A Collaborative Recommendation System for Online Courses Recommendations
A collaborative recommender system that recommends online courses for students based on similarities of students' course history is presented and it is noticed that clustering students into similar groups based on their respective course selections play a vital role in generating association rules of high quality when compared with the association rules generated using the whole set of courses and students.
Abstract
In this paper, we present a collaborative recommender system that recommends online courses for students based on similarities of students' course history. The proposed system employs data mining techniques to discover patterns between courses. Consequently, we have noticed that clustering students into similar groups based on their respective course selections play a vital role in generating association rules of high quality when compared with the association rules generated using the whole set of courses and students. In particular, the Apriori algorithm was used to generate association rules; once using the whole dataset and once using the clusters which are formed based on students' choices of courses. The results reveal that the coverage of the rules generated on clusters are better. Also, to assess the effect of course dependency on recommendations, we applied the SPADE algorithm on course sequences. The results are in harmony with the results obtained when Apriori was applied.