IRT METHODOLOGY AND DIAGNOSTICS OF COGNITIVE MISCONCEPTIONS IN MATHEMATICS EDUCATION: PSYCHOMETRIC ANALYSIS AND COGNITIVE INTERVENTION

Authors

  • Bozorov Murot Nashvandovich Associate Professor, University of Economics and Pedagogy

Keywords:

mathematical misconceptions, IRT methodology, 2PL model, cognitive diagnostic assessment, PISA, TIMSS, PSM, cognitive conflict.

Abstract

 

        This article analyzes the psychometric foundations of the IRT methodology for identifying cognitive misconceptions of 7th-9th graders. Based on Uzbekistan’s PISA-2022 and TIMSS-2023 results, low performance in the “reasoning” cognitive domain is linked to misconceptions. A comparative analysis of IRT models (1PL, 2PL, 3PL), possibilities of cognitive diagnostic assessment (CDA) and the Q-matrix, PSM methods, and cognitive intervention strategies (“cognitive conflict”) are presented. The analysis serves as a methodological basis for identifying systemic gaps and developing intervention programs.

References

OECD. PISA 2018 Assessment and Analytical Framework. - Paris: OECD Publishing, 2019.

Mullis I. V. S., Martin M. O. (Eds.). TIMSS 2019 Assessment Frameworks. - Boston: TIMSS & PIRLS International Study Center, 2017.

Ojose B. Common Misconceptions in Mathematics: Strategies to Correct Them. - Lanham: University Press of America, 2015.

Sheffield L. J., Cruikshank D. E. Teaching and Learning Mathematics: A Guide to Research. - New York: Routledge, 2015.

Gierl M. J., Haladyna T. M. (Eds.). Cognitive Diagnostic Assessment for Education. - Cambridge University Press, 2009.

Piaget J. Piaget’s theory // Carmichael’s Manual of Child Psychology. - 3rd ed. - New York: Wiley, 1970. - Vol. 1. - P. 703-732.

Anderson J. R. Cognitive Psychology and Its Implications. - 5th ed. - Worth Publishers, 2000.

Rasch G. Probabilistic Models for Some Intelligence and Attainment Tests. - Copenhagen: Danish Institute for Educational Research, 1960.

Hambleton R. K., Swaminathan H. Item Response Theory: Principles and Applications. - Boston: Kluwer-Nijhoff Publishing, 1985.

Embretson S. E., Reise S. P. Item Response Theory for Psychologists. - Mahwah, NJ: Lawrence Erlbaum, 2000.

Baker F. B. The Basics of Item Response Theory. - 2nd ed. - College Park, MD: ERIC Clearinghouse on Assessment and Evaluation, 2001.

Kolen M. J., Brennan R. L. Test Equating, Scaling, and Linking. - 3rd ed. - New York: Springer, 2014.

Bock R. D., Aitkin M. Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm // Psychometrika. - 1981. - Vol. 46, No. 4. - P. 443-459.

DeMars C. Item Response Theory. - Oxford: Oxford University Press, 2010.

Rizopoulos D. ltm: An R package for latent variable modelling and item response theory analyses // Journal of Statistical Software. - 2006. - Vol. 17, No. 5. - P. 1-25.

Anderson L. W., Krathwohl D. R. (Eds.). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. - New York: Longman, 2001.

Rosenbaum P. R., Rubin D. B. The central role of the propensity score in observational studies for causal effects // Biometrika. - 1983. - Vol. 70, No. 1. - P. 41-55.

Austin P. C. An introduction to propensity score methods for reducing the effects of confounding in observational studies // Multivariate Behavioral Research. - 2011. - Vol. 46, No. 3. - P. 399-424.

Crocker L., Algina J. Introduction to Classical and Modern Test Theory. - New York: Holt, Rinehart and Winston, 1986.

Yuldashev Q. H. Matematik savodxonlikni baholashning zamonaviy metodlari. - Toshkent: Fan, 2018.

Identifying and dealing with student errors in the mathematics classroom. - PMC, 2022. - Article ID: 9798414.

Published

2026-05-08

How to Cite

IRT METHODOLOGY AND DIAGNOSTICS OF COGNITIVE MISCONCEPTIONS IN MATHEMATICS EDUCATION: PSYCHOMETRIC ANALYSIS AND COGNITIVE INTERVENTION. (2026). Universal International Scientific Journal, 3(5), 51-57. https://universaljurnal.uz/index.php/jurnal/article/view/4013