Abstract
In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.
Highlights
Academic Editor: Jamal Jokar ArsanjaniReceived: 24 December 2020Accepted: 8 February 2021Published: 11 February 2021Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Recently, we have witnessed an enormous research expansion in Recommender Systems (RS)
This study conducted a systematic review of publications from 2016 to June 2020 on course RS to identify the state of the art in methodologies RS
After preliminary filtering based on the abstract, title of the article and inclusion and exclusion criteria 43 papers were selected for final review
Summary
Academic Editor: Jamal Jokar ArsanjaniReceived: 24 December 2020Accepted: 8 February 2021Published: 11 February 2021Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Recently, we have witnessed an enormous research expansion in Recommender Systems (RS). RS are information filtering systems which provide facilities to perform predictions. They are beneficial to many industries, such as book recommendation (Amazon), Google Play Store Apps suitable for the user, YouTube videos and music recommendation of Apple Music [1]. Effective career counselling services received by a student are crucial to their overall academic performance and it can make a countless impact on their career success. These counselling decisions should incorporate several factors such as perceived employment opportunities, student interests, academic results, attitudes and aptitudes and course selection is challenging.
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