Understanding Professional Challenges Faced by Iranian Teachers of English
Bibliographic record
Abstract
The objective of the present study is to understand professional challenges faced by Iranian high school teachers of English through exploring their viewpoints. To this end, it benefits from both qualitative and quantitative modes of inquiry. First, grounded theory method was used to conduct some interviews with 20 EFL teachers and members of educational groups in the Education Organization, Shiraz, Iran. After coding the obtained data, a number of concepts and categories were identified and a model was developed. Next, a questionnaire was designed out of the findings of grounded theory procedures. It was filled out by 130 EFL teachers and the collected data was subjected to both descriptive and inferential statistics. The results confirmed the existence of educational, social, economic and temporal challenges in the profession. They further revealed that variables of gender, years of experience and educational districts had no significant effects on teachers’ viewpoints. Generally, the current EFL situation has led to teacher burnout. In order to improve the situation, some modifications seem necessary. With regard to this, a number of solutions have been offered at the end of this study.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".