  This paper introduces a novel computational approach for analyzing nonverbal
social behavior in educational settings. Integrating multimodal behavioral
cues, including facial expressions, gesture intensity, and spatial dynamics,
the model assesses the nonverbal immediacy (NVI) of teachers from RGB classroom
videos. A dataset of 400 30-second video segments from German classrooms was
constructed for model training and validation. The gesture intensity regressor
achieved a correlation of 0.84, the perceived distance regressor 0.55, and the
NVI model 0.44 with median human ratings. The model demonstrates the potential
to provide a valuable support in nonverbal behavior assessment, approximating
the accuracy of individual human raters. Validated against both questionnaire
data and trained observer ratings, our models show moderate to strong
correlations with relevant educational outcomes, indicating their efficacy in
reflecting effective teaching behaviors. This research advances the objective
assessment of nonverbal communication behaviors, opening new pathways for
educational research.
