TODAY, MODERN EDUCATIONAL MODELS ARE CONCERNED WITH THE DEVELOPMENT OF THE TEACHER-STUDENT EXPERIENCE AND THE POTENTIAL OPPORTUNITIES IT PRESENTS. USER-CENTRIC ANALYSES ARE USEFUL BOTH IN TERMS OF THE SOCIO-TECHNICAL PERSPECTIVE ON DATA USAGE WITHIN THE EDUCATIONAL DOMAIN AND THE POSITIVE IMPACT THAT DATA-DRIVEN METHODS HAVE. MOREOVER, THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) IN EDUCATION AND PROCESS INNOVATION HAS EMERGED DUE TO THE STRATEGIC PERSPECTIVES AND THE PROCESS MONITORING THAT HAVE SHOWN TO BE MISSING WITHIN THE TRADITIONAL EDUCATION CURRICULA. THIS STUDY SHOWS THAT THERE IS AN UNPRECEDENTED INCREASE IN THE AMOUNT OF TEXT-BASED DATA IN DIFFERENT ACTIVITIES WITHIN THE EDUCATIONAL PROCESSES, WHICH CAN BE LEVERAGED TO PROVIDE USEFUL STRATEGIC INTELLIGENCE AND IMPROVEMENT INSIGHTS. EDUCATORS CAN APPLY THE RESULTANT METHODS AND TECHNOLOGIES, PROCESS INNOVATIONS, AND CONTEXTUAL-BASED INFORMATION FOR AMPLE SUPPORT AND MONITORING OF THE TEACHING-LEARNING PROCESSES AND DECISION MAKING. TO THIS EFFECT, THIS PAPER PROPOSES AN EDUCATIONAL PROCESS AND DATA MINING (EPDM) MODEL THAT LEVERAGES THE PERSPECTIVES OR OPINIONS OF THE STUDENTS TO PROVIDE USEFUL INFORMATION THAT CAN BE USED TO ENHANCE THE END-TO-END PROCESSES WITHIN THE EDUCATIONAL DOMAIN. THEORETICALLY, THIS STUDY APPLIES THE MODEL TO DETERMINE HOW THE STUDENTS EVALUATE THEIR TEACHERS BY CONSIDERING THE GENDER OF THE TEACHERS. WE ANALYZED THE UNDERLYING PATTERNS AND DETERMINED THE EMOTIONAL VALENCE OF THE STUDENTS BASED ON THEIR COMMENTS IN THE STUDENTS EVALUATION OF TEACHING (SET). THUS, THIS WORK IMPLEMENTS THE PROPOSED EPDM MODEL USING SET COMMENTS CAPTURED IN A SETTING OF HIGHER EDUCATION.
INTRODUCING COMPUTATIONAL MODELING INTO STEM CLASSROOMS CAN PROVIDE OPPORTUNITIES FOR THE SIMULTANEOUS LEARNING OF COMPUTATIONAL THINKING (CT) AND STEM. THIS PAPER DESCRIBES THE C2STEM MODELING ENVIRONMENT FOR LEARNING PHYSICS, AND THE PROCESSES STUDENTS CAN APPLY TO THEIR LEARNING AND MODELING TASKS. WE USE AN UNSUPERVISED LEARNING METHOD TO CHARACTERIZE STUDENT LEARNING BEHAVIORS AND HOW THESE BEHAVIORS RELATE TO LEARNING GAINS IN STEM AND CT.
IN ONLINE DISCUSSIONS, INDIVIDUAL CONTRIBUTIONS DIRECTLY INFLUENCE GROUP PERFORMANCE AND THE DEEP ANALYSIS ON INDIVIDUAL LEVEL COULD BE USED TO EXPLAIN WHAT HAPPENS WITHIN GROUPS. HOWEVER, MEASURING INDIVIDUAL CONTRIBUTIONS IN ONLINE COLLABORATIVE DISCUSSION IS A COMPLEX TASK DUE TO THE VARIETY OF ELEMENTS AND FACTORS. THE PURPOSE OF THIS STUDY IS TO PROVIDE A FRAMEWORK TO MEASURE INDIVIDUAL CONTRIBUTIONS IN ONLINE COLLABORATIVE DISCUSSIONS USING QUANTITATIVE METHODS BOTH FROM KNOWLEDGE CONTENT AND PARTICIPATION BEHAVIORS. THEN THE FRAMEWORK IS VALIDATED BY AN EMPIRICAL STUDY. MOREOVER, WE VISUALIZE THE INFORMATION OF INDIVIDUAL CONTRIBUTIONS TO GIVE A DIRECT OBSERVATION FOR TEACHERS.
WHILE OTHER FIELDS SUCH AS STATISTICS AND EDUCATION HAVE EXAMINED VARIOUS ISSUES WITH QUANTITATIVE WORK, FEW STUDIES IN PHYSICS EDUCATION RESEARCH (PER) HAVE DONE SO. WE CONDUCTED A TWO-PHASE STUDY TO IDENTIFY AND TO UNDERSTAND THE EXTENT OF THESE ISSUES IN QUANTITATIVE PER. DURING PHASE 1, WE CONDUCTED A FOCUS GROUP OF THREE EXPERTS IN THIS AREA, FOLLOWED BY SIX INTERVIEWS. SUBSEQUENT INTERVIEWS REFINED OUR PLAN. BOTH THE FOCUS GROUP AND INTERVIEWS REVEALED ISSUES REGARDING THE LACK OF DETAILS IN SAMPLE DESCRIPTIONS, LACK OF INSTITUTIONAL OR COURSE CONTEXTUAL INFORMATION, LACK OF REPORTING ON LIMITATION, AND OVERGENERALIZATION OR OVERSTATEMENT OF CONCLUSIONS. DURING PHASE 2, WE EXAMINED 72 MANUSCRIPTS THAT USED FOUR CONCEPTUAL OR ATTITUDINAL ASSESSMENTS (FORCE CONCEPT INVENTORY, CONCEPTUAL SURVEY OF ELECTRICITY AND MAGNETISM, BRIEF ELECTRICITY AND MAGNETISM ASSESSMENT, AND COLORADO LEARNING ATTITUDES ABOUT SCIENCE SURVEY). MANUSCRIPTS WERE CODED ON WHETHER THEY FEATURED VARIOUS SAMPLE DESCRIPTIONS, INSTITUTIONAL OR COURSE CONTEXT INFORMATION, LIMITATIONS, AND WHETHER THEY OVERGENERALIZED CONCLUSIONS. WE ALSO ANALYZED THE DATA TO SEE IF REPORTING HAS CHANGED FROM THE EARLIER PERIODS TO MORE RECENT TIMES. WE FOUND THAT NOT MUCH HAS CHANGED REGARDING SAMPLE DESCRIPTIONS AND INSTITUTIONAL OR COURSE CONTEXT INFORMATION, BUT REPORTING AND OVERGENERALIZING CONCLUSIONS HAVE IMPROVED OVER TIME. WE OFFER SOME QUESTIONS FOR RESEARCHERS, REVIEWERS, AND READERS IN PER TO CONSIDER WHEN CONDUCTING OR USING QUANTITATIVE WORK.
EDUCATORS IN HIGHER EDUCATION INSTITUTES OFTEN USE STATISTICAL RESULTS OBTAINED FROM THEIR ONLINE LEARNING MANAGEMENT SYSTEM (LMS) DATASET, WHICH HAS LIMITATIONS, TO EVALUATE STUDENT ACADEMIC PERFORMANCE. THIS STUDY DIFFERS FROM THE CURRENT BODY OF LITERATURE BY INCLUDING AN ADDITIONAL DATASET THAT ADVANCES THE KNOWLEDGE ABOUT FACTORS AFFECTING STUDENT ACADEMIC PERFORMANCE. THE KEY AIMS OF THIS STUDY ARE FOURFOLD. FIRST, IS TO FILL THE EDUCATIONAL LITERATURE GAP BY APPLYING MACHINE LEARNING TECHNIQUES IN EDUCATIONAL DATA MINING, MAKING USE OF THE INTERNET USAGE BEHAVIOUR LOG FILES AND LMS DATA. SECOND, LMS DATA AND INTERNET USAGE LOG FILES WERE ANALYSED WITH MACHINE LEARNING TECHNIQUES FOR PREDICTING AT-RISK-OF-FAILURE STUDENTS, WITH GREATER EXPLANATION ADDED BY COMBINING STUDENT DEMOGRAPHIC DATA. THIRD, THE DEMOGRAPHIC FEATURES HELP TO EXPLAIN THE PREDICTION IN UNDERSTANDABLE TERMS FOR EDUCATORS. FOURTH, THE STUDY USED A RANGE OF INTERNET USAGE DATA, WHICH WERE CATEGORIZED ACCORDING TO TYPE OF USAGE DATA AND TYPE OF WEB BROWSING DATA TO INCREASE PREDICTION ACCURACY.
WITH THE APPLICATION OF LEARNING MANAGEMENT SYSTEMS(LMS) IN ONLINE LEARNING PLATFORMS, THERE IS ALREADY A LARGE AMOUNT OF DATA TO RECORD STUDENT OPERATIONS. MANY RESEARCHERS HAVE DONE SOME RESEARCH BASED ON THIS TYPE OF DATA AND OBTAINED SOME VALID PREDICTORS. FIRST, WE ANALYZE THE THEORETICAL FEASIBILITY OF THIS RESEARCH AND COMBINE OTHER ESTABLISHED RESEARCH RESULTS TO DETERMINE OUR RESEARCH OBJECTIVES. THEN OUR DATA COMES FROM A LEARNING PLATFORM BASED ON MOODLE LMS IN CHONGQING. THE MAIN RESEARCH OBJECTS INCLUDE 8 BLENDED COURSES AND 638 STUDENTS. AFTER SOME EXPERIMENTS, WE ESTABLISHED A GENERAL MULTIPLE LINEAR REGRESSION MODEL WITH INTERACTION EFFECTS TO ANALYZE THE RELATIONSHIP BETWEEN PREDICTOR AND FINAL GRADE. AFTER SOME EXPERIMENTS, WE ESTABLISH A ORDINARY MULTIPLE LINEAR REGRESSION MODEL WITH INTERACTION EFFECTS TO ANALYZE THE RELATIONSHIP BETWEEN PREDICTORS AND FINAL GRADES. OUR MODEL HAS A GOOD TRACK RECORD IN PREDICTING STUDENT ACHIEVEMENT. THEREFORE, WE HOPE TO ESTABLISH A SYSTEM OF ACADEMIC SUPERVISION AND REMINDERS ON THE BASIS OF THIS MODEL TO HELP STUDENTS BETTER MANAGE THEIR OWN LEARNING AND STRENGTHEN THE SUPERVISION OF TEACHERS. SINCE THE DATA MAY VARY GREATLY AT DIFFERENT LEVELS OF EDUCATION, WE NEED TO IMPROVE THE MODEL IN THE FUTURE TO ACHIEVE GREATER PORTABILITY AND SUPPORT MORE DATA INFORMATION.
THE DEBATE ABOUT REAL OBJECTIVES OF ASSESSMENT IS NOT NEW; HOWEVER, CURRENTLY STILL PREDOMINATING SOME ASPECTS, SUCH AS THE ENHANCEMENT OF MEMORY AND PRACTICE THAT REFLECTS A TRADITIONALIST AND LIMITED APPROACH. IT IS NECESSARY TO OVERCOME THE EXCLUSIVE USE OF TRADITIONAL ASSESSMENTS IN ORDER TO ADOPT A FORMATIVE ASSESSMENT. THIS MODALITY SEEKS TO IDENTIFY STUDENT GAPS AND NEEDS IN THE EDUCATIONAL PROCESS, TO RECEIVE FEEDBACK AND TAKE NECESSARY DECISIONS TO IMPROVE TEACHING AND LEARNING PROCESSES. TO MEET THIS IMMINENT NEED AND SEEKING TO ASSIST TEACHERS IN IMPLEMENTING A FORMATIVE EVALUATION, SOME TECHNOLOGICAL RESOURCES HAVE BEEN DEVELOPED. THIS STUDY AIMED TO DESCRIBE THE POTENTIAL OF THREE APPLICATIONS (ARMET, THE CELL, AND 3DCLASS) IN THE FORMATIVE EVALUATION PROCESS, THROUGH THE DATABASES ASSOCIATED WITH THEM. RESULTS SHOW THAT THESE APPLICATIONS PROVIDE MORE TRANSPARENT AND CONSISTENT EVALUATION METRICS, ENABLING THE TEACHER TO SYSTEMATIZE CRITERIA AND INDICATORS, REDUCING THE SUBJECTIVITY OF THE FORMATIVE ASSESSMENT PROCESS AND THE TIME SPENT FOR PROCESSING, TABULATION, AND DATA ANALYSIS. CONSIDERING THAT IT IS POSSIBLE TO CHECK WHAT ISSUES WERE RECURRING ERRORS, TOTAL QUANTITY OF MISTAKES, WHAT QUESTIONS HAD MORE LEVEL OF SUCCESS AND HOW LONG THEY TOOK TO DEVELOP THE ACTIVITY. THEY ALSO ALLOW ASCERTAINING WHETHER THERE HAS BEEN GROWTH OVER TIME OR EVEN IDENTIFYING WHERE ERRORS OCCUR; ALLOWING A THOROUGH INVESTIGATION ON THE POSSIBLE CAUSES, STUDENT'S FEEDBACK, AS WELL AS POINTED AND DIRECTED TEACHER INTERVENTIONS.
THIS ARTICLE PRESENTS A STUDY OF ESSAYCRITIC, A COMPUTER-BASED WRITING AID FOR ENGLISH AS A FOREIGN LANGUAGE (EFL) THAT PROVIDES FEEDBACK ON THE CONTENT OF ENGLISH ESSAYS. WE COMPARED TWO FEEDBACK CONDITIONS: AUTOMATED FEEDBACK FROM ESSAYCRITIC (TARGET CLASS) AND FEEDBACK FROM COLLABORATING PEERS (COMPARISON CLASS). WE USED A MIXED METHODS APPROACH TO COLLECT AND ANALYZE THE DATA, COMBINING INTERACTION ANALYSIS OF CLASSROOM CONVERSATIONS DURING THE WRITING PROCESS AND STATISTICAL ANALYSIS OF STUDENTS' GRADES. THE GRADES OF STUDENTS IN BOTH CLASSES IMPROVED FROM PRE-TEST TO POST-TEST BUT IN DIFFERENT WAYS. THE STUDENTS IN THE TARGET CLASS INCLUDED MORE IDEAS (CONTENT) IN THEIR ESSAYS, WHEREAS THE STUDENTS IN THE COMPARISON CLASS PUT MORE EMPHASIS ON THE ORGANIZATION OF THEIR IDEAS. WE DISCUSS OUR FINDINGS TO IDENTIFY STRENGTHS AND WEAKNESSES OF OUR APPROACH, AND WE END THE PAPER BY SUGGESTING SOME DIRECTIONS FOR FURTHER RESEARCH.
THIS PAPER EXAMINES VISIONS OF 'LEARNING' ACROSS HUMANS AND MACHINES IN A NEAR-FUTURE OF INTENSIVE DATA ANALYTICS. BUILDING UPON THE CONCEPT OF 'LEARNIFICATION', PRACTICES OF 'LEARNING' IN EMERGING BIG DATA-DRIVEN ENVIRONMENTS ARE DISCUSSED IN TWO SIGNIFICANT WAYS: THE TRAINING OF MACHINES, AND THE NUDGING OF HUMAN DECISIONS THROUGH DIGITAL CHOICE ARCHITECTURES. FIRSTLY, 'MACHINE LEARNING' IS DISCUSSED AS AN IMPORTANT EXAMPLE OF HOW DATA-DRIVEN TECHNOLOGIES ARE BEGINNING TO INFLUENCE EDUCATIONAL ACTIVITY, BOTH THROUGH SOPHISTICATED TECHNICAL EXPERTISE AND A GROUNDING IN BEHAVIOURAL PSYCHOLOGY. SECONDLY, WE EXPLORE HOW EDUCATIONAL SOFTWARE DESIGN INFORMED BY BEHAVIOURAL ECONOMICS IS INCREASINGLY INTENDED TO FRAME LEARNER CHOICES TO INFLUENCE AND 'NUDGE' DECISIONS TOWARDS OPTIMAL OUTCOMES. THROUGH THE GROWING INFLUENCE OF 'DATA SCIENCE' ON EDUCATION, BEHAVIOURIST PSYCHOLOGY IS INCREASINGLY AND POWERFULLY INVESTED IN FUTURE EDUCATIONAL PRACTICES. FINALLY, IT IS ARGUED THAT FUTURE EDUCATION MAY TEND TOWARD VERY SPECIFIC FORMS OF BEHAVIOURAL GOVERNANCE - A 'MACHINE BEHAVIOURISM' - ENTAILING COMBINATIONS OF RADICAL BEHAVIOURIST THEORIES AND MACHINE LEARNING SYSTEMS, THAT APPEAR TO WORK AGAINST NOTIONS OF STUDENT AUTONOMY AND PARTICIPATION, SEEKING TO INTERVENE IN EDUCATIONAL CONDUCT AND SHAPING LEARNER BEHAVIOUR TOWARDS PREDEFINED AIMS.
CURRENTLY, BIG DATA ANALYTICS HELP EDUCATORS BETTER ANALYZE STUDENTS AND MONITOR THEIR PROGRESS AND LIKELIHOOD OF SUCCESS. THE ONLINE LEARNING EXPERIENCE APPLIES TO THE EXPERIENCES IN THE ONLINE COMPUTER-MEDIATED DIGITAL SYSTEM TO COLLECT INFORMATION. AN ESSENTIAL PART OF EDUCATION TRAINING PROGRAMS IS CHALLENGING AND EFFECTIVELY IMPLEMENTED BETWEEN TEACHERS AND STUDENTS IN THE ONLINE LEARNING FEEDBACK SYSTEM. HENCE, A BIG DATA VISUALIZATION ASSISTED MULTIMODAL FEEDBACK FRAMEWORK (BDVMFF) IS PROPOSED TO BOOST STUDENTS' CONFIDENCE, SELF-CONSCIOUSNESS, AND MOTIVATION IN THE ONLINE LEARNING PLATFORM. THE BDVMFF OFFERS THE TEACHER A DIGITAL WORKFLOW TO EFFECTIVELY EXCHANGE BOTH THE WRITING AND THE INPUT TO USE MULTIMODAL FEEDBACK EFFECTIVELY. THIS SYSTEM PROVIDES TEACHERS AND STUDENTS WITH AN EFFECTIVE AND STRAIGHTFORWARD DIGITAL LEARNING ENVIRONMENT. THE SIMULATION RESULTS OF BDVMFF SHOW THE HIGHEST PERFORMANCE RATIO (97.9%), THE EFFICIENCY RATIO (96.1%), THE GRADE ANALYSIS RATIO (93.5%), THE COMPUTATION RATIO (95.3%), AND THE LOWEST RESPONSE TIME COMPARED TO EXISTING METHODS.
WHILE MANY STRATEGIES FOR PROTECTING PERSONAL PRIVACY RELY ON REGULATORY FRAMEWORKS, CONSENT, AND ANONYMIZING DATA, THEY ARE NOT ALWAYS EFFECTIVE. TERMS AND CONDITIONS OFTEN LAG BEHIND ADVANCES IN TECHNOLOGY, SOFTWARE, AND USER BEHAVIOURS, AND CONSENT TO USE DATA FOR A RANGE OF UNCLEAR PURPOSES MAY BE PROVIDED UNWITTINGLY. AS THE COMMERCIAL MARKET FOR (STUDENT) DATA EXPANDS, SO DOES THE NUMBER OF BROKERS WHO MOVE, SHARE AND SELL DATA ACROSS CONTINENTS AND LEGISLATIVE ENVIRONMENTS. THIS PAPER REVIEWS FOUR MASSIVE OPEN ONLINE COURSE (MOOC) PROVIDERS FROM DIFFERENT GEOPOLITICAL AND REGULATORY CONTEXTS. IT EXPLORES HOW CONSENT TO COLLECT AND USE DATA IS DESCRIBED TO POTENTIAL USERS, AND HOW THAT CONSENT APPLIES AT MICRO, MESO, AND MACRO LEVELS. THIS PAPER PROPOSES A NEED FOR GREATER TRANSPARENCY AROUND THE IMPLICATIONS OF USERS GRANTING CONSENT AT THE POINT OF REGISTRATION. FURTHER, IT HIGHLIGHTS THAT THOUGH MOOC PROVIDERS HAVE A RESPONSIBILITY TO MAKE CLEAR THE POTENTIAL USES AND SHARING OF USER DATA, USERS THEMSELVES SHOULD ALSO BE MORE AWARE AND CONSIDER HOW MEANINGFUL STUDENT AGENCY CAN BE ACHIEVED.
DIGITAL TEXTBOOK ANALYTICS ARE A NEW METHOD OF COLLECTING STUDENT-GENERATED DATA IN ORDER TO BUILD PREDICTIVE MODELS OF STUDENT SUCCESS. PREVIOUS RESEARCH USING SELF-REPORT OR LABORATORY MEASURES OF READING SHOW THAT ENGAGEMENT WITH THE TEXTBOOK WAS RELATED TO STUDENT LEARNING OUTCOMES. WE HYPOTHESIZED THAT AN ENGAGEMENT INDEX BASED ON DIGITAL TEXTBOOK USAGE DATA WOULD PREDICT STUDENT COURSE GRADES. LINEAR REGRESSION ANALYSES WERE CONDUCTED USING DATA FROM 233 STUDENTS TO DETERMINE WHETHER DIGITAL TEXTBOOK USAGE METRICS PREDICTED FINAL COURSE GRADES. A CALCULATED LINEAR INDEX OF TEXTBOOK USAGE METRICS WAS SIGNIFICANTLY PREDICTIVE OF FINAL COURSE GRADES AND WAS A STRONGER PREDICTOR OF COURSE OUTCOMES THAN PREVIOUS ACADEMIC ACHIEVEMENT. HOWEVER, TIME SPENT READING, ONE OF THE VARIABLES THAT MAKE UP THE INDEX WAS MORE STRONGLY PREDICTIVE OF COURSE OUTCOMES. ADDITIONALLY, STUDENTS WHO WERE IN THE TOP 10TH PERCENTILE IN NUMBER OF HIGHLIGHTS HAD SIGNIFICANTLY HIGHER COURSE GRADES THAN THOSE IN THE LOWER 90TH PERCENTILE. THESE FINDINGS SUGGEST THAT DIGITAL TEXTBOOK ANALYTICS ARE AN EFFECTIVE EARLY WARNING SYSTEM TO IDENTIFY STUDENTS AT RISK OF ACADEMIC FAILURE. THESE DATA CAN BE COLLECTED UNOBTRUSIVELY AND AUTOMATICALLY AND PROVIDE STRONGER PREDICTION OF OUTCOMES THAN PRIOR ACADEMIC ACHIEVEMENT (WHICH TO THIS POINT HAS BEEN THE SINGLE STRONGEST PREDICTOR OF STUDENT SUCCESS). (C) 2015 ELSEVIER INC. ALL RIGHTS RESERVED.
DISTANCE LEARNING HAS HAD A HUGE IMPACT ON THE EDUCATIONAL PROCESS OF UNIVERSITIES AND SCHOOLS. THE MAIN PLATFORM THROUGH WHICH THE E-LEAMING PROCESS AT MOSCOW AVIATION INSTITUTE (NATIONAL RESEARCH UNIVERSITY) (MAI) IS CARRIED OUT IS LMS MAI MOODLE. IT SHOULD BE NOTED THAT ON THE BASIS OF THIS PLATFORM, A WEBSITE WITH COURSES LMS.MAI.RU WAS CREATED. THE SECOND IN POPULARITY AND FREQUENCY OF USE IS MICROSOFT TEAMS PLATFORM (MT). IT IS WORTH NOTING THAT THANKS TO THE PLATFORMS FOR DISTANCE LEARNING, THE EDUCATIONAL PROCESS WAS NOT ONLY NOT DISRUPTED OR STOPPED, BUT WAS ALSO SUPPLEMENTED WITH SUCH ADVANTAGES AS AUTONOMY AND FLEXIBILITY IN ACQUIRING KNOWLEDGE. IN CONNECTION WITH THE CORONAVIMS PANDEMIC, ON MARCH 17, 2020, MAI COMPLETELY SWITCHED TO DISTANCE LEARNING. AT THE MOMENT, THE EPIDEMIOLOGICAL SITUATION IN RUSSIA IS IMPROVING AND UNIVERSITIES ARE AGAIN CONDUCTING FACE-TO-FACE CLASSROOM STUDIES. BUT WHAT WAS HAPPENING WITH TWO PLATFORMS MENTIONED ABOVE AFTER THE END OF THE QUARANTINE? STUDENTS AND TEACHERS OF MAI SAY THAT THE USE OF PLATFORMS HAS DECREASED, BUT CLASSES ARE STILL HELD IN THIS FORMAT. WE DISCUSSED THE PROSPECTS FOR FURTHER USE OF MT AND LMS MAI IN THIS ARTICLE.
DATA FROM STUDENTS AND LEARNING PRACTICES ARE ESSENTIAL FOR FEEDING THE ARTIFICIAL INTELLIGENCE SYSTEMS USED IN EDUCATION. RECURRENT DATA TRAINS THE ALGORITHMS SO THAT THEY CAN BE ADAPTED TO NEW SITUATIONS, EITHER TO OPTIMIZE COURSEWORK OR TO MANAGE REPETITIVE TASKS. AS THE ALGORITHMS SPREAD IN DIFFERENT LEARNING CONTEXTS AND THE ACTIONS WHICH THEY PERFORM EXPAND, PEDAGOGICAL INTERPRETATIVE FRAMEWORKS ARE REQUIRED TO USE THEM PROPERLY. BASED ON CASE ANALYSES AND A LITERATURE REVIEW, THE PAPER ANALYSES THE LIMITS OF LEARNING PRACTICES BASED ON THE MASSIVE USE OF DATA FROM A PEDAGOGICAL APPROACH. THE FOCUS IS ON DATA CAPTURE, BIASES ASSOCIATED WITH DATASETS, AND HUMAN INTERVENTION BOTH IN THE TRAINING OF ARTIFICIAL INTELLIGENCE ALGORITHMS AND IN THE DESIGN OF MACHINE LEARNING PIPELINES. IN ORDER TO FACILITATE THE ADEQUATE USE OF DATA-DRIVEN LEARNING PRACTICES, IT IS PROPOSED TO FRAME APPROPRIATE HEURISTICS TO DETERMINE THE PEDAGOGICAL SUITABILITY OF ARTIFICIAL INTELLIGENCE SYSTEMS AND ALSO THEIR EVALUATION BOTH IN TERMS OF ACCOUNTABILITY AND OF THE QUALITY OF THE TEACHING-LEARNING PROCESS. THUS, FINALLY, A SET OF TOP-DOWN PROPOSED RULES THAT CAN CONTRIBUTE TO FILL THE IDENTIFIED GAPS TO IMPROVE THE EDUCATIONAL USE OF DATA-DRIVEN EDUCATIONAL ALGORITHMS IS DISCUSSED.
AN ASSOCIATION BETWEEN STUDENTS' LEARN-ONLINE ENGAGEMENT AND ACADEMIC PERFORMANCE WAS INVESTIGATED FOR A THIRD-YEAR WATER RESOURCES SYSTEMS DESIGN COURSE AT THE UNIVERSITY OF SOUTH AUSTRALIA IN 2017. AS THE PATTERNS OF DATA WERE NON-PARAMETRIC, MANN-WHITNEY AND KRUSKAL-WALLIS TESTS WERE PERFORMED USING SPSS. IT WAS REVEALED FROM THE TEST RESULTS THAT DISTRIBUTIONS OF STUDENTS' LOGINS TO LEARN-ONLINE SITE FOR ALL CATEGORIES AND SUB-CATEGORIES INCLUDING GENDER, INTERNATIONAL/DOMESTIC STUDENTS AND GRADES WERE ALMOST SIMILAR. THEREFORE, IT IS RELATIVELY UNREALISTIC TO USE LEAN-ONLINE ENGAGEMENT DATA TO PREDICT STUDENTS' PERFORMANCES. A CORRELATION TEST WAS FURTHER PERFORMED TO VALIDATE THE HYPOTHESIS TESTING RESULTS AND A WEAK RELATIONSHIP (PEARSON'S R = 0.29) BETWEEN LOGIN TO LEARN-ONLINE SITE AND GRADE WAS OBSERVED. THE SMALLER F RATIOS OF ONE WAY ANOVA ALSO VALIDATED THE TEST RESULTS. MANN-WHITNEY AND KRUSKAL-WALLIS TESTS CAN BE APPLIED TO COURSE ANALYTICS DATA FOR FACE-TO-FACE AND ONLINE COURSES TO UNDERSTAND A BETTER PICTURE ABOUT THE USES OF LEARN-ONLINE ENGAGEMENT DATA.
WHILE A PANDEMIC IS CURRENTLY SWEEPING THE WORLD, TECHNOLOGY BECOMES A NECESSITY IN ALMOST THE ENTIRE ASPECTS OF LIFE, PARTICULARLY IN LEARNING ACTIVITIES. ONE OF CONVENTIONAL LEARNING-BASED PLATFORMS THAT ADOPT ONLINE SYSTEM IS MOODLE LEARNING MANAGEMENT SYSTEM. THIS STUDY EXPLORED THE ARGUMENTATION SKILLS OF PROSPECTIVE TEACHERS BASED ON COMMOGNITIVE APPROACH USING THE LMS. THIS QUALITATIVE RESEARCH USED ONLINE-BASED SEMI-STRUCTURED QUESTIONNAIRE AND INTERVIEW DURING A DISCUSSION GROUP VIA LMS PLATFORM. THE SUBJECTS OF THIS STUDY WERE 38 PROSPECTIVE TEACHERS. THE RESEARCH INSTRUMENT WAS A QUESTIONNAIRE WITH EIGHT ITEMS. IT WAS INITIALLY ASSESSED WITH CRONBACH'S ALPHA TEST IN WHICH THE VALUE INDICATED THE QUESTIONNAIRE WAS RELIABLE (0.852). THE FINDINGS OF THIS STUDY INDICATE THE ARGUMENTATION SKILLS OF PROSPECTIVE TEACHERS CORRELATE WITH THE COMMOGNITIVE FRAMEWORK, INCLUDING CLAIM WITH ENDORSED NARRATIVES; DATA WITH VISUAL MEDIATORS; WARRANT WITH ROUTINE, WORD USE, AND ENDORSED NARRATIVES; AND BACKING WITH ENDORSED NARRATIVES.
LIBRARY ANALYTICS IN ACADEMIC LIBRARIES, ESPECIALLY IN THE UNITED STATES, ARE PART OF THE EXPANDING WEB OF SOCIETYWIDE SURVEILLANCE. THE USE OF LIBRARY ANALYTICS SYSTEMS POSES SERIOUS THREATS TO THE WELL-BEING OF MANY ACTORS, ESPECIALLY STUDENTS. ACADEMIC LIBRARIANS ARE FACED WITH NEW MORAL CHALLENGES BY SUCH SYSTEMS AS DESCRIBED HERE AND MUST DETERMINE THE EXTENT TO WHICH THEY WISH TO PARTICIPATE IN THE SURVEILLANCE OF STUDENTS FOR WHOM THEY ARE RESPONSIBLE. THE COLUMN CONCLUDES WITH SOME RECOMMENDATIONS FROM THE LITERATURE AND WITH CONSIDERATION OF THE ROLES OF FREE INQUIRY, FREEDOM FROM SURVEILLANCE, AND EXERCISE OF AUTONOMY ESSENTIAL TO HIGHER EDUCATION AND FULL CITIZENSHIP.
THIS PAPER EXPLORES DATA ANALYTICS APPLIED TO URBAN EDUCATION, FOCUSING IN PARTICULAR ON ISSUES OF REPRESENTATIONALISM, THE VIEW THAT REPRESENTATIONS (HERE, DIGITAL DATA) STAND IN MIMETIC RELATION TO SOME EXTERNAL REALITY FROM WHICH THEY ARE ONTOLOGICALLY DISTINCT. BASED ON INTERVIEWS CONDUCTED OVER THE 2016-2017 SCHOOL YEAR WITH A TEAM OF DATA PROFESSIONALS EMPLOYED BY A CHARTER MANAGEMENT ORGANIZATION (CMO) THAT OPERATES ONE DOZEN SCHOOLS IN SOUTH AND EAST LOS ANGELES, THIS PAPER SHOWS THAT RESPONDENTS ENDORSE A RANGE OF VIEWS ABOUT HOW DATA CAN STAND IN FOR THINGS, EVENTS, AND PEOPLE. DATA PROFESSIONALS CHARGED WITH THE CREATION AND MANAGEMENT OF STUDENT-LEVEL DATA EXPRESSED VIEWS CONSISTENT WITH A REPRESENTATIONALIST UNDERSTANDING OF DIGITAL DATA; BY CONTRAST, THOSE CHARGED CHIEFLY WITH AGGREGATING AND ANALYZING HETEROGENEOUS STREAMS OF DATA EXPRESSED GREATER SKEPTICISM TOWARD REPRESENTATIONALIST COMMITMENTS. DESPITE THIS HETEROGENEOUS SET OF VIEWS, ALL OF THE DATA PROFESSIONALS INTERVIEWED RELIED ON THE SAME MEDIUM TO COMMUNICATE THEIR INTERPRETATIONS OF DATA TO OTHER MEMBERS OF THE ORGANIZATION, INCLUDING SCHOOL PERSONNEL: A BESPOKE PLATFORM THAT CONSISTS OF 125 DASHBOARDS. CRITICALLY, GRAPHICAL MEANS OF VISUALIZATION EXPRESSED OBJECTIVITY, CERTAINTY, AND ACTUARIAL FORESIGHT, EVEN IN CASES WHERE DATA PROFESSIONALS EXPRESSED AMBIVALENCE ABOUT THE REPRESENTATIONAL POWER OF A GIVEN SOURCE OF DATA.
WE REVIEW OUR RESEARCH WITH DEGREE AND ANALYSE HOW OUR WORK HAS IMPACTED THE COLLABORATIVE LEARNING COMMUNITY SINCE 2000. OUR RESEARCH IS FRAMED WITHIN THE CONTEXT OF COMPUTER-BASED INTERACTION ANALYSIS AND THE DEVELOPMENT OF COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) TOOLS. WE IDENTIFY SOME ASPECTS OF OUR WORK WHICH HAVE BEEN FOLLOWED UP BY OTHER RESEARCHERS AND HIGHLIGHT SOME ISSUES THAT REMAIN PENDING. FINALLY WE PRESENT A PERSPECTIVE OF COMPUTER-BASED INTERACTION ANALYSIS EVOLUTION IN RELATION TO A WIDER TECHNOLOGICAL ENHANCED LEARNING CONTEXT.
FLIPPED LEARNING HAS BEEN COMMONLY USED TO PROVIDE STUDENTS WITH LEARNING CONTENTS INSIDE/OUTSIDE CLASSROOMS. IT ENCOURAGES STUDENTS TO PREVIEW LEARNING CONTENTS BEFORE THEIR CLASSES. THEREAFTER, LEARNING ACTIVITIES ARE TAKEN IN CLASSES WITH INSTRUCTIONS OR HELP FROM THE TEACHER. AN IMPORTANT ISSUE IN THE SUCCESS OF FLIPPED LEARNING IS THE EFFECTIVENESS OF STUDENTS' PREVIEW BECAUSE IT AFFECTS THE SUBSEQUENT LEARNING ACTIVITIES IN THEIR CLASSES. FORTUNATELY, FOR EPUB3 EBOOKS USED IN FLIPPED CLASSES, THE EMBEDDED TRACK AND TEST FUNCTIONS CAN BE USED TO TRACK THE ACCESSES AND TESTS TAKEN ON THESE EBOOKS. AS SUCH, THE TEACHER CAN CAPTURE THE EFFECTIVENESS OF STUDENTS' PREVIEW BY CHECKING THEIR PRE-CLASS ACCESSES AND TESTS, AND HENCE TAKE ADEQUATE ACTIONS FOR SUBSEQUENT CLASS ACTIVITIES. IN THIS PAPER, WE PRESENT AN ANALYTICS APPROACH INVESTIGATING THE EFFECTIVENESS OF STUDENTS' PREVIEW. THEIR ACCESSES AND TESTS ON EPUB3 EBOOKS ARE TRACKED, RECORDED, AND ANALYSED USING A BIG DATA BOOCS (EBOOK OPEN ONLINE COURSES) PLATFORM. THE APPROACH IS APPLIED TO A 3-CLASS COURSE AT A UNIVERSITY IN TAIWAN WITH 71 FIRST-YEAR STUDENTS ENROLLED. AS THE RESULTS ILLUSTRATE, THE APPROACH IS USEFUL FOR SUPPORTING TEACHERS TO TAKE ADEQUATE ACTIONS IN CLASSES. STUDENTS' PREVIEW EFFECTIVENESS IS GRADUALLY ENHANCED IN LATER CLASSES.
DURING INSTRUCTION, USING INFORMATION THAT IS DIRECTLY RELEVANT TO STUDENTS WITHIN A LESSON CREATES A MORE PERSONALIZED LESSON THAT MAY BETTER RESONATE WITH THE STUDENTS. HOWEVER, IN A CLASS, EACH STUDENT HAS HIGHLY VARIED INDIVIDUAL EXPERIENCES AND IT IS UNTENABLE FOR THE TEACHER TO REMEMBER ALL OF THE EXPERIENCES. FURTHERMORE, TEACHING ALREADY INVOLVES SUBSTANTIAL MULTITASKING THAT MAY BE QUITE COGNITIVELY OVERWHELMING, EVEN WITH NON-PERSONALIZED LESSONS. THEREFORE, PROVIDING SUPPORT TO THE TEACHER TO INCORPORATE STUDENTS' INDIVIDUAL EXPERIENCES IS CRUCIAL TO ENABLE TRULY PERSONALIZED TEACHING. WE PROPOSE THAT VISUALIZING STUDENTS' INDIVIDUAL EXPERIENCES FOR THE TEACHER DURING INSTRUCTION MAY HELP HIM OR HER ADAPT THE LESSON IN REAL-TIME TO THE STUDENTS IN THE CLASS. THIS PAPER INVESTIGATES THE KIND OF VISUALIZATION THAT WOULD ENABLE A TEACHER TO UPTAKE A STUDENT'S EXPERIENCES THE MOST EFFECTIVELY. WE CONDUCTED A WITHIN-SUBJECTS STUDY TO ASSESS THE IMPACT THAT THREE FORMS OF VISUALIZATION (PLAIN TEXT, GRAPHICAL MIND MAP, AND STORY ILLUSTRATION) WOULD HAVE ON VISUAL EFFICIENCY DURING A HYPOTHETICAL TEACHING SCENARIO. VISUAL EFFICIENCY WAS MEASURED IN TERMS OF RESPONSE ACCURACY, RESPONSE TIME, AND MENTAL EFFORT. RESULTS SUGGESTED THAT VISUAL EFFICIENCY CAN BE IMPROVED BY USING PROPER VISUALIZATION DESIGN. WE DISCUSS THE IMPLICATIONS FOR SUPPORTING REAL-TIME PERSONALIZED INSTRUCTION.
CONTEXT . CONCEPTUAL DEVELOPMENTS IN OUR UNDERSTANDING OF KNOWLEDGE ARE MERGING WITH MACHINE-LEARNING METHODS FOR MAKING SENSE OF DATA. THIS CREATES NEW, AND INTERESTING WAYS IN WHICH WE CAN DOCUMENT AND ANALYSE KNOWLEDGE, AND CONCEPTUAL CHANGE. PROBLEM . CURRENTLY, THE STUDY OF CONCEPTUAL CHANGE IS OFTEN LIMITED TO SMALL SAMPLE SIZES BECAUSE OF THE LABORIOUS NATURE OF EXISTING, PURELY QUALITATIVE APPROACHES. METHOD . WE PRESENT ASSOCIATION RULE MINING TO BETTER MEASURE AND UNDERSTAND CHANGES IN STUDENTS' THINKING AT THE CLASSROOM LEVEL, BASED ON DATA COLLECTED WHILE IMPLEMENTING A CONSTRUCTIONIST LEARNING ACTIVITY IN A US COLLEGE CLASSROOM. ASSOCIATION RULE MINING IS USED ON A SET OF QUALITATIVELY CODED STUDENT RESPONSES. WE THEN LOOK AT CHANGES IN THE ASSOCIATION RULES BETWEEN STUDENTS' RESPONSES BEFORE AND AFTER A LEARNING ACTIVITY TO BETTER UNDERSTAND STUDENTS'CONCEPTUAL CHANGE AT THE CLASSROOM LEVEL. RESULTS . WE FIND THAT STUDENTS CONVERGE ON A MORE COMPLETE AND ACCURATE SET OF CAUSAL CLAIMS IN THEIR POST-RESPONSES. FINDING THESE CHANGES WOULD HAVE BEEN DIFFICULT OR IMPOSSIBLE WITHOUT ASSOCIATION RULE MINING, OR A SIMILAR APPROACH. THIS SUGGESTS THAT ASSOCIATION RULE MINING IS A POTENTIALLY FRUITFUL APPROACH TO ANALYSING CONCEPTUAL CHANGE AT THE CLASSROOM LEVEL. CONSTRUCTIVIST CONTENT . ASSOCIATION RULE MINING IS AGNOSTIC WITH REGARD TO THE ONTOLOGY OF ITS DATA. THIS MAKES ASSOCIATION RULE MINING A PARTICULARLY SUITABLE ANALYSIS METHOD WHEN TAKING A CONSTRUCTIVIST VIEW OF LEARNING
CONTRIBUTION: THE MAIN CONTRIBUTION OF THIS WORK IS A WAY TO IMPROVE EXISTING DEGREES WITH STATE-OF-THE-ART TECHNOLOGIES SO THAT THEY STAY INTERESTING AND USEFUL FOR STUDENTS. IT IS OF INTEREST TO FACULTY IN ANY UNIVERSITY WORLDWIDE, ESPECIALLY IN ENGINEERING EDUCATION, WHERE THE CONSTANT EVOLUTION OF TECHNOLOGY MAKES FACULTY DEVOTE EFFORT TO KEEP COURSES UP-TO-DATE. BACKGROUND: OVER LAST FEW YEARS, BIG DATA HAS BEEN ONE OF THE MOST DEMANDED TECHNOLOGIES BY COMPANIES IN ANY FIELD. IN ORDER TO FILL THIS NEED OF BIG DATA PROFESSIONALS, UNIVERSITIES MUST CREATE NEW DEGREES OR UPDATE EXISTING DEGREES SO THAT THEIR GRADUATED STUDENTS ARE READY TO PERFORM THIS JOB. FOR THIS, THE DEGREE IN COMPUTER ENGINEERING OF OUR UNIVERSITY, WHICH IS TAUGHT TOTALLY ONLINE, HAS BEEN UPDATED TO INCLUDE BIG DATA TECHNOLOGIES. THESE TECHNOLOGIES ARE INCLUDED IN A DATABASES COURSE. INTENDED OUTCOMES: THE PROPOSAL ALLOWS FACULTY FROM ANY UNIVERSITY WORLDWIDE TO KEEP DEGREES UP-TO-DATE WITH MINIMAL ADMINISTRATIVE EFFORT. APPLICATION DESIGN: BIG DATA TECHNOLOGIES HAVE BEEN INCLUDED IN A PRACTICAL EXERCISE, SINCE DEEPER MODIFICATIONS IN THE COURSE WOULD REQUIRE A LOT ADMINISTRATIVE OF EFFORT. THIS WORK PRESENTS THIS EXERCISE FOCUSING ON THE LEARNING OBJECTIVES IT PURSUES. FINDINGS: A NUMBER OF DIMENSIONS ARE ANALYZED OVER SEVERAL ACADEMIC YEARS, RESULTING THAT ALMOST ALL SUBMITTING STUDENTS HAVE ACHIEVED ALL THE LEARNING OUTCOMES OF THE EXERCISE, THE COURSE EVALUATION AND SUCCESS RATES HAVE IMPROVED, AND STUDENTS VALUE POSITIVELY THE EXERCISE, THE MATERIALS, AND THE KNOWLEDGE ACQUIRED.
PREDICTING STUDENTS' LINE OF ACTIONS HELPS EDUCATORS GIVE ADEQUATE GUIDANCE TO STUDENTS, BUT THIS REMAINS A CHALLENGE IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM) EDUCATION. GIVEN THIS, THERE IS A SCARCITY OF RELATED RESEARCH THAT WILL HELP IMPROVE TEACHERS' PREDICTION CAPABILITIES ON STUDENTS' LINE OF ACTIONS WHEN TACKLING ILL-DEFINED PROBLEMS (IDPS), AS WELL AS HOW EMERGING DATA MINING TECHNIQUES COULD CONTRIBUTE TO SUCH PREDICTION. THE PRESENT STUDY AIMS TO FILL THE GAP BY MEASURING THE QUALITY OF TEACHERS' PREDICTIONS (LABELED EXPERT PREDICTION), WHERE 43 ELEMENTARY TEACHERS PREDICT STUDENTS' STEP-BY-STEP ACTIONS WHEN SOLVING AN IDP THROUGH THE LIGHT PATH TASK (LPT), AND THEN COMPARING ITS QUALITY WITH THAT OF MACHINE PREDICTION, EXECUTED VIA SEQUENTIAL PATTERN MINING TECHNIQUES. DATA ON STUDENTS' LINES OF ACTION WERE COLLECTED FROM 501 5TH- AND 6TH-GRADE STUDENTS, AGED 11-12. THE RESULTS SHOWED THE SIGNIFICANTLY LOWER ACCURACY OF EXPERT PREDICTION COMPARED TO MACHINE PREDICTION, WHICH HIGHLIGHTS THE ADVANTAGES OF USING DATA MINING IN PREDICTING STUDENTS' ACTIONS AND SHOWS ITS POSSIBLE APPLICATION AS A RECOMMENDATION SYSTEM TO PROVIDE ADAPTIVE GUIDANCE IN FUTURE STEM EDUCATION.
THIS ARTICLE PRESENTS THE CURRENT PERCEPTIONS ON THE VALUE OF ANALYTICS AND THEIR POSSIBLE CONTRIBUTION TO THE HIGHER EDUCATION SECTOR IN NEW ZEALAND. SEVEN OUT OF EIGHT RESEARCH-INTENSIVE PUBLIC UNIVERSITIES IN NEW ZEALAND TOOK PART IN THE STUDY. PARTICIPANTS INCLUDED SENIOR MANAGEMENT AND THOSE WHO HAVE SOME ROLE ASSOCIATED WITH DECISION-MAKING WITHIN HIGHER EDUCATION (N = 82). THE STUDY FOUND INCONSISTENT UNDERSTANDING OF THE MEANING OF ANALYTICS ACROSS PARTICIPANTS. IN PARTICULAR, THREE FORMS OF PERCEPTIONS OF ANALYTICS WERE IDENTIFIED: STRUCTURAL; FUNCTIONAL AND STRUCTURAL-FUNCTIONAL. IT WAS EVIDENT THAT SOME PARTICIPANTS VIEWED ANALYTICS IN ITS STRUCTURAL ELEMENTS SUCH AS STATISTICS, METRICS, TRENDS, NUMBERS, GRAPH, AND ANY RELEVANT INFORMATION/DATA TO ENHANCE BETTER DECISION-MAKING, WHEREAS OTHER PARTICIPANTS PERCEIVED THE NOTION OF ANALYTICS IN TERMS OF FUNCTIONAL ASPECT; AS MEANS TO AN END, A PROCESS TO USE THE DATA TO GAIN INSIGHTS AND TAKING ACTION ON COMPLEX PROBLEMS, YET A THIRD GROUP VIEWED ANALYTICS FROM BOTH STRUCTURAL-FUNCTIONAL PERSPECTIVES. THESE KINDS OF PERCEPTIONS HAVE TO A LARGER EXTENT INFLUENCED PARTICIPANTS' VIEWS ON THE VALUE OF ANALYTICS IN SHAPING POLICY AND PRACTICE. ALSO, LITERATURE HAS ADDRESSED A NUMBER OF POSSIBLE CHALLENGES ASSOCIATED WITH THE LARGE-SCALE INSTITUTIONAL IMPLEMENTATION OF ANALYTICS. THESE CHALLENGES WERE: DIFFICULTIES IN EXTRACTING DATA FROM MULTIPLE DATABASES, MAINTAINING DATA QUALITY, ETHICAL AND PRIVACY ISSUES, AND LACK OF PROFESSIONAL DEVELOPMENT OPPORTUNITIES. THIS ARTICLE AIMS TO BROADLY CONTRIBUTE TO A BETTER UNDERSTANDING OF CURRENT PERCEPTION AND VALUE OF ANALYTICS IN HIGHER EDUCATION, AND IN PARTICULAR WITHIN THE NEW ZEALAND CONTEXT.
THE PURPOSE OF THIS PAPER IS TO IDENTIFY AT-RISK ONLINE STUDENTS EARLIER, MORE OFTEN, AND WITH GREATER ACCURACY USING TIME-SERIES CLUSTERING. THE CASE STUDY SHOWED THAT THE PROPOSED APPROACH COULD GENERATE MODELS WITH HIGHER ACCURACY AND FEASIBILITY THAN THE TRADITIONAL FREQUENCY AGGREGATION APPROACHES. THE BEST PERFORMING MODEL CAN START TO CAPTURE AT-RISK STUDENTS FROM WEEK 10. IN ADDITION, THE FOUR PHASES IN STUDENT'S LEARNING PROCESS DETECTED HOLIDAY EFFECT AND ILLUSTRATE AT-RISK STUDENTS' BEHAVIORS BEFORE AND AFTER A LONG HOLIDAY BREAK. THE FINDINGS ALSO ENABLE ONLINE INSTRUCTORS TO DEVELOP CORRESPONDING INSTRUCTIONAL INTERVENTIONS VIA COURSE DESIGN OR STUDENT-TEACHER COMMUNICATIONS.
THE COVID-19 PANDEMIC LED CATALAN UNIVERSITIES TO DO ALL TEACHING AND EVALUATION ONLINE FROM 11 MARCH 2020 UNTIL THE END OF TERM ON 30 JULY. CONVENTIONAL UNIVERSITIES MADE THE TRANSITION TO ONLINE TEACHING IN JUST A FEW DAYS AND SUDDENLY VIRTUAL PLATFORMS BECOME THE CENTRE OF INTERACTION BETWEEN LECTURERS AND STUDENTS. DATA THAT WERE OBTAINED FROM THE VIRTUAL PLATFORMS GAVE EXTREMELY VALUABLE INFORMATION ABOUT WHAT WAS BEING DONE IN CLASS. THIS PAPER ANALYSES DATA TAKEN FROM ATENEA, THE MOODLE VIRTUAL PLATFORM AT THE UNIVERSITAT POLITECNICA DE CATALUNYA (UPC), DURING QUARANTINE. THE KEY INDICATORS AND A DATA ANALYSIS DESIGN FOR MOODLE HAVE BEEN PROPOSED, WHICH REVEAL TEACHING DEVELOPMENTS AT VARIOUS LEVELS (OVERALL AND AT THE CENTRE AND SUBJECT LEVEL). THIS IS APPLIED TO STUDY DATA FROM THE UPC MOODLE AND THE RESULTS ARE DISCUSSED. THE METHODOLOGY CAN BE EXTRAPOLATED TO OTHER UNIVERSITIES WITH MOODLE PLATFORMS BECAUSE THE UPC IS A SET OF SMALL CAMPUSES AND CENTRES.
THIS PAPER INTRODUCES VISEN, A NOVEL VISUAL NARRATIVE FRAMEWORK THAT HAS BEEN SHOWN TO FACILITATE, SUPPORT, AND ENHANCE STUDENT ENGAGEMENT IN AN ADAPTIVE ONLINE LEARNING ENVIRONMENT (OLE). VISEN PROVIDES EXPLORABLE VISUAL NARRATIVES PERSONALIZED TO STUDENTS IN ORDER TO SUPPORT THEM IN ENGAGING WITH COURSE CONTENT. THE EVALUATION OF VISEN SHOWED THAT THE EXPLORABLE VISUAL NARRATIVES ENCOURAGED THE MAJORITY OF 'IMPROVING ENGAGEMENT STUDENTS', THAT COMPLETED THE INFORMATION MANAGEMENT AND DATA ENGINEERING MODULE AS PART OF THEIR UNDERGRADUATE DEGREE, TO ENGAGE WITH ASSIGNED ACTIVITIES, AND SUBSEQUENTLY THESE LEARNERS ENHANCED THEIR ENGAGEMENT LEVELS. VISUALIZATIONS HAVE BEEN USED IN OLES TO SUPPORT STUDENTS BY PRESENTING STUDENT DATA. INFORMATION VISUALIZATION RESEARCH HAS DEMONSTRATED THE VALUE OF VISUAL NARRATIVES IN COMMUNICATING A MESSAGE, BY HIGHLIGHTING FACTS AND MAKING THE MESSAGE MORE MEMORABLE. IN ADDITION, VISUAL DATA EXPLORATION CAN SUPPORT USERS IN UNDERSTANDINGS THE MESSAGE. HOWEVER, IN OLES, EXPLORABLE VISUAL NARRATIVES HAVE NOT BEEN USED TO DATE TO SUPPORT STUDENT ENGAGEMENT OR TO GUIDE LEARNERS THROUGH A MESSAGE THAT THEY COULD EXPLORE AND UNDERSTAND. THIS PAPER EVALUATES THE IMPACT THAT EXPLORABLE VISUAL NARRATIVES HAD ON STUDENT COURSE ENGAGEMENT DURING TWO SUCCESSIVE ACADEMIC YEARS.
THIS EXPLANATORY SEQUENTIAL MIXED METHODS STUDY EXPLORES THE PERCEPTIONS OF ACADEMIC AND ADMINISTRATIVE MANAGERS RESPONSIBLE FOR TEACHER TRAINING AT A PUBLIC UNIVERSITY IN COLOMBIA, AS WELL AS THEIR VIEWS ON IMPROVING SUCH TRAINING AFTER LEARNING ABOUT THE PERFORMANCE OF TEACHERS STUDENT TEACHERS IN THE 2019 SABER PRO TEST, THE DIFFERENCES IN THEIR TEST SCORES, AND THE RELATIONSHIPS AND STATISTICAL CORRELATIONS BETWEEN THESE OUTCOMES AND THE STUDENTS' PERSONAL, FAMILY, SOCIOECONOMIC AND ACADEMIC CHARACTERISTICS. OUR FINDINGS SHOW SIGNIFICANT DIFFERENCES IN THE STUDENT TEACHERS' MEAN SCORES AND PERFORMANCE WHEN DATA ARE GROUPED ACCORDING TO PERSONAL, SOCIOECONOMIC AND ACADEMIC CONDITIONS; A SIGNIFICANT RELATIONSHIP BETWEEN PERFORMANCE AND STUDENT TEACHER CHARACTERISTICS; AND CORRELATIONS BETWEEN CRITICAL READING SCORES AND THE OTHER COMPETENCIES ASSESSED. OUR DATA ALSO HIGHLIGHT THE LACK OF KNOWLEDGE AMONG ACADEMIC AND ADMINISTRATIVE MANAGERS ABOUT STUDENTS' LIFE CIRCUMSTANCES AND THE DIVERSITY OF FACTORS THAT MAY IMPACT THEIR PERFORMANCE; THE IMPORTANCE OF CORRELATIONAL DATA; THE DIFFERENCE BETWEEN EXPECTED AND TRUE OUTCOMES; THE INEQUITY UNDER WHICH STUDENTS SEEM TO PURSUE THEIR EDUCATION; THE LIMITATIONS IN ACCESS TO RESOURCES; THE TRAINING REQUIRED FOR TEACHERS TO BE ABLE TO ANALYZE QUANTITATIVE DATA AND USE SPECIFIC SOFTWARE; THE IMPACT OF TEACHERS' CRITICAL READING SKILLS ON STUDENT OUTCOMES; THE IMPORTANCE OF DATA-DRIVEN DECISION-MAKING; AND THE NEED FOR TEACHERS TO ENGAGE IN QUANTITATIVE RESEARCH PRACTICES.
PLAGIARISM DETECTION FOR COMPUTER PROGRAMMING EXERCISES IS A DIFFICULT PROBLEM. A TRADITIONAL STRATEGY HAS BEEN TO COMPARE THE SUBMISSIONS FROM ALL OF THE STUDENTS IN A CLASS, SEARCHING FOR SIMILARITIES BETWEEN SUBMISSIONS SUGGESTIVE OF COPYING. AUTOMATED TOOLS EXIST THAT COMPARE SUBMISSIONS IN ORDER TO HELP WITH THIS SEARCH. INCREASINGLY, HOWEVER, INSTRUCTORS HAVE ALLOWED STUDENTS TO SUBMIT MULTIPLE SOLUTIONS, RECEIVING FORMATIVE FEEDBACK BETWEEN SUBMISSIONS, WITH FEEDBACK OFTEN GENERATED BY AUTOMATED ASSESSMENT SYSTEMS. ALLOWING MULTIPLE SUBMISSIONS ALLOWS FOR A FUNDAMENTALLY NEW WAY TO DETECT PLAGIARISM. SPECIFICALLY, STUDENTS MAY STRUGGLE WITH AN EXERCISE UNTIL FRUSTRATION LEADS THEM TO SUBMIT WORK THAT IS NOT THEIR OWN. WE PRESENT A METHOD FOR DETECTING PLAGIARISM FROM THE SEQUENCE OF SUBMISSIONS MADE BY AN INDIVIDUAL STUDENT. WE HAVE EXPLORED A VARIETY OF MEASURES OF PROGRAM CHANGE OVER SUBMISSIONS, AND WE HAVE FOUND A SET OF FEATURES THAT CAN BE TRANSFORMED, USING LOGISTIC REGRESSION, INTO A SCORE CAPTURING THE LIKELIHOOD OF PLAGIARISM. WE HAVE APPLIED THIS METHOD TO DATA FROM FOUR EXERCISES FROM AN UNDERGRADUATE PROGRAMMING CLASS. WE SHOW THAT OUR AUTOMATICALLY GENERATED SCORES ARE STRONGLY CORRELATED WITH THE ASSESSMENTS OF PLAGIARISM MADE BY AN EXPERT INSTRUCTOR. THUS, THE SCORES CAN ACT AS A POWERFUL TOOL FOR SEARCHING FOR CASES OF ACADEMIC DISHONESTY.
