The Perceptions of Saudi EFL Students Toward the Effect of Anxiety on Their Oral Communication
Bibliographic record
Abstract
Oral communication is one of the most important skills of communication among individuals with each other. It refers to communication between the students and their teachers through the spoken language, which includes a group of distinctive words, phrases, and sentences which are associated with specific meanings. However, there are many barriers that affect this communication. Therefore, this study addresses some of the barriers of anxiety when communicating using the English language in which it constitutes a major problem for EFL students and hinders their goals in learning the English language. This study aims to investigate the EFL students’ perceptions towards the effect of anxiety on their oral communication in various Saudi universities. Moreover, it explores some strategies that may help EFL students to overcome this problem. The research instrument used in this study was a questionnaire that was modified for the purposes of the study. The questionnaire consisted of 20 items divided into two parts. Furthermore, 102 EFL students participated in this study. The results showed that EFL students suffer from anxiety while speaking English, whether in a discussion class with the teacher or when presenting a presentation. The participants also agreed with most of the strategies stated in the survey. Based on the findings, recommendations and suggestions for further research are provided.
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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 | 0.004 |
| 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 | 0.002 | 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".