Bringing data science into the classroom

Work Package 4 (WP4) transformed the DataScEd4CiEn conceptual framework and Professional Development Programme into authentic classroom practice. Building on the knowledge, pedagogical approaches and digital tools introduced in WP3, teachers from partner and associated schools collaborated to design, implement, evaluate and refine interdisciplinary STEAM learning scenarios that integrate Data Science, civic engagement and social justice.

Developed through a Design-Based Research (DBR) approach, the scenarios underwent multiple cycles of classroom implementation, evaluation and refinement. Teachers worked alongside researchers and teacher educators throughout the process to ensure that every scenario was pedagogically robust, curriculum-aligned and adaptable to different educational contexts. This collaborative process resulted in a collection of high-quality, classroom-tested educational resources that are now openly available to educators across Europe.

Key Achievements

Work Package 4 successfully:

✅ Developed a common DataScEd4CiEn STEAM Scenario Template to support the design of high-quality interdisciplinary learning experiences.

✅ Supported teachers in collaboratively designing, implementing, evaluating and refining STEAM learning scenarios through an iterative Design-Based Research methodology.

✅ Produced a collection of classroom-tested interdisciplinary STEAM learning scenarios integrating Data Science, civic engagement and social justice.

✅ Developed comprehensive educational resource packages including teacher guides, lesson plans, student worksheets, authentic datasets, assessment materials and supporting resources.

✅ Published the final learning scenarios as multilingual Open Educational Resources available in English, Greek, German and Spanish.

from professional development to classroom practice

The STEAM Learning Scenarios represent the practical application of the DataScEd4CiEn Professional Development Programme. After completing Module 5, interdisciplinary teacher teams applied the knowledge and skills developed throughout the course to design innovative classroom scenarios addressing authentic social, environmental and civic challenges.

Each scenario was developed using the DataScEd4CiEn STEAM Scenario Template, which provided a common framework for integrating Data Science, STEAM education, civic engagement and social justice while allowing teachers to adapt activities to their own national curriculum and classroom context.

The scenarios were subsequently implemented in partner and associated schools, where teachers collected classroom evidence through observations, student feedback, surveys, interviews and reflective practice. The findings informed a comprehensive evaluation of each scenario, enabling teachers and researchers to refine the learning activities before producing the final classroom-tested versions now available through the project platform

The Development Process

Step 1 – Scenario Design

Teachers worked collaboratively to design interdisciplinary STEAM learning scenarios using the DataScEd4CiEn STEAM Scenario Template. Each scenario addressed an authentic societal challenge and combined Data Science practices with inquiry-based and project-based learning approaches.

Step 2 – Classroom Implementation

The scenarios were implemented in classrooms across the partner countries, providing teachers and students with authentic opportunities to explore real-world issues through data-driven inquiry. Classroom implementation generated valuable evidence regarding student engagement, learning, curriculum integration and classroom feasibility.

Step 3 – Evaluation and Refinement

Teachers, teacher educators and researchers analysed classroom observations, teacher reflections, student feedback and evaluation data to identify strengths and opportunities for improvement. The evaluation highlighted the importance of authentic datasets, collaborative learning, inquiry-based pedagogy and meaningful connections between Data Science and social justice, while informing revisions that strengthened the educational quality of every scenario.

Step 4 – Final Classroom-Tested Resources

The revised scenarios were implemented again and finalised as comprehensive classroom-tested educational resources. The complete collection is now available as multilingual Open Educational Resources that teachers can adapt and implement within their own educational settings.

The DataScEd4CiEn STEAM Scenario Template

To support consistency and quality across all partner countries, the consortium developed the DataScEd4CiEn STEAM Scenario Template. The template guides teachers through every stage of scenario development, from defining authentic real-world questions and curriculum objectives to planning learning activities, selecting digital tools, integrating Data Science practices, and designing assessment strategies.

By providing a common design framework while allowing flexibility for national curricula and local educational contexts, the template supported teachers in creating innovative interdisciplinary learning experiences that place Data Science at the centre of STEAM education for civic engagement and social justice

The Learning Scenario Collection

The DataScEd4CiEn learning scenarios address a diverse range of contemporary social, environmental and civic challenges while promoting Data Science literacy through interdisciplinary STEAM education.

