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Due to time constraints, course instructors often need to selectivelyparticipate in student discussion threads, due to their limited bandwidth andlopsided student--instructor ratio on online forums. We propose the first deeplearning models for this binary prediction problem. We propose novel attentionbased models to infer the amount of latent context necessary to predictinstructor intervention. Such models also allow themselves to be tuned toinstructor's preference to intervene early or late. Our three proposedattentive model variants to infer the latent context improve over thestate-of-the-art by a significant, large margin of 11% in F1 and 10% in recall,on average. Further, introspection of attention help us better understand whataspects of a discussion post propagate through the discussion thread thatprompts instructor intervention.

When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums