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<p>The online teaching effectiveness evaluation for college database technology courses is a complex multiple-attribute decision-making (MADM) challenge. Recent studies have adopted logarithmic TODIM (LogTODIM) and EDAS for MADM, but IAEE evaluations involve inherent uncertainty (e.g., vague feedback on teaching effectiveness or ambiguous judging criteria). Z-numbers address this by pairing numerical values with reliability measures, making them ideal for capturing unclear assessment data.​ This study introduces an integrated Z-number-based logarithmic TODIM-EDAS (ZN-LogTODIM-EDAS) approach. First, it uses Z-numbers to quantify uncertain online teaching effectiveness indicators. Then, LogTODIM calculates the dominance degree of each assessment object by considering decision-makers’ risk preferences, while EDAS evaluates alternatives via positive/negative distance from the average solution. The combination mitigates limitations of single methods—LogTODIM’s sensitivity to risk and EDAS’s reliance on average values.​ To validate this methodology, a numerical example for online teaching effectiveness evaluation for college database technology is presented and ZN-LogTODIM-EDAS is utilized to rank the online teaching effectiveness evaluation, and compares results with traditional techniques. The consistency and accuracy of the integrated method confirm its applicability and effectiveness for online teaching effectiveness evaluation.</p>

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A fuzzy multiple-attribute decision-making under Z-number environment for online teaching effectiveness evaluation for college database technology courses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Xinyang Ying,
  • Leilei Dong

摘要

The online teaching effectiveness evaluation for college database technology courses is a complex multiple-attribute decision-making (MADM) challenge. Recent studies have adopted logarithmic TODIM (LogTODIM) and EDAS for MADM, but IAEE evaluations involve inherent uncertainty (e.g., vague feedback on teaching effectiveness or ambiguous judging criteria). Z-numbers address this by pairing numerical values with reliability measures, making them ideal for capturing unclear assessment data.​ This study introduces an integrated Z-number-based logarithmic TODIM-EDAS (ZN-LogTODIM-EDAS) approach. First, it uses Z-numbers to quantify uncertain online teaching effectiveness indicators. Then, LogTODIM calculates the dominance degree of each assessment object by considering decision-makers’ risk preferences, while EDAS evaluates alternatives via positive/negative distance from the average solution. The combination mitigates limitations of single methods—LogTODIM’s sensitivity to risk and EDAS’s reliance on average values.​ To validate this methodology, a numerical example for online teaching effectiveness evaluation for college database technology is presented and ZN-LogTODIM-EDAS is utilized to rank the online teaching effectiveness evaluation, and compares results with traditional techniques. The consistency and accuracy of the integrated method confirm its applicability and effectiveness for online teaching effectiveness evaluation.