Teaching Competencies and Job Satisfaction among Basic Education Teachers
Bibliographic record
Abstract
This study aimed to identify the teaching competencies and job satisfaction among basic education teachers.To achieve the objectives of the study, two study tools, teaching competencies questionnaire and job satisfaction scale, were applied. The study followed a descriptive methodology; its sample consisted of (65) male and female teachers from public schools at Al-jam'a directorate of education in Amman, for the spring semester of the academic year 2014/2015; the participants were chosen randomly. For the purpose of statistical analysis, means and standard deviations were computed. However, the validity and reliability tests were conducted for the study tools. Thus, Cronbach alpha coefficient were applied to measure the reliability of first tool and second tool of the study, which were (0.869 and 0.857) respectively. The results of the study revealed that the level of the teaching competencies possessed by physical education teachers on the total score was high, and the level of job satisfaction possessioned by physical education teachers on the total score was moderate. The results also showed that there were no statistically significant differences at the level of significance (α = 0.05) in the three domains of teaching competencies (planning competencies, implementation competencies, and evaluation competencies). In the light of the results of the study, the study concluded a number of recommendations including: inclusion the teaching competencies to be met by physical education teachers in preparation and teacher training programs; and pay attention to the moral, economic, and social aspects of physical education teachers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".