A Comprehensive Review of Course Recommendation Systems for MOOCs
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Date
2024
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Abstract
In recent years, many students have accepted Massive Open Online Courses (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 includes 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. This
paper contributes to examining the cutting-edge course Recommendation System in depth, examining recent developments, difficulties,
and future work in this field.