Frontiers in psychology · 2026
Uncovering heterogeneity in South African students' mathematics competence: latent profile analysis and multinomial logistic regression.
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Aziku Millicent, Tahri Dahman, Djekourmane Denis
Аннотация
INTRODUCTION: Mathematics competence is multidimensional, and students may demonstrate different patterns of performance across mathematical content and cognitive domains. This study examined heterogeneity in mathematics competence among South African Grade 8 students and investigated factors associated with membership in distinct mathematics competence profiles. Guided by Bronfenbrenner's ecological theory of human development, the study considered individual characteristics and features of students' immediate home and school environments.
METHODS: The study used data from 11,012 South African Grade 8 students who participated in the Trends in International Mathematics and Science Study (TIMSS) 2023. Latent Profile Analysis (LPA) was used to identify distinct mathematics competence profiles across seven domains: Number, Algebra, Geometry, Data and Probability, Knowing, Applying, and Reasoning. A three-step multinomial logistic regression was subsequently conducted to examine individual- and contextual-level factors associated with profile membership.
RESULTS: Four mathematics competence profiles were identified: Low, Lower-middle, Upper-middle, and High competence. Profile membership was differentially associated with students' characteristics and immediate learning environments. In particular, mathematics confidence, home educational resources, school belonging, and bullying were notably associated with membership in the higher-competence profiles, although the magnitude and direction of these associations varied across profiles.
DISCUSSION: The findings demonstrate substantial heterogeneity in students' mathematics competence and suggest that mathematics performance is associated with a combination of individual and contextual factors. The results highlight the importance of recognizing differentiated patterns of mathematics competence when designing educational policies and support strategies and of considering students' home and school environments alongside individual characteristics.
METHODS: The study used data from 11,012 South African Grade 8 students who participated in the Trends in International Mathematics and Science Study (TIMSS) 2023. Latent Profile Analysis (LPA) was used to identify distinct mathematics competence profiles across seven domains: Number, Algebra, Geometry, Data and Probability, Knowing, Applying, and Reasoning. A three-step multinomial logistic regression was subsequently conducted to examine individual- and contextual-level factors associated with profile membership.
RESULTS: Four mathematics competence profiles were identified: Low, Lower-middle, Upper-middle, and High competence. Profile membership was differentially associated with students' characteristics and immediate learning environments. In particular, mathematics confidence, home educational resources, school belonging, and bullying were notably associated with membership in the higher-competence profiles, although the magnitude and direction of these associations varied across profiles.
DISCUSSION: The findings demonstrate substantial heterogeneity in students' mathematics competence and suggest that mathematics performance is associated with a combination of individual and contextual factors. The results highlight the importance of recognizing differentiated patterns of mathematics competence when designing educational policies and support strategies and of considering students' home and school environments alongside individual characteristics.