[Submitted on 22 Sep 2026]
Title:EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues
View a PDF of the paper titled EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues, by Julian Bernado and 3 other authors
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Abstract:Large language models have allowed the rapid deployment of pedagogical annotations corresponding to constructs of interest, allowing a natural language interface for generating classifications on a conversational dataset. However due to the opaque nature of LLM reasoning, we have no verifiable, mechanistic insight into why a model chose a label for an utterance. We introduce the EduBehaviors framework, an interpretable, scalable approach to annotating educational data that uses LLMs to measure repeated observable behaviors relevant to many constructs of interest and then learns a classifier for the construct based on these observable behaviors. We evaluate the framework on the TalkMoves dataset, predicting the Teacher TalkMoves labels. Our best configuration results in a macro-F1 of 0.673 and 0.688 Cohen's kappa, proving competitive with direct prompting approaches. In addition, we release EduBehaviors Toolkit, two tools allowing researchers to operationalize the EduBehaviors framework in their own data.
Subjects:
Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
Cite as: arXiv:2609.27043 [cs.CL]
(or arXiv:2609.27043v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.27043
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ana Trindade Ribeiro [view email] [v1] Tue, 22 Sep 2026 20:38:54 UTC (869 KB)
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