Modeling student knowledge while students are acquiring new concepts is a crucial stepping stone towards providing personalized automated feedback at scale. We believe that rich information about a student's learning is captured within her responses to open-ended problems with unbounded solution spaces, such as programming exercises. In addition, sequential snapshots of a student's progress while she is solving a single exercise can provide valuable insights into her learning behavior. Creating representations for a student's knowledge state is a challenging task, but with recent advances in machine learning, there are more promising techniques to learn representations for complex entities. In our work, we feed the embedded program submission sequence into a recurrent neural network and train it on two tasks of predicting the student's future performance. By training on these tasks, the model learns nuanced representations of a student's knowledge, exposes patterns about a student's learning behavior, and reliably predicts future student performance. Even more importantly, the model differentiates within a pool of poorly performing students and picks out students who have true knowledge gaps, giving teachers early warnings to provide assistance. [For the full proceedings, see ED596512.]