1.3 Using Open Data as OER Higher Education

Content adapted from:

Atenas, J. and Havemann, L. (2015) Open Data as Open Educational Resources: Case Studies of Emerging Practice http://dx.doi.org/10.6084/m9.figshare.1590031

Atenas, J., Havemann, L. and Priego, E. (2015) ‘Open Data as Open Educational Resources: Towards transversal skills and global citizenship’, Open Praxis, 7(4), p. 377-389. https://doi.org/10.5944/openpraxis.7.4.233.

Introduction

Education, as Freire (1970) states, is never neutral; it either reproduces dominant power relations or becomes “a practice of freedom”. In this respect, the integration of open data (OD) and open government data (OGD) into higher education aligns with Giroux’s (2010) call for critical pedagogy: enabling learners to interrogate and challenge structures of power (Foucault, 1980).

Open data—defined as data “freely used, re-used and redistributed by anyone”—constitutes a Digital Public Good when it is openly licensed, accessible, interoperable, and designed to advance social inclusion. When used pedagogically, OD effectively becomes OER, providing authentic, real-world material that allows students to think “as researchers, journalists, scientists, and policy-makers”

Open data is not inherently educational; it becomes OER when embedded in pedagogical practice.

The use of open data as OER should not be treated in isolation, but as part of an interconnected open knowledge ecosystem comprising:

• Open Data (OD / OGD)
• Open Educational Resources (OER)
• Free and Open Source Software (FOSS)
• Open Science (OS)
• Civic Engagement and Participation

The pedagogical value of open data as a Digital Public Good

Open data supports:

• Authenticity: engagement with real-world datasets and problems
• Transparency: understanding of public policies and governance
• Equity: democratised access to knowledge resources
• Participation: enabling students to contribute back to the public domain

The quadrangle of literacies for open data education

Literacy	Description	Pedagogical purpose
Statistical literacy	Understanding and interpreting data	Evidence-based reasoning
Political literacy	Understanding institutions and governance	Democratic engagement
Media literacy	Critically analysing information	Combat misinformation
Data literacy	Working with datasets and tools	Technical and analytical competence

Using open data in HE contributes to:

• Data commons (shared knowledge resources)
• Civic infrastructures (participatory governance)
• Open science ecosystems

Students become co-creators of knowledge, feeding outputs back into open ecosystems:

Students…can contribute to the open data ecosystem and engage with the wider community.

However, Open data must be framed critically, data are never neutral…they can amplify discrimination and segregation

Principle	Application in teaching
Avoid harm	De-identify sensitive data
Equity	Avoid biased interpretations
Transparency	Explain data provenance
Sovereignty	Respect community ownership
Accountability	Critically assess algorithms

Pedagogical Approaches

The pedagogical use of open data as OER demands a shift from transmissive models of teaching towards participatory, inquiry-driven, and critically engaged learning designs. Within this paradigm, learning is structured around authentic engagement with data as a form of knowledge production, rather than passive content consumption.

Drawing on critical pedagogy (Freire, 1970; Giroux, 2010), such approaches position students not merely as learners but as producers, interpreters, and communicators of knowledge, capable of interrogating the social, political, and epistemic dimensions of data. In this sense, open data becomes both an epistemic resource and a pedagogical catalyst, enabling learners to engage with real-world complexity, uncertainty, and power relations embedded in data infrastructures.

The integration of open data within HE therefore requires pedagogical designs that:

• support experiential and problem-oriented learning,
• scaffold data and critical literacies, and
• connect learning activities to societal challenges and civic participation.

flowchart TD

A["Open Ecosystem<br>(Open Data + OER + FOSS + Open Science)"]
--> B[Curriculum Design]

B --> C1["Define learning outcomes<br>(critical, civic, analytical skills)"]
B --> C2["Embed literacies<br>(data, statistical, media, political)"]
B --> C3["Align with SDGs / societal challenges"]

C1 --> D[Identify datasets & tools]
C2 --> D
C3 --> D

D --> E1[Select Open Data portals]
D --> E2["Select FOSS tools<br>(e.g. Python, R, QGIS)"]
D --> E3[Assess licensing & ethics]

E1 --> F[Design authentic learning tasks]
E2 --> F
E3 --> F

F --> G1[Problem-based learning]
F --> G2[Data expeditions]
F --> G3[Collaborative projects]
F --> G4[Civic data investigations]

G1 --> H[Student data practices]
G2 --> H
G3 --> H
G4 --> H

H --> I1[Data cleaning & analysis]
H --> I2[Visualisation & storytelling]
H --> I3[Interdisciplinary collaboration]

