Connecting language proficiency to teaching ability: A meta-analysis
Bibliographic record
Abstract
Most English language teachers around the world speak English as an additional language, and their level of English proficiency is often a matter of concern for them and their employers who associate higher levels of language proficiency with more effective teaching skills. To this end, several studies have examined the relationship between language proficiency and teachers’ beliefs about their pedagogical capabilities, commonly known as self-efficacy. While generally studies show a positive relationship between language proficiency and self-perceived teaching ability, findings regarding the strength of the relationship, the role of specific language skills (e.g. speaking, listening), and how they interact with different teaching abilities (e.g. classroom management) are inconsistent. By combining data from 19 studies, this meta-analytic study examined the relationship between language proficiency and teaching self-efficacy and analysed the role of various moderators such as teaching degree, teaching experience, and type of self-efficacy/proficiency measures. Findings reveal a moderate relationship ( r = .37) between language proficiency and teaching self-efficacy, with some moderator variables showing significant differences across correlations. The results indicate that only a small percentage of the variance in self-efficacy can be accounted for by teachers’ language proficiency, suggesting that while language proficiency is important, there is more to self-efficacy than just language proficiency.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.039 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".