Publikationen
2024
Fleischer, Y., Podworny, S. & Biehler, R. (2024). Teaching and learning to construct data-based decision trees using data cards as the first introduction to machine learning in middle school. Statistics Education Research Journa 23(1), Article 3. https://doi.org/10.52041/serj.v23i1.450
Fleischer, Y., Podworny, S., Biehler, R. (2024). Datenbasiertes Entscheiden. Wie TikTok dein wahres Alter herausfinden kann. Mathewelt - das Arbeitsheft, mathematik lehren 244.
Katherine M. Miller, Joseph L. Polman, Susan A. Yoon, Jooeun Shim, Vivian Y. Leung, Yen Nguyen, Andee Rubin, Andee Rubin, Traci Higgins, Jessica M. Karch, James K.L. Hammerman, Camillia Matuk, Kayla DesPortes, Anna Amato, Suzanne Dikker, Xavier Ochoa, Esteban Romero, Susanne Podworny, Yannik Fleischer, Rolf Biehler, Justice T. Walker, Amanda Barany, Alex Acquah, Andi Scarola, Sayed Reza, Trang C. Tran, Ralph Vacca, Megan Silander, Peter J. Woods, Cassia Fernandez, Adelmo Eloy, Paulo Blikstein, Roseli de Deus Lopes, Josh Radinsky, Iris Tabak, Victor R. Lee, Dorottya Demszky, Sarah Levine, Josephine Louie (2024). Data and Social Worlds: How Data Science Education Supports Civic Participation and Social Discourse. In Robb Lindgren, Tutaleni Asino, Eleni A. Kyza, Chee-Kit Looi, D. Teo Keifert & Enrique Suárez (Eds.), ISLS Annual Meeting 2024 June 10-14, 2024 Learning as a Cornerstone of Healing, Resilience, and Community, pp 1863-1870. URL
Podworny, S. (2024). Studentisches Feedback zu Online-Gruppenarbeiten. die hochschullehre 9(1). https://dx.doi.org/10.3278/HSL2317W
Podworny, S. (2024). Eine qualitative Studie zu Data Science Education: Schülerinnen und Schüler analysieren multivariate Daten. Stochastik in der Schule 44(1), 2-10.
Podworny, S. (2024). Preface. In: S. Podworny, D. Frischemeier, M. Dvir & D. Ben-Zvi (Eds.), Reasoning with data models and modeling in the big data era. Universitätsbibliothek Paderborn, Paderborn. http://dx.doi.org/10.17619/UNIPB/1-1815
Podworny, S., Fleischer, Y., Biehler, R. (2024). Lebensmittel mit Entscheidungsbäumen klasifizieren. mathematik lehren 244, 8-13.
Podworny, S., Frischemeier, D., Dvir, M. & Ben-Zvi, D. (Eds..) (2024). Reasoning with data models and modeling in the big data era. Universitätsbibliothek Paderborn, Paderborn. http://dx.doi.org/10.17619/UNIPB/1-1815
Podworny, S. & Frischemeier, D. (2024). Young learners' perspectives on the concept of data as a model: what are data and what are they used for? In: S. Podworny, D. Frischemeier, M. Dvir & D. Ben-Zvi (Eds.), Reasoning with data models and modeling in the big data era (pp 15-22). Universitätsbibliothek Paderborn, Paderborn. http://dx.doi.org/10.17619/UNIPB/1-1815
2023
Podworny, S. (2023). Studentisches Feedback zu Online-Gruppenarbeiten. In: N. Vöing & D. Bücker (Hrsg). Paderbroner Beiträge 2023. die hochschullehre - Themenheft 2023. DOI: 10.3278/HSLT2301W
Podworny, S. (2023). Statistics and Probability Education in Germany. In: Burrill, G. F., de Oliveria Souza, L., Reston, E. (eds.), Research on Reasoning with Data and Statistical Thinking: International Perspectives. Advances in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-29459-4_4
Podworny, S. & Frischemeier, D. (2023). Minisymposium Data Science. In: IDMI-Primar Goethe-Universität Frankfurt (Hrsg.), 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.
Wilkerson, M. H., Ben-Zvi, D., Clegg, T., Dvir, M., Matuk, C., Podworny, S., Stephens, A. & Zapata-Cardona, L. (2023). K-12 Data Science Education: Outcomes of a National Workshop; International Perspectives; and Next Steps for the Learning Sciences. In J. D. Slottag & E. S. Charles (Eds.), General Proceedings of the ISLS Annual Meeting: Building Knowledge and Sustaining our Community (pp 76-79). ISLS: Montreal, Canada.
