CA-informed Interactional Feature Analysis of Conversations in Textbooks Used for Teaching English Speaking in Thai Secondary Schools
Bibliographic record
Abstract
With Conversation Analysis (CA) insights, this paper examines the textbooks used to teach oral English communication to Thai EFL learners in secondary schools. In an attempt to raise the awareness of features of naturally-occurring conversation and help increase the learners’ exposure to these features, two textbook series, hereafter A and B, were purposively sampled for a close examination of their model conversations and related exercises. Six textbooks, three from each series, were obtained from secondary school teachers voluntarily joining a CA-informed English conversation-teaching workshop in lower southern Thailand. The findings showed that textbook series A contains action-driven, function-based communicative content, whereas B is theme/situation-based, being organized around topics or events likely faced by learners in daily life. Both textbook series put more focus on face-to-face dialogues, offering a significantly smaller number of phone and multi-party conversations. The model conversations in both series are presented with punctuation symbols of written language and without any representations of spoken language features such as stress and intonation. Some of the conversations in series B are sequentially incomplete, and while offering students conversations with various types of action sequences, both series can integrate more opening and closing sequences as well as sequences with dispreferred responses into their model conversations. To raise learners’ awareness of features of natural conversation, more instances of repair and overlap may also be integrated into both audio and printed materials. Finally, to achieve the communicative unit goal, more scaffolding exercises can be provided to allow students to practice not only word and sentence pronunciation in isolation, but in relation to achieving a particular interactional goal via the construction of turns in more manageable, meaningful sequences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".