Using authentic datasets, digital technologies and inquiry-based learning, students investigate real-world problems, analyse evidence, communicate findings and propose solutions that contribute to more sustainable, equitable and socially responsible communities.

The collection includes scenarios focusing on:

• Poverty and Economic Justice
• Food Waste and Sustainable Consumption
• Fast Fashion and the Environmental Impact of Clothing
• Droughts and Climate Change
• Earthquakes and Disaster Preparedness
• Migration and Global Citizenship
• Gender Equality in Sport
• Robotics for Social Innovation
• Wildfire Detection and Environmental Protection
• Nutrition and Sustainable Food Systems

Fire Alert! – Detecting Wildfires, Protecting Our Future

School: La Salle Buen Consejo School, Puerto Real, Spain

Grade: 6 (Ages 11–12)

Subjects: Science, Mathematics, Social Sciences, Computing & Robotics, Spanish, Citizenship Education

Wildfires are becoming increasingly frequent and severe across the world, threatening ecosystems, biodiversity, communities and livelihoods. In this interdisciplinary STEAM learning scenario, students investigate the causes, consequences and geographical distribution of forest fires while exploring how data science and technology can contribute to protecting both people and the environment. Through authentic environmental data, students develop an understanding of the links between climate change, sustainability, civic responsibility and social justice.

Students begin by exploring real-world questions about the increasing number of wildfires worldwide before analysing authentic datasets from international platforms such as Global Forest Watch and the Copernicus Programme. Using interactive maps, graphs and official databases, they identify wildfire patterns, compare trends across countries, interpret environmental data and critically evaluate the evidence. Rather than simply reading graphs, students are encouraged to distinguish between observations and interpretations, recognise scientific uncertainty, identify missing information and make evidence-based decisions while considering equity and the needs of vulnerable communities.

Building on their data analysis, students investigate how technology can support environmental protection before designing, constructing and programming a Micro:bit fire-detection robot equipped with light and temperature sensors. Working collaboratively, they develop, test and refine their prototype before presenting it to younger students alongside awareness campaigns promoting responsible environmental action. By integrating data science, robotics, mathematical reasoning and environmental education, the project transforms authentic data into meaningful action, empowering students to become informed citizens capable of using technology creatively to address real-world sustainability challenges.

Embracing the Foreigner in a Globalized World where Distance is Relative

School: La Salle Buen Consejo School, Puerto Real (Spain)

Grade: 8 (Ages 13–14)

Subjects: Geography & History, Mathematics, Spanish Language & Literature, English, Technology & Digitalisation, and Civic & Ethical Values

Authors: Inmaculada Catalán Sánchez, Álvaro Domínguez Álvarez, María Carmen Fernández Santamaría, María Julia Linares Martínez & Ana Serradó Bayés

What can data teach us about migration, inclusion and human rights? In this interdisciplinary STEAM learning scenario, students investigate migration from both historical and contemporary perspectives, exploring how globalisation has shaped human movement across time. Through authentic migration datasets, historical sources and ethical inquiry, students examine the experiences of migrants and refugees while reflecting on the importance of empathy, diversity and global citizenship in an increasingly interconnected world.

Students analyse official migration statistics using CODAP, interpret graphs and compare migration patterns, calculate the distances travelled by refugees using geographical data and mathematical modelling, and investigate how digital technologies can help visualise migration routes. Alongside their data analysis, they explore historical events such as the conquest of the Americas, examine literary texts that promoted the rights of Indigenous peoples, and discuss the relationship between migration, human rights and social justice. Through inquiry-based learning and computational thinking, students learn to interpret evidence critically while recognising how data can challenge stereotypes and support informed decision-making.

The project culminates in students reflecting on the concept of “relative distance”—not only as a geographical measure but also as a symbol of empathy, solidarity and inclusion. Through essays, discussions and multimedia presentations, they challenge prejudice, explore the rights of migrants and refugees, and consider how individuals and communities can promote respectful and inclusive societies. By combining Data Science, humanities, mathematics and civic education, the scenario empowers students to use evidence to understand complex social issues while developing the knowledge, critical thinking and values needed for active global citizenship.

Is a Mixed-Gender Professional Football League Viable?

