Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers

TL;DR

This paper demonstrates that Vision Transformers, especially Swin Transformer, can effectively classify children's engagement in educational settings with high accuracy, enabling scalable automated analysis.

Contribution

It introduces a transformer-based approach for detecting behavioral and collaborative engagement in children using visual cues, outperforming existing models.

Findings

Swin Transformer achieved 97.58% accuracy in engagement classification.

Transformer models effectively capture local and global visual cues.

The approach shows promise for real-world educational engagement analysis.

Abstract

In early childhood education, accurately detecting collaborative and behavioral engagement is essential to foster 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 ChildPlay 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 six state of the art transformer models: Vision Transformer (ViT), Data efficient Image Transformer (DeiT), Swin Transformer, VitGaze, APVit and GazeTR. Among these, the Swin Transformer achieved the highest classification performance with an accuracy of 97.58 percent, 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.