Publikationen

Unsere Veröffentlichungen zu Themen aus dem Projekt

2026

Berger, U., Biehler, R., Binder, K., Elmer, C., Ertz, F., Hotz, T., Huber, S., Ickstadt, K., Kauermann, G., Küchenhoff, H., Lübke, K., Münnich, R., Schüller, K., Skill, T., Weihs, C., Weinert, H., & Weisser, C. (2026). Daten, Künstliche Intelligenz und Evidenz – neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung. AStA Wirtschafts- und Sozialstatistisches Archiv. https://doi.org/10.1007/s11943-026-00372-0
Fleischer, Y., & Biehler, R. (2026). Informal approaches to predictive modeling: Fostering data literacy and critical engagement through decision tree construction. ZDM – Mathematics Education. https://doi.org/10.1007/s11858-026-01806-3 
Hüsing, S., Musaeus, L. H., Caspersen, M. E., Schulte, C., Barendsen, E., Grgurina, N., Hauswirth, M., Lonati, V., Mani, M., Monga, M., Nobles, H., Reckinger, S., Silvia, D. W., & Sparmann, S. (2026). Epistemic Programming as a Scientific Field: Building a Community of Practice and an Interaction-Based Framework. Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education V. 2. https://doi.org/10.1145/3803401.3812051
Jotzo, L. & Podworny, S. (2026). Data-driven Climate Change Exploration in an Interdisciplinary Grade-10 Classroom. Proceedings of the 12th International Conference on Teaching Statistics (ICOTS12). Brisbane, Australien.  
Weiland, T., Biehler, R., Gal, I., & Pfannkuch, M. (2026). Conceptualizing Data Literacy for Citizenship (Survey paper). ZDM Mathematics Education, 58(6).

2025

Biehler, R., Engel, J., Frischemeier, D., & Podworny, S. (2025). Civic Statistical Literacy: Konzept und praxisnahe Umsetzung am Beispiel des Klimawandels. mathematica didactica, 48.
Biehler, R., Kawakami, T., Lampen, E., Weiland, T., & Zapata-Cardona, L. (2025). Statistics and data science education as a vehicle for empowering citizens – short summary of a survey. EMS Magazine.https://doi.org/10.4171/MAG/257
Biehler, R., & Schulte, C. (2025). Lessons Learned from the ProDaBi Project: Shaping Perspectives at the Intersection of Data, AI, and Education Titles. Symposium on Integrating AI and Data Science into School Education across Disciplines. https://openreview.net/forum?id=TGsuaghKD3
Biehler, R., & Wilkerson, M., H. (2025). The new role of data in society: Challenges and opportunities for mathematics education in the age of data science and AI. In M. B. Casabò, S. Carreira, G. Bolondi, M. Gaidoschick, & C. Spagnolo (Eds.), Proceedings of the Fourteenth Congress of the European Society for Research in Mathematics Education (CERME14) (pp. 59–96). Free University of Bozen-Bolzano; ERME
Binder, K., & Rößner, M. (2025). Software-Supported and Simulation-Based Introduction to Significance Tests. Symposium on Integrating AI and Data Science into School Education across Disciplines. https://openreview.net/forum?id=fJCRhPUH5b
Fergusson, A.-M. G., Podworny, S., Fleischer, Y., Hüsing, S., Puloka, M. S., Biehler, R., Pfannkuch, M., Budgett, S., & Dalrymple, M. (2025). 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. IASE 2025. https://doi.org/10.52041/iase25.138
Fleischer, Y., & Biehler, R. (2025). Analyzing Students’ Informal Approaches to Creating Decision Trees in the Classroom. In C. Cornejo, P. Felmer, D. M. Gómez, P. Dartnell, P. Araya, A. Peri, & V. Randolph (Eds.), Proceedings of the 48th Conference of the International Group for the Psychology of Mathematics Education: Research Reports, Vol. 1 (pp. 267–274).
Fleischer, Y., & Biehler, R. (2025). Exploring students’ constructions of data-based decision trees after an introductory teaching unit on machine learning. ZDM – Mathematics Education, 57(1), 153–173. https://doi.org/10.1007/s11858-025-01663-6
Frischemeier, D., & Biehler, R. (2025). Förderung von statistischem Denken im Mathematikunterricht der Primarstufe: Bedeutsame Ideen und Förderungsmöglichkeiten. Stochastik in der Schule, 45(1), 22–33.
Höper, L. (2025). Entwicklung und Evaluation des Konzepts Datenbewusstsein für informatische Bildung[Universität Paderborn]. https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2420
Höper, L., & Fleischer, Y. (2025). Empowering Students in a Data-Driven World: Explanatory Models for Understanding Data-Driven Technologies from Everyday Life. Symposium on Integrating AI and Data Science into School Education across Disciplines. https://openreview.net/forum?id=uwenNz2YMI
Höper, L., & Schulte, C. (2025). ReVEAL model and its application to revealing viewpoints on educational approaches to learning about data and AI. Computer Science Education, 1–33. https://doi.org/10.1080/08993408.2025.2516957
Hüsing, S., & Podworny, S. (2025). 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. Symposium on Integrating AI and Data Science into School Education across Disciplines.https://openreview.net/forum?id=pJtX42JGnT
Podworny, S., Biehler, R., & Fleischer, Y. (2025). Young students’ engagement with data to create decision trees. ZDM – Mathematics Education, 57(1), 175–191. https://doi.org/10.1007/s11858-024-01649-w
Podworny, S., Fleischer, Y., & Biehler, R. (2025). Explorative Datenanalyse in der Schule – Analyse der Mediennutzung von Jugendlichen mit den YOU‑PB Daten. Stochastik in der Schule, 45(2), 9–16.
Schönbrodt, S., & Podworny, S. (2025). Shaping the Future of Education: AI and Data Science Literacy as a Civic Imperative in Education. Symposium on Integrating AI and Data Science into School Education across Disciplines. https://openreview.net/forum?id=KVi7XcIx7N
Stoppel, H., & Hüsing, S. (2025). Konstruktionistisches Geometrielernen durch epistemisches Programmieren in Scratch. Beiträge zum Mathematikunterricht; 58. https://doi.org/10.17877/DE290R-25995