School: La Salle Buen Consejo School, Puerto Real (Spain)

Grade: 9 (Ages 14–15)

Subjects: Mathematics, Physical Education, Science, Technology and Language

Authors: Idea by José Antonio González Sayagues (student teacher), developed by Ana Serradó Bayés

Can data help us answer complex social questions? In this interdisciplinary STEAM learning scenario, students investigate whether a mixed professional football league could be viable by examining issues of gender equity, athletic performance, economics and public interest. Rather than relying on opinions or stereotypes, students learn to analyse authentic data, evaluate evidence and develop informed conclusions about one of the most debated topics in modern sport.

Using official football datasets together with digital tools such as CODAP, GeoGebra and ChatGPT, students compare the performance of male and female players, explore statistical models, investigate player market values, and examine the reliability of AI-generated information. Throughout the project, they question assumptions, identify bias, recognise uncertainty and compare evidence from official sources with responses generated by artificial intelligence, developing healthy scepticism towards AI while strengthening their data literacy, statistical reasoning and critical thinking skills.

The project culminates in an evidence-based structured debate, where students defend or challenge the viability of a mixed professional football league using data rather than opinion. Working collaboratively, they construct mathematical models, interpret visualisations and communicate well-supported arguments while acknowledging uncertainty and alternative perspectives. By integrating Data Science, mathematics, sport and civic education, the scenario empowers students to explore questions of equality and social justice through evidence-based reasoning, demonstrating how data can support informed public debate and responsible decision-making.

What is the true cost of your clothes

School: The English School, Nicosia (Cyprus)

Grade: Year 8 (Ages 13–14)

Subjects: PSHCE, Mathematics, Science, Computing, Art & Design, Design & Technology

Authors: Leoni Hadjithoma, Michalis Gavrielides, Stavroula Neocleous, Harris Evangelou, Maria Christodoulou, Nicoletta Stavrides & Eleni Skoulia

What lies behind the clothes we wear every day? In this interdisciplinary STEAM learning scenario, students investigate the environmental, ethical and economic impact of the fast fashion industry through the Design Thinking process. By exploring authentic data, scientific evidence and personal consumption habits, they develop empathy for garment workers and communities affected by textile production while examining how everyday consumer choices influence people and the planet.

Students analyse real-world datasets using CODAP, investigate the environmental footprint of clothing production, critically evaluate scientific research on school uniforms, and explore issues such as reliability, bias and evidence-based decision-making. Working across Mathematics, Science, PSHCE, Art, Computing and Design & Technology, they develop statistical reasoning, critical thinking and digital literacy while applying the five stages of Design Thinking—Empathise, Define, Ideate, Prototype and Test—to generate practical solutions that promote more sustainable fashion.

The project culminates in students transforming their ideas into meaningful action through upcycled clothing products, bookmark collages created from recycled textiles, and awareness campaigns designed to encourage responsible consumption. Students communicate their findings through digital presentations, websites, posters or short videos, demonstrating how data can be used to raise awareness, challenge unsustainable practices and inspire positive change. By integrating Data Science, STEAM education and social justice, the scenario empowers students to become informed consumers capable of making ethical, evidence-based decisions that contribute to a more sustainable future.

Every Bite counts: Tackling food waste together

School: The English School, Nicosia (Cyprus)

Grade: Year 9 (Ages 13–14)

Subjects: PSHCE, Mathematics, Science, ICT, Design & Technology, English and Art

Authors: Leoni Hadjithoma, Michalis Gavrielides, Stavroula Neocleous, Harris Evangelou, Maria Christodoulou & Nicoletta Stavrides

Food waste is one of today’s greatest environmental and social challenges, yet every individual has the power to make a difference. In this interdisciplinary STEAM learning scenario, students investigate the causes and consequences of food waste through authentic data analysis, scientific inquiry and Design Thinking, exploring how informed consumer choices can contribute to a more sustainable future.

Working across multiple subject areas, students analyse food waste data using CODAP, investigate the environmental and social impact of food waste, calculate their own household food footprint, and explore sustainable solutions such as composting and biogas production. Through the Design Thinking process, they empathise with those affected by food insecurity, define key challenges, generate innovative solutions and use evidence to support their decisions. Alongside developing data literacy, statistical reasoning and digital competence, students strengthen critical thinking, collaboration and problem-solving skills while exploring the connections between sustainability, social justice and responsible citizenship.

