Examining Foreign Language Learners’ Speaking Anxiety: The Case of English L2 Learners
Bibliographic record
Abstract
Examining speaking anxiety in learners of foreign languages is important not only for the learners but also for teachers and curriculum designers. The study aimed to examine foreign language learners’ speaking anxiety in 100 M.A. students at Prince Sattam bin Abdulaziz University in Saudi Arabia. The survey was directed to M.A. students. The study used the PSCAS. The findings demonstrated that a variety of factors, including a limited vocabulary, pronunciation difficulties, social pressure, a lack of confidence, and negative past experiences, might contribute to speaking anxiety. Together, these components create a complex web of worry that keeps students from being open to vocal communication. However, the study also discovered several practical strategies that M.A. candidates could employ to boost their speaking confidence and lessen their speaking anxiety. These strategies included role-playing games, conversations in small groups, the use of technology, group projects involving collaborative speaking, regular constructive criticism, availability of interactive language labs, practical language application, progressively more difficult assignments, cultural immersion programs, and workshops on public speaking. By using these strategies, students can establish a supportive learning environment that promotes language proficiency and confidence. Based on the findings of this study, several recommendations were proposed, such as integration of supportive learning environments, utilization of technology, implementation of practical language application, training in public speaking, gradual complexity in assignments, and promotion of positive thinking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".