Math Anxiety and Gender Differences in Additional Mathematics among Islamic Residential School Students

Mathematics anxiety is a major psychological barrier to student achievement, especially when students are faced with advanced and high-stakes subjects such as Additional Mathematics. There has been a lot of research on this issue in regular public schools, but little is known about how these emotional pressures play out in specialized educational settings. The objectives of this study were to determine the level of math anxiety and gender differences among students in an Islamic residential school in Terengganu, Malaysia. The sample was based on localized case study design where data was collected from 70 Form 5 students. A subset of 70 fully completed responses was then subjected to descriptive and inferential analyses. The descriptive results reveal an alarming reality, which is that 65.7% of the students suffer from high levels of math anxiety. An item-level analysis revealed a surprising paradox; students reported feeling very comfortable, focused and cooperative during everyday classroom learning but experienced high levels of emotional distress over high-stakes testing, the fear of public inaccuracy and unexpected classroom evaluation. Moreover, the independent samples t-test revealed a statistically significant gender difference, showing that female students suffer from a significantly heavier emotional burden and higher anxiety levels compared to their male counterparts. The high math anxiety in elite, dual-curriculum boarding settings is a psychological roadblock, not a reflection of a student’s lack of effort. Educators need to actively shift classroom cultures away from high-stakes tactics like unexpected “cold-calling” and instead focus on normalizing mistakes as a natural part of learning to protect student well-being and ensure equitable participation in STEM pathways.

Additional Mathematics, Math anxiety, Islamic residential schools, Gender differences

1. Ashcraft, M. H. (2020). Cognitive consequences of math anxiety: A twenty-year review. Educational Psychology Review, 32(4), 911–925. [Google Scholar] [Crossref]

2. Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Purpura, D. J. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134–168. [Google Scholar] [Crossref]

3. Beilock, S. L., & Maloney, E. A. (2015). Math anxiety: A factor in math achievement not to be ignored. Policy Insights from the Behavioral and Brain Sciences, 2(1), 4–12. [Google Scholar] [Crossref]

4. Carey, E., Devine, A., Hill, F., Dowker, A., McLellan, R., & Szűcs, D. (2021). Understanding mathematics anxiety: Loss of confidence and explicit triggers in secondary students. Frontiers in Psychology, 12, Article 642310. [Google Scholar] [Crossref]

5. Dowker, A., Cheriton, O., Horton, R., & Miletic, V. (2020). Mathematics anxiety in secondary school students: Affective barriers to STEM. Journal of Educational Psychology, 112(6), 1155–1168. [Google Scholar] [Crossref]

6. Foley, A. E., Herts, J. B., Borgonovi, F., Guerriero, S., Levine, S. C., & Beilock, S. L. (2024). The experimental mechanics of evaluation panic: How working memory is hijacked in testing environments. Trends in Neuroscience and Education, 34, Article 100218. [Google Scholar] [Crossref]

7. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Education. [Google Scholar] [Crossref]

8. Mazana, Y. M., Montero, C. S., & Casmir, R. O. (2020). Investigating students’ attitude towards learning mathematics. International Electronic Journal of Mathematics Education, 15(1), Article em0561. [Google Scholar] [Crossref]

9. Mutodi, P. (2023). The residential pressure-cooker: Academic stress and psychological bottlenecks in boarding school systems. Journal of Residential Education, 19(2), 88–104. [Google Scholar] [Crossref]

10. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]

11. Orbach, L., Herzog, M., & Fritz, A. (2020). State- and trait-math anxiety and their relation to math performance in secondary school students. PLoS ONE, 15(3), Article e0229979. [Google Scholar] [Crossref]

12. Picho, K., & Akansha, A. (2025). Empathy in the STEM classroom: Instructional strategies to mitigate affective barriers. Instructional Science, 53(1), 45–67. [Google Scholar] [Crossref]

13. Ramirez, G., Shaw, S. T., & Maloney, E. A. (2022). Math anxiety: Past research, promising interventions, and a new framework for the math classroom. Educational Psychologist, 57(2), 118–132. [Google Scholar] [Crossref]

14. Recber, S., Isiksal, M., & Koç, Y. (2022). Investigating self-efficacy, gender, and mathematics anxiety as predictors of mathematics achievement. International Journal of Science and Mathematics Education, 20(3), 515–534. [Google Scholar] [Crossref]

15. Rico-Bautista, D., Maestre-Góngora, G., & Guerrero, C. (2021). Cognitive deficits vs. affective blocks: A restructuring of mathematical difficulties in secondary education. Journal of Physics: Conference Series, 1712(1), Article 012015. [Google Scholar] [Crossref]

16. Saraswathi, K., & Naidoo, J. (2024). Additional mathematics as a gatekeeper for science and engineering fields: An empirical tracking. South African Journal of Higher Education, 38(2), 204–221. [Google Scholar] [Crossref]

17. Szczygieł, M. (2020). When do gender differences in math anxiety emerge? A longitudinal study on secondary school cohorts. Journal of Early Adolescence, 40(7), 1012–1036. [Google Scholar] [Crossref]

18. Tan, L. S., & Chin, L. C. (2022). Challenges in secondary STEM pathways: Parsing the density of Additional Mathematics. Malaysian Journal of Learning and Instruction, 19(1), 143–171. [Google Scholar] [Crossref]

19. Van Mier, H. I., Schleepen, L. M., & Van den Berg, F. C. (2023). Gender differences in math anxiety and performance: The role of internalized academic perfectionism. Frontiers in Education, 8, Article 110943. [Google Scholar] [Crossref]

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