Patterns of Corrective Feedback and Uptake in an Adult ESL Classroom
Bibliographic record
Abstract
This article begins by synthesizing ndings from observational class-room research on corrective feedback and then presents an observa-tional study of patterns of error treatment in an adult ESL classroom. The study examines the range and types of feedback used by the teacher and their relationship to learner uptake and immediate repair of error. The database consists of 10 hours of transcribed interaction, comprising 1,716 student turns and 1,641 teacher turns, coded in accordance with the categories identi ed in Lyster and Ranta’s (1997) model of corrective discourse. The results reveal a clear preference for implicit types of reformulative feedback, namely, recasts and transla-tion, leaving little opportunity for other feedback types that encourage learner-generated repair. Consequently, rates of learner uptake and immediate repair of error are low in this classroom. These results are discussed in relation to the hypothesis that L2 learners may bene t more from retrieval and production processes than from only hearing target forms in the input. Corrective feedback has recently gained prominence in studies of ESLand other L2 education contexts, as a number of researchers have looked speci cally into its nature and role in L2 teaching and learning
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".