Challenges in online English language learning: a study of an English medium instruction school in Thailand
Bibliographic record
Abstract
The challenges in online English learning in a Thai secondary school context are the focus of the study where English is used as a medium language for instruction by adapting Hijazi and AlNatour’s (2021) and Sukman and Mhunkongdee’s (2021) frameworks to explore the challenges in online English learning perceived by students and students’ challenges perceived by teachers in a secondary school in Samut Sakhon province. The participants in this study were 77 Thai students and 20 teachers including 10 Thais, eight Filipinos, one Canadian, and one Australian. The adapted questionnaires were completed by the participants. Afterwards, the data gained from the questionnaires were analysed using SPSS statistics. The results showed that the biggest challenge in online English learning for both groups of the participants was social aspects. Motivation and willingness came up as the second biggest challenges among the groups. However, the third challenge for students was teaching methods, whereas the third challenge perceived by teachers was online English learning. The findings of this study may be useful for future planning for schools, teachers, students, and other stakeholders. Recommendations and implications of the findings are also provided.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".