@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}},
}

@inbook{63801,
  author       = {{Gildehaus, Lara and Liebendörfer, Michael and Hüsing, Sven and Gottschalk, Rebecca and Strauß, Franzisca}},
  booktitle    = {{Realitätsbezüge im Mathematikunterricht}},
  isbn         = {{9783662699881}},
  issn         = {{2625-3550}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Normatives Modellieren im Kontext des Klimawandels – wie viel CO2 stoßen Lebensmittel aus?}}},
  doi          = {{10.1007/978-3-662-69989-8_6}},
  year         = {{2025}},
}

@article{63805,
  author       = {{Stoppel, Hannes and Hüsing, Sven}},
  journal      = {{Beiträge zum Mathematikunterricht; 58}},
  publisher    = {{Gesellschaft für Didaktik der Mathematik}},
  title        = {{{Konstruktionistisches Geometrielernen durch epistemisches Programmieren in Scratch}}},
  doi          = {{10.17877/DE290R-25995}},
  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}},
}

@inproceedings{52379,
  author       = {{Hüsing, Sven and Schulte, Carsten and Sparmann, Sören and Bolte, Mario}},
  booktitle    = {{Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1}},
  publisher    = {{ACM}},
  title        = {{{Using Worked Examples for Engaging in Epistemic Programming Projects}}},
  doi          = {{10.1145/3626252.3630961}},
  year         = {{2024}},
}

@inproceedings{54796,
  author       = {{Hüsing, Sven and Sparmann, Sören and Schulte, Carsten and Bolte, Mario}},
  booktitle    = {{Proceedings of the 2024 Symposium on Eye Tracking Research and Applications}},
  publisher    = {{ACM}},
  title        = {{{Identifying K-12 Students' Approaches to Using Worked Examples for Epistemic Programming}}},
  doi          = {{10.1145/3649902.3655094}},
  year         = {{2024}},
}

@inproceedings{57738,
  author       = {{Hüsing, Sven and Schönbrodt, Sarah}},
  title        = {{{Förderung von Epistemic Agency – Entwicklung von Computational Essays bei der Bearbeitung datengetriebener, realer Problemstellungen}}},
  doi          = {{10.17877/DE290R-25018}},
  year         = {{2024}},
}

@inproceedings{52380,
  author       = {{Sparmann, Sören and Hüsing, Sven and Schulte, Carsten}},
  booktitle    = {{Proceedings of the 23rd Koli Calling International Conference on Computing Education Research}},
  publisher    = {{ACM}},
  title        = {{{JuGaze: A Cell-based Eye Tracking and Logging Tool for Jupyter Notebooks}}},
  doi          = {{10.1145/3631802.3631824}},
  year         = {{2023}},
}

@inbook{40511,
  author       = {{Hüsing, Sven and Schulte, Carsten and Winkelnkemper, Felix}},
  booktitle    = {{Computer Science Education}},
  isbn         = {{9781350296916}},
  publisher    = {{Bloomsbury Academic}},
  title        = {{{Epistemic Programming}}},
  doi          = {{10.5040/9781350296947.ch-022}},
  year         = {{2023}},
}

@article{32335,
  abstract     = {{Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.}},
  author       = {{PODWORNY, SUSANNE and Hüsing, Sven 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        = {{{A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}}},
  doi          = {{10.52041/serj.v21i2.46}},
  volume       = {{21}},
  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}},
}

@inproceedings{40510,
  abstract     = {{<jats:p>Decision-making processes are often based on data and data-driven machine learning methods in different areas such as recommender systems, medicine, criminalistics, etc. Well-informed citizens need at least a minimal understanding and critical reflection of corresponding data-driven machine learning methods. Decision trees are a method that can foster a preformal understanding of machine learning. We developed an exploratory teaching unit introducing decision trees in grade 6 along the question “How can Artificial Intelligence help us decide whether food is rather recommendable or not?” Students’ performances in an assessment task and self-assessment show that young learners can use a decision tree to classify new items and that they found the corresponding teaching unit informative.</jats:p>}},
  author       = {{Podworny, Susanne and Fleischer, Yannik and Hüsing, Sven}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Grade 6 Students’ Perception and Use of Data-Based Decision Trees}}},
  doi          = {{10.52041/iase.icots11.t2h3}},
  year         = {{2022}},
}

