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Die Universität Paderborn im Februar 2023 Bildinformationen anzeigen

Die Universität Paderborn im Februar 2023

Foto: Universität Paderborn, Hannah Brauckhoff

Sven Hüsing

Kontakt
Publikationen
 Sven Hüsing

Didaktik der Informatik (DDI)

Wissenschaftlicher Mitarbeiter

Telefon:
+49 5251 60-6614
Fax:
+49 5251 60-6623
Büro:
F2.201
Besucher:
Fürstenallee 11
33102 Paderborn

Liste im Research Information System öffnen

2023

Epistemic Programming

S. Hüsing, C. Schulte, F. Winkelnkemper, in: Computer Science Education, Bloomsbury Academic, 2023

DOI


2022

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

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.


Grade 6 Students’ Perception and Use of Data-Based Decision Trees

S. Podworny, Y. Fleischer, S. Hüsing, in: Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics, International Association for Statistical Education, 2022

<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>


Jupyter Notebooks for Teaching, Learning, and Doing Data Science

Y. Fleischer, S. Hüsing, R. Biehler, S. Podworny, C. Schulte, in: Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics, International Association for Statistical Education, 2022

<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>


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

<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>


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)


Faszination 3D-Film: Entwicklung einer 3D-Konstruktion

S. Hüsing, N. Weiser, R. Biehler, mathematik lehren (2021), 2021(228), pp. 23–27


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