Synchronous computer-mediated recasts, auditory processing, and categorical perception of VOT in stops: evidence from L2 Mandarin of Indonesian learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it