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A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation

Author

Listed:
  • Chonghui Zhang

    (Zhejiang Gongshang University)

  • Weihua Su

    (Zhejiang Gongshang University)

  • Sichao Chen

    (Zhejiang Gongshang University)

  • Shouzhen Zeng

    (Ningbo University)

  • Huchang Liao

    (Sichuan University)

Abstract

Massive open online courses (MOOC) are free learning courses based on online platforms for higher education, which not only promote the open sharing of learning resources, but also lead to serious information overload. However, there are many courses on MOOCs, and it can be difficult for users to choose courses that match their individual or group preferences. Therefore, a combined weighting based large-scale group decision-making approach is proposed to implement MOOC group recommendations. First, based on the MOOC operation mode, we decompose the course content into three stages, namely pre-class, in-class, and post-class, and then the curriculum-arrangement-movement- performance evaluation framework is constructed. Second, the probabilistic linguistic criteria importance through intercriteria correlation method is employed to obtain the objective weighting of the criterion. Meanwhile, the word embedding model is utilized to vectorize online reviews, and the subjective weighting of the criteria are acquired by calculating the text similarity. The combined weighting then can be obtained by fusing the subjective and objective weighting. Based on this, the PL-MULTIMIIRA approach and Borda rule is employed to rank the alternatives for group recommendation, and an easy-to-use formula for group satisfaction is proposed to evaluate the effect of the proposed method. Furthermore, a case study is conducted to group recommendations for statistical MOOCs. Finally, the robustness and effectiveness of the proposed approach were verified through sensitivity analysis as well as comparative analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:grdene:v:32:y:2023:i:3:d:10.1007_s10726-023-09816-2
    DOI: 10.1007/s10726-023-09816-2
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    References listed on IDEAS

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    1. Dandan Luo & Shouzhen Zeng & Ji Chen, 2020. "A Probabilistic Linguistic Multiple Attribute Decision Making Based on a New Correlation Coefficient Method and its Application in Hospital Assessment," Mathematics, MDPI, vol. 8(3), pages 1-16, March.
    2. Han-Saem Park & Moon-Hee Park & Sung-Bae Cho, 2015. "Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 317-338.
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