@inproceedings{64625,
  author       = {{Fergusson, Anna and Podworny, Susanne and Fleischer, Yannik and Hüsing, Sven and Puloka, Malia S. and Biehler, Rolf and Pfannkuch, Maxine and Budgett, Stephanie and Dalrymple, Michelle}},
  booktitle    = {{Proceedings of the IASE 2025 Satellite Conference - Statistics and Data Science Education in STEAM}},
  publisher    = {{International Association for Statistics Education}},
  title        = {{{Branching out data science education: Developing task and computational environment design principles for teaching data science at the high school level through an international research collaboration}}},
  doi          = {{10.52041/iase25.138}},
  year         = {{2026}},
}

@article{58353,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>Statistics and machine learning are critical because they play an essential role in our everyday lives and the careers we may pursue in the future. It may be beneficial to introduce machine learning, such as decision trees (DTs), at an early stage of education. The data-based construction of DTs is an example of a machine learning process, which can be addressed in mathematics or statistics teaching because of relatively low prior knowledge requirements. This paper focuses on investigating how sixth-grade students create and evaluate data-based DTs. The basis is a teaching unit that aims to lay the foundation for machine learning and enhance students’ understanding of the process. We investigate students’ processes in detail while they build DTs with data cards about food items to predict whether a new item is recommendable. After the teaching unit, an interview study examines students’ strategies for creating decision trees. The findings contribute to understanding students’ learning processes and the challenges when working with decision trees.</jats:p>}},
  author       = {{Podworny, Susanne and Biehler, Rolf and Fleischer, Yannik}},
  issn         = {{1863-9690}},
  journal      = {{ZDM – Mathematics Education}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Young students’ engagement with data to create decision trees}}},
  doi          = {{10.1007/s11858-024-01649-w}},
  year         = {{2025}},
}

@inproceedings{63803,
  author       = {{Hüsing, Sven and Podworny, Susanne}},
  booktitle    = {{Symposium on integrating AI and data science into school education across disciplines}},
  title        = {{{Empowering Students to Gain Insights within Data Exploration Projects in the Classroom - Using, Modifying, and Creating Data Moves through a Scaffolded Use of Digital Tools}}},
  year         = {{2025}},
}

@article{60495,
  author       = {{Podworny, Susanne and Fleischer, Yannik and Biehler, Rolf}},
  journal      = {{Stochastik in der Schule}},
  number       = {{2}},
  pages        = {{9--16}},
  title        = {{{Explorative Datenanalyse in der Schule – Analyse der Mediennutzung von Jugendlichen mit den YOU‑PB Daten}}},
  volume       = {{45}},
  year         = {{2025}},
}

@article{55667,
  abstract     = {{<jats:p>This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test data. They learned to manually construct decision trees to classify food items as recommendable or not. They utilized data cards with a heuristic that is a simplified form of a machine learning algorithm. We report on evidence that this topic is teachable to middle school students, along with insights for refining our teaching approach and broader implications for teaching machine learning at the school level.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Podworny, Susanne and Biehler, Rolf}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  number       = {{1}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning to Construct Data-Based Decision Trees Using Data Cards as the First Introduction to Machine Learning in Middle School}}},
  doi          = {{10.52041/serj.v23i1.450}},
  volume       = {{23}},
  year         = {{2024}},
}

