Data Literacy Competence Models

What does it mean to be data literate? The answer to this question is not a simple one, as opinions differ. The majority of definitions seem to lean toward an instrumental framing. Increasingly, definitions are developed that take a more critical stance. However, understanding data is often neglected or underdeveloped. On March 22 2022, the Joint Research Centre of the European Commission published a new version of the Digital Competence Framework (DigComp) which is one of the most widely used frameworks.The new version 2.2 aims to ‘(...) engage citizens confidently and safely with digital technologies, taking account of emerging technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), (and) datafication’. In our contribution we aim to critically analyse the DigComp 2.2 framework. We do this by focusing on an analysis of the examples, as they are meant to: ‘(...) motivate education and training providers to update their curriculum and course material to face today’s challenges’. We have selected 84 examples out of the total 259 examples which have any bearing on data, algorithms and AI and have coded them. The main outcome of our analysis is that DigComp 2.2 manages to introduce new themes related to data and AI into its framework, be it with a very strong focus on AI. Much more emphasis is put on Understanding Data than on Using Data. DigComp 2.2. strongly leans towards a social science perspective on data literacy and less on a STEM perspective of data literacy

Big data · Curriculum · Data science · Digital Literacy · Digital transformation · Knowledge management · Literacy · Pedagogy · Sociology · World Wide Web · Computer Science · Data Quality and Management · Engineering · Psychology · Research Data Management Practices