Understanding the Impact of Emotional Engagement on Learning Outcomes in Online Education: An Automated Analysis Approach

Abstract Online education offers flexibility but often suffers from reduced learner engagement. This study developed an automated method to detect emotional engagement using an optimized Vision Transformer model with transfer learning. Facial data from 40 undergraduates produced a dataset of 71,185 labeled images across three engagement levels. The proposed model achieved 93.8% classification accuracy, surpassing conventional machine learning and deep learning baselines. Analysis showed engagement typically declined after six minutes of learning, with a modest rebound near session end. Pearson correlation revealed a significant positive relationship between engagement and learning outcomes, indicating that emotionally engaged learners achieved higher academic performance. These results demonstrate the feasibility of deep learning–based approaches for scalable monitoring of learner engagement and highlight the central role of emotional states in shaping online learning effectiveness. The findings provide practical insights for designing adaptive interventions to sustain attention and optimize digital learning environments.