As the offer of Massive Open Online Courses (MOOCs) continues to grow around the world, a great deal of MOOC research has focused on their low success rates and used indicators that might be more appropriate for traditional degree-seeking students than for MOOC learners, who, because of the openness of MOOCs, represent a more diverse clientele who exhibit different characteristics and behaviours. In this study, conducted in a French MOOC that is part of the EDUlib initiative, we systematically classified MOOC user profiles based on their behaviour in the open-source learning management system (LMS) — in this case, Sakai — and studied their survival in the MOOC. After formatting the logs in ordinal variables in order to reflect a continuum of participation central to the behavioural engagement concept (Fredricks, Blumenfeld, & Paris, 2004), we incrementally executed a two-step cluster analysis procedure that led us to identify five different user profiles, after having manually excluded Ghots : Browser, Self-Assessor, Serious Reader, Active-Independent, and Active-Social. These five profiles differed both qualitatively and quantitatively on the continuum of engagement, and a significant proportion of the less active profiles did not drop out of the MOOC. Our results confirm the importance of social behaviours, as in recent typologies, but also point out a new Self-Assessor category. The implications of these profiles for MOOC design are discussed.
- Distance Education,
- Open Learning,
- participant profiles,
- survival analysis,
- behavioural engagement,
- cluster analysis
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- Alberti, C., Timsit, J.-F., & Chevret, S. (2005). Analyse de survie: Le test du logrank [Survival Analysis: The log-rank test]. Revue des Maladies Respiratoires, 22(5), 829-832. doi: 10.1016/S0761-8425(05)85644-X
- Anderson, T. (2013, April 1). Promise and/or peril: MOOCs and open and distance education [Blog post]. Retrieved from https://landing.athabascau.ca/file/view/274885/promise-andor-peril-moocs-and-open-and-distance-education
- Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004, April). Off-task behavior in the cognitive tutor classroom: when students game the system. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 383-390). ACM. doi: 10.1145/985692.985741
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall, Inc.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman.
- Bernard, R., & Amundsen, C. L. (2008). Antecedents to dropout in distance education: Does one model fit all? International Journal of E-Learning & Distance Education, 4(2), 25-46. Retrieved from http://www.ijede.ca/index.php/jde/article/viewFile/530/716
- Carré, P. (2001). Accompagner des formations ouvertes: conférence de consensus. Paris: L'Harmattan.
- Clark, R. E. (1999). The CANE model of work motivation: A two-stage model of commitment and necessary mental effort. In J. Lowyck (Ed.), Trends in corporate training. Leuven, Belgium: University of Leuven Press.
- Diaz, D. P., & Cartnal, R. B. (1999). Students' learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching, 47(4), 130-135.
- Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132. doi: 10.1146/annurev.psych.53.100901.135153
- European Commission. (2015). Open education Europa. Open Education Scoreboard. Retrieved from https://www.openeducationeuropa.eu/en
- Ferguson, R., & Clow, D. (2015). Examining engagement: Analysing learner subpopulations in massive open online courses (MOOCs). In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 51-58). ACM. doi: 10.1145/2723576.2723606
- Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. doi: 10.3102/00346543074001059
- Gagne, R. M., Wager, W. W., Golas, K. C., Keller, J. M., & Russell, J. D. (2005). Principles of instructional design. Performance Improvement, 44(2), 44-46.
- Garson, G. D. (2014). Cluster analysis. Asheboro, NC: Statistical Associates Publishers.
