Teacher Talk Moves in K12 Mathematics Lessons: Automatic Identification, Prediction Explanation, and Characteristic Exploration

Talk moves have been shown to facilitate enriched discussion and conversation in classrooms, leading to improved student learning outcomes. To support teachers in enhancing discursive techniques and providing timely feedback on their classroom discourse, this paper proposed two BERT-based deep learning models for automatically identifying teacher talk moves in K12 mathematics lessons. However, the proposed discourse models have complex structures and cannot offer clear explanations on the prediction of teacher utterance, potentially leading teachers to distrust the model. To address this issue, this paper employed three model-agnostic interpreting methods from explainable artificial intelligence and transparently explained to teachers how the model predictions were made by computing and displaying the word relevance. The analysis results confirmed the validity of these explanations. Further, the paper investigated the interpreting results to uncover key characteristics of each type of teacher talk moves. The findings indicated that simple words and phrases could serve as representative indicators of talk moves (e.g., phrases centering on "agree" and "disagree" in the case of talk move getting students to relate to another's idea), which shows the potential to assist teachers in mastering discursive techniques. We believe that the paper built a solid step towards building an automated classroom discourse analysis system and fully addressing the interpretability concerns of deep learning-based discourse models.