Computational thinking and assignment resubmission predict persistence in a computer science MOOC. - Chen, Chen; Sonnert, Gerhard; Sadler, Philip M.; Malan, David J. (Journal of Computer Assisted Learning, 2020)
Massive open online course (MOOC) studies have shown that precourse skills (such as precomputational thinking) and course engagement measures (such as making multiple submission attempts with assignments when the initial submission is incorrect) predict students' grade performance, yet little is known about whether these factors predict students' course retention. In applying survival analysis to a sample of more than 20,000 participants from one popular computer science MOOC, we found that students' precomputational thinking skills and their perseverance in assignment submission strongly predict their persistence in the MOOC. Moreover, we discovered that precomputational thinking skills, programming experience, and gender, which were previously considered to be constant predictors of students' retention, have effects that attenuate over the course milestones. This finding suggests that MOOC educators should take a growth perspective towards students' persistence: As students overcome the initial hurdles, their resilience grows stronger. Lay Description: What is already known about this topicMOOCs have very low retention rates, ranging between 1% to 15%.Background and engagement factors have been shown to predict MOOC dropout.Precomputational thinking style has been shown to predict computer science performance, but no research has investigated its effect on retention in either traditional classrooms or in MOOCs.Auto‐adaptive feedback is a quickly and widely adopted assistant feature in MOOCs, particularly in computer science, but its impact on retention is not well understood.What this paper addsStudents' precomputational thinking skills and their perseverance in assignment submission strongly predict their persistence in the MOOC.This study identified predictors of dropout whose effects did not diminish over time, including foreign status, gaming hours on the computer, self‐reported extrovert personality, and motivation.This study discovered predictors of dropout whose effects were seen early in the course, but diminished in importance over the milestone sequence. These predictors were gender, age, precomputational thinking skills, and prior computer science experience.Implications for practice and/or policyDesign a gradual learning curve for beginners so that they can adapt to the course mindset and framework.Assure students who are frustrated in the beginning of the course that once they adapt to the course mindset, their lack of background knowledge will not define their future experience and persistence.Assisted with the auto‐adaptive feedback features, multiple trials in the course assignment can be an engaging and rewarding experience.MOOCs should develop pedagogies that explicitly encourage student to use a trial‐and‐error strategy, testing different scenarios and experimenting with different solutions, to make the most use of auto‐adaptive feedback features.MOOC educators should not only take a growth perspective towards students' knowledge and skill development, but also a growth perspective towards students' persistence: As students overcome the initial hurdles, their resilience grows stronger.