A FUZZY INFERENCE FRAMEWORK FOR INTEGRATING PISA AND TIMSS INDICATORS IN THE GLOBAL EVALUATION OF MATHEMATICS EDUCATION
Abstract
Abstract
This study analyses the mathematical performance of 36 countries based on the 2022 PISA Mathematics scores and the 2023 TIMSS Grade 8 Mathematics scores. The objective of this study is to provide a more flexible and comprehensive evaluation of countries’ performance by addressing the limitations of conventional crisp classification methods. To achieve this, a Mamdani fuzzy inference system is employed, where both PISA and TIMSS scores are modelled using fuzzy sets consisting of low, medium, and high categories. The fuzzy approach allows each country to be evaluated based on degrees of membership, enabling a more nuanced interpretation of performance levels. The results indicate that most countries fall into the medium performance category, while only a limited number are classified as high or low. Several countries are also identified as borderline cases, highlighting the presence of uncertainty and gradual transitions in performance levels. These findings demonstrate that the fuzzy approach provides a more realistic and flexible framework for analysing educational data, and it can serve as an alternative method to support more informed decision-making in education policy.
References
Boesen, J., Helenius, O., Bergqvist, E., Bergqvist, T., Lithner, J., Palm, T., & Palmberg, B. (2014). Developing mathematical competence: From the intended to the enacted curriculum. The Journal of Mathematical Behavior, 33, 72–87. https://doi.org/10.1016/j.jmathb.2013.10.001
Bolstad, O. H. (2023). Lower secondary students’ encounters with mathematical literacy. Mathematics Education Research Journal, 35(1), 237–253. https://doi.org/10.1007/s13394-021-00386-7
Doz, D., Felda, D., & Cotič, M. (2022). Combining Students’ Grades and Achievements on the National Assessment of Knowledge: A Fuzzy Logic Approach. Axioms, 11(8), 359. https://doi.org/10.3390/axioms11080359
Goyal, M., Gupta, C., & Gupta, V. (2022). A meta-analysis approach to measure the impact of project-based learning outcome with program attainment on student learning using fuzzy inference systems. Heliyon, 8(8), e10248. https://doi.org/10.1016/j.heliyon.2022.e10248
Ergin, H., & Nasibov, E. (2025). Analyzing Students’ Academic Performance Based on Fuzzy Inference System. Applied Sciences, 15(23), 12755. https://doi.org/10.3390/app152312755
Fischman, G. E., Topper, A. M., Silova, I., Goebel, J., & Holloway, J. L. (2019). Examining the influence of international large-scale assessments on national education policies. Journal of Education Policy, 34(4), 470–499. https://doi.org/10.1080/02680939.2018.1460493
Ina V.S. Mullis, Michael O. Martin, & Matthias von Davier. (2024). TIMSS 2023 International Results in Mathematics and Science.
Kumari, K., & Mohan, B. M. (2024). Modelling and Simulation of Interval Type-2 Fuzzy PID Controllers using Mamdani Minimum Inference. IFAC-PapersOnLine, 57, 286–291. https://doi.org/10.1016/j.ifacol.2024.05.049
Laukaityte, I., Rolfsman, E., & Wiberg, M. (2024). TIMSS vs. PISA: what can they tell us about student success?—a comparison of Swedish and Norwegian TIMSS and PISA 2015 results with a focus on school factors. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1323687
Liu, X., Hansen, K. Y., Valcke, M., & De Neve, J. (2024). A decade of PISA: student perceived instructional quality and mathematics achievement across European countries. ZDM – Mathematics Education, 56(5), 859–891. https://doi.org/10.1007/s11858-024-01630-7
Niss, M., & Højgaard, T. (2019). Mathematical competencies revisited. Educational Studies in Mathematics, 102(1), 9–28. https://doi.org/10.1007/s10649-019-09903-9
Organization for Economic Co-operation and Development (OECD) PISA 2022 Results. (2023). In The State of Learning and Equity in Education (Vol. 1). OECD Publishing.
Pathak, B. K. (2025). Assessing Student Academic Performance withFuzzy Expert System. International Journal of Modern Education and Computer Science, 17(2), 111–122. https://doi.org/10.5815/ijmecs.2025.02.05
Pratama, Y., Pasaribu, M., Nababan, J., Sihombing, D., & Gultom, D. (2021). Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic. Journal of Physics: Conference Series, 1933(1), 012069. https://doi.org/10.1088/1742-6596/1933/1/012069
Sellar, S., & Lingard, B. (2014). The OECD and the expansion of PISA: new global modes of governance in education. British Educational Research Journal, 40(6), 917–936. https://doi.org/10.1002/berj.3120
Selvachandran, G., Quek, S. G., Lan, L. T. H., Son, L. H., Giang, N. L., Ding, W., Abdel-Basset, M., & de Albuquerque, V. H. C. (2021). A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems. IEEE Transactions on Fuzzy Systems, 29(4), 716–730. https://doi.org/10.1109/TFUZZ.2019.2961350
Thanh Loan, D. T., Duy Tho, N., Huu Nghia, N., Chien, V. D., & Anh Tuan, T. (2024). Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression. International Journal of Modern Education and Computer Science, 16(1), 1–10. https://doi.org/10.5815/ijmecs.2024.01.01
Yang, X., Chan, M. C. E., Kaur, B., Deng, J., Luo, J., & Wen, Y. (2025). International comparative studies in mathematics education: a scoping review of the literature from 2014 to 2023. ZDM – Mathematics Education, 57(4), 745–761. https://doi.org/10.1007/s11858-025-01700-4
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 asido saragih, Jaya Santoso, Ana Muliyana, Winner J. Pardede, Ony J. Hutapea, Winda R. Siagian
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution 4.0 International License
