The Effects of Direct and Indirect Corrective Feedback on Accuracy in Second Language Writing
Bibliographic record
Abstract
The effectiveness of providing Corrective Feedback (CF) on L2 writing has long been a matter of considerable debate. A growing body of research has been conducted to investigate the value of various types of CF on improving grammatical accuracy in the writing of English as a second or foreign language. This article is mainly concerned with the role of Corrective Feedback (CF) in developing the L2 writers’ ability to produce an accurate text, and argues that CF is considered to be one of the fundamental techniques in teaching second language (L2) writing. Bearing this in mind, it attempts to maintain the effectiveness of CF on the L2 students’ abilities to develop the accuracy of their written output. This topic has recently produced a significant interest among both teachers and researchers in the areas of L2 writing and second language acquisition. A key issue to be addressed is the degree to which CF effectively helps the second language writers obtain long-term accuracy. Currently, the author of this paper has been conducting a PhD study on the effect of direct and indirect corrective feedback on the academic writing accuracy of Kurdish EFL university students, and the data was collected from writing testing samples (pre-test, post-test and delayed post-test) produced by105 undergraduate students of English department from two public universities. The results could be obtained from the study should have important implications for L2 writing practitioners and researchers.
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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 | 0.006 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".