Learning Portfolio Analysis and Mining for SCORM Compliant Environment

1. Learning Portfolio Analysis and Mining for SCORM Compliant Environment Presenter : Su, Wun-Huei • Authors : Jun-Ming Su, Shian-Shyong Tseng, Wei Wang and Jui-FengWeng • Jin Tan David Yang • Wen-Nung Tsai Pattern Recognition (PR, 2010)

2. Outline • Motivation • Objective • Methodology • Implement • Experiments • Conclusion • Comments

3. Motivation • With vigorous development of the Internet, e-learning system has become more and more popular. • Sharable Content Object Reference Model (SCORM, 2004) • how to provide customized course • how to create, represent and maintain the activity tree • Learning portfolio can help teacher understand the reason why a learner got high or low grade

4. Objectives we apply data mining approaches to extract learning features from learning portfolio and then adaptively construct personalized activity trees

5. Methodology– Overview The Framework of Learning Portfolio Mining (LPM) 5

6. Methodology– User Model Definition Phase • Learner L= (ID, LC, LS) • LC = <c1c2…cn> • LS = <s1s2…sn> • L=(35, <F, M, S Y, H, FD, D, T, H>, < A, AA, AAA, AAB, AB>) 6

7. Methodology– Learning Pattern Extraction Phase Learning Pattern Extraction Phase 7

8. Methodology– Learning Pattern Extraction Phase • Sequential Pattern Mining Process • We use GSP algorithm to extract the frequent learning patterns from learning portfolio 8

9. Methodology– Learning Pattern Extraction Phase • Feature Transforming Process • based upon maximal learning patterns in Table 3, the original learning sequences of every learner can be mapped into a bit vector 9

10. Methodology– Learning Pattern Extraction Phase • Learner Clustering Process • we can apply clustering algorithm to group learners into several clusters according to learning features of learners • K-means algorithm(it difficult determine the number of clusters ) • ISODATA clustering approach to group learners into different clusters(can dynamically change the number of clusters by lumping and splitting procedures and iteratively change the number of clusters for better result) 10

11. Methodology– Decision Tree Construction Phase • how to assign a new learner to a suitable cluster according to her/his learning characteristics and capabilities is an issue to be solved • we can apply decision tree induction algorithm, ID3 (Quinlan, 1986), to create a decision tree. 11

12. Methodology– Learning Pattern Extraction Phase Activity Tree Generation Phase 12

13. Implementation 13

14. Implementation 14

15. Experimental 15

16. Conclusions • How to provide customized course according to individual learning characteristics , and how to create the activity tree in SCORM 2004 • we propose a four phase Learning Portfolio Mining (LPM) Approach • predict which group a new learner belongs to • also propose an algorithm to create personalized activity tree which can be used in SCORM compliant learning environment. • The analysis of experimental results by performing the t-test also shows that this LPM approach is workable and beneficial for learners

17. Comments • Advantage • A good application • Drawback • Application • Analysis portfolio record of e-learning system and provide learners with more personalized learning guidance