Enable collaborative learning: an improved e-learning social network exploiting approach

  • Authors:

  • Zhi-Mei Wang;Ling-Ning Li

  • Affiliations:

  • Wenzhou Vocational & Technical College, Department of Computer, Wenzhou, Zhejiang, China;Shanghai JiaoTong University, Department of Computer Science and Engineering, Shanghai, China

  • Venue:
  • ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
  • Year:
  • 2007

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Abstract

In this paper we propose an improved E-Learning Social Network Exploiting Approach based on Hebbian Learning Law, which can automatically group distributed e-learners with similar interests and make proper recommendations, which can finally enhance the collaborative learning among similar e-learners. Through similarity discovery, trust weights update and potential neighbors adjustment, the algorithm implements an automatic-adapted trust relationship with gradually enhanced satisfactions. It avoids dicult design work required for user preference representation or user similarity calculation. Hence it is suitable for open and distributed e-learning environments. Experimental results have shown that the algorithm has preferable prediction accuracy and user satisfaction. In addition, we achieve an improvement on both satisfaction and scalability.