2014 Poster Sessions : Understanding Attrition in MOOCs

Student Name : Sherif Halawa
Advisor : John Mitchell
Research Areas: Theory
Massive open online courses (MOOCs) offer educational data at an unprecedented scale. The wealth of data collected for each learner and the large number of learners allow us to build models that predict learner persistence and reasons for disengagement. This poster summarizes our work on modeling affect and ability factors that control learner persistence and performance, and how such models can be used to drive intervention design in online courses.

Sherif Halawa is a PhD student in the School of Engineering, and a member of the Lytics lab co-directed by Pr. John Mitchell. Sherif's interest is in using educational data from online courses and machine learning to drive interventions that increase student persistence and performance. Sherif participated in developing a number of learning platforms at Stanford including Class2Go, CourseWare, and the ClassX interactive video streaming system.