A Multimodal Analytics Framework for Predicting Behavior Change in Special Education

Abstract

This paper employs data-driven multimodal analytics to anticipate changes in special education behavior. The research employs physiological markers, facial expressions, speech patterns, and classroom interactions to offer a comprehensive understanding of student behavior. In order to identify patterns associated with emotion, cognition, and behavior, these numerous inputs are subjected to rigorous machine learning algorithms. The method that has been demonstrated enables educators and caregivers to promptly identify significant changes and implement personalized corrections. The findings indicate that multimodal fusion is more effective at predicting complex behavioral processes than unimodal techniques, rendering it a superior method for comprehending such processes. The research contributes to the development of adaptive and distinctive support systems that can enhance the academic performance and affective well-being of special education students.