Patterns of uptake and repair following recasts and prompts in an EFL context: Does feedback explicitness play a role?
Bibliographic record
Abstract
This study sought to examine the effectiveness of two categories of feedback, namely recasts and prompts. Also, the study focused on the relationship between subsets of each feedback type and the extent to which they led to learner uptake and repair in an EFL context. Data were collected through non-participant observations of three intact upper-intermediate EFL classes where 36 hours of interactions among 59 students and three teachers were audiotaped, transcribed, and analyzed in terms of pre-specified coding systems that addressed four different subtypes of prompts – clarification requests, repetitions, elicitations, and metalinguistic clues – and two recast subtypes – explicit and implicit recasts. Data analysis showed that among prompts, clarification requests led to the highest percentage of uptake whereas elicitations were associated with the highest repair percentage. As for recasts, more explicit ones led to higher percentages of uptake and repair. The results of the study may contribute to a more in-depth understanding of the patterns of uptake and repair in an EFL context. The study confirms the role of feedback explicitness in such a context.
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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 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".