Conversation Analysis as Discourse Approaches to Teaching EFL Speaking
Bibliographic record
Abstract
Conversation has been of primary interest to language researchers; since natural, unplanned, everyday conversation is the most commonly occurring and universal language “genre”, in that conversation is a speech activity in which all which all members of a community routinely participate Among approaches to discourse analysis in speaking, conversation analysis is one of the practical devices in teaching spoken English in EFL classroom. This paper tries to look at the theoretical basis for conversational analysis and explore the feasibility of applying a discourse approach to speaking in teaching a group of learners. Key words: Conversation Analysis; Discourse Approach; EFL; Spoken English Resume: La conversation a ete d'un interet primordial pour les chercheurs de langue, parce qu'une conversation naturelle, non planifiee et quotidienne est le genre de langue le plus frequent et universel. La conversation est une activite de discours dans lequelle tous les membres d'une communaute participent regulierement. Parmi les approches d'analyse du discours dans l'oral, l'analyse de conversation est l'un des dispositifs pratiques dans l'enseignement de l'anglais en oral pour les etudiants qui apprennent l'anglais comme une langue etrangere. Cet article tente d'examiner les fondements theoriques de l'analyse de conversation et d'explorer la possibilite d'appliquer une approche discursive a l'oral dans l'enseignement d'un groupe d'apprenants.Mots-cles: analyse de conversation; approche discursive; anglais en tant qu'une langue etrangere; anglais parle
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".