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

Chonghui Zhang; Weihua Su; Sichao Chen; Shouzhen Zeng; Huchang Liao

doi:10.1007/s10726-023-09816-2

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

Group Decis Negot. 2023;32(3):537-567. doi: 10.1007/s10726-023-09816-2. Epub 2023 Feb 20.

Authors

Chonghui Zhang¹, Weihua Su¹, Sichao Chen¹, Shouzhen Zeng², Huchang Liao³

Affiliations

¹ College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018 China.
² School of Business, Ningbo University, Ningbo, 315211 China.
³ Business School, Sichuan University, Chengdu, 610064 China.

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.

Keywords: Combined weighting; Group recommendation; Large scale group decision making; MOOC; Online reviews.

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