{"id":3770,"date":"2024-07-08T06:09:26","date_gmt":"2024-07-08T06:09:26","guid":{"rendered":"https:\/\/datasc4ed.euc.ac.cy\/wp-2-conceptual-framework-for-data-science-within-steam-education\/"},"modified":"2024-07-08T06:09:26","modified_gmt":"2024-07-08T06:09:26","slug":"wp-2-conceptual-framework-for-data-science-within-steam-education","status":"publish","type":"page","link":"https:\/\/datasc4ed.euc.ac.cy\/es\/wp-2-conceptual-framework-for-data-science-within-steam-education\/","title":{"rendered":"WP2 – Conceptual framework for Data Science within STEAM Education"},"content":{"rendered":"\n

Work Package 2 (WP2)<\/strong> established the scientific and pedagogical foundation of the DataScEd4CiEn project through the development of the Data Inquiry for Civic Engagement in STEAM (DICE) Conceptual Framework<\/strong>. Designed for learners aged 9\u201315, the framework provides a coherent model for integrating Data Science into STEAM education while fostering data literacy, critical thinking, civic engagement, and social justice.<\/p>\n\n\n\n

Developed through an extensive review of international research and an iterative design process involving all project partners, the framework identifies the key competencies, pedagogical principles, and data science practices needed to support meaningful, problem-driven learning using authentic data. It also provides practical guidance for teachers to integrate Data Science into interdisciplinary STEAM contexts.<\/p>\n\n\n\n

As the theoretical backbone of the project, the DICE Framework informed the design of the Professional Development Programme (WP3), the development of the STEAM learning scenarios (WP4), and their implementation and evaluation across partner schools. By positioning Data Science at the intersection of STEAM education, civic engagement and social justice, the framework provides a coherent educational model that supports responsible citizenship and evidence-based decision-making.<\/p>\n\n\n\n

Key achievements:<\/h3>\n\n\n\n
    \n
  • Developed the DataScEd4CiEn DICE Conceptual Framework.<\/li>\n\n\n\n
  • Defined the core competencies and data science practices for learners aged 9\u201315. <\/li>\n\n\n\n
  • Established the pedagogical foundation for the project’s Professional Development Programme and STEAM learning scenarios. <\/li>\n\n\n\n
  • Integrated Data Science, STEAM Education, Civic Engagement and Social Justice into a coherent educational model.<\/li>\n<\/ul>\n\n\n\n

    <\/p>\n\n\n\n

    What WP2 Delivers<\/strong><\/h3>\n\n\n\n

    WP2 produced a comprehensive set of research-based resources that provide the foundation for integrating Data Science into STEAM education:<\/p>\n\n\n\n

    \ud83d\udccc The DICE Conceptual Framework<\/strong>, defining the key principles, competencies and pedagogical approaches for teaching Data Science through authentic, interdisciplinary learning.<\/p>\n\n\n\n

    \ud83d\udccc A comprehensive research report<\/strong>, synthesising international literature and existing educational frameworks to establish a robust theoretical foundation for Data Science education in STEAM.<\/p>\n\n\n\n

    \ud83d\udccc A competency framework<\/strong> describing the knowledge, skills and dispositions that learners aged 9\u201315 need to develop data literacy, critical thinking, ethical reasoning and civic engagement.<\/p>\n\n\n\n

    \ud83d\udccc Teacher guidelines and professional development resources<\/strong>, supporting educators in implementing Data Science through inquiry-based, real-world STEAM learning.<\/p>\n\n\n\n

    \ud83d\udccc A pedagogical foundation<\/strong> that informed the development of the project’s Professional Development Programme (WP3) and interdisciplinary STEAM learning scenarios (WP4).<\/p>\n\n\n\n

    Together, these outputs establish a coherent educational model that enables teachers to integrate Data Science into everyday classroom practice while empowering students to investigate real-world challenges, make evidence-based decisions, and become active, informed citizens.<\/p>\n\n\n\n

    <\/p>\n\n\n\n

    the DataScEd4CiEn <\/strong>(DICE) Framework<\/h2>\n\n\n\n

    In an increasingly data-driven world, the ability to collect, analyse, interpret and communicate data has become an essential competency for informed decision-making, problem-solving and active citizenship. The Data Inquiry for Civic Engagement in STEAM (DICE) Conceptual Framework<\/strong> was developed through the DataScEd4CiEn project to support the meaningful integration of Data Science into STEAM education for learners aged 9\u201315.<\/p>\n\n\n\n

    The framework provides a research-based model that combines Data Science<\/strong>, STEAM Education<\/strong>, Civic Engagement<\/strong> and Social Justice<\/strong>, enabling students to investigate authentic real-world challenges using data. By working with real datasets, students develop data literacy, statistical reasoning, computational thinking, critical thinking and ethical decision-making while learning to use evidence to understand and address issues affecting their communities.<\/p>\n\n\n\n

    Designed for teachers as well as learners, the framework offers practical guidance for creating interdisciplinary, inquiry-based learning experiences that connect classroom learning with real-world contexts and responsible citizenship.<\/p>\n\n\n\n

    \n\n\n\n

    Why Data Science in STEAM Education?<\/strong><\/h2>\n\n\n\n

    The rapid growth of data in every aspect of society has transformed the way we learn, work and make decisions. From climate change and public health to artificial intelligence and social media, data shapes our understanding of the world. However, simply having access to information is not enough. Young people must also develop the ability to question, analyse, interpret and communicate data critically and responsibly.<\/p>\n\n\n\n