2022
Fleischer, Y., Hüsing, S., Biehler, R., Podworny, S., & Schulte, C. (2022). Jupyter Notebooks for Teaching, Learning, and Doing Data Science. 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. https://doi.org/10.52041/iase.icots11.T10E3
Fleischer, Y. & Podworny, S. (2022). Teaching machine learning with decision trees in middle school using CODAP. In U.T. Jankvist, R. Elicer, A. Clark-Wilson, H.-G. Weigand, & M. Thomsen (Eds.), Proceedings of the 15th international conference on technology in mathematics teaching (ICTMT 15) (pp. 280–281). Danish School of Education, Aarhus University.
Frischemeier, D., Podworny, S. & Biehler, R. (2022). Data Visualization Packages for Non-inferential Civic Statistics in High School Classrooms. In: Ridgway, J. (Eds), Statistics for Empowerment and Social Engagement. Springer, Cham. https://doi.org/10.1007/978-3-031-20748-8_9
Podworny, S. (2022). Vokabeln lernen im Schlaf? Statistische Testprozeduren verstehen. mathematik lehren 232, 36-40.
Podworny, S. & Fleischer, Y. (2022). An approach to teaching data science in middle school. In U.T. Jankvist, R. Elicer, A. Clark-Wilson, H.-G. Weigand, & M. Thomsen (Eds.), Proceedings of the 15th international conference on technology in mathematics teaching (ICTMT 15) (pp. 308–315). Danish School of Education, Aarhus University.
Podworny, S., Fleischer, Y., & Hüsing, S. (2022). Grade 6 students‘ perception and use of data-based decision trees. 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. https://doi.org/10.52041/iase.icots11.T2H3
Podworny S., Fleischer, Y., Stroop, D. & Biehler R. (2022). An example of rich, real and multivariate survey data for use in school. Twelfth Congress of the European Society for Research in Mathematics Education (CERME12), Bozen-Bolzano, Italy. hal-03751842
Podworny, S., Frischemeier, D. & Biehler, R. (2022). Civic Statistics for Prospective Teachers: Developing Content and Pedagogical Content Knowledge Through Project Work. In: Ridgway, J. (Eds), Statistics for Empowerment and Social Engagement. Springer, Cham. https://doi.org/10.1007/978-3-031-20748-8_15
Podworny, S., Hüsing, S. & Schulte, C. (2022). A place for data science introduction in school: between statistics and programming. Statistics Education Research Journal 21(2), Special Issue on Data Science.
2021
Frischemeier, D., Biehler, R., Podworny, S. & Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data. Teaching Statistics 43, 182-189.
Frischemeier, D., Podworny, S. & Biehler, R. (2021). Integration fachwissenschaftlicher und fachdidaktischer Komponenten in der Lehramtsausbildung Mathematik Grundschule am Beispiel einer Veranstaltung zur Leitidee "Daten, Häufigkeit und Wahrscheinlichkeit". In R. Biehler, A. Eichler, R. Hochmuth, S. Rach & N. Schaper (Hrsg.) (2021). Lehrinnovationen in der Hochschulmathematik: praxisrelevant – didaktisch fundiert – forschungsbasiert (pp. 227-249). Wiesbaden: Springer Spektrum.
Höper, L., Podworny, S., Hüsing, S., Schulte, C., Fleischer, Y., Biehler, R., Frischemeier, D. & Malatyali, H., (2021). Zur neuen Bedeutung von Daten in Data Science und künstlicher Intelligenz. In: Humbert, L. (Hrsg.), INFOS 2021 – 19. GI-Fachtagung Informatik und Schule. Gesellschaft für Informatik, Bonn. (S. 345-345). DOI: 10.18420/infos2021_a230
Höper, L., Podworny, S., Schulte, C. & Frischemeier, D. (2021). Exploration of location data: Real data in the context of interaction with a cellular network. In: R. Helenius, E. Falck (Eds.), Statistics Education in the Era of Data Science. Proceedings of the Satellite conference of the International Association for Statistical Education (IASE), Aug-Sept 2021, Online conference.
