Group recommendation in online learning environment using community detection Online publication date: Tue, 31-Mar-2015
by V. Senthil Kumaran; A. Sankar; K. Kiruthikaa
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 2, No. 1, 2015
Abstract: The success of any e-learning system depends on quality of assistance provided to its students in the learning process. So, analysing the student's academic skills is essential. Also, students should be able to extend their knowledge in various other fields which might be connected to their current interests. This paper proposes a novel approach through which students are grouped based on several factors including their academic interests and also motivate the students to enhance their knowledge by providing appropriate recommendations by students in the same group. It is already proved that (Reihaneh Rabbany et al., 2011b; Yang et al., 2009) neither link information nor content information individually is sufficient to form student communities. So, we use both together, for community detection. We also intend to recommend courses that are similar to the current interests of the student to widen their knowledge. Experimental results show that the proposed method outperforms the existing methods that use data mining techniques.
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