Work Package 2 (WP2) is a key pillar of the DataScEd4CiEn project, dedicated to developing a comprehensive framework and competency model for integrating Data Science into STEAM education. Designed for students aged 9-15, this framework identifies the essential skills and knowledge needed to navigate the increasingly data-driven world. It also serves as the foundation for teacher professional development, ensuring that educators are equipped to guide students in using data for problem-solving, critical thinking, and civic engagement.

As the theoretical backbone of the project, WP2 informs all subsequent work packages, including teacher training (WP3) and the creation of data-driven STEAM learning scenarios (WP4). By positioning Data Science at the intersection of STEAM disciplines and civic education, the framework supports real-world, inquiry-based learning that fosters data literacy, ethical reasoning, and social awareness.

Key Objectives of WP2

✅ Develop a research-based framework for Data Science in STEAM education, with a focus on teacher training and student competency development.
✅ Identify the core skills students need to analyze, interpret, and apply data in meaningful ways.

What WP2 Delivers

📌 A research report outlining key concepts in Data Science, statistics education, and STEAM, including best practices for teacher education and student learning.
📌 Guidelines for educators, detailing how to integrate Data Science into STEAM subjects through innovative teaching methods and curriculum design.

Through this work, WP2 lays the groundwork for a new approach to STEAM education, where data-driven learning empowers students to tackle real-world challenges, advocate for social justice, and become informed, responsible citizens.

the DataScEd4CiEn Framework

In today’s data-driven world, understanding how to collect, analyze, and interpret data is essential for problem-solving, decision-making, and civic engagement. The DataScEd4CiEn project is an Erasmus+ initiative that aims to integrate data science into STEAM (Science, Technology, Engineering, Arts, and Mathematics) education for students aged 9-15.

By engaging with real-world, messy data, students will develop the skills needed to critically analyze information, navigate digital spaces responsibly, and use data to address societal challenges.

Why Data Science in STEAM Education?

The explosion of data in modern society presents both opportunities and challenges. While open access to information fosters innovation and knowledge-sharing, many students lack the ability to critically assess, interpret, and make informed decisions based on data.

The DataScEd4CiEn framework is designed to bridge this gap by equipping young learners with the knowledge, skills, and ethical awareness required to become:
✔️ Critical Thinkers – Able to analyze and interpret data-driven narratives.
✔️ Problem-Solvers – Applying data science to tackle real-world challenges.
✔️ Active Citizens – Using data literacy to advocate for social and environmental justice.

Key Components of the DataScEd4CiEn Framework

🔹 1. Data Science in STEAM Learning

The framework highlights data science as an interdisciplinary field that enhances STEAM education by providing learners with hands-on experience in data collection, analysis, and interpretation.

💡 What students learn:

  • How to work with real-world, non-traditional datasets
  • The role of data-driven decision-making in science and society
  • The ethical use of data in civic engagement and digital citizenship

🔹 2. Core Data Science Competencies for Young Learners

To ensure a structured learning experience, the framework focuses on essential data science skills, including:

✔️ Data Collection & Cleaning – Understanding how to source and refine raw data.
✔️ Exploratory Data Analysis (EDA) – Identifying trends and making sense of information.
✔️ Statistical & Mathematical Thinking – Using probability, statistics, and modeling for problem-solving.
✔️ Data Visualization – Presenting data-driven stories through graphs and charts.
✔️ Programming & Big Data Technologies – Learning the basics of coding and computational analysis.
✔️ Ethics & Digital Citizenship – Discussing misinformation, bias, and privacy in the digital age.

🔹 3. Problem-Based Learning & Social Justice in Data Science

The DataScEd4CiEn project encourages students to apply their data science knowledge to address pressing social justice issues, including:

🌍 Climate Change & Sustainability – Analyzing environmental data to explore pollution, energy use, and conservation strategies.
🛡️ Digital Citizenship & Online Privacy – Understanding how personal data is collected, shared, and used in digital spaces.
📊 Social Inequality & Public Health – Using data to investigate disparities in healthcare, education, and economic systems.

Teaching Approach: How Students Learn

The DataScEd4CiEn framework is built on three key teaching strategies:

1️⃣ Problem-Driven Learning

  • Students work on authentic data science problems that are relevant to their lives.
  • Example: “How does online activity shape digital identity and privacy?”

2️⃣ Statistical Inquiry & Data Science Cycles

  • Students follow structured data science investigation models like PPDAC (Problem, Plan, Data, Analysis, Conclusion) to explore, analyze, and interpret data.

3️⃣ Digital Tools & Interactive Learning

  • Students use digital platforms such as:
    ✔️ Scratch & Tynker for block-based coding
    ✔️ Google Trends & CODAP for real-world data exploration
    ✔️ Python & R for advanced data analysis

How This Framework Prepares Students for the Future

By implementing the DataScEd4CiEn conceptual framework, schools and educators will:

✔️ Develop students’ 21st-century skills – Enhancing critical thinking, problem-solving, and digital literacy.
✔️ Bridge the gap between data science and civic engagement – Encouraging responsible data use and ethical decision-making.
✔️ Prepare the next generation of data-literate citizens – Empowering students to navigate the complex digital world with confidence.

Join the Data Science in STEAM Education Movement!

Are you an educator, student, or policymaker interested in bringing data science into classrooms? The DataScEd4CiEn project is paving the way for a new generation of data-literate, socially engaged learners.

🔗 Contact us to learn more about implementing this framework in your school!