Classroom teachers utilize many nonverbal activities, such as gesturing and walking, to maintain student attention. Quantifying instructor behaviors in a live classroom environment has traditionally been done through manual coding, a prohibitively time-consuming process which precludes providing timely, fine-grained feedback to instructors. Here we propose an automated method for assessing teachers’ non-verbal behaviors using video-based motion estimation tailored for classroom applications. Motion was estimated by subtracting background pixels that varied little from their mean values, and then noise was reduced using filters designed specifically with the movements and speeds of teachers in mind. Camera pan and zoom events were also detected, using a method based on tracking the correlations between moving points in the video. Results indicated the motion estimation method was effective for predicting instructors’ non-verbal behaviors, including gestures (kappa =.298), walking (kappa =.338), and camera pan (an indicator of instructor movement; kappa =.468), all of which are plausibly related to student attention. We also found evidence of predictive validity, as these automated predictions of instructor behaviors were correlated with students’ mean self-reported level of attention (e.g., r =.346 for walking), indicating that the proposed method captures the association between instructors’ non-verbal behaviors and student attention. We discuss the potential for providing timely, fine-grained, automated feedback to teachers, as well as opportunities for future classroom studies using this method.