Systematic literature review on time management of educational activities in learning management systems
DOI:
https://doi.org/10.17398/1695-288X.19.1.63Keywords:
Literature Review, Educational Data Mining, Time Management, Learning Analytics, Learning Management SystemsAbstract
The use of learning management systems is becoming frequent as a form of learning. This is because it allows greater flexibility of time and space if we compare to face-to-face learning. Thus, this work aims to present how the field of Educational Data Mining and Learning Analytics are contributing to the extraction of knowledge from the self-regulation of time management in e-learning environments. For this, we considered the concept of time management by Pintrich (2000) and carried out a systematic review of the literature. With that, it was possible to notice that most of the analyzed works do not study only time management. We also realized that the data, which represents time management, are aggregated data, that is, the phenomenon is not studied over time. With these results, you can see an overview of how Learning Analytics and Educational Data Mining are supporting the extraction of knowledge about self-regulation of time management in online environments.
Downloads
References
Bittencourt, I. I., & Isotani, S. (2018). Informática na Educação baseada em Evidências: Um Manifesto. Revista Brasileira de Informática Na Educação, 26(3), 108. https://doi.org/10.5753/rbie.2018.26.03.108
Boroujeni, M. S., Sharma, K., Kidziński, Ł., Lucignano, L., & Dillenbourg, P. (2016). How to Quantify Student’s Regularity? In Adaptive and Adaptable Learning (pp. 277–291). Springer International Publishing. https://doi.org/10.1007/978-3-319-45153-4_21
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01403
Claessens, B. J. C., van Eerde, W., Rutte, C. G., & Roe, R. A. (2007). A review of the time management literature. Personnel Review, 36(2), 255–276. https://doi.org/10.1108/00483480710726136
Dunlosky, J., & Ariel, R. (2011). The influence of agenda-based and habitual processes on item selection during study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(4), 899–912. https://doi.org/10.1037/a0023064
Feldmann, B. (2014). Two Decades of e-learning in Distance Teaching – From Web 1.0 to Web 2.0 at the University of Hagen. In Communications in Computer and Information Science (pp. 163–172). Springer International Publishing. https://doi.org/10.1007/978-3-319-10671-7_16
Hadwin, A., Järvelä, S., & Miller, M. (2017, August 31). Self-Regulation, Co-Regulation, and Shared Regulation in Collaborative Learning Environments. Retrieved from https://www.routledgehandbooks.com/doi/10.4324/9781315697048.ch6
Jo, I.-H., Kim, D., & Yoon, M. (2014). Analyzing the log patterns of adult learners in LMS using learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge - LAK ’14. ACM Press. https://doi.org/10.1145/2567574.2567616
Jo, I.-H., Yu, T., Lee, H., & Kim, Y. (2014). Relations between Student Online Learning Behavior and Academic Achievement in Higher Education: A Learning Analytics Approach. In Emerging Issues in Smart Learning (pp. 275–287). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_38
Jo, Il-Hyun & Kim, Dongho & Yoon, Meehyun. (2015). Constructing proxy variables to measure adult learners' time management strategies in LMS. Educational Technology and Society. 18. 214-225.
Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers & Education, 127, 233–251. https://doi.org/10.1016/j.compedu.2018.08.023
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Tech. Rep. EBSE 2007-001, Keele University and Durham University Joint Report.
Lee, Y. (2018). Effect of uninterrupted time-on-task on students’ success in Massive Open Online Courses (MOOCs). Computers in Human Behavior, 86, 174–180. https://doi.org/10.1016/j.chb.2018.04.043
Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48. https://doi.org/10.1016/j.iheduc.2015.12.003
Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1), 121–131. https://doi.org/10.1037/a0033546
Nadinloyi, K. B., Hajloo, N., Garamaleki, N. S., & Sadeghi, H. (2013). The Study Efficacy of Time Management Training on Increase Academic Time Management of Students. Procedia - Social and Behavioral Sciences, 84, 134–138. https://doi.org/10.1016/j.sbspro.2013.06.523
Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00422
Pintrich, P. R. (2000). The Role of Goal Orientation in Self-Regulated Learning. In Handbook of Self-Regulation (pp. 451–502). Elsevier. https://doi.org/10.1016/b978-012109890-2/50043-3
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
Rasid, N., Nohuddin, P. N. E., Alias, H., Hamzah, I., & Nordin, A. I. (2017). Using Data Mining Strategy in Qualitative Research. In Advances in Visual Informatics (pp. 100–111). Springer International Publishing. https://doi.org/10.1007/978-3-319-70010-6_10
Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. ACM Press. https://doi.org/10.1145/2330601.2330661
Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74. https://doi.org/10.1016/j.compedu.2015.08.004
Wolters, C. A., Won, S., & Hussain, M. (2017). Examining the relations of time management and procrastination within a model of self-regulated learning. Metacognition and Learning, 12(3), 381–399. https://doi.org/10.1007/s11409-017-9174-1
Won You, Ji. (2015). Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning. Educational Technology and Society. 18. 64-74.
Yen, C.-J., Tu, C.-H., Sujo-Montes, L. E., Armfield, S. W. J., & Chan, J.-Y. (2013). Learner Self-Regulation and Web 2.0 Tools Management in Personal Learning Environment. International Journal of Web-Based Learning and Teaching Technologies, 8(1), 46–65. https://doi.org/10.4018/jwltt.2013010104
You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003
Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53. https://doi.org/10.1016/j.iheduc.2015.05.002
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (2000). Attaining Self-Regulation. In Handbook of Self-Regulation (pp. 13–39). Elsevier. https://doi.org/10.1016/b978-012109890-2/50031-7
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
Downloads
Published
Issue
Section
License
Authors who publish in this journal accept the following conditions:
Authors retain copyright over their works and grant the journal the right of first publication. Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows third parties to share, copy, distribute, publicly communicate, adapt, transform, and reuse the work in any medium or format, including for commercial purposes, provided that authorship is properly acknowledged, the original source is cited, a link to the license is included, and any changes made are indicated. Note: This license applies to articles published from Vol. 25, No. 2, 2026 onwards.
Authors may enter into separate and additional contractual arrangements for the non-exclusive distribution of the published version of the article —for example, its deposit in an institutional repository or its subsequent inclusion in a book—, provided that it is clearly stated that the work was first published in this journal.
Authors are permitted and encouraged to deposit and disseminate their work on the Internet, for example, in institutional repositories, institutional websites, or personal websites before, during, and after the review and publication process, as this may foster scholarly exchange, increase the visibility of the work, and enable broader and faster dissemination of the published research.





