Crossing Deserts and Oceans: Professional Development Routes of English Teachers in Arab Gulf Countries
Bibliographic record
Abstract
This study explores expatriate English teachers' professional development (PD) experiences in the Arab Gulf Cooperation Council (GCC) countries. It focuses on how teaching experience and age influence their perceptions of PD programmes. As English proficiency becomes increasingly vital for academic and professional success in the region, PD has emerged as a critical mechanism for enhancing teaching quality and supporting teachers' career growth. However, many expatriate teachers face challenges related to limited access to PD opportunities, a lack of institutional support, and misalignment between PD programmes and their specific needs. This research investigates these issues through a survey, collecting data from 144 expatriate teachers working in the Gulf. Findings reveal that more experienced teachers view PD as beneficial for career growth but express concerns about its relevance to their day-to-day teaching contexts and limited impact on career advancement. The study underscores the need for tailored PD programmes that address the distinct needs of expatriate teachers and align with the region's unique educational challenges. It also highlights the importance of institutional support in fostering a culture of continuous learning among expatriate educators. Policymakers and academic leaders are recommended to improve PD programmes and enhance the professional growth of expatriate teachers in the Arab Gulf countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".