ERIC Number: EJ1162109
Record Type: Journal
Publication Date: 2017-Dec
Abstractor: As Provided
Different Underlying Motivations and Abilities Predict Student versus Teacher Persistence in an Online Course
Higashi, Ross M.; Schunn, Christian D.; Flot, Jesse B.
Educational Technology Research and Development, v65 n6 p1471-1493 Dec 2017
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.
Descriptors: Online Courses, Large Group Instruction, Dropout Rate, Academic Persistence, Teacher Persistence, Predictor Variables, Elementary Secondary Education, Summer Programs, Programming, Student Motivation, Teacher Motivation, Prior Learning, Interests, Goal Orientation, Identification (Psychology)
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Publication Type: Journal Articles; Reports - Research; Tests/Questionnaires
Education Level: Elementary Secondary Education
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: N/A