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

arXiv: https://arxiv.org/abs/2508.15782 · HTML: https://mlsanity.com/paper/2508.15782/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [ChildPlay: A New Benchmark for Understanding Children's Gaze Behaviour](https://mlsanity.com/paper/2307.01630/) (2023)
- [Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines](https://mlsanity.com/paper/2302.00431/) (2023)
- [ViTGaze: Gaze Following with Interaction Features in Vision Transformers](https://mlsanity.com/paper/2403.12778/) (2024)
- [Class-attention Video Transformer for Engagement Intensity Prediction](https://mlsanity.com/paper/2208.07216/) (2022)
- [Measuring Student Behavioral Engagement using Histogram of Actions](https://mlsanity.com/paper/2307.09420/) (2023)
- [Vision Beyond Boundaries: An Initial Design Space of Domain-specific Large Vision Models in Human-robot Interaction](https://mlsanity.com/paper/2404.14965/) (2024)

## Built on by (most cited first, 3 of 3)

- [Gaze to Insight: A Scalable AI Approach for Detecting Gaze Behaviours in Face-to-Face Collaborative Learning](https://mlsanity.com/paper/2604.03317/) (2026)
- [Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention](https://mlsanity.com/paper/2608.24580/) (2026)
- [ChildGaze: A Benchmark Dataset for Collaborative Behavior Understanding in Children](https://mlsanity.com/paper/2609.06353/) (2026)

## Abstract (arXiv)

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.