Exploring the Relationship Between Learning Motivation and L2 WTC in an EFL Classroom Among Thai EFL Learners
Bibliographic record
Abstract
The present study investigates the relationship between university learners’ English learning motivation and their willingness to communicate in English (L2 WTC) in EFL classroom under the big environment of Thailand joining the AEC (ASEAN Economic Community). By applying mixed methods, data is collected and the findings can be summarized as follows. Firstly, the university learners including males and females all have high motivation towards English learning, especially with higher instrumental motivation. Besides, the university learners in total have intermediate level on their willingness to communicate in English in EFL classrooms without significant gender difference. More than 50% of students are more willing to communicate in English with friends than with teachers for they believe that friends are easier to communicate and understand. Thirdly, university learners’ English learning motivation has strong positively correlation with their L2 WTC. In English learning motivation, instrumental motivation has stronger positively correlation with their L2 WTC than integrative counterpart and is better predictor of students’ L2 WTC in EFL classroom. These findings have implications for teachers teaching English in EFL context who should take the big environment in society and their distance with students into account, and shed some lights on the research of L2 WTC in the future.
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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 | 0.000 |
| 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) | 0.002 | 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".