    The DICE Conceptual Framework addresses this need by integrating Data Science within STEAM education through authentic, interdisciplinary learning experiences. By investigating real-world issues using data, students develop the knowledge, skills and ethical awareness needed to become informed, responsible and active citizens.<\/p>\n\n\n\n

    The framework supports students in becoming:<\/p>\n\n\n\n

    \u2714\ufe0f Critical Thinkers<\/strong> \u2013 analysing, interpreting and evaluating data to make evidence-based decisions.<\/p>\n\n\n\n

    \u2714\ufe0f Problem Solvers<\/strong> \u2013 applying data science practices to investigate authentic societal and environmental challenges.<\/p>\n\n\n\n

    \u2714\ufe0f Responsible Digital Citizens<\/strong> \u2013 understanding the ethical use of data, artificial intelligence and digital technologies.<\/p>\n\n\n\n

    \u2714\ufe0f Active Citizens<\/strong> \u2013 using data to explore issues related to sustainability, social justice and civic engagement, and to propose informed solutions.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Key Components of the DataScEd4CiEn Framework<\/strong><\/h2>\n\n\n\n

    The DICE (Data Inquiry for Civic Engagement in STEAM) Conceptual Framework brings together four interconnected dimensions that support meaningful, interdisciplinary learning through Data Science.<\/p>\n\n\n\n

    \n\n\n\n

    \ud83d\udcca 1. Data Science Practices<\/h2>\n\n\n\n

    Students learn to work with authentic data by asking meaningful questions, collecting and cleaning data, analysing patterns, interpreting evidence, and communicating findings responsibly.<\/p>\n\n\n\n

    Key learning areas include:<\/strong><\/p>\n\n\n\n

    \u2714 Data collection and preparation<\/p>\n\n\n\n

    \u2714 Exploratory data analysis<\/p>\n\n\n\n

    \u2714 Statistical and mathematical reasoning<\/p>\n\n\n\n

    \u2714 Data visualisation<\/p>\n\n\n\n

    \u2714 Computational thinking and digital tools<\/p>\n\n\n\n

    \u2714 Ethical use of data and Artificial Intelligence<\/p>\n\n\n\n

    \n\n\n\n

    \ud83d\udd2c 2. STEAM Learning<\/h2>\n\n\n\n

    Data Science is embedded within interdisciplinary STEAM learning, allowing students to investigate authentic problems that connect science, technology, engineering, the arts and mathematics.<\/p>\n\n\n\n

    Students engage in:<\/p>\n\n\n\n

    \u2714 Inquiry-based learning<\/p>\n\n\n\n

    \u2714 Project-based investigations<\/p>\n\n\n\n

    \u2714 Collaborative problem-solving<\/p>\n\n\n\n

    \u2714 Real-world data analysis<\/p>\n\n\n\n

    \u2714 Creative design and innovation<\/p>\n\n\n\n

    \n\n\n\n

    \ud83c\udf0d 3. Civic Engagement & Social Justice<\/h2>\n\n\n\n

    The framework encourages learners to use data to understand and investigate issues affecting their communities and the wider world.<\/p>\n\n\n\n

    Example contexts include:<\/p>\n\n\n\n

    \ud83c\udf31 Climate change and sustainability<\/p>\n\n\n\n

    \ud83c\udf7d Food waste<\/p>\n\n\n\n

    \ud83d\udc55 Fast fashion<\/p>\n\n\n\n

    \ud83c\udf0d Migration<\/p>\n\n\n\n

    \u2696 Social inequality<\/p>\n\n\n\n

    \ud83d\udcbb Digital citizenship<\/p>\n\n\n\n

    \ud83e\udd16 Artificial Intelligence<\/p>\n\n\n\n

    \n\n\n\n

    \ud83d\udca1 4. Twenty-First Century Competencies<\/h2>\n\n\n\n

    The framework supports the development of the knowledge, skills and dispositions required for responsible participation in a data-rich society.<\/p>\n\n\n\n

    These include:<\/p>\n\n\n\n

    \u2714 Critical thinking<\/p>\n\n\n\n

    \u2714 Statistical reasoning<\/p>\n\n\n\n

    \u2714 Data literacy<\/p>\n\n\n\n

    \u2714 Communication<\/p>\n\n\n\n

    \u2714 Collaboration<\/p>\n\n\n\n

    \u2714 Ethical reasoning<\/p>\n\n\n\n

    \u2714 Evidence-based decision-making<\/p>\n\n\n\n

    \n\n\n\n

    Teaching Approach: How Students Learn<\/h1>\n\n\n\n

    The DICE Conceptual Framework promotes an inquiry-based, student-centred approach in which learners investigate authentic societal challenges through the collection, analysis and interpretation of data. Rather than learning data science in isolation, students apply it within meaningful STEAM contexts that encourage collaboration, critical thinking and evidence-based decision-making.<\/p>\n\n\n\n

    1. Inquiry- and Problem-Based Learning<\/h2>\n\n\n\n

    Students investigate authentic questions linked to issues that affect their lives and communities, using data to explore problems, evaluate evidence and propose informed solutions.<\/p>\n\n\n\n

    Examples include:<\/strong><\/p>\n\n\n\n