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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ju, C., Wang, X., Li, B. (2006). Knowledge Representing and Clustering in e-Learning. In: Pan, Z., Aylett, R., Diener, H., Jin, X., Göbel, S., Li, L. (eds) Technologies for E-Learning and Digital Entertainment. Edutainment 2006. Lecture Notes in Computer Science, vol 3942. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736639_23
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DOI: https://doi.org/10.1007/11736639_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33423-1
Online ISBN: 978-3-540-33424-8
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