Negotiation of Form, Recasts, and Explicit Correction in Relation to Error Types and Learner Repair in Immersion Classrooms
Bibliographic record
Abstract
This study investigated specific patterns of a reactive approach to form‐focused instruction: namely, corrective feedback and its relationship to error types and immediate learner repair. The database is drawn from transcripts of audio recordings made in four French immersion class‐rooms at the elementary level, totaling 18.3 hours and including 921 error sequences. The 921 learner errors were coded as grammatical, lexical, or phonological, or as unsolicited uses of L1. Corrective feedback moves were coded as explicit correction, recast, or negotiation of form (i.e., elicitation, metalinguistic clues, clarification requests, or repetition of error). In contrast with previous studies of error treatment in L2 classrooms, which showed that teachers' use of corrective feedback was relatively unsystematic, this study revealed a certain degree of systematicity in the teachers' treatment of specific types of oral errors. First, the proportion of error types receiving corrective feedback from the teachers reflected the rate at which these various error types actually occurred. Second, the teachers tended to provide feedback on phonological and lexical errors with a certain amount of consistency (at rates of 70% and 80%, respectively); grammatical errors received corrective feedback at a lower rate, but accounted for the highest number of corrective feedback moves in the database nonetheless. Third, the teachers tended to select feedback types in accordance with error types: namely, recasts after grammatical and phonological errors and negotiation of form after lexical errors. Overall, the negotiation of form proved to be more effective at leading to immediate repair than recasts or explicit correction, particularly in the case of lexical errors and also in the case of grammatical errors and unsolicited uses of L1, but not in the case of phonological errors; the latter clearly benefit from recasts. This pattern suggests (a) that the teachers were on the right track in their decisions to recast phonological errors and to negotiate lexical errors and (b) that perhaps teachers could draw more frequently on the negotiation of form in response to grammatical errors, because almost two thirds of all grammatical repairs resulted from this type of feedback. A preference for providing feedback in this way is supported by de Bot's (1996) argument that language learners are likely to benefit more from being “pushed” (Swain, 1995) to retrieve target language forms than from merely hearing the forms in the input, because the retrieval and subsequent production stimulate the development of connections in memory.
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".