Executive functions and mathematics performance in primary school students: A systematic review
Bibliographic record
Abstract
Executive functions (EF)—working memory, inhibitory control, cognitive flexibility and planning—constitute the cognitive infrastructure of mathematical learning. Motivated by recent international assessments in which many Latin-American pupils scored below basic numeracy standards; this systematic review examines how EF relate to mathematics performance in primary school. Following PRISMA 2020 guidelines, we searched six major databases, screened 1,490 records and retained 37 empirical studies published between 2009 and 2025 . Methodological quality was appraised using the Newcastle–Ottawa Scale, with 78 % of studies were rated high quality. Across longitudinal and cross-sectional research, studies consistently reported small-to-moderate associations between EF and mathematical performance. Visuospatial and verbal-numeric working memory showed the strongest relationships, while inhibitory control and cognitive flexibility contributed to problem-solving and strategy use. Socio-economic disadvantage was commonly linked to weaker EF development and lower mathematics outcomes, whereas bilingual experience appeared to confer advantages via enhanced inhibition and flexibility. Intervention studies integrating EF-focused activities into math instruction reported improvements in arithmetic fluency, attention control and strategy switching, consistent with near transfer, understood here as improvements in skills closely related to the specifically trained EF component. By integrating recent studies published in English and Spanish, applying uniform quality criteria, and incorporating both correlational and intervention research, this review refines the current understanding of how EF support mathematical learning. Findings highlight the potential of embedding EF-supportive practices into everyday numeracy instruction, particularly in socioeconomically vulnerable contexts.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".