Andre, J., Le, T. K., & Webster, R. (2019). Academic Leadership and Learning Analytics in Higher Education in Vietnam. Leadership and Management in Higher Education: Driving Change with Global Trends, 16.
 Bandura, A. (2007). Reflections on an Agentic Theory of Human Behavior. Journal of the Norwegian Psychological Association, 44(8)
 Bandura, A. (2018). Toward a Psychology of Human Agency: Pathways and Reflections. Perspectives on Psychological Science, 13(2), 130–136. https://doi. org/10.1177/1745691617699280
 Bloom, B. S. (1974). Time and learning. American Psychologist, 29(9), 682
 Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29. https://doi.org/10.1109/ TLT.2016.2616312
 Devi, B., Khandelwal, D. B., & Das, M. (2017). Application of Bandura’s social cognitive theory in the technology enhanced, blended learning environment. International Journal of Applied Research, 3(1), 721– 724.
 Dodge, B., Whitmer, J., & Frazee, J. P. (2015). Improving undergraduate student achievement in large blended courses through data-driven interventions. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK ’15, 412– 413. https://doi.org/10.1145/2723576.2723657
 Elbadrawy, A., Studham, R. S., & Karypis, G. (2015). Collaborative multi-regression models for predicting students’ performance in course activities. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 103–107. https:// doi.org/10.1145/2723576.2723590
 Gong, L., Liu, Y., & Zhao, W. (2018). Using Learning Analytics to Promote Student Engagement and Achievement in Blended Learning: An Empirical Study. 6.
 Hellings, J., & Haelermans, C. (2020). The effect of providing learning analytics on student behaviour and performance in programming: A randomised controlled experiment. Higher Education. https://doi. org/10.1007/s10734-020-00560-z
 Henrich, J. (2020). The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous (Illustrated edition). Farrar, Straus and Giroux
 Hlosta, M., Papathoma, T., & Herodotou, C. (2020). Explaining Errors in Predictions of At-Risk Students in Distance Learning Education. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education (Vol. 12164, pp. 119–123). Springer International Publishing. https:// doi.org/10.1007/978-3-030-52240-7_22
 Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469–478. https://doi.org/10.1016/j.chb.2014.04.002
 Ifenthaler, D., & Yau, J. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 2020(June). https://doi. org/10.1007/s11423-020-09788-z
 Iglesias-Pradas, S., Ruiz-de-Azcárate, C., & AgudoPeregrina, Á. F. (2015). Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies. Computers in Human Behavior, 47, 81–89. https:// doi.org/10.1016/j.chb.2014.09.065
 Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014). Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. Journal of Learning Analytics, 1(1), 6–47. https://doi.org/10.18608/jla.2014.11.3
 Jo, I.-H., Yu, T., Lee, H., & Kim, Y. (2015). Relations between Student Online Learning Behavior and Academic Achievement in Higher Education: A Learning Analytics Approach. In G. Chen, V. Kumar, Kinshuk, R. Huang, & S. C. Kong (Eds.), Emerging Issues in Smart Learning (pp. 275–287). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3- 662-44188-6_38
 Leitner, P., Ebner, M., & Ebner, M. (2019). Learning Analytics Challenges to Overcome in Higher Education Institutions. In D. Ifenthaler, D.-K. Mah, & J. Y.-K. Yau (Eds.), Utilizing Learning Analytics to Support Study Success (pp. 91–104). Springer International Publishing. https://doi.org/10.1007/978- 3-319-64792-0_6
 Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2019). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 101202. https://doi.org/10.1016/j.learninstruc.2019.04.003
 Lipsey, M. W., Puzio, K., Yun, C., Herbert, M. A., Steinka-Fry, K., Cole, M. W., Roberts, M., Anthony, K. S., & Busick, M. D. (2012). Translating the Statistical Representation of the Effects of Education Interventions Into More Readily Interpretable Forms. Institute of Education Sciences.
 Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., & Yang, S. J. H. (2018). Applying Learning Analytics for the Early Prediction of Students’ Academic Performance in Blended Learning. Educational Technology & Society, 21(2)
 Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588– 599. https://doi.org/10.1016/j.compedu.2009.09.008
 Manninen, B. (2018). False Cause: 100 of the Most Important Fallacies in Western Philosophy (pp. 338– 341). https://doi.org/10.1002/9781119165811.ch79
 Mellenbergh, G. (2019). Random Assignment (pp. 39–54). https://doi.org/10.1007/978-3-030-12272-0_4
 Merwin, J. C. (1969). Historical review of changing concepts of evaluation. In Educational evaluation: New roles, new methods: The sixty-eighth yearbook of the National Society for the Study of Education, Part II. University of Chicago Press.
 Park, Y., & Jo, I.-H. (2015). Development of the Learning Analytics Dashboard to Support Students’ Learning Performance. Journal of Universal Computer Science, 21(1)
 Rafaeli, S., & Ravid, G. (1997). OnLine, Web Based Learning Environment for an Information Systems course: Access logs, Linearity and Performance. ISECON 97. Information Systems Education Conference.
 Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472. https://doi.org/10.1016/j. compedu.2013.06.009
 Society for Learning Analytics Research. (2020, November 13). [Webinar] What Do We Mean by Rigour in Learning Analytics? https://www.youtube.com/watch ?v=RxGa6MvCX6Y&feature=youtu.be
 Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness.
 Trowler, P., & Trowler, V. (2010). Student engagement evidence summary. The Higher Education Academy.
 Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
 Webster, R., Andre, J., & Trinh, T. T. G. (2019). Industry 4.0 and higher education: Combining learning analytics and learning science to transform the undergraduate learning experience in Vietnam. Leadership and Management in Higher Education: Driving Change with Global Trends. Leadership and Management in Higher Education: Driving Change with Global Trends, Ho Chi Minh City.
 Yang, T.-Y., Brinton, C. G., Joe-Wong, C., & Chiang, M. (2017). Behavior-Based Grade Prediction for MOOCs via Time Series Neural Networks. IEEE Journal of Selected Topics in Signal Processing, 1–1. https://doi. org/10.1109/JSTSP.2017.2700227
 Zimmerman, W. A., & Johnson, G. (2017). Exploring Factors Related to Completion of an Online Undergraduate-Level Introductory Statistics Course. Online Learning, 21(3). https://doi.org/10.24059/olj. v21i3.1017