1.4 Data Literacy, Open Data & Civic Participation

Note: Content in this section is adapted from:

Atenas, J. & Havemann, L. (2015). Open data as open educational resources: Towards transversal skills and global citizenship. Open Praxis, 7(4), 377. https://doi.org/10.5944/openpraxis.7.4.233

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

Data Literacy, Open Data & Civic Participation

Driving Question

Is data literacy the next frontier of digital literacy—and how can it empower civic participation and social justice?

This unit explores how open data practices, when integrated into education, can foster not only technical competencies but also critical, ethical, and civic capabilities.

flowchart TD

    %% Structure
    A[Open Ecosystem] --> B[Open Education]
    A --> C[Open Data]
    A --> D[Civic Participation]

    B --> B1[Learning resources]
    B --> B2[Open teaching practices]

    C --> C1[Public datasets]
    C --> C2[Transparency]

    D --> D1[Community engagement]
    D --> D2[Policy participation]

    B --> E[Key Skills]
    C --> E
    D --> E

    E --> E1[Data literacy]
    E --> E2[Critical thinking]
    E --> E3[AI literacy]
    E --> E4[Data storytelling]
    E --> E5[Ethical awareness]

    E --> F[Informed citizens]


    %% Pastel styling with black text
    classDef ecosystem fill:#d6eaf8,color:#000000,stroke:#7fb3d5;
    classDef education fill:#d5f5e3,color:#000000,stroke:#82e0aa;
    classDef data fill:#fdebd0,color:#000000,stroke:#f8c471;
    classDef civic fill:#ebdef0,color:#000000,stroke:#c39bd3;
    classDef skills fill:#f9e79f,color:#000000,stroke:#f7dc6f;
    classDef outcome fill:#d1f2eb,color:#000000,stroke:#76d7c4;

    class A ecosystem;
    class B,B1,B2 education;
    class C,C1,C2 data;
    class D,D1,D2 civic;
    class E,E1,E2,E3,E4,E5 skills;
    class F outcome;

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Why Data Literacy Matters (Critical Framing)

Nowadays knowledge is built through the critical analysis of diverse information sources and the ability to understand, interpret and work with data. Open Data is defined as data that is accessible, interoperable, reusable and universal, and is typically made publicly available in non-proprietary, machine-readable formats at the lowest possible level of detail (Johnson, 2014). It is seen as a means of strengthening democracy, as publishing government data can empower citizens to exercise their rights (Huijboom and Van de Broek, 2011).

Data literacy, as defined by Prado and Marzal (2013) “enables individuals to access, interpret, critically assess, manage, handle and ethically use data” (p. 126) whereas, critical data literacy is defined by Sander (2020) as “the ability to critically engage with datafication by reflecting on the societal implications of data processing and implementing this understanding in practice” (p. 2). To critically interact with data, we followed Kellner’s (2003) ideas of “education for democratising and reconstructing education to meet the challenges of a global and technological society” (p.1), as critical theory can enable us to identify how socio-economic structures are being produced and reproduced in a datafied society, allowing us toand rethink our pedagogies using a critical, feminist and data justice approach (Braidotti, 2016; Dencik et al., 2016; Dencik & Sanchez-Monedero, 2022; D’Ignazio & Klein, 2020; Heeks & Swain, 2018; hooks, 1994; Taylor, 2017)

For scholarly communities, Open Data is highly valuable: it supports open scientific inquiry, encourages diverse analysis and perspectives, and enables new research as well as the testing of alternative hypotheses and methods (Arzberger et al., 2004). Furthermore, improved transparency, reproducibility and efficiency in research—and ultimately greater societal benefit—are closely linked to open data practices (Molloy, 2011). However, despite these advantages, the open data process—from publication to reuse—faces significant socio-technical challenges that can hinder its effectiveness (Zuiderwijk et al., 2012).

Data is often perceived as objective—but it is deeply embedded in power, politics, and social structures.

• Data collection reflects priorities and biases
• Algorithms can amplify inequalities
• Lack of skills creates data exclusion

Davies (2010) argues that there is an increasing need for both governments and society to develop the capacity to interpret and debate the meaning of data, and to use Open Data responsibly in democratic contexts. Although the availability and use of Open Data are expanding in civil society and the business sector, its application in education remains limited. Greater public engagement is likely to depend on educators fostering understanding of these datasets as sites of enquiry, alongside supporting the development of relevant analytical skills.

