Using Discourse Signals for Robust Instructor Intervention Prediction


  • Muthu Kumar Chandrasekaran National University of Singapore
  • Carrie Epp University of Pittsburgh
  • Min-Yen Kan National University of Singapore
  • Diane Litman University of Pittsburgh



MOOC discussion forum, MOOC, Instructor intervention, PDTB discourse relations


We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses.




How to Cite

Chandrasekaran, M. K., Epp, C., Kan, M.-Y., & Litman, D. (2017). Using Discourse Signals for Robust Instructor Intervention Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1).