Title: Learning portfolio analysis and mining for SCORM compliant environment
Authors: Su, JM
Tseng, SS
Wang, W
Weng, JF
Yang, JTD
Tsai, WN
Department of Computer Science
Keywords: learning portfolio analysis;SCORM;data mining;personalized learning environment
Issue Date: 2006
Abstract: With vigorous development of the Internet, e-learning system has become more and more popular. Sharable Content Object Reference Model (SCORM) 2004 provides the Sequencing and Navigation (SN) Specification to define the course sequencing behavior, control the sequencing, selecting and delivering of course, and organize the content into a hierarchical structure, namely Activity Tree. Therefore, how to provide customized course according to individual learning characteristics and capabilities, and how to create, represent and maintain the activity tree with appropriate associated sequencing definition for different learners become two important issues. However, it is almost impossible to design personalized learning activities trees for each learner manually. The information of learning behavior, called learning portfolio, can help teacher understand the reason why a learner got high or low grade. Thus, in this paper, we propose a Learning Portfolio Mining (LPM) Approach including four phases: 1. User Model Definition Phase: define the learner profile based upon existing articles and pedagogical theory. 2. Learning Pattern Extraction Phase: apply sequential pattern mining technique to extract the maximal frequent learning patterns from the learning sequence, transform original learning sequence into a bit vector, and then use distance based clustering approach to group learners with good learning performance into several clusters. 3. Decision Tree Construction Phase: use two third of the learner profiles with corresponding cluster labels as training data to create a decision tree, and the remainings are the testing data. 4. Activity Tree Generation Phase: use each created cluster including several learning patterns as sequencing rules to generate personalized activity tree with associated sequencing rules of SN. Finally, for evaluating our proposed approach of learning portfolio analysis, an experiment has been done and the results show that generated personalized activity trees with sequencing rules are workable and beneficial for learners.
URI: http://hdl.handle.net/11536/14379
ISSN: 1436-4522
Volume: 9
Issue: 1
Begin Page: 262
End Page: 275
Appears in Collections:Articles