Pronunciation teaching in minority languages: perspectives of elementary school teachers in a Chinese-English bilingual program in Canada
Bibliographic record
Abstract
Despite an increasing interest in pronunciation instruction in English as a majority language or international lingua franca, less is known about pronunciation learning in non-English minority languages, especially among child learners. Bilingual education programs provide a unique context to address this research gap, as they involve immersive education in minority languages. Teachers in these programs thus are insightful informants. The current study focuses on the context of a Mandarin-English bilingual program in Canada and addresses two research questions: What factors do teachers believe influence students’ Mandarin pronunciation learning? What are teachers’ strategies and needs when teaching Mandarin pronunciation? Semi-structured interviews were conducted with twelve Chinese teachers with diverse language backgrounds. The teachers discussed multifaceted factors that influenced bilingual students’ pronunciation learning, including speech targets, individual factors, and language environments at school and in society. Teachers shared a wide array of pronunciation teaching techniques, although they expressed concerns related to policies and resources. This study demonstrates the complexity of teaching the pronunciation of a minority language, whose speech system is distinctly different from English, in a bilingual classroom setting. It shares teaching strategies among bilingual teachers and identifies future directions for policymaking and research.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".