stanford-nlpxed/verbal_sanction_model

Model Card for Verbal Sanction Model

Model Details

• Developed by: Mei Tan, EduNLP Lab @ Stanford University Graduate School of Education
• Release Date: 2025-11-17
• Paper: Tan, Mei, and Dorottya Demszky. (2025). Do As I Say: What Teachers’ Language Reveals About Classroom Management Practices. (EdWorkingPaper: 23-844). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/9yj6-jn52

Model Description

This model is a RoBERTa-base classifier fine-tuned to predict binary labels from teacher utterances in classroom transcripts. It was trained on 5720 annotated teacher utterances from elementary math classroom transcripts from the NCTE dataset [1]. It is intended for research on teachers' classroom discourse.

The model classifies whether a teacher utterance is an instance of verbal sanctioning language. Verbal Sanctions are defined as a subset of behavior management that involves “only telling.” These are verbal responses, statements or directives with no mention of material consequences. Examples include short desists, commands, reprimands, and threats. These submoves are categorized in a separate model.

Intended Uses

Not intended for evaluation of teaching quality. What is appropriate in a given classroom is highly contextual and relational in a way that this model does not capture.

Data Formatting

The expected input is a formatted sequence of a single teacher utterance. Example: "Student D, put that away."

Generalizability

The training data for this model come from ~200 observations sampled from the original NCTE study [2], which represents 1652 includes observations of 317 fourth- and fifth-grade mathematics classrooms across 53 schools in New England that were primarily serving low-income students of color. The utterances in this dataset are roughly sentence-length and human-transcribed.

Applying this model to new datasets generalizing to other contexts should involve validation: annotate a sample from the new data context to assess model generalizability.

[1] Demszky, D., & Hill, H. (2023). The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts. In 18th Workshop on Innovative Use of NLP for Building Educational Applications.

[2] Kane, Thomas, Hill, Heather, and Staiger, Douglas. National Center for Teacher Effectiveness Main Study. Inter-university Consortium for Political and Social Research [distributor], 2022-06-16. https://doi.org/10.3886/ICPSR36095.v4