INFORMATION SERVICE PROVIDERS OFTEN REQUIRE EVIDENCE FROM MULTIPLE, HETEROGENEOUS INFORMATION SOURCES TO BETTER CHARACTERIZE USERS AND OFFER PERSONALIZED SERVICE. IN MANY CASES, STATISTIC INFORMATION (FOR EXAMPLE, USERS' PROFILES) AND SEQUENTIALLY DYNAMIC INFORMATION (FOR EXAMPLE, LOGS OF INTERACTION WITH INFORMATION SYSTEMS) ARE TWO PROMINENT SOURCES THAT CAN BE COMBINED TO ACHIEVE OPTIMIZED RESULTS. PREVIOUS ATTEMPTS IN COMBINING THESE TWO SOURCES MAINLY EXPLOITED MODELS DESIGNED FOR EITHER STATIC OR SEQUENTIAL INFORMATION, BUT NOT BOTH. THIS STUDY AIMS TO FILL THE GAP BY PROPOSING A NOVEL JOINT NEURAL NETWORK MODEL THAT CAN NATURALLY FIT BOTH STATIC AND SEQUENTIAL USER DATA. TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED METHOD, THIS STUDY USES THE PROBLEM OF AT-RISK STUDENT PREDICTION AS AN EXAMPLE WHERE BOTH STATIC DATA (PERSONAL PROFILES) AND SEQUENTIAL DATA (EVENT LOGS) ARE INVOLVED. A THOROUGH EVALUATION WAS CONDUCTED ON AN OPEN DATA SET, WITH COMPARISONS TO A RANGE OF EXISTING APPROACHES INCLUDING BOTH STATIC AND SEQUENTIAL MODELS. THE RESULTS REVEAL SUPERB PERFORMANCES OF THE PROPOSED METHOD. IMPLICATIONS OF THE FINDINGS ON FURTHER RESEARCH AND APPLICATIONS OF JOINT MODELS ARE DISCUSSED.
THIS PAPER PRESENTS A WEB-BASED SOFTWARE TOOL FOR TUTORING SUPPORT OF ENGINEERING STUDENTS WITHOUT ANY NEED OF DATA SCIENTIST BACKGROUND FOR USAGE. THIS TOOL IS FOCUSED ON THE ANALYSIS OF STUDENTS' PERFORMANCE, IN TERMS OF THE OBSERVABLE SCORES AND OF THE COMPLETION OF THEIR STUDIES. FOR THAT PURPOSE, IT USES A DATA SET THAT ONLY CONTAINS FEATURES TYPICALLY GATHERED BY UNIVERSITY ADMINISTRATIONS ABOUT THE STUDENTS, DEGREES AND SUBJECTS. THE WEB-BASED TOOL PROVIDES ACCESS TO RESULTS FROM DIFFERENT ANALYSES. CLUSTERING AND VISUALIZATION IN A LOW-DIMENSIONAL REPRESENTATION OF STUDENTS' DATA HELP AN ANALYST TO DISCOVER PATTERNS. THE COORDINATED VISUALIZATION OF AGGREGATED STUDENTS' PERFORMANCE INTO HISTOGRAMS, WHICH ARE AUTOMATICALLY UPDATED SUBJECT TO CUSTOM FILTERS SET INTERACTIVELY BY AN ANALYST, CAN BE USED TO FACILITATE THE VALIDATION OF HYPOTHESES ABOUT A SET OF STUDENTS. CLASSIFICATION OF STUDENTS ALREADY GRADUATED OVER THREE PERFORMANCE LEVELS USING EXPLORATORY VARIABLES AND EARLY PERFORMANCE INFORMATION IS USED TO UNDERSTAND THE DEGREE OF COURSE-DEPENDENCY OF STUDENTS' BEHAVIOR AT DIFFERENT DEGREES. THE ANALYSIS OF THE IMPACT OF THE STUDENT'S EXPLANATORY VARIABLES AND EARLY PERFORMANCE IN THE GRADUATION PROBABILITY CAN LEAD TO A BETTER UNDERSTANDING OF THE CAUSES OF DROPOUT. PRELIMINARY EXPERIMENTS ON DATA OF THE ENGINEERING STUDENTS FROM THE 6 INSTITUTIONS ASSOCIATED TO THIS PROJECT WERE USED TO DEFINE THE FINAL IMPLEMENTATION OF THE WEB-BASED TOOL. PRELIMINARY RESULTS FOR CLASSIFICATION AND DROP-OUT WERE ACCEPTABLE SINCE ACCURACIES WERE HIGHER THAN 90% IN SOME CASES. THE USEFULNESS OF THE TOOL IS DISCUSSED WITH RESPECT TO THE STATED GOALS, SHOWING ITS POTENTIAL FOR THE SUPPORT OF EARLY PROFILING OF STUDENTS. REAL DATA FROM ENGINEERING DEGREES OF EU HIGHER EDUCATION INSTITUTIONS SHOW THE POTENTIAL OF THE TOOL FOR MANAGING HIGH EDUCATION AND VALIDATE ITS APPLICABILITY ON REAL SCENARIOS.
THE MEASUREMENT OF STUDENT PERFORMANCE DURING THEIR PROGRESS THROUGH UNIVERSITY STUDY PROVIDES ACADEMIC LEADERSHIP WITH CRITICAL INFORMATION ON EACH STUDENT'S LIKELIHOOD OF SUCCESS. ACADEMICS HAVE TRADITIONALLY USED THEIR INTERACTIONS WITH INDIVIDUAL STUDENTS THROUGH CLASS ACTIVITIES AND INTERIM ASSESSMENTS TO IDENTIFY THOSE "AT RISK" OF FAILURE/WITHDRAWAL. HOWEVER, MODERN UNIVERSITY ENVIRONMENTS, OFFERING EASY ON-LINE AVAILABILITY OF COURSE MATERIAL, MAY SEE REDUCED LECTURE/TUTORIAL ATTENDANCE, MAKING SUCH IDENTIFICATION MORE CHALLENGING. MODERN DATA MINING AND MACHINE LEARNING TECHNIQUES PROVIDE INCREASINGLY ACCURATE PREDICTIONS OF STUDENT EXAMINATION ASSESSMENT MARKS, ALTHOUGH THESE APPROACHES HAVE FOCUSSED UPON LARGE STUDENT POPULATIONS AND WIDE RANGES OF DATA ATTRIBUTES PER STUDENT. HOWEVER, MANY UNIVERSITY MODULES COMPRISE RELATIVELY SMALL STUDENT COHORTS, WITH INSTITUTIONAL PROTOCOLS LIMITING THE STUDENT ATTRIBUTES AVAILABLE FOR ANALYSIS. IT APPEARS THAT VERY LITTLE RESEARCH ATTENTION HAS BEEN DEVOTED TO THIS AREA OF ANALYSIS AND PREDICTION. WE DESCRIBE AN EXPERIMENT CONDUCTED ON A FINAL-YEAR UNIVERSITY MODULE STUDENT COHORT OF 23, WHERE INDIVIDUAL STUDENT DATA ARE LIMITED TO LECTURE/TUTORIAL ATTENDANCE, VIRTUAL LEARNING ENVIRONMENT ACCESSES AND INTERMEDIATE ASSESSMENTS. WE FOUND POTENTIAL FOR PREDICTING INDIVIDUAL STUDENT INTERIM AND FINAL ASSESSMENT MARKS IN SMALL STUDENT COHORTS WITH VERY LIMITED ATTRIBUTES AND THAT THESE PREDICTIONS COULD BE USEFUL TO SUPPORT MODULE LEADERS IN IDENTIFYING STUDENTS POTENTIALLY "AT RISK."
ENGAGEMENT IS A CRUCIAL GOAL IN BOTH INTERACTIVE SYSTEM DESIGN AND EDUCATION. WHEREAS ENGAGEMENT IN DIGITAL SYSTEMS ARE BASED ON CONCEPTS SUCH AS IMMERSION AND USER EXPERIENCE, ENGAGEMENT IN EDUCATION IS DESCRIBED IN THREE DIMENSIONS OF BEHAVIORAL, COGNITIVE AND EMOTIONAL ENGAGEMENTS. IN THIS PAPER A RESEARCH APPROACH TO UNDERSTAND USER ENGAGEMENT IN DIGITAL EDUCATION IS PRESENTED. THE APPROACH IS BASED ON A MICROLEVEL STUDENT ENGAGEMENT MEASUREMENT THAT TAKES INTO ACCOUNT ENGAGEMENT IN HCI AND ALSO IN E-LEARNING, FACING THE CHALLENGE OF INTEGRATING THE TWO POINTS OF VIEW. THE RESEARCH FOLLOWED AN ACTION RESEARCH METHODOLOGY WHICH EMPHASIZES INVESTIGATION AND IMPROVEMENT OF A REAL-WORLD SITUATION WITH ITERATIVE PLAN-ACT-REFLECT CYCLES. THE APPROACH WAS TESTED IN A FULLY ONLINE UNIVERSITY WHERE WE DESIGNED AND DEVELOPED A MODULE FOR CAPTURING ENGAGEMENT DATA. THE RESULTS SHOW THAT USER ENGAGEMENT CAN BE CAPTURED IN ONLINE EDUCATIONAL SETTINGS PROVIDING A USEFUL AND ACTIONABLE UNDERSTANDING OF USER ENGAGEMENT IN DIGITAL EDUCATION.
NEXT-TERM GRADE PREDICTION IS A CHALLENGING PROBLEM. THE OBJECTIVE OF THIS PROBLEM IS TO PREDICT STUDENTS GRADES IN NEW COURSES, GIVEN THEIR GRADES IN COURSES THEY HAVE PREVIOUSLY TAKEN. ADOPTING VARIOUS MACHINE LEARNING ALGORITHMS IS A VERY COMMON AND STRAIGHTFORWARD APPROACH TO TACKLING THIS PROBLEM. HOWEVER, SUCH MODELS ARE VERY DIFFICULT TO INTERPRET. THAT IS, IT IS DIFFICULT TO EXPLAIN TO A STUDENT (OR A TEACHER) WHY THE MODEL PREDICTED GRADE B FOR A GIVEN STUDENT FOR EXAMPLE. IN THIS WORK, WE SHED LIGHT ON THE IMPORTANCE OF BUILDING INTERPRETABLE MODELS FOR EDUCATIONAL DATA MINING TASKS. SPECIFICALLY, WE PROPOSE A NOVEL INTERPRETABLE FRAMEWORK FOR MULTI-CLASS GRADE PREDICTION THAT IS BASED ON AN OPTIMAL RULE-LIST MINING ALGORITHM. ADDITIONALLY, WE EVALUATE OUR PROPOSED FRAMEWORK ON TWO PRIVATE DATASETS AND COMPARE OUR RESULTS WITH BASELINE MODELS. OUR FINDINGS SHOW THAT OUR PROPOSED FRAMEWORK IS CAPABLE OF ACHIEVING HIGHER PREDICTION AND INTERPRETABILITY VALUES WHEN COMPARED TO BLACK-BOX MODELS.
ANALYTICS HELPS ORGANIZATIONS EXCEL. MANY ORGANIZATIONAL LEADERS DO NOT BELIEVE THEIR ORGANIZATIONS CAN BENEFIT FROM ANALYTICS, IN PART BECAUSE OF THE PERCEIVED COMPLEXITY OF ANALYTICS OUTPUTS. ANALYTICS OUTPUTS ARE THE ARTIFACTS PRODUCED BY ANALYTICS SYSTEMS, INCLUDING DATA, INFORMATION, DECISIONS, AND CHANGES IN BEHAVIORS, TO NAME A FEW EXAMPLES. THESE OUTPUTS DETERMINE THE VALUE AND ROLES ANALYTICS PLAY IN SERVING INDIVIDUALS AND THE ORGANIZATION. THIS ARTICLE USES AN INTEGRATIVE LITERATURE REVIEW OF THE INFORMATION-SYSTEMS LITERATURE TO DEVELOP AN ANALYTICS SYSTEM MODEL AND HIERARCHY OF OUTPUTS. IT EXAMINES GENERAL AND HUMAN-RESOURCE-DEVELOPMENT ANALYTICS DEFINITIONS TO ASSESS THE FIT OF THE ANALYTICS OUTPUTS IDENTIFIED TO THE HIERARCHY OF OUTPUTS FROM THE ANALYSIS OF THE LITERATURE. IT PROVIDES GUIDANCE FOR HUMAN-RESOURCE DEVELOPMENT AND PERFORMANCE IMPROVEMENT PRACTITIONERS TO IDENTIFY THE CAPABILITIES OF ANALYTICS AND THE FIT OF THESE SYSTEMS TO THEIR NEEDS. IT IDENTIFIES RESEARCH EXTENSIONS BASED ON THE MODELS PRESENTED HERE.
TEACHERS' ONLINE DISCUSSION TEXT DATA SHED LIGHT ON THEIR REFLECTIVE THINKING. WITH THE GROWING SCALE OF TEXT DATA, THE TRADITIONAL WAY OF MANUAL CODING, HOWEVER, HAS BEEN CHALLENGED. IN ORDER TO PROCESS THE LARGE-SCALE UNSTRUCTURED TEXT DATA, IT IS NECESSARY TO INTEGRATE THE INDUCTIVE CONTENT ANALYSIS METHOD AND EDUCATIONAL DATA MINING TECHNIQUES. AN INDUCTIVE CONTENT ANALYSIS ON SAMPLES TAKEN FROM 17,624 POSTS WAS IMPLEMENTED AND THE CATEGORIES OF TEACHERS' REFLECTIVE THINKING WERE OBTAINED. BASED ON THE RESULTS OF INDUCTIVE CONTENT ANALYSIS, WE IMPLEMENTED A SINGLE-LABEL TEXT CLASSIFICATION ALGORITHM TO CLASSIFY THE SAMPLE DATA. THEN, WE APPLIED THE TRAINED CLASSIFICATION MODEL ON A LARGE-SCALE AND UNEXPLORED ONLINE DISCUSSION TEXT DATA SET AND TWO TYPES OF VISUALIZATIONS OF THE RESULTS WERE PROVIDED. BY USING THE CATEGORIES GAINED FROM INDUCTIVE CONTENT ANALYSIS TO CREATE A RADAR MAP, TEACHERS' REFLECTION LEVEL WAS REPRESENTED. IN ADDITION, A CUMULATIVE ADJACENCY MATRIX WAS CREATED TO CHARACTERIZE THE EVOLUTION OF TEACHERS' REFLECTIVE THINKING. THIS STUDY COULD PARTLY EXPLAIN HOW TEACHERS REFLECTED IN ONLINE PROFESSIONAL LEARNING ENVIRONMENTS AND BROUGHT AWARENESS TO EDUCATIONAL POLICY MAKERS, TEACHER TRAINING MANAGERS, AND EDUCATION RESEARCHERS.
THIS ARTICLE ARGUES FOR THE ESTABLISHMENT OF BIG DATA EARLY ALERT SYSTEMS THAT INFORM DATA-DRIVEN STUDENT SUPPORT MECHANISMS IN UNIVERSITIES. IT PROPOSES A GUIDING FRAMEWORK FOR INTEGRATED BIG DATA TO ENHANCE STUDENT SUCCESS PREMISED ON A COMPREHENSIVE UNDERSTANDING OF STUDENTS AS PEOPLE IN THE WORLD WHO ARRIVE AT UNIVERSITIES WITH VARIOUS COMPLEX LIFE PROBLEMS THAT MAY DISRUPT THEIR LEARNING OPPORTUNITIES. IT ARGUES THAT VARIOUS DATA COMPONENTS SHOULD BE LINKED TOGETHER TO FOSTER COHERENCE AND SEAMLESSNESS IN UNDERSTANDING STUDENT SOCIO-ECONOMIC AND ACADEMIC NEEDS TO DEVELOP RESPONSIVE LEARNING-ENHANCEMENT INTERVENTION PROGRAMMES. THIS IS BASED ON ACTION RESEARCH CONDUCTED THROUGH PROJECTS LAUNCHED AT THE UNIVERSITY OF WITWATERSRAND IN 2015 AND AT THE UNIVERSITY OF ZULULAND IN 2018. THE SYSTEMS WERE LAUNCHED, AND DATA WAS COLLECTED USING THE PROPOSED STUDENT PERFORMANCE TRACKING SYSTEM. THIS ARTICLE EXPLORES CONCEPTUAL AND THEORETICAL UNDERPINNINGS OF ESTABLISHING BIG DATA-BASED STUDENT SUPPORT SYSTEMS IN SOUTH AFRICAN UNIVERSITIES. A BIG STUDENT DATA MODEL IS PROPOSED FOR WIDER USE IN SOUTH AFRICAN UNIVERSITIES.
RESEARCH STUDIES ON EDUCATIONAL DATA MINING ARE ON THE INCREASE DUE TO THE BENEFITS OBTAINED FROM THE KNOWLEDGE ACQUIRED FROM MACHINE LEARNING PROCESSES WHICH HELP TO IMPROVE DECISION MAKING PROCESSES IN HIGHER INSTITUTIONS OF LEARNING. IN THIS STUDY, PREDICTIVE ANALYSIS WAS CARRIED OUT TO DETERMINE THE EXTENT TO WHICH THE FIFTH YEAR AND FINAL CUMULATIVE GRADE POINT AVERAGE (CGPA) OF ENGINEERING STUDENTS IN A NIGERIAN UNIVERSITY CAN BE DETERMINED USING THE PROGRAM OF STUDY, THE YEAR OF ENTRY AND THE GRADE POINT AVERAGE (GPA) FOR THE FIRST THREE YEARS OF STUDY AS INPUTS INTO A KONSTANZ INFORMATION MINER (KNIME) BASED DATA MINING MODEL. SIX DATA MINING ALGORITHMS WERE CONSIDERED, AND A MAXIMUM ACCURACY OF 89.15% WAS ACHIEVED. THE RESULT WAS VERIFIED USING BOTH LINEAR AND PURE QUADRATIC REGRESSION MODELS, AND R-2 VALUES OF 0.955 AND 0.957 WERE RECORDED FOR BOTH CASES. THIS CREATES AN OPPORTUNITY FOR IDENTIFYING STUDENTS THAT MAY GRADUATE WITH POOR RESULTS OR MAY NOT GRADUATE AT ALL, SO THAT EARLY INTERVENTION MAY BE DEPLOYED.
KNOWLEDGE ELABORATION PLAYS A CRITICAL ROLE IN PROMOTING KNOWLEDGE ACQUISITION AND FACILITATING THE RETENTION OF TARGET KNOWLEDGE IN ONLINE COLLABORATIVE DISCUSSIONS. ADOPTING A KEY-TERM-BASED AUTOMATED ANALYSIS APPROACH, WE PROPOSED AN INDICATOR FRAMEWORK TO MEASURE THE LEVEL OF KNOWLEDGE ELABORATION IN TERMS OF COVERAGE, ACTIVATION, AND EQUITABILITY. AN INTERACTIVE WEB-BASED TOOL WAS DEVELOPED TO PROVIDE A MULTIDIMENSIONAL VIEW OF STUDENTS' KNOWLEDGE ELABORATION, ALLOWING TEACHERS TO HAVE A REAL-TIME, IN-DEPTH UNDERSTANDING OF STUDENTS' MASTERY OF DOMAIN KNOWLEDGE. USING STUDENTS' DISCUSSION POSTS ON A PROBLEM-SOLVING TASK AS THE DATA SOURCE, A CASE STUDY WAS CONDUCTED, AND THE RESULTS SHOWED THAT THE PROPOSED METHODOLOGY WAS EFFECTIVE IN EXAMINING GROUP KNOWLEDGE ELABORATION IN ONLINE COLLABORATIVE DISCUSSIONS.
THE ANALYSIS OF LITERATURE WITHIN A RESEARCH DOMAIN CAN PROVIDE SIGNIFICANT VALUE DURING PRELIMINARY RESEARCH. WHILE LITERATURE REVIEWS MAY PROVIDE AN IN-DEPTH UNDERSTANDING OF CURRENT STUDIES WITHIN AN AREA, THEY ARE LIMITED BY THE NUMBER OF STUDIES WHICH THEY TAKE INTO ACCOUNT. IMPORTANTLY, WHILST PUBLICATIONS IN HOT AREAS ABOUND, IT IS NOT FEASIBLE FOR AN INDIVIDUAL OR TEAM TO ANALYSE A LARGE VOLUME OF PUBLICATIONS WITHIN A REASONABLE AMOUNT OF TIME. ADDITIONALLY, MAJOR PUBLICATIONS WHICH HAVE GAINED A LARGE NUMBER OF CITATIONS ARE MORE LIKELY TO BE INCLUDED IN A REVIEW, WITH RECENT OR FRINGE PUBLICATIONS RECEIVING LESS INCLUSION. WE PROVIDE THUS AN AUTOMATIC METHODOLOGY FOR THE LARGE-SCALE ANALYSIS OF LITERATURE WITHIN THE INTELLIGENT TUTORING SYSTEMS (ITS) DOMAIN, WITH THE AIM OF IDENTIFYING TRENDS AND AREAS OF RESEARCH FROM A CORPUS OF PUBLICATIONS WHICH IS SIGNIFICANTLY LARGER THAN IS TYPICALLY PRESENTED IN CONVENTIONAL LITERATURE REVIEWS. WE ILLUSTRATE THIS BY A NOVEL ANALYSIS OF 20 YEARS OF ITS RESEARCH. THE RESULTING ANALYSIS INDICATES A SIGNIFICANT SHIFT OF THE STATUS QUO OF RESEARCH IN RECENT YEARS WITH THE ADVENT OF NOVEL NEURAL NETWORK ARCHITECTURES AND THE INTRODUCTION OF MOOCS.
THIS INNOVATIVE PRACTICE FULL PAPER PRESENTS OUR WORK ON USING MACHINE LEARNING TO ESTIMATE STUDENT MASTERY OF CALCULUS SKILLS BASED ON THEIR PERFORMANCE USING AN ONLINE PROBLEM SOLVING LEARNING ENVIRONMENT. WE HAVE SHOWN IN EARLIER PAPERS THAT WE CAN MAKE ACCURATE PREDICTIONS OF STUDENT PERFORMANCE AT AN AGGREGATE LEVEL USING THE PERFORMANCE FACTORS ANALYSIS (PFA) APPROACH. WE HAD AN EXPERT TEACHER LABEL 243 CALCULUS PROBLEMS WITH THE SKILLS REQUIRED TO SOLVE EACH PROBLEM AND THEN USE PFA TO PREDICT STUDENT PERFORMANCE USING A DATASET WITH 1609 STUDENTS. IN THIS PAPER, WE IMPROVED OUR LABELING OF THE SKILLS BY ADDING NEW DIFFICULTY TAGS AS WELL AS SHOWING HOW TO APPLY INTERACTIVE MACHINE LEARNING TO HELP THE EXPERT TEACHER IMPROVE THEIR LABELING OF THE SKILLS REQUIRED FOR EACH PROBLEM. THIS CAN THEN IMPROVE THE PREDICTION ACCURACY FOR FUTURE CLASSES DOING THE SAME PROBLEMS. IT CAN ALSO HELP THE EXPERT TEACHER IMPROVE THE DEFINITION OF THE SKILLS TO MAKE THEM MORE EFFECTIVE FEATURES FOR PREDICTING STUDENT PERFORMANCE. THE TECHNIQUE WE PRESENT IS TO USE A HUMAN-IN-THE-LOOP HILL CLIMBING PROCESS WHERE A SKILL IS ADDED OR REMOVED, AND WE TEST TO SEE WHETHER THIS SIGNIFICANTLY IMPROVES THE PREDICTIONS FOR THAT PROBLEM OVER ALL STUDENTS. IF SO, THEN THE INSTRUCTOR IS ASKED TO VERIFY WHETHER INDEED THAT PARTICULAR SKILL SHOULD BE REMOVED OR ADDED. THE NEW SKILL COULD BE THE RESULT OF THE EXPERT MAKING A MISTAKE IN LABELLING A PROBLEM WITH SKILLS, BUT IT COULD ALSO SUGGEST THAT A MODIFICATION OF THE SKILL DEFINITION WOULD YIELD MORE ACCURATE PREDICTIONS. THE KEY IDEA BEHIND THIS APPROACH IS TO HARNESS THE STRENGTHS OF BOTH THE INSTRUCTOR AND THE MACHINE LEARNING ALGORITHMS TO IMPROVE THE EFFECTIVENESS OF THE PEDAGOGICAL SYSTEM.
MODELING IS UBIQUITOUS IN ENGINEERING, BUT DATA ON MODEL USE AND USER SOPHISTICATION IN ENGINEERING-DRIVEN FIRMS ARE REMARKABLY SPARSE. SOME AUTHORS HAVE PREDICTED A SEA CHANGE IN ENGINEERING WORKFLOW THROUGH MODEL-BASED SYSTEMS ENGINEERING. DESPITE BENEFITS, THE PREVALENCE OF EFFECTIVE MODELING TECHNIQUES IN INDUSTRY IS AN OPEN QUESTION. MASSIVE OPEN ONLINE COURSES AND SIMILAR ONLINE OFFERINGS ARE A NEW OPPORTUNITY TO LEARN ABOUT INDUSTRY PRACTICES. IN THIS PAPER, WE DRAW ON DATA FROM THE "ARCHITECTURE AND SYSTEMS ENGINEERING: MODELS AND METHODS TO MANAGE COMPLEX SYSTEMS" ONLINE PROGRAM FROM THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY, WHICH HAS ENROLLED 4200 PARTICIPANTS, TO UNDERSTAND HOW MODELS ARE USED IN PRACTICE. WE USE AGGREGATED DATA FROM QUESTIONS TO INVESTIGATE MODEL USE BY PARTICIPANTS AND THEIR ORGANIZATIONS. THE RESULTS DEMONSTRATE THAT WHEREAS MODELING IS WIDELY RECOGNIZED FOR ITS POTENTIAL, THE ACTUAL DEPLOYMENT AGAINST PROBLEMS OF INTEREST IS SUBSTANTIALLY LOWER THAN THE PARTICIPANT-STATED POTENTIAL. WE ALSO FIND THAT MANY PARTICIPANTS REPORTED LOW USE OF BASIC MODELING BEST PRACTICES, SUCH AS SPECIALIZED PROGRAMS. WE PROPOSE THAT THIS RESEARCH IS A FRESH USE OF DESCRIPTIVE ANALYSIS TO UNCOVER ACTUAL MODELING PRACTICES. FUTURE RESEARCH SHOULD TARGET MORE DETAIL ABOUT MODELING PRACTICES IN ENGINEERING-DRIVEN FIRMS, AND THE REASONS WHY MODELING ADOPTION IS LOW OR OVERSTATED.
THE PURPOSE OF THIS CASE STUDY IS TO DEMONSTRATE HOW TO UTILIZE MACHINE LEARNING APPROACHES TO ANALYZE STUDENT PROCESS DATA FOR VALIDATING AND INFORMING DIGITAL GAME-BASED ASSESSMENTS (DGBAS) WITH AN EVIDENCE-CENTERED GAME DESIGN (ECGD). THE FIRST ANALYSIS WAS CONDUCTED TO EXAMINE WHETHER STUDENTS' MASTERY OF THE OVERALL SKILL REQUIRED BY THE GAME CAN BE WELL PREDICTED BY TASK-RELATED BEHAVIORAL FEATURES AND IF THE SELECTED KEY FEATURES MAP ONTO THE EVIDENCE MODEL OF THE ECGD. SPECIFICALLY, WE EXTRACTED 27 BEHAVIORAL FEATURES AS THE INDICATORS OF STUDENTS' GAMEPLAY ACTIVITIES FROM THE EVIDENCE TRACE FILES AND MODELLED THEM USING A MACHINE LEARNING ALGORITHM-SUPPORT VECTOR MACHINE WITH RECURSIVE FEATURE ELIMINATION-TO IDENTIFY THE KEY FEATURES FOR PREDICTION. THE KEY FEATURES WERE IN TURN USED TO PREDICT STUDENTS' MASTERY OF THE OVERALL SKILL. RESULTS SHOWED THAT STUDENTS' RETRY ATTEMPTS ON TWO ASSESSMENT TASKS WERE FOUND TO BE MOST INFLUENTIAL FOR PREDICTION WITH A MODERATE TO HIGH TRAINING AND TESTING ACCURACY. THE SECOND ANALYSIS WAS CONDUCTED TO EXAMINE WHETHER THE NUMBER OF LEARNING OPPORTUNITIES IS SUFFICIENT FOR EVALUATING STUDENTS' MASTERY OF THE OVERALL SKILL AS WELL AS DETERMINE THE OPTIMAL NUMBER OF LEARNING OPPORTUNITIES FOR EVALUATION. THE APPROACH OF LONG SHORT-TERM MEMORY NETWORKS WAS USED TO MODEL STUDENTS' TIME-SERIES BEHAVIORAL FEATURES ACROSS MULTIPLE LEARNING OPPORTUNITIES FOR PREDICTING THEIR ACQUISITION OF THE OVERALL SKILL. RESULTS SUGGESTED THAT FIVE LEARNING OPPORTUNITIES WERE A GOOD BALANCE BETWEEN EVALUATION ACCURACY AND PRACTICAL FEASIBILITY, AND THEY WERE SUFFICIENT FOR EVALUATING STUDENTS' MASTERY OF THE OVERALL SKILL GIVEN THE DGGA TASKS.
PERFORMANCE DATA IN HIGHER EDUCATION HAS GONE THROUGH A MAJOR DEVELOPMENT IN THE LAST FEW DECADES. SIMPLE INPUT MEASURES HAVE GIVEN WAY TO INCREASINGLY NUANCED AND DYNAMIC OUTPUT MEASURES AND PERFORMANCE INDICATORS HAVE BECOME AN INTEGRAL PART OF MANAGEMENT AT THE ORGANISATIONAL AND SYSTEM LEVEL. THE EVOLUTION OF HIGHER EDUCATION PERFORMANCE MEASUREMENT SHOWS A REITERATIVE RELATIONSHIP BETWEEN DATA AVAILABILITY, ITS PURPOSE IN A GOVERNANCE SYSTEM AND ITS TARGET AUDIENCE. DIGITALISATION OF LEARNING, MANAGEMENT AND COMMUNICATION SYSTEMS HAS REVOLUTIONISED DATA AVAILABILITY, CREATING NEW POSSIBILITIES FOR 'BIG DATA' USE. BASED ON INSIGHTS FROM THE PAST EVOLUTION, CURRENT EXPERIMENTS WITH 'BIG DATA' AND LESSONS FROM OTHER SECTORS, THE ARTICLE EXPLORES WHAT THE NEW 'BIG DATA' ERA MIGHT MEAN FOR HIGHER EDUCATION GOVERNANCE. THE HIGH VOLUME OF DATA BUT ALSO ITS SPEED OF ACCUMULATION AND RELATED ANALYTICAL TECHNIQUES, ARE LIKELY TO SUBSTANTIALLY TRANSFORM THE CURRENT RELATIONSHIP BETWEEN DATA AND PERFORMANCE BUT ALSO CREATE SOME TECHNICAL, ETHICAL AND POLICY CHALLENGES.
TINKERING IS OFTEN VIEWED AS ARBITRARY PRACTICE THAT SHOULD BE AVOIDED. HOWEVER, TINKERING CAN BE PERFORMED AS PART OF A SOUND REASONING PROCESS. IN THIS ETHNOMETHODOLOGICAL STUDY, WE INVESTIGATED TINKERING AS A REASONING PROCESS THAT CONSTRUES LOGICAL INFERENCES. THIS IS A NEW ASSET-BASED APPROACH THAT CAN BE APPLIED IN COMPUTER SCIENCE EDUCATION. WE ANALYZED ARTIFACT-BASED INTERVIEWS, VIDEO OBSERVATIONS, REFLECTIONS, AND SCAFFOLDING ENTRIES FROM THREE PAIRS OF EARLY CHILDHOOD TEACHER CANDIDATES TO DOCUMENT HOW THEY ENGAGED IN REASONING WHILE TINKERING. ABDUCTIVE REASONING OBSERVED DURING TINKERING IS DISCUSSED IN DETAIL.
THE CONCEPT OF OPENNESS AND INFORMATION SHARING (LINKING) TOGETHER WITH INCREASING AMOUNTS OF DATA AVAILABLE SIGNIFICANTLY AFFECT THE CURRENT EDUCATIONAL SYSTEM. INSTITUTIONS AS WELL AS OTHER STAKEHOLDERS ARE FACING CHALLENGES HOW TO SUCCESSFULLY DEAL WITH THEM AND POTENTIALLY PROFIT FROM THEM. IN THIS REGARD, THIS PAPER EXPLORES OPPORTUNITIES OF BIG AND OPEN LINKED DATA ANALYTICS IN THE EDUCATIONAL PROCESS INTENDED TO DEVELOP THE NEW SET OF SKILLS. A COMPREHENSIVE LITERATURE REVIEW RESULTED IN A FRAMEWORK OF RELEVANT SKILLS, NAMELY SOFT, HARD, AND DATA ANALYTICS SKILLS. THEIR IMPORTANCE WAS EVALUATED USING A DELPHI METHOD. IN ORDER TO DETERMINE THE RELATIONSHIPS BETWEEN INVOLVED STAKEHOLDERS, THEIR ROLES AND REQUIREMENTS, A STAKEHOLDER THEORY IS UTILIZED. IT RESULTED IN THE IDENTIFICATION OF CURRENT AND EMERGING ROLES OF STAKEHOLDERS IN THE DATA ANALYTICS ECOSYSTEM. A STRUCTURAL CLASSIFICATION OF STAKEHOLDERS' INFLUENCES AND IMPACTS THEN REPRESENTS A NECESSARY BACKGROUND FOR ESTABLISHING STRATEGIES FOR THE DEVELOPMENT OF THE RIGHT SKILLS NEEDED TO GAIN THE VALUE FROM THESE DATA. THIS PAPER PROVIDES A COMPREHENSIVE VIEW ON BIG AND OPEN LINKED DATA ANALYTICS IN THE EDUCATIONAL CONTEXT, DEFINES AND INTERLINKS DATA-RELATED WITH CURRENT ROLES AS WELL AS THE SKILLS REQUIRED TO PERFORM DATA ANALYTICS.
GROUP FORMATION IS ONE OF THE ESSENTIAL STAGES OF COLLABORATIVE LEARNING. THIS PAPER PROPOSES AN INTELLIGENT COMPUTATIONAL APPROACH TO OPTIMIZE THE GROUP FORMATION PROCESS TAKING INTO ACCOUNT MULTIPLE CRITERIA: INTER-HOMOGENEITY, INTRA-HETEROGENEITY, AND EMPATHY. MORE SPECIFICALLY, IT USES A GENETIC ALGORITHM TO MAXIMIZE THE NUMBER OF DIFFERENT STUDENT PROFILES IN EACH GROUP. THE PROPOSED METHOD WAS EVALUATED REGARDING ITS COMPUTATIONAL PERFORMANCE COMPARING AGAINST THREE BASELINES; AND IN A REAL EDUCATIONAL APPLICATION, WHERE IT WAS COMPARED WITH RANDOM AND SELF-ORGANIZED METHODS. THE RESULTS SHOWED THE POTENTIAL OF THE PROPOSED METHOD FROM BOTH THE COMPUTATIONAL AND PEDAGOGICAL POINTS OF VIEW.
BIG DATA IS AN ESSENTIAL ASPECT OF INNOVATION WHICH HAS RECENTLY GAINED MAJOR ATTENTION FROM BOTH ACADEMICS AND PRACTITIONERS. CONSIDERING THE IMPORTANCE OF THE EDUCATION SECTOR, THE CURRENT TENDENCY IS MOVING TOWARDS EXAMINING THE ROLE OF BIG DATA IN THIS SECTOR. SO FAR, MANY STUDIES HAVE BEEN CONDUCTED TO COMPREHEND THE APPLICATION OF BIG DATA IN DIFFERENT FIELDS FOR VARIOUS PURPOSES. HOWEVER, A COMPREHENSIVE REVIEW IS STILL LACKING IN BIG DATA IN EDUCATION. THUS, THIS STUDY AIMS TO CONDUCT A SYSTEMATIC REVIEW ON BIG DATA IN EDUCATION IN ORDER TO EXPLORE THE TRENDS, CLASSIFY THE RESEARCH THEMES, AND HIGHLIGHT THE LIMITATIONS AND PROVIDE POSSIBLE FUTURE DIRECTIONS IN THE DOMAIN. FOLLOWING A SYSTEMATIC REVIEW PROCEDURE, 40 PRIMARY STUDIES PUBLISHED FROM 2014 TO 2019 WERE UTILIZED AND RELATED INFORMATION EXTRACTED. THE FINDINGS SHOWED THAT THERE IS AN INCREASE IN THE NUMBER OF STUDIES THAT ADDRESS BIG DATA IN EDUCATION DURING THE LAST 2 YEARS. IT HAS BEEN FOUND THAT THE CURRENT STUDIES COVERED FOUR MAIN RESEARCH THEMES UNDER BIG DATA IN EDUCATION, MAINLY, LEARNER'S BEHAVIOR AND PERFORMANCE, MODELLING AND EDUCATIONAL DATA WAREHOUSE, IMPROVEMENT IN THE EDUCATIONAL SYSTEM, AND INTEGRATION OF BIG DATA INTO THE CURRICULUM. MOST OF THE BIG DATA EDUCATIONAL RESEARCHES HAVE FOCUSED ON LEARNER'S BEHAVIOR AND PERFORMANCES. MOREOVER, THIS STUDY HIGHLIGHTS RESEARCH LIMITATIONS AND PORTRAYS THE FUTURE DIRECTIONS. THIS STUDY PROVIDES A GUIDELINE FOR FUTURE STUDIES AND HIGHLIGHTS NEW INSIGHTS AND DIRECTIONS FOR THE SUCCESSFUL UTILIZATION OF BIG DATA IN EDUCATION.
BIG DATA REFERS TO LARGE AND DISPARATE VOLUMES OF DATA GENERATED BY PEOPLE, APPLICATIONS AND MACHINES. IT IS GAINING INCREASING ATTENTION FROM A VARIETY OF DOMAINS, INCLUDING EDUCATION. WHAT ARE THE CHALLENGES OF ENGAGING WITH BIG DATA RESEARCH IN EDUCATION? THIS PAPER IDENTIFIES A WIDE RANGE OF CRITICAL ISSUES THAT RESEARCHERS NEED TO CONSIDER WHEN WORKING WITH BIG DATA IN EDUCATION. THE ISSUES IDENTIFIED INCLUDE DIVERSITY IN THE CONCEPTION AND MEANING OF BIG DATA IN EDUCATION, ONTOLOGICAL, EPISTEMOLOGICAL DISPARITY, TECHNICAL CHALLENGES, ETHICS AND PRIVACY, DIGITAL DIVIDE AND DIGITAL DIVIDEND, LACK OF EXPERTISE AND ACADEMIC DEVELOPMENT OPPORTUNITIES TO PREPARE EDUCATIONAL RESEARCHERS TO LEVERAGE OPPORTUNITIES AFFORDED BY BIG DATA. THE GOAL OF THIS PAPER IS TO RAISE AWARENESS ON THESE ISSUES AND INITIATE A DIALOGUE. THE PAPER WAS INSPIRED PARTLY BY INSIGHTS DRAWN FROM THE LITERATURE BUT MOSTLY INFORMED BY EXPERIENCE RESEARCHING INTO BIG DATA IN EDUCATION.
IN THIS ARTICLE, WE IDENTIFY AND EXAMINE OPPORTUNITIES FOR FORMATIVE ASSESSMENT PROVIDED BY INFORMATION TECHNOLOGIES (IT) AND THE CHALLENGES WHICH THESE OPPORTUNITIES PRESENT. WE ADDRESS SOME OF THESE CHALLENGES BY EXAMINING KEY ASPECTS OF ASSESSMENT PROCESSES THAT CAN BE FACILITATED BY IT: DATAFICATION OF LEARNING; FEEDBACK AND SCAFFOLDING; PEER ASSESSMENT AND PEER FEEDBACK. WE THEN CONSIDER HOW THESE PROCESSES MAY BE APPLIED IN RELATION TO THE ASSESSMENT OF HORIZONTAL, GENERAL COMPLEX 21ST CENTURY SKILLS (21ST CS), WHICH ARE STILL PROVING CHALLENGING TO INCORPORATE INTO CURRICULA AS WELL AS TO ASSESS. 21ST CS SUCH AS CREATIVITY, COMPLEX PROBLEM SOLVING, COMMUNICATION, COLLABORATION AND SELF-REGULATED LEARNING CONTAIN COMPLEX CONSTRUCTS INCORPORATING MOTIVATIONAL AND AFFECTIVE COMPONENTS. OUR ANALYSIS HAS ENABLED US TO MAKE RECOMMENDATIONS FOR POLICY, PRACTICE AND FURTHER RESEARCH. WHILE THERE IS CURRENTLY MUCH INTEREST IN AND SOME PROGRESS TOWARDS THE DEVELOPMENT OF LEARNING/ASSESSMENT ANALYTICS FOR ASSESSING 21ST CS, THE COMPLEXITY OF ASSESSING SUCH SKILLS, TOGETHER WITH THE NEED TO INCLUDE AFFECTIVE ASPECTS MEANS THAT USING IT-ENABLED TECHNIQUES WILL NEED TO BE COMBINED WITH MORE TRADITIONAL METHODS OF TEACHER ASSESSMENT AS WELL AS PEER ASSESSMENT FOR SOME TIME TO COME. THEREFORE LEARNERS, TEACHERS AND SCHOOL LEADERS MUST LEARN HOW TO MANAGE THE GREATER VARIETY OF SORTS AND SOURCES OF FEEDBACK INCLUDING RESOLVING TENSIONS OF INCONSISTENT FEEDBACK FROM DIFFERENT SOURCES.
FACTORS INFLUENCING CHOICE OF MAJOR IN ELECTRICAL ENGINEERING AND LATER CURRICULAR AND PROFESSIONAL CHOICES ARE INVESTIGATED. STUDIES INCLUDE BOTH QUANTITATIVE AND QUALITATIVE ANALYSES VIA STUDENT TRANSCRIPTS, SURVEYS, AND FOCUS GROUPS. STUDENT MOTIVATION FOR CHOOSING AN ELECTRICAL ENGINEERING MAJOR AND LATER SUBDISCIPLINE IN THE FIELD IS INTERPRETED THROUGH EXPECTANCY-VALUE THEORY, WHERE PRIMARY FACTORS OF STRONG PERCEIVED VALUE OF FUTURE PROFESSIONAL OPPORTUNITIES AND STRONG INFLUENCE OF COURSE INSTRUCTORS ARE IDENTIFIED. PERFORMANCES IN SELECT REQUIRED ELECTRICAL ENGINEERING COURSES APPEAR TO SERVE AS PREDICTORS FOR STUDENT CHOICE OF SUBDISCIPLINE EMPHASIS. IN CONTRAST, PARTICIPATION IN STUDENT PROFESSIONAL ACTIVITIES DOES NOT SHOW STATISTICALLY SIGNIFICANT CORRELATIONS WITH SUBDISCIPLINE. CURRICULAR AND PROFESSIONAL CHOICES APPEAR TO BE EXPLAINED BY EXPECTANCY-VALUE THEORY WITH INCLUSION OF SOCIALIZERS. THE FINDINGS SUGGEST THAT EARLY AND INTEGRATIVE EXPOSURE OF ALL ELECTRICAL ENGINEERING TECHNICAL AREAS, INCLUDING HIGH-QUALITY TEACHING, MAY PROVIDE AN OPTIMAL BASIS FOR STUDENTS TO MAKE FUTURE DECISIONS ON ACADEMIC PATH AND PARTICIPATION IN PROFESSIONAL ACTIVITIES.
RECENT INCREASE IN THE AVAILABILITY OF LEARNING DATA HAS GIVEN EDUCATIONAL DATA MINING AN IMPORTANCE AND MOMENTUM, IN ORDER TO BETTER UNDERSTAND AND OPTIMIZE THE LEARNING PROCESS AND ENVIRONMENTS IN WHICH IT OCCURS. THE AIM OF THIS PAPER IS TO PROVIDE A COMPREHENSIVE ANALYSIS AND COMPARISON OF STATE OF THE ART SUPERVISED MACHINE LEARNING TECHNIQUES APPLIED FOR SOLVING THE TASK OF STUDENT EXAM PERFORMANCE PREDICTION, I.E. DISCOVERING STUDENTS AT A "HIGH RISK" OF DROPPING OUT FROM THE COURSE, AND PREDICTING THEIR FUTURE ACHIEVEMENTS, SUCH AS FOR INSTANCE, THE FINAL EXAM SCORES. FOR BOTH CLASSIFICATION AND REGRESSION TASKS, THE OVERALL HIGHEST PRECISION WAS OBTAINED WITH ARTIFICIAL NEURAL NETWORKS BY FEEDING THE STUDENT ENGAGEMENT DATA AND PAST PERFORMANCE DATA, WHILE THE USAGE OF DEMOGRAPHIC DATA DID NOT SHOW SIGNIFICANT INFLUENCE ON THE PRECISION OF PREDICTIONS. TO EXPLOIT THE FULL POTENTIAL OF THE STUDENT EXAM PERFORMANCE PREDICTION, IT WAS CONCLUDED THAT ADEQUATE DATA ACQUISITION FUNCTIONALITIES AND THE STUDENT INTERACTION WITH THE LEARNING ENVIRONMENT IS A PREREQUISITE TO ENSURE SUFFICIENT AMOUNT OF DATA FOR ANALYSIS.
PURPOSE WITH A GROWING NEED TO ASSESS MULTIPLE ASPECTS OF HEALTHCARE EDUCATION, THE GOAL OF THIS STUDY WAS TO DEVELOP AN INNOVATIVE WEB-BASED APPLICATION TO STREAMLINE ASSESSMENT PROCESSES AND MEET THE INCREASINGLY COMPLEX ROLE OF THE EDUCATIONAL MANAGER. DESIGN/METHODOLOGY/APPROACH AARDVARC (AUTOMATED APPROACH TO REVIEWING AND DEVELOPING VALUABLE ASSESSMENT RESOURCES FOR YOUR CURRICULUM) WAS CREATED WITH THE CORE FUNCTION OF STANDARDIZING COURSE SYLLABI THROUGH THE USE OF A WEB-BASED PORTAL AND THE ABILITY TO QUERY FIELDS WITHIN THE PORTAL TO COLLECT MULTIPLE POINTS OF DATA. AARDVARC PERMITS QUICK AND EFFICIENT GATHERING OF PROGRAMMATIC, CURRICULAR, FACULTY, TEACHING, PRECEPTOR AND FINANCIAL DATA TO FACILITATE MEANINGFUL CHANGE AND A SHARED RESPONSIBILITY OF ASSESSMENT. THIS SOFTWARE HAS ALLOWED AUTOMATIC COMPLETION OF COMPLEX ANALYTICS EACH SEMESTER, INCLUDING COVERAGE OF PROGRAM OUTCOMES, COURSE LEARNING OBJECTIVES, TEACHING AND ASSESSMENT METHODS, COURSE READINGS, TOPICS COVERED IN THE CURRICULUM, FACULTY TEACHING HOURS, EXPERIENTIAL ACTIVITIES, COVERAGE OF DISEASE STATES AND SCHEDULING OF PEER OBSERVATION OF TEACHING. FINDINGS THREE YEARS AFTER ITS INITIAL LAUNCH, AARDVARC IS NOW USED BY 520 FACULTY, 60 STAFF, 44 PRECEPTORS AND OVER 2,000 STUDENTS ACROSS MULTIPLE HEALTH PROFESSION AND SCIENCE PROGRAMS. DATA ANALYTICS THROUGH AARDVARC HAVE ALLOWED THE SCHOOL TO REIMAGINE HOW ASSESSMENT CAN BE CONDUCTED AND HAVE PROVIDED A PATHWAY FOR MAKING EVIDENCE-BASED PROGRAMMATIC AND CURRICULAR CHANGES. ORIGINALITY/VALUE THIS ORIGINAL SOFTWARE HAS PROVIDED AN INNOVATIVE APPROACH TO CONDUCT ASSESSMENT THAT COMBINES BEST PRACTICES IN CURRICULUM, ASSESSMENT, DATA ANALYTICS AND EDUCATIONAL TECHNOLOGY WHILE IMPROVING THE OVERALL QUALITY, SPEED, AND EFFICIENCY OF ACADEMIC AND BUSINESS OPERATIONS.
PREDICTIVE USES OF DATA ARE BECOMING WIDESPREAD IN INSTITUTIONAL SETTINGS AS ACTORS SEEK TO ANTICIPATE PEOPLE AND THEIR ACTIVITIES. PREDICTIVE MODELING IS INCREASINGLY THE SUBJECT OF SCHOLARLY AND PUBLIC CRITICISM. LESS COMMON, HOWEVER, IS SCRUTINY DIRECTED AT THE DATA THAT INFORM PREDICTIVE MODELS BEYOND CONCERNS ABOUT HOMOGENOUS TRAINING DATA OR GENERAL EPISTEMOLOGICAL CRITIQUES OF DATA. IN THIS PAPER, I DRAW FROM A QUALITATIVE CASE STUDY SET IN HIGHER EDUCATION IN THE UNITED STATES TO INVESTIGATE THE MAKING OF DATA. DATA ANALYTICS PROJECTS AT UNIVERSITIES HAVE BECOME MORE PERVASIVE AND INTENSIVE TO BETTER UNDERSTAND AND ANTICIPATE UNDERGRADUATE STUDENT BODIES. DRAWING FROM 12 MONTHS OF ETHNOGRAPHIC RESEARCH AT A LARGE PUBLIC UNIVERSITY, I ANALYZE THE WAYS DATA PERSONNEL AT THE INSTITUTION-DATA SCIENTISTS, ADMINISTRATORS, AND PROGRAMMERS-SORT STUDENT DATA INTO "ATTRIBUTES" AND "BEHAVIORS," WHERE "ATTRIBUTES" ARE DEMOGRAPHIC DATA THAT STUDENTS "CAN'T CHANGE." "BEHAVIORS," IN CONTRAST, ARE DATA DEFINED AS REFLECTIVE OF WHAT STUDENTS CAN CHOOSE: ATTENDING AND PAYING ATTENTION IN CLASS, STUDYING ON CAMPUS, AMONG OTHER DATA WHICH PERSONNEL CATEGORIZE AS WHAT STUDENTS HAVE CONTROL OVER. THIS DISCURSIVE SPLIT ENABLES THE INSTITUTION NUDGE STUDENTS TO MAKE RESPONSIBLE CHOICES ACCORDING TO BEHAVIOR DATA THAT CORRELATE WITH SUCCESS IN THE PREDICTIVE MODEL. IN DISCUSSING HOW PERSONNEL TYPE, SORT, STABILIZE, AND NUDGE ON BEHAVIOR DATA, THIS PAPER EXAMINES THE CONTINGENCIES OF DATA MAKING PROCESSES AND IMPLICATIONS FOR THE APPLICATION OF STUDENT DATA.
THIS ARTICLE EXAMINES DIGITAL DATA USE WITHIN THREE AUSTRALIAN SECONDARY SCHOOLS, DRAWING ON IN-DEPTH INTERVIEWS WITH 50 SCHOOL STAFF TO EXPLORE TENSIONS BETWEEN: (I) ESTABLISHED LOGICS OF "DATA-DRIVEN" SCHOOLING; AND (II) EMERGING "DATAFIED" PRACTICES ASSOCIATED WITH DIGITAL SYSTEMS, PLATFORMS AND DEVICES. USING SOCIOLOGICAL THEORISATION OF INSTITUTIONAL LOGICS, THE ARTICLE EXAMINES HOW PROMISES OF DIGITAL "DATAISM" ARE THWARTED BY THE ENTRENCHED TEMPORAL ORGANISATION OF SCHOOLING, AND TEACHER-CENTRED UNDERSTANDINGS OF STUDENTS AS COERCED SUBJECTS. AS SUCH, PREVAILING LOGICS OF STATE BUREAUCRACY AND PROFESSIONALISM COMBINE IN WAYS THAT TEMPER THE PROSPECT OF INDIVIDUALISM, SELF-REGULATION, CONTINUOUS FEEDBACK, AND OTHER IMPLICIT PROMISES OF DIGITAL DATA. THE PAPER CONSIDERS THE EXTENT TO WHICH SCHOOL DATA LOGICS CAN ENDURE AMID THE INCREASED DIGITISATION OF K-12 EDUCATION.
THIS STUDY EXAMINES INSTRUCTOR STRATEGIES FOR DESIGNING AND PARTICIPATING IN ONLINE DISCUSSIONS AND HOW THESE STRATEGIES INFLUENCE THEIR STUDENTS' PARTICIPATION AND COURSE PERFORMANCE IN UNIVERSITY INTRODUCTORY ONLINE MATHEMATICS COURSES. THE STUDY LEVERAGED DATA AND TEXT MINING TECHNIQUES TO EXAMINE FIVE YEARS OF ONLINE DISCUSSION DATA AUTOMATICALLY COLLECTED BY A LEARNING MANAGEMENT SYSTEM FROM 72 INTRODUCTORY ONLINE MATHEMATICS COURSES INVOLVING OVER 2800 INSTRUCTORS AND STUDENTS WHO COLLECTIVELY CONTRIBUTED OVER 22,000 POSTS. A CLASSIFICATION AND REGRESSION TREE (CART) ANALYSIS OF INSTRUCTOR STRATEGIES REVEALED FOUR INSTRUCTOR PARTICIPATION VARIABLES (OPEN-ENDED DISCUSSION, GRADING, DISCUSSION SETTING, ELABORATED FEEDBACK) THAT IMPACTED STUDENT COURSE PERFORMANCE AND NINE THAT DID NOT. FURTHER, STUDENTS IN COURSES WHERE INSTRUCTORS USED OPEN-ENDED DISCUSSION PROMPTS AND GRADED POSTS HAD HIGHER AVERAGE FINAL COURSE GRADES. AN EXAMINATION OF HOW INSTRUCTOR DISCUSSION STRATEGIES INFLUENCED STUDENT PARTICIPATION FOUND THAT SUBGROUPS OF STUDENTS WITH LOWER AVERAGE FINAL COURSE GRADES TENDED TO ENGAGE IN LESS ONLINE SPEAKING AND LISTENING, WHILE ALSO CONTRIBUTING MORE POSTS CATEGORIZED AS SOCIAL INTERACTIONS AND FEWER RELATED TO COURSE CONTENT. STUDENTS IN SUBGROUPS WITH HIGHER AVERAGE FINAL COURSE GRADES TENDED TO ENGAGE IN MORE ONLINE LISTENING AND CONTRIBUTED MORE POSTS CODED AS HIGHER IN KNOWLEDGE CONSTRUCTION.
THE ACADEMIC PERFORMANCE OF A STUDENT IN A UNIVERSITY IS DETERMINED BY A NUMBER OF FACTORS, BOTH ACADEMIC AND NON-ACADEMIC. STUDENT THAT PREVIOUSLY EXCELLED AT THE SECONDARY SCHOOL LEVEL MAY LOSE FOCUS DUE TO PEER PRESSURE AND SOCIAL LIFESTYLE WHILE THOSE WHO PREVIOUSLY STRUGGLED DUE TO FAMILY DISTRACTIONS MAY BE ABLE TO FOCUS AWAY FROM HOME, AND AS A RESULT EXCEL AT THE UNIVERSITY. UNIVERSITY ADMISSION IN NIGERIA IS TYPICALLY BASED ON COGNITIVE ENTRY CHARACTERISTICS OF A STUDENT WHICH IS MOSTLY ACADEMIC, AND MAY NOT NECESSARILY TRANSLATE TO EXCELLENCE ONCE IN THE UNIVERSITY. IN THIS STUDY, THE RELATIONSHIP BETWEEN THE COGNITIVE ADMISSION ENTRY REQUIREMENTS AND THE ACADEMIC PERFORMANCE OF STUDENTS IN THEIR FIRST YEAR, USING THEIR CGPA AND CLASS OF DEGREE WAS EXAMINED USING SIX DATA MINING ALGORITHMS IN KNIME AND ORANGE PLATFORMS. MAXIMUM ACCURACIES OF 50.23% AND 51.9% RESPECTIVELY WERE OBSERVED, AND THE RESULTS WERE VERIFIED USING REGRESSION MODELS, WITH R-2 VALUES OF 0.207 AND 0.232 RECORDED WHICH INDICATE THAT STUDENTS' PERFORMANCE IN THEIR FIRST YEAR IS NOT FULLY EXPLAINED BY COGNITIVE ENTRY REQUIREMENTS.
