Translanguaging for critical multilingual language awareness: preparing teacher candidates to support multilingual learners in classrooms
Bibliographic record
Abstract
This study brings together translanguaging and critical multilingual language awareness (CMLA) (García, 2015, 2017) to examine how teacher candidates (TCs) prepare to support multilingual learners in elementary classrooms. Data was drawn from four TCs in a teacher education course on supporting multilingual learners in Ontario, Canada. Research questions guiding this study were: (1) What are TCs’ stances toward translanguaging, and what factors shape their developing translanguaging stance? (2) How do TCs plan for translanguaging in their lessons? (3) What are the challenges/limitations to TCs’ planning for translanguaging? Data sources, which included TCs’ unit and lesson plans, course assignments, reflections, and interviews were analyzed deductively to identify themes related to their translanguaging stance, and how they planned for translanguaging in their coursework. The findings demonstrate that TCs’ language learning experiences, challenges and identities were factors that shaped their developing translanguaging stance. TCs incorporated many translanguaging strategies and resources to support learners’ socioemotional wellbeing and language learning, but saw translanguaging as a temporary scaffold rather than a way to de-center the hegemony of English in curriculum and assessment. The findings provide implications for how teacher educators can foster TCs’ critical engagement with translanguaging to disrupt linguistic hierarchies and democratize the classroom.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".