Corrective Feedback in CALL: Individual Voices Speak Out Through Think-Alouds
Bibliographic record
Abstract
This paper presents research conducted as part of a larger computer-mediated experiment which investigated the effects of preprogrammed computer-delivered corrective feedback on foreign language development. The research also explored learners’ attitudes regarding corrective feedback and how it may help them notice the gap between their interlanguage (IL) and the target language (TL), focusing on the relationship between types of corrective feedback and level of awareness. It is this last aspect of the study that will be addressed in this paper. Specifically, concurrent verbal reports (think-alouds protocols) were employed to investigate whether learner processing of corrective feedback differs depending on the type of feedback and type of activity. While the small sample size (N=17) limits the generalizability of the findings, the results offer valuable insights into the role of corrective feedback in language learning and its potential to enhance learners’ metalinguistic awareness. Overall, corrective feedback encouraged reflection and appeared to help learners recognize their errors. The results indicate that explicit feedback in receptive tasks was particularly effective in promoting higher levels of awareness and facilitating successful self-correction, although varying levels of awareness were observed across all treatment groups.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".