Chinese EFL Learners' Perceptions of English Speaking Difficulties in Thailand
Bibliographic record
Abstract
This study aimed to investigate (1) the factors affecting Chinese EFL learners' difficulties when speaking English, (2) the levels of English speaking difficulties experienced by Chinese EFL learners, and (3) how they perceive teachers' help to overcome such difficulties. Eighty-five non-English major Chinese postgraduate students at a Thai university participated in this study. This study used a mixed-methods design that included a questionnaire and a semi-structured interview. Percentage, mean, and standard deviation were used to analyze the data obtained from the questionnaire. Content analysis was used to look for themes that emerged from the data obtained from the semi-structured interview. The finding indicated that linguistic factors were the most influential for non-English major Chinese postgraduate students at a Thai university when speaking English. Moreover, this study also found that the overall mean score of 85 non-English major Chinese postgraduate students at a Thai university who participated in the survey was 3.66 out of 5 in English speaking difficulties, which indicated that the 85 participants generally experienced a high level of English speaking difficulties. Furthermore, the results revealed in the semi-structured interviews can be divided into five themes: teaching pronunciation, teaching vocabulary, offering more opportunities for English speaking, reducing criticism, and giving positive feedback.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".