Stress Effects on Exam Performance using EEG
Bibliographic record
Abstract
Mental stress can be one of the most prominent factors of failure or poor performance in students. The traditional method of examination involves evaluating performance of students in limited time that may increase their stress level and may deteriorate their performance. Electroencephalogram (EEG) is one of the most commonly used methods to measure stress using brain waves. In this study, we investigate the influence of time limitation in exam on the performance of students and use EEG to explore the contribution of stress towards the change in performance. For this purpose, students performed mental arithmetic task (MAT) based on Montreal Imaging Stress Task of same difficulty level twice; once with time limitation accompanied by feedback for every question to induce stress and once without any time limitation and feedback. We observe vast difference in performance of the students for the two MAT tests and significant change in the power spectral density of theta, alpha, and beta frequency bands associated with increase in stress level for three chosen electrodes in EEG results. Our results show that stress may be one of the major factors for bad performance of the students in the exam resulting in failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".