The project culminates in students designing sustainable food packaging that promotes responsible consumption and includes practical advice on reducing food waste. Each package features an original recipe created from leftover ingredients together with a QR code linking to a student-produced cooking video, demonstrating how everyday food can be creatively reused instead of discarded. By combining Data Science, STEAM education, digital technologies and design innovation, the project empowers students to transform data into meaningful action while inspiring their school community to value every bite and reduce food waste.

Reducing Food Waste: Learning to Act with Data

School: 1st Gymnasium of Vyron (Greece)

Grade: 7–8 (Ages 12–14)

Subjects: PSHCE, Mathematics, Data Science, Environmental Studies, Art and ICT

Authors: Vasiliki Boltsi, Vasiliki Papaioannou, Olga Loukopoulou, Dionysia Mastrogiannopoulou, Katerina Soufli (1st Gymnasium of Vyron), Dionysia Bakogianni & Nikolaos Ververas (National and Kapodistrian University of Athens)

Food waste is one of the world’s most pressing sustainability challenges, with significant environmental, economic and social consequences. In this interdisciplinary STEAM learning scenario, students investigate food waste through the lens of data science, exploring how authentic evidence can help individuals and communities make more sustainable decisions. Beginning with thought-provoking questions about inequality, consumption and responsibility, students develop investigable research questions, analyse international datasets and learn how evidence can drive meaningful civic action.

Using authentic datasets from organisations such as Eurostat, the European Commission, FAO and Our World in Data, students explore patterns in food waste across countries and sectors. Working in CODAP, they visualise data, investigate relationships between variables such as income, food waste and CO₂ emissions, identify trends and outliers, construct simple explanatory models, make justified predictions and critically evaluate the uncertainty and limitations of their findings. Throughout the project, students learn to distinguish between opinion and evidence, correlation and causation, while developing statistical reasoning, critical thinking and responsible decision-making skills.

The scenario concludes with students transforming their data analyses into creative civic interventions. They design evidence-based awareness campaigns, comics, digital posters and interactive educational games using tools such as Canva, BookCreator and Roblox Studio, communicating sustainability messages supported by real data rather than opinion alone. By combining scientific inquiry, data literacy, digital creativity and active citizenship, students discover how data science can empower them to tackle real-world sustainability challenges and become informed, responsible citizens.

The Journey of Our Food: From Farm to Bin

School: Mary Immaculate College (Ireland)

Year Group: 5th–6th Class (Ages 11–12)

Subjects: Social, Environmental and Scientific Education (SEE), Mathematics, Geography, Science, English, Arts Education

Authors: Teachers at Mary Immaculate College

In this scenario, students explore the journey of food from production to consumption and waste, connecting their everyday experiences with wider environmental and social issues through data science, inquiry, and creative expression.

In Social, Environmental and Scientific Education (SEE), students reflect on their own food habits at home and in school, exploring how food is used, shared, or wasted. Through discussion and drama-based activities, they develop empathy and begin to understand the social and ethical dimensions of food waste.

In Geography and Science, students investigate where food comes from, how it is produced, and how it travels to reach them. They explore food systems, global connections, and the environmental impact of food production, transport, and waste.

In Mathematics, students work with real-world datasets on food waste across Europe and Ireland. They interpret graphs, compare countries, identify patterns, and generate their own data through school-based observations and simple surveys.

To deepen their understanding, students use digital tools such as CODAP to explore datasets interactively, supporting their ability to make data-informed observations and distinguish between patterns and explanations .

In English, students develop their communication skills through discussion, report writing, and persuasive texts, using evidence from data to support their ideas and raise awareness about food waste.

In Arts Education, students use drama, visual art, and poetry to explore and communicate the “hidden journey” of food. Inspired by artistic and literary examples, they create meaningful messages that connect data with human experience.

The learning process follows a design thinking approach, guiding students to identify problems, generate ideas, and develop creative responses. This culminates in students designing and presenting actions or messages aimed at reducing food waste in their school community, making learning both meaningful and impactful.

