Abstract
Some researchers have questioned the use of dropout metrics to assess the quality of MOOCs. The main reason for this doubt is that participants register for online courses with different intentions. Therefore, it is proposed to use a learner-centred approach and to study the learner intention-fulfilment. Researchers studied the effect of result-oriented intention on MOOC completion. However, studies in traditional educational settings have shown that a more significant predictor of behavior is not result-oriented intention, but action-oriented intention. In our paper, we expand the study of the intention-to-behavior relation in MOOCs and identify the role of strong positive action-oriented intentions in MOOCs. As strong positive action-oriented intentions, we identified two types of intention: intention to watch all the video lectures and intention to complete all the tasks. The research database consists of trace data and survey data collected among participants of 5 MOOCs launched in the spring semester of 2017. Survey data recorded one result-oriented intention (to earn a certificate) and the two strong positive action-oriented intentions. The results showed, first, a significant relationship between strong positive action-oriented intentions and behavior in MOOCs. Secondly, we found that the intention to watch lectures and to complete tasks are conceptually different intentions: the intention to watch lectures does not play a significant role in course completion compared to the intention to complete all the tasks. Thirdly, we found that the strong positive action-oriented intention to complete all the tasks is a more powerful predictor of course completion than the result-oriented intention. These results can be used to adjust interventions that are embedded in the courses to increase their effectiveness.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The dataset used during the current study is available from the corresponding author on reasonable request.
Code Availability
The code used during the current study is available from the corresponding author on the reasonable request.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Bache, S. M., & Wickham, H. (2014). magrittr: A Forward-Pipe Operator for R. R package version 1.5. Vienna, Austria: The R Foundation. Retrieved from https://CRAN. R-project. org/package= magrittr. Retrieved from: https://cran.r-project.org/web/packages/magrittr/index.html
Bagozzi, R. P., & Yi, Y. (1989). The degree of intention formation as a moderator of the attitude-behavior relationship. Social Psychology Quarterly. https://doi.org/10.2307/2786991
Brooker, A., Corrin, L., De Barba, P., Lodge, J., & Kennedy, G. (2018). A tale of two MOOCs: How student motivation and participation predict learning outcomes in different MOOCs. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.3237
DeBoer, J., Ho, A. D., Stump, G. S., & Breslow, L. (2014). Changing “course” reconceptualizing educational variables for massive open online courses. Educational Researcher, 43(2), 74–84. https://doi.org/10.3102/0013189X14523038
Egloffstein, M., & Schwerer, F. (2019). Participation and achievement in enterprise MOOCs for professional development: Initial findings from the openSAP University. Learning technologies for transforming large-scale teaching, learning, and assessment (pp. 91–103). Cham: Springer.
Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review (No. w23744). National Bureau of Economic Research. Retrieved from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3031695
Fox, J., Weisberg, S., Adler, D., Bates, D., Baud-Bovy, G., Ellison, S., ... & Heiberger, R. (2012). Package ‘car’. Vienna: R Foundation for Statistical Computing. Retrieved from: https://r-forge.r-project.org/projects/car/
Gohel, D. Flextable: Functions for Tabular Reporting, 2018. URL https://CRAN. R-project. org/package= flextable. R package version 0.4, 4, 3. Retrieved from: https://davidgohel.github.io/flextable/
Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. European review of social psychology, 4(1), 141–185.
Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493–503. https://doi.org/10.1037/0003-066X.54.7.493
Handoko, E., Gronseth, S. L., McNeil, S. G., Bonk, C. J., & Robin, B. R. (2019). Goal setting and MOOC completion: A study on the role of self-regulated learning in student performance in massive open online courses. International Review of Research in Open and Distributed Learning, 20(3), 39–58. https://doi.org/10.19173/irrodl.v20i4.4270
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. https://doi.org/10.1080/01587919.2017.1369006
Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/j.compedu.2019.103724
Ho, A., Chuang, I., Reich, J., Coleman, C., Whitehill, J., Northcutt, C., & Petersen, R. (2015). HarvardX and MITx: Two years of open online courses fall 2012 summer 2014. Doi: https://doi.org/10.2139/ssrn.2586847
Huin, L., Bergheaud, Y., Caron, P. A., Codina, A., & Disson, E. (2016). Measuring completion and dropout in MOOCs: A learner-centered model. In Khalil, M., Ebner, M., Kopp, M., Lorenz, A., & Kalz, M. (Eds.). Proceedings of the European MOOC Stakeholder Summit, (pp. 55–68).
