Mathematics anxiety and problem-solving ability in numerical methods courses: A repeated cross-sectional study across two Southeast Asian universities
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
Mathematics anxiety (MA) impedes mathematical learning by taxing cognitive resources, negatively affecting problem-solving ability (PSA). Some evidence suggests that the strength and nature of this negative association may vary across educational and cultural contexts. However, cross-cultural studies on MA and PSA, especially in Southeast Asia, remain limited. This study investigated cohort-level patterns of MA and PSA in a transnational Numerical Methods course at Universiti Tun Hussein Onn Malaysia (UTHM) and Universitas Pahlawan (UP), Indonesia. The study spanned four independent cohorts from 2021 to 2024 and focused on institutional differences and temporal variation during a period of instructional transition. Using a repeated cross-sectional design, a total of 139 undergraduate students across four independent cohorts (UTHM: n=23; UP: n=116) were assessed during the 2021–2024 academic years using the Mathematics Anxiety Rating Scale and standardized problem-solving tests. Two-way factorial ANOVA examined the effects of university and cohort year on MA and PSA, and Pearson correlation analysis examined the association between the two constructs. A significant main effect of university indicated that UP students reported higher MA and lower PSA scores than UTHM students (p < 0.001). Significant year effects were observed for MA and PSA scores across successive cohorts (p ≤ 0.01), during a period characterized by a transition in instructional modalities from online to hybrid learning. Significant University×Year interactions indicated differing trends by institution. A moderate negative correlation between MA and PSA was observed (r = –0.49, p < 0.001).
INTRODUCTION
Mathematics forms the basis of higher studies and contributes significantly to one’s success at the university level. It is also a foundational discipline in higher education and essential for success across many academic and professional domains. However, previous studies reported it as the most anxiety-provoking subject(Ashcraft, 2002; Ramirez et al., 2016). Mathematics Anxiety (MA), a psychological phenomenon characterized by tension and apprehension related to math tasks, remains a significant barrier to learning(Carey et al., 2017; Hendriana et al., 2026; Xiang et al., 2026). MA has been studied extensively over decades, with evidence consistently linking it to cognitive and affective barriers in learning(Diponegoro et al., 2024; Dowker et al., 2016; Listiawati et al., 2025). MA negatively impacts working memory capacity, reduces confidence, and fosters avoidance behaviors, consequently lowering mathematical performance and problem-solving ability(Delima et al., 2024; Maloney & Beilock, 2012; Schoenfeld, 1982).
Theoretical frameworks for understanding MA generally cluster into three perspectives. Cognitive theories highlight how anxiety consumes working memory resources, limiting the capacity to process mathematical information effectively(Ashcraft & Kirk, 2001). MA is associated with avoidance behaviors and reduced mathematical performance, a pattern observed across diverse student populations(Maloney & Beilock, 2012). Affective-motivational theories focus on self-efficacy, motivation, and attribution beliefs as key mediators of anxiety responses(Bandura, 1997; Pajares & Miller, 1994). In addition, sociocultural perspectives emphasize the role of classroom environments, cultural norms, and educational traditions in shaping observed differences in mathematics anxiety(Beilock et al., 2010; Gutierrez, 2002). These models collectively explain why MA can be both a personal and culturally situated experience.
In this context, problem-solving ability (PSA), defined as the application of mathematical concepts to new and complex tasks, is closely tied to competencies in related areas(Jonassen, 2010). The ability to solve problems was considered the hallmark of mathematical competence(Jonassen, 2017). It required more than mere procedural fluency, including conceptual understanding, logical reasoning, and ability to apply knowledge in a new situation(Ashcraft, 2002; Ramirez et al., 2016; Schoenfeld, 1982). Prior studies have reported that PSA was influenced by cognitive dimensions (e.g., prior knowledge, metacognition), affective motivation and anxiety(Ahmed, 2018). High levels of MA impaired PSA because problem-solving activities triggered anxious thoughts, which interrupted attentional focus, and reduced persistence on challenging tasks due to increased avoidance behavior(Beilock & Willingham, 2014). Furthermore, cross-cultural studies showed that educational traditions significantly influenced the development of PSA. High levels of MA tend to interfere with PSA, resulting in a vicious cycle where performance declines, further increasing anxiety(Wigfield & Eccles, 1989). In addition, this has made the relationship between the two variables important for any intervention to work. PSA in mathematics includes conceptual understanding, logical reasoning, and the application of knowledge to novel situations(Jonassen, 2010; Schoenfeld, 2017). Importantly, MA and PSA are reciprocally related: heightened anxiety can disrupt focus and persistence during problem solving process, while frequent difficulties exacerbate anxiety, creating a negative feedback loop that hinders learning progress(Ramirez et al., 2018).
