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<p>The development of brain oscillation patterns during knowledge acquisition has gained attention, yet studies in realistic learning contexts remain limited. This study investigated dynamic brain activity across an 11-lesson biology course simulating a MOOC environment. Twenty undergraduates wore 14-channel Electroencephalography (EEG) headsets while completing lecture, virtual lab, and quiz tasks across three progressive stages. EEG signals from six participants (after quality screening) were analyzed using amplitude, power spectral density (PSD), and phase-locking index (PLI). Wilcoxon rank sum tests revealed significant stage- and task-related differences despite the small sample size, including increased frontal theta during quizzes, parietal alpha suppression during lectures, and high-beta enhancements in later stages of labs and quizzes. Machine learning models trained on EEG features achieved a classification accuracy of 83% for three learning stage discrimination, validating that the brain presents nonidentical functional patterns during cognitive learning. These results underscore the potential for real-time EEG-based personalized educational interventions.</p>

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EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Hui Xie,
  • Chunli Jia,
  • Yanxia Luo,
  • Jiangshan He,
  • Zexiao Dong,
  • Dan Liang,
  • Ziqi Ren,
  • Mingzhe Jiang,
  • Xinbo Gao,
  • Xueli Chen

摘要

The development of brain oscillation patterns during knowledge acquisition has gained attention, yet studies in realistic learning contexts remain limited. This study investigated dynamic brain activity across an 11-lesson biology course simulating a MOOC environment. Twenty undergraduates wore 14-channel Electroencephalography (EEG) headsets while completing lecture, virtual lab, and quiz tasks across three progressive stages. EEG signals from six participants (after quality screening) were analyzed using amplitude, power spectral density (PSD), and phase-locking index (PLI). Wilcoxon rank sum tests revealed significant stage- and task-related differences despite the small sample size, including increased frontal theta during quizzes, parietal alpha suppression during lectures, and high-beta enhancements in later stages of labs and quizzes. Machine learning models trained on EEG features achieved a classification accuracy of 83% for three learning stage discrimination, validating that the brain presents nonidentical functional patterns during cognitive learning. These results underscore the potential for real-time EEG-based personalized educational interventions.