The Impact of Corrective Feedback on L2 Pragmatics Production in Face-to-face and Technology-mediated Settings
Bibliographic record
Abstract
This paper presents findings from a quasi-experimental study that examined the effect of corrective feedback (CF) on L2 pragmatics, specifically comparing Face-to-Face (FF) and Technology-Mediated (TM) modes. The study involved a total of forty-four ESL students from three parallel intact classes. The primary focus of this paper is to report the results obtained from data collected through production tasks employing Role-play scenarios. To analyze the data, a mixed-model Analysis of Variance was conducted, examining the main and interaction effects of CF, delivery mode (FF and TM), speech act type (request and refusal), and time (pre-test, post-test, and delayed post-test). The results demonstrated that CF had a substantial positive effect on L2 pragmatic production, resulting in significant overall improvement. Furthermore, the results showed that both FF and TM modes of CF were similarly effective for enhancing pragmatic production. Additionally, the study demonstrated that the effects of CF on pragmatic production were durable and long-lasting. Altogether, these findings support the utilization of corrective feedback in technology-mediated language instruction within L2 classrooms.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".