2013 Poster Sessions : Tools for Predicting Drop-off in Massively Open Online Classes

Student Name : Justin Cheng
Advisor : Scott Klemmer
Research Areas: Graphics/HCI
This poster describes two diagnostic tools to predict students are at risk of dropping out from an online class. While thousands of students have been attracted to large online classes, keeping them motivated has been challenging. Experiments on a large, online HCI class suggest that the tools these paper introduces can help identify students who will not complete assignments, with an $F_1$ score of $0.46$ and $0.73$ three days before the assignment due date.

Justin Cheng is a PhD student in Computer Science. His research focuses on the design and analysis of large online collaborative systems and social networks.