Kizilcec, R. F., & Cohen, G. L. (2017). Eight-minute self-regulation intervention raises educational attainment at scale in individualist but not collectivist cultures. Proceedings of the National Academy of Sciences, 114(17), 4348–4353. https://doi.org/10.1073/pnas.1611898114
Kizilcec, R. F., & Halawa, S. (2015). Attrition and achievement gaps in online learning. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 57–66). Doi: https://doi.org/10.1145/2724660.2724680
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 (TOCHI), 22(2), 1–24. https://doi.org/10.1145/2699735
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. https://doi.org/10.1016/j.compedu.2016.10.001
Lamb, A., Smilack, J., Ho, A., & Reich, J. (2015). Addressing common analytic challenges to randomized experiments in MOOCs: Attrition and zero-inflation. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 21–30). Doi: https://doi.org/10.1145/2724660.2724669
Lüdecke, D. (2017). Package ‘esc’. Retrieved from: https://github.com/strengejacke/esc
Lüdecke, D. (2018). sjPlot: Data visualization for statistics in social science. R package version, 2(1). Retrieved from: https://cran.r-project.org/package=sjPlot
Maya-Jariego, I., Holgado, D., González-Tinoco, E., Castaño-Muñoz, J., & Punie, Y. (2020). Typology of motivation and learning intentions of users in MOOCs: The MOOCKNOWLEDGE study. Educational Technology Research and Development, 68(1), 203–224. https://doi.org/10.1007/s11423-019-09682-3
Rabin, E., Kalman, Y. M., & Kalz, M. (2019). An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. International Journal of Educational Technology in Higher Education, 16(1), 1–20. https://doi.org/10.1186/s41239-019-0144-3
Reich, J. (2014). MOOC completion and retention in the context of student intent. EDUCAUSE Review Online, 8. Retrieved from: https://er.educause.edu/articles/2014/12/mooc-completion-and-retention-in-the-context-of-student-intent
Renz, J., Schwerer, F., & Meinel, C. (2016). openSAP: Evaluating xMOOC usage and challenges for scalable and open enterprise education. International Journal of Advanced Corporate Learning, 9, 34–39.
Rieber, L. P. (2017). Participation patterns in a massive open online course (MOOC) about statistics. British Journal of Educational Technology, 48(6), 1295–1304. https://doi.org/10.1111/bjet.12504
Robinson, C., Yeomans, M., Reich, J., Hulleman, C., & Gehlbach, H. (2016). Forecasting student achievement in MOOCs with natural language processing. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 383–387). Doi: https://doi.org/10.1145/2883851.2883932
Rohloff, T., & Meinel, C. (2018). Towards personalized learning objectives in MOOCs. European Conference on Technology Enhanced Learning (pp. 202–215). Cham: Springer.
Sheeran, P. (2002). Intention—behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12(1), 1–36. https://doi.org/10.1080/14792772143000003
Sheeran, P., & Orbell, S. (1999). Implementation intentions and repeated behaviour: Augmenting the predictive validity of the theory of planned behaviour. European Journal of Social Psychology, 29(2–3), 349–369. https://doi.org/10.1002/(SICI)1099-0992(199903/05)29:2/3%3c349::AID-EJSP931%3e3.0.CO;2-Y
Sheeran, P., Orbell, S., & Trafimow, D. (1999). Does the temporal stability of behavioral intentions moderate intention-behavior and past behavior-future behavior relations? Personality and Social Psychology Bulletin, 25(6), 724–734. https://doi.org/10.1177/0146167299025006007
Verplanken, B., Aarts, H., Van Knippenberg, A. D., & Moonen, A. (1998). Habit versus planned behaviour: A field experiment. British Journal of Social Psychology, 37(1), 111–128. https://doi.org/10.1111/j.2044-8309.1998.tb01160.x
Walji, S., Deacon, A., Small, J., & Czerniewicz, L. (2016). Learning through engagement: MOOCs as an emergent form of provision. Distance Education, 37(2), 208–223. https://doi.org/10.1080/01587919.2016.1184400
Wang, Y., & Baker, R. (2018). Grit and intention: Why do learners complete MOOCs? The International Review of Research in Open and Distributed Learning. https://doi.org/10.19173/irrodl.v19i3.3393
Warnes, G. R., Bolker, B., Lumley, T.,Johnson, R. C. (2018). Package ‘gmodels’. Retrieved from: http://mirrors.ucr.ac.cr/CRAN/web/packages/gmodels/gmodels.pdf
Wickham, H. (2016). rvest: Easily harvest (scrape) web pages. R package version 0.3, 2. Retrieved from: https://CRAN.R-project.org/package=rvest
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., ... & Kuhn, M. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 2019. Retrieved from: https://joss.theoj.org/papers/https://doi.org/10.21105/joss.01686
Zhu, H. (2018). KableExtra: Construct complex table with ’kable’ and pipe syntax. Retrieved from: https://cran.r-project.org/web/packages/kableExtra/index.html
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The author declares that there are no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1
Rights and permissions
About this article
Cite this article
Semenova, T. Not Only the Intention to Complete: The Role of Action-Oriented Intentions in MOOC Completion. Tech Know Learn 27, 707–719 (2022). https://doi.org/10.1007/s10758-021-09534-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10758-021-09534-1