Toward Adaptive Unsupervised Dialogue Act Classification in Tutoring by Gender and Self-Efficacy

For tutorial dialogue systems, classifying the dialogue act (such as questions, requests for feedback, and statements) of student natural language utterances is a central challenge. Recently, unsupervised machine learning approaches are showing great promise; however, these models still have much room for improvement in terms of accuracy. To address this challenge, this paper presents a new unsupervised dialogue act modeling approach that leverages non-cognitive factors of gender and selfefficacy to better model students’ utterances during tutorial dialogue. The experimental findings show that for females, leveraging learner characteristics within dialogue act classification significantly improves performance of the models, producing better accuracy. This line of investigation will inform the design of next-generation tutorial dialogue systems, which leverage machine-learned models to adapt to their users with the help of non-cognitive factors.