Since the COVID-19 pandemic, online courses have expanded access to education, yet the absence of direct instructor support challenges learners' ability to self-regulate attention and engagement. Mind wandering and disengagement can be detrimental to learning outcomes, making their automated detection via video-based indicators a promising approach for real-time learner support. However, machine learning-based approaches often require sharing sensitive data, raising privacy concerns. Federated learning offers a privacy-preserving alternative by enabling decentralized model training while also distributing computational load. We propose a framework exploiting cross-device federated learning to address different manifestations of behavioral and cognitive disengagement during remote learning, specifically behavioral disengagement, mind wandering, and boredom. We fit video-based cognitive disengagement detection models using facial expressions and gaze features. By adopting federated learning, we safeguard users' data privacy through privacy-by-design and introduce a novel solution with the potential for real-time learner support. We further address challenges posed by eyeglasses by incorporating related features, enhancing overall model performance. To validate the performance of our approach, we conduct extensive experiments on five datasets and benchmark multiple federated learning algorithms. Our results show great promise for privacy-preserving educational technologies promoting learner engagement.

翻译：自COVID-19疫情以来，在线课程扩大了教育可及性，但直接教师支持的缺失对学习者自我调节注意力与参与度的能力提出了挑战。走神与脱离状态可能损害学习成效，这使得通过视频指标进行自动化检测成为实时学习支持的一种可行途径。然而，基于机器学习的方法通常需要共享敏感数据，引发了隐私担忧。联邦学习通过实现去中心化模型训练并分散计算负载，提供了一种隐私保护的替代方案。我们提出一个利用跨设备联邦学习的框架，以应对远程学习中行为与认知脱离的不同表现形式，具体包括行为脱离、走神及厌倦状态。我们采用面部表情与注视特征构建基于视频的认知脱离检测模型。通过采用联邦学习，我们以隐私保护设计原则保障用户数据隐私，并提出一种具备实时学习支持潜力的创新解决方案。我们进一步通过纳入眼镜相关特征应对眼镜带来的挑战，从而提升模型整体性能。为验证方法的性能，我们在五个数据集上开展大量实验，并对多种联邦学习算法进行基准测试。结果表明，该隐私保护教育技术在促进学习者参与度方面具有广阔前景。