Foreign language anxiety in EFL classrooms: teachers' perceptions, challenges, and strategies for mitigation
Bibliographic record
Abstract
This study investigates Foreign Language Anxiety (FLA) in English as a foreign language (EFL) classrooms in Egyptian higher education. The study was conducted at multiple universities in Egypt, where English is widely taught and learned as a foreign language. Data was collected through a questionnaire completed by 49 EFL instructors, supplemented by five one-to-one semi-structured interviews with participants from the questionnaire. The results demonstrate that EFL teachers generally acknowledge and frequently observe FLA among students, particularly during speaking activities. Specific sub-skills such as summarizing and presenting were identified as anxiety-inducing. Although some instructors consider FLA motivating, others view it as detrimental to student progress. Instructors also suggest creating supportive environments where mistakes are valued as part of the learning process to mitigate FLA. Interestingly, it was found that instructors themselves may experience FLA, particularly when speaking with native speakers or teaching in a second language (L2). Strategies suggested by instructors to alleviate FLA include group discussions, role plays, individual activities with preparation, and peer support. Overall, instructors' attitudes, rapport, and feedback play a crucial role in managing FLA levels in the classroom. This study contributes to raising awareness among stakeholders toward FLA in Egypt and the broader EFL context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".