Degree of having Teachers of Mathematics for the Secondary Stage of Evaluating Competences at Schools of Mafraq Governorate / Jordan from Their Point of View
Bibliographic record
Abstract
This study aimed at revealing and investigating the degree which teachers of mathematics at the secondary stage, the calendar competences at the schools of Mafraq governorate in Jordan from their point of view (gender variables, educational qualifications and teaching experience). To achieve the objective of this study, the researcher prepared a questionnaire including two areas of the competences of the teachers' personal and cognitive competences, consisting of (30) items divided into domains (15) for each. The sample consisted of (62) female and male teachers who were teaching at secondary stage. To extract the results, the researcher used the analysis of the three way ANOVA and multivariate variance in line with means and standard deviations. The results showed that the degree of having secondary teachers of mathematics at schools of Mafraq governorate for evaluation competences of students' learning was medium, while the total mean was of (3,37). The results also showed that there were no statistically significant differences at the level of (α ≤ 0.05) to the extent of having secondary teachers of mathematics at schools of Mafraq governorate for evaluation competences of learning from their point of view due to gender variable. Whereas, the results showed that there were statistically significant differences at the level of (α ≤ 0.05) of the degree of having evaluation competences in experience and qualification variables in favor of experience (more than 5 years) and for the scientific qualification which is higher than the bachelor degree.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".