A Comprehensive Review of Course Recommendation Systems for MOOCs

Abstract

In recent years, many students have accepted MOOCs as a means of education.. Due to the enormous number of courses available through MOOC, students need help in identifying and selecting an appropriate course based on their profile and interests. To address this issue, MOOCs incorporate a course recommendation system that generates a list of courses based on the student's prerequisites. This literature review attempts to detect and assess trends, processes employed, and developments in MOOC course RS through an exhaustive analysis of academic literature published between January 1, 2016 and November 31, 2023. The study include the various methodologies employed, the datasets used for evaluations, the performance measures used, and the many issues encountered by Recommendation Systems. Literature published in ScienceDirect, Wiley, Springer, ACM, and IEEE, were chosen for review. After applying inclusion and exclusion criteria, 76 articles from the aforementioned databases, including journals, conferences, and book chapters, were selected. The investigation found that methods from Machine Learning and Deep Learning were widely deployed. Traditional approaches like content-based filtering, collaborative filtering, and hybrid filtering were frequently employed in conjunction with other algorithms for more accurate and precise suggestions. It also underlines the need to take data sparsity, the cold start problem, data overload, and user preferences into account when designing a course recommendation system. The literature study examines cutting-edge course Recommendation System in depth, examining recent developments, difficulties, and future work in this field.