E-ISSN: 3125-0130

Articles

STEM Approaches in Primary Education: A Bibliometric Analysis of Global Research Trends

Keywords

  • Bibliometric analysis
  • Educational robotics
  • Primary education
  • Research trends
  • STEM education

How to Cite

STEM Approaches in Primary Education: A Bibliometric Analysis of Global Research Trends. (2026). Journal of Education for Sustainability and Transformation, 1(4), 65-78. https://journal.serral.org/jestra/article/view/42

Abstract

STEM approaches have become a prominent strategy for building foundational scientific, technological, and mathematical competencies in primary education, yet the structure of the research field remains insufficiently mapped. This study analyzed global publication and citation trends, leading contributors, collaboration patterns, and thematic evolution in research on STEM approaches in primary education. A bibliometric design combining performance analysis and science mapping was applied to 55 journal articles published between 2017 and 2026 and analyzed with bibliometrix and Biblioshiny. The results show an annual growth rate of 31.8%, with 89.1% of the articles published since 2021, and an average of 9.04 citations per document, with citation impact peaking for the 2021 cohort. Production is widely dispersed: 43 of 48 sources published a single article, no author contributed more than two documents, and Türkiye, China, the United States, and Malaysia led corresponding-author output. Only 16.36% of documents involved international co-authorship. Thematic analysis identified STEM education, primary education, and primary schools as basic themes; educational robotics, engineering, mathematics, and computational thinking as motor themes; and inquiry-based and project-based learning as developed but isolated niche themes. Educational robotics dominated early work, whereas STEM learning attitudes, educational technology, and environmental awareness emerged most recently. The findings reveal a rapidly growing but fragmented field and point to opportunities for stronger international collaboration, closer integration of pedagogy and technology, and links with sustainability education.

References

  1. Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11. https://doi.org/10.1016/j.edurev.2016.11.002
  2. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  3. Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
  4. Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006
  5. Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020
  6. Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.
  7. Bybee, R. W. (2010). Advancing STEM education: A 2020 vision. Technology and Engineering Teacher, 70(1), 30–35.
  8. Callon, M., Courtial, J.-P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22(1), 155–205. https://doi.org/10.1007/BF02019280
  9. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002
  10. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  11. English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education, 3, Article 3. https://doi.org/10.1186/s40594-016-0036-1
  12. Herzamzam, D. A., Dwiprabowo, R., Oktaviana, E., Faujiah, E., & Salim, F. (2024). Analysis of algebraic thinking process in terms of learning styles of elementary school students. In Proceedings of the 7th International Conference on Learning Innovation and Quality Education (ICLIQE 2023) (pp. 566–575). Atlantis Press. https://doi.org/10.2991/978-2-38476-301-6_54
  13. Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3, Article 11. https://doi.org/10.1186/s40594-016-0046-z
  14. Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education, 7, Article 11. https://doi.org/10.1186/s40594-020-00207-6
  15. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.
  16. Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: A systematic literature review. International Journal of STEM Education, 6, Article 2. https://doi.org/10.1186/s40594-018-0151-2
  17. Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
  18. Salim, F., Purwanto, A., & Lestari, I. (2024). Improving students’ science problem solving ability through the implementation of problem based learning models assisted by animation media. International Journal of Elementary Education, 8(2), 269–278. https://doi.org/10.23887/ijee.v8i2.76925
  19. Wibowo, F. C., Darman, D. R., Salim, F., Prahani, B. K., & Samsudin, A. (2026). Knowledgeable integration hybrid AI joins augmented reality and science literacy: Prospects and challenges. Social Sciences & Humanities Open, 13, Article 102566. https://doi.org/10.1016/j.ssaho.2026.102566
  20. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
  21. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629