L2 learners' interpretation and understanding of written corrective feedback: insights from their metalinguistic reflections
Bibliographic record
Abstract
The impact written corrective feedback (WCF) has on second language development is still a subject of much debate. While some believe it leads to improvement, others are more sceptical. But in order for WCF to lead to second language improvement, learners must first be able to not only correctly interpret the WCF but also understand the linguistic information provided through this feedback. The study reported in this article was designed to look at English as a second language (ESL) learners' verbalisations about language produced immediately after revising their texts. Forty-nine (n = 49) high school French-speaking learners produced four texts over a four-month period. Two types of WCF (direct, providing the correct form above or next to the error and indirect, indicating that an error was produced by underlining it) were alternatively used when correcting the texts in order to create balanced conditions. After revising their corrected text, participants completed a questionnaire. Their answers were coded by creating semantic categories and an interrater agreement was calculated. The results show that although the participants understood the WCF they received, some corrections nevertheless led to erroneous hypotheses about the intent of the correction. Additionally, there appear to be differences in the participants' verbalisations according to the feedback received.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".