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Discovering Collaborative Patterns in eLearning through Temporal Data Mining and Machine Learning

frayne avatar
frayne
32 diapositives · 471 Views · Sep 02, 2014

This study explores innovative methods for understanding collaborative eLearning patterns using machine learning and temporal data mining. By analyzing huge volumes of asynchronous and synchronous data, we investigate user engagement, learning styles, and collaborative activities within online platforms. We discuss fuzzy rules for data analysis, focusing on duration, frequency, and anomaly detection in learning sessions. Our findings contribute to enhancing courseware technology, assessment methodologies, and collaborative engagement, providing insights for effective eLearning strategies and improving user experience.

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Discovering Collaborative Patterns in eLearning through Temporal Data Mining and Machine Learning
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  1. Discovering Collaborative Patterns in eLearning from Meta-code Subsequence Name: Yip Chi Kin Date: 15-01-2005

  2. Motivation ․Collaborative eLearning ․Machine Learning Assess ․Temporal Data Mining ․Generalization Usage

  3. eLearning ․Learning Style & Model ․Collaborative activity ․Courseware ․Technology ․Assessment

  4. Temporal Data ․Asynchronousness ․Irregularity ․Huge Volume ․Streaming Data ․Distributed analysis ․Heterogeneous data types Synchronous Dataset

  5. Temporal Mining Prediction Correlation Regression Benchmarking Causality Analysis Periodic Pattern Mining Sequential Event Patterns Temporal Association Finding Clustering and Classification Threshold selection Frequency Analysis Anomaly Detection Bioinformatics approach

  6. Tracking Techniques ․Full Screen HyperCard ․Click tracking of user ․Timeout timestamp ․Referrer timestamp ․Full-loaded timestamp ․Machine hanging ․Diminish / Enlarge Platform Window ․Delay capture session code

  7. Temporal Timestamp Event ID User ID Timestamp From Referrer 22345 101 20030620160000 S21 t12 22346 66 20030620160008 PLI ipi 22347 101 20030620160010 T12 cmi S21, t12, … are HTML page codes ipi, cmi, … are communicative sessions

  8. Temporal Grouping ID User ID Timestamp Group_id 223 101 20030620160000 21 224 66 20030620160008 35 225 101 20030620160010 42 Members could be join to another group Anytime in the eLearning Platform

  9. Data Cleaning ․Hacking Problem ․Graphics Learning ․Session Errors ․Double Clicks

  10. Duration Duration(Page) = Starting Timestamp(Page)  Ending Timestamp(Page) = StEt = Dt = Duration of each page ․Fuzzy rule: if Dt1 second then Dt= 0 Browse weight Bc =s ( c Dt ) s = Number of students, where 1 s 163 t = Timestamp c = Pagecode

  11. Frequency Ps = Frequency(Page) of each student fc = s Ps=Total frequence of page ․Fuzzy rule: if Dt 3 seconds then fc = fc + 1 s = Number of students, where 1 s 163 t = Timestamp c = Pagecode where

  12. Weighting Navigation of Page where • Bc is Duration of Page Code • fc is Frequency of Page Code • c is Linguistic variable of each Events

  13. Navigation Pages Statistics Raw Browsing data After Logarithm

  14. Normalization ․Unique Interval Normalization of Page Code ( 0 wn 1 ) where xn is source value and wn is weight value maxx macima and minx minima for all data

  15. Events Coding A = Concepts B = Individual C = Collaboration D = Technique E = eLearning X = Idle Time Theory, Courseware Video, Skill

  16. Information Granules ․Linguistic variables ․Fuzzy Reasoning ․Interval Valued ․Super Subsequence

  17. Meta-Code Code about code Tri-event pattern Fuzzy Rules Interval Valued Linguistic variables Information Granulation Events Code MetaCode

  18. Frequency & Time Temporal Reasoning Linguistic Variables Temporal Database Fuzzy Rules Fuzzy Rules Granular Computing Timestamp Event Weight Time Partitioning Code Sequence Contiguous Sequence … C D C B B A A C Discretization

  19. Mutual event window size B B A A A A A X X X X X A A A A D D D A A A A A D C A A A B B B B D D A A A C C A D B B B A D D Group Size A A A A C X A A A A A D D D D D A A A A A A A A D D A A B A A B B B A A A C C C C D D D B B B B C B A A X X X A A A C C B B B B C C D D D C C C Communication Window Individual Personalization Profile … … … … … Collaborative Window

  20. Collaborative Link Increasing weights from collaborative links Member #1 Member #2 Synchronous Communication Sc =ncWc Member #3 Member #4 where n is Numbers of Links W is weight of events Member #5

  21. Synchronous Communication Synchronous Weight Syne = Sc where e is events c is member of group

  22. Asynchronous Communication Communication Asyne = Ac Asynchronous Weight where W is weight of events n is Numbers of Asynchronous Communications

  23. Capture Windows Study period subsequence 6480000 sec Capture direction One day period 86400 sec Minimum weighting & Maximum weighting Effective communications 100000 to 1400000 sec Points of Weight = (Asyne + Syne)

  24. Minimum Windowing

  25. Maximum Windowing

  26. Result Applications • Curriculum Planning Coursework (#5) studying period should be more than 6 days • Collaborative Assessment Range of Collaboration benchmark is 77920 to 176497 points of weight • Effective Communication Necessary communication period is 4 days

  27. Project Enhancement ․Huge Volume Implementation (Apply special algorithms) ․Rewrite C++ Programs (Generalization Usage) ․Data Organization (One day  Members = 86400  163)․Visualization of Patterns

  28. n MetaCode SubSequence … A A B B B D C A A A A C C C C A A A A B B D D D … A B D C A C A A B D MetaCode Events SubSequence MetaCode Modeling Time interval = 1 second , weblog duration = 75 days , Code length of Personalization Profile = n = 75246060 = 6480000

  29. Tri-event Pattern Mutual relationships of tri-event pattern in sub-sequence ․Comparison of good/bad tri-event patterns ․Frequent sequential pattern finding (tri-event) ․Longest common subsequence ․Super Subsequence ․Sequential events prediction ․Sequence reconstruction ․Viterbi algorithm (hamming distance + Transformational grammar )

  30. 010 110 011 000 0001110100 001 111 101 100 Super Subsequence set of tri-event: {000, 001, 010, 011, 100, 101, 110, 111} concatenation Super Subsequence : 000 001 010 011 100 101 110 111 Super Subsequence

  31. Further Research ․Automata and Computability ․Implementing Algorithms ․Fuzzy Linguistic Associative Rules ․Fuzzy Reasoning Partitioning ․Subsequence Matching ․MetaCode Grammar

  32. Conclusions ․Assess Collaboration ․Granular Modeling ․Generalization Usage