Correlation Between the Cortical Activation Studied by Functional Near Infrared Spectroscopy Neuroimaging (fNIRS) With Performance of 3rd Grade Students
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".