@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 = {{2024}}, } @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}}, } @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{46186, author = {{Höper, Lukas and Schulte, Carsten}}, issn = {{0025-5866}}, journal = {{MNU journal}}, number = {{4}}, pages = {{314--320}}, publisher = {{Verlag Klaus Seeberger}}, title = {{{Paradigmenwechsel vom klassischen zum datengetriebenen Problemlösen im Informatikunterricht}}}, volume = {{76}}, year = {{2023}}, } @article{47151, abstract = {{When it comes to mastering the digital world, the education system is more and more facing the task of making students competent and self-determined agents when interacting with digital artefacts. This task often falls to computing education. In the traditional fields of computing education, a plethora of models, guidelines, and principles exist, which help scholars and teachers identify what the relevant aspects are and which of them one should cover in the classroom. When it comes to explaining the world of digital artefacts, however, there is hardly any such guiding model. The ARIadne model introduced in this paper provides a means of explanation and exploration of digital artefacts which help teachers and students to do a subject analysis of digital artefacts by scrutinizing them from several perspectives. Instead of artificially separating aspects which target the same phenomena within different areas of education (like computing, ICT or media education), the model integrates technological aspects of digital artefacts and the relevant societal discourses of their usage, their impacts and the reasons behind their development into a coherent explanation model.}}, author = {{Winkelnkemper, Felix and Höper, Lukas and Schulte, Carsten}}, issn = {{1648-5831}}, journal = {{Informatics in Education}}, keywords = {{Computer Science Applications, Communication, Education, General Engineering}}, publisher = {{Vilnius University Press}}, title = {{{ARIadne – An Explanation Model for Digital Artefacts}}}, doi = {{10.15388/infedu.2024.09}}, year = {{2023}}, } @article{49655, abstract = {{In today's digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This article addresses these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students' data awareness. The study involves a teaching unit on data awareness framed by a pretest-posttest design using a questionnaire on students' awareness and understanding of and reflection on data practices of data-driven digital artefacts. The study's findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and AI literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.}}, author = {{Höper, Lukas and Schulte, Carsten}}, issn = {{2398-5348}}, journal = {{Information and Learning Sciences}}, keywords = {{Library and Information Sciences, Computer Science Applications, Education}}, publisher = {{Emerald}}, title = {{{The data awareness framework as part of data literacies in K-12 education}}}, doi = {{10.1108/ils-06-2023-0075}}, year = {{2023}}, } @inproceedings{47448, abstract = {{In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.}}, author = {{Terfloth, Lutz and Schaffer, Michael and Buhl, Heike M. and Schulte, Carsten}}, isbn = {{978-3-031-44069-4}}, location = {{Lisboa}}, publisher = {{Springer, Cham}}, title = {{{Adding Why to What? Analyses of an Everyday Explanation}}}, doi = {{10.1007/978-3-031-44070-0_13}}, 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{31407, abstract = {{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.}}, 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}}, } @inproceedings{38158, author = {{Winkelnkemper, Felix and Huhmann, Tobias and Bechinie, Dominik and Eilerts, Katja and Lenke, Michael and Schulte, Carsten}}, booktitle = {{Society for Information Technology & Teacher Education International Conference}}, keywords = {{⛔ No DOI found}}, pages = {{1407–1413}}, title = {{{Supporting Geometry Learning Digitally-an Interdisciplinary Project to Foster Spatial Competences and Individual Learning Paths by Using Adaptable Algorithmic Feedback Capabilities}}}, year = {{2022}}, } @inbook{39080, author = {{Schulte, Carsten and Winkelnkemper, Felix}}, booktitle = {{Theologie im Übergang - Identität - Digitalisierung - Dialog}}, pages = {{117–135}}, publisher = {{Herder}}, title = {{{Digitalisierung als Chance und Herausforderung - Bemerkungen aus der Didaktik der Informatik}}}, year = {{2022}}, } @inproceedings{38160, author = {{Huhmann, Tobias and Winkelnkemper, Felix}}, booktitle = {{EDULEARN22 Proceedings}}, pages = {{10017–10026}}, title = {{{SUPPORTING GEOMETRY LEARNING DIGITALLY THROUGH ADAPTABLE ALGORITHMIC FEEDBACK-CHALLENGES AND SOLUTIONS}}}, doi = {{10.21125/edulearn.2022.2416}}, year = {{2022}}, } @article{38162, author = {{Huhmann, Tobias and Eilerts, Katja and Winkelnkemper, Felix}}, journal = {{Mathematik differenziert}}, keywords = {{⛔ No DOI found}}, number = {{4-2022}}, pages = {{42–45}}, title = {{{Pentomino Digital - Mit Einer App Geometrie Lernen}}}, year = {{2022}}, } @inproceedings{40510, abstract = {{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.}}, 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{35674, abstract = {{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.}}, author = {{Fleischer, 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{30937, abstract = {{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.}}, 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}}, } @article{35672, abstract = {{This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.}}, author = {{Fleischer, Yannik and Biehler, Rolf 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 = {{{Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}}}, doi = {{10.52041/serj.v21i2.61}}, volume = {{21}}, 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{25523, author = {{Podworny, Susanne and Höper, Lukas and Fleischer, Yannik and Hüsing, Sven and Schulte, Carsten}}, booktitle = {{19. GI-Fachtagung Informatik und Schule, INFOS 2021, Wuppertal, Germany, September 8-10, 2021}}, editor = {{Humbert, Ludger}}, pages = {{327}}, publisher = {{Gesellschaft für Informatik, Bonn}}, title = {{{Data Science ab Klasse 5 - Konkrete Unterrichtsvorschläge für künstliche Intelligenz unplugged und Datenbewusstsein}}}, doi = {{10.18420/infos2021\_w278}}, volume = {{{P-313}}, year = {{2021}}, }