GIVEN THE FOCUS ON BOOSTING RETENTION RATES AND THE POTENTIAL BENEFITS OF PROACTIVE AND EARLY IDENTIFICATION OF STUDENTS WHO MAY REQUIRE SUPPORT, HIGHER EDUCATION INSTITUTIONS ARE LOOKING AT THE DATA ALREADY CAPTURED IN UNIVERSITY SYSTEMS. STUDENT EARLY ALERT SYSTEMS ARE PART OF FORMAL, PROACTIVE, EARLY INTERVENTION COMMUNICATION INITIATIVES THAT INSTITUTIONS HAVE PUT INTO PLACE TO HELP WITH THE TIMELY IDENTIFICATION AND INTERVENTION (ALERT) OF AT-RISK STUDENTS. THE SIGNIFICANCE OF STUDENT EARLY ALERT SYSTEMS IS THAT SUPPORT COULD BE OFFERED TO HIGH-RISK STUDENTS WHILE THEY ARE STILL ENROLLED IN THE UNIT AND ABLE TO INFLUENCE THEIR SUCCESS/FAILURE BEFORE THE UNIT COMPLETES. DELIVERING TIMELY INTERVENTIONS TO STUDENTS VIA A STUDENT EARLY ALERT SYSTEM TYPICALLY REQUIRES TEACHING STAFF TO IDENTIFY AT-RISK STUDENTS, AND ACT UPON THAT INFORMATION IN A WAY THAT WOULD ENCOURAGE STUDENTS TO CHANGE THEIR BEHAVIOURS. HOWEVER, LITTLE IS UNDERSTOOD REGARDING TEACHERS' NEEDS AND ATTITUDES TOWARDS THE USE OF SUCH A SYSTEM. IN THE CONTEXT OF A PROTOTYPE EARLY ALERT SYSTEM, THIS ARTICLE REPORTS THE PRACTICES AND OPINIONS OF A RANGE OF TEACHING STAFF ACROSS ALL FACULTIES WITHIN AN INSTITUTION. THE ARTICLE SHEDS LIGHT ON HOW TEACHERS MEASURE STUDENT PERFORMANCE AND ENGAGEMENT IN THEIR UNITS, AND THE PERCEIVED BENEFITS AND BARRIERS OF USING THE EARLY ALERT SYSTEM TO IDENTIFY AND MANAGE AT-RISK STUDENTS.
ACCURATELY TRACING THE STATE OF LEARNER KNOWLEDGE CONTRIBUTES TO PROVIDING HIGH-QUALITY INTELLIGENT SUPPORT FOR COMPUTER-SUPPORTED PROGRAMMING LEARNING. HOWEVER, KNOWLEDGE TRACING IS DIFFICULT WHEN LEARNERS HAVE ONLY HAD A FEW PRACTICE OPPORTUNITIES, WHICH IS OFTEN COMMON IN BLOCK-BASED PROGRAMMING. THIS ARTICLE PROPOSED TWO KNOWLEDGE TRACING MODELS THAT CAN EXPLOIT THE PROBLEM-SOLVING PROCESS DATA GENERATED BY LEARNERS FROM A SINGLE PROGRAMMING TASK. A NOVEL METRIC, THE APPROACHING INDEX, WAS DEVELOPED USING THE TREE EDIT DISTANCE IN ABSTRACT SYNTAX TREES TO MEASURE THE SIMILARITIES BETWEEN THE LEARNERS' INTERMEDIATE SOLUTIONS AND THE OPTIMAL SOLUTION. THE PROPOSED METHOD ALLOWS FOR EACH LEARNER'S PROGRAMMING PATH TO BE REPRESENTED AS A RAW APPROACHING INDEX SEQUENCE (AISEQ) OR AS A SINGLE VARIABLE (AISCORE) BY AVERAGING THE AISEQ. A LOGISTIC REGRESSION MODEL WAS FIRST DESIGNED TO PREDICT THE LEARNERS' PERFORMANCES USING THEIR AISCORE, THE NUMBER OF ATTEMPTS, AND THEIR CURRENT PERFORMANCE. A SECOND MODEL, A RECURRENT NEURAL NETWORK MODEL, WAS ALSO DEVELOPED TO DIRECTLY USE THE AISEQ AND TO MAKE PREDICTIONS. TO VERIFY THE EFFECTIVENESS OF THESE MODELS, A SERIES OF STATISTICAL ANALYSES AND EXPERIMENTS WERE CONDUCTED ON TWO EXISTING LARGE-SCALE BLOCK-BASED PROGRAMMING DATASETS, THE RESULTS FROM WHICH REVEALED THAT THE PROPOSED MODELS WERE COMPETITIVE WITH FOUR STATE-OF-THE-ART MODELS ON MULTIPLE METRICS, SUCH AS THE PRECISION-RECALL CURVE, ACCURACY, SPECIFICITY, AND COHEN'S KAPPA. ESPECIALLY, THE PROPOSED MODELS WERE FOUND TO BE MORE ROBUST THAN THE COMPARED MODELS IN PREDICTING WHO WOULD FAIL TO COMPLETE THE TASKS.
NOWADAYS, THE AMOUNT OF INFORMATION SPREAD OVER NETWORKS IS EXTREMELY LARGE, AND MANY SENSIBLE DATA ARE GRANTED BY LEGITIMATE OWNERS AIMING TO EXPLOIT DIFFERENT NETWORKING SERVICES. IN PARTICULAR, THE MAJORITY OF PEOPLE GIVE THEIR OWN CONSENT FOR PROCESSING PERSONAL DATA WITHOUT UNDERSTANDING HOW NETWORK PROVIDERS WILL MANAGE THEM, AND IF THEY WILL BE SHARED AMONG DIFFERENT NETWORK PROVIDERS. IN THIS PAPER, WE PROPOSE A TOOL EXPLOITING VISUALIZATION TECHNIQUES IN ORDER TO MAKE A USER AWARE OF HOW HIS/HER PERSONAL DATA ARE EXCHANGED AND SHARED DURING DAILY WEB BROWSING ACTIVITIES. IN PARTICULAR, THE PROPOSED TOOL ENABLES A USER TO INTERACTIVELY VISUALIZE THE COMMUNICATION FLOWS DURING THE AFORESAID BROWSING PROCESS, AND TO DISCOVER POSSIBLY HIDDEN NETWORK PROVIDERS INVOLVED IN IT. MOREOVER, THE GRAPHICAL INTERFACE ALSO PROVIDES REAL-TIME SUMMARY GRAPHS, WHICH SHOW THE AMOUNT OF INFORMATION ACQUIRED FROM THE NETWORK. FINALLY, WE PERFORMED SEVERAL USERS STUDIES AIMING TO ANALYSE HOW THE TOOL CAN IMPROVE THE USER'S PERCEPTION ON THE PRIVACY ISSUES THAT S/HE IS EXPOSED TO. RESULTS DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED TOOL.
NOWADAYS, INSTRUCTORS APPLY A LARGE VARIETY OF LEARNING METHODOLOGIES TO HELP LEARNERS TO ACHIEVE THE LEARNING OUTCOMES AND TO ASSESS THE KNOWLEDGE ACQUIRED ACROSS THE COURSE. FORMATIVE AND SUMMATIVE ASSESSMENT MODELS ARE MAINLY APPLIED IN MULTIPLE COMBINATIONS INDEPENDENTLY OF THE LEARNING ENVIRONMENT (ON-SITE, ONLINE OR BLENDED). WHEN WE MOVE TO AN ADAPTIVE LEARNING, THE ADAPTION TENDS TO BE IN THE LEARNING PROCESS (LEARNING PATH, ACTIVITIES, EDUCATIONAL RESOURCES) MAINLY RELATED TO FORMATIVE MODELS BUT LITTLE ADAPTION CAN BE FOUND RELATED TO SUMMATIVE MODELS AND VERY RESTRICTIVE. IN THE LATTER CASE, GRADE FORMULAS DEPENDING ON PERFORMED ASSESSMENT ACTIVITIES ARE TYPICALLY DEFINED TO PROVIDE A PERSONALIZED LEARNING PROCESS. IN THIS PAPER, WE INTRODUCE THE BASIS OF AN INNOVATIVE PERSONALIZED SUMMATIVE MODEL BASED ON LEARNER'S PREFERENCES AND EFFORT. ALTHOUGH THIS MODEL CONCEPTUALLY MAY ALLOW PASSING A COURSE WITHOUT EVALUATING ALL LEARNING OUTCOMES, IT IS NOT FAR FROM CONVENTIONAL SUMMATIVE MODELS WHERE A CERTAIN GRADE IS REQUIRED TO PASS THE COURSE AND THE LEARNER MAY NOT HAVE ACQUIRED ALL THE KNOWLEDGE TAUGHT IN THE COURSE. THE PAPER INTRODUCES THE MODEL AND IT ALSO ANALYSES AN OPINION SURVEY ON INSTRUCTORS AND LEARNERS.
DIMENSIONALITY REDUCTION (DR) TECHNIQUES ARE WIDELY USED TO ANALYZE AND MAKE SENSE OF HIGH-DIMENSIONAL DATA. EACH METHOD IS GEARED TOWARDS PRESERVING A DIFFERENT ASPECT OF THE DATA. FOR EXAMPLE, SOME TECHNIQUES FAVOR NEIGHBORHOOD PRESERVATION WHEREAS OTHERS FAVOR DISTANCE PRESERVATION. WHILE THESE DR TECHNIQUES HELP USERS TO REPRESENT THEIR DATA, IT MAKES A COMPLEX TASK TO SELECT A SUITABLE DR. ALSO, MOST DR TECHNIQUES HAVE ADDITIONAL PARAMETERS THAT AFFECT THE RESULTS, WHICH MAKE THE TASK OF CHOOSING A TECHNIQUE MORE DIFFICULT. EXISTING METHODS COMPARE DR TECHNIQUES USING SOME QUALITY METRICS, AND SOME OF THEM COMBINE DR OUTPUTS BY AVERAGING PROJECTIONS. HOWEVER, IT DOES NOT YET PROVIDE ENOUGH MECHANISMS TO CREATE A NEW DR ACCORDING TO USER REQUIREMENTS. IN THIS PAPER, WE PRESENT A WAY TO ANALYZE AND COMPARE DIFFERENT DR TECHNIQUES. IT IS AN INTERACTIVE ASSESSMENT METHOD THAT ALLOWS A USER TO EXPLORE KNOWN DR TECHNIQUES, IDENTIFY THE DIFFERENCES BETWEEN THEM, AND CREATE A NEW DR TECHNIQUE THAT COMBINES EXISTING TECHNIQUES TO MATCH USER EXPECTATIONS.
THE FREQUENCY OF MICROSACCADES IS OFTEN USED AS A MEASUREMENT OF EYE MOVEMENT IN ORDER TO ESTIMATE THE LEVEL OF A COGNITIVE MENTAL WORKLOAD DURING AN EXPERIMENTAL TASK. WHEN THE EXPERIMENT INCLUDES SEVERAL LEVELS OF MANIPULATION TASKS, OCULAR INFORMATION INCLUDING MICROSACCADES, SACCADES AND PUPIL DIAMETERS CAN BE MEASURED AND COMPARED. THE RESULTS SHOW THAT THE MICROSACCADE RATE REFLECTS A PARTICIPANT'S SUBJECTIVE IMPRESSIONS. IN ADDITION TO MEASUREMENTS OF OCULO-MOTORS, MICROSACCADE RATES ALSO REFLECT THE LEVEL OF EFFORT REQUIRED BY A MENTAL WORKLOAD, AND THE MUTUAL RELATIONSHIPS BETWEEN THESE RATES ARE ANALYZED USING CAUSAL ANALYSIS.
MANAGEMENT AND MAINTENANCE OF MOBILE NETWORKS IS A CHALLENGING ACTIVITY: KEY POINT INDICATORS RELATED TO DIFFERENT DIMENSIONS NEED TO BE EVALUATED IN ORDER TO MAKE BUSINESS DECISIONS AND/OR TO MANAGE PROBLEMS ARISING FROM THE NETWORK AND THE RELATIVE IMPACT ON BUSINESS. THIS PAPER PRESENTS A VISUAL ANALYTICS SOLUTION ABLE TO GEO-REFERENCE UP TO THREE KEY POINT INDICATORS, AIMING AT SUPPORTING OPERATORS IN CHARGE OF ANALYZING THE MOBILE NETWORK, DETECTING POSSIBLE MOBILE NETWORK FAILURES AND MANAGING RESULTING BUSINESS ORIENTED DECISIONS. THE PROPOSED SYSTEM HAS BEEN DEVELOPED COLLABORATIVELY BY UNIVERSITY OF ROME "LA SAPIENZA" AND TIM.
IN THIS PAPER, ELECTROCARDIOGRAM (ECG) ANALYSES WERE USED AS VALUABLE A TOOL IN THE EVALUATION OF COGNITIVE TASKS ALSO GIVEN BY THE ELECTROENCEPHALOGRAMS (EEG). BY TAKING AND ANALYZING MEASUREMENTS IN LARGE QUANTITIES, WE TRIED TO BETTER UNDERSTAND THE FUNCTIONING OF HUMAN PHYSIOLOGICAL SYSTEMS. THIS STUDY EXAMINED THE COGNITIVE AND CARDIOVASCULAR SYSTEM FUNCTION SIMULTANEOUSLY. THE PURPOSE OF THIS PAPER WAS TO SEEK STATISTICAL CAUSALITY IN THE SENSE OF GRANGER BETWEEN THE EEG AND ECG SIGNALS BASED ON TIME SERIES AND AUTO-REGRESSIVE VECTOR PROCESSES (VAR). FOR THIS PURPOSE, 24 HOURS WERE RECORDED AND DURING THE TESTS, RANDOM AND NONSTATIONARY PORTIONS OF THE ECG AND EEG WERE EXTRACTED. THE RESULTS INDICATED THAT THERE WAS GRANGER CAUSALITY BETWEEN THE SIGNALS. THIS ALLOWED US TO FORECAST AND PREDICT TRAFFIC SPOTS WITHIN AND BETWEEN THE ECG AND EEG SIGNALS.
360 DEGREES VIDEOS ARE INCREASINGLY USED FOR MEDIA AND ENTERTAINMENT, BUT THE BEST PRACTICES FOR EDITING THEM ARE NOT YET WELL ESTABLISHED. IN THIS PAPER, WE PRESENT A STUDY IN WHICH WE INVESTIGATED THE USER EXPERIENCE OF 360 DEGREES MUSIC VIDEOS VIEWED ON COMPUTER MONITOR AND VR GOGGLES. THE RESEARCH WAS CONDUCTED IN THE FORM OF A LABORATORY EXPERIMENT WITH 20 TEST PARTICIPANTS. DURING THE WITHIN-SUBJECT STUDY, PARTICIPANTS WATCHED AND EVALUATED FOUR VERSIONS OF THE SAME 360 DEGREES MUSIC VIDEO WITH A DIFFERENT CUTTING RATE. BASED ON THE RESULTS, AN AVERAGE CUTTING RATE OF 26 SECONDS DELIVERED THE HIGHEST-QUALITY USER EXPERIENCE BOTH FOR COMPUTER MONITOR AND VR GOGGLES. THE CUTTING RATE MATCHED WITH PARTICIPANTS' MENTAL MODELS, AND THERE WAS ENOUGH TIME TO EXPLORE THE ENVIRONMENT WITHOUT GETTING BORED. FASTER CUTTING RATES MADE THE USERS NERVOUS, AND A VIDEO CONSISTING OF A SINGLE SHOT WAS CONSIDERED TO BE TOO STATIC AND BORING.
IN THIS PAPER, WE PROPOSE A NEW ALGORITHM FOR DATA EXTRACTION FROM TIME SERIES DATA, AND FURTHERMORE AUTOMATIC CALCULATION OF HIGHLY INFORMATIVE DEEP FEATURES TO BE USED IN FAULT DETECTION. IN DATA EXTRACTION ELEVATOR START AND STOP EVENTS ARE EXTRACTED FROM SENSOR DATA INCLUDING BOTH ACCELERATION AND MAGNETIC SIGNALS. IN ADDITION, A GENERIC DEEP AUTOENCODER MODEL IS ALSO DEVELOPED FOR AUTOMATED FEATURE EXTRACTION FROM THE EXTRACTED PROFILES. AFTER THIS, EXTRACTED DEEP FEATURES ARE CLASSIFIED WITH RANDOM FOREST ALGORITHM FOR FAULT DETECTION. SENSOR DATA ARE LABELLED AS HEALTHY AND FAULTY BASED ON THE MAINTENANCE ACTIONS RECORDED. THE REMAINING HEALTHY DATA ARE USED FOR VALIDATION OF THE MODEL TO PROVE ITS EFFICACY IN TERMS OF AVOIDING FALSE POSITIVES. WE HAVE ACHIEVED NEARLY 100% ACCURACY IN FAULT DETECTION ALONG WITH AVOIDING FALSE POSITIVES BASED ON NEW EXTRACTED DEEP FEATURES, WHICH OUTPERFORMS RESULTS USING EXISTING FEATURES. EXISTING FEATURES ARE ALSO CLASSIFIED WITH RANDOM FOREST TO COMPARE RESULTS. OUR DEVELOPED ALGORITHM PROVIDES BETTER RESULTS DUE TO THE NEW DEEP FEATURES EXTRACTED FROM THE DATASET WHEN COMPARED TO EXISTING FEATURES. THIS RESEARCH WILL HELP VARIOUS PREDICTIVE MAINTENANCE SYSTEMS TO DETECT FALSE ALARMS, WHICH WILL IN TURN REDUCE UNNECESSARY VISITS OF SERVICE TECHNICIANS TO INSTALLATION SITES.
IN CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEMS, THE VISUALIZATION OF QUERIES ALLOWS TO ADD THE HUMAN VISUAL PERCEPTION IN THE ANALYSIS PROCESS AND FACILITATE THE DISCOVERY OF KNOWLEDGE. CONTENT-BASED QUERIES CAN BE PERFORMED COMPARING FEATURES EXTRACTED FROM IMAGES, SUCH AS COLOR, TEXTURE, AND SHAPE. IN THIS PAPER WE PROPOSE WAYS TO DELIMIT THE REGION OF INTEREST TO BE VISUALIZED IN THE EXECUTION OF QUERIES BY SIMILARITY IN COMPLEX DATASETS. LIMITING THE AMOUNT OF DATA TO BE VISUALIZED ALLOWS KEEPING THE DISTRIBUTION OF MAPPED DATA CLOSER TO THE REAL DISTRIBUTION, BESIDES ALLOWING THE APPLICATION OF MORE EXPENSIVE COMPUTATIONAL METHODS FOR MULTIDIMENSIONAL PROJECTION. THE PROPOSED TECHNIQUES WERE IMPLEMENTED IN A PROTOTYPE THAT ALLOWS VISUALIZING ONLY THE REGION IN WHICH THE QUERY IS BEING PERFORMED, MAPPING THE DATA IN THREE-DIMENSIONAL SPACES AND ALLOWING USERS TO INTERACT WITH THEM, BEING FAVORED BY HUMAN PERCEPTION TO IMPROVE THE ANALYSIS AND UNDERSTANDING OF THE DATA.
THERE HAS BEEN AN INCREASING DEMAND TO UNDERSTAND AND DESCRIBE EVOLUTIONARY COMPUTING TECHNIQUES. INFORMATION VISUALIZATION MAY CONTRIBUTE WITH INTERACTIVE DATA VISUALIZATIONS THAT HELP EXPLORE THE POPULATION OF INDIVIDUAL SOLUTIONS OVER THE DATA SEARCH SPACE AND GENERATIONS, CONVERGENT BEHAVIOR, INDIVIDUAL FITNESS, THE DYNAMIC OF THE EVOLUTIONARY PROCESS AMONG OTHER POSSIBLE SCENARIOS. ALTHOUGH THERE ARE PREVIOUS WORKS ON THE USE OF VISUALIZATION TO ANALYZE EVOLUTIONARY TECHNIQUES, THERE HAS BEEN LITTLE DIVERSITY AMONG THE APPROACHED VISUALIZATION TECHNIQUES. ALSO, MOST RELATED WORKS CONSIDER ONLY GENETIC ALGORITHMS AND IGNORE OTHER EVOLUTIONARY APPROACHES. THEREFORE THE GOAL OF THIS PAPER IS TO SUGGEST THE APPROPRIATE INFOVIS TECHNIQUES FOR THE ANALYZED SCENARIOS TO BETTER UNDERSTAND THE BEHAVIOR OF EVOLUTIONARY COMPUTING ALGORITHMS. FURTHERMORE, WE PRESENT A CASE STUDY THAT APPLIES THE PROPOSED SCENARIOS TO AUTOCLUSTERING, A TOOL BASED ON ESTIMATION OF DISTRIBUTION ALGORITHMS. WE HOPE THE PROPOSED SCENARIOS AND TECHNIQUES PROVIDE A SET OF GOOD PRACTICES FOR THE ANALYSIS OF EVOLUTIONARY COMPUTING TECHNIQUES.
ELEMENT OVERLAP IN VISUALIZATION TECHNIQUES IS A KNOWN PROBLEM, AND HIGH AMOUNTS OF DATA AND LACK OF AVAILABLE VISUAL SPACE POTENTIALIZE THIS ISSUE. MANY STUDIES HAVE APPLIED TECHNIQUES TO REDUCE OCCLUSION LEVELS IN DATA VISUALIZATIONS, SUCH AS RANDOM JITTER, ELEMENT TRANSPARENCY, LAYOUT REARRANGEMENT, AND FOCUS+CONTEXT TECHNIQUES. HOWEVER, FEW STUDIES FOCUS ON THE PRESENCE OF OCCLUSION, WHICH IS A RELEVANT TOPIC FOR VISUALIZATIONS WHERE SOME DEGREE OF OVERLAP IS INEVITABLE OR PURPOSEFULLY EXPLORED. THIS PAPER TAKES A STEP IN THIS DIRECTION, AND PRESENTS A COMPARATIVE STUDY OF VISUAL VARIABLES, MEASURING THEIR ROBUSTNESS TO OVERLAP AND NUMBER OF UNIQUE VALUES. THE STUDY USED A GRID LAYOUT TO DISPLAY VISUAL VARIABLES (HUE, SATURATION, SHAPE, TEXT, ORIENTATION, AND TEXTURE), AND VARIED PERCENTAGE OF OCCLUSION (0%, 50%, 60%, AND 70%) AND NUMBER OF UNIQUE VALUES (3, 4, AND 5) TO MEASURE THE EFFECT THEY CAUSE ON THE SPEED AND ACCURACY TO LOCATE THE VISUAL VARIABLES. HENCE, 48 VOLUNTEERS PERFORMED LOCATE TASKS ON A TOOL THAT AUTOMATICALLY GENERATE A GRID OF VISUAL VARIABLES AND COLLECT THEIR ANSWERS. THE RESULTS REVEALED THAT HUE AND SHAPE WERE ROBUST TO HIGH OCCLUSION LEVELS AND A HIGH NUMBER OF UNIQUE VALUES. TEXT AND TEXTURE HAD MEDIUM LOSS OF PERFORMANCE, WHILE SATURATION AND ORIENTATION WERE THE MOST NEGATIVELY AFFECTED.
LEARNERS REQUIRE A CERTAIN EFFORT TO ACQUIRE A SPECIFIC SKILL OR COMPETENCE. THE INVESTED EFFORT CAN BE AFFECTED BY MANY FACTORS AS PREVIOUS KNOWLEDGE, ABILITIES OR TIME AVAILABLE FOR LEARNING. THE EVALUATION OF THE EFFORT HAS BEEN MAINLY RELATED TO COGNITIVE SCIENCE OR INSTRUCTIONAL PSYCHOLOGY DUE TO THE RELATION BETWEEN EFFORT AND MENTAL WORK. THIS PAPER FOCUSES ON HOW THE EFFORT CAN BE ESTIMATED ON E-LEARNING SYSTEMS. THIS INFORMATION CAN ENHANCE INSTRUCTIONAL PROCESS SINCE TEACHERS CAN ANALYZE THE TOTAL TIME LEARNERS INVEST ON ACQUIRING KNOWLEDGE AND THEY CAN ADJUST BETTER THE COMPLEXITY OF THE COURSE. THE PAPER CONTRIBUTES ON PRINCIPLES TO DESIGN AN EFFORT-BASED SYSTEM. FINALLY, A PARTICULAR APPLICATION TO AN INTELLIGENT TUTORING SYSTEM IS PERFORMED.
ARTIFICIAL NEURAL NETWORKS ARE MACHINE-LEARNING ALGORITHMS INSPIRED BY BIOLOGICAL NEURAL NETWORKS. THEIR MAIN INCONVENIENT IS THEIR "BLACK-BOX" NATURE: WHILE THEY ARE VERY EFFICIENT FOR MAKING PREDICTIONS, IT IS DIFFICULT TO EXPLAIN THESE PREDICTIONS. IN THIS PAPER, WE PROPOSE A VISUAL TRANSLATION OF THE REASONING PERFORMED BY SIMPLE NEURAL NETWORKS, I.E. WITHOUT HIDDEN LAYERS. THIS VISUALIZATION RELIES ON RAINBOW BOXES, A RECENTLY-INTRODUCED TECHNIQUE FOR SET VISUALIZATION, AND ON THREE IMPROVEMENTS WE PROPOSE FOR RAINBOW BOXES, INCLUDING INTERACTIVITY. WE ALSO PRESENT A SMALL APPLICATION OF THE PROPOSED APPROACH TO DECISION SUPPORT IN ANTIBIOTHERAPY, FOR HELPING A PHYSICIAN TO CHOOSE AN ANTIBIOTIC IN URINARY INFECTIONS.
THE MANUAL ANALYSIS OF SURVEILLANCE VIDEOS IS UNFEASIBLE DUE TO THE EXCESSIVE AMOUNT OF DATA, THE ASSOCIATED SUBJECTIVITY, OR THE EVENTUAL PRESENCE OF DISTRACTING NOISE. AUTOMATIC SUMMARIZATION APPROACHES PROVIDE LITTLE/NO USER INTERACTION, LIMITING HIS/HER COMPREHENSION REGARDING THE INVOLVED PHENOMENA. VISUAL ANALYTICS TECHNIQUES REPRESENT A POTENTIAL TOOL FOR SUCH ANALYSIS, PROVIDING VIDEO REPRESENTATIONS THAT CLEARLY COMMUNICATE THEIR CONTENT, POTENTIALLY REVEALING PATTERNS THAT MAY REPRESENT EVENTS OF INTEREST. IN THIS PAPER, WE PRESENT A METHODOLOGY FOR VISUAL ANALYSIS OF SURVEILLANCE VIDEO THAT COMBINES POINT-PLACEMENT TECHNIQUES AND TEMPORAL SELF-SIMILARITY MAPS (TSSMS) TO REVEAL THE EVENTS OCCURRENCE STRUCTURE AND TO ENHANCE THE COMPREHENSION OF THEIR TEMPORAL PROPERTIES. EXPERIMENTS IN SEVERAL SURVEILLANCE SCENARIOS DEMONSTRATE THAT OUR PROPOSED METHODOLOGY PROVIDES AN EFFECTIVE EVENTS SUMMARIZATION AND THE EXPLORATION OF BOTH THE STRUCTURE OF EACH EVENT AND THE RELATIONSHIP AMONG THEM, ALLOWING THE SECURITY AGENT TO FILTER/EXPLORE THOSE THAT REPRESENT POTENTIAL ALERT SITUATIONS.
CURRENTLY THERE ARE NUMEROUS REPOSITORIES OF LINKED DATA AND OPEN DATA THAT STORE INFORMATION FROM VERY VARIED DOMAINS. THESE REPOSITORIES CAN BE CONSULTED TO RETRIEVE THE DATA THEY STORE USING QUERY LANGUAGES. NORMALLY, THE RESULT OF THE QUERIES GENERATES A FILE CONTAINING THE REQUESTED DATA REPRESENTED IN SOME INFORMATION REPRESENTATION FORMAT. THIS ARTICLE PRESENTS A PROCESS MODEL TO CREATE LEARNING RESOURCES IN A SIMPLE AND FAST WAY USING THESE REPOSITORIES CAN BE USED AS EXAMPLE OF THIS MODEL, IT HAS BEEN CREATED A WEB APPLICATION IN PYTHON THAT SUGGESTS PAINTINGS AND OTHER ARTISTIC OBJECTS FROM THE MUSEUMS OF THE CITY OF MADRID AND ALLOWS TO BUILD, IN A SEMI-AUTOMATIC WAY, A SET OF PROPOSALS FOR LEARNING ACTIVITIES ORIENTED TO THEIR STUDENTS. IN ORDER TO IT, THE APPLICATION EXPLOITS SEVERAL REPOSITORIES OF OPEN DATA PORTALS HAVE BEEN EXPLOITED: WIKIDATA AND THE MADRID CITY COUNCIL OPEN DATA PORTAL.
THE TEXTUAL DESCRIPTION OF DATA CHARTS IS A COMPLEX TASK. A CHART PRESENTS DIFFERENT VISUAL CHARACTERISTICS FOR THE INFORMATION REPRESENTED, WHICH CAN BE INFLUENCED BY THE TECHNIQUE SELECTED AND THE COMBINATION OF VISUAL ELEMENTS. THERE ARE CROWDSOURCING INITIATIVES TO CREATE DESCRIPTIONS FOR CHARTS AVAILABLE ON THE WEB, BUT THE DESCRIPTIONS CAN HAVE FAILURES, CONSIDERING THAT THEY ARISE FROM THE UNDERSTANDING OF THE PERSON. IN THIS CONTEXT, METHODS TO AUTOMATICALLY EXTRACT DATA FROM CHART IMAGES ALLOW PRODUCING DESCRIPTIONS FOR USE IN THESE SCENARIOS. HOWEVER, THERE IS NO STANDARD WAY OF VOCALIZING THE CHART CONTENT. FOR THIS, THE TEXTUAL DESCRIPTION MUST BE BASED ON A TEMPLATE, SO THAT THE CHART CAN BE COMPLETELY UNDERSTOOD. THUS, THIS PAPER PRESENTS TEMPLATES THAT ALLOW VERBALIZING THE DATA EXTRACTED FROM VERTICAL AND GROUPED BAR CHARTS IN AN INTELLIGIBLE WAY. EVALUATIONS WERE PERFORMED WITH USERS TO VERIFY THE EASE OF UNDERSTANDING TEXTUAL DESCRIPTIONS. THE RESULTS SHOWED THAT THE PROPOSED TEMPLATES WERE SUITABLE FOR VOCALIZATION THE CONTENTS OF BAR CHARTS.
THIS PAPER OUTLINES A FRAMEWORK FOR THE ANALYSIS OF DATA VISUALIZATIONS. INVESTIGATING THE SEMIOTIC POTENTIALS OF DATA VISUALIZATIONS MEANS THAT WE FIRST HAVE TO INVENTORIZE WHAT IS WITHIN THEM. USING A SOCIAL SEMIOTIC APPROACH, THE PAPER COLLATES THE GRAPHIC RESOURCES AND MODES DEPLOYED IN DATA VISUALIZATIONS, THEIR AFFORDANCES AND COMMUNICATIVE TASKS. THE FRAMEWORK COMBINES THEORETICAL CONSIDERATIONS AND EMPIRICAL RESEARCH. IT IS BASED ON BOTH A THOROUGH LITERATURE REVIEW OF RELEVANT PUBLICATIONS IN THE FIELD OF DATA VISUALIZATION AND VISUAL COMMUNICATION AND AN ANALYSIS OF A CORPUS OF DATA VISUALIZATIONS TAKEN FROM JOURNALISM. THE AIM OF THE PAPER IS TO LINK THE AESTHETIC FORM TO ITS SOCIAL MEANING IN A SPECIFIC CONTEXT. THE FRAMEWORK ADVANCES UNDERSTANDING OF HOW SEMIOTIC RESOURCES SHAPE THE MEANING OF A DATA VISUALIZATION.
ADDITIVE MANUFACTURING TECHNIQUES HAS GREAT POTENTIAL FOR MANUFACTURING METAL OR POLYMER COMPONENTS WITH VERY HIGH GEOMETRIC COMPLEXITY. THIS FAMILY OF PROCESSES IS NOW EXPERIENCING SIGNIFICANT GROWTH AND IS AT THE ORIGIN OF INTENSE RESEARCH ACTIVITY (OPTIMIZATION OF TOPOLOGY, BIOMEDICAL APPLICATIONS, ETC.). ONE OF THE CHARACTERISTICS OF THIS METHOD IS THAT THE GEOMETRIC COMPLEXITY IS FREE. THE COMPLEXITY OF A CAD MODEL IS ALSO A FIELD OF RESEARCH. THE BASIC IDEA IS THAT THE COMPLEXITY OF A COMPONENT HAS IMPLICATIONS IN DESIGN AND ESPECIALLY IN MANUFACTURING. INDEED, INDUSTRIAL COMPETITIVENESS IN THE MECHANICAL FIELD GENERATED THE NEED TO PRODUCE INCREASINGLY COMPLEX SYSTEMS AND PARTS (IN TERMS OF TOPOLOGY, FUNCTIONALITY...). IN THE PRESENT WORK, WE PROPOSE A COMPLEXITY METRIC MODEL BASED SOLELY ON THE GEOMETRIC INFORMATION FOUND IN COMPUTER-AIDED DESIGN (CAD) FILE. THE PROPOSED METRIC IS A MULTIPLICATIVE MODEL. OUR INVESTIGATION IS BASED ON THE ANALYSIS OF DIFFERENT PARTS PICKED FROM OUR TECHNICAL DOCUMENT DATABASE. THE FIRST RESULTS OF OUR WORK DEMONSTRATE THAT OUR MODEL IS HIGHLY CORRELATED TO A PART'S EVALUATED COMPLEXITY. NONETHELESS, WITH ITS CURRENT QUALITY, OUR MODEL COULD HELP ENGINEERING TEAMS IDENTIFY HIGH-COMPLEXITY PRODUCTS AS EARLY AS THE DESIGN PHASE.
THE AVAILABILITY OF DETAILED VIRTUAL 3D BUILDING MODELS INCLUDING REPRESENTATIONS OF INDOOR ELEMENTS, ALLOWS FOR A WIDE NUMBER OF APPLICATIONS REQUIRING EFFECTIVE EXPLORATION AND NAVIGATION FUNCTIONALITY. DEPENDING ON THE APPLICATION CONTEXT, USERS SHOULD BE ENABLED TO FOCUS ON SPECIFIC OBJECTS-OF-INTERESTS (OOIS) OR IMPORTANT BUILDING ELEMENTS. THIS REQUIRES APPROACHES TO FILTERING BUILDING PARTS AS WELL AS TECHNIQUES TO VISUALIZE IMPORTANT BUILDING OBJECTS AND THEIR RELATIONS. FOR IT, THIS PAPER EXPLORES THE APPLICATION AND COMBINATION OF INTERACTIVE RENDERING TECHNIQUES AS WELL AS THEIR SEMANTICALLYDRIVEN CONFIGURATION IN THE CONTEXT OF 3D INDOOR MODELS.
STATE OF THE ART GENOMIC METHODS PRODUCE AN ABUNDANCE OF DATA WHICH ULTIMATELY INCREASES THE QUANTITY WITHIN AND OF DATA REPOSITORIES. THEREBY, OPEN DATA IS OF MUCH IMPORTANCE TO BOOST SCIENTIFIC STUDIES FOR COMBATING DISEASE. MORE AND MORE OFTEN, THERE ARE SEVERAL DATA SOURCES AVAILABLE, OFFERING DIVERSE SAMPLE DATA. PARTICULARLY, INTERPRETATION OF GENOMIC DATA REMAINS A CHALLENGE DUE TO DATA SIZE AND DIFFERING QUALITY. WE PRESENT AN APPROACH FOR VISUALIZING UNCERTAINTY OF HETEROGENEOUS DATA SOURCES ON MUTATION RATES IN GENOMIC DATA FOR PEDIATRIC CANCER ANALYSIS. VISUALIZATION AS METHOD FOR KNOWLEDGE DISCOVERY WILL BE OF GREAT IMPORTANCE IN ORDER TO PUT INHOMOGENEITY OF SAMPLE DATA INTO PERSPECTIVE.
THE DIVISION OF ASTRONOMY AND ASTROPHYSICS OF COMENIUS UNIVERSITY WAS GRANTED RESOURCES FROM THE FIRST PLAN FOR EUROPEAN COOPERATING STATES (PECS) PROJECT AS THE NINTH EUROPEAN COOPERATING STATE WITH EUROPEAN SPACE AGENCY (ESA) TO TRANSFORM A NEWLY ACQUIRED TELESCOPE TO A PROFESSIONAL OBSERVATION DEVICE. ONE OF THE GOALS OF THE TRANSFORMATION WAS THE DESIGN AND DEVELOPMENT OF AN IMAGE PROCESSING PIPELINE WHICH WOULD BE ABLE TO PROCESS AN ACQUIRED RAW IMAGE OF SPACE DEBRIS INTO OBJECT OBSERVATIONS IN TIME (TRACKLETS), FURTHER CORRELATE THEM WITH SELECTED CATALOGS AND IDENTIFY THEM. THE SYSTEM CONTAINS 9 IMAGE PROCESSING ELEMENTS (IPES) IN TOTAL.
VISUALIZATION OF PROPORTIONS IS A VERY COMMON NEED AND THERE ARE MANY TECHNIQUES FOR IT. THE PIE CHART IS POPULAR AMONG PRACTITIONERS AND GENERAL AUDIENCE, BUT MANY PROMINENT EXPERTS ADVISE AGAINST USING IT. THIS PAPER REPORTS AN EXPERIMENT WHERE THE PIE CHART IS COMPARED TO STACKED BAR CHARTS WITH A BASELINE CONDITION OF TABLE. THE AIM IS TO STUDY IF THE PERFORMANCE DIFFERENCES JUSTIFY THE BAD REPUTATION OF PIE CHARTS. IN THE EXPERIMENT THE PARTICIPANTS WERE REQUESTED TO LIST OUT THE ELEMENTS OF A VISUALIZATION IN DESCENDING ORDER OF SIZE. THE TASK TIME AND THE ANSWER WERE RECORDED. THE RESULTS SHOW THAT THE PIE CHART IS SLOWER AND LESS ACCURATE THAN THE STACKED BAR CHART, ESPECIALLY WHEN THE DIFFERENCE BETWEEN THE ELEMENTS IS SMALL, BUT THE PARTICIPANTS FIND IT SLIGHTLY MORE PLEASANT TO USE. THE PARTICIPANTS ALSO PERCEIVE THE STACKED BAR CHART AS THE MOST EFFECTIVE VISUALIZATION.
VISUAL LANGUAGES ARE STUDIED IN MANY DIFFERENT DISCIPLINES INCLUDING FORMAL LOGIC. SEVERAL DIAGRAM METHODS HAVE BEEN PROPOSED FOR THE VISUAL REPRESENTATION OF THE LOGICAL RELATIONS AND IN PARTICULAR FOR FIRST ORDER PREDICATE LOGIC (FOPL) FORMULAS. AMONG THESE, LOGICAL SYMBOLISM, EULER DIAGRAMS, SEMANTIC NETWORKS, CONCEPTUAL GRIDS, CONCEPTUAL SPACES AND SO ON. IT IS SHOWN THAT THESE REPRESENTATIONS ARE FORMALLY EQUIVALENT AND CAN BE INTER-TRANSLATED ALGORITHMICALLY, BUT PROVIDE DIFFERENT AND COMPLEMENTARY VISUALIZATIONS SUCH THAT THE USE OF MULTIPLE REPRESENTATIONS MAY PROVIDE GREATER INSIGHT THAN ANY ALONE. WE PRESENT A NEW VISUAL LANGUAGE, V-LOGIC, WHICH SUPPORT DIFFERENT VISUAL REPRESENTATION SCHEMES: SPATIAL, GRAPHICAL AND SYMBOLIC NOTATIONS. IT SPECIFIES RULES FOR MAPPING FOPL FORMULAS IN SPECIAL SEMANTICALLY EQUIVALENT DIAGRAMS, NAMED V-DIAGRAMS. LOGICAL INFERENCE BASED ON THE INTERPRETATION OF A V-DIAGRAM IS ESSENTIALLY A TRANSLATION OF THE DIAGRAM INTO A LOGICAL FORMALISM. SUCH A TRANSLATION IS NATURAL AND COULD BE USED TO TEACH FOPL. WE PERFORMED A PRELIMINARY STUDY WITH 10 STUDENTS. RESULTS PROVIDED US WITH OVERALL POSITIVE FEEDBACK ABOUT THE EFFECTIVENESS OF OUR APPROACH AS WELL AS FURTHER DIRECTIONS TO EXPLORE.
THE CONSISTENT ARRANGEMENT OF MAP FEATURES IN ACCORDANCE WITH THE MAP SCALE HAS RECENTLY BEEN TECHNICALLY IMPORTANT IN DIGITAL CARTOGRAPHIC GENERALIZATION. THIS IS PRIMARILY DUE TO THE RECENT DEMAND FOR INFORMATIVE MAPPING SYSTEMS, ESPECIALLY FOR USE IN SMARTPHONES AND TABLETS. HOWEVER, SUCH SOPHISTICATED GENERALIZATION HAS USUALLY BEEN CONDUCTED MANUALLY BY EXPERT CARTOGRAPHERS AND THUS RESULTS IN A TIME-CONSUMING AND ERROR-PRONE PROCESS. IN THIS PAPER, WE FOCUS ON THE DISPLACEMENT PROCESS WITHIN CARTOGRAPHIC GENERALIZATION AND FORMULATE THEM AS A CONSTRAINED OPTIMIZATION PROBLEM TO PROVIDE AN ASSOCIATED ALGORITHM IMPLEMENTATION AND ITS EFFECTIVE SOLUTION. WE FIRST IDENTIFY THE UNDERLYING SPATIAL RELATIONSHIPS AMONG MAP FEATURES, SUCH AS POINTS AND LINES, ON EACH MAP SCALE AS CONSTRAINTS AND OPTIMIZE THE COST FUNCTION THAT PENALIZES EXCESSIVE DISPLACEMENT OF THE MAP FEATURES IN TERMS OF THE MAP SCALE. SEVERAL EXAMPLES ARE ALSO PROVIDED TO DEMONSTRATE THAT THE PROPOSED APPROACH ALLOWS US TO MAINTAIN CONSISTENT MAPPING REGARDLESS OF CHANGES TO THE MAP SCALE.
WE HYPOTHESIZE THAT VISUAL INTERVENTIONS FOR LIFE AND CAREER DESIGN, IN CONTRAST TO CURRENT VERBAL AND WRITTEN PRACTICES, CAN POSITIVELY SUPPORT LIFE DESIGN SESSIONS. OUR EXPLORATIVE EXPERIMENTAL STUDY SHOWS A POSITIVE IMPACT OF VISUAL TREATMENTS IN INCREASING PARTICIPANTS' SELF-ESTEEM, EFFICACY, AND LIFE DESIGN SKILLS AND IN REDUCING ANXIETY TOWARD THE FUTURE. FEMALE PARTICIPANTS SEEMS TO FIND GREATER BENEFITS THAN MALES: FURTHER STUDIES SHOULD INVESTIGATE THE ROLE OF GENDER AS A POTENTIAL MODERATOR IN VISUALIZATION'S EFFECTIVENESS FOR LIFE DESIGN.
VISUAL ENVIRONMENT TECHNIQUES CAN SUPPORT AN EASY EXPLORATION OF LARGE DATASETS. A VISUAL ENVIRONMENT HAS BEEN DEVELOPED TO EXPLORE THE BREEDING DATA OF A MONOGAMOUS AND NEST-SITE FAITHFUL SEABIRD, THE SCOPOLI'S SHEARWATER. THE AIM WAS TO PROVIDE A TOOL, EASY-TO-USE FOR ORNITHOLOGISTS, TO EXAMINE THE HISTORY OF THE NESTS, AND THE RELATIONSHIPS OF THE VARIOUS FACTORS AFFECTING THE VARIABILITY IN THE BREEDING SUCCESS DURING A FOUR YEAR TIME PERIOD. THE TOOL IS BASED ON FOUR COORDINATED VIEWS, LINKED TO EACH OTHER AND TO THE DATASET, EACH SHOWING A DIFFERENT GROUPING OF DATA ACCORDING WITH SPATIAL, TEMPORAL, AND CATEGORICAL CRITERIA. THIS TOOL CAN HELP THE ORNITHOLOGISTS TO PICK UP EASILY THE SIMILARITIES AND DIFFERENCES BETWEEN THE NESTS AND OFFERS A NEW INSTRUMENT FOR THE FIRST EXPLORATION OF DATA.
TAXONOMIC TREES HAVE BEEN USED FOR DECADES TO VISUALIZE TAXONOMIES; HOWEVER, TAXONOMIES INVOLVING MANY TAXA MAY BE DIFFICULT TO INTERPRET DUE TO PROBLEMS RELATED TO OVER-PLOTTING. THIS PROBLEM CAN MANIFEST ITSELF IN METAGENOMICS STUDIES, WHERE THE SET OF DETECTED TAXA CAN HAVE A CARDINALITY IN THE HUNDREDS OR THOUSANDS. WE PRESENT A METHOD BY WHICH A PHYLOGENETIC TREE'S COMPLEXITY MAY BE REDUCED BY REMOVING NODES WITH LITTLE SUPPORT OR WITH TRIVIAL OUT-DEGREE (AS DEFINED BY THE USER). A DIFFUSIVE MODEL IS THEN USED TO COLOR THE NODES BASED ON THEIR TAXONOMIC PROXIMITY; THE COLOR OF ANCESTOR NODES ARE A MIXTURE OF THEIR DESCENDENTS. THIS METHOD RESULTS IN COMPACT TAXONOMIC TREES WHOSE COLOR GRADUALLY DIFFUSES TO WHITE AT THE ROOT. OUR SECOND EXAMPLE APPLICATION OF THE TECHNIQUE IS A MORE GENERALIZED STRUCTURE OF PEDIGREES. WE SHOW THAT RELATED TAXA CAN BE EASILY LOCATED BY REDUCING THE COMPLEXITY OF THE GRAPH VIA PRUNING AND COLORING OF RELATED VERTICES.
THE DECISION-MAKING PROCESS AND THE DEVELOPMENT OF DECISION SUPPORT SYSTEMS (DSS) HAVE BEEN ENHANCED BY A VARIETY OF METHODS ORIGINATED FROM INFORMATION SCIENCE, COGNITIVE PSYCHOLOGY AND ARTIFICIAL INTELLIGENCE OVER THE PAST YEARS. SITUATED VISUALIZATION (SV) IS A METHOD TO PRESENT DATA REPRESENTATIONS IN CONTEXT. ITS MAIN CHARACTERISTIC IS TO DISPLAY DATA REPRESENTATIONS NEAR THE DATA REFERENT. AS AUGMENTED REALITY (AR) IS BECOMING MORE MATURE, AFFORDABLE AND WIDESPREAD, USING IT AS A TOOL FOR SV BECOMES FEASIBLE IN SEVERAL SITUATIONS. IN ADDITION, IT MAY PROVIDE A POSITIVE CONTRIBUTION TO MORE EFFECTIVE AND EFFICIENT DECISION-MAKING, AS THE USERS HAVE CONTEXTUAL, RELEVANT AND APPROPRIATE INFORMATION TO ENDORSE THEIR CHOICES. AS NEW CHALLENGES AND OPPORTUNITIES ARISE, IT IS IMPORTANT TO UNDERSTAND THE RELEVANCE OF INTERTWINING THESE FIELDS. BASED ON A LITERATURE ANALYSIS, THIS PAPER ADDRESSES AND DISCUSSES CURRENT AREAS OF APPLICATION, BENEFITS, CHALLENGES AND OPPORTUNITIES OF USING SV THROUGH AR TO VISUALIZE DATA IN CONTEXT AND TO SUPPORT A DECISION-MAKING PROCESS AND ITS IMPORTANCE IN FUTURE DSS.
MULTIPLE DESCRIPTORS ARE EMPLOYED TO REPRESENT IMAGES IN CONTENT-BASED IMAGE RETRIEVAL (CBIR) SYSTEMS. EACH DESCRIPTOR CONSISTS OF A FEATURE EXTRACTOR ASSOCIATED WITH A DISTANCE FUNCTION. AN EXTRACTOR IS GENERALLY SUITABLE FOR REPRESENTING A SPECIFIC SUBSET OF IMAGES ON A DATABASE. THE BOUNDARY CONDITIONS ARE INFORMATION USED TO DETECT THIS SUBSET. THE USE OF VISUALIZATION IN CBIR HELPS TO REPRESENT THE SIMILARITY RELATIONSHIP BETWEEN IMAGES, IMPROVING THE USER'S UNDERSTANDING OF THE CBIR SYSTEM, ALLOWING THEM TO MODIFY PARAMETERS TO OBTAIN BETTER RESULTS. IT IS PROVEN THAT THE USE OF MULTIPLE DESCRIPTORS WITH BOUNDARY CONDITIONS TENDS TO IMPROVE THE PRECISION OF CBIR QUERIES, BUT THERE IS NO DATA ON THE IMPACT THAT THE TECHNIQUE GENERATES ON VISUALIZATION. THIS PAPER USES MULTIPLE DESCRIPTORS WITH BOUNDARY CONDITIONS TO GENERATE A NEIGHBOR JOINING SIMILARITY TREE-BASED VIEW. TESTS HAVE SHOWN THAT THE QUALITY OF THE VISUALIZATION MAY BE RELATED TO THE QUALITY OF THE QUERY RESULT. IN THE CONTEXT OF SIMILARITY TREES, THE USE OF MULTIPLE DESCRIPTORS CONTRIBUTED TO A BETTER-ORGANIZED VISUALIZATION.
THIS PAPER PRESENTS A VISUALIZATION TECHNIQUE FOR MULTIDIMENSIONAL DATASETS CONTAINING REAL AND CATEGORICAL VARIABLES. SUPPOSING MULTIDIMENSIONAL DATASETS CONTAINING REAL AND CATEGORICAL VALUES, THIS TECHNIQUE DISPLAYS A SET OF AXES CORRESPONDING TO THE DIMENSIONS OF REAL VALUES. THE TECHNIQUE EVENLY DIVIDES THE AXES INTO SEVERAL RANGES AND DISPLAYS COMPONENT BAR CHARTS THERE. IT BRIGHTLY DRAWS THE COMPONENT BAR CHARTS IF ASSOCIATION RULES ARE APPLIED AT THE CORRESPONDING RANGES OF THE DIMENSIONS OF REAL VALUES. AS A RESULT, THIS TECHNIQUE HIGHLIGHTS ASSOCIATION RULES SO THAT USERS CAN DISCOVER IMPORTANT RELATIONSHIPS BETWEEN REAL AND CATEGORICAL VARIABLES IN MULTIDIMENSIONAL DATASETS. THIS PAPER INTRODUCES AN APPLICATION OF THE PRESENTED TECHNIQUE TO PAINTING IMAGE COLLECTIONS. THIS APPLICATION VISUALIZES IMAGE FEATURES AND CATEGORICAL INFORMATION OF PAINTING IMAGES AND PROVIDES A USER INTERFACE TO BROWSE THE PAINTING IMAGES ASSOCIATED WITH THE MULTIDIMENSIONAL VALUES. THIS PAPER ALSO INTRODUCES USER EVALUATION RESULTS OF THE USER INTERFACES FOR PAINTING IMAGE COLLECTIONS.
THE MANAGEMENT OF A FULLY ONLINE COURSE IS A KEY TO MAINTAINING LEARNING EFFECTIVENESS. SOME FACTORS OF PARTICIPANTS AND THEIR LEARNING ACTIVITIES WERE ANALYSED. FEATURES OF STUDENTS WHO PREFERRED JOINING FULLY ONLINE COURSES WERE EXTRACTED AND COMPARED ACROSS SEVERAL YEARS. NOTE TAKING ACTIVITY DURING THE COURSE WAS LEXICALLY ANALYSED AND THE FEATURES OF NOTES WERE EXTRACTED. THE RELATIONSHIPS BETWEEN THESE FEATURES AND LEARNING PERFORMANCE WAS COMPARED. FIVE TYPICAL NOTE TAKING STYLES WERE EXTRACTED, AND REFLECTIONS OF LEARNING PERFORMANCE AND PARTICIPANTS' CHARACTERISTICS WERE EXAMINED.
HERITAGE VISUALISATIONS ARE INTERPRETATIONS SITUATED IN PLACE AND TIME. THEY ARE SHAPED BY THE ASSUMPTIONS OF THE DAY AND CONTAIN INFORMED SPECULATIONS. THIS PAPER DESCRIBES ARTISTIC LICENSE: VR SYDNEY COVE CIRCA 1800 WHICH IS A VR ARTWORK THAT DIRECTLY ADDRESSES THE LIMINAL SPACES OF HERITAGE VISUALISATION. IT EXAMINES THE TENSIONS BETWEEN ACCURACY AND AUTHENTICITY IN HERITAGE VISUALISATION AND IT DISCUSSES VISUAL TROPES IN ART. IT CONCLUDES BY ARGUING THAT THE FOREGROUNDING OF THE INTERPRETIVE NATURE OF HERITAGE VISUALISATION BY ARTISTIC LICENSE: VR SYDNEY COVE CIRCA 1800 DIRECTLY ENGAGES WITH AUDIENCES TO BROADEN THE DEBATE ABOUT THE ROLE OF HERITAGE IN SOCIETY.
TIMESERIES MODELS ARE USED EXTENSIVELY IN FINANCIAL SERVICES, FOR EXAMPLE, TO QUANTIFY RISK AND PREDICT ECONOMICS. HOWEVER, ANALYSTS ALSO NEED TO COMPREHEND THE STRUCTURE AND BEHAVIOR OF THESE MODELS TO BETTER UNDERSTAND AND EXPLAIN RESULTS. WE PRESENT A METHODOLOGY, DERIVED FROM EXTENSIVE INDUSTRY EXPERIENCE, TO AID EXPLANATION THROUGH INTEGRATED INTERACTIVE VISUALIZATIONS THAT REVEAL MODEL STRUCTURE AND BEHAVIOR OF CONSTITUENT TIMESERIES FACTORS, THEREBY INCREASING UNDERSTANDING OF THE MODEL, THE DOMAIN AND THE SENSITIVITIES. EXPERT FEEDBACK INDICATES ALIGNMENT WITH MENTAL MODELS.
SCATTERPLOT MATRIX AND PARALLEL COORDINATE PLOTS ARE WELL-USED MULTI-DIMENSIONAL DATA VISUALIZATION TECHNIQUES. THESE TECHNIQUES HAVE A PROBLEM THAT THEY NEED A VERY LARGE SCREEN SPACE WHEN AN INPUT DATASET HAS AN ENORMOUS NUMBER OF DIMENSIONS. TO SOLVE THIS PROBLEM, WE PROPOSE A METHOD FOR SELECTING IMPORTANT SCATTERPLOTS FROM ALL SCATTERPLOTS GENERATED FROM INPUT DATASETS AND FOR DRAWING THE SCATTERPLOTS AS "OUTLIERS" AND "REGIONS ENCLOSING NON-OUTLIER PLOTS." THE TECHNIQUE IS USEFUL FOR USERS TO DETERMINE WHETHER TO DELETE OUTLIERS FROM THE DATASETS AND FORM MATHEMATICAL MODELS OF NON-OUTLIER PLOTS. THIS PAPER INTRODUCES AN EXAMPLE OF VISUALIZATION USING THIS TECHNIQUE WITH A RETAIL TRANSACTION DATASET AND CLIMATE VALUES.
THIS PAPER PRESENTS AN INTERACTIVE RENDERING TECHNIQUE FOR DETAIL+OVERVIEW VISUALIZATION OF 3D DIGITAL TERRAIN MODELS USING INTERACTIVE CLOSE-UPS. A CLOSE-UP IS AN ALTERNATIVE PRESENTATION OF INPUT DATA VARYING WITH RESPECT TO GEOMETRICAL SCALE, MAPPING, APPEARANCE, AS WELL AS LEVEL-OF-DETAIL (LOD) AND LEVEL-OF-ABSTRACTION (LOA) USED. THE PRESENTED 3D CLOSE-UP APPROACH ENABLES IN-SITU COMPARISON OF MULTIPLE REGIONOF-INTERESTS (ROIS) SIMULTANEOUSLY. WE DESCRIBE A GPU-BASED RENDERING TECHNIQUE FOR THE IMAGE-SYNTHESIS OF MULTIPLE CLOSE-UPS IN REAL-TIME.
THE NEXT GENERATION SCIENCE STANDARDS (NGSS) AND OTHER NATIONAL FRAMEWORKS ARE CALLING FOR MUCH MORE SOPHISTICATED APPROACHES TO STEM EDUCATION, CENTERED AROUND THE INTEGRATION OF COMPLEX EXPERIMENTATION (INCLUDING REAL LABS, NOT JUST SIMULATIONS), DATA COLLECTION AND ANALYSIS, MODELING, AND DATA-DRIVEN ARGUMENTATION, I.E., STUDENTS CAN BEHAVE LIKE REAL SCIENTISTS. HOW TO IMPLEMENT SUCH COMPLEX APPROACHES IN SCALABLE WAYS IS AN UNSOLVED CHALLENGE - BOTH FOR PRESENTIAL AND DISTANCE EDUCATION. HERE WE REPORT ON THE ITERATIVE DESIGN AND LARGE-SCALE DEPLOYMENT OF AN OPEN ONLINE COURSE WITH A "BIOLOGY CLOUD EXPERIMENTATION LAB" (USING LIVING CELLS) THAT ENGAGED REMOTE LEARNERS (> 300 STUDENTS) IN THE SCIENTIFIC PRACTICES OF EXPERIMENTATION, MODELING AND DATA ANALYSIS TO INVESTIGATE THE PHOTOTAXIS OF A MICROORGANISM. WE DEMONSTRATE (1) THE ROBUSTNESS AND SCALABILITY OF THE CLOUD LAB TECHNOLOGY (> 2,300 EXPERIMENTS RUN), (2) THE DESIGN PRINCIPLES AND SYNERGISTIC INTEGRATION OF MULTIPLE UI AND LEARNING ACTIVITIES AND SUITABLE DATA FORMATS TO FACILITATE NGSS-ALIGNED SCIENCE ACTIVITIES, AND (3) DESIGN FEATURES THAT LEVERAGES THE NATURAL VARIABILITY OF REAL BIOLOGY EXPERIMENTS TO INSTIGATE AUTHENTIC INQUIRY. THIS PLATFORM AND COURSE CONTENT ARE NOW SUITED FOR LARGE-SCALE ADAPTATION IN FORMAL K-16 EDUCATION; AND WE PROVIDE RECOMMENDATIONS FOR INQUIRY-BASED SCIENCE LEARNING IN GENERAL.
