Translanguaging in Action: Authentic Teaching Practices in the High School English as an Additional Language Classroom
Bibliographic record
Abstract
ABSTRACT Translanguaging has recently gained much currency in second language (L2) education. While the conceptualization of this theory stems from the goal of enhancing L2 education for learners, one challenge scholars have underlined is the need for more responsive, practical classroom activities consistent with translanguaging principles and stance. Taking this challenge as a departure point, first, we identify the salient characteristics of translingual practices by reviewing relevant literature. Then, drawing on an action research project and using vignettes from the actual teaching practices of Author 1 of this paper, we report our experiences/findings of how translanguaging practices in a high school L2 classroom can be enacted. Thus, this paper aims to help educators envision practices derived from translingual principles and contribute to a repertoire of concrete ideas for classroom practices that L2 teachers can adapt in their teaching. Given that the focus on the experiences of a translanguaging approach discussed in the paper is those of only one secondary teacher in Western Canada, we recognize that practitioners may need to adjust for their own contexts. We hope the findings reported in this paper will provide insights into how to intentionally enact translanguaging practices in L2 education in a way that honors pedagogical translanguaging.
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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 | 0.006 | 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 | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".