EVOLUTION. SOCIAL PARTICIPATION LEADS TO TRANSMISSION OF IDEAS FROM ONE INDIVIDUAL TO ANOTHER AND CAN BE GAUGED IN THE SAME WAY AS EVALUATING DISEASES. THE TEMPORAL DYNAMICS OF THE SOCIAL PARTICIPATION CAN BE MODELED THROUGH THE LENS OF EPIDEMIOLOGICAL MODELING. TO TEST THE PLAUSIBILITY OF THIS FRAMEWORK, WE INVESTIGATED SOCIAL PARTICIPATION ON TWITTER USING THE TWEET POSTING PATTERNS OF INDIVIDUALS IN THREE ACADEMIC CONFERENCES AND ONE LONG TERM CHAT SPACE. WE USED A BASIC SIR EPIDEMIOLOGICAL MODEL, WHERE THE RATE PARAMETERS WERE ESTIMATED THROUGH EULER'S SOLUTIONS TO SIR MODEL AND NON-LINEAR LEAST SQUARES OPTIMIZATION TECHNIQUE. WE DISCUSS THE DIFFERENCES IN THE SOCIAL PARTICIPATION AMONG INDIVIDUALS IN THESE SPACES BASED ON THEIR TRANSITION BEHAVIOR INTO DIFFERENT CATEGORIES OF THE SIR MODEL. WE ALSO MADE INFERENCES ON HOW THE TOTAL LIFETIME OF THESE DIFFERENT TWITTER SPACES AFFECTS THE ENGAGEMENT AMONG INDIVIDUALS. WE CONCLUDE BY DISCUSSING IMPLICATIONS OF THIS STUDY AND PLANNED FUTURE RESEARCH OF REFINING THE LIFESPAN OF AN IDEA FRAMEWORK.
LEARNING DESIGN RESEARCH HAS PREDOMINATELY RELIED UPON SURVEY- AND INTERVIEW-BASED METHODOLOGIES, BOTH OF WHICH ARE SUBJECT TO LIMITATIONS OF SOCIAL DESIRABILITY AND RECALL. AN ALTERNATIVE APPROACH IS OFFERED IN THIS MANUSCRIPT, WHEREBY PHYSICAL AND ONLINE LEARNING ACTIVITY DATA IS ANALYSED USING EPISTEMIC NETWORK ANALYSIS. USING A SAMPLE OF 6,040 COURSE OFFERINGS FROM 10 FACULTIES ACROSS A FOUR YEAR PERIOD (2016-2019), THE UTILITY OF NETWORKS TO UNDERSTAND LEARNING DESIGN IS ILLUSTRATED. SPECIFICALLY, THROUGH THE ADOPTION OF A NETWORK ANALYTIC APPROACH, THE FOLLOWING WAS FOUND: UNIVERSITIES ARE CLEARLY COMMITTED TO BLENDED LEARNING, BUT THERE ARE CONSIDERABLE DIFFERENCES BOTH BETWEEN AND WITHIN DISCIPLINES.
IN AN ERA THAT WILL INCREASINGLY DEPEND UPON LIFELONG LEARNING, THE LA COMMUNITY WILL NEED TO FACILITATE THE MOVEMENT AND SHARING OF DATA AND INFORMATION ACROSS INSTITUTIONAL AND GEOGRAPHIC BOUNDARIES. THIS WILL HELP US TO RECOGNISE PRIOR LEARNING (RPL) AND TO PERSONALISE THE LEARNER EXPERIENCE. HERE, WE EXPLORE THE UTILITY OF SKILLS-BASED CURRICULUM ANALYTICS AND HOW IT MIGHT FACILITATE THE PROCESS OF AWARDING RPL BETWEEN TWO INSTITUTIONS. WE EXPLORE THE POTENTIAL UTILITY OF COMBINING NATURAL LANGUAGE PROCESSING AND SKILLS TAXONOMIES TO MAP BETWEEN SUBJECT DESCRIPTIONS FOR THESE TWO DIFFERENT INSTITUTIONS, PRESENTING TWO ALGORITHMS WE HAVE DEVELOPED TO FACILITATE RPL AND EVALUATING THEIR PERFORMANCE. WE DRAW ATTENTION TO SOME OF THE ISSUES THAT ARISE, LISTING AREAS THAT WE CONSIDER RIPE FOR FUTURE WORK IN A SURPRISINGLY UNDEREXPLORED AREA.
AN ONGOING STUDY IS BEING RUN TO ENSURE THAT THE MCGRAW-HILL EDUCATION LEARNSMART PLATFORM TEACHES STUDENTS AS EFFICIENTLY AS POSSIBLE. THE FIRST STEP IN DOING SO IS TO IDENTIFY WHAT KNOWLEDGE COMPONENTS (KCS) EXIST IN THE CONTENT; WHILE THE CONTENT IS TAGGED BY EXPERTS, THESE TAGS NEED TO BE RE-CALIBRATED PERIODICALLY. LEARNSMART COURSES ARE ORGANIZED INTO CHAPTERS CORRESPONDING TO THOSE FOUND IN A TEXTBOOK; EACH CHAPTER CAN HAVE ANYWHERE FROM ABOUT A HUNDRED TO A FEW THOUSAND QUESTIONS. THE KC EXTRACTION ALGORITHMS PROPOSED BY BARNES [1] AND DESMARAIS ET AL [3] ARE APPLIED ON A CHAPTER-BY-CHAPTER BASIS. TO ASSESS THE ABILITY OF EACH MINED Q MATRIX TO DESCRIBE THE OBSERVED LEARNING, THE PFA MODEL OF PAVLIK ET AL [4] IS FITTED TO IT AND A CROSS-VALIDATED AUC IS CALCULATED. THE MODELS ARE ASSESSED BASED ON WHETHER PFA'S PREDICTIONS OF STUDENT CORRECTNESS ARE ACCURATE. EARLY RESULTS SHOW THAT BOTH ALGORITHMS DO A REASONABLE JOB OF DESCRIBING STUDENT PROGRESS, BUT Q MATRICES WITH VERY DIFFERENT NUMBERS OF KCS FIT OBSERVED DATA SIMILARLY WELL. CONSEQUENTLY, FURTHER CONSIDERATION IS REQUIRED BEFORE AUTOMATED EXTRACTION IS PRACTICAL IN THIS CONTEXT.
THE FIELD OF LEARNING ANALYTICS NEEDS TO ADOPT A MORE RIGOROUS APPROACH FOR PREDICTIVE MODEL EVALUATION THAT MATCHES THE COMPLEX PRACTICE OF MODEL-BUILDING. IN THIS WORK, WE PRESENT A PROCEDURE TO STATISTICALLY TEST HYPOTHESES ABOUT MODEL PERFORMANCE WHICH GOES BEYOND THE STATE-OF-THE-PRACTICE IN THE COMMUNITY TO ANALYZE BOTH ALGORITHMS AND FEATURE EXTRACTION METHODS FROM RAW DATA. WE APPLY THIS METHOD TO A SERIES OF ALGORITHMS AND FEATURE SETS DERIVED FROM A LARGE SAMPLE OF MASSIVE OPEN ONLINE COURSES (MOOCS). WHILE A COMPLETE COMPARISON OF ALL POTENTIAL MODELING APPROACHES IS BEYOND THE SCOPE OF THIS PAPER, WE SHOW THAT THIS APPROACH REVEALS A LARGE GAP IN DROPOUT PREDICTION PERFORMANCE BETWEEN FORUM-, ASSIGNMENT-, AND CLICKSTREAM-BASED FEATURE EXTRACTION METHODS, WHERE THE LATTER IS SIGNIFICANTLY BETTER THAN THE FORMER TWO, WHICH ARE IN TURN INDISTINGUISHABLE FROM ONE ANOTHER. THIS WORK HAS METHODOLOGICAL IMPLICATIONS FOR EVALUATING PREDICTIVE OR AI-BASED MODELS OF STUDENT SUCCESS, AND PRACTICAL IMPLICATIONS FOR THE DESIGN AND TARGETING OF AT-RISK STUDENT MODELS AND INTERVENTIONS.
IN THIS WORK, WE PRESENT (1) A METHODOLOGY FOR COLLECTING, EVALUATING, AND UTILIZING HUMAN-ANNOTATED DATA ABOUT COURSE SYLLABI IN PREDICTIVE MODELS OF STUDENT SUCCESS, AND (2) AN EMPIRICAL ANALYSIS OF THE PREDICTIVENESS OF SUCH FEATURES AS THEY RELATE TO OTHERS IN MODELING END-OF-COURSE GRADES IN TRADITIONAL HIGHER EDUCATION COURSES. WE PRESENT A TWO-STAGE APPROACH TO (1) THAT ADDRESSES SEVERAL CHALLENGES UNIQUE TO THE ANNOTATION TASK, AND ADDRESS (2) USING VARIABLE IMPORTANCE METRICS FROM A SERIES OF EXPLORATORY MODELS. WE DEMONSTRATE THAT THE PROCESS OF SUPPLEMENTING TRADITIONAL COURSE DATA WITH HUMAN-ANNOTATED DATA CAN POTENTIALLY IMPROVE PREDICTIVE MODELS WITH INFORMATION NOT CONTAINED IN UNIVERSITY RECORDS, AND HIGHLIGHT SPECIFIC FEATURES THAT DEMONSTRATE THESE POTENTIAL INFORMATION GAINS.
IN ONLINE SOCIAL LEARNING ENVIRONMENTS, TAGGING HAS DEMONSTRATED ITS POTENTIAL TO FACILITATE SEARCH, TO IMPROVE RECOMMENDATIONS AND TO FOSTER REFLECTION AND LEARNING.STUDIES HAVE SHOWN THAT SHARED UNDERSTANDING NEEDS TO BE ESTABLISHED IN THE GROUP AS A PREREQUISITE FOR LEARNING. WE HYPOTHESISE THAT THIS CAN BE FOSTERED THROUGH TAG RECOMMENDATION STRATEGIES THAT CONTRIBUTE TO SEMANTIC STABILIZATION. IN THIS STUDY, WE INVESTIGATE THE APPLICATION OF TWO TAG RECOMMENDERS THAT ARE INSPIRED BY MODELS OF HUMAN MEMORY: (I) THE BASE-LEVEL LEARNING EQUATION BLL AND (II) MINERVA. BLL MODELS THE FREQUENCY AND RECENCY OF TAG USE WHILE MINERVA IS BASED ON FREQUENCY OF TAG USE AND SEMANTIC CONTEXT. WE TEST THE IMPACT OF BOTH TAG RECOMMENDERS ON SEMANTIC STABILIZATION IN AN ONLINE STUDY WITH 56 STUDENTS COMPLETING A GROUP-BASED INQUIRY LEARNING PROJECT IN SCHOOL. WE FIND THAT DISPLAYING TAGS FROM OTHER GROUP MEMBERS CONTRIBUTES SIGNIFICANTLY TO SEMANTIC STABILIZATION IN THE GROUP, AS COMPARED TO A STRATEGY WHERE TAGS FROM THE STUDENTS' INDIVIDUAL VOCABULARIES ARE USED. TESTING FOR THE ACCURACY OF THE DIFFERENT RECOMMENDERS REVEALED THAT ALGORITHMS USING FREQUENCY COUNTS SUCH AS BLL PERFORMED BETTER WHEN INDIVIDUAL TAGS WERE RECOMMENDED. WHEN GROUP TAGS WERE RECOMMENDED, THE MINERVA ALGORITHM PERFORMED BETTER. WE CONCLUDE THAT TAG RECOMMENDERS, EXPOSING LEARNERS TO EACH OTHER'S TAG CHOICES BY SIMULATING SEARCH PROCESSES ON LEARNERS' SEMANTIC MEMORY STRUCTURES, SHOW POTENTIAL TO SUPPORT SEMANTIC STABILIZATION AND THUS, INQUIRY-BASED LEARNING IN GROUPS.
THE FOLLOWING PAPER IS A PROOF-OF-CONCEPT DEMONSTRATION OF A NOVEL BAYESIAN FRAMEWORK FOR MAKING INFERENCES ABOUT INDIVIDUAL STUDENTS AND THE CONTEXT IN WHICH THEY ARE LEARNING. IT HAS IMPLICATIONS FOR BOTH EFFORTS TO AUTOMATE PERSONALIZED INSTRUCTION AND TO PROBABILISTICALLY MODEL EDUCATIONAL CONTEXT. BY MODELLING STUDENTS AS BAYESIAN LEARNERS, INDIVIDUALS WHO WEIGH THEIR PRIOR BELIEF AGAINST CURRENT CIRCUMSTANTIAL DATA TO REACH CONCLUSIONS, IT BECOMES POSSIBLE TO BOTH GENERATE ESTIMATES OF PERFORMANCE AND THE IMPACT OF THE EDUCATIONAL ENVIRONMENT IN PROBABILISTIC TERMS. THIS FRAMEWORK IS TESTED THROUGH A BAYESIAN ALGORITHM THAT CAN BE USED TO CHARACTERIZE STUDENT PRIOR KNOWLEDGE IN COURSE MATERIAL AND PREDICT STUDENT PERFORMANCE. THIS IS DEMONSTRATED USING BOTH SIMULATED DATA. THE ALGORITHM GENERATES ESTIMATES THAT BEHAVE QUALITATIVELY AS EXPECTED ON SIMULATED DATA AND PREDICT STUDENT PERFORMANCE SUBSTANTIALLY BETTER THAN CHANCE. A DISCUSSION OF THE RESULTS AND THE CONCEPTUAL BENEFITS OF THE FRAMEWORK FOLLOW.
IN THIS PAPER, WE PERFORM A PREDICTIVE ANALYSIS OF A CURRICULUM-INTEGRATED MATH GAME, ST MATH, TO SUGGEST A PARTIAL ORDERING FOR THE GAME'S CURRICULUM SEQUENCE. WE ANALYZED THE SEQUENCE OF ST MATH OBJECTIVES PLAYED BY ELEMENTARY SCHOOL STUDENTS IN 5 U.S. DISTRICTS AND GROUPED EACH OBJECTIVE INTO DIFFICULT AND EASY CATEGORIES ACCORDING TO HOW MANY RETRIES WERE NEEDED FOR STUDENTS TO MASTER AN OBJECTIVE. WE OBSERVED THAT RETRIES ON SOME OBJECTIVES WERE HIGH IN ONE DISTRICT AND LOW IN ANOTHER DISTRICT WHERE THE OBJECTIVES ARE PLAYED IN A DIFFERENT ORDER. MOTIVATED BY THIS OBSERVATION, WE INVESTIGATED WHAT MAKES AN EFFECTIVE CURRICULUM SEQUENCE. TO INFER A NEW PARTIALLY-ORDERED SEQUENCE, WE PERFORMED AN EXPANDED REPLICATION STUDY OF A NOVEL PREDICTIVE ANALYSIS BY A PRIOR STUDY TO FIND PREDICTIVE RELATIONSHIPS BETWEEN 15 OBJECTIVES PLAYED IN DIFFERENT SEQUENCES BY 3,328 STUDENTS FROM 5 DISTRICTS. BASED ON THE PREDICTIVE ABILITIES OF OBJECTIVES IN THESE DISTRICTS, WE FOUND 17 SUGGESTED OBJECTIVE ORDERINGS. AFTER DERIVING THESE ORDERINGS, WE CONFIRMED THE VALIDITY OF THE ORDER BY EVALUATING THE IMPACT OF THE SUGGESTED SEQUENCE ON CHANGES IN RATES OF RETRIES AND CORRESPONDING PERFORMANCE. WE OBSERVED THAT WHEN THE OBJECTIVES WERE PLAYED IN THE SUGGESTED SEQUENCE, WE RECORD A DRASTIC REDUCTION IN RETRIES, IMPLYING THAT THESE OBJECTIVES ARE EASIER FOR STUDENTS. THIS INDICATES THAT OBJECTIVES THAT COME EARLIER CAN PROVIDE PREREQUISITE KNOWLEDGE FOR LATER OBJECTIVES. WE BELIEVE THAT DATA-INFORMED SEQUENCES, SUCH AS THE ONES WE SUGGEST, MAY IMPROVE EFFICIENCY OF INSTRUCTION AND INCREASE CONTENT LEARNING AND PERFORMANCE.
REFLECTION ASSESSMENT IS A CRITICAL COMPONENT OF HEALTH PROFESSIONS EDUCATION THAT CAN BE USED FOR PERSONALIZED LEARNING SUPPORT. HOWEVER, REFLECTION ASSESSMENT AT SCALE REMAINS A CHALLENGE DUE TO THE DEMANDING NATURE OF TASKS AND THE COMMON USE OF SIMPLIFIED CRITERIA OF QUALITY. THIS STUDY ADDRESSED THIS ISSUE BY DEVELOPING A MULTI-DIMENSIONAL AUTOMATED ASSESSMENT THAT USES LINGUISTIC MODELS TO CLASSIFY REFLECTIONS BY OVERALL QUALITY (DEPTH) AND THE PRESENCE OF SIX CONSTITUENT ELEMENTS DENOTING QUALITY (DESCRIPTION, ANALYSIS, FEELING, PERSPECTIVE, EVALUATION, AND OUTCOME). 1500 REFLECTIONS FROM 369 DENTAL STUDENTS WERE MANUALLY CODED TO ESTABLISH GROUND TRUTH. CLASSIFIERS FOR EACH OF THE SIX ELEMENTS WERE TRAINED AND TESTED BASED ON LINGUISTIC FEATURES EXTRACTED USING THE LIWC TOOL APPLYING BOTH SINGLE-LABEL AND MULTI-LABEL CLASSIFICATION APPROACHES. CLASSIFIERS FOR DEPTH WERE BUILT BOTH DIRECTLY FROM LINGUISTIC FEATURES AND BASED ON THE PRESENCE OF THE SIX ELEMENTS. RESULTS SHOWED THAT LINGUISTIC MODELING CAN BE USED TO RELIABLY DETECT THE PRESENCE OF REFLECTION ELEMENTS AND THE LEVEL OF DEPTH. HOWEVER, THE DEPTH CLASSIFIER SHOWED A HEAVY RELIANCE ON COGNITIVE ELEMENTS (DESCRIPTION, ANALYSIS, AND EVALUATION) RATHER THAN THE OTHERS. THESE FINDINGS INDICATE THE FEASIBILITY OF IMPLEMENTING MULTIDIMENSIONAL AUTOMATED ASSESSMENT IN HEALTH PROFESSIONS EDUCATION AND THE NEED TO RECONSIDER HOW QUALITY OF REFLECTION IS CONCEPTUALIZED.
COLLABORATIVE PROBLEM SOLVING IS DEFINED AS HAVING COGNITIVE AND SOCIAL DIMENSIONS. WHILE NETWORK ANALYTIC TECHNIQUES SUCH AS EPISTEMIC NETWORK ANALYSIS (ENA) AND SOCIAL NETWORK ANALYSIS (SNA) HAVE BEEN SUCCESSFULLY USED TO INVESTIGATE THE PATTERNS OF COGNITIVE AND SOCIAL CONNECTIONS THAT DESCRIBE CPS, FEW ATTEMPTS HAVE BEEN MADE TO COMBINE THE TWO APPROACHES. BUILDING ON PRIOR WORK THAT USED ENA AND SNA METRICS AS INDEPENDENT PREDICTORS OF COLLABORATIVE LEARNING, WE PROPOSE AND TEST THE INTEGRATED SOCIAL-EPISTEMIC NETWORK SIGNATURE (ISENS), AN APPROACH THAT AFFORDS THE SIMULTANEOUS INVESTIGATION OF COGNITIVE AND SOCIAL CONNECTIONS. WE TESTED ISENS ON DATA COLLECTED FROM MILITARY TEAMS PARTICIPATING IN TRAINING SCENARIOS. OUR RESULTS SUGGEST THAT (1) THESE TEAMS ARE DEFINED BY SPECIFIC PATTERNS OF COGNITIVE AND SOCIAL CONNECTIONS, (2) ISENS NETWORKS ARE ABLE TO CAPTURE THESE PATTERNS, AND (3) ISENS IS A BETTER PREDICTOR OF TEAM OUTCOMES COMPARED TO ENA ALONE, SNA ALONE, AND A NON-INTEGRATED SENS APPROACH.
IN THIS PAPER, WE PROPOSE A METHOD FOR PREDICTING FINAL GRADES OF STUDENTS BY A RECURRENT NEURAL NETWORK (RNN) FROM THE LOG DATA STORED IN THE EDUCATIONAL SYSTEMS. WE APPLIED THIS METHOD TO THE LOG DATA FROM 108 STUDENTS AND EXAMINED THE ACCURACY OF PREDICTION. FROM THE EXPERIMENTAL RESULTS, COMPARING WITH MULTIPLE REGRESSION ANALYSIS, IT IS CONFIRMED THAT AN RNN IS EFFECTIVE TO EARLY PREDICTION OF FINAL GRADES.
HERE WE PRESENT THE EXPERIENCE OF THE STELLAE RESEARCH GROUP (UNIVERSITY OF SANTIAGO DE COMPOSTELA) IN THE USE OF ACADEMIC SOCIAL NETWORKS AS A MEANS FOR TEACHING AND LEARNING IN HIGHER EDUCATION. IT IS AN EXPERIENCE USING STUDENT E-PORTFOLIOS AS ARTEFACTS SPANNING THE BREADTH OF TEACHING, LEARNING AND ASSESSMENT PROCESSES. THE TEACHING APPROACH RESPECTS STUDENT DIVERSITY, PROMOTES AUTONOMY, PROPOSES AN ELIMINATION OF BARRIERS BETWEEN FORMAL AND INFORMAL LEARNING SPACES AND AIMS TO ENCOURAGE SELF AND SOCIO-REGULATED LEARNING. THE STUDY AIMS TO ANALYSE THE SOCIAL NETWORK CONDITIONS THAT STIMULATE THE DEVELOPMENT OF THESE PROCESSES AND HOW TO USE DATA GENERATED IN THIS CONTEXT TO IMPROVE PROPOSALS. WE WORKED FROM A QUALITATIVE PERSPECTIVE COMBINED WITH LEARNING ANALYTICS QUANTITATIVE TECHNIQUES. THE RESULTS SHOW IMPROVED TEACHER ACCESS TO DATA REGARDING STUDENT LEARNING PROCESSES, AND BETTER KNOWLEDGE OF STUDENT NETWORK BEHAVIOUR AND MOTIVATION DATA. WE CONCLUDE THAT THE USE OF SOCIAL NETWORKS CREATES A PARTICIPATION STRUCTURE AMONG STUDENTS THAT PROVIDES GREAT OPPORTUNITIES, FOSTERS SELF AND SOCIO-REGULATION SKILLS AND PROMOTES A POSITIVE CORRELATION BETWEEN THE NUMBER OF INTERACTIONS AND MARKS OBTAINED.
INTELLIGENT TUTORING SYSTEMS (ITSS) ARE COMMONLY DESIGNED TO ENHANCE STUDENT LEARNING. HOWEVER, THEY ARE NOT TYPICALLY DESIGNED TO MEET THE NEEDS OF TEACHERS WHO USE THEM IN THEIR CLASSROOMS. ITSS GENERATE A WEALTH OF ANALYTICS ABOUT STUDENT LEARNING AND BEHAVIOR, OPENING A RICH DESIGN SPACE FOR REAL-TIME TEACHER SUPPORT TOOLS SUCH AS DASHBOARDS. WHEREAS REAL-TIME DASHBOARDS FOR TEACHERS HAVE BECOME POPULAR WITH MANY LEARNING TECHNOLOGIES, WE ARE NOT AWARE OF PROJECTS THAT HAVE DESIGNED DASHBOARDS FOR ITSS BASED ON A BROAD INVESTIGATION OF TEACHERS' NEEDS. WE CONDUCTED DESIGN INTERVIEWS WITH TEN MIDDLE SCHOOL MATH TEACHERS TO EXPLORE THEIR NEEDS FOR ON-THE-SPOT SUPPORT DURING BLENDED CLASS SESSIONS, AS A FIRST STEP IN A USER-CENTERED DESIGN PROCESS OF A REAL-TIME DASHBOARD. BASED ON MULTI-METHODS ANALYSES OF THIS INTERVIEW DATA, WE IDENTIFY SEVERAL OPPORTUNITIES FOR ITSS TO BETTER SUPPORT TEACHERS' NEEDS, NOTING THAT THE ANALYTICS COMMONLY GENERATED BY EXISTING TEACHER SUPPORT TOOLS DO NOT STRONGLY ALIGN WITH THE ANALYTICS TEACHERS EXPECT TO BE MOST USEFUL. WE HIGHLIGHT KEY TENSIONS AND TRADEOFFS IN THE DESIGN OF SUCH REALTIME SUPPORTS FOR TEACHERS, AS REVEALED BY "SPEED DATING" POSSIBLE FUTURES WITH TEACHERS. THIS PAPER HAS IMPLICATIONS FOR OUR ONGOING CO-DESIGN OF A REAL-TIME DASHBOARD FOR ITSS, AS WELL AS BROADER IMPLICATIONS FOR THE DESIGN OF ITSS THAT CAN EFFECTIVELY COLLABORATE WITH TEACHERS IN CLASSROOM SETTINGS.
WE PROPOSE A DATA DRIVEN METHOD FOR DECOMPOSING POPULATION LEVEL LEARNING CURVE MODELS INTO MUTUALLY EXCLUSIVE DISTINCTIVE GROUPS EACH CONSISTING OF SIMILAR LEARNING TRAJECTORIES. WE VALIDATE THIS METHOD ON SIX KNOWLEDGE COMPONENTS FROM THE LOG DATA FROM AN ONLINE TUTORING SYSTEM ASSISTMENT. PRELIMINARY ANALYSIS REVEALS INTERPRETABLE PATTERNS OF "SKILL GROWTH" THAT CORRELATE WITH STUDENTS' PERFORMANCE IN THE SUBSEQUENTLY ADMINISTERED STATE STANDARDIZED TESTS.
WHILE HELP SEEKING HAS BEEN EXTENSIVELY STUDIED USING SELF REPORT SURVEY DATA AND MODELS, THERE IS A LACK OF CONTENT ANALYSIS TECHNIQUES THAT CAN BE DIRECTLY APPLIED TO CLASSIFY HELP SEEKING BEHAVIOUR. IN THIS PRELIMINARY WORK WE PROPOSE A CODING SCHEME WHICH IS THEN APPLIED TO AN OPEN DATASET THAT WE HAVE CREATED BY CAREFULLY SELECTING SUB GROUPS FROM TWO POPULAR DISCUSSION SITES (REDDIT AND STACKEXCHANGE). WE THEN EXPLORE THE POSSIBILITY FOR AUTOMATICALLY CLASSIFYING HELP SEEKING BEHAVIOUR USING MACHINE LEARNING MODELS. A PRELIMINARY MODEL PROVIDES GOOD INITIAL RESULTS, SUGGESTING THAT IT MAY INDEED BE POSSIBLE TO CONSTRUCT STUDENT SUPPORT SYSTEMS THAT BUILD OFF OF AN ACCURATE CLASSIFIER.
THIS STUDY EXAMINES THE IMPACT OF CONTENT-BASED NETWORK PARTITIONING AND TIE DEFINITION ON SOCIAL NETWORK STRUCTURES AND INTERPRETATION FOR MOOC DISCUSSION FORUMS. USING DYNAMIC INTERRELATED POST AND THREAD CATEGORIZATION [5] BASED ON A PREVIOUSLY DEVELOPED NATURAL LANGUAGE MODEL [27], 817 THREADS CONTAINING 3124 DISCUSSION POSTS FROM 567 LEARNERS IN A MOOC ON THE USE OF STATISTICS IN MEDICINE WERE CHARACTERIZED AS EITHER RELATED TO THE LEARNING OF COURSE CONTENT OR NOT. CONTENT-RELATED, NON-CONTENT, AND UNPARTITIONED INTERACTION NETWORKS WERE CONSTRUCTED BASED ON FIVE DIFFERENT TIE DEFINITIONS: DIRECT REPLY, STAR, DIRECT REPLY+STAR, LIMITED COPRESENCE, AND TOTAL COPRESENCE. RESULTS SHOWED CONTENT-RELATED AND NON-CONTENT NETWORKS TO HAVE DISTINCT CHARACTERISTICS AT THE NETWORK, COMMUNITY, AND INDIVIDUAL NODE LEVELS, VALIDATING THE USEFULNESS OF THE CONTENT/NON-CONTENT DISTINCTION AS AN ANALYTIC TOOL. NETWORK PROPERTIES WERE LESS SENSITIVE TO DIFFERENCES IN TIE DEFINITION WITH THE EXCEPTION OF TOTAL COPRESENCE, WHICH SHOWED DISTINCT CHARACTERISTICS PRESENTING DANGERS FOR GENERAL USE, BUT USEFULNESS FOR DETECTING INFLATED SOCIAL STATUS DUE TO "SUPERTHREAD" INITIATION.
IN THIS PAPER, WE REPORT ON A MODEL THAT USES A MATHEMATICALLY AND COGNITIVELY AUGMENTED LATENT SEMANTIC ANALYSIS METHOD TO AUTOMATICALLY ASSESS ASPECTS OF WRITTEN ARGUMENTATION, PRODUCED BY STUDENTS IN A SCIENCE COMMUNICATION COURSE.
IN THIS POSTER, WE PRESENT THE RESULTS OF THE STUDY WHICH EXAMINED THE RELATIONSHIP BETWEEN STUDENT DIFFERENCES IN THEIR USE OF THE AVAILABLE TECHNOLOGY AND THEIR PERCEIVED LEVELS OF COGNITIVE PRESENCE WITHIN THE MOOC CONTEXT. THE COGNITIVE PRESENCE IS A CONSTRUCT USED TO MEASURE THE LEVEL OF PRACTICAL INQUIRY IN THE COMMUNITIES OF INQUIRY MODEL. OUR RESULTS REVEALED THE EXISTENCE OF THREE CLUSTERS BASED ON STUDENT TECHNOLOGY USE. THE CLUSTERS SIGNIFICANTLY DIFFERED IN TERMS OF THEIR LEVELS OF COGNITIVE PRESENCE, MOST NOTABLY THEY DIFFERED ON THE LEVELS OF PROBLEM RESOLUTION.
STUDENT CLICKSTREAM DATA CAN PROVIDE VALUABLE INSIGHTS ABOUT STUDENT ACTIVITIES IN AN ONLINE LEARNING ENVIRONMENT AND HOW THESE ACTIVITIES INFORM THEIR LEARNING OUTCOMES. HOWEVER, GIVEN THE NOISY AND COMPLEX NATURE OF THIS DATA, AN ONGOING CHALLENGE INVOLVES DEVISING STATISTICAL TECHNIQUES THAT CAPTURE CLEAR AND MEANINGFUL ASPECTS OF STUDENTS' CLICK PATTERNS. IN THIS PAPER, WE UTILIZE STATISTICAL CHANGE DETECTION TECHNIQUES TO INVESTIGATE STUDENTS' ONLINE BEHAVIORS. USING CLICKSTREAM DATA FROM TWO LARGE UNIVERSITY COURSES, ONE FACE-TO-FACE AND ONE ONLINE, WE ILLUSTRATE HOW THIS METHODOLOGY CAN BE USED TO DETECT WHEN STUDENTS CHANGE THEIR PREVIEWING AND REVIEWING BEHAVIOR, AND HOW THESE CHANGES CAN BE RELATED TO OTHER ASPECTS OF STUDENTS' ACTIVITY AND PERFORMANCE.
CURRENTLY, THE DISSEMINATION OF OPEN DATA IN CONJUNCTION WITH EDUCATIONAL DATA MINING (EDM), LEARNING ANALYTICS, E-LEARNING, INTELLIGENT SYSTEMS, INTELLIGENT TUTORS, AND ONLINE JUDGE TECHNIQUES HAVE HAD MADE USEFUL CONTRIBUTIONS TO THE FIELD OF EDUCATION THROUGH THE KNOWLEDGE GENERATED FROM DATA ANALYSIS. IDENTIFYING FACTORS THAT ALLOW US TO UNDERSTAND HOW STUDENTS LEARN AND THEIR BEHAVIOR HAS AIDED MANAGERS AND TEACHING PROFESSIONALS TO IDENTIFY THE BEST TEACHING SETTINGS. THIS STUDY AIMS TO DO A COMPARISON OF ACADEMIC SUCCESS WITH OTHER STUDIES IN THE LITERATURE. TWO THOUSAND FOUR HUNDRED NINETY-NINE STUDENTS WERE ANALYZED FOR OVER 11 YEARS. THESE STUDENTS BELONG TO A BRAZILIAN UNIVERSITY AND THREE UNDERGRADUATE COURSES OF COMPUTING (COMPUTER SCIENCE, SOFTWARE ENGINEERING AND INFORMATION SYSTEMS). THE STATISTICAL AND DATA MINING TECHNIQUES WERE USED TO EXTRACT INFORMATION THAT CAN VALIDATE THE HYPOTHESES OF THIS STUDY. OUR MAIN OBJECTIVE IS TO SEEK WHICH FACTORS TEND TO CONTRIBUTE TO STUDENTS' RETENTION, DROPOUT, DIFFICULTIES, AND ACADEMIC SUCCESS. FOR REACH THIS OBJECTIVE, WE COMPARE GENDER EFFECTIVENESS AND COURSE CURRICULUM GRADE. THE DATA SHOWED THAT SOME FACTORS, NOT PREVIOUSLY ANALYZED BY OTHER STUDIES, TEND TO INFLUENCE STUDENT PERFORMANCE.
MASSIVE OPEN ONLINE COURSES (MOOCS) ARE USUALLY SIX TO TEN WEEKS LONG. PARTICIPATION TENDS TO DECREASE AS THE COURSES PROGRESS, LEADING TO LOW COMPLETION RATES. THIS LED TO THE QUESTION: COULD SHORTER MOOCS CONTRIBUTE TO LEARNERS' ENGAGEMENT, RETENTION AND SUCCESS? THIS PAPER COMPARES TWO VERSIONS OF STUDY SKILLS MOOC, WHICH SHARED THE SAME CONTENT BUT WERE DELIVERED IN DIFFERENT LENGTH FORMATS. ONE WAS DEPLOYED AS A SINGLE SIX-WEEK COURSE AND THE OTHER AS TWO THREE-WEEK BLOCKS. IN TOTAL, 617 PEOPLE REGISTERED FOR THE TWO VERSIONS. DATA SOURCES INCLUDED LEARNING ANALYTICS, SURVEYS AND THE SPANISH VERSION OF THE GENERAL SELF-EFFICACY SCALE. BOTH VERSIONS OF THE STUDY SKILLS MOOC RESULTED IN INCREASED PARTICIPANTS' SELF-EFFICACY. HOWEVER, LEARNERS ENROLLED IN THE VERSION COMPOSED OF TWO THREE-WEEK BLOCKS WERE ALSO MORE ENGAGED WITH COURSE CONTENT, OTHER STUDENTS AND THE FACILITATORS. THEIR RETENTION AND COMPLETION RATES WERE HIGHER THAN THOSE IN THE LONGER VERSION OF THE COURSE. REASONS LINKED TO GOAL PROXIMITY, MOTIVATION, INTERACTIONS AND SOCIAL MODELLING ARE DISCUSSED
THIS PAPER DESCRIBES THE DESIGN AND EVALUATION OF PERSONALIZED VISUALIZATIONS TO SUPPORT YOUNG LEARNERS' SELF-REGULATED LEARNING (SRL) IN ADAPTIVE LEARNING TECHNOLOGIES (ALTS). OUR LEARNING PATH APP COMBINES THREE PERSONALIZED VISUALIZATIONS (PV) THAT ARE DESIGNED AS AN EXTERNAL REFERENCE TO SUPPORT LEARNERS' INTERNAL REGULATION PROCESS. THE PERSONALIZED VISUALIZATIONS ARE BASED ON THREE PILLARS: GROUNDING IN SRL THEORY, THE USAGE OF TRACE DATA AND THE PROVISION OF CLEAR ACTIONABLE RECOMMENDATIONS FOR LEARNERS TO IMPROVE REGULATION. THIS QUASI-EXPERIMENTAL PRE-POSTTEST STUDY FINDS THAT LEARNERS IN THE PERSONALIZED VISUALIZATION CONDITION IMPROVED THE REGULATION OF THEIR PRACTICE BEHAVIOR, AS INDICATED BY HIGHER ACCURACY AND LESS COMPLEX MOMENT-BY-MOMENT LEARNING CURVES COMPARED TO LEARNERS IN THE CONTROL GROUP. LEARNERS IN THE PV CONDITION SHOWED BETTER TRANSFER ON LEARNING. FINALLY, STUDENTS IN THE PERSONALIZED VISUALIZATIONS CONDITION WERE MORE LIKELY TO UNDER-ESTIMATE INSTEAD OF OVER-ESTIMATE THEIR PERFORMANCE. OVERALL, THESE FINDINGS INDICATES THAT THE PERSONALIZED VISUALIZATIONS IMPROVED REGULATION OF PRACTICE BEHAVIOR, TRANSFER OF LEARNING AND CHANGED THE BIAS IN RELATIVE MONITORING ACCURACY.
LEARNING ANALYTICS (LA) LOOKS FOR A BETTER UNDERSTANDING OF LEARNING AND WAYS TO OPTIMIZE BOTH THE LEARNING AND THE ENVIRONMENTS IN WHICH IT OCCURS. ONE OF ITS KEY RESEARCH AREAS IS FOCUSED ON DATA INTEROPERABILITY, SPECIFICALLY ON HOW TO COLLECT AND STORE LEARNING DATA. PROPRIETARY SYSTEMS USUALLY STORE DATA IN THEIR OWN UNIQUE FORMAT AND THUS MAKE IT DIFFICULT TO REUSE LA SOLUTIONS. SOME APPROACHES HAVE APPEARED IN THE LAST YEARS TO OVERCOME THIS ISSUE AND THE EXPERIENCE API (XAPI) HAS BEEN THE MOST SUCCESSFUL IN THIS AREA, PRIMARILY BECAUSE OF ITS GENERIC APPROACH NOT TIED TO ANY LEARNING MANAGEMENT SYSTEM (LMS). HOWEVER, THE XAPI SPECIFICATION IS INFORMAL, WITH SOME LOOSE DEFINITIONS, THAT MAY LEAD TO UNEXPECTED MISTAKES. IN ORDER TO AVOID AMBIGUITY, IN THIS PAPER WE PRESENT AN XAPI ONTOLOGY THAT CAPTURES THE CONCEPTS AND SEMANTICS OF THE SPECIFICATION, AND HAS BEEN VALIDATED WITH TWO DATASETS OF THE XAPI COMMUNITY. IN ADDITION, A WEB CLIENT HAS BEEN DEVELOPED TO PROVIDE A VALIDATION TOOL THAT CAN CHECK THE CORRECTNESS AND CONFORMANCE OF INDIVIDUAL XAPI FILES AS WELL AS COMPLETE XAPI DATASETS.
PEOPLE IN GENERAL AND STUDENTS IN PARTICULAR HAVE A TENDENCY TO MISINTERPRET THEIR OWN ABILITIES. SOME TEND TO UNDERESTIMATE THEIR SKILLS, WHILE OTHERS TEND TO OVERESTIMATE THEM. THIS PAPER INVESTIGATES THE DEGREE TO WHICH METACOGNITION IS ASYMMETRIC IN REAL-WORLD LEARNING AND EXAMINES THE CHANGE OF A STUDENTS' CONFIDENCE OVER THE COURSE OF A SEMESTER AND ITS IMPACT ON THE STUDENTS' ACADEMIC PERFORMANCE. OUR FINDINGS, CONDUCTED USING 129,644 STUDENTS LEARNING IN EIGHT COURSES WITHIN THE LEARNSMART PLATFORM, INDICATE THAT POOR OR UNREALISTIC METACOGNITION IS ASYMMETRIC. THESE STUDENTS ARE BIASED IN ONE DIRECTION: THEY ARE MORE LIKELY TO BE OVERCONFIDENT THAN UNDERCONFIDENT. ADDITIONALLY, WHILE THE EXAMINATION OF THE TEMPORAL ASPECTS OF CONFIDENCE REVEALS NO SIGNIFICANT CHANGE THROUGHOUT THE SEMESTER, CHANGES ARE MORE APPARENT IN THE FIRST AND THE LAST FEW WEEKS OF THE COURSE. MORE SPECIFICALLY, THERE IS A SHARP INCREASE IN UNDERCONFIDENCE AND A SIMULTANEOUS DECREASE IN REALISTIC EVALUATION TOWARD THE END OF THE SEMESTER. FINALLY, BOTH OVERCONFIDENCE AND UNDERCONFIDENCE SEEM TO BE CORRELATED WITH STUDENTS' OVERALL COURSE PERFORMANCE. AN INCREASE IN OVERCONFIDENCE IS RELATED TO HIGHER OVERALL PERFORMANCE, WHILE AN INCREASE IN UNDERCONFIDENCE IS ASSOCIATED WITH LOWER OVERALL PERFORMANCE.
PREDICTING THE DECREASE OF STUDENTS' ENGAGEMENT IN TYPICAL MOOC TASKS SUCH AS WATCHING LECTURE VIDEOS OR SUBMITTING ASSIGNMENTS IS KEY TO TRIGGER TIMELY INTERVENTIONS IN ORDER TO TRY TO AVOID THE DISENGAGEMENT BEFORE IT TAKES PLACE. THIS PAPER PROPOSES AN APPROACH TO BUILD THE NECESSARY PREDICTIVE MODELS USING STUDENTS' DATA THAT BECOMES AVAILABLE DURING A COURSE. THE APPROACH WAS EMPLOYED IN AN EXPERIMENTAL STUDY TO PREDICT THE DECREASE OF THREE DIFFERENT ENGAGEMENT INDICATORS IN A MOOC. THE RESULTS SUGGEST ITS FEASIBILITY WITH VALUES OF AREA UNDER THE CURVE FOR DIFFERENT PREDICTORS RANGING FROM 0.718 TO 0.914.
THERE HAVE BEEN NUMEROUS EFFORTS TO INCREASE STUDENTS' ACADEMIC SUCCESS. ONE DATA-DRIVEN APPROACH IS TO HIGHLIGHT THE IMPORTANT LEARNING OBJECTIVES IN A COURSE. IN THIS PAPER, WE USED VISUALIZATION AND THREE FEATURE SELECTION METHODS TO HIGHLIGHT THE IMPORTANT LEARNING OBJECTIVES IN A COURSE. IDENTIFYING IMPORTANT LEARNING OBJECTIVES AS WELL AS THE RELATION AMONG THE LEARNING OBJECTIVES HAVE MULTIPLE EDUCATIONAL ADVANTAGES. FIRST, IT INFORMS THE INSTRUCTORS AND STUDENTS OF THE IMPORTANT TOPICS IN THE COURSE; WITHOUT LEARNING THEM PROPERLY STUDENTS WILL NOT BE SUCCESSFUL. SECOND, IT HIGHLIGHTS ANY INCONSISTENCIES IN DEFINING THE LEARNING OBJECTIVE, HOW THEY ARE BEING ASSESSED, AND DESIGN OF THE COURSE. THUS, THIS APPROACH CAN BE USED AS A COURSE DESIGN DIAGNOSTIC TOOL.
IN THIS PAPER, VIDEO ANNOTATION TOOL FOR LEARNING JOB INTERVIEW IS PROPOSED. TO VISUALIZE THE DIFFERENCE OF OBTAINED DESCRIPTIONS, THE PROPOSED TOOL USES CORRESPONDENCE ANALYSIS. THE RESULTS OF CORRESPONDENCE ANALYSIS ARE USED TO GIVE FEEDBACK TO LEARNERS. BY THE RESULTS, THE LEARNER CAN UNDERSTAND THE CHARACTERISTICS OF HIS/HER DESCRIPTIONS AMONG THE OTHERS.
ADDITIVE FACTORS MODEL IS A WIDELY USED STUDENT MODEL, WHICH IS PRIMARILY USED FOR REFINING KNOWLEDGE COMPONENT MODELS (Q-MATRICES). WE EXPLORE THE ROBUSTNESS AND GENERALIZABILITY OF THE MODEL. WE EXPLICITLY FORMULATE SIMPLIFYING ASSUMPTIONS THAT THE MODEL MAKES AND WE DISCUSS METHODS FOR VISUALIZING LEARNING CURVES BASED ON THE MODEL. WE ALSO REPORT ON AN APPLICATION OF THE MODEL TO DATA FROM A LEARNING SYSTEM FOR INTRODUCTORY PROGRAMMING; THESE EXPERIMENTS ILLUSTRATE POSSIBLY MISLEADING INTERPRETATION OF MODEL RESULTS DUE TO DIFFERENCES IN ITEM DIFFICULTY. OVERALL, OUR RESULTS SHOW THAT GREATER CARE HAS TO BE TAKEN IN THE APPLICATION OF THE MODEL AND IN THE INTERPRETATION OF RESULTS OBTAINED WITH THE MODEL.
EDUCATIONAL TESTS CAN BE USED TO ESTIMATE PUPILS' ABILITIES AND THEREBY GIVE AN INDICATION OF WHETHER THEIR SCHOOL TYPE IS SUITABLE FOR THEM. HOWEVER, TESTS IN EDUCATION ARE USUALLY CONDUCTED FOR EACH CONTENT AREA SEPARATELY WHICH MAKES IT DIFFICULT TO COMBINE THESE RESULTS INTO ONE SINGLE SCHOOL ADVICE. TO HELP WITH SCHOOL ADVICE, WE PROVIDE A COMPARISON BETWEEN BOTH DOMAIN-SPECIFIC AND DOMAIN-AGNOSTIC METHODS FOR PREDICTING SCHOOL TYPES. BOTH USE DATA FROM A PUPIL MONITORING SYSTEM IN THE NETHERLANDS, A SYSTEM THAT KEEPS TRACK OF PUPILS' EDUCATIONAL PROGRESS OVER SEVERAL YEARS BY A SERIES OF TESTS MEASURING MULTIPLE SKILLS. A DOMAIN-SPECIFIC ITEM RESPONSE THEORY (IRT) MODEL IS CALIBRATED FROM WHICH AN ABILITY SCORE IS EXTRACTED AND IS SUBSEQUENTLY PLUGGED INTO A MULTINOMIAL LOG-LINEAR REGRESSION MODEL. SECOND, WE TRAIN DOMAIN-AGNOSTIC MACHINE LEARNING (ML) MODELS. THESE ARE A RANDOM FOREST (RF) AND A SHALLOW NEURAL NETWORK (NN). FURTHERMORE, WE APPLY CASE WEIGHTING TO GIVE EXTRA ATTENTION TO PUPILS WHO SWITCHED BETWEEN SCHOOL TYPES. WHEN CONSIDERING THE PERFORMANCE OF ALL PUPILS, RFS PROVIDED THE MOST ACCURATE PREDICTIONS FOLLOWED BY NNS AND IRT RESPECTIVELY. WHEN ONLY LOOKING AT THE PERFORMANCE OF PUPILS WHO SWITCHED SCHOOL TYPE, IRT PERFORMED BEST FOLLOWED BY NNS AND RFS. CASE WEIGHTING PROVED TO PROVIDE A MAJOR IMPROVEMENT FOR THIS GROUP. LASTLY, IRT WAS FOUND TO BE MUCH EASIER TO EXPLAIN IN COMPARISON TO THE OTHER MODELS. THUS, WHILE ML PROVIDED MORE ACCURATE RESULTS, THIS COMES AT THE COST OF A LOWER EXPLAINABILITY IN COMPARISON TO IRT.
SINCE TEACHERS ARE NOT PHYSICALLY PRESENT IN AN ONLINE CLASS, INSTRUCTIONAL VIDEO IS THE MAJOR CARRIER OF COURSE CONTENTS IN AN ONLINE LEARNING ENVIRONMENT. THIS PAPER AIMS TO INVESTIGATE HOW COURSE-LEVEL EXPLORATORY VIDEO RETENTION ANALYSIS CAN BE USED FOR IDENTIFYING MOMENTS WITH ABNORMAL WATCHING BEHAVIORS AND REVISING VIDEOS FOR A HIGHER VIDEO RETENTION. WE HAVE EMPIRICALLY EVALUATED THE EFFECTIVENESS OF VIDEO ANALYSIS AND REVISIONS, BASED ON EVALUATING RETENTIONS OF REVISED VIDEOS.
COLLABORATIVE PROBLEM SOLVING (CPS) IS A PROCESS THAT RELIES ON BOTH COGNITIVE AND SOCIAL SKILLS CONTRIBUTIONS BY THOSE INVOLVED IN THE JOINT ACTIVITY. IF A STUDENT IS MATCHED WITH A PROBLEMATIC GROUP OF PEERS, THEN THERE WILL BE NO VALID MEASUREMENT OF THE CPS SKILLS. IN THE HUMAN-AGENT SETTINGS. CPS SKILLS ARC MEASURED BY PAIRING EACH INDIVIDUAL STUDENT WITH A COMPUTER AGENT OR AGENTS THAT CAN BE PROGRAMMED TO ACT AS TEAM MEMBERS WITH VARYING CHARACTERISTICS RELEVANT TO DIFFERENT CPS SKILLS AND CONTEXTS. THIS PAPER DESCRIBES CURRENT RESEARCH ON MEASURING CPS SKILLS THROUGH HUMAN-AGENT INTERACTIONS IN A PROTOTYPE OF A COLLABORATIVE EDUCATIONAL GAME.
EMOTIONS FORM AN INTEGRAL PART OF OUR COGNITIVE FUNCTION. PAST RESEARCH HAS DEMONSTRATED CONCLUSIVE ASSOCIATIONS BETWEEN EMOTIONS AND LEARNING ACHIEVEMENT [7, 26, 27]. THIS PAPER USED A PERSON-CENTERED APPROACH TO EXPLORE STUDENTS' (N = 65) FACIAL BEHAVIOR, EMOTIONS, LEARNER TRAITS AND LEARNING. AN AUTOMATIC FACIAL EXPRESSION RECOGNITION SYSTEM WAS USED TO DETECT BOTH MIDDLE SCHOOL AND UNIVERSITY STUDENTS' REAL-TIME FACIAL MOVEMENTS WHILE THEY LEARNED SCIENTIFIC TASKS IN A 3D NARRATIVE VIDEO GAME. THE RESULTS INDICATED A STRONG STATISTICAL RELATIONSHIP BETWEEN THREE SPECIFIC FACIAL MOVEMENTS (I.E., OUTER BROW RAISING, LIP TIGHTENING AND LIP PRESSING), STUDENT SELF-REGULATORY LEARNING STRATEGY AND LEARNING PERFORMANCE. OUTER BROW RAISING (AU2) HAD STRONG PREDICTIVE POWER WHEN A STUDENT IS CONFRONTED WITH OBSTACLES AND DOES NOT KNOW HOW TO PROCEED. BOTH LIP TIGHTENING AND PRESSING (AU23 AND AU24) WERE PREDICTIVE WHEN A STUDENT ENGAGED IN A TASK THAT REQUIRES A DEEP LEVEL OF INCOMING INFORMATION PROCESSING AND SHORT MEMORY ACTIVATION. THE FINDINGS ALSO SUGGESTED A CORRELATIONAL RELATIONSHIP BETWEEN STUDENT SELF-REGULATORY LEARNING STRATEGY USE AND NEUTRAL STATE. IT IS HOPED THAT THIS STUDY WILL PROVIDE EMPIRICAL EVIDENCE FOR HELPING US DEVELOP A DEEPER UNDERSTANDING OF THE RELATIONSHIP BETWEEN FACIAL BEHAVIOR AND COMPLEX LEARNING ESPECIALLY IN EDUCATIONAL GAMES.
THERE IS AN INCREASING AWARENESS THAT PREDICTIVE ANALYTICS HELPS UNIVERSITIES TO EVALUATE STUDENTS' PERFORMANCES. BIG DATA ANALYTICS, SUCH AS STUDENT DEMOGRAPHIC DATASETS, CAN PROVIDE INSIGHT THAT HELPS TO SUPPORT ACADEMIC SUCCESS AND COMPLETION RATES. FOR EXAMPLE, LEARNING ANALYTICS IS AN ESSENTIAL COMPONENT OF BIG DATA IN UNIVERSITIES THAT CAN PROVIDE STRATEGIC DECISION MAKERS WITH THE OPPORTUNITY TO PERFORM A TIME SERIES ANALYSIS OF LEARNING ACTIVITIES. A TWO-YEAR RETROSPECTIVE ANALYSIS OF STUDENT LEARNING DATA FROM THE UNIVERSITY OF HA'IL WAS CONDUCTED FOR THIS STUDY. PREDICTIVE DEEP LEARNING TECHNIQUES, THE BIDIRECTIONAL LONG SHORT TERM MODEL (BLSTM), WERE UTILIZED TO INVESTIGATE STUDENTS WHOSE RETENTION WAS AT RISK. THE MODEL HAS DIVERSE FEATURES WHICH CAN BE UTILIZED TO ASSESS HOW NEW STUDENTS WILL PERFORM AND THUS CONTRIBUTES TO EARLY PREDICTION OF STUDENT RETENTION AND DROPOUT. FURTHER, THE CONDITION RANDOM FIELD (CRF) METHOD FOR SEQUENCE LABELING WAS USED TO PREDICT EACH STUDENT LABEL INDEPENDENTLY. EXPERIMENTAL RESULTS OBTAINED WITH THE PREDICTIVE MODEL INDICATES THAT PREDICTION OF STUDENT RETENTION IS POSSIBLE WITH A HIGH LEVEL OF ACCURACY USING BLSTM AND CRF DEEP LEARNING TECHNIQUES.
INTERNET AND ORGANIZATIONAL NETWORK SECURITY IS STILL THREATENED BY DEVASTATING MALICIOUS ACTIVITIES. GIVEN THE CONTINUOUS ESCALATION OF SUCH ATTACKS IN TERMS OF THEIR FREQUENCY, SOPHISTICATION AND STEALTHINESS, IT IS OF PARAMOUNT IMPORTANCE TO GENERATE EFFECTIVE CYBER THREAT INTELLIGENCE THAT AIM AT INFERRING, ATTRIBUTING, CHARACTERIZING AND MITIGATING SUCH MISDEMEANORS. NEVERTHELESS, SUCH IMPERATIVE TASKS ARE PARTIALLY IMPEDED BY THE LACK OF APPROACHES THAT CAN PRODUCE PROMPT AND ACCURATE ACTIONABLE INTELLIGENCE BY INVESTIGATING VARIOUS NETWORK TRAFFIC SOURCES. IN THIS PAPER, WE PROPOSE AND EVALUATE A BIG DATA ARCHITECTURE THAT IS ROOTED IN REAL-TIME NETWORK TRAFFIC PROCESSING, DISTRIBUTED MESSAGING AND SCALABLE DATA STORAGE. THE KEY BENEFIT OF THE PROPOSED ARCHITECTURE IS THAT IT AUTOMATES THE AGGREGATION AND TRANSFORMATIONS OF HETEROGENEOUS NETWORK DATA, ALLOWING FOR GREATER FOCUS ON CYBER THREAT INTELLIGENCE ANALYTICS, RATHER THAN DATA MANAGEMENT, AGGREGATION, RECONCILIATION AND FORMATTING. EMPIRICAL EVALUATIONS INVESTIGATING THE APPLICATION OF MACHINE LEARNING ANALYTICS BY EXPLOITING THE ARTIFACTS RESULTING FROM A PROTOTYPE OF THE PROPOSED ARCHITECTURE AND BY USING 100 GB OF REAL NETWORK TRAFFIC, INDEED DEMONSTRATE THE PRACTICALITY, EFFECTIVENESS, AND ADDED-VALUE OF THE PROPOSED ARCHITECTURE.
PERSONALIZED LANGUAGE LEARNING (PLL), A POPULAR APPROACH TO PRECISION LANGUAGE EDUCATION, PLAYS AN INCREASINGLY ESSENTIAL ROLE IN EFFECTIVE LANGUAGE EDUCATION TO MEET DIVERSE LEARNER NEEDS AND EXPECTATIONS. RESEARCH ON PLL HAS BECOME AN ACTIVE SUB-FIELD OF RESEARCH ON TECHNOLOGY-ENHANCED LANGUAGE LEARNING AND ARTIFICIAL INTELLIGENCE APPLICATIONS IN EDUCATION. BASED ON THE PLL LITERATURE FROM THE WEB OF SCIENCE AND SCOPUS DATABASES, THIS STUDY IDENTIFIED TRENDS AND PROMINENT RESEARCH ISSUES WITHIN THE FIELD FROM 2000 TO 2019 USING STRUCTURAL TOPIC MODELING AND BIBLIOMETRICS. TREND ANALYSIS OF ARTICLES DEMONSTRATED INCREASING INTEREST IN PLL RESEARCH. JOURNALS SUCH AS EDUCATIONAL TECHNOLOGY & SOCIETY AND COMPUTERS & EDUCATION HAD CONTRIBUTED MUCH TO PLL RESEARCH. PLL ASSOCIATED CLOSELY WITH MOBILE LEARNING, GAME-BASED LEARNING, AND ONLINE/WEB-BASED LEARNING. MOREOVER, PERSONALIZED FEEDBACK AND RECOMMENDATIONS WERE IMPORTANT ISSUES IN PLL. ADDITIONALLY, THERE WAS AN INCREASING INTEREST IN ADOPTING LEARNING ANALYTICS AND ARTIFICIAL INTELLIGENCE IN PLL RESEARCH. RESULTS OBTAINED COULD HELP PRACTITIONERS AND SCHOLARS BETTER UNDERSTAND THE TRENDS AND STATUS OF PLL RESEARCH AND BECOME AWARE OF THE HOT TOPICS AND FUTURE DIRECTIONS.
LEARNING FROM VISUAL AND SOCIAL MEDIA MAKES A COMPLEX AREA OF STUDY AND A VITAL PART OF UNDERSTANDING THE DEVELOPMENT OF 21ST CENTURY SKILLS. THE ACTIVE VIDEO WATCHING (AVW) PLATFORM WAS DEVELOPED IN ORDER TO SCAFFOLD STUDENTS' ACTIVE LEARNING OF SOFT SKILLS FROM VIDEOS, BY ENCOURAGING USERS TO ENGAGE WITH THE CONTENT (E.G. MARKING IMPORTANT ASPECTS AND WRITING COMMENTS). PREVIOUS STUDIES OF AVW USED LEARNING ANALYTICS TO IDENTIFY STUDENT COMMENTS WHICH CAN BE USED IN "INTELLIGENT NUDGES" FOR TRIGGERING REFLECTION AMONG OTHERS WHO WATCH THE SAME VIDEO. HERE, WE DESCRIBE THE METHODOLOGY AND REASONING FOR CONDUCTING A QUALITATIVE THEMATIC ANALYSIS OF SUCH COMMENTS, WITH RESPECT TO LEARNING PRESENTATION SKILLS. OUR AIM IS TO UNCOVER ADDITIONAL LEARNING OPPORTUNITIES FROM THE DATA AND HOW THEY MIGHT BE EXPLAINED WITHIN A BROADER THEORETICAL FRAMEWORK OF OBSERVATIONAL LEARNING. AS A BASIS FOR DISCUSSION, WE PRESENT A PRELIMINARY THEMATIC MAP OF THE RESULTS AND HOW STUDENTS' REMARKS ON GOOD/BAD EXAMPLES IN THE VIDEOS RELATE TO THE TYPES OF KNOWLEDGE THEY GAIN FROM IT. WE SUGGEST SEVERAL RESULTING TOPICS FOR FUTURE STUDY.
THIS PAPER PRESENTS A LITERATURE REVIEW ON THE USE OF A LARGE ARRAY OF DATA ABOUT STUDENTS AND COURSES THAT WAS COLLECTED BY INSTITUTIONS AND LEARNING ANALYTICS TO IMPROVE STUDENTS SUCCESS AND RETENTION. ACADEMIC ANALYTICS IS GETTING NOTABLE ATTENTION, BECAUSE IT ASSISTS EDUCATIONAL INSTITUTIONS IN IMPROVING STUDENT ACHIEVEMENT AND SUCCESS, INCREASING STUDENT RETENTION, AND REDUCE THE LOAD OF LIABILITY AND ACCOUNTABILITY. THE PURPOSE OF THIS PAPER IS TO PROVIDE A BRIEF OVERVIEW OF HOW ACADEMIC ANALYTICS HAS BEEN USED IN EDUCATIONAL INSTITUTIONS, WHAT TOOLS ARE AVAILABLE, AND HOW INSTITUTION CAN PREDICT STUDENT PERFORMANCE AND ACHIEVEMENT. IN ADDITION, THE STUDY WILL DISCUSS ITS APPLICATIONS, GOALS, EXAMPLES, AND WHY INSTRUCTORS WANT TO MAKE USE OF ACADEMIC ANALYTICS. FINALLY, THIS PAPER WILL PROPOSE AN INTELLIGENT RECOMMENDATION INTERVENTION TO IMPROVE STUDENTS' ACHIEVEMENT THAT WILL BE BASED ON TWO OUTCOMES; PERFORMANCE AS MEASURED BY FINAL GRADE, AND STUDENTS' INFORMATION DATA SUCH AS ATTENDANCE, PREREQUISITE SUBJECT, ENGLISH AND MATHEMATICS MARKS, AND SUGGESTS THE USE OF ARTIFICIAL NEURAL NETWORK AND DECISION TREE FOR PREDICTIVE MODELING.
THE VALUE OF STUDENTS DEVELOPING THE CAPACITY TO MAKE ACCURATE JUDGEMENTS ABOUT THE QUALITY OF THEIR WORK AND THAT OF OTHERS HAS BEEN WIDELY RECOGNISED IN HIGHER EDUCATION LITERATURE. HOWEVER, DESPITE THIS RECOGNITION, LITTLE ATTENTION HAS BEEN PAID TO THE DEVELOPMENT OF TOOLS AND STRATEGIES WITH THE POTENTIAL BOTH TO FOSTER EVALUATIVE JUDGEMENT AND TO SUPPORT EMPIRICAL RESEARCH INTO ITS GROWTH. THIS PAPER PROVIDES A DEMONSTRATION OF HOW EDUCATIONAL TECHNOLOGIES MAY BE USED TO FILL THIS GAP. IN PARTICULAR, WE INTRODUCE THE ADAPTIVE LEARNING SYSTEM RIPPLE AND DESCRIBE HOW IT AIMS TO (1) DEVELOP EVALUATIVE JUDGEMENT IN LARGE-CLASS SETTINGS THROUGH SUGGESTED STRATEGIES FROM THE LITERATURE SUCH AS THE USE OF RUBRICS, EXEMPLARS AND PEER REVIEW AND (2) ENABLE LARGE EMPIRICAL STUDIES AT LOW COST TO DETERMINE THE EFFECT-SIZE OF SUCH STRATEGIES. A CASE STUDY DEMONSTRATING HOW RIPPLE HAS BEEN USED TO ACHIEVE THESE GOALS IN A SPECIFIC CONTEXT IS PRESENTED.
THE DIAGNOSIS OF DISEASE OFTEN REQUIRES ANALYSIS OF A BIOPSY. MANY DIAGNOSES DEPEND NOT ONLY ON THE PRESENCE OF CERTAIN FEATURES BUT ON THEIR LOCATION WITHIN THE TISSUE. RECENTLY, A NUMBER OF DEEP LEARNING DIAGNOSTIC AIDS HAVE BEEN DEVELOPED TO CLASSIFY DIGITIZED BIOPSY SLIDES. CLINICAL WORKFLOWS OFTEN INVOLVE PROCESSING OF MORE THAN 500 SLIDES PER DAY. BUT, CLINICAL USE OF DEEP LEARNING DIAGNOSTIC AIDS WOULD REQUIRE A PREPROCESSING WORKFLOW THAT IS COST-EFFECTIVE, FLEXIBLE, SCALABLE, RAPID, INTERPRETABLE, AND TRANSPARENT. HERE, WE PRESENT SUCH A WORKFLOW, OPTIMIZED USING DASK AND MIXED PRECISION TRAINING VIA APEX, CAPABLE OF HANDLING ANY PATCH-LEVEL OR SLIDE LEVEL CLASSIFICATION AND PREDICTION PROBLEM. THE WORKFLOW USES A FLEXIBLE AND FAST PREPROCESSING AND DEEP LEARNING ANALYTICS PIPELINE, INCORPORATES MODEL INTERPRETATION AND HAS A HIGHLY STORAGE-EFFICIENT AUDIT TRAIL. WE DEMONSTRATE THE UTILITY OF THIS PACKAGE ON THE ANALYSIS OF A PROTOTYPICAL ANATOMIC PATHOLOGY SPECIMEN, LIVER BIOPSIES FOR EVALUATION OF HEPATITIS FROM A PROSPECTIVE COHORT. THE PRELIMINARY DATA INDICATE THAT PATHFLOWAI MAY BECOME A COST-EFFECTIVE AND TIME-EFFICIENT TOOL FOR CLINICAL USE OF ARTIFICIAL INTELLIGENCE (AI) ALGORITHMS.
EMOTIONS PLAY A CRITICAL ROLE DURING LEARNING AND PROBLEM SOLVING WITH ADVANCED LEARNING TECHNOLOGIES (ALTS). DESPITE THEIR IMPORTANCE, RELATIVELY FEW ATTEMPTS HAVE BEEN MADE TO UNDERSTAND LEARNERS' EMOTIONAL MONITORING AND REGULATION BY USING DATA VISUALIZATIONS OF THEIR OWN (AND OTHERS') COGNITIVE, AFFECTIVE, METACOGNITIVE, AND MOTIVATIONAL (CAMM) SELF-REGULATED LEARNING (SRL) PROCESSES TO POTENTIALLY FOSTER THEIR EMOTION REGULATION (ER). WE PRESENT A THEORETICALLY BASED AND EMPIRICALLY DRIVEN CONCEPTUAL FRAMEWORK THAT ADDRESSES ER BY PROPOSING THE USE OF VISUALIZATIONS OF ONE'S OWN AND OTHERS' CAMM SRL MULTICHANNEL DATA TO FACILITATE LEARNERS' MONITORING AND REGULATION OF EMOTIONS DURING LEARNING WITH ALTS. WE USE AN EXAMPLE WITH EYE-TRACKING DATA TO ILLUSTRATE THE MAPPING BETWEEN THEORETICAL ASSUMPTIONS, ER STRATEGIES, AND THE TYPES OF DATA VISUALIZATIONS THAT CAN ENHANCE LEARNERS' ER, INCLUDING KEY PROCESSES SUCH AS EMOTION FLEXIBILITY, EMOTION ADAPTIVITY, AND EMOTION EFFICACY. WE CONCLUDE WITH FUTURE DIRECTIONS LEADING TO A SYSTEMATIC INTERDISCIPLINARY RESEARCH AGENDA THAT ADDRESSES OUTSTANDING ER-RELATED ISSUES BY INTEGRATING MODELS, THEORIES, METHODS, AND ANALYTICAL TECHNIQUES FOR THE COGNITIVE, LEARNING, AND AFFECTIVE SCIENCES; HUMAN COMPUTER INTERACTION (HCI); DATA VISUALIZATION; BIG DATA; DATA MINING; AND SRL.
SURGICAL TRAINING HAS UNDERGONE SUBSTANTIAL CHANGE IN THE LAST FEW DECADES. AS TECHNOLOGY AND PATIENT COMPLEXITY CONTINUES TO INCREASE, DEMANDS FOR NOVEL APPROACHES TO ENSURE COMPETENCY HAVE ARISEN. VIRTUAL REALITY SYSTEMS AUGMENTED WITH MACHINE LEARNING REPRESENTS ONE SUCH APPROACH. THE ABILITY TO OFFER ON DEMAND TRAINING, INTEGRATE CHECKLISTS, AND PROVIDE PERSONALIZED, SURGEON-SPECIFIC FEEDBACK IS PAVING THE WAY TO A NEW ERA OF SURGICAL TRAINING. MACHINE LEARNING ALGORITHMS THAT IMPROVE OVER TIME AS THEY ACQUIRE MORE DATA WILL CONTINUE TO REFINE THE EDUCATION THEY PROVIDE. FURTHER, FULLY IMMERSIVE SIMULATED ENVIRONMENTS COUPLED WITH MACHINE LEARNING ANALYTICS PROVIDE REAL-WORLD TRAINING OPPORTUNITIES IN A SAFE ATMOSPHERE AWAY FROM THE POTENTIAL TO HARM PATIENTS. CAREFUL IMPLEMENTATION OF THESE TECHNOLOGIES HAS THE POTENTIAL TO INCREASE ACCESS AND IMPROVE QUALITY OF SURGICAL TRAINING AND PATIENT CARE AND ARE POISED TO CHANGE THE LANDSCAPE OF CURRENT SURGICAL TRAINING. HEREIN, WE DESCRIBE THE CURRENT STATE OF VIRTUAL REALITY COUPLED WITH MACHINE LEARNING FOR SURGICAL TRAINING, FUTURE DIRECTIONS, AND EXISTING LIMITATIONS OF THIS TECHNOLOGY. (C) 2020 ELSEVIER INC. ALL RIGHTS RESERVED.
COLLABORATIVE LEARNING WITH EDUCATIONAL GAMES ON MULTI-TOUCH TABLETOP DEVICES OPENS CHANCES, CHALLENGES AND QUESTIONS WHICH MAKE CONTEMPORARY APPROACHES IN LEARNING ANALYTICS MEET THEIR BOUNDARIES. MULTI-MODALITY MIGHT HELP HERE, AND EYE-TRACKING IS A PROMISING DATA SOURCE IN THE EFFORT TO A BETTER UNDERSTANDING OF THE LEARNERS' BEHAVIOUR. THIS ARTICLE DESCRIBES OUR PREVIOUS WORK REGARDING SERIOUS GAMES ON LARGE MULTI-TOUCH TABLETOP DISPLAYS, DEVELOPED TO TEACH COMPUTER SCIENCE THEORY TOPICS IN A COLLABORATIVE AND ENTERTAINING WAY, OUR PREVIOUS RESEARCH EFFORTS AND CHALLENGES AND OBSTACLES WE MET ON OUR WAY. EYE-TRACKING WILL IMPROVE OUR UNDERSTANDING OF THE LEARNERS' BEHAVIOUR WHILE THEY ARE NOT INTERACTING WITH THE GAME, ENHANCE THE CONSTRUCTION OF COHERENT LEARNER MODELS AND MIGHT EVEN PROVIDE A SUBTLE WAY OF CONTROL ON A MEDIUM OF PUBLIC INTERACTION. THE BENEFITS OF OUR WORK CAN BE USED TO ENHANCE THE GAME MECHANICS AND SUPPORT SHY OR STUDENTS WITH DISABILITIES. WE PRESENT OUR PLAN OF ACTION WHICH FOLLOWS A DESIGN-BASED RESEARCH APPROACH AND INCLUDES THE MOTIVATION FOR OUR WORK AND OUR SHORT-AND LONG-TERM GOALS.
LEARNING ENVIRONMENTS HAVE BEEN PROFOUNDLY RESHAPED BY PERVASIVE TECHNOLOGY. NEW EDUCATIONAL METHODOLOGIES TAKE FULL ADVANTAGE OF ICT IN A MOBILE CUSTOMIZED USER-FRIENDLY ENVIRONMENT, TO SUPPORT LEARNING AND STIMULATE INDIVIDUALS' POTENTIAL. UNFORTUNATELY, TECHNOLOGY-ENHANCED LEARNING TOOLS ARE NOT OFTEN DESIGNED WITH ACCESSIBILITY IN MIND, ALTHOUGH THEY CAN GREATLY BENEFIT THE PERSONAL EMPOWERMENT AND INCLUSION OF SPECIAL-NEEDS PEOPLE. TO ADDRESS THIS GAP, A WEB PLATFORM HAS BEEN CREATED FOR DELIVERING ACCESSIBLE GAMES TO PEOPLE WITH DOWN SYNDROME. SINCE PERSONALIZATION, ORDERLINESS AND POSITIVE REINFORCEMENT ARE CRUCIAL TO LEARNING IN THESE SUBJECTS, THE PLATFORM OFFERS A PERSONALIZED SAFE ENVIRONMENT FOR LEARNING, CONFORMING TO BEHAVIORAL ANALYSIS PRINCIPLES. LEARNING ANALYTICS ARE INCORPORATED IN THE PLATFORM FOR EASY MONITORING OF STUDENT PROGRESS VIA WEB INTERFACES. THE PARTICIPATORY DESIGN DRIVING THE DEVELOPMENT OF THE LEARNING PLATFORM ALLOWED THE CUSTOMIZATION OF THE GAMES' DISCRIMINATIVE STIMULI, DIFFICULTY LEVELS AND REINFORCEMENT, AS WELL AS THE CREATION OF A GAME "ENGINE" TO EASILY SET UP NEW PERSONALIZED EXERCISES. THESE CUSTOMIZATION FEATURES MAKE THE GAME PLATFORM USABLE BY A LARGER AUDIENCE, INCLUDING INDIVIDUALS WITH LEARNING DIFFICULTIES AND AUTISM.
GROUPING STUDENTS WITH SIMILAR PAST ACHIEVEMENT TOGETHER (TRACKING) MIGHT AFFECT THEIR READING ACHIEVEMENT. MULTILEVEL ANALYSES OF 208,057 FOURTH GRADE STUDENTS IN 40 COUNTRIES SHOWED THAT CLUSTERING STUDENTS IN SCHOOLS BY PAST ACHIEVEMENT WAS LINKED TO HIGHER READING ACHIEVEMENT, CONSISTENT WITH THE BENEFITS OF CUSTOMIZED, TARGETED INSTRUCTION. MEANWHILE, STUDENTS HAD HIGHER READING ACHIEVEMENT WITH GREATER DIFFERENCES (VARIANCES) AMONG CLASSMATES' PAST ACHIEVEMENT, READING ATTITUDES, OR FAMILY SES; THESE RESULTS ARE CONSISTENT WITH THE VIEW THAT GREATER STUDENT DIFFERENCES YIELD MORE HELP OPPORTUNITIES (HIGHER ACHIEVERS HELP LOWER ACHIEVERS, SO THAT BOTH LEARN), AND FOSTER LEARNING FROM THEIR DIFFERENT RESOURCES, ATTITUDES AND BEHAVIORS. ALSO, A STUDENT HAD HIGHER READING ACHIEVEMENT WHEN CLASSMATES HAD MORE RESOURCES (SES, HOME EDUCATIONAL RESOURCES, READING ATTITUDE, PAST ACHIEVEMENT), SUGGESTING THAT CLASSMATES SHARED THEIR RESOURCES AND HELPED ONE ANOTHER. MODELING OF NON-LINEAR RELATIONS AND ACHIEVEMENT SUBSAMPLES OF STUDENTS SUPPORTED THE ABOVE INTERPRETATIONS. PRINCIPALS CAN USE THESE RESULTS AND A SIMPLER VERSION OF THIS METHODOLOGY TO RE-ALLOCATE STUDENTS AND RESOURCES INTO DIFFERENT COURSE SECTIONS AT LITTLE COST TO IMPROVE STUDENTS' READING ACHIEVEMENT.
CLASSROOM EXPERIMENTS THAT EVALUATE THE EFFECTIVENESS OF EDUCATIONAL TECHNOLOGIES DO NOT TYPICALLY EXAMINE THE EFFECTS OF CLASSROOM CONTEXTUAL VARIABLES (E.G., OUT-OF-SOFTWARE HELP-GIVING AND EXTERNAL DISTRACTIONS). YET THESE VARIABLES MAY INFLUENCE STUDENTS' INSTRUCTIONAL OUTCOMES. IN THIS PAPER, WE INTRODUCE THE SPATIAL CLASSROOM LOG EXPLORER (SPACLE): A PROTOTYPE TOOL THAT FACILITATES THE RAPID DISCOVERY OF RELATIONSHIPS BETWEEN WITHIN-SOFTWARE AND OUT-OF-SOFTWARE EVENTS. UNLIKE PREVIOUS TOOLS FOR RETROSPECTIVE ANALYSIS, SPACLE REPLAYS MOMENT-BY-MOMENT ANALYTICS ABOUT STUDENT AND TEACHER BEHAVIORS IN THEIR ORIGINAL SPATIAL CONTEXT. WE PRESENT A DATA ANALYSIS WORKFLOW USING SPACLE AND DEMONSTRATE HOW THIS WORKFLOW CAN SUPPORT CAUSAL DISCOVERY. WE SHARE THE RESULTS OF OUR INITIAL REPLAY ANALYSES USING SPACLE, WHICH HIGHLIGHT THE IMPORTANCE OF CONSIDERING SPATIAL FACTORS IN THE CLASSROOM WHEN ANALYZING ITS LOG DATA. WE ALSO PRESENT THE RESULTS OF AN INVESTIGATION INTO THE EFFECTS OF STUDENT-TEACHER INTERACTIONS ON STUDENT LEARNING IN K-12 BLENDED CLASSROOMS, USING OUR WORKFLOW, WHICH COMBINES REPLAY ANALYSIS WITH SPACLE AND CAUSAL MODELING. OUR FINDINGS SUGGEST THAT STUDENTS' AWARENESS OF BEING MONITORED BY THEIR TEACHERS MAY PROMOTE LEARNING, AND THAT "GAMING THE SYSTEM" BEHAVIORS MAY EXTEND OUTSIDE OF EDUCATIONAL SOFTWARE USE.
THERE ARC SOME IMPLEMENTATIONS TOWARDS UNDERSTANDING STUDENTS' EMOTIONAL STATES THROUGH AUTOMATED SYSTEMS WITH MACHINE LEARNING MODELS. HOWEVER, GENERIC AL MODELS OF EMOTIONS LACK ENOUGH ACCURACY TO AUTONOMOUSLY AND MEANINGFULLY TRIGGER ANY INTERVENTIONS. COLLECTING SELF-LABELS FROM STUDENTS AS THEY ASSESS THEIR INTERNAL STATES CAN BE A WAY TO COLLECT LABELED SUBJECT SPECIFIC DATA NECESSARY TO OBTAIN PERSONALIZED EMOTIONAL ENGAGEMENT MODELS. IN THIS PAPER, WE OUTLINE PRELIMINARY ANALYSIS ON EMOTIONAL SELF-LABELS COLLECTED FROM STUDENTS WHILE USING A LEARNING PLATFORM.
STUDENTS' CONFUSION IS A BARRIER FOR LEARNING, CONTRIBUTING TO LOSS OF MOTIVATION AND TO DISENGAGEMENT WITH COURSE MATERIALS. HOWEVER, DETECTING STUDENTS' CONFUSION IN LARGE-SCALE COURSES IS BOTH TIME AND RESOURCE INTENSIVE. THIS PAPER PROVIDES A NEW APPROACH FOR CONFUSION DETECTION IN ONLINE FORUMS THAT IS BASED ON HARNESSING THE POWER OF STUDENTS' SELF-REPORTED AFFECTIVE STATES (REPORTED USING A SET OF PRE-DEFINED HASHTAGS). IT PRESENTS A RULE FOR LABELING CONFUSION, BASED ON STUDENTS' HASHTAGS IN THEIR POSTS, THAT IS SHOWN TO ALIGN WITH TEACHERS' JUDGEMENT. WE USE THIS LABELING RULE TO INFORM THE DESIGN OF AN AUTOMATED CLASSIFIER FOR CONFUSION DETECTION FOR THE CASE WHEN THERE ARE NO SELF-REPORTED HASHTAGS PRESENT IN THE TEST SET. WE DEMONSTRATE THIS APPROACH IN A LARGE SCALE BIOLOGY COURSE USING THE NOTA BENE ANNOTATION PLATFORM. THIS WORK LAYS THE FOUNDATION TO EMPOWER TEACHERS WITH BETTER SUPPORT TOOLS FOR DETECTING AND ALLEVIATING CONFUSION IN ONLINE COURSES.
THE SCHOLARLY INVESTIGATION OF DISCOURSE IN TEACHING AND LEARNING IS MULTI-DISCIPLINARY, THEORETICALLY RICH, AND HIGHLY TECHNICAL. RESEARCHERS WITH BACKGROUNDS IN EDUCATION, COGNITIVE PSYCHOLOGY, COMPUTER SCIENCE, AND THE SOCIAL SCIENCES APPLY A DIVERSE SET OF TECHNIQUES TO UNDERSTAND HOW STUDENT DISCUSSIONS AFFECT LEARNING. AIDED BY BIG DATA COMING FROM LEARNING CONTENT MANAGEMENT AND MASSIVE OPEN ONLINE COURSE SYSTEMS, THESE RESEARCHERS HAVE AN UNPARALLELED OPPORTUNITY FOR INSIGHT INTO THE TEACHING AND LEARNING PROCESS. IN THIS PAPER WE SUMMARIZE SOME OF THE CHALLENGES AND OPPORTUNITIES ARISING OUT OF THREE WORKSHOPS ON EDUCATIONAL DISCOURSE. THESE WORKSHOPS CONVENED BOTH EXPERT AND EMERGING SCHOLARS TO DISCUSS THE (I) ETHICAL, (II) TECHNICAL, AND (III) INFRASTRUCTURE BARRIERS TO BUILDING A RESEARCH COMMUNITY FOCUSED ON COMPUTER-MEDIATED EDUCATIONAL DISCOURSE. OF PARTICULAR NOTE IS THAT WHILE COMPUTATIONAL INFRASTRUCTURE EXISTS FOR STORING AND MANIPULATING EDUCATIONAL DISCOURSE, THERE IS A NEED FOR A SOCIOTECHNICAL INFRASTRUCTURE UPON WHICH COMMUNITY MEMBERS CAN COME TOGETHER TO ENGAGE IN JOINT WORK.
ELEMENTS OF APPLIED STATISTICS AND COMPUTER SCIENCE ARE QUICKLY INTEGRATING AND BEING APPLIED TO A DIVERSE SET OF PROBLEMS IN ACADEMIA AND INDUSTRY. HERE I EXPLORE THE POTENTIAL VALUE OF THIS MULTI -DISCIPLINARY APPROACH TO APPLICATIONS IN HIGHER EDUCATION BY APPLYING IT TO FORECASTING COURSE LEVEL OUTCOMES FOR INDIVIDUAL STUDENTS AT ALL OF PENN STATE'S CAMPUSES. UTILIZING HUNDREDS OF DATA SOURCES ON INDIVIDUAL STUDENTS, RANGING FROM PAST PERFORMANCE TO CURRENT COURSE ENGAGEMENT, I DEMONSTRATE THE POTENTIAL ACCURACY OF FORECASTING TECHNIQUES AT IDENTIFYING HIGH RISK STUDENTS EARLY IN THE COURSE TERM. OUR PRELIMINARY RESULTS SUGGEST THAT %50 OF STUDENTS THAT EARNED A D OR F IN 2015 COULD HAVE BEEN IDENTIFIED PRIOR TO THE START OF THE COURSE.
SELF-REGULATED LEARNING (SRL) IS A PROCESS THAT HIGHLY FLUCTUATES AS STUDENTS ACTIVELY DEPLOY THEIR METACOGNITIVE AND COGNITIVE PROCESSES DURING LEARNING. IN THIS PAPER, WE APPLY AN EXTENSION OF LATENT PROFILING, LATENT TRANSITION ANALYSIS (LTA), WHICH INVESTIGATES THE LONGITUDINAL DEVELOPMENT OF STUDENTS' SRL LATENT CLASS MEMBERSHIPS OVER TIME. WE WILL BRIEFLY REVIEW THE THEORETICAL FOUNDATIONS OF SRL AND DISCUSS THE VALUE OF USING LTA TO INVESTIGATE THIS MULTIDIMENSIONAL CONCEPT. THIS STUDY IS BASED ON COLLEGE STUDENTS (N = 75) LEARNING ABOUT THE HUMAN CIRCULATORY SYSTEM WHILE USING METATUTOR, AN INTELLIGENT TUTORING SYSTEM THAT ADAPTIVELY SUPPORTS SRL AND TARGETS SPECIFIC METACOGNITIVE SRL PROCESSES INCLUDING JUDGMENT OF LEARNING (JOL) AND CONTENT EVALUATION (CE). PRELIMINARY RESULTS IDENTIFY TRANSITIONAL PROBABILITIES OF SRL PROFILES FROM FOUR DISTINCT EVENTS ASSOCIATED WITH THE USE OF SRL.
IN THIS PAPER, WE EXAMINED WHETHER PREVIOUS FINDINGS ON EDUCATIONAL BIG DATA CONSISTING OF E-BOOK LOGS FROM A GIVEN ACADEMIC COURSE CAN BE REPRODUCED WITH DIFFERENT DATA FROM OTHER ACADEMIC COURSES. THE PREVIOUS FINDINGS SHOWED THAT (1) STUDENTS WHO ATTAINED CONSISTENTLY GOOD ACHIEVEMENT MORE FREQUENTLY BROWSED DIFFERENT E-BOOKS AND THEIR PAGES THAN LOW ACHIEVERS AND THAT (2) THIS DIFFERENCE WAS FOUND ONLY FOR LOGS OF PREPARATION FOR COURSE SESSIONS (PREVIEW), NOT FOR REVIEWING MATERIAL (REVIEW). PRELIMINARILY, WE ANALYZED E-BOOK LOGS FROM FOUR COURSES. THE RESULTS WERE REPRODUCED IN ONLY ONE COURSE AND ONLY PARTIALLY, THAT IS, (1) HIGH ACHIEVERS MORE FREQUENTLY CHANGED E-BOOKS THAN LOW ACHIEVERS (2) FOR PREVIEW. THIS FINDING SUGGESTS THAT TO ALLOW EFFECTIVE USAGE OF LEARNING AND TEACHING ANALYSES, WE NEED TO CAREFULLY CONSTRUCT AN EDUCATIONAL ENVIRONMENT TO ENSURE REPRODUCIBILITY.
WITH INCREASING NUMBERS OF STUDENTS IN TERTIARY STEM EDUCATION AND STAGNATING NUMBERS OF AVAILABLE TEACHING STAFF, LARGE CLASS LECTURES OFTEN REMAIN THE LAST RESORT. LECTURES ARE NOT INHERENTLY BAD, BUT THEY TEND TO FOSTER NEGATIVE LEARNING ATTITUDES AMONG STUDENTS LIKE PASSIVITY AND A FEELING OF ISOLATION. FURTHERMORE, SOME TOPICS ARE BETTER TAUGHT USING OTHER FORMATS THAN LECTURES. TECHNOLOGY IN THE FORM OF EDUCATIONAL SOFTWARE ENABLES A MORE APPROPRIATE LEARNING AND VARIOUS TEACHING FORMATS DEPLOYABLE IN LARGE CLASSES. THIS ARTICLE FIRST IDENTIFIES CORE COMPONENTS OF EDUCATIONAL SOFTWARE BASED ON TODAY'S INFORMATION TECHNOLOGY AND SECOND DEMONSTRATES HOW THESE COMPONENTS CAN BE COMBINED IN DIFFERENT MANNERS RESULTING IN VARIOUS LEARNING AND TEACHING FORMATS. THE CORE COMPONENTS ARE: DOCUMENT MANAGEMENT, WORKFLOW MANAGEMENT, CONTENT ENRICHMENT, INPUT INTERACTIONS, LEARNING ANALYTICS, AND SYSTEM MESSAGING. THE CONTRIBUTION OF THIS ARTICLE IS THREEFOLD: CORE COMPONENTS OF EDUCATIONAL SOFTWARE ARE PROPOSED AND MOTIVATED, FOUR TEACHING METHODS AND FOUR LEARNING AND TEACHING FORMATS ARE PROPOSED THAT BUILD UPON THE AFOREMENTIONED CORE COMPONENTS, AND FIELD EVALUATIONS OF THREE OF THESE FORMATS ARE REPORTED ABOUT.
THE PRESENT STUDY EXAMINES BEHAVIORAL PATTERNS, MOTIVATIONS, AND SELF-REGULATED LEARNING STRATEGIES OF RETURNING LEARNERS A SPECIAL LEARNER SUBPOPULATION IN MASSIVE OPEN ONLINE COURSES (MOOCS). TO THIS END, DATA WERE COLLECTED FROM A TEACHER PROFESSIONAL DEVELOPMENT MOOC THAT HAS BEEN OFFERED FOR SEVEN ITERATIONS DURING 2014-2016. DATA ANALYSIS IDENTIFIED MORE THAN 15% OF ALL REGISTRANTS AS RETURNING LEARNERS. FINDINGS FROM CLICK LOG ANALYSIS IDENTIFIED POSSIBLE MOTIVATIONS OF RE-ENROLLMENT INCLUDING IMPROVING GRADES, REFRESHING THEORETICAL UNDERSTANDING, AND SOLVING PRACTICAL PROBLEMS. FURTHER ANALYSIS UNCOVERED EVIDENCE OF SELF-REGULATED LEARNING STRATEGIES AMONG RETURNING LEARNERS. TAKEN TOGETHER, THIS STUDY CONTRIBUTES TO ONGOING INQUIRY INTO MOOCS LEARNING PATHWAYS, INFORMS FUTURE MOOC DESIGN, AND SHEDS LIGHT ON THE EXPLORATION OF MOOCS AS A VIABLE OPTION FOR TEACHER PROFESSIONAL DEVELOPMENT.
A PRIMARY GOAL OF HIGHER EDUCATION IS TO PROMOTE MEANINGFUL LEARNING: THE DELIVERY OF CORE ACADEMIC CONTENT TO STUDENTS IN INNOVATIVE WAYS THAT ALLOW THEM TO LEARN AND THEN APPLY WHAT THEY HAVE LEARNED. AS A PEDAGOGICAL STRATEGY, WRITING-TO-LEARN (WTL) INTENDS TO USE WRITING TO IMPROVE STUDENTS' UNDERSTANDING OF COURSE CONTENT AND CONCEPTS. TO IMPROVE STUDENTS' MEANINGFUL LEARNING OF CONCEPTUAL KNOWLEDGE IN WTL ACTIVITIES, THE PROJECT PROPOSES TO DEVELOP THE WRITE-AND-LEARN SYSTEM TO GENERATE AUTOMATED FORMATIVE FEEDBACK BY TAKING ADVANTAGE OF THE CONCEPT MAPS CONSTRUCTED FROM INSTRUCTORS' LECTURE NOTES AND INDIVIDUAL STUDENTS' WRITING ASSIGNMENTS. THE PROPOSED RESEARCH AIMS TO PROVIDE INSIGHTS INTO HOW TO APPLY CONCEPT MAPS INTO WTL ACTIVITIES TO GENERATE EFFECTIVE FORMATIVE FEEDBACK ON THE ACQUISITION AND DEVELOPMENT OF CONCEPTUAL KNOWLEDGE, AND EXPLORE HOW AND TO WHAT EXTENT CONCEPT MAP-BASED FORMATIVE FEEDBACK CAN BE UTILIZED TO SCAFFOLD AND PROMOTE MEANINGFUL LEARNING IN WTL ACTIVITIES.
WE EXPLORE THE RELATIONSHIP BETWEEN VIDEO PLAYBACK SPEED AND STUDENT LEARNING OUTCOMES. USING AN EXPERIMENTAL DESIGN, WE PRESENT THE RESULTS OF A PRE-REGISTERED STUDY THAT ASSIGNS USERS TO WATCH VIDEOS AT EITHER 1.0X OR 1.25X SPEED. WE FIND THAT STUDENTS WHO CONSUME SPED CONTENT ARE MORE LIKELY TO GET BETTER GRADES IN A COURSE, ATTEMPT MORE CONTENT, AND OBTAIN MORE CERTIFICATES. WE ALSO FIND THAT WHEN VIDEOS ARE SPED UP, STUDENTS SPEND LESS TIME CONSUMING VIDEOS AND ARE MARGINALLY MORE LIKELY TO COMPLETE MORE VIDEO CONTENT. THESE FINDINGS SUGGEST THAT FUTURE STUDY OF PLAYBACK SPEED AS A TOOL FOR OPTIMIZING VIDEO CONTENT FOR MOOCS IS WARRANTED. APPLICATIONS FOR REINFORCEMENT LEARNING AND ADAPTIVE CONTENT ARE DISCUSSED.
ASYNCHRONOUS ONLINE DISCUSSIONS ARE BROADLY USED TO SUPPORT PEER INTERACTION IN ONLINE AND HYBRID COURSES. IN THIS PAPER, WE ARGUE THAT THE ANALYSIS OF ONLINE PEER INTERACTIONS WOULD BENEFIT FROM THE FOCUS ON RELATIONAL EVENTS THAT ARE TEMPORAL AND OCCUR DUE TO A RANGE OF FACTORS. TO DEMONSTRATE THE POSSIBILITY, WE APPLIED RELATIONAL EVENT MODELING (REM) TO A DATASET FROM ONLINE DISCUSSIONS IN SEVEN ONLINE CLASSES. INFORMED BY A CONCEPTUAL MODEL OF SOCIAL INTERACTION IN ONLINE DISCUSSIONS, THIS MODELING INCLUDED (A) A LEARNER ATTRIBUTE CAPTURING ASPECTS OF TEMPORAL PARTICIPATION, (B) SOCIAL DYNAMICS FACTORS SUCH AS PREFERENTIAL ATTACHMENT AND RECIPROCITY, AND (C) TURNBY-TURN SEQUENTIAL PATTERNS. RESULTS SHOWED THAT LEARNER ACTIVITY AND FAMILIARITY FROM RECENT INTERACTIONS AFFECTED THEIR PROPENSITY TO FORM TIES. TURN-BY-TURN SEQUENTIAL PATTERNS, THAT CAPTURE INDIVIDUAL POSTING IN BURSTS, EXPLAIN HOW TWO-STAR NETWORK PATTERNS FORM. SINCE TWO-STAR NETWORK PATTERNS COULD FURTHER FACILITATE SMALL GROUP FORMATION IN THE NETWORK, WE EXPECTED THE MODELS TO ALSO CAPTURE COMMUNICATION IN TRIADS (I.E. TRIADIC CLOSURE). YET, MODELS, DEVOID OF THE CONTENT OF EXCHANGES, DID NOT CAPTURE THE SOCIAL DYNAMICS WELL, AND FAILED TO PREDICT PATTERNS FOR COMMUNICATION ACROSS TRIADS. BY BRINGING IN DISCOURSE FEATURES, FUTURE WORK CAN INVESTIGATE THE ROLE OF KNOWLEDGE BUILDING BEHAVIOURS IN TRIADIC CLOSURE OF DIGITAL NETWORKS. THIS STUDY CONTRIBUTES FRESH INSIGHTS INTO SOCIAL INTERACTION IN ONLINE DISCUSSIONS, CALLS FOR ATTENTION TO MICRO-LEVEL TEMPORAL PATTERNS, AND MOTIVATES FUTURE WORK TO SCAFFOLD LEARNER PARTICIPATION IN SIMILAR CONTEXTS.
UNTIL RECENTLY AN ASSUMPTION WITHIN THE PREDICTIVE MODELLING COMMUNITY HAS BEEN THAT COLLECTING MORE STUDENT DATA IS ALWAYS BETTER. BUT IN REACTION TO RECENT HIGH PROFILE DATA PRIVACY SCANDALS, MANY EDUCATORS, SCHOLARS, STUDENTS AND ADMINISTRATORS HAVE BEEN QUESTIONING THE ETHICS OF SUCH A STRATEGY. SUGGESTIONS ARE GROWING THAT THE MINIMUM AMOUNT OF DATA SHOULD BE COLLECTED TO AID THE FUNCTION FOR WHICH A PREDICTION IS BEING MADE. YET, MACHINE LEARNING ALGORITHMS ARE PRIMARILY JUDGED ON METRICS DERIVED FROM PREDICTION ACCURACY OR WHETHER THEY MEET PROBABILISTIC CRITERIA FOR SIGNIFICANCE. THEY ARE NOT ROUTINELY JUDGED ON WHETHER THEY UTILIZE THE MINIMUM NUMBER OF THE LEAST SENSITIVE FEATURES, PRESERVING WHAT WE NAME HERE AS DATA COLLECTION PARSIMONY. WE BELIEVE THE ABILITY TO ASSESS DATA COLLECTION PARSIMONY WOULD BE A VALUABLE ADDITION TO THE SUITE OF EVALUATIONS FOR ANY PREDICTION STRATEGY AND TO THAT END, THE FOLLOWING PAPER PROVIDES AN INTRODUCTION TO DATA COLLECTION PARSIMONY, DESCRIBES A NOVEL METHOD FOR QUANTIFYING THE CONCEPT USING EMPIRICAL BAYES ESTIMATES AND THEN TESTS THE METRIC ON REAL WORLD DATA. BOTH THEORETICAL AND EMPIRICAL BENEFITS AND LIMITATIONS OF THIS METHOD ARE DISCUSSED. WE CONCLUDE THAT FOR THE PURPOSE OF MODEL BUILDING THIS METRIC IS SUPERIOR TO OTHERS IN SEVERAL WAYS, BUT THERE ARE SOME HURDLES TO EFFECTIVE IMPLEMENTATION.
THE COVID-19 PANDEMIC TRIGGERED UNPRECEDENTED ADOPTION OF ONLINE EDUCATION IN UNIVERSITIES AND DIGITAL TECHNOLOGIES ARE INCREASINGLY BECOMING AN ESSENTIAL PART OF THE LEARNING AND TEACHING EXPERIENCE. FROM WITHIN AN ONGOING PANDEMIC 'LOCKDOWN', THIS STUDY INVESTIGATES THE CURRENT LANDSCAPE OF ONLINE EDUCATION WITH A FOCUS ON THE LEARNING AND TEACHING EXPERIENCE IN VIRTUAL CLASSROOMS COMPARED TO PHYSICAL CLASSROOMS. WE FOUND THAT INSTRUCTORS TYPICALLY COMBINE MULTIPLE TECHNOLOGIES TO CREATE A HOLISTIC LEARNING EXPERIENCE FOR STUDENTS. THE BENEFITS OF VIRTUAL CLASSROOMS INCLUDED SELF-PACED LEARNING, INCREASED OPPORTUNITIES TO GAIN PROCEDURAL KNOWLEDGE, DIVERSIFIED COMMUNICATION CHANNELS, AND GREATER OPPORTUNITIES FOR LEARNING ANALYTICS. COMPARED TO PHYSICAL CLASSROOMS, THE DRAWBACKS OF VIRTUAL CLASSROOMS INCLUDED THE LACK OF BOTH TIMELY ACCESS TO ALL INFORMATION THROUGH A SINGLE INTERFACE AND NATURAL INTERPERSONAL INTERACTION. BASED ON OUR FINDINGS WE RECOMMEND INSTRUCTORS TO REMAIN FLEXIBLE WHEN SELECTING TECHNOLOGIES AND TO ADAPT THE LEVEL OF OFFLINE LEARNING TO FIT THEIR STUDENTS' LEARNING STYLES AND ABILITIES. WE ALSO IDENTIFIED OPPORTUNITIES FOR VIRTUAL REALITY TECHNOLOGY TO MITIGATE THE DRAWBACKS OF ONLINE EDUCATION BY HELPING TO TRANSLATE IN-CLASS EXPERIENCE TO VIRTUAL SPACES.
STUDENT SUCCESS EFFICACY STUDIES ARE AIMED AT ASSESSING INSTRUCTIONAL PRACTICES AND LEARNING ENVIRONMENTS BY EVALUATING THE SUCCESS OF AND CHARACTERIZING STUDENT SUBGROUPS THAT MAY BENEFIT FROM SUCH MODALITIES. WE PROPOSE AN ENSEMBLE LEARNING APPROACH TO PERFORM THESE ANALYTICS TASKS WITH SPECIFIC FOCUS ON ESTIMATING INDIVIDUALIZED TREATMENT EFFECTS (ITE). ITE ARE A MEASURE FROM THE PERSONALIZED MEDICINE LITERATURE THAT CAN, FOR EACH STUDENT, QUANTIFY THE IMPACT OF THE INTERVENTION STRATEGY ON STUDENT PERFORMANCE, EVEN THOUGH THE GIVEN STUDENT EITHER DID OR DID NOT EXPERIENCE THIS INTERVENTION (I.E., IS EITHER IN THE TREATMENT GROUP OR IN THE CONTROL GROUP). WE ILLUSTRATE OUR LEARNING ANALYTICS METHODS IN THE STUDY OF A SUPPLEMENTAL INSTRUCTION COMPONENT FOR A LARGE ENROLLMENT INTRODUCTORY STATISTICS COURSE RECOGNIZED AS A CURRICULUM BOTTLENECK AT SAN DIEGO STATE UNIVERSITY. AS PART OF THIS APPLICATION, WE SHOW HOW THE ENSEMBLE ESTIMATE OF THE ITE MAY BE USED TO ASSESS THE PEDAGOGICAL REFORM (SUPPLEMENTAL INSTRUCTION), ADVISE STUDENTS INTO SUPPLEMENTAL INSTRUCTION AT THE BEGINNING OF THE COURSE, AND QUANTIFY THE IMPACT OF THE SUPPLEMENTAL INSTRUCTION COMPONENT ON AT-RISK SUBGROUPS.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE LEFT THEIR MARK ON THE FACE OF EDUCATION DURING THE RECENT YEARS. AT THE HASSO PLATTNER INSTITUTE (HPI) IN POTSDAM, GERMANY, WE ARE ACTIVELY DEVELOPING A MOOC PLATFORM, WHICH PROVIDES OUR RESEARCH WITH A PLETHORA OF E-LEARNING TOPICS, SUCH AS LEARNING ANALYTICS, AUTOMATED ASSESSMENT, PEER ASSESSMENT, TEAM-WORK, ONLINE PROCTORING, AND GAMIFICATION. WE RUN SEVERAL INSTANCES OF THIS PLATFORM. ON OPENHPI, WE PROVIDE OUR OWN COURSES FROM WITHIN THE HPI CONTEXT. FURTHER INSTANCES ARE OPENSAP, OPENWHO, AND MOOC.HOUSE, WHICH IS THE SMALLEST OF THESE PLATFORMS, TARGETING CUSTOMERS WITH A LESS EXTENSIVE COURSE PORTFOLIO. IN 2013, WE STARTED TO WORK ON THE GAMIFICATION OF OUR PLATFORM. BY NOW, WE HAVE IMPLEMENTED ABOUT TWO THIRDS OF THE FEATURES THAT WE INITIALLY HAVE EVALUATED AS USEFUL FOR OUR PURPOSES. ABOUT A YEAR AGO WE ACTIVATED THE IMPLEMENTED GAMIFICATION FEATURES ON MOOC.HOUSE. BEFORE ACTIVATING THE FEATURES ON OPENHPI AS WELL, WE EXAMINED, AND RE-EVALUATED OUR INITIAL CONSIDERATIONS BASED ON THE DATA WE COLLECTED SO FAR AND THE CHANGES IN OTHER CONTEXTS OF OUR PLATFORMS.
A COMPARATIVE ANALYSIS HAS BEEN CARRIED OUT IN ORDER TO STUDY THE SIMILARITIES AND DIFFERENCES IN THE USE AND MANAGEMENT OF SPACE AND TIME BY STUDENTS, BASED ON THEIR GENDER. THE STUDY ANALYSES THE DATA RECORDS GENERATED BY STUDENTS FROM THREE DIFFERENT DEGREE COURSES IN TWO UNIVERSITIES, THROUGHOUT FOUR ACADEMIC YEARS. THE METHODOLOGY INCLUDES EXPLORATORY STATISTICAL ANALYSIS AND LEARNING ANALYTICS FOR THE DETECTION OF SPATIAL, TEMPORAL AND BEHAVIOURAL PATTERNS. RESULTS SHOW A CONSISTENT CONVERGENCE IN MOST CASES, ALTHOUGH THEY ALSO SHOW MARGINAL BEHAVIORAL TRENDS, BOTH FOR THE DAYS OF THE WEEK, THE HOURS OF THE DAY, AND FOR THE DIFFERENT CONTENTS OF THE VIRTUAL CAMPUSES. THIS IS MORE EVIDENT IN ITS SPATIAL ASPECT, WHICH HIGHLIGHTS SOME CLEAR DIFFERENCES IN THE PROCESSES OF DISTRIBUTION AND CONCENTRATION OF THE EVENTS UNDER STUDY: AT LEAST IN THIS CASE, WOMEN DO NOT ACT IN THE SAME WAY AS MEN. ULTIMATELY, THE STUDY PROPOSES NEW FORMS OF SYNERGY BETWEEN EDUCATIONAL WORK AND THE APPLICATION OF THE SOCIAL SCIENCES' DISCIPLINARY CONTENTS THUS STRENGTHENING THE TRANSFER OF KNOWLEDGE FROM SPECIFIC DIDACTICS ON BOTH EDUCATIONAL CURRICULUM AND TEACHER TRAINING.
WE INVESTIGATE HOW THE TECHNOLOGY ACCEPTANCE AND LEARNING EXPERIENCE OF THE DIGITAL EDUCATION PLATFORM HPI SCHUL-CLOUD (HPI SCHOOL CLOUD) FOR GERMAN SECONDARY SCHOOL TEACHERS CAN BE IMPROVED BY PROPOSING A USER-CENTERED RESEARCH AND DEVELOPMENT FRAMEWORK. WE HIGHLIGHT THE IMPORTANCE OF DEVELOPING DIGITAL LEARNING TECHNOLOGIES IN A USER-CENTERED WAY TO TAKE DIFFERENCES IN THE REQUIREMENTS OF EDUCATORS AND STUDENTS INTO ACCOUNT. WE SUGGEST APPLYING QUALITATIVE AND QUANTITATIVE METHODS TO BUILD A SOLID UNDERSTANDING OF A LEARNING PLATFORM'S USERS, THEIR NEEDS, REQUIREMENTS, AND THEIR CONTEXT OF USE. AFTER CONCEPT DEVELOPMENT AND IDEA GENERATION OF FEATURES AND AREAS OF OPPORTUNITY BASED ON THE USER RESEARCH, WE EMPHASIZE ON THE APPLICATION OF A MULTIATTRIBUTE UTILITY ANALYSIS DECISION-MAKING FRAMEWORK TO PRIORITIZE IDEAS RATIONALLY, TAKING RESULTS OF USER RESEARCH INTO ACCOUNT. AFTERWARD, WE RECOMMEND APPLYING THE PRINCIPLE BUILD-LEARN-ITERATE TO BUILD PROTOTYPES IN DIFFERENT RESOLUTIONS WHILE LEARNING FROM USER TESTS AND IMPROVING THE SELECTED OPPORTUNITIES. LAST BUT NOT LEAST, WE PROPOSE AN APPROACH FOR CONTINUOUS SHORT- AND LONG-TERM USER EXPERIENCE CONTROLLING AND MONITORING, EXTENDING EXISTING WEB- AND LEARNING ANALYTICS METRICS.
ONE NEW USAGE OF LEARNING ANALYTICS IN INTELLIGENT TUTORING SYSTEMS (ITS) IS SEQUENCING BASED ON PERFORMANCE PREDICTION, WHICH INFORMS SEQUENCERS WHETHER A STUDENT MASTERED OR NOT A SPECIFIC SET OF SKILLS. MATRIX FACTORIZATION (MF) PERFORMANCE PREDICTION IS PARTICULARLY APPEALING BECAUSE IT DOES NOT REQUIRE TAGGING INVOLVED SKILLS IN TASKS. HOWEVER, MF'S DIFFICULT INTERPRETABILITY DOES NOT ALLOW TO SHOW THE STUDENT'S STATE EVOLUTION, I. E. HIS/HER PROGRESS OVER TIME. IN THIS PAPER WE PRESENT A NOVEL PROGRESS MODELING TECHNIQUE INTEGRATING THE MOST FAMOUS CONTROL THEORY STATE MODELER, THE KALMAN FILTER, AND MATRIX FACTORIZATION. OUR METHOD, THE SKILL DEFICIENCY AWARE KALMAN STATE ESTIMATION FOR MATRIX FACTORIZATION, (1) UPDATES AT EACH INTERACTION THE STUDENT'S STATE OUTPERFORMING THE BASELINE BOTH IN PREDICTION ERROR AND IN COMPUTATIONAL REQUIREMENTS ALLOWING FASTER ONLINE INTERACTIONS; (2) MODELS THE INDIVIDUALIZED PROGRESS OF THE STUDENTS OVER TIME THAT COULD BE LATER USED TO DEVELOP NOVEL SEQUENCING POLICIES. OUR RESULTS ARE TESTED ON DATA OF A COMMERCIAL ITS WHERE OTHER STATE OF THE ART METHODS WERE NOT APPLICABLE.
VIRTUAL REALITY PRESENTS EXCITING NEW PROSPECTS FOR THE DELIVERY OF EDUCATIONAL MATERIALS TO STUDENTS. BY COMBINING THIS TECHNOLOGY WITH BIOLOGICAL SENSORS, A STUDENT IN A VIRTUAL EDUCATIONAL ENVIRONMENT CAN BE MONITORED FOR PHYSIOLOGICAL MARKERS OF ENGAGEMENT OR MORE COGNITIVE STATES OF LEARNING. WITH THIS INFORMATION, THE VIRTUAL REALITY ENVIRONMENT CAN BE ADAPTIVELY ALTERED TO REFLECT THE STUDENT'S STATE, ESSENTIALLY CREATING A CLOSED-LOOP FEEDBACK SYSTEM. THIS PAPER EXPLORES THESE CONCEPTS, AND PRESENTS PRELIMINARY DATA ON A COMBINED EEG-VR WORKING MEMORY EXPERIMENT AS A FIRST STEP TOWARD A BROADER IMPLEMENTATION OF AN INTELLIGENT ADAPTIVE LEARNING SYSTEM. THIS FIRST-PASS NEURAL TIME-SERIES AND OSCILLATORY DATA SUGGEST THAT WHILE AN EEG-BASED NEUROFEEDBACK SYSTEM IS FEASIBLE, MORE WORK ON REMOVING ARTIFACTS AND IDENTIFYING RELEVANT AND IMPORTANT FEATURES WILL LEAD TO HIGHER PREDICTION ACCURACY.
IN THIS INTEGRATED STUDY OF DYNAMICS IN MOOCS DISCUSSION FORUMS, WE ANALYZE THE INTERPLAY OF TEMPORAL PATTERNS, DISCUSSION CONTENT, AND THE SOCIAL STRUCTURE EMERGING FROM THE COMMUNICATION USING MIXED METHODS. A SPECIAL FOCUS IS ON THE YET UNDER-EXPLORED ASPECT OF TIME DYNAMICS AND INFLUENCE OF THE COURSE STRUCTURE ON FORUM PARTICIPATION. OUR ANALYSES SHOW DEPENDENCIES BETWEEN THE COURSE STRUCTURE (VIDEO OPENING TIME AND ASSIGNMENT DEADLINES) AND THE OVERALL FORUM ACTIVITY WHEREAS SUCH A CLEAR LINK COULD ONLY BE PARTIALLY OBSERVED CONSIDERING THE DISCUSSION CONTENT. FOR ANALYZING THE SOCIAL DIMENSION WE APPLY ROLE MODELING TECHNIQUES FROM SOCIAL NETWORK ANALYSIS. WHILE THE TYPES OF USER ROLES BASED ON CONNECTION PATTERNS ARE RELATIVELY STABLE OVER TIME, THE HIGH FLUCTUATION OF ACTIVE CONTRIBUTORS LEAD TO FREQUENT CHANGES FROM ACTIVE TO PASSIVE ROLES DURING THE COURSE. HOWEVER, WHILE MOST USERS DO NOT CREATE MANY SOCIAL CONNECTIONS THEY CAN PLAY AN IMPORTANT ROLE IN THE CONTENT DIMENSION TRIGGERING DISCUSSIONS ON THE COURSE SUBJECT. FINALLY, WE SHOW THAT FORUM ACTIVITY LEVEL CAN BE PREDICTED ONE WEEK IN ADVANCE BASED ON THE COURSE STRUCTURE, FORUM ACTIVITY HISTORY AND ATTRIBUTES OF THE COMMUNICATION NETWORK WHICH ENABLES IDENTIFICATION OF PERIODS WHEN INCREASED TUTOR SUPPORTS IN THE FORUM IS NECESSARY.
HEALTHCARE SIMULATIONS ARE HANDS-ON LEARNING EXPERIENCES AIMED AT ALLOWING STUDENTS TO PRACTICE ESSENTIAL SKILLS THAT THEY MAY NEED WHEN WORKING WITH REAL PATIENTS IN CLINICAL WORKPLACES. SOME CLINICAL CLASSROOMS ARE EQUIPPED WITH PATIENT MANIKINS THAT CAN RESPOND TO ACTIONS OR THAT CAN BE PROGRAMMED TO DETERIORATE OVER TIME. STUDENTS CAN PERFORM ASSESSMENTS AND INTERVENTIONS, AND ENHANCE THEIR CRITICAL THINKING AND COMMUNICATION SKILLS. THERE IS AN OPPORTUNITY TO EXPLOIT THE STUDENTS' DIGITAL TRACES THAT THESE MANIKINS CAN PERVASIVELY CAPTURE TO MAKE KEY ASPECTS OF THE LEARNING PROCESS VISIBLE. THE SETTING CAN BE AUGMENTED WITH SENSORS TO CAPTURE TRACES OF GROUP INTERACTION. THESE MULTIMODAL DATA CAN BE USED TO GENERATE VISUALISATIONS OR FEEDBACK FOR STUDENTS OR TEACHERS. THIS PAPER REPORTS ON AN AUTHENTIC CLASSROOM STUDY USING ANALYTICS TO INTEGRATE MULTIMODAL DATA OF STUDENTS' INTERACTIONS WITH THE MANIKINS AND THEIR PEERS IN SIMULATION SCENARIOS. WE REPORT ON THE CHALLENGES ENCOUNTERED IN DEPLOYING SUCH ANALYTICS 'IN THE WILD', USING AN ANALYSIS FRAMEWORK THAT CONSIDERS THE SOCIAL, EPISTEMIC AND PHYSICAL DIMENSIONS OF COLLOCATED GROUP ACTIVITY.
TO MASTER A SKILL, STUDENTS GENERALLY PRACTICE THE CONTENT OF THE SKILL IN A BLOCKING MANNER. WHILE PRACTICING IN A BLOCKED FASHION, STUDENTS KNOW THE CONTEXT OF THE PROBLEMS AND ALSO WHICH STRATEGY IS NEEDED TO ARRIVE AT A SOLUTION. HOWEVER, IN REAL LIFE STANDARDIZED TESTS, WHERE PROBLEMS FROM VARIOUS SKILLS ARE GROUPED TOGETHER, STUDENTS OFTEN FIND IT CHALLENGING TO IDENTIFY THE CORRECT STRATEGY TO SOLVE THE PROBLEMS. THIS IS BECAUSE, DURING LEARNING, STUDENTS OFTEN PRACTICE THE CONTENT IN ISOLATION. IT HINDERS THEIR ABILITY TO DISCRIMINATE AMONG THE CONTEXTS OF THE PROBLEM. IN THIS WORK, USING TUTOR FUNTOOT, WE PRESENT STUDENTS WORKING ON THE TOPIC ADDITION WORD PROBLEMS WITH A SUBTRACTION WORD PROBLEM AND INVESTIGATE HOW THEY PERFORM IN THE OUT-OF-CONTEXT SUBTRACTION WORD PROBLEM. WE FIND THAT STUDENTS' PERFORMANCE IN THE TOPIC ADDITION WORD PROBLEMS IS A STRONG PREDICTOR OF THEIR PERFORMANCE IN THIS OUT-OFCONTEXT PROBLEM. OUR RESULTS SUGGEST THAT IT IS A STRONGER PREDICTOR FOR HIGHER GRADES (4TH AND 5TH) COMPARED TO THE LOWER (2ND AND 3RD) GRADES.
MODELING AND PREDICTING STUDENT LEARNING IS AN IMPORTANT TASK IN COMPUTER-BASED EDUCATION. A LARGE BODY OF WORK HAS FOCUSED ON REPRESENTING AND PREDICTING STUDENT KNOWLEDGE ACCURATELY. EXISTING TECHNIQUES ARE MOSTLY BASED ON STUDENTS' PERFORMANCE AND ON TIMING FEATURES. HOWEVER, RESEARCH IN EDUCATION, PSYCHOLOGY AND EDUCATIONAL DATA MINING HAS DEMONSTRATED THAT STUDENTS' CHOICES AND STRATEGIES SUBSTANTIALLY INFLUENCE LEARNING. IN THIS PAPER, WE INVESTIGATE THE IMPACT OF STUDENTS' EXPLORATION STRATEGIES ON LEARNING AND PROPOSE THE USE OF A PROBABILISTIC MODEL JOINTLY REPRESENTING STUDENT KNOWLEDGE AND STRATEGIES. OUR ANALYSES ARE BASED ON DATA COLLECTED FROM AN INTERACTIVE COMPUTER-BASED GAME. OUR RESULTS SHOW THAT EXPLORATION STRATEGIES ARE A SIGNIFICANT PREDICTOR OF THE LEARNING OUTCOME. FURTHERMORE, THE JOINT MODELS OF PERFORMANCE AND KNOWLEDGE SIGNIFICANTLY IMPROVE THE PREDICTION ACCURACY WITHIN THE GAME AS WELL AS ON EXTERNAL POST-TEST DATA, INDICATING THAT THIS COMBINED REPRESENTATION PROVIDES A BETTER PROXY FOR LEARNING.
IN THIS PAPER, WE DESCRIBE A TABLET APPLICATION DESIGNED AROUND AN INTERACTIVE GAME-BASED SCIENCE MUSEUM EXHIBIT. IT IS AIMED TO HELP PROVIDE MUSEUM DOCENTS USEFUL INFORMATION ABOUT THE VISITORS' ACTIONS, IN A WAY THAT IS ACTIONABLE, AND ENABLES DOCENTS TO PROVIDE ASSISTANCE AND PROMPTS TO VISITORS THAT ARE MORE MEANINGFUL, COMPARED TO WHAT THEY ARE TYPICALLY ABLE TO DO WITHOUT THIS INTERFACE AUGMENTATION.
WIKIGLASS IS A LEARNING ANALYTIC TOOL FOR VISUALIZING COLLABORATIVE WORK ON WILDS BUILT BY GROUPS OF SECONDARY OR PRIMARY SCHOOL STUDENTS. THIS POSTER PRESENTS NEW FEATURES OF WIKIGLASS DEVELOPED RECENTLY BASED ON REQUESTS FROM TEACHERS, INCLUDING FLEXIBLE SELECTION OF DATE RANGE, REVISION NETWORK, AND THINKING ORDER DETECTION. CURRENTLY THE NEW FEATURES ARE USED AND EVALUATED IN TWO SECONDARY SCHOOLS IN HONG KONG.
THE INTEREST IN DEVELOPING LEARNING ANALYTICS TOOLS THAT CAN BE INTEGRATED INTO THE WELL-KNOWN MOODLE COURSE MANAGEMENT SYSTEMS IS INCREASING NOWADAYS. THESE TOOLS GENERALLY PROVIDE SOME TYPE OF BASIC ANALYTICS AND GRAPHS ABOUT USERS' INTERACTION IN THE COURSE. HOWEVER, THEY DO NOT ENABLE A VARIED SET OF DATA MINING TECHNIQUES TO BE APPLIED, SUCH AS APPROACHES FOR CLASSIFICATION, CLUSTERING, OR ASSOCIATION. TO ADDRESS THIS ISSUE, A NEW AND FREELY AVAILABLE MOODLE DATA MINING TOOL, NAMED MDM, HAS BEEN PROPOSED IN THIS PAPER. THE PROPOSED TOOL EASES THE WHOLE KNOWLEDGE DISCOVERY PROCESS, INCLUDING TASKS SUCH AS SELECTION, DATA PRE-PROCESSING, AND DATA MINING FROM MOODLE COURSES. THE PROPOSED MDM TOOL HAS BEEN DEVELOPED IN PHP PROGRAMMING LANGUAGE, SO IT CAN BE EASILY INTEGRATED INTO MOODLE AS A MODULE FOR A SPECIFIC COURSE. ITS MAIN FEATURES AND ARCHITECTURE ARE DESCRIBED IN DEPTH, AND A TUTORIAL IS ALSO PROVIDED AS A PRACTICAL WAY OF USING THE MDM INTERFACE. FINALLY, SOME EXPERIMENTAL RESULTS USING A REAL-LIFE SAMPLE DATASET OF MECHANICAL ENGINEERING STUDENTS ARE ANALYZED. (C) 2017 WILEY PERIODICALS, INC.
WE INVESTIGATE MULTI-SENSOR MODELING OF TEACHERS' INSTRUCTIONAL SEGMENTS (E.G., LECTURE, GROUP WORK) FROM AUDIO RECORDINGS COLLECTED IN 56 CLASSES FROM EIGHT TEACHERS ACROSS FIVE MIDDLE SCHOOLS. OUR APPROACH FUSES TWO SENSORS: A UNIDIRECTIONAL MICROPHONE FOR TEACHER AUDIO AND A PRESSURE ZONE MICROPHONE FOR GENERAL CLASSROOM AUDIO. WE SEGMENT AND ANALYZE THE AUDIO STREAMS WITH RESPECT TO DISCOURSE TIMING, LINGUISTIC, AND PARALINGUISTIC FEATURES. WE TRAIN SUPERVISED CLASSIFIERS TO IDENTIFY THE FIVE INSTRUCTIONAL SEGMENTS THAT COLLECTIVELY COMPRISED A MAJORITY OF THE DATA, ACHIEVING TEACHER-INDEPENDENT F-1 SCORES RANGING FROM 0.49 TO 0.60. WITH RESPECT TO INDIVIDUAL SEGMENTS, THE INDIVIDUAL SENSOR MODELS AND THE FUSED MODEL WERE ON PAR FOR QUESTION & ANSWER AND PROCEDURES & DIRECTIONS SEGMENTS. FOR SUPERVISED SEATWORK, SMALL GROUP WORK, AND LECTURE SEGMENTS, THE CLASSROOM MODEL OUTPERFORMED BOTH THE TEACHER AND FUSION MODELS. ACROSS ALL SEGMENTS, A MULTI-SENSOR APPROACH LED TO AN AVERAGE 8% IMPROVEMENT OVER THE STATE OF THE ART APPROACH THAT ONLY ANALYZED TEACHER AUDIO. WE DISCUSS IMPLICATIONS OF OUR FINDINGS FOR THE EMERGING FIELD OF MULTIMODAL LEARNING ANALYTICS.
COLLABORATIVE FILTERING BASED ALGORITHMS, INCLUDING RECURRENT NEURAL NETWORKS (RNN), TEND TOWARDS PREDICTING A PERPETUATION OF PAST OBSERVED BEHAVIOR. IN A RECOMMENDATION CONTEXT, THIS CAN LEAD TO AN OVERLY NARROW SET OF SUGGESTIONS LACKING IN SERENDIPITY AND INADVERTENTLY PLACING THE USER IN WHAT IS KNOWN AS A "FILTER BUBBLE." IN THIS PAPER, WE GRAPPLE WITH THE ISSUE OF THE FILTER BUBBLE IN THE CONTEXT OF A COURSE RECOMMENDATION SYSTEM IN PRODUCTION AT A PUBLIC UNIVERSITY. OUR APPROACH IS TO PRESENT COURSE RESULTS THAT ARE NOVEL OR UNEXPECTED TO THE STUDENT BUT STILL RELEVANT TO THEIR INTERESTS. WE BUILD ONE SET OF MODELS BASED ON COURSE CATALOG DESCRIPTIONS (BOW) AND ANOTHER SET INFORMED BY ENROLLMENT HISTORIES (COURSE2VEC). WE COMPARE THE PERFORMANCE OF THESE MODELS ON OFF-LINE VALIDATION SETS AND AGAINST THE SYSTEM'S EXISTING RNN-BASED RECOMMENDATION ENGINE IN AN ONLINE USER STUDY OF UNDERGRADUATES (N = 70) WHO RATED THEIR COURSE RECOMMENDATIONS ALONG SIX CHARACTERISTICS RELATED TO SERENDIPITY. RESULTS OF THE USER STUDY SHOW A DRAMATIC LACK OF NOVELTY IN RNN RECOMMENDATIONS AND DEPICT THE CHARACTERISTIC TRADE-OFFS THAT MAKE SERENDIPITY DIFFICULT TO ACHIEVE. WHILE THE MACHINE LEARNED COURSE2VEC MODELS PERFORMED BEST ON OFF-LINE VALIDATION TASKS, IT WAS THE SIMPLE BAG-OF-WORDS BASED RECOMMENDATIONS THAT STUDENTS RATED AS MORE SERENDIPITOUS. WE DISCUSS THE ROLE OF THE KIND OF INFORMATION PRESENTED BY THE SYSTEM IN A STUDENT'S DECISION TO ACCEPT A RECOMMENDATION FROM EITHER ALGORITHM.
VIDEO IS BECOMING A DOMINANT MEDIUM FOR THE DELIVERY OF EDUCATIONAL MATERIAL. DESPITE THE WIDESPREAD USE OF VIDEO FOR LEARNING, THERE IS STILL A LACK OF UNDERSTANDING ABOUT HOW BEST TO HELP PEOPLE LEARN IN THIS MEDIUM. THIS STUDY DEMONSTRATES THE USE OF THERMAL CAMERA AS COMPARED TO TRADITIONAL SELF-REPORTED METHODS FOR ASSESSING LEARNERS' COGNITIVE LOAD WHILE WATCHING VIDEO LECTURES OF DIFFERENT STYLES. WE EVALUATED OUR APPROACH IN A STUDY WITH 78 UNIVERSITY STUDENTS VIEWING TWO VARIANTS OF SHORT VIDEO LECTURES ON TWO DIFFERENT TOPICS. TO INCORPORATE SUBJECTIVE MEASURES, THE STUDENTS REPORTED ON MENTAL EFFORT, INTEREST, PRIOR KNOWLEDGE, CONFIDENCE, AND CHALLENGE. MOREOVER, THROUGH A PHYSICAL SLIDER DEVICE, THE STUDENTS COULD CONTINUOUSLY REPORT ON THEIR PERCEIVED LEVEL OF DIFFICULTY. LASTLY, WE USED THERMAL SENSOR AS AN ADDITIONAL INDICATOR OF STUDENTS' LEVEL OF DIFFICULTY AND ASSOCIATED COGNITIVE LOAD. THIS WAS ACHIEVED THROUGH, CONTINUOUS REAL-TIME MONITORING OF STUDENTS BY USING A THERMAL IMAGING CAMERA. THIS STUDY AIMS TO ADDRESS THE FOLLOWING: FIRSTLY, TO ANALYZE IF VIDEO STYLES DIFFER IN TERMS OF THE ASSOCIATED COGNITIVE LOAD. SECONDLY, TO ASSESS THE EFFECTS OF COGNITIVE LOAD ON LEARNING OUTCOMES; COULD AN INCREASE IN THE COGNITIVE LOAD BE ASSOCIATED WITH POORER LEARNING OUTCOMES? THIRD, TO SEE IF THERE IS A MATCH BETWEEN STUDENTS' PERCEIVED DIFFICULTY LEVELS AND A BIOLOGICAL INDICATOR. THE RESULTS SUGGEST THAT THERMAL IMAGING COULD BE AN EFFECTIVE TOOL TO ASSESS LEARNERS' COGNITIVE LOAD, AND AN IN-CREASED COGNITIVE LOAD COULD LEAD TO POORER PERFORMANCE. MOREOVER, IN TERMS OF THE LECTURE STYLES, THE ANIMATED VIDEO LECTURES APPEAR TO BE A BETTER TOOL THAN THE TEXT-ONLY LECTURES (IN THE CONTENT AREAS TESTED HERE). THE RESULTS OF THIS STUDY MAY GUIDE FUTURE WORKS ON EFFECTIVE VIDEO DESIGNS, ESPECIALLY THOSE THAT CONSIDER THE COGNITIVE LOAD.
LECTURE VIDEOS ARE AMONGST THE MOST COMMONLY USED INSTRUCTIONAL METHODS IN PRESENT MASSIVE OPEN ONLINE COURSES (MOOCS). AS THE MAIN FORM OF INSTRUCTION, STUDENTS' ENGAGEMENT BEHAVIOUR WITH MOOC VIDEOS DIRECTLY IMPACTS THE STUDENTS' SUCCESS OR FAILURE. THIS RESEARCH FOCUSES ON AN IN-DEPTH ANALYSIS OF 1.5 MILLION VIDEO INTERACTIONS (E.G. PAUSE, SEEK VIDEO) OF A PROGRAMMING MOOC. OUR VIDEO-BY-VIDEO ANALYSIS EXPLORES THE RATIONALE BEHIND THE TIME-WISE VARIATION OF VIDEO INTERACTIONS. WE AIM TO ANALYSE DISCOURSE FEATURES (E.G. SYNTACTIC SIMPLICITY OF TEXT, AND SPEAKING RATE) AND THEIR CORRELATION WITH THE VIDEO INTERACTION PATTERNS. THIS PAPER PRESENTS PRELIMINARY RESULTS AND EDUCATIONAL VIDEO DESIGN IMPLICATIONS.
STUDENT KNOWLEDGE MODELING IS AN IMPORTANT PART OF MODERN PERSONALIZED LEARNING SYSTEMS, BUT TYPICALLY RELIES UPON VALID MODELS OF THE STRUCTURE OF THE CONTENT AND SKILL IN A DOMAIN. THESE MODELS ARE OFTEN DEVELOPED THROUGH EXPERT TAGGING OF SKILLS TO ITEMS. HOWEVER, CONTENT CREATORS IN CROWDSOURCED PERSONALIZED LEARNING SYSTEMS OFTEN LACK THE TIME (AND SOMETIMES THE DOMAIN KNOWLEDGE) TO TAG SKILLS THEMSELVES. FULLY AUTOMATED APPROACHES THAT RELY ON THE COVARIANCE OF CORRECTNESS ON ITEMS CAN LEAD TO EFFECTIVE SKILL-ITEM MAPPINGS, BUT THE RESULTANT MAPPINGS ARE OFTEN DIFFICULT TO INTERPRET. IN THIS PAPER WE PROPOSE AN ALTERNATE APPROACH TO AUTOMATICALLY LABELING SKILLS IN A CROWDSOURCED PERSONALIZED LEARNING SYSTEM USING CORRELATED TOPIC MODELING, A NATURAL LANGUAGE PROCESSING APPROACH, TO ANALYZE THE LINGUISTIC CONTENT OF MATHEMATICS PROBLEMS. WE FIND A RANGE OF POTENTIALLY MEANINGFUL AND USEFUL TOPICS WITHIN THE CONTEXT OF THE ASSISTMENTS SYSTEM FOR MATHEMATICS PROBLEM-SOLVING.
THE FACILITATION OF INTERPERSONAL RELATIONSHIPS WITHIN A RESPECTFUL LEARNING CLIMATE IS AN IMPORTANT ASPECT OF TEACHING PRACTICE. HOWEVER, IN LARGE-SCALE ONLINE CONTEXTS, SUCH AS MOOCS, THE NUMBER OF LEARNERS AND HIGHLY ASYNCHRONOUS NATURE MILITATES AGAINST THE DEVELOPMENT OF A SENSE OF BELONGING AND DYADIC TRUST. GIVEN THESE CHALLENGES, INSTEAD OF CONVENTIONAL INSTRUMENTS THAT REFLECT LEARNERS' AFFECTIVE PERCEPTIONS, WE SUGGEST A SET OF INDICATORS THAT CAN BE USED TO EVALUATE SOCIAL ACTIVITY IN RELATION TO THE PARTICIPATION STRUCTURE. THESE GROUP-LEVEL INDICATORS CAN THEN HELP TEACHERS TO GAIN INSIGHTS INTO THE EVOLUTION OF SOCIAL ACTIVITY SHAPED BY THEIR FACILITATION CHOICES. FOR THIS STUDY, GROUP-LEVEL INDICATORS WERE DERIVED FROM MEASURING INFORMATION EXCHANGE ACTIVITY BETWEEN THE RETURNING MOOC POSTERS. BY CONCEPTUALIZING THIS GROUP AS AN IDENTITY-BASED COMMUNITY, WE CAN APPLY EXPONENTIAL RANDOM GRAPH MODELLING TO EXPLAIN THE NETWORK'S STRUCTURE THROUGH THE CONFIGURATIONS OF DIRECT RECIPROCITY, TRIADIC-LEVEL EXCHANGE, AND THE EFFECT OF PARTICIPANTS DEMONSTRATING SUPER-POSTING BEHAVIOR. THE FINDINGS PROVIDE NOVEL INSIGHTS INTO NETWORK AMPLIFICATION, AND HIGHLIGHT THE DIFFERENCES BETWEEN THE COURSES WITH DIFFERENT FACILITATION STRATEGIES. DIRECT RECIPROCATION WAS CHARACTERISTIC OF NON-FACILITATED GROUPS. EXCHANGE AT THE LEVEL OF TRIADS WAS MORE PROMINENT IN HIGHLY FACILITATED ONLINE COMMUNITIES WITH INSTRUCTOR'S INVOLVEMENT. SUPER-POSTING ACTIVITY WAS LESS PRONOUNCED IN NETWORKS WITH HIGHER TRIADIC EXCHANGE, AND MORE PRONOUNCED IN NETWORKS WITH HIGHER DIRECT RECIPROCITY.
WE HAVE DEVELOPED A METHOD CALLED SKILL2VEC, WHICH APPLIES BIG DATA TECHNIQUES TO AUTOMATICALLY ANALYZE THE LEARNING DATA TO DISCOVER SKILL RELATIONSHIP, LEADING TO A MORE OBJECTIVE AND DATA-INFORMED DECISION MAKING. SKILL2VEC IS A NEURAL NETWORK ARCHITECTURE WHICH CAN TRANSFORM A SKILL TO A NEW VECTOR SPACE CALLED EMBEDDING. THE EMBEDDING CAN FACILITATE THE COMPARISON AND VISUALIZATION OF DIFFERENT SKILLS AND THEIR RELATIONSHIP. WE CONDUCTED A PILOT EXPERIMENT USING BENCHMARK DATASET TO DEMONSTRATE THE EFFECTIVENESS OF OUR METHOD.
MOOC IS GAINING MOMENTUM IN MALAYSIA AS IT IS SEEN AS A GAME CHANGER IN THE DELIVERY OF UNIVERSITY COURSES TO A WIDER AUDIENCE, BEYOND THE CAMPUS WALLS. AT UNIVERSITI MALAYSIA SARAWAK, THE FIRST MOOC COURSE WAS DESIGNED AND DEVELOPED TO DELIVER LEARNING ABOUT ICT COMPETENCIES TO UNIVERSITY STUDENTS AT ALL PUBLIC UNIVERSITIES THROUGHOUT THE COUNTRY. THE STUDY EXAMINED THE PERCEPTIONS AND SATISFACTION LEVELS AMONG ON-CAMPUS STUDENTS WHO HAVE ENROLLED INTO THE MOOC COURSE, AS A FIRST STEP TO INVESTIGATE THE IMPACT OF THE WEB-BASED INSTRUCTIONAL APPROACH ON HIGHER LEARNING. TWO KEY ASPECTS WERE FOCUSED; THE PEDAGOGICAL AND TECHNICAL ASSEMBLY OF THE MOOC. THE PEDAGOGICAL ASPECT LOOKED INTO THE INSTRUCTIONAL DESIGN AND ASSESSMENT OF CONTENTS IN THE COURSE, WHILE THE TECHNICAL ASPECT COVERED AREAS SUCH AS USER INTERFACE, PRESENTATION OF VIDEO CONTENT, PROVISION OF LEARNING AND SOCIAL TOOLS, AND LEARNING ANALYTICS. FINDINGS INDICATED A SIGNIFICANT RELATIONSHIP BETWEEN EACH OF THESE ELEMENTS WITH PARTICIPANTS' SATISFACTION LEVELS. IT IS CLEAR THAT THESE ELEMENTS ARE CRUCIAL IN MAKING MOOC A SUCCESSFUL INSTRUCTIONAL SUPPLEMENT FOR UNDERGRADUATE LEARNING IN MALAYSIA.
EDUCATIONAL REMOTE LABORATORIES ARE SOFTWARE AND HARDWARE TOOLS THAT ALLOW STUDENTS TO REMOTELY ACCESS REAL EQUIPMENT LOCATED IN UNIVERSITIES AS IF THEY WERE IN A HANDS-ON-LAB SESSION. SINCE MOST REMOTE LABS SHARE CERTAIN MANAGEMENT TASKS (AUTHENTICATION, LEARNING ANALYTICS, SCHEDULING, ETC.), SOFTWARE SYSTEMS IMPLEMENTING THEM ON TOP OF WHICH REMOTE LABS COULD BE IMPLEMENTED WERE DEVELOPED AND CALLED REMOTE LAB MANAGEMENT SYSTEMS (RLMS). A KEY FEATURE PROVIDED BY CERTAIN RLMSS IS SHARING A REMOTE LABORATORY BETWEEN TWO SYSTEMS DEPLOYED IN TWO INSTITUTIONS. THIS WAY, IT BECOMES POSSIBLE TO HAVE MULTIPLE RLMS INSTANCES (WHICH ARE PURE SOFTWARE) IN A CLOUD ENVIRONMENT, CUSTOMIZED FOR DIFFERENT SCHOOLS OR UNIVERSITIES. EACH SCHOOL WOULD HAVE ITS OWN RLMS, WITH ALL THE MANAGEMENT FEATURES (E.G., MANAGING ITS OWN STUDENTS), AND IN THE END, THE RLMS WOULD CONNECT TO THE RLMS WHICH HAS THE PHYSICAL EQUIPMENT. THE FOCUS OF THIS CONTRIBUTION IS TO DETAIL HOW THIS "RLMS AS A SERVICE" IS BEING IMPLEMENTED IN THE CASE OF WEBLAB-DEUSTO AS PART OF THE MCLOUD PROJECT, FROM A TECHNICAL POINT OF VIEW.
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 WHICH COLLECTS THEIR ONLINE LEARNING FOOTAGE. IT ENABLES AND ENCOURAGES EDUCATIONAL RESEARCH, LEARNING SOFTWARE APPLICATION 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 IMPACTS 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 IN THEIR EDUCATION PRACTICE. EDUCATORS AND INSTITUTIONS SHOULD ALSO RECOGNIZE THE REALISTIC DIVERSITIES OF INDIVIDUAL STUDENTS' LEARNING STYLES AND VARIABLE LEARNING PROGRESS, CONTEXTUALLY DEPENDENT LEARNING ACCESSIBILITY, AND THEIR CORRESPONDENT SUPPORT NEEDS FOR THE FINE-GRAINED LEARNING ACTIVITIES. WE ARGUE THAT THE STRATEGICALLY BALANCED PRACTICES AND INNOVATED LEARNING TECHNOLOGY ARE CRUCIAL TOWARDS AN OPTIMIZED LEARNING EXPERIENCE FOR THE LEARNERS.
ALTHOUGH MANY EFFORTS ARE BEING MADE TO PROVIDE EDUCATORS WITH DASHBOARDS AND TOOLS TO UNDERSTAND STUDENT BEHAVIORS WITHIN SPECIFIC TECHNOLOGICAL ENVIRONMENTS (LEARNING ANALYTICS), THERE IS A LACK OF WORK IN SUPPORTING EDUCATORS IN MAKING DATA-INFORMED DESIGN DECISIONS WHEN DESIGNING A BLENDED COURSE AND PLANNING LEARNING ACTIVITIES. IN THIS PAPER, WE INTRODUCE CONCEPT-LEVEL DESIGN ANALYTICS, A KNOWLEDGE-BASED VISUALIZATION, WHICH UNCOVERS FACETS OF THE LEARNING ACTIVITIES THAT ARE BEING AUTHORED. THE VISUALIZATION IS INTEGRATED INTO A (BLENDED) LEARNING DESIGN AUTHORING TOOL, EDCRUMBLE. THIS NEW APPROACH IS EXPLORED IN THE CONTEXT OF A HIGHER EDUCATION PROGRAMMING COURSE, WHERE TEACHING ASSISTANTS DESIGN LABS AND HOME PRACTICE SESSIONS WITH ONLINE SMART LEARNING CONTENT ON A WEEKLY BASIS. WE PERFORMED A WITHIN-SUBJECTS USER STUDY TO COMPARE THE USE OF THE DESIGN TOOL BOTH WITH AND WITHOUT THE VISUALIZATION. WE STUDIED THE DIFFERENCES IN TERMS OF COGNITIVE LOAD, DESIGN OUTCOMES AND USER ACTIONS WITHIN THE SYSTEM TO COMPARE BOTH CONDITIONS TO THE OBJECTIVE OF EVALUATING THE IMPACT OF USING DESIGN ANALYTICS DURING THE DECISION-MAKING PHASE OF COURSE DESIGN.
THE DIFFUSION OF LEARNING TECHNOLOGIES HAS FOSTERED THE USE OF MOBILE AND WEB-BASED APPLICATIONS TO ASSESS THE KNOWLEDGE LEVEL OF LEARNERS. IN PARALLEL, AN INCREASING RESEARCH INTEREST HAS BEEN DEVOTED TO STUDYING NEW LEARNING ANALYTICS TOOLS ABLE TO SUMMARIZE THE CONTENT OF LARGE SETS OF LEARNING DOCUMENTS. TO BRIDGE THE GAP BETWEEN FORMATIVE ASSESSMENT TOOLS AND DOCUMENT SUMMARIZATION SYSTEMS, THIS PAPER ADDRESSES THE PROBLEM OF RECOMMENDING SHORT SUMMARIES OF LARGE SETS OF LEARNING DOCUMENTS BASED ON THE OUTCOMES OF MULTIPLE-CHOICE TESTS. SPECIFICALLY, IT PRESENTS A NEW METHODOLOGY FOR INTEGRATING FORMATIVE ASSESSMENT THROUGH MOBILE APPLICATIONS AND SUMMARIZATION OF LEARNING DOCUMENTS IN TEXTUAL FORM. THE CONTENT OF THE MULTIPLE-CHOICE TESTS IS EXPLOITED TO DRIVE THE GENERATION OF DOCUMENT SUMMARIES TAILORED TO SPECIFIC TOPICS. FURTHERMORE, THE OUTCOMES OF THE TESTS ARE USED TO AUTOMATICALLY RECOMMEND THE GENERATED SUMMARIES TO LEARNERS BASED ON THEIR ACTUAL NEEDS. AS A CASE STUDY, WE PERFORMED AN EVALUATION EXPERIENCE OF STUDENTS' PROGRESSES, WHICH WAS CONDUCTED IN THE CONTEXT OF A UNIVERSITY-LEVEL COURSE. THE ACHIEVED RESULTS SHOW THE APPLICABILITY OF THE PROPOSED METHODOLOGY.
DATA WAREHOUSES AND BUSINESS INTELLIGENCE HAVE BECOME POPULAR FIELDS OF RESEARCH IN RECENT YEARS. UNFORTUNATELY, IN DAILY PRACTICE MANY DATA WAREHOUSE AND BUSINESS INTELLIGENCE SOLUTIONS STILL FAIL TO HELP ORGANIZATIONS MAKE BETTER DECISIONS AND INCREASE THEIR PROFITABILITY, DUE TO INTRANSPARENT COMPLEXITIES AND PROJECT INTERDEPENDENCIES. IN ADDITION, EMERGING APPLICATION DOMAINS SUCH AS MOBILE LEARNING & ANALYTICS HEAVILY DEPEND ON A WELL-STRUCTURED DATA FOUNDATION WITH A LONGITUDINALLY PREPARED ARCHITECTURE. THEREFORE, THIS RESEARCH PRESENTS THE DATA WAREHOUSE CAPABILITY MATURITY MODEL (DWCMM) WHICH ENCOMPASSES BOTH TECHNICAL AND ORGANIZATIONAL ASPECTS INVOLVED IN DEVELOPING A DATA WAREHOUSE ENVIRONMENT. THE DWCMM CAN BE USED TO HELP ORGANIZATIONS ASSESS THEIR CURRENT DATA WAREHOUSE SOLUTION AND PROVIDE THEM WITH GUIDELINES FOR FUTURE IMPROVEMENTS. THE DWCMM CONSISTS OF A MATURITY MATRIX AND A MATURITY ASSESSMENT QUESTIONNAIRE WITH 60 QUESTIONS. THE DWCMM HAS BEEN EVALUATED EMPIRICALLY THROUGH EXPERT INTERVIEWS AND CASE STUDIES. WE CONCLUDE THAT THE DWCMM CAN BE SUCCESSFULLY APPLIED IN PRACTICE AND THAT ORGANIZATIONS CAN INTELLIGIBLY UTILIZE THE DWCMM AS A QUICKSCAN INSTRUMENT TO JUMPSTART THEIR DATA WAREHOUSE AND BUSINESS INTELLIGENCE IMPROVEMENT PROCESSES.
FACED WITH THE STATE OF EMERGENCY RESTRICTIONS DUE TO THE GLOBAL COVID-19 PANDEMIC, THE 2020 TEACHING MUSIC ONLINE IN HIGHER EDUCATION CONFERENCE WAS ADAPTIVELY MODIFIED TO EXIST AS A SYNCHRONOUS, ONLINE EVENT. EMBRACING THE OPPORTUNITY TO MODEL ONLINE MUSIC PEDAGOGY IN ITS ONLINE FORMAT, ORGANIZERS TRANSFORMED THE CONFERENCE TO UTILIZE LIVE-STREAMING, VIDEO RECORDINGS AND OTHER ONLINE ACTIVE LEARNING STRATEGIES. A TOTAL OF 143 TERTIARY MUSIC INSTRUCTORS AND GRADUATE STUDENTS FROM 66 INSTITUTIONS LOCATED AROUND THE WORLD TOOK PART IN THE CONFERENCE AT A TIME OF UNPRECEDENTED RESTRICTION ON FACE-TO-FACE LEARNING AND TRAVEL RESTRICTIONS. THIS REPORT IS BOTH IMPORTANT AND TIMELY, AS IT PROVIDES INSIGHTS INTO COMPONENTS THAT WERE FOUND TO BE BOTH NECESSARY AND FUNDAMENTAL TO THE SUCCESS OF THE EVENT FOR MUSIC RESEARCHERS, TEACHERS AND OTHER DELEGATES IN ATTENDANCE. KEY AREAS RELATED TO ORGANIZATION, COMMUNICATION, STRUCTURE, PROTOCOLS AND ACTIVITIES WERE EXPLORED THROUGH LEARNING ANALYTICS AND A CONFERENCE EVALUATION. STRATEGIES AND RECOMMENDATIONS ARE INCLUDED TO ASSIST OTHERS WHO WISH TO CREATE AND PRESENT AN ONLINE CONFERENCE THAT EXEMPLIFIES ONLINE PEDAGOGY PRINCIPLES.
ONLINE EDUCATION HAS BROADENED THE AVENUES OF RESEARCH ON STUDENT BEHAVIOR AND PERFORMANCE. HI THIS PAPER, WE SHED LIGHT ON HOW STUDENT INTERACTION LOGS ON VIRTUAL LEARNING ENVIRONMENTS HAVE BEEN ANALYZED. WE CONDUCTED TWO STUDIES: INTERVIEWS WITH INSTRUCTORS AND A SYSTEMATIC MAPPING OF 136 PAPERS ON EDUCATION DATA MINING AND LEARNING ANALYTICS, PUBLISHED BETWEEN 2010 AND FEB/2019. THEN, WE TRIANGULATE THE RESULTS BETWEEN THE INSTRUCTORS' ANSWERS AND RESEARCH RESULTS FOUND IN THE MAPPING. THE MAIN CONTRIBUTIONS OF THIS PAPER ARE: (I) AN OVERVIEW OF BRAZILIAN INSTRUCTORS' EXPERIENCE WITH VIRTUAL LEARNING ENVIRONMENTS AND REQUIREMENTS TO IMPROVE STUDENT ANALYSIS; (II) AN OVERVIEW AND CLASSIFICATION OF THE PAPERS IN THIS TOPIC; (III) A RANKING OF IMPORTANCE OF THE INSTRUCTORS' MAIN STATEMENTS AND PAPER RESULTS; AND (IV) GUIDELINES BASED ON THE REQUIREMENTS MENTIONED BY INSTRUCTORS FOR DESIGNING TOOLS THAT SUPPORT THEIR ANALYSES. FINALLY, WE DISCUSS GAPS IN EXISTING WORK THAT CAN GROUND AND GUIDE FUTURE RESEARCH, SUCH AS TOOLS TO EVALUATE STUDENT INTERACTION LOGS WITH VIDEO LECTURES AND TO ANALYZE INSTRUCTOR INTERACTION LOGS ON VIRTUAL LEARNING ENVIRONMENTS.
EDUCATION 4.0 IS BEING EMPOWERED MORE AND MORE BY ARTIFICIAL INTELLIGENCE (AI) METHODS. WE OBSERVE A CONTINUOUSLY GROWING DEMAND FOR ADAPTIVE AND PERSONALIZED EDUCATION. IN THIS PAPER WE PRESENT AN INNOVATIVE APPROACH TO PROMOTING AI IN EDUCATION 4.0. OUR FIRST CONTRIBUTION IS AI ASSISTED HIGHER EDUCATION PROCESS WITH SMART SENSORS AND WEARABLE DEVICES FOR SELF-REGULATED LEARNING. SECONDLY WE DESCRIBE OUR FIRST RESULTS OF EDUCATION 4.0 DIDACTIC METHODS IMPLEMENTED WITH LEARNING ANALYTICS AND MACHINE LEARNING ALGORITHMS. THE AIM OF THIS CASE STUDY IS TO PREDICT THE FINAL SCORE OF STUDENTS BEFORE PARTICIPATING IN FINAL EXAMINATION. WE PROPOSE AN EARLY RECOGNITION SYSTEM EQUIPPED WITH REAL DATA CAPTURED IN A BLENDED LEARNING COURSE WITH A PERSONALIZED TEST AT THE BEGINNING OF THE SEMESTER, AN ADAPTIVE LEARNING ENVIRONMENT BASED ON AUTO TUTOR BY N. A. CROWDER THEORY WITH ADAPTIVE SELF-ASSESSMENT FEEDBACK. IT IS OBVIOUS THAT FOCUSING ON STUDENTS' SUCCESS AND THEIR EXPERIENCES IS A WIN-WIN SCENARIO FOR STUDENTS AND PROFESSORS AS WELL AS FOR THE ADMINISTRATION.
THE AGE OF DATA ANALYTICS REQUIRES "DATA SCIENTISTS" ACROSS A WIDE RANGE OF BUSINESS DISCIPLINES WITH DEEP KNOWLEDGE OF HOW TO MANAGE AND ANALYSE VAST AMOUNTS OF DATA TO SUPPORT DECISION-MAKING. AS A RESULT, NEW ANALYTICAL TOOLS ARE BEING TAUGHT IN THE MANAGEMENT INFORMATION SYSTEMS (MIS) OR BUSINESS ANALYTICS (BA) PROGRAMS TO FOSTER STUDENTS' DEVELOPMENT OF THIS CRITICAL COMPETENCY. THREE WAVES OF ANALYTICS TOOLS ARE CONSIDERED FROM AN EXPERIENTIAL PERSPECTIVE INCLUDING HOW TO INTRODUCE ANALYTICS TO FIRST-YEAR BUSINESS STUDENTS USING AN ADVANCED DATA ANALYTICS SOFTWARE PACKAGE WITH MULTIPLE TECHNIQUES AND DATA FLOW INTERFACE. SAS ENTERPRISE MINER IS USED TO TEACH TECHNICAL TOPICS AND PERFORM BUSINESS-CENTRIC DATA ANALYTICS. AN INNOVATIVE EXERCISE IS DESIGNED IN SUCH A WAY THAT IT GUIDES FIRST-YEAR BUSINESS STUDENTS THROUGH A LEARNING ANALYTICS JOURNEY TO MOTIVATE THEM TO UNDERSTAND THE NOTION OF DATA ANALYTICS AND TO DEVELOP SKILLS AROUND THIS EMERGING AREA WITHOUT DELVING TOO MUCH INTO EXTRANEOUS TECHNICAL DETAILS. A POST-COURSE SURVEY INDICATED THAT THE EXERCISE HELPED STUDENTS UNDERSTAND THE OPPORTUNITIES AS WELL AS THE CHALLENGES IN DOING ANALYTICS.
LEARNING ANALYTICS A VARIANT OF EDUCATIONAL DATA MINING IS A PROCESS OF COLLECTION, ANALYSIS AND REPORTING OF DATA ABOUT LEARNERS AND THEIR CONTEXTS. ANALYZING PERFORMANCE OF STUDENTS IS A CHALLENGING AND IMPORTANT TASK. USE OF TEMPORAL ASSOCIATION MINING METHODS OF DATA MINING TECHNIQUE CAN BE A BETTER SOLUTION FOR REAL TIME STUDENT PERFORMANCE ANALYSIS. BY USING ASSOCIATION RULE MINING APPROACH, STUDENTS' PERFORMANCE IN COURSES AND FACULTY PERFORMANCE IN COURSE CONDUCTION CAN BE ANALYZED TO DETERMINE THE VARIATION IN COURSE PERFORMANCE. THIS PAPER ATTEMPTS AN APPROACH OF EXPLOITING ASSOCIATION MINING TECHNIQUES TO DETERMINE REAL TIME PATTERNS IN STUDENTS' DATA TO ANALYZE STUDENTS' PERFORMANCE BY USING THE PERFORMANCE OF STUDENTS. THIS ANALYSIS HELPS IN TAKING REMEDIAL ACTIONS FOR THE FORTHCOMING BATCH STUDENTS' PERFORMANCE. IN THIS PAPER WE HAVE DONE THE ANALYSIS, HOW STUDENTS' PERFORMANCES VARY WITH RESPECT TO DIFFERENT BLOOMS KNOWLEDGE LEVEL MAPPED QUESTIONS. WE HAVE PERFORMED THE PATTERN ANALYSIS OF STUDENTS PERFORMANCE WITH RESPECT TO BLOOMS LEVEL MAPPED QUESTIONS USING APRIORI ALGORITHM OF ASSOCIATION RULE MINING TO PROVIDE THE RECOMMENDATIONS.
PRIVACY AND CONFIDENTIALITY ARE CORE CONSIDERATIONS IN EDUCATION, WHILE AT THE SAME TIME, USING AND SHARING DATA-AND, MORE BROADLY, OPEN SCIENCE-IS INCREASINGLY VALUED BY EDITORS, FUNDING AGENCIES, AND THE PUBLIC. THIS MANUSCRIPT RESPONDS TO AN EMPIRICAL INVESTIGATION OF STUDENTS' PERCEPTIONS OF THE USE OF THEIR DATA IN LEARNING ANALYTICS SYSTEMS BY IFENTAHLER AND SCHUMACHER (EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT, 64: 923-938, 2016). WE SUMMARIZE THEIR WORK IN THE CONTEXT OF THE COVID-19 PANDEMIC AND THE RESULTING SHIFT TO DIGITAL MODES OF TEACHING AND LEARNING BY MANY TEACHERS, USING THE TENSION BETWEEN PRIVACY AND OPEN SCIENCE TO FRAME OUR RESPONSE. WE OFFER INFORMED RECOMMENDATIONS FOR EDUCATIONAL TECHNOLOGY RESEARCHERS IN LIGHT OF IFENTAHLER AND SCHUMACHER'S FINDINGS AS WELL AS STRATEGIES FOR NAVIGATING THE TENSION BETWEEN THESE IMPORTANT VALUES. WE CONCLUDE WITH A CALL FOR EDUCATIONAL TECHNOLOGY SCHOLARS TO MEET THE CHALLENGE OF STUDYING LEARNING (AND DISRUPTIONS TO LEARNING) IN LIGHT OF COVID-19 WHILE PROTECTING THE PRIVACY OF STUDENTS IN WAYS THAT GO BEYOND WHAT INSTITUTIONAL REVIEW BOARDS CONSIDER TO BE WITHIN THEIR PURVIEW.
ACCIDENTS CAUSED BY FAULTS ON OVERHEAD POWER LINES HAVE BEEN MORE FREQUENTLY REPORTED UNDER EXTREME WEATHER CONDITIONS AND MAY STRONGLY THREATEN THE SAFETY AND STABILITY OF THE POWER GRIDS, E.G., MASSIVE WILDFIRES CAUSED BY THE ELECTRICAL ARCS OR LINES GETTING IN TOUCH WITH VEGETATION, RELAY MISS-OPERATIONS, ETC. IT HAS BEEN WIDELY RECOGNIZED THAT THE ELECTRIC SAFETY CONCERNS ENGENDERED BY OVERHEAD LINE FAULTS HAVE TO BE TIMELY AND PROPERLY ADDRESSED TO MINIMIZE THE SUBSEQUENT RISKS AND DAMAGES. THE EXISTING MONITORING DEVICES AND PROTECTIVE RELAYS CAN BARELY DETECT HIGH IMPEDANCE FAULTS (HIFS) AND ARE UNABLE TO WARN THE SYSTEM OPERATORS UNTIL SERIOUS ABNORMALITIES OR DAMAGES ARE OBSERVED. AIMING AT AVOIDING THE DAMAGING CONSEQUENCES OF HIFS, AN ONLINE MONITORING SYSTEM EMBEDDED WITH MACHINE LEARNING ANALYTICS IS PROPOSED THAT ENSURES A FAST AND ACCURATE DETECTION OF HIFS IN POWER SYSTEMS. THE PERFORMANCE OF THE PROPOSED ARTIFICIAL INTELLIGENCE ENGINE IS TESTED UNDER A VARIETY OF SIMULATED CONDITIONS AND THE NUMERICAL RESULTS DEMONSTRATE ITS EFFICACY AND SUPERIORITY OVER THE STATE-OF-THE-ART ADVANCEMENTS.
COMPUTER SUPPORTED COLLABORATIVE LEARNING (CSCL) ENVIRONMENTS ARE FREQUENTLY EMPLOYED IN VARIOUS EDUCATIONAL SCENARIOS. AT THE SAME TIME, LEARNING ANALYTICS TOOLS ARE FREQUENTLY USED TO QUANTIFY ACTIVE LEARNERS' PARTICIPATION, COLLABORATION, AND EVOLUTION OVER TIME IN CSCL ENVIRONMENTS. THE AIM OF THIS PAPER IS TO INTRODUCE A NOVEL METHOD TO CLUSTER UTTERANCES FROM ONLINE CONVERSATIONS INTO ZONES BASED ON DIFFERENT LEVELS OF COLLABORATION. THIS METHOD DEPENDS ON TIME SERIES ANALYSES, GROUNDED IN DIALOGISM AND FOCUSES ON THE UNDERLYING SEMANTIC CHAINS THAT ARE ENCOUNTERED IN ADJACENT CONTRIBUTIONS. OUR APPROACH USES CROSS-REFERENCE PATTERNS (CRP) APPLIED ON THE CONVERGENCE FUNCTION BETWEEN TWO UTTERANCES WHICH CAPTURES THEIR SEMANTIC RELATEDNESS. TWO METHODS FOR CLUSTERING UTTERANCES INTO CONVERGENCE REGIONS ARE TESTED: CLUSTERING BY UNIFORMITY AND HIERARCHICAL CLUSTERING. WE FOUND THAT HIERARCHICAL CLUSTERING SURPASSES CLUSTERING BY UNIFORMITY BY CONSIDERING ONLY HIGHLY RELATED CONTRIBUTIONS AND PROVIDING A MORE STRAIGHTFORWARD UNIFICATION MECHANISM. A VALIDATION ANALYSIS ON THE HIERARCHICAL CLUSTERING MODEL WAS PERFORMED ON A CORPUS OF 10 CHAT CONVERSATION REPORTING VARIANCE IN TERMS OF F1 SCORES. THE MODEL AND ENCOUNTERED PROBLEMS ARE DISCUSSED IN DETAIL.
WITH TODAYS INCREASED USE OF DIGITIZATION IN EDUCATION AND SOCIETY THERE ARE NEEDS FOR INNOVATIVE EDUCATIONAL STRATEGIES FOR ENHANCING LEARNING AND TEACHING. IN ADDITION, THERE ARE RISING DEMANDS ON EMBRACING NEW DIGITAL LEARNING AND EDUCATIONAL ENVIRONMENTS AND INTEGRATION OF THE SOCIAL CONTEXT, SUCH AS SOCIAL INTERACTIONS OUTSIDE THE FORMAL INSTITUTIONAL SETTINGS AND SOCIAL MEDIA TO ENHANCE NEW DIGITAL EDUCATIONAL PRACTICES. THIS PAPER INTRODUCE THE CONCEPT OF NEXT GENERATION DIGITAL LEARNING ENVIRONMENT (NGDLE), AND EXPLORE ONE INITITAVE NAMED EUREKOS, FROM MENTORIX, DENMARK, BUT NOW FROM EUREKOS, WHICH HAS BEEN TESTED AND IN PRACTICAL USE FOR MANY LEARNERS AND COURSES, ESPECILLY IN THE NETHERLANDS, AND DENMARK. THE ARCHITECTS BEHIND EUREKOS FOLLOW THE SUCCESSFUL CHARACTERISTIC OF A PERSONAL LEARNING ENVIRONMENT (PLE), I.E. LEARN WITH OTHER PEOPLE, CONTROL OF OWN LEARNING RESOURCES, INTEGRATE LEARNING FROM DIFFERENT INSTITUTIONS AND SOURCES, AND MANAGE ACTIVITIES. EUREKOS PROVIDES COURSE BUILDING FACILITIES, LEARNING TEAMS, AND A SOCIAL HUB INTEGRATED WITH SOCIAL MEDIA. EUREKOS CAN BE LIKENED TO A LEGO BASEPLATE. IT INTEGRATES INTEROPERABILITY, PERSONALIZATION, LEARNING ANALYTICS, COLLABORATION, AND A UNIVERSAL DESIGN WITH SOCIAL MEDIA AND THE NECESSARY SOFTWARE.
THE DECISION-MAKING PROCESS IN THE EDUCATIONAL CONTEXT HAS BEEN WIDELY INVESTIGATED AS AN EFFECTIVE MECHANISM TO SUPPORT EDUCATORS AND ADMINISTRATORS IN DISTANCE AND BLENDED LEARNING. PATTERN DISCOVERY IN DATA PRODUCED BY LEARNING MANAGEMENT SYSTEMS (LMS) ELICITS IMPORTANT INFORMATION ABOUT THE EDUCATIONAL PROCESS, ESPECIALLY WHEN CONSIDERING CORRELATIONS BETWEEN STAKEHOLDERS INTERACTING IN DIFFERENT PERSPECTIVES OF THE LMS LIKE GRADES, ACCESS, FORUM OR MESSAGE COMMUNICATIONS. IN THIS PAPER, WE PROPOSE A DATA AND METADATA MODEL FOR LEARNING ANALYTICS APPLICATIONS USING DATA FROM LMS. THIS MODEL IS LMS INDEPENDENT AND SUPPORTS BUSINESS INTELLIGENCE AND DATA MINING APPLICATIONS FOR DECISION-MAKING IN THE EDUCATIONAL CONTEXT. OUR MODEL CONSIDERS THE USE OF HISTORICAL DATA FROM LMS TREATED UNDER THREE PERSPECTIVES THAT CAN BE ANALYZED INDIVIDUALLY OR CORRELATED. THE MODEL ALSO ALLOWS ANALYSIS CONSIDERING DIFFERENT STAKEHOLDERS (STUDENT, EDUCATOR, ETC.) AND LEVELS OF GRANULARITY (INSTITUTION, DEPARTMENT, COURSE, DISCIPLINE, ETC.). IN THIS SENSE, THE PROPOSED MODEL ALLOWS CONSISTENT AND CORRELATED ANALYSIS OF DATA FROM LMSS. AS A RESULT, OUR MODEL EFFECTIVE COLLABORATES WITH THE EDUCATIONAL PROCESS THROUGH AN EFFICIENT DECISION-MAKING.
IN A SCHOOL CONTEXT, LEARNING IS USUALLY DETECTED BY REPEATED MEASUREMENTS OF THE SKILL OF INTEREST THROUGH A SEQUENCE OF SPECIALLY DESIGNED TESTS; IN PARTICULAR, THIS IS THE CASE WITH TRACKING IMPROVEMENT IN ORAL READING FLUENCY IN ELEMENTARY SCHOOL CHILDREN IN THE U.S. RESULTS PRESENTED IN THIS PAPER SUGGEST THAT IT IS POSSIBLE AND FEASIBLE TO DETECT IMPROVEMENT IN ORAL READING FLUENCY USING DATA COLLECTED DURING CHILDREN'S INDEPENDENT READING OF A BOOK USING THE RELAY READER (TM) APP. WE ARE THUS A STEP CLOSER TO THE VISION OF HAVING A CHILD READ FOR THE STORY, NOT FOR A TEST, YET BEING ABLE TO UNOBTRUSIVELY ASSESS THEIR PROGRESS IN ORAL READING FLUENCY.
EMPIRICAL EVIDENCE OF HOW BACKGROUND MUSIC BENEFITS OR HINDERS LEARNING BECOMES THE CRUX OF OPTIMIZING MUSIC RECOMMENDATION IN EDUCATIONAL SETTINGS. THIS STUDY AIMS TO FURTHER PROBE THE UNDERLYING MECHANISM THROUGH AN EXPERIMENT IN NATURALISTIC SETTING. 30 PARTICIPANTS WERE RECRUITED TO JOIN A FIELD EXPERIMENT WHICH WAS CONDUCTED IN THEIR OWN STUDY PLACES FOR ONE WEEK. DURING THE EXPERIMENT, PARTICIPANTS WERE ASKED TO CONDUCT LEARNING SESSIONS WITH MUSIC IN THE BACKGROUND AND COLLECT MUSIC TRACKS THEY DEEMED SUITABLE FOR LEARNING USING A NOVEL MOBILE-BASED MUSIC DISCOVERY APPLICATION. A SET OF PARTICIPANT-RELATED, CONTEXT-RELATED, AND MUSIC-RELATED DATA WERE COLLECTED VIA A PRE-EXPERIMENT QUESTIONNAIRE, SURVEYS POPPED UP IN THE MUSIC APP, AND THE LOGGING SYSTEM OF THE MUSIC APP. PRELIMINARY RESULTS REVEAL CORRELATIONS BETWEEN CERTAIN MUSIC CHARACTERISTICS AND LEARNERS' TASK ENGAGEMENT AND PERCEIVED TASK PERFORMANCE. THIS STUDY IS EXPECTED TO PROVIDE EVIDENCE FOR UNDERSTANDING COGNITIVE AND EMOTIONAL DIMENSIONS OF BACKGROUND MUSIC DURING LEARNING, AS WELL AS IMPLICATIONS FOR THE ROLE OF PERSONALIZATION IN THE SELECTION OF BACKGROUND MUSIC FOR FACILITATING LEARNING.
THIS PAPER PRESENTS AN APPROACH TO AUTOMATIC LABELING OF THE CONTENT OF MESSAGES IN ONLINE DISCUSSION ACCORDING TO THE CATEGORIES OF SOCIAL PRESENCE. TO ACHIEVE THIS GOAL, THE PROPOSED APPROACH IS BASED ON A COMBINATION OF TRADITIONAL TEXT MINING FEATURES AND WORD COUNTS EXTRACTED WITH THE USE OF ESTABLISHED LINGUISTIC FRAMEWORKS (I.E., LIWC AND COH-METRIX). THE BEST PERFORMING CLASSIFIER OBTAINED 0.95 AND 0.88 FOR ACCURACY AND COHEN'S KAPPA, RESPECTIVELY. THIS PAPER ALSO PROVIDES SOME THEORETICAL INSIGHTS INTO THE NATURE OF SOCIAL PRESENCE BY LOOKING AT THE CLASSIFICATION FEATURES THAT WERE MOST RELEVANT FOR DISTINGUISHING BETWEEN THE DIFFERENT CATEGORIES. FINALLY, THIS STUDY ADOPTED EPISTEMIC NETWORK ANALYSIS TO INVESTIGATE THE STRUCTURAL CONSTRUCT VALIDITY OF THE AUTOMATIC CLASSIFICATION APPROACH. NAMELY, THE ANALYSIS SHOWED THAT THE EPISTEMIC NETWORKS PRODUCED BASED ON MESSAGES MANUALLY AND AUTOMATICALLY CODED PRODUCED NEARLY IDENTICAL RESULTS. THIS FINDING THUS PRODUCED EVIDENCE OF THE STRUCTURAL VALIDITY OF THE AUTOMATIC APPROACH.
STUDENTS' QUESTIONS ARE ESSENTIAL TO HELP TEACHERS IN ASSESSING THEIR UNDERSTANDING AND ADAPTING THEIR PEDAGOGY. HOWEVER, IN A FLIPPED CLASSROOM CONTEXT WHERE MANY QUESTIONS ARE ASKED ONLINE TO BE ADDRESSED IN CLASS, SELECTING QUESTIONS CAN BE DIFFICULT FOR TEACHERS. TO HELP THEM IN THIS TASK, WE PRESENT HERE THREE ALTERNATIVE WAYS OF ORGANIZING QUESTIONS: ONE BASED ON PEDAGOGICAL NEEDS, ONE BASED ON ESTIMATED STUDENTS' PROFILES AND ONE MIXING BOTH APPROACHES. RESULTS OF A SURVEY FILLED BY 37 TEACHERS IN A FLIPPED CLASSROOM PEDAGOGY SHOW NO CONSENSUS OVER A SINGLE ORGANIZATION. A CLUSTER ANALYSIS BASED ON TEACHERS' FLIPPED CLASSROOM EXPERIENCE ALLOWED US TO DISTINGUISH TWO PROFILES, BUT THEY WERE NOT ASSOCIATED WITH ANY PARTICULAR QUESTION ORGANIZATION PREFERENCE. QUALITATIVE RESULTS SUGGEST THE NEED FOR DIFFERENT ORGANIZATIONS MAY RELY MORE ON A PEDAGOGICAL PHILOSOPHY AND ADVOCATES FOR DIFFERENTIATED DASHBOARDS.
THIS PAPER PRESENTS A STUDY THAT EXAMINED AUTOMATED CROSS-LANGUAGE CLASSIFICATION OF ONLINE DISCUSSION MESSAGES FOR THE LEVELS OF COGNITIVE PRESENCE, A KEY CONSTRUCT FROM THE WIDELY USED COMMUNITY OF INQUIRY (COI) MODEL OF ONLINE LEARNING. SPECIFICALLY, WE EXAMINED THE CLASSIFICATION OF 1,500 PORTUGUESE LANGUAGE DISCUSSION MESSAGES USING A CLASSIFIER TRAINED ON A CORPUS OF THE 1,747 ENGLISH LANGUAGE DISCUSSION MESSAGES. IN THE STUDY, A RANDOM FOREST CLASSIFIER WAS DEVELOPED USING A SMALL SET OF 108 VALIDATED INDICATORS OF PSYCHOLOGICAL PROCESSES, LINGUISTIC COHERENCE, AND ONLINE DISCUSSION STRUCTURE. THE CLASSIFIER OBTAINED 67% ACCURACY AND COHEN'S K OF 0.32, SHOWING A MODERATE LEVEL OF INTER-RATER AGREEMENT ABOVE CHANCE AND THE GENERAL VIABILITY OF THE PROPOSED APPROACH. MOST IMPORTANTLY, THE FINDINGS SUGGEST THAT CERTAIN ASPECTS OF COGNITIVE PRESENCE CONSTRUCT ARE HIGHLY GENERALIZABLE AND TRANSFER ACROSS DIFFERENT LANGUAGES. FINALLY, THE PAPER ALSO PRESENTS A NOVEL METHOD FOR ADDRESSING CLASS IMBALANCE PROBLEM USING A GENERIC ALGORITHM HEURISTIC TECHNIQUE, WHICH PROVIDED SUBSTANTIAL IMPROVEMENTS OVER THE USE OF IMBALANCED DATASET. RESULTS AND PRACTICAL IMPLICATIONS ARE FURTHER DISCUSSED.
PERFORMANCE AND CONSISTENCY PLAY A LARGE ROLE IN LEARNING. THIS STUDY ANALYZES THE RELATION BETWEEN CONSISTENCY IN STUDENTS' ONLINE WORK HABITS AND ACADEMIC PERFORMANCE IN A BLENDED COURSE. WE UTILIZE THE DATA FROM LOGS RECORDED BY A LEARNING MANAGEMENT SYSTEM (LMS) IN TWO INFORMATION TECHNOLOGY COURSES. THE TWO COURSES REQUIRED THE COMPLETION OF MONTHLY ASYNCHRONOUS ONLINE DISCUSSION TASKS AND WEEKLY ASSIGNMENTS, RESPECTIVELY. WE MEASURE CONSISTENCY BY USING DATA TIME WARPING (DTW) DISTANCE FOR TWO SUCCESSIVE TASKS (ASSIGNMENTS OR DISCUSSIONS), AS AN APPROPRIATE MEASURE TO ASSESS SIMILARITY OF TIME SERIES, OVER 11-DAY TIMELINE STARTING 10 DAYS BEFORE AND UP TO THE SUBMISSION DEADLINE. WE FOUND MEANINGFUL CLUSTERS OF STUDENTS EXHIBITING SIMILAR BEHAVIOR AND WE USE THESE TO IDENTIFY THREE DISTINCT CONSISTENCY PATTERNS: HIGHLY CONSISTENT, INCREMENTALLY CONSISTENT, AND INCONSISTENT USERS. WE ALSO FOUND EVIDENCE OF SIGNIFICANT ASSOCIATIONS BETWEEN THESE PATTERNS AND LEARNER'S ACADEMIC PERFORMANCE.
