STEM Education Landscapes: A Comparative Bibliometric and Pedagogical Overview of Canada and Ukraine
Bibliographic record
Abstract
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.092 | 0.213 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".