Different underlying motivations and abilities predict student versus teacher persistence in an online course
| Autor: | Christian D. Schunn, Jesse B. Flot, Ross Higashi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Persistence (psychology)
Goal orientation 05 social sciences Educational technology 050301 education Contrast (statistics) 050109 social psychology Education Subject matter Online course ComputingMilieux_COMPUTERSANDEDUCATION 0501 psychology and cognitive sciences Psychology Programmer 0503 education Social psychology Dropout (neural networks) Cognitive psychology |
| Zdroj: | Educational Technology Research and Development. 65:1471-1493 |
| ISSN: | 1556-6501 1042-1629 |
| DOI: | 10.1007/s11423-017-9528-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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