Multilingual pedagogies and digital technologies to support learning STEM in schools in France and Canada
Bibliographic record
Abstract
For many years, French and Canadian schools have welcomed students from around the world. This article presents the Binogi/ESCAPE project, which supports the integration of a multilingual digital resource in the classroom that presents STEM content through a multilingual lens with associated animated videos and quizzes. The study aims to encourage the inclusion of multiple languages in STEM content in both language-based (FSL/ESL) and content-based (STEM) classrooms. Researchers collected data during the 2020–2022 school years through focus groups, interviews, logs, observations, and questionnaires. Study participants included 17 teachers in France and 18 teachers in Canada. The results show that opening up to languages through a multilingual resource works as a springboard, allowing teachers and their students to find innovative ways to include other languages. Teachers who have used the resource have also appreciated the use of Binogi for instructional differentiation. Binogi's multilingual features supported translanguaging activities in the classroom, linking STEM content to literacy activities. However, more research is needed to understand how to train teachers to use multilingual resources to better support newcomer students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".