In the case of Indonesia, the educational approach lacked cohesion and was applied within the context of greater decentralization, regional variety, and uneven budgetary frameworks. Universitas Pahlawan (UP) is a private developing school, with tighter community connections, strong academic support, and rapid growth. It was due to the difficulties such as inadequate technological resources, divergent regional practices, and a greater focus on teacher-led learning encountered in the educational context. In many areas, rote learning persists and is often a barrier to developing problem solving skills. Additionally, a correlation has been established between rigid pedagogy, high-stakes examinations, and limited support systems in students with relatively high MA(Chen et al., 2023).
In contrast, Universiti Tun Hussein Onn Malaysia (UTHM) represents a more established public technical university within Malaysia’s comparatively centralized higher education system, with broader access to institutional resources and technological infrastructure. This contrast between UTHM and UP provides a meaningful comparative context for examining how institutional resources and educational environments may shape mathematics learning experiences over time.
Institutional and educational contexts may differ between the participating universities in terms of resource availability, curriculum implementation, and learning support. However, these contextual variables were not directly measured in the present study and therefore cannot be used to explain observed institutional differences.
Several Western studies had been conducted on MA, although less was known about the longitudinal development of cultural and instructional influences in Southeast Asia. This gap was addressed by reviewing how university origin (Malaysia versus Indonesia), and temporal changes in instructional conditions (2021-2024) interacted to influence changes in MA and PSA over time. The education system in Malaysia focused on examination results, bilingualism, and prescribed pedagogy, alongside the use of e-learning. However, Indonesia encountered certain problems, including large class sizes, different regional practices, and rote learning reliance. These systemic differences influenced ways of understanding MA and managing problem-solving in a supportive manner.
Institutional and contextual factors may contribute to observed differences between participating groups in Southeast Asia, a region characterized by diverse educational systems and cultural contexts. Despite cultural and linguistic similarities, Malaysia and Indonesia, differ in educational governance structures, resource distribution, and implementation of instructional policies, which may influence students’ learning experiences(OECD, 2024; Padilla-Valdez, 2019). Such contrasts offer a valuable context for comparative study to better understand how cultural and institutional factors influence mathematics learning outcomes.
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The author is responsible for acquiring the permission(s) to reproduce any copyrighted figures, tables, data, or text that are being used in the submitted paper. Authors should note that text quotations of more than 250 words from a published or copyrighted work will require grant of permission from the original publisher to reprint. The written permission letter(s) must be submitted together with the manuscript.References
Ahmed, W. (2018). Developmental trajectories of math anxiety during adolescence: Associations with STEM career choice. Journal of Adolescence, 67(1), 158–166. https://doi.org/10.1016/j.adolescence.2018.06.010
Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11(5), 181–185. https://doi.org/10.1111/1467-8721.00196
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224–237. https://doi.org/10.1037/0096-3445.130.2.224
Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243–248. https://doi.org/10.3758/bf03194059
Ashcraft, M. H., & Ridley, K. S. (2005). Math anxiety and its cognitive consequences: A tutorial review. In J. I. D. Campbell (Ed.), The Handbook of Mathematical Cognition (pp. 231–252). Psychology Press. https://doi.org/10.4324/9780203998045-21
Bandura, A. (1997). Self-efficacy: The exercise of control. Worth Publishers.
Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers’ math anxiety affects girls’ math achievement. Proceedings of the National Academy of Sciences, 107(5), 1860–1863. https://doi.org/10.1073/pnas.0910967107
Beilock, S. L., & Willingham, D. T. (2014). Math anxiety: Can teachers help students reduce it? ask the cognitive scientist. American Educator, 38(2), 28–33.
Bozkurt, A., & Sharma, R. C. (2020). Emergency remote teaching in a time of global crisis due to CoronaVirus pandemic. Asian journal of distance education, 15(1), i–vi.
Carey, E., Devine, A., Hill, F., & Szűcs, D. (2017). Differentiating anxiety forms and their role in academic performance from primary to secondary school. PLoS One, 12(3), e0174418. https://doi.org/10.1371/journal.pone.0174418
Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7(11), E537. https://doi.org/10.3978/j.issn.2072-1439.2015.10.63
Chen, M. Y. K., Jamaludin, A., & Tan, A. L. (2023). Behavioural predictors of math anxiety [Preprint]. ArXiv. https://doi.org/10.48550/arXiv.2301.13639
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587
Delima, N., Kusuma, D. A., & Paulus, E. (2024). The students' mathematics self-regulated learning and mathematics anxiety based on the use of chat GPT, music, study program, and academic achievement. Infinity Journal, 13(2), 349–362. https://doi.org/10.22460/infinity.v13i2.p349-362
Diponegoro, A. M., Khalil, I. A., & Prahmana, R. C. I. (2024). When religion meets mathematics: From mathematical anxiety to mathematical well-being for minority group student. Infinity Journal, 13(2), 413–440. https://doi.org/10.22460/infinity.v13i2.p413-440
Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in psychology, 7, 00508. https://doi.org/10.3389/fpsyg.2016.00508
Foley, A. E., Herts, J. B., Borgonovi, F., Guerriero, S., Levine, S. C., & Beilock, S. L. (2017). The math anxiety-performance link. Current Directions in Psychological Science, 26(1), 52–58. https://doi.org/10.1177/0963721416672463
Gutierrez, R. (2002). Enabling the practice of mathematics teachers in context: Toward a new equity research agenda. Mathematical thinking and learning, 4(2-3), 145–187. https://doi.org/10.1207/s15327833mtl04023_4
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21(1), 33–46. https://doi.org/10.2307/749455
Hendriana, H., Chen, X., Korenova, L., & Wijaya, T. T. (2026). Profiles of K–12 mathematics teachers’ innovative behavior: A latent profile analysis. Infinity Journal, 15(3), 757–772. https://doi.org/10.22460/infinity.v15i3.p757-772
Jonassen, D. H. (2010). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge. https://doi.org/10.4324/9780203847527
Jonassen, D. H. (2017). Learning to solve complex scientific problems. Routledge.
