Sven Hüsing

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Publikationen

Didaktik der Informatik (DDI)

Wissenschaftlicher Mitarbeiter

VCard

Telefon:

+49 5251 60-6614

Fax:

+49 5251 60-6623

E-Mail:

sven.huesing(at)upb(dot)de

Büro:

F2.201

Besucher:

Fürstenallee 11
33102 Paderborn

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2022

Computational Essays as an Approach for Reproducible Data Analysis in lower Secondary School

S. Hüsing, S. Podworny, in: Proceedings of the IASE 2021 Satellite Conference, International Association for Statistical Education, 2022

Abstract

DOI

<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>

A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING

S. PODWORNY, S. Hüsing, C. SCHULTE, STATISTICS EDUCATION RESEARCH JOURNAL (2022), 21(2), 6

Abstract

DOI

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.

2021

Using data cards for teaching data based decision trees in middle school

S. Podworny, Y. Fleischer, S. Hüsing, R. Biehler, D. Frischemeier, L. Höper, C. Schulte, in: Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research, Joensuu, Finland, November 18 - 21, 2021, ACM, 2021, pp. 39:1-39:3

DOI

Epistemic Programming - An insight-driven programming concept for Data Science

S. Hüsing, in: Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research, Joensuu, Finland, November 18 - 21, 2021, ACM, 2021, pp. 42:1-42:3

DOI

Künstliche Intelligenz und maschinelles Lernen im Informatikunterricht der Sek. I mit Jupyter Notebooks und Python am Beispiel von Entscheidungsbäumen und künstlichen neuronalen Netzen

K. Bovermann, Y. Fleischer, S. Hüsing, C. Opitz, in: 19. GI-Fachtagung Informatik und Schule, INFOS 2021, Wuppertal, Germany, September 8-10, 2021, Gesellschaft für Informatik, Bonn, 2021, pp. 319

DOI

Methodik für Datenprojekte im Informatikunterricht

L. Höper, S. Hüsing, H. Malatyali, C. Schulte, L. Budde, LOG IN (2021), 41(1), pp. 31-38

Data Science ab Klasse 5–Konkrete Unterrichtsvorschläge für künstliche Intelligenz unplugged und Datenbewusstsein

S. Podworny, L. Höper, Y. Fleischer, S. Hüsing, C. Schulte, INFOS 2021–19. GI-Fachtagung Informatik und Schule (2021)

Zur neuen Bedeutung von Daten in Data Science und künstlicher Intelligenz

L. Höper, S. Podworny, S. Hüsing, C. Schulte, Y. Fleischer, R. Biehler, D. Frischemeier, H. Malatyali, INFOS 2021–19. GI-Fachtagung Informatik und Schule (2021)

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