The Degree of Educational Supervisors’ Commitment to Apply the Classroom-Visits Techniques
Bibliographic record
Abstract
The study aimed to investigate the degree of educational supervisors’ commitment to apply the classroom-visits techniques (before, during, after the visit, and the total). The study population consisted of (652) teachers of the elementary stage in Petra; (169) males and (483) females, in addition to their educational supervisors, which consisted of (16) educational supervisors. About (35%) of the total teachers were randomly selected while keeping on all the educational supervisors. To achieve the study objectives, the analytical and descriptive methodologies were used, the researchers used a questionnaire as a tool for the study which consisted of three domains of the classroom-visit techniques: before, during and after the visit.The study results showed that the teachers and educational supervisors believe that the degree of educational supervisors’ commitment to apply classroom-visit techniques is high. The results also showed that there are significant differences at the level of (a≤0.05) between the means of teachers’ views and the educational supervisors’ views to the degree of educational supervisors’ commitment to apply the classroom-visit techniques in favor of the educational supervisors. They see that they apply the classroom-visit techniques with a higher degree than that of the teachers’ views. The results also indicated no statistical significance according to the variables of gender and years of experience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".