Enhancing children’s numeracy and executive functions via their explicit integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Executive functions (EF) are crucial to regulating learning and are predictors of emerging mathematics. However, interventions that leverage EF to improve mathematics remain poorly understood. 193 four-year-olds (mean age = 3 years; 11 months pre-intervention; 111 female, 69% White) were assessed 5 months apart, with 103 children randomised to an integrated EF and mathematics intervention. Our pre-registered hypotheses proposed that the intervention would improve mathematics more than practice as usual. Multi-level modelling and network analyses were applied to the data. The intervention group improved more than the control group in overall numeracy, even when controlling for differences across settings in EF and mathematics-enhancing practices. EF and mathematics measures showed greater interconnectedness post-intervention. In addition, disadvantaged children in the intervention group made greater gains than in the control group. Our findings emphasise the need to consider EFs in their integration with co-developing functions, and in their educational and socio-economic context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it