Abstract
E-learning systems , as an education pattern, are becoming more and more popular. In e-learning systems, courseware management is an indispensable part. As the number of various courseware increases, how to find the courseware or learning materials that are most suitable to users and users of e-learning systems are most interested in is a practical problem. In this paper, we apply the idea of knowledge discovery techniques to make personalized recommendation for courseware. We design the courseware recommendation algorithm which combines contents filtering and collaborative filtering techniques. Also we propose the architecture of courseware management system with courseware recommendation, which is seamlessly integrated in our E-learning system. The experiment shows that our algorithm is able to truly reflect users’ interests with high efficiency.
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References
Anane, R., Crowther, S., Beadle, J., et al.: eLearning Content Provision. In: Proceedings of the 15th International Workshop on Database and Expert Systems Applications (DEXA 2004), August-September 2004, pp. 420–425 (2004)
Mallinson, B., Sewry, D.: Elearning at Rhodes University – a case study. In: Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2004), August-September 2004, pp. 708–711 (2004)
CELTS-3,42: China E-learning Technology Standard, http://www.celtsc.edu.cn/
Data Exchange Standard and Interfaces in E-learning integration platform
Dublin Core Metadata Initiative, http://dublincore.org/
Badrul, S., Georage, K., Joseph, K., John, R.: Item-Based Collaborative Filtering Recommendation Algorithms [J]. In: Proceedings of the tenth international conference on World Wide Web, pp. 285–295. ACM Press, Hong Kong (2001)
Zeng, C., Xing, C., Zhou, L.: A Personalized Search Algorithm by Using Content-Based Filtering. Journal of Software 14(5) (2003)
Zhao, Z., Yuan, W.: Research on the User Collaborative Recommendation Algorithm in Personalized Search Engine. Microelectronics and computer 22(6) (2005)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM (December 1992)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of CSCW 1994, Chapel Hill, NC (1994)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating’ Word of Mouth. In: Proceedings of CHI 1995, Denver, Co (1995)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and Evaluating Choices in a Virtual Community of Use. In: Proceedings of CHI 1995 (1995)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
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Liang, G., Weining, K., Junzhou, L. (2006). Courseware Recommendation in E-Learning System. In: Liu, W., Li, Q., W.H. Lau, R. (eds) Advances in Web Based Learning – ICWL 2006. ICWL 2006. Lecture Notes in Computer Science, vol 4181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925293_2
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DOI: https://doi.org/10.1007/11925293_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49027-2
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