Using Association Rules for Course Recommendation

Narimel Bendakir, Esma Aïmeur

Students often need guidance in choosing adequate courses to complete their academic degrees. Course recommender systems have been suggested in the literature as a tool to help students make informed course selections. Although a variety of techniques have been proposed in these course recommender systems, combining data mining with user ratings in order to improve the recommendation has never been done before. This paper presents RARE, a course Recommender system based on Association RulEs, which incorporates a data mining process together with user ratings in recommendation. Starting from a history of real data, it discovers significant rules that associate academic courses followed by former students. These rules are later used to infer recommendations. In order to benefit from the current students' opinions, RARE also offers to users the possibility to rate the recommendations, thus leading to an improvement of the rules. Therefore, RARE combines the benefits of both former students' experience and current students' ratings in order to recommend the most relevant courses to its users.

Subjects: 1.3 Computer-Aided Education; 12. Machine Learning and Discovery

Submitted: May 17, 2006


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