Future non-native English as a second language teachers : the impact of language proficiency on their use of English in the classroom
Bibliographic record
Abstract
The main purpose of this study was to better understand the perceptions of non-native future ESL teachers in relation to their language proficiency in English and the impact their proficiency had on their use of English with their pupils while on practicum. However, the impacts that were uncovered were not necessarily related to the language proficiency of the student teachers who participated in the study, but rather linked to the use of English as the language of instruction in the context of language teaching. This study took place in a medium-size French regional university in Québec where several pre-service teachers were surveyed using an online questionnaire that they took at their convenience. As a follow up to the questionnaire, a few of these future teachers agreed to be interviewed. Even though English is one of the two national languages of Canada, most ESL teachers in the province of Québec are non-native English speakers. For the most part, they live in French communities and teach in French schools, which means communicating in French is necessary outside their classrooms. The research data were analysed and interpreted using a descriptive approach for the questionnaire and a thematic analysis approach for the semi-structured interviews. The study revealed some of the challenges of being a future non-native ESL teacher in Québec and the perceptions they have towards their English proficiency. Although all the participants indicated that they were very fluent in English, some of them reported that they needed to use French at times to teach English, which reduced the amount of time spent using English in the classroom. Finally, the findings also exposed various factors that can account for the use of the mother tongue of the pupils in the classroom.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".