Training Curriculum Development Based on the Guiding Concept to Promote the Ability to Organize Learning Experiences on Executive Functions in Early Childhood for Childcare Teachers
Bibliographic record
Abstract
This research aimed to develop an instructional coaching model to enhance early childhood educators’ abilities in designing learning experiences for brain management. This research employed a mixed-methods approach, combining quantitative and qualitative methods and consisted of two phases: 1) developing an instructional coaching framework, and 2) validating the framework with seven expert raters, the expert panel consisted of individuals with expertise in curriculum and instruction, research and curriculum development, measurement and evaluation, and early childhood education. The selection criteria for the experts were: a master’s degree or higher, experience in curriculum development or coaching and willingness to participate in the research. A total of seven experts were selected through purposive sampling. Data was collected using a 5-point Likert scale. The findings revealed that: 1) The developed instructional coaching framework comprised six components: (1) background and significance, (2) principles, (3) objectives, (4) instructional structure, (5) media and learning resources, and (6) evaluation. 2) The expert raters highly agreed on the framework’s suitability (Mean = 4.64, S.D. = 0.54). However, they suggested further refinements, particularly in selecting activities that align with the context of brain management in early childhood and that can effectively connect to age-appropriate developmental learning.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".