Confirmatory Factors Analysis Practice Level and Guidelines for Developing Teachers' Performance Standards and Conduct Standards
Bibliographic record
Abstract
The objectives of this research are 1) to analyze the confirmatory factors performance standards and conduct standards of teachers who have been certified through the Teachers' Council of Professional Knowledge Standards training 2) to study the level of compliance with performance standards and conduct standards, and 3) to study the development guidelines according to performance standards and conduct standards. The samples consisted of 348 persons who passed the teachers' professional knowledge training standard of the Teachers Council of Thailand by Multi-Stage Random Sampling. The instruments used to collect data are: quality-validated questionnaires from experts and try out before use. Analyze data with Second-Order Confirmatory Factor Analysis, S.D., and Content Analysis. The results of the research were as follows: 1) The second-order confirmatory factors analysis on performance standards showed that the model was consistent with the empirical data. It shows that the performance standard consisted of 12 factors. In terms of conduct standards, it found that the model was consistent with the empirical data additionally. It showed that the standard of conduct consisted of 5 factors. 2) Compliance with overall performance standards is at a high level and conduct standards are at a very high level. 3) There are seven development guidelines according to performance standards and six guidelines for development according to the conduct standards which the experts have approved.
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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 | 0.124 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".