TEACHERS PLAY AN ESSENTIAL ROLE DURING COLLABORATIVE LEARNING. TO PROVIDE EFFECTIVE SUPPORT, TEACHERS HAVE TO BE CONSTANTLY AWARE OF STUDENTS' ACTIVITIES AND MAKE FAST DECISIONS ABOUT WHICH GROUP TO OFFER SUPPORT, WITHOUT DISRUPTING STUDENTS' COLLABORATIVE PROCESS. TEACHER DASHBOARDS ARE VISUAL DISPLAYS THAT PROVIDE ANALYTICS ABOUT LEARNERS TO HELP TEACHERS INCREASE THEIR AWARENESS OF THE SITUATION. HOWEVER, IF TEACHERS ARE NOT ABLE TO EFFICIENTLY AND EFFECTIVELY DISTILL INFORMATION FROM THE DASHBOARD, THE DASHBOARD CAN BECOME AN OBSTACLE INSTEAD OF AN AID. IN THE PRESENT STUDY, WE COMPARED DASHBOARDS THAT PROVIDE INFORMATION (MIRRORING) TO DASHBOARDS THAT PROVIDE INFORMATION AND ALERT THE TEACHER TO GROUPS THAT ARE IN NEED OF SUPPORT (ADVISING). TEACHERS WERE SHOWN STANDARDIZED, FICTITIOUS COLLABORATIVE SITUATIONS ON ONE OF THE TYPES OF DASHBOARDS AND WERE ASKED TO DETECT THE GROUP THAT WAS IN NEED OF SUPPORT. THE RESULTS SHOWED THAT TEACHERS IN THE ADVISING CONDITION MORE OFTEN DETECTED THE PROBLEMATIC GROUP, NEEDED LESS EFFORT TO DO SO, AND WERE MORE CONFIDENT OF THEIR DECISIONS. THE TEACHER-DASHBOARD INTERACTION PATTERNS SHOWED THAT TEACHERS IN THE ADVISING CONDITION GENERALLY STARTED BY CHECKING THE GIVEN ALERT, BUT ALSO THAT THEY TRIED TO LOOK AT AS MUCH INFORMATION ABOUT OTHER GROUPS AS THEY COULD. IN THE MIRRORING CONDITION, TEACHERS GENERALLY STARTED BY EXAMINING INFORMATION FROM CLASS OVERVIEWS, BUT DID NOT ALWAYS HAVE TIME TO CHECK INFORMATION FOR INDIVIDUAL GROUPS. THESE FINDINGS ARE DISCUSSED IN LIGHT OF THE ROLE OF A TEACHER DASHBOARD IN TEACHERS' DECISION MAKING IN THE CONTEXT OF STUDENT COLLABORATION.
THIS PAPER PRESENTS THE INTEGRATED METHODOLOGICAL FRAMEWORK (IMF) WHICH USES SOCIAL NETWORK ANALYSIS (SNA) TO STRUCTURALLY IDENTIFY COMMUNITIES IN HIGHER EDUCATION ONLINE LEARNING (HEOL). DECADES OF RESEARCH SPEAKS FOR THE VALUE OF COMMUNITY-BASED LEARNING ALBEIT IN TRADITIONAL, BLENDED, OR ONLINE ENVIRONMENTS. THE COMMUNITIES OF PRACTICE (COP) AND COMMUNITY OF INQUIRY (COI) ARE WELL-ESTABLISHED, EMPIRICALLY TESTED FRAMEWORKS THAT HAVE BEEN EFFECTIVELY USED FOR EXPLORATION OF COMMUNITY-BASED LEARNING IN PROFESSIONAL AND EDUCATIONAL CONTEXTS. TYPICALLY, RESEARCH USING THESE FRAMEWORKS HAS REQUIRED EXTENSIVE QUALITATIVE ANALYSIS MAKING IT TEDIOUS AND TIME-CONSUMING. PIVOTING ON STRUCTURAL SIMILARITIES BETWEEN NETWORKS AND COMMUNITIES, THE IMF EMBEDS SNA CONSTRUCTS IN STRUCTURAL COMPONENTS OF THE COP AND COI FRAMEWORKS. BY STRUCTURALLY IDENTIFYING A COP AND COI, THE IMF ALLOWS TARGETED, SELECTIVE QUALITATIVE ANALYSIS THUS REDUCING THE EXTENT OF QUALITATIVE ANALYSIS REQUIRED PREVIOUSLY IN RESEARCH USING THE COP AND COI FRAMEWORKS. APPLICATION OF THE IMF IS DEMONSTRATED IN A CASE STUDY ON AN ONLINE BLOGGING NETWORK. THE STUDY SUBSTANTIATES THE IMF AS AN EFFECTIVE FRAMEWORK FOR STRUCTURAL IDENTIFICATION OF A COP AND COI. THE VALIDITY AND ROBUSTNESS OF THE IMF IS BEING FURTHER TESTED IN ONGOING RESEARCH.
ONLINE EDUCATION CAN BE USED AS A CATALYST FOR GAINING KNOWLEDGE ON LEARNING AND LEARNING PROCESSES DUE TO ITS GENERATION OF MASSIVE CORPORA OF DATA ON STUDENT BEHAVIOUR. THIS KNOWLEDGE CAN THEN BE UTILIZED TO IMPROVE THE QUALITY OF EDUCATION. SINCE 2013 THE DELFT UNIVERSITY OF TECHNOLOGY (TU DELFT) MASSIVE OPEN ONLINE COURSES (MOOC) PROGRAMME HAS CREATED AND RUN OVER FORTY MOOCS, GAINING MORE THAN ONE MILLION ENROLLED STUDENTS IN THE PROCESS. AS TU DELFT'S MOOC PROGRAMME DEVELOPED AND EXPANDED OVER TIME, AN ORGANISATIONAL STRUCTURE WAS CREATED IN WHICH EDUCATIONAL PROCESSES AND RESEARCH ACTIVITIES WERE ALIGNED AND INTEGRATED. THROUGH THIS INTEGRATED ORGANISATIONAL STRUCTURE, DATA IS COLLECTED AND ANALYSED, WHICH HAS RESULTED IN A SUBSTANTIAL NUMBER OF EVALUATION REPORTS, ANALYSES, AND ACADEMIC PAPERS. TU DELFT HAS BENEFITED FROM THE INSIGHTS FROM THIS COMBINED OUTPUT AND HAS ADAPTED TO THE FINDINGS BOTH IN ONLINE AND ON-CAMPUS COURSE DESIGN. THIS WAY TU DELFT'S MOOC PROGRAMME PROVIDES A VALUABLE ENVIRONMENT FOR INNOVATING EDUCATIONAL DESIGN EXPERIENCE AND DEVELOPING NEW EDUCATIONAL DELIVERY STRATEGIES THAT CAN ALSO BE USED TO IMPROVE ON-CAMPUS EDUCATION.
PURPOSE THIS STUDY AIMS TO INVESTIGATE THE FEASIBILITY OF DEVELOPING GENERAL PREDICTIVE MODELS FOR USING THE LEARNING MANAGEMENT SYSTEM (LMS) DATA TO PREDICT STUDENT PERFORMANCES IN VARIOUS COURSES. THE AUTHORS FOCUSED ON EXAMINING THREE PRACTICAL BUT IMPORTANT QUESTIONS: ARE THERE A COMMON SET OF STUDENT ACTIVITY VARIABLES THAT PREDICT STUDENT PERFORMANCE IN DIFFERENT COURSES? WHICH MACHINE-LEARNING CLASSIFIERS TEND TO PERFORM CONSISTENTLY WELL ACROSS DIFFERENT COURSES? CAN THE AUTHORS DEVELOP A GENERAL MODEL FOR USE IN MULTIPLE COURSES TO PREDICT STUDENT PERFORMANCE BASED ON LMS DATA? DESIGN/METHODOLOGY/APPROACH THREE MANDATORY UNDERGRADUATE COURSES WITH LARGE CLASS SIZES WERE SELECTED FROM THREE DIFFERENT FACULTIES AT A LARGE WESTERN CANADIAN UNIVERSITY, NAMELY, FACULTIES OF SCIENCE, ENGINEERING AND EDUCATION. COURSE-SPECIFIC MODELS FOR THESE THREE COURSES WERE BUILT AND COMPARED USING DATA FROM TWO SEMESTERS, ONE FOR MODEL BUILDING AND THE OTHER FOR GENERALIZABILITY TESTING. FINDINGS THE INVESTIGATION HAS LED THE AUTHORS TO CONCLUDE THAT IT IS NOT DESIRABLE TO DEVELOP A GENERAL MODEL IN PREDICTING COURSE FAILURE ACROSS VARIABLE COURSES. HOWEVER, FOR THE SCIENCE COURSE, THE PREDICTIVE MODEL, WHICH WAS BUILT ON DATA FROM ONE SEMESTER, WAS ABLE TO IDENTIFY ABOUT 70% OF STUDENTS WHO FAILED THE COURSE AND 70% OF STUDENTS WHO PASSED THE COURSE IN ANOTHER SEMESTER WITH ONLY LMS DATA EXTRACTED FROM THE FIRST FOUR WEEKS. ORIGINALITY/VALUE THE RESULTS OF THIS STUDY ARE PROMISING AS THEY SHOW THE USABILITY OF LMS FOR EARLY PREDICTION OF STUDENT COURSE FAILURE, WHICH HAS THE POTENTIAL TO PROVIDE STUDENTS WITH TIMELY FEEDBACK AND SUPPORT IN HIGHER EDUCATION INSTITUTIONS.
IN THE CONTEXT OF ONLINE PROGRAMMING HOMEWORK FOR A UNIVERSITY COURSE, WE EXPLORE THE EXTENT TO WHICH LEARNERS ENGAGE WITH OPTIONAL PROMPTS TO SELF-EXPLAIN ANSWERS THEY CHOOSE FOR PROBLEMS. SUCH PROMPTS ARE KNOWN TO BENEFIT LEARNING IN LABORATORY AND CLASSROOM SETTINGS [4], BUT THERE ARE LESS DATA ABOUT THE EXTENT TO WHICH STUDENTS ENGAGE WITH THEM WHEN THEY ARE OPTIONAL ADDITIONS TO ONLINE HOMEWORK. WE REPORT DATA FROM A DEPLOYMENT OF SELF-EXPLANATION PROMPTS IN ONLINE PROGRAMMING HOMEWORK, PROVIDING INSIGHT INTO HOW THE FREQUENCY OF WRITING EXPLANATIONS IS CORRELATED WITH DIFFERENT VARIABLES, SUCH AS HOW EARLY STUDENTS START HOMEWORK, WHETHER THEY GOT A PROBLEM CORRECT, AND HOW PROFICIENT THEY ARE IN THE LANGUAGE OF INSTRUCTION. WE ALSO REPORT SUGGESTIVE RESULTS FROM A RANDOMIZED EXPERIMENT COMPARING SEVERAL METHODS FOR INCREASING THE RATE AT WHICH PEOPLE WRITE EXPLANATIONS, SUCH AS INCLUDING MORE THAN ONE KIND OF PROMPT. THESE FINDINGS PROVIDE INSIGHT INTO PROMISING DIMENSIONS TO EXPLORE IN UNDERSTANDING HOW REAL STUDENTS MAY ENGAGE WITH PROMPTS TO EXPLAIN ANSWERS.
THIS PAPER DESCRIBES WORK IN PROGRESS TO ANSWER THE QUESTION OF HOW WE CAN IDENTIFY AND MODEL THE DEPTH AND QUALITY OF STUDENT PARTICIPATION IN CLASS DISCUSSION FORUMS USING THE CONTENT OF THE DISCUSSION FORUM MESSAGES. WE LOOK AT TWO WIDELY-STUDIED FRAMEWORKS FOR ASSESSING CRITICAL DISCOURSE AND COGNITIVE ENGAGEMENT: THE ICAP AND COMMUNITY OF INQUIRY (COI) FRAMEWORKS. OUR GOAL IS TO DISCOVER WHERE THEY AGREE AND WHERE THEY OFFER COMPLEMENTARY PERSPECTIVES ON LEARNING. IN THIS STUDY, WE TRAIN PREDICTIVE CLASSIFIERS FOR BOTH FRAMEWORKS ON THE SAME DATA SET IN ORDER TO DISCOVER WHICH ATTRIBUTES ARE MOST PREDICTIVE AND HOW THOSE CORRELATE WITH THE FRAMEWORK LABELS. WE FIND THAT GREATER DEPTH AND QUALITY OF PARTICIPATION IS ASSOCIATED WITH LONGER AND MORE COMPLEX MESSAGES IN BOTH FRAMEWORKS, AND THAT THE THREADED REPLY STRUCTURE MATTERS MORE THAN TEMPORAL ORDER. WE FIND SOME IMPORTANT DIFFERENCES AS WELL, PARTICULARLY IN THE TREATMENT OF MESSAGES OF AFFIRMATION.
THIS PAPER REPORTS ON EARLY STAGES OF A MACHINE LEARNING RESEARCH PROJECT, WHERE PHASES OF COGNITIVE PRESENCE IN MOOC DISCUSSIONS WERE MANUALLY CODED IN PREPARATION FOR TRAINING AUTOMATED COGNITIVE CLASSIFIERS. WE PRESENT A MANUAL-CLASSIFICATION RUBRIC COMBINING GARRISON, ANDERSON AND ARCHER'S [11] CODING SCHEME WITH PARK'S [25] REVISED VERSION FOR A TARGET MOOC. THE INTER-RATER RELIABILITY BETWEEN TWO RATERS ACHIEVED 95.4% AGREEMENT WITH A COHEN'S WEIGHTED KAPPA OF 0.96, DEMONSTRATING OUR CLASSIFICATION RUBRIC IS PLAUSIBLE FOR THE TARGET MOOC DATASET. THE CLASSIFICATION RUBRIC, ORIGINALLY INTENDED FOR FOR-CREDIT, UNDERGRADUATE COURSES, CAN BE APPLIED TO A MOOC CONTEXT. WE FOUND THAT THE MAIN DISAGREEMENTS BETWEEN TWO RATERS LAY ON ADJACENT COGNITIVE PHASES, IMPLYING THAT ADDITIONAL CATEGORIES MAY EXIST BETWEEN COGNITIVE PHASES IN SUCH MOOC DISCUSSION MESSAGES. OVERALL, OUR RESULTS SUGGEST A RELIABLE RUBRIC FOR CLASSIFYING COGNITIVE PHASES IN DISCUSSION MESSAGES OF THE TARGET MOOC BY TWO RATERS. THIS INDICATES WE ARE IN A POSITION TO APPLY MACHINE LEARNING ALGORITHMS WHICH CAN ALSO CATER FOR DATA WITH INTER-RATER DISAGREEMENTS IN FUTURE AUTOMATED CLASSIFICATION STUDIES.
MODELING STUDENT KNOWLEDGE IS IMPORTANT FOR ASSESSMENT DESIGN, ADAPTIVE TESTING, CURRICULUM DESIGN, AND PEDAGOGICAL INTERVENTION. THE ASSESSMENT DESIGN COMMUNITY HAS PRIMARILY FOCUSED ON CONTINUOUS LATENT-SKILL MODELS WITH STRONG CONDITIONAL INDEPENDENCE ASSUMPTIONS AMONG KNOWLEDGE ITEMS, WHILE THE PREREQUISITE DISCOVERY COMMUNITY HAS DEVELOPED MANY MODELS THAT AIM TO EXPLOIT THE INTERDEPENDENCE OF DISCRETE KNOWLEDGE ITEMS. THIS PAPER ATTEMPTS TO BRIDGE THE GAP BY ASKING, "WHEN DOES MODELING ASSESSMENT ITEM INTERDEPENDENCE IMPROVE PREDICTIVE ACCURACY?" A NOVEL ADAPTIVE TESTING EVALUATION FRAMEWORK IS INTRODUCED THAT IS AMENABLE TO TECHNIQUES FROM BOTH COMMUNITIES, AND AN EFFICIENT ALGORITHM, DIRECTED ITEM-DEPENDENCE AND CONFIDENCE THRESHOLDS (DIDACT), IS INTRODUCED AND COMPARED WITH AN ITEM-RESPONSE-THEORY BASED MODEL ON SEVERAL REAL AND SYNTHETIC DATASETS. EXPERIMENTS SUGGEST THAT ASSESSMENTS WITH CLOSELY RELATED QUESTIONS BENEFIT SIGNIFICANTLY FROM MODELING ITEM INTERDEPENDENCE.
AT PRESENT, UNIVERSITIES COLLECT STUDY-RELATED DATA ABOUT THEIR STUDENTS. THIS INFORMATION CAN BE USED TO SUPPORT STUDENTS AT RISK OF FAILING THEIR STUDIES. AT THE FACULTY OF MECHANICAL ENGINEERING (FME), CZECH TECHNICAL UNIVERSITY IN PRAGUE (CTU), THE GROUP OF THE FIRST-YEAR STUDENTS IS THE MOST VULNERABLE. THE MOST CRITICAL PART OF THE FIRST YEAR IS THE WINTER EXAM PERIOD WHEN STUDENTS USUALLY DIVIDE INTO THOSE WHO WILL PASS AND FAIL. ONE OF THE MOST IMPORTANT ABILITIES, STUDENTS NEED TO LEARN, IS EXAM PLANNING, AND OUR RESEARCH AIMS AT THE EXPLORATION OF THE EXAM STRATEGIES OF SUCCESSFUL STUDENTS. THESE STRATEGIES CAN BE USED FOR IMPROVING FIRST-YEAR STUDENTS RETENTION. THE OUTGOING RESEARCH ON THE ANALYSIS OF EXAM STRATEGIES OF THE FIRST-YEAR STUDENTS IN THE ACADEMIC YEAR 2017/2018 IS REPORTED. FROM A TOTAL OF 361 FIRST-YEAR STUDENTS, SUCCESSFUL STUDENTS HAVE BEEN SELECTED. THE SUCCESSFUL STUDENT IS THE ONE WHO FINISHED ALL THREE MANDATORY EXAMS BEFORE THE END OF THE FIRST EXAM PERIOD. FROM THE EXAM SEQUENCES OF 153 SELECTED STUDENTS, A "LAYERED" MARKOV CHAIN PROBABILISTIC MODEL HAS BEEN CONSTRUCTED. IT UNCOVERED THE MOST COMMON EXAM STRATEGIES TAKEN BY THOSE STUDENTS.
PROBLEM SOLVING, PARTICULARLY IN DISCIPLINES SUCH AS GENETICS, IS AN ESSENTIAL BUT DIFFICULT COMPETENCY FOR STUDENTS TO MASTER. PRIOR WORK INDICATED THAT TRACE DATA CAN BE LEVERAGED TO MEASURE THE INVISIBLE COGNITIVE PROCESSES THAT UNDERGIRD LEARNING ACTIVITIES SUCH AS PROBLEM SOLVING. BUILDING ON PRIOR WORK AND GIVEN THE IMPORTANCE AND DIFFICULTIES ASSOCIATED WITH GENETICS PROBLEM SOLVING, WE USED UNSUPERVISED STATISTICAL METHODS (K-MEANS CLUSTERING AND FEATURE SELECTION) TO CHARACTERIZE THE PATTERNS OF PROCESSES STUDENTS USE DURING GENETICS PROBLEM SOLVING AND THE RELATIONSHIP TO PROXIMAL AND DISTAL OUTCOMES. AT THE LEVEL OF THE INDIVIDUAL PROBLEM, WE FOUND THAT CONCLUSION PROCESSES, SUCH AS MAKING CLAIMS AND ELIMINATING POSSIBLE SOLUTIONS, WAS AN IMPORTANT INTERIM STEP AND ASSOCIATED WITH GETTING A PARTICULAR PROBLEM CORRECT. SURPRISINGLY, WE NOTED THAT A DIFFERENT SET OF PROCESSES WAS ASSOCIATED WITH COURSE OUTCOMES. STUDENTS WHO PERFORMED MULTIPLE METACOGNITIVE STEPS (E.G. MONITORING, CHECKING, PLANNING) IN A ROW OR WHO ENGAGED IN EXECUTION STEPS (E.G. USING INFORMATION, DRAWING A PICTURE, RESTATING THE PROCESS) AS PART OF PROBLEM SOLVING DURING THE SEMESTER PERFORMED BETTER ON FINAL ASSESSMENTS. WE FOUND A THIRD SET OF PRACTICES, MAKING CONSECUTIVE CONCLUSION PROCESSES, METACOGNITIVE PROCESSES PRECEDING REASONING AND REASONING PRECEDING CONCLUSIONS TO BE IMPORTANT FOR SUCCESS AT BOTH THE PROBLEM LEVEL AND ON FINAL ASSESSMENTS. THIS SUGGESTS THAT DIFFERENT PROBLEM-SOLVING PROCESSES ARE ASSOCIATED WITH SUCCESS ON DIFFERENT COURSE BENCHMARKS. THIS WORK RAISES PROVOCATIVE QUESTIONS REGARDING BEST PRACTICES FOR TEACHING PROBLEM SOLVING IN GENETICS CLASSROOMS.
HERE WE PRESENT THE EXPERIENCE OF THE STELLAE RESEARCH GROUP (UNIVERSITY OF SANTIAGO DE COMPOSTELA) IN THE USE OF ACADEMIC SOCIAL NETWORKS AS A MEANS FOR TEACHING AND LEARNING IN HIGHER EDUCATION. IT IS AN EXPERIENCE USING STUDENT E-PORTFOLIOS AS ARTEFACTS SPANNING THE BREADTH OF TEACHING, LEARNING AND ASSESSMENT PROCESSES. THE TEACHING APPROACH RESPECTS STUDENT DIVERSITY, PROMOTES AUTONOMY, PROPOSES AN ELIMINATION OF BARRIERS BETWEEN FORMAL AND INFORMAL LEARNING SPACES AND AIMS TO ENCOURAGE SELF AND SOCIO-REGULATED LEARNING. THE STUDY AIMS TO ANALYSE THE SOCIAL NETWORK CONDITIONS THAT STIMULATE THE DEVELOPMENT OF THESE PROCESSES AND HOW TO USE DATA GENERATED IN THIS CONTEXT TO IMPROVE PROPOSALS. WE WORKED FROM A QUALITATIVE PERSPECTIVE COMBINED WITH LEARNING ANALYTICS QUANTITATIVE TECHNIQUES. THE RESULTS SHOW IMPROVED TEACHER ACCESS TO DATA REGARDING STUDENT LEARNING PROCESSES, AND BETTER KNOWLEDGE OF STUDENT NETWORK BEHAVIOUR AND MOTIVATION DATA. WE CONCLUDE THAT THE USE OF SOCIAL NETWORKS CREATES A PARTICIPATION STRUCTURE AMONG STUDENTS THAT PROVIDES GREAT OPPORTUNITIES, FOSTERS SELF AND SOCIO-REGULATION SKILLS AND PROMOTES A POSITIVE CORRELATION BETWEEN THE NUMBER OF INTERACTIONS AND MARKS OBTAINED.
PROMOTED BY THE GROWING ACCESS TO MOBILE DEVICES AND THE EMPHASIS ON SITUATED LEARNING, LOCATION-BASED TOOLS ARE BEING USED INCREASINGLY IN EDUCATION. MULTIPLE STAKEHOLDERS COULD BENEFIT FROM UNDERSTANDING THE LEARNING AND TEACHING PROCESSES TRIGGERED BY THESE TOOLS, SUPPORTED BY DATA ANALYTICS. FOR INSTANCE, PRACTITIONERS COULD USE ANALYTICS TO MONITOR AND REGULATE THE IMPLEMENTATION OF THEIR LEARNING DESIGNS (LD), AS WELL AS TO ASSESS THEIR IMPACT AND EFFECTIVENESS. ALSO, THE COMMUNITY AROUND SPECIFIC TOOLS-SUCH AS RESEARCHERS, MANAGERS OF EDUCATIONAL INSTITUTIONS, AND DEVELOPERS-COULD USE ANALYTICS TO FURTHER IMPROVE THE TOOLS AND BETTER UNDERSTAND THEIR ADOPTION. THIS PAPER REPORTS THE CO-DESIGN PROCESS OF A LOCATION-BASED AUTHORING TOOL THAT INCORPORATES MULTI-STAKEHOLDER ANALYTICS FOR LD FEATURES. IT CONTRIBUTES TO THE RESEARCH COMMUNITY THROUGH A CASE STUDY THAT INVESTIGATES HOW ANALYTICS CAN SUPPORT SPECIFIC LD NEEDS OF DIFFERENT STAKEHOLDERS OF LOCATION-BASED TOOLS. RESULTS EMPHASISE OPPORTUNITIES AND IMPLICATIONS OF ALIGNING ANALYTICS AND LD IN LOCATION-BASED LEARNING.
DISCUSSION BOARDS CAN PROVIDE A GLIMPSE INTO THE REGULAR AND SUBSTANTIVE INTERACTION REQUIRED IN ONLINE COURSES. ADVANCES IN TECHNOLOGY AND AN INCREASED INTEREST IN LEARNING ANALYTICS NOW PROVIDES RESEARCHERS WITH BILLIONS OF DATA POINTS ABOUT INSTRUCTOR AND STUDENT INTERACTION WITHIN A LEARNING MANAGEMENT SYSTEM (LMS). THIS STUDY USED LMS DATA TO EXPLORE THE FREQUENCY OF INTERACTION BETWEEN INSTRUCTORS AND STUDENTS IN DISCUSSION BOARDS IN ONLINE COURSES AT ONE INSTITUTION. OVERALL, 415 COURSES WERE ANALYZED SPANNING TWO SEMESTERS. RESULTS FROM THE STUDY FOUND THAT THE AVERAGE NUMBER OF POSTS BY AN INSTRUCTOR WAS 32.9. THE AVERAGE INSTRUCTOR INTERACTION WAS 1.49 INSTRUCTOR POSTS PER STUDENT. 23% OF COURSES HAD NO INSTRUCTOR POSTS. STUDENT POSTS AVERAGED 470 PER COURSE AND THE AVERAGE POSTS PER STUDENT WAS 19.9. BASED ON THE DISCUSSION BOARD ACTIVITY, THE MOST DISCUSSION INTERACTION OCCURRED DURING THE FIRST TWO WEEKS OF THE SEMESTER. RESULTS ALSO SUGGESTED THAT THERE IS NO RELATIONSHIP BETWEEN STUDENT SATISFACTION AND THE NUMBER OF TOTAL POSTS IN A COURSE. THE PAPER CONCLUDES WITH IMPLICATIONS FOR RESEARCH AND PRACTICE.
THERE IS A GROWING BODY OF LITERATURE THAT RECOGNIZES THE IMPORTANCE OF APPLYING GAMIFICATION IN EDUCATIONAL SETTINGS. THIS RESEARCH DEVELOPED AN APPLICATION TO GAMIFY STUDENTS' HOMEWORK TO ADDRESS THE CONCERN OF THE STUDENTS' INABILITY TO COMPLETE THEIR HOMEWORK. THIS RESEARCH AIMS TO INVESTIGATE STUDENTS' PERFORMANCE IN DOING THEIR HOMEWORK, AND REFLECTIONS AND PERCEPTIONS OF THE GAMEFUL EXPERIENCE IN GAMIFIED HOMEWORK EXERCISES. BASED ON THE DATA GATHERED FROM EXPERIMENTAL AND CONTROL GROUPS (N = 84) VIA LEARNING ANALYTICS, SURVEY, AND INTERVIEW, THE RESULTS SHOW A HIGH LEVEL OF SATISFACTION ACCORDING TO STUDENTS' FEEDBACK. THE MOST NOTICEABLE FINDING TO EXTRACT FROM THE ANALYSIS IS THAT STUDENTS CAN TAKE ON A PERSONA, EARN POINTS, AND EXPERIENCE A DEEPER SENSE OF ACHIEVEMENT THROUGH DOING THE GAMIFIED HOMEWORK. MOREOVER, THE STUDENTS, ON THE WHOLE, ARE LIKELY TO BE INTRINSICALLY MOTIVATED WHENEVER THE HOMEWORK IS ATTRIBUTED TO FACTORS UNDER THEIR OWN CONTROL, WHEN THEY CONSIDER THAT THEY HAVE THE EXPERTISE TO BE SUCCESSFUL LEARNERS TO ACHIEVE THEIR DESIRED OBJECTIVES, AND WHEN THEY ARE INTERESTED IN DEALING WITH THE HOMEWORK FOR LEARNING, NOT JUST ACHIEVING HIGH GRADES.
THE DEVELOPMENT OF E-LEARNING PLATFORMS AND THEIR EXTENSIVE USE IN SCHOOLS AND UNIVERSITIES ALLOWS STUDENTS FROM ISOLATED LOCATIONS TO REMOTELY PARTICIPATE IN EDUCATIONAL ACTIVITIES. THIS ARTICLE PROVIDES AN ANALYSIS OF PROMINENT INTEROPERABILITY STANDARDS FOR E-LEARNING, EMPHASIZING THEIR ROLE IN THE DEVELOPMENT OF THE CLASSROOM OF THE FUTURE. ALTHOUGH CURRENT E-LEARNING INFRASTRUCTURES SUPPORT THE REMOTE ACCESS TO LEARNING RESOURCES AND ON-LINE ASSESSMENT, THE CLASSROOM OF THE FUTURE WILL REPOSITION THE PROFESSORS' AND STUDENTS' APPROACH TO EDUCATION, AND WILL INCLUDE TOOLS AND TECHNOLOGIES FOR PERSONALIZED LEARNING, VIRTUAL AND AUGMENTED REALITY, CLOUD COMPUTING, 3D-PRINTING, DEEP LEARNING AND LEARNING ANALYTICS. ACHIEVING INTEROPERABILITY AMONG E-LEARNING INFRASTRUCTURES IS ESSENTIAL FOR THE CLASSROOM OF THE FUTURE. HOWEVER, THE FIELD OF E-LEARNING HAS GROWN WITHOUT A CLEAR VISION ON HOW HETEROGENEOUS E-LEARNING SYSTEMS SHOULD INTER-RELATE, AND INTEROPERABILITY REMAINS A BIG CHALLENGE. WE ANALYZE RELEVANT STANDARDIZATION APPROACHES WHICH IN TURN SHAPES AN OVERVIEW OF ENABLING TECHNOLOGIES FOR E-LEARNING. FINALLY WE LOOK AT CHALLENGES FOR E-LEARNING INFRASTRUCTURES INTEROPERABILITY THAT CAN SHAPE FUTURE RESEARCH, TACKLING INCLUSIVE EDUCATION.
THIS PAPER DESCRIBES THE IMPLEMENTATION AND TESTING OF OUR CONCEPT OF ADAPTABLE RUBRICS, DEFINED AS ANALYTICAL RUBRICS THAT ARRANGE ASSESSMENT CRITERIA AT MULTIPLE LEVELS THAT CAN BE EXPANDED ON DEMAND. BECAUSE OF ITS ADAPTABLE NATURE, THESE RUBRICS CANNOT BE IMPLEMENTED IN PAPER FORMATS, NEITHER ARE THEY SUPPORTED BY CURRENT LEARNING MANAGEMENT SYSTEMS (LMS). THE MAIN CONTRIBUTION OF THIS WORK INVOLVES THE ADAPTABLE CAPABILITY OF DIFFERENT LEVELS OF DETAIL, WHICH CAN BE EXPANDED FOR EACH RUBRIC CRITERION AS NEEDED. OUR RUBRICS PLATFORM PROVIDES SPECIALIZED AND INTUITIVE TOOLS TO CREATE AND MODIFY RUBRICS AS WELL AS MANAGING METADATA TO SUPPORT LEARNING ANALYTICS. AS AN EXAMPLE OF A PRACTICAL ASSESSMENT SITUATION, A CASE STUDY ON MECHANICAL COMPUTER AIDED DESIGN (MCAD) SYSTEMS TRAINING IS PRESENTED. THE VALIDATION PROCESS IN THIS SCENARIO PROVED THE EFFECTIVENESS OF OUR ADAPTABLE RUBRIC PLATFORM FOR SUPPORTING FORMATIVE ASSESSMENT IN A MULTIFACETED AND COMPLEX FIELD SUCH AS MCAD. THE SYSTEM ALSO SHOWED THE POTENTIAL OF COLLECTING USER INTERACTION METADATA, WHICH CAN BE USED TO ANALYZE THE EVALUATION PROCESS AND GUIDE FURTHER IMPROVEMENTS IN THE TEACHING STRATEGY.
LEARNING ANALYTICS IS VALUABLE SOURCES OF UNDERSTANDING STUDENTS' BEHAVIOR AND GIVING FEEDBACK TO THEM SO THAT WE CAN IMPROVE THEIR LEARNING ACTIVITIES. ANALYZING COMMENT DATA WRITTEN BY STUDENTS AFTER EACH LESSON HELPS TO GRASP THEIR LEARNING ATTITUDES AND SITUATIONS. THEY CAN BE A POWERFUL SOURCE OF DATA FOR ALL FORMS OF ASSESSMENT. IN THE CURRENT STUDY, WE BREAK DOWN STUDENT COMMENTS INTO DIFFERENT TOPICS BY EMPLOYING TWO TOPIC MODELS: PROBABILISTIC LATENT SEMANTIC ANALYSIS (PLSA), AND LATENT DIRICHLET ALLOCATION (LDA), TO DISCOVER THE TOPICS THAT HELP TO PREDICT FINAL STUDENT GRADES AS THEIR PERFORMANCE. THE OBJECTIVES OF THIS PAPER ARE TWOFOLD: FIRST, DETERMINE HOW THE THREE TIME-SERIES ITEMS: P-, C- AND N-COMMENTS AND THE DIFFICULTY OF A SUBJECT AFFECT THE PREDICTION RESULTS OF FINAL STUDENT GRADES. SECOND, EVALUATE THE RELIABILITY OF PREDICTING STUDENT GRADES BY CONSIDERING THE DIFFERENCES BETWEEN PREDICTION RESULTS OF TWO CONSECUTIVE LESSONS. THE RESULTS OBTAINED CAN HELP TO UNDERSTAND STUDENT BEHAVIOR DURING THE PERIOD OF THE SEMESTER, GRASP PREDICTION ERROR OCCURRED IN EACH LESSON, AND ACHIEVE FURTHER IMPROVEMENT OF THE STUDENT GRADE PREDICTION.
MASSIVE OPEN ONLINE COURSES (MOOCS) ARE A PROMISING APPROACH FOR SCALABLE KNOWLEDGE DISSEMINATION. HOWEVER, THEY ALSO FACE MAJOR CHALLENGES SUCH AS LOW ENGAGEMENT, LOW RETENTION RATE, AND LACK OF PERSONALIZATION. WE PROPOSE ATTENTIVELEARNER(2) , A MULTIMODAL INTELLIGENT TUTOR RUNNING ON UNMODIFIED SMARTPHONES, TO SUPPLEMENT TODAY'S CLICKSTREAM-BASED LEARNING ANALYTICS FOR MOOCS. ATTENTIVELEARNER(2) USES BOTH THE FRONT AND BACK CAMERAS OF A SMARTPHONE AS TWO COMPLEMENTARY AND FINE-GRAINED FEEDBACK CHANNELS IN REAL TIME: THE BACK CAMERA MONITORS LEARNERS' PHOTOPLETHYSMOGRAPHY (PPG) SIGNALS AND THE FRONT CAMERA TRACKS THEIR FACIAL EXPRESSIONS DURING MOOC LEARNING. ATTENTIVELEARNER(2) IMPLICITLY INFERS LEARNERS' AFFECTIVE AND COGNITIVE STATES DURING LEARNING FROM THEIR PPG SIGNALS AND FACIAL EXPRESSIONS. THROUGH A 26-PARTICIPANT USER STUDY, WE FOUND THAT: (1) ATTENTIVELEARNER(2) CAN DETECT 6 EMOTIONS IN MOBILE MOOC LEARNING RELIABLY WITH HIGH ACCURACY (AVERAGE ACCURACY = 84.4%); (2) THE DETECTED EMOTIONS CAN PREDICT LEARNING OUTCOMES (BEST R-2 = 50.6%); AND (3) IT IS FEASIBLE TO TRACK BOTH PPG SIGNALS AND FACIAL EXPRESSIONS IN REAL TIME IN A SCALABLE MANNER ON TODAY'S UNMODIFIED SMARTPHONES.
THE STUDY AIMS AT INVESTIGATING UNIVERSITY STUDENTS' ACCEPTANCE OF A STATISTICS LEARNING PLATFORM TO SUPPORT THE LEARNING OF STATISTICS IN A BLENDED LEARNING CONTEXT. THREE KINDS OF DIGITAL RESOURCES, WHICH ARE SIMULATIONS, ONLINE VIDEOS, AND ONLINE QUIZZES, WERE PROVIDED ON THE PLATFORM. PREMISED ON THE TECHNOLOGY ACCEPTANCE MODEL, WE ADOPTED A REVISED MODEL CONSISTING OF FOUR EXTERNAL FACTORS (SELF-EFFICACY, FACILITATING CONDITIONS, SUBJECTIVE NORM, AND ANXIETY) THAT MAY INFLUENCE STUDENTS' PERCEPTIONS AND ACCEPTANCE OF THE PLATFORM. A MIXED RESEARCH METHOD WAS USED. A TOTAL OF 102 PARTICIPANTS WERE INVOLVED IN THIS STUDY. DATA COLLECTION INCLUDES QUESTIONNAIRES SURVEY, INDIVIDUAL INTERVIEWS, AND FOCUS GROUP DISCUSSIONS. THE FINDINGS SHOW THAT STUDENTS' INTENTION TO USE THE PLATFORM IS AFFECTED BY THEIR ATTITUDE TOWARD THE PLATFORM, WHICH IS SIGNIFICANTLY INFLUENCED BY PERCEIVED USEFULNESS. FURTHER SUGGESTIONS REGARDING THE DESIGN AND IMPLEMENTATION OF A LEARNING PLATFORM ARE PROVIDED BASED ON THE OBSERVATIONS AND RESULTS OF THE STUDY. THIS PRELIMINARY STUDY PROVIDES VALUABLE INSIGHTS INTO OUR FURTHER REFINEMENT OF THE PLATFORM AND DEVELOPMENT OF A LEARNING ANALYTICS PLATFORM IN THE FUTURE FOR BETTER LEARNING OF STUDENTS IN STATISTICS.
STUDENT LEARNING OUTCOMES WITHIN COURSES FORM THE BASIS FOR COURSE COMPLETION AND TIME-TO-GRADUATION STATISTICS, WHICH ARE OF GREAT IMPORTANCE IN EDUCATION, PARTICULARLY HIGHER EDUCATION. BUDGET PRESSURES HAVE LED TO LARGE CLASSES IN WHICH STUDENT-TO-INSTRUCTOR INTERACTION IS VERY LIMITED. MOST OF THE CURRENT EFFORTS TO IMPROVE STUDENT PROGRESS IN LARGE CLASSES, SUCH AS "LEARNING ANALYTICS," (LA) FOCUS ON THE ASPECTS OF STUDENT BEHAVIOR THAT ARE FOUND IN THE LOGS OF LEARNING MANAGEMENT SYSTEMS (LMS), FOR EXAMPLE, FREQUENCY OF SIGNING IN, TIME SPENT ON EACH PAGE, AND GRADES. THESE ARE IMPORTANT, BUT ARE DISTANT FROM PROVIDING HELP TO THE STUDENT MAKING INSUFFICIENT PROGRESS IN A COURSE. WE DESCRIBE A COMPUTER ANALYTICAL METHODOLOGY WHICH INCLUDES A DISSECTION OF THE CONCEPTS IN THE COURSE, EXPRESSED AS A DIRECTED GRAPH, THAT ARE APPLIED TO TEST QUESTIONS, AND USES PERFORMANCE ON THESE QUESTIONS TO PROVIDE FORMATIVE FEEDBACK TO EACH STUDENT IN ANY COURSE FORMAT: FACE-TO-FACE, BLENDED, FLIPPED, OR ONLINE. EACH STUDENT RECEIVES INDIVIDUALIZED ASSISTANCE IN A SCALABLE AND AFFORDABLE MANNER. IT WORKS WITH ANY CLASS DELIVERY TECHNOLOGY, TEXTBOOK, AND LEARNING MANAGEMENT SYSTEM.
OBJECTIVES: TO DESCRIBE PEER-ASSISTED LEARNING (PAL) GROUPS FORMED BY DENTAL UNDERGRADUATE STUDENTS IN A BIOMEDICAL COURSE AND TO INVESTIGATE THE ASSOCIATION OF INDIVIDUAL AND GROUP CHARACTERISTICS WITH ACADEMIC PERFORMANCE. SUBJECTS AND METHODS: IN 2015, 92 FOURTH-YEAR STUDENTS (43 MALES AND 49 FEMALES) IN THE COLLEGE OF DENTISTRY, UNIVERSITY OF DAMMAM, SAUDI ARABIA, WERE INVITED TO FORM PAL GROUPS TO STUDY A UNIT OF A BIOMEDICAL COURSE. AN EXAMINATION WAS USED TO ASSESS THEIR KNOWLEDGE AFTER 2 WEEKS. IN ADDITION, A QUESTIONNAIRE AND SOCIAL NETWORK ANALYSIS WERE USED TO INVESTIGATE (1) INDIVIDUAL STUDENT ATTRIBUTES: GENDER, ROLE, SUBJECT MATTER KNOWLEDGE, GRADE IN PREVIOUS YEAR, TEAMING WITH FRIENDS, PREVIOUS COMMUNICATION WITH TEAMMATES, AND CONTENT DISCUSSION, AND (2) GROUP ATTRIBUTES: GROUP TEACHER'S PREVIOUS GRADE, NUMBER OF COLLEAGUES WITH WHOM A STUDENT CONNECTED, TEAMING WITH FRIENDS, SIMILARITY OF TEAMMATES' PREVIOUS GRADES, AND TEACHER HAVING HIGHER PREVIOUS GRADES THAN OTHER TEAMMATES. REGRESSION ANALYSIS WAS USED TO ASSESS THE ASSOCIATION OF EXAMINATION SCORES WITH INDIVIDUAL AND GROUP ATTRIBUTES. RESULTS: THE RESPONSE RATE WAS 80.4% (74 STUDENTS: 36 MALES AND 38 FEMALES). STUDENTS WHO PREVIOUSLY SCORED GRADES A AND B HAD HIGHER EXAMINATION SCORES THAN STUDENTS WITH GRADES C/LESS (REGRESSION COEFFICIENT = 18.50 AND 13.39) WITHIN THE GROUPS. HIGHER SCORES WERE NOT ASSOCIATED WITH WORKING IN GROUPS INCLUDING FRIENDS ONLY (REGRESSION COEFFICIENT = 1.17) OR WHEN ALL STUDENTS HAD SIMILAR PREVIOUS GRADES (REGRESSION COEFFICIENT = 0.85). CONCLUSIONS: STUDENTS WITH PREVIOUS HIGH GRADES BENEFITED TO A GREATER EXTENT FROM WORKING IN PAL GROUPS. SIMILARITY OF TEAMMATES IN PAL GROUPS WAS NOT ASSOCIATED WITH BETTER SCORES. (C) 2017 S. KARGER AG, BASEL
IN THIS STUDY, WE INVESTIGATED THE DEGREE TO WHICH THE COGNITIVE PROCESSES IN WHICH STUDENTS ENGAGE DURING READING COMPREHENSION COULD BE EXAMINED THROUGH DYNAMICAL ANALYSES OF THEIR NATURAL LANGUAGE RESPONSES TO TEXTS. HIGH SCHOOL STUDENTS (N = 142) GENERATED TYPED SELF-EXPLANATIONS WHILE READING A SCIENCE TEXT. THEY THEN COMPLETED A COMPREHENSION TEST THAT MEASURED THEIR COMPREHENSION AT BOTH SURFACE AND DEEP LEVELS. THE RECURRENT PATTERNS OF THE WORDS IN STUDENTS' SELF-EXPLANATIONS WERE FIRST VISUALIZED IN RECURRENCE PLOTS. THESE VISUALIZATIONS ALLOWED US TO QUALITATIVELY ANALYZE THE DIFFERENT SELF-EXPLANATION PROCESSES OF SKILLED AND LESS SKILLED READERS. THESE RECURRENCE PLOTS THEN ALLOWED US TO CALCULATE RECURRENCE INDICES, WHICH REPRESENTED THE PROPERTIES OF THESE TEMPORAL WORD PATTERNS. RESULTS OF CORRELATION AND REGRESSION ANALYSES REVEALED THAT THESE RECURRENCE INDICES WERE SIGNIFICANTLY RELATED TO THE STUDENTS' COMPREHENSION SCORES AT BOTH SURFACE- AND DEEP LEVELS. ADDITIONALLY, WHEN COMBINED WITH SUMMATIVE METRICS OF WORD USE, THESE INDICES WERE ABLE TO ACCOUNT FOR 32% OF THE VARIANCE IN STUDENTS' OVERALL TEXT COMPREHENSION SCORES. OVERALL, OUR RESULTS SUGGEST THAT RECURRENCE QUANTIFICATION ANALYSIS CAN BE UTILIZED TO GUIDE BOTH QUALITATIVE AND QUANTITATIVE ASSESSMENTS OF STUDENTS' COMPREHENSION.
THE NETWORKED IMPROVEMENT LEARNING AND SUPPORT (NILS) PLATFORM IS AN ONLINE TOOL DESIGNED TO ACCELERATE THE INITIATION AND DEVELOPMENT OF NETWORKED IMPROVEMENT COMMUNITIES IN A DISCIPLINED MANNER. ITS MAIN GOAL IS TO PROMOTE SOCIAL, ORGANIZATIONAL LEARNING THROUGH CURATION AND SYNTHESIS AND TACIT TO EXPLICIT KNOWLEDGE CONVERSION TO FACILITATE KNOWLEDGE CONSTRUCTION AND OWNERSHIP BY THE COMMUNITIES REGARDING IMPROVEMENT PRACTICE IN EDUCATION. IN THIS PROPOSAL WE WILL DISCUSS THE NILS PLATFORM, A FEW USE CASES, AND A PLAN OF ANALYTICS DEVELOPMENT THAT ADVANCES KNOWLEDGE DISSEMINATION AND MONITORS THE HEALTH STATUS OF NETWORKS.
DESPITE THEIR IMPORTANCE FOR EDUCATIONAL PRACTICE, REFLECTIVE WRITINGS ARE STILL MANUALLY ANALYSED AND ASSESSED, POSING A CONSTRAINT ON THE USE OF THIS EDUCATIONAL TECHNIQUE. RECENTLY, RESEARCH STARTED TO INVESTIGATE AUTOMATED APPROACHES FOR ANALYSING REFLECTIVE WRITING. FOUNDATIONAL TO MANY AUTOMATED APPROACHES IS THE KNOWLEDGE OF WORDS THAT ARE IMPORTANT FOR THE GENRE. THIS RESEARCH PRESENTS KEYWORDS THAT ARE SPECIFIC TO SEVERAL CATEGORIES OF A REFLECTIVE WRITING MODEL. THESE KEYWORDS HAVE BEEN DERIVED FROM EIGHT DATASETS, WHICH CONTAIN SEVERAL THOUSAND INSTANCES USING THE LOG-LIKELIHOOD METHOD. BOTH PERFORMANCE MEASURES, THE ACCURACY AND THE COHEN'S KAPPA FOR THESE KEYWORDS WERE ESTIMATED WITH TEN-FOLD CROSS VALIDATION. THE RESULTS REACHED AN ACCURACY OF 0.78 ON AVERAGE FOR ALL EIGHT CATEGORIES AND A FAIR TO GOOD INTERRATER RELIABILITY FOR MOST CATEGORIES EVEN THOUGH IT DID NOT MAKE USE OF ANY SOPHISTICATED RULE-BASED MECHANISMS OR MACHINE LEARNING APPROACHES. THIS RESEARCH CONTRIBUTES TO THE DEVELOPMENT OF AUTOMATED REFLECTIVE WRITING ANALYTICS THAT ARE BASED ON DATADRIVEN EMPIRICAL FOUNDATIONS.
IT IS BECOMING INCREASINGLY IMPORTANT NOT ONLY IN HIGHER EDUCATIONAL INSTITUTIONS BUT ALSO IN PRIMARY AND SECONDARY SCHOOLS FOR STUDENTS TO LEARN PROACTIVELY AND AUTONOMOUSLY. WITH THIS TYPE OF LEARNING, IT IS ESSENTIAL FOR STUDENTS TO CONSTANTLY REFLECT ON THEIR LEARNING IN ORDER TO ADJUST TASKS AND PROGRESS TO THE SUBSEQUENT LEARNING. HOWEVER, WHEN STUDENTS REFLECT ON LEARNING, THEY OFTEN STICK TO THE FACTS AND THEIR IMPRESSIONS OF VARIOUS ACTIVITIES. THUS, IT IS NECESSARY FOR TEACHERS TO GRASP THE INDIVIDUAL SITUATION OF EACH STUDENT, IDEALLY THROUGH READING SENTENCES OF REFLECTION ON LEARNING WRITTEN BY THE STUDENT, AND TO FACILITATE REFLECTION ON LEARNING APPROPRIATELY. ONE PROMISING DIRECTION IS TECHNOLOGIES SUCH AS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING THAT COULD BE USED TO FACILITATE LEARNING BY ANALYZING LARGE AMOUNTS OF ACCUMULATED DATA. THE PURPOSE OF THIS STUDY IS TO DEVELOP A SYSTEM THAT FACILITATES STUDENT REFLECTION ON LEARNING BY USING A CUSTOM MACHINE LEARNING MODEL. THIS MODEL AUTOMATICALLY CLASSIFIES WRITTEN SENTENCES OF REFLECTION ON LEARNING WITH A FOCUS ON THE DIFFERENT PHASES OF REFLECTION. WE THEN FACILITATE FURTHER REFLECTION ON LEARNING BY GRASPING THE SITUATION FROM THE CLASSIFICATION RESULT. IN THIS PAPER, WE DESCRIBE THE DESIGN AND DEVELOPMENT OF THE SYSTEM FUNCTIONS.
THIS PAPER INTRODUCES A SYSTEM THAT SUPPORTS STUDENT-CENTERED ONLINE ONE-TO-ONE TUTORING AND EVALUATES THE PRACTICAL VALUE OF THE SYSTEM BY RUNNING AN EXPERIMENT WITH 64 EXPERIENCED MATHEMATICS TEACHERS AND 810 STUDENTS IN GRADE 7. THE EXPERIMENT LASTED FOR 50 DAYS. A COMPREHENSIVE EVALUATION WAS PERFORMED USING STUDENTS' ACADEMIC PERFORMANCE BEFORE AND AFTER USAGE OF THE SYSTEM AND THE SYSTEM LOG FILES. BY CLASSIFYING THE STUDENTS INTO ACTIVE AND INACTIVE USAGE GROUPS, IT WAS DETERMINED THAT ACTIVE STUDENTS SIGNIFICANTLY OUTPERFORMED INACTIVE STUDENTS ON POSTTESTS, BUT WITH A SMALL EFFECT SIZE. THE RESULTS ALSO SUGGESTED THAT HIGH PRIOR KNOWLEDGE STUDENTS TENDED TO BENEFIT MORE FROM USING THE SYSTEM THAN LOW PRIOR KNOWLEDGE STUDENTS. AN EXPLANATION FOR THIS RESULT WAS THAT STUDENTS WITH A HIGH LEVEL OF PRIOR KNOWLEDGE WERE MORE LIKELY TO HAVE GOOD-QUALITY INTERACTIONS WITH THEIR TEACHERS. THEREFORE, ALTHOUGH SOME ADVANTAGES OF THIS TYPE OF STUDENT-CENTERED ONLINE ONE-TO-ONE TUTORING ARE OBSERVED, IN THIS SYSTEM, BOTH THE STUDENTS AND THE TEACHERS NEED TO BE FURTHER FACILITATED TO PRODUCE MORE EFFECTIVE TUTORING INTERACTIONS.
PROBLEM-BASED LEARNING IS A WIDELY USED LEARNING METHODOLOGY IN THE FIELD OF TECHNOLOGICAL DISCIPLINES, ESPECIALLY IN DISTANCE EDUCATION ENVIRONMENTS. IN THESE ENVIRONMENTS, THE MOST USED TOOLS, WHICH PROVIDE LEARNING SCENARIOS, ARE REMOTE AND VIRTUAL LABORATORIES. INTERNET OF THINGS (IOT) DEVICES CAN BE USED AS REMOTE OR VIRTUAL LABORATORIES. IN ADDITION TO THIS, THEY CAN BE ORGANIZED/ORCHESTRATED TO BUILD REMOTE MAKER SPACES THROUGH THE WEB. THESE TYPES OF SPACES ARE CALLED THE WEB OF THINGS (WOT). THIS PAPER PROPOSES THE USE OF THESE TYPES OF SPACES AND THEIR INTEGRATION AS PRACTICAL ACTIVITIES INTO THE CURRICULA OF TECHNOLOGICAL SUBJECTS. THIS APPROACH WILL ALLOW US TO ACHIEVE TWO FUNDAMENTAL OBJECTIVES: (1) TO IMPROVE THE ACADEMIC RESULTS (GRADES) OF STUDENTS; AND (2) TO INCREASE ENGAGEMENT AND INTEREST OF STUDENTS IN THE STUDIED TECHNOLOGIES, INCLUDING IOT DEVICES. THESE PLATFORMS ARE MODELED USING ARCHETYPES BASED ON DIFFERENT TYPOLOGIES AND USAGE SCENARIOS. IN PARTICULAR, THESE USAGE SCENARIOS WILL IMPLEMENT A LEARNING STRATEGY FOR EACH PROBLEM TO BE SOLVED. THE CURRENT WORK SHOWS THE EVOLUTION OF THESE ARCHETYPES AND THEIR APPLICATION IN THE TEACHING OF DISCIPLINES/SUBJECTS DEFINED IN COMPUTER SCIENCE, SUCH AS DISTRIBUTED COMPUTING AND CYBERSECURITY.
THIS PAPER DEMONSTRATED THE USE OF THE SUPERVISED MACHINE LEARNING (ML) FOR TEXT CLASSIFICATION TO PREDICT STUDENTS' FINAL COURSE GRADES IN A HYBRID ADVANCED STATISTICS COURSE AND EXHIBITED THE POTENTIAL OF USING ML CLASSIFIED MESSAGES TO IDENTIFY STUDENTS AT RISK OF COURSE FAILURE. WE BUILT THREE CLASSIFICATION MODELS WITH TRAINING DATA OF 76,936 POSTS FROM TWO LARGE ONLINE FORUMS AND APPLIED THE MODELS TO CLASSIFY MESSAGES INTO STATISTICS-RELATED AND NON-STATISTICS-RELATED POSTS IN A PRIVATE FACEBOOK GROUP. THREE ML ALGORITHMS WERE COMPARED IN TERMS OF CLASSIFICATION EFFECTIVENESS AND CONGRUENCY WITH HUMAN CODING. STUDENTS WITH MORE MESSAGES ENDORSED BY TWO OR MORE ML ALGORITHMS AS STATISTICS-RELATED HAD HIGHER FINAL COURSE GRADES. STUDENTS WHO FAILED THE COURSE ALSO HAD SIGNIFICANTLY FEWER MESSAGES ENDORSED BY ALL THREE ML ALGORITHMS THAN THOSE WHO PASSED. RESULTS SUGGEST THAT ML CAN BE USED FOR IDENTIFYING STUDENTS IN NEED OF SUPPORT WITHIN THE PERSONAL LEARNING ENVIRONMENT AND FOR QUALITY CONTROL OF THE LARGE-SCALE EDUCATIONAL DATA.
ADVANCES IN INFORMATION AND COMMUNICATIONS TECHNOLOGY (ICT) HAVE INCREASED THE GROWTH OF MASSIVE OPEN ONLINE COURSES (MOOCS) APPLIED IN DISTANCE LEARNING ENVIRONMENTS. VARIOUS TOOLS HAVE BEEN UTILIZED TO DELIVER INTERACTIVE CONTENT INCLUDING PICTURES, FIGURES, AND VIDEOS THAT CAN MOTIVATE THE LEARNERS TO BUILD NEW COGNITIVE SKILLS. HIGH RANKING UNIVERSITIES HAVE ADOPTED MOOCS AS AN EFFICIENT DASHBOARD PLATFORM WHERE LEARNERS FROM AROUND THE WORLD CAN PARTICIPATE IN SUCH COURSES. THE STUDENTS LEARNING PROGRESS IS EVALUATED BY USING SET COMPUTER-MARKED ASSESSMENTS. IN PARTICULAR, THE COMPUTER GIVES IMMEDIATE FEEDBACK TO THE STUDENT ONCE HE OR SHE COMPLETES THE ONLINE ASSESSMENTS. THE RESEARCHERS CLAIM THAT STUDENT SUCCESS RATE IN AN ONLINE COURSE CAN BE RELATED TO THEIR PERFORMANCE AT THE PREVIOUS SESSION IN ADDITION TO THE LEVEL OF ENGAGEMENT. INSUFFICIENT ATTENTION HAS BEEN PAID BY LITERATURE TO EVALUATE WHETHER STUDENT PERFORMANCE AND ENGAGEMENT IN THE PRIOR ASSESSMENTS COULD AFFECT STUDENT ACHIEVEMENT IN THE NEXT ASSESSMENTS. IN THIS PAPER, TWO PREDICTIVE MODELS HAVE BEEN DESIGNED NAMELY STUDENTS' ASSESSMENTS GRADES AND FINAL STUDENTS' PERFORMANCE. THE MODELS CAN BE USED TO DETECT THE FACTORS THAT INFLUENCE STUDENTS' LEARNING ACHIEVEMENT IN MOOCS. THE RESULT SHOWS THAT BOTH MODELS GAIN FEASIBLE AND ACCURATE RESULTS. THE LOWEST RSME GAIN BY RF ACQUIRE A VALUE OF 8.131 FOR STUDENTS ASSESSMENTS GRADES MODEL WHILE GBM YIELDS THE HIGHEST ACCURACY IN FINAL STUDENTS' PERFORMANCE, AN AVERAGE VALUE OF 0.086 WAS ACHIEVED.
AS COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) GAINS A BROADER USAGE, THE NEED FOR AUTOMATED TOOLS CAPABLE OF SUPPORTING TUTORS IN THE TIME-CONSUMING PROCESS OF ANALYZING CONVERSATIONS BECOMES MORE PRESSING. MOREOVER, COLLABORATION, WHICH PRESUMES THE INTERTWINING OF IDEAS OR POINTS OF VIEW AMONG PARTICIPANTS, IS A CENTRAL ELEMENT OF DIALOGUE PERFORMED IN CSCL ENVIRONMENTS. THEREFORE, STARTING FROM DIALOGISM AND A COHESION-BASED MODEL OF DISCOURSE, WE PROPOSE AND VALIDATE TWO COMPUTATIONAL MODELS FOR ASSESSING COLLABORATION. THE FIRST MODEL IS BASED ON A COHESION GRAPH AND CAN BE PERCEIVED AS A LONGITUDINAL ANALYSIS OF THE ONGOING CONVERSATION, THUS ACCOUNTING FOR COLLABORATION FROM A SOCIAL KNOWLEDGE-BUILDING PERSPECTIVE. IN THE SECOND APPROACH, COLLABORATION IS REGARDED FROM A DIALOGICAL PERSPECTIVE AS THE INTERTWINING OR SYNERGY OF VOICES PERTAINING TO DIFFERENT SPEAKERS, THEREFORE ENABLING A TRANSVERSAL ANALYSIS OF SUBSEQUENT DISCUSSION SLICES.
TEAMWORK IS AN IMPORTANT COMPETENCY FOR 21ST CENTURY LEARNER. HOWEVER, EQUIPPING STUDENTS WITH AN AWARENESS OF THEIR TEAMWORK BEHAVIORS IS DIFFICULT. THIS PAPER THEREFORE AIMS TO DEVELOP A MODEL THAT WILL ANALYZE STUDENT DIALOGUE TO IDENTIFY TEAMWORK INDICATORS THAT WILL SERVE AS FORMATIVE FEEDBACK FOR STUDENTS. FOUR DIMENSIONS OF TEAMWORK NAMELY COORDINATION, MUTUAL PERFORMANCE MONITORING, CONSTRUCTIVE CONFLICT AND TEAM EMOTIONAL SUPPORT ARE MEASURED. IN ADDITION, THE PAPER EXPLORES MULTI-LABEL CLASSIFICATION APPROACHES COMBINED WITH FEATURE ENGINEERING TECHNIQUES TO CLASSIFY STUDENT CHAT DATA. THE RESULTS SHOW THAT BY INCORPORATING LINGUISTIC FEATURES, IT IS POSSIBLE TO ACHIEVE BETTER PERFORMANCE IN IDENTIFYING THE TEAMWORK INDICATORS IN STUDENT DIALOGUE.
ONE OF THE MAIN CHALLENGES FOR ONLINE LEARNERS IS KNOWING HOW TO EFFECTIVELY MANAGE THEIR TIME. HIGHLY AUTONOMOUS SETTINGS, SUCH AS MASSIVE OPEN ONLINE COURSES (MOOCS), PUT ADDITIONAL PRESSURE ON LEARNERS IN THIS REGARD. HOWEVER, LITTLE IS KNOWN ABOUT HOW LEARNERS ORGANISE THEIR TIME IN TERMS OF SESSIONS OR BLOCKS OF TIME ACROSS A MOOC. THIS STUDY EXAMINED SESSION BEHAVIOURAL DATA OF 9272 LEARNERS IN A MOOC AND ITS RELATION TO THEIR ENGAGEMENT, GRADE AND SELF-REPORT DATA MEASURING ASPECTS OF SELF-REGULATED LEARNING (SRL). FROM AN EXPLORATORY TEMPORAL APPROACH USING CLUSTERING AND GROUP COMPARISON TESTS, WE EXAMINED HOW LEARNERS DISTRIBUTED SESSIONS IN RELATION TO THEIR LENGTH AND FREQUENCY ACROSS THE COURSE (MACRO ASPECT), AND WHICH TYPES OF ACTIVITIES THEY PRIORITISED WITHIN THESE SESSIONS (MICRO ASPECT). WE THEN INVESTIGATED IF THESE PATTERNS OF SESSIONS WERE RELATED TO LEARNERS' LEVEL OF ENGAGEMENT, ACHIEVEMENT AND USE OF SELF-REGULATED LEARNING (SRL) SKILLS. WE FOUND THAT SUCCESSFUL LEARNERS HAD MORE FREQUENT AND LONGER SESSIONS ACROSS THE COURSE, MIXED UP ACTIVITIES WITHIN SESSIONS, AND CHANGED THE FOCUS OF ACTIVITIES WITHIN SESSIONS ACROSS THE COURSE. IN ADDITION, SESSION DISTRIBUTION WAS FOUND TO BE A MEANINGFUL PROXY FOR LEARNERS' USE OF SRL SKILLS RELATED TO TIME MANAGEMENT AND EFFORT REGULATION. THAT IS, LEARNERS WITH HIGHER LEVELS OF TIME MANAGEMENT AND EFFORT REGULATION HAD LONGER AND MORE SESSIONS ACROSS THE COURSE. BASED ON THE RESULTS, IMPLICATIONS FOR SUPPORTING SPECIFIC SESSION BEHAVIOURS TO PROMOTE EFFECTIVE LEARNING IN MOOCS ARE DISCUSSED.
THE AIMS OF THIS LONGITUDINAL STUDY WERE TO DESCRIBE UNDERGRADUATES' LEARNING BEHAVIORAL TYPES IN E-LEARNING AND TO INVESTIGATE THEIR RELATIONSHIP TO LEARNING OUTCOMES OVER AN ENTIRE SEMESTER. IN THE FIRST PART OF THE STUDY, 441 STUDENTS AT A NATIONAL UNIVERSITY IN JAPAN WERE ANALYZED WITH REGARD TO THEIR VISUALIZATION OF LEARNING PROGRESS. SEVEN DISTINCT TYPES OF LEARNING BEHAVIOR WERE IDENTIFIED: (1) PROCRASTINATION, (2) LEARNING HABIT, (3) RANDOM, (4) DIMINISHED DRIVE, (5) EARLY BIRD, (6) CHEVRON, AND (7) CATCH-UP. IN THE SECOND PART OF THIS STUDY, DATA FROM 226 STUDENTS WERE ANALYZED. THE RESULTS SHOWED SIGNIFICANT RELATIONSHIPS BETWEEN THEIR LEARNING TYPE AND ULTIMATE LEARNING OUTCOMES. THE STUDENTS WHO EXHIBITED THE LEARNING HABIT TYPE SCORED SIGNIFICANTLY HIGHER ON THE TEST THAN THOSE STUDENTS OF THE PROCRASTINATION TYPE. THE RESULTS IMPLY THAT REGULATED LEARNING (I.E., FORMING A LEARNING HABIT) COULD INCREASE LEARNING EFFECTIVENESS AND LEAD TO BETTER LEARNING OUTCOMES IN E-LEARNING. (C) 2014 ELSEVIER INC. ALL RIGHTS RESERVED.
BACKGROUND FAILURE TO EFFECTIVELY ORGANIZE AND MANAGE LEARNING TIME IS AN IMPORTANT FACTOR INFLUENCING ONLINE LEARNERS' PERFORMANCE. INVESTIGATION OF TIME-INVESTMENT PATTERNS FOR ONLINE LEARNING WILL PROVIDE EDUCATORS WITH USEFUL KNOWLEDGE OF HOW LEARNERS ENGAGE IN AND REGULATE THEIR ONLINE LEARNING AND SUPPORT THEM IN TAILORING ONLINE COURSE DESIGN AND TEACHING. HOWEVER, UNDERSTANDING OF HOW LEARNERS INVEST AND MANAGE THEIR TIME DURING ONLINE LEARNING REMAINS LIMITED. OBJECTIVES THIS STUDY AIMS TO DISCOVER THE TYPICAL TIME-INVESTMENT PATTERNS OF MOOC LEARNERS AND THEIR TEMPORAL-LEARNING CHARACTERISTICS BASED ON A SYSTEMATIC TIME-INVESTMENT ANALYSIS FRAMEWORK AND THEIR RELATIONSHIP WITH LEARNING PERFORMANCE. METHODS BASED ON A PROPOSED TIME-INVESTMENT-ANALYSIS FRAMEWORK, THIS STUDY APPLIED STATISTICAL, CLUSTER AND LAG SEQUENTIAL ANALYSES TO INVESTIGATE LEARNERS' TIME-INVESTMENT PATTERNS AND THEIR RELATIONSHIPS WITH LEARNING PERFORMANCE, SESSION TIME ALLOCATION, AND LEARNING SEQUENCES BY ANALYSING THE LEARNING DATA FROM 12,463 PARTICIPANTS OF A MASSIVE OPEN ONLINE COURSE (MOOC) IN CHINA. RESULTS AND CONCLUSIONS SEVEN TIME-INVESTMENT PATTERNS OF MOOC LEARNERS WERE DEFINED, AND LEARNING PERFORMANCE WAS FOUND TO DIFFER AMONG THEM. FURTHER ANALYSIS SHOWS THAT HIGH PERFORMERS INVESTED TIME THROUGHOUT THE WHOLE COURSE AND ALLOCATED TIME TO MULTIPLE ACTIVITIES, EXAM-TAKERS PERFORMED BETTER IN TIME MANAGEMENT AND PRODUCED MORE BEHAVIOURAL SEQUENCES RELATED TO COGNITIVE STRATEGY AND RECOURSE USE, AND LEARNERS' MOTIVATION AND PRIOR KNOWLEDGE AFFECTED THE MANAGEMENT AND EFFECTIVENESS OF THEIR TIME INVESTMENT. IMPLICATIONS THE RESULTS SUPPORT THE RECOGNITION AND EVALUATION OF ONLINE LEARNING TIME-INVESTMENT PATTERNS AND SUGGEST RELEVANT CUES FOR IMPROVING MOOC DESIGN AND TEACHING.
THE INTEGRATION OF SOCIAL MEDIA IN EDUCATION HAS BEEN RAISING NEW CHALLENGES FOR TEACHERS, STUDENTS AND ORGANIZATIONS, IN BOTH TRADITIONAL AND TECHNOLOGY-MEDIATED LEARNINGS SETTINGS. FORMAL HIGHER EDUCATION CONTEXTS ARE STILL MOSTLY ANCHORED AND LOCKED UP IN INSTITUTIONAL LMS, DESPITE THE INNUMEROUS EDUCATIONAL DIGRESSIONS THAT EDUCATORS HAVE BEEN CONDUCTING THROUGHOUT SOCIAL MEDIA NETWORKS. ONE OF THE BIGGEST CHALLENGES IN CONTEMPORARY EDUCATIONAL NEEDS CONSISTS ON MANAGING THE INTEGRATION, VALIDATION AND REPORTING ON EDUCATIONAL PROCESSES, GOALS AND STUDENT PERFORMANCE, WHEN THEY ARE WIDELY SPREAD IN SEVERAL FORMAL AND INFORMAL CONTEXTS. IN THIS CHAPTER A SYSTEM FOR THE INTEGRATION OF LMS AND SOCIAL MEDIA IS PRESENTED, AS WELL AS EVIDENCE ON ITS PRACTICAL USAGE. A SET OF SOCIAL NETWORK ANALYTICS ARE ALSO BROUGHT FORWARD AS FEATURES THAT ARE CURRENTLY BEING ADDED TO THE REFERRED SYSTEM.
WHILE MANY STRATEGIES FOR PROTECTING PERSONAL PRIVACY HAVE RELIED ON REGULATORY FRAMEWORKS, CONSENT AND ANONYMIZING DATA, SUCH APPROACHES ARE NOT ALWAYS EFFECTIVE. FRAMEWORKS AND TERMS AND CONDITIONS OFTEN LAG USER BEHAVIOUR AND ADVANCES IN TECHNOLOGY AND SOFTWARE; CONSENT CAN BE PROVISIONAL AND FRAGILE; AND THE ANONYMIZATION OF DATA MAY IMPEDE PERSONALIZED LEARNING. THIS PAPER REPORTS ON A DIALOGICAL MULTI-CASE STUDY METHODOLOGY OF FOUR MASSIVE OPEN ONLINE COURSE (MOOC) PROVIDERS FROM DIFFERENT GEOPOLITICAL AND REGULATORY CONTEXTS. IT EXPLORES HOW THE PROVIDERS (1) DEFINE 'PERSONAL DATA' AND WHETHER THEY ACKNOWLEDGE A CATEGORY OF 'SPECIAL' OR 'SENSITIVE' DATA; (2) ADDRESS THE ISSUE AND SCOPE OF STUDENT CONSENT (AND DEFINE THAT SCOPE); AND (3) USE STUDENT DATA IN ORDER TO INFORM PEDAGOGY AND/OR ADAPT THE LEARNING EXPERIENCE TO PERSONALISE THE CONTEXT OR TO INCREASE STUDENT RETENTION AND SUCCESS RATES. THIS STUDY FOUND THAT LARGE AMOUNTS OF PERSONAL DATA CONTINUE TO BE COLLECTED FOR PURPOSES SEEMINGLY UNRELATED TO THE DELIVERY AND SUPPORT OF COURSES. THE CAPACITY FOR USERS TO WITHDRAW OR WITHHOLD CONSENT FOR THE COLLECTION OF CERTAIN CATEGORIES OF DATA SUCH AS SENSITIVE PERSONAL DATA REMAINS SEVERELY CONSTRAINED. THIS PAPER PROPOSES THAT USER CONSENT AT THE TIME OF REGISTRATION SHOULD BE RECONSIDERED, AND THAT THERE IS A PARTICULAR NEED FOR CONSENT WHEN SENSITIVE PERSONAL DATA ARE USED TO PERSONALIZE LEARNING, OR FOR PURPOSES OUTSIDE THE ORIGINAL INTENTION OF OBTAINING CONSENT.
SHARING THE SAME PHILOSOPHY OF "RELATIONS MATTER" WITH COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL), SOCIAL NETWORK ANALYSIS (SNA) HAS BECOME A COMMON METHODOLOGY IN THE CSCL RESEARCH. IN THIS RESEARCH, I USE SNA METHODS FROM RELATIONAL TIES, NETWORK MODES, AND INTEGRATED METHODS PERSPECTIVES TO UNDERSTAND ATTRIBUTES OF RELATIONS IN CSCL. I DESIGN, CONDUCT, AND EVALUATE THREE SNA ANALYTICS ON THE SAME DATASET FROM AN ONLINE COURSE TO UNDERSTAND CSCL ENTITIES, RELATIONS, AND PROCESSES. THIS ONLINE COLLABORATIVE DISCUSSION IN THIS COURSE STRESSES STUDENTS' KNOWLEDGE INQUIRY, CONSTRUCTION, AND BUILDING THROUGH PEER INTERACTIONS. RESULTS SHOW THAT COMPARED TO TRADITIONAL SNA METHODS, THESE THREE SNA APPROACHES CAN REVEAL MORE DETAILED, RICHER PICTURE OF THE COLLABORATIVE LEARNING PROCESSES, PARTICULARLY, THE INTERACTIONAL, MULTI-MODAL, AND TEMPORAL ASPECTS. MOREOVER, THESE SNA APPROACHES ARE GENERALIZABLE FOR UNDERSTANDING SIMILAR CSCL SETTINGS. BASED ON THE RESULTS, THIS RESEARCH PROPOSES METHODOLOGICAL IMPLICATIONS TO FURTHER APPLY AND DEVELOP SNA IN THE CSCL FIELD.
THE NEED FOR TRAINING STUDENTS IN PRESENTATION SKILLS HAS BEEN RAISED. SKILLS IN THE CREATION AND ELABORATION OF SLIDES ARE PARTICULARLY IMPORTANT BUT IT IS ACTUALLY DIFFICULT TO TRAIN STUDENTS IN SUCH SKILLS. IT IS IMPORTANT FOR STUDENTS TO PROPERLY UNDERSTAND DEFECTIVE PORTIONS IN SLIDES SO THAT THEY CAN POLISH THE SLIDES BY THEMSELVES. HOWEVER, IT IS DIFFICULT FOR NOVICES TO DETECT DEFECTIVE PORTIONS AND IT GENERALLY DEPENDS ON INDIVIDUAL WORK BY EXPERIENCED PEOPLE SUCH AS PROFESSORS. SUCH WORK IMPOSES A HUGE BURDEN ON PROFESSORS AND PREVENTS DETAILED GUIDANCE INCLUDING THE LINKAGE OF ORAL EXPLANATIONS, GESTURES, AND SLIDE DESCRIPTIONS. THIS RESEARCH WAS AIMED AT DEVELOPING A MECHANISM FOR EXTRACTING THE DEFECTIVE PORTIONS IN SLIDES AND PROVIDING THEM TO STUDENTS ON THE BASIS OF HYBRID ANALYSIS OF TWO PROMISING SOURCES: (1) CONSTITUTIVE FEATURES BEING INHERENT IN THE SLIDE DATA AND (2) AUDIENCES' VISUAL TRACKING BEHAVIORS OF SLIDE CONTENT. THIS PAPER MAINLY DESCRIBES METHODS OF EXTRACTING CANDIDATES FOR PROBLEMS THAT INHIBIT UNDERSTANDING FROM PRESENTATION SLIDES BY AUDIENCES ON THE BASIS OF CONSTITUTIVE FEATURES OF SLIDES AND AUDIENCE GAZE.
A GROWING BODY OF EMPIRICAL EVIDENCE SUGGESTS THAT THE ADAPTIVE CAPABILITIES OF COMPUTER-BASED LEARNING ENVIRONMENTS CAN BE IMPROVED THROUGH THE USE OF EDUCATIONAL DATA MINING TECHNIQUES. LOG-FILE TRACE DATA PROVIDES A WEALTH OF INFORMATION ABOUT LEARNER BEHAVIORS THAT CAN BE CAPTURED, MONITORED, AND MINED FOR THE PURPOSES OF DISCOVERING NEW KNOWLEDGE AND DETECTING PATTERNS OF INTEREST. THIS STUDY AIMS TO LEVERAGE THESE ANALYTICAL TECHNIQUES TO MINE LEARNER BEHAVIORS IN RELATION TO BOTH DIAGNOSTIC REASONING PROCESSES AND OUTCOMES IN BIOWORLD, A COMPUTER-BASED LEARNING ENVIRONMENT THAT SUPPORT LEARNERS TO PRACTICE SOLVING MEDICAL PROBLEMS AND RECEIVE FORMATIVE FEEDBACK. IN DOING SO, HIDDEN MARKOV MODELS ARE USED TO MODEL BEHAVIORAL INDICATORS OF PROFICIENCY DURING PROBLEM SOLVING, WHILE AN ENSEMBLE OF TEXT CLASSIFICATION ALGORITHMS ARE APPLIED TO WRITTEN CASE SUMMARIES THAT LEARNERS' WRITE AS AN OUTCOME OF SOLVING A CASE IN BIOWORLD. THE APPLICATION OF THESE ALGORITHMS CHARACTERIZE LEARNER BEHAVIORS AT DIFFERENT PHASES OF PROBLEM SOLVING WHICH PROVIDES CORROBORATING EVIDENCE IN SUPPORT OF WHERE REVISIONS CAN BE MADE TO PROVIDE DESIGN GUIDELINES OF THE SYSTEM. WE CONCLUDE BY DISCUSSING THE INSTRUCTIONAL DESIGN AND PEDAGOGICAL IMPLICATIONS FOR THE NOVICE-EXPERT OVERLAY SYSTEM IN BIOWORLD, AND HOW THE FINDINGS INFORM THE DELIVERY OF FEEDBACK TO LEARNERS BY HIGHLIGHTING SIMILARITIES AND DIFFERENCES BETWEEN THE NOVICE AND EXPERT TRAJECTORY TOWARD SOLVING PROBLEMS.
LOGIC AS A KNOWLEDGE REPRESENTATION AND REASONING LANGUAGE IS A FUNDAMENTAL TOPIC OF AN ARTIFICIAL INTELLIGENCE (AI) COURSE AND INCLUDES A NUMBER OF SUB-TOPICS. ONE OF THEM, WHICH BRINGS DIFFICULTIES TO STUDENTS TO DEAL WITH, IS CONVERTING NATURAL LANGUAGE (NL) SENTENCES INTO FIRST-ORDER LOGIC (FOL) FORMULAS. TO ASSIST STUDENTS TO OVERCOME THOSE DIFFICULTIES, WE DEVELOPED THE NLTOFOL SYSTEM AND EQUIPPED IT WITH A STRONG ASSISTANCE AND FEEDBACK MECHANISM. IN THIS WORK, FIRST, WE PRESENT THAT FEEDBACK MECHANISM. THE MECHANISM CAN PROVIDE ASSISTANCE BEFORE AN ANSWER IS SUBMITTED, IF REQUESTED, BUT MAINLY IT PROVIDES ASSISTANCE AFTER AN ANSWER IS SUBMITTED. TO THAT END, IT CHARACTERIZES THE ANSWER IN TERMS OF COMPLETENESS AND ACCURACY TO DETERMINE THE LEVEL OF INCORRECTNESS, BASED ON AN ANSWER CATEGORIZATION SCHEME, INTRODUCED IN THIS PAPER. THE AUTOMATICALLY GENERATED NATURAL LANGUAGE FEEDBACK SEQUENCES GROW FROM GENERAL TO SPECIFIC AND CAN INCLUDE STATEMENTS ON A STUDENT'S METACOGNITIVE STATE. FEEDBACK IS PROVIDED AS NATURAL LANGUAGE SENTENCES AUTOMATICALLY GENERATED THROUGH A TEMPLATE-BASED NATURAL LANGUAGE GENERATION MECHANISM. SECOND, WE PRESENT AN EXTENSIVE EVALUATION OF THE EFFECTIVENESS OF THE ASSISTANCE AND FEEDBACK MECHANISM ON STUDENTS' LEARNING. THE EVALUATION OF FEEDBACK WITH STUDENTS SHOWED THAT FULL FEEDBACK SEQUENCES LEAD TO GREATER LEARNING GAINS THAN SEQUENCES CONSISTING OF ONLY FLAG FEEDBACK AND BOTTOM-OUT HINTS (N = 226), AND THAT GENERIC, TEMPLATE-BASED FEEDBACK SEQUENCES ARE COMPARABLE TO THE UTILITY OF PROBLEM-SPECIFIC HINTS GENERATED BY HUMAN TUTORS (N = 120).
ENGINEERING DEGREES ARE OFTEN REGARDED AS COMPLEX AND ONE USUAL ISSUE IS THAT STUDENTS STRUGGLE AND FEEL DISCOURAGED DURING THE LEARNING PROCESS. GAMIFICATION IS STARTING TO PLAY AN IMPORTANT ROLE IN EDUCATION WITH THE OBJECTIVE OF PROVIDING ENGAGEMENT AND IMPROVING THE MOTIVATION OF STUDENTS. ONE SPECIFIC EXAMPLE IS THE USE OF BADGES. THE ANALYSIS OF USERS' INTERACTIONS AND BEHAVIORS WITH THE BADGE SYSTEM CAN BE USED TO IMPROVE THE LEARNING PROCESS, E.G., BY ADAPTING THE LEARNING MATERIALS AND GIVING GAME-BASED ACTIVITIES TO STUDENTS DEPENDING ON THEIR INTEREST TOWARD BADGES. IN THIS WORK, WE PROPOSE SOME METRICS THAT PROVIDE INFORMATION REGARDING THE BEHAVIOR OF STUDENTS WITH BADGES, INCLUDING IF THEY ARE INTENTIONALLY EARNING THEM, THE CONCENTRATION FOR ACHIEVING THEM AND THEIR TIME EFFICIENCY. WE VALIDATE THESE METRICS BY PROVIDING AN EXTENSIVE ANALYSIS OF 291 DIFFERENT STUDENTS INTERACTING WITH A LOCAL INSTANCE OF KHAN ACADEMY WITHIN OUR COURSES FOR FRESHMEN AT UNIVERSIDAD CARLOS III DE MADRID. THIS ANALYSIS INCLUDES RELATIONSHIP MINING BETWEEN BADGE INDICATORS AND OTHERS RELATED TO THE LEARNING PROCESS, THE ANALYSIS OF SPECIFIC ARCHETYPAL PROFILES OF STUDENTS THAT REPRESENT A BROADER POPULATION AND ALSO BY CLUSTERING STUDENTS BY THEIR BADGE INDICATORS WITH THE OBJECTIVE OF CUSTOMIZING LEARNING EXPERIENCES. WE FINALIZE BY DISCUSSING THE IMPLICATIONS OF THE RESULTS FOR ENGINEERING EDUCATION, PROVIDING GUIDELINES INTO HOW INSTRUCTORS CAN TAKE ADVANTAGE OF THE FINDINGS OF THE RESEARCH AND HOW RESEARCHERS CAN REPLICATE EXPERIMENTS SIMILAR TO THIS ONE IN OTHER GENERAL CONTEXTS.
AMONG THE LITERATURE ON SELF-REGULATED LEARNING AND SOCIAL NETWORKING, THE STUDIES, WHICH EXPLORE THE IMPACT OF SOCIAL NETWORKS ON LEARNING REGARDING CONNECTION SIZES AND RELATIONSHIP-ESTABLISHING FACTORS, ARE RARELY SEEN IN THE CONTEXT OF SOCIAL NETWORKING AMONG STRANGERS. THIS DESCRIPTIVE STUDY ADDRESSES THE GAP BY EXPLORING DATA FROM 468 CHINESE JUNIOR HIGH SCHOOL GRADUATES IN AN ONLINE LEARNING RESOURCE PLATFORM WITH AN INTEGRATED SOCIAL NETWORK. THE DATA IS DIGITALLY GENERATED WHEN THE GRADUATES ENGAGED IN ONLINE SELF-REGULATED LEARNING ACTIVITIES FOR AN AVERAGE OF 36 DAYS WITHOUT ANY FACILITATIONS. THE DATA ANALYSIS EXPLORES THE CONNECTION SIZES AND TYPES OF FOLLOW LINKS, TYPES OF SELF-REGULATED LEARNERS, AND THEIR RELATIONSHIP WITH LESSON COMPLETION. THE STUDY REVEALS THAT SOCIAL NETWORKS TRIGGER DIFFERENT LEVELS OF LEARNING ENGAGEMENT. SPECIFICALLY, THE GRADUATES WITH BIDIRECTIONAL FOLLOW LINKS AND THE OPTIMAL CONNECTION SIZE OF FIVE COMPLETE MORE LESSONS THAN OTHER GRADUATES. THE STUDY ALSO FINDS THAT ACADEMIC FACTORS (SIMILAR LEARNING GOALS AND ACHIEVEMENT GAPS) ARE MORE IMPORTANT THAN SOCIAL FACTORS (COMMON IDENTITY) IN ESTABLISHING SOCIAL CONNECTIONS TO SUPPORT SELF-REGULATED LEARNING ACTIVITIES. THESE FINDINGS HAVE DIRECT IMPLICATIONS FOR THE DESIGN OF SOCIAL NETWORKING THAT FACILITATES SELF-REGULATED LEARNING, AND ENHANCES STUDENTS' SELF-REGULATED LEARNING EFFICACY IN ONLINE LEARNING ENVIRONMENTS.
IN GENERAL TERMS, THE AIMS OF FORMATIVE ASSESSMENT ARE TO GATHER AND ANALYZE INFORMATION ABOUT THE PROGRESS OF STUDENTS, WITH THE INTENTION OF IMPROVING INSTRUCTION IN REAL TIME. THIS CAN ALSO HELP TEACHERS TO IDENTIFY THOSE ELEMENTS THAT STUDENTS ARE STRUGGLING WITH, AND THEREFORE TO DETERMINE WHERE ADJUSTMENTS MUST BE MADE. THERE ARE, HOWEVER, SOME ISSUES THAT MUST BE ADDRESSED BEFORE THIS APPROACH CAN BECOME MORE WIDESPREAD. IN THIS RESEARCH, AND WITH MATHEMATICS AS OUR SPECIFIC SCOPE, WE FOCUS ON TWO OF THESE ISSUES: WE PRESENT A FRAMEWORK TO HELP TEACHERS IMPLEMENT ASSESSMENT ITEMS THAT ARE HIGHLY DYNAMIC AND FLEXIBLE, BUT THAT ALLOWS THEM AT THE SAME TIME TO MODEL COMMON MISCONCEPTIONS AND TO PROVIDE SUITABLE FEEDBACK. FOLLOWING THIS, WE CARRIED OUT AN EXPERIMENT TO DETERMINE (I) WHETHER THE STUDENTS PERCEIVED SUCH FEEDBACK TO BE USEFUL; (II) WHETHER THEY ACTUALLY USED IT; AND ULTIMATELY (III) WHETHER IT PRODUCED A DIFFERENCE IN THEIR PERFORMANCE. TO FIND THE ANSWERS TO THESE QUESTIONS, WE USED AN ONLINE & X2018;PRE-CALCULUS & X2019; COURSE WITH 458 STUDENTS AND COLLECTED DATA DIRECTLY FROM THOSE STUDENTS VIA BOTH A PERCEPTION SURVEY AND FROM THEIR INTERACTION WITH THE LMS. BASED ON THESE DATA, WE PERFORMED SEVERAL ANALYSES, INCLUDING PROCESS MINING AND STATISTICAL HYPOTHESIS TESTS. THE RESULTS DEMONSTRATED THAT ALTHOUGH MANY OF THE STUDENTS HAD A POSITIVE OPINION OF THE FEEDBACK RECEIVED, IT WAS NOT ALWAYS FOLLOWED. NEVERTHELESS, WHEN IT WAS (OR AT LEAST WHEN STUDENTS SAID IT WAS), THE STUDENTS PERFORMED BETTER.
LEARNING IS A NATURAL HUMAN ACTIVITY THAT IS SHAPED BY PERSONAL EXPERIENCES, COGNITIVE AWARENESS, PERSONAL BIAS, OPINIONS, CULTURAL BACKGROUND, AND ENVIRONMENT. LEARNING HAS BEEN DEFINED AS A STABLE AND PERSISTENT CHANGE IN WHAT A PERSON KNOWS AND CAN DO. LEARNING IS FORMED THROUGH AN INDIVIDUAL'S INTERACTIONS, INCLUDING THE CONVEYANCE OF KNOWLEDGE AND SKILLS FROM OTHERS AND EXPERIENCES. SO, LEARNING IS A PERSONALIZED EXPERIENCE THAT ALLOWS ONE TO EXPAND THEIR KNOWLEDGE, PERSPECTIVE, SKILLS, AND UNDERSTANDING. THEREFORE, PERSONALIZED LEARNING MODELS CAN HELP TO MEET INDIVIDUAL NEEDS AND GOALS. FURTHERMORE, TO PERSONALIZE THE LEARNING EXPERIENCE, TECHNOLOGY INTEGRATION CAN PLAY A CRUCIAL ROLE. THIS PAPER PROVIDES A REVIEW OF THE RECENT RESEARCH LITERATURE ON PERSONALIZED LEARNING AS TECHNOLOGY IS CHANGING HOW LEARNING CAN BE EFFECTIVELY PERSONALIZED. THE EMPHASIS IS ON THE TERMS USED TO CHARACTERIZE LEARNING AS THOSE CAN SUGGEST A FRAMEWORK FOR PERSONALIZED AND WILL EVENTUALLY BE USED IN META-ANALYSES OF RESEARCH ON PERSONALIZED LEARNING, WHICH IS BEYOND THE SCOPE OF THIS PAPER.
PERSISTENCE HAS PROVEN TO BE A GREAT CHALLENGE IN ONLINE LEARNING ENVIRONMENTS. GAMING AND INTERACTIVITY HAVE BEEN SUGGESTED AS ESSENTIAL FEATURES IN REDUCING DROPOUT AND INCREASING PERSISTENCE IN ONLINE LEARNING. YET IN INTERACTIVE GAME-BASED LEARNING ENVIRONMENTS, PERSISTENCE IN MOVING FORWARD IN THE GAME MAY COME AT THE EXPENSE OF INVESTING IN EACH OF THE GAME'S LEVELS. THAT IS, THE MOTIVATION TO COMPLETE THE GAME MAY HAVE A DELETERIOUS EFFECT ON LEARNING AT SPECIFIC LEVELS AND HENCE ON LEARNING FROM THE GAME IN GENERAL. THEREFORE, WE HAVE CHOSEN TO FOCUS ON MICROLEVEL PERSISTENCE (I.E., PERSISTENCE DURING EACH COMPONENT OF THE LEARNING PROCESS). WE STUDY MICROLEVEL PERSISTENCE IN THE CONTEXT OF ACQUIRING COMPUTATIONAL THINKING-THE THOUGHT PROCESS OF SOLVING PROBLEMS THROUGH ABSTRACTION-WHICH IS A KEY COMPONENT OF THE NEW LITERACIES NEEDED FOR TOMORROW'S CITIZENS. IN THIS STUDY, WE ANALYZE DATA COLLECTED FROM AN ONLINE, GAME-BASED LEARNING ENVIRONMENT (CODEMONKEY (TM)). THE DATA DOCUMENT THE ACTIVITY OF FIRST TO SIXTH GRADERS (N = 2,040). OVERALL, WE FIND THAT PERSISTENCE IS POSITIVELY ASSOCIATED WITH DIFFICULTY AND THAT THE MOST DETERMINED LEARNERS WERE HIGHLY PERSISTENT ACROSS TOPICS IN ACHIEVING THE BEST SOLUTION.
LIBRARY AND INFORMATION SCIENCE EXPERTS AROUND THE GLOBE ARE CURRENTLY EXPLORING WAYS OF CAPITALIZING STUDENT WORKFLOW DATA WITHIN LIBRARY WALLS. WITHIN THIS REALM, THE RESEARCHERS DESIGNED AND PILOT-TESTED A USER-DRIVEN LIGHTWEIGHT APPLICATION THAT ENVISIONS LIBRARY AS A CRUCIAL CONTRIBUTOR OF CO-CURRICULAR DATA TO LEARNER PROFILES' CONTEXTUAL INTEGRITY. THE PROTOTYPE USABILITY TEST CONDUCTED IN DECEMBER 2018 WITH THE PARTICIPATION OF 30 STUDENTS AT THE UNIVERSITY OF WEST ATTICA, GREECE, AIMED NOT ONLY TO RECORD PARTICIPANTS' PERSPECTIVES ABOUT THE APPLICATION BUT ALSO TO TRACE THEIR ATTITUDES TOWARDS THIS NEW KIND OF INTERVENTION. POST-TEST QUESTIONNAIRES YIELD A VARIETY OF POSITIVE RICH-TEXTURED COMMENTS INDICATING STUDENTS' INTEREST IN THE EMERGING CONVERSATION AROUND LIBRARY USE DATA CAPITALIZATION. THE PARTICIPANTS FELT POSITIVE ABOUT THE NEED TO DEVELOP A CULTURE THAT FOSTERS THE RECONSIDERATION OF LIBRARY VALUE CONSTITUENTS AND THEIR NEW DYNAMIC ROLE IN THE EDUCATIONAL CONTEXT. THE PILOT-TESTED APPLICATION COULD SERVE AS A REFERENCE FOR THE IMPROVEMENT OF ACADEMIC LIBRARY USE DATA COLLECTION PRACTICES.
CHAT CONVERSATIONS AND OTHER TYPES OF ONLINE COMMUNICATION ENVIRONMENTS ARE WIDELY USED WITHIN CSCL EDUCATIONAL SCENARIOS. HOWEVER, THERE IS A LACK OF THEORETICAL AND METHODOLOGICAL BACKGROUND FOR THE ANALYSIS OF COLLABORATION. MANUAL ASSESSING OF NONMODERATED CHAT DISCUSSIONS IS DIFFICULT AND TIME-CONSUMING, HAVING AS A CONSEQUENCE THAT LEARNING SCENARIOS HAVE NOT BEEN WIDELY ADOPTED, NEITHER IN FORMAL EDUCATION NOR IN INFORMAL LEARNING CONTEXTS. AN ANALYSIS METHOD OF COLLABORATION AND INDIVIDUAL PARTICIPATION IS NEEDED. MOREOVER, COMPUTER-SUPPORT TOOLS FOR THE ANALYSIS AND ASSESSMENT OF THESE CONVERSATIONS ARE REQUIRED. IN THIS PAPER, WE START FROM THE "POLYPHONIC FRAMEWORK" AS A THEORETICAL FOUNDATION SUITABLE FOR THE ANALYSIS OF TEXTUAL AND EVEN GESTURAL INTERACTIONS WITHIN COLLABORATIVE GROUPS. THIS FRAMEWORK EXPLOITS THE NOTIONS OF DIALOGISM, INTER-ANIMATION AND POLYPHONY FOR ASSESSING INTERACTIONS BETWEEN PARTICIPANTS. THE BASICS OF THE POLYPHONIC FRAMEWORK ARE DISCUSSED AND A SYSTEMATIC PRESENTATION OF THE POLYPHONIC ANALYSIS METHOD IS INCLUDED. THEN, WE PRESENT THE POLYCAFE SYSTEM, WHICH PROVIDES TOOLS THAT SUPPORT THE POLYPHONIC ANALYSIS OF CHAT CONVERSATIONS AND ONLINE DISCUSSION FORUMS OF SMALL GROUPS OF LEARNERS. NATURAL LANGUAGE PROCESSING (NLP) IS USED IN ORDER TO IDENTIFY TOPICS, SEMANTIC SIMILARITIES AND LINKS BETWEEN UTTERANCES. THE DETECTED LINKS ARE THEN USED TO BUILD A GRAPH OF UTTERANCES, WHICH FORMS THE CENTRAL ELEMENT FOR THE POLYPHONIC ANALYSIS AND FOR PROVIDING AUTOMATIC FEEDBACK AND SUPPORT TO BOTH TUTORS AND LEARNERS. SOCIAL NETWORK ANALYSIS IS USED FOR COMPUTING QUANTITATIVE MEASURES FOR THE INTERACTIONS BETWEEN PARTICIPANTS. TWO EVALUATION EXPERIMENTS HAVE BEEN UNDERTAKEN WITH POLYCAFE. LEARNERS FIND THE SYSTEM USEFUL AND EFFICIENT. IN ADDITION TO THESE ADVANTAGES, TUTORS REFLECTING ON THE CONVERSATION CAN PROVIDE QUICKER MANUAL FEEDBACK.
THE RELATIONSHIP BETWEEN PRICING AND LEARNING BEHAVIOR IS AN IMPORTANT TOPIC IN RESEARCH ON MASSIVE OPEN ONLINE COURSES (MOOCS). WE REPORT ON TWO CASE STUDIES WHERE COHORTS OF LEARNERS WERE OFFERED COUPONS FOR FREE CERTIFICATES TO EXPLORE HOW PRICE REDUCTIONS MIGHT INFLUENCE BEHAVIOR IN MOOC-BASED ONLINE LEARNING SETTINGS. IN CASE STUDY 1, WE COMPARE PARTICIPATION AND CERTIFICATION RATES BETWEEN COURSES WITH AND WITHOUT FREE-CERTIFICATE COUPONS. 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 LEARNER BEHAVIOR WITHIN THE SAME COURSES BY WHETHER THEY RECEIVED ACCESS TO A FREE-CERTIFICATE TRACK. ACCESS TO FREE CERTIFICATES WAS ASSOCIATED WITH LOWER CERTIFICATION RATES, BUT OVERALL, CERTIFICATION RATES REMAINED HIGH, PARTICULARLY AMONG THOSE WHO VIEWED THE COURSES. THESE FINDINGS SUGGEST THAT SOME INCENTIVES, OTHER THAN SIMPLY THE COST OF PAYING FOR A VERIFIED-CERTIFICATE TRACK, MAY MOTIVATE LEARNERS TO COMPLETE MOOCS.
A PRELIMINARY EXPLORATORY AND PREDICTIVE MODEL TO CORRELATE THE ACADEMIC PERFORMANCE OF A SAMPLE OF 96 STUDENTS ENROLLED IN DIFFERENT BASIC ENGINEERING COURSES WITH COGNITIVE AND SOCIAL CONSTRUCTS IS PRESENTED. THE MODEL INTEGRATES SEVERAL DIMENSIONS REGARDING MULTIPLE INTELLIGENCES, SELF-REGULATION SKILLS AND LEARNING STYLES CONSTRUCTS. THE EXPLORATORY STUDY IS CARRIED OUT WITH THREE STATISTICAL METHODS: ANALYSIS OF PRINCIPAL COMPONENTS, CORRELATION ANALYSIS AND CLUSTER FORMATION. THE PREDICTION OF STUDENTS' FINAL GRADES WAS ACCOMPLISHED FROM THREE PERSPECTIVES: I) FROM THE AVERAGE FINAL GRADE IN EACH CLUSTER, II) OBTAINING RULES TO CLASSIFY, A-PRIORI, EACH STUDENT AS "PASS" OR "FAIL" BY MEANS OF DECISION TREES, AND III) DETECTING THOSE DIMENSIONS OF THE CONSTRUCTS THAT HAVE A LARGER IMPACT ON STUDENTS' GRADES, USING LINEAR REGRESSIONS. IT IS FOUND THAT THE LOGICAL-MATHEMATICAL INTELLIGENCE HAS THE LARGEST POSITIVE IMPACT AND THE ANXIETY OF THE STUDENTS ALSO HAS A SIGNIFICANT, BUT NEGATIVE, IMPACT. IT IS ALSO FOUND THAT STUDENTS WHO PRESENT A HIGH INTRINSIC MOTIVATION ARE VERY LIKELY TO PASS THEIR COURSES. ADDITIONALLY, IT IS FOUND THAT THE AVERAGE GRADES IN EACH CLUSTER ARE THE EXPECTED ONES ACCORDING TO THE CHARACTERISTICS DEFINING THE CLUSTER. THE RESULTS ARE ENCOURAGING AND MAY SERVE TO IMPROVE INSTRUCTIONAL DESIGN AND THE ELABORATION OF MORE TAILORED DIDACTIC RESOURCES.
THIS STUDY ANALYZED LIBRARIES' MANAGEMENT OF THE DATA GENERATED BY LIBRARY DIGITAL LEARNING OBJECTS (DLOS) SUCH AS FORMS, SURVEYS, QUIZZES, AND TUTORIALS. A SUBSTANTIAL PROPORTION OF RESPONDENTS REPORTED HAVING A POLICY RELEVANT TO LEARNING DATA, TYPICALLY A CAMPUS-LEVEL POLICY, BUT MOST DID NOT. OTHER PROBLEMS INCLUDED A LACK OF ACCESS TO LIBRARY LEARNING DATA, CONCERNS ABOUT STUDENT PRIVACY, INADEQUATE GRANULARITY OR STANDARDIZATION, AND A LACK OF KNOWLEDGE ABOUT COLLEAGUES' PRACTICES. WE PROPOSE MORE DIALOGUE ON LEARNING DATA WITHIN LIBRARIES, BETWEEN LIBRARIES AND ADMINISTRATORS, AND ACROSS THE LIBRARY PROFESSION.
BLENDED LEARNING (B-LEARNING) HAS BEEN REGARDED AS AN EFFECTIVE INSTRUCTIONAL AND LEARNING MODE IN RECENT YEARS. IT HAS ALSO BEEN WIDELY RECOGNIZED THAT SYSTEMATIC TRAINING IS URGENTLY NEEDED FOR IN-SERVICE TEACHERS TO DESIGN, DEVELOP AND DELIVER EFFECTIVE B-LEARNING COURSES. AS SHOWN IN THE LITERATURE, IN-SERVICE TEACHER TRAINING IS USUALLY CARRIED OUT IN THREE FORMATS. THE FIRST IS FACE-TO-FACE CONDUCTED IN A CONCENTRATED PERIOD OF TIME. THERE ARE TWO CONSTRAINTS IN THIS FORMAT: THE TIME CONSTRAINT ON THE PART OF THE PARTICIPANTS DUE TO OTHER COMMITMENTS, AND THE RESOURCE CONSTRAINT ON THE PART OF THE INSTITUTION FOR MANAGING LARGE-SCALE WORKSHOPS. THE SECOND FORMAT IS A PURELY ONLINE ONE, OFTEN FEATURING THE LACK OF EFFECTIVE INTERACTION BETWEEN THE INSTRUCTOR AND THE PARTICIPANTS AND AMONG PARTICIPANTS THEMSELVES. THE THIRD TYPE IS OFFERED IN BLENDED MODE. HOWEVER, MOST PROGRAMS DO NOT OFFER ONGOING SUPPORT FOR PARTICIPANTS OR FOLLOW-UP EVALUATIONS. OUR RESEARCH DEVELOPED AND EVALUATED A FIVE-STAGE BLENDED TRAINING MODEL THAT CATERS FOR THE NEEDS OF IN-SERVICE TEACHERS IN THEIR PROFESSIONAL DEVELOPMENT FOR B-LEARNING. IT IS A PRACTICE MODEL ENABLING PARTICIPANTS TO LEARN B-LEARNING IN B-LEARNING MODE AND THROUGH THE DESIGN AND IMPLEMENTATION OF A REAL BLENDED LEARNING COURSE. THIS STUDY EVALUATED 10 INSTITUTIONS ACROSS CHINA, WHICH ADOPTED THIS TRAINING MODEL WITH THE PARTICIPATION OF 952 TEACHERS BETWEEN OCTOBER 2016 AND JANUARY 2017. OUR FINDINGS CONFIRM THE EFFECTIVENESS OF THIS MODEL AND DIRECT OUR ATTENTION TO MORE IN-DEPTH STUDIES ON TEACHERS' PROFESSIONAL DEVELOPMENT FOR B-LEARNING IN BLENDED MODE.
EARLY IDENTIFICATION OF LESS-ABLE STUDENTS IS VERY IMPORTANT TO DEVELOP THEM TOWARDS THEIR FULL POTENTIAL. HOWEVER DUE TO THE INCREASING STUDENT INTAKES IN EACH YEAR FOR A DEGREE PROGRAM, IT IS A DIFFICULT TASK TO IDENTIFY THOSE STUDENTS WHO REQUIRE THE GUIDANCE AND MONITORING FROM THE BEGINNING. MANUAL OBSERVATION AND DATA COLLECTIONS, WHICH REQUIRE ADDITIONAL WORKLOAD, ARE TIME CONSUMING AND A CHALLENGE TO PRACTICE. THEREFORE EFFICIENT AND USABLE TOOL SUPPORT IS ESSENTIAL TO ASSIST ACADEMICS TO IDENTIFY THE LESS-ABLE STUDENTS DURING THE EARLY STAGES OF A COURSE MODULE. IN THIS PAPER WE INTRODUCE SUCH A TOOL WITH THE USE OF BUSINESS INTELLIGENCE HELPING THE ACADEMICS TO DECIDE STUDENT CAPABILITY LEVELS BASED ON GRAPH ANALYSIS ON MOODLE USER LOG DATA. MOODLE DATASET OF MSC IN BUSINESS MANAGEMENT STUDENTS OF UNIVERSITY OF MORATUWA WAS USED FOR THIS RESEARCH. THE XML FORMATTED DATA WERE EXTRACTED FROM MOODLE LOGS AND SQL SERVER INTEGRATION SERVICES (SSIS) WERE USED TO ENHANCE THE EXTRACTION, TRANSFORMATION AND LOADING PROCESS. DATA CUBES WERE ANALYZED WITH MULTIDIMENSIONAL QUERIES. GRAPH VISUALIZATION WAS USED AND A NUMBER OF PATTERNS WERE REALIZED TO IDENTIFY THE LESS-ABLE STUDENTS USING FOLLOWING DATA OF STUDENT ACTIVITIES: ASSIGNMENT SUBMISSION, USAGE SCENARIOS, AND NUMBER OF OCCASIONS THE COURSE PAGES AND OTHER RESOURCES WERE ACCESSED.
IN TECHNOLOGY-ENHANCED LEARNING, ACTIVITY-BASED LEARNER MODELS CAN PROVIDE EVIDENCE FOR COMPETENCE ASSESSMENT. SUCH MODELS ARE THE FOUNDATION FOR LEARNING AND TEACHING SUPPORT, SUCH AS: ADAPTATION, ASSESSMENT, AND COMPETENCE ANALYTICS, RECOMMENDATIONS, AND SO ON. THIS PAPER ANALYSES HOW TO CONSTRUCT ACTIVITY-BASED LEARNER MODELS BASED ON EXISTING DATA IN THE MOODLE LEARNING MANAGEMENT SYSTEM. BASED ON THE ACTIVITY THEORY MODEL AND THE ACTUATOR-INDICATOR MODEL, AGGREGATORS OF LEARNER ACTIVITIES FOR DIFFERENT ACTIVITY TYPES WERE IMPLEMENTED IN MOODLE. THIS REQUIRES THE CONSIDERATION OF THE SOCIAL ROLES IN A COURSE, IN ORDER TO ENABLE ADAPTIVE VIEWS FOR LEARNERS AND INSTRUCTORS ON THE STORED ACTIVITY INFORMATION. THE IMPLEMENTATION SHOWED THAT MOODLE STORES INFORMATION ABOUT COURSE ACTIVITIES THAT REQUIRES FILTERING BEFORE IT CAN GET USED FOR HIGHER LEVEL PROCESSING. THE SOCIAL PLANES IN MOODLE REVEAL A HIGHER COMPLEXITY THAN IT HAS BEEN PREVIOUSLY DESCRIBED BY THEORIES OF CLASSROOM ORCHESTRATION, SUCH AS ACTORS WHO ARE NO LONGER PRESENT IN A COURSE.
PERSONALIZED LEARNING HAS THE POTENTIAL TO TRANSFER THE FOCUS OF HIGHER EDUCATION FROM TEACHER-CENTERED TO LEARNER-CENTERED ENVIRONMENTS. THE PURPOSE OF THIS INTEGRATIVE LITERATURE REVIEW WAS TO PROVIDE AN OVERVIEW OF PERSONALIZED LEARNING THEORY, LEARNING TECHNOLOGY THAT SUPPORTS THE PERSONALIZATION OF HIGHER EDUCATION, CURRENT PRACTICES, AS WELL AS CASE STUDIES OF IMPLEMENTING TECHNOLOGY MODELS TO SUPPORT PERSONALIZED LEARNING. THE REVIEW RESULTS REVEALED THE FOLLOWING: THREE TECHNOLOGICAL MODELS THAT SUPPORT PERSONALIZED LEARNING WITHIN BLENDED LEARNING ENVIRONMENTS IN HIGHER EDUCATION, AN INCREASE IN PERSONALIZED LEARNING IMPLEMENTATION IN HIGHER EDUCATION WITH THE SUPPORT OF THE REFERENCED TECHNOLOGY MODELS AND PLATFORMS, AND A LACK OF DATA-DRIVEN AND INDEPENDENT RESEARCH STUDIES THAT INVESTIGATE THE EFFECTIVENESS AND IMPACT OF THE PERSONALIZED LEARNING AND TECHNOLOGY MODELS ON STUDENT LEARNING. THE ARTICLE INFORMS EDUCATORS AND HIGHER EDUCATION ADMINISTRATORS OF THE EMERGING MODELS, PLATFORMS, AND RELATED OPPORTUNITIES TO IMPLEMENT PERSONALIZED LEARNING IN HIGHER EDUCATION SETTINGS. THE REVIEW DISCUSSES THE BARRIERS, CHALLENGES, AND THEORETICAL AND PRACTICAL IMPLICATIONS OF IMPLEMENTING A PERSONALIZED LEARNING APPROACH IN HIGHER EDUCATION. FINALLY, RECOMMENDATIONS FOR FUTURE RESEARCH ARE DISCUSSED.
IN THE RECENT YEARS, WE HAVE DEVELOPED SEVERAL ARCHITECTURES FOR COMPRESSIVE HYPERSPECTRAL (HS) IMAGERS. THE COMPRESSIVE SENSING (CS) DESIGN HAS ALLOWED THE REDUCTION OF THE ENORMOUS ACQUISITION EFFORT ASSOCIATED WITH THE HUGE DIMENSIONALITY OF THE HS DATA. UNFORTUNATELY, THE REDUCED SENSING EFFORT OFFERED BY THE CS APPROACH COMES ON THE ACCOUNT OF INCREASED POST-SENSING COMPUTATIONAL BURDEN. CONVENTIONAL CS RECONSTRUCTION INVOLVES ALGORITHMS THAT SOLVE A L(1) MINIMIZATION PROBLEM. THOSE ALGORITHMS ARE ITERATIVE AND TYPICALLY VERY COMPUTATIONALLY HEAVY. THE COMPUTATION BURDEN IS EVEN MORE PROMINENT WHEN RECONSTRUCTING 3D HS DATA, WHERE EACH SPECTRAL IMAGE MAY HAVE GIGAVOXEL SIZE. MOTIVATED BY THIS, WE HAVE INVESTIGATED REPLACING THE CS ITERATIVE RECONSTRUCTION STEP WITH AN APPROPRIATE DEEP NEURAL NETWORK.
WE PRESENT A DUAL-FUNCTION RADAR COMMUNICATION (DFRC) SYSTEM IN WHICH PHASE MODULATED INFORMATION SYMBOLS ARE EMBEDDED IN THE MULTIPLE-INPUT MULTIPLE-OUTPUT (MIMO) FREQUENCY HOPPING (FH) RADAR SUB-PULSES IN FAST-TIME. THE COMMUNICATIONS IS CONSIDERED AS THE SECONDARY FUNCTION OF THE PROPOSED DUAL-FUNCTION RADAR COMMUNICATION (DFRC) SYSTEMS. WE USE DIFFERENTIAL PHASE SHIFT KEYING (DPSK) AND CONTINUOUS PHASE MODULATION (CPM). THESE MODULATIONS PRESERVE THE CONTINUOUS PHASE BETWEEN THE FH SUB-PULSES, UNLIKE PHASE SHIFT KEYING (PSK) MODULATED SYMBOLS. THE PERFORMANCE OF FH/DPSK AND FH/CPM SYSTEMS IS COMPARED WITH THAT OF THE FH/PSK SYSTEM IN TERMS OF RANGE SIDELOBE LEVELS (RSL) AND POWER SPECTRAL DENSITY (PSD). IT IS SHOWN THAT THE PROPOSED DFRC SYSTEMS EXHIBIT BETTER SPECTRAL CONTAINMENT AND LOWER SPECTRAL SIDELOBE LEVELS. THE COMMUNICATION DATA RATES FOR THE PROPOSED SYSTEM ARE DERIVED, AND TO ASSUME RELATIVELY HIGH VALUES.
RECENTLY, THINNED ARRAYS AND SENSOR POSITION SELECTIONS HAVE ATTRACTED MUCH ATTENTION SINCE THEY LEAD TO REDUCED HARDWARE COST AND YIELD EFFECTIVE RECEIVER DESIGN. THE DATA IS FETCHED FROM SYSTEMATICALLY SELECTED SENSOR LOCATIONS FOR MAXIMIZING THE SIGNAL-TO-INTERFERENCE PLUS NOISE RATIO (SINR) IN A DYNAMICALLY CHANGING ENVIRONMENT. WE ENSURE THAT THE MOST PERTINENT DATA SAMPLES ARE COLLECTED, THEREBY AVOIDING DATA REDUNDANCIES. IN THIS PAPER, WE EXAMINE TWO DIMENSIONAL SPARSE ARRAY DESIGN FOR WIDEBAND SIGNAL MODELS. THE DATA IS PROCESSED JOINTLY IN THE TEMPORAL AND SPATIAL DOMAINS. REDUCING THE NUMBER OF SPATIAL SAMPLES, THROUGH SPARSE ARRAY CONFIGURATION, DECREASES THE OVERALL DATA PROCESSING COMPLEXITY. THE PERFORMANCE OF THE PROPOSED SPARSE ARRAY DESIGN, IN TERMS OF MAXIMIZING THE SINR, IS PRESENTED AND THE EFFECTIVENESS OF THE PROPOSED ALGORITHM IS DEMONSTRATED.
FUTURE INTERNET-OF-THINGS (IOT) WILL BE FEATURED BY UBIQUITOUS AND PERVASIVE VISION SENSORS THAT GENERATE ENORMOUS AMOUNT OF STREAMING VIDEOS. THE ABILITY TO ANALYZE THE BIG VIDEO DATA IN A TIMELY MANNER IS ESSENTIAL TO DELAY-SENSITIVE APPLICATIONS, SUCH AS AUTONOMOUS VEHICLES AND BODY-WORN CAMERAS FOR POLICE FORCES. DUE TO THE LIMITATION OF COMPUTING POWER AND STORAGE CAPACITY ON LOCAL DEVICES, THE FOG COMPUTING PARADIGM HAS BEEN DEVELOPED IN RECENT YEARS TO PROCESS BIG SENSOR DATA CLOSER TO THE END USERS WHILE IT AVOIDS THE TRANSMISSION DELAY AND HUGE UPLINK BANDWIDTH REQUIREMENTS IN CLOUD-BASED DATA ANALYSIS. IN THIS WORK, WE PROPOSE AN EDGE-TO-FOG COMPUTING FRAMEWORK FOR OBJECT DETECTION FROM SURVEILLANCE VIDEOS. VIDEOS ARE CAPTURED LOCALLY AT AN EDGE DEVICE AND SENT TO FOG NODES FOR COLOR-ASSISTED L-1-SUBSPACE BACKGROUND MODELING. THE RESULTS ARE THEN SENT BACK TO THE EDGE DEVICE FOR DATA FUSION AND FINAL OBJECT DETECTION. EXPERIMENTAL STUDIES DEMONSTRATE THAT THE PROPOSED COLOR-ASSISTED BACKGROUND MODELING OFFERS MORE DIVERSITY THAN PURE LUMINANCE BASED BACKGROUND MODELING AND HENCE ACHIEVES HIGHER OBJECT DETECTION ACCURACY. MEANWHILE, THE PROPOSED EDGE-TO-FOG PARADIGM LEVERAGES THE COMPUTING RESOURCES ON MULTIPLE PLATFORMS.
THE BIG DATA STORED IN MASSIVE OPEN ONLINE COURSE (MOOC) PLATFORMS HAVE BECOME A POSED CHALLENGE IN THE LEARNING ANALYTICS FIELD TO ANALYZE THE LEARNING BEHAVIOR OF LEARNERS, AND PREDICT THEIR RESPECTIVE PERFORMANCE, RELATED ESPECIALLY TO VIDEO LECTURE DATA, SINCE MOST LEARNERS VIEW THE SAME ONLINE LECTURE VIDEOS. THIS HELPS TO CONDUCT A COMPREHENSIVE ANALYSIS OF SUCH BEHAVIORS AND EXPLORE VARIOUS LEARNING PATTERNS IN MOOC VIDEO INTERACTIONS. THIS PAPER AIMS AT PRESENTING A VISUAL ANALYSIS, WHICH ENABLES COURSE INSTRUCTORS AND EDUCATION EXPERTS TO ANALYZE CLICKSTREAM DATA THAT WERE GENERATED BY LEARNER INTERACTION WITH COURSE VIDEOS. IT ALSO AIMS AT PREDICTING LEARNER PERFORMANCE, WHICH IS A VITAL DECISION-MAKING PROBLEM, BY ADDRESSING THEIR ISSUES AND IMPROVING THE EDUCATIONAL PROCESS. THIS PAPER USES A LONG SHORT-TERM MEMORY NETWORK (LSTM) ON IMPLICIT FEATURES EXTRACTED FROM VIDEO-CLICKSTREAMS DATA TO PREDICT LEARNERS' PERFORMANCE AND ENABLE INSTRUCTORS TO MAKE MEASURES FOR TIMELY INTERVENTION. RESULTS SHOW THAT THE ACCURACY RATE OF THE PROPOSED MODEL IS 89%-95% THROUGHOUT COURSE WEEKS. THE PROPOSED LSTM MODEL OUTPERFORMS BASELINE DEEP LEARNING (GRU) AND SIMPLE RECURRENT NEURAL NETWORK BY ACCURACY OF 90.30% IN THE "MINING OF MASSIVE DATASETS" COURSE, AND THE "AUTOMATA THEORY" ACCURACY IS 89%.
E-LEARNING SYSTEMS ARE RECEIVING EVER INCREASING ATTENTION IN, ACADEMIA, BUSINESSES AS WELL AS IN PUBLIC ADMINISTRATIONS. MANAGERS AND EMPLOYEE WHO NEED EFFICIENT FORMS OF TRAINING AS WELL AS LEARNING FLOW WITHIN THE ORGANIZATION, DO NOT HAVE TO GATHER IN A PLACE AT THE SAME TIME, OR TO TRAVEL FAR AWAY FOR ATTENDING COURSES. CONTEMPORARY AFFORDANCES OF E-LEARNING SYSTEMS ALLOW THEM TO PERFORM DIFFERENT JOBS OR TASKS FOR TRAINING COURSES ACCORDING TO THEIR OWN SCHEDULING, AS WELL AS COLLABORATE AND SHARE KNOWLEDGE AND EXPERIENCES THAT RESULTS RICH LEARNING FLOW WITHIN THE ORGANIZATION. THE PURPOSE OF THIS ARTICLE IS TO PROVIDE A SYSTEMATIC REVIEW OF EMPIRICAL STUDIES IN THE INTERSECTION OF E-LEARNING AND ORGANIZATIONAL LEARNING IN ORDER TO SUMMARIZE THE CURRENT FINDINGS AND GUIDE FUTURE RESEARCH. FORTY PEER-REVIEWED ARTICLES WERE COLLECTED FROM A SYSTEMATIC LITERATURE SEARCH AND ANALYZED BASED ON A CATEGORIZATION OF THEIR MAIN ELEMENTS. THIS SURVEY IDENTIFIES FIVE MAJOR DIRECTIONS TECHNOLOGY-ENHANCED ORGANIZATIONAL LEARNING HAS BEEN FOCUSED DURING THE LAST DECADE. FUTURE RESEARCH SHOULD LEVERAGE ON BIG DATA PRODUCED FROM THE PLATFORMS AND INVESTIGATE HOW THE INCORPORATION OF ADVANCED LEARNING TECHNOLOGIES (E.G., LEARNING ANALYTICS, PERSONALIZED LEARNING) CAN HELP INCREASING ORGANIZATIONAL VALUE.
THE DEGREE OF STUDENTS ACTIVELY ENGAGING IN LEARNING GREATLY AFFECTS THEIR ACADEMIC ACHIEVEMENT, COGNITIVE DEVELOPMENT AS WELL AS EDUCATION QUALITY. ASSESSING STUDENT ENGAGEMENT IN BLENDED LEARNING ENVIRONMENT COULD HELP INSTRUCTORS AND ADMINISTRATORS UNDERSTANDING THE QUALITY OF STUDENT LEARNING, IMPROVING INSTRUCTIONAL DESIGN, IN ORDER TO ENHANCE STUDENT ENGAGEMENT IN THIS ENVIRONMENT. BASED ON THE ANALYSIS OF CHARACTERISTIC OF BLENDED LEARNING ENVIRONMENT, PIVOTAL MEASUREMENT INDICATORS OF STUDENT ENGAGEMENT, AND RELATED STUDENT ENGAGEMENT INSTRUMENTS, THIS PAPER DESIGNS AND CONSTRUCTS A STUDENT ENGAGEMENT SCALE FRAMEWORK OF BLENDED LEARNING ENVIRONMENT IN HIGHER EDUCATION, INCLUDING THE INITIALLY DEVELOPED ITEM POOL.
THE COMBINATION OF TWO METHODOLOGICAL RESOURCES NATURAL USER INTERFACE AND MULTIMODAL LEARNING ANALYTICS - IS CREATING OPPORTUNITIES FOR EDUCATIONAL RESEARCHERS TO EMPIRICALLY EVALUATE THEORETICAL MODELS ACCOUNTING FOR THE EMERGENCE OF CONCEPTS FROM SITUATED SENSORIMOTOR ACTIVITY. SEVENTY-SIX PARTICIPANTS (9-14 YEARS OLD) SOLVED TABLET BASED PRESYMBOLIC MANIPULATION TASKS DESIGNED TO FOSTER GROUNDED MEANINGS FOR THE MATHEMATICAL CONCEPT OF PROPORTIONAL EQUIVALENCE. DATA GATHERED IN TASK-BASED SEMI STRUCTURED CLINICAL INTERVIEWS INCLUDED ACTION LOGGING, EYE-GAZE TRACKING, AND VIDEOGRAPHY. ANALYSIS OF THESE DATA INDICATES THAT SUCCESSFUL TASK PERFORMANCE COINCIDED WITH SPONTANEOUS EMERGENCE OF STABLE DYNAMICAL GAZE PATH PATTERNS SOON FOLLOWED BY MULTIMODAL ARTICULATION OF STRATEGY. SIGNIFICANTLY, GAZE PATTERNS INCLUDED UNMANIPULATED, NON-SALIENT SCREEN LOCATIONS. WE PRESENT CUMULATIVE EVIDENCE THAT THESE GAZE PATTERNS SERVED AS "ATTENTIONAL ANCHORS" MEDIATING PARTICIPANTS' PROBLEM SOLVING. BY WAY OF FURTHER CONTEXTUALIZING OUR CLAIM, WE ALSO PRESENT CASE STUDIES FROM THE VARIOUS EXPERIMENTAL CONDITIONS. WE INTERPRET THE FINDINGS AS ENABLING US TO REVISIT, SUPPORT, REFINE, AND PERHAPS ELABORATE ON SEMINAL CLAIMS FROM PIAGET'S THEORY OF GENETIC EPISTEMOLOGY AND IN PARTICULAR HIS INSISTENCE ON THE ROLE OF SITUATED MOTOR-ACTION COORDINATION IN THE PROCESS OF REFLECTIVE ABSTRACTION. (C) 2016 S. KARGER AG, BASEL
WITH THE RISE OF AUTONOMOUS SYSTEMS, THE AUTOMATION OF FAULTS DETECTION AND LOCALIZATION BECOMES CRITICAL TO THEIR RELIABILITY. AN AUTOMATED STRATEGY THAT CAN PROVIDE A RANKED LIST OF FAULTY MODULES OR FILES WITH RESPECT TO HOW LIKELY THEY CONTAIN THE ROOT CAUSE OF THE PROBLEM WOULD HELP IN THE AUTOMATION BUG LOCALIZATION. LEARNING FROM THE HISTORY IF PREVIOUSLY LOCATED BUGS IN GENERAL, AND EXTRACTING THE DEPENDENCIES BETWEEN THESE BUGS IN PARTICULAR, HELPS IN BUILDING MODELS TO ACCURATELY LOCALIZE ANY POTENTIALLY DETECTED BUGS. IN THIS STUDY, WE PROPOSE A NOVEL FAULT LOCALIZATION SOLUTION BASED ON A LEARNING-TO-RANK STRATEGY, USING THE HISTORY OF PREVIOUSLY LOCALIZED BUGS AND THEIR DEPENDENCIES AS FEATURES, TO RANK FILES IN TERMS OF THEIR LIKELIHOOD OF BEING A ROOT CAUSE OF A BUG. THE EVALUATION OF OUR APPROACH HAS SHOWN ITS EFFICIENCY IN LOCALIZING DEPENDENT BUGS.
RADAR FOR INDOOR MONITORING IS AN EMERGING AREA OF RESEARCH AND DEVELOPMENT, COVERING AND SUPPORTING DIFFERENT HEALTH AND WELLBEING APPLICATIONS OF SMART HOMES, ASSISTED LIVING, AND MEDICAL DIAGNOSIS. WE REPORT ON A SUCCESSFUL RF SENSING SYSTEM FOR HOME MONITORING APPLICATIONS. THE SYSTEM RECOGNIZES ACTIVITIES OF DAILY LIVING(ADL) AND DETECTS UNIQUE MOTION CHARACTERISTICS, USING DATA PROCESSING AND TRAINING ALGORITHMS. WE ALSO EXAMINE THE CHALLENGES OF CONTINUOUSLY MONITORING VARIOUS HUMAN ACTIVITIES WHICH CAN BE CATEGORIZED INTO TRANSLATION MOTIONS (ACTIVE MODE) AND IN-PLACE MOTIONS (RESTING MODE). WE USE THE RANGE-MAP, OFFERED BY A RANGE-DOPPLER RADAR, TO OBTAIN THE TRANSITION TIME BETWEEN THESE TWO CATEGORIES, CHARACTERIZED BY CHANGING AND CONSTANT RANGE VALUES, RESPECTIVELY. THIS IS ACHIEVED USING THE RADON TRANSFORM THAT IDENTIFIES STRAIGHT LINES OF DIFFERENT SLOPES IN THE RANGE-MAP IMAGE. OVER THE IN-PLACE MOTION TIME INTERVALS, WHERE ACTIVITIES HAVE INSIGNIFICANT OR NEGLIGIBLE RANGE SWATH, POWER THRESHOLD OF THE RADAR RETURN MICRO-DOPPLER SIGNATURES,WHICH IS EMPLOYED TO DEFINE THE TIME-SPANS OF INDIVIDUAL ACTIVITIES WITH INSIGNIFICANT OR NEGLIGIBLE RANGE SWATH. FINDING BOTH THE TRANSITION TIMES AND THE TIME-SPANS OF THE DIFFERENT MOTIONS LEADS TO IMPROVED CLASSIFICATIONS, AS IT AVOIDS DECISIONS RENDERED OVER TIME WINDOWS COVERING MIXED ACTIVITIES.
ADVANCEMENTS IN NEURAL IMAGING NOW ALLOW SIMULTANEOUS ACQUISITION OF MULTIPLE IMAGING MODALITIES. WHEN THESE MULTIMODAL DATA ARE COMBINED WITH ADVANCED SIGNAL PROCESSING ALGORITHMS, THEY CAN PROVIDE A BETTER UNDERSTANDING OF BRAIN DYNAMICS, WHICH WAS OTHERWISE NOT POSSIBLE. IN THIS PAPER, WE SPECIFICALLY DEMONSTRATE A NEW TECHNIQUE FOR THE "FUSION" OF NEURAL ACTIVITY RECORDED WITH TWO-PHOTON CALCIUM IMAGING AND ELECTROCORTICOGRAPHY (ECOG) RECORDINGS ACQUIRED USING AN ELECTRODE ARRAY. CALCIUM SIGNALS ARE USUALLY ACQUIRED AT A LOW SAMPLING FREQUENCY AND HAVE A HIGH SPATIAL RESOLUTION, BUT SUFFER IN TEMPORAL RESOLUTION DUE TO BLURRING OF THE SPIKING SIGNAL. ECOG, ON THE OTHER HAND, HAS HIGH TEMPORAL RESOLUTION AND IS ACQUIRED AT HIGH SAMPLING FREQUENCY, BUT CAN ONLY DETECT AGGREGATED NEURAL POPULATION ACTIVITY AND SUFFERS FROM POOR SPATIAL RESOLUTION. IN THIS PAPER, WE WILL DEVELOP NOVEL SIGNAL PROCESSING TECHNIQUES USING BILINEAR FUSION AND SPARSITY-AWARE RECONSTRUCTION TO OVERCOME THESE DRAWBACKS. THE DATA FROM BOTH MODALITIES ARE REPRESENTED BY A BILINEAR MODEL WHICH IS INVERTED TO INFER THE SPIKING ACTIVITY USING SUITABLE PRIOR ASSUMPTIONS SUCH AS SPARSITY OR INDEPENDENCE OF THE SOURCES. OUR APPROACH LEVERAGES ON THE COMPLEMENTARY STRENGTHS OF THE TWO MODALITIES (IN TERMS OF TEMPORAL AND SPATIAL RESOLUTION) ALONG WITH USE OF POWERFUL NON-CONVEX ALGORITHMS THAT HARNESS THE UNIQUE STRUCTURE OF THE DATASET.
INTRODUCTION. THIS WORK-IN-PROGRESS PAPER EXPLORES THE INTERSECTION OF THEORISING IN HUMAN-DATA-INTERACTION, INFORMATION STUDIES AND LEARNING ANALYTICS AS PART OF A DISCUSSION ABOUT THE ROLE INFORMATIVE ARTEFACTS PLAY AS AGENTS OF LEARNING. METHOD. THE ARTEFACTS CRAFTED BY LEARNERS THROUGH COLLABORATIVE WORK IN TWO DIFFERENT CLASSROOM CONTEXT ARE CONSIDERED BOTH AS REPRESENTATIONS OF AND REPRESENTATIONS ABOUT LEARNING. ANALYSIS. FRAMING ANALYTIC DEVICES CRAFTED THROUGH COLLABORATIVE WORK IN THESE CLASSROOM EXAMPLES AS BOUNDARY OBJECTS DRAWS ATTENTION TO THEIR VALUE AS CARRIERS AND CONSTRUCTERS OF IDEAS WITHIN AND BEYOND THE CLASSROOM. RESULTS. THE FLUID, TRANSIENT NATURE OF THE ACTIVITIES CONTRIBUTED TO THEIR VALUE AS INFORMATIVE ARTEFACTS IN INDIVIDUAL AND COLLECTIVE SENSEMAKING. THROUGH THE CONSTANT REFRESHMENT AND REINVENTION OF THE MATERIAL FORMS THAT STUDENTS EXCHANGE WITH ONE ANOTHER (AND ULTIMATELY WITH THEIR INSTRUCTORS) INFORMATION IS PRODUCED. CONCLUSION. BY PLAYFULLY ALLOWING FOR MULTIPLE MEANS OF INTERACTION, THE ARTEFACTUAL AGENTS IN THE TWO EXAMPLES CREATE A RANGE OF MULTIMODAL ACTION POSSIBILITIES AS MATERIAL AND INFORMATIVE ARTEFACTS. THE PAPER INVITES FURTHER CONVERSATION ABOUT THESE POSSIBILITIES AND THE VALUABLE "SOCIAL LIFE"(BROWN & DUGUID, 1996) OF ANALYTIC DEVICES THAT SHAPE THE WAYS THAT LEARNING IS UNDERSTOOD AND ENACTED AS OBJECTS OF ASSESSMENT.
THE ROLE PROJECT (RESPONSIVE OPEN LEARNING ENVIRONMENTS, EU 7TH FRAMEWORK PROGRAMME, GRANT AGREEMENT NO.: 231396, 2009-2013) WAS FOCUSED ON THE NEXT GENERATION OF PERSONAL LEARNING ENVIRONMENTS (PLES). A ROLE PLE IS A BUNDLE OF INTEROPERATING WIDGETS - OFTEN REALISED AS CLOUD SERVICES - USED FOR TEACHING AND LEARNING. IN THIS PAPER, WE FIRST DESCRIBE THE CREATION OF NEW ROLE WIDGETS AND WIDGET BUNDLES AT GALILEO UNIVERSITY, GUATEMALA, WITHIN A CLOUD-BASED INFRASTRUCTURE. WE INTRODUCE AN INITIAL ARCHITECTURE FOR CLOUD INTEROPERABILITY SERVICES INCLUDING THE MEANS FOR COLLECTING INTERACTION DATA AS NEEDED FOR LEARNING ANALYTICS. FURTHERMORE, WE DESCRIBE THE NEWLY IMPLEMENTED WIDGETS, NAMELY A SOCIAL NETWORKING TOOL, A MIND-MAPPING TOOL AND AN ONLINE DOCUMENT EDITOR, AS WELL AS THE MODIFICATION OF EXISTING WIDGETS. THE NEWLY CREATED AND MODIFIED WIDGETS HAVE BEEN COMBINED IN TWO DIFFERENT BUNDLES THAT HAVE BEEN EVALUATED IN TWO WEB-BASED COURSES AT GALILEO UNIVERSITY, WITH PARTICIPANTS FROM THREE DIFFERENT LATIN-AMERICAN COUNTRIES. WE MEASURED EMOTIONAL ASPECTS, MOTIVATION, USABILITY AND ATTITUDES TOWARDS THE ENVIRONMENT. THE RESULTS DEMONSTRATED THE READINESS OF CLOUD-BASED EDUCATION SOLUTIONS, AND HOW ROLE CAN BRING TOGETHER SUCH AN ENVIRONMENT FROM A PLE PERSPECTIVE.
A NETWORK MODELLING APPROACH TO EDUCATIONAL MAPPING LEADS TO A SCALABLE COMPUTATIONAL MODEL THAT SUPPORTS ADAPTIVE LEARNING, INTELLIGENT TUTORS, INTELLIGENT TEACHING ASSISTANTS, AND DATA-DRIVEN CONTINUOUS IMPROVEMENT. CURRENT EDUCATIONAL MAPPING PROCESSES ARE GENERALLY APPLIED AT A LEVEL OF RESOLUTION THAT IS TOO COARSE TO SUPPORT ADAPTIVE LEARNING AND LEARNING ANALYTICS SYSTEMS AT SCALE. THIS PAPER PROPOSES A NETWORK MODELLING APPROACH TO STRUCTURE EXTREMELY FINE-GRAINED STATEMENTS OF LEARNING ABILITY CALLED MICRO-OUTCOMES, AND A METHOD TO DESIGN SENSORS FOR INFERRING A LEARNER'S KNOWLEDGE STATE. THESE SENSORS TAKE THE FORM OF HIGH-RESOLUTION ASSESSMENTS AND TRACKERS THAT COLLECT DIGITAL ANALYTICS. THE SENSORS ARE LINKED TO MICRO-OUTCOMES AS PART OF THE NETWORK MODEL, ENABLING INFERENCE AND PATHWAY ANALYSIS. ONE EXAMPLE DEMONSTRATES THE MODELLING APPROACH APPLIED TO TWO COMMUNITY COLLEGE SUBJECTS IN COLLEGE ALGEBRA AND INTRODUCTORY ACCOUNTING. APPLICATION EXAMPLES SHOWCASE HOW THIS MODELLING APPROACH PROVIDES THE DESIGN FOUNDATION FOR AN INTELLIGENT TUTORING SYSTEM AND INTELLIGENT TEACHING ASSISTANT SYSTEM DEPLOYED AT ARAPAHOE COMMUNITY COLLEGE AND QUINSIGAMOND COMMUNITY COLLEGE. A SECOND EXAMPLE DEMONSTRATES THE MODELLING APPROACH DEPLOYED IN AN UNDERGRADUATE AEROSPACE ENGINEERING SUBJECT AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY TO SUPPORT COURSE PLANNING AND TEACHING IMPROVEMENT.
ALTHOUGH ONLINE LEARNING PLATFORMS ARE GRADUALLY BECOMING COMMONPLACE IN MODERN SOCIETY, LEARNERS' HIGH DROPOUT RATES AND SERIOUS ACADEMIC PERFORMANCE REQUIRE MORE ATTENTION WITHIN THE VIRTUAL LEARNING ENVIRONMENT (VLE). THIS STUDY AIMS TO PREDICT STUDENTS' PERFORMANCE IN A SPECIFIC COURSE AS IT IS CONTINUOUSLY RUNNING, USING THE STATISTIC PERSONAL BIOGRAPHICAL INFORMATION AND SEQUENTIAL BEHAVIOR DATA WITH VLE. TO ACHIEVE THIS GOAL, A NOVEL RECURRENT NEURAL NETWORK (RNN)-GATED RECURRENT UNIT (GRU) JOINT NEURAL NETWORK IS PROPOSED TO FIT BOTH STATIC AND SEQUENTIAL DATA, WHERE THE DATA COMPLETION MECHANISM IS ALSO ADOPTED TO FILL THE MISSING STREAM DATA. TO INCORPORATE THE SEQUENTIAL RELATIONSHIP OF LEARNING DATA, THREE KINDS OF TIME-SERIES DEEP NEURAL NETWORK ALGORITHMS: SIMPLE RNN, GRU, AND LSTM ARE FIRST TAKEN INTO CONSIDERATION AS BASELINE MODELS. THEIR PERFORMANCES ARE COMPARED IN IDENTIFYING AT-RISK STUDENTS. EXPERIMENTAL RESULTS ON OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD) SHOW THAT SIMPLE METHODS LIKE GRU AND SIMPLE RNN HAVE BETTER RESULTS THAN THE RELATIVELY COMPLEX LSTM MODEL. THE RESULTS ALSO REVEAL THAT DIFFERENT MODELS HAVE DIFFERENT PEAK PERFORMANCE TIME, WHICH RESULTS IN THE PROPOSED JOINT MODEL THAT ACHIEVES OVER 80% PREDICTION ACCURACY OF AT-RISK STUDENTS AT THE END OF THE SEMESTER.
AIMED AT A MASSIVE OUTREACH AND OPEN ACCESS EDUCATION, MASSIVE OPEN ONLINE COURSES (MOOC) HAS EVOLVED INCREDIBLY ENGAGING MILLIONS OF LEARNERS' OVER THE YEARS. THESE COURSES PROVIDE AN OPPORTUNITY FOR LEARNING ANALYTICS WITH RESPECT TO THE DIVERSITY IN LEARNING ACTIVITY. INSPITE OF ITS GROWTH, HIGH DROPOUT RATE OF THE LEARNERS', IT IS EXAMINED TO BE A PARAMOUNT FACTOR THAT MAY OBSTRUCT THE DEVELOPMENT OF THE E-LEARNING PLATFORMS. FABRICATING ON THE EXISTING EFFORTS OF RETAINING LEARNERS' ENGAGEMENT PRIOR TO LEARNING, THE STUDY EXPLORES TO DECIPHER THE ATTRIBUTES OF STUDENT RETENTION IN E- LEARNING. THE STUDY PROPOSES A CLEAR RATIONALE OF SIGNIFICANT ATTRIBUTES USING CLASSIFICATION ALGORITHMS (DECISION TREE) IN ORDER TO IMPROVE COURSE DESIGN AND DELIVERY FOR DIFFERENT MOOC PROVIDERS AND LEARNERS'. USING THE THREE MOOC DATASETS, THIS RESEARCH WORK ANALYSES THE APPROACH AND RESULTS OF APPLYING THE DATA MINING TECHNIQUES TO ONLINE LEARNERS', BASED ON THEIR IN-COURSE BEHAVIOUR. FINALLY, IT PREDICTS THE ATTRIBUTES THAT LEAD TO MINIMISE ATTRITION RATE AND ANALYSE THE DIFFERENT COHORT BEHAVIOUR AND ITS IMPACTS FOR DROPOUTS USING DATA MINING TECHNIQUE. IT FOCUSES TO BUILD A MORE INTEGRATED ENVIRONMENT FOR THESE LEARNERS'.
THIS PAPER PRESENTS, TO THE BEST OF OUR KNOWLEDGE, THE FIRST STUDY WITH REAL-WORLD TRAFFIC DATA FROM A NATIONAL K-12 EDUCATION SERVICE PROVIDER (ESP). THIS ESP, WHICH SUPPORTS A ONE-TO-ONE COMPUTING PROGRAM IN URUGUAY, IS IN CHARGE OF THE WI-FI INTERNET ACCESS AT ALL K-12 SCHOOLS IN THE COUNTRY. NETWORK USERS INCLUDE TEACHERS AND STUDENTS BETWEEN 6 AND 18 YEARS OLD. THIS WORK IS FOCUSED ON IDENTIFYING THE TYPICAL NETWORK USAGE PATTERNS, AND STUDYING THEIR EVOLUTION THROUGHOUT THE YEAR, IN ORDER TO SHED LIGHT ON THE USER BEHAVIOR IN SUCH A NOVEL SCENARIO. BASED ON SPECIFICALLY DESIGNED FEATURES AND PROPER DISTANCE MEASURES, FOUR MAIN WI-FI USAGE PROFILES ARE IDENTIFIED. THESE CLUSTERS ARE FOUND TO BE STABLE THROUGHOUT THE YEAR, WHICH MAKES IT POSSIBLE TO IMPLEMENT A WI-FI ACTIVITY MONITORING SYSTEM TRAINED WITH DATA COLLECTED DURING THE FIRST WEEKS OF THE YEAR ONLY. THE RESULTS SUGGEST THAT NETWORK TRAFFIC ANALYSIS IS AN ESSENTIAL TOOL FOR EVIDENCE-BASED DECISION MAKING BY THE ESP, NOT ONLY FROM THE NETWORK OPERATOR PERSPECTIVE, BUT ALSO PROVIDING VITAL INFORMATION FOR LEARNING ANALYTICS PURPOSES.
THE EMERGENCE OF NEW TECHNOLOGIES SUCH AS IOT AND BIG DATA, THE CHANGE IN THE BEHAVIOR OF SOCIETY IN GENERAL AND THE YOUNGER GENERATION IN PARTICULAR, REQUIRE HIGHER EDUCATION INSTITUTIONS TO "LOOK" FOR TEACHING DIFFERENTLY. THIS STATEMENT IS COMPLEMENTED BY THE PREDICTION OF THE FUTURIST THOMAS FREY, WHO POSTULATES THAT "IN 14 YEARS IT WILL BE A BIG DEAL WHEN STUDENTS LEARN FROM ROBOT TEACHERS OVER THE INTERNET". THUS, IT IS NECESSARY TO URGENTLY BEGIN A DISRUPTION OF CURRENT TEACHING MODELS, TO BE ABLE TO INCLUDE IN THESE PROCESSES THE NEW TECHNOLOGIES AND THE DAILY HABITS OF THE NEW GENERATIONS. THE EARLY USAGE OF MOBILE DEVICES AND THE CONSTANT CONNECTION TO THE INTERNET (SOCIAL NETWORKS, AMONG OTHERS) MEAN THAT THE CURRENT GENERATION OF YOUNG PEOPLE, WHO ARE REACHING HIGHER EDUCATION, HAS THE MOST TECHNOLOGICAL LITERACY EVER. IN THIS NEW CONTEXT, THIS ARTICLE PRESENTS A DISRUPTIVE CONCEPTUAL APPROACH TO HIGHER EDUCATION, USING INFORMATION GATHERED BY IOT AND BASED ON BIG DATA & CLOUD COMPUTING AND LEARNING ANALYTICS ANALYSIS TOOLS. THIS APPROACH WILL, FOR EXAMPLE, ALLOW INDIVIDUALIZED SOLUTIONS TAKING INTO ACCOUNT THE CHARACTERISTICS OF THE STUDENTS, TO HELP THEM CUSTOMIZE THEIR CURRICULUM AND OVERCOME THEIR LIMITATIONS AND DIFFICULTIES, THROUGHOUT THE LEARNING PROCESS
TRANSFORMATIVE POTENTIAL AND THE POWER OF TECHNOLOGY IS CHANGING THE WAYS OF LEARNING PROCESSES. NEW LEARNING TRENDS SHOW THAT LEARNING SHOULD BE MORE CONNECTED, INDIVIDUALIZED, GAMIFIED, ADAPTABLE AND EASY TO REGULATE. IT IS THE REASON THAT HUMANS ARE PLAYFUL, INVENTOR AND PERFORM IN REFERENCE TO THEIR INDIVIDUAL DIFFERENCES. IN TODAY'S WORLD, TECHNOLOGICAL DEVICES, HUMANS AND EVEN HUMANOID ARTIFICIAL INTELLIGENCES ARE CONNECTED WITH EACH OTHER AND THE PHYSICAL ENVIRONMENT BY THE HELP OF SENSORS AND WIRELESS NETWORKS. IN THIS NEW INTERNET OF THINGS ERA, THERE IS A STRONG CYCLICAL LINKAGE BETWEEN WEARABLE DEVICES, LEARNING ANALYTICS, ARTIFICIAL INTELLIGENCE AND ADAPTIVITY. THIS STUDY INTENDS TO REVIEW AND FIND THE CURRENT AND FUTURE DEVELOPMENTS ABOUT PERSONAL DIGITAL ASSISTANTS ALONG WITH WEARABLE TECHNOLOGIES IN EDUCATIONAL CONTEXTS. FOR THIS PURPOSE, A QUALITATIVE REVIEW OF THE LITERATURE WAS PERFORMED. RELATED RESOURCES AND ARTICLES ABOUT PERSONAL DIGITAL ASSISTANTS AND WEARABLES WERE EXAMINED ACCORDING TO POSSIBLE EDUCATIONAL USE AND APPLICATIONS. FORESIGHTS OF THE STUDIES, FINDINGS AND CONCLUSION SECTIONS OF THE DOCUMENTS WERE ANALYZED AND SYNTHESIZED AFTER DESCRIBING THE CURRENT PERSONAL DIGITAL ASSISTANTS AND WEARABLES IN USE. EDUCATIONAL POTENTIALS OF THE TECHNOLOGIES MENTIONED BEFORE WERE SPECIFIED AND DISCUSSED FOR THE FURTHER RESEARCH ABOUT EDUCATIONAL TECHNOLOGIES.
BIG DATA HAS BEEN ONE OF THE HOTTEST TOPICS OF SCIENTIFIC DISCUSSIONS IN THE RECENT YEARS. IN EARLY 2000S, AN INDUSTRY ANALYST ATTEMPTED TO DESCRIBE BIG DATA AS THE THREE VS: VOLUME, VELOCITY, AND VARIABILITY. WITH THE NEW TECHNOLOGIES SUCH AS HADOOP, IT IS NOW FEASIBLE TO STORE AND USE EXTREMELY LARGE VOLUMES OF DATA THAT COMES IN AT AN UNPRECEDENTED VELOCITY. THE VARIABILITY OF THIS DATA CAN BE LARGE AS IT CAN COME IN DIFFERENT FORMATS SUCH AS TEXT DOCUMENTS, VOICE OR VIDEO, AND FINANCIAL TRANSACTIONS. BIG DATA ANALYTICS HAS BEEN PROVEN TO BE USEFUL IS VARIOUS FIELDS SUCH AS SCIENCE, SPORTS, ADVERTISING, HEALTH CARE, GENOMIC SEQUENCE DATA, AND MEDICAL IMAGING. THIS STUDY PRESENTS A BRIEF OVERVIEW OF BIG DATA ANALYTICS IN MEDICAL IMAGING APPROACHES WITH CONSIDERING THE IMPORTANCE OF CONTEMPORARY MACHINE LEARNING TECHNIQUES SUCH AS DEEP LEARNING.
ONE OF THE MAIN CURRENT CHALLENGES IN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS IS THE PORTABILITY OR TRANSFERABILITY OF PREDICTIVE MODELS OBTAINED FOR A PARTICULAR COURSE SO THAT THEY CAN BE APPLIED TO OTHER DIFFERENT COURSES. TO HANDLE THIS CHALLENGE, ONE OF THE FOREMOST PROBLEMS IS THE MODELS' EXCESSIVE DEPENDENCE ON THE LOW-LEVEL ATTRIBUTES USED TO TRAIN THEM, WHICH REDUCES THE MODELS' PORTABILITY. TO SOLVE THIS ISSUE, THE USE OF HIGH-LEVEL ATTRIBUTES WITH MORE SEMANTIC MEANING, SUCH AS ONTOLOGIES, MAY BE VERY USEFUL. ALONG THIS LINE, WE PROPOSE THE UTILIZATION OF AN ONTOLOGY THAT USES A TAXONOMY OF ACTIONS THAT SUMMARISES STUDENTS' INTERACTIONS WITH THE MOODLE LEARNING MANAGEMENT SYSTEM. WE COMPARE THE RESULTS OF THIS PROPOSED APPROACH AGAINST OUR PREVIOUS RESULTS WHEN WE USED LOW-LEVEL RAW ATTRIBUTES OBTAINED DIRECTLY FROM MOODLE LOGS. THE RESULTS INDICATE THAT THE USE OF THE PROPOSED ONTOLOGY IMPROVES THE PORTABILITY OF THE MODELS IN TERMS OF PREDICTIVE ACCURACY. THE MAIN CONTRIBUTION OF THIS PAPER IS TO SHOW THAT THE ONTOLOGICAL MODELS OBTAINED IN ONE SOURCE COURSE CAN BE APPLIED TO OTHER DIFFERENT TARGET COURSES WITH SIMILAR USAGE LEVELS WITHOUT LOSING PREDICTION ACCURACY.
CLINICAL CARE INCREASINGLY RELIES ON INDIVIDUALIZED, HIGHLY MULTIDIMENSIONAL DATA FOR PATIENT PHENOTYPING. THIS REVIEW FOCUSSES ON TWO TECHNOLOGIES WHICH ARE CONTRIBUTING TO THIS TREND AND, IN THEIR MUTUAL OVERLAP, MAY PROVIDE A NOVEL AND POWERFUL CONTRIBUTION TO FUTURE CLINICAL CARE. ARTIFICIAL INTELLIGENCE (AI), IN PARTICULAR THE TECHNIQUE OF DEEP LEARNING, IS STEADILY MAKING INROADS TO CLINICAL CARE AND HAS DEMONSTRATED THE ABILITY TO EXTRACT CLINICALLY RELEVANT FEATURES FROM COMPLEX DATASETS. LIKEWISE, MASS SPECTROMETRY (MS), IN PARTICULAR HIGH-RESOLUTION MS, IS GAINING THE ABILITY TO PROVIDE HIGHLY MULTIDIMENSIONAL DATASETS FROM PATIENT SAMPLES WITH THE QUALITY NECESSARY FOR CLINICAL DECISION MAKING. THE CONVERGENCE OF THESE TRENDS HAS RESULTED IN THE NASCENT APPLICATION OF DEEP LEARNING ANALYTICS TO HIGH RESOLUTION MASS SPECTRA. DUE TO THE LARGE VOLUMES OF CONTENT PROVIDED BY HIGH RESOLUTION MASS SPECTRA AND THE ABILITY OF MULTILAYER NEURAL NETWORKS TO EXTRACT SUBTLE FEATURES FROM COMPLEX DATASETS, FUTURE INTEGRATION OF THEIR MUTUAL APPLICATION IS IDENTIFIED AS A RESEARCH AREA OF HIGH PRIORITY FOR CLINICAL CARE. FACTORS ARE IDENTIFIED THAT HAVE CONTRIBUTED TO HISTORICAL PROGRESS FOR EACH TECHNOLOGY AND RECOMMENDATIONS ARE PROVIDED TO ACCELERATE THE MUTUAL APPLICATION OF DEEP LEARNING AND MASS SPECTROMETRY TECHNIQUES TO PATIENT CARE.
BACKGROUND: TODAY'S GOLD STANDARD FOR IDENTIFYING WHAT WORKS, THE RANDOMIZED CONTROLLED TRIAL, POORLY SERVES EACH AND ANY INDIVIDUAL LEARNER. ELEMENTS OF MY ARGUMENT PROVIDE GROUNDS FOR PROPOSED REMEDIES IN CASES WHERE SOFTWARE CAN LOG EXTENSIVE DATA ABOUT OPERATIONS EACH LEARNER APPLIES TO LEARN AND EACH BIT OF INFORMATION TO WHICH A LEARNER APPLIES THOSE OPERATIONS. PURPOSE OF STUDY: ANALYSES OF SUCH BIG DATA CAN PRODUCE LEARNING ANALYTICS THAT PROVIDE RAW MATERIAL FOR SELF-REGULATING LEARNERS, FOR INSTRUCTORS TO PRODUCTIVELY ADAPT INSTRUCTIONAL DESIGNS, AND FOR LEARNING SCIENTISTS TO ADVANCE LEARNING SCIENCE. I DESCRIBE AN EXAMPLE OF SUCH A SOFTWARE SYSTEM, NSTUDY. RESEARCH DESIGN: I DESCRIBE AND ANALYZE FEATURES OF NSTUDY, INCLUDING BOOKMARKS, QUOTES, NOTES, AND NOTE ARTIFACTS THAT CAN BE USED TO GENERATE TRACE DATA. RESULTS: BY USING SOFTWARE LIKE NSTUDY AS THEY STUDY, LEARNERS CAN PARTNER WITH INSTRUCTORS AND LEARNING SCIENTISTS IN A SYMBIOTIC AND PROGRESSIVE ECOLOGY OF AUTHENTIC EXPERIMENTATION. CONCLUSION: I ARGUE THAT SOFTWARE TECHNOLOGIES LIKE NSTUDY OFFER SIGNIFICANT VALUE IN SUPPORTING LEARNERS AND ADVANCING LEARNING SCIENCE. A RATIONALE AND RECOMMENDATIONS FOR THIS APPROACH ARISE FROM MY CRITIQUE OF PSEUDO-RANDOM CONTROLLED TRIALS.
THE ADVANCEMENT OF INTERNET OF THINGS (IOT) TECHNOLOGIES, SUCH AS LOW-COST EMBEDDED SINGLE BOARD COMPUTERS WHICH INTEGRATE SENSORS, COMMUNICATION HARDWARE, AND PROCESSING POWER IN ONE UNIT, HAS GIVEN MORE TRACTION TO THE CONCEPT OF SMART CITIES. HAVING CHEAPER PROCESSING POWER AT THEIR DISPOSAL, THE SENSING UNITS ARE CAPABLE OF GATHERING INCREASINGLY LARGER AMOUNTS OF RAW DATA LOCALLY, WHICH MUST BE PROCESSED BEFORE BEING USABLE. ONE CONCERN FOR THIS SCHEME IS THE AMOUNT OF INFRASTRUCTURE AND NETWORK BANDWIDTH NEEDED TO TRANSFER THE DATA FROM THE ACQUISITION LOCATION TO A SERVER, WHICH MAY BE MILES AWAY, FOR FURTHER PROCESSING. THE BANDWIDTH AVAILABLE TO THE SENSOR NETWORK, DISTRIBUTED THROUGH THE CITY, IS EXPANDING IN A LOWER RATE THAN THE SIZE AND BANDWIDTH DEMAND OF THE NETWORK IT SERVES. THEREFORE, TRANSFERRING THE UNPROCESSED DATA TO A CENTRAL SERVER DOES NOT SEEM FEASIBLE UNLESS MAJOR COMPROMISES ARE MADE IN TERMS OF DATA RESOLUTION AND SIZE. THIS PAPER PROPOSES A LOCAL BIG DATA BASED PREPROCESSING SCHEME BEFORE THE DATA IS TRANSFERRED TO THE STORAGE. USING THIS SCHEME CAN FREE UP THE NETWORK BANDWIDTH, EXPLOIT THE OTHERWISE WASTED LOCAL PROCESSING POWER, AND RELEASE PROCESSING LOAD FROM THE CENTRAL SERVER, ALLOWING IT TO SERVE A LARGER NETWORK WITHOUT THE NEED FOR MORE POWERFUL HARDWARE. BY MAKING EFFICIENT USE OF NETWORK INFRASTRUCTURE THE SMART CITY APPLICATIONS ARE MORE AFFORDABLE AND SCALABLE.
MULTI-MODAL TENSOR DATA SETS ARISE WITH INCREASING FREQUENCY IN MODERN DAY SCIENTIFIC AND ENGINEERING APPLICATIONS, FOR EXAMPLE IN BIOMEDICAL SCIENCES AND AUTONOMOUS ENGINEERED SYSTEMS. OVER THE PAST TWENTY YEARS, TENSOR-DOMAIN DATA ANALYSIS HAS BEEN ATTEMPTED PRIMARILY IN THE CONTEXT OF STANDARD (L-2-NORM) EIGENVECTOR DECOMPOSITIONS ACROSS TENSOR DOMAINS. THE ALGORITHMS ARE NOT JOINT-TENSOR-DOMAIN OPTIMAL AND EXHIBIT THE FAMILIAR SENSITIVITY TO FAULTY/CORRUPTED/MISSING MEASUREMENTS THAT CHARACTERIZES ALL L-2-NORM PRINCIPAL-COMPONENT ANALYSIS METHODS. IN THIS WORK, WE PRESENT A ROBUSTIFIED METHOD TO EVALUATE THE CONFORMITY OF TENSOR DATA ENTRIES WITH RESPECT TO THE WHOLE ACCESSIBLE DATA SET. CONFORMITY EVALUATION IS BASED ON A CONTINUOUSLY REFINED SEQUENCE OF CALCULATED L-1-NORM TENSOR SUBSPACES. THE THEORETICAL DEVELOPMENTS ARE ILLUSTRATED IN THE CONTEXT OF A MULTISENSOR LOCALIZATION APPLICATION THAT INDICATES UNPRECEDENTED ESTIMATION PERFORMANCE AND RESISTANCE TO INTERMITTENT DISTURBANCES. AN ELECTROENCEPHALOGRAM (EEG) DATA ANALYSIS EXPERIMENT IS ALSO PRESENTED.
AT THE CHANGHWA FUHAI WIND FARM, THE HIGH-RESOLUTION NUMERICAL MODELS FOR WIND, WAVE AND CURRENT SIMULATIONS ARE CRUCIAL TO PROVIDE DAILY FORECASTS AS A DECISION-SUPPORT SYSTEM FOR TAIWAN'S OFFSHORE WIND ENERGY INDUSTRY. IN THIS STUDY, THE TWO-STAGE METHOD, CONSISTING OF NUMERICAL SIMULATION AND DEEP-LEARNING CORRECTION BASED ON THE OBSERVED DATA AT FUHAI WIND FARM SITE, IS IMPLEMENTED TO FURTHER IMPROVE THE METEOROLOGICAL AND MARINE FORECAST ACCURACY. THE RECURRENT NEURAL NETWORK-LONG SHORT-TERM MEMORY (RNN-LSTM) MODEL IS ADOPTED IN THE DEEP LEARNING ANALYTICS. CURRENTLY, THIS SYSTEM IS CAPABLE OF DAILY PROVIDING THE NEXT 4-DAY WIND FIELD FORECAST AND THE NEXT 7-DAY WAVE AND CURRENT FIELD FORECAST. THROUGH THE TESTING, IT IS FOUND THAT THE ERROR CORRECTION OF WIND SIMULATIONS CAN REDUCE ROOT MEAN SQUARE ERROR (RMSE) BY UP TO 36.7%. FURTHERMORE, THROUGH ADOPTING THE SQL AND HBASE HYBRID DATABASE SYSTEM, A WIDE RANGE OF HISTORICAL DATA AS WELL AS DAILY FORECAST DATA CAN BE QUICKLY QUERIED AND DISPLAYED ON THE WEB INTERFACE TO PROVIDE IMPORTANT DECISION SUPPORT TO THE RELEVANT WIND POWER COMPANIES IN SCHEDULING OFFSHORE CONSTRUCTION PLANS, FORECASTING WIND ENERGY PRODUCTION, AND ARRANGING WIND TURBINES' SHUTDOWN AND MAINTENANCE SEQUENCE.
THE PURPOSE OF THIS REVIEW IS TO INVESTIGATE THE TRENDS IN THE BODY OF RESEARCH ON MACHINE LEARNING IN EDUCATIONAL TECHNOLOGIES, PUBLISHED BETWEEN 2007 AND 2017. THE CRITERIA FOR ARTICLE SELECTION WERE AS FOLLOWS: (1) STUDY ON MACHINE LEARNING IN EDUCATIONAL/LEARNING TECHNOLOGIES, (2) PUBLISHED BETWEEN 2007-2017, (3) PUBLISHED IN A PEER-REVIEWED OUTLET, AND (4) AN EMPIRICAL STUDY, LITERATURE REVIEW, OR META-ANALYSIS. EIGHTY-NINE ARTICLES WERE CHOSEN, AFTER THE FIRST ROUND OF THE ARTICLE SELECTION PROCESS. THROUGH A SECOND LOOK AT THE ARTICLES, FIFTEEN ARTICLES THAT DID NOT MATCH THE CRITERIA WERE ELIMINATED. AFTER THE CLOSE EXAMINATION OF THE SEVENTY-FOUR ARTICLES, CERTAIN DEMOGRAPHICAL AND THEMATIC TRENDS EMERGED. THE TOP CONTRIBUTORS TO THE BODY OF RESEARCH WERE TAIWAN AND THE UNITED STATES WHILE THE MOST PRODUCTIVE YEAR WAS 2017. THE MOST UTILIZED MACHINE LEARNING METHODS WERE VECTORS AND DECISION TREES. COMMONLY RESEARCHED AREAS, ON THE OTHER HAND, WERE AUTOMATION, COGNITIVE PROCESS ASSESSMENT, PREDICTION, INTELLIGENT TUTORING SYSTEMS, AND OPPORTUNITIES AND CHALLENGES IN THE USE OF BIG DATA & LEARNING ANALYTICS. RECOMMENDATIONS FOR FUTURE RESEARCH FOCUS ON EXPANDING GEOGRAPHICAL DIVERSITY, INCORPORATING BAYESIAN AND FUZZY LOGIC METHODS MORE IN EDUCATIONAL MACHINE LEARNING WORK.
THE MAIN GOAL OF THIS ARTICLE IS TO ANALYSE THE EVOLUTION AND CHALLENGES OF E-LEARNING, WITH SPECIAL EMPHASIS ON THE NEED TO MOVE FROM A CLOSED SPACE-THE VIRTUAL CLASSROOM-TO INCORPORATE ADAPTIVE TOOLS THAT GUARANTEE A MUCH MORE PERSONALIZED USE. THE ARTICLE DESCRIBES THE EVOLUTION OF E-LEARNING FROM A PEDAGOGICAL AND TECHNOLOGICAL POINT OF VIEW AND SHOWS HOW THE TRAINING SPACES HAVE DIVERSIFIED BEYOND THE VIRTUAL CLASSROOM. E-LEARNING IS INCORPORATING MORE OPEN PEDAGOGICAL DESIGNS IN WHICH DIFFERENT PEOPLE CAN SHARE CONTENT, ACTIVITIES AND EXPERIENCES IN FORMAL AND INFORMAL SITUATIONS. RESEARCH IN THIS FIELD SHOWS THAT STUDENTS WITH LESS SELF-REGULATION CAPABILITIES TEND TO FAIL. ON THE CONTRARY, THE MOST SUCCESSFUL STUDENTS SHOW GREATER EFFICIENCY AND EFFICIENCY IN SELF-REGULATED LEARNING. RESEARCH ON THE ROLE OF STUDENTS IN VIRTUAL LEARNING ENVIRONMENTS HIGHLIGHTS THE IMPORTANCE OF SELF-REGULATION PROCESSES IN THE SUCCESS OF THE RESULTS. EMERGING TECHNOLOGIES SUCH AS THE USE OF ADAPTIVE SYSTEMS, INTELLIGENT AGENTS AND THE LEARNING ANALYTICS CAN FACILITATE THE PROCESSES OF ADAPTATION AND SELF-REGULATION. THE ADOPTION OF THESE TECHNOLOGY CAN PROVIDE REAL-TIME INFORMATION TO LEARNERS AND TO FACILITATE STRATEGIES FOR SCAFFOLDING DURING THE LEARNING PROCESS.
INTEGRATING THE TWO DOMINANT THEORIES OF SELF-REGULATED LEARNING (SRL) AND COGNITIVE ENGAGEMENT COULD ADVANCE OUR UNDERSTANDING OF WHAT MAKES STUDENTS MORE EFFICIENT, EFFECTIVE LEARNERS. AN INTEGRATION OF THESE THEORIES HAS YET TO BE EXPLORED, AND THIS PAPER ADDRESSES THIS GAP BY PROPOSING A NOVEL INTEGRATIVE MODEL OF SRL ENGAGEMENT. SPECIFICALLY, WE IDENTIFIED THE NATURE OF COGNITIVE ENGAGEMENT (I.E., CHANGING CONSECUTIVELY, CONTEXT-DEPENDENT, COMPRISING QUANTITATIVE AND QUALITATIVE DIMENSIONS, OCCURRING CONSCIOUSLY OR UNCONSCIOUSLY), BASED ON WHICH WE COMPARED THE CONCEPTUAL DIFFERENCES AND SIMILARITIES BETWEEN COGNITIVE ENGAGEMENT AND SRL. WE REVIEWED THREE MODELS THAT HAVE INVESTIGATED COGNITIVE ENGAGEMENT WITHIN THE FRAMEWORKS OF SRL, ANALYZED THEIR FEATURES AND WEAKNESSES, AND PROPOSED AN EXTENSION OF PREVIOUS MODELS LINKING SRL AND COGNITIVE ENGAGEMENT. THE PROPOSED MODEL IS ONE OF THE FIRST TO CLARIFY THE MECHANISMS OF HOW SRL PHASES AND SUBPROCESSES RELATE TO THE FUNCTIONING OF COGNITIVE ENGAGEMENT. IN ADDITION TO ADDING TO THE THEORETICAL DISCUSSIONS OF THE RELATIONS BETWEEN COGNITIVE ENGAGEMENT AND SRL, THE MODEL INFORMS THE DESIGN OF ADAPTIVE SCAFFOLDINGS AND THE PRACTICE OF LEARNING ANALYTICS. SEVERAL RECOMMENDATIONS ARE PRESENTED FOR FUTURE RESEARCH IN THIS AREA TO TEST THIS NEW MODEL EMPIRICALLY.
LEARNING ANALYTICS AND VISUALIZATIONS MAKE IT POSSIBLE TO EXAMINE AND COMMUNICATE LEARNERS' ENGAGEMENT, PERFORMANCE, AND TRAJECTORIES IN ONLINE COURSES TO EVALUATE AND OPTIMIZE COURSE DESIGN FOR LEARNERS. THIS IS PARTICULARLY VALUABLE FOR WORKFORCE TRAINING INVOLVING EMPLOYEES WHO NEED TO ACQUIRE NEW KNOWLEDGE IN THE MOST EFFECTIVE MANNER. THIS PAPER INTRODUCES A SET OF METRICS AND VISUALIZATIONS THAT AIM TO CAPTURE KEY DYNAMICAL ASPECTS OF LEARNER ENGAGEMENT, PERFORMANCE, AND COURSE TRAJECTORIES. THE METRICS ARE APPLIED TO IDENTIFY PROTOTYPICAL BEHAVIOR AND LEARNING PATHWAYS THROUGH AND INTERACTIONS WITH COURSE CONTENT, ACTIVITIES, AND ASSESSMENTS. THE APPROACH IS EXEMPLIFIED AND EMPIRICALLY VALIDATED USING MORE THAN 30 MILLION SEPARATE LOGGED EVENTS THAT CAPTURE ACTIVITIES OF 1,608 BOEING ENGINEERS TAKING THE MITXPRO COURSE, "ARCHITECTURE OF COMPLEX SYSTEMS,"DELIVERED IN FALL 2016. VISUALIZATION RESULTS SHOW COURSE STRUCTURE AND PATTERNS OF LEARNER INTERACTIONS WITH COURSE MATERIAL, ACTIVITIES, AND ASSESSMENTS. TREE VISUALIZATIONS ARE USED TO REPRESENT COURSE HIERARCHICAL STRUCTURES AND EXPLICIT SEQUENCE OF CONTENT MODULES. LEARNER TRAJECTORY NETWORKS REPRESENT PATHWAYS AND INTERACTIONS OF INDIVIDUAL LEARNERS THROUGH COURSE MODULES, REVEALING PATTERNS OF LEARNER ENGAGEMENT, CONTENT ACCESS STRATEGIES, AND PERFORMANCE. RESULTS PROVIDE EVIDENCE FOR INSTRUCTORS AND COURSE DESIGNERS FOR EVALUATING THE USAGE AND EFFECTIVENESS OF COURSE MATERIALS AND INTERVENTION STRATEGIES.
THE FOOD SYSTEMS APPROACH HAS GAINED RENEWED PROMINENCE IN RECENT YEARS, DUE TO ITS ROLE TOWARDS GAINING AN UNDERSTANDING OF FOOD INSECURITY AND MALNUTRITION. A "FOOD SYSTEMS" LENS HAS THEREFORE BECOME ESSENTIAL TO BETTER DESIGN DEVELOPMENT INTERVENTIONS AND INNOVATIONS THAT CAN POSITIVELY IMPACT FOOD SYSTEMS OUTCOMES. THIS STUDY PROVIDES EVIDENCE ON THE DYNAMICS ACROSS FOOD SYSTEM DIMENSIONS WITHIN DEVELOPMENT PROJECTS SUPPORTED BY THE INTERNATIONAL FUND FOR AGRICULTURAL DEVELOPMENT (IFAD). A CUSTOM TAXONOMY WAS DEVELOPED AND MACHINE LEARNING TECHNIQUES PRIMARILY FOCUSED ON SUPERVISED TEXT MINING, NETWORK ANALYSIS AND LASSO REGRESSION WERE APPLIED TO IFAD PROJECT DOCUMENTATION TO EXTRACT ANALYTICS ABOUT FOOD SYSTEMS' SPATIAL AND TEMPORAL THEMATIC REPRESENTATION OVER 40 YEARS OF PROJECT IMPLEMENTATION. THE PAPER THUS PROVIDES INSIGHTS ABOUT THE DYNAMICS AS WELL AS TRANSFORMATIONS OF FOOD SYSTEMS WITHIN IFAD'S STATED ACTIVITIES, PROVIDING A HISTORICAL OVERVIEW OF HOW THE FUND HAS TACKLED FOOD SYSTEMS OVER FOUR DECADES OF PROJECT LIFE CYCLES. FINDINGS SHOW AN OVERALL INCREASE IN REPORTING AGAINST FOOD SYSTEM DIMENSIONS AND CONSOLIDATE THE APPLICABILITY OF MACHINE LEARNING ANALYTICS TO UNCOVER TRENDS ABOUT INTERNATIONAL AGENCIES' ACTIVITIES AND ACCELERATE KNOWLEDGE GENERATION AROUND STRATEGIC THEMES.
ADVANCES IN HUMAN COMPUTER INTERACTION (HCI) ARE ENABLING INCREASINGLY MORE HUMAN-LIKE INTERACTIONS WITH COMPUTERS. IN THIS POSITION PAPER, WE EXPLORE THE IMPACT OF SEVERAL SUCH ADVANCES IN HCI ON THE DESIGN OF AN INTELLIGENT TUTORING SYSTEM (ITS), WITH THE HYPOTHESIS THAT SUCH SYSTEMS MAY DRIVE DEEPER ENGAGEMENT AND HENCE IMPROVE LEARNING OUTCOMES. RESEARCHERS HAVE MADE CLAIMS REGARDING LEARNING GAINS RESULTING FROM SELF-EXPLANATIONS, FREQUENT ADDRESSING OF ERRORS AND IMPASSES, RICH NATURAL LANGUAGE UNDERSTANDING AND DIALOG, APPROPRIATE DEGREE OF INTERACTIVITY, AND USE OF MULTIPLE REPRESENTATIONS. HOWEVER, MANY STUDIES ON CURRENT ITSS THAT HAVE EMBODIED ONE OR MORE OF THE ABOVE FEATURES ARE SHOWING LITTLE TO NO DISCERNIBLE IMPACT ON LEARNING. THIS IS POSSIBLY PARTLY DUE TO THE POOR USER EXPERIENCE. OUR TUTORING SYSTEM IS AIMED AT ADDRESSING LEARNING CHALLENGES FOR K-12 STUDENTS, BY INTEGRATING A SUITE OF DIFFERENTIATING TECHNOLOGIES AROUND INTERACTIVITY, DIALOG, AUTOMATED QUESTION GENERATION, AND LEARNING ANALYTICS. IN THIS PAPER, WE FIRST REVIEW LEARNING THEORIES AND INSIGHTS GLEANED FROM PRIOR RESEARCH ON ITSS THAT HAVE INSPIRED OUR DESIGN. WE THEN DESCRIBE THE FUNCTIONAL ARCHITECTURE OF OUR TUTORING SYSTEM, FOLLOWED BY A PRELIMINARY REPORT ON THE STATUS OF THE PROTOTYPE CURRENTLY BEING BUILT.
THE NUMBER OF INFORMATION SYSTEMS USING ADAPTATION RULES IS INCREASING QUICKLY. THESE SYSTEMS ARE USUALLY FOCUSED ON IMPLEMENT NICE AND COMPLEX FUNCTIONALITY FOR ADAPTATION OF CONTENTS, LINKS OR PRESENTATION, SO SOFTWARE ENGINEERING METHODOLOGIES FOR THE DESCRIPTION OF RULES ARE REQUIRED. IN ADDITION, THE DISTRIBUTED SERVICE ORIENTED INTERNET PHILOSOPHY PRESENTS THE CHALLENGE OF COMBINING DIFFERENT RULES FROM INDEPENDENT INTERNET SOURCES. MOREOVER, EASY AUTHORING, RULE REUSE AND COLLABORATIVE DESIGN SHOULD BE ENABLED. THIS PAPER PRESENTS THE AR (ADAPTATION RULES) MODEL, A NEW SOFTWARE ENGINEERING MODEL FOR THE DESCRIPTION OF RULES FOR ADAPTATION. THESE RULES CAN BE COMPOSED AS A SET OF SMALLER ATOMIC, REUSABLE, PARAMETRIC, INTERCHANGEABLE AND INTEROPERABLE RULES, WITH CLEAR RESTRICTIONS IN THEIR COMBINATIONS. OUR MODEL ENABLES THE DISTRIBUTION OF RULES AS WELL AS RULE REUSE AND COLLABORATION AMONG RULE CREATORS. WE ILLUSTRATE OUR APPROACH WITH THE APPLICATION OF THIS MODEL TO A HINTING ADAPTIVE E-LEARNING SYSTEM THAT GENERATES EXERCISES WITH HINTS, WHICH CAN BE ADAPTED BASED ON DEFINED RULES. ADVANTAGES OF THE AR MODEL ARE CONFIRMED WITH AN EVALUATION THAT HAS BEEN DONE WITH TEACHERS AND LEARNING ANALYTICS EXPERTS FOR ADAPTIVE E-LEARNING.