@inproceedings{59467,
  author       = {{Miller, Katherine M and Polman, Joseph L and Yoon, Susan A and Shim, Jooeun and Leung, Vivian Y and Nguyen, Yen and Rubin, Andee and Higgins, Traci and Karch, Jessica M and Hammerman, James KL and Matuk, Camillia and DesPortes, Kayla and Amato, Anna and Dikker, Suzanne and Ochoa, Xavier and Romero, Esteban and Podworny, Susanne and Fleischer, Yannik and Biehler, Rolf and Walker, Justice T. and Barany, Amanda and Acquah, Alex and Scarola, Andi and Reza, Sayed and Tran, Trang C. and Vacca, Ralph and Silander, Megan and Woods, Peter J. and Fernandez, Cassia and Eloy, Adelmo and Blikstein, Paulo and de Deus Lopes, Roseli and Radinsky, Josh and Tabak, Iris and Lee, Victor R. and Demszky, Dorottya and Levine, Sarah and Louie, Josephine}},
  booktitle    = {{Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024}},
  editor       = {{Lindgren, R. and Asino, T. I. and Kyza, E. A. and Looi, C. K. and Keifert, D. T. and Suárez, E.}},
  pages        = {{1863--1870}},
  publisher    = {{International Society of the Learning Sciences}},
  title        = {{{Data and Social Worlds: How Data Science Education Supports Civic Participation and Social Discourse}}},
  year         = {{2024}},
}

@inproceedings{48100,
  author       = {{Wilkerson, Michelle and Ben-Zvi, Dani and Dvir, Michal and Matuk, Camilla and Podworny, Susanne and Stephens, Amy and Zapata-Cardona, Lucia}},
  booktitle    = {{General Proceedings of the ISLS Annual Meeting: Building Knowledge and Sustaining our Community}},
  editor       = {{Slotta, J.D. and Charles, E.S.}},
  pages        = {{76--79}},
  publisher    = {{ISLS}},
  title        = {{{K-12 Data Science Education: Outcomes of a National Workshop; International Perspectives; and Next Steps for the Learning Sciences}}},
  year         = {{2023}},
}

@inproceedings{48101,
  author       = {{Podworny, Susanne and Frischemeier, Daniel}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2022: 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik vom 29.08.2022 bis 02.09.2022 in Frankfurt am Main}},
  editor       = {{IDMI-Primar Goethe-Universität Frankfurt, .}},
  publisher    = {{WTM-Verlag}},
  title        = {{{ Minisymposium Data Science}}},
  year         = {{2023}},
}

@inbook{48106,
  author       = {{Podworny, Susanne and Fleischer, Yannik}},
  booktitle    = {{Proceedings of the 15th international conference on technology in mathematics teaching (ICTMT 15)}},
  editor       = {{Jankvist, U.T. and Elicer, R. and Clark-Wilson, A and Weigand, Hans-Georg and Thomson, M}},
  pages        = {{308--315}},
  publisher    = {{Danish School of Education}},
  title        = {{{ An approach to teaching data science in middle school}}},
  year         = {{2022}},
}

@inproceedings{48107,
  author       = {{Podworny, Susanne and Fleischer, Yannik and Stroop, Dietlinde and Biehler, Rolf}},
  location     = {{Bozen-Bolzano, Italy}},
  title        = {{{An example of rich, real and multivariate survey data for use in school}}},
  year         = {{2022}},
}

@article{48104,
  author       = {{Podworny, Susanne}},
  journal      = {{mathematik lehren}},
  pages        = {{36--40}},
  title        = {{{Vokabeln lernen im Schlaf? Statistische Testprozeduren verstehen}}},
  volume       = {{232}},
  year         = {{2022}},
}

@inproceedings{35674,
  abstract     = {{<jats:p>We report on our work with students in our data science courses, focusing on the analysis of students’ results. This study represents an in-depth analysis of students’ creation and documentation of machine learning models. The students were supported by educationally designed Jupyter Notebooks, which are used as worked examples. Using the worked example, students document their results in a so-called computational essay. We examine which aspects of creating computational essays are difficult for students to find out how worked examples should be designed to support students without being too prescriptive. We analyze the computational essays produced by students and draw consequences for redesigning our worked example.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Hüsing, Sven and Biehler, Rolf and Podworny, Susanne and Schulte, Carsten}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  editor       = {{Peters, S. A. and Zapata-Cardona, L. and Bonafini, F. and Fan, A.}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Jupyter Notebooks for Teaching, Learning, and Doing Data Science}}},
  doi          = {{10.52041/iase.icots11.t10e3}},
  year         = {{2022}},
}

