Corrective feedback, learner uptake, and feedback perception in a Chinese as a foreign language classroom
Bibliographic record
Abstract
The role of corrective feedback in second language classrooms has received considerable research attention in the past few decades. However, most of this research has been conducted in English-teaching settings, either ESL or EFL. This study examined teacher feedback, learner uptake as well as learner and teacher perception of feedback in an adult Chinese as a foreign language classroom. Ten hours of classroom interactions were videotaped, transcribed and coded for analysis. Lyster and Ranta’s (1997) coding system involving six types of feedback was initially used to identify feedback frequency and learner uptake. However, the teacher was found to use a number of additional feedback types. Altogether, 12 types of feedback were identified: recasts, delayed recasts, clarification requests, translation, metalinguistic feedback, elicitation, explicit correction, asking a direct question, repetition, directing question to other students, re-asks, and using L1-English. Differences were noted in the frequency of some of the feedback types as well as learner uptake compared to what had been reported in some previous ESL and EFL studies. With respect to the new feedback types, some led to noticeable uptake. As for the students’ and teacher’s perceptions, they did not match and both the teacher and the students were generally not accurate in perceiving the frequency of each feedback type. The findings are discussed in terms of the role of context in affecting the provision and effectiveness of feedback and its relationship to student and teacher perception of feedback.
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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 | 0.005 | 0.015 |
| 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 | 0.001 |
| 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".