HYDRAULIC FRACTURING HAS GREATLY IMPACTED THE OIL AND GAS INDUSTRY AND IS A LARGE COMPONENT OF FUTURE OIL PRODUCTION. PROPER OPERATION IS DEPENDENT ON SUPERVISORS MONITORING DATA FOR SIGNS OF DANGEROUS PRESSURE SPIKES. HUMAN ERROR BECOMES A LARGE FACTOR IN PROCESSING SUCH A LARGE AMOUNT OF DATA IN REAL TIME. THE SYSTEM PROPOSED IN THIS PAPER IS ABLE TO READ AND INTERPRET THE DATA FROM A WELL TO MAKE ACCURATE PREDICTIONS ON WHEN POTENTIAL PRESSURE SPIKES WILL OCCUR WITHIN THE WELL, SAVING TIME AND MONEY ON PROJECTS THAT ARE PUSHED TO THE LIMIT.
ALTHOUGH HYPERSPECTRAL TECHNOLOGY HAS CONTINUED TO IMPROVE OVER THE YEARS, ITS USE IS OFTEN STILL LIMITED DUE TO SIZE, WEIGHT AND POWER (SWAP) CONSTRAINTS. ONE OF THE MORE TAXING REQUIREMENTS, IS THE NEED TO SAMPLE A LARGE NUMBER OF VERY FINE SPECTRAL BANDS. THE PROHIBITIVELY LARGE SIZE OF HYPERPSECTRAL DATA CREATES CHALLENGES IN BOTH ARCHIVAL AND PROCESSING. COMPRESSIVE SENSING IS AN ENABLING TECHNOLOGY FOR REDUCING THE OVERALL PROCESSING AND SWAP REQUIREMENTS. THIS PAPER EXPLORES THE VIABILITY OF PERFORMING CLASSIFICATION ON SPARSELY SAMPLED HYPERSPECTRAL DATA WITHOUT THE NEED OF PERFORMING SPARSE RECONSTRUCTION. IN PARTICULAR, A SPATIAL-SPECTRAL CLASSIFIER BASED ON A SUPPORT VECTOR MACHINE (SVM) AND EDGE-PRESERVING FILTERS (EPFS) IS APPLIED DIRECTLY IN THE COMPRESSED DOMAIN. THE WELL-KNOWN RESTRICTED ISOMETRY PROPERTY (RIP) AND A RANDOM SPECTRAL SAMPLING STRATEGY ARE USED TO EVALUATE ANALYTICALLY, THE ERROR BETWEEN THE COMPRESSED CLASSIFIER AND THE FULL BAND CLASSIFIER. THE MATHEMATICAL ANALYSIS PRESENTED SHOWS THAT THE CLASSIFICATION ERROR CAN BE EXPRESSED IN TERMS OF THE RESTRICTED ISOMETRY CONSTANT (RIC) AND THAT IT IS INDEED POSSIBLE TO ACHIEVE FULL CLASSIFICATION PERFORMANCE IN THE COMPRESSED DOMAIN, GIVEN THAT SUFFICIENT SAMPLING CONDITIONS ARE MET. A SET OF EXPERIMENTS ARE PERFORMED TO EMPIRICALLY DEMONSTRATE COMPRESSED CLASSIFICATION. IMAGES FROM BOTH THE AIRBORNE VISIBLE / INFRARED IMAGING SPECTROMETER (AVIRIS) AND REFLECTIVE OPTICS SYSTEM IMAGING SPECTROMETER (ROSIS) ARE EXAMINED TO DRAW INFERENCES ON THE IMPACT OF SCENE COMPLEXITY. THE RESULTS PRESENTED CLEARLY DEMONSTRATE THE POSSIBILITY OF COMPRESSED CLASSIFICATION AND LEAD TO SEVERAL OPEN RESEARCH QUESTIONS TO BE ADDRESSED IN FUTURE WORK.
UNOBTRUSIVE HUMAN ACTIVITY MONITORING IS IMPORTANT IN ELDERCARE AND ASSISTED LIVING TO ALERT THE CAREGIVERS OF ANY POTENTIAL ACCIDENTS. CURRENT RESEARCH EFFORTS ON NON-WEARABLE MONITORING TECHNOLOGIES RELY ON FEATURES EXTRACTED FROM RADAR AND OPTICAL CAMERA SYSTEMS; THE LATTER HAS PRIVACY CONCERNS. AS AN ALTERNATIVE, WE INVESTIGATE A DIFFERENT KIND OF ELECTRO-OPTICAL SENSOR - COMPRESSIVE ACTIVE SENSING ELECTRO-OPTICAL (CASEO) SENSOR. A CASEO SYSTEM CONSISTS OF AN INFRARED SPATIAL LIGHT MODULATION DEVICES (SLM) BASED ILLUMINATOR AND A SINGLE-ELEMENT PHOTODIODE (PD) BASED RECEIVER. THE SCENE INFORMATION IS ENCODED THROUGH A SEQUENCE OF CODED ILLUMINATION PATTERNS VIA THE SLM ILLUMINATOR AND THE CORRESPONDING MEASUREMENTS CAPTURED BY THE PD. SUCH COMPRESSIVE MEASUREMENTS CAN BE USED IN CLASSIFICATION IN LIEU OF THE ACTUAL VIDEO FRAMES. IN THIS WORK, WE EXPLORE THE FEASIBILITY OF USING CASEO IN THE IDENTIFICATION OF DIFFERENT HUMAN ACTIVITIES. WE HAVE DEVELOPED A CASEO PROCESSING FRAMEWORK. SIMULATIONS USING UIUC HUMAN ACTIVITY RECOGNITION TEST DATASET WERE CONDUCTED TO VALIDATE THIS FRAMEWORK.
THE RISE OF THE INTERNET HAS ENABLED FAKE NEWS TO REACH LARGER AUDIENCES MORE QUICKLY. AS MORE PEOPLE TURN TO SOCIAL MEDIA FOR NEWS, THE ACCURACY OF INFORMATION ON THESE PLATFORMS IS ESPECIALLY IMPORTANT. TO HELP ENABLE CLASSIFICATION OF THE ACCURACY NEWS ARTICLES AT SCALE, MACHINE LEARNING MODELS HAVE BEEN DEVELOPED AND TRAINED TO RECOGNIZE FAKE ARTICLES. PREVIOUS LINGUISTIC WORK SUGGESTS PART-OF-SPEECH AND N-GRAM FREQUENCIES ARE OFTEN DIFFERENT BETWEEN FAKE AND REAL ARTICLES. TO COMPARE HOW THESE FREQUENCIES RELATE TO THE ACCURACY OF THE ARTICLE, A DATASET OF 260 NEWS ARTICLES, 130 FAKE AND 130 REAL, WAS COLLECTED FOR TRAINING NEURAL NETWORK CLASSIFIERS. THE FIRST MODEL RELIES SOLELY ON PART-OF-SPEECH FREQUENCIES WITHIN THE BODY OF THE TEXT AND CONSISTENTLY ACHIEVED 82% ACCURACY. AS THE PROPORTION OF THE DATASET USED FOR TRAINING GREW SMALLER, ACCURACY DECREASED, AS EXPECTED. THE TRUE NEGATIVE RATE, HOWEVER, REMAINED HIGH. THUS, SOME ASPECT OF THE FAKE ARTICLES WAS READILY IDENTIFIABLE, EVEN WHEN THE CLASSIFIER WAS TRAINED ON A LIMITED NUMBER OF EXAMPLES. THE SECOND MODEL RELIES ON THE MOST COMMONLY OCCURRING N-GRAM FREQUENCIES. THE NEURAL NETS WERE TRAINED ON N-GRAMS OF DIFFERENT LENGTH. INTERESTINGLY, THE ACCURACY WAS NEAR 61% FOR EACH N-GRAM SIZE. THIS SUGGESTS SOME OF THE SAME INFORMATION MAY BE ASCERTAINABLE ACROSS N-GRAMS OF DIFFERENT SIZES.
A COMMON TOOL TO PROCESS AND INTERPRET MULTIMODAL DATA IS TO REPRESENT THE DATA IN A SPARSE TUCKER FORMAT, DECOMPOSED AS A SPARSE CORE TENSOR AND DICTIONARY MATRICES FOR EACH MODAL DIMENSION. IN REAL-WORLD APPLICATIONS ONE MAY BE PRESENTED WITH A COMPOSITION OF SEVERAL TENSORS, EACH WITH ITS OWN SPARSE TUCKER REPRESENTATION AND COLLECTION OF DICTIONARIES. THE TUCKER MODEL AND ASSOCIATED RECOVERY ALGORITHMS STRUGGLE TO ACCURATELY SEPARATE COMPOSITE TENSORS IN THIS SITUATION, EITHER HAVING DIFFICULTY WITH THE OVERCOMPLETE DICTIONARIES OR NOT FULLY TAKING ADVANTAGE OF THE SPECIAL STRUCTURE OF THE DECOMPOSITION. TO ADDRESS THESE DEFICIENCIES, WE INTRODUCE AN OVERCOMPLETE SPARSE TUCKER MODEL AND AN ITERATIVE ALGORITHM TO SEPARATE A COMPOSITE SPARSE TUCKER TENSOR. THE METHOD, WHICH IS BASED ON SOFT-THRESHOLDING SHRINKAGE TECHNIQUES, IS DEMONSTRATED TO EFFECTIVELY SEPARATE OVERCOMPLETE TENSORS AND RECOVER THE SPARSE COMPONENT TENSORS ON REAL-WORLD DATASETS, AND TO DO SO MORE ACCURATELY THAN OTHER TUCKER METHODS.
INVESTIGATING THE FACTORS AFFECTING STUDENTS' ACADEMIC FAILURE IN ONLINE AND/OR BLENDED COURSES BY ANALYZING STUDENTS' LEARNING BEHAVIOR DATA GATHERED FROM LEARNING MANAGEMENT SYSTEMS (LMS) IS A CHALLENGING AREA IN INTELLIGENT LEARNING ANALYTICS AND EDUCATION DATA MINING AREA. IT HAS BEEN ARGUED THAT THE ACTUAL COURSE DESIGN AND THE INSTRUCTOR'S INTENTIONS IS CRITICAL TO DETERMINE WHICH VARIABLES MEANINGFULLY REPRESENT STUDENT EFFORT THAT SHOULD BE INCLUDED/EXCLUDED FROM THE LIST OF PREDICTING FACTORS. IN THIS PAPER WE DESCRIBE SUCH AN APPROACH FOR IDENTIFYING STUDENTS AT RISK OF FAILURE IN ONLINE COURSES. FOR THE PROOF OF OUR CONCEPT WE USED THE DATA OF TWO COHORTS OF AN ONLINE COURSE IMPLEMENTED IN MOODLE LMS. USING THE DATA OF THE FIRST COHORT WE DEVELOPED A PREDICTION MODEL BY EXPERIMENTING WITH CERTAIN BASE CLASSIFIERS AVAILABLE IN WEKA. TO IMPROVE THE OBSERVED PERFORMANCE OF THE EXPERIMENTED BASE CLASSIFIERS, WE ENHANCED FURTHER OUR MODEL WITH THE MAJORITY VOTING ENSEMBLE CLASSIFIER. THE FINAL MODEL WAS USED AT THE NEXT COHORT OF STUDENTS IN ORDER TO IDENTIFY THOSE AT RISK OF FAILURE BEFORE THE FINAL EXAM. THE PREDICTION ACCURACY OF THE MODEL WAS HIGH WHICH SHOW THAT THE FINDINGS OF SUCH A PROCESS CAN BE GENERALIZED.
ONLINE LEARNING HAS BEEN ADOPTED RAPIDLY BY EDUCATIONAL INSTITUTIONS AND ORGANIZATIONS. DESPITE ITS MANY ADVANTAGES, INCLUDING 24/7 ACCESS, HIGH FLEXIBILITY, RICH CONTENT, AND LOW COST, ONLINE LEARNING SUFFERS FROM HIGH DROPOUT RATES THAT HAMPER PEDAGOGICAL AND ECONOMIC GOAL OUTCOMES. ENHANCED STUDENT DROPOUT PREDICTION TOOLS WOULD HELP PROVIDERS PROACTIVELY DETECT STUDENTS AT RISK OF LEAVING AND IDENTIFY FACTORS THAT THEY MIGHT ADDRESS TO HELP STUDENTS CONTINUE THEIR LEARNING EXPERIENCE. THEREFORE, THIS STUDY SEEKS TO IMPROVE STUDENT DROPOUT PREDICTIONS, WITH THREE MAIN CONTRIBUTIONS. FIRST, IT BENCHMARKS A RECENTLY PROPOSED LOGIT LEAF MODEL (LLM) ALGORITHM AGAINST EIGHT OTHER ALGORITHMS, USING A REAL-LIFE DATA SET OF 10,554 STUDENTS OF A GLOBAL SUBSCRIPTION-BASED ONLINE LEARNING PROVIDER. THE LLM OUTPERFORMS ALL OTHER METHODS IN FINDING A BALANCE BETWEEN PREDICTIVE PERFORMANCE AND COMPREHENSIBILITY. SECOND, A NEW MULTILEVEL INFORMATIVE VISUALIZATION OF THE LLM ADDS NOVEL BENEFITS, RELATIVE TO A STANDARD LLM VISUALIZATION. THIRD, THIS RESEARCH SPECIFIES THE IMPACTS OF STUDENT DEMOGRAPHICS; CLASSROOM CHARACTERISTICS; AND ACADEMIC, COGNITIVE, AND BEHAVIORAL ENGAGEMENT VARIABLES ON STUDENT DROPOUT. IN REVIEWING LLM SEGMENTS, THESE RESULTS SHOW THAT DIFFERENT INSIGHTS EMERGE FOR VARIOUS STUDENT SEGMENTS WITH DIFFERENT LEARNING PATTERNS. THIS NOTABLE RESULT CAN BE USED TO PERSONALIZE STUDENT RETENTION CAMPAIGNS.
UNIVERSITY TEACHERS, WHO GENERALLY FOCUS THEIR INTEREST ON PEDAGOGY AND STUDENTS, MAY FIND IT DIFFICULT TO MANAGE E-LEARNING PLATFORMS WHICH PROVIDE LEARNING ANALYTICS AND DATA. BUT LEARNING INDICATORS MIGHT HELP TEACHERS WHEN THE AMOUNT OF INFORMATION TO PROCESS GROWS EXPONENTIALLY. THE INDICATORS CAN BE COMPUTED BY THE AGGREGATION OF DATA AND BY USING TEACHERS' KNOWLEDGE WHICH IS OFTEN IMPRECISE AND UNCERTAIN. POSSIBILITY THEORY PROVIDES A SOLUTION TO HANDLE THESE DRAWBACKS. POSSIBILISTIC NETWORKS ALLOW US TO REPRESENT THE CAUSAL LINK BETWEEN THE DATA BUT THEY REQUIRE THE DEFINITION OF ALL THE PARAMETERS OF CONDITIONAL POSSIBILITY TABLES. UNCERTAIN GATES ALLOW THE AUTOMATIC CALCULATION OF THESE CONDITIONAL POSSIBILITY TABLES BY USING FOR EXAMPLE THE LOGICAL COMBINATION OF INFORMATION. THE CALCULATION TIME TO PROPAGATE NEW EVIDENCE IN POSSIBILISTIC NETWORKS CAN BE IMPROVED BY COMPILING POSSIBILISTIC NETWORKS. IN THIS PAPER, WE WILL PRESENT AN EXPERIMENTATION OF COMPILING POSSIBILISTIC NETWORKS TO COMPUTE COURSE INDICATORS. INDEED, THE LMS MOODLE PROVIDES A LARGE SCALE OF DATA ABOUT LEARNERS THAT CAN BE MERGED TO PROVIDE INDICATORS TO TEACHERS IN A DECISION MAKING SYSTEM. THUS, TEACHERS CAN PROPOSE DIFFERENTIATED INSTRUCTION WHICH, BETTER CORRESPONDS TO THEIR STUDENT'S EXPECTATIONS AND THEIR LEARNING STYLE.
PROLIFERATION OF SOCIAL NETWORKS AND ITS USE IN EDUCATION AND HIGHER LEARNING HAVE BECOME AN INTERESTING TOPIC. IN ORDER TO ACCOMMODATE DIVERSE STUDENT COHORTS IN THE AGE OF MASSIFICATION OF EDUCATION MARKET, THIS APPROACH HAS BECOME ATTRACTIVE. RECENT ADVANCES IN LEARNING ANALYTICS AND DATA VISUALIZATION FURTHER PROVED TO BE USEFUL IN ENCOURAGING COLLABORATIVE LEARNING. ON THE OTHER HAND, INFORMATION OF SOCIAL NETWORKS CAN BE TOO COMPLEX TO VISUALIZE OFTEN OVERLOADING USERS WITH TOO MUCH POSSIBLY UNWANTED INFORMATION. THE QUESTION IS WHAT TO SHOW AND WHAT NOT TO SHOW WHEN WE VISUALIZE SOCIAL RELATIONSHIPS OF USERS. IN THIS PAPER, WE PROPOSE A NEW VISUALIZATION MODEL CALLED LEARNING SPACE AND DEVELOP A METHOD FOR VISUALIZING LEARNING ACTIVITIES ON SOCIAL NETWORK IN A 3D VIRTUAL LEARNING SPACE. WE EVALUATE THE METHOD USING QUESTIONNAIRES TO SEE WHETHER VISUALIZATION OF SOCIAL RELATIONS OF LEARNING WILL IMPROVE THE LEARNING IN THE FOLLOWING WAYS OR NOT: MAKE IT MORE FUN, MAKE IT EASIER, MOTIVATE MORE. THE RESULT SHOWS THAT OUR METHOD OF VISUALIZATION OF LEARNING ACTIVITIES IN SOCIAL NETWORK MADE LEARNING MORE FUN AND EASIER. THE RESULT ALSO SHOWS THAT IT HELPS STUDENT ENGAGE AND MOTIVATE ON THE SUBJECTS.
EXISTING RADAR ALGORITHMS ASSUME STATIONARY STATISTICAL CHARACTERISTICS FOR ENVIRONMENT/CLUTTER. IN PRACTICAL SCENARIOS, THE STATISTICAL CHARACTERISTICS OF THE CLUTTER CAN DYNAMICALLY CHANGE DEPENDING ON WHERE THE RADAR IS OPERATING. NONSTATIONARITY IN THE STATISTICAL CHARACTERISTICS OF THE CLUTTER MAY NEGATIVELY AFFECT THE RADAR PERFORMANCE. COGNITIVE RADAR THAT CAN SENSE THE CHANGES IN THE CLUTTER STATISTICS, LEARN THE NEW STATISTICAL CHARACTERISTICS, AND ADAPT TO THESE CHANGES HAS BEEN PROPOSED TO OVERCOME THESE SHORTCOMINGS. WE HAVE RECENTLY DEVELOPED TECHNIQUES FOR DETECTION OF STATISTICAL CHANGES AND LEARNING THE NEW CLUTTER DISTRIBUTION FOR COGNITIVE RADAR. IN THIS WORK, WE WILL EXTEND THE LEARNING COMPONENT. MORE SPECIFICALLY, IN OUR PREVIOUS WORK, WE HAVE DEVELOPED A SPARSE RECOVERY BASED CLUTTER DISTRIBUTION IDENTIFICATION TO LEARN THE DISTRIBUTION OF THE NEW CLUTTER CHARACTERISTICS AFTER THE DETECTED CHANGE IN THE STATISTICS OF THE CLUTTER. IN OUR METHOD, WE HAVE BUILT A DICTIONARY OF CLUTTER DISTRIBUTIONS AND USED THIS DICTIONARY IN ORTHOGONAL MATCHING PURSUIT TO PERFORM SPARSE RECOVERY OF THE CLUTTER DISTRIBUTION ASSUMING THAT THE DICTIONARY INCLUDES THE NEW DISTRIBUTION. IN THIS WORK, WE PROPOSE A HYPOTHESIS TESTING BASED APPROACH TO DETECT WHETHER THE NEW DISTRIBUTION OF THE CLUTTER IS INCLUDED IN THE DICTIONARY OR NOT, AND SUGGEST A METHOD TO DYNAMICALLY UPDATE THE DICTIONARY. WE ENVISION THAT THE SUCCESSFUL OUTCOMES OF THIS WORK WILL BE OF HIGH RELEVANCE TO THE ADAPTIVE LEARNING AND COGNITIVE AUGMENTATION OF THE RADAR SYSTEMS THAT ARE USED IN REMOTELY PILOTED VEHICLES FOR SURVEILLANCE AND RECONNAISSANCE OPERATIONS.
THIS REVIEW IS A MAJOR UPDATE OF THE CHAPTER ON ONLINE LEARNING COMMUNITIES IN K-12 SETTINGS IN THE FIRST EDITION OF THIS HANDBOOK. THIS CHAPTER CLARIFIES THE CONCEPT AND BOUNDARY OF ONLINE LEARNING COMMUNITIES, IDENTIFIES MAJOR TRENDS OF RESEARCH, AND SUGGESTS PERTINENT ISSUES FOR FUTURE RESEARCH. FOUR ESTABLISHED ONLINE LEARNING COMMUNITIES, WHICH HAVE SUSTAINED FOR MORE THAN A DECADE AND ARE SUPPORTED BY RESEARCH, ARE REVIEWED ON THEIR CHANGES AND EVOLUTIONS OVER THE LAST DECADE. THEY INCLUDE KNOWLEDGE BUILDING COMMUNITIES, QUEST ATLANTIS, VIRTUAL MATH TEAM, ANDWEB-BASED INQUIRY SCIENCE ENVIRONMENT. THE SUCCESS OF THE INTENDED DESIGN PRINCIPLES AND CHARACTERISTICS OF THESE LEARNING ENVIRONMENTS ARE EVALUATED ALONG THE COGNITIVE, SOCIAL, AND TECHNOLOGICAL DIMENSIONS. TWO EMERGENT ONLINE LEARNING COMMUNITY PLATFORMS, CANVAS K-12 AND BRIGHTSPACE, ARE ALSO REVIEWED. THESE PLATFORMS MAKE USE OF EMERGING TECHNOLOGIES SUCH AS SOCIAL AND INTERACTIVE NETWORKING, WHICH ARE NEW TRENDS THAT ONLINE LEARNING COMMUNITIES ARE GRAVITATING TOWARD. POTENTIAL ISSUES SURROUNDING K-12 ONLINE LEARNING COMMUNITIES ARE DISCUSSED. THESE ISSUES INCLUDE INFRASTRUCTURE, CULTURE AND PRACTICES OF SCHOOL TEACHING AND LEARNING, DEVELOPMENT AND ASSESSMENT PROCESS OF AUTHENTIC KNOWLEDGE, USAGE AND ETHICS OF LEARNING ANALYTICS, AND POSSIBILITIES AND IMPACT ON EDUCATION DUE TO ADVANCES IN TECHNOLOGIES.
EDUCATIONAL INSTITUTIONS AND GOVERNING PRACTICES ARE INCREASINGLY AUGMENTED WITH DIGITAL DATABASE TECHNOLOGIES THAT FUNCTION AS NEW KINDS OF POLICY INSTRUMENTS. THIS ARTICLE SURVEYS AND MAPS THE LANDSCAPE OF DIGITAL POLICY INSTRUMENTATION IN EDUCATION AND PROVIDES TWO DETAILED CASE STUDIES OF NEW DIGITAL DATA SYSTEMS. THE LEARNING CURVE IS A MASSIVE ONLINE DATA BANK, PRODUCED BY PEARSON EDUCATION, WHICH DEPLOYS HIGHLY SOPHISTICATED DIGITAL INTERACTIVE DATA VISUALIZATIONS TO CONSTRUCT KNOWLEDGE ABOUT EDUCATION SYSTEMS. THE SECOND CASE CONSIDERS LEARNING ANALYTICS' PLATFORMS THAT ENABLE THE TRACKING AND PREDICTING OF STUDENTS' PERFORMANCES THROUGH THEIR DIGITAL DATA TRACES. THESE DIGITAL POLICY INSTRUMENTS ARE EVIDENCE OF HOW DIGITAL DATABASE INSTRUMENTS AND INFRASTRUCTURES ARE NOW AT THE CENTRE OF EFFORTS TO KNOW, GOVERN AND MANAGE EDUCATION BOTH NATIONALLY AND GLOBALLY. THE GOVERNING OF EDUCATION, AUGMENTED BY TECHNIQUES OF DIGITAL EDUCATION GOVERNANCE, IS BEING DISTRIBUTED AND DISPLACED TO NEW DIGITIZED CENTRES OF CALCULATION', SUCH AS PEARSON AND KNEWTON, WITH THE TECHNICAL EXPERTISE TO CALCULATE AND VISUALIZE THE DATA, PLUS THE PREDICTIVE ANALYTICS CAPACITIES TO ANTICIPATE AND PRE-EMPT EDUCATIONAL FUTURES. AS PART OF A DATA-DRIVEN STYLE OF GOVERNING, THESE EMERGING DIGITAL POLICY INSTRUMENTS PREFIGURE THE EMERGENCE OF REAL-TIME' AND FUTURE-TENSE' TECHNIQUES OF DIGITAL EDUCATION GOVERNANCE.
IN THE LAST DECADE, THE INCREASING PLURALITY OF MATERIALS, MEDIA TYPES AND SOFTWARE TOOLS WITHIN THE INTERNET HAS ESTABLISHED FIRST STEPS TOWARDS MORE INDIVIDUALIZED LEARNING APPROACHES. HOWEVER, THE DEVELOPMENT OF UTILIZING BIG DATA-BASED ALGORITHMS OF THE NEXT INTERNET GENERATION, THE SO-CALLED 'INTERNET OF THINGS', LEADS TO A COMPREHENSIVE APPROACH OF PERSONALIZED LEARNING FOR VERY DIFFERENT TARGET GROUPS. UNDERSTANDING THE LEARNER'S PROFILE AND INTERESTS, LEARNING GOALS AND LEARNING DIFFICULTIES, 'INTELLIGENT AGENTS' ACCOMPANY AND GUIDE THE LEARNER DURING THE LEARNING PROCESS IN THE FUTURE. IN PARTICULAR, FOR STUDENTS WITH LEARNING DISABILITIES SUCH AS DYSLEXIA AND DYSCALCULIA, THE NEW DEVELOPMENT OF 'LEARNING ANALYTICS' BASED ON BIG DATA ALGORITHMS HAS A LARGE POTENTIAL. THE GOAL IS TO OVERCOME THE SEVERE GAP BETWEEN THEIR EXISTING INTELLECTUAL POTENTIALS AND THEIR OFTEN UNSUCCESSFUL LEARNING BIOGRAPHIES IN SCHOOLS AND UNIVERSITIES. THE CHANGES OF DAILY ENVIRONMENTS TOWARDS A 4.0-SOCIETY, IN PARTICULAR THROUGH FULLY NETWORKED SMART CITIES, RESULT IN AN INCREASING NUMBER OF DATA FROM VERY HETEROGENEOUS SOURCES. DUE TO TECHNOLOGIES SUCH AS, E.G., SMART WEARABLES COMBINED WITH DATA GENERATED THROUGH TRAFFIC AND MOBILITY OR LOGISTICS, SMART GEOGRAPHICAL PLACES WILL BE ENABLED TO ACT AS A BASIS FOR INTELLIGENT LEARNING AND EVALUATION MECHANISMS.
THE DATA PRESENTED IN THIS ARTICLE COMPRISE AN EDUCATIONAL DATASET COLLECTED FROM THE STUDENT INFORMATION SYSTEM (SIS), THE LEARNING MANAGEMENT SYSTEM (LMS) CALLED MOODLE, AND VIDEO INTERACTIONS FROM THE MOBILE APPLICATION CALLED "EDIFY." THE DATASET, FROM THE HIGHER EDUCATIONAL INSTITUTION (HEI) IN SULTANATE OF OMAN, COMPRISES FIVE MODULES OF DATA FROM SPRING 2017 TO SPRING 2021. THE DATASET CONSISTS OF 326 STUDENT RECORDS WITH 40 FEATURES IN TOTAL, INCLUDING THE STUDENTS' ACADEMIC INFORMATION FROM SIS (WHICH HAS 24 FEATURES), THE STUDENTS' ACTIVITIES PERFORMED ON MOODLE WITHIN AND OUTSIDE THE CAMPUS (COMPRISING 10 FEATURES), AND THE STUDENTS' VIDEO INTERACTIONS COLLECTED FROM EDIFY (CONSISTING OF SIX FEATURES). THE DATASET IS USEFUL FOR RESEARCHERS WHO WANT TO EXPLORE STUDENTS' ACADEMIC PERFORMANCE IN ONLINE LEARNING ENVIRONMENTS, AND WILL HELP THEM TO MODEL THEIR EDUCATIONAL DATAMINING MODELS. MOREOVER, IT CAN SERVE AS AN INPUT FOR PREDICTING STUDENTS' ACADEMIC PERFORMANCE WITHIN THE MODULE FOR EDUCATIONAL DATAMINING AND LEARNING ANALYTICS. FURTHERMORE, RESEARCHERS ARE HIGHLY RECOMMENDED TO REFER TO THE ORIGINAL PAPERS FOR MORE DETAILS. DATASET:HTTPS://ZENODO.ORG/RECORD/5591907 (ACCESSED ON 18 OCTOBER 2021). DATASET LICENSE: CC-BY 4.0.
THIS STUDY EXPLORES AND CHARACTERIZES LEARNERS' PARTICIPATION PATTERNS IN MOOC FORUMS, AS WELL AS THE FACTORS THAT CORRELATE WITH LEARNERS' PARTICIPATION. EDUCATIONAL DATA MINING AND LEARNING ANALYTICS METHODS WERE USED TO RETRIEVE AND ANALYZE THE LEARNERS' INTERPERSONAL INTERACTION DATA, WHICH HAD ACCUMULATED IN THE COURSERA LOG FILES. THE CONTENT IN THE FORUMS WAS CATEGORIZED BASED ON HENRI'S CRITERIA AND CONVERTED INTO QUANTITATIVE VALUES THAT COULD BE COMPARED AND VISUALIZED. IT WAS FOUND THAT ONLY 20% OF THE LEARNERS WERE COLLABORATING IN THE FORUMS THROUGHOUT THE ENTIRE COURSE AND WERE RESPONSIBLE FOR 50% OF THE TOTAL POSTS. A PORTION OF THEM EARNED THE NAME "SUPER ACTIVE." THE ANALYSES NOT ONLY DEMONSTRATED THE VOLUME OF ACTIVITY AND ITS PATTERN BUT ALSO REVEALED THE CONTENT OF THE DISCUSSIONS, WHICH HELPED TO HIGHLIGHT THE NEEDS AND REASONS FOR STUDENTS' USAGE OF THE FORUMS. THE CONTENT ANALYSIS SHOWED INTENSITY IN THE "COGNITIVE" AND "DISCIPLINE" CATEGORIES. THUS, FORUM PARTICIPANTS BENEFIT FROM DISCUSSIONS NOT ONLY SOCIALLY BUT DISCIPLINARILY AND COGNITIVELY AS WELL. FURTHERMORE, EVEN THOUGH A STRONG SIGNIFICANT CORRELATION WAS FOUND BETWEEN THE LEARNERS' COMPLETION STATUS AND THEIR ACTIVITY IN THE FORUMS, A GROUP OF LEARNERS, WHO DID NOT COMPLETE THE COURSE, WAS HIGHLY ACTIVE.
THE PAPER IS AIMED AT CURRENT APPROACHES TOWARD USING OF INFORMATION AND COMMUNICATION TECHNOLOGY IN EDUCATION. FIRSTLY, NOWADAYS USAGE OF COMPUTERS IN EDUCATION IS INTRODUCED ON THE BASIS OF AN OVERVIEW AND SURVEY OF THE RESEARCHES PRESENTED ON THE RECENT CONFERENCE COMPUTERS IN EDUCATION ORGANIZED BY THE ASIA-PACIFIC SOCIETY FOR COMPUTERS IN EDUCATION. LAST RESEARCH IN THE FOLLOWING AREAS IS DISCUSSED HERE: COMPUTER-SUPPORTED PERSONALIZED AND COLLABORATIVE LEARNING, SEAMLESS LEARNING, LEVEL OF ATTENTION AND LEARNING EFFICIENCY, LEARNING ANALYTICS, APPLICATION OF INNOVATIVE EDUCATIONAL TECHNOLOGIES IN STEM EDUCATION, AND ONLINE GAME-BASED LEARNING IN SCHOOL EDUCATION. DESPITE THE GEOGRAPHICAL ORIENTATION MANY ISSUES MAY BE INSPIRATIONAL FOR SCHOLARS FROM WESTERN COUNTRIES. ON THE OTHER HAND, FROM RESEARCH PAPERS PRESENTED BY RESEARCHERS FROM ASIA-PACIFIC REGION IT IS EVIDENT THAT THEY HAVE BEEN FOCUSING ON VERY SIMILAR TOPICS IN EDUCATION AS SCIENTISTS IN WESTERN COUNTRIES DO. SECONDLY, SOME OBSERVATION CONCERNING USAGE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN EGYPT EDUCATION AS WELL AS RECENT RESEARCH WORKS EXAMINED WITHIN THE DOCTORAL STUDY PROGRAM INFORMATION AND COMMUNICATION TECHNOLOGY IN EDUCATION PROVIDED BY THE UNIVERSITY OF HRADEC KRALOVE, CZECH REPUBLIC, ARE PRESENTED IN THE PAPER IN THE LIGHT OF THE MENTIONED SURVEY.
WE PRESENT A NEAR-FIELD SOURCE LOCALIZATION METHOD FOR USE WITH A RECENTLY PROPOSED INFLATABLE ARRAY-BASED SONAR SYSTEM. THE SENSING SYSTEM, KNOWN AS THE UNDERWATER INFLATABLE CO-PRIME SONAR ARRAY, CAN BE EASILY DEPLOYED FROM AN UNMANNED UNDERWATER VEHICLE (UUV), AS IT CAN BE STORED AS A FOLDED PACKAGE WITH COMPACT STOWED DIMENSION. THIS PACKAGE CAN BE DETACHED FROM THE UUV AND IT CAN MORPH INTO ITS FINAL STRUCTURAL FORM AT THE DESTINATION. THE CO-PRIME SPARSE ARRAY CONFIGURATION OFFERS THE CAPABILITY TO RESOLVE A MUCH HIGHER NUMBER OF SOURCES AS COMPARED TO A CONVENTIONAL UNIFORM HALF-WAVELENGTH-SPACED ARRAY. USING REAL-DATA MEASUREMENTS IN A WATER TANK WITH A CO-PRIME ARRAY PROTOTYPE, WE DEMONSTRATE THE CAPABILITY OF THE PROPOSED SYSTEM TO LOCALIZE WIDEBAND ACOUSTIC SOURCES.
ARTIFICIAL INTELLIGENCE (AI) AND REMOTE SURGERY GO TOGETHER IN DEFINING THE FUTURE OF INTELLIGENT MEDICINE. THE ADVANCEMENT OF ROBOTIC SURGERY BECAME A POSSIBILITY BY LEVERAGING INTELLIGENT SENSORS, MACHINE LEARNING (ML), AND RELIABLE WIRELESS CONNECTIVITY IN ADDITION TO LOW-LATENCY RESPONSE TO SURGICAL COMMANDS AND AUTOMATED RESPONSES TO PATIENT'S STATUS RESULTING IN AN ACCURATE AUTOMATED SYSTEM. THE TREND IS TO DEVELOP CYBER-MEDICAL SYSTEMS THROUGH THE INTEGRATION OF MEDICAL KNOWLEDGE AND ENGINEERING APPLICATIONS IN ORDER TO CREATE CUSTOMIZABLE INTELLIGENT HEALTHCARE PROCEDURES. SECURITY PLAYS A CRITICAL ROLE WHILE THE SYSTEMS NAVIGATE THE DYNAMICS OF HUMAN-CONTROLLED CONNECTIVITY VERSUS AUTONOMY, AND REMOTE COMMANDS VERSUS AUTOMATED RESPONSES TO SENSORS. THE SYSTEMS NEED TO "LEARN" FROM EXPERIENCE AND TRANSFERRED KNOWLEDGE FROM OTHER SYSTEMS WHILE PROTECTING AGAINST LEARNING FROM FALSE DATA. THIS PAPER ADDRESSES THE CYBER SECURITY RISKS INTRODUCED BY ADAPTING THE EMERGING TECHNOLOGIES AS WELL AS PROVIDING POTENTIAL SOLUTIONS THAT ARE BASED ON BEST PRACTICES. THE ML MODEL USED IN CYBER MEDICINE SHOULD BE AS SIMPLE AS POSSIBLE PROVIDING THE REQUIRED ACCURACY TO PRODUCE THE DESIRED OUTCOME, UNDERSTANDING THAT MORE COMPLEX MODELS HAVE HIGHER CHANCES OF SUFFERING THE DEGRADATION EFFECTS OF OVERFITTING.
THIS PAPER BUILDS ON PREVIOUS RESEARCH THAT EVALUATED THE IMPACT OF AN EMBEDDED ONLINE ACADEMIC AND INFORMATION LITERACY MODULE INTO THE LEARNING MANAGEMENT SYSTEM (LMS) OF A FIRST-YEAR UNIVERSITY BUSINESS COURSE. THE RESEARCH FINDINGS CONCLUDED THAT THE RESOURCE CONTRIBUTED TO STUDENT SUCCESS AND HELPED TO DEMONSTRATE THE VALUE OF THE LIBRARY AT GRIFFITH UNIVERSITY TO THE WIDER COMMUNITY. SINCE THE ORIGINAL MODULE WAS EMBEDDED INTO THE LMS FOR A SINGLE COURSE IN 2014, THE PROVISION OF THESE BESPOKE ONLINE MODULES HAS EXPANDED. DATA HAS BEEN GATHERED OVER THE YEARS FROM 2014 TO 2017 ON UNIQUE USERS AND PAGE VIEWS OF THE MODULES. USAGE HAS EXPLODED FROM 4442 PAGE VISITS BY 910 UNIQUE USERS IN ONE COURSE IN 2014, TO 271,556 PAGE VISITS BY 12,456 UNIQUE USERS IN 45 DIFFERENT COURSES IN 2017. WHILST THE LEARNING ANALYTICS SUPPORT THE CONTINUED AND INCREASED PROVISION OF THESE RESOURCES, OTHER ISSUES AROUND SUSTAINABILITY HAVE ARISEN AND NEED TO BE ADDRESSED. THE AIM OF THIS PAPER IS TO EXAMINE HOW THE USE OF AN E-LEARNING MOD& CAN HELP ADDRESS ISSUES OF SUSTAINABILITY THAT ARISE AS PART OF SUPPORTING AN ONLINE APPROACH TO ACADEMIC AND INFORMATION LITERACY SUPPORT.
UNSUPERVISED TARGET GENERATION FOR HYPERSPECTRAL IMAGERY (HSI) HAVE GENERATED GREAT INTEREST IN THE HYPERSPECTRAL COMMUNITY. HOWEVER, MOST OF THE CURRENT UNSUPERVISED TARGET GENERATION ALGORITHMS HAVE TO PROCESS LARGE HSI DATA, WHICH IS ACQUIRED USING THE TRADITIONAL NYQUIST-SHANNON SAMPLING THEOREM, RESULTING IN DATA WITH HIGH BAND-TO-BAND CORRELATION. AS A CONSEQUENCE, THESE ALGORITHMS END UP PROCESSING REDUNDANT INFORMATION, RAISING THE DEMAND FOR LARGE MEMORY STORAGE, PROCESSING TIME, AND TRANSMISSION BANDWIDTH. IN THE PAST, SOME EFFORTS HAVE BEEN DEDICATED TO DEALING WITH THE REDUNDANT INFORMATION VIA DATA REDUCTION (DR) OR DATA COMPRESSION POST-ACQUISITION. HOWEVER, TO THE BEST OF OUR KNOWLEDGE, THIS CHALLENGE HAS BEEN ADDRESSED OUTSIDE THE CONTEXT OF COMPRESSIVE SENSING (CS). THIS PAPER APPLIES CS DATA ACQUISITION PROCESS AT THE SENSOR LEVEL SO THAT THE REDUNDANT INFORMATION IS REMOVED AT THE EARLY STAGE OF THE DATA PROCESSING CHAIN. THE MAIN ADVANTAGE OF OUR APPROACH IS THAT IT EMPLOYS A RANDOM SENSING PROCESS, AND THE CONCEPT OF UNIVERSALITY, TO RANDOMLY SENSE THE HSI BANDS AND PRODUCE DATA CONTAINING THE BARE MINIMUM INFORMATION. WE TAKE ADVANTAGE OF CS RESTRICTED ISOMETRIC PROPERTIES (RIP), RESTRICTED CONFORMAL PROPERTIES (RCP), AND NEWLY DERIVED ORTHOGONAL SUB-SPACE PROJECTION (OSP) PROPERTIES TO PERFORM AUTOMATIC TARGET GENERATION PROCESS (ATGP) IN THE COMPRESSIVELY SENSED BAND DOMAIN (CSBD), INSTEAD OF IN THE ORIGINAL DATA SPACE (ODS), WHERE THE HSI DATA CONTAINS FULL SPECTRAL BANDS. OUR EXPERIMENTAL RESULTS SHOW THAT, BY WORKING IN THE CSBD, WE AVOID PROCESSING REDUNDANT DATA AND STILL MAINTAIN PERFORMANCE RESULTS THAT ARE COMPARABLE WITH THE PERFORMANCE RESULTS OBTAINED IN THE ODS.
MOST COMMONLY USED CLASSIFICATION ALGORITHMS PROCESS DATA IN THE FORM OF VECTORS. AT THE SAME TIME, MODERN DATASETS OFTEN COMPRISE MULTIMODAL MEASUREMENTS THAT ARE NATURALLY MODELED AS MULTI-WAY ARRAYS, ALSO KNOWN AS TENSORS. PROCESSING MULTI-WAY DATA IN THEIR TENSOR FORM CAN ENABLE ENHANCED INFERENCE AND CLASSIFICATION ACCURACY. TUCKER DECOMPOSITION IS A STANDARD METHOD FOR TENSOR DATA PROCESSING, WHICH HOWEVER HAS DEMONSTRATED SEVERE SENSITIVITY TO CORRUPTED MEASUREMENTS DUE TO ITS L2-NORM FORMULATION. IN THIS WORK, WE PRESENT A SELECTION OF CLASSIFICATION METHODS THAT EMPLOY AN L1-NORM-BASED, CORRUPTION-RESISTANT REFORMULATION OF TUCKER (L1-TUCKER). OUR EXPERIMENTAL STUDIES ON MULTIPLE REAL DATASETS CORROBORATE THE CORRUPTION-RESISTANCE AND CLASSIFICATION ACCURACY AFFORDED BY L1-TUCKER.
IN THIS PAPER, WE PROPOSE A COLLABORATIVE SENSING SCHEME FOR SOURCE LOCALIZATION AND IMAGING IN AN UNMANNED AERIAL VEHICLE (UAV) NETWORK. A TWO-STAGE IMAGE FORMATION APPROACH, WHICH COMBINES THE ROBUST ADAPTIVE BEAMFORMING TECHNIQUE AND SPARSITY-BASED RECONSTRUCTION STRATEGY, IS PROPOSED TO ACHIEVE ACCURATE MULTI-SOURCE LOCALIZATION. IN ORDER TO MINIMIZE THE COMMUNICATION TRAFFIC IN THE UAV NETWORK, EACH UAV NODE ONLY TRANSMITS THE COARSE-RESOLUTION IMAGE, IN LIEU OF THE LARGE VOLUME OF RAW SAMPLED DATA. THE PROPOSED METHOD MAINTAINS THE ROBUSTNESS IN THE PRESENCE OF MODEL MISMATCH WHILE PROVIDING A HIGH-RESOLUTION IMAGE.
THIS STUDY EXPLORED YOUTH WITH AUTISM SPECTRUM DISORDER (ASD) LEARNING SOCIAL COMPETENCE IN THE CONTEXT OF INNOVATIVE 3D VIRTUAL LEARNING ENVIRONMENT AND THE EFFECTS OF GAMING AS A CENTRAL ELEMENT OF THE LEARNING EXPERIENCE. THE EMPIRICAL STUDY RETROSPECTIVELY COMPARED THE SOCIAL INTERACTIONS OF 11 ADOLESCENTS WITH ASD IN GAME-AND NONGAME-BASED 3D COLLABORATIVE LEARNING ACTIVITIES IN THE SAME SOCIAL COMPETENCE TRAINING CURRICULUM. WE EMPLOYED A LEARNING ANALYTICS APPROACH - ASSOCIATION RULE MINING TO UNCOVER THE ASSOCIATIVE RULES OF VERBAL SOCIAL INTERACTION AND NONVERBAL SOCIAL INTERACTION CONTRIBUTORS FROM THE LARGE DATASET OF THE CODED SOCIAL BEHAVIORS. BY COMPARING THE RULES ACROSS THE GAME AND NONGAME ACTIVITIES, WE FOUND A SIGNIFICANT DIFFERENCE IN YOUTH WITH ASD'S SOCIAL PERFORMANCE. THE RESULTS OF THE GROUP COMPARISON STUDY INDICATED THAT THE CO-OCCURRENCE OF VERBAL AND NONVERBAL BEHAVIORS IS MUCH STRONGER IN THE GAME-BASED LEARNING ACTIVITIES. THE GAME ACTIVITIES ALSO YIELDED MORE DIVERSE SOCIAL INTERACTION BEHAVIOR PATTERNS. ON THE OTHER HAND, IN THE NONGAME ACTIVITIES, STUDENTS' SOCIAL INTERACTION BEHAVIOR PATTERNS ARE MUCH MORE LIMITED. FURTHERMORE, THE IMPACT OF GAME DESIGN PRINCIPLES ON LEARNING IS THEN DISCUSSED IN THIS PAPER.
THE AIM OF THIS WORK IS TO PRESENT AN INNOVATIVE FRAMEWORK TO ENHANCED LEARNER ENGAGEMENT AND MOTIVATION ON MASSIVE OPEN ONLINE LEARNING ENVIRONMENTS. THE PAPER EXPLORES ON THE IMPACT OF APPLYING EDUCATIONAL DATA MINING IN COMBINATION WITH LEARNING ANALYTICS TECHNIQUES, THROUGH A FRAMEWORK CALLED DIGITAL LEARNING ECOSYSTEM (DLE) TO PROVIDE STUDENTS A COMPLETE AND DIFFERENT LEARNING EXPERIENCE ALONG THE COURSE. THIS WORK PRESENTS IN DETAIL THE DESIGN AND APPLYING OF A DLE WITH DIFFERENT STRATEGIES TO HELP STUDENTS OVERCOME THE LEARNING PROCESS DIFFICULTIES THAT ARISE IN A MASSIVE, OPEN, AND ONLINE COURSE. THE PROPOSED FRAMEWORK IS BASED ON THE IMPORTANCE OF CREATING ENGAGING EXPERIENCES BEFORE, DURING AND AFTER A COURSE COMPLETION TO INCREASE LEARNERS' PARTICIPATION AND REDUCE DROPOUT RATES. THIS STUDY ANALYZED DATA OBTAINED FROM EDX COURSE "JAVA FUNDAMENTALS FOR ANDROID DEVELOPMENT" DEVELOPED BY A GROUP OF PROFESSORS OF THE FACULTY OF COMPUTER ENGINEERING, AND THE DEPARTMENT OF RESEARCH AND DEVELOPMENT (GES) OF GALILEO UNIVERSITY. THE DATA SET CONSISTED OF A POST-COURSE SURVEY, PARTICIPANTS DEMOGRAPHIC, SOLUTION ACTIVITIES SUBMISSION, AND FINAL GRADES. THE RESULTS OBTAINED ARE ANALYZED IN TERMS OF STUDENT PERCEIVED USEFULNESS, MOTIVATION, STUDENT'S USAGE OF TOOLS AND STUDENTS' ATTITUDE TOWARDS DLE, DEGREE OF SATISFACTION, AND ENGAGEMENT.
THE AIM OF THIS STUDY WAS TO INVESTIGATE HOW TO ENGAGE UPPER SECONDARY LEVEL STUDENTS WHEN LEARNING IN A BLENDED LEARNING ENVIRONMENT. DIGITAL COURSE MATERIAL ABOUT ENTREPRENEURSHIP AND GLOBAL BUSINESS WAS CREATED ON A DIGITAL LEARNING PLATFORM CALLED CLANED. AS A RESULT, 4 DIGITAL BUSINESS COURSES WERE ESTABLISHED. WITH AN AIM TO DESIGN COURSE MATERIALS THAT PROMOTE ENGAGING LEARNING, A SPECIFIC PEDAGOGICAL SCRIPT WAS FIRST PREPARED BASED ON THE ENGAGING LEARNING ENVIRONMENT-MODEL AND THEN PILOTED IN PRACTICE [1] [2]. THIS STUDY EXAMINES THE RESULTS OF THE PILOT PHASE AND REVEALS HOW THE COURSE MATERIAL, BASED ON PEDAGOGICAL SCRIPT, MANAGED TO ENGAGE STUDENTS AND PROMOTE LEARNING. THE DATA WAS COLLECTED DURING THE PILOT PHASE OF THE COURSES, WHERE 261 STUDENTS ANSWERED OPEN-ENDED QUESTIONS ABOUT HOW THEY HAD EXPERIENCED THE CONTENT OF DIFFERENT ASSIGNMENTS FROM THE PERSPECTIVE OF THEIR OWN LEARNING. AFTER THIS, THE DATA WAS CONTENT ANALYZED AND THE RESULTS, IN A FORM OF AN EXAMPLE ASSIGNMENT, WERE COMPARED WITH THE RATINGS PROVIDED BY THE LEARNING ANALYTICS OF CLANED. THE RESULTS SHED LIGHT ON THE IMPORTANCE OF PEDAGOGICAL PRE-PLANNING WHEN DESIGNING NEW EDUCATIONAL CONTENTS AND PROVIDED CONCRETE EXAMPLES ON HOW THIS CAN BE DONE IN PRACTICE.
THIS ARTICLE PRESENTS FINDINGS FROM A CASE STUDY ON A FULLY ONLINE BACHELOR'S LEVEL COURSE AT AN AUSTRALIAN UNIVERSITY. THE STUDY WAS UNDERTAKEN TO DEMONSTRATE THE EFFECTIVENESS OF THE INTEGRATED METHODOLOGICAL FRAMEWORK (IMF) IN STRUCTURALLY EXPLORING AND IDENTIFYING ONLINE COMMUNITIES OF INQUIRY (COI). THE IMF EMPLOYS SOCIAL NETWORK ANALYSIS (SNA) AS THE KEY METHODOLOGY FOR EXPLORING COMMUNITY-BASED LEARNING IN LIGHT OF THE COMMUNITIES OF PRACTICE (COP) AND COI FRAMEWORKS. THE CASE STUDY WAS CONDUCTED ON TWO OFFERINGS OF THE SAME ONLINE COURSE WITH SOME VARIATIONS IN THE DESIGN. IN LINE WITH THE INTENTIONS OF THE LECTURER TO ENGAGE STUDENTS IN A COI, THE COURSE INCLUDED GUIDED, FACILITATED, AND GRADED WEEKLY DISCUSSION ACTIVITIES. ON APPLICATION OF THE IMF, NETWORK DIAGRAMS AND SNA MEASURES CLEARLY SHOWED THE IMPACT OF THE DIFFERENT LEARNING DESIGNS ON STUDENT ONLINE ENGAGEMENT WITHIN THE DISCUSSION FORUMS IN EACH SEMESTER. BASED ON STRUCTURAL COMPONENTS OF A COI WITHIN THE IMF, A COMPARATIVE ANALYSIS OF THE NETWORKS OBTAINED INDICATED THE FORMATION OF AN UNIDENTIFIED COMMUNITY IN S2 AND A COI IN S3. THE ARTICLE DISCUSSES FINDINGS IN TERMS OF EFFECTIVENESS OF THE IMF, IMPACT OF LEARNING DESIGN ON COMMUNITY FORMATION AND LEARNING ANALYTICS IN ONLINE LEARNING.
STUDENTS HAVE DIVERSE CIRCUMSTANCES, EXPERIENCES, AND GOALS. THE CHALLENGES THEY FACE ARE OFTEN INVISIBLE TO THE EDUCATORS AND STAFF WORKING TO SUPPORT THEM. IN THIS PAPER WE DESCRIBE THE DESIGN OF A NOVEL TOOL TO SUPPORT EACH STUDENT TO REPRESENT THEIR OWN STUDY JOURNEY. THIS HAS BEEN CREATED WITH THE PARTICIPATION OF STUDENTS AND OTHER STAKEHOLDERS THROUGHOUT THE DESIGN PROCESS, AND WAS INSPIRED BY PREVIOUS RESEARCH ON THE BARRIERS FACED BY DISABLED STUDENTS AND THE IMPACTS OF THESE ON THEIR EXPERIENCES AND ATTAINMENT. THE TOOL (OUR JOURNEY) CAN BE USED ONLINE OR IN A PRINTED FORM. THE BASIS OF THE DESIGN IS THE CREATION OF CARDS THAT DESCRIBE PARTICULAR EVENTS THAT WERE IMPORTANT TO THE STUDENT. STUDENTS CHOOSE AN EVENT TYPE AND AN EMOTIONAL STATE, AND PROVIDE SHORT DESCRIPTIONS. THROUGH THIS THEY BUILD A STRUCTURED NARRATIVE THAT HAS SEVERAL POTENTIAL USES. OUR JOURNEY PROVIDES AN ACCESSIBLE AND ENGAGING MEANS FOR DIALOGUE AND REFLECTION ON THE NATURE OF STUDY JOURNEYS WHICH COULD BE BENEFICIAL FOR STUDENTS. JOURNEY DATA COLLECTED AT SCALE COULD PROVIDE INSIGHTS INTO AREAS SUCH AS STUDENT MENTAL HEALTH AND WELLBEING, CHALLENGES COMMONLY FACED IN STUDY, AND A HOLISTIC VIEW OF THE LIVES OF STUDENTS.
IN-DEPTH ANALYSIS OF VIEWING BEHAVIOR IS AN IMPORTANT FACTOR INFORMING EDUCATIONAL VIDEO DESIGN. RESEARCH HAS LARGELY ADDRESSED THE SUPPORT OF SUCH ANALYSES FOR INDIVIDUAL LEARNERS OR HAS RELIED ON AGGREGATING USER INTERACTIONS WITH VIDEO PLAYERS TO DERIVE POTENTIAL POINTS OF INTEREST. ONLY IN RECENT YEARS, WITH RISING USE OF COMMERCIAL VIDEO HOSTING PLATFORM, THE DATA NECESSARY TO ANALYZE VIEWING BEHAVIOR ON A LARGER SCALE AND INDEPENDENTLY OF ACTIVE USER INTERACTIONS WITH LEARNING CONTENT HAS BECOME WIDELY AVAILABLE. THIS ARTICLE PROPOSES TO EXAMINE AUDIENCE RETENTION DATA TO OBTAIN IN-DEPTH INFORMATION ABOUT THE USAGE OF EDUCATIONAL VIDEOS AND CALCULATE METRICS THAT CAN SERVE AS A STARTING POINT FOR QUALITATIVELY EXAMINING THE EFFECTS OF DIDACTIC VIDEO DESIGN DECISIONS. WE PROPOSE A SET OF METRICS AND PRESENT A TOOLCHAIN FOR EXTRACTING THE NECESSARY DATA FROM YOUTUBE ANALYTICS AND CALCULATING THESE METRICS. THE USEFULNESS OF THESE METRICS IS SHOWCASED ON THREE DIFFERENT SCENARIOS OF USE THAT ARE BACKED WITH REAL-WORLD ANALYTICS DATA FROM FLIPPED CLASSROOM COURSES. OUR RESULTS SHOW THAT THE DETAILED AUDIENCE RETENTION DATA PROVIDED BY VIDEO HOSTING PLATFORMS CAN PROVIDE INSIGHTS THAT GO BEYOND THE CURRENT STATE-OF-THE-ART IN IN-VIDEO LEARNING ANALYTICS.
MASSIVE OPEN ONLINE COURSES HAVE EMERGED AS AN ALTERNATIVE TO THE TRADITIONAL EDUCATIONAL SYSTEM BECAUSE OF THE FLEXIBILITY IN TIMINGS AND ALSO IT OVERCOMES THE ECONOMIC AND GEOGRAPHICAL BARRIERS FOR THE USERS. MOOCS ALSO HELP LEARNERS FROM DIVERSE BACKGROUND TO COMMUNICATE AND EXCHANGE KNOWLEDGE IN MOOCS FORUMS. THE NUMBER OF LEARNERS ENROLLING IN SUCH COURSES IS VERY HIGH. DESPITE THE UNRESTRICTED ACCESSIBILITY, THE COMPLETION RATE IS VERY LOW. VARIOUS FACTORS AFFECT THE COMPLETION OF THE COURSE BY THE STUDENTS SUCH AS INTEREST IN THE SUBJECT, THE PURPOSE OF ENROLLING IN THE SUBJECT, WHETHER THE LECTURER IS ABLE TO CONVEY HIS KNOWLEDGE TO THE STUDENTS OR NOT. EDM (EDUCATIONAL DATA MINING) AND LA (LEARNING ANALYTICS) ARE THE FIELDS IN WHICH DATA OF STUDENTS LEARNING ACTIVITY IS ANALYZED TO OBTAIN CERTAIN VITAL INFORMATION OR CAN BE USED IN PREDICTION USING EDM TOOLS AND TECHNIQUES. DATA ANALYSIS SHOWS THAT THERE IS A STRONG RELATIONSHIP BETWEEN THE NUMBER OF EVENTS SUCH AS CLICK EVENT, VIDEO WATCHED, FORUM POST AND THE SUCCESSFUL LEARNER'S OUTCOME. MACHINE LEARNING ALGORITHMS ARE APPLIED ON THE DATASET FROM HARVARDX AND THE RESULT SHOWS THAT RANDOM FOREST GIVES AN OPTIMUM RESULT WITH THE HIGHEST PERFORMANCE.
BACKGROUND: VIRTUAL SCENARIOS ARE E-LEARNING RESOURCES THAT PRESENT JOB-REALISTIC SITUATIONS, PROMOTE TAKING ACTIONS AND PROVIDE LEARNING VIA THE CONSEQUENCES OF DECISIONS MADE. THE WAVES PROJECT ATTEMPTS TO WIDEN ACCESS TO VIRTUAL SCENARIOS FOR EDUCATORS AND LEARNERS. AIMS: THE AIM OF THIS PAPER IS TO PRESENT DIFFERENT FACETS OF SIMPLIFYING ACCESS TO VIRTUAL SCENARIOS THAT STRUCTURE THE DEVELOPMENTS IN WAVES. METHODS: THE DEVELOPMENTS ARE DRIVEN BY USER NEEDS AND SHAPED BY A TECHNICAL REFERENCE GROUP TO FOLLOW CURRENT TRENDS IN INFORMATION TECHNOLOGIES. THEY ALSO MEET THE CONSTRAINTS OF THE LEGACY CODE OF TWO EXEMPLARY AUTHORING SYSTEMS (OPENLABYRINTH AND CASUS) AND FIT THE LIMITS OF ALLOCATED TIME RESOURCES. RESULTS: THE PAPER CHARACTERIZES EIGHT TASKS THAT ADDRESS ACCESSIBILITY, USABILITY AND INTEGRATION CHALLENGES RELATED TO VIRTUAL SCENARIOS. THESE INVOLVE ENHANCEMENTS IN SUCH TOPICS AS INTERNATIONALIZATION, RESPONSIVENESS, STREAMLINING OF WORKFLOWS, JUST-IN-TIME GUIDANCE, SUPPORT IN INTERACTION AND REFLECTION, SINGLE SIGN-ON SECURITY, LEARNING ANALYTICS AND MICROSERVICES. CONCLUSIONS: THE AUTHORS DESCRIBE THE FEATURES CHARACTERISTIC TO SCENARIO-BASED LEARNING AND OUTLINE DEVELOPMENT DIRECTIONS TO IMPROVE ACCESS TO VIRTUAL SCENARIOS. THE EXAMPLES DEMONSTRATED USING TWO AUTHORING TOOLS ARE INTENDED TO INFLUENCE IMPROVEMENTS IN SIMILAR E-LEARNING SYSTEMS.
PROJECT-BASED COURSES CAN PROVIDE VALUABLE LEARNING EXPERIENCES FOR COMPUTING MAJORS AS WELL AS FOR FACULTY AND COMMUNITY PARTNERS. HOWEVER, PROPER COORDINATION BETWEEN STUDENTS, STAKEHOLDERS AND THE ACADEMIC TEAM IS VERY DIFFICULT TO ACHIEVE. WE PRESENT AN INTEGRAL STUDY CONSISTING OF A TWOFOLD APPROACH. FIRST, WE PROPOSE A PROVEN CAPSTONE COURSE FRAMEWORK IMPLEMENTATION IN CONJUNCTION WITH AN EDUCATIONAL SOFTWARE TOOL TO SUPPORT AND ENSURE PROPER FULFILLMENT OF MOST ACADEMIC AND ENGINEERING NEEDS. SECOND, WE PROPOSE AN APPROACH FOR MINING PROCESS DATA FROM THE INFORMATION GENERATED BY THIS TOOL AS A WAY OF UNDERSTANDING THESE COURSES AND IMPROVING SOFTWARE ENGINEERING EDUCATION. MOREOVER, WE PROPOSE VISUALIZATIONS, METRICS AND ALGORITHMS USING PROCESS MINING TO PROVIDE AN INSIGHT INTO PRACTICES AND PROCEDURES FOLLOWED DURING VARIOUS PHASES OF A SOFTWARE DEVELOPMENT LIFE CYCLE. WE MINE THE EVENT LOGS PRODUCED BY THE EDUCATIONAL SOFTWARE TOOL AND DERIVE ASPECTS SUCH AS COOPERATIVE BEHAVIORS IN A TEAM, COMPONENT AND STUDENT ENTROPY, PROCESS COMPLIANCE AND VERIFICATION. THE PROPOSED VISUALIZATIONS AND METRICS ( LEARNING ANALYTICS) PROVIDE A MULTI-FACETED VIEW TO THE ACADEMIC TEAM SERVING AS A TOOL FOR FEEDBACK ON DEVELOPMENT PROCESS AND QUALITY BY STUDENTS.
THE INTERACTION WITH THE VARIOUS LEARNERS IN A MASSIVE OPEN ONLINE COURSE (MOOC) IS OFTEN COMPLEX. CONTEMPORARY MOOC LEARNING ANALYTICS RELATE WITH CLICK-STREAMS, KEYSTROKES AND OTHER USER-INPUT VARIABLES. SUCH VARIABLES HOWEVER, DO NOT ALWAYS CAPTURE LEARNERS' LEARNING AND BEHAVIOR (E.G., PASSIVE VIDEO WATCHING). IN THIS PAPER, WE PRESENT A STUDY WITH 40 STUDENTS WHO WATCHED A MOOC LECTURE WHILE THEIR EYE-MOVEMENTS WERE BEING RECORDED. WE THEN PROPOSED A METHOD TO DEFINE STIMULI-BASED GAZE VARIABLES THAT CAN BE USED FOR ANY KIND OF STIMULUS. THE PROPOSED STIMULI-BASED GAZE VARIABLES INDICATE STUDENTS' ATTENTION (I.E., WITH-ME-NESS), AT THE PERCEPTUAL (FOLLOWING TEACHER'S DEICTIC ACTS) AND CONCEPTUAL LEVELS (FOLLOWING TEACHER DISCOURSE). IN OUR EXPERIMENT, WE IDENTIFIED A SIGNIFICANT MEDIATION EFFECT OF THE TWO LEVELS OF WITH-ME-NESS ON THE RELATION BETWEEN STUDENTS' MOTIVATION AND THEIR LEARNING PERFORMANCE. SUCH VARIABLES ENABLE COMMON MEASUREMENTS FOR THE DIFFERENT KIND OF STIMULI PRESENT IN DISTINCT MOOCS. OUR LONG-TERM GOAL IS TO CREATE STUDENT PROFILES BASED ON THEIR PERFORMANCE AND LEARNING STRATEGY USING STIMULI-BASED GAZE VARIABLES AND TO PROVIDE STUDENTS GAZE-AWARE FEEDBACK TO IMPROVE OVERALL LEARNING PROCESS.
IN A WIDE RANGE OF FIELDS, PROFESSIONAL PRACTICE IS BEING TRANSFORMED BY THE INCREASING INFLUENCE OF DIGITAL ANALYTICS: THE MASSIVE VOLUMES OF BIG DATA, AND SOFTWARE ALGORITHMS THAT ARE COLLECTING, COMPARING AND CALCULATING THAT DATA TO MAKE PREDICTIONS AND EVEN DECISIONS. RESEARCHERS IN A NUMBER OF SOCIAL SCIENCES HAVE BEEN CALLING ATTENTION TO THE FAR-REACHING AND ACCELERATING CONSEQUENCES OF THESE FORCES, CLAIMING THAT MANY PROFESSIONALS, RESEARCHERS, POLICY-MAKERS AND THE PUBLIC ARE JUST BEGINNING TO REALISE THE ENORMOUS POTENTIALS AND CHALLENGES THESE ANALYTICS ARE PRODUCING. YET, OUTSIDE OF PARTICULAR AREAS OF RESEARCH AND PRACTICE, SUCH AS LEARNING ANALYTICS, THERE HAS BEEN LITTLE DISCUSSION OF THIS TO DATE IN THE BROADER EDUCATION LITERATURE. THIS ARTICLE AIMS TO SET OUT SOME KEY ISSUES PARTICULARLY RELEVANT TO THE UNDERSTANDINGS OF PROFESSIONAL PRACTICE, KNOWLEDGE AND LEARNING POSED BY THE LINKAGES OF BIG DATA AND SOFTWARE CODE. IT BEGINS BY OUTLINING DEFINITIONS, FORMS AND EXAMPLES OF THESE ANALYTICS, THEIR POTENTIALITIES AND SOME OF THE HIDDEN IMPACT, AND THEN PRESENTS ISSUES FOR RESEARCHERS AND EDUCATORS. IT SEEKS TO CONTRIBUTE TO AND EXTEND DEBATES TAKING PLACE IN CERTAIN QUARTERS TO A BROADER PROFESSIONAL EDUCATION AND WORK AUDIENCE.
MASSIVE OPEN ONLINE COURSE (MOOC) PROVISION HAS PROLIFERATED OVER THE PAST FOUR YEARS AND MILLIONS OF LEARNERS HAVE PARTICIPATED. HOWEVER, IT IS WIDELY ACKNOWLEDGED THAT THE NUMBER OF COMPLETIONS IS DISTURBINGLY LOW AND MOOC DROP-OUT RATES HAVE BEEN A PROMINENT ISSUE OF DISCUSSION. CURRENTLY, MOST MOOC ANALYSIS FOCUSES ON ASPECTS SUCH AS PATTERNS OF ENGAGEMENT AND PREDICTION OF DROPOUT USING DATA OBTAINED BY APPLYING LEARNING ANALYTICS TO THE LARGE AMOUNTS OF DATA GATHERED. IN CONTRAST, THE EFFECTIVENESS OF TEACHING AND LEARNING PROGRAMS IN TRADITIONAL FACE-TO-FACE HIGHER EDUCATION (HE) SETTINGS IS INCREASINGLY BEING ASSESSED BY MEASURES LINKED TO THE PROMOTION OF STUDENT ENGAGEMENT. INTERNATIONALLY, STUDENT ENGAGEMENT SURVEYS ARE BEING USED TO PROVIDE PROFILES OF THE EFFECTIVENESS OF TEACHING AND LEARNING WITHIN AND BETWEEN COURSES. IN THIS PAPER WE CONSIDER MOOC PARTICIPATION AND DROP-OUT ISSUES EMERGING FROM THE LITERATURE, AND PROVIDE AN OVERVIEW OF THE STUDENT ENGAGEMENT SURVEYS NOW WIDELY USED IN HE. MOOC PEDAGOGY IS EXAMINED FROM THE PERSPECTIVE OF STUDENT ENGAGEMENT AND WE IDENTIFY THE NEED FOR A SIMILAR MODEL ADAPTED TO MOOCS WHICH COULD PROVIDE BOTH A FRAMEWORK FOR COURSE DEVELOPMENT AND AN INSTRUMENT TO ASSESS ASPECTS OF TEACHING AND LEARNING IN EXISTING COURSES.
WITH THE ADVENT OF NEW TECHNOLOGICAL ADVANCES, IT IS POSSIBLE TO STUDY SOCIAL INTERACTIONS AT A MICROLEVEL WITH UNPRECEDENTED ACCURACY. HIGH FREQUENCY SENSORS, SUCH AS EYE-TRACKERS, ELECTRODERMAL ACTIVITY WRISTBANDS, EEG BANDS, AND MOTION SENSORS PROVIDE OBSERVATIONS AT THE MILLISECOND LEVEL. THIS LEVEL OF PRECISION ALLOWS RESEARCHERS TO COLLECT LARGE DATASETS ON SOCIAL INTERACTIONS. IN THIS PAPER, I DISCUSS HOW MULTIPLE EYE-TRACKERS CAN CAPTURE A FUNDAMENTAL CONSTRUCT IN SOCIAL INTERACTIONS, JOINT VISUAL ATTENTION (JVA). JVA HAS BEEN STUDIED BY DEVELOPMENTAL PSYCHOLOGISTS TO UNDERSTAND HOW CHILDREN ACQUIRE LANGUAGE, LEARNING SCIENTISTS TO UNDERSTAND HOW SMALL GROUPS OF LEARNERS WORK TOGETHER, AND SOCIAL SCIENTISTS TO UNDERSTAND INTERACTIONS IN SMALL TEAMS. THIS PAPER DESCRIBES A METHODOLOGY FOR CAPTURING JVA IN COLOCATED SETTINGS USING MOBILE EYE-TRACKERS. IT PRESENTS SOME EMPIRICAL RESULTS AND DISCUSSES IMPLICATIONS OF CAPTURING MICROOBSERVATIONS TO UNDERSTAND SOCIAL INTERACTIONS.
A RISING EPISTEMOLOGICAL PARADIGM IN THE COGNITIVE SCIENCES-EMBODIED COGNITION-HAS BEEN STIMULATING INNOVATIVE APPROACHES, AMONG EDUCATIONAL RESEARCHERS, TO THE DESIGN AND ANALYSIS OF STEM TEACHING AND LEARNING. THE PARADIGM PROMOTES THEORIZATIONS OF COGNITIVE ACTIVITY AS GROUNDED, OR EVEN CONSTITUTED, IN GOAL-ORIENTED MULTIMODAL SENSORIMOTOR PHENOMENOLOGY. CONCEPTUAL LEARNING, PER THESE THEORIES, COULD EMANATE FROM, OR BE TRIGGERED BY, EXPERIENCES OF ENACTING OR WITNESSING PARTICULAR MOVEMENT FORMS, EVEN BEFORE THESE MOVEMENTS ARE EXPLICITLY SIGNIFIED AS ILLUSTRATING TARGET CONTENT. PUTTING THESE THEORIES TO PRACTICE, NEW TYPES OF LEARNING ENVIRONMENTS ARE BEING EXPLORED THAT UTILIZE INTERACTIVE TECHNOLOGIES TO INITIALLY FOSTER STUDENT ENACTMENT OF CONCEPTUALLY ORIENTED MOVEMENT FORMS AND ONLY THEN FORMALIZE THESE GESTURES AND ACTIONS IN DISCIPLINARY FORMATS AND LANGUAGE. IN TURN, NEW RESEARCH INSTRUMENTS, SUCH AS MULTIMODAL LEARNING ANALYTICS, NOW ENABLE RESEARCHERS TO AGGREGATE, INTEGRATE, MODEL, AND REPRESENT STUDENTS' PHYSICAL MOVEMENTS, EYE-GAZE PATHS, AND VERBAL-GESTURAL UTTERANCE SO AS TO TRACK AND EVALUATE EMERGING CONCEPTUAL CAPACITY. WE-A COHORT OF COGNITIVE SCIENTISTS AND DESIGN-BASED RESEARCHERS OF EMBODIED MATHEMATICS-SURVEY A SET OF EMPIRICALLY VALIDATED FRAMEWORKS AND PRINCIPLES FOR ENHANCING MATHEMATICS TEACHING AND LEARNING AS DIALOGIC MULTIMODAL ACTIVITY, AND WE SYNTHETIZE A SET OF PRINCIPLES FOR EDUCATIONAL PRACTICE.
THIS PAPER IS BASIC RESEARCH FOCUSED ON THE ANALYSIS OF SCIENTIFIC ADVANCES RELATED TO SMART LEARNING ENVIRONMENTS (SLE). OUR MAIN OBJECTIVE IS TO SINGLE OUT THE COMMON ASPECTS TO PROPOSE A NEW DEFINITION WHICH WILL CONSTITUTE THE STARTING POINT TO DESIGN AN INNOVATIVE MODEL WHICH WE CAN APPLY TO THE ANALYSIS OF REAL CASES AND GOOD PRACTICES. FOR THIS, WE HAVE PROPOSED A QUALITATIVE METHODOLOGY THAT HAS BEEN IMPLEMENTED IN TWO PHASES: ON THE ONE HAND, A DOCUMENTARY ANALYSIS OF THE EXISTING DEFINITIONS FOR SLE USING THE NVIVO PROGRAM (FREQUENCY OF WORDS, CODING AND CROSS-REFERENCES) AND, ON THE OTHER, AN EXPERT JUDGEMENT BY MEANS OF THE DELPHI METHOD IN ORDER TO VALIDATE THE PROPOSED MODEL. THE MAIN RESULTS ARE REFLECTED IN THE COALESCENCE OF A NEW DEFINITION OF SLE AND THE PROPOSAL OF THE MODEL ENTITLED SLE-5. WITH THE PRESENT RESEARCH, WE HAVE BEEN ABLE TO PROVIDE A MODEL, DEFINED IN FIVE DIMENSIONS AND OTHER KEY ELEMENTS IN SLE SUCH AS ERGONOMICS AND LEARNING ANALYTICS, WHICH TRANSCENDS THE TECHNOLOGICAL-PEDAGOGICAL GAP OF THE SLE AND OFFERS A FRAMEWORK FOR THE DESIGN AND ANALYSIS OF DIDACTIC PROPOSALS BASED ON THIS MODEL.
IN THIS PAPER THE ISSUE OF BIAS-VARIANCE TRADE-OFF IN BUILDING AND OPERATING MOODLE MACHINE LEARNING (ML) MODELS ARE DISCUSSED TO AVOID TRAPS OF GETTING UNRELIABLE PREDICTIONS. MOODLE IS ONE OF THE WORLD'S MOST POPULAR OPEN-SOURCE LEARNING MANAGEMENT SYSTEM (LMS) WITH MILLIONS OF USERS. ALTHOUGH SINCE MOODLE 3.4 RELEASE IT IS POSSIBLE TO CREATE ML MODELS WITHIN THE LMS SYSTEM VERY FEW STUDIES HAVE BEEN PUBLISHED SO FAR ABOUT THE CONDITIONS OF ITS PROPER APPLICATION. USING THESE MODELS AS BLACK BOXES HOLD SERIOUS RISKS TO GET UNRELIABLE PREDICTIONS AND FALSE ALARMS. FROM A COMPREHENSIVE STUDY OF DIFFERENTLY BUILT MACHINE LEARNING MODELS ELABORATED AT THE UNIVERSITY OF DUNAUJVAROS IN HUNGARY, ONE SPECIFIC ISSUE IS ADDRESSED HERE, NAMELY THE INFLUENCE OF THE SIZE AND THE ROW-COLUMN RATIO OF THE PREDICTOR MATRIX ON THE GOODNESS OF THE PREDICTIONS. IN THE SO-CALLED TIME SPLITTING METHOD IN MOODLE LEARNING ANALYTICS THE EFFECT OF VARYING NUMBERS OF TIME SPLITS AND OF PREDICTORS HAS ALSO BEEN STUDIED TO SEE THEIR INFLUENCE ON THE BIAS AND THE VARIANCE OF THE MODELS. AN APPLIED STATISTICS COURSE IS USED TO DEMONSTRATE THE CONSEQUENCES OF THE DIFFERENT MODEL SET UP.
THIS RESEARCH FULL PAPER AIMS TO INVESTIGATE THE LEARNING ANALYTICS OF STUDENTS' PROBLEM SOLVING WHEN WORKING ON DISTRIBUTED PROGRAMMING PRACTICES. TYPICAL PROGRAMMING PRACTICE ACTIVITIES (I.E. ASSIGNMENTS) IN A LECTURE-DOMINANT COURSE MAY VIOLATE THE PRINCIPLES OF DISTRIBUTED RETRIEVAL PRACTICE. HOWEVER, THERE ARE TRADEOFFS BETWEEN MANAGING DEPTH AND BREADTH OF THE CONTENT AND CLASSROOM DISRUPTIONS WITH THE MODERN PLATFORMS AND TECHNOLOGIES. IN THIS WORK, WE INVESTIGATE STUDENTS' BEHAVIORAL ANALYTICS IN DISTRIBUTED PROGRAMMING PRACTICES. A CLASSROOM STUDY WAS CONDUCTED IN AN INTRODUCTORY PROGRAMMING COURSE AND THE LEARNERS' PATTERNS WERE OBSERVED. RESULTS SHOWED THAT THERE WERE THREE DISTINCT PATTERNS FOUND: AFFIRMATIVE, EXPERIMENTAL, AND SURRENDERING. BETTER-PERFORMING STUDENTS DEMONSTRATED MORE AFFIRMATIVE BEHAVIORS AND FEWER SURRENDERING ACTS; BELOW-AVERAGE STUDENTS SHOWED A LACK OF PERSISTENCE IN DISTRIBUTED PRACTICES. ADDITIONALLY, THE STUDY RECONFIRMED THE VALUE OF SPACING EFFECTS ON LEARNING, WHICH IS THE IMPORTANCE OF SPENDING TIME AND TO SPREADING THE WORKING SESSIONS TO SOLVE DIVERSE QUIZZES. INEFFECTIVE TRIAL-AND-ERROR STRATEGY AND NEGLECT THE POWER OF PRACTICES CAN HE TWO ALARMING BEHAVIORS IN DISTRIBUTED PROGRAMMING PRACTICES. FINALLY, PREDICTIVE MODELS OF PERFORMANCE WERE PRESENTED BASED ON THE BEHAVIORAL PATTERNS.
OVER THE PAST SEVERAL YEARS, THERE HAS BEEN A RISE IN ONLINE LEARNING PLATFORMS AND WEBSITES FOR SKILL ACQUISITION. HOWEVER, TRADITIONAL LEARNING ANALYTICS TO EVALUATE THE EFFECTIVENESS OF PRESENTED MATERIAL ARE LIMITED IN THAT THEY DO NOT ACQUIRE INFORMATION REGARDING THE USER'S BIOPHYSICAL STATE DURING THIS LEARNING PROCESS. IN THIS PAPER, WE PROPOSE AN INEXPENSIVE SYSTEM FOR EVALUATING ONLINE LEARNERS' ENGAGEMENT AND PERFORMANCE VIA ELECTROENCEPHALOGRAPHY (EEG), EYE-TRACKING, AND BEHAVIORAL DATA METHODS. SUCH INFORMATION IS OF INTEREST TO (I) NEUROSCIENTISTS AND PSYCHOLOGISTS AIMING TO QUANTIFY COMPUTERIZED LEARNING PROCESSES AND DEVELOPING MODELS TO REPRESENT LEARNING STATES, AS WELL AS TO (II) INDIVIDUALS DESIGNING EDUCATIONAL CONTENT TO BETTER UNDERSTAND HOW INFORMATION IS ACCESSED AND UTILIZED BY USERS. WE FIRST DESCRIBE THE SELECTION, DESIGN, AND INTEGRATION OF THE BIO-SIGNAL COMPONENTS. WE THEN DEMONSTRATE THE COMBINED UTILITY OF OUR SYSTEM THROUGH RECORDING SIGNALS WHILE PARTICIPANTS (N=22) COMPLETED GERMAN LANGUAGE LESSONS ON THE FREE WEB-BASED PLATFORM, DUOLINGO. AS LOW-COST HARDWARE IS UTILIZED IN OUR SYSTEM, DATA ACQUISITION CAN READILY BE SCALED TO MULTIPLE RESEARCH SITES OR REMOTE COLLECTION, ALLOWING FOR ACCESS TO MORE NATURALISTIC DATASETS NOT TYPICALLY STUDIED USING TRADITIONAL LABORATORY RESEARCH SYSTEMS.
PURPOSE THE PURPOSE OF THIS PAPER IS TO INVESTIGATE AN EFFICIENT MEANS OF ESTIMATING THE ABILITY OF STUDENTS SOLVING PROBLEMS IN THE COMPUTER-BASED LEARNING ENVIRONMENT. DESIGN/METHODOLOGY/APPROACH ITEM RESPONSE THEORY (IRT) AND TRUESKILL WERE APPLIED TO SIMULATED AND REAL PROBLEM SOLVING DATA TO ESTIMATE THE ABILITY OF STUDENTS SOLVING HOMEWORK PROBLEMS IN THE MASSIVE OPEN ONLINE COURSE (MOOC). BASED ON THE ESTIMATED ABILITY, DATA MINING MODELS PREDICTING WHETHER STUDENTS CAN CORRECTLY SOLVE HOMEWORK AND QUIZ PROBLEMS IN THE MOOC WERE DEVELOPED. THE PREDICTIVE POWER OF IRT- AND TRUESKILL-BASED DATA MINING MODELS WAS COMPARED IN TERMS OF AREA UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE. FINDINGS THE CORRELATION BETWEEN STUDENTS' ABILITY ESTIMATED FROM IRT AND TRUESKILL WAS STRONG. IN ADDITION, IRT- AND TRUESKILL-BASED DATA MINING MODELS SHOWED A COMPARABLE PREDICTIVE POWER WHEN THE DATA INCLUDED A LARGE NUMBER OF STUDENTS. WHILE IRT FAILED TO ESTIMATE STUDENTS' ABILITY AND COULD NOT PREDICT THEIR PROBLEM SOLVING PERFORMANCE WHEN THE DATA INCLUDED A SMALL NUMBER OF STUDENTS, TRUESKILL DID NOT EXPERIENCE SUCH PROBLEMS. ORIGINALITY/VALUE ESTIMATING STUDENTS' ABILITY IS CRITICAL TO DETERMINE THE MOST APPROPRIATE TIME FOR PROVIDING INSTRUCTIONAL SCAFFOLDING IN THE COMPUTER-BASED LEARNING ENVIRONMENT. THE FINDINGS OF THIS STUDY SUGGEST THAT TRUESKILL CAN BE AN EFFICIENT MEANS FOR ESTIMATING THE ABILITY OF STUDENTS SOLVING PROBLEMS IN THE COMPUTER-BASED LEARNING ENVIRONMENT REGARDLESS OF THE NUMBER OF STUDENTS.
JUTGE.ORG IS AN OPEN EDUCATIONAL ONLINE PROGRAMMING JUDGE DESIGNED FOR STUDENTS AND INSTRUCTORS, FEATURING A REPOSITORY OF PROBLEMS THAT IS WELL ORGANIZED BY COURSES, TOPICS, AND DIFFICULTY. INTERNALLY, JUTGE.ORG USES A SECURE AND EFFICIENT ARCHITECTURE AND INTEGRATES MODERN VERIFICATION TECHNIQUES, FORMAL METHODS, STATIC CODE ANALYSIS, AND DATA MINING. JUTGE.ORG HAS EXHAUSTIVELY BEEN USED DURING THE LAST DECADE AT THE UNIVERSITAT POLITECNICA DE CATALUNYA TO STRENGTHEN THE LEARNING-BY-DOING APPROACH IN SEVERAL COURSES. THIS PAPER PRESENTS THE MAIN CHARACTERISTICS OF JUTGE.ORG AND SHOWS ITS USE AND IMPACT ON A WIDE RANGE OF COURSES COVERING BASIC PROGRAMMING, DATA STRUCTURES, ALGORITHMS, ARTIFICIAL INTELLIGENCE, FUNCTIONAL PROGRAMMING, AND CIRCUIT DESIGN.
LEARNING ACTIVITIES USE AN INCREASING NUMBER OF SOFTWARE TOOLS. THESE MEDIATE THE INTERACTIONS AMONG PARTICIPANTS AND OF THESE WITH RESOURCES LIKE DOCUMENTS AND TOOLS. THESE TOOLS CONSTITUTE A VALUABLE SOURCE OF INFORMATION ABOUT THE ACTUAL LEARNING PROCESSES. HOWEVER, THIS USE FACES MULTIPLE PROBLEMS. LECTURERS NEED TO GAIN EXPERTISE ON THE DATA EACH TOOL PROVIDES AND HOW TO ANALYSE THEM, EACH TOOL ONLY OFFERS A PARTIAL VIEW OF THE PROCESS, STUDENTS HAVE DIFFERENT PROFILES AND USE DIFFERENT TOOLS, AND TIME TO MAKE ANALYSES IS LIMITED. TO ADDRESS THIS SITUATION, THIS WORK PROPOSES THE USE OF ASSISTANTS FOR LEARNING ACTIVITIES (ALAS), I.E. SEMI-AUTOMATED TOOLS THAT USE SOCIAL KNOWLEDGE TO INTEGRATE DIFFERENT SOURCES OF INFORMATION AND INTERPRET THEIR DATA. THIS KNOWLEDGE IS EXTRACTED FROM LITERATURE ON LEARNING, AND SPECIFIED AS SOCIAL PROPERTIES. THESE PROPERTIES DESCRIBE PATTERNS THAT APPEAR IN INFORMATION AND THEIR INTERPRETATION IN TERMS OF THE LEARNING-RELATED ACTIVITIES. THEIR SPECIFICATION RELIES ON A SPECIFIC MODELLING LANGUAGE ORIENTED TO SOCIAL ACTIVITIES AND THEIR CONTEXT. IT IS DESIGNED TO FACILITATE COMMUNICATION WITH THE TARGET LEARNING COMMUNITIES. WRAPPERS FOR SOFTWARE TOOLS GET THE RAW DATA, TRANSFORM THEM INTO FACTS FOR THIS LANGUAGE, AND ASSERT THEM IN AN INFORMATION BASE. THEN, A PATTERN MATCHING ALGORITHM FINDS INSTANCES OF THE SOCIAL PROPERTIES AMONG THESE FACTS, GIVING AN INTERPRETATION OF THE ORIGINAL DATA. A CASE STUDY ON TEAMWORK IN A PROJECT-BASED LEARNING CONTEXT OF A UNIVERSITY USING SEVERAL SOFTWARE TOOLS ILLUSTRATES THE APPROACH. IT SHOWS THE FEASIBILITY OF ADAPTING THE ANALYSIS THROUGH THE MODIFICATION OF THE CONSIDERED PROPERTIES, AND HOW THESE CAN EXPLAIN THE OBSERVED DATA.
E-LEARNING STUDENTS TEND TO GET JADED AND EASILY DROPOUT FROM ONLINE COURSES. IMPROVING THE LEARNERS' EXPERIENCE AND DECREASING DROPOUT RATES IN THESE E-LEARNING BASED SCENARIOS IS THE MAIN PURPOSE OF THIS STUDY. THIS PAPER SHARES THE RESULTS OBTAINED SO FAR AND CRAFTS AN INITIAL COMPARISON WITH NEW AND EMERGING COMMERCIAL SOLUTIONS. IN A FIRST STAGE, THE OBJECTIVE WAS TO FOCUS THE STUDY AND RESEARCH MADE UP TILL NOW (BACKGROUND, STATE OF THE ART, SURVEY AND INTERVIEWS TO DETECT MOTIVES AND BEHAVIOR PATTERNS). IN A SECOND PHASE, THE DEVELOPMENT, TESTS AND VALIDATION OF A FUNCTIONAL PROTOTYPE OF AN INTELLIGENT ALERTING SYSTEM TO ALLOW AND EVALUATE CONCEPTS, COLLECT STATISTICAL DATA ON ITS EFFECTIVENESS, ANALYZE AND DISCOVER IF COURSE COMPLETION RATES ARE REALLY IMPROVED. FINALLY, SOME RELATED WORK IS FOCUSED, AS WELL AS THE ENLIGHTENMENT OF NEW, EMERGING AND IMPROVED COMMERCIAL SOLUTIONS, CONCLUSIONS AND AN OPENING COMPARISON WITH THE PROPOSED SOLUTION.
IN THE LAST DECADE, THE USE OF SERIOUS GAMES AS A TEACHING AND LEARNING TOOL HAS STEADILY INCREASED IN MANY DISCIPLINES. NEVERTHELESS, SERIOUS GAMES ARE STILL FACING CRUCIAL CHALLENGES, SUCH AS THEIR INTEGRATION IN THE GLOBAL LEARNING PROCESS. ON THE OTHER HAND, WITH THE INCREASED ADOPTION OF ONLINE APPLICATIONS AND COURSES, IT IS BECOMING POSSIBLE TO COLLECT AND CENTRALIZE LARGE AMOUNTS OF TRACE DATA GENERATED BY PLAYERS. SUCH DATA MAY BE USED TO PRODUCE STATISTICS ON STUDENTS' BEHAVIORS INSIDE PEDAGOGICAL SERIOUS GAMES, BOTH AS INDIVIDUALS AND AGGREGATED AS GROUPS (E.G., CLASSROOMS). IN THIS PAPER WE PROPOSE A CLASSIFICATION OF POTENTIAL USES OF STATISTICS IN SERIOUS GAMES AND GIVE NEW INSIGHTS INTO HOW STATISTICAL ANALYSIS OF GROUPS' BEHAVIOR MAY IMPACT POSITIVELY ON THE LEARNING PROCESS. WE ALSO PRESENT EXPERIMENTAL RESULTS OBTAINED DURING A LARGE-SCALE GAME DEPLOYMENT USING THE WEGAS PLATFORM, OUR OPEN SOURCE PLATFORM FOR GAME AUTHORING AND EXECUTION.
[CONTEXT] THE USE OF MOOCS HAS GENERATED AN INCREASING AMOUNT OF DATA THAT, IF PROPERLY EXPLORED, CAN PROVIDE AN UNDERSTANDING OF HOW STUDENTS INTERACT THROUGHOUT THEIR LEARNING, IN ADDITION TO IDENTIFYING STRATEGIES THAT CAN BE USED IN THE PROCESS OF BUILDING ENVIRONMENTS. THAT ENHANCE THE CONSTRUCTION OF KNOWLEDGE IN A SHARED WAY. [OBJECTIVE] THE OBJECTIVE OF THIS ARTICLE IS TO PROPOSE A CONCEPTUAL COLLABORATIVE FRAMEWORK (MOOCOLAB), BASED ON LEARNING ANALYTICS MECHANISMS AND RECOMMENDATION SYSTEMS TO IMPROVE COLLABORATIVE LEARNING IN THE ENVIRONMENT. [METHODOLOGY] FOR THE DEVELOPMENT OF THE FRAMEWORK, THE DESIGN SCIENCE RESEARCH MODEL WAS USED FOR THE ANALYSIS, DEVELOPMENT AND EVALUATION OF MOOCOLAB. [RESULTS] AN EXPERIMENT WAS CARRIED OUT WITH TWO SAMPLES: A CONTROL GROUP (WHICH DID NOT USE THE FRAMEWORK) AND AN EXPERIMENTAL GROUP (WHICH WENT THROUGH THE SAME COURSE USING MOOCOLAB). [CONCLUSION] FROM THE RESULTS OBTAINED IN THE RESEARCH, IT IS EVIDENT THAT THE IMPLEMENTATION OF FRAMEWORK IDENTIFYING THE INDIVIDUALITIES OF EACH STUDENT WITH THE DISCOVERY OF BEHAVIORAL PATTERNS AND THEIR RESPECTIVE SKILLS TO ADAPT THE ENVIRONMENT WITH THE RECOMMENDATION OF PEERS, IN ORDER TO IMPROVE THE MUTUAL EXCHANGE OF INFORMATION BETWEEN STUDENTS INVOLVED IN THE LEARNING PROCESS.
SELF-REGULATED LEARNING (SRL) REFERS TO HOW LEARNERS STEER THEIR OWN LEARNING. SUPPORTING SRL HAS BEEN SHOWN TO ENHANCE THE USE OF SRL STRATEGIES AND LEARNING PERFORMANCE IN COMPUTER-BASED LEARNING ENVIRONMENTS. HOWEVER, LITTLE IS KNOWN ABOUT SUPPORTING SRL IN MASSIVE OPEN ONLINE COURSES (MOOCS). IN THIS STUDY, WEEKLY SRL PROMPTS WERE EMBEDDED AS VIDEOS IN A MOOC. WE EMPLOYED A SEQUENTIAL PATTERN MINING ALGORITHM, SEQUENTIAL PATTERN DISCOVERY USING EQUIVALENCE CLASSES (CSPADE), ON GATHERED LOG DATA TO EXPLORE WHETHER DIFFERENCES EXIST BETWEEN LEARNERS WHO VIEWED THE SRL-PROMPT VIDEOS AND THOSE WHO DID NOT. RESULTS SHOWED THAT SRL-PROMPT VIEWERS INTERACTED WITH MORE COURSE ACTIVITIES AND COMPLETED THESE ACTIVITIES IN A MORE SIMILAR SEQUENTIAL PATTERN THAN NON SRL-PROMPT VIEWERS. ALSO, SRL-PROMPT VIEWERS TENDED TO FOLLOW THE COURSE STRUCTURE, WHICH HAS BEEN IDENTIFIED AS A BEHAVIORAL CHARACTERISTIC OF STUDENTS WHO SCORED HIGHER ON SRL (I.E., COMPREHENSIVE LEARNERS) IN PREVIOUS RESEARCH. BASED ON THE RESULTS, IMPLICATIONS FOR SUPPORTING SRL IN MOOCS ARE DISCUSSED.
RUBRICS HAVE BEEN SUGGESTED AS A MEANS TO FOSTER STUDENTS' EVALUATIVE JUDGEMENT, THE CAPACITY TO APPRAISE THEIR OWN WORK AND THAT OF OTHERS; HOWEVER, EMPIRICAL EVIDENCE OF RUBRICS' EFFECTIVENESS IS STILL EMERGING. THIS PAPER CONTRIBUTES FINDINGS FROM A RANDOMISED CONTROLLED EXPERIMENT ON THE EFFECT OF RUBRICS ON EVALUATIVE JUDGEMENT. PARTICIPANTS WERE RANDOMLY ASSIGNED TO ONE OF TWO GROUPS: A CONTROL GROUP WHICH EVALUATED PEER-AUTHORED LEARNING RESOURCES WITHOUT THE USE OF A RUBRIC AND AN EXPERIMENT GROUP WHICH CARRIED OUT THE EVALUATION USING A THREE-ITEM RUBRIC BASED ON (1) ALIGNMENT WITH COURSE CONTENT, (2) ACCURACY AND (3) CLARITY. BOTH GROUPS WERE ASKED TO RATE THEIR CONFIDENCE AND PROVIDE COMMENTS TO JUSTIFY THEIR SCORING. THE RESULTS SHOWED A SMALL EFFECT SIZE IN INCREASING AVERAGE AGREEMENT ON THE QUALITY OF LEARNING RESOURCES IN THE EXPERIMENT GROUP. ANALYSIS OF COMMENTS REVEALS THAT CRITERIA IN AND BEYOND THE RUBRIC GUIDED PARTICIPANTS' RATINGS OF QUALITY. THE STUDY PROVIDES EVIDENCE OF THE IMPACT OF RUBRICS ON STUDENTS' EVALUATIVE JUDGEMENT AND AN EXAMPLE OF HOW DATA-DRIVEN APPROACHES AND LEARNING ANALYTICS CAN INFORM ACTIONABLE DESIGN CHOICES FOR EMBEDDING PEDAGOGICALLY SUPPORTED STRATEGIES DERIVED FROM THE LITERATURE INTO ACTIVELY OPERATING EDUCATIONAL TECHNOLOGIES.
AS PART OF THE LARGE-SCALE IMPLEMENTATION OF LEARNING DESIGN AT THE OPEN UNIVERSITY, THE UK'S LARGEST HIGHER EDUCATION INSTITUTION, A TAXONOMY OF LEARNING ACTIVITIES INFORMS THE DEVELOPMENT OF COURSE MODULES. THE TAXONOMY IS ALSO USED TO MAP A MODULE'S LEARNING DESIGN, TO CATEGORIZE ITS LEARNING ACTIVITIES, AFTER IT HAS BEEN DEVELOPED. THIS ENABLES COURSE TEAMS TO COMPARE A MODULE'S LEARNING DESIGN WITH STUDENT OUTCOMES, IN ORDER TO DETERMINE WHICH LEARNING DESIGNS ARE MOST EFFECTIVE AND IN WHICH CIRCUMSTANCES. HOWEVER, THE MAPPING PROCESS IS LABOR-INTENSIVE AND OPEN TO INCONSISTENCIES, MAKING THE OUTCOMES LESS TRUSTWORTHY AND LESS USEFUL FOR LEARNING ANALYTICS. IN THIS PAPER, WE PRESENT AN EXPLORATORY STUDY THAT INVESTIGATES THE AUTOMATIZATION OF THE MAPPING PROCESS BY MEANS OF BOTH UNSUPERVISED AND SUPERVISED MACHINE LEARNING APPROACHES. FOR THE SUPERVISED MACHINE LEARNING (LOGISTIC REGRESSION), WE USE A LABELLED SET OF 35,000 ACTIVITY DESCRIPTIONS CLASSIFIED AS EITHER REFLECTIVE OR NON-REFLECTIVE (I.E., WHETHER OR NOT THE ACTIVITY INVOLVES STUDENT REFLECTION) DRAWN FROM 267 MODULES. OUR OUTCOMES, WITH SIMILAR TO 79% ACCURACY, ARE SUFFICIENTLY PROMISING FOR THIS APPROACH TO MERIT FURTHER WORK, EXTENDING IT IN PARTICULAR TO A LARGER SET OF LEARNING DESIGN ACTIVITIES.
EDUCATION IS A MAJOR FORCE FOR ECONOMIC AND SOCIAL WELLBEING. DESPITE HIGH ASPIRATIONS, EDUCATION AT ALL LEVELS CAN BE EXPENSIVE AND INEFFECTIVE. THREE GRAND CHALLENGES ARE IDENTIFIED: (1) ENABLE PEOPLE TO LEARN ORDERS OF MAGNITUDE MORE EFFECTIVELY, (2) ENABLE PEOPLE TO LEARN AT ORDERS OF MAGNITUDE LESS COST, AND (3) DEMONSTRATE SUCCESS BY EXEMPLARY INTERDISCIPLINARY EDUCATION IN COMPLEX SYSTEMS SCIENCE. A TEN YEAR 'MAN-ON-THE-MOON' PROJECT IS PROPOSED IN WHICH FUTURICT'S UNIQUE COMBINATION OF COMPLEXITY, SOCIAL AND COMPUTING SCIENCES COULD PROVIDE AN URGENTLY NEEDED TRANSDISCIPLINARY LANGUAGE FOR MAKING SENSE OF EDUCATIONAL SYSTEMS. IN CLOSE DIALOGUE WITH EDUCATIONAL THEORY AND PRACTICE, AND GROUNDED IN THE EMERGING DATA SCIENCE AND LEARNING ANALYTICS PARADIGMS, THIS WILL TRANSLATE INTO PRACTICAL TOOLS (BOTH ANALYTICAL AND COMPUTATIONAL) FOR RESEARCHERS, PRACTITIONERS AND LEADERS; GENERATIVE PRINCIPLES FOR RESILIENT EDUCATIONAL ECOSYSTEMS; AND INNOVATION FOR RADICALLY SCALABLE, YET PERSONALISED, LEARNER ENGAGEMENT AND ASSESSMENT. THE PROPOSED EDUCATION ACCELERATOR WILL SERVE AS A 'WIND TUNNEL' FOR TESTING THESE IDEAS IN THE CONTEXT OF REAL EDUCATIONAL PROGRAMMES, WITH AN INTERNATIONAL VIRTUAL CAMPUS DELIVERING COMPLEX SYSTEMS EDUCATION EXPLOITING THE NEW UNDERSTANDING OF COMPLEX, SOCIAL, COMPUTATIONALLY ENHANCED ORGANISATIONAL STRUCTURE DEVELOPED WITHIN FUTURICT.
WHILE ADAPTIVE LEARNING IS EMERGING AS A PROMISING TECHNOLOGY TO PROMOTE ACCESS AND QUALITY AT A LARGE SCALE IN HIGHER EDUCATION (BECKER ET AL., 2018), THE IMPLEMENTATION OF ADAPTIVE LEARNING IN TEACHING AND LEARNING IS STILL SPORADIC, AND IT IS UNCLEAR HOW TO BEST DESIGN AND TEACH AN ADAPTIVE LEARNING COURSE IN A HIGHER EDUCATION CONTEXT. AS EARLY ADOPTERS, A TEAM OF INSTRUCTORS, INSTRUCTIONAL DESIGNERS, AND ADMINISTRATORS AT THE UNIVERSITY OF CENTRAL FLORIDA (UCF) IDENTIFIED FIVE KEY DESIGN FEATURES AS AN ADAPTIVE LEARNING DESIGN FRAMEWORK TO GUIDE THE UNIQUE COURSE DESIGN PROCESS. THESE FIVE FEATURES INVOLVE DELIBERATE DESIGN AND DEVELOPMENT EFFORTS THAT COULD BRING SIGNIFICANT BENEFITS TO STUDENT LEARNING. THE PURPOSE OF THIS FIELD NOTE IS TO PRESENT A DESIGN FRAMEWORK AND BEST PRACTICES FOR TEACHING FROM BOTH A SYSTEMS AND A PEDAGOGICAL APPROACH IN THE CONTEXT OF IMPLEMENTATION AT UCF. WE ALSO SHARE THE RATIONALE AND CLASSIFICATION FRAMEWORK UCF HAS ADOPTED TO ENSURE THE TERM "ADAPTIVE LEARNING" IS UNIVERSALLY UNDERSTOOD ACROSS CAMPUS. THIS PAPER OFFERS INSIGHTS INTO THE DESIGN, DELIVERY, AND IMPLICATIONS OF UTILIZING ADAPTIVE LEARNING SYSTEMS IN HIGHER EDUCATION COURSES AT A PUBLIC RESEARCH UNIVERSITY AND ATTEMPTS TO CAPTURE THE INTIMACY OF LESSONS LEARNED AND BEST PRACTICES GATHERED SINCE THE PROJECT'S INCEPTION IN 2014.
ASSESSMENT USUALLY PLAYS AN INDISPENSABLE ROLE IN THE EDUCATION AND IT IS THE PRIME INDICATOR OF STUDENT LEARNING ACHIEVEMENT. EXAM QUESTIONS ARE THE MAIN FORM OF ASSESSMENT USED IN LEARNING. SETTING APPROPRIATE EXAM QUESTIONS TO ACHIEVE THE DESIRED OUTCOME OF THE COURSE IS A CHALLENGING WORK FOR THE EXAMINER. THEREFORE THIS RESEARCH IS MAINLY FOCUSED TO CATEGORIZE THE EXAM QUESTIONS AUTOMATICALLY INTO ITS LEARNING LEVELS USING BLOOM'S TAXONOMY. NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES SUCH AS TOKENIZATION, STOP WORD REMOVAL, LEMMATIZATION AND TAGGING WERE USED PRIOR TO GENERATING THE RULE SET TO BE USED FOR THIS CLASSIFICATION. WORDNET SIMILARITY ALGORITHMS WITH NLTK AND COSINE SIMILARITY ALGORITHM WERE DEVELOPED TO GENERATE A UNIQUE SET OF RULES TO IDENTIFY THE QUESTION CATEGORY AND THE WEIGHT FOR EACH EXAM QUESTION ACCORDING TO BLOOM'S TAXONOMY. THESE DERIVED RULES MAKE IT EASY TO ANALYZE THE EXAM QUESTIONS. EVALUATORS CAN REDESIGN THEIR EXAM PAPERS BASED ON THE OUTCOME OF THIS CLASSIFICATION PROCESS. A SAMPLE OF EXAMINATION QUESTIONS OF THE DEPARTMENT OF COMPUTING AND INFORMATION SYSTEMS, WAYAMBA UNIVERSITY, SRI LANKA WAS USED FOR THE EVALUATION; WEIGHT ASSIGNMENT WAS DONE BASED ON THE TOTAL VALUE GENERATED FROM BOTH WORDNET ALGORITHM AND THE COSINE ALGORITHM. IDENTIFIED QUESTION CATEGORIES WERE CONFIRMED BY A DOMAIN EXPERT. THE GENERATED RULE SET INDICATED OVER 70% ACCURACY.
THE USE OF VARIOUS LEARNING APPS IN SCHOOL SETTINGS IS GROWING AND THUS PRODUCING AN INCREASING AMOUNT OF USAGE GENERATED DATA. HOWEVER, THIS USAGE GENERATED DATA HAS ONLY TO A VERY LITTLE EXTEND BEEN USED FOR MONITORING AND PROMOTING LEARNING PROGRESS. WE TEST IF APPLICATION USAGE GENERATED DATA FROM A READING APP HOLDS POTENTIAL FOR MEASURING READING ABILITY, READING SPEED PROGRESS AND FOR POINTING OUT FEATURES IN A SCHOOL SETTING THAT PROMOTES LEARNING. WE ANALYZE NEW DATA FROM THREE DIFFERENT SOURCES: (1) USAGE GENERATED DATA FROM A WIDELY USED READING APP, (2) DATA FROM A NATIONAL READING ABILITY TEST, AND (3) REGISTER DATA ON STUDENT BACKGROUND AND FAMILY CHARACTERISTICS. FIRST, WE FIND THAT READING APP GENERATED DATA TO SOME DEGREE TELLS THE SAME STORY ABOUT READING ABILITY AS DOES THE FORMAL NATIONAL READING ABILITY TEST. SECOND, WE FIND THAT THE READING APP DATA HAS THE POTENTIAL TO MONITOR READING SPEED PROGRESS. FINALLY, WE TESTED SEVERAL MODELS INCLUDING MACHINE LEARNING MODELS. TWO OF THESE WERE ABLE TO IDENTIFY VARIABLES ASSOCIATED WITH READING SPEED PROGRESS WITH SOME DEGREE OF SUCCESS AND TO POINT AT CERTAIN CONDITIONS THAT PROMOTES READING SPEED PROGRESS. WE DISCUSS THE RESULTS AND AVENUES FOR FURTHER RESEARCH ARE PRESENTED.
THIS RESEARCH REVEALED THE ANTECEDES OF TWO LEARNER-CENTERED OUTCOME MEASURES OF SUCCESS IN MASSIVE OPEN ONLINE COURSES (MOOCS): LEARNER SATISFACTION AND LEARNER INTENTION-FULFILLMENT. PREVIOUS STUDIES USED SUCCESS CRITERIA FROM FORMAL EDUCATION CONTEXTS PLACING RETENTION AND COMPLETION RATES AS THE ULTIMATE OUTCOME MEASURES. WE ARGUE THAT THE SUGGESTED LEARNER-CENTERED OUTCOMES ARE MORE APPROPRIATE FOR MEASURING SUCCESS IN NON-FORMAL LIFELONG LEARNING SETTINGS BECAUSE THEY ARE FOCUSED ON THE LEARNER'S INTENTIONS, RATHER THAN THE INTENTIONS OF THE COURSE DEVELOPER. THE BEHAVIOURAL MEASURES OF 125 MOOC PARTICIPANTS WHO ANSWERED A PRE- AND A POST-QUESTIONNAIRE WERE HARVESTED. THE ANALYSIS REVEALED THAT LEARNER SATISFACTION WAS DIRECTLY AFFECTED BY: THE IMPORTANCE OF THE MOOC'S BENEFITS; ONLINE SELF-REGULATED LEARNING - GOAL SETTING; NUMBER OF VIDEO LECTURES ACCESSED; AND, PERCEIVED COURSE USABILITY. AGE AND THE NUMBER OF QUIZZES ACCESSED INDIRECTLY EFFECTED LEARNER SATISFACTION, THROUGH PERCEIVED COURSE USABILITY AND THROUGH NUMBER OF VIDEO LECTURES ACCESSED. INTENTION-FULFILLMENT WAS DIRECTLY AFFECTED BY: GENDER; THE IMPORTANCE OF THE MOOC'S BENEFITS; ONLINE SELF-REGULATED LEARNING - GOAL SETTING; THE NUMBER OF QUIZZES ACCESSED; THE DURATION OF PARTICIPATION; AND, PERCEIVED COURSE USABILITY. PREVIOUS EXPERIENCE WITH MOOCS AND THE IMPORTANCE OF MOOC'S BENEFITS, INDIRECTLY AFFECTED INTENTION-FULFILLMENT THROUGH THE NUMBER OF QUIZZES ACCESSED AND PERCEIVED COURSE USABILITY.
THIS LONGITUDINAL STUDY INVESTIGATES THE DIFFERENCES IN LEARNERS' EFFORTFUL BEHAVIOUR OVER TIME DUE TO RECEIVING METACOGNITIVE HELP-IN THE FORM OF ON-DEMAND TASK-RELATED VISUAL ANALYTICS. SPECIFICALLY, LEARNERS' INTERACTIONS (N = 67) WITH THE TASKS WERE TRACKED DURING FOUR SELF-ASSESSMENT ACTIVITIES, CONDUCTED AT FOUR DISCRETE POINTS IN TIME, OVER A PERIOD OF 8 WEEKS. THE CONSIDERED AND CODED TIME POINTS WERE: (A) PRIOR TO PROVIDING THE METACOGNITIVE HELP; (B) WHILE THE TASK-RELATED VISUAL ANALYTICS WERE AVAILABLE (TREATMENT); (C) AFTER THE REMOVAL OF THE TREATMENT; AND (D) WHILE THE OPTION TO RECEIVE METACOGNITIVE HELP WAS AVAILABLE AGAIN. TO MEASURE LEARNERS' EFFORTFUL BEHAVIOUR ACROSS THE SELF-ASSESSMENT ACTIVITIES, THIS STUDY UTILIZED LEARNERS' RESPONSE-TIMES TO CORRECTLY/WRONGLY COMPLETE THE TASKS AND ON-TASK EFFORT EXPENDITURE. THE PANEL DATA ANALYSIS SHOWN THAT THE USAGE OF METACOGNITIVE HELP CAUSED STATISTICALLY SIGNIFICANT CHANGES IN LEARNERS' EFFORTFUL BEHAVIOUR, MOSTLY IN THE THIRD AND FOURTH PHASE. STATISTICALLY SIGNIFICANT CHANGES WERE DETECTED ALSO IN THE USAGE OF METACOGNITIVE HELP. THESE RESULTS PROVIDE EMPIRICAL EVIDENCE ON THE BENEFITS OF TASK-RELATED VISUAL ANALYTICS TO SUPPORT LEARNERS' ON-TASK ENGAGEMENT, AND SUGGEST RELEVANT CUES ON HOW METACOGNITIVE HELP COULD BE DESIGNED AND PROMPTED BY FOCUSING ON THE "TASK", INSTEAD OF THE "SELF".
DYNAMIC ASSESSMENT IS AN INTERACTIVE APPROACH TO PSYCHOLOGICAL OR PSYCHOEDUCATIONAL ASSESSMENT WHERE INTERVENTION IS EMBEDDED WITHIN THE ASSESSMENT PROCEDURE. A NUMBER OF DYNAMIC ASSESSMENT PROCEDURES HAVE BEEN DEVELOPED ALLOWING FOR DYNAMIC ESTIMATION OF ABILITIES AND MORE ACCURATE PREDICTION OF LEARNERS' DIFFICULTIES. THE PAPER PROPOSES A METHOD FOR DYNAMIC, INSTRUCTOR-SUPERVISED, REALTIME ASSESSMENT AND ADAPTATION OF LEARNING ACTIVITIES IN THE CONTEXT OF UNICORDER (TM) INTEGRAL GAME-BASED LEARNING METHOD DESCRIBED IN PREVIOUS AUTHOR'S PUBLICATION. THE IPT CHALLENGE UNICORDER (TM) SYSTEM OPERATIONALIZES THE PROPOSED METHOD AND ALLOWS THE STUDENTS TO CONTINUOUSLY LOG THEIR PROGRESS ALONG THE LEARNING PATH OF CHALLENGES, MISSIONS, AND ACTIONS. THIS LOGGING IS DONE IN MULTIMODAL (GESTURE, TEXT, AUDIO, VIDEO), LOW EFFORT GRAPHICAL USER INTERFACE. THE COLLECTED DATA IS USED TO GAMIFY THE LEARNING PROCESS, TO ALLOW IMMEDIATE FEEDBACK AND ACHIEVEMENT RECOGNITION, AS WELL AS TO PRESENT MULTIMEDIA "STORIES" ABOUT THE ACHIEVEMENTS. IT USES CONTINUOUS REAL-TIME ASSESSMENT OF LEARNERS' PERFORMANCE FOR DYNAMIC SEQUENTIAL AND STRUCTURAL ADAPTATION OF THE PRESENTED CHALLENGE ACTIONS.
EMBODIED COGNITION THEORISTS BELIEVE THAT MATHEMATICS THINKING IS EMBODIED IN PHYSICAL ACTIVITY, LIKE GESTURING WHILE EXPLAINING MATH SOLUTIONS. THIS RESEARCH ASKS THE QUESTION WHETHER EXPERTISE IN MATHEMATICS CAN BE DETECTED BY ANALYZING STUDENTS' RATE AND TYPE OF MANUAL GESTURES. THE RESULTS REVEAL SEVERAL UNIQUE FINDINGS, INCLUDING THAT MATH EXPERTS REDUCED THEIR TOTAL RATE OF GESTURING BY 50%, COMPARED WITH NON-EXPERTS. THEY ALSO DYNAMICALLY INCREASED THEIR RATE OF GESTURING ON HARDER PROBLEMS. ALTHOUGH EXPERTS REDUCED THEIR RATE OF GESTURING OVERALL, THEY SELECTIVELY PRODUCED 62% MORE ICONIC GESTURES. ICONIC GESTURES ARE STRATEGIC BECAUSE THEY ASSIST WITH RETAINING SPATIAL INFORMATION IN WORKING MEMORY, SO THAT INFERENCES CAN BE EXTRACTED TO SUPPORT CORRECT PROBLEM SOLVING. THE PRESENT RESULTS ON REPRESENTATION-LEVEL GESTURE PATTERNS ARE CONVERGENT WITH RECENT FINDINGS ON SIGNAL-LEVEL HANDWRITING, WHILE ALSO CONTRIBUTING A CAUSAL UNDERSTANDING OF HOW AND WHY EXPERTS ADAPT THEIR MANUAL ACTIVITY DURING PROBLEM SOLVING.
SELF-REGULATED LEARNING (SRL) SKILLS ARE ESPECIALLY IMPORTANT IN MASSIVE OPEN ONLINE COURSES (MOOCS), WHERE TEACHER GUIDANCE IS SCARCE, AND LEARNERS MUST ENGAGE IN THEIR LEARNING PROCESS TRYING TO SUCCEED AND ACHIEVE THEIR LEARNING GOALS. HOWEVER, DEVELOPING SRL STRATEGIES IS DIFFICULT FOR LEARNERS GIVEN THE AUTONOMY THAT IS REQUIRED IN THIS KIND OF COURSES. IN ORDER TO SUPPORT LEARNERS ON THIS PROCESS, RESEARCHERS HAVE PROPOSED A VARIETY OF TOOLS DESIGNED TO SUPPORT CERTAIN ASPECTS OF SELF-REGULATION IN ONLINE LEARNING ENVIRONMENTS. NEVERTHELESS, THERE IS A LACK OF STUDY TO UNDERSTAND WHAT THE COMMONALITIES AND DIFFERENCES IN TERMS OF DESIGN ARE, WHAT THE RESULTS IN TERMS OF THE EFFECT ON LEARNERS' SELF-REGULATION ARE AND WHICH OF THEM COULD BE APPLIED IN MOOCS. THOSE ARE THE QUESTIONS THAT SHOULD BE FURTHER EXPLORED. IN THIS PAPER WE PRESENT A SYSTEMATIC LITERATURE REVIEW WHERE 22 TOOLS DESIGNED TO SUPPORT SRL IN ONLINE ENVIRONMENTS WERE ANALYZED. OUR FINDINGS INDICATE THAT: (1) MOST OF THE STUDIES DO NOT EVALUATE THE EFFECT ON LEARNERS' SRL STRATEGIES; (2) THE USE OF INTERACTIVE VISUALIZATIONS HAS A POSITIVE EFFECT ON LEARNERS' MOTIVATION; (3) THE USE OF THE SOCIAL COMPARISON COMPONENT HAS A POSITIVE EFFECT ON ENGAGEMENT AND TIME MANAGEMENT; AND (4) THERE IS A LACK OF MODELS TO MATCH LEARNERS' ACTIVITY WITH THE TOOLS WITH SRL STRATEGIES. FINALLY, WE PRESENT THE LESSONS LEARNED FOR GUIDING THE COMMUNITY IN THE IMPLEMENTATION OF TOOLS TO SUPPORT SRL STRATEGIES IN MOOCS.
THIS PAPER FOCUSES ON A DESIGN BASED RESEARCH STUDY ABOUT STEM (SCIENCE, TECHNOLOGY, ENGINEERING AND MATHS) LEARNING BY MAKING THROUGH COLLABORATION AND PRODUCTION. THIS STUDY EXAMINES LEARNING BY MAKING BY STUDENTS TO EXPLORE STEM USING A CONSTRUCTIONIST APPROACH WITH A PARTICULAR FOCUS ON COMPUTER SCIENCE AND ENGINEERING. THE USE OF IOT AS A TECHNOLOGY ENHANCED LEARNING (TEL) TOOL CREATED THE LEARNING CONDITIONS TO BE STUDIED: (A) COLLABORATIVE: NO ONE PERSON HAD THE KNOWLEDGE TO COMPLETE THE PROJECT ALONE, (B) PROBLEM-BASED: NO OFF THE SHELF SOLUTION WAS USED, AND (C) MULTIDISCIPLINARY: THE LEARNING CONTEXT PUSHED THE BOUNDARIES ACROSS THE SUBJECTS. THE STUDY INVESTIGATED THE LEARNING CONDITIONS AND INDICATORS OF COLLABORATION AND PRODUCTION TAKING PLACE WHEN LEARNING ABOUT STEM. THE RESULTS WERE USED TO INFORM THE DESIGN OF EFFECTIVE DATA ANALYTICS AND VISUALIZATION TOOLS FOR THE PELARS PROJECT TO ADVANCE PRACTICE-BASED LEARNING ACTIVITIES IN STEM TEACHING. HOWEVER, MORE SPECIFICALLY, THE FINDINGS PROVIDE INSIGHT INTO THE KNOWLEDGE CONSTRUCTION PROCESS WHEN LEARNING THROUGH MAKING IN COMPLEX ENVIRONMENTS. THESE INSIGHTS ILLUSTRATE THE COMBINED PEDAGOGICAL VALUE OF COLLABORATION AND PRODUCTION SUPPORTING THE MULTIDISCIPLINARY LEARNING OPPORTUNITIES. THE IMPORTANCE OF COMMUNITY KNOWLEDGE CONSTRUCTION AND ITS RELATIONSHIP TO THE PEDAGOGICAL APPROACH IS EXAMINED. THE SIGNIFICANCE OF THESE FINDINGS IN THE CONTEXT OF IOT TEL TOOLS IN EDUCATION IS EXPLORED.
WHILE IT IS WELL ACCEPTED THAT INTEGRATING ACADEMIC SKILLS INTO THE CURRICULA IS BEST PRACTICE, IN REALITY IT IS NOT ALWAYS ACCEPTED BY INSTITUTIONS. SUCH INTEGRATION INVOLVES BOTH CONTEXTUALISATION AND APPROPRIATE TIMING. THIS PRESENTS REAL ISSUES FOR ADJUNCT ACADEMIC LANGUAGE AND LEARNING CENTRES THAT ARE CENTRALISED, RATHER THAN PART OF A SPECIFIC FACULTY OR COURSE. TO SOME DEGREE, THE ISSUES ARISING FROM THIS DISJOINT CAN BE MINIMISED BY THE USE OF UNIVERSITY-WIDE SUBMISSION DATA INDICATING WHEN STUDENTS ARE REQUIRED TO SUBMIT THEIR ASSESSMENTS. THE OVERALL OBJECTIVE OF THIS PAPER WAS TO IDENTIFY HOW SUBMISSION DATA COULD BE USED TO CONTEXTUALISE AND EMBED ACADEMIC SUPPORT SERVICES AND IMPROVE THE AVAILABILITY OF THOSE SERVICES DURING PEAK TIMES. SPECIFICALLY, IT USED A MIXED METHODS APPROACH TO: A) INVESTIGATE HOW ASSESSMENT SUBMISSION DATA COULD BE EMPLOYED TO PROVIDE BETTER TIMED AND CONTEXTUALISED WORKSHOPS; AND B) HOW THAT DATA COULD BE APPLIED TO PREDICT THE DEMAND ON AN ACADEMIC DROP-IN CENTRE. THE RESULTS OF THE STUDY WERE TWOFOLD: FIRST, THAT ASSESSMENT SUBMISSION DATA CAN BE USED TO INFORM WHEN AND HOW CONTEXTUALISED ACADEMIC SUPPORT SHOULD BE PROVIDED. SECONDLY, THAT ASSESSMENT SUBMISSION DATA CAN BE USED TO INFORM WHEN STUDENTS REQUIRE ACADEMIC SUPPORT SERVICES, AND CONSEQUENTLY STAFF THOSE SERVICES APPROPRIATELY.
NATIONAL RESEARCH AND EDUCATION NETWORKS (NRENS) ARE FOR SHARING RESOURCES AMONG MEMBER ORGANISATIONS. NRENS PLAY AN IMPORTANT ROLE IN E-LEARNING ACTIVITIES AS THEY ENABLE CROSS-ORGANISATIONAL COMMUNICATION. HETEROGENEOUS INFRASTRUCTURES USED FOR E-LEARNING ARE A CHALLENGE FOR SYSTEM DESIGN, DATA INTEGRITY, AND INTEROPERABILITY. THIS PAPER PROPOSES AN NREN E-LEARNING REFERENCE ARCHITECTURE AND AFFILIATED NREN E-LEARNING DESIGN PATTERNS THAT CONSIDER COMMUNICATION, RELIABLE ACCESS, A COLLABORATION AND INTEROPERABILITY BETWEEN E-LEARNING INFRASTRUCTURES. RESEARCH EXISTS ABOUT DIFFERENT ARCHITECTURES INCLUDING TRADITIONAL E-LEARNING SYSTEMS, SERVICE-ORIENTED CLOUD COMPUTING SUCH AS THE SERVICE-ORIENTED ARCHITECTURE (SOA) ADAPTATIONS FOR EDUCATION SYSTEMS, E-LEARNING SYSTEMS AND A SERVICE-ORIENTED CLOUD COMPUTING ARCHITECTURE (SOCCA). HOWEVER, NO RESEARCH HAS BEEN CARRIED OUT FOR NREN E-LEARNING ARCHITECTURES. FOR DEEPER INSIGHT, WE INVESTIGATE IN THIS PAPER AN E-LEARNING ARCHITECTURE BASED ON THE AFGHANISTAN RESEARCH AND EDUCATION NETWORK (AFGREN). THE REFERENCE E-LEARNING ARCHITECTURE BASED ON AFGREN PROVIDES GUIDANCE FOR THE RAPID DEVELOPMENT OF DIFFERENT E-LEARNING SERVICES AND LEARNING-ANALYTICAL INFRASTRUCTURE FOR AFGHANISTAN.
MIDDLE SCHOOL IS AN IMPORTANT PHASE IN THE ACADEMIC TRAJECTORY, WHICH PLAYS A MAJOR ROLE IN THE PATH TO SUCCESSFUL POST-SECONDARY OUTCOMES SUCH AS GOING TO COLLEGE. DESPITE THIS, RESEARCH ON FACTORS LEADING TO COLLEGE-GOING CHOICES DO NOT YET UTILIZE THE EXTENSIVE FINE-GRAINED DATA NOW BECOMING AVAILABLE ON MIDDLE SCHOOL LEARNING AND ENGAGEMENT. THIS PAPER USES INTERACTION-BASED DATA-MINED ASSESSMENTS OF STUDENT BEHAVIOR, ACADEMIC EMOTIONS AND KNOWLEDGE FROM A MIDDLE SCHOOL ONLINE LEARNING ENVIRONMENT, AND EVALUATES THEIR RELATIONSHIPS WITH DIFFERENT OUTCOMES IN HIGH SCHOOL AND COLLEGE. THE DATA-MINED MEASURES OF STUDENT BEHAVIOR, EMOTIONS, AND KNOWLEDGE ARE USED IN THREE ANALYSES: (1) TO DEVELOP A PREDICTION MODEL OF COLLEGE ATTENDANCE; (2) TO EVALUATE THEIR RELATIONSHIPS TO INTERMEDIATE OUTCOMES ON THE PATH TO COLLEGE ATTENDANCE SUCH AS MATH AND SCIENCE COURSE-TAKING DURING HIGH SCHOOL; (3) TO DEVELOP AN OVERALL PATH MODEL BETWEEN THE EDUCATIONAL EXPERIENCES STUDENTS HAVE DURING MIDDLE SCHOOL, THEIR HIGH SCHOOL EXPERIENCES, AND THEIR EVENTUAL COLLEGE ATTENDANCE. THIS GIVES A RICHER PICTURE OF THE COGNITIVE AND NON-COGNITIVE MECHANISMS THAT STUDENTS EXPERIENCE THROUGHOUT VARIED PHASES IN THEIR YEARS IN SCHOOL, AND HOW THEY MAY BE RELATED TO ONE ANOTHER. SUCH UNDERSTANDING MAY PROVIDE EDUCATORS WITH INFORMATION ABOUT STUDENTS' TRAJECTORIES WITHIN THE COLLEGE PIPELINE.
TO ESTIMATE THE SUCCESS OF A PROJECT IT IS NECESSARY TO COLLECT AND ANALYSE DATA. DEPENDING ON THE PHASE AFTER WHICH THE EVALUATION IS CARRIED OUT IT GIVES INFORMATION ABOUT THE STATUS OF THE PROJECT OR ABOUT THE PERSPECTIVES IN THE FUTURE. IT PROVES WHICH MEASURES ARE SUCCESSFUL AND WORTH TO CONTINUE. THE PROBLEM IS TO FIND OUT WHICH FACTORS PROVIDE USEFUL INFORMATION AND HOW TO INTERPRET AND USE THEM. THIS PAPER IS ABOUT THE DEVELOPMENT AND EFFECTIVENESS OF A PROJECT WHICH SUPPORTS STUDENTS IN THE STUDY ENTRY PHASE. IT WAS MONITORED AND DIFFERENT MEASURES OF EVALUATION WERE USED. ONE OF THEM WAS TO OBSERVE THE WRITTEN EXAM RESULTS OF ALL BACHELOR STUDY PROGRAMS OF OUR UNIVERSITY. IN THE PROCESS OF INTERPRETING THE DATA, IT WAS DISCOVERED THAT EACH STUDY PROGRAM WAS A LITTLE BIT DIFFERENT AND THE CHALLENGE WAS TO FIND A METHOD TO SUIT THE REQUIREMENT OF ALL. THIS PAPER DISCUSSES THIS APPROACH, SHOWS WHAT STATEMENTS CAN BE MADE AND WHERE THE LIMITS OF THIS EVALUATION LIES.
THE RAPID DEVELOPMENT AND THE WIDESPREAD USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) HAVE AN ENORMOUS INFLUENCE ON THE WAY OF LIVING, WORKING, AND LEARNING. EXPECTING ICT TO HELP STUDENTS TO MEET THE CHALLENGES OF THIS FAST-CHANGING WORLD, ICT HAS ENTERED THE EDUCATIONAL FIELD IN A PERVASIVE WAY. RESULTING CHANGES IN THE EDUCATIONAL SECTOR HAVE MAJOR IMPLICATION NOT ONLY FOR THE PROCESS OF LEARNING BUT ALSO FOR THE TEACHING PROFESSION ITSELF. THE BIGGEST CHANGE AREA OF TODAY'S CLASSROOM IS THE ROLE OF THE TEACHER IN THE CLASSROOM. TEACHERS OF TODAY NEED TO BE A FACILITATOR OF LEARNING, A PARTNER OR CO-PARTICIPANT IN THE LEARNING PROCESS BY TAKING INTO ACCOUNT STUDENTS' STRENGTHS AND WEAKNESSES WHEN PLANNING AND DELIVERING LEARNING ACTIVITIES. THE MAIN AIM OF THE EUROPEAN PROJECT NEXT-TELL (WWW. NEXT-TELL. EU), AN EU-FUNDED PROJECT IS TO SUPPORT TEACHERS IN FACING EXACTLY THESE CHALLENGES, OR IN MORE GENERAL THE CHALLENGES OF THE 21ST CENTURY EDUCATION. THIS PAPER PRESENTS AN ACTIVITY TRACKING TOOL CALLED MYCLASS THAT HAS BEEN DEVELOPED IN THE CONTEXT OF THE NEXT-TELL PROJECT. FURTHERMORE, A REPORT OUTLINES EXPLICIT USER FEEDBACK GIVEN BY TEACHERS WHO HAVE APPLIED AND USED MYCLASS IN THE CONTEXT OF A TRAINING WORKSHOP.
IT IS CRUCIALLY IMPORTANT THAT EDUCATIONAL PRACTITIONERS AND RESEARCHERS PAY ATTENTION TO THE STUDENTS WITH POOR ACADEMIC PERFORMANCE AND HELP IMPROVE THEIR LEARNING. THE BEHAVIORS OF THE STUDENTS USING ONLINE LEARNING SYSTEMS CAN BE USED AS ONE IMPORTANT DATA SOURCE TO ANALYZE THE STUDENTS' LEARNING BEHAVIORS. FROM THE VIEWPOINT OF FORMATIVE ASSESSMENT, WE DEFINE ONE STUDENT AS A STUDENT WITH POOR ACADEMIC PERFORMANCE DURING ONE LEARNING PERIOD IF THIS STUDENT IS CLASSIFIED INTO A STUDENT WITH POOR ACADEMIC PERFORMANCE IN THREE QUARTERS OF ALL EXAMINATIONS DURING THIS PERIOD. AFTER SCREENING ALL STUDENTS' MATHEMATIC EXAM SCORES FROM GRADE ONE TO GRADE THREE OF ONE EXPERIMENT CLASS IN A JUNIOR HIGH SCHOOL IN CHINA, SIX STUDENTS ARE IDENTIFIED AS STUDENTS WITH POOR ACADEMIC PERFORMANCE IN MATH EDUCATION DURING NEARLY THREE YEARS' STUDY PERIOD. THEY PERFORMED WORSE IN MOST EXAMINATIONS, BUT NOT BAD IN SOME EXAMINATIONS. BASED ON THE OLAI (ONLINE LEARNING ACTIVITY INDEX) MODEL PROPOSED BY JIA AND YU (2017) TO DESCRIBE THE STUDENTS' ONLINE LEARNING ACTIVITIES, WE ANALYZE THE STUDENTS' ONLINE QUIZ ACTIVITY IN A WEB-BASED INTERACTIVE LEARNING SYSTEM BY COMPARING THE VALUES OF THE OLAI DIMENSIONS OF THE STUDENTS. THE DATA ANALYSIS SHOWS THAT EVERY STUDENT HAD HIS OR HER OWN FEATURE, AND THUS INDIVIDUAL APPROACHES TO HELP EACH STUDENT ARE SUGGESTED. ALL THE POOR STUDENTS HAD A BAD PERFORMANCE IN THE STARTING POINT, I.E. THE FIRST EXAM. THEIR DEFICIENCY IN PREVIOUS STUDY PREVENTED THEM FROM UNDERSTANDING NEW KNOWLEDGE AND SHOULD BE OVERCOME AT FIRST. OVERALL, THEIR ONLINE PERFORMANCE IS POSITIVELY CORRELATED WITH THE NORMAL EXAM PERFORMANCE. THE ONLINE QUIZ ACTIVITIES WITH INSTANT FEEDBACK IS HELPFUL FOR THE STUDENTS WITH POOR ACADEMIC PERFORMANCE IN THEIR NORMAL EXAMS. THE MORE CHALLENGING QUIZZES AND THE FREQUENT HELP FROM TEACHING ASSISTANTS ONLINE MUST NOT LEAD TO BETTER PERFORMANCE IN NORMAL EXAMS.
THIS ARTICLE ANALYSES FIVE TECHNOLOGY-ENHANCED LEARNING-RELATED TERMS ON WIKIPEDIA, ASSESSING THEIR USEFULNESS IN RELATION TO ACADEMIC JOURNAL ARTICLES CONCERNING THE SAME TERMS. DATA WERE OBTAINED ABOUT THE WORD LENGTHS OF THE WIKIPEDIA ARTICLES, THE NUMBERS OF WIKIPEDIA EDITS AND NUMBERS OF ACADEMIC JOURNAL PUBLICATIONS OVER THE FIRST 5 YEARS AFTER THE CREATION OF THE FIRST WIKIPEDIA ENTRY. DATA WERE GATHERED DIRECTLY FROM WIKIPEDIA, AS WELL AS THE ACADEMIC JOURNAL AGGREGATOR ACADEMIC SEARCH COMPLETE, AND WERE ANALYSED IN IBM MISS 23. THE ARTICLE ARGUES THAT WIKIPEDIA CAN BE MORE USEFUL THAN ACADEMIC JOURNAL ARTICLES IN THE NEW AND EMERGING PHASES OF A TECHNOLOGY, BECAUSE OF THE VOLUME OF INFORMATION MADE AVAILABLE, TOGETHER WITH THE SPEED OF ITS PUBLICATION AND THE UPDATING OF ITS CONTENTS.
RECENT INTEREST IN ONLINE ASSESSMENT OF SCIENTIFIC INQUIRY HAS LED TO SEVERAL NEW ONLINE SYSTEMS THAT ATTEMPT TO ASSESS THESE SKILLS, BUT PRODUCING MODELS THAT DETECT WHEN STUDENTS ARE SUCCESSFULLY PRACTISING THESE SKILLS CAN BE CHALLENGING. IN THIS PAPER, WE STUDY MODELS THAT ASSESS STUDENT INQUIRY IN AN IMMERSIVE VIRTUAL ENVIRONMENT, WHERE A STUDENT NAVIGATES AN AVATAR AROUND A WORLD, SPEAKING TO IN-GAME CHARACTERS, COLLECTING SAMPLES AND CONDUCTING SCIENTIFIC TESTS WITH THOSE SAMPLES IN THE VIRTUAL LABORATORY. TO THIS GOAL, WE LEVERAGE LOG FILE DATA FROM NEARLY 2000 MIDDLE SCHOOL STUDENTS USING VIRTUAL PERFORMANCE ASSESSMENT (VPA), A SOFTWARE SYSTEM WHERE STUDENTS PRACTICE INQUIRY SKILLS IN DIFFERENT VIRTUAL SCENARIOS. WE DEVELOP MODELS OF STUDENT INTERACTION WITHIN VPA THAT PREDICT WHETHER A STUDENT WILL SUCCESSFULLY CONDUCT SCIENTIFIC INQUIRY. SPECIFICALLY, WE IDENTIFY BEHAVIOURS THAT LEAD TO DISTINGUISHING CAUSAL FROM NON-CAUSAL FACTORS TO IDENTIFY A CORRECT FINAL CONCLUSION AND TO DESIGN A CAUSAL EXPLANATION ABOUT THESE CONCLUSIONS. WE THEN DEMONSTRATE THAT THESE MODELS CAN BE ADAPTED WITH MINIMAL EFFORT BETWEEN VPA SCENARIOS. WE CONCLUDE WITH DISCUSSIONS OF HOW THESE MODELS SERVE AS A TOOL FOR BETTER UNDERSTANDING SCIENTIFIC INQUIRY IN VIRTUAL ENVIRONMENTS AND AS A PLATFORM FOR THE FUTURE DESIGN OF EVIDENCE-BASED INTERVENTIONS.
A LOT OF RESEARCH HAS BEEN DONE IN THE FIELD OF INFORMATION AND COMMUNICATION TECHNOLOGY FOR DEVELOPMENT (ICT4D) INVESTIGATING AND MEASURING THE IMPACT OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) INVESTMENTS ON HUMAN DEVELOPMENT. EDUCATION IS A MAJOR COMPONENT OF THE HUMAN DEVELOPMENT INDEX (HDI) WHICH AFFECTS THE CORE OF HUMAN DEVELOPMENT. THIS RESEARCH INVESTIGATES THE RELATIVE EFFICIENCY OF ICT INFRASTRUCTURE UTILIZATION WITH RESPECT TO THE EDUCATIONAL COMPONENT OF THE HUMAN DEVELOPMENT INDEX (HDI). A NOVEL CONCEPTUAL MODEL IS PROPOSED, AND THE DATA ENVELOPMENT ANALYSIS (DEA) METHODOLOGY USED TO MEASURE THE RELATIVE EFFICIENCY OF THE COMPONENTS OF ICT INFRASTRUCTURE (INPUTS) AND THE COMPONENTS OF EDUCATION (OUTPUTS). RESULTS SHOW A STRONG IMPACT OF ICT INFRASTRUCTURE ON EDUCATIONAL ATTAINMENT AND ADULT LITERACY RATES, A STRONG CORRELATION BETWEEN THIS INFRASTRUCTURE AND LITERACY RATES AS WELL AS PROVIDE A THEORETICAL SUPPORT FOR THE ARGUMENT OF INCREASING ICT INFRASTRUCTURE TO PROVIDE AN INCREASE IN HUMAN DEVELOPMENT ESPECIALLY WITHIN THE EDUCATIONAL CONTEXT.
THIS ARTICLE PRESENTS THE INITIAL DEVELOPMENT OF A BIOMASS SENSOR TO MONITOR THE GROWTH OF MACROALGAE (SEAWEEDS) IN THE INTEGRATED MULTI-TROPHIC AQUACULTURE (IMTA) AT HARBOR BRANCH OCEANOGRAPHIC INSTITUTE AT FLORIDA ATLANTIC UNIVERSITY. THE SENSOR UTILIZES A COMBINED OPTICAL/ACOUSTIC MEANS TO QUANTIFY THE SEAWEED BIOMASS. SUCH CONFIGURATION ENSURES THE SENSOR PROVIDING ROBUST COVERAGE UNDER DIFFERENT AMBIENT CONDITIONS AND BIOMASS DENSITIES. AFTER THE BIOMASS SENSOR'S PERFORMANCE HAS BEEN VALIDATED IN THE LAB ENVIRONMENT, WE DEPLOYED THE SENSOR AT THE MACROALGAL SEAWEED CULTIVATION RACEWAY TO QUANTIFY THE SEAWEED DENSITY. THE DATA PROCESSING PROCEDURES ARE DOCUMENTED, AND THE FIELD TEST RESULTS ARE PRESENTED. FINALLY, THE ADVANTAGES AND DISADVANTAGES OF THIS APPROACH AND FUTURE APPLICATION OF THE SENSOR TO DRIVE A MACHINE LEARNING-BASED PREDICTION BIOMASS MODEL ARE DISCUSSED.
KNOWLEDGE TRACING MODELS THE COGNITIVE PROCESS OF SKILL ACQUISITION OF A STUDENT TO PREDICT THE CURRENT KNOWLEDGE STATE. BASED ON COGNITIVE PROCESSING THEORY, WE REGARD STUDENT KNOWLEDGE STATE IN DICHOTOMOUS VIEW IN ALIGNMENT WITH PERFORMANCE FACTOR ANALYSIS (PFA). ASSUMING THAT A STUDENT'S CORRECT AND INCORRECT RESPONSES ARE FUNDAMENTALLY DIFFERENT FOR MODELING A STUDENT'S KNOWLEDGE STATE, WE PROPOSE A DICHOTOMOUS KNOWLEDGE TRACING (DIKT), A NOVEL KNOWLEDGE TRACING NETWORK WITH A DICHOTOMOUS PERSPECTIVE ON A STUDENT'S KNOWLEDGE STATE. WE MODIFY THE NETWORK'S VALUE MEMORY BY DIVIDING IT INTO TWO MEMORIES, EACH ENCODING RECALLABLE AND UNRECALLABLE KNOWLEDGE TO PRECISELY CAPTURE THE STUDENT KNOWLEDGE STATE. WITH THE PROPOSED ARCHITECTURE, OUR MODEL GENERATES A KNOWLEDGE TRAJECTORY THAT INSTANTLY AND ACCURATELY PORTRAYS A STUDENT'S KNOWLEDGE LEVEL BASED ON LEARNING HISTORY. EMPIRICAL EVALUATIONS DEMONSTRATE THAT OUR PROPOSED MODEL ACHIEVES COMPARABLE PERFORMANCE ON BENCHMARK EDUCATIONAL DATASETS.
ANALYTICS IN EDUCATION HAS BEEN RECEIVED MUCH ATTENTION OVER THE PAST DECADE. IT IS NECESSARY TO MAINTAIN HIGH RETENTION RATE IN ANY INSTITUTIONS OF HIGHER EDUCATION, THEREFORE SEVERAL ATTEMPTS ON THE APPLICATION OF ANALYTICS HAVE BEEN DONE FOR THIS PROBLEM. TO DETECT STUDENTS AT HIGH DROP-OUT RISK EARLY AND INTERVENE THEM EFFECTIVELY, UTILIZING THE EDUCATIONAL BIG DATA CAN BE USEFUL. IN THIS PAPER, AN AUTOMATIC DETECTION METHOD OF ACADEMICALLY AT-RISK STUDENTS BY USING LOG DATA OF LEARNING MANAGEMENT SYSTEMS IS CONSIDERED. SOME WELL-KNOWN MACHINE LEARNING METHODS ARE USED TO BUILD A PREDICTIVE MODEL OF STUDENT PERFORMANCE EVALUATED BY GPA. BY USING ACTUAL DATA SET, WE INVESTIGATE AN AVAILABILITY OF THE PROPOSED METHOD AND DISCUSS ITS ABILITY TO EARLY DETECTION OF OFF-TASK BEHAVIOR. THE EXPERIMENTAL RESULTS INDICATED THAT SOME CHARACTERISTICS OF BEHAVIOR ABOUT LEARNING WHICH AFFECT THE LEARNING OUTCOMES CAN BE DETECTED WITH ONLY THE ONLINE LOG DATA. FURTHERMORE, COMPARATIVE IMPORTANCE OF EXPLANATORY VARIABLES OBTAINED BY THE APPROACH WOULD HELP TO ESTIMATE WHICH VARIABLE AFFECTS COMPARATIVELY TO THE LEARNING OUTCOME AND IT CAN BE USED IN INSTITUTIONAL RESEARCH.
THE LEARNING OBJECTIVES, LEARNING ACTIVITIES AND ASSESSMENT ARE VERY MUCH INTERRELATED. ASSESSMENT HELPS TO EVALUATE STUDENTS LEARNING ACHIEVEMENT. POORLY DESIGNED ASSESSMENTS USUALLY FAIL TO EXAMINE THE ACHIEVEMENT OF INTENDED LEARNING OUTCOME OF A COURSE. THERE ARE DIFFERENT TAXONOMIES THAT HAVE BEEN DEVELOPED TO IDENTIFY THE LEVEL OF THE ASSESSMENT BEING PRACTICED SUCH AS BLOOM'S AND SOLO. IN THIS RESEARCH WE HAVE STUDIED THE USE OF WORDNET WITH COSINE SIMILARITY ALGORITHM FOR CLASSIFYING A GIVEN EXAM QUESTION ACCORDING TO BLOOM'S TAXONOMY LEARNING LEVELS. WORDNET SIMILARITY ALGORITHM DEPENDS ON THE EXTRACTED VERBS FROM EXAM QUESTION. COSINE SIMILARITY ALGORITHM WAS BASED ON IDENTIFICATION OF QUESTION PATTERNS OF EXAM QUESTION. IT CONSISTS OF TAG PATTERN GENERATION MODULE, GRAMMAR GENERATION MODULE, PARSER GENERATION AND COSINE SIMILARITY CHECKING MODULE. THIS ALGORITHM WAS HELPFUL TO CLASSIFY THE EXAM QUESTION WHERE VERBS WERE NOT PRESENT IN EXAM QUESTIONS. EXAM QUESTIONS TAKEN FROM COURSES AT THE DEPARTMENT OF COMPUTING AND INFORMATION SYSTEMS AT WAYAMBA UNIVERSITY WERE USED AS A BASIS FOR A PERFORMANCE COMPARISON, WITH THE AUTONOMOUS SYSTEM PROVIDING CLASSIFICATIONS THAT WERE CONSISTENT WITH THOSE PROVIDED BY DOMAIN EXPERTS ON APPROXIMATELY 71% OF OCCASIONS.
BLENDED LEARNING IS OFTEN ASSOCIATED WITH STUDENT-ORIENTED LEARNING IN WHICH STUDENTS HAVE VARYING DEGREES OF CONTROL OVER THEIR LEARNING PROCESS. HOWEVER, THE CURRENT NOTION OF BLENDED LEARNING IS OFTEN A TEACHER-ORIENTED APPROACH IN WHICH THE TEACHER IDENTIFIES THE USED LEARNING TECHNOLOGIES AND THEREBY OFFERS STUDENTS A BLENDED TEACHING COURSE INSTEAD OF A BLENDED LEARNING COURSE (GEORGE-WALKER & KEEFFE, 2010). A MORE STUDENT-ORIENTED APPROACH IS NEEDED WITHIN EDUCATIONAL DESIGN OF BLENDED LEARNING COURSES SINCE PREVIOUS RESEARCH SHOWS THAT STUDENTS SHOW A LARGE VARIATION IN THE WAY THEY USE THE DIFFERENT DIGITAL LEARNING RESOURCES TO SUPPORT THEIR LEARNING. THERE IS LITTLE INSIGHT INTO WHY STUDENTS SHOW DISTINCT PATTERNS IN THEIR USE OF THESE LEARNING RESOURCES AND WHAT THE CONSEQUENCES OF THESE (UN) CONSCIOUS DIFFERENCES ARE IN RELATION TO STUDENT PERFORMANCE. THE CURRENT STUDY EXPLORES DIFFERENT USAGE PATTERNS OF LEARNING RESOURCES BY STUDENTS IN A BLENDED COURSE. IT TRIES TO ESTABLISH CAUSES FOR THESE DIFFERENCES BY USING DISPOSITIONAL DATA AND DETERMINES THE EFFECT OF DIFFERENT USAGE PATTERNS ON STUDENT PERFORMANCE.
COMPUTING EDUCATION RESEARCHERS HAVE BECOME INCREASINGLY INTERESTED IN LEVERAGING LOG DATA AUTOMATICALLY COLLECTED WITHIN COMPUTER PROGRAMMING ENVIRONMENTS IN ORDER TO UNDERSTAND STUDENTS' LEARNING PROCESSES AND TAILOR INSTRUCTION TO STUDENT NEEDS. WHILE DATA ON STUDENTS' PROGRAMMING ACTIVITIES HAS BEEN POSITIVELY CORRELATED WITH THEIR LEARNING OUTCOMES, THOSE DATA TELL ONLY PART OF THE STORY. ANOTHER PART OF THE STORY LIES IN STUDENTS' SOCIAL ACTIVITIES, WHICH, ACCORDING TO SOCIAL LEARNING THEORY, CAN ALSO BE PREDICTIVE OF STUDENTS' LEARNING OUTCOMES. IN ORDER TO GAIN FURTHER INSIGHT INTO HOW COMPUTING STUDENTS' LEARNING PROCESSES INFLUENCE THEIR LEARNING OUTCOMES, WE PRESENT AN EMPIRICAL STUDY THAT EXPLORES THE INTERPLAY OF STUDENTS' SOCIAL ACTIVITIES, PROGRAMMING ACTIVITIES, AND COURSE OUTCOMES IN AN EARLY COMPUTING COURSE. BY ANALYZING LOG DATA COLLECTED THROUGH A PROGRAMMING ENVIRONMENT AUGMENTED WITH A SOCIAL NETWORKING-STYLE ACTIVITY STREAM, WE FOUND THAT ANSWERS TO QUESTIONS POSED THROUGH THE ACTIVITY STREAM WERE POSITIVELY CORRELATED WITH STUDENTS' ABILITY TO MAKE PROGRAMMING PROGRESS, AND THEIR EVENTUAL SUCCESS IN THE COURSE. BASED ON OUR FINDINGS, WE PRESENT RECOMMENDATIONS FOR THE DESIGN OF PEDAGOGICAL ENVIRONMENTS TO SUPPORT A MORE SOCIAL PROGRAMMING PROCESS.
THIS PAPER DESCRIBES RESEARCH AND DEVELOPMENT AROUND TWO GAMEFUL COURSES THAT REIMAGINED THEIR ASSESSMENT SYSTEMS TO BETTER SUPPORT STUDENT AUTONOMY AND PROMOTE ENGAGEMENT. WE PRESENT RESULTS FROM AN ONGOING CLASSROOM-BASED RESEARCH STUDY THAT SIGNALS THE SUCCESS OF THESE DESIGNS AND, IN SO DOING, EXPLORE KEY ELEMENTS OF WHAT WE CALL GAMEFUL DESIGN: THE PROCESS OF REDESIGNING CORE ELEMENTS OF A LEARNING ENVIRONMENT TO BETTER SUPPORT INTRINSIC MOTIVATION. WE DESCRIBE THIS PROCESS AND DISCUSS A SET OF PROMISING PRACTICES FOR THE DESIGN OF GAMEFUL COURSES. RESULTS FROM THREE STUDIES INDICATE THAT GAMEFUL COURSE DESIGN IS POSITIVELY RELATED TO STUDENTS WORKING HARDER AND FEELING MORE IN CONTROL OF THEIR CLASS PERFORMANCE.
THIS PAPER INVESTIGATES MULTI-AGENT SYSTEMS (MAS) RELATED DATA ANALYTICS. MAS IS USED FOR MODELING COMPLEX, DECENTRALIZED, AND REAL-WORLD TASKS SUCH AS PACKAGE DELIVERY BY UNMANNED AIRCRAFT SYSTEMS (UAS), ENVIRONMENTAL MONITORING, PRECISION AGRICULTURE, SECURITY, DISASTER MANAGEMENT, UAS TRAFFIC MANAGEMENT (UTM) AMONG OTHERS. FISH FARMING IS ONE SUCH AREA, WHERE THE DEPLOYMENT OF UAS PLATFORMS COULD DRASTICALLY IMPROVE THE CURRENT LABOR-INTENSIVE AND RESOURCE-CONSTRAINT OPERATIONS. THIS RESEARCH ADDRESSES THE DESIGN OF MISSION CONTROL AND PATH PLANNING STRATEGIES FOR UASS DEPLOYED ON THE FISH FARM. THE PROPOSED STRATEGY ENABLES PERIODIC MONITORING OF MISSION-CRITICAL PARAMETERS. A CONTROL STRATEGY IS DESIGNED TO ADDRESS THE TRACKING CONTROL OF THE UAS UNDER WIND CONDITIONS.
NOTIFICATION SERVICES ARE WIDELY USED NOWADAYS IN MANY SCENARIOS. FOR INSTANCE, THEY ARE USEFUL IF A PATIENT NEEDS CARE OR AN IT INCIDENT NEEDS SUPPORT. THERE LACKS OF A SIMPLE, AFFORDABLE AND EFFICIENT NOTIFICATION SYSTEM THAT CAN DELIVER IMPORTANT MESSAGES ACROSS VARIOUS CHANNELS TO A CERTAIN NUMBER OF PEOPLE. THEREFORE, WE DEVELOPED A SERVERLESS 1-CLICK NOTIFICATION SYSTEM USING THE AMAZON WEB SERVICES (AWS) IOT PLATFORM. THE SYSTEM ALLOWS A USER TO NOTIFY THE DESIGNATED PERSONNEL(S) WITH A SIMPLE CLICK USING CONFIGURABLE IOT BUTTONS VIA WI-FI. AN AWS CLOUD SERVER RECEIVES THE REQUEST SENT BY THE BUTTONS, PROCESSES THE REQUEST AND SENDS THE NOTIFICATION SUCH AS EMAILS, TEXT MESSAGES OR PHONE CALLS TO THE DESIGNATED SERVICE MEMBERS BASED ON THE PREPROGRAMED PROCEDURES AND DATABASE. THE SYSTEM IS USER FRIENDLY AND EASY TO IMPLEMENT SINCE NO HARDWARE INSTALLATION IS REQUIRED. THE SERVERLESS SYSTEM AND THE PAY-AS-YOU-GO POLICY MAKE THE SYSTEM VERY COST EFFECTIVE. THE SYSTEM IS RELIABLE AS IT IS SUPPORTED BY THE AWS. IN THIS ARTICLE WE WILL DESCRIBE A FEW KEY CLOUD FUNCTIONS AND SERVICES THAT WE ADOPTED IN THE NOTIFICATION SYSTEM AND DISCUSS TWO POTENTIAL USE CASES IN CLASSROOM TECHNICAL SUPPORT AND HOME-BASED LONG-TERM CARE SETTINGS. WE VALIDATED THE SYSTEM FUNCTIONALITY AT TEXAS CHRISTIAN UNIVERSITY'S PATIENT CARE SIMULATION CENTER.
RECENT ADVANCES IN NEUROSCIENCE HAVE REVEALED MANY PRINCIPLES ABOUT NEURAL PROCESSING. IN PARTICULAR, MANY BIOLOGICAL SYSTEMS WERE FOUND TO RECONFIGURE/RECRUIT SINGLE NEURONS TO GENERATE MULTIPLE KINDS OF DECISIONS. SUCH FINDINGS HAVE THE POTENTIAL TO ADVANCE OUR UNDERSTANDING OF THE DESIGN AND OPTIMIZATION PROCESS OF ARTIFICIAL NEURAL NETWORKS. PREVIOUS WORK DEMONSTRATED THAT DENSE NEURAL NETWORKS ARE NEEDED TO SHAPE COMPLEX DECISION SURFACES REQUIRED FOR AI-LEVEL RECOGNITION TASKS. WE INVESTIGATE THE ABILITY TO MODEL HIGH DIMENSIONAL RECOGNITION PROBLEMS USING SINGLE OR SEVERAL NEURONS NETWORKS THAT ARE RELATIVELY EASIER TO TRAIN. BY EMPLOYING THREE DATASETS, WE TEST THE USE OF A POPULATION OF SINGLE NEURON NETWORKS IN PERFORMING MULTI-CLASS RECOGNITION TASKS. SURPRISINGLY, WE FIND THAT SPARSE NETWORKS CAN BE AS EFFICIENT AS DENSE NETWORKS IN BOTH BINARY AND MULTI-CLASS TASKS. MOREOVER, SINGLE NEURON NETWORKS DEMONSTRATE SUPERIOR PERFORMANCE IN BINARY CLASSIFICATION SCHEME AND COMPETING RESULTS WHEN COMBINED FOR MULTI-CLASS RECOGNITION.
WE ANALYSE ENGAGEMENT AND PERFORMANCE DATA ARISING FROM PARTICIPANTS' INTERACTIONS WITH AN IN-HOUSE LMS AT IMPERIAL COLLEGE LONDON WHILE A COHORT OF STUDENTS FOLLOW TWO COURSES ON A NEW FULLY ONLINE POSTGRADUATE DEGREE IN MANAGEMENT. WE IDENTIFY AND INVESTIGATE TWO MAIN QUESTIONS RELATING TO THE RELATIONSHIPS BETWEEN ENGAGEMENT AND PERFORMANCE, DRAWING RECOMMENDATIONS FOR IMPROVED GUIDELINES TO INFORM THE DESIGN OF SUCH COURSES.
TARGET DETECTION IS AN IMPORTANT PROBLEM IN REMOTE-SENSING WITH CRUCIAL APPLICATIONS IN LAW-ENFORCEMENT, MILITARY AND SECURITY SURVEILLANCE, SEARCH-AND-RESCUE OPERATIONS, AND AIR TRAFFIC CONTROL, AMONG OTHERS. OWING TO THE RECENTLY INCREASED AVAILABILITY OF COMPUTATIONAL RESOURCES, DEEP-LEARNING BASED METHODS HAVE DEMONSTRATED STATE-OF-THE-ART PERFORMANCE IN TARGET DETECTION FROM UNIMODAL AERIAL IMAGERY. IN ADDITION, OWING TO THE AVAILABILITY OF REMOTE-SENSING DATA FROM VARIOUS IMAGING MODALITIES, SUCH AS RGB, INFRARED, HYPER-SPECTRAL, MULTI-SPECTRAL, SYNTHETIC APERTURE RADAR, AND LIDAR, RESEARCHERS HAVE FOCUSED ON LEVERAGING THE COMPLEMENTARY INFORMATION OFFERED BY THESE VARIOUS MODALITIES. OVER THE PAST FEW YEARS, DEEP-LEARNING METHODS HAVE DEMONSTRATED ENHANCED PERFORMANCE USING MULTI-MODAL DATA. IN THIS WORK, WE PROPOSE A METHOD FOR VEHICLE DETECTION FROM MULTI-MODAL AERIAL IMAGERY, BY MEANS OF A MODIFIED YOLOV3 DEEP NEURAL NETWORK THAT CONDUCTS MID-LEVEL FUSION. TO THE BEST OF OUR KNOWLEDGE, THE PROPOSED MID-LEVEL FUSION ARCHITECTURE IS THE FIRST OF ITS KIND TO BE USED FOR VEHICLE DETECTION FROM MULTI-MODAL AERIAL IMAGERY USING A HIERARCHICAL OBJECT DETECTION NETWORK. OUR EXPERIMENTAL STUDIES CORROBORATE THE ADVANTAGES OF THE PROPOSED METHOD.
CONVOLUTIONAL NEURAL NETWORKS (CNNS) BECOME VERY USEFUL TOOLS IN CLASSIFICATION AND FEATURE EXTRACTION APPLICATIONS. IN THIS RESEARCH, WE PRESENT A COMPARABLE STUDY OF SEVERAL COMMONLY-USED CNNS IN TERMS OF PERFORMANCE. MOST RECENTLY DEVELOPED CNNS ARE SELECTED IN OUR STUDY, WHICH INCLUDE NASNET-LARGE, INCEPTION-RESNET-V2, DENSENET201, NASNET-MOBILE, MOVILENET-V2 AS WELL AS WELL-KNOWN RESNET50 AND VGG19 FOR COMPARISONS. IN OUR CLASSIFICATION EXPERIMENTS THERE ARE EIGHT DIFFERENT GEOMETRICAL SHAPES, EACH OF WHICH INCLUDES 486 TO 620 COMPUTER-GENERATED IMAGES. TWO BASIC SHAPES, TRIANGLE AND SQUARE, VARY WITH SOLID OR HOLLOW SHAPES, AND THEN OVERLAPPING WITH OR WITHOUT THREE-DISK DISTRACTORS. CNNS TRAINING AND TESTING BOTH CAN USE THE SHAPE IMAGES AS THE EXPERIMENTS CONDUCTED ON THE IMAGENET. ON THE OTHER HAND, WE CAN USE THE PRETRAINED CNNS ON IMAGENET TO EXTRACT FEATURES, THEN TRAIN A MULTICLASS SUPPORT VECTOR MACHINE (SVM) TO DO CLASSIFICATION. TRAINING IMAGES MAY INCLUDE FOUR SHAPES OR TWO CATEGORIES (SOLID OR HOLLOW), WHILE TESTING IMAGES ARE FOUR SHAPES OR TWO CATEGORIES WITH DISTRACTORS. THE PERFORMANCE OF CNNS INCLUDES CLASSIFICATION ACCURACIES AND TIME COSTS IN TRAINING AND TESTING. THE EXPERIMENTAL RESULTS WILL PROVIDE GUIDANCE IN SELECTING CNN MODELS.
PREDICTIVE MODELS TO EVALUATE STUDENT'S PERFORMANCE HAVE BEEN WIDELY USED IN THE PAST. THESE MODELS HAVE BEEN BASICALLY USED AS A STATISTICAL TOOL TO PREDICT WHETHER STUDENTS WILL PASS A COURSE BASED ON PREVIOUS BACKGROUND VARIABLES SUCH AS PRIOR-LEARNING OR ACADEMIC RECORDS. THESE MODELS HAVE A LARGE POTENTIAL TO GIVE SUPPORT TO TEACHERS AND LEARNERS DURING THE LEARNING PROCESS IN REAL TIME. THIS PAPER FOCUSES ON THE DESIGN FOUNDATIONS OF A PREDICTIVE CORE SYSTEM. THIS CORE SYSTEM IS THE ESSENTIAL COMPONENT TO BUILD IN THE FUTURE A PREDICTIVE SUPPORT FRAMEWORK. ADDITIONALLY, EXPERIMENTAL RESULTS ARE SHOWN TO VALIDATE THE QUALITY OF THE DESIGNED SYSTEM.
SWALLOWING DYSFUNCTION, OR DYSPHAGIA, OCCURS SECONDARY TO MANY UNDERLYING ETIOLOGIES SUCH AS STROKE AND CAN LEAD TO PNEUMONIA. THE UPPER ESOPHAGEAL SPHINCTER (UES) IS A MAJOR ANATOMICAL LANDMARK THAT ALLOWS THE PASSAGE OF SWALLOWED MATERIALS INTO THE ESOPHAGUS DURING SWALLOWING. DELAYED UES OPENING OR REDUCED DURATION OF OPENING CAN LEAD TO THE ACCUMULATION OF PHARYNGEAL RESIDUE, WHICH CAN INCREASE RISK OF ASPIRATION. UES OPENING IS OBSERVED THROUGH THE INSPECTION OF RADIOGRAPHIC EXAMS, KNOWN AS VIDEOFLUOROSCOPY SWALLOW STUDIES (VFSSS), WHICH EXPOSE PATIENTS TO IONIZING RADIATION AND DEPEND ON SUBJECTIVE CLINICIAN INTERPRETATIONS. HIGH RESOLUTION CERVICAL AUSCULTATION (HRCA) IS A NON-INVASIVE SENSOR-BASED TECHNOLOGY THAT HAS BEEN RECENTLY INVESTIGATED TO DEPICT SWALLOWING PHYSIOLOGY. HRCA HAS BEEN PROPOSED FOR DETECTING UES OPENING DURATION THROUGH A DEEP LEARNING FRAMEWORK. HOWEVER, THE PROPOSED FRAMEWORK WAS ONLY VALIDATED OVER SWALLOWS FROM PATIENTS. FOR SUCH AN ALGORITHM TO BE ROBUST, IT HAS TO BE PROVEN EQUALLY RELIABLE FOR THE DETECTION OF UES OPENING DURATION IN SWALLOWS FROM BOTH PATIENTS AND HEALTHY SUBJECTS. IN THIS STUDY, WE INTEND TO INVESTIGATE THE ROBUSTNESS OF THE HRCA-BASED FRAMEWORK TO DETECT THE UES OPENING IN SIGNALS COLLECTED FROM A DIVERSE POPULATION. THE FRAMEWORK SHOWED COMPARABLE PERFORMANCE REGARDING THE UES OPENING DETECTION WITH AN AVERAGE AREA UNDER THE ROC CURVE OF 95%. THE RESULTS INDICATE THAT THE HRCA-BASED UES OPENING DETECTION CAN PROVIDE SUPERIOR PERFORMANCE ON SWALLOWS FROM DIVERSE POPULATIONS WHICH DEMONSTRATES THE CLINICAL POTENTIAL OF HRCA AS A NON-INVASIVE SWALLOWING ASSESSMENT TOOL.
THIS ARTICLE TRIES TO EXPLORE NEW COLLEGE ENGLISH TEACHING AND LEARNING MODES BASED ON THE BLENDED TEACHING APPROACH IN EFL. UNDER THE DIRECTION OF "EDUCATION INFORMATIONIZATION 'THE THIRTEENTH FIVE-YEAR' PLAN" AND "THE GUIDELINES ON COLLEGE ENGLISH TEACHING", THIS PAPER HOLDS THAT IT IS VITAL AND INDISPENSABLE TO INTEGRATE EDUCATION TECHNOLOGY INTO COLLEGE ENGLISH TEACHING. WITH THE REFERENCE OF SOME TEACHING PRACTICE AT SOME COLLEGE, THE AUTHOR PROVIDES SOME IDEAS TO CONSTRUCT A NEW COLLEGE ENGLISH TEACHING AND LEARNING MODES TO PROMOTE PERSONALIZED LEARNERS' LEARNING AND OPTIMIZE TEACHING METHODS AS WELL. THE PAPER ENDS WITH A CONCLUSION THAT THE ORGANIC CONNECTION OF ON-LINE AND OFF-LINE TEACHING MODELS ENSURE THE QUALITY OF ENGLISH BLENDED TEACHING; FOSTERING VARIOUS TYPES OF COMMUNICATION IN INTERACTION IS THE KEY FACTOR IN THE PROCESS OF ENGLISH BLENDED TEACHING; THE BASIC PRINCIPLE OF ENGLISH BLENDED TEACHING IS TO HIGHLIGHT THE AUTONOMOUS ROLE OF STUDENTS IN LEARNING AND THE SUPPLY OF VARIOUS AND HIGH QUALITY LEARNING RESOURCES IS A SIGNIFICANT METHOD EMPLOYED IN BLENDED ENGLISH TEACHING.
THIS PAPER DESCRIBES THE ANALYSIS OF THE SOCIAL AND SEMANTIC STRUCTURE OF DISCUSSION FORUMS IN MASSIVE OPEN ONLINE COURSES (MOOCS) IN TERMS OF INFORMATION EXCHANGE AND USER ROLES. TO THAT END, WE ANALYSE A NETWORK OF FORUM USERS BASED ON INFORMATION-GIVING RELATIONS EXTRACTED FROM THE FORUM DATA. CONNECTION PATTERNS THAT APPEAR IN THE INFORMATION EXCHANGE NETWORK OF FORUM USERS ARE USED TO DEFINE SPECIFIC USER ROLES IN A SOCIAL CONTEXT. SEMANTIC ROLES ARE DERIVED BY IDENTIFYING THEMATIC AREAS IN WHICH AN ACTOR SEEKS FOR INFORMATION (PROBLEM AREAS) AND THE AREAS OF INTEREST IN WHICH AN ACTOR PROVIDES INFORMATION TO OTHERS (EXPERTISE). THE INTERPLAY OF SOCIAL AND SEMANTIC ROLES IS ANALYSED USING A SOCIO-SEMANTIC BLOCKMODELLING APPROACH. THE RESULTS SHOW THAT SOCIAL AND SEMANTIC ROLES ARE NOT STRONGLY INTERDEPENDENT. THIS INDICATES THAT COMMUNICATION PATTERNS AND INTERESTS OF USERS DEVELOP SIMULTANEOUSLY ONLY TO A MODERATE EXTENT. IN ADDITION TO THE CASE STUDY, THE METHODOLOGICAL CONTRIBUTION IS IN COMBINING TRADITIONAL BLOCKMODELLING WITH SEMANTIC INFORMATION TO CHARACTERISE PARTICIPANT ROLES.
WRITING ANALYTICS FOCUSES ON THE MEASUREMENT AND ANALYSIS OF WRITTEN TEXTS FOR THE PURPOSE OF UNDERSTANDING WRITING PROCESSES AND PRODUCTS, IN THEIR EDUCATIONAL CONTEXTS, AND IMPROVING THE TEACHING AND LEARNING OF WRITING. THIS WORKSHOP ADOPTS A CRITICAL, HOLISTIC PERSPECTIVE IN WHICH THE DEFINITION OF "THE SYSTEM" AND "SUCCESS" IS NOT RESTRICTED TO IR METRICS SUCH AS PRECISION AND RECALL, BUT RECOGNIZES THE MANY WIDER ISSUES THAT AID OR OBSTRUCT ANALYTICS ADOPTION IN EDUCATIONAL SETTINGS, SUCH AS THEORETICAL AND PEDAGOGICAL GROUNDING, USABILITY, USER EXPERIENCE, STAKEHOLDER DESIGN ENGAGEMENT, PRACTITIONER DEVELOPMENT, ORGANIZATIONAL INFRASTRUCTURE, POLICY AND ETHICS.
THERE HAS BEEN A SHARP RISE IN THE AMOUNT OF DATA AVAILABLE FOR ANALYSIS IN MANY PROFESSIONAL FIELDS IN RECENT YEARS. IN THE MEDICAL SECTOR, THIS SIGNIFICANT INCREASE IN DATA CAN HELP DETECT AND CONFIRM UNDERLYING SYMPTOMS IN PATIENTS THAT WOULD OTHERWISE REMAIN UNDETECTED. MACHINE LEARNING TECHNIQUES HAVE BEEN APPLIED IN THE MEDICAL SECTOR AND CAN HELP DIAGNOSE IRREGULARITIES WHEN DATA IS PROVIDED FOR THE SPECIFIC AREA ON WHICH THE SYSTEM HAS BEEN TRAINED. LEVERAGING THE NEWFOUND AMOUNT OF BIG DATA AND ADVANCED DIAGNOSTIC TECHNIQUES, HIGHER DIMENSIONAL DATA FEATURE EXTRACTION CAN BE BETTER ANALYZED. THE ALGORITHM PRESENTED IN THIS PAPER UTILIZES A CONVOLUTIONAL NEURAL NETWORK TO CATEGORIZE ELECTROCARDIOGRAM (ECG) DATA BY PROCESSING THE ORIGINAL DATA IMPLEMENTING THE FAST FOURIER TRANSFORM (FFT) AND PRINCIPAL COMPONENT ANALYSIS (PCA) TO REDUCE DIMENSIONALITY WHILE MAINTAINING PERFORMANCE. THE PAPER PROPOSES THREE INTELLIGENT IDENTIFICATION ALGORITHMS THAT CAN BE FED INTO ANOTHER SPECIALIZED MACHINE LEARNING SYSTEM OR ANALYZED USING TRADITIONAL DIAGNOSTIC PROCEDURES.
HOBBYIST ELECTRONICS HAVE GREATLY IMPROVED IN BOTH QUALITY AND CAPABILITY OVER THE PAST SEVERAL YEARS. IT IS NOW POSSIBLE TO SOLVE COMPUTATIONALLY CHALLENGING PROBLEMS WITH EQUIPMENT COSTING LESS THAN A FEW HUNDRED US DOLLARS. BOTH OF THE DATALOGGER CONCEPTS PRESENTED IN THIS WORK LEVERAGE THIS IMPROVEMENT BY USING OFF-THE-SHELF TECHNOLOGY TO REPLACE WHAT COULD PREVIOUSLY ONLY BE DONE WITH EXPENSIVE CUSTOM HARDWARE. THE PROCESSORS USED IN THESE CONCEPTS, THE TEENSY 4.0 AUDIO ADAPTER AND THE CTAG BEAST, WERE ORIGINALLY DESIGNED FOR MUSICIANS WHO REQUIRE THE ABILITY TO MANIPULATE MULTIPLE CHANNELS OF AUDIO SIMULTANEOUSLY. THIS CAPABILITY, HOWEVER, ALSO ENABLES THE CONSTRUCTION OF DATALOGGERS CAPABLE OF RECORDING PERFECTLY SYNCHRONIZED MULTI-CHANNEL AUDIO - A REQUIREMENT FOR PASSIVE PHASED SONAR ARRAYS. EACH DATALOGGER CARRIES THE ADDITIONAL BENEFIT OF LOW POWER CONSUMPTION, PERMITTING THE ARRAY TO BE DEPLOYED FOR SEVERAL HOURS BEFORE REQUIRING RECOVERY. FUTURE VERSIONS OF THE DATALOGGERS ARE EXPECTED TO HAVE MISSION DURATIONS COMPARABLE TO EXISTING COMMERCIAL SYSTEMS. THIS PAPER FOLLOWS THE DEVELOPMENT PROCESS OF BOTH OF THESE CONCEPTS AND COMPARES EACH OF THEIR PERFORMANCE. THE FIRST CONCEPT, THE TEENSY, CONSISTS OF TWO TEENSY 4.0 CONTROL BOARDS, EACH SANDWICHED BETWEEN TWO TEENSY AUDIO ADAPTERS. THIS ASSEMBLY IS CAPABLE OF RECORDING UP TO EIGHT CHANNELS OF AUDIO IN NEAR PERFECT SYNC. THE SECOND CONCEPT, THE BEAST, CONSISTS OF A BEAGLEBONE BLACK SINGLE BOARD COMPUTER AUGMENTED WITH A CTAG BEAST CAPE. THIS SYSTEM IS CAPABLE OF RECORDING EIGHT CHANNELS OF PERFECTLY SYNCHRONIZED AUDIO. BOTH OF THESE SYSTEMS WERE TESTED IN THE FIELD WITH A FOUR-ELEMENT CO-PRIME SONAR ARRAY. THE DATA IS ANALYZED AND THE RESULTS OF THE COMPARISON ARE PRESENTED IN THIS WORK. FINALLY, SOME OPERATIONAL RECOMMENDATIONS AND POSSIBILITIES FOR IMPROVEMENT ARE ALSO DISCUSSED.
THE ADAPTIVITY OF INTELLIGENT TUTORING SYSTEMS RELIES ON THE ACCURACY OF THE STUDENT MODEL AND THE DESIGN OF THE INSTRUCTIONAL POLICY. RECENTLY AN INSTRUCTIONAL POLICY HAS BEEN PRESENTED THAT IS COMPATIBLE WITH ALL COMMON STUDENT MODELS. IN THIS WORK WE PRESENT THE NEXT STEP TOWARDS A UNIVERSAL INSTRUCTIONAL POLICY. WE INTRODUCE A NEW POLICY THAT IS APPLICABLE TO AN EVEN WIDER RANGE OF STUDENT MODELS INCLUDING DBNS MODELING SKILL TOPOLOGIES AND FORGETTING. WE THEORETICALLY AND EMPIRICALLY COMPARE OUR POLICY TO PREVIOUS POLICIES. USING SYNTHETIC AND REAL WORLD DATA SETS WE SHOW THAT OUR POLICY CAN EFFECTIVELY HANDLE WHEEL-SPINNING STUDENTS AS WELL AS FORGETTING ACROSS A WIDE RANGE OF STUDENT MODELS.
