Learning English as Thai Adult Learners: An Insight into Experience in Using Learning Strategies
Bibliographic record
Abstract
This research aims to understand language learning strategies of Thai adult learners and factors affecting their strategy use. The participants are forty officers of General Service Division of the Council of State of Thailand, attending an English training course for developing their work potential. The data were collected through the questionnaire adapted from the Strategy Inventory for Language Learning (SILL). To further explore personal views about their experience in learning strategies and factors influential on their strategy use, fifteen participants were selected for individual interviews. Findings revealed that the learners reported an overall preference for the use of social strategies. Analysis of the qualitative data confirmed most of the SILL responses and revealed additional strategies and factors affecting the strategy use. Individual learners chose strategies suitable for the achievement of their goals and take account of their affective needs and work context. Overall, this study has emphasized the necessity of having qualitative data which can enrich and illuminate the findings of quantitative data and could be valuable resources for considering appropriate ways in which English proficiency of Thai adult learners could be developed. Implications are drawn regarding the language learning strategies of adult learners and their strategy use as professional engagement as well as recommendations for future research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".