Needs for the Development of Competency-Based Instruction of Small-Sized Secondary Schools’ Teachers Under The Office of Nakhon Ratchasima Secondary Education
Bibliographic record
Abstract
This study aimed to investigate the needs and develop guidelines for improving competency-based instruction for teachers in small-sized secondary schools under the Office of Nakhon Ratchasima Secondary Education. The research gathered data from 216 teachers drawn by using a stratified random sampling method, the sample size was determined by Krejcie and Morgan’s table (1970). Data were collected through a five-level rating scale questionnaire with an Index of Item Objective Congruence (IOC) between 0.80 and 1.00 and a reliability score of 0.96. Statistical analysis methods included frequency, percentage, mean, standard deviation, t-test, and the Modified Priority Needs Index (PNIModified). The results revealed two significant areas of need: (1) improving teachers’ knowledge and skills in designing competency-based learning management and (2) enhancing their ability to implement competency-based instruction in classrooms. Based on these findings, three key development guidelines were recommended: (1) practical training workshops, (2) coaching and mentoring programs, and (3) collaborative lesson development through professional learning communities. The study highlighted the critical importance of competency-based instruction in equipping students with essential 21st-century skills. It underscored the need for structured teacher development programs, particularly in resource-limited educational settings.
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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 | 0.003 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".