Beyond the walls of classrooms: Exploring the pedagogical effectiveness of Text-To-Speech-based Shadowing (TTS-S) on the development of Mandarin tones
Bibliographic record
Abstract
This study examines the pedagogical effectiveness of using Text-To-Speech synthesis (TTS) combined with Shadowing (TTS-S) for self-regulated learning of Mandarin tones 1 and 4. The aim is to determine the probability of success of this innovative approach that uses TTS to generate audio for shadowing practice. The research was guided by the following research question: can TTS-S help L2 learners raise their awareness and improve their perception and production of the target Mandarin tones over a period of six weeks? Over six week, ten participants engaged in self-regulated activities using TTS-S to learn the two pronunciation targets. By means of pre-/post-tests (to assess effectiveness in pronunciation) participants were asked to complete: (1) an awareness task in which they verbalized their metacognitive knowledge of Mandarin tones; (2) ABX tasks to assess their perception of Mandarin tones; and (3) a production task to evaluate their production of the target tones. Our findings are inconclusive regarding the effectiveness of TTS-S for improving awareness, perception, and production of Mandarin tones 1 and 4 among L2 learners. They also indicate that while certain aspects of phonological development, specifically production, showed some improvements, the overall impact was not statistically significant.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".