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This study investigates the feasibility of using a single-channel EEG device to assess cognitive load during online learning, addressing the challenge of lacking visual cues in remote education. By employing a hybrid CNN+LSTM+Attention model, the researchers achieved an accuracy of 78.5% in distinguishing between easy and difficult educational video content, significantly outperforming conventional classifiers. The authors emphasize the need for subject-independent evaluation and provide a reproducible evaluation pipeline alongside a tool for educators to visualize cognitive load in real-time.
A single-channel EEG can accurately assess cognitive load in online learning, revealing critical insights into student engagement with educational content.
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 waveform with band-power features. In a within-subject setting, the model reaches up to 78.5% accuracy, compared with 55% for conventional feature-based classifiers; regularization (dropout and L2) closes the large gap between training and validation accuracy that we observe without it, keeping validation accuracy stable at roughly 68-73%. We are deliberately cautious about these numbers: with only nine subjects, within-subject evaluation is optimistic, and we argue that subject-independent evaluation -- in which no learner appears in both training and test data -- should be the standard for this task. To that end we release a reproducible evaluation pipeline. We frame the work as a feasibility study rather than a deployable system, and pair it with an open, notebook-based tool that records EEG, runs inference, and visualizes estimated cognitive load as a heatmap over the video timeline to help educators locate potentially challenging segments.