CHRONOVIEW IS A VISUALIZATION METHOD OF REPRESENTING PERIODIC FEATURES OF THE OCCURRENCE OF EVENTS. IT EXPRESSES A SET OF TIME STAMPS IN THE POSITION ON A PLANE. ALTHOUGH CHRONOVIEW OFFERS HIGH SPACE EFFICIENCY, IT CAN GENERATE AMBIGUOUS REPRESENTATIONS. TO SOLVE THIS PROBLEM, GLYPHS HAVE BEEN EXPLOITED AS CHRONOVIEW MARKERS. THIS PAPER EXPLAINS A USER STUDY CONDUCTED TO INVESTIGATE THE EFFECTIVENESS OF THE STAR GLYPH AND RING GLYPH. THE STUDY SHOWS THAT GLYPHS CONTRIBUTE TO AN ACCURATE READING OF TEMPORAL FEATURES. IT ALSO SHOWS THAT STAR GLYPH AND RING GLYPH HAVE DIFFERENT FEATURES. IT IS CLEAR THAT, WHEREAS RING GLYPH DOMINATES THE TIME RANGE READING, STAR GLYPH DOMINATES THE COMPARISON OF FREQUENCIES IN THE UNIT OF TIME.
EVALUATING USER EXPERIENCE (UX) IS NOT A TRIVIAL TASK, AND UX SPECIALISTS HAVE USED A VARIETY OF TOOLS TO ANALYZE DATA COLLECTED FROM USER TESTS, WHICH CAUSES DIFFICULTY IN SYNCHRONIZING THE DATA. THIS PAPER PRESENTS UXMOOD, A TOOL THAT CONDENSES MULTIPLE DISTINCT DATA TYPES (AUDIO, VIDEO, TEXT, AND EYE-TRACKING) IN A DASHBOARD OF COORDINATED VISUALIZATIONS TO EASE THE ANALYSIS PROCESS AND ALLOW TO MANAGE SEVERAL PROJECTS WHERE EACH PROJECT HAS SEVERAL LOGS OF USER INTERACTION. THE TOOL REPLAYS SESSIONS OF TESTS AND USES A COMBINATION OF DIFFERENT SENTIMENT ANALYSIS TECHNIQUES TO PRESENT A SUGGESTION OF USER SENTIMENT AT ANY GIVEN TIME DURING THE TASKS. THE VISUALIZATIONS SUPPORT BRUSHING AND DETAILS-ON-DEMAND INTERACTIONS AND ARE SYNCHRONIZED WITH A TEMPORAL SLIDER, ALLOWING ANALYSTS TO SEE SPECIFIC MOMENTS OF THE TESTS FREELY. ALSO, THE USES OF THE SENTIMENT ANALYSIS IN THE COLLECTED DATA MAY IMPROVED THE QUALITATIVE ANALYSIS OF UX.
IN THIS PAPER WE DESCRIBE AN INTERACTIVE WEB-BASED VISUAL ANALYSIS TOOL FOR FORMULA ONE RACES. IT FIRST PROVIDES AN OVERVIEW ABOUT ALL RACES ON A YEARLY BASIS IN A CALENDAR-LIKE REPRESENTATION. FROM THIS STARTING POINT, RACES CAN BE SELECTED AND VISUALLY INSPECTED IN DETAIL. WE SUPPORT A DYNAMIC RACE POSITION DIAGRAM AS WELL AS A MORE DETAILED LAP TIMES LINE PLOT FOR SHOWING THE DRIVERS' LAP TIMES IN COMPARISON. MANY INTERACTION TECHNIQUES ARE SUPPORTED LIKE SELECTIONS, FILTERING, HIGHLIGHTING, COLOR CODING, OR DETAILS-ON-DEMAND. WE ILLUSTRATE THE USEFULNESS OF OUR VISUALIZATION TOOL BY APPLYING IT TO A FORMULA ONE DATASET WHILE WE DESCRIBE THE DIFFERENT DYNAMIC VISUAL RACING PATTERNS FOR A NUMBER OF SELECTED RACES AND DRIVERS.
VISUAL CLUTTERING IS STILL A SEVERE PROBLEM OF LARGE-SCALE NETWORK VISUALIZATION TECHNIQUES, AND THEREFORE, MANY IMPROVEMENTS IN NETWORK VISUALIZATION HAVE BEEN PRESENTED. MEANWHILE, MANY NETWORK DATASETS IN DAILY LIFE CONTAIN LABEL INFORMATION. WE DEVELOP A NETWORK VISUALIZATION TECHNIQUE WHICH OVERLAYS A NODE-LINK DIAGRAM FOR VISUALIZING THE CONNECTIVITY, AND A SET VISUALIZATION FOR REPRESENTING THE LABEL INFORMATION. THE TECHNIQUE FIRST PLACES A SET OF NODES IN AN INPUT DATASET INTO A 3D SPACE, AND PROVIDES AN INTERACTIVE MECHANISM TO MANIPULATE VIEWPOINTS IN THE 3D SPACE. THE TECHNIQUE THEN PROJECTS THE NODES ONTO A 2D PLANE, AND GENERATES A DELAUNAY TRIANGULAR MESH CONNECTING THE NODES WHICH HAVE THE USERSPECIFIED LABEL ON THE 2D PLANE. FINALLY, THE TECHNIQUE DISPLAYS THE OUTER BOUNDARY OF THE TRIANGULAR MESH TO REPRESENT THE REGION ENCLOSING THE NODES WHICH HAVE THE LABEL. IN ADDITION, SUPPOSING THAT MULTIPLE WEIGHTED LABELS CAN BE ASSIGNED TO THE SAME NODE, THE IMPLEMENTATION DRAWS NODES LIKE PIE CHARTS TO CLEARLY REPRESENT THE WEIGHTS OF THE LABELS ASSIGNED TO THE NODES. THIS PAPER SHOWS EXAMPLES OF THE VISUALIZATION, BY APPLYING A CO-AUTHORSHIP NETWORK DATASET.
THE DESIGN OF PRODUCTION PLANNING TOOLS IS PRIMARILY BASED ON CONVENTIONS WHICH CAN BE FOUND IN RESEARCH ON VISUAL PERCEPTION AND HOW DATA IS TRADITIONALLY REPRESENTED. STANDARDIZED FORMS HAVE BECOME EVERYDAY TOOLS AND ARE AN ESSENTIAL PART OF OUR VISUAL CULTURE. IN THE FIRST PART OF THE 20TH CENTURY, THE GANTT CHART WAS INTRODUCED AND WAS PRIMARILY USED FOR CHARTING WORKPLACE EFFICIENCY. IT HAS BEEN USED IN VARIOUS FORMS EVER SINCE, IN PARALLEL WITH OTHER STAGE-GATE MODELS. VISUAL MANAGEMENT HAS BEEN DEVELOPED IN COMPANIES THAT WORK WITH LEAN PRODUCTION SYSTEMS. FOR GOVERNANCE AND CONTROL OF DAILY ACTIVITIES, A SO-CALLED LEAN BOARD IS USED, WHICH CONSISTS OF A WHITE BOARD. IN THIS PAPER WE DISCUSS HOW DESPITE RAPID TECHNOLOGICAL DEVELOPMENT AND DIGITALIZATION IN MANY FIELDS OUR PERCEPTIONS, VISUAL REPRESENTATIONS, AND ORGANIZATION OF TIME SEEM TO REMAIN ROOTED IN THE PAST.
THE RELATIONSHIP BETWEEN FEATURES OF AN ELDERLY DRIVER'S EYE FIXATIONS AND COGNITIVE PERFORMANCE WAS ANALYSED. THE PARTICIPANTS WERE 29 MOTORISTS AGED OVER 60 YEARS WHO DROVE FREQUENTLY. THEIR EYE MOVEMENTS WERE OBSERVED DURING 16 SPECIFIC SITUATIONS ALONG A TEST COURSE. THEIR COGNITIVE PERFORMANCE WAS MEASURED USING COMPUTER BASED TESTS PRIOR TO DRIVING. SINCE SOME FEATURES OF EYE FIXATIONS DURING DRIVING CORRELATE WITH THE SCORES OF COGNITIVE TESTS, THE POSSIBILITY OF PREDICTING THE COGNITIVE SCORES WAS EXAMINED USING FEATURES OF EYE FIXATIONS AND A MULTIPLE REGRESSION ANALYSIS TECHNIQUE. PREDICTION PERFORMANCE WAS ALSO AFFECTED BY SPECIFIC FEATURES OF THE FIXATIONS AND THEIR BEHAVIOUR.
AS HUMANS, WE HAVE DEVELOPED TO PROCESS HIGHLY COMPLEX VISUAL DATA FROM OUR SURROUNDINGS. THIS IS WHY DATA VISUALIZATION AND INTERACTION IS ONE OF THE QUICKEST WAYS TO FACILITATE INVESTIGATION AND COMMUNICATE UNDERSTANDING. TO PERFORM VISUAL ANALYTICS EFFECTIVELY AT THE BIG DATA SCALE IT IS CRUCIAL THAT WE DEVELOP AN INTEGRATED PROCESSING AND VISUALIZATION ECOSYSTEM. HOWEVER, TO DATE, IN LARGE HIGH-RESOLUTION DISPLAY (LHRD) ENVIRONMENTS THE WORLDS OF DATA PROCESSING AND VISUALIZATION REMAIN LARGELY DISCONNECTED. IN THIS PAPER, WE PROPOSE A COMMON ARCHITECTURAL APPROACH TO ENABLE INTEGRATED DATA PROCESSING AND DISTRIBUTED VISUALIZATION VIA THE COMPOSITION OF DISCRETE MICROSERVICES. EACH OF THESE MICROSERVICES PROVIDES A VERY SPECIFIC CLEARLY-DEFINED FUNCTION, SUCH AS ANALYZING DATA, CREATING A VISUALIZATION, SHARDING DATA OR PROVIDING A SYNCHRONIZATION SOURCE. BY DEFINING COMMON TRANSPORT, DATA AND API FORMATS WE ENABLE THE COMPOSITION OF THESE MICROSERVICES FROM PROCESSING RAW DATA THROUGH TO ANALYTICS, VISUALIZATION AND RENDERING. THIS COMPOSITIONALITY, INSPIRED BY SUCCESSFUL DATA-DRIVEN VISUALIZATION FRAMEWORKS PROVIDES A COMMON PLATFORM FOR IMMERSIVE SOCIAL VISUAL ANALYTICS.
THE PHENOMENON OF ESPORTS HAS BEEN INCREASING OVER THE YEARS, INCLUDING THE INTEREST IN ONLINE VIDEO GAMES, BY PLAYERS, COACHES AND ANALYSTS. ONE OF THE MOST POPULAR GENRES OF THESE GAMES IS THE MULTIPLAYER ONLINE BATTLE ARENA (MOBA) AND GAMES LIKE LEAGUE OF LEGEND (LOL) HAVE INCREASED ITS PLAYER BASE AND EVENT VIEWERSHIP. SIMILARLY TO OTHER SPORTS, PLAYERS AND COACHES ANALYZE THE GAMES AND ALL THE GAMES EVENTS, SUCH AS PLAYERS' MOVEMENTS AND SPATIAL, TEMPORAL EVENTS IN WHICH THEY PARTICIPATE, TO UNDERSTAND HOW THEY PLAY AND TO DEFINE NEW STRATEGIES TO HELP THEM PROGRESS. THIS PAPER PRESENTS THE VISUALEAGUE II TOOL THAT HELPS PLAYERS AND COACHES TO ANALYZE LOL GAME DATA USING ANIMATED MAPS AND OTHER VISUALIZATIONS; AND PRESENTS A COMPARATIVE STUDY BETWEEN VISUALEAGUE II AND OTHER TWO TOOLS (LOL REPLAY SYSTEM AND OP.GG) TO UNDERSTAND IF THE PROPOSED VISUALIZATION TECHNIQUES ARE ADEQUATE FOR ANALYZING PLAYER'S PERFORMANCE. THE RESULTS SUGGEST THAT PLAYERS ARE CAPABLE OF VISUALIZING GAME-RELATED DATA THROUGH THE USE OF ANIMATED MAPS, TO SOLVE COMMON ANALYTIC TASKS AND THE COMPARISON SUGGESTS THAT PARTICIPANTS PREFER TO VISUALIZE SPATIO-TEMPORAL DATA DYNAMICALLY, I.E., USING ANIMATED MAPS OR THE REPLAY SYSTEM.
INDEPENDENT COMPONENT ANALYSIS (ICA) HAS EMERGED AS A USEFUL METHOD FOR SEPARATION OF COMPONENTS, SUCH AS IN REMOVING NOISE FROM DATA. WE EXAMINE ONE OF THE CHALLENGES OF ICA - INSTABILITY, PARTICULARLY IN HIGH DIMENSIONS, WHEN THE INDEPENDENT COMPONENTS VARY, EACH TIME WHEN ICA IS PERFORMED. THIS MAY BE DUE TO VARIOUS CAUSES INCLUDING THE STOCHASTIC NATURE OF THE ALGORITHM AND THE ADDITIVE NOISE. THE OBJECTIVE OF THIS STUDY IS TO EXAMINE DENOISING AND STABILITY ISSUES OF ICA IN HIGH DIMENSIONS AND MAKE A COMPARATIVE EVALUATION OF SELECT APPROACHES. WE TAKE A CHALLENGING ELECTROCARDIOGRAM DATASET WHICH IS A HIGH-DIMENSIONAL TIME SERIES OF MULTIPLE SENSORS. WE EXPERIMENT WITH A MIX OF APPROACHES AND METHODS - FOR RESAMPLING, CLUSTERING, ICA ALGORITHMS AND DIMENSIONALITY. WE CHECK THE INTERNAL VALIDITY USING THE ICASSO STABILITY INDEX, THE AMARI SEPARATION PERFORMANCE INDEX AND THE MINIMUM DISTANCE (MD) INDEX. THE FIRST KEY CONTRIBUTION OF THIS WORK IS THAT IT FINDS COUNTER-EVIDENCE TO THE CLAIM THAT RESAMPLING (BOOTSTRAPPING) TACKLES THE QUESTION OF STABILITY. THE SECOND CONTRIBUTION IS THAT IT FINDS EVIDENCE OF AN IMPORTANT LIMITATION OF THE MINIMUM DISTANCE INDEX WHEN DEALING WITH HIGH DIMENSIONAL DATA - THE INDEX MAY BECOME HIGHLY CONCENTRATED AND MAY REMAIN SUB-OPTIMAL AT ALL DIMENSIONS. SELECTIVELY REMOVING NOISE COMPONENTS BY VISUAL INSPECTION CAN IMPROVE THE AMARI, THE ICASSO OR THE MD INDEX VALUES. SOME AUTOMATED TOOLS EXIST, BUT IN HIGH DIMENSIONS, VISUAL INSPECTION OF THE INDIVIDUAL COMPONENTS IS STILL REQUIRED FOR EFFECTIVE DENOISING - DATA DRIVEN METHODS ARE NOT GOOD ENOUGH.
WE INTRODUCE SONGVIS, A VISUALIZATION THAT REPRESENTS MUSIC'S SEMANTIC DESCRIPTORS. SONGVIS USES EMOJIS, COLORS, LINES AND SHAPES TO EMBODY THE SEMANTIC CONTENT OF A SONG. IT AIMS TO AID USERS ON TASKS RELATED TO EXPLORATION/BROWSING OF MUSIC LIBRARIES AND AT QUERIES FOR SIMILAR TRACKS BASED ON VISUAL CHARACTERISTICS. WE FIRST COLLECTED THE DESCRIPTORS AFTER SURVEYING PAPERS ON THE TOPIC OF "MUSIC VISUALIZATION", AND USED A QUESTIONNAIRE TO RANK THE TERMS BY CONSULTING THE PUBLIC. THEN, THE FEATURES: MOOD, DANCEABILITY, TEMPO, MUSIC GENRE AND INSTRUMENT, WERE EXTRACTED USING STATE-OF-THE-ART MUSIC INFORMATION RETRIEVAL ALGORITHMS AND WE CONSIDERED THEIR VISUALS. THEN, WE DISCUSS POTENTIAL IMPROVEMENTS TO BE MADE. WITH SONGVIS WE PERFORM A STEP FORWARD TOWARDS VISUALLY REPRESENTING SEMANTIC DESCRIPTORS AND EXPECT THAT THE MUSIC INFORMATION RESEARCH, THE INFORMATION VISUALIZATION FIELDS AND THE PUBLIC CAN BENEFIT FROM THIS WORK.
EHEALTH HAS ALWAYS BEEN AN INTERDISCIPLINARY FIELD AND INVOLVES A VARIETY OF STAKEHOLDERS COMING FROM DIFFERENT WORKING AREAS BUT HAVE TO COLLABORATE FOR A GREATER GOAL. THIS SETUP CAN ALSO BE FOUND IN RESEARCH PROJECTS AND TEAMS LEADING TO THE PROBLEM, THAT RESEARCH DATA, MEASUREMENTS AND OTHER INFORMATION - WHEN NOT PUBLISHED - ARE OFTEN NOT STORED PERMANENTLY OR NOT ACCESSIBLE FOR OTHERS OR FUTURE TASKS. IN THE AGE OF KNOWLEDGE DATA HAVE BEEN DISCOVERED AS HIDDEN AND PRECIOUS RESOURCES WHEN PUTTING THEM IN NEW CONTEXTS - VALUE CAN EVEN BE GENERATED WHEN NOT LOOKING FOR IT. THIS PAPER ADDRESSES THE DYNAMIC FIELD OF RESEARCH, WHERE THE GATHERING OF NEW KNOWLEDGE IS OF SPECIAL INTEREST. THE PRESENTED APPROACH REFERS TO AN ALREADY EXISTING LABORATORY INFRASTRUCTURE AND AIMS TO COLLECT DATA FROM VARIOUS LINKED RESOURCES. THEREFORE SYSTEM REQUIREMENTS HAVE BEEN ELABORATED AS WELL AS A PROTOTYPICAL SOLUTION FOR SETTING UP A PLATFORM THAT REALISES AN INTEGRATED LABORATORY INFRASTRUCTURE. HAVING A CENTRAL INTERFACE ALLOWS RESEARCHERS TO BENEFIT FROM ALREADY AVAILABLE DATA RESOURCES. THE SOLUTION IS BASED ON STATE-OF-THE-ART-TECHNOLOGIES WHICH WILL ALSO COVER ARISING FUTURE DEMANDS.
SINCE PATHOLOGY IS SUPPORTED BY INFORMATION TECHNOLOGY NEW OPPORTUNITIES AND QUESTIONS HAVE ARISEN. THE DIGITAL AGE ENABLES ANALYZING HISTOPATHOLOGICAL DATA WITH ARTIFICIAL INTELLIGENCE METHODS TO REVEAL FURTHER INFORMATION AND CORRELATIONS. IN THIS PAPER EXISTING APPROACHES TO VISUALIZATION OF MEDICAL DECISION PROCESSES ARE PRESENTED AS WELL AS THE RELEVANCE OF EXPLAINABILITY IN DECISION MAKING. THE FIRST STEP FOR IMPLEMENTING DECISION-PATHS IN SYSTEMS IS TO RETRACE AN EXPERIENCED PATHOLOGIST'S DIAGNOSIS FINDING PROCESS. RECORDING A ROUTE THROUGH A LANDSCAPE COMPOSED OF HUMAN TISSUE IN TERMS OF A ROADBOOK IS ONE POSSIBLE APPROACH TO COLLECT INFORMATION ON HOW DIAGNOSES ARE FOUND. CHOOSING THE ROADBOOK METAPHOR PROVIDES A SIMPLE SCHEMA, THAT HOLDS BASIC DIRECTIONS ENRICHED WITH METADATA REGARDING LANDMARKS ON A RALLY - IN THE CONTEXT OF PATHOLOGY SUCH LANDMARKS PROVIDE INFORMATION ON THE DECISION FINDING PROCESS.
THE VIRTUAL ST PAUL'S CATHEDRAL PROJECT (HTTPS://VPCP.CHASS.NCSU.EDU/) DEMONSTRATES THE CAPABILITIES OF DIGITAL MODELING TECHNOLOGY FOR VISUALIZING THE PAST THROUGH SIMULTANEOUS INTEGRATION OF MULTIPLE SETS AND KINDS OF DATA. IT ALSO ILLUSTRATES THE CHALLENGES MODEL-MAKERS FACE WHEN THE HISTORIC DATA ON WHICH THEY DEPEND FOR ACCURACY IS FRAGMENTARY, MISSING, OR CONTRADICTORY. THE VIRTUAL CATHEDRAL PROJECT ADDRESSES THESE CHALLENGES BY DISTINGUISHING BETWEEN PORTIONS OF THE VISUAL MODEL BASED ON VERIFIABLE DATA (SUCH AS MEASUREMENTS OF THE ORIGINAL STRUCTURES EITHER SURVIVING FROM CONTEMPORARY SURVEYS OR DERIVED FROM ARCHAEOLOGICAL EXAMINATION OF RUINS) AND PORTIONS BASED ON REPRESENTATIVE DATA (SUCH AS INFORMATION DERIVED FROM OTHER EXAMPLES OF THE SAME KIND OF STRUCTURE). OFTEN, THE MODELING OF ANY SPECIFIC STRUCTURE INCLUDES BOTH KINDS OF DATA; OUR MODEL OF THE CATHEDRAL'S DEANERY, FOR EXAMPLE, COMBINES DESCRIPTIVE DATA ABOUT SIZE AND ARRANGEMENT OF ROOMS DERIVED FROM A CONTEMPORARY SURVEY OF THE PROPERTY WITH REPRESENTATIONAL DATA FROM OTHER CATHEDRAL DEANERIES OF THAT TIME ABOUT CONSTRUCTION MATERIALS AND OTHER FEATURES OF THE BUILDING'S APPEARANCE. THIS PRESENTATION USES THE PROCESS WE EMPLOYED IN RECREATING THE CATHEDRAL'S WEST FRONT TO ILLUSTRATE HOW WE SUPPORT OUR CLAIMS TO HAVE CREATED A HISTORICALLY ACCURATE MODEL OF ST PAUL'S CATHEDRAL AND ITS ENVIRONS WHILE ACKNOWLEDGING THAT OUR USE OF THE WORD "ACCURATE" IS RELATIVE TO THE QUALITY OF THE DATA AVAILABLE TO US FOR VISUALIZATION.
VIRTUAL REALITY (VR) PROVIDES IMMERSIVE ENVIRONMENTS WHERE USERS WATCH OBJECTS AND SCENES AS IF THEY WERE IN FRONT OF USERS. THIS IS A SIGNIFICANT ADVANTAGE WHILE APPLYING TO 3D VISUALIZATION SYSTEMS. HOWEVER, IT IS OFTEN DIFFICULT FOR NOVICE USERS TO MAKE THE BEST USE OF VR TO REPRODUCE THE OBJECTS AND SCENES PRECISELY AND THE COMPREHENSIVELY DEPENDING ON THE SKILLS OF USERS AND THE COMPLEXITY OF OBJECTS AND SCENES. ONE OF THE PROBLEMS IS THAT THE ALLOWED OPERATIONS IN VR ARE TOO FLEXIBLE. HERE, WE PROPOSE A VIEWPOINT SELECTION METHOD THAT ALLOWS EVERY USER TO WATCH THE OBJECTS AND SCENES EASILY AND CAREFULLY IN A VR SPACE. THE VIEWPOINT IS NOT FIXED BUT CAN BE SHIFTED TO THE MOST FAVORABLE POSITION ACCORDING TO THE DEMAND OF USERS. REGARDING THE SELECTED VIEWPOINT AS THE INITIAL POSITION, USERS CAN EASILY GO TOWARDS THE TARGET PART WITH FEWER MOVEMENTS. THIS SYSTEM HELPS USERS TO UNDERSTAND THE COMPLEX OBJECTS IN THE VR SPACE PRECISELY WITH LESS TIME. WE DEMONSTRATE OUR SYSTEM ON THE OBSERVATION OF MODE WATER PAIRS AND DISCUSS THE COMPARATIVE VISUALIZATION RESULTS.
A FUNDAMENTAL TASK IN 3D GEOVISUALIZATION AND GIS APPLICATIONS IS THE VISUALIZATION OF VECTOR DATA THAT CAN REPRESENT FEATURES SUCH AS TRANSPORTATION NETWORKS OR LAND USE COVERAGE. MAPPING OR DRAPING VECTOR DATA REPRESENTED BY GEOMETRIC PRIMITIVES (E.G., POLYLINES OR POLYGONS) TO 3D DIGITAL ELEVATION OR 3D DIGITAL TERRAIN MODELS IS A CHALLENGING TASK. WE PRESENT AN INTERACTIVE GPU-BASED APPROACH THAT PERFORMS GEOMETRY-BASED DRAPING OF VECTOR DATA ON PER-FRAME BASIS USING AN IMAGE-BASED REPRESENTATION OF A 3D DIGITAL ELEVATION OR TERRAIN MODEL ONLY.
PATIENT JOURNEYS ARE THE KEY FOR TREATMENT DEVELOPMENT, QUALITY INSURANCE AND BETTER UNDERSTANDING OF DISEASE PROGRESSION. THEREFORE, MANY VISUALIZATION TOOLKITS EXIST TO MONITOR INDIVIDUAL PATIENT LIVES. HOWEVER, THERE IS NO VISUALIZATION THAT RELIES ON THE OVERALL PATTERNS OF PATIENT JOURNEYS. WE PROPOSE A NEW FRAMEWORK PROGVIZ THAT VISUALIZES PATTERNS OF PATIENT JOURNEYS, WHICH RELY ON FINITE STATE MODELS. THESE MODELS ARE ABLE TO ILLUSTRATE PROGRESSION OF DISEASES, TREATMENT AND OUTCOMES OF WHOLE POPULATIONS AS WELL AS VERY SPECIFIC ANALYSES. FINALLY, WE PROPOSE A METHOD TO EVALUATE HOW TREATMENT AFFECTS DISEASE PROGRESSION.
MANY ANALYTICAL TASKS, SUCH AS SOCIAL NETWORK ANALYSIS, DEPEND ON COMPARING GRAPHS. EXISTING METHODS ARE SLOW, OR CAN BE DIFFICULT TO UNDERSTAND. TO ADDRESS THESE CHALLENGES, THIS PAPER PROPOSES GRAGNOSTICS, A SET OF 10 FAST, LAYPERSON-UNDERSTANDABLE GRAPH-LEVEL FEATURES. EACH CAN BE COMPUTED IN LINEAR TIME. TO EVALUATE THE ABILITY OF THESE FEATURES TO DISCRIMINATE DIFFERENT TOPOLOGIES AND TYPES OF GRAPHS, THIS PAPER COMPARES A MACHINE LEARNING CLASSIFIER USING GRAGNOSTICS TO ALTERNATIVE CLASSIFIERS, AND THE EVALUATION FINDS THAT THE GRAGNOSTICS CLASSIFIER ACHIEVES HIGHER PERFORMANCE. TO EVALUATE GRAGNOSTICS' UTILITY IN INTERACTIVE VISUALIZATION TOOLS, THIS PAPER PRESENTS CHIRON, A GRAPH VISUALIZATION TOOL THAT ENABLES USERS TO EXPLORE THE SUBGRAPHS OF A LARGER GRAPH. EXAMPLE USAGE SCENARIOS OF CHIRON DEMONSTRATE THAT USING GRAGNOSTICS IN A RANK-BY-FEATURE FRAMEWORK CAN BE EFFECTIVE FOR FINDING INTERESTING SUBGRAPHS.
THE PAPER PRESENTS ONGOING RESEARCH AIMING TO EASE THE INTERACTION WITH LARGE AMOUNTS OF PUBLIC SECTOR DATA. THE PROJECT FOCUSES ON THE DEVELOPMENT OF A MODULAR ONLINE PLATFORM FOR BOTH DESKTOP AND MOBILE DEVICES EXPLOITING VISUAL ANALYTICS TO OFFER CITIZENS, RESEARCHERS AND POLICYMAKERS CROSS-CUTTING READING OF HETEROGENEOUS INFORMATION. WE EXPERIMENT NEW WAYS TO INTEGRATE, ANALYZE, AND VISUALIZE ADMINISTRATIVE, LEGAL, AND ECONOMIC DATA AS THEY APPEAR TO BE A POWERFUL ALLY NOT ONLY TO INCREASE THE TRANSPARENCY OF GOVERNMENT ACTIVITIES BUT ALSO TO ENHANCE EVIDENCE-BASED POLICY MAKING AND AGENDA SETTING.
INFOGRAPHICS IS A TOOL FOR DATA VISUALIZATION. IT MAKES DATA EASY TO UNDERSTAND AND INTERPRET. AN INFOGRAPHIC IS DEFINED AS A VISUAL REPRESENTATION FOR DATA LIKE A CHART OR A DIAGRAM. INFOGRAPHICS CAN HELP IN MANY FIELDS INCLUDING EDUCATION. THIS IS DUE TO THE FACT THAT INFORMATION CAN BE EASILY MEMORIZED AND UNDERSTOOD IF WAS GIVEN IN A VISUAL FORM. INFOGRAPHICS ARE FOUND EVERYWHERE AND USED IN MANY DIFFERENT FIELDS. FACEBOOK TIMELINE IS CONSIDERED AS AN INFOGRAPHIC. ON ANOTHER HAND, ONLINE JOURNALISM IS INCREASINGLY GAINING POPULARITY. IT IS ALSO CONSIDERED AS A SOURCE OF BIG DATA THAT IS RAPIDLY EXPANDING. ONLINE NEWSPAPERS AND MAGAZINES PROVIDE A LARGE POPULATION WITH DAILY IMPORTANT INFORMATION. THE AIM OF THE WORK IS TO USE DATA VISUALIZATION, INFOGRAPHICS AND NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES IN ONLINE JOURNALISM. THE AIM OF THE WORK IS TO AUTOMATICALLY VISUALIZE THE INFORMATION IN AN ARTICLE IN THE FORM OF INFOGRAPHICS.
DYSCALCULIA SHOULD BE DETECTED IN THE THIRD PRIMARY CLASS, BUT IN MANY CASES THIS DISTURB IS DIAGNOSED IN LATER AGES. IN THIS PAPER WE PRESENT A TOOL FOR SUPPORTING THE CLINICIAN IN THE GENERATION OF RESPONSIVE WEB-BASED TESTS FOR INDIVIDUATING PEOPLE WITH DISORDER IN BASIC NUMERICAL AND ARITHMETIC SKILLS. TESTS ARE CREATED BY COMBINING SPECIFIC KINDS OF QUESTIONS WHICH ARE DELIVERED TO A SAMPLE OF PEOPLE BELONGING TO A SPECIFIC TARGET. THE TOOL PROVIDES ALSO SUPPORT IN THE DATA ANALYSIS AND IN SETTING THE ALERT THRESHOLDS FOR THE SELECTED USER TARGET. RESULTS MAY BE GRAPHICALLY VISUALIZED IN SUMMARIZED AND SINGLE USER WAY. ONCE THE SETTING PROCESS IS TERMINATED, THE TEST MAY BE ADOPTED. A CASE STUDY ON ADULT PEOPLE IS ALSO PRESENTED.
THE INCREASING AVAILABILITY OF DIGITAL MULTIMEDIA CONTENT HAS LED TO THE NEED OF NEW APPROACHES FOR THE ANALYSIS OF LARGE DATABASES CONTAINING VIDEO AND ASSOCIATED DATA, FOR EXAMPLE, SUBTITLES. VISUALIZATION PROVIDES VALUABLE INSIGHTS OF SUCH DATASET, COMPLEMENTING APPROACHES SOLELY BASED ON TECHNIQUES FOR KNOWLEDGE DISCOVERY IN DATABASES AND INFORMATION RETRIEVAL. HENCE, VISUAL ANALYTICS, COMBINING AUTOMATIC PROCESSING WITH INTERACTIVE DATA VISUALIZATION, HAS PROVEN TO BE AN EFFECTIVE MEANS TO EXPLORE AND INTERPRET SUCH DATA. THE ANALYSIS OF NEWS CORPORA REPRESENTS A TYPICAL TASK FOR SUCH A SCENARIO. DOMAIN EXPERTS SUCH AS JOURNALISTS AND SOCIAL SCIENCE SCHOLARS REQUIRE AN OVERVIEW OF IMPORTANT TOPICS, THE TEMPORAL COHERENCE OF EVENTS, AND THEY SHOULD BE ABLE TO COMPARE DIFFERENT TOPICS. WE PRESENT A VISUAL ANALYTICS APPROACH THAT AIMS TO SUPPORT THESE TASKS WITH AUTOMATIC VIDEO PREPROCESSING, TOPIC EXTRACTION, CLUSTERING, AND DIMENSIONALITY REDUCTION. COORDINATED LINKED VIEWS SUPPORT THE FLEXIBLE INSPECTION OF THE DATASET AND THE PROCESSED RESULTS. WE FURTHER DISCUSS THE APPLICATION OF OUR APPROACH IN A USAGE SCENARIO, INSPECTING THE DATASET OF A DAILY NEWS BROADCAST OF THE YEAR 2015.
THE AWARENESS OF EMERGING TECHNOLOGIES IS ESSENTIAL FOR STRATEGIC DECISION MAKING IN ENTERPRISES. EMERGING AND DECREASING TECHNOLOGICAL TRENDS COULD LEAD TO STRENGTHENING THE COMPETITIVENESS AND MARKET POSITIONING. THE EXPLORATION, DETECTION AND IDENTIFICATION OF SUCH TRENDS CAN BE ESSENTIALLY SUPPORTED THROUGH INFORMATION VISUALIZATION, TREND MINING AND IN PARTICULAR THROUGH THE COMBINATION OF THOSE. COMMONLY, TRENDS APPEAR FIRST IN SCIENCE AND SCIENTIFIC DOCUMENTS. HOWEVER, THOSE DOCUMENTS DO NOT PROVIDE SUFFICIENT INFORMATION FOR ANALYZING AND IDENTIFYING EMERGING TRENDS. IT IS NECESSARY TO ENRICH DATA, EXTRACT INFORMATION FROM THE INTEGRATED DATA, MEASURE THE GRADIENT OF TRENDS OVER TIME AND PROVIDE EFFECTIVE INTERACTIVE VISUALIZATIONS. WE INTRODUCE IN THIS PAPER AN APPROACH FOR INTEGRATING, ENRICHING, MINING, ANALYZING, IDENTIFYING AND VISUALIZING EMERGING TRENDS FROM SCIENTIFIC DOCUMENTS. OUR APPROACH ENHANCES THE STATE OF THE ART IN VISUAL TREND ANALYTICS BY INVESTIGATING THE ENTIRE ANALYSIS PROCESS AND PROVIDING AN APPROACH FOR ENABLING HUMAN TO EXPLORE UNDETECTED POTENTIALLY EMERGING TRENDS.
IN RECENT YEARS, THE INTEGRATION OF RESEARCHES IN COMPUTER SCIENCE AND MEDICAL FIELDS HAS MADE AVAILABLE TO THE SCIENTIFIC COMMUNITY AN ENORMOUS AMOUNT OF DATA, STORED IN DATABASES. IN THIS PAPER, WE ANALYZE THE DATA AVAILABLE IN THE PARKINSON'S PROGRESSION MARKERS INITIATIVE (PPMI), A COMPREHENSIVE OBSERVATIONAL, MULTI-CENTER STUDY DESIGNED TO IDENTIFY PROGRESSION BIOMARKERS IMPORTANT FOR BETTER TREATMENTS FOR PARKINSON'S DISEASE. THE DATA OF PPMI PARTICIPANTS ARE COLLECTED THROUGH A COMPREHENSIVE BATTERY OF TESTS AND ASSESSMENTS INCLUDING MAGNETIC RESONANCE IMAGING AND DATSCAN IMAGING, COLLECTION OF BLOOD, CEREBRAL SPINAL FLUID, AND URINE SAMPLES, AS WELL AS COGNITIVE AND MOTOR EVALUATIONS. TO THIS AIM, WE PROPOSE A TECHNIQUE TO IDENTIFY A CORRELATION BETWEEN THE BIOMEDICAL DATA IN THE PPMI DATASET FOR VERIFYING THE CONSISTENCY OF MEDICAL REPORTS FORMULATED DURING THE VISITS AND ALLOW TO CORRECTLY CATEGORIZE THE VARIOUS PATIENTS. TO CORRELATE THE INFORMATION OF EACH PATIENT'S MEDICAL REPORT, INFORMATION RETRIEVAL TECHNIQUES HAVE BEEN ADOPTED, INCLUDING THE LATENT SEMANTIC ANALYSIS TECHNIQUE SUITABLE FOR CONSTRUCTING A CONCEPT SPACE ON PATIENT INFORMATION. THEN, PATIENTS ARE GROUPED AND CLASSIFIED INTO AFFECTED OR NOT BY USING CLUSTERING ALGORITHMS ACCORDING TO THE SIMILARITY OF MEDICAL REPORTS PROJECTED IN THE CONCEPT SPACE. RESULTS REVEALED THAT THE PROPOSED TECHNIQUE REACHED 95% OF EFFECTIVENESS IN THE CLASSIFICATION OF PATIENTS.
BACKGROUND TINKERING, AN AD HOC APPROACH TO SOLVING A PROBLEM, INVOLVES MANIPULATING OBJECTS TO CHARACTERIZE AND BUILD KNOWLEDGE ABOUT A PARTICULAR SYSTEM IN AN EXPLORATORY WAY, OFTEN WITH THE GOAL OF GETTING SOME PRODUCT/IDEA TO PRODUCE DESIRED BEHAVIOR. TINKERING CONTRASTS MORE DELIBERATE ACTIVITY TOWARD UNDERSTANDING HOW A PHENOMENON WORKS. PURPOSE SOME RESEARCHERS HAVE ARGUED THAT TINKERING IS AN UNPRODUCTIVE ENGINEERING DESIGN PRACTICE BECAUSE IT DOES NOT ALWAYS LEAD TO PROGRESS IN DESIGN AND/OR CONCEPTUAL LEARNING WHILE OTHERS HAVE ARGUED FOR TINKERING AS A PRODUCTIVE DESIGN PRACTICE. WE SPEAK TO THIS DISAGREEMENT REGARDING THE PRODUCTIVITY OF TINKERING FOR NOVICE DESIGNERS BY CLOSELY ANALYZING TINKERING EPISODES. DESIGN/METHOD WE PRESENT A MICROGENETIC ACCOUNT OF TWO TINKERING EPISODES TO CONTRIBUTE TO A MORE REFINED UNDERSTANDING OF WHAT TINKERING IS. THE RESEARCH SETTING IS IN A SUMMER CAMP FOR HIGH SCHOOL GIRLS USING A DESIGN-BASED ARDUINO COMPONENT. RESULTS OUR ANALYSES DEMONSTRATE THAT TINKERING IS NEITHER UNIVERSALLY PRODUCTIVE NOR UNPRODUCTIVE IN ENGINEERING DESIGN; WE SHOW THAT TINKERING CAN HELP PARTICIPANTS MAKE PROGRESS TOWARD SOME INSTRUCTIONAL GOALS SUCH AS SUPPORTING THE ENGAGEMENT OF STUDENTS IN SOME ENGINEERING DESIGN PRACTICES. WE ALSO SHOW THAT TINKERING CAN HINDER PROGRESS TOWARD OTHER GOALS SUCH AS DEVELOPING A ROBUST SOLUTION TO A DESIGN PROBLEM. CONCLUSIONS WE ARGUE FOR A MORE NUANCED UNDERSTANDING OF PRODUCTIVITY (OF TINKERING AND OTHER DESIGN PRACTICES), WHICH IS LOCAL AND DEFINED WITH RESPECT TO SPECIFIC GOALS AND ACTORS.
THE VISUAL ANALYSIS OF MULTIVARIATE GRAPHS IS A CHALLENGING PROBLEM. WE ADDRESS THE PARTICULAR TASK OF STUDYING RELATIONS BETWEEN THE STRUCTURE OF A GRAPH AND THE MULTIVARIATE ATTRIBUTES ASSOCIATED WITH IT. TO FACILITATE THIS TASK, WE PROPOSE A NOVEL INTERACTIVE VISUALIZATION APPROACH. THE CORE IDEA IS TO SHOW STRUCTURE AND CALCULATED ATTRIBUTE SIMILARITY IN AN INTEGRATED FASHION AS A MATRIX. A TABLE CAN BE ATTACHED TO THE MATRIX ON DEMAND TO VISUALIZE THE UNDERLYING ATTRIBUTE VALUES IN DETAIL. TO SUPPORT THE VISUAL COMPARISON OF STRUCTURE AND ATTRIBUTES AT DIFFERENT LEVELS, SEVERAL INTERACTION TECHNIQUES ARE PROVIDED, INCLUDING MATRIX REORDERING, SELECTION AND EMPHASIS OF SUBSETS, REARRANGEMENT OF SUB-MATRICES, AND COLUMN ROTATION FOR DETAILED COMPARISON. TO DEMONSTRATE THE UTILITY OF OUR TECHNIQUES, WE APPLY THEM TO EXPLORE RELATIONS BETWEEN STRUCTURE AND ATTRIBUTES IN A NETWORK OF SOCCER PLAYERS.
PARALLEL COORDINATES ARE COMMONLY USED FOR NON-TEMPORAL MULTIVARIATE DATA, BUT THERE IS LITTLE SUPPORT FOR THEIR USABILITY FOR DISPLAYING TEMPORAL MULTIVARIATE DATA. IN THIS PAPER, WE INTRODUCE A STUDY EVALUATING THE USABILITY OF 2D AND 3D PARALLEL COORDINATES FOR PATTERN IDENTIFICATION IN TEMPORAL MULTIVARIATE DATA. THE RESULTS INDICATE THAT 3D PARALLEL COORDINATES HAVE HIGHER USABILITY, AS MEASURED WITH HIGHER ACCURACY AND FASTER RESPONSE TIME AS WELL AS SUBJECTIVE RATINGS, COMPARED TO 2D.
VISUALIZATION TOOLS ALLOW VISUAL AND INTERACTIVE DATA EXPLORATION FOR FACILITATING THE INTERPRETATION OF COMPLEX DATA SETS. THE CENTER FOR RESEARCH AND AMBULATORY CARE OF THE SICKLE CELL DISEASE (CERPAD), LEADING THE NEONATAL SCREENING OF SICKLE CELL PROGRAM IN SAINT-LOUIS OF SENEGAL, AIMS AT SETTING UP A LONG TERM DATABASE ON WHICH DATA ANALYSIS AND REPORTING TOOLS ARE BUILT FOR EPIDEMIOLOGICAL AND SOCIOANTHROPOLOGICAL STUDIES. IN THIS PAPER, WE PROPOSE A TOOL FOR THE PROPORTIONAL VISUALIZATION OF GENOTYPES AND PHENOTYPES, USING RAINBOW BOXES, A RECENTLY INTRODUCED SET VISUALIZATION TECHNIQUE. WE PROPOSE AN IMPROVEMENT FOR RAINBOW BOXES, FEATURING AREA-PROPORTIONAL BOXES INSTEAD OF HEIGHT-PROPORTIONAL BOXES. THIS NEW APPROACH IS INTEGRATED IN THE SIMENS MODULE OF THE CERPAD FOR THE VISUALIZATION OF SICKLE CELL DATA PROPORTION ACCORDING TO GENOTYPES AND PHENOTYPES, AND BY ETHNIC GROUPS. A QUALITATIVE EVALUATION IS PROVIDED BY TWO DOMAIN EXPERTS.
FOR SEVERAL YEARS, THE RECONSTRUCTION OF COMPUTER AIDED DESIGN (CAD) MODELS FROM A DEFORMED MESH GET MORE AND MORE ATTENTION. THIS CAD MODEL IS USED IN ORDER TO VISUALIZE 3D OBJECTS THAT WERE SCANNED AND APPROXIMATE THEIR SHAPES BY MATHEMATICAL FORMULATIONS. IT REPRESENTS THE GEOMETRIC SUPPORT USED IN MANY OTHER ACTIVITIES (ANALYSIS, MANUFACTURING, ASSEMBLY, ETC.). SURFACE RECONSTRUCTION IS THE MOST DIFFICULT PROBLEM OF CAD MODEL RECONSTRUCTION. THERE ARE TWO TYPES OF SURFACES: PRIMITIVE SURFACES AND COMPLEX SURFACES. IN THIS PAPER, WE PROPOSE A METHOD TO RECONSTRUCT COMPLEX SURFACES. OUR ALGORITHM IS BASED ON THIN PLATE SPLINE (TPS) METHOD TO OPTIMIZE LOCATIONS OF CONTROL POINTS OF A B-SPLINE SURFACE. ONCE SURFACES ARE APPROXIMATED, THE GEOMETRIC MODEL CAN BE RECONSTRUCTED. WE EVALUATE EVERY STEP OF OUR APPROACH USING MECHANICAL MODELS AND SHOW THAT WE CAN ACHIEVE GOOD RESULTS AND MEANINGFUL APPROXIMATED CONTROL POINTS COMPARING WITH OTHER METHODS.
WITH THE TECHNOLOGICAL ADVANCEMENT IN THE FIELD OF COMPUTER AIDED DESIGN SUCH AS THE RAPID DEVELOPMENT OF SCANNING TECHNOLOGIES, THE RECONSTRUCTION OF COMPLETE AND INCOMPLETE CYLINDERS GIVEN NOISY POINT CLOUDS WITH FORM DEFECTS BECOMES AN IMPORTANT ISSUE. IN FACT, CYLINDRICAL SURFACES ARE FOUND IN DOMESTIC TO INDUSTRIAL CONTEXTS. IN THIS PAPER, A COMPARATIVE STUDY OF CYLINDER FITTING ALGORITHMS MANUFACTURED IN THE LIPPS LABORATORY IS PROPOSED. THE AIM OF THE PROPOSED APPROACH IS TO DETERMINE THE DIAMETER OF CYLINDRICAL FEATURE FOR MINIMIZING ROUNDNESS ERROR FROM EXPERIMENTAL DATA-POINTS. THE ROUNDNESS ERROR IS EVALUATED USING TWO INTERNATIONALLY DEFINED METHODS: MINIMUM CIRCUMSCRIBED CYLINDER (MCC) AND MAXIMUM INSCRIBED CYLINDER (MIC). ALL ALGORITHMS GIVE SIMILAR RESULTS IN THE CASE WHERE THE SCANNED CYLINDER IS COMPLETE AND WITHOUT FORM DEFECTS, BUT IN THE CASE OF MISSING DATA SOME ALGORITHMS GIVE UNACCEPTABLE RESULTS. THE TWO REFERENCE CYLINDERS HAVE BEEN INDEPENDENTLY ANALYZED, RESPECTING SIX CRITERIA (CALCULATION COMPLEXITY, DAMPING PARAMETER, INITIAL GUESS, TIME, CIRCULARITY ERROR AND COMPLEXITY CYLINDER). THE RESULTS OF ALGORITHMS ARE ALSO COMPARED TO HELP MANUFACTURERS AND INSPECTORS FACILITATE AND IMPROVE THE APPLICATION OF THESE METHODS AND TO SELECT THE APPROPRIATE ALGORITHM FOR SIZE AND FORM EVALUATION.
INTERACTIVE FICTION IS A PART OF DIGITAL LITERATURE THAT PROMOTES THE ACTIVE ROLE OF THE READER IN THE READING PROCESS OF AN ELECTRONIC BOOK (E-BOOK). FOR THIS PURPOSE, MULTIMEDIA RESOURCES ARE INCLUDED WITHIN THE CONTENT OF THE E-BOOK, WITH WHICH THE READER HAS TO INTERACT. AS A CONSEQUENCE, A RICHER READING EXPERIENCE IS PROMOTED, ACCORDING TO WHICH THE READER MUST DECIDE HOW TO CONTINUE THE STORY HE/SHE IS READING, AND, IN TURN, THESE DECISIONS CONDITION THE FUTURE COURSE OF THE STORY. HOWEVER, THE MAIN PROBLEM FACED BY WRITERS OF INTERACTIVE FICTION IS THE LACK OF EDITORS THAT MAKE IT EASIER FOR THEM, WHO ARE NOT NECESSARILY EXPERTS IN INTERACTIVE CONTENT DESIGN, TO USE THESE STRATEGIES IN THE CONTENTS OF THEIR E-BOOKS, AND WHICH IN TURN OFFER THEM ENOUGH NARRATIVE FLEXIBILITY. FOR THIS PURPOSE, WE HAVE DEVELOPED IFDBMAKER, AN INTERACTIVE FICTION AUTHORING TOOL ORIENTED TO WRITERS WITH NO PRIOR KNOWLEDGE IN DIGITAL CONTENT PRODUCTION. THIS TOOL IS EQUIPPED WITH A WEB-BASED INTERFACE AND LETS NON-TECHNICAL WRITERS USE SOPHISTICATED NARRATIVE RESOURCES WHILE AUTHORING INTERACTIVE FICTION E-BOOKS WITH THE SOLE SUPPORT OF A SIMPLE WEB BROWSER.
DUE TO ITS GEOGRAPHICAL POSITION, THE APULIA REGION, IS USED TO HOUSE MIGRANTS FROM ALL OVER THE WORLD WHO ARRIVED OVER THE CENTURIES. APULIA IS ALSO A TRANSIT LAND FOR MIGRANTS THAT WANT TO REACH OTHER ITALIAN REGIONS OR EUROPEAN COUNTRIES. ONE OF THE MAIN ISSUES OF MIGRATION FLOWS IS RELATED TO THE HEALTH SERVICES NETWORK. IN THIS CONTEXT, THE APULIA REGION, IN COLLABORATION WITH OTHER PRIVATE AND PUBLIC ORGANISATIONS, PROPOSED THE PREVENZIONE 4.0 (PREVENTION) PROJECT THAT AIMS AT CREATING AN E-HEALTH ENVIRONMENT TO EMPOWER THE SERVICES OF THE NATIONAL HEALTH SERVICE FOR MIGRANTS. TECHNOLOGICAL SOLUTIONS AND LEARNING PATHS WILL BE IMPLEMENTED TO REDUCE THE NUMBER OF USERS WHO DAILY ASK FOR HEALTH CARE SERVICES. THE ACTIONS WILL BE AVAILABLE FOR BOTH MIGRANTS AND PROFESSIONAL FIGURES INVOLVED IN THE MANAGEMENT OF MIGRANTS' RECEPTION PROCESSES. THIS PAPER PRESENTS A MOBILE APPLICATION DESIGNED TO HELP THE MIGRANT CENTRES TO PROVIDE MEDICAL AND PSYCHOLOGICAL SUPPORT TO THEIR GUESTS. THE APP FOSTERS THE MIGRANTS' EMPOWERMENT TO MAKE THEM ABLE TO TAKE CARE OF THEIR HEALTH WITHOUT INVOLVING THE NATIONAL HEALTH SERVICE WHEN NOT STRICTLY NECESSARY.
WE PRESENT A SYSTEM THAT COMBINES AMBIENT VISUALIZATION, INFORMATION RETRIEVAL AND MACHINE LEARNING TO FACILITATE THE EASE AND QUALITY OF DOCUMENT CLASSIFICATION BY SUBJECT MATTER EXPERTS FOR THE PURPOSE OF ORGANIZING DOCUMENTS BY "TAGS" INFERRED BY THE RESULTANT CLASSIFIERS. THIS SYSTEM INCLUDES DATA COLLECTION, A LANGUAGE MODEL, QUERY EXPLORATION, FEATURE SELECTION, SEMI-SUPERVISED MACHINE LEARNING AND A VISUAL ANALYTIC WORKFLOW ENABLING NON-DATA SCIENTISTS TO RAPIDLY DEFINE, VERIFY, AND REFINE HIGH-QUALITY DOCUMENT CLASSIFIERS.
DUE TO THE DIFFUSION OF MOBILE DEVICES, MORE AND MORE PEOPLE ACCESS E-LEARNING PLATFORMS FROM MOBILE PHONES. STUDENTS LEARN FROM DIGITAL BOOKS AND HAVE ACCESS TO INFORMATION ANYTIME AND ANYWHERE. HOWEVER, WITH BILLIONS OF MOBILE USERS WORLDWIDE, AS WELL AS BILLIONS OF UNDER-PROTECTED INTERNET OF THINGS (IOT) DEVICES, THE RISK OF BEING THE TARGET OF MALWARE, CYBERCRIME AND SOPHISTICATED ATTACKS IS HIGH. THIS PAPER PROPOSES AND DISCUSSES A SET OF VISUALIZATION TECHNIQUES APPLIED TO A DATASET GENERATED BY DREBIN, A MALWARE DETECTION TOOL THAT PERFORMS A STATIC ANALYSIS ON APPS INSTALLED TO ANDROID DEVICES. ON THE BASE OF DATASET, WE APPLIED TEXT, TREE AND GRAPH VISUALIZATION TECHNIQUES TO IDENTIFY MALWARE PATTERNS. THE VISUAL FINDINGS CAN HELP THE CYBERSECURITY ANALYST IN DETECTING MALICIOUS APP BEHAVIOR.
3D DESIGN SOFTWARE IS A COMMONLY USED INTERACTIVE LEARNING TOOL IN STEM EDUCATION. IT IS GENERALLY BELIEVED THAT 3D DESIGN CAN EFFECTIVELY TRAIN STUDENTS' SPATIAL THINKING AND ENGINEERING DESIGN ABILITIES. THERE HAVE BEEN MANY THEORETICAL AND PRACTICAL ACHIEVEMENTS IN THE APPLICATION OF 3D PRINTING EDUCATION, BUT THE EVALUATION OF 3D DESIGN IS MAINLY BASED ON DIAGNOSTIC AND SUMMATIVE EVALUATIONS, WHICH IGNORES THE GROWTH OF STUDENTS' SPATIAL THINKING AND ENGINEERING DESIGN ABILITY. STUDENTS' CREATIVITY AND PROBLEM-SOLVING ABILITIES ARE LIMITED, WHILE TEACHERS HAVE NOT BEEN ABLE TO TEACH STUDENTS IN ACCORDANCE WITH THEIR APTITUDE. IN THIS STUDY, WE PROPOSE A 3D PRINTING COURSE DATA COLLECTION MODEL BASED ON XAPI SPECIFICATIONS, AND USE GEEKCAD DESIGN SOFTWARE TO IMPLEMENT THE XAPI PROFILE AND APPLY IT TO THE 3D PRINTING COURSE. THE SUCCESSFUL IMPLEMENTATION OF THIS MODEL AT LI JUN PRIMARY SCHOOL PROVED THAT XAPI CAN COMPLETELY RECORD THE LEARNING PATHS OF STUDENTS. THROUGH THE ANALYSIS OF INTERACTIVE DATA, WE FOUND THAT STUDENTS HAVE DIFFERENT OPERATING HABITS AND LEARNING PATHS WHICH PROVIDE THE BASIS FOR THE EVALUATION OF STUDENTS' SPATIAL THINKING ABILITY AND ENGINEERING DESIGN SKILL IN INTERACTIVE LEARNING ENVIRONMENT.
THIS PAPER PRESENTS A SYSTEM THAT EMPLOYS INFORMATION VISUALIZATION TECHNIQUES TO VISUALLY ANALYSE LABOUR ACCIDENT DATA, WHICH REPRESENTS A SERIOUS SOCIAL PROBLEM. OUR SYSTEM USES ASSOCIATED GEOGRAPHICAL AND TEMPORAL DATA INFORMATION TO CREATE A HIERARCHICAL INTERACTIVE LAYOUT THAT FOCUSES ON HIGHLIGHTING THE EVOLUTION OF ACCIDENT OCCURRENCES IN LOCALITIES OVER TIME. WE PRESENT THE RESULTS OF APPLYING OUR PROPOSED SYSTEM ON DATA PROVIDED BY THE BRAZILIAN FEDERAL LABOUR PROSECUTION OFFICE, DEMONSTRATING THE POTENTIAL OF OUR STRATEGY IN ANALYSING THE EVOLUTION OF OCCURRENCES OVER TIME, HIGHLIGHTING TRENDS, SEASONAL EVENTS, ABNORMAL BEHAVIOUR, AS WELL AS IN COMPARING THE BEHAVIOUR OF LOCALITIES IN THE SAME/DISTINCT HIERARCHICAL LEVELS, AMONG OTHER TASKS. WE BELIEVE THAT THE PROPOSED SYSTEM PROVIDES AN EFFECTIVE ENVIRONMENT THAT MAY GUIDE THE GOVERNORS IN CREATING PUBLIC POLICIES TO REDUCE THESE ACCIDENTS AND GRANT SAFETY FOR EMPLOYEES, AND THAT MAY ENCOURAGE CITIZEN PARTICIPATION AND TRANSPARENCY IN GOVERNMENTS.
