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<p>The rapid evolution of E-learning platforms in dental education is a multi-criteria decision problem that demands vigorous decision-making under ambiguity. This paper posits an innovative Multi-Criteria Decision-Making (MCDM) model underpinning Dual Hesitant Fuzzy Sets (DHFS), entropy weights, and an enhanced TOPSIS algorithm to manage expert rating-dependent dual-layer ambiguity. Both membership and value hesitation are retained, with the added semantic depth compared with available fuzzy systems. An exemplar underpinning five dental E-learning platforms evaluated on seven alternatives verify the superiority of the innovative DHFS-Entropy-TOPSIS procedure compared with the standard Fuzzy decision making techniques. The sensitivity analysis verifies the robustness of the procedure about variations in the weights. The results validate the DHFS-MCDM as an effective and large-scale solution to the optimization of digital learning websites, and future possibilities aim at exploiting machine learning-based dynamic, adaptive decision-making.</p>

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A dual hesitant fuzzy entropy-TOPSIS framework for multi-criteria evaluation of medical E-learning systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Nabilah Abughazalah,
  • Majid Khan

摘要

The rapid evolution of E-learning platforms in dental education is a multi-criteria decision problem that demands vigorous decision-making under ambiguity. This paper posits an innovative Multi-Criteria Decision-Making (MCDM) model underpinning Dual Hesitant Fuzzy Sets (DHFS), entropy weights, and an enhanced TOPSIS algorithm to manage expert rating-dependent dual-layer ambiguity. Both membership and value hesitation are retained, with the added semantic depth compared with available fuzzy systems. An exemplar underpinning five dental E-learning platforms evaluated on seven alternatives verify the superiority of the innovative DHFS-Entropy-TOPSIS procedure compared with the standard Fuzzy decision making techniques. The sensitivity analysis verifies the robustness of the procedure about variations in the weights. The results validate the DHFS-MCDM as an effective and large-scale solution to the optimization of digital learning websites, and future possibilities aim at exploiting machine learning-based dynamic, adaptive decision-making.