Semester-long simulated-MOOC EEG dataset from an 11-lesson biology course¶
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Evidence · 4 claims (4 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 4 claims rest on one study
Description¶
A dataset of semester-long, stage-labeled 14-channel EEG recordings from six undergraduates completing lecture, virtual-lab, and quiz activities in an 11-lesson simulated-MOOC biology course, released by Hui Xie et al. for machine-learning and learning-stage analysis.
Components¶
Claims¶
- Machine learning models classify the three learning stages from EEG features with over 80% test accuracy, SVM highest at 82.6% [+W]
- Relative PSD shows stage- and task-specific band patterns, including higher high-beta in Stage 3 of labs and quizzes [+W]
- EEG average amplitude differs significantly across learning stages within each task, and across tasks at Stages 1 and 3, during a semester-long course [+W]
- Global efficiency declined across stages where tested while local efficiency differed significantly between stages in each task [+W]
Related Products and Programmes¶
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Key Sources¶
- Hui Xie, Chunli Jia, Yanxia Luo, Jiangshan He, Zexiao Dong, Dan Liang, Ziqi Ren, Mingzhe Jiang, Xinbo Gao & Xueli Chen. (2025). EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment. npj Science of Learning. https://doi.org/10.1038/s41539-025-00376-5
Page metadata
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product |
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semester-long-simulated-mooc-eeg-dataset-from-an-11-lesson-biology-course |
title |
Semester-long simulated-MOOC EEG dataset from an 11-lesson biology course |
description |
A dataset of semester-long, stage-labeled 14-channel EEG recordings from six undergraduates completing lecture, virtual-lab, and quiz activities in an 11-lesson simulated-MOOC biology course, released by Hui Xie et al. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-26 |
trust |
unverified — no one has confirmed this page's content |
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