Connecting Learner Motivation to Learner Progress and Completion in Massive Open Online Courses | Relier la motivation de l’apprenant à ses progrès et à l’achèvement des cours en ligne ouverts à tous


  • Hedieh Najafi University of Toronto
  • Carol Rolheiser
  • Laurie Harrison
  • Will Heikoop



Mooc, self-efficacy, task-value, self-paced, online-learning


We examined how massive open online courses (MOOC) learners’ motivational factors, self-efficacy, and task-value related to their course progress and achievement, as informed by learners’ initial course completion intention. In three individual MOOCs, learners completed a pre-course survey to report their levels of task-value and self-efficacy and to indicate their intention to complete each course topic. Using clustering techniques, we identified two distinct groups of learners in the three MOOCs based on self-efficacy and task-value variables: higher-motivation group and lower-motivation group. The higher-motivation group achieved significantly higher grades in two of the MOOCs, and also adhered to their initial completion intention significantly more so than the lower-motivation group. We posit that MOOC completion research should consider learners’ topic-level interest as one success criterion. Further research can clarify perceived task-value in relation to learners’ existing knowledge, their learning goals, and learning outcomes related to the MOOC participation.

Nous avons examiné comment, dans les cours en ligne ouverts à tous (CLOT), les facteurs de motivation des apprenants, leur autoefficacité et leur valeur tâche étaient reliés à leurs progrès et à leur achèvement du cours selon l’intention initiale d’achèvement du cours des apprenants. Dans trois CLOT, les apprenants ont rempli un sondage avant le début du cours pour indiquer leur degré de valeur tâche et d’autoefficacité, ainsi que leur intention de compléter chaque sujet du cours. À l’aide de techniques agglomératives, nous avons cerné deux groupes distincts d’apprenants dans trois CLOT selon les variables de la valeur tâche et de l’autoefficacité : un groupe à plus forte motivation, et un groupe dont la motivation était plus faible. Le groupe dont la motivation était plus élevée a obtenu des notes considérablement plus élevées dans deux CLOT et, dans deux cours, ont adhéré à leur intention initiale d’achèvement considérablement plus que le groupe dont la motivation était moindre. Nous posons en principe que la recherche sur l’achèvement des CLOT devrait tenir compte de l’intérêt des apprenants sur le plan des sujets comme étant un critère de réussite. De plus amples recherches pourraient clarifier la valeur tâche perçue relativement aux connaissances préalables des apprenants, à leurs objectifs d’apprentissage et aux résultats d’apprentissage liés à la participation aux CLOT.


Artino, A. R. (2012). Academic self-efficacy: From educational theory to instructional practice. Perspectives on Medical Education, 1(2), 76–85.

Artino, A. R., & McCoach, D. B. (2008). Development and initial validation of the online learning value and self-efficacy scale. Journal of Educational Computing Research, 38(3), 279-303. doi: 10.2190/EC.38.3.c

Author (2014)

Cho, M.-H., & Heron, M. L. (2015). Self-regulated learning: the role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course. Distance Education, 36(1), 80–99.

Crossley, S., Paquette, L., Dascalu, M., McNamara, D. S., & Baker, R. S. (2016, April). Combining click-stream data with NLP tools to better understand MOOC completion. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 6-14). ACM. DOI: 10.1145/2883851.2883931

Csizér, K., & Jamieson, J. (2012). Cluster Analysis. In the Encyclopedia of Applied Linguistics. Blackwell Publishing Ltd.

de Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218–231.

Engle, D., Mankoff, C., & Carbrey, J. (2015). Coursera’s introductory human physiology course: Factors that characterize successful completion of a MOOC. The International Review Of Research In Open And Distributed Learning, 16(2). doi:

Gerlich, N. R., Mills, L. H., & Sollosy, M. (2009). An evaluation of predictors of achievement on selected outcomes in a self-paced online course. Journal of Research in Higher Education, 4, 1-14.

Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11(1), 19-42.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.

Ho, A. D., Chuang, I., Reich, J., Coleman, C. A., Whitehill, J., Northcutt, C. G., Williams, J. J., Hansen, J. D., Lopez, G., & Petersen, R. (2015). Harvardx and Mitx: Two years of open online courses fall 2012-summer 2014.

Kim, P. Y., Allbritton, D. W., Keri, R. A., Mieyal, J. J., & Wilson-Delfosse, A. L. (2015). Supplemental online pharmacology modules increase recognition and production memory in a hybrid problem-based learning (PBL) curriculum. Medical Science Educator, 1–9.

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.

Kizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners: Toward data-driven design with the OLEI scale. ACM Transactions on Computer-Human Interaction, 22(2), 6. DOI: 10.1145/2699735

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.

Milligan, G. W. (1996). Clustering validation: Results and implications for applied analyses. In P. Arabie, L. J. Hubert, and G. De Soto (Eds.) Clustering and Classification (pp. 341-375). River Edge, NJ: World Scientific.

Milligan, C., & Littlejohn, A. (2017). Why Study on a MOOC? The Motives of Students and Professionals. The International Review Of Research In Open And Distributed Learning, 18(2).

Norušis, M. (2012). IBM SPSS statistics 19 procedures companion. Upper Saddle River, NJ: Prentice Hall.

Pachlhofer, K. & Vander Putten, J. (2014). Undergraduate student motivation in a self-paced developmental mathematics course.

Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459-470.

Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., & Velegol, D. (2016). Understanding MOOC students: Motivations and behaviours indicative of MOOC completion. Journal of Computer Assisted Learning, 32(3), 202-217. DOI: 10.1111/jcal.12131

Robinson, C., Yeomans, M., Reich, J., Hulleman, C., & Gehlbach, H. (2016, April). Forecasting student achievement in MOOCs with natural language processing. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 383-387). ACM. DOI: 10.1145/2883851.2883932

Russell, M., Kleiman, G., Carey, R., & Douglas, J. (2009). Comparing self-paced and cohort-based online courses for teachers. Journal of Research on Technology in Education, 41(4), 443-466.

Saumure, K. & Given, L. (2008). Convenience sample. In L. M. Given (Ed.), The SAGE encyclopedia of qualitative research methods (pp. 125-125). Thousand Oaks, CA: SAGE Publications Ltd.

Southard, S., Meddaugh, J., & France-Harris, A. (2015). Can SPOC (Self-Paced Online Course) live long and prosper? A comparison study of a new species of online course delivery. Online Journal of Distance Learning Administration, 18(2).

von Eye, A., & Bogat, G. A. (2006). Person-oriented and variable-oriented research: Concepts, results, and development. Merrill-Palmer Quarterly, 52(3), 390-420.

Wilkowski, J., Deutsch, A., & Russell, D. M. (2014). Student skill and goal achievement in the mapping with Google MOOC. In Proceedings of the first ACM conference on Learning@ scale conference (pp. 3-10). DOI: 10.1145/2556325.2566240