Dropout Prediction in Massive Open Online Courses

• In Event: Poster Session 8
  In Poster Session: Motivation, Emotion, and Perception in Technology-Based Learning Environments

Abstract

Massive open online courses (MOOCs) have recently taken center stage in discussions surrounding online education, both in terms of their potential as well as their high dropout rates. This study designs a mechanism for the early and accurate identification of students at risk for dropping out. Focusing on struggling students who remain active in discussion forums and who are already more likely to finish a course, we propose a temporal modeling approach, one which prioritizes the at-risk students in order of their likelihood to drop out. In identifying only a small subset of at-risk students, we seek to provide systematic insight for instructors so they may better provide targeted support for those students most in need of intervention.

Authors

• Wanli Xing, University of Missouri - Columbia

• Yonghe Wu, East China Normal University