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<p>In the era of big data, the evaluation of precise teaching quality in vocational college English leverages data-driven insights to transform traditional assessment methods. By analyzing diverse datasets—such as student performance, engagement metrics, and learning behaviors—educators can objectively measure teaching effectiveness tailored to specific vocational contexts. This approach enables personalized feedback, identifies learning gaps in real-time, and optimizes instructional strategies. Ultimately, it ensures alignment with industry demands, enhancing both student outcomes and the practical relevance of English education in vocational training. The assessment of precise English teaching quality in vocational colleges, situated within the big data context, inherently constitutes a Multiple-Attribute Decision-Making (MADM) challenge. To address such complex issues, the integrated TODIM and TOPSIS methodology has recently gained traction. This study utilizes Type-2 Neutrosophic Numbers (T2NNs) as a sophisticated tool for representing the inherent uncertainty and ambiguity present in the evaluation data. A novel hybrid model, the T2NN-TODIM-TOPSIS technique, is developed herein to effectively manage MADM problems within the T2NN environment. The practical application and robustness of this proposed technique are subsequently demonstrated through a detailed numerical example focused on evaluating the effectiveness of precision English teaching in a vocational college setting, leveraging big data analytics. Furthermore, a comprehensive comparative analysis with existing methods is conducted to substantiate the validity and advantages of the T2NN-TODIM-TOPSIS approach.</p>

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Modified TODIM-TOPSIS technique for type-2 neutrosophic number multiple-attribute decision-making and applications to precise teaching quality evaluation in vocational college english

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Ye Bao,
  • Lei Zhang,
  • Aotegensubude,
  • Hasieerdeni

摘要

In the era of big data, the evaluation of precise teaching quality in vocational college English leverages data-driven insights to transform traditional assessment methods. By analyzing diverse datasets—such as student performance, engagement metrics, and learning behaviors—educators can objectively measure teaching effectiveness tailored to specific vocational contexts. This approach enables personalized feedback, identifies learning gaps in real-time, and optimizes instructional strategies. Ultimately, it ensures alignment with industry demands, enhancing both student outcomes and the practical relevance of English education in vocational training. The assessment of precise English teaching quality in vocational colleges, situated within the big data context, inherently constitutes a Multiple-Attribute Decision-Making (MADM) challenge. To address such complex issues, the integrated TODIM and TOPSIS methodology has recently gained traction. This study utilizes Type-2 Neutrosophic Numbers (T2NNs) as a sophisticated tool for representing the inherent uncertainty and ambiguity present in the evaluation data. A novel hybrid model, the T2NN-TODIM-TOPSIS technique, is developed herein to effectively manage MADM problems within the T2NN environment. The practical application and robustness of this proposed technique are subsequently demonstrated through a detailed numerical example focused on evaluating the effectiveness of precision English teaching in a vocational college setting, leveraging big data analytics. Furthermore, a comprehensive comparative analysis with existing methods is conducted to substantiate the validity and advantages of the T2NN-TODIM-TOPSIS approach.