The Effect of Implicit Corrective Feedback on English Writing of International Second Language Learners
Bibliographic record
Abstract
Debate about the value and the effect of both kinds of corrective feedback, explicit and implicit on second language writing has been prominent in recent years. Second language writing researchers investigate whether written implicit corrective feedback facilitates the acquisition of linguistic features. In contrast, L2 writing researchers generally emphasize the question of whether written corrective feedback helps student writers improve their writing texts and reduces their language errors. Understanding these differences is important because it provides guidelines for English language writing teachers on what are the best way to provide feedback for student writers. A quasi-experimental study was conducted to investigate the effects of implicit corrective feedback on the English writing of international second language learners in a UK educational context. It scrutinizes the application of teacher implicit written feedback in relation to the advancement of the writing skill of second language learners within a short-term period. A case study consisting of a small group of international students received implicit written feedback through codes representing specific types of writing errors. Participants were also interviewed to understand their views regarding teacher implicit written feedback and their reactions towards it. The results of the study revealed that teacher implicit written feedback helped correcting particular type of errors while other errors mandated the intervention of the teacher oral 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 | 0.004 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".