Intelligent Database Systems Lab N.Y.U.S.T. I. M. Learning Portfolio Analysis and Mining for SCORM Compliant Environment Pattern Recognition (PR, 2010) Presenter : Su, Wun-Huei Authors : Jun-Ming Su, Shian-Shyong Tseng, Wei Wang and Jui-Feng Weng Jin Tan David Yang Wen-Nung Tsai

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology Implement Experiments Conclusion Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives we apply data mining approaches to extract learning features from learning portfolio and then adaptively construct personalized activity trees 4

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Overview 5 The Framework of Learning Portfolio Mining (LPM)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – User Model Definition Phase 6 Learner L= (ID, LC, LS)  LC =  LS =  L=(35,, )

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Learning Pattern Extraction Phase 7 Learning Pattern Extraction Phase

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Learning Pattern Extraction Phase 8 Sequential Pattern Mining Process  We use GSP algorithm to extract the frequent learning patterns from learning portfolio

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Learning Pattern Extraction Phase 9 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

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Learning Pattern Extraction Phase 10 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)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Decision Tree Construction Phase 11 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Learning Pattern Extraction Phase 12 Activity Tree Generation Phase

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Implementation 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Implementation 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experimental 15

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 16

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments Advantage  A good application Drawback Application  Analysis portfolio record of e-learning system and provide learners with more personalized learning guidance 17