How to Quantify Student’s Regularity?
Studies carried out in classroom-based learning context, have consistently shown a positive relation between students’ conscientiousness and their academic success. We hypothesize that time management and regularity are main constructing blocks of students’ conscientiousness in the context of online education. In online education, despite intuitive arguments supporting on-demand courses as more flexible delivery of knowledge, completion rate is higher in the courses with rigid temporal constraints and structure. In this study, we further investigate how students’ regularity affects their learning outcome in MOOCs. We propose several measures to quantify students regularity. We validate accuracy of these measures as predictors of students’ performance in the course.
KeywordsRegulation Self-regulation Time management Massive open online courses Procrastination Engagement
- 3.Dillenbourg, P., Li, N., Kidziński, Ł.: The complications of the orchestration clock. In: From Books to MOOCs? Emerging Models of Learning and Teaching in Higher Education. Portland Press (2016)Google Scholar
- 4.Eyal, N.: Hooked: How to Build Habit-Forming Products. Penguin Canada, Toronto (2014)Google Scholar
- 5.Ferrari, J.R., Ware, C.B.: Academic procrastination: personality. J. Soc. Behav. Pers. 7(3), 495–502 (1992)Google Scholar
- 7.Kennedy, G., Coffrin, C., de Barba, P., Corrin, L.: Predicting success: how learners’ prior knowledge, skills and activities predict mooc performance. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 136–140. ACM (2015)Google Scholar
- 8.Kizilcec, R.F., Halawa, S.: Attrition and achievement gaps in online learning. In: Proceedings of the Second ACM Conference on Learning@Scale, pp. 57–66. ACM (2015)Google Scholar
- 10.Lauría, E.J., Baron, J.D., Devireddy, M., Sundararaju, V., Jayaprakash, S.M.: Mining academic data to improve college student retention: an open source perspective. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 139–142. ACM (2012)Google Scholar
- 12.Li, N., Kidziński, Ł., Jermann, P., Dillenbourg, P.: MOOC video interaction patterns: what do they tell us? In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, E. (eds.) Design for Teaching and Learning in a Networked World. LNCS, vol. 9307, pp. 197–210. Springer, Heidelberg (2015)Google Scholar
- 14.McAuley, A., Stewart, B., Siemens, G., Cormier, D.: The MOOC model for digital practice (2010)Google Scholar
- 15.Nawrot, I., Doucet, A.: Building engagement for MOOC students: introducing support for time management on online learning platforms. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 1077–1082 (2014)Google Scholar
- 17.Paredes, W.C., Chung, K.S.K.: Modelling learning & performance: a social networks perspective. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 34–42. ACM (2012)Google Scholar
- 20.Boroujeni, M.S., Kidziński, Ł., Dillenbourg, P.: How employment constrains participation in MOOCS? In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 376–377 (2016)Google Scholar
- 24.Vetterli, M., Kovačević, J., Goyal, V.K.: Foundations of Signal Processing. Cambridge University Press, Cambridge (2014)Google Scholar
- 25.Wolff, A., Zdrahal, Z., Nikolov, A., Pantucek, M.: Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 145–149. ACM (2013)Google Scholar