Different underlying motivations and abilities predict student versus teacher persistence in an online course
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.
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