The Effect of School Administration and Educational Supervision on Teachers teaching performance: Training Programs as a Mediator Variable
Bibliographic record
Abstract
The aim of the present study was to investigate the effect of educational supervision as an independent variable on English language teachers’ teaching performance as a dependent variable as well as the role of training programs as a mediator variable. The investigation reported in the study was carried out among Libyan school teachers of English in the city of Zawiya in Libya. Reaching an overall number (N=3036) of teachers as reported by the Ministry of Education, the study sample was (N=351) teachers who were chosen using a random sampling technique. In order to achieve this, the study used a quantitative analysis, specifically a confirmatory factor analysis (CFA) to test the validity of the assumed relationships among these variables as well as structural equation modeling (SEM) through AMOS. The study obtained several results, most important of which indicated that educational supervision was positively correlated with teachers’ performance in teaching English. In addition, the results showed that training programs played an important role as a mediator variable in a making a higher indirect positive impact of educational supervision on teachers’ teaching performance. Based on these results, in order to enable the Libyan school teachers of English address their weaknesses and identify their strengths, educational supervisors should organize more effective activities related to teacher training. Their interests should not be exclusive to teachers’ imperfections, but they should interact and communicate with teachers for better identifications of their strengths and weaknesses. By so doing, teachers can promote their professional knowledge, skills and experiences.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".