Cognitive Diagnostic Modelling of Students’ Mathematical Skills in Southwestern Nigeria Secondary Schools

Authors

Adediwura Alaba Adeyemi

Department of Educational Foundations Faculty of Education Obafemi Awolowo University, Ile-Ife (Nigeria)

Babatimehin Temitope

Department of Educational Foundations Faculty of Education Obafemi Awolowo University, Ile-Ife (Nigeria)

Babayemi Beatrice Oluwakemi

Department of Arts and Social Science Education Faculty of Education Obafemi Awolowo University, Ile-Ife (Nigeria)

Article Information

DOI: 10.47772/IJRISS.2026.100600397

Subject Category: Education

Volume/Issue: 10/6 | Page No: 5680-5692

Publication Timeline

Submitted: 2026-04-30

Accepted: 2026-05-06

Published: 2026-06-25

Abstract

The current study used Cognitive Diagnostic Modelling (CDM) to analyse the proficiency of Senior Secondary School Two students in mathematics skills in Southwestern Nigeria. Using the 100-item Mathematics Achievement Test (MAT), which measured proficiency in 15 cognitive attributes, 1,200 students from selected schools took part in the study. The items were mapped onto the cognitive attributes using the Q-Matrix, which was previously established by subject experts. The G-DINA model was adopted for data analysis, and mastery attribute profile estimates, model fit, and classification accuracies were obtained. The results showed very high proficiency with simple skills like arithmetic and fractions, while low proficiency with higher-level cognitive skills like algebra, probability, and problem-solving. It was found that there are no homogeneous mastery attribute profiles, and many students have partial mastery of attributes. The results obtained suggest that the G-DINA model fits well with the data collected

Keywords

Cognitive Diagnostic Modelling (CDM); G-DINA Model; Mathematics Skills; Attribute Mastery; Secondary Education Nigeria

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References

1. Adegoke, B. A., & Okebukola, P. A. (2021). Predictors of students’ achievement in secondary school mathematics in Nigeria. African Journal of Educational Research, 25(2), 45–60. [Google Scholar] [Crossref]

2. Adeleke, M. A., Adeyemi, B. A., & Oladipo, S. O. (2024). Diagnostic assessment and students’ learning outcomes in mathematics: Evidence from Southwest Nigeria. Journal of Educational Measurement and Evaluation, 9(1), 112–130. [Google Scholar] [Crossref]

3. Awofala, A. O. A. (2022). Problem-solving strategies and students’ performance in mathematics in Nigeria. International Journal of Mathematical Education in Science and Technology, 53(4), 789–805. [Google Scholar] [Crossref]

4. de la Torre, J., & Minchen, N. (2021). Cognitively diagnostic assessment and the G-DINA model: Recent developments. Psychometrika, 86(4), 1020–1045. [Google Scholar] [Crossref]

5. Guo, X., et al. (2024). Enhancing assessment practices through cognitive diagnostic models. Computers & Education. [Google Scholar] [Crossref]

6. Lim, Y. S. (2024). Gauging Q-matrix design and model selection in applied cognitive diagnosis. Applied Measurement in Education, 37(4), 412–429. [Google Scholar] [Crossref]

7. Ma, W., & de la Torre, J. (2022). GDINA: An R package for cognitive diagnosis modeling. Journal of Statistical Software, 93(14), 1–26. [Google Scholar] [Crossref]

8. Mji, A., & Makgato, M. (2022). Factors associated with high school learners’ poor performance in mathematics. African Journal of Research in Mathematics, Science and Technology Education, 26(1), 15–27. [Google Scholar] [Crossref]

9. Nallasamy, S., & Khairani, A. (2022). Application of cognitive diagnostic assessment in educational contexts. e-Bangi Journal of Social Sciences & Humanities. [Google Scholar] [Crossref]

10. Ndlovu, M., & Mji, A. (2021). Students’ difficulties in solving non-routine mathematical problems. South African Journal of Education, 41(2), 1–10. [Google Scholar] [Crossref]

11. Ogunniyi, M. B., & Fakunle, O. (2023). Enhancing conceptual understanding in mathematics through innovative teaching strategies. African Educational Review, 20(3), 210–228. [Google Scholar] [Crossref]

12. Okoye, R. O., & Adebayo, F. A. (2022). Assessment reforms in Nigeria: The need for diagnostic approaches. Nigerian Journal of Educational Evaluation, 21(1), 67–82. [Google Scholar] [Crossref]

13. Ravand, H., et al. (2025). Measuring cognitive levels in high-stakes testing: A cognitive diagnostic modelling approach. Frontiers in Education. [Google Scholar] [Crossref]

14. Ravand, H., & Baghaei, P. (2023). Cognitive diagnostic modeling: Applications and recent trends. Educational Psychology Review, 35(2), 567–589. [Google Scholar] [Crossref]

15. Tao, J., et al. (2024). Cognitive diagnosis method via Q-matrix-embedded neural networks. Applied Sciences, 14(22), 10380. [Google Scholar] [Crossref]

16. Williamson, J. (2023). Cognitive diagnostic models and how they can be useful. Cambridge Assessment. [Google Scholar] [Crossref]

17. Xu, X., & Wang, C. (2022). The role of cognitive diagnostic assessment in personalized learning. Computers & Education, 182, 104456. [Google Scholar] [Crossref]

18. Zhang, Y., et al. (2025). Development of a computerized adaptive assessment system based on cognitive diagnosis. Educational Technology Research. [Google Scholar] [Crossref]

19. Zhang, Y. (2025). Cognitive diagnostic analysis of mathematics key competencies. PLOS ONE. [Google Scholar] [Crossref]

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