Investigation of Teaching Competencies to Enhance Students’ EFL Learning at Taif University
Bibliographic record
Abstract
The paper aimed to investigate the teaching competencies implemented by instructors to enhance EFL students at Taif University in Saudi Arabia. The first two questions discussed the degree of implementing the teaching competencies, which either enhance or hinder learning. The third and fourth questions were an attempt to examine if there were any significant differences at (a=0.05) due to instructors’ qualifications and years of experience. The researcher developed an observation checklist to investigate the degree of implementing the competencies. There were four domains: preparation, instruction, assessment, and educational climate. The findings of the first two questions showed that preparation and educational climate include the competencies where the instructors displayed satisfactory performance. However, instruction and assessment include the competencies which were rarely or not demonstrated. The results of the third question showed that there were significant differences. However, there were no significant differences due to years of experience. The findings of the first two questions could be due whether or not instructors have undergone professional development to equip them with the competencies required to enhance students’ learning. The results of the third question could be due to the fact that instructors having a diploma after the bachelor degree helped them make difference. The result of the fourth question could be due to the educational context that didn’t support instructors’ 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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".