Teaching and Learning English in Thailand and the Integration of Conversation Analysis (CA) into the Classroom
Bibliographic record
Abstract
This paper provides an overview of English language teaching and learning, specifically as it pertains to teaching English conversational skills in Thailand. The paper examines the shortcomings of the Communicative Language Teaching (CLT) approach, the current dominant pedagogical approach in the nation, and explores how the integration of Conversation Analysis (CA) can potentially address those shortcomings. It is argued that CA can be used as a teaching tool to raise awareness of the mechanisms of conversation, which are potentially critical to a successful interaction, but oftengo unnoticed by both teachers and learners. This paper also posits that CA can serve as a diagnostic tool for examining talk and identifying problems that can hinder students from achieving targeted communicative teaching and learning goals. It is recommended that English teachers be trained to deploy CA in conjunction with CLT so as to increase students’ overall communicative competence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".