Effects of Recasts and Elicitations in Dyadic Interaction and the Role of Feedback Explicitness
Bibliographic record
Abstract
The present study investigated the effects of two categories of interactional feedback—recasts and elicitations—on learning linguistic forms that arose incidentally in dyadic interaction. The study also identified implicit and explicit forms of each feedback type and examined their subsequent effects immediately after interaction and after 2 weeks. Data came from 42 adult English as a second language learners who participated in task‐based interaction with two native‐ speaker English language teachers and received various forms of recasts and elicitations on their nontargetlike output. The effects of feedback were measured by means of learner‐specific preinteraction scenario descriptions and immediate and delayed postinteraction error identification/correction tasks. The results showed a higher degree of immediate postinteraction correction for recasts than for elicitations. The results also showed that in both cases the more explicit forms of each feedback type led to higher rates of immediate and delayed postinteraction correction than the implicit forms. However, the effects of explicitness were more pronounced for recasts than for elicitations. These latter findings suggest that although both recasts and elicitations may be beneficial for second language learning, their effectiveness might be closely, but differentially, related to their degree of explicitness.
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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 | 0.007 | 0.056 |
| 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 | 0.000 | 0.002 |
| 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".