  In a Massive Open Online Course (MOOC), predictive models of student behavior
can support multiple aspects of learning, including instructor feedback and
timely intervention. Ongoing courses, when the student outcomes are yet
unknown, must rely on models trained from the historical data of previously
offered courses. It is possible to transfer models, but they often have poor
prediction performance. One reason is features that inadequately represent
predictive attributes common to both courses. We present an automated
transductive transfer learning approach that addresses this issue. It relies on
problem-agnostic, temporal organization of the MOOC clickstream data, where,
for each student, for multiple courses, a set of specific MOOC event types is
expressed for each time unit. It consists of two alternative transfer methods
based on representation learning with auto-encoders: a passive approach using
transductive principal component analysis and an active approach that uses a
correlation alignment loss term. With these methods, we investigate the
transferability of dropout prediction across similar and dissimilar MOOCs and
compare with known methods. Results show improved model transferability and
suggest that the methods are capable of automatically learning a feature
representation that expresses common predictive characteristics of MOOCs.