PATCH-BASED IMAGE DENOISING APPROACHES HAVE GAINED POPULARITY RECENTLY. WE PROPOSE AN IMAGE DENOISING APPROACH USING SUBSPACES THAT ARE FIT USING AN L-1-NORM CRITERION. THIS NEW APPROACH IS COMPETITIVE WITH EXISTING APPROACHES IN TERMS OF OBJECTIVE ERROR METRICS AND VISUAL FIDELITY, AND HAS THE ADDED BENEFIT THAT IT CAN BE IMPLEMENTED IN PARALLEL FOR LARGE-SCALE APPLICATIONS.
STRUCTURED MULTIDIMENSIONAL DATA IS OFTEN EXPRESSED IN A TENSOR FORMAT. HOWEVER, DUE TO THE LARGE NUMBER OF TERMS, IT CAN BE DIFFICULT TO PROCESS, STORE, INTERPRET, OR EXTRACT PATTERNS FROM DATA IN A RAW TENSOR FORMAT. TO ALLEVIATE THIS, VARIOUS TYPES OF TENSOR DECOMPOSITIONS HAVE BEEN DEVELOPED TO REDUCE THE NUMBER OF TERMS USED TO REPRESENT MULTIDIMENSIONAL DATA, AS WELL AS TO REVEAL UNDERLYING STRUCTURE AND RELATIONSHIPS AMONG THE VARIABLES. THIS ARTICLE WILL EXPLORE VARIATIONS ON TWO TYPES OF TENSOR DECOMPOSITIONS, THE CANDECOMP/PARAFAC AND THE TUCKER DECOMPOSITIONS, AND PERFORM BASELINE COMPARISONS OF THEIR ASSOCIATED ALGORITHMS ON COMMON DATASETS WITH SIMILAR CHOICES OF PARAMETERS. WE PERFORM NUMERICAL EXPERIMENTS ON SYNTHETIC AND REAL-WORLD DATA TO DIRECTLY COMPARE MULTIPLE ALGORITHMS TO APPROXIMATE THE TWO TENSOR DECOMPOSITION TYPES. WE ALSO PRESENT RESULTS COMPARING THE TWO DECOMPOSITION MODELS AND THEIR ALGORITHMS FOR IMAGE DENOISING AND COMPLETION EXAMPLES.
THIS WORK INTRODUCES A MONTE CARLO BASED TECHNIQUE POWERED WITH THE ABILITY TO ESTIMATE THE INDIVIDUAL UNCERTAINTY CONTRIBUTIONS OF EACH MODEL PARAMETER. THE PROPOSED TECHNIQUE UTILIZES THE SO-CALLED PARAMETER SPACE ANALYSIS TO IDENTIFY THE IMPORTANCE OF INFLUENTIAL DEGREES OF FREEDOM (DOFS) WITH RESPECT TO THE UNCERTAINTY QUANTIFICATION PROBLEM. ONCE DETERMINED, THESE DOFS CAN BE USED TO DEFINE AND SOLVE A LINEAR SYSTEM OF EQUATIONS BASED ON LINEARIZING THE MODEL OF INTEREST TO DETERMINE THE UNCERTAINTY CONTRIBUTION OF EACH DOFS IN CONJUNCTION WITH THE MONTE CARLO BASED SAMPLES.
VIDEO CODING IS THE PROCESS OF REDUCING THE HUGE VOLUME OF VIDEO DATA TO A SMALL NUMBER OF BITS. HIGH CODING EFFICIENCY REDUCES THE BANDWIDTH REQUIRED FOR VIDEO STREAMING, AND THE SPACE REQUIRED TO STORE THE VIDEO DATA ON ELECTRONIC DEVICES, WHILE MAINTAINING THE FIDELITY OF THE DECOMPRESSED VIDEO SIGNAL. IN RECENT YEARS, DEEP LEARNING HAS BEEN EXTENSIVELY APPLIED IN THE FIELD OF VIDEO CODING. HOWEVER, IT REMAINS CHALLENGING HOW TO EXPLORE THE INTRA- AND INTER-FRAME CORRELATIONS IN DEEP LEARNING-BASED VIDEO CODING SYSTEMS TO IMPROVE THE CODING EFFICIENCY. IN THIS WORK, WE PROPOSE A HIERARCHICAL MOTION ESTIMATION AND COMPENSATION NETWORK FOR VIDEO COMPRESSION. THE VIDEO FRAMES ARE TAGGED AS INTRA-FRAMES AND INTER-FRAMES. WHILE INTRA-FRAMES ARE COMPRESSED INDEPENDENTLY, THE INTER-FRAMES ARE HIERARCHICALLY PREDICTED BY ADJACENT FRAMES USING A BI-DIRECTIONAL MOTION PREDICTION NETWORK, WHICH RESULTS IN HIGHLY SPARSE AND COMPRESSIBLE RESIDUE. THE RESIDUE FRAMES ARE THEN COMPRESSED VIA SEPARATELY TRAINED RESIDUE CODING NETWORKS. EXPERIMENTAL RESULTS DEMONSTRATE THAT THE PROPOSED HIERARCHICAL DEEP VIDEO COMPRESSION NETWORK OFFERS SIGNIFICANTLY HIGHER CODING EFFICIENCY AND SUPERIOR VISUAL QUALITY COMPARED TO PRIOR ARTS.
BIG DATA HAS BEEN DRIVING PROFESSIONAL SPORTS OVER THE LAST DECADE. IN OUR DATA-DRIVEN WORLD, IT BECOMES IMPORTANT TO FIND ADDITIONAL METHODS FOR THE ANALYSIS OF BOTH GAMES AND ATHLETES. THERE IS AN ABUNDANCE OF VIDEOS TAKEN IN PROFESSIONAL AND AMATEUR SPORTS. PLAYER DATASETS CAN BE CREATED UTILIZING COMPUTER VISION TECHNIQUES. WE PROPOSE A NOVEL APPROACH BY CREATING AN AUTONOMOUS MASKING ALGORITHM THAT CAN RECEIVE LIVE OR PREVIOUSLY RECORDED VIDEO FOOTAGE OF SPORTING EVENTS. THIS PROCEDURE CAN IDENTIFY GRAPHICAL OVERLAYS TO OPTIMIZE FURTHER PROCESSING BY TRACKING AND TEXT RECOGNITION ALGORITHMS FOR REAL-TIME ANALYSIS.
MASSIVE OPEN ONLINE COURSES ARE GAINING POPULARITY WITH MILLIONS OF STUDENTS ENROLLED, THOUSANDS OF COURSES AVAILABLE AND HUNDREDS OF LEARNING INSTITUTIONS INVOLVED. DUE TO THE HIGH NUMBER OF STUDENTS AND THE RELATIVELY SMALL NUMBER OF TUTORS, STUDENT ASSESSMENT, ESPECIALLY FOR COMPLEX TASKS, IS A TYPICAL ISSUE OF SUCH COURSES. THUS, PEER ASSESSMENT IS BECOMING INCREASINGLY POPULAR TO SOLVE SUCH A PROBLEM AND SEVERAL APPROACHES HAVE BEEN PROPOSED SO FAR TO IMPROVE THE RELIABILITY OF ITS OUTCOMES. AMONG THE MOST PROMISING, THERE IS FUZZY ORDINAL PEER ASSESSMENT (FOPA) THAT ADOPTS MODELS COMING FROM FUZZY SET THEORY AND GROUP DECISION MAKING. IN THIS PAPER WE PROPOSE AN EXTENSION OF FOPA SUPPORTING MULTI-CRITERIA ASSESSMENT BASED ON RUBRICS. STUDENTS ARE ASKED TO RANK A SMALL NUMBER OF PEER SUBMISSIONS AGAINST SPECIFIED CRITERIA, THEN PROVIDED RANKINGS ARE TRANSFORMED IN FUZZY PREFERENCE RELATIONS, EXPANDED TO OBTAIN MISSING VALUES AND AGGREGATED TO ESTIMATE FINAL GRADES. RESULTS OBTAINED ARE PROMISING IF COMPARED TO OTHER PEER ASSESSMENT TECHNIQUES BOTH IN THE RECONSTRUCTION OF THE CORRECT RANKING AND ON THE ESTIMATION OF STUDENTS' GRADES.
WE DEVELOP A NEW MODEL AND ALGORITHMS FOR MACHINE LEARNING-BASED LEARNING ANALYTICS, WHICH ESTIMATE A LEARNER'S KNOWLEDGE OF THE CONCEPTS UNDERLYING A DOMAIN, AND CONTENT ANALYTICS, WHICH ESTIMATE THE RELATIONSHIPS AMONG A COLLECTION OF QUESTIONS AND THOSE CONCEPTS. OUR MODEL REPRESENTS THE PROBABILITY THAT A LEARNER PROVIDES THE CORRECT RESPONSE TO A QUESTION IN TERMS OF THREE FACTORS: THEIR UNDERSTANDING OF A SET OF UNDERLYING CONCEPTS, THE CONCEPTS INVOLVED IN EACH QUESTION, AND EACH QUESTION'S INTRINSIC DIFFICULTY. WE ESTIMATE THESE FACTORS GIVEN THE GRADED RESPONSES TO A COLLECTION OF QUESTIONS. THE UNDERLYING ESTIMATION PROBLEM IS ILL-POSED IN GENERAL, ESPECIALLY WHEN ONLY A SUBSET OF THE QUESTIONS ARE ANSWERED. THE KEY OBSERVATION THAT ENABLES A WELL-POSED SOLUTION IS THE FACT THAT TYPICAL EDUCATIONAL DOMAINS OF INTEREST INVOLVE ONLY A SMALL NUMBER OF KEY CONCEPTS. LEVERAGING THIS OBSERVATION, WE DEVELOP BOTH A BI-CONVEX MAXIMUM-LIKELIHOOD-BASED SOLUTION AND A BAYESIAN SOLUTION TO THE RESULTING SPARSE FACTOR ANALYSIS (SPARFA) PROBLEM. WE ALSO INCORPORATE USER-DEFINED TAGS ON QUESTIONS TO FACILITATE THE INTERPRETABILITY OF THE ESTIMATED FACTORS. EXPERIMENTS WITH SYNTHETIC AND REAL-WORLD DATA DEMONSTRATE THE EFFICACY OF OUR APPROACH. FINALLY, WE MAKE A CONNECTION BETWEEN SPARFA AND NOISY, BINARY-VALUED (1-BIT) DICTIONARY LEARNING THAT IS OF INDEPENDENT INTEREST.
THIS DEMO PRESENTS WIKIGLASS, A LEARNING ANALYTIC TOOL FOR VISUALIZING THE STATISTICS AND TIMELINES OF COLLABORATIVE WIKIS BUILT BY SECONDARY SCHOOL STUDENTS DURING THEIR GROUP PROJECT IN INQUIRY-BASED LEARNING. THE TOOL ADOPTS A MODULAR STRUCTURE FOR THE FLEXIBILITY OF REUSE WITH DIFFERENT DATA SOURCES. THE CLIENT SIDE IS BUILT WITH THE MODEL-VIEW-CONTROLLER FRAMEWORK AND THE ANGULARJS LIBRARY WHEREAS THE SERVER SIDE MANAGES THE DATABASE AND DATA SOURCES. THE TOOL IS CURRENTLY USED BY SECONDARY TEACHERS IN HONG KONG AND IS UNDERGOING EVALUATION AND IMPROVEMENT.
ACQUIRING FIELD DATA FOR MACHINE LEARNING-BASED IMAGE ENHANCEMENT TECHNIQUES CAN BE CHALLENGING. THESE TECHNIQUES OFTEN USE A SUPERVISED LEARNING ARCHITECTURE THAT REQUIRES PAIRS OF DEGRADED AND TARGET IMAGES THAT WOULD BE UNFEASIBLE TO ACQUIRE IN THE FIELD. ONE ALTERNATIVE APPROACH IS TO EMPLOY SIMULATION MODELS THAT CAN ACCURATELY CAPTURE THE UNIQUE CHARACTERISTICS OF A DEGRADED VISUAL ENVIRONMENT (DVE). THE FIDELITY OR ACCURACY OF THESE MODELS DETERMINES THE EFFECTIVENESS OF THE FULLY TRAINED IMAGE ENHANCEMENT ALGORITHM. THIS PAPER EXPLORES THE BENEFITS AND DEFICITS OF UTILIZING SIMULATION SOFTWARE TO PROPERLY PORTRAY UNDERWATER LIDAR CAPTURE IN DVES. THIS IS ACCOMPLISHED BY EMPLOYING 3DSMAX STUDIOS TO GENERATE 3D RENDERINGS OF UNDERWATER TARGETS. THE IMAGE SYSTEMS ENGINEERING TOOLBOX FOR CAMERAS (ISETCAM) IS THEN USED TO SYNTHETICALLY GENERATE A TRAINING DATASET SUBJECTIVE AND OBJECTIVE METRICS ARE DEVISED TO MEASURE THE EFFECTIVENESS OF THESE APPROACHES IN TRAINING A GAN-BASED UNDERWATER LIDAR IMAGE ENHANCEMENT ALGORITHM.
CLUSTERING IS AN UNSUPERVISED MACHINE LEARNING TECHNIQUE THAT SERVES TO EXTRACT PATTERNS IN UNLABELED DATASETS BY GROUPING THEIR ELEMENTS BASED ON A SIMILARITY MEASURE. A PRIORI KNOWLEDGE OF THE NUMBER OF CLUSTERS IS NEEDED IN MOST OF THE CLUSTERING TECHNIQUES, WHICH IS BOTH DIFFICULT AND NECESSARY FOR AN EFFECTIVE AND ACCURATE PATTERN RECOGNITION AND LATENT (NOT DIRECTLY OBSERVABLE) FEATURE ANALYSIS. RECENTLY, GRAPH BASED SYMMETRIC NONNEGATIVE MATRIX FACTORIZATION (SYMMNMF) HAS BEEN DEMONSTRATED TO PERFORM BETTER THAN K-MEANS AND SPECTRAL CLUSTERING. HERE, WE PRESENT A CONSENSUS CLUSTERING BASED ON ROBUST RESAMPLING TECHNIQUE WHICH IN CONJUNCTION WITH SYMMNMF AND PROPORTION OF AMBIGUOUS CLUSTERING (PAC) CRITERION PERFORMS A ROBUST GRAPHICAL CLUSTERING AND ACCURATE IDENTIFICATION OF THE NUMBER OF CLUSTERS IN SEVERAL NON-CONVEX BENCHMARK DATASETS.
ALTHOUGH SINGLE-FRAME MACHINE LEARNING IMAGE RESTORATION TECHNIQUES HAVE BEEN SHOWN TO BE EFFECTIVE, THE PROPOSED MULTIFRAME APPROACH TAKES ADVANTAGE OF BOTH SPATIAL AND TEMPORAL INFORMATION TO RESOLVE HIGH-RESOLUTION AND HIGH-DYNAMICRANGE IMAGES. THE PROPOSED ALGORITHM IS AN EXTENSION OF THE PREVIOUSLY PROPOSED ALGORITHM DEBLURGAN-C AND AIMS TO FURTHER IMPROVE THE CAPABILITIES OF IMAGE RESTORATION IN DEGRADED VISUAL ENVIRONMENTS. THE MAIN CONTRIBUTIONS OF THE PROPOSED TECHNIQUES INCLUDE: 1) DEVELOPMENT OF AN EFFECTIVE FRAMEWORK TO GENERATE A MULTI-FRAME TRAINING DATASET TYPICAL OF DEGRADED VISUAL ENVIRONMENTS; 2) ADOPTING A MULTI-FRAME IMAGE RESTORATION FRAMEWORK THAT GENERATES A SINGLE RESTORED IMAGE AS THE OUTPUT; 3) CONDUCTING SUBSTANTIAL EXPERIMENTS AGAINST THE GENERATED MULTI-FRAME TRAINING DATASET AND DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED MULTI-FRAME IMAGE ENHANCEMENT ALGORITHM.
IN THIS WORK, WE CONSIDER COMPRESSED SENSING BASED POOLED TESTING, WHERE K OUT OF N ITEMS ARE DEFECTIVE (WITH A NON-ZERO STATE). EACH TIME, A SUBSET OF ITEMS ARE MIXED TOGETHER, AND A REAL-VALUED QUANTITATIVE MEASUREMENT IS OBTAINED, WHERE THE MEASUREMENT EQUALS A RANDOM LINEAR COMBINATION OF THE STATES OF THE MIXED ITEMS. OUR OBJECTIVE IS TO DETECT THE K DEFECTIVE ITEMS BASED ON THE QUANTITATIVE MEASUREMENTS. THIS PROBLEM ARISES IN A VARIETY OF APPLICATIONS, INCLUDING VIRAL INFECTION DIAGNOSIS, NETWORK STATE INFERENCE, ETC. WE ASSUME THAT EACH ITEM CAN BE MIXED IN A LIMITED NUMBER OF TESTS, AND THE MIXING COEFFICIENTS ARE DRAWN INDEPENDENTLY ACCORDING TO A STANDARD GAUSSIAN DISTRIBUTION. WE OBTAIN SUFFICIENT CONDITIONS ON THE NUMBER OF TESTS REQUIRED FOR THE EXACT DETECTION OF THE DEFECTIVE ITEMS USING AN EXHAUSTIVE SEARCH DECODER. OUR RESULT INDICATES THAT THE SAMPLE COMPLEXITY SCALES IN THE ORDER OF O(K/(RHO) OVER CAP LOG(N/K), WHERE (RHO) OVER CAP IS APPROXIMATELY THE MINIMUM OF THE N PROPORTIONS OF TESTS THAT INCLUDE INDIVIDUAL ITEMS. OUR RESULT RECOVERS THE OPTIMAL SAMPLE COMPLEXITY IN COMPRESSIVE SENSING WHEN (RHO) OVER CAP = 1. THE PERFORMANCE OF THE EXHAUSTIVE SEARCH DECODER IS EVALUATED NUMERICALLY UNDER VARIOUS ASSUMPTIONS ON THE MIXING CONSTRAINTS, SIGNAL TO NOISE RATIO, AND SPARSITY LEVEL.
THE TECHNIQUES FOR FUSION OF MULTI-SPECTRAL SENSOR DATASETS CONTAINING COMPLEMENTARY INFORMATION ABOUT THE SUBJECT OF OBSERVATION FACILITATE VISUALIZATION OF THE DATA INTO A FORM MORE EASILY INTERPRETED BY BOTH HUMANS AND ALGORITHMS. MANY APPLICATIONS OF FEATURE-LEVEL FUSION SEEK TO COMBINE EDGES AND TEXTURES, ACROSS THE BANDWIDTH OF THE SENSORY SPECTRUM. VISUALIZATION TECHNIQUES CAN BE SKEWED BY THE INTRODUCTION OF CORRUPTION AND REDUNDANCIES INDUCED BY HARMONICS. A MAJORITY OF IMAGE FUSION TECHNIQUES RELY ON INTENSITY HUE SATURATION (IHS) TRANSFORMS, PRINCIPAL COMPONENT ANALYSIS (PCA), AND GRAM SCHMIDT PROCESS. PCA'S ABILITY TO REMOVE THE REDUNDANCY FROM A SET OF CORRELATED DATA WHILE PRESERVING THE VARIANCE AND ITS RESISTANCE TO COLOR DISTORTION LENDS ITSELF TO THIS DATA-FUSION APPLICATION. PCA ALSO HAS A LOWER SPECTRAL DISTORTION AS COMPARED TO IHS AND HAS BEEN FOUND TO CREATE SUPERIOR IMAGE FUSION. THE APPLICATION OF NEURAL NETWORK CONTROL TECHNIQUES HAS BEEN SHOWN TO MORE ACCURATELY RECREATE RESULTS SIMILAR TO THOSE FOUND BY HUMAN INFERENCE. OVER THE YEARS, INCREASED COMPUTATION POWER HAS GIVEN RISE TO THE SPREAD OF NEURAL NETWORKS INTO ROLES PREVIOUSLY CARRIED OUT BY HUMANS. SELECT ADVANCED IMAGE PROCESSING TECHNIQUES HAVE BENEFITED GREATLY FROM THEIR IMPLEMENTATION. WE PROPOSE A NOVEL METHOD OF UTILIZING PCA IN CONJUNCTION WITH A NEURAL NETWORK TO ACHIEVE A HIGHER QUALITY OF IMAGE FUSION. IMPLEMENTATION OF AN AUTOENCODER NEURAL NETWORK TO FUSE THIS INFORMATION CREATES A HIGHER LEVEL OF DATA VISUALIZATION WHEN COMPARED TO TRADITIONAL WEIGHTED FUSION TECHNIQUES.
THIS PAPER CONSIDERS HUMAN ACTIVITY CLASSIFICATION FOR AN INDOOR RADAR SYSTEM. HUMAN MOTIONS GENERATE NON-STATIONARY RADAR RETURNS WHICH REPRESENT DOPPLER AND MICRO-DOPPLER SIGNALS. THE TIME-FREQUENCY (TF) ANALYSIS OF MICRO-DOPPLER SIGNALS CAN DISCERN SUBTLE VARIATIONS ON THE MOTION BY PRECISELY REVEALING VELOCITY COMPONENTS OF VARIOUS MOVING BODY PARTS. WE CONSIDER RADAR FOR ACTIVITY MONITORING USING TF-BASED MACHINE LEARNING APPROACH EXPLOITING BOTH TEMPORAL AND SPATIAL DEGREES OF FREEDOM. THE PROPOSED APPROACH CAPTURES DIFFERENT HUMAN MOTION REPRESENTATIONS MORE VIVIDLY IN JOINT-VARIABLE DATA DOMAINS ACHIEVED THROUGH BEAMFORMING AT THE RECEIVER. THE RADAR DATA IS COLLECTED USING REAL TIME MEASUREMENTS AT 77 GHZ USING FOUR RECEIVE ANTENNAS, AND SUBSEQUENTLY MICRO-DOPPLER SIGNATURES ARE ANALYZED THROUGH MACHINE LEARNING ALGORITHM FOR CLASSIFICATIONS OF HUMAN WALKING MOTIONS. WE PRESENT THE PERFORMANCE OF THE PROPOSED MULTI ANTENNA APPROACH IN SEPARATING AND CLASSIFYING TWO CLOSELY WALKING PERSONS MOVING IN OPPOSITE DIRECTIONS.
DUE TO THE HIGH NUMBER OF STUDENTS ENROLLED AND THE RELATIVELY SMALL NUMBER OF AVAILABLE TUTORS, THE ASSESSMENT OF COMPLEX ASSIGNMENTS IS DEEMED AS ONE OF THE MOST CRITICAL TASKS IN MASSIVE OPEN ON-LINE COURSES (MOOCS). PEER ASSESSMENT IS BECOMING AN INCREASINGLY POPULAR TOOL TO FACE THIS PROBLEM AND MANY APPROACHES HAVE BEEN PROPOSED SO FAR TO MAKE ITS OUTCOMES MORE RELIABLE. A PROMISING APPROACH IS FOPA (FUZZY ORDINAL PEER ASSESSMENT) THAT ADOPTS AND INTEGRATES MODELS COMING FROM FUZZY SET THEORY AND GROUP DECISION MAKING. IN THIS PAPER WE PROPOSE A FOPA EXTENSION SUPPORTING MULTI-CRITERIA ASSESSMENT BASED ON RUBRICS. STUDENTS ARE ASKED TO RANK A SMALL NUMBER OF PEER SUBMISSIONS AGAINST SPECIFIED CRITERIA, PROVIDED RANKINGS ARE THEN TRANSFORMED IN FUZZY PREFERENCE RELATIONS, EXPANDED TO OBTAIN MISSING VALUES AND AGGREGATED TO ESTABLISH A GLOBAL RANKING BETWEEN STUDENTS' WORKS WITH RESPECT TO EACH CRITERION AND GLOBALLY. THE ABSOLUTE GRADES OF ALL SUBMISSIONS ARE THEN CALCULATED.
CONSIDERABLE ATTENTION HAS BEEN GIVEN TO METHODS FOR KNOWLEDGE ESTIMATION, A CATEGORY OF METHODS FOR AUTOMATIC ASSESSMENT OF A STUDENT'S DEGREE OF SKILL MASTERY OR KNOWLEDGE AT A SPECIFIC TIME. KNOWLEDGE ESTIMATION IS FREQUENTLY USED TO MAKE DECISIONS ABOUT WHEN A STUDENT HAS REACHED MASTERY AND IS READY TO ADVANCE TO NEW MATERIAL, BUT THERE HAS BEEN LITTLE WORK TO FORECAST HOW FAR A STUDENT IS FROM MASTERY OR PREDICT HOW MUCH MORE PRACTICE THE STUDENT WILL NEED BEFORE HE OR SHE WILL REACH MASTERY. THIS ARTICLE PRESENTS A METHOD FOR PREDICTING THE POINT AT WHICH A STUDENT WILL REACH SKILL MASTERY WITHIN AN ADAPTIVE LEARNING SYSTEM, BASED ON CURRENT APPROACHES TO ESTIMATING STUDENT KNOWLEDGE. WE APPLY THIS TECHNIQUE TO TWO POPULAR METHODS OF MODELING STUDENT LEARNING - BAYESIAN KNOWLEDGE TRACING AND PERFORMANCE FACTORS ANALYSIS - AND COMPARE PREDICTION CORRECTNESS. POTENTIAL APPLICATIONS AND FUTURE STEPS FOR IMPROVING THE METHOD ARE DISCUSSED.
IN THIS PAPER, WE PRESENT THE RESULTS OF AN EXPLORATORY STUDY THAT EXAMINED THE PROBLEM OF AUTOMATING CONTENT ANALYSIS OF STUDENT ONLINE DISCUSSION TRANSCRIPTS. WE LOOKED AT THE PROBLEM OF CODING DISCUSSION TRANSCRIPTS FOR THE LEVELS OF COGNITIVE PRESENCE, ONE OF THE THREE MAIN CONSTRUCTS IN THE COMMUNITY OF INQUIRY (COI) MODEL OF DISTANCE EDUCATION. USING COH-METRIX AND LIWC FEATURES, TOGETHER WITH A SET OF CUSTOM FEATURES DEVELOPED TO CAPTURE DISCUSSION CONTEXT, WE DEVELOPED A RANDOM FOREST CLASSIFICATION SYSTEM THAT ACHIEVED 70.3% CLASSIFICATION ACCURACY AND 0.63 COHEN'S KAPPA, WHICH IS SIGNIFICANTLY HIGHER THAN VALUES REPORTED IN THE PREVIOUS STUDIES. BESIDES IMPROVEMENT IN CLASSIFICATION ACCURACY, THE DEVELOPED SYSTEM IS ALSO LESS SENSITIVE TO OVERFITTING AS IT USES ONLY 205 CLASSIFICATION FEATURES, WHICH IS AROUND 100 TIMES LESS FEATURES THAN IN SIMILAR SYSTEMS BASED ON BAG-OF-WORDS FEATURES. WE ALSO PROVIDE AN OVERVIEW OF THE CLASSIFICATION FEATURES MOST INDICATIVE OF THE DIFFERENT PHASES OF COGNITIVE PRESENCE THAT GIVES AN ADDITIONAL INSIGHTS INTO THE NATURE OF COGNITIVE PRESENCE LEARNING CYCLE. OVERALL, OUR RESULTS SHOW GREAT POTENTIAL OF THE PROPOSED APPROACH, WITH AN ADDED BENEFIT OF PROVIDING FURTHER CHARACTERIZATION OF THE COGNITIVE PRESENCE CODING SCHEME.
RESEARCH IN FORMAL EDUCATION HAS REPEATEDLY OFFERED EVIDENCE OF THE IMPORTANCE OF SOCIAL INTERACTIONS FOR STUDENT LEARNING. HOWEVER, IT REMAINS UNCLEAR WHETHER THE DEVELOPMENT OF SUCH INTERPERSONAL RELATIONSHIPS HAS THE SAME INFLUENCE ON LEARNING IN THE CONTEXT OF LARGE-SCALE OPEN ONLINE LEARNING. FOR INSTANCE, IN MOOCS GROUP MEMBERS FREQUENTLY CHANGE AND THE VOLUME OF INTERACTIONS CAN QUICKLY AMASS TO CHAOS, THEREFORE IMPEDING AN INDIVIDUAL'S PROPENSITY TO FOSTER MEANINGFUL RELATIONSHIPS. THIS PAPER EXAMINED A MOOC FOR ITS POTENTIAL TO DEVELOP SOCIAL PROCESSES. AS IT IS EXCEEDINGLY DIFFICULT TO ESTABLISH A RELATIONSHIP WITH SOMEBODY WHO SELDOM ACCESSES A MOOC DISCUSSION, WE SINGLED OUT A COHORT DEFINED BY ITS PARTICIPANTS' REGULARITY OF FORUM PRESENCE. THE STUDY, ANALYSED THIS 'COHORT' AND ITS DEVELOPMENT, IN COMPARISON TO THE ENTIRE MOOC LEARNER NETWORK. MIXED METHODS OF SOCIAL NETWORK ANALYSIS (SNA), CONTENT ANALYSIS AND STATISTICAL NETWORK MODELLING, REVEALED THE POTENTIAL FOR UNFOLDING SOCIAL PROCESSES AMONG A MORE PERSISTENT GROUP OF LEARNERS IN THE MOOC SETTING.
MANY PEOPLE WHO DO NOT KNOW ENGLISH HAVE MOVED TO ENGLISH-SPEAKING COUNTRIES TO LEARN ENGLISH. ONCE THERE, THEY LEARN ENGLISH THROUGH FORMAL AND INFORMAL METHODS. WHILE CONSIDERABLE WORK HAS STUDIED THE EXPERIENCES OF ENGLISH LANGUAGE LEARNERS IN DIFFERENT LEARNING ENVIRONMENTS, WE HAVE YET TO SEE ANALYTICS THAT DETAIL THE EXPERIENCES OF THIS POPULATION WITHIN FORMAL AND INFORMAL LEARNING ENVIRONMENTS. THIS STUDY USED THE EXPERIENCE SAMPLING METHODOLOGY TO CAPTURE THE INFORMATION THAT IS NEEDED TO DETAIL THE COMMUNICATION AND AFFECTIVE EXPERIENCES OF ADVANCED ENGLISH LANGUAGE LEARNERS. THE COLLECTED DATA REVEALS DIFFERENCES IN HOW ENGLISH LANGUAGE LEARNERS PERCEIVED THEIR COMMUNICATION SUCCESS BASED ON THEIR LEARNING CONTEXT, WITH HIGHER LEVELS OF COMMUNICATIVE SUCCESS EXPERIENCED IN FORMAL LEARNING SETTINGS. NO SUCH DIFFERENCES WERE FOUND FOR LEARNERS', HIGHLY NEGATIVE, AFFECT. THE DATA SUGGEST A NEED FOR ADDITIONAL EMOTIONAL SUPPORT WITHIN FORMAL AND INFORMAL LEARNING ENVIRONMENTS AS WELL AS A NEED FOR ORAL COMMUNICATION SUPPORT WITHIN INFORMAL CONTEXTS.
THIS STUDY ATTEMPTS TO INVESTIGATE TO WHAT EXTENT INDICATORS OF ACADEMIC WRITING AND COGNITIVE THINKING CAN HELP MEASURE THE WRITING QUALITY OF GROUP COLLABORATIVE WRITINGS ON WIKIS. PARTICULARLY, COMPARISONS WERE MADE ON WIKI CONTENT IN DIFFERENT STAGES OF THE PROJECTS. PRELIMINARY RESULTS FROM A MULTIPLE LINEAR REGRESSION ANALYSIS REVEAL THAT LINGUISTIC INDICATORS SUCH AS ENGAGEMENT MARKERS AND SELF-MENTION WERE SIGNIFICANT PREDICTORS IN EARLIER STAGES TO THE PROJECTS, WHEREAS VERBS INDICATING COGNITIVE THINKING IN THE EVALUATION LEVEL WERE SIGNIFICANT IN LATER PROJECT STAGES.
DIGITAL PLATFORMS AND LEARNING ANALYTICS ARE BECOMING INCREASINGLY WIDESPREAD IN THE EDUCATION SECTOR: COMMERCIAL CORPORATIONS ARGUE THEIR BENEFITS FOR TEACHING AND LEARNING, THEREBY ENDORSING THE CONTINUOUS AUTOMATED COLLECTION AND PROCESSING OF STUDENT DATA FOR MEASUREMENT, ASSESSMENT, MANAGEMENT, AND IDENTITY FORMATION. LARGELY MISSING IN THESE DISCOURSES, HOWEVER, ARE THE POTENTIAL COSTS OF DATAFICATION FOR PUPILS' AND TEACHERS' AGENCY AND THE MEANING OF EDUCATION ITSELF. THIS ARTICLE EXPLORES THE GENERAL DISCURSIVE FRAMING BY WHICH THESE SURVEILLANT PRACTICES IN EDUCATION HAVE COME TO SEEM NATURAL. THROUGH A STUDY OF COMMERCIAL SUPPLIERS OF EDUCATIONAL PLATFORMS, WE SHOW HOW THE PREVAILING VISION OF DATAFICATION IN THEIR DISCOURSES CATEGORISES SOFTWARE SYSTEMS, NOT TEACHERS, AS CENTRAL TO EDUCATION, REIMAGINING SPACE, TIME, AND AGENCY WITHIN EDUCATIONAL PROCESSES AROUND THE ORGANISATION OF DATA SYSTEMS AND THE DEMANDS OF COMMERCIAL DATA PRODUCTION. NOT ONLY DOES THIS LEGITIMATE THE NEW CONNECTIVE ENVIRONMENT OF DATAVEILLANCE (THAT IS, SURVEILLANCE THROUGH DATA PROCESSING), BUT IT ALSO NATURALISES A WIDERNORMATIVEENVIRONMENT IN WHICH TEACHERS AND STUDENTS ARE ASSIGNED NEW ROLES AND RESPONSIBILITIES. IN THE PROCESS, THE PANOPTIC POSSIBILITIES OF UBIQUITOUS COMMERCIAL ACCESS TO PERSONAL EDUCATIONAL DATA ARE PRESENTED AS PART OF A VIRTUOUS CIRCLE OF KNOWLEDGE PRODUCTION AND EVEN TRAINING FOR GOOD CITIZENSHIP. THIS BROADER RETHINKING OF EDUCATIONTHROUGHSURVEILLANCE MUST ITSELF BE CRITIQUED.
STUDENTS OFTEN ENTER HIGHER EDUCATION ACADEMICALLY UNPREPARED AND WITH UNREALISTIC PERCEPTIONS AND EXPECTATIONS REGARDING ACADEMIC COMPETENCIES FOR THEIR STUDIES. HOWEVER, PREPAREDNESS AND REALISTIC PERCEPTIONS ARE IMPORTANT FACTORS FOR STUDENT RETENTION. WITH REGARD TO A PROPOSED MODEL OF FIVE ACADEMIC COMPETENCIES (TIME MANAGEMENT, LEARNING SKILLS, TECHNOLOGY PROFICIENCY, SELF-MONITORING, AND RESEARCH SKILLS), INCOMING STUDENTS' PERCEPTIONS CONCERNING ACADEMIC STAFF SUPPORT AND STUDENTS' SELF-REPORTED CONFIDENCE AT A GERMAN UNIVERSITY WERE EXAMINED. USING QUANTITATIVE DATA, AN INITIAL EXPLORATORY STUDY WAS CONDUCTED (N = 155), WHICH REVEALED FIRST-YEAR STUDENTS' PERCEPTIONS OF THE ROLE OF ACADEMIC STAFF IN SUPPORTING THEIR DEVELOPMENT, ESPECIALLY IN RESEARCH SKILLS, AS WELL AS LOW SELF-REPORTED CONFIDENCE IN THIS COMPETENCE. THUS, A FOLLOW UP STUDY (N = 717) WAS CONDUCTED TO CONFIRM THESE FINDINGS AS WELL AS TO PROVIDE AN IN-DEPTH UNDERSTANDING OF RESEARCH SKILLS. UNDERSTANDING STUDENTS' PERCEPTIONS IS CRUCIAL IF HIGHER EDUCATION INSTITUTIONS ARE TO MEET STUDENTS' NEEDS AND PROVIDE ADEQUATE SUPPORT SERVICES IN THE CHALLENGING FIRST YEAR. THUS, IN ORDER TO INCREASE STUDENT RETENTION, IT IS SUGGESTED THAT UNIVERSITIES ASSIST FIRST-YEAR STUDENTS IN DEVELOPING ACADEMIC COMPETENCIES THROUGH PERSONALISED COMPETENCE-BASED PROGRAMS AND WITH THE HELP OF EMERGING RESEARCH FIELDS AND EDUCATIONAL TECHNOLOGIES SUCH AS LEARNING ANALYTICS AND DIGITAL BADGES.
THE PURPOSE OF THIS PAPER IS TO INTRODUCE THE CONSENSUAL ASSESSMENT TECHNIQUE (CAT) TO LIBRARY, INFORMATION, AND LEARNING SPACES AS AN APPROACH FOR ASSESSING THE CREATIVITY, INNOVATIVENESS, AND VALUE OF PRODUCTS. THIS PAPER PROPOSES ADAPTATION OF THE CAT TO ACCOMMODATE EVERYDAY, REAL-WORLD ACTIVITIES INTEGRAL TO LIBRARY AND LEARNING PRACTICES, RESEARCH, AND SUPPORT FOR TECHNOLOGY-RICH LEARNING ENVIRONMENTS. BASED ON CASE STUDY RESEARCH, AN AWARE-ENABLED SOCIAL RADIO TOOL IN A POSTSECONDARY VIRTUAL DISTRIBUTED EDUCATIONAL ENVIRONMENT IS USED AS AN EXAMPLE TO ILLUSTRATE THE APPLICATION AND POTENTIAL OF THE TECHNIQUE. CONTRIBUTING TO THE LITERATURE ACROSS MULTIPLE DOMAINS (E.G., ASSESSMENT, CREATIVITY, INFORMATION SCIENCE, LEARNING, AND INNOVATION), THIS PAPER HAS THE POTENTIAL TO PROVOKE NEW OR REVISED APPROACHES TO ASSESSMENT FOR PRACTICE AND RESEARCH. THIS PAPER ALSO GIVES RISE TO NEW PARTNERING AND COLLABORATIVE OPPORTUNITIES FOR LIBRARIES, LEARNERS, AND EDUCATORS IN RELATION TO DATA AND LEARNING ANALYTICS AND OTHER EMERGING DIGITAL LEARNING ISSUES. A CONCEPTUAL FRAMEWORK FOR INTEGRATION OF THE CAT INTO LIBRARY AND LEARNING SPACES IS ADVANCED. THE PROPOSED FRAMEWORK IS EXPECTED TO IGNITE INTEREST IN THE CAT, OPENING THE WAY FOR EXTENSION OF THE TECHNIQUE TO 21ST CENTURY TECHNOLOGY-RICH LIBRARY AND LEARNING SPACES WITH IMPLICATIONS FOR PRACTICE, RESEARCH, AND METHODOLOGY.
A GREAT AMOUNT OF WORK HAS BEEN DONE USING DEEP LEARNING STRATEGIES IN SEQUENCE MODELING, SUCH AS THE ENCODERDECODER LSTM. MOST SOLUTIONS FOR THIS TYPE OF PROBLEM INVOLVE RECURRENT NEURAL NETWORKS. THIS APPROACH, WHILE OFTEN YIELDING SUPERIOR RESULTS, HAS ITS DOWNSIDES. TRAINING TIME CAN SOMETIMES BE PROHIBITIVELY LONG AND THE ONLY REMEDY FOR THAT IS HIGHER COMPUTING POWER. ADDITIONALLY, THESE MODELS ARE EXTREMELY DIFFICULT TO INTERPRET, REGARDLESS OF THEIR PERFORMANCE. THE DEEPER AND MORE COMPLEX THE NETWORK, THE HARDER IT IS TO MAKE SENSE OF IT. NEURAL NETWORKS LACK A SIMPLE REPRESENTATION OF THE KNOWLEDGE THEY LEARN. RULE-BASED LEARNERS, HOWEVER, ARE THE OPPOSITE IN THIS REGARD. THEY REPRESENT THE KNOWLEDGE THEY LEARN THROUGH RELATIONAL RULES, WHICH ARE EASILY DIGESTED BY A HUMAN. WHILE MOST RULE-BASED LEARNERS ARE DESIGNED WITH THE INTENT OF DISCOVERING DEPENDENCIES BETWEEN VARIABLES, SUCH AS IN ASSOCIATION RULE LEARNING, SOMETIMES RULE-BASED LEARNERS CAN BE IMPLEMENTED IN A SUPERVISED SETTING. FOR SEQUENCE MODELING, THOUGH, RULE-BASED SOLUTIONS ARE SCARCE, ESPECIALLY IF WE CONSIDER THE CASE WHERE INPUTS AND TARGETS ARE VARIABLE LENGTH SEQUENCES. WE PROPOSE A TYPE OF ARCHITECTURE THAT UTILIZES BINARY TREES AND EVOLUTIONARY ALGORITHMS TO DISCOVER RULES. THESE MODELS MAKE INFERENCES THROUGH SEQUENTIAL ACTIONS, SIMILARLY TO HOW REINFORCEMENT LEARNING AGENTS PROGRESS THROUGH TASKS BY CHOOSING ACTIONS BASED ON THE STATE OF THE ENVIRONMENT. THESE NEW LEARNERS CAN PREDICT ON VARIOUS DATA TYPES, INCLUDING MULTI-OUTPUT AND SEQUENCES OF VARIABLE LENGTH. WE CONSIDER A SIMPLE PROBLEM WHERE THE INPUTS AND TARGETS ARE VARIABLE LENGTH SEQUENCES. OUR STRATEGY PERFECTLY LEARNS THE RULES FROM A SYNTHETIC DATASET. LASTLY, WE DISCUSS HOW TO APPLY THE STRATEGY MORE GENERALLY.
COMPLETION RATES FOR MASSIVE OPEN ONLINE CLASSES (MOOCS) ARE NOTORIOUSLY LOW. IDENTIFYING STUDENT PATTERNS RELATED TO COURSE COMPLETION MAY HELP TO DEVELOP INTERVENTIONS THAT CAN IMPROVE RETENTION AND LEARNING OUTCOMES IN MOOCS. PREVIOUS RESEARCH PREDICTING MOOC COMPLETION HAS FOCUSED ON CLICK-STREAM DATA, STUDENT DEMOGRAPHICS, AND NATURAL LANGUAGE PROCESSING (NLP) ANALYSES. HOWEVER, MOST OF THESE ANALYSES HAVE NOT TAKEN FULL ADVANTAGE OF THE MULTIPLE TYPES OF DATA AVAILABLE. THIS STUDY COMBINES CLICK-STREAM DATA AND NLP APPROACHES TO EXAMINE IF STUDENTS' ON-LINE ACTIVITY AND THE LANGUAGE THEY PRODUCE IN THE ONLINE DISCUSSION FORUM IS PREDICTIVE OF SUCCESSFUL CLASS COMPLETION. WE STUDY THIS ANALYSIS IN THE CONTEXT OF A SUBSAMPLE OF 320 STUDENTS WHO COMPLETED AT LEAST ONE GRADED ASSIGNMENT AND PRODUCED AT LEAST 50 WORDS IN DISCUSSION FORUMS, IN A MOOC ON EDUCATIONAL DATA MINING. THE FINDINGS INDICATE THAT A MIX OF CLICK-STREAM DATA AND NLP INDICES CAN PREDICT WITH SUBSTANTIAL ACCURACY (78%) WHETHER STUDENTS COMPLETE THE MOOC. THIS PREDICTIVE POWER SUGGESTS THAT STUDENT INTERACTION DATA AND LANGUAGE DATA WITHIN A MOOC CAN HELP US BOTH TO UNDERSTAND STUDENT RETENTION IN MOOCS AND TO DEVELOP AUTOMATED SIGNALS OF STUDENT SUCCESS.
DATA-DRIVEN METHODS HAVE PREVIOUSLY BEEN USED IN INTELLIGENT TUTORING SYSTEMS TO IMPROVE STUDENT LEARNING OUTCOMES AND PREDICT STUDENT LEARNING METHODS. WE HAVE BEEN INCORPORATING DATA-DRIVEN METHODS FOR FEEDBACK AND PROBLEM SELECTION INTO DEEP THOUGHT, A LOGIC TUTOR WHERE STUDENTS PRACTICE CONSTRUCTING DEDUCTIVE LOGIC PROOFS. IN THIS LATEST STUDY WE HAVE IMPLEMENTED OUR DATA-DRIVEN PROFICIENCY PROFILER (DDPP) INTO DEEP THOUGHT AS A PROOF OF CONCEPT. THE DDPP DETERMINES STUDENT PROFICIENCY WITHOUT EXPERT INVOLVEMENT BY COMPARING RELEVANT STUDENT RULE SCORES TO PREVIOUS STUDENTS WHO BEHAVED SIMILARLY IN THE TUTOR AND SUCCESSFULLY COMPLETED IT. THE RESULTS SHOW THAT THE DDPP DID IMPROVE IN PERFORMANCE WITH ADDITIONAL DATA AND PROVED TO BE AN EFFECTIVE PROOF OF CONCEPT.
BACKGROUND PROBLEM-SOLVING FOLLOWED BY INSTRUCTION (PS-I) IS A POWERFUL DESIGN SHOWN TO TRANSFORM STUDENTS' CONCEPTUAL UNDERSTANDING AND TRANSFER. WITHIN PS-I, NO RESEARCH HAS EXAMINED HOW MOMENT-BY-MOMENT DETERMINANTS OF AFFECTIVE STATES IMPACT THE PROBLEM-SOLVING PHASE AND POSTTEST PERFORMANCE. METHODS I DEVELOP A MULTIMODAL LEARNING ANALYTICS PIPELINE TO (A) INFER AFFECTIVE STATES IN PS-I VIA OBSERVABLE FACIAL MOVEMENTS, (B) UNDERSTAND HOW THE INCIDENCE AND TEMPORAL DYNAMICS OF THESE STATES VARY BASED ON MANIPULATING THE PROBLEM-SOLVING CONTEXT WITH SCAFFOLDING STRATEGIES (FAILURE-DRIVEN, SUCCESS-DRIVEN, NONE) IN AN EXPERIMENTAL STUDY (N = 132), AND (C) ASSESS THE EXTENT TO WHICH AFFECTIVE STATES MIGHT EXPLAIN LEARNING. FINDINGS STUDENTS EXPOSED TO FAILURE-DRIVEN SCAFFOLDING SHOW EXCLUSIVE DYNAMICS COMPRISING SHAME, A SELF-CONSCIOUS EMOTION ASSOCIATED WITH METACOGNITIVE AND COGNITIVE BENEFITS. FAILURE-DRIVEN SCAFFOLDING ALSO CREATES OPPORTUNITIES FOR RELATIVELY GREATER EMOTIONAL DISPLAYS OF KNOWLEDGE EMOTIONS (E.G., SURPRISE, INTEREST). HOSTILE EMOTIONS DIFFERENTIALLY IMPACT LEARNING IN PS-I, WITH THE INCIDENCE OF ANGER AND DISGUST SHOWING POSITIVE ASSOCIATIONS AND THE INCIDENCE OF CONTEMPT SHOWING A NEGATIVE ASSOCIATION. FINALLY, PLEASURABLE EMOTIONS (E.G., HAPPINESS) POSITIVELY ASSOCIATE WITH ISOMORPHIC POSTTEST PERFORMANCE BUT NEGATIVELY ASSOCIATE WITH NON-ISOMORPHIC AND TRANSFER POSTTESTS. CONTRIBUTION OVERT CHANGES IN FACIAL MOVEMENTS REFLECTIVE OF STUDENTS EXPERIENCING NEGATIVE EMOTIONAL STATES ACT AS CATALYSTS FOR LEARNING.
WELFARE AND ECONOMIC DEVELOPMENT IS DIRECTLY DEPENDENT ON THE AVAILABILITY OF HIGHLY SKILLED AND EDUCATED INDIVIDUALS IN SOCIETY. IN THE UK, HIGHER EDUCATION IS ACCESSED BY A LARGE PERCENTAGE OF HIGH SCHOOL GRADUATES (50% IN 2017). STILL, IN BRAZIL, A LIMITED NUMBER OF PUPILS LEAVING HIGH SCHOOLS CONTINUE THEIR EDUCATION (UP TO 20%). INITIAL PIONEERING EFFORTS OF UNIVERSITIES AND COMPANIES TO SUPPORT PUPILS FROM UNDERPRIVILEGED BACKGROUNDS, TO BE ABLE TO SUCCEED IN BEING ACCEPTED BY UNIVERSITIES INCLUDE PERSONALISED LEARNING SOLUTIONS. HOWEVER, INITIAL FINDINGS SHOW THAT TYPICAL DISTANCE LEARNING PROBLEMS OCCUR WITH THE PUPIL POPULATION: ISOLATION, DEMOTIVATION, AND LACK OF ENGAGEMENT. THUS, RESEARCHERS AND COMPANIES PROPOSED GAMIFICATION. HOWEVER, GAMIFICATION DESIGN IS TRADITIONALLY EXCLUSIVELY BASED ON THEORY-DRIVEN APPROACHES AND USUALLY IGNORE THE DATA ITSELF. THIS PAPER TAKES A DIFFERENT APPROACH, PRESENTING A LARGE-SCALE STUDY THAT ANALYSED, STATISTICALLY AND VIA MACHINE LEARNING (DEEP AND SHALLOW), THE FIRST BATCH OF STUDENTS TRAINED WITH A BRAZILIAN GAMIFIED INTELLIGENT LEARNING SOFTWARE (CALLED CAMALEON), TO ESTABLISH, VIA A GRASSROOTS METHOD BASED ON LEARNING ANALYTICS, HOW GAMIFICATION ELEMENTS IMPACT ON STUDENT ENGAGEMENT. THE EXERCISE RESULTS IN A NOVEL PROPOSAL FOR REALTIME MEASUREMENT ON MASSIVE OPEN ONLINE COURSES (MOOCS), POTENTIALLY LEADING TO ITERATIVE IMPROVEMENTS OF STUDENT SUPPORT. IT ALSO SPECIFICALLY ANALYSES THE ENGAGEMENT PATTERNS OF AN UNDERSERVED COMMUNITY.
MACHINE LEARNING (ML) IS EXPECTED, IN THE NEAR FUTURE, TO PROVIDE VARIOUS VENUES AND EFFECTIVE TOOLS TO IMPROVE EDUCATION IN GENERAL, AND SCIENCE-TECHNOLOGY-ENGINEERING-MATHEMATICS (STEM) EDUCATION IN PARTICULAR. THE GARTNER ANALYTICS ASCENDANCY MODEL REQUIRES THE USE OF FOUR TYPES OF DATA ANALYTICS TO BE CONSIDERED COMPREHENSIVE: DESCRIPTIVE, DIAGNOSTIC, PREDICTIVE AND PRESCRIPTIVE DATA ANALYTICS. THIS PAPER PRESENTS THE OUTCOMES OF A RESEARCH AND DEVELOPMENT PROJECT AT BRADLEY UNIVERSITY (PEORIA, IL, USA) AIMED AT THE SETUP AND BENCHMARKING OF EIGHT ML ALGORITHMS FOR PREDICTIVE LEARNING ANALYTICS, SPECIFICALLY, A PREDICTION OF STUDENT ACADEMIC PERFORMANCE IN A COURSE. THE ANALYZED AND TESTED ML ALGORITHMS INCLUDE LINEAR REGRESSION, LOGISTIC REGRESSION, K-NEAREST NEIGHBOR CLASSIFICATION, NAIVE BAYES CLASSIFICATION, ARTIFICIAL NEURAL NETWORK REGRESSION AND CLASSIFICATION, DECISION TREE CLASSIFICATION, RANDOM FOREST CLASSIFICATION, AND SUPPORT VECTOR MACHINE CLASSIFICATION. BASED ON THE OBTAINED ACCURACY OF THE ANALYZED AND TESTED ML ALGORITHMS, WE HAVE FORMULATED A SET OF RECOMMENDATIONS FOR FACULTY AND PRACTITIONERS IN TERMS OF SELECTION, SETUP AND UTILIZATION OF ML ALGORITHMS IN PREDICTIVE ANALYTICS IN STEM EDUCATION. WE ALSO PERFORMED FORMATIVE AND SUMMATIVE SURVEYS OF UNDERGRADUATE AND GRADUATE STUDENTS IN COMPUTER SCIENCE AND COMPUTER INFORMATION SYSTEMS COURSES TO UNDERSTAND THEIR OPINION ABOUT UTILIZATION OF ML-BASED PREDICTIVE ANALYTICS IN EDUCATION; A SUMMARY OF OBTAINED STUDENT FEEDBACK IS PRESENTED IN THIS PAPER.
6 MODERN ONLINE EDUCATION (ELEARNING) NEEDS ARE BEING EVOLVED ACCORDINGLY WITH MORE AND MORE DEMANDING PEDAGOGICAL AND TECHNOLOGICAL REQUIREMENTS. ON ONE HAND, ADVANCED LEARNING RESOURCES, SUCH AS INTERACTIVE VIDEO-LECTURES, 3D SIMULATIONS, SERIOUS GAMES AND VIRTUAL LABORATORIES ARE BASED ON COSTLY COMPUTATIONAL INFRASTRUCTURES. ON THE OTHER HAND, ELEARNING NEEDS INCLUDE SUPPORTING THE LATEST LEARNING METHODOLOGIES AND STRATEGIES, SUCH AS LEARNING ANALYTICS, GAMIFICATION AND FORMATIVE ASSESSMENT, WHICH REQUIRE EFFECTIVE REAL-TIME PROCESSING AND ANALYSIS OF MASSIVE DATA AS WELL AS INTEROPERABILITY WITH EXTERNAL SYSTEMS. HOWEVER, THESE FUNCTIONAL ELEARNING ADVANCES ARE ESPECIALLY FRUSTRATING WHEN NON-FUNCTIONAL REQUIREMENTS ARE NOT MET APPROPRIATELY, SUCH AS SCALABILITY, PERFORMANCE AND INTEROPERABILITY, HAVING CONSIDERABLE REPERCUSSIONS ON THE LEARNING OUTCOMES AS THEIR LACK IMPEDES THE EXPECTED LEARNING FLOW. THIS PAPER PRESENTS AN OVERVIEW OF THE EFFORTS TACKLED SO FAR OF USING DISTRIBUTED COMPUTING FOR THE ENHANCEMENT OF CURRENT ELEARNING BY SHOWING THE APPROACHES AND THE RESULTS ACHIEVED OF SOME REAL APPLICATIONS OF THESE TECHNOLOGIES TO REAL CONTEXT OF ELEARNING. THE NOVELTY OF THIS APPROACH IS TO COMBINE THE PROVISION OF COMPLEX AND ADVANCED SOFTWARE SUPPORT TO MEET CHALLENGING FUNCTIONAL ELEARNING NEEDS WITH THE BENEFITS OF PROVIDING POWERFUL DISTRIBUTED AND HIGH PERFORMANCE COMPUTING TO ALLEVIATE DEMANDING NON-FUNCTIONAL REQUIREMENTS ALSO TO BE MET IN THIS CONTEXT.
THE COLLECTION OF EVIDENCE FROM AUTHENTIC CLASSROOM PRACTICE IS IMPORTANT BOTH FOR TEACHER PROFESSIONAL DEVELOPMENT (TPD) AND EDUCATIONAL RESEARCH. HOWEVER, MOST CURRENT LEARNING ANALYTICS (LA) FAILS TO CAPTURE THE PHYSICAL OCCURRENCES OF THE CLASSROOM, AND DO NOT ALWAYS ADDRESS INDIVIDUAL TEACHERS' NEEDS. MORE CUSTOMIZABLE FORMS OF CLASSROOM DATA COLLECTION (E.G., THROUGH VIDEO-RECORDINGS OR BY HUMAN OBSERVERS) ARE TIME-CONSUMING AND EXPENSIVE TO IMPLEMENT. NAVIGATING THIS TRADEOFF BETWEEN THE PERSONALIZATION OF DATA COLLECTION AND THE STRICT TIME/EFFORT CONSTRAINTS OF CLASSROOM PRACTICE IS STILL AN UNSOLVED CHALLENGE FOR DESIGNERS OF LA AND TEACHING ANALYTICS (TA) SYSTEMS TO BE USED IN FACE-TO-FACE CLASSROOMS. IN THIS PAPER, WE EXTRACT LESSONS LEARNT FROM A DESIGN-BASED RESEARCH PROCESS THAT EXPLORES THIS TRADEOFF, TOWARDS THE DEVELOPMENT OF TEACHER-DRIVEN, PERSONALIZABLE DATA COLLECTION TOOLS. THROUGH A SURVEY OF 15 EXPERT TEACHERS AND PAPER AND SOFTWARE PROTOTYPE TESTING WITH A TOTAL OF 14 TEACHERS IN SIMULATED AND AUTHENTIC SETTINGS, WE GATHERED INFORMATION ABOUT TEACHERS' PREFERENCES FOR TEACHER-DRIVEN DATA COLLECTION IN CLASSROOMS, AND DERIVED DESIGN INSIGHTS (E.G., A COLD-START PROBLEM AND THE NEED FOR MULTIPLE LAYERS IN THE PERSONALIZATION), WHICH CAN BE USEFUL FOR THE DESIGN OF TA/LA TOOLS THAT COLLECT PERSONALIZED DATA FROM EVERYDAY CLASSROOMS.
INNOVATIVE PEDAGOGIES HAVE SIGNIFICANTLY IMPACTED HEALTH PROFESSIONS' EDUCATION, DENTAL EDUCATION INCLUDED. IN THIS CONTEXT, FACULTY, DEFINED IN THIS STUDY AS INSTRUCTOR IN HIGHER EDUCATION, HAS BEEN INCREASINGLY REQUIRED TO HONE THEIR INSTRUCTIONAL SKILLS. THE PURPOSE OF THIS EXPLORATORY STUDY WAS TO SHARE THE DESIGN, IMPLEMENTATION AND PRELIMINARY OUTCOMES OF TWO PROGRAMMES TO ENHANCE DENTAL FACULTY'S INSTRUCTIONAL SKILLS, THE TEACHING AND LEARNING SEMINAR SERIES AND THE COURSE DIRECTOR ORIENTATION. DATA SOURCES INCLUDED FACULTY AND STUDENT SURVEYS DEVELOPED AND ADMINISTERED BY THE RESEARCHERS; DATA EXTRACTED FROM THE LEARNING MANAGEMENT SYSTEM; REPORTS FROM THE LEARNING ANALYTICS TOOL; AND CLASSROOM OBSERVATIONS. PARTICIPANTS' SATISFACTION, SELF-REPORTED LEARNING, INSTRUCTIONAL BEHAVIOURAL CHANGE, AND IMPACT ON STUDENT LEARNING BEHAVIOURS AND INSTITUTIONAL PRACTICE WERE ASSESSED BORROWING FROM KIRKPATRICK'S 4-LEVEL MODEL OF EVALUATION OF PROFESSIONAL DEVELOPMENT EFFECTIVENESS. INITIAL FINDINGS SHOWED THAT FACULTY IN BOTH PROGRAMMES REPORTED POSITIVE LEARNING EXPERIENCES. PARTICIPANTS REPORTED THAT THE PROGRAMMES MOTIVATED THEM TO IMPROVE INSTRUCTIONAL PRACTICE AND IMPROVED THEIR KNOWLEDGE OF INSTRUCTIONAL INNOVATION. SOME FACULTY REPORTED IMPLEMENTATION OF NEW INSTRUCTIONAL STRATEGIES AND TOOLS, WHICH HELPED CREATE AN ACTIVE AND INTERACTIVE LEARNING ENVIRONMENT THAT WAS WELCOMED BY THEIR STUDENTS. THE STUDY CONTRIBUTES TO LITERATURE AND BEST PRACTICE IN HEALTH SCIENCES FACULTY DEVELOPMENT IN PEDAGOGY AND MAY GUIDE OTHER DENTAL SCHOOLS IN DESIGNING PROFESSIONAL DEVELOPMENT PROGRAMMES.
THE DATA STORED IN E-LEARNING AND ACADEMIC SOCIAL NETWORKING SYSTEM DATABASES CAN REVEAL USEFUL INSIGHT INTO THE BEHAVIOR OF THE USERS OF THESE SYSTEMS. THE STATISTICAL ANALYSIS AND MACHINE LEARNING TECHNIQUES CAN BE USED TO MAKE MEANINGFUL INFERENCES FROM THIS DATA. MOREOVER, EDUCATION DATA MINING (EDM) AND LEARNING ANALYTICS (LA) PRACTICES CAN HELP EDUCATORS AND ADMINISTRATORS TO TAKE EDUCATED DECISIONS RELATED TO THE WELLBEING OF LEARNERS AND THE LEARNING SYSTEM OVERALL. IN THIS PAPER, WE PRESENT DESCRIPTIVE STATISTICAL ANALYSIS OF SCHOLAT E-LEARNING DATA TO EXPOSE INTERESTING INFORMATION ABOUT THE BEHAVIOR OF LEARNERS AND THE DATA GATHERING PRACTICES. THE DATA WAS FETCHED FROM SCHOLAT COURSES MODULE DATABASE, AND THEN UNIVARIATE AND BIVARIATE ANALYSIS WAS PERFORMED TO GET AN INSIGHT. THIS DATA REPRESENTS THE ACTIVITIES OF STUDENTS WHEN THEY WERE UNDERGOING A BLENDED LEARNING COURSE. THE ANALYSIS REVEALED THAT STUDENTS' INTEREST LIES IN ACTIVITIES WHICH ARE RELATED TO IMPROVE THEIR GRADES, E.G. SUBMITTING HOMEWORK ON TIME WHERE MEAN VALUE APPROACHES THE MAXIMUM VALUE. HOWEVER, THEY DO NOT SHOW THE SAME ENTHUSIASM IN OTHER ACTIVITIES. MOREOVER, A LOOPHOLE WAS ALSO DISCOVERED IN DATA GATHERING PRACTICES AS A RESULT OF ANALYSIS. THE ANALYSIS LAYS A FOUNDATION FOR MORE COMPLICATED ANALYSIS IN FUTURE AND IMPROVING THE SYSTEM'S DATA GATHERING PRACTICES.
RECENT ADVANCES IN DEEP LEARNING HAVE ACHIEVED GREAT SUCCESS IN FUNDAMENTAL COMPUTER VISION TASKS SUCH AS CLASSIFICATION, DETECTION AND SEGMENTATION. NEVERTHELESS, THE RESEARCH EFFORT IN DEEP LEARNING-BASED VIDEO CODING IS STILL IN ITS INFANCY. STATE-OF-THE-ART DEEP VIDEO CODING NETWORKS EXPLORE TEMPORAL CORRELATIONS BY MEANS OF FRAME-LEVEL MOTION ESTIMATION AND MOTION COMPENSATION, WHICH REQUIRE HIGH COMPUTATIONAL COMPLEXITY DUE TO THE FRAME SIZE, WHILE EXISTING BLOCK-LEVEL INTER-FRAME PREDICTION SCHEMES UTILIZE ONLY THE CO-LOCATED BLOCKS IN PRECEDING FRAMES, WHICH DID NOT CONSIDER OBJECT MOTIONS. IN THIS WORK, WE PROPOSE A NOVEL MOTION-AWARE DEEP VIDEO CODING NETWORK, IN WHICH INTER-FRAME CORRELATIONS ARE EFFECTIVELY EXPLORED VIA A BLOCK-LEVEL MOTION COMPENSATION NETWORK. EXPERIMENTAL RESULTS DEMONSTRATE THAT THE PROPOSED INTER-FRAME DEEP VIDEO CODING MODEL SIGNIFICANTLY IMPROVES THE DECODING QUALITY UNDER THE SAME COMPRESSION RATIO.
E-LEARNING IS A RESPONSE TO THE NEW EDUCATIONAL NEEDS OF SOCIETY AND AN IMPORTANT DEVELOPMENT IN INFORMATION AND COMMUNICATION TECHNOLOGIES BECAUSE IT REPRESENTS THE FUTURE OF THE TEACHING AND LEARNING PROCESSES. HOWEVER, THIS TREND PRESENTS MANY CHALLENGES, SUCH AS THE PROCESSING OF ONLINE FORUMS WHICH GENERATE A HUGE NUMBER OF MESSAGES WITH AN UNORDERED STRUCTURE AND A GREAT VARIETY OF TOPICS. THESE FORUMS PROVIDE AN EXCELLENT PLATFORM FOR LEARNING AND CONNECTING STUDENTS OF A SUBJECT BUT THE DIFFICULTY OF FOLLOWING AND SEARCHING THE VAST VOLUME OF INFORMATION THAT THEY GENERATE MAY BE COUNTERPRODUCTIVE. THE MAIN GOAL OF THIS PAPER IS TO REVIEW THE APPROACHES AND TECHNIQUES RELATED TO ONLINE COURSES IN ORDER TO PRESENT A SET OF LEARNING ANALYTICS TECHNIQUES AND A GENERAL ARCHITECTURE THAT SOLVE THE MAIN CHALLENGES FOUND IN THE STATE OF THE ART BY MANAGING THEM IN A MORE EFFICIENT WAY: 1) EFFICIENT TRACKING AND MONITORING OF FORUMS GENERATED; 2) DESIGN OF EFFECTIVE SEARCH MECHANISMS FOR QUESTIONS AND ANSWERS IN THE FORUMS; AND 3) EXTRACTION OF RELEVANT KEY PERFORMANCE INDICATORS WITH THE OBJECTIVE OF CARRYING OUT AN EFFICIENT MANAGEMENT OF ONLINE FORUMS. IN OUR PROPOSAL, NATURAL LANGUAGE PROCESSING, CLUSTERING, INFORMATION RETRIEVAL, QUESTION ANSWERING, AND DATA MINING TECHNIQUES WILL BE USED.
IN RECENT YEARS, MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BECOME A PHENOMENON OFFERING THE POSSIBILITY TO TEACH THOUSANDS OF PARTICIPANTS SIMULTANEOUSLY. IN THE SAME TIME THE PLATFORMS USED TO DELIVER THESE COURSES ARE STILL IN THEIR FLEDGLING STAGES. WHILE COURSE CONTENT AND DIDACTICS OF THOSE MASSIVE COURSES ARE THE PRIMARY KEY FACTORS FOR THE SUCCESS OF COURSES, STILL A SMART PLATFORM MAY INCREASE OR DECREASE THE LEARNERS EXPERIENCE AND HIS LEARNING OUTCOME. THE PAPER AT HAND PROPOSES THE USAGE OF AN A/B TESTING FRAMEWORK THAT IS ABLE TO BE USED WITHIN AN MICRO-SERVICE ARCHITECTURE TO VALIDATE HYPOTHESES ABOUT HOW LEARNERS USE THE PLATFORM AND TO ENABLE DATA-DRIVEN DECISIONS ABOUT NEW FEATURES AND SETTINGS. TO EVALUATE THIS FRAMEWORK THREE NEW FEATURES (ONBOARDING TOUR, REMINDER MAILS AND A PINBOARD DIGEST) HAVE BEEN IDENTIFIED BASED ON A USER SURVEY. THEY HAVE BEEN IMPLEMENTED AND INTRODUCED ON TWO LARGE MOOC PLATFORMS AND THEIR INFLUENCE ON THE LEARNERS BEHAVIOR HAVE BEEN MEASURED. FINALLY THIS PAPER PROPOSES A DATA DRIVEN DECISION WORKFLOW FOR THE INTRODUCTION OF NEW FEATURES AND SETTINGS ON E-LEARNING PLATFORMS.
FROM MASSIVE OPEN ONLINE COURSES (MOOC) TO THE SMALLER SCALE USE OF LEARNING MANAGEMENT SYSTEMS, MANY STUDENTS INTERACT WITH ONLINE PLATFORMS THAT ARE MEANT TO SUPPORT LEARNING. INVESTIGATIONS INTO THE USE OF THESE SYSTEMS HAVE CONSIDERED HOW WELL STUDENTS LEARN WHEN CERTAIN APPROACHES ARE EMPLOYED. HOWEVER, WE DO NOT FULLY UNDERSTAND HOW COURSE TYPE OR SYSTEM DESIGN INFLUENCE STUDENT ACTIONS AND EXPERIENCES, MEANING PRIOR FINDINGS CANNOT BE PROPERLY INTERPRETED AND USED BECAUSE WE DO NOT UNDERSTAND HOW THESE FACTORS INFLUENCE THOSE FINDINGS. ACCORDINGLY, WE CONDUCTED A STUDY TO COMPARE STUDENT EXPERIENCES AND BEHAVIORS ACROSS LEARNING MANAGEMENT SYSTEMS AND COURSES FROM A LEARNING ANALYTICS PERSPECTIVE. DIFFERENCES IN STUDENT BEHAVIORS AND EXPERIENCES HIGHLIGHT HOW SYSTEM DESIGN AND THE NATURE OF THE COURSE INTERACT: STUDENTS REPORTED INCREASED LEARNING SUPPORT WHEN USING A SYSTEM THAT FOREGROUNDS STUDENT INTERACTION THROUGH DISCUSSION FORUMS, BUT THIS RELATIONSHIP DID NOT HOLD ACROSS ALL COURSE TYPES. SIMILARLY, STUDENTS FROM THE CONTENT-DELIVERY FOCUSED SYSTEM SPENT MORE TIME ONLINE WHILE FEELING LESS SUPPORTED REGARDLESS OF WHICH TYPE OF COURSE THEY WERE TAKING. THIS NEWLY FOUND EVIDENCE FOR THE OFTEN-INTERRELATED INFLUENCE THAT THE COURSE AND SYSTEM HAVE ON STUDENT EXPERIENCES AND BEHAVIORS SHOULD THEREFORE BE CONSIDERED WHEN SELECTING A SYSTEM TO MEET PARTICULAR PEDAGOGICAL GOALS.
EMERGING TECHNOLOGIES OFFER AN OPPORTUNITY FOR THE DEVELOPMENT, AT THE INSTITUTIONAL LEVEL, OF QUALITY PROCESSES WITH GREATER CAPACITY TO ENHANCE LEARNING IN HIGHER EDUCATION THAN AVAILABLE THROUGH CURRENT QUALITY PROCESSES. THESE SYSTEMS OFFER THE POTENTIAL TO EXTEND USE OF LEARNING ANALYTICS IN INSTITUTIONAL-LEVEL QUALITY PROCESSES IN ADDITION TO THE WIDESPREAD FOCUS ON BUSINESS ANALYTICS, AND TO DELIVER WELL-CONSTRUCTED MIXES OF INFORMATION FROM DIFFERENT DATA SOURCES. BORROWED FROM MUSIC AMPLIFICATION, THE TERM FOLD-BACK IS PROPOSED AS A WAY TO DESCRIBE SUCH A MIX. THIS PAPER BEGINS THE DESIGN-RESEARCH PROJECT OF DESIGNING EFFECTIVE FOLD-BACK SYSTEMS BY EXPANDING THE THEORETICAL ASSUMPTIONS ABOUT LEARNING EMBEDDED IN HIGHER EDUCATION QUALITY PROCESSES. A NUMBER OF THEORIES BUILDING ON VYGOTSKY'S CULTURAL-HISTORICAL APPROACH ARE DISCUSSED TO IMAGINE QUALITY IN HIGHER EDUCATION IN TERMS OF WHAT STUDENTS ACTUALLY DO AND HOW THEY ENGAGE IN ADDITION TO WHAT THE INSTITUTION DOES. THE DISCUSSION IS SUMMARISED IN A FOLD-BACK MATRIX CAPTURING THE SORTS OF EVALUATION QUESTIONS THE SYSTEMS MIGHT ADDRESS. THE PAPER CONCLUDES BY PROVIDING TWO INITIAL DESIGN SKETCHES FOR RE-PURPOSING EMERGING TECHNOLOGIES WITH THE CAPACITY TO SUPPORT EXPANDED QUALITY PROCESSES IN EDUCATION. THESE SKETCHES ARE BASED ON THE EXPERIENCE APPLICATION PROGRAMMING INTERFACE (XAPI) AND DEDOOSE TECHNOLOGIES.
WE STUDY HOW THE USE OF ONLINE LEARNING SYSTEMS STIMULATE COGNITIVE ACTIVITIES, BY CONDUCTING AN EXPERIMENT WITH THE USE OF EYE TRACKING TECHNOLOGY TO MONITOR EYE FIXATIONS OF 60 FINAL YEAR STUDENTS ENGAGING IN ONLINE INTERACTIVE TUTORIALS AT THE START OF THEIR FINAL YEAR PROJECT MODULE. OUR FINDINGS SHOW THAT THE STUDENTS' VISUAL SCANNING BEHAVIOURS FALL INTO THREE DIFFERENT TYPES OF EYE FIXATION PATTERNS, AND THE DATA CORRESPONDING TO THE DIFFERENT TYPES RELATES TO THE PERFORMANCE OF THE STUDENTS IN OTHER RELATED ACADEMIC MODULES. WE CONCLUDE THAT THIS METHOD OF STUDYING EYE FIXATION PATTERNS CAN IDENTIFY DIFFERENT TYPES OF LEARNERS WITH RESPECT TO COGNITIVE ACTIVITIES AND ACADEMIC POTENTIALS, ALLOWING EDUCATORS TO UNDERSTAND HOW THEIR INSTRUCTIONAL DESIGN USING ONLINE LEARNING ENVIRONMENTS CAN STIMULATE HIGHER-ORDER COGNITIVE ACTIVITIES.
STUDENT MISTAKES ARE OFTEN NOT RANDOM BUT, RATHER, REFLECT THOUGHTFUL YET INCORRECT STRATEGIES. IN ORDER FOR EDUCATIONAL TECHNOLOGIES TO MAKE FULL USE OF STUDENTS' PERFORMANCE DATA TO ESTIMATE THE KNOWLEDGE OF A STUDENT, IT IS IMPORTANT TO MODEL NOT ONLY THE CONCEPTIONS BUT ALSO THE MISCONCEPTIONS THAT A STUDENT'S PARTICULAR PATTERN OF SUCCESSES AND ERRORS MAY INDICATE. THE STUDENT MODELS THAT DRIVE THE "OUTER LOOP" OF INTELLIGENT TUTORING SYSTEMS TYPICALLY DO NOT REPRESENT OR TRACK MISCONCEPTIONS. HERE, WE PRESENT A METHOD OF REPRESENTING MISCONCEPTIONS IN THE KNOWLEDGE COMPONENT MODELS, OR Q-MATRICES, THAT ARE USED BY STUDENT MODELS TO ESTIMATE LATENT KNOWLEDGE. WE SHOW, IN A CASE STUDY ON A FRACTION ARITHMETIC DATASET, THAT INCORPORATING A MISCONCEPTION INTO THE KNOWLEDGE COMPONENT MODEL DRAMATICALLY IMPROVES THE OVERALL MODEL'S FIT TO DATA. WE ALSO DERIVE QUALITATIVE INSIGHTS FROM COMPARING PREDICTED LEARNING CURVES ACROSS MODELS THAT INCORPORATE VARYING MISCONCEPTION-RELATED PARAMETERS. FINALLY, WE SHOW THAT THE INCLUSION OF A MISCONCEPTION IN THE KNOWLEDGE COMPONENT MODEL CAN YIELD INDIVIDUAL STUDENT ESTIMATES OF MISCONCEPTION STRENGTH THAT ARE SIGNIFICANTLY CORRELATED WITH OUT-OF-TUTOR MEASURES OF STUDENT ERRORS.
PREREQUISITE SKILL STRUCTURES HAVE BEEN CLOSELY STUDIED IN PAST YEARS LEADING TO MANY DATA-INTENSIVE METHODS AIMED AT REFINING SUCH STRUCTURES. WHILE MANY OF THESE PROPOSED METHODS HAVE YIELDED SUCCESS, DEFINING AND REFINING HIERARCHIES OF SKILL RELATIONSHIPS ARE OFTEN DIFFICULT TASKS. THE RELATIONSHIP BETWEEN SKILLS IN A GRAPH COULD EITHER BE CAUSAL, THEREFORE, A PREREQUISITE RELATIONSHIP (SKILL A MUST BE LEARNED BEFORE SKILL B). THE RELATIONSHIP MAY BE NONCAUSAL, IN WHICH CASE THE ORDERING OF SKILLS DOES NOT MATTER AND MAY INDICATE THAT BOTH SKILLS ARE PREREQUISITES OF ANOTHER SKILL. IN THIS STUDY, WE PROPOSE A SIMPLE, EFFECTIVE METHOD OF DETERMINING THE STRENGTH OF PRE-TO-POST-REQUISITE SKILL RELATIONSHIPS. WE THEN COMPARE OUR RESULTS WITH A TEACHER-LEVEL SURVEY ABOUT THE STRENGTH OF THE RELATIONSHIPS OF THE OBSERVED SKILLS AND FIND THAT THE SURVEY RESULTS LARGELY CONFIRM OUR FINDINGS IN THE DATA-DRIVEN APPROACH.
MODELS OF LEARNING IN EDM AND LAK ARE PUSHING THE BOUNDARIES OF WHAT CAN BE MEASURED FROM LARGE QUANTITIES OF HISTORICAL DATA. WHEN CONTROLLED RANDOMIZATION IS PRESENT IN THE LEARNING PLATFORM, SUCH AS RANDOMIZED ORDERING OF PROBLEMS WITHIN A PROBLEM SET, NATURAL QUASI-RANDOMIZED CONTROLLED STUDIES CAN BE CONDUCTED, POST-HOC. DIFFICULTY AND LEARNING GAIN ATTRIBUTION ARE AMONG FACTORS OF INTEREST THAT CAN BE STUDIED WITH SECONDARY ANALYSES UNDER THESE CONDITIONS. HOWEVER, MUCH OF THE CONTENT THAT WE MIGHT LIKE TO EVALUATE FOR LEARNING VALUE IS NOT ADMINISTERED AS A RANDOM STIMULUS TO STUDENTS BUT INSTEAD IS BEING SELF-SELECTED, SUCH AS A STUDENT CHOOSING TO SEEK HELP IN THE DISCUSSION FORUMS, WIKI PAGES, OR OTHER PEDAGOGICALLY RELEVANT MATERIAL IN ONLINE COURSEWARE. HELP SEEKERS, BY VIRTUE OF THEIR MOTIVATION TO SEEK HELP, TEND TO BE THE ONES WHO HAVE THE LEAST KNOWLEDGE. WHEN PRESENTED WITH A COHORT OF STUDENTS WITH A BI-MODAL OR UNIFORM KNOWLEDGE DISTRIBUTION, THIS CAN PRESENT PROBLEMS WITH MODEL INTERPRETABILITY WHEN A SINGLE POINT ESTIMATION IS USED TO REPRESENT COHORT PRIOR KNOWLEDGE. SINCE RESOURCE ACCESS IS INDICATIVE OF A LOW KNOWLEDGE STUDENT, A MODEL CAN TEND TOWARDS ATTRIBUTING THE RESOURCES WITH LOW OR NEGATIVE LEARNING GAIN IN ORDER TO BETTER EXPLAIN PERFORMANCE GIVEN THE HIGHER AVERAGE PRIOR POINT ESTIMATE. IN THIS PAPER WE PRESENT SEVERAL INDIVIDUALIZED PRIOR STRATEGIES AND DEMONSTRATE HOW LEARNING EFFICACY ATTRIBUTION VALIDITY AND PREDICTION ACCURACY IMPROVE AS A RESULT. LEVEL OF EDUCATION ATTAINED, RELATIVE PAST ASSESSMENT PERFORMANCE, AND THE PRIOR PER STUDENT COLD START HEURISTIC WERE EMPLOYED AND COMPARED AS PRIOR KNOWLEDGE INDIVIDUALIZATION STRATEGIES.
CYBER SECURITY OFFERS DIGITAL DATA SETS THAT ALLOW LEARNING ANALYSIS, FOR EXAMPLE, TO USE TRACKS TO ENHANCE THE LEARNING PROCESS AND ENVIRONMENT THAT LEARNERS LEAVE BEHIND. FOCUS ON USING LEARNING ANALYTICS IN DATA SECURITY TRAINING TO PROVIDE A MORE EVIDENCE-BASED SYSTEMIC METHOD FOR EVALUATING THE EFFECTS OF LEARNING AND DEVELOPING MORE SUCCESSFUL LEARNING. FOR EDUCATORS, ORGANIZERS, AND CYBER BUSINESS ENTREPRENEURS, THERE IS A CRUCIAL FACTOR TO REMEMBER. THE ARTICLE AIMS PRIMARILY TO EXPLORE ALL CURRENT APPROACHES TO DATA HACKING THAT USE CYBERSECURITY LEARNING. SPECIFICALLY, THE PROPOSED UM-LSS-CS SOLUTION PROVIDES A WARNING INFORMATION-BASED USER-CENTERED MACHINE LEARNING SYSTEM ARCHITECTURE TO DETERMINE USER RISK. THE STRATEGY HELPS SECURITY ANALYSTS TO OBTAIN A FULL RISK RANKING FOR A DEVICE, AND A SAFETY ANALYST WILL CONCENTRATE ON USERS WITH HIGH-RISK RATINGS. FINALLY, THE APPROACHES OF THE PROPOSED METHOD TO BE OBTAINED IN UM-LSS-CS ARE AN INCREASING NUMBER OF LEARNER'S ACTIVITIES RATIO IS 86.2%, PERFORMANCE ORIENTATION OF LEARNERS' IN ML RATIO IS 89.05%, INSECURE INFORMATION REDUCED VIA CYBERSECURITY RATIO IS 88.6%, THE ACCURACY OF THE LEARNING SYSTEM IN USER-CENTRIC APPROACHES RATIO IS 91.3%, AND OVERALL PERFORMANCE RATIO IS 97.2% ARE ESTIMATED.
THIS CHAPTER REVIEWS ACTIONABLE DATA USE-BOTH AS AN UMBRELLA TERM AND AS A SPECIFIC CONCEPT-DEVELOPED IN THREE DIFFERENT TRADITIONS THAT DATA/INFORMATION CAN INFORM AND GUIDE P-20 EDUCATIONAL PRACTICE TOWARD BETTER OUTCOMES. THE LITERATURES REVIEWED ARE KNOWN AS DATA-DRIVEN DECISION MAKING (DDDM), EDUCATION DATA MINING (EDM), AND LEARNING ANALYTICS (LA). DDDM IS GROUNDED IN K-12 SETTINGS, HAS A SOCIAL ORIENTATION, AND IS SHAPED BY POLICY. EDM AND LA BEGAN IN HIGHER EDUCATION USING DATA PROVIDED BY INSTRUCTIONAL TOOLS. THIS REVIEW OF MORE THAN 1,500 PUBLICATIONS TRACED PATTERNS IN THESE COMMUNITIES REVEALING DISCIPLINARY DISCONNECTS BETWEEN DDDM AND EDM/LA. RECOGNIZING INFORMATION'S SYSTEMIC NATURE, THIS REVIEW EXPANDED THE ANALYSIS FROM TEACHER PRACTICE TO EDUCATOR PRACTICE. WHILE METHODOLOGICAL PROGRESS HAS BEEN MADE IN ALL AREAS, STUDIES OF IMPACT WERE CONCENTRATED IN DDDM. EDM AND LA FOCUS ON TOOLS FOR CURRENT/FUTURE EDUCATIONAL SETTINGS AND LEVERAGING DATA HARVESTED FOR BASIC RESEARCH WHILE RECONCEIVING LEARNING PRACTICES. THE DDDM IMPACT STUDIES DID NOT SUPPORT A DIRECTLY BENEFICIAL MODEL FOR DATA USE. RATHER, LONG TIMESCALE CAPACITY FACTORS, INCLUDING CULTURAL AND ORGANIZATIONAL PROCESSES THAT IMPACT DATA USE WERE REVEALED. A COMPLEMENTARY MODEL OF COMPONENTS, INFRASTRUCTURE, AND CAPACITY IS ADVANCED WITH RECOMMENDATIONS FOR SCHOLARSHIP IN EDUCATION'S SOCIOTECHNICAL FUTURE.
ONLINE EDUCATION HAS BEEN GOING THROUGH NUMEROUS TRANSFORMATIONS AS NEW AND INNOVATIVE TECHNOLOGIES INFLUENCE AND SHAPE NEW E-LEARNING PORTALS. DIFFERENTIATED E-LEARNING PROMISES TO ADD VALUE AND ENHANCE THE EDUCATIONAL SERVICES PROVIDED BY AN ACADEMIC INSTITUTION. IN THIS PAPER WE PRESENT OUR ONLINE LEARNING MODEL THAT ADVOCATES AND ENDORSES DIFFERENTIATED LEARNING AS AN E-LEARNING AFFORDANCE THAT HAS BEEN FACILITATED THROUGH THE DEVELOPMENT OF NEW LEARNING TECHNOLOGIES. WE DEMONSTRATE HOW THE ONLINE PORTAL ENABLES AND SUPPORTS MULTIPLE INSTANCES WHEREBY DIFFERENTIATED LEARNING IS APPLIED AND PRACTICED, INCLUDING THROUGH THE USE OF A NOVEL ANALYTICS TOOL THAT SUMS UP THE OVERALL LEARNER EFFORT IN ONE VISUAL. THE PAPER ADVANCES THE NOTION OF "PRODUCTIVE DIVERSITY" IN LEARNING, REPLACING THE TEMPLATED SAMENESS CHARACTERIZING THE COMMUNICATIVE PRACTICES OF "DIDACTIC PEDAGOGY," INCLUDING TEXTBOOKS, LECTURES, TESTS. TODAY'S COMPUTER-MEDIATED, NETWORKED LEARNING ENVIRONMENTS CAN SUPPORT DIFFERENTIATED LEARNING ON A NUMBER OF DIMENSIONS, WHERE STUDENTS ARE ABLE TO WORK AT THEIR OWN PACE, CHOOSE THEIR OWN TOPICS WITHIN A GENERAL DISCIPLINARY RUBRIC, AND OFFER EACH OTHER FEEDBACK IN SUCH A WAY THAT DIFFERENCES IN PERSPECTIVE BECOME A VALUABLE RESOURCE FOR LEARNING. THE PAPER CONCLUDES BY DEMONSTRATING A TECHNOLOGY THAT ATTEMPTS TO TRANSLATE THESE PRINCIPLES INTO PRACTICE-THE CGSCHOLAR PLATFORM, INCLUDING THE HIGH LEVEL PROGRESS VISUALIZATIONS IT OFFERS IN ITS LEARNING ANALYTICS.
THIS PAPER PRESENTS AN ANALYTICS DASHBOARD THAT HAS BEEN DEVELOPED FOR DESIGNERS OF INTERACTIVE E-BOOKS. THIS IS PART OF THE EU-FUNDED MC SQUARED PROJECT THAT IS DEVELOPING A PLATFORM FOR AUTHORING INTERACTIVE EDUCATIONAL E-BOOKS. THE PRIMARY OBJECTIVE IS TO DEVELOP TECHNOLOGIES AND RESOURCES THAT ENHANCE CREATIVE THINKING FOR BOTH DESIGNERS (AUTHORS) AND LEARNERS. THE LEARNING MATERIAL IS EXPECTED TO OFFER LEARNERS OPPORTUNITIES TO ENGAGE CREATIVELY WITH MATHEMATICAL PROBLEMS AND DEVELOP CREATIVE MATHEMATICAL THINKING. THE ANALYTICS DASHBOARD IS DESIGNED TO INCREASE AUTHORS' AWARENESS SO THAT THEY CAN MAKE INFORMED DECISIONS ON HOW TO REDESIGN AND IMPROVE THE E-BOOKS. THIS PAPER PRESENTS ARCHITECTURAL AND DESIGN DECISIONS ON KEY FEATURES OF THE DASHBOARD AND DISCUSSES FUTURE STEPS WITH RESPECT TO THE POTENTIAL FOR EXPLORATORY DATA ANALYSIS.
THIS PAPER EXPLORES A LONGITUDINAL APPROACH TO COMBINING ENGAGEMENT, PERFORMANCE AND SOCIAL CONNECTIVITY DATA FROM A MOOC USING THE FRAMEWORK OF EXPONENTIAL RANDOM GRAPH MODELS (ERGMS). THE IDEA IS TO MODEL THE SOCIAL NETWORK IN THE DISCUSSION FORUM IN A GIVEN WEEK NOT ONLY USING PERFORMANCE (ASSIGNMENT SCORES) AND OVERALL ENGAGEMENT (LECTURE AND DISCUSSION VIEWS) COVARIATES WITHIN THAT WEEK, BUT ALSO ON THE SAME PERSON-LEVEL COVARIATES FROM ADJACENT PREVIOUS AND SUBSEQUENT WEEKS. WE FIND THAT OVER ALL EIGHT WEEKLY SESSIONS, THE SOCIAL NETWORKS CONSTRUCTED FROM THE FORUM INTERACTIONS ARE RELATIVELY SPARSE AND LACK THE TENDENCY FOR PREFERENTIAL ATTACHMENT. BY ANALYZING DATA FROM THE SECOND WEEK, WE ALSO FIND THAT INDIVIDUALS WITH HIGHER PERFORMANCE SCORES FROM CURRENT, PREVIOUS, AND FUTURE WEEKS TEND TO BE MORE CONNECTED IN THE SOCIAL NETWORK. ENGAGEMENT WITH LECTURES HAD SIGNIFICANT BUT SOMETIMES PUZZLING EFFECTS ON SOCIAL CONNECTIVITY. HOWEVER, THE RELATIONSHIPS BETWEEN SOCIAL CONNECTIVITY, PERFORMANCE, AND ENGAGEMENT WEAKENED OVER TIME, AND RESULTS WERE NOT STABLE ACROSS WEEKS.
WHILE MACHINE LEARNING-BASED IMAGE RESTORATION TECHNIQUES HAVE BEEN THE FOCUS IN RECENT YEARS, THESE ALGORITHMS ARE NOT ADEQUATE TO ADDRESS THE EFFECTS OF A DEGRADED VISUAL ENVIRONMENT. AN ALGORITHM THAT SUCCESSFULLY MITIGATES THESE ISSUES IS PROPOSED. THE ALGORITHM IS BUILT UPON THE STATE-OF-THE-ART DEBLURGAN ALGORITHM BUT OVERCOMES SEVERAL OF ITS DEFICIENCIES. THE KEY CONTRIBUTIONS OF THE PROPOSED TECHNIQUES INCLUDE: 1)DEVELOPMENT OF AN EFFECTIVE FRAMEWORK TO GENERATE TRAINING DATASETS TYPICAL OF A DEGRADED VISUAL ENVIRONMENT; 2) ADOPTING A CORRENTROPY BASED LOSS FUNCTION TO INTEGRATE WITH THE ORIGINAL VGG16 BASED PERCEPTUAL LOSS FUNCTION AND AN L1 LOSS FUNCTION; 3) CONDUCTING SUBSTANTIAL EXPERIMENTS AGAINST IMAGES FROM THE ARTIFICIAL TRAINING DATASETS AND DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED ALGORITHM.
THREE-DIMENSIONAL (3-D) RADAR IMAGING IS AN IMPORTANT TASK UNDERPINNING MANY APPLICATIONS THAT SEEK TARGET DETECTION, LOCALIZATION, AND CLASSIFICATION. HOWEVER, TRADITIONAL 3-D RADAR IMAGING IS COMPUTATIONALLY CUMBERSOME AND NEEDS A LARGE MEMORY SPACE TO STORE THE CUBIC DATA MATRIX. THESE CHALLENGES HAVE HINDERED 3-D RADAR IMAGING DEVELOPMENT AND LIMITED ITS APPLICATIONS. OWING TO THE SPARSITY OF THE RADAR IMAGE OF TYPICAL TARGETS, THE DATA SIZE COULD BE SIGNIFICANTLY REDUCED BY EXPLOITING COMPRESSIVE SENSING AND SPARSE RECONSTRUCTION TECHNIQUES. THESE TECHNIQUES PROVE IMPORTANT TO MITIGATE THE SIDELOBE LEVELS THAT ARISE FROM SUCCESSIVE FOURIER-BASED PROCESSING. TOWARDS THIS END, WE FIRST PERFORM POLAR REFORMATTING TO THE 2-D DATA MATRIX IN AZIMUTH AND RANGE. THEN, A SERIES OF IMAGES OVER DIFFERENT VALUES OF THE VERTICAL VARIABLE Z ARE GENERATED BY USING DIFFERENT FOCUSING FILTERS. SPARSE OPTIMIZATION IS AFTERWARDS APPLIED TO THE 3-D DATA CUBE TO PRODUCE HIGH-RESOLUTION, SIGNIFICANTLY REDUCED SIDELOBE 3-D IMAGE. COMPARED WITH THE CONVENTIONAL 3-D RADAR IMAGING METHODS, THE PROPOSED METHOD, IN ADDITION TO HIGH FIDELITY IMAGES, REQUIRES FEWER DATA MEASUREMENTS AND OFFERS COMPUTATIONALLY EFFICIENT PROCESSING. NUMERICAL SIMULATIONS ARE PROVIDED TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED METHOD.
OBTAINING THE EFFECTIVE CONTOUR FROM AN IMAGE TAKEN BY FISHEYE LENS IS IMPORTANT FOR THE FOLLOWING TRANSACTIONS. MANY STUDIES TRY TO DEVELOP SUITABLE METHODS TO GET ACCURATE CONTOURS OF FISHEYE IMAGES. USING THE TRADITIONAL LEVEL SET METHOD (CV MODEL) IS HARD TO MEET THE DESIRE TASK THAT THE FINAL SEGMENTATION REGION IS A CIRCLE. THEREFORE, THE PREPROCESSING OF FISHEYE IMAGES AND THE IMPROVEMENT OF TRADITIONAL LEVEL SET METHOD ARE REDESIGNED TO GET A FINAL CIRCULAR SEGMENTATION WHICH MAY BE SUITABLE TO OTHER APPLICATIONS. IN THIS PAPER, WE USE THE LOCAL ENTROPY METHOD TO MAKE THE VALUE OF PIXELS BE EVEN INSIDE THE EFFECTIVE CIRCULAR REGION, FURTHER THRESHOLD METHOD TO REMOVE THE HOLE(S), AND AT LAST THE EXPLICIT CIRCULAR LEVEL SET METHOD TO GET FINAL SEGMENTATION. THE FINAL EXPERIMENTAL RESULTS SHOW THAT THE SEGMENTATION IS EFFECTIVE.
COLLABORATIVE PROBLEM SOLVING (CPS) IS SEEN AS A KEY SKILL IN K-12 EDUCATION-IN COMPUTER SCIENCE AS WELL AS OTHER SUBJECTS. EFFORTS TO INTRODUCE CHILDREN TO COMPUTING RELY ON PAIR PROGRAMMING AS A WAY OF HAVING YOUNG LEARNERS ENGAGE IN CPS. CHARACTERISTICS OF QUALITY COLLABORATION ARE JOINT EXPLORING OR UNDERSTANDING, JOINT REPRESENTATION, AND JOINT EXECUTION. WE PRESENT A DATA DRIVEN APPROACH TO ASSESSING AND ELUCIDATING COLLABORATION THROUGH MODELING OF MULTIMODAL STUDENT BEHAVIOR AND PERFORMANCE DATA.
BAYESIAN KNOWLEDGE TRACING (BKT) IS ONE OF THE MOST POPULAR KNOWLEDGE INFERENCE MODELS DUE TO ITS INTERPRETABILITY AND ABILITY TO INFER STUDENT KNOWLEDGE. A PROPER STUDENT MODELING CAN HELP GUIDE THE BEHAVIOR OF A COGNITIVE TUTOR SYSTEM AND PROVIDE INSIGHT TO RESEARCHERS ON UNDERSTANDING HOW STUDENTS LEARN. USING FOUR DIFFERENT DATASETS WE STUDY THE RELATIONSHIP BETWEEN THE ERROR COMING FROM FITTING THE PARAMETERS AND THE DIFFICULTY INDEX OF THE SKILLS AND THE EFFECT OF THE SIZE OF THE DATASET IN THIS RELATIONSHIP. THE RELATIONSHIP BETWEEN THE FITTING ERROR AND THE DIFFICULTY INDEX CAN BE VERY EASY MODELED AND MIGHT BE INDICATING SOME PROBLEMS WITH BKTS PERFORMANCE. HOWEVER, LARGE DATASETS ARE REQUIRED TO CLEARLY SEE THIS CONNECTION AS THERE IS AN IMPORTANT SAMPLE SIZE EFFECT.
DATA ARE CRUCIAL TO IMPROVE DECISION-MAKING AND OBTAIN GREATER BENEFITS IN ANY TYPE OF ACTIVITY. HOWEVER, THE LARGE AMOUNT OF INFORMATION GENERATED BY NEW TECHNOLOGIES HAS MADE DATA ANALYSIS AND KNOWLEDGE GENERATION A COMPLEX TASK. NUMEROUS TOOLS HAVE EMERGED TO FACILITATE THIS GENERATION OF KNOWLEDGE, SUCH AS DASHBOARDS. ALTHOUGH DASHBOARDS ARE USEFUL TOOLS, THEIR EFFECTIVENESS CAN BE AFFECTED BY POOR DESIGN OR BY NOT TAKING INTO ACCOUNT THE CONTEXT IN WHICH THEY ARE PLACED. THEREFORE, IT IS NECESSARY TO DESIGN AND CREATE CUSTOM DASHBOARDS ACCORDING TO THE AUDIENCE AND DATA DOMAIN. THIS PAPER PRESENTS AN APPLICATION OF THE SOFTWARE PRODUCT LINE PARADIGM AND THE INTEGRATION OF THIS APPROACH INTO A WEB SERVICE TO ALLOW USERS TO REQUEST SOURCE CODE FOR CUSTOMIZED INFORMATION DASHBOARDS. THE MAIN GOAL IS TO INTRODUCE THE IDEA OF CREATING A HOLISTIC ECOSYSTEM OF DIFFERENT SERVICES TO CRAFT AND INTEGRATE INFORMATION VISUALIZATIONS IN A VARIETY OF CONTEXTS. ONE OF THE CONTEXTS THAT CAN BE ESPECIALLY FAVORED BY THIS APPROACH IS THE EDUCATIONAL CONTEXT, WHERE LEARNING ANALYTICS, DATA ANALYSIS OF STUDENT PERFORMANCE, AND DIDACTIC TOOLS ARE BECOMING VERY RELEVANT. THREE DIFFERENT USE CASES OF THIS APPROACH ARE PRESENTED TO ILLUSTRATE THE BENEFITS OF THE DEVELOPED GENERATIVE SERVICE.
LEARNING ANALYTICS PROCESSES STUDENT DATA, EVEN FOR STUDENTS UNDER 18. THE ANALYTICAL CYCLE CONSISTS OF COLLECTING DATA, STORING IT FOR LONG PERIODS AND USING IT FOR ANALYSIS AND VISUALIZATION. THE MORE DATA, THE BETTER THE ANALYSIS. THIS ANALYSIS CAN BE DESCRIPTIVE, PREDICTIVE AND, EVEN, PRESCRIPTIVE, WHICH INVOLVES THE MANAGEMENT, PROCESSING AND USE OF PERSONAL DATA. THE EDUCATIONAL CONTEXT IS, THUS, VERY SENSITIVE, UNLIKE INDIVIDUAL CONTEXTS WHERE ANALYSIS IS USED AT WILL. IT IS NOT CLEAR HOW STUDENT DATA ARE BEING USED BY TECHNOLOGY COMPANIES SERVING EDUCATION AND WHO IS ACTUALLY BENEFITING, HOW THIS WILL AFFECT STUDENTS IN THE SHORT AND LONG-TERM FUTURE, OR WHAT LEVEL OF PRIVACY OR SECURITY IS APPLIED TO PROTECT STUDENT DATA. THEREFORE, AND IN RELATION TO THE ABOVE, ANALYZING EDUCATIONAL DATA IMPLIES A SENSITIVE AND FRAGILE CONTEXT IN THE MANAGEMENT AND ANALYSIS OF PERSONAL DATA OF STUDENTS, INCLUDING MINORS, IN WHICH PRECAUTIONS MUST BE MAXIMIZED. THIS SYSTEMATIC REVIEW OF THE LITERATURE EXPLORES THE IMPORTANCE OF PERSONAL DATA PROTECTION AND SECURITY IN THE FIELD OF EDUCATION THROUGH THE EMERGING PROMISES OF THOSE INTERESTED IN USING BLOCKCHAIN TECHNOLOGY. THE RESULTS SHOW THAT IT IS IMPORTANT TO UNDERSTAND THE IMPLICATIONS AND RISKS DERIVED FROM THE USE OF EMERGING TECHNOLOGIES IN EDUCATION, THEIR RELATIONSHIP WITH SOCIETY AND THE CURRENT LEGISLATION.
THE USE OF OPEN TEXTBOOKS IN UNIVERSITIES IS ACCORDING TO SOME ORGANISATIONS CHANGING THE HIGHER EDUCATION LANDSCAPE AND IS PROMISING FOR THE MAINSTREAM ADOPTION OF OER. THE AIM OF THIS PAPER IS TO ANALYSE AUTHORS' VIEWS OF AGENCY AND EMPOWERMENT WHEN THEY PLAN, CREATE AND REFLECT ON THEIR OPEN TEXTBOOKS, THEIR TEACHING AND STUDENTS' LEARNING. ANOTHER AIM IS TO ANALYSE THE WAYS IN WHICH KNOWLEDGE SUPPORTS AUTHORS' CREATION OF OPEN TEXTBOOKS AND TENSIONS INHERENT IN THIS PRACTICE. THIS QUALITATIVE STUDY USED A MODIFIED VERSION OF A VALIDATED QUESTIONNAIRE FOR 1 H LONG SEMI-STRUCTURED INTERVIEWS WITH FOUR INTERVIEWEES, CONDUCTED OVER 4 DAYS. THE DATA FROM THE INTERVIEWS WERE ANALYSED IN THREE STEPS COMBINING TWO CODING STRUCTURES, FOR SELF-REGULATED LEARNING AND LEVELS OF CONTRADICTIONS. THE RESULTS SUGGEST THAT THE FOUR AUTHORS ENGENDERED A SENSE OF RELATIONAL AGENCY IN THE CREATION PROCESS. THEY INDICATED THAT THEY VALUED OPENNESS AND THE PEDAGOGICAL PROJECT ITSELF BY HIGHLIGHTING BOTH TEACHERS' AND LEARNERS' PERCEPTION OF AGENCY AND EMPOWERMENT. THE DATA ALSO SUPPORT THE AUTHORS' CONCERNS ABOUT THE DISRUPTIVE NATURE OF OPEN TEXTBOOKS REGARDING STABILITY VERSUS ADAPTATION AND DATA OWNERSHIP AS IT RELATES TO USE OF LEARNING ANALYTICS AND COMMERCIAL INTERESTS, INDICATING CONSIDERABLE CONTRADICTIONS IN OPEN TEXTBOOK PRACTICES.
THE CURRENT TENDENCY OF HUMAN LEARNING AND TEACHING IS TARGETED TO DEVELOPMENT AND INTEGRATION OF DIGITAL TECHNOLOGIES (LIKE CLOUD SOLUTIONS, MOBILE TECHNOLOGY, LEARNING ANALYTICS, BIG DATA, AUGMENTED REALITY, NATURAL INTERACTION TECHNOLOGIES, ETC.). OUR SCIENCE GATEWAY (HTTP://SCIGATE.IMP.KIEV.UA) IN COLLABORATION WITH HIGH PERFORMANCE COMPUTING CENTER (HTTP://HPCC.KPI.UA) IS AIMED ON THE CLOSE COOPERATION AMONG THE MAIN ACTORS IN LEARNING AND RESEARCHING WORLD (TEACHERS, STUDENTS, SCIENTISTS, SUPPORTING PERSONNEL, VOLUNTEERS, ETC.) WITH INDUSTRY AND ACADEMIA TO PROPOSE THE NEW FRAMEWORKS AND INTEROPERABILITY REQUIREMENTS FOR THE BUILDING BLOCKS OF A DIGITAL ECOSYSTEM FOR LEARNING (INCLUDING INFORMAL LEARNING) THAT DEVELOPS AND INTEGRATES THE CURRENT AND NEW TOOLS AND SYSTEMS. IT IS THE PORTAL FOR MANAGEMENT OF DISTRIBUTED COURSES (WORKFLOWS), TOOLS, RESOURCES, AND USERS, WHICH IS CONSTRUCTED ON THE BASIS OF THE LIFERAY FRAMEWORK AND GUSE/WS-PGRADE TECHNOLOGY. IT IS BASED ON DEVELOPMENT OF MULTI-LEVEL APPROACH (AS TO METHODS/ALGORITHMS) FOR EFFECTIVE STUDY AND RESEARCH THROUGH FLEXIBLE SELECTION AND COMBINATION OF UNIFIED MODULES ("GAMING" WITH MODULES AS WITH LEGO-BRICKS). IT ALLOWS US TO PROVIDE THE FLEXIBLE AND ADJUSTABLE FRAMEWORK WITH DIRECT INVOLVEMENT IN REAL-WORLD AND SCIENTIFIC USE CASES MOTIVATED BY THE EDUCATIONAL AIMS OF STUDENTS AND REAL SCIENTIFIC AIMS IN LABS.
DESPITE THE 30 YEARS OF RESEARCH ON THE EFFECTS OF USING COMPUTER-BASED MAPPING TOOLS TO CONSTRUCT CONCEPT MAPS, THE RESEARCH HAS PROVIDED LITTLE INSIGHT INTO THE COGNITIVE PROCESSES AND STRATEGIES STUDENTS USE TO CREATE MORE ACCURATE MAPS AND THE TYPES OF BEHAVIORS THAT CAN BE MINED IN COMPUTER-BASED MAPPING TOOLS TO REVEAL AND SUPPORT PROCESSES THAT IMPROVE STUDENT PERFORMANCE. THIS STUDY USED VIDEO ANALYSIS TO IDENTIFY, OPERATIONALLY DEFINE, AND CODE PROCESSES OF REASONING STUDENTS USED WHILE USING A COMPUTER-BASED TOOL TO CONSTRUCT CAUSAL MAPS. THE PROCESS DATA WAS THEN USED TO CONDUCT A PATH ANALYSIS TO DETERMINE WHICH AND HOW SPECIFIC PROCESSES IMPACT THE QUALITY OF STUDENTS' CAUSAL MAPS AND TO DETERMINE HOW STUDENTS' PRIOR KNOWLEDGE ON THE GIVEN PROBLEMS INFLUENCE WHICH PROCESSES STUDENTS USE. THE FINDINGS REVEAL THAT STUDENTS EXHIBITING THE TENDENCY TO USE MORE BACKWARD THAN FORWARD PROCESSING AND USE MORE BREADTH THAN DEPTH-FIRST PROCESSING PRODUCED HIGHER QUALITY CAUSAL MAPS, AND THAT THE INFLUENCE OF PRIOR CONTENT KNOWLEDGE ON WHAT PROCESSES STUDENTS USE ARE NOT CONCLUSIVE. THE FINDINGS AND THE METHODS DEVELOPED IN THIS STUDY PRESENT NEW IDEAS ON WAYS TO INTEGRATE LEARNING ANALYTICS INTO COMPUTER-BASED MAPPING TOOLS TO MODEL AND MANAGE THE COGNITIVE PROCESSES STUDENTS USE TO CONSTRUCT BETTER COGNITIVE MAPS.
THE COST OF DATA-MOVEMENT IS ONE OF THE FUNDAMENTAL ISSUES WITH MODERN COMPUTE SYSTEMS PROCESSING BIG DATA WORKLOADS. ONE APPROACH TO MOVE THE COMPUTATION CLOSER TO DATA IS TO EQUIP THE STORAGE OR MEMORY DEVICES WITH PROCESSING POWER. THE NOTION OF MOVING COMPUTATION TO DATA IS KNOWN AS NEAR DATA PROCESSING (NDP). IN THIS WORK, WE RE-EXAMINE THE IDEA OF REDUCING THE DATA MOVEMENT BY PROCESSING DATA DIRECTLY IN THE STORAGE DEVICES. WE EVALUATE ASTOR, A COMPUTE FRAMEWORK ON AN ACTIVE STORAGE PLATFORM, WHICH INCORPORATES A SOFTWARE STACK AND A DEDICATED MULTI-CORE PROCESSOR FOR IN-STORAGE PROCESSING. ASTOR UTILIZES THE PROCESSING POWER OF STORAGE DEVICES BY USING AN ARRAY OF ACTIVE DRIVE (TM) DEVICES TO SIGNIFICANTLY REDUCE THE BANDWIDTH REQUIREMENT ON THE NETWORK. WE EVALUATE THE PERFORMANCE AND SCALABILITY OF ASTOR FOR DISTRIBUTED PROCESSING OF BIG DATA WORKLOADS. WE CONCLUDE BY DISCUSSING A COMPARATIVE STUDY OF OTHER EXISTING DATA-CENTRIC APPROACHES.
THIS PAPER AIMS TO DEVELOP A ROBUST DISSOLVED OXYGEN (DO) PREDICTION MODEL OF WATER QUALITY TO SUPPORT THE HYBRID AERIAL UNDERWATER ROBOTICS SYSTEM (HAUCS) PROJECT. MANY CHALLENGES ARISE IN DEVELOPING SUCH A MODEL USING THE FISH FARM DATA COLLECTED, SUCH AS A SMALL DATASET CONTAINING MISSING DATA AND NOISY MEASUREMENTS TAKEN IN AN IRREGULAR INTERVAL. AN ATTEMPT TO DEAL WITH THESE ISSUES TO OBTAIN A ROBUST PREDICTION IS DISCUSSED. MACHINE LEARNING TECHNIQUES, SUCH AS LONG SHORT-TERM MEMORY (LSTM) AND PHASED LSTM (PLSTM), ARE PRESENTED AND MOTIVATED FOR DEALING WITH THE PROBLEM. THE PERFORMANCES OF LSTM AND PLSTM AGAINST A LARGER AND LESS PROBLEMATIC WATER QUALITY DATASET ARE FIRST INVESTIGATED. THE ATTEMPTS TO TRANSFER THE KNOWLEDGE OF THE MODELS TRAINED ON THIS LARGE DATASET FOR FISH FARM DO DATA PREDICTION THROUGH TRANSFER LEARNING ARE THEN REPORTED. TO MITIGATE THE NOISY MEASUREMENT DATA, A LOSS FUNCTION WHICH CAN BETTER DEAL WITH GAUSSIAN NOISE: THE CORRENTROPY LOSS IS ADOPTED. THE LONG-RANGE PREDICTION EXPERIMENTAL RESULTS USING THIS TRANSFER LEARNING TECHNIQUE AND THE CORRENTROPY LOSS FUNCTION ARE PRESENTED.
OVER THE LAST DECADES, CRIMINAL ACTIVITIES HAVE PROGRESSIVELY EXPANDED INTO THE INFORMATION TECHNOLOGY (IT) WORLD, ADDING TO THE "TRADITIONAL" CRIMINAL ACTIVITIES, IGNORING POLITICAL BOUNDARIES AND LEGAL JURISDICTIONS. BUILDING UPON THE POSSIBILITIES OF TECHNOLOGIES LIKE BIG DATA ANALYTICS, REPRESENTATIONAL MODELS, MACHINE LEARNING, SEMANTIC REASONING AND AUGMENTED INTELLIGENCE, OUR WORK PRESENTED IN THIS PAPER, WHICH HAS BEEN PERFORMED WITHIN THE COLLABORATIVE RESEARCH PROJECT MAGNETO (TECHNOLOGIES FOR PREVENTION, INVESTIGATION, AND MITIGATION IN THE CONTEXT OF THE FIGHT AGAINST CRIME AND TERRORISM), CO-FUNDED BY THE EUROPEAN COMMISSION WITHIN HORIZON 2020 PROGRAMME, IS GOING TO SUPPORT LEAS IN THEIR CRITICAL NEED TO EXPLOIT ALL AVAILABLE RESOURCES AND HANDLING THE LARGE AMOUNT OF DIVERSIFIED MEDIA MODALITIES TO EFFECTIVELY CARRY OUT CRIMINAL INVESTIGATION. THE PAPER AT HAND FOCUSES AT THE APPLICATION OF MACHINE LEARNING SOLUTIONS FOR INFORMATION FUSION AND CLASSIFICATION TOOLS INTENDED TO SUPPORT LEA'S INVESTIGATIONS. THE PERSON FUSION TOOL WILL BE RESPONSIBLE FOR FINDING IN AN UNDERLYING KNOWLEDGE GRAPH DIFFERENT PERSON INSTANCES THAT REFER TO THE SAME PERSON AND FUSE THESE INSTANCES. THE GENERAL APPROACH, THE SIMILARITY METRICS, THE ARCHITECTURE OF THE TOOL AND DESIGN CHOICES AS WELL AS MEASURES TO IMPROVE THE EFFICIENCY OF THE TOOL WILL BE PRESENTED. THE TOOL FOR CLASSIFYING MONEY TRANSFER TRANSACTIONS USES DECISION TREES. THIS IS DUE TO A REQUIREMENT OF EASY EXPLAINABILITY OF THE CLASSIFICATION RESULTS, WHICH IS DEMANDED FROM THE ETHICAL AND LEGAL PERSPECTIVE OF THE MAGNETO PROJECT. THE DESIGN OF THE TOOL, THE SELECTED IMPLEMENTATION AND AN EVALUATION BASED ON ANONYMIZED FINANCIAL DATA RECORDS WILL BE PRESENTED.
THE EXTRACTION OF USEFUL INFORMATION FROM LARGE, DISPARATE, AND HETEROGENEOUS DATA SETS REQUIRES A GOOD SET OF THEORETICAL AND COMPUTATIONAL TOOLS. THE METHODS BASED ON THE IDEAS OF INFORMATION GEOMETRY (IG) OFFER AN UNDERSTANDING OF THE HIDDEN PATTERNS INHERENT IN THE DATA AS WELL AS HELP IN THEIR VISUALIZATION. FISHER INFORMATION IS SUCH A TOOL. IT HAS BEEN USED WIDELY IN MANY AREAS OF SOCIAL AND ECONOMIC RESEARCH LEADING TO AN IMPROVED UNDERSTAND OF TRENDS AND HIDDEN PATTERNS. HERE WE OUTLINE ITS USEFULNESS IN UNDERSTANDING LARGE AND DISPARATE DATA SETS.
AS AN IMPORTANT PART OF SUSTAINABLE SOCIETY, SMART CAMPUSES HAVE ATTRACTED MORE AND MORE ATTENTION. BY ASSOCIATING ARTIFICIAL INTELLIGENCE WITH SCHOOL INFRASTRUCTURE, IT MAKES TEACHING SERVICES MORE CONVENIENT AND PERSONALIZED. SMART CLASSROOM IS ONE OF THE IMPORTANT SOLUTIONS OF SMART CAMPUSES BECAUSE TEACHING AND LEARNING ARE ALWAYS THE PRIMARY TASKS OF SMART CAMPUSES. AT PRESENT, MANY INTELLIGENT TECHNOLOGIES HAVE BEEN PUT INTO SMART CLASSROOMS FOR MONITORING AND ANALYZING TEACHING ACTIVITIES, BUT THE COMPLEXITY OF CLASSROOM BEHAVIOR STILL IMPEDES THE DEVELOPMENT OF SMART CLASSROOMS. EXISTING RESEARCHES ON LEARNING BEHAVIOR ANALYTICS ONLY FOCUS ON STUDENT ACTIONS. HOWEVER, STUDENT LEARNING BEHAVIORS ARE OFTEN CLOSELY RELATED TO TEACHER BEHAVIORS, SO IT IS NOT OBJECTIVE AND ACCURATE TO MEASURE ENGAGEMENT ONLY BY ANALYZING STUDENT ACTIONS. IN THIS PAPER, WE PROPOSE A TEACHER-STUDENT BEHAVIORAL ENGAGEMENT PATTERN (TSBEP) TO SYNTHETICALLY MEASURE STUDENT ENGAGEMENT BY ADDING TEACHER BEHAVIORS. AND THE DECISION TREE MODEL BASED ON CLASSIFICATION AND REGRESSION TREE (CART) IS USED TO PREDICT ENGAGEMENT LEVELS REFERRING TO THE TSBEP. EXPERIMENTAL RESULTS SHOW THAT THE PROPOSED TSBEP CAN MEASURE THE TEACHING AND LEARNING MORE ACCURATELY. AND IT IS HELPFUL TO SUSTAINABLY DEPLOY AND IMPLEMENT LEARNING ANALYTICS TO IMPROVE THE QUALITY OF LEARNING AND TEACHING IN SMART CLASSROOMS.
LEARNING ANALYTICS (LA) HAS A MAJOR INTEREST IN EXPLORING AND UNDERSTANDING THE LEARNING PROCESS OF HUMANS AND, FOR THIS PURPOSE, BENEFITS FROM BOTH COGNITIVE SCIENCE, WHICH STUDIES HOW HUMANS LEARN, AND MACHINE LEARNING, WHICH STUDIES HOW ALGORITHMS LEARN FROM DATA. USUALLY, MACHINE LEARNING IS EXPLOITED AS A TOOL FOR ANALYZING DATA COMING FROM EXPERIMENTAL STUDIES, BUT IT HAS BEEN RECENTLY APPLIED TO HUMANS AS IF THEY WERE ALGORITHMS THAT LEARN FROM DATA. ONE EXAMPLE IS THE APPLICATION OF RADEMACHER COMPLEXITY, WHICH MEASURES THE CAPACITY OF A LEARNING MACHINE, TO HUMAN LEARNING, WHICH LED TO THE FORMULATION OF HUMAN RADEMACHER COMPLEXITY (HRC). IN THIS LINE OF RESEARCH, WE PROPOSE HERE A MORE POWERFUL MEASURE OF COMPLEXITY, THE HUMAN ALGORITHMIC STABILITY (HAS), AS A TOOL TO BETTER UNDERSTAND THE LEARNING PROCESS OF HUMANS. THE EXPERIMENTAL RESULTS FROM THREE DIFFERENT EMPIRICAL STUDIES, ON MORE THAN 600 ENGINEERING STUDENTS FROM THE UNIVERSITY OF GENOA, SHOWED THAT HAS (I) CAN BE MEASURED WITHOUT THE ASSUMPTIONS REQUIRED BY HRC, (II) DEPENDS NOT ONLY ON THE KNOWLEDGE DOMAIN, AS HRC, BUT ALSO ON THE COMPLEXITY OF THE PROBLEM, AND (III) CAN BE EXPLOITED FOR BETTER UNDERSTANDING OF THE HUMAN LEARNING PROCESS. (C) 2016 ELSEVIER B.V. ALL RIGHTS RESERVED.
HOW DO IMPORTANT MEMBERS OF ONLINE QUESTION & ANSWER COMMUNITIES (WHO WE CALL EXPERTS) BEHAVE? AND HOW DO THEY INFLUENCE THE DISCUSSIONS IN WHICH THEY TAKE PART? THIS WORK REPORTS ON AN INVESTIGATION INTO THESE QUESTIONS, WHICH WE ANSWER THROUGH ANALYSES EXPLORING METRICS, MACHINE LEARNING CLASSIFIERS, AND RECOMMENDATIONS. WE REPORT ON SEVERAL FINDINGS: THE DEGREE OF EXPERTISE CORRELATES TO BEHAVIORAL PATTERNS, WHEREBY EXPERTS WOULD RARELY ASK FOR HELP, AND INSTEAD, PREDOMINANTLY PROVIDE HELP TO OTHER COMMUNITY MEMBERS; THE INCLUSION OF AN EXPERT RESULTS IN LONGER DISCUSSIONS. WE PROPOSE A METRIC (THE WEIGHTED SUM), WHICH ENABLES US TO BETTER QUANTIFY THE REPUTATIONS OF EXPERT MEMBERS OF THE COMMUNITY. WE DESCRIBE THE USE OF FOUR MACHINE LEARNING CLASSIFIERS FOR THE IDENTIFICATION OF BOTH EXPERT USERS AND THE MOST SIGNIFICANT CONVERSATIONS WITHIN THESE COMMUNITIES. WE PROPOSE A NOVEL APPROACH FOR A RECOMMENDATION SYSTEM, WHICH UTILIZES SEMANTIC ANNOTATIONS TO IDENTIFY TOPICAL EXPERTS AND TO ASCERTAIN THEIR RESPECTIVE AREA OF SPECIALISM. WE FORESEE THE SUITABILITY OF OUR EXPERTISE-FINDING METHODS AND FINDINGS TO SUPPORT LEARNING ANALYTICS, AND IN SCENARIOS WHERE USERS MAY APPLY LESSONS LEARNT FROM OUR RESULTS TO IMPROVE THEIR STATUS IN A COMMUNITY. OUR FINDINGS CAN ALSO INFORM SYSTEMS FOR RECOMMENDING EXPERTS AND DISCUSSIONS.
STUDENT MODELING TECHNIQUES ARE EVALUATED MOSTLY USING HISTORICAL DATA. RESEARCHERS TYPICALLY DO NOT PAY ATTENTION TO DETAILS OF THE ORIGIN OF THE USED DATA SETS. HOWEVER, THE WAY DATA ARE COLLECTED CAN HAVE IMPORTANT IMPACT ON EVALUATION AND INTERPRETATION OF STUDENT MODELS. WE DISCUSS IN DETAIL TWO WAYS HOW DATA COLLECTION IN EDUCATIONAL SYSTEMS CAN INFLUENCE RESULTS: MASTERY ATTRITION BIAS AND ADAPTIVE CHOICE OF ITEMS. WE SYSTEMATICALLY DISCUSS PREVIOUS WORK RELATED TO THESE BIASES AND ILLUSTRATE THE MAIN POINTS USING BOTH SIMULATED AND REAL DATA. WE SUMMARIZE SPECIFIC CONSEQUENCES FOR PRACTICE {NOT JUST FOR DOING EVALUATION OF STUDENT MODELS, BUT ALSO FOR DATA COLLECTION AND PUBLICATION OF DATA SETS.
TO PROMOTE HEALTHY AWARENESS AND ACTIVITY LEARNING, WE GAVE 12- TO 14-YEAR-OLD YOUTH ACTIVITY MONITORS (FITBITS) TO TRACK THEIR PHYSICAL ACTIVITY, WHICH WAS THEN INTEGRATED INTO A VIDEOGAME WE CREATED. THE PLAYERS' REAL-WORLD STEPS TRANSFORM INTO IN-GAME RESOURCES NEEDED FOR GAMEPLAY. IN ADDITION TO REQUIRING REAL-WORLD STEPS FOR VARIOUS IN-GAME ACTIVITIES, A DASHBOARD IN THIS GAME PRESENTS VISUAL REPRESENTATIONS OF ACTIVITY PATTERNS, OSTENSIBLY INFORMING STUDENTS ABOUT PATTERNS OF THEIR OWN ACTIVITY. IN THIS PAPER AND POSTER, WE DISCUSS CHALLENGES IN INITIAL DESIGNS OF OUR DASHBOARD. WE PRESENT FINDINGS AND CHALLENGES IN THE PROCESS OF CREATING A USER-CENTERED DASHBOARD AND CONCLUDE WITH OUR FUTURE DESIGN GOALS.
GIVEN THE IMPORTANCE OF READING PROFICIENCY AND HABITS FOR YOUNG STUDENTS, AN ONLINE E-QUIZ BANK, READING BATTLE, WAS LAUNCHED IN 2014 TO FACILITATE READING IMPROVEMENT FOR PRIMARY-SCHOOL STUDENTS. WITH MORE THAN TEN THOUSAND QUESTIONS IN BOTH ENGLISH AND CHINESE, THE SYSTEM HAS ATTRACTED NEARLY FIVE THOUSAND LEARNERS WHO HAVE MADE ABOUT HALF A MILLION QUESTION ANSWERING RECORDS. IN AN EFFORT TOWARDS DELIVERING PERSONALIZED LEARNING EXPERIENCE TO THE LEARNERS, THIS STUDY AIMS TO DISCOVER POTENTIALLY USEFUL KNOWLEDGE FROM LEARNERS' READING AND QUESTION ANSWERING RECORDS IN THE READING BATTLE SYSTEM, BY APPLYING ASSOCIATION RULE MINING AND CLUSTERING ANALYSIS. THE RESULTS SHOW THAT LEARNERS COULD BE GROUPED INTO THREE CLUSTERS BASED ON THEIR SELF-REPORTED READING HABITS. THE RULES MINED FROM DIFFERENT LEARNER CLUSTERS CAN BE USED TO DEVELOP PERSONALIZED RECOMMENDATIONS TO THE LEARNERS. IMPLICATIONS OF THE RESULTS ON EVALUATING AND FURTHER IMPROVING THE READING BATTLE SYSTEM ARE ALSO DISCUSSED.
THE COVID-19 PANDEMIC RADICALLY AND WITHOUT WARNING CHANGED THE LABORATORY LEARNING ENVIRONMENT FOR STUDENTS AND INSTRUCTORS. STUDENTS WERE FACED WITH HAVING TO BE RECEPTIVE TO NEW LEARNING METHODS; INSTRUCTORS SCRAMBLED TO DEVISE INNOVATIVE WAYS OF PROVIDING A REALISTIC LAB EXPERIENCE FOR STUDENTS. THE DEMAND FOR CREATIVE ONLINE TEACHING STRATEGIES AND THE EXPANSION OF GAMIFIED TRAINING PLATFORMS CREATED AN OPPORTUNITY FOR THE DEVELOPMENT OF NEW AND INTERACTIVE LAB EXPERIENCES. CURRENT ONLINE LABS OFFER SOME ELEMENTS OF A "REAL" LAB EXPERIENCE, BUT A SYSTEM THAT INCORPORATES ALL THE TOOLS NEEDED TO CREATE A REALISTIC, IMMERSIVE LAB ENVIRONMENT HAS YET TO BE DEVELOPED. THIS STUDY EXAMINES USING DIFFERENT GAMIFICATION ELEMENTS IMPLEMENTED IN A POWERPOINT-BASED PLATFORM. THERE WAS NO COST ASSOCIATED WITH THE VIRTUAL LAB AND IT COULD BE EASILY DOWNLOADED, INCREASING ACCESSIBILITY. IN TRUE GAMING STYLE, A STUDENT COULD "PLAY" WITHOUT RESTRICTION, WITHOUT THE LIMITATIONS THAT ACCOMPANY WET LABS. STUDENTS WERE CHALLENGED WITH VARIOUS SCENARIOS THROUGHOUT THE LAB, MAKING CHOICES AND RECEIVING FEEDBACK THROUGH THE PROCESS. THESE FEATURES POSITIVELY IMPACTED STUDENT OUTCOMES AND IMPROVED ENGAGEMENT, AS EXPRESSED IN END-OF-COURSE EVALUATIONS. THE IMPLEMENTATION ALSO STRESSED THE NEED FOR FURTHER DEVELOPMENT OF EMBEDDED ASSESSMENTS, COMPETITIVE AND INTERACTIVE OPPORTUNITIES FOR STUDENTS, AND ACCESS TO DETAILED LEARNING ANALYTICS FOR INSTRUCTORS.
EARLY DETECTION OF AT-RISK STUDENTS IS ESSENTIAL, ESPECIALLY IN THE UNIVERSITY ENVIRONMENT. MOREOVER, PERSONALIZED LEARNING HAS BEEN SHOWN TO INCREASE MOTIVATION AND LOWER STUDENT DROPOUT RATES. AT PRESENT, THE AVERAGE DROPOUT RATES AMONG STUDENTS FOLLOWING COURSES LEADING TO THE AWARD OF SPANISH UNIVERSITY DEGREES ARE AROUND 18% AND 42.8% FOR PRESENTIAL TEACHING AND ONLINE COURSES, RESPECTIVELY. THE OBJECTIVES OF THIS STUDY ARE: (1) TO DESIGN AND TO IMPLEMENT A MODULAR OBJECT-ORIENTED DYNAMIC LEARNING ENVIRONMENT (MOODLE) PLUGIN, "EORIENTATION", FOR THE EARLY DETECTION OF AT-RISK STUDENTS; (2) TO TEST THE EFFECTIVENESS OF THE "EORIENTATION" PLUGIN ON UNIVERSITY STUDENTS. WE WORKED WITH 279 THIRD-YEAR STUDENTS FOLLOWING HEALTH SCIENCES DEGREES. A PROCESS FOR EXTRACTING INFORMATION RECORDS WAS ALSO IMPLEMENTED. IN ADDITION, A LEARNING ANALYTICS MODULE WAS DEVELOPED, THROUGH WHICH BOTH SUPERVISED AND UNSUPERVISED MACHINE LEARNING TECHNIQUES CAN BE APPLIED. ALL THESE MEASURES FACILITATED THE PERSONALIZED MONITORING OF THE STUDENTS AND THE EASIER DETECTION OF STUDENTS AT ACADEMIC RISK. THE USE OF THIS TOOL COULD BE OF GREAT IMPORTANCE TO TEACHERS AND UNIVERSITY GOVERNING TEAMS, AS IT CAN ASSIST THE EARLY DETECTION OF STUDENTS AT ACADEMIC RISK. FUTURE STUDIES WILL BE AIMED AT TESTING THE PLUGIN USING THE MOODLE ENVIRONMENT ON DEGREE COURSES AT OTHER UNIVERSITIES.
THE PREVALENCE OF EARLY ALERT SYSTEMS (EAS) AT TERTIARY INSTITUTIONS IS INCREASING. THESE SYSTEMS ARE DESIGNED TO ASSIST WITH TARGETED STUDENT SUPPORT IN ORDER TO IMPROVE STUDENT RETENTION. THEY ALSO REQUIRE CONSIDERABLE HUMAN AND CAPITAL RESOURCES TO IMPLEMENT, WITH SIGNIFICANT COSTS INVOLVED. IT IS THEREFORE AN IMPERATIVE THAT THE SYSTEMS CAN DEMONSTRATE QUANTIFIABLE FINANCIAL BENEFITS TO THE INSTITUTION. THE PURPOSE OF THIS PAPER IS TO REPORT ON THE FINANCIAL IMPLICATIONS OF IMPLEMENTING AN EAS AT AN AUSTRALIAN UNIVERSITY AS A CASE STUDY. THE CASE STUDY INSTITUTION IMPLEMENTED AN EAS IN 2011 USING DATA GENERATED FROM A DATA WAREHOUSE. THE DATA SET IS COMPRISED OF 16,124 STUDENTS ENROLLED BETWEEN 2011 AND 2013. USING A TREATMENT EFFECTS APPROACH, THE STUDY FOUND THAT THE COST OF A STUDENT DISCONTINUING WAS ON AVERAGE $4,687. STUDENTS IDENTIFIED BY THE EAS REMAINED ENROLLED FOR LONGER, WITH THE INSTITUTION BENEFITING WITH APPROXIMATELY AN ADDITIONAL $4,004 IN REVENUE PER STUDENT OVER THE LENGTH OF ENROLMENT. ALL SCHOOLS HAD A SIGNIFICANT POSITIVE EFFECT ASSOCIATED WITH THE EAS AND THE EAS SHOWED SIGNIFICANT VALUE TO THE INSTITUTION REGARDLESS OF THE TIMING WHEN THE STUDENT WAS IDENTIFIED. THE RESULTS INDICATE THAT EAS HAD SIGNIFICANT FINANCIAL BENEFITS TO THIS INSTITUTION AND THAT THE BENEFITS EXTENDED TO THE ENTIRE INSTITUTION BEYOND THE FIRST YEAR OF ENROLMENT.
THE ADEQUATE EMOTIONAL STATE OF STUDENTS HAS PROVED TO BE ESSENTIAL FOR FAVORING LEARNING. THIS PAPER EXPLORES THE POSSIBILITY OF OBTAINING STUDENTS' FEEDBACK ABOUT THE EMOTIONS THEY FEEL IN CLASS IN ORDER TO DISCOVER POTENTIAL EMOTION PATTERNS THAT MIGHT INDICATE LEARNING FAILS. THIS PAPER PRESENTS A VISUAL DASHBOARD THAT ALLOWS STUDENTS TO TRACK THEIR EMOTIONS AND FOLLOW UP ON THEIR EVOLUTION DURING THE COURSE. WE HAVE COMPILED THE PRINCIPAL CLASSROOM RELATED EMOTIONS AND DEVELOPED A TWO-PHASE INQUIRY PROCESS TO: VERIFY THE POSSIBILITY TO MEASURE STUDENTS' EMOTIONS IN CLASSROOM; DISCOVER HOW EMOTIONS CAN BE DISPLAYED TO PROMOTE SELF-REFLECTION; AND CONFIRM THE IMPACT OF EMOTIONS ON LEARNING PERFORMANCE. OUR RESULTS SUGGEST THAT STUDENTS' EMOTIONS IN CLASS ARE RELATED TO EVALUATION MARKS. THIS SHOWS THAT EARLY INFORMATION ABOUT STUDENTS' EMOTIONS CAN BE USEFUL FOR TEACHERS AND STUDENTS TO IMPROVE CLASSROOM RESULTS AND LEARNING OUTCOMES.
WRITING TRAINING SYSTEMS HAVE BEEN DEVELOPED TO PROVIDE STUDENTS WITH INSTRUCTION AND DELIBERATE PRACTICE ON THEIR WRITING. ALTHOUGH GENERALLY SUCCESSFUL IN PROVIDING ACCURATE SCORES, A COMMON CRITICISM OF THESE SYSTEMS IS THEIR LACK OF PERSONALIZATION AND ADAPTIVE INSTRUCTION. IN PARTICULAR, THESE SYSTEMS TEND TO PLACE THE STRONGEST EMPHASIS ON DELIVERING ACCURATE SCORES, AND THEREFORE, TEND TO OVERLOOK ADDITIONAL INDICES THAT MAY CONTRIBUTE TO STUDENTS' SUCCESS, SUCH AS THEIR AFFECTIVE STATES DURING WRITING PRACTICE. THIS STUDY TAKES AN INITIAL STEP TOWARD ADDRESSING THIS GAP BY BUILDING A PREDICTIVE MODEL OF STUDENTS' AFFECT USING INFORMATION THAT CAN POTENTIALLY BE COLLECTED BY COMPUTER SYSTEMS. WE USED INDIVIDUAL DIFFERENCE MEASURES, TEXT INDICES, AND KEYSTROKE ANALYSES TO PREDICT ENGAGEMENT AND BOREDOM IN 132 WRITING SESSIONS. THE RESULTS SUGGEST THAT THESE THREE CATEGORIES OF INDICES WERE SUCCESSFUL IN MODELING STUDENTS' AFFECTIVE STATES DURING WRITING. TAKEN TOGETHER, INDICES RELATED TO STUDENTS' ACADEMIC ABILITIES, TEXT PROPERTIES, AND KEYSTROKE LOGS WERE ABLE CLASSIFY HIGH AND LOW ENGAGEMENT AND BOREDOM IN WRITING SESSIONS WITH ACCURACIES BETWEEN 76.5% AND 77.3%. THESE RESULTS SUGGEST THAT INFORMATION READILY AVAILABLE IN WRITING TRAINING SYSTEMS CAN INFORM AFFECT DETECTORS AND ULTIMATELY IMPROVE STUDENT MODELS WITHIN INTELLIGENT TUTORING SYSTEMS.
WE PRESENT OUR APPROACH TO DESIGNING AND EVALUATING TOOLS THAT CAN ASSIST TEACHERS IN CLASSROOM SETTINGS WHERE STUDENTS ARE USING EXPLORATORY LEARNING ENVIRONMENTS (ELES), USING AS OUR CASE STUDY THE MIGEN SYSTEM, WHICH TARGETS 1114 YEAR OLD STUDENTS' LEARNING OF ALGEBRA. WE DISCUSS THE CHALLENGING ROLE OF TEACHERS IN EXPLORATORY LEARNING SETTINGS AND MOTIVATE THE NEED FOR VISUALISATION AND NOTIFICATION TOOLS THAT CAN ASSIST TEACHERS IN FOCUSING THEIR ATTENTION ACROSS THE WHOLE CLASS AND INFORM THEIR INTERVENTIONS. WE PRESENT THE DESIGN AND EVALUATION APPROACH FOLLOWED DURING THE DEVELOPMENT OF MIGEN'S TEACHER ASSISTANCE TOOLS, DRAWING PARALLELS WITH THE RECENTLY PROPOSED LATUX WORKFLOW BUT ALSO DISCUSSING HOW WE GO BEYOND THIS TO INCLUDE A LARGE NUMBER OF TEACHER PARTICIPANTS IN OUR EVALUATION ACTIVITIES, SO AS TO GAIN THE BENEFIT OF DIFFERENT VIEW POINTS. WE DISCUSS THE RESULTS OF THE EVALUATIONS, WHICH SHOW THAT PARTICIPANTS APPRECIATED THE CAPABILITIES OF THE TOOLS AND WERE MOSTLY ABLE TO USE THEM QUICKLY AND ACCURATELY.
COMPUTERIZED EDUCATIONAL SYSTEMS ARE INCREASINGLY PROVIDED AS OPEN ONLINE SERVICES WHICH PROVIDE ADAPTIVE PERSONALIZED LEARNING EXPERIENCE. TO FULLY EXPLOIT POTENTIAL OF SUCH SYSTEMS, IT IS NECESSARY TO THOROUGHLY EVALUATE DIFFERENT DESIGN CHOICES. HOWEVER, BOTH OPENNESS AND ADAPTIVITY MAKE PROPER EVALUATION DIFFICULT. WE PROVIDE A DETAILED REPORT ON EVALUATION OF AN ONLINE SYSTEM FOR ADAPTIVE PRACTICE OF GEOGRAPHY, AND USE THIS CASE STUDY TO HIGHLIGHT METHODOLOGICAL ISSUES WITH EVALUATION OF OPEN ONLINE LEARNING SYSTEMS, PARTICULARLY ATTRITION BIAS. TO FACILITATE EVALUATION OF LEARNING, WE PROPOSE TO USE RANDOMIZED REFERENCE QUESTIONS. WE ILLUSTRATE APPLICATION OF SURVIVAL ANALYSIS AND LEARNING CURVES FOR DECLARATIVE KNOWLEDGE. THE RESULT PROVIDE AN INTERESTING INSIGHT INTO THE IMPACT OF ADAPTIVITY ON LEARNER BEHAVIOUR AND LEARNING.
IN THE FIELD OF LEARNING ANALYTICS, MINING THE REGULARITIES OF SOCIAL INTERACTION AND COGNITIVE PROCESSING HAVE DRAWN INCREASING ATTENTION. NEVERTHELESS, IN MOOCS, THERE IS A LACK OF INVESTIGATIONS ON THE COMBINATION OF SOCIAL AND COGNITIVE BEHAVIORAL PATTERNS. TO FILL IN THIS GAP, THIS STUDY AIMED TO UNCOVER THE RELATIONSHIP BETWEEN SOCIAL INTERACTION, COGNITIVE PROCESSING, AND LEARNING ACHIEVEMENTS IN A MOOC DISCUSSION FORUM. SPECIFICALLY, WE COLLECTED THE 3925 PARTICIPANTS' FORUM DATA THROUGHOUT 16 WEEKS. SOCIAL NETWORK ANALYSIS AND EPISTEMIC NETWORK ANALYSIS WERE JOINTLY ADOPTED TO INVESTIGATE DIFFERENCES IN SOCIAL INTERACTION, COGNITIVE PROCESSING BETWEEN TWO ACHIEVEMENT GROUPS, AND THE DIFFERENCES IN COGNITIVE PROCESSING NETWORKS BETWEEN TWO TYPES OF COMMUNITIES. FINALLY, MODERATION ANALYSIS WAS EMPLOYED TO EXAMINE THE MODERATING EFFECT OF COMMUNITY TYPES BETWEEN COGNITIVE PROCESSING AND LEARNING ACHIEVEMENTS. RESULTS INDICATED THAT: (1) THE HIGH- AND LOW-ACHIEVING GROUPS PRESENTED SIGNIFICANT DIFFERENCES IN TERMS OF DEGREE, BETWEENNESS, AND EIGENVECTOR CENTRALITY; (2) THE STRONGER COGNITIVE CONNECTIONS WERE FOUND WITHIN THE HIGH-ACHIEVING GROUP AND THE INSTRUCTOR-LED COMMUNITY; (3) THE COGNITIVE PROCESSING INDICATORS INCLUDING INSIGHT, DISCREPANCY, AND TENTATIVE WERE SIGNIFICANTLY NEGATIVE PREDICTORS OF LEARNING ACHIEVEMENTS, WHEREAS INHIBITION AND EXCLUSIVE WERE SIGNIFICANTLY POSITIVE PREDICTORS; (4) THE COMMUNITY TYPE MODERATED THE RELATIONSHIP BETWEEN COGNITIVE PROCESSING AND LEARNING ACHIEVEMENTS.
MASSIVE OPEN ONLINE COURSES (MOOCS) ALLOW LEARNING TO TAKE PLACE ANYTIME AND ANYWHERE WITH LITTLE EXTERNAL MONITORING BY TEACHERS. CHARACTERISTICALLY, HIGHLY DIVERSE GROUPS OF LEARNERS ENROLLED IN MOOCS ARE REQUIRED TO MAKE DECISIONS RELATED TO THEIR OWN LEARNING ACTIVITIES TO ACHIEVE ACADEMIC SUCCESS. THEREFORE, IT IS CONSIDERED IMPORTANT TO SUPPORT SELF-REGULATED LEARNING (SRL) STRATEGIES AND ADAPT TO RELEVANT HUMAN FACTORS (E.G., GENDER, COGNITIVE ABILITIES, PRIOR KNOWLEDGE). SRL SUPPORTS HAVE BEEN WIDELY INVESTIGATED IN TRADITIONAL CLASSROOM SETTINGS, BUT LITTLE IS KNOWN ABOUT HOW SRL CAN BE SUPPORTED IN MOOCS. VERY FEW EXPERIMENTAL STUDIES HAVE BEEN CONDUCTED IN MOOCS AT PRESENT. TO FILL THIS GAP, THIS PAPER PRESENTS A SYSTEMATIC REVIEW OF STUDIES ON APPROACHES TO SUPPORT SRL IN MULTIPLE TYPES OF ONLINE LEARNING ENVIRONMENTS AND HOW THEY ADDRESS HUMAN FACTORS. THE 35 STUDIES REVIEWED SHOW THAT HUMAN FACTORS PLAY AN IMPORTANT ROLE IN THE EFFICACY OF SRL SUPPORTS. FUTURE STUDIES CAN USE LEARNING ANALYTICS TO UNDERSTAND LEARNERS AT A FINE-GRAINED LEVEL TO PROVIDE SUPPORT THAT BEST FITS INDIVIDUAL LEARNERS. THE OBJECTIVE OF THE PAPER IS TWOFOLD: (A) TO INFORM RESEARCHERS, DESIGNERS AND TEACHERS ABOUT THE STATE OF THE ART OF SRL SUPPORT IN ONLINE LEARNING ENVIRONMENTS AND MOOCS; (B) TO PROVIDE SUGGESTIONS FOR ADAPTIVE SELF-REGULATED LEARNING SUPPORT.
THE USE OF VIDEO-BASED OPEN EDUCATIONAL RESOURCES IS WIDESPREAD, AND INCLUDES MULTIPLE APPROACHES TO IMPLEMENTATION. IN THIS PAPER, THE TERM "WITH-VIDEO ASSIGNMENTS" IS INTRODUCED TO PORTRAY VIDEO LEARNING RESOURCES ENHANCED WITH ASSIGNMENTS. THE GOAL OF THIS STUDY IS TO EXAMINE THE FACTORS THAT INFLUENCE STUDENTS' INTENTION TO ADOPT WITH-VIDEO ASSIGNMENTS. EXTENDING THE TECHNOLOGY ACCEPTANCE MODEL BY INCORPORATING STUDENTS' EMOTIONS, WE APPLIED PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING BASED ON A SAMPLE OF 73 STUDENTS WHO SYSTEMATICALLY EXPERIENCED WITH-VIDEO ASSIGNMENTS IN THEIR STUDIES. IN ADDITION, STUDENTS' ACTIVITY WAS ANALYZED USING AGGREGATED TIME SERIES VISUALIZATIONS BASED ON VIDEO ANALYTICS. LEARNING ANALYTICS INDICATE THAT STUDENTS MAKE VARYING USE OF WITH-VIDEO ASSIGNMENTS, DEPENDING ON WHEN THEY ACCESS THEM. STUDENTS ARE MORE LIKELY TO WATCH A GREATER PROPORTION OF THE VIDEO WHEN THEY USE WITH-VIDEO ASSIGNMENTS DURING THE SEMESTER, AS OPPOSED TO DURING THE EXAMS. FURTHER, THE FINDINGS HIGHLIGHT THE IMPORTANT ROLE OF STUDENTS' EMOTIONS IN ADOPTING WITH-VIDEO ASSIGNMENTS. IN ADDITION, PERCEIVED USEFULNESS OF WITH-VIDEO ASSIGNMENTS INCREASES THEIR POSITIVE EMOTIONS AND INTENTION TO ADOPT THIS MEDIUM, WHILE PERCEIVED EASE OF USE INCREASES ONLY THEIR INTENTIONS. TOGETHER, THESE CONSTRUCTS EXPLAIN 68% OF THE VARIANCE IN STUDENTS' INTENTION TO ADOPT WITH-VIDEO ASSIGNMENTS.
THIS STUDY ADDRESSES THE ISSUES OF OVERLOAD AND CHAOS IN MOOC DISCUSSION FORUMS BY DEVELOPING A MODEL TO CATEGORIZE AND IDENTIFY THREADS BASED ON WHETHER OR NOT THEY ARE SUBSTANTIALLY RELATED TO THE COURSE CONTENT. CONTENT-RELATED POSTS WERE DEFINED AS THOSE THAT GIVE/SEEK HELP FOR THE LEARNING OF COURSE MATERIAL AND SHARE/COMMENT ON RELEVANT RESOURCES. A LINGUISTIC MODEL WAS BUILT BASED ON MANUALLY-CODED STARTING POSTS IN THREADS FROM A STATISTICS MOOC (N=837) AND TESTED ON THREAD STARTING POSTS FROM THE SECOND OFFERING OF THE SAME COURSE (N=304) AND A DIFFERENT STATISTICS COURSE (N=298). THE NUMBER OF VIEWS AND VOTES THREADS RECEIVED WERE TESTED TO SEE IF THEY HELPED CLASSIFICATION. RESULTS SHOWED THAT CONTENT-RELATED POSTS IN THE STATISTICS MOOC HAD DISTINCT LINGUISTIC FEATURES WHICH APPEARED TO BE UNRELATED TO THE SUBJECT-MATTER DOMAIN; THE LINGUISTIC MODEL DEMONSTRATED GOOD CROSS-COURSE RELIABILITY (ALL RECALL AND PRECISION > .77) AND WAS USEFUL ACROSS ALL TIME SEGMENTS OF THE COURSES; NUMBER OF VIEWS AND VOTES WERE NOT HELPFUL FOR CLASSIFICATION.
DESPITE THE PREVALENCE OF E-LEARNING SYSTEMS IN SCHOOLS, MOST OF TODAY'S SYSTEMS DO NOT PERSONALIZE EDUCATIONAL DATA TO THE INDIVIDUAL NEEDS OF EACH STUDENT. THIS PAPER PROPOSES A NEW ALGORITHM FOR SEQUENCING QUESTIONS TO STUDENTS THAT IS EMPIRICALLY SHOWN TO LEAD TO BETTER PERFORMANCE AND ENGAGEMENT IN REAL SCHOOLS WHEN COMPARED TO A BASELINE APPROACH. IT IS BASED ON USING KNOWLEDGE TRACING TO MODEL STUDENTS' SKILL ACQUISITION OVER TIME, AND TO SELECT QUESTIONS THAT ADVANCE THE STUDENT'S LEARNING WITHIN THE RANGE OF THE STUDENT'S CAPABILITIES, AS DETERMINED BY THE MODEL. THE ALGORITHM IS BASED ON A BAYESIAN KNOWLEDGE TRACING (BKT) MODEL THAT INCORPORATES PARTIAL CREDIT SCORES, REASONING ABOUT MULTIPLE ATTEMPTS TO SOLVE PROBLEMS, AND INTEGRATING ITEM DIFFICULTY. THIS MODEL IS SHOWN TO OUTPERFORM OTHER BKT MODELS THAT DO NOT REASON ABOUT (OR REASON ABOUT SOME BUT NOT ALL) OF THESE FEATURES. THE MODEL WAS INCORPORATED INTO A SEQUENCING ALGORITHM AND DEPLOYED IN TWO CLASSES IN DIFFERENT SCHOOLS WHERE IT WAS COMPARED TO A BASELINE SEQUENCING ALGORITHM THAT WAS DESIGNED BY PEDAGOGICAL EXPERTS. IN BOTH CLASSES, STUDENTS USING THE BKT SEQUENCING APPROACH SOLVED MORE DIFFICULT QUESTIONS AND ATTRIBUTED HIGHER PERFORMANCE THAN DID STUDENTS WHO USED THE EXPERT-BASED APPROACH. STUDENTS WERE ALSO MORE ENGAGED USING THE BKT APPROACH, AS DETERMINED BY THEIR INTERACTION TIME AND NUMBER OF LOG-INS TO THE SYSTEM, AS WELL AS THEIR REPORTED OPINION. WE EXPECT OUR APPROACH TO INFORM THE DESIGN OF BETTER METHODS FOR SEQUENCING AND PERSONALIZING EDUCATIONAL CONTENT TO STUDENTS THAT WILL MEET THEIR INDIVIDUAL LEARNING NEEDS.
THE "DOER EFFECT" IS AN ASSOCIATION BETWEEN THE NUMBER OF ONLINE INTERACTIVE PRACTICE ACTIVITIES STUDENTS' DO AND THEIR LEARNING OUTCOMES THAT IS NOT ONLY STATISTICALLY RELIABLE BUT HAS MUCH HIGHER POSITIVE EFFECTS THAN OTHER LEARNING RESOURCES, SUCH AS WATCHING VIDEOS OR READING TEXT. SUCH AN ASSOCIATION SUGGESTS A CAUSAL INTERPRETATION--MORE DOING YIELDS BETTER LEARNING--WHICH REQUIRES RANDOMIZED EXPERIMENTATION TO MOST RIGOROUSLY CONFIRM. BUT SUCH EXPERIMENTS ARE EXPENSIVE, AND ANY SINGLE EXPERIMENT IN A PARTICULAR COURSE CONTEXT DOES NOT PROVIDE RIGOROUS EVIDENCE THAT THE CAUSAL LINK WILL GENERALIZE TO OTHER COURSE CONTENT. WE SUGGEST THAT ANALYTICS OF INCREASINGLY AVAILABLE ONLINE LEARNING DATA SETS CAN COMPLEMENT EXPERIMENTAL EFFORTS BY FACILITATING MORE WIDESPREAD EVALUATION OF THE GENERALIZABILITY OF CLAIMS ABOUT WHAT LEARNING METHODS PRODUCE BETTER STUDENT LEARNING OUTCOMES. WE ILLUSTRATE WITH ANALYTICS THAT NARROW IN ON A CAUSAL INTERPRETATION OF THE DOER EFFECT BY SHOWING THAT DOING WITHIN A COURSE UNIT PREDICTS LEARNING OF THAT UNIT CONTENT MORE THAN DOING IN UNITS BEFORE OR AFTER. WE ALSO PROVIDE GENERALIZABILITY EVIDENCE ACROSS FOUR DIFFERENT COURSES INVOLVING OVER 12,500 STUDENTS THAT THE LEARNING EFFECT OF DOING IS ABOUT SIX TIMES GREATER THAN THAT OF READING.
DIFFERENCES IN LEARNERS' BEHAVIOR HAVE A DEEP IMPACT ON THEIR EDUCATIONAL PERFORMANCE. CONSEQUENTLY, THERE IS A NEED TO DETECT AND IDENTIFY THESE DIFFERENCES AND BUILD SUITABLE LEARNER MODELS ACCORDINGLY. IN THIS PAPER, WE REPORT ON THE RESULTS FROM AN ALTERNATIVE APPROACH FOR DYNAMIC STUDENT BEHAVIORAL MODELING BASED ON THE ANALYSIS OF TIME-BASED STUDENT-GENERATED TRACE DATA. THE GOAL WAS TO UNOBTRUSIVELY CLASSIFY STUDENTS ACCORDING TO THEIR TIME-SPENT BEHAVIOR. WE APPLIED 5 DIFFERENT SUPERVISED LEARNING CLASSIFICATION ALGORITHMS ON THESE DATA, USING AS TARGET VALUES (CLASS LABELS) THE STUDENTS' PERFORMANCE SCORE CLASSES DURING A COMPUTER-BASED ASSESSMENT (CBA) PROCESS, AND COMPARED THE OBTAINED RESULTS. THE PROPOSED APPROACH HAS BEEN EXPLORED IN A STUDY WITH 259 UNDERGRADUATE UNIVERSITY PARTICIPANT STUDENTS. THE ANALYSIS OF THE FINDINGS REVEALED THAT A) THE LOW MISCLASSIFICATION RATES ARE INDICATIVE OF THE ACCURACY OF THE APPLIED METHOD AND B) THE ENSEMBLE LEARNING (TREEBAGGER) METHOD PROVIDES BETTER CLASSIFICATION RESULTS COMPARED TO THE OTHERS. THESE PRELIMINARY RESULTS ARE ENCOURAGING, INDICATING THAT A TIME-SPENT DRIVEN DESCRIPTION OF THE STUDENTS' BEHAVIOR COULD HAVE AN ADDED VALUE TOWARDS DYNAMICALLY RESHAPING THE RESPECTIVE MODELS.
THIS RESEARCH INVESTIGATES WHETHER STUDENTS' LEVEL OF DOMAIN EXPERTISE CAN BE DETECTED DURING AUTHENTIC LEARNING ACTIVITIES BY ANALYZING THEIR PHYSICAL ACTIVITY PATTERNS. MORE EXPERT STUDENTS REDUCED THEIR MANUAL ACTIVITY BY A SUBSTANTIAL 50%, WHICH WAS EVIDENT IN FINE-GRAINED SIGNAL ANALYSES AND TOTAL RATE OF GESTURING. THE QUALITY OF EXPERTS' DISCRETE HAND MOVEMENTS ALSO AVERAGED SHORTER IN DISTANCE, BRIEFER IN DURATION, AND SLOWER IN VELOCITY THAN THOSE OF NON-EXPERTS. INTERESTINGLY, EXPERTS ADAPTED BY NEARLY ELIMINATING GESTURES ON EASIER PROBLEMS, WHILE SELECTIVELY INCREASING THEM ON HARDER ONES. THEY ALSO STRATEGICALLY PRODUCED 62% MORE ICONIC GESTURES, WHICH SERVE TO RETAIN SPATIAL INFORMATION IN WORKING MEMORY WHILE EXTRACTING INFERENCES REQUIRED TO SOLVE PROBLEMS CORRECTLY. THESE FINDINGS HIGHLIGHT THE CLOSE RELATION BETWEEN HAND MOVEMENTS AND MENTAL STATE AND, MORE SPECIFICALLY, THAT HAND MOVEMENTS PROVIDE AN UNUSUALLY CLEAR WINDOW ON STUDENTS' LEVEL OF DOMAIN EXPERTISE. EMBODIED COGNITION AND LIMITED RESOURCE THEORIES ONLY PARTIALLY ACCOUNT FOR THE PRESENT FINDINGS, WHICH SPECIFY FUTURE DIRECTIONS FOR THEORETICAL WORK.
THIS STUDY EXAMINES THE EXTENT TO WHICH THE LEARNING ORIENTATIONS IDENTIFIED BY STUDENT SELF-REPORTS AND THE OBSERVATION OF THEIR ONLINE LEARNING EVENTS WERE RELATED TO EACH OTHER AND TO THEIR ACADEMIC PERFORMANCE. THE PARTICIPANTS WERE 322 FIRST-YEAR ENGINEERING UNDERGRADUATES, WHO WERE ENROLLED IN A BLENDED COURSE. USING STUDENTS' SELF-REPORT ON A QUESTIONNAIRE ABOUT THEIR APPROACHES TO LEARNING AND PERCEPTIONS OF THE BLENDED LEARNING ENVIRONMENT, 'UNDERSTANDING' AND 'REPRODUCING' LEARNING ORIENTATIONS WERE IDENTIFIED. USING OBSERVATIONS OF STUDENT ACTIVITY ONLINE, A HIDDEN MARKOV MODEL (HMM) AND AGGLOMERATIVE SEQUENCE CLUSTERING DETECTED FOUR QUALITATIVELY DIFFERENT PATTERNS OF ONLINE LEARNING ORIENTATIONS. CROSS-TABULATIONS SHOWED SIGNIFICANT AND LOGICAL ASSOCIATIONS AMONGST THE LEARNING ORIENTATIONS DERIVED BY THE SELF-REPORT AND OBSERVATIONAL METHODS. SIGNIFICANT DIFFERENCES WERE ALSO CONSISTENTLY FOUND IN THE STUDENTS' ACADEMIC PERFORMANCE ACROSS THE MID-TERM AND FINAL ASSESSMENTS BASED ON THEIR LEARNING ORIENTATIONS DETECTED BY BOTH SELF-REPORT AND OBSERVATIONAL METHODS, RESULTS WHICH HAVE IMPORTANT IMPLICATIONS FOR LEARNING RESEARCH.
THE CURRENT STUDY INTRODUCES A MODEL FOR MEASURING STUDENT DILIGENCE USING ONLINE BEHAVIORS DURING INTELLIGENT TUTORING SYSTEM USE. THIS MODEL IS VALIDATED USING A FULL ACADEMIC YEAR DATASET TO TEST ITS PREDICTIVE VALIDITY AGAINST LONG-TERM ACADEMIC OUTCOMES INCLUDING END-OF-YEAR GRADES AND TOTAL WORK COMPLETED BY THE END OF THE YEAR. THE MODEL IS ADDITIONALLY VALIDATED FOR ROBUSTNESS TO TIME-SAMPLE LENGTH AS WELL AS DATA SAMPLING FREQUENCY. WHILE THE MODEL IS SHOWN TO BE PREDICTIVE AND ROBUST TO TIME-SAMPLE LENGTH, THE RESULTS ARE INCONCLUSIVE FOR ROBUSTNESS IN DATA SAMPLING FREQUENCY. IMPLICATIONS FOR RESEARCH ON INTERVENTIONS, AND UNDERSTANDING THE INFLUENCE OF SELF-CONTROL, MOTIVATION, METACOGNITION, AND COGNITION ARE DISCUSSED.
THE WORK IN PROGRESS PRESENTS A STUDY ON UNDERGRADUATE ENGINEERING STUDENTS AND PROJECT-BASED LEARNING (PBL) TO UNDERSTAND COMMUNITY IMPACT THAT SUPPORTS RESEARCH TO-PRACTICE USING DATA ANALYTICS IN HIGHER EDUCATION. THIS STUDY EXPANDS ON DATA ANALYSIS AND VISUALIZATION APPLICATIONS IN PBL USING WATSON ANALYTICS. IN APPLYING THE DECISION TECHNIQUES, THE CLASSROOM ENVIRONMENT OUTCOMES AND COMMUNITY IMPACT ARE KEY TO ENGINEERING EDUCATION GOALS. OUR APPROACH WILL PROVIDE A FRAMEWORK TO EXPLORE PREDICTIVE MODELS AND THE OUTCOME OF PBL TECHNIQUES IN ENGINEERING EDUCATION TO DRAW A CONCLUSION WITH REGARD TO DECISION METHODS AND FACTORS (E.G., THE ENGINEER DESIGN PROCESS). IN USING THE SMART DATA ANALYSIS AND VISUALIZATION TOOLS SUCH AS WATSON ANALYTICS, THE SPECIFIC AREAS ANALYZED COULD CONTRIBUTE AND AID IN THE LEARNING ENVIRONMENT. FROM THIS ASSESSMENT, THE PBL DECISION TECHNIQUES WITHIN THE CLASSROOM ENVIRONMENT AND THE OVERALL RELATIONSHIP TO THE INDUSTRY CREATED NEW METHODOLOGIES (TECHNIQUES) TO EXPLORE COMMUNITY IMPACT USING DATA ANALYTICS. THESE PRACTICES ARE ASSESSED FROM DETAIL STATISTICAL ANALYSIS AND THE DATASET FOR PREDICTIVE MODELING OF THE USER COMMUNITY. AS SUCH, THIS STUDY SERVES AS A BASELINE IN DEVELOPING MODELS TO IMPROVE PBL METHODS USING DATA ANALYSIS IN ENGINEERING AND AVIATION EDUCATION FOR THE EXAMINATION OF COMMUNITY IMPACT.
EDUCATIONAL VIDEOS ARE INCREASINGLY USED TO LET STUDENTS PREPARE LESSON MATERIAL AT HOME PRIOR TO IN-CLASS ACTIVITIES IN FLIPPED CLASSROOMS. THE MAIN CHALLENGE OF THIS TEACHING STRATEGY IS TO STIMULATE STUDENTS TO WATCH THESE VIDEOS ATTENTIVELY BEFORE GOING TO CLASS. THIS PAPER DESCRIBES THE USE OF QUESTIONS THAT POP-UP WITHIN RELATIVELY LONG EDUCATIONAL VIDEOS OF 16 MIN ON AVERAGE AND DESIGNED TO ENHANCE STUDENTS' ENGAGEMENT AND UNDERSTANDING WHEN PREPARING FOR IN-CLASS ACTIVITIES. THE EFFECTS OF SUCH POP-UP QUESTIONS ON STUDENTS' LEARNING PERFORMANCE WERE STUDIED WITHIN A FLIPPED COURSE IN MOLECULAR BIOLOGY. STUDENTS HAD ACCESS TO VIDEOS WITH OR WITHOUT A VARIABLE SET OF POP-UP QUESTIONS. THE EXPERIMENTAL GROUP WITH POP-UP QUESTIONS SHOWED SIGNIFICANTLY HIGHER TEST RESULTS COMPARED TO THE GROUP WITHOUT POP-UP QUESTIONS. INTERESTINGLY, STUDENTS THAT ANSWERED POP-UP QUESTIONS ON CERTAIN CONCEPTS DID NOT SCORE BETTER ON ITEMS TESTING THESE SPECIFIC CONCEPTS THAN THE CONTROL GROUP. THESE RESULTS SUGGEST THAT MERELY THE PRESENCE OF POP-UP QUESTIONS ENHANCES STUDENTS' LEARNING. ADDITIONAL DATA FROM INTERVIEWS, SURVEYS, AND LEARNING ANALYTICS SUGGEST THAT POP-UP QUESTIONS INFLUENCE VIEWING BEHAVIOR, LIKELY BY PROMOTING ENGAGEMENT. IT IS CONCLUDED THAT POP-UP QUESTIONS STIMULATE LEARNING WHEN STUDYING VIDEOS OUTSIDE CLASS THROUGH AN INDIRECT TESTING EFFECT.
AS A DIGITAL LEARNING MEDIUM, SERIOUS GAMES CAN BE POWERFUL, IMMERSIVE EDUCATIONAL VEHICLES AND PROVIDE LARGE DATA STREAMS FOR UNDERSTANDING PLAYER BEHAVIOR. EDUCATIONAL DATA MINING AND LEARNING ANALYTICS CAN EFFECTIVELY LEVERAGE BIG DATA IN THIS CONTEXT TO HEIGHTEN INSIGHT INTO STUDENT TRAJECTORIES AND BEHAVIOR PROFILES. IN APPLICATION OF THESE METHODS, DISTILLING EVENT-STREAM DATA DOWN TO A SET OF SALIENT FEATURES FOR ANALYSIS (I.E. FEATURE ENGINEERING) IS A VITAL ELEMENT OF ROBUST MODELING. THIS PAPER PRESENTS A PROCESS FOR SYSTEMATIC GAME-BASED FEATURE ENGINEERING TO OPTIMIZE INSIGHT INTO PLAYER BEHAVIOR: THE IDEFA FRAMEWORK (INTEGRATED DESIGN OF EVENT-STREAM FEATURES FOR ANALYSIS). IDEFA ALIGNS GAME DESIGN AND DATA COLLECTION FOR HIGH-RESOLUTION FEATURE ENGINEERING, HONED THROUGH CRITICAL, ITERATIVE INTERPLAY WITH ANALYSIS. BUILDING ON RECENT RESEARCH IN GAME-BASED DATA MINING, WE EMPIRICALLY INVESTIGATE IDEFA APPLICATION IN SERIOUS GAMES. RESULTS SHOW THAT BEHAVIORAL MODELS WHICH USED A FULL FEATURE SET PRODUCED MORE MEANINGFUL RESULTS THAN THOSE WITH NO FEATURE ENGINEERING, WITH GREATER INSIGHT INTO IMPACTFUL LEARNING INTERACTIONS, AND PLAY TRAJECTORIES CHARACTERIZING GROUPS OF PLAYERS. THIS DISCOVERY OF EMERGENT PLAYER BEHAVIOR IS FUELED BY THE DATA FRAMEWORK, RESULTANT BASE DATA STREAM, AND RIGOROUS FEATURE CREATION PROCESS PUT FORWARD IN IDEFA-INTEGRATING ITERATIVE DESIGN, FEATURE ENGINEERING, AND ANALYSIS FOR OPTIMAL INSIGHT INTO SERIOUS PLAY.
THIS PAPER PROPOSES A SET OF INDICATORS TO CAPTURE THE SOCIAL CONTEXT BETWEEN REGULAR FORUM POSTERS IN MASSIVE OPEN ONLINE COURSES (MOOCS). SUCH INDICATORS ADVANCE LEARNING ANALYTICS RESEARCH OF SOCIAL RELATIONS IN ONLINE SETTINGS, AS THEY ENABLE TO COMPARE INTERACTIONS IN DIFFERENT COURSES. PROPOSED INDICATORS WERE DERIVED THROUGH EXPONENTIAL RANDOM GRAPH MODELLING (ERGM) TO THE NETWORKS OF REGULAR POSTERS IN FOUR MOOC FORUMS. MODELLING DEMONSTRATED THAT SOCIAL CONTEXT CAN BE CAPTURED BY THE NETWORK PATTERNS OF RECIPROCITY. IN SOME MOOCS, NETWORK FORMATION IS DRIVEN BY THE HIGHER PROPENSITY FOR DIRECT RECIPROCITY AND LOWER PROPENSITY FOR INDIRECT AND TRIADIC-LEVEL RECIPROCITY. THE SOCIAL CONTEXT IN A HIGHLY MODERATED COURSE WAS DESCRIBED BY LOWER PROPENSITY FOR DIRECT RECIPROCITY AND HIGHER PROPENSITY FOR INDIRECT AND TRIADIC-LEVEL RECIPROCITY. WE CONCLUDE THAT PATTERNS OF DIRECT, INDIRECT, AND TRIADIC-LEVEL RECIPROCITY REFLECT VARIATIONS OF BEHAVIOUR IN KNOWLEDGE EXCHANGE ON MOOC FORUMS. THESE THREE TYPES OF PATTERNS CAN BE THEORISED AS DYADIC INFORMATION EXCHANGE, SOCIAL SOLIDARITY, AND GRADUAL AMPLIFICATION OF INFORMATION FLOW. RE-MODELLING THE SAME FOUR MOOC NETWORKS WITHOUT THE STAFF AND TEACHING ASSISTANTS SUGGESTED THAT THESE NETWORK ACTORS PLAY A ROLE IN THE FORMATION OF INDIRECT AND TRIADIC-LEVEL RECIPROCITY PATTERNS, RELATED TO GROUP COHESION.
THE FORGING ONLINE EDUCATION THROUGH FIRE (FORGE) INITIATIVE PROVIDES EDUCATORS AND LEARNERS IN HIGHER EDUCATION WITH ACCESS TO WORLD-CLASS FIRE TESTBED INFRASTRUCTURE. FORGE SUPPORTS EXPERIMENTALLY DRIVEN RESEARCH IN AN ELEARNING ENVIRONMENT BY COMPLEMENTING TRADITIONAL CLASSROOM AND ONLINE COURSES WITH INTERACTIVE REMOTE LABORATORY EXPERIMENTS. THE PROJECT HAS ACHIEVED ITS OBJECTIVES BY DEFINING AND IMPLEMENTING A FRAMEWORK CALLED FORGEBOX. THIS FRAMEWORK OFFERS THE METHODOLOGY, ENVIRONMENT, TOOLS AND RESOURCES TO SUPPORT THE CREATION OF HTML-BASED ONLINE EDUCATIONAL MATERIAL CAPABLE ACCESSING VIRTUALIZED AND PHYSICAL FIRE TESTBED INFRASTRUCTURE EASILY. FORGEBOX ALSO CAPTURES VALUABLE QUANTITATIVE AND QUALITATIVE LEARNING ANALYTIC INFORMATION USING QUESTIONNAIRES AND LEARNING ANALYTICS THAT CAN HELP OPTIMISE AND SUPPORT STUDENT LEARNING. TO DATE, FORGE HAS PRODUCED COURSES COVERING A WIDE RANGE OF NETWORKING AND COMMUNICATION DOMAINS. THESE ARE FREELY AVAILABLE FROM FORGEBOX.EU AND HAVE RESULTED IN OVER 24,000 EXPERIMENTS UNDERTAKEN BY MORE THAN 1,800 STUDENTS ACROSS 10 COUNTRIES WORLDWIDE. THIS WORK HAS SHOWN THAT THE USE OF REMOTE HIGH-PERFORMANCE TESTBED FACILITIES FOR HANDS-ON REMOTE EXPERIMENTATION CAN HAVE A VALUABLE IMPACT ON THE LEARNING EXPERIENCE FOR BOTH EDUCATORS AND LEARNERS. ADDITIONALLY, CERTAIN CHALLENGES IN DEVELOPING FIRE-BASED COURSEWARE HAVE BEEN IDENTIFIED, WHICH HAS LED TO A SET OF RECOMMENDATIONS IN ORDER TO SUPPORT THE USE OF FIRE FACILITIES FOR TEACHING AND LEARNING PURPOSES.
FROM E-COMMERCE TO E-LEARNING, RECOMMENDATION SYSTEMS HAVE GIVEN BIRTH TO AN IMPORTANT AND THRIVING RESEARCH NICHE AND HAVE BEEN DEPLOYED IN A VARIETY OF APPLICATION AREAS OVER THE LAST DECADE. IN PARTICULAR, IN THE TECHNOLOGY-ENHANCED LEARNING (TEL) FIELD, RECOMMENDATION SYSTEMS HAVE ATTRACTED INCREASING INTEREST, ESPECIALLY WITH THE RISE OF EDUCATIONAL DATA MINING AND BIG DATA LEARNING ANALYTICS. GENERALLY, TEL RECOMMENDATION SYSTEMS ARE USED TO SUPPORT LEARNERS IN LOCATING RELEVANT EDUCATIONAL CONTENT ACCORDING TO THEIR PROFILES. THESE SYSTEMS MAY INVOLVE SEVERAL PHASES, SUCH AS DATA ACQUISITION AND PREPARATION, MODELING, AND RECOMMENDATION COMPUTATION, PHASES, WHICH TOGETHER, CAN DESCRIBE A TEL RECOMMENDATION SYSTEM AND DISTINGUISH IT FROM OTHERS. HOWEVER, SUCH A DESCRIPTION NEEDS TO BE EXPANDED AND GENERALIZED IN ORDER TO COVER MOST OF THE TEL RECOMMENDATION SYSTEMS, ESPECIALLY, IN THE CONTEXT OF ANYWHERE AND ANYTIME LEARNING BASED ON VARIOUS WEB-BASED LEARNING ENVIRONMENTS, INCLUDING LEARNING OBJECT REPOSITORY (LOR), OPEN COURSEWARE (OCW), OPEN EDUCATIONAL RESOURCES (OER), LEARNING MANAGEMENT SYSTEMS (LMS), MASSIVE OPEN ONLINE COURSES (MOOC), EDUCATIONAL WIDGETS, EDUCATIONAL MOBILE APPLICATIONS, ETC. IN THIS CHAPTER, WE PROVIDE A GENERIC META-LEVEL FRAMEWORK FOR A COMMON DESCRIPTION OF TEL RECOMMENDATION SYSTEMS. THEN, WE PRESENT AN ANALYSIS OF SEVERAL EXISTING TEL RECOMMENDATION SYSTEMS WITH RESPECT TO OUR DEFINED FRAMEWORK.
WE CONSIDER THE PROBLEM OF DETECTING A CHANGE IN AN ARBITRARY VECTOR PROCESS BY EXAMINING THE EVOLUTION OF CALCULATED DATA SUBSPACES. IN OUR DEVELOPMENTS, BOTH THE DATA SUBSPACES AND THE CHANGE IDENTIFICATION CRITERION ARE NOVEL AND FOUNDED IN THE THEORY OF L1-NORM PRINCIPAL-COMPONENT ANALYSIS (PCA). THE OUTCOME IS HIGHLY ACCURATE, RAPID DETECTION OF CHANGE IN STREAMING DATA THAT VASTLY OUTPERFORMS CONVENTIONAL EIGENVECTOR SUBSPACE METHODS (L2-NORM PCA). IN THIS PAPER, ILLUSTRATIONS ARE OFFERED IN THE CONTEXT OF ARTIFICIAL DATA AND REAL ELECTROENCEPHALOGRAPHY (EEG) AND ELECTROMYOGRAPHY (EMG) DATA SEQUENCES.
