Weakly Supervised Learning of Dialogue Structure in MOOC Forum Threads

In this paper we present a new method for understanding discussions between students in MOOC forums. In particular, we introduce a machine learning method for discovering instances in which a response relation exists between a pair of posts in a forum thread, for example when one student provides the answer to a question or comments on something another student previously said. Research has shown that understanding conversational structure between students is paramount to evaluating the productivity of the collaboration and estimating outcomes. However, previous methods often rely on human supplied dialogue act labels or discourse parsing algorithms requiring large labeled datasets. Our method, which utilizes a fast, exact optimization process known as spectral optimization, does not require manually annotated training data and is highly scalable and generalizable. Empirical results are given using real world datasets consisting of conversations between students participating in Coursera courses, and we see predictive accuracy above 90% - nearing the human inter-annotator agreement rate for these datasets.