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A Systematic Review and Meta-Analysis of Fuzzy Logic for Students’ Performance Assessment
Open accessAbstract
Assessment of students’ performance using fuzzy logic provides high flexibility and reliability in education. This study aims to apply a systematic review and meta-analysis to synthesize the findings of selected studies in this field, with a focus on the effects of fuzzy logic system configurations on students’ performance assessment outcomes, focusing on subgroup analyses to explore variations across input and output factors. A total of 109 articles were retrieved from databases, including ScienceDirect, Springer, IEEE, Scopus, and Google Scholar, for qualitative and quantitative syntheses. Among these, 46 studies reported both fuzzy and non-fuzzy median scores and were included in the meta-analysis. Results showed that output member functions achieved the highest median scores (≥ 89.50), and subgroup analyses revealed significant heterogeneity across studies (I² ≥ 75%, p <0.01). Frequency-based combinations of fuzzy variables generally outperformed non-frequency configurations, enhancing system granularity and accuracy. These findings highlight the importance of optimizing fuzzy logic system designs to improve student performance assessment.
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“Recent applications of fuzzy logic to grading and evaluation contexts similarly report improvements in fairness, consistency, and interpretability when compared with conventional aggregation-based assessment models (Barlybayev et al., 2025; Loan et al., 2024; Tuan et al., 2025).”
From Reducing classification risk in developmental performance assessment using fuzzy logic: evidence from preservice mathematics teacher education (2026) · cites this paper as Tuan et al., 2025
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Naccache, H., & Hleiss, R. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1840093
Metadata
- Authors
- Tuan, T. A., Tho, N. D., Nghia, N. H., & Loan, D. T. T.
- Affiliations
- Walailak University (TH)
- Venue
- International Journal of Engineering Trends and Technology
- Published
- 2025-09-30
- Volume
- 73
- Issue
- 9
- ISSN
- 2231-5381 (ISSN-L)
- OpenAlex
- W7084745783
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- FWCI
- 0.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Engineering Trends and Technology (publishedVersion)
- Funders
- Walailak University
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- fuzzy logic, student assessment, academic performance evaluation, educational data mining
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