INNOVATIVE INFORMATION AND COMMUNICATION TECHNOLOGIES HAVE REFORMED HIGHER EDUCATION FROM THE TRADITIONAL WAY TO SMART LEARNING. SMART LEARNING APPLIES TECHNOLOGICAL AND SOCIAL DEVELOPMENTS AND FACILITATES EFFECTIVE PERSONALIZED LEARNING WITH INNOVATIVE TECHNOLOGIES, ESPECIALLY SMART DEVICES AND ONLINE TECHNOLOGIES. SMART LEARNING HAS ATTRACTED INCREASING RESEARCH INTEREST FROM THE ACADEMIA. THIS STUDY AIMS TO COMPREHENSIVELY REVIEW THE RESEARCH FIELD OF SMART LEARNING BY CONDUCTING A TOPIC MODELING ANALYSIS OF 555 SMART LEARNING PUBLICATIONS COLLECTED FROM THE SCOPUS DATABASE. IN PARTICULAR, IT SEEKS ANSWERS TO (1) WHAT THE MAJOR RESEARCH TOPICS CONCERNING SMART LEARNING WERE, AND (2) HOW THESE TOPICS EVOLVED. RESULTS DEMONSTRATE SEVERAL MAJOR RESEARCH ISSUES, FOR EXAMPLE, INTERACTIVE AND MULTIMEDIA LEARNING, STEM (SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS) EDUCATION, ATTENDANCE AND ATTENTION RECOGNITION, BLENDED LEARNING FOR SMART LEARNING, AND AFFECTIVE AND BIOMETRIC COMPUTING. FURTHERMORE, SEVERAL EMERGING TOPICS WERE IDENTIFIED, FOR EXAMPLE, SMART LEARNING ANALYTICS, SOFTWARE ENGINEERING FOR E-LEARNING SYSTEMS, IOT (INTERNET OF THINGS) AND CLOUD COMPUTING, AND STEM EDUCATION. ADDITIONALLY, POTENTIAL INTER-TOPIC DIRECTIONS WERE HIGHLIGHTED, FOR INSTANCE, ATTENDANCE AND ATTENTION RECOGNITION AND IOT AND CLOUD COMPUTING, SEMANTICS AND ONTOLOGY AND MOBILE LEARNING, FEEDBACK AND ASSESSMENT AND MOOCS (MASSIVE OPEN ONLINE COURSES) AND COURSE CONTENT MANAGEMENT, AS WELL AS BLENDED LEARNING FOR SMART LEARNING AND ECOSYSTEM AND AMBIENT INTELLIGENCE.
WITH THE POPULARITY OF ONLINE LEARNING, MORE AND MORE RESEARCHERS HAVE ATTACHED GREAT IMPORTANCE TO THE RELATIONSHIP BETWEEN LEARNING EFFECT AND INFLUENCE FACTORS IN THE ONLINE COURSES. IN LITERATURE WORKS, LOGISTIC STEPWISE REGRESSION ALGORITHM IS THE MOST USED METHOD. BUT THIS METHOD HAS LIMITATION IN RUN TIME ESPECIALLY WHEN THE DIMENSION OF DATA IS LARGE. BESIDES THAT, IT CAN'T RANK THE IMPORTANCE OF FACTORS. AIMING AT THE ABOVE SHORTCOMINGS, THIS PAPER PROPOSES A NOVEL APPROACH TO ANALYZE THE INFLUENCING FACTORS OF ONLINE LEARNING, WHICH IS BASED ON THE COMBINATION OF DECISION TREE AND RECURSIVE FEATURE ELIMINATION. FIRSTLY, THE FEATURE SORTING ALGORITHM IS BASED ON DECISION TREE TO CONDUCT THE PRELIMINARY SCREENING, WHICH IS TO FORM A CANDIDATE FEATURE SET. THEN, RECURSIVE FEATURE ELIMINATION IS USED TO RANK THE CANDIDATE FEATURES BY THEIR IMPORTANCE. AT THIS STAGE, LOGISTIC REGRESSION (LR), SUPPORT VECTOR MACHINE (SVM) AND DECISION TREE (DT) MODELS ARE USED SEPARATELY TO OBTAIN EACH COLLATING SEQUENCE OF IMPORTANCE. BY AVERAGING THESE COLLATING SEQUENCES, THE FINAL IMPORTANCE RANKING OF CANDIDATE FEATURES IS ACHIEVED. FINALLY, AN EXPERIMENT IS CARRIED OUT ON THE OPEN UNIVERSITY LEARNING ANALYTICS DATASET, AND THE RESULTS SHOW THAT LEARNING BEHAVIOR HAS AN IMPORTANT IMPACT ON THE LEARNING EFFECT. POSITIVE LEARNING BEHAVIORS CAN LEAD TO BETTER LEARNING EFFECT.
IT IS ARGUED THAT WE ARE WITNESSING A PARADIGMATIC SHIFT TOWARD CONSTRUCTIONIST GAMING IN WHICH STUDENTS DESIGN GAMES INSTEAD OF JUST CONSUMING THEM. HOWEVER, ONLY A LIMITED NUMBER OF STUDIES HAVE EXPLORED TEACHING OF EDUCATIONAL GAME DESIGN (GD). THIS PAPER REPORTS A CASE STUDY IN WHICH LEARNING BY DESIGNING GAMES STRATEGY WAS USED TO TEACH DIFFERENT VIEWPOINTS OF EDUCATIONAL GD. IN ORDER TO SUPPORT DESIGN ACTIVITIES, WE PROPOSED A CIMDELA (CONTENT, INSTRUCTION, MECHANICS, DYNAMICS, ENGAGEMENT, LEARNING ANALYTICS) FRAMEWORK THAT AIMS TO ALIGN GAME DESIGN AND INSTRUCTIONAL DESIGN ASPECTS. THIRTY UNDER-GRADUATE STUDENTS PARTICIPATED IN THE GAMIFIED WORKSHOP AND DESIGNED MATH GAMES IN TEAMS. THE ACTIVITIES WERE DIVIDED INTO EIGHT ROUNDS CONSISTING OF DESIGN DECISIONS AND GAME TESTING. THE WORKSHOP ACTIVITIES WERE OBSERVED AND THE DESIGNED GAMES SAVED. MOST OF THE STUDENTS WERE ENGAGED IN THE DESIGN ACTIVITIES AND PARTICULARLY THE APPROACH THAT ALLOWED STUDENTS TO TEST THE EVOLVING GAME AFTER EACH ROUND, MOTIVATED STUDENTS. OBSERVATIONS REVEALED THAT SOME OF THE STUDENTS HAD ISOLATED DESIGN MINDSET IN THE BEGINNING AND THEY HAD PROBLEMS TO CONSIDER DESIGN DECISIONS FROM GAME DESIGN AND INSTRUCTIONAL PERSPECTIVES, BUT TEAM-BASED DESIGN ACTIVITIES OFTEN LED TO FRUITFUL DEBATE WITH CO-DESIGNERS AND HELPED SOME STUDENTS TO EXPAND THEIR MINDSETS.
STATISTICAL PREDICTIVE MODELS PLAY AN IMPORTANT ROLE IN LEARNING ANALYTICS. IN THIS WORK, WE SEEK TO HARNESS THE POWER OF PREDICTIVE MODELING METHODOLOGY FOR THE DEVELOPMENT OF AN ANALYTICS FRAMEWORK IN STEM STUDENT SUCCESS EFFICACY STUDIES. WE DEVELOP NOVEL PREDICTIVE ANALYTICS TOOLS TO PROVIDE STAKEHOLDERS AUTOMATED AND TIMELY INFORMATION TO ASSESS STUDENT PERFORMANCE TOWARD A STUDENT SUCCESS OUTCOME, AND TO INFORM PEDAGOGICAL DECISIONS OR INTERVENTION STRATEGIES. IN PARTICULAR, WE TAKE ADVANTAGE OF THE RANDOM FOREST MACHINE LEARNING ALGORITHM, PROPOSING A NUMBER OF INNOVATIONS TO IDENTIFY KEY INPUT THRESHOLDS, QUANTIFY THE IMPACT OF INPUTS ON STUDENT SUCCESS, EVALUATE STUDENT SUCCESS AT BENCHMARKS IN A PROGRAM OF STUDY, AND OBTAIN A STUDENT SUCCESS SCORE. THE PROPOSED MACHINERY CAN ALSO TAILOR INFORMATION FOR ADVISERS TO IDENTIFY THE RISK LEVELS OF INDIVIDUAL STUDENTS IN EFFORTS TO ENHANCE STEM PERSISTENCE AND STEM GRADUATION SUCCESS. WE ADDITIONALLY PRESENT OUR PREDICTIVE ANALYTICS PIPELINE, MOTIVATED BY AND ILLUSTRATED IN A PARTICULAR STEM STUDENT SUCCESS STUDY AT SAN DIEGO STATE UNIVERSITY. WE HIGHLIGHT THE PROCESS OF DESIGNING, IMPLEMENTING, VALIDATING, AND DEPLOYING ANALYTICAL TOOLS OR DASHBOARDS, AND EMPHASIZE THE ADVANTAGE OF LEVERAGING THE UTILITIES OF BOTH STATISTICAL ANALYSES AND BUSINESS INTELLIGENCE TOOLS IN ORDER TO MAXIMIZE FUNCTIONALITY AND COMPUTATIONAL CAPACITY.
EASY JAVASCRIPT SIMULATIONS (EJSS) IS AN OPEN-SOURCE TOOL THAT ALLOWS TEACHERS WITH LIMITED PROGRAMMING EXPERIENCE TO STRAIGHTFORWARDLY BUNDLE AN INTERACTIVE COMPUTER SCIENCE OR ENGINEER SIMULATION IN AN HTML+ JAVASCRIPT WEBPAGE. ITS PROMINENT PLACE IN PHYSICS (WHERE IT HAS WON SEVERAL PRIZES) SHOULD NOT HINDER ITS APPLICATION IN OTHER FIELDS (SUCH AS BUILDING THE FRONT-END OF REMOTE LABORATORIES OR LEARNING ANALYTICS) AFTER HAVING ADAPTED PART OF THE FUNCTIONALITY OF EJSS TO THEM. TO FACILITATE THE FUTURE INCLUSION OF NEW FUNCTIONALITIES IN EJSS, THIS PAPER PRESENTS A NEW VERSION OF THIS TOOL THAT ALLOWS THE ENHANCEMENT OF EJSS, LETTING IT INCORPORATE NEW TOOLS AND CHANGE ITS GRAPHICAL USER INTERFACE, BY MEANS OF EXTENSION PLUGINS (SPECIAL SOFTWARE LIBRARIES). TO ILLUSTRATE THE BENEFITS OF THIS DISTRIBUTABLE SELF-CONTAINED NON-INTRUSIVE STRATEGY, THE PAPER (A) DISCUSSES THE NEW METHODOLOGICAL POSSIBILITIES THAT THE PLUGINS BRING TO EJSS DEVELOPERS AND USERS, AND (B) PRESENTS THREE PLUGINS: ONE TO SUPPORT THE PLUGIN MANAGEMENT AND THE OTHERS TO EASILY SET UP A STREAMLINED REMOTE LABORATORY. MOREOVER, THE PAPER ALSO PRESENTS THE MAIN CHARACTERISTICS OF THAT REMOTE LAB TO ALLOW READERS TAKE ADVANTAGE OF EJSS AND THE THREE PLUGINS TO SET UP NEW ONLINE EXPERIMENTS FOR THEIR STUDENTS QUICKLY.
GENE MICROARRAY DATA GENERALLY INCLUDES HIGH DIMENSION, SMALL SAMPLE DATASETS PRONE TO NOISE. ANALYZING THIS DATA USING SUPERVISED AND NON-SUPERVISED LEARNING ALGORITHMS IS EXTREMELY USEFUL FOR GENE CHARACTERIZATION, DISEASE DIAGNOSIS, AND GENETIC THERAPY IN THE MEDICAL FIELD. FOR MANY YEARS, PRINCIPAL COMPONENT ANALYSIS (PCA) HAS BEEN USED AS A TOOL IN ALGORITHMS FOR GENE EXPRESSION CLASSIFICATION. PREVIOUS SOLUTIONS UTILIZE L-2 NORM BASED PCA; HOWEVER, WITH ITS SUPERIOR RESISTANCE TO OUTLIER DATA, L-1 NORM PCA OFFERS IMPROVED RESULTS. BOTH METHODS ARE COMPARED USING SUPPORT VECTOR MACHINES (SVM) TO CLASSIFY GENETIC MUTATIONS AND CO-REGULATION IN SEVERAL PUBLICLY AVAILABLE DATASETS. METHODS UTILIZING L-1 PCA RESULT IN IMPROVED ACCURACY COMPARED TO L-2 PCA WHEN USED AS A PRE-PROCESSING STEP TO SVM CLASSIFICATION FOR GENE MICROARRAY DATA.
ALTHOUGH AN INCREASING NUMBER OF ETHICAL DATA SCIENCE AND AI COURSES IS AVAILABLE, WITH MANY FOCUSING SPECIFICALLY ON TECHNOLOGY AND COMPUTER ETHICS, PEDAGOGICAL APPROACHES EMPLOYED IN THESE COURSES RELY EXCLUSIVELY ON TEXTS RATHER THAN ON ALGORITHMIC DEVELOPMENT OR DATA ANALYSIS. IN THIS PAPER WE RECOUNT A RECENT EXPERIENCE IN DEVELOPING AND TEACHING A TECHNICAL COURSE FOCUSED ON RESPONSIBLE DATA SCIENCE, WHICH TACKLES THE ISSUES OF ETHICS IN AI, LEGAL COMPLIANCE, DATA QUALITY, ALGORITHMIC FAIRNESS AND DIVERSITY, TRANSPARENCY OF DATA AND ALGORITHMS, PRIVACY, AND DATA PROTECTION. INTERPRETABILITY OF MACHINE-ASSISTED DECISION-MAKING IS AN IMPORTANT COMPONENT OF RESPONSIBLE DATA SCIENCE THAT GIVES A GOOD LENS THROUGH WHICH TO SEE OTHER RESPONSIBLE DATA SCIENCE TOPICS, INCLUDING PRIVACY AND FAIRNESS. WE PROVIDE EMERGING PEDAGOGICAL BEST PRACTICES FOR TEACHING TECHNICAL DATA SCIENCE AND AI COURSES THAT FOCUS ON INTERPRETABILITY, AND TIE RESPONSIBLE DATA SCIENCE TO CURRENT LEARNING SCIENCE AND LEARNING ANALYTICS RESEARCH. WE FOCUS ON A NOVEL METHODOLOGICAL NOTION OF THE OBJECT-TO-INTERPRET-WITH, A REPRESENTATION THAT HELPS STUDENTS TARGET METACOGNITION INVOLVING INTERPRETATION AND REPRESENTATION. IN THE CONTEXT OF INTERPRETING MACHINE LEARNING MODELS, WE HIGHLIGHT THE SUITABILITY OF "NUTRITIONAL LABELS"-A FAMILY OF INTERPRETABILITY TOOLS THAT ARE GAINING POPULARITY IN RESPONSIBLE DATA SCIENCE RESEARCH AND PRACTICE.
WITH THE WIDE EXPANSION OF DISTRIBUTED LEARNING ENVIRONMENTS THE WAY WE LEARN BECAME MORE DIVERSE THAN EVER. THIS POSES AN OPPORTUNITY TO INCORPORATE DIFFERENT DATA SOURCES OF LEARNING TRACES THAT CAN OFFER BROADER INSIGHTS INTO LEARNER BEHAVIOR AND THE INTRICACIES OF THE LEARNING PROCESS. WE ARGUE THAT COMBINING ANALYTICS ACROSS DIFFERENT E-LEARNING SYSTEMS CAN POTENTIALLY MEASURE THE EFFECTIVENESS OF LEARNING DESIGNS AND MAXIMIZE LEARNING OPPORTUNITIES IN DISTRIBUTED SETTINGS. AS A STEP TOWARD THIS GOAL, IN THIS STUDY, WE CONSIDERED HOW TO BROADEN THE CONTEXT OF A SINGLE LEARNING ENVIRONMENT INTO A LEARNING ECOSYSTEM THAT INTEGRATES THREE SEPARATE E-LEARNING SYSTEMS. WE PRESENT A CROSS-PLATFORM ARCHITECTURE THAT CAPTURES, INTEGRATES, AND STORES LEARNING-RELATED DATA FROM THE LEARNING ECOSYSTEM. TO DEMONSTRATE THE FEASIBILITY AND THE BENEFITS OF CROSS-PLATFORM ARCHITECTURE, WE USED REGRESSION AND CLASSIFICATION TECHNIQUES TO GENERATE INTERPRETABLE MODELS WITH ANALYTICS THAT CAN BE RELEVANT FOR INSTRUCTORS IN UNDERSTANDING LEARNING BEHAVIOR AND SENSEMAKING OF THE INSTRUCTIONAL METHOD ON LEARNER PERFORMANCE. THE RESULTS SHOW THAT COMBINING DATA ACROSS THREE E-LEARNING SYSTEMS IMPROVE THE CLASSIFICATION ACCURACY COMPARED TO DATA FROM A SINGLE LEARNING SYSTEM BY A FACTOR OF 5. THIS ARTICLE HIGHLIGHTS THE VALUE OF CROSS-PLATFORM LEARNING ANALYTICS AND PRESENTS A SPRINGBOARD FOR THE CREATION OF NEW CROSS-SYSTEM DATA-DRIVEN RESEARCH PRACTICES.
DATA ARE CRUCIAL TO IMPROVE DECISION-MAKING AND TO OBTAIN GREATER BENEFITS IN ANY TYPE OF ACTIVITY. HOWEVER, THE LARGE AMOUNT OF INFORMATION GENERATED BY NEW TECHNOLOGIES HAS MADE DATA ANALYSIS AND KNOWLEDGE GENERATION A COMPLEX TASK. NUMEROUS TOOLS HAVE EMERGED TO FACILITATE THIS KNOWLEDGE GENERATION, SUCH AS DASHBOARDS. ALTHOUGH DASHBOARDS ARE VERY POWERFUL TOOLS, THEIR EFFECTIVENESS CAN BE AFFECTED BY A BAD DESIGN OR BY NOT TAKING INTO ACCOUNT THE CONTEXT IN WHICH THEY ARE PLACED. THEREFORE, IT IS NECESSARY TO DESIGN AND CREATE TAILORED DASHBOARDS ACCORDING TO THE AUDIENCE AND DATA DOMAIN. CREATING TAILORED DASHBOARDS CAN BE VERY BENEFICIAL, BUT ALSO A COSTLY PROCESS IN TERMS OF TIME AND RESOURCES. THIS PAPER PRESENTS AN APPLICATION OF THE SOFTWARE PRODUCT LINE PARADIGM TO GENERATE DASHBOARDS ADAPTED TO ANY CONTEXT IN A MORE STRAIGHTFORWARD WAY BY REUSING BOTH SOFTWARE COMPONENTS AND KNOWLEDGE. ONE OF THE CONTEXTS THAT CAN BE ESPECIALLY FAVORED BY THIS APPROACH IS THE EDUCATIONAL CONTEXT, WHERE LEARNING ANALYTICS AND THE ANALYSIS OF STUDENT PERFORMANCE TO IMPROVE LEARNING METHODOLOGIES ARE BECOMING VERY POPULAR. HAVING TAILORED DASHBOARDS FOR ANY ROLE (STUDENT, TEACHER, ADMINISTRATOR, ETC.) CAN IMPROVE DECISION MAKING PROCESSES BY SHOWING EACH USER THE INFORMATION THAT INTERESTS THEM MOST IN THE WAY THAT BEST ENABLES THEM TO UNDERSTAND IT.
THIS PAPER TREATS A 3D VISUALIZATION TOOL CALLED TIME-TUNNEL, ESPECIALLY DESCRIBES ITS ASPECTS AS 3D PARALLEL COORDINATES BY SHOWING ITS ACTUAL VISUALIZATION EXAMPLES. ORIGINALLY, TIME-TUNNEL IS A MULTIDIMENSIONAL DATA VISUALIZATION TOOL AND IT WAS EXTENDED TO SUPPORT PARALLEL COORDINATES CALLED PCTT(PARALLEL COORDINATES VERSION OF TIME-TUNNEL). FURTHERMORE, AS ITS ASPECTS OF 3D PARALLEL COORDINATES, 2DTO2D VISUALIZATION FUNCTIONALITY WAS ADDED. ALTHOUGH PCTT CAN VISUALIZE NETWORK DATA BECAUSE IP PACKETS CONSIST OF MANY ATTRIBUTES AND SUCH MULTIPLE-ATTRIBUTES DATA CAN BE VISUALIZED USING PARALLEL COORDINATES, 2DTO2D VISUALIZATION FUNCTIONALITY CAN MORE EFFECTIVELY VISUALIZE PATTERNS OF IP PACKETS THAT SEEM NETWORK ATTACKS. THE AUTHORS HAVE ALREADY PROPOSED THE COMBINATORIAL USE OF PCTT AND 2DTO2D VISUALIZATION FOR THE INTRUSION DETECTION OF THE INTERNET. THIS PAPER ALSO INTRODUCES SPLINE PARALLEL COORDINATES REPRESENTATION AS ONE OF THE NEW FEATURES OF PCTT. THE AUTHORS ALSO PROPOSED THE USE OF PCTT FOR LEARNING ANALYTICS BY VISUALIZING LEANERS' LEARNING ACTIVITY DATA AND INTRODUCED 3D MODE INTO PCTT TO VISUALIZE EACH LEARNER'S LEARNING PATTERN MORE EFFICIENTLY. THIS 3D MODE IS REGARDED AS 3D PARALLEL COORDINATES. HOWEVER, BECAUSE SUCH 3D MODE WAS NOT ENOUGH TO DISTINGUISH EACH LEARNER'S LEANING PATTERN, THE AUTHORS IMPLEMENTED MORE EFFECTIVE 3D MODE, AND CLARIFY THE USEFULNESS OF THE NEW 3D MODE BY SHOWING VISUALIZATION RESULTS.
MOOCS' HIGH DROPOUT RATE HAS BEEN WIDELY CRITICIZED. IT IS ONE OF THE MEASURES TO REDUCE THE DROPOUT RATE BY ESTABLISHING A PERFECT LEARNING SUPPORT SERVICE SYSTEM AND ENHANCING THE LEARNING SUPPORT SERVICE CAPACITY. THIS STUDY TOOK 621 COURSES OF 14 MAJOR MOOC PLATFORMS IN CHINA AS THE RESEARCH OBJECT, INVESTIGATED THEIR LEARNING SUPPORT CONDITION, AND COMPARED THE TYPES AND QUANTITY OF LEARNING SUPPORT SERVICES (LSS) PROVIDED BY DIFFERENT TYPES OF COURSES. THE STUDY FOUND THAT, IN GENERAL, CHINA'S MOOCS SUPPLY VERY FEW TYPES OF LSS, AND THE OVERALL LEVEL OF SUPPORT SERVICE WAS LOW. THERE ARE BIG DIFFERENCES IN LEARNING SUPPORT AMONG MOOCS WITH DIFFERENT TEACHING MODELS. INQUIRY-BASED COURSES DESIGNED BETTER LEARNING SUPPORT THAN LECTURE-BASED COURSES. FLIPPED-CLASSROOM MOOCS USED LEARNING SUPPORT MORE OFTEN. THE LEARNING SUPPORT IN MOOCS WAS MORE TRADITIONALLY DESIGNED. COURSES WITH WELL-DESIGNED CERTIFICATE SYSTEM HAD BETTER LEARNING SUPPORT. ACCORDING TO THE RESEARCH RESULTS, WE SUGGEST THAT IN ORDER TO ADVANCE THE CONSTRUCTION AND APPLICATION, MOOCS SHOULD ENHANCE THE LEARNING SUPPORT SERVICE CAPACITY, PROMOTE THE PRACTICAL APPLICATION OF LEARNING ANALYTICS, PAY MORE ATTENTION TO THE TIMELINESS AND VALIDITY OF TEACHING INTERACTION, CONSTRUCT THE INQUIRY LEARNING ENVIRONMENT, AND PROMOTE FLIPPED-CLASSROOM TEACHING MODEL. WITH
PURPOSE OF REVIEW THE FIELD OF NEUROTRAUMA RESEARCH FACES A REPRODUCIBILITY CRISIS. IN RESPONSE, RESEARCH LEADERS IN TRAUMATIC BRAIN INJURY (TBI) AND SPINAL CORD INJURY (SCI) ARE LEVERAGING DATA CURATION AND ANALYTICS METHODS TO ENCOURAGE TRANSPARENCY, AND IMPROVE THE RIGOR AND REPRODUCIBILITY. HERE WE REVIEW THE CURRENT CHALLENGES AND OPPORTUNITIES THAT COME FROM EFFORTS TO TRANSFORM NEUROTRAUMA'S BIG DATA TO KNOWLEDGE. RECENT FINDINGS THREE PARALLEL MOVEMENTS ARE DRIVING DATA-DRIVEN-DISCOVERY IN NEUROTRAUMA. FIRST, LARGE MULTICENTER CONSORTIA ARE COLLECTING LARGE QUANTITIES OF NEUROTRAUMA DATA, REFINING COMMON DATA ELEMENTS (CDES) THAT CAN BE USED ACROSS STUDIES. INVESTIGATORS ARE NOW TESTING THE VALIDITY OF CDES IN DIVERSE RESEARCH SETTINGS. SECOND, DATA SHARING INITIATIVES ARE WORKING TO MAKE NEUROTRAUMA DATA FINDABLE, ACCESSIBLE, INTEROPERABLE, AND REUSABLE (FAIR). THESE EFFORTS ARE REFLECTED BY RECENT OPEN DATA REPOSITORY PROJECTS FOR PRECLINICAL AND CLINICAL NEUROTRAUMA. THIRD, MACHINE LEARNING ANALYTICS ARE ALLOWING RESEARCHERS TO UNCOVER NOVEL DATA-DRIVEN-HYPOTHESES AND TEST NEW THERAPEUTICS IN MULTIDIMENSIONAL OUTCOME SPACE. WE ARE ON THE THRESHOLD OF A NEW ERA IN DATA COLLECTION, CURATION, AND ANALYSIS. THE NEXT PHASE OF BIG DATA IN NEUROTRAUMA RESEARCH WILL REQUIRE RESPONSIBLE DATA STEWARDSHIP, A CULTURE OF DATA-SHARING, AND THE ILLUMINATION OF 'DARK DATA'.
THE SYSTEMATIC APPLICATION OF ANALYTICAL METHODS ON HUMAN RESOURCES (HR)-RELATED (BIG) DATA IS REFERRED TO AS HR ANALYTICS OR PEOPLE ANALYTICS. TYPICAL PROBLEMS IN HR ANALYTICS INCLUDE THE ESTIMATION OF CHURN RATES, THE IDENTIFICATION OF KNOWLEDGE AND SKILL IN AN ORGANIZATION, AND THE PREDICTION OF SUCCESS ON A JOB. HR ANALYTICS, AS OPPOSED TO THE SIMPLE USE OF KEY PERFORMANCE INDICATORS, IS A GROWING FIELD OF INTEREST BECAUSE OF THE RAPID GROWTH OF VOLUME, VELOCITY, AND VARIETY OF HR DATA, DRIVEN BY THE DIGITALIZATION OF WORK PROCESSES. PERSONNEL FILES USED TO BE IN STEEL LOCKERS IN THE PAST. THEY ARE NOW STORED IN COMPANY SYSTEMS, ALONG WITH DATA FROM HIRING PROCESSES, EMPLOYEE SATISFACTION SURVEYS, E-MAILS, AND PROCESS DATA. WITH THE GROWING PREVALENCE OF HR ANALYTICS, A DISCUSSION AROUND ITS ETHICS NEEDS TO OCCUR. THE OBJECTIVE OF THIS PAPER IS TO DISCUSS THE ETHICAL IMPLICATIONS OF THE APPLICATION OF SOPHISTICATED ANALYTICAL METHODS TO QUESTIONS IN HR MANAGEMENT. THIS PAPER BUILDS ON PREVIOUS LITERATURE IN ALGORITHMIC FAIRNESS THAT FOCUSES ON TECHNICAL OPTIONS TO IDENTIFY, MEASURE, AND REDUCE DISCRIMINATION IN DATA ANALYSIS. THIS PAPER APPLIES TO HR ANALYTICS THE ETHICAL FRAMEWORKS DISCUSSED IN OTHER FIELDS INCLUDING MEDICINE, ROBOTICS, LEARNING ANALYTICS, AND COACHING.
ONLINE LABORATORIES CAN EXHIBIT SMARTER BEHAVIOR WHEN INFORMATION ABOUT THE USER AND HIS/HER CONTEXT IS GIVEN AS AN INPUT TO THE LAB. PART OF THIS DATA CAN BE GATHERED FROM A VIRTUAL LEARNING ENVIRONMENT (VLE) OR ANY OTHER EDUCATIONAL SYSTEM THAT CONTAINS INFORMATION ABOUT THE USER. INFORMATION PROVIDED FOR THESE SYSTEMS INCLUDE: THE TYPE OF USER (STUDENT, TEACHER, ADMINISTRATOR), USER CURRENT LEVEL OF EXPERIENCE (AMOUNT OF TIME PERFORMING LAB EXPERIMENTS, BACKGROUND KNOWLEDGE) AND RESULTS OBTAINED IN PREVIOUS LAB EXPERIENCES. ADDITIONAL CONTEXT INFORMATION CAN BE PROVIDED BY THE USER DEVICE, SUCH AS THE GEOGRAPHICAL LOCATION, DEVICE TYPE, BANDWIDTH, DATE AND TIME, AMONG OTHERS. WITH THIS INFORMATION, THE ONLINE LABORATORY SYSTEM CAN OFFER DIFFERENT VARIATIONS OF LABORATORIES TO EACH USER AND CUSTOMIZE THE EXPERIMENT INTERFACES AND ACTIVITIES IN TERMS OF TASK DIFFICULTY LEVEL, SITUATIONAL CONTEXT AND SUPPORT INFORMATION. THE USER INTERACTIONS AND ADMINISTRATIVE INFORMATION CAN BE COLLECTED AND STORED IN THE LEARNER RECORD STORE (LRS) IN THE FORM OF XAPI STATEMENTS, READY TO BE ACCESSED BY THE VLE TO UPDATE THE CURRENT STATE OF THE USER INFORMATION AND EXTRACT/PERFORM LEARNING ANALYTICS. THIS PAPER PRESENTS THE DESIGN AND IMPLEMENTATION OF A MODEL TO INTEGRATE XAPI TECHNOLOGY WITH ONLINE LABORATORIES DEMONSTRATIONS THAT INCLUDES THE VLE INTEGRATION AND USER CONTEXT INFORMATION AS PART OF THE ONLINE LAB EXPERIENCE.
DIGITAL EDUCATIONAL DATA MANAGEMENT HAS BECOME AN INTEGRAL PART OF SCHOOL PRACTICES. ACCESSING SCHOOL DATABASE BY TEACHERS, STUDENTS, AND PARENTS FROM MOBILE DEVICES PROMOTES DATA-DRIVEN EDUCATIONAL INTERACTIONS BASED ON REAL-TIME INFORMATION. THIS PAPER ANALYSES MOBILE ACCESS OF EDUCATIONAL DATABASE IN A LARGE SAMPLE OF 429 SCHOOLS DURING AN ENTIRE ACADEMIC YEAR. USING LEARNING ANALYTICS APPROACH, THE STUDY COMPARES STUDENTS, THEIR MOTHERS' AND FATHERS' MOBILE LOGINS ONTO THE DATABASE BETWEEN SCHOOLS WITH FREQUENT, OCCASIONAL, AND NO MOBILE (I.E., COMPUTER ONLY) TEACHER ACCESS. IN ADDITION, THIS PAPER EXPLORES GENDER DIFFERENCES IN PARENTAL INVOLVEMENT THROUGH MOBILE MONITORING OF THEIR CHILDREN' FUNCTION IN SCHOOL. THE RESULTS SUPPORTED BOTH STUDY HYPOTHESES. (1) MOBILE ACCESSING OF THE DATABASE BY TEACHERS PROMOTED MOBILE ACCESSING OF THE DATABASE BY THEIR STUDENTS, MOTHERS, AND FATHERS. IT SEEMS THAT UBIQUITOUS MOBILE DATA MANAGEMENT IS A MODELING PROCESS IN WHICH STUDENTS AND PARENTS LEARN FROM TEACHERS. (2) COMPARED TO FATHERS, SIGNIFICANTLY MORE MOTHERS USED THE MOBILE SCHOOL DATABASE. MOREOVER, AMONG PARENTS-USES, MOTHERS ACCESSED EDUCATIONAL DATA OF THEIR CHILDREN SIGNIFICANTLY MORE FREQUENTLY THAN FATHERS. THE RESULTS SUGGEST THAT MOTHERS ARE STILL MORE ACTIVELY INVOLVED THAN FATHERS IN MOBILE MONITORING OF HOW THEIR CHILDREN FUNCTION IN SCHOOL. THE RESULTS ARE DISCUSSED IN TERMS OF SCHOOL COMMUNITY OF INNOVATION MODEL AND TECHNOLOGICAL DETERMINISM APPROACH.
BACKGROUND: THE CAPACITY OF INSTRUCTIONAL TECHNOLOGIES TO PERSONALIZE INSTRUCTION HAS PROGRESSIVELY IMPROVED OVER THE LAST DECADE, IN CONJUNCTION WITH CHANGES IN LEARNING THEORIES THAT DICTATE WHAT, WHEN, AND HOW TO SUPPORT LEARNERS. FOCUS OF STUDY: THIS PAPER REVIEWS SEVERAL TECHNOLOGY-RICH LEARNING ENVIRONMENTS THAT ARE INVESTIGATED BY MEMBERS OF THE LEARNING ENVIRONMENTS ACROSS DISCIPLINES PARTNERSHIP, INCLUDING NEWTON'S PLAYGROUND, THE WAR OF 1812 IHISTORY TOURS, CRYSTAL ISLAND, BIOWORLD, AND METATUTOR. THE ADAPTIVE CAPABILITIES OF THESE SYSTEMS ARE DISCUSSED IN TERMS OF THE METAPHORS OF USING COMPUTERS AS COGNITIVE, METACOGNITIVE, AND AFFECTIVE TOOLS. RESEARCH DESIGN: RESEARCHERS RELY ON CONVERGENT METHODOLOGIES TO COLLECT DATA VIA MULTIPLE MODALITIES TO GAIN A BETTER UNDERSTANDING OF WHAT LEARNERS KNOW, FEEL, AND UNDERSTAND. THE DESIGN GUIDELINES OF THESE LEARNING ENVIRONMENTS ARE USED TO SITUATE THIS UNDERSTANDING AS A MEANS TO GENERALIZE BEST PRACTICES IN PERSONALIZING INSTRUCTION. CONCLUSIONS: THE FINDINGS OF THESE INVESTIGATIONS HAVE SIGNIFICANT IMPLICATIONS FOR THE METAPHOR OF USING TECHNOLOGY AS A TOOL TO AUGMENT OUR THINKING. THE CHALLENGE IS NOW TO BROADEN LEARNING THEORIES WHILE TAKING INTO CONSIDERATION THE SOCIAL AND EMOTIONAL PERSPECTIVE OF LEARNING, AS WELL AS TO LEVERAGE RECENT ADVANCES IN LEARNING ANALYTICS AND DATA-MINING TECHNIQUES TO ITERATIVELY IMPROVE THE DESIGN OF TECHNOLOGY-RICH LEARNING ENVIRONMENTS.
KEYWORDS LIKE FLEXIBLE LEARNING REVOLUTION, E-LEARNING, LIFE-LONG LEARNING, LEARNING MANAGEMENT SYSTEMS (LMS), BLENDED LEARNING, FLIPPED CLASSROOMS, LEARNING ANALYTICS, OR MASSIVE OPEN ONLINE COURSES (MOOCS) REPRESENT JUST FEW OF THE HEADLINES DOMINATING THE LAST TWO DECADES OF THE EDUCATION DEVELOPMENT. ALL OF THEM ARE STRONGLY INFLUENCED AND CONDITIONED BY THE INTERNET PHENOMENON. OF COURSE, THIS LIST IS STILL FAR FROM BEING COMPLETE. ONE COULD CONTINUE WITH NUMEROUS MORE RECENT KEYWORDS AS CLOUD COMPUTING, INTERNET OF THINGS, OR INDUSTRY 4.0 THAT ARE AGAIN STRONGLY RELATED TO THE INTERNET. ALL THESE WORDS APPEARED AS SLOGANS OF NUMEROUS CAMPAIGNS IN THE SOCIETY DEVELOPMENT TYPICALLY HAVING A HYPECYCLE FEATURES. THE PRESENTATION COMMEMORATES SEVERAL MOMENTS OF PREVIOUS DEVELOPMENT, THE GOOD, THE BAD AND THE UGLY. IN SUCH A CONTEXT IT INTRODUCES THE LATEST INITIATIVE PRESENTED AS INDUSTRY 4.0 AND ITS IMPORTANCE FOR THE AUTOMOTIVE INDUSTRY IN SLOVAKIA. THEN IT RAISES SEVERAL QUESTIONS CONCERNING SUSTAINABILITY OF THE FUTURE DEVELOPMENT. BASED ON PREVIOUS EXPERIENCE IT CONCLUDES BY THE STATEMENT THAT THE SLOGAN INDUSTRY 4.0 IS NOT COMPLETE. IF IT WILL NOT SIMULTANEOUSLY INCLUDE ALSO MASSIVE EXTENSION TO INDUSTRY.4.UNIVERSITY AND INDUSTRY.4.TEACHERS ACTIVITIES, A SUSTAINABLE DEVELOPMENT WILL BE SERIOUSLY JEOPARDIZED, AT LEAST IN THE CENTRAL EUROPE.
AS TECHNOLOGICAL ADVANCEMENT AND INNOVATION CONTINUALLY RISE GLOBALLY, THE APPLICATIONS OF SUCH PROGRESS IS EVIDENT IN MANY FIELDS, MOST ESPECIALLY IN EDUCATION. KNOWING THAT EDUCATION IS ESSENTIAL TO FOSTER HOLISTIC GROWTH ESPECIALLY IN CHILDREN, THE RESEARCHERS AIM TO MAXIMIZE THE POTENTIAL OF TECHNOLOGY AS A WAY TO FACILITATE LEARNING IN A CHILD'S GROWING YEARS. FROM BOOKS TO EDUCATIONAL APPLICATIONS AND GAMES, VARIOUS EDUCATIONAL MOBILE TOOLS FOR CHILDREN HAVE NOW BEEN DEVELOPED. HOWEVER, MOST OF THESE APPLICATIONS LACK SIGNIFICANT FEATURES FOR LEARNING, SUCH AS AN ASSESSMENT OF ONE'S PERFORMANCE, AS WELL AS THE INVOLVEMENT OF THE TEACHERS AND PARENTS OF THE USERS. SKILLVILLE AIMS TO PROPOSE SOLUTIONS TO THESE PROBLEMS. SKILLVILLE IS AN EDUCATIONAL MOBILE GAME DEVELOPED USING CORONA SDK AND PUBLISHED IN ANDROID PLATFORM. THIS PAPER DISCUSSES SKILLVILLE AS AN AID FOR LEARNING FOCUSED ON THREE (3) BASIC AND MOST COMMON VISUAL PERCEPTION SKILLS NAMELY LANGUAGE AND SPELLING, SEARCHING AND SORTING, AND MEMORY SKILLS. LEARNING ANALYTICS IS USED FOR MONITORING PERFORMANCE AND POSSIBLE IMPROVEMENT OF USERS PER GAME PLAYED. THIS ALSO INCLUDES DISCUSSION ABOUT EVALUATION OF THE APPLICATION BY SCHOOL TEACHERS AND USER EXPERIENCE TESTING BY CHILDREN OF AGES 4-7. TEST RESULTS SHOW SKILLVILLE'S HIGH POTENTIAL AS A LEARNING TOOL FOR CHILDREN.
THE U.S. FOOD AND DRUG ADMINISTRATION (FDA) HAS APPROVED TWO DIGITAL PATHOLOGY SYSTEMS FOR PRIMARY DIAGNOSIS. THESE SYSTEMS PRODUCE AND CONSUME WHOLE SLIDE IMAGES (WSIS) CONSTRUCTED FROM GLASS SLIDES USING ADVANCED DIGITAL SLIDE SCANNERS. WSIS CAN GREATLY IMPROVE THE WORKFLOW OF PATHOLOGISTS THROUGH THE DEVELOPMENT OF NOVEL IMAGE ANALYTICS SOFTWARE FOR AUTOMATIC DETECTION OF CELLULAR AND MORPHOLOGICAL FEATURES AND DISEASE DIAGNOSIS USING HISTOPATHOLOGY SLIDES. HOWEVER, THE GIGABYTE SIZE OF A WSI POSES A SERIOUS CHALLENGE FOR STORAGE AND RETRIEVAL OF MILLIONS OF WSIS. IN THIS PAPER, WE PROPOSE A SYSTEM FOR SCALABLE STORAGE OF WSIS AND FAST RETRIEVAL OF IMAGE TILES USING DRAM. A WSI IS PARTITIONED INTO TILES AND SUB-TILES USING A COMBINATION OF A SPACE-FILLING CURVE, RECURSIVE PARTITIONING, AND DEWEY NUMBERING. THEY ARE THEN STORED AS A COLLECTION OF KEY-VALUE PAIRS IN DRAM. DURING RETRIEVAL, A TILE IS FETCHED USING KEY-VALUE LOOKUPS FROM DRAM. THROUGH PERFORMANCE EVALUATION ON A 24-NODE CLUSTER USING 100 WSIS, WE OBSERVED THAT, COMPARED TO APACHE SPARK, OUR SYSTEM WAS THREE TIMES FASTER TO STORE THE 100 WSIS AND 1,000 TIMES FASTER TO ACCESS A SINGLE TILE ACHIEVING MILLISECOND LATENCY. SUCH FAST ACCESS TO TILES IS HIGHLY DESIRABLE WHEN DEVELOPING DEEP LEARNING-BASED IMAGE ANALYTICS SOLUTIONS ON MILLIONS OF WSIS.
ANALYZING THE BREATHING DATA TO CLASSIFY THE RESPIRATORY STATES HAS VARIOUS APPLICATIONS IN THE AREAS OF DRUG OVERDOSE AND MEDICAL DIAGNOSIS OF SEVERAL OTHER RESPIRATORY MEDICAL CONDITIONS. TRACHEAL SOUNDS HAVE SHOWN TO PROVIDE ACCURATE BREATHING DATA WITH HIGH SIGNAL TO NOISE RATIO FOR MEASURING AIR FLOW. THE HEARTBEAT SIGNAL IS A SOURCE OF INTERFERENCE FOR THE TRACHEAL SOUND MEASUREMENTS AND HENCE IS OFTEN FILTERED OUT PRIOR TO THE TRACHEAL SOUND DATA ANALYSIS. FILTERING THE HEARTBEAT SIGNAL, HOWEVER, REMOVES A PART OF THE TRACHEAL SOUND DATA ALONG WITH ITS ENERGY AND STATISTICAL INFORMATION. WE PROPOSE AN ALGORITHM TO CLASSIFY THE RESPIRATORY STATES FROM THE TRACHEAL SOUND DATA DESPITE THE PRESENCE OF HEARTBEAT SIGNALS. THIS ALGORITHM USES THE DATA HISTOGRAM AS WELL AS THE DATA AUTOCORRELATION FUNCTION (ACF) FOR FEATURE EXTRACTIONS, AND SHOWS THAT THESE FEATURES, WHEN USED BY A SOFTMAX CLASSIFIER, CAN PROPERLY DISCRIMINATE AMONG BREATHING CONDITIONS WITH CLASSIFICATION RATES EXCEEDING 97%.
A CRITICAL LIMITATION IN THE APPLICATION OF DEEP LEARNING TO RADAR SIGNAL CLASSIFICATION IS THE LACK OF SUFFICIENT DATA TO TRAIN VERY DEEP NEURAL NETWORKS. THE DEPTH OF A NEURAL NETWORK IS ONE OF THE MORE SIGNIFICANT NETWORK PARAMETERS THAT AFFECTS ACHIEVABLE CLASSIFICATION ACCURACY. ONE WAY TO OVERCOME THIS CHALLENGE IS TO GENERATE SYNTHETIC SAMPLES FOR TRAINING DEEP NEURAL NETWORKS (DNNS). IN PRIOR WORK OF THE AUTHORS, TWO METHODS HAVE BEEN DEVELOPED: 1) DIVERSIFIED MICRO-DOPPLER SIGNATURE GENERATION VIA TRANSFORMATIONS OF THE UNDERLYING SKELETAL MODEL DERIVED FROM VIDEO MOTION CAPTURE (MOCAP) DATA, AND 2) AUXILIARY CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS (ACGANS) WITH KINEMATIC SIFTING. WHILE DIVERSIFIED MOCAP HAS THE ADVANTAGE OF GREATER ACCURACY IN GENERATING SIGNATURES THAT SPAN TO THE PROBABLE TARGET SPACE OF EXPECTED HUMAN MOTION FOR DIFFERENT BODY SIZES, SPEEDS, AND INDIVIDUALIZED GAIT, THE METHOD CANNOT CAPTURE DATA ARTIFACTS DUE TO SENSOR IMPERFECTIONS OR CLUTTER. IN CONTRAST, ADVERSARIAL LEARNING HAS BEEN SHOWN TO BE ABLE TO CAPTURE NON-TARGET RELATED ARTIFACTS, HOWEVER, THE ACGANS CAN ALSO GENERATE MISLEADING SIGNATURES THAT ARE KINEMATICALLY IMPOSSIBLE. THIS PAPER PROVIDES AN IN-DEPTH PERFORMANCE COMPARISON OF THE TWO METHODS ON A THROUGH-THE-WALL RADAR DATA SET OF HUMAN ACTIVITIES OF DAILY LIVING (ADL) IN THE PRESENCE OF CLUTTER AND SENSOR ARTIFACTS.
WITH AGILE METHODOLOGY AND INTERNATIONAL COLLABORATION HAVING BECOME INDUSTRY STANDARDS NOT ONLY IN SOFTWARE ENGINEERING, HOW WOULD WE ENABLE STUDENTS IN HIGHER EDUCATION TO GATHER THESE EXPERIENCES? ONE POSSIBLE APPROACH PRESENTS OUR INTENSIVE COURSE ON AGILE SOFTWARE DEVELOPMENT, APPLYING GAMES IN ENGINEERING EDUCATION, WITHIN THE FRAMEWORK OF A ONE-WEEK PAN-EUROPEAN EXCHANGE PROGRAM. FIRST, WE BRIEFLY INTRODUCE THE PROGRAM THAT'S HELD TWICE A YEAR, OFFERING ABOUT 60 SCIENTIFIC INTENSIVE COURSES IN EACH SESSION. TOPICS COME FROM THE RESEARCH FIELDS AT THE PARTNER INSTITUTIONS AND FOCUS ON ENGINEERING, NATURAL SCIENCES, AND MATHEMATICS. ROUGHLY 4,000 STUDENTS FROM 24 UNIVERSITIES IN 15 EUROPEAN COUNTRIES PARTICIPATE IN THE PROGRAM ANNUALLY. SECOND, WE REPORT ON THE CONCEPT AND DESIGN OF OUR INTENSIVE COURSE ON AGILE SOFTWARE DEVELOPMENT. WE APPLY A FLIPPED CLASSROOM SETTING TO ACQUIRE THE BASIC KNOWLEDGE; WE USE GAME-BASED LEARNING FOR IN-CLASS EXERCISES; WE FOSTER PROJECT-BASED AND COLLABORATIVE LEARNING FOR PRACTICAL IMPLEMENTATION. FINALLY, WE OUTLINE THE PROJECT WORK IN OUR COURSE AND SHARE THE MOST INSPIRING STUDENT PROJECTS DIVING INTO DATA-DRIVEN VISUALIZATIONS AND LEARNING ANALYTICS. THUS, THE PARTICIPANTS GAIN INSIGHTS INTO THE REAL-LIFE APPLICATION OF AGILE PROJECT MANAGEMENT METHODS AND INTERNATIONAL COLLABORATION IN HETEROGENEOUS TEAMS DESPITE THE LIMITED COURSE LENGTH.
LIFELONG LEARNERS' ACTIVITIES ARE SCATTERED ALONG THE DAY, IN DIFFERENT LOCATIONS AND MAKING USE OF MULTIPLE DEVICES. MOST OF THE TIMES THEY HAVE TO MERGE LEARNING, WORK AND EVERYDAY LIFE MAKING DIFFICULT TO HAVE AN ACCOUNT ON HOW MUCH TIME IS DEVOTED TO LEARNING ACTIVITIES AND LEARNING GOALS. INDEED, LEARNING EXPERIENCES ARE DISRUPTED AND THERE IS A LACK OF SOLUTIONS TO INTEGRATE DAILY LIFE ACTIVITIES AND LEARNING IN THE SAME PROCESS. ON THE OTHER HAND, SMARTPHONES ARE BECOMING A UNIVERSAL LEARNING DEVICE FACILITATING NEW TOOLS AND WAYS OF INTERACTION THAT CAN BE SMOOTHLY EMBEDDED INTO DAILY LIFE. THIS MANUSCRIPT PRESENTS THE NFC LEARNTRACKER, A MOBILE TOOL PROPOSING THE USER TO INTROSPECT HIS AUTOBIOGRAPHY AS A LEARNER TO IDENTIFY SUCCESSFUL PHYSICAL LEARNING ENVIRONMENTS, MARK THEM WITH SENSOR TAGS, BIND THEM TO SELF-DEFINED LEARNING GOALS, KEEP TRACK OF THE TIME INVESTED ON EACH GOAL WITH A NATURAL INTERFACE, AND MONITOR THE LEARNING ANALYTICS. THIS WORK IMPLIES A SUITABLE TOOL FOR LIFELONG LEARNERS TO BIND SCATTERED ACTIVITIES KEEPING THEM IN A CONTINUING LEARNING FLOW. THE NFC LEARNTRACKER IS RELEASED UNDER OPEN ACCESS LICENCE WITH THE AIM TO FOSTER ADAPTATION TO FURTHER COMMUNITIES AS WELL AS TO FACILITATE THE EXTENSION TO THE INCREASING NUMBER OF SENSOR AND NFC TAGS EXISTENT IN THE MARKET.
PREVIOUS STUDIES ON LEARNING CHALLENGES IN THE FIELD OF MODELING FOCUS ON COGNITIVE PERSPECTIVES, SUCH AS MODEL UNDERSTANDING, MODELING LANGUAGE KNOWLEDGE AND PERCEPTUAL PROPERTIES OF GRAPHICAL NOTATION BY NOVICE BUSINESS ANALYSTS AS MAJOR SOURCES AFFECTING MODEL QUALITY. IN THE EDUCATIONAL CONTEXT OUTCOME FEEDBACK IS USUALLY APPLIED TO IMPROVE LEARNING ACHIEVEMENTS. HOWEVER, NOT MANY RESEARCH PUBLICATIONS HAVE BEEN WRITTEN OBSERVING THE CHARACTERISTICS OF A MODELING PROCESS ITSELF THAT CAN BE ASSOCIATED WITH BETTER/WORSE LEARNING OUTCOMES, NOR HAVE ANY EMPIRICALLY VALIDATED RESULTS BEEN REPORTED ON THE OBSERVATIONS OF MODELING ACTIVITIES IN THE EDUCATIONAL CONTEXT. THIS PAPER ATTEMPTS TO COVER THIS GAP FOR CONCEPTUAL MODELING. WE ANALYZE MODELING BEHAVIOR (CONCEPTUAL MODELING EVENT DATA OF 20 CASES, 10.000 EVENTS IN TOTAL) USING EXPERIMENTAL LOGGING FUNCTIONALITY OF THE JMERMAID MODELING TOOL AND PROCESS MINING TECHNIQUES. THE OUTCOMES OF THE WORK INCLUDE MODELING PATTERNS THAT ARE INDICATIVE FOR WORSE/BETTER LEARNING PERFORMANCE. THE RESULTS CONTRIBUTE TO (1) IMPROVING TEACHING GUIDANCE FOR CONCEPTUAL MODELING TARGETED AT PROCESS-ORIENTED FEEDBACK, (2) PROVIDING RECOMMENDATIONS ON THE TYPE OF DATA THAT CAN BE USEFUL IN OBSERVING A MODELING BEHAVIOR FROM THE PERSPECTIVE OF LEARNING OUTCOMES. IN ADDITION, THE STUDY PROVIDES FIRST INSIGHTS FOR LEARNING ANALYTICS RESEARCH IN THE DOMAIN OF CONCEPTUAL MODELING. (C) 2014 ELSEVIER LTD. ALL RIGHTS RESERVED.
THIS PAPER PRESENTS THE FINDINGS OF A STUDY ON A FULLY ONLINE BACHELOR'S LEVEL COURSE IN HEALTH SCIENCES AT A EUROPEAN UNIVERSITY CONDUCTED TO EXPLORE THE INFLUENCE OF LEARNING DESIGN ON THE FORMATION AND EVOLUTION OF DIFFERENT TYPES OF COMMUNITIES OF LEARNING. THE IMPETUS FOR THE STUDY CAME FROM THE WELL-ESTABLISHED EFFECTIVENESS OF COMMUNITY-BASED LEARNING, A NEED FOR UNDERSTANDING LEARNING DESIGN AND ANALYTICS WITHIN NETWORKED STRUCTURES AND, THE LACK OF THEORETICAL GROUNDING FOR SOCIAL NETWORK ANALYSIS (SNA) IN PREVIOUS LITERATURE. OUR STUDY USES THE INTEGRATED METHODOLOGICAL FRAMEWORK (IMF) WHICH EMPLOYS SNA AS THE KEY METHODOLOGY FOR EXPLORING COMMUNITY-BASED LEARNING IN LIGHT OF THE COMMUNITIES OF PRACTICE (COP) AND COMMUNITY OF INQUIRY (COI) FRAMEWORKS. THE COURSE COMPRISED OF THREE DIFFERENTLY DESIGNED SUCCESSIVE DISCUSSION FORUMS SPANNING THREE WEEKS EACH. NETWORK DIAGRAMS AND SNA MEASURES CLEARLY SHOWED THE IMPACT OF THE DIFFERENT LEARNING DESIGNS ON STUDENT ENGAGEMENT IN THE DISCUSSION FORUMS. BASED ON COP AND COI STRUCTURAL COMPONENTS WITHIN THE IMF, A COMPARATIVE ANALYSIS OF WHOLE-NETWORK PROPERTIES OF THE THREE NETWORKS INDICATED THE FORMATION OF A COP, INITIATED AND MEDIATED BY THE TUTOR IN DISCUSSION 1, SUSTAINED BY THE STUDENTS IN DISCUSSION 2, AND DISINTEGRATED DUE TO LACK OF GUIDANCE AND FACILITATION IN DISCUSSION 3. QUALITATIVE ANALYSIS ON THE CONTENT OF DISCUSSION POSTS REVEALED THE IMPORTANCE OF GROUP ORIENTED MESSAGES IN THE FORMATION OF THE COP. THE PAPER DISCUSSES FINDINGS IN TERMS OF IMPLICATIONS FOR LEARNING DESIGN AND ANALYTICS IN ONLINE LEARNING AND THE ROLE OF THE TUTOR IN COMMUNITY FORMATION.
EDUCATORS NEED TO CHANGE THEIR PRACTICE TO ADAPT TO A SHIFTING EDUCATIONAL CONTEXT. BY VISUALISING LEARNING DESIGN DECISIONS, THIS ARTICLE HIGHLIGHTS THE NEED TO CAPTURE EDUCATORS' "TACIT" KNOWLEDGE RELATING TO COURSE MATERIAL, ACTIVITY TYPES AND WORKLOAD, THROUGH EMPLOYING LEARNING ANALYTICS METHODS IN ORDER TO ANALYSE THE LEARNING DESIGNS OF COURSES TAKEN BY 60,000+ STUDENTS, COMMON PEDAGOGICAL PATTERNS ARE IDENTIFIED. WHEN ANALYSING 157 LEARNING DESIGNS USING A TAXONOMY OF SEVEN DIFFERENT LEARNING ACTIVITIES, WE FOUND THAT THE MAJORITY OF EDUCATORS USED TWO TYPES OF LEARNING ACTIVITIES MOST WIDELY, NAMELY ASSIMILATIVE ACTIVITIES (READING, WATCHING VIDEOS AND LISTENING TO AUDIO) AND ASSESSMENT ACTIVITIES. SURPRISINGLY, EDUCATORS DO NOT CHOOSE DIFFERENT ACTIVITY TYPES BASED UPON FUNCTION (EG, REPLACE ONE TYPE OF STUDENT-ACTIVATING ACTIVITY BY ANOTHER), BUT PATTERNS CAN BE SEEN WHERE EDUCATORS COMBINE ASSIMILATIVE, PRODUCTIVE AND ASSESSMENT ACTIVITIES OR ASSIMILATIVE, FINDING AND HANDLING INFORMATION AND COMMUNICATION TASKS. WHILE EDUCATORS RELY HEAVILY ON ASSIMILATIVE AND ASSESSMENT ACTIVITIES, NO POSITIVE CORRELATION WAS FOUND BETWEEN ANY OF THE SEVEN LEARNING DESIGN ACTIVITY TYPES AND STUDENT OUTCOMES. OUR INITIAL FINDINGS SUGGEST THAT STUDENT OUTCOMES ARE NEGATIVELY CORRELATED WITH A HIGH PROPORTION OF ASSIMILATIVE ACTIVITIES. FURTHER STUDIES ARE NEEDED TO ESTABLISH WHETHER PARTICULAR LEARNING DESIGN DECISIONS ARE RELATED TO STUDENT OUTCOMES AND WHETHER THESE FINDINGS CAN BE REPLICATED IN DIFFERENT RESEARCH SETTINGS.
PUBLIC SPEAKING IS AN IMPORTANT SKILL, THE ACQUISITION OF WHICH REQUIRES DEDICATED AND TIME CONSUMING TRAINING. IN RECENT YEARS, RESEARCHERS HAVE STARTED TO INVESTIGATE AUTOMATIC METHODS TO SUPPORT PUBLIC SPEAKING SKILLS TRAINING. THESE METHODS INCLUDE ASSESSMENT OF THE TRAINEE'S ORAL PRESENTATION DELIVERY SKILLS WHICH MAY BE ACCOMPLISHED THROUGH AUTOMATIC UNDERSTANDING AND PROCESSING OF SOCIAL AND BEHAVIORAL CUES DISPLAYED BY THE PRESENTER. IN THIS STUDY, WE PROPOSE AN AUTOMATIC SCORING SYSTEM FOR PRESENTATION DELIVERY SKILLS USING A NOVEL ACTIVE DATA REPRESENTATION METHOD TO AUTOMATICALLY RATE SEGMENTS OF A FULL VIDEO PRESENTATION. WHILE MOST APPROACHES HAVE EMPLOYED A TWO STEP STRATEGY CONSISTING OF DETECTING MULTIPLE EVENTS FOLLOWED BY CLASSIFICATION, WHICH INVOLVE THE ANNOTATION OF DATA FOR BUILDING THE DIFFERENT EVENT DETECTORS AND GENERATING A DATA REPRESENTATION BASED ON THEIR OUTPUT FOR CLASSIFICATION, OUR METHOD DOES NOT REQUIRE EVENT DETECTORS. THE PROPOSED DATA REPRESENTATION IS GENERATED UNSUPERVISED USING LOW-LEVEL AUDIOVISUAL DESCRIPTORS AND SELF-ORGANIZING MAPPING AND USED FOR VIDEO CLASSIFICATION. THIS REPRESENTATION IS ALSO USED TO ANALYSE VIDEO SEGMENTS WITHIN A FULL VIDEO PRESENTATION IN TERMS OF SEVERAL CHARACTERISTICS OF THE PRESENTER'S PERFORMANCE. THE AUDIO REPRESENTATION PROVIDES THE BEST PREDICTION RESULTS FOR SELF-CONFIDENCE AND ENTHUSIASM, POSTURE AND BODY LANGUAGE, STRUCTURE AND CONNECTION OF IDEAS, AND OVERALL PRESENTATION DELIVERY. THE VIDEO DATA REPRESENTATION PROVIDES THE BEST RESULTS FOR PRESENTATION OF RELEVANT INFORMATION WITH GOOD PRONUNCIATION, USAGE OF LANGUAGE ACCORDING TO AUDIENCE, AND MAINTENANCE OF ADEQUATE VOICE VOLUME FOR THE AUDIENCE. THE FUSION OF AUDIO AND VIDEO DATA PROVIDES THE BEST RESULTS FOR EYE CONTACT. APPLICATIONS OF THE METHOD TO PROVISION OF FEEDBACK TO TEACHERS AND TRAINEES ARE DISCUSSED.
RUNNING MACHINE LEARNING ANALYTICS OVER GEOGRAPHICALLY DISTRIBUTED DATASETS IS A RAPIDLY ARISING PROBLEM IN THE WORLD OF DATA MANAGEMENT POLICIES ENSURING PRIVACY AND DATA SECURITY. VISUALIZING HIGH DIMENSIONAL DATA USING TOOLS SUCH AS T-DISTRIBUTED STOCHASTIC NEIGHBOR EMBEDDING (TSNE) AND UNIFORM MANIFOLD APPROXIMATION AND PROJECTION (UMAP) BECAME A COMMON PRACTICE FOR DATA SCIENTISTS. BOTH TOOLS SCALE POORLY IN TIME AND MEMORY. WHILE RECENT OPTIMIZATIONS SHOWED SUCCESSFUL HANDLING OF 10,000 DATA POINTS, SCALING BEYOND MILLION POINTS IS STILL CHALLENGING. WE INTRODUCE A NOVEL FRAMEWORK: SKETCH AND SCALE (SNS). IT LEVERAGES A COUNT SKETCH DATA STRUCTURE TO COMPRESS THE DATA ON THE EDGE NODES, AGGREGATES THE REDUCED SIZE SKETCHES ON THE MASTER NODE, AND RUNS VANILLA TSNE OR UMAP ON THE SUMMARY, REPRESENTING THE DENSEST AREAS, EXTRACTED FROM THE AGGREGATED SKETCH. WE SHOW THIS TECHNIQUE TO BE FULLY PARALLEL, SCALE LINEARLY IN TIME, LOGARITHMICALLY IN MEMORY AND COMMUNICATION, MAKING IT POSSIBLE TO ANALYZE DATASETS WITH MANY MILLIONS, POTENTIALLY BILLIONS OF DATA POINTS, SPREAD ACROSS SEVERAL DATA CENTERS AROUND THE GLOBE. WE DEMONSTRATE THE POWER OF OUR METHOD ON TWO MID-SIZE DATASETS: CANCER DATA WITH 52 MILLION 35-BAND PIXELS FROM MULTIPLEX IMAGES OF TUMOR BIOPSIES; AND ASTROPHYSICS DATA OF 100 MILLION STARS WITH MULTI-COLOR PHOTOMETRY FROM THE SLOAN DIGITAL SKY SURVEY (SDSS).
WITH THE STEADY DEVELOPMENT OF ONLINE EDUCATION AND ONLINE LEARNING ENVIRONMENTS, POSSIBILITIES TO SUPPORT SOCIAL INTERACTIONS BETWEEN STUDENTS HAVE ADVANCED SIGNIFICANTLY. THIS STUDY EXAMINED THE RELATIONSHIP BETWEEN INDICATORS OF SOCIAL PRESENCE AND ACADEMIC PERFORMANCE. SOCIAL PRESENCE IS DEFINED AS STUDENTS' ABILITY TO ENGAGE SOCIALLY WITH AN ONLINE LEARNING COMMUNITY. THE RESULTS OF A MULTIPLE REGRESSION ANALYSIS SHOWED THAT CERTAIN INDICATORS OF SOCIAL PRESENCE WERE SIGNIFICANT PREDICTORS OF FINAL GRADES IN A MASTER'S LEVEL COMPUTER SCIENCE ONLINE COURSE. MOREOVER, THE STUDY ALSO REVEALED THAT TEACHING PRESENCE MODERATED THE ASSOCIATION BETWEEN SOCIAL PRESENCE AND ACADEMIC PERFORMANCE, INDICATING THAT A COURSE DESIGN THAT INCREASED THE LEVEL OF MEANINGFUL INTERACTIONS BETWEEN STUDENTS HAD A SIGNIFICANT IMPACT ON THE DEVELOPMENT OF SOCIAL PRESENCE, AND THUS COULD POSITIVELY AFFECT STUDENTS' ACADEMIC PERFORMANCE. THIS IS ESPECIALLY IMPORTANT IN SITUATIONS WHEN DISCUSSIONS ARE INTRODUCED TO PROMOTE THE DEVELOPMENT OF LEARNING OUTCOMES ASSESSED IN COURSES. ANOTHER IMPLICATION OF OUR RESULTS IS THAT INDICATORS OF SOCIAL PRESENCE CAN BE USED FOR EARLY DETECTION OF STUDENTS AT RISK OF FAILING A COURSE. FINDINGS INFORM RESEARCH AND PRACTICE IN THE EMERGING FIELD OF LEARNING ANALYTICS BY PROMPTING THE OPPORTUNITIES TO OFFER ACTIONABLE INSIGHTS INTO THE REASONS WHY CERTAIN STUDENTS ARE LAGGING BEHIND.
TO EFFECTIVELY AND EFFICIENTLY IMPLEMENT BLENDED SCIENCE AND TECHNOLOGY EDUCATION, TEACHERS SHOULD BE ABLE TO FIND EDUCATIONAL RESOURCES THAT SUIT THEIR NEED, FIT WITH THEIR CURRICULA, AND THAT CAN BE EASILY EXPLOITED IN THEIR CLASSROOM. THE EUROPEAN UNION HAS SUPPORTED THE FP7 GO-LAB INTEGRATED PROJECT (2012-2016) AND THEN THE H2020 NEXT-LAB INNOVATION ACTION (2017-2019) TO DEVELOP AND DISSEMINATE INQUIRY LEARNING SPACES AS OPEN EDUCATIONAL RESOURCES INTEGRATING ONLINE LABS. THIS PAPER PRESENTS THE TECHNICAL ECOSYSTEM SUPPORTING THESE INITIATIVES AND COMBINING LOOSELY-COUPLED CLOUD SERVICES AND PLATFORMS. THE GOLABZ.EU SHARING PLATFORM IS A REPOSITORY OFFERING ONLINE LABS, SCAFFOLDING APPS, AND INQUIRY LEARNING SPACES CREATED BY TEACHERS FOR TEACHERS. THE GRAASP. EU AUTHORING PLATFORM IS A SOCIAL MEDIA ENABLING COLLABORATIVE CREATION, AGILE PERSONALIZATION AND SECURE EXPLOITATION AT SCHOOL, AS WELL AS EXCHANGE OF BEST PRACTICES BETWEEN TEACHERS.
THIS PAPER PROVIDES A COMPREHENSIVE REVIEW OF THE FEATURES AND TRENDS OF PERSONALISED LEARNING. THE REVIEW COVERS A TOTAL OF 203 JOURNAL ARTICLES COLLECTED FROM SCOPUS, WHICH WERE PUBLISHED FROM 2001 TO 2018 AND INVOLVED PERSONALISED LEARNING PRACTICES. COMPARING THE PRACTICES BETWEEN 2001-2009 AND 2010-2018, THERE WAS A CLEAR TREND THAT THEY BECAME MORE DIVERSIFIED IN TERMS OF THE EDUCATIONAL LEVELS AND MODES OF EDUCATION, AS WELL AS THE MEANS TO ACHIEVE PERSONALISED LEARNING, THEIR OBJECTIVES AND THE DEVICES USED. IN MOST PRACTICES, THE USE OF TECHNOLOGY WAS CLEARLY EMPHASISED, SUCH AS VARIOUS INTELLIGENT LEARNING/TUTORING SYSTEMS, MOBILE DEVICES, LEARNING ANALYTICS, AND AUGMENTED/VIRTUAL REALITY APPLICATIONS. LEARNING METHODS, MATERIALS AND THE TIME AND PLACES FOR LEARNING WERE THE ASPECTS MOST FREQUENTLY ADDRESSED. THE OBJECTIVES OF THE PRACTICES FOCUSED MAINLY ON INCREASING LEARNING EFFECTIVENESS, FOLLOWED BY ENHANCING LEARNERS' MOTIVATION, ENGAGEMENT AND SATISFACTION. THE FACTORS LEADING TO SUCCESSFUL PERSONALISED LEARNING WERE IDENTIFIED FROM THE ARTICLES REVIEWED, AMONG WHICH MOST FACTORS WERE RELATED TO LEARNERS' CHARACTERISTICS, LEARNING PROGRESS AND TECHNOLOGY. RESEARCH ISSUES WHICH NEED FURTHER INVESTIGATION ARE RECOMMENDED, PARTICULARLY ON THE CHALLENGES ARISING FROM THE ADOPTION OF EMERGING TECHNOLOGIES, AND THE CHANGING ROLE OF TEACHERS IN THE TREND TOWARDS STUDENT-CENTRED PERSONALISED LEARNING.
THE PURPOSE OF THIS STUDY WAS TO IDENTIFY THE COURSE DESIGN, ASSESSMENT AND EVALUATION, AND FACILITATION PRACTICES FROM THE PERSPECTIVES OF AWARD-WINNING ONLINE FACULTY. ALIGNED WITH THIS PURPOSE, WE DEVELOPED A CONCEPTUAL FRAMEWORK FOCUSED ON ONLINE COURSE DESIGN, ASSESSMENT AND EVALUATION, AND FACILITATION; AND REVIEW RELEVANT LITERATURE IN LIGHT OF THIS FRAMEWORK. WE INTERVIEWED EIGHT AWARD-WINNING ONLINE FACULTY MEMBERS FROM ACROSS THE UNITED STATES. THESE FACULTY RECEIVED ONLINE TEACHING AWARDS FROM ONE OF THE FOLLOWING PROFESSIONAL ASSOCIATIONS: ONLINE LEARNING CONSORTIUM (OLC), ASSOCIATION FOR EDUCATIONAL COMMUNICATIONS AND TECHNOLOGY (AECT), OR UNITED STATES DISTANCE LEARNING ASSOCIATION. BASED ON THE INTERVIEWS, IT WAS FOUND THAT ONLINE INSTRUCTORS USE A SYSTEMATIC DESIGN PROCESS, BACKWARDS DESIGN, CONSIDERED LEARNER NEEDS, AND DESIGNED LEARNER INTERACTION DURING THE DESIGN PROCESS. FACULTY RECOMMENDED USING A VARIETY OF ASSESSMENTS, USING TRADITIONAL AND AUTHENTIC ASSESSMENTS AND USED RUBRICS TO ASSESS STUDENTS, COURSE TEMPLATES AND QUALITY ASSURANCE PROCESS AND SURVEYS, LEARNING ANALYTICS, AND PEER REVIEWS FOR ASSESSMENT AND EVALUATION. TIMELY RESPONSE AND FEEDBACK, AVAILABILITY AND PRESENCE, AND PERIODIC COMMUNICATION WERE SOME FACILITATION STRATEGIES THE AWARD-WINNING INSTRUCTORS USED. WE DISCUSS THESE FINDINGS AND PROVIDE SUGGESTIONS FOR FUTURE RESEARCH AND PRACTICE. THESE FINDINGS CAN ADD TO WHAT IS KNOWN ABOUT EFFECTIVE ONLINE TEACHING BEST PRACTICES, STANDARDS, AND COMPETENCIES.
MASSIVE OPEN ONLINE COURSES (MOOCS) SHOW GREAT POTENTIAL TO TRANSFORM TRADITIONAL EDUCATION THROUGH THE INTERNET. HOWEVER, THE HIGH ATTRITION RATES IN MOOCS HAVE OFTEN BEEN CITED AS A SCALE-EFFICACY TRADEOFF. TRADITIONAL EDUCATIONAL APPROACHES ARE USUALLY UNABLE TO IDENTIFY SUCH LARGE-SCALE NUMBER OF AT-RISK STUDENTS IN DANGER OF DROPPING OUT IN TIME TO SUPPORT EFFECTIVE INTERVENTION DESIGN. WHILE BUILDING DROPOUT PREDICTION MODELS USING LEARNING ANALYTICS ARE PROMISING IN INFORMING INTERVENTION DESIGN FOR THESE AT-RISK STUDENTS, RESULTS OF THE CURRENT PREDICTION MODEL CONSTRUCTION METHODS DO NOT ENABLE PERSONALIZED INTERVENTION FOR THESE STUDENTS. IN THIS STUDY, WE TAKE AN INITIAL STEP TO OPTIMIZE THE DROPOUT PREDICTION MODEL PERFORMANCE TOWARD INTERVENTION PERSONALIZATION FOR AT-RISK STUDENTS IN MOOCS. SPECIFICALLY, BASED ON A TEMPORAL PREDICTION MECHANISM, THIS STUDY PROPOSES TO USE THE DEEP LEARNING ALGORITHM TO CONSTRUCT THE DROPOUT PREDICTION MODEL AND FURTHER PRODUCE THE PREDICTED INDIVIDUAL STUDENT DROPOUT PROBABILITY. BY TAKING ADVANTAGE OF THE POWER OF DEEP LEARNING, THIS APPROACH NOT ONLY CONSTRUCTS MORE ACCURATE DROPOUT PREDICTION MODELS COMPARED WITH BASELINE ALGORITHMS BUT ALSO COMES UP WITH AN APPROACH TO PERSONALIZE AND PRIORITIZE INTERVENTION FOR AT-RISK STUDENTS IN MOOCS THROUGH USING INDIVIDUAL DROP OUT PROBABILITIES. THE FINDINGS FROM THIS STUDY AND IMPLICATIONS ARE THEN DISCUSSED.
EDUCATION IS ONE OF IMPORTANT PROGRAM THAT HAS ALWAYS BEEN THE GOAL OF THE COUNTRY'S STRATEGY TO ADVANCE THE SOCIETY. TECHNOLOGY IS ONE OF VARIOUS ASPECT THAT SUPPORT THE EDUCATION SECTOR TO IMPROVE ITS QUALITY, SUCH AS BUSINESS INTELLIGENCE. BUSINESS INTELLIGENCE IS A TECHNOLOGY THAT IS DEVELOPING TODAY AND CAN HELP IN THE DECISION-MAKING PROCESS FORM LARGE NUMBER OF DATA OR KNOWN AS BIG DATA ANALYTICS. THIS STUDY IDENTIFIES THE APPLICATION OF BUSINESS INTELLIGENCE TO THE EDUCATION SECTOR, ESPECIALLY IN TERMS OF THE TECHNOLOGY, CONTRIBUTION, AND APPLICATION USING PRISMA. TECHNOLOGY OF BUSINESS INTELLIGENCE APPLICATION (Q1) IS CATEGORIZED INTO TWO, THAT ARE TECHNIQUES AND TOOLS. FOR THE FIRST CATEGORY, THERE ARE FOUR KIND OF TECHNIQUES. THE TECHNIQUES ARE DATA MINING, VIABLE SYSTEM MODEL (VSM), LEARNING ANALYTICS, CLOUD COMPUTING, AND BEHAVIOURAL ANALYTICS. FOR THE OTHER CATEGORY, THERE ARE SEVERAL TOOLS THAT USED TO APPLY THE BUSINESS INTELLIGENCE. THE TOOLS ARE HADOOP, GEPHI, BIGDATA BY IBM, AND WEB-BASED. CONTRIBUTIONS OF BUSINESS INTELLIGENCE APPLICATION (Q2) ARE KNOWLEDGE TRANSFER, INNOVATION, AND EVALUATION. DOMAIN APPLICATION OF BUSINESS INTELLIGENCE (Q3) ARE THE RESULT OF THE GENERAL FORM OF APPLYING BUSINESS INTELLIGENCE TO VARIOUS FIELDS SUCH AS RESEARCH, CURRICULUM, ASSESSMENT, BEHAVIOUR ANALYSIS, STUDENT ENROLMENT, AND RESOURCE MANAGEMENT.
THIS STUDY INVESTIGATED THE ROLE AND PROCESS OF SELF-DIRECTED REFLECTIVE ASSESSMENT (SDRA) ENHANCED BY LEARNING ANALYTICS TO SUPPORT PRE-SERVICE TEACHERS' (PTS') COLLECTIVE EMPOWERMENT IN A KNOWLEDGE-BUILDING (KB) CLASSROOM. THE PARTICIPANTS WERE 43 SECOND-YEAR PTS FROM A COMPULSORY COURSE TAUGHT BY A TEACHER WHO HAD 2 YEARS' TEACHING EXPERIENCE. A COMPARISON CLASS OF 47 PTS, TAUGHT BY THE SAME TEACHER AND STUDYING THE SAME TOPICS IN A REGULAR KB ENVIRONMENT, ALSO PARTICIPATED. STATISTICAL ANALYSIS REVEALED SIGNIFICANT DIFFERENCES IN PARTICIPATION AND DOMAIN UNDERSTANDING BETWEEN THE EXPERIMENTAL CLASS AND THE COMPARISON CLASS. QUALITATIVE TRACING OF THE SDRA GROUP'S ONLINE DISCOURSE INDICATED THAT THE PTS WERE EMPOWERED AND THAT THEIR COLLECTIVE EMPOWERMENT INCREASED GRADUALLY OVER TIME. ANALYSIS OF THE PTS' PROMPT SHEETS REVEALED THAT ANALYTIC-SUPPORTED SDRA HELPED THE PTS ENGAGE IN COLLECTIVE DECISION MAKING TO CHOOSE AND JUDGE PROMISING IDEAS, AND IN COLLECTIVE SYNTHESIS AND "RISE-ABOVE" OF IDEAS, THUS HELPING THEM ENGAGE IN HIGH-LEVEL COLLECTIVE EMPOWERMENT. THE FINDINGS HAVE IMPORTANT IMPLICATIONS FOR THE DESIGN OF TECHNOLOGY-RICH ENVIRONMENTS AS METACOGNITIVE TOOLS TO SUPPORT LEARNERS' EMPOWERMENT, AND THEY SHED LIGHT ON HOW TEACHERS CAN USE SUCH TOOLS TO ENGAGE LEARNERS IN METACOGNITIVE PRACTICES TO INCREASE THEIR EMPOWERMENT.
THE UNIVERSITY OF SYDNEY LIBRARY PILOTED AN INITIATIVE IN 2017 THAT USED FEEDBACK AND LEARNING ANALYTICS TO ADDRESS REFERENCING ERRORS IN UNDERGRADUATE STUDENT ASSIGNMENTS. WE COLLABORATED WITH TEACHING STAFF TO DELIVER STANDARDISED FEEDBACK AND LEARNING MATERIALS TO STUDENTS, COMBINING SEVERAL EDUCATIONAL TECHNOLOGIES IN USE AT THE UNIVERSITY. WE DISCUSS OUR PILOT THROUGH THREE LENSES: TECHNOLOGY, COLLABORATION AND EVALUATING IMPACT. KEY FINDINGS INCLUDE THAT TECHNOLOGY AFFORDS OPPORTUNITIES FOR STUDENT SUPPORT HOWEVER INTEGRATION ISSUES CAN COMPLICATE THE STREAMLINING OF WORKFLOWS. MOREOVER, IT IS NOT POSSIBLE FOR NON-EMBEDDED LIBRARY STAFF TO ALIGN TECHNOLOGIES WITH PEDAGOGY AND LEARNING OUTCOMES. COLLABORATIONS BETWEEN LIBRARY AND TEACHING STAFF INCLUDE A SHARED UNDERSTANDING OF THE VALUE OF REFERENCING AS AN ETHICAL PRACTICE. TEACHING STAFF, HOWEVER, MAY BE UNABLE TO DELIVER REFERENCING FEEDBACK IN A WAY THAT IS CONSISTENT WITH AN EXTERNALLY DESIGNED INITIATIVE. WHEN EVALUATING THE IMPACT OF OUR PILOT WE FOUND THAT STUDENTS OFTEN DISPLAY A LACK OF UNDERSTANDING ABOUT THE ROLE OF FEEDBACK IN THEIR LEARNING. RECOMMENDATIONS INCLUDE THAT TEACHING STAFF SHOULD DRIVE THE USE OF TECHNOLOGIES WITHIN THEIR UNITS OF STUDY, AND THAT INSTRUCTIONALLY DESIGNED FEEDBACK PLANNING IS KEY IN DELIVERING CONSISTENT FEEDBACK, DEVELOPING STUDENT FEEDBACK LITERACY AND IN SUPPORTING REFERENCING AS ETHICAL PRACTICE.
THIS DATA ARTICLE PRESENTS COMPARISONS OF ENERGY GENERATION COSTS FROM GAS-FIRED TURBINE AND DIESEL-POWERED SYSTEMS OF DISTRIBUTED GENERATION TYPE OF ELECTRICAL ENERGY IN COVENANT UNIVERSITY, OTA, NIGERIA, A SMART UNIVERSITY CAMPUS DRIVEN BY INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT). CUMULATIVE MONTHLY DATA OF THE ENERGY GENERATION COSTS, FOR CONSUMPTION IN THE INSTITUTION, FROM THE TWO MODES ELECTRIC POWER, WHICH WAS PRODUCED AT LOCATIONS CLOSED TO THE COMMUNITY CONSUMING THE ENERGY, WERE RECORDED FOR THE PERIOD SPANNING JANUARY TO DECEMBER 2017. BY THESE, ENERGY GENERATION COSTS FROM THE TURBINE SYSTEM PROCEED FROM THE GAS-FIRING WHEREAS THE GENERATION COST DATA FROM THE DIESEL-POWERED GENERATOR ALSO INCLUDE DATA ON MAINTENANCE COST FOR THIS MODE OF ELECTRICAL POWER GENERATION. THESE ENERGY GENERATION COST DATA THAT WERE PRESENTED IN TABLES AND GRAPHS EMPLOY DESCRIPTIVE PROBABILITY DISTRIBUTION AND GOODNESS-OF-FIT TESTS OF STATISTICAL SIGNIFICANCE AS THE METHODS FOR THE DATA DETAILING AND COMPARISONS. INFORMATION DETAILS FROM THIS DATA OF ENERGY GENERATION COSTS ARE USEFUL FOR FURTHERING RESEARCH DEVELOPMENTS AND AIDING ENERGY STAKEHOLDERS AND DECISION-MAKERS IN THE FORMULATION OF POLICIES ON ENERGY GENERATION MODES, ECONOMIC VALUATION IN TERMS OF COSTING AND MANAGEMENT FOR ATTAINING ENERGY-EFFICIENT/ SMART EDUCATIONAL ENVIRONMENT. (C) 2018 THE AUTHORS. PUBLISHED BY ELSEVIER INC.
CONTEXTUAL FACTORS IN WHICH LEARNING OCCURS ARE CRUCIAL ASPECTS THAT LEARNING ANALYTICS AND RELATED DISCIPLINES AIM TO UNDERSTAND FOR OPTIMIZING LEARNING AND THE ENVIRONMENTS IN WHICH LEARNING OCCURS. IN FOREIGN VOCABULARY DEVELOPMENT, TAKING THE NOTES OR MEMOS OF LEARNING CONTEXTS ALONG WITH OTHER FACTORS, PLAY AN ESSENTIAL ROLE IN QUICK MEMORIZATION AND REFLECTION. HOWEVER, CONVENTIONAL TOOLS FAIL TO AUTOMATE THE LEARNING CONTEXTS GENERATION PROCESS AS LEARNERS STILL NEED TO TAKE MEMOS OR E-NOTES TO DESCRIBE THEIR VOCABULARY LEARNING CONTEXTS. THIS PAPER PRESENTS THE IMAGE UNDERSTANDING PROJECT (HEREAFTER IUECOSYSTEM) THAT COULD PRODUCE SMARTLY-GENERATED LEARNING CONTEXTS PRIMARILY IN A LEARNER'S TARGET LANGUAGES. THE IUECOSYSTEM USES VISUAL CONTENT ANALYSIS OF LIFELOGGING IMAGES AS THE SENSOR DATA TO PRODUCE SMARTLY-GENERATED LEARNING CONTEXTS THAT COULD BE USED AS AN ALTERNATIVE TO HANDWRITTEN MEMOS OR ELECTRONIC NOTES. THE IUECOSYSTEM USES APPLIED ARTIFICIAL INTELLIGENCE TO PRODUCE SMARTLY-GENERATED LEARNING CONTEXTS. THIS INTELLIGENT LEARNING ENVIRONMENT COLLECTS A LEARNER'S LEARNING SATISFACTION AND INTERACTION DATA AND, LATER ON, ANALYZES THEM TO PRODUCE TIME-BASED NOTIFICATIONS FOR ENHANCING RETENTION. FURTHERMORE, A NEW LEARNING DESIGN IS PRESENTED THAT AIMS TO MAP A LEARNER'S PRIOR VOCABULARY KNOWLEDGE WITH NEW LEARNING VOCABULARIES TO BE LEARNED. THIS LEARNING DESIGN WOULD HELP LEARNERS TO REVIEW AND RECALL PRIOR KNOWLEDGE WHILE LEARNING NEW VOCABULARY.
ATTRACTING STUDENTS TO GERONTOLOGICAL SOCIAL WORK HAS LONG BEEN DIFFICULT. A POSSIBLE STRATEGY IS TO PROVIDE STUDENTS WITH EXPERIENTIAL LEARNING OPPORTUNITIES WITH OLDER ADULTS THAT PROVIDE THEM WITH SUPPORT WHILE REFLECTING ON THIS WORK. OUR TEAM MENTORED REFLECTIVE PRACTICE MODEL REPRESENTS AN EFFORT TO ENHANCE STUDENTS' REFLECTIVE PRACTICE SKILLS IN THE CONTEXT OF A CLINICAL RESEARCH PROJECT IN WHICH BOTH INSTRUCTORS AND STUDENTS FORM A SUPPORTIVE TEAM. STUDENTS REPORTED A NUMBER OF BENEFITS THAT INCLUDED THE DEVELOPMENT OF MEANINGFUL RELATIONSHIPS WITH OLDER CLIENTS, CLINICAL SKILLS THAT RELATED TO MEMORY LOSS, AND THE INTEGRATION OF RESEARCH AND PRACTICE. STUDENTS WHO WERE ENGAGED IN THE TEAM MENTORED PROCESS OF REFLECTION EVIDENCED CHANGES IN HOW THEY WERE THINKING ABOUT CLIENTS. THESE NEW UNDERSTANDINGS HAVE THE POTENTIAL TO IMPACT THE WAYS IN WHICH STUDENTS WORK WITH OLDER ADULTS.
THE RESEARCH PRESENTS PRECISION EDUCATION THAT AIMS TO REGULATE STUDENTS' BEHAVIORS THROUGH THE LEARNING ANALYTICS DASHBOARD (LAD) IN THE AI-SUPPORTED SMART LEARNING ENVIRONMENT (SLE). THE LAD BASICALLY TRACKS AND VISUALIZES TRACES OF LEARNING ACTIONS TO MAKE STUDENTS AWARE OF THEIR LEARNING BEHAVIORS AND REFLECT THESE AGAINST THE AGREED GOALS. THIS RESEARCH AIMS TO REALIZE THE DIGITAL TRANSFORMATION OF THE LEARNING SPACE, THEREBY IMPROVING STUDENTS' LEARNING OUTCOMES WITH THE ASSISTANCE OF THE LEARNING DASHBOARD. TO EXAMINE WHETHER THERE WAS A CLOSE RELATIONSHIP BETWEEN THE FREQUENCY OF USING THE WHOLE PLATFORM AND ACADEMIC RESULTS, THE DATA WAS COLLECTED FROM 50 FIRST-YEAR UNIVERSITY STUDENTS WHO REGISTERED WITH THE INNOVATIVE THINKING COURSE. BASED ON THE DATA, WE CONSTRUCTED THE TECHNOLOGY ACCEPTANCE MODEL (TAM) QUESTIONNAIRE AND INTERVIEW GUIDE TO REALIZE THE STUDENTS' ACCEPTANCE AND FEEDBACK TOWARDS THE SLE. STUDENTS WERE CLUSTERED INTO HIGH-MARK AND LOW-MARK GROUPS BASED ON THEIR FINAL RESULTS. THE WILCOXON RANK-SUM TEST IS USED TO IDENTIFY A SIGNIFICANT DIFFERENCE BETWEEN THE TWO GROUPS USING THE PRECISION EDUCATION PLATFORM. SUBSEQUENTLY, THE PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING (PLS-SEM) IS FURTHER UTILIZED TO ANALYZE THE RELATIONSHIP BETWEEN SYSTEM QUALITY, PERCEIVED EASE OF USE, AND PERCEIVED USEFULNESS ON BEHAVIORAL INTENTION AND LEARNING TRANSFER.
THIS PAPER PRESENTS AFFECT DATA COLLECTED FROM PERIODIC EMOTION DETECTION SURVEYS THROUGHOUT AN INTRODUCTORY STATISTICS MOOC CALLED "I HEART STATS." THIS IS THE FIRST MOOC, TO OUR KNOWLEDGE, TO CAPTURE VALUABLE STUDENT AFFECT DATA THROUGH SELF-REPORTED SURVEYS. TO COLLECT STUDENT AFFECT, WE USED TWO SELF-REPORTING METHODS: (1) THE SELF-ASSESSMENT MANIKIN AND (2) A DISCRETE EMOTION LIST. WE FOUND THAT THE MOST COMMON REPORTED MOOC EMOTION WAS HOPE FOLLOWED BY ENJOYMENT AND CONTENTMENT. THERE WERE SUBSTANTIAL SHIFTS IN AFFECTIVE STATES OVER THE COURSE, NOTABLY WITH ANXIETY AND PRIDE. THE MOST VALUABLE RESULT OF OUR STUDY IS A PRELIMINARY DESCRIPTION OF THE METHODS FOR COLLECTING SELF-REPORTED STUDENT AFFECT AT SCALE IN A MOOC SETTING.
THIS PAPER DESCRIBES THE DEVELOPMENT AND EVALUATION OF AN AFFECT-AWARE INTELLIGENT SUPPORT COMPONENT THAT IS PART OF A LEARNING ENVIRONMENT KNOWN AS ITALK2LEARN. THE INTELLIGENT SUPPORT COMPONENT IS ABLE TO TAILOR FEEDBACK ACCORDING TO A STUDENT'S AFFECTIVE STATE, WHICH IS DEDUCED BOTH FROM SPEECH AND INTERACTION. THE AFFECT PREDICTION IS USED TO DETERMINE WHICH TYPE OF FEEDBACK IS PROVIDED AND HOW THAT FEEDBACK IS PRESENTED (INTERRUPTIVE OR NON-INTERRUPTIVE). THE SYSTEM INCLUDES TWO BAYESIAN NETWORKS THAT WERE TRAINED WITH DATA GATHERED IN A SERIES OF ECOLOGICALLY-VALID WIZARD-OF-OZ STUDIES, WHERE THE EFFECT OF THE TYPE OF FEEDBACK AND THE PRESENTATION OF FEEDBACK ON STUDENTS' AFFECTIVE STATES WAS INVESTIGATED. THIS PAPER REPORTS RESULTS FROM AN EXPERIMENT THAT COMPARED A VERSION THAT PROVIDED AFFECT-AWARE FEEDBACK (AFFECT CONDITION) WITH ONE THAT PROVIDED FEEDBACK BASED ON PERFORMANCE ONLY (NON-AFFECT CONDITION). RESULTS SHOW THAT STUDENTS WHO WERE IN THE AFFECT CONDITION WERE LESS BORED AND LESS OFF-TASK, WITH THE LATTER BEING STATICALLY SIGNIFICANT. IMPORTANTLY, STUDENTS IN BOTH CONDITIONS MADE LEARNING GAINS THAT WERE STATISTICALLY SIGNIFICANT, WHILE STUDENTS IN THE AFFECT CONDITION HAD HIGHER LEARNING GAINS THAN THOSE IN THE NON-AFFECT CONDITION, ALTHOUGH THIS RESULT WAS NOT STATISTICALLY SIGNIFICANT IN THIS STUDY'S SAMPLE. TAKEN ALL TOGETHER, THE RESULTS POINT TO THE POTENTIAL AND POSITIVE IMPACT OF AFFECT-AWARE INTELLIGENT SUPPORT.
THE MAIN GOAL OF THIS PAPER IS TO PROVIDE AN INTERVENTION MECHANISM EMBEDDED INTO A LEARNING INTELLIGENT SYSTEM IN ORDER TO SUPPORT LEARNERS TO SUCCEED IN THEIR LEARNING PROCESS IN ONLINE LEARNING ENVIRONMENTS BY USING FEEDBACK AND PREDICTIVE ANALYTICS. THE SYSTEM, NAMED LEARNING INTELLIGENT SYSTEM (LIS), IS CURRENTLY UNDER DEVELOPMENT IN THE CONTEXT OF A 3-YEAR PROJECT AT THE UNIVERSITET OBERTA DE CATALUNYA (UOC). IN ORDER TO FULFILL THIS OBJECTIVE, TO BEGIN WITH, DIFFERENT TYPES OF INTERVENTION MEASURES WILL BE IDENTIFIED AND REVIEWED. FOLLOWING THIS, AN EVALUATION OF THESE INTERVENTION MECHANISMS WILL BE PROVIDED TAKING INTO ACCOUNT THE REQUIREMENTS AND DATA AVAILABLE AT UOC. THIS EVALUATION WILL ALLOW THE DESIGN OF AN INTERVENTION MECHANISM TO BE IMPLEMENTED IN LIS SYSTEM. THE CONTRIBUTION OF THIS PAPER IS BASED ON THE DESIGN AND APPLICATION OF AN INTERVENTION MECHANISM THROUGH LIS SYSTEM WHICH WILL HELP TEACHERS, BUT MAINLY LEARNERS TO SUCCEED IN THEIR ONLINE COURSES. BY PREDICTING THEIR SUCCESS RATE USING DATA FROM THE SYSTEM, AND THE PROVISION OF FEEDBACK, THE SYSTEM WILL HELP AND ENCOURAGE LEARNERS TO ACHIEVE THEIR LEARNING GOALS, AND CONSEQUENTLY THEIR COURSES CERTIFICATION. THE FINDINGS WILL CONTRIBUTE, NOT ONLY IMPROVING THE TEACHER-LEARNING PROCESS AT UOC, BUT ALSO IN MANY OTHER BLENDED AND ONLINE ENVIRONMENTS.
THIS DATA ARTICLE PRESENTS DATA OF ACADEMIC PERFORMANCES OF UNDERGRADUATE STUDENTS IN SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) DISCIPLINES IN COVENANT UNIVERSITY, NIGERIA. THE DATA SHOWS ACADEMIC PERFORMANCES OF MALE AND FEMALE STUDENTS WHO GRADUATED FROM 2010 TO 2014. THE TOTAL POPULATION OF SAMPLES IN THE OBSERVATION IS 3046 UNDERGRADUATES MINED FROM BIOCHEMISTRY (BCH), BUILDING TECHNOLOGY (BLD), COMPUTER ENGINEERING (CEN), CHEMICAL ENGINEERING (CHE), INDUSTRIAL CHEMISTRY (CHM), COMPUTER SCIENCE (CIS), CIVIL ENGINEERING (CVE), ELECTRICAL AND ELECTRONICS ENGINEERING (EEE), INFORMATION AND COMMUNICATION ENGINEERING (ICE), MATHEMATICS (MAT), MICROBIOLOGY (MCB), MECHANICAL ENGINEERING (MCE), MANAGEMENT AND INFORMATION SYSTEM (MIS), PETROLEUM ENGINEERING (PET), INDUSTRIAL PHYSICS ELECTRONICS AND IT APPLICATIONS (PHYE), INDUSTRIAL PHYSICS-APPLIED GEOPHYSICS (PHYG) AND INDUSTRIAL PHYSICS-RENEWABLE ENERGY (PHYR). THE DETAILED DATASET IS MADE AVAILABLE IN FORM OF A MICROSOFT EXCEL SPREADSHEET IN THE SUPPLEMENTARY MATERIAL OF THIS ARTICLE. (C) 2018 THE AUTHORS. PUBLISHED BY ELSEVIER LNC.
THIS WORK IS SITUATED IN THE CONTEXT OF THE LEARNING DESIGN RESEARCH AREA. IT REVIEWS DIFFERENT FORMS IN WHICH LEARNING DESIGNS CAN BE REPRESENTED BOTH IN TEXTUAL OR IN A GRAPHICAL WAY. THE DIFFERENT FORMS HAVE BEEN GATHERED FROM THE OPTIONS USED BY TEACHERS IN CURRENT PRACTICE AND FROM PROPOSALS OF THE RESEARCH COMMUNITY. THE FOLLOWING CATEGORIES HAVE BEEN DEFINED TO CLASSIFY THE SEVERAL REPRESENTATION TYPES INTO A CLEAR AND CONCISE CLASSIFICATION: NARRATIVE TEXT; FORMS/TEMPLATES; TABLE REPRESENTATIONS/MATRICES; CONCEPT MAPS, MIND MAPS AND TREE BASED REPRESENTATIONS; FLOW DIAGRAMS; SEQUENTIAL DIAGRAMS; AND AD-HOC DIAGRAMS. FOR EACH TYPE OF REPRESENTATION SPECIFIC EXAMPLES AND USES ARE PROVIDED, SHOWING THE BENEFITS AND CONTEXTS IN WHICH THEY ARE USED. TEXT-BASED REPRESENTATIONS ARE COMMON IN THE REAL TEACHERS' PRACTICE, WHILE GRAPHICAL REPRESENTATIONS HAVE BEEN TAKEN FROM RESEARCH INITIATIVES. THE GOAL OF THE PAPER IS TO ATTRACT ATTENTION TO THE FACT THAT NO TYPE OF REPRESENTATION FOR LEARNING DESIGNS HAS BEEN ADOPTED AS A MAINSTREAM, YET. A COMMON LANGUAGE IN THIS DOMAIN WOULD BE DESIRABLE AND COULD OFFER GREAT BENEFITS RELATED TO THE COMMUNICATION AND SHARING OF TEACHING AND LEARNING RESULTS. THIS COULD BE VERY USEFUL FOR THE REPRESENTATION OF LEARNING DESIGNS AND TEACHING PRACTICES IN LEARNING ANALYTICS DASHBOARDS.
PROBLEM SOLVING IS CONSIDERED A FUNDAMENTAL HUMAN SKILL. HOWEVER, LARGE-SCALE ASSESSMENT OF PROBLEM SOLVING IN K-12 EDUCATION REMAINS A CHALLENGING TASK. RESEARCHERS HAVE ARGUED FOR THE DEVELOPMENT OF AN ENHANCED ASSESSMENT APPROACH THROUGH JOINT EFFORT FROM MULTIPLE DISCIPLINES. IN THIS STUDY, A THREE-STAGE APPROACH BASED ON AN EVIDENCE-CENTERED DESIGN FRAMEWORK IS PROPOSED TO ANALYZE PROBLEM-SOLVING BEHAVIOR, ABILITIES, AND PERFORMANCE. THE APPROACH IS APPLIED TO ASSESS THE ONLINE PROBLEM SOLVING OF 554 STUDENTS IN A SHANGHAI PRIMARY SCHOOL. THE STUDY REVEALS FOUR CLUSTERS WITH DISTINCTIVE PROBLEM-SOLVING BEHAVIOR, ABILITIES, AND PERFORMANCE. THE FINDINGS OF THIS APPROACH ALSO CORROBORATE THE RESULTS OF THE PROGRAMME FOR INTERNATIONAL STUDENT ASSESSMENT OF SHANGHAI STUDENTS' PROBLEM-SOLVING PERFORMANCE. THE IMPLICATIONS AND LIMITATIONS OF THIS STUDY ARE ALSO DISCUSSED.
ASSESSMENT IS AN ESSENTIAL ACTIVITY TO ACHIEVE THE OBJECTIVE OF THE COURSE BEING TAUGHT AND TO IMPROVE THE TEACHING AND LEARNING PROCESS. THERE ARE SEVERAL EDUCATIONAL TAXONOMIES THAT CAN BE USED TO ASSESS THE EFFICACY OF ASSESSMENT IN ENGINEERING LEARNING BY ALIGNING THE ASSESSMENT TASKS IN LINE WITH THE INTENDED LEARNING OUTCOMES AND TEACHING AND LEARNING ACTIVITIES. THIS RESEARCH IS FOCUSED ON USING A LEARNING TAXONOMY THAT FITS WELL FOR COMPUTER SCIENCE AND ENGINEERING TO CATEGORIZE AND ASSIGN WEIGHTS TO EXAM QUESTIONS ACCORDING TO THE TAXONOMY LEVELS. EXISTING NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES, WORDNET SIMILARITY ALGORITHMS WITH NLTK AND WORDNET PACKAGE WERE USED AND A NEW SET OF RULES WERE DEVELOPED TO IDENTIFY THE CATEGORY AND THE WEIGHT FOR EACH EXAM QUESTION ACCORDING TO BLOOM'S TAXONOMY. USING THE RESULT THE EVALUATORS CAN ANALYZE AND DESIGN THE QUESTION PAPERS TO MEASURE THE STUDENT KNOWLEDGE FROM VARIOUS ASPECTS AND LEVELS. PRIOR EVALUATION WAS CONDUCTED TO IDENTIFY MOST SUITABLE NLP PREPROCESSING TECHNIQUES TO THE CONTEXT. A SAMPLE SET OF END SEMESTER EXAMINATION QUESTIONS OF THE DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING (CSE), UNIVERSITY OF MORATUWA WAS USED TO EVALUATE THE ACCURACY OF THE QUESTION CLASSIFICATION; WEIGHT ASSIGNMENT AND THE MAIN CATEGORY ASSIGNMENT WERE VALIDATED AGAINST THE MANUAL CLASSIFICATION BY A DOMAIN EXPERT. THE OUTCOME OF CLASSIFICATION IS A SET OF WEIGHTS ASSIGNED UNDER EACH TAXONOMY CATEGORY, INDICATING THE LIKELIHOOD OF A QUESTION TO FALL INTO A CERTAIN CATEGORY. THE HIGHEST WEIGHT CATEGORY WAS CONSIDERED AS THE MAIN CATEGORY OF THE EXAM QUESTION. ACCORDING TO THE GENERATED RULE SET THE ACCURACY OF DETECTING THE CORRECT MAIN CATEGORY OF A QUESTION IS 82%.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE GAINED ENORMOUS POPULARITY IN RECENT YEARS AND HAVE ATTRACTED LEARNERS WORLDWIDE. HOWEVER, MOOCS FACE A CRUCIAL CHALLENGE IN THE HIGH DROPOUT RATE, WHICH VARIES BETWEEN 91%-93%. AN INTERPLAY BETWEEN DIFFERENT LEARNING ANALYTICS STRATEGIES AND MOOCS HAVE EMERGED AS A RESEARCH AREA TO REDUCE DROPOUT RATE. MOST EXISTING STUDIES USE CLICK-STREAM FEATURES AS ENGAGEMENT PATTERNS TO PREDICT AT-RISK STUDENTS. HOWEVER, THIS STUDY USES A COMBINATION OF CLICK-STREAM FEATURES AND THE INFLUENCE OF THE LEARNER'S FRIENDS BASED ON THEIR DEMOGRAPHICS TO IDENTIFY POTENTIAL DROPOUTS. EXISTING PREDICTIVE MODELS ARE BASED ON SUPERVISED LEARNING TECHNIQUES THAT REQUIRE THE BULK OF HAND-LABELLED DATA TO TRAIN MODELS. IN PRACTICE, HOWEVER, SCARCITY OF MASSIVE LABELLED DATA MAKES TRAINING DIFFICULT. THEREFORE, THIS STUDY USES SELF-TRAINING, A SEMI-SUPERVISED LEARNING MODEL, TO DEVELOP PREDICTIVE MODELS. EXPERIMENTAL RESULTS ON A PUBLIC DATA SET DEMONSTRATE THAT SEMISUPERVISED MODELS ATTAIN COMPARABLE RESULTS TO STATE-OFTHE-ART APPROACHES, WHILE ALSO HAVING THE FLEXIBILITY OF UTILIZING A SMALL QUANTITY OF LABELLED DATA. THIS STUDY DEPLOYS SEVEN WELL-KNOWN OPTIMIZERS TO TRAIN THE SELF-TRAINING CLASSIFIERS, OUT OF WHICH, STOCHASTIC GRADIENT DESCENT (SGD) OUTPERFORMED OTHERS WITH THE VALUE OF F1 SCORE AT 94.29%, AFFIRMING THE RELEVANCE OF THIS EXPOSITION.
THE ARTICLE REPORTS THE EXPERIENCE GAINED WHILE ORGANIZING A REGIONAL ONLINE COMPETITION IN UNDERSTANDING ENGLISH TEXTS FOR HIGH SCHOOL STUDENTS IN THE PILSEN REGION, THE CZECH REPUBLIC. THE COMPETITION WAS CONDUCTED USING THE MOODLE LEARNING MANAGEMENT SYSTEM (LMS). TWO SUCCESSIVE YEARS OF THE COMPETITION HAVE BEEN PERFORMED AND THE COMPETITION HAS BEEN GRADUALLY IMPROVED, RECEIVING FEEDBACK FROM PUPILS, TEACHERS AND THE EVALUATION OF LEARNING ANALYTICS. IN THE FIRST YEAR OF THE SCHOOL YEAR 2018/2019, THE COMPETITION WAS ENTERED BY 1105 STUDENTS FROM 27 SCHOOLS, AND IN THE SECOND YEAR, 1707 STUDENTS FROM 20 SCHOOLS. THERE WERE FOUR SCHOOL ONLINE ROUNDS, AN ONLINE SEMIFINAL AND FACE-TO-FACE FINALS. IN THE SCHOOL ROUNDS, THE COMPETITION WAS SUPERVISED BY TRAINED TEACHERS IN SCHOOLS. PUPILS COULD ACCESS THE ONLINE SYSTEM FROM THE SCHOOL COMPUTER ROOM OR FROM MOBILE DEVICES (TABLETS, SMARTPHONES). THE SEMIFINALS AND FINALS AT THE UNIVERSITY OF WEST BOHEMIA IN PILSEN WERE ATTENDED BY THE WINNERS OF THE SCHOOL ROUNDS. THE ARTICLE DESCRIBES THE DIVISION OF STUDENTS INTO CATEGORIES ACCORDING TO THE TYPE OF SCHOOL AND AGE AND PRESENTS TECHNICAL SOLUTIONS IN THE FORM OF ONLINE TEST TASKS IN COURSES IN LMS MOODLE, AS WELL AS POSSIBLE PROSPECTS FOR FUTURE YEARS OF THE COMPETITION.
GRAPHOGAME TEACHER TRAINING SERVICE IS A MOBILE-BASED SOLUTION FOR PROVIDING TEACHERS WITH SCIENTIFICALLY VALIDATED PEDAGOGICAL TRAINING IN LITERACY INSTRUCTION. IN MANY AFRICAN COUNTRIES TEACHERS CURRENTLY HAVE INSUFFICIENT KNOWLEDGE TO TEACH LITERACY IN LOCAL LANGUAGES AND LEARNING MATERIALS ARE SCARCE, ESPECIALLY FOR CHILDREN WITH LEARNING DIFFICULTIES. AS PART OF THE GRAPHOWORLD NETWORK, CAPOLSA/UNIVERSITY OF ZAMBIA IS DEVELOPING NEW MOBILE-BASED METHOD FOR PROVIDING IN-SERVICE TRAINING FOR TEACHERS IN LITERACY INSTRUCTION AND ASSISTING CHILDREN WITH LEARNING DIFFICULTIES. GRAPHOGAME TEACHER TRAINING SERVICE WAS PILOTED IN OCTOBER 2014 IN RURAL ZAMBIA. AN ORIENTATIVE WORKSHOP WAS GIVEN TO 24 TEACHERS WHO LEARNED ABOUT LITERACY INSTRUCTION METHODOLOGY AND THEN ORGANIZED A GRAPHOGAME INTERVENTION TO RANDOMLY SELECTED 2ND GRADE CHILDREN EITHER AT HOME OR IN A SCHOOL ENVIRONMENT. PARENTS OF THE CHILDREN IN THE HOME INTERVENTION GROUP WERE ALSO ENCOURAGED TO PLAY GRAPHOGAME. GRAPHOGAME LEARNING ANALYTICS SHOWS THAT BOTH THE CHILDREN AND THEIR PARENTS IMPROVED THEIR WORD READING SKILLS. CHILDREN WHO PLAYED GRAPHOGAME PERFORMED BETTER THAN THEIR NON-PLAYING CLASSMATES IN THE EGRA LETTER-SOUND KNOWLEDGE TEST AT THE END OF THE INTERVENTION. TEACHERS, PARENTS AND CHILDREN WERE ALL MOTIVATED TO USE ICT-BASED LITERACY LEARNING TOOLS AND THEIR LITERACY SKILLS LEVELS SHOW HIGH DEMAND FOR SUPPORT SERVICES FOR LITERACY INSTRUCTION.
THIS STUDY EXAMINES WHETHER INCLUDING MORE CONTEXTUAL INFORMATION IN DATA ANALYSIS COULD IMPROVE OUR ABILITY TO IDENTIFY THE RELATION BETWEEN STUDENTS' ONLINE LEARNING BEHAVIOR AND OVERALL PERFORMANCE IN AN INTRODUCTORY PHYSICS COURSE. WE CREATED FOUR LINEAR REGRESSION MODELS CORRELATING STUDENTS' PASS-FAIL EVENTS IN A SEQUENCE OF ONLINE LEARNING MODULES WITH THEIR NORMALIZED TOTAL COURSE SCORE. EACH MODEL TAKES INTO ACCOUNT AN ADDITIONAL LEVEL OF CONTEXTUAL INFORMATION THAN THE PREVIOUS ONE, SUCH AS STUDENT LEARNING STRATEGY AND DURATION OF ASSESSMENT ATTEMPTS. EACH OF THE LATTER THREE MODELS IS ALSO ACCOMPANIED BY A VISUAL REPRESENTATION OF STUDENTS' INTERACTION STATES ON EACH LEARNING MODULE. WE FOUND THAT THE BEST PERFORMING MODEL IS THE ONE THAT INCLUDES THE MOST CONTEXTUAL INFORMATION, INCLUDING INSTRUCTION CONDITION, INTERNAL CONDITION, AND LEARNING STRATEGY. THE MODEL SHOWS THAT WHILE MOST STUDENTS FAILED ON THE MOST CHALLENGING LEARNING MODULE, THOSE WITH NORMAL LEARNING BEHAVIOR ARE MORE LIKELY TO OBTAIN HIGHER TOTAL COURSE SCORES, WHEREAS STUDENTS WHO RESORTED TO GUESSING ON THE ASSESSMENTS OF SUBSEQUENT MODULES TENDED TO RECEIVE LOWER TOTAL SCORES. OUR RESULTS SUGGEST THAT CONSIDERING MORE CONTEXTUAL INFORMATION RELATED TO EACH EVENT CAN BE AN EFFECTIVE METHOD TO IMPROVE THE QUALITY OF LEARNING ANALYTICS, LEADING TO MORE ACCURATE AND ACTIONABLE RECOMMENDATIONS FOR INSTRUCTORS.
IN RESEARCHING STUDENT LEARNING EXPERIENCE IN HIGHER EDUCATION, A DEARTH OF STUDIES HAS INVESTIGATED COGNITIVE, SOCIAL, AND MATERIAL DIMENSIONS SIMULTANEOUSLY WITH DIE SAME POPULATION. FROM AN ECOLOGICAL PERSPECTIVE OF LEARNING, THIS STUDY EXAMINED THE INTERRELATEDNESS AMONGST KEY ELEMENTS IN THESE DIMENSIONS OF 365 UNDERGRADUATES PERSONALISED LEARNING NETWORKS. DATA WERE COLLECTED FIRM QUESTIONNAIRES, LEARNING ANALYTICS, AND COURSE MARKS TO MEASURE THESE ELEMENTS IN THE BLENDED LEARNING EXPERIENCE AND ACADEMIC PERFORMANCE. STUDENTS REPORTED QUALITATIVELY DIFFERENT COGNITIVE ENGAGEMENT BETWEEN AN UNDERSTANDING AND A REPRODUCING LEARNING ORIENTATION TOWARDS LEARNING, WHICH WHEN COMBINED WITH THEIR CHOICES OF COLLABORATION, GENERATED FIVE QUALITATIVELY DIFFERENT PATTERNS OF COLLABORATION. THE RESULTS REVEALED THAT STUDENTS HAD AN UNDERSTANDING LEARNING ORIENTATION AND CHOSE TO COLLABORATE WITH STUDENTS OF SIMILAR LEARNING ORIENTATION TENDED TO HAVE MORE SUCCESSFUL BLENDED LEARNING EXPERIENCE. THEIR PERSONALISED LEARNING NETWORKS WERE CHARACTERIZED BY SELF REPORTED ADOPTION OF DEEP APPROACHES TO FACE-TO-FACE AND ONLINE LEARNING; POSITIVE PERCEPTIONS OF THE INTEGRATION BETWEEN ONLINE ENVIRONMENT AND THE COURSE DESIGN; THE WAY THEY COLLABORATED AND POSITIONED THEMSELVES IN THEIR COLLABORATIVE NETWORKS; AND THEY WERE MORE ENGAGED WITH ONLINE LEARNING ACTIVITIES IN THE COURSE. THE STUDY HAD SIGNIFICANT IMPLICATIONS TO INFORM THEORY DEVELOPMENT IN LEARNING ECOLOGY RESEARCH AND TO GUIDE CURRICULUM DESIGN, TEACHING, AND LEARNING.
BOTH EDUCATIONAL DATA MINING (EDM) AND LEARNING ANALYTICS (LA) FOCUS ON APPLYING ANALYTICS AND DATA MINING TECHNIQUES TO EXTRACT USEFUL INFORMATION FROM LARGE DATA SETS. EDM IS GENERALLY MORE INTERESTED IN AUTOMATED METHODS FOR DISCOVERY WITHIN THE EDUCATIONAL DATA WHILE LA IS RELATIVELY KEEN ON APPLYING HUMAN-LED METHODS TO UNDERSTAND THE INVOLVED LEARNING PROCESSES. AMONG THE VARIOUS FIELDS OF CHALLENGING STUDIES IN EDM, DOMAIN STRUCTURE DISCOVERY IS AIMED TO FIND THE STRUCTURE OF KNOWLEDGE IN AN EDUCATIONAL DOMAIN, SUCH AS FORMULATING THE PREREQUISITE REQUIREMENTS AMONG VARIOUS KNOWLEDGE COMPONENTS THROUGH ONLINE EDUCATIONAL RESOURCES. HOWEVER, WITH THE VAST AMOUNT OF KNOWLEDGE COMPONENTS IN SPECIFIC SUBJECTS, THE PROCESS OF SUCH FORMULATION IS VERY COMPLICATED AND TIME-CONSUMING NO MATTER BEING DONE MANUALLY OR SEMI-AUTOMATICALLY. IN THIS WORK, WE PROPOSE A SYSTEMATIC FRAMEWORK OF A SEMI-SUPERVISED LEARNING APPROACH IN WHICH A CONCEPT-BASED CLASSIFIER IS CO-TRAINED WITH AN EXPLICIT SEMANTIC ANALYSIS (ESA) CLASSIFIER TO DERIVE A COMMON SET OF PREREQUISITE RULES BASED ON A DIVERSE SET OF ONLINE EDUCATIONAL RESOURCES. TO DEMONSTRATE ITS FEASIBILITY, A WORKING PROTOTYPE IS BUILT WITH SOME IMPRESSIVE RESULTS OBTAINED IN SPECIFIC ENGINEERING SUBJECTS. MORE IMPORTANTLY, OUR PROPOSAL SHEDS LIGHT ON MANY POSSIBLE DIRECTIONS FOR FUTURE EXPLORATION.
THIS STUDY INVESTIGATED TO WHAT EXTENT MULTIMODAL DATA CAN BE USED TO DETECT MISTAKES DURING CARDIOPULMONARY RESUSCITATION (CPR) TRAINING. WE COMPLEMENTED THE LAERDAL QCPR RESUSCIANNE MANIKIN WITH THE MULTIMODAL TUTOR FOR CPR, A MULTI-SENSOR SYSTEM CONSISTING OF A MICROSOFT KINECT FOR TRACKING BODY POSITION AND A MYO ARMBAND FOR COLLECTING ELECTROMYOGRAM INFORMATION. WE COLLECTED MULTIMODAL DATA FROM 11 MEDICAL STUDENTS, EACH OF THEM PERFORMING TWO SESSIONS OF TWO-MINUTE CHEST COMPRESSIONS (CCS). WE GATHERED IN TOTAL 5254 CCS THAT WERE ALL LABELLED ACCORDING TO FIVE PERFORMANCE INDICATORS, CORRESPONDING TO COMMON CPR TRAINING MISTAKES. THREE OUT OF FIVE INDICATORS, CC RATE, CC DEPTH AND CC RELEASE, WERE ASSESSED AUTOMATICALLY BY THE REUSCIANNE MANIKIN. THE REMAINING TWO, RELATED TO ARMS AND BODY POSITION, WERE ANNOTATED MANUALLY BY THE RESEARCH TEAM. WE TRAINED FIVE NEURAL NETWORKS FOR CLASSIFYING EACH OF THE FIVE INDICATORS. THE RESULTS OF THE EXPERIMENT SHOW THAT MULTIMODAL DATA CAN PROVIDE ACCURATE MISTAKE DETECTION AS COMPARED TO THE RESUSCIANNE MANIKIN BASELINE. WE ALSO SHOW THAT THE MULTIMODAL TUTOR FOR CPR CAN DETECT ADDITIONAL CPR TRAINING MISTAKES SUCH AS THE CORRECT USE OF ARMS AND BODY WEIGHT. THUS FAR, THESE MISTAKES WERE IDENTIFIED ONLY BY HUMAN INSTRUCTORS. FINALLY, TO INVESTIGATE USER FEEDBACK IN THE FUTURE IMPLEMENTATIONS OF THE MULTIMODAL TUTOR FOR CPR, WE CONDUCTED A QUESTIONNAIRE TO COLLECT VALUABLE FEEDBACK ASPECTS OF CPR TRAINING.
ONLINE INITIATIVES, A TERM THAT DESCRIBES GOAL-ORIENTED SYSTEMS SUCH AS CROWDSOURCING, PEER PRODUCTION, AND ONLINE COMMUNITIES, ARE INCREASINGLY POPULAR WAYS TO SOURCE WORK FROM AND HARNESS THE SKILLS OF DISTRIBUTED POPULATIONS. THE EXPLOSION OF ONLINE INITIATIVES IN RECENT YEARS HAS INSPIRED SCHOLARSHIP EXAMINING WHY PEOPLE CONTRIBUTE TO THESE INITIATIVES. WHILE THERE IS AN ABUNDANCE OF CASE STUDIES ON MOTIVATION, THERE IS A DEARTH OF RESEARCH THAT EXPLORES MOTIVATION BETWEEN INITIATIVE TYPES. THROUGH CASE STUDIES OF TWO ONLINE INITIATIVES, MY DISSERTATION RESEARCH EXPLORES RELATIONSHIPS BETWEEN INITIATIVE STRUCTURE, ENGAGEMENT, AND PARTICIPANT ROLE ON MOTIVATION.
INTELLIGENT TUTORING SYSTEMS (ITSS) AIM TO PROVIDE PERSONALIZED CONTENTS TO STUDENTS CONTRIBUTING TO CREATE AN ADAPTIVE AND INNOVATIVE CHOICE TO LEARN. THESE CONTENTS ARE USUALLY EXERCISES. ITSS MAKE USE OF DIFFERENT TYPES OF DATA, STUDENT MODELS AND MATHEMATICAL AND PROBABILISTIC ALGORITHMS TO ACHIEVE THE ADAPTATION. ONE OF THE MAIN INFORMATION USED FOR ADAPTATION IS THE SKILLS OF THE STUDENT. THIS PAPER MAKES A REVIEW OF FOUR MAIN GENERAL METHODS USED FOR SKILL MODELLING AND ADAPTATION: KNOWLEDGE SPACES, ITEM RESPONSE THEORY, BAYESIAN NETWORKS AND SEMANTIC SOLUTIONS. IN ADDITION, WE PROVIDE EXAMPLES ABOUT ITSS THAT USE THESE TECHNIQUES AND ANALYZE THE ADVANTAGES AND DISADVANTAGES OF EACH METHOD.
ADVANCES IN TECHNOLOGY IN RECENT YEARS HAVE CHANGED THE LEARNING BEHAVIORS OF LEARNERS AND RESHAPED TEACHING METHODS. THIS HAD RESULTED IN SEVERAL CHALLENGES FACED BY CURRENT EDUCATIONAL SYSTEMS, SUCH AS AN INCREASED FOCUS ON INFORMAL LEARNING, A GROWING GAP OF PRIOR KNOWLEDGE AMONG STUDENTS IN CLASSROOMS AND A MISMATCH BETWEEN INDIVIDUAL CAREER CHOICES AND THE DEVELOPMENT OF THE WORK FORCE. THIS PAPER LOOKS AT THESE CHALLENGES WITH A VIEW TOWARDS REVOLUTIONIZING CURRENT LEARNING ENVIRONMENTS TO SMART LEARNING ENVIRONMENTS AND PROVIDES NEW SUGGESTIONS FOR TECHNOLOGICAL SOLUTIONS. FURTHERMORE, THIS PAPER ARGUES FOR A TRANSFORMATION FROM THE CURRENT LEARNING ENVIRONMENTS TO SMART LEARNING ENVIRONMENTS. THIS IS TO BE ACHIEVED BY REENGINEERING THE FUNDAMENTAL STRUCTURE AND OPERATIONS OF CURRENT EDUCATIONAL SYSTEMS TO BETTER INTEGRATE THESE NEW TECHNOLOGIES WITH THE REQUIRED PEDAGOGICAL SHIFT. THE FUTURE PERSPECTIVES OF SMART LEARNING ENVIRONMENTS ARE REVIEWED AND SHARED, THROUGH EXAMPLES OF EMERGING INNOVATIONS SUCH AS THE FLIPPED CLASSROOM, GAME BASED LEARNING, GESTURE BASED LEARNING, ALONG WITH PEDAGOGICAL SHIFTS, SUCH AS LIFE-LONG LEARNING PORTFOLIO MAINTENANCE, TEAM TEACHING, AND SEPARATION OF LEARNING AND COMPETENCY ASSESSMENT.
COURSE INSTRUCTORS NEED TO BE ABLE TO IDENTIFY STUDENTS IN NEED OF ASSISTANCE AS EARLY IN THE COURSE AS POSSIBLE. RECENT WORK HAS SUGGESTED THAT MACHINE LEARNING APPROACHES APPLIED TO SNAPSHOTS OF SMALL PROGRAMMING EXERCISES MAY BE AN EFFECTIVE SOLUTION TO THIS PROBLEM. HOWEVER, THESE RESULTS HAVE BEEN OBTAINED USING DATA FROM A SINGLE INSTITUTION, AND PRIOR WORK USING FEATURES EXTRACTED FROM STUDENT CODE HAS BEEN HIGHLY SENSITIVE TO DIFFERENCES IN CONTEXT. THIS WORK PROVIDES TWO CONTRIBUTIONS: FIRST, A PARTIAL REPRODUCTION OF PREVIOUSLY PUBLISHED RESULTS, BUT IN A DIFFERENT CONTEXT, AND SECOND, AN EXPLORATION OF THE EFFICACY OF NEURAL NETWORKS IN SOLVING THIS PROBLEM. OUR FINDINGS CONFIRM THE IMPORTANCE OF TWO FEATURES (THE NUMBER OF STEPS REQUIRED TO SOLVE A PROBLEM AND THE CORRECTNESS OF KEY PROBLEMS), INDICATE THAT MACHINE LEARNING TECHNIQUES ARE RELATIVELY STABLE ACROSS CONTEXTS (BOTH ACROSS TERMS IN A SINGLE COURSE AND ACROSS COURSES), AND SUGGEST THAT NEURAL NETWORK BASED APPROACHES ARE AS EFFECTIVE AS THE BEST BAYESIAN AND DECISION TREE METHODS. FURTHERMORE, NEURAL NETWORKS CAN BE TUNED TO BE RELIABLY PESSIMISTIC, SO THEY MAY SERVE A COMPLEMENTARY ROLE IN SOLVING THE PROBLEM OF IDENTIFYING STUDENTS WHO NEED ASSISTANCE.
THIS PAPER EXPLORES OPINION MINING USING SUPERVISED LEARNING ALGORITHMS TO FIND THE POLARITY OF THE STUDENT FEEDBACK BASED ON PRE-DEFINED FEATURES OF TEACHING AND LEARNING. THE STUDY CONDUCTED INVOLVES THE APPLICATION OF A COMBINATION OF MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING TECHNIQUES ON STUDENT FEEDBACK DATA GATHERED FROM MODULE EVALUATION SURVEY RESULTS OF MIDDLE EAST COLLEGE, OMAN. IN ADDITION TO PROVIDING A STEP BY STEP EXPLANATION OF THE PROCESS OF IMPLEMENTATION OF OPINION MINING FROM STUDENT COMMENTS USING THE OPEN SOURCE DATA ANALYTICS TOOL RAPID MINER, THIS PAPER ALSO PRESENTS A COMPARATIVE PERFORMANCE STUDY OF THE ALGORITHMS LIKE SVM, NAIVE BAYES, K NEAREST NEIGHBOR AND NEURAL NETWORK CLASSIFIER. THE DATA SET EXTRACTED FROM THE SURVEY IS SUBJECTED TO DATA PREPROCESSING WHICH IS THEN USED TO TRAIN THE ALGORITHMS FOR BINOMIAL CLASSIFICATION. THE TRAINED MODELS ARE ALSO CAPABLE OF PREDICTING THE POLARITY OF THE STUDENT COMMENTS BASED ON EXTRACTED FEATURES LIKE EXAMINATION, TEACHING ETC. THE RESULTS ARE COMPARED TO FIND THE BETTER PERFORMANCE WITH RESPECT TO VARIOUS EVALUATION CRITERIA FOR THE DIFFERENT ALGORITHMS.
COMPUTER PROGRAMMING IS ESSENTIAL IN ENGINEERING EDUCATION. WE ARE DEVELOPING A PROGRAMMING EDUCATION SUPPORT TOOL PGTRACER IN ORDER TO FACILITATE LEARNING PROCESS OF COMPUTER PROGRAMMING. PGTRACER UTILIZES FILL-IN-THE-BLANK QUESTION COMPOSED OF A PAIR OF A SOURCE PROGRAM AND A TRACE TABLE. WE PROPOSE A SET OF FEEDBACK FUNCTIONS FOR THE STUDENTS IN THIS PAPER. PGTRACER AUTOMATICALLY COLLECTS LEARNING LOG OF THE STUDENTS WHEN THEY FILL A BLANK. THE FEEDBACK FUNCTIONS PROVIDE VARIOUS ANALYSIS RESULT OF THE COLLECTED LOG SO THAT STUDENTS CAN EASILY UNDERSTAND THEIR ACHIEVEMENT LEVEL AND WEAK POINTS. MANY STUDENTS HIGHLY APPRECIATE THE FEEDBACK FUNCTIONS THROUGH AN EXPERIMENTAL EVALUATION OF THE FUNCTIONS.
MASSIVE OPEN ONLINE COURSES (MOOCS) CAME INTO THE EDUCATIONAL ECOSYSTEM ATTRACTING THE ATTENTION OF THE PUBLIC MEDIA, BUSINESSES, TEACHERS, AND LEARNERS FROM ALL OVER THE WORLD. THE ORIGINAL COURSES WERE COMPLETELY OPEN AND FREE, TARGETING THE WORLDWIDE POPULATION. HOWEVER, CURRENT MOOC PROVIDERS HAVE PIVOTED TOWARDS MORE PRIVATE DIRECTIONS, AND WE OFTEN FIND THAT MOOC MATERIALS ARE COMPLETELY CLOSED WITHIN THEIR HOSTING PLATFORMS AND CANNOT BE RETRIEVED FROM THEM BY THEIR LEARNERS. THIS DIMINISHES THE POTENTIAL OF MOOCS BY MAKING CONTENT AVAILABLE TO A SMALL PROPORTION OF LEARNERS AND SEVERELY LIMITS THE REUSABILITY OF THE EDUCATIONAL RESOURCES. IN THIS PAPER, WE PRESENT A PROCESS THAT WE CALL 'UNMOOCING', IN WHICH WE TRANSFORM THE RESOURCES OF A MOOC INTO OERS. WE TAUGHT A MOOC ON OPEN EDUCATION IN THE UNED ABIERTA PLATFORM, AND WE 'UNMOOCED' ALL OF ITS EDUCATIONAL RESOURCES, MAKING THEM AVAILABLE TO DOWNLOAD BY THE LEARNERS THAT ARE TAKING THE COURSE. THE RESULTS OF THE UNMOOCING WERE VERY ENCOURAGING: THE POSSIBILITY OF DOWNLOADING THE COURSE RESOURCES WAS THE MOST HIGHLY RATED COMPONENT OF THE COURSE. ADDITIONALLY, THE TWO UNMOOCED MATERIALS THAT WERE CONSIDERED AS MOST USEFUL (PRESENTATIONS AND CONTENTS IN A PDF) WERE DOWNLOADED BY 90% OF THE LEARNERS. NOW THAT THE MAJORITY OF MOOC PROVIDERS ARE MOVING TOWARDS A MORE CLOSED EDUCATIONAL APPROACH, WE BELIEVE THAT THIS PAPER SENDS A POWERFUL MESSAGE FOR BRINGING BACK THE ORIGINAL MOOC CONCEPT OF 'OPENNESS' WITH THE UNMOOCING PROCESS, THUS CONTRIBUTING TO THE WIDER DISSEMINATION AND DEMOCRATIZATION OF EDUCATION ACROSS THE GLOBE.
THE US ARMY MUST UPDATE ITS VEHICLE FLEET TO BE BETTER EQUIPPED FOR POTENTIAL FUTURE MILITARY CONFLICTS IN NORTHERN CLIMATES (US ARMY, 2017). THIS PROCESS INVOLVES CONSIDERING MANNED, OPTIONALLY MANNED, AND UNMANNED VEHICLES AS VIABLE OPTIONS IN THE FUTURE. OPTIONALLY MANNED AND UNMANNED VEHICLES IN THE ARMED FORCES HAVE SUBSTANTIAL BENEFITS BECAUSE THEY CAN OPERATE WITHOUT DIRECT DRIVER INPUT OR ARE ABLE TO PERFORM MISSIONS DEEMED TOO DANGEROUS FOR TROOPS. OPTIONALLY MANNED VEHICLES ALLOW THE DRIVER TO SHIFT SOME, OR ALL, FOCUS AWAY FROM THE TASK OF DRIVING THE VEHICLE. IN SOME CASES, THESE AUTONOMOUS VEHICLES MAY PERFORM BETTER THAN A HUMAN DRIVER BY RAPIDLY SENSING AND REACTING TO TERRAIN CHANGES. ONBOARD SENSING AND DECISION MAKING ARE EQUALLY APPLICABLE TO BOTH FULLY AUTONOMOUS AND TELEOPERATED VEHICLES. THIS WORK WILL FOCUS ON THE TERRAIN SENSING, WAYPOINT NAVIGATION, AND TELEOPERATION POTENTIAL OF AN OPTIONALLY MANNED OR UNMANNED VEHICLE. RESULTS FROM A VEHICLE DEMONSTRATION ON TWO DIFFERENT TERRAIN CONDITIONS WILL PROVIDE THE BASIS FOR ADDITIONAL TERRAIN SENSING AND AUTONOMOUS VEHICLE DEVELOPMENT WORK IN THE COMING YEAR. (C) 2021 PUBLISHED BY ELSEVIER LTD ON BEHALF OF ISTVS.
RUBRICS ARE WIDELY USED THROUGHOUT POSTSECONDARY EDUCATION AS MEANS FOR AIDING IN INSTRUCTION AND EVALUATION. HOWEVER, DESPITE THEIR BROAD GLOBAL ADOPTION, VERY LITTLE IS KNOWN ABOUT THE QUALITY OF RUBRICS IN USE. WE DEVELOP TWO MEASURES TO ASSESS THE QUALITY OF RUBRICS: (1) A CHECKLIST IDENTIFYING CRITERIA OF HIGH-QUALITY RUBRICS BASED ON ANALYTIC RUBRIC DESIGN BEST PRACTICES AND (2) A SET OF LMS-DERIVED FEATURES THAT ARE HYPOTHESIZED TO REPRESENT STRUCTURAL COMPONENTS THAT ARE, IN GENERAL, NECESSARY BUT NOT SUFFICIENT FOR HIGH QUALITY RUBRICS. THE VALIDITY OF USING THE FEATURE-GENERATED SCORES AS PROXIES FOR IDENTIFYING RUBRIC QUALITY IS EVALUATED THROUGH SEVERAL MEANS. FIRST, THE FEATURE-GENERATED SCORES ARE CALCULATED FOR A SET OF EXTERNAL EXEMPLARY RUBRICS OF KNOWN HIGH QUALITY. SECOND, THE FEATURE-SCORES FOR A SUBSET OF INTERNAL RUBRICS ARE COMPARED TO AVERAGE HUMAN RATER SCORES OF RUBRIC QUALITY BASED ON THE CHECKLIST. WE DISCUSS THE RESULTS, PRACTICAL APPLICATIONS, AND A LARGER RESEARCH PROGRAM SURROUNDING THE FEATURE-GENERATED SCORES.
HOW DO THE FEATURES OF A LEARNING ENVIRONMENT'S USER INTERFACE IMPACT LEARNERS' AGENCY AND, FURTHER, THEIR LEARNING? WE EXPLORED THIS QUESTION IN THE CONTEXT OF DECIMAL POINT, A DIGITAL LEARNING GAME DESIGNED TO SUPPORT MIDDLE SCHOOL STUDENTS IN LEARNING DECIMALS. PREVIOUS STUDIES OF THE GAME SHOWED THAT GIVING STUDENTS THE ABILITY TO CHOOSE THE ORDER AND NUMBER OF MINI-GAMES TO PLAY DID NOT SIGNIFICANTLY IMPACT THEIR LEARNING OUTCOMES COMPARED TO A CONDITION WITHOUT CHOICE. IN THIS PAPER WE EXPLORE WHETHER SOME ELEMENTS OF THE GAME'S INTERFACE MAY HAVE INADVERTENTLY EXERTED INDIRECT CONTROL OVER STUDENTS' CHOICE, LEADING TO THE PREVIOUS EFFECTS. WE CONDUCTED A CLASSROOM STUDY USING A NEW VERSION OF THE GAME THAT VARIED WHETHER STUDENTS SAW A VISUAL PATH CONNECTING MINI-GAMES ON THE GAME MAP TO MODULATE THE LEVEL OF INDIRECT CONTROL STUDENTS WOULD EXPERIENCE WITH AN IMPLIED ORDERING. ULTIMATELY, WE FOUND THAT STUDENTS IN THE NO-LINE CONDITION EXERCISED SIGNIFICANTLY MORE AGENCY BUT DID NOT LEARN ANY LESS THAN THE LINE CONDITION. THESE RESULTS SUGGEST THAT INDIRECT CONTROL CAN BE A SUBTLE BUT POWERFUL WAY TO DIRECT STUDENT ATTENTION IN DIGITAL LEARNING GAMES.
TEACHERS ARE OFTEN ENCOURAGED TO ADOPT DIFFERENT POSITIONING STRATEGIES AT VARIOUS STAGES OF A CLASSROOM LESSON AS EACH CAN INFLUENCE LEARNERS IN DIFFERENT WAYS. HOWEVER, LITTLE WORK HAS BEEN DONE TO MAKE EVIDENCE OF THE USE OF CLASSROOMS VISIBLE TO TEACHERS AND STUDENTS. AS SENSORS DROP IN PRICE, IT IS BECOMING MORE VIABLE TO CAPTURE TRACES OF THE USE OF THE PHYSICAL CLASSROOM SPACE AUTOMATICALLY. IN THIS PAPER, WE BUILD ON THE NOTION OF SPATIAL PEDAGOGY TO PROPOSE AN APPROACH TO VISUALISE DIGITAL TRACES OF TEACHER POSITIONING IN THE CLASSROOM. WE ILLUSTRATE OUR APPROACH THROUGH AN AUTHENTIC CASE STUDY OF A TEACHER ENACTING THREE DISTINCTIVE LEARNING DESIGNS. WE DOCUMENT THE TEACHER'S AND STUDENTS' REACTIONS TO VISUAL REPRESENTATIONS OF POSITIONING DATA TO EXPLORE THEIR POTENTIAL AS PROXIES OF SPATIAL PEDAGOGY.
GENERAL CHEMISTRY IS A GATEWAY COURSE THAT IMPACTS THE STEM TRAJECTORY OF TENS OF THOUSANDS OF STUDENTS EACH YEAR, AND ITS ROLE IN THE INTRODUCTORY CURRICULUM AS WELL AS ITS PEDAGOGICAL DESIGN ARE THE CENTER OF AN ONGOING DEBATE. TO INVESTIGATE THE ROLE OF GENERAL CHEMISTRY IN THE CURRICULUM, WE REPORT THE RESULTS OF A POSTHOC ANALYSIS OF 10 YEARS OF ARCHIVED STUDENT DATA (CA. 12,000 STUDENTS) AIMED AT DISCOVERING WHETHER TAKING COLLEGE-LEVEL GENERAL CHEMISTRY HAS AN IMPACT ON STUDENT PERFORMANCE IN AND PROGRESSION TO SUBSEQUENT CHEMISTRY COURSES. WE INTRODUCE REGRESSION DISCONTINUITY; A QUASI-EXPERIMENTAL METHOD THAT ORIGINATED IN EDUCATIONAL PSYCHOLOGY AND CAN BE USED TO ESTIMATE CAUSAL IMPACTS OF EDUCATIONAL TREATMENT WHEN EX ANTE RANDOMIZATION IS NOT FEASIBLE. USING REGRESSION DISCONTINUITY, WE ESTIMATED A POSITIVE IMPACT OF PRIOR GENERAL CHEMISTRY COURSEWORK OF ABOUT ONE-FOURTH OF A LETTER GRADE IN TWO SUBSEQUENT COURSES, ORGANIC CHEMISTRY I AND A PHYSICAL GENERAL CHEMISTRY COURSE FOR NONMAJORS. WE ALSO FOUND THAT TAKING GENERAL CHEMISTRY HAD NO IMPACT ON THE LIKELIHOOD THAT STUDENTS WILL PROGRESS TO THE FINAL COURSE IN THE NONMAJORS SEQUENCE.
IT IS DIFFICULT TO OVERSTATE THE IMPORTANCE OF LITERACY FOR ADEQUATE FUNCTIONING IN SOCIETY, FROM EDUCATIONAL ATTAINMENT AND EMPLOYMENT OPPORTUNITIES TO HEALTH OUTCOMES. WE CREATED A READING APP WITH THE GOAL OF HELPING READERS IMPROVE THEIR READING SKILL WHILE READING FOR MEANING AND PLEASURE, AND USED IT TO COLLECT UNIQUE DATA ON CHILDREN'S EXTENDED READING. ANALYSIS OF THE DATA REVEALS THE IMPORTANCE OF A BEHAVIORAL FACTOR IN UNDERSTANDING OBSERVED READING PERFORMANCE.
THIS PAPER PROPOSES A NEW APPROACH TO TRANSLATE LEARNER DATA INTO SELF-REGULATED LEARNING SUPPORT. LEARNING PHASES IN BLENDED CLASSROOMS PLACE UNIQUE REQUIREMENTS ON STUDENTS' SELF-REGULATED LEARNING (SRL). LEARNING PATH GRAPHS MERGE MOMENT-BY-MOMENT LEARNING CURVES AND LEARNING PHASE DATA TO UNDERSTAND STUDENT' SRL SUPPORT NEEDS. RESULTS INDICATE 4 GROUPS WITH DIFFERENT SRL SUPPORT NEEDS. STUDENTS IN THE SELF-REGULATED LEARNING GROUP ARE CAPABLE OF LEARNING WITHOUT EXTERNAL REGULATION. IN THE TEACHER REGULATION GROUP STUDENTS NEED INITIAL TEACHER REGULATION BUT RELY ON SRL THEREAFTER. STUDENTS IN THE SYSTEM REGULATION GROUP REQUIRE TEACHER AND SYSTEM REGULATION TO LEARN. FINALLY, THE ADVANCED SYSTEM SUPPORT GROUP IS IN NEED OF SUPPORT BEYOND THE CURRENT LEVEL OF SYSTEM REGULATION. BASED ON THESE INSIGHTS, THE APPLICATION OF PERSONALIZED DASHBOARDS AND HYBRID HUMAN-SYSTEM REGULATION IS FURTHER SPECIFIED.
GAPS BETWEEN KNOWLEDGE SOURCES ARE INTERESTING TO VARIOUS STAKEHOLDERS: THEY MIGHT INDICATE POTENTIAL MISCONCEPTIONS AWAITING CORRECTION, COMPLEX OR NOVEL KNOWLEDGE THAT REQUIRES CAREFUL DELIVERY OR STUDYING. MOTIVATED BY THESE UNDERLYING VALUES, THIS STUDY EXPLORES THE KNOWLEDGE GAP PHENOMENON IN THE CONTEXT OF STUDENT TEXTUAL RESPONSES. IN THE METHOD PROPOSED IN THIS STUDY, DISCOURSES ARE FIRST MAPPED INTO STRUCTURED KNOWLEDGE SPACES WHERE GAPS BETWEEN CORRECT/INCORRECT RESPONSES AND ASSESSED KNOWLEDGE ARE MEASURED BY NETWORK-BASED METRICS. EMPIRICAL RESULTS DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED METHOD IN MEASURING GAPS IN STUDENT RESPONSES. THE NETWORKED REPRESENTATION OF TEXTS PROPOSED IN THIS STUDY IS NOVEL IN QUANTITATIVELY FRAMING GAPS OF KNOWLEDGE. IT ALSO OFFERS A SET OF VALIDATED METRICS FOR ANALYZING STUDENT RESPONSES IN RESEARCH AND PRACTICE.
THIS STUDY EXPLORES THE APPLICATION OF COMPUTATIONAL TECHNIQUES TO EXTRACT INFORMATION ABOUT DENTAL STUDENTS' DEVELOPING CONCEPTIONS OF THEIR PROFESSION FROM DIGITAL REFLECTIVE JOURNAL ENTRIES. TOP CONCEPT NETWORKS WERE CREATED FOR TWO COHORTS OF STUDENTS AT THE BEGINNING AND END OF THEIR FOUR-YEAR PROGRAM. A SHIFT FROM A COLLECTION OF GENERAL NOTIONS ABOUT BECOMING A PROFESSIONAL TO A MORE INTEGRATED, PATIENT-CENTERED CONCEPTUALIZATION WAS FOUND FOR BOTH COHORTS. THE TWO GROUPS INITIALLY DIFFERED IN THEIR PERCEPTION OF DENTAL SCHOOL (A MECHANISM FOR BEING ABLE TO WORK AS A DENTIST VERSUS A PLACE TO LEARN THE SKILLS TO SERVE PATIENTS WELL) AND SUBSEQUENTLY IN THE EXTENT OF ATTENTION THEY PAID TO THE FEELINGS OF THEIR PATIENTS AND THEMSELVES, AS WELL AS THE CONTINUAL GROWTH OF SKILL AFTER GRADUATION. SEVERAL USEFUL LINGUISTIC MARKERS WERE IDENTIFIED FOR EXAMINING THESE SAME ISSUES IN OTHER COHORTS. THE RESULTS SUGGEST THAT TOP CONCEPT NETWORKS CAN OFFER A USEFUL WINDOW INTO STUDENTS' DEVELOPING CONCEPTIONS OF THEIR PROFESSION. THIS KIND OF INFORMATION CAN SUPPORT STUDENT SUCCESS ON A MACRO LEVEL BY OFFERING FEEDBACK ON EXISTING CURRICULA / INFORMING LEARNING DESIGNS TO CULTIVATE DESIRED CONCEPTIONS, AND ON A MICRO LEVEL THROUGH IDENTIFYING PARTICULAR WAYS INDIVIDUALS ALIGN WITH AND DIVERGE FROM THE COMMON TRAJECTORIES.
THIS STUDY EXAMINES THE EXTENT TO WHICH INSTRUCTIONS TO SELF-EXPLAIN VS. OTHER-EXPLAIN A TEXT LEAD READERS TO PRODUCE DIFFERENT FORMS OF EXPLANATIONS. NATURAL LANGUAGE PROCESSING WAS USED TO EXAMINE THE CONTENT AND CHARACTERISTICS OF THE EXPLANATIONS PRODUCED AS A FUNCTION OF INSTRUCTION CONDITION. UNDERGRADUATE STUDENTS (N = 146) TYPED EITHER SELF-EXPLANATIONS OR OTHER-EXPLANATIONS WHILE READING A SCIENCE TEXT. THE LINGUISTIC PROPERTIES OF THESE EXPLANATIONS WERE CALCULATED USING THREE AUTOMATED TEXT ANALYSIS TOOLS. MACHINE LEARNING CLASSIFIERS IN COMBINATION WITH THE FEATURES WERE USED TO PREDICT INSTRUCTION CONDITION (I.E., SELF-OR OTHER-EXPLANATION). THE BEST MACHINE LEARNING MODEL PERFORMED AT RATES ABOVE CHANCE (KAPPA = .247; ACCURACY = 63%). FOLLOW-UP ANALYSES INDICATED THAT STUDENTS IN THE SELF-EXPLANATION CONDITION GENERATED EXPLANATIONS THAT WERE MORE COHESIVE AND THAT CONTAINED WORDS THAT WERE MORE RELATED TO SOCIAL ORDER (E.G., ETHICS). OVERALL, THE RESULTS SUGGEST THAT NATURAL LANGUAGE PROCESSING TECHNIQUES CAN BE USED TO DETECT SUBTLE DIFFERENCES IN STUDENTS' PROCESSING OF COMPLEX TEXTS.
ESTABLISHING SMALL LEARNING GROUPS IN ONLINE COURSES IS A POSSIBLE WAY TO FOSTER COLLABORATIVE KNOWLEDGE BUILDING IN AN ENGAGING AND EFFECTIVE LEARNING COMMUNITY. TO ENABLE GROUP ACTIVITIES IT IS NOT ENOUGH TO DESIGN COLLABORATIVE TASKS AND TO PROVIDE COLLABORATION TOOLS FOR ONLINE SCENARIOS. COLLABORATION IN SUCH LEARNING GROUPS IS PRONE TO FAIL OR EVEN NOT TO BE INITIATED WITHOUT EXPLICIT GUIDANCE. IN THE TARGET SITUATIONS, INTERVENTIONS AND GUIDING MECHANISMS HAVE TO SCALE WITH A GROWING NUMBER OF COURSE PARTICIPANTS. TO ACHIEVE THIS UNDER PRIVACY CONSTRAINTS, WE AIM AT IDENTIFYING TARGET INDICATORS FOR WELL-FUNCTIONING GROUP WORK THAT DO NOT RELY ON ANY KIND OF INFORMATION ABOUT INDIVIDUAL LEARNERS.
IDENTIFYING ENROLLMENT PATTERNS ASSOCIATED WITH COURSE SUCCESS CAN HELP EDUCATORS DESIGN BETTER DEGREE PLANS, AND STUDENTS MAKE INFORMED DECISIONS ABOUT FUTURE ENROLLMENTS. WHILE DISCRIMINATING PATTERN MINING TECHNIQUES CAN BE USED TO ADDRESS THIS PROBLEM, COURSE ENROLLMENT PATTERNS INCLUDE SEQUENCE AND QUANTITY (GRADES) INFORMATION. NONE OF THE EXISTING METHODSWERE DESIGNED TO ACCOUNT FOR BOTH FACTORS. IN THIS WORK WE PRESENT UPM, A UNIVERSAL DISCRIMINATING PATTERN MINING FRAMEWORK THAT SIMULTANEOUSLY MINES VARIOUS TYPES OF ENROLLMENT PATTERNS WHILE ACCOUNTING FOR SEQUENCE AND QUANTITY USING AN EXPANSION-SPECIFIC APPROACH. UNLIKE THE EXISTING METHODS, UPM EXPANDS A GIVEN PATTERN WITH AN ITEM BYFINDING A MINIMUM-ENTROPY SPLIT OVER THE ITEM'S QUANTITIES. WE THEN USE UPM TO EXTRACT DISCRIMINATING ENROLLMENT PATTERNS FROM THE HIGH AND THE LOW PERFORMING STUDENT GROUPS. THESE PATTERNS CAN BE UTILIZED BY EDUCATORS FOR DEGREE PLANNING. TO EVALUATE THE QUALITY OF THE EXTRACTED PATTERNS, WE ADOPT A SUPERVISED CLASSIFICATION APPROACH WHERE WE APPLY VARIOUS CLASSIFICATION TECHNIQUES TO LABEL STUDENTS ACCORDING THO THEIR PERFORMANCE BASED ON THE EXTRACTED PATTERNS. OUR EVALUATION SHOWS THAT THE CLASSIFICATION ACCURACIES OBTAINED USING THE UPM EXTRACTED PATTERNS ARE HIGHER THAN THE ACCURACIES OBTAINED USING PATTERNS EXTRACTED BY OTHER TECHNIQUES. ACCURACY IMPROVES SIGNIFICANTLY FOR STUDENTS WITH LARGER NUMBERS OF PATTERNS. MOREOVER, EXPANSION-SPECIFIC QUANTITATIVE MINING LEADS TO MORE ACCURATE CLASSIFICATIONS THAN THE METHODS THAT DO NOT ACCOUNT FOR QUANTITIES (GRADES).
YONSEI UNIVERSITY IN KOREA LAUNCHED AN EDUCATIONAL INNOVATION PROJECT ENTITLED "DATA-DRIVEN SMART-CONNECTED CAMPUS LIFE SERVICE", FOR WHICH STUDENT-RELATED DATA HAVE BEEN ACCUMULATED AT UNIVERSITY LEVEL SINCE SPRING OF 2015, AND DESCRIPTIVE, PREDICTIVE AND PRESCRIPTIVE MODELING HAVE BEEN CONDUCTED TO OFFER INNOVATIVE EDUCATION SERVICE TO STUDENTS. THE DATASET COVERS NOT ONLY CONVENTIONAL STUDENT INFORMATION, STUDENT QUESTIONNAIRE SURVEY, AND UNIVERSITY ADMINISTRATIVE DATA, BUT ALSO UNCONVENTIONAL DATA SETS SUCH AS STUDENT LOCATION DATA AND LEARNING MANAGEMENT SYSTEM (LMS) LOG DATA. BASED ON THE DATASETS, WITH RESPECT TO 4,000+ FRESHMAN STUDENTS AT RESIDENTIAL COLLEGE, WE CONDUCTED PRELIMINARY IMPLEMENTATION OF DESCRIPTIVE AND PREDICTIVE MODELING FOR STUDENT ACHIEVEMENT, SATISFACTION, AND MENTAL HEALTH. THE RESULTS WERE OVERALL PROMISING. FIRST, DESCRIPTIVE AND PREDICTIVE MODELING OF GPA FOR STUDENT ACHIEVEMENT PRESENTED A LIST OF SIGNIFICANT PREDICTIVE VARIABLES FROM STUDENT LOCATIONS AND LMS ACTIVITIES. SECOND, DESCRIPTIVE MODELING OF STUDENT SATISFACTION REVEALED INFLUENTIAL VARIABLES SUCH AS "IMPROVEMENT OF CREATIVITY" AND "ABILITY OF COOPERATION". THIRD, SIMILAR DESCRIPTIVE MODELING WAS APPLIED TO STUDENTS' MENTAL HEALTH CHANGES BY SEMESTERS, AND THE STUDY UNCOVERED INFLUENTIAL FACTORS SUCH AS "DIFFICULTY WITH RELATIONSHIP" AND "TIME SPENT WITH FRIENDS INCREASED' AS KEY DETERMINANTS OF STUDENT MENTAL HEALTH. ALTHOUGH THE EDUCATIONAL INNOVATION PROJECT IS STILL IN ITS EARLY STAGES, WE HAVE THREE STRATEGIES OF THE FUTURE MODELLING EFFORTS: THEY ARE: (1) STEP-BY-STEP IMPROVEMENT FROM DESCRIPTIVE, PREDICTIVE, TO PRESCRIPTIVE MODELLING; (2) FULL USE OF RECURRING DATA ACQUISITION; (3) DIFFERENT LEVEL OF SEGMENTATION.
THERE HAS BEEN LONG-STANDING STEREOTYPES ON MEN AND WOMEN'S COMMUNICATION STYLES, SUCH AS MEN USING MORE ASSERTIVE OR AGGRESSIVE LANGUAGE AND WOMEN SHOWING MORE AGREEABLENESS AND EMOTIONS IN INTERACTIONS. IN THE CONTEXT OF COLLABORATIVE LEARNING, MALE LEARNERS OFTEN BELIEVED TO BE MORE ACTIVE PARTICIPANTS WHILE FEMALE LEARNERS ARE LESS ENGAGED. TO FURTHER EXPLORE GENDER DIFFERENCES IN LEARNERS COMMUNICATION BEHAVIOR AND WHETHER IT HAS CHANGED IN THE CONTEXT OF ONLINE SYNCHRONOUS COLLABORATION, WE EXAMINED STUDENTS INTERACTIONS AT A SOCIOCOGNITIVE LEVEL WITH A METHODOLOGY CALLED GROUP COMMUNICATION ANALYSIS (GCA). WE FOUND THAT THERE WERE NO SIGNIFICANT DIFFERENCES BETWEEN MEN AND WOMEN IN THE DEGREE OF PARTICIPATION. HOWEVER, WOMEN EXHIBITED SIGNIFICANTLY HIGHER AVERAGE SOCIAL IMPACT, RESPONSIVITY AND INTERNAL COHESION COMPARED TO MEN. WE ALSO COMPARED THE PROPORTION OF LEARNERS INTERACTION PROFILES, AND RESULTS SUGGEST THAT WOMEN ARE MORE LIKELY TO BE EFFECTIVE AND COHESIVE COMMUNICATORS. WE DISCUSSED IMPLICATIONS OF THESE FINDINGS FOR PEDAGOGICAL PRACTICES TO PROMOTE INCLUSIVITY AND EQUITY IN COLLABORATIVE LEARNING ONLINE.
SCIENCE EDUCATION REFORMS IN THE UNITED STATES CALL FOR AN EMPHASIS ON TEACHING OF SCIENTIFIC PRACTICES, SUCH AS INQUIRY. PREVIOUS WORK EXAMINED EXPERT VERSUS NOVICE PRACTICES IN AUTHENTIC SCIENCE INQUIRY AND FOUND ALTHOUGH EXPERTS HAVE FAIRLY CONSISTENT INQUIRY STRATEGIES, NOVICES EXIST ON A CONTINUUM. IN THIS PAPER, WE EXTEND OUR PREVIOUS QUALITATIVE WORK TO QUANTITATIVELY ANALYZE DIFFERENCES IN INQUIRY PRACTICES AMONG NOVICES. USING CLUSTERING ANALYSIS, WE FOUND THAT NON-SCIENCE MAJORS WHO PERFORMED SIMPLE INVESTIGATIONS TENDED TO CLUSTER TOGETHER AND BIOLOGY MAJORS WHO PERFORMED COMPLEX INVESTIGATIONS ALSO TENDED TO CLUSTER TOGETHER. WE OBSERVED TWO ADDITIONAL CLUSTERS THAT CONTAIN BOTH NON-SCIENCE MAJORS AND BIOLOGY MAJORS, BUT WHO PERFORMED DISTINCT INQUIRY STRATEGIES. THIS RAISES SOME CRITICAL QUESTIONS ABOUT HOW TO PEDAGOGICALLY TARGET STUDENTS WITHIN EACH CLUSTER.
IT IS WIDELY ACKNOWLEDGED LANGUAGE WE USE REFLECTS NUMEROUS PSYCHOLOGICAL CONSTRUCTS, INCLUDING OUR THOUGHTS, FEELINGS, AND DESIRES. CAN THE SO CALLED "NONCOGNITIVE" TRAITS WITH KNOWN LINKS TO SUCCESS, SUCH AS GROWTH MINDSET, LEADERSHIP ABILITY, AND INTRINSIC MOTIVATION, HE SIMILARLY REVEALED THROUGH LANGUAGE? WE INVESTIGATED THIS QUESTION BY ANALYZING STUDENTS' 150-WORD OPEN-ENDED DESCRIPTIONS OF THEIR OWN EXTRACURRICULAR ACTIVITIES OR WORK EXPERIENCES INCLUDED IN THEIR COLLEGE APPLICATIONS. WE USED THE COMMON APPLICATION-NATIONAL STUDENT CLEARINGHOUSE DATA SET, A SIX-YEAR LONGITUDINAL DATASET THAT INCLUDES COLLEGE APPLICATION DATA AND GRADUATION OUTCOMES FOR 278,201 U.S. HIGH-SCHOOL STUDENTS. WE FIRST DEVELOPED A CODING SCHEME FROM A STRATIFIED SAMPLE OF 4,000 ESSAYS AND USED IT TO CODE SEVEN TRAITS: GROWTH MINDSET, PERSEVERANCE, GOAL ORIENTATION, LEADERSHIP, PSYCHOLOGICAL CONNECTION (INTRINSIC MOTIVATION), SELF-TRANSCENDENT (PROSOCIAL) PURPOSE, AND TEAM ORIENTATION, ALONG WITH EARNED ACCOLADES. THEN, WE USED STANDARD CLASSIFIERS WITH HAG-OF-N-GRAMS AS FEATURES AND DEEP LEARNING TECHNIQUES (RECURRENT NEURAL NETWORKS) WITH WORD EMBEDDINGS TO AUTOMATE THE CODING. THE MODELS DEMONSTRATED CONVERGENT VALIDITY WITH THE HUMAN CODING WITH AUCS RANGING FROM.770 TO.925 AND CORRELATIONS RANGING FROM.418 TO.734. THERE WAS ALSO EVIDENCE OF DISCRIMINANT VALIDITY IN THE PATTERN OF INTER CORRELATIONS (7.- S BETWEEN-.206 TO.306) FOR BOTH HUMAN- AND MODEL- CODED TRAITS. FINALLY, THE MODELS DEMONSTRATED INCREMENTAL PREDICTIVE VALIDITY IN PREDICTING SIX-YEAR GRADUATION OUTCOMES NET OF SOCIODEMOGRAPHICS, INTELLIGENCE, ACADEMIC ACHIEVEMENT, AND INSTITUTIONAL GRADUATION RATES. WE CONCLUDE THAT LANGUAGE PROVIDES A LENS INTO NONCOGNITIVE TRAITS IMPORTANT FOR COLLEGE SUCCESS, WHICH CAN BE CAPTURED WITH AUTOMATED METHODS.
STUDENT LEARNING ACTIVITY IN MOOCS CAN BE VIEWED FROM MULTIPLE PERSPECTIVES. WE PRESENT A NEW ORGANIZATION OF MOOC LEARNER ACTIVITY DATA AT A RESOLUTION THAT IS IN BETWEEN THE FINE GRANULARITY OF THE CLICKSTREAM AND COARSE ORGANIZATIONS THAT COUNT ACTIVITIES, AGGREGATE STUDENTS OR USE LONG DURATION TIME UNITS. A DETAILED ACCESS TRAJECTORY (DAT) CONSISTS OF BINARY VALUES AND IS TWO DIMENSIONAL WITH ONE AXIS THAT IS A TIME SERIES, AND THE OTHER THAT IS A CHRONOLOGICALLY ORDERED LIST OF A MOOC COMPONENT TYPE'S INSTANCES, VIDEOS IN INSTRUCTIONAL ORDER, FOR EXAMPLE. MOST POPULAR MOOC PLATFORMS GENERATE DATA THAT CAN BE ORGANIZED AS DETAILED ACCESS TRAJECTORIES (DATS). WE EXPLORE THE VALUE OF DATS BY CONDUCTING FOUR EMPIRICAL MINI-STUDIES. OUR STUDIES SUGGEST DATS CONTAIN RICH INFORMATION ABOUT STUDENTS' LEARNING BEHAVIORS AND FACILITATE MOOC LEARNING ANALYSES.
THIS EXPLORATORY STUDY CHALLENGES THE CURRENT PRACTICES IN COGNITIVE LOAD MEASUREMENT BY USING MULTICHANNEL DATA TO INVESTIGATE COGNITIVE LOAD AFFORDANCES DURING ONLINE COMPLEX PROBLEM SOLVING. MOREOVER, IT IS AN ATTEMPT TO INVESTIGATE HOW COGNITIVE LOAD IS RELATED TO STRATEGY USE. ACCORDINGLY, IN THE CURRENT STUDY A WELL-AND AN ILL-STRUCTURED PROBLEM WERE DEVELOPED IN A VIRTUAL LEARNING ENVIRONMENT. ONLINE SUPPORT WAS PROVIDED. PARTICIPANTS WERE 15 STUDENTS FROM THE TEACHER TRAINING PROGRAM. THIS STUDY INCORPORATED SUBJECTIVE MEASUREMENTS OF STUDENTS' COGNITIVE LOAD (I.E., INTRINSIC, EXTRANEOUS, GERMANE LOAD AND THEIR MENTAL EFFORT) COMBINED WITH PHYSIOLOGICAL DATA CONTAINING GALVANIC SKIN RESPONSE (GSR) AND SKIN TEMPERATURE (ST). A FIRST AIM WAS TO INVESTIGATE WHETHER THERE WAS A SIGNIFICANT DIFFERENCE FOR THE SUBJECTIVE MEASUREMENTS, PHYSIOLOGICAL DATA AND CONSULTATION OF SUPPORT BETWEEN THE WELL-AND ILL-STRUCTURED PROBLEM. SECONDLY THIS STUDY INVESTIGATED HOW INDIVIDUAL DIFFERENCES OF SUBJECTIVE MEASUREMENTS ARE RELATED TO INDIVIDUAL DIFFERENCES OF PHYSIOLOGICAL DATA AND CONSULTATION OF SUPPORT. RESULTS REVEAL SIGNIFICANT DIFFERENCES FOR INTRINSIC LOAD, MENTAL EFFORT BETWEEN A WELL-AND ILL-STRUCTURED PROBLEM. MOREOVER, WHEN INVESTIGATING INDIVIDUAL DIFFERENCES, FINDINGS REVEAL THAT GSR MIGHT BE RELATED TO MENTAL EFFORT. ADDITIONALLY, RESULTS INDICATE THAT COGNITIVE LOAD INFLUENCES STRATEGY USE. FUTURE RESEARCH WITH LARGER SAMPLE SIZES SHOULD VERIFY THESE FINDINGS IN ORDER TO HAVE MORE INSIGHT INTO HOW WE CAN MEASURE COGNITIVE LOAD AND HOW ITS RELATED TO SELF-DIRECTED LEARNING. THESE INSIGHTS SHOULD ALLOW US TO PROVIDE ADAPTIVE SUPPORT IN VIRTUAL LEARNING ENVIRONMENTS.
IN RECENT YEARS, SEVERAL DATA-DRIVEN METHODS HAVE BEEN DEVELOPED TO HELP UNDERGRADUATE STUDENTS DURING COURSE SELECTION AND SEQUENCING. THESE METHODS TEND TO UTILIZE THE WHOLE SET OF PAST COURSE REGISTRATION DATA, REGARDLESS OF THE PAST STUDENTS' GRADUATION GPA AND TIME TO DEGREE (TTD). THOUGH SOME PREVIOUS WORK HAS SHOWN THROUGH THE RESULTS OF THEIR DEVELOPED MODELS THAT STUDENTS OF DIFFERENT GPA TEND TO TAKE COURSES IN DIFFERENT SEQUENCE, THE ACTUAL ANALYSIS OF THE DEGREE PLANS AND HOW/IF THEY RELATE TO THE STUDENTS' GRADUATION GPA AND TIME-TO-DEGREE HAS NOT RECEIVED MUCH ATTENTION. THIS STUDY ANALYZES HOW THE STUDENT'S ACADEMIC LEVEL WHEN THEY TAKE DIFFERENT COURSES, AS WELL AS THE PAIRWISE DEGREE SIMILARITY BETWEEN PAIRS OF STUDENTS RELATE TO THE STUDENTS' GRADUATION GPA AND TTD. OUR STUDY USES A LARGE-SCALE DATASET THAT CONTAINS 25 MAJORS FROM DIFFERENT COLLEGES AT THE UNIVERSITY OF MINNESOTA AND SPANS 16 YEARS. THE ANALYSIS SHOWS THAT TTD IS HIGHLY CORRELATED WITH BOTH THE TIMING AND ORDERING OF COURSES THAT STUDENTS FOLLOW IN THEIR DEGREE PLANS, WHILE THE CORRELATION BETWEEN GRADUATION GPA AND THE COURSE TIMING AND ORDERING IS NOT AS HIGH. WE ALSO PERFORM A CASE STUDY THAT USES COURSE TIMING AND ORDERING FEATURES TO PREDICT WHETHER THE STUDENT AT EACH SEMESTER WILL GRADUATE ON-TIME OR OVERTIME. THE RESULTS SHOW THAT CAREFUL CURRICULUM PLANNING IS NEEDED TO IMPROVE GRADUATION RATES IN UNIVERSITIES.
MOOCS HAVE DEVELOPED INTO MULTIPLE LEARNING DESIGN MODELS WITH A WIDE RANGE OF OBJECTIVES. TEACH-OUTS ARE ONE SUCH EXAMPLE, AIMING TO DRIVE MEANINGFUL DISCUSSIONS AROUND TOPICS OF PRESSING SOCIAL URGENCY WITHOUT THE USE OF FORMAL ASSESSMENTS. GIVEN THIS APPROACH, IT IS CRUCIAL TO EVALUATE LEARNERS' ENGAGEMENT IN THE DISCUSSION FORUM TO UNDERSTAND THEIR EXPERIENCES. THIS PAPER PRESENTS A PILOT STUDY THAT APPLIED UNSUPERVISED NATURAL LANGUAGE PROCESSING TECHNIQUES TO UNDERSTAND WHAT AND HOW STUDENTS ENGAGE IN DIALOGUE IN A TEACH-OUT. WE USED TOPIC MODELING TO DISCOVER THE EMERGING TOPICS IN THE DISCUSSION FORUMS AND EVALUATED THE ON-TOPICNESS OF THE DISCUSSIONS (I.E. THE DEGREE TO WHICH DISCUSSIONS WERE RELEVANT TO THE TEACH-OUT CONTENT). WE ALSO APPLIED CONTENT ANALYSIS TO INVESTIGATE THE SENTIMENTS ASSOCIATED WITH THE DISCUSSIONS. WE HAVE TAKEN A STEP TOWARD EXTRACTING STRUCTURE FROM STUDENTS' DISCUSSIONS TO UNDERSTAND LEARNING BEHAVIORS HAPPEN IN THE DISCUSSION FORUM. THIS IS THE FIRST STUDY TO ANALYZE DISCUSSION FORUMS IN A TEACH-OUT.
LEARNING MANAGEMENT SYSTEM (LMS) WEB LOGS PROVIDE GRANULAR, NEAR-REAL-TIME RECORDS OF STUDENT BEHAVIOR AS LEARNERS INTERACT WITH ONLINE COURSE MATERIALS IN DIGITAL LEARNING ENVIRONMENTS. HOWEVER, IT REMAINS UNCLEAR WHETHER LMS ACTIVITY INDEED REFLECTS BEHAVIORAL PROPERTIES OF STUDENT ENGAGEMENT, AND IT ALSO REMAINS UNCLEAR HOW TO DEAL WITH VARIABILITY IN LMS USAGE ACROSS A DIVERSITY OF COURSES. IN THIS STUDY, WE EVALUATE WHETHER INSTRUCTORS' SUBJECTIVE RATINGS OF THEIR STUDENTS' ENGAGEMENT ARE RELATED TO FEATURES OF LMS ACTIVITY FOR 9,021 STUDENTS ENROLLED IN 473 FOR-CREDIT COURSES. WE FIND THAT ESTIMATORS DERIVED FROM LMS WEB LOGS ARE CLOSELY RELATED TO INSTRUCTOR RATINGS OF ENGAGEMENT, HOWEVER, WE ALSO OBSERVE THAT THERE IS NOT A SINGLE GENERIC RELATIONSHIP BETWEEN ACTIVITY AND ENGAGEMENT, AND WHAT CONSTITUTES THE BEHAVIORAL COMPONENTS OF "ENGAGEMENT" WILL BE CONTINGENT ON COURSE STRUCTURE. HOWEVER, FOR MANY OF THESE COURSES, MODELED ENGAGEMENT SCORES ARE COMPARABLE TO INSTRUCTORS' RATINGS IN THEIR SENSITIVITY FOR PREDICTING ACADEMIC PERFORMANCE. AS LONG AS THEY ARE TUNED TO THE DIFFERENCES BETWEEN COURSES, ACTIVITY INDICES FROM LMS WEB LOGS CAN PROVIDE A VALID AND USEFUL PROXY MEASURE OF STUDENT ENGAGEMENT.
WITH CROSS-DISCIPLINARY ACADEMIC INTERESTS INCREASING AND ACADEMIC ADVISING RESOURCES OVER CAPACITY, THE IMPORTANCE OF EXPLORING DATA-ASSISTED METHODS TO SUPPORT STUDENT DECISION MAKING HAS NEVER BEEN HIGHER. WE BUILD ON THE FINDINGS AND METHODOLOGIES OF A QUICKLY DEVELOPING LITERATURE AROUND PREDICTION AND RECOMMENDATION IN HIGHER EDUCATION AND DEVELOP A NOVEL RECURRENT NEURAL NETWORK-BASED RECOMMENDATION SYSTEM FOR SUGGESTING COURSES TO HELP STUDENTS PREPARE FOR TARGET COURSES OF INTEREST, PERSONALIZED TO THEIR ESTIMATED PRIOR KNOWLEDGE BACKGROUND AND ZONE OF PROXIMAL DEVELOPMENT. WE VALIDATE THE MODEL USING TESTS OF GRADE PREDICTION AND THE ABILITY TO RECOVER PREREQUISITE RELATIONSHIPS ARTICULATED BY THE UNIVERSITY. IN THE THIRD VALIDATION, WE RUN THE FULLY PERSONALIZED RECOMMENDATION FOR STUDENTS THE SEMESTER BEFORE TAKING A HISTORICALLY DIFFICULT COURSE AND OBSERVE DIFFERENTIAL OVERLAP WITH OUR WOULD-BE SUGGESTIONS. WHILE NOT PROOF OF CAUSAL EFFECTIVENESS, THESE THREE EVALUATION PERSPECTIVES ON THE PERFORMANCE OF THE GOAL-BASED MODEL BUILD CONFIDENCE AND BRING US ONE STEP CLOSER TO DEPLOYMENT OF THIS PERSONALIZED COURSE PREPARATION AFFORDANCE IN THE WILD.
PRIVACY CONCERNS MAY LEAD PEOPLE TO OPT-IN OR OPT-OUT OF HAVING THEIR EDUCATIONAL DATA COLLECTED. THESE DECISIONS MAY IMPACT THE PERFORMANCE OF EDUCATIONAL PREDICTIVE MODELS. TO UNDERSTAND THIS, WE CONDUCTED A SURVEY TO DETERMINE THE PROPENSITY OF STUDENTS TO WITHHOLD OR GRANT ACCESS TO THEIR DATA FOR THE PURPOSES OF TRAINING PREDICTIVE MODELS. WE SIMULATED THE EFFECTS OF OPT-OUT ON THE ACCURACY OF EDUCATIONAL PREDICTIVE MODELS BY DROPPING A RANDOM SAMPLE OF DATA OVER A RANGE OF INCREMENTS, AND THEN CONTEXTUALIZE OUR FINDINGS USING THE SURVEY RESULTS. WE FIND THAT GRADE PREDICTIVE MODELS ARE FAIRLY ROBUST AND THAT KAPPA SCORES DO NOT DECREASE UNLESS THERE IS SIGNIFICANT OPT-OUT, BUT WHEN THERE IS, THE DETERIORATING PERFORMANCE DISPROPORTIONATELY AFFECTS CERTAIN SUBPOPULATIONS.
MOOCS AND ONLINE COURSES HAVE NOTORIOUSLY HIGH ATTRITION [1]. ONE CHALLENGE IS THAT IT CAN BE DIFFICULT TO TELL IF STUDENTS FAIL TO COMPLETE BECAUSE OF DISINTEREST OR BECAUSE OF COURSE DIFFICULTY. UTILIZING A DEEP KNOWLEDGE TRACING FRAMEWORK, WE ACCOUNT FOR STUDENT ENGAGEMENT BY INCLUDING COURSE INTERACTION COVARIATES. WITH THESE, WE FIND THAT WE CAN PREDICT A STUDENT'S NEXT ITEM RESPONSE WITH OVER 88% ACCURACY. USING THESE PREDICTIONS, TARGETED INTERVENTIONS CAN BE OFFERED TO STUDENTS AND TARGETED IMPROVEMENTS CAN BE MADE TO COURSES. IN PARTICULAR, THIS APPROACH WOULD ALLOW FOR GATING OF CONTENT UNTIL A STUDENT HAS REASONABLE LIKELIHOOD OF SUCCEEDING.
VIRTUAL HUMANS ARE FREQUENTLY USED TO HELP MEDICAL STUDENTS PRACTICE COMMUNICATION SKILLS. HERE, WE SHOW THAT COMMUNICATION SKILLS FEATURES DRAWN FROM THE LITERATURE ON BEST PRACTICES FOR DOCTOR-PATIENT COMMUNICATION CAN BE USED TO PREDICT STUDENT INTERVIEWERS' SUCCESS IN A GIVEN DOMAIN SKILL. WE ALSO DEMONSTRATE THE VIABILITY OF BAYESIAN RULE LISTS, AN INTERPRETABLE MACHINE LEARNING MODEL, FOR THIS USE CASE. BAYESIAN RULE LISTS' PREDICTIVE PERFORMANCE IS COMPARABLE TO THAT OF OTHER OTHER COMMONLY USED ALGORITHMS, INCLUDING DECISION TREES. THIS SUGGESTS THAT BAYESIAN RULE LISTS, WHICH PRODUCE SIMPLE, HUMAN-READABLE TRAINED BINARY CLASSIFIERS, MAY BE SUITABLE FOR PROVIDING FEEDBACK FOR EDUCATIONAL PURPOSES.
