Abstract
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).
Bio Lukas Höper
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.
Bio Carsten Schulte
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.
