Learning Strategies and Academic Performance in Mathematics of Senior High School Students

This study investigated learning strategies in Mathematics among Grade 11 students in relation to their academic performance. The study involved a total of 292 Grade 11 students enrolled during the school year 2025–2026 in one of the secondary schools in a selected School Divisions in Negros Occidental. It employed a quantitative-descriptive and correlational research design. The respondents’ learning strategies was assessed using the short version of the Motivated Strategies for Learning Questionnaire (MSLQ-PTS) developed by Morais et al. (2025), and their academic performance was determined through their Fourth Quarter grades in Mathematics. The results showed that the students exhibited a high level of learning strategy utilization where Help seeking recorded the highest mean score while critical thinking registered the lowest mean scores among its subconcepts. The level of academic performance in Mathematics was very satisfactory. It was also revealed that there is a significant relationship between learning strategies in Mathematics and the academic performance of the Grade 11 students.

Mathematics education is fundamental in developing learners

1. Chick, N. L., Gurung, R. a. R., Haynie, A., & Ciccone, A. A. (2023). Exploring signature pedagogies. https://doi.org/10.4324/9781003444732 [Google Scholar] [Crossref]

2. Doyle, T. (2023). Helping students learn in a learner-centered environment: A guide to facilitating learning in higher education. Taylor & Francis. [Google Scholar] [Crossref]

3. Khasawneh, E. (2025). Empowering learning: The role of inquiry-based learning in enhancing mathematics achievement. In Mathematics-Teaching and Learning. IntechOpen. [Google Scholar] [Crossref]

4. Madan, M. D., & Singh, P. A. (2024). THEORIES OF LEARNING: PIAGET AND VYGOTSKY. PERSPECTIVES IN EDUCATION, CURRICULUM AND PEDAGOGY, 291. [Google Scholar] [Crossref]

5. Schunk, D. H., & Zimmerman, B. J. (2022). Self-regulation of learning and performance. https://doi.org/10.4324/9780203763353 [Google Scholar] [Crossref]

6. A. Magazines / Journals / Publications / Articles / Researches [Google Scholar] [Crossref]

7. Akahsah, N. M. (2025). The Influence of Resource Management Strategies on Students’ Use of Cognitive Components and Metacognitive Self-Regulation among Pre-University Students. Pakistan Journal of Life and Social Sciences (PJLSS), 23(1). https://doi.org/10.57239/pjlss-2025-23.1.00339 [Google Scholar] [Crossref]

8. AlAli, R., & Wardat, Y. (2024). Innovative teaching: building engagement, critical thinking, and lifelong learning skills. International Journal of Religion, 5(10), 2152–2163. https://doi.org/10.61707/fdpxd919 [Google Scholar] [Crossref]

9. Almulla, M. A. (2023). Constructivism learning theory: A paradigm for students’ critical thinking, creativity, and problem solving to affect academic performance in higher education. Cogent Education, 10(1). https://doi.org/10.1080/2331186x.2023.2172929 [Google Scholar] [Crossref]

10. Alsraisri, N., & Amjad, F. (2025). EXPLORING REMEDIAL TEACHING INTERVENTIONS FOR STUDENTS FACING PERSISTENT LEARNING CHALLENGES. Lex Localis, 23(11), 47-67. [Google Scholar] [Crossref]

11. Biwer, F., de Bruin, A., & Persky, A. (2022). Study smart–impact of a learning strategy training on students’ study behavior and academic performance. Advances in Health Sciences Education, 28(1), 147. https://pmc.ncbi.nlm.nih.gov/articles/PMC9397154/ [Google Scholar] [Crossref]

12. Büchele, S. (2023). Navigating success in higher education: engagement as a mediator between learning strategies and performance in mathematics. Assessment & Evaluation in Higher Education, 48(8), 1356–1370. https://doi.org/10.1080/02602938.2023.2230387 [Google Scholar] [Crossref]

13. Cabral-Gouveia, C., Menezes, I., & Neves, T. (2023). Educational strategies to reduce the achievement gap: a systematic review. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1155741 [Google Scholar] [Crossref]

14. Cakir, M. (2022). Constructivist approaches to learning in science and their implications for science pedagogy: A literature review. International journal of environmental and science education. https://surl.li/iapsni [Google Scholar] [Crossref]

