Using Discourse Signals for Robust Instructor Intervention Prediction
This work addresses the problem of enhancing instructor support in online education, but it is incremental as it builds on existing methods with discourse features.
The paper tackled predicting instructor intervention in MOOC discussion forums by using automatically obtained discourse relations, which improved prediction performance compared to a state-of-the-art baseline across 14 MOOC offerings.
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.