1. B. Voss, “Massive open online courses (MOOCs): A Primer for University and College Board Members,” Association of Governing Boards of Universities and Colleges [White Paper], 2013. Available: agb.org/sites/agb.org/files/report_2013_MOOCs.pdf.
2. D. Shah, “By the numbers: MOOCS in 2017,” 2018. Available: https://www.class-central.com/report/mooc-stats-2017/
3. M. F. M. Yaakob, and M. M. M. Zalli, “An Overview of Massive Open Online Courses (MOOCs), Pedagogies, and Distance Education,” in Redesigning Higher Education Initiatives for Industry 4.0: IGI Global, 2019, pp. 56-68.
4. N. Rubaai, and H. Hashim, “Polytechnic ESL lecturers’ acceptance of using Massive Open Online Course (MOOC) for teaching English as a Second Language (ESL),” International Journal of Innovative Technology and Exploring Engineering, vol. 8(9), 2019, pp. 114-121.
5. R. F. Kizilcec, M. Perez-Sanagustín, and J. J. Maldonado, “Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses,” Computers and Education, vol. 104, 2017, pp. 18–33.
6. S. Lajoie, and R. Azevedo, “Teaching and learning in technology-rich environments,” Handbook of Educational Psychology, vol. 2, 2006, pp. 803–821.
7. K. F. Hew, and W. S. Cheung, “Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges,” Educational Research Review, vol. 12, 2014, pp. 45–58.
8. R. F. Kizilcec, and S. Halawa, “Attrition and achievement gaps in online learning,” in Proceedings of the Second ACM Conference on Learning@Scale, 2015, pp. 57–66.
9. E. Rabin, Y. M. Kalman, and M. Kalz, “An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs,” International Journal of Educational Technology in Higher Education, vol. 16(1), 2019, pp. 14.
10. L. Barnard, V. Paton, and W. Lan, “Online self-regulatory learning behaviors as a mediator in the relationship between online course perceptions with achievement,” International Review of Research in Open and Distance Learning, vol. 9(2), 2008, pp. 1–15.
11. N. Dabbagh, and A. Kitsantas, “Supporting self-regulation in student-centered web-based learning environments,” International Journal on E-Learning, vol. 3(1), 2004, pp. 40-47.
12. B. J. Zimmerman, “Attaining self-regulation: A social cognitive perspective,” in Handbook of self-regulation: Academic Press, 2000, pp. 13-39.
13. A. Kitsantas, and B. J. Zimmerman, “Comparing self-regulatory processes among novice, non-expert, and expert volleyball players: A microanalytic study,” Journal of Applied Sport Psychology, vol. 14(2), 2002, pp. 91-105.
14. J. L. Whipp, and S. Chiarelli, “Self-regulation in a Web-based course: A case study,” Educational Technology Research and Development, vol. 52(4), 2004, pp. 5-22.
15. L. Song, E. S. Singleton, J. R. Hill, and M. H. Koh, “Improving online learning: Student perceptions of useful and challenging characteristics,” The Internet and Higher Education, vol. 7(1), 2004, pp. 59-70.
16. E. Yukselturk, and S. Bulut, “Predictors for student success in an online course,” Educational Technology and Society, vol. 10(2), 2007, pp. 71-83.
17. R. F. Kizilcec, and E. Schneider, “Motivation as a lens to understand online learners: Toward data-driven design with the OLEI Scale,” Transactions on Computer-Human Interactions, vol. 22(2), 2015, pp.24.
18. K. Ley, and D. B. Young, “Instructional principles for self-regulation,” Educational Technology Research and Development, vol. 49(2), 2001, pp. 93-103.
19. P. C. Sun, R. J. Tsai, G. Finger, Y. Y. Chen, and D. Yeh, “What drives a successful elearning? An empirical investigation of the critical factors influencing learner satisfaction,” Computers and Education, vol. 50(4), 2008, pp. 1183–1202.
20. R. W. Brislin, “Back-translation for cross-cultural research,” Journal of Cross-cultural Psychology, vol. 1, 1970, pp. 185-216.
21. J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis: A Global Perspective (7th edition). Upper Saddle River, New Jersey: Pearson Education Inc., 2010.
22. J. F. Hair, G. T. M. Hult, C. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). London: Thousand Oaks: SAGE, 2017.
23. D. W. Barclay, C. Higgins, and R. Thompson, “The partial least square (PLS) approach to causal modeling: Personal computer adoption and use as an illustration,” Technology Studies, vol. 2(2), 1995, pp.285–309.
24. C. Fornell, and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, vol. 18(1), 1981, pp. 39–50.
25. Z. Awang, SEM Made Simple: A Gentle Approach to Learning Structural Equation Modelling. Bandar Baru Bangi: MPWS Rich Resources, 2015.
26. L. Breslow, D. E. Pritchard, J. DeBoer, G. Stump, A. D. Ho, and D. Seaton, “Studying learning in the worldwide classroom: Research into edX’s first MOOC,” Research and Practice in Assessment, vol. 8, 2013, pp. 13–25.
27. A. Ogan, E. Walker, R. Baker, M. M. T. Rodrigo, J. C. Soriano, and M. J. Castro, “Towards understanding how to assess help-seeking behavior across cultures,” International Journal of Artificial Intelligence in Education, vol. 25(2), 2015, pp. 229–248.