Exploring the Influence of Socio-Cultural Factors on Teacher Development in the Saudi EFL Setting
Bibliographic record
Abstract
This research article explores the complex relationship between socio-cultural factors and teacher development within the English as a Foreign Language (EFL) context in Saudi Arabia. Acknowledging the pivotal role of teacher growth in enhancing language education, the study seeks to unravel the influence of socio-cultural elements on the professional development of EFL educators in the unique Saudi Arabian educational landscape. Through the lens of qualitative research, this investigation employs in-depth interviews with 10 Saudi teachers actively engaged in teaching English at the university level. The thematic analysis method was chosen for its suitability in identifying patterns and recurring themes within the qualitative data, allowing for a comprehensive exploration of the socio-cultural dynamics influencing teacher development. Themes such as societal perceptions of the teaching profession, the influence of traditional teacher-development approaches, group learning, community support, cultural sensitivity, and Islamic values emerge as crucial elements shaping the participants’ experiences. The study highlights the intricate ways in which cultural norms and societal expectations interact with teachers’ professional journeys, impacting their teaching methodologies, beliefs, and overall development. The findings offer valuable insights for educators, policymakers, and researchers seeking to enhance teacher development programs within diverse cultural and linguistic contexts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".