Person:

Chen, Chen

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.

Participants’ engagement in massive online open courses (MOOCs) is highly irregular and self-directed. It is well known in the field of television media that substantial parts of the audience tend to drop out at major episodic, or seasonal, closures, which makes creating cliff-hangers a crucial strategy to retain viewers (Bakker, 1993; Cazani, 2016; Thompson, 2003). Could there be an analogous pattern in MOOCs—with an elevated probability of dropout at major chapter transitions? Applying disjoint survival analysis on a sample of 12,913 students in a popular astronomy MOOC that built participants’ cultural capital (hobbyist pursuits), we found a significant increase in dropout rates at chapter closures. Moreover, the latter the chapter closure was positioned in the course sequence, the higher the dropout rate became. We found this pattern replicated in a sample of 20,134 students in a popular computer science MOOC that introduced participants to programming.