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Record W4414502197 · doi:10.37213/cjal.2025.34394

The impact of timing in written corrective feedback on collaborative writing and L2 accuracy development

2025· article· en· W4414502197 on OpenAlex
Gabriel Michaud, Kim McDonough

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Applied Linguistics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia UniversityUniversité de Montréal
Fundersnot available
KeywordsCorrective feedbackCollaborative writingTask (project management)Writing processPeer feedbackSecond language writingWriting assessmentGroup (periodic table)Task analysis

Abstract

fetched live from OpenAlex

This study examines the impact of written corrective feedback (WCF) timing on the collaborative writing process and writing accuracy development of adult learners of French as a second language. Forty-eight learners were divided into three groups to complete a collaborative writing task in pairs. The first group received immediate WCF via Google Docs while writing, the second group received delayed WCF one week later with 10 minutes allocated for error correction, and the third group performed the writing task without receiving any feedback. All discussions during the collaborative writing were recorded and analyzed for language-related episodes (LREs). Writing accuracy was assessed through pretests, immediate posttests, and delayed posttests using story-retelling tasks. The findings revealed that the delayed feedback group engaged in more extensive discussions about linguistic forms compared to the other two groups. In terms of writing accuracy, the immediate feedback group showed the most significant improvements over time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.279
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it