MISCONCEPTIONS AND IRT METHODOLOGY IN MATHEMATICS EDUCATION: RELEVANCE AND THEORETICAL BASIS
Keywords:
mathematical misconceptions, IRT methodology, 2PL model, mathematical training, TIMSS, PISA, cognitive diagnostic assessmentAbstract
This article analyzes the relevance and theoretical foundations of typical errors (misconceptions) of 7th-9th grade students in mathematics education and the modern psychometric method of their detection - the Item Response Theory (IRT) methodology. Based on data from international studies (TIMSS, PISA), systemic gaps in the mathematical training of Uzbek schoolchildren have been identified, including typical errors related to fractions, percentages, and linear equations. The article highlights the advantages of IRT (1PL, 2PL, 3PL models), its superiority over classical test theory, and the possibilities of cognitive diagnostic assessment (CDA). The presented analysis serves as a methodological basis for correcting gaps in mathematical training 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.
O‘zbekiston Respublikasining «Ta’lim to‘g‘risida»gi Qonuni. - Toshkent, 2020.
O‘zbekiston Respublikasi Milliy o‘quv dasturi. - Toshkent, 2020.
Yuldashev Q. H. Matematik savodxonlikni baholashning zamonaviy metodlari. - Toshkent: Fan, 2018.
O‘zbekiston Respublikasi Vazirlar Mahkamasi huzuridagi Ta’lim sifatini nazorat qilish davlat inspeksiyasi materiallari. - 2021.
Gierl M. J., Haladyna T. M. (Eds.). Cognitive Diagnostic Assessment for Education. - Cambridge University Press, 2009.
Sheffield L. J., Cruikshank D. E. Teaching and Learning Mathematics: A Guide to Research. - New York: Routledge, 2015.
Ojose B. Common Misconceptions in Mathematics: Strategies to Correct Them. - Lanham: University Press of America, 2015.
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.
Hambleton R. K., Swaminathan H. Item Response Theory: Principles and Applications. - Boston: Kluwer-Nijhoff Publishing, 1985.
DeMars C. Item Response Theory. - Oxford: Oxford University Press, 2010.
Crocker L., Algina J. Introduction to Classical and Modern Test Theory. - New York: Holt, Rinehart and Winston, 1986.
Embretson S. E., Reise S. P. Item Response Theory for Psychologists. - Mahwah, NJ: Lawrence Erlbaum, 2000.
Rasch G. Probabilistic Models for Some Intelligence and Attainment Tests. - Copenhagen: Danish Institute for Educational Research, 1960.
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.
Kolen M. J., Brennan R. L. Test Equating, Scaling, and Linking. - 3rd ed. - New York: Springer, 2014.
Cohen J. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit // Psychological Bulletin. - 1968. - Vol. 70, No. 4. - P. 213-220.
Creswell J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. - 4th ed. - Thousand Oaks, CA: Sage, 2014.
Cochran W. G. Sampling Techniques. - 3rd ed. - New York: John Wiley & Sons, 1977.
Lohr S. L. Sampling: Design and Analysis. - 2nd ed. - Boston: Brooks/Cole, 2010.
Patton M. Q. Qualitative Research & Evaluation Methods. - 4th ed. - Thousand Oaks, CA: Sage, 2015.
Shadish W. R., Cook T. D., Campbell D. T. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. - Boston: Houghton Mifflin, 2002.
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.
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.
Livingston S. A., Zieky M. J. Passing Scores: A Manual for Setting Standards of Performance on Educational and Occupational Tests. - Princeton, NJ: Educational Testing Service, 1982.
Birnbaum A. Some latent trait models and their use in inferring an examinee's ability // Lord F. M., Novick M. R. (Eds.) Statistical Theories of Mental Test Scores. - Reading, MA: Addison-Wesley, 1968. - P. 397-479.
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.
R Core Team. R: A Language and Environment for Statistical Computing. - Vienna, Austria: R Foundation for Statistical Computing, 2024.
Chalmers R. P. mirt: A multidimensional item response theory package for the R environment // Journal of Statistical Software. - 2012. - Vol. 48, No. 6. - P. 1-29.
Massey F. J. The Kolmogorov-Smirnov test for goodness of fit // Journal of the American Statistical Association. - 1951. - Vol. 46, No. 253. - P. 68-78.
Student. The probable error of a mean // Biometrika. - 1908. - Vol. 6, No. 1. - P. 1-25.
Mann H. B., Whitney D. R. On a test of whether one of two random variables is stochastically larger than the other // The Annals of Mathematical Statistics. - 1947. - Vol. 18, No. 1. - P. 50-60.
Jonckheere A. R. A distribution-free k-sample test against ordered alternatives // Biometrika. - 1954. - Vol. 41, No. 1-2. - P. 133-145.
Raudenbush S. W., Bryk A. S. Hierarchical Linear Models: Applications and Data Analysis Methods. - 2nd ed. - Thousand Oaks, CA: Sage, 2002.
Field A. Discovering Statistics Using IBM SPSS Statistics. - 5th ed. - London: Sage, 2018.
Cronbach L. J. Coefficient alpha and the internal structure of tests // Psychometrika. - 1951. - Vol. 16, No. 3. - P. 297-334.