Written corrective feedback at university
Bibliographic record
Abstract
This study explores the written corrective feedback provided by two university teachers in the academic texts written by their students. Specifically, it focuses on the relationship established between the linguistic and discursive errors identified by the teachers, the forms of feedback provided (direct, indirect, metalinguistic and metadiscursive) and the impact of feedback on a second version of the texts revised by the students. A total of 142 texts (two versions of 71 texts) submitted by two groups of students taking a primary education degree at two Spanish universities (71 students) were analyzed. These were coded according to the errors detected, the form of feedback provided, and the way in which they incorporated the feedback into a second version. The results show that the errors detected in the highest numbers by the teachers were discursive, followed by morpho-syntactic and spelling mistakes. The most common feedback was indirect, followed by metalinguistic, although the two teachers were found to take distinct approaches. Regarding its impact, the students incorporated a high percentage (80%) of the feedback provided.
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 | 0.000 | 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 | 0.001 | 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".