The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
Сomparative analysis of grading models using fuzzy logic to enhance fairness and consistency in student performance evaluation
Open accessAbstract
The article examines the continuous assessment of student performance as a crucial element of modern educational processes for achieving learning objectives. Traditional assessment methods, such as exams and grading systems, do not always reflect the diversity of learning styles and individual characteristics of students, creating gaps in fairness and accuracy. As a solution to this issue, the study proposes a Fuzzy Logic Model (FLM), which serves as an innovative approach to assessing student performance. The study, conducted on a sample of 33 students enrolled in the ‘Object-Oriented Programming in Java’ course, compares the effectiveness of the FLM with traditional grading systems such as national standards, arithmetic mean, as well as institutional schemes including the U.S. Grade Point Average system and India’s Central Board of Secondary Education system. The advantage of the FLM lies in its ability to model uncertainty and subjective elements of assessment, making the system more flexible and comprehensive. The results of the study show that the FLM can provide a fairer, more accurate and individualized assessment, better reflecting the complexity and multifaceted nature of student performance. The article emphasizes the importance of continuously improving assessment methods to meet modern educational demands, highlighting the relevance of using adaptive models such as fuzzy logic to enhance educational outcomes and increase the fairness of assessments.
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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 Barlybayev et al., 2025
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Research works printed but not linked: 7
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Zhao, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-17738-z · Cited by 15
Otarbay, Z., & Kyzyrkanov, A. E. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1691929 · Cited by 13
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Kyzyrkanov, A. E., Otarbay, Z., & Yersultanova, Z. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.264 · Cited by 2
Suswanto, H., Aripriharta, A., Handayani, A. N., Mufti, N., Musta’ámal, A. H., & Prasetyo, M. W. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2598910 · Cited by 1
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A hybrid fuzzy neural model for corpus-based English language teaching
Xu, Z., & Ma, L. (2026) · Discover Computing · doi:10.1007/s10791-026-09921-1
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Enhancing awareness of cultural influences on requirements engineering
Muzammel, C. S., Spichkova, M., & Harland, J. (2026) · Requirements Engineering · doi:10.1007/s00766-026-00464-x
George, M. A., George, A. M., Varghese, S. G., Kurian, C. P., Srividya, R., Yadav, A., Bhoi, D., Dutta, H. V., & Jain, K. (2026) · IEEE Access · doi:10.1109/access.2026.3724607
Multimodal Collaborative Assessment of English Speech Based on LLM and Fuzzy Logic
Yao, W. (2026) · Systems and Soft Computing · doi:10.1016/j.sasc.2026.200620
Kyzyrkanov, A. E., Otarbay, Z., Assel, K., Tursynova, N., Saltanat, A., Yersultanova, Z., & Tulegenova, E. N. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.08.401
Self-citation
Metadata
- Authors
- Barlybayev, A., Razakhova, B., Sharipbay, A., Nazyrova, A., Tursynova, N., Zulkhazhav, A., & Yelibayeva, G.
- Affiliations
- L. N. Gumilyov Eurasian National University (KZ)
- Faculty of Information Technologies, L.N. Gumilyov Eurasian National University
- Corresponding author
- Nazyrova, Aizhan
- ORCID
- Venue
- Cogent Education
- Publisher
- Informa UK Limited
- Published
- 2025-03-24
- Volume
- 12
- Issue
- 1
- ISSN
- 2331-186X (ISSN-L)
- OpenAlex
- W4408819908
- Cited by
- 12
- References
- 33
- Citations by year
- 2026: 8 · 2025: 4
- FWCI
- 12.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cogent Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant АР19678613)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- student assessment, continuous assessment, grading fairness, grading consistency, grade point average
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