Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract)


  • Deliang Wang The University of Hong Kong



Explainable Artificial Intelligence, Artificial Intelligence In Education, Classroom Dialogue Analysis, Talk Move


This paper explores proposing interpreting methods from explainable artificial intelligence to address the interpretability issues in deep learning-based models for classroom dialogue. Specifically, we developed a Bert-based model to automatically detect student talk moves within classroom dialogues, utilizing the TalkMoves dataset. Subsequently, we proposed three generic interpreting methods, namely saliency, input*gradient, and integrated gradient, to explain the predictions of classroom dialogue models by computing input relevance (i.e., contribution). The experimental results show that the three interpreting methods can effectively unravel the classroom dialogue analysis, thereby potentially fostering teachers' trust.



How to Cite

Wang, D. (2024). Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23676-23678.