EFL Students’ and Teachers’ Attitudes toward Foreign Language Speaking Anxiety: A Look at NESTs and Non-NESTs
Bibliographic record
Abstract
Native English Speaking Teachers (NESTs) have been employed in various English language teaching (ELT) positions and departments at private and state universities in Turkey, particularly over the last three decades. However, undergraduate EFL students’ attitudes toward NESTs and Non-Native English Speaking Teachers (Non-NESTs) remain seriously under-investigated. The purpose of this study is to examine the impact of communication classes given by NESTs and Non-NESTs on students’ foreign language speaking anxiety (FLSA). Forty-eight undergraduate EFL students attending communication classes taught by (American) NESTs and (Turkish) Non-NESTs were given a questionnaire to examine their attitudes toward foreign language speaking anxiety (FLSA). Further, a sub-sample of students was interviewed to investigate their feelings, beliefs and opinions about the relationship between the FLSA they experienced and their communication classes given by NESTs and Non-NESTs. Similarly, the teachers were interviewed to examine their feelings about the FLSA their students experience in their communication classes. Quantitatively, the results showed no significant difference in attitude toward FLSA between the students who attended classes taught by NESTs and Non-NESTs, although a significant difference was observed between the two classes taught by Non-NESTs. Further, female and male students did not differ significantly in terms of attitudes toward FLSA in NESTs’ and Non-NESTs’ classes. The qualitative findings revealed that both teachers and students had positive attitudes toward mistakes made during the oral production of the foreign language (FL). Finally, the correction strategies employed by the teachers in the classroom are believed to have an impact on student attitudes toward FLSA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".