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Title |
Personalized recommender system for e-Learning environment based on student’s preferences |
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Author |
Hanaa EL FAZAZI, Mohammed QBADOU, Intissar SALHI, Khalifa MANSOURI |
| Citation |
Vol. 18 No. 10 pp. 173-178 |
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Abstract |
Nowadays, new technologies and the fast increase of the Internet have made access to information easier for all kind of people, building new challenges for education when utilizing the Internet as a tool. One of the best examples is how to personalize an e-learning system according to the learner’s requirements and knowledge level in a learning process. This system should adapt the learning experience according to the goals of the individual learner. In this paper, we present a recommender e-learning approach which utilizes recommendation techniques for educational data mining specifically for identifying e-Learners’ learning preferences. The proposed approach is based on three modules, a domain module which contains all the knowledge for a particular area, a learner module which uses to identify learners’ learning preferences and activities and a recommendation module which pre-processes data to create a suitable recommendation list and predicting performances. Recommended resources are obtained by using level of knowledge of learners in different steps and the range of recommendation techniques based on content-based filtering and collaborative approaches. Several techniques such as classification, clustering and association rules are used to improve personalization with filtering techniques to provide a recommendation and assist learners to improve their performance. |
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Keywords |
E-learning, recommender system, educational data mining, collaborative filtering, learning objects |
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