@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{31407,
  abstract     = {{<jats:p>Students are not aware and have little understanding of collecting and processing personal data in their everyday contexts of interaction with data-driven digital artifacts. To be aware of where, how and why data are collected and processed is important to be self-determined. Therefore, we develop and evaluate a teaching sequence to provide reasoning about data as a fundamental aspect of statistical literacy. This teaching sequences deals with the context of interaction with a cellular network where location data are collected. Students get real location data from an unknown person which can be explored with the aim to characterize the person. Students gain different insights by using different basic filters and explain how they achieve these. The results of the exploratory study indicate that students learned to gain insights by exploring given location data and that these insights may describe the person with detailed aspects that may not necessarily be true.</jats:p>}},
  author       = {{Höper, Lukas and Podworny, Susanne and Schulte, Carsten and Frischemeier, Daniel}},
  booktitle    = {{Proceedings of the IASE 2021 Satellite Conference}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Exploration of Location Data: Real Data in the Context of Interaction with a Cellular Network}}},
  doi          = {{10.52041/iase.nkppy}},
  year         = {{2022}},
}

@proceedings{25521,
  editor       = {{Schulte, Carsten and A. Becker, Brett and Divitini, Monica and Barendsen, Erik}},
  isbn         = {{978-1-4503-8397-4}},
  publisher    = {{ACM}},
  title        = {{{ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education, Virtual Event, Germany, June 26 - July 1, 2021 - Working Group Reports}}},
  doi          = {{10.1145/3456565}},
  year         = {{2021}},
}

@proceedings{25522,
  editor       = {{Schulte, Carsten and A. Becker, Brett and Divitini, Monica and Barendsen, Erik}},
  isbn         = {{978-1-4503-8214-4}},
  publisher    = {{ACM}},
  title        = {{{ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education, Virtual Event, Germany, June 26 - July 1, 2021}}},
  doi          = {{10.1145/3430665}},
  year         = {{2021}},
}

@inproceedings{25525,
  author       = {{Große-Bölting, Gregor and Gerstenberger, Dietrich Karl-Heinz and Gildehaus, Lara and Mühling, Andreas and Schulte, Carsten}},
  booktitle    = {{ICER 2021: ACM Conference on International Computing Education Research, Virtual Event, USA, August 16-19, 2021}},
  editor       = {{J. Ko, Amy and Vahrenhold, Jan and McCauley, René and Hauswirth, Matthias}},
  pages        = {{169--183}},
  publisher    = {{ACM}},
  title        = {{{Identity in K-12 Computer Education Research: A Systematic Literature Review}}},
  doi          = {{10.1145/3446871.3469757}},
  year         = {{2021}},
}

@article{25527,
  author       = {{Schulte, Carsten and A. Becker, Brett}},
  journal      = {{ACM SIGCSE Bull.}},
  number       = {{3}},
  pages        = {{3--4}},
  title        = {{{ITiCSE 2021 recap}}},
  doi          = {{10.1145/3483403.3483405}},
  volume       = {{53}},
  year         = {{2021}},
}

@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}},
}

@inproceedings{29707,
  author       = {{Bechinie, Dominik and Eilerts, Katja and Huhmann, Tobias and Lenke, Michael and Schulte, Carsten and Winkelnkemper, Felix}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2021}},
  publisher    = {{WTM Verlag, Münster}},
  title        = {{{Geometrielernen digital unterstützen - Räumliche Kompetenzen und individuelle Lernwege mittels adaptierbarer algorithmischer Rückmeldemöglichkeiten fördern}}},
  year         = {{2021}},
}

@article{29708,
  author       = {{Gerstenberger, Dietrich Karl-Heinz and Winkelnkemper, Felix and Schulte, Carsten}},
  journal      = {{9. Fachtagung Hochschuldidaktik Informatik (HDI)}},
  pages        = {{49}},
  title        = {{{Nutzung der Personas-Methode zum Umgang mit der Heterogenität von Informatik-Studierenden}}},
  year         = {{2021}},
}

@phdthesis{27499,
  author       = {{Budde, Lea}},
  publisher    = {{University of Paderborn, Germany}},
  title        = {{{Entwicklung und Rekonstruktion einer interaktionsgeprägten Sichtweise auf das komplementäre Mensch-Maschine-Verhältnis}}},
  year         = {{2021}},
}

@inbook{29720,
  author       = {{Passey, Don and Brinda, Torsten and Cornu, Bernard and Holvikivi, Jaana and Lewin, Cathy and Magenheim, Johannes and Morel, Raymond and Osorio, Javier and Tatnall, Arthur and Thompson, Barrie and Webb, Mary}},
  booktitle    = {{Advancing Research in Information and Communication Technology}},
  editor       = {{Goedicke, Michael and Neuhold, Erich  and Rannenberg, Kai}},
  isbn         = {{978-3-030-81700-8}},
  issn         = {{1868-422X}},
  keywords     = {{Educational technologies, Education and technologies, Digital technologies and education, Information technologies, Communication technologies, Educational technologies and research, Educational technologies and pedagogical practices, Educational technologies and policy, Educational management and technologies, Professional development and educational technologies}},
  pages        = {{129--152}},
  publisher    = {{Springer, Cham}},
  title        = {{{Computers and Education – Recognising Opportunities and Managing Challenges}}},
  doi          = {{10.1007/978-3-030-81701-5_5}},
  volume       = {{AICT-600}},
  year         = {{2021}},
}

@article{24456,
  abstract     = {{One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently.}},
  author       = {{Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and Horwath, Ilona and Hüllermeier, Eyke and Kern, Friederike and Kopp, Stefan and Thommes, Kirsten and Ngonga Ngomo, Axel-Cyrille and Schulte, Carsten and Wachsmuth, Henning and Wagner, Petra and Wrede, Britta}},
  issn         = {{2379-8920}},
  journal      = {{IEEE Transactions on Cognitive and Developmental Systems}},
  keywords     = {{Explainability, process ofexplaining andunderstanding, explainable artificial systems}},
  number       = {{3}},
  pages        = {{717--728}},
  title        = {{{Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}}},
  doi          = {{10.1109/tcds.2020.3044366}},
  volume       = {{13}},
  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}},
}

@inproceedings{29706,
  author       = {{Höper, Lukas and Schulte, Carsten}},
  booktitle    = {{51. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2021 - Computer Science & Sustainability, Berlin, Germany, 27. September - 1. Oktober, 2021}},
  isbn         = {{978-3-88579-708-1}},
  location     = {{Bonn}},
  pages        = {{1623--1632}},
  publisher    = {{Gesellschaft für Informatik}},
  title        = {{{Datenbewusstsein im Kontext digitaler Kompetenzen für einen selbstbestimmten Umgang mit datengetriebenen digitalen Artefakten}}},
  doi          = {{10.18420/informatik2021-136}},
  year         = {{2021}},
}

@inproceedings{27491,
  abstract     = {{ Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.}},
  author       = {{Höper, Lukas}},
  booktitle    = {{21st Koli Calling International Conference on Computing Education Research}},
  isbn         = {{9781450384889}},
  keywords     = {{data awareness, machine learning, data science education, data-driven digital artifacts, artificial intelligence}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Developing and Evaluating the Concept Data Awareness for K12 Computing Education}}},
  doi          = {{10.1145/3488042.3490509}},
  year         = {{2021}},
}