Litcius/Paper detail

Going over the cliff: MOOC dropout behavior at chapter transition

Chen Chen, Gerhard Sonnert, Philip M. Sadler, Dimitar Sasselov, Colin Fredericks, David J. Malan

2020Distance Education41 citationsDOI

Abstract

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.

Topics & Concepts

Dropout (neural networks)Sample (material)Disjoint setsMathematics educationDrop outComputer sciencePsychologyMultimediaMathematicsChemistryCombinatoricsDemographic economicsEconomicsMachine learningChromatographyOnline Learning and AnalyticsSocial Media and PoliticsImpact of Technology on Adolescents