  In online collaborative learning environments, students create content and
construct their own knowledge through complex interactions over time. To
facilitate effective social learning and inclusive participation in this
context, insights are needed into the correspondence between
student-contributed artifacts and their subsequent popularity among peers. In
this study, we represent student artifacts by their (a) contextual action logs
(b) textual content, and (c) set of instructor-specified features, and use
these representations to predict artifact popularity measures. Through a
mixture of predictive analysis and visual exploration, we find that the neural
embedding representation, learned from contextual action logs, has the
strongest predictions of popularity, ahead of instructor's knowledge, which
includes academic value and creativity ratings. Because this representation can
be learnt without extensive human labeling effort, it opens up possibilities
for shaping more inclusive student interactions on the fly in collaboration
with instructors and students alike.
