Enhancing equitable access to education for English language learners: evaluating the impact of a digital multilingual STEM resource in Canada
Bibliographic record
Abstract
In Canada, the substantial increase in English language learners has underscored the pressing need for educational resources that enable them to receive an education appropriate to their academic level while learning the school language. This issue carries significant implications for equitable access to quality education. In this study, we describe a novel multilingual digital resource for grades 6–9 in STEM. The resource offers content in the school languages as well as in Dari, Tigrinya, Arabic, Somali, and Spanish, among others. We examined the factors influencing the use and appreciation of this resource and its impact on students’ attitude towards STEM. This study involved 15 teachers and 161 students from three Canadian provinces. We used data collected by the platform over two years, complemented by student questionnaires, to investigate the relationship between teacher investment in the resource and students’ attitudes towards its content, the relationship between students’ attitudes and their resource use, and the impact of language availability on students’ use of the resource. Our results reveal that using student language as a content transfer tool is a powerful strategy for maximising student engagement in the resource but that the teacher plays a key role in its effectiveness.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".