Why it matters
Universities are using learning and engagement data to identify students who may need support early, with the aim of improving retention and outcomes.
Data can show early signs that a student is struggling, such as missed sessions or late submissions. The value comes from timely, supportive contact from staff, with students understanding how their data is used.
By the numbers
43%of bachelor's students complete within the expected time across OECD countries 1
70%complete within three extra years 1
Within expected duration
Within three extra years
Source: OECD, Education at a Glance 2025: who is expected to complete tertiary education, September 2025
What's changing
- Learning platforms provide engagement signals.
- Advisers use dashboards to prioritise outreach.
- Ethical frameworks govern the use of student data.
How leaders are responding
- Combining data with personal outreach from advisers.
- Being transparent with students about how data is used.
- Evaluating interventions as well as predictions.
What good looks like
- Early-warning signals agreed with academics and support teams
- Interventions tested for impact, not just risk scores
- Students told what data is used and how
What to watch
- Integration of wellbeing and academic data.
- AI assistants for student support.
- Ethical guidance on learning analytics.
Where to start
Pilot early alerts in one programme, train advisers to follow up, and evaluate the effect on progression.
Questions for your leadership team
- Which signals best show that a student needs support?
- How do students feel about this use of their data?
- Which interventions actually help?
