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。

利用话语信号进行教师干预的可靠性预测

Using Discourse Signals for Robust Instructor Intervention Prediction

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 forum's 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
problem's sensitivity to instructors' intervention preferences. We conclude
this paper with our take on the future research issues in intervention.

本论文研究 MOOC 授课中关于其有限带宽下教师何时干预讨论，设计了一个二元分类器，在分类过程中结合论坛类型信息，通过人类评判验证表明该问题的难度，且结果受教师干预偏好影响较大，并指出了未来相关研究问题。