Leveraging EdTech in Creating Refugee-Inclusive Classrooms in Canada
Bibliographic record
Abstract
As Canada experiences a growing number of newcomer students with refugee backgrounds, K-12 educators face challenges to meet students’ unique academic, linguistic, and psychosocial needs. This paper examines the role of educational technology (EdTech) to bridge the resource and training gap by enhancing teacher preparedness through an accessible, inclusive, and trauma-informed digital resource. This study presents a qualitative case study methodology to analyze the interactive online manual, Supporting Teachers to Address the Mental Health of Students from War Zones. The research utilizes three data sources: feedback from 110 educators through a questionnaire, observational data from 69 students from two separate pre-service teacher cohorts, and an expert evaluation report conducted by university curriculum specialists. Findings suggest that successful EdTech for refugee-background student initiatives must be trauma-informed, strength-based, culturally responsive, and designed with usability and accessibility in mind. Furthermore, collaboration between K-12 educators, researchers, and developers is vital to ensure that there is alignment of pedagogy and technology.
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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 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| 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".