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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4758

    Title: Learning portfolio analysis and mining for SCORM compliant environment
    Authors: Jun-Ming Su;S. S. Tseng;Wei Wang;J. F. Weng;J.T. D. Yang;Wen-Nung Tsai
    Contributors: Department of Information Science and Applications
    Keywords: Learning Portfolio Analysis;SCORM;Data Mining, Personalized Learning.
    Date: 2005
    Issue Date: 2009-11-30 16:03:26 (UTC+8)
    Publisher: Asia University
    Abstract: With vigorous development of the Internet, e-learning system has become more and more popular. Sharable content object reference model (SCORM) 1.3 provides the sequencing and navigation to define the course sequencing behavior, control the sequencing, select and deliver 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 capability, 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 phase: 1) user model definition phase: define the learner profile based upon 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 remaining 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, several experiments have been done and the results show that generated personalized activity trees with sequencing rules are workable for learners.
    Relation: Journal of Educational Technology & Society
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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