  With large student enrollment, MOOC instructors face the unique challenge in
deciding when to intervene in forum discussions with their limited bandwidth.
We study this problem of instructor intervention. Using a large sample of forum
data culled from 61 courses, we design a binary classifier to predict whether
an instructor should intervene in a discussion thread or not. By incorporating
novel information about a forum's type into the classification process, we
improve significantly over the previous state-of-the-art.
  We show how difficult this decision problem is in the real world by
validating against indicative human judgment, and empirically show the
problem's sensitivity to instructors' intervention preferences. We conclude
this paper with our take on the future research issues in intervention.
