The Amount and Usefulness of Written Corrective Feedback Across Different Educational Contexts and Levels
Bibliographic record
Abstract
This study examined and compared different written corrective feedback techniques used by English as a second language (ESL) teachers in three different educational contexts and levels (primary, secondary, and college) in Quebec, Canada. In particular, it examined whether there were any differences in the types of errors made, the kind and degree of feedback provided, as well as the students’ ability to incorporate the feedback while revising their texts. Data were collected at the three aforementioned contexts from six ESL teachers in their intact classes when they corrected their students’ (N = 128) written essays (drafts and revisions). Results revealed an important difference across the three levels in terms of students’ errors, teachers’ feedback, and students’ revisions. They showed that (a) while grammatical errors were made more frequently by primary students, lexical errors were made more frequently by college students; (b) primary and secondary students received more direct than indirect feedback, while college students received more indirect feedback; (c) the secondary and college students were more successful in incorporating the feedback into their revisions than primary students. La présente étude a examiné et comparé plusieurs techniques de rétroactions correctives écrites utilisées par des enseignants d’anglais langue seconde (ALS) dans trois contextes et niveaux d’éducation différents (primaire, secondaire et collégial) au Québec, au Canada. En particulier, elle a examiné s’il existait des différences dans les types d’erreurs qui étaient faites, quelle sorte et quel niveau de rétroaction étaient fournis ainsi que la capacité des élèves à intégrer la rétroaction lorsqu’ils révisaient leurs textes. On a recueilli des données dans les trois contextes susmentionnés auprès de six enseignants d’ALS dans leurs classes intactes lorsqu’ils corrigeaient les rédactions (brouillons et révisions) de leurs élèves (N = 128). Les résultats ont révélé une différenc importante dans les trois niveaux en ce qui concerne les erreurs des élèves, la rétroaction des enseignants et les révisions des élèves. Les résultats ont montré que (a), alors que les élèves de primaire faisaient plus d’erreurs grammaticales, les élèves de collège faisaient plus d’erreurs lexicales; (b) les élèves de primaire et de secondaire recevaient plus de rétroaction directe qu’indirecte, alors que les élèves de collège recevaient plus de rétroaction indirecte; (c) les élèves de secondaire et de collège réussissaient mieux à incorporer la rétroaction dans leurs révisions que les élèves de primaire.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".