Analyzing the Early Literacy Skills and Visual Motor Integration Levels of Kindergarten Students
Bibliographic record
Abstract
Early childhood education aims to support children’s whole development and their school readiness. Children develop a knowledge about reading, writing and learning before elementary school. This knowledge is called early literacy and it’s a key factor for school readiness. This study delves to investigate the early literacy skills of children and their visual motor integration. For this purpose, kindergarten students’ level of early literacy skills and visual motor integration was determined and the correlation between the two was analyzed. Eighty children at the age of five (40 females and 40 males) attending kindergarten were purposively chosen. In the study, 38% of the participants in the kindergarten was in the inadequate level of early literacy skills and 62% was in the instructional level. Children in the instructional level also showed that they had better visual motor coordination skills as well. These children presented better visual motor coordination skills in the fine manipulative skills category, print awareness category, and expressive and receptive language skills category. It was seen that visual motor integration development is a valuable factor to supports kindergarten children’s early literacy skills. For this reason, children’s visual motor coordination skills should be taken into consideration and supported for their early literacy skills development.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".