THIS PAPER SHEDS LIGHT ON THE ROLE OF VISUAL THINKING TOOLS WITHIN THE PROCESS OF DESIGNING YOUR LIFE, THE APPLICATION OF DESIGN THINKING TO LIFE DESIGN. IT PROVIDES A CONCEPTUAL FRAMEWORK SHOWING ILLUSTRATIVE EXAMPLES OF VISUAL TEMPLATES FOR EACH PHASE INDICATING ITS FUNCTIONS AND BENEFITS. BASED ON A THOROUGH UNDERSTANDING OF THE FUNCTIONS AND BENEFITS OF KNOWLEDGE VISUALIZATION IN GENERAL, THIS PAPER SETS OUT TO PROVIDE A FIRST OVERVIEW OF THE USE OF VISUAL THINKING TOOLS IN DESIGNING YOUR LIFE INFORMING AND SUPPORTING PRACTITIONERS AND RESEARCHER FOR MORE CONSCIOUS SELECTIONS OF VISUALIZATION METHODS IN THEIR LIFE DESIGN EFFORTS.
CREATING A VISUALIZATION THAT CONVEYS A NARRATIVE REQUIRES CHOOSING THE DIMENSIONS AND FEATURES THAT HELP TELL THE STORY. TIME AND SPACE ARE TWO OF THESE STORYTELLING ATTRIBUTES WHICH ARE COMMONLY PRESENT IN THE STORY'S STRUCTURE. THUS, THESE SHOULD BE CONSIDERED IN THE CREATION PROCESS. NARRATIVE VISUALIZATION IS STILL A NEW FIELD IN INFORMATION VISUALIZATION RESEARCH, AND WHILE THERE ARE GUIDELINES FOR DESIGNING VISUALIZATIONS, SPECIFIC ONES FOR THIS NEW AREA ARE STILL LACKING. THEREFORE, SUPPORTED BY PREVIOUS RESEARCH ON BROAD RECOMMENDATIONS FOR DESIGNING VISUALIZATIONS, WE PROPOSE A SPECIFIC SET OF GUIDELINES TO STRUCTURE EFFECTIVE VISUAL NARRATIVES DIVIDED INTO FOUR DECISION CATEGORIES: INTENT, SPATIO-TEMPORAL, INTERACTION, AND NARRATIVE ELEMENTS.
THIS PAPER BUILDS ON PREVIOUS WORK INTO GRAPH-BASED VISUALISATION OF MELODIC RELATIONSHIPS (TUNE FAMILIES) WITHIN COLLECTIONS OF TRADITIONAL MUSIC. HERE, USING A COMMUNITY DETECTION ALGORITHM, POTENTIAL TUNE FAMILIES CAN BE READILY IDENTIFIED. HOWEVER, THE RICHER THE INFORMATION CONTAINED IN THE GRAPH, THE MORE DIFFICULT IT IS FOR THE VISUALISATION ALGORITHMS TO OPERATE SUCCESSFULLY. THEREFORE, AN APPROACH IS PROPOSED WHICH USES MODIFIED VERSIONS OF THE GRAPH BOTH TO ENHANCE THE COMMUNITY DETECTION RESULTS AND, MORE IMPORTANTLY, RESTRUCTURE THE GRAPH, BY CREATING A COMPOUND GRAPH, TO REVEAL THE COMMUNITIES VISUALLY. FINALLY, THE WIDER APPLICABILITY OF THE TECHNIQUE IS CONSIDERED.
WITH THE FAST DEVELOPMENT OF MOBILE COMPUTING AND INCREASING COMPUTING CAPACITIES OF MOBILE DEVICES, NEW COLLABORATIVE APPLICATIONS AND PLATFORMS ARE APPEARING TO SUPPORT COLLABORATION ON THE MOVE. INDEED, NOWADAYS, MEMBERS OF A TEAM CAN BE NOT ONLY GEOGRAPHICALLY DISTRIBUTED BUT THEY CAN ALSO WORK ANY-TIME AND ANYWHERE THANKS TO THE USE OF MOBILE DEVICES. OFTEN, HOWEVER, TEAM MEMBERS WOULD LIKE TO WORK EITHER ONLINE OR OFFLINE ON A COMMON PROJECT; LIKEWISE, THEY MAY WISH TO SWITCH AMONG VARIOUS DEVICES SUCH AS LAPTOPS, TABLETS AND MOBILE PHONES AND STILL WORK IN THE SAME APPLICATION ENVIRONMENT, SHARING THE SAME DATA, ETC. IN THIS PAPER WE PRESENT A PLATFORM THAT ENABLES APPLICATION AND SERVICES MIGRATION AT RUNTIME BETWEEN DIFFERENT PLATFORMS USING THE WEBRTC (WEB REAL-TIME COMMUNICATION) FRAMEWORK. WE HAVE STUDIED APPLICATIONS MIGRATION BOTH THROUGH A CENTRAL SERVER AND THROUGH A DISTRIBUTED (PEER-TO-PEER) MODEL. VARIOUS ISSUES THAT ARISE IN APPLICATION MIGRATION SUCH AS PROFILE MATCHING, APPLICATION CONTEXT, DATA SYNCHRONISATION AND CONSISTENCY ARE DISCUSSED. THE EFFICIENCY AND SCALABILITY OF THE WEBRTC FRAMEWORK AND MOBILE DEVICES (PEERS) UNDER ANDROID IN A REAL COMPUTING INFRASTRUCTURE ARE STUDIED. SOME EXPERIMENTAL RESULTS ON THE APPLICATION MIGRATION TIME ACCORDING TO APPLICATION STATE DATA SIZE ARE REPORTED.
EDUCATIONAL RESEARCH IS ONE OF THE MANY FIELDS OF KNOWLEDGE THAT FREQUENTLY USE CASE STUDIES AS A RESEARCH METHOD, PARTICULARLY WHEN APPLYING AN INTERPRETIVE APPROACH. BASED ON LITERATURE REVIEWS AND A SYSTEMATIC ANALYSIS OF CURRENT SCIENTIFIC LITERATURE, THIS PAPER EXAMINES THE PREVALENCE AND CHARACTERISTICS OF THE CASE STUDY AS A METHODOLOGY FOR RESEARCH ON MOOCS. NINETY-TWO DOCUMENTS WERE SELECTED FROM THE SEARCH RESULTS RETURNED BY TWO OF THE MOST PRESTIGIOUS SCIENTIFIC DATABASES: WEB OF SCIENCE (WOS) AND SCOPUS. FINDINGS SHOWED THAT (A) EVEN WHEN SEARCHING SOLELY FOR CASE STUDIES, QUANTITATIVE RESEARCH PARADIGMS WERE MORE PREVALENT THAN INTERPRETIVE APPROACHES; (B) GEOGRAPHICAL DISTRIBUTION OF THESE STUDIES WAS PARTIALLY BIASED; (C) CASE STUDIES WERE LESS PREVALENT IN THESE DATABASES THAN OTHER EMPIRICAL INVESTIGATIONS ON MOOCS; (D) THE DATA COLLECTION AND DATA ANALYSIS METHODS MOST FREQUENTLY USED IN THE CASE STUDIES WERE MORE ALIGNED WITH A QUANTITATIVE APPROACH; AND (E) THERE IS STILL VERY LITTLE INSTRUCTOR-FOCUSED RESEARCH USING THIS METHODOLOGY. IN THE LIGHT OF THESE FINDINGS AND THEIR DISCUSSION, FUTURE DIRECTIONS FOR RESEARCH USING CASE STUDY METHODOLOGY ARE PROPOSED, GIVEN THE POTENTIAL OF THIS METHOD TO ILLUSTRATE CERTAIN ISSUES FOR WHICH OTHER APPROACHES HAVE PROVED INADEQUATE OR INSUFFICIENT.
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
THE ARTICLE HIGHLIGHTS THE NEED FOR IMPROVEMENT IN THE EVALUATION OF DASHBOARD IMPLEMENTATIONS INTENDED TO SUPPORT QUALITY PROCESSES IN HIGHER EDUCATION AND PROPOSES A SYSTEMS THINKING FRAMEWORK FOR THIS IMPROVEMENT. A REVIEW OF THE LITERATURE ON QUALITY ENHANCEMENT PROCESSES IN HIGHER EDUCATION AND DASHBOARDS RESEARCH HIGHLIGHTS THAT QUALITY ENHANCEMENT IN HIGHER EDUCATION MIGHT BE MORE APPROPRIATELY UNDERSTOOD THROUGH THE LENS OF COMPLEXITY MANAGEMENT. THE VIABLE SYSTEMS MODEL IS ARGUED TO BE USEFUL FOR THIS PURPOSE AND, IN TERMS OF THE ROLE OF DASHBOARDS IN SUPPORTING THESE PROCESSES, ENCOURAGES FOCUS ON THEIR ROLE IN SUPPORTING COMMUNICATION AND INFORMATION FLOW FOR EFFECTIVE DECISION-MAKING FOR ADAPTATION. SOME INITIAL SYSTEMS MODELLING IS CONDUCTED TO ILLUSTRATE HOW THE APPLICATION OF THE MODEL COULD GUIDE EVALUATION, PROVIDING THE FOUNDATION FOR CASE STUDY EVALUATION AND RESEARCH.
MASSIVE OPEN ONLINE COURSES (MOOCS) AROSE AS A WAY OF TRANSCENDING FORMAL HIGHER EDUCATION BY REALIZING TECHNOLOGY-ENHANCED FORMATS OF LEARNING AND INSTRUCTION AND BY GRANTING ACCESS TO A WIDE AUDIENCE WAY BEYOND STUDENTS ENROLLED IN ANY ONE HIGHER EDUCATION INSTITUTION. HOWEVER, WHILE MOOCS HAVE BEEN REPORTED AS AN EFFICIENT AND IMPORTANT EDUCATIONAL TOOL, YET THERE IS A NUMBER OF ISSUES AND PROBLEMS RELATED TO THEIR EDUCATIONAL IMPACT. MORE SPECIFICALLY, THERE IS AN IMPORTANT NUMBER OF DROP OUTS DURING A COURSE, LITTLE PARTICIPATION, AND LACK OF STUDENTS' MOTIVATION AND ENGAGEMENT OVERALL. TO OVERCOME THESE LIMITATIONS, CONVERSATIONAL PEDAGOGICAL AGENTS HAVE ARISEN TO GUIDE AND SUPPORT STUDENT DIALOGUE USING NATURAL LANGUAGE BOTH IN INDIVIDUAL AND COLLABORATIVE SETTINGS. CONVERSATIONAL AGENTS HAVE BEEN PRODUCED TO MEET A WIDE VARIETY OF APPLICATIONS AND STUDIES EXPLORING THE USAGE OF SUCH AGENTS HAVE LED TO POSITIVE RESULTS. INTEGRATING THIS TYPE OF ARTIFICIAL AGENTS INTO MOOCS IS EXPECTED TO TRIGGER PRODUCTIVE PEER INTERACTION IN DISCUSSION GROUPS. IN THIS PAPER, WE PRESENT A STATE-OF-THE-ART STUDY OF THE USE OF CONVERSATIONAL AGENTS TO SUPPORT COLLABORATIVE LEARNING IN THE CONTEXT OF MOOCS. THE ULTIMATE GOAL OF THIS STUDY IS TO ANALYZE THE POTENTIAL OF CONVERSATIONAL AGENTS TO CONSIDERABLY INCREASE THE ENGAGEMENT AND THE COMMITMENT OF MOOC STUDENTS, REDUCING CONSEQUENTLY, THE OVERALL MOOCS DROPOUT RATE. THE RESEARCH REPORTED IN THIS PAPER IS CURRENTLY UNDERTAKEN WITHIN THE RESEARCH PROJECT COLMOOC FUNDED BY THE EUROPEAN COMMISSION.
THE INTEGRATION OF COMPUTATIONAL MODELLING IN SCIENCE CLASSROOMS PROVIDES A UNIQUE OPPORTUNITY TO PROMOTE KEY 21ST CENTURY SKILLS INCLUDING COMPUTATIONAL THINKING (CT) AND COLLABORATION. THE OPEN-ENDED, PROBLEM-SOLVING NATURE OF THE TASK REQUIRES GROUPS TO GRAPPLE WITH THE COMBINATION OF TWO DOMAINS (SCIENCE AND COMPUTING) AS THEY COLLABORATIVELY CONSTRUCT COMPUTATIONAL MODELS. WHILE THIS APPROACH HAS PRODUCED SIGNIFICANT LEARNING GAINS FOR STUDENTS IN BOTH SCIENCE AND CT IN K-12 SETTINGS, THE COLLABORATIVE LEARNING PROCESSES STUDENTS USE, INCLUDING LEARNER REGULATION, ARE NOT WELL UNDERSTOOD. IN THIS PAPER, WE PRESENT A SYSTEMATIC ANALYSIS FRAMEWORK THAT COMBINES NATURAL LANGUAGE PROCESSING (NLP) OF COLLABORATIVE DIALOGUE, LOG FILE ANALYSES OF STUDENTS' MODEL-BUILDING ACTIONS, AND FINAL MODEL SCORES. THIS ANALYSIS IS USED TO BETTER UNDERSTAND STUDENTS' REGULATION OF COLLABORATIVE PROBLEM SOLVING (CPS) PROCESSES OVER A SERIES OF COMPUTATIONAL MODELLING TASKS OF VARYING COMPLEXITY. THE RESULTS SUGGEST THAT THE COMPUTATIONAL MODELLING CHALLENGES AFFORD OPPORTUNITIES FOR STUDENTS TO A) EXPLORE RESOURCE-INTENSIVE PROCESSES, SUCH AS TRIAL AND ERROR, TO MORE SYSTEMATIC PROCESSES, SUCH AS DEBUGGING MODEL ERRORS BY LEVERAGING DATA TOOLS, AND B) LEARN FROM EACH OTHER USING SOCIALLY SHARED REGULATION (SSR) AND PRODUCTIVE COLLABORATION. THE USE OF SUCH SSR PROCESSES CORRELATED POSITIVELY WITH THEIR MODEL-BUILDING SCORES. OUR PAPER AIMS TO ADVANCE OUR UNDERSTANDING OF COLLABORATIVE, COMPUTATIONAL MODELLING IN K-12 SCIENCE TO BETTER INFORM CLASSROOM APPLICATIONS.
COLLABORATIVE PROBLEM-SOLVING (CPS) HAS BECOME AN ESSENTIAL COMPONENT OF TODAY'S KNOWLEDGE-BASED, INNOVATION-CENTRED ECONOMY AND SOCIETY. AS SUCH, COMMUNICATION AND CPS ARE NOW CONSIDERED CRITICAL 21ST CENTURY SKILLS AND INCORPORATED INTO EDUCATIONAL PRACTICE, POLICY, AND RESEARCH. DESPITE GENERAL AGREEMENT THAT THESE ARE IMPORTANT SKILLS, THERE IS LESS AGREEMENT ON HOW TO CAPTURE SOCIOCOGNITIVE PROCESSES AUTOMATICALLY DURING TEAM INTERACTIONS TO GAIN A BETTER UNDERSTANDING OF THEIR RELATIONSHIP WITH CPS OUTCOMES. THE AVAILABILITY OF NATURALLY OCCURRING EDUCATIONAL DISCOURSE DATA WITHIN ONLINE CPS PLATFORMS PRESENTS A GOLDEN OPPORTUNITY TO ADVANCE UNDERSTANDING ABOUT ONLINE LEARNER SOCIOCOGNITIVE ROLES AND ECOLOGIES. IN THIS PAPER, WE EXPLORE THE RELATIONSHIP BETWEEN EMERGENT SOCIOCOGNITIVE ROLES, COLLABORATIVE PROBLEM-SOLVING SKILLS, AND OUTCOMES. GROUP COMMUNICATION ANALYSIS (GCA) - A COMPUTATIONAL LINGUISTIC FRAMEWORK FOR ANALYZING THE SEQUENTIAL INTERACTIONS OF ONLINE TEAM COMMUNICATION - WAS APPLIED TO A LARGE CPS DATASET IN THE DOMAIN OF SCIENCE (PARTICIPANT N = 967; TEAM N = 480). THE ETS COLLABORATIVE SCIENCE ASSESSMENT PROTOTYPE (ECSAP) WAS USED TO MEASURE LEARNERS' CPS SKILLS, AND CPS OUTCOMES. CLUSTER ANALYSES AND LINEAR MIXED-EFFECTS MODELLING WERE USED TO DETECT LEARNER ROLES, AND ASSESS THE RELATIONSHIP BETWEEN THOSE ROLES ON CPS SKILLS AND OUTCOMES. IMPLICATIONS FOR FUTURE RESEARCH AND PRACTICE ARE DISCUSSED REGARDING SOCIOCOGNITIVE ROLES AND COLLABORATIVE PROBLEM-SOLVING SKILLS.
METRICS INCLUDING COHEN'S KAPPA, PRECISION, RECALL, AND F-1 ARE COMMON MEASURES OF PERFORMANCE FOR MODELS OF DISCRETE STUDENT STATES, SUCH AS A STUDENT'S AFFECT OR BEHAVIOUR. THIS STUDY EXAMINED DISCRETE MODEL METRICS FOR PREVIOUSLY PUBLISHED STUDENT MODEL EXAMPLES TO IDENTIFY SITUATIONS WHERE METRICS PROVIDED DIFFERING PERSPECTIVES ON MODEL PERFORMANCE. SIMULATED MODELS ALSO SYSTEMATICALLY SHOWED THE EFFECTS OF IMBALANCED CLASS DISTRIBUTIONS IN BOTH DATA AND PREDICTIONS, IN TERMS OF THE VALUES OF METRICS AND THE CHANCE LEVELS (VALUES OBTAINED BY MAKING RANDOM PREDICTIONS) FOR THOSE METRICS. RANDOM CHANCE LEVEL FOR F-1 WAS ALSO ESTABLISHED AND EVALUATED. RESULTS FOR EXAMPLE STUDENT MODELS SHOWED THAT OVERPREDICTION OF THE CLASS OF INTEREST (POSITIVE CLASS) WAS RELATIVELY COMMON. CHANCE-LEVEL F-1 WAS INFLATED BY OVER-PREDICTION; CONVERSELY, MAXIMUM POSSIBLE VALUES FOR F-1 AND KAPPA WERE NEGATIVELY IMPACTED BY OVER-PREDICTION OF THE POSITIVE CLASS. ADDITIONALLY, NORMALIZATION METHODS FOR F-1 RELATIVE TO CHANCE ARE DISCUSSED AND COMPARED TO KAPPA, DEMONSTRATING AN EQUIVALENCE BETWEEN KAPPA AND NORMALIZED F-1. FINALLY, IMPLICATIONS OF RESULTS FOR CHOICE OF METRICS ARE DISCUSSED IN THE CONTEXT OF COMMON STUDENT MODELLING GOALS, SUCH AS AVOIDING FALSE NEGATIVES FOR STUDENT STATES THAT ARE NEGATIVELY RELATED TO LEARNING.
LARGE-SCALE ONLINE LEARNING ENVIRONMENTS SUCH AS MOOCS PROVIDE AN OPPORTUNITY TO EVALUATE THE EFFICACY OF LEARNING STRATEGIES IN AN INFORMAL LEARNING CONTEXT WITH A DIVERSE LEARNER POPULATION. HERE, WE EVALUATE THE EXTENT TO WHICH RETRIEVAL PRACTICE - RECOGNIZED AS ONE OF THE MOST EFFECTIVE LEARNING STRATEGIES - FACILITATES LONG-TERM KNOWLEDGE RETENTION (AND THUS LEARNING OUTCOMES) AMONG MOOC LEARNERS USING AN INSTRUCTIONAL INTERVENTION. WE OBSERVED NO EFFECT ON LEARNING OUTCOMES AND HIGH LEVELS OF TREATMENT NON-COMPLIANCE. IN ADDITION, WE CONDUCTED A SERIES OF EXPLORATORY STUDIES INTO LONG-TERM RECALL OF KNOWLEDGE ACQUIRED IN MOOCS. SURPRISINGLY, BOTH PASSING AND NON-PASSING LEARNERS SCORED SIMILARLY ON A KNOWLEDGE POST-TEST, RETAINING APPROXIMATELY TWO-THIRDS OF WHAT THEY LEARNED OVER THE LONG TERM.
ONE FEATURE OF SELF-PACED ONLINE COURSES IS GREATER LEARNER CONTROL OVER THE TIMING OF THEIR WORK IN A COURSE. HOWEVER, THE GREATER TIMING FLEXIBILITY THAT LEARNERS ENJOY IN SUCH ENVIRONMENTS MAY PLAY A DIFFERENT ROLE IN THE LEARNING PROCESS THAN HAS BEEN PREVIOUSLY OBSERVED IN FORMAL ONLINE OR FACE-TO-FACE COURSES. AS SUCH, THE STUDY OF WORK TIMING MERITS FURTHER INVESTIGATION. TOWARD THIS GOAL, THIS STUDY FORWARDS TWO MEASURES THAT REPRESENT THE TIMING OF COURSEWORK: 1) THE TIMING INDEX, OR THE DEGREE TO WHICH A PARTICIPANT COMPLETES 50% OF THEIR WORK, AND 2) THE SPACING COUNT, THE FREQUENCY OF WORK PERFORMED ACROSS THE COURSE TIMEFRAME. IN THIS STUDY, THE AUTHORS DEMONSTRATE THE USE OF THESE MEASURES FROM A DATA SET OF 42 U.S. MIDDLE-SCHOOL TEACHERS WHO PARTICIPATED IN A SELF-PACED, ONLINE PROFESSIONAL DEVELOPMENT COURSE TO SUPPORT TEACHER IMPLEMENTATION OF A NEW BLENDED-LEARNING CURRICULUM. USING THE TWO MEASURES, THE AUTHORS IDENTIFY TIMING BEHAVIOURS OF PARTICIPANTS AND EXAMINE THE EFFECTS THAT TIMING HAS ON TEACHER SELF-EFFICACY AFTER COMPLETING THE COURSE. THE TWO MEASURES AND VISUALIZATIONS DEMONSTRATED IN THIS PAPER YIELD USEFUL INDIVIDUAL-LEVEL VARIABLES FOR COURSE TIMING THAT CAN BE USED FOR FURTHER STUDY ON THE EFFECTS ON LEARNING OUTCOMES.
THE PAPER PRESENTS INNOVATIVE TRUSTWORTHY SERVICES TO SUPPORT SECURE E-ASSESSMENT IN WEB-BASED COLLABORATIVE LEARNING GRIDS. ALTHOUGH E-LEARNING HAS BEEN WIDELY ADOPTED, THERE EXIST STILL DRAWBACKS WHICH LIMIT THEIR POTENTIAL. AMONG THESE LIMITATIONS, WE INVESTIGATE INFORMATION SECURITY REQUIREMENTS IN ON-LINE ASSESSMENT LEARNING ACTIVITIES, (E-ASSESSMENT). IN PREVIOUS RESEARCH, WE PROPOSED A TRUSTWORTHINESS MODEL TO SUPPORT SECURE E-ASSESSMENT REQUIREMENTS FOR E-LEARNING. IN THIS PAPER, WE PRESENT EFFECTIVE APPLICATIONS OF OUR APPROACH BY INTEGRATING FLEXIBLE AND INTEROPERABLE WEB BASED SECURE E-LEARNING SERVICES BASED ON OUR TRUSTWORTHINESS MODEL INTO E-ASSESSMENT ACTIVITIES IN ON-LINE COLLABORATIVE LEARNING COURSES. MOREOVER, WE LEVERAGE GRID TECHNOLOGY TO MEET FURTHER DEMANDING REQUIREMENTS OF COLLABORATIVE LEARNING APPLICATIONS IN TERMS OF COMPUTATION PERFORMANCE AND MANAGEMENT OF LARGE DATA SETS, IN ORDER FOR THE TRUSTWORTHY COLLABORATIVE LEARNING SERVICES TO BE CONTINUOUSLY ADAPTED, ADJUSTED, AND PERSONALISED TO EACH SPECIFIC TARGET LEARNING GROUP. EVALUATION IN A REAL CONTEXT IS PROVIDED WHILE IMPLICATIONS OF THIS STUDY ARE EVENTUALLY REMARKED AND DISCUSSED.
EDUCATIONAL VIRTUAL WORLDS (EVWS) PROVIDE AN INTERESTING AND ENGAGING EDUCATIONAL PLATFORM TO PROMOTE STUDENT LEARNING. VIRTUAL CLASSROOMS SHOULD BE DESIGNED TO BE RESPONSIVE AND ADAPTIVE ENOUGH TO JUDGE THE EMOTIONAL LEVEL OF THE LEARNER AND SHOULD BE CAPABLE OF PROVIDING REQUIRED SUPPORT AND FEED-BACK TO MAINTAIN THE ENGAGEMENT OF THE LEARNER TO ACHIEVE THEIR LEARNING GOALS. VIRTUAL AGENTS WITH AFFECTIVE CAPABILITIES HAVE POTENTIAL TO IMPROVE THE EFFECTIVENESS AND EFFICIENCY OF THE E-LEARNING AND VIRTUAL LEARNING PLATFORMS. THIS RESEARCH STUDY WILL EXPLORE UNDERSTANDING OF LEARNERS' EMOTIONAL STATES THAT EVOLVE DURING THE LEARNING PROCESS WITHIN A EVW BY DRAWING ON THEORIES RELATED TO EPISTEMIC EMOTIONS AND DEVELOP APPROACHES TO RECOGNISE AND INTERVENE IN A TIMELY MANNER BASED ON THE INDIVIDUAL'S FEELINGS BEING EXPERIENCED AT THAT MOMENT. TO DERIVE THAT EMOTIONAL STATE, THIS PAPER ALSO PRESENTS A MULTI-DATA AND MULTIMODAL APPROACH TO JUDGE THE UNDERLYING EMOTIONAL STATE OF THE LEARNER WITHIN EVW AND THE MOST APPROPRIATE INTERVENTION FOR THAT LEARNER.
FEATURED APPLICATION THIS WORKS CAN HELP TO CHOOSE THE BETTER AND MORE EFFICIENT VIDEOS FOR PROFESSORS OF PHYSICS COURSES. DIGITAL VIDEOS HAVE AN IMPORTANT AND INCREASING PRESENCE IN STUDENT LEARNING. THEY PLAY A KEY ROLE ESPECIALLY IN SUBJECTS WITH HIGH MATHEMATICAL CONTENT, SUCH AS PHYSICS. HOWEVER, CREATING VIDEOS IS A TIME-CONSUMING ACTIVITY FOR TEACHERS, WHO ARE USUALLY NOT EXPERTS IN VIDEO CREATION. THEREFORE, IT IS IMPORTANT TO KNOW WHICH KINDS OF VIDEOS ARE PERCEIVED AS MORE USEFUL BY STUDENTS AND WHY. IN THIS PAPER WE ANALYZE STUDENTS' PERCEPTION OF VIDEOS IN AN INTRODUCTORY PHYSICS COURSE OF ENGINEERING WITH OVER 200 FIRST YEAR STUDENTS IN A 100% ONLINE UNIVERSITY, UNIVERSITAT OBERTA DE CATALUNYA (UOC). STUDENTS HAD 142 VIDEOS AVAILABLE OF SEVERAL TYPES. WE FOLLOWED A QUALITATIVE METHODOLOGY FROM A GROUND THEORY PERSPECTIVE AND PERFORMED SEMI-STRUCTURED INTERVIEWS. RESULTS SHOW THAT STUDENTS FOUND VIDEOS AS THE MOST VALUED RESOURCE, ALTHOUGH THEY CONSIDERED THAT VIDEOS CANNOT SUBSTITUTE TEXT DOCUMENTS. STUDENTS VALUED HUMAN ELEMENTS AND FOUND THEM IN VIDEOS WHERE THE HANDS OF THE PROFESSOR APPEAR. FINALLY, STUDENTS CONSUMED VIDEOS ACCORDING TO THE COURSE SCHEDULE, VISUALIZED THE WHOLE VIDEO THE FIRST TIME, AND CONSUMED IT LATER ACCORDING TO FURTHER DELIVERIES AND EXAMS. THE MAIN CONTRIBUTIONS OF THIS PAPER WERE ANALYZING THE PERCEPTION OF STUDENTS FROM A QUALITATIVE PERSPECTIVE IN AN INTRODUCTORY COURSE OF PHYSICS IN ENGINEERING, OBTAINING THE MAIN ELEMENTS THAT MAKE VIDEOS USEFUL FOR STUDENTS AND SHOWING THAT VIDEOS WITH HANDS ARE VALUED BY STUDENTS.
SOCIAL NETWORKS ANALYSIS HAS BECOME A COMMON TREND AMONG SCHOLARS AND RESEARCHERS WORLDWIDE. A GREAT NUMBER OF COMPANIES, INSTITUTIONS AND ORGANISATIONS ARE INTERESTED IN SOCIAL NETWORKS DATA MINING. INFORMATION PUBLISHED ON MANY SOCIAL NETWORKS, LIKE FACEBOOK, TWITTER OR INSTAGRAM CONSTITUTE AN IMPORTANT ASSET IN MANY APPLICATION FIELDS, OVERALL SENTIMENT ANALYSIS, BUT ALSO ECONOMICS ANALYSIS, POLITICS ANALYSIS AND SO ON. SOCIAL NETWORKS ANALYSIS COMPREHENDS MANY DISCIPLINES AND INVOLVES THE APPLICATION OF DIFFERENT METHODOLOGIES AND TECHNIQUES TO DEFINE THE CRITERIA FOR GENERATING THE ANALYTICS, ACCORDING TO THE PURPOSE OF THE STUDY. IN THIS WORK, WE FOCUSED ON THE SEMANTIC ANALYSIS OF THE CONTENT OF TEXTUAL INFORMATION OBTAINED FROM SOCIAL MEDIA, AIMING AT EXTRACTING HOT TOPICS FROM SOCIAL NETWORKS. WE CONSIDERED, AS CASE STUDY, REVIEWS FROM THE YELP SOCIAL NETWORK. THE SAME METHODOLOGY CAN BE ALSO APPLIED FOR SOCIAL AND POLITICAL OPINION MINING CAMPAIGNS.
THIS PAPER ADDRESSES DYNAMICAL INTERDEPENDENCE AMONG THE ACTIONS OF GROUP MEMBERS. I ASSUME THAT THE ACTIONS OF EACH MEMBER CAN BE REPRESENTED AS NODES OF A DYNAMICAL NETWORK AND THEN COLLECT THE NODES INTO DISJOINT SUBSETS (COMPONENTS) REPRESENTING THE INDIVIDUAL GROUP MEMBERS. INTERDEPENDENCE AMONG GROUP MEMBERS' ACTIONS CAN THEN BE DEFINED WITH REFERENCE TO A K-PARTITE NETWORK, IN WHICH THE PARTITIONS CORRESPOND TO THE GROUP MEMBER COMPONENTS. INDEPENDENCE AMONG GROUP MEMBERS' ACTIONS CAN BE DEFINED WITH REFERENCE TO A NETWORK IN WHICH THE GROUP MEMBER COMPONENTS ARE DISCONNECTED FROM ONE ANOTHER. THE DEGREE TO WHICH THE INTERACTIONS OF ACTUAL GROUPS CORRESPOND TO EITHER OF THESE THEORETICAL NETWORK STRUCTURES CAN BE CHARACTERIZED USING MODIFIED VERSIONS OF EXISTING NETWORK STATISTICS. TAKING THIS APPROACH, I PROPOSE A NUMBER OF NETWORK-BASED MEASURES OF DYNAMICAL INTERDEPENDENCE, DISCUSS THE INTERPRETATION OF THE PROPOSED MEASURES, AND CONSIDER HOW TO ASSESS THEIR RELIABILITY AND VALIDITY. THESE IDEAS ARE ILLUSTRATED USING AN EXAMPLE IN WHICH DYADS COLLABORATED VIA ONLINE CHAT TO COMPLETE A GRADE 12 LEVEL MATHEMATICS ASSESSMENT.
THE PURPOSE OF THIS STUDY WAS TO PROPOSE A MIXED-METHODS APPROACH TO ANALYZING SHARED EPISTEMIC AGENCY IN JIGSAW INSTRUCTION FROM MULTIPLE TEMPORAL PERSPECTIVES, AND TO EVALUATE ITS EFFECTIVENESS BY EXAMINING ACTUAL DATASETS. WE EMPLOYED A COMBINATION OF SOCIO-SEMANTIC NETWORK ANALYSIS (SSNA) AND IN-DEPTH DIALOGICAL DISCOURSE ANALYSIS AS A MIXED-METHODS APPROACH, AND ANALYZED DISCOURSE TRANSCRIPTS BY UNIVERSITY STUDENTS ENGAGED IN JIGSAW INSTRUCTION. FIRST, WE GRAPHICALLY DEPICTED A QUANTITATIVE MEASURE OF SHARED EPISTEMIC AGENCY AT THE GROUP LEVEL AND IDENTIFIED PIVOTAL POINTS OF DISCOURSE WHERE STUDENTS MIGHT ENGAGE IN AN EPISTEMIC ACTION TOWARD ALLEVIATING A LACK OF KNOWLEDGE. THEN, WE CONDUCTED DIALOGICAL DISCOURSE ANALYSIS OF THE SEGMENTS AROUND THE PIVOTAL POINTS TO DESCRIBE STUDENTS' COLLABORATION PRACTICES. SSNA REPRESENTED THE QUANTITATIVE NATURE OF SHARED EPISTEMIC AGENCY WITH 60% ACCURACY AND PROVIDED A NEW WAY TO LOOK AT IT AS A DISTRIBUTION OF PIVOTAL POINTS FOR ALLEVIATING A LACK OF KNOWLEDGE ACROSS ALL PROCESSES OF JIGSAW GROUP ACTIVITIES. THE DIALOGICAL DISCOURSE ANALYSIS OF THE DISCOURSE SEGMENTS IDENTIFIED BY SSNA FURTHER DESCRIBED DIALOGICAL PATTERNS IN THE SHARED EPISTEMIC AGENCY AND EACH STUDENT'S CONTRIBUTION TO THEM.
IF WE WISH TO EMBED ASSESSMENT FOR ACCOUNTABILITY WITHIN INSTRUCTION, WE NEED TO BETTER UNDERSTAND THE RELATIVE CONTRIBUTION OF DIFFERENT TYPES OF LEARNER DATA TO STATISTICAL MODELS THAT PREDICT SCORES AND DISCRETE ACHIEVEMENT LEVELS ON ASSESSMENTS USED FOR ACCOUNTABILITY PURPOSES. THE PRESENT WORK SCALES UP AND EXTENDS PREDICTIVE MODELS OF MATH TEST SCORES AND ACHIEVEMENT LEVELS FROM EXISTING LITERATURE AND SPECIFIES SIX CATEGORIES OF MODELS THAT INCORPORATE INFORMATION ABOUT STUDENT PRIOR KNOWLEDGE, DEMOGRAPHICS, AND PERFORMANCE WITHIN THE MATHIA INTELLIGENT TUTORING SYSTEM. LINEAR REGRESSION, ORDINAL LOGISTIC REGRESSION, AND RANDOM FOREST REGRESSION AND CLASSIFICATION MODELS ARE LEARNED WITHIN EACH CATEGORY AND GENERALIZED OVER A SAMPLE OF 23,000+ LEARNERS IN GRADES 6, 7, AND 8 OVER THREE ACADEMIC YEARS IN MIAMI-DADE COUNTY PUBLIC SCHOOLS. AFTER BRIEFLY EXPLORING HIERARCHICAL MODELS OF THIS DATA, WE DISCUSS A VARIETY OF TECHNICAL AND PRACTICAL APPLICATIONS, LIMITATIONS, AND OPEN QUESTIONS RELATED TO THIS WORK, ESPECIALLY CONCERNING TO THE POTENTIAL USE OF INSTRUCTIONAL PLATFORMS LIKE MATHIA AS A REPLACEMENT FOR TIME-CONSUMING STANDARDIZED TESTS.
WE DEVELOPED A METHOD FOR USING COH-METRIX TO AUTOMATICALLY ANALYZE TUTORIAL DIALOGUES. COH-METRIX, A WEB-BASED TOOL FOR AUTOMATICALLY EVALUATING TEXT, IS FREELY AVAILABLE TO RESEARCHERS. WE APPLIED THE METHOD TO 190 TUTORIAL DIALOGUES BETWEEN WOMEN AND BRCA GIST FROM TWO EXPERIMENTS. BRCA GIST IS AN INTELLIGENT TUTORING SYSTEM (ITS) TO HELP WOMEN MAKE DECISIONS ABOUT GENETIC TESTING FOR BREAST CANCER RISK. TUTORIAL DIALOGUES SCORED HIGH ON MEASURES OF TEXTUAL COHESION (DEEP COHESION, REFERENTIAL COHESION, AND THE COMPOSITE VARIABLE FORMALITY). THEY ALSO SCORED HIGH ON MEASURES OF THE SITUATION MODEL (LSA VERB OVERLAP AND CAUSAL VERB AND CAUSAL PARTICLE). HOWEVER, THERE WAS MIXED SUPPORT FOR THE HYPOTHESIS THAT HIGHER SCORES ON COH-METRIX VARIABLES WOULD PREDICT SUBSEQUENT COMPREHENSION. A COH-METRIX PRINCIPLE IS THAT THE OBSERVABLE COHESION OF A TEXT IS A RELIABLE GUIDE TO THE COHERENCE OF THE READER'S MENTAL REPRESENTATION OF THAT TEXT. THUS IT APPEARS THAT INTERACTING WITH BRCA GIST HELPED PEOPLE FORM COHERENT MENTAL REPRESENTATIONS OF COMPLEX MEDICAL MATERIALS. WE CONCLUDE THAT COH-METRIX CAN BE USED TO RELIABLY ASSESS TUTORIAL DIALOGUES AND MAKE INFERENCES ABOUT THE MENTAL REPRESENTATIONS OF PEOPLE ENGAGED IN CONVERSATION WITH AN ITS BASED ON OBSERVABLE CHARACTERISTICS OF THE STATEMENTS PEOPLE MAKE.
WE INTRODUCE A NOVEL APPROACH TO VISUALIZING TEMPORAL CLICKSTREAM BEHAVIOUR IN THE CONTEXT OF A DEGREE-SATISFYING ONLINE COURSE, HABITABLE WORLDS, OFFERED THROUGH ARIZONA STATE UNIVERSITY. THE CURRENT PRACTICE FOR VISUALIZING BEHAVIOUR WITHIN A DIGITAL LEARNING ENVIRONMENT IS TO GENERATE PLOTS BASED ON HAND-ENGINEERED OR CODED FEATURES USING DOMAIN KNOWLEDGE. WHILE THIS APPROACH HAS BEEN EFFECTIVE IN RELATING BEHAVIOUR TO KNOWN PHENOMENA, FEATURES CRAFTED FROM DOMAIN KNOWLEDGE ARE NOT LIKELY WELL SUITED TO MAKING UNFAMILIAR PHENOMENA SALIENT AND THUS CAN PRECLUDE DISCOVERY. WE INTRODUCE A METHODOLOGY FOR ORGANICALLY SURFACING BEHAVIOURAL REGULARITIES FROM CLICKSTREAM DATA, CONDUCTING AN EXPERT IN-THE-LOOP HYPERPARAMETER SEARCH, AND IDENTIFYING ANTICIPATED AS WELL AS NEWLY DISCOVERED PATTERNS OF BEHAVIOUR. WHILE THESE VISUALIZATION TECHNIQUES HAVE BEEN USED BEFORE IN THE BROADER MACHINE-LEARNING COMMUNITY TO BETTER UNDERSTAND NEURAL NETWORKS AND RELATIONSHIPS BETWEEN WORD VECTORS, WE APPLY THEM TO ONLINE BEHAVIOURAL LEARNER DATA AND GO A STEP FURTHER, EXPLORING THE IMPACT OF THE PARAMETERS OF THE MODEL ON PRODUCING TANGIBLE, NON-TRIVIAL OBSERVATIONS OF BEHAVIOUR THAT SUGGEST PEDAGOGICAL IMPROVEMENT TO THE COURSE DESIGNERS AND INSTRUCTORS. THE METHODOLOGY INTRODUCED IN THIS PAPER LED TO AN IMPROVED UNDERSTANDING OF PASSING AND NON-PASSING STUDENT BEHAVIOUR IN THE COURSE AND IS APPLICABLE TO OTHER DATASETS OF CLICKSTREAM ACTIVITY WHERE INVESTIGATORS AND STAKEHOLDERS WISH TO ORGANICALLY SURFACE PRINCIPAL PATTERNS OF BEHAVIOUR.
LEARNING ANALYSTS OFTEN CONSIDER WHETHER LEARNING PROCESSES ACROSS TIME ARE RELATED 1) TO ONE ANOTHER OR 2) TO LEARNING OUTCOMES AT HIGHER LEVELS. FOR EXAMPLE, ARE A GROUP'S TEMPORAL SEQUENCES OF TALK (E.G., CORRECT EVALUATION -> CORRECT, NEW IDEA) DURING ITS PROBLEM SOLVING RELATED TO ITS GROUP SOLUTION? I SHOW HOW TO ADDRESS THESE ISSUES WITH 1) A HIGHER-LEVEL OUTCOME REGRESSION AND 2) A LOWER-LEVEL PROCESS REGRESSION, APPLYING BOTH TO 3,234 TURNS OF TALK BY 80 STUDENTS WORKING IN 20 GROUPS TO SOLVE AN ALGEBRA PROBLEM. THE EASY-TO-USE, OUTCOME-LEVEL ANALYSIS OF GROUP SOLUTION SCORE HAS THE FOLLOWING PROBLEMS: MULTICOLLINEARITY, POSSIBLY LOW STATISTICAL POWER, CANNOT TEST FOR LINKS AMONG SEQUENCE COMPONENTS, AND CANNOT MODEL OUTCOMES AT MULTIPLE LEVELS. THE COMPLEX, PROCESS-LEVEL ANALYSIS FOR TURNS OF TALK OVERCOMES THESE SHORTCOMINGS WITH MULTILEVEL ANALYSIS, VECTOR AUTO-REGRESSION, AND OUTCOME-LEVEL REGRESSION RESIDUALS. THESE RESULTS SUGGEST A COMBINED PROCEDURE. FIRST, RUN AN OUTCOME-LEVEL ANALYSIS. IF THE RESULTS ARE SIGNIFICANT, THEN THE OUTCOME-LEVEL RESULTS SUFFICE. OTHERWISE, NON-SIGNIFICANT RESULTS MIGHT REFLECT MULTICOLLINEARITY, WHICH THEN REQUIRES A PROCESS-LEVEL ANALYSIS. THIS PROCEDURE CAN HELP TEST A COMPREHENSIVE MODEL OF HOW LEARNING PROCESSES OR THEIR TEMPORAL SEQUENCES ARE RELATED TO LEARNING OUTCOMES AT THE TURN-, TIME PERIOD-, INDIVIDUAL-, GROUP-, CLASS-, AND SCHOOL-LEVELS.
EDUCATIONAL TECHNOLOGIES, SUCH AS TEACHER DASHBOARDS, ARE BEING DEVELOPED TO SUPPORT TEACHERS' INSTRUCTION AND STUDENTS' LEARNING. SPECIFICALLY, DASHBOARDS SUPPORT TEACHERS IN PROVIDING THE JUST-IN-TIME INSTRUCTION NEEDED BY STUDENTS IN COMPLEX CONTEXTS SUCH AS SCIENCE INQUIRY. IN THIS STUDY, WE USED THE INQ-BLOTTER TEACHER-ALERTING DASHBOARD TO INVESTIGATE WHETHER TEACHER SUPPORT ELICITED BY THE TECHNOLOGY INFLUENCED STUDENTS' INQUIRY PERFORMANCE IN A SCIENCE INTELLIGENT TUTORING SYSTEM, INQ-ITS. RESULTS INDICATED THAT STUDENTS' INQUIRY IMPROVED AFTER RECEIVING TEACHERS' HELP, ELICITED BY THE INQ-BLOTTER ALERTS. THIS INQUIRY IMPROVEMENT WAS SIGNIFICANTLY GREATER THAN FOR MATCHED STUDENTS WHO DID NOT RECEIVE HELP FROM THE TEACHER IN RESPONSE TO ALERTS. EPISTEMIC NETWORK ANALYSES WERE THEN USED TO INVESTIGATE THE PATTERNS IN THE DISCURSIVE SUPPORTS PROVIDED TO STUDENTS BY TEACHERS. THESE ANALYSES REVEALED SIGNIFICANT DIFFERENCES IN THE TYPES OF SUPPORT THAT FOSTERED (VERSUS DID NOT FOSTER) STUDENT IMPROVEMENT; DIFFERENCES ACROSS TEACHERS WERE ALSO FOUND. OVERALL, THIS STUDY USED INNOVATIVE TOOLS AND ANALYSES TO UNDERSTAND HOW TEACHERS USE THIS TECHNOLOGICAL GENRE OF ALERTING DASHBOARDS TO DYNAMICALLY SUPPORT STUDENTS IN SCIENCE INQUIRY.
COLLABORATION ANALYTICS OFTEN FOCUSES ON ASSESSING AND MONITORING INDIVIDUALS DURING COLLABORATIVE PROBLEM-SOLVING (CPS). A DEFINING FEATURE OF CPS IS THE INTERDEPENDENCE THAT EXISTS BETWEEN INDIVIDUALS WHEN THEY WORK TOGETHER THAT IS, HOW THEY RESPOND TO AND INFLUENCE ONE ANOTHER OVER TIME. WHILE MODELS THAT ACCOUNT FOR THE IMPACT OF INTERDEPENDENCE AT THE INDIVIDUAL LEVEL OF ANALYSIS (INTERDEPENDENT MODELS) EXIST, THEY ARE OFTEN HIGHLY COMPLEX. THIS COMPLEXITY MAKES THEM POTENTIALLY DIFFICULT TO USE IN ASSESSMENTS AND SYSTEMS THAT NEED TO BE EXPLAINABLE FOR EDUCATORS, LEARNERS, AND OTHER RESEARCHERS. MEASURES OF THE IMPACT OF INTERDEPENDENCE AT THE INDIVIDUAL LEVEL OF ANALYSIS COULD INFORM DECISIONS AS TO WHETHER INTERDEPENDENT MODELS SHOULD BE USED, OR WHETHER SIMPLER MODELS WILL SUFFICE. SUCH MEASURES COULD ALSO BE USED TO INVESTIGATE SPECIFIC QUESTIONS ABOUT INTERDEPENDENCE IN COLLABORATIVE SETTINGS. IN THIS PAPER, I PRESENT A NOVEL METHOD OF MEASURING THE IMPACT OF INTERDEPENDENCE ON INDIVIDUALS USING EPISTEMIC NETWORK ANALYSIS. TO PROVIDE EVIDENCE OF THE VALIDITY OF THE MEASURE, I COMPARE IT TO QUALITATIVE FINDINGS THAT DESCRIBE THE IMPACT OF INTERDEPENDENCE ON INDIVIDUALS PARTICIPATING IN TEAM TRAINING SCENARIOS. TO DEMONSTRATE THE VALUE OF THE MEASURE, I USE IT TO ASSESS THE IMPACT OF INTERDEPENDENCE IN THESE DATA OVERALL AND TO TEST HYPOTHESES REGARDING THE COLLABORATIVE TASK DESIGN. MY RESULTS SUGGEST THAT THE MEASURE CAN DISTINGUISH BETWEEN INDIVIDUALS WHO HAVE BEEN IMPACTED BY INTERDEPENDENCE DIFFERENTLY, THAT INTERDEPENDENCE IS IMPACTFUL IN THESE DATA OVERALL, AND THAT ASPECTS OF THE TASK DESIGN MAY HAVE AFFECTED HOW SOME INDIVIDUALS WERE IMPACTED BY INTERDEPENDENCE.
THIS PAPER EXPLORES A SYSTEM THAT ATTEMPTS TO MAXIMIZE HIGH SCHOOL STUDENTS' SENSE OF CHOICE WHEN SELECTING ELECTIVE SUBJECTS. WE PROPOSE THAT INDIVIDUAL SCHOOLS CAN TAILOR THE COMBINATIONS OF SUBJECTS THEY OFFER IN ORDER TO MAXIMIZE THE NUMBER OF PROSPECTIVE STUDENTS WHO CAN STUDY THEIR PREFERRED SUBJECTS, POTENTIALLY INCREASING ENROLMENT NUMBERS AND ACADEMIC OUTCOMES WHILE ALSO REDUCING ADMINISTRATIVE OVERHEADS. WE ANALYZE THE UNDERLYING COMPUTATIONAL PROBLEM ENCOUNTERED IN THIS TASK AND DESCRIBE A SUITABLE AL-BASED OPTIMIZATION ALGORITHM THAT WE HAVE MADE AVAILABLE FOR FREE DOWNLOAD. WE ALSO DISCUSS SOME OUTCOMES OF USING THIS METHOD ON A SMALL NUMBER OF CASE STUDY SCHOOLS.
CONFUSION IS AN IMPORTANT EPISTEMIC EMOTION BECAUSE IT CAN HELP STUDENTS FOCUS THEIR ATTENTION AND EFFORT WHEN SOLVING COMPLEX LEARNING TASKS. HOWEVER, UNRESOLVED CONFUSION CAN BE DETRIMENTAL BECAUSE IT MAY RESULT IN STUDENTS' DISENGAGEMENT. THIS IS ESPECIALLY CONCERNING IN SIMULATION ENVIRONMENTS USING DISCOVERY-BASED LEARNING, WHICH PUTS MORE OF THE ONUS FOR LEARNING ON THE STUDENTS. THUS, STUDENTS WITH MISCONCEPTIONS MAY BECOME CONFUSED. IN THIS STUDY, THE POSSIBLE MOMENTS OF CONFUSION IN A SIMULATION-BASED PREDICT-OBSERVE-EXPLAIN (POE) ENVIRONMENT WERE INVESTIGATED. LOG-BASED INTERACTION PATTERNS OF UNDERGRADUATE STUDENTS FROM A FULLY ONLINE COURSE WERE ANALYZED. IT WAS FOUND THAT POE ENVIRONMENTS CAN OFFER A LEVEL OF DIFFICULTY THAT POTENTIALLY TRIGGERS SOME CONFUSION, AND A LIKELY MOMENT OF STUDENTS' CONFUSION WAS THE OBSERVE TASK. IT WAS ALSO FOUND THAT CONFIDENCE IN PRIOR KNOWLEDGE IS AN IMPORTANT FACTOR THAT CAN CONTRIBUTE TO STUDENTS' CONFUSION. STUDENTS MOSTLY STRUGGLED WHEN THEY DISCOVERED A MISMATCH BETWEEN THE SUBJECTIVE AND OBJECTIVE CORRECTNESS OF THEIR RESPONSES. THE EFFECTS OF SUCH A MISMATCH WERE MORE PRONOUNCED WHEN CONFUSION MARKERS WERE ANALYZED THAN WHEN STUDENTS' LEARNING OUTCOMES WERE OBSERVED. THESE FINDINGS MAY GUIDE FUTURE WORKS TO BRIDGE THE KNOWLEDGE GAPS THAT LEAD TO CONFUSION IN POE ENVIRONMENTS.
EDUCATIONAL ENVIRONMENTS CONTINUE TO EVOLVE RAPIDLY TO ADDRESS THE NEEDS OF DIVERSE, GROWING STUDENT POPULATIONS WHILE EMBRACING ADVANCES IN PEDAGOGY AND TECHNOLOGY. IN THIS CHANGING LANDSCAPE, ENSURING CONSISTENCY AMONG THE ASSESSMENTS FOR DIFFERENT OFFERINGS OF A COURSE (WITHIN OR ACROSS TERMS), PROVIDING MEANINGFUL FEEDBACK ABOUT STUDENT ACHIEVEMENTS, AND TRACKING STUDENT PROGRESS OVER TIME ARE ALL CHALLENGING TASKS, PARTICULARLY AT SCALE. HERE, A COLLECTION OF VISUAL TOPIC DEPENDENCY MODELS (TDMS) IS PROPOSED TO HELP ADDRESS THESE CHALLENGES. IT USES STATISTICAL MODELS TO DETERMINE AND VISUALIZE STUDENT ACHIEVEMENTS ON ONE OR MORE TOPICS AND THEIR DEPENDENCIES AT A COURSE LEVEL REFERENCE TDM (E.G., CS 100) AS WELL AS ASSESSMENT DATA AT THE CLASSROOM LEVEL (E.G., STUDENTS IN CS 100 TERM 1 2016 SECTION 001), BOTH AT ONE POINT IN TIME (STATIC) AND OVER TIME (DYNAMIC). THE COLLECTION OF TDMS SHARE A COMMON TWO-WEIGHTED GRAPH FOUNDATION. EXEMPLAR ALGORITHMS ARE PRESENTED FOR THE CREATION OF THE COURSE REFERENCE AND SELECTED CLASS (STATIC AND DYNAMIC) TDMS; THE ALGORITHMS ARE ILLUSTRATED USING A COMMON SYMBOLIC EXAMPLE. STUDIES ON THE APPLICATION OF THE TDM COLLECTION ON DATASETS FROM TWO UNIVERSITY COURSES ARE PRESENTED; THESE CASE STUDIES UTILIZE THE OPEN-SOURCE, PROOF OF CONCEPT TOOL UNDER DEVELOPMENT.
IN THIS STUDY, WE DEVELOP AND TEST FOUR MEASURES FOR CONCEPTUALIZING THE POTENTIAL IMPACT OF CO-ENROLLMENT IN DIFFERENT COURSES ON STUDENTS' CHANGING RISK FOR ACADEMIC DIFFICULTY AND RECOVERY FROM ACADEMIC DIFFICULTY IN A FOCAL COURSE. WE OFFER FOUR PREDICTORS, TWO RELATED TO INSTRUCTIONAL COMPLEXITY AND TWO RELATED TO STRUCTURAL COMPLEXITY (THE ORGANIZATION OF THE CURRICULUM) THAT HIGHLIGHT DIFFERENT TRENDS IN STUDENT EXPERIENCE OF THE FOCAL COURSE. COURSE DIFFICULTY, DISCIPLINE OF MAJOR, TIME IN SEMESTER, AND SIMULTANEOUS DIFFICULTY ACROSS COURSES WERE ALL SIGNIFICANTLY RELATED TO ENTERING A MODERATE AND HIGH-RISK CLASSIFICATION IN THE EARLY WARNING SYSTEM (EWS). COURSE DIFFICULTY, DISCIPLINE OF MAJOR, AND TIME IN SEMESTER WERE RELATED TO EXITING ACADEMIC DIFFICULTY CLASSIFICATIONS. WE OBSERVE A SNOWBALL EFFECT, WHEREBY STUDENTS WHO ARE EXPERIENCING DIFFICULTY IN THE FOCAL COURSE ARE AT INCREASED RISK OF EXPERIENCING DIFFICULTY IN THEIR OTHER COURSES. OUR FINDINGS SUGGEST THAT DIFFERENT METRICS MAY BE NEEDED TO IDENTIFY RISK FOR ACADEMIC DIFFICULTY AND RECOVERY FROM ACADEMIC DIFFICULTY. OUR RESULTS DEMONSTRATE WHAT A MORE HOLISTIC ASSESSMENT OF ACADEMIC FUNCTIONING MIGHT LOOK LIKE IN EARLY WARNING SYSTEMS AND COURSE RECOMMENDER SYSTEMS, AND SUGGEST THAT ACADEMIC PLANNERS CONSIDER THE RELATIONSHIP BETWEEN COURSE CO-ENROLLMENT AND STUDENT ACADEMIC SUCCESS.
THIS PAPER PRESENTS A PLATFORM CALLED RIPPLE (RECOMMENDATION IN PERSONALISED PEER-LEARNING ENVIRONMENTS) THAT RECOMMENDS PERSONALIZED LEARNING ACTIVITIES TO STUDENTS BASED ON THEIR KNOWLEDGE STATE FROM A POOL OF CROWDSOURCED LEARNING ACTIVITIES THAT ARE GENERATED BY EDUCATORS AND THE STUDENTS THEMSELVES. RIPPLE INTEGRATES INSIGHTS FROM CROWDSOURCING, LEARNING SCIENCES, AND ADAPTIVE LEARNING, AIMING TO NARROW THE GAP BETWEEN THESE LARGE BODIES OF RESEARCH WHILE PROVIDING A PRACTICAL PLATFORM-BASED IMPLEMENTATION THAT INSTRUCTORS CAN EASILY USE IN THEIR COURSES. THIS PAPER PROVIDES A DESIGN OVERVIEW OF RIPPLE, WHICH CAN BE EMPLOYED AS A STANDALONE TOOL OR EMBEDDED INTO ANY LEARNING MANAGEMENT SYSTEM (LMS) OR ONLINE PLATFORM THAT SUPPORTS THE LEARNING TOOLS INTEROPERABILITY (LTI) STANDARD. THE PLATFORM HAS BEEN EVALUATED BASED ON A PILOT IN AN INTRODUCTORY COURSE WITH 453 STUDENTS AT THE UNIVERSITY OF QUEENSLAND. INITIAL RESULTS SUGGEST THAT THE USE OF THE RIPPLE PLATFORM LED TO MEASURABLE LEARNING GAINS AND THAT STUDENTS PERCEIVED THE PLATFORM AS BENEFICIALLY SUPPORTING THEIR LEARNING.