MASSIVE ONLINE COURSES OCCUPY AN IMPORTANT PLACE IN THE EDUCATIONAL LANDSCAPE OF TODAY. WE STUDY AN APPROACH TO SCALE PREDICTIVE ANALYTIC MODELS DERIVED FROM ONLINE COURSE DISCUSSION FOR A SPECIFICALLY THAT OF CONFUSION DETECTION-ONTO OTHER COURSES. THE PRIMARY CHALLENGE HERE IS THE LACK OF LABELED EXAMPLES IN A NEW COURSE AND THIS CALLS FOR UNSUPERVISED DOMAIN ADAPTATION (DA). AS A FIRST STEP IN EXPLORING DA IN THE EDUCATION DOMAIN, WE PROPOSE A SIMPLE ALGORITHM, DIAD, WHICH ADAPTS A CLASSIFIER TRAINED ON A COURSE WITH LABELED DATA BY SELECTIVELY CHOOSING INSTANCES FROM A NEW COURSE (WITH NO LABELED DATA) THAT ARE MOST DISSIMILAR TO THE COURSE WITH LABELED DATA AND ON WHICH THE CLASSIFIER IS VERY CONFIDENT OF CLASSIFICATION. OUR ALGORITHM IS EMPIRICALLY VALIDATED ON THE CONFUSION DETECTION TASK ACROSS MULTIPLE ONLINE COURSES. WE FIND THAT DIAD OUTPERFORMS OTHER METHODS ON THE TARGET DOMAIN, WHILE SHOWING A COMPARABLE PERFORMANCE TO A POPULAR METHOD THAT USES LABELED DATA FROM THE TARGET DOMAIN.
THIS STUDY EXAMINES FIDELITY OF RANKING AND RATING SCALES IN THE CONTEXT OF ONLINE PEER REVIEW AND ASSESSMENT. USING THE MONTE-CARLO SIMULATION TECHNIQUE, WE DEMONSTRATED THAT RATING SCALES OUTPERFORM RANKING SCALES IN REVEALING THE RELATIVE "TRUE" LATENT QUALITY OF THE PEER-ASSESSED ARTIFACTS VIA THE OBSERVED AGGREGATE PEER ASSESSMENT SCORES. OUR ANALYSIS FOCUSED ON A SIMPLE, SINGLE-ROUND PEER ASSESSMENT PROCESS AND TOOK INTO ACCOUNT PEER ASSESSMENT NETWORK TOPOLOGY, NETWORK SIZE, THE NUMBER OF ASSESSMENTS PER ARTIFACT, AND THE CORRELATION STATISTICS USED. THIS METHODOLOGY ALLOWS TO SEPARATE THE EFFECTS OF STRUCTURAL COMPONENTS OF PEER ASSESSMENT FROM COGNITIVE EFFECTS.
IT IS A COMMON PHENOMENON FOR STUDENTS TO LISTEN TO BACKGROUND MUSIC WHILE STUDYING. HOWEVER, THERE ARE MIXED AND INCONCLUSIVE K INDINGS IN THE LITERATURE, LEAVING IT UNCLEAR WHETHER AND IN WHICH CIRCUMSTANCES BACKGROUND MUSIC CAN FACILITATE OR HINDER LEARNING. THIS PAPER REPORTS A STUDY INVESTIGATING THE EFFECTS OF FIVE DIFFERENT TYPES OF BACKGROUND AUDIO (FOUR TYPES OF MUSIC AND ONE ENVIRONMENTAL SOUND) ON READING COMPREHENSION. AN EXPERIMENT WAS CONDUCTED WITH 33 GRADUATE STUDENTS, WHERE A SERIES OF COGNITIVE, METACOGNITIVE, AFFECTIVE VARIABLES AND PHYSIOLOGICAL SIGNALS WERE COLLECTED AND ANALYZED. PRELIMINARY RESULTS SHOW THAT THERE WERE DIFFERENCES ON THESE VARIABLES ACROSS DIFFERENT MUSIC TYPES. THIS STUDY CONTRIBUTES TO THE UNDERSTANDING AND OPTIMIZING OF BACKGROUND MUSIC FOR FACILITATING LEARNING.
THE CURRENT STUDY COMPARES TEXTUAL AND SYMBOLIC ELABORATED, RESPONSE-BASED FEEDBACK IN MATHEMATICS. WE USE A RANDOMIZED EXPERIMENT IN KHAN ACADEMY TO MEASURE FEEDBACK EFFECT IN FOUR DIFFERENT TOPICS. OVERALL, WE POINT OUT TO THE SUPERIORITY OF SYMBOLIC FEEDBACK.
MASSIVE OPEN ONLINE COURSES (MOOCS) OFTEN INCORPORATE LECTURE-BASED LEARNING ALONG WITH LECTURE NOTES, TEXTBOOKS, AND VIDEOS TO STUDENTS. MOREOVER, MOOCS ALSO INCORPORATE PRACTICE ACTIVITIES AND QUIZZES. STUDENT LEARNING IN MOOCS CAN BE TRACKED AND IMPROVED USING STATE-OF-THE-ART STUDENT MODELING. CURRENTLY, THIS MEANS EMPLOYING CONVENTIONAL STUDENT MODELS THAT ARE CONSTRUCTED AROUND INTELLIGENT TUTORING SYSTEMS (ITS). TRADITIONAL ITS SYSTEMS ONLY UTILIZE STUDENTS PERFORMANCE INTERACTIONS (QUIZ, PROBLEM-SOLVING OR PRACTICE ACTIVITIES). THEREFORE, TEXT INTERACTIONS ARE ENTIRELY IGNORED WHILE MODELING STUDENTS PERFORMANCE IN MOOCS USING THESE COGNITIVE MODELS. IN THIS WORK, WE PROPOSE A COMPREHENSION FACTOR ANALYSIS MODEL (CFM) FOR ONLINE COURSES, WHICH INTEGRATES STUDENT READING INTERACTIONS IN STUDENT MODELS TO TRACK AND PREDICT LEARNING OUTCOMES. OUR MODEL EVALUATION SHOWS THAT CFM OUTPERFORMS STATE-OF-THE-ART MODELS IN PREDICTING STUDENTS' PERFORMANCE IN A MOOC. THESE MODELS CAN HELP BETTER STUDENT-WISE ADAPTATION IN THE CONTEXT OF MOOCS.
IN A MASSIVE OPEN ONLINE COURSE (MOOC), PREDICTIVE MODELS OF STUDENT BEHAVIOR CAN SUPPORT MULTIPLE ASPECTS OF LEARNING, INCLUDING INSTRUCTOR FEEDBACK AND TIMELY INTERVENTION. ONGOING COURSES, WHEN THE STUDENT OUTCOMES ARE YET UNKNOWN, MUST RELY ON MODELS TRAINED FROM THE HISTORICAL DATA OF PREVIOUSLY OFFERED COURSES. IT IS POSSIBLE TO TRANSFER MODELS, BUT THEY OFTEN HAVE POOR PREDICTION PERFORMANCE. ONE REASON IS FEATURES THAT INADEQUATELY REPRESENT PREDICTIVE ATTRIBUTES COMMON TO BOTH COURSES. WE PRESENT AN AUTOMATED TRANSDUCTIVE TRANSFER LEARNING APPROACH THAT ADDRESSES THIS ISSUE. IT RELIES ON PROBLEM-AGNOSTIC, TEMPORAL ORGANIZATION OF THE MOOC CLICKSTREAM DATA, WHERE, FOR EACH STUDENT, FOR MULTIPLE COURSES, A SET OF SPECIFIC MOOC EVENT TYPES IS EXPRESSED FOR EACH TIME UNIT. IT CONSISTS OF TWO ALTERNATIVE TRANSFER METHODS BASED ON REPRESENTATION LEARNING WITH AUTO-ENCODERS: A PASSIVE APPROACH USING TRANSDUCTIVE PRINCIPAL COMPONENT ANALYSIS AND AN ACTIVE APPROACH THAT USES A CORRELATION ALIGNMENT LOSS TERM. WITH THESE METHODS, WE INVESTIGATE THE TRANSFERABILITY OF DROPOUT PREDICTION ACROSS SIMILAR AND DISSIMILAR MOOCS AND COMPARE WITH KNOWN METHODS. RESULTS SHOW IMPROVED MODEL TRANSFERABILITY AND SUGGEST THAT THE METHODS ARE CAPABLE OF AUTOMATICALLY LEARNING A FEATURE REPRESENTATION THAT EXPRESSES COMMON PREDICTIVE CHARACTERISTICS OF MOOCS.
WE STUDY THE USAGE OF A SELF-GUIDED ONLINE TUTORING PLATFORM CALLED ALGEBRA NATION, WHICH IS WIDELY BY MIDDLE SCHOOL AND HIGH SCHOOL STUDENTS WHO TAKE THE END-OF-COURSE ALGEBRA I EXAM AT THE END OF THE SCHOOL YEAR. THIS ARTICLE AIMS TO STUDY HOW THE PLATFORM CONTRIBUTES TO INCREASING STUDENTS' EXAM SCORES BY EXAMINING USERS' LOGS OVER A THREE YEAR PERIOD. THE PLATFORM UNDER CONSIDERATION WAS USED BY MORE THAN 36,000 STUDENTS IN THE FIRST YEAR, TO NEARLY 67,000 BY THE THIRD YEAR, THUS ENABLING US TO EXAMINE HOW USAGE PATTERNS EVOLVED AND INFLUENCED STUDENTS' PERFORMANCE AT SCALE. WE FIRST IDENTIFY WHICH ALGEBRA NATION USAGE FACTORS IN CONJUNCTION WITH MATH OVERALL PREPARATION AND SOCIOECONOMIC FACTORS CONTRIBUTE TO THE STUDENTS' EXAM PERFORMANCE. SUBSEQUENTLY, WE INVESTIGATE THE EFFECT OF INCREASED TEACHER FAMILIARITY LEVEL WITH THE ALGEBRA NATION ON STUDENTS' SCORES ACROSS DIFFERENT GRADES THROUGH MEDIATION ANALYSIS. THE RESULTS SHOW THAT THE INDIRECT EFFECT OF TEACHER'S FAMILIARITY WITH THE PLATFORM THROUGH INCREASING STUDENT'S USAGE DOSAGE IS MORE SIGNIFICANT IN HIGHER GRADES.
ONLINE LECTURES ARE AN INCREASINGLY POPULAR TOOL FOR LEARNING, YET RESEARCH ON INSTRUCTOR VISIBILITY DURING AN ONLINE LECTURE, AND STUDENTS' ENVIRONMENTAL SETTINGS, HAS NOT BEEN WELL-EXPLORED. THE CURRENT STUDY ADDRESSES THIS GAP IN THE LITERATURE BY EXPERIMENTALLY MANIPULATING ONLINE DISPLAY FORMAT AND SOCIAL LEARNING SETTINGS TO UNDERSTAND THEIR INFLUENCE ON STUDENT LEARNING AND MIND-WANDERING EXPERIENCES. RESULTS SUGGEST THAT INSTRUCTOR VISIBILITY WITHIN AN ONLINE LECTURE DOES NOT IMPACT STUDENTS' MW OR RETENTION PERFORMANCE. HOWEVER, WE FOUND SOME EVIDENCE THAT STUDENTS' SOCIAL SETTING DURING VIEWING HAS AN IMPACT ON MW (P =.05). SPECIFICALLY, STUDENTS WHO WATCHED THE LECTURE IN A CLASSROOM WITH OTHERS REPORTED SIGNIFICANTLY MORE MW THAN STUDENTS WHO WATCHED THE LECTURE ALONE. FINALLY, SOCIAL SETTING ALSO MODERATED THE NEGATIVE RELATIONSHIP BETWEEN MW AND MATERIAL RETENTION. OUR RESULTS DEMONSTRATE THAT LEARNING EXPERIENCES DURING ONLINE LECTURES CAN VARY BASED ON WHERE, AND WITH WHOM, THE LECTURES ARE WATCHED.
A PROMINENT ISSUE FACED BY THE EDUCATION RESEARCH COMMUNITY IS THAT OF STUDENT ATTRITION. WHILE LARGE RESEARCH EFFORTS HAVE BEEN DEVOTED TO STUDYING COURSE-LEVEL ATTRITION, WIDELY REFERRED TO AS DROPOUT, LESS RESEARCH HAS BEEN FOCUSED ON FINER-GRAINED ASSIGNMENT-LEVEL ATTRITION COMMONLY OBSERVED IN K-12 CLASSROOMS. THIS LATER INSTANTIATION OF ATTRITION, REFERRED TO IN THIS PAPER AS "STOPOUT," IS CHARACTERIZED BY STUDENTS FAILING TO COMPLETE THEIR ASSIGNED WORK, BUT THE CAUSE OF SUCH BEHAVIOR ARE NOT OFTEN KNOWN. THIS BECOMES A LARGE PROBLEM FOR EDUCATORS AND DEVELOPERS OF LEARNING PLATFORMS AS STUDENTS WHO GIVE UP ON ASSIGNMENTS EARLY ARE MISSING OPPORTUNITIES TO LEARN AND PRACTICE THE MATERIAL WHICH MAY AFFECT FUTURE PERFORMANCE ON RELATED TOPICS; SIMILARLY, IT IS DIFFICULT FOR RESEARCHERS TO DEVELOP, AND SUBSEQUENTLY DIFFICULT FOR COMPUTER-BASED SYSTEMS TO DEPLOY INTERVENTIONS AIMED AT PROMOTING PRODUCTIVE PERSISTENCE ONCE A STUDENT HAS CEASED INTERACTION WITH THE SOFTWARE. THIS DIFFICULTY HIGHLIGHTS THE IMPORTANCE TO UNDERSTAND AND IDENTIFY EARLY SIGNS OF STOPOUT BEHAVIOR IN ORDER TO PROVIDE AID TO STUDENTS PRE-EMPTIVELY TO PROMOTE PRODUCTIVE PERSISTENCE IN THEIR LEARNING. WHILE MANY CASES OF STUDENT STOPOUT MAY BE ATTRIBUTABLE TO GAPS IN STUDENT KNOWLEDGE AND INDICATIVE OF STRUGGLE, STUDENT ATTRIBUTES SUCH AS GRIT AND PERSISTENCE MAY BE FURTHER AFFECTED BY OTHER FACTORS. THIS WORK FOCUSES ON IDENTIFYING DIFFERENT FORMS OF STOPOUT BEHAVIOR IN THE CONTEXT OF MIDDLE SCHOOL MATH BY OBSERVING STUDENT BEHAVIORS AT THE SUB-PROBLEM LEVEL. WE FIND THAT STUDENTS EXHIBIT DISPROPORTIONATE STOPOUT ON THE FIRST PROBLEM OF THEIR ASSIGNMENTS IN COMPARISON TO STOPOUT ON SUBSEQUENT PROBLEMS, IDENTIFYING A BEHAVIOR THAT WE CALL "REFUSAL," AND USE THE EMERGING PATTERNS OF STUDENT ACTIVITY TO BETTER UNDERSTAND THE POTENTIAL CAUSES UNDERLYING STOPOUT BEHAVIOR EARLY IN AN ASSIGNMENT.
THERE ARE NUMEROUS STUDIES HAVE REPORTED THE EFFECTIVENESS OF EXAMPLE-BASED PROGRAMMING LEARNING. HOWEVER, LESS IS EXPLORED RECOMMENDING CODE EXAMPLES WITH ADVANCED MACHINE LEARNING-BASED MODELS. IN THIS WORK, WE PROPOSE A NEW METHOD TO EXPLORE THE SEMANTIC ANALYTICS BETWEEN PROGRAMMING CODES AND THE ANNOTATIONS. WE HYPOTHESIZE THAT THESE SEMANTICS ANALYTICS WILL CAPTURE MASS AMOUNT OF VALUABLE INFORMATION THAT CAN BE USED AS FEATURES TO BUILD PREDICTIVE MODELS. WE EVALUATED THE PROPOSED SEMANTIC ANALYTICS EXTRACTION METHOD WITH MULTIPLE DEEP LEARNING ALGORITHMS. RESULTS SHOWED THAT DEEP LEARNING MODELS OUTPERFORMED OTHER MODELS AND BASELINE IN MOST CASES. FURTHER ANALYSIS INDICATED THAT IN SPECIAL CASES, THE PROPOSED METHOD OUTPERFORMED DEEP LEARNING MODELS BY RESTRICTING FALSE-POSITIVE CLASSIFICATIONS.
WE STUDY THE IMPACT OF AN ONLINE TUTORING PROGRAM, ANIMALWATCH, FOR ALGEBRA READINESS ON MATHEMATICS ACHIEVEMENTS OF GRADE 6 STUDENTS. WE USE THE DATA FROM A RANDOMIZED EXPERIMENTAL DESIGN CONDUCTED ON 69 TEACHERS AND 2025 STUDENTS IN CALIFORNIA IN THE ACADEMIC YEARS 2011-2012. AFTER A BRIEF DESCRIPTION OF THE EXPERIMENTAL DESIGN AND THE SYSTEM IMPLEMENTATION, WE ANALYZE THE TREATMENT EFFECT OF EMPLOYING ANIMALWATCH USING THE POPULAR HIERARCHICAL LINEAR MODELS AND FIND A SMALL POSITIVE EFFECT. WE FURTHER USE THE LOGGED SYSTEM USAGE DATA SUCH AS TIME SPENT IN THE SYSTEM, MODULES COMPLETED, CORRECT/INCORRECT/NO-ANSWERS RECORDS OF STUDENTS IN EACH LOGIN TO ANALYZE HOW SYSTEM IMPLEMENTATION AND USAGE HELPED DIFFERENT STUDENTS. OUR RESULTS PROVIDE INSIGHTS INTO THE LIMITATIONS IN IMPLEMENTING SUCH A STUDY IN A REAL WORLD SETTING AND SUGGESTS RECOMMENDATIONS FOR FUTURE RESEARCH.
THIS PAPER SUGGESTS A NOVEL DATA MINING ALGORITHM FOR THE EVALUATION OF E-LEARNING COURSES FROM A LEARNING MANAGEMENT SYSTEM. THIS NEW ALGORITHM, WHICH IS CALLED S-ALGO+ (SUPERPOSITION ALGORITHM), TAKES AS INPUT THE COURSE RANKINGS AND THE SUGGESTION RESULTS FROM ANY KIND OF RANKING/HIERARCHICAL ALGORITHMS AND EVALUATES THE VALIDITY OF A COURSE RANKING POSITION. THE RANKING ALGORITHMS ESTIMATE THE QUANTITY AND QUALITY OF THE COURSE CONTENT ACCORDING TO USERS' ACTIONS AND INTEREST. S-ALGO+ GENERATES AN IMPROVED FINAL RANKING SUGGESTION OUTPUT, COMBINING THE BEST RESULTS OF THE SOURCE RANKING ALGORITHMS USING STATISTICAL AND MATHEMATIC TECHNIQUES. IN THIS WAY, THE RESEARCHERS AND COURSE INSTRUCTORS CAN USE MORE ACCURATE RESULTS. THE EFFICIENCY AND APPLICABILITY OF THE S-ALGO+ ALGORITHM WAS EVALUATED SUCCESSFULLY WITH A CROSS-COMPARISON QUANTITATIVE AND QUALITATIVE PROCESS IN A CASE STUDY AT A GREEK UNIVERSITY. OUR NEW PROPOSED S-ALGO+ ALGORITHM MAY LEAD TO BOTH THEORETICAL AND PRACTICAL ADVANTAGES. IT MAY ALSO APPLY NOT ONLY FOR COURSE EVALUATION BUT FOR ANY KIND OF WEB APPLICATION SUCH AS E-COMMERCE.
IN THIS PAPER, WE EXPLORE HOW WE CAN MODEL STUDENTS' RESPONSE TIMES TO PREDICT STUDENT PERFORMANCE IN INTELLIGENT TUTORING SYSTEMS. RELATED RESEARCH SUGGESTS THAT RESPONSE TIME CAN PROVIDE INFORMATION WITH RESPECT TO CORRECTNESS. HOWEVER, TIME IS NOT CONSISTENTLY USED WHEN MODELING STUDENTS' PERFORMANCE. HERE, WE BUILD ON PREVIOUS WORK THAT INDICATED THAT THE RELATIONSHIP BETWEEN RESPONSE TIME AND STUDENT PERFORMANCE IS NON-LINEAR. BASED ON THIS CONCEPT, WE COMPARE THREE MODELS: A STANDARD ADDITIVE FACTORS ANALYSIS MODEL (AFM), AN AFM MODEL ENHANCED WITH A LINEAR STEP DURATION PARAMETER AND AN AFM MODEL ENHANCED WITH A QUADRATIC, STEP DURATION PARAMETER. THE RESULTS OF THIS COMPARISON SHOW THAT THE AFM MODEL THAT IS ENHANCED WITH THE QUADRATIC STEP DURATION PARAMETER OUTPERFORMS THE OTHER MODELS OVER FOUR DIFFERENT DATASETS AND FOR MOST OF THE METRICS WE USED TO EVALUATE THE MODELS IN CROSS VALIDATION AND PREDICTION.
THE DECISION ON WHAT ITEM TO LEARN NEXT IN A COURSE CAN BE SUPPORTED BY A RECOMMENDER SYSTEM (RS), WHICH AIMS AT MAKING THE LEARNING PROCESS MORE EFFICIENT AND EFFECTIVE. HOWEVER, LEARNERS AND LEARNING ACTIVITIES FREQUENTLY CHANGE OVER TIME. THE QUESTION IS: HOW ARE TIMELY APPROPRIATE RECOMMENDATIONS OF LEARNING RESOURCES ACTUALLY EVALUATED AND HOW CAN THEY BE COMPARED? RESEARCHERS HAVE FOUND THAT, IN ADDITION TO A STANDARDIZED DATASET DEFINITION, THERE IS ALSO A LACK OF STANDARDIZED DEFINITIONS OF EVALUATION PROCEDURES FOR RS IN THE AREA OF TECHNOLOGY ENHANCED LEARNING. THIS PAPER ARGUES THAT, IN A CLOSED-COURSE SETTING, A TIME-DEPENDENT SPLIT INTO THE TRAINING SET AND TEST SET IS MORE APPROPRIATE THAN THE USUAL CROSS-VALIDATION TO EVALUATE THE TOP-N RECOMMENDED LEARNING RESOURCES AT VARIOUS POINTS IN TIME. MOREOVER, A NEW MEASURE IS INTRODUCED TO DETERMINE THE TIMELINESS DEVIATION BETWEEN THE POINT IN TIME OF AN ITEM RECOMMENDATION AND THE POINT IN TIME OF THE ACTUAL ACCESS BY THE USER. DIFFERENT RECOMMENDER ALGORITHMS, INCLUDING TWO NOVEL ONES, ARE EVALUATED WITH THE TIME-DEPENDENT EVALUATION FRAMEWORK AND THE RESULTS, AS WELL AS THE APPROPRIATENESS OF THE FRAMEWORK, ARE DISCUSSED.
REVISION IS IMPORTANT BUT CHALLENGING FOR NOVICE WRITERS, PARTICULARLY IN POST-SECONDARY EDUCATION WHERE OPPORTUNITIES FOR PERSONALIZED FEEDBACK ARE LIMITED. INEXPERIENCED WRITERS TYPICALLY OVERLOOK REVISION; WHEN THEY DO REVISE, THEY FOCUS ON SURFACE ERRORS RATHER THAN GLOBAL REVISIONS THAT ENHANCE MEANING AND COHERENCE. WRITING ANALYTICS CAN AUTOMATE PERSONALIZED PROMPTS TO GUIDE REVISION. WE USE TOPIC MODELLING LDA AS GROUNDS FOR AN ANALYTIC TO SCAFFOLD HOLISTIC REVISION AT PARAGRAPH AND ESSAY LEVELS. THE ANALYTIC VISUALIZES TOPIC DISTRIBUTION AND GENERATES THREE TYPES OF PROMPTS: INTRODUCTION, PARAGRAPH AND CONCLUSION. FEEDBACK ENCOURAGES REVISIONS FOCUSING ON SEQUENCING TOPICS, EXPANDING UNDERDEVELOPED IDEAS, AND MAKING HOLISTIC REVISIONS TO IMPROVE CLARITY AND COHERENCE OF PARAGRAPHS. MODEL FEEDBACK WAS EVALUATED USING UNDERGRADUATE STUDENT ESSAYS ON VARIOUS TOPICS SCORED BY HUMAN EVALUATORS. MODEL ACCURACY WAS STRONG FOR ALL TYPES OF FEEDBACK. THIS OPENS NEW BRANCHES OF RESEARCH TO EXPLORE GENERATING PERSONALIZED FEEDBACK AT PARAGRAPH AND ESSAY LEVELS.
MINING BIG DATA PRODUCED BY STUDENTS LEARNING THROUGH MOBILE LEARNING (M-LEARNING) AND UBIQUITOUS LEARNING (U-LEARNING) CAN PROMOTE INSTRUCTIONAL EFFECTIVENESS. SO, IN LEARNING PHYSICS EXPERIMENTS, THE BIG DATA SHOULD BE RECORDED, MINED AND USED. THIS PAPER ANALYSES THE RESEARCH RESULTS ON M-LEARNING, U-LEARNING AND EDUCATIONAL BIG DATA. IT INCLUDES FIVE PARTS. FIRSTLY, IT DEALS WITH THE PROMOTION OF PERSONALISED ADAPTIVE LEARNING, WHERE EDUCATIONAL DATA MINING AND LEARNING ANALYTICS CAN BE USED TO HELP STUDENTS FIND THE BEST LEARNING METHODS AND RESOURCES FOR PHYSICS EXPERIMENTS, WHEN NEEDED. SECONDLY, IT CONSIDERS THE DIGITISING OF A UNIVERSITY PHYSICS EXPERIMENT COURSE FOR RECORDING RESOURCE USAGE AND THE EXPERIMENTAL OPERATION PROCESS. THIRDLY, IT EXAMINES THE REFORM OF THE TEACHING OF PHYSICS EXPERIMENTS, IN WHICH TEACHERS PROVIDE RICH E-LEARNING RESOURCES AND A USEFUL COMMUNICATION PLATFORM FOR RECORDING THE DATA PRODUCED BY STUDENTS AND ADJUST THEIR TEACHING METHODS AND STRATEGIES FOR DIFFERENT STUDENTS. FOURTHLY, IT DISCUSSES THE REFORM OF THE METHOD FOR LEARNING PHYSICS EXPERIMENTS, IN WHICH STUDENTS ADOPT BLENDED LEARNING WHICH COMBINES INFORMAL AFTER-CLASS LEARNING AND FORMAL CLASSROOM EXPERIMENT LEARNING, AND USES THE PREDICTION FUNCTION OF BIG DATA TO CHANGE THEIR LEARNING METHOD FOR DIFFERENT EXPERIMENTS. FINALLY, THE PAPER LOOKS AT THE REFORM OF THE EVALUATION METHOD FOR PHYSICS EXPERIMENTS TO REFLECT MORE OBJECTIVELY STUDENTS' ACTUAL LEVELS OF PERFORMANCE BY ANALYSING THE WHOLE PROCESS.
MANY CHANGES IN TEACHING PRACTICES (SUCH AS INTRODUCTION OF E-ASSESSMENT) ARE INITIATED BY SCHOOL MANAGEMENT, OR BY A LEAD TEACHER, BUT HAVE DIRECT IMPACT ON THE LEARNING DESIGNS OF OTHERS. HOWEVER, MODELS OF TEACHERS AS INNOVATORS, CONDUCTING EVIDENCE-BASED INQUIRY INTO THEIR STUDENTS' LEARNING, VIEW THE TEACHER AS INITIATOR OF CHANGE IN THEIR OWN PRACTICE (NORMALLY AS INDIVIDUAL). THIS PAPER ADDRESSES THE QUESTION OF WHETHER SUCH MODELS CAN ALSO BE APPLIED TO CHANGE INTRODUCED BY TOP-DOWN PROCESSES. IN THIS PAPER, WE EXAMINE TEACHER INQUIRY IN SUCH A CONTEXT. WE DISCUSS A CASE STUDY IN WHICH WE WORKED WITH THE HEAD OF SCIENCE (HOS) OF A SECONDARY SCHOOL. THE HOS DESIGNED A CROSS-CURRICULAR SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS (STEM) PROJECT WITH 57 STEM TEACHERS AND INTRODUCED GOOGLE FORMS AS A TOOL FOR FORMATIVE ASSESSMENT. WE SUPPORTED THE HOS TO DESIGN AND CONDUCT AN INQUIRY THAT INVOLVED ALL TEACHERS. WE EXAMINE THE CONTEXT AND PROCESS OF INQUIRY AND HOW IT WAS APPLIED. OUR FINDINGS PROVIDE INSIGHT INTO TEACHERS' USE OF TOOLS AND REPRESENTATIONS WHEN COMMUNICATING ABOUT LEARNING DESIGN, AND THE IMPLICATIONS OF THIS FOR THE DEVELOPMENT OF LEARNING ANALYTICS TOOLS. THEY ALSO INFORM THE DEVELOPMENT OF A METHOD FOR COORDINATING TEACHER INQUIRY IN CROSS-CURRICULAR PROJECTS AND, MORE GENERALLY, IN THE CONTEXT OF TOP-DOWN INTRODUCTION OF CHANGE.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE GAINED A LOT OF ATTENTION IN THE LAST YEARS AS A NEW TECHNOLOGY-ENHANCED LEARNING (TEL) APPROACH IN HIGHER EDUCATION. MOOCS PROVIDE MORE EDUCATIONAL OPPORTUNITIES TO A MASSIVE NUMBER OF LEARNERS TO ATTEND FREE ONLINE COURSES AROUND THE GLOBE. DISCUSSIONS AROUND MOOCS HAVE BEEN FOCUSING ON THE POTENTIAL, SOCIAL, INSTITUTIONAL, TECHNOLOGICAL, RELEVANCE, AND MARKETING ISSUES AND LESS ON THE QUALITY DESIGN OF MOOC ENVIRONMENTS. SEVERAL STUDIES HAVE REPORTED A HIGH DROP-OUT RATE IN AVERAGE OF 95% OF COURSE PARTICIPANTS AND OTHER PEDAGOGICAL PROBLEMS CONCERNING ASSESSMENT AND FEEDBACK. THUS, THE QUALITY OF MOOCS DESIGN IS WORTH ADDITIONAL INVESTIGATION. ALTHOUGH SEVERAL STUDIES IDENTIFIED A LARGE SET OF CRITERIA TO THE SUCCESSFUL DESIGN OF TEL SYSTEMS IN GENERAL, NOT ALL OF THEM CAN BE USED IN THE MOOC CONTEXT, DUE TO SOME UNIQUE FEATURES OF MOOCS. THIS STUDY IS A FIRST STEP TOWARDS IDENTIFYING SPECIFIC CRITERIA THAT NEED TO BE CONSIDERED WHEN DESIGNING AND IMPLEMENTING MOOCS. THE RESULTS OF THIS EMPIRICAL STUDY ARE BASED ON A LARGE SURVEY TARGETING LEARNERS AS WELL AS PROFESSORS, BOTH WITH MOOC EXPERIENCE. AS A RESULT, WE IDENTIFIED AND RATED 74 INDICATORS CLASSIFIED INTO OUR TWO MAIN DIMENSIONS OF PEDAGOGICAL AND TECHNOLOGICAL CRITERIA DISTRIBUTED OVER SIX CATEGORIES. FROM THESE, THE LEARNING ANALYTICS AND ASSESSMENT CATEGORIES WERE FOUND TO BE THE KEY FEATURES FOR EFFECTIVE MOOCS.
THE DIVERSE PROFILE OF STUDENTS AND LARGE CLASS SIZES IN AUSTRALIAN UNIVERSITIES TODAY RAISE A CHALLENGE FOR INSTRUCTORS TO INDIVIDUALLY SCAFFOLD THE DEVELOPMENT OF CAPABILITIES SUCH AS ACADEMIC AND INFORMATION LITERACIES, WHICH ARE REQUIRED FOR THE SUCCESSFUL COMPLETION OF A STUDY PROGRAM. THIS IS ESPECIALLY TRUE FOR PROGRAMS SUCH AS SOCIAL WORK WHERE STUDENTS CAN BE PROFILED AS COMING FROM A RANGE OF AGE GROUPS, LINGUISTIC AND CULTURAL BACKGROUNDS AS WELL AS LIFE EXPERIENCES. THIS PAPER REPORTS ON A PROJECT THAT WAS UNDERTAKEN IN AN AUSTRALIAN UNIVERSITY TO ADDRESS SUCH CHALLENGES AND PROVIDE STUDENTS WITH AUTHENTIC EXPERIENTIAL LEARNING OPPORTUNITIES. THE PROJECT ALSO SOUGHT TO AFFORD STUDENTS WITH GREATER CONTROL OVER THE PROGRESSION OF THEIR LEARNING SKILLS DEVELOPMENT AND OPPORTUNITIES FOR TAILORING THEIR STUDIES TO SUIT PERSONAL CIRCUMSTANCES. THE UNIVERSITY'S DIGITAL ENVIRONMENT FACILITATED THE CREATION OF ISPRINGS AS E-LEARNING RESOURCES THAT SCAFFOLDED THE DEVELOPMENT OF ACADEMIC AND INFORMATION LITERACIES ACROSS FOUR COURSES AND TWO YEAR LEVELS. THE EVALUATION OF THE EFFICACY OF THESE RESOURCES FROM STUDENT AND STAFF FEEDBACK AS WELL AS FROM LEARNING ANALYTICS INDICATED THAT THERE WAS A CONSISTENT POSITIVE IMPACT ON STUDENT LEARNING. THERE WAS AN INCREASE IN DISTINCTIONS AND HIGH DISTINCTIONS AND THERE WAS A DECREASE IN F1S, ESPECIALLY IN THE TWO FIRST YEAR COURSES. TUTORS AND COURSE COORDINATORS IN ALL FOUR COURSES REPORTED A MARKED IMPROVEMENT IN STUDENTS' WRITING AND RESEARCH SKILLS.
INFORMATION VISUALISATION STRATEGIES CAN BE APPLIED IN A VARIETY OF DOMAINS. IN THE CONTEXT OF TEMPORAL NETWORKS, I.E., NETWORKS IN WHICH INTERACTIONS BETWEEN INDIVIDUALS OCCUR THROUGHOUT TIME, EFFORTS HAVE BEEN CONDUCTED TO DEVELOP VISUAL APPROACHES THAT ALLOW FINDING INTERACTION PATTERNS, ANOMALIES, AND OTHER BEHAVIOURS NOT PREVIOUSLY PERCEIVED IN THE DATA. THIS PAPER PRESENTS TWO CASE STUDIES INVOLVING REAL-WORLD EDUCATION NETWORKS FROM A PRIMARY SCHOOL AND A HIGH SCHOOL. FOR THIS PURPOSE, WE USED THE MASSIVE SEQUENCE VIEW (MSV) LAYOUT WITH THE COMMUNITY-BASED NODE ORDERING (CNO) METHOD, TWO WELL ESTABLISHED APPROACHES FOR VISUAL ANALYSIS OF TEMPORAL NETWORKS. OUR RESULTS SHOW THAT THE IDENTIFIED PATTERNS INVOLVING STUDENTS/STUDENTS AND STUDENTS/TEACHERS REPRESENT IMPORTANT INFORMATION TO BENEFIT AND SUPPORT DECISION MAKING ABOUT SCHOOL MANAGEMENT AND TEACHING STRATEGIES, ESPECIALLY THOSE RELATED TO STRATEGIC GROUP FORMATION.
E-LEARNING SYSTEMS ARE RECEIVING EVER INCREASING ATTENTION IN ACADEMIA, BUSINESS AND PUBLIC ADMINISTRATION. MAJOR CRISES, LIKE THE PANDEMIC, HIGHLIGHT THE TREMENDOUS IMPORTANCE OF THE APPROPRIATE DEVELOPMENT OF E-LEARNING SYSTEMS AND ITS ADOPTION AND PROCESSES IN ORGANIZATIONS. MANAGERS AND EMPLOYEES WHO NEED EFFICIENT FORMS OF TRAINING AND LEARNING FLOW WITHIN ORGANIZATIONS DO NOT HAVE TO GATHER IN ONE PLACE AT THE SAME TIME OR TO TRAVEL FAR AWAY TO ATTEND COURSES. CONTEMPORARY AFFORDANCES OF E-LEARNING SYSTEMS ALLOW USERS TO PERFORM DIFFERENT JOBS OR TASKS FOR TRAINING COURSES ACCORDING TO THEIR OWN SCHEDULING, AS WELL AS TO COLLABORATE AND SHARE KNOWLEDGE AND EXPERIENCES THAT RESULT IN RICH LEARNING FLOWS WITHIN ORGANIZATIONS. THE PURPOSE OF THIS ARTICLE IS TO PROVIDE A SYSTEMATIC REVIEW OF EMPIRICAL STUDIES AT THE INTERSECTION OF E-LEARNING AND ORGANIZATIONAL LEARNING IN ORDER TO SUMMARIZE THE CURRENT FINDINGS AND GUIDE FUTURE RESEARCH. FORTY-SEVEN PEER-REVIEWED ARTICLES WERE COLLECTED FROM A SYSTEMATIC LITERATURE SEARCH AND ANALYZED BASED ON A CATEGORIZATION OF THEIR MAIN ELEMENTS. THIS SURVEY IDENTIFIES FIVE MAJOR DIRECTIONS OF THE RESEARCH ON THE CONFLUENCE OF E-LEARNING AND ORGANIZATIONAL LEARNING DURING THE LAST DECADE. FUTURE RESEARCH SHOULD LEVERAGE BIG DATA PRODUCED FROM THE PLATFORMS AND INVESTIGATE HOW THE INCORPORATION OF ADVANCED LEARNING TECHNOLOGIES (E.G., LEARNING ANALYTICS, PERSONALIZED LEARNING) CAN HELP INCREASE ORGANIZATIONAL VALUE.
WITH THE RAPID ADVANCEMENTS IN TECHNOLOGY, MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BECOME THE MOST POPULAR FORM OF ONLINE EDUCATIONAL DELIVERY, LARGELY DUE TO THE REMOVAL OF GEOGRAPHICAL AND FINANCIAL BARRIERS FOR PARTICIPANTS. A LARGE NUMBER OF LEARNERS GLOBALLY ENROL IN SUCH COURSES. DESPITE THE FLEXIBLE ACCESSIBILITY, RESULTS INDICATE THAT THE COMPLETION RATE IS QUITE LOW. EDUCATIONAL DATA MINING AND LEARNING ANALYTICS ARE EMERGING FIELDS OF RESEARCH THAT AIM TO ENHANCE THE DELIVERY OF EDUCATION THROUGH THE APPLICATION OF VARIOUS STATISTICAL AND MACHINE LEARNING APPROACHES. AN EXTENSIVE LITERATURE SURVEY INDICATES THAT NO SIGNIFICANT RESEARCH IS AVAILABLE WITHIN THE AREA OF MOOC DATA ANALYSIS, IN PARTICULAR CONSIDERING THE BEHAVIOURAL PATTERNS OF USERS. IN THIS PAPER, THEREFORE, TWO SETS OF FEATURES, BASED ON LEARNER BEHAVIOURAL PATTERNS, WERE COMPARED IN TERMS OF THEIR SUITABILITY FOR PREDICTING THE COURSE OUTCOME OF LEARNERS PARTICIPATING IN MOOCS. OUR EXPLORATORY DATA ANALYSIS DEMONSTRATES THAT THERE IS STRONG CORRELATION BETWEEN CLICK STREAM ACTIONS AND SUCCESSFUL LEARNER OUTCOMES. VARIOUS MACHINE LEARNING ALGORITHMS HAVE BEEN APPLIED TO ENHANCE THE ACCURACY OF CLASSIFIER MODELS. SIMULATION RESULTS FROM OUR INVESTIGATION HAVE SHOWN THAT RANDOM FOREST ACHIEVED VIABLE PERFORMANCE FOR OUR PREDICTION PROBLEM, OBTAINING THE HIGHEST PERFORMANCE OF THE MODELS TESTED. CONVERSELY, LINEAR DISCRIMINANT ANALYSIS ACHIEVED THE LOWEST RELATIVE PERFORMANCE, THOUGH REPRESENTED ONLY A MARGINAL REDUCTION IN PERFORMANCE RELATIVE TO THE RANDOM FOREST.
THE USE OF TECHNOLOGY IN EDUCATION HAS RISEN SO RAPIDLY THAT MANY OF THE E-LEARNING TOOLS HAVE BECOME A GREAT SOURCE FOR DATA GATHERING. IN RESPONSE TO THE GROWTH, LEARNING ANALYTICS ARE DEVELOPED TO EXTRACT THE MEANING FROM EXTENSIVELY LARGE DATASETS AND OPTIMIZE LEARNING OPPORTUNITIES FOR LEARNERS. UNDERSTANDING USERS' BEHAVIORS IS ONE OF THE KEY FACTORS THAT CAN HELP EDUCATIONAL INSTITUTES IMPROVE THEIR CURRICULUM DESIGN OR NON-INSTRUCTIONAL INTERVENTION. THIS PAPER, THEREFORE, EXPLORES LMS'S USER BEHAVIORS THROUGH THE STUDY OF COURSES' PERIODIC BEHAVIORS. IT ASSUMES THAT THERE IS A WEEKLY PATTERN IN MOST COLLEGE-LEVEL COURSES WHERE CLASSES MEET ON A WEEKLY BASIS, AND THAT THE PATTERN IS REFLECTED THROUGH THE NUMBER OF DAILY ACTIVITIES THE COURSES PRODUCE. COURSES THAT SHARE SIMILAR WEEKLY PATTERNS ARE GROUPED INTO THE SAME CLUSTER USING AN UNSUPERVISED CLUSTERING ALGORITHM. THE KNOWLEDGE OBTAINED FROM THE CLUSTERING CAN BE USED TO DESCRIBE THE PATTERN IN OTHER COURSES. THIS PAPER CONTINUES TO DEMONSTRATE ONE OF COURSE PERIODIC MODELLING'S APPLICATIONS BY PROPOSING A METHOD TO PREDICT THE ACTIVITIES THAT OCCUR IN AN LMS. AS A RESULT, THE WEEKLY PATTERN FOUND IN EACH COURSE, WHEN AGGREGATED, CAN REPRESENT THE WEEKLY BEHAVIOR OF THE OVERALL SYSTEM. IT CAN ALSO PREDICT THE FUTURE TREND OF ACTIVITIES WITH CORRECT SHAPE AND ACCURACY WITHIN THE RANGE OF 82-86 PERCENT.
IN THIS PAPER WE PRESENT A CASE STUDY OF TRACKING A) STUDENTS' PERCEPTIONS OF PERFORMANCE AND GOAL EXPECTANCY BEFORE TAKING A COMPUTER-BASED TEST, B) THEIR PERCEPTION OF PERFORMANCE AFTER TAKING THE TEST, C) THEIR ACTUAL PERFORMANCE AS IT IS CALCULATED BY THE TESTING ENVIRONMENT ITSELF AND D) THEIR TIME-SPENT BEHAVIOR DURING TEST. OUR GOAL IS TO EXPLORE WHETHER STUDENTS' TIME-SPENT BEHAVIOR DURING COMPUTER-BASED TESTING - EXPRESSING "(UN-)CERTAINTY" - CAN REVEAL ANY DIFFERENCES BETWEEN WHAT THEY BELIEVE THEY KNOW, AND WHAT THEY ACTUALLY KNOW. FURTHERMORE, WE INVESTIGATE THE CORRELATION BETWEEN STUDENTS' GOAL-EXPECTANCY AND THEIR "(UN-) CERTAINTY". WE CONDUCTED A CASE STUDY WITH A SIMPLIFIED VERSION OF THE LAERS ASSESSMENT ENVIRONMENT. WE USED STATISTICAL METHODS AND STRUCTURAL EQUATION MODELING (SEM) FOR THE CONSTRUCTION OF A PREDICTIVE MODEL WHICH EXPLAINS THE RESULTS, BASED ON STUDENTS' TIME-SPENT ON ANSWERING EACH QUESTION OF A MULTIPLE CHOICE QUIZ. INITIAL RESULTS INDICATE THAT A) STUDENTS' PERCEPTIONS OF PERFORMANCE AND THEIR ACTUAL PERFORMANCE SIGNIFICANTLY DIFFER, BOTH PRE AND POST TEST, B) STUDENTS' TEMPORAL BEHAVIOR CAN EXPLAIN SATISFACTORILY WHAT THEY ACTUALLY KNOW, AND C) GOAL-EXPECTANCY HAS AN INDIRECT EFFECT ON STUDENTS' "(UN-) CERTAINTY".
IN THIS STUDY, A LEARNING-DATA COLLECTION SYSTEM TO ACQUIRE THE DATA REGARDING DEVELOPMENTAL STAGES AND LEARNING ACTIVITIES HAS BEEN DEVELOPED FOR THE PURPOSE OF SELF-DIRECTED CHOOSING OF LEARNING TASKS BY CHILDREN. THE SKILLS NECESSARY FOR THE CHILDREN TO CHOOSE THEIR OWN LEARNING TASKS ARE CATEGORIZED INTO THREE SEGMENTS-"COMPUTATIONAL THINKING," "ICT LITERACY," AND "SOCIAL PERSPECTIVE"-THAT CORRESPOND TO LEARNING THE VARIOUS ACTIVITIES THAT THE AUTHORS PROMOTE. THESE LEARNING ACTIVITIES ARE "PROGRAMMING CLASSROOM," "IT ADVISEMENT SESSION," AND "CHILDREN'S CITY." THIS STUDY PROPOSES A SYSTEM DESIGN OF THE LEARNING-DATA COLLECTION SYSTEM. THE LEARNING-DATA ARE COLLECTED IN THE PROGRAMMING CLASSROOMS. THE RESEARCH QUESTION OF THIS STUDY IS TO FIND OUT HOW THE LEARNING FREQUENCY IN PROGRAMMING EDUCATION FOR CHILDREN AFFECTS THE DEPTH OF REFLECTION AND THE DIFFERENCES IN THE QUALITY OF LEARNING. AS A RESULT OF ANALYZED LEARNING-DATA, IT WAS CONFIRMED THAT THE MORE FREQUENT THE LEARNING, THE DEEPER REFLECTION WAS POSSIBLE TO PROMOTE MORE SELF-DIRECTED LEARNING.
ONLINE EDUCATION DEVELOPS DYNAMICALLY IN RUSSIA AND IN THE WORLD. HOWEVER, ITS USE IS ASSOCIATED WITH A NUMBER OF SPECIFIC PROBLEMS CAUSED BY A LARGE NUMBER OF STUDENTS AND THE LACK OF PERSONAL CONTACT WITH THEM. AMONG THESE PROBLEMS ARE - LOSS OF MOTIVATION AND EARLY TERMINATION OF EDUCATION, DIFFICULTIES IN MASTERING CERTAIN SIGNIFICANT ELEMENTS OF THE COURSE AND, AS A RESULT, LOW RESULTS OF THE COURSE AS A WHOLE. ANALYSIS OF THE PROGRESS IN TRAINING AND FORECASTING THE RESULTS OF THE COURSE ARE IMPORTANT TASKS IN SOLVING THE ABOVE PROBLEMS. THE PAPER CONSIDERS A MODEL FOR ANALYZING CURRENT PERFORMANCE AND PREDICTING THE RESULTS OF FINAL TESTS USING ELEMENTS OF ARTIFICIAL INTELLIGENCE. A DATA MODEL BASED ON THE DIGITAL FOOTPRINT OF ONLINE COURSES STUDENTS HAS BEEN BUILT. AN ALGORITHM FOR ANALYZING AND PREDICTING THE EFFECTIVENESS AND SUCCESS OF TRAINING BASED ON NEURAL NETWORKS SUCH AS KOHONEN SOM (SELF-ORGANIZING MAP) AND MULTILAYER PERCEPTRON HAS BEEN DEVELOPED. BASED ON THE METHODS OF INFORMATION THEORY, THE MUTUAL INFORMATION CONTAINED IN THE DATA ON CURRENT PERFORMANCE AND THE RESULTS OF THE FINAL TESTING WERE EVALUATED. A MODEL HAS BEEN DEVELOPED FOR ESTIMATING THE THEORETICAL LIMITS OF FORECASTING ACCURACY FOR THE RESULTS OF FINAL TESTING BASED ON LEARNING ANALYTICS. THE MODELS AND ALGORITHMS ARE TESTED ON REAL DATA FROM THE ONLINE COURSE OF THE URAL FEDERAL UNIVERSITY, PRESENTED ON THE OPEN EDUCATION PLATFORM.
THE PURPOSE OF THIS PAPER IS TO PRESENT A SPECIFIC CASE OF CALL TASK DESIGN ADOPTING THE PATTERNS-BASED LEARNING DESIGN APPROACH. THE APPROACH PROVIDES A COMMON FRAMEWORK/VOCABULARY FOR EDUCATIONAL DESIGNERS, PARTICULARLY TEACHERS, TO SYSTEMATICALLY CAPTURE, ARTICULATE, MAKE EXPLICIT, SHARE AND REUSE EXPERT KNOWLEDGE OF TEACHING AND GOOD DESIGN. OVER THE PAST 10-15 YEARS, CONSIDERABLE ADVANCES HAVE BEEN MADE IN BOTH LEARNING DESIGN (LD) AND LEARNING ANALYTICS (LA) RESEARCH IN TERMS OF RESEARCH OUTPUTS. HOWEVER, THE ANTICIPATED IMPACT AND BENEFITS, E.G. THE PATTERNS-BASED APPROACH, TO THE EVERYDAY PRACTICE OF TEACHERS AND LEARNERS HAVE BEEN INVISIBLE OR MOSTLY UNREALIZED. RESEARCH TO EXPLORE CALL TASK DESIGN WITH REFERENCE TO LD AND LA IS UNDERREPRESENTED IN THE LITERATURE. THE AIM OF THIS PAPER IS TO EXPLAIN THE CHALLENGES FACED BY CALL DESIGNERS/TEACHERS IN TASK DESIGN AND REFLECT ON THEM METHODOLOGICALLY WITH RELATION TO THE PATTERNS-BASED APPROACH. IT FIRSTLY PROVIDES AN OVERVIEW OF THE CALL TASK DESIGN LITERATURE TO HIGHLIGHT THE GAPS AND WEAKNESSES IN THE RESEARCH AND PRACTICE, FOLLOWED BY AN INTRODUCTION OF THE CONCEPT OF DESIGN PATTERNS. THIS PAPER THEN EXAMINES THREE CALL TASKS FROM TWO CURRENT ONLINE LANGUAGE LEARNING BEGINNERS COURSES IN A NEW ZEALAND UNIVERSITY, AND DEVELOPS THEM INTO DESIGN PATTERNS. EXPLANATION OF HOW THEY WERE DEVELOPED AND DISCUSSION OF THE POSSIBLE ADDED VALUE OF DESIGN PATTERN TO THE DESIGN OF CALL TASKS ARE PROVIDED.
WITH THE RAPID ADVANCEMENT OF VIRTUAL LEARNING ENVIRONMENTS (VLE) IN HIGHER EDUCATION, THE AMOUNT OF AVAILABLE STUDENT DATA GROWS. UNIVERSITIES COLLECT THE INFORMATION ABOUT STUDENTS, THEIR DEMOGRAPHICS, THEIR STUDY RESULTS AND THEIR BEHAVIOUR IN THE ONLINE ENVIRONMENT. BY APPLYING MODELLING AND PREDICTIVE ANALYSIS METHODS IT IS POSSIBLE TO PREDICT STUDENT OUTCOME OR DETECT BOTTLENECKS IN COURSE DESIGN. OUR WORK AIMS AT STATISTICAL SIMULATION OF STUDENT BEHAVIOUR IN THE VLE IN ORDER TO IDENTIFY BEHAVIOURAL PATTERNS LEADING TO DROP-OUT OR PASSIVE WITHDRAWAL I.E. THE STATE WHEN A STUDENT IS NOT STUDYING, BUT HE HAS NOT ACTIVELY WITHDRAWN FROM STUDIES. FOR THAT PURPOSE, THE METHOD CALLED MARKOV CHAIN MODELLING HAS BEEN USED. RECORDED STUDENT ACTIVITIES IN VLE (VLE LOGS) HAS BEEN USED FOR CONSTRUCTING OF PROBABILISTIC REPRESENTATION THAT STUDENTS WILL PERFORM SOME ACTIVITY IN THE NEXT WEEK BASED ON THEIR ACTIVITIES IN THE CURRENT WEEK. THE RESULT IS AN INSTANCE OF THE FAMILY OF ABSORBING MARKOV CHAINS, WHICH CAN BE ANALYSED USING THE PROPERTY CALLED TIME TO ABSORPTION. THE PRELIMINARY RESULTS SHOW THAT INTERESTING PATTERNS IN STUDENT VLE BEHAVIOUR CAN BE UNCOVERED, ESPECIALLY WHEN COMBINED WITH THE INFORMATION ABOUT SUBMISSION OF THE FIRST ASSESSMENT. OUR ANALYSIS HAS BEEN PERFORMED USING OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD) AND RESEARCH NOTES ARE AVAILABLE ONLINE (HTTPS://BIT.LY/2JRY5ZV).
IN THIS PAPER THE FOCUS IS ON THE CONSTRUCTION, USE, PEDAGOGICAL POTENTIAL, AND LONG-TERM SUSTAINABILITY OF CERTAIN WEB-BASED TOOLS DESIGNED FOR TEACHING LOGIC. A SERIES OF WEB-BASED TOOLS HAVE BEEN IMPLEMENTED AS A TWO-PART SYSTEM, AND THE TOOLS HAVE BEEN TESTED AND EVALUATED IN A NUMBER OF PRACTICAL EXPERIMENTS. THE FIRST PART OF THE SYSTEM IS A STUDENT-FACING JAVA-APPLET RUNNING IN THE STUDENT'S BROWSER, IMPLEMENTED USING THE PROLOG PROGRAMMING LANGUAGE AS EMBODIED IN A JAVA IMPLEMENTATION CALLED PROLOG+CG. THE SECOND PART IS A TEACHER-ORIENTED, SERVER-BASED BACKEND FOR LOGGING THE PROGRESS OF STUDENTS. IN THE PAPER, WE PROVIDE A PRESENTATION OF THE PEDAGOGICAL AND TECHNICAL IDEAS OF CONSTRUCTION THAT UNDERPIN THE TOOLS WHICH HAVE BEEN MADE SO FAR. IT IS EXPLAINED HOW THE TOOLS CAN BE DEVELOPED AS WEB APPLICATIONS USING GAMIFIED QUIZZING. WE THEN PROVIDE AN EVALUATION OF THE POTENTIAL OF LOG DATA AS LEARNING ANALYTICS OFFERED BY THESE TOOLS IN THE CONTEXT OF UNIVERSITY COURSES INTRODUCING BASIC LOGIC AND FORMAL ASPECTS OF ARGUMENTATION. WE ALSO DESCRIBE HOW WE HAVE USED AND EVALUATED THE TOOLS IN A REAL LEARNING CONTEXT, USING BOTH QUANTITATIVE, LOG-BASED DATA AND QUALITATIVE INTERVIEW. THE EVALUATION ALSO INCLUDES A DISCUSSION OF THE ETHICAL ASPECTS CONCERNING THE LOGGING OF STUDENT DATA. WE ALSO PROVIDE SOME INSIGHTS ON HOW THE TOOLS CAN AND HAVE BEEN MADE SUSTAINABLE.
DUE TO A HIGH NUMBER OF PARTICIPANTS AND LIMITED HUMAN TUTORING CAPACITIES, CLOSE SUPERVISION AND HUMAN GUIDANCE IS HARDLY POSSIBLE IN LARGE ONLINE COURSES. THUS, THE AIM OF THE IKARION PROJECT IS TO DEVELOP METHODS FOR INTELLIGENT AND AUTOMATED DIAGNOSIS AND INTERVENTION IN ONLINE LEARNING ENVIRONMENTS. IN THIS PAPER, WE PRESENT OUR DISTRIBUTED AND AUTONOMOUS FEEDBACK SYSTEM AIMING TO IMPROVE TEAM COLLABORATION AND INTERACTION BY MEANS OF INTERVENTIONS. THE FEEDBACK SYSTEM DETECTS AND DIAGNOSES EXISTING AND EMERGING PROBLEMS WITHIN THE GROUPS BASED ON THEIR INTERACTION PATTERNS. BASED ON PRINCIPLES OF PEDAGOGY AND EDUCATIONAL PSYCHOLOGY, ANALYSIS METRICS AND APPROPRIATE MEASURES ARE PROVIDED FOR DIFFERENT TYPES OF FEEDBACK INCLUDING MIRRORING FOR EXPLORATORY AND INFORMATIVE PROMPTS PROVIDING APPROPRIATE INFORMATION ABOUT PROGRESS AND ACTUAL STATE FOR THE TEAMWORK, AND GUIDING PROMPTS AIMING TO MODIFY THE INTERACTION AND COLLABORATION OF A TEAM OR AN INDIVIDUAL STUDENT. THE DISTRIBUTED FEEDBACK SYSTEM CONSISTS OF THREE ESSENTIAL COMPONENTS: MOODLE-BASED LEARNING MANAGEMENT SYSTEM ALONG WITH VARIOUS PROMPTS IMPLEMENTED AS MOODLE PLUGINS, LEARNING ANALYTICS BACKEND, AND RULE-BASED INTERVENTION SYSTEM BASED ON AN EXPERT SYSTEM. WE DESCRIBE, ON AN ARCHITECTURAL LEVEL, THE DISTRIBUTED FEEDBACK SYSTEM AND ITS COMPONENTS. AS A RESULT, WE SHOW MEASUREMENTS OF TWO LONG TERM EXPERIMENTS CARRIED OUT IN TWO ONLINE COURSES AT UNIVERSITY LEVEL WITH A DURATION OF ONE SEMESTER EACH. WE CONCLUDE THE PAPER WITH AN OUTLOOK ON FUTURE WORK.
THE DEVELOPMENT OF WEB APPLICATIONS IS AN ESSENTIAL AND CRITICAL SKILL FOR COMPUTER SCIENCE ENGINEERS. NOWADAYS, THIS IS A CORE SUBJECT IN THE COMPUTER SCIENCE ENGINEERING STUDIES AND AN INCREASINGLY DEMANDED SUBJECT IN OTHER ENGINEERING STUDIES. WITH THE AIM TO FACILITATE INTRODUCTORY KNOW-HOW IN THIS AREA, THE UNIVERSITY AUTONOMA OF MADRID OFFERS AT EDX PLATFORM THE MASSIVE OPEN ONLINE COURSE (MOOC): "INTRODUCTION TO DEVELOPMENT OF WEB APPLICATIONS". SEVERAL STUDIES HAVE EMPHASISED THE POSITIVE IMPACT OF METHODS, SUCH AS THE USE OF CONTINUOUS FEEDBACK, TO IMPROVE AND PROMOTE LEARNING PERSISTENCE AND ENGAGEMENT IN ONLINE COURSES. WE PROPOSE A SYSTEM FOR PERIODICALLY PROVIDING MOOC LEARNERS INFORMATION ABOUT THEIR PROGRESS AND SUGGESTIONS ABOUT HOW CAN THEY CAN IMPROVE THEIR LEARNER PERFORMANCE BASED ON THEIR ACHIEVEMENTS AND INTERACTIONS IN MOOCS. THIS SYSTEM IS CALLED LEARNING INTERVENTION SYSTEM FOR EDX MOOCS (EDX-LIS). TO TEST THE INTERVENTION STRATEGY ON LEARNERS' BEHAVIOUR SUPPORTED BY EDX-LIS, A RESEARCH STUDY WAS CONDUCTED WITH THE LEARNERS OF THE MENTIONED MOOC. THE RESULTS OBTAINED FROM THIS STUDY HAVE DEMONSTRATED THAT THE INTERVENTION STRATEGY SUPPORTED BY THE EDX-LIS HAS A POSITIVE IMPACT ON THE MOTIVATION, PERSISTENCE AND ENGAGEMENT OF THE LEARNERS IN THE MOOC.
IN RECENT YEARS THE AMOUNT OF DATA STORED IN EDUCATIONAL DATABASE IS GROWING RAPIDLY. THE STORED DATABASE CONTAINS HIDDEN INFORMATION WHICH IF USED AIDS IMPROVEMENT OF STUDENT'S PERFORMANCE AND BEHAVIOUR. IN THIS PAPER PREDICTIVE MODELLING APPROACH IS USED FOR EXTRACTING THIS HIDDEN INFORMATION. DATA IS COLLECTED, A PREDICTIVE MODEL IS FORMULATED, PREDICTIONS ARE MADE, AND THE MODEL IS VALIDATED AS ADDITIONAL DATA BECOMES AVAILABLE. THE PREDICTIVE MODELS WILL HELP THE INSTRUCTOR TO UNDERSTAND HOW WELL OR HOW POORLY THE STUDENTS IN HIS/HER CLASS WILL PERFORM, AND HENCE THE INSTRUCTOR CAN CHOOSE PROPER PEDAGOGICAL AND INSTRUCTIONAL INTERVENTIONS TO ENHANCE STUDENT LEARNING OUTCOMES. THE IMPLEMENTATION IS DONE IN HADOOP FRAMEWORK WITH MAPREDUCE AND REVOLUTIONARY R ENTERPRISE RRE.
IN A COLLABORATIVE LEARNING ENVIRONMENT, THE PROMOTION AND SUPPORT OF WELL-BALANCED STUDENT PARTICIPATION IS AN IMPORTANT STEP TOWARDS THE ACHIEVEMENT OF LEARNING OUTCOMES. AMBIENT INFORMATION VISUALISATIONS CAN HELP RAISE AWARENESS OF THE BALANCE OF DISTRIBUTION IN MEETINGS AND SMALL LEARNER GROUPS. THIS PAPER EXPLORES THE USE OF AMBIENT INFORMATION IN THE CLASSROOM, WHERE WE ATTEMPT TO ENCOURAGE A PROPER BALANCE OF FEEDBACK BETWEEN STUDENT GROUPS DURING 'DESIGN CRITIQUE' STUDIO SESSIONS. THE CONTRIBUTION OF THE PAPER IS TWO-FOLD: (1) WE PRESENT NECESSARY DESIGN CHOICES FOR AMBIENT VISUALISATIONS THAT PROMOTE FEEDBACK BALANCE IN CLASSROOMS, MOTIVATING UNDER-PARTICIPATORS WHILE LIMITING OVER-PARTICIPATION (2) WE SHOW THE EFFECTS ON STUDENT PERCEPTION AND FEEDBACK PARTICIPATION THROUGH THE ACTUAL DEPLOYMENT OF SUCH VISUALISATIONS IN ` REAL CLASSROOM SESSIONS'.
THIS PAPER PRESENTS AN EXAMPLE OF SCAFFOLDING DURING THE DEVELOPMENT OF AN ENGINEERING COURSE, IN WHICH STUDENTS ARE SUPPORTED BY TEACHERS AND OTHER STUDENTS. THIS PROPOSAL COVERS THE BENEFITS OF THE USE OF SHARED KNOWLEDGE REPOSITORIES IN WHICH CONTENT WAS CREATED BY STUDENTS. TEAMWORK IS THE TRANSVERSAL COMPETENCE THAT IS CONSIDERED TO BE THE CENTRAL KNOWLEDGE TOPIC. THE COOPERATION AMONG STUDENTS THROUGH TEAMWORK METHODOLOGY HAS GENERATED MORE THAN 500 LEARNING RESOURCES AND A KNOWLEDGE MANAGEMENT SYSTEM, BRACO, WHICH HAS BEEN CREATED WITH THESE RESOURCES TO MANAGE INFORMATION AND CONDUCT SEARCHES ACCORDING TO EACH STUDENT'S PROFILE AND NEEDS. THE GENERATED KNOWLEDGE SPIRAL IS COMPOSED OF KNOWLEDGE CIRCLES THAT INCREASE DURING EACH ITERATION OF THE ACTION-RESEARCH IMPLEMENTATION. THE REFLECTION PHASE OF THIS RESEARCH CONSISTS OF THE EVALUATION OF THE IMPACT ON LEARNING FOR STUDENTS IN THE EXPERIMENTAL GROUP AFTER USING THE KNOWLEDGE RESOURCES GENERATED BY STUDENTS IN RELATION WITH TEAMWORK COMPETENCE, IN CONTRAST WITH THE CONTROL GROUP THAT DOES NOT EXPERIENCE THIS INTERVENTION. WITH REGARD TO THE ASSESSMENTS, SEVERAL SURVEYS AND A LEARNING ANALYTICS SYSTEM, THIS PAPER EXPLAINS THE UNDERLYING METHODOLOGICAL FOUNDATIONS AND THE EMPIRICAL STUDY. IN COMPARISON TO THE CONTROL GROUP, THE EXPERIMENTAL GROUP OBTAINED BETTER RESULTS IN RELATION TO INDICATORS OF POSITIVE LEARNING RESULTS, SUCH AS STUDENT-STUDENT INTERACTION, TEAMWORK DEVELOPMENT AND FINAL GRADES DURING THE TEAMWORK PROCESS.
TO MAINTAIN KNOWLEDGEABLE, WELL SKILLED EMPLOYEES AND SUSTAIN THEIR COMPETITIVE ADVANTAGE, GOVERNMENT AGENCIES AND THE CORPORATE SECTOR ADOPT ONLINE COURSEWARE DESIGNED FOR EDUCATION AND TRAINING. THE INSTRUCTIONAL DESIGN OF RESULTING HUMAN-COMPUTER INTERACTION (HCI), WHICH OCCURS DURING A TRAINING SESSION, IS LEFT AS AN AFTERTHOUGHT. NO ATTENTION IS GIVEN TO THE EMERGENT SOCIALIZED WORK-PLACE WHERE ON THE JOB TRAINING OFTEN INVOLVES COLLABORATIVE PARTNERSHIPS. THERE ARE NO MEASURABLE ACCOUNTS OF THE EFFECTIVENESS OF SHARING SUCH KNOWLEDGE AND EXPERTISE WITH NEW EMPLOYEES. TO THIS END, AN EXPERIMENTAL RESEARCH STUDY WAS DESIGNED TO INVESTIGATE THE INTERACTIVE EFFECT OF INSTRUCTIONAL STRATEGIES AUGMENTED WITH EITHER A DIGITAL INSTRUCTIONAL ASSISTANT OR A TRADITIONAL CLASS-ROOM TUTOR, AND PARTICIPANTS' PREFERENCE FOR TRAINING-MODE ON THE ACQUISITION OF INTRODUCTORY ETHICS KNOWLEDGE. PARTICIPANTS WERE GIVEN THE OBJECT-SPATIAL IMAGERY AND VERBAL QUESTIONNAIRE (OSIVQ) TO ESTABLISH THEIR COGNITIVE STYLE [1]. THE QUEST INTERACTIVE TEST ANALYSIS SYSTEM PROVIDED THE COGNITIVE PERFORMANCE MEASURING TOOL [2], TO DEFINE THE LEARNING ANALYTICS AND TO ENSURE THERE WERE NO MEASUREMENT ERRORS IN THE INTRODUCTORY ETHICS KNOWLEDGE TESTING INSTRUMENTS. THEREFORE, RELIABILITY OF THE TESTING TOOLS WAS SECURED THROUGH THE QUEST CALIBRATION TECHNIQUES, THEREBY SAFEGUARDING THE PREDICTABILITY OF THE RESEARCH DESIGN. THE METHODOLOGY EMBRACED BY THIS EXPERIMENTAL RESEARCH LINKS HCI WITH THE DISCIPLINES OF INSTRUCTIONAL SCIENCE, COGNITIVE PSYCHOLOGY AND OBJECTIVE MEASUREMENT TO AUTHENTICATE VALUABLE MECHANISMS FOR ADOPTION BY THE EDUCATION, TRAINING AND SKILLS DEVELOPMENT SECTORS.
THE WIDESPREAD USE OF ONLINE DISCUSSION FORUMS IN EDUCATIONAL SETTINGS PROVIDES A RICH SOURCE OF DATA FOR RESEARCHERS INTERESTED IN HOW COLLABORATION AND INTERACTION CAN FOSTER EFFECTIVE LEARNING. SUCH ONLINE BEHAVIOUR CAN BE UNDERSTOOD THROUGH THE COMMUNITY OF INQUIRY FRAMEWORK, AND THE COGNITIVE PRESENCE CONSTRUCT IN PARTICULAR CAN BE USED TO CHARACTERISE THE DEPTH OF A STUDENT'S CRITICAL ENGAGEMENT WITH COURSE MATERIAL. AUTOMATED METHODS HAVE BEEN DEVELOPED TO SUPPORT THIS TASK, BUT MANY STUDIES USED SMALL DATA SETS, AND THERE HAVE BEEN FEW REPLICATION STUDIES. IN THIS WORK, WE PRESENT FINDINGS RELATED TO THE ROBUSTNESS AND GENERALISABILITY OF AUTOMATED CLASSIFICATION METHODS FOR DETECTING COGNITIVE PRESENCE IN DISCUSSION FORUM TRANSCRIPTS. WE CLOSELY EXAMINED ONE PUBLISHED STATE-OF-THE-ART MODEL, COMPARING DIFFERENT APPROACHES TO MANAGING UNBALANCED CLASSES IN THE DATA. BY DEMONSTRATING HOW COMMONLY-USED DATA PREPROCESSING PRACTICES CAN LEAD TO OVER-OPTIMISTIC RESULTS, WE CONTRIBUTE TO THE DEVELOPMENT OF THE FIELD SO THAT THE RESULTS OF AUTOMATED CONTENT ANALYSIS CAN BE USED WITH CONFIDENCE.
EDUCATIONAL INSTITUTIONS ARE ACQUIRING NOVEL TECHNOLOGIES TO HELP MAKE THEIR PROCESSES MORE EFFICIENT AND SERVICES MORE ATTRACTIVE FOR BOTH STUDENTS AND FACULTY. BIOMETRIC TECHNOLOGY IS ONE SUCH EXAMPLE THAT HAS BEEN IMPLEMENTED IN EDUCATIONAL INSTITUTIONS WITH EXCELLENT RESULTS. IN ADDITION TO IDENTIFYING STUDENTS, ACCESS CONTROL, AND PERSONAL DATA MANAGEMENT, IT HAS CRITICAL APPLICATIONS TO IMPROVE THE ACADEMIC DOMAIN'S TEACHING/LEARNING PROCESSES. IDENTITY MANAGEMENT SYSTEM, CLASS ATTENDANCE, E-EVALUATION, SECURITY, STUDENT MOTIVATIONS, AND LEARNING ANALYTICS ARE AREAS IN WHICH BIOMETRIC TECHNOLOGY IS MOST HEAVILY EMPLOYED. A LITERATURE REVIEW IS PERFORMED TO PRESENT AN OVERVIEW OF BIOMETRIC TECHNOLOGY APPLICATIONS FOR EDUCATIONAL PURPOSES, CHALLENGES THAT MUST OVERCOME TO IMPLEMENT BIOMETRIC TECHNOLOGY, AND POTENTIALLY FORESHADOWING TRENDS EFFECTIVELY. THE FUTURE SEEMS PROMISING FOR BIOMETRIC TECHNOLOGY; THE BIOMETRIC TECHNOLOGY MARKET IS EXPECTED TO REACH A VALUE OF USD 94 BILLION BY 2025 AT A COMPOUND ANNUAL GROWTH RATE OF 36%. NEW CHARACTERISTICS ARE UNDER DEVELOPMENT FOR COMMERCIAL APPLICATIONS, SUCH AS VASCULAR PATTERN RECOGNITION, EAR SHAPE RECOGNITION, FACIAL THERMOGRAPHY, ODOR SENSING, GAIT RECOGNITION, HEARTBEAT AUTHENTICATION, BRAIN WAVES, AND HUMAN BODY BIOACOUSTICS. THE BIGGEST CHALLENGE THIS TECHNOLOGY MUST OVERCOME IS SECURITY AND PRIVACY ISSUES, WHICH MUST BE ADDRESSED TO FULLY DEVELOP THE TECHNOLOGY TO ITS FULL POTENTIAL. IT IS DESIRABLE THAT THIS LITERATURE REVIEW CAN PROVIDE RESEARCHERS WITH A SOUND VISION OF THE POTENTIAL THAT BIOMETRIC TECHNOLOGY WILL HAVE IN EDUCATION.
IN HIGHER EDUCATION, PREDICTING THE ACADEMIC PERFORMANCE OF STUDENTS IS ASSOCIATED WITH FORMULATING OPTIMAL EDUCATIONAL POLICIES THAT VEHEMENTLY IMPACT ECONOMIC AND FINANCIAL DEVELOPMENT. IN ONLINE EDUCATIONAL PLATFORMS, THE CAPTURED CLICKSTREAM INFORMATION OF STUDENTS CAN BE EXPLOITED IN ASCERTAINING THEIR PERFORMANCE. IN THE CURRENT STUDY, THE TIME-SERIES SEQUENTIAL CLASSIFICATION PROBLEM OF STUDENTS' PERFORMANCE PREDICTION IS EXPLORED BY DEPLOYING A DEEP LONG SHORT-TERM MEMORY (LSTM) MODEL USING THE FREELY ACCESSIBLE OPEN UNIVERSITY LEARNING ANALYTICS DATASET. IN THE PASS/FAIL CLASSIFICATION JOB, THE DEPLOYED LSTM MODEL OUTPERFORMED THE STATE-OF-THE-ART APPROACHES WITH 93.46% PRECISION AND 75.79% RECALL. ENCOURAGINGLY, OUR MODEL SUPERSEDED THE BASELINE LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS BY 18.48% AND 12.31%, RESPECTIVELY, WITH 95.23% LEARNING ACCURACY. WE DEMONSTRATED THAT THE CLICKSTREAM DATA GENERATED DUE TO THE STUDENTS' INTERACTION WITH THE ONLINE LEARNING PLATFORMS CAN BE EVALUATED AT A WEEK-WISE GRANULARITY TO IMPROVE THE EARLY PREDICTION OF AT-RISK STUDENTS. INTERESTINGLY, OUR MODEL CAN PREDICT PASS/FAIL CLASS WITH AROUND 90% ACCURACY WITHIN THE FIRST 10 WEEKS OF STUDENT INTERACTION IN A VIRTUAL LEARNING ENVIRONMENT (VLE). A CONTRIBUTION OF OUR RESEARCH IS AN INFORMED APPROACH TO ADVANCED HIGHER EDUCATION DECISION-MAKING TOWARDS SUSTAINABLE EDUCATION. IT IS A BOLD EFFORT FOR STUDENT-CENTRIC POLICIES, PROMOTING THE TRUST AND THE LOYALTY OF STUDENTS IN COURSES AND PROGRAMS.
A BIBLIOMETRIC ANALYSIS WAS APPLIED IN THIS PAPER TO QUANTITATIVELY EVALUATE THE SOCIAL NETWORK ANALYSIS RESEARCH FROM 1975 TO 2017 BASED ON 7311 BIBLIOGRAPHIC RECORDS COLLECTED FROM THE SCIENCE CITATION INDEX (SCI) DATABASE. FIRSTLY, A COMPREHENSIVE ANALYSIS WAS CONDUCTED TO REVEAL THE CURRENT LANDSCAPES SUCH AS SCIENTIFIC OUTPUTS, INTERNATIONAL COLLABORATION, SUBJECT CATEGORIES, AND RESEARCH PERFORMANCES BY INDIVIDUALS, WE THEN USE INNOVATIVE METHODS SUCH AS BURST DETECTION, REFERENCED PUBLICATION YEARS SPECTROSCOPY AND KEYWORD SEMANTIC CLUSTERING TO PROVIDE A DYNAMIC VIEW OF THE EVOLUTION OF SOCIAL NETWORK ANALYSIS RESEARCH HOTPOTS AND TRENDS FROM VARIOUS PERSPECTIVES. RESULTS SHOWS THAT SOCIAL NETWORK ANALYSIS RESEARCH HAS DEVELOPED RAPIDLY IN THE PAST FOUR DECADES AND IS IN THE GROWTH PERIOD WITH A MATURITY OF 50.00%, THE TOTAL OF 7311 ARTICLES COVER 120 COUNTRIES (REGIONS) AND THE TOP FIVE MOST PRODUCTIVE COUNTRIES ARE USA, ENGLAND, CHINA, CANADA AND GERMANY. AMONG THE 1181 MAJOR JOURNAL RELATED TO SOCIAL NETWORK ANALYSIS, UNIVERSITY OF ILLINOIS, UNIVERSITY OF SYDNEY AND CARNEGIE MELLON UNIVERSITY RANKED AS THE TOP THREE. IN ADDITION BURST KEYWORDS SUCH AS KNOWLEDGE MANAGEMENT, CENTRALITY, MODULARITY, COMMUNITY, LINK PREDICTION, LEARNING ANALYTICS AND BIG DATA DEMONSTRATE THE TRENDS OF THIS FIELD. THE RESULT PROVIDES A DYNAMIC VIEW OF THE EVOLUTION OF SOCIAL NETWORK ANALYSIS RESEARCH LANDSCAPES, HOTSPOTS AND TRENDS FROM VARIOUS PERSPECTIVES WHICH MAY SERVE AS A POTENTIAL GUIDE FOR FUTURE RESEARCH.
BIG DATA IS CONFIGURED AS A TECHNOLOGICAL ELEMENT AND OF INCREASING EDUCATIONAL INTEREST. THE NEED TO ADVANCE THE QUALITY OF ACADEMIC INCLUSION HAS LED TO AN UNPRECEDENTED EXPANSION OF EDUCATIONAL PROCESSES AND FEATURES. THUS, COLLECTING MASSIVE DATA ON EDUCATIONAL INFORMATION IS PART OF TEACHERS' DAILY LIVES AND EDUCATIONAL INSTITUTIONS THEMSELVES. THERE IS AN INTENSE DEBATE ABOUT THE POTENTIAL OF BIG DATA IN THE EDUCATIONAL CONTEXT, ESPECIALLY THROUGH LEARNING ANALYTICS THAT FAVOR THE APPROPRIATE, RESPONSIBLE, AND INCLUSIVE USE OF THE DATA COLLECTED. THE MAIN AIM OF THIS ARTICLE IS TO ANALYZE USER PROFILES AND THE TENDENCY TO USE BIG DATA AND SEE WHAT FACTORS INFLUENCE ITS APPLICABILITY. THIS STUDY EMPLOYS AN INCIDENTAL SAMPLE OF 265 STUDENTS OF EDUCATIONAL SCIENCES FROM ANDALUSIAN UNIVERSITIES, (SPAIN), USING AN AD-HOC SURVEY. A CLUSTER ANALYSIS WAS CONDUCTED TOGETHER WITH ORDINAL REGRESSION ANALYSIS AND DECISION TREE. THE RESULTS ALLOW US TO CONFIRM THE EXISTENCE OF TWO DIFFERENT STUDENT PROFILES, IN TERMS OF THEIR PERCEPTIONS AND APPRAISAL OF BIG DATA AND ITS IMPLICATIONS IN EDUCATION. CONSEQUENTLY, A HIGHER SCORE IS FOUND FOR THAT PROFILE THAT CONTEMPLATES AND POSITIVELY CONCEIVES BIG DATA IN TERMS OF LEARNING OPPORTUNITIES AND IMPROVEMENT OF EDUCATIONAL QUALITY. THE RESEARCH DEMONSTRATES THE NEED TO PROMOTE BIG DATA TRAINING WITHIN THE CONTEXT OF UNIVERSITY, AIDING THE ACQUISITION OF DIGITAL AND TRANSVERSAL SKILLS.
SELF-REGULATED LEARNING THEORIES ARE USED TO UNDERSTAND THE REASONS FOR DIFFERENT LEVELS OF UNIVERSITY STUDENT ACADEMIC PERFORMANCE. SIMILARLY, LEARNING ANALYTICS RESEARCH PROPOSES THE COMBINATION OF DETAILED DATA TRACES DERIVED FROM TECHNOLOGY-MEDIATED TASKS WITH A VARIETY OF ALGORITHMS TO PREDICT STUDENT ACADEMIC PERFORMANCE. THE FORMER APPROACH IS DESIGNED TO PROVIDE MEANINGFUL PEDAGOGICAL GUIDANCE, WHILE THE LATTER IS DESIGNED TO IDENTIFY EVENT PATTERNS AND RELATIONS THAT CAN BE TRANSLATED INTO ACTIONABLE REMEDIATION. THE BENEFITS OF BOTH APPROACHES HAVE MOTIVATED THIS STUDY TO INVESTIGATE IF A COMBINATION OF THE SELF-REPORT DATA AND DATA ARISING FROM AN OBSERVATION OF THE ENGAGEMENT OF STUDENTS WITH ONLINE LEARNING EVENTS OFFERS A DEEPER UNDERSTANDING AND EXPLANATION OF WHY SOME STUDENTS ACHIEVE RELATIVELY HIGHER LEVELS OF ACADEMIC PERFORMANCE. IN THIS PAPER WE EXPLORE HOW TO COMBINE DATA ABOUT SELF-REGULATED LEARNING SKILLS WITH OBSERVABLE MEASURES OF ONLINE ACTIVITY IN A BLENDED LEARNING COURSE TO INCREASE PREDICTIVE CAPABILITIES OF STUDENT ACADEMIC PERFORMANCE FOR THE PURPOSES OF INFORMING TEACHING AND TASK DESIGN. A CASE STUDY IN A COURSE WITH 145 STUDENTS SHOWED THAT THE VARIATION OF THE STUDENTS' FINAL SCORE FOR THEIR COURSE IS BETTER EXPLAINED WHEN FACTORS FROM BOTH APPROACHES ARE CONSIDERED. THE RESULTS POINT TO THE POTENTIAL OF ADOPTING A COMBINED USE OF SELF-REPORT AND OBSERVED DATA TO GAIN A MORE COMPREHENSIVE UNDERSTANDING OF SUCCESSFUL UNIVERSITY STUDENT LEARNING.
MANY CONTEMPORARY STUDIES REALIZED IN THE LEARNING ANALYTICS RESEARCH FIELD PROVIDE SUBSTANTIAL INSIGHTS INTO THE VIRTUAL LEARNING ENVIRONMENT STAKEHOLDERS' BEHAVIOUR ON SINGLE-COURSE OR SMALL-SCALE LEVEL. THEY USED DIFFERENT KNOWLEDGE DISCOVERY TECHNIQUES, INCLUDING FREQUENT PATTERNS ANALYSIS. HOWEVER, THERE ARE ONLY A FEW STUDIES THAT HAVE EXPLORED THE STAKEHOLDERS' BEHAVIOUR OVER A MORE EXTENDED PERIOD OF SEVERAL ACADEMIC YEARS IN DETAIL. THIS ARTICLE CONTRIBUTES TO FILLING IN THIS GAP AND PROVIDES A NOVEL APPROACH TO USING HOMOGENEOUS GROUPS OF FREQUENT PATTERNS FOR IDENTIFYING THE CHANGES IN STAKEHOLDERS' BEHAVIOUR FROM THE PERSPECTIVE OF TIME. THE NOVELTY OF THIS APPROACH LIES IN FACT, THAT EVEN THOUGH THE TIME VARIABLE IS NOT DIRECTLY INVOLVED, IDENTIFICATION OF HOMOGENEOUS GROUPS OF FREQUENT ITEMSETS ALLOWS ANALYSIS AND COMPARISON OF THE STAKEHOLDERS' BEHAVIORAL PATTERNS AND THEIR CHANGES OVER DIFFERENT OBSERVED PERIODS. FOUND HOMOGENEOUS GROUPS OF FREQUENT ITEMSETS, WHICH CONFORM MINIMAL THRESHOLD OF SELECTED MEASURES, SHOWED, THAT IT IS POSSIBLE TO UNCOVER THE CHANGES IN STAKEHOLDERS' BEHAVIOUR THROUGHOUT THE OBSERVED LONGER PERIOD. AS A RESULT, THESE HOMOGENOUS GROUPS OF FOUND FREQUENT PATTERNS ALLOW A BETTER UNDERSTANDING OF THE HIDDEN CHANGES IN SEASONALITY OR TRENDS IN STAKEHOLDERS' BEHAVIOUR OVER SEVERAL ACADEMIC YEARS. THIS ARTICLE DISCUSSES THE POSSIBLE IMPLICATIONS OF THE RESULTS AND PROPOSED APPROACH IN THE CONTEXT OF VIRTUAL LEARNING ENVIRONMENT MANAGEMENT AND EDUCATIONAL CONTENT IMPROVEMENT.
TO THE BEST OF OUR KNOWLEDGE, THIS PAPER PRESENTS THE FIRST INTERNET DOMAIN NAME SYSTEM (DNS) QUERIES DATA STUDY FROM A NATIONAL K-12 EDUCATION SERVICE PROVIDER. THIS PROVIDER, CALLED PLAN CEIBAL, SUPPORTS A ONE-TO-ONE COMPUTING PROGRAM IN URUGUAY. ADDITIONALLY, IT HAS DEPLOYED AN INFORMATION AND COMMUNICATIONS TECHNOLOGY (ICT) INFRASTRUCTURE IN ALL OF URUGUAY'S PUBLIC SCHOOLS AND HIGH-SCHOOLS, IN ADDITION TO MANY PUBLIC SPACES. THE MAIN DEVELOPMENT IS WIRELESS CONNECTIVITY, WHICH ALLOWS ALL THE STUDENTS (WHOSE AGES RANGE BETWEEN 6 AND 18 YEARS OLD) TO CONNECT TO DIFFERENT RESOURCES, INCLUDING INTERNET ACCESS. IN THIS ARTICLE, WE USE 9,125,888,714 DNS-QUERY RECORDS, COLLECTED FROM MARCH TO MAY 2019, TO STUDY PLAN CEIBAL USER'S INTERNET BEHAVIOR APPLYING UNSUPERVISED MACHINE LEARNING TECHNIQUES. FIRSTLY, WE CONDUCTED A STATISTICAL ANALYSIS AIMING AT DEPICTING THE DISTRIBUTION OF THE DATA. THEN, TO UNDERSTAND USERS' INTERNET BEHAVIOR, WE PERFORMED PRINCIPAL COMPONENT ANALYSIS (PCA) AND CLUSTERING METHODS. THE RESULTS SHOW THAT INTERNET USE BEHAVIOR IS INFLUENCED BY AGE-GROUP AND TIME OF THE DAY. HOWEVER, IT IS INDEPENDENT OF THE GEOGRAPHICAL LOCATION OF THE USERS. INTERNET USE BEHAVIOR ANALYSIS IS OF PARAMOUNT IMPORTANCE FOR EVIDENCE-BASED DECISION MAKING BY ANY EDUCATION NETWORK PROVIDER, NOT ONLY FROM THE NETWORK-OPERATOR PERSPECTIVE BUT ALSO FOR PROVIDING CRUCIAL INFORMATION FOR LEARNING ANALYTICS PURPOSES.
WITH THE EMERGENCE OF ONLINE LEARNING, MANY SIMILAR COURSES ARE OFFERED ON THE INTERNET. THE INTERNET IS AN ESSENTIAL TOOL FOR IMPROVING LEARNING EFFICIENCY BY ANALYZING HOW LEARNERS SELECT COURSES; IT IS ALSO A CONVENIENT CHANNEL TO EXTRACT LEARNER PREFERENCES. WE ADOPT DYNAMIC LEARNING DATA FROM A POPULAR MASSIVE OPEN ONLINE COURSE (MOOC) WEBSITE IN CHINA, IMOOC.COM, TO DETECT THE ONLINE LEARNING BEHAVIOR PATTERN OF CHINESE ONLINE LEARNERS AND ESTIMATE HERDING BEHAVIOR. CONSIDERING THE LARGE NUMBER OF SIMILAR ONLINE COURSES, THIS STUDY DETERMINES HOW A SUITABLE COURSE CAN BE SELECTED. WE FIRST EVINCE THE EXISTENCE OF HERDING IN ONLINE LEARNING AND THEN DIVIDE HERDING BEHAVIOR INTO DIFFERENT GROUPS: SOME LEARNERS PASSIVELY MIMIC THEIR PEERS (IRRATIONAL HERDING), WHEREAS SOME LEARNERS ENGAGE IN ACTIVE OBSERVATIONAL LEARNING ACCORDING TO THEIR NEEDS (RATIONAL HERDING). THE RESULTS REVEAL THAT RATIONAL HERDING DOMINATES ONLINE LEARNING. SUBSEQUENTLY, SOME VARIABLES AMPLIFY THE HERDING EFFECT, SUCH AS THE DIFFICULTY LEVEL OF THE COURSE AND THE EXPERIENCE OF THE LEARNERS. WE CONCLUDE THAT THE HIGHER THE DIFFICULTY LEVEL OF A COURSE, THE MORE THE RATIONAL HERDING OF THE LEARNERS AND THAT THE MORE EXPERIENCED THE LEARNERS, THE HIGHER THE RATIONAL HERDING. THIS STUDY PROVIDES IMPLICATIONS FOR THE WAY IN COURSE SELECTION IN MOOC, SPECIFICALLY IN THE LEARNING ANALYTICS OF THE ONLINE LEARNERS. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
THE AIM OF THE PAPER IS TO CONSIDER THE ROLE EDUCATION PLAYS IN EVOLVING INDUSTRIAL CONTEXTS AND THE NEED OF APPLYING TRANSFORMATIVE APPROACH TO THE PROCESSES OF INTEGRATION OF INDUSTRY AND EDUCATION. EVOLUTION OF EDUCATION HAS PROVEN ITS CONSERVATIVE NATURE AND A HIGH LEVEL OF RESISTANCE TO CHANGES AND TRANSFORMATION. THE GAP BETWEEN THE INDUSTRY'S NEEDS AND THE SCOPE AND QUALITY OF COMPETENCIES OFFERED GENERALLY BY EDUCATION IS REVEALING THE INCREASING NEED TO FIND NEW EDUCATIONAL MODELS TO FACE THE NEW ERA OF SMART DIGITAL CONNECTED WORLD. AUTHORS UNDERTOOK A LITERATURE REVIEW ON THE MAIN STAGES OF INDUSTRIAL AND EDUCATIONAL SHIFTS THROUGH THE HISTORY. MAPPING THE INDUSTRIAL PECULIARITIES OF EACH STAGE WITH THE EDUCATIONAL CONTEXTS ALLOWED THE AUTHORS TO IDENTIFY THE CRITICAL ASPECTS OF EACH MILESTONE AND FIND THE DISPARITIES THAT IN MANY CASES CAUSE THE INABILITY OF EDUCATION TO COPE WITH THE CHALLENGES CREATED BY INDUSTRY 4.0. IN CONCLUSION, AUTHORS EMPHASISE THE URGENT NEED FOR SUITABLE EDUCATIONAL MODELS IN SHAPING THE ENVIRONMENTS IN WHICH STUDENTS LEARN AS WELL AS THE CONTENT THEY ARE LEARNING TO FACE THE FUTURE CHALLENGES OF THE NEW INDUSTRY ERA. IN THIS CONTEXT, THE AUTHORS REFER TO LEARNING ANALYTICS - AN EMERGING INTERDISCIPLINARY AREA - AS A MAJOR SOPHISTICATED TOOL TO SUPPORT EDUCATION IN COMPLYING WITH THE GROWING NEED FOR ADVANCED SKILLS IN INDUSTRY.
ADVANCES IN SCIENCE AND TECHNOLOGY, THE INTERNET OF THINGS, AND THE PROLIFERATION OF MOBILE APPS ARE CRITICAL FACTORS TO THE CURRENT INCREASE IN THE AMOUNT, STRUCTURE, AND SIZE OF INFORMATION THAT ORGANIZATIONS HAVE TO STORE, PROCESS, AND ANALYZE. TRADITIONAL DATA STORAGES PRESENT TECHNICAL DEFICIENCIES WHEN HANDLING HUGE VOLUMES OF DATA AND ARE NOT ADEQUATE FOR PROCESS MODELING AND BUSINESS INTELLIGENCE; TO COPE WITH THESE DEFICIENCIES, NEW METHODS AND TECHNOLOGIES HAVE BEEN DEVELOPED UNDER THE UMBRELLA OF BIG DATA. HOWEVER, THERE IS STILL THE NEED IN HIGHER EDUCATION INSTITUTIONS (HEIS) OF A TECHNOLOGICAL TOOL THAT CAN BE USED FOR BIG DATA PROCESSING AND KNOWLEDGE MANAGEMENT (KM). TO OVERCOME THIS ISSUE, IT IS ESSENTIAL TO DEVELOP AN INFORMATION INFRASTRUCTURE THAT ALLOWS THE CAPTURING OF KNOWLEDGE AND FACILITATES EXPERIMENTATION BY HAVING CLEANED AND CONSISTENT DATA. THUS, THIS PAPER PRESENTS A HYBRID INFORMATION INFRASTRUCTURE FOR BUSINESS INTELLIGENCE AND ANALYTICS (BI&A) AND KM BASED ON AN EDUCATIONAL DATA WAREHOUSE (EDW) AND AN ENTERPRISE ARCHITECTURE (EA) REPOSITORY THAT ALLOWS THE DIGITIZATION OF KNOWLEDGE AND EMPOWERS THE VISUALIZATION AND THE ANALYSIS OF DISSIMILAR ORGANIZATIONAL COMPONENTS AS PEOPLE, PROCESSES, AND TECHNOLOGY. THE PROPOSED INFRASTRUCTURE WAS CREATED BASED ON RESEARCH AND WILL SERVE TO RUN DIFFERENT EXPERIMENTS TO ANALYZE EDUCATIONAL DATA AND ACADEMIC PROCESSES AND FOR THE CREATION OF EXPLICIT KNOWLEDGE USING DIFFERENT ALGORITHMS AND METHODS OF EDUCATIONAL DATA MINING, LEARNING ANALYTICS, ONLINE ANALYTICAL PROCESSING (OLAP), AND EA ANALYTICS.
WE PRESENT AN INNOVATIVE, COOPERATIVE, AND COMPETITIVE MULTIPLAYER SERIOUS GAME, SUITED FOR THE EDUCATIONAL NEEDS OF SUPPLY CHAIN AND OPERATION MANAGEMENT POST-GRADUATE STUDENTS. HENCE, THE OBJECTIVE IS TO SATISFY THE EVER-INCREASING REQUIREMENT OF STUDENTS TO HAVE THE ABILITY TO EXPERIENCE AND PRACTICE THE THEORY LEARNED IN TRADITIONAL WAYS, FOR ACTIVE KNOWLEDGE ACQUISITION. TO COPE WITH SUCH NEEDS, WE DESIGNED AND IMPLEMENTED A MULTIPLAYER ONLINE SERIOUS GAME, THAT PROVIDES PLAYERS WITH A REALISTIC INDUSTRIAL EXPERIENCE, AND TEACHES THEM HOW TO TAKE A WHOLE RANGE OF DAY-TO DAY AND MEDIUM-TERM CHALLENGING DECISIONS. LEARNERS ARE DIVIDED INTO TEAMS, EACH ONE REPRESENTING AN ORIGINAL EQUIPMENT MANUFACTURER (OEM), IN EVERY TEAM THE STUDENTS WILL COLLABORATE AND WILL COMPETE IN THE SAME MARKET, AND SHARING A LIMITED SET OF SUPPLIERS. TO THIS AIM THEY HAVE TO DEFINE A STRATEGY TO TARGET THE BEST MARKET SEGMENTATION. TEACHERS HAVE THE POSSIBILITY TO INVESTIGATE THE DECISION PATTERS OF THE LEARNERS, ANALYZE KPIS AND LEARNING ANALYTICS, TO BETTER UNDERSTAND THE LEARNING PROCESS AND GUIDE THE LEARNERS IN THEIR EDUCATIONAL JOURNEY. BY MEANS OF A PRELIMINARY QUESTIONNAIRE, THE INTEREST IN USING THE SERIOUS GAME TO STUDY OPERATION MANAGEMENT WAS CONFIRMED. IN ADDITION, THE GAME WAS TESTED BY A SMALL GROUP OF STUDENTS, WHO ACKNOWLEDGED THE EFFECTIVENESS OF THE GAME'S DYNAMICS AS A TOOL TO COMPLEMENT TRADITIONAL TEACHING METHODS.
THERE IS CONTINUAL EVIDENCE OF INEFFECTIVE E-LEARNING PROGRAMMES THAT ARE SET AMID EMERGING INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) TOOLS BY HIGHER EDUCATION (HE) PROVIDERS. WHILE MANY OF THE EXISTING ACCOUNTS OUTLINE THE POTENTIAL OF INTEGRATING SUCH EDUCATIONAL TECHNOLOGY INTO THEIR TEACHING AND LEARNING PRACTICE, OTHER STUDIES POINT OUT THE ADOPTION CHALLENGES OF SUCH PROGRAMMES. THIS CHAPTER TACKLES THIS DILEMMA IN TWO RESPECTS. FIRSTLY, THROUGH AN EXAMINATION OF THE LIMITATIONS SURROUNDING THE INSTRUCTIONAL SYSTEMS DESIGN (ISD) MODELS WHILE URGING THE NEED FOR EMPIRICAL EVIDENCE AND RATIFICATION PROCESSES TO SUBSTANTIATE THESE MODELS AS THEY RELATE TO ONLINE INSTRUCTIONAL ENVIRONMENTS. SECONDLY, THROUGH THE INVESTIGATION OF THE EFFECTIVENESS OFFERED BY ICT TOOLS UNDER DIFFERENT INSTRUCTIONAL ENVIRONMENTS IN ORDER TO FACILITATE THE EFFECTIVE APPLICATION OF E-LEARNING. FIELD EVALUATION IN THE FORM OF A SERIES OF 2X3 FACTORIAL QUASIEXPERIMENTS WAS CONDUCTED AT FOUR HIGHER EDUCATION INSTITUTIONS IN SAUDI ARABIA. THE EMPIRICAL RESULTS CONFIRM THE VALIDITY OF THE ISD MODEL AND RELIABLY CAPTURED ITS EFFECTS IN IMPROVING LEARNERS' PERFORMANCE UNDER THREE INSTRUCTIONAL DELIVERY MODES. THE EMPIRICAL EVIDENCE REVEALS THE EXTENT OF EFFECTIVENESS OF THE PROPOSED PRESCRIPTIVE ISD MODEL ENABLING AN IMPROVED DESIGN OF ICT-BASED HE INSTRUCTIONAL STRATEGIES. ON A MANAGERIAL LEVEL, THE FINDINGS FACILITATE THE DELIVERY MODE DECISION MAKING BY HE PROVIDERS IN TERMS OF THE CONGRUENCE OF TECHNOLOGY INTEGRATION UNDER EACH OF THE THREE LEARNING EXPERIENCES. THE CALIBRATED ASSESSMENT MEASURES PROVIDE A DISCUSSION TO EXTEND THE PRACTICAL IMPLICATION OF THE CURRENT E-PEDAGOGICAL PRACTICE IN THE E-LEARNING INDUSTRY.
THE FOCUS ON ASSESSMENT OF LEARNING EXPERIENCES HAS SHIFTED FROM KNOWLEDGE TO COMPETENCES. UNFORTUNATELY, ASSESSING CERTAIN COMPETENCES IS MAINLY A SUBJECTIVE TASK, BEING PROBLEMATIC FOR BOTH THE EVALUATORS AND THE EVALUATED. ADDITIONALLY, WHEN THE LEARNING PROCESS IS COMPUTER-SUPPORTED AND THE NUMBER OF STUDENTS INCREASES, TRADITIONAL ASSESSMENT PROCEDURES SUFFER FROM SCALABILITY PROBLEMS. IN THIS PAPER WE INTRODUCE A SYSTEM THAT SUPPORTS GRADING LEARNING COMPETENCES ACCORDING TO STUDENTS' PERFORMANCE IN AN ONLINE COURSE. WE AUTOMATICALLY EXTRACT DIFFERENT OBJECTIVE INDICATORS ABOUT STUDENTS' WORK IN A LEARNING MANAGEMENT SYSTEM (LMS). EVALUATORS CAN USE AN ASSESSMENTSPECIFIC QUERY LANGUAGE TO EXPRESS A NUMBER OF REQUIRED INDICATORS. SUCH INDICATORS ARE AUTOMATICALLY EXTRACTED FROM THE ACTIVITY LOGS GENERATED BY THE LMS. A CASE STUDY WITH MOODLE LMS-BASED COURSES IS CARRIED OUT TO EXPLAIN HOW SUCH INDICATORS CAN BE OBTAINED AND HOW TO ANALYZE THE ASSESSMENT RESULTS.
RECENT YEARS HAVE WITNESSED THE RAPID DEVELOPMENT OF DIGITAL DISTANCE LEARNING TOOLS SUCH AS OPEN EDUCATIONAL RESOURCES (OERS) AND MASSIVE OPEN ONLINE COURSES (MOOCS), ALL OF WHICH PRESENT TRADITIONAL EDUCATION SYSTEMS AND STRUCTURES WITH EXCITING NEW OPPORTUNITIES, BUT ALSO SOME CHALLENGES. PROPERLY EXPLOITED, THESE NEW RESOURCES THUS HAVE A HUGE AMOUNT TO OFFER: THEY CAN PROMOTE KNOWLEDGE-SHARING AND INTERCULTURAL COMMUNICATION; THEY CAN BROADEN ACCESS TO EDUCATION, BY ENABLING TEACHING AND LEARNING TO TAKE PLACE VIRTUALLY ANYWHERE; AND IN THEIR ABILITY TO REACH LARGE NUMBERS OF LEARNERS, INCLUDING BY CREATING VIRTUAL COMMUNITIES OF COMMON INTEREST, THEY CAN INCREASE COST-EFFICIENCY. FURTHERMORE, BY THEIR VERY NATURE THEY CAN PROMOTE INNOVATION IN EDUCATION AND NEW METHODOLOGIES. AT A BROADER LEVEL, THE EU IS IN DANGER OF LAGGING BEHIND OTHER REGIONS OF THE WORLD IN THIS FIELD. THEREFORE THERE IS A NEED FOR THINKING "OUT OF THE BOX" AND TRY TO CREATE LARGE SCALE AND NATIONWIDE RESEARCH AND TESTING ENVIRONMENTS.
EARCHERS SHOULD KEEP IN MIND THAT INTERACTIVE STRATEGIES NEED TO BE APPLIED IN ORDER TO MAINTAIN AND EVEN INCREASE LOW PROFICIENCY STUDENT ATTENTION THROUGHOUT THE LECTURES. IN THIS STUDY, WE PROVIDE STR-TEXTS TO NON-NATIVE ENGLISH SPEAKING STUDENTS DURING ENGLISH LECTURES TO FACILITATE LEARNING, ATTENTION, AND MEDITATION. WE CARRY OUT AN EXPERIMENT TO TEST THE FEASIBILITY OF OUR APPROACH. OUR RESULTS SHOW THAT THE PARTICIPANTS IN THE EXPERIMENTAL GROUP BOTH OUTPERFORM THOSE IN THE CONTROL GROUP ON THE POST-TESTS AND HAVE HIGHER ATTENTION AND MEDITATION. IN ADDITION, OUR RESULTS DEMONSTRATE THAT LOWER ABILITY STUDENTS WHO USED STR-TEXTS HAD HIGHER LEARNING PERFORMANCE, ATTENTION, AND MEDITATION THAN LOWER ABILITY STUDENTS IN THE CONTROL GROUP. THE RESULTS ALSO SHOW THAT ATTENTION AND MEDITATION ARE DYNAMIC AND CHANGE THROUGHOUT THE LECTURES. FURTHERMORE, WE FOUND THAT ATTENTION TRENDLINES HAVE A NEGATIVE SLOPE, WHEREAS MEDITATION TRENDLINES HAVE A POSITIVE SLOPE. OUR RESULTS SUGGEST THAT STR-TEXTS ARE USEFUL FOR NON-NATIVE SPEAKING STUDENTS, ESPECIALLY THOSE WITH LOWER LANGUAGE PROFICIENCY, AND CAN ENHANCE THEIR COMPREHENSION OF ENGLISH LECTURES AND ALSO RAISE THEIR ATTENTION AND MEDITATION. HOWEVER, EDUCATORS AND RES
MOBILE TECHNOLOGY IS BECOMING AN INTEGRAL PART OF STUDENT'S LIFE. THE NEW MOBILE LANDSCAPE SHIFTS THE EDUCATIONAL GROUND DEMANDING FOR INNOVATIVE WAYS TO CREATE GAMES FOR EDUCATIONAL PURPOSES. LOCATION-BASED GAMES PROVIDE NEW OPPORTUNITIES FOR EFFECTIVE LEARNING IN AUTHENTIC ENVIRONMENTS. ON THE OTHER HAND, THIS GAME GENRE POSES NEW CHALLENGES TO EDUCATIONAL PRACTITIONERS THAT ARE CLOSELY RELATED TO TECHNOLOGICAL ADVANCES AND ESTABLISHED PEDAGOGICAL PRACTICES. THE AIM OF THIS STUDY IS TO INVESTIGATE ON THE LIMITATIONS AND CHALLENGES OF AUTHORING TOOLS REGARDING THE SUPPORT OF NEW TRENDS AND STANDARDS. IN THE FIRST PLACE, WE EXPLORE EDUCATIONAL SCENARIOS IMPLEMENTED IN THE FORM OF LOCATION-BASED GAMES AND A GENERAL CONCEPTUAL MODEL THAT COMPRISES COMMON GAME ELEMENTS IS FORMULATED. SECONDLY, TWO TYPICAL AUTHORING TOOLS THAT ACTIVELY SUPPORT LOCATION-BASED EXPERIENCES AND GAMES ARE PRESENTED WITH A CRITICAL VIEW WITH REGARD TO THE MODEL PRESENTED. FINALLY, WE DRAW CONCLUSIONS IN ORDER TO INFORM THE DESIGN OF NEXT GENERATION AUTHORING TOOLS. THE PLATFORMS HAVE BEEN USED SUCCESSFULLY IN EDUCATIONAL SETTINGS BY NON-TECHNICAL EDUCATIONAL PRACTITIONERS, HOWEVER THEY MISS THE OPPORTUNITY ARISING FROM INTEGRATING CURRENT TRENDS AND EMERGING STANDARDS FOCUSED ON INCREASING LEARNING TECHNOLOGIES INTEROPERABILITY. SUCH TRENDS AFFORD GAME LEARNING ANALYTICS AS THE NEXT GENERATION MOVE AND NEW WAYS OF INTEGRATING AUTHORING AND ANALYTICS TOOLS NEED TO BE EXPLORED IN ORDER TO MAKE EDUCATIONAL GAMES EVEN MORE IMPACTFUL.
ADVANCED BY POWERFUL VENTURE PHILANTHROPIES, EDUCATIONAL TECHNOLOGY COMPANIES, AND THE US DEPARTMENT OF EDUCATION, A GROWING MOVEMENT TO APPLY 'BIG DATA' THROUGH 'LEARNING ANALYTICS' TO CREATE 'PERSONALIZED LEARNING' IS CURRENTLY UNDERWAY IN K-12 EDUCATION IN THE UNITED STATES. WHILE SCHOLARS HAVE OFFERED VARIOUS CRITIQUES OF THE CORPORATE SCHOOL REFORM AGENDA, THE ROLE OF PERSONALIZED LEARNING TECHNOLOGY IN THE CORPORATIZATION OF PUBLIC EDUCATION HAS NOT BEEN EXTENSIVELY EXAMINED. THROUGH A CONTENT ANALYSIS OF US DEPARTMENT OF EDUCATION REPORTS, PERSONALIZED LEARNING ADVOCACY WHITE PAPERS, AND PUBLISHED RESEARCH MONOGRAPHS, THIS PAPER DETAILS HOW BIG DATA AND ADAPTIVE LEARNING SYSTEMS ARE FUNCTIONING TO REDEFINE EDUCATIONAL POLICY, TEACHING, AND LEARNING IN WAYS THAT TRANSFER EDUCATIONAL DECISIONS FROM PUBLIC SCHOOL CLASSROOMS AND TEACHERS TO PRIVATE CORPORATE SPACES AND AUTHORITIES. THE ANALYSIS SHOWS THAT ALL THREE TYPES OF DOCUMENTS POSITION EDUCATION WITHIN A REDUCTIVE SET OF ECONOMIC RATIONALITIES THAT EMPHASIZE HUMAN CAPITAL DEVELOPMENT, THE EXPANSION OF DATA-DRIVEN INSTRUCTION AND DECISION-MAKING, AND A NARROW CONCEPTION OF LEARNING AS THE ACQUISITION OF DISCRETE SKILLS AND BEHAVIOR MODIFICATION DETACHED FROM BROADER SOCIAL CONTEXTS AND CULTURALLY RELEVANT FORMS OF KNOWLEDGE AND INQUIRY. THE PAPER CONCLUDES BY DRAWING OUT THE CONTRADICTIONS INHERENT TO PERSONALIZED LEARNING TECHNOLOGY AND CORPORATIZATION OF SCHOOLING. IT ARGUES THAT THESE CONTRADICTIONS NECESSITATE A BROAD RETHINKING OF THE VALUE AND PURPOSE OF NEW EDUCATIONAL TECHNOLOGY.
COURSE INSTRUCTORS NEED TO ASSESS THE EFFICACY OF THEIR TEACHING METHODS, BUT EXPERIMENTS IN EDUCATION ARE SELDOM POLITICALLY, ADMINISTRATIVELY, OR ETHICALLY FEASIBLE. QUASI-EXPERIMENTAL TOOLS, ON THE OTHER HAND, ARE OFTEN PROBLEMATIC, AS THEY ARE TYPICALLY TOO COMPLICATED TO BE OF WIDESPREAD USE TO EDUCATORS AND MAY SUFFER FROM SELECTION BIAS OCCURRING DUE TO CONFOUNDING VARIABLES SUCH AS STUDENTS' PRIOR KNOWLEDGE. WE DEVELOPED A MACHINE LEARNING ALGORITHM THAT ACCOUNTS FOR STUDENTS' PRIOR KNOWLEDGE. OUR ALGORITHM IS BASED ON SYMBOLIC REGRESSION THAT USES NON-EXPERIMENTAL DATA ON PREVIOUS SCORES COLLECTED BY THE UNIVERSITY AS INPUT. IT CAN PREDICT 60-70 PERCENT OF VARIATION IN STUDENTS' EXAM SCORES. APPLYING OUR ALGORITHM TO EVALUATE THE IMPACT OF TEACHING METHODS IN AN ORDINARY DIFFERENTIAL EQUATIONS CLASS, WE FOUND THAT CLICKERS WERE A MORE EFFECTIVE TEACHING STRATEGY AS COMPARED TO TRADITIONAL HANDWRITTEN HOMEWORK; HOWEVER, ONLINE HOMEWORK WITH IMMEDIATE FEEDBACK WAS FOUND TO BE EVEN MORE EFFECTIVE THAN CLICKERS. THE NOVELTY OF OUR FINDINGS IS IN THE METHOD (MACHINE LEARNING-BASED ANALYSIS OF NON-EXPERIMENTAL DATA) AND IN THE FACT THAT WE COMPARE THE EFFECTIVENESS OF CLICKERS AND HANDWRITTEN HOMEWORK IN TEACHING UNDERGRADUATE MATHEMATICS. EVALUATING THE METHODS USED IN A CALCULUS CLASS, WE FOUND THAT ACTIVE TEAM WORK SEEMED TO BE MORE BENEFICIAL FOR STUDENTS THAN INDIVIDUAL WORK. OUR ALGORITHM HAS BEEN INTEGRATED INTO AN APP THAT WE ARE SHARING WITH THE EDUCATIONAL COMMUNITY, SO IT CAN BE USED BY PRACTITIONERS WITHOUT ADVANCED METHODOLOGICAL TRAINING.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE ATTRACTED MILLIONS OF PEOPLE WHO ARE GEOGRAPHICALLY DISPERSED. MOOCS ARE MAINLY AUTHORED IN ENGLISH. HOWEVER, A BIG PROPORTION OF THE PARTICIPANTS SPEAKS ENGLISH AS A FOREIGN LANGUAGE. THERE ARE STUDIES REPORTING THAT SOME PARTICIPANTS STRUGGLE WITH UNDERSTANDING THE LANGUAGE IN THE VIDEO LECTURES AND ARE RELUCTANT TO COMMUNICATE WITH OTHER FELLOW LEARNERS. PERSONALISATION SERVICES HAVE BEEN PROPOSED TO HELP LEARNERS WHO ARE HAVING VARIOUS DIFFICULTIES IN MOOCS. IN ORDER TO PROVIDE HELP TO ENGLISH AS A SECOND LANGUAGE SPEAKERS (ESL) IN MOOCS, THERE IS A NEED FOR IDENTIFYING THEM IN CASE THEIR LANGUAGE INFORMATION IS NOT PROVIDED. THE AIM OF OUR RESEARCH IS TO I) IDENTIFY ESL PARTICIPANTS, II) INVESTIGATE THE BEHAVIOURS OF ESL PARTICIPANTS, AND III) PREDICT THEIR PERFORMANCE BASED ON THEIR PREVIOUS ACTIVITIES IN THE COURSE TO ENABLE A PERSONALISED INTERVENTION. IN OUR PREVIOUS STUDIES, MOOC LEARNERS WERE GROUPED BASED ON THEIR LOCATION AND THEIR COMMENTS. THIS PAPER INVESTIGATES THE OVERALL SOCIAL ENGAGEMENT OF PARTICIPANTS IN A MOOC.
THERE HAS BEEN DEBATES RELATED TO ONLINE AND BLENDED LEARNING FROM A PERSPECTIVE OF LEARNER EXPERIENCES IN TERMS OF STUDENT SATISFACTION, ENGAGEMENT AND PERFORMANCES. IN THIS PAPER, WE ANALYZE STUDENT FEEDBACK AND REPORT THE FINDINGS OF A STUDY OF THE RELATIONSHIPS BETWEEN STUDENT SATISFACTION AND THEIR ENGAGEMENT IN AN ONLINE COURSE WITH THEIR OVERALL PERFORMANCES. THE MODULE WAS OFFERED ONLINE TO 844 UNIVERSITY STUDENTS IN THE FIRST YEAR ACROSS DIFFERENT DISCIPLINES, NAMELY ENGINEERING, SCIENCE, HUMANITIES, MANAGEMENT AND AGRICULTURE. IT WAS ASSESSED MAINLY THROUGH CONTINUOUS ASSESSMENTS AND WAS DESIGNED USING A LEARNING-BY-DOING PEDAGOGICAL APPROACH. THE FOCUS WAS ON THE ACQUISITION OF NEW SKILLS AND COMPETENCIES, AND THEIR APPLICATION IN AUTHENTIC MINI PROJECTS THROUGHOUT THE MODULE. STUDENT FEEDBACK WAS CODED AND ANALYZED FOR 665 STUDENTS BOTH FROM A QUANTITATIVE AND QUALITATIVE PERSPECTIVE. THE ASSOCIATION BETWEEN SATISFACTION AND ENGAGEMENT WAS SIGNIFICANT AND POSITIVELY CORRELATED. FURTHERMORE, THERE WAS A WEAK BUT POSITIVE SIGNIFICANT CORRELATION BETWEEN SATISFACTION AND ENGAGEMENT WITH THEIR OVERALL PERFORMANCES. STUDENTS WERE GENERALLY SATISFIED WITH THE LEARNING DESIGN PHILOSOPHY, IRRESPECTIVE OF THEIR PERFORMANCE LEVELS. STUDENTS, HOWEVER, REPORTED ISSUES RELATED TO LACK OF TUTOR SUPPORT AND EXPERIENCING TECHNICAL DIFFICULTIES ACROSS GROUPS. THE FINDINGS RAISE IMPLICATIONS FOR INSTITUTIONAL E-LEARNING POLICY MAKING TO IMPROVE STUDENT EXPERIENCES. THE FACTORS THAT ARE IMPORTANT RELATE TO THE OBJECT OF SUCH POLICIES, LEARNING DESIGN MODELS, STUDENT SUPPORT AND COUNSELING, AND LEARNING ANALYTICS.
THIS RESEARCH TO PRACTICE FULL PAPER INTRODUCES AN APPROACH FOR THE EARLY IDENTIFICATION OF STUDENTS AT RISK OF DROPPING OUT OF THEIR DISTANCE LEARNING COURSES. STUDENTS DROPOUT IN UNIVERSITY COURSES HAVE LONG BEEN A MAJOR ISSUE LEADING TO SOCIAL, ACADEMIC AND FINANCIAL IMPACTS. PREDICTIVE MODELLING ANALYSIS HAS BEEN USED AS A TOOL TO IDENTIFY AT AN EARLY STAGE PROBABLE CASES OF DROPOUT. IN DISTANCE LEARNING, THIS TYPE OF ANALYSIS IS MADE POSSIBLE MOSTLY BECAUSE OF THE INCREASING ADOPTION RATE OF VIRTUAL LEARNING ENVIRONMENTS (VLES) WHERE DATA ON THE STUDENT ACADEMIC ACTIVITIES CAN BE CONTINUOUSLY RECORDED. THIS PAPER DISCUSSES THE DESIGN AND VALIDATION OF A LEARNING ANALYTICS SYSTEM FIR EARLY IDENTIFICATION OF STUDENTS AT RISK OF DROPPING OUT. THE CASE STUDY PRESENTED RELIES ON DATA COLLECTED FROM TWO POSTGRADUATE COURSES AS PART OF BRAZILS OPEN UNIVERSITY. THE METHODOLOGY FOR THE SYSTEM DESIGN AND VALIDATION COMPRISES THE STEPS TO BUILD AN INTELLIGENT DATA PIPELINE. JUPYTER NOTEBOOKS HAVE BEEN D USED AS THE DATA SCIENCE ANALYSIS ENVIRONMENT IN ORDER TO CREATE DATA PIPELINES AND HAVE THEIR PERFORMANCE EVALUATED. RESULTS OBTAINED FROM THE VALIDATION OF MODELS BUILT OUT OF EIGHT MACHINE LEARNING TECHNIQUES SHOW AN AVERAGE ACCURACY OF 84% AMONG THE SET OF ML TECHNIQUES TESTED. THE HIGHEST ACCURACY IS DELIVERED BY THE EXTRA TREES CLASSIFIER WITH 88%. LOGISTIC REGRESSION PERFORMED THE WORST PERFORMANCE WITH AN ACCURACY OF 79%.
FORMATIVE ASSESSMENT IS REGARDED AS ONE OF THE MAIN CHALLENGES IN MOOC RESEARCH AND PRACTICE. WHILE MOOC PARTICIPANTS NEED FORMATIVE ASSESSMENT AND FEEDBACK TO SELF-REGULATE THEIR OWN LEARNING, PROVIDING TIMELY AND PERSONALISED FEEDBACK TO LARGE COHORTS OF STUDENTS POSES ISSUES IN TERMS OF SCALABILITY AND SUSTAINABILITY THAT REQUIRE DESIGNERS TO WORK OUT TECHNOLOGICAL SOLUTIONS TO PRODUCE EFFECTIVE FEEDBACK. THIS PAPER PUTS FORWARD A PROPOSAL FOR A TYPE OF FEEDBACK WHICH IS PARTICULARLY SUITED TO ASSESS NON DECLARATIVE KNOWLEDGE, LIKE CRITICAL THINKING ABOUT A COMPLEX SUBJECT. THE FEEDBACK PROPOSED RELIES ON THE POWER OF COMPARISON WITH PEERS' OPINIONS AND PRACTICE, AND IS COMPLEMENTARY TO THE MOST WELL-KNOWN TECHNIQUE OF PEER REVIEW, WHICH REQUIRES SOME DEGREE OF SYNCHRONICITY BETWEEN PARTICIPANTS. THE PROPOSED FEEDBACK CONSISTS IN VISUALISING A TABLE CONTAINING THE ANSWERS AND BEHAVIOURS OF INDIVIDUAL PARTICIPANTS SIDE-BY-SIDE TO DESCRIPTIVE STATISTICS OF THE ANALOGOUS DATA CONCERNING THE WHOLE COHORT. QUANTITATIVE AND QUALITATIVE DATA WERE COLLECTED TO INVESTIGATE SELF-REPORTED USEFULNESS AND POTENTIAL OF THE FEEDBACK. WHILE USEFULNESS WAS STATISTICALLY HIGHER THE MID-POINT OF THE SCALE, NO SIGNIFICANT DIFFERENCE WAS FOUND WHEN CONSIDERING THE NATURE OF DATA (ANSWERS TO SURVEYS VS ACTIONS CARRIED OUT) AS AN INDEPENDENT VARIABLE. A FEW SUGGESTIONS ON HOW TO IMPROVE THIS FEEDBACK WERE ALSO IDENTIFIED.
THE TECHNOLOGICAL IMPROVEMENTS OF RECENT YEARS MADE POSSIBLE TO GIVE A BOOST TO IMMERSIVE TECHNOLOGIES SUCH AS VIRTUAL OR AUGMENTED REALITY. THIS IMPULSE HAS LOWERED COSTS, MAKING IT POSSIBLE FOR ANYONE TO CREATE CONTENT IN 360 DEGREES. IN EDUCATION, THIS ALLOWS THE STUDENT TO BE EASILY PLACED IN CONTEXTS WHERE OTHERWISE A LARGE NUMBER OF RESOURCES WOULD BE NEEDED. THERE ARE CURRENTLY MANY APPLICATIONS FOR THE CREATION OF INTERACTIVE ACTIVITIES, BUT FEW TOTALLY FREE AND EVEN LESS WITH THE ABILITY TO INTEGRATE INTO DIFFERENT LEARNING MANAGEMENT SYSTEMS. IT IS DIFFICULT FOR TEACHERS TO IMPLEMENT INTERACTIVE LEARNING EXPERIENCES AND EVEN MORE SO TO OBTAIN DETAILED FEEDBACK FROM STUDENTS. THIS ARTICLE DETAILS THE CREATION OF AN INTERACTIVE LEARNING AND ASSESSMENT ACTIVITY, COMBINING TEXT, INTERACTIVE VIDEOS AND 360-DEGREE SCENES, CAPABLE OF PROVIDING INFORMATION ON STUDENT INTERACTION THROUGH AN XAPI. THIS INFORMATION WILL BE COLLECTED IN A LEARNING RECORD STORE THAT CAN BE TAKEN AS A DATA SOURCE BY LEARNING AND CONTENT MANAGEMENT SYSTEMS, OPENING THE OPPORTUNITY TO APPLY LEARNING ANALYTICS TECHNIQUES. AS AN EXAMPLE, WE USED A TOOL FOR CREATING A SERIOUS GAME THAT SERVES TO PREPARE A LABORATORY PRACTICE OF THE SUBJECT LOCAL NETWORKS, PROVIDING A FAST, INTUITIVE AND USEFUL WAY TO APPLY THE METHODOLOGY FLIP-GAME ENGINEERING AND TECHNOLOGY (FLIP-GET) DEVELOPED BY RESEARCHERS OF THE UNIVERSITY OF CADIZ.
IN RECENT YEARS, THE U.S. HAS BEEN EXPERIENCING A SHIFT IN TECHNICAL AND PROFESSIONAL EDUCATION. INDUSTRY, EDUCATION AND GOVERNMENT STAKEHOLDERS ARE REDEFINING THEIR PARTNERSHIPS AND WORKING TOGETHER TO CREATE COMPETENCY-BASED, INDUSTRY-DRIVEN EDUCATION AT THE LOCAL, STATE, AND NATIONAL LEVELS. THESE FORM THE FUNDAMENTAL PRINCIPLES OF A PARADIGM SHIFT IN EDUCATION NEEDED TO COMBINE AND ENHANCE INNOVATIVE EDUCATIONAL APPROACHES FROM DIFFERENT RESEARCH FIELDS, AS WELL AS BEST-PRACTICE MODELS FROM OTHER COUNTRIES. THIS PARADIGM SHIFT PRESENTS A CHALLENGE, AS EDUCATION AND PUBLIC SECTORS ARE TYPICALLY NOT FAMILIAR WITH MATURED (PROJECT) MANAGEMENT PRINCIPLES, AND OFTEN FOLLOW THE "GARBAGE CAN-MODEL"(COHEN, 1964), WHILE, SIMULTANEOUSLY, INDUSTRY EXPERTS LACK EXPERIENCE AND KNOWLEDGE ABOUT THE ACADEMIC AND PUBLIC WORLD. BUT BOTH HAVE TO ANSWER QUESTIONS LIKE: HOW CAN PM MEASURE PROGRESS IN AN EDUCATIONAL PROJECT? WHICH PM METHODS ARE MOST/LEAST HELPFUL? USING A CASE STUDY APPROACH TO THE RESEARCH, THIS PAPER WILL ANALYZE SEVERAL HIGHLY VISIBLE AND INNOVATIVE STAKEHOLDER PARTNERSHIPS THAT ARE BREAKING TRADITIONAL CURRICULUM AND ASSESSMENT MODELS. USING INDUSTRY SUBJECT MATTER EXPERTS, BOTH THE MICHIGAN ADVANCED TECHNICIAN TRAINING (MAT2) PROGRAM HAVE DEVELOPED A COMPETENCY-BASED CURRICULUM THAT INCORPORATES A SOPHISTICATED METHODOLOGY OF ASSESSMENTS USING THE INDUSTRY-DRIVEN DACUM APPROACH AS THE FOUNDATION. THE NEW MODEL REQUIRES A SOPHISTICATED METHODOLOGY FOR TRACKING LEARNING ANALYTICS INCLUDING PERFORMANCE, COMPLETION RATES, AND LEARNING GAPS. THIS PAPER WILL PROPOSE A SOLUTION.
WE PRESENT A SURVEY ON THE CURRENT STATUS OF EDUCATION ANALYTICS. OVER 80 STATE-OF-THE-ART RESEARCH PROJECTS AND REPORTS, PUBLISHED FROM 2013 TO 2017 AND ARCHIVED IN IEEE XPLORE, ARE LISTED. FOCUSING ON STUDENT PERFORMANCE PREDICTION AND INTERVENTION, WE HIGHLIGHT THE DATA SETS USED, TECHNIQUES EMPLOYED, ALGORITHMS UTILIZED, AND PARTICULAR INFORMATION IN EACH PAPER/PROJECT.
THIS PAPER ANALYZES STUDENTS' SELF-PERCEPTION OF SUCCESS AND LEARNING EFFECTIVENESS AFTER USING NON-COMPULSORY GAMIFICATION IN AN ONLINE CYBCOURSE. FOR THIS PURPOSE, WE DESIGNED A CYBERSECURITY GAME BASED ON COGNITIVE CONSTRUCTIVISM LEARNING THEORY. WE BUILT THE GAME SCENES USING METAPHORS TO PRESENT THE MAIN CYBERSECURITY CONTENTS TO THE STUDENTS. WE DELIVERED THE GAME IN A REGULAR COURSE WITH TWO OBJECTIVES: FIRST, TO FIND THE PRIMARY DESIGN FACTORS THAT AFFECT STUDENTS' SELF-PERCEPTION OF SUCCESS. WE PROPOSE A STRUCTURAL EQUATION MODEL TO FIND OUT THE ELEMENTS WITH THE MOST SIGNIFICANT IMPACT ON THE STUDENTS' SELF-PERCEPTION OF SUCCESS. THE RESULTS SHOW THAT THE REALISTIC GAME DESIGN AND THE CONTEXTUALIZATION OF THE GAME DO HAVE A NOTABLE INFLUENCE. THEY ARE BOTH EXAMPLES OF BEST PRACTICES IN GAME DESIGN; SECOND, TO EVALUATE THE LEARNING EFFECTIVENESS OF THE GAME. THE RESULTS SUGGEST A HIGH CORRELATION BETWEEN PLAYING THE GAME AND SUCCEEDING IN THE COURSE. MOREOVER, CHRONOLOGICAL ANALYSIS OF THE PERFORMANCE REVEALS THAT THE INTENTION TO PLAY THE GAME COULD BE A SIMPLE DROPOUT PREDICTOR. THUS, INTRODUCING THE GAME IN THE EDUCATIONAL CURRICULA IMPROVES STUDENT ENGAGEMENT AND CONSOLIDATES THEIR KNOWLEDGE ON CYBERSECURITY.
THIS PAPER PRESENTS AN APPLICATION SCENARIO OF A RULE-BASED RECOMMENDER MODEL (LIME) TO A PSLC COGNITIVE TUTOR ANALYTICS DATABASE. LIME IS A RECOMMENDATION MODEL WHICH PROVIDES A FORMATIVE SUPPORT TO STUDENTS, TUTORS AND LECTURERS, THANKS TO A VISUAL INTERACTION BETWEEN USERS AND THE ELEARNING SYSTEM. ON THE OTHER SIDE, COGNITIVE COMPUTER TUTORS, KINDLY AVAILABLE FROM THE PITTSBURG SCIENCE LEARNING CENTRE (PSLC) AND THE CARNEGIE MELLON UNIVERSITY (CMU) LEARNING ANALYTICS REPOSITORY, PROVIDE INTELLIGENT AND VISUAL INTERACTIVE ACTIVITIES THAT HELP MIDDLE-SCHOOL STUDENTS IMPROVE THEIR DAILY ACADEMIC SKILLS. THEY ALSO OFFER ON-DEMAND STEP-BY-STEP FEEDBACK DURING PROBLEM SOLVING AND REPORT ON STUDENT PROGRESS FOR TEACHERS AND STUDENTS. EVERY SINGLE LEARNER INTERACTION WITH A TUTOR IS LOGGED AND IS FREELY AVAILABLE WITHIN SPECIFIC DATABASES EXPRESSED IN THE PSLC LOGGING FORMAT (ALSO KNOWN AS TUTOR MESSAGE FORMAT). THIS FORMAT IS WELL DEFINED IN AN XML SCHEMA DEFINITION (XSD FILE) AND A DOCUMENT TYPE DEFINITION (DTD FILE). WE BEGIN OUR TEXT BY PRESENTING ALL THESE TECHNOLOGIES (LIME MODEL, TUTORS AND THE PSLC LOGGING FORMAT). WE THEN FORGE SOME EXAMPLE CONFIGURATIONS OF OUR RECOMMENDER MODEL, AND APPLY THEM TO A DATABASE OF OVER 1250 STUDENTS, 2250 INTERACTION HOURS AND ALMOST 1 GB OF LOGGED DATA. THE RESULT OF THIS PROCESS IS A POTENTIAL RECOMMENDATION TO EACH OF THESE STUDENTS AFTER THE USE PERIOD OF THE INTERACTIVE TUTOR SOFTWARE. FINALLY, WE PRESENT THE RESULTS OF THIS PRACTICAL IMPLEMENTATION, AND DISCUSS THE VALIDITY OR OUR MODEL IN THE RELATED TUTORING ENVIRONMENTS.
ACADEMIC WORK: GRADING STUDENT ASSIGNMENTS OR CONDUCTING LITERATURE SURVEYS ENTAILS EXTENSIVE READING, WHICH IS BOTH A TIME CONSUMING AND COGNITIVELY DEMANDING TASK. THE CHALLENGE INCREASES PROPORTIONALLY WITH THE INCREASE OF VOLUME OF THE TEXTUAL CONTENT. IN THIS PAPER, WE PROPOSE A NOVEL APPROACH TO VISUALIZATION OF TEXTUAL DATA THAT DEPICTS INFORMATION ON A CONTINUUM (TEMPORAL OR SPATIAL) ALLOWING INFERENCES TO BE MADE ABOUT THEMATIC ORGANIZATION OF A DOCUMENT AND ITS STRUCTURE. OUR VISUALIZATION METHOD TERMED THEMETRACK CREATES A VISUAL MAP: DELINEATING KEY THEMES AND TRACKING THEIR PRESENCE THROUGHOUT THE TEXT, HIGHLIGHTING THEIR VARIATIONS AND RELATIONSHIPS. IT AIMS TO MAKE THE REVIEW OF TEXTUAL DATA MORE EFFICIENT. TO ASSESS THE VIABILITY OF THE PROPOSED APPROACH, A SERIES OF EXPERIMENTS WERE CONDUCTED USING GRADUATE-LEVEL THESES AND PUBLISHED ARTICLES IN THE PEER REVIEWED JOURNALS. THE APPLICATIONS OF THE PROPOSED METHOD ARE DISCUSSED AND THE REAL-WORD EXAMPLES ARE PROVIDED.
MACHINE LEARNING TECHNIQUES ARE APPLIED ON HIGHER EDUCATION DATA FOR ANALYZING THE INTERACTION BETWEEN THE STUDENTS AND ELECTRONIC LEARNING SYSTEMS. THIS TYPE OF ANALYSIS SERVES IN PREDICTING STUDENTS' SCORES, IN ALERTING STUDENTS-AT-RISK, AND IN MANAGING THE DEGREE OF STUDENT ENGAGEMENT TO EDUCATIONAL SYSTEM. THE APPROACHES IN THIS WORK IMPLEMENTS THE DIVIDE AND CONQUER ALGORITHM ON FEATURE SET OF AN EDUCATIONAL DATA SET TO ENHANCE THE ANALYSIS AND PREDICTION ACCURACY. IT DIVIDES THE FEATURE SET INTO A NUMBER OF LOGICAL SUBGROUPS BASED ON THE PROBLEM DOMAIN. EACH SUBGROUP IS ANALYZED SEPARATELY AND THE FINAL RESULT IS THE COMBINATION OF THE RESULTS OF THE ANALYSIS OF THESE SUBGROUPS. THE CLASSIFIER THAT SHOWS THE BEST PREDICTION ACCURACY IS DEPENDENT ON THE LOGICAL NON-STATISTICAL NATURE OF THE FEATURES IN EACH GROUP. BOTH TRADITIONAL AND BOOSTING CLASSIFIERS ARE UTILIZED ON EACH DATASET, FROM WHICH A COMPARISON IS CONDUCTED TO SHOW THE BEST CLASSIFIERS ALONG WITH THE BEST DATASET. THIS APPROACH PROVIDES THE POSSIBILITY TO APPLY A BRUTE FORCE ALGORITHM IN THE SELECTION OF THE BEST FEATURE SUBGROUPS WITH A LOW COMPUTATIONAL COMPLEXITY. THE EXPERIMENTAL WORK SHOWS A HIGH PREDICTION ACCURACY OF THE STUDENTS-AT-RISK RELATIVE TO THE CURRENT RESEARCH, AND PROVIDES A LIST OF NEW IMPORTANT FEATURES IN THE FIELD OF ELECTRONIC LEARNING SYSTEMS.
THE COMPUTING EDUCATION RESEARCH LITERATURE CONTAINS A WIDE VARIETY OF METHODS THAT CAN BE USED TO IDENTIFY STUDENTS WHO ARE EITHER AT RISK OF FAILING THEIR STUDIES OR WHO COULD BENEFIT FROM ADDITIONAL CHALLENGES. MANY OF THESE ARE BASED ON MACHINE-LEARNING MODELS THAT LEARN TO MAKE PREDICTIONS BASED ON PREVIOUSLY OBSERVED DATA. HOWEVER, IN EDUCATIONAL CONTEXTS, DIFFERENCES BETWEEN COURSES SET HUGE CHALLENGES FOR THE GENERALIZABILITY OF THESE METHODS. FOR EXAMPLE, TRADITIONAL MACHINE-LEARNING METHODS ASSUME IDENTICAL DISTRIBUTION IN ALL DATA- IN OUR TERMS, TRADITIONAL MACHINE-LEARNING METHODS ASSUME THAT ALL TEACHING CONTEXTS ARE ALIKE. IN PRACTICE, DATA COLLECTED FROM DIFFERENT COURSES CAN BE VERY DIFFERENT AS A VARIETY OF FACTORS MAY CHANGE, INCLUDING GRADING, MATERIALS, TEACHING APPROACH, AND THE STUDENTS. TRANSFER-LEARNING METHODOLOGIES HAVE BEEN CREATED TO ADDRESS THIS CHALLENGE. THEY RELAX THE STRICT ASSUMPTION OF IDENTICAL DISTRIBUTION FOR TRAINING AND TEST DATA. SOME SIMILARITY BETWEEN THE CONTEXTS IS STILL NEEDED FOR EFFICIENT LEARNING. IN THIS WORK, WE REVIEW THE CONCEPT OF TRANSFER LEARNING ESPECIALLY FOR THE PURPOSE OF PREDICTING THE OUTCOME OF AN INTRODUCTORY PROGRAMMING COURSE AND CONTRAST THE RESULTS WITH THOSE FROM TRADITIONAL MACHINE-LEARNING METHODS. THE METHODS ARE EVALUATED USING DATA COLLECTED IN SITU FROM TWO SEPARATE INTRODUCTORY PROGRAMMING COURSES. WE EMPIRICALLY SHOW THAT TRANSFER-LEARNING METHODS ARE ABLE TO IMPROVE THE PREDICTIONS, ESPECIALLY IN CASES WITH LIMITED AMOUNT OF TRAINING DATA, FOR EXAMPLE, WHEN MAKING EARLY PREDICTIONS FOR A NEW CONTEXT. THE DIFFERENCE IN PREDICTIVE POWER IS, HOWEVER, RATHER SUBTLE, AND TRADITIONAL MACHINE-LEARNING MODELS CAN BE SUFFICIENTLY ACCURATE ASSUMING THE CONTEXTS ARE CLOSELY RELATED AND THE FEATURES DESCRIBING THE STUDENT ACTIVITY ARE CAREFULLY CHOSEN TO BE INSENSITIVE TO THE FINE DIFFERENCES.
ENGLISH IS THE IMPORTANT INTERNATIONAL LANGUAGE IN THE WORLD. IN ASIA, MANY NON-ENGLISH-SPEAKING COUNTRIES REGARD ENGLISH WRITING EDUCATION AS ONE OF THE PRIMARY GOALS OF ENGLISH EDUCATION REFORM. FACED WITH THIS TREND, TEACHERS TRIED TO TRAIN STUDENTS TO MASTER THE SKILLS OF ENGLISH WRITING TO COPE WITH THE GLOBAL VILLAGES IN THE TWENTY FIRST CENTURY. IN THE FLIPPED CLASSROOM, ALTHOUGH STUDENTS COULD EFFECTIVELY RECEIVE OUT-OF-CLASS AND IN-CLASS OPPORTUNITIES FOR EXERCISES TO IMPROVE LEARNING; HOWEVER, SUMMARIZING, ORGANIZING, EVEN EVALUATING OTHERS' WRITING REMAIN DIFFICULT TASKS FOR STUDENTS. IN ORDER TO DEVELOP STUDENTS' LEARNER AUTONOMY OR HIGH-LEVEL THINKING SKILLS TO ACHIEVE THE GOAL OF ENGLISH TEACHING, THE USE OF MIND-MAPPING LEARNING STRATEGY IS KNOWN AN EFFECTIVE KNOWLEDGE CONSTRUCTION TOOL FOR HELPING STUDENTS' ORGANIZATIONAL THINKING AND PARAPHRASING IN ENGLISH WRITING SKILLS. BESIDES, MANY PREVIOUS STUDIES HAVE CONSIDERED THE PEER ASSESSMENT AN EFFECTIVE LEARNING STRATEGY IN THE WRITING CLASSROOMS TO PROVIDE STUDENTS WITH A TEACHER'S PERSPECTIVE VIEW TO THINK AND EVALUATE WRITING. THEREFORE, THIS RESEARCH DEVELOPED AN ONLINE PEER ASSESSMENT APPROACH TO SUPPORTING MIND-MAPPING FLIPPED CLASSROOMS. MOREOVER, AN EXPERIMENT HAS BEEN CONDUCTED TO EVALUATE THE ADVANTAGES AND DISADVANTAGES OF THE PROPOSED APPROACH ON STUDENTS' ENGLISH LEARNING ANALYTICS SUCH AS TIME INVOLVEMENT AND LEARNING REFLECTIONS. IT IS IMPORTANT THAT EDUCATORS COULD CONTINUOUSLY CREATE THE ONLINE PEER ASSESSMENT LEARNING ENVIRONMENT FOR LEARNERS AND AIM FOR THE GOALS TO HELP LEARNERS BECOME MORE CRITICAL, INDEPENDENT, AND AUTONOMOUS IN ENGLISH LANGUAGE LEARNING.
THE PAPER IS A STUDY ABOUT THE MOOC EXPERIENCE AT THE SPANISH NATIONAL UNIVERSITY OF DISTANCE EDUCATION (UNED), WHERE WE HAVE COLLECTED INITIAL AND FINAL INFORMATION ABOUT LEARNERS' PROFILES AND OPINIONS, AS WELL AS ENROLMENT, COMPLETION AND CERTIFICATION RATES. IT IS A SURVEY-BASED STUDY COVERING 17 MOOCS OFFERED IN UNED'S OWN PLATFORM, AND COLLECTS INFORMATION FROM A SAMPLE OF MORE THAN 24000 LEARNERS (INITIAL SURVEY) AND 2003 LEARNERS (FINAL SURVEY). THE PAPER FIRST PRESENTS AN OVERVIEW OF THE MOOC EXPERIENCE AT UNED, INTRODUCING THE MAIN FEATURES OF THESE COURSES. AFTERWARDS, IT FOCUSES ON THE METHODOLOGY USED IN THE STUDY AND IN THE INFORMATION GATHERED IN THE SECOND EDITION OF UNED MOOCS, WHICH TOOK PLACE FROM NOVEMBER 2013 UNTIL MARCH 2014. LEARNERS' AVERAGE PROFILE IS A SPANISH FEMALE, APPROXIMATELY 37 YEARS OLD, WITH A UNIVERSITY DEGREE, AND GENERALLY EMPLOYED. FOR MANY OF THE PARTICIPANTS, UNED MOOCS WERE THEIR FIRST EXPERIENCES WITH THESE SORTS OF COURSES, AND THE MAIN REASONS FOR ENROLMENT WERE THE COURSE TOPIC AND THE PERCEIVED USEFULNESS FOR PROFESSIONAL DEVELOPMENT. THE EXPECTATIONS REGARDING COMPLETION AND CERTIFICATION WHERE INITIALLY VERY HIGH, BUT COMPLETION RATES REMAIN BELOW 14%. IN THE FINAL SURVEY, THE OVERALL EXPERIENCE IN THE MOOCS AND THE DIFFERENT TOOLS USED IN THE COURSES WERE HIGHLY VALUED BY LEARNERS, EXCEPT THE SUPPORT FIGURES ( CURATOR, FACILITATOR, PEERS), WHICH RECEIVED LOWER RATINGS. THESE FINDINGS ARE OF INTEREST FOR THE INSTITUTION AND FURTHER RESEARCH, REFINING LEARNING ANALYTICS, IS ENCOURAGED.
OVER THE LAST FEW YEARS, THE USE OF MOBILE TECHNOLOGIES HAS BROUGHT THE FORMULATION OF LOCATION-BASED LEARNING APPROACHES SHAPING NEW OR ENHANCED EDUCATIONAL ACTIVITIES. INVOLVING TEACHERS IN THE DESIGN OF THESE ACTIVITIES IS IMPORTANT BECAUSE THE DESIGNS NEED TO BE ALIGNED WITH THE REQUIREMENTS OF THE SPECIFIC EDUCATIONAL SETTINGS. YET ANALYSING THE IMPLEMENTATION OF THE ACTIVITIES WITH STUDENTS IS ALSO CRITICAL, NOT ONLY FOR ASSESSMENT PURPOSES BUT ALSO FOR ENABLING THE IDENTIFICATION OF LEARNING DESIGN ELEMENTS THAT SHOULD BE REVISED AND IMPROVED. THIS PAPER STUDIES A CASE THAT APPLIES VISUALIZATIONS TO SUPPORT STUDENTS' SELF-ASSESSMENT AND TEACHERS' INQUIRY OF A MOBILE LEARNING DESIGN. THE DESIGN IS A GAMIFIED LOCATION-BASED LEARNING ACTIVITY COMPOSED BY GEOLOCATED QUESTIONS AND IMPLEMENTED WITH THE QUESTINSITU: THE GAME MOBILE APPLICATION. THE ACTIVITY WAS DESIGNED BY SEVEN TEACHERS AND ENACTED BY 81 SECONDARY EDUCATION STUDENTS ORGANIZED IN A TOTAL OF 23 GROUPS. LOG FILES, GATHERED FROM QUESTINSITU: THE GAME, PROVIDED THE DATA FOR THE VISUALIZATIONS, WHICH REPRESENTED RELEVANT ASPECTS OF THE GROUP ACTIVITY ENACTMENT (BOTH TIME USED TO ANSWER QUESTIONS AND TO REACH THE GEOGRAPHICAL ZONE OF THE QUESTIONS, SCORES OBTAINED PER ZONE, ETC). ON THE ONE HAND, THE VISUALIZATIONS WERE DISCUSSED WITH THE TEACHERS AS A LEARNING ANALYTICS TOOL POTENTIALLY USEFUL TO CONSIDER WHEN REDESIGNING THE ACTIVITY, IF NEEDED. ON THE OTHER HAND, THE STUDY SHOWS THAT THE VISUALIZATIONS LED STUDENTS TO MAKE A BETTER DIAGNOSIS OF THEIR OWN ACTIVITY PERFORMANCE.
TRADITIONALLY, THE QUALITY OF A COURSE OFFERING IS MEASURED BASED ON LEARNER FEEDBACK AT THE END OF THE OFFERING. THIS CHAPTER OFFERS A METHOD TO MEASURE THE QUALITY OF A COURSE OFFERING-CONTINUALLY, FORMATIVELY, AND SUMMATIVELY-USING FACTORS SUCH AS THE QUALITY OF RESOURCES USED, LEARNER MOTIVATION, LEARNER CAPACITY, LEARNER COMPETENCY GROWTH, AND INSTRUCTOR COMPETENCE. THESE FACTORS ARE REPRESENTED IN A BAYESIAN BELIEF NETWORK (BBN) IN A SYSTEM CALLED MI-IDEM. MI-IDEM RECEIVES STREAMS OF DATA CORRESPONDING TO THESE FACTORS AS AND WHEN THEY BECOME AVAILABLE, WHICH LEADS TO ESTIMATES OF QUALITY OF THE COURSE OFFERING BASED ON INDIVIDUAL FACTORS AS WELL AS AN OVERALL QUALITY OF THE OFFERING. CONTINUOUS, FORMATIVE, AND SUMMATIVE COURSE QUALITY MEASUREMENTS ARE IMPERATIVE TO IDENTIFY WEAKNESSES IN THE LEARNING PROCESS OF STUDENTS AND TO ASSIST THEM WHEN THEY NEED HELP. THIS CHAPTER PROFESSES THE NEED FOR A COMPREHENSIVE MEASUREMENT OF COURSE QUALITY AND ENSUING INITIATIVES TO PERSONALIZE AND ADAPT COURSE OFFERINGS. IT PRESENTS TWO CASE STUDIES OF THIS NOVEL APPROACH: FIRST, MEASUREMENT OF THE QUALITY OF A COURSE OFFERING IN A BLENDED ONLINE LEARNING ENVIRONMENT AND SECOND, MEASUREMENT OF THE QUALITY OF TRAINING COURSE OFFERING IN AN INDUSTRY ENVIRONMENT.
PURPOSE - THE PURPOSE OF THIS PAPER IS TO PROPOSE A CONCEPTUAL MODEL OF ENGAGEMENT, APPROPRIATED FROM SOCIAL MEDIA MARKETING, AS A SENSE-MAKING FRAMEWORK TO UNDERSTAND ENGAGEMENT AS A MEASURABLE PROCESS THROUGH THE DEVELOPMENT OF ENGAGEMENT PROFILES. TO EXPLORE ITS POTENTIAL APPLICATION TO EDUCATION THE PAPER FOLLOWS PREVIOUS WORK WITH PERSONALISED LEARNING STRATEGIES TO PLACE EMPHASIS ON THE PROMOTION OF THE LEARNER VOICE - THEIR ABILITY TO INFLUENCE DECISIONS AFFECTING THEM AND THEIR COMMUNITY. DESIGN/METHODOLOGY/APPROACH - THIS PAPER WILL POSITION ENGAGEMENT AS A SOCIOCULTURAL PROCESS AND ADOPT AN ACTIVITY THEORY BASED METHODOLOGY DEMONSTRATED THROUGH A DESK ANALYSIS OF VLE DATA FROM A FURTHER EDUCATION COLLEGE. FINDINGS - THE ANALYSIS SUGGESTS THAT THE APPROACH CAN YIELD INSIGHTS THAT MAY BE ELUSIVE IN TRADITIONAL MEASURES REINFORCING THE OVERALL CONCEPTUAL PROPOSAL FOR A MULTI-METHOD APPROACH TO PROFILING LEARNER ENGAGEMENT. RESEARCH LIMITATIONS/IMPLICATIONS - THE PAPER HAS FOCUSED ON PRESENTATION AND EXPLORATION OF THE CONCEPTUAL APPROACH, WHICH HAS LIMITED THE SCOPE TO BROADEN THE DISCUSSION OF THE DESK ANALYSIS AND WIDER FINDINGS THAT THIS APPROACH REVEALS. PRACTICAL IMPLICATIONS - IT IS INTENDED THAT THE APPROACH OFFERS A GENERALIZABLE MODEL THAT CAN BE ADOPTED BY INSTITUTIONS PLANNING TO MEASURE ENGAGEMENT OR DEVELOP LEARNER ACTIVITY PROFILES. SEVERAL AREAS OF IMMEDIATE POTENTIAL ARE IDENTIFIED THROUGHOUT THE PAPER. ORIGINALITY/VALUE - THIS PAPER CONTRIBUTES A MULTI-METHOD APPROACH TO ENGAGEMENT AS ARGUED FOR IN RECENT ENGAGEMENT LITERATURE. THIS SHOULD OFFER INSTITUTIONS A WAY TO REALISE VALUE FROM EMERGING IDEAS WITHIN RELATED DOMAINS OF LEARNING DESIGN AND LEARNING ANALYTICS.
THE RESEARCH OF MULTI-CATEGORY LEARNING BEHAVIORS IS A HOT ISSUE IN INTERACTIVE LEARNING ENVIRONMENT, AND THERE ARE MANY CHALLENGES IN DATA STATISTICS AND RELATIONSHIP MODELING. WE SELECT THE MASSIVE LEARNING BEHAVIORS DATA OF MULTIPLE PERIODS AND COURSES AND STUDY THE DECISION APPLICATION OF REGRESSION ANALYSIS. FIRST, BASED ON THE DEFINITION OF DATA STRUCTURE AND RELATIONSHIP FEATURES, THE DATA ARE NORMALIZED; SECONDLY, A MULTI-CATEGORY LEARNING BEHAVIOR PREDICTION PROCESS IS DESIGNED, THE RESULTS OF THE DATA ANALYSIS ARE EFFECTIVE; COMPUTER PROGRAM AND GEODA ARE USED FOR DECISION ANALYSIS, AND THE MULTIPLE REGRESSION EQUATION OF LEARNING BEHAVIORS IS CONSTRUCTED, SO AS TO ACHIEVE THE FEASIBLE PREDICTION DIRECTION AND EFFECTIVE DECISION TREND. THE DECISION APPLICATION MECHANISM IS A USEFUL METHOD TO STUDY MULTI-CATEGORY LEARNING BEHAVIORS, WHICH CAN BE REFERENCED BY MODEL DESIGN AND TECHNOLOGY APPLICATION FOR INTERACTIVE LEARNING ENVIRONMENT.
COMPREHENSION IS AN IMPORTANT COGNITIVE STATE FOR LEARNING. HUMAN TUTORS RECOGNIZE COMPREHENSION AND NON-COMPREHENSION STATES BY INTERPRETING LEARNER NON-VERBAL BEHAVIOR (NVB). EXPERIENCED TUTORS ADAPT PEDAGOGY, MATERIALS, AND INSTRUCTION TO PROVIDE ADDITIONAL LEARNING SCAFFOLD IN THE CONTEXT OF PERCEIVED LEARNER COMPREHENSION. NEAR REAL-TIME ASSESSMENT FOR E-LEARNER COMPREHENSION OF ON-SCREEN INFORMATION COULD PROVIDE A POWERFUL TOOL FOR BOTH ADAPTATION WITHIN INTELLIGENT E-LEARNING PLATFORMS AND APPRAISAL OF TUTORIAL CONTENT FOR LEARNING ANALYTICS. HOWEVER, LITERATURE SUGGESTS THAT NO EXISTING METHOD FOR AUTOMATIC CLASSIFICATION OF LEARNER COMPREHENSION BY ANALYSIS OF NVB CAN PROVIDE A PRACTICAL SOLUTION IN AN E-LEARNING, ON-SCREEN, CONTEXT. THIS PAPER PRESENTS DESIGN, DEVELOPMENT, AND EVALUATION OF COMPASS, A NOVEL NEAR REAL-TIME COMPREHENSION CLASSIFICATION SYSTEM FOR USE IN DETECTING LEARNER COMPREHENSION OF ON-SCREEN INFORMATION DURING E-LEARNING ACTIVITIES. COMPASS USES A NOVEL DESCRIPTIVE ANALYSIS OF LEARNER BEHAVIOR, IMAGE PROCESSING TECHNIQUES, AND ARTIFICIAL NEURAL NETWORKS TO MODEL AND CLASSIFY AUTHENTIC COMPREHENSION INDICATIVE NON-VERBAL BEHAVIOR. THIS PAPER PRESENTS A STUDY IN WHICH 44 UNDERGRADUATE STUDENTS ANSWERED ON-SCREEN MULTIPLE CHOICE QUESTIONS RELATING TO COMPUTER PROGRAMMING. USING A FRONT-FACING USB WEB CAMERA THE BEHAVIOR OF THE LEARNER IS RECORDED DURING READING AND APPRAISAL OF ON-SCREEN INFORMATION. THE RESULTANT DATASET OF NON-VERBAL BEHAVIOR AND QUESTION-ANSWER SCORES HAS BEEN USED TO TRAIN ARTIFICIAL NEURAL NETWORK (ANN) TO CLASSIFY COMPREHENSION AND NON-COMPREHENSION STATES IN NEAR REAL-TIME. THE TRAINED COMPREHENSION CLASSIFIER ACHIEVED NORMALIZED CLASSIFICATION ACCURACY OF 75.8 PERCENT.
THE STUDY COMPARES TRADITIONAL AND SOCIAL MEDIA MARKETING STRATEGIES IN LAUNCHING A NEW ONLINE 24-WEEK CISCO CERTIFIED NETWORK ASSOCIATE (CCNA) COURSE. THE RESEARCH COURSE IS NON-CREDIT AND IS OFFERED BY THE VIRTUAL ACADEMY (VIRTAC) IN THE DEPARTMENT OF INFORMATION SCIENCES AND TECHNOLOGY (IST) OF THE VOLGENAU SCHOOL OF ENGINEERING (VSE) AT GEORGE MASON UNIVERSITY (MASON). THE RESEARCH COURSE IS FUNDED BY A GRANT FROM CISCO. IT IS A FULLY ONLINE HYBRID DISTANCE LEARNING COURSE THAT USES THE ASYNCHRONOUS AND SYNCHRONOUS DELIVERY METHODS. THE MARKETING TEAM IS CROSS-DISCIPLINARY WITH RESOURCES WITHIN MASON FROM THE MARKETING AND IST DEPARTMENTS. THE LESSONS LEARNED IN THE AREAS OF MARKET SEGMENTATION, TARGETING, AND POSITIONING OF THE ONLINE COURSE ARE ANALYZED. THE MARKETING MIX USING THE FOUR PS OF PRICE, PROMOTION, PLACE OR DELIVERY AND PRODUCT ARE APPLIED USING BOTH TRADITIONAL AND NEW SOCIAL MEDIA STRATEGIES. A TRADITIONAL "BROADLY CAST" MARKETING STRATEGY WAS USED. IT INCLUDED PRINT, POSTER, FLYER, TELEMARKETING AND OTHER MEDIA. NEWER SOCIAL MEDIA MARKETING TECHNIQUES THAT WERE MORE "NARROWLY CAST" ALSO WERE USED. THESE INCLUDE LINKED-IN, FACEBOOK, TWITTER, AND VARIOUS FORUMS. COMPARATIVE METRICS AND MARKETING RETURN ON INVESTMENTS WERE ANALYZED AND ARE SUMMARIZED IN THE PAPER. CONTINUOUS QUALITY IMPROVEMENT STRATEGIES FROM THE INITIAL COURSE OFFERING ARE DISCUSSED INCLUDING MARKETING AND LEARNING ANALYTICS. THE PAPER INCLUDES THE METHODS, FINDINGS, AND BEST PRACTICES FOR MARKETING FUTURE OFFERINGS, AS WELL AS, CHANGES IN THE SECOND CCNA RESEARCH COURSE OFFERING.
RESEARCH HAS INDICATED STRONG RELATIONSHIPS BETWEEN LEARNERS' AFFECT AND THEIR LEARNING. EMOTIONS RELATE CLOSELY TO STUDENTS' WELL-BEING, LEARNING QUALITY, PRODUCTIVITY, AND INTERACTION. DIGITAL GAME-BASED LEARNING (DGBL) HAS BEEN WIDELY RECOGNIZED TO BE EFFECTIVE IN ENHANCING LEARNING EXPERIENCES AND INCREASING STUDENT MOTIVATION. THE FIELD OF EMOTIONS IN DGBL HAS BECOME AN ACTIVE RESEARCH FIELD WITH ACCUMULATED LITERATURE AVAILABLE, WHICH CALLS FOR A COMPREHENSIVE UNDERSTANDING OF THE UP-TO-DATE LITERATURE CONCERNING EMOTIONS IN VIRTUAL DGBL AMONG STUDENTS AT ALL EDUCATIONAL LEVELS. BASED ON 393 RESEARCH ARTICLES COLLECTED FROM THE WEB OF SCIENCE, THIS STUDY, FOR THE FIRST TIME, EXPLORES THE CURRENT ADVANCES AND TOPICS IN THIS FIELD. SPECIFICALLY, THEMATIC EVOLUTION ANALYSIS IS CONDUCTED TO EXPLORE THE EVOLUTION OF TOPICS THAT ARE CATEGORIZED INTO FOUR DIFFERENT GROUPS (I.E., GAMES, EMOTIONS, APPLICATIONS, AND ANALYTICAL TECHNOLOGIES) IN THE CORPUS. SOCIAL NETWORK ANALYSIS EXPLORES THE CO-OCCURRENCES BETWEEN TOPICS TO IDENTIFY THEIR RELATIONSHIPS. INTERESTING RESULTS ARE OBTAINED. FOR EXAMPLE, WITH THE INTEGRATION OF DIVERSE APPLICATIONS (E.G., MOBILES) AND ANALYTICAL TECHNOLOGIES (E.G., LEARNING ANALYTICS AND AFFECTIVE COMPUTING), INCREASING TYPES OF AFFECTIVE STATES, SOCIO-EMOTIONAL FACTORS, AND DIGITAL GAMES ARE INVESTIGATED. ADDITIONALLY, IMPLICATIONS FOR FUTURE RESEARCH INCLUDE 1) CHILDREN'S ANXIETY/ATTITUDE AND ENGAGEMENT IN COLLABORATIVE GAMEPLAY, 2) INDIVIDUAL PERSONALITIES AND CHARACTERISTICS FOR PERSONALIZED SUPPORT, 3) EMOTION DYNAMICS, 4) MULTIMODAL DATA USE, 5) GAME CUSTOMIZATION, 6) BALANCE BETWEEN LEARNERS' SKILL LEVELS AND GAME CHALLENGE AS WELL AS REWARDS AND LEARNING ANXIETY.
THIS STUDY INVESTIGATES WHETHER ICT TECHNOLOGY CAN ENHANCE APPLIED BEHAVIOR ANALYSIS (ABA) REHABILITATION THERAPY FOR CHILDREN WITH AUTISM. A TECHNOLOGY-ENHANCED REHABILITATION SYSTEM TO SUPPORT THE DAILY WORK OF ABA TUTORS, PARENTS AND TEACHERS WAS CREATED, INVOLVING ABA EXPERTS AND PARENTS OF AUTISTIC CHILDREN IN THE ENTIRE DESIGN PHASE, IN ORDER TO BETTER UNDERSTAND THE SYSTEM'S FUNCTIONAL REQUIREMENTS AND ENHANCE ITS USABILITY. THUS, COMBINING THE ABA PROGRAMS WITH A LEARNING ANALYTICS TOOL, AN OPEN-SOURCE WEB APPLICATION , ABCD SW, WAS IMPLEMENTED FOR TEACHING AND MONITORING LEARNING IN LOW-FUNCTIONING AUTISTIC CHILDREN. IN A SMALL PILOT STUDY THE SYSTEM WAS TESTED ON SEVEN CHILDREN WITH AUTISM ENROLLED IN AN INTENSIVE INTERVENTION LASTING 9 MONTHS. THE CHILDREN WERE ASSESSED BEFORE AND AFTER THE PILOT TEST, USING VINELAND ADAPTIVE BEHAVIOR SCALES TO MEASURE THEIR PERSONAL AND SOCIAL SKILLS. TEST PARTICIPANTS SHOWED IMPROVED COMMUNICATION, ESPECIALLY IN THE EXPRESSIVE CATEGORY (P < 0.05). SUBJECTIVE FEEDBACK FROM THE ABA TEAM INVOLVED IN THE USER TEST CONFIRMED THE CHILDREN'S IMPROVEMENT IN SOCIALIZATION, COMMUNICATION AND BEHAVIOR. ABCD SW EXPEDITES THE INTERVENTION (THUS INCREASING ITS EFFICIENCY) AND MAKES IT MORE PLEASANT FOR THE CHILDREN. FURTHERMORE, ABCD SW ENABLES CAREGIVERS TO EASILY CONDUCT AND PERSONALIZE THE INTERVENTION, REDUCING ITS COST. THE STUDY SEEMS TO SUGGEST THAT ABCD SW, AND ICT TECHNOLOGY IN GENERAL, CAN ENHANCE ABA REHABILITATION THERAPY FOR CHILDREN WITH AUTISM, ENCOURAGING FURTHER INVESTIGATION OF THIS PROMISING RESEARCH FIELD.
MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BEEN EXPERIENCING INCREASING USE AND POPULARITY IN HIGHLY RANKED UNIVERSITIES IN RECENT YEARS. THE OPPORTUNITY OF ACCESSING HIGH QUALITY COURSEWARE CONTENT WITHIN SUCH PLATFORMS, WHILE ELIMINATING THE BURDEN OF EDUCATIONAL, FINANCIAL, AND GEOGRAPHICAL OBSTACLES HAS LED TO A RAPID GROWTH IN PARTICIPANT NUMBERS. THE INCREASING NUMBER AND DIVERSITY OF PARTICIPATING LEARNERS HAS OPENED UP NEW HORIZONS TO THE RESEARCH COMMUNITY FOR THE INVESTIGATION OF EFFECTIVE LEARNING ENVIRONMENTS. LEARNING ANALYTICS HAS BEEN USED TO INVESTIGATE THE IMPACT OF ENGAGEMENT ON STUDENT PERFORMANCE. HOWEVER, THE EXTENSIVE LITERATURE REVIEW INDICATES THAT THERE IS LITTLE RESEARCH ON THE IMPACT OF MOOCS, PARTICULARLY IN ANALYZING THE LINK BETWEEN BEHAVIORAL ENGAGEMENT AND MOTIVATION AS PREDICTORS OF LEARNING OUTCOMES. IN THIS PAPER, WE CONSIDER A DATASET, WHICH ORIGINATES FROM ONLINE COURSES PROVIDED BY HARVARD UNIVERSITY AND THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY, DELIVERED THROUGH THE EDX PLATFORM. TWO SETS OF EMPIRICAL EXPERIMENTS ARE CONDUCTED USING BOTH STATISTICAL AND MACHINE LEARNING TECHNIQUES. STATISTICAL METHODS ARE USED TO EXAMINE THE ASSOCIATION BETWEEN ENGAGEMENT LEVEL AND PERFORMANCE, INCLUDING THE CONSIDERATION OF LEARNER EDUCATIONAL BACKGROUNDS. THE RESULTS INDICATE A SIGNIFICANT GAP BETWEEN SUCCESS AND FAILURE OUTCOME LEARNER GROUPS, WHERE SUCCESSFUL LEARNERS ARE FOUND TO READ AND WATCH COURSE MATERIAL TO A HIGHER DEGREE. MACHINE LEARNING ALGORITHMS ARE USED TO AUTOMATICALLY DETECT LEARNERS WHO ARE LACKING IN MOTIVATION AT AN EARLY TIME IN THE COURSE, THUS PROVIDING INSTRUCTORS WITH INSIGHT IN REGARDS TO STUDENT WITHDRAWAL.
ADVANCEMENTS IN ADAPTIVE EDUCATIONAL TECHNOLOGIES, SPECIFICALLY THE ADAPTIVE LEARNING SYSTEM, HAVE MADE IT POSSIBLE TO AUTOMATICALLY OPTIMIZE THE SEQUENCING OF THE PEDAGOGICAL INSTRUCTIONS ACCORDING TO THE NEEDS OF INDIVIDUAL LEARNERS. THE CRUX OF SUCH SYSTEMS LIES IN THE INSTRUCTIONAL SEQUENCING POLICY, WHICH RECOMMENDS PERSONALIZED LEARNING MATERIAL BASED ON THE LEARNING EXPERIENCES OF THE LEARNER TO MAXIMIZE THEIR LEARNING OUTCOMES. HOWEVER, LIMITED AVAILABLE INFORMATION SUCH AS COGNITIVE, AFFECTIVE STATES, AND COMPETENCE LEVELS OF THE LEARNERS ONGOING KNOWLEDGE POINTS SERVERS CRITICAL CHALLENGES TO OPTIMIZING INDIVIDUAL-SPECIFIC PEDAGOGICAL INSTRUCTIONS IN REAL-TIME. MOREOVER, MAKING SUCH DECISIONS POLICY FOR EVERY LEARNER WITH A UNIQUE KNOWLEDGE PROFILE DEMANDS A TRADE-OFF BETWEEN LEARNER CURRENT KNOWLEDGE AND CURIOSITY TO LEARN NEXT KNOWLEDGE POINT. TO ADDRESS THESE CHALLENGES, THIS PAPER PROPOSES A PERSONALIZED ADAPTABILITY KNOWLEDGE EXTRACTION STRATEGY (PAKES) USING COGNITIVE DIAGNOSIS AND REINFORCEMENT LEARNING (RL). WE APPLY THE GENERAL DIAGNOSTIC MODEL TO TRACK THE CURRENT KNOWLEDGE STATE OF THE LEARNERS. SUBSEQUENTLY, AN RL-BASED Q-LEARNING ALGORITHM IS EMPLOYED TO RECOMMEND OPTIMAL PEDAGOGICAL INSTRUCTIONS FOR INDIVIDUALS TO MEET THEIR LEARNING OBJECTIVES WHILE MAINTAINING EQUILIBRIUM AMONG THE LEARNER-CONTROL AND TEACHING TRAJECTORIES. THE RESULTS INDICATE THAT THE LEARNING ANALYTICS OF THE PROPOSED FRAMEWORK CAN FAIRLY DELIVER THE OPTIMAL PEDAGOGICAL PATHS FOR THE LEARNERS BASED UPON THEIR LEARNING PROFILES. A 62% LEARNING PROGRESS SCORE WAS ACHIEVED WITH THE PEDAGOGICAL PATHS RECOMMENDED BY THE PAKES, SHOWING A 20% IMPROVEMENT COMPARED TO THE BASELINE MODEL.
THE DIVERSITY OF STUDENT NEEDS HAS BEEN SHOWN TO IMPACT ON THEIR PROFICIENCY IN THE USE OF DIGITAL TECHNOLOGIES AND CALLS INTO QUESTION THE POPULAR RHETORIC THAT STUDENTS ENTERING UNIVERSITIES TODAY ARE ALREADY LITERATE IN DIGITAL SKILLS AND THUS ABLE TO ENGAGE EFFECTIVELY IN TECHNOLOGY ENHANCED LEARNING. PERSONALIZED LEARNING ENVIRONMENTS (PLES) ARE SEEN TO OFFER GREAT PROMISE IN MEETING THE INDIVIDUAL NEEDS OF LEARNERS FROM DIVERSE BACKGROUNDS AND LEARNING ANALYTICS CAN PLAY AN IMPORTANT ROLE IN INFORMING THE DESIGN AND DEVELOPMENT OF CURRICULA TO ACCOMMODATE SUCH DIVERSITY. THIS PAPER DRAWS ON THE LITERATURE AND PREVIOUS RESEARCH UNDERTAKEN BY THE AUTHOR TO OUTLINE A HOLISTIC MODEL TO THE DESIGN AND DEVELOPMENT OF INCLUSIVE TECHNOLOGY ENHANCED LEARNING ENVIRONMENTS. A RECENTLY FUNDED AUSTRALIAN LEARNING AND TEACHING PROJECT(I), WHICH EMPLOYS THE PROPOSED APPROACH IS OUTLINED AND A CONCEPTUAL MODEL OF A PERSONALIZED LEARNING ENVIRONMENT UNDER DEVELOPMENT IS DESCRIBED. THE PLE UNDER DEVELOPMENT AIMS TO ADDRESSES THE DIVERSITY OF STUDENTS LEARNING STYLES, DIGITAL LITERACY, ENGLISH LANGUAGE PROFICIENCY, ACCESS TO TECHNOLOGIES, AND ACCESSIBILITY REQUIREMENTS THROUGH A RESPONSIVE LEARNING SYSTEM THAT HAS BEEN INFORMED BY AND BASED ON THE GLOBAL PUBLIC INCLUSIVE INFRASTRUCTURE (GPII)(II) SOFTWARE AND SERVICE ENHANCEMENT PROJECT, WHICH 'AIMS TO ALLOW USERS TO INVOKE AND USE THE ACCESS FEATURES THEY NEED ANYWHERE, ANYTIME, ON ANY DEVICE' [1]. THE FINAL SECTION OF THE PAPER DISCUSSES THE PROPOSED DIRECTION OF THE PROJECT AND THE POTENTIAL BENEFITS IT AFFORDS IN IMPROVING LEARNING OUTCOMES AND PROMOTING THE SCHOLARSHIP OF TEACHING AND LEARNING.
WHILE OPEN-ENDED AND TINKERING-BASED LEARNING ENVIRONMENTS OFFER CONSIDERABLE SUPPORT FOR DEVELOPING STEM-BASED REASONING AND COLLABORATION SKILLS, UNDERSTANDING HOW AND WHEN LEARNERS ARE ENGAGED IN PRODUCTIVE OR UNPRODUCTIVE EXPLORATION IS A PRESSING CHALLENGE. THIS IS PARTICULARLY TRUE IN MUSEUMS WHERE DWELL TIMES ARE SHORT AND VISITORS CAN ENTER AND EXIT EXHIBITS AT VARYING TIMES. IN RESPONSE, THIS WORK AIMED TO ANSWER TWO QUESTIONS: 1) HOW CAN WE COMBINE LEARNING ANALYTICS AND QUALITATIVE APPROACHES TO UNDERSTAND HOW LEARNERS MOVE FROM UNPRODUCTIVE TO PRODUCTIVE STATES DURING OPEN-ENDED INQUIRY? 2) WHAT ROLE DO INTERACTIONS WITH OTHERS PLAY IN SUPPORTING PARTICIPANTS' TRANSITIONS TO PRODUCTIVE STATES? TO ANSWER THESE QUESTIONS, THIS STUDY EXAMINED HOW COMBINING A HIDDEN MARKOV MODEL (HMM) AND INTERACTION ANALYSIS TOGETHER CAN REVEAL IMPORTANT ELEMENTS OF PARTICIPANTS' COLLABORATION AND EXPLORATION THAT WOULD LIKELY BE LOST IF EACH METHOD WERE APPLIED IN ISOLATION. THE METHODS WERE APPLIED TO VISITORS PARTICIPATING AT AN INTERACTIVE MULTI-TOUCH EXHIBIT (NAMED OZTOC). THE APPLICATION OF HMM SUCCESSFULLY CAPTURED WHEN PARTICIPANTS TRANSITIONED FROM PERSISTENT UNPRODUCTIVE TO PRODUCTIVE STATES, WHILE INTERACTION ANALYSIS USING THE DIVERGENT COLLABORATIVE LEARNING MECHANISMS FRAMEWORK (DCLM) SHOWED HOW SPECIFIC DIVERGENT COLLABORATIVE INTERACTIONS SUPPORTED THESE TRANSITIONS. IN PARTICULAR, WE REVEAL THE ROLE THAT VISITORS ENGAGING IN BOUNDARY SPANNING PERCEPTION AND BOUNDARY SPANNING ACTION PLAYED IN THESE TRANSITIONS. MORE BROADLY, THIS WORK SHOWS HOW DESIGNS THAT PROVIDE OPPORTUNITIES FOR THESE KINDS OF INTERACTIONS MAY HELP LEARNERS EFFECTIVELY TRANSITION OUT OF SUSTAINED STATES OF UNPRODUCTIVE PERSISTENCE.
