The first iteration of the case studies and learning stories is finished. To guide the further development of case studies and learning stories, the project consortium organised a series of three expert validation workshops. The discussions in the workshop helped identifying the most prominent questions around education data use.

The case studies analyse actions at various levels (school, municipality, national) related to education data analytics, exploring the perspective of various education actors. As more practice-oriented articles, the learning stories, concretely describe data use practices of teachers, school leaders, EdTech and local authorities.

                                                 

The three Expert Validation Workshops occurred in May and June 2024, uniting researchers from various European universities and educators to tackle shared challenges in education data use. Participants were invited to deliberate on the issues and challenges highlighted by the cross-analysis of the initial case study and learning story drafts.

These expert discussions enabled the Agile EDU partners to pinpoint common questions relevant to all stakeholders. The case studies are currently analysed in terms of what implications they have for these common questions and how more information could be collected to better answer those questions. Based on this analysis, the case studies will be developed further in autumn 2024. After two more cycles of peer and expert review in the first half of 2025, they will be made publicly accessible at the project's conclusion.

Need a change in mindset

A common theme raised by participants was the need for a change in mindset in education stakeholders, from policy makers to parents. In policy making, there is a need to base policy decisions more on evidence from research. Overall, there is an "old world view" of data, which may hinder effective handling and exploitation of data. For instance, data is often viewed as something that should be copied for storage and ownership by different organisations, although data could be called from a single source, reducing redundancy and improving cost-efficiency. The public concern about data privacy is another example where existing perspectives can pose a challenge. Due to a lack of trust in the public, including parents sending their children to school, the debate over education data focuses more on data privacy than on how to best analyse this data to inform educators to make improvements in their practices. Perhaps one way to improve public trust is to involve the departments of statistics more, an often trusted institution, as a mediator between education data and the public.

EdTech procurement

The workshops also generated discussions about the challenges in the agreements between EdTech and schools. There is a need for more mediation between schools and EdTech, guidelines in procurement and standards or minimal requirements to certify the pedagogical benefits of EdTech services. Moreover, the informed consent model has been considered flawed, because there is a power imbalance between EdTech companies and the individual user, on top of the daunting task of reading the detailed terms and conditions of EdTech agreements. The workshop discussions also revealed the differences in EdTech procurement in different education systems. Decentralization leads to fragmented choice of tools and services, limited exchange of data between systems, and a lot of variation in schools and municipalities' resources to tackle data-related challenges. On the other hand, the more centralized a system is, the more inflexible and sluggish it can be when there is the need to adopt tools for specific situations.

 

Adopting a critical approach

The current debate on education data analytics puts emphasis on "digital data" and using data "to improve", which can lead to overreliance on quantitative data or a reduction of complex learning processes. Workshop participants acknowledged the need for a more critical approach to data use and data literacy in education both for initial teacher education and in schools for students, to help them navigate a data-driven world as active citizens. Critical data literacy activities for students were mentioned, where students not only learn how to critically evaluate the quality of data, but also learn how their personal data as a citizen can be used by various governmental or public institutions. Playful learning methodologies were also discussed as a way to develop data literacy with hands-on, off-screen and student-centered activities. Teachers can benefit from a critical look at the data they collect, and an inquiry-based approach, formulating their research question, which can lead to more creative ways of collecting and interpreting student data.

Way forward

These are a few of the many discussion points that emerged in the Expert Validation Workshops. The project is now analysing the case studies and learning stories in terms of how they could further have implications for the themes discussed in the workshops by experts and practitioners. Ultimately, the aim of the project is to also develop policy recommendations based on these common themes and how they have been addressed in the case studies.