@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}},
}

@article{59622,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>This study explores how high school students construct decision trees using data cards and the software CODAP (codap.concord.org) in interviews after attending a teaching unit. We conceptualized data-based decision tree construction using nine key aspects that we intended to teach, tested variations of two design elements in teaching, and analyzed the interviews qualitatively to compare student behavior to intended outcomes. We found high alignment to intentions but also deviations in data activities and informal or context-based rather than data-based reasoning. The design element of context-free (blinded) data seems to enhance data-based reasoning, while the design element of data card use showed diagnostic potential.</jats:p>}},
  author       = {{Fleischer, Yannik and Biehler, Rolf}},
  issn         = {{1863-9690}},
  journal      = {{ZDM – Mathematics Education}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Exploring students’ constructions of data-based decision trees after an introductory teaching unit on machine learning}}},
  doi          = {{10.1007/s11858-025-01663-6}},
  volume       = {{57}},
  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}},
}

@inproceedings{60860,
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf}},
  booktitle    = {{Proceedings of the 48th Conference of the International Group for the Psychology of Mathematics Education: Research Reports, Vol. 1 }},
  editor       = {{Cornejo, C. and Felmer, P. and Gomez, D.M. and Dartnell, P. and Araya, P. and Peri, A. and Randolph, V.}},
  pages        = {{267--274}},
  title        = {{{ANALYZING STUDENTS’INFORMAL APPROACHES TO CREATING DECISION TREES IN THE CLASSROOM}}},
  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}},
}

@inbook{57950,
  author       = {{Fleischer, Yannik and Biehler, Rolf}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2024 - 57. Jahrestagung der Gesellschaft für Didaktik der Mathematik.}},
  editor       = {{Ebers, P. and Rösken, F. and Barzel, B. and Büchter, A. and Schacht, F. and Scherer, P.}},
  pages        = {{167--170}},
  publisher    = {{WTM-Verlag}},
  title        = {{{Intuitiver Zugang zu datenbasierten Entscheidungsbäumen}}},
  doi          = {{10.37626/GA9783959872782.0}},
  year         = {{2024}},
}

@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}},
}

@article{35672,
  abstract     = {{<jats:p>This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf and Schulte, Carsten}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}}},
  doi          = {{10.52041/serj.v21i2.61}},
  volume       = {{21}},
  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}},
}

@article{35737,
  author       = {{Biehler, Rolf and Fleischer, Franz Yannik}},
  issn         = {{0141-982X}},
  journal      = {{Teaching Statistics}},
  keywords     = {{Education, Statistics and Probability}},
  pages        = {{S133--S142}},
  publisher    = {{Wiley}},
  title        = {{{Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks}}},
  doi          = {{10.1111/test.12279}},
  volume       = {{43}},
  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}},
}

@inbook{35823,
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2020}},
  editor       = {{Siller, H.-S. and Weigel, W. and Wörler, J. F.}},
  publisher    = {{WTM-Verlag}},
  title        = {{{Automatisierte Entscheidungsverfahren als Thema im allgemeinbildenden Mathematikunterricht}}},
  doi          = {{10.17877/DE290R-21301}},
  year         = {{2020}},
}

@inbook{35821,
  author       = {{Budde, Lea and Frischemeier, Daniel and Biehler, Rolf and Fleischer, Franz Yannik 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), July 2020}},
  editor       = {{Arnold, P.}},
  publisher    = {{ISI/IASE}},
  title        = {{{Data Science Education in Secondary School: How to Develop Statistical Reasoning When Exploring Data Using CODAP}}},
  year         = {{2020}},
}