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2.3 Critical Data Literacy for Citizenship through Open Data
Note: Content in this section is adapted from:
Atenas, J., Havemann, L., Rodés, V., & Podetti, M. (2023). Critical data literacy in praxis: An open education approach for academic development. Edutec. Revista Electrónica de Tecnología Educativa, (85), 49–67. https://doi.org/10.21556/edutec.2023.85.2851
Atenas, J., Havemann, L., & Timmermann, C. (2020). Critical literacies for a datafied society: Academic development and curriculum design in higher education. Research in Learning Technology, 28. https://doi.org/10.25304/rlt.v28.2468
A guide for academics
We live in a datafied society, where everyday actions are translated into data and used to shape decisions about our lives.
Yet, data is not neutral—it reflects choices, omissions, and power. HE has a social responsibility for forming critical and active citizens (Di Nauta et al., 2018; Pee, & Vululleh,2020); therefore, it must enable spaces to foster critical citizenship connecting society, industry, innovation and research by developing transversal skills, which are defined as critical, innovative, interpersonal and intrapersonal skills and global citizenship (UNESCO, 2015). HE should therefore bridge educational processes with issues such as human rights; economy; migration; environment and sustainable development, and for that, the use of OpenData can be an effective tool to facilitate the interaction between teaching, research and society.In research, we often say that data is rather benignly ‘collected’, suggesting that, like wild flowers or berries, it occurs in nature and belongs to no-one. The language of the business of data also implies that data is already present, but it tends to be discussed as a raw material which, through technological innovation, can more aggressively be mined or extracted (Mezzadra & Neilson, 2017). Without for a moment wishing to conflate the purposes of researchers and tech companies, we note that both ways of speaking about data buy into, and rather conveniently reinforce, the ‘common sense’ idea that data comes from, and represents or reflects reality -that it transparently reports on the nature of the real, rather than being something made by human or machine.
Van Es and Schäfer (2017) note that ‘students need to be educated to become critical data practitioners who are both capable of working with data and of critically questioning the big myths that frame the datafied society’ (p. 12).Therefore, we aim to support academics in developing critical and inquisitive relationships with data (Atenas et al., 2020; Holmes etal., 2022) to question it uses to address the data-literacy gap, to prevent widening the inequalities and power dynamics embedded in data-practices (Richterich, 2018).
Our approach in building critical data literacies aims at facilitating the Continuing Professional Development (CPD) for academics, supporting them in developing evidence-based learning and teaching programs and activities driven by OD, fostering critically and collaboratively research skills by studying their socio-political environment, cultural relationships and interactions between groups to uncover power relations and inequalities.
For example
You begin by noticing the data that surrounds you—often invisible, embedded in daily life.
Track your data production over 1–2 days:
- Platform logins
- Location tracking
- Online purchases
- Institutional systems
Reflect:
- Who collects this data?
- Did you consent?
- Who benefits?
We aim at upskilling educators and learners across HE on data and datafication linking technical and socially-driven data skills across the data cycle to address issues and understand the ethical dimensions of data (Louie et al., 2022), to critically apply ideas of social and data justice into research-based learning, building on the idea of agency, so participants can acknowledge the problems and conundrums of datafication of society to develop innovation skills in educators using open and emerging practices creating spaces for dialogue and research between students, educators, civil society and experts in OD to enhance education (Zamorski, 2002).
flowchart TD
%% MAIN FLOW
A[Daily Activities]
--> B[Data Generated]
--> C[Collected by Platforms]
--> D[Processed by Algorithms]
--> E[Influences Decisions]
--> F[User Awareness & Reflection]
%% EMPHASIS LAYER
C --> C1[Data Capture]
D --> D1[Algorithmic Processing]
E --> E1[Behavioural Influence]
%% STYLING (pastel + black font)
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#FFF5F5,color:#000
style E fill:#F3E6FF,color:#000
style F fill:#E0F7FA,color:#000
style C1 fill:#F5FFF7,color:#000
style D1 fill:#FFF7F7,color:#000
style E1 fill:#F9F0FF,color:#000
According to Dalton, Taylor, and Thatcher (2016), people are often unaware why, how or even that data about themselves are being collected, analysed and ‘shared’ with additional parties. For Ozga (2008) and Ball (2015), the data we produce are used to classify us as individuals, allocating us into categories that define our worth in society, our perceived effectiveness and our potential to pose risks. Using algorithms, our behaviours, successes and failures are then predicted according to the categories in which we have been placed (Harel Ben Shahar 2017; Maull, Godsiff, and Mulligan 2014; Schildkamp, Karbautzki, and Vanhoof 2013; Schouten 2017).
Data does not speak for itself—it must be interpreted within its context.
