The use of data is a powerful and ever increasing force in the modern world. Vast datasets drive many of the applications of computing that affect our day to day lives, and it is is increasingly accepted that modern scientific research is a data-driven field. Whilst many computing educators and researchers recognise the importance of data literacy, it is an area that is often not covered in great detail in computing curricula in schools. Researchers Andreas Grillenberger and Ralf Romieke set out to develop a competency model for data literacy to help educators to address this important area.
Originally published in Issue 9 of Hello World: The computing and digital making magazine for educators. Available free at helloworld.cc (Shared under Creative Commons CC BY NC SA).
Content and process
This model has been created based on a review of a wide range of research literature into data management, data science, data ethics, and the practicalities of handling and processing data. The model splits data literacy into two categories; content and process. However, these are interleaved to emphasise the strong links between them. The intention is that students should be supported to develop strong skills in working with data, and that these skills should be underpinned by a good understanding of the nature of the content area for the data they are working with. The links between adjacent content and process areas are perhaps most obvious, for example the content area ‘data and information (C1)’ and the process area ‘gathering, modeling and cleansing (P1)’. All content and process areas can be usefully linked though, so understanding ‘data ethics and protection (C4)’ is also important when ‘gathering, modeling and cleansing (P1)’.
In the classroom
The authors used this process to design a series of lessons that were aimed at developing students understanding of both the content areas and the process areas. An example that clearly illustrates this approach is a lesson based on analysing anonymised data from Portuguese students. This data contained attributes of the students such as habits and family situations, as well as their exam grades. The students analyzed this data using the tool Orange, which allows analysis using a graphical interface. Students were able to explore aspects of the data that interested them, and develop their ‘analysing, visualising and interpreting’ processes. What they found also prompted discussions around the limits of data and the ethics of using factors such as family situation to predict student achievement. The lesson developed practical skills but also engaged students in exploration of the nature of data as content by using this interleaved model.
Developing data literacy
This data literacy competency model provides a framework for educators to plan to develop data literacy that is based in research. The model supports educators to consider how they develop data literacy in ways that allow students to develop practical skills and a deep understanding of the content involved in data literacy.
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