Nonverbal Immediacy Analysis in Education: A Multimodal Computational Model

TL;DR

This paper presents a multimodal computational model that analyzes nonverbal social behaviors in educational videos, accurately assessing teacher immediacy and correlating with educational outcomes.

Contribution

It introduces a novel multimodal approach for nonverbal behavior analysis in classrooms, utilizing RGB video data and achieving high correlation with human ratings.

Findings

Gesture intensity regressor correlation: 0.84

Perceived distance regressor correlation: 0.55

NVI model correlation with human ratings: 0.44

Abstract

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.