Teacher Practices and Students’ Preferences for Written Corrective Feedback and Their Implications on Writing Instruction
Bibliographic record
Abstract
The discrepancy between students’ preferences and teacher practices for feedback on writing has created difficulty on the side of teachers and confusion on the side of the students. What teachers believe and practice as effective feedback for students may not be the one that students perceive as useful and effective feedback for them. This paper investigates the types of written feedback preferred by the students and the types of feedback provided by the teachers on students’ writing. This study employed a survey design which involved 54 students and 22 teachers using convenience sampling technique. The instrument used in collecting data was a questionnaire in the form of Feedback Scale. The results showed that there were some points of compatibility between students’ preferences and teachers’ practices and some other points were incompatible. The data showed that both students and teachers preferred to have or to give direct feedback but the data also indicated that students liked to have more direct feedback than the teacher could provide. It was also found that the teachers provided more indirect feedback than the students expected to have. The students also preferred unfocused feedback to focused feedback. The findings of the study have crucial implications on writing instruction. There is a need to design writing instructions which accommodate both teachers’ practices and students’ preferences for written feddback. Based on the profile of students’ preference and teachers’ practices, a model of feedback provision in teaching writing is proposed. This model is called preference-based feedback on writing instruction.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".