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The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Author

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  • Deepani B. Guruge

    (Melbourne Institute of Technology (MIT), School of IT and Engineering (SITE), Melbourne, VIC 3000, Australia)

  • Rajan Kadel

    (Melbourne Institute of Technology (MIT), School of IT and Engineering (SITE), Melbourne, VIC 3000, Australia)

  • Sharly J. Halder

    (Melbourne Institute of Technology (MIT), School of IT and Engineering (SITE), Melbourne, VIC 3000, Australia)

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.

Suggested Citation

  • Deepani B. Guruge & Rajan Kadel & Sharly J. Halder, 2021. "The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research," Data, MDPI, vol. 6(2), pages 1-30, February.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:2:p:18-:d:497968
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    References listed on IDEAS

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    1. Halevi, Gali & Moed, Henk & Bar-Ilan, Judit, 2017. "Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the Literature," Journal of Informetrics, Elsevier, vol. 11(3), pages 823-834.
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    4. Carolyn Watters & Hong Wang, 2000. "Rating news documents for similarity," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(9), pages 793-804.
    5. Xiaoliang Chen & Jianzhong Zheng & Yajun Du & Mingwei Tang, 2020. "Intelligent Course Plan Recommendation for Higher Education: A Framework of Decision Tree," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-11, January.
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    1. Sahan Bulathwela & María Pérez-Ortiz & Emine Yilmaz & John Shawe-Taylor, 2022. "Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    2. Shadi Atalla & Mohammad Daradkeh & Amjad Gawanmeh & Hatim Khalil & Wathiq Mansoor & Sami Miniaoui & Yassine Himeur, 2023. "An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling," Mathematics, MDPI, vol. 11(5), pages 1-25, February.

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