The Development Executive Functions for Early Childhood in 21st Century
Bibliographic record
Abstract
Developing executive functions (EF) is a crucial issue in early childhood development which is also important in 21st-century learning. EF guideline was developed by Permsubhirunya and co to provide a guide for teachers who seeks to develop learning activities that could improve executive functions in children under the age of 6. The purpose of the study was to investigate the effect of the utilization of the EF guideline on the development of early childhood teachers’ abilities in designing learning experiences for EF development. The study was conducted with a qualitative approach. The participants were 65 teachers in public early childhood centers in Thailand. The participants were purposively selected using the criteria of their work status and experience. The instruments were the EF guideline, an evaluation form for learning management design ability and learning material selection, and an interview form. The data were analyzed using content analysis and thematic analysis. The results of the study were in the descriptive presentation. The findings could be summarized that the use of the EF guideline was beneficial in developing teachers’ knowledge of the early childhood executive functions, abilities to design learning management, and ability in selecting or creating learning material. The findings also suggested that teachers perceived the EF guideline as potential learning material that help them make a connection between the plan, activities, and evaluation. Therefore, it also helped them control the process of learning management and contribute to a meaningful learning environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".