Written corrective feedback and learner engagement: A case study of a French as a second language program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Within the context of second language (L2) writing, learner engagement with feedback has elicited significant theoretical and empirical interest (e.g., Zhang & Hyland, 2018; Zheng & Yu, 2018). Research has highlighted the dynamic nature of learner engagement with corrective feedback (WCF), but the ways in which learner and contextual factors impact such engagement with WCF in authentic classrooms are still underexplored (Han, 2019). Furthermore, little is known about how L2 learners engage with WCF from an ecological perspective, which considers the relationships between learners and their surrounding environments (Bronfenbrenner,1993; van Lier, 2000). \n \nSituated in an adult French as a second language (FSL) setting in Canada, this study adopted an ecological perspective to analyze the influence of learner and contextual factors on learners’ affective, cognitive, and behavioural engagement with WCF on linguistic errors. Participants in this study were five adult students registered in an FSL program in the francophone province of Quebec. Data were collected from multiple sources, including students’ drafts with written feedback provided, semi-structured interviews, retrospective verbal reports, and other class documents. \n \nFindings show that learner and contextual factors influence learners’ affective, cognitive, and behavioural engagement with WCF in a number of complex ways.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it