Evaluating Educational Groups’ Function of High Schools in Tehran on the Basis of Krek Patrick
Bibliographic record
Abstract
Nowadays, checking human sciences situation in valuation educational system in Iran is very important. They who educated in Iran’s educational system, then tested higher education one of the most sophisticated countries, find well that one of the most important distinctions between two systems is difference of situation and human science value. The aim of this research is evaluating educational groups’ function of human sciences area in high schools in Tehran due to Krek Patrick Model in 4 levels (reaction, behavior, learning an results). Statistical population of all teachers of educational groups of schools in Tehran by number… and sample volume according to Cochran formula is 359 that selected by clustered sampling among teachers who are members of educational groups of human sciences area. To collect data from questionnaire, researcher uses four above levels. Formal and content validity are accepted by 15 authorities and reliability that is calculated by using Cronbach Alpha in 4 levels (reaction, behavior, learning and results) is 0.823, 0.795, 0.861, and 0.742 respectively. Study method is descriptive and survey and adding to descriptive indexes, to analyze data, one group t, independent t, analyzing multivariable variance and regression are used. Results of one group t test showed that educational groups’ function of human sciences area is suitable due to Krek Patrick Model in four studied levels. Also results of independent test t and analyzing multivariable variance showed that there is a meaningful difference in educational groups’ function according to Krek Patrick Model and also in various levels among women and men. Finally, results of regression showed that history of work predicts educational groups’ function according to Krek Patrick model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".