Scholarlink

<p>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 important 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Understanding the impact of emotional engagement on learning outcomes in online education: an automated analysis approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

查看全文
  • Guanyu Chen,
  • Guangxin Han,
  • Juan Niu,
  • Juhou He

摘要

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 important 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.