Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education

This paper proposes a federated learning framework for privacy-preserving detection of mind wandering, behavioral disengagement, and boredom in online learning environments using facial expressions and gaze features from webcam video. The approach is validated across five datasets and benchmarks multiple federated learning algorithms for automated learner state detection.

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 le

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