Self-Regulated Learning as a Correlate of Mathematics Achievement among Secondary School Students in Meru South Sub-County, Kenya
Authors
Department of Educational Psychology, Kenyatta University (Kenya)
Department of Educational Psychology, Kenyatta University (Kenya)
Article Information
DOI: 10.47772/IJRISS.2025.910000252
Subject Category: Education
Volume/Issue: 9/10 | Page No: 3142-3149
Publication Timeline
Submitted: 2025-10-08
Accepted: 2025-10-14
Published: 2025-11-10
Abstract
This study investigated the relationship between self-regulated learning and mathematics achievement among Form Three students in Meru South Sub-County, Kenya, a region characterized by persistent underperformance in the subject. Grounded in Bandura's Social Cognitive Theory, the study sought to determine the extent to which students' strategic learning behaviors are associated with their academic outcomes. A correlational research design was employed, involving a sample of 276 Form Three students selected from public secondary schools. Data on self-regulated learning were collected using the Academic Self-Regulated Learning Questionnaire (ASRLQ), while mathematics achievement was measured using standardized examination scores. Data were analyzed using the Pearson product-moment correlation coefficient. The results revealed a strong, positive, and statistically significant relationship between self-regulated learning and mathematics achievement (ρ = .766, p < .001). This finding indicates that students who more frequently employ self-regulatory strategies such as goal setting, self-evaluation, and environmental structuring tend to achieve higher scores in mathematics. The study concludes that self-regulated learning is a critical component of academic success in mathematics and recommends the explicit integration of self-regulated learning strategy instruction into secondary school mathematics pedagogy to better equip students for academic success.
Keywords
Self-Regulated Learning, Mathematics Achievement
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References
1. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. [Google Scholar] [Crossref]
2. Duru, D. C., & Okeke, S. O. C. (2021). Self regulated learning skill as a predictor of mathematics achievement: A focus on ability levels. Malikussaleh Journal of Mathematics Learning, 4(1), 20-27. [Google Scholar] [Crossref]
3. El-Adl, A., & Alkharusi, H. (2020). Relationships between self-regulated learning strategies, learning motivation and mathematics achievement. Cypriot Journal of Educational Sciences, 15(1), 104-111. [Google Scholar] [Crossref]
4. Harding, S.-M., English, N., Nibali, N., Griffin, P., Graham, L., Alom, B., & Zhang, Z. (2019). Self-regulated learning as a predictor of mathematics and reading performance: A picture of students in Grades 5 to 8. Australian Journal of Education, 63(1), 74–97. https://doi.org/10.1177/0004944119830153 [Google Scholar] [Crossref]
5. Llagoso, G. (2017). Self-regulated learning strategies in mathematics. Academia.edu. https://www.academia.edu/41053963/self_regulated_learning_strategies_in_mathematics [Google Scholar] [Crossref]
6. Mabena, N., Mokgosi, P. N., & Ramapela, S. S. (2021). Factors contributing to poor learner performance in mathematics: A case of selected schools in Mpumalanga Province, South Africa. Problems of Education in the 21st Century, 79(3), 451–466. https://doi.org/10.33225/pec/21.79.451 [Google Scholar] [Crossref]
7. Magno, C. (2010). Assessing academic self-regulated learning among Filipino college students: The factor structure and item fit. SSRN Electronic Journal. https://www.google.com/search?q=https://doi.org/10.2139/ssrn.2287208 [Google Scholar] [Crossref]
8. Ochieng’, W. (2015). Self-efficacy and academic achievement among secondary schools in Kenya. Journal of Education and Practice, 6(24), 62-78. [Google Scholar] [Crossref]
9. Ong'uti, C., Aloka, P., & Nyakinda, J. (2019). Metacognitive monitoring as predictor of mathematics achievement among students in public secondary schools in Kenya. International Journal of Psychology and Behavioral Sciences, 9(1), 1–7. [Google Scholar] [Crossref]
10. Richards, E. (2020, February 28). U.S. students lag other countries in math. The reason likely lies in how schools teach it. USA Today. https://www.usatoday.com/story/news/education/2020/02/28/math-scores-high-school-lessons-freakonomics-pisa-algebra-geometry/4835742002/ [Google Scholar] [Crossref]
11. Sorensen, C. (2009). Introduction to research in education (8th ed.). Wadsworth: Cengage Learning. [Google Scholar] [Crossref]
12. Zimmerman, B. J., & Labuhn, A. S. (2012). Self-regulation of learning: Process approaches to personal development. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook, Vol. 1: Theories, constructs, and critical issues (pp. 399–425). American Psychological Association. [Google Scholar] [Crossref]
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