EACH TIME A LEARNER IN A SELF-PACED ONLINE COURSE SEEKS TO ANSWER AN ASSESSMENT QUESTION, IT TAKES SOME TIME FOR THE STUDENT TO READ THE QUESTION AND ARRIVE AT AN ANSWER TO SUBMIT. IF MULTIPLE ATTEMPTS ARE ALLOWED, AND THE FIRST ANSWER IS INCORRECT, IT TAKES SOME TIME TO PROVIDE A SECOND ANSWER. HERE WE STUDY THE DISTRIBUTION OF SUCH "RESPONSE TIMES." WE FIND THAT THE LOG-NORMAL STATISTICAL MODEL FOR SUCH TIMES, PREVIOUSLY SUGGESTED IN THE LITERATURE, HOLDS FOR ONLINE COURSES. USERS WHO, ACCORDING TO THIS MODEL, TEND TO TAKE LONGER ON SUBMITS ARE MORE LIKELY TO COMPLETE THE COURSE, HAVE A HIGHER LEVEL OF ENGAGEMENT, AND ACHIEVE A HIGHER GRADE. THIS FINDING CAN BE THE BASIS FOR DESIGNING INTERVENTIONS IN ONLINE COURSES, SUCH AS MOOCS, WHICH WOULD ENCOURAGE "FAST" USERS TO SLOW DOWN.
SCHOLARS HAVE APPLIED AUTOMATIC CONTENT ANALYSIS TO STUDY COMPUTER-MEDIATED COMMUNICATION IN COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL). SINCE CSCL ALSO TAKES PLACE IN FACE-TO-FACE INTERACTIONS, WE STUDIED THE AUTOMATIC CODING ACCURACY OF MANUALLY TRANSCRIBED FACE-TO-FACE COMMUNICATION. WE CONDUCTED OUR STUDY IN AN AUTHENTIC HIGHER-EDUCATION PHYSICS CONTEXT WHERE COMPUTER-SUPPORTED COLLABORATIVE INQUIRY-BASED LEARNING (CSCIL) IS A POPULAR PEDAGOGICAL APPROACH. SINCE LEARNERS' NEEDS FOR SUPPORT IN CSCIL VARY IN THE DIFFERENT INQUIRY PHASES (ORIENTATION, CONCEPTUALIZATION, INVESTIGATION, CONCLUSION, AND DISCUSSION), WE STUDIED, FIRST, HOW THE CODING ACCURACY OF FIVE COMPUTATIONAL MODELS (BASED ON WORD EMBEDDINGS AND DEEP NEURAL NETWORKS WITH ATTENTION LAYERS) DIFFERED IN THE VARIOUS INQUIRY-BASED LEARNING (IBL) PHASES WHEN COMPARED TO HUMAN CODING. SECOND, WE INVESTIGATED HOW THE DIFFERENT FEATURES OF THE BEST PERFORMING COMPUTATIONAL MODEL IMPROVED THE CODING ACCURACY. THE STUDY INDICATED THAT THE ACCURACY OF THE BEST PERFORMING COMPUTATIONAL MODEL (DIFFERENTIATED ATTENTION WITH PRE-TRAINED STATIC EMBEDDINGS) WAS SLIGHTLY BETTER THAN THAT OF THE HUMAN CODER (58.9% VS. 54.3%). WE ALSO FOUND THAT CONSIDERING THE PREVIOUS AND FOLLOWING UTTERANCES, AS WELL AS THE RELATIVE POSITION OF THE UTTERANCE, IMPROVED THE MODEL'S ACCURACY. OUR METHOD ILLUSTRATES HOW COMPUTATIONAL MODELS CAN BE TRAINED FOR SPECIFIC PURPOSES (E.G., TO CODE IBL PHASES) WITH SMALL DATA SETS BY USING PRE-TRAINED MODELS.
IN THIS PAPER, WE EVALUATE THE COMPLETE UNDERGRADUATE CO-ENROLLMENT NETWORK OVER A DECADE OF EDUCATION AT A LARGE AMERICAN PUBLIC UNIVERSITY. WE PROVIDE DESCRIPTIVE AND EXPLORATORY ANALYSES OF THE NETWORK, DEMONSTRATING THAT THE CO-ENROLLMENT NETWORKS EVALUATED FOLLOW POWER-LAW DEGREE DISTRIBUTIONS SIMILAR TO MANY OTHER LARGE-SCALE NETWORKS; THAT THEY REVEAL STRONG PERFORMANCE-BASED ASSORTATIVITY; AND THAT NETWORK-BASED FEATURES CAN IMPROVE GPA-BASED STUDENT PERFORMANCE PREDICTORS. WE MODEL THE UNIVERSITY-WIDE UNDERGRADUATE CO-ENROLLMENT NETWORK AS AN UNDIRECTED GRAPH, AND IMPLEMENT MULTIPLE NETWORK-AUGMENTED APPROACHES TO STUDENT GRADE PREDICTION, INCLUDING AN ADAPTION OF THE STRUCTURAL MODELLING APPROACH FROM (GETOOR, 2005; LU & GETOOR, 2003A). WE COMPARE THE PERFORMANCE OF THIS PREDICTOR TO TRADITIONAL METHODS USED FOR GRADE PREDICTION IN UNDERGRADUATE UNIVERSITY COURSES, AND DEMONSTRATE THAT A MULTI-VIEW ENSEMBLING APPROACH OUTPERFORMS BOTH PRIOR "FLAT" AND NETWORK-BASED MODELS FOR GRADE PREDICTION ACROSS SEVERAL CLASSIFICATION METRICS. THESE FINDINGS DEMONSTRATE THE USEFULNESS OF COMBINING DIVERSE APPROACHES IN MODELS OF STUDENT SUCCESS, AND DEMONSTRATE SPECIFIC NETWORK-BASED MODELLING STRATEGIES THAT ARE LIKELY TO BE MOST EFFECTIVE FOR GRADE PREDICTION.
THE ANALYSIS OF STUDENT QUESTIONS CAN BE USED TO IMPROVE THE LEARNING EXPERIENCE FOR BOTH STUDENTS AND TEACHERS. WE INVESTIGATED QUESTIONS (N = 6457) ASKED BEFORE THE CLASS BY FIRST-YEAR MEDICINE/PHARMACY STUDENTS ON AN ONLINE PLATFORM, USED BY PROFESSORS TO PREPARE FOR Q&A SESSIONS. OUR LONG-TERM OBJECTIVES ARE TO HELP PROFESSORS IN CATEGORIZING THOSE QUESTIONS, AND TO PROVIDE STUDENTS WITH FEEDBACK ON THE QUALITY OF THEIR QUESTIONS. TO DO SO, WE DEVELOPED A CODING SCHEME AND THEN USED IT FOR AUTOMATIC ANNOTATION OF THE WHOLE CORPUS. WE IDENTIFIED STUDENT CHARACTERISTICS FROM THE TYPOLOGY OF QUESTIONS THEY ASKED USING THE K-MEANS ALGORITHM OVER FOUR COURSES. STUDENTS WERE CLUSTERED BASED ON QUESTION DIMENSIONS ONLY. THEN, WE CHARACTERIZED THE CLUSTERS BY ATTRIBUTES NOT USED FOR CLUSTERING, SUCH AS STUDENT GRADE, ATTENDANCE, AND NUMBER AND POPULARITY OF QUESTIONS ASKED. TWO SIMILAR CLUSTERS ALWAYS APPEARED (LOWER THAN AVERAGE STUDENTS WITH POPULAR QUESTIONS, AND HIGHER THAN AVERAGE STUDENTS WITH UNPOPULAR QUESTIONS). WE REPLICATED THESE ANALYSES ON THE SAME COURSES ACROSS DIFFERENT YEARS TO SHOW THE POSSIBILITY OF PREDICTING STUDENT PROFILES ONLINE. THIS WORK SHOWS THE USEFULNESS AND VALIDITY OF OUR CODING SCHEME AND THE RELEVANCE OF THIS APPROACH TO IDENTIFY DIFFERENT STUDENT PROFILES.
MASTERY BEHAVIOURS - SEEKING OUT CHALLENGING TASKS AND CONTINUING TO WORK ON THEM DESPITE DIFFICULTIES - ARE INTEGRAL TO ACHIEVEMENT BUT DIFFICULT TO MEASURE WITH PRECISION. THE CURRENT STUDY REPORTS ON THE DEVELOPMENT AND VALIDATION OF THE COMPUTER-BASED PERSISTENCE, EFFORT, RESILIENCE, AND CHALLENGE-SEEKING (PERC) TASK IN TWO DEMOGRAPHICALLY DIVERSE SAMPLES OF ADOLESCENTS (TOTAL N = 3,188). WE PRESENT EVIDENCE FOR CONVERGENT VALIDITY WITH SELF-REPORTED MASTERY BEHAVIOURS AND LEARNING MINDSETS, DISCRIMINANT VALIDITY FROM THEORETICALLY UNRELATED CONSTRUCTS, AND INCREMENTAL PREDICTIVE VALIDITY FOR GRADE POINT AVERAGE (GPA). RESULTS SUGGEST THAT THE PERC TASK MAY BE A USEFUL TOOL FOR ASSESSING MASTERY BEHAVIOURS AT SCALE.
THIS PAPER DESCRIBES A COLLABORATION ORGANIZED AROUND EXCHANGING DATA BETWEEN TWO TECHNOLOGICAL SYSTEMS TO SUPPORT TEACHERS' INSTRUCTIONAL DECISION-MAKING. THE GOALS OF THE COLLABORATION AMONG RESEARCHERS, TECHNOLOGY DEVELOPERS, AND PRACTITIONERS WERE NOT ONLY TO SUPPORT TEACHERS' INSTRUCTIONAL DECISION-MAKING BUT ALSO TO DOCUMENT THE CHALLENGES AND OPPORTUNITIES ASSOCIATED WITH BRINGING TOGETHER DATA FROM INSTRUCTION- AND ASSESSMENT-FOCUSED TECHNOLOGIES. THE APPROACH DESCRIBED IN THIS PAPER ILLUSTRATES THE POTENTIAL IMPORTANCE OF ANCHORING DATA PRODUCTS THAT COMBINE DATA BETWEEN TWO SYSTEMS IN THE NEEDS OF TEACHERS AS WELL AS ALIGNING THE CONTENT THAT STUDENTS LEARN AND ARE ASSESSED ON BETWEEN SYSTEMS. THE INCREASING PRESENCE OF DATA STANDARDS HAS MADE SHARING COMPLEX DATA INCREASINGLY MORE FEASIBLE. THE EXAMPLE COLLABORATION DESCRIBED IN THIS PAPER DEMONSTRATES THE ROLE THAT NONTECHNICAL ACTIVITIES CAN PLAY IN SUPPORTING THE EXCHANGE AND USE OF LEARNER EVENT DATA.
IN CLASSROOMS, SOME TRANSITIONS BETWEEN ACTIVITIES IMPOSE (QUASI-)SYNCHRONICITY, MEANING THERE IS A NEED FOR LEARNERS TO MOVE BETWEEN ACTIVITIES AT THE SAME TIME. TO MAKE REAL-TIME DECISIONS ABOUT WHEN TO MOVE TO THE NEXT ACTIVITY, TEACHERS NEED TO BE ABLE TO BALANCE THE PROGRESS OF THEIR STUDENTS AS THEY WORK AT DIFFERENT PACES. IN THIS PAPER, WE PRESENT A SET OF ESTIMATORS THAT CAN BE USED IN REAL TIME TO PREDICT THE PROGRESS AND COMPLETION RATES OF STUDENTS WORKING ON COMPUTER-SUPPORTED ACTIVITIES THAT CAN BE DIVIDED INTO SEQUENTIAL SUBTASKS. WITH OUR ESTIMATORS, WE INVESTIGATE WHAT EFFECT THE AVERAGE PROGRESS RATE OF THE CLASS, A GIVEN NUMBER OF PREVIOUS STEPS, OR WEIGHTING THE PROPORTION OF PROGRESS ASSIGNED TO EACH SUBTASK HAS ON PREDICTIONS OF STUDENTS' PROGRESS. WE FIND THAT ACCOUNTING FOR THE AVERAGE CLASS PROGRESS RATE NEAR THE BEGINNING OF THE ACTIVITY CAN IMPROVE PREDICTIONS OVER BASELINE. ADDITIONALLY, WEIGHTED SUBTASKS DECREASE PREDICTION ACCURACY FOR ACTIVITIES WHERE THE BEHAVIOUR OF FASTER STUDENTS DIVERGES FROM THE AVERAGE BEHAVIOUR OF THE CLASS. THIS PAPER CONTRIBUTES TO OUR ABILITY TO PROVIDE ACCURATE STUDENT PROGRESS PREDICTIONS AND TO UNDERSTAND THE BEHAVIOUR OF STUDENTS AS THEY PROGRESS THROUGH THE ACTIVITY. THESE REAL-TIME PREDICTIONS CAN ENABLE TEACHERS TO OPTIMIZE LEARNING TIME IN THEIR CLASSROOMS.
TEACHER DASHBOARDS ARE A SPECIFIC FORM OF ANALYTICS IN WHICH VISUAL DISPLAYS PROVIDE TEACHERS WITH INFORMATION ABOUT THEIR STUDENTS; FOR EXAMPLE, CONCERNING STUDENT PROGRESS AND PERFORMANCE ON TASKS DURING LESSONS OR LECTURES. IN THE PRESENT PAPER, WE FOCUS ON THE ROLE OF TEACHER DASHBOARDS IN THE CONTEXT OF TEACHER DECISION-MAKING IN K-12 EDUCATION. THERE IS LARGE VARIATION IN TEACHER DASHBOARD USE IN THE CLASSROOM, WHICH COULD BE EXPLAINED BY TEACHER CHARACTERISTICS. THEREFORE, WE INVESTIGATE THE ROLE OF TEACHER CHARACTERISTICS - SUCH AS EXPERIENCE, AGE, GENDER, AND SELF-EFFICACY - IN HOW TEACHERS USE DASHBOARDS. MORE SPECIFICALLY, WE PRESENT TWO CASE STUDIES TO UNDERSTAND HOW DIVERSITY IN TEACHER DASHBOARD USE IS RELATED TO TEACHER CHARACTERISTICS. SURPRISINGLY, IN BOTH CASE STUDIES, TEACHER CHARACTERISTICS WERE NOT ASSOCIATED WITH DASHBOARD USE. BASED ON OUR FINDINGS, WE PROPOSE AN INITIAL FRAMEWORK TO UNDERSTAND WHAT CONTRIBUTES TO DIVERSITY OF DASHBOARD USE. THIS FRAMEWORK MIGHT SUPPORT FUTURE RESEARCH TO ATTRIBUTE DIVERSITY IN DASHBOARD USE. THIS PAPER SHOULD BE SEEN AS A FIRST STEP IN EXAMINING THE ROLE OF TEACHER CHARACTERISTICS IN DASHBOARD USE IN K-12 EDUCATION.
THIS STUDY BUILDS ON PRIOR RESEARCH BY LEVERAGING NATURAL LANGUAGE PROCESSING (NLP), CLICK-STREAM ANALYSES, AND SURVEY DATA TO PREDICT STUDENTS' MATHEMATICS SUCCESS AND MATH IDENTITY (NAMELY, SELF-CONCEPT, INTEREST, AND VALUE OF MATHEMATICS). SPECIFICALLY, WE COMBINE NLP TOOLS DESIGNED TO MEASURE LEXICAL SOPHISTICATION, TEXT COHESION, AND SENTIMENT WITH ANALYSES OF STUDENT CLICK-STREAM DATA WITHIN AN ONLINE MATHEMATICS TUTORING SYSTEM. WE COMBINE THESE DATA SOURCES TO PREDICT ELEMENTARY STUDENTS' SUCCESS WITHIN THE SYSTEM AS WELL AS COMPONENTS OF THEIR MATH IDENTITY AS MEASURED THOUGH A STANDARDIZED SURVEY. DATA FROM 147 STUDENTS WAS EXAMINED LONGITUDINALLY OVER A YEAR OF STUDY. THE RESULTS INDICATED LINKS BETWEEN MATH SUCCESS AND NON-COGNITIVE MEASURES OF MATH IDENTITY. ADDITIONALLY, THE RESULTS INDICATE THAT MATH IDENTITY WAS STRONGLY PREDICTED BY CLICK-STREAM VARIABLES AND THE PRODUCTION OF MORE LEXICALLY SOPHISTICATED AND COHESIVE LANGUAGE. IN ADDITION, SIGNIFICANT VARIANCE IN MATH IDENTITY WAS EXPLAINED BY AFFECTIVE AND COGNITIVE VARIABLES. THE RESULTS INDICATE THAT NLP AND CLICK-STREAM DATA CAN COMBINE TO PROVIDE INSIGHTS INTO NON-COGNITIVE CONSTRUCTS SUCH AS MATH IDENTITY.
IN THE LAST DECADES, DIFFERENT MODELS HAVE BEEN DEVELOPED IN ORDER TO PREDICT STUDENT'S SUCCESS. THESE MODELS AIMED TO PREDICT BASED ON PREVIOUS PERFORNANCE DATA WHETHER NEW STUDENTS WILL BE ABLE TO PASS THE PRESENT COURSE. THESE MODELS HAVE A LARGE POTENTIAL IN ADAPTIVE SYSTEMS WHERE DIFFERENT LEARNING PATHS CAN BE OFFERED TO THE LEARNER BASED ON HER CURRENT PROGRESSION THROUGH THE COURSE AND COMPARING WITH THE STANDARD PERFORMANCE OF STUDENTS WHO HAVE PASSED THE COURSE. IN THIS PAPER, WE DESCRIBE THE DESIGN OF A PREDICTION SYSTEM AND DIFFERENT MODELS THAT CAN BE USED TO HELP TO PREDICT STUDENT'S SUCCESS BETTER. THE PRESENTED MODELS HAVE PARTICULARLY DESIGNED FOR AN INTRODUCTORY COURSE TO LEARN DIGITAL SYSTEMS DESIGN.
NO LONGER BOUND BY THE CONFINES OF THE CAMPUS, WORKPLACE, OR VIRTUAL LEARNING ENVIRONMENT, LEARNING ON AND THROUGH THE INTERNET HAS REDEFINED WHO LEARNS WHAT, FROM WHOM, WHERE, WHEN, AND UNDER WHAT CIRCUMSTANCES. OFFLINE, ONLINE, AND IN COMBINATION, THE NEW E-LEARNING IS REWRITING COMMUNICATION NETWORKS, CREATING NEW SPACES AND RELATIONSHIPS, RESTRUCTURING KNOWLEDGE NETWORKS, CHALLENGING IDENTITIES, AND CHANGING THE LOCATION, EVALUATION, AND ACCESSIBILITY OF INFORMATION RESOURCES AND PEOPLE. IN KEEPING WITH THE 'RETHINKING COMMUNICATION, SPACE AND IDENTITY' THEME OF THE 2015 AUSTRALIA AND NEW ZEALAND COMMUNICATION ASSOCIATION CONFERENCE WHERE A TALK ON THIS TOPIC WAS FIRST GIVEN, THIS PAPER CALLS FOR A RETHINKING OF LEARNING THAT RECOGNIZES AND BUILDS ON THE WAY NEW INTERNET-ENABLED SPACES, STRUCTURES, AND COMMUNICATION ROUTES ARE SUPPORTING AND TRANSFORMING KNOWLEDGE EXCHANGE AND PRODUCTION. DRAWING ON RESEARCH AND PERSPECTIVES ON ONLINE COMMUNICATION, LEARNING, AND SOCIAL NETWORKS, THE PAPER PRESENTS A VIEW OF CONTEMPORARY LEARNING PRACTICE, I.E., SOCIAL, NETWORK BASED, AND NETWORK SHARED. THIS PERSPECTIVE CALLS FOR A CHANGE IN THE QUESTIONS WE ASK ABOUT LEARNING AND MEDIA USE, AND THUS HOW WE VALUE AND WORK WITH NEW CONFIGURATIONS OF LEARNING NETWORKS. THIS PAPER ARGUES THAT TAKING A NETWORK VIEW NOT ONLY HELPS UNDERSTAND CONTEMPORARY CHANGES IN LEARNING, KNOWLEDGE, AND COMMUNICATION PRACTICES, BUT ALSO PROVIDES INSIGHT INTO THE DEVELOPMENT OF NEW PRACTICES FOR LEARNING, KNOWLEDGE, AND EDUCATION IN THE TWENTY-FIRST CENTURY.
AUTOMATED TEXT DETECTION AND ANALYSIS HOLDS INCREDIBLE POTENTIAL FOR RESEARCH IN HIGHER EDUCATION. IT IS CHALLENGING BECAUSE HIGHER EDUCATION INSTITUTES PRODUCE AN ENORMOUS AMOUNT AND VARIETY OF TEXTS, LETTERS, ARTICLES, BOOKS, REPORTS ETC. FUTURISTIC E-LEARNING BASED EDUCATION REPLACES THE DIFFICULTY OF UNDERSTANDING THE SEMANTIC MEANING OF THE LEARNING CONTENT FROM VIDEOS WHICH IS MOST PROMINENT SOURCE USED BY THE LEANERS TO ACQUIRE KNOWLEDGE. THEREFORE, CONTENT BASED VIDEO RETRIEVAL HAS BECOME THE CHALLENGING RESEARCH AREA UNDER PATTERN RECOGNITION AND COMPUTER VISION IN HIGHER EDUCATION THROUGH MASSIVE OPEN ONLINE COURSES (MOOCS). TEXT PLAYS A DYNAMIC ROLE IN UNDERSTANDING THE TRUE MEANING OF BEHAVIOR OF THE VIDEO. HENCE, IT IS CHALLENGING TO DETECT AND IDENTIFY THE TEXT IN VIDEO DUE TO VARIABLE COMPLEX BACKGROUND, LOW CONTRAST, BLUR, POOR ILLUMINATION, FONT SIZE, FONT-STYLE, OCCLUSIONS. THE TRADITIONAL APPROACH OF END-TO-END CONVOLUTION NEURAL NETWORK (CNN) PERFORMS SATISFACTORY IN DETECTING VIDEO TEXT. HOWEVER, IT IS ALSO IMPORTANT TO DEAL WITH THE VIDEO SIZE, THEREFORE, WE HAVE ADOPTED MAP REDUCE TECHNIQUE TO STORE THE VIDEO CONTENT AND UTILIZE IT EFFICIENTLY BY PARALLEL COMPUTING. FOLLOWED BY THIS, WE EMPLOYED NOVEL APPROACH TO CLEAN UP THE VIDEO FRAMES TO FEED TO NEURAL NETWORK MODEL BASED ON REGION PROPOSAL NETWORK (RPN) WITH CNN BY FINDING APPROPRIATE ANCHOR RATIOS TO EXTRACT THE TEXT CANDIDATES. FINALLY, WE TRAIN OUR MODEL WITH EXTRACTED FRAMES TO PREDICT FOR THE TEST VIDEOS. THE PROPOSED METHOD IS EVALUATED ON ICDAR VIDEO TEXT BENCHMARK DATASETS AND FEW PUBLICLY AVAILABLE TEST DATASETS TO ACHIEVE HIGH RECALL.
ONLINE LEARNING HAS DEVELOPED RAPIDLY, BUT THE SUCCESS RATE IS VERY LOW. HENCE, IT IS OF GREAT SIGNIFICANCE TO CONSTRUCT A LEARNING RESULT PREDICTING MODEL, AND TO QUICKLY AND ACCURATELY IDENTIFY STUDENTS AT RISK OF FAILING THEIR COURSE. IN ORDER TO MINE THE DYNAMIC FEATURES OF LEARNING BEHAVIORS AND USE THEM TO IMPROVE THE ACCURACY OF DETECTION OF AT-RISK STUDENTS, WE PROPOSE A LONG-SHORT TERM MEMORY (LSTM) NETWORK BASED APPROACH TO IDENTIFY AT-RISK STUDENTS. TO VALIDATE THE PERFORMANCE OF THIS APPROACH, WE FIRST EXTRACTED THE BEHAVIOR DATA OF ONE COURSE FROM A PUBLIC DATASET, AND GENERATE TWO TYPES OF DATASETS, THE AGGREGATED DATASETS AND THE SEQUENTIAL DATASETS. AFTER THAT, WE USED EIGHT CLASSIC MACHINE LEARNING METHODS TO TRAIN PREDICTING MODEL ON THESE DATASETS AND EXPLORED WHETHER THE MODELS TRAINED ON SEQUENTIAL DATASETS ARE MORE ACCURATE THAN THE MODELS TRAINED ON AGGREGATED DATASETS. THE RESULTS SHOW THAT THE MODELS TRAINED ON SEQUENTIAL DATASETS ARE MORE ACCURATE WHEN NAIVE BAYES, CLASSIFICATION AND REGRESSION TREE, RANDOM FOREST (RF), ITERATIVE DICHOTOMISER 3 AND MULTILAYER PERCEPTION ARE USED. FINALLY, WE USED THE LSTM TO TRAIN PREDICTING MODELS ON SEQUENTIAL DATASETS, AND COMPARED THEM WITH THE BEST MODELS TRAINED BY RF. THE RESULTS SHOW THAT THE MODELS TRAINED BY THE LSTM ARE MORE ACCURATE, WHICH PROVES THE EFFECTIVENESS OF THE PROPOSED APPROACH AT CERTAIN EXTENT.
NEW AFFORDANCES FOR ONLINE DISCUSSION FORUMS ARE INCREASINGLY EXPLORED. IN THIS REGARD, GAMIFICATION ELEMENTS SUCH AS BADGES, PROGRESS BARS AND PROGRESS VISUALIZATIONS ARE USED IN ORDER TO INCREASE STUDENTS' ENGAGEMENT AND PARTICIPATION. THIS STUDY USES THE COMMUNITY OF INQUIRY MODEL TO EXPLORE STUDENTS' CRITICAL THINKING UNDER THE EFFECT OF A) A COMMUNITY AND B) INDIVIDUAL GAMIFICATION ELEMENTS, OVER TWO DISCUSSIONS. REACHING THE LAST DISCUSSION OF THE COURSE, THE STUDENTS, WHO WERE AWARDED INDIVIDUAL GAMIFICATION ELEMENTS, ATTAINED HIGHER LEVELS OF CRITICAL THINKING COMPARED TO THE STUDENTS THAT WERE AWARDED COMMUNITY GAMIFICATION ELEMENTS. TEACHING PRESENCE HAS BEEN INFLUENCED BY THE GAMIFICATION ELEMENTS PROVIDED, REGARDLESS OF THEIR TYPE. THE RESULTS HIGHLIGHT THE VALUE OF GAMIFICATION ON THE STUDENT'S CONSCIOUS AWARENESS OF THEIR OWN THINKING AND LEARNING.
SOCIAL NETWORK ANALYSIS (SNA) IS WIDELY USED TO ANALYZE SOCIAL INTERACTIONS IN ASYNCHRONOUS DISCUSSIONS. A WELL ESTABLISHED MODEL THAT REPRESENTS A CLEAR PICTURE OF THE KNOWLEDGE-BUILDING PROCESSES OCCURRING IN ONLINE DISCUSSIONS IS THE COMMUNITY OF INQUIRY. SNA HAS RECENTLY EMERGED AS AN ANALYTICAL TOOL FOR EXPLORING THE DEVELOPMENT OF A COMMUNITY OF INQUIRY IN ONLINE LEARNING. HOWEVER, THE APPLICATION OF SNA HAS BEEN LIMITED AND LACKS APPROPRIATE PEDAGOGICAL GROUNDING. IN THIS PAPER, WE INVESTIGATE A) THE RELATIONSHIP BETWEEN SOCIAL NETWORKS AND COMMUNITY LEARNING AS WELL AS B) THE IMPACT OF GAMIFICATION, BASED ON SOCIAL NETWORK METRICS, ON COMMUNITY LEARNING. ACCORDING TO THE RESULTS, STRONG RELATIONSHIPS HAVE BEEN REVEALED MAINLY BETWEEN SNA METRICS AND THE SOCIAL PRESENCE, AS WELL AS THE EXPLORATION PHASE OF THE COGNITIVE PRESENCE OF A COMMUNITY OF INQUIRY. THE EVIDENCE ALSO HIGHLIGHTS THE POTENTIAL OF GAMIFICATION FOR EMPOWERING THE EVOLUTION OF A COMMUNITY OF INQUIRY.
WE DISCUSS THE NEW CHALLENGES AND DIRECTIONS FACING THE USE OF BIG DATA AND ARTIFICIAL INTELLIGENCE (AI) IN EDUCATION RESEARCH, POLICY-MAKING, AND INDUSTRY. IN RECENT YEARS, APPLICATIONS OF BIG DATA AND AI IN EDUCATION HAVE MADE SIGNIFICANT HEADWAYS. THIS HIGHLIGHTS A NOVEL TREND IN LEADING-EDGE EDUCATIONAL RESEARCH. THE CONVENIENCE AND EMBEDDEDNESS OF DATA COLLECTION WITHIN EDUCATIONAL TECHNOLOGIES, PAIRED WITH COMPUTATIONAL TECHNIQUES HAVE MADE THE ANALYSES OF BIG DATA A REALITY. WE ARE MOVING BEYOND PROOF-OF-CONCEPT DEMONSTRATIONS AND APPLICATIONS OF TECHNIQUES, AND ARE BEGINNING TO SEE SUBSTANTIAL ADOPTION IN MANY AREAS OF EDUCATION. THE KEY RESEARCH TRENDS IN THE DOMAINS OF BIG DATA AND AI ARE ASSOCIATED WITH ASSESSMENT, INDIVIDUALIZED LEARNING, AND PRECISION EDUCATION. MODEL-DRIVEN DATA ANALYTICS APPROACHES WILL GROW QUICKLY TO GUIDE THE DEVELOPMENT, INTERPRETATION, AND VALIDATION OF THE ALGORITHMS. HOWEVER, CONCLUSIONS FROM EDUCATIONAL ANALYTICS SHOULD, OF COURSE, BE APPLIED WITH CAUTION. AT THE EDUCATION POLICY LEVEL, THE GOVERNMENT SHOULD BE DEVOTED TO SUPPORTING LIFELONG LEARNING, OFFERING TEACHER EDUCATION PROGRAMS, AND PROTECTING PERSONAL DATA. WITH REGARD TO THE EDUCATION INDUSTRY, RECIPROCAL AND MUTUALLY BENEFICIAL RELATIONSHIPS SHOULD BE DEVELOPED IN ORDER TO ENHANCE ACADEMIA-INDUSTRY COLLABORATION. FURTHERMORE, IT IS IMPORTANT TO MAKE SURE THAT TECHNOLOGIES ARE GUIDED BY RELEVANT THEORETICAL FRAMEWORKS AND ARE EMPIRICALLY TESTED. LASTLY, IN THIS PAPER WE ADVOCATE AN IN-DEPTH DIALOG BETWEEN SUPPORTERS OF "COLD" TECHNOLOGY AND "WARM" HUMANITY SO THAT IT CAN LEAD TO GREATER UNDERSTANDING AMONG TEACHERS AND STUDENTS ABOUT HOW TECHNOLOGY, AND SPECIFICALLY, THE BIG DATA EXPLOSION AND AI REVOLUTION CAN BRING NEW OPPORTUNITIES (AND CHALLENGES) THAT CAN BE BEST LEVERAGED FOR PEDAGOGICAL PRACTICES AND LEARNING.
PURPOSE THE PURPOSE OF THIS PAPER IS TO EXAMINE THE ROLE OF DATA ANALYTICS IN ADDRESSING CONTEMPORARY CHALLENGES AND OPPORTUNITIES FACING INSTITUTIONS OF HIGHER EDUCATION, WITH A SPECIAL EMPHASIS ON ENROLLMENT MANAGEMENT FOR THE EXAMPLE OF A COLLEGE OF BUSINESS ADMINISTRATION IN A HISPANIC-SERVING FOUR-YEAR INSTITUTION OF HIGHER EDUCATION. DESIGN/METHODOLOGY/APPROACH AFTER REVIEWING PREVIOUS RESEARCH ON THIS SUBJECT, THE AUTHORS PROPOSE ADDING A NEW STRATUM TO HIGHER EDUCATION ANALYTICS, ENROLLMENT MANAGEMENT ANALYTICS, WHICH UNIQUELY IDENTIFIES THE ACTIONS ASSOCIATED WITH ATTRACTING AND RECRUITING STUDENTS, ACCOUNTS FOR INSTITUTIONAL DIFFERENCES AND LEADS TO STUDENTS' SUCCESSFUL MATRICULATION AT INSTITUTIONS. FINDINGS THE AUTHORS DEMONSTRATE ON THE EXAMPLE OF A PROFESSIONAL COLLEGE IN A FOUR-YEAR INSTITUTION OF HIGHER EDUCATION HOW THE UTILIZATION OF MODERN ANALYTICAL TOOLS AND TECHNIQUES DEVELOP ACTIONABLE DECISIONS IN THE STUDENTS' RECRUITMENT PROCESS, WHICH LEADS TO THEIR MATRICULATION. ORIGINALITY/VALUE A DEFINITION AND A NEW STRATUM IN HIGHER EDUCATION ANALYTICS ARE PROPOSED FOR ENROLLMENT MANAGEMENT ANALYTICS.
WHILE DATA-DRIVEN DECISION-MAKING HAS BECOME A NEW PARADIGM FOR SCHOOL DEVELOPMENT AND ACCOUNTABILITY, RESEARCH ON THE UNDERLYING ICT INFRASTRUCTURES AND THE ICT MANAGEMENT PROCESSES HAVE BEEN LESS PROMINENT. WITH THE TREND OF DATAFICATION, EDUCATIONAL MANAGEMENT GAINS NEW OPTIONS BUT ALSO REQUIRES ADEQUATE CONTROLLING MECHANISMS TO TAKE CARE OF THE DATA AND TO ACCOUNT FOR PRIVACY AND SECURITY. THE INFORMATION MANAGEMENT CYCLE CAN BE USED TO DEFINE ALL RELEVANT ASPECTS OF THE MANAGEMENT PROCESS. THIS IS ADOPTED TO THE SPECIFIC SITUATION OF EDUCATIONAL INSTITUTIONS AND FURTHERMORE EXTENDED TO ACCOUNT FOR THE DIFFERENT LEVELS OF EDUCATIONAL GOVERNANCE. THIS LEADS TO A NEW CONCEPT OF EDUCATIONAL TECHNOLOGY GOVERNANCE AS A NECESSARY FRAME FOR SUPPORTING DATAFICATION PROCESSES.
EDUCATIONAL TECHNOLOGIES HAVE CAPTURED THE ATTENTION OF RESEARCHERS, POLICY MAKERS, AND PARENTS. EACH YEAR, CONSIDERABLE EFFORT AND MONEY ARE INVESTED INTO NEW TECHNOLOGIES, HOPING TO FIND THE NEXT EFFECTIVE LEARNING TOOL. HOWEVER, TECHNOLOGY CHANGES RAPIDLY AND LITTLE ATTENTION IS PAID TO THE CHANGES AFTER THEY OCCUR. THIS PAPER PROVIDES AN OVERALL PICTURE OF THE CHANGING TRENDS IN EDUCATIONAL TECHNOLOGY BY ANALYZING THE HORIZON REPORTS' PREDICTIONS OF THE MOST INFLUENTIAL EDUCATIONAL TECHNOLOGIES FROM 2011 TO 2021, IDENTIFYING LARGER TRENDS ACROSS THESE YEARLY PREDICTIONS, AND BY USING BIBLIOMETRIC ANALYSIS TO EVALUATE THE ACCURACY OF THE IDENTIFIED TRENDS. THE RESULTS SUGGEST THAT MOBILE AND ANALYTICS TECHNOLOGIES TRENDED CONSISTENTLY ACROSS THE PERIOD, THERE WAS A TREND TOWARDS MAKER TECHNOLOGIES AND GAMES IN THE EARLY PART OF THE DECADE, AND EMERGING TECHNOLOGIES (E.G., VR, AI) ARE PREDICTED TO TREND IN THE FUTURE. OVERALL, THE SPECIFIC TECHNOLOGIES FOCUSED ON BY THE HRS' PREDICTIONS AND BY EDUCATIONAL RESEARCHERS' PUBLICATIONS SEEM TO COINCIDE WITH THE AVAILABILITY OF CONSUMER GRADE TECHNOLOGIES, SUGGESTING THAT THE MARKETPLACE AND TECHNOLOGY INDUSTRY IS DRIVING TRENDS (CF., PEDAGOGY OR THEORY).
EFFORTS TO RAISE THE BAR OF HIGHER EDUCATION SO AS TO RESPOND TO DYNAMIC SOCIETAL/INDUSTRY NEEDS HAVE LED TO A NUMBER OF INITIATIVES, INCLUDING ARTIFICIAL NEURAL NETWORK (ANN) BASED EDUCATIONAL DATA MINING (EDM) INCLUSIVE. WITH ANN-BASED EDM, HUMONGOUS AMOUNT OF STUDENT DATA IN HIGHER INSTITUTIONS COULD BE HARNESSED FOR INFORMED ACADEMIC ADVISORY THAT PROMOTES ADAPTIVE LEARNING FOR PURPOSES OF STUDENT RETENTION, STUDENT PROGRESSION, AND COST SAVING. MINING STUDENTS' DATA OPTIMALLY REQUIRES PREDICTIVE DATA MINING TOOL AND MACHINE LEARNING TECHNIQUE LIKE ANN. HOWEVER, DESPITE ACKNOWLEDGING THE CAPABILITY OF ANN-BASED EDM FOR EFFICIENTLY CLASSIFYING STUDENTS' LEARNING BEHAVIOR AND ACCURATELY PREDICTING STUDENTS' PERFORMANCE, THE CONCEPT HAS RECEIVED LESS THAN COMMENSURATE ATTENTION IN THE LITERATURE. THIS SEEMS TO SUGGEST THAT THERE ARE GAPS AND CHALLENGES CONFRONTING ANN-BASED EDM IN HIGHER EDUCATION. IN THIS STUDY, WE USED THE SYSTEMATIC LITERATURE REVIEW TECHNIQUE TO GAUGE THE PULSE OF RESEARCHERS FROM THE VIEWPOINT OF MODELING, LEARNING PROCEDURE, AND COST FUNCTION OPTIMIZATION USING RESEARCH STUDIES. WE AIM TO UNEARTH THE GAPS AND CHALLENGES WITH A VIEW TO OFFERING RESEARCH DIRECTION TO UPCOMING RESEARCHERS THAT WANT TO MAKE INVALUABLE CONTRIBUTIONS TO THIS RELATIVELY NEW FIELD. WE ANALYZED 190 STUDIES CONDUCTED IN 2010-2018. OUR FINDINGS REVEAL THAT HARDWARE CHALLENGES, TRAINING CHALLENGES, THEORETICAL CHALLENGES, AND QUALITY CONCERNS ARE THE BANE OF ANN-BASED EDM IN HIGHER EDUCATION AND OFFER WINDOWS OF OPPORTUNITIES FOR FURTHER RESEARCH. WE ARE OPTIMISTIC THAT ADVANCES IN RESEARCH ALONG THIS DIRECTION WILL MAKE ANN-BASED EDM IN HIGHER EDUCATION MORE VISIBLE AND RELEVANT IN THE QUEST FOR HIGHER EDUCATION-DRIVEN SUSTAINABLE DEVELOPMENT.
THE FORMATIVE ASSESSMENT OF WRITTEN REFLECTION PROVIDES OPPORTUNITIES FOR STUDENTS TO IMPROVE THEIR PRACTICE IN AN ITERATIVE MANNER USING REFLECTIVE WRITING. HOWEVER, MANUAL FORMATIVE ASSESSMENT OF WRITTEN REFLECTION IS TIME CONSUMING AND SUBJECTIVE. WHILE PROGRESS HAS BEEN MADE IN DEPLOYING WRITING ANALYTICS TOOLS TO PROVIDE AUTOMATED, FORMATIVE FEEDBACK, FEW APPROACHES TO AUTOMATED ASSESSMENT ARE GROUNDED IN A VALIDATED, THEORY-BASED, FORMATIVE ASSESSMENT MODEL. TO ADDRESS THIS, WE PROPOSE A FIVE-FACTOR MODEL OF THE CAPABILITY FOR WRITTEN REFLECTION (CWREF), GROUNDED IN THE SCHOLARSHIP OF REFLECTIVE WRITING PEDAGOGY. THIS PAPER USES CONFIRMATORY FACTOR ANALYSIS TO VALIDATE THE CWREF MODEL BY EXAMINING THE RELATIVE CONTRIBUTIONS OF TEXTUAL FEATURES, DERIVED FROM WRITING ANALYTICS, TO EACH FACTOR IN THE MODEL, AND THEIR CONTRIBUTIONS TO CWREF. THE MODEL WAS EVALUATED WITH TWO REFLECTIVE WRITING CORPORA, SHOWING WHICH TEXTUAL FEATURES, DERIVED USING ACADEMIC WRITING ANALYTICS AND LINGUISTIC INQUIRY & WORD COUNT, WERE SIGNIFICANT INDICATORS OF FACTORS IN BOTH CORPORA. IN ADDITION, IT WAS FOUND THAT THE REFLECTIVE WRITING CONTEXT WAS AN IMPORTANT FACTOR INFLUENCING THE VALIDITY OF THE CWREF MODEL. FINALLY, WE CONSIDER HOW THIS NEW ANALYTICAL ASSESSMENT MODEL COULD ENABLE IMPROVED TRACKING OF PROGRESSION IN REFLECTIVE WRITING, PROVIDING THE BASIS FOR IMPROVED FORMATIVE FEEDBACK.
SOCIAL RELATIONS ARE AN ESSENTIAL COMPONENT OF GROUP LEARNING ONLINE, BECAUSE THROUGH THEM, THE PROCESS IS MANAGED AND CONTROLLED. IN ADDITION, LEARNING CANNOT BE UNDERSTOOD IN ISOLATION FROM ITS SITUATED NATURE. TO IDENTIFY PATTERNS OF PARTICIPATION AND SOCIAL CONNECTIONS IN ONLINE GROUP LEARNING, THIS STUDY ANALYZES THE SOCIAL NETWORK AMONG UNIVERSITY STUDENTS WHO WORK IN ONLINE DISCUSSION FORUMS AND ARE REQUIRED TO SOLVE HIGHER ORDER LEARNING TASKS. DISCUSSION FORUMS WERE USED, FOCUSING ON THREE TYPES OF TASKS: ANALYZING CASE STUDIES, EVALUATING WEBSITES, AND CREATING WEBQUEST. THE STRUCTURES OF SOCIAL NETWORKS THAT AROSE OUT OF 96 DISCUSSION FORUMS OVER THREE ACADEMIC YEARS WERE ANALYZED. THE RESULTS DEMONSTRATE THAT AS THE OPENING (LOWER LEVEL OF STRUCTURATION) OF THE LEARNING TASK INCREASES, SO TOO DOES THE NEED FOR SOCIAL INTERACTION IN THE GROUP. THE SOCIAL STRUCTURE OF THE GROUPS IS MODULATED BY THE TYPE OF LEARNING TASK. FINALLY, THE EDUCATIONAL IMPLICATIONS OF THE RESULTS ARE DISCUSSED.
AUTOMATIC SCORING AND FEEDBACK TOOLS HAVE BECOME CRITICAL COMPONENTS OF ONLINE LEARNING PROLIFERATION. THESE TOOLS RANGE FROM MULTIPLE-CHOICE QUESTIONS TO GRADING ESSAYS USING MACHINE LEARNING (ML). LEARNING ENVIRONMENTS SUCH AS MASSIVE OPEN ONLINE COURSES (MOOCS) WOULD NOT BE POSSIBLE WITHOUT THEM. THE USAGE OF THIS MECHANISM HAS BROUGHT MANY EXCITING AREAS OF STUDY, FROM THE DESIGN OF QUESTIONS TO THE ML GRADING TOOLS' PRECISION AND ACCURACY. THIS PAPER ANALYZES THE FINDINGS OF 125 STUDIES PUBLISHED IN JOURNALS AND PROCEEDINGS BETWEEN 2016 AND 2020 ON THE USAGES OF AUTOMATIC SCORING AND FEEDBACK AS A LEARNING TOOL. THIS ANALYSIS GIVES AN OVERVIEW OF THE TRENDS, CHALLENGES, AND OPEN QUESTIONS IN THIS RESEARCH AREA. THE RESULTS INDICATE THAT AUTOMATIC SCORING AND FEEDBACK HAVE MANY ADVANTAGES. THE MOST IMPORTANT BENEFITS INCLUDE ENABLING SCALING THE NUMBER OF STUDENTS WITHOUT ADDING A PROPORTIONAL NUMBER OF INSTRUCTORS, IMPROVING THE STUDENT EXPERIENCE BY REDUCING THE TIME BETWEEN SUBMISSION GRADING AND FEEDBACK, AND REMOVING BIAS IN SCORING. ON THE OTHER HAND, THESE TECHNOLOGIES HAVE SOME DRAWBACKS. THE MAIN PROBLEM IS CREATING A DISINCENTIVE TO DEVELOP INNOVATIVE ANSWERS THAT DO NOT MATCH THE EXPECTED ONE OR HAVE NOT BEEN CONSIDERED WHEN PREPARING THE PROBLEM. ANOTHER DRAWBACK IS POTENTIALLY TRAINING THE STUDENT TO ANSWER THE QUESTION INSTEAD OF LEARNING THE CONCEPTS. WITH THIS, GIVEN THE EXISTENCE OF A CORRECT ANSWER, SUCH AN ANSWER COULD BE LEAKED TO THE INTERNET, MAKING IT EASIER FOR STUDENTS TO AVOID SOLVING THE PROBLEM. OVERALL, EACH OF THESE DRAWBACKS PRESENTS AN OPPORTUNITY TO LOOK AT WAYS TO IMPROVE TECHNOLOGIES TO USE THESE TOOLS TO PROVIDE A BETTER LEARNING EXPERIENCE TO STUDENTS.
THIS STUDY PROVIDES A SYSTEMATIC REVIEW OF THE RECENT ADVANCES IN DESIGNING THE INTELLIGENT TUTORING ROBOT (ITR) AND SUMMARIZES THE STATUS QUO OF APPLYING ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES. WE FIRST ANALYZE THE ENVIRONMENT OF THE ITR AND PROPOSE A RELATIONSHIP MODEL FOR DESCRIBING INTERACTIONS OF ITR WITH THE STUDENTS, THE SOCIAL MILIEU, AND THE CURRICULUM. THEN, WE TRANSFORM THE RELATIONSHIP MODEL INTO THE PERCEPTION-PLANNING-ACTION MODEL TO EXPLORE WHAT AI TECHNIQUES ARE SUITABLE TO BE APPLIED IN THE ITR. THIS ARTICLE PROVIDES INSIGHTS ON PROMOTING A HUMAN-ROBOT TEACHING-LEARNING PROCESS AND AI-ASSISTED EDUCATIONAL TECHNIQUES, WHICH ILLUSTRATES THE DESIGN GUIDELINES AND FUTURE RESEARCH PERSPECTIVES IN INTELLIGENT TUTORING ROBOTS.
FUTURE LEARNING SPACES (FLSS), SUCH AS IMMERSIVE VIRTUAL ENVIRONMENTS, HAVE EXPANDED THE ARCHITECTURAL STUDIO BEYOND TRADITIONAL SPACES, OFFERING NEW AFFORDANCES FOR LEARNING COMMUNITIES (LCS) TO ENCULTURATE DESIGN PRACTICES. THESE DEVELOPMENTS RAISE A NEED TO ASSESS HOW DIFFERENT SPACES FOSTER INTENDED LEARNING ACTIVITIES. IN RESPONSE, WE DEVELOPED A MODEL TO ASSESS THE WAY (LCS) ARE SUPPORTED DURING THE DESIGN PROCESS. OUR MODEL AGGREGATES LEARNERS' DESIGN DECISIONS, ALONG WITH THEIR PHYSICAL, VIRTUAL AND SOCIAL SETTINGS, INTO UNITS CALLED KNOWLEDGE CONSTRUCTION ACTIVITIES. THESE ARE THEN LINKED WITH SUBSEQUENT UNITS, TO CREATE DESIGN DEVELOPMENT GRAPHS. THE MODEL WAS APPLIED TO TWO STUDIO COURSES THAT USED A CONVENTIONAL CLASSROOM AND A TECHNOLOGY-ENHANCED IMMERSIVE FLS. ANALYSES SHOW THAT EACH SPACE SUPPORTED DIFFERENT LEARNING ACTIVITIES. NOTABLY, THE IMMERSIVE FLS WAS SHOWN TO SUPPORT DESIGN CONVERGENCE, CONSIDERED TO BE AN ESSENTIAL SKILL. WHILE ADVANCING CONCEPTS RELATED TO DESIGN PEDAGOGY, THE MODEL CAN SUPPORT ADDITIONAL DESIGNERS OF FLS FOR (LCS) WHEN INTEGRATING MULTIPLE SPACES.
IS MY PROFESSIONAL KNOWLEDGE OUTDATED? DO I HAVE THE SKILLS NEEDED FOR THE NEW CHALLENGES OF THE SOCIETY? WHAT KNOWLEDGE DO I LACK TO QUALIFY FOR A JOB I LIKE? WHAT UNIVERSITIES CAN I ADDRESS TO GET KNOWLEDGE THAT IMPROVES MY EMPLOYMENT EXPECTATIONS? THESE ARE RELEVANT QUESTIONS THAT ALL EMPLOYEES HAVE DONE IN ANY MOMENT OF THEIR LIFE. IN ADDITION, WHEN THERE ARE HIGH RATES OF UNEMPLOYMENT AND JOB OFFERS THAT KEEP UNFILLED, THE ANSWERS TO THESE QUESTIONS ARE EVEN MORE RELEVANT. ANSWERING SUCH QUESTIONS OPEN NEW OPPORTUNITIES FOR EMPLOYED AND UNEMPLOYED PEOPLE, BY ALLOWING THEM TO DESIGN A FORMATIVE PLAN ACCORDING TO THEIR SKILLS AND EXPECTATIONS. IT ALSO PROVIDES EVIDENCES TO EMPLOYERS ABOUT THE SKILLS AND KNOWLEDGE OF THE SOCIETY, MAKING THEM MORE AWARE OF THE SKILLS OF THEIR POTENTIAL FUTURE EMPLOYEES. THE COMPANIES ALSO WILL HAVE MORE KNOWLEDGE TO DESIGN THE PROFESSIONAL CAREER OF THEIR EMPLOYEES ACCORDING TO THE COMPANY NEEDS AND THE KNOWLEDGE AND SKILLS OF THEIR EMPLOYEES. THIS PAPER PROPOSES A SYSTEM THAT HELPS PEOPLE BY SHOWING WHICH KNOWLEDGE AND SKILLS A PERSON MISSES FOR A GIVEN JOB POSITION AND WHAT UNIVERSITY COURSES THE PERSON CAN TAKE TO ACQUIRE THE REQUIRED SKILLS AND KNOWLEDGE. THE SYSTEM HAS BEEN IMPLEMENTED AS A RECOMMENDER SYSTEM THAT HELPS USERS IN PLANNING THEIR LIFE-LONG LEARNING. THE PAPER SHOWS THE ARCHITECTURE OF THE PROPOSED SYSTEM, A CASE STUDY TO EXPLAIN HOW IT WORKS, A SURVEY TO VALIDATE ITS USEFULNESS AND USABILITY AND SOME CONCLUSIONS AFTER ITS FIRST EXPERIMENTATION.
AS A METRIC, CUSTOMER LIFETIME VALUE (CLV) IS IMPORTANT IN BUSINESS ENVIRONMENTS IN ORDER TO PRIORITIZE CUSTOMER VALUE TO THE ORGANIZATION SO THAT EFFORT AND RESOURCES CAN BE EFFECTIVELY UTILIZED BASED ON THE PROFITABILITY OF EACH CUSTOMER. IN AN ACADEMIC SETTING THERE IS AN EQUAL INTEREST TO CLASSIFY STUDENTS BASED ON THEIR ENGAGEMENT, SO THAT HIGH PERFORMING STUDENTS, WEAK PERFORMERS WHO MAY LIKELY BE DROPOUTS AND THE REST OF THE STUDENTS IN THE SPECTRUM CAN BE CLEARLY IDENTIFIED. IN THIS PAPER WE PRESENT A FACTOR MODEL FOR TRANSLATING THE CLV INTO A LEARNING ORIENTED STUDENT ENGAGEMENT VALUE (SEV), WHICH DEFINES AN INDICATOR OF ENGAGEMENT. TO ANALYZE ITS UTILITY WE APPLIED THE SEV AT THE OPEN UNIVERSITY OF CATALONIA, BY SETTING AN INITIAL SET OF VARIABLES TO CALCULATE THE SEV AND CALCULATE THEIR VALUES IN A REAL CONTEXT. THE INFORMATION PROVIDED FOR THE SEV ALLOWS TO PERFORM PERSONALIZED AND RELEVANT FEEDBACK AND ASSISTANCE TO STUDENTS.
KEYSTROKE LOGGERS FACILITATE RESEARCHERS TO COLLECT FINE-GRAINED PROCESS DATA AND OFFER SUPPORT IN ANALYZING THESE DATA. KEYSTROKE LOGGING HAS BECOME POPULAR IN WRITING RESEARCH, AND STUDY BY STUDY WE ARE NOW PAVING THE PATH TO A BETTER UNDERSTANDING OF WRITING PROCESS DATA. HOWEVER, FEW RESEARCHERS HAVE CONCENTRATED ON HOW TO BRING KEYSTROKE LOGGING TO THE CLASSROOM. NOT BECAUSE THEY ARE NOT CONVINCED THAT WRITING DEVELOPMENT COULD BENEFIT FROM A MORE PROCESS-ORIENTED PEDAGOGY, BUT BECAUSE 'TRANSLATING' COMPLEX AND LARGE DATA SETS TO AN EDUCATIONAL CONTEXT IS CHALLENGING. THEREFORE, WE HAVE DEVELOPED A NEW FUNCTION IN INPUTLOG, SPECIFICALLY AIMING TO FACILITATE WRITING TUTORS IN PROVIDING PROCESS FEEDBACK TO THEIR STUDENTS. BASED ON AN XML- LOGFILE, THE SO-CALLED 'REPORT' FUNCTION AUTOMATICALLY GENERATES A PDF-FILE ADDRESSING DIFFERENT PERSPECTIVES OF THE WRITING PROCESS: PAUSING, REVISION, SOURCE USE, AND FLUENCY. THESE PERSPECTIVES ARE REPORTED EITHER QUANTITATIVELY OR VISUALLY. BRIEF INTRODUCTORY TEXTS EXPLAIN THE INFORMATION PRESENTED. INPUTLOG PROVIDES A DEFAULT FEEDBACK REPORT, BUT USERS CAN ALSO CUSTOMIZE THE REPORT. THIS PAPER DESCRIBES THE PROCESS REPORT AND DEMONSTRATES THE USE OF IT IN AN INTERVENTION. WE ALSO PRESENT SOME ADDITIONAL PEDAGOGICAL SCENARIOS TO ACTIVELY USE THIS TYPE OF FEEDBACK IN WRITING CLASSES.
EARLY WARNING SYSTEMS HAVE BEEN PROGRESSIVELY IMPLEMENTED IN HIGHER EDUCATION INSTITUTIONS TO PREDICT STUDENT PERFORMANCE. HOWEVER, THEY USUALLY FAIL AT EFFECTIVELY INTEGRATING THE MANY INFORMATION SOURCES AVAILABLE AT UNIVERSITIES TO MAKE MORE ACCURATE AND TIMELY PREDICTIONS, THEY OFTEN LACK DECISION-MAKING REASONING TO MOTIVATE THE REASONS BEHIND THE PREDICTIONS, AND THEY ARE GENERALLY BIASED TOWARD THE GENERAL STUDENT BODY, IGNORING THE IDIOSYNCRASIES OF UNDERREPRESENTED STUDENT POPULATIONS (DETERMINED BY SOCIO-DEMOGRAPHIC FACTORS SUCH AS RACE, GENDER, RESIDENCY, OR STATUS AS A FRESHMEN, TRANSFER, ADULT, OR FIRST-GENERATION STUDENTS) THAT TRADITIONALLY HAVE GREATER DIFFICULTIES AND PERFORMANCE GAPS. THIS PAPER PRESENTS A MULTIVIEW EARLY WARNING SYSTEM BUILT WITH COMPREHENSIBLE GENETIC PROGRAMMING CLASSIFICATION RULES ADAPTED TO SPECIFICALLY TARGET UNDERREPRESENTED AND UNDERPERFORMING STUDENT POPULATIONS. THE SYSTEM INTEGRATES MANY STUDENT INFORMATION REPOSITORIES USING MULTIVIEW LEARNING TO IMPROVE THE ACCURACY AND TIMING OF THE PREDICTIONS. THREE INTERFACES HAVE BEEN DEVELOPED TO PROVIDE PERSONALIZED AND AGGREGATED COMPREHENSIBLE FEEDBACK TO STUDENTS, INSTRUCTORS, AND STAFF TO FACILITATE EARLY INTERVENTION AND STUDENT SUPPORT. EXPERIMENTAL RESULTS, VALIDATED WITH STATISTICAL ANALYSIS, INDICATE THAT THIS MULTIVIEW LEARNING APPROACH OUTPERFORMS TRADITIONAL CLASSIFIERS. LEARNING OUTCOMES WILL HELP INSTRUCTORS AND POLICY-MAKERS TO DEPLOY STRATEGIES TO INCREASE RETENTION AND IMPROVE ACADEMICS.
