A Systematic Literature Review on Recommender Systems for MOOCs

Abstract

In recent years, MOOCs (Massive Open Online Courses) have become popular and the online learning resources are increasing, they are an offered courses by schools and universities, which are accessible to everyone and free of charge on the internet, they offer the possibility to teach a very group of students, in the same course, at the same time, even if they are not in the same location. There are many MOOCs platforms with different characteristics, they contain a huge amount of data, so the learner does not know which course to take and can choose irrelevant MOOCs. Therefore, he will waste the time and also the motivation. Recommender systems give a solution to this problem, they suggest learning resources to learners according to their interests and needs, so learner will be satisfied because he finds an appropriate course. In this paper, we give a systematic literature review of MOOCs recommender systems, based on published papers in the past ten years, between 2012 and 2022. We have selected 123 papers from five databases, IEEE Xplore, Springer Link, Science Direct, Google Scholar and ACM Library. We have divided the data analysis in two parts, the quantitative analysis, and the qualitative analysis. In the quantitative analysis, we have studied first the evolution of papers by year and the distribution of papers on databases by type. Then, in the qualitative analysis, we have based principally on the distribution of papers by the existed areas in MOOCs. We have found that there are six main fields, course recommendation, peer recommender, MOOC provider, video recommendation, learning activities and OER, paid activities recommender system and other papers in various types. A high number of articles have been published in the field of courses, which confirms that this domain is very important and crucial for learners.