Investigating the impact of task complexity on uptake and noticing of corrective feedback recasts
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it