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OLAT Log Analysis
- 1. Introduction Mapping Log To MXML Algorithms Case Study Discussions Outline A Log-based Learning Content Creation (Part I) OLAT Course Log Analysis Yi Guo Supervised by: Prof. Harald Gall Universit¨t Z¨rich a u Institut f¨r Informatik u SEAL IFI Soft Talks, 2009 1 / 48
- 2. Introduction Mapping Log To MXML Algorithms Case Study Discussions 1 Introduction Motivation Objective 2 Mapping Log To MXML Raw Logs MXML Format Mapping 3 Algorithms Process Mining Overview Heuristic Mining Fuzzy Mining 4 Case Study 5 Discussions 2 / 48
- 3. Introduction Mapping Log To MXML Motivation Algorithms Objective Case Study Discussions Motivation Requirement of Legacy LMS The monitoring solution of legacy LMS is incomplete To analyze course activities it is necessary to correctly set up the data recording when creating a new OLAT course. — OLAT 6.1 User Manual Abstract the course schema from course contents go to olat courses table Next generation e-learning courses need clearer reference and schema 3 / 48
- 4. Introduction Mapping Log To MXML Motivation Algorithms Objective Case Study Discussions Objective Have an accurate view of the ”learning patterns” Construct a clearer model of user behaviors 4 / 48
- 5. Introduction Mapping Log To MXML Raw Logs Algorithms MXML Format Case Study Mapping Discussions Raw Logs 5 / 48
- 6. Introduction Mapping Log To MXML Raw Logs Algorithms MXML Format Case Study Mapping Discussions MXML Format Figure: MXML Class Diagram 6 / 48
- 7. Introduction Mapping Log To MXML Raw Logs Algorithms MXML Format Case Study Mapping Discussions Mapping Assumptions 1 Single user 2 Single session 3 No noisy data Figure: OLAT Log and MXML 7 / 48
- 8. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions Process Mining supports/ “world” controls business processes software people machines system components organizations records specifies events, e.g., configures messages, models implements transactions, verification analyzes analyzes etc. process/ discovery event system logs conformance model Figure: Process mining: link logs to models 8 / 48
- 9. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions A Heuristic Algorithm Advantage Less sensitive for noise and the incompleteness of logs can handle some limitations of the α-algorithm 9 / 48
- 10. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions Construction of the dependency/frequency table A, B: sample event B #B #B<A #A>B $A →L B $A → B Metric Calculation $A →L B = (#A>B − #B>A)/(#A>B + #B>A + 1) (1) $A → B = $A →L B × δ n (2) δ: fall factor n: the intermediary event number DS(X , Y ) = (($X →L Y )2 + ($X → Y )2 ) (3) 10 / 48
- 11. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions Dependency/Frequency graph Figure: D/F graph 11 / 48
- 12. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions Fuzzy Mining Why Spaghetti-like ? 1 Less-structured process 2 2 assumptions 1 reliablity 2 existence 12 / 48
- 13. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions Fuzzy Mining Algorithms Metric Matrix Unary Binary Significance Frequency Frequency 3 principles Routing Distance Correlation x Proximity 1 Aggregation x Endpoint 2 Abstraction x Originator 3 Emphasis x Data Type x Data Value Table: Metric matrix 13 / 48
- 14. Introduction Mapping Log To MXML Process Mining Overview Algorithms Heuristic Mining Case Study Fuzzy Mining Discussions Result nodes in cluster 32 Figure: A fuzzy graph example 14 / 48
- 15. Introduction Mapping Log To MXML Algorithms Case Study Discussions Data Collection Course Name Event No. Inst No. Time CareOL CBZ Home 1329 111 2009 eCF Basic I 623648 585 HS08 eCF Advanced II 97551 427 FS08 GEO 112 Humangeographie I 49794 278 2007-2009 PTO - Psychologie Taught Online 441126 1286 2008-2009 Sprachliche Interaktion im Raum 2243 25 2009 Table: Courses collected from University of Zurich 15 / 48
- 16. Introduction Mapping Log To MXML Algorithms Case Study Discussions Corporate Finance II Figure: eCF II Schema Figure: eCF Fuzzy models 16 / 48
- 17. Introduction Mapping Log To MXML Algorithms Case Study Discussions Discussions Algorithm Improvement Mapping log case to process instance supporting collaborative learning activities Supporting multiple sessions Result Evaluation What are proper thresholds? Result Application How to reflect the analysis result to the course creation Other Perspectives Social network Performance analysis 17 / 48
- 18. Introduction Mapping Log To MXML Algorithms Case Study Discussions Discussions Questions and suggestions? 18 / 48
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- 42. Introduction Mapping Log To MXML Algorithms Case Study Discussions Discussions Questions and suggestions? Thank you! 42 / 48
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