TWO RECENT PUBLICATIONS REPORT THE EMERGING TECHNOLOGIES THAT ARE LIKELY TO HAVE A SIGNIFICANT IMPACT ON LEARNING AND INSTRUCTION: (A) NEW MEDIA CONSORTIUM'S 2011 HORIZON REPORT (JOHNSON, SMITH, WILLIS, LEVINE & HAYWOOD, 2011), AND (B) A ROADMAP FOR EDUCATION TECHNOLOGY FUNDED BY THE NATIONAL SCIENCE FOUNDATION IN THE USA (TO DOWNLOAD THE REPORT SEE HTTP://WWW.CRA.ORG/CCC/EDTECH.PHP). SOME OF THE COMMON TECHNOLOGIES MENTIONED IN BOTH REPORTS INCLUDE PERSONALIZED LEARNING, MOBILE TECHNOLOGIES, DATA MINING, AND LEARNING ANALYTICS. THIS PAPER ANALYZES AND SYNTHESIZES THESE TWO REPORTS. TWO ADDITIONAL SOURCES ARE CONSIDERED IN THE DISCUSSION: (A) THE IEEE TECHNICAL COMMITTEE ON LEARNING TECHNOLOGY'S REPORT ON CURRICULA FOR ADVANCED LEARNING TECHNOLOGY, AND, (B) THE EUROPEAN STELLAR PROJECT THAT IS BUILDING THE FOUNDATION FOR A NETWORK OF EXCELLENCE FOR TECHNOLOGY ENHANCED LEARNING. THE ANALYSIS FOCUSES ON ENABLERS OF (E.G., DYNAMIC ONLINE FORMATIVE ASSESSMENT FOR COMPLEX LEARNING ACTIVITIES) AND BARRIERS TO (E.G., ACCESSIBILITY AND PERSONALIZABILITY) TO SUSTAINED AND SYSTEMIC SUCCESS IN IMPROVING LEARNING AND INSTRUCTION WITH NEW TECHNOLOGIES. IN ADDITION, TWO CRITICAL ISSUES CUTTING ACROSS EMERGING EDUCATIONAL TECHNOLOGIES ARE IDENTIFIED AND EXAMINED AS LIMITING FACTORS NAMELY, POLITICAL AND POLICY ISSUES. PROMISING EFFORTS BY SEVERAL GROUPS (E.G., THE NATIONAL TECHNOLOGY LEADERSHIP COALITION, THE IEEE TECHNICAL COMMITTEE ON LEARNING TECHNOLOGY, NETWORKS OF EXCELLENCE, ETC.) WILL BE INTRODUCED AS ALTERNATIVE WAYS FORWARD. IMPLICATIONS FOR RESEARCH AND PARTICULAR FOR ASSESSMENT AND EVALUATION ARE INCLUDED IN THE DISCUSSION AS MEANS TO ESTABLISH CREDIBLE CRITERIA FOR IMPROVEMENT.
SOCIAL NETWORK ANALYSIS (SNA) MAY BE OF SIGNIFICANT VALUE IN STUDYING ONLINE COLLABORATIVE LEARNING. SNA CAN ENHANCE OUR UNDERSTANDING OF THE COLLABORATIVE PROCESS, PREDICT THE UNDER-ACHIEVERS BY MEANS OF LEARNING ANALYTICS, AND UNCOVER THE ROLE DYNAMICS OF LEARNERS AND TEACHERS ALIKE. AS SUCH, IT CONSTITUTES AN OBVIOUS OPPORTUNITY TO IMPROVE LEARNING, INFORM TEACHERS AND STAKEHOLDERS. BESIDES, IT CAN FACILITATE DATA-DRIVEN SUPPORT SERVICES FOR STUDENTS. THIS STUDY INCLUDED FOUR COURSES AT QASSIM UNIVERSITY. ONLINE INTERACTION DATA WERE COLLECTED AND PROCESSED FOLLOWING A STANDARD DATA MINING TECHNIQUE. THE SNA PARAMETERS RELEVANT TO KNOWLEDGE SHARING AND CONSTRUCTION WERE CALCULATED ON THE INDIVIDUAL AND THE GROUP LEVEL. THE ANALYSIS INCLUDED QUANTITATIVE NETWORK ANALYSIS AND VISUALIZATION, CORRELATION TESTS AS WELL AS PREDICTIVE AND EXPLANATORY REGRESSION MODELS. OUR RESULTS SHOWED A CONSISTENT MODERATE TO STRONG POSITIVE CORRELATION BETWEEN PERFORMANCE, INTERACTION PARAMETERS AND STUDENTS' CENTRALITY MEASURES ACROSS ALL THE STUDIED COURSES, REGARDLESS OF THE SUBJECT MATTER. IN EACH OF THE STUDIED COURSES, STUDENTS WITH STRONGER TIES TO PROMINENT PEERS (BETTER SOCIAL CAPITAL) IN SMALL INTERACTIVE AND COHESIVE GROUPS TENDED TO DO BETTER. THE RESULTS OF CORRELATION TESTS WERE CONFIRMED USING REGRESSION TESTS, WHICH WERE VALIDATED USING A NEXT YEAR DATASET. USING SNA INDICATORS, WE WERE ABLE TO CLASSIFY STUDENTS ACCORDING TO ACHIEVEMENT WITH HIGH ACCURACY (93.3%). THIS DEMONSTRATES THE POSSIBILITY OF USING INTERACTION DATA TO PREDICT UNDERACHIEVERS WITH REASONABLE RELIABILITY, WHICH IS AN OBVIOUS OPPORTUNITY FOR INTERVENTION AND SUPPORT.
OVER THE LAST 10 YEARS, LEARNING ANALYTICS HAVE PROVIDED EDUCATORS WITH BOTH DASHBOARDS AND TOOLS TO UNDERSTAND STUDENT BEHAVIORS WITHIN SPECIFIC TECHNOLOGICAL ENVIRONMENTS. HOWEVER, THERE IS A LACK OF WORK TO SUPPORT EDUCATORS IN MAKING DATA-INFORMED DESIGN DECISIONS WHEN DESIGNING A BLENDED COURSE AND PLANNING APPROPRIATE LEARNING ACTIVITIES. IN THIS PAPER, WE INTRODUCE KNOWLEDGE-BASED DESIGN ANALYTICS THAT UNCOVER FACETS OF THE LEARNING ACTIVITIES THAT ARE BEING CREATED. A KNOWLEDGE-BASED VISUALIZATION IS INTEGRATED INTO EDCRUMBLE, A (BLENDED) LEARNING DESIGN AUTHORING TOOL. THIS NEW APPROACH IS EXPLORED IN THE CONTEXT OF A HIGHER EDUCATION PROGRAMMING COURSE, WHERE INSTRUCTORS DESIGN LABS AND HOME PRACTICE SESSIONS WITH ONLINE SMART LEARNING CONTENT ON A WEEKLY BASIS. WE PERFORMED A WITHIN-SUBJECTS USER STUDY TO COMPARE THE USE OF THE DESIGN TOOL BOTH WITH AND WITHOUT VISUALIZATION. WE STUDIED THE DIFFERENCES IN TERMS OF COGNITIVE LOAD, CONTROLLABILITY, CONFIDENCE AND EASE OF CHOICE, DESIGN OUTCOMES, AND USER ACTIONS WITHIN THE SYSTEM TO COMPARE BOTH CONDITIONS WITH THE OBJECTIVE OF EVALUATING THE IMPACT OF USING DESIGN ANALYTICS DURING THE DECISION-MAKING PHASE OF COURSE DESIGN. OUR RESULTS INDICATE THAT THE USE OF A KNOWLEDGE-BASED VISUALIZATION ALLOWS THE TEACHERS TO REDUCE THE COGNITIVE LOAD (ESPECIALLY IN TERMS OF MENTAL DEMAND) AND THAT IT FACILITATES THE CHOICE OF THE MOST APPROPRIATE ACTIVITIES WITHOUT AFFECTING THE OVERALL DESIGN TIME. IN CONCLUSION, THE USE OF KNOWLEDGE-BASED DESIGN ANALYTICS IMPROVES THE OVERALL LEARNING DESIGN QUALITY AND HELPS TEACHERS AVOID COMMITTING DESIGN ERRORS.
NOWADAYS, THE LEARNING PROCESS IS SUPPORTED BY DIFFERENT INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) TOOLS WHICH PROVIDE MORE FLEXIBLE METHODS OF DELIVERY OF LEARNING CONTENT AND STUDENTS' EVALUATION. THE LEARNING MANAGEMENT SYSTEMS (LMS) ARE USED TO INTEGRATE A WIDE RANGE OF PEDAGOGICAL AND COURSE ADMINISTRATION TOOLS. THE MOST WIDELY USED LMS SYSTEM IS MOODIE. ITS INTEGRATION WITH DIFFERENT TOOLS IMPROVES AND SUPPORTS THE LEARNING PROCESS. KEY COMPONENT OF THE SYSTEM IS THE COURSE, WHICH CAN BE ORGANIZED IN DIFFERENT FORMATS AND CAN INCLUDE DIFFERENT ACTIVITIES AND RESOURCES. STUDENTS ARE BEING ENROLLED TO A SPECIFIC COURSE. AFTER A SUCCESSFUL COURSE DELIVERY, THERE EXIST WELL-DEFINED ASSESSMENT METHODS, WHICH GIVE A LOT OF OPPORTUNITIES. ONE OF THE FACED CHALLENGES IS TO APPLY DIFFERENT LEARNING ANALYTICS METHODS IN ORDER TO TAKE DECISIONS AND CONCLUSIONS FOR COURSE CONTENT FROM ONE SIDE AND STUDENTS RESULTS, AND MOTIVATION FROM ANOTHER. FOR THAT PURPOSE, PERSONAL INFORMATION IS STORED AND PROCESSED. THE MAIN GOAL OF THE PAPER IS TO REVIEW THE GDPR REGULATION AND TO APPLY IT TO A LIFE LMS SYSTEM (MOODIE). THERE EXIST SOME PLUG-INS FOR MANAGING AND PROCESSING PERSONAL DATA WITHIN MOODIE SYSTEM. THE RESEARCH HERE FOCUSES ON LIFE MOODIE SYSTEM AND POSSIBLE PLACES (OUTSIDE OF THE SYSTEM ITSELF), WHERE PERSONAL INFORMATION CAN BE STORED. A CHAPTER FIVE DESCRIBES THE AUTHORS APPROACH FOR DATA PROCESSING WITH TWO PROCESSES - FOR DATA ANONYMIZATION AND FOR DATA SHARING WITH EXTERNAL SYSTEMS.
THE ASSESSMENT OF INDIVIDUAL STUDENT LEARNING IS DIFFICULT AND TIME-CONSUMING, AND THERE IS THE NEED FOR IMPROVED MONITORING OF STUDENT PERFORMANCE INFORMATION. THE VIRTUOSO ENTERPRISE LEARNING AND INSTRUCTIONAL SUPPORT (ELIS) PLATFORM, DEVELOPED BY CINGLEVUE INTERNATIONAL, IS A BRAND NEW SOFTWARE SOLUTION THAT MAKES IT POSSIBLE TO COLLECT DATA ABOUT THE WIDE RANGE OF STUDENT LEARNING EXPERIENCES FROM KINDERGARTEN TO TERTIARY. DRAWING ON APPLIED RESEARCH, THE EVOLUTION AND DEVELOPMENT OF THE SOFTWARE IS INFORMED BY EVIDENCE-BASED DATA AND RESEARCH ANALYSIS. POWERED BY DATA MINING AND INNOVATIVE ANALYTICS, SUCH AS PERFORMANCE TRACKING, LEARNING ANALYTICS, AND SOCIAL NETWORK ANALYSIS, THE PLATFORM FACILITATES THE DEVELOPMENT AND MANAGEMENT OF INDIVIDUAL LEARNING STRATEGIES FOR STUDENTS. INDIVIDUAL LEARNING PLANS, STUDENT PROGRESS AND LEARNING OUTCOMES, COLLABORATIVE LEARNING, AND EVIDENCE-BASED FEEDBACK, ARE JUST SOME OF THE ACTIVITIES THAT CAN NOW BE DOCUMENTED AND COMMUNICATED BETWEEN STUDENTS, GUARDIANS, TEACHERS, AND ADMINISTRATORS. THE SOFTWARE EQUIPS TEACHERS WITH THE TECHNOLOGY TO MONITOR STUDENT PROGRESS AND TO IDENTIFY AND ASSESS STUDENTS AT RISK OF ACADEMIC AND SCHOOL DISENGAGEMENT, INCLUDING THOSE IN NEED OF EXTENSION. THIS WILL NOT ONLY SUPPORT A MORE ACCURATE TARGETING OF STUDENTS WITH THE HIGHEST NEED, BUT ALSO THE IMPLEMENTATION OF EVIDENCE-BASED ASSESSMENT AND INTERVENTION STRATEGIES. THE PLATFORM HAS THE POTENTIAL TO TRANSFORM EDUCATION AND TO MAKE A DIFFERENCE TO THE EXPERIENCES OF STUDENTS, PARENTS, TEACHERS, AND ADMINISTRATORS, ALTHOUGH THIS WILL DEPEND ON THE UNIQUE SITUATION AND GOALS OF EACH ORGANISATION IMPLEMENTING THE SOFTWARE.
AN UNDERGRADUATE LEVEL SOFTWARE ENGINEERING COURSES GENERALLY CONSISTS OF A TEAM-BASED SEMESTER LONG PROJECT AND EMPHASIZES ON BOTH TECHNICAL AND MANAGERIAL SKILLS. SOFTWARE ENGINEERING IS A PRACTICE-ORIENTED AND APPLIED DISCIPLINE AND HENCE THERE IS AN EMPHASIS ON HANDS-ON DEVELOPMENT, PROCESS, USAGE OF TOOLS IN ADDITION TO THEORY AND BASIC CONCEPTS. WE PRESENT AN APPROACH FOR MINING THE PROCESS DATA (PROCESS MINING) FROM SOFTWARE REPOSITORIES ARCHIVING DATA GENERATED AS A RESULT OF CONSTRUCTING SOFTWARE BY STUDENT TEAMS IN AN EDUCATIONAL SETTING. WE PRESENT AN APPLICATION OF MINING THREE SOFTWARE REPOSITORIES: TEAM WIKI (USED DURING REQUIREMENT ENGINEERING), VERSION CONTROL SYSTEM (DEVELOPMENT AND MAINTENANCE) AND ISSUE TRACKING SYSTEM (CORRECTIVE AND ADAPTIVE MAINTENANCE) IN THE CONTEXT OF AN UNDERGRADUATE SOFTWARE ENGINEERING COURSE. WE PROPOSE VISUALIZATIONS, METRICS AND ALGORITHMS TO PROVIDE AN INSIGHT INTO PRACTICES AND PROCEDURES FOLLOWED DURING VARIOUS PHASES OF A SOFTWARE DEVELOPMENT LIFE-CYCLE. THE PROPOSED VISUALIZATIONS AND METRICS (LEARNING ANALYTICS) PROVIDE A MULTI-FACETED VIEW TO THE INSTRUCTOR SERVING AS A FEEDBACK TOOL ON DEVELOPMENT PROCESS AND QUALITY BY STUDENTS. WE MINE THE EVENT LOGS PRODUCED BY SOFTWARE REPOSITORIES AND DERIVE INSIGHTS SUCH AS DEGREE OF INDIVIDUAL CONTRIBUTIONS IN A TEAM, QUALITY OF COMMIT MESSAGES, INTENSITY AND CONSISTENCY OF COMMIT ACTIVITIES, BUG FIXING PROCESS TREND AND QUALITY, COMPONENT AND DEVELOPER ENTROPY, PROCESS COMPLIANCE AND VERIFICATION. WE PRESENT OUR EMPIRICAL ANALYSIS ON A SOFTWARE REPOSITORY DATASET CONSISTING OF 19 TEAMS OF 5 MEMBERS EACH AND DISCUSS CHALLENGES, LIMITATIONS AND RECOMMENDATIONS.
THE RESEARCH ON MASSIVE OPEN ONLINE COURSES (MOOCS) DATA ANALYTICS HAS MUSHROOMED RECENTLY BECAUSE OF THE RAPID DEVELOPMENT OF MOOCS. THE MOOC DATA NOT ONLY CONTAINS LEARNER PROFILES AND LEARNING OUTCOMES, BUT ALSO SEQUENTIAL INFORMATION ABOUT WHEN AND WHICH TYPE OF LEARNING ACTIVITIES EACH LEARNER PERFORMS, SUCH AS REVIEWING A LECTURE VIDEO BEFORE UNDERTAKING AN ASSIGNMENT. LEARNING SEQUENCE ANALYTICS COULD HELP UNDERSTAND THE CORRELATIONS BETWEEN LEARNING SEQUENCES AND PERFORMANCES, WHICH FURTHER CHARACTERIZE DIFFERENT LEARNER GROUPS. HOWEVER, FEW WORKS HAVE EXPLORED THE SEQUENCE OF LEARNING ACTIVITIES, WHICH HAVE MOSTLY BEEN CONSIDERED AGGREGATED EVENTS. A VISUAL ANALYTICS SYSTEM CALLED VISEQ IS INTRODUCED TO RESOLVE THE LOSS OF SEQUENTIAL INFORMATION, TO VISUALIZE THE LEARNING SEQUENCE OF DIFFERENT LEARNER GROUPS, AND TO HELP BETTER UNDERSTAND THE REASONS BEHIND THE LEARNING BEHAVIORS. THE SYSTEM FACILITATES USERS IN EXPLORING LEARNING SEQUENCES FROM MULTIPLE LEVELS OF GRANULARITY. VISEQ INCORPORATES FOUR LINKED VIEWS: THE PROJECTION VIEW TO IDENTIFY LEARNER GROUPS, THE PATTERN VIEW TO EXHIBIT OVERALL SEQUENTIAL PATTERNS WITHIN A SELECTED GROUP, THE SEQUENCE VIEW TO ILLUSTRATE THE TRANSITIONS BETWEEN CONSECUTIVE EVENTS, AND THE INDIVIDUAL VIEW WITH AN AUGMENTED SEQUENCE CHAIN TO COMPARE SELECTED PERSONAL LEARNING SEQUENCES. CASE STUDIES AND EXPERT INTERVIEWS WERE CONDUCTED TO EVALUATE THE SYSTEM.
COMPUTER NETWORKS ARE A FUNDAMENTAL COMPONENT OF INFORMATION TECHNOLOGY SYSTEMS TODAY AND THEREFORE OFTEN INCLUDED AS A SUBJECT IN COMPUTER SCIENCE, ELECTRONICS, AND ENGINEERING COURSES. AS UNDERSTANDING OF NETWORK PROTOCOLS CAN BE GREATLY DEEPENED BY SEEING THEM IN ACTION, WE PRESENT IN THIS PAPER A WEB-BASED TCP FLOW TOOLBOX TO ENGAGE STUDENTS IN AN ACTIVE FASHION TOWARDS "LEARNING BY DOING". THE PRESENTED TOOLBOX ALLOWS ACTIVELY DRAWING INDIVIDUAL PACKET EXCHANGES USING TIME-SEQUENCE DIAGRAMS IN ORDER TO LEARN THE EFFECTS OF SLOW START AND CONGESTION CONTROL, IMMEDIATE VERSUS DELAYED ACKNOWLEDGMENT, FLOW CONTROL, PROCESSING DELAYS, AND PACKETS HAVING DELAY OR GETTING LOST. BY USING THESE TIME-SEQUENCE DIAGRAMS, STUDENTS CAN VISUALIZE HOW ENTITIES COMMUNICATE WITH ONE ANOTHER AND INTERACTIVELY DRAW BY USING DRAG AND DROP THE SEQUENCE OF MESSAGES EXCHANGED BETWEEN THE ENTITIES NEEDED TO CARRY OUT THE TCP FUNCTIONALITY OF DIFFERENT SPECIFIC SCENARIOS. THE TCP FLOW TOOLBOX IS THE FIRST AND UNIQUE IN ITS KIND, AND COMPLEMENTS TRADITIONAL LABORATORY EXERCISES. TODAY, NO INTERACTIVE LEARNING MATERIAL EXISTS TO LEARN THE INNER WORKING OF THE COMPLEX TCP PROTOCOL. DUE TO ITS WEB-BASED IMPLEMENTATION AND STEP BY STEP FEEDBACK MODUS, THE TOOLBOX CAN ALSO BE USED IN E-LEARNING AND BLENDED LEARNING SETTINGS. BY ADDING LEARNING ANALYTICS AND COLLECTING DATA FROM THE LEARNING BEHAVIOR OF THE STUDENTS, THE LECTURER CAN EASILY MONITOR THE PROGRESS OF HIS STUDENTS AND USE THIS TO ATTUNE THE CONTENT OF HIS CLASSES TO THE DIFFICULTIES EXPERIENCED BY THE STUDENTS.
THIS RESEARCH WAS MOTIVATED BY AN INTEREST IN UNDERSTANDING HOW SOCIAL MEDIA ARE APPLIED IN TEACHING IN HIGHER EDUCATION. DATA WERE COLLECTED USING AN ONLINE QUESTIONNAIRE, COMPLETED BY 333 INSTRUCTORS IN HIGHER EDUCATION, THAT ASKED ABOUT GENERAL SOCIAL MEDIA USE AND SPECIFIC USE IN TEACHING. EDUCATION AND LEARNING THEORIES SUGGEST THREE POTENTIAL REASONS FOR INSTRUCTORS TO USE SOCIAL MEDIA IN THEIR TEACHING: (1) EXPOSING STUDENTS TO PRACTICES, (2) EXTENDING THE RANGE OF THE LEARNING ENVIRONMENT, AND (3) PROMOTING LEARNING THROUGH SOCIAL INTERACTION AND COLLABORATION. ANSWERS TO OPEN-ENDED QUESTIONS ABOUT HOW SOCIAL MEDIA WERE USED IN TEACHING, AND RESULTS OF A FACTOR ANALYSIS OF CODED RESULTS, REVEALED SIX DISTINCT FACTORS THAT ALIGN WITH THESE REASONS FOR USE: (1) FACILITATING STUDENT ENGAGEMENT, (2) INSTRUCTOR'S ORGANIZATION FOR TEACHING, (3) ENGAGEMENT WITH OUTSIDE RESOURCES, (4) ENHANCING STUDENT ATTENTION TO CONTENT, (5) BUILDING COMMUNITIES OF PRACTICE, AND (6) RESOURCE DISCOVERY. THESE FACTORS ACCORD WITH A USES AND GRATIFICATIONS PERSPECTIVE THAT DEPICTS ADOPTERS AS ACTIVE MEDIA USERS CHOOSING AND SHAPING MEDIA USE TO MEET THEIR OWN NEEDS. RESULTS PROVIDE A MORE COMPREHENSIVE PICTURE OF SOCIAL MEDIA USE THAN FOUND IN PREVIOUS WORK, ENCOMPASSING NOT ONLY THE ARRAY OF MEDIA USED BUT ALSO THE RANGE OF PURPOSES ASSOCIATED WITH USE OF SOCIAL MEDIA IN CONTEMPORARY TEACHING INITIATIVES.
LEARNING DESIGN IS A RESEARCH FIELD WHICH STUDIES HOW TO BEST SUPPORT TEACHERS AS DESIGNERS OF TECHNOLOGY ENHANCED LEARNING (TEL) SITUATIONS. ALTHOUGH SUBSTANTIAL WORK HAS BEEN DONE IN THE ARTICULATION OF THE LEARNING DESIGN PROCESS, LITTLE IS KNOWN ABOUT HOW LEARNING DESIGNS ARE EXPERIENCED BY STUDENTS AND TEACHERS, ESPECIALLY IN THE CONTEXT OF SCHOOLS. THIS PAPER EMPIRICALLY EXAMINES IF A TEACHER INQUIRY MODEL, AS A TOOL FOR SYSTEMATIC RESEARCH BY TEACHERS INTO THEIR OWN PRACTICE, FACILITATES THE CONNECTION BETWEEN THE DESIGN AND DATA-INFORMED REFLECTION ON TEL INTERVENTIONS IN TWO SCHOOL COMMUNITIES. HIGH SCHOOL TEACHERS PARTICIPATED IN A LEARNING DESIGN PROFESSIONAL DEVELOPMENT PROGRAM SUPPORTED BY A WEB-BASED COMMUNITY PLATFORM INTEGRATING A TEACHER INQUIRY TOOL (TILE). A MULTIPLE CASE STUDY WAS CONDUCTED AIMED AT UNDERSTANDING: (A) CURRENT TEACHER PRACTICE AND (B) TEACHER INVOLVEMENT IN INQUIRY CYCLES OF DESIGN AND CLASSROOM IMPLEMENTATIONS WITH TECHNOLOGIES. MULTIPLE DATA SOURCES WERE USED OVER A ONE YEAR PERIOD INCLUDING FOCUS GROUPS TRANSCRIPTS, TEACHER INTERVIEW PROTOCOLS, DIGITAL ARTIFACTS, AND QUESTIONNAIRES. SHARING TEACHER-LED INQUIRIES TOGETHER WITH LEARNING ANALYTICS WAS PERCEIVED AS BEING USEFUL FOR CONNECTING PEDAGOGICAL INTENTIONS WITH THE EVALUATION OF THEIR ENACTMENT WITH LEARNERS, AND THIS DIFFERED FROM THEIR CURRENT PRACTICE. TEACHERS' REFLECTIONS ABOUT THEIR DESIGNS FOCUSED ON THE TIME MANAGEMENT OF LEARNING ACTIVITIES AND THEIR FAMILIARITY WITH THE ENACTMENT AND ANALYTICS TOOLS. RESULTS INFORM HOW TECHNOLOGY CAN SUPPORT TEACHER-LED INQUIRY AND COLLECTIVE REFLECTIVE PRACTICE IN SCHOOLS.
CHALLENGES OF BROADENING ACCESS, ESCALATING COST, MAINTAINING DESIRABLE QUALITY AND ENHANCING MEANINGFUL LEARNING EXPERIENCES IN AFRICAN HIGHER EDUCATION (HE) HAVE SPURRED DEBATES ON HOW TO RESTRUCTURE HIGHER EDUCATION DELIVERY TO MEET THE DIVERSE NEEDS OF HETEROGENEOUS LEARNERS AND ADAPT PEDAGOGICAL MODELS TO THE EDUCATIONAL REALITIES OF LOW-INCOME AFRICAN COUNTRIES. IN VIEW OF THESE COMPLEXITIES, MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE BEEN ADVANCED BY WESTERN CONSORTIA, UNIVERSITIES AND ONLINE PLATFORM PROVIDERS AS PANACEAS FOR DISRUPTING/TRANSFORMING EXISTING EDUCATION MODELS AFRICAN UNIVERSITIES. MOOCS HAVE BEEN TOUTED AS DISRUPTIVE INNOVATIONS WITH THE POTENTIAL TO CREATE NEW NICHE MARKETS FOR HE COURSES, DISRUPT TRADITIONAL MODELS OF INSTRUCTION AND CONTENT DELIVERY AND CREATE NEW REVENUE STREAMS FOR HIGHER EDUCATION. YET ACADEMIC ELITISM WHICH MANIFESTS IN THE EXCLUSIVE SELECTION OF TOP AMERICAN UNIVERSITIES TO DEVELOP, HOST AND DELIVER MOOCS, MOOC PROVIDERS' USE OF UNIVERSITY BRAND AND REPUTATION AS BENCHMARKS FOR CHARGING RECRUITMENT FEES ON HEADHUNTERS RECRUITING MOOC GRADUATES AND THEIR COMPLEX BUSINESS MODELS INVOLVING THE SALE OF STUDENTS' BIG DATA (E.G. LEARNING ANALYTICS) FOR PROFIT SEEM TO BE INCONSISTENT WITH CLAIMS ABOUT PHILANTHROPIC AND EGALITARIAN DRIVE OF MOOCS. DRAWING ON DISRUPTIVE INNOVATION THEORY AND A REVIEW OF MAINSTREAM LITERATURE ON MOOCS ADOPTION IN AMERICAN AND AFRICAN TERTIARY SECTORS, THIS STUDY ARGUES THAT BEHIND THE MOOC RHETORIC OF DISRUPTING AND DEMOCRATIZING HIGHER EDUCATION LIES THE PROJECTION OF TOP ACADEMIC BRANDS ON THE MARKETING PEDESTAL, FINANCIAL PIGGYBACKING ON THE HYPE AND POLITICS OF ACADEMIC EXCLUSION.
THIS EMOTIONS PLAY A SIGNIFICANT ROLE IN DETERMINING HUMAN BEHAVIOR, INCLUDING THE INVOLVEMENT AND MOTIVATION OF STUDENTS, IN THE LEARNING PROCESS. A DIGITAL GAME HAVE BECOME AN IMPORTANT OPTION, AS A PEDAGOGICAL TOOL, FOR THE DEVELOPMENT OF SKILLS CURRENTLY REQUIRED FOR THE INSERTION OF AN INDIVIDUAL IN LABOR MARKET. THIS PAPER REPORTS A RESEARCH ON A COMPUTATIONAL ARCHITECTURE, BASED ON THE TECHNIQUES AND METHODS OF LEARNING ANALYTICS, TO ASSIST EDUCATORS, WHO USE DIGITAL GAMES AS PEDAGOGICAL TOOLS, IN ASSESSING THE IMPACTS OF A PLAYER'S EMOTIONAL STATES ON HIS MOTIVATION TO PLAY AND, CONSEQUENTLY, IN THEIR LEARNING. SUCH ARCHITECTURE AIMS TO IDENTIFY DATA ON PLAYERS' EMOTIONAL STATES, THROUGH THEIR FACIAL EXPRESSIONS, AND CROSS THEM WITH DATA ON THE PLAYERS 'ACTIONS, GAME EVENTS OR STATES, WHICH CAN EVIDENCE THEIR LEARNING. A DEEP LEARNING NEURAL NETWORK IS USED IN THE IDENTIFICATION OF THE PLAYERS 'FACIAL EXPRESSIONS, THROUGH IMAGES OF THE FACES OF THE STUDENTS PLAYING. THE CAPTURE OF THESE IMAGES IS DONE BY A WEBCAM, INSTALLED ON THE COMPUTER WHERE THE GAME IS PLAYING, WHICH IS TRIGGERED BY AN INTELLIGENT SOFTWARE AGENT, ACCORDING TO PRE-ESTABLISHED CONDITIONS. THIS INTELLIGENT SOFTWARE AGENT ALSO CAPTURES THE DATA ABOUT THE LEARNING EVIDENCES. A SOFTWARE MODULE, TO ANALYZE LEARNING AND THE POSSIBLE IMPACTS OF PLAYERS' EMOTIONAL STATES ON THEIR LEARNING, COMPLETES THE PROPOSED COMPUTATIONAL ARCHITECTURE. THE WORK WAS DEVELOPED IN THE DISCIPLINE ON DIGITAL GAMES APPLIED TO EDUCATION IN A POSTGRADUATE COURSE IN COMPUTER SCIENCE.
BACKGROUND: ASSISTIVE AUTOMATIC SEIZURE DETECTION CAN EMPOWER HUMAN ANNOTATORS TO SHORTEN PATIENT MONITORING DATA REVIEW TIMES. WE PRESENT A PROOF-OF-CONCEPT FOR A SEIZURE DETECTION SYSTEM THAT IS SENSITIVE, AUTOMATED, PATIENT-SPECIFIC, AND TUNABLE TO MAXIMISE SENSITIVITY WHILE MINIMIZING HUMAN ANNOTATION TIMES. THE SYSTEM USES CUSTOM DATA PREPARATION METHODS, DEEP LEARNING ANALYTICS AND ELECTROENCEPHALOGRAPHY (EEG) DATA. METHODS: SCALP EEG DATA OF 365 PATIENTS CONTAINING 171,745 S ICTAL AND 2,185,864 S INTERICTAL SAMPLES OBTAINED FROM CLINICAL MONITORING SYSTEMS WERE ANALYSED AS PART OF A CROWDSOURCED ARTIFICIAL INTELLIGENCE (AI) CHALLENGE. PARTICIPANTS WERE TASKED TO DEVELOP AN ICTAL/INTERICTAL CLASSIFIER WITH HIGH SENSITIVITY AND LOW FALSE ALARM RATES. WE BUILT A CHALLENGE PLATFORM THAT PREVENTED PARTICIPANTS FROM DOWNLOADING OR DIRECTLY ACCESSING THE DATA WHILE ALLOWING CROWDSOURCED MODEL DEVELOPMENT. FINDINGS: THE AUTOMATIC DETECTION SYSTEM ACHIEVED TUNABLE SENSITIVITIES BETWEEN 75.00% AND 91.60% ALLOWING A REDUCTION IN THE AMOUNT OF RAW EEG DATA TO BE REVIEWED BY A HUMAN ANNOTATOR BY FACTORS BETWEEN 142X, AND 22X RESPECTIVELY. THE ALGORITHM ENABLES INSTANTANEOUS REVIEWER-MANAGED OPTIMIZATION OF THE BALANCE BETWEEN SENSITIVITY AND THE AMOUNT OF RAW EEG DATA TO BE REVIEWED. INTERPRETATION: THIS STUDY DEMONSTRATES THE UTILITY OF DEEP LEARNING FOR PATIENT-SPECIFIC SEIZURE DETECTION IN EEG DATA. FURTHERMORE, DEEP LEARNING IN COMBINATION WITH A HUMAN REVIEWER CAN PROVIDE THE BASIS FOR AN ASSISTIVE DATA LABELLING SYSTEM LOWERING THE TIME OF MANUAL REVIEW WHILE MAINTAINING HUMAN EXPERT ANNOTATION PERFORMANCE. (C) 2021 THE AUTHORS. PUBLISHED BY ELSEVIER B.V.
ARTIFICIAL INTELLIGENCE (AI), ALTHOUGH BEING OPPOSED TO THE NATURAL INTELLIGENCE, HAS BECOME A VITAL AND, SOMETIMES, AN INTEGRAL PART OF MODERN SOCIETY. NOWADAYS WE SEE THAT AI AND ITS DISTINCT FEATURES ARE BEING APPLIED IN THE GROWING NUMBER OF AREAS, BOTH IN INDUSTRY AND SERVICES, PUBLIC HEALTHCARE AND EDUCATION. IN THIS ARTICLE WE PROVIDE AN ANALYSIS OF THE CURRENT STATE OF AI TECHNOLOGIES APPLIED IN THE AREA OF LANGUAGE TEACHING. THE LANGUAGE TEACHING PROCESS REQUIRES AN INDIVIDUAL APPROACH TO EVERY PARTICIPANT OF THE LANGUAGE CLASS - AT THE CURRENT STATE OF EDUCATIONAL SYSTEM, IT IS SOMETIMES HARD OR EVEN IMPOSSIBLE FOR A TEACHER TO PROVIDE A SUFFICIENT ATTENTION AND SUPPORT TO EVERY MEMBER OF THE CLASS. IT MAY RESULT IN NEGATIVE QUALITY OF LANGUAGE TEACHING PROCESS. WE BELIEVE THAT A BROADER USE OF MODERN TECHNOLOGIES SUCH AS AI MIGHT HELP IMPROVE THIS SITUATION. IN THIS ARTICLE WE FOCUS OUR ATTENTION ON INVESTIGATION OF NATURAL LANGUAGE PROCESSING (NLP), MACHINE LEARNING (ML), AND LEARNING ANALYTICS (LA) IN EDUCATION, HOW IT IS BEING IMPLEMENTED IN LANGUAGE TEACHING AND WHAT POSSIBLE WAYS FOR IMPROVEMENT EXIST. WE OBSERVE THE CURRENT PROBLEMS OF NLP, ML, LA AND ARTIFICIAL INTELLIGENCE IN GENERAL, FROM THE PERSPECTIVE OF NATURAL LANGUAGES. BASED ON THAT OBSERVATION, WE SUGGEST HOW AI CAN IMPROVE THE QUALITY OF LANGUAGE TEACHING PROCESS AND WHAT PROBLEMS THE CURRENT SOCIETY NEEDS TO SOLVE IN ORDER FOR AI TO HAVE FRUITFUL IMPACT ON EDUCATION.
THIS ARTICLE PRESENTS A REVIEW OF WORKS THAT CENTER THEIR INTEREST IN ELEARNING PLATFORMS AND THE DATA MINING OF PARTICIPANTS' ACTIVITY. THE STUDIES IN THIS RESEARCH AREA GENERATE INFORMATION, THROUGH THE ANALYSIS OF SUCH LOGS AND DATA, THAT IS PROVIDED TO THE STUDENTS IN REAL TIME TO HELP THEM TO COLLABORATE AND LEARN THROUGH COLLABORATION ON THE PLATFORM. THERE ARE STUDIES FROM DIFFERENT AREAS OF STUDY SUCH AS LEARNING ANALYTICS, EDUCATIONAL DATA MINING, GROUP AWARENESS TOOLS OR INTERACTION ANALYSIS TOOLS. THE REVIEW TAKES A DOUBLE PERSPECTIVE: I) TO ANALYZE THE DATA EXTRACTED FROM ACTIVITY LOGS, THEIR PROCESSING, THE INFORMATION GENERATED AND THE WAYS TO COMMUNICATE IT; AND II) TO EXPLORER THE MODEL AND THE INSTRUMENTS USED TO ASSESS HOW THE INFORMATION PROVIDED IMPACT ON ONLINE COLLABORATIVE PROCESSES AND/OR THE LEARNING. THE CONCLUSIONS EMPHASIZE THAT THE MODELS OF COLLABORATIVE LEARNING THAT JUSTIFIES THE SELECTION OF THE DATA EXTRACTED FROM THE ACTIVITY LOGS, THE PROCESSING, THE INFORMATION GENERATED AND PROVIDED TO THE STUDENTS AND THE WAY OF COMMUNICATING IT, ARE NOT EXPLICITLY STATED. IN ADDITION, IMPORTANT BIASES ARE DETECTED BECAUSE OF NOT CONSIDERING THE MULTIDIMENSIONAL NATURE OF THE COLLABORATIVE LEARNING PROCESSES. ALSO, FEW STUDIES ANALYZE THE RELATIONS BETWEEN STUDENTS' USES OF THE INFORMATION PROVIDED AND THE QUALITY OF THEIR COLLABORATIVE PROCESSES AND LEARNING RESULTS. THE VERY FEW STUDIES THAT DO ANALYZE SUCH RELATION DO NOT GO INTO DEPTH ON THE CHANGES IN GROUP DYNAMICS CAUSED BY INFORMATION.
CONTEXTTHIS ARTICLE DESCRIBES THE SCHOLARLY WORK THAT HAS ADDRESSED THE FIFTH RECOMMENDATION OF THE 1988 WORLD CONFERENCE ON MEDICAL EDUCATION: TRAIN TEACHERS AS EDUCATORS, NOT CONTENT EXPERTS ALONE, AND REWARD EXCELLENCE IN THIS FIELD AS FULLY AS EXCELLENCE IN BIOMEDICAL RESEARCH OR CLINICAL PRACTICE'. PROGRESSOVER THE PAST 30 YEARS, SCHOLARS HAVE DEFINED THE PREPARATION NEEDED FOR TEACHING AND OTHER EDUCATOR ROLES, AND CREATED FACULTY DEVELOPMENT DELIVERY SYSTEMS TO TRAIN TEACHERS AS EDUCATORS. TO REWARD THE EXCELLENCE OF EDUCATORS, SCHOLARS HAVE EXPANDED DEFINITIONS OF SCHOLARSHIP, DEFINED EDUCATOR ROLES AND CRITERIA FOR JUDGING EXCELLENCE, AND DEVELOPED EDUCATOR PORTFOLIOS TO MAKE ACHIEVEMENTS VISIBLE FOR PEER REVIEW. DESPITE THESE EFFORTS, THE SCHOLARSHIP OF DISCOVERY CONTINUES TO BE MORE HIGHLY PRIZED AND REWARDED THAN THE SCHOLARSHIP OF TEACHING. THESE VALUES ARE DEEPLY EMBEDDED IN UNIVERSITY CULTURE AND POLICIES. CHALLENGESTO REMEDY THE STRUCTURAL INEQUALITIES BETWEEN RESEARCHERS AND EDUCATORS, A HOLISTIC APPROACH TO REWARDING THE BROAD RANGE OF EDUCATIONAL ROLES AND EDUCATIONAL SCHOLARSHIP IS NEEDED. THIS REQUIRES STRONG ADVOCACY TO CREATE CHANGES IN ACADEMIC REWARDS AND SUPPORT POLICIES, PROVIDE A CLEAR CAREER TRAJECTORY FOR EDUCATORS USING LEARNING ANALYTICS, EXPAND PROGRAMMES FOR FACULTY DEVELOPMENT, SUPPORT HEALTH PROFESSIONS EDUCATION SCHOLARSHIP UNITS AND ACADEMIES OF MEDICAL EDUCATORS, AND CREATE MECHANISMS TO ENSURE HIGH STANDARDS FOR ALL EDUCATORS. THE AUTHORS DESCRIBE SCHOLARLY RESEARCH AND INSTITUTIONAL CHANGES DESIGNED TO DEVELOP AND REWARD TEACHERS AS EDUCATORS AND SCHOLARS BUT ASSERT THAT CHALLENGES REMAIN TO TREAT EDUCATORS AS EQUITABLY AS RESEARCHERS.
THIS PAPER EXPLORES PEDAGOGICAL PRACTICES AND PARTICIPANTS' ENGAGEMENT IN LEARNING ACTIVITIES DURING THE FIRST INTERNATIONAL MASSIVE OPEN ONLINE COURSE (MOOC) OFFERED BY THE UNIVERSITY OF OSLO THROUGH THE FUTURELEARN PLATFORM IN 2015. THE DATA WERE COLLECTED USING PRE- AND POST-COURSE SURVEYS AND PARTICIPANT OBSERVATIONS. WE USED THE ACQUISITION AND PARTICIPATION METAPHORS OF LEARNING PROPOSED BY SFARD (1998) AS A CONCEPTUAL FRAMEWORK TO INFORM OUR ANALYSES AND DISCUSSIONS. THE DATA INDICATED THAT NEW PEDAGOGICAL PRACTICES ARE IN THE MAKING FOR ONLINE LEARNING, INVOLVING ELEMENTS OF EXISTING PRACTICES AND RADICALLY NEW ONES. THE INSTRUCTORS HAD SOLE AUTHORITY IN DEVELOPING AND CURATING COURSE CONTENTS, THUS FOLLOWING THE ACQUISITION METAPHOR OF TEACHING AND LEARNING. IN ADDITION, THE DATA INDICATED THAT, OVERALL, THE LEARNERS HAD A POSITIVE EXPERIENCE OF LEARNING BY PARTICIPATING IN THE MOOC. THE LEARNERS ENGAGED IN ONLINE DISCUSSION FORUMS, INTERACTING ASYNCHRONOUSLY WITH FELLOW LEARNERS AND MENTORS. THE DISCUSSION FORUMS PROMOTED KNOWLEDGE SHARING AND COLLABORATIVE LEARNING ACTIVITIES AMONG DIVERSE GROUPS (JOINERS, SURVEYORS, AND SOCIAL LEARNERS). THE APPARENT CONTRADICTION BETWEEN TEACHING ACCORDING TO THE ACQUISITION METAPHOR AND THE LEARNERS' PREFERENCES FOR THE PARTICIPATION METAPHOR WAS RESOLVED BY SOME OF THE LEARNERS THROUGH SELF-ORGANISED SCAFFOLDING. THE TEACHERS DID NOT INTERACT ENOUGH WITH THE LEARNERS AND SO, TO COMPENSATE, SOME LEARNERS TOOK ON FACILITATING ROLES. WE DISCUSS OUR FINDINGS IN TERMS OF THE RELATED WORK AND CONTEMPORARY TRENDS IN ONLINE LEARNING AND HIGHER EDUCATION RESEARCH, INCLUDING LEARNING ANALYTICS, FORMATIVE ASSESSMENT, PERSONALIZATION, COLLABORATION SUPPORT, AND LIFELONG LEARNING.
THE FOCUS OF THIS PAPER ARE THE ETHICAL, LEGAL AND SOCIAL CHALLENGES FOR ENSURING THE RESPONSIBLE USE OF "BIG BRAIN DATA"-THE RECORDING, COLLECTION AND ANALYSIS OF INDIVIDUALS' BRAIN DATA ON A LARGE SCALE WITH CLINICAL AND CONSUMER-DIRECTED NEUROTECHNOLOGICAL DEVICES. FIRST, I HIGHLIGHT THE BENEFITS OF BIG DATA AND MACHINE LEARNING ANALYTICS IN NEUROSCIENCE FOR BASIC AND TRANSLATIONAL RESEARCH. THEN, I DESCRIBE SOME OF THE TECHNOLOGICAL, SOCIAL AND PSYCHOLOGICAL BARRIERS FOR SECURING BRAIN DATA FROM UNWARRANTED ACCESS. IN THIS CONTEXT, I THEN EXAMINE WAYS IN WHICH SAFEGUARDS AT THE HARDWARE AND SOFTWARE LEVEL, AS WELL AS INCREASING "DATA LITERACY" IN SOCIETY, MAY ENHANCE THE SECURITY OF NEUROTECHNOLOGICAL DEVICES AND PROTECT THE PRIVACY OF PERSONAL BRAIN DATA. REGARDING ETHICAL AND LEGAL RAMIFICATIONS OF BIG BRAIN DATA, I FIRST DISCUSS EFFECTS ON THE AUTONOMY, THE SENSE OF AGENCY AND AUTHENTICITY, AS WELL AS THE SELF THAT MAY RESULT FROM THE INTERACTION BETWEEN USERS AND INTELLIGENT, PARTICULARLY CLOSED-LOOP, NEUROTECHNOLOGICAL DEVICES. I THEN DISCUSS THE IMPACT OF THE "DATAFICATION" IN BASIC AND CLINICAL NEUROSCIENCE RESEARCH ON THE JUST DISTRIBUTION OF RESOURCES AND ACCESS TO THESE TRANSFORMATIVE TECHNOLOGIES. IN THE LEGAL REALM, I EXAMINE POSSIBLE LEGAL CONSEQUENCES THAT ARISES FROM THE INCREASING ABILITIES TO DECODE BRAIN STATES AND THEIR CORRESPONDING SUBJECTIVE PHENOMENOLOGICAL EXPERIENCES ON THE HITHERTO INACCESSIBLE PRIVACY OF THESE INFORMATION. FINALLY, I DISCUSS THE IMPLICATIONS OF BIG BRAIN DATA FOR NATIONAL AND INTERNATIONAL REGULATORY POLICIES AND MODELS OF GOOD DATA GOVERNANCE.
EDUCATIONAL TECHNOLOGY (EDTECH) IS A DYNAMIC, EVOLVING FIELD AND AS SUCH, IN IDENTIFYING AND MAPPING RESEARCH PATTERNS IN THIS FIELD, A SYSTEMATIC APPROACH IS REQUIRED. STARTING FROM WHEN THE WORLD WIDE WEB BECAME PUBLICLY AVAILABLE, THIS STUDY CONDUCTS A SYSTEMATIC REVIEW OF EDUCATIONAL TECHNOLOGY RESEARCH PATTERNS. THE REVIEW SHOWED THAT AFTER 1993, THERE WAS A SUDDEN INCREASE IN THE NUMBER OF EDUCATIONAL TECHNOLOGY PUBLICATIONS, AND THAT IN TERMS OF SUBJECT AREAS, SOCIAL SCIENCES DOMINATE THE FIELD, WHICH SUGGESTS THAT THERE IS A NEED FOR MORE INTERDISCIPLINARY RESEARCH. REGARDING THE GEOGRAPHICAL DISTRIBUTION OF THE RESEARCH, THE REVIEW FOUND THAT MOST OF THE CONTRIBUTIONS COME FROM THE SAME DEVELOPED COUNTRIES. THE FOLLOWING THEMES FROM OVER THE COURSE OF ALMOST THREE DECADES WERE IDENTIFIED: 1993-1999 MULTIMEDIA LEARNING AND INSTRUCTIONAL DESIGN; 2000-2004 CONVERGENCE OF EDUCATIONAL TECHNOLOGY, DISTANCE EDUCATION AND ONLINE LEARNING ENVIRONMENTS, AND EDUCATIONAL TECHNOLOGY INTEGRATION IN TRADITIONAL LEARNING SETTINGS; 2005-2009 REVISING CURRICULUM FOR EDUCATIONAL TECHNOLOGY, EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION AND DISTANCE EDUCATION, AND THE BOTTLENECK OF THE SIGNIFICANT DIFFERENCES IN EDUCATIONAL TECHNOLOGY RESEARCH; 2010-2014 ONLINE LEARNING AND HIGHER EDUCATION, INTEGRATION OF ICT AND FULL POTENTIAL OF EDUCATIONAL TECHNOLOGY; AND 2015-2019 DATA-DRIVEN, SMART EDUCATIONAL TECHNOLOGY, BIG DATA, AND LEARNING ANALYTICS. WHILE CRITICAL VIEWS ARE INCREASING, THIS STUDY ALSO OBSERVED THAT SOME DISCOURSE, SUCH AS ARGUMENTS THAT EDTECH WILL CHANGE EDUCATION AND REPLACE TEACHERS, ARE CONSTANTLY ARTICULATED THROUGHOUT THE LITERATURE.
EVEN THOUGH THE RECENT INFLUX OF TABLETS IN PRIMARY EDUCATION GOES TOGETHER WITH THE VISION THAT EDUCATIONAL TECHNOLOGIES WILL REVOLUTIONIZE EDUCATION, EMPIRICAL RESULTS SUPPORTING THIS CLAIM ARE SCARCE. THE ADAPTIVE EDUCATIONAL TECHNOLOGY IN THIS RESEARCH IS USED DAILY IN PRIMARY CLASSROOMS AND INCLUDES TEACHER DASHBOARDS. WHILE STUDENTS PRACTICE ON THE TABLET, THE TECHNOLOGY DISPLAYS REAL-TIME DATA OF LEARNER PROGRESS AND PERFORMANCE IN TEACHER DASHBOARDS. THIS STUDY EXAMINES HOW TEACHERS USE THE DASHBOARDS DURING LESSONS APPLYING THE VERBERTS' LEARNING ANALYTIC PROCESS MODEL. TEACHER DASHBOARD CONSULTATIONS AND RESULTING PEDAGOGICAL ACTIONS WERE OBSERVED IN MATHEMATICS LESSONS. IN A FOLLOWING STIMULATED RECALL INTERVIEW, A TEACHER WAS ASKED TO ELABORATE ON THE KNOWLEDGE HE/SHE ACTIVATED AND HIS/HER REASONING IN INTERPRETING THE DASHBOARD. THE RESULTS INDICATE THAT TEACHERS CONSULT THE DASHBOARD ON AVERAGE 8,3 TIMES PER LESSON, BUT GREAT VARIATION AMONG TEACHERS WAS FOUND. TEACHERS ACTIVATE EXISTING KNOWLEDGE ABOUT THE CLASS AND STUDENTS TO INTERPRET DASHBOARD DATA. THE PEDAGOGICAL ACTIONS TEACHERS TAKE AFTER DASHBOARD CONSULTATION ARE MAINLY PROVIDING INDIVIDUAL FEEDBACK AND ADDITIONAL INSTRUCTION. THE RESULTS SHOW THAT PEDAGOGICAL ACTIONS PREFORMED AT TEACHERS' OWN INITIATIVE ARE MOSTLY DIRECTED TO LOW ABILITY STUDENTS, WHEREAS ACTIONS AFTER CONSULTING THE DASHBOARD ARE MORE DIRECTED AT MIDDLE AND HIGH ABILITY STUDENTS. THESE RESULTS INDICATE THAT EXTRACTED LEARNING ANALYTICS, IN THE FORM OF TEACHER DASHBOARDS ARE INDEED INFLUENCING TEACHERS' PEDAGOGICAL ACTIONS IN DAILY CLASSROOM ACTIVITIES AND MAY INITIATE BEHAVIOR CHANGES IN TEACHING PRACTICES.
FAST ADVANCEMENT OF TECHNOLOGY HAS LED TO AN INCREASED INTEREST FOR USING INFORMATION TECHNOLOGY TO PROVIDE FEEDBACK BASED ON LEARNING BEHAVIOR OBSERVATIONS. THIS WORK OUTLINES A NOVEL APPROACH FOR ANALYZING BEHAVIORAL LEARNER DATA THROUGH THE APPLICATION OF PROCESS MINING TECHNIQUES SPECIFICALLY TARGETING A COMPLEX PROBLEM SOLVING PROCESS. WE REALIZE THIS IN THE CONTEXT OF ONE PARTICULAR LEARNING CASE, NAMELY, DOMAIN MODELING. THIS WORK EXTENDS OUR PREVIOUS RESEARCH ON PROCESS-MINING ANALYSIS OF DOMAIN MODELING BEHAVIOR OF NOVICES BY ELABORATING WITH NEW INSIGHTS FROM A REPLICATION STUDY ENHANCED WITH AN EXTRA OBSERVATION ON HOW NOVICES VERIFY/VALIDATE MODELS. THE FINDINGS INCLUDE A SET OF TYPICAL MODELING AND VALIDATION PATTERNS THAT CAN BE USED TO IMPROVE TEACHING GUIDANCE FOR DOMAIN MODELING COURSES. FROM A SCIENTIFIC VIEWPOINT, THE RESULTS CONTRIBUTE TO IMPROVING OUR KNOWLEDGE ON THE COGNITIVE ASPECTS OF PROBLEM-SOLVING BEHAVIOR OF NOVICES IN THE AREA OF DOMAIN MODELING, SPECIFICALLY REGARDING PROCESS-ORIENTED FEEDBACK AS OPPOSED TO TRADITIONAL OUTCOME FEEDBACK (IS A SOLUTION CORRECT? WHY (NOT)?) USUALLY APPLIED IN THIS TYPE OF COURSES. ULTIMATELY, THE OUTCOMES OF THE WORK CAN BE INSPIRATIONAL OUTSIDE OF THE AREA OF DOMAIN MODELING AS LEARNING EVENT DATA IS BECOMING READILY AVAILABLE THROUGH VIRTUAL LEARNING ENVIRONMENTS AND OTHER INFORMATION SYSTEMS. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
IN THIS STUDY, WE REVIEW 1830 RESEARCH ARTICLES ON ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED), WITH THE AIM OF PROVIDING A HOLISTIC PICTURE OF THE KNOWLEDGE EVOLUTION IN THIS INTERDISCIPLINARY RESEARCH FIELD FROM 2010 TO 2019. A NOVEL THREE-STEP APPROACH IN THE ANALYSIS OF THE KEYWORD CO-OCCURRENCE NETWORKS (KCN) IS PROPOSED TO IDENTIFY THE KNOWLEDGE STRUCTURE, KNOWLEDGE CLUSTERS AND TRENDING KEYWORDS WITHIN AIED OVER TIME. THE RESULTS REVEAL CONSIDERABLE RESEARCH DIVERSITY IN THE AIED FIELD, CENTERING AROUND TWO SUSTAINED THEMES: INTELLIGENT TUTORING SYSTEMS (2010-19) AND MASSIVE OPEN ONLINE COURSES (SINCE 2014). THE FOCAL EDUCATIONAL CONCERNS REFLECTED IN AIED RESEARCH ARE: (1) ONLINE LEARNING; (2) GAME-BASED LEARNING; (3) COLLABORATIVE LEARNING; (4) ASSESSMENT; (5) AFFECT; (6) ENGAGEMENT; AND (7) LEARNING DESIGN. THE HIGHLY CONNECTED KEYWORDS RELEVANT TO ANALYTIC TECHNIQUES WITHIN THIS FIELD INCLUDE NATURAL LANGUAGE PROCESSING, EDUCATIONAL DATA MINING, LEARNING ANALYTICS AND MACHINE LEARNING. NEURAL NETWORK, DEEP LEARNING, EYE TRACKING, AND PERSONALIZED LEARNING ARE TRENDING KEYWORDS IN THIS FIELD AS THEY HAVE EMERGED WITH KEY STRUCTURAL ROLES IN THE LATEST TWO-YEAR PERIOD ANALYZED. THIS IS THE FIRST ARTICLE PROVIDING A SYSTEMATIC REVIEW OF A LARGE BODY OF LITERATURE ON ARTIFICIAL INTELLIGENCE IN EDUCATION, AND IN IT WE UNCOVER THE UNDERLYING PATTERNS OF KNOWLEDGE CONNECTIVITY WITHIN THE FIELD, AS WELL AS PROVIDE INSIGHT INTO ITS FUTURE DEVELOPMENT. THE THREE-STEP MULTI-SCALE (MACRO, MESO, MICRO) FRAMEWORK PROPOSED IN THIS STUDY CAN ALSO BE APPLIED TO MAP THE KNOWLEDGE DEVELOPMENT IN OTHER SCIENTIFIC RESEARCH AREAS.
LEARNING PROGRAMMING HAS BECOME MORE AND MORE POPULAR AND ORGANIZING INTRODUCTORY MASSIVE OPEN ONLINE COURSES (MOOCS) ON PROGRAMMING CAN BE ONE WAY TO BRING THIS EDUCATION TO THE MASSES. WHILE PROGRAMMING MOOCS USUALLY USE AUTOMATED ASSESSMENT TO GIVE FEEDBACK ON THE SUBMITTED CODE, THE LACK OF UNDERSTANDING OF CERTAIN ASPECTS OF THE TASKS AND FEEDBACK GIVEN BY THE AUTOMATED ASSESSMENT SYSTEM CAN BE ONE PERSISTENT PROBLEM FOR MANY PARTICIPANTS. THIS PAPER INTRODUCES TROUBLESHOOTERS, WHICH ARE HELP SYSTEMS, STRUCTURED LIKE DECISION TREES, FOR GIVING HINTS AND EXAMPLES OF CERTAIN ASPECTS OF THE COURSE TASKS. THE GOAL OF THIS PAPER IS TO GIVE AN OVERVIEW OF USABILITY (BENEFITS AND DANGERS) OF, AND THE PARTICIPANTS' FEEDBACK ON, USING TROUBLESHOOTERS. TROUBLESHOOTERS HAVE BEEN USED FROM THE YEAR 2016 IN TWO DIFFERENT PROGRAMMING MOOCS FOR ADULTS IN ESTONIA. THESE MOOCS ARE CHARACTERIZED BY HIGH COMPLETION RATES (50-70%), WHICH IS UNUSUAL FOR MOOCS. DATA IS GATHERED FROM THE LEARNING ANALYTICS INTEGRATED INTO THE TROUBLESHOOTERS' ENVIRONMENT, LETTERS FROM THE PARTICIPANTS, QUESTIONNAIRES, AND TASKS CONDUCTED THROUGH THE COURSES. AS IT WAS NOT COMPULSORY TO USE TROUBLESHOOTERS, THE RESULTS INDICATE THAT ONLY 19.8% OF THE USERS DID NOT USE TROUBLESHOOTERS AT ALL AND 10% OF THE PARTICIPANTS DID NOT FIND TROUBLESHOOTERS HELPFUL AT ALL. THE MAIN DIFFERENCE THAT APPEARED IS THAT THE NUMBER OF QUESTIONS ASKED FROM THE ORGANIZERS ABOUT THE PROGRAMMING TASKS DURING THE COURSES VIA HELPDESK DECLINED ABOUT 29%.
WE WILL PRESENT THE PRELIMINARY RESULTS OF AN INVESTIGATION ON THE USE OF A VARIETY OF LEARNING TOOLS LOOSELY INTEGRATED BY THE NEXT GENERATION DIGITAL LEARNING ENVIRONMENT (NGDLE) OF THE UP2U EU-FUNDED PROJECT. STUDENTS PERFORM INDIVIDUAL AND GROUP ACTIVITIES, BOTH FORMAL AND INFORMAL ONES, WITHIN A PROJECT OF FIXED DURATION, TO ACCOMPLISH A COMMON TASK ESTABLISHED BY A TEACHER OR CHOSEN BY THE LEARNERS ON THEIR OWN INITIATIVE. LEARNING-RELATED DATA ARE FED TO A CENTRAL STORE BY A POPULAR LMS, BY A COLLABORATIVE PLATFORM SUPPORTING PROJECT-ORIENTED AND ARTIFACT-ORIENTED LEARNING AND BY MORE SPECIALIZED TOOLS ABLE TO TRACK A SET OF LESS STRUCTURED ACTIVITIES. DATA ARE THEN PROCESSED IN VARIOUS WAYS TO ANALYSE MEANINGFUL CORRELATIONS BETWEEN ACTIVITIES MEDIATED BY DIFFERENT TOOLS AND ARE FINALLY COMPARED TO INFORMATION ACQUIRED IN MORE TRADITIONAL WAYS ABOUT SOME SKILL LEVELS, BEFORE AND AFTER THE PROJECT EXECUTION. THE INVESTIGATION PUTS A SPECIAL FOCUS ON ACTIONS RELATED TO THE SET-UP AND THE COORDINATION OF A PROJECT GROUP AND ON THE ACTIVITY AIMED AT FINDING EDUCATIONAL RESOURCES ON THE WEB. THE TOOLS IN THE NGDLE USE THE XAPI PROTOCOL TO SEND STATEMENTS OF USER ACTIONS. A COMMON LEARNING RECORD STORE (LRS) IS PROVIDED BY THE NGDLE ITSELF. PROCESSING OF ANALYTICS DATA IS PERFORMED BOTH WITH NATIVE FUNCTIONS OF THE LRS AND WITH EXTERNAL TOOLS BEING FED WITH EXPORTED DATA.
IN THE 21ST CENTURY LEARNING INCREASINGLY HAPPENS ON THE SOCIAL WEB. LEARNING HAS EVOLVED INTO AN INTERACTIVE SOCIAL PROCESS PRODUCING LARGE AMOUNTS OF DATA ACROSS A MULTITUDE OF INHOMOGENEOUS SYSTEMS. TO IDENTIFY THE ROLE OF INDIVIDUAL ACTORS AND GROUPS OF ACTORS THE WHOLE GLOBAL LEARNING NETWORK NEEDS TO BE ANALYZED. THE WORK PRESENTED IN THIS PAPER INGESTS LEARNING DATA INTO A CLOUD HOSTED DISTRIBUTED TEMPORAL GRAPH MODEL WITH A SUPPORTING DISTRIBUTED PROCESSING FRAMEWORK TO CALCULATE GLOBAL GRAPH METRICS. THE PRESENTED SIMPLE ARCHITECTURAL APPROACH BUILDS UPON THE XAPI SPECIFICATION TO ENSURE COMPATIBILITY AND FLEXIBILITY. BASED ON THE GLOBAL GRAPH METRICS WE CAN DETECT COMMUNITIES AND IDENTIFY INFORMATION BROKERS. THIS INFORMATION ENHANCES THE UNDERSTANDING OF THE LEARNER'S PERSONAL LEARNING NETWORK AND ITS DEVELOPMENT OVER TIME. IT CONTRIBUTES TO ONGOING EFFORTS TO GUIDE LEARNERS' THROUGH THE TANGLED UNDERGROWTH OF THE GLOBAL SOCIAL LEARNING NETWORK TOWARDS INDIVIDUALS AND COMMUNITIES RELEVANT TO THEIR INTERESTS, SKILLS AND APTITUDES.
CRITICAL THINKING AND PROBLEM -SOLVING ARE TWO OF THE MOST FREQUENTLY DESIRED GOALS FOR DENTAL STUDENT LEARNING, BUT GREATER UNDERSTANDING IS NEEDED OF HOW STUDENTS' CRITICAL THINKING SKILLS DEVELOP FROM THE BEGINNING TO THE END OF THE CURRICULUM. THE AIM OF THIS STUDY WAS TO USE STUDENTS' PERFORMANCE ON CRITICAL THINKING ASSESSMENTS AT FIVE TIME POINTS FROM THE FIRST TO THE FOURTH YEARS TO LONGITUDINALLY ASSESS THEIR CRITICAL THINKING DEVELOPMENT ACROSS THE CURRICULUM. IN 2014-16, THE PERFORMANCE OF STUDENTS IN TWO SUCCESSIVE GRADUATING CLASSES AT ONE U.S. DENTAL SCHOOL WAS COLLECTED FROM FIVE ASSIGNMENTS DESIGNED TO, AMONG OTHER THINGS, ASSESS CRITICAL THINKING. THOSE ASSIGNMENTS RANGED FROM A CASE STUDY IN THE CARIOLOGY I COURSE IN FALL OF THE FIRST YEAR TO AN OSCE COMPLETED BEFORE GRADUATION. STUDENTS WERE DIVIDED INTO FOUR QUARTILES FROM LOWEST- TO HIGHEST -ACHIEVING BASED ON THE FIRST (BASELINE) ASSESSMENT, AND THOSE QUARTILES WERE TRACKED TO DETERMINE EACH STUDENT'S PROGRESS OVER THE FOUR YEARS. THE RESULTS SHOWED THAT, FOR BOTH COHORTS, STUDENTS WHO RANKED IN THE TOP QUARTILE REMAINED IN THE TOP OF THE CLASS FOR ALL SUBSEQUENT ASSESSMENTS. ALSO IN BOTH COHORTS, THE STUDENTS WHO HAD THE LOWEST SCORES ON THE INITIAL ASSESSMENT OF CRITICAL THINKING SKILLS SHOWED STATISTICALLY SIGNIFICANT IMPROVEMENTS OVER TIME, INCREASING THEIR SCORES BY 9.4% (COHORT 1, P<0.001) AND 4.6% (COHORT 2, P=0.003) IN EACH CONSECUTIVE ASSESSMENT. THESE RESULTS SUGGEST THAT IMPLEMENTING CRITICAL THINKING ASSESSMENTS EARLY IN THE CURRICULUM CAN ASSIST IN PREDICTING STUDENTS' FUTURE PERFORMANCE ON CRITICAL THINKING ASSESSMENTS. FURTHERMORE, A DENTAL CURRICULUM INTENTIONALLY DESIGNED TO PROMOTE CRITICAL THINKING CAPACITIES CAN HELP STUDENTS IMPROVE THESE SKILLS OVER TIME, WHATEVER THEIR LEVEL AT THE BEGINNING.
BULLYING IS A SERIOUS SOCIAL PROBLEM AT SCHOOLS, VERY PREVALENT INDEPENDENTLY OF CULTURE AND COUNTRY, AND PARTICULARLY ACUTE FOR TEENAGERS. WITH THE IRRUPTION OF ALWAYS-ON COMMUNICATIONS TECHNOLOGY, THE PROBLEM, NOW TERMED CYBERBULLYING, IS NO LONGER RESTRICTED TO SCHOOL PREMISES AND HOURS. THERE ARE MANY DIFFERENT APPROACHES TO ADDRESS CYBERBULLYING, SUCH AS SCHOOL BUDDIES, EDUCATIONAL VIDEOS, OR INVOLVING POLICE IN COUNSELING; HOWEVER, AWARENESS CONTINUES TO BE INSUFFICIENT. WE HAVE DEVELOPED CONECTADO, A SERIOUS GAME TO BE USED IN THE CLASSROOM TO INCREASE AWARENESS ON BULLYING AND CYBERBULLYING IN SCHOOLS. WHILE PLAYING THE GAME, STUDENTS GAIN A FIRST-HAND IMMERSIVE EXPERIENCE OF THE PROBLEM AND THE ASSOCIATED EMOTIONS, FOSTERING AWARENESS AND EMPATHY WITH VICTIMS. THIS PAPER DESCRIBES CONECTADO AND PRESENTS ITS VALIDATION WITH ACTUAL STUDENTS USING GAME ANALYTICS.
INTRODUCTION NARRATIVE ASSESSMENT DATA ARE VALUABLE IN UNDERSTANDING STRUGGLES IN RESIDENT PERFORMANCE. HOWEVER, IT REMAINS UNKNOWN WHICH THEMES IN NARRATIVE DATA THAT OCCUR EARLY IN TRAINING MAY INDICATE A HIGHER LIKELIHOOD OF STRUGGLES LATER IN TRAINING, ALLOWING PROGRAMS TO INTERVENE SOONER. METHODS USING LEARNING ANALYTICS, WE IDENTIFIED 26 INTERNAL MEDICINE RESIDENTS IN THREE COHORTS THAT WERE BELOW EXPECTED ENTRUSTMENT DURING TRAINING. WE COMPILED ALL NARRATIVE DATA IN THE FIRST 6 MONTHS OF TRAINING FOR THESE RESIDENTS AS WELL AS 13 TYPICALLY PERFORMING RESIDENTS FOR COMPARISON. NARRATIVE DATA WERE BLINDED FOR ALL 39 RESIDENTS DURING INITIAL PHASES OF AN INDUCTIVE THEMATIC ANALYSIS FOR INITIAL CODING. RESULTS MANY SIMILARITIES WERE IDENTIFIED BETWEEN THE TWO COHORTS. CODES THAT DIFFERED BETWEEN TYPICAL AND LOWER ENTRUSTED RESIDENTS WERE GROUPED INTO TWO TYPES OF THEMES: THREE EXPLICIT/MANIFEST AND THREE IMPLICIT/LATENT WITH SIX TOTAL THEMES. THE EXPLICIT/MANIFEST THEMES FOCUSED ON SPECIFIC ASPECTS OF RESIDENT PERFORMANCE WITH ASSESSORS DESCRIBING 1) GAPS IN ATTENTION TO DETAIL, 2) COMMUNICATION DEFICITS WITH PATIENTS, AND 3) DIFFICULTY RECOGNIZING THE "BIG PICTURE" IN PATIENT CARE. THREE IMPLICIT/LATENT THEMES, FOCUSED ON HOW NARRATIVE DATA WERE WRITTEN, WERE ALSO IDENTIFIED: 1) FEEDBACK DESCRIBED AS A DEFICIENCY RATHER THAN AN OPPORTUNITY TO IMPROVE, 2) NORMATIVE COMPARISONS TO IDENTIFY A RESIDENT AS BEING BEHIND THEIR PEERS, AND 3) WARNING OF POSSIBLE RISK TO PATIENT CARE. DISCUSSION CLINICAL COMPETENCY COMMITTEES (CCCS) USUALLY RELY ON ACCUMULATED DATA AND TRENDS. USING THE THEMES IN THIS PAPER WHILE REVIEWING NARRATIVE COMMENTS MAY HELP CCCS WITH EARLIER RECOGNITION AND BETTER ALLOCATION OF RESOURCES TO SUPPORT RESIDENTS' DEVELOPMENT.
THE UNIVERSITY IS CURRENTLY A COMPLEX AND A KEY SYSTEM FOR THE DEVELOPMENT OF THE SCIENTIFIC KNOWLEDGE AND THE TRAINING OF FUTURE PROFESSIONAL WORKERS. IT IS HAPPENING A GLOBAL DIGITAL INCLUSION, SPECIFICALLY IN THE UNIVERSITY CONTEXT. THIS DIGITAL TRANSFORMATION IS DEFINED IN DIFFERENT TYPES OF RESOURCES. THE USE OF DIGITAL PLATFORMS IS ONE OF THE CLEAREST MAINSTREAMING. THE DIGITAL INNOVATION AT UNIVERSITIES POINTS OUT A CHALLENGE TO THEIR COMMUNITY. THE CHALLENGE AT THE INSTITUTIONS CONSISTS OF TRANSFORMING THE FACE-TO-FACE TRAINING MODEL INTO A FLEXIBLE MODEL, EVEN A MIXED MODEL COMBINING FACE-TO-FACE WITH ONLINE TRAINING. IN FACT, IT IS NECESSARY TO TALK ABOUT A CHANGE OF PARADIGM WHEN WE CONCEPTUALISE THE UNIVERSITY TRAINING TOWARDS NEW APPROACHES IN ORDER TO SEARCH FOR A SUSTAINABLE TEACHING. THIS CONTRIBUTION AIMS TO SHOW THE NEWEST ASPECTS FROM THE EDUCATIONAL RESEARCH ABOUT THE CHANGES IN THE SPANISH UNIVERSITY TEACHING. FROM THE DIFFERENT REVIEWED REPORTS, IT IS ADDED SOME DATA ABOUT THE IMPLEMENTATION OF VIRTUAL OR DIGITAL PLATFORMS INTO THE SPANISH UNIVERSITY SYSTEM. THIS REVIEW AFFIRMS THE GENERALISED USE OF THIS TYPE OF RESOURCE. THERE ARE OTHER ANALYSED ASPECTS ABOUT THE IMPACT OF THIS TECHNOLOGICAL TOOL TO THE TEACHING. SOME PROPOSALS ARE ADDED TO CONSIDER IN FUTURE LINES OF RESEARCH, SUCH US THE EMERGING TECHNOLOGIES WHICH START TO BE USED IN EDUCATIONAL CONTEXTS. WE TALK ABOUT LEARNING ANALYTICS, MAKERSPACES, ACTIVE LEARNING OR ADAPTATIVE LEARNING. IN SHORT, IT IS AFFIRMED THE TECHNOLOGICAL TRANSFORMATION TOWARDS THE IMPROVEMENT OF A MORE SCALABLE AND SUSTAINABLE SYSTEM.
REPEATED MACHINE DOWNTIME LEADS TO LOST PRODUCTIVITY, LATE DELIVERIES, AND DISSATISFIED CUSTOMERS. PLANTS OFTEN STRUGGLE WITH MEETING DELIVERY COMMITMENT BECAUSE THEY LACK ADVANCED PREDICTIVE TECHNOLOGIES. BECAUSE OF LIMITED INFORMATION FROM CURRENT PRODUCTION PRACTICE, IT IS VERY CHALLENGING TO CONTROL PRODUCT QUALITY, MEET THE TIGHT TOLERANCE, AND ELIMINATE SCRAP PARTS, WHICH IS ESPECIALLY CRITICAL FOR EXPENSIVE AEROENGINE COMPONENTS. WITH THE RECENT DEVELOPMENT OF INDUSTRIAL INTERNET OF THINGS AND OPERATION TECHNOLOGIES, IT IS NOW FEASIBLE TO DIGITIZE THE PRODUCTION PROCESS AND TAKE MACHINE RELIABILITY AND PERFORMANCE TO A NEW LEVEL. IN THIS STUDY, THE MACHINING PROCESS IS SIMULATED IN THE VIRTUAL ENVIRONMENT, AND THE OUTPUT RESPONSE IS USED AS A REFERENCE DIGITAL THREAD OF THE PROCESS. IN REAL-TIME MACHINING, THE COMPUTER NUMERICAL CONTROL (CNC) CONTROL SIGNALS TOGETHER WITH EXISTING MACHINERY SENSORS AND HISTORICAL DATA ARE MONITORED AND FUSED TOGETHER TO OBSERVE THE MACHINING CONDITIONS. COMPARING THE REFERENCE SIGNATURE WITH SUPERVISED MACHINE LEARNING ANALYTICS, ANY SUBTLE DEVIATIONS IN OPERATING BEHAVIOR, WHICH ARE OFTEN THE EARLY WARNING SIGNS OF PROBLEMS, CAN BE IDENTIFIED. ALSO, WITH THE LOOK-AHEAD FUNCTION, IT GIVES THE OPERATORS BETTER VISIBILITY INTO THE MACHINING PROCESS. WITH THE PROPOSED METHOD, WITHOUT ADDING ANY MORE HARDWARE TO EXISTING PRODUCTION MACHINERIES, IT IS FEASIBLE TO CAPTURE DATA IN REAL TIME, CONDUCT AUTOMATED ANALYSIS OF THE INFORMATION, AND CREATE VISUALIZATIONS FOR TEAM MEMBERS. PLUS, THE REAL-TIME MONITORING OF ANY DEVIATION IN OPERATING BEHAVIOR DURING PRODUCTION CAN PROTECT MACHINE AND CUTTING TOOLS FROM EXCESSIVE LOAD AND DAMAGE, THEREBY REDUCING OPERATING COSTS AND INCREASING EFFECTIVE MANUFACTURING CAPACITY.
THE COLLECTION AND ANALYSIS OF DATA ARE CENTRAL TO MEDICAL EDUCATION AND MEDICAL EDUCATION SCHOLARSHIP. ALTHOUGH THE TECHNICAL ABILITY TO COLLECT MORE DATA, AND MEDICAL EDUCATION'S DEPENDENCE ON DATA, HAVE NEVER BEEN GREATER, IT IS GETTING HARDER FOR MEDICAL SCHOOLS AND EDUCATIONAL SCHOLARS TO COLLECT AND USE DATA, PARTICULARLY IN TERMS OF THE REGULATIONS, SECURITY ISSUES, AND GROWING RELUCTANCE OF LEARNERS AND OTHERS TO PARTICIPATE IN DATA COLLECTION ACTIVITIES. THESE TWO COUNTERVAILING TRENDS PRESENT A GROWING THREAT TO THE VIABILITY OF MEDICAL EDUCATION SCHOLARSHIP. IN RESPONSE, THERE MUST EITHER BE A MORE CONDUCIVE DATA ENVIRONMENT FOR MEDICAL EDUCATION SCHOLARSHIP OR MEDICAL EDUCATION MUST MOVE TO BECOME LESS DEPENDENT ON DATA. THERE IS, THEREFORE, A GROWING NEED FOR A SYSTEM-WIDE CORRECTION: A SHIFT IN PRACTICE THAT MAKES DATA USE MORE VIABLE AND PRODUCTIVE WHILE MAINTAINING HIGH PROFESSIONAL STANDARDS. THERE ARE FIVE CORE AREAS THAT CAN CONTRIBUTE TO A SYSTEM-WIDE CORRECTION: GREATER CLARITY OVER WHAT CAN BE USED AS DATA; GREATER CLARITY ON WHAT CONSTITUTES "GOOD" DATA; CHANGES TO THE WAYS IN WHICH DATA ARE COLLECTED; BETTER STRATEGIC STEWARDSHIP OF EXISTING DATA; AND DELIBERATE AND STRATEGIC ATTENTION TO "DATA READINESS" IN SUPPORT OF MEDICAL EDUCATION AND MEDICAL EDUCATION SCHOLARSHIP. THESE SOLUTIONS ARE PRIMARILY PRACTICAL AND CONCEPTUAL CHANGES IN THE FACE OF WHAT ARE MAINLY REGULATORY CHALLENGES. HOWEVER, MEDICAL EDUCATORS ALSO NEED TO ENGAGE WITH EMERGING AREAS OF PRACTICE SUCH AS LEARNING ANALYTICS, AND THEY NEED TO CONSIDER THE SHIFTING SOCIAL CONTRACT FOR USING DATA IN MEDICAL EDUCATION.
CURRICULUM MAPPING IS THE PROCESS OF DESIGNING A MULTIDIMENSIONAL MODEL OF AN EDUCATIONAL PROGRAMME FOR A COMPLETE, MORE TRANSPARENT AND BETTER-INTEGRATED LEARNING EXPERIENCE. MANY UNIVERSITIES WORLDWIDE ARE BUILDING OR EXPANDING THEIR TECHNICAL INFRASTRUCTURE TO MANAGE THEIR CURRICULA. OUR AIM WAS TO DELIVER A SYNOPSIS OF CURRENT PRACTICES AND DESCRIBE THE FOCUS OF RESEARCH INTEREST IN IMPLEMENTING CURRICULUM MAPPING TOOLS FOR MEDICAL EDUCATION. AS PART OF THE BUILDING CURRICULUM INFRASTRUCTURE IN MEDICAL EDUCATION (BCIME) PROJECT, WE CONDUCTED A STATE-OF-THE-ART NARRATIVE REVIEW OF THE LITERATURE. A SYSTEMATISED SEARCH OF THE PUBMED/MEDLINE DATABASE FOR THE YEARS 2013-2019 RESULTED IN 352 ABSTRACTS, FROM WHICH 23 FULL-TEXT PAPERS WERE INCLUDED IN THE FINAL REVIEW. FROM THESE, WE EXTRACTED GUIDANCE ON 12 KEY CHARACTERISTICS OF CURRICULUM MAPPING TOOLS. THE COLLECTED EXPERIENCES FORMED FOUR THEMATIC CATEGORIES: VISUALISATIONS, TEXT DESCRIPTIONS AND ANALYSIS, THE OUTCOME-BASED APPROACH AND ADAPTABILITY IN CURRICULUM MAPPING. AS RESULT OF THE REVIEW, WE SUMMARISED TOPICS REGARDING WAYS OF: IMPLEMENTATING NEW COMPETENCY-BASED CATALOGUES (LIKE NKLM) IN CURRICULUM MAPPING SOFTWARE (E. G., USING DYNAMIC CHECKLISTS), METHODS OF STREAMLINING THE AUTHORING PROCESS (E. G., BY AUTOMATIC DETECTION AND ALIGNMENT OF ACTION VERBS IN LEARNING OBJECTIVES DESCRIPTIONS) AND GRAPHICAL FORMS OF PRESENTING CURRICULUM DATA (E. G., NETWORK VISUALISATIONS USING AUTOMATIC CLUSTERING OF RELATED PARTS OF A CURRICULUM BASED ON SIMILARITIES BETWEEN TEXTUAL DESCRIPTIONS). WE EXPECT FURTHER DEVELOPMENTS IN TEXT-MINING METHODS AND VISUAL/LEARNING ANALYTICS IN CURRICULUM MAPPING. THE COLLECTED DATA INFORMED THE DESIGN OF A NEW CURRICULUM MANAGEMENT SYSTEM CALLED EDUPORTFOLIO, WHICH IS CURRENTLY BEING IMPLEMENTED BY THE BCIME PROJECT.
EDUCATORS SHOW GREAT INTEREST IN PARTICIPATING IN SOCIAL-MEDIA COMMUNITIES, SUCH AS TWITTER, TO SUPPORT THEIR PROFESSIONAL DEVELOPMENT AND LEARNING. THE MAJORITY OF THE RESEARCH INTO TWITTER BASED PROFESSIONAL LEARNING COMMUNITIES HAS INVESTIGATED WHY EDUCATORS CHOOSE TO USE TWITTER FOR PROFESSIONAL DEVELOPMENT AND LEARNING AND WHAT THEY ACTUALLY DO IN THESE COMMUNITIES. HOWEVER, FEW STUDIES HAVE EXAMINED WHY CERTAIN COMMUNITY MEMBERS REMAIN COMMITTED AND OTHERS GRADUALLY DROP OUT. TO FILL THIS GAP IN THE RESEARCH, THIS STUDY INVESTIGATED HOW SOME KEY FEATURES OF ONLINE DISCOURSE INFLUENCED THE CONTINUED PARTICIPATION OF THE MEMBERS OF A TWITTER BASED PROFESSIONAL LEARNING COMMUNITY. MORE THAN 600,000 TWEETS GENERATED OVER SIX YEARS UNDER THE HASHTAG #EDCHAT WERE GATHERED. ONLINE DISCOURSE WAS DECONSTRUCTED TO THE COGNITIVE DIMENSION, THE INTERACTIVE DIMENSION, AND THE SOCIAL DIMENSION. TEXT-MINING METHODS WERE THEN USED TO AUTOMATICALLY IDENTIFY THESE DIMENSIONS IN THE TWEETS. FINALLY, SURVIVAL ANALYSIS WAS USED TO QUANTIFY THE INFLUENCES OF THESE DIMENSIONS ON USERS' COMMITMENT TIME TO THE TWITTER COMMUNITY. THE IMPLICATIONS OF THE RESULTS AND FINDINGS ARE THEN DISCUSSED. WANLI XING IS AN ASSISTANT PROFESSOR IN INSTRUCTIONAL TECHNOLOGY AT TEXAS TECH UNIVERSITY, USA WITH BACKGROUND IN LEARNING SCIENCES, STATISTICS, COMPUTER SCIENCE AND MATHEMATICAL MODELING. HIS RESEARCH INTERESTS ARE EDUCATIONAL DATA MINING, LEARNING ANALYTICS, AND CSCL. GAO FEI IS AN ASSOCIATE PROFESSOR AT BOWLING GREEN STATE UNIVERSITY. HER CURRENT RESEARCH INVOLVES EXAMINING THE TYPES OF INTERACTION AND LEARNING ENABLED BY ONLINE SOCIAL TECHNOLOGIES, DESIGNING TECHNOLOGY-MEDIATED ENVIRONMENTS THAT ENCOURAGE MEANINGFUL SOCIAL INTERACTION, AND EXPLORING PEDAGOGICAL METHODS THAT PROMOTE DEEP LEARNING IN SUCH ENVIRONMENTS.
PURPOSE THERE IS A PAUCITY OF DATA AVAILABLE RELATING TO THE MISCONDUCT OF POLICE OFFICERS IN LARGER POLICING AGENCIES, TYPICALLY RESULTING IN CASE STUDY APPROACHES AND LIMITED INSIGHT INTO THE FACTORS ASSOCIATED WITH SERIOUS MISCONDUCT. THIS PAPER SEEKS TO CONTRIBUTE TO THE EMERGING KNOWLEDGE BASE ON POLICE MISCONDUCT THROUGH ANALYSIS OF 28,429 COMPLAINTS AMONG 3,830 OFFICERS IN THE NEW YORK POLICE DEPARTMENT, BETWEEN 2000 AND 2019. DESIGN/METHODOLOGY/APPROACH THIS STUDY UTILIZED A DATA SET CONSISTING OF OFFICER AND COMPLAINANT DEMOGRAPHICS, AND OFFICER COMPLAINT RECORDS. MACHINE LEARNING ANALYTICS WERE EMPLOYED, SPECIFICALLY RANDOM FOREST, TO CONSIDER WHICH VARIABLES WERE MOST ASSOCIATED WITH SERIOUS MISCONDUCT AMONG OFFICERS THAT COMMITTED MISCONDUCT. PARTIAL DEPENDENCE PLOTS WERE EMPLOYED AMONG VARIABLES IDENTIFIED AS IMPORTANT TO CONSIDER THE POINTS AT WHICH MISCONDUCT WAS MOST, AND LEAST LIKELY TO OCCUR. FINDINGS PRIOR INSTANCES OF SERIOUS MISCONDUCT WERE PARTICULARLY ASSOCIATED WITH FURTHER INSTANCES OF SERIOUS MISCONDUCT, WHILE REMEDIAL ACTION DID NOT APPEAR TO HAVE AN IMPACT IN PREVENTING FURTHER MISCONDUCT. INEXPERIENCE, BOTH IN RANK AND AGE, WAS ASSOCIATED WITH MISCONDUCT. SPECIFIC PRIOR COMPLAINTS, SUCH AS MINOR USE OF FORCE, DID NOT APPEAR TO BE PARTICULARLY ASSOCIATED WITH INSTANCES OF SERIOUS MISCONDUCT. THE CHARACTERISTICS OF THE COMPLAINANT HELD MORE IMPORTANCE THAN THE CHARACTERISTICS OF THE OFFICER. ORIGINALITY/VALUE THE ABILITY TO ANALYZE A DATA SET OF THIS SIZE IS UNUSUAL AND IMPORTANT TO PROGRESSING THE KNOWLEDGE AREA REGARDING POLICE MISCONDUCT. THIS STUDY CONTRIBUTES TO THE GROWING USE OF MACHINE LEARNING IN UNDERSTANDING THE POLICE MISCONDUCT ENVIRONMENT, AND MORE ACCURATELY TAILORING MISCONDUCT PREVENTION POLICY AND PRACTICE.
RECENT WORK HIGHLIGHTS THE DIFFICULTIES THAT ACADEMIC STAFF ENCOUNTER IN APPLYING INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) TO DEVELOP HIGHER ORDER ACADEMIC SKILLS SUCH AS CRITICAL THINKING, PROBLEM SOLVING AND CREATIVITY. ALTHOUGH HIGHER EDUCATION INSTITUTIONAL MISSION STATEMENTS, LEARNING STRATEGIES AND COURSE OUTLINES PURPORT THE VALUE OF THESE HIGHER ORDER SKILLS, THERE IS STILL SOME QUESTION ABOUT HOW WELL ACADEMICS ARE EQUIPPED TO UTILIZE NEW TECHNOLOGIES IN ASSESSING THEM. DESPITE A RHETORIC AVOWING THE BENEFITS OF THESE HIGHER ORDER SKILLS, INDEED THAT THEY CONSTITUTE THE 'HIGHER' IN HIGHER EDUCATION, IT HAS BEEN SUGGESTED IN RECENT WORK THAT ACADEMICS SET ICT-BASED ASSESSMENT TASKS UP IN SUCH A WAY AS TO INADVERTENTLY LEAD STUDENTS ON THE PATH TOWARDS LOWER ORDER OUTCOMES. THIS IS A CONTROVERSIAL CLAIM, AND ONE THAT THIS PAPERS SEEKS TO EXPLORE AND CRITIQUE IN TERMS OF CHALLENGING THE CONCEPTUAL BASIS OF ASSESSING HIGHER ORDER SKILLS THROUGH THESE TECHNOLOGIES. IT IS ARGUED THAT THE USE OF DIGITAL MEDIA IN HIGHER EDUCATION IS LEADING TO A FOCUS ON STUDENTS' ABILITY TO USE AND MANIPULATE OF THESE PRODUCTS AS AN INDEX OF THEIR FLEXIBILITY AND ADAPTABILITY TO THE DEMANDS OF THE KNOWLEDGE ECONOMY. 'NEW PARADIGM' ICT ASSESSMENT, INCLUDING LEARNING ANALYTICS AND INTEGRATED FORMATS, DESIGNED TO FOSTER THESE NEW ADAPTIVE AND CREATIVE SKILLS HAVE BEEN HAILED AS THE WAY FORWARD. HOWEVER, THIS CONNECTIVIST VIEW OF LEARNING CAN EASILY SLIDE INTO FOCUSING ON COMPETENCIES RATHER THAN HIGHER ORDER SKILLS. THE PAPER SEEKS TO ADDRESS AND SITUATE THE PLACE OF ICT IN ASSESSING HIGHER ORDER ACADEMIC SKILLS IN TERMS OF PEDAGOGICAL PHILOSOPHY.
LEARNING GAMES HAVE GREAT POTENTIAL TO BECOME AN INTEGRAL PART OF NEW CLASSROOMS OF THE FUTURE. ONE OF THE KEY REPORTED BENEFITS IS THE CAPACITY TO KEEP STUDENTS DEEPLY ENGAGED DURING THEIR LEARNING PROCESS. THEREFORE, IT IS NECESSARY TO DEVELOP MODELS THAT CAN MEASURE QUANTITATIVELY HOW LEARNERS ARE ENGAGING WITH LEARNING GAMES TO INFORM GAME DESIGNERS AND EDUCATORS, AND TO FIND WAYS TO MAXIMIZE LEARNER ENGAGEMENT. IN THIS ARTICLE, WE PRESENT OUR PROPOSAL TO MULTIDIMENSIONALLY MEASURE ENGAGEMENT IN A LEARNING GAME OVER FOUR DIMENSIONS: GENERAL ACTIVITY, SOCIAL, EXPLORATION, AND QUESTS. WE APPLY METRICS FROM THESE DIMENSIONS TO DATA FROM THE RADIX ENDEAVOR, AN INQUIRY-BASED ONLINE GAME FOR STEM LEARNING THAT HAS BEEN TESTED IN K-12 CLASSROOMS AS PART OF A PILOT STUDY ACROSS NUMEROUS SCHOOLS. BASED ON THESE DIMENSIONS, WE APPLY CLUSTERING AND REPORT FOUR DIFFERENT ENGAGEMENT PROFILES THAT WE DEFINE AS "INTEGRALLY ENGAGED," "LONE ACHIEVER," "SOCIAL EXPLORER," AND "NONENGAGED." WE ALSO USE THREE VARIABLES (ACCOUNT TYPE, CLASS GRADE, AND GENDER) TO PERFORM A CROSS-SECTIONAL ANALYSIS FINDING INTERESTING, STATISTICALLY SIGNIFICANT DIFFERENCES IN ENGAGEMENT. FOR EXAMPLE, IN-SCHOOL STUDENTS AND ACCOUNTS REGISTERED TO MALES ENGAGED SOCIALLY MUCH MORE THAN OUT-OF-SCHOOL LEARNERS OR ACCOUNTS REGISTERED TO FEMALES, AND THAT OLDER STUDENTS HAVE BETTER PERFORMANCE METRICS THAN YOUNGER ONES.
THIS PAPER CONSIDERS THE ROLE OF LIFELONG LEARNING IN MODERN SOCIETY WITHIN A TECHNOLOGY ENHANCED LEARNING MEDIATED ENVIRONMENT AND THE IMPACT OF THE DEPENDENCE ON TECHNOLOGY ON THE STRATEGIC DESIGN OF LEARNING SYSTEMS. THE ROLE OF LIFELONG LEARNING IN MODERN ECONOMIES AND THE DIVERSITY OF ACTIVITIES ASSOCIATED WITH LIFELONG LEARNING REQUIRES TARGETED RESOURCING AND UNDERSTANDING OF THE MEANING OF LIFELONG LEARNING. THE HEGEMONY OF TECHNOLOGY ENHANCED LEARNING IN MODERN EDUCATION IS ACCEPTED AS A DE-FACTO COMPONENT IN THE DESIGN OF ANY LEARNING PROGRAMME, HOWEVER THE PEDAGOGICAL PARADIGMS MAY NOT BE FULLY UNDERSTOOD. THE LITERATURE ON THE NEXUS BETWEEN THE TECHNOLOGY ENHANCED LEARNING AND THE SMART CITY EMPHASISES THE TECHNOLOGY WHILE FOCUSING ON LEARNING ANALYTICS AND BIG DATA APPLICATIONS. EVIDENCE OF THE PEDAGOGICAL PARADIGM REQUIREMENTS IS NOT QUITE SO VISIBLE AND THIS LACK OF UNDERSTANDING OF THE COMPLETE MODEL HAS THE POTENTIAL TO CREATE TENSIONS IN THE DESIGN OF LIFELONG LEARNING SYSTEMS. A PROJECT-BASED METHODOLOGY OVER 2 YEARS WAS EMPLOYED VIA 8 COLLABORATIVE LEARNING PROGRAMMES INVOLVING MORE THAN 500 PARTICIPANTS DRAWN FROM HIGH SCHOOL AND COLLEGE GRADUATE LEVELS. ALL PROJECTS WERE DESIGNED TO PROVIDE AUTHENTIC LEARNING EXPERIENCES WHERE PARTICIPANTS WERE DIVIDED INTO GROUPS OF 6 TO 8 IN A SUPPORTED OPEN LEARNING COMMUNITY ENVIRONMENT. EACH AUTHENTIC PROJECT WAS DESIGNED TO IMMERSE THE PARTICIPANTS IN A PRACTICAL ORIENTED AND SOCIALLY SIGNIFICANT PROGRAMME OF LEARNING. DURING THE PROCESS THE PARTICIPANTS DEMONSTRATED MASTERY AND SOCIALLY AWARE INTERACTION. THE AGENCY OF ACTIVE LEARNING IS CONSIDERED IN THE SENSE OF THE TRIUNE OF HUMAN, EDUCATION AND ECONOMIC, SYSTEMS FOR THE SUSTAINABLE GROWTH OF A KNOWLEDGE ECONOMY.
TOLERANCE EDUCATION AIMS TO PROMOTE THE CULTURE OF PEACE AND ACCEPTANCE OF OTHERS FOR WELL-BEING AND DIVERSITY MANAGEMENT. THEREFORE, IT IS IMPORTANT TO CREATE LEARNING ENVIRONMENTS, CONTENT, AND FACILITIES FOR TOLERANCE EDUCATION IN LINE WITH THE UNIVERSAL VALUES IN EDUCATION. HOWEVER, LITTLE IS KNOWN ABOUT HOW THE LITERATURE INVESTIGATED AND SUMMARIZED THE FINDINGS RELATED TO TOLERANCE EDUCATION. THEREFORE, THIS STUDY CONDUCTS A SYSTEMATIC REVIEW ABOUT THIS TOPIC, WHERE 75 REVIEWED STUDIES WERE ANALYZED ACCORDING TO THE CULTURAL-HISTORICAL ACTIVITY THEORY (CHAT) COMPONENTS. FINDINGS HIGHLIGHTED THAT: (1) FROM THE SUBJECT COMPONENT, FUTURE RESEARCH SHOULD INVOLVE MORE STAKEHOLDERS, LIKE PARENTS AND POLICYMAKERS, WHEN DISCUSSING TOLERANCE EDUCATION; (2) FROM THE OBJECT COMPONENT, TOLERANCE EDUCATION SHOULD NOT ONLY FOCUS ON THE PHYSICAL ENVIRONMENT, BUT ALSO ON THE VIRTUAL ENVIRONMENT; (3) FROM THE TOOLS COMPONENT, LIMITED DIGITAL TOOLS AND TECHNOLOGY HAVE BEEN USED TO PROMOTE TOLERANCE EDUCATION; (4) FROM THE COMMUNITY COMPONENT, MORE INVESTIGATION OF TOLERANCE EDUCATION SHOULD BE OUTSIDE OF SCHOOL CONTEXTS; (5) FROM THE RULES COMPONENT, MORE ICT TRAINING SHOULD BE PROVIDED. THE USE OF BIG DATA AND LEARNING ANALYTICS COULD BE ONE OF THE POTENTIAL METHODS TO ASSESS STUDENTS IN TOLERANCE EDUCATION; (6) FROM THE LABOR DIVISION COMPONENT, SCHOOL FAMILY INTERACTION ROLE IS VERY IMPORTANT TO MANAGE RISKS AND DIVERSITIES THROUGH TOLERANCE EDUCATION; AND (7) FROM THE OUTCOMES COMPONENT, TOLERANCE EDUCATION STUDIES SHOULD GO BEYOND SIMPLY INVESTIGATING THE AWARENESS AND ATTITUDES OF LEARNERS, AND INVESTIGATE, FOR INSTANCE, THE BEHAVIORAL CHANGES OF LEARNERS. FINALLY, RECOMMENDATIONS WERE MADE UNDER EACH CHAT COMPONENT.
FEEDBACK HAS LONG BEEN IDENTIFIED AS A CORE COMPONENT IN THE LEARNING PROCESS AND REMAINS A REGULAR SUBJECT OF ACADEMIC RESEARCH. IT CAN BE PERFORMED IN SEVERAL FORMS, FOR DIFFERENT PURPOSES BUT THE LITERATURE EMPHASIZES THE UNDENIABLE POWER OF FEEDBACK ON THE ACHIEVEMENT AND DEVELOPMENT OF LEARNING OUTCOMES. THIS ARTICLE PRESENTS A REFLEXION ABOUT CURRENT TRENDS ON FEEDBACK IN HIGHER EDUCATION AND SEEKS TO CONTRIBUTE TO DEEPENING THE THEME AND THE DIVERSE ISSUES RELATED, IN A COMPREHENSIVE AND INTEGRATIVE PERSPECTIVE OF THE VARIOUS ASPECTS INVOLVED. DIFFERENT APPROACHES TO THE CONCEPT OF FEEDBACK, THE CHARACTERISTICS OF EFFECTIVE FEEDBACK, STRATEGIES OF SUCCESSFUL FEEDBACK EXAMPLES AND BEST PRACTICES WILL BE CONSIDERED. WE WILL ADDRESS RECENT ISSUES IN THE LITERATURE ON THE POWER AND CHALLENGES OF PROVIDING FEEDBACK IN A CONNECTED AGE THROUGH TECHNOLOGY. THERE ARE FEW INSTRUMENTS THAT ASSESS THE FREQUENCY, TYPE AND IMPACT OF FEEDBACK IN CLASSROOMS, HOWEVER THE ENORMOUS QUANTITY OF DATA PRODUCED SURROUNDING THE INTERACTIONS IN VIRTUAL LEARNING ENVIRONMENTS PROVIDES A VALUABLE MATERIAL TO THE LEARNING ANALYTICS FIELD THAT CAN BE USED TO IMPROVE FEEDBACK PRACTICES. THIS RESEARCH SEEKS TO ENHANCE THE LEARNING PROCESS OVER THE ANALYSIS OF DATASET TO PROVIDE INFORMATIVE FEEDBACK TO LEARNERS AND EDUCATORS. IN ADDITION, BASED ON A REFLEXIVE APPROACH SUPPORTED BY OUR PROFESSIONAL EXPERIENCE PERFORMED IN THE MOODLE PLATFORM WE PRESENT SOME RESULTS OF HOW FEEDBACK CAN BE USED TO OPTIMIZE MATHEMATICAL UNDERSTANDING. WE WILL ALSO PRESENT A BRIEF ANALYSIS OF THE DATA COLLECTED IN A SURVEY THAT SEEKS TO INCREASE THE KNOWLEDGE ABOUT STUDENTS' PERCEPTIONS REGARDING THIS ISSUE.
DRAMATIC ADVANCES IN THE DESIGN AND DEPLOYMENT OF SENSORS, EDGE COMMUNICATION AND COMPUTING IN RECENT YEARS HAVE ENABLED A SIGNIFICANT SHIFT AND EVOLUTION IN EDUCATION. PERVASIVE LEARNING IN VARIOUS LEARNING ENVIRONMENTS, INCLUDING FACE-TO-FACE CLASSROOMS, ONLINE LEARNING, VIRTUAL LEARNING, AND HYBRID LEARNING, IS BECOMING OUR DOMINANT LEARNING PARADIGM FOR STUDENTS WHO EXPECT HIGHLY FLEXIBLE AND EFFICIENT OPTIONS FOR STUDYING AT THEIR OWN TIME, PLACE, AND PACE. ESPECIALLY, IN OUR POST-PANDEMIC PRESENT, ONLINE LEARNING WILL CONTINUE TO GAIN IN IMPORTANCE AS A SIGNIFICANT AVAILABLE AND REQUIRED LEARNING COMPONENT. THIS ARTICLE PROVIDES A COMPREHENSIVE REVIEW OF THE STATE-OF-THE-ART SYSTEMS AND STUDIES FOR ASSISTING STUDENT LEARNING AND ATTEMPTING TO CAPTURE STUDENT ATTENTION AND ENGAGEMENT IN MANY DIFFERENT WAYS. IT FOCUSES ON SENSORS AND HARDWARE, RANGING FROM COMMERCIAL MULTI-SENSOR EYE-TRACKING DEVICES TO OPEN-SOURCE, LOW-COST SYSTEMS. WE ALSO EXPLORE AND PRESENT SYSTEM INFRASTRUCTURE, DATA FEATURES, DATA PROCESSING TECHNIQUES, TOOLS AND SOFTWARE, AND KEY TECHNOLOGIES THAT ARE USEFUL FOR ENHANCING MANY STUDENT LEARNING ENVIRONMENTS. ADDITIONALLY, THE ADVANTAGES, USE CASES, FEATURES, AND LIMITATIONS OF DIFFERENT SYSTEMS AND TECHNIQUES ARE EXPLORED AND CONTRASTED, WHERE THE RESULTS OF OUR COMPARATIVE ANALYSIS ARE SUMMARIZED IN TABLES FOR READERS. THIS REVIEW ARTICLE COULD ASSIST BOTH TEACHERS AND RESEARCHERS TO HAVE A BETTER UNDERSTANDING OF CURRENT SENSORS, MULTI-SENSOR SYSTEMS, AND RELATED TECHNOLOGIES FOR CAPTURING STUDENT ATTENTION AND UNDERSTANDING THEIR PERFORMANCE. IT CAN ALSO BE APPLIED TO PROVIDING PRACTICAL INFORMATION FOR NOVEL SYSTEM DESIGN, PROMOTING ONGOING RESEARCH STUDIES, AND FOSTERING SENSING AND TECHNOLOGY INNOVATIONS FOR SMART EDUCATION.
INTERACTIVE DISPLAYS ARE BECOMING INCREASINGLY POPULAR IN INFORMAL LEARNING ENVIRONMENTS AS AN EDUCATIONAL TECHNOLOGY FOR IMPROVING STUDENTS' LEARNING AND ENHANCING THEIR ENGAGEMENT. INTERACTIVE DISPLAYS HAVE THE POTENTIAL TO REINFORCE AND MAINTAIN COLLABORATION AND RICH-INTERACTION WITH THE CONTENT IN A NATURAL AND ENGAGING MANNER. DESPITE THE INCREASED PREVALENCE OF INTERACTIVE DISPLAYS FOR LEARNING, THERE IS LIMITED KNOWLEDGE ABOUT HOW STUDENTS COLLABORATE IN INFORMAL SETTINGS AND HOW THEIR COLLABORATION AROUND THE INTERACTIVE SURFACES INFLUENCES THEIR LEARNING AND ENGAGEMENT. WE PRESENT A DUAL EYE-TRACKING STUDY, INVOLVING 36 PARTICIPANTS, A TWO-STAGED WITHIN-GROUP EXPERIMENT WAS CONDUCTED FOLLOWING SINGLE-GROUP TIME SERIES DESIGN, INVOLVING REPEATED MEASUREMENT OF PARTICIPANTS' GAZE, VOICE, GAME-LOGS AND LEARNING GAIN TESTS. VARIOUS CORRELATION, REGRESSION AND COVARIANCE ANALYSES EMPLOYED TO INVESTIGATE STUDENTS' COLLABORATION, ENGAGEMENT AND LEARNING GAINS DURING THE ACTIVITY. THE RESULTS SHOW THAT COLLABORATIVELY, PAIRS WHO HAVE HIGH GAZE SIMILARITY HAVE HIGH LEARNING OUTCOMES. INDIVIDUALLY, PARTICIPANTS SPENDING HIGH PROPORTIONS OF TIME IN ACQUIRING THE COMPLEMENTARY INFORMATION FROM IMAGES AND TEXTUAL PARTS OF THE LEARNING MATERIAL ATTAIN HIGH LEARNING OUTCOMES. MOREOVER, THE RESULTS SHOW THAT THE SPEECH COULD BE AN INTERESTING COVARIATE WHILE ANALYZING THE RELATION BETWEEN THE GAZE VARIABLES AND THE LEARNING GAINS (AND TASK-BASED PERFORMANCE). WE ALSO SHOW THAT THE GAZE IS AN EFFECTIVE PROXY TO COGNITIVE MECHANISMS UNDERLYING COLLABORATION NOT ONLY IN FORMAL SETTINGS BUT ALSO IN INFORMAL LEARNING SCENARIOS.
EXPLORATORY LEARNING ENVIRONMENTS, SUCH AS VIRTUAL LABS, SUPPORT DIVERGENT LEARNING PATHWAYS. HOWEVER, DUE TO THEIR COMPLEXITY, BUILDING COMPUTATIONAL MODELS OF LEARNING IS CHALLENGING AS IT IS DIFFICULT TO IDENTIFY FEATURES THAT (I) ARE INFORMATIVE WITH RESPECT TO COMMON LEARNING STRATEGIES, (II) ABSTRACT SIMILAR ACTIONS BEYOND SURFACE DIFFERENCES, AND (III) DIFFERENTIATE GROUPS OF LEARNERS. IN THIS PAPER, WE PRESENT A VISUALIZATION TOOL THAT ADDRESSES THESE CHALLENGES BY FACILITATING A NOVEL ANALYTIC APPROACH TO AID IN THE KNOWLEDGE ENGINEERING PROCESS, FOCUSING ON FIVE MAIN CAPABILITIES: DATA-DRIVEN HYPOTHESES RAISING, VISUALIZING BEHAVIOR OVER TIME, EASILY GROUPING RELATED ACTIONS, CONTRASTING LEARNERS' BEHAVIORS ON THESE ACTIONS, AND COMPARING THE BEHAVIORS OF GROUPS OF LEARNERS. WE APPLY THIS ANALYTIC APPROACH TO BETTER UNDERSTAND HOW STUDENTS WORK WITH A POPULAR INTERACTIVE PHYSICS VIRTUAL LAB. BY SPLITTING LEARNERS BY LEARNING GAINS, WE FOUND THAT PRODUCTIVE LEARNERS PERFORMED MORE ACTIVE TESTING AND ADAPTED MORE QUICKLY TO THE TASK AT HAND BY FOCUSING ON MORE RELEVANT TESTING INSTRUMENTS. IMPLICATIONS FOR ONLINE VIRTUAL LABS AND A BROADER CLASS OF COMPLEX LEARNING ENVIRONMENTS ARE DISCUSSED THROUGHOUT.
AFFECT DYNAMICS, THE STUDY OF HOW AFFECT DEVELOPS AND MANIFESTS OVER THE COURSE OF LEARNING, HAS BECOME A POPULAR AREA OF RESEARCH IN LEARNING ANALYTICS. DESPITE SOME SHARED METRICS AND RESEARCH QUESTIONS, RESEARCHERS IN THIS AREA HAVE SOME DIFFERENCES IN HOW THEY PRE-PROCESS THE DATA FOR ANALYSIS [17]. SPECIFICALLY, RESEARCHERS DIFFER IN HOW THEY TREAT CASES WHERE A STUDENT REMAINS IN THE SAME AFFECTIVE STATE IN TWO SUCCESSIVE OBSERVATIONS, REFERRED TO AS SELF-TRANSITIONS. WHILE MOST RESEARCHERS INCLUDE THESE CASES IN THEIR DATA, D'MELLO AND OTHERS HAVE ARGUED OVER THE LAST FEW YEARS THAT THESE CASES SHOULD BE REMOVED PRIOR TO ANALYSIS. WHILE THIS CHOICE REFLECTS THE INTENDED FOCUS IN THEIR RESEARCH PARADIGM ON THE TRANSITIONS OUT OF AN AFFECTIVE STATE, THIS DIFFERENCE IN DATA PREPROCESSING CHANGES THE MEANING OF THE METRIC USED. FOR AROUND A DECADE, THE COMMUNITY HAS USED THE METRIC L TO EVALUATE THE PROBABILITY OF TRANSITIONS IN AFFECT. L IS LARGELY BELIEVED TO HAVE A VALUE OF 0 WHEN A TRANSITION IS AT CHANCE, AND THIS IS TRUE FOR THE ORIGINAL USE OF THE METRIC. HOWEVER, THIS PAPER PROVIDES MATHEMATICAL EVIDENCE THAT THIS METRIC DOES NOT HAVE A VALUE OF 0 AT CHANCE IF SELF-TRANSITIONS ARE REMOVED. THIS SHIFT IS PROBLEMATIC BECAUSE PREVIOUSLY PUBLISHED STATISTICAL ANALYSES COMPARING L VALUES TO THE VALUE AT CHANCE HAVE USED THE WRONG VALUE, INCORRECTLY PRODUCING LOWERED P VALUES AND IN MANY CASES REPORTING TRANSITIONS AS SIGNIFICANTLY MORE LIKELY THAN CHANCE WHEN THEY ARE ACTUALLY LESS FREQUENT.
LEARNING ANALYTICS IS QUICKLY EVOLVING. OLD FASHIONED DASHBOARDS WITH DESCRIPTIVE INFORMATION AND TRENDS ABOUT WHAT HAPPENED IN THE PAST ARE SLIGHTLY SUBSTITUTED BY NEW DASHBOARDS WITH FORECASTING INFORMATION AND PREDICTING RELEVANT OUTCOMES ABOUT LEARNING. ARTIFICIAL INTELLIGENCE IS AIDING THIS REVOLUTION. THE ACCESSIBILITY TO COMPUTATIONAL RESOURCES HAS INCREASED, AND SPECIFIC TOOLS AND PACKAGES FOR INTEGRATING ARTIFICIAL INTELLIGENCE TECHNIQUES LEVERAGE SUCH NEW ANALYTICAL TOOLS. HOWEVER, IT IS CRUCIAL TO DEVELOP TRUSTWORTHY SYSTEMS, ESPECIALLY IN EDUCATION WHERE SKEPTICISM ABOUT THEIR APPLICATION IS DUE TO THE RISK OF TEACHERS' REPLACEMENT. HOWEVER, ARTIFICIAL INTELLIGENCE SYSTEMS SHOULD BE SEEN AS COMPANIONS TO EMPOWER TEACHERS DURING THE TEACHING AND LEARNING PROCESS. DURING THE PAST YEARS, THE UNIVERSITAT OBERTA DE CATALUNYA HAS ADVANCED DEVELOPING A DATA MART WHERE ALL DATA ABOUT LEARNERS AND CAMPUS UTILIZATION ARE STORED FOR RESEARCH PURPOSES. THE EXTENSIVE COLLECTION OF THESE EDUCATIONAL DATA HAS BEEN USED TO BUILD A TRUSTWORTHY EARLY WARNING SYSTEM WHOSE INFRASTRUCTURE IS INTRODUCED IN THIS PAPER. THE INFRASTRUCTURE SUPPORTS SUCH A TRUSTWORTHY SYSTEM BUILT WITH ARTIFICIAL INTELLIGENCE PROCEDURES TO DETECT AT-RISK LEARNERS EARLY ON IN ORDER TO HELP THEM TO PASS THE COURSE. TO ASSESS THE SYSTEM'S TRUSTWORTHINESS, WE CARRIED OUT AN EVALUATION ON THE BASIS OF THE SEVEN REQUIREMENTS OF THE EUROPEAN ASSESSMENT LIST FOR TRUSTWORTHY ARTIFICIAL INTELLIGENCE (ALTAI) GUIDELINES THAT RECOGNIZE AN ARTIFICIAL INTELLIGENCE SYSTEM AS A TRUSTWORTHY ONE. RESULTS SHOW THAT IT IS FEASIBLE TO BUILD A TRUSTWORTHY SYSTEM WHEREIN ALL SEVEN ALTAI REQUIREMENTS ARE CONSIDERED AT ONCE FROM THE VERY BEGINNING DURING THE DESIGN PHASE.
IN SPITE OF THE HIGH IMPACT OF MASSIVE OPEN ONLINE COURSES (MOOCS), LEARNERS FREQUENTLY DISENGAGE FROM THE COURSE CONTENTS AND ACTIVITIES DUE TO UNEXPECTED PROBLEMS OF DIFFERENT NATURES, SUCH AS CONTENT-RELATED OR TECHNICAL ISSUES. FEEDBACK HAS BEEN IDENTIFIED AS AN IMPORTANT ASPECT OF THE LEARNING PROCESS DIRECTLY CONNECTED WITH LEARNERS' ENGAGEMENT. HOWEVER, THE MASSIVE AND IMPERSONAL NATURE OF MOOCS HINDERS THE PROVISION OF EFFICIENT AND TIMELY FEEDBACK TO THOSE LEARNERS FACING PROBLEMS. THIS PAPER EXAMINES HOW MOOC PRACTITIONERS IDENTIFY AND SUPPORT LEARNERS FACING PROBLEMS, WHAT CHALLENGES THEY ENCOUNTER, AND WHAT STRATEGIES THEY APPLY TO OVERCOME SUCH CHALLENGES. ADDITIONALLY, THE CURRENT STUDY AIMS TO COMPARE THE LEARNERS' PROBLEMS AND PRACTITIONERS' EXPERIENCES BETWEEN ENGINEERING-RELATED AND NONENGINEERING RELATED MOOCS. A QUALITATIVE PHENOMENOLOGICAL STUDY HAS BEEN CONDUCTED THROUGH SEMISTRUCTURED INTERVIEWS WITH 14 MOOC PRACTITIONERS. THE EVIDENCE GATHERED SHOWS DIVERSE LEARNERS' AND PRACTITIONERS' PROBLEMS SHARED AMONG ENGINEERING AND NONENGINEERING COURSES AND A GENERAL CONCERN ON HOW TO ADDRESS INDIVIDUAL LEARNERS' NEEDS IN TIME. A COMMON PRACTICE OF PROBLEM IDENTIFICATION REGARDS CHECKING THE SELF-REPORTED ISSUES IN COMMUNICATION FORUMS. IDENTIFICATION STRATEGIES WITH THE USE OF LEARNING ANALYTICS ARE LIMITED DUE TO PLATFORM RESTRICTIONS OR LACK OF PRACTITIONERS' SKILLS IN INTERPRETING THE PROVIDED INFORMATION. THE SYNTHESIS OF MOOC PRACTITIONERS FROM BOTH ENGINEERING AND NONENGINEERING DISCIPLINES MAY PROVIDE INSIGHTS THAT ARE EITHER GLOBALLY APPLICABLE TO ALL DISCIPLINES OR SPECIFIC TO ENGINEERING. THE RESULTS COULD BE SHAPED INTO CONCEPTUAL AND TECHNOLOGICAL SOLUTIONS TO HELP MOOC STAKEHOLDERS (E.G., RESEARCHERS, PRACTITIONERS) IDENTIFY POTENTIAL LEARNERS FACING DIFFICULTIES AND SUPPORT THEM DURING THE COURSE PROCESS.
HIGHER EDUCATION INSTITUTIONS MUST ENABLE STUDENTS TO ACQUIRE SKILLS AND CAPACITIES THAT PREPARE THEM FOR WORKING LIFE AND ENHANCE THEIR EMPLOYABILITY. THIS WILL LEAD TO AN APPLIED LEARNING- AND TEACHING-ENHANCEMENT-ORIENTED SUSTAINABLE HIGHER EDUCATION SYSTEM. THIS RESEARCH AIMS TO CONTRIBUTE TO THAT GOAL BY ANALYZING STUDENT INTERACTIONS IN A COLLABORATIVE LEARNING COMMUNITY. IT ASSESSES THE IMPACT OF VISUAL TOOLS ON ACADEMIC PERFORMANCE AND STUDENT SATISFACTION IN EMPLOYMENT-FOCUSED BLENDED STUDIES, IN WHICH ENROLLEES WERE GEOGRAPHICALLY DISPERSED UNDERGRADUATES WITH A DIVERSITY OF PROFILES. A FINANCIAL STUDIES LEARNING COMMUNITY WAS CREATED TO TEST STUDENTS' INTERACTIONS IN A MODEL CONDUCIVE TO PARTICIPATION AS VISUAL CONTENT CREATORS AND USERS. THREE SURVEYS (PRE-PROJECT, APPRAISAL OF CLASSMATES' VISUAL EXERCISES, AND POST-PROJECT) WERE CONDUCTED TO ASSESS PROJECT IMPACT. FIRST, WE USED A UNIVARIATE APPROACH, FOCUSED ON STUDENTS' CHARACTERISTICS, COURSE AND PROJECT APPRAISALS, AND THE EFFECTS OF THE PROJECT ON ACADEMIC PERFORMANCE AND EXPECTATIONS. SECONDLY, A BIVARIATE APPROACH WAS CONDUCTED TO DETECT RELATIONSHIPS BETWEEN RESPONDENTS' APPRAISALS AND PERSONAL CHARACTERISTICS AND TO DETERMINE WHETHER THEIR MEAN SCORES WERE THE SAME IRRESPECTIVE OF SUCH CHARACTERISTICS. THE FINDINGS SHOWED THAT: (1) STUDENTS' PREFERENCES CONCUR WITH THOSE OF THEIR EMPLOYERS; (2) PARTICIPATION IN INNOVATIVE INITIATIVES IMPROVES STUDENTS' PERCEPTION OF COURSE PROCEDURES; (3) VISUAL TOOLS HAVE A POSITIVE IMPACT ON LEARNING, IN TERMS OF BOTH ACADEMIC PERFORMANCE AND STUDENT SATISFACTION. THE STUDY CONCLUDES BY PROVIDING SUPPORT FOR EDUCATIONAL INSTITUTIONS ' DECISION-MAKING AROUND COURSES AND THE OVERALL CURRICULA BY DEFINING THE FACTORS DETERMINING ACADEMIC PERFORMANCE AND STUDENT SATISFACTION.
IN HIGHER EDUCATION, QUALITY FEEDBACK FOR STUDENTS IS REGARDED AS ONE OF THE MAIN CONTRIBUTORS TO IMPROVE STUDENT LEARNING. FEEDBACK TO SUPPORT STUDENTS' DEVELOPMENT INTO SELF-REGULATED LEARNERS, WHO SET THEIR OWN GOALS, SELF-MONITOR THEIR ACTUAL PERFORMANCE ACCORDING TO THESE GOALS, AND ADJUST LEARNING STRATEGIES IF NECESSARY, IS SEEN AS AN IMPORTANT ASPECT OF CONTEMPORARY FEEDBACK PRACTICE. HOWEVER, ONLY THOSE STUDENTS WHO ARE AWARE OF THE COURSE DEMANDS AND THE IMPACT OF CERTAIN STUDY BEHAVIORS ON THEIR FINAL ACHIEVEMENT ARE IN A POSITION TO SELF-REGULATE THEIR LEARNING ON AN INFORMED BASIS. LEARNING ANALYTICS IS AN EMERGING FIELD PRIMARILY CONCERNED WITH USING PREDICTIVE MODELS TO INFORM EDUCATIONAL INSTRUCTORS OR LEARNERS ABOUT PROJECTED STUDY OUTCOMES. IN A SCOPING STUDY, OVER 200 STUDENTS OF AN INTRODUCTORY PROGRAMMING COURSE (CS1) WERE SUPPLIED WITH INFORMATION REVEALING PERFORMANCE INDICATORS FOR DIFFERENT STAGES ON THE COURSE AND PROJECTING FINAL PERFORMANCE FOR VARIOUS ACHIEVEMENT LEVELS. THE STUDY WAS SET OUT TO EXPLORE THE IMPACT OF THIS TYPE OF FEEDBACK IN THE CONFINED CONTEXT OF A CS1 COURSE AS WELL AS TO LEARN ABOUT STUDENTS' ATTITUDES TOWARD DIAGNOSTIC COURSE DATA IN GENERAL. THE RESULTS FROM THE STUDY SUGGEST THAT STUDENTS VALUED THE INFORMATION, BUT, DESPITE HIGH ENGAGEMENT WITH THE INFORMATION, STUDENTS' STUDY BEHAVIOR AND LEARNING OUTCOME REMAINED RATHER UNAFFECTED FOR THE ASPECTS INVESTIGATED. GIVEN THESE MULTI-LAYERED RESULTS, WE SUGGEST FURTHER EXPLORATION ON THE PROVISION OF FEEDBACK BASED ON DIAGNOSTIC COURSE DATA - A VITAL STEP TOWARD MORE TRANSPARENCY FOR STUDENTS TO FOSTER THEIR ACTIVE ROLE IN THE LEARNING PROCESS.
OPEN, ONLINE ENVIRONMENTS LIKE SOCIAL MEDIA ARE NOW A MAINSTAY OF LIFE-LONG INFORMAL LEARNING. SOCIAL MEDIA LIKE TWITTER HELP PEOPLE GATHER INFORMATION, SHARE RESOURCES, AND DISCUSS WITH OTHER PARTICIPANT-LEARNERS WITH SIMILAR INTERESTS. THIS PAPER SEEKS TO TEST AND VALIDATE THE 'LEARNING IN THE WILD' CODING SCHEMA IN THE CONTEXT OF DISCUSSIONS ON TWITTER, AN APPROACH FIRST DEVELOPED FOR STUDYING LEARNING COMMUNITIES ON REDDIT. THE SCHEMA CONSIDERS HOW PARTICIPANT-LEARNERS ARE LEVERAGING SOCIAL MEDIA TO FACILITATE SELF-DIRECTED INFORMAL LEARNING PRACTICES, EXPLORATORY DIALOGUE, AND COMMUNICATIVE EXCHANGES. WE APPLY THE CODING SCHEMA ON A SAMPLE OF TWEETS (N=594) FROM THE HISTORY TWITTERSPHERE COMMUNITY (#TWITTERSTORIANS) TO PROVIDE A MORE NUANCED UNDERSTANDING OF THE DIFFERENT KINDS OF DISCURSIVE PRACTICES, RESOURCE EXCHANGES, AND IDEAS BEING SHARED AND COMMUNICATED OUTSIDE TRADITIONAL CLASSROOM SETTINGS.
THIS RESEARCH TO PRACTICE FULL PAPER IS DRIVEN BY THE QUESTION: HOW CAN A TEACHER OF A LARGE CLASS BE EFFECTIVELY SUPPORTED FOR FORMATIVE ASSESSMENT? THE PAPER SUGGESTS A FRAMEWORK FOR AUTOMATIC CLASSIFIERS / PREDICTIVE MODELS AND ITS DATA INTEGRATION TOOL FOR TEACHERS. TO DESIGN AN INPUT DATA SET FOR AUTOMATIC CLASSIFIER, THE TOOL ENABLES THE TEACHERS TO INTEGRATE DATA EXTRACTED FROM PAPER-BASED EXAMS; COMPUTER ASSISTED FORMATIVE ASSESSMENTS AND LMSS, WITH LEARNING DISPOSITION DATA, COLLECTED BY APPLYING THE BUCKINGHAM SHUM AND DEAKIN CRICK'S THEORETICAL FRAMEWORK FOR DISPOSITIONAL LEARNING ANALYTICS. THE SUGGESTED FRAMEWORK AND ITS TOOL ARE TESTED WITH THE REAL ASSESSMENT DATA OF 129 STUDENTS COLLECTED DURING CONDUCTING THE COMPUTER ORGANIZATION (HEREAFTER CO) COURSE. THE RESULTS OF THIS CO TEST SCENARIO SHOW HOW TEACHERS CAN INTERPRET THE OUTCOMES OF THE AUTOMATIC CLASSIFIERS AS DECISIONSUPPORT RECOMMENDATIONS TO IMPROVE THE PLANNING FOR FORMATIVE ASSESSMENT. AS AN EXAMPLE, IT IS PRESENTED AND DISCUSSED HOW THE CO TEACHER CAN IMPROVE HIS/HER STRATEGIES IN FORMATIVE ASSESSMENT FOR DIFFERENT STUDENTS GROUPS (AT-RISK STUDENTS, MEDIUM STUDENTS, GOOD STUDENTS AND EXCELLENT STUDENTS). THE PAPER SHOWS ALSO HOW REPORTS FOR CLASSIFIERS ACCURACY COMPARISON CAN BE PRODUCED AND UNDERSTOOD BY TEACHERS. RELATED WORKS ARE DISCUSSED TO SHOW THE DIFFERENCES AND BENEFITS OF THE PRESENTED FRAMEWORK. MAIN ADVANTAGES ARE: THE POSSIBILITY TO USE ITS AUTOMATIC CLASSIFICATION ALGORITHMS INSTEAD OF THE STATISTICAL REGRESSION ALGORITHMS AND THE POSSIBILITY TO USE ITS INTEGRATION TOOL TO INTEGRATE DATA COLLECTED FROM APPLYING A THEORETICAL FRAMEWORK WITH DATA FROM ELEARNING /E-ASSESSMENT AS INPUT DATA SETS FOR AUTOMATIC CLASSIFIERS.
THIS PAPER FOLLOWS OUR STREAM OF RESEARCH ON SERENDIPITY ENGINEERING FOR SERIES EVENTS CONNECTED IN TIME AND SPACE. OCCURRING BY RATHER HIDDEN AND UNEXPECTED CONNECTIONS SERENDIPITY ENGINEERING IS ALSO RELATED TO THE BIPOLAR DECISION MAKING PROCESS: HESITATE BEFORE TAKING THE RISK, ACCEPT AND ENGAGE IN SERENDIPITY OR DENY IT. ALTHOUGH RESEARCH EXISTS WITHIN THE GLOBAL SYSTEMS SCIENCE FIELD FOR SERENDIPITY IDENTIFICATION AND ENGINEERING, THE RESULTS ARE NOT ENCOURAGING AS SUCH RESEARCH NOW FAILS IN PREDICTIONS. MASSIVE GLOBAL RESEARCH AND APPARENT IMPACT IS EVIDENT AND REACHABLE IN SOCIAL MEDIA SUCH AS TWITTER, FACEBOOK, INSTAGRAM ETC. FURTHERMORE, UTILISING A RANGE OF TOOLS, METHODOLOGIES AND TECHNIQUES, IT MAY BE POSSIBLE TO IDENTIFY ACTIVITIES AND HIDDEN CONNECTIONS IN BOTH BIG AND SMALL DATA AND PREDICT, AID OR EVEN INTERFERE IN THE DECISION MAKING PROCESS. SUCH INTERFERENCE BASED ON PREDICTION CAN ACTUALLY ALTER THE FUTURE DECISION MAKING USERS' DOMINO OF EVENTS AND ITEMS SELECTED AND ORCHESTRATE SERIES OF EVENTS THAT MAY HAVE POSSIBLE UTILITY IN PSYCHOLOGY, MARKETING, SERENDIPITY SYSTEMS DESIGN AND ENGINEERING DESIGN OR OTHER INFLUENTIAL DISCIPLINES. OUR RESEARCH MAIN AIM IS THE IDENTIFICATION, COLLECTION AND ANALYSIS OF BOTH BIG AND SMALL DATA IN ORDER TO SHED LIGHT IN SERENDIPITY CONNECTIONS IN CHRONO-SPATIAL INTELLIGENCE AND EVEN ENGINEER IT, CALLED CHRONO-SPATIAL INTELLIGENCE FOR SERENDIPITY ENGINEERING (CSISE). A SECOND AIM IS TO IDENTIFY TOOLS, METHODOLOGIES AND EVALUATION TECHNIQUES TO FACILITATE SUCH DEEP UNDERSTANDING VIA CHRONO-SPATIAL INTELLIGENCE ANALYTICS. HERE, WE PROPOSE A SERENDIPITY ENGINEERING MODEL FOR LEARNING INSIGHTS ANCHORED IN BIG AND SMALL DATA METHODS, TOOLS AND LEARNING ANALYTICS.
NEOLIBERALISM IS A GLOBAL PEDAGOGICAL PROJECT AIMED AT THE DISPOSSESSION OF FREE TIME SO THAT ALL OF LIFE BECOMES PRODUCTIVE, AND EDUCATION IS A CENTRAL INSTITUTIONAL MEANS FOR ITS REALISATION. THIS PROJECT AIMS AT MARKETISING ALL OF SOCIAL LIFE, SO THAT LIFE BECOMES PREDICATED UPON THE EXTRACTION OF VALUE. IN PART THE DEPLOYMENT OF TECHNOLOGIES, TECHNICAL SERVICES, AND TECHNIQUES ENABLES EDUCATION TO BE CO-OPTED AS AN INSTITUTIONAL MEANS FOR PRODUCTION AND CONTROL. THIS OCCURS INSIDE BOTH FORMAL AND INFORMAL EDUCATIONAL INSTITUTIONS AND SPACES, LIKE UNIVERSITIES AND MASSIVE OPEN ON-LINE COURSES, AS ONE MECHANISM TO OFFSET THE TENDENCY FOR THE RATE OF PROFIT TO FALL AND TO RE-ESTABLISH ACCUMULATION. THIS PEDAGOGIC PROJECT ALSO TENDS TO RECALIBRATE AND ENCLOSE THE ROLES OF STAFF AND STUDENTS AS ENTREPRENEURIAL SUBJECTS, WHOSE LABOUR IS ENABLED THROUGH TECHNOLOGY. THIS IS ACHIEVED THROUGH LEARNING ANALYTICS, BIG DATA, MOBILITY AND FLEXIBILITY OF PROVISION, AND SO ON. AT ISSUE IS THE EXTENT TO WHICH THIS NEOLIBERAL PROJECT CAN BE RESISTED OR REFUSED, AND ALTERNATIVES DESCRIBED. THIS ARTICLE WILL ANALYSE THE RELATIONSHIPS BETWEEN TECHNOLOGY, PEDAGOGY, AND THE CRITICAL SUBJECT IN THE NEOLIBERAL UNIVERSITY, IN ORDER TO ARGUE FOR THE USE OF TECHNOLOGY INSIDE A CO-OPERATIVE PEDAGOGY OF STRUGGLE. THIS DEMANDS THAT WE ASK WHAT EDUCATION IS, BEFORE WE ASK WHAT IT IS FOR, OR THE PLACE OF TECHNOLOGY-ENHANCED LEARNING IN THE UNIVERSITY. THE ARTICLE CONSIDERS WHETHER IT IS POSSIBLE TO UNCOVER WAYS IN WHICH EDUCATION MIGHT BE USED FOR CO-OPERATION RATHER THAN COMPETITION, AND WHAT TECHNOLOGY-ENHANCED CO-OPERATIVE EDUCATION MIGHT LOOK LIKE?
ONLINE DISCOURSE IS WIDELY USED IN DIVERSE CONTEXTS OF LEARNING AND PROFESSIONAL TRAINING, BUT SUPERFICIAL INTERACTIONS AND DIGRESSION OFTEN OCCUR. IN THE FACE OF THESE PROBLEMS AND THE LARGE-SCALE UNSTRUCTURED TEXT DATA, THE TRADITIONAL WAY OF LEARNING ANALYTICS HAS BEEN CHALLENGED IN TERMS OF PROVIDING TIMELY INTERVENTION AND FEEDBACK. IN THIS PAPER, A WORKFLOW FOR AUTOMATICALLY DETECTING IN-SERVICE TEACHERS' COGNITIVE ENGAGEMENT IN AN ONLINE PROFESSIONAL LEARNING COMMUNITY IS DESCRIBED. DISCOURSE DATA OF 1834 IN-SERVICE TEACHERS INVOLVED IN A TEACHER PROFESSIONAL DEVELOPMENT PROGRAM WAS COLLECTED AND PROCESSED USING THE WORD2VEC TOOLKIT TO GENERATE LEXICAL VECTORS. THE METHOD OF VECTOR SPACE PROJECTION WAS USED TO CALCULATE THE NEW INFORMATION CONTAINED IN EACH POST, COSINE SIMILARITY WAS USED TO CALCULATE TOPIC RELEVANCE, AND CLUSTER ANALYSIS WAS USED TO EXPLORE IN-SERVICE TEACHERS' DISCOURSE CHARACTERISTICS. RESULTS SHOWED THAT IN-SERVICE TEACHERS' AVERAGE CONTRIBUTION WAS 4.59 POSTS AND THE AVERAGE LENGTH OF EACH POST WAS 39.47 CHARACTERS IN CHINESE. IN THE MATHEMATICS ONLINE PROFESSIONAL LEARNING COMMUNITY, THE AVERAGE AMOUNT OF NEW INFORMATION CONTAINED IN EACH POST WAS 0.221 AND IN-SERVICE TEACHERS' POSTS CONTAINED MUCH NEW INFORMATION IN THE EARLY STAGES OF ONLINE DISCOURSE. MOST IN-SERVICE TEACHERS' POSTS WERE RELEVANT TO THE DISCUSSION TOPIC. CLUSTER ANALYSIS SHOWED THREE DIFFERENT GROUPS OF POSTS WITH UNIQUE CHARACTERISTICS: HIGH TOPIC RELEVANCE WITH MUCH NEW INFORMATION, HIGH TOPIC RELEVANCE WITH LITTLE NEW INFORMATION, AND LOW TOPIC RELEVANCE WITH LITTLE NEW INFORMATION. FINALLY, LIMITATIONS ARE DISCUSSED AND FUTURE RESEARCH DIRECTIONS ARE PROPOSED.
UNDER THE VIGOROUS DEVELOPMENT OF GLOBAL ANTICIPATORY COMPUTING IN RECENT YEARS, THERE HAVE BEEN NUMEROUS APPLICATIONS OF ARTIFICIAL INTELLIGENCE (AI) IN PEOPLE'S DAILY LIVES. LEARNING ANALYTICS OF BIG DATA CAN ASSIST STUDENTS, TEACHERS, AND SCHOOL ADMINISTRATORS TO GAIN NEW KNOWLEDGE AND ESTIMATE LEARNING INFORMATION; IN TURN, THE ENHANCED EDUCATION CONTRIBUTES TO THE RAPID DEVELOPMENT OF SCIENCE AND TECHNOLOGY. EDUCATION IS SUSTAINABLE LIFE LEARNING, AS WELL AS THE MOST IMPORTANT PROMOTER OF SCIENCE AND TECHNOLOGY WORLDWIDE. IN RECENT YEARS, A LARGE NUMBER OF ANTICIPATORY COMPUTING APPLICATIONS BASED ON AI HAVE PROMOTED THE TRAINING PROFESSIONAL AI TALENT. AS A RESULT, THIS STUDY AIMS TO DESIGN A SET OF INTERACTIVE ROBOT-ASSISTED TEACHING FOR CLASSROOM SETTING TO HELP STUDENTS OVERCOMING ACADEMIC DIFFICULTIES. TEACHERS, STUDENTS, AND ROBOTS IN THE CLASSROOM CAN INTERACT WITH EACH OTHER THROUGH THE ARCS MOTIVATION MODEL IN PROGRAMMING. THE PROPOSED METHOD CAN HELP STUDENTS TO DEVELOP THE MOTIVATION, RELEVANCE, AND CONFIDENCE IN LEARNING, THUS ENHANCING THEIR LEARNING EFFECTIVENESS. THE ROBOT, LIKE A TEACHING ASSISTANT, CAN HELP STUDENTS SOLVING PROBLEMS IN THE CLASSROOM BY ANSWERING QUESTIONS AND EVALUATING STUDENTS' ANSWERS IN NATURAL AND RESPONSIVE INTERACTIONS. THE NATURAL INTERACTIVE RESPONSES OF THE ROBOT ARE ACHIEVED THROUGH THE USE OF A DATABASE OF EMOTIONAL BIG DATA (GOOGLE FACIAL EXPRESSION COMPARISON DATASET). THE ROBOT IS LOADED WITH AN EMOTION RECOGNITION SYSTEM TO ASSESS THE MOODS OF THE STUDENTS THROUGH THEIR EXPRESSIONS AND SOUNDS, AND THEN OFFER CORRESPONDING EMOTIONAL RESPONSES. THE ROBOT IS ABLE TO COMMUNICATE NATURALLY WITH THE STUDENTS, THEREBY ATTRACTING THEIR ATTENTION, TRIGGERING THEIR LEARNING MOTIVATION, AND IMPROVING THEIR LEARNING EFFECTIVENESS.
ASSESSING COMPETENCIES AND LEARNING PROGRESS IS A VITAL TASK FOR DIGITAL LEARNING GAMES. SPECIFICALLY, THE ADAPTATION AND PERSONALIZATION OF GAMING AND LEARNING ACTIVITIES, WHICH IS CONSIDERED ONE OF KEY FEATURES TO ASSURE APPROPRIATE LEARNING, REQUIRES A ROBUST AND THEORY GROUNDED ASSESSMENT. THIS ESTABLISHES A BASIS FOR THE GAME, AS AN AUTONOMOUS ENTITY, TO GAIN A CERTAIN LEVEL OF UNDERSTANDING OF THE INDIVIDUAL LEARNERS / PLAYERS. A SECOND ASPECT IN THE CONTEXT OF GAME-BASED ASSESSMENT IS ASSESSMENT WITH GAMES. OVER THE PAST YEARS, DIGITAL GAMES HAVE BEEN IDENTIFIED AS AN IDEAL VEHICLE TO ASSESS VARIOUS TYPES OF APTITUDE (SUCH AS COMPETENCIES) BUT ALSO FURTHER CHARACTERISTICS SUCH AS PERSONALITY TRAITS. VAL SHUTE, IN HER INFLUENTIAL WORK, OFTEN ARGUES THAT ASSESSMENT AND PERSONALIZATION IS OFTEN TOO SIMPLIFIED, ABSTRACT, AND DECONTEXTUALIZED TO SUIT THE NEEDS OF SUCCESSFUL INDIVIDUAL LEARNING SUPPORT. IN THIS CONTRIBUTION, I WANT TO HIGHLIGHT THE LINKS OF GAME-BASED ASSESSMENT TO TECHNIQUES OF LEARNING ANALYTICS AND PSYCHOMETRIC METHODOLOGIES. I PRESENT A COMBINATORIAL, STRUCTURAL APPROACH TO THE MEASUREMENT OF COMPETENCIES AND INDIVIDUAL LEARNING PATHS BASED ON KNOWLEDGE SPACE THEORY, EXTENDED BY MICRO ADAPTIVITY, A WELL-ELABORATED APPROACH TO LEARNER MODELLING AND IN-GAME ADAPTATION. I COMPLEMENT THE IDEAS OF STRUCTURAL ASSESSMENT MODELS WITH PSYCHOMETRIC CONSIDERATIONS, IN PARTICULAR THE ASPECTS OF ASSESSMENT ACCURACY, RELIABILITY, AND VALIDITY. THE IDEA IS TO CAPITALIZE ON THE METHODOLOGICAL ACCURACY OF ITEM RESPONSE THEORY SCALING, WHERE A NUMBER OF ITEMS ARE ARRANGED ON A LINEAR DIMENSION OF "DIFFICULTY" AND IMPROVE THE ACCURACY OF THE MULTI-DIMENSIONAL MICRO ADAPTIVE ASSESSMENT MODELS.
ANATOMY IS AN ESSENTIAL SUBJECT OF THE MEDICAL CURRICULUM. DESPITE ITS RELEVANCE, THE CURRICULAR TIME AND LOGISTICAL RESOURCES DEVOTED TO TEACHING ANATOMY ARE IN DECLINE, FAVORING THE INTRODUCTION OF NEW PEDAGOGICAL APPROACHES BASED ON COMPUTER-ASSISTED LEARNING (CAL). THIS NEW PEDAGOGICAL APPROACH PROVIDES AN INSIGHT INTO STUDENTS' LEARNING PROFILES AND FEATURES, WHICH ARE CORRELATED WITH KNOWLEDGE ACQUISITION. THE AIM OF THIS STUDY WAS TO UNDERSTAND HOW TRAINING WITH CAL PLATFORMS CAN INFLUENCE MEDICAL STUDENTS' ANATOMY PERFORMANCE. A TOTAL OF 611 MEDICAL STUDENTS ATTENDING MUSCULOSKELETAL ANATOMY (MA) AND CARDIOVASCULAR ANATOMY (CA) COURSES WERE ALLOCATED TO ONE OF THREE GROUPS (MA GROUP, CA GROUP, AND MA + CA GROUP). AN ASSOCIATION BETWEEN THE PERFORMANCE IN THESE ANATOMY COURSES AND THE NUMBER OF CAL TRAINING SESSIONS WAS DETECTED. IN THE MA GROUP (R = 0.761,P < 0.001) AND THE MA + CA GROUP (R = 0.786,P < 0.001), A LARGE POSITIVE CORRELATION WAS OBSERVED BETWEEN MUSCULOSKELETAL ANATOMY PERFORMANCE AND THE NUMBER OF CAL TRAINING SESSIONS. SIMILARLY, IN THE CA GROUP (R = 0.670,P < 0.001) AND THE MA + CA GROUP (R = 0.772,P < 0.001), A LARGE POSITIVE CORRELATION WAS OBSERVED BETWEEN CARDIOVASCULAR ANATOMY PERFORMANCE AND THE NUMBER OF CAL TRAINING SESSIONS. MULTIPLE LINEAR REGRESSION MODELS WERE PERFORMED, CONSIDERING EITHER MUSCULOSKELETAL OR CARDIOVASCULAR ANATOMY PERFORMANCE AS THE DEPENDENT VARIABLE. THE RESULTS SUGGEST THAT USING CAL PLATFORMS TO STUDY HAS A POSITIVE DOSE-DEPENDENT EFFECT ON ANATOMY PERFORMANCE. UNDERSTANDING STUDENTS' INDIVIDUAL FEATURES AND ACADEMIC BACKGROUND MAY CONTRIBUTE TO THE OPTIMIZATION OF THE LEARNING PROCESS.
SCIENTISTS ARE INCREASINGLY ENGAGING THE WEB TO PROVIDE FORMAL AND INFORMAL SCIENCE EDUCATION OPPORTUNITIES. DESPITE THE PROLIFIC GROWTH OF WEB-BASED RESOURCES, SYSTEMATIC EVALUATION AND ASSESSMENT OF THEIR EFFICACY REMAINS LIMITED. WE USED CLICKSTREAM ANALYTICS, A WIDELY AVAILABLE METHOD FOR TRACKING WEBSITE VISITORS AND THEIR BEHAVIOR, TO EVALUATE >60,000 VISITS OVER THREE YEARS TO AN EDUCATIONAL WEBSITE FOCUSED ON ECOLOGY. VISITS ORIGINATING FROM SEARCH ENGINE QUERIES WERE A SMALL PROPORTION OF THE TRAFFIC, SUGGESTING THE NEED TO ACTIVELY PROMOTE WEBSITES TO DRIVE VISITATION. HOWEVER, THE NUMBER OF VISITS REFERRED TO THE WEBSITE PER SOCIAL MEDIA POST VARIED DEPENDING ON THE SOCIAL MEDIA PLATFORM AND THE QUALITY OF THOSE VISITS (E.G., TIME ON SITE AND NUMBER OF PAGES VIEWED) WAS SIGNIFICANTLY LOWER THAN VISITS ORIGINATING FROM OTHER REFERRING WEBSITES. IN PARTICULAR, VISITORS REFERRED TO THE WEBSITE THROUGH TARGETED PROMOTION (E. G., INCLUSION IN A WEBSITE LISTING CLASSROOM TEACHING RESOURCES) HAD HIGHER QUALITY VISITS. ONCE ENGAGED IN THE SITE'S CORE CONTENT, VISITOR RETENTION WAS HIGH; HOWEVER, VISITORS RARELY USED THE TUTORIAL RESOURCES THAT SERVE TO EXPLAIN THE SITE'S USE. OUR RESULTS DEMONSTRATE THAT SIMPLE CHANGES IN WEBSITE DESIGN, CONTENT AND PROMOTION ARE LIKELY TO INCREASE THE NUMBER OF VISITORS AND THEIR ENGAGEMENT. WHILE THERE IS A GROWING EMPHASIS ON USING THE WEB TO BROADEN THE IMPACTS OF BIOLOGICAL RESEARCH, TIME AND RESOURCES REMAIN LIMITED. CLICKSTREAM ANALYTICS PROVIDES AN EASILY ACCESSIBLE, RELATIVELY FAST AND QUANTITATIVE MEANS BY WHICH THOSE ENGAGING IN EDUCATIONAL OUTREACH CAN IMPROVE UPON THEIR EFFORTS.
ALONG WITH SIGNIFICANT OPPORTUNITIES, MASSIVELY OPEN ONLINE COURSES (MOOCS) PROVIDE MAJOR CHALLENGES TO STUDENTS (KEEPING TRACK OF COURSE MATERIALS AND EFFECTIVELY INTERACTING WITH TEACHERS AND FELLOW STUDENTS), TEACHERS (MANAGING THOUSANDS OF STUDENTS AND SUPPORTING THEIR LEARNING PROGRESS), RESEARCHERS (UNDERSTANDING HOW STUDENTS INTERACT WITH MATERIALS AND EACH OTHER), AND MOOC PLATFORM DEVELOPERS (SUPPORTING EFFECTIVE COURSE DESIGN AND DELIVERY IN A SCALABLE WAY). THIS ARTICLE DEMONSTRATES THE USE OF DATA ANALYSIS AND VISUALIZATION AS A MEANS TO EMPOWER STUDENTS, TEACHERS, RESEARCHERS, AND PLATFORM DEVELOPERS BY MAKING LARGE VOLUMES OF DATA EASY TO UNDERSTAND. FIRST, WE INTRODUCE THE INSIGHT NEEDS OF DIFFERENT STAKEHOLDER GROUPS. SECOND, WE COMPARE THE WIDE VARIETY OF DATA PROVIDED BY MAJOR MOOC PLATFORMS. THIRD, WE PRESENT A NOVEL FRAMEWORK THAT DISTINGUISHES VISUALIZATIONS BY THE TYPE OF QUESTIONS THEY ANSWER. WE THEN REVIEW THE STATE OF THE ART MOOC VISUAL ANALYTICS USING A TABULATION OF STAKEHOLDER NEEDS VERSUS VISUAL ANALYTICS WORKFLOW TYPES. FINALLY, WE PRESENT NEW DATA ANALYSIS AND VISUALIZATION WORKFLOWS FOR STATISTICAL, GEOSPATIAL, AND TOPICAL INSIGHTS. THE WORKFLOWS HAVE BEEN OPTIMIZED AND VALIDATED IN THE INFORMATION VISUALIZATION MOOC (IVMOOC) ANNUALLY TAUGHT AT INDIANA UNIVERSITY SINCE 2013. ALL WORKFLOWS, SAMPLE DATA, AND VISUALIZATIONS ARE PROVIDED AT HTTP://CNS.IU.EDU/2016-MOOCVIS.HTML.
AS A YOUNG DISCIPLINE COMPUTER SCIENCE SUFFERS FROM A CRISIS OF IDENTITY WHEN TRYING TO BEST APPROACH RESEARCH PROBLEMS AND CONDUCT QUALITY AND RIGOROUS SCIENTIFIC WORK. THIS IS COMMONLY HANDLED BY BORROWING SCIENTIFIC METHODS FROM MATURE DISCIPLINES, SUCH AS MATHEMATICS AND LOGIC THEORIES. HOWEVER, WHILST THE OBJECT OF INVESTIGATION IN COMPUTER SCIENCE CHANGES BOTH THE CONSTRUCTION OF THEORIES DESCRIBING IT AND THE GROWING PRACTICAL EXPERIENCE IN ITS USAGE, ACCEPTED SCIENTIFIC METHODS DO NOT RESPOND WELL TO INTERPLAYING WITH THEORETICAL AND PRACTICAL APPROACHES. PARTICULARLY, EMERGENT INTERDISCIPLINARY FIELDS WITHIN COMPUTER SCIENCE, SUCH AS ONLINE EDUCATION, SHOW NO CONSENSUS IN LITERATURE ABOUT WELL-DEFINED METHODS TO CONDUCT SYSTEMATIC RESEARCH, THUS FACING DIFFICULTIES TO DEAL WITH INTERDISCIPLINARY RESEARCH AND METHODOLOGICAL GAPS. THE ULTIMATE GOAL OF THIS STUDY IS TO SHED LIGHT ON THESE DIFFICULTIES WHILST PROPOSING METHODOLOGICAL GUIDELINES AND GOOD PRACTICES IN ORDER TO FOSTER SOUND RESEARCH IN THE ONLINE EDUCATION FIELD.
MOST RESEARCH IN PRIMARY AND SECONDARY COMPUTING EDUCATION HAS FOCUSED ON UNDERSTANDING LEARNERS WITHIN FORMAL CLASSROOM COMMUNITIES, LEAVING ASIDE THE GROWING NUMBER OF PROMISING INFORMAL ONLINE PROGRAMMING COMMUNITIES WHERE YOUNG USERS CONTRIBUTE, COMMENT, AND COLLABORATE ON PROGRAMS TO FACILITATE LEARNING. IN THIS ARTICLE, WE EXAMINED TRENDS IN COMPUTATIONAL PARTICIPATION IN SCRATCH, AN ONLINE COMMUNITY WITH OVER 1 MILLION REGISTERED YOUTH DESIGNERS. DRAWING ON A RANDOM SAMPLE OF 5,004 YOUTH PROGRAMMERS AND THEIR ACTIVITIES OVER 3 MONTHS IN EARLY 2012, WE EXAMINED PROGRAMMING CONCEPTS USED IN PROJECTS IN RELATION TO LEVEL OF PARTICIPATION, GENDER, AND LENGTH OF MEMBERSHIP OF SCRATCH PROGRAMMERS. LATENT CLASS ANALYSIS RESULTS IDENTIFIED THE SAME FOUR GROUPS OF PROGRAMMERS IN EACH MONTH BASED ON THE USAGE OF DIFFERENT PROGRAMMING CONCEPTS AND SHOWED HOW MEMBERSHIP IN THESE GROUPS SHIFTED IN DIFFERENT WAYS ACROSS TIME. STRIKINGLY, THE LARGEST GROUP OF PROJECT CREATORS (NAMED LOOPS) USED THE SIMPLEST AND FEWEST PROGRAMMING CONCEPTS. FURTHER, THIS GROUP WAS THE MOST STABLE IN MEMBERSHIP AND WAS DISPROPORTIONATELY FEMALE. IN CONTRAST, THE MORE COMPLEX PROGRAMMING GROUPS (NAMED VARIABLES, LOW BOOLEANS, AND HIGH BOOLEANS) SHOWED MUCH MOVEMENT ACROSS TIME. FURTHER, THE LOW BOOLEANS AND HIGH BOOLEANS GROUPS, THE ONLY GROUPS TO USE "AND," "OR," AND "NOT" STATEMENTS IN THEIR PROGRAMS, WERE DISPROPORTIONATELY MALE. IN THE DISCUSSION, WE ADDRESS THE CHALLENGES OF ANALYZING YOUNG LEARNERS' PROGRAMMING IN INFORMAL ONLINE COMMUNITIES AND OPPORTUNITIES FOR DESIGNING MORE EQUITABLE COMPUTATIONAL PARTICIPATION.
A CRITICAL UNDERSTANDING OF DIGITAL TECHNOLOGIES IS AN EMPOWERING COMPETENCE FOR CITIZENS OF ALL AGES. IN THIS PAPER WE INTRODUCE AN OPEN EDUCATIONAL APPROACH OF ARTIFICIAL INTELLIGENCE (AI) FOR EVERYONE. THROUGH A HYBRID AND PARTICIPATIVE MOOC WE AIM TO DEVELOP A CRITICAL AND CREATIVE PERSPECTIVE ABOUT THE WAY AI IS INTEGRATED IN THE DIFFERENT DOMAINS OF OUR LIVES. WE HAVE BUILT AND NOW OPERATE A MOOC IN AI FOR ALL THE CITIZENS FROM 15 YEARS OLD. THE MOOC AIMS TO HELP UNDERSTANDING AI FOUNDATIONS AND APPLICATIONS, INTENDED FOR A LARGE PUBLIC BEYOND THE SCHOOL DOMAIN, WITH MORE THAN 20,000 PARTICIPANTS ENGAGED IN THE MOOC AFTER NINE MONTHS. THIS STUDY ADDRESSES THE PEDAGOGICAL METHODS FOR DESIGNING AND EVALUATING THE MOOC IN AI. THROUGH THIS STUDY WE RAISE FOUR QUESTIONS REGARDING CITIZEN EDUCATION IN AI: WHY (I.E., TO WHICH AIM) SHARING SUCH CITIZEN FORMATION? WHAT IS THE DISCIPLINARY KNOWLEDGE TO BE SHARED? WHAT ARE THE COMPETENCIES TO DEVELOP? HOW CAN IT BE SHARED AND EVALUATED? WE FINALLY SHARE LEARNING ANALYTICS, QUANTITATIVE AND QUALITATIVE EVALUATIONS AND EXPLAIN TO WHICH EXTENT EDUCATIONAL SCIENCE RESEARCH HELPS ENLIGHTEN SUCH LARGE SCALE INITIATIVES. THE ANALYSIS OF THE MOOC IN AI HELPS TO IDENTIFY THAT THE MAIN FEEDBACK RELATED TO AI IS "FEAR", BECAUSE AI IS UNKNOWN AND MYSTERIOUS TO THE PARTICIPANTS. AFTER DEVELOPING PLAYFUL AI SIMULATIONS, THE AI MECHANISMS BECOME FAMILIAR FOR THE MOOC PARTICIPANTS AND THEY CAN OVERCOME THEIR MISCONCEPTION ON AI TO DEVELOP A MORE CRITICAL POINT OF VIEW. THIS CONTRIBUTION DESCRIBES A K-12 AI EDUCATIONAL PROJECT OR INITIATIVES OF A CONSIDERABLE IMPACT, VIA THE FORMATION OF TEACHERS AND OTHER EDUCATORS.
CURRENTLY, MEDICAL EDUCATION CONTEXT POSES DIFFERENT CHALLENGES TO ANATOMY, CONTRIBUTING TO THE INTRODUCTION OF NEW PEDAGOGICAL APPROACHES, SUCH AS COMPUTER-ASSISTED LEARNING (CAL). THIS APPROACH PROVIDES INSIGHT INTO STUDENTS' LEARNING PROFILES AND SKILLS THAT ENHANCE ANATOMY KNOWLEDGE ACQUISITION. TO UNDERSTAND THE INFLUENCE OF ANATOMY CAL ON SPATIAL ABILITIES, A STUDY WAS CONDUCTED. A TOTAL OF 671 MEDICAL STUDENTS ATTENDING MUSCULOSKELETAL (MA) AND CARDIOVASCULAR ANATOMY (CA) COURSES, WERE ALLOCATED TO ONE OF THREE GROUPS (MA GROUP, CA GROUP, MA+CA GROUP). STUDENTS' PRE-TRAINING AND POST-TRAINING SPATIAL ABILITIES WERE ASSESSED THROUGH MENTAL ROTATIONS TEST (MRT), WITH SCORES RANGING BETWEEN 0-24. AFTER CAL TRAINING SESSIONS, STUDENTS' SPATIAL ABILITIES PERFORMANCE IMPROVED (9.72 +/- 4.79 VS. 17.05 +/- 4.57, P<0.001). ALTHOUGH MALE STUDENTS IN BOTH MA GROUP AND CA GROUP SHOW BETTER BASELINE SPATIAL ABILITIES, NO SEX DIFFERENCES WERE FOUND AFTER CAL TRAINING. THE IMPROVEMENT IN SPATIAL ABILITIES SCORE BETWEEN SESSIONS (DELTA MRT) WAS CORRELATED WITH MUSCULOSKELETAL ANATOMY TRAINING SESSIONS IN MA GROUP (R=0.333, P<0.001) AND MA+CA GROUP (R=0.342, P<0.001), AND WITH CARDIOVASCULAR ANATOMY TRAINING SESSIONS IN CA GROUP (R=0.461, P=0.001) AND MA+CA GROUP (R=0.324, P=0.001). MULTIPLE LINEAR REGRESSION MODELS WERE USED, CONSIDERING THE DELTA MRT AS DEPENDENT VARIABLE. AN ASSOCIATION OF DELTA MRT TO THE AMOUNT OF CAL TRAINING AND THE BASELINE SPATIAL ABILITIES WAS OBSERVED. THE RESULTS SUGGEST THAT CAL TRAINING IN ANATOMY HAS POSITIVE DOSE-DEPENDENT EFFECT ON SPATIAL ABILITIES.
TO ACHIEVE BETTER RESEARCH OUTCOMES, ALL COUNTRIES, ESPECIALLY THE DEVELOPING COUNTRIES, FOCUS ON IMPROVEMENT IN THE RESEARCH SKILLS OF THE ACADEMIA, IN PARTICULAR THAT OF NOVICE STUDENTS. RESEARCH METHODS (RM) COURSE IS THE COMMON FIRST STEP WHEREBY STUDENTS GAIN THEIR INITIAL SKILLS IN DESIGNING, CONDUCTING AND REPORTING RESEARCH. THIS RESEARCH AIMS TO SIGNIFICANTLY IMPROVE THE WAY RESEARCH METHODS COURSE IS TAUGHT IN HIGHER EDUCATION SETTINGS. AS PART OF THIS RESEARCH, THIS STUDY INVESTIGATES THE FOLLOWING RESEARCH QUESTIONS. FIRST, CAN LEAN-AGILE PRACTICES INFUSED IN A RESEARCH METHODS COURSE HELP IMPROVE THE RESEARCH SKILLS? SECOND, IF SO, WHAT KIND OF CUSTOMIZATION IS NEEDED TO MAKE IT A) ACCEPTABLE TO THE STUDENT RESEARCHERS (SRS) AND RESEARCH SUPERVISORS (RSS), AND B) MEASURABLE AND SUITABLE FOR LEARNING RESEARCH METHODS? FINALLY, IS THERE AN ADDITIVE CAUSAL INFLUENCE ON LEARNING EFFICIENCY FROM LEAN-AGILE METHODS AND SELF-REGULATED LEARNING METHODS? THIS RESEARCH HAS DESIGNED AN ONLINE EDUCATIONAL PLATFORM FOR A RESEARCH METHODS (RM) COURSE THAT IS INFUSED WITH NOVEL PEDAGOGICAL APPROACH / PROCESSES, TO SCAFFOLD THEIR LEARNING PROCESSES WITH ANALYTICAL SOLUTIONS AND INTERACTIVE VISUALIZATIONS. THE FIRST ITERATION OF THIS DESIGN SCIENCE RESEARCH (DSR) COMMENCED WITH A SURVEY OF SRS AND RSS IN A YOUNG UNIVERSITY IN INDIA, AS PART OF THE RELEVANCE CYCLE; AS PART OF RIGOR CYCLE, SYSTEMATIC & EXPLORATIVE LITERATURE REVIEWS ARE UNDERWAY. DESIGN CYCLE TO BUILD AN MVP BASED ON SURVEY INPUTS IS ALSO IN PROGRESS; EVALUATION CYCLE IS BEING PLANNED. THIS DC PAPER ELABORATES ON THESE RESEARCH PROCESSES AND IDENTIFIES A FEW CHALLENGES FACING A DSR-BASED APPROACH.
BACKGROUND: EARLY PREDICTION OF STUDENTS' LEARNING PERFORMANCE USING DATA MINING TECHNIQUES IS AN IMPORTANT TOPIC THESE DAYS. THE PURPOSE OF THIS LITERATURE REVIEW IS TO PROVIDE AN OVERVIEW OF THE CURRENT STATE OF RESEARCH IN THAT AREA. METHOD: WE CONDUCTED A LITERATURE REVIEW FOLLOWING A TWO-STEP PROCEDURE, LOOKING FOR PAPERS USING THE MAJOR SEARCH ENGINES AND SELECTION BASED ON CERTAIN CRITERIA. RESULTS: THE DOCUMENT SEARCH PROCESS YIELDED 133 RESULTS, 82 OF WHICH WERE SELECTED IN ORDER TO ANSWER SOME ESSENTIAL RESEARCH QUESTIONS IN THE AREA. THE SELECTED PAPERS WERE GROUPED AND DESCRIBED BY THE TYPE OF EDUCATIONAL SYSTEMS, THE DATA MINING TECHNIQUES APPLIED, THE VARIABLES OR FEATURES USED, AND HOW EARLY ACCURATE PREDICTION WAS POSSIBLE. CONCLUSIONS: MOST OF THE PAPERS ANALYZED WERE ABOUT ONLINE LEARNING SYSTEMS AND TRADITIONAL FACE-TO-FACE LEARNING IN SECONDARY AND TERTIARY EDUCATION; THE MOST COMMONLY-USED PREDICTIVE ALGORITHMS WERE J48, RANDOM FOREST, SVM, AND NAIVE BAYES (CLASSIFICATION), AND LOGISTIC AND LINEAR REGRESSION (REGRESSION). THE MOST IMPORTANT FACTORS IN EARLY PREDICTION WERE RELATED TO STUDENT ASSESSMENT AND DATA OBTAINED FROM STUDENT INTERACTION WITH LEARNING MANAGEMENT SYSTEMS. FINALLY, HOW EARLY IT WAS POSSIBLE TO MAKE PREDICTIONS DEPENDED ON THE TYPE OF EDUCATIONAL SYSTEM.
THE LEARNING MANAGEMENT SYSTEMS PROVIDE A SET OF FACILITIES FOR THE LECTURER TO MANAGE HIS COURSES. UNFORTUNATELY, THEY HAVE LIMITATIONS WHEN IT COMES TO ASSESSING GENERIC SKILLS. IN MOST OF THEM, EVERY ACTIVITY IS ASSESSABLE BUT JUST WITH A SIMPLE GRADE AND THERE IS NOT A DIRECT LINK BETWEEN ACTIVITIES AND GENERIC SKILLS. IN THIS WORK WE PRESENT TWO ALTERNATIVES TO SOLVE THIS ISSUE: AN ASSISTED METHOD BASED ON A MODEL-DRIVEN ARCHITECTURE APPROACH AND A REST WEB SERVICE THAT FACILITATES THE ASSESSMENT OF GENERIC SKILLS. WE APPLY BOTH APPROACHES TO A CASE STUDY CONSISTING IN A MOODLE-BASED COURSE WHERE WE ASSESS THE ABILITY OF PLAN AND MANAGE TIME OF EACH STUDENT. RESULTS SHOW THAT THE APPROACHES ARE COMPLEMENTARY, THE WEB SERVICE PROVIDES MORE DETAILED FORMATIVE FEEDBACK, BUT THE MODEL-DRIVEN APPROACH SEEMS MORE SCALABLE FOR COURSES WITH A HIGH NUMBER OF STUDENTS, WHERE IT IS MORE DIFFICULT TO ASSESS THEIR GENERIC SKILLS.
MANY RESEARCHERS HAVE INVESTIGATED THE DIFFICULTIES FACED BY NOVICE PROGRAMMERS. HOWEVER, THESE APPROACHES HAVE SO FAR FOCUSED PRIMARILY ON THE IDENTIFICATION AND CORRECTION OF COMMON SYNTAX ERRORS, OR THAT OF TOPIC DIFFICULTY IN THE CS1 CURRICULUM. MEANWHILE, POOR CODING PRACTICES ADOPTED BY STUDENTS HAVE GONE MOSTLY UNADDRESSED. WHILE THESE PRACTICES MAY NOT NECESSARILY LEAD TO ERRONEOUS CODE, THEY MAY NONETHELESS INDICATE AREAS OF DIFFICULTY AND LEAD TO POORLY STRUCTURED PROGRAMS. TO ADDRESS THESE ISSUES, OUR PROJECT EXAMINES STUDENTS' CODING HABITS AND COMMON ERRORS IN CS1 EXERCISES GATHERED FROM 77 FIRST-YEAR STUDENTS. THIS DATA WAS COLLECTED IN REAL TIME SO THAT WE MAY LATER RECONSTRUCT THE THOUGHT PROCESS OF THE STUDENT WHILE SOLVING THE PROGRAMMING EXERCISES. TO ASSIST OUR ANALYSIS, WE BUILT A CODE VISUALIZER THAT ANIMATES THE PROGRAMMING PROCESS DYNAMICALLY AND SUMMARIZES ERROR METRICS SIMULTANEOUSLY. OUR ULTIMATE GOAL IS TO USE THE CODE VISUALIZER TO HELP EITHER AN INSTRUCTOR OR A STUDENT TO IDENTIFY POOR PROGRAMMING PRACTICES DURING THE CODING PROCESS. WITH THE ERROR METRICS GATHERED, AN INSTRUCTOR CAN INSPECT POTENTIAL IMPROVEMENTS IN CODING BEHAVIORS FOR AN INDIVIDUAL STUDENT AT A GIVEN POINT IN TIME OR OVER TIME, AND IDENTIFY BAD CODING HABITS COMMON TO POPULATIONS OF STUDENTS.
MOOCS HAVE TRADITIONALLY BEEN SEEN AS PROVIDING AN INDIVIDUAL LEARNING EXPERIENCE, HOWEVER THERE IS AN INCREASING TREND TOWARDS ENABLING SOCIAL LEARNING IN MOOCS. TO MAKE ONLINE LEARNING AT SCALE MORE SOCIAL AND COLLABORATIVE, SOME MOOCS HAVE INTRODUCED COHORTS. THE INTERACTION BETWEEN A SMALLER NUMBER OF LEARNERS, WITHIN A COHORT, FACILITATES A RICHER EXCHANGE OF EXPERIENCES AND IDEAS AS COMPARED TO THE EFFECT OF "DRINKING FROM THE FIRE HOSE" FELT IN MOOCS WITHOUT COHORTS. TRADITIONALLY, THESE COHORTS HAVE BEEN FORMED RANDOMLY. IN THIS PAPER, WE EXAMINE THE MOOC "INQUIRY AND TECHNOLOGY FOR TEACHERS" , WHERE WE FORMED COHORTS BASED ON STUDENT DEMOGRAPHICS RELEVANT TO OUR COURSE DESIGN. FURTHERMORE, THESE COHORTS (WHICH WE CALLED SPECIAL INTEREST GROUPS, SIGS) CONTAINED A NESTED SOCIAL STRUCTURE OF SMALL TEAMS THAT WORKED TOGETHER ON CO-CREATING A FINAL ARTIFACT. THE DIFFERENT SOCIAL PLANES (WHOLE COURSE, SIGS, TEAMS, AND INDIVIDUALS) WERE LINKED TOGETHER BY PEDAGOGICAL SCRIPTS THAT ORCHESTRATED THE MOVEMENT OF IDEAS AND ARTIFACTS VERTICALLY AND HORIZONTALLY. IN THIS CONTRIBUTION, WE ANALYZED THE INTERACTION BETWEEN THESE SOCIAL PLANES TO CONTEXTUALIZE THE CO-CREATION OF ARTEFACTS.
EDUCATIONAL ANALYTICS HAVE A GREAT POTENTIAL TO PERSONALIZE LEARNING PROCESSES IN THE DEVELOPING WORLD. MOST SCHOOLS IN THE DEVELOPING WORLD FOLLOW FIXED CURRICULA IN THEIR SCHOOLS WITH LITTLE ROOM FOR PERSONALIZATION. INFRASTRUCTURE AND COST CONSTRAINTS MAKE THE USE OF 1-1 COMPUTER-BASED TUTORS TO PERSONALIZE STUDENT LEARNING UNREALISTIC IN THESE CONTEXTS. HOWEVER, PERVASIVE PRESENCE OF MOBILE AND TABLET DEVICES HAS MADE IT POSSIBLE TO COLLECT STUDENT ASSESSMENT DATA, AND TO PERSONALIZE DECISION-MAKING FOR TEACHERS AND EDUCATIONAL ADMINISTRATORS. THIS PAPER PRESENTS AN EDUCATIONAL ANALYTICS METHOD BASED ON KNOWLEDGE SPACE THEORY (KST) AND K-MEANS TO GROUP STUDENTS BASED ON THEIR ZONE OF PROXIMAL DEVELOPMENT (ZPD). THE PAPER ALSO PRESENTS A CASE STUDY APPLYING THIS TECHNIQUE TO SUPPORT EDUCATIONAL ADMINISTRATORS AND TEACHERS IN SEMI-RURAL SCHOOLS IN A DEVELOPING COUNTRY.
THE ORCHESTRATION OF COLLABORATIVE LEARNING ACTIVITIES IN TECHNOLOGY-ENHANCED CLASSROOMS HAS BECOME A NONTRIVIAL ENDEAVOR FOR EDUCATORS. DEPENDING ON THE BEHAVIORS AND NEEDS OF STUDENTS THAT EMERGE IN REAL EDUCATIONAL SITUATIONS, EDUCATORS MAY NEED TO ORCHESTRATE ACTIVITY ADAPTATIONS ON THE FLY. THESE ADAPTATIONS MAY RANGE FROM THE PROVISION OF ADDITIONAL SCAFFOLDING BY THE EDUCATOR (E.G., THE EDUCATOR'S PARTICIPATION IN A GROUP DISCUSSION) TO A CHANGE IN THE PLANNED PEDAGOGICAL SCENARIO (E.G., THE DURATION). THIS ARTICLE AIMS TO CONTRIBUTE TO THE ORCHESTRATION OF TECHNOLOGY-MEDIATED COLLABORATIVE LEARNING SESSIONS IN A CLASSROOM CONTEXT. WE PRESENT THE DESIGN, IMPLEMENTATION, AND EVALUATION OF A TEACHER-FACING DASHBOARD THAT SUPPORTS TEACHERS IN ORCHESTRATING SCRIPTED COLLABORATION. EVALUATION STUDIES WERE CONDUCTED IN 16 CLASSROOM SESSIONS. THE FINDINGS INDICATE THAT TEACHERS FOUND THE INFORMATION ON THE DASHBOARD TO BE ACTIONABLE AND HELP FACILITATE JUST IN TIME SUPPORT TO STUDENT GROUPS.
DUE TO HIGH VOLUME, VELOCITY, AND VARIETY (REFERRED TO AS "3V") OF DATA PRODUCTION, THE TERM "BIG DATA" HAS RECENTLY EMERGED. BECAUSE OF SUCH FAST DATA GROWTH, DATA CENTERS OF EDUCATIONAL INSTITUTIONS ARE SEEMINGLY EXPLODING AS WELL. THEREFORE, BIG DATA IS MAKING ITS WAY TO THE FIELD OF E-LEARNING. NOWADAYS LARGE EDUCATIONAL INSTITUTIONS FEEL THE URGENT NEED TO ANALYZE THIS FLOOD OF DATA TO PROVIDE STUDENTS WITH HIGHER QUALITY OF SERVICES. DESPITE THE FACT THAT THERE IS HUGE VOLUME AND VARIETY OF DATA WITH HIGH VELOCITY OF PRODUCTION, THE EXISTING TRADITIONAL METHODS ARE UNABLE TO ANALYZE SUCH DATA. IN OTHER WORDS, THERE IS LITTLE DONE ON IMPLEMENTING LEARNING ANALYTICS ON BIG DATA ENGINES. THE AUTHOR CREATED A SCALABLE MACHINE LEARNING SYSTEM ON A CLUSTER OF THREE MACHINES SUITABLE FOR LARGE DATASET OPERATIONS USING BIG DATA ENGINE AND IMPLEMENTED AN EARLY WARNING PROCESS WITH MACHINE LEARNING TECHNIQUES. THE DATASET WAS OBTAINED FROM ONLINE AND SEMIONLINE COURSES FROM THE UNIVERSITY OF TEHRAN. MANY FEATURES LIKE ACCESS TIME, HISTORY OF ACCESS AND HISTORY OF ACTIONS OF STUDENTS WERE EXTRACTED AND USED FOR THE CLASSIFICATION TASK. A PARALLEL DISTRIBUTED CLASSIFICATION PROCESS WAS RUN TO PREDICT STUDENTS' SUCCESS AND FAILURES. IT OFFERS A FAST, SCALABLE AND IN-MEMORY PROCESS WITH APACHE SPARK. EVENTUALLY, THE RESEARCH COMPARES THE EFFICIENCY OF PROPOSED METHOD WITH CURRENT AND COMMON METHODS'. THE RESULTS REVEAL THAT OUR MODEL HAD A HIGH CLASSIFICATION ACCURACY AND THE LARGER THE DATASET, THE MORE EFFICIENT OUR PROCESS WILL BE. IT ALSO DISCUSSES A NEW TERM "LEARNING ANALYTIC" AND "BIG DATA" THAT ARE COMPLETELY NEW AND WORTH READING. MY PAPER CONTAINS WORTHY INFORMATION ABOUT IMPLEMENTING ELEARNING DATA MINING ON BIG DATA ENGINES.
