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

Chonghui Zhang1, Weihua Su1, Sichao Chen1

  • 1College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018 China.

Group Decision and Negotiation
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Summary

This study introduces a novel group recommendation system for massive open online courses (MOOCs) to combat information overload. The approach effectively combines subjective and objective criteria weighting for personalized course selection.

Keywords:
Combined weightingGroup recommendationLarge scale group decision makingMOOCOnline reviews

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Area of Science:

  • Educational Technology
  • Computer Science
  • Decision Sciences

Background:

  • Massive Open Online Courses (MOOCs) offer accessible higher education but suffer from information overload, making course selection challenging.
  • Existing recommendation systems often fail to address the complexities of group preferences in MOOC environments.
  • The need for a robust method to guide users through the vast MOOC landscape is critical.

Purpose of the Study:

  • To propose a combined weighting based large-scale group decision-making approach for MOOC group recommendations.
  • To develop a framework for evaluating MOOCs based on pre-class, in-class, and post-class stages.
  • To enhance user experience by facilitating the selection of suitable MOOCs for individuals and groups.

Main Methods:

  • Decomposition of MOOC content into pre-class, in-class, and post-class stages, forming a curriculum-arrangement-movement-performance evaluation framework.
  • Utilizing the Probabilistic Linguistic Criteria Importance Through Intercriteria Correlation (PL-MICR) method for objective criterion weighting.
  • Employing word embedding models for vectorizing online reviews to derive subjective criterion weighting based on text similarity.

Main Results:

  • A fused combined weighting was obtained by integrating subjective and objective weights.
  • The Probabilistic Linguistic Multi-Criteria Decision Making (PL-MULTIMIIRA) approach and Borda rule were used for group recommendation ranking.
  • A group satisfaction formula was proposed and validated through a case study on statistical MOOCs, demonstrating robustness and effectiveness via sensitivity and comparative analyses.

Conclusions:

  • The proposed group decision-making approach effectively addresses information overload in MOOCs.
  • The method provides a reliable mechanism for group recommendations, enhancing user satisfaction.
  • The study validates the effectiveness and robustness of the novel approach for MOOC selection.