The Pedagogy of Error Correction: Surviving the Written Corrective Feedback Challenge
Bibliographic record
Abstract
Should we correct our students’ language errors? Most ESL teachers would an- swer this question with a resounding Yes while at the same time wondering how to meet the challenge. The collaborative project reported below was designed to provide ESL teacher trainees with an opportunity to experience the ups and downs of providing corrective feedback on writing and develop their awareness in this regard. To this end, the teacher trainees acted as corrective-feedback tutors to high school learners during one school semester. During the course of the proj- ect, they kept journals documenting their reflections in regard to this demanding pedagogical practice. Time constraints, motivation, and fear of making mistakes themselves or of not providing adequate guidance to the learners were among the major hurdles encountered by the tutors. In interviews conducted at the end of the project, the tutors offered suggestions for overcoming these difficulties and surviving the corrective-feedback trials and tribulations. The survival tips pre- sented were drawn from the tutors’ recommendations as well as from insights from corrective-feedback research.Devrait-on corriger les erreurs de langue de nos étudiants? La plupart des en- seignants d’ALS répondraient à cette question par un oui catégorique tout en se demandant comment relever ce défi. Le projet collaboratif décrit ci-dessous a été conçu pour fournir aux stagiaires en ALS une occasion de vivre les hauts et les bas liés au fait de présenter de la rétroaction corrective aux travaux écrits, et de se conscientiser à cet égard. À cette fin, les stagiaires ont joué le rôle de tuteurs fournissant de la rétroaction corrective à des élèves du secondaire pendant un se- mestre. Au cours de projet, ils ont tenu un journal pour noter leurs réflexions relatives à cette pratique pédagogique exigeante. Parmi les obstacles les plus im- portants auxquels les tuteurs ont fait face, notons les contraintes de temps, la motivation et la peur de se tromper eux-mêmes ou de ne pas fournir des conseils adéquats aux élèves. Lors d’entrevues qui ont eu lieu à la fin du projet, les tuteurs ont offert des suggestions pour surmonter ces difficultés et survivre aux vicissi- tudes de la rétroaction corrective. Les conseils de survie présentés sont tirés des recommandations des tuteurs et des perspectives découlant de la recherche sur la rétroaction corrective.
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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 | 0.027 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".