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