Developing a theoretically founded data literacy competency model

Abstract

Today, data is everywhere: Our digitalized world depends on enormous amounts of data that are captured by and about everyone and considered a valuable resource. Not only in everyday life, but also in science, the relevance of data has clearly increased in recent years: Nowadays, data-driven research is often considered a new research paradigm. Thus, there is general agreement that basic competencies regarding gathering, storing, processing and visualizing data, often summarized under the term data literacy, are necessary for every scientist today. Moreover, data literacy is generally important for everyone, as it is essential for understanding how the modern world works. Yet, at the moment data literacy is hardly considered in CS teaching at schools. To allow deeper insight into this field and to structure related competencies, in this work we develop a competency model of data literacy by theoretically deriving central content and process areas of data literacy from existing empirical work, keeping a school education perspective in mind. The resulting competency model is contrasted to other approaches describing data literacy competencies from different perspectives. The practical value of this work is emphasized by giving insight into an exemplary lesson sequence fostering data literacy competencies.