Fair Trade, Fair World – Understanding Poverty Through Data

School: La Salle – Buen Consejo (Spain)

Grade: 4 (Ages 9–10)

Subjects: Social Sciences, Mathematics and Arts

Authors: María Lucia Vargas & Ana Serradó Bayés

How can data help us better understand global poverty and inspire us to create a fairer world? In this thought-provoking STEAM learning scenario, students explore the relationship between economy, justice and social responsibility by investigating global poverty through authentic data, maps, machine learning and real-world case studies.

Students analyse World Bank datasets, interpret poverty maps, compare living conditions across countries using the Dollar Street project, and train a machine learning model to recognise different levels of household wealth while reflecting on bias in artificial intelligence. Through these activities, they develop data literacy, mathematical reasoning, critical thinking and digital skills while questioning inequality and exploring the meaning of fairness.

Their learning culminates in the creation of a Fair Trade Market, where students design logos, investigate fair pricing, create data visualisations, develop awareness campaigns and present practical actions that promote ethical consumption and global solidarity. By combining data science with civic engagement, the project empowers students to become informed, compassionate and active global citizens

Clean Air for All – How Clean Is the Air We Breathe?

School: University of Münster (Germany)

Grade: 4 (Ages 9–10)

Subjects: Mathematics, Science and German

Authors: Eva Hollmann & Maleen Klee

Clean air is essential for healthy communities, yet air pollution continues to affect people and the environment in different ways. In this interdisciplinary STEAM learning scenario, students investigate the quality of the air they breathe by exploring authentic air pollution data collected through the OpenSenseMap platform.

Using a real-world inquiry approach, students compare air quality in urban and rural areas, collect and organise particulate matter (PM2.5) data, calculate averages, interpret tables and bar charts, and use evidence to answer the question: How clean is the air we breathe, and what can we do to improve it?

Throughout the project, students develop data literacy, mathematical reasoning, scientific understanding and collaborative problem-solving skills while reflecting on the relationship between environmental quality, public health and social responsibility. Their learning culminates in the creation of awareness campaigns and creative products that encourage individuals and communities to take action to reduce air pollution and protect the environment.

Insects in danger – How can we help?

School: University of Münster (Germany)

Grade: 4 (Ages 9–10)

Subjects: Science, Mathematics and Arts

Authors: Franziska Lücking, Dana Stegemann & Nina Hoyer

What can data tell us about the decline of insect populations, and how can young people help protect biodiversity? In this interdisciplinary STEAM learning scenario, students investigate one of today’s most pressing environmental challenges by combining scientific inquiry, data literacy and civic action.

Students first explore the essential role insects play in ecosystems as pollinators, natural pest controllers, decomposers and a vital food source for many species. They then analyse authentic datasets and graphs showing changes in insect populations over the past decade, interpreting evidence through the Reading the Data, Reading Between the Data, and Reading Beyond the Data framework. By examining the causes of insect decline, students develop statistical reasoning, critical thinking and an understanding of the human impact on biodiversity.

Working collaboratively, students research insect-friendly plants, design a biodiversity-friendly school garden and evaluate different solutions to support local ecosystems. The project culminates in hands-on environmental action as students plant selected flowers and construct an insect hotel, transforming data-informed decisions into meaningful community action. Through this process, they strengthen data literacy, problem-solving, creativity, collaboration and environmental responsibility while discovering how small local actions can contribute to global sustainability

mission trash: Investigating and reducing waste at school

School: University of Münster (Germany)

Grade: 4 (Ages 9–10)

Subjects: Mathematics, Science and Arts

Authors: Mailin Holzkamp & Rika Thiemann

How much waste does our school produce, and what can we do to reduce it? In this STEAM learning scenario, students investigate the growing environmental challenge of waste production by collecting and analysing authentic data from their own school environment. Through an inquiry-based approach, they explore how everyday waste affects the environment while developing mathematical reasoning, data literacy and environmental responsibility.

Working collaboratively as young researchers, students design their own investigation, formulate research questions and collect real-world data by weighing and categorising different types of waste produced in their classroom over the course of a week. They compare alternative methods of data collection, organise their findings using tables and bar charts, and interpret the results to identify patterns in waste production. Using the Reading the Data, Reading Between the Data, and Reading Beyond the Data framework, students move from describing the evidence to drawing conclusions about consumption habits and evaluating the environmental consequences of waste generation.

