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Record W4410942377 · doi:10.5430/wjel.v15n6p184

Crossing Deserts and Oceans: Professional Development Routes of English Teachers in Arab Gulf Countries

2025· article· en· W4410942377 on OpenAlexvenueno aff
Konstantinos M. Pitychoutis, Ahmed Al Rawahi, Filomachi Spathopoulou

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyPolitical scienceOceanography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.249
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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