Effects of Form‐Focused Instruction and Corrective Feedback on L2 Pronunciation Development of /ɹ/ by Japanese Learners of English
Bibliographic record
Abstract
Sixty‐five Japanese learners of English participated in the current study, which investigated the acquisitional value of form‐focused instruction (FFI) with and without corrective feedback (CF) on learners’ pronunciation development. All students received a 4‐hr FFI treatment designed to encourage them to notice and practice the target feature of English /ɹ/ in meaningful discourse, except those in the control group ( n = 11), who received comparable instruction but without FFI on English /ɹ/. During FFI, the instructors provided CF only to students in the FFI + CF group ( n = 29) by recasting their mispronunciation or unclear pronunciation of /ɹ/, whereas no CF was provided to those in the FFI‐only group ( n = 25). Acoustic analyses were conducted on frequency values of the third formant (F3) of English /ɹ/ tokens elicited via pretest and posttest measures targeting familiar items and a generalizability test targeting unfamiliar items. The results showed that: (a) F3 values of the FFI + CF group significantly declined after the intervention, not only at a controlled‐speech level but also a spontaneous‐speech level, regardless of following vowel contexts; (b) change in F3 values of the FFI‐only group and the control group was not statistically significant; and (c) the generalizability of FFI to novel tokens remained unclear.
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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 | 0.001 | 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 | 0.000 |
| 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".