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Due to time constraints, course instructors often need to selectively participate in student discussion threads, due to their limited bandwidth and lopsided student--instructor ratio on online forums. We propose the first deep learning models for this binary prediction problem. We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention. Such models also allow themselves to be tuned to instructors preference to intervene early or late. Our three proposed attentive model variants to infer the latent context improve over the state-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 what aspects of a discussion post propagate through the discussion thread that prompts instructor intervention.
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 fro
Social learning, i.e., students learning from each other through social interactions, has the potential to significantly scale up instruction in online education. In many cases, such as in massive open online courses (MOOCs), social learning is facil
Many context-sensitive data flow analyses can be formulated as a variant of the all-pairs Dyck-CFL reachability problem, which, in general, is of sub-cubic time complexity and quadratic space complexity. Such high complexity significantly limits the
Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus it may not capture import
Underground online forums are platforms that enable trades of illicit services and stolen goods. Carding forums, in particular, are known for being focused on trading financial information. However, little evidence exists about the sellers that are p