Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse

Jie Cao, Abhijit Suresh, Jennifer Jacobs, Charis Clevenger, Amanda Howard, Chelsea Brown, Brent Milne, Tom Fischaber, Tamara Sumner, James Martin

Abstract:

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.