Reconceptualising the Cognition–Achievement Relationship in Secondary Mathematics: Evidence from a Classroom-Based Correlational Study

Ananda Safitri; Attin Warmi; Hanifah Hanifah

doi:10.31949/dm.v8i1.16979

Authors

Ananda Safitri Universitas Singaperbangsa Karawang, Indonesia
Attin Warmi Universitas Singaperbangsa Karawang, Indonesia
Hanifah Hanifah Universitas Singaperbangsa Karawang, Indonesia

DOI:

https://doi.org/10.31949/dm.v8i1.16979

Abstract

This study aimed to examine the strength and direction of the relationship between mathematical thinking processes and mathematics learning outcomes among ninth-grade junior high school students. The research employed a quantitative nonexperimental correlational design to identify associative patterns between variables without inferring causal relationships. The population consisted of 476 students across twelve classes, and the sample was selected using purposive sampling, resulting in one intact class comprising 36 students. Data on mathematical thinking processes were collected through four constructed-response (essay) items designed to measure reasoning, conceptual linkage, and structured problem-solving abilities. Mathematics learning outcomes were obtained from students’ midterm examination scores as indicators of curriculum-based academic achievement. Data analysis was conducted in several stages, including descriptive statistical analysis and normality testing using the Shapiro–Wilk test. Due to non-normal distribution in one variable, the relationship between variables was analysed using Spearman’s rank correlation coefficient as a non-parametric alternative. The results indicated that students demonstrated relatively adequate levels of mathematical thinking; however, the correlation between mathematical thinking processes and learning outcomes was positive but weak and statistically non-significant (ρ = 0.211, p > 0.05). These findings suggest that mathematical thinking, while conceptually important, may not function as a strong standalone predictor of examination-based academic performance. The study implies that mathematics learning outcomes are influenced by multiple interacting cognitive and contextual factors, highlighting the importance of aligning assessment practices with higher-order thinking competencies to enhance the measurable contribution of mathematical reasoning to student achievement.

Keywords:

Mathematical thinking processes, Mathematics learning outcomes, Correlational study, Secondary education, Classroom-based assessment

Downloads

Download data is not yet available.

References

Agu, I. C., Obi, U. S., Mbachu, C. O., et al. (2025). Does academic excellence predict non-cognitive competencies? Insights from a situational judgement test among medical students in Nigeria. BMC Medical Education, 25, 623. https://doi.org/10.1186/s12909-025-07167-x

Andrijati, N., Yulianto, S., & Sukasih, S. (2025). Problem-based learning in developing mathematical reasoning assessment model for sustainable development. In Proceedings of the 8th International Conference on Education Innovation (ICEI 2024) (pp. 637–650). Atlantis Press. https://doi.org/10.2991/978-2-38476-360-3_58

Arthur, Y. D., Dogbe, C. S. K., & Asiedu-Addo, S. K. (2022). Enhancing performance in mathematics through motivation, peer-assisted learning, and teaching quality: The mediating role of student interest. Eurasia Journal of Mathematics, Science and Technology Education, 18(2), em2072. https://doi.org/10.29333/ejmste/11509

Austin, B., French, B., Adesope, O., & Gotch, C. (2017). Use of standard deviations as predictors in models using large-scale international data sets. The Journal of Experimental Education, 85(4), 559–573. https://doi.org/10.1080/00220973.2016.1260522

Bascones, G. Y., Yunzal, A. N., Jr., & Casinillo, L. F. (2024). Exploring contextual factors affecting student performance in mathematics: A sequential explanatory research. Canadian Journal of Family and Youth, 16(3), 210–234. https://doi.org/10.29173/cjfy30045

Cook, B. G., & Cook, L. (2008). Nonexperimental quantitative research and its role in guiding instruction. Intervention in School and Clinic, 44(2), 98–104. https://doi.org/10.1177/1053451208321565

He, G., Lin, H., & Su, A. (2024). Longitudinal and reciprocal links between metacognition, mathematical modeling competencies, and mathematics achievement in grades 7–8: A cross-lagged panel analysis. Metacognition and Learning, 19, 967–995. https://doi.org/10.1007/s11409-024-09397-8

Hu, J., & Qian, S. (2025). Correlations and comparisons of teacher expectations, achievement motivation, academic achievement, and creativity. Frontiers in Psychology, 16, 1516405. https://doi.org/10.3389/fpsyg.2025.1516405

Johnson, H. L., Donovan, C., & McClintock, E. (2025). A case of crossover mixed analyses in mathematics education research. Methods in Psychology, 100205. https://doi.org/10.1016/j.metip.2025.100205

Kain, C., Koschmieder, C., Matischek-Jauk, M., & Bergner, S. (2024). Mapping the landscape: A scoping review of 21st century skills literature in secondary education. Teaching and Teacher Education, 151, 104739. https://doi.org/10.1016/j.tate.2024.104739

