Knowledge representing and clustering in e-learning

Authors:
Chunhua Ju;Xun Wang;Biwei Li
Affiliations:
College of Computer & Information Engineering, Zhejiang Gongshang University, Hangzhou, P.R. China;College of Computer & Information Engineering, Zhejiang Gongshang University, Hangzhou, P.R. China;College of Computer & Information Engineering, Zhejiang Gongshang University, Hangzhou, P.R. China
Venue:
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
Year:
2006

Citing 3
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Abstract

For e-Learning, traditional navigator or searching engine has inherent weaknesses, so individualized intelligent learning is difficult to be realized. This paper proposed a hybrid knowledge structure reflecting the relationships among knowledge modules. A series of association knowledge items were gathered by standardized inputting and knowledge clustering based on association rules. Based on the mapping of knowledge items to knowledge domain, the proposed knowledge clustering and representation could intelligently provide learner clues of interrelated learning. The simulation results showed that the proposed plan is an effective scheme of intelligent learning.