Different underlying motivations and abilities predict student versus teacher persistence in an online course
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Free online courses, including Massively Open Online Courses, have great potential to increase the inclusiveness of education, but suffer from very high course dropout rates. A study of 172 K-12 students and 114 K-12 teachers taking the same free, online, summertime programming course finds that student and teacher populations have different underlying motivational models that predict rates of persistence in the course despite having generally similar motivational levels. Student persistence is predicted by prior programming knowledge, intrinsic interest in the subject matter, and mastery approach goals. By contrast, teacher persistence is similarly predicted by intrinsic interest, but then also by self-identity as a programmer, performance approach goals, and negatively by performance avoidance goals. This sub-population discrepancy in predictive factors is novel, and may be reflective of differing environmental conditions or internal mechanisms between students and teachers. Future design of free choice learning environments can take these factors into account to increase rates of user persistence for different target user populations.
KeywordsMOOC Programming Students Teachers Motivation Persistence
Work on this project was funded by a Grant from the National Science Foundation. We also thank the operators of the online course we studied for ongoing technical support.
Compliance with ethical standards
Conflict of interest
Ross Higashi and Jesse Flot were involved in the development of the Robotics Academy curriculum used in the study. Jesse owned stock in Robomatter, Inc. at the time the data was collected, which is the worldwide commercial distributor of curriculum developed at the Robotics Academy. Christian Schunn declares that he has no conflict of interest.
- Breslow, L., 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(1), 13–25.Google Scholar
- Darling-Hammond, L., Holtzman, D. J., Gatlin, S. J., & Heilig, J. V. (2005). Does teacher preparation matter? Evidence about teacher certification, teach for America, and teacher effectiveness. Education policy analysis archives, 13(42), n42.Google Scholar
- DeBoer, J., Stump, G. S., Seaton, D., & Breslow, L. (2013). Diversity in MOOC students’ backgrounds and behaviors in relationship to performance in 6.002x. In the Proceedings of the Sixth Learning International Networks Consortium Conference.Google Scholar
- Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Springer.Google Scholar
- Dillahunt, T. R., Wang, B. Z., & Teasley, S. (2014). Democratizing higher education: Exploring MOOC use among those who cannot afford a formal education. The International Review of Research in Open and Distributed Learning, 15(5).Google Scholar
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.Google Scholar
- Gütl, C., Rizzardini, R. H., Chang, V., & Morales, M. (2014). Attrition in MOOC: Lessons learned from drop-out students. In Learning Technology for Education in Cloud. MOOC and Big Data (pp. 37–48). New York: Springer.Google Scholar
- Ho, A. D., Chuang, I., Reich, J., Coleman, C., Whitehill, J., Northcutt, C., Williams, J. J., Hansen, J., Lopez, G., & Petersen, R. (2015). HarvardX and MITx: Two years of open online courses (HarvardX Working Paper No. 10). doi: 10.2139/ssrn.2586847.
- Ho, A. D., Reich, J., Nesterko, S., Seaton, D. T., Mullaney, T., Waldo, J., & Chuang, I. (2014). HarvardX and MITx: The first year of open online courses (HarvardX and MITx Working Paper No. 1).Google Scholar
- Hulleman, C. S., & Senko, C. (2010). Up around the bend: Forecasts for achievement goal theory and research in 2020. In The decade ahead: Theoritical perspectives on motivation and achievement (Vol. 16, pp. 71–104). Emerald Group Publishing Limited.Google Scholar
- Nesterko, S. O., Seaton, D. T., Kashin, K., Han, Q., Reich, J., Waldo, J., Chuang I., & Ho, A. D. (2014). Education Levels Composition (HarvardX Insights).Google Scholar
- Nesterko, S. O., Seaton, D. T., Kashin, K., Han, Q., Reich, J., Waldo, J., Chuang I., & Ho, A. D. (2014). World Map of Education Composition (HarvardX Insights).Google Scholar
- Reich, J. (2014). MOOC completion and retention in the context of student intent. EDUCAUSE Review Online.Google Scholar
- Senko, C. (2016). Achievement goal theory. Handbook of Motivation at School, 75.Google Scholar
- Smith, M. (2016). Computer science for all | whitehouse.gov. Retrieved from https://obamawhitehouse.archives.gov/blog/2016/01/30/computer-science-all.
- The College Board. (2016). AP computer science principles. New York, NY. Retrieved from CS Principles website: https://secure-media.collegeboard.org/digitalServices/pdf/ap/ap-computer-science-principles-course-and-examdescription.pdf
- Wiebe, E., Williams, L., Yang, K., & Miller, C. (2003). Computer science attitude survey. Computer Science, 14(25), 0–86.Google Scholar
- Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Self-regulated learning and academic achievement: Theoretical perspectives. New York: Routledge.Google Scholar