Defining data use in education and turning guidelines into practice

In Autumn 2023, Country Dialogue Labs were organised in Denmark, Norway, Spain, Sweden and Portugal, bringing together policy and practice organisations and other stakeholders to exchange views and ideas. These national events culminated in a first European Dialogue Lab, with participants from organisations representing policy makers, researchers, EdTech developers, students, parents and school leaders.

At the European Dialogue Lab key discussion points included:

• The need to clarify terms and concepts used in datafication, especially those relating to data in teaching and learning.
• Mediation to match strategies, frameworks and guidelines developed at national level to schools' contexts and needs, giving examples of good practice for example.

• Breaking down ‘silo mentalities', to encourage more dialogue between policy makers, EdTech developers and practitioners to, for example, help small companies understand the issues and integrate products into different platforms.

• Given the vast amounts of data being generated, what type of data is most needed to improve teaching and learning? Can AI help? How can education systems deal with the increased commercial value of data?
• Capacity building in all aspects of datafication, including data literacy, the ethical use of data and exercising agency.
• Ensuring roles and responsibilities to govern and manage datafication are clear and in place at all levels.

 

Comparative overview of countries

The spiral of agile transformation in the project includes several iterations of a survey of ministries of education and national agencies to investigate their views on the topic (vision, enablers, obstacles, main players and their relationships) as well as relevant practice examples. The first cycle of agile information capture was organized as one of the first project activity and two more cycles of agile information collection will follow in 2024 and 2025. In the preliminary collection of information, from May to June 2022 we collected information about data regulation, governance, management, and use from fourteen member countries: Belgium-FR, Belgium-NL, Croatia, Cyprus, Finland, France, Greece, Hungary, Ireland, Portugal, Serbia, Slovakia, Slovenia and Spain, in the 1st comparative overview report.

The analytical framework of data use in education

A starting point for the Agile EDU project is to identify the key studies that can define an analytical framework to be used for the project.

To develop this framework, the Scientific Advisory Board of Agile EDU (represented by Kristiina Kumpulainen, Olga Viberg, Ben Williamson, Jo Tondeur, with additional contributions from Sarah Howard) conducted a literature review on datafication in education, coordinated by the Faculty of Education, University of Oslo (Ola Erstad, Øystein Gilje, Greta Björk Gudmundsdottir, Rebekka Baunbæk Wagstaffe). This review brings together literature and research in this field in order to highlight some key issues and questions, with a distinct European focus on an emerging area of interest for many stakeholders – that of data, datafication and education.  This review clarifies concepts and provide operational definitions, identifies existing evidence from research; and finally, defines the conceptual framework for the Agile-EDU project.

The framework places learners and teachers at the centre of all data practices in education and consists of three pillars:

• Regulations, rights, privacy – Addresses the questions related to data ownership, privacy and regulations that facilitate the safe and ethical use of data between schools and EdTech. How is student data used by companies for commercial purposes? Do data-driven education technologies benefit all students equally? Are current regulations able to address the needs of schools ?
• Data in use for teaching and learning – Addresses the questions evaluating what kind of data meaningfully benefits teachers and students, at classroom, school, municipal/regional and national level. What kind of learning analytics practices are actually useful? What kind of data literacy skills are necessary for teachers and students to make use of data to plan and regulate teaching and learning? What data practices promote student learning without reducing opportunities to develop other skills?
• Data governance – Concerns questions around improving practices of collecting, aggregating and sharing data across multiple levels and institutions to facilitate the use of data in education. How can EdTech and governments collaborate for mutually beneficial improvement of data governance? How can we facilitate safe data exchange between institutions?

Finally, the analytical framework and the literature review were discussed and adapted by researchers and practitioners as part of an Expert Validation Workshop, leading to an updated literature review and framework. The literature review will be enriched yearly till the end of the 3-year project

Case studies and learning stories identifying important practices in the field

Having validated the analytical framework, the Agile EDU project is currently working on case studies and learning stories, taking the framework as a basis for analysing the practices they identified in their countries.

The practices cover a wide range of topics, such as:

1. Playful learning to teach data literacy in the classroom.
2. Giving more access to education data on a nationwide digital platform to parents to enable them to understand how their children are learning.
3. Developing a generative AI based chatbot that addresses the needs of teachers and keeps student input safe and private.
4. Using adaptive learning technologies in an effective way in the classroom.
5. Implementing digital textbooks and related teacher training in a safe and scalable manner.
6. Analysing data from schools to identify regions, students and schools with vulnerabilities to support them for more equity.

Each case study in Agile EDU will present an in-depth investigation of a phenomenon related to data use in education within its real-life context. The learning story will explain the actions of the practitioner (a teacher, a teacher educator/trainer, an ICT administrator at school or municipality level, a policy maker, etc) and the lessons learned from those actions that could help other practitioners to improve their practices related to data use in education. In 2024, the project will develop the first version of 9 case studies and 18 learning stories, to be reviewed and updated in an "agile" manner in two iterations of review and revision. The 2^nd and 3^rd wave of dialogue labs and expert validation workshops will fuel these revisions.

 

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