Hüsing, S. & Podworny, S. (2021). Computational essays as an approach for reproducible data analysis in lower secondary school. In: R. Helenius, E. Falck (Eds.), Statistics Education in the Era of Data Science. Proceedings of the Satellite conference of the International Association for Statistical Education (IASE), Aug-Sept 2021, Online conference.
Podworny, S. & Biehler, R. (2021). The process of actively building a model for a randomization test – insights into learners’ modeling activities based on a case study. Mathematical Thinking and Learning.
Podworny, S., Fleischer, Y., Hüsing, S., Biehler, R., Frischemeier, D., Höper, L. & Schulte, C. (2021). Using data cards for teaching data based decision trees in middle school. In 21st Koli Calling International Conference on Computing Education Research (Koli Calling '21), November 18-21, 2021, Joensuu, Finland. ACM, New York, NY, USA.
Podworny, S., Höper, L., Fleischer, Y., Hüsing, S. & Schulte, C., (2021). Data Science ab Klasse 5 – Konkrete Unterrichtsvorschläge für künstliche Intelligenz unplugged und Datenbewusstsein. In: Humbert, L. (Hrsg.), INFOS 2021 – 19. GI-Fachtagung Informatik und Schule. Gesellschaft für Informatik, Bonn. (S. 327-327). DOI: 10.18420/infos2021_w278
2020
Biehler, R., Fleischer, Y., Budde, L., Frischemeier, D., Gerstenberger, D., Podworny, S. & Schulte, C. (2020). 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. 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, Online. Voorborg, The Netherlands.
Biehler, R., Frischemeier, D., Podworny, S., Wassong, T., Schulte, C., Opel, S. & Schlichtig, M. (2020). Substantielle Digitale Bildung statt nur Anwendung digitaler Werkzeuge – Impulse aus einem Pilotprojekt zu Data Science in der Sekundarstufe. In: Beiträge zum Mathematikunterricht 2019 (pp. 133-136). Münster: WTM-Verlag.
Budde, L., Frischemeier, D., Biehler, R., Fleischer, Y., Gerstenberger, D., Podworny, S., Schulte, C. (2020). Data science education in secondary school: How to develop statistical reasoning when exploraing data using CODAP. 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, Online. Voorborg, The Netherlands.
2019
Engel, J., Biehler, R., Frischemeier, D., Podworny, S., Schiller, A. & Martignon, L. (2019). Zivilstatistik: Konzept einer neuen Perspektive auf Data Literacy und Statistical Literacy. AStA Wirtschafts-und Sozialstatistisches Archiv, 1-32.
Frischemeier, D. & Podworny, S. (2019). Implementation der Software TinkerPlots zur Datenanalyse und zur Simulation von Zufallsexperimenten in der Grundschullehrerausbildung in Stochastik. In: D. Walter & R. Rink (Hrsg), Digitale Medien in der Lehrerbildung Mathematik (pp. 73-94), Münster: WTM-Verlag.
Frischemeier, D., Podworny, S. & Biehler, R. (2019). Chancen und Herausforderungen für die Implementation von Zivilstatistiken in der Lehramtsausbildung. Stochastik in der Schule 39(1), 26-33.
Opel, S., Schlichtig, M., Schulte, C., Biehler, R., Frischemeier, D., Podworny, S. & Wassong, T. (2019). Entwicklung und Reflexion einer Unterrichtssequenz zum Maschinellen Lernen als Aspekt von Data Science in der Sekundarstufe II. In: A. Pasternak (Hrsg.), Proceedings zur 18. GI-Fachtagung Informatik und Schule "Informatik für Alle” (S. 285-294). Bonn: Gesellschaft für Informatik.
Podworny, S. (2019). Connecting context, statistics and software for understanding a randomization test: a case study. In: U. T. Jankvist, M. Van den Heuvel-Panhuizen, & M. Veldhuis (Eds.). Proceedings of the Eleventh Congress of the European Society for Research in Mathematics Education (CERME11, February 6 – 10, 2019). Utrecht, the Netherlands: Freudenthal Group & Freudenthal Institute, Utrecht University and ERME.
Podworny, S. (2019). Simulationen und Randomisierungstests mit der Software TinkerPlots. Wiesbaden: Springer Spektrum.
Podworny, S. (2019). Synthesis of elements for conducting a randomization test. In J. M. Contreras, M. Gea, M. M. López-Martin & E. Molina-Portillo (Eds.), Actas des Tercer Congreso International Virtual de Educación.
