Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers
This work addresses the need for scalable, automated engagement analysis in real-world educational settings, though it is incremental as it applies existing transformer models to a specific domain.
The paper tackled the problem of automatically detecting behavioral and collaborative engagement in early childhood education using Vision Transformers, achieving a classification accuracy of 97.58% with the Swin Transformer model.
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.