Select a dataset and ask:
What is included? What is excluded? Who created it and why? What assumptions are embedded?
flowchart TD
%% MAIN FLOW
A[Dataset]
--> B[Identify Source]
--> C[Check Context]
--> D[Analyse Bias]
--> E[Identify Missing Data]
--> F[Interpret Critically]
%% SUPPORTING LAYER
B --> B1["Who collected the data?"]
C --> C1["Why and how was it created?"]
D --> D1[Bias and representation]
E --> E1[Gaps and exclusions]
%% STYLING (pastel + black font)
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
style E fill:#E0F7FA,color:#000
style F fill:#FDEDEC,color:#000
style B1 fill:#F5FAFF,color:#000
style C1 fill:#F5FFF7,color:#000
style D1 fill:#F9F0FF,color:#000
style E1 fill:#F3FDFF,color:#000
In HE, we must look beyond data capabilities, and include critical thinking, citizenship and innovation skills (Gray, Gerlitz, and Bounegru 2018; Van Es and Schäfer 2017), foster skills to evaluate, analyse and interpret data (Prado and Marzal 2013; Schield 2004), and ground teaching and learning in addressing real problems (Atenas and Havemann 2019; Fung 2017), to understand issues such as commodification, surveillance and privacy (Gray 2016; Kellner and Share 2009; Matthews 2016). In light of this, it is crucial that academics and students across disciplines in HE adopt as foundational a critical data studies perspective (Iliadis and Russo 2016). Such an approach can empower students to question the ethics, structures and economics of data use, and fundamentally, the apparent inevitability of the surveillance and datafication of all aspects of daily life.
Data is linked to real issues—inequality, environment, education.
Choose a societal issue Find open datasets Analyse patterns and gaps
flowchart TD
%% MAIN FLOW
A[Identify Issue]
--> B[Find Open Data]
--> C[Analyse Data]
--> D[Question Narratives]
--> E[Create Output]
--> F[Share & Discuss]
%% SUPPORTING LAYER
B --> B1[Data portals and sources]
C --> C1[Methods and tools]
D --> D1[Challenge assumptions]
E --> E1[Visuals and storytelling]
%% STYLING (pastel + black font)
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style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
style E fill:#E0F7FA,color:#000
style F fill:#FDEDEC,color:#000
style B1 fill:#F5FAFF,color:#000
style C1 fill:#F5FFF7,color:#000
style D1 fill:#F9F0FF,color:#000
style E1 fill:#F3FDFF,color:#000
To develop critical perspectives which examine and challenge power dynamics through data, our approach was built in layers which examine particular perspectives including data ethics, data feminism, agency and social justice and data justice. We contend that this approachs upports educators in enhancing their teaching practice, providing them with the strategies necessary to design learning activities that help their students develop a critical understanding of the ethical dimension of data-dynamics and identify problematic data practices. In turn, we hope that students will be empowered and inspired to examine and challenge the power structures that perpetuate intersectional oppressions, and adopt a data justice lens when considering issues related to privacy, consent, personal agency, and data sovereignty
Data literacy must include ethics, agency, and justice.
Consider:
Who is represented? Who is excluded? Does this reinforce inequality? What is a fair interpretation?
flowchart TD
%% MAIN FLOW
A[Dataset Analysis]
--> B[Identify Stakeholders]
--> C[Check Representation]
--> D[Assess Bias]
--> E[Reflect on Impacts]
--> F[Propose Ethical Use]
%% SUPPORTING LAYER
B --> B1["Who is involved or affected?"]
C --> C1["Who is represented or excluded?"]
D --> D1[Bias in data and methods]
E --> E1[Social and ethical consequences]
%% STYLING (pastel + black font)
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style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
style E fill:#E0F7FA,color:#000
style F fill:#FDEDEC,color:#000
style B1 fill:#F5FAFF,color:#000
style C1 fill:#F5FFF7,color:#000
style D1 fill:#F9F0FF,color:#000
style E1 fill:#F3FDFF,color:#000
| Element | Activity | Dynamic | Expected outcome |
|---|---|---|---|
| Data inequalities | Work with students in assessing data from their governments to identify who is misrepresented in the picture | Engage students in reviewing open datasets to understand who is represented, how, and who may be missing | Raise awareness of how data is collected, portrayed, and how inequalities in data emerge |
| Social issues | Present a social problem and ask students to find reports and press coverage | Engage students in political and legal deliberations at local and global levels by analysing related data | Enable understanding of policymaking processes through analysis of policies, data, and official reports |
| Data justice | Engage students in evaluating facts and contrasting information in news media | Encourage students to monitor political activity and critically assess reports through data analysis | Support students in identifying societal problems using data and comparing local and global contexts |
| Social participation | Support students in identifying organisations involved in citizenship issues | Foster collaboration with civil society and local communities through student projects | Promote engagement through dissertations based on open data analysis, encouraging open publication of findings |