Kim, M. K., & Cho, M. K. (2015). The study of constructed-response assessment of elementary mathematics education in Korea. Eurasia Journal of Mathematics, Science and Technology Education, 11(2), 299–311. https://doi.org/10.12973/eurasia.2015.1331a

Koh, K., Chapman, O., Gierus, B., et al. (2025). Assessment of higher-level mathematical thinking skills: A scoping review. ZDM–Mathematics Education, 57, 665–678. https://doi.org/10.1007/s11858-025-01695-y

Li, R., & Qi, C. (2025). Factors and pathways shaping mathematics achievement in China: Evidence from a decade of regional quality monitoring. Canadian Journal of Science, Mathematics and Technology Education, 25, 691–703. https://doi.org/10.1007/s42330-025-00414-9

Liu, X., Valcke, M., Yang Hansen, K., & De Neve, J. (2022). Does school-level instructional quality matter for school mathematics performance? Comparing teacher data across seven countries. Sustainability, 14(9), 5267. https://doi.org/10.3390/su14095267

Lu, P., Qiu, L., Yu, W., Welleck, S., & Chang, K. W. (2023). A survey of deep learning for mathematical reasoning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 14605–14631). https://doi.org/10.18653/v1/2023.acl-long.817

Mahzari, M., AlNahedh, T., Ahmed, A. A., Al Rumyyan, A., Shaban, S., & Magzoub, M. E. (2023). Practical guide to undergraduate medical curriculum alignment and mapping. Advances in Medical Education and Practice, 14, 1001–1012. https://doi.org/10.2147/AMEP.S424815

Miller, W. (2013). Non-parametric statistics. In Statistics and measurement concepts with OpenStat. Springer. https://doi.org/10.1007/978-1-4614-5743-5_6

Monteleone, C., Miller, J., & Warren, E. (2023). Conceptualising critical mathematical thinking in young students. Mathematics Education Research Journal, 35, 339–359. https://doi.org/10.1007/s13394-023-00445-1

Patmawati, H., & Unaenah, E. (2025). Mathematical thinking process of gifted elementary school students in Tasikmalaya City: A case study. Prisma Sains, 13(4), 1133–1149. https://doi.org/10.33394/j-ps.v13i4.17685

Putra, F. G., Sunyono, S., Haenilah, E. Y., Hariri, H., Lengkana, D., & Nurhanurawati, N. (2023). A correlational study on representational abilities and mathematical connections in junior high school. Al-Ishlah: Jurnal Pendidikan, 15(2), 1511–1518. https://doi.org/10.35445/alishlah.v15i2.2321

Quainoo, E. A., Asamoah, D., Baidoo-Anu, D., Ye, J., & Akuamoah Boateng, C. (2025). Examining profiles of classroom assessment literacy among basic school teachers. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2558163

Robinson, R. S. (2023). Purposive sampling. In F. Maggino (Ed.), Encyclopedia of quality of life and well-being research. Springer. https://doi.org/10.1007/978-3-031-17299-1_2337

Saglam Karakoc, R., & Michou, A. (2025). Mathematics teachers’ perceptions about the end-of-secondary school external assessment in two educational contexts: Implications for teaching and learning. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2592360

Schweizer, G., & Furley, P. (2016). Reproducible research in sport and exercise psychology: The role of sample sizes. Psychology of Sport and Exercise, 23, 114–122. https://doi.org/10.1016/j.psychsport.2015.11.005

Suleiman, I. B., Okunade, O. A., Dada, E. G., et al. (2024). Key factors influencing students’ academic performance. Journal of Electrical Systems and Information Technology, 11, 41. https://doi.org/10.1186/s43067-024-00166-w

Susanti, E., Fadillah, S., & Jamilah, J. (2025). Analysis of junior high school students’ critical mathematical thinking ability in solving similarity problems. Union: Jurnal Ilmiah Pendidikan Matematika, 13(3), 745–760. https://doi.org/10.30738/union.v13i3.19759

Sydänmaanlakka, A., Häsä, J., Holm, M. E., & Hannula, M. S. (2024). Mathematics-related achievement emotions: Interaction between learning environment and students' mathematics performance. Learning and Individual Differences, 113, 102486. https://doi.org/10.1016/j.lindif.2024.102486

Wang, X. S., Perry, L. B., & Malpique, A., et al. (2023). Factors predicting mathematics achievement in PISA: A systematic review. Large-Scale Assessments in Education, 11, 24. https://doi.org/10.1186/s40536-023-00174-8

Wilson Fadiji, A., & Reddy, V. (2023). Well-being and mathematics achievement: What is the role of gender, instructional clarity, and parental involvement? Frontiers in Psychology, 13, 1044261. https://doi.org/10.3389/fpsyg.2022.1044261

Zulaiha, R., Rahdiani, D., Rahman, A., & Al Anfal, M. F. (2021). Analysis of difficulty level and discriminating power between multiple-choice and essay items on math test. In Proceedings of the International Conference on Educational Assessment and Policy (ICEAP 2020) (pp. 62–68). Atlantis Press. https://doi.org/10.2991/assehr.k.210423.065