Processing immediate written corrective feedback during online collaborative writing: A depth of processing perspective
Bibliographic record
Abstract
This study investigates how learners process immediate written corrective feedback (IWCF) during collaborative writing tasks in a synchronous, computer-mediated environment. Drawing on depth of processing (DoP) as an analytical lens, the study examines how feedback type—direct, indirect, and metalinguistic—influences learner engagement and immediate accuracy. Data were collected from intermediate-level learners of French as a second language (L2) using screen recordings, chat logs, and Google Docs revisions during online writing tasks. Results indicate that feedback type significantly shaped learners’ engagement. Direct feedback led to high rates of immediate correction but was typically associated with minimal cognitive engagement. In contrast, metalinguistic feedback prompted deeper processing, characterized by hypothesis testing, rule recall, and collaborative negotiation. Indirect feedback produced mixed results: while some learners overlooked it, others engaged in collaborative problem-solving to interpret and revise errors. The synchronous context appeared to amplify the impact of feedback by enabling real-time interaction, visibility, and reinvestment of teacher comments into the writing process. These findings highlight the importance of tailoring feedback types to task goals and learner needs, particularly within task-based language teaching (TBLT) frameworks. The study underscores the pedagogical potential of combining real-time feedback with structured peer collaboration in digital environments to support both accuracy and autonomy in L2 writing. Implications are discussed for optimizing corrective feedback practices in online, task-based instructional settings.
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.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".