@inproceedings{30937,
  abstract     = {{<jats:p>Data Science has become an increasingly important aspect of our everyday lives as we gain a lot of different insights from data analyses, for example in the context of environmental issues. In order to make the process of data analyses comprehensible for lower secondary school students, we developed a data analysis project for computer science classes, focusing on gaining insights from environmental data by using the concept of epistemic programming. In this article, we report on the second implementation of this project, which was conducted in a ninth-grade computer science class. Concretely, we want to examine, how far the students were able to create computational essays to conduct reproducible data analyses on their own. In this regard, the computational essays created with the help of the professional tool Jupyter Notebooks will be examined in terms of aspects of reproducibility.</jats:p>}},
  author       = {{Hüsing, Sven and Podworny, Susanne}},
  booktitle    = {{Proceedings of the IASE 2021 Satellite Conference}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Computational Essays as an Approach for Reproducible Data Analysis in lower Secondary School}}},
  doi          = {{10.52041/iase.zwwoh}},
  year         = {{2022}},
}

@inproceedings{27494,
  author       = {{Hüsing, Sven}},
  booktitle    = {{Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research, Joensuu, Finland, November 18 - 21, 2021}},
  editor       = {{Seppälä, Otto and Petersen, Andrew}},
  pages        = {{42:1--42:3}},
  publisher    = {{ACM}},
  title        = {{{Epistemic Programming - An insight-driven programming concept for Data Science}}},
  doi          = {{10.1145/3488042.3490510}},
  year         = {{2021}},
}

@inproceedings{27495,
  author       = {{Bovermann, Klaus and Fleischer, Yannik and Hüsing, Sven and Opitz, Christian}},
  booktitle    = {{19. GI-Fachtagung Informatik und Schule, INFOS 2021, Wuppertal, Germany, September 8-10, 2021}},
  editor       = {{Humbert, Ludger}},
  pages        = {{319}},
  publisher    = {{Gesellschaft für Informatik, Bonn}},
  title        = {{{Künstliche Intelligenz und maschinelles Lernen im Informatikunterricht der Sek. I mit Jupyter Notebooks und Python am Beispiel von Entscheidungsbäumen und künstlichen neuronalen Netzen}}},
  doi          = {{10.18420/infos2021\_w283}},
  volume       = {{P-313}},
  year         = {{2021}},
}

@article{35763,
  author       = {{Hüsing, Sven and Weiser, Niklas and Biehler, Rolf}},
  journal      = {{mathematik lehren}},
  number       = {{228}},
  pages        = {{23–27}},
  publisher    = {{Friedrich Verlag}},
  title        = {{{Faszination 3D-Film: Entwicklung einer 3D-Konstruktion}}},
  volume       = {{2021}},
  year         = {{2021}},
}

@article{29702,
  author       = {{Höper, Lukas and Hüsing, Sven and Malatyali, Hülya and Schulte, Carsten and Budde, Lea}},
  journal      = {{LOG IN}},
  number       = {{1}},
  pages        = {{31--38}},
  publisher    = {{LOG IN Verlag}},
  title        = {{{Methodik für Datenprojekte im Informatikunterricht}}},
  volume       = {{41}},
  year         = {{2021}},
}

@article{29710,
  author       = {{Podworny, Susanne and Höper, Lukas and Fleischer, Yannik and Hüsing, Sven and Schulte, Carsten}},
  journal      = {{INFOS 2021–19. GI-Fachtagung Informatik und Schule}},
  publisher    = {{Gesellschaft für Informatik, Bonn}},
  title        = {{{Data Science ab Klasse 5–Konkrete Unterrichtsvorschläge für künstliche Intelligenz unplugged und Datenbewusstsein}}},
  year         = {{2021}},
}

@article{29712,
  author       = {{Höper, Lukas and Podworny, Susanne and Hüsing, Sven and Schulte, Carsten and Fleischer, Yannik and Biehler, Rolf and Frischemeier, Daniel and Malatyali, Hülya}},
  journal      = {{INFOS 2021–19. GI-Fachtagung Informatik und Schule}},
  publisher    = {{Gesellschaft für Informatik, Bonn}},
  title        = {{{Zur neuen Bedeutung von Daten in Data Science und künstlicher Intelligenz}}},
  year         = {{2021}},
}

@article{40512,
  author       = {{Hüsing, Sven and Weiser, Niklas and Biehler, Rolf}},
  journal      = {{mathematik lehren}},
  number       = {{228}},
  pages        = {{23–27}},
  publisher    = {{Friedrich Verlag}},
  title        = {{{Faszination 3D-Film: Entwicklung einer 3D-Konstruktion}}},
  volume       = {{2021}},
  year         = {{2021}},
}