We aim to enable more effective risk-based decision making
Data and intelligence can give insight into the performance of institutions and programmes.
Using externally produced and verified data (rather than data and information provided solely by providers) benefits the provider, as it reduces duplication in reporting the same data to different organisations, and the HCPC, as we receive independent information about provider performance.
Using data and intelligence as a key part of our quality assurance model allows us to be:
- Proactive – where data and intelligence identifies risks, we can trigger some form of engagement with providers
- Risk-based – have an evidence-based understand of risks for providers
- Proportionate – use risk profiling to undertake bespoke and right touch regulatory interventions
We have a structured data and intelligence model and framework to define and give insight into institution-level and specific risks. The framework defines how we apply the model, including what actions to take based on data and intelligence.
Data
In this section we explore:
- The data points that we use to inform our judgements
- The methodology for inclusion of data points
- Benchmarking
We consider the following data points, which are linked to a range of performance areas:
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Performance area |
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Data point / comparison |
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Reason for inclusion |
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Resourcing |
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Total intended learner numbers compared to total enrolment numbers |
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Conclusions can be drawn from what is intended and what is happening, including popularity of the institution, and ongoing financial viability |
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Performance indicator |
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Aggregation of percentage not continuing |
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Established performance indicators used by Higher Education Statistics Agency |
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Performance indicator |
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Aggregation of percentage in employment / further study |
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Established performance indicators used by Higher Education Statistics Agency |
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| Learner satisfaction |
Aggregation of National Student Survey results across seven themes |
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Nationally recognised results for learners on undergraduate programmes |
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We have produced a briefing document which explains our use of data in more detail, including how we arrive at data points we use, limitations, and future developments.
Benchmarking
Benchmarking allows significant differences in performance to be highlighted, whilst considering that certain learner characteristics can impact on data points. We use benchmarking from relevant organisations as a comparison point when considering data.
Four country context
Four country context is not built into HESA or NSS data benchmarking. We aim to understand the four country context in a qualitative way, and use this understanding when considering how data points and performance scoring contribute to risk.
Application
The institution performance data is used as part of the evidence through assessment activities:
- We will ask providers to reflect on data points through our performance review process
- We consider this information internally, and use it to come to judgements about how processes should be applied
- We will use the information and our judgements to frame assessments for visitors
We will also consider the specifics of the provider, model of learning, and country context in decision-making, to ensure we are giving appropriate weight to data points.