计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6): 39-49.doi: 10.11896/jsjkx.250600153

• 智能教育技术 • 上一篇    下一篇

基于前额叶EEG驱动的自适应学科学习效果评估模型研究

谢晖1,2,3, 梁丹1,2, 杨慧婷1,2, 贾春丽1,2, 贺江山1,2, 董泽骁1,2, 任子琪1,2, 蒋明哲1,2,4, 陈雪利1,2,3,4   

  1. 1 西安电子科技大学生命科学技术学院先进诊疗技术与装备陕西省高等学校重点实验室生物医学光子学与分子影像实验室 西安 710126
    2 西安电子科技大学生命科学技术学院西安市跨尺度生命信息智能感知与调控重点实验室 西安 710126
    3 高性能电子装备机电集成制造全国重点实验室光电集成与医学装备实验中心 西安 710071
    4 西安电子科技大学广州研究院光电集成与医学装备实验室 广州 510555
  • 收稿日期:2025-06-23 修回日期:2025-10-31 出版日期:2026-06-15 发布日期:2026-06-09
  • 通讯作者: 陈雪利(xlchen@xidian.edu.cn)
  • 作者简介:(hxie@xidian.edu.cn)
  • 基金资助:
    国家自然科学基金(62275210);国家领军人才计划;国家青年拔尖人才计划;中国博士后科学基金会博士后创新人才支持计划(GZB20230561);西安市科技计划项目(23ZDCYJSGG0026-2023);中央高校基本科研业务费专项资金

Research on Adaptive Disciplinary Learning Effectiveness Evaluation Model Driven by PrefrontalEEG

XIE Hui1,2,3, LIANG Dan1,2, YANG Huiting1,2, JIA Chunli1,2, HE Jiangshan1,2, DONG Zexiao1,2, REN Ziqi1,2, JIANG Mingzhe1,2,4, CHEN Xueli1,2,3,4   

  1. 1 Center for Biomedical-photonics and Molecular Imaging,Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province,School of Life Science and Technology,Xidian University,Xi'an 710126,China
    2 Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information,School of Life Science and Technology,Xidian University,Xi'an 710126,China
    3 Center for Bioptoelectronic-integration and Medical Instrumentation,State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment,Xidian University,Xi'an 710071,China
    4 Bioptoelectronic-integration and Medical Instrumentation Laboratory,Guangzhou Institute of Technology,Xidian University,Guangzhou 510555,China
  • Received:2025-06-23 Revised:2025-10-31 Published:2026-06-15 Online:2026-06-09
  • About author:XIE Hui,born in 1986,Ph.D,professor.His main research interests include brain functional cognition research and multimodal data online learning effect multidimensional evaluation research.
    CHEN Xueli,born in 1984,Ph.D,professor,Ph.D supervisor.His main research interests include biomedical photonics and molecular imaging,particularly in stimulated Raman scattering microscopy.
  • Supported by:
    National Natural Science Foundation of China(62275210),National Leading Talent Program, National Young Top-notch Talent Program, Postdoctoral Innovative Talents Support Program of the China Postdoctoral Science Foundation(GZB20230561),Xi'an Science and Technology Program(23ZDCYJSGG0026-2023) and Fundamental Research Funds for the Central Universities of Ministry of Education of China.

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摘要： 我国目前拥有多种在线学习平台,在线教育用户超过3亿人,在线教育成为了人们生活必不可少的一部分。在线教育在为人们提供便利的同时,其学习效果评估仍面临许多挑战:一是传统方法难以捕捉学科间神经响应的差异,二是缺乏融合多维度行为数据的动态评估指标。针对这些问题,基于比格兰学科分类模型,设计了多学科拟真在线学习实验,系统比较了学生学习不同学科时前额叶的EEG特征差异,同时设计了包含实验答题时间与答题准确率的学习效果标签,建立了EEG特征数据集并训练了在16个学习视频、8门课程与四大学科层面的学习效果分类模型。实验结果表明,学生学习人文科学与硬学科(自然科学、应用科学)在前额叶的响应模式有较大差异;在按四大学科统计学习效果时可以实现约90%的学习效果分类准确率,验证了便携式EEG设备在教育评估中的可行性,为未来教育智能评估中的个性化肖像生成提供可借鉴方案。

关键词: 在线学习评价, 多学科学习, EEG, 机器学习, 个性化评估

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

中图分类号: 

  • TP391
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