Technology
Withdrawn Study: Wearable Sensors Monitored Cognitive Load in Vocational Students
A withdrawn arXiv paper explored using EEG and heart rate variability from wearable devices to assess cognitive load in secondary vocational students during different learning tasks.
A research paper submitted to arXiv in June 2024 and later withdrawn examined how wearable monitoring technology could assess cognitive load in real-time using physiological signals from secondary vocational students.
Study Design#
The research by Yanxin Chen combined two physiological indicators measured at high temporal resolution (1-second intervals):
- Electroencephalogram (EEG) data from the FP1 channel
- Heart rate variability (HRV) data
Researchers conducted two experiments to validate their approach. The first used an N-BACK task to train a random forest classification model, which achieved 97% accuracy in distinguishing different levels of cognitive load. The second tested the model's cross-task transferability using the National Computer Rank Examination (Level-1).
Findings and Application#
The study demonstrated that the classification model could generalize across different learning contexts, suggesting potential for practical classroom applications. At the time of submission, the researchers indicated the findings were undergoing trial implementation in their school as a tool for optimizing teaching resource allocation.
Status#
The paper was withdrawn after its third revision in June 2026, with no PDF available through arXiv's standard archive. The withdrawal suggests the authors may have had concerns about the work's readiness or methodology for publication.
