Abstracts
Abstract
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.
Keywords:
- MOOC,
- learner behavioral engagement,
- tailored intervention,
- latent class analysis
Download the article in PDF to read it.
Download
Appendices
Bibliography
- 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