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Using data and intelligence

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 the provider) 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: 

Performance area

 

Data point / comparison 

 

Reason for inclusion 

 

Resourcing / institutional appeal

 

Total intended learner numbers compared to total enrolment numbers 

 

Conclusions can be drawn from what is intended and what is happening, including popularity of the institution, and ongoing financial viability 

 

Performance indicator 

 

Aggregation of percentage not continuing 

 

Established performance indicators used by HESA

 

Performance indicator

 

Aggregation of percentage in employment / further study 

 

Established performance indicators used by HESA 

 

Teaching quality 

 

HCPC performance review cycle length 

 

Internal judgement of the riskiness of an institution, based on detailed assessment of the institution in previous AEPM 

 

Performance indicator

 

Teaching Excellence Framework (TEF) award

 

Externally produced and verified data which gives insight into the teaching quality at the institution

 

Learner / graduate satisfaction  

National Student Survey (NSS) overall satisfaction score (Q27) 

 

High level satisfaction rating of institutions, comparable against benchmark

 

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.

 

Page updated on: 06/09/2021
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