@inbook{43231,
  author       = {{Podworny, Susanne and Frischemeier, Daniel and Biehler, Rolf}},
  booktitle    = {{Statistics for Empowerment and Social Engagement}},
  isbn         = {{9783031207471}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Civic Statistics for Prospective Teachers: Developing Content and Pedagogical Content Knowledge Through Project Work}}},
  doi          = {{10.1007/978-3-031-20748-8_15}},
  year         = {{2022}},
}

@inbook{43230,
  author       = {{Frischemeier, Daniel and Podworny, Susanne and Biehler, Rolf}},
  booktitle    = {{Statistics for Empowerment and Social Engagement}},
  isbn         = {{9783031207471}},
  pages        = {{199--236}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Data Visualization Packages for Non-inferential Civic Statistics in High School Classrooms}}},
  doi          = {{10.1007/978-3-031-20748-8_9}},
  year         = {{2022}},
}

@inproceedings{39064,
  author       = {{Podworny, Susanne and Fleischer, Franz Yannik and Stroop, Dietlinde and Biehler, Rolf}},
  location     = {{Bozen-Bolzano, Italy}},
  title        = {{{An example of rich, real and multivariate survey data for use in school}}},
  year         = {{2022}},
}

@inproceedings{48162,
  author       = {{Höper, Lukas and Podworny, Susanne and Hüsing, Sven and Schulte, Carsten and Fleischer, Yannik and Biehler, Rolf and Frischemeier, Daniel and Malatyali, Hülya}},
  isbn         = {{978-3-88579-707-4}},
  location     = {{Wuppertal}},
  pages        = {{345--345}},
  title        = {{{ Zur neuen Bedeutung von Daten in Data Science und künstlicher Intelligenz}}},
  year         = {{2021}},
}

@article{35781,
  author       = {{Podworny, Susanne and Biehler, Rolf}},
  issn         = {{1098-6065}},
  journal      = {{Mathematical Thinking and Learning}},
  keywords     = {{Developmental and Educational Psychology, Education, General Mathematics}},
  number       = {{4}},
  pages        = {{291--311}},
  publisher    = {{Informa UK Limited}},
  title        = {{{The process of actively building a model for a randomization test – insights into learners’ modeling activities based on a case study}}},
  doi          = {{10.1080/10986065.2021.1922837}},
  volume       = {{24}},
  year         = {{2021}},
}

@article{35751,
  author       = {{Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Budde, Lea}},
  issn         = {{0141-982X}},
  journal      = {{Teaching Statistics}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{S1}},
  pages        = {{S182--S189}},
  publisher    = {{Wiley}},
  title        = {{{A first introduction to data science education in secondary schools: Teaching and learning about data exploration with<scp>CODAP</scp>using survey data}}},
  doi          = {{10.1111/test.12283}},
  volume       = {{43}},
  year         = {{2021}},
}

@inbook{56204,
  author       = {{Frischemeier, Daniel and Podworny, Susanne and Biehler, Rolf}},
  booktitle    = {{Konzepte und Studien zur Hochschuldidaktik und Lehrerbildung Mathematik}},
  isbn         = {{9783662628539}},
  issn         = {{2197-8751}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Integration fachwissenschaftlicher und fachdidaktischer Komponenten in der Lehramtsausbildung Mathematik Grundschule am Beispiel einer Veranstaltung zur Leitidee „Daten, Häufigkeit und Wahrscheinlichkeit“}}},
  doi          = {{10.1007/978-3-662-62854-6_11}},
  year         = {{2021}},
}

@inproceedings{35814,
  author       = {{Biehler, Rolf and Fleischer, Franz Yannik and Budde, Lea and Frischemeier, Daniel and Gerstenberger, Dietrich and Podworny, Susanne and Schulte, Carsten}},
  booktitle    = {{New Skills in the Changing World of Statistics Education Proceedings of the Roundtable conference of the International Association for Statistical Education (IASE)}},
  editor       = {{Arnold, P.}},
  publisher    = {{ISI/IASE}},
  title        = {{{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}}},
  year         = {{2020}},
}