Development a Program to Enhance Curriculum and Learning Management Competency of Private Primary School Teachers
Bibliographic record
Abstract
The aims of this research were: 1) to study the factors and indicators to enhance curriculum and learning management competency of private primary school teachers, 2) to study current situations and desirable situations and techniques, 3) to develop a program and 4) to study the effects of a program. The study comprised 4 phases: Phase 1, Studying the factors and indicators to enhance curriculum and learning management competency, Phase 2, Studying current situations and desirable situations and techniques, Phase 3. Developing a program and Phase 4, Studying the effects of a program. The 32 subjects were the teachers of Aekpanya School, private school in Somdej district, Kalasin province. The instruments used for data collection were: a suitable evaluation form, a questionnaire, a satisfied evaluation form, a competency evaluation form and a test. The statistics used for data analysis were percentage, mean, standard deviation and data description presentation. The findings found that there were five factors including with 5 learning themes. The current situations of the curriculum and learning management competency was in ‘moderate’ level in overall, and the desirable situations of the curriculum and learning management competency of private primary school teachers was in ‘very much’ level in overall. The five techniques of development the curriculum and learning management competency were ranked as follows: workshop, self-learning, coaching, mentoring and supervision. The effects of a program to enhance curriculum and learning management competency found that it increased significantly and it was in ‘very much’ level.
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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 | 0.001 | 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) | 0.006 | 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".