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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. 


In this section we explore: 

  • The data points that we use to inform our institutional performance scoring
  • The methodology for inclusion of data points 
  • Benchmarking
  • Institution performance scoring – data points used

We arrive at institution level performance scores which we use as part of evidence and information when considering ongoing approval. We consider the following data points, which are linked to a range of performance areas: 

Performance area


Data point / comparison 


Reason for inclusion 


Weighting for performance scoring

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



Performance scores can range from 0 to 1. Each data point is weighted based on the ‘weightings’ column, before being subtracted from the performance score (which starts at 1). Weightings were arrived at through the pilot of the Education quality assurance model, which took place in the 2020-21 academic year, and are based on impact of underperforming areas on overall institution risk.

If data points are greater than the benchmark, the overall performance score is not positively impacted. This is so overperforming areas do not compensate for underperforming ones, hiding potential problems. 

A performance score of 1 means an institution is performing well. This score can be reached when each score with an available benchmark is at or above the benchmark, the TEF award is Gold, and the HCPC AEPM cycle length is at the maximum. 

This model has been tested using training data, using existing internal data, publicly available data sources, and information from providers. 


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. 


The institution performance score 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 the specific data points or overall performance scores. Some institutions are by nature more risky, which may reasonably mean that a high performance score cannot be achieved. 


Page updated on: 06/09/2021