A Study of Discourse in Relation to Language Learning in English Classes Co-Taught by Native English-Speaking Teachers and Local Teachers in Taiwan
Bibliographic record
Abstract
This study attempts to explore the nature and the potential of various discourse structures and linguistic functions that may facilitate students’ learning in English classes co-taught by a native English-speaking teacher (NEST) and a local English teacher in Taiwanese elementary schools. Considering the nature of the study, the author employed a case-study approach to investigate the classroom discourse. In the study, data were analyzed based on a theoretical framework combining discourse analysis schemes, systemic functional theory of language, sociocultural theory of mind and activity theory. The study reveals that repetition drills were commonly used in the classrooms in spite of the difference in the learners’ levels, and the Initiating-Responding model was the dominant feature of the classroom discourse structure. The target language, i.e., English, was used by the teachers for demanding information or action, while by the students it was used for repeating and imitating. In light of the findings, the author makes suggestions on co-taught English classes of this kind, for instance, the necessity of creating interactional context for language use, encouraging individual responses from students, and using alternative discourse strategies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".