@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://preview.aclanthology.org/fix-sig-urls/2025.coling-main.513/",
    pages = "7671--7684",
    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."
}