SOCIOECONOMIC AND PSYCHOLOGICAL CORRELATES OF MATHEMATICAL LITERACY IN SOUTHEAST ASIA: AN ANALYSIS OF PISA 2022 DATA
Keywords:
Mathematical Literacy, ESCS, Mathematics Anxiety, Mathematical Confidence, Teacher Support, PISA 2022Abstract
Mathematical literacy remains a key indicator of students’ capacity to apply mathematical knowledge in real-life contexts. This study examines how teacher support, mathematical confidence, mathematics anxiety, economic, social, and cultural status (ESCS), and gender are associated with students’ mathematical literacy in four Southeast Asian countries: Indonesia, Malaysia, Thailand, and Singapore. Using PISA 2022 student data, the study included 35,609 students: 13,439 from Indonesia, 7,069 from Malaysia, 6,606 from Singapore, and 8,495 from Thailand. The reported analyses used descriptive statistics, reliability testing, country comparisons, and multiple linear regression. The unweighted results show substantial cross-country differences in mathematics achievement, with Singapore recording the highest sample mean and Indonesia the lowest. The pooled regression model explained 39.0% of the variance in the unweighted mathematics score. ESCS showed the largest standardized association with mathematical literacy, whereas mathematics anxiety showed a negative association. Mathematical confidence was positively associated with achievement in Malaysia, Singapore, and Thailand but had a negative coefficient in Indonesia. Teacher support was positively associated with achievement in Malaysia, Singapore, and Thailand but was not statistically significant in Indonesia. Because the original analysis averaged the ten plausible values at the student level rather than applying the full PISA estimation framework, all numerical estimates should be interpreted with appropriate caution. Subject to this limitation, the findings suggest that policies should address socioeconomic disparities, strengthen students’ mathematical self-efficacy, and reduce mathematics anxiety through targeted learning support.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Samsul Irpan, Mohammad Naim Bahar, Noor Mohammad Azizi, Zohaib Hassan Sain

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
