Examining Teacher Candidates’ Pedagogical Practices and Stances Towards Translanguaging and Multimodality in Writing
Bibliographic record
Abstract
This study examines pre-service teacher candidates’ (TCs) stances and use of translanguaging and multimodality to support K-12 multilingual learners’ writing. Data were drawn from a course on supporting multilingual learners in a teacher education program in Ontario. Data sources were responses to the Pedagogical Content Knowledge for Language- Inclusive Teaching (PeCK–LIT) Test, and TCs’ unit plans and lesson plans. Analytical codes were derived from the literature on translanguaging: monolingual and translanguaging stance, translanguaging as a scaffold and resource, teacher-directed and student-directed, intentional and spontaneous translanguaging, and supporting monomodality and multimodality. Findings demonstrate the use of translanguaging strategies such as multilingual word walls and online translation tools. However, there were constraints to TCs’ stances, such as allowing translanguaging as a temporary scaffold towards English-only instruction and approaching writing as a discrete rather than multimodal skill. The paper recommends ways TCs can be supported in developing a holistic understanding of translanguaging and multimodality.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".