The Supervision Model to Promote Curriculum Administration Emphasizes Learner Competency of Schools under the Office of Primary Educational Service Area
Bibliographic record
Abstract
This research aimed to develop a supervision model to promote curriculum administration that emphasized learner competency in schools under the Office of Primary Educational Service Area, using the Research and Development (R&D) methodology. The study consisted of two phases. The first phase investigated the current and desired status and the need for developing supervision to promote curriculum administration emphasizing learner competency. This involved document analysis, interviews with five educational supervision experts who were obtained through purposive sampling, and surveys of 370 school administrators and heads of academic teachers selected through stratified sampling determined using Taro Yamane’s formula. The researchers developed and validated the supervision model with 11 experts selected through purposive sampling for the second phase. Research tools included document analysis verified by the advisor, questionnaires with IOC between .80 and 1.00 and Cronbach’s alpha coefficient of .986, interviews, the supervision model, implementation manuals, and evaluation forms. Data analysis utilized percentage, mean, standard deviation, PNImodified, and content analysis. The results indicated high levels of both current and desired statuses for supervision-promoting curriculum administration that emphasizes learner competency. The overall necessity for development, measured by the PNImodified value, is .102, with evaluation, creation, reflection, information, and action ranked from highest to lowest. The supervision model included principles, objectives, and implementation methods with a five-step supervision process (information, creation, action, reflection, and evaluation) and outcomes promoting curriculum administration in four areas (curriculum preparation, curriculum use, curriculum supervision and monitoring, and curriculum evaluation and improvement), model evaluation, and conditions for use. The model’s suitability and feasibility were rated as highly appropriate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".