Executive Functioning and Early Math Skills in Young Children at Risk for Mathematical Difficulties: Evaluation of Interventions Efficacy and Transfer Effects
Bibliographic record
Abstract
Research Findings: Executive Function (EF) and Early Math (EM) are foundational skills for children’s school success. Interventions have shown to foster these skills, but their effectiveness in less developed countries remains unknown. This study examined the initial efficacy of an eight-week EF and an EM skills program for young Iranian children at risk for mathematical difficulties. Participants included 88 five- to six-year-old children who were randomly assigned into three conditions: Business-As-Usual (BAU) control group, EF training group, or an EM training group. The experimental groups received 24 sessions over two months. In the EF group, children had significantly higher working memory and planning scores at posttest, which were stable five months later. No significant group difference was found on inhibitory control. In the EM group, children demonstrated stronger math performance compared to BAU children at the posttest and follow-up, suggesting stable math improvement. There was no significant effect of EF training on math performance or significant effect of math training on EF skills. Practice or Policy: Findings suggest that the EF training was related to stronger EF and EM training was related to better math performance. However, there were no significant transfer effects of EF on math and vice versa.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".