SERIOUS OR EDUCATIONAL GAMES GAIN INCREASING RESEARCH INTEREST AS TOOLS TO AUGMENT TRADITIONAL INSTRUCTIONAL APPROACHES ON SCHOLASTIC LEARNING, ESPECIALLY IN MATHEMATICS EDUCATION. IN THIS STUDY, WE INVESTIGATED WHETHER GAME-BASED APPROACHES MAY NOT ONLY BE USEFUL TO FOSTER NUMERICAL LEARNING BUT MAY ALSO BE VALID AS AN ASSESSMENT TOOL. TO MEASURE THEIR CONCEPTUAL KNOWLEDGE OF FRACTIONS ELEVEN-YEAR-OLD STUDENTS PLAYED A MATH GAME ON TABLET COMPUTERS USING TILT-CONTROL TO NAVIGATE AN AVATAR ALONG A NUMBER LINE FOR A TOTAL OF 30 MIN. FINDINGS INDICATED THAT HALLMARK EFFECTS OF FRACTION MAGNITUDE PROCESSING TYPICALLY OBSERVED IN BASIC RESEARCH, SUCH AS THE NUMERICAL DISTANCE EFFECT, WERE SUCCESSFULLY REPLICATED USING THE GAME-BASED ASSESSMENT. MOREOVER, FRACTION COMPARISON PERFORMANCE AS WELL AS FRACTION ESTIMATION ACCURACY CORRELATED SIGNIFICANTLY WITH STUDENTS' MATH GRADES. THEREFORE, THE RESULTS OF THE CURRENT STUDY SUGGEST THAT GAME-BASED LEARNING ENVIRONMENTS FOR FRACTION EDUCATION (EVEN USING TILT CONTROL) MAY ALSO ALLOW FOR A VALID ASSESSMENT OF STUDENTS' FRACTION KNOWLEDGE. (C) 2017 ELSEVIER LTD. ALL RIGHTS RESERVED.
TEACHERS REGULATING GROUPS OF STUDENTS DURING COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) FACE THE CHALLENGE OF ORCHESTRATING THEIR GUIDANCE AT STUDENT, GROUP, AND CLASS LEVEL. DURING CSCL, TEACHERS CAN MONITOR ALL STUDENT ACTIVITY AND INTERACT WITH MULTIPLE GROUPS AT THE SAME TIME. NOT MUCH IS KNOWN ABOUT THE WAY TEACHERS DIAGNOSE STUDENT PROGRESS AND DECIDE UPON APPROPRIATE INTERVENTIONS WHEN THEY REGULATE MULTIPLE GROUPS SYNCHRONOUSLY. THIS EXPLORATIVE STUDY DESCRIBES THE STRATEGIES AND EXPERIENCES RELATED TO REGULATING THE ACTIVITIES OF SEVEN GROUPS OF STUDENTS, AS REPORTED BY TWO TEACHERS, AND AIMED TO WIDEN THE FRAMEWORK FOR DESCRIBING TEACHER REGULATION OF CSCL SETTINGS THAT ARE CHARACTERIZED BY SYNCHRONICITY. RECURRING THEMES INCLUDED THE HIGH AMOUNT OF INFORMATION LOAD TEACHERS EXPERIENCED WHILE DIAGNOSING STUDENTS' NEEDS, THE FOCUS AND LEVEL OF REGULATION, AND THE WAY THE TEACHERS USED PRIOR KNOWLEDGE OF STUDENTS TO DECIDE ON AN INTERVENTION AFTER DIAGNOSIS. BOTH TEACHERS VALUED THE ABILITY TO MONITOR STUDENT PROGRESS ONLINE, AND MENTIONED THE NECESSITY OF STUDENTS BEING ABLE TO FOLLOW THE TEACHER'S ACTIVITY AS WELL. THEORETICAL IMPLICATIONS ARE DESCRIBED IN TERMS OF UNDERSTANDING TEACHER REGULATION, SYNCHRONICITY, AND INFORMATION LOAD. PRACTICAL IMPLICATIONS ARE DESCRIBED FOR LOWERING INFORMATION LOAD. (C) 2015 ELSEVIER LTD. ALL RIGHTS RESERVED.
FLIPPED CLASSROOMS ARE BECOMING INCREASINGLY POPULAR, PARTICULARLY WHEN TEACHING NON-NATIVE SPEAKING STUDENTS. EXISTING RESEARCH HAS LARGELY FOCUSED ON EXAMINING ACADEMIC PERFORMANCE AND STUDENTS' PERCEPTIONS OF THE LEARNING PROCESS. THIS EXPLORATORY STUDY USES LOG-FILE DATA TO IDENTIFY HIDDEN PATTERNS OF STUDENT ONLINE BEHAVIOUR IN A FLIPPED CLASSROOM ENVIRONMENT FOR A COHORT OF STUDENTS, WITH SPECIAL ATTENTION TO THEIR COMMAND OF THE HOME LANGUAGE OF THE INSTITUTION. USING CLUSTER ANALYSIS, CATEGORIES WERE IDENTIFIED REGARDING WHEN AND HOW OFTEN ONLINE FLIPPED LESSONS WERE ACCESSED: (I) BEFORE CLASS, (II) AFTER CLASS AND BEFORE A WEEKLY EXAM, AND (III) AFTER THE WEEKLY EXAM BUT BEFORE AN ASSIGNMENT. GENDER AND THE AVERAGE NUMBER OF MINUTES THE LESSONS WERE ACCESSED BEFORE EACH TIME PERIOD WERE ALSO CONSIDERED. FINDINGS INDICATED THAT THERE WAS SUSTAINED ACCESS TO FLIPPED MATERIALS THROUGHOUT THE SEMESTER FOR ALL STUDENTS. IN ADDITION TO ACCESSING ONLINE LESSONS PRIOR TO CLASS, STUDENTS ALSO ACCESSED ONLINE LESSONS PRIOR TO WEEKLY EXAMS AND PROJECT SUBMISSION DEADLINES, INDICATING THE VALUE OF SUCH MATERIAL FOR REVISION. INTERESTINGLY, TWO CLUSTERS OF NON-NATIVE ENGLISH-SPEAKERS WERE IDENTIFIED WHERE ONE GROUP ACCESSED THE MATERIAL MORE OFTEN THAN NATIVE STUDENTS, AND THE SECOND GROUP ACCESSED THE MATERIAL LESS FREQUENTLY THAN THE NATIVE ENGLISH-SPEAKING STUDENTS.
INTRODUCTORY PROGRAMMING PLAYS A CENTRAL ROLE IN SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS ( STEM) EDUCATION, BOTH IN LOWER AND HIGHER EDUCATION. TEACHING AND LEARNING PROGRAMMING ACCORDING TO PEDAGOGICAL THEORY IS A HIGHLY RESEARCHED TOPIC AND A VARIETY OF LEARNING TOOLS ARE BEING IMPLEMENTED TO ASSIST STUDENTS WHO REQUIRE SUPPORT IN UNDERSTANDING THE MECHANICS OF PROGRAMMING. A MAJORITY OF THESE TOOLS COMBINES PEDAGOGICAL ASPECTS OF LEARNING PROGRAMMING WITH THE TYPICAL FEATURES OF PROGRAMMING ENVIRONMENTS. THIS SURVEY LOOKS AT VARIOUS STRATEGIES AND RECOMMENDATIONS FROM THE PROGRAMMING EDUCATION AREA AND COMPARES DIFFERENT TYPES OF PROGRAMMING TOOLS AVAILABLE THAT ADHERE TO THESE STRATEGIES AND CLASSIFIES THE DIFFERENT TYPES OF TOOLS UNDER THEIR MAIN FOCUS. THE STUDY ALSO AIMS TO GENERATE RECOMMENDATIONS BASED ON THE OBSERVATIONS OF TOOL DESIGN THAT COULD BE HIGHLY BENEFICIAL TO THE TEACHERS AND THE STUDENTS IN PROGRAMMING COURSES. BY ANALYZING 50 PROGRAMMING TOOLS THAT CAN BE CLASSIFIED AS INTELLIGENT TUTORING SYSTEMS, VISUALIZATION TOOLS, INTELLIGENT PROGRAMMING ENVIRONMENTS, AND SIMULATION TOOLS, IT CAN BE SEEN THAT MANY OF THESE TOOLS FOCUS ON A SPECIFIC TYPE OF LEARNING AND THAT THEY INCORPORATE SOME OF THE LEARNING STRATEGIES IDENTIFIED. PEDAGOGICAL RESEARCH EMPHASIZE THE NEED OF STUDENT NETWORKING, PEER REVIEW, EXAMPLE BASED LEARNING AND SOLUTION PLANNING APPROACHES TO BE PRESENT IN THE LEARNING PROCESS. SOME TOOLS EXCEL IN THESE PEDAGOGICALLY SOUND FEATURES WHILE OTHERS ARE IN NEED OF FURTHER IMPROVEMENT. THIS STUDY FOCUSES ON PRESENTING THE FEATURES OF TOOLS AND RECOMMENDATIONS FOR LEARNING SO THAT NOVICE LEARNERS OR TEACHERS CAN MAKE INFORMED DECISIONS ABOUT THE TYPES OF TOOLS THEY WISH TO USE AND HOW TO UTILIZE THEM IN EFFECTIVE WAYS OF LEARNING.
VISUAL ANALYTICS COMBINES AUTOMATED DATA ANALYSIS AND HUMAN INTELLIGENCE THROUGH VISUALISATION TECHNIQUES TO ADDRESS THE COMPLEXITY OF CURRENT REAL-WORLD PROBLEMS. THIS REVIEW USES THE LENS OF VISUAL ANALYTICS TO EXAMINE FOUR DIMENSIONS OF VISUAL REPRESENTATIONS FOR ANALYSING COLLABORATIVE DISCOURSE: GOALS, DATA SOURCES, VISUALISATION DESIGNS, AND ANALYTICAL TECHNIQUES BASED ON 89 STUDIES. WE FOUND VISUAL ANALYSIS APPROACHES TO BE SUITABLE AND ADVANTAGEOUS FOR DECOMPOSING THE TEMPORALITY OF COLLABORATIVE DISCOURSE. HOWEVER, IT HAS BEEN CHALLENGING FOR CURRENT RESEARCH TO SIMULTANEOUSLY CONSIDER LEARNING THEORIES AND FOLLOW VISUALISATION DESIGN PRINCIPLES WHEN ADOPTING VISUALISATIONS TO ANALYSE COLLABORATIVE DISCOURSE. AT THE SAME TIME, EXISTING VISUAL ANALYSIS APPROACHES HAVE MAINLY TARGETED LEARNERS OR RESEARCHERS IN ONLINE CONTEXTS AND MAINLY FOCUSED ON MIRRORING COLLABORATIVE DISCOURSE RATHER THAN PROVIDING ADVANCED AFFORDANCES SUCH AS ALERTING OR ADVISING. INFORMED BY THESE FINDINGS, WE PROPOSE A POSSIBLE FUTURE RESEARCH AGENDA AND OFFER SUGGESTIONS FOR THE FEATURES OF SUCCESSFUL COLLABORATION TO GUIDE THE DESIGN OF ADVANCED AFFORDANCES.
THE DEVELOPMENT OF CREATIVITY IS A MAJOR CHALLENGE FOR ENGINEERING EDUCATION. CREATIVITY NOT ONLY AFFECTS ORIGINALITY AND INNOVATION BUT ALSO INCREASES THE UTILITY OF PRODUCTS AND IDEAS, THUS YIELDING A COMPETITIVE ADVANTAGE IN THE MARKET. ALTHOUGH STUDIES HAVE DEMONSTRATED THAT CREATIVITY CAN BE PRACTICALLY ASSESSED THROUGH STUDENT INNOVATION OR RESPONSES TO CREATIVITY QUESTIONNAIRES, FEW STUDIES HAVE EXPLORED HOW CREATIVITY IS DEVELOPED. THEREFORE, THIS STUDY DEVELOPED A MODEL FOR PREDICTING CREATIVITY BASED ON THE DISCUSSION RECORDS OF STUDENTS DURING CREATIVE ACTIVITIES. THE MODEL WAS DESIGNED USING THE CHINESE KNOWLEDGE INFORMATION PROCESSING TOKENIZATION SYSTEM, A TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY METHOD, AND SUPPORT VECTOR MACHINES. THE EXPERIMENTAL RESULTS REVEALED A MODEL ACCURACY OF 93 %, THUS DEMONSTRATING THE MODEL'S FEASIBILITY FOR PREDICTING CREATIVITY. FURTHERMORE, THE STUDENTS' KEY TERMS WERE OBTAINED BY USING K-MEANS CLUSTERING WITH THE PROPOSED MODEL, THUS YIELDING VALUABLE SUPPLEMENTARY INFORMATION FOR EXPLORING THE DEVELOPMENT OF CREATIVE IDEAS. THESE RESULTS REVEALED THAT DATA MINING HAS SUBSTANTIAL VALUE IN CREATIVITY EDUCATION. THEREFORE, THE PROPOSED PREDICTIVE MODEL IS SUITABLE FOR PREDICTING CREATIVITY IN LEARNING ACTIVITIES BASED ON DISCUSSION RECORDS. IT CAN SUPPORT TEACHERS' ASSESSMENT OF STUDENT PERFORMANCE AND PROVISION OF TIMELY GUIDANCE AND FEEDBACK TO ENHANCE CREATIVE THINKING AND STIMULATE IMAGINATION AMONG ENGINEERING STUDENTS.
THIS PAPER SURVEYS THE STATE OF THE ART ON PREDICTION IN MOOCS THROUGH A SYSTEMATIC LITERATURE REVIEW (SLR). THE MAIN OBJECTIVES ARE: FIRST, TO IDENTIFY THE CHARACTERISTICS OF THE MOOCS USED FOR PREDICTION, SECOND, TO DESCRIBE THE PREDICTION OUTCOMES, THIRD, TO CLASSIFY THE PREDICTION FEATURES, FOURTH, TO DETERMINE THE TECHNIQUES USED TO PREDICT THE VARIABLES, AND, FIFTH, TO IDENTIFY THE METRICS USED TO EVALUATE THE PREDICTIVE MODELS. RESULTS SHOW THERE IS STRONG INTEREST IN PREDICTING DROPOUTS IN MOOCS. A VARIETY OF PREDICTIVE MODELS ARE USED, THOUGH REGRESSION AND SUPPORT VECTOR MACHINES STAND OUT. THERE IS ALSO WIDE VARIETY IN THE CHOICE OF PREDICTION FEATURES, BUT CLICKSTREAM DATA ABOUT PLATFORM USE STANDS OUT. FUTURE RESEARCH SHOULD FOCUS ON DEVELOPING AND APPLYING PREDICTIVE MODELS THAT CAN BE USED IN MORE HETEROGENEOUS CONTEXTS (IN TERMS OF PLATFORMS, THEMATIC AREAS, AND COURSE DURATIONS), ON PREDICTING NEW OUTCOMES AND MAKING CONNECTIONS AMONG THEM (E.G., PREDICTING LEARNERS' EXPECTANCIES), ON ENHANCING THE PREDICTIVE POWER OF CURRENT MODELS BY IMPROVING ALGORITHMS OR ADDING NOVEL HIGHER-ORDER FEATURES (E.G., EFFICIENCY, CONSTANCY, ETC.).
ACCORDING TO VARIOUS INTERNATIONAL REPORTS, ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED) IS ONE OF THE CURRENTLY EMERGING FIELDS IN EDUCATIONAL TECHNOLOGY. WHILST IT HAS BEEN AROUND FOR ABOUT 30 YEARS, IT IS STILL UNCLEAR FOR EDUCATORS HOW TO MAKE PEDAGOGICAL ADVANTAGE OF IT ON A BROADER SCALE, AND HOW IT CAN ACTUALLY IMPACT MEANINGFULLY ON TEACHING AND LEARNING IN HIGHER EDUCATION. THIS PAPER SEEKS TO PROVIDE AN OVERVIEW OF RESEARCH ON AI APPLICATIONS IN HIGHER EDUCATION THROUGH A SYSTEMATIC REVIEW. OUT OF 2656 INITIALLY IDENTIFIED PUBLICATIONS FOR THE PERIOD BETWEEN 2007 AND 2018, 146 ARTICLES WERE INCLUDED FOR FINAL SYNTHESIS, ACCORDING TO EXPLICIT INCLUSION AND EXCLUSION CRITERIA. THE DESCRIPTIVE RESULTS SHOW THAT MOST OF THE DISCIPLINES INVOLVED IN AIED PAPERS COME FROM COMPUTER SCIENCE AND STEM, AND THAT QUANTITATIVE METHODS WERE THE MOST FREQUENTLY USED IN EMPIRICAL STUDIES. THE SYNTHESIS OF RESULTS PRESENTS FOUR AREAS OF AIED APPLICATIONS IN ACADEMIC SUPPORT SERVICES, AND INSTITUTIONAL AND ADMINISTRATIVE SERVICES: 1. PROFILING AND PREDICTION, 2. ASSESSMENT AND EVALUATION, 3. ADAPTIVE SYSTEMS AND PERSONALISATION, AND 4. INTELLIGENT TUTORING SYSTEMS. THE CONCLUSIONS REFLECT ON THE ALMOST LACK OF CRITICAL REFLECTION OF CHALLENGES AND RISKS OF AIED, THE WEAK CONNECTION TO THEORETICAL PEDAGOGICAL PERSPECTIVES, AND THE NEED FOR FURTHER EXPLORATION OF ETHICAL AND EDUCATIONAL APPROACHES IN THE APPLICATION OF AIED IN HIGHER EDUCATION.
THIS ARTICLE PRESENTS THE FINDINGS OF A SYSTEMATIC QUALITATIVE ANALYSIS OF RESEARCH IN THE ETHICS OF DIGITAL TRACE DATA USE IN LEARNING AND EDUCATION. FROM THE RESULTING ANALYSIS OF 77 PEER-REVIEWED STUDIES, WE (1) MAP THE CHARACTERISTICS OF RESEARCH BY STUDY TYPE, ACADEMIC COMMUNITY, INSTITUTIONAL SETTING, AND NATIONAL CONTEXT; (2) IDENTIFY THE PRIMARY ETHICAL CONCERNS AND RELATED RESPONSES; AND (3) HIGHLIGHT THE RESEARCH GAPS. FOUR AREAS OF FOCUS ARE IDENTIFIED IN THIS EMERGING AREA: (1) PRIVACY, INFORMED CONSENT, AND DATA OWNERSHIP; (2) VALIDITY AND INTEGRITY; (3) ETHICAL DECISION MAKING; AND (4) GOVERNANCE AND ACCOUNTABILITY. WE HIGHLIGHT THE LACK OF EVIDENCE PARTICULARLY FOR PRESCHOOL AND SCHOOL-AGED CHILDREN AND THE DISPARATE COMMUNITIES WORKING IN THIS DOMAIN, AND WE SUGGEST A MORE COHESIVE APPROACH, WHERE THE WIDER LEARNING AND EDUCATIONAL ECOSYSTEM IS RECOGNIZED, EXPLICIT ENGAGEMENT WITH ETHICAL THEORY IS CENTRAL, AND MID- TO LONG-TERM ETHICAL ISSUES ARE CONSIDERED ALONGSIDE IMMEDIATE CONCERNS.
IN A DYNAMIC MARKETPLACE, NEW PRODUCT DEVELOPMENT (NPD) IS CONSIDERED THE PIVOT OF AN ORGANIZATION'S COMPETITIVE STRATEGY. HOWEVER, LITTLE RESEARCH HAS BEEN CONDUCTED ON THE TEACHING MATERIAL, PEDAGOGICAL APPROACH, AND LEARNING OBJECTIVE THAT SHOULD BE PART OF A COURSE ON NPD. THE STUDY INTRODUCES A METHODOLOGY FOR DESIGNING A CURRICULUM WITH LIMITED PRIOR KNOWLEDGE. FIRST, LATENT SEMANTIC ANALYSIS WAS APPLIED TO EXTRACT THE MAIN RESEARCH THEMES FROM JOURNAL ARTICLES ON THE SUBJECT, WHICH ARE CONSIDERED POTENTIAL TEACHING MATERIALS. NEXT, THE MODIFIED DELPHI METHOD WAS APPLIED TO IDENTIFY THEIR ELIGIBILITY AS TEACHING MATERIALS. FINALLY, BOTH A REVISED BLOOM'S TAXONOMY AND ANALYTIC HIERARCHY PROCESS WERE APPLIED TO ESTABLISH LEARNING OBJECTIVES AND DETERMINE THE PRIORITIES IN TEACHING MATERIALS AND PEDAGOGICAL APPROACHES, RESPECTIVELY. THE PAPER PRESENTS A CASE STUDY TO SHOW THAT THE PROPOSED METHODOLOGY CAN INDUCE FACULTIES TO CONSIDER STUDENT REQUIREMENTS, EVALUATE THE FEASIBILITY OF TEACHING MATERIALS AND PEDAGOGICAL APPROACHES, AND DEVELOP ASSESSMENTS WITH CONCRETE LEARNING OBJECTIVES. MOREOVER, THE STUDY FOUND THAT THE PEDAGOGICAL APPROACHES OF EXPERIENTIAL LEARNING AND CONSTRUCTIVIST LEARNING WERE EFFECTIVE FOR TEACHING INNOVATION MANAGEMENT, KNOWLEDGE MANAGEMENT, PROJECT MANAGEMENT, AND RISK MANAGEMENT IN COURSES AT BUSINESS SCHOOLS IN NEW PRODUCT DEVELOPMENT.
THE SELF-ASSESSMENT SYSTEM WAS DEVELOPED BASED ON THE ASSESSMENT AS LEARNING (AAL) APPROACH, IN WHICH LEARNERS TAKE RESPONSIBILITY FOR THEIR OWN LEARNING AND USE FEEDBACK FOR SELF-INTERVENTION. THE FEEDBACK AS THE SYSTEM'S PRINCIPAL COMPONENT WAS PRODUCED USING ASSESSMENT DATA IN THE SYSTEM. IT IS ESSENTIAL FOR THE ASSESSMENT AS LEARNING THAT STUDENTS CONSTRUCT THEIR INTERVENTIONS TO THEIR LEARNING PROCESSES BY USING ASSESSMENT FEEDBACK. FROM THIS POINT, THIS STUDY AIMED TO EVALUATE THE WEB-BASED SELF-ASSESSMENT SYSTEM THROUGH THE LENS OF STUDENT PERCEIVED SELF-INTERVENTION. THE ONE-SHOT CASE STUDY DESIGN (ONE-GROUP POST-TEST) MODEL, ONE OF WEAK EXPERIMENTAL DESIGN, WAS USED AS A RESEARCH DESIGN. THE WEB-BASED SELF-ASSESSMENT SYSTEM WAS EMPLOYED OVER A 10-WEEK PERIOD TO SUPPORT FACE-TO-FACE LESSONS. AT THE BEGINNING OF THE IMPLEMENTATION PROCESS, STUDENTS TAKING THE "MEASUREMENT AND EVALUATION IN EDUCATION" LESSON IN THE EDUCATION FACULTY WERE INFORMED ABOUT THE SYSTEM. AFTER THE IMPLEMENTATION PROCESS, THE PERCEIVED SELF-INTERVENTION SCALE WAS USED TO DETERMINE THE STUDENTS' PERCEPTION. A TOTAL OF 214 STUDENTS (71%) TOOK AT LEAST ONE TEST IN THE SYSTEM AND GAINED ACCESS TO THE FEEDBACK MODULE. THE HIGH LEVEL OF PARTICIPATION IN A NON-MANDATORY SYSTEM MAY BE CONSIDERED A SUCCESS. LEARNERS APPEARED TO HAVE UTILIZED FEEDBACK GAINED FROM THE SYSTEM IN PLANNING SELF-INTERVENTION AND MONITORING AND EVALUATING THE PLANNED INTERVENTION'S EFFECTIVENESS.
IDENTIFYING AT-RISK STUDENTS AS SOON AS POSSIBLE IS A CHALLENGE IN EDUCATIONAL INSTITUTIONS. DECREASING THE TIME LAG BETWEEN IDENTIFICATION AND REAL AT-RISK STATE MAY SIGNIFICANTLY REDUCE THE RISK OF FAILURE OR DISENGAGE. IN SMALL COURSES, THEIR IDENTIFICATION IS RELATIVELY EASY, BUT IT IS IMPRACTICAL ON LARGER ONES. CURRENT LEARNING MANAGEMENT SYSTEMS STORE A LARGE AMOUNT OF DATA THAT COULD HELP TO GENERATE PREDICTIVE MODELS TO EARLY IDENTIFICATION OF STUDENTS IN ONLINE AND BLENDED LEARNING. THE CONTRIBUTION OF THIS PAPER IS TWOFOLD: FIRST, A NEW ADAPTIVE PREDICTIVE MODEL IS PRESENTED BASED ONLY ON STUDENTS' GRADES SPECIFICALLY TRAINED FOR EACH COURSE. A DEEP ANALYSIS IS PERFORMED IN THE WHOLE INSTITUTION TO EVALUATE ITS PERFORMANCE ACCURACY. SECOND, AN EARLY WARNING SYSTEM IS DEVELOPED, FOCUSING ON DASHBOARDS VISUALIZATION FOR STAKEHOLDERS (I.E., STUDENTS AND TEACHERS) AND AN EARLY FEEDBACK PREDICTION SYSTEM TO INTERVENE IN THE CASE OF AT-RISK IDENTIFICATION. THE EARLY WARNING SYSTEM HAS BEEN EVALUATED IN A CASE STUDY ON A FIRST-YEAR UNDERGRADUATE COURSE IN COMPUTER SCIENCE. WE SHOW THE ACCURACY OF THE CORRECT IDENTIFICATION OF AT-RISK STUDENTS, THE STUDENTS' APPRAISAL, AND THE MOST COMMON FACTORS THAT LEAD TO AT-RISK LEVEL.
THE DECISION TO TRUST A MEDICAL TRAINEE WITH THE CRITICAL RESPONSIBILITY TO CARE FOR A PATIENT IS FUNDAMENTAL TO CLINICAL TRAINING. WHEN CAREFULLY AND DELIBERATELY MADE, SUCH DECISIONS CAN SERVE AS SIGNIFICANT STIMULI FOR LEARNING AND ALSO SHAPE THE ASSESSMENT OF TRAINEES. HOLDING BACK ENTRUSTMENT DECISIONS TOO MUCH MAY HAMPER THE TRAINEE'S DEVELOPMENT TOWARD UNSUPERVISED PRACTICE. WHEN CARELESSLY MADE, HOWEVER, THEY JEOPARDIZE PATIENT SAFETY. ENTRUSTMENT DECISION-MAKING PROCESSES, THEREFORE, DESERVE CAREFUL ANALYSIS. MEMBERS (INCLUDING THE AUTHORS) OF THE INTERNATIONAL COMPETENCY-BASED MEDICAL EDUCATION COLLABORATIVE CONDUCTED A CONTENT ANALYSIS OF THE ENTRUSTMENT DECISION-MAKING PROCESS IN HEALTH CARE TRAINING DURING A TWO-DAY SUMMIT IN SEPTEMBER 2013 AND SUBSEQUENTLY REVIEWED THE PERTINENT LITERATURE TO ARRIVE AT A DESCRIPTION OF THE CRITICAL FEATURES OF THIS PROCESS, WHICH INFORMS THIS ARTICLE. THE AUTHORS DISCUSS THEORETICAL BACKGROUNDS AND TERMINOLOGY OF TRUST AND ENTRUSTMENT IN THE CLINICAL WORKPLACE. THE COMPETENCY-BASED MOVEMENT AND THE INTRODUCTION OF ENTRUSTABLE PROFESSIONAL ACTIVITIES FORCE EDUCATORS TO RETHINK THE GROUNDS FOR ASSESSMENT IN THE WORKPLACE. ANTICIPATING A DECISION TO GRANT AUTONOMY AT A DESIGNATED LEVEL OF SUPERVISION APPEARS TO ALIGN BETTER WITH HEALTH CARE PRACTICE THAN DO MOST CURRENT ASSESSMENT PRACTICES. THE AUTHORS DISTINGUISH DIFFERENT MODES OF TRUST AND ENTRUSTMENT DECISIONS AND ELABORATE FIVE CATEGORIES, EACH WITH RELATED FACTORS, THAT DETERMINE WHEN DECISIONS TO TRUST TRAINEES ARE MADE: THE TRAINEE, SUPERVISOR, SITUATION, TASK, AND THE RELATIONSHIP BETWEEN TRAINEE AND SUPERVISOR. THE AUTHORS' AIM IN THIS ARTICLE IS TO LAY A THEORETICAL FOUNDATION FOR A NEW APPROACH TO WORKPLACE TRAINING AND ASSESSMENT.
THE FAST DEVELOPMENT IN MOBILE TECHNOLOGIES AND THE CONTINUOUS IMPROVEMENTS IN SMART MOBILE DEVICES, PROPELLED BY WIDE ADOPTION OF MOBILE CLOUD COMPUTING, HAVE PROMPTED FOR THE DEVELOPMENT OF A NEW GENERATION OF MOBILE APPLICATIONS AIMED AT SUPPORTING TEAM WORK. SUCH APPLICATIONS PROVIDE GREAT FLEXIBILITY TO WORKFORCE, ESPECIALLY GROUPS OF PEOPLE DEVELOPING TEAMWORK BOTH GEOGRAPHICALLY DISTRIBUTED AND ON THE MOVE. THE ABILITY OF BEING ABLE TO WORK ACCORDING TO THE A3 PARADIGM, NAMELY, ANYTIME, ANYWHERE, AWARENESS, SIGNIFICANTLY IMPROVES THE TEAMWORK PERFORMANCE. RELEVANT EXAMPLES OF SUCH APPLICATIONS ARE FOUND IN BUSINESSES-ESPECIALLY SMALL AND MEDIUM SIZE ENTERPRISES-, ONLINE COLLABORATIVE LEARNING IN VIRTUAL CAMPUSES, SOCIAL NETWORKING, DISASTER MANAGEMENT SCENARIOS, ETC. HOWEVER, DESIGNING AND DEVELOPING SUCH MOBILE COLLABORATIVE APPLICATIONS REQUIRES OVERCOMING MANY ISSUES AND CHALLENGES. AMONG THESE, THERE ARE THREE MAIN PILLARS OF SUCH APPLICATIONS, NAMELY, SHARING, CONTROLLING AND COORDINATION. IN THIS PAPER, WE PRESENT MOBILEPEERDROID A PLATFORM THAT SUPPORT SHARING, CONTROLLING AND COORDINATION IN MOBILE ANDROID TEAM. THE PLATFORM IS BASED ON P2P ARCHITECTURE TO ACHIEVE FLEXIBILITY, EFFICIENCY AND SCALABILITY. THE PLATFORM HAS BEEN INSTANTIATED FOR TEAMS OF MEMBERS USING MOBILE DEVICES UNDER ANDROID. IN ADDITION, IT ENABLES EXTENSIONS (VIA SEPARATION OF CONCERNS AND MODULARITY) WITH NEW FEATURES FOR SUPPORTING NEW APPLICATION DEVELOPMENT.
THE PORTABILITY OF PREDICTIVE MODELS OF ACADEMIC PERFORMANCE HAS BEEN WIDELY STUDIED IN THE FIELD OF LEARNING PLATFORMS, BUT THERE ARE FEW STUDIES IN WHICH THE RESULTS OF PREVIOUS EVALUATIONS ARE USED AS FACTORS. THE AIM OF THIS WORK WAS TO ANALYZE PORTABILITY PRECISELY IN THIS CONTEXT, WHERE PRECEDING PERFORMANCE IS USED AS A KEY PREDICTOR. THROUGH A STUDY DESIGNED TO CONTROL THE MAIN CONFOUNDING FACTORS, THE RESULTS OF 170 STUDENTS EVALUATED OVER TWO ACADEMIC YEARS WERE ANALYZED, DEVELOPING VARIOUS PREDICTIVE MODELS FOR A BASE GROUP (BG) OF 39 STUDENTS. AFTER THE FOUR BEST MODELS WERE SELECTED, THEY WERE VALIDATED USING DIFFERENT STATISTICAL TECHNIQUES. FINALLY, THESE MODELS WERE APPLIED TO THE REMAINING GROUPS, CONTROLLING THE NUMBER OF DIFFERENT FACTORS WITH RESPECT TO THE BG. THE RESULTS SHOW THAT THE MODELS' PERFORMANCE VARIES CONSISTENTLY WITH WHAT WAS EXPECTED: AS THEY MOVE AWAY FROM THE BG (FEWER COMMON CHARACTERISTICS), THE SPECIFICITY OF THE FOUR MODELS TENDS TO DECREASE.
VULNERABLE (E.G., LGBTQ, HOMELESS, DISABLED, RACIAL/ETHNIC MINORITY, AND/OR POOR) YOUTH DISPROPORTIONALLY REPORT CHALLENGES AT SCHOOL COMPARED TO THEIR MAJORITY COUNTERPARTS, BUT WE ARE NOT ALWAYS SURE OF THE BEST WAYS TO SUPPORT THESE STUDENTS. HOW MIGHT BIG DATA HELP TO AMELIORATE EXPERIENCES FOR VULNERABLE STUDENTS WHO ARE NOT PART OF THE MAJORITY (E.G., WHITE, MIDDLE CLASS, STRAIGHT)? WE REVIEW CURRENT WAYS THAT USING BIG DATA CAN PROMOTE STUDENT ENGAGEMENT SPECIFIC TO SCHOOL EXPERIENCES WHERE VULNERABLE YOUTH SHARE A DISPROPORTIONAL AMOUNT OF BURDEN. WE REVIEW EXTANT USES OF BIG DATA TO TRACK, INVOLVE, AND MONITOR STUDENT PROGRESS AND ATTENDANCE. ADDITIONALLY, WE REVIEW THE POTENTIAL PRIVACY IMPLICATIONS AND THREATS TO STUDENTS' CIVIL LIBERTIES.
THE CONCEPT OF BIG DATA HAS BEEN EXTENSIVELY CONSIDERED AS A TECHNOLOGICAL MODERNISATION IN ORGANISATIONS AND EDUCATIONAL INSTITUTES. THUS, THE PURPOSE OF THIS STUDY IS TO DETERMINE WHETHER THE MODIFIED TECHNOLOGY ACCEPTANCE MODEL (MTAM) IS VIABLE FOR EVALUATING THE PERFORMANCE OF LIBRARIANS IN THE USE OF BIG DATA ANALYTICS IN ACADEMIC LIBRARIES. THIS STUDY USED AN EMPIRICAL RESEARCH METHOD FOR COLLECTING DATA FROM 211 LIBRARIANS WORKING IN PAKISTAN'S UNIVERSITIES. ON THE BASIS OF THE FINDINGS OF THE MTAM ANALYSIS BY STRUCTURAL EQUATION MODELLING, THE PERFORMANCES OF THE ACADEMIC LIBRARIES WERE COMPREHENDED THROUGH THE PROCESS OF BIG DATA. THE MAIN INFLUENTIAL COMPONENTS OF THE PERFORMANCE ANALYSIS IN THIS STUDY WERE THE BIG DATA ANALYTICS CAPABILITIES, PERCEIVED EASE OF ACCESS AND THE USEFULNESS OF BIG DATA PRACTICES IN ACADEMIC LIBRARIES. SUBSEQUENTLY, THE UTILISATION OF BIG DATA WAS SIGNIFICANTLY AFFECTED BY SKILLS, PERCEIVED EASE OF ACCESS AND THE USEFULNESS OF ACADEMIC LIBRARIES. THE RESULTS ALSO SUGGESTED THAT THE VARIOUS COMPONENTS OF THE ACADEMIC LIBRARIES LEAD TO EFFECTIVE ORGANISATIONAL PERFORMANCE WHEN LINKED TO BIG DATA ANALYTICS.
THE DROPOUT RATES IN THE EUROPEAN COUNTRIES IS ONE OF THE MAJOR ISSUES TO BE FACED IN A NEAR FUTURE AS STATED IN THE EUROPE 2020 STRATEGY. IN 2017, AN AVERAGE OF 10.6% OF YOUNG PEOPLE (AGED 18-24) IN THE EU-28 WERE EARLY LEAVERS FROM EDUCATION AND TRAINING ACCORDING TO EUROSTAT'S STATISTICS. THE MAIN AIM OF THIS REVIEW IS TO IDENTIFY STUDIES WHICH USES EDUCATIONAL DATA MINING TECHNIQUES TO PREDICT UNIVERSITY DROPOUT IN TRADITIONAL COURSES. IN SCOPUS AND WEB OF SCIENCE (WOS) CATALOGUES, WE IDENTIFIED 241 STUDIES RELATED TO THIS TOPIC FROM WHICH WE SELECTED 73, FOCUSING ON WHAT DATA MINING TECHNIQUES ARE USED FOR PREDICTING UNIVERSITY DROPOUT. WE IDENTIFIED 6 DATA MINING CLASSIFICATION TECHNIQUES, 53 DATA MINING ALGORITHMS AND 14 DATA MINING TOOLS.
FLIPPED CLASSROOM (FC) IS AN ACTIVE LEARNING DESIGN REQUIRING STUDENTS TO COMPLETE ASSIGNED PRE-CLASS LEARNING ACTIVITIES IN PREPARATION FOR FACE-TO-FACE SESSIONS. STUDENTS' TIMELY, REGULAR, AND PRODUCTIVE ENGAGEMENT WITH THE PRE-CLASS ACTIVITIES IS CONSIDERED CRITICAL FOR THE SUCCESS OF THE OVERALL FC DESIGN, AS THESE ACTIVITIES SERVE TO PREPARE STUDENTS FOR EFFECTIVE PARTICIPATION IN FACE-TO-FACE SESSIONS. HOWEVER, THERE IS LIMITED EMPIRICAL EVIDENCE ON THE STRENGTH OF ASSOCIATION BETWEEN STUDENTS' REGULARITY OF ENGAGEMENT WITH THE PRE-CLASS ACTIVITIES AND THEIR LEARNING PERFORMANCE IN A FC COURSE. HENCE, THE CURRENT STUDY USES LEARNING TRACE DATA FROM THREE CONSECUTIVE OFFERINGS OF A FC COURSE TO EXAMINE STUDENTS' REGULARITY OF PRE-CLASS LEARNING ACTIVITIES AND ITS ASSOCIATION WITH THE STUDENTS' COURSE PERFORMANCE. IN PARTICULAR, THE STUDY DERIVES SEVERAL INDICATORS OF REGULARITY FROM THE TRACE DATA, INCLUDING INDICATORS RELATED TO TIME MANAGEMENT AND THOSE REFLECTING REGULARITY IN THE PATTERN OF ENGAGEMENT WITH PRE-CLASS LEARNING ACTIVITIES. THE ASSOCIATION WITH COURSE PERFORMANCE IS EXAMINED BY BUILDING PREDICTIVE REGRESSION MODELS WITH THE DEFINED INDICATORS AS FEATURES. TO EXAMINE THE RELEVANCE OF INCORPORATING THE SPECIFICITIES OF THE INSTRUCTIONAL DESIGN IN PREDICTIVE MODELS, WE DESIGNED AND COMPARED TWO KINDS OF INDICATORS: GENERIC (I.E. COURSE-DESIGN-AGNOSTIC) AND COURSE-DESIGN-SPECIFIC INDICATORS. THE STUDY IDENTIFIED SEVERAL INDICATORS OF REGULARITY OF PRE-CLASS ACTIVITIES AS SIGNIFICANT PREDICTORS OF COURSE PERFORMANCE. IT ALSO DEMONSTRATED THAT PREDICTIVE MODELS WITH ONLY GENERIC INDICATORS WERE ABLE TO EXPLAIN ONLY A SMALL PORTION OF THE OVERALL VARIABILITY IN THE STUDENTS' COURSE PERFORMANCE, AND WERE SIGNIFICANTLY OUTPERFORMED BY MODELS THAT INCORPORATED COURSESPECIFIC INDICATORS. FINALLY, THE STUDY FINDINGS POINT TO THE IMPORTANCE OF ASSISTING STUDENTS IN REGULATING THEIR USE OF LEARNING RESOURCES DURING CLASS PREPARATION ACTIVITIES IN A FC.
THERE IS A WIDESPREAD NOTION THAT EDUCATIONAL SYSTEMS SHOULD EMPOWER LEARNERS WITH SKILLS AND COMPETENCES TO COPE WITH A CONSTANTLY CHANGING LANDSCAPE. REFERENCE IS OFTEN MADE TO SKILLS SUCH AS CRITICAL THINKING, PROBLEM SOLVING, COLLABORATIVE SKILLS, INNOVATION, DIGITAL LITERACY, AND ADAPTABILITY. WHAT IS NEGOTIABLE IS HOW BEST TO ACHIEVE THE DEVELOPMENT OF THOSE SKILLS, IN PARTICULAR WHICH TEACHING AND LEARNING APPROACHES ARE SUITABLE FOR FACILITATING OR ENABLING COMPLEX SKILLS DEVELOPMENT. IN THIS PAPER, WE BUILD ON OUR PREVIOUS WORK OF EXPLORING NEW FORMS OF PEDAGOGY FOR AN INTERACTIVE WORLD, AS DOCUMENTED IN OUR INNOVATING PEDAGOGY REPORT SERIES. WE PRESENT A SET OF INNOVATIVE PEDAGOGICAL APPROACHES THAT HAVE THE POTENTIAL TO GUIDE TEACHING AND TRANSFORM LEARNING. AN INTEGRATED FRAMEWORK HAS BEEN DEVELOPED TO SELECT PEDAGOGIES FOR INCLUSION IN THIS PAPER, CONSISTING OF THE FOLLOWING FIVE DIMENSIONS: (A) RELEVANCE TO EFFECTIVE EDUCATIONAL THEORIES, (B) RESEARCH EVIDENCE ABOUT THE EFFECTIVENESS OF THE PROPOSED PEDAGOGIES, (C) RELATION TO THE DEVELOPMENT OF TWENTY-FIRST CENTURY SKILLS, (D) INNOVATIVE ASPECTS OF PEDAGOGY, AND (E) LEVEL OF ADOPTION IN EDUCATIONAL PRACTICE. THE SELECTED PEDAGOGIES, NAMELY FORMATIVE ANALYTICS, TEACHBACK, PLACE-BASED LEARNING, LEARNING WITH DRONES, LEARNING WITH ROBOTS, AND CITIZEN INQUIRY ARE EITHER ATTACHED TO SPECIFIC TECHNOLOGICAL DEVELOPMENTS, OR THEY HAVE EMERGED DUE TO AN ADVANCED UNDERSTANDING OF THE SCIENCE OF LEARNING. EACH ONE IS PRESENTED IN TERMS OF THE FIVE DIMENSIONS OF THE FRAMEWORK.
MASTERY ASSESSMENTS ARE USED IN MANY DIGITAL LEARNING ENVIRONMENTS TO CONTROL LEARNING PROGRESSION AND SUPPORT LOW-ACHIEVING STUDENTS RELEARNING IMPORTANT CONTENT. BUT RESEARCH ON PERSONALIZED FEEDBACK FOLLOWING A MASTERY ASSESSMENT IS SCARCE AND YIELDED AMBIGUOUS EFFECTS. THEREFORE, THIS STUDY SEEKS TO CONTRIBUTE TO THIS AREA OF RESEARCH BY INVESTIGATING THE EFFECT OF TWO DIFFERENT FEEDBACK MESSAGES IN SHORT ONLINE COURSES FOR GERMAN AND ENGLISH LANGUAGE GRAMMAR AND SPELLING IN SECONDARY CLASSROOMS (GRADES 6-10). THE STUDY COMPARES A REWARD-BASED FEEDBACK MESSAGE TO A SELF-REFERENCED FEEDBACK MESSAGE. THE REWARD-BASED MESSAGE INDICATES IF THE LEARNER REACHES THE MASTERY CRITERION AND HOW MANY POINTS THE LEARNING APP AWARDED FOR COMPLETING THE TEST. THE SELF-REFERENCED FEEDBACK MESSAGE APPLIES MOTIVATION THEORY TO STRENGTHEN STUDENTS' INTERNAL ATTRIBUTION OF CAUSES FOR THE TEST PERFORMANCE. THE WEB-APP MASTERYX (WWW.MASTERYX.DE) RANDOMLY ASSIGNED STUDENTS TO EITHER THE REWARD-BASED OR THE SELF-REFERENCED FEEDBACK MESSAGE. THE STUDY SAMPLED 620 STUDENTS (309 FEMALE, 311 MALE) IN 53 CLASSROOMS FROM 27 SECONDARY SCHOOLS. IT ANALYZED THE EFFECT OF THE TWO TYPES OF FEEDBACK MESSAGES ON THE LEVEL OF TEST-RETEST SEQUENCES (N = 2450). RESULTS INDICATE SMALL THOUGH SIGNIFICANT POSITIVE EFFECTS OF THE SELF REFERENCED FEEDBACK MESSAGE ON SUBSEQUENT LEARNING BEHAVIOR (READING ELABORATED ITEM FEEDBACK, TRAINING BEHAVIOR). HOWEVER, A MULTILEVEL REGRESSION MODEL SHOWED SMALL TO MEDIUM EFFECTS OF THE REWARD-BASED FEEDBACK MESSAGE ON THE HIGHER COURSE LEVELS' FOLLOW-UP MASTERY ASSESSMENT SCORE. THE FINDINGS EMPHASIZE THE COMPLEXITY OF DESIGNING PERSONALIZED FEEDBACK STRATEGIES IN ONLINE LEARNING ENVIRONMENTS WITH MASTERY ASSESSMENTS.
COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) HAS BEEN HIGHLIGHTED AS A BENEFICIAL LEARNING EXPERIENCE FOR STUDENTS IN BLENDED AND ONLINE SETTINGS. IN HIGHLY DIVERSE AND INTERNATIONAL CONTEXTS, CSCL ALSO ALLOWS STUDENTS THE OPPORTUNITY TO ENCOUNTER NEW IDEAS AND VALUES FROM PEERS WITH DIFFERENT BACKGROUNDS. HOWEVER, PREVIOUS RESEARCH HAS HIGHLIGHTED THAT THERE ARE WIDE VARIATIONS IN STUDENT PARTICIPATION LEVELS IN CSCL ACTIVITIES AND THAT MANY STUDENTS EXPERIENCE SOCIAL AND CULTURAL TENSIONS WHEN WORKING WITH DIVERSE PEERS. THESE ISSUES MAY DAMAGE THE QUALITY OF ONLINE COLLABORATION AND LIMIT THE POTENTIAL GAINS OF CSCL. IN THIS STUDY, WE EXPLORED ONE EVIDENCE-BASED SOLUTION FOR ENCOURAGING CSCL PARTICIPATION: THE INTERNATIONALISATION OF THE ONLINE ACADEMIC CONTENT USED FOR COLLABORATIVE ACTIVITIES. USING A RANDOMISED CONTROL TRIAL METHOD WITH 428 UNDERGRADUATE STUDENTS IN AN INTRODUCTORY STATISTICS COURSE, WE COMPARED INDIVIDUAL AND GROUP-LEVEL PARTICIPATION IN AN ONLINE COLLABORATIVE TASK WHEN STUDENTS USED CONTENT FROM THE LOCAL CONTEXT COMPARED TO CONTENT FROM INTERNATIONAL CONTEXTS. OUR FINDINGS SUGGEST THAT INTERNATIONALISATION OF ONLINE CONTENT CAN ENCOURAGE INDIVIDUAL-LEVEL PARTICIPATION AND DECREASE THE DISPARITY OF PARTICIPATION WITHIN SMALL GROUPS WHEN THE CONTENT IS SITUATED IN COUNTRIES THAT ARE PERSONALLY RELEVANT TO STUDENTS' OWN BACKGROUNDS. AT THE SAME TIME, PARTICIPATION WAS INFLUENCED BY INDIVIDUAL DEMOGRAPHICS AND GROUP DYNAMICS.
EDUCATIONAL DATA MINING IS USED TO DISCOVER SIGNIFICANT PHENOMENA AND RESOLVE EDUCATIONAL ISSUES OCCURRING IN THE CONTEXT OF TEACHING AND LEARNING. THIS STUDY PROVIDES A SYSTEMATIC LITERATURE REVIEW OF EDUCATIONAL DATA MINING IN MATHEMATICS AND SCIENCE EDUCATION. A TOTAL OF 64 ARTICLES WERE REVIEWED IN TERMS OF THE RESEARCH TOPICS AND DATA MINING TECHNIQUES USED. THIS REVIEW REVEALED THAT DATA MINING IN MATHEMATICS AND SCIENCE EDUCATION HAS BEEN COMMONLY USED TO UNDERSTAND STUDENTS' BEHAVIOR AND THINKING PROCESS, IDENTIFY FACTORS AFFECTING STUDENT ACHIEVEMENTS, AND PROVIDE AUTOMATED ASSESSMENT OF STUDENTS' WRITTEN WORK. RECENTLY, RESEARCHERS HAVE TENDED TO USE SUCH DATA MINING TECHNIQUES AS TEXT MINING TO DEVELOP LEARNING SYSTEMS FOR SUPPORTING TEACHERS' INSTRUCTION AND STUDENTS' LEARNING. WE ALSO FOUND THAT CLASSIFICATION, TEXT MINING, AND CLUSTERING ARE MAJOR DATA MINING TECHNIQUES RESEARCHERS HAVE USED. STUDIES USING DATA MINING WERE MORE LIKELY TO BE CONDUCTED IN THE FIELD OF SCIENCE EDUCATION THAN IN THE FIELD OF MATHEMATICS EDUCATION. WE DISCUSS THE MAIN RESULTS OF OUR REVIEW IN COMPARISON WITH THE PREVIOUS REVIEWS OF EDUCATIONAL DATA MINING (EDM) LITERATURE AND WITH EDM STUDIES CONDUCTED IN THE CONTEXT OF SCIENCE AND MATHEMATICS EDUCATION. FINALLY, WE PROVIDE IMPLICATIONS FOR RESEARCH AND TEACHING AND LEARNING OF SCIENCE AND MATHEMATICS AND SUGGEST POTENTIAL RESEARCH DIRECTIONS.
MASSIVE OPEN ONLINE COURSES (MOOCS) HOLD THE PROMISE OF DEMOCRATIZING THE LEARNING PROCESS. HOWEVER, PROVIDING EFFECTIVE FEEDBACK HAS PROVEN HARD TO OFFER AT SCALE SINCE MOST METHODS REQUIRE A TEACHER OR TUTOR. LEVERAGING BIG DATA IN MOOCS OFFERS A MECHANISM TO DEVELOP PREDICTIVE MODELS THAT CAN INFORM COMPUTER-BASED PEDAGOGICAL TUTORS. WE REVIEW RESEARCH ON GRADE PREDICTION AND EXAMINE THE PREDICTIVE POWER OF A MODEL BASED ON USER VIDEO-WATCHING BEHAVIOR. IN A MOOC ORGANIZED AROUND WEEKLY ASSIGNMENTS, WE FIND THAT FREQUENCY OF VIDEO VIEWING PER WEEK IS A BETTER PREDICTOR THAN INDIVIDUAL VIEWING FEATURES SUCH AS PLAYS, PAUSES, SEEKING, AND RATE CHANGES. THIS FINDING IS USEFUL FOR MOOCS THAT USE ASSIGNMENTS FOR COURSE EVALUATIONS IN ADDITION OR TO THE EXCLUSION OF IN-VIDEO QUIZZES FOR FORMATIVE ASSESSMENT. ENGAGING, WELL-CRAFTED ASSIGNMENTS IN MOOCS HAVE THE POTENTIAL OF BOOSTING STUDENT RETENTION AND COURSE COMPLETION BY FOSTERING A DEEPER UNDERSTANDING THROUGH APPLICATION AND PRACTICE.
THE MOST DRAMATIC FACTOR SHAPING THE FUTURE OF HIGHER EDUCATION IS BIG DATA AND ANALYTICS. IN THE BIG DATA ERA, THE EXPLOSIVE GROWTH OF MASSIVE DATA MANIPULATIONS IMPOSES A HEAVY BURDEN ON COMPUTATION, STORAGE, AND COMMUNICATION IN DATA CENTERS. INCREASING UNCERTAINTIES IN INFORMATION SYSTEM AVAILABILITY HAVE BECOME A DAILY SERIOUS PROBLEM. AN APPROPRIATE EVALUATION AND SELECTION OF THE RIGHT INFORMATION SYSTEM DISASTER RECOVERY (DR) SITE CAN ENSURE BUSINESS CONTINUITY AND INVESTMENT OPTIMIZATION. HOWEVER, MOST ACADEMIC INSTITUTES ALWAYS NEGLECT THE IMPORTANCE OF DR. NOT TO MENTION THE DR SITES IN THE ERA OF BIG DATA. EXISTING RESEARCH RESULTS DO NOT EVALUATE OR SELECT DR SITES IN GENERAL OR THOSE FOR ACADEMIC BIG DATA APPLICATIONS IN PARTICULAR. THEREFORE, THIS RESEARCH AIMS TO ESTABLISH AN ANALYTIC FRAMEWORK FOR EVALUATING, SELECTING DR SITES FOR ACADEMIC BIG DATA. THE PROPOSED ANALYTIC FRAMEWORK IS CONSISTING OF THE DECISION-MAKING TRIAL AND EVALUATION LABORATORY (DEMATEL), DEMATEL-BASED NETWORK PROCESS (DNP) AND VISEKRITERIJUMSKO KOMPROMISNO RANGIRANJE (VIKOR) METHODS. AN EMPIRICAL STUDY BASED ON A REAL BIG DATA DR APPLICATION OF AN ASIAN HIGH-PERFORMANCE COMPUTER CENTER'S EVALUATION AND SELECTION OF DR SITES FOR ACADEMIC BIG DATA IS USED TO ILLUSTRATE THE FEASIBILITY OF THE PROPOSED FRAMEWORK. THE ANALYTIC RESULTS CAN SERVE AS A FOUNDATION FOR INFORMATION TECHNOLOGY ADMINISTRATORS' STRATEGIES TO REDUCE THE PERFORMANCE GAPS OF A DR SITE FOR BIG DATA MANIPULATIONS IN GENERAL, AND ACADEMIC BIG DATA MANIPULATIONS IN SPECIAL.
WITH THE POPULARITY AND CONVENIENCE OF SMART PHONES AND INTERNET, MORE AND MORE USERS PARTICIPATE IN MOOC (MASSIVE OPEN ONLINE COURSES) VIA MOBILE PHONES OR TABLET COMPUTERS. THUS HOW TO DESIGN MOOC FOR MOBILE LEARNING DESERVES THEORETICAL STUDY AND PRACTICAL EXPLORATION. BASED ON THE REVIEW OF RELATED LITERATURE AND THE ASSESSMENT OF THE APPS PROVIDED BY SIX MOSTLY USED MOOC PLATFORMS, WE PROPOSED SIX GUIDELINES FOR THE MOBILE MOOC DESIGN, I.E. CLEAR NAVIGATION, SIMPLE LAYOUT, LINEAR DISPLAY, HARMONIOUS COLORING, SMOOTH VIDEO, AND FULL INTERACTION. TO VERIFY THE RATIONALITY OF THOSE GUIDELINES, WE DESIGNED A CORRESPONDING SURVEY TO INVESTIGATE THE LEARNERS' PERCEPTION AND ATTITUDE TOWARD THE SIX GUIDELINES IMPLEMENTED IN ONE MOOC IN CHINA. THE ANALYSIS OF THE SURVEY RESULT DEMONSTRATES THAT THE MOOC LEARNERS BELIEVED THOSE GUIDELINES ARE VALUABLE IN MOBILE MOOC DESIGN AND HELPFUL FOR MOOC LEARNING.
THE ADVENT OF THE SOCIAL WEB BROUGHT WITH IT CHALLENGES AND OPPORTUNITIES FOR RESEARCH ON LEARNING AND KNOWLEDGE CONSTRUCTION. USING THE ONLINE-ENCYCLOPEDIA WIKIPEDIA AS AN EXAMPLE, WE DISCUSS SEVERAL METHODS THAT CAN BE APPLIED TO ANALYZE THE DYNAMIC NATURE OF KNOWLEDGE-RELATED PROCESSES IN MASS COLLABORATION ENVIRONMENTS. THESE METHODS CAN HELP IN THE ANALYSIS OF THE INTERACTIONS BETWEEN THE TWO LEVELS THAT ARE RELEVANT IN COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL) RESEARCH: THE INDIVIDUAL LEVEL OF LEARNERS AND THE COLLECTIVE LEVEL OF THE GROUP OR COMMUNITY. IN LINE WITH CONSTRUCTIVIST THEORIES OF LEARNING, WE ARGUE THAT THE DEVELOPMENT OF KNOWLEDGE ON BOTH LEVELS IS TRIGGERED BY PRODUCTIVE FRICTION, THAT IS, THE PROLIFIC RESOLUTION OF SOCIO-COGNITIVE CONFLICTS. BY DESCRIBING THREE PROTOTYPICAL METHODS THAT HAVE BEEN USED IN PREVIOUS WIKIPEDIA RESEARCH, WE REVIEW HOW THESE TECHNIQUES CAN BE USED TO EXAMINE THE DYNAMICS ON BOTH LEVELS AND ANALYZE HOW THESE DYNAMICS CAN BE PREDICTED BY THE AMOUNT OF PRODUCTIVE FRICTION. WE ILLUSTRATE HOW THESE STUDIES MAKE USE OF TEXT CLASSIFIERS, SOCIAL NETWORK ANALYSIS, AND CLUSTER ANALYSIS IN ORDER TO OPERATIONALIZE THE THEORETICAL CONCEPTS. WE CONCLUDE BY DISCUSSING IMPLICATIONS FOR THE ANALYSIS OF DYNAMIC KNOWLEDGE PROCESSES FROM A LEARNING SCIENCES PERSPECTIVE.
