Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach
arXiv:2607.01795v1 Announce Type: new Abstract: Monitoring cognitive load during online learning could help instructors identify content that learners find difficult, but remote settings remove the visual cues that support this judgement in a classroom. We study whether a single-channel, consumer-grade EEG device (the NeuroSky MindWave Mobile 2) can distinguish easy from difficult educational-video content, using the publicly available dataset of Wang et al. [24] (ten learners, one excluded for excessive noise, leaving nine). We implement a hybrid CNN+LSTM+Attention model that combines the raw
The proliferation of online learning platforms and accessible wearable neurotechnology creates an immediate need and opportunity for non-invasive cognitive load assessment.
This research could lead to more adaptive and effective online educational systems, improving learning outcomes and potentially expanding access to complex subjects for a wider audience.
The ability to objectively measure cognitive load in remote learning environments changes how educational content is designed, delivered, and personalized without requiring direct observation.
- · Online education platforms
- · Ed-tech companies
- · Neurotechnology manufacturers
- · Learners
- · Traditional static curriculum designers
Adaptive online learning systems gain a new dimension for personalized content delivery.
Increased engagement and retention in remote learning could improve global educational attainment rates.
The application of accessible neuro-sensing for performance assessment may expand beyond education into other white-collar training and productivity monitoring.
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Read at arXiv cs.LG