A Hierarchical and Finite Mixture Modeling Approach to ‎Mathematics Achievement: Evidence from PISA 2022

  • Authors

    • Pei-Ching Chao Center for Teacher Education, Fu Jen Catholic University, New Taipei City, Taiwan https://orcid.org/0000-0002-5696-9916
    • Joseph Meng-chun Chin Graduate Institute of Educational Administration and Policy, National Chengchi University, Taipei City, ‎Taiwan
    • Gregory S. Ching Graduate Institute of Educational Administration and Policy, National Chengchi University, Taipei City, ‎Taiwan https://orcid.org/0000-0001-9148-0019
    https://doi.org/10.14419/tc3wv638

    Received date: November 21, 2025

    Accepted date: December 19, 2025

    Published date: December 23, 2025

  • Mathematics Achievement; Hierarchical Linear Modeling; Gaussian Mixture Model; Clustering and Classification; Bayesian Information Crite-‎Rion; Mul-tigroup Structural Equation Modeling; Educational Data Science

  • Abstract

    This study investigates the combined effects of cognitive, psychosocial, and cross-subject academic indicators on students’ mathematics ‎achievement across 38 countries that participated in the PISA 2022 creative thinking assessment. Drawing on data from 144,446 students, ‎we employed hierarchical linear modeling to examine how reading and science proficiency, engagement in creative activities (in and out of ‎school), perseverance, curiosity, and socioeconomic status (ESCS) predict mathematical performance. The results show that reading and ‎science are robust predictors of mathematics scores. ESCS and perseverance also demonstrated consistent positive effects, while creativity ‎showed context-specific associations, positive in some clusters and negative in others. To identify latent cross-national typologies, we ap-‎plied both K-means clustering and Gaussian Mixture Modeling (GMM) to country-level aggregates. Model comparison using the Bayesian ‎Information Criterion (BIC) favored the GMM solution, which was subsequently used to group countries for multigroup structural equation ‎modeling (MG-SEM). Results revealed significant variations in predictor effects across clusters, highlighting heterogeneity in pathways to ‎mathematics success. This study contributes to comparative education research by integrating hierarchical regression and latent classification ‎techniques, offering implications for instructional design and international education policy aimed at promoting mathematical literacy across ‎diverse systems‎.

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    Chao, P.-C., Chin, J. M.- chun, & Ching, G. S. (2025). A Hierarchical and Finite Mixture Modeling Approach to ‎Mathematics Achievement: Evidence from PISA 2022. International Journal of Basic and Applied Sciences, 14(8), 520-530. https://doi.org/10.14419/tc3wv638