Efficacy of Written Corrective Feedback as a Tool to Reduce Learners’ Errors on L2 Writing
Bibliographic record
Abstract
Over the last few decades, considerable research has been done to investigate the role of written corrective feedback in SLA classrooms. However, early researches suffered from major design flaws and consequently failed to draw any definite conclusions. In order to move this line of research it is important to analyze the issue in EFL/ESL settings. This research study, by applying quantitative research design, seeks to investigate the effectiveness of WCF on 30 low-intermediate EFL learners and their error reduction rate on pre, post and delayed posttest. Two different types of WCF (direct and indirect metalinguistic) were provided on two error categories, i.e. articles and past tense. Statistical analysis indicated that both treatment groups performed significantly better than control group on subsequent drafts. Thus, the present study by proving the efficacy of WCF at least on above mentioned error categories strengthens the case in favor of WCF in L2 classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".