Massive Open Online Courses (MOOCs) have a high attrition rate: most students who register for a course do not complete it. By examining a student's history of actions during a course, we can predict whether or not they will drop out in the next week, facilitating interventions to improve retention. We compare predictions resulting from several modeling techniques and several features based on different student behaviors. Our best predictor uses a Hidden Markov Model (HMM) to model sequences of student actions over time, and encodes several continuous features into a single discrete observable state using a simple cross-product method. It yielded an ROC AUC (Receiver Operating Characteristic Area Under the Curve score) of 0.710, considerably better than a random predictor. We also use simpler HMM models to derive information about which student behaviors are most salient in determining student retention.
Title
Predicting Student Retention in Massive Open Online Courses using Hidden Markov Models
Published
2013-05-17
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2013-109
Type
Text
Extent
13 p
Archive
The Engineering Library
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