CoChat
Sign Up
Cited by 12Educational Technology and AssessmentStudent Assessment and FeedbackEdcuational Technology Systems

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 access
Barlybayev, Alibek, Razakhova, Bibigul, Sharipbay, Altynbek, Nazyrova, Aizhan, Tursynova, Nazira, Zulkhazhav, Altanbek, Yelibayeva, Gaziza·2025·Cogent Education
DOI 10.1080/2331186x.2025.2481008
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1080/2331186x.2025.2481008

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 7

  1. Arora, N., & Saini, J. R. (2013). A fuzzy probabilistic neural network for student’s academic performance prediction. International Journal of Innovative Research in Science, Engineering and Technology, 2(9), 4425–4432.
  2. 10.30495/jedu.2021.22870.4633 · DOI
  3. Daud, W. S. W., Aziz, K. A. A., & Sakib, E. (2011). An evaluation of students’ performance in oral presentation using fuzzy approach. Organization, 5(3.67), 0–73.
  4. Ingoley, S., & Bakal, J. W. (2012). Use of fuzzy logic in evaluating students’ learning achievement. International Journal on Advanced Computer Engineering and Communication Technology (IJACECT), 1(2), 47–54.
  5. Kharola, A., Kunwar, S., & Choudhury, G. B. (2015). Students performance evaluation: A fuzzy logic reasoning approach. PM World Journal, 4(9), 1–11.
  6. Latah, M. (2016). ANFIS approach with genetic feature selection for prediction of students’ academic performance in distance education environment. International Journal of Engineering Applied Sciences and Technology, 1(8), 2455–2143.
  7. Meenakshi, N., & Pankaj, N. (2015). Application of fuzzy logic for evaluation of academic performance of students of computer application course. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 3(10), 260–267.

Cited by

Sorted by citations

See all 12

Related

See all

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)
DOI
10.1080/2331186x.2025.2481008
OpenAlex
W4408819908
Cited by
12
References
33
Citations by year
2026: 8 · 2025: 4
FWCI
12.58
Type
journal article
Language
en
Open access
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review