Online translanguaging and multiliteracies strategies to support K‐12 multilingual learners: Identity texts, linguistic landscapes, and photovoice
Bibliographic record
Abstract
The COVID‐19 pandemic has given rise to the burgeoning of online, blended, and hybrid classrooms. The transition to virtual learning has been a challenge for many teachers and learners, but for multilingual learners (MLs) who have to navigate the virtual learning environment in a new language, online learning can be particularly difficult. Translanguaging (García et al., 2017) and multiliteracies (Cope & Kalantzis, 2015) theories call for teachers to support MLs by activating their prior knowledge, connecting to their lives, integrating their home languages and cultures, and engaging them in learning through multiple modalities. This theory‐based practice article discusses three pedagogical strategies based on translanguaging and multiliteracies theories which are designed for multilingual K‐12 classrooms with an online learning component: (1) digital identity texts, (2) linguistic landscapes, and (3) photovoice. The examples presented in the article were developed through the authors' collaborative and reflective engagement with each other, and drawn from their respective work with K‐12 MLs and the preservice teachers preparing to teach MLs in mainstream classrooms in Ontario, Canada. The authors offer suggestions for how the proposed translanguaging and multilingual strategies can challenge monolingual practices, develop critical language awareness, and expand students' diverse language and literacies practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".