15. Casteel, A., & Bridier, N. L. (2021). Describing populations and samples in doctoral student research. International journal of doctoral studies, 16(1). https://openurl.ebsco.com/EPDB%3Agcd%3A16%3A343120/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A154190436&crl=c&link_origin=scholar.google.com [Google Scholar] [Crossref]

16. Chew, S. L., & Cerbin, W. J. (2020). The cognitive challenges of effective teaching. The Journal of Economic Education, 52(1), 17–40. https://doi.org/10.1080/00220485.2020.1845266 [Google Scholar] [Crossref]

17. Cogliano, M., Bernacki, M. L., & Kardash, C. M. (2020). A metacognitive retrieval practice intervention to improve undergraduates’ monitoring and control processes and use of performance feedback for classroom learning. Journal of Educational Psychology, 113(7), 1421–1440. https://doi.org/10.1037/edu0000624 [Google Scholar] [Crossref]

18. Doabler, C. T., Clarke, B., Kosty, D., Turtura, J. E., Sutherland, M., Maddox, S. A., & Smolkowski, K. (2021). Using direct observation to document “practice-based evidence” of evidence-based mathematics instruction. Journal of Learning Disabilities, 54(1), 20-35. [Google Scholar] [Crossref]

19. Dutton, J. (2023, June 28). Repositório Digital de Publicações Científicas: How to Enhance Students’ Self-Regulation. https://dspace.uevora.pt/rdpc/handle/10174/35303 [Google Scholar] [Crossref]

20. Ghanad, A. (2023). An overview of quantitative research methods. International journal of multidisciplinary research and analysis, 6(08), 3794-3803. [Google Scholar] [Crossref]

21. Goyibova, N., Muslimov, N., Sabirova, G., Kadirova, N., & Samatova, B. (2025). Differentiation approach in education: Tailoring instruction for diverse learner needs. MethodsX, 14, 103163. https://doi.org/10.1016/j.mex.2025.103163 [Google Scholar] [Crossref]

22. Gu, Y., He, J., Huang, W., & Sun, B. (2025). Professional Development for Teachers in the Digital Age: A comparative analysis of online training programs and policy implementation. Behavioral Sciences, 15(8), 1076. https://doi.org/10.3390/bs15081076 [Google Scholar] [Crossref]

23. Guo, J., Yang, L., Zhang, J., & Gan, Y. (2021). Academic self-concept, perceptions of the learning environment, engagement, and learning outcomes of university students: relationships and causal ordering. Higher Education, 83(4), 809–828. https://doi.org/10.1007/s10734-021-00705-8 [Google Scholar] [Crossref]

24. He, X., Fang, J., Cheng, H. N. H., Men, Q., & Li, Y. (2023). Investigating online learners’ knowledge structure patterns by concept maps: A clustering analysis approach. Education and Information Technologies, 28(9), 11401–11422. https://doi.org/10.1007/s10639-023-11633-8 [Google Scholar] [Crossref]

25. Ishak, M., Oderinde, I., & Ahmad, S. (2025). The role of Metacognitive Strategies in Enhancing learning Outcomes and Educational Efficiency: A Systematic review of Quantitative, Qualitative and Mixed-Method studies. International Journal of Academic Research in Business and Social Sciences, 15(4). https://doi.org/10.6007/ijarbss/v15-i4/24964 [Google Scholar] [Crossref]

26. Jaja, B., & Ofubaraibim, C. E. (2025). Reassessing school administrators’ role in supporting teacher autonomy and professional growth. In Ignatius Ajuru University of Education, International Journal of Scientific Research in Education (pp. 519–537) [Journal-article]. https://www.ijsre.com.ng/assets/vol.%2C-18(3)-jaja%2C-b.%2C---ofubaraibim%2C-c.-e.pdf [Google Scholar] [Crossref]

27. Korompis, F. L. S. (2023). Piaget’s Theory in Mathematics Education in Elementary School. International Journal of Research and Review, 10(6), 82–92. https://doi.org/10.52403/ijrr.20230612 [Google Scholar] [Crossref]

