Learners’ Perceptions of Synchronous Written Corrective Feedback in Videoconferenced Collaborative Writing
Bibliographic record
Abstract
Second language (L2) research suggests that synchronous written corrective feedback (SWCF) in online collaborative writing tasks can help improve L2 linguistic knowledge and writing skills. Following the rise of online collaborative writing in the wake of the COVID-19 pandemic, this exploratory study examines L2 learners’ perceptions of receiving SWCF during collaborative writing tasks completed on an online text-editing platform (Google Docs) and mediated by videoconferencing (Zoom). Adult learners (N = 46) enrolled in advanced online French as a Second Language courses took part in two collaborative writing tasks, during which their teachers (N = 3) provided SWCF. Learners’ screen activity was recorded. After the experiment, a perception survey was distributed and selected participants took part in semi-structured interviews to further discuss their experience. Results indicate that learners viewed the provision of SWCF through computer-mediated communication as an effective way to improve their L2 writing compared to traditional, delayed written feedback. Pedagogical implications for the implementation of videoconferences collaborative writing tasks involving teacher SWCF are discussed.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".