Open Data has clear educational value within research- and scenario-based learning, where it can strengthen digital and information literacies and promote critical, analytical, collaborative and civic skills. As an Open Educational Resource, it can also enable collaboration and engagement with local communities, supporting the development of global citizens. Research-based learning involves enquiry-driven activities grounded in the scientific method, including questioning, data analysis and ethical presentation of findings, thereby encouraging reflective practice (Gilardi & Lozza, 2009; Ambrose et al., 2010; Wagner, 2014). Through the use of real-world scenarios, educators can help develop data-literate learners, skilled professionals and informed, active citizens (Bindé & Matsuura, 2005; Borne et al., 2009; Littlejohn, Beetham & McGill, 2012; Eve, 2013).

Open Education as a Foundation

Open education promotes democratic access to knowledge through:

• Open Educational Resources (OER)
• Open Data
• Open Science
• Open platforms and tools

Goals of Open Education

Goal	Description
Access	Reduce educational inequality
Quality	Improve learning materials
Participation	Enable collaboration
Inclusion	Broaden opportunities

flowchart LR
    A[Open Education] --> B[OER]
    A --> C[Open Science]
    A --> D[Open Data]

    D --> E[Data Literacy]
    E --> F[Critical Skills]
    F --> G[Ethical Awareness]
    G --> H[Civic Engagement]
    H --> I[Social Justice]

    %% 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 G fill:#EBF5FB,color:#000
    style H fill:#E8F8F5,color:#000
    style I fill:#F5EEF8,color:#000

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Open Data: Beyond Access

Definition

Open Data refers to datasets that anyone can:

• Use
• Reuse
• Share

Provided attribution is maintained.

Principles of Open Data

Dimension	Criteria	Implication
Legal	Open license	Free reuse
Technical	Machine-readable	Enables automation
Social	Non-discriminatory	Promotes inclusion
Temporal	Up-to-date	Maintains relevance
Structural	Interoperable	Cross-system use

Our design rationale is grounded in debates on the datafication of society and data justice, arguing that HE should take a leading role in developing critical socio-technical pedagogies that build capacity for engaging with data-driven systems and claims (Schäfer & van Es, 2017; Dencik et al., 2016; Taylor, 2017). Critical data literacies are essential to challenge what Ball (2015) defines the tyranny of numbers and to strengthen both individual and collective agency.

This requires to prioritise social justice and participation, recognising that data practices can marginalise data‑illiterate groups by reducing their experiences to mere data points. As Open Data cannot inherently guarantee justice, it is vital to equip learners with the skills to critically interpret and debate data and to use it responsibly within democratic contexts, particularly to avoid harm to vulnerable groups (Gurstein, 2011; Atenas et al., 2015; Johnson, 2014; Taylor, 2017).

Principles of Open Data

Dimension	Criteria	Implication
Legal	Open license	Free reuse
Technical	Machine-readable	Enables automation
Social	Non-discriminatory	Promotes inclusion
Temporal	Up-to-date	Maintains relevance
Structural	Interoperable	Cross-system use

Data Literacy as a Multi-Dimensional Competency

Definition

Data literacy includes the ability to:

• Read and understand data
• Analyse and interpret data
• Communicate insights
• Act ethically with data

Discipline‑independent competencies that students can develop through research‑based learning with open datasets include

• Critical thinking: engaging in argumentative discourse to construct knowledge, supported by creative and innovative teaching approaches (Weinberger & Fischer, 2006; Silberman, 1973; Papert, 1987).
• Data curation skills: organising, managing and analysing data using appropriate tools, alongside planning and documenting research processes (Mazon et al., 2014; Baker & Duerr, 2015).
• Research skills: developing enquiry, problem-solving and effective communication through collaboration and engagement with real-world issues (Uhlir & Schröder, 2007; Zamorski, 2002; Barrie, 2004).
• Statistical literacy: interpreting and critically evaluating data as a core component of information literacy (Schield, 2004; Wallman, 1993; Watson & Callingham, 2003).
• Teamwork skills: collaborating to solve complex problems and clearly communicate findings, including visualisation of data (Duch, Groh and Allen, 2001). Global citizenship: fostering critical awareness of local and global issues, while recognising that Open Data may exclude marginalised groups unless inclusive measures are taken (Evans & Nation, 1993; Willems & Bossu, 2012; Johnson, 2014; Gurstein, 2011).

