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 automaticadapted 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.