Development of Folk Wisdom Curriculum Course for Enhancing Early Childhoodsof their Developmental Domains at the Child Development Centers (CDCs) in Bueng Kan’s Local Administrative Organization
Bibliographic record
Abstract
Designing the Research & Development (R&D) method in four phases for inventing to develop of folk wisdom curriculum course for enhancing early childhoods of their five developmental domain skills at the Child Development Centers (CDCs) in Bueng Kan's Local Administrative Organization was invented to make sense the instructional local classroom learning environment using the innovative lesson plans on the Folk Wisdom Curriculum Course (FWCC) is the instructional tool with the Interview Form, the 30-item Questionnaire on Folk Wisdom Curriculum Course (QLWCC) on 5 scales, and the Lesson Experiencing Plan Innovation (LEPI) Assessment on four instructional lesson plans were assessed of 150 educational personnel (EP) at 16 Child Development Centers under the Bueng Kan's Provincial Administrative Organization with the local folk wisdom who are expert professional on culture, local festival, local language, storytelling etc., were participated in five main developing early childhood of their physical, mental, emotional, social, and language.The average mean score indicated that of 3.40, αreliability ranged from 0.81 to 0.85 for the QLWCC.The EPs' assessing outcomes with the LEPI are differentiated significantly at 0.01 with pretest-posttest-design model.The interviewees' responses of their opinions indicated that of the caregivers and teachers lack knowledge and understanding of early childhood education management principles, they didn't understand the curriculum for early childhood, Development… Supangjit Kanlayakaew.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".