Session #05

Lukas Höper and Carsten Schulte (Germany) & Orit Hazzan and Koby Mike (Israel)

Data Awareness: Be aware of the data! - Lukas Höper and Carsten Schulte (Germany)

In data and in digital literacy one core issue is to enable students to cope with the datafication of their everyday lives, and hence to become data literate. Based on this line of reasoning the debate then focusses on what skills to teach. We add to this discussion by trying a slightly different angle: to make students aware of the data flows they create and probably can influence when interacting with digital artefacts like social messengers, recommendation system of video streaming portals, or simply when using a mobile phone.

In order to enable students to understand these processes of data collection and processing when using data-driven technologies, we developed the framework data awareness. The goal is to enable students to become aware of and understand the collection and processing of data about them during interaction with digital artefacts. It also aims to provide students with appropriate skills and adequate knowledge to apply this to their own daily lives, and to enable them to evaluate data-driven systems and their impact. This is intended to create the basis for them to be able to shape the data-driven world (in the sense of agency).

In this talk we will first introduce this framework data awareness. We will then present two exemplary teaching units for fostering data awareness. The first is about exploring the mobile phone system and location data traces of one user; the second is about a recommender system for movies and how it works (e.g., using the k-nearest-neighbour method).

Lukas Höper is PhD student for computing education research at Paderborn University, Germany.

The main research interest is to develop the concept data awareness for computing education and evaluate this within design-based research by developing and empirically examining teaching materials in practice. Since 2020, he has been working on data awareness in the ProDaBi project, in which curriculum ideas, teaching materials and teacher education approaches regarding data awareness, artificial intelligence and data science in schools are developed.

Carsten Schulte is professor for computing education research at Paderborn University, Germany.

Work and research interests are: Philosophy of computing education and empirical research into teaching-learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) in the ProDaBi project, in which Data Science and Artificial Intelligence are prepared as teaching topics. He is also PI in the collaborative research centre ‘Constructing Explainability’ on explainable AI.

Recordings and Slides

To get access to the recordings and slides of the session, please enter the password we sent you in the confirmation email for your registration.

If you have any questions or problems with entering the password, please contact us via prodabi@mail.uni-paderborn.de.

Teaching Core Principles of Machine Learning with a Simple Machine Learning Algorithm: The Case of the KNN Algorithm  - Orit Hazzan and Koby Mike (Israel)

Data science is a new interdisciplinary science that focuses on extracting insights and value from data. Upon scanning introductory data science courses, one usually finds that they include several machine learning algorithms of different kinds.

In this talk, we propose that only one simple algorithm may be sufficient for such courses, illustrating our approach using the KNN algorithm. The main reason we propose the KNN algorithm is that it is simple to understand both from a mathematical perspective and from an algorithmic perspective. This approach is implemented in the basic level of the data science unit of the Israeli high school computer science curriculum. We highlight our approach from three perspectives: Computational, cognitive and pedagogical. We show that despite the simplicity of the KNN algorithm, it enables to expose novice data science learners to the main ideas of machine learning and to pose interesting questions that address its core concepts. We also discuss how such an approach may eliminate barriers, which new teachers may encounter, to both learning the topic and teaching it.  In the discussion, we invite the audience to suggest other algorithms that may serve as the sole algorithm taught in introductory data science courses.

Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education.

Within this framework she researches cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. She has published about 130 papers in professional refereed journals and conference proceedings, and seven books.
In 2006–2008 she served as the Technion’s Associate Dean of Undergraduate Studies. In 2007-2010 she chaired the High School Computer Science Curriculum Committee assigned by the Israeli Ministry of Education. In 2011-2015 Hazzan was the faculty Dean. From 2017 to 2019, Hazzan served the Technion Dean of Undergraduate Studies.

Additional details can be found on her personal homepage.

Koby Mike is a Ph.D. student in the Technion’s Department of Education in Science and Technology under the supervision of Professor Orit Hazzan.

He holds a B.Sc. and a M.Sc. in electrical engineering. Koby’s doctoral research focuses on data science education. As part of his research, he teaches data science in high school and at Tel Aviv University. Prior to his doctoral studies, Koby has gained an extensive experience in the Israeli hi-tech industry.

Recordings and Slides

To get access to the recordings and slides of the session, please enter the password we sent you in the confirmation email for your registration.

If you have any questions or problems with entering the password, please contact us via prodabi@mail.uni-paderborn.de.

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