Development of an Integrated Learning Experience Management Model through Storytelling to Promote Executive Functions for Early Childhood
Bibliographic record
Abstract
This research aimed to develop an integrated learning experience model through storytelling to promote executive functions in early childhood and investigate children’s executive tasks. Using purposive sampling, the target group consisted of 40 kindergarten children in their third year at Khon Kaen Kindergarten School, during the first semester of the 2024 academic year. The experiment lasted eight weeks, two days per week, and 60 minutes per day. The research tools used included an integrated learning experience model through storytelling to promote executive functions and the MU.EF-101 form. A single-group study was conducted, with measurements taken only after the experiment. Data were analyzed using percentages. The research findings revealed: 1) The integrated learning experience model through storytelling to promote executive functions for early childhood comprises five components: (1) Background, theories, principles, objectives, and content; (2) A four-step experiential process including create curiosity interest, analytical thinking and experiential connection; brainstorm to create a model symbol; and action and Reflection; (3) Evaluation; (4) Application of the model; and (5) Supporting factors. Expert evaluation indicated that the model was highly appropriate. 2) Early childhood children who experienced the model demonstrated improved executive functions at a good level or higher (T-Score 56–60), accounting for 85 percent.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".