The Effects of Direct Written Corrective Feedback (WCF) on Language Preparatory School Students` IELTS Independent Writing Section Score
Bibliographic record
Abstract
A wealth of research has examined the effects of varied types of Written Corrective Feedback (WCF) in Academic Writing courses.However, there has been an ongoing discussion on adopting one of them.In this regard, this study investigated the effects of direct feedback on IELTS Academic Writing Section Score of Foundation English students` over 24 weeks using pre-test and post-test.Considering this aim, 40 students were separated into two groups as control or experimental group through systematic sampling method.Experimental group students were exposed to direct feedback, whereas control group students were engaged in metalinguistic feedback.Each student wrote 6 essays related to IELTS Writing Task 2 regardless of being in control or experimental group.Quantitative data were analyzed by IBM SPSS 23 through independent sample t test and paired sample t test.Independent sample t test and paired sample t test post test results were recorded as .001respectively, indicating that there were highly significant differences in experimental group.On the other hand, control group progressed to an extent, but it was not as significant as the experimental group.Likewise, the questionnaire and interview analysis show that direct feedback was supported by more students when compared to metalinguistic feedback.Findings of this study may have some insightful points for educators who have been covering or managing Academic English courses at universities globally.
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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 | 0.002 | 0.014 |
| 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) | 0.002 | 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".