Synchronous computer-mediated recasts, auditory processing, and categorical perception of VOT in stops: evidence from L2 Mandarin of Indonesian learners
Bibliographic record
Abstract
Purpose This study investigated the benefits of synchronous computer-mediated recasts for Indonesian-speaking learners in improving the categorical perception of voice onset time (VOT) in Mandarin stops. It also examined how individual differences in auditory processing predicted these benefits.Methodology Using an interventional design with pre- and posttests, 64 beginning Indonesian learners of second language (L2) Mandarin participated in a 17-week synchronous computer-mediated communication course. Half of the participants received one-on-one recasts for their nontarget-like utterances of Mandarin stops (/ph/-/p/, /th/-/t/, /kh/-/k/), while the other half served as the control group and received no such feedback. Classical categorical perception tests on a VOT continuum from Mandarin /ph/ to /p/ were administered through identification and discrimination tasks before, immediately after, and four weeks post-treatment. Auditory processing tests were also conducted to measure participants’ ability to encode spectral and temporal sound details.Findings Results showed that the recast group exhibited more pronounced improvement in VOT categorization, with significantly narrower boundary width and better between-category discrimination in both posttests compared to the control group. Regression analysis confirmed that individual differences in auditory processing significantly predicted the benefits of recasts.Originality/value These findings suggest that optimal, profile-matched instruction in a synchronous computer-mediated communication context can maximize L2 speech learning, aiding educators and researchers in setting evidence-based expectations and goals.
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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 | 0.001 |
| 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) | 0.002 | 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".