Nonverbal Immediacy Analysis in Education: A Multimodal Computational Model
This research provides an objective assessment tool for nonverbal communication in educational settings, potentially supporting teacher training and evaluation, though it is incremental in applying existing multimodal methods to a specific domain.
The paper tackled the problem of analyzing nonverbal social behavior in education by developing a multimodal computational model that integrates facial expressions, gesture intensity, and spatial dynamics from classroom videos, achieving correlations up to 0.84 for gesture intensity and 0.44 for overall nonverbal immediacy with human ratings.
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.