Web Data Mining

Topics: Data mining, World Wide Web, Web page Pages: 10 (3139 words) Published: April 18, 2013
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 jhsun@xmut.edu.cn

Yanqi Xie
Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China yqxie@xmut.edu.cn

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 users find interesting items [2], them predict a user's preference and suggest items by analyzing the past preference information of users, e-learning system is applied on the basis of the method. The user’s learning route is given and then provides the relevant learners useful messages through dynamically searching for the appropriate learning profile. This paper recommends learners the studying activities or learning profile through the technology of Web Mining with the purpose of helping they adopt a proper learning profile, we describe a framework that aims at solution to e-learning to discover the hidden insight of learning profile and web data. We demonstrate how data mining technology can be effectively applied in an e-learning environment. The framework we propose takes the results of the data mining process as input, and converts these results into actionable knowledge, by enriching them with information that can be

The route where the learner browses through the web pages will be noted down in Web log, carries on the technology of Web mining through Learning Profile and Web log, and analyzes from the materials related to association rule. It can be found the best learning profile from this information. These learning profiles combine with the Agent and put them on the learning website. Furthermore, the Agent recommends the function of learning profiles on learning website. Therefore, the learner will acquire a better learning profile. This chapter briefly illustrates the relevant contents including: e-Learning, Learning Profile, Agent, Web Data mining and Association rule. A. E-learning E-learning is the online delivery of information for purposes of education, training, or knowledge management. In the Information age skills and knowledge need to be continually updated and refreshed to keep up with today’s fastpaced study environment. E-learning is also growing as a delivery method for information in the education field and is becoming a major...

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.
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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.
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