classroom_management_model

Model Card for Classroom Management 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 10354 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 classroom management language. Classroom management language is defined as that which reflects teacher actions aimed at creating and maintaining an orderly environment conducive to academic learning. This includes organizing lessons, managing transitions, explaining rules, monitoring progress, facilitating group interactions, maintaining accountability, and redirecting attention or behavior to support instructional goals.

Examples include: “Please stand up, push in your seats, and line up quietly”; “I want you to get your books; you may lay anywhere on the floor and read quietly”; “Make sure that you have your homework out to give to me as you’re going out the door over there”;“You need to be in your seats by the count of five”.

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.

By using this model, you agree that you will not use this model for commercial purposes.

Data Formatting

The expected input is a formatted sequence of utterances comprised of the target utterance to be classified and three utterances before and after. The seven utterances should be marked with the respective speakers, and the central target utterance should be marked with [TARGET][/TARGET].

Example: "Teacher: Uh, no, I’ll help you. Student: Somebody wrote w-w-w that [inaudible]. Teacher: All right, everybody get a worksheet. [TARGET]Teacher: I will give you time to make yours, also.[/TARGET] Student: Ooh, yes. Student: Ooh. Teacher: Don’t use the same numbers from yesterday – it’s a different graph."

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