Cognitive Theoretical Perspectives of Corrective Feedback
Bibliographic record
Abstract
The role of corrective feedback (CF) in the L2 learning process has for decades remained a dominant issue in the (I)SLA strands of research, albeit some overlapping between these two contexts. Indeed, there are several cognitive theoretical underpinnings cited by empirical CF studies to account for the role or lack thereof of CF in the L2 learning process, for example, the Monitor Model (Krashen, 1982), the Interaction Hypothesis (Long, 1996), the Noticing Hypothesis (Schmidt, 1990), the Output Hypothesis (Swain, 2005), Skill Acquisition Theory (DeKeyser, 2015), and the Model of the L2 learning process in ISLA (Leow, 2015), be it oral, written, or computerized or digital. This chapter (1) traces the early roots of CF, (2) presents a coarse-grained theoretical feedback processing framework to discuss the cognitive theoretical underpinnings postulated to account for the role of CF in L2 development, (3) provides a list of cognitive processes assumed to play a role during CF appropriation, and (4) reports on each theoretical underpinning followed by a commentary on their ability to account for the role of CF in L2 development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".