The differential effect of individual and collaborative processing of written corrective feedback on French as a foreign language learners’ engagement
Bibliographic record
Abstract
An increasing number of studies have explored the effects of collaborative writing on written outcomes; however, few studies have examined the influence of collaborative processing of feedback. This study addresses this gap by focusing on learner engagement. While collaborative writing involves co-authoring a text, which requires negotiation and idea sharing, collaborative processing of feedback focuses on jointly interpreting and responding to feedback. Utilizing a mixed-methods design, this study examined 24 learners of French as a Foreign Language (FFL) over an 8-week period. It compared their engagement with written corrective feedback (WCF) when processed individually versus in pairs. The findings provide insights into how different feedback processing modes influence learner engagement and highlight the potential benefits of collaborative feedback processing. The instructor provided indirect WCF, and learners revised their essays with think-aloud sessions. The study examined cognitive and behavioral engagement through writing analysis and used think-aloud reports examining affective engagement. Results indicated that learners’ cognitive engagement varied between individual and collaborative processing, with individuals employing fewer high-depth and low-depth processing strategies. However, affective engagement was found to be independent of task completion mode, and behavioral engagement did not differ between individual and collaborative processing of WCF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".