Lots of data about HE in England!

This one is for the wonks.

HEFCE has published a set of interactive charts on its website on Higher Education Provision in England.

According to the HEFCE site, Professor Madeleine Atkins, HEFCE Chief Executive, said:

‘As the Government seeks to ensure that economic recovery and growth is more evenly shared across different localities and industry sectors, universities and colleges continue to play a critical role in supplying a highly educated and skilled workforce, providing opportunities for individuals while meeting the needs of the economy and society.

‘The data shows us that the issues associated with HE cold spots can often be complex. Higher education providers, working collaboratively with their local enterprise partnerships, will be able to use this powerful new toolkit to establish a detailed picture of HE in their localities, enabling them to identify any gaps in provision, participation and the supply of graduates. This provides a strong evidence base to explore potential solutions for delivering local economic recovery and growth.

‘Universities and colleges play a key role as economic and social ‘anchors’ in their local and wider communities. Working with local partners in this way to reach a joint understanding of the issues that affect them collectively, they can make an important contribution to the ongoing development of Strategic Economic Plans, and also, of course, to decisions about where and when to invest different forms of funding.’

The maps show that there appear to be HE “cold spots” in: border areas between England and Wales; along the Cumbrian coast; Humberside and North Yorkshire; from Kent to the Wash, and the south-west.

Looking at the interactive maps in detail we can see that for the West Midlands students traveled an average of 41 miles from home to their institution, and 30 miles from home to graduate employment. Only students in London traveled less from home to work.

71% of graduates whose home was the West Midlands ended up working in this region (only the North East and North West have a higher number of graduates returning to work in their home region. However, when looking at where graduates who studied in the West Midlands ended up working, then only 57% were retained in the region.

Looking at subject level data, it’s notable that in terms of student numbers, Staffordshire punches above its weight in terms of the numbers of STEM students (I’ve excluded Birkbeck from this sheet just so I could get a screengrab), but there are few unis above us in terms of numbers enrolled.hefceparticipation1


We’re also pretty big in terms of new entrants to technology awards too.


So, in summary, here’s a resource from HEFCE that brings together data that is available elsewhere, for instance HEIDI and  DLHE,  but possibly less easy to visualise. Using the website and the downloadable app for graphing means that some quick comparisons can be created to show how, for instance, relative strengths of subject areas.





Westminster Forum – Higher Education Data Landscape

I recently attended this event in London, which provide some great speakers, and useful networking opportunities, as well as showing what others are doing with HE data, and where we might want to do more. These notes taken at the event provide an insight for colleagues; I also have the slides from the presentations for those who want to look in more detail.

The event was opened by Sir Tim Wilson, former VC of University of Hertfordshire, who referenced the 2011 white paper which had asked to lessen the burden on information provision, but noted the level of complexity and diversity due to different providers in a more heterogeneous sector

Paul Greatrix (Registrar at University of Nottingham)

Paul introduced the ideas behind redesigning information landscape. He raised his concern about regulatory landscape also and government requirements. He identified that provision of more information does not necessarily mean better decision making

Scale of challenge for HEIs was the need to respond to 550 different external reporting requirements in addition to any internal reporting

In reference to league table providers, Dr Greatrix identified plenty if objections, but HEIs care because they have  impact on potential students and the wider public, even though league table results can lead to perverse behaviours such as VCs and senior managers focusing on the wrong things.

In conclusion, he identified an uncertain future but with grounds for optimism. The fundamental issues were around regulation and the need for proper data, organised in the right way. It was not that there is not too little information re HE but that it needs to be underpinned with proper IAG for those with no previous family HE participation.

Malcolm Scott ( BIS Digital economy directorate)

Everyone is talking about data but there is nothing new about big data.  The reason we a etalking about if now is due to the actual volume of data, massive increase in volume, growth of technology to sort data and bring it together, and the ability to get value out of it. Data can provide value to existing and new industries.


Government t has designated big data as one of 8 great technologies and has Looked at skills, infrastructure and hygiene factors that must be right to be able to exploit data. Major investments have already been made such as the Turing Institute and large km array telescope

Importantly he raised this issue: How do we get managers to realise data can make org better?

This can be alternatively expressed as the organisation will lose advantage of it doesn’t use data properly.

He suggested that on the supply side there is shortage of big data skills, expecting 13-23% rise in demand for big data staff in UK by 2017, noting that the people needed are not just computer scientists

Johnny Rich (push.co.uk)

Johnny pointed out that the information landscape for students is confusing and that Students don’t know what they need to know, Eg what’s it really like to study x at y?
As students go through jungle they will latch on to things that they recognise, eg league tables, names of courses. These may not actually be the useful things, as they will only spend a maximum of 1/3 of their time studying

He proposed that many information requirements are an unnecessary burden but a necessary evil. Sometimes data or information provided can be a part of marketing, but not what we want to know about- eg traffic light health guidelines on a sandwich wrapper. Regulation and the manufacturer might want this, but the customer isn’t likely to make a purchase decision on this. Is KIS is like this?

