A Comprehensive Review of Course Recommender Systems in e-Learning 

Abstract

The amount of information available online for higher education could be overwhelming to students in deciding the appropriate and relevant courses they should take. This issue has led to the need for automated and intelligent adaptive mechanisms to assist students in finding resources that match their individual goals, interests, and current knowledge. Although various course recommendation approaches have been introduced in recent years, they suffer from complexity, efficiency, and trust issues. This paper aims to categorize various course recommendation approaches put forward by researchers in recent years and to review the strengths and limitations of them.

Keywords: collaborative filtering (CF), content-based (CB), knowledge-based (KB), course recommendation system (CRS), online learning