Self-Regulated Learning as a Mediator of the Effect of Personalized Learning on Academic Achievement among Chinese University Students

This study examined self-regulated learning as a mediator of the effect of personalized learning on academic achievement among graduate students in China. A quantitative cross-sectional survey design was employed. Data were collected from 450 graduate students in China through an electronic questionnaire, and 446 valid responses were retained after Mahalanobis distance screening. The research instrument consisted of 30 items measuring personalized learning, self-regulated learning, and perceived academic achievement. Data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity, with all outer loadings exceeding .70, Cronbach’s alpha values ranging from .930 to .937, composite reliability values ranging from .941 to .947, and AVE values ranging from .615 to .640. The structural model explained 65.4% of the variance in academic achievement and 51.6% of the variance in self-regulated learning. The results showed that personalized learning had a significant positive direct effect on academic achievement (β = .386, t = 8.101, p < .001) and self-regulated learning (β = .718, t = 25.018, p < .001). Self-regulated learning also significantly predicted academic achievement (β = .486, t = 10.138, p < .001). Mediation analysis confirmed that self-regulated learning significantly mediated the relationship between personalized learning and academic achievement (β = .349, t = 9.629, p < .001). The findings suggest that personalized learning improves graduate students’ academic achievement partly by enhancing students’ ability to regulate their learning process.

Personalized learning; self-regulated learning

1. Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose(s)? Educational Psychology Review, 33, 1675–1715. [Google Scholar] [Crossref]

2. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. [Google Scholar] [Crossref]

3. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE. [Google Scholar] [Crossref]

4. Dent, A. L., & Koenka, A. C. (2016). The relation between self-regulated learning and academic achievement across childhood and adolescence: A meta-analysis. Educational Psychology Review, 28, 425–474. [Google Scholar] [Crossref]

5. Dumont, H., & Ready, D. D. (2023). On the promise of personalized learning for educational equity. npj Science of Learning, 8, Article 26. [Google Scholar] [Crossref]

6. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE. [Google Scholar] [Crossref]

7. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. [Google Scholar] [Crossref]

8. Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2022). Advanced issues in partial least squares structural equation modeling (2nd ed.). SAGE. [Google Scholar] [Crossref]

9. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. [Google Scholar] [Crossref]

10. Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84. https://doi.org/10.1016/j.edurev.2015.11.002 [Google Scholar] [Crossref]

11. Major, L., Francis, G. A., & Tsapali, M. (2021). The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis. British Journal of Educational Technology, 52(5), 1935–1964. [Google Scholar] [Crossref]

12. Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. [Google Scholar] [Crossref]

13. Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, Article 422. https://doi.org/10.3389/fpsyg.2017.00422 [Google Scholar] [Crossref]

14. Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued progress: Promising evidence on personalized learning. RAND Corporation. [Google Scholar] [Crossref]

15. Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. [Google Scholar] [Crossref]

16. Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x [Google Scholar] [Crossref]

17. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838 [Google Scholar] [Crossref]

18. Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2022). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010. [Google Scholar] [Crossref]

19. Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, Article 101976. [Google Scholar] [Crossref]

20. Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4–5), 356–373. [Google Scholar] [Crossref]

21. Xu, Z., Zhang, Y., & Li, H. (2023). A meta-analysis of the efficacy of self-regulated learning interventions on academic achievement in online and blended environments in K–12 and higher education. Behaviour & Information Technology, 42(16), 2911–2928. [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]

23. 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]

• 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