Comparison of English Teacher Feedback and Automated Writing Feedback on the Quality of English Language Learners’ Essay Revision
Bibliographic record
Abstract
This study compared the effects of teacher feedback (TF) and online automated feedback (AF) on the quality of revision of English writing. It also examined the strengths and weaknesses of the two types of feedback perceived by English language learners (ELLs) as a foreign language (FL). Sixty-eight Chinese students from two English classes participated the study. The two classes received TF and online AF (Scoring Network) respectively upon completion of their draft essays. While the two classes did not differ on the English writing proficiency, the class receiving TF obtained significantly higher scores on essay revision, indicating the better effect of TF. Students’ responses showed that, overall, TF was more positively commented upon because the encouraging words motivated students to revise. In contrast, the students receiving online AF criticized the Scoring Network for their difficulty to comprehend the feedback they were provided. The results suggest that English teachers may consider using TF as a major source of feedback in English writing for ELLs in China.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".