Numbers that reach the student in time.
The HESF expects providers to monitor progression and act on it. We help you build learning analytics that identify students needing support early, inform course review, and treat student data with the care it deserves.
Learning Analytics, as we practise it.
- Retention, progression and completion analytics across cohorts and subgroups
- At-risk identification with timely referral into support - not just reporting
- Course and unit insight feeding periodic review and external referencing
- Dashboards and reporting for academic boards and executives
- Ethics of student data - consent, transparency and proportionate use
- Data foundations - definitions, quality and lineage you can defend
Three moves, in order.
Frame
We define the questions worth answering - progression, equity, engagement - and the decisions they should trigger.
Build
Measures, dashboards and alerting designed with teaching and support staff, not just the data team.
Act
Intervention pathways and governance so insight becomes support, and support becomes evidence.
Deliverables you can put in front of a board.
Analytics readiness review
Where your data, definitions and tooling stand - and the shortest path to insight you can act on.
Discuss this Data & AICohort insight model
A monitoring model for retention, progression and completion, including subgroup outcomes.
Discuss this Data & AIStudent-data ethics framework
Principles and controls for the fair, transparent use of learner data.
Discuss thisPut learning analytics on the roadmap.
Tell us where you are and where the institution needs to be - we will bring the map.