Program Development of English Teacher’s Competency Enhancement for Learning in 21st-Century of Secondary Schools in the Northeastern Region
Bibliographic record
Abstract
The English language is a global language and crucial for all people, and English teachers’ competency development affects high-quality students, the objectives of the research were 1) to study the elements and indicators of English teachers’ competency 2) to study the current conditions, desirable conditions, and needs to enhance English teachers’ competency 3) to create and develop the English teachers’ competency enhancement program, and 4) to study the results of implementing the English teachers’ competency enhancement program. Research Methodology was Research and Development which were conducted in 4 phases according to research objectives. The findings of the study showed that: I. The elements and indicators of English teachers’ competency consisted of 3 aspects, 14 elements, and 67 indicators including 1) knowledge, 2) skills, and 3) characteristics insisted on by the 9 experts. In all aspects, 14 elements were appropriate at the highest level. II. In current conditions competency of English teachers in all aspects were at a high level. Overall desirable conditions were at the highest level, and their competencies needed were knowledge, skills, and characteristics, respectively. III. The English teachers’ competency enhancement program consisted of 1) rationale 2) objectives 3) contents: Module 1 (18 hours), Module 2 (12 hours), Module 3 (6 hours) 4) procedure and sub-activities 5) test and measurement. The 9 experts’ evaluation of the program is at the most appropriate, feasibility, and utility level. IV. The result of implementing the English teachers’ competency enhancement program showed that 1) Knowledge: the post-development average was higher than the pre-development average. 2) Skills: assessed by the school supervising teacher, the first time their overall performances were at the average level, the second time and third times were at a high level, and the highest respectively. 3) Characteristics: evaluated by the director and the head of the Foreign Languages Department, the first time supervision was at the average level, the second time and third times were at a high level, and the highest, respectively. 4) the overall satisfaction of English teachers who joined the program was at the highest 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 | 0.003 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".