Survey of the Teaching of Pronunciation in Adult ESL Programs in Canada, 2010
Bibliographic record
Abstract
This follow-up study reexamines the state of the teaching of pronunciation in ESL classes across Canada. The purpose of the survey was twofold: to gain a snapshot of current practices and to compare this with the picture of 10 years ago. We based the current work on Breitkreutz, Derwing, and Rossiter’s (2001) survey asking teachers about resources, approaches, and beliefs about teaching pronunciation. We also asked for background information about the instructors’ formal education and teaching experience. For the most part, instruction in pronunciation in Canada has not changed substantially in the last decade. More training opportunities are available, although these are still not enough according to many of our respondents. The number of pronunciation courses offered in English-as-asecond-language (ESL) programs has also increased. Teachers’ beliefs about pronunciation instruction remained largely the same, with a similar focus on suprasegmentals and segmentals. However, we did find a slight difference in how teachers approached these two aspects of pronunciation. Ten years ago, teachers reported emphasizing both aspects in class, whereas today there seemed to be a slightly greater focus on segmentals. Finally, we offer several recommendations for TESL programs, ESL programs, and ESL instructors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".