Abstract
In today’s digital infrastructures and applications, data about users is continuously collected, processed, and used. As a result, users are subjects of data practices – often without understanding them. This raises a challenge for contemporary data science and AI education: how can we empower students to understand and navigate a world shaped by data-driven systems? This includes enabling students to make sense of data flows embedded in everyday digital applications and to make informed, self-determined decisions about their interactions with such systems.
From 2020 to 2025, I conducted a design-based research project as part of my dissertation, focusing on the development of the data awareness framework. Data awareness responds to this challenge by equipping school students with conceptual tools to uncover and understand data practices embedded in digital technologies. At its core, the framework introduces an explanatory model of data-driven technologies. It aims to enable students to use this model to explore the role of data in daily digital applications. Our empirical studies suggest four theoretical ideas that describe data awareness: recognizing when data about oneself is being collected (“aha moment“); shifting focus toward questioning how and why personal data is collected and used; analyzing and reflecting on these data practices; and becoming more empowered to make informed and responsible decisions in interactions with data-driven technologies.
In this talk, I will discuss the meaning of data awareness based on these four theoretical ideas, position it in relation to data and AI literacies, and illustrate it with examples from classroom practice.
Lukas Höper
Dr. Lukas Höper received his PhD in Computing Education from Paderborn University, Germany, under the supervision of Prof. Dr. Carsten Schulte. He currently works as a secondary school teacher of computing and mathematics. His main research interest is empirical research on teaching and learning processes in K-12 computing education. For his dissertation, he developed and evaluated the data awareness framework. From 2020 to 2025, he worked on data awareness and other topics in AI and data science education as part of the ProDaBi project.