Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse
This work addresses the resource-intensive problem of analyzing tutoring discourse for educators and researchers, but it is incremental as it builds on existing classroom datasets and methods.
The paper tackled the challenge of scaling talk moves analysis in mathematics tutoring by introducing the SAGA22 dataset and exploring modeling strategies, showing that pretraining on classroom data improves performance with longer context and speaker information.
Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves - a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling.