Web Data Mining

A Recommender System Based On Web Data Mining for Personalized E-learning
Jinhua Sun
Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China [e-mail]

Yanqi Xie
Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China [e-mail]

Abstract—In this paper, we introduce a web data mining solution to e-learning system to discover hidden patterns strategies from their learners and web data, describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable e-learning system, propose a new framework based on data mining technology for building a Web-page recommender system, and demonstrate how data mining technology can be effectively applied in an e-learning environment. Keywords--Data mining; web log,;e-learning; recommender

readily interpreted by the analyst. A virtual e-learning framework is proposed, and how to enhance e-learning through web data mining is discussed. II. RELATED WORK

I.

INTRODUCTION

With the rapid development of the World Wide Web, Web data mining has been extensively used in the past for analyzing huge collections of data, and is currently being applied to a variety of domains [1]. In the recent years, e-learning is becoming common practice and widespread in China. With the development of e-Learning, massive amounts of learning courses are available on the e-Learning system. When entering e-Learning System, the learners are unable to know where to begin to learn with various courses. Therefore, learners waste a lot of time on e-Learning system, but don’t get the effective learning result. It is very difficult and time consuming for educators to thoroughly track and assess all the activities performed by all learners. In order to overcome such a problem, the recommender learning system is required. Recommender systems are used on many web sites to help

References: [1] [2] D.J.H and, H.Mannila, and P.Smyth. Principles of Data Mining. MIT Press, 2000. J.B.Schafer, J.A.Konstan, and J.Riedl. Recommender systems in ecommerce. In ACM Conference on Electronic Commerce, pages 158166, 1999. Liaw, S. & Hung ,H. How Web Technology Can facilitate Learning. Information Systems Management, 2002. Choonho Kim and Juntae Kim, A Recommendation Algorithm Using Multi-Level Association Rules, Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, p.524, October 13-17, 2003. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaurmann Publishers, 2000 Za¨ıane, O. R. & Luo, J. Towards evaluating learners’ behaviour in a web-based distance learning environment. In Proc. of IEEE International Conference on Advanced Learning Technologies (ICALT01), p. 357– 360, 2001. Sarwar, B., Karypis, G., Konstan, J.A., & Reidl, J. Item-based Collaborative Filtering Recommendation Algorithms. Proceedings of the Tenth International Conference on World Wide Web, pp. 285 - 295, 2001. R.Agrawal et al., Fast Discovery of Association Rules, Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, Calif., 1996, chap. 12. L.Breiman et al., Classification and Regression Trees, Wadsworth, Belmont, Calif., 1984. MacQueen, J. B. Some Methods for classification and Analysis of Multivariate Observations. In Proceedings of of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp.281297. Cristóbal Romero, Sebastián Ventura and Jose A. Delgado et al., Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems, Creating New Learning Experiences on a Global Scale,2007, pp. 292-306. Inselberg, A. Multidimensionl detective, In IEEE Symposium on Information Visualization, 1997, vol.00, p.100-110 . Ware, C. Information Visualization: Perception for Design,Morgan Kaufmann, New York, 2000. [3] [4] [5] [6] [7] [8] [9] [10] Recommender systems have emerged as powerful tools for helping users find and evaluate items of interest. The research work presented in this paper makes several contributions to the recommender systems for personalized e-learning. First of all, we propose a new framework based on web data mining technology for building a Web-page recommender system. Additionally, we demonstrate how web data mining technology can be effectively applied in an e-learning environment. [11] [12] [13]