28. Koskinen, R., & Pitkäniemi, H. (2022). Meaningful Learning in Mathematics: A Research Synthesis of teaching Approaches. International Electronic Journal of Mathematics Education, 17(2), em0679. https://doi.org/10.29333/iejme/11715 [Google Scholar] [Crossref]

29. Kwarikunda, D., Schiefele, U., Muwonge, C. M., & Ssenyonga, J. (2022). Profiles of learners based on their cognitive and metacognitive learning strategy use: occurrence and relations with gender, intrinsic motivation, and perceived autonomy support. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01322-1 [Google Scholar] [Crossref]

30. Lee, J., & Paul, N. (2023). A review of pedagogical approaches for improved engagement and learning outcomes in mathematics. Journal of Student Research, 12(3), 1-9. [Google Scholar] [Crossref]

31. Lo, K. W. K., Ngai, G., Chan, S. C. F., & Kwan, K. (2022). How Students’ motivation and learning experience Affect their Service-Learning Outcomes: A Structural equation modeling analysis. Frontiers in Psychology, 13, 825902. https://doi.org/10.3389/fpsyg.2022.825902 [Google Scholar] [Crossref]

32. Loyens, S. M. M., Van Meerten, J. E., Schaap, L., & Wijnia, L. (2023). Situating Higher-Order, Critical, and Critical-Analytic Thinking in Problem- and Project-Based Learning Environments: A Systematic review. Educational Psychology Review, 35(2). https://doi.org/10.1007/s10648-023-09757-x [Google Scholar] [Crossref]

33. Mariappan, L. (2023). Empowering passive learners: Enhancing the teaching and learning process with scenario-based learning. The English Teacher, 52(2). http://meltajournals.com/index.php/TET/issue/view/7 [Google Scholar] [Crossref]

34. Martín-Arbós, S., Castarlenas, E., & Dueñas, J. (2021). Help-Seeking in an Academic context: A Systematic review. Sustainability, 13(8), 4460. https://doi.org/10.3390/su13084460 [Google Scholar] [Crossref]

35. Mbarawii, W., & Amabibi, F. I. (2024). Managing student enrollment for goal attainment in public senior secondary schools: The role of monitoring and evaluation, and resource allocation planning. In International Journal of Economics, Environmental Development and Society,, 3(3), 326–346. https://www.ijeeds.com.ng/assets/vol.%2C-5(3)-mbarawii%2C-w.%2C---amabibi%2C-f.-i..pdf [Google Scholar] [Crossref]

36. Mensah, S., Asare, B., & Aboagye, S. K. (2026). Exploring the predictive power of Problem-Solving skills, conceptual understanding, and logical reasoning on mathematics achievement. International Journal of Human Research and Social Science Studies., 03(03). https://doi.org/10.55677/ijhrsss/08-2026-vol03i03 [Google Scholar] [Crossref]

37. Morais, E., Santos, A. C., & Mouraz, A. (2025). Validation and proposal of a short version of the Motivated Strategies for Learning Questionnaire (MSLQ) for Portuguese adolescent students. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1445548 [Google Scholar] [Crossref]

38. Mustafa, N. C., Idris, N., Ismail, S., Isa, H. M., Amali, N. a. K., & Rahmat, N. H. (2025). Learning Strategies in Language learning Influence of resource management in language learning. International Journal of Research and Innovation in Social Science, IX(IX), 8320–8331. https://doi.org/10.47772/ijriss.2025.909000679 [Google Scholar] [Crossref]

39. Naz, B., & Qayyum, A. (2025). Effectiveness of Problem-Solving as a teaching strategy in enhancing students’ conceptual understanding and critical thinking in mathematics education. Research Journal for Social Affairs, 3(6), 693–702. https://doi.org/10.71317/rjsa.003.06.0500 [Google Scholar] [Crossref]

40. Nilimaa, J. (2023). New examination approach for Real-World Creativity and Problem-Solving Skills in Mathematics. Trends in Higher Education, 2(3), 477–495. https://doi.org/10.3390/higheredu2030028 [Google Scholar] [Crossref]

41. Nobis, M. (2025). Cracking the Math Code: Challenges and Strategies for Success among future math Educators. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5213804 [Google Scholar] [Crossref]

