Abstract: China currently hosts diverse online learning platforms serving over 300 million users,making digital education an integral part of modern life.However,challenges persist in ensuring learning effectiveness and evaluating proficiency levels through conventional grade-based assessment methods,which often fail to capture neural response differences across disciplines and lack dynamic evaluation metrics integrating multidimensional behavioral data.This study designs a multidisciplinary simulated online learning experiment based on the Biglan discipline classification model to address these limitations.EEG signals are recorded from participants during learning sessions,with comparative analysis performed on neural patterns across different courses and disciplines.A composite learning effectiveness metric integrating response time and answer accuracy is developed to label the EEG feature dataset.Classification models are trained to predict learning outcomes at three granularity levels:16 instructional videos,8 courses,and 4 major disciplines.Key findings reveal distinct prefrontal cortex activation patterns between humanities and STEM subjects(natural/applied sciences).The discipline-level classification achieves 90% accuracy in predicting learning effectiveness.These results demonstrate the feasibility of portable EEG devices for educational assessment and provide methodolo-gical insights for developing personalized learning profiles in intelligent evaluation systems.The experimental protocol successfully captures neurocognitive differences across academic domains while maintaining practical applicability in real-world educational settings.

Key words: Online learning evaluation, Multidisciplinary learning, EEG, Machine learning, Personalized assessment