Unwillingness to Communicate in English During Group Discussions Among Thai Postgraduate Students: Level and Coping Strategies
Bibliographic record
Abstract
This study aimed to investigate the level of Unwillingness to Communicate (UWTC) in English during group discussions among 37 Thai postgraduate students, as well as the coping strategies they typically employ to manage their UWTC. Burgoon’s (1976) Willingness to Communicate (WTC) questionnaire was used to measure the participants’ UWTC levels, and semi-structured interviews were conducted with six selected participants to explore their coping strategies in greater depth. The results revealed that the majority of participants (73%) exhibited a moderate level of UWTC, while 27% demonstrated a low level. Notably, no participants reported a high level of UWTC, indicating an absence of strong reluctance to communicate. The interview data further revealed that students managed their UWTC through strategies such as thorough preparation, positive self-talk, and collaboration with peers. These coping mechanisms helped reduce anxiety, boost confidence, and encourage more active participation in academic discussions. The findings highlight the importance of fostering supportive, low-anxiety classroom environments to enhance students’ willingness to communicate in English. This study offers practical implications for educators aiming to promote communicative engagement by addressing both the linguistic and affective factors influencing student participation in English-medium settings.
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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 | 0.002 | 0.010 |
| 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 | 0.002 |
| 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".