Self-Regulation and How It Influences Grit, Motivational Beliefs, and Cognitive Strategy Use In Learning

This study examines how self-regulation influences grit, motivational beliefs, and cognitive strategy use in learning among university students. Recognizing that academic success is shaped not only by cognitive ability but also by non-cognitive factors, the study focuses on how learners regulate their learning processes and how this relates to persistence, motivation, and learning strategies. A quantitative survey design was employed using a 5-point Likert scale instrument adapted from Martin et al. (2022). The instrument consisted of several sections measuring grit, motivational beliefs, and self-regulated learning strategies. A total of 61 university students participated in the study. Descriptive statistics and correlation analysis using SPSS were conducted to examine students’ perceptions and the relationships among the variables. The findings reveal that students demonstrate generally high levels of self-regulation, particularly in planning, monitoring, and persisting in their learning tasks. Students also showed moderate to high levels of grit, motivational beliefs, and cognitive strategy use. Correlation analysis indicated significant positive relationships between self-regulation and grit, self-regulation and motivational beliefs, and self-regulation and cognitive strategy use. These results suggest that students who actively regulate their learning are more likely to demonstrate perseverance, maintain positive beliefs about their learning abilities, and apply effective cognitive strategies. Overall, the study highlights the important role of self-regulation in supporting students’ motivation, persistence, and strategic learning behaviours. The findings also provide useful insights for educators in promoting self-regulated learning strategies to enhance students’ engagement and academic performance.

self-regulation, grit

1. Ahmad, N., Alias, F. A., Hamat, M., & Mohamed, S. A. (2024). Reliability Analysis: Application of Cronbach’s Alpha in REsearch Instruments. . Unit Penerbitan Jabatan Sains Komputer & Matematik. [Google Scholar] [Crossref]

2. Bandura, A. (1971). Social Learning Theory. General Learning Press. [Google Scholar] [Crossref]

3. Bandura, A., & National Inst of Mental health. (1986). Social Foundations of Thought and action: A Social Cognitive Theory. Prentice-Hall, Inc. [Google Scholar] [Crossref]

4. Chen, J., & Alibakhshi, G. (2026). AI-Powered Applications’ Effects on English Language Learners’ Cognitive, Metacognitive, and Resource Management Strategies, and Language Achievement. Journal of Computer Assisted Learning, 42(1). https://doi.org/10.1002/jcal.70171 [Google Scholar] [Crossref]

5. Guo, W., Bai, B., Zang, F., Wang, T., & Song, H. (2023). Influences of motivation and grit on students’ self-regulated learning and English learning achievement: A comparison between male and female students. System, 114. https://doi.org/10.1016/j.system.2023.103018 [Google Scholar] [Crossref]

6. Ha, C., Roehrig, A. D., & Zhang, Q. (2023). Self-regulated learning strategies and academic achievement in South Korean 6thgraders: A two-level hierarchical linear modeling analysis. PLoS ONE, 18(4 April). https://doi.org/10.1371/journal.pone.0284385 [Google Scholar] [Crossref]

7. He, L. (2024). The Application of SPSS Correlation Analysis in the Study of Precision Teaching of English in Universities. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1371 [Google Scholar] [Crossref]

8. Khairuddin, Z., Amir Johan, S. J., Md. Ngadiran, N., Sukiman, S. L., & Rahmat, N. H. (2025). INVESTIGATING PERCEPTION ON GRIT, MOTIVATION,AND SELF- REGULATED LEARNING AMONG MALAYSIAN UNDERGRADUATES. Asian People Journal (APJ), 8(2), 154–170. https://doi.org/10.37231/apj.2025.8.2.823 [Google Scholar] [Crossref]

9. Martin, H., Craigwell, R., & Ramjarrie, K. (2022). Grit, motivational belief, self-regulated learning (SRL), and academic achievement of civil engineering students. European Journal of Engineering Education, 47(4), 535–557. https://doi.org/10.1080/03043797.2021.2021861 [Google Scholar] [Crossref]

10. Mateo, J. D. (2024). Path Analysis of Grit, Self-Efficacy, Self-Regulation, and Science Academic Performance in Online Learning. Asian Journal of Education and Social Studies, 50(10), 299–314. https://doi.org/10.9734/ajess/2024/v50i101621 [Google Scholar] [Crossref]

11. Mikami, H., & Shiozawa, T. (2024). A self-regulation perspective on L2 grit development and its impact on language achievement. Language Teaching Research. https://doi.org/10.1177/13621688241287501 [Google Scholar] [Crossref]

12. Muhamad Yew, N. A., Abd Hamid, N. A., Maktiar Singh, K. K., & Rahmat, N. H. (2023). Exploring Motivational Beliefs and Self-Regulated Learning Strategies in Learning among Undergraduates. International Journal of Academic Research in Business and Social Sciences, 13(7). https://doi.org/10.6007/ijarbss/v13-i7/17043 [Google Scholar] [Crossref]

13. Peltzer, N., Kuhn, J., Stadler, M., Cera, R., Hoyer, C., Flegr, S., Fischer, M. R., & Richters, C. (2026). The Interplay of Self-regulation, Cognitive Load and Learner Resources: Implications for Learning Outcomes in Virtual Reality. Computers & Education, 105612. https://doi.org/10.1016/j.compedu.2026.105612 [Google Scholar] [Crossref]

14. Piaget, J. (1936). Origins of intelligence in the Childen . Routledge. [Google Scholar] [Crossref]

15. Ryan, R. M., & Deci, E. L. (2000). Self-Determination theory and the facilitation of intrinsic , social development , and well-being. American Psychologist, 55(1), 68–78. [Google Scholar] [Crossref]

16. Townley-Flores, C., Sulik, M. J., Bardack, S., & Obradović, J. (2022). Interplay of motivational beliefs and self-regulation with achievement across economic risk. Journal of Applied Developmental Psychology, 82. https://doi.org/10.1016/j.appdev.2022.101442 [Google Scholar] [Crossref]

17. Vetter, T. R. (2017). Descriptive Statistics: Reporting the Answers to the 5 Basic Questions of Who, What, Why, When, Where, and a Sixth, so What? Anesthesia and Analgesia, 125(5), 1797–1802. https://doi.org/10.1213/ANE.0000000000002471 [Google Scholar] [Crossref]

18. Wang, L., Zou, B., Wang, C., & Huang, F. (2025). Understanding learner motivation and intention to use ChatGPT for English language learning. Discover Computing, 28(1). https://doi.org/10.1007/s10791-025-09805-w [Google Scholar] [Crossref]

19. Ziegenfuss, J. Y., Easterday, C. A., Dinh, J. M., JaKa, M. M., Kottke, T. E., & Canterbury, M. (2021). Impact of demographic survey questions on response rate and measurement: A randomized experiment. Survey Practice, 14(1), 1–11. https://doi.org/10.29115/sp-2021-0010 [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