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Record W4403789942 · doi:10.4018/ijtee.357995

Correlation Between the Cortical Activation Studied by Functional Near Infrared Spectroscopy Neuroimaging (fNIRS) With Performance of 3rd Grade Students

2024· article· en· W4403789942 on OpenAlexaff
Elazab Elshazly, Hussein Mostafa, Mohammed Safi

Bibliographic record

VenueInternational Journal of Technology-Enhanced Education · 2024
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsFunctional near-infrared spectroscopyNeuroimagingFunctional neuroimagingPsychologyNeuroscienceCorrelationFunctional Brain ImagingCognitionMathematics

Abstract

fetched live from OpenAlex

The cortical activation and performance of 3rd grade students were investigated using Functional near infrared spectroscopy (fNIRS) during reading Arabic language. The main cortical activation parameter tested was hemoglobin difference (HbDiff), while the performance was evaluated according to the number of committed errors and latency. The recorded HbDiff concentrations for the typically developed (TD) were significantly higher (0.05) than that of students with dyslexia (Dys), confirming higher brain activities for TD students. TD students committed less errors and need less time to finish the task. Moreover, Pearson correlation analysis performed showed that there was a negative correlation between cortical activation parameters and performance. Limited studies explored the use of fNIRS to investigate the cortical activation of Arabic students' brains or correlated between the cortical activation and performance variables. Therefore, this current research is novel and showed the potential utilization of the fNIRS in the field of educational neuroscience.

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 score0.122
Threshold uncertainty score0.422

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 integrity0.0000.001
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 head0.010
GPT teacher head0.335
Teacher spread0.324 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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