Building on their data analysis, students collaboratively develop practical strategies to reduce waste within their school community. They present and justify their proposals using evidence collected during the investigation, critically evaluate the feasibility of different solutions, and agree on actions that can be implemented at classroom or school level. Through this process, students strengthen data literacy, critical thinking, collaboration, creativity and civic engagement while recognising how data-informed decisions can contribute to more sustainable schools and communities.

Numbers Speak Louder – What Can We Do About Food Waste in Our Daily School Life?

School: University of Münster (Germany)

Grade: 4 (Ages 9–10)

Subjects: Mathematics, Science and Arts

Authors: Carolin Hohmann & Sonja Holdmann

Food waste is one of today’s most pressing environmental and social challenges, yet many children are unaware of where most food is wasted or how their own actions can make a difference. In this interdisciplinary STEAM learning scenario, students investigate authentic data on food waste across different sectors and countries to understand where food waste occurs most frequently and why it matters. Using real datasets in CODAP, they learn to read, compare and interpret data while recognising that private households generate the largest proportion of food waste.

Through the DataScEd4CiEn approach, students progress from Reading the Data by exploring food waste datasets and creating graphs, to Reading Between the Data by comparing countries and sectors, calculating averages and identifying meaningful patterns. Finally, they engage in Reading Beyond the Data, discussing the causes and consequences of food waste and considering how evidence can inform sustainable action. Throughout the investigation, mathematics becomes a tool for understanding a real-world sustainability issue rather than an isolated school subject.

The project culminates in a genuine civic engagement activity in which students design a Food Sharing Shelf for their school. Inspired by the concept of food sharing, they investigate how surplus food can be redistributed instead of discarded, collaboratively establish rules for using the shelf, determine appropriate food items, create information materials and signage, and develop practical solutions for implementation within their school community. By combining data literacy, sustainability education, design thinking and collaborative problem-solving, students develop critical thinking, creativity and responsible citizenship while demonstrating how data can inspire meaningful action towards reducing food waste.

Our Class in Motion: How active are we during the school day?

School: University of Münster (Germany)

Grade: 3 (Ages 8–9)

Subjects: Mathematics, Science, Physical Education and Arts

Authors: Charlotta Thoms & Maja Meinschäfer

How active are children during a typical school day, and how can data help them make healthier choices? In this interdisciplinary STEAM learning scenario, students investigate their own movement patterns by collecting and analysing real data about their physical activity throughout the school day. Combining mathematics, physical education, science and digital tools, they explore the relationship between movement, health and wellbeing while developing essential data literacy skills.

Using stopwatches and digital devices, students record their own activity levels, organise the collected data into tables and graphs, and compare their findings with their initial predictions. Following the DataScEd4CiEn framework, they progress from Reading the Data by measuring and representing movement data, to Reading Between the Data by identifying patterns, comparing activity levels during lessons and breaks, and interpreting differences across the class. Finally, through Reading Beyond the Data, they reflect on the wider implications of physical inactivity and consider how evidence can support healthier lifestyles and more active learning environments.

Building on their findings, students collaboratively design practical strategies to increase physical activity during the school day. They develop classroom movement breaks, create posters promoting healthy habits, and explore the importance of balanced nutrition through activities based on the healthy eating pyramid. By connecting personal data with real-life decision-making, the project empowers students to become active participants in improving their own wellbeing while recognising that healthy lifestyles are supported through both individual choices and collaborative action within the school community.

Our Sustainable School – Reducing Waste, Creating Change

School: University of Münster (Germany)

Grade: 4 (Ages 9–10)

Subjects: Mathematics, Science, Arts and English

This interdisciplinary STEAM learning scenario encourages students to investigate how waste is produced in their everyday lives and empowers them to take meaningful action towards creating a more sustainable school. Through authentic data collection, environmental inquiry and collaborative problem-solving, students explore how individual choices can contribute to reducing waste and protecting the environment.

Students collect and analyse data about the waste produced in their homes and classrooms, using digital tools such as CODAP and BookCreator to organise, visualise and interpret their findings. They compare their own data with national waste statistics, developing an understanding of the differences between personal and large-scale datasets while strengthening their ability to read the data, read between the data and read beyond the data.

