THE EFFECTS OF CORRECTIVE FEEDBACK ON INSTRUCTED L2 SPEECH PERCEPTION
Bibliographic record
Abstract
To what extent do second language (L2) learners benefit from instruction that includes corrective feedback (CF) on L2 speech perception? This article addresses this question by reporting the results of a classroom-based experimental study conducted with 32 young adult Korean learners of English. An instruction-only group and an instruction + CF group were exposed to five 1-hr form-focused lessons that drew learners’ attention to the nonnative phonemic contrast /i/-/ɪ/, but only the instruction + CF group was given relevant feedback. Forced-choice identification tasks were completed by participants in a pretest, an immediate posttest, and a delayed posttest. The two groups showed similar accuracy on the pretest; however, the instruction + CF group outperformed the instruction-only group on the immediate and delayed posttests as well as on unfamiliar words. The significant predictors for these differences turned out to be perceptual accuracy vis-à-vis /ɪ/-natural and /ɪ/-synthesized sounds. These findings are discussed in terms of the pivotal role played by CF in developing accuracy in L2 speech perception.
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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 | 0.000 | 0.007 |
| 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 | 0.000 |
| 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".