The impact of a feedback intervention on university students’ second language writing feedback literacy
Bibliographic record
Abstract
This study evaluated the effect of a combined feedback activity (peer feedback, computer-generated feedback and teacher feedback) on students’ second language (L2) writing feedback literacy. One hundred and eighty-two Chinese university students participated in this research. Findings revealed that the intervention significantly improved students’ literacy in Appreciating Feedback, Acknowledging Different Feedback Sources and Managing Affect, but not Making Judgements and Taking Action. L2 proficiency levels affected the literacy development. Low-proficiency students’ feedback literacy did not change significantly. Middle-proficiency students improved significantly in Appreciating Feedback, Acknowledging Different Feedback Sources, Managing Affect, and Taking Action. High-proficiency students only improved significantly in Appreciating Feedback. Findings further reveal different degrees of difficulty for students to improve feedback literacy along its five dimensions. This study bears implications for developing students’ feedback literacy in L2 writing and in other disciplinary areas, particularly regarding how teachers could use multiple feedback sources and address students’ varied proficiency levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".