Written Languaging and Engagement with Written Corrective Feedback: The Results of Reflective Teaching
Bibliographic record
Abstract
For corrective feedback (CF) to contribute to second language (L2) development, some cognitive processes need to be completed. Learners need to notice and comprehend the CF, reflect on and deeply process it, and finally integrate it into their interlanguage (Gass, 1997). Written languaging (WL), which requires learners to explicitly explain to themselves why they have received CF, has been proposed as a technique which can stimulate deep cognitive processing of the written CF. In an effort to improve learners’ writing accuracy, I adopted WL, whereby upon receiving online direct corrections, learners typed their selfexplanations regarding the underlying reasons for their writing mistakes. Then, I engaged in systematic reflection and journaling during a 10-week semester to critically analyze the affordances and limitations of WL. The conclusion, drawn from my perceptions of the usefulness of WL originating from my journal writing, is that WL has the potential to not only facilitate learning for students but also can provide teachers with a rich description of learners’ cognitive and affective engagement with CF. Some recommendations are made for better implementation of this instructional technique. 
 Pour que la rétroaction corrective (RC) contribue au développement de la langue seconde (L2), des processus cognitifs doivent se produire. Les apprenants doivent remarquer et comprendre la RC, y réfléchir, la traiter profondément et pour finir, l’intégrer dans leur interlangue (Gass, 1997). La mise en langue écrite (MLE), qui exige des apprenants qu’ils s’expliquent à eux-mêmes de façon explicite pourquoi ils ont reçu de la RC, a été proposée comme une technique qui peut stimuler le traitement cognitif profond de la RC écrite. Dans le but d’améliorer la précision de la rédaction des apprenants, j’ai adopté la MLE, où, lorsqu’ils recevaient les corrections directes en ligne, les apprenants tapaient leurs propres explications sur les raisons pour lesquelles ils avaient commis des erreurs écrites. Ensuite, je me suis employé à réfléchir systématiquement et à tenir un journal pendant les 10 semaines que durait le semestre, afin d’analyser de façon critique les opportunités et les limitations de la MLE. La conclusion, tirée de mes perceptions de l’utilité de la MLE puisées dans mon journal, est que la MLE a non seulement le potentiel de faciliter l’apprentissage des apprenants, mais peut aussi fournir aux enseignants une riche description de l’engagement cognitif et affectif des apprenants eu égard à la RC. Des recommandations sont faites pour une meilleure mise en œuvre de cette technique d’enseignement.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".