Investigating the impact of task complexity on uptake and noticing of corrective feedback recasts
Bibliographic record
Abstract
Abstract This study investigated the relationship between task complexity, second language (L2) learners’ response and awareness of corrective feedback provided in the form of recasts during teacher–student interaction. Drawing on Robinson’s Triadic Componential Framework, the study examined how degrees of task complexity created by two specific task characteristics had an impact on learners’ responses (referred to as uptake), and their reported noticing of grammatical and lexical recasts. Data documenting learners’ uptake, operationalized as changes in response to feedback during interactions and noticing of recasts, as indicated in students’ self reports of detection and attention to recasts, were collected during one-on-one interaction sessions and stimulated recall sessions with ESL learners in Canada. Frequency analysis and Cochran’s Q analysis with multiple McNemar post hoc tests were carried out to compare the uptake and noticing of recasts across different tasks. The results revealed that tasks with different degrees of complexity impacted uptake and noticing of recasts differently. The results also showed that linguistic target, i.e., lexical or grammatical features, modulated the relationship between task complexity and recast uptake and noticing. The study calls for a more nuanced approach to investigating task complexity in research, and for practitioners to consider task complexity in decision making related to the use of corrective feedback and the design of classroom-based tasks.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".