Some Key Challenges for Data Literacy

Frank, Mark S.; Walker, Johanna

doi:10.15353/joci.v12i3.3288

Cited by 10 publications

(2 citation statements)

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“…This, however, assumes a high level of knowledge about data, processing, structuring, analysing and presenting data. Over the years, this community started to realise that not every citizen has the necessary knowledge to deal with data in this way (Carlson and Stowell Bracke, 2015; Frank and Walker, 2016). As Boychuk et al (2016) note:…”

Section: The Discussion On Data Literacymentioning

confidence: 99%

Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data

Van Audenhove,

Vermeire,

Van den Broeck

et al. 2024

ILS

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Purpose The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

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Section: The Discussion On Data Literacymentioning

confidence: 99%

Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data

Van Audenhove,

Vermeire,

Van den Broeck

et al. 2024

ILS

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“…It is often presented from the perspective of community informatics. Frank and Walker (2016) defined data literacy as “… enabling community to take advantage of data … and it is inseparable from its function within a community” (p. 233). Data literacy is also “the understanding of how data are applied for benefit or detriment, within the cultural context of security and privacy” (Crusoe, 2016, p. 38).…”

Section: Conceptualizations Of Data Literacy In the Literaturementioning

confidence: 99%

Conceptions and perspectives of data literacy in secondary education

Gebre

2022

Brit J Educational Tech

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Data literacy has been suggested as an important competence that individuals need to succeed in a data-intensive society. However, there is no common understanding as to what data literacy entails and how it could be developed. Instructional emphasis on developing competence of individuals fails to capture learners' relationship to data in everyday life and limits what they can possibly achieve in data-rich environments. This paper critically reviews conceptualizations of data literacy in the literature with a focus in K-12 education. The analysis determined four orientations of data literacy: development of competence, inquiry with data, awareness of personal data and civic engagement. I proposed a broader conceptualization of data literacy that integrates conceptions, competencies and contexts. The study offers holistic and context-oriented framing of data literacy for researchers and educators.

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