2024

Biehler, R., Fleischer, Y. (2024). Datenbasiertes Entscheiden. Vom Zufall zur optimalen Strategie: Bestärkendes maschinelles Lernen am Beispiel Bauernschach. Mathewelt – das Arbeitsheft, mathematik lehren 244.
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.
Höper, L. & Schulte, C. (2024). Empowering Students for the Data-Driven World: A Qualitative Study of the Relevance of Learning about Data-Driven Technologies. Informatics in Education 23 (3), S. 593-624. https://doi.org/10.15388/infedu.2024.19
Höper, L. & Schulte, C. (2024). New Perspectives on the Future of Computing Education: Teaching and Learning Explanatory Models. In Koli Calling 2024: Proceedings of the 24th Koli Calling International Conference on Computing Education Research. https://doi.org/10.1145/3699538.3699558
Höper, L., Schulte, C., & Benzmüller, C. (2024). Verantwortung. In U. Furbach, E. Kitzelmann, T. Michaeli, & U. Schmid (Hrsg.), Künstliche Intelligenz für Lehrkräfte (S. 219–236). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44248-4_16
Höper, L., Schulte, C., & Mühling, A. (2024). Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing Education. In: Proceedings of the 2024 ACM Conference on International Computing Education Research – Volume 1 (ICER 2024), 326–342. https://doi.org/10.1145/3632620.3671118
Höper, L., Schulte, C., & Mühling, A. (2024). Students’ Motivation and Intention to Engage with Data-Driven Technologies from a CS Perspective in Everyday Life. In Proceedings of the 2024 Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2024). https://doi.org/10.1145/3649217.3653625
Hüsing, S., & Schönbrodt, S. (2024). Förderung von Epistemic Agency – Entwicklung von Computational Essays bei der Bearbeitung datengetriebener, realer Problemstellungen. Beiträge zum Mathematikunterricht 57. https://doi.org/10.17877/DE290R-25018
Hüsing S., Schulte C., Sparmann S., Bolte M. (2024), Using Worked Examples for Engaging in Epistemic Programming Projects. In SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, Pages 443–44.
Hüsing, S., Sparmann, S., Schulte, C., & Bolte, M. (2024). Identifying K-12 Students’ Approaches to Using Worked Examples for Epistemic Programming. Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, 1-7. https://doi.org/10.1145/3649902.3655094
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), Eine qualitative Studie zu Data Science Education: Schülerinnen und Schüler analysieren multivariate Daten. Stochastik in der Schule 44(1), 2-10.
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.
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

2023

Höper, L. & Schulte, C. (2023), The data awareness framework as part of data literacies in K-12 education, Information and Learning Sciences, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ILS-06-2023-0075 (online publication date: 18 December 2023)
Hüsing, S., Schulte, C., & Winkelnkemper, F. (2023). Epistemic Programming. In S. Sentance, E. Barendsen, N. R. Howard, & C. Schulte (Eds.), Computer Science Education: Perspectives on Teaching and Learning in School (2nd ed., pp. 291–304). Bloomsbury Academic; Bloomsbury Collections. http://dx.doi.org/10.5040/9781350296947.ch-022
Höper, L., & Schulte, C. (2023). Paradigmenwechsel vom klassischen zum datengetriebenen Problemlösen im Informatikunterricht. MNU journal, 76(4), 314–320.
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.
Sparmann, S., Hüsing, S., & Schulte, C. (2023). JuGaze: A Cell-based Eye Tracking and Logging Tool for Jupyter Notebooks. Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, 1–11. https://doi.org/10.1145/3631802.3631824
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

