Utilizing Technology in Multicultural Education: Experiences from Canadian Schools
Bibliographic record
Abstract
Background. In the context of increasingly diverse classrooms, Canadian schools face both the challenge and opportunity of fostering inclusive learning environments through multicultural education. As technology becomes more integrated into pedagogy, its role in addressing linguistic, cultural, and social differences gains prominence.Purpose. This study explores how educators in Canadian schools utilize digital tools to support multicultural education and promote equity among students from diverse backgrounds. The research aims to identify effective strategies, tools, and practices that enhance cultural inclusivity and student engagement through technology-enhanced instruction.Method. A qualitative multiple-case study approach was employed, involving interviews with 28 teachers, classroom observations across six schools, and analysis of institutional technology integration plans. Results. The findings indicate that technology, when used intentionally, facilitates culturally responsive teaching through language support apps, collaborative platforms, and digital storytelling tools. However, the study also reveals disparities in access, digital literacy, and institutional readiness, which hinder equitable outcomes.Conclusion. The study concludes that leveraging technology for multicultural education requires not only pedagogical innovation but also systemic support, teacher training, and inclusive design principles. These insights offer practical implications for educators and policymakers seeking to enhance diversity and inclusion in digital learning environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.036 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".