Podworny, S. & Frischemeier, D. (2019). Krankenhäuser, Unfälle und Statistik. Mini-Projekte zur Thematisierung von Zivilstatistik im Unterricht. Stochastik in der Schule 39(1), 13-19.
Schlichtig, M., Opel, S., Schulte, C., Biehler, R., Frischemeier, D., Podworny, S. & Wassong, T. (2019). Maschinelles Lernen im Unterricht mit Jupyter Notebook. In: A. Pasternak (Hrsg.), Proceedings zur 18. GI-Fachtagung Informatik und Schule “Informatik für Alle” (S. 385). Bonn: Gesellschaft für Informatik.
2018
Biehler, R., Budde, L., Frischemeier, D., Heinemann, B., Podworny, S., Schulte, C. & Wassong, T. (Hrsg) (2018). Paderborn Symposium on Data Science Education on School Level 2017: The collected extended abstracts. Paderborn: Universitätsbibliothek Paderborn.
Biehler, R., Frischemeier, D. & Podworny, S. (2018). Civic Engagement in Higher Education: A university course in civic statistics for mathematics preservice teachers. Zeitschrift für Hochschulentwicklung, 13(2), 169-182.
Biehler, R., Frischemeier, D. & Podworny, S. (2018). Elementary preservice teachers’ reasoning about statistical modeling in a civic statistics context. ZDM Mathematics Education. https://doi.org/10.1007/s11858-018-1001-x
Biehler, R., Frischemeier, D., Podworny, S., Wassong, T., Budde, L., Heinemann, B., Schulte, C. (2018). Data science and big data in upper secondary schools: A module to build up first components of statistical thinking in a data science curriculum. Archives of Data Science, Series A (Online First) 5(1), 19-28.
Frischemeier, D., Podworny, S. & Biehler, R. (2018). Activities for promoting civic statistical knowledge of preservice teachers. Proceedings of the conference on "Challenges and Innovations in Statistics Education". Multiplier Conference of ProCivicStat. University of Szeged Faculty of Economics and Business Administration, September 7-9, 2017. ISBN: 978-963-306-575-4
Heinemann, B., Budde, L., Schulte, C., Biehler, R., Frischemeier, D., Podworny, S., & Wassong, T. (2018). Data Science and Big Data in Upper Secondary Schools: What Should Be Discussed From a Perspective of Computer Science Education? Archives of Data Science, Series A, 5(1), S. 18-26.
Heinemann, B., Opel, S., Budde, L., Schulte, C., Frischemeier, D., Biehler, R., Podworny, S., Wassong, T. (2018). Drafting a Data Science Curriculum for Secondary Schools. In: 18th Koli Calling International Conference on Computing Education Research (Koli Calling ’18), November 22–25, 2018, Koli, Finland. ACM, New York, NY, USA.
Podworny, S. (2018). Simulationen und Randomisierungstests mit der Software TinkerPlots. Theoretische Werkzeuganalyse zur stochastischen Simulation und explorative Fallstudie zum statistischen Schließen mit Randomisierungstests. Paderborn: Universität Paderborn.
Podworny, S. (2018). Students' Refelctions About a Course for Learning Inferential Reasoning Via Simulations. In: C. Batanero & E. J. Chernoff (Eds.), Teaching and Learning Stochastics: ICME-13 Monographs (pp. 333-349), Springer International Publishing AG.
Podworny, S., Frischemeier, D. & Biehler, R. (2018). Enhancing civic statistical knowledge of secondary preservice teachers for mathematics. In M. A. Sorto, A. White, & L. Guyot (Eds.), Looking back, looking forward. Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018), Kyoto, Japan. Voorburg, The Netherlands: International Statistical Institute.
Podworny, S., Frischemeier, D. & Biehler, R. (2018). Zivilstatistisches Wissen in der Lehramtsausbildung fördern: Design und Durchführung eines universitären Seminars für Lehramtsstudierende der Mathematik. In: Fachgruppe Didaktik der Mathematik der Universität Paderborn (Hrsg.), Beiträge zum Mathematikunterricht 2018 (pp. 1423-1426). Münster: WTM-Verlag.
Wassner, C., Podworny, S. & Biehler R. (2018). Reale Datenkompetenz im Unterricht fördern. In Fachgruppe Didaktik der Mathematik der Universität Paderborn (Hrsg.), Beiträge zum Mathematikunterricht 2018 (pp. 1923-1926). Münster: WTM-Verlag.
