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.

研究如何使用自动获取的话语关系来预测在线课程的讨论区中教师干预的情况，并且发现使用 PDTB 话语特征可以提高分类器的鲁棒性和多样性。