Developing a Course Recommender by Combining Clustering and Fuzzy Association Rules

Each semester, students go through the process of selecting appropriate courses. It is difficult to find information about each course and ultimately make decisions. The objective of this paper is to design a course recommender model which takes student characteristics into account to recommend appropriate courses. The model uses clustering to identify students with similar interests and skills. Once similar students are found, dependencies between student course selections are examined using fuzzy association rules mining. The application of clustering and fuzzy association rules results in appropriate recommendations and a predicted score. In this study, a collection of data on undergraduate students at the Management and Accounting Faculty of College of Farabi in University of Tehran is used. The records are from 2004 to 2015. The students are divided into two clusters according to Educational background and demographics. Finally, recommended courses and predicted scores are given to students. The mined rules facilitate decision-making regarding course selection.

Keywords:

Course Recommender Model , Course Selection , Clustering , K-means , Fuzzy Association Rules