I1 --> J[Knowledge production]
I2 --> J
I3 --> J

J --> K1["Open outputs<br>(reports, datasets, dashboards)"]
J --> K2[Contribution to Open Science]
J --> K3[Community engagement]

K1 --> L[Civic participation & impact]
K2 --> L
K3 --> L

L --> M[Feedback into open ecosystem]

M --> A

%% Pastel colour styles (black text)
classDef ecosystem fill:#E8F4F8,stroke:#444,color:#000;
classDef curriculum fill:#F3E8FF,stroke:#444,color:#000;
classDef literacy fill:#FFF4E6,stroke:#444,color:#000;
classDef data fill:#E8F8EA,stroke:#444,color:#000;
classDef design fill:#FDE8E8,stroke:#444,color:#000;
classDef pedagogy fill:#FFFBE6,stroke:#444,color:#000;
classDef practice fill:#E8F0FE,stroke:#444,color:#000;
classDef output fill:#F0E8F8,stroke:#444,color:#000;
classDef impact fill:#E9F7F6,stroke:#444,color:#000;

%% Apply styles
class A ecosystem;
class B curriculum;
class C1,C2,C3 literacy;
class D,E1,E2,E3 data;
class F design;
class G1,G2,G3,G4 pedagogy;
class H,I1,I2,I3 practice;
class J,K1,K2,K3 output;
class L,M impact;

Loading

Design principles

• First, authenticity is central: students benefit from working with real, often incomplete and “messy” datasets, which mirror professional and civic uses of data and foster deeper engagement with analytical processes.

• Second, openness operates as both method and outcome. Not only are the datasets openly available, but the learning process itself encourages the sharing of results, enabling students to contribute to wider knowledge ecosystems and experience the social value of their work.

• Third, interdisciplinarity emerges as a necessary condition, as meaningful engagement with data frequently requires the integration of technical, analytical, and contextual knowledge across domains. This is particularly evident in activities that combine computational tools with social or environmental inquiry.

• Fourth, ethical reflexivity must be embedded throughout the learning design. Students need to critically interrogate how data are produced, represented, and used, recognising the potential for bias, exclusion, and harm.

• Finally, the most effective designs foreground student agency, granting learners autonomy to select datasets, define questions, and shape outputs. This not only increases motivation but also cultivates the critical confidence required for engaged citizenship.

Discipline	Example Activity	Description	Open Data Portals (Full URL)
Social Sciences & Policy	Inequality analysis	Compare global inequality trends and indicators	http://data.worldbank.org/
Social Sciences & Policy	Education access study	Analyse literacy, enrolment, and education inequalities	http://uis.unesco.org/
Social Sciences & Policy	Civic monitoring	Track public spending and project implementation	https://opencoesione.gov.it/
Environmental Sciences	Climate data modelling	Analyse temperature, emissions and climate change data	https://climate.copernicus.eu/
Environmental Sciences	Biodiversity analysis	Study ecosystems and species distribution	https://environment.data.gov.uk/
Environmental Sciences	Sustainability indicators	Compare SDG-related datasets across countries	https://data.unep.org/
Computer Science & Data Science	Data-driven applications	Build apps using real-time APIs from government data	https://data.gov.uk/
Computer Science & Data Science	Smart city data	Analyse transport, infrastructure, and urban systems	https://www.smartcitiesworld.net/open-data
Computer Science & Data Science	Machine learning projects	Train models using open datasets	https://archive.ics.uci.edu/ml/index.php
Health & Public Health	Epidemiological analysis	Study disease trends and global health data	https://www.who.int/data
Health & Public Health	Health inequality mapping	Analyse regional differences in health outcomes	https://digital.nhs.uk/data-and-information
Urban Studies & Geography	Transport modelling	Analyse mobility and transport infrastructure	https://tfl.gov.uk/info-for/open-data-users/
Urban Studies & Geography	Housing analysis	Study housing affordability and urban inequality	https://data.london.gov.uk/
Urban Studies & Geography	GIS mapping	Conduct spatial analysis using geospatial datasets	https://www.openstreetmap.org/
Humanities & Media Studies	Data journalism	Produce narratives from open datasets	https://www.theguardian.com/data
Humanities & Media Studies	Cultural analytics	Explore digital heritage and cultural datasets	https://www.europeana.eu/
Cross-disciplinary	Urban inequality project	Combine global and local data for policy insights	http://data.worldbank.org/ + https://data.gov.uk/