Executive functioning effects the development of writing skills
Bibliographic record
Abstract
This study examines the relations between development of the central executive and writing skills in young children. Furthering understandings about the development of an executive system of cognitive resources in young children is important when designing early interventions, yet little is understood about the organization of the system as it emerges in children. Baddley (1999) proposes a model in which the central executive, consisting of the phonological loop and visuospatial sketchpad, effects text generation which in turn effects both transcription and translation. To test this, children (n=178) were administered measures of transcription (handwriting), translation (propositional density), visual working memory, and verbal working memory 4 times between the ages of 4 and 7 years. Analysis using multilevel modelling showed that growth rates in visual and verbal working memory predicted propositional density. However, only growth rates in visual working memory predicted handwriting performance. The study concludes that the executive system that is differentiated over the course of the first 3 years in school influences translation and transcription. This research shows that writing is not domain specific (involving only language). Transferring ideas into written form uses visual executive processes while more complex ideas in writing requires a suite of executive functions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".