Multiple-Draft/Multiple-Party Feedback Practices in an EFL Tertiary Writing Course: Teachers’ and Students’ Perspectives
Bibliographic record
Abstract
Based on various sources of data collection for a qualitative research project, the study reported in this paper set out to examine four teachers’ and sixteen students’ perceptions of a multiple-draft/multiple-party feedback approach to English as a Foreign Language (EFL) student writing. This approach had been implemented as a trial in a tertiary setting in Vietnam. Three sources of feedback at three phases were provided. These included (1) peer/group written and oral feedback on the students’ first drafts, (2) a teaching assistant’s written and oral feedback on their second drafts and (3) the lecturer’s written feedback on their final drafts. Content analysis of the data revealed that all participants valued this multiple feedback approach because of its practicality and the quality of the feedback which participants believed contributed to writing improvement. Based on the participants’ reactions, the study highlights the potential of multiple-draft/multiple-party feedback practices for improving English language writing in a tertiary context.
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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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".