Enhancing Early Childhood Teachers’ Competency in Organizing Learning Experiences: The Impact of a Training Curriculum on Executive Function (EF) Development
Bibliographic record
Abstract
Executive Function (EF) is crucial for early childhood development, supporting cognitive abilities and self-regulation. However, many early childhood educators lack the necessary training to foster EF skills in the classroom. This study aims to evaluate the impact of a training curriculum designed to enhance early childhood educators’ competencies in organizing learning experiences that promote EF development. A mixed-methods research design was employed, incorporating both quantitative and qualitative data. The training curriculum included 24 hours of theoretical instruction over four days, followed by five days of supervised implementation, during which researchers observed classrooms and engaged teachers in reflective discussions. Moreover, the study targeted 10 kindergarten teachers from levels 1–3 at Wat Srijan School in Khon Kaen province, selected through purposive sampling based on their regular involvement in teaching kindergarten classes and willingness to participate in professional development training. The school was chosen for its initiative to become a model private institution in enhancing children’s brain development and life skills. The study utilized four primary instruments: cognitive achievement tests, lesson-planning assessments, teaching practice evaluations, and teacher satisfaction surveys. Quantitative results showed significant cognitive improvement with all participants exceeding the 80% passing threshold, achieving a 100% success rate. Lesson-planning assessments showed high scores across all components, particularly in defining learning outcomes and instructional objectives. Competency evaluations indicated strong teaching preparation, instructional techniques, and effective use of teaching aids. Qualitative results revealed three key themes: (1) empowerment through teacher training, (2) impact of classroom management techniques, and (3) practical application in the classroom. Furthermore, the participants provided positive feedback on the training’s relevance and effectiveness, valuing the blend of theoretical knowledge and practical application, which boosted their confidence in implementing EF strategies. This study’s contribution lies in its integrated approach, combining structured training with hands-on classroom application to support the sustainable development of EF practices in early childhood education.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".