Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates.
Anthony, G., & Ord, K. (2008). Change-of-career secondary teachers: Motivations, expectations and intentions. Asia-Pacific Journal of Teacher Education, 36(4), 359–376. https://doi.org/10.1080/13598660802395865
Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: Examining aspects of language and social engagement. Computers & Education, 94, 49–60. https://doi.org/10.1016/j.compedu.2015.11.010
Breslow, L. B., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research & Practice in Assessment, 8, 13–25. http://www.rpajournal.com/studying-learning-in-the-worldwide-classroom-research-into-edxs-first-mooc/
Brookhart, S. M., & Freeman, D. J. (1992). Characteristics of entering teacher candidates. Review of Educational Research, 62(1), 37–60. https://doi.org/10.2307/1170715
Brophy, J. E. (2013). Motivating students to learn. Routledge.
Chaw, L. Y., & Tang, C. M. (2019). Driving high inclination to complete massive open online courses (MOOCs): Motivation and engagement factors for learners. The Electronic Journal of e-Learning, 17(2), 118–130. https://eric.ed.gov/?id=EJ1221333
Daza, V., Makriyannis, N., & Rovira Riera, C. (2013). MOOC attack: Closing the gap between pre-university and university mathematics. Open Learning: The Journal of Open, Distance and e-Learning, 28(3), 227–238. https://doi.org/10.1080/02680513.2013.872558
Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic rewards and intrinsic motivation in education: Reconsidered once again. Review of Educational Research, 71(1), 1–27. https://doi.org/10.3102/00346543071001001
Douglas, K. A, Merzdorf, H. E., Hicks, N. M., Sarfraz, M. I., & Bermel, P. (2020). Challenges to assessing motivation in MOOC learners: An application of an argument-based approach. Computers & Education, 150. https://doi.org/10.1016/j.compedu.2020.103829
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Evans, B. J., Baker, R. B., & Dee, T. S. (2016). Persistence patterns in massive open online courses (MOOCs). The Journal of Higher Education, 87(2), 206–242. https://doi.org/10.1080/00221546.2016.11777400
Feklistova, L., Lepp, M., & Luik, P. (2019). Completers' engagement clusters in programming MOOC: The case of Estonia. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), ICERI 2019 Proceedings: 12th annual international conference of education, research and innovation (pp. 1119−1126). IATED.
Gallén, R. C., & Caro, E. T. (2017). An exploratory analysis of why a person enrolls in a massive open online course within MOOCKnowledge data collection. IEEE Global Engineering Education Conference (pp. 1600–1605). https://doi.org/10.1109/EDUCON.2017.7943062
Green, J., Liem, G. D., Martin, A. J., Colmar, S., Marsh, H. W., & McInerney, D. (2012). Academic motivation, self-concept, engagement, and performance in high school: Key processes from a longitudinal perspective. Journal of Adolescence, 35, 1111–1122. https://doi.org/10.1016/j.adolescence.2012.02.016
Grover, S., Franz, P., Schneider, E., & Pea, R. (2013). The MOOC as distributed intelligence: Dimensions of a framework & evaluation of MOOCs. 10th International Conference on Computer Supported Collaborative Learning. https://www.researchgate.net/publication/275771115_The_MOOC_as_Distributed_Intelligence_Dimensions_of_a_Framework_Evaluation_of_MOOCs
Huitt, W. (2011). Motivation to learn: An overview. Educational Psychology Interactive. Valdosta State University. http://www.edpsycinteractive.org/topics/motivation/motivate.html
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of participant behavior in a massive open online course. International Review of Research in Open and Distributed Learning, 18(6), 1–18. https://doi.org/10.19173/irrodl.v18i6.3087
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 (Eds.), Proceedings of the third international conference on learning analytics and knowledge (pp. 170–179). https://doi.org/10.1145/2460296.2460330
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 Interactions, 22(2). https://doi.org/10.1145/2699735
Lepp, M., Luik, P., Palts, T., Papli, K., Suviste, R., Säde, M., & Tõnisson, E. (2017). MOOC in programming: A success story. In L. Campbel & R. Hartshorne (Eds.), Proceedings of the International Conference on e-Learning (ICEL; pp. 138−147). Academic Publishing International (API).
Luik, P., Lepp, M., Palts, T., Säde, M., Suviste, R., Tõnisson, E., & Gaiduk, M. (2018). Completion of Programming MOOC or Dropping out: Are There any Differences in Motivation? In K. Ntalianis, A. Andreatos, and C. Sgouropoulou (Eds.), Proceedings of the 17th European Conference on e-Learning ECEL 2018 (pp. 329–337). Academic Conferences and Publishing International Limited Reading.
Luik, P., Suviste, R., Lepp, M., Palts, T., Tõnisson, E., Säde, M., & Papli, K. (2019). What motivates enrolment in programming MOOCs? British Journal of Educational Technology, 50(1), 153−165. https://doi.org/10.1111/bjet.12600
Macdonald, P., & Ahern, T. C. (2015). Exploring the instructional value and worth of a MOOC. Journal of Educational Computing Research, 52(4), 496–513. https://doi.org/10.1177/0735633115571927
Maya-Jariego, I., Holgado, D., González-Tinoco, E., Castaño-Muñoz, J., & Punie, Y. (2019). Typology of motivation and learning intentions of users in MOOCs: The MOOCKNOWLEDGE study. Educational Technology Research and Development, 68, 203–224. https://doi.org/10.1007/s11423-019-09682-3
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). https://doi.org/10.19173/irrodl.v18i2.3033
Orhan Özen, S. (2017). The effect of motivation on student achievement. In E. Karadag (Ed.), The factors effecting student achievement (pp. 35–56). https://doi.org/10.1007/978-3-319-56083-0_3
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667–686. https://doi.org/10.1037/0022-06126.96.36.1997
Reparaz, C., Aznárez-Sanado, M., & Mendoza, G. (2020). Self-regulation of learning and MOOC retention. Computers in Human Behavior, 111. https://doi.org/10.1016/j.chb.2020.106423
Tomšik, R. (2016). Choosing teaching as a career: Importance of the type of motivation in career choices. TEM Journal, 5(3), 396–400. https://doi.org/10.18421/TEM53-21
Tseng, S., Tsao, Y., Yu, L., Chan, C., & Lai, K. R. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning, 11(8), 1–11. https://doi.org/10.1186/s41039-016-0033-5
Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs? Journal Of Online Learning & Teaching, 11(1), 17–30. https://jolt.merlot.org/vol11no1/Wang_0315.pdf
Watted, A., & Barak, M. (2018). Motivating factors of MOOC completers: Comparing between university-affiliated students and general participants. The Internet and Higher Education, 37, 11–20. https://doi.org/10.1016/j.iheduc.2017.12.001
White, S., Davis, H., Dickens, K., Leon, M., & Sanchez-Vera, M. M. (2015). MOOCs: What motivates the producers and participants? In S. Zvacek, M. Restivo, J. Uhomoibhi, & M. Helfert (Eds.), Computer supported education: CSEDU 2014 (pp. 99–114). https://doi.org/10.1007/978-3-319-25768-6_7
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015
Wigfield, A., Eccles, J. S., & Möller, J. (2020). How dimensional comparisons help to understand linkages between expectancies, values, performance, and choice. Educational Psychology Review, 32(3), 657–680. https://doi.org/10.1007/s10648-020-09524-2
Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding student motivation, behaviors and perceptions in MOOCs. Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 1882–1895). https://doi.org/10.1145/2675133.2675217
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.