Modeling the Relationship Between Cognitive Strategies, Effort Regulation, and Metacognitive Writing Strategies: The Mediating Role of Affective Factors

Academic writing is a complex process demanding cognitive control as well as motivation, persistence and emotional control. While some research has focused on the strategies used for writing alone, fewer studies have considered the combination of cognitive, affective, and self-regulatory factors as a whole to support writing development. This study presents and tests a model exploring the relationships between cognitive strategies, effort regulation, affective strategies and metacognitive writing strategies of university students in Malaysia. A quantitative research design was adopted, which included 202 undergraduate and postgraduate students of academic writing in a public university. The Writing Strategy Scale was adapted from Raoofi et al. (2017) and used to gather data, which was then analysed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) by SmartPLS 4. Significant positive relationships were found between effort regulation and affective strategies, effort regulation and metacognitive strategies, cognitive strategies and affective strategies, and cognitive strategies and metacognitive strategies. Affective strategies were also shown to mediate in the proposed model to a significant degree. The model accounted for 50.2% of the variance in the affective strategies and 53.4% of the variance in the metacognitive writing strategies. The results of this study indicate that the problem-solving strategies are not sufficient for successful academic writing. Emotions regulation and sustained efforts help the students plan, monitor and evaluate their writing well. The study provides practical examples for language teaching and learning and adds to the ongoing debates on SLL in higher education.

academic writing, cognitive strategies, effort regulation, affective strategies, metacognitive strategies

1. Bruning, R., Dempsey, M., Kauffman, D. F., McKim, C., & Zumbrunn, S. (2013). Examining dimensions of self-efficacy for writing. Journal of Educational Psychology, 105(1), 25–38. [Google Scholar] [Crossref]

2. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906 [Google Scholar] [Crossref]

3. Fornell, C. and Bookstein. F.L. (1982) Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research, 19(4), 440-452. https://www.jstor.org/stable/3151718 [Google Scholar] [Crossref]

4. Goodman, S.N. (1999). Toward evidence-based medical statistics: 2. The Bayes factor. Clinical Trials, 6(3), 229–24 Bayes Factors, 233-240. DOI: 10.7326/0003-4819-130-12-199906150-00019 [Google Scholar] [Crossref]

5. Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https:// doi. org/ 10. 1504/ IJMDA. 2017.087624 [Google Scholar] [Crossref]

6. Hauf, S., Rickter, N. F., Saretedt, M., Ringle.,C. M. (2024). The Importance and Performance in PLS-SEM. Journal of Retailing and Consumer Services, 78(2024) (100723). https://epub.ub.uni-muenchen.de/109073/1/1- s2.0-S0969698924000195-main.pdf [Google Scholar] [Crossref]

7. Hayes, J. R. (1996). A new framework for understanding cognition and affect in writing. In C. M. Levy & S. Ransdell (Eds.), The science of writing (pp. 1–27). Lawrence Erlbaum. [Google Scholar] [Crossref]

8. Hayes, J. R., & Flower, L. (1980). Identifying the organization of writing processes. In L. W. Gregg & E. R. Steinberg (Eds.), Cognitive processes in writing (pp. 3–30). Lawrence Erlbaum. [Google Scholar] [Crossref]

9. Kurniash, Mukminaties,N., Arianto, M.A., Sari,R.N.S. (2022) Affective Factors in Online Writing Performance: Do They Matter? MEXTESOL Journal, 46(2), 1-9. [Google Scholar] [Crossref]

10. Liu,X. (2023) The Effect of Affective Filter Hypothesis on College Students’ English Writing Teaching and Its Enlightenment. Open Access Library Journal, 10(9), https://doi.org/10.4236/oalib.1110671 [Google Scholar] [Crossref]

11. Miller, G. (1956) The magical number seven, plus or minus two: Some Limits on our capacity for processing information. The Psychological Review, 63, 81-97. [Google Scholar] [Crossref]

12. Ramayah, T., Cheah, F. Ting, H., & Memon, M.A. (2018). Partial least Equation Structural Equation Modeling (PLS-SEM) using SEM 3.0: An Updated Guide and Practical Guide to Statistical Analysis. Kuala Lumpur, Malaysia: Pearson [Google Scholar] [Crossref]

13. Raoofi,S. Miri,A., Gharibi,J. & Malaki, B. (2017) Assessing and Validating a Writing Strategy Scale for Undergraduate Students. Journal of Language Teaching and Research, Vol 8(3), pp 624-633. Retrieved from http://www.academypublication.com/issues2/jltr/vol08/03/23.pdf [Google Scholar] [Crossref]

14. Ringle, C. M., & Sarstedt, M. (2016). Using importance-performance matrix analysis (IPMA) in PLS-SEM: Using SmartPLS 3. Journal of Business Research, 69(8), 3169-3177. http://dx.doi.org/ 10.1108/IMDS-10-2015-0449 [Google Scholar] [Crossref]

15. Rosdiana,L.A., Damaianti,V.S., Mulyati,Y., and Sastromiharjo,A. (2023) The Role of Metacognitive Strategies in Academci Writing Skills in Higher Education. International J ournal of Learning, Teaching and Educational Research, 22(6), 328-344. https://doi.org/10.26803/ijlter.22.6.18 [Google Scholar] [Crossref]

16. Sagredo-Ortiz S and Kloss S (2025) Academic writing strategies in university students from three disciplinary areas: design and validation of an instrument. Front. Educ. 10:1600497. doi: 10.3389/feduc.2025.1600497 [Google Scholar] [Crossref]

17. Savasci,M. (2026). Understanding L2 Academic Writing Trajectories: Writing Self-Efficacy, Motivation, Attitudes, and Achievement Emotions. TESL-EJ, 29(4), https://doi.org/10.55593/ej.29116a1 [Google Scholar] [Crossref]

18. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1016/0364-0213(88)90023-7 [Google Scholar] [Crossref]

19. Thung,N.T. (2023) A Study of Writing Strategies used by Students of Different Levels of Proficiency at A University in Vietnam. International Journal of Social Science and Human Research, 6(5), 2722-2729. https://ijsshr.in/v6i5/25.php [Google Scholar] [Crossref]

20. Zhang M, He Q, Du J, Liu F, Huang B. Learners' perceived advantages and social-affective dispositions toward online peer feedback in academic writing. Front Psychol. 2022 Sep 20;13:973478. doi: 10.3389/fpsyg.2022.973478. PMID: 36204744; PMCID: PMC9531959. [Google Scholar] [Crossref]

21. Ziegenfuss, J. Y., Casey A. E., Jennifer M. D., Meghan M. J., Thomas E. K, and Marna, C. (202) Impact of Demographic Survey Questions on Response Rate and Measurement: A Randomized Experiment. Survey Practice 14 (1), https://doi.org/10.29115/SP-2021-0010. [Google Scholar] [Crossref]

22. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. [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