Sindhuja Penchala, Saketh Reddy Kontham, Prachi Bhattacharjee et al.
In early childhood education, accurately detecting behavioral and collaborative engagement is essential for fostering meaningful learning experiences. This paper presents an AI-driven approach that leverages Vision Transformers (ViTs) to automatically classify children's engagement using visual cues such as gaze direction, interaction, and peer collaboration. Utilizing the Child-Play gaze dataset, our method is trained on annotated video segments to classify behavioral and collaborative engagement states (e.g., engaged, not engaged, collaborative, not collaborative). We evaluated three state-of-the-art transformer models: Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), and Swin Transformer. Among these, the Swin Transformer achieved the highest classification performance with an accuracy of 97.58%, demonstrating its effectiveness in modeling local and global attention. Our results highlight the potential of transformer-based architectures for scalable, automated engagement analysis in real-world educational settings.