Executive functions and mathematical ability in early elementary school children: The moderating role of family socioeconomic status
Bibliographic record
Abstract
Children’s executive functions (EFs) and family socioeconomic status (SES) play critical roles in the development of mathematical ability in early elementary education. However, the potential interplay between EFs and SES remains underexplored. This study addressed this gap by comprehensively investigating the moderating role of SES in the relationship between EF subcomponents (i.e., interference inhibition, response inhibition, and working memory) and children’s concurrent and future mathematical abilities (i.e., arithmetic operations and logical–visuospatial skills). A total of 172 participants ( M age = 6.78 years; 107 boys) took part in the study at the beginning of first grade in elementary school (T1) and 20 months later (T2). We measured EFs, SES, and mathematical ability at T1 and mathematical ability at T2. Results from hierarchical linear regression models indicated that working memory was positively associated with T1 arithmetic operations and logical–visuospatial skills as well as with T2 arithmetic operations. Furthermore, family SES was positively associated with arithmetic operations at both T1 and T2. Notably, we found a significant interaction effect between interference inhibition and SES on T1 arithmetic operations and logical–visuospatial skills. Specifically, interference inhibition was positively related to T1 arithmetic operations and logical–visuospatial skills for children from low- and middle-SES families, but not for children from high-SES families. Our findings contribute to a nuanced understanding of how cognitive and environmental factors jointly influence mathematical development, underscoring the need for targeted interventions for children from different SES backgrounds to support their mathematical ability development.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".