This study examines Arduino-supported STEM education as a pedagogical approach for developing selected professional competencies of future physics teachers and explores its relevance to technology-enhanced physics teacher education. A two-phase quantitative design combined a systematic review and meta-analysis with an empirical comparative study. Eighteen empirical studies from Web of Science, Scopus, and Google Scholar were synthesized using standardized mean differences in Comprehensive Meta-Analysis 4.0. The empirical phase involved 60 future physics teachers in three Arduino-supported formats: Project-Based Learning, Inquiry/Research-Based Learning, and Laboratory/Experimental Learning. Professional competence was assessed across research competence, engineering and design thinking, practical action skills, and theory–practice connection. Group differences were analyzed using Jamovi and Welch’s ANOVA. The meta-analysis showed a positive overall effect of Arduino-supported interventions, with variation across educational contexts. In the empirical phase, the Project-Based Learning group obtained the highest professional-competency scores. Meta-regression showed that instructional format did not significantly moderate the overall effect. Arduino-supported STEM activities may provide a useful context for linking physics concepts with inquiry, engineering design, experimentation, and practical problem solving. The empirical findings are limited to the four assessed competency dimensions and the specific study context. Further research with validated instruments, stronger designs, and broader samples is needed. The study also presents a wind-energy instructional model that integrates Arduino technology, experimental physics, and engineering-oriented STEM activities for future physics teacher preparation.
6 authors ·
15 co-authorship links
· 2 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
Ahmet Yesevi University
Sakarya University
Affiliation unknown
Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-author
Organisation
Joint articles
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.