Abstract
In recent years, network-based education has been growing rapidly in size and complexity. Therefore, knowledge clustering becomes more and more important in personalized information retrieval for e-learning. This paper introduces a clustering coefficient partition algorithm for providing e-learning personalization service after the learners’ knowledge has been classified with clustering. Through automatic analysis of learners’ behaviors, their partition with similar knowledge level and interests can be discovered in order to provide learners with contents that best match their educational needs for collaborative learning. Experimental results show that our algorithm is efficient and effective in extracting clusters from large set of contents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, C.-M., Chung, C.-J.: Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education 51(2), 624–645 (2008)
Chen, C.-M., Duh, L.-J.: Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems with Applications 34(4), 2298–2315 (2008)
Tzouveli, P., Mylonas, P., Kollias, S.: An intelligent e-learning system based on learner profiling and learning resources adaptation. Computers & Education 51(1), 224–238 (2008)
Meo, P.D., Garro, A., Terracina, G., Ursino, D.: Personalizing learning programs with X-Learn, an XML-based. “user-device” adaptive multi-agent system, Information Sciences 177(8), 1729–1770 (2007)
Shee, D.Y., Wang, Y.-S.: Multi-criteria: evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education 50(3), 894–905 (2008)
Kritikou, Y., Demestichas, P., Adamopoulou, E., Demestichas, K., Theologou, M., Paradia, M.: User Profile Modeling in the context of web-based learning management systems. Journal of Network and Computer Applications, Corrected Proof (November 19, 2007) (in press)
Chen, C.-M., Liu, C.-Y., Chang, M.-H.: Personalized curriculum sequencing utilizing modified item response theory for web-based instruction. Expert Systems with applications 30(2), 378–396 (2006)
Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16, 645–678 (2005)
Fu, H., Foghlu, M.: A conceptual subspace clustering algorithm in e-learning. In: 10th International Conference on Advanced Communication Technology, ICACT 2008 - Proceedings, pp. 1983–1988 (2008)
Zakrzewska, D.: Cluster analysis for users’ modeling in intelligent E-learning systems. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS, vol. 5027, pp. 209–214. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Li, X. (2009). Personalization Service Research of the E-Learning System Based on Clustering Coefficient Partition Algorithm. In: Chen, L., et al. Advances in Web and Network Technologies, and Information Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03996-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-03996-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03995-9
Online ISBN: 978-3-642-03996-6
eBook Packages: Computer ScienceComputer Science (R0)