The Strategies of Competency-Based Learning Management for Schools Administration in Special Areas Schools, Lampang, Thailand
Bibliographic record
Abstract
The purpose of this research is to create and evaluate the use of competency-based learning management strategies of school administrators in special areas of Lampang, Thailand using the SOAR concept. Thirty participants are selected for providing information consisting of six school administrators, twelve teachers, and twelve school committee members. Environmental analysis for competency-based learning is performed by six school administrators, three administrators in the education service area office, three educational supervisors, six teachers, six the school committees, and three experts. The content validity and appropriateness of the draft strategy have been done by nine experts. Evaluating the use of strategies include questionnaires, interviews, assessments, and focus group recordings provided by six school administrators and twelve teachers. All data is analyzed using frequency, percentage, mean, standard deviation and descriptive narrative. The research results found that there are three main strategies, ten minor strategies, and four success factors in managing competency-based learning of school administrators in special areas, Lampang, Thailand. Evaluating results revealed that the use of strategies is feasible and useful at a high level (4.31±0.69).
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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 | 0.001 | 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 | 0.000 | 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".