The Effect of Pragmatic Instruction on Developing Learners’ Use of Request Modifiers in the EFL Context
Bibliographic record
Abstract
The aim of this study is to evaluate the effects of three teaching approaches: a deductive teaching approach, an inductive teaching approach, and an inductive-deductive teaching approach on facilitating Chinese EFL learners’ use of request modifiers. Written discourse completion tasks were employed to collect learners’ request data and a follow-up interview reported Chinese EFL learners’ overall positive attitudes towards pragmatic instruction with a preference for the deductive approach. The findings presented the necessity for instructions of request in EFL contexts and reveal the superiority of the inductive-deductive teaching approach on pragmatic knowledge. Combing the results of the experiment with learners’ perceptions, it indicates that practitioners should consider incorporating both deductive and inductive instructions to fit learners’ preferences of instructional styles and learning needs. Besides, in terms of learners’ pragmatic competence, such a teaching approach would also guarantee the treatment effect in both short and long runs.
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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 | 0.004 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".