COMPUTATIONAL THINKING (CT) REFERS TO COGNITIVE SKILLS OF LOGICAL REASONING, ABSTRACTION, AND PROBLEM-SOLVING THAT CAN BE AUTOMATED. STUDENTS WITH INTELLECTUAL DISABILITIES HAVE COGNITIVE DEFICITS THAT CAN BE IMPROVED BY WORKING THE CT. TO ENSURE THAT STUDENTS DEVELOP THIS ABILITY IT IS IMPORTANT TO USE APPROPRIATE AND ACCESSIBLE TOOLS FOR EVERYONE AND EDUCATIONAL DIGITAL GAMES ARE PRESENTED AS A POSSIBILITY TO TEACH CT IN A FUN AND ENGAGING WAY. THE PRESENT STUDY AIMS TO UNDERSTAND THE RELATIONSHIP BETWEEN THE LEARNING OF CHILDREN WITH INTELLECTUAL DISABILITIES AND THE EXISTING GUIDELINES FOR THE DEVELOPMENT OF ACCESSIBLE EDUCATIONAL DIGITAL GAMES, TO PROMOTE CT FOR THEM. ALSO, A SET OF THESE GUIDELINES IS PROVIDED FOR DESIGNING EDUCATIONAL DIGITAL GAMES AIMED AT STUDENTS WITH INTELLECTUAL DISABILITIES.
ADMINISTRATIVE PROCESSES THAT NEED TO BE COMPLETED TO MAINTAIN A BASIC STANDARD OF LIVING, TO STUDY, OR TO ATTAIN EMPLOYMENT, ARE PERCEIVED TO CREATE BURDENS FOR DISABLED PEOPLE. THE NAVIGATION OF INFORMATION, FORMS, COMMUNICATIONS, AND ASSESSMENTS TO ACHIEVE A PARTICULAR GOAL RAISES DIVERSE ACCESSIBILITY ISSUES. IN THIS PAPER WE EXPLORE THE DIFFERENT TYPES OF IMPACTS THESE PROCESSES HAVE ON DISABLED UNIVERSITY STUDENTS. WE BEGIN BY SURVEYING LITERATURE THAT HIGHLIGHTS THE SYSTEMIC CHARACTERISTICS OF ADMINISTRATIVE BURDENS AND BARRIERS FOR DISABLED PEOPLE. WE THEN DESCRIBE HOW A PARTICIPATORY RESEARCH EXERCISE WITH STUDENTS LED TO THE DEVELOPMENT OF A SURVEY ON THESE ISSUES. THIS WAS COMPLETED BY 104 RESPONDENTS WITH A DIVERSE RANGE OF DECLARED DISABILITIES. THIS PROVIDES EVIDENCE FOR A RANGE OF IMPACTS, AND UNDERSTANDING OF THE PERCEIVED LEVEL OF CHALLENGE OF COMMONLY EXPERIENCED PROCESSES. THE MOST COMMON NEGATIVE IMPACT REPORTED WAS ON STRESS LEVELS. OTHER COMMONLY REPORTED IMPACTS INCLUDE EXACERBATION OF EXISTING CONDITIONS, TIME LOST FROM STUDY, AND INSTANCES WHERE SUPPORT WAS NOT AVAILABLE IN A TIMELY FASHION. PROCESSES TO APPLY FOR DISABILITY-RELATED SUPPORT WERE MORE COMMONLY CHALLENGING THAN OTHER TYPES OF PROCESSES. WE USE THIS RESEARCH TO SUGGEST DIRECTIONS FOR IMPROVING ACCESSIBILITY AND EMPOWERMENT IN THIS SPACE.
GAMIFICATION HAS BEEN WIDELY EMPLOYED IN EDUCATIONAL CONTEXTS TO IMPROVE STUDENTS' ENGAGEMENT AND ENHANCE THE LEARNING PROCESS. IN THIS SENSE, THE PRESENT WORK INVESTIGATES THE IMPACT OF GAMIFICATION ON STUDENTS' BEHAVIOR AND PERFORMANCE THROUGH LEARNING PATHS, I.E., SEQUENCES OF LEARNING OBJECTS FOLLOWED BY STUDENTS WHILE INTERACTING WITH A VIRTUAL LEARNING ENVIRONMENT. TO ACHIEVE THIS, WE CAPTURED, ANALYZED AND VISUALLY REPRESENTED LEARNING PATHS FROM 139 STUDENTS ENROLLED IN AN ONLINE COURSE. AS RESULT, WE OBSERVED THAT STUDENTS USING GAMIFICATION INTERACTED MORE WITH THE COURSE ELEMENTS, HAD LONGER PATHS AND BETTER GRADES. THE MAIN CONTRIBUTION OF THIS WORK IS TO PRESENT LEARNING PATHS AS A TOOL TO INVESTIGATE STUDENTS' INTERACTION IN A VIRTUAL LEARNING ENVIRONMENT WITH GAMIFIED ELEMENTS AND TO ANALYZE THESE ELEMENTS' IMPACT ON STUDENTS' BEHAVIOR AND PERFORMANCE.
THIS PAPER PROPOSES A TRUSTWORTHINESS-BASED APPROACH FOR THE DESIGN OF SECURE LEARNING ACTIVITIES IN ONLINE LEARNING GROUPS. ALTHOUGH COMPUTER-SUPPORTED COLLABORATIVE LEARNING HAS BEEN WIDELY ADOPTED IN MANY EDUCATIONAL INSTITUTIONS OVER THE LAST DECADE, THERE EXIST STILL DRAWBACKS THAT LIMIT ITS POTENTIAL. AMONG THESE LIMITATIONS, WE INVESTIGATE ON INFORMATION SECURITY VULNERABILITIES IN LEARNING ACTIVITIES, WHICH MAY BE DEVELOPED IN ONLINE COLLABORATIVE LEARNING CONTEXTS. ALTHOUGH SECURITY ADVANCED METHODOLOGIES AND TECHNOLOGIES ARE DEPLOYED IN LEARNING MANAGEMENT SYSTEMS, MANY SECURITY VULNERABILITIES ARE STILL NOT SATISFACTORILY SOLVED. TO OVERCOME THESE DEFICIENCIES, WE FIRST PROPOSE THE GUIDELINES OF A HOLISTIC SECURITY MODEL IN ONLINE COLLABORATIVE LEARNING THROUGH AN EFFECTIVE TRUSTWORTHINESS APPROACH. HOWEVER, AS LEARNERS' TRUSTWORTHINESS ANALYSIS INVOLVES LARGE AMOUNT OF DATA GENERATED ALONG LEARNING ACTIVITIES, PROCESSING THIS INFORMATION IS COMPUTATIONALLY COSTLY, ESPECIALLY IF REQUIRED IN REAL TIME. AS THE MAIN CONTRIBUTION OF THIS PAPER, WE EVENTUALLY PROPOSE A PARALLEL PROCESSING APPROACH, WHICH CAN CONSIDERABLY DECREASE THE TIME OF DATA PROCESSING, THUS ALLOWING FOR BUILDING RELEVANT TRUSTWORTHINESS MODELS TO SUPPORT LEARNING ACTIVITIES EVEN IN REAL TIME. COPYRIGHT (C) 2014 JOHN WILEY & SONS, LTD.
THIS PAPER PROPOSES A TRUSTWORTHINESS MODEL FOR THE DESIGN OF SECURE LEARNING ASSESSMENT IN ON-LINE WEB COLLABORATIVE LEARNING GROUPS. ALTHOUGH COMPUTER SUPPORTED COLLABORATIVE LEARNING HAS BEEN WIDELY ADOPTED IN MANY EDUCATIONAL INSTITUTIONS OVER THE LAST DECADE, THERE EXIST STILL DRAWBACKS WHICH LIMIT THEIR POTENTIAL IN COLLABORATIVE LEARNING ACTIVITIES. AMONG THESE LIMITATIONS, WE INVESTIGATE INFORMATION SECURITY REQUIREMENTS IN ON-LINE ASSESSMENT, (E-ASSESSMENT), WHICH CAN BE DEVELOPED IN COLLABORATIVE LEARNING CONTEXTS. DESPITE INFORMATION SECURITY ENHANCEMENTS HAVE BEEN DEVELOPED IN RECENT YEARS, TO THE BEST OF OUR KNOWLEDGE, INTEGRATED AND HOLISTIC SECURITY MODELS HAVE NOT BEEN COMPLETELY CARRIED OUT YET. EVEN WHEN SECURITY ADVANCED METHODOLOGIES AND TECHNOLOGIES ARE DEPLOYED IN LEARNING MANAGEMENT SYSTEMS, TOO MANY TYPES OF VULNERABILITIES STILL REMAIN OPENED AND UNSOLVED. THEREFORE, NEW MODELS SUCH AS TRUSTWORTHINESS APPROACHES CAN OVERCOME THESE LACKS AND SUPPORT E-ASSESSMENT REQUIREMENTS FOR E-LEARNING. TO THIS END, A HOLISTIC SECURITY MODEL IS DESIGNED, IMPLEMENTED AND EVALUATED IN A REAL CONTEXT OF E-LEARNING. IMPLICATIONS OF THIS STUDY ARE REMARKED FOR SECURE ASSESSMENT IN ON-LINE COLLABORATIVE LEARNING THROUGH EFFECTIVE TRUSTWORTHINESS APPROACHES.
THE ACCEPTANCE OF EDUCATIONAL DATA MINING (EDM) TECHNOLOGY IS ON THE RISE DUE TO, ITS ABILITY TO EXTRACT NEW KNOWLEDGE FROM LARGE AMOUNTS OF STUDENTS' DATA. THIS KNOWLEDGE IS IMPORTANT FOR EDUCATIONAL STAKEHOLDERS, SUCH AS POLICY MAKERS, EDUCATORS, AND STUDENTS THEMSELVES TO ENHANCE EFFICIENCY AND ACHIEVEMENTS. HOWEVER, PREVIOUS STUDIES ON EDM HAVE FOCUSED MORE ON TECHNICAL ASPECTS, SUCH AS EVALUATING METHODS AND TECHNIQUES, WHILE IGNORING THE END-USERS' ACCEPTANCE OF THE TECHNOLOGY. REALISING ITS IMPORTANCE, THIS STUDY HAS ANALYSED THE DETERMINANTS THAT COULD INFLUENCE THE ACCEPTANCE OF EDM TECHNOLOGY, PARTICULARLY AMONG UNDERGRADUATE STUDENTS SINCE THEY ARE THE MOST AFFECTED BY THE TECHNOLOGY. FOR THIS REASON, 11 HYPOTHESES HAVE BEEN FORMULATED BASED ON DETERMINANTS OF TECHNOLOGY READINESS INDEX (TRI) AND TECHNOLOGY ACCEPTANCE MODEL 3 (TAM3), WHICH COULD RENDER AN IN-DEPTH INSIGHT REGARDING EDM ACCEPTANCE. A SURVEY WAS CONDUCTED ON 211 UNDERGRADUATE STUDENTS FROM SIX PUBLIC UNIVERSITIES IN MALAYSIA FOR A PERIOD OF 6 MONTHS (MAY TO OCTOBER 2014) USING QUESTIONNAIRES AS THE INSTRUMENT TO COLLECT DATA TO TEST THE HYPOTHESISED RELATIONSHIPS. THE PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING (PLS-SEM) APPROACH WAS USED TO ANALYSE THE PROPOSED ACCEPTANCE MODEL, WHICH WAS RUN USING SMARTPLS, VERSION 3 SOFTWARE. THE FINDINGS HAVE REVEALED THAT 'RELEVANCE FOR ANALYSING', 'SELF-EFFICACY', 'FACILITATING CONDITIONS', 'PERCEIVED USEFULNESS', 'PERCEIVED EASE OF USE', 'OPTIMISM' AND 'DISCOMFORT' HAVE INFLUENCED THE ACCEPTANCE OF EDM TECHNOLOGY AMONG UNDERGRADUATE STUDENTS.
WE EXAMINED THE WITHIN-DOMAIN STRUCTURE OF THE FIVE DOMAINS OF PERSONALITY MEASURED BY THE BIG FIVE INVENTORY-2 WITH DATA COLLECTED FROM AN ADOLESCENT SAMPLE (N = 838). THREE POSSIBLE FACTOR MODELS WERE TESTED: A SINGLE FACTOR, CORRELATED FACETS, AND A SINGLE FACTOR WITH CORRELATED RESIDUALS. WE EXAMINED EACH MODEL CONTROLLING FOR ACQUIESCENCE, A RESPONSE BIAS IN WHICH RESPONDENTS TEND TO AGREE/DISAGREE REGARDLESS OF ITEM CONTENT, USING TWO APPROACHES: ACQUIESCENCE FACTOR AND WITHIN-PERSON CENTERING OF ITEM-LEVEL RESPONSES. ACROSS EACH DOMAIN, RESULTS INDICATED BOTH THE CORRELATED FACETS AND CORRELATED RESIDUALS MODELS DEMONSTRATED ACCEPTABLE FIT. ACCOUNTING FOR ACQUIESCENT RESPONDING WAS GENERALLY ASSOCIATED WITH IMPROVED MODEL FIT. HOWEVER, CONSISTENT WITH PAST STRUGGLES IN MEASURING OPEN-MINDEDNESS IN ADOLESCENTS, THE CORRELATED RESIDUALS MODEL WITH ACQUIESCENCE AS A FACTOR FOR OPEN-MINDEDNESS FAILED TO CONVERGE. REGULARIZED STRUCTURAL EQUATION MODELING WAS CONDUCTED ON THIS MODEL FOR OPEN-MINDEDNESS AND SUGGESTED CERTAIN RESIDUAL COVARIANCES THAT CONTRIBUTED TO ESTIMATION DIFFICULTIES SHOULD BE CONSTRAINED TO ZERO. ADVANTAGES OF MODELS ARE DISCUSSED WITH IMPLICATIONS FOR STUDYING THE BIG FIVE PERSONALITY DOMAINS IN ADOLESCENTS. PUBLIC SIGNIFICANCE STATEMENT MANY PERSONALITY MEASURES ARE DEVELOPED AND VALIDATED USING ADULT SAMPLES AND HAVE NOT BEEN VALIDATED WITH ADOLESCENTS. OUR FINDINGS SUGGEST THAT BOTH THE CORRELATED FACETS AND THE CORRELATED RESIDUALS MODEL ARE APPROPRIATE FACTOR STRUCTURES FOR THE BIG FIVE INVENTORY-2 WITH A SAMPLE OF ADOLESCENTS, THOUGH OPEN-MINDEDNESS WAS MORE ELUSIVE. STUDYING PERSONALITY AMONG ADOLESCENTS NECESSITATES AN APPROPRIATE PERSONALITY INVENTORY, AND WE HOPE FINDINGS MAY INFORM SUBSEQUENT EFFORTS TO ACHIEVE THIS AIM.
LEARNER'S MOTIVATION DIFFICULTIES ARE RECOGNIZED AS A PROBLEM IN DIVERSE EDUCATIONAL SCENARIOS, REACHING UP TO UNIVERSITY DEGREES. AMONG OTHER TECHNIQUES THAT ARE OFTEN APPLIED BY INSTRUCTORS TO COUNTERACT THIS ISSUE, THOSE RELATED TO THE USE OF GAMING ELEMENTS SEEM TO VERY PROMISING. IN THIS CONTEXT, CONSIDERING THE USE OF GAME-LIKE PROPERTIES IN LEARNING SCENARIOS, KNOWN AS GAMIFICATION, HAS RECEIVED INCREASING INTEREST BY ACADEMIA IN RECENT YEARS. HOWEVER, ITS APPLICATION IN HIGHER EDUCATION CAN BE CHALLENGING, DUE TO SOME UNWANTED EFFECTS CAUSED BY THE LACK OF PROVEN DESIGN METHODOLOGIES HAVE BEEN DETECTED. CHOOSING THE ADEQUATE FORMAL PROCESS FOR GAMIFICATION DESIGN HAS BECOME AN IMPORTANT SUCCESS REQUIREMENT. THIS WORK PRESENTS A SYSTEMATIC REVIEW OF THE GAMIFICATION DESIGN FRAMEWORKS DISCUSSED IN THE LITERATURE, PROVIDING A USEFUL RESOURCE TO EDUCATIONAL PRACTITIONERS AS WELL AS GAMIFICATION DESIGNERS AND RESEARCHERS. A TOTAL OF 2314 UNIQUE WORKS ARE INITIALLY RECORDED, BASED ON QUERIES IN DATABASES, LIBRARIES, JOURNALS AND SEARCH ENGINES. AFTER APPLYING A SYSTEMATIC FILTERING PROCESS, A DEFINITIVE LIST OF 40 WORKS IS MORE CLOSELY ANALYSED. NEXT TO REVIEW OVER RELEVANT LITERATURE, AN ASSESSMENT OF THE MAIN FEATURES FOUND IN THE DISCUSSED APPROACHES IS GIVEN, WHILE ALSO CATEGORIZING THEM ACCORDING TO THEIR MAIN APPLICATION FIELD AND ITS SUITABILITY IN HIGHER EDUCATIONAL ENVIRONMENTS.
THIS PAPER PRESENTS RESULTS FROM A QUESTIONNAIRE (N=333) DESIGNED TO GAIN AN UNDERSTANDING OF INSTRUCTOR MOTIVATIONS AND EXPERIENCE WITH SOCIAL MEDIA USE IN EDUCATIONAL PRACTICE. DATA ON OVERALL USE OF SOCIAL MEDIA, AND INSTRUCTORS' USE OF SOCIAL MEDIA IN CLASSES ARE APPLIED TO ASSESS FACTORS LEADING TO PRESENT AND FUTURE USE OF SOCIAL MEDIA IN TEACHING, USING A FRAMEWORK BASED ON THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT2) MODEL. OUR FINDINGS SHOW USE IN TEACHING IS DRIVEN BY FACTORS ASSOCIATED WITH UTAUT'S PERFORMANCE EXPECTANCY CONSTRUCT, I.E., PERSONAL ENGAGEMENT WITH SOCIAL MEDIA, AND MODERATING CONDITION OF AGE, WITH OLDER PARTICIPANTS MAKING GREATER USE OF SOCIAL MEDIA IN TEACHING. OTHER CONSTRUCTS ASSOCIATED WITH USE ARE HABIT (EXPERIENCE TEACHING ONLINE), SOCIAL INFLUENCE (COLLEAGUES USING SOCIAL MEDIA), EFFORT EXPECTANCY (AWARENESS OF BARRIERS, STAYING INFORMED), FACILITATING CONDITIONS (INSTITUTIONAL TECHNOLOGY SUPPORT) AND MODERATING CONDITIONS (TEACHING AT A TWO-YEAR COLLEGE).
TRUSTWORTHINESS AND TECHNOLOGICAL SECURITY SOLUTIONS ARE CLOSELY RELATED TO ONLINE COLLABORATIVE LEARNING AND THEY CAN BE COMBINED WITH THE AIM OF REACHING INFORMATION SECURITY REQUIREMENTS FORE-LEARNING PARTICIPANTS AND DE. SIGNERS. MOREOVER, MOBILE COLLABORATIVE LEARNING IS AN EMERGING EDUCATIONAL MODEL DEVOTED TO PROVIDING THE LEARNER WITH THE ABILITY TO ASSIMILATE LEARNING ANY TIME AND ANYWHERE. IN THIS PAPER, WE JUSTIFY THE NEED OF TRUSTWORTHINESS MODELS AS A FUNCTIONAL REQUIREMENT DEVOTED TO IMPROVING INFORMATION SECURITY. TO THIS END, WE PROPOSE A METHODOLOGICAL APPROACH TO MODELLING TRUSTWORTHINESS IN ONLINE COLLABORATIVE LEARNING. OUR PROPOSAL SETS OUT TO BUILD A THEORETICAL APPROACH WITH THE AIM TO PROVIDE E-LEARNING DESIGNERS AND MANAGERS WITH GUIDELINES FOR INCORPORATING SECURITY INTO MOBILE ONLINE COLLABORATIVE ACTIVITIES THROUGH TRUSTWORTHINESS ASSESSMENT AND PREDICTION. (C) 2015 ELSEVIER B.V. ALL RIGHTS RESERVED.
DRAWING ON SELF-DETERMINATION THEORY (SDT), THE STUDY PROPOSES A MODEL TO REVEAL THE UNDERLYING MECHANISM BY WHICH SOCIAL INTERACTION SHAPES LEARNING ENGAGEMENT. THIS STUDY USES SURVEY DATA FROM 318 MASSIVE OPEN ONLINE COURSES (MOOCS) LEARNERS AND STRUCTURAL EQUATION MODELING METHOD TO ASSESS THE PROPOSED MODEL. THE RESULTS SHOW THAT IMMERSIVE EXPERIENCE AND THREE PSYCHOLOGICAL NEEDS SATISFACTION (I.E., COMPETENCE, RELATEDNESS, AND AUTONOMY NEEDS) FULLY MEDIATE THE EFFECT OF INTERACTION ON MOOC LEARNING ENGAGEMENT. PSYCHOLOGICAL NEEDS SATISFACTION DEMONSTRATES DIFFERENT EFFECTIVENESS ON LEARNING ENGAGEMENT. SPECIFICALLY, COMPETENCE NEED SATISFACTION IS SHOWN TO BE THE STRONGEST PREDICTOR OF MOOC LEARNING ENGAGEMENT, WHILE AUTONOMY NEED SATISFACTION HAS THE WEAKEST EFFECTIVENESS. FURTHER, THE RESULTS DEMONSTRATE THAT PEER LEARNING GROUP IDENTIFICATION STRENGTHENS THE EFFECT OF SOCIAL INTERACTION ON PSYCHOLOGICAL NEEDS SATISFACTION. THE STUDY IS AMONG THE FIRST TO SPECIFICALLY FOCUS ON EXPLORING THE MECHANISM HOW PSYCHOLOGICAL NEEDS SATISFACTION MEDIATES THE RELATIONSHIP BETWEEN SOCIAL INTERACTION AND LEARNING ENGAGEMENT AND HAS IMPORTANT MANAGERIAL IMPLICATIONS FOR MOOC PLATFORM DESIGN AND OPERATION IN ORDER TO FACILITATE MOOC ENGAGEMENT.
ADAPTIVE EDUCATIONAL GAMES PROVIDE NEW OPPORTUNITIES TO TRAIN EARLY NUMERICAL SKILLS. HOWEVER, EMPIRICAL EVIDENCE FOR THE EFFECTIVENESS OF ADAPTIVE EDUCATIONAL GAMES IS SCARCE. THIS STUDY INVESTIGATED THE EFFECTIVENESS OF AN ADAPTIVE GAME COMPARED TO A NONADAPTIVE GAME IN TERMS OF COGNITIVE, NONCOGNITIVE AND EFFICIENCY OUTCOMES. IN TOTAL, 84 CHILDREN WERE RANDOMLY ASSIGNED TO A CONDITION IN WHICH CHILDREN TRAINED EARLY NUMERICAL SKILLS WITH AN ADAPTIVE VERSION OF THE NUMBER SENSE GAME (NSG), OR TO A CONDITION IN WHICH THEY TRAINED WITH A NONADAPTIVE VERSION. EARLY NUMERACY WAS EVALUATED BEFORE THE TRAINING, IMMEDIATELY AFTER THE TRAINING AND 3 WEEKS AFTER THE TRAINING. MATH ANXIETY (MA) WAS ASSESSED BEFORE AND 3 WEEKS AFTER THE TRAINING. THE TIME CHILDREN PRACTICED WITH THE NSG WAS USED TO ASSESS EFFICIENCY. RESULTS REVEALED THAT CHILDREN IN BOTH CONDITIONS IMPROVED ON EARLY NUMERICAL ABILITY, WITH SUSTAINED EFFECTS 3 WEEKS AFTER THE TRAINING. IN BOTH CONDITIONS, CHILDREN'S MA SCORES WERE LOWER AFTER THE TRAINING. CHILDREN IN THE ADAPTIVE CONDITION LEARNED MORE EFFICIENTLY COMPARED TO THE NONADAPTIVE CONDITION, AND THE INTERACTION BETWEEN PRIOR KNOWLEDGE AND CONDITION HAS SHOWN THAT CHILDREN WITH LOW PRIOR KNOWLEDGE BENEFITED MORE FROM A NONADAPTIVE TRAINING WHILE CHILDREN WITH HIGH PRIOR KNOWLEDGE BENEFITED MORE FROM AN ADAPTIVE TRAINING IN TERMS OF LEARNING EFFICIENCY. THESE RESULTS CONFIRM THAT ADAPTIVE EDUCATIONAL GAMES CAN OFFER SOLACE IN TERMS OF THE NEED FOR DIFFERENTIATION.
INCORPORATION OF INCREASED FLEXIBILITY AND ADAPTIVITY IN COURSES CAN SUPPORT FRESHMEN WITH TAKING RESPONSIBILITY FOR THEIR LEARNING PROCESS. THIS IS ESPECIALLY BENEFICIAL IN THE CASE OF CHEMISTRY COURSES GIVEN THEIR TYPICALLY ABSTRACT NATURE AND ASSOCIATED NEED FOR ENGAGING IN PRACTICE AND HIGHER ORDER THINKING. BLENDED LEARNING CAN BE USED TO ACHIEVE COURSE FLEXIBILITY AND ADAPTIVITY, AND INCREASED STUDENT ENGAGEMENT. ADOPTING BLENDED LEARNING REQUIRES TEACHERS TO RECONSIDER AND REDESIGN THEIR COURSE SETUP. THE WILLINGNESS FOR THIS WILL LARGELY BE DICTATED BY PERCEIVED PRACTICALITY, HOWEVER. TO THIS END, OUR AIM IS TO DEVELOP PRACTICAL REDESIGN GUIDELINES FOR FLEXIBLE BLENDED LEARNING. SUCH GUIDELINES NEED TO BE INSTRUMENTAL, CONGRUENT, AND OF 'LOW COST' FOR A TEACHER. THE RESULTING FOUR GUIDELINES WERE FINE-TUNED IN TWO PHASES OVER 5 YEARS USING TWO INTRODUCTORY CHEMISTRY COURSES AND INCORPORATED VARIOUS BLENDED LEARNING ELEMENTS, SUCH AS SLIDE-CAST LECTURES, LIVE LECTURES, FORMATIVE IN-CAST MULTIPLE CHOICE QUESTIONS, TUTORIALS, FORMATIVE ELECTRONIC TUTORIAL QUESTIONS, AND STUDENT VOTING ON THE COURSE SCHEDULE. THE OUTCOME OF OUR MULTIYEAR ENDEAVOR WAS POSITIVE AND SUSTAINABLE BY ALL METRICS USED, INCLUDING ONLINE ENGAGEMENT BY STUDENTS, LEARNING OUTCOMES, AND STUDENT EVALUATIONS. THE TEACHERS IN PHASE II LARGELY EMBRACED THE GUIDELINES EMERGING FROM PHASE I, AND IN THEIR COURSE EXPERIENCED THEM PREDOMINANTLY AS PRACTICAL WHILE LIMITATIONS WERE ALSO IDENTIFIED. WE HOPE THAT OUR GUIDELINES AND EXPERIENCES CAN PROVIDE FELLOW TEACHERS WITH A BLUEPRINT FOR PRACTICAL INCORPORATION OF FLEXIBILITY AND ADAPTIVITY USING BLENDED LEARNING IN THEIR CHEMISTRY COURSES.
GAMIFICATION IS INCREASINGLY BECOMING A PERTINENT ASPECT OF ANY UI AND UX DESIGN. HOWEVER, A CANONICAL DEARTH IN RESEARCH AND APPLICATION OF GAMIFICATION HAS BEEN RELATED TO THE ROLE OF INDIVIDUAL DIFFERENCES IN SUSCEPTIBILITY TO GAMIFICATION AND ITS VARIED DESIGNS. TO ADDRESS THIS GAP, THIS STUDY REVIEWS THE EXTANT CORPUS OF RESEARCH ON TAILORED GAMIFICATION (42 STUDIES). THE FINDINGS OF THE REVIEW INDICATE THAT MOST STUDIES ON THE FIELD ARE MOSTLY FOCUSED ON USER MODELING FOR A FUTURE PERSONALIZATION, ADAPTATION, OR RECOMMENDATION OF GAME ELEMENTS. THIS USER MODEL USUALLY CONTAINS THE USERS' PREFERENCES OF PLAY (I.E., PLAYER TYPES), AND IS MOSTLY APPLIED IN EDUCATIONAL SETTINGS. THE MAIN CONTRIBUTIONS OF THIS PAPER ARE A STANDARDIZED TERMINOLOGY OF THE GAME ELEMENTS USED IN TAILORED GAMIFICATION, THE DISCUSSION ON THE MOST SUITABLE GAME ELEMENTS FOR EACH USERS' CHARACTERISTIC, AND A RESEARCH AGENDA INCLUDING DYNAMIC MODELING, EXPLORING MULTIPLE CHARACTERISTICS SIMULTANEOUSLY, AND UNDERSTANDING THE EFFECTS OF OTHER ASPECTS OF THE INTERACTION ON USER EXPERIENCE.
REFLECTING ON 50 YEARS OF EDUCATIONAL TECHNOLOGY RESEARCH, A CONTENT AND AUTHORSHIP ANALYSIS WAS CONDUCTED OF 1777 RESEARCH ARTICLE TITLES AND ABSTRACTS, PUBLISHED IN THE BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY (BJET) FROM 1970 TO ISSUE 3, 2018. A TEXT-MINING TOOL (LEXIMANCER) WAS USED TO IDENTIFY KEY CONCEPTS AND THEMES EMERGING THROUGHOUT EACH OF THE FIVE DECADES, WHICH WERE THEN COMPARED TO THOSE FOUND IN A PREVIOUS ANALYSIS OF COMPUTERS & EDUCATION, AS WELL AS THE MOST CITED BJET PUBLICATIONS IN EACH DECADE. THE FRAMEWORK OF WEST AND RICH (2012) WAS THEN USED TO PROVIDE GUIDANCE ON BJET'S RIGOUR, IMPACT AND PRESTIGE. COMMON THEMES IN BJET THROUGHOUT THE PAST 50 YEARS HAVE INCLUDED THE EVOLUTION OF TEACHING AND LEARNING IN DISTANCE EDUCATION, THE EMERGENCE OF INSTRUCTIONAL DESIGN, MISUNDERSTANDING BETWEEN PRACTITIONERS AND LEARNING DESIGNERS, ISSUES OF PRE AND IN-SERVICE TEACHER EDUCATION AND TECHNOLOGY UPTAKE BY EDUCATORS AND STUDENTS, INCLUDING THE CONFIDENCE TO DO SO, THE TECHNOLOGY SKILLS OF EDUCATORS AND STUDENTS, AS WELL AS A LACK OF INSTITUTIONAL SUPPORT TO PROVIDE SPACE AND TIME FOR TRAINING AND INTEGRATION TO OCCUR. THE AUTHORSHIP ANALYSIS REVEALED AN ENCOURAGING INCREASE IN INTERNATIONAL IDENTITY OVER TIME, WITH 60% OF ARTICLES IN BJET PUBLISHED BY AN AUTHOR OUTSIDE OF THE UK OVER THE PAST 50 YEARS, AND 79% IN THE PAST DECADE. DESPITE THIS, CONTRIBUTIONS FROM SOUTH AMERICA, THE MIDDLE EAST AND AFRICA ARE VASTLY UNDERREPRESENTED, AND AUTHORS FROM THESE REGIONS NEED FURTHER SUPPORT FROM THE FIELD. SUGGESTED FUTURE RESEARCH AREAS INCLUDE FINDING ALTERNATIVE MODELS OF EDUCATOR PROFESSIONAL DEVELOPMENT, FURTHER EXPLORATION OF THE ROLE OF THEORY AND POLICY, AS WELL AS THE RISE OF CO-AUTHORSHIP AND COLLABORATIVE RESEARCH.
GAMIFICATION HAS BEEN WIDELY EMPLOYED IN THE EDUCATIONAL DOMAIN OVER THE PAST EIGHT YEARS WHEN THE TERM BECAME A TREND. HOWEVER, THE LITERATURE STATES THAT GAMIFICATION STILL LACKS FORMAL DEFINITIONS TO SUPPORT THE DESIGN OF GAMIFIED STRATEGIES. THIS PAPER AIMS TO CREATE A TAXONOMY FOR THE GAME ELEMENTS, BASED ON GAMIFICATION EXPERTS' OPINIONS. AFTER A BRIEF REVIEW FROM EXISTING WORK, WE EXTRACT FIRST THE GAME ELEMENTS FROM THE CURRENT STATE OF THE ART, AND THEN EVALUATE THEM VIA A SURVEY WITH 19 GAMIFICATION AND EDUCATION EXPERTS. THE RESULTING TAXONOMY TAXONOMY INCLUDED THE DESCRIPTION OF 21 GAME ELEMENTS AND THEIR QUANTITATIVE AND QUALITATIVE EVALUATION BY THE EXPERTS. OVERALL, THE PROPOSED TAXONOMY WAS IN GENERAL WELL ACCEPTED BY MOST OF THE EXPERTS. THEY ALSO SUGGESTED EXPANDING IT WITH THE INCLUSION OF NARRATIVE AND STORYTELLING GAME ELEMENTS. THUS, THE MAIN CONTRIBUTION OF THIS PAPER IS PROPOSING A NEW, CONFIRMED TAXONOMY TO STANDARDISE THE TERMINOLOGY USED TO DEFINE THE GAME ELEMENTS AS A MEAN TO DESIGN AND DEPLOY GAMIFICATION STRATEGIES IN THE EDUCATIONAL DOMAIN.
GAMIFICATION APPLIES GAME ELEMENTS IN NON-GAME CONTEXTS TO IMPROVE USERS' EXPERIENCE. ONE OF THE CONTEXTS THAT HAVE MOSTLY TAKEN ADVANTAGE OF GAMIFICATION IS THE EDUCATIONAL ONE. HOWEVER, THE STUDENTS' EXPERIENCE IS UNIQUE, SINCE IT VARIES ACCORDING TO THEIR PROFILE. THEREFORE, THE INDIVIDUALITIES OF EACH STUDENT MUST BE CONSIDERED TO IMPROVE THE STUDENTS' EXPERIENCE. THIS PAPER AIMS TO EXPLORE THE GAMIFICATION PROPERTIES AND ANALYZE THE RESULTS OF THE USER-CENTERED APPLICATION. FOR THIS, WE PROPOSED A FRAMEWORK FOCUSED ON THE USER-CENTERED GAMIFICATION IN THE EDUCATIONAL CONTEXT, TAKING INTO ACCOUNT PERSONAL, FUNCTIONAL, PSYCHOLOGICAL, TEMPORAL, PLAYFUL, IMPLEMENTABLE AND EVALUATIVE PROPERTIES. AFTER APPLYING THE FRAMEWORK IN THE E-LEARNING SYSTEM, THE CONTROLLED EXPERIMENT WITH 139 STUDENTS REVEALED AN INCREASE IN STUDENTS' INTERACTION, ENGAGEMENT AND SATISFACTION. THUS, OUR MAIN CONTRIBUTIONS ARE THE IMPROVEMENT OF THE STUDENTS' EXPERIENCE WITH THE USER-CENTERED GAMIFICATION AND THE DEFINITION OF A FRAMEWORK TO ASSIST IN ITS APPLICATION. RESEARCH HIGHLIGHTS THIS PAPER PROPOSES A USER-CENTERED GAMIFICATION FRAMEWORK FOR EDUCATIONAL CONTEXT IT HAS BEEN APPLIED IN AN E-LEARNING SYSTEM AND A CONTROLLED EXPERIMENT WAS CONDUCTED A TOTAL OF 139 STUDENTS ENROLLED FOR AN ONLINE COURSE, AND HALF OF THEM USED THE GAMIFIED VERSION RESULTS INDICATE AN INCREASE IN THE STUDENTS' INTERACTION, ENGAGEMENT AND SATISFACTION
THIS CONTRIBUTION PROPOSES SOFTWARE INFRASTRUCTURE TO SUPPORT NEW TYPES OF LEARNING METHODOLOGIES AND RESOURCES BASED ON COLLABORATIVE KNOWLEDGE ENGINEERING BY MEANS OF AN INNOVATIVE APPLICATION FRAMEWORK CALLED THE VIRTUALIZED COLLABORATIVE SESSIONS FRAMEWORK (VCSF). THE VCSF HELPS MEET CHALLENGING COLLABORATIVE KNOWLEDGE ENGINEERING REQUIREMENTS IN ONLINE LEARNING, SUCH AS INCREASING GROUP MEMBERS' LEARNING PERFORMANCE DURING THE ON-LINE COLLABORATIVE LEARNING PROCESS. IN TURN, SYSTEMATIC APPLICATION OF THE VCSF PLATFORM ENRICHED WITH SEMANTIC KNOWLEDGE ENGINEERING TECHNOLOGIES ENABLES E-LEARNING DEVELOPERS TO LEVERAGE SUCCESSFUL COLLABORATIVE LEARNING EXPERIENCES IN A SOFTWARE REUSE FASHION WHILE SAVING DEVELOPMENT TIME AND EFFORT. THE FRAMEWORK IS PROTOTYPED AND SUCCESSFULLY TESTED IN REAL ENVIRONMENTS, THUS SHOWING THE SOFTWARE REUSE CAPABILITY AND THE COLLABORATIVE KNOWLEDGE ENGINEERING BENEFITS OF THE VCSF APPROACH. THE RESEARCH REPORTED IN THIS PAPER WAS UNDERTAKEN WITHIN THE ALICE PROJECT FUNDED THROUGH THE EUROPEAN 7TH FRAMEWORK PROGRAM (FP7).
ADAPTIVE LEARNING SYSTEMS AIM TO PROVIDE LEARNING ITEMS TAILORED TO THE BEHAVIOR AND NEEDS OF INDIVIDUAL LEARNERS. HOWEVER, ONE OF THE OUTSTANDING CHALLENGES IN ADAPTIVE ITEM SELECTION IS THAT OFTEN THE CORRESPONDING SYSTEMS DO NOT HAVE INFORMATION ON INITIAL ABILITY LEVELS OF NEW LEARNERS ENTERING A LEARNING ENVIRONMENT. THUS, THE PROFICIENCY OF THOSE NEW LEARNERS IS VERY DIFFICULT TO BE PREDICTED. THIS HEAVILY IMPAIRS THE QUALITY OF PERSONALIZED ITEMS' RECOMMENDATION DURING THE INITIAL PHASE OF THE LEARNING ENVIRONMENT. IN ORDER TO HANDLE THIS ISSUE, KNOWN AS THE COLD-START PROBLEM, WE PROPOSE A SYSTEM THAT COMBINES ITEM RESPONSE THEORY (IRT) WITH MACHINE LEARNING. SPECIFICALLY, WE PERFORM ABILITY ESTIMATION AND ITEM RESPONSE PREDICTION FOR NEW LEARNERS BY INTEGRATING IRT WITH CLASSIFICATION AND REGRESSION TREES BUILT ON LEARNERS' SIDE INFORMATION. THE GOAL OF THIS WORK IS TO BUILD A LEARNING SYSTEM THAT INCORPORATES IRT AND MACHINE LEARNING INTO A UNIFIED FRAMEWORK. WE COMPARE THE PROPOSED HYBRID MODEL TO ALTERNATIVE APPROACHES BY CONDUCTING EXPERIMENTS ON TWO EDUCATIONAL DATA SETS. THE OBTAINED RESULTS AFFIRMED THE POTENTIAL OF THE PROPOSED METHOD. IN PARTICULAR, THE OBTAINED RESULTS INDICATE THAT IRT COMBINED WITH RANDOM FORESTS PROVIDES THE LOWEST ERROR FOR THE ABILITY ESTIMATION AND THE HIGHEST ACCURACY IN TERMS OF RESPONSE PREDICTION. THIS WAY, WE DEDUCE THAT THE EMPLOYMENT OF MACHINE LEARNING IN COMBINATION WITH IRT COULD INDEED ALLEVIATE THE EFFECT OF THE COLD START PROBLEM IN AN ADAPTIVE LEARNING ENVIRONMENT.
THE POTENTIAL OF GAMIFICATION TO IMPROVE USERS' MOTIVATION AND ENGAGEMENT INFLUENCED MANY RESEARCHERS AND PROFESSIONALS TO ANALYSE ITS EFFECTS IN EDUCATIONAL SETTINGS. WHILE SOME STUDIES FOCUS ON ADAPTING GAME ELEMENTS ACCORDING TO DEMOGRAPHIC AND BEHAVIOURAL INFORMATION OF THE USER PROFILE, FEW OF THEM EXPLORE (OR EVEN CONSIDER) CULTURAL FACTORS. THESE CULTURAL FACTORS PLAY AN ESSENTIAL ROLE IN OUR SOCIETIES' DEVELOPMENT. THUS, THIS WORK PROPOSES AND EVALUATES A REPRESENTATIVE MODEL TO UNDERSTAND BETTER THE RELATIONSHIP BETWEEN CULTURAL FACTORS AND GAMIFICATION WITHIN EDUCATIONAL DOMAINS, NAMELY THE GAMIFICATION FOR CULTURAL STUDIES MODEL (GAMICSM). THROUGH A QUALITATIVE APPROACH, WE MAP HOFSTEDE'S CULTURAL DIMENSIONS (I.E., POWER DISTANCE, INDIVIDUALISM/COLLECTIVISM, UNCERTAINTY AVOIDANCE, MASCULINITY/FEMININITY, LONG/SHORT-TERM ORIENTATION, AND INDULGENCE/RESTRAINT) WITH A TAXONOMY OF GAMIFICATION ELEMENTS FOR EDUCATION (TGEEE), A RECENT MODEL FOR GAMIFICATION ELEMENTS FOR EDUCATIONAL ENVIRONMENTS. FURTHERMORE, WE ADAPTED A SURVEY TO EVALUATE THE RESULTANT MODEL WITH EIGHT DOMAIN EXPERTS IN GAMIFICATION AND EDUCATION. BASED ON THIS EVALUATION, WE ARE ABLE TO PROPOSE A STARTING MODEL, CONTAINING SOME ADDITIONAL REFINEMENTS AND IMPROVEMENTS. THUS, THE MAIN CONTRIBUTIONS OF THIS WORK ARE: (I) THE FIRST MODEL TO RELATE GAME ELEMENTS AND CULTURAL DIMENSIONS WITHIN EDUCATIONAL DOMAINS AND (II) A STATE-OF-THE-ART EMPIRICAL STUDY INTERSECTING CULTURE, GAMIFICATION AND EDUCATION.
ON ANY TYPE OF COURSE (ON SITE OR ONLINE), A LEARNER IS EVALUATED WHETHER HE HAS ACQUIRED THE KNOWLEDGE AND COMPETENCES PROVIDED IN THE COURSE. THE EVALUATION SHOULD BE PERFORMED BY EVALUATING HIS PROGRESSION BY MEANS OF THE INTERACTION IN THE CLASSROOM OR ASSESSMENT ACTIVITIES. MOSTLY, ASSESSMENT ACTIVITIES ARE USED TO CHECK THE LEVEL OF EXPERTISE OF THE LEARNER. TYPICALLY, THE ASSESSMENT MODEL AND ASSESSMENT ACTIVITIES OF SUBJECTS IN OFFICIAL PROGRAMMES ARE THE SAME FOR ALL THE LEARNERS, SINCE THEY SHOULD BE EVALUATED HAVING THE SAME OPPORTUNITIES AND CONDITIONS. HOWEVER, WHEN THE LEARNER IS EVALUATED BASED ON A CONTINUOUS ASSESSMENT MODEL, HE IS DEMONSTRATING ON EACH ACTIVITY HIS KNOWLEDGE AND PROFICIENCY LEVEL AND, AT THE SAME TIME, HIS REPUTATION COULD BE ALSO BUILT BASED ON THE ACTIONS HE IS PERFORMING WITHIN THE COURSE. THEREFORE, THE ASSESSMENT MODEL CAN BE PARTICULARLY ADAPTED FOR EACH LEARNED BASED ON THIS INFORMATION. IN THIS PAPER, WE PRESENT A GENERAL SYSTEM TO ADAPT ANY COMPONENT OF THE ASSESSMENT PROCESS (MODEL, ACTIVITY, QUESTION.) BASED ON DIFFERENT EVIDENCES GATHERED FROM THE LEARNING PROCESS OF THE LEARNER.
SIGNIFICANT GENDERED PERFORMANCE DIFFERENCES ARE SIGNALS OF SYSTEMIC INEQUITY IN HIGHER EDUCATION. UNDERSTANDING OF THESE INEQUITIES HAS BEEN HAMPERED BY THE LOCAL NATURE OF PRIOR STUDIES; CONSISTENT MEASURES OF PERFORMANCE DISPARITY ACROSS MANY DISCIPLINES AND INSTITUTIONS HAVE NOT BEEN AVAILABLE. HERE, WE REPORT THE FIRST WIDE-RANGING, MULTI-INSTITUTION MEASURES OF GENDERED PERPRMANCE DIFFERENCE, EXAMINING MORE THAN A MILLION STUDENT ENROLLMENTS IN HUNDREDS OF COURSES AT FIVE UNIVERSITIES. AFTER CONTROLLING FOR FACTORS THAT RELATE TO ACADEMIC PERFORMANCE USING OPTIMAL MATCHING, WE IDENTIFY PATTERNS OF GENDERED PERFORMANCE DIFFERENCE THAT ARE CONSISTENT ACROSS THESE UNIVERSITIES. BIOLOGY, CHEMISTRY, PHYSICS, ACCOUNTING, AND ECONOMICS LECTURE COURSES REGULARLY EXHIBIT GENDERED PERFORMANCE DIFFERENCES THAT ARE STATISTICALLY AND MATERIALLY SIGNIFICANT, WHEREAS LAB COURSES IN THE SAME SUBJECTS DO NOT. THESE RESULTS REINFORCE THE IMPORTANCE OF BROAD INVESTIGATION OF PERFORMANCE DISPARITIES ACROSS HIGHER EDUCATION. THEY ALSO HELP FOCUS EQUITY RESEARCH ON THE STRUCTURE AND EVALUATIVE SCHEMES OF THESE LECTURE COURSES.
COMPETITIVE ORGANIZATIONS HAVE IMPLEMENTED SYSTEMS OF BUSINESS INTELLIGENCE IN ORDER TO HELP EMPLOYEES IN THE PROCESS OF EVIDENCE-BASED DECISION-MAKING. USING THESE SYSTEMS IN UNIVERSITY WILL PROVIDE A SET OF ANALYTICAL TOOLS THAT SUPPORT DECISION-MAKING OF ACADEMICS FOCUSED TO THE IMPROVEMENT OF THEIR RESEARCH AND TEACHING ACTIVITIES. IN THE CASE OF TEACHERS, FOR EXAMPLE, IT MAY HELP TO BETTER UNDERSTAND STUDENTS, HOW THEY LEARN AND HOW TO IMPROVE THE LEARNING PROCESSES ACCORDING TO EVIDENCES. TO IMPLEMENT THESE SYSTEMS EFFICIENTLY IT IS NECESSARY TO GATHER DATA ABOUT THE ACTIVITIES STUDENTS AND TEACHERS PERFORM DURING THE LEARNING-TEACHING PROCESS. CURRENTLY, MOST UNIVERSITIES PROVIDE VIRTUAL LEARNING ENVIRONMENTS (VLE) WHERE STUDENTS PERFORM MOST OF THEIR LEARNING ACTIVITIES. THESE ENVIRONMENTS MAY STORE DATA ABOUT THE INTERACTION OF THEIR USERS AND, THEREFORE, GATHER INFORMATION OF ALL THE AGENTS DURING THE TEACHING-LEARNING PROCESS. OUR PROPOSAL IS TO ADOPT THE STRATEGIES OF BUSINESS INTELLIGENCE, WHICH RESULTED USEFUL IN ORGANIZATIONS, TO UNIVERSITIES. BY APPLYING ANALYTIC TECHNIQUES ON THE LARGE VOLUME OF DATA STORED IN THE VLE, WE PROPOSE TO BUILD DASHBOARDS FOR TEACHERS AND ACADEMIC PROGRAM MANAGERS IN ORDER TO HELP THEM TO TAKE DECISIONS THAT IMPROVE TEACHING IN THE SHORT, MEDIUM AND LONG TERM.
ADAPTIVE LEARNING SYSTEMS HAVE RECEIVED AN INCREASING ATTENTION AS THEY ENABLE TO PROVIDE PERSONALIZED INSTRUCTIONS TAILORED TO THE BEHAVIORS AND NEEDS OF INDIVIDUAL LEARNERS. IN ORDER TO REACH THIS GOAL, IT IS DESIRED TO HAVE AN ASSESSMENT SYSTEM, MONITORING EACH LEARNER'S ABILITY CHANGE IN REAL TIME. THE ELO RATING SYSTEM (ERS), A POPULAR SCORING ALGORITHM FOR PAIRED COMPETITIONS, HAS RECENTLY BEEN CONSIDERED AS A FAST AND FLEXIBLE METHOD THAT CAN ASSESS LEARNING PROGRESS IN ONLINE LEARNING ENVIRONMENTS. HOWEVER, IT HAS BEEN ARGUED THAT A STANDARD ERS MAY BE PROBLEMATIC DUE TO THE MULTIDIMENSIONAL NATURE OF THE ABILITIES EMBEDDED IN LEARNING MATERIALS. IN ORDER TO HANDLE THIS ISSUE, WE PROPOSE A SYSTEM THAT INCORPORATES A MULTIDIMENSIONAL ITEM RESPONSE THEORY MODEL (MIRT) IN THE ERS. THE BASIC IDEA IS THAT INSTEAD OF UPDATING A SINGLE ABILITY PARAMETER FROM THE RASCH MODEL, OUR METHOD ALLOWS A SIMULTANEOUS UPDATE OF MULTIPLE ABILITY PARAMETERS BASED ON A COMPENSATORY MIRT MODEL, RESULTING IN A MULTIDIMENSIONAL EXTENSION OF THE ERS ("M-ERS"). TO EVALUATE THE APPROACH, THREE SIMULATION STUDIES WERE CONDUCTED. RESULTS SUGGEST THAT THE ERS THAT INCORRECTLY ASSUMES UNIDIMENSIONALITY HAS A SERIOUSLY LOWER PREDICTION ACCURACY COMPARED TO THE M-ERS. ACCOUNTING FOR BOTH SPEED AND ACCURACY IN M-ERS IS SHOWN TO PERFORM BETTER THAN USING ACCURACY DATA ONLY. AN APPLICATION FURTHER ILLUSTRATES THE METHOD USING REAL-LIFE DATA FROM A POPULAR EDUCATIONAL PLATFORM FOR EXERCISING MATH SKILLS.
ELECTRONIC LEARNING SYSTEMS HAVE RECEIVED INCREASING ATTENTION BECAUSE THEY ARE EASILY ACCESSIBLE TO MANY STUDENTS AND ARE CAPABLE OF PERSONALIZING THE LEARNING ENVIRONMENT IN RESPONSE TO STUDENTS' LEARNING NEEDS. TO THAT END, USING FAST AND FLEXIBLE ALGORITHMS THAT KEEP TRACK OF THE STUDENTS' ABILITY CHANGE IN REAL TIME IS DESIRABLE. RECENTLY, THE ELO RATING SYSTEM (ERS) HAS BEEN APPLIED AND STUDIED IN BOTH RESEARCH AND PRACTICAL SETTINGS (BRINKHUIS & MARIS, 2009; KLINKENBERG, STRAATEMEIER, & VAN DER MAAS IN COMPUTERS & EDUCATION, 57, 1813-1824, 2011). HOWEVER, SUCH ADAPTIVE ALGORITHMS FACE THE COLD-START PROBLEM, DEFINED AS THE PROBLEM THAT THE SYSTEM DOES NOT KNOW A NEW STUDENT'S ABILITY LEVEL AT THE BEGINNING OF THE LEARNING STAGE. THE COLD-START PROBLEM MAY ALSO OCCUR WHEN A STUDENT LEAVES THE E-LEARNING SYSTEM FOR A WHILE AND RETURNS (I.E., A BETWEEN-SESSION PERIOD). BECAUSE EXTERNAL EFFECTS COULD INFLUENCE THE STUDENT'S ABILITY LEVEL DURING THE PERIOD, THERE IS AGAIN MUCH UNCERTAINTY ABOUT ABILITY LEVEL. TO ADDRESS THESE PRACTICAL CONCERNS, IN THIS STUDY WE PROPOSE ALTERNATIVE APPROACHES TO COLD-START ISSUES IN THE CONTEXT OF THE E-LEARNING ENVIRONMENT. PARTICULARLY, WE PROPOSE MAKING THE ERS MORE EFFICIENT BY USING AN EXPLANATORY ITEM RESPONSE THEORY MODELING TO ESTIMATE STUDENTS' ABILITY LEVELS ON THE BASIS OF THEIR BACKGROUND INFORMATION AND PAST TRAJECTORIES OF LEARNING. A SIMULATION STUDY WAS CONDUCTED UNDER VARIOUS CONDITIONS, AND THE RESULTS SHOWED THAT THE PROPOSED APPROACH SUBSTANTIALLY REDUCES ABILITY ESTIMATION ERRORS. WE ILLUSTRATE THE APPROACH USING REAL DATA FROM A POPULAR LEARNING PLATFORM.
A POPULAR ACTIVITY AMONG YOUNG CHILDREN IS THE USE OF MOBILE DEVICES AND APPS. YET, THE IMPACT OF MOBILE DEVICES ON LEARNING AND DEVELOPMENT IS RATHER UNDEREXPLORED. THE LIMITED STUDIES IDENTIFIED EXPLORE EFFECTS ON LITERACY DEVELOPMENT AND COMMUNICATION AND REPORT ON MIXED FINDINGS. A CONSIDERABLE GAP IS OBSERVED AS TO HOW THE USE OF MOBILE APPS RELATES TO YOUNG CHILDREN'S UNDERSTANDING IN DIVERSE DOMAINS INCLUDING SCIENCE LEARNING, AND TO EXTEND, WHETHER AND HOW MOBILE APPS SHOULD BE USED AND HOW IN EARLY YEARS' SETTINGS. THE AIM OF THIS PAPER IS TO SHED LIGHT ON THIS AREA BY EXAMINING THE LEARNING EFFECTS OF TOUCH SCREEN MOBILE GAME APPLICATIONS, IN PARTICULAR THE GAME ANGRY BIRDS, ON TWO GROUPS OF PRESCHOOLERS 4 AND 5 YEARS OLD RESPECTIVELY. EVIDENCE FROM A COMPARATIVE STUDY WITH 32 PARTICIPANTS REVEAL SIGNIFICANT DIFFERENCES BETWEEN THE TWO GROUPS IN TERMS OF GAME SKILLS AND THEIR UNDERSTANDING OF PROJECTILE MOTION. IMPLICATIONS FOR EDUCATIONAL STAKEHOLDERS, PARENTS AND APP DESIGNERS ARE DISCUSSED ALONG WITH FUTURE RESEARCH DIRECTIONS.
WITH THE PROLIFERATION OF MOOCS AND THE LARGE AMOUNT OF DATA COLLECTED, A LOT OF QUESTIONS HAVE BEEN ASKED ABOUT THEIR VALUE AND EFFECTIVENESS. ONE OF THE KEY ISSUES EMERGING IS THE DIFFICULTY IN THE SENSE-MAKING FROM THE DATA AVAILABLE. THE USE OF ANALYTIC DASHBOARDS HAS BEEN SUGGESTED TO PROVIDE QUICK INSIGHTS AND DISTIL THE LARGE VOLUME OF LEARNER INTERACTION DATA GENERATED. THESE DASHBOARDS HOLD THE PROMISE OF PROVIDING A CONTEXTUALIZED VIEW OF DATA AND FACILITATING USEFUL RESEARCH EXPLORATION. HOWEVER, LITTLE HAS BEEN DONE IN DEFINING HOW THESE DASHBOARDS SHOULD BE CREATED, OFTEN RESULTING IN A PROLIFERATION OF SYSTEMS FOR EACH NEW RESEARCH AGENDA. WE PRESENT OUR EXPERIENCE OF BUILDING MOOC DASHBOARDS FOR TWO DIFFERENT PLATFORMS, NAMELY COURSERA AND FUTURELEARN, MOTIVATED BY A SET OF DESIGN GOALS WITH INPUT FROM A DIVERSE SET OF STAKEHOLDERS. WE DEMONSTRATE THE FEATURES OF THE SYSTEM AND HOW IT HAS SERVED TO MAKE DATA ACCESSIBLE AND USEABLE. WE REPORT ON PROBLEMS FACED, DRAWING ON ANALYSES OF THINK-ALOUD SESSIONS CONDUCTED WITH REAL EDUCATORS, WHICH HAVE INFORMED OUR DASHBOARD PROCESS.
TECHNOLOGY IS ADVANCING AT AN EVER-INCREASING SPEED. THE BACKEND CAPABILITIES AND THE FRONTEND MEANS OF INTERACTION ARE REVOLUTIONIZING ALL KINDS OF APPLICATIONS. IN THIS PAPER, WE ANALYZE HOW THE TECHNOLOGICAL BREAKTHROUGHS SEEM TO MAKE EDUCATIONAL INTERACTIONS LOOK SMARTER AND MORE HUMAN. AFTER DEFINING EDUCATION 4.0 FOLLOWING THE INDUSTRY 4.0 IDEA, WE IDENTIFY THE KEY BREAKTHROUGHS OF THE LAST DECADE IN EDUCATIONAL TECHNOLOGY, BASICALLY REVOLVING AROUND THE CONCEPT CLOUD COMPUTING, AND IMAGINE A NEW WAVE OF EDUCATIONAL TECHNOLOGIES SUPPORTED BY MACHINE LEARNING THAT ALLOWS DEFINING EDUCATIONAL SCENARIOS WHERE COMPUTERS INTERACT AND REACT MORE AND MORE LIKE HUMANS.