Interdisciplinary Teacher Collaboration in English for Specific Purposes Subjects in a Thai University
Bibliographic record
Abstract
The purpose of this research study was to investigate the roles of English teachers and subject teachers engaged in the collaborative process of interdisciplinary teaching in English for Specific Purposes subjects at a Thai university and explore the benefits and drawbacks of implementing such collaborations. In addition, students’ attitudes towards interdisciplinary teacher collaboration (ITC) in ESP classrooms were explored. Participants were English teachers, subject teachers, and students studying on ESP subjects. This research study used a mixed methods approach from four sources of data. The findings revealed the extensive roles taken on by both teachers involved in the ITCs. Roles for the English teacher involved being a lesson planner, teacher, learning organizer, and class activities designer. The subject teacher’s role was identified as a consultant or informant, supporter, monitor, and facilitator. The benefits were that an English teacher gained confidence, reduced worry in teaching ESP subjects, and received instant feedback from the subject teacher. The drawbacks were that it was challenging to balance the different schedules of both teachers and that lesson planning was time consuming. Students showed positive attitudes towards this method of teaching. They liked to study because of the enjoyable and knowledgeable activities and the teacher’s confidence.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".