Rhythmic training, literacy, and graphomotor skills in kindergarteners
Bibliographic record
Abstract
Introduction: The aim of this manuscript is twofold: first, to investigate the relationship between rhythmic, phonological and graphomotor skills in kindergarten children; and second, to evaluate the possible impact of rhythmic training on the two other skills. Methods: To that end, we selected a sample of 78 children in Québec. Forty-two children received rhythmic training (experimental group) and 34 arts training (active control group) during the same period (10 weeks). Before and after training, children in both groups were assessed for general skills (forward and backward memory span, vocabulary, non-verbal ability), rhythmic skills (synchronization and discrimination tasks), literacy skills (phonological skills - syllable counting, syllable deletion, rhyme discrimination - and invented spelling skills) and graphomotor skills (legibility of letter writing, quality of copying of geometric shapes). Results: Results showed correlations between the child's rhythmic and literacy skills, as well as between rhythm synchronization and pen pressure. In addition, rhythmic training showed improvement in rhythmic abilities, but this did not transfer to literacy or graphomotor development (apart from a significant increase in the duration of pauses in both groups at post-test, with a larger improvement for the rhythm group). Discussion: These results are discussed in terms of duration and intensity of learning, and they highlight the possible benefits of informal rhythm practices in the classroom.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".