Heterogeneity of Learners’ Behavioral Patterns of Watching Videos and Completing Assessments in Massive Open Online Courses (MOOCs): A Latent Class Analysis
Massive open online courses (MOOCs) have been touted as an effective way to make higher education accessible for free or for only a small fee, thus addressing the problem of unequal access and providing new opportunities to young people in middle and low income groups. However, many critiques of MOOCs have indicated that low completion rates are a major concern. Using a latent class analysis (LCA), a more advanced methodology to identify latent subgroups, this study examined the heterogeneity of learners’ behavioral patterns in a MOOC, categorized them into distinctive subgroups, and ultimately determined the optimal number of latent subgroups in a MOOC. The five subgroups identified in this study were: completing (6.6%); disengaging (4.8%); auditing (4.6%); sampling (21.1%); and enrolling (62.8%). Results indicated this was the optimal number of subgroups. Given the characteristics of the three at-risk subgroups (disengaging, sampling, and enrolling), tailored instructional strategies and interventions to improve behavioral engagement are discussed.
Anderson, T. (2013, March). Promise and/or peril: MOOCs and open and distance education. Commonwealth of Learning, 3, 1-9. Retrieved from http://www.ethicalforum.be/sites/default/files/MOOCsPromisePeril.pdf
Bergner, Y., Kerr, D., & Pritchard, D. E. (2015). Methodological challenges in the analysis of MOOC data for exploring the relationship between discussion forum views and learning outcomes. In J. G. Boticario, & O. C. Santos (Eds.), Proceedings of the Eighth International Conference on Educational Data Mining (pp. 234–241). Retrieved from https://www.educationaldatamining.org/EDM2015/proceedings/full234-241.pdf
Bote-Lorenzo, M. L., & Gómez-Sánchez, E. (2017). Predicting the decrease of engagement indicators in a MOOC. In A. Wise, P. H. Winne, & G. Lynch (Chairs), Proceedings of the Seventh International Conference on Learning Analytics and Knowledge (pp. 143–147). doi: 10.1145/3027385.3027387
Cassidy, D., Breakwell, N., & Bailey, J. (2014). Keeping them clicking: Promoting student engagement in MOOC design. The All Ireland Journal of Teaching and Learning in Higher Education, 6(2), 1–15. Retrieved from http://www.icep.ie/wp-content/uploads/2013/12/CassidyBreakwellBailey.pdf
Chen, Q., Luo, W., Palardy, G. J., Glaman, R., & McEnturff, A. (2017). The efficacy of common fit indices for enumerating classes in growth mixture models when nested data structure is ignored: A Monte Carlo study. SAGE Open, 7(1), 1–19. doi: 10.1177/2158244017700459
Chen, Z., Alcorn, B., Christensen, G., Eriksson, N., Koller, D., & Emanuel, E. J. (2015, September 22). Who’s benefiting from MOOCs, and why. Harvard Business Review. Retrieved from https://hbr.org/2015/09/whos-benefiting-from-moocs-and-why
College Board (2016). Trends in college pricing 2016. Retrieved from https://trends.collegeboard.org/sites/default/files/2016-trends-college-pricing-web_1.pdf
Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (Grit–S). Journal of Personality Assessment, 91(2), 166-174. doi: 10.1080/00223890802634290
Ferguson, R., & Clow, D. (2015). Examining engagement: Analysing learner subpopulations in massive open online courses (MOOCs). In J. Baron, G. Lynch, & N. Marziarz (Chairs), Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 51-68). doi: 10.1145/2723576.2723606
Ferguson, R., Clow, D., Beale, R., Cooper, A. J., Morris, N., Bayne, S., & Woodgate, A. (2015). Moving through MOOCS: Pedagogy, learning design and patterns of engagement. In G. Conole, T. Klobučar, C. Rensing, J. Konert, & E. Lavoué (Eds.), lecture notes in computer science: Vol. 9307. Design for teaching and learning in a networked world (pp. 70-84). Springer, Cham. doi: 10.1007/978-3-319-24258-3_6
Hamori, M. (2019, May 21). MOOCs at work: What induces employer support for them? The International Journal of Human Resource Management. doi: 10.1080/09585192.2019.1616593
Henderikx, M. A., Kreijns, K., & Kalz, M. (2017). Refining success and dropout in massive open online courses based on the intention-behavior gap. Distance Education, 38(3), 353-368. doi: 10.1080/01587919.2017.1369006
Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1), 133-160. doi: 10.19173/irrodl.v15i1.1651
Jung, Y., & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCs). Computers & Education, 122, 9-22. doi: 10.1016/j.compedu.2018.02.013
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18-33. doi: 10.1016/j.compedu.2016.10.001
Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 170-179). doi: 10.1145/2460296.2460330
Koller, D., Ng, A., Do, C., & Chen, Z. (2013). Retention and intention in massive open online courses. Educause Review, 48(3), 62-63. Retrieved from https://er.educause.edu/articles/2013/6/retention-and-intention-in-massive-open-online-courses
Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 671-694. doi: 10.1080/10705510701575602
Li, Q., & Baker, R. (2016). Understanding engagement in MOOCs. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the Ninth International Conference on Educational Data Mining (pp. 605-606). Retrieved from https://www.educationaldatamining.org/EDM2016/proceedings/edm2016_proceedings.pdf
Magidson, J., & Vermunt, J. (2002). Latent class models for clustering: A comparison with k-means. Canadian Journal of Marketing Research, 20(1), 37-44. Retrieved from https://pdfs.semanticscholar.org/6add/265688cde63766bed6b920c4546e7c11ab99.pdf
Moore, R. L., & Wang, C. (2020, June 12). Influence of learner motivation dispositions on MOOC completion. Journal of Computing in Higher Education. doi: 10.1007/s12528-020-09258-8
Phan, T., McNeil, S. G., & Robin, B. R. (2016). Students’ patterns of engagement and course performance in a massive open online course. Computers & Education, 95, 36-44. doi: 10.1016/j.compedu.2015.11.015
Poquet, O., Dowell, N., Brooks, C., & Dawson, S. (2018). Are MOOC forums changing? In M. Hatala (Ed.), Proceedings of the Eighth International Conference on Learning Analytics and Knowledge (pp. 340-349). doi: 10.1145/3170358.3170416
Ramesh, A., Goldwasser, D., Huang, B., Daume III, H., & Getoor, L. (2014). Learning latent engagement patterns of students in online courses. In C. E. Brodley, & P. Stone (Eds.), Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (pp. 1272-1278). Retrieved from https://dl.acm.org/doi/10.5555/2893873.2894071
Reich, J. (2014, December 8). MOOC completion and retention in the context of student intent. Educause Review Online. Retrieved from https://er.educause.edu/articles/2014/12/mooc-completion-and-retention-in-the-context-of-student-intent
Waddoups, C. J. (2016). Did employers in the United States back away from skills training during the early 2000s? Industrial and Labor Relations Review, 69(2), 405-434. doi: 10.1177/0019793915619904
Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs? Journal of Online Learning and Teaching, 11(1), 17-30. Retrieved from https://jolt.merlot.org/vol11no1/Wang_0315.pdf
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.