  This study employs cutting-edge wearable monitoring technology to conduct
high-precision, high-temporal-resolution (1-second interval) cognitive load
assessment on electroencephalogram (EEG) data from the FP1 channel and heart
rate variability (HRV) data of secondary vocational students. By jointly
analyzing these two critical physiological indicators, the research delves into
their application value in assessing cognitive load among secondary vocational
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 secondary
vocational 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 (Level-1), 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.
