@inproceedings{ganti-etal-2025-cross,
title = "Cross Domain Classification of Education Talk Turns",
author = "Ganti, Achyutarama R. and
Wilson, Steven R. and
Wing-Yue, Geoffrey Louie",
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.461/",
pages = "6897--6917",
abstract = "The study of classroom discourse is essential for enhancing child development and educational outcomes in academic settings. Prior research has focused on the annotation of conversational talk-turns within the classroom, offering a statistical analysis of the various types of discourse prevalent in these environments. In this work, we explore the generalizability and transferability of text classifiers trained to predict these discourse codes across educational domains. We examine two distinct English-language classroom datasets from the domains: literacy and math. Our results show that models exhibit high accuracy and generalizability when the training and test datasets originate from the same or similar domains. In situations where limited training data is available in new domains, few shot and zero shot exhibit more resiliency and aren`t as effected as their supervised counterparts. We also observe that accompanying each talk turn with dialog-level context improves the accuracy of the generative models. We conclude by offering suggestions on how to enhance the generalization of these methods to novel domains, proposing directions for future studies to investigate new methods for boosting the model adaptability across domains."
}
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%0 Conference Proceedings
%T Cross Domain Classification of Education Talk Turns
%A Ganti, Achyutarama R.
%A Wilson, Steven R.
%A Wing-Yue, Geoffrey Louie
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ganti-etal-2025-cross
%X The study of classroom discourse is essential for enhancing child development and educational outcomes in academic settings. Prior research has focused on the annotation of conversational talk-turns within the classroom, offering a statistical analysis of the various types of discourse prevalent in these environments. In this work, we explore the generalizability and transferability of text classifiers trained to predict these discourse codes across educational domains. We examine two distinct English-language classroom datasets from the domains: literacy and math. Our results show that models exhibit high accuracy and generalizability when the training and test datasets originate from the same or similar domains. In situations where limited training data is available in new domains, few shot and zero shot exhibit more resiliency and aren‘t as effected as their supervised counterparts. We also observe that accompanying each talk turn with dialog-level context improves the accuracy of the generative models. We conclude by offering suggestions on how to enhance the generalization of these methods to novel domains, proposing directions for future studies to investigate new methods for boosting the model adaptability across domains.
%U https://aclanthology.org/2025.coling-main.461/
%P 6897-6917
Markdown (Informal)
[Cross Domain Classification of Education Talk Turns](https://aclanthology.org/2025.coling-main.461/) (Ganti et al., COLING 2025)
ACL
- Achyutarama R. Ganti, Steven R. Wilson, and Geoffrey Louie Wing-Yue. 2025. Cross Domain Classification of Education Talk Turns. In Proceedings of the 31st International Conference on Computational Linguistics, pages 6897–6917, Abu Dhabi, UAE. Association for Computational Linguistics.