Why Data Literacy Matters

Data is often perceived as objective—but it is deeply embedded in power, politics, and social structures.

• Data collection reflects priorities and biases
• Algorithms can amplify inequalities
• Lack of skills creates data exclusion

Key Risks

Risk	Description	Example
Bias	Reinforces inequalities	Predictive policing
Exclusion	Marginalises groups	Lack of data skills
Misinterpretation	Leads to incorrect conclusions	Misleading charts
Exploitation	Data misuse without consent	Surveillance systems

Domains of Competence

Domain	Skills	Outcomes
Technical	Analysis, visualisation	Evidence-based conclusions
Cognitive	Interpretation, critique	Deep understanding
Ethical	Privacy, fairness	Responsible use
Civic	Participation, advocacy	Social engagement

Data Literacy as a Multi-Dimensional Competency

To develop critical perspectives which examine and challenge power dynamics through data, our approach is built in layers which examine particular perspectives including data ethics, data feminism, agency and social justice and data justice supporting students develop a critical understanding of the ethical dimension of data-dynamics and identify problematic data practices to 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 Skills Lifecycle

flowchart LR
    A[Data Collection]
    --> B[Data Curation]
    --> C[Data Analysis]
    --> D[Data Interpretation]
    --> E[Data Visualisation]
    --> F[Data Communication]
    --> G[Data Publication]
    --> H[Societal Impact]

    %% 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 G fill:#EBF5FB,color:#000
    style H fill:#E8F8F5,color:#000

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Skills Progression Matrix

The following matrix outlines the progression of core data literacy skills across four proficiency levels. It illustrates how learners evolve from basic recognition and use of data to advanced, critical, and transformative practices.

Skill Area	Beginner	Intermediate	Advanced	Expert
Collection	Recognises data	Gathers data	Evaluates sources	Designs strategies
Curation	Basic cleaning	Organises data	Ensures quality	Creates reusable datasets
Analysis	Uses simple tools	Applies methods	Critically analyses	Builds models
Interpretation	Reads charts	Explains trends	Critiques data	Connects systems
Visualisation	Creates basic charts	Chooses appropriate formats	Designs narratives	Influences decisions
Communication	Shares findings	Adapts messages	Engages audiences	Drives change
Publication	Shares outputs	Uses platforms	Publishes openly	Advocates transparency

Ethical Competency Matrix

This matrix presents the development of ethical competencies in data practice, emphasising the importance of responsible, fair, and socially aware data use at all levels of proficiency.

Competency	Beginner	Intermediate	Advanced	Expert
Privacy	Awareness	Applies rules	Evaluates risks	Designs safeguards
Bias	Recognises	Identifies	Mitigates	Challenges systems
Equity	Awareness	Applies inclusion	Adjusts data use	Advocates justice
Power	Awareness	Identifies structures	Critiques	Transforms systems
Consent	Understands	Applies	Designs processes	Advocates rights

Critical Thinking & Civic Impact Matrix

This matrix highlights how critical thinking and civic engagement skills develop alongside data literacy, enabling learners to move from basic understanding to active participation and leadership in society.

Dimension	Beginner	Intermediate	Advanced	Expert
Critical Thinking	Questions data	Evaluates sources	Identifies bias	Challenges narratives
Collaboration	Teamwork	Interdisciplinary	Community engagement	Co-creation
Civic Awareness	Awareness	Uses data socially	Engages communities	Leads initiatives
Impact	Limited	Local	Broader	Systemic change

We recommend that academics intending to implement research-based activities using open data should:

• Identify and describe the learning outcomes for the intended activities;
• Identify the portals which will source the data;
• Clearly identify and describe the challenges students might face;
• Provide training materials for the software students will need to analyse the data;
• Support students in communicating their findings to local or wider communities.