Understanding Student Language: An Unsupervised Dialogue Act Classification Approach

Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language understanding of educational dialogues. Automatic dialogue act modeling has long been addressed with supervised classification techniques that require substantial manual time and effort. Recently, there is emerging interest in unsupervised dialogue act classification, which addresses the challenges related to manually labeling corpora. This paper builds on the growing body of work in unsupervised dialogue act classification and reports on the novel application of an information retrieval technique, the Markov Random Field, for the task of unsupervised dialogue act classification. Evaluation against manually labeled dialogue acts on a tutorial dialogue corpus in the domain of introductory computer science demonstrates that the proposed technique outperforms existing approaches to education-centered unsupervised dialogue act classification. Unsupervised dialogue act classification techniques have broad application in educational data mining in areas such as collaborative learning, online message boards, classroom discourse, and intelligent tutoring systems.