Enhancing teachers’ TPACK for data science in STEAM education: Insights from a pilot professional development program in Cyprus
DOI:
https://doi.org/10.52041/iase25.136Abstract
In today’s data-driven world, education systems face growing pressure to equip students with data science literacy and critical thinking skills. While STEAM education offers a natural interdisciplinary setting for fostering these competencies, teachers often lack the confidence, training, and pedagogical strategies needed to integrate data science into their practice. The EU-funded DataScEd4CiEn project responds to this challenge by designing and implementing a Professional Development (PD) program tailored mostly for STEAM educators. This paper presents an exploratory study of a pilot implementation in Cyprus, aimed at investigating how the program supports teachers in adopting data- driven, interdisciplinary approaches. Using a Design-Based Research (DBR) methodology, the study iteratively refines the PD model while collecting mixed-methods data to examine changes in teacher knowledge, instructional practices, and collaborative engagement. Preliminary findings highlight the transformative potential of targeted PD in advancing data science integration and empowering teachers to meet the evolving demands of 21st-century education.References
Avalos, B. (2011). Teacher professional development in Teaching and Teacher Education over ten years. Teaching and Teacher Education, 27(1), 10–20. https://doi.org/10.1016/j.tate.2010.08.007
Borko, H. (2004). Professional development and teacher learning: Mapping the terrain. Educational Researcher, 33(8), 3–15. https://doi.org/10.3102/0013189X033008003
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102/0013189X032001009
Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute. https://learningpolicyinstitute.org/product/effective- teacher-professional-development-report
Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38(3), 181–199. https://doi.org/10.3102/0013189X08331140
Engel, J. (2017). Statistical literacy for active citizenship: A call for data science education. Statistics Education Research Journal, 16(1), 44–49. https://doi.org/10.52041/serj.v16i1.213
Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Yoon, K. S. (2001). What makes professional development effective? Results from a national sample of teachers. American Educational Research Journal, 38(4), 915–945. https://doi.org/10.3102/00028312038004915
Guskey, T. R. (2002). Professional development and teacher change. Teachers and Teaching, 8(3), 381–391. https://doi.org/10.1080/135406002100000512
Gutstein, E. (2006). Reading and writing the world with mathematics: Toward a pedagogy for social justice. Routledge. https://doi.org/10.4324/9780203112946
Honey, M., Pearson, G., & Schweingruber, H. (Eds.). (2014). STEM integration in K–12 education: Status, prospects, and an agenda for research. National Academies Press. https://doi.org/10.17226/18612
Kennedy, M. M. (2016). How does professional development improve teaching? Review of Educational Research, 86(4), 945–980. https://doi.org/10.3102/0034654315626800
National Research Council. (2012). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press. https://doi.org/10.17226/13165
Wineburg, S. S., Martin, D., & Monte-Sano, C. (2012). Reading like a historian: Teaching literacy in middle and high school history classrooms. Teachers College Press.
Wing, J. M. (2019). The data life cycle. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.e26845b4