Although the massive and open nature of
MOOCs necessitates more technology support for instructors,
existing prediction research has been barely capable of offering
real-world solutions. One critical case where MOOC
instructors could benefit from real-time technological support
is the design of collaborative activities, in particular group
formation. In this regard, this research work investigated the
use of in-situ learning technique to produce useful and
actionable information that could assist instructors in group
formation while the course continues. Focusing on a particular
MOOC context, a predictive model was created to compute the
probability that students would participate in group
discussions or not. Using these probability scores, actual group
behavior was also predicted for three cases: at least 2, 3 or 4
different students would post in group discussions. According
to the results, the model was able to accurately predict
individual student behavior as well as group behavior before
the actual collaborative activity had taken place, suggesting its
potential for real-time use. Future research involves the
exploration of other approaches for creating actionable
predictions and the application of the predictions in practice in
an ongoing MOOC.
