Comprehensive Evaluation of Students' Mathematics Performance Based on Fuzzy Clustering Analysis
DOI:
https://doi.org/10.54097/ps0fry97Keywords:
Fuzzy clustering analysis, Performance evaluation, Fuzzy C-meansAbstract
It is introduced the fuzzy clustering analysis method to construct a multidimensional comprehensive evaluation model for students' mathematics grades. Firstly, summarize the basic principles of fuzzy clustering analysis and related theories of educational evaluation; Secondly, 86 students from two classes were selected as the research subjects, and data on seven indicators including unit tests, mid-term and final grades, homework completion rates, and classroom participation were collected; Once again, the fuzzy C-means (FCM) algorithm was used to perform cluster analysis on students, and the optimal number of clusters was determined to be 3, resulting in three typical student profiles: 30 stable and excellent students, 35 potential and developmental students, and 21 students with weak foundations; Finally, based on the clustering results, differentiated teaching strategies and dynamic tracking mechanisms are proposed. Comparative analysis shows that the fuzzy clustering method can more scientifically reveal the structural differences in student performance compared to the traditional average segmentation.
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[1] Qadir, A. M. (2023). Clustering algorithms in educational data mining: A review of applications and trends (2013–2023). Education and Information Technologies, 28, 14403-14445. https://doi.org/10.1007/s10639-023-11756-6
[2] Hasan, M., Rahman, S., Hossain, M., & Khan, R. (2025). Clustering student performance data using K-means for adaptive learning strategies. International Journal of Evaluation and Research in Education, 14(1), 504-513.
[3] Bustomi, Y., Lestari, R., Kusnadi, A., & Firdaus, A. (2022). Application of fuzzy C-means clustering for junior high school student grouping based on academic and behavioral indicators. Journal of Physics: Conference Series, 2394, Article 012016. https://doi.org/10.1088/1742-6596/2394/1/012016
[4] Rustam, Z., & Usman, M. (2026). Fuzzy C-means clustering for interpretable learning trajectory modeling in outcomes-based education. Barekeng: Jurnal Ilmu Matematika dan Terapan, 20(1), 109-122.
[5] Shi, Z. H. (2019). Research and practice on performance prediction based on multi-source data comprehensive analysis [Master's thesis]. Inner Mongolia University.
[6] Xie, Y. T. (2019). Research on course correlation and student comprehensive quality evaluation based on academic performance [Master's thesis]. Central China Normal University.
[7] Wang, S. X. (2018). Analysis of middle school students' performance based on fuzzy inference model [Master's thesis]. Minzu University of China.
[8] Zhao, J. L. (2018). Comprehensive evaluation and application research on middle school academic performance based on fuzzy clustering analysis [Master's thesis]. Northwest Normal University.
[9] Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press. https://doi.org/10.1007/978-1-4757-0450-1
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