A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation
Chonghui Zhang (),
Weihua Su (),
Sichao Chen (),
Shouzhen Zeng () and
Huchang Liao ()
Additional contact information
Chonghui Zhang: Zhejiang Gongshang University
Weihua Su: Zhejiang Gongshang University
Sichao Chen: Zhejiang Gongshang University
Shouzhen Zeng: Ningbo University
Huchang Liao: Sichuan University
Group Decision and Negotiation, 2023, vol. 32, issue 3, No 2, 537-567
Abstract:
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: MOOC; Group recommendation; Large scale group decision making; Online reviews; Combined weighting (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10726-023-09816-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:grdene:v:32:y:2023:i:3:d:10.1007_s10726-023-09816-2
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10726/PS2
DOI: 10.1007/s10726-023-09816-2
Access Statistics for this article
Group Decision and Negotiation is currently edited by Gregory E. Kersten
More articles in Group Decision and Negotiation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().