Exploring the Sources of English as a Foreign Language Teachers’ Stress at Secondary High School
Bibliographic record
Abstract
Stress is a psychological and physiological response to perceived challenges or threats, often resulting in emotional or physical tension that can lead to feelings of frustration, anger, or nervousness. For non-native English teachers, teaching English as a foreign language (EFL) presents unique stressors that differ from those faced by first or second-language instructors. Research suggests that EFL teachers experience higher levels of anxiety and job-related pressure due to linguistic barriers, cultural differences, and institutional expectations. This study aims to investigate the primary sources of stress among EFL teachers at the secondary high school level and to identify key factors that contribute to their occupational strain. A quantitative research approach was adopted for this study, with primary data collected through a structured survey questionnaire distributed to EFL teachers. Using purposive sampling, 32 secondary school English teachers were selected to participate, ensuring that respondents had relevant experience in EFL instruction. The collected data were analyzed using SPSS software to identify significant patterns and correlations. The findings highlight multiple stressors, including teachers’ lack of capability, economic constraints, political instability in the country, students’ poor academic performance, inadequate school infrastructure, and restrictive school management policies. The study underscores the need for institutional support and policy reforms to mitigate stress among EFL teachers. Addressing these stressors could improve teacher well-being, reduce attrition rates, and enhance the overall quality of English language education. By understanding the specific challenges faced by EFL educators, schools, and policymakers can implement targeted interventions to create a more sustainable and supportive teaching environment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".