THAI UNIVERSITY STUDENTS’ ATTITUDES TOWARDS ELF IN CLASSROOM CONTEXTS: A MIXED-METHODS STUDY
Bibliographic record
Abstract
English has long acted as a powerful language in the world since it is spoken by various people from diverse cultural backgrounds and places around the globe. As a number of non-native speakers (NNSs) are rapidly growing, English as a Lingua Franca (ELF) has become more widespread among English users. This study aimed to investigate Thai university students’ attitudes towards ELF. Particularly, attitudes towards learning English, varieties of English, and ELF used as a medium of instruction in the classroom were examined. Employed a mixed-methods research design, the questionnaire and semi-structured interview were used to collect the data. 23 undergraduate students studying at Xavier Learning Community (XLC), Chiang Rai, Thailand, participated in this study. The findings revealed that effective communication with other English speakers was the paramount goal of learning English. Moreover, the students have begun to express positive attitudes towards different varieties of English used in the classroom as they showed their interest in non-native cultures. This study suggested that supportive learning environments where the students could expose to various varieties of English both inside and outside the classroom would raise their awareness of ELF. Also, ELF teachers should adjust their teaching strategies to meet students’ needs 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".