Self-Efficacy of English Language Teachers\nin Ontario: The Impact of Language Proficiency,\nTeaching Qualifications, Linguistic Identity,\nand Teaching Experience
Bibliographic record
Abstract
Les auteurs analysent l’incidence des niveaux autodéclarés de compétences langagières en anglais, des diplômes de formation à l’enseignement en langues, de l’identité linguistique (par exemple, celle d’enseignant non anglophone) et de l’expérience en enseignement sur le sentiment d’autoefficacité des enseignants de l’anglais. Ils conçoivent une nouvelle échelle d’autoefficacité des enseignants à partir de données provenant des enseignants de l’anglais en Ontario. Une analyse de régression multiple révèle que la compétence, l’identité linguistique et l’expérience en enseignement ont toutes une incidence sur l’autoefficacité des enseignants. Toutefois, pour ce groupe d’enseignants, un diplôme de maîtrise en formation à l’enseignement en langues n’a pas d’incidence marquée sur la confiance autodéclarée des enseignants. Ces résultats sont comparés à ceux des études antérieures, et des recommandations quant aux pistes de recherches futures sont mises de l’avant. Abstract: This study investigates the impact of self-reported level of English language proficiency, Language Teacher Education (LTE) qualifications, linguistic identity (e.g., non-native English speakers), and teaching experience on English language teachers’ self-efficacy beliefs. Drawing on data from English language teachers in Ontario, a newly formed English language teacher self-efficacy scale was utilized in this study. Multiple regression analysis indicated proficiency, linguistic identity, and teaching experience all to have an impact on teachers’ self-efficacy. However, for this group of teachers, a Masters-level LTE qualification did not have a substantial impact on their self-reported teaching confidence. These results are discussed in relation to previous literature, and recommendations for future research are highlighted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".