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<p>The European Union (“EU”) faces a significant challenge in upskilling and reskilling its workforce to meet current and future demands. In response, it has invested in a wide range of policies and strategic frameworks focused on skill development, many of which are documented in structured databases categorized by content and form. While recent literature acknowledges the importance of skill identification in educational policy, extracting skills directly from policy texts remains a novel and underexplored approach. This study applies network analysis to EU educational policies, published in EUR-Lex—the official EU policy database—to identify emerging and high-impact skills, offering a data-driven perspective on trends and skill clusters within the policy landscape. By conceptualizing the EU’s social policy network, we reveal patterns and communities of interconnected skills, providing empirical insights that inform both pedagogical practice and strategic policymaking. Our findings have practical implications for educators, curriculum designers, and institutional planners. Skill clusters identified through co-occurrence and centrality analysis can guide more relevant curriculum design and policy alignment. Finally, this study supports targeted investments in education and workforce development while highlighting critical skill gaps and offering a foundation for future research on data-informed educational transformation.</p>

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Extracting knowledge and highly demanded skills from European Union educational policies: a network analysis approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Dimitrios Christos Kavargyris,
  • Konstantinos Georgiou,
  • Nikolaos Mittas,
  • Lefteris Angelis

摘要

The European Union (“EU”) faces a significant challenge in upskilling and reskilling its workforce to meet current and future demands. In response, it has invested in a wide range of policies and strategic frameworks focused on skill development, many of which are documented in structured databases categorized by content and form. While recent literature acknowledges the importance of skill identification in educational policy, extracting skills directly from policy texts remains a novel and underexplored approach. This study applies network analysis to EU educational policies, published in EUR-Lex—the official EU policy database—to identify emerging and high-impact skills, offering a data-driven perspective on trends and skill clusters within the policy landscape. By conceptualizing the EU’s social policy network, we reveal patterns and communities of interconnected skills, providing empirical insights that inform both pedagogical practice and strategic policymaking. Our findings have practical implications for educators, curriculum designers, and institutional planners. Skill clusters identified through co-occurrence and centrality analysis can guide more relevant curriculum design and policy alignment. Finally, this study supports targeted investments in education and workforce development while highlighting critical skill gaps and offering a foundation for future research on data-informed educational transformation.