Executive Functions in 5- to 8-Year Olds: Developmental Changes and Relationship to Academic Achievement
Bibliographic record
Abstract
Pronounced improvements in executive functions (EF) during preschool years have been documented incross-sectional studies. However, longitudinal evidence on EF development during the transition to school andpredictive associations between early EF and later school achievement are still scarce. This study examineddevelopmental changes in EF across three time-points, the predictive value of EF for mathematical, reading andspelling skills and explored children’s specific academic attainment as a function of early EF. Participants were323 children following regular education; 160 children were enrolled in prekindergarten (younger cohort: 69months) and 163 children in kindergarten (older cohort: 78.4 months) at the first assessment. Various tasks of EFwere administered three times with an interval of one year each. Mathematical, reading and spelling skills weremeasured at the last assessment. Individual background characteristics such as vocabulary, non-verbalintelligence and socioeconomic status were included as control variables. In both cohorts, changes in EF weresubstantial; improvements in EF, however, were larger in preschoolers than school-aged children. EF assessed inpreschool accounted for substantial variability in mathematical, reading and spelling achievement two years later,with low EF being especially associated with significant academic disadvantages in early school years. Giventhat EF continue to develop from preschool into primary school years and that starting with low EF is associatedwith lower school achievement, EF may be considered as a marker or risk for academic disabilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".