Transformer-Based Student Engagement Recognition Using Few-Shot Learning
Wejdan Alarefah, Salma Kammoun Jarraya, Nihal Abuzinadah · Computers · 2025
Improving the recognition of online learning engagement is a critical issue in educational information technology, due to the complexities of student behavior and varying assessment standards. Additionally, the scarcity of publicly available datasets for engagement recognition exacerbates this challenge. The majority of existing methods for detecting student engagement necessitate significant amounts of annotated data to capture variations in behaviors and interaction patterns. To address these limitations, we investigate few-shot learning (FSL) techniques to reduce the dependency on extensive training data. Transformer-based models have shown comprehensive results for video-based facial recognition tasks, thus paving new ground for understanding complicated patterns. In this research, we propose an innovative FSL model that employs a prototypical network with the vision transformer (ViT) model pre-trained on a face recognition dataset (e.g., MS1MV2) for spatial feature extraction, followed by an LSTM layer for temporal feature extraction. This approach effectively addresses the challenges of limited labeled data in engagement recognition. Our proposed approach achieves state-of-the-art performance on the EngageNet dataset, demonstrating its efficacy and potential in advancing engagement recognition research.