Computational Thinking Skills as Predictors of Mathematics Performance among Secondary School Students in Ondo State, Nigeria
Keywords:
Abstraction, Algorithmic Thinking, Computational Thinking, Decomposition, Mathematics Performance, Pattern Recognition, Secondary School StudentsAbstract
This study examined the relationship between computational thinking skills and Mathematics performance among secondary school students in Ondo State, Nigeria. Specifically, the study determined the levels of students’ decomposition, pattern recognition, abstraction, and algorithmic thinking skills and examined their relationships with Mathematics performance. A correlational survey design was adopted, with a criterion-referenced Mathematics Performance Test used to measure students’ performance. The study involved 400 secondary school students in Ondo State. Data were collected using a Computational Thinking Skills Scale and a Mathematics Performance Test. The instruments were administered to the participants, and the data were analysed using descriptive statistics, Pearson Product-Moment Correlation Coefficient, and multiple linear regression at the 0.05 level of significance. The findings showed that students demonstrated high levels of decomposition (M = 3.92, SD = 1.08), pattern recognition (M = 3.82, SD = 1.08), abstraction (M = 3.86, SD = 1.09), and algorithmic thinking (M = 3.83, SD = 1.08). Students’ Mathematics performance had a mean score of 31.00 out of 50, representing 62.00% of the maximum obtainable score. Significant positive relationships were found between Mathematics performance and decomposition (r = .521), pattern recognition (r = .486), abstraction (r = .548), and algorithmic thinking (r = .583), all at p < .001. Multiple regression analysis further showed that the four computational-thinking components jointly significantly predicted Mathematics performance, R = .678, R² = .460, adjusted R² = .455, F(4, 395) = 84.04, p < .001. Algorithmic thinking made the strongest relative contribution (β = .289). The study concludes that computational thinking skills are significantly associated with and jointly predictive of students’ Mathematics performance.
References
Abe, T. O., Gbore, L. O., Osakuade, J. O., Owabumoye, O. S., Ajele, A. J., & Ogunbadejo, O. (2026). Appropriate use of correlation coefficient in educational research. Commonwealth Journal of Academic Research, 7(1), 1–23. https://doi.org/10.5281/zenodo.18722362
Akinwumi, M. O., & Adodo, S. O. (2025). Critical thinking as predictors of students’ academic achievement and confidence in physics in secondary schools in Ekiti State. In S. O. Adodo, A. O. Omoniyi, A. M. Laleye, & A. A. Akingbemisilu (Eds.), Repositioning science education for global sustainable development: a Gedenkschrift in honour of Prof. B. O. Duyilemi, Dr. J. O. Agbayewa, Prof. S. O. Daramola, Dr. T. A. Oluwateleure, & Dr. T. Orimogunje (pp. 92–107). Department of Science Education, Adekunle Ajasin University.
Berk, G., & Gülcü, A. (2024). The effect of computer-supported STEM applications on secondary students’ achievement and computational thinking skills. Participatory Educational Research, 11(4), 160–183. https://doi.org/10.17275/per.24.54.11.4
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 Annual Meeting of the American Educational Research Association, 1–25.
Chytas, C., van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. L. J. (2024). Computational thinking in secondary mathematics education with GeoGebra: insights from an intervention in calculus lessons. Digital Experiences in Mathematics Education, 10, 228–259. https://doi.org/10.1007/s40751-024-00141-0
Fauzi, A. L., Kusumah, Y. S., Nurlaelah, E., & Juandi, D. (2024). Computational thinking in mathematics education: a systematic literature review on its implementation and impact on students’ learning. Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran, 10(2), 640–653. https://doi.org/10.33394/jk.v10i2.11140
Ghosh, J. B. (2024). Mathematical explorations enabled by technology: a rich context for integrating computational and mathematical thinking in secondary school. Far East Journal of Mathematical Education, 26(2), 79–98. https://doi.org/10.17654/0973563124009
Lehmann, T. H. (2024). How current perspectives on algorithmic thinking can be applied to students’ engagement in algorithmatizing tasks. Mathematics Education Research Journal, 36, 609–643. https://doi.org/10.1007/s13394-023-00462-0
Looi, C. K., Chan, S.-W., Wu, L., Huang, W., Kim, M. S., & Sun, D. (2024). Exploring computational thinking in the context of mathematics learning in secondary schools: dispositions, engagement and learning performance. International Journal of Science and Mathematics Education, 22, 993–1011. https://doi.org/10.1007/s10763-023-10419-1
Mulyono, B., Hapizah, & Sukanda, D. C. (2024). Computational thinking skills in mathematics: a study of social arithmetic. Journal of Honai Math, 7(3), 451–468. https://doi.org/10.30862/jhm.v7i3.759
Olojo, O. J., & Abe, T. O. (2024). Investigating the impact of computational thinking integration on mathematical problem-solving skills of senior secondary school students in Akure South Local Government Area of Ondo State. The Seybold Report, 19(1), 524–543. https://doi.org/10.5281/zenodo.10616816
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. Basic Books.
Piaget, J. (1970). Science of education and the psychology of the child. Orion Press.
Top, O., & Arabacıoğlu, T. (2024). Integrating computational thinking into mathematics education: its effects on achievement, motivation, and learning strategies. Bayburt Eğitim Fakültesi Dergisi, 19(42), 2034–2066. https://doi.org/10.35675/befdergi.1385749
Vygotsky, L. S. (1978). Mind in society: the development of higher psychological processes. Harvard University Press.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Ye, H., Liang, B., Ng, O.-L., & Chai, C. S. (2023). Integration of computational thinking in K–12 mathematics education: a systematic review on CT-based mathematics instruction and student learning. International Journal of STEM Education, 10, Article 3. https://doi.org/10.1186/s40594-023-00396-w
