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[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 View PDF HTML (experimental) 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) Full-text links: Access Paper: View a PDF of the paper titled EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues, by Julian Bernado and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.CY cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)