Exploring English-Speaking Anxiety, Vocabulary Size, and Their Relationship Among High- and Low-Performing Thai University Students
Bibliographic record
Abstract
This study examines English speaking anxiety, vocabulary size, and the relationship between English language speaking anxiety and vocabulary size among Thai university students, specifically analyzing differences between high- and low-performance groups. A sample of 438 undergraduate students enrolled in a Communicative English course participated in the research, utilizing the Updated Vocabulary Level Test (UVLT) to measure vocabulary size and a speaking anxiety questionnaire to evaluate anxiety levels. The Rasch analysis shows that the speaking anxiety questionnaire indicates model fit, one-dimensionality with an eigenvalue of 1.85, local independence, and person reliability of 0.894 (p < .001), reflecting the good quality of the instrument. The findings show that both high- and low-performing students experience significant levels of speaking anxiety, with mean anxiety scores ranging from 73% to 75%. Interestingly, similar levels of English speaking anxiety are exhibited by two groups. It also shows that only the high-performance group is likely to mastery the first 1000 words level and progress to another level. A significant relationship was observed between vocabulary size and English speaking anxiety. Although vocabulary size has a minimally positive correlation with speaking anxiety, it suggests a complex interrelationship between vocabulary size and speaking proficiency, with trait worry leading to high cognitive load and difficulty maintaining performance. This study underscores the critical need for language educators to address both vocabulary enhancement and anxiety reduction strategies in their teaching practices. Future studies should explore specific interventions aimed at mitigating anxiety and enhancing vocabulary acquisition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".