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

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.