Abstract

In today’s data-driven world, the ability to interpret, analyze, and make decisions based on data is a critical skill across many fields of life. This makes data science not just an asset but a necessity for navigating modern challenges (Wise, 2019). One of the most pressing questions in education today is how to effectively cultivate data science in school (Biehler & Schulte, 2018). However, to achieve this, it is essential first to equip teachers with the skills and knowledge they need to integrate data science into their teaching practices, particularly in STEM/STEAM contexts. The current situation, however, reveals a significant gap in university curricula for teacher education, where programs promoting data science aspects for pre-service teachers in STEM/STEAM contexts appear to be rare.
The project “Promoting Data Science Education for Teacher Education at the University level (DataSETUP)” addresses this gap by embedding data science into teacher education, thereby enhancing educators’ capacity to teach data science effectively. The core of DataSETUP is the development of a comprehensive framework to guide the creation of educational resources and innovative teaching modules specifically tailored to the university level for teacher education programs. These modules are developed using a design-based research approach and are guided by the principles of the Statistical Reasoning Learning Environment (SRLE) (Garfield & Ben-Zvi, 2008), which integrates teaching, technology, curriculum, and assessment in statistics education. In particular, the DataSETUP modules focus on experiencing specific data moves within an investigative cycle when exploring real and open data with digital tools.
Moreover, the DataSETUP framework not only supports the integration of data science in teacher education but also promotes interdisciplinary learning. It encourages a global perspective on data science, recognizing the importance of equipping students to navigate and critically evaluate data in a globally interconnected world. By fostering these competencies, the DataSETUP project contributes first steps to the cultivation of a new generation of teachers who are able to navigate and teach the complexities of our data-rich world.
We will present the DataSETUP framework in detail, alongside concrete materials and activities that can be implemented in teacher education courses at the university level. These resources are designed to empower future teachers to bring data science into their classrooms, thus contributing to the development of a data-literate society.

References
Biehler, R., & Schulte, C. (2018). Perspectives for an interdesciplinary data science curriculum at German secondary schools. In R. Biehler, L. Budde, D. Frischemeier, B. Heinemann, S. Podworny, C. Schulte, & T. Wassong (Eds.), Paderborn symposium on data science education at school level 2017: The collected extended abstracts (pp. 2-14). Universitätsbibliothek Paderborn. https://doi.org/http://doi.org/10.17619/UNIPB/1-374
Garfield, J., & Ben-Zvi, D. (2008). Developing students' statistical reasoning: Connecting research and teaching practice. Springer Science+Business Media.
Wise, A. F. (2019). Educating data scientists and data literate citizens for a new generation of data. Journal of the Learning Sciences, 29(1), 165-181. https://doi.org/10.1080/10508406.2019.1705678