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Register a new userWhen to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums
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Added by
Muthu Kumar Chandrasekaran
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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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.
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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 forums 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 problems sensitivity to instructors intervention preferences. We conclude this paper with our take on the future research issues in intervention.
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