Written Corrective Feedback: What Do Students and Teachers Prefer and Why?.
Bibliographic record
Abstract
A growing body of research has examined the effectiveness of written corrective feedback (WCF) for L2 writing. An area that has attracted considerable attention recently is how students and teachers perceive the usefulness of WCF. However, research in this area has largely focused on students’ perspectives, with fewer studies comparing students’ and teachers’ opinions. This study investigated how ESL students and teachers perceive the usefulness of different types and amounts of WCF, and also the reasons they have for their preferences. Qualitative and quantitative data was collected from 31 ESL teachers and 33 ESL students by means of written questionnaires. The results showed that while there were some areas of agreement between teachers and students, important discrepancies in their opinions did occur, not only in how WCF should be provided but also why. Pedagogical implications of the study are discussed. Resume De plus en plus etudes examinent l’efficacite des remarques ecrites des professeurs dans l’ecriture des etudiants de langue seconde. Un domaine d’etude qui attire de plus en plus attention recemment est l’un de la maniere dans lequel les etudiants et les professeurs percoivent l’utilite des remarques ecrites. La plupart des etudes dans cet domaine examinent les opinions des etudiants. Moins souvent ces etudes comparent les opinions des etudiants et des professeurs. Cet etude examine comment les etudiants et les professeurs de langue seconde percoivent l’eficacite des differents genres et quantites de remarques ecrites, et examine aussi les raisons pour ces perceptions. Employions un sondage, les responses qualitatives et quantitatives de 31 professeurs et de 33 etudiants ont ete ramasse. Les resultats montrent que bien qu’il y a des accords entre les opinions des etudiants et les opinions des professeurs dans quelques domaines, il y a aussi des desaccords importants, non seulement dans les perceptions concernant la maniere dans lequel les professeurs devrais remarquer sur l’ecriture des etudiants, mais aussi concernant les raisons pour lequel les professeurs devrais remarquer sur l’ecriture des etudiants. Les implications pedagogiques de cet etude sont discuter. CJAL * RCLA Amrhein & Nassaji 95
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".