Record keeping
Understanding what works – whether to enhance learning, close outcome gaps, or boost engagement – requires tracking student progress over time, ideally in comparison to peers. For care-experienced and estranged students, this is challenging: small populations and inconsistent definitions make robust analysis difficult. Encouragingly, national datasets such as NSS, Student Academic Experience Survey, and Global Student Living Index now include flags for these groups, and HESA is exploring how to publish relevant data. Over time, this will provide a national benchmark against which institutions can measure their own insights.
The starting point, however, is effective record-keeping: what tools and practices does your university use to monitor and track these students and how often do you use this data to assess the impact of your interventions?
Higher Education Access Tracker – HEAT
Overview: The Higher Education Access Tracker (HEAT) is a membership organisation for educational institutions, collaborative outreach groups, and charities like Sutton Trust or Unite Foundation, that are heavily engaged in HE widening participation initiatives. The HEAT database has been predominantly used to capture and then track the entry into HE, of participants of outreach activities like summer schools. It works through a year on year ‘matching’ exercise, the Higher Education or HE Track, that connects a record on the database with the student’s formal HESA record, returning all the material held on progression to and within HE, including WP characteristics like polar quintiles.
Much like the human brain, the functionality of the HEAT database is wider than simply tracking into the university ‘entry’ data point – valuable though that certainly is. Post-entry intervention activities can also be captured and participants monitored through to degree completion, and indeed into post-graduate outcomes and employment destinations (Graduate Outcome Survey).
The Unite Foundation is an affiliate member of HEAT and has uploaded all scholarship applicants, distinguishing between selected and not selected, for a natural comparator group. Suggested actions for existing HEAT members to build their long-term data diligence on care experienced and estranged students:
- Ensure outreach functions routinely capture flags for care experience and, separately, estranged
- Build HEAT consent into bursary and service access across both populations; in this way HEAT can deliver a professional and secure institutional participation platform (whilst still enabling excel usage locally).
- Add an ‘activity’ for each #HomeAtUniversity measure e.g. rent guarantor scheme, summer in halls, housing plan, and record the participation/uptake by student.
- Annually access HEAT’s HE Track downloads that prompt investigation and learning dependent on pattern of interventions (e.g. comparing care experienced to estranged if offers do not apply to both, examining intersectionality, developing insight into national returns trends.)
Resourcing: HEAT is a subscription-based research collaboration which is owned and steered by the members; of which there are varying types. Only one subscription per institution is required – check the HEAT membership lists here to see if your university already has access to this data resource and find out who the principal user is amongst your colleagues (known as the HEAT ’Database Point of Contact’. Your outreach team is a good first port of call.
The data entry process is supported by excel-based templates which facilitate bulk uploading. The frequency of data entry can be flexible but naturally with a minimum of annually, ahead of each HESA matching window. Upload of student personal data must be GDPR compliant, and this important aspect is supported with guidance for both privacy policies and participant forms.
Monitoring/Evaluation: Each HE Track matched return can be downloaded and imported into a Power BI dashboard for ease of analysis. Every step toward this analysis has supporting guides, and the dashboard now includes pre-built comparator populations as well as an intuitive sliding segmentation tool that automates cutting your own data set by levels of engagement in interventions, activity characteristics as well as student characteristics. Each ‘slice’ on the database generates a clear graphic for ease of cut and paste into data communication content. Delays with the HESA Data Futures project has caused a temporary backlog of matching returns but this is set to resume predictable regularity in early 2026.
