Agile EDU: the first 18 months
In the first 18 months of the Agile EDU project, several key activities were successfully completed.
Developing an analytical framework: This framework covers various dimensions of using educational data in schools, including data regulation, rights, privacy, teaching, learning, and governance. It also considers the growing use of generative AI in education. The framework is represented by a Venn diagram, with each circle corresponding to a pillar. Tools, content, and platforms, which generate and process educational data, intersect these pillars. Regulations and privacy issues influence how these tools are used and governed, and in turn, data governance affects their practical application in schools.
Producing a focused literature review: This review addresses the three pillars of the conceptual framework: regulation and rights, data use in teaching and learning, and data governance. It clarifies key concepts like ‘datafication,' ‘platformisation,' and data literacy, providing partners with the necessary understanding to implement project activities. The review will be updated in the second and third years to reflect rapid developments in the field.
Drafting case studies and learning stories: Partners selected case studies and learning stories based on the project's analytical framework and their own priorities. These cover a wide range of technologies, issues, and practices related to educational data, such as using adaptive learning technologies to improve teaching, relationships between education authorities and commercial publishers, and customising generative AI applications to safeguard student data. Equity, inclusion, and continuous professional development are addressed across all case studies. Case studies offer detailed descriptions and analyses for policymakers and education leaders, while learning stories provide concise, practical examples for school heads and teachers. The first iteration of these documents has been completed, with a second iteration planned for January 2025.
Ensuring the framework's relevance and effectiveness: The ongoing process of validating the analytical framework and discussing key issues guarantees its applicability in addressing the challenges and opportunities of using educational data. During validation workshops, experts have examined and debated the critical points identified from a cross-analysis of the initial drafts of case studies and learning stories.
Creating communities of stakeholders through Dialogue Labs: These one-day participatory workshops brought together diverse actors, including local authority officials, school leaders, teachers, and parents, to discuss the use of educational data. Despite differing views, participants engaged in structured dialogues, leading to the identification of common issues across countries. These included variations in conceptual understanding, the need for more research on the pedagogical benefits of data, and the lack of professional development for using data in teaching and learning. Insights from these country-level discussions were then shared at a European Dialogue Lab, which included European and international organizations, civil society, EdTech companies, and research networks. The same stakeholders will participate in three more Lab series in the project's second half, fostering a sense of community among those working on educational data.
Interconnected activities shape the project's progress. The activities are designed to inform and enhance each other. For instance, key points from the initial case studies were used to develop reflective questions for expert validation workshops. Discussions from these workshops will help set the agenda for future dialogue labs and contribute to the second iteration of case studies and learning stories. This iterative process ensures that findings continuously shape and enrich the project's next steps.
First findings
Agile EDU's first findings stem from three key activities: case studies and learning stories, dialogue labs, and expert validation workshops. These activities, guided by the project's analytical framework, reveal the current practices and challenges in using educational data.
Conceptual understanding varies significantly. Discussions at the dialogue labs highlighted, that terms like ‘data,' ‘datafication,' ‘platformisation,' and ‘learning analytics' are often unclear and used inconsistently. This ambiguity makes it difficult to define, for instance, what data literacy means for school leaders, teachers, and students, as noted in Agile EDU's literature review.
Developing an educational data ecosystem faces multiple challenges. These include interoperability issues between different platforms, lack of data flow connections between education levels, and disparities in data collection and analysis capabilities. Larger education authorities, such as those in Nordic countries, have more resources to handle large datasets, creating inequalities with smaller municipalities. Additionally, technology companies pose challenges through licensing, ethical issues, and the accessibility and reliability of their products.
Trust in the technology sector is a major issue. Participants at the dialogue labs and expert validation workshops highlighted a flawed consent model, which creates a power imbalance between technology providers and users. Additionally, the pedagogical benefits of many tools and platforms remain unproven. The commercial use of student data often leads to suspicions of vested interests, resulting in a mindset where data privacy and data collection are seen as conflicting activities, causing data to go unused due to distrust.
These challenges are cumulative and exacerbated by current debates. Issues such as generative AI, digital education, screen time, and mobile phone use in schools add to the complexity. Schools are tasked with developing students' multiliteracies (digital, data, AI) while also reverting to traditional teaching methods. Playful learning approaches, like those in the Agile EDU case study in Denmark, where students use hands-on activities and analogue tools to learn data literacy, can help address these challenges.
More research is needed on how data can improve teaching and learning. There is ongoing debate about what types of data should be collected to have the greatest impact, and whether the use of data increases teachers' agency and action potential. Some school actors worry that overuse of technology could lead to a ‘mechanisation' of education, reducing personal interactions and teacher awareness of student learning processes. Experts also question the pedagogical benefits of data-driven tools and platforms, noting the lack of quality standards and the premature spread of educational technology solutions.
Professional development is crucial for effective data use in education. Both experts and participants at the dialogue labs identified this as a key challenge. Different actors have varying needs for data literacy skills training, and these needs are intertwined with ethical concerns. For example, some school actors are concerned about data types like brain signals, eye tracking, and video analysis of social and emotional behaviour. Improving parents' digital competences and ‘data culture' is also important, as highlighted by the Agile EDU case study from Norway, where a pilot program gives parents access to the schools' digital platform.
| These activities and achievements have delivered the project's expected short-term impact:
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