Task-based language teaching and the timing of written corrective feedback: The role of language aptitude and working memory
Bibliographic record
Abstract
Immediate written corrective feedback (CF) is increasingly popular in second language (L2) classrooms because it allows teachers to provide CF to students while they are writing (Aubrey & Shintani, 2021). However, this can increase cognitive load as students process CF while writing (Kellogg, 1996). Research indicates that working memory's role varies with different types of CF (Li & Roshan, 2019). Grounded in Task-Based Language Teaching, which emphasizes meaningful communication and task engagement (Ellis, 2003), this study examined the influence of working memory and language aptitude on the effectiveness of immediate and delayed CF during collaborative writing tasks. Seventy-six university students learning French as an L2 participated in two collaborative writing tasks under three conditions, focusing on the French past tense. The first group received immediate written CF with metalinguistic comments during writing, the second received the same CF one week later, and the third, a task-only group, did not receive any CF. Furthermore, students individually wrote three texts as pretests, immediate posttests, and delayed posttests, and completed the LLAMA F test for language aptitude and a backwards digit span task for working memory. Results indicated that working memory predicted posttest performance only for the immediate CF group. For the delayed CF group, higher language aptitude negatively predicted posttest performance.
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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 | 0.001 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".