STUDIES ON THE PREDICTION OF STUDENT SUCCESS IN DISTANCE LEARNING HAVE EXPLORED MAINLY DEMOGRAPHICS FACTORS AND STUDENT INTERACTIONS WITH THE VIRTUAL LEARNING ENVIRONMENTS. HOWEVER, IT IS REMARKABLE THAT A VERY LIMITED NUMBER OF STUDIES USE INFORMATION ABOUT THE ASSIGNMENTS SUBMITTED BY STUDENTS AS INFLUENTIAL FACTOR TO PREDICT THEIR ACADEMIC ACHIEVEMENT. THIS PAPER AIMS TO EXPLORE THE REAL IMPORTANCE OF ASSIGNMENT INFORMATION FOR SOLVING STUDENTS' PERFORMANCE PREDICTION IN DISTANCE LEARNING AND EVALUATE THE BENEFICIAL EFFECT OF INCLUDING THIS INFORMATION. WE INVESTIGATE AND COMPARE THIS FACTOR AND ITS POTENTIAL FROM TWO INFORMATION REPRESENTATION APPROACHES: THE TRADITIONAL REPRESENTATION BASED ON SINGLE INSTANCES AND A MORE FLEXIBLE REPRESENTATION BASED ON MULTIPLE INSTANCE LEARNING (MIL), FOCUS ON HANDLE WEAKLY LABELED DATA. A COMPARATIVE STUDY IS CARRIED OUT USING THE OPEN UNIVERSITY LEARNING ANALYTICS DATASET, ONE OF THE MOST IMPORTANT PUBLIC DATASETS IN EDUCATION PROVIDED BY ONE OF THE GREATEST ONLINE UNIVERSITIES OF UNITED KINGDOM. THE STUDY INCLUDES A WIDE SET OF DIFFERENT TYPES OF MACHINE LEARNING ALGORITHMS ADDRESSED FROM THE TWO DATA REPRESENTATION COMMENTED, SHOWING THAT ALGORITHMS USING ONLY INFORMATION ABOUT ASSIGNMENTS WITH A REPRESENTATION BASED ON MIL CAN OUTPERFORM MORE THAN 20% THE ACCURACY WITH RESPECT TO A REPRESENTATION BASED ON SINGLE INSTANCE LEARNING. THUS, IT IS CONCLUDED THAT APPLYING AN APPROPRIATE REPRESENTATION THAT ELIMINATES THE SPARSENESS OF DATA ALLOWS TO SHOW THE RELEVANCE OF A FACTOR, SUCH AS THE ASSIGNMENTS SUBMITTED, NOT WIDELY USED TO DATE TO PREDICT STUDENTS' ACADEMIC PERFORMANCE. MOREOVER, A COMPARISON WITH PREVIOUS WORKS ON THE SAME DATASET AND PROBLEM SHOWS THAT PREDICTIVE MODELS BASED ON MIL USING ONLY ASSIGNMENTS INFORMATION OBTAIN COMPETITIVE RESULTS COMPARED TO PREVIOUS STUDIES THAT INCLUDE OTHER FACTORS TO PREDICT STUDENTS PERFORMANCE.</P>
BACKGROUND: STUDENT ENGAGEMENT IS ESSENTIAL TO QUALITY LEARNING. REGULAR FORMATIVE ASSESSMENT TASKS CAN SUPPORT POSITIVE STUDENT ENGAGEMENT ATTITUDES AND BEHAVIOURS TOWARDS LEARNING. OBJECTIVES: THIS STUDY AIMED TO EVALUATE THE RELATIONSHIPS OF REGULAR, RECURRENT DELIVERY OF ONLINE QUIZZES IN PROMOTING STUDENT ENGAGEMENT AND ACADEMIC PERFORMANCE. DESIGN: CONCURRENT EXPLORATORY MIXED-METHOD DESIGN. SETTING: A LARGE METROPOLITAN UNIVERSITY IN WESTERN SYDNEY. PARTICIPANTS: DATA FROM 1037 FINAL-YEAR UNDERGRADUATE NURSING STUDENTS ENROLLED IN A CORE THEORETICAL UNIT RELATED TO PALLIATIVE NURSING. METHOD: A SERIES OF NEW WEEKLY PARTICIPATION TASK (WPT), CONSISTING OF MULTIPLE ONLINE QUIZZES WAS EMBEDDED INTO THE UNIT. ADMINISTRATIVE DATA, INCLUDING DATA RETRIEVED FROM THE LEARNING ANALYTICS, WAS USED FOR QUANTITATIVE DATA ANALYSIS. QUALITATIVE DATA WERE RETRIEVED FROM OPEN-ENDED QUESTIONS WITHIN THE INSTITUTIONAL STUDENT FEEDBACK ON UNIT SURVEY. RESULTS: EACH STUDENT'S OVERALL MEAN NUMBER OF ATTEMPTS WAS 4.6, ACHIEVING A MEAN QUIZ SCORE OF 97.6%. STUDENTS WITH HIGH QUIZ ATTEMPTS WERE MORE LIKELY TO ALSO HAD HIGH TUTORIAL ATTENDANCE (AOR: 1.42, 95% CI: 1.05 TO 1.90), ACHIEVED MAXIMUM QUIZZES SCORES (AOR: 1.78, 95% CI: 1.26 TO 2.51), BUT INTERESTINGLY, LOWER GRADE POINT AVERAGE (AOR: 1.73, 95% CI: 1.28 TO 2.35). THE WPT RECEIVED 111 (26%) POSITIVE COMMENTS IN STUDENTS' OPEN-ENDED RESPONSES, AND ALL THREE (BEHAVIOURAL, EMOTIONAL AND COGNITIVE) STUDENT ENGAGEMENT DIMENSIONS WERE EVIDENT AS STUDENTS' SOURCES OF SATISFACTION. CONCLUSIONS: THE WPTS ARE A SUCCESSFUL FORMATIVE ASSESSMENT TASK THAT SUPPORTS STUDENT ENGAGEMENT ACROSS ALL THREE DIMENSIONS, AND CONTRIBUTES SIGNIFICANTLY TO REDUCING STUDENT STRESS AND INCREASING PREPAREDNESS FOR AND PARTICIPATION IN FACE-TO-FACE TUTORIAL SESSIONS.
IN THIS PAPER WE PRESENT OUR FINDINGS FROM THE FOODWEB2.0 PROJECT ABOUT SUCCESS INDICATORS AND RESTRAINTS WHILE IMPLEMENTING WEB2.0 BASED LEARNING PROCESSES. WE CONDUCTED TWO COURSES WITH THE SAME CONTENT WITH TWO DIFFERENT TARGET GROUPS FROM THE GERMAN FOOD INDUSTRY: ONE WITH REGULAR EMPLOYEES AND ONE WITH TEACHERS OF DEDICATED EDUCATION FACILITIES. COMPARING THE PERFORMANCE OF THE TWO COURSES BY TRIANGULATING METHODS FROM SOCIAL NETWORK ANALYSIS AND QUANTITATIVE AND QUALITATIVE SURVEYS, WE IDENTIFIED INDICATORS FOR THE SUCCESSFUL IMPLEMENTATION AND DIFFERENCES IN THE MOTIVATION OF LEARNERS AND TEACHERS. THESE FINDINGS ILLUSTRATE THE NEED FOR STRATEGIES INVOLVING AND MOTIVATING TEACHERS WHEN INTRODUCING MODERN LEARNING METHODS AND TOOLS WITHIN THE FOOD INDUSTRY AND OTHER RELATED BRANCHES.
THE USE OF DIGITAL EDUCATIONAL MATERIAL IN CLASSROOMS HAS INCREASED IN RECENT YEARS, NOW CONSTITUTING A MAJOR MARKET FOR PUBLISHERS AND COMPANIES. THE PRESENT RESEARCH ADDRESSES A DIGITAL MATERIAL ASSESSMENT IN TWO DIFFERENT AREAS. FIRST, THE STUDY THROUGH AN EVALUATION OF INTERNAL DATA COLLECTED ON THE PLATFORM SHOWS AN ANALYSIS OF THE USE AND IMPLEMENTATION OF DIGITAL MATERIAL INTO THE SCHOOL. SECONDLY, IT EXPOSES AN EXTERNAL EXAMINATION OF THE EFFECT OF USING DIGITAL MATERIAL AT SCHOOL ON STUDENTS' EXECUTIVE FUNCTIONS. THE SAMPLE CONSISTED OF 168 CHILDREN AGED 8-11 FROM A STATE SCHOOL IN THE COMMUNITY OF MADRID (SPAIN). THE INSTRUMENT SELECTED FOR THE EXTERNAL ASSESSMENT OF ACADEMIC PROGRESS WAS THE EVALUACION NEUROPSICOLOGICA DE LAS FUNCIONES EJECUTIVAS EN NINOS (ENFEN) [NEUROPSYCHOLOGICAL ASSESSMENT OF EXECUTIVE FUNCTIONS IN CHILDREN]. THE LEARNING ANALYTICS SYSTEM OF THE SMILE AND LEARN PLATFORM RECORDED THE STUDENTS' USE DURING CLASS. ACCORDING TO THE USAGE ANALYSIS, THE RESULTS OBTAINED SHOW PREFERENCE OF USING ACTIVITIES FROM LOGIC AND SPATIAL WORLDS. IN THE EXTERNAL ANALYSIS OF THE EFFECT OF THE LEARNING MATERIAL, THE RESULTS RECORD A SIGNIFICANT EFFECT USING ACTIVITIES IN LOGIC AND SPATIAL WORLDS WITH THE GRAY TRAILS TASK, WHICH INVOLVES SPATIAL PERCEPTION, PROCESSING SPEED, AND WORKING MEMORY, AMONG OTHERS. A SECOND ANALYSIS TO CONTRAST THE RESULTS WITH A POST HOC DESIGN APPROACHES RELATIONSHIPS AMONG EXECUTIVE FUNCTIONS AS INVOLVED IN TASKS LIKE GRAY TRAILS, INTERFERENCE, AND RING TASKS WITHIN THE USAGE OF SPATIAL AND LOGIC ACTIVITIES. THE NEED FOR FURTHER RESEARCH TO IMPROVE THESE MATERIALS FOR ENHANCED LEARNING AND THE EXTRAPOLATION OF TRAINING FROM EXECUTIVE FUNCTIONS TO OTHER TASKS IS DISCUSSED. LIKEWISE, LIMITATIONS OF THE IMPLEMENTATION AND DESIGN OF THESE MATERIALS ARE POINTED OUT.
MASSIVE OPEN ONLINE COURSES (MOOCS) ALLOW STUDENTS AND INSTRUCTORS TO DISCUSS THROUGH MESSAGES POSTED ON A FORUM. HOWEVER, THE INSTRUCTORS SHOULD LIMIT THEIR INTERACTION TO THE MOST CRITICAL TASKS DURING MOOC DELIVERY SO, TEACHER-LED SCAFFOLDING ACTIVITIES, SUCH AS FORUM-BASED SUPPORT, CAN BE VERY LIMITED, EVEN IMPOSSIBLE IN SUCH ENVIRONMENTS. IN ADDITION, STUDENTS WHO TRY TO CLARIFY THE CONCEPTS THROUGH SUCH COLLABORATIVE TOOLS COULD NOT RECEIVE USEFUL ANSWERS, AND THE LACK OF INTERACTIVITY MAY CAUSE A PERMANENT ABANDONMENT OF THE COURSE. THE PURPOSE OF THIS PAPER IS TO REPORT THE EXPERIMENTAL FINDINGS OBTAINED EVALUATING THE PERFORMANCE OF A TEXT CATEGORIZATION TOOL CAPABLE OF DETECTING THE INTENT, THE SUBJECT AREA, THE DOMAIN TOPICS, THE SENTIMENT POLARITY, AND THE LEVEL OF CONFUSION AND URGENCY OF A FORUM POST, SO THAT THE RESULT MAY BE EXPLOITED BY INSTRUCTORS TO CAREFULLY PLAN THEIR INTERVENTIONS. THE PROPOSED APPROACH IS BASED ON THE APPLICATION OF ATTENTION-BASED HIERARCHICAL RECURRENT NEURAL NETWORKS, IN WHICH BOTH A RECURRENT NETWORK FOR WORD ENCODING AND AN ATTENTION MECHANISM FOR WORD AGGREGATION AT SENTENCE AND DOCUMENT LEVELS ARE USED BEFORE CLASSIFICATION. THE INTEGRATION OF THE DEVELOPED CLASSIFIER INSIDE AN EXISTING TOOL FOR CONVERSATIONAL AGENTS, BASED ON THE ACADEMICALLY PRODUCTIVE TALK FRAMEWORK, IS ALSO PRESENTED AS WELL AS THE ACCURACY OF THE PROPOSED METHOD IN THE CLASSIFICATION OF FORUM POSTS.
THERE IS GROWING DEMAND FOR ONLINE LEARNING ACTIVITIES THAT OFFER FLEXIBILITY FOR STUDENTS TO STUDY ANYWHERE, ANYTIME, AS ONLINE STUDENTS FIT STUDY AROUND WORK AND FAMILY COMMITMENTS. WE DESIGNED A SERIES OF ONLINE ACTIVITIES AND EVALUATED HOW, WHERE AND WITH WHAT DEVICES STUDENTS USED THE ACTIVITIES, AS WELL AS THEIR LEVELS OF ENGAGEMENT AND DEEP LEARNING WITH THE ACTIVITIES. A MIXED-METHODS DESIGN WAS USED TO EXPLORE STUDENTS' INTERACTIONS WITH THE ONLINE ACTIVITIES. THIS METHOD INTEGRATED LEARNING ANALYTICS DATA WITH RESPONSES FROM 63 SURVEY RESPONDENTS, NINE INTERVIEWS AND 16 FOCUS GROUP PARTICIPANTS. WE FOUND THAT STUDENTS USED A COMBINATION OF MOBILE DEVICES TO ACCESS THE ONLINE LEARNING ACTIVITIES ACROSS A VARIETY OF LOCATIONS DURING OPPORTUNISTIC STUDY SESSIONS IN ORDER TO FIT STUDY INTO THEIR DAILY ROUTINES. THE ONLINE ACTIVITIES WERE PERCEIVED POSITIVELY, FACILITATING AFFECTIVE, COGNITIVE, AND BEHAVIOURAL ENGAGEMENT AS WELL AS STIMULATING DEEP LEARNING. ACTIVITIES THAT WERE AUTHENTIC, PROMOTED PROBLEM-SOLVING, APPLIED THEORY TO REAL-LIFE SCENARIOS AND INCREASED STUDENTS' FEELINGS OF BEING SUPPORTED WERE PERCEIVED AS MOST BENEFICIAL TO LEARNING. THESE FINDINGS HAVE IMPLICATIONS FOR THE FUTURE DESIGN OF ONLINE ACTIVITIES, WHERE ACTIVITIES NEED TO ACCOMMODATE STUDENTS' NEED FOR FLEXIBILITY AS STUDENTS' STUDY HABITS BECOME MORE MOBILE. IMPLICATIONS FOR PRACTICE OR POLICY: THE HIGHER EDUCATION SECTOR NEEDS TO RECOGNISE STUDENTS' INCREASING NEED FOR FLEXIBLE ONLINE LEARNING ACTIVITIES THAT ACCOMMODATE STUDY AROUND WORK AND FAMILY COMMITMENTS. ACADEMICS NEED TO DESIGN ONLINE ACTIVITIES THAT ARE COMPATIBLE WITH MULTIPLE DEVICES AND ARE OFFERED IN DIFFERENT FORMATS TO ALLOW STUDENTS TO STUDY IN OPPORTUNISTIC SESSIONS ACROSS A VARIETY OF SETTINGS. LECTURERS NEED TO CONTEXTUALISE ONLINE ACTIVITIES WITHIN SUBJECT CONTENT AND CREATE AUTHENTIC TASKS, WITH REAL-LIFE APPLICATIONS WHICH MAKE STUDENTS FEEL SUPPORTED.
INTRODUCTION: DUE TO SCIENTIFIC AND TECHNOLOGICAL DEVELOPMENT, MEDICAL EDUCATION HAS BEEN READJUSTING ITS FOCUS AND STRATEGIES. MEDICAL CURRICULUM HAS BEEN ADOPTING A VERTICAL INTEGRATION MODEL, IN WHICH BASIC AND CLINICAL SCIENCES COEXIST DURING MEDICAL INSTRUCTION. THIS CONTEXT FAVOURS THE INTRODUCTION OF NEW COMPLEMENTARY TECHNOLOGY-BASED PEDAGOGICAL APPROACHES. THUS, EVEN TRADITIONAL CORE SCIENCES OF MEDICAL CURRICULUM, LIKE ANATOMY, ARE REFOCUSING THEIR TEACHING/LEARNING PARADIGM. MATERIAL AND METHODS: WE PERFORMED A BIBLIOGRAPHIC REVIEW AIMING TO REFLECT ON MEDICAL EDUCATION'S CURRENT PEDAGOGICAL TREND, BY ANALYSING THE ADVANTAGES OF THE INTRODUCTION AND DIVERSIFICATION OF PEDAGOGICAL APPROACHES IN ANATOMY EDUCATION. RESULTS: ANATOMY EDUCATION'S STATUS QUO IS CHARACTERIZED BY: LESS AVAILABLE TEACHING TIME, INCREASING DEMANDS FROM RADIOLOGY AND ENDOSCOPY IMAGING AND OTHER INVASIVE AND NON-INVASIVE MEDICAL TECHNIQUES, INCREASING NUMBER OF MEDICAL STUDENTS AND OTHER LOGISTICAL RESTRAINS EXPOSED BY THE CURRENT MEDICAL EDUCATION SCENARIO. THE TRADITIONAL LEARNING APPROACH, MAINLY BASED ON CADAVERIC DISSECTION, IS DRIFTING TO COMPLEMENTARY NEWER TECHNOLOGIES-SUCH AS 3D MODELS OR 2D/3D DIGITAL IMAGING-TO EXAMINE THE ANATOMY OF THE HUMAN BODY. ALSO, KNOWLEDGE TRANSFER IS TAKING DIFFERENT CHANNELS, AS LEARNING MANAGEMENT SYSTEMS, SOCIAL NETWORKS AND COMPUTER-ASSISTED LEARNING AND ASSESSMENT ARE ASSUMING RELEVANT ROLES. DISCUSSION: THE FUTURE HOLDS PROMISING APPROACHES FOR EDUCATION MODELS. THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE, VIRTUAL REALITY AND LEARNING ANALYTICS COULD PROVIDE ANALYTIC TOOLS TOWARDS A REAL-TIME AND PERSONALIZED LEARNING PROCESS. CONCLUSION: A REFLECTION ON ANATOMY EDUCATION, AS A COMPREHENSIVE MODEL, ALLOWS US TO UNDERSTAND MEDICAL EDUCATION'S COMPLEXITY. THEREFORE, THE PRESENT MEDICAL EDUCATION CONTEXT FAVOURS A BLENDED LEARNING APPROACH, IN WHICH MULTI-MODALITY PEDAGOGICAL STRATEGIES MAY BECOME THE LANDMARK.
THIS PAPER REPORTS ON THE RESULTS OF AN ANALYSIS OF THE RESEARCH PROPOSALS SUBMITTED TO THE MOOC RESEARCH INITIATIVE (MRI) FUNDED BY THE GATES FOUNDATION AND ADMINISTERED BY ATHABASCA UNIVERSITY. THE GOAL OF MRI WAS TO MOBILIZE RESEARCHERS TO ENGAGE INTO CRITICAL INTERROGATION OF MOOCS. THE SUBMISSIONS - 266 IN PHASE 1, OUT OF WHICH 78 WAS RECOMMENDED FOR RESUBMISSION IN THE EXTENDED FORM IN PHASE 2, AND FINALLY, 28 FUNDED - WERE ANALYZED BY APPLYING CONVENTIONAL AND AUTOMATED CONTENT ANALYSIS METHODS AS WELL AS CITATION NETWORK ANALYSIS METHODS. THE RESULTS REVEALED THE MAIN RESEARCH THEMES THAT COULD FORM A FRAMEWORK OF THE FUTURE MOOC RESEARCH: I) STUDENT ENGAGEMENT AND LEARNING SUCCESS, II) MOOC DESIGN AND CURRICULUM, III) SELF-REGULATED LEARNING AND SOCIAL LEARNING, IV) SOCIAL NETWORK ANALYSIS AND NETWORKED LEARNING, AND V) MOTIVATION, ATTITUDE AND SUCCESS CRITERIA. THE THEME OF SOCIAL LEARNING RECEIVED THE GREATEST INTEREST AND HAD THE HIGHEST SUCCESS IN ATTRACTING FUNDING. THE SUBMISSIONS THAT PLANNED ON USING LEARNING ANALYTICS METHODS WERE MORE SUCCESSFUL. THE USE OF MIXED METHODS WAS BY FAR THE MOST POPULAR. DESIGN-BASED RESEARCH METHODS WERE ALSO SUGGESTED COMMONLY, BUT THE QUESTIONS ABOUT THEIR APPLICABILITY AROSE REGARDING THE FEASIBILITY TO PERFORM MULTIPLE ITERATIONS IN THE MOOC CONTEXT AND RATHER A LIMITED FOCUS ON TECHNOLOGICAL SUPPORT FOR INTERVENTIONS. THE SUBMISSIONS WERE DOMINATED BY THE RESEARCHERS FROM THE FIELD OF EDUCATION (75% OF THE ACCEPTED PROPOSALS). NOT ONLY WAS THIS A POSSIBLE CAUSE OF A COMPLETE LACK OF SUCCESS OF THE EDUCATIONAL TECHNOLOGY INNOVATION THEME, BUT IT COULD BE A WORRYING SIGN OF THE FRAGMENTATION IN THE RESEARCH COMMUNITY AND THE NEED TO INCREASED EFFORTS TOWARDS ENHANCING INTERDISCIPLINARITY.
MACHINE LEARNING PROPOSES INNOVATIVE METHODS FOR STUDENTS' LEARNING ANALYSIS AND NEW WAYS FOR MODELING THE LEARNING PROCESS AND ITS REALIZATION. LEARNING ANALYTICS TAKES ADVANTAGE OF THIS FACT AND PROCESSES DATA ACCORDING TO ACCEPTED OR EMERGING ALGORITHMS THAT LEADS TO CREATION OF ANALYTICAL AND PREDICTIVE MODELS. LEARNING PERFORMANCE IS CONNECTED TO A SET OF BEHAVIORAL ACTIVITIES IN EDUCATIONAL ENVIRONMENT CONCERNING IMPROVEMENT OF KNOWLEDGE AND SKILLS. IT IS A VERY IMPORTANT CRITERION FOR STUDENTS' PROGRESS AND FOR THE FORMATION OF THE FINAL STUDENTS' OUTCOMES. FOR ACHIEVING BETTER LEARNING PERFORMANCE, THE ACTIVITIES SHOULD LEAD TO THE LEARNING OPTIMIZATION IN CONTEXT OF TIME DURATION, EDUCATIONAL TASKS ORGANIZATION, CONTENT PRESENTATION AND MANAGEMENT. ACTIVITIES THAT SUPPORT LEARNING ARE ORIENTED TO SELF-DEPENDENT AND SELF-REGULATED LEARNING AS WELL AS SOCIALLY-ORIENTED AND GROUP-DRIVEN LEARNING. THE AIM OF THE PAPER IS TO PRESENT AN EXPLORATION FOCUSING ON THE INFLUENCE OF SELF-DEPENDENT ACTIVITIES IN THE FORM OF SELF-ASSESSMENT ON LEARNING PERFORMANCE. AN EXPERIMENT IS CONDUCTED WITH STUDENTS WHO HAVE HAD THE POSSIBILITY TO DIRECT AND ORGANIZE THEIR SELF-ASSESSMENT ACTIVITIES IN THE LEARNING MANAGEMENT SYSTEM. SELF-ASSESSMENT ACTIVITIES ARE NOT GRADED AND THEY ARE NOT INCLUDED IN THE FORMATION OF THE FINAL COURSE MARK. THE STUDENTS' BEHAVIOR IS TRACED DURING ONE SEMESTER AND MACHINE LEARNING ALGORITHMS ARE UTILIZED TO ANALYZE THE QUALITY AND QUANTITY OF THE TAKEN SELF-ASSESSMENT ACTIVITIES. ON THIS BASE ANALYTICAL AND PREDICTIVE MODELS REGARDING LEARNING PERFORMANCE AND THE ACHIEVED ACADEMIC RESULTS ARE CREATED. THE PATTERNS AND ANOMALIES ARE OUTLINED AND THEY ARE USED TO POINT OUT THE DIRECTIONS FOR LEARNING PERFORMANCE AND FINAL OUTCOMES IMPROVEMENT.
[BACKGROUND] VIRTUAL REALITY (VR) TECHNOLOGY CAN PROVIDE UNIQUE IMMERSIVE EXPERIENCES FOR GROUP USERS, AND ESPECIALLY FOR ANALYTICS TASKS WITH VISUAL INFORMATION IN LEARNING. PROVIDING A SHARED CONTROL/VIEW MAY IMPROVE THE TASK PERFORMANCE AND ENHANCE THE USER EXPERIENCE DURING VR COLLABORATION. [OBJECTIVES] THEREFORE, THIS RESEARCH EXPLORES THE EFFECT OF COLLABORATIVE MODES AND USER POSITION ARRANGEMENTS ON TASK PERFORMANCE, USER ENGAGEMENT, AND COLLABORATION BEHAVIORS AND PATTERNS IN A VR LEARNING ENVIRONMENT THAT SUPPORTS IMMERSIVE COLLABORATIVE TASKS. [METHOD] THE STUDY INVOLVED TWO COLLABORATIVE MODES (SHARED AND NON-SHARED VIEW AND CONTROL) AND THREE POSITION ARRANGEMENTS (SIDE-BY-SIDE, CORNER-TO-CORNER, AND BACK-TO-BACK). A USER STUDY WAS CONDUCTED WITH 30 PARTICIPANTS DIVIDED INTO THREE GROUPS (SINGLE, SHARED, AND NON-SHARED) USING A VR APPLICATION THAT ALLOWED USERS TO EXPLORE THE STRUCTURAL AND TRANSFORMATIONAL PROPERTIES OF 3D GEOMETRIC SHAPES. [RESULTS] THE RESULTS SHOWED THAT THE SHARED MODE WOULD LEAD TO HIGHER TASK PERFORMANCE THAN SINGLE USERS FOR LEARNING ANALYTICS TASKS IN VR. BESIDES, THE SIDE-BY-SIDE POSITION GOT A HIGHER SCORE AND MORE FAVOR FOR ENHANCING THE COLLABORATIVE EXPERIENCE. [CONCLUSION] THE SHARED VIEW WOULD BE MORE SUITABLE FOR IMPROVING TASK PERFORMANCE IN COLLABORATIVE VR. IN ADDITION, THE SIDE-BY-SIDE POSITION MAY PROVIDE A HIGHER USER EXPERIENCE WHEN COLLABORATING IN LEARNING VR. FROM THESE RESULTS, A SET OF GUIDELINES FOR THE DESIGN OF COLLABORATIVE VISUALIZATIONS FOR VR ENVIRONMENTS ARE DISTILLED AND PRESENTED AT THE END OF THE PAPER. ALL IN ALL, ALTHOUGH OUR EXPERIMENT IS BASED ON A COLOCATED SETTING WITH TWO USERS, THE RESULTS ARE APPLICABLE TO BOTH COLOCATED AND DISTRIBUTED COLLABORATIVE SCENARIOS WITH TWO OR MORE USERS.</P>
IN THIS PAPER, WE PRESENT AN OVERVIEW OF THE RESULTS ACHIEVED BY A FEW APPLICATIONS OF EDUCATIONAL RESEARCH TOWARDS OVERCOMING THE DIFFICULTIES IN THE AMAZON REGION'S EDUCATIONAL SCENARIOS FOR A BETTER HIGHER EDUCATION. MANAUS, THE CAPITAL OF THE BRAZILIAN STATE OF AMAZONAS, DEMANDS AN URGENT INCREASE IN THE NUMBER OF QUALIFIED PROFESSIONALS, MOSTLY FOR ITS INDUSTRIAL POLE. HOWEVER, AMAZONAS HAS A POOR RECORD OF EDUCATIONAL PERFORMANCE IN ALL SCHOLASTIC GRADES. THE SITUATION IS ESPECIALLY CRITICAL IN THE RURAL AREAS, WHERE THE POPULATION, SEPARATED BY MILES OF UNCHARTED JUNGLE, IS USUALLY ONLY ACCESSIBLE THROUGH THE RIVERS. THIS GEOGRAPHIC ISOLATION TRANSFORMS THE SIMPLEST EDUCATIONAL ACTIVITIES INTO A DAILY CHALLENGE. A SUSTAINABLE SOLUTION TO THE LACK OF PROFESSIONALS NEEDS THE ABILITY TO REACH THE STUDENTS WITH LEARNING ACTIVITIES WHEREVER THEY ARE. IN THIS WORK WE SHARE EXPERIENCES IN TWO DIFFERENT SCENARIOS: THE CAPITAL AND THE RURAL AREAS. FOR THE CAPITAL, THE AUTHORS BEGAN A LARGE-SCALE PROGRAM TO ADDRESS THREE ASPECTS: (I) REDUCING STUDENT DROPOUTS; (II) ENLARGING THE REACHABLE COMMUNITY; (III) OFFERING DIFFERENT LEVELS OF KNOWLEDGE. FOUR DIFFERENT LEARNING ACTIVITIES WERE PREPARED: UNDERGRADUATE AND GRADUATE CLASSES, A TALENT DEVELOPMENT PROGRAM, SHORT-TERM EVENTS AND INTENSIVE TRAINING. FOR THE RURAL TOWNS, THE AUTHORS DESCRIBE THE EDUCATIONAL EXPERIENCE WITH UNDERGRADUATE AND GRADUATE DISTANCE LEARNING AND THE ONGOING RESEARCH USING LEARNING ANALYTICS AND ADAPTIVE MODELS BASED ON THE LMS MOODLE. THE RESULTS ARE ENCOURAGING, AS THE MEASUREMENT OF THE POSITIVE IMPACTS OF THE REPORTED INITIATIVE CLEARLY INDICATES THAT A STRUCTURED PROGRAM WOULD BRING SOLID AND LONG LASTING BENEFITS. IT IS ALSO WORTH NOTICING THAT THE PROPOSED SOLUTIONS MAY ALSO FIT OTHER LEARNING SCENARIOS.
FOLLOWING AN ACCELERATING PACE OF TECHNOLOGICAL CHANGE, MASSIVE OPEN ONLINE COURSES (MOOCS) HAVE EMERGED AS A POPULAR EDUCATIONAL DELIVERY PLATFORM, LEVERAGING UBIQUITOUS CONNECTIVITY AND COMPUTING POWER TO OVERCOME LONGSTANDING GEOGRAPHICAL AND FINANCIAL BARRIERS TO EDUCATION. CONSEQUENTLY, THE DEMOGRAPHIC REACH OF EDUCATION DELIVERY IS EXTENDED TOWARDS A GLOBAL ONLINE AUDIENCE, FACILITATING LEARNING AND DEVELOPMENT FOR A CONTINUALLY EXPANDING PORTION OF THE WORLD POPULATION. HOWEVER, AN EXTENSIVE LITERATURE REVIEW INDICATES THAT THE LOW COMPLETION RATE IS A MAJOR ISSUE RELATED TO MOOCS. THIS IS CONSIDERED TO BE A LACK OF PERSON TO PERSON INTERACTION BETWEEN INSTRUCTORS AND LEARNERS ON SUCH COURSES AND, THE ABILITY OF TUTORS TO MONITOR LEARNERS IS IMPAIRED, OFTEN LEADING TO LEARNER WITHDRAWALS. TO ADDRESS THIS PROBLEM, LEARNER DROP OUT PATTERNS ACROSS FIVE COURSES OFFERED BY HARVARD AND MIT UNIVERSITIES ARE INVESTIGATED IN THIS PAPER. LEARNING ANALYTICS IS APPLIED TO ADDRESS KEY FACTORS BEHIND PARTICIPANT DROPOUT EVENTS THROUGH THE COMPARISON OF ATTRITION DURING THE FIRST AND LAST WEEKS OF EACH COURSE. THE RESULTS SHOW THAT THE ATTRITION OF PARTICIPANTS DURING THE FIRST WEEK OF THE COURSE IS HIGHER THAN DURING THE LAST WEEK, LOW PERCENTAGES OF LEARNERS' ATTRITION ARE FOUND PRIOR TO COURSE CLOSING DATES. THIS COULD INDICATE THAT ASSESSMENT FEES MAY NOT REPRESENT A SIGNIFICANT REASON FOR LEARNERS' WITHDRAWAL. WE INTRODUCE SUPERVISED MACHINE LEARNING ALGORITHMS FOR THE ANALYSIS OF LEARNER RETENTION AND ATTRITION WITHIN A MOOC PLATFORM. RESULTS SHOW THAT MACHINE LEARNING REPRESENTS A VIABLE DIRECTION FOR THE PREDICTIVE ANALYSIS OF MOOCS OUTCOMES, WITH THE HIGHEST PERFORMANCES YIELDED BY BOOSTED TREE CLASSIFICATION FOR INITIAL ATTRITION AND NEURAL NETWORK BASED CLASSIFICATION FOR FINAL ATTRITION.
THIS PAPER EXPLORES THE ROLE OF THEORY AND INTERDISCIPLINARITY IN TECHNOLOGY ENHANCED INFORMAL LEARNING, AND THE RESEARCH COMMUNITY. WE CONSIDER CULTURAL HISTORICAL ACTIVITY THEORY (CHAT) BUT WE STRONGLY FEEL THAT OUR ARGUMENT HAS BROADER APPLICATION TO THE USE OF THEORY AS PART OF THE INTELLECTUAL 'SELF-DEFENCE TOOLKIT' THAT RESEARCHERS AND PRACTITIONERS IN THE CRITICAL TEL COMMUNITY NEED TO CONSIDER IF THEY ARE TO 'RESIST' THE CRISES ARISING FROM EDUCATIONAL GLOBALISATION. THEORY OFFERS US THE HISTORY, SCOPE, POWER, AND LANGUAGE, THAT WE NEED TO BE REFLEXIVELY AWARE OF BOTH OUR OWN INTERESTS AND THOSE OF OTHERS WHO ARE ACTORS IN THE SETTINGS IN WHICH WE ARE WORKING. THE PAPER AROSE FROM OUR CONCERN TO PROVIDE EVIDENCE THAT OUR WORK IN VIRTUAL RESEARCH COMMUNITIES (IN THE INTER-LIFE PROJECT) - TO HELP YOUNG PEOPLE PURSUE THEIR OWN RESEARCH AGENDAS AND FIND THEIR RESEARCH 'VOICES' - WAS ACTUALLY EFFECTIVE IN SERVING THEIR INTERESTS AS WELL AS OUR OWN. OUR RESEARCH REVEALED THE CENTRALITY OF LEARNING SPACE, COMMUNITY SUPPORT, AND CREATIVE/VISUAL ARTEFACT PRODUCTION IN THE SUSTAINABILITY OF THESE COMMUNITIES. WE ILLUSTRATE THE INTERACTIONS ACROSS THESE REALMS THROUGH SELECTED EXAMPLES FROM OUR INTER-LIFE FINDINGS. WE EXPLORE HOW AN INTERDISCIPLINARY APPROACH IN TEL CAN SUPPORT THE CREATIVE CONFLUENCE OF LEARNING IN SCIENCE AND THE VISUAL ARTS, SERVING DIVERSE CULTURAL COMMUNITIES OF LEARNERS. FINALLY, WE CONSIDER RESISTANCE TO THE LARGE-SCALE INDUSTRIALISATION OF TEL, AND ITS IDEOLOGIES, THAT SEEMS TO US TO BE A KEY ISSUE IN TEL RESEARCH. FOR EXAMPLE, WE ARE CONCERNED BY THE POTENTIAL FOR SURVEILLANCE AND CONTROL THAT IS CONTAINED IN THE WHOLE LEARNING ANALYTICS MOVEMENT IN WHICH SO MANY TEL RESEARCHERS ARE BECOMING INVOLVED.
LIFE LONG TRAINING AND EDUCATION IN FOOD SAFETY IS A CRUCIAL ISSUE IN PARTICULAR FOR PROFESSIONALS WORKING IN THE FIELD. AT THE SAME TIME THERE IS A NEED TO EVALUATE THE EFFECTIVENESS OF THAT TRAINING IN PARTICULAR WHEN IS GIVEN IN E-LEARNING. THE PURPOSE OF THIS EXPLORATIVE WORK IS TO EVALUATE THE FOOD SAFETY AND QUALITY E-LEARNING COURSE WITHIN A MASTER PROGRAM OFFERED IN THE NATIONAL DISTANCE LEARNING UNIVERSITY IN PORTUGAL. FOLLOWING A CASE STUDY METHODOLOGY A MIXED METHODS APPROACHED WAS USED, BASED ON AN ONLINE QUESTIONNAIRE SURVEY TO STUDENTS THAT ENROLLED THE COURSE, FOLLOWED BY A FOCUS GROUP DISCUSSION TO BETTER DISCUSS THE WEAK POINTS ADDRESSED BY THE STUDENTS IN THE QUESTIONNAIRE. THE QUESTIONNAIRE AIMED TO ASSESS STUDENTS PERCEPTIONS ABOUT THE COURSE ORGANIZATION, RECOURSES, ASSIGNMENTS, ACQUIRED COMPETENCES, CHANGE ATTITUDES AND BEHAVIOUR AND LINK WITH THE PROFESSIONAL ACTIVITY STUDENTS WHO ENROLLED THE COURSE DURING 2016/17 AND 2017/18 WERE SURVEYED IN THE FOURTH SEMESTER OF THE CURRICULAR YEAR, IN THE PERIOD OF DEVELOPMENT OF THE MASTER THESIS DISSERTATION. IN ADDITION A QUANTITATIVE ANALYSIS WAS CONDUCTED IN THE TEMPORAL DISTRIBUTIONS OF MESSAGES PUBLISHED ON THE MOODLE PLATFORM (LEARNING ANALYTICS) TO EVALUATE STUDENTS' INTERACTIONS AND ENGAGEMENT DURING THE COURSE. THIS STUDY HAS SHOWN THAT OVERALL, STUDENTS ARE VERY SATISFIED WITH THE COURSE, HOWEVER THEY SUGGESTED IMPROVEMENTS THAT ARE NEEDED CONSIDERING THE PEDAGOGICAL MODEL USED AND THEIR AVAILABILITY TO LEARN AS STUDENTS WITH FULL TIME JOBS, MOST RELATED WITH THE FOOD SCIENCE. THIS EXPLORATIVE RESEARCH AIMED TO CONTRIBUTE TO THE IMPROVEMENT OF FOOD SAFETY AND QUALITY TRAINING BASED ALSO ON RECENT TOOLS AND RECOMMENDATIONS WITHIN E-LEARNING IN SCIENCE EDUCATION.
MULTI-VIEW LEARNING IS A MACHINE LEARNING APP0ROACH AIMING TO EXPLOIT THE KNOWLEDGE RETRIEVED FROM DATA, REPRESENTED BY MULTIPLE FEATURE SUBSETS KNOWN AS VIEWS. CO-TRAINING IS CONSIDERED THE MOST REPRESENTATIVE FORM OF MULTI-VIEW LEARNING, A VERY EFFECTIVE SEMI-SUPERVISED CLASSIFICATION ALGORITHM FOR BUILDING HIGHLY ACCURATE AND ROBUST PREDICTIVE MODELS. EVEN THOUGH IT HAS BEEN IMPLEMENTED IN VARIOUS SCIENTIFIC FIELDS, IT HAS NOT ADEQUATELY USED IN EDUCATIONAL DATA MINING AND LEARNING ANALYTICS, SINCE THE HYPOTHESIS ABOUT THE EXISTENCE OF TWO FEATURE VIEWS CANNOT BE EASILY IMPLEMENTED. SOME NOTABLE STUDIES HAVE EMERGED RECENTLY DEALING WITH SEMI-SUPERVISED CLASSIFICATION TASKS, SUCH AS STUDENT PERFORMANCE OR STUDENT DROPOUT PREDICTION, WHILE SEMI-SUPERVISED REGRESSION IS UNCHARTED TERRITORY. THEREFORE, THE PRESENT STUDY ATTEMPTS TO IMPLEMENT A SEMI-REGRESSION ALGORITHM FOR PREDICTING THE GRADES OF UNDERGRADUATE STUDENTS IN THE FINAL EXAMS OF A ONE-YEAR ONLINE COURSE, WHICH EXPLOITS THREE INDEPENDENT AND NATURALLY FORMED FEATURE VIEWS, SINCE THEY ARE DERIVED FROM DIFFERENT SOURCES. MOREOVER, WE EXAMINE A WELL-ESTABLISHED FRAMEWORK FOR INTERPRETING THE ACQUIRED RESULTS REGARDING THEIR CONTRIBUTION TO THE FINAL OUTCOME PER STUDENT/INSTANCE. TO THIS PURPOSE, A PLETHORA OF EXPERIMENTS IS CONDUCTED BASED ON DATA OFFERED BY THE HELLENIC OPEN UNIVERSITY AND REPRESENTATIVE MACHINE LEARNING ALGORITHMS. THE EXPERIMENTAL RESULTS DEMONSTRATE THAT THE EARLY PROGNOSIS OF STUDENTS AT RISK OF FAILURE CAN BE ACCURATELY ACHIEVED COMPARED TO SUPERVISED MODELS, EVEN FOR A SMALL AMOUNT OF INITIALLY COLLECTED DATA FROM THE FIRST TWO SEMESTERS. THE ROBUSTNESS OF THE APPLYING SEMI-SUPERVISED REGRESSION SCHEME ALONG WITH SUPERVISED LEARNERS AND THE INVESTIGATION OF FEATURES' REASONING COULD HIGHLY BENEFIT THE EDUCATIONAL DOMAIN.
THIS PAPER DESCRIBES THE BACKGROUND AND DISCUSSION POINTS TO A POSTER PRESENTATION, WHICH AIMS TO HIGHLIGHT AND SCRUTINIZE GOOD EXAMPLES OF EDUCATIONAL SOFTWARE. THE EXPLORATION OF THIS QUESTION - IN THE EXACT FORMULATION "WHAT ARE GOOD EXAMPLES OF EDUCATIONAL SOFTWARE?" - DATES BACK TO 2015, WHEN THE AUTHOR POSTED IT FOR OPEN DISCUSSION ON RESEARCHGATE, A SOCIAL NETWORK FOR RESEARCHERS. AT THE TIME OF WRITING, THIS QUESTION HAS MORE THAN 6.800 READS AND RECEIVED 27 RESPONSES CONTAINING LISTS, EXAMPLES, LINKS, RECOMMENDATIONS AND MOTIVATIONS FOR 40 (TYPES OF) NAMED SOFTWARE, FROM PROFESSIONAL RESEARCHERS AND EDUCATORS IN THE FIELD. AN ATTENDANT AIM IS TO BRING THESE SUGGESTIONS INTO AN OVERVIEW THAT CAN TELL SOMETHING MORE PRECISE ABOUT WHAT MAKES FOR "GOOD" EDUCATIONAL SOFTWARE, TAKING INTO ACCOUNT BOTH THE RESEARCH LITERATURE AND THE RESPONDENTS' MOTIVATIONS. FOR THIS PURPOSE, THE EXAMPLES WERE CATEGORIZED BY THEIR MAIN INSTRUCTIONAL FUNCTION AS SUGGESTED IN PREVIOUS LITERATURE, AND FURTHER ASSESSED BY APPLYING MY RECENTLY INTRODUCED CONCEPT OF INTEGRAL DIGITAL VALUES (IDV). THE RESULT IS A CONCRETIZATION OF HOW WE CAN RECOGNIZE THE IMPLEMENTATION OF RELEVANT COGNITIVE AND PEDAGOGICAL PRINCIPLES IN WELL-DESIGNED EDUCATIONAL SOFTWARE. SOME IDENTIFIED ASPECTS WERE HOW THE SOFTWARE MADE USE OF MEANINGFUL REPRESENTATIONS, EFFECTIVE FEEDBACK, ADAPTIVELY AND NOVEL SOCIAL CONFIGURATIONS. MORE COMPLEX SOFTWARE SYSTEMS WERE SUGGESTED AS "GOOD EXAMPLES" WITH REFERENCE TO THEIR USE OF AI-TECHNIQUES, CONCEPTUAL MODELLING AND/OR LEARNING ANALYTICS. THESE NON-CONCLUSIVE RESULTS SERVE TO INFORM THE ON-GOING WORK OF FORMULATING SCIENTIFICALLY GROUNDED CRITERIA FOR IDENTIFYING AND ASSESSING KEY FEATURES OF EDUCATIONAL TECHNOLOGIES, SUCH AS THE REVIEWED SOFTWARE. IN ORDER TO PURSUING THIS DISCUSSION FURTHER, A NUMBER OF RESULTING QUESTIONS ARE SUGGESTED.
AS ELEARNING IS MORE AND MORE EMPLOYED NOWADAYS AS AN ALTERNATIVE TO TRADITIONAL LEARNING SCENARIOS, A MULTITUDE OF ELEARNING SOLUTIONS AND PROVIDERS HAVE EMERGED ON THE MARKET. MOREOVER, THIS DOMAIN HAS ADVANCED TO SUCH LENGTHS THAT MOST ORGANIZATIONS POSSESS ALREADY A STANDARD SUITE OF SUCH APPLICATIONS. THIS PAPER INTRODUCES AN ELEAMING SOLUTION THAT IS FOCUSED ON EXPOSING SPECIALIZED AND PERSONALIZED CONTENT DESCRIBING THE USAGE OF TELEMEDICINE SERVICES ACROSS WIDELY SPREAD GEOGRAPHICAL AREAS. THE PROPOSED ELEAMING SOLUTION MAINLY CONSISTS OF INTERACTIVE DASHBOARDS THAT PRESENT IN A SIMPLE, INTUITIVE AND HIGHLY VISUAL MANNER THE RESULTS OF SOME COMPLEX CDR (CALL DETAIL RECORDS) ANALYSES. THEREFORE, THE SYSTEM IS FED BY AN ANALYTICAL PLATFORM, WHOSE ARCHITECTURAL DESIGN WILL BE FURTHER DESCRIBED IN THIS PAPER, TOGETHER WITH AN ARGUMENTATION WHY THE PROPOSED DATA MODELLING APPROACH AND TECHNIQUES ARE OPTIMAL AND ARE READY TO RESPOND TO THE PURSUED AREAS OF INTEREST. OVERALL, THE AIM OF THE ELEARNING SOLUTION IS TO HELP DECISION MAKERS TO BETTER PERCEIVE THEIR BUSINESS FROM A GLOBAL AND ANALYTICAL POINT OF VIEW AND TO UNDERSTAND BEHAVIOURS IN TERMS OF TELEMEDICINE USAGE TRENDS. THIS PAPER FOCUSES ON A LINE OF RESEARCH THAT HAS NOT BEEN YET PURSUED, BUT THAT COULD IMPACT THE OPTIMIZATION OF TELEMEDICINE SERVICES OFFERING. IN A NUTSHELL, EXPOSING THE ANALYSIS OF TERABYTES OF CDRS THROUGH SPECIFIC DATA-MINING TECHNIQUES IN A CLEAR AND SIMPLIFIED MANNER SUPPORTS LEARNING ABOUT BUSINESS FACTS AND TRENDS. MOREOVER, THE ASSIMILATION OF THIS TYPE OF KNOWLEDGE IS VITAL IN ENABLING INFORMED DECISIONS ABOUT THE OPTIMIZATION OF PROVISIONING TELEMEDICINE SERVICES IN CONFORMITY WITH THEIR ACTUAL USAGE. IN ADDITION, WE ENVISION AN ANALOGY TO TRADITIONAL LEARNING ANALYTICS TOOLS CENTRED ON PERSONALIZED DATA VISUALIZATION.
THE COVID-19 OUTBREAK, ALONG WITH POST-PANDEMIC IMPACT HAS PROMPTED INTERNET PLUS EDUCATION TO RE-EXAMINE NUMEROUS FACETS OF TECHNOLOGY-ORIENTED ACADEMIC RESEARCH, PARTICULARLY EDUCATIONAL BIG DATA (EBD). HOWEVER, THE UNEXPECTED TRANSITION FROM FACE-TO-FACE OFFLINE EDUCATION TO ONLINE LESSONS HAS URGED TEACHERS TO INTRODUCE EDUCATIONAL TECHNOLOGY INTO TEACHING PRACTICE, WHICH HAS HAD AN OVERWHELMING IMPACT ON TEACHERS' PROFESSIONAL AND PERSONAL LIVES. THE AIM OF THIS PRESENT WORK IS TO FATHOM WHICH RESEARCH FOCI CONSTRUCT EBD IN A COMPREHENSIVE MANNER AND HOW POSITIVE PSYCHOLOGICAL INDICATORS FUNCTION IN THE TECHNOSTRESS SUFFERED BY LESS AGENTIC TEACHERS. TO THIS END, CITESPACE 5.7 AND VOSVIEWER WERE APPLIED TO EXAMINE A LONGITUDINAL STUDY OF THE LITERATURE FROM WEB OF SCIENCE CORE COLLECTION WITH THE OBJECTIVE OF UNCOVERING THE EXPLICIT PATTERNS AND KNOWLEDGE STRUCTURES IN SCIENTIFIC NETWORK KNOWLEDGE MAPS. THOUSAND SEVEN HUNDRED AND EIGHT ARTICLES CONCERNED WITH EDUCATIONAL DATA THAT MET THE CRITERIA WERE EXTRACTED AND ANALYZED. RESEARCH SPANNING 15 YEARS WAS CONDUCTED TO REVEAL THAT THE KNOWLEDGE BASE HAS ACCUMULATED DRAMATICALLY AFTER MANY GOVERNMENTS' INITIATIVES SINCE 2012 WITH AN ACCELERATING ANNUAL GROWTH AND DECREASING GEOGRAPHIC IMBALANCE. THE REVIEW ALSO IDENTIFIED SOME INFLUENTIAL AUTHORS AND JOURNALS WHOSE EFFECTS WILL CONTINUE TO HAVE FUTURE IMPLICATIONS. THE AUTHORS IDENTIFIED SEVERAL TOPICAL FOCI SUCH AS DATA MINING, STUDENT PERFORMANCE, LEARNING ENVIRONMENT AND PSYCHOLOGY, LEARNING ANALYTICS, AND APPLICATION. MORE SPECIFICALLY, THE AUTHORS IDENTIFIED THE SCIENTIFIC SHIFT FROM DATA MINING APPLICATION TO DATA PRIVACY AND EDUCATIONAL PSYCHOLOGY, FROM GENERAL SCAN TO SPECIFIC INVESTIGATION. AMONG THE CONCLUSIONS, THE RESULTS HIGHLIGHTED THE IMPORTANT INTEGRATION OF EDUCATIONAL PSYCHOLOGY AND TECHNOLOGY DURING CRITICAL PERIODS OF EDUCATIONAL DEVELOPMENT.</P>
INTELLIGENT LEARNING ENVIRONMENTS CAN BE DESIGNED TO SUPPORT THE DEVELOPMENT OF LEARNERS' COGNITIVE SKILLS, STRATEGIES, AND METACOGNITIVE PROCESSES AS THEY WORK ON COMPLEX DECISION-MAKING AND PROBLEM-SOLVING TASKS. HOWEVER, THE COMPLEXITY OF THE TASKS MAY IMPEDE THE PROGRESS OF NOVICE LEARNERS. PROVIDING ADAPTIVE FEEDBACK TO LEARNERS WHO FACE DIFFICULTIES REQUIRES LEARNER MODELING APPROACHES THAT CAN IDENTIFY LEARNERS' PROFICIENCIES AND THE DIFFICULTIES THEY FACE IN EXECUTING REQUIRED SKILLS, STRATEGIES, AND METACOGNITIVE PROCESSES. THIS PAPER DISCUSSES A MULTILEVEL HIERARCHICAL LEARNER MODELING SCHEME THAT ANALYZES AND CAPTURES LEARNERS' COGNITIVE PROCESSES AND PROBLEM-SOLVING STRATEGIES ALONG WITH THEIR PERFORMANCE ON ASSIGNED TASKS IN A GAME-BASED ENVIRONMENT CALLED URBANSIM THAT REQUIRES COMPLEX DECISION MAKING FOR DEALING WITH COUNTERINSURGENCY SCENARIOS. AS THE SCENARIO EVOLVES IN A TURN-BY-TURN FASHION, URBANSIM EVALUATES THE LEARNERS' MOVES USING A NUMBER OF PERFORMANCE MEASURES. OUR LEARNER MODELING SCHEME INTERPRETS THE REPORTED PERFORMANCE VALUES BY ANALYZING THE LEARNERS' ACTIVITIES CAPTURED IN LOG FILES TO DERIVE LEARNERS' PROFICIENCIES IN ASSOCIATED COGNITIVE SKILLS AND STRATEGIES, AND UPDATES THE LEARNER MODEL. WE DISCUSS THE DETAILS OF THE LEARNER MODELING ALGORITHMS IN THIS PAPER, AND THEN DEMONSTRATE THE EFFECTIVENESS OF OUR APPROACH BY PRESENTING RESULTS FROM A STUDY WE CONDUCTED AT VANDERBILT UNIVERSITY.
IN DISTANCE EDUCATION LEARNERS ARE ENCOURAGED TO BE AUTONOMOUS LEARNERS BY LEARNING AT THEIR OWN PLACE AND PACE. HOWEVER, THEY REQUIRE RELEVANT SUPPORTS AND TIMELY FEEDBACK TO MOTIVATE THEM PROGRESS EFFECTIVELY IN THEIR STUDIES. IN ABSENCE OF FACE TO FACE INTERACTIONS, TUTORS ARE UNABLE TO UNDERSTAND CLEARLY THE PROBLEMS FACED BY THE LEARNERS AND THUS THEY ARE NOT GIVEN THE RIGHT TIMELY FEEDBACK. THIS RESEARCH IS PART OF A DOCTORAL RESEARCH AND THE AIM OF THIS PAPER IS TO FIRSTLY, INVESTIGATE THE TYPES OF SUPPORTS THAT LEARNERS LOOK FOR IN A COMPLETELY ONLINE COURSE AND SECONDLY, IDENTIFY A CONVENIENT ENVIRONMENT TO ENABLE TUTORS TRACK THE LEARNERS' PROGRESS AND GIVE PERSONALIZED ASSISTANCE. AS METHODOLOGY, A CONTENT ANALYSIS OF FOUR SYNCHRONOUS TUTORIAL CHATS, OF 998 LINES OF INTERACTIONS, WAS DONE BASED ON ACTIVITY THEORY OF ENGESTROM. AN ONLINE TUTOR AND HIS TEN LEARNERS PARTICIPATED IN THE CHATS, ENABLING THEM TO INTERACT FREELY WITH THE FOCUS OF SUCCESSFULLY COMPLETING THE ASSIGNMENT SET. RESULT DEPICTS THE HUMAN BEHAVIOUR OF LEARNERS ENROLLED IN A COMPLETELY ONLINE COURSE: THEY QUERIED ON THE RULES, THE PROCEDURES OF THE COURSE AS WELL AS ON THE EXPECTATIONS OF THE TUTOR. THE INTERACTIONS WERE FOCUSED TOWARDS MEETING THE OBJECTIVES OF THE COURSE BUT THEY WERE NOT DOMINATED BY META-COGNITIVE INTERACTIONS. THUS, WE PROPOSE FOR RELEVANT VARIABLES TO BE LOGGED TO ENABLE TUTORS TO UNDERSTAND LEARNER'S PROGRESS AND TO GIVE EFFICIENT SUPPORT BASED ON THE ANALYSIS OF LEARNING ANALYTICS OF LEARNERS' PAST INTERACTIONS. LEARNERS' BEHAVIOURS VIA THEIR INTERACTIONS, WILL ENABLE TUTORS IDENTIFY LURKERS, ACTIVE AND PASSIVE LEARNERS. THIS RESEARCH IS A STEPPING STONE IN IDENTIFYING PATTERNS WITH REGARDS TO TUTORS AND LEARNERS' INTERACTIONS.
THIS ARTICLE DESCRIBES THE DETERMINATION OF STUDENT PROFILES BASED ON THE CONSTRUCTS OF MULTIPLE INTELLIGENCES AND ON LEARNING AND AFFECTIVE STRATEGIES, IN ORDER TO IDENTIFY THE MOST IMPORTANT CHARACTERISTICS FOR ENSURING THE ACADEMIC SUCCESS OF ENGINEERING STUDENTS. THE TWO CONSTRUCTS WERE ORGANIZED IN TERMS OF EIGHT DIMENSIONS EACH: THE BASIS FOR DEVELOPING TWO QUESTIONNAIRES THAT WERE COMPLETED BY 618 UNDERGRADUATE ENGINEERING STUDENTS, IN AN ATTEMPT TO DEFINE THEIR STUDENT PROFILE. THREE ALTERNATIVE MEASURES WERE DESIGNED TO DETERMINE NUMERICAL VALUES FOR EACH DIMENSION, ACCORDING TO THEIR CAPACITY TO PREDICT ACADEMIC PERFORMANCE IN TERMS OF FINAL GRADES, USING REGRESSION ANALYSIS. ACCORDING TO THE STUDY'S FINDINGS, THE LOGICAL/MATHEMATICAL DIMENSION PLAYS AN IMPORTANT ROLE IN STUDENT PERFORMANCE, WHILE ANXIETY HAS A NEGATIVE EFFECT ON FINAL GRADES. THE DEFINITION OF APPROPRIATE MEASURES TO DETERMINE STUDENTS' COGNITIVE, AFFECTIVE, AND SELF-REGULATORY PROFILES CAN PROVIDE INSTRUCTORS WITH TIMELY INFORMATION TO IMPLEMENT APPROPRIATE TEACHING STRATEGIES IN THEIR GROUPS.
THIS PROPOSAL IS TO PUT THE USE OF ICT-ENHANCED WRITING INTO A BIGGER PICTURE IN HIGHER EDUCATION, NAMELY, IN THE REALM OF EPORTFOLIO FOR ACADEMIC LEARNING AND CAREER FOR LIFE-LONG, IN WHICH THE PROGRESS AND ITS PROCESS OF LEARNING LEADS TO THE BENEFIT OF CAREER DESIGN AND LIFE-LONG LEARNING AFTER GRADUATION, WHICH IS, I.E., THE MAIN PURPOSE OF THE MISSION OF THE HIGHER EDUCATION. IT HAS BEEN PROVED THAT SUCH SKILLS CAN BE DEVELOPED THROUGH THE LEARNING PROCESS OF WRITING. WHILE IN THE PAST, PAPER AND A PEN WERE THE OPTIMAL TECHNOLOGIES TO REFLECT THE EVIDENCE OF LEARNING OR THE MIRROR OF THE LEARNING MIND, CURRENTLY DIGITAL MEDIA HAVE BEEN DOMINANT. IN OTHER WORDS, THE DIGITAL MEDIA HAVE BEEN PROVIDING STUDENTS WITH RICHER WAYS OF COMMUNICATION AND PRESENTATION FOR EXPRESSING THE MIND. IT IS PROPOSED THAT, THE STUDENTS' META-COGNITIVE REFLECTION BEING THE KEY TO ACTIVE LEARNING, THE DEVELOPMENT OF THE STUDENTS' EXPRESSIVE SKILLS IN LITERACY WILL INDEED BRING ABOUT A SUCCESS IN LIFE. BY NURTURING THE STUDENTS' COMPREHENSIVE WRITING SKILLS AS THE ARTIFACT OR EVIDENCE FOR ACADEMIC LEARNING THROUGH META-COGNITIVE ACTIVITIES, WHICH IS IN TURN ARCHIVED IN EPORTFOLIO, IS IT POSSIBLE TO CONDUCT LEARNING ANALYTICS AND PLAN ACADEMIC ADVICE FURTHER IN A FRUITFUL WAY. OF COURSE, ARCHIVED ARTIFACT MUST BE BASED ON THE HONEST WORK OF STUDENTS, WHERE THE AUTHENTICATION FEATURE OF ICT WILL PLAY THE IMPORTANT ROLE. THIS PAPER ELABORATES THE FUNDAMENTAL ISSUES SURROUNDING THE MISSION OF THE HIGHER EDUCATION TO LEAD TO LIFE-LONG LEARNING AND CAREER AND THE ASSESSMENT STRATEGIES FOR MEASURING THE LEARNING OUTCOME FOR DESIGNING THE FUTURE EDUCATION.
PURPOSE FOR STUDIES IN EDUCATIONAL DATA MINING OR LEARNING ANALYTICS, THE PREDICTION OF STUDENT'S PERFORMANCE OR EARLY WARNING IS ONE OF THE MOST POPULAR RESEARCH TOPICS. HOWEVER, RESEARCH GAPS INDICATE A PAUCITY OF RESEARCH USING MACHINE LEARNING AND DEEP LEARNING (DL) MODELS IN PREDICTIVE ANALYTICS THAT INCLUDE BOTH BEHAVIORS AND TEXT ANALYSIS. DESIGN/METHODOLOGY/APPROACH THIS STUDY COMBINED BEHAVIORAL DATA AND DISCUSSION BOARD CONTENT TO CONSTRUCT EARLY WARNING MODELS WITH MACHINE LEARNING AND DL ALGORITHMS. IN TOTAL, 680 COURSE SECTIONS, 12,869 STUDENTS AND 14,951,368 LOGS WERE COLLECTED FROM A K-12 VIRTUAL SCHOOL IN THE USA. THREE ROUNDS OF EXPERIMENTS WERE CONDUCTED TO DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED APPROACH. FINDINGS THE DL MODEL PERFORMED BETTER THAN MACHINE LEARNING MODELS AND WAS ABLE TO CAPTURE 51% OF AT-RISK STUDENTS IN THE EIGHTH WEEK WITH 86.8% OVERALL ACCURACY. THE COMBINATION OF BEHAVIORAL AND TEXTUAL DATA FURTHER IMPROVED THE MODEL'S PERFORMANCE IN BOTH RECALL AND ACCURACY RATES. THE TOTAL WORD COUNT IS A MORE GENERAL INDICATOR THAN THE TEXTUAL CONTENT FEATURE. SUCCESSFUL STUDENTS SHOWED MORE WORDS IN ANALYTIC, AND AT-RISK STUDENTS SHOWED MORE WORDS IN AUTHENTIC WHEN TEXT WAS IMPORTED INTO A LINGUISTIC FUNCTION WORD ANALYSIS TOOL. THE BALANCED THRESHOLD WAS 0.315, WHICH CAN CAPTURE UP TO 59% OF AT-RISK STUDENTS. ORIGINALITY/VALUE THE RESULTS OF THIS EXPLORATORY STUDY INDICATE THAT THE USE OF STUDENT BEHAVIORS AND TEXT IN A DL APPROACH MAY IMPROVE THE PREDICTIVE POWER OF IDENTIFYING AT-RISK LEARNERS EARLY ENOUGH IN THE LEARNING PROCESS TO ALLOW FOR INTERVENTIONS THAT CAN CHANGE THE COURSE OF THEIR TRAJECTORY.
AS SOCIAL MEDIA BECOME A STAPLE FOR KNOWLEDGE DISCOVERY AND SHARING, QUESTIONS ARISE ABOUT HOW SELF-ORGANIZING COMMUNITIES MANAGE LEARNING OUTSIDE THE DOMAIN OF ORGANIZED, AUTHORITY-LED INSTITUTIONS. YET EXAMINATION OF SUCH COMMUNITIES IS CHALLENGED BY THE QUANTITY OF POSTS AND VARIETY OF MEDIA NOW USED FOR LEARNING. THIS PAPER ADDRESSES THE CHALLENGES OF IDENTIFYING (1) WHAT INFORMATION, COMMUNICATION, AND DISCURSIVE PRACTICES SUPPORT SUCCESSFUL ONLINE COMMUNITIES, (2) WHETHER SUCH PRACTICES ARE SIMILAR ON TWITTER AND REDDIT, AND (3) WHETHER MACHINE LEARNING CLASSIFIERS CAN BE SUCCESSFULLY USED TO ANALYZE LARGER DATASETS OF LEARNING EXCHANGES. THIS PAPER BUILDS ON EARLIER WORK THAT USED MANUAL CODING OF LEARNING AND EXCHANGE IN REDDIT 'ASK' COMMUNITIES TO DERIVE A CODING SCHEMA WE REFER TO AS 'LEARNING IN THE WILD'. THIS SCHEMA OF EIGHT CATEGORIES: EXPLANATION WITH DISAGREEMENT, AGREEMENT, OR NEUTRAL PRESENTATION; SOCIALIZING WITH NEGATIVE, OR POSITIVE INTENT; INFORMATION SEEKING; PROVIDING RESOURCES; AND COMMENTS ABOUT FORUM RULES AND NORMS. TO COMPARE ACROSS MEDIA, RESULTS FROM CODING REDDIT'S ASKHISTORIANS ARE COMPARED TO RESULTS FROM CODING A SAMPLE OF #TWITTERSTORIANS TWEETS (N = 594). HIGH AGREEMENT BETWEEN CODERS AFFIRMED THE APPLICABILITY OF THE CODING SCHEMA TO THIS DIFFERENT MEDIUM. LIWC LEXICON-BASED TEXT ANALYSIS WAS USED TO BUILD MACHINE LEARNING CLASSIFIERS AND APPLY THESE TO CODE A LARGER DATASET OF TWEETS (N = 69,101). THIS RESEARCH SHOWS THAT THE 'LEARNING IN THE WILD' CODING SCHEMA HOLDS ACROSS AT LEAST TWO DIFFERENT PLATFORMS, AND IS PARTIALLY SCALABLE TO STUDY LARGER ONLINE LEARNING COMMUNITIES.
THE SEMINARS ARE A LEARNING MECHANISM THAT SERVE TO COMPLEMENT REGULAR EDUCATION WITH MORE SPECIALIZED CONTENT. DIFFERENT LEARNING MATERIALS ARE GENERATED FROM THE SEMINARS ACTIVITY, SUCH AS DOCUMENTATION, PRESENTATIONS, VIDEOS, EXERCISES... THESE MATERIALS CAN ALSO BE USED FOR TEACHING PURPOSES IN OTHER CONTEXTS. THE MANAGEMENT AND DISTRIBUTION OF THESE MATERIALS IS NORMALLY DONE BY HAND OR STORED IN A DIGITAL REPOSITORY. THE PROBLEM WITH THESE TOOLS IS THAT THEY FACILITATE STORAGE MANAGEMENT BUT ARE NOT FOCUSED TO SUPPORT THEIR FORMATIVE APPLICATION. THIS ARTICLE DESCRIBES THE EXPERIENCE OF CREATING A WEB TOOL THAT ALLOWS TO TAKE PROFIT OF THE MATERIALS GENERATED IN A SEMINAR CALLED ZARAGOZA LINGUISTICS FOR LATER TEACHING USE. THE FUNCTIONALITY HAS BEEN IMPLEMENTED USING WEB SCRAPING TECHNIQUES AND THE YOUTUBE WEB SERVICES API.
PURPOSE THIS STUDY AIMS TO EXPLORE THE DIGITAL DIVIDE BETWEEN STUDENTS LIVING IN METROPOLITAN AND NON-METROPOLITAN AREAS IN THE ANTIOQUIA REGION OF COLOMBIA. THIS IS ACHIEVED BY COLLECTING DATA ABOUT STUDENT INTERACTIONS FROM THE MOODLE LEARNING MANAGEMENT SYSTEM (LMS), AND SUBSEQUENTLY APPLYING SUPERVISED MACHINE LEARNING MODELS TO INFER THE GAP BETWEEN STUDENTS IN METROPOLITAN AND NON-METROPOLITAN AREAS. DESIGN/METHODOLOGY/APPROACH THIS WORK USES THE WELL-ESTABLISHED CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING METHODOLOGY, WHICH COMPRISES SIX PHASES, VIZ., PROBLEM UNDERSTANDING, DATA UNDERSTANDING, DATA PREPARATION, MODELLING, EVALUATION AND IMPLEMENTATION. IN THIS CASE, STUDENT DATA WAS COLLECTED FROM THE MOODLE PLATFORM FROM THE ANTIOQUIA CAMPUS OF THE UNAD DISTANCE LEARNING UNIVERSITY. FINDINGS THE DIGITAL DIVIDE IS EVIDENT IN THE CLASSIFICATION MODEL WHEN OBSERVING DIFFERENCES IN VARIABLES SUCH AS THE NUMBER OF ACCESSES TO THE LMS, THE TOTAL TIME SPENT AND THE NUMBER OF DISTINCT IP ADDRESSES USED, AS WELL AS THE NUMBER OF SYSTEM MODIFICATION EVENTS. ORIGINALITY/VALUE THIS STUDY PROVIDES CONCLUSIONS REGARDING THE PROBLEMS STUDENTS IN VIRTUAL EDUCATION MAY FACE AS A RESULT OF THE DIGITAL DIVIDE IN COLOMBIA WHICH HAVE BECOME INCREASINGLY VISIBLE SINCE THE IMPLEMENTATION OF MACHINE LEARNING METHODOLOGIES ON LMS SUCH AS MOODLE. HOWEVER, THESE PRACTICES MAY BE REPLICATED IN ANY VIRTUAL EDUCATIONAL CONTEXT AND FURTHERMORE BE EXTENDED TO ENABLE PERSONALISATION OF VARIOUS ASPECTS OF THE MOODLE PLATFORM TO MEET THE INDIVIDUAL NEEDS OF STUDENTS.
THE DEVELOPMENT OF NETWORKS AND WEB BASED ENVIRONMENTS COMBINED WITH MODERN LEARNING THEORIES COMPOSE THE NEW AREA OF LEARNING SPACES AIMING AT ENHANCING INTERACTIVE PROCESSES BETWEEN EDUCATORS, THE NET GENERATION'S LEARNERS AND THE EDUCATIONAL CONTENT. COLLABORATION AT A DISTANCE (E-COLLABORATION) SUPPORTED BY ADVANCED LEARNING TECHNOLOGIES IS TODAY FEASIBLE AND EFFECTIVE AT A LARGE SCALE OPENING UP NEW PERSPECTIVES IN HIGHER EDUCATION. THE PAPER PRESENTS A CASE STUDY AT THE HELLENIC OPEN UNIVERSITY REGARDING THE DEVELOPMENT OF A LEARNING SEQUENCE AIMED AT ACHIEVING HIGH DEGREES OF INTERACTIONS AMONG STUDENTS, STUDENTS AND EDUCATORS, AND ALSO AT GUIDING THEM BETWEEN FACE-TO-FACE MEETINGS WHEN THEY PREPARE AN ESSAY. THE LEARNING SPACE OF THE CASE STUDY IS REALIZED BY AUTHORING SEQUENCES OF LEARNING ACTIVITIES USING AN LMS LAMS. EXTENDING THE CASE STUDY, A METHODOLOGY IS ILLUSTRATED TO AUTHOR COLLABORATIVE SEQUENCES USING LMS ADDRESSING TO OPEN UNIVERSITIES IN PARTICULAR AND ALSO TO CONVENTIONAL UNIVERSITIES IN GENERAL.
HIGH-QUALITY FEEDBACK EXERTS A CRUCIAL INFLUENCE ON LEARNING NEW SKILLS AND IT IS ONE OF THE MOST COMMON PSYCHOLOGICAL INTERVENTIONS. HOWEVER, KNOWING HOW TO DELIVER FEEDBACK EFFECTIVELY IS CHALLENGING FOR EDUCATORS IN BOTH TRADITIONAL AND ONLINE CLASSROOM ENVIRONMENTS. THIS STUDY USES PSYCHOPHYSIOLOGICAL METHODOLOGY TO INVESTIGATE ATTENTION ALLOCATION TO DIFFERENT FEEDBACK VALENCES (I.E., POSITIVE AND NEGATIVE FEEDBACK), AS THE EYE TRACKER PROVIDES ACCURATE INFORMATION ABOUT INDIVIDUALS' LOCUS OF ATTENTION WHEN THEY PROCESS FEEDBACK. WE COLLECTED LEARNING ANALYTICS VIA A BEHAVIORAL ASSESSMENT GAME AND EYE-MOVEMENT MEASURES VIA AN EYE TRACKER TO INFER UNDERGRADUATE STUDENTS' COGNITIVE PROCESSING OF FEEDBACK THAT IS ASSIGNED TO THEM AFTER COMPLETING A TASK. THE EYE MOVEMENTS OF N = 30 UNDERGRADUATES AT A UNIVERSITY IN WESTERN CANADA WERE TRACKED BY THE EYELINK 1000 PLUS EYE TRACKER WHILE THEY PLAYED POSTERLET, A DIGITAL GAME-BASED ASSESSMENT. IN POSTERLET, STUDENTS DESIGNED THREE POSTERS AND RECEIVED CRITICAL (NEGATIVE) OR CONFIRMATORY (POSITIVE) FEEDBACK FROM VIRTUAL CHARACTERS IN THE GAME AFTER COMPLETING EACH POSTER. ANALYSES SHOWED THAT, OVERALL, STUDENTS ATTENDED TO CRITICAL FEEDBACK MORE THAN TO CONFIRMATORY FEEDBACK, AS MEASURED BY THE TIME SPENT ON FEEDBACK IN TOTAL, PER WORD, AND PER LETTER, AND BY THE NUMBER OF FEEDBACK FIXATIONS AND REVISITS. HOWEVER, THERE WAS NO DIFFERENCE IN DWELL TIME BETWEEN VALENCES PRIOR TO ANY FEEDBACK REVISITS, SUGGESTING THAT RETURNING TO READ CRITICAL FEEDBACK MORE OFTEN THAN CONFIRMATORY FEEDBACK ACCOUNTS FOR THE OVERALL DWELL TIME DIFFERENCE BETWEEN VALENCES WHEN FEEDBACK IS ASSIGNED TO STUDENTS. THE STUDY SUMMARIZES THE EYE MOVEMENT RECORD ON CRITICAL AND CONFIRMATORY FEEDBACK, RESPECTIVELY. IMPLICATIONS OF THIS RESEARCH INCLUDE ENHANCING OUR UNDERSTANDING OF THE DIFFERENTIAL TEMPORAL COGNITIVE PROCESSING OF FEEDBACK VALENCES THAT MAY ULTIMATELY IMPROVE THE DELIVERY OF FEEDBACK.
TODAY'S ORGANIZATIONS, INCLUDING ONLINE BUSINESSES, USE THE ART OF DATA-DRIVEN DECISION-MAKING I.E. BUSINESS-INTELLIGENCE (BI) TO BENEFIT FROM ALL THE DATA OUT IN THE OPEN. GIVEN THE CURRENT MARKET DEMAND FOR BI SKILL-SETS, INCLUDING THE KNOWLEDGE OF DIFFERENT SOURCES AND TOOLS FOR DATA-COLLECTION PLUS PROCESSING, TODAY'S YOUTH NEED A BASIC UNDERSTANDING OF DATA-DRIVEN INTELLIGENCE, AND AN AWARENESS OF BIG-DATA RELATED ETHICS AND PRIVACY. HOWEVER, THERE HAS BEEN LIMITED RESEARCH AND DEVELOPMENT WORK TOWARDS DESIGNING AN EFFECTIVE EDUCATIONAL MODULE IN THIS REGARD AT THE K-12 LEVEL. WE INTEND TO ADDRESS THIS PARTICULAR LIMITATION BY PRESENTING A UNIQUELY ENGAGING MIDDLE-SCHOOL LEARNING MODULE BASED UPON A COMBINATION OF USEFUL TOPICS, LIKE DATA-MINING, PREDICTIVE-ANALYTICS, DATA-VISUALIZATION, BIG-DATA, ETHICS AND PRIVACY, USING THE FREE RAPIDMINER SOFTWARE-TOOL. THE NOVELTY OF OUR MODULE LIES IN THE USE OF A GUI-BASED VISUAL HANDS-ON PLATFORM (RAPIDMINER), A HOLLYWOOD MOVIE-THEME BASED EDUCATIONAL ACTIVITY, AS WELL AS AN ADDED FOCUS ON BIG-DATA ETHICS AND PRIVACY, AND ITS CONCEPTUAL MAPPING TO THE NSA-GENCYBER SECURITY-FIRST PRINCIPLES. WE DISCUSS AND ANALYZE THE SURVEY DATA OBTAINED FROM OVER HUNDRED PARTICIPANTS THROUGH SEVERAL OFFERINGS OF OUR MODULE AS AN EDUCATIONAL WORKSHOP THROUGH OUR GOOGLE-IGNITECS AND NSA-GENCYBER PROGRAMS. THE COLLECTED LEARNING-ANALYTICS DATA INDICATE THAT OUR MODULE CAN BECOME A SIMPLE YET EFFECTIVE MEANS FOR INTRODUCING DATA-MINING, BIG-DATA, ETHICAL AND PRIVACY ISSUES, AND GENCYBER SECURITY-FIRST PRINCIPLES AT THE MIDDLE-SCHOOL LEVEL. OUR RESULTS SHOW PROSPECTS OF MOTIVATING MIDDLE-SCHOOL PARTICIPANTS TOWARDS FURTHER LEARNING OF TOPICS IN DATA-SCIENCE, DATA-ETHICS AND DATA-SECURITY, WHICH IS NECESSARY TODAY IN A VARIETY OF PROFESSIONS.
THIS PAPER IS A SURVEY OF THE AVAILABILITY OF OPEN DATA SETS GENERATED FROM MASSIVELY OPEN ONLINE COURSES (MOOCS). THIS LOG DATA ALLOWS RESEARCHERS TO ANALYZE AND PREDICT STUDENT PERFORMANCE. OFTEN, THE GOAL OF THE ANALYSIS IS TO FOCUS ON AT-RISK STUDENTS WHO ARE NOT LIKELY TO FINISH A COURSE. THERE IS A GROWING GAP BETWEEN THE AVERAGE RESEARCHER (WHO DOES NOT HAVE ACCESS TO PROPRIETARY DATA) AND THE READY AVAILABILITY OF DATA SETS FOR ANALYSIS. MOST RESEARCH PAPERS STUDYING AND PREDICTING STUDENT PERTBRMANCE IN MOOCS ARE DONE ON PROPRIETARY DATA SETS THAT ARE NOT ANONYMIZED (DEIDENTIFIED) OR RELEASED FOR GENERAL STUDY. THERE ARE NO STANDARDIZED TOOLS THAT PROVIDE A GATEWAY TO ACCESS USABLE DATA SETS; INSTEAD, THE RESEARCHER MUST NAVIGATE A MAZE OF SITES WITH DIFFERENT DATA STRUCTURES AND VARYING DATA ACCESS POLICIES. TO OUR KNOWLEDGE, NO OPEN DATA SETS ARE BEING PRODUCED, AND HAVE NOT BEEN SINCE 2016. THE AUTHORS SURVEY THE HISTORY OF MOOC DATA SHARING, IDENTIFY THE FEW AVAILABLE OPEN DATA SETS, AND DISCUSS A PATH FORWARD TO INCREASE THE REPRODUCIBILITY OF MOOC RESEARCH.
OVER THE LAST 10 YEARS, AS A RESULT OF THE INCREASED COMMUNICATION AND COLLABORATION FACILITIES OFFERED BY THE WEB, THERE HAS BEEN A PRONOUNCED WORLDWIDE TENDENCY TOWARDS MAKING SCIENTIFIC RESEARCH AND ITS OUTCOMES ACCESSIBLE TO SOCIETY. WE REFER HERE TO THE EASY ACCESS FOR BOTH RESEARCHERS AND THE LARGE PUBLIC TO RESEARCH RESULTS IN VARIOUS TOP-LEVEL DOMAINS SUCH AS BIO- OR NANOTECHNOLOGIES, DRUG DESIGN OR ASTROPHYSICS, AS WELL AS TO THE INVOLVEMENT OF NON-SPECIALISTS IN DIFFERENT MAJOR RESEARCH PROJECTS IN THESE DOMAINS AT THE SAME TIME, WE OBSERVE AN EXPONENTIAL GROWTH OF OPEN EDUCATIONAL RESOURCES (OER), ONLINE COURSES, MOOCS, BOOKS AND OTHER MATERIALS ORGANIZED IN DIGITAL LIBRARIES, ENCYCLOPAEDIAS, GAMES AND MULTIMEDIA APPLICATIONS, AS WELL AS ATTEMPTS TO BUILD A SUSTAINABLE INFRASTRUCTURE TO ACHIEVE OPEN EDUCATION THROUGH NEW PEDAGOGIES SUCH AS OPEN CORE COURSEWARE, WEB 2.0 APPROACHES, BLENDED LEARNING AND LEARNING ANALYTICS. IF, UNTIL RECENTLY, THE ACCENT WAS SET ON OERS, IN THE LAST YEARS THE FOCUS SHIFTED TOWARDS THE OPEN EDUCATIONAL PRACTICE (OEP), I.E. THE WAY IN WHICH OERS CAN BE USED FOR TEACHING AND LEARNING IN ORDER TO INNOVATE THE LEARNING PROCESS. IN THE LONG RUN, THE SUSTAINABILITY OF OPEN EDUCATION (OE) AND/OR OPEN SCIENCE (OS) IS UNDOUBTEDLY ENSURED BY A CULTURAL AND/OR BEHAVIOURAL CHANGE OF SOCIETY'S MEMBERS, ESPECIALLY OF THE CURRENT YOUNG GENERATION WHICH WILL BE THE RESEARCHERS OR NON-SPECIALIZED PRODUCERS/CONSUMERS OF SCIENCE IN THE FUTURE. IN THIS PAPER, WE HIGHLIGHT HOW WE CAN EDUCATE STUDENTS/FUTURE TEACHERS FOR A WORLD OF OPEN SCIENCE AND OPEN EDUCATION, BOTH THROUGH THE USE/REUSE/CREATE OF OERS AND THE USE OF OEP, AS WELL AS RAISING AWARENESS OF THE IMPACT OF OPENNESS ON THE DEVELOPMENT OF SCIENCE AND THE WAY IT IS ABSORBED BY SOCIETY.
THE INTERACTION WITH THE VARIOUS LEARNERS IN A MASSIVE OPEN ONLINE COURSE (MOOC) IS OFTEN COMPLEX. CONTEMPORARY MOOC LEARNING ANALYTICS RELATE WITH CLICK-STREAMS, KEYSTROKES AND OTHER USER-INPUT VARIABLES. SUCH VARIABLES HOWEVER, DO NOT ALWAYS CAPTURE USERS' LEARNING AND BEHAVIOR (E.G., PASSIVE VIDEO WATCHING). IN THIS PAPER, WE PRESENT A STUDY WITH 40 STUDENTS WHO WATCHED A MOOC LECTURE WHILE THEIR EYE-MOVEMENTS WERE BEING RECORDED. WE THEN PROPOSED A METHOD TO DEFINE STIMULI-BASED GAZE VARIABLES THAT CAN BE USED FOR ANY KIND OF STIMULUS. THE PROPOSED STIMULI-BASED GAZE VARIABLES INDICATE STUDENTS' CONTENT-COVERAGE (IN SPACE AND TIME) AND READING PROCESSES (AREA OF INTEREST BASED VARIABLES) AND ATTENTION (I.E., WITH-ME-NESS), AT THE PERCEPTUAL (FOLLOWING TEACHER'S DEICTIC ACTS) AND CONCEPTUAL LEVELS (FOLLOWING TEACHER DISCOURSE). IN OUR EXPERIMENT, WE IDENTIFIED A SIGNIFICANT MEDIATION EFFECT OF THE CONTENT COVERAGE, READING PATTERNS AND THE TWO LEVELS OF WITH-ME-NESS ON THE RELATION BETWEEN STUDENTS' MOTIVATION AND THEIR LEARNING PERFORMANCE. SUCH VARIABLES ENABLE COMMON MEASUREMENTS FOR THE DIFFERENT KIND OF STIMULI PRESENT IN DISTINCT MOOCS. OUR LONG-TERM GOAL IS TO CREATE STUDENT PROFILES BASED ON THEIR PERFORMANCE AND LEARNING STRATEGY USING STIMULI-BASED GAZE VARIABLES AND TO PROVIDE STUDENTS GAZE-AWARE FEEDBACK TO IMPROVE OVERALL LEARNING PROCESS. ONE KEY INGREDIENT IN THE PROCESS OF ACHIEVING A HIGH LEVEL OF ADAPTATION IN PROVIDING GAZE-AWARE FEEDBACK TO THE STUDENTS IS TO USE ARTIFICIAL INTELLIGENCE (AI) ALGORITHMS FOR PREDICTION OF STUDENT PERFORMANCE FROM THEIR BEHAVIOUR. IN THIS CONTRIBUTION, WE ALSO PRESENT A METHOD COMBINING STATE-OF-THE-ART AI TECHNIQUE WITH THE EYE-TRACKING DATA TO PREDICT STUDENT PERFORMANCE. THE RESULTS SHOW THAT THE STUDENT PERFORMANCE CAN BE PREDICTED WITH AN ERROR OF LESS THAN 5%.
PURPOSE - RECENTLY, A LAYERED AND LATERAL FRAMEWORK THAT PROVIDES OPPORTUNITIES FOR STUDENTS TO DESIGN THE PEDAGOGY THAT CREATES A BROAD BASED, FLEXIBLE AND PERSONALIZED LEARNING IS INTRODUCED BY THE TAYLOR'S UNIVERSITY SCHOOL OF ENGINEERING. THIS APPROACH CREATES AVENUE FOR THE STUDENTS TO DESIGN THEIR OWN CURRICULUM WITHIN THE FRAMEWORK THAT IS DEVELOPED WITH INPUTS FROM THE INDUSTRY. THE FRAMEWORK, THE KEY COMPONENTS, AND THE TRACKING OF THE PERFORMANCE TO ALIGN THE SET OUTCOMES THROUGH ASSESSMENT TOOL IS PRESENTED. METHODOLOGY - MY STUDY PLAN (MYSP) A MACRO-ENABLED SOFTWARE IS USED TO DEVELOP A FOUR-YEAR TIER MODEL FOR THE STUDENTS TO DESIGN THE CURRICULUM WITH THE HELP OF A MENTOR AT THE START OF THE PROGRAMME. STUDENT'S LEARNING IS MONITORED ON A SEMESTER-BASED LEARNING ANALYTICS PROCESS IMPLEMENTING THE PROGRAMME OUTCOMES (PO) SCORECARD. THE AUGUST 2018 COHORT OF THE ELECTRICAL AND ELECTRONIC ENGINEERING PROGRAMME IS USED TO PILOT THIS PEDAGOGY. FINDINGS - CURRENTLY THE PO MEASUREMENT IS DONE FOR THE GRADUATING STUDENT AND FOCUS ONLY ON THE EFFECTIVENESS OF THE PROGRAMME. THE PROGRAMME OUTCOME ASSESSMENT REVIEW (POAS) USED IN THIS STUDY TO MONITOR THE MYSP THE CONTINUAL MONITORING EACH SEMESTER RATHER TOWARDS THE END OF GRADUATION. INITIAL SIMULATION OF EXISTING PO ATTAINMENT RECORD OF STUDENTS SUGGEST THAT THIS CAN GENERATE INTEREST TOWARDS THE LEARNING PROGRESS ON A SEMESTER BASIS. SIGNIFICANCE - THE SUCCESSFUL ADOPTION SEES AN EFFECTIVENESS IN THE LONG RUN IN DEVELOPING GRADUATES THROUGH THE CONTINUAL MONITORING OF THE PROGRAMME OUTCOMES. THE GRADUATING STUDENTS CARRY THE PO SCORECARD THAT COMPLEMENTS A SECOND TRANSCRIPT FOR VALUE ADDITION FOR THEIR STUDIES. PROSPECTIVE EMPLOYERS CAN USE THE PO SCORECARD AS A METRIC FOR INITIAL RECRUITMENT PURPOSES, A MEASURE FOR THE SOFT SKILLS ACHIEVEMENTS OF THE APPLICANT.
THERE IS A STRONG NEED TO MAKE RESEARCH MORE MOREOVER EFFICIENT RESULTING IN MORE IMPACTFUL OUTCOMES, ACROSS THE WORLD AND MORE SO IN DEVELOPING COUNTRIES. TO IMPROVE RESEARCH OUTCOMES AND IMPACT ON PRACTITIONERS, EFFECTIVENESS OF STUDENT RESEARCHERS (SRS), RESEARCH SUPERVISORS (RSS) AND COMMUNICATION OF RESEARCH FINDINGS NEED IMPROVEMENT. THE STUDY ATTEMPTS TO FIND OUT 1. CAN LEAN HELP IMPROVING THESE ELEMENTS? 2. IF SO, WHAT KIND OF CUSTOMIZATION IS NEEDED TO MAKE IT: ACCEPTABLE TO THE SRS AND RSS, MEASURABLE AND SUITABLE FOR THE FIELD OF RESEARCH? 3. HOW CAN 'DATA ANALYTICS ORIENTED LEAN RESEARCH MANAGEMENT PROCESSES' HELP IMPROVING THE EFFICIENCY? 4. IS THERE A BIDIRECTIONAL INFLUENCE BETWEEN FOLLOWING LEAN AND BEING "SELF-REGULATED"? CONSIDERING THE NUMEROUS CONFOUNDING VARIABLES AND ETHICAL CONSIDERATIONS, IDENTIFYING SRS FOR TREATMENT GROUP(S) AND CONTROL GROUP CAN BE CHALLENGING. TO ADDRESS THESE CHALLENGES EFFECTIVELY, "PROPENSITY SCORE ANALYSIS" WILL BE USED. RESEARCH METHODS (RM) EDUCATIONAL PLATFORM THAT A) FACILITATES AND ENHANCES PEDAGOGICAL APPROACH / PROCESSES IN IMPROVING RESEARCH COMPETENCES B) PROVIDES ASSOCIATED TECHNICAL PRACTICES, ANALYTICAL SOLUTIONS AND VISUALIZATION HELPFUL TO SRS AND RSS C) FACILITATES AND IMPROVES RESEARCHER COMMUNICATION WITH THE COMMON PUBLIC ARE SOME OF THE OUTCOMES THIS STUDY AIMS FOR.
REFLECTIVE WRITING IS AN IMPORTANT EDUCATIONAL PRACTICE TO TRAIN REFLECTIVE THINKING. CURRENTLY, RESEARCHERS MUST MANUALLY ANALYZE THESE WRITINGS, LIMITING PRACTICE AND RESEARCH BECAUSE THE ANALYSIS IS TIME AND RESOURCE CONSUMING. THIS STUDY EVALUATES WHETHER MACHINE LEARNING CAN BE USED TO AUTOMATE THIS MANUAL ANALYSIS. THE STUDY INVESTIGATES EIGHT CATEGORIES THAT ARE OFTEN USED IN MODELS TO ASSESS REFLECTIVE WRITING, AND THE EVALUATION IS BASED ON 76 STUDENT ESSAYS (5080 SENTENCES) THAT ARE LARGELY FROM THIRD- AND SECOND-YEAR HEALTH, BUSINESS, AND ENGINEERING STUDENTS. TO TEST THE AUTOMATED ANALYSIS OF REFLECTION IN WRITINGS, MACHINE LEARNING MODELS WERE BUILT BASED ON A RANDOM SAMPLE OF 80% OF THE SENTENCES. THESE MODELS WERE THEN TESTED ON THE REMAINING 20% OF THE SENTENCES. OVERALL, THE STANDARDIZED EVALUATION SHOWS THAT FIVE OUT OF EIGHT CATEGORIES CAN BE DETECTED AUTOMATICALLY WITH SUBSTANTIAL OR ALMOST PERFECT RELIABILITY, WHILE THE OTHER THREE CATEGORIES CAN BE DETECTED WITH MODERATE RELIABILITY (COHEN'S RANGES BETWEEN .53 AND .85). THE ACCURACIES OF THE AUTOMATED ANALYSIS WERE ON AVERAGE 10% LOWER THAN THE ACCURACIES OF THE MANUAL ANALYSIS. THESE FINDINGS ENABLE REFLECTION ANALYTICS THAT IS IMMEDIATE AND SCALABLE.
THIS RESEARCH CATEGORY FULL PAPER PRESENTS AN APPROACH FOR CATEGORIZING STUDENT MISCONCEPTIONS ABOUT DYNAMICS AND HEAT TRANSFER USING TEXT CLASSIFICATION. RESEARCH IN EDUCATIONAL ENGINEERING DESCRIBES HOW SCIENCE CONCEPTS CAN BE ONTOLOGICALLY CATEGORIZED INTO TWO MAJOR CATEGORIES (SUBSTANCES AND PROCESSES) ACCORDING TO THEIR NATURE. STUDENTS CAN ACQUIRE MISCONCEPTIONS BY INCORRECTLY CATEGORIZING CONCEPTS. PREDICATE TESTS HELP TO DETERMINE WHEN MISCONCEPTIONS HAVE OCCURRED AND AID IN CUSTOMIZING CURRICULUM CONTENT. HOWEVER PREDICATE TESTS RELY ON TIME-CONSUMING, MANUAL LABOR. THE MAIN GOAL OF THIS RESEARCH WAS TO SHOW HOW PREDICATE TESTS COULD BE AUTOMATED WITH TEXT CLASSIFICATION MODELS USING A PREVIOUSLY ANNOTATED DATASET. WE COMPARED CLASSIFIER PERFORMANCE BETWEEN WEKA'S SUPPORT VECTOR MACHINES, MULTINOMIAL NAIVE BAYES, LOGISTIC REGRESSION, AND BAYESIAN LOGISTIC REGRESSION IMPLEMENTATIONS USING THE EMERGENT PROCESS AND SEQUENTIAL PROCESS ONTOLOGICAL CATEGORIES AS LABELS. WE COMPARED MODEL PERFORMANCE USING WEKA'S N-GRAM TOKENIZER WITH 3-GRAMS VS. USING JAVA'S WORDTOKENIZER TO CONVERT OUR WORD DATASET TO NUMERICAL VECTORS. MODELS WERE EVALUATED USING 10-FOLD CROSS-VALIDATION CONSIDERING ACCURACY, F-MEASURE, AND KAPPA COEFFICIENT AS MEASURES OF PERFORMANCE. WE HAVE SHOWN THE FEASIBILITY OF USING TEXT CLASSIFICATION FOR MISCONCEPTION ASSESSMENT. OUR IMPLEMENTATION OF PREDICATE TEST AUTOMATION CAN PLAY AN IMPORTANT ROLE IN SPEEDING UP MISCONCEPTION ASSESSMENT RESEARCH AND CURRICULUM DESIGN RESEARCH.
CLASSIFYING THE COGNITIVE LEVELS OF ASSESSMENT QUESTIONS ACCORDING TO BLOOM'S TAXONOMY CAN HELP INSTRUCTORS DESIGN EFFECTIVE ASSESSMENTS THAT ARE WELL ALIGNED WITH THE INTENDED LEARNING OUTCOMES. HOWEVER, THE CLASSIFICATION PROCESS IS TIME CONSUMING AND REQUIRES EXPERIENCE. MANY STUDIES HAVE ATTEMPTED TO AUTOMATE THE PROCESS BY UTILIZING DIFFERENT MACHINE LEARNING AND TEXT MINING APPROACHES, BUT NONE HAS EXAMINED THE CLASSIFICATION OF ARABIC QUESTIONS. THE PURPOSE OF THIS STUDY IS TO EXAMINE THIS RESEARCH GAP AND TO INTRODUCE A NEW FEATURE EXTRACTION METHOD THAT WOULD BETTER SUIT ARABIC QUESTIONS AND THEIR UNIQUE CHARACTERISTICS. IT ALSO AIMS TO PROVIDE ARAB INSTRUCTORS WITH A TOOL THAT CAN HELP THEM AUTOMATICALLY CLASSIFY THEIR ASSESSMENT QUESTIONS. TO ACCOMPLISH THIS PURPOSE, THE STUDY DEVELOPED A DATASET OF MORE THAN 600 ARABIC ASSESSMENT QUESTIONS. IT THEN PROPOSED A MODIFIED TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) METHOD FOR EXTRACTING FEATURES FROM ARABIC QUESTIONS. UNLIKE THE TRADITIONAL TF-IDF, THE PROPOSED METHOD WAS DESIGNED TO TAKE THE NATURE OF ASSESSMENT QUESTIONS INTO CONSIDERATION. IT WAS EVALUATED BY COMPARING IT TO TWO METHODS THAT HAVE BEEN USED FOR CLASSIFYING ENGLISH QUESTIONS, I.E., THE TRADITIONAL TF-IDF AND A MODIFIED TF-IDF METHOD CALLED TERM FREQUENCY PART-OF-SPEECH-INVERSE DOCUMENT FREQUENCY (TFPOS-IDF). A T-TEST WAS UTILIZED TO EXAMINE WHETHER THE DIFFERENCE IN PERFORMANCE BETWEEN THE THREE METHODS WAS STATISTICALLY SIGNIFICANT. THE PROPOSED METHOD OUTPERFORMED THE TWO OTHER METHODS. THE OVERALL ACCURACY, PRECISION, AND RECALL SCORED BY THE PROPOSED METHOD WERE SIGNIFICANTLY HIGHER THAN THOSE SCORED BY THE TRADITIONAL TF-IDF AND TFPOS-IDF METHODS. THE EVALUATION RESULTS INDICATE THE PROMISING POTENTIAL OF THE PROPOSED METHOD, WHICH CAN BE EXTENDED TO OTHER LANGUAGES.
THIS ARTICLE INVESTIGATES THE IMPACT OF EDUCATIONAL CONTEXTS ON AUTOMATIC CLASSIFICATION OF ONLINE DISCUSSION MESSAGES ACCORDING TO COGNITIVE PRESENCE, AN ESSENTIAL CONSTRUCT OF THE COMMUNITY OF INQUIRY MODEL. IN PARTICULAR, THE WORK REPORTED IN THE ARTICLE ANALYZED ONLINE DISCUSSION MESSAGES WRITTEN IN BRAZILIAN PORTUGUESE FROM TWO DIFFERENT COURSES THAT WERE FROM DIFFERENT SUBJECT AREAS (BIOLOGY AND TECHNOLOGY) AND HAD DIFFERENT TEACHING PRESENCE IN THE ONLINE DISCUSSIONS. THE STUDY EXPLORED A SET OF 127 FEATURES OF ONLINE DISCUSSION MESSAGES AND A RANDOM FOREST CLASSIFIER TO AUTOMATICALLY RECOGNIZE THE PHASES OF THE COGNITIVE PRESENCE IN ONLINE DISCUSSION MESSAGES. THE RESULTS SHOWED THAT THE CLASSIFIER ACHIEVED BETTER PERFORMANCE WHEN APPLIED TO THE ENTIRE DATASET. IT REVEALS THAT WHEN A CLASSIFIER IS CREATED FOR A SPECIFIC COURSE IT IS NOT GENERIC ENOUGH TO BE APPLIED TO A COURSE FROM A DIFFERENT FIELD OF KNOWLEDGE. THE RESULTS ALSO SHOWED THE IMPORTANCE OF THE FEATURES THAT WERE PREDICTIVE OF THE PHASES OF THE COGNITIVE PRESENCE IN THE EDUCATIONAL CONTEXT. BASED ON THE FINDINGS OF THIS STUDY, FUTURE WORK SHOULD ADOPT THE SAME FEATURE SET AS USED IN THE CURRENT STUDY, BUT IT SHOULD TRAIN THE CLASSIFIER OF THE COGNITIVE PRESENCE ON DATASETS IN SUBJECT AREAS RELATED TO THE TOPIC OF THE DISCUSSIONS.
THIS CHAPTER DEFINES ANALYTICS AND TRACES ITS EVOLUTION FROM ITS ORIGIN IN 1988 TO ITS CURRENT STAGE-COGNITIVE ANALYTICS. WE DISCUSS TYPES OF LEARNING AND DESCRIBE CLASSES OF MACHINE LEARNING ALGORITHMS. GIVEN THIS BACKDROP, WE PROPOSE A REFERENCE ARCHITECTURE FOR COGNITIVE ANALYTICS AND INDICATE WAYS TO IMPLEMENT THE ARCHITECTURE. A FEW COGNITIVE ANALYTICS APPLICATIONS ARE BRIEFLY DESCRIBED. THE CHAPTER CONCLUDES BY INDICATING CURRENT TRENDS AND FUTURE RESEARCH DIRECTION.
A CHALLENGE THAT COURSE AUTHORS FACE WHEN REVIEWING THEIR CONTENTS IS TO DETECT HOW TO IMPROVE THEIR COURSES IN ORDER TO MEET THE EXPECTATIONS OF THEIR LEARNERS. IN THIS PAPER, WE PROPOSE AN ANALYTICAL APPROACH THAT EXPLOITS LEARNERS' LOGS OF READING TO PROVIDE AUTHORS WITH INSIGHTFUL DATA ABOUT THE CONSUMPTION OF THEIR COURSES. WE FIRST MODEL READING ACTIVITY USING THE CONCEPT OF READING-SESSION AND PROPOSE A NEW AND EFFICIENT SESSION IDENTIFICATION. WE THEN ELABORATE A LIST OF INDICATORS COMPUTED USING LEARNERS' READING SESSIONS THAT ALLOW TO REPRESENT THEIR BEHAVIOUR AND TO INFER THEIR NEEDS. WE EVALUATE OUR PROPOSALS WITH COURSE AUTHORS AND LEARNERS USING LOGS FROM A MAJOR E-LEARNING PLATFORM. INTERESTING RESULTS WERE FOUND. THIS DEMONSTRATES THE EFFECTIVENESS OF THE APPROACH IN IDENTIFYING ASPECTS AND PARTS OF A COURSE THAT MAY PREVENT IT FROM BEING EASILY READ AND UNDERSTOOD, AND FOR GUIDING THE AUTHORS THROUGH THE ANALYSIS AND REVIEW TASKS.
E-LEARNING ENVIRONMENTS CAN STORE HUGE AMOUNTS OF DATA ON THE INTERACTION OF LEARNERS WITH THE CONTENT, ASSESSMENT AND DISCUSSION. YET, AFTER THE IDENTIFICATION OF MEANINGFUL PATTERNS OR LEARNING BEHAVIOUR IN THE DATA, IT IS NECESSARY TO USE THESE PATTERNS TO IMPROVE LEARNING ENVIRONMENTS. IT IS NOTABLE THAT DESIGNS TO BENEFIT FROM THESE PATTERNS HAVE BEEN DEVELOPED PARTICULARLY WITH THE USE OF EDUCATIONAL DATA MINING AND LEARNING ANALYTICS IN THE RECENT TIMES. ON THE OTHER HAND, MULTI-CRITERIA DECISION-MAKING METHODS PROVIDE OPPORTUNITIES TO RESEARCHERS TO DISCOVER AND USE THE PATTERNS IN THE DATA OBTAINED FROM LEARNING ENVIRONMENTS. THIS STUDY SEEKS TO DISCOVER THE PATTERNS IN THE INTERACTION DATA GATHERED FROM E-LEARNING ENVIRONMENTS. IN THIS CONTEXT, THE RESEARCH HAS TWO MAIN OBJECTIVES. FIRSTLY, IT AIMS TO UTILIZE THE ELECTRE TRI METHOD, WHICH IS ONE OF THE MULTI-CRITERIA DECISION-MAKING METHODS DESIGNED TO CLASSIFY THE LEARNERS BASED ON THE INTERACTION DATA IN DIFFERENT UNITS. SECONDLY, IT AIMS TO ANALYSE THE RELATIONSHIP BETWEEN THE CLASSIFICATION BASED ON THE ELECTRE TRI METHOD AND THE CLASSIFICATION IN THE REAL LIFE. TO THAT END, TWO DIFFERENT INTERACTION DATA SETS OBTAINED FROM LEARNING MANAGEMENT SYSTEMS AT DIFFERENT TIMES ARE USED IN THIS STUDY. THE FIRST DATA SET CONSISTS OF THE DATA ON 11 CRITERIA AND 78 STUDENTS WHEREAS THE SECOND DATA SET CONSISTS OF THE DATA ON 25 CRITERIA AND 65 STUDENTS. THREE DIFFERENT CATEGORIES ARE IDENTIFIED IN THE FIRST DATA SET BY THE ELECTRE TRI METHOD. BASED ON THIS FINDING, THE CLASSIFICATION IN THE ELECTRE TRI METHOD IS COMPARED TO THE REAL-LIFE CLASSIFICATION, WHICH SHOWS A MEDIUM-LEVEL CORRELATION. TWO DIFFERENT CATEGORIES ARE IDENTIFIED IN THE SECOND DATA SET. THERE IS A MEDIUM-LEVEL CORRELATION BETWEEN THESE CATEGORIES AND THE REAL-LIFE CLASSIFICATION AS WELL. IN CONCLUSION, THIS STUDY PRESENTS DISCUSSIONS ON THE USE OF MULTI-CRITERIA DECISION-MAKING METHODS TO IMPROVE E-LEARNING ENVIRONMENTS.
THE AUTOMATIC DETECTION OF DESIGN SMELLS HAS EVOLVED IN PARALLEL TO THE EVOLUTION OF AUTOMATIC REFACTORING TOOLS. THERE WAS A HUGE RISE IN RESEARCH ACTIVITY REGARDING DESIGN SMELL DETECTION FROM 2010 TO THE PRESENT. HOWEVER, IT SHOULD BE NOTED THAT THE ADOPTION OF DESIGN SMELL DETECTION IN REAL SOFTWARE DEVELOPMENT PRACTICE IS NOT COMPARABLE TO THE ADOPTION OF AUTOMATIC REFACTORING TOOLS. ON THE BASIS OF THE ASSUMPTION THAT IT IS THE OBJECTIVENESS OF A REFACTORING OPERATION AS OPPOSED TO THE SUBJECTIVITY IN DEFINITION AND IDENTIFICATION OF DESIGN SMELLS THAT MAKES THE DIFFERENCE, IN THIS PAPER, THE LACK OF AGREEMENT BETWEEN DIFFERENT EVALUATORS WHEN DETECTING DESIGN SMELLS IS EMPIRICALLY STUDIED. TO DO SO, A SERIES OF EXPERIMENTS AND STUDIES WERE DESIGNED AND CONDUCTED TO ANALYSE THE CONCORDANCE IN DESIGN SMELL DETECTION OF DIFFERENT PERSONS AND TOOLS, INCLUDING A COMPARISON BETWEEN THEM. THIS WORK FOCUSES ON TWO WELL KNOWN DESIGN SMELLS: GOD CLASS AND FEATURE ENVY. CONCORDANCE ANALYSIS IS BASED ON THE KAPPA STATISTIC FOR INTER-RATER AGREEMENT (PARTICULARLY KAPPA-FLEISS). THE RESULTS OBTAINED SHOW THAT THERE IS NO AGREEMENT IN DETECTION IN GENERAL, AND, IN THOSE CASES WHERE A CERTAIN AGREEMENT APPEARS, IT IS CONSIDERED TO BE A FAIR OR POOR DEGREE OF AGREEMENT, ACCORDING TO A KAPPA-FLEISS INTERPRETATION SCALE. THIS SEEMS TO CONFIRM THAT THERE IS A SUBJECTIVE COMPONENT WHICH MAKES THE RATERS EVALUATE THE PRESENCE OF DESIGN SMELLS DIFFERENTLY. THE STUDY ALSO RAISES THE QUESTION OF A LACK OF TRAINING AND EXPERIENCE REGARDING DESIGN SMELLS.
PREDICTING THE PERFORMANCE OF STUDENTS BECOMES MORE INTERESTING DUE TO THE INCREASE IN THE NUMBER OF STUDENTS OPTING FOR ENGINEERING COURSES AND PARENTS' INCLINATION TO SEE THEIR WARDS BECOMING ENGINEERS AND GETTING PLACED IN OUTSTANDING COMPANIES WITH GOOD PACKAGES. THIS PAPER AIMS AT PERFORMANCE ANALYSIS OF STUDENTS BY APPLYING DIFFERENT MACHINE LEARNING TECHNIQUES. THE COLLECTED DATA CONTAINS THE STUDENTS' PERSONAL INFORMATION, FAMILY BACKGROUND, FRIENDS, STUDY TIME AND OTHER INFORMATION WHICH CONTAINS 45 QUESTIONS. VARIOUS CLASSIFIERS LIKE RANDOM FOREST, SVM AND LOGICAL REGRESSION WERE APPLIED ON THE COLLECTED DATA BY HARD-CODING IN PYTHON USING JUPYTER NOTEBOOK. BASED ON THE CLASSIFICATION ALGORITHMS, STATISTICS ARE GENERATED AND COMPARISON OF ALL THREE CLASSIFIERS IS MADE SO AS TO ENVISAGE THE TRUTHFULNESS AND TO DISCOVER THE FINEST ACTING ALGORITHM. RANDOM FOREST CLASSIFIER GIVES 96% ACCURACY, LOGISTIC REGRESSION GIVES 86% ACCURACY AND SVM CLASSIFIER GIVES 84% ACCURACY. SO, THE BEST RESULT IS SHOWN BY RANDOM FOREST CLASSIFIER.
NATIONAL GUIDELINES ADVOCATE FOR A MORE SOPHISTICATED STEM EDUCATION THAT INTEGRATES COMPLEX AND AUTHENTIC SCIENTIFIC PRACTICES, E.G., EXPERIMENTATION, DATA COLLECTION, DATA ANALYSIS, AND MODELING. HOW TO ACHIEVE THAT IS CURRENTLY UNCLEAR FOR BOTH PRESENTIAL AND DISTANCE EDUCATION. WE RECENTLY DEVELOPED A SCALABLE CLOUD LAB THAT ENABLES MANY ONLINE USERS TO PERFORM PHOTOTAXIS EXPERIMENT WITH REAL, LIVING EUGLENA CELLS (OPPOSED TO JUST SIMULATIONS). HERE WE ITERATIVELY DESIGNED AND DEPLOYED AN OPEN COURSE ON THE EDX PLATFORM INCLUDING SUITABLE USER INTERFACES THAT FACILITATES INQUIRY-BASED LEARNING ON THIS CLOUD LAB: ONLINE STUDENTS (>300) RUN REAL EXPERIMENTS (>2,300), PERFORMED DATA ANALYSIS, EXPLORED MODELS, AND EVEN FORMULATED AND EXPERIMENTALLY TESTED THEIR OWN HYPOTHESES. PLATFORM AND COURSE CONTENT ARE NOW SUITED FOR GLOBAL ADAPTATION IN FORMAL K-16 EDUCATION. WE WILL DEMO OUR CLOUD LAB AT THE CONFERENCE.
ONLINE LEARNING ENVIRONMENTS (OLE) INCLUDING LEARNING MANAGEMENT SYSTEMS (LMS) AND MASSIVE OPEN ONLINE COURSES (MOOCS) ARE GAINING POPULARITY AS THE BEST MODERN ALTERNATE SOLUTIONS AVAILABLE FOR EDUCATION IN THE CURRENT ERA. THE LUXURY TO LEARN IRRESPECTIVE OF GEOGRAPHICAL AND TEMPORAL RESTRICTIONS MAKES IT AN ATTRACTIVE RESOURCE. AT THE START OF 2020, THE GLOBAL PANDEMIC ENFORCED SOCIAL DISTANCE PRACTICE WORLDWIDE, CHANGING THE WORK ENVIRONMENT DYNAMICS, LEAVING OPTIONS LIKE ONLINE TRADING, WORK FROM HOME, AND ONLINE EDUCATION. ONLINE LEARNING ENVIRONMENTS GAINED PARTICULAR ATTENTION IN THE EDUCATIONAL SECTOR, WHERE USERS COULD ACCESS THE ONLINE LEARNING RESOURCES TO FULFIL THEIR ACADEMIC REQUIREMENTS DURING THE LOCKDOWN. FROM MASSIVELY AVAILABLE CONTENT SUCH AS MOOC, LEARNERS ARE OVERWHELMED WITH THE AVAILABLE CHOICES. IN THIS SCENARIO, RECOMMENDER SYSTEMS (RS) COME TO THE RESCUE TO HELP THE LEARNER MAKE APPROPRIATE CHOICES FOR COMPLETING THE ENROLLED COURSE. THERE IS TREMENDOUS SCOPE AND A MULTITUDE OF OPPORTUNITIES AVAILABLE FOR RESEARCHERS TO FOCUS ON THIS DOMAIN. AN EXHAUSTIVE ANALYSIS IS REQUIRED TO SPOTLIGHT THE OPPORTUNITIES IN THIS REALM. VARIOUS STUDIES HAVE BEEN PERFORMED TO PROVIDE SUCH SOLUTIONS IN MULTIPLE AREAS OF THE MOOC RECOMMENDATION SYSTEMS (MOOCRS) SUCH AS COURSE RECOMMENDATION, LEARNER PEER RECOMMENDATION, RESOURCE RECOMMENDATIONS, TO NAME A FEW. THIS IS A COMPENDIOUS STUDY INTO THE RESEARCH CONDUCTED IN THIS AREA, IDENTIFYING 670 ARTICLES OUT OF 116 SELECTED FOR ANALYSIS PUBLISHED FROM 2013 TO 2021. IT ALSO HIGHLIGHTS MULTIPLE AREAS IN MOOC, WHERE THE RECOMMENDATION IS REQUIRED, AS WELL AS TECHNOLOGIES USED BY OTHER RESEARCHERS TO PROVIDE SOLUTIONS OVER TIME.
STATE-OF-THE-ART SCENARIOS, SUCH AS INTERNET OF THINGS (IOT) AND SMART CITIES, HAVE RECENTLY ARISEN. THEY INVOLVE THE PROCESSING OF HUGE DATA SETS UNDER STRICT TIME REQUIREMENTS, RENDERING THE USE OF CLOUD RESOURCES UNFEASIBLE. FOR THIS REASON, FOG COMPUTING HAS BEEN PROPOSED AS A SOLUTION; HOWEVER, THERE REMAINS A NEED FOR INTELLIGENT ALLOCATION DECISIONS, IN ORDER TO MAKE IT A FULLY USABLE SOLUTION IN SUCH CONTEXTS. IN THIS PAPER, A NETWORK-AWARE SCHEDULING ALGORITHM IS PRESENTED, WHICH AIMS TO SELECT THE FOG NODE MOST SUITABLE FOR THE EXECUTION OF AN APPLICATION WITHIN A GIVEN DEADLINE. THIS DECISION IS MADE TAKING THE STATUS OF THE NETWORK INTO ACCOUNT. THIS SCHEDULING ALGORITHM WAS IMPLEMENTED AS AN EXTENSION TO THE KUBERNETES DEFAULT SCHEDULER, AND COMPARED WITH EXISTING PROPOSALS IN THE LITERATURE. THE COMPARISON SHOWS THAT OUR PROPOSAL IS THE ONLY ONE THAT CAN EXECUTE ALL THE SUBMITTED JOBS WITHIN THEIR DEADLINES (I.E., NO JOB IS REJECTED OR EXECUTED EXCEEDING ITS DEADLINE) WITH CERTAIN CONFIGURATIONS IN SOME OF THE SCENARIOS TESTED, THUS OBTAINING AN OPTIMAL SOLUTION IN SUCH SCENARIOS.
HIGHER EDUCATIONAL INSTITUTES (HEI) ARE ADOPTING UBIQUITOUS AND SMART EQUIPMENT SUCH AS MOBILE DEVICES OR DIGITAL GADGETS TO DELIVER EDUCATIONAL CONTENT IN A MORE EFFECTIVE MANNER THAN THE TRADITIONAL APPROACHES. IN PRESENT WORKS, A LOT OF SMART CLASSROOM APPROACHES HAVE BEEN DEVELOPED, HOWEVER, THE STUDENT LEARNING EXPERIENCE IS NOT YET FULLY EXPLORED. MOREOVER, MODULE HISTORICAL DATA OVER TIME IS NOT CONSIDERED WHICH COULD PROVIDE INSIGHT INTO THE POSSIBLE OUTCOMES IN THE FUTURE, LEADING NEW IMPROVEMENTS AND WORKING AS AN EARLY DETECTION METHOD FOR THE FUTURE RESULTS WITHIN THE MODULE. THIS PAPER PROPOSES A FRAMEWORK BY TAKING INTO ACCOUNT MODULE HISTORICAL DATA IN ORDER TO PREDICT MODULE PERFORMANCE, PARTICULARLY THE MODULE RESULT BEFORE THE COMMENCEMENT OF CLASSES WITH THE GOAL OF IMPROVING MODULE PASS PERCENTAGE. FURTHERMORE, A VIDEO STREAMING SERVER ALONG WITH BLENDED LEARNING ARE SEQUENTIALLY INTEGRATED WITH THE DESIGNED FRAMEWORK TO ENSURE CORRECTNESS OF TEACHING AND LEARNING PEDAGOGY. SIMULATION RESULTS DEMONSTRATE THAT BY CONSIDERING MODULE HISTORICAL DATA USING TIME SERIES FORECASTING HELPS IN IMPROVING MODULE PERFORMANCE IN TERMS OF MODULE DELIVERY AND RESULT OUTCOME IN TERMS OF PASS PERCENTAGE. FURTHERMORE, THE PROPOSED FRAMEWORK PROVIDES A MECHANISM FOR FACULTIES TO ADJUST THEIR TEACHING STYLE ACCORDING TO STUDENT PERFORMANCE LEVEL TO MINIMIZE THE STUDENT FAILURE RATE.
SERIOUS GAMES (SG) HAVE BEEN SHOWN TO HAVE INSTRUCTIONAL POTENTIAL AND A NUMBER OF FORMAL MODELS, FRAMEWORKS AND METHODOLOGIES HAVE EMERGED TO SUPPORT THEIR DESIGN AND ANALYSIS. THE ACTIVITY THEORY-BASED MODEL OF SERIOUS GAMES (ATMSG) FACILITATES A SYSTEMATIC AND DETAILED REPRESENTATION OF EDUCATIONAL SG DESCRIBING HOW GAME ELEMENTS ARE CONNECTED TOGETHER TO CONTRIBUTE TO PEDAGOGICAL GOALS. THIS PAPER PROPOSES AND PRESENTS AN EXTENSION TO THE ATMSG FRAMEWORK TO FACILITATE THE IDENTIFICATION, SELECTION AND INTEGRATION OF ANALYTICS INTO SERIOUS GAMES. A PRACTICAL EXAMPLE OF THE APPROACH IN USE IN THE ANALYSIS AND DESIGN PHASE OF A SG FOR ENGINEERING IS DEMONSTRATED.
THE DEVELOPMENT OF STUDENTS' EARLY ALERT SYSTEMS (EAS), OR STUDENTS' LEARNING ALARM SYSTEMS, HAS LONG BEEN DISCUSSED BY ADMINISTRATORS AND RESEARCHERS IN HIGHER EDUCATION. THEY ARE CONSIDERED A POWERFUL MECHANISM FOR EVALUATING STUDENTS' LEARNING EFFECTIVENESS, PREDICTING POTENTIAL LEARNING DIFFICULTIES, AND IDENTIFYING ACADEMICALLY AT-RISK STUDENTS. THE RAPID GROWTH OF INFORMATION TECHNOLOGY HAS MADE THE EAS ADVANCED IN SCALE AND PRACTICE. HOWEVER, ALTHOUGH MOST UNIVERSITIES IN TAIWAN HAVE ESTABLISHED SIMILAR PREVENTIVE SYSTEMS OR MECHANISMS, FEW STUDIES HAVE FOCUSED ON EXPLORING ACTUAL USES OF AND POSSIBLE RESTRICTIONS ON EXISTING EAS. THEREFORE, THE MAIN PURPOSE OF THIS STUDY WAS TO OUTLINE THE CURRENT SITUATION OF EAS IN TAIWANESE UNIVERSITIES. WE EXPLORED THE FOLLOWING QUESTIONS: (1) WHAT TYPES OF EAS HAVE BEEN ESTABLISHED IN UNIVERSITIES IN TAIWAN? (2) WHAT ARE THE MAIN FUNCTIONS AND MECHANISMS OF THE CURRENT EAS? (3) WHAT ARE THE ACTUAL USES OF EAS IN UNIVERSITIES? (4) WHAT ARE THE POSSIBLE RESTRICTIONS ON AND SOLUTIONS FOR USING EAS? DATA WERE GATHERED FROM 56 PUBLIC AND PRIVATE UNIVERSITIES IN TAIWAN. EXPERTS FAMILIAR WITH OR IN CHARGE OF EAS DESIGN, DEVELOPMENT, AND USE IN THESE UNIVERSITIES WERE INTERVIEWED BY TELEPHONE OR FACE-TO-FACE. WE FOUND THAT ALTHOUGH THE UTILITY RATE OF EAS IN MOST UNIVERSITIES TENDED TO BE HIGH, FACULTY RESISTANCE AND DIFFICULTY IN FOLLOW-UP ASSESSMENTS ON EAS UTILIZATION WERE THE TWO COMMON RESTRICTIONS THAT OCCURRED ACROSS CAMPUSES. TO ENHANCE THE USE EFFECTIVENESS OF EAS, FACULTY AND ADMINISTRATORS FROM BOTH DEPARTMENTAL AND INSTITUTIONAL LEVELS SHOULD BE INVOLVED. IN ADDITION, THIS STUDY SUGGESTED THAT IN THE FUTURE, UNIVERSITIES MUST ATTEMPT TO STRENGTHEN COLLABORATION AMONG UNITS RELATED TO EAS, IMPROVE THE EVALUATION OF EAS EFFECTIVENESS, AND PROVIDE ADDITIONAL EARLY ALERT INDICATORS OF STUDENTS' ACADEMIC PERFORMANCE.
THIS STUDY EXAMINED THE INFLUENCE OF THE NURTURANCE OF YOUNG FACULTY ON IMPROVED JOB ENGAGEMENT AND THE MODERATING ROLE OF INSTITUTIONAL CITIZENSHIP BEHAVIOUR. THIS STUDY USED A QUANTITATIVE RESEARCH DESIGN, ADOPTING A SURVEY METHOD. YOUNG FACULTY (GRADUATE ASSISTANTS-LECTURER 1) WERE CONSIDERED FOR THIS SURVEY. A TOTAL OF 261 YOUNG FACULTY WERE DRAWN FROM ALL THE COLLEGES OF THE THREE RANDOMLY SELECTED PRIVATE UNIVERSITIES IN OGUN STATE, NIGERIA. STRUCTURAL EQUATION MODELLING, SMART PARTIAL LEAST SQUARE (3.0) WAS USED TO SHOW THE LEVEL OF RELATIONSHIPS AND THE RESULTANT EFFECT OF YOUNG FACULTY NURTURING INITIATIVES ON IMPROVED JOB ENGAGEMENT WITH THE MODERATING ROLE OF INSTITUTIONAL CITIZENSHIP BEHAVIOUR. THE FINDINGS HIGHLIGHT A SIGNIFICANT INFLUENCE OF NURTURANCE OF YOUNG FACULTY ON IMPROVED JOB ENGAGEMENT (TEACHING, RESEARCH AND COMMUNITY SERVICE), WHILE THE INSTITUTIONAL CITIZENSHIP BEHAVIOUR SIGNIFICANTLY MODERATED THE RELATIONSHIP BETWEEN NURTURANCE OF YOUNG FACULTY AND IMPROVED JOB ENGAGEMENT. BASED ON THE EMPIRICAL EVIDENCE FROM THE STUDY, IT IS CONCLUDED THAT VIRTUAL LEARNING SUPPORT INDIRECTLY INFLUENCED IMPROVED JOB ENGAGEMENT AMONG THE YOUNG SCHOLARS IN NIGERIA PRIVATE UNIVERSITIES. THIS STUDY CONTRIBUTES SIGNIFICANTLY BY FILLING A RESEARCH GAP IN STRATEGIC HUMAN RESOURCE MANAGEMENT AND ORGANISATIONAL BEHAVIOUR LITERATURE IN THE INSTITUTION OF HIGHER LEARNING. THE EMPIRICAL STUDIES ON THE RELATIONSHIP BETWEEN NYF, ICB AND IJE HAVE NOT BEEN SUFFICIENTLY RESEARCHED, PARTICULARLY WITHIN THE CONTEXT OF NIGERIA EDUCATIONAL SECTOR.
RECENT REVIEWS ADDRESSING THE IMPACT OF NOISE EXPOSURE IN TEACHING AND LEARNING SITUATIONS CONCLUDE NEGATIVE EFFECTS ON LEARNING PERFORMANCE. PROVIDING OBJECTIVE REAL-TIME FEEDBACK ON NOISE IS A KEY FOR TEACHERS AND STUDENTS TO ADJUST IT INTO SUITABLE LEVELS. THIS EXPERIMENTAL WORK PRESENTS THE RESULTS FROM A STUDY EXPLORING THE VISUAL FEEDBACK BASED ON NOISE LEVEL AND THE IMPACT ON STUDENTS' (N = 198) PERCEIVED LEARNING PERFORMANCE COLLECTED IN 24 SESSIONS. THE RESULTS SUGGEST PERSUASIVE EFFECTS OF THE AMBIENT DISPLAY ON THE GROUPS AND AN IMPROVEMENT OF NOISE AWARENESS IN STUDENTS. MEASUREMENTS OF PERCEIVED LEARNING-PERFORMANCE, AND PERCEIVED NOISE WERE COLLECTED AND CORRELATED WITH THE OBJECTIVE NOISE SAMPLES CONCLUDING POORER PERCEIVED LEARNING PERFORMANCE IN NOISIEST GROUPS. FINALLY, IMPLICATIONS FOR FURTHER RESEARCH, AS WELL AS LESSONS LEARNED TO MODERATE NOISE LEVELS IN CLASSROOMS USING AMBIENT DISPLAYS ARE DISCUSSED.
THIS PAPER DESCRIBES THE DESIGN AND VALIDATION OF A GAME BASED ON A PLATFORM FOR EASY DEPLOYMENT OF COLLABORATIVE EDUCATIONAL GAMES, NAMED BECO GAMES PLATFORM. AS AN EXAMPLE OF ITS POTENTIAL, A LEARNING EXPERIENCE FOR AN ECONOMICS SUBJECT WAS CREATED THROUGH A COLLABORATIVE GAME TO UNDERSTAND THE CONCEPT OF COMMON GOODS. THE EFFECTIVENESS OF THE GAME WAS TESTED BY COMPARING THE PERFORMANCE OF BACHELOR STUDENTS WHO USED THE PLATFORM AND THOSE WHO DID NOT (137 STUDENTS VS. 92 STUDENTS). IN ADDITION, IT WAS CONTROLLED THAT IN PREVIOUS YEARS WHEN STUDENTS PLAYED THE GAME THROUGH FORUMS AND AN EXCEL SHEET, THESE DIFFERENCES DID NOT EXIST. RESULTS INDICATE THAT THE PERFORMANCE DIFFERENCES BETWEEN STUDENTS WHO PARTICIPATED IN THE ONLINE GAME AND THOSE WHO DID NOT WERE GREATER THAN IN PREVIOUS YEARS. IN ADDITION, A SATISFACTION SURVEY WAS DELIVERED TO THE STUDENTS TO UNDERSTAND THEIR IMPRESSIONS BETTER. THIS SURVEY ASSESSED STUDENT OPINION ABOUT THE PLATFORM, ABOUT THE EDUCATIONAL EXPERIENCE, AND ABOUT THEIR BEHAVIOR DURING THE GAME.
IT IS UNDENIABLE THAT SOCIAL MEDIA IS ONE OF THE MOST IMPORTANT TOOLS INTRODUCED TO SOCIETY AS IT IS USED INTENSIVELY IN EVERYDAY DIALOGUES. THE MOST POPULAR SOCIAL MEDIA CHANNELS ARE FACEBOOK, INSTAGRAM, TWITTER, WHATSAPP, ETC. THEY ARE HELPFUL TOOLS FOR PEOPLE, ESPECIALLY WHEN IT COMES TO COMMUNICATION, AND THEY ARE ALSO VERY BENEFICIAL FOR EXCHANGING OR SHARING OPINIONS, KNOWLEDGE AND EXPERIENCE. IN RECENT YEARS, THE USE OF SOCIAL MEDIA HAS SPREAD TO MANY FIELDS, BOTH FORMAL AND INFORMAL, IN ORDER TO ACHIEVE CERTAIN GOALS. THE FIELD OF EDUCATION IS ONE OF THE MOST COMMON FORMAL AREAS THAT USES SOCIAL MEDIA AS AN EDUCATIONAL TOOL FOR GUIDING STUDENTS, MOTIVATING THEM, AND PROVIDING THEM BOTH LECTURES NOTES AND FEEDBACK. HOWEVER, THE CONTINUOUS USE OF EDUCATIONAL METHODS BASED ON SOCIAL MEDIA HAS LED TO AN INCREASE IN THE AMOUNT OF DATA. IN THE LITERATURE, IT IS DEFINED AS BIG DATA. THIS LEADS TO SIGNIFICANT DIFFICULTIES IN ANALYSING AND USING THIS DATA TO ENHANCE EDUCATIONAL PERFORMANCE, ITS EFFECTIVENESS, AND SUPPORTING PRIMARY RESEARCHES THAT ARE RELATED TO EDUCATION, WHICH ARE DIFFICULT AND SOMETIMES IMPOSSIBLE TO PERFORM MANUALLY. FOR THESE REASONS, BIG DATA ANALYTIC TECHNIQUES MUST BE USED TO ACHIEVE THIS GOAL. THE MAIN AIM OF THIS LITERATURE REVIEW PAPER IS TO PROVIDE INFORMATION ABOUT BIG DATA ALONG WITH ITS ADVANTAGES AND CHALLENGES. ALSO, BIG DATA ANALYTIC TECHNIQUES WILL BE EXPLAINED AND THEIR USAGE IN EDUCATION WILL BE DISCUSSED. WE HOPE THAT THIS PAPER WILL HELP EDUCATIONAL INSTITUTIONS TO SELECT SUITABLE/EFFECTIVE BIG DATA ANALYTIC TECHNIQUES FOR THEIR NEEDS AND TO ANALYSE THEIR BIG DATA TO HELP STUDENTS.
THIS PAPER PRESENTS AN ANALYSIS OF DATA REFERRING TO THE PROFILE OF BRAZILIAN STUDENTS IN THE YEAR 2016, ACCORDING TO THE HIGHER EDUCATION CENSUS. THE INFORMATION PROVIDED BY THIS CENSUS IS USED TO CARRY OUT VARIOUS ANALYSES RELATED TO THE CURRENT SITUATION OF BRAZILIAN EDUCATION, AS WELL AS THE PROFILE OF STUDENTS AND INSTITUTIONS. IN THIS WORK, WE ANALYZE THE MODALITY OF COURSES OFFERED BY EDUCATIONAL INSTITUTIONS, INVESTIGATING THE PROFILE AND THE QUANTITATIVE OF STUDENTS WHO HAVE SCHOLARSHIP, AND HOW THEY ENTER THE COURSES. WE ALSO INVESTIGATE THE DISTRIBUTION OF STUDENTS ACCORDING TO THE INFORMED SKIN COLOR/RACE. THE METHOD USED TO CONDUCT THIS RESEARCH INVOLVED THE APPLICATION OF THE STEPS PROPOSED BY THE CRISP-DM REFERENCE MODEL AND THE USE OF THE APRIORI ASSOCIATION ALGORITHM TO IDENTIFY AND ANALYZE THE DATA SET. THE RESULTS SHOW THAT 30% OF STUDENTS CHOOSE NOT TO REPORT THEIR SKIN COLOR; OF THOSE WHO REPORTED, 37.4% ARE WHITE AND 6% ARE BLACK. APPROXIMATELY 82% OF THE STUDENTS ENTERED HIGHER EDUCATION THROUGH THE TRADITIONAL METHOD (ENTRANCE EXAMINATION), AND 82.3% OF THE MALE AND 77.2% OF THE FEMALE OPTED FOR FACE-TO-FACE COURSES.
IN RECENT YEARS, THERE HAS BEEN AN INCREASING INTEREST IN ANALYZING ONLINE LEARNING BEHAVIOR DATA TO UNDERSTAND STUDENTS' LEARNING PATTERNS. HOWEVER, LITTLE EMPIRICAL EVIDENCE HAS BEEN REPORTED ABOUT THE ASSOCIATIONS AMONG MEANINGFUL POSTS AND INSTRUCTOR INTERACTION OF MIDDLE SCHOOL STUDENTS WITH DIFFERENT LEARNING PERFORMANCES. THIS STUDY EXAMINED THE DIFFERENCES OF MEANINGFUL POSTS AND INSTRUCTOR INTERACTIONS BETWEEN STUDENTS WITH HIGH AND LOW ACADEMIC PERFORMANCE. BESIDES, TO FURTHER UNDERSTAND THE ASSOCIATIONS AMONG LEARNING BEHAVIORS, THE RELATIONSHIPS OF THE NUMBER OF MEANINGFUL POSTS, INSTRUCTOR INTERACTIONS AND OTHER LEARNING BEHAVIORS OF MIDDLE SCHOOL STUDENTS WERE DISCUSSED. A TOTAL OF 108 MIDDLE SCHOOL STUDENTS PARTICIPATED IN THIS STUDY AND THEIR DATA WAS COLLECTED BY A LEARNING MANAGEMENT SYSTEM, NAMED E-CLOUD BAG. TO EXPLORE THE DIFFERENCES AND RELATIONSHIPS, A MIXED METHOD WAS USED TO ANALYZE THE DATA. THE RESULTS FOUND HIGH-SCORE STUDENTS SIGNIFICANTLY HIGHER IN THE PERCENTAGE OF MEANINGFUL POSTS THAN LOW-SCORE STUDENTS. ALSO, SOME MEDIUM CORRELATIONS WERE FOUND BETWEEN THE NUMBER OF MEANINGFUL POSTS, THE NUMBER OF INSTRUCTOR INTERACTION, AND SOME OTHER ONLINE LEARNING BEHAVIORS.
THIS ARTICLE DESCRIBES AN EXAMPLE OF DATA MINING TECHNIQUES APPLIED TO AN OPEN EDUCATIONAL ENVIRONMENT. THESE NOVEL ASSESSMENT METHODS IN THE EDUCATIONAL ROBOTICS (ER) FIELD PROVIDE EMPIRICAL EVIDENCE OF PROBLEM-SOLVING STYLES BEHIND THE KEY TASKS OF PROPOSED ACTIVITIES WITHIN REAL OPERATIVE SCENARIOS. A SUPERVISED, MIXED MACHINE LEARNING (ML) APPROACH WAS APPLIED TO DATA FROM SEVEN ITALIAN SECONDARY SCHOOLS (197 STUDENTS), AND FOUR ML TECHNIQUES [LOGISTIC REGRESSION (LR), SUPPORT VECTOR MACHINE (SVM), K-NEAREST NEIGHBORS (KNN), AND RANDOM FOREST (RF)] WERE EXPLORED TO PREDICT STUDENTS' SUCCESS.
THIS RESEARCH FULL PAPER PRESENTS RECENT RESEARCH ON THE EDUCATIONAL DATA MINING (EDM) FIELD. IN RECENT YEARS, EDM HAS CONTRIBUTED SIGNIFICANTLY TO THE PREVENTION OF VARIOUS CHALLENGES IN ACADEMIA. THIS PAPER PRESENTS AN ANALYSIS OF CLASSIFIERS FOR A COMPARATIVE STUDY OF EDM IMPACT, USING INSTITUTIONAL DATA AND TRACE DATA GENERATED BY A VIRTUAL LEARNING ENVIRONMENT TO PREDICT ACADEMIC SUCCESS/FAILURE. FOR THIS, A MODEL OF EDUCATIONAL DATA MINING USING LOGISTIC REGRESSION, SUPPORT VECTOR MACHINE, NAIVE BAYES AND J48 AS CLASSIFIERS AND CROSS VALIDATION AS A TEST METHOD, WAS ELABORATED AND WAS USED TO COMPARE THE PREDICTION ACCURACY AND THE EXECUTION TIME OF EACH CLASSIFIER. THE MODEL WAS APPLIED ON A PUBLIC DATASET WITH 32,593 STUDENTS, DISTRIBUTED AMONG SEVEN COURSES. THE RESULTS ON ACCURACY AND EXECUTION TIME OF EACH CLASSIFIER ALLOWED US TO MAKE RECOMMENDATIONS ON THE SUITABILITY OF USING THEM. THE RESULTS ALSO REVEALED THAT IT IS BETTER TO SEPARATE THE TRACE DATA FROM THE INSTITUTIONAL DATA IN THE MODEL APPLICATION REGARDLESS OF THE CLASSIFIER.
THE AIM OF THIS WORK IS TO PRESENT THE DATA MINING TECHNIQUES FOR CLASSIFYING STOCK KEEPING UNITS TO ABC CLASSIFICATION IN SPARE PARTS WAREHOUSE IN A FACTORY OF THAILAND. ABC ANALYSIS IS AN "INVENTORY CATEGORIZATION METHOD" THAT DIVIDE ITEMS INTO THREE CATEGORIES (A, B, AND C), WHICH ARE THE LABEL OF DATA. THE DATA WERE COLLECTED FROM SAP INVENTORY MANAGEMENT IN THE YEAR OF 2015-2019. THERE WERE TOTALLY 21,222 INSTANCES AND 28 ATTRIBUTES. THE DATASET IS PROCESSED WITH FIVE POPULAR ENSEMBLES TECHNIQUES INCLUDING RANDOMFOREST, BAGGING, ADABOOST, DAGGING AND DECORATE. THE RESULTS OF THIS STUDY GIVE INSIGHT INTO TECHNIQUES FOR ACCURATELY CLASSIFYING ABC CATEGORIZATION FOR INVENTORY MANAGEMENT, COMPARE THE PRECISION, RECALL, F-MEASURE AND ACCURACY OF FIVE ENSEMBLE ALGORITHMS THAT CAN BE DEPLOYED TO THE INVENTORY PROCESS FOR THE FACTORY.
IN THE WORLD OF TECHNOLOGY TODAY, THE HARDWARE, APPLICATIONS, AND ONLINE COMPUTING SERVICE HAVE TRANSFORMED INSTRUCTIONAL APPROACHES AND CLASSROOM CHANGES. IN VARIOUS WAYS, ARTIFICIAL INTELLIGENCE HAS SHOWN ITS FUNCTION AS TRIGGERING TRANSFORMATIONS IN THE MULTIMEDIA EDUCATIONAL PLATFORM IN AN UNIMAGINABLE MANNER. IN THIS PAPER, THE MULTIMEDIA SYSTEMS CAN BE PROGRAMMED TO COMMUNICATE WITH THE STUDENTS USING INTELLECTUAL COMPUTER-AIDED ARTIFICIAL INTELLIGENCE (ICA-AI) FRAMEWORK THROUGH VISUAL VISION, VOICE RECOGNITION, AND INTELLIGENT BEHAVIOR THAT THE RESEARCHERS CONSIDER INHERENTLY HUMAN. THE ICA-AI SCHEME FACILITATES A VARIETY OF CURRICULA FOR TEACHING AND STUDYING DIVERSE TOPICS IN INFORMATION TECHNOLOGY. IN GENERAL, IT OFFERS MULTIMEDIA-BASED LECTURES, DRILLS, RESEARCH, REVIEWS, AND DEBATE MODERATION BASED ON STUDENTS' SUCCESS AND DEVELOPMENT IN HIGHER EDUCATION PLATFORMS. THE FRAMEWORK, INSTRUCTIONAL DESIGN, STUDENTS' MODELING, AND THE KNOWLEDGE BASE'S ORGANIZATION ARE DEFINED EFFECTIVELY USING ICA-AI. THE GENETIC ALGORITHM HAS BEEN DESIGNED FOR INTEGRATION INTO THE INSTRUCTION MECHANISM OF ALL RECORDS, INFORMATION, AND MULTIMEDIA OBJECTS USING ICA-AI. THE CASE STUDY HAS BEEN COMPARED BASED ON THE STUDENTS' PERFORMANCE RATIO AND FEEDBACK FOLLOWING CONVENTIONAL METHODS TO VALIDATE THE PROPOSED METHOD'S RELIABILITY.
THIS PAPER PRESENTS THE COLLABORATIVE EXPERIENCE THAT IS UNDER DEVELOPMENT AS THE EUROPEAN ERASMUS+ PROJECT SPEET (STUDENT PROFILE FOR ENHANCING ENGINEERING TUTORING). THIS PROJECT GOAL EMERGES FROM THE POTENTIAL SYNERGY AMONG A) THE HUGE AMOUNT OF ACADEMIC DATA ACTUALLY EXISTING AT THE ACADEMIC DEPARTMENTS OF FACULTIES AND SCHOOLS, AND B) THE MATURITY OF DATA SCIENCE IN ORDER TO PROVIDE ALGORITHMS AND TOOLS TO ANALYSE AND EXTRACT INFORMATION FROM THE AVAILABLE LARGE AMOUNT OF DATA. A RICH PICTURE CAN BE EXTRACTED FROM THIS DATA IF CONVENIENTLY PROCESSED. THE MAIN PURPOSE OF THIS PROJECT IS TO APPLY DATA MINING ALGORITHMS TO PROCESS THIS DATA IN ORDER TO EXTRACT INFORMATION ABOUT AND TO IDENTIFY STUDENT PROFILES. SOME EXAMPLES OF THE STUDENT PROFILE WE ARE REFERRING TO WITHIN THE PROJECT SCOPE IS, FOR EXAMPLE: STUDENTS THAT FINISH DEGREE ON TIME, STUDENTS THAT ARE BLOCKED ON A CERTAIN SET OF SUBJECTS, STUDENTS THAT LEAVE DEGREE EARLIER, ETC.
IN PRACTICE, THE HIGH QUALITY OF EDUCATIONAL PROCESSES AND CONTENT CAN HARDLY BE ACHIEVED AND MAINTAINED WITHOUT MONITORING. AND SINCE TRACKING THE RESULTS IS NOT ENOUGH, IT IS NECESSARY TO FIND EXISTING FLAWS AND IDENTIFY THEIR CAUSES. IN THIS PAPER, IT IS ARGUED THAT EVEN SMALL DATA SETS SUCH AS STUDENTS' TEST RESULTS CAN PROVIDE VALUABLE INFORMATION USEFUL TO IMPROVE FURTHER ITERATIONS OF TEACHING AND LEARNING OUTCOMES VERIFICATION PROCESSES AND EDUCATIONAL MATERIALS. NEXT, THE APPROPRIATENESS OF GRAPHICAL ANALYSIS FOR THIS PURPOSE IS POINTED OUT TAKING ITS SIMPLICITY EVEN FOR NON-STATISTICIANS AND ITS ABILITY TO VISUALIZE ENTIRE SMALL DATA SETS WITH HIGH PRECISION INTO ACCOUNT. HOWEVER, THE PRIMARY AIM OF THIS PAPER IS TO PROVIDE PRACTICAL EXAMPLES SHOWING THAT THE PYTHON PROGRAMMING LANGUAGE (WITH A SELECTION OF SPECIALIZED MODULES) CAN BE USED IN A CONVENIENT AND EFFECTIVE MANNER FOR GRAPHICAL ANALYSIS OF SMALL EDUCATIONAL DATA SETS. FOR THE PURPOSE OF DEMONSTRATION CASE STUDY, ACTUAL TEST RESULTS WERE USED. ANALYSIS EXAMINED: CORRECTNESS AND CONFIDENCE OF STUDENTS ANSWERING RESPECTIVE TEST QUESTIONS, CORRELATIONS BETWEEN RESULTS BASED ON QUESTION TYPE AND RELEVANT CONTENT AREA, AND ALSO SIMILARITIES OF ANSWERS GIVEN BY DIFFERENT STUDENTS. ALSO, A PYTHON-BASED SOFTWARE ENVIRONMENT FOR GRAPHICAL ANALYSIS HAS BEEN COMPARED TO MICROSOFT EXCEL.
WE DISCUSS IF AND HOW IT IS POSSIBLE TO DEVELOP MEANINGFUL E-ASSESSMENTS IN ENGINEERING MECHANICS. THE FOCUS IS ON COMPLEX EXAMPLE PROBLEMS, RESEMBLING TRADITIONAL PAPER-PENCIL EXAMS. MOREOVER, THE SWITCH TO E-ASSESSMENTS SHOULD BE AS TRANSPARENT AS POSSIBLE FOR THE STUDENTS, I.E., IT SHOULDN'T LEAD TO ADDITIONAL DIFFICULTIES, WHILE STILL MAINTAINING SUFFICIENTLY HIGH DISCRIMINATION INDICES FOR ALL QUESTIONS. EXAMPLE PROBLEMS HAVE BEEN DESIGNED IN SUCH A WAY, THAT IT IS POSSIBLE TO ACCOUNT FOR A GREAT VARIETY OF INPUTS RANGING FROM GRAPHICAL TO NUMERICAL AND ALGEBRAIC AS WELL AS STRING INPUT TYPES. THANKS TO THE IMPLEMENTATION OF RANDOM VARIABLES IT IS EVEN POSSIBLE TO CREATE AN INDIVIDUAL SET OF INITIAL VALUES FOR EVERY PARTICIPANT. ADDITIONALLY, WHEN DEALING WITH COMPLEX EXAMPLE PROBLEMS ERRORS CARRIED FORWARD HAVE TO BE TAKEN INTO ACCOUNT. DIFFERENT APPROACHES TO DO SO ARE DETAILED AND DISCUSSED, E.G., PRE-DEFINED PATHS FOR SUB-QUESTIONS, USAGE OF STUDENTS' PREVIOUS INPUTS OR DECISION TREES. THE MAIN FINDING IS THAT COMPLEX EXAMPLE PROBLEMS IN ENGINEERING MECHANICS CAN VERY WELL BE USED IN E-ASSESSMENTS IF THE DESIGN OF THESE QUESTIONS IS WELL STRUCTURED INTO MEANINGFUL SUB-QUESTIONS AND ERRORS CARRIED FORWARD ARE ACCOUNTED FOR.
DEVELOPING TOOLS TO SUPPORT STUDENTS AND LEARNING IN A TRADITIONAL OR ONLINE SETTING IS A SIGNIFICANT TASK IN TODAY'S EDUCATIONAL ENVIRONMENT. THE INITIAL STEPS TOWARD ENABLING SUCH TECHNOLOGIES USING MACHINE LEARNING TECHNIQUES FOCUSED ON PREDICTING THE STUDENT'S PERFORMANCE IN TERMS OF THE ACHIEVED GRADES. HOWEVER, THESE APPROACHES DO NOT PERFORM AS WELL IN PREDICTING POOR-PERFORMING STUDENTS. THE OBJECTIVE OF OUR WORK IS TWOFOLD. FIRST, IN ORDER TO OVERCOME THIS LIMITATION, WE EXPLORE IF POORLY PERFORMING STUDENTS CAN BE MORE ACCURATELY PREDICTED BY FORMULATING THE PROBLEM AS BINARY CLASSIFICATION, BASED ON DATA PROVIDED BEFORE THE START OF THE SEMESTER. SECOND, IN ORDER TO GAIN INSIGHTS AS TO WHICH ARE THE FACTORS THAT CAN LEAD TO POOR PERFORMANCE, WE ENGINEERED A NUMBER OF HUMAN-INTERPRETABLE FEATURES THAT QUANTIFY THESE FACTORS. THESE FEATURES WERE DERIVED FROM THE STUDENTS' GRADES FROM THE UNIVERSITY OF MINNESOTA, AN UNDERGRADUATE PUBLIC INSTITUTION. BASED ON THESE FEATURES, WE PERFORM A STUDY TO IDENTIFY DIFFERENT STUDENT GROUPS OF INTEREST, WHILE AT THE SAME TIME, IDENTIFY THEIR IMPORTANCE. AS THE RESULTING MODELS PROVIDE US WITH DIFFERENT SUBSETS OF CORRECT PREDICTIONS, THEIR COMBINATION CAN BOOST THE OVERALL PERFORMANCE.
THE ACQUISITION OF COMPETENCES FOR THE DEVELOPMENT OF COMPUTER PROGRAMS IS ONE OF THE MAIN CHALLENGES FACED BY COMPUTER SCIENCE STUDENTS. AS A RESULT OF NOT BEING ABLE TO DEVELOP THE ABILITIES NEEDED (FOR EXAMPLE, ABSTRACTION), STUDENTS DROP OUT THE SUBJECTS AND SOMETIMES EVEN THE COURSE. THERE IS A NEED TO STUDY THE CAUSES OF STUDENT SUCCESS (OR FAILURE) IN INTRODUCTORY CURRICULAR UNITS TO CHECK FOR BEHAVIOURS OR CHARACTERISTICS THAT MAY BE DETERMINANT AND THUS TRY TO PREVENT AND CHANGE SAID CAUSES. THE STUDENTS OF ONE PROGRAMMING CURRICULAR UNIT WERE INVITED TO ANSWER FOUR SURVEYS. WE USE MACHINE LEARNING TECHNIQUES TO TRY TO PREDICT THE STUDENTS' GRADES BASED ON THE ANSWERS OBTAINED ON THE SURVEYS. THE RESULTS OBTAINED ENABLE US TO PLAN THE SEMESTER ACCORDINGLY, BY ANTICIPATING HOW MANY STUDENTS MIGHT NEED EXTRA SUPPORT. WE HOPE TO INCREASE THE STUDENTS' MOTIVATION AND, WITH THIS, INCREASE THEIR INTEREST ON THE SUBJECT. THIS WAY WE AIM TO ACCOMPLISH OUR ULTIMATE GOAL: REDUCING THE DROP OUT AND INCREASING THE OVERALL AVERAGE STUDENT PERFORMANCE.
THIS PAPER DESCRIBES THE EVALUATION ASSET, WHICH IS A TOOL THAT SUPPORTS THE EVALUATION OF APPLIED GAMES. THE OVERALL GOAL IS TO PROVIDE A MODULAR APPROACH TO GAME EVALUATION THAT CAN EASILY BE APPLIED AND INTEGRATED BY GAME DEVELOPERS IN THEIR GAME PROJECTS. THE IDEA OF THIS ASSET IS TO UTILISE GAME-BASED USER DATA AND TRANSLATE IT INTO MEANINGFUL INFORMATION ABOUT THE QUALITY OF A GAME. THIS ASSET HAS ADDED VALUE FOR VARIOUS STAKEHOLDERS DEVELOPERS, WHO GET AN INSTRUMENT TO EASILY EVALUATE THEIR SOFTWARE, TRAINING PROVIDERS, WHO GET INFORMATION ABOUT THE PEDAGOGICAL VALUE OF GAMES, AND THE PLAYERS, WHO ARE ENABLED TO PROVIDE QUICK FEEDBACK WITHOUT DISTRACTION. RELEVANT RELATED WORK FROM THE FIELD OF LEARNING AND GAME ANALYTICS IS SUMMARISED. THE TECHNICAL IMPLEMENTATION OF THE ASSET CONSISTS IN A CLIENT-SIDE AND A SERVER-SIDE COMPONENT FOR COLLECTING AND PRE-PROCESSING INTERACTION DATA FROM GAMES. THIS DATA IS THEN ANALYSED IN TERMS OF MEANINGFUL EVALUATION VARIABLES ON WHICH AUTOMATIC REPORTS AND VISUALISATIONS CAN BE RETRIEVED. THE EVALUATION ASSET COMES WITH CONFIGURABLE EVALUATION METRICS FOR TRANSLATING THE DATA INTO MEANINGFUL INFORMATION ABOUT ASPECTS ON USABILITY AND GAME EXPERIENCE. IN ADDITION, THE EVALUATION ASSET MAY BE LINKED TO OTHER ASSETS, TO MAKE USE OF THEIR DATA FOR ADDITIONAL INFORMATION ON THE QUALITY OF A GAME. THE TECHNICAL IMPLEMENTATION OF THE ASSET IS DESCRIBED, AS WELL AS ITS INTEGRATION AND APPLICATION IN THE CONTEXT OF A GAME. THE EVALUATION ASSET, AS AN EVALUATION INSTRUMENT COMPLEMENTING MORE TRADITIONAL TECHNIQUES (E.G. QUESTIONNAIRES), WILL BE INCORPORATED IN THE APPLIED GAMES DEVELOPED IN RAGE AND APPLIED IN THE CONTEXT OF THE PILOT STUDIES.
PREDICTING STUDENT PERFORMANCE AS EARLY AS POSSIBLE AND ANALYSING TO WHICH EXTENT INITIAL STUDENT BEHAVIOUR COULD LEAD TO FAILURE OR SUCCESS IS CRITICAL IN INTRODUCTORY PROGRAMMING (CS1) COURSES, FOR ALLOWING PROMPT INTERVENTION IN A MOVE TOWARDS ALLEVIATING THEIR HIGH FAILURE RATE. HOWEVER, IN CS1 PERFORMANCE PREDICTION, THERE IS A SERIOUS LACK OF STUDIES THAT INTERPRET THE PREDICTIVE MODEL'S DECISIONS. IN THIS SENSE, WE DESIGNED A LONG-TERM STUDY USING VERY FINE-GRAINED LOG-DATA OF 2056 STUDENTS, COLLECTED FROM THE FIRST TWO WEEKS OF CS1 COURSES. WE EXTRACT FEATURES THAT MEASURE HOW STUDENTS DEAL WITH DEADLINES, HOW THEY FIX ERRORS, HOW MUCH TIME THEY SPEND PROGRAMMING, AND SO FORTH. SUBSEQUENTLY, WE CONSTRUCT A PREDICTIVE MODEL THAT ACHIEVED CUTTING-EDGE RESULTS WITH AREA UNDER THE CURVE (AUC) OF.89, AND AN AVERAGE ACCURACY OF 81.3%. TO ALLOW AN EFFECTIVE INTERVENTION AND TO FACILITATE HUMAN-AI COLLABORATION TOWARDS PRESCRIPTIVE ANALYTICS, WE, FOR THE FIRST TIME, TO THE BEST OF OUR KNOWLEDGE, GO A STEP FURTHER THAN THE PREDICTION ITSELF AND LEVERAGE THIS FIELD BY PROPOSING AN APPROACH TO EXPLAINING OUR PREDICTIVE MODEL DECISIONS INDIVIDUALLY AND COLLECTIVELY USING A GAME-THEORY BASED FRAMEWORK (SHAP), (LUNDBERG ET AL., 2020) THAT ALLOWS INTERPRETING OUR BLACK-BOX NON-LINEAR MODEL LINEARLY. IN OTHER WORDS, WE EXPLAIN THE FEATURE EFFECTS, CLEARLY BY VISUALISING AND ANALYSING INDIVIDUAL PREDICTIONS, THE OVERALL IMPORTANCE OF FEATURES, AND IDENTIFICATION OF TYPICAL PREDICTION PATHS. THIS METHOD CAN BE FURTHER APPLIED TO OTHER EMERGING COMPETITIVE MODELS, AS THE CS1 PREDICTION FIELD PROGRESSES, ENSURING TRANSPARENCY OF THE PROCESS FOR KEY STAKEHOLDERS: ADMINISTRATORS, TEACHERS, AND STUDENTS.
INVESTIGATING AND EXPLAINING THE PATTERNS OF LEARNERS' ENGAGEMENT IN ADAPTIVE LEARNING CONDITIONS IS A CORE ISSUE TOWARDS IMPROVING THE QUALITY OF PERSONALIZED LEARNING SERVICES. THIS ARTICLE COLLECTS LEARNER DATA FROM MULTIPLE SOURCES DURING AN ADAPTIVE LEARNING ACTIVITY, AND EMPLOYS A FUZZY SET QUALITATIVE COMPARATIVE ANALYSIS (FSQCA) APPROACH TO SHED LIGHT TO LEARNERS' ENGAGEMENT PATTERNS, WITH RESPECT TO THEIR LEARNING PERFORMANCE. SPECIFICALLY, THIS ARTICLE MEASURES AND CODES LEARNERS' ENGAGEMENT BY FUSING AND COMPILING CLICKSTREAMS (E.G., RESPONSE TIME), PHYSIOLOGICAL DATA (E.G., EYE-TRACKING, ELECTROENCEPHALOGRAPHY, ELECTRODERMAL ACTIVITY), AND SURVEY DATA (E.G., GOAL-ORIENTATION) TO DETERMINE WHAT CONFIGURATIONS OF THOSE DATA EXPLAIN WHEN LEARNERS CAN ATTAIN HIGH OR MEDIUM/LOW LEARNING PERFORMANCE. FOR THE EVALUATION OF THE APPROACH, AN EMPIRICAL STUDY WITH 32 UNDERGRADUATES WAS CONDUCTED. THE ANALYSIS REVEALED SIX CONFIGURATIONS THAT EXPLAIN LEARNERS' HIGH PERFORMANCE AND THREE THAT EXPLAIN LEARNERS' MEDIUM/LOW PERFORMANCE, DRIVEN BY ENGAGEMENT MEASURES COMING FROM THE MULTIMODAL DATA. SINCE FSQCA EXPLAINS THE OUTCOME OF INTEREST ITSELF, RATHER THAN ITS VARIANCE, THESE FINDINGS ADVANCE OUR UNDERSTANDING ON THE COMBINED EFFECT OF THE MULTIPLE INDICATORS OF ENGAGEMENT ON LEARNERS' PERFORMANCE. LIMITATIONS AND POTENTIAL IMPLICATIONS OF THE FINDINGS ARE ALSO DISCUSSED.
THE RECENT DEVELOPMENTS IN SCHOOLS HAVE BROUGHT IN SIGNIFICANT CHANGES TO THE TRADITIONAL CLASS ROOM TEACHING PRACTICES. THE REVIEW FOCUSES ON THE FOUR DIMENSIONS OF THE WORK SYSTEM, I.E., ENVIRONMENT, TECHNOLOGY, COURSE, AND HUMAN-FACTOR THROUGH WHICH THE TEACHING-LEARNING OCCURS. THE MANMACHINE INTERACTIONS DEMANDS BOTH PHYSICAL AND COGNITIVE DEMAND AND INFLUENCE THE ERGONOMIC OUTCOMES AND PERFORMANCE OUTCOMES. THE REVIEW INDICATES THAT FOR EFFECTIVE LEARNING, STUDENT'S NEW LEARNING PATTERNS, EVOLVING EDUCATION TECHNOLOGIES AND METHODOLOGIES NEEDS TO BE BETTER UNDERSTOOD BY ALL STAKEHOLDERS, I.E., STUDENTS, TEACHERS, ADMINISTRATORS AND EVALUATORS, TECHNOLOGY AND COMMUNICATION PROVIDERS. FEW ISSUES RELATING TO USER CENTRED DISPLAY-CONTROL DEVICES; SPAN OF ATTENTION IN A BLENDED MULTI-MEDIA INTERACTIVE ENVIRONMENT; COGNITIVE LOADS AND THE ROLE PLAYED BY SOFT SYSTEMS; CONFIGURATION OF TECHNOLOGY VIS-A-VIS USER AND COURSE; ERGONOMIC DESIGN METHODS THAT ACCOUNTS FOR 'PHYSICAL CONFIGURATIONS', FLEXIBLE 'COURSE' AND 'USER' DIMENSIONS; AND SUPPORT AT INSTITUTIONAL LEVEL FOR TECHNOLOGY ADOPTION.
STUDENTS WILL EXPERIENCE A COMPLEX MIXTURE OF MENTAL STATES DURING DISCUSSION, INCLUDING CONCENTRATION, CONFUSION, FRUSTRATION, AND BOREDOM, WHICH HAVE BEEN WIDELY ACKNOWLEDGED AS CRUCIAL COMPONENTS FOR REVEALING A STUDENT'S LEARNING STATES. IN THIS STUDY, WE PROPOSE USING MULTIMODAL DATA TO DESIGN AN INTELLIGENT MONITORING AGENT THAT CAN ASSIST TEACHERS IN EFFECTIVELY MONITORING THE MULTIPLE MENTAL STATES OF STUDENTS DURING DISCUSSION. WE FIRSTLY DEVELOPED AN ADVANCED MULTI-SENSOR-BASED SYSTEM AND APPLIED IT IN A REAL UNIVERSITY'S RESEARCH LAB TO COLLECT A MULTIMODAL "IN-THE-WILD" TEACHER-STUDENT CONVERSATION DATASET. THEN, WE DERIVED A SET OF PROXY FEATURES FROM FACIAL, HEART RATE, AND ACOUSTIC MODALITIES AND USED THEM TO TRAIN SEVERAL SUPERVISED LEARNING CLASSIFIERS WITH DIFFERENT MULTIMODAL FUSION APPROACHES SINGLE-CHANNEL-LEVEL, FEATURE-LEVEL, AND DECISION-LEVEL FUSION TO RECOGNIZE STUDENTS' MULTIPLE MENTAL STATES IN CONVERSATIONS. WE EXPLORED HOW TO DESIGN MULTIMODAL ANALYTICS TO AUGMENT THE ABILITY TO RECOGNIZE DIFFERENT MENTAL STATES AND FOUND THAT FUSING HEART RATE AND ACOUSTIC MODALITIES YIELDS BETTER RECOGNIZE THE STATES OF CONCENTRATION (AUC = 0.842) AND CONFUSION (AUC = 0.695), WHILE FUSING THREE MODALITIES YIELD THE BEST PERFORMANCE IN RECOGNIZING THE STATES OF FRUSTRATION (AUC = 0.737) AND BOREDOM (AUC = 0.810). OUR RESULTS ALSO EXPLORED THE POSSIBILITY OF LEVERAGING THE ADVANTAGES OF THE REPLACEMENT CAPABILITIES BETWEEN DIFFERENT MODALITIES TO PROVIDE HUMAN TEACHERS WITH SOLUTIONS FOR ADDRESSING THE CHALLENGES WITH MONITORING STUDENTS IN DIFFERENT REAL-WORLD EDUCATION ENVIRONMENTS.
IN AN ATTEMPT TO IMPROVE UNDERGRADUATE MEDICAL STUDENT PREPARATION FOR AND LEARNING FROM DISSECTION SESSIONS, DISSECTION AUDIO-VISUAL RESOURCES (DAVR) WERE DEVELOPED. DATA FROM E-LEARNING MANAGEMENT SYSTEMS INDICATED DAVR WERE ACCESSED BY 28% +/- 10 (MEAN +/- SD FOR NINE DAVR ACROSS THREE YEARS) OF STUDENTS PRIOR TO THE CORRESPONDING DISSECTION SESSIONS, REPRESENTING AT MOST 58% +/- 20 OF ASSIGNED DISSECTORS. APPROXIMATELY 50% OF STUDENTS ACCESSED ALL AVAILABLE DAVR BY THE END OF SEMESTER, WHILE 10% ACCESSED NONE. NINETY PERCENT OF SURVEY RESPONDENTS (RESPONSE RATE 58%) GENERALLY AGREED THAT DAVR IMPROVED THEIR PREPARATION FOR AND LEARNING FROM DISSECTION WHEN USED. OF SEVERAL LEARNING RESOURCES, ONLY DAVR USAGE HAD A SIGNIFICANT POSITIVE CORRELATION (P = 0.002) WITH FEELING PREPARED FOR DISSECTION. RESULTS ON CADAVERIC ANATOMY PRACTICAL EXAMINATION QUESTIONS IN YEAR 2 (Y2) AND YEAR 3 (Y3) COHORTS WERE 3.9% (P < 0.001, EFFECT SIZE D = 20.32) AND 0.3% LOWER, RESPECTIVELY, WITH DAVR AVAILABLE COMPARED TO PREVIOUS YEARS. HOWEVER, THERE WERE POSITIVE CORRELATIONS BETWEEN STUDENTS' CADAVERIC ANATOMY QUESTION SCORES WITH THE NUMBER AND TOTAL TIME OF DAVR VIEWED (Y2, R = 0.171, 0.090, P = 0.002, N.S., RESPECTIVELY; AND Y3, R = 0.257, 0.253, BOTH P < 0.001). STUDENTS ACCESSING ALL DAVR SCORED 7.2% AND 11.8% HIGHER THAN THOSE ACCESSING NONE (Y2, P = 0.015, D = 0.48; AND Y3, P = 0.005, D = 0.77, RESPECTIVELY). FURTHER DEVELOPMENT AND PROMOTION OF DAVR ARE NEEDED TO IMPROVE ENGAGEMENT AND LEARNING OUTCOMES OF MORE STUDENTS. (C) 2016 AMERICAN ASSOCIATION OF ANATOMISTS.
IN THE IDEAL CS1 CLASSROOM, WE SHOULD UNDERSTAND PROGRAMMING PROCESS-HOW STUDENT CODE EVOLVES OVER TIME. HOWEVER, FOR GRAPHICS-BASED PROGRAMMING ASSIGNMENTS, THE TASK OF UNDERSTANDING AND GRADING FINAL SOLUTIONS, LET ALONE THOUSANDS OF INTERMEDIATE STEPS, IS INCREDIBLY LABOR-INTENSIVE. IN THIS WORK, WE PRESENT A CHALLENGE, A DATASET, AND A PROMISING FIRST SOLUTION TO AUTONOMOUSLY USE IMAGE OUTPUT TO IDENTIFY FUNCTIONAL, INTERMEDIATE STAGES OF A STUDENT SOLUTION. BY USING COMPUTER VISION TECHNIQUES TO ASSOCIATE VISUAL OUTPUT OF INTERMEDIATE STUDENT CODE WITH FUNCTIONAL PROGRESS, WE SUPPLEMENT A LOT OF THE TEACHER LABOR ASSOCIATED WITH UNDERSTANDING GRAPHICS-BASED, OPEN-ENDED ASSIGNMENTS. WE HOPE OUR PUBLICATION OF THE DATASET USED IN OUR CASE STUDY SPARKS DISCUSSION IN THE COMMUNITY ON HOW TO ANALYZE PROGRAMS WITH VISUAL PROGRAM OUTPUT.
PREDICTING STUDENT LEARNING ACHIEVEMENT IN BLENDED COURSES IS AN EMERGING FIELD IN EDUCATION RESEARCH. UNLIKE FULLY ONLINE LEARNING, WHEN PREDICTING LEARNING ACHIEVEMENT IN BLENDED COURSES BASED ON STUDENT ONLINE BEHAVIOR DATA, EXTRA ATTENTION NEEDS TO BE PAID ON THE NATURE OF BLENDED COURSES. FOCUSING ON STUDENT ONLINE LEARNING BEHAVIOR IN BLENDED COURSES, THIS STUDY TRIES TO EXPLORE THE PATTERNS IN STUDENT ONLINE LEARNING BEHAVIOR AND BRING INSIGHT INTO THE PROCESS WITH WHICH MACHINE LEARNING ALGORITHMS BUILD PREDICTIVE MODEL OF FINAL GRADE. THE RESULTS OF THIS STUDY ARE AS FOLLOWS: 1) STUDENT ONLINE LEARNING BEHAVIORS CAN BE CLUSTERED INTO 5 CATEGORIES; 2) DIFFERENT PATTERNS OF LEARNING BEHAVIOR AFFECT THE ACCURACY OF LEARNING OUTCOME PREDICTION; 3) USING RANDOM FOREST ALGORITHM, PREDICTIVE MODELS WITH SMALL MAX TREE DEPTH ACHIEVE HIGHER ACCURACY.
ADAPTIVE ONLINE LEARNING CAN FACILITATE STUDENTS' SUPPORT BY RESPONDING IMMEDIATELY TO THE USER'S INTERACTIONS. GOOD FEEDBACK TO STUDENTS HELPS CLOSING THE GAP BETWEEN ACTUAL AND DESIRED PERFORMANCE. IN THIS PAPER WE ANALYZE HOW TO INTRODUCE ONLINE ADAPTIVE FORMATIVE LEARNING IN COMPUTER SCIENCE, A DISCIPLINE WITH WELL DOCUMENTED CHALLENGES THAT ARE HARD TO TACKLE WITH TRADITIONAL CLASSROOM METHODS. SPECIFICALLY, WE DEVELOPED ILLUSTRATIVE LEARNING ITEMS TEACHING MODEL-DRIVEN DESIGN AND IMPLEMENTED THEM IN AN ONLINE SYSTEM THAT IMPLEMENTS A MODEL FOR AUTOMATIC FORMATIVE ASSESSMENT DEVELOPED BY UNIVERSITY OF TORINO. THE MODEL TAKES ADVANTAGE OF AN AUTOMATIC ASSESSMENT SYSTEM INITIALLY DESIGNED FOR STEM DISCIPLINES, THEN ADOPTED FOR TEACHING LANGUAGES AND OTHER DISCIPLINES TOO. THE KEY FEATURES OF THE ADAPTIVE MODEL SUPPORTED BY THE ONLINE SYSTEM ARE ALGORITHMIC QUESTIONS, AVAILABILITY, CONTEXTUALIZATION, IMMEDIATE FEEDBACK, INTERACTIVE FEEDBACK, AND OPEN ANSWERS. THESE FEATURES ARE PORTABLE ACROSS SUBJECT DOMAINS, SO THE SYSTEM CAN BE ADAPTED TO INCLUDE NEW SUBJECTS. WE CHOSE MDD BECAUSE IT IS A TOPIC OF COMPUTER SCIENCE EDUCATION CONNECTED WITH COMPUTATIONAL THINKING, SOFTWARE DESIGN, AND FORMAL METHODS, WHICH ARE THREE OF THE CORE AREAS IN NEED OF ENHANCED SUPPORT.
INFORMATION VISUALIZATION CONCERNS EFFORTS ON CREATING BETTER SOLUTIONS TO REPRESENT DATA IN VISUAL FORMATS AND CONSEQUENTLY SUPPORT THE USER INTERPRETATION ON IT. THE LITERATURE HAS SHOWN THAT THERE IS A GAP IN THE DEVELOPMENT OF EDUCATIONAL DATA VISUALIZATIONS WHICH FULFILL END-USER NEEDS. THIS PAPER PRESENTS VIS2LEARNING A GUIDE OF RECOMMENDATIONS TO SUPPORT THE BUILDING OF VISUALIZATIONS APPLIED TO EDUCATIONAL DATA IN E-LEARNING CONTEXT. VIS2LEARNING PROVIDES A SET OF SCENARIOS FROM WHICH EDUCATIONAL DATA VISUALIZATIONS CAN BE DEVELOPED. EACH SCENARIO COMPRISES (I) A VISUAL FORMAT OF VISUALIZATION AND ITS CHARACTERISTICS; (II) EXAMPLES OF HOW TO USE IT; AND (III) OVERALL RECOMMENDATIONS TO ENHANCE THE USER'S UNDERSTANDING ON THE DATA. WE CARRIED OUT AN ONLINE QUESTIONNAIRE FROM WHICH 34 END-USERS (BRAZILIAN TEACHERS) EVALUATED VISUALIZATIONS WHICH WERE CONSTRUCTED BY USING THE GUIDE. THE RESULTS REVEAL THAT THE RECOMMENDATIONS MADE THE VISUALIZATIONS SUITABLE TO BE APPLIED IN THE E-LEARNING CONTEXT. HOWEVER, OUR FINDINGS SHOWED THAT THE PARTICIPANTS WHO DID NOT HAVE A FORMER CONTACT WITH VISUALIZATIONS IN E-LEARNING CONTEXT STRUGGLED TO UNDERSTAND SOME VISUAL FORMATS WHICH ARE NOT WELL-KNOWN BY THE AUDIENCE.
THIS COMPARATIVE STUDY PAPER SEEKS TO DOCUMENT HOW WITH THE HELP OF BIG DATA DIFFERENT ASPECTS OF INTERNATIONAL ACCREDITATIONS ARE PERCEIVED BY BOTH PLEKHANOV RUSSIAN UNIVERSITY OF ECONOMICS AND HAN UNIVERSITY OF APPLIED SCIENCES (ARNHEM BUSINESS SCHOOL), THE NETHERLANDS FACULTY AND HIGHER MANAGEMENT. THIS PAPER IS LOOKING AT BRINGING ADVICE AND HELPING THE CHOSEN UNIVERSITIES WITH THEIR INTERNATIONAL ACCREDITATIONS PROCESSES BY DEMONSTRATING THE IMPORTANCE OF BIG DATA. THE IMPORTANCE OF BIG DATA AND INTERNATIONAL ACCREDITATIONS TO BOTH UNIVERSITIES WILL BE ACCOUNTED FOR IN THIS PAPER. IN COMPARISON TO CURRENT RESEARCH ON BIG DATA IN HIGHER EDUCATION, THIS WORK FOCUSES ON THE GOAL OF PREPARING THE UNIVERSITIES FOR FUTURE ACADEMIC INTERNATIONAL ACCREDITATIONS. IT IS A COMPARATIVE STUDY WHERE IS MEANT TO LEARN FROM BEST PRACTICES RATHER THAN TO GENERALIZE AND EXTRAPOLATE RESULTS. ON A CONCEPTUAL WAY, THIS PAPER CONTRIBUTES TO KNOWLEDGE BY ATTEMPTING TO DEVELOP A STRATEGIC PLANNING OF THE INTERNATIONAL ACCREDITATIONS PROCESS BY DETERMINING THE BEST PRACTICES USING BIG DATA WHILE CREATING A PROCESS FOR INTERNATIONALIZATION TO INCREASE THE UNIVERSITIES' GLOBAL COMPETITIVENESS.
AN IMPORTANT ROLE OF OPEN LEARNER MODELS (OLMS) IS TO SUPPORT SELF-REFLECTION. WE EXPLORE HOW TO DO THIS FOR AN OLM BASED ON FINE-GRAINED LONG TERM PHYSICAL ACTIVITY TRACKER DATA THAT MANY PEOPLE ARE ACCUMULATING. WE AIM TO TACKLE TWO WELL-DOCUMENTED CHALLENGES THAT PEOPLE FACE, IN MAKING EFFECTIVE USE OF AN OLM FOR REFLECTION. 1. WE CREATED A TUTORIAL TO SCAFFOLD SENSE-MAKING NEEDED TO UNDERSTAND THE MEANING OF THE OLM. 2. WE INTEGRATED AN INTERFACE SCAFFOLD TO HELP USERS CONSIDER KEY QUESTIONS FOR EFFECTIVE REFLECTION. WE REPORT THE RESULTS OF A QUALITATIVE THINK-ALOUD LAB STUDY WITH 21 PARTICIPANTS VIEWING THEIR OWN LONG TERM OLM. TO EVALUATE THE TUTORIAL SCAFFOLDING, WE SPLIT PARTICIPANTS INTO AN EXPERIMENTAL GROUP, WHO DID A TUTORIAL BEFORE EXPLORING THE OLM AND A CONTROL GROUP WHICH EXPLORED THE INTERFACE WITHOUT THE TUTORIAL. TO EVALUATE THE REFLECTION SCAFFOLDING, ALL PARTICIPANTS FIRST EXPLORED THE INTERFACE AS THEY WISHED. WE THEN PROVIDED GOAL PROMPTS TO SCAFFOLD REFLECTION. OUR STUDY REVEALED THAT, UNDER LAB CONDITIONS, THE TUTORIAL SCAFFOLDING WAS NOT NEEDED - ALL PARTICIPANTS IN BOTH GROUPS COULD READILY UNDERSTAND THE OLM. HOWEVER, WE FOUND THAT SEVERAL OF THE GOAL PROMPTS WERE IMPORTANT TO HELP PARTICIPANTS CONSIDER KEY QUESTIONS FOR EFFECTIVE REFLECTION. OUR KEY CONTRIBUTION IS INSIGHTS INTO THE DESIGN OF SCAFFOLDING FOR REFLECTION IN A LIFE-LONG LEARNING CONTEXT OF GAINING INSIGHTS AND SETTING GOALS FOR PHYSICAL ACTIVITY.