使用复杂的大脑网络和可解释机器学习的学习者的多层次认知状态分类
Xiuling He1,2, Yue Li1,2, Xiong Xiao1,2
1National Engineering Research Center of Educational Big Data, Central China Normal University, Luoyu Road, Wuhan, 430079 Hubei China.
Cognitive neurodynamics
|January 6, 2025
概括
这项研究使用脑电图 (EEG) 分析学习过程中的大脑网络,通过 eXtreme Gradient Boosting (XGBoost) 用88.07%的准确度识别认知状态. 这些发现突出了前脑,脑和中脑连接,这些连接对于高阶思维技能 (HOTS) 至关重要.
科学领域:
- 认知神经科学 认知神经科学
- 教育心理学教育心理学
- 机器学习 机器学习
背景情况:
- 了解学习者的认知状态对于发展高阶思维技能 (HOTS) 至关重要.
- 以前的脑电图 (EEG) 研究往往集中在单个通道上,忽视了通道间的连接.
- 需要先进的方法来分析不同学习活动期间复杂的大脑动态.
研究的目的:
- 在使用EEG的不同学习活动中调查整个大脑网络动态.
- 根据功能性大脑网络特征对认知状态进行分类.
- 识别与更高认知状态相关的关键大脑区域和连接.
主要方法:
- 根据Bloom的分类学和ICAP框架设计了三个学习活动 (视频观看,关键词提取,文章创作).
- 在这些活动中记录了31名大学生的EEG信号.
- 应用了EEG微态序列分析,网络构建的相锁定值和机器学习分类器 (SVM,KNN,随机森林,XGBoost).
主要成果:
- 极端梯度提升 (XGBoost) 在分类认知状态方面取得了最高的准确性 (88.07%).
- 脑电图微态序列分析揭示了与学习活动相对应的大脑网络的动态变化.
- 莎普利添加式扩展 (SHAP) 确定了额头,和中部大脑区域连接对于高认知状态至关重要.
结论:
- 该研究成功地使用基于EEG的功能性大脑网络分析对认知状态进行了分类.
- XGBoost 和 SHAP 分析为理解与学习相关的大脑动态提供了有效的工具.
- 研究结果支持设计认知指导的教学策略,以增强学习者的HOTS.
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