Characterizing and evaluating student dropout through understanding student’s journey in a MicroMasters program

Abstract

The latest evolution of Massive Online Open Courseware (MOOC) includes MOOC based programs where students across the world can complete a series of MOOC courses to achieve microcredentials. However, the high dropout problem in MOOCs also persists in MOOC based programs, with the additional complexity of open enrollment.

This study provides a framework to characterize dropout in a MOOC based program using the following: understanding the students’ course taking behavior through a student’s course journey model, understanding students’ return behavior between courses using time-to-event analysis, and proposing a metric, time to dropout, that defines what a dropout is in the context of a MOOC based program.

We demonstrate that students’ course journey representations, in conjunction with the t_dropout metric, can define dropout in a MOOC based program and allow dropout analysis based on student’s course registration behaviors. We also demonstrate that course journey visualization can be used to understand student’s course journey for manual intervention, such as in an educational dashboard for identifying at-risk students.

Results show that students who are further along in their course sequence are more likely to return to take the next course and more likely to return faster to take the next course. Also, results suggest that students’ choice in course order may impact return behaviors, where taking courses in order has a higher return rate at most stages of the program.