Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909719854055424 |
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| author | Cao, Jie Suresh, Abhijit Jacobs, Jennifer Clevenger, Charis Howard, Amanda Brown, Chelsea Milne, Brent Fischaber, Tom Sumner, Tamara Martin, James H. |
| author_facet | Cao, Jie Suresh, Abhijit Jacobs, Jennifer Clevenger, Charis Howard, Amanda Brown, Chelsea Milne, Brent Fischaber, Tom Sumner, Tamara Martin, James H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse Cao, Jie Suresh, Abhijit Jacobs, Jennifer Clevenger, Charis Howard, Amanda Brown, Chelsea Milne, Brent Fischaber, Tom Sumner, Tamara Martin, James H. Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2412.13395 |