Native or Non-native-speaking Teaching for L2 Pronunciation Teaching?—An Investigation on Their Teaching Effect and Students’ Preferences
Bibliographic record
Abstract
<p>This study investigated L2 leaners’ preferences between native-speaking teachers (NST) and non-native-speaking teachers (NNST) as their English pronunciation teacher, and examined the participants’ accentedness and comprehensibility in L2-English pronunciation after being taught by a NST and a NNST. The participants were 30 undergraduates who were doing non-English majors at a university in China. They went through 4-month English pronunciation classes. In the first 2 months, they were taught by a NST. From the 3rd to the 4th month, they were taught by a NNST. Their accentedness and comprehensibility of spoken English were tested at the beginning of the programme (pre-test), at the end of the 2nd month (middle test), and at the end of the 4th month (post-test). Information on their evaluation of the NST and NNST as a pronunciation teacher was collected with questionnaires at the end of the experiment. According to the results, (1) compared with that in pre-test, the participants’ accentedness and comprehensibility both improved slightly in middle test; (2) compared with that in middle test, the participants received significant improvement both in comprehensibility and accentedness; (3) the majority of the participants prefer a NST to a NNST to be their English pronunciation teacher.</p>
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How this classification was reachedexpand
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".