Analyzing the Embedded Learning-Based Movement Education Program’s Effects on Preschool Children’s Visual-Motor Coordination and Self-Regulation
Bibliographic record
Abstract
Preschool children’s visual-motor coordination skills are very valuable for their academic and social learnings. Using these skills, children have a broad movement repertoire, display better academic skills, participate in classroom activities and social relations, and develop self-regulation. Self-regulation is required for children to set their goals, purposeful planning, monitoring, and adapting. Children are natural players and they enjoy to move and play. Early childhood programs should consider supporting the visual-motor coordination in joint play situations to enhance children’s behaviors. This study used movement activities designed with embedded learning instruction to create cooperative play and increased group interactions among children. The purpose of this research was to analyze the embedded learning-based movement education program’s effects on preschool children’s visual-motor coordination and self-regulation development. For this purpose, an experimental research design with pretest-posttest, control group constructed. Control group children followed their traditional (MoNE, 2013) preschool education program while the experiment group pursued embedded learning-based movement education. Results indicated that both groups of children had significantly better (p.≤.0.05) visual-motor coordination and self-regulation skills. The difference was greater in experience group of children and the correlation was stronger between visual-motor coordination and self-regulation. It has been found that embedded learning-based movement education program positively affected preschool children’s visual-motor coordination and self-regulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".