Scientific modeling and translanguaging: A multilingual and multimodal approach to support science learning and engagement
Bibliographic record
Abstract
Abstract Research suggests that translanguaging can be transformative for teaching and learning by making students' diverse linguistic resources a meaningful part of classroom discourse. Building on this study, researchers have explored how translanguaging practices can support learning in STEM (science, technology, engineering, and mathematics), primarily in the context of bilingual classrooms. However, in the United States, most students learn in English‐dominant classrooms. In response, researchers and educators have begun to explore strategies for inviting and leveraging translanguaging in English‐dominant classrooms, primarily focusing on literacy learning. Less is known about supporting translanguaging in English‐dominant STEM classrooms, particularly with monolingual teachers. In an English‐dominant sixth‐grade STEM classroom engaging in a 9‐week ecology unit, we explored how scientific modeling could not only provide a context for inviting translanguaging, but how it could also provide a setting where modeling and translanguaging could be experienced as analogous meaning‐making practices. Our findings demonstrate that translanguaging has the potential to support new kinds of learning in English‐dominant STEM classrooms, not only about STEM content and practices but also about what counts as legitimate and valuable participation in these spaces.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".