He proposed that KIS…

  • ignores the information needs of the disenfranchised
  • only tells them what they think they want to know
  • is better than nothing

and thta the NSS was about:

  • Satisfaction is not quality
  • Enhancement not choice

The NSS  however indirectly affects choice as it feeds into league tables.

He proposed a Marketing 101 approach – find your point of difference eg shampoo adverts, and start from there.

Graeme Wise (NUS assistant director policy)

Graeme introduced the policy context in which HE data is used and alignment of interests between data provides, collectors and users.

He was anticipating research on financial outcomes of HE from different institutions, using combined data from SLC, ,tax records as these linked data sets will provide model of earnings and outcomes from different unis and subjects which would be a driver for further marketisation
Looking at other public policy agenda- public sector data and fashion for analytics- he proposed the provocative thought of industries moving from production to service (eg software was previously a product, now a subscription service, eg Microsoft, media industries) and will education become that kind of industry, with a move from students as consumers to students as producers.
He looked at the ecology of data collection that spans student life cycle with the consolidation of data at output end, whereas an area for most attention is on the daily experience of students.
Fashion for analytics is not a passing fad and this is a challenge to sector – it is possible to get itvery wrongly not being there, or by doing it badly..
He proposed that in ideal world, everything collected for external purposes should be available for internal purposes, and need people with insight and experience as well as analysis skills to apply to real world student experience issues, eg retention, learning space utilisation, curricula. He suggested we involve student representatives to legitimise and shape the work


Phil Richards (JISC)

Phil had 2 main messages – Overcoming barriers to sharing, and making data a priority for senior management

The barriers to sharing were proposed under three headings: Co opetiton, Compulsion and Coherence.

Making data a priority for senior management came under 4 Rs:

  • Reputation– league tables, Unistats, research metrics, Which? Guide,
  • Recruitment and retention-this is an area for  investment decision for software- for example the ability to identify at risk students either before they arrive or how they behave when on campus
  • Risk mitigation – in a new HE paradigm we need detailed scenario planning (however who had considered removal of SNC in last year’s planning?)

Andy Youell (Director HEDIIP)

Andy started by considering the future of data and information, noting that many organisations were not designed to keep up with technology meaning that ad hoc solutions emerge, frequently in silos, which provide a sort term result only.

He asked- What is “here” like? There are Over 500 data collections which lead to duplication, inconsistency, lack of data sharing, lack of comparability across collection eg in sometjign simple such as the definitional difference between a course and a programme

Also highlighted were data management and governance issues,,eg security, quality, accessibility. There is often low awareness of where data is held in institution, low awareness of where is being supplied from and to whom.

The HEDIIP vision was one of new systems that reduce burden for data providers and improve quality, timeliness and accessibility of data and info about HE

The benefits are to: reduce the cost of data (duplication, inefficiencies); increase value of data (analytical capability, quality and timelines linking using standard identifiers), and improve information (clarity).

John Gledhill (Tribal – supplier of SITS)

John pointed out that we tend to sum up 3-4 yrs of education in snapshot data and that student data collection is low resolution and low frame rate currently. In future we might need to capture data that we think might be worthless, for instance working in areas of unstructured data eg Facebook, Twitter, RSS, as well as structured databases and file systems.

Steve Egan (HEFCE)

Steve talked of the need for accurate data definitions to protect those who want to play the game properly, but questioned how we can produce timely data, eg HESA? For example, for widening participation, the  data is 2 years out of date, and this has implication for funding.

Since students make decisions on range of information some of which is influenced by data, then they need to be able to trust the data, for example claims for employability, noting the weaknesses of DLHE data.

Government also needs good data to be able to identify what is happening with part time students, SIV subjects and accountability. Better and more timely information will lead to better decision making

Summary by Sir Tim Wilson

Are we using external data internally?
Is data collection and analysis a cost or an investment?
Have to change because if we don’t it won’t get better. Some people enjoy being victims and complaining. Has to change to make things better for students and all stakeholders
Willingness to move to a common good, which is not the same as uniformity.
We have the power and knowledge to do data analysis which needs transformational leadership, vision and innovation.

The Importance of Maths

A new publication from the Higher Education Academy this week looks at the
needs of students for an understanding of mathematics and statistics when undertaking undergraduate studies in various disciplines.

The discipline areas studied were: Business and Management, Chemistry, Computing, Economics, Geography, Sociology and Psychology. The  suggestion in the report is that many of the discipline specific