Biehler, R. (2022). Revisiting Fundamental Ideas for Statistics Education From the Perspective of Machine Learning and Its Applications. 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.
Biehler, R., De Veaux, R., Engel, J., Kazak, S., & Frischemeier, D. (2022). Editorial: Research on Data Science Education. Statistics Education Research Journal, 21(2).
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.
Fleischer, Y., Biehler, R., & Schulte, C. (2022). Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. Statistics Education Research Journal, 21(2).
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.
Höper, L., Podworny, S., Schulte, C., & Frischemeier, D. (2022). Exploration of Location Data: Real Data in the Context of Interaction with a Cellular Network. Proceedings of the IASE 2021 Satellite Conference.
Hüsing, S., & Podworny, S. (2022). Computational Essays as an Approach for Reproducible Data Analysis in lower Secondary School. Proceedings of the IASE 2021 Satellite Conference. IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science.
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., Hüsing, S., & Schulte, C. (2022). A place for a data science project in school: Between statistics and epistemic programming. Statistics Education Research Journal, 21(2), 6.
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.
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

2021

Biehler, R., & Fleischer, Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. Teaching Statistics, 43(S1), S133-S142.
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. ISI/IASE.
Bovermann, K., Fleischer, Y., Hüsing, S. & Opitz, C., (2021). Künstliche Intelligenz und maschinelles Lernen im Informatikunterricht der Sek. I mit Jupyter Notebooks und Python am Beispiel von Entscheidungsbäumen und künstlichen neuronalen Netzen. In: Humbert, L. (Hrsg.), INFOS 2021 – 19. GI-Fachtagung Informatik und Schule. Gesellschaft für Informatik, Bonn. (S. 319-319). DOI: 10.18420/infos2021_w283
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(S1), S182-S189.
Höper, L. (2021). Developing and evaluating the concept data awareness for K12 computing education. In 21st Koli Calling International Conference on Computing Education Research (Koli Calling ’21). Association for Computing Machinery, New York, NY, USA, Article 41, 1–3.
Höper, L., Hüsing, S., Malatyali, H., Schulte, C., & Budde, L. (2021). Methodik für Datenprojekte im Informatikunterricht. LOG IN: Vol. 41, No. 1.
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. & Schulte, C., (2021). Datenbewusstsein: Aufmerksamkeit für die eigenen Daten. In: Humbert, L. (Hrsg.), INFOS 2021 – 19. GI-Fachtagung Informatik und Schule. Gesellschaft für Informatik, Bonn. (S. 73-82). DOI: 10.18420/infos2021_f235
Höper, L., & Schulte, C. (2021). Datenbewusstsein im Kontext digitaler Kompetenzen für einen selbstbestimmten Umgang mit datengetriebenen digitalen Artefakten. INFORMATIK 2021.
Hüsing, S. (2021). Epistemic Programming – An insight-driven programming concept for Data Science. In 21st Koli Calling International Conference on Computing Education Research (Koli Calling ’21). Association for Computing Machinery, New York, NY, USA, Article 42, 1–3.
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). Association for Computing Machinery, New York, NY, USA, Article 39, 1–3.
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., 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.
Fleischer, Y., & Biehler, R. (2020). Automatisierte Entscheidungsverfahren als Thema im allgemeinbildenden Mathematikunterricht. In H.-S. Siller, W. Weigel, & J. F. Wörler (Eds.), Beiträge zum Mathematikunterricht 2020 (pp. 273-276). Münster: WTM-Verlag.

2019

Biehler, R. (2019). Software for learning and for doing statistics and probability – Looking back and looking forward from a personal perspective. In J. M. Contreras, M. M. Gea, M. M. López-Martín, & E. Molina-Portillo (Eds.), Proceedings of the Third International Virtual Congress of Statistical Education. University of Granada.
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.
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. (Eds.). (2018). Paderborn Symposium on Data Science Education at School Level 2017: The Collected Extended Abstracts. Paderborn: Universitätsbibliothek Paderborn. doi.org/10.17619/UNIPB/1-374
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), 28.
Biehler, R., & Schulte, C. (2018). Perspectives for an interdisciplinary data science curriculum at German secondary schools. In R. Biehler, L. Budde, D. Frischemeier, B. Heinemann, S. Podworny, C. Schulte, & T. Wassong (Eds.), Paderborn Symposium on Data Science Education at School Level 2017: The Collected Extended Abstracts (pp. 2-14). Universitätsbibliothek Paderborn.
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), P26,-18.
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. Proceedings of the 18th Koli Calling International Conference on Computing Education Research – Koli Calling ’18, (17), 1–5.