Implementing CA-T Model Lessons in Schools: A Preliminary Study in Southern Border Provinces of Thailand
Bibliographic record
Abstract
Encouraged by previous studies which recommended incorporating insights from Conversation Analysis (CA) into English conversation teaching to improve EFL students' oral proficiency, this paper reports on the findings from Phase I of a longitudinal study designed to investigate the impact of employing a CA-informed teaching (CA-T) model to improve Thai students' oral English proficiency. The aim of Phase I of this study was to engage local teachers in co-developing and piloting the CA-T model. In this phase, 16 purposively sampled primary and secondary English teachers from Thailand’s southern provinces participated in an intensive 6-day workshop designed to (1) familiarize them with the instructional value of CA insights and key features of the CA-T model and (2) assist these teachers in creating CA-T lesson plans. Following the workshop, teachers piloted the lesson plans, provided feedback on the implementation process, reported on the perceived effects of the lessons, and offered recommendations for improving the CA-T model. This paper describes the content of the workshop, shares teachers' feedback about the CA-T lessons and implementation process, and presents preliminary findings as to the potential challenges and benefits of employing the CA-T model in Thai primary and secondary classrooms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".