MULTIMODAL ANALYSIS OF INTER-BRAIN AND BEHAVIORAL SYNCHRONY IN REMOTE LEARNING

Abstract

This paper examines multimodal analysis of behavioral and inter-brain synchronization in remote learning environments to determine students' social and cognitive alignment when using digital platforms to participate. The paper integrates neurophysiological signals like EEG-based inter-brain coherence with behavioral indicators like eye movements, facial expressions, and interaction patterns to determine synchrony. Advanced signal processing and machine learning integrate diverse data sources. Next, learning outcomes, teamwork, and participation patterns are identified. Increased inter-brain synchrony improves academic performance, group attention, and communication even when people are absent. Synchrony-aware analytics improves virtual collaboration, adaptive training, and academic success, according to this paper. Additionally, it improves intelligent remote learning system design.