STEM Education Landscapes: A Comparative Bibliometric and Pedagogical Overview of Canada and Ukraine
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Notice bibliographique
Résumé
This chapter provides a comprehensive comparative analysis of STEM education in Canada and Ukraine, integrating bibliometric mapping, qualitative content analysis, and comparative synthesis. Drawing on over 21,000 publications indexed in the Web of Science, including more than 850 Canadian and 85 Ukrainian works, the study examines national trajectories, institutional priorities, and pedagogical innovations. The findings demonstrate that Canada has solidified its position as a global leader, bolstered by mature research infrastructures, comprehensive policy frameworks, and extensive international collaborations. Canadian STEM education emphasizes teacher preparation, equity, diversity, and inclusion (EDI), the integration of Indigenous and multicultural knowledge systems, and a strong tradition of informal learning environments such as science museums and afterschool clubs. Emerging trends highlight digital pedagogy, immersive technologies, and gamified approaches, situating STEM as a vehicle for civic engagement, sustainability, and social justice. In Ukraine, STEM education has evolved rapidly, despite economic and wartime challenges, reflecting the country's adaptability and innovation under constraint. Pedagogical universities integrate STEM modules into teacher training, emphasizing visualization, modeling, and competency-based approaches. Practices include cloud-based platforms, augmented and virtual reality, and experimental adoption of generative AI to foster research skills and reflective thinking among pre-service teachers. Ukraine also demonstrates strong alignment with the Sustainable Development Goals (SDGs), expanding STEM into non-formal settings through STREAM centers, gamification, and project-based initiatives that extend access to underserved and displaced learners. The comparative analysis reveals complementary strengths. Canada offers institutional maturity, inclusivity, and ethically grounded pedagogy, while Ukraine exemplifies resilience, rapid digital adoption, and crisis-driven innovation. Equity and inclusion remain divergent: Canada benefits from systemic frameworks for gender and refugee support, while Ukraine continues to face acute regional and infrastructural disparities. Nevertheless, both contexts demonstrate the potential of STEM education to act as a driver of societal transformation, whether through stability and policy coherence (Canada) or through adaptive experimentation and resilience (Ukraine). The chapter concludes that cross-national collaboration between Canada and Ukraine could generate mutually beneficial outcomes. Canada may learn from Ukraine’s agile innovation cycles, while Ukraine could adapt Canada’s inclusive and policy-supported models. Together, they offer distinct but complementary pathways for reimagining STEM education as context-sensitive, ethically informed, and future-oriented, capable of addressing the intertwined challenges of technological disruption, sustainability, and social resilience.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,019 | 0,019 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle