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Abstract

Classroom management critically affects students’ academic and behavioral outcomes, yet we lack quantitative methods for observing these practices at scale. This study develops and validates language-based measures of classroom management—such as responding to student behavior and issuing verbal or material sanctions—using natural language processing on 1,652 elementary mathematics classroom transcripts. On average, classroom management language comprises 24% of teacher talk, and behavior management comprises 7%, with wide variation across teachers. Novice teachers use more reprimands, and command frequency increases with experience. Classrooms with more Black students experience more language reflecting exclusionary discipline. These measures offer a new lens for studying and supporting classroom management as it unfolds in real time.

Keywords

artificial intelligence classroom management descriptive analysis discourse analysis equity instructional practices natural language processing observational research research methodology teacher education/development

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Do as I Say: What Teachers’ Language Reveals About Classroom Management Practices

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