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The high level of attrition and low rate of certification in Massive OpenOnline Courses (MOOCs) has prompted a great deal of research. Prior researchershave focused on predicting dropout based upon behavioral features such asstudent confusion, click-stream patterns, and social interactions. However, fewstudies have focused on combining student logs with forum data.  In this work, we use data from two different offerings of the same MOOC. Weconduct a survival analysis to identify likely dropouts. We then examine twoclasses of features, social and behavioral, and apply a combination of modelingand feature-selection methods to identify the most relevant features to predictboth dropout and certification. We examine the utility of three different modeltypes and we consider the impact of different definitions of dropout on thepredictors. Finally, we assess the reliability of the models over time byevaluating whether or not models from week 1 can predict dropout in week 2, andso on. The outcomes of this study will help instructors identify studentslikely to fail or dropout as soon as the first two weeks and provide them withmore support.

Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features