Factors Impacting EFL Teachers’ Self-efficacy: A Theoritical Perspective
Bibliographic record
Abstract
EFL teachers working in Saudi Arabian universities are often from different social, cultural and academic backgrounds. The factors that affect their self-efficacy can vary from one teacher to another because of their different social and cultural associations. This research is carried out in the light of the existing literature to determine some of the factors and variables which may influence the level of EFL teachers’ self-efficacy who teach at the universities in Saudi Arabia. Some of the factors are related to the personal traits of the teachers such as competence, experience, flexibility, hard work, perseverance, motivation, attitude, self-efficacy, resourcefulness and how they see themselves as teachers; whereas others are associated with their cultural, social and academic background, the working environment of an organization, and the attitudes of learners. Teacher self-efficacy is prone to be affected either negatively or positively by all these factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".