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

This paper proposes a federated learning framework to detect mind wandering, behavioral disengagement, and boredom in online learning environments using video-based facial expressions and gaze features while preserving student privacy. The system aims to enable real-time learner support by detecting cognitive and behavioral disengagement without sharing sensitive student data.

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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