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<p>This study analyzes and evaluates prominent teaching modes to identify the optimal approach for implementation, aiming to enhance pedagogical outcomes. It proposes a systematic framework for teaching evaluation and mode innovation through a multi-criteria decision-making (MCDM) methodology that integrates q-rung orthopair fuzzy sets (q-ROFS) with the Ranking of Alternatives based on Trace to Median Index (RATMI) approach. This integrated method selects the optimal C programming language teaching mode to maximize instructional effectiveness, where RATMI evaluates teaching modes, while q-ROFS reduces uncertainty in expert assessments. In addition, a novel q-ROFS score function derived from the proposed similarity measure is developed to enhance discrimination capability and overcome data limitations. Furthermore, teaching factor weights are computed by combining subjectively assigned weights from teaching experts with objectively derived weights generated through a new entropy measure. Finally, a case study on C programming language teaching modes validates the model’s scientific rigor and operational effectiveness via comprehensive sensitivity and comparative analyses.</p>

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An Integrated Fuzzy Multi-criteria Decision-Making Framework Incorporating RATMI for Teaching Mode Evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Xindong Peng,
  • Linhui Yu,
  • Wenquan Li

摘要

This study analyzes and evaluates prominent teaching modes to identify the optimal approach for implementation, aiming to enhance pedagogical outcomes. It proposes a systematic framework for teaching evaluation and mode innovation through a multi-criteria decision-making (MCDM) methodology that integrates q-rung orthopair fuzzy sets (q-ROFS) with the Ranking of Alternatives based on Trace to Median Index (RATMI) approach. This integrated method selects the optimal C programming language teaching mode to maximize instructional effectiveness, where RATMI evaluates teaching modes, while q-ROFS reduces uncertainty in expert assessments. In addition, a novel q-ROFS score function derived from the proposed similarity measure is developed to enhance discrimination capability and overcome data limitations. Furthermore, teaching factor weights are computed by combining subjectively assigned weights from teaching experts with objectively derived weights generated through a new entropy measure. Finally, a case study on C programming language teaching modes validates the model’s scientific rigor and operational effectiveness via comprehensive sensitivity and comparative analyses.