The Role of an Instructor’s Asynchronous Feedback in Promoting Students’ Interaction and Text Revisions
Bibliographic record
Abstract
This study focused on teacher asynchronous feedback and how it engaged 15 English as Foreign language (EFL) learners in revising their writing. The data was collected from (1) the teacher’s feedback, (2) students’ responses and, (3) students’ drafts of writing. The data analysis showed that the teacher provided a number of 832 items of feedback which were categorized into “question” as the most dominating category, followed by “statement”, “suggestion”, “directive” and “correction”. Three hundred and eighty (380;46%) of the feedback items were direct, while 452 (54%) were indirect. The feedback focused on content, organization, vocabulary, grammar, mechanics and more general/not specific comments. Teacher’s feedback (1) engaged the learners in text revisions (overall=1102 comments), (2) stimulated their responses (overall=1032 comments) and (3) extended their online asynchronous interactions. Of the total amount of text revisions (1102), 362 (33%) were made based on the teacher’s direct feedback, and 427 (38%) of them were based on the teacher’s indirect feedback, while 313 (29%) were based on peers’ and self-corrections. The learners revised their writing in terms of content, organization, vocabulary, grammar and mechanics. The findings have implications for teacher interactive feedback practices in writing and the role of asynchronous tools in facilitating such practices.
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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 | 0.005 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".