  We study student behavior and performance in two Massive Open Online Courses
(MOOCs). In doing so, we present two frameworks by which video-watching
clickstreams can be represented: one based on the sequence of events created,
and another on the sequence of positions visited. With the event-based
framework, we extract recurring subsequences of student behavior, which contain
fundamental characteris- tics such as reflecting (i.e., repeatedly playing and
pausing) and revising (i.e., plays and skip backs). We find that some of these
behaviors are significantly associated with whether a user will be Correct on
First Attempt (CFA) or not in answering quiz questions. With the position-based
framework, we then devise models for performance. In evaluating these through
CFA prediction, we find that three of them can substantially improve prediction
quality in terms of accuracy and F1, which underlines the ability to relate
behavior to performance. Since our prediction considers videos individually,
these benefits also suggest that our models are useful in situations where
there is limited training data, e.g., for early detection or in short courses.
