A dual hesitant fuzzy entropy-TOPSIS framework for multi-criteria evaluation of medical E-learning systems
Optimizing Medical E-Learning Platform Selection with Advanced AI Decision Frameworks
This analysis leverages a Dual Hesitant Fuzzy Entropy-TOPSIS framework to rigorously evaluate and rank medical E-learning platforms, specifically for dental education. It addresses the inherent ambiguity and expert hesitation in multi-criteria decision-making by integrating DHFS, entropy-based weighting, and an enhanced TOPSIS algorithm. The model provides a robust, objective, and transparent method for selecting optimal digital learning solutions.
For enterprises in the medical education sector, this framework offers a decisive competitive advantage by enabling the selection of E-learning platforms that are precisely aligned with pedagogical objectives and operational efficiencies. It mitigates risks associated with subjective platform choices and ensures optimal investment in digital learning infrastructure, leading to enhanced learner engagement and superior educational outcomes.
Executive Impact at a Glance
Our framework quantifies the strategic advantages of an optimized E-learning platform selection, delivering clear, measurable benefits to your organization.
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Reduction in Subjectivity
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Improvement in Platform Alignment
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Confidence in Decision Making
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview
The rapid evolution of E-learning platforms in dental education presents a multi-criteria decision problem requiring robust evaluation. This paper introduces an innovative Multi-Criteria Decision-Making (MCDM) model that combines Dual Hesitant Fuzzy Sets (DHFS), entropy weights, and an enhanced TOPSIS algorithm. This model is designed to manage expert rating-dependent dual-layer ambiguity, retaining both membership and value hesitation to provide deeper semantic depth compared with traditional fuzzy systems. It aims to optimize digital learning websites and pave the way for adaptive decision-making.
Methodology
The DHFS-Entropy-TOPSIS framework involves several key steps. First, expert evaluations are captured using Dual Hesitant Fuzzy Sets (DHFS) to account for multiple membership and non-membership degrees. These evaluations are then aggregated using a Dual Hesitant Fuzzy Weighted Average (DHFWA) operator. Objective weights for criteria are derived using an entropy-based method, where lower entropy indicates higher consensus and importance. Finally, an extended TOPSIS algorithm ranks alternatives based on their proximity to ideal positive and negative solutions, ensuring a robust decision process.
Results & Discussion
The model was applied to five dental E-learning platforms evaluated on seven criteria. Coursera (P2) ranked first with a Closeness Coefficient (CC) of 0.568, followed by EdX (P3) at 0.505, and Moodle (P1) at 0.468. Content Quality (C1) and Pedagogical Effectiveness (C5) were identified as the most influential criteria. A sensitivity analysis confirmed the robustness of the rankings under weight perturbations, with Spearman's rank correlation consistently at 1.0, indicating high stability. The framework proved superior to standard Fuzzy decision-making techniques.
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Closeness Coefficient for Top Platform (Coursera)
Enterprise Process Flow
Expert DHFS Evaluation
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DHFWA Aggregation
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Entropy Weighting
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Extended TOPSIS Ranking
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Sensitivity Analysis
| Criterion | DHFS-MCDM Advantage |
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| Uncertainty Handling |
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| Cost Criterion Handling |
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| Weighting Method |
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| Robustness Verification |
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| Semantic Depth |
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Impact on Dental Education Platform Selection
In a real-world application, five dental E-learning platforms (P1-Moodle, P2-Coursera, P3-EdX, P4-Custom, P5-Blackboard) were evaluated against seven criteria. The DHFS-Entropy-TOPSIS framework successfully ranked Coursera as the most preferable platform, citing its high content quality and pedagogical effectiveness. This systematic approach minimized subjective bias and provided clear, defensible recommendations for optimizing digital learning investments.
Outcome: The framework validated Coursera (P2) as the top choice (CC=0.568), followed by EdX (P3) and Moodle (P1), ensuring data-driven selection for medical E-learning.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrate DHFS-MCDM into your enterprise decision-making processes.
Phase 1: Data Collection & Expert Elicitation
Gather expert evaluations of E-learning platforms against defined criteria using the Dual Hesitant Fuzzy Sets (DHFS) scale. This involves engaging subject matter experts in dental education.
Phase 2: Aggregation & Weight Calculation
Apply the Dual Hesitant Fuzzy Weighted Average (DHFWA) to aggregate expert judgments. Subsequently, calculate objective criterion weights using the entropy method to quantify uncertainty and importance.
Phase 3: Platform Ranking & Validation
Utilize the extended TOPSIS algorithm to rank platforms based on their proximity to ideal solutions. Perform a comprehensive sensitivity analysis to ensure the robustness and stability of the rankings under varying conditions.
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