Cross-Domain Classification of Education Talk-Turns
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 these discourse codes across different educational domains via automatic text classifiers. We examine two distinct English-language classroom datasets from the domains of literacy and mathematics. Our results show that models exhibit high accuracy and generalizability when the training and test datasets originate from the same or similar domains. However, as the distance between the training and test domains increases in terms of subject matter and teaching methodology, we observe a decline in model performance. 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 and techniques for boosting the model adaptability across varied educational domains.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: Educational Applications
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data analysis
Languages Studied: English
Submission Number: 496
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