- Gartner. (2019). Hype cycle research methodology (Blog post). Retrieved from http://www.gartner.com/en/research/methodologies/gartner-hype-cycle
- Hill, P. (2013, March 6). Emerging Patterns in MOOCs: A graphical view. [Blog post]. Retrieved from http://mfeldstein.com/emerging_student_patterns_in_moocs_graphical_view/
- Ho, A. D., Reich, J., Nesterko, S., Seaton, D. T., Mullaney, T., Waldo, J., & Chuang, I. (2014). HarvardX and MITx: The first year of open online courses. HarvardX and MITx Working Paper No. 1. doi: 10.2139/ssrn.2381263
- Jiang, S., Williams, A., Schenke, K., Warschauer, M., & O' Dowd, D. (2014, July). Predicting MOOC performance with week 1 behavior. In Proceedings of the 7th International Conference on Educational Data Mining 2014. Retrieved from http://educationaldatamining.org/conferences/index.php/EDM/2014/paper/viewFile/1444/1410/
- Jordan, K. (2013). A research summary on MOOC completion rates [Blog post]. Retrieved from http://edlab.tc.columbia.edu/index.php?q=node/899
- Jordan, K. (2014). Initial trends in enrolment and completion of Massive Open Online Courses. The International Review of Research in Open and Distance Learning, 15(1), 133-160. doi: 10.19173/irrodl.v15i1.1651
- Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of participant behavior in a massive open online course. The International Review of Research in Open and Distributed Learning, 18(6). 1-18. doi: 10.19173/irrodl.v18i6.3087
- Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114-132. doi: 10.1007/s12528-016-9126-9
- Kizilcec, R. F., Piech, C., & Schneider, E. (2013, April). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 170-179). doi: 10.1145/2460296.2460330
- Kovanović, V., Joksimović, S., Gašević, D., Owers, J., Scott, A. M., & Woodgate, A. (2016). Profiling MOOC course returners: How does student behavior change between two course enrollments? In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (pp. 269-272).
- Linnenbrink, E. A., & Pintrich, P. R. (2003). The role of self-efficacy beliefs in student engagement and learning in the classroom, Reading and Writing Quarterly: Overcoming Learning Difficulties, 19(2), 119-137. doi: 110.1080/10573560308223
- Milligan, C. (2012). Change 11 SRL-MOOC study: Initial findings [Blog post]. Retrieved from http://worklearn.wordpress.com/2012/12/19/change-11-srl-mooc-study-initial-findings/
- Molinari, G., Poellhuber, B., Heutte, J., Lavoué, E., Widmer, D. S., & Caron, P. A. (2016). L'engagement et la persistance dans les dispositifs de formation en ligne: regards croisés. Distances et médiations des savoirs. Distance and Mediation of Knowledge, 13.
- Peters, D. (2018, Feb 22). MOOCs are not dead, but evolving. [Blog post]. Retrieved from https://www.universityaffairs.ca/news/news-article/moocs-not-dead-evolving/
- Pintrich, P. R. (2003). Motivation and classroom learning. In Reynolds, W. M. and Miller, G. E. (Eds), Handbook of Psychology, vol 7: Educational Psychology, Hoboken, NJ: John Wiley & Sons, Inc., 2003, pp. 103-122. doi: 10.1002/0471264385.wei0706
- Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ), Ann Arbor, MI: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning. doi: 10.1080/10573560308223
- Poellhuber, B., Roy, N., & Levasseur, C. (2017). MOOC: A vector of accessibility for higher education in developing countries? In AERA Proceedings, San Antonio, TX.Shah, D. (2018, Nov. 12). By The Numbers: MOOCs in 2018. In Class-Central. Retrieved from https://www.class-central.com/report/
- Roy, N., Poellhuber, B., & Bouchoucha, I. (2015). Différences régionales à travers le monde des étudiants inscrits dans un MOOC francophone: portrait d'un cas issu de l'initiative EDUlib [Worldwide regional differences among students enrolled in a francophone MOOC: A portrait of a case under the EDUlib initiative]. Revue internationale des technologies en pédagogie universitaire/International Journal of Technologies in Higher Education, 12(1 2), 75-92.
- Simpson, O. (2003). Student retention in online, open and distance learning. London: Routledge. doi: 10.4324/9780203416563
- Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. New York, USA: Allyn & Bacon/Pearson Education.
- Tseng, S. F., Tsao, Y. W., Yu, L. C., Chan, C. L., & Lai, K. R. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning, 11(8). doi: 10.1186/s41039-016-0033-5
- Tufféry, S. (2011). Cluster analysis. In S. Tufféry (Ed.). Data mining and statistics for decision making (pp. 685). Chichester: Wiley. doi: 10.1002/9780470979174.ch9
- Viau, R. (2003). La motivation en contexte scolaire [Motivation in school context]. Louvain-la-neuve, Belgium: De Boeck Supérieur.
- White, B. (2014, August). Is "MOOC-Mania" over? In International Conference on Hybrid Learning and Continuing Education (pp. 11-15). Springer, Cham.
- Whitmer, J., Schiorring, E., James, P., & Miley, S. (2015). How students engage with a remedial English writing MOOC: A case study in learning analytics with big data. Retrieved from https://library.educause.edu/~/media/files/library/2015/3/elib1502-pdf.pdf