K-12 Data Science Education: Outcomes of a National Workshop; International Perspectives; and Next Steps for the Learning Sciences
M. Wilkerson, D. Ben-Zvi, M. Dvir, C. Matuk, S. Podworny, A. Stephens, L. Zapata-Cardona, in: J.D. Slotta, E.S. Charles (Eds.), General Proceedings of the ISLS Annual Meeting: Building Knowledge and Sustaining Our Community, ISLS, Montreal, Canada, 2023, pp. 76–79.

 Minisymposium Data Science
S. Podworny, D. Frischemeier, in: . IDMI-Primar Goethe-Universität Frankfurt (Ed.), Beiträge Zum Mathematikunterricht 2022: 56. Jahrestagung Der Gesellschaft Für Didaktik Der Mathematik Vom 29.08.2022 Bis 02.09.2022 in Frankfurt Am Main, WTM-Verlag, Münster, 2023.

 An approach to teaching data science in middle school
S. Podworny, Y. Fleischer, in: U.T. Jankvist, R. Elicer, A. Clark-Wilson, H.-G. Weigand, M. Thomson (Eds.), Proceedings of the 15th International Conference on Technology in Mathematics Teaching (ICTMT 15), Danish School of Education, Aarhus, Denmark, 2022, pp. 308–315.

An example of rich, real and multivariate survey data for use in school
S. Podworny, Y. Fleischer, D. Stroop, R. Biehler, in: 2022.

Vokabeln lernen im Schlaf? Statistische Testprozeduren verstehen
S. Podworny, mathematik lehren 232 (2022) 36–40.

Jupyter Notebooks for Teaching, Learning, and Doing Data Science
Y. Fleischer, S. Hüsing, R. Biehler, S. Podworny, C. Schulte, in: S.A. Peters, L. Zapata-Cardona, F. Bonafini, A. Fan (Eds.), Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics, International Association for Statistical Education, 2022.

Civic Statistics for Prospective Teachers: Developing Content and Pedagogical Content Knowledge Through Project Work
S. Podworny, D. Frischemeier, R. Biehler, in: Statistics for Empowerment and Social Engagement, Springer International Publishing, Cham, 2022.

Data Visualization Packages for Non-inferential Civic Statistics in High School Classrooms
D. Frischemeier, S. Podworny, R. Biehler, in: Statistics for Empowerment and Social Engagement, Springer International Publishing, Cham, 2022, pp. 199–236.

An example of rich, real and multivariate survey data for use in school
S. Podworny, Y. Fleischer, D. Stroop, R. Biehler, in: 2022.

 Zur neuen Bedeutung von Daten in Data Science und künstlicher Intelligenz
L. Höper, S. Podworny, S. Hüsing, C. Schulte, Y. Fleischer, R. Biehler, D. Frischemeier, H. Malatyali, in: 2021, pp. 345–345.

The process of actively building a model for a randomization test – insights into learners’ modeling activities based on a case study
S. Podworny, R. Biehler, Mathematical Thinking and Learning 24 (2021) 291–311.

A first introduction to data science education in secondary schools: Teaching and learning about data exploration with<scp>CODAP</scp>using survey data
D. Frischemeier, R. Biehler, S. Podworny, L. Budde, Teaching Statistics 43 (2021) 2–9.

Data science education in secondary schools: Teaching and learning decision trees with CODAP and Jupyter Notebooks as an example of integrating machine learning into statistics education
R. Biehler, Y. Fleischer, L. Budde, D. Frischemeier, D. Gerstenberger, S. Podworny, C. Schulte, in: P. Arnold (Ed.), New Skills in the Changing World of Statistics Education Proceedings of the Roundtable Conference of the International Association for Statistical Education (IASE), ISI/IASE, Voorborg, The Netherlands, 2020.

Data Science Education in Secondary School: How to Develop Statistical Reasoning When Exploring Data Using CODAP
L. Budde, D. Frischemeier, R. Biehler, Y. Fleischer, D. Gerstenberger, S. Podworny, C. Schulte, in: P. Arnold (Ed.), New Skills in the Changing World of Statistics Education: Proceedings of the Roundtable Conference of the International Association for Statistical Education (IASE), July 2020, ISI/IASE, Voorborg, The Netherlands, 2020.