Cognitive Knowledge, Strategic Deployment, Metacognitive Regulation, and Mindset as Predictors of Students’ Problem – Solving Skills

This study examined the influence of cognitive knowledge, strategic deployment, metacognitive regulation, and mindset on the mathematical problem-solving performance of Grade 7 students in public secondary schools. Using a quantitative-correlational design, data were collected from selected students in the Banga East District, South Cotabato. Instruments included a researcher-developed questionnaire and open-ended problem-solving tasks aligned with Polya’s framework. Findings revealed that all four factors significantly contribute to students’ problem-solving performance, with metacognitive regulation and strategic deployment emerging as strong predictors. Students demonstrated moderate proficiency in conceptual understanding but showed difficulties in planning, monitoring, and adapting strategies in non-routine tasks. Results also indicated that growth mindset and self-efficacy positively influence persistence and engagement. The study highlights the importance of integrating cognitive, strategic, metacognitive, and affective components in mathematics instruction. A holistic approach is recommended to enhance students’ ability to solve both routine and real-life mathematical problems.

cognitive knowledge, strategic deployment, metacognitive regulation, mindset

1. Aguila, M. C. (2020). Enhancing students’ mathematical problem-solving skills through strategy-based instruction. Journal of Mathematics Education, 13(2), 45–60. [Google Scholar] [Crossref]

2. DepEd. (2015). DepEd Order No. 8, s. 2015: Policy guidelines on classroom assessment for the K to 12 Basic Education Program. Department of Education. [Google Scholar] [Crossref]

3. DepEd. (2020). National Achievement Test (NAT) results and analysis report. Department of Education. [Google Scholar] [Crossref]

4. Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students. Metacognition and Learning, 3(3), 231–264. https://doi.org/10.1007/s11409-008-9029-x [Google Scholar] [Crossref]

5. Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087 [Google Scholar] [Crossref]

6. Dweck, C. S. (2016). Mindset: The new psychology of success (Updated ed.). Random House. [Google Scholar] [Crossref]

7. Gilmore, C., Keeble, S., Richardson, S., & Cragg, L. (2017). The relationship between symbolic and non-symbolic number skills and mathematical achievement. Developmental Science, 20(4), e12401. https://doi.org/10.1111/desc.12401 [Google Scholar] [Crossref]

8. Mayer, R. E. (2018). Learning and instruction (3rd ed.). Pearson. [Google Scholar] [Crossref]

9. National Council of Teachers of Mathematics. (2014). Principles to actions: Ensuring mathematical success for all. NCTM. [Google Scholar] [Crossref]

10. National Council of Teachers of Mathematics. (2020). Catalyzing change in middle school mathematics: Initiating critical conversations. NCTM. [Google Scholar] [Crossref]

11. OECD. (2019). PISA 2018 results (Volume I): What students know and can do. OECD Publishing. https://doi.org/10.1787/5f07c754-en [Google Scholar] [Crossref]

12. Panadero, E., & Alonso-Tapia, J. (2014). How do students self-regulate? Review of Zimmerman’s cyclical model of self-regulated learning. Educational Psychology Review, 26(4), 450–479. https://doi.org/10.1007/s10648-014-9282-0 [Google Scholar] [Crossref]

13. Rittle-Johnson, B., & Schneider, M. (2015). Developing conceptual and procedural knowledge of mathematics. In R. Cohen Kadosh & A. Dowker (Eds.), The Oxford handbook of numerical cognition (pp. 1118–1134). Oxford University Press. [Google Scholar] [Crossref]

14. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033 [Google Scholar] [Crossref]

15. Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense-making in mathematics. Journal of Education, 196(2), 1–38. [Google Scholar] [Crossref]

16. Stanton, J. D. (2021). Enhancing students’ problem-solving skills through metacognitive strategies. Journal of STEM Education, 22(3), 15–25. [Google Scholar] [Crossref]

17. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2 [Google Scholar] [Crossref]

18. Zimmerman, B. J. (2017). Self-regulated learning and academic achievement: An overview. In D. H. Schunk & B. J. Zimmerman (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 1–15). Routledge. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study