2017
Biehler, R., Frischemeier, D. & Podworny, S. (2017). Design, realization and evaluation of a university course for preservice teachers on developing statistical reasoning and literacy with a focus on civic statistics. Invited paper für Invited Paper Session auf dem World Statistics Congress 61, Marrakech, Morocco.
Biehler, R., Frischemeier, D. & Podworny S. (2017). Editorial: Reasoning about models and modelling in the context of informal statistical inference. Statistics Education Research Journal, 16(2), 8-12.
Biehler, R., Frischemeier, D. & Podworny S. (2017). Elementary preservice teachers´reasoning about modeling a "family factory" with TinkerPlots - A pilot study. Statistics Education Research Journal, 16(2), 244-289.
Podworny, S., Frischemeier, D. & Biehler, R. (2017). Design, realization and evaluation of a statistics course for preservice teachers for primary school in Germany. In: A. Molnar (Ed.), Teaching Statistics in a Data Rich World. Proceedings of the Satellite Conference of the International Association of Statistics Education (IASE), July 2017, Rabat, Morocco.
2016
Podworny, Susanne (2016). Design of a course for learning probability via simulations with TinkerPlots. Invited Paper for Presentation in Topic Study Group 14 at the 13th International Congress on Mathematical Education (ICME 13), Hamburg.
Biehler, Rolf; Frischemeier, Daniel & Podworny, Susanne (2016). Stochastische Simulationen mit TinkerPlots - Von einfachen Zufallsexperimenten zum informellen Hypothesentesten. Stochastik in der Schule, 36(1), 22-27.
2015
Biehler, Rolf; Frischemeier, Daniel & Podworny, Susanne (2015). Informelles Hypothesentesten mit Simulationsunterstützung in der Sekundarstufe I. Praxis der Mathematik, 66(6), 21-25.
Biehler, Rolf; Frischemeier, Daniel & Podworny, Susanne (2015). Preservice Teachers´ Reasoning about Uncertainty in the Context of Randomization Tests. In A. Zieffler & E. Fry (Eds.): Reasoning about Uncertainty: Learning and Teaching Informal Inferential Reasoning (pp. 129-162). Catalyst Press.
Biehler, Rolf; Frischemeier, Daniel & Podworny, Ssusanne (2015). Elementary preservice teachers´ reasoning about modeling a "family factory" with TinkerPlots - A pilot study. Paper presented at the Ninth International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-9). University of Paderborn, Waldhotel Nachtigall.
2014
Podworny, Susanne & Biehler, Rolf (2014). A learning trajectory on hypothesis testing with TinkerPlots - design and exploratory evaluation. In K. Makar; B. de Sousa; R. Gould (Hrsg.): Sustainability in statistics education. Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014), Flagstaff, Arizona, USA.ISA: Voorburg, The Netherlands.
Frischemeier, Daniel & Podworny, Susanne (2014). Explorative Datenanalyse und stochastische Simulationen mit TinkerPlots - erste Einsätze in Kassel & Paderborn. Erscheint in: Wassong, Thomas; Frischemeier, Daniel; Fischer, Pascal R.; Hochmuth, Reinhard; Bender, Peter (Hrsg.) (2014): Mit Werkzeugen Mathematik und Stochastik lernen - Using Tools for Learning Mathematics and Statistics.Wiesbaden: Springer Spektrum. Festschrift zum 60. Geburtstag von Rolf Biehler.
2013
Biehler, Rolf; Frischemeier, Daniel & Podworny, Susanne (2013). Preservice teachers´ reasoning about uncertainty in the context of randomization tests. Paper presented at the Eighth International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-8). University of Minnesota, Two Harbors (MN).
Biehler, Rolf; Frischemeier, Daniel & Podworny, Susanne (2013). TinkerPlots 2.0 - von realen Handlungen über Computersimulationen zum stochastischen Denken. In G. Greefrath, F. Käpnick, & M. Stein: Beiträge zum Mathematikunterricht 2013, WTM Verlag, Münster. 144-147.
Podworny, Susanne (2013). Mit TinkerPlots vom einfachen Simulieren zum informellen Hypothesentesten. In G. Greefrath, F. Käpnick, & M. Stein: Beiträge zum Mathematikunterricht 2013, WTM Verlag, Münster. 324-327.