Listiawati, E., Juniati, D., & Ekawati, R. (2025). Understanding mathematics prospective teachers' comprehension of function derivatives based on APOS theory: Insights from low mathematics anxiety levels. Infinity Journal, 14(2), 483–512. https://doi.org/10.22460/infinity.v14i2.p483-512
Luttenberger, S., Wimmer, S., & Paechter, M. (2018). Spotlight on math anxiety. Psychology Research and Behavior Management, 11, 311–322. https://doi.org/10.2147/prbm.S141421
Maloney, E. A., Ansari, D., & Fugelsang, J. A. (2011). Rapid communication: The effect of mathematics anxiety on the processing of numerical magnitude. Quarterly Journal of Experimental Psychology, 64(1), 10–16. https://doi.org/10.1080/17470218.2010.533278
Maloney, E. A., & Beilock, S. L. (2012). Math anxiety: Who has it, why it develops, and how to guard against it. Trends in Cognitive Sciences, 16(8), 404–406. https://doi.org/10.1016/j.tics.2012.06.008
Núñez-Peña, M. I., Suárez-Pellicioni, M., & Bono, R. (2013). Effects of math anxiety on student success in higher education. International Journal of Educational Research, 58, 36–43. https://doi.org/10.1016/j.ijer.2012.12.004
OECD. (2019). Education at a glance 2019: OECD indicators. OECD Publishing. https://doi.org/10.1787/f8d7880d-en
OECD. (2024). Transforming education in Indonesia: Examining the landscape of current reforms. OECD Publishing. https://doi.org/10.1787/9ff8d407-en
Padilla-Valdez, N. (2019). Education in Southeast Asia: Shaping the Asian intellectuals. SALTeL Journal (Southeast Asia Language Teaching and Learning), 1(2), 15–20. https://doi.org/10.35307/saltel.v1i2.10
Pajares, F., & Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of educational psychology, 86(2), 193–203. https://doi.org/10.1037/0022-0663.86.2.193
Quarteroni, A., Sacco, R., & Saleri, F. (2007). Numerical mathematics. Springer. https://doi.org/10.1007/b98885
Ramirez, G., Chang, H., Maloney, E. A., Levine, S. C., & Beilock, S. L. (2016). On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies. Journal of Experimental Child Psychology, 141, 83–100. https://doi.org/10.1016/j.jecp.2015.07.014
Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist, 53(3), 145–164. https://doi.org/10.1080/00461520.2018.1447384
Reichardt, C. S. (2002). Experimental and quasi‐experimental designs for generalized causal inference. by William R. Shadish, Thomas D. Cook, and Donald T. Campbell. Boston: Houghton Mifflin, 2002. Pp. 623. $60.36 (paper). Social Service Review, 76(3), 510–514. https://doi.org/10.1086/345281
Richardson, F. C., & Suinn, R. M. (1972). The mathematics anxiety rating scale: Psychometric data. Journal of Counseling Psychology, 19(6), 551–554. https://doi.org/10.1037/h0033456
Schoenfeld, A. H. (1982). Measures of problem-solving performance and of problem-solving instruction. Journal for Research in Mathematics Education, 13(1), 31–49. https://doi.org/10.5951/jresematheduc.13.1.0031
Schoenfeld, A. H. (2017). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics (Reprint). Journal of Education, 196(2), 1–38. https://doi.org/10.1177/002205741619600202
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
Wigfield, A., & Eccles, J. S. (1989). Test anxiety in elementary and secondary school students. Educational Psychologist, 24(2), 159–183. https://doi.org/10.1207/s15326985ep2402_3
Xiang, J. W. S., Shaafi, N. F., & Maryani, I. (2026). Mathematics anxiety, achievement, and gender among secondary school students in Kota Kinabalu, Sabah. Infinity Journal, 15(3), 699–716. https://doi.org/10.22460/infinity.v15i3.p699-716
Yusof, Y. b. M., & Tall, D. (1998). Changing attitudes to university mathematics through problem solving. Educational Studies in Mathematics, 37(1), 67–82. https://doi.org/10.1023/a:1003456104875