CRS: A course recommender system

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


Most problems facing Distance Education (DE) academic advising can be overcome using a course recommender system. Such a system can overcome the problem of students who do not know their interest in courses from merely their titles or descriptions provided in course catalogues. The authors introduce in this chapter an XML user-based Collaborative Filtering (CF) system called CRS. The system aims at predicting a DE student's academic performance and interest on a course based on a collection of profiles of students who have similar interests and academic performance in prior courses. The system advises a student to take courses that were taken successfully by students who have the same interests and academic performance as the active student. The framework of CRS identifies a set of course features for every academic major. The authors experimentally evaluate CRS. Results show marked improvement.

Original languageBritish English
Title of host publicationStudent Engagement and Participation
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
Number of pages18
ISBN (Electronic)9781522525851
StatePublished - 19 Jun 2017


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