Building on their analysis, students explore recycling, waste separation, composting and the principles of the circular economy. They investigate practical strategies for reducing waste, create awareness campaigns through informative posters, and work collaboratively to design and build a composting system for their school. By combining mathematics, environmental science, digital technologies and creative communication, the project transforms data into action, enabling students to become active contributors to a more sustainable school community while developing the knowledge, skills and sense of responsibility needed for responsible citizenship.

Design Thinking: Droughts! No?

School: La Salle – Buen Consejo (Spain)

Grade: 10 (Ages 15–16)

Subjects: Geography, Science, Mathematics, Technology and Arts

Authors: Ana Serradó Bayés, María Lucia Vargas & Manuel Pavón Iglesias

How can schools respond to one of the greatest environmental challenges of our time? In this interdisciplinary STEAM project, students investigate the causes and consequences of drought and water scarcity in their local community before applying Design Thinking to develop practical, sustainable solutions. Through the five stages of Empathize, Define, Ideate, Prototype and Test, students become active problem-solvers, exploring how data can support environmental decision-making and civic action.

Using authentic weather data, students formulate investigative questions, collect information from official sources, analyse precipitation patterns with CODAP, build statistical and prediction models, and evaluate relationships between climate variables. Throughout the investigation they develop data literacy, critical thinking and mathematical reasoning while learning to interpret evidence, identify trustworthy sources and understand the impact of climate change on their own locality.

The project culminates in the design and construction of a rainwater collection cistern using Tinkercad, enabling students to transform data-driven research into an innovative engineering solution for their school. They calculate the prototype’s capacity and cost, present their ideas to classmates and local stakeholders, and reflect on how Design Thinking, Data Science and collaborative problem-solving can contribute to environmental sustainability. By combining scientific inquiry, engineering design and civic engagement, the scenario empowers students to see themselves as active contributors to addressing real-world challenges.

Civically engaged to ensure that an earthquake is not a social issue!

School: La Salle – Buen Consejo (Spain)

Grade: 10 (Ages 15–16)

Subjects: Biology, Geology, Mathematics and Digitalization

Authors: Manuel Pavón Iglesias & Ana Serradó Bayés

Can data help us understand natural disasters and make our communities safer? In this interdisciplinary STEAM project, students investigate the science behind earthquakes while using authentic seismic data to determine whether earthquakes pose a real risk to their local community. Combining biology, mathematics, data science and digital technologies, students explore how earthquakes occur, how they are measured, and how scientists monitor seismic activity around the world.

Working with official earthquake datasets from the Spanish National Geographic Institute (IGN), students collect, analyse and visualise real earthquake data using CODAP. They identify patterns in historical seismic events, investigate probability, construct decision tree machine learning models to explore earthquake prediction, and critically evaluate the strengths and limitations of scientific models. Along the way, they develop essential data science competencies, including data interpretation, statistical reasoning, evidence-based argumentation, and ethical communication of scientific information.

The learning experience combines scientific inquiry with practical investigations, including hands-on activities exploring seismic waves, locating earthquake epicentres, understanding the Earth’s interior, and interpreting geological evidence. Students conclude the project by producing a journalistic article entitled “Are You Afraid of an Earthquake in Puerto Real, Yes or No?”, using data visualisations, statistical evidence and machine learning predictions to support an informed position. Through this authentic civic challenge, students learn how data can be used not only to understand natural phenomena but also to communicate scientific evidence responsibly and help communities make informed decisions.

impact

The implementation and evaluation of the DataScEd4CiEn STEAM learning scenarios demonstrated the value of integrating Data Science into interdisciplinary STEAM education through authentic, socially relevant contexts.

Teachers reported that the scenarios increased student engagement, strengthened critical thinking and collaboration, promoted evidence-based reasoning and enabled students to connect classroom learning with real-world societal challenges. They particularly valued the use of authentic datasets, the interdisciplinary nature of the activities and the balance between theoretical foundations and practical classroom application. The iterative Design-Based Research process ensured that the final scenarios are not only research-informed but also classroom-tested and adaptable for diverse educational contexts.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.