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<p>High school students’ educational achievement must be assessed by considering a range of theoretical and practical tests administered throughout a semester. In addition to being susceptible to subjective bias or inconsistent grading standards, traditional assessment systems frequently miss the subtleties of performance changes, such as abrupt increases or decreases. Based on Type-2 Fuzzy (T2F) sets, this work presents a Balanced Evaluation Model (BEM) to improve the precision and dependability of student performance evaluation. At fuzzy level 2, the suggested model separates imbalanced evaluation inputs, distinguishes between performance improvements and falls, and pinpoints the underlying causes of these changes. To ensure a fair and open assessment of subjects, students, and tests, a final optimization phase is implemented. According to experimental results, compared to traditional assessment methods, there is a 13.06% improvement in identifying high-performance students, an 11.14% reduction in evaluation errors, and a 7.14% decrease in average evaluation time. By combining T2F theory with a multi-examination evaluation framework, this study is new in that it improves fairness, better manages uncertainty, and offers more insightful suggestions for students’ growth. For complex educational assessment situations, the suggested method provides a reliable, open, and adaptable solution.</p>

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Educational performance evaluation based on fuzzy evaluation model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月31日

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  • Jing Hu,
  • Haoxin Wang,
  • Dajie Ji

摘要

High school students’ educational achievement must be assessed by considering a range of theoretical and practical tests administered throughout a semester. In addition to being susceptible to subjective bias or inconsistent grading standards, traditional assessment systems frequently miss the subtleties of performance changes, such as abrupt increases or decreases. Based on Type-2 Fuzzy (T2F) sets, this work presents a Balanced Evaluation Model (BEM) to improve the precision and dependability of student performance evaluation. At fuzzy level 2, the suggested model separates imbalanced evaluation inputs, distinguishes between performance improvements and falls, and pinpoints the underlying causes of these changes. To ensure a fair and open assessment of subjects, students, and tests, a final optimization phase is implemented. According to experimental results, compared to traditional assessment methods, there is a 13.06% improvement in identifying high-performance students, an 11.14% reduction in evaluation errors, and a 7.14% decrease in average evaluation time. By combining T2F theory with a multi-examination evaluation framework, this study is new in that it improves fairness, better manages uncertainty, and offers more insightful suggestions for students’ growth. For complex educational assessment situations, the suggested method provides a reliable, open, and adaptable solution.