Language teacher self-efficacy beliefs for pronunciation instruction
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Studies on language teachers’ self-efficacy (LTSE) have gained attention in recent years; however, limited research has explored LTSE in specific domains of language instruction, particularly pronunciation. The present study employs a domain-specific survey to measure English as a second language (ESL) teachers’ self-efficacy in pronunciation instruction (PI) in Canadian classrooms. Data from the survey and follow-up interviews were analyzed to explore ESL teachers’ overall self-efficacy beliefs, relationships with language, and pronunciation proficiencies. The findings reveal that ESL teachers in Canada generally report high levels of self-efficacy for teaching pronunciation. While the correlation between general language proficiency and self-efficacy was not significant, the correlation between their pronunciation proficiency and self-efficacy for teaching pronunciation was significant, even though it falls below the benchmark for small effect size.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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