Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse

• URL: http://arxiv.org/abs/2412.13395v1
• Date: Wed, 18 Dec 2024 00:13:04 GMT
• Title: Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse
• Authors: Jie Cao, Abhijit Suresh, Jennifer Jacobs, Charis Clevenger, Amanda Howard, Chelsea Brown, Brent Milne, Tom Fischaber, Tamara Sumner, James H. Martin,
• Abstract summary: This paper focuses on analyzing mathematics tutoring discourse using talk moves. scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task.
• Score: 6.1701318546149
• License:
• 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.

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