Second Language Writing Anxiety of Thai EFL Undergraduate Students: Dominant Causes, Levels and Coping Strategies
Bibliographic record
Abstract
Using a mixed-methods research design, this study explored the dominant causes, levels, and coping strategies of second language writing anxiety among 55 second-year Thai EFL undergraduate students majoring in English for International Communication. Data were collected from the Causes of Writing Anxiety Inventory (CWAI) questionnaire developed by Rezaei and Jafari (2014), the Second Language Writing Anxiety Inventory (SLWAI) developed by Cheng (2004), and a stimulated recall interview. Descriptive statistics were applied to analyze the data obtained from CWAI and SLWAI, while thematic analysis was used to identify themes from the stimulated recall interview. The results revealed that the predominant cause of writing anxiety was writing assignments, affecting 77.09% of students, followed by linguistic difficulties and fear of writing tests, each affecting 70.09%. Additionally, the study found that the level of writing anxiety among these students was high according to the writing anxiety questionnaire. Furthermore, during the stimulated recall interview, participants revealed five strategies typically used to manage their writing anxiety, namely positive self-talk, starting with a plan, relaxation techniques, goal setting, and seeking social support.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".