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UPDATE: POLAR whitewash fails to cover all surfaces.

The debate about how best to measure widening access to our universities is heating up. This is in the period leading to the impending deadline for the consultation on university data requirements by HESA. The hastily arranged consultation has been extended and now closes on the 3rd May 2019. It follows the suspension of the Data Futures programme for at least a year due to pressure from the universities.  The consultation itself looks like a further attempt to pre-empt the deliberations of the delayed Augar ‘Review of Post-18 Education and Funding’ whose call for evidence closed a year ago. Gathering the right data is at the core of assessing widening access to our universities. It must not be covered in more ‘whitewash’.

The recent debate on the relevance of using POLAR methodology to gauge the success of widening access
to our universities has reached a crescendo on social media. Rather than lead, the government is reacting and trying to wriggle out of the situation. But the reaction by the Minister of State for Universities, Science, Research and Innovation, Chris Skidmore on social media in the last few days shows for the first time a realisation that things must change. In a reply to the BBC education correspondent, Sean Coughlan on Twitter he says “Effective reform to improving access and participation in HE can only take place if we are prepared to look again at how we can best reach out to those groups still not accessing HE- we need the most relevant and accurate data in order to identify where further reform needed”. Indeed, better meaningful data and fewer misleading statistics are needed.

It all ‘kicked off’ with links to a new report from the 'Durham University Evidence Centre for Education' (DECE) called ‘Using contextualised admissions to widen access to higher education: a guide to the evidence base’ and discussed by TEFS on 19th April 2019 in ‘Grade inflation and contextualised admissions to university are stirring up a wasp’s nest.’ Within the DECE paper lay a tripwire that set off a total demolition of the use of POLAR methodology. They simply compared data from those on free school meals (FSM) with the POLAR areas that they came from. The conclusion is that FSM students are distributed across the various POLAR areas and that POLAR is not an appropriate proxy for identifying disadvantage. Those that equate a post-code with disadvantage in university access are deliberately misleading us.

What is POLAR.

POLAR stands for 'Participation of Local Areas' in university access. It is an invention of the Higher Education Statistical Agency (HESA) and the Office for Students (OfS) and is defined as “The POLAR classification places local areas into five quintiles, based on the higher education participation rates of 18 year olds in the locality” (see also TEFS 6th April 2018 ‘Flying over the UK on a POLAR expedition. The distant cracks in university access are widening’).  However, this is a grandiose name for a company owned by the universities themselves to gather data for them. The other key source of data is UCAS that is also owned by the universities as discussed last year by TEFS 20th April 2018 with ‘The Social Mobility agenda in the UK - Who counts the beans?’.

Although conforming to the Office for National Statistics (ONS) requirements and regulated by the UK Statistics Authority (see ‘Exploring the public value of statistics about post-16 education and skills in England’),
HESA is not governed by the ONS and is not subject to freedom of information. There is an inherent conflict in the role of HESA, that describes itself as “the trusted source of HE data and analysis, we play a key role in supporting and enhancing the competitive strength of the sector”, and the need for gathering data for the general societal good.

The idea behind POLAR is simple. All students are classified according to their post-code into five different geographical types, or ‘quintiles’,
based upon rates of university participation. Few from Quintile 1 areas go to university, whereas the vast majority from Quintile 5 areas do attend. The advantage for universities is simply that they might only have to collect enough ‘post-codes’ in their database to reach pre-set widening access targets. The flawed assumption made by many is that POLAR is a sound proxy for disadvantage. It isn’t and it is now exposed as something that actually does what it says on the tin; ‘whitewash’.

It’s the individual that counts

Anyone close enough to the educational front in universities will know that, when a student arrives with problems in their office or lab, they present themselves as an ‘individual’. Those gathering data sometimes seem to forget this simple fact. Disadvantage affects individuals not post-codes in this respect. Thus, as a scientist, it seems obvious that in measuring widening access, the data must be collated from individual inputs. Solving a need for widening access must be applied equally to individuals and their circumstances if it is to succeed. Therefore, a radical rethink is needed.

The ‘whitewash’ is corrosive and is burning the fingers of universities.

Many observers this week will be forgiven for thinking that government announcements about ‘success’ in widening access to university is illusory. They might be right because it is based upon marginal improvements in numbers from POLAR Quintile 1 areas. This is against the backdrop of greater student numbers overall from the more advantaged areas. They make up the bulk of the increase in student numbers in recent years; whatever way you look at the figures.

With the smoke from Augar on the horizon, and more debate about the usefulness of data on widening access, many universities may now regret not taking a more proactive approach to gathering widening access data on their students as individuals. The use of POLAR access data to cover their failings and create an illusion of success will surely burn their fingers.


Mike Larkin, retired from Queen's University Belfast after 37 years teaching Microbiology, Biochemistry and Genetics.

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