  The high level of attrition and low rate of certification in Massive Open
Online Courses (MOOCs) has prompted a great deal of research. Prior researchers
have focused on predicting dropout based upon behavioral features such as
student confusion, click-stream patterns, and social interactions. However, few
studies have focused on combining student logs with forum data.
  In this work, we use data from two different offerings of the same MOOC. We
conduct a survival analysis to identify likely dropouts. We then examine two
classes of features, social and behavioral, and apply a combination of modeling
and feature-selection methods to identify the most relevant features to predict
both dropout and certification. We examine the utility of three different model
types and we consider the impact of different definitions of dropout on the
predictors. Finally, we assess the reliability of the models over time by
evaluating whether or not models from week 1 can predict dropout in week 2, and
so on. The outcomes of this study will help instructors identify students
likely to fail or dropout as soon as the first two weeks and provide them with
more support.
