This study employs cutting-edge wearable monitoring technology to conduct
high-precision, high-temporal-resolution cognitive load assessment on EEG data
from the FP1 channel and heart rate variability (HRV) data of secondary
vocational students(SVS). By jointly analyzing these two critical physiological
indicators, the research delves into their application value in assessing
cognitive load among SVS students and their utility across various tasks. The
study designed two experiments to validate the efficacy of the proposed
approach: Initially, a random forest classification model, developed using the
N-BACK task, enabled the precise decoding of physiological signal
characteristics in SVS students under different levels of cognitive load,
achieving a classification accuracy of 97%. Subsequently, this classification
model was applied in a cross-task experiment involving the National Computer
Rank Examination, demonstrating the method's significant applicability and
cross-task transferability in diverse learning contexts. Conducted with high
portability, this research holds substantial theoretical and practical
significance for optimizing teaching resource allocation in secondary
vocational education, as well as for cognitive load assessment methods and
monitoring. Currently, the research findings are undergoing trial
implementation in the school.

通过使用前沿的可穿戴监测技术，该研究在 FP1 通道的脑电图数据和次要职业学生的心率变异性数据上进行高精度、高时间分辨率的认知负荷评估，综合分析这两个关键生理指标，研究其在评估次要职业学生认知负荷中的应用价值以及在不同任务中的效用。该研究设计了两个实验证明了所提出方法的有效性：首先，使用 N-Back 任务开发的随机森林分类模型能够对处于不同认知负荷水平下的次要职业学生的生理信号特征进行精确解码，实现了 97% 的分类准确率。随后，在国家计算机等级考试中应用该分类模型进行交叉任务实验，展示了该方法在不同学习环境中的显著适用性和跨任务的可转移性。该研究具有高可移植性，对于优化次要职业教育中的教学资源配置以及认知负荷评估方法和监测具有重要的理论和实践意义。目前，研究结果正在学校进行试行实施。