Teachers’ attitudes and classroom management practices critically affect students’ academic and behavioral outcomes, contributing to the persistent issue of racial disparities in school discipline. Yet, identifying and improving classroom management at scale is challenging, as existing methods require expensive classroom observations by experts. We apply natural language processing methods to elementary math classroom transcripts to computationally measure the frequency of teachers’ classroom management language in instructional dialogue and the degree to which such language is reflective of punitive attitudes. We find that the frequency and punitiveness of classroom management language show strong and systematic correlations with human-rated observational measures of instructional quality, student and teacher perceptions of classroom climate, and student academic outcomes. Our analyses reveal racial disparities and patterns of escalation in classroom management language. We find that classrooms with higher proportions of Black students experience more frequent and more punitive classroom management. The frequency and punitiveness of classroom management language escalate over time during observations, and these escalations occur more severely for classrooms with higher proportions of Black students. Our results demonstrate the potential of automated measures and position everyday classroom management interactions as a critical site of intervention for addressing racial disparities, preventing escalation, and reducing punitive attitudes.
classroom management, artificial intelligence, natural language processing, instructional practices, observational research, research methodology, equity
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