Volume 7 Issue 1 - May 2016

  • 1. Clustering based approach to overcome cold start problem in intelligent e-learning system

    Authors : Gopal Sakarkar, Dr. S. P. Deshpande

    Pages : 1-13

    DOI : http://dx.doi.org/10.21172/1.71.001

    Keywords : Cold start problem, Recommendation, KNN, K-Means, Socioeconomics ,Personalization, e-learning

    Abstract :

    Online learning and teaching is new methodology adapted by both learners as well as teachers. Recommendation is the recent demanding trend in every online services provider. Along with various e-business and e-commerce service providers, e-learning websites are willing to start and deliver customize and recommend based learning systems. Proper and accurate recommendation is very challenging and demanding research area in 21st century as a numbers of web sites are increasing dramatically every day. But to deliver the essential product to accurate user is basic obligation of good recommendation system. One of the most challenging task for developing intelligent e-learning system is to overcome the cold start problem that occurred for new learners. In this study , data generated from the learners was analyzed using K-Nearest Neighbour , K-means algorithm and Apriori algorithm . To develop a better recommendation systems , we are considering learners past educational data, parental information and his current technical knowledge. Result of data analysis reveals that socio-economic background and educational academic past data plays important role in recommendation system.

    Citing this Journal Article :

    Gopal Sakarkar, Dr. S. P. Deshpande, "Clustering based approach to overcome cold start problem in intelligent e-learning system", https://www.ijltet.org/journal_details.php?id=901&j_id=3017, Volume 7 Issue 1 - May 2016, 1-13, #ijltetorg