This document provides practical guidelines for teachers, school leaders, and other education professionals on how to use education data ethically, inclusively, and meaningfully to enhance students' academic and socio-emotional development and drive school improvement. It draws on nine case studies, 18 learning stories, a literature review, and a series of workshops for education stakeholders conducted under the Agile EDU project, supported by evidence from research.

 

The guidelines promote a nuanced approach to the competent and reflective professional use of data—both digital and non-digital—with an emphasis on learning. They encourage self-reflection, professional and organisational learning, rather than viewing data solely as a tool for accountability and efficiency.

 

The guidelines can help schools and educators to:

 

These principles are organised around 12 key ideas:

 

1. Start by defining the question before looking for data

Identify the challenges you wish to address before collecting or analysing data. Beginning with clear inquiry questions ensures that data collection remains purposeful, reduces unnecessary workload, and protects privacy.

 

2. Address data literacy in professional development

Both teachers and school leaders should seek modular, ongoing training in data literacy to interpret data effectively and address inclusion challenges. Peer learning communities and long-term mentoring can support the meaningful adoption of data-informed practices.

 

3. Collaborate with students

Involving students in collecting and analysing data builds their motivation, sense of belonging, and ownership. It also enhances their data literacy and generates creative ideas. Student participation—through surveys, projects, or playful inquiry—helps schools co-create solutions that improve wellbeing, competence development, and inclusion.

 

4. Analyse group data to identify trends and patterns

Instead of focusing solely on individual performance, schools should analyse group data to identify patterns in teaching and learning. Aggregated analysis supports reflection on teaching methods and promotes equity in assessment.

 

5. Keep student data private

Schools must ensure that all tools comply with privacy laws and minimise data sharing with third parties. Using open-source or collectively procured tools can strengthen data security and promote ownership and privacy within schools.

 

6. Establish a data team in the school

A cross-functional data team can manage inquiry cycles, analyse data, and translate findings into concrete actions for student growth and school improvement. This collaborative structure fosters shared understanding, capacity building, and sustainable data use.

 

7. Develop a school data strategy

Schools should develop a written data strategy that integrates data use into broader digital action plans, linking teacher professional development, data ethics, and innovation.

 

8. Empower students to think about their learning

When students interpret their own learning data, they develop self-regulation and metacognitive skills. Teachers can guide this process to ensure that data supports reflection and dialogue rather than competition or anxiety.

 

9. Develop students' critical data literacy skills

Students should understand how data shapes society and influences equity. Engaging activities that compare digital and non-digital data, or explore local civic data, can help students become informed and responsible digital citizens.

 

10. Reflect on how data-driven EdTech shapes practice

Schools should evaluate how digital tools affect pedagogy, student wellbeing, and inclusion. Data-driven technologies can provide value but may also change classroom behaviour and learning culture in unintended ways.

 

11. Go beyond performance indicators

Schools should look beyond test scores to include wellbeing, inclusion, and environmental factors when reflecting on progress. A narrow focus on performance can increase stress, foster exclusion, and reduce educational breadth and depth.

 

12. Triangulate data sources

Combining multiple data types—quantitative and qualitative, digital and observational—provides more valid and nuanced insights. Triangulation supports evidence-informed reflection while acknowledging the complexity of learning.