Enhancing Writing Proficiency: The Role of Model Essays as Corrective Feedback Tools in IELTS Writing Task Achievement and Coherence/Cohesion
Bibliographic record
Abstract
Proficiency in IELTS writing tasks is crucial for obtaining high scores in this internationally recognized test. This quasi-experimental study investigated the effectiveness of corrective feedback in improving the gain scores of both academic writing task 1 and task 2. Sixty Iranian students participated in pre/post-test administrations, with the experimental group receiving instruction based on the analysis made on 10 model essays, and the control group received reformulation on their own produced texts. Two skilled raters assessed the students' typewritten texts in terms of task response and "coherence/cohesion" – two writing band descriptors. After conducting ANOVA and Bonferroni post hoc tests, the results demonstrated that the treatment group achieved significantly higher scores in the two mentioned components. In both the post-test and delayed post-test, the Experimental Group (EG) consistently outperformed the Control Group (CG) in task response sub-scale of academic writing task 1 & 2 (p < 0.05). Further examination, employing a multi-mediator approach, accentuates the substantial impact of these writing rubrics on the overall IELTS score. Additionally, within the EG, there was a significant increase in coherence scores between the pretest and posttest (p < 0.05). However, no statistically significant change was observed between the post-test and the delayed post test (p > 0.05).
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".