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

Different underlying motivations and abilities predict student versus teacher persistence in an... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Educational Technology Research and Development Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Association for Educational Communications and Technology
Subject
Education; Educational Technology; Learning and Instruction
ISSN
1042-1629
eISSN
1556-6501
D.O.I.
10.1007/s11423-017-9528-z
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Educational Technology Research and DevelopmentSpringer Journals

Published: May 2, 2017

References

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