Which Computational Thinking Components Are Emphasized in Science and Mathematics Education? A Bibliometric and Thematic Mapping Study
Keywords:
Computational Thinking, Mathematics education, Science EducationAbstract
Computational thinking (CT) has become a key competency in contemporary science and mathematics education, yet prior research often conceptualizes CT as a uniform construct with limited attention to disciplinary differences. This study aims to systematically map how CT components are emphasized across science/STEM and mathematics education in order to clarify domain-specific patterns and research directions. A bibliometric and thematic mapping approach was employed using peer-reviewed publications retrieved from the Dimensions AI database. Following PRISMA 2020 guidelines, 421 articles were included in the final dataset. Bibliometric analysis was conducted to examine publication trends and domain distribution, while co-word analysis and thematic mapping using VOSviewer were applied to identify thematic structures and the relative prominence of CT components across domains. The findings reveal six major thematic clusters representing conceptual problem solving, classroom pedagogy, technology and artificial intelligence, core CT components, programming and digital tools, and learning outcomes and affective dimensions. Comparative analysis shows distinct domain-responsive emphases: science/STEM education prioritizes algorithmic procedures, modeling and simulation, and data-oriented practices, whereas mathematics education more strongly emphasizes abstraction, generalization, pattern recognition, and formal algorithmic reasoning. These results indicate that CT should be conceptualized as a discipline-responsive construct rather than a generic set of skills. The study concludes by highlighting implications for curriculum design, teacher education, and assessment practices, and by identifying gaps for future research on the integration of computational thinking in domain-sensitive ways.
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