An Examination of Self-Regulation and Higher-Order Cognitive Skills as Predictors of Preschool Children’s Early Academic Skills
Bibliographic record
Abstract
In this study, the direct and indirect relationships of children’s self-regulation skills and their higher-order cognitive skills of cognitive flexibility and abstraction skills with their early academic competencies are examined. Within the scope of the study, the mediating role of self-regulation skills with early academic competencies is investigated. In the study, inhibitory control, behaviour regulation, effortful control and cognitive flexibility are focused on as the main components of executive functions which develop in the early childhood period. The research data were obtained from 185 children aged 60-72 months attending preschool education institutions in the central province of Bursa, and from 16 of these children’s teachers. For determining the children’s self-regulation skills, effortful control and behaviour regulation were evaluated. Stepwise multiple regression analysis was used to test whether or not the children’s self-regulation and higher-order cognitive skills predicted their early academic competencies. As a result of the research, it was determined that the self-regulation skills of effortful control and behaviour regulation predicted early academic skills and attitudes. A statistically significant relationship of cognitive flexibility and abstraction skills with early academic success scale scores was not found. The findings are discussed in terms of the relationship of behaviour regulation and effortful control with early academic success and competencies.
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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 | 0.004 |
| 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 | 0.000 | 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".