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The success of any e-learning system depends on quality and the quantity of assistance provided to its students, in the learning process. Hence, it is essential to analyze a student’s academic skills in order to personalize the education provided both vertically and horizontally. This paper proposes a novel approach through which initially students are grouped based on several factors including their academic interests and further motivate the students to enhance their knowledge by providing appropriate recommendations made based on students belonging to their group. It has been proved that neither link information nor content information individually is sufficient to form student communities (Rabbany et al., 2011). Therefore, the approach includes both together for community detection. Further, the approach also intends to recommend courses based on the ratings for courses given by other students with similar skill sets in the same group. Experimental results highlight the quality or relevance of the recommendations made within communities, which in turn reflects on the accuracy of the proposed community detection method.


community based recommendation e-learning recommender system in learning

Article Details

Author Biographies

V Senthil kumaran, PSG College of Technology

V. Senthil Kumaran received his MSc in Computer Science from Madurai Kamaraj University, Madurai, India. He is currently working as an Assistant Professor at the Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, India. Since 2008, he is working towards his PhD in Computer Science from the Faculty of Science and Humanities, PSG College of Technology. His research is focused on semantic web, data mining, intelligent information retrieval and e-learning.

A Sankar, PSG College of Technology

A. Sankar received his PhD in Computer Science from Bharathiar University, Coimbatore, India, in 2003. He is currently working as an Associate Professor at the Department of Computer Applications, PSG College of Technology, Coimbatore, India. He has more than 24 years of teaching and ten years of research experience. His research interests include agile software engineering, data mining, e-learning and networks.

K Kiruthikaa, PSG College of Technology

Kiruthikaa K, is currently pursuing her 5th year of the five year integrated M.Sc Software Engineering in the Department of Applied Mathematics and Computational Sciences at PSG College of Technology, Coimbatore, India. She has worked as a six month intern for a reputed organization and is currently involved in research domains like Semantic Web, Information Retrieval and Software Design Patterns.
How to Cite
Senthil kumaran, V., Sankar, A., & Kiruthikaa, K. (2014). Community based recommendation in e-learning systems. Journal of E-Learning and Knowledge Society, 10(1).