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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(5): 411-416 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.446

A Personalized e-Learning System Based on EGL

Jie Gao and Bo Song
Abstract—With the development of Internet technology, information resource is being spread widely, thus the issue of information overload formed. It becomes more difficult for users to retrieve their needed information from so enormous information space. To solve the issue of information overload, recommender system emerges as the times require. This paper presents the personalized e-learning system based on Collaborative Filtering Recommender Algorithm. And the system will be implemented based on EGL with the advantages of developers paying attention to the business issues without caring for software technical details. By the solution of EGL, the function of personalized information recommendation of e-learning system will be achieved and personalized information will be recommended to users to help them improve learning efficiency. The results show that the solution of EGL is more flexible and simple, reduce the development cycle and cost, improved the real-time requirement of e-learning system.

Index Terms—EGL, recommender system, personalized, e-learning, architecture.

The authors are with Software College, Shenyang Normal University, Shenyang, Liaoning, China (corresponding author: Song Bo; e-mail: songbo63@aliyun.com, gaojiexy@126.com).

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Cite: Jie Gao and Bo Song, "A Personalized e-Learning System Based on EGL," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 411-416, 2014.

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