“English‐Only Is Not the Way to Go”: Teachers’ Perceptions of Plurilingual Instruction in an English Program at a Canadian University
Bibliographic record
Abstract
Although recent calls have been made for a plurilingual shift in language learning, particularly in countries with linguistically and culturally diverse populations, teachers are still unsure about how to apply plurilingualism in the classroom. There remains a paucity of studies investigating the disconnect between the theory and implementation of the plurilingual shift. This quasi‐experimental study addressed these challenges by implementing plurilingual instruction in one English language program in a Canadian university and examining teachers’ perceptions of this type of instruction compared to English‐only. Seven teachers, all co‐researchers of the study, taught two groups of students with different approaches: One group received plurilingual instruction, and the other group received English‐only instruction. A deductive analysis of semistructured interviews with the teachers and an inductive analysis of classroom observations were conducted. Results show several affordances of plurilingual instruction, such as engaging students in language learning, advancing agentive power, and developing a safe space. Moreover, although none of the teachers had received training in plurilingualism, they unanimously reported preference for plurilingual instruction. Challenges resulted mainly from teachers’ history with the English‐only teaching tradition. This study is significant because it pioneered research aiming to bridge the gap between the theory and practice of plurilingualism, contributing pedagogical directions in TESOL.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".