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Record W2911089196 · doi:10.1109/icet.2018.8603652

Stress Effects on Exam Performance using EEG

2018· article· en· W2911089196 on OpenAlexaboutno aff
Muhammad Adeel Hafeez, Sadia Shakil, Sobia Jangsher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
FundersHigher Education Commision, PakistanHigher Education Commission, Pakistan
KeywordsElectroencephalographyStress (linguistics)Task (project management)Mental stressMental arithmeticAudiologyPsychologyComputer scienceAlpha (finance)Significant differenceCognitive psychologyDevelopmental psychologyMathematicsStatisticsMedicinePsychometricsNeuroscienceEngineeringHeart rateLinguistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.290
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2018
Admission routes1
Has abstractyes

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