The Level of Performance of Science Teachers in the Sultanate of Oman in Light of NSTA Standards from their Point of View
Bibliographic record
Abstract
The study aimed to know The Level of Performance of Science Teachers in the Sultanate of Oman in Light of NSTA Standards from their Point of View. The study sample consisted of (126) science male and female teachers in the al-dakhliah governorate in the Sultanate of Oman for the academic year 2016/2017. The researchers developed a questionnaire to determine the level of performance of science teachers in light of NSTA standards, which consisted of 83 paragraphs divided into five standards. The results of the study indicate that the level of performance of science teachers was moderate. The results indicated that the highest level performance of science teachers was in teachers' knowledge and understanding of content, followed by the standard of understanding how students learn and use different methods to develop their knowledge. While the lowest level of performance of science teachers in the effective planning of educational units standard. The results also showed no significant differences in the performance of science teachers attributed to the gender variable or specialization variable, while there are differences of statistical significance attributed to the variable qualification scientific and for the benefit of Masters, and the existence of differences of statistical significance attributed to the variable number of years of service and for teachers who have years of experience more than ten years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".