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
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.
BibTeX
@inproceedings{cao-etal-2025-enhancing,
title = "Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse",
author = "Cao, Jie and
Suresh, Abhijit and
Jacobs, Jennifer and
Clevenger, Charis and
Howard, Amanda and
Brown, Chelsea and
Milne, Brent and
Fischaber, Tom and
Sumner, Tamara and
Martin, James H.",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.513/",
pages = "7671--7684"
}