A Study of Effective Teacher Feedback in English Continuation Writing Classes
Bibliographic record
Abstract
As an important part of classroom discourse, effective teacher feedback ensures the quality of classroom discourse. Most studies nowadays take reading or writing classes as research subjects, but the research on teacher feedback in continuation writing classes has not been discussed yet. Besides, most researchers pay less attention to the effective use of teacher feedback. Therefore, in order to find out the effective use of teacher feedback in continuation writing classes, this paper will explore the characteristics of effective teacher feedback by observation and discourse analysis methods in five excellent English continuation writing classes. The main findings are as follows: experienced teachers attach great importance to the use of feedback; second, experienced teachers prefer to use mixed feedback; third, teachers are supposed to use different types of teacher feedback, especially the Question Closely, Repetition plus Question Closely and Recast plus Question Closely. These findings will remind teachers of the importance of teacher feedback, and provide useful guidelines for teachers who are confused about how to use teacher feedback in continuation writing class.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".