Enhancing English Language Learners’ Conversation Abilities via CA-informed Sitcom Lessons
Bibliographic record
Abstract
The study investigated the effectiveness of Conversation Analysis (CA)-informed sitcom lessons in enhancing conversation abilities of Thai learners of English. The participants included 42 high school students enrolled in an English for Communication course at a public high school in Southern Thailand. Through 15-week sitcom lessons, they were taught how to construct conversation sequences to accomplish such sequential actions as greeting and leave-taking, dis/agreement, new announcement, compliment, invitation, and request, as well as to collaboratively analyze conversations from the sitcoms and role-play them at the end of each lesson. Before and after the series of lessons, the participants were engaged in role-play conversations that were videotaped for subsequent assessment of their conversation abilities. The findings from both comparative statistical and close single-case analyses revealed significant improvements in all the aspects assessed especially regarding grammar and appropriacy. Therefore, it is recommended that EFL teachers should apply CA principles to teaching English conversation, integrating conversations from authentic materials such as sitcoms to strengthen English language learners’ conversation abilities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".