Introduction
The DataScEd4CiEn Professional Development Programme (Work Package 3) was developed to strengthen teachers’ capacity to integrate Data Science into STEAM education for civic engagement and social justice. Building on the DICE Conceptual Framework developed in WP2, the programme equips primary and lower secondary teachers with the knowledge, pedagogical approaches, digital tools and practical resources needed to implement authentic, data-driven learning experiences in their classrooms.
Key Achievements
The Professional Development Programme successfully:
✅ Developed a comprehensive blended-learning professional development programme for primary and lower secondary teachers.
✅ Created five research-based learning modules combining theory, pedagogy and classroom practice.
✅ Produced an extensive teacher toolkit including study guides, presentations, authentic datasets, digital tools and implementation resources.
✅ Established a collaborative professional learning network connecting teachers, researchers and teacher educators across Europe.
what wP3 delivers
The Professional Development Programme provides educators with a complete set of resources for implementing Data Science in STEAM education, including:
📌 Five structured professional development modules
📌 Study guides and presentation materials
📌 Teacher toolkit with authentic datasets and digital resources
📌 Practical classroom activities and implementation guidance
📌 Digital tools supporting inquiry-based data investigations
📌 Professional learning resources available in multiple languages
The Professional development Journey
The DataScEd4CiEn Professional Development Programme is designed as a progressive learning journey that supports teachers in developing the knowledge, pedagogical approaches and practical skills needed to integrate Data Science into STEAM education for civic engagement and social justice.
Building on the DICE Conceptual Framework, participants progress from understanding the theoretical foundations of data-centred STEAM education to designing and implementing their own interdisciplinary learning scenarios. Throughout the programme, teachers engage with authentic datasets, digital technologies, Artificial Intelligence, collaborative learning activities and reflective practice, enabling them to confidently translate research into meaningful classroom experiences.
The Professional Development Journey
Understand the DICE Conceptual Framework
Explore the theoretical foundations of Data Science in STEAM education and examine how data can be used to promote civic engagement, social justice and responsible citizenship.
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Discover Data-centred STEAM Pedagogy
Develop an understanding of inquiry-based, problem-based and project-based learning approaches that support authentic, interdisciplinary learning.
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Explore Digital Tools and Authentic Data
Gain practical experience using digital tools such as CODAP, Gapminder and World Bank datasets while learning how to formulate statistical questions, identify appropriate datasets and support meaningful data exploration.
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Develop AI and Data Literacy
Investigate the role of Artificial Intelligence and Machine Learning in society, explore ethical issues such as bias and privacy, and design classroom activities that promote responsible and critical use of AI technologies.
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Design and Implement Classroom Scenarios
Apply the knowledge and skills developed throughout the programme by designing, implementing and evaluating curriculum-aligned STEAM scenarios that address authentic social justice challenges through data-driven inquiry.
Course structure
The DataScEd4CiEn Professional Development Programme consists of five interconnected modules that progressively develop teachers’ knowledge, pedagogical understanding and practical skills for integrating Data Science into STEAM education. Together, the modules provide a balanced combination of theoretical foundations, classroom-ready resources and opportunities for reflection and application.
The programme has been designed for primary and lower secondary teachers and can be used flexibly to support both individual professional learning and collaborative professional development within schools and teacher education programmes.
module 1 – The DataScEd4CiEn Conceptual Framework
Module 1 provides opportunities to learn about the DataScEd4CiEn project and the theoretical concepts that have informed its design. Using a case-based approach, the module provides insights into the components of the DataScEd4CiEn conceptual framework that supports teachers and students need when working with data derived from problem-driven STEAM contexts. By providing insights into the core components of the DataScEd4CiEn conceptual framework, the module will support learner’s in engaging in problem-driven inquiry through the exploration of multivariate data on a range of topics related to citizenship education. Such inquiry can equip the younger generation with the knowledge and skills required to cope with the demands of modern society and become “tomorrow’s progressive leaders, productive workers, and responsible citizens” (Ge et al., 2015, p. 384)..
Resources
Module 2 – Data-centred STEAM Education
This module introduces the pedagogical approaches that underpin the DataScEd4CiEn framework. Participants explore inquiry-based, problem-based and project-based learning, examine the principles of integrated STEAM education, and investigate how data science can be embedded within authentic contexts to promote critical thinking, collaboration and responsible citizenship.
Resources
Module 3 – The DataScEd4CiEn Teacher Toolkit
This practical module equips teachers with the digital tools, datasets and classroom resources needed to implement data-centred STEAM learning. Participants learn how to locate authentic datasets, formulate statistical questions, analyse data using digital tools such as CODAP, and design engaging classroom activities that promote data literacy and evidence-based reasoning.
Resources
Module 4 – Data Science, Artificial Intelligence and Active Citizenship
This module explores the relationship between data science, Artificial Intelligence and Machine Learning within the context of civic engagement and ethical responsibility. Participants investigate how AI systems are developed, examine issues such as algorithmic bias, data privacy and fairness, and develop strategies for helping students become informed, critical and responsible users of AI technologies.
Resources
Module 5 Developing STEAM scenarios
The final module supports participants in applying their learning by designing curriculum-aligned STEAM scenarios for students aged 9–15. Teachers develop interdisciplinary learning experiences that integrate authentic datasets, digital technologies and real-world societal challenges while promoting civic engagement, sustainability and social justice. The module encourages reflection, adaptation and classroom implementation, enabling teachers to confidently translate theory into practice.
These modules provide educators with the knowledge, strategies, and tools to integrate data science into STEAM education, preparing students for data-driven decision-making and civic engagement.
Resources
Impact and Legacy
The DataScEd4CiEn Professional Development Programme has established a sustainable framework for supporting teachers in the integration of Data Science within STEAM education. Through its research-informed design, practical classroom focus and multilingual resources, the programme has equipped educators with the knowledge, skills and confidence to develop data-driven learning experiences that promote civic engagement, critical thinking and social justice.
Following its successful implementation and refinement across the partner countries, the programme remains openly available to educators, teacher educators and schools, ensuring that its resources continue to support professional learning and classroom innovation beyond the lifetime of the project.
Access the Full Professional Development Programme
The study guides provide an overview of each Professional Development module. To access the complete course materials, resources and learning activities, please register on the DataScEd4CiEn e-Platform.
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.