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Solutions and trends of recommendation systems for massive open online courses

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  • Campos, Rodrigo
  • Santos, Rodrigo Pereira dos
  • Oliveira, Jonice

Abstract

Massive Open Online Courses (MOOCs) have been widely disseminated due to the arrival of Web 2.0. In recent years, recommendation systems have been applied to support MOOCs users in choosing suitable learning materials (e.g., courses and videos) in this modality. However, implementing such systems remains a challenge since several recommendation aspects should be considered. In this work, we identify and analyze such aspects (e.g., inputs, approaches, and outputs), investigating several implementation possibilities based on the literature. Results show that collaborative filtering and content-based are the most used approaches. More than 60 techniques are used to support recommendations in MOOCs, and most of the studies focus on recommending courses. The main contributions are: (1) a better understanding of the aspects, benefits, and limitations of MOOCs recommendation; and (2) the identification of open issues and research trends, providing an analysis that can support the implementation of emerging recommendation systems for MOOCs.

Suggested Citation

  • Campos, Rodrigo & Santos, Rodrigo Pereira dos & Oliveira, Jonice, 2025. "Solutions and trends of recommendation systems for massive open online courses," Technological Forecasting and Social Change, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:tefoso:v:217:y:2025:i:c:s0040162525001490
    DOI: 10.1016/j.techfore.2025.124118
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    References listed on IDEAS

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    1. Michail N. Giannakos & Patrick Mikalef & Ilias O. Pappas, 2022. "Systematic Literature Review of E-Learning Capabilities to Enhance Organizational Learning," Information Systems Frontiers, Springer, vol. 24(2), pages 619-635, April.
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    3. Kaplan, Andreas M. & Haenlein, Michael, 2016. "Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster," Business Horizons, Elsevier, vol. 59(4), pages 441-450.
    4. Chonghui Zhang & Weihua Su & Sichao Chen & Shouzhen Zeng & Huchang Liao, 2023. "A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation," Group Decision and Negotiation, Springer, vol. 32(3), pages 537-567, June.
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