42. Ortiz-De-Villate, C., Rodríguez-Santero, J., & Torres-Gordillo, J. (2021). Contextual, personal and family factors in Explaining Academic Achievement: a Multilevel study. Sustainability, 13(20), 11297. https://doi.org/10.3390/su132011297 [Google Scholar] [Crossref]

43. Paethrangsi, N., Teekasap, S., Khiewpan, R., & Jandaboue, W. (2024). Empowering Students’ Autonomous Learning through Self-regulation, Metacognitive Strategies, and Collaborative Learning Environments. Journal of Liberal Arts RMUTT, 5(1), 69–79. https://doi.org/10.60101/jla.2024.5.1.4065 [Google Scholar] [Crossref]

44. Prihandono, D., Abiprayu, K. B., & Wijaya, A. P. (2024). ENCOURAGING SUCCESS: THE MULTIFACETED IMPACT OF EFFECTIVE LEADERSHIP ON ACADEMIC CULTURE AND PERFORMANCE. proceedings.unnes.ac.id. https://doi.org/10.15294/snkpti.v1i1.3831 [Google Scholar] [Crossref]

45. Rabillas, A., Kilag, O. K., Canete, N., Trazona, M., Calope, M. L., & Kilag, J. (2023). Elementary math learning through Piaget's cognitive development stages. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(4), 128-142. [Google Scholar] [Crossref]

46. Rukshana, M. M. M., Gunavathi, M. S., & Prithiyangadevi, M. S. (2025). FROM Procedures To Conceptual Insight: A Review of Teaching Strategies in Mathematics. Contemporary Techniques in Math Education, 3. [Google Scholar] [Crossref]

47. Singh, A. R. Y. D. C. K. (2026, January 26). An exploration of teaching strategies that promote Problem-Solving and Higher-Order thinking skills. https://www.ijesh.com/j/article/view/528 [Google Scholar] [Crossref]

48. Suleiman, I. B., Okunade, O. A., Dada, E. G., & Ezeanya, U. C. (2024). Key factors influencing students’ academic performance. Journal of Electrical Systems and Information Technology, 11(1). https://doi.org/10.1186/s43067-024-00166-w [Google Scholar] [Crossref]

49. Tañola, M. D., & Lomibao, L. S. (2024). Understanding How Students Learn Mathematics: A systematic literature review of contemporary learning strategies in Mathematics education post-2020. Journal of Innovations in Teaching and Learning, 4(1), 66-75. [Google Scholar] [Crossref]

50. Tao, Y., Meng, Y., Gao, Z., & Yang, X. (2022). Perceived teacher support, student engagement, and academic achievement: a meta-analysis. Educational Psychology, 42(4), 401–420. https://doi.org/10.1080/01443410.2022.2033168 [Google Scholar] [Crossref]

51. Vale, I., & Barbosa, A. (2023). Active learning strategies for an effective mathematics teaching and learning. European Journal of Science and Mathematics Education, 11(3), 573–588. https://doi.org/10.30935/scimath/13135 [Google Scholar] [Crossref]

52. Velez, A. J., Dayaganon, D. G., Robigid, J., Demorito, J., Villegas, J., & Gomez, D. (2023). Difficulties and Coping Strategies in Understanding Mathematical Concepts in a Private Higher Education in Tagum City, Davao del Norte, Philippines. Davao Research Journal, 14(1), 45–54. https://doi.org/10.59120/drj.v14i1.10 [Google Scholar] [Crossref]

53. Vu, H. X. (2025). Leveraging Technology for Higher-Order Thinking Development: Instructional Design Strategies in Higher Education. The International Journal of Technologies in Learning, 32(2), 25–46. https://doi.org/10.18848/2327-0144/cgp/v32i02/25-46 [Google Scholar] [Crossref]

54. Wild, S., & Neef, C. (2023). Analyzing the associations between motivation and academic performance via the mediator variables of specific mathematic cognitive learning strategies in different subject domains of higher education. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00423-w [Google Scholar] [Crossref]

55. Zare, J., & Al-Issa, A. (2024). Exploring task engagement strategies in DDL-enhanced tasks: Insights from EFL learners. Contemporary Educational Psychology, 78, 102299. https://doi.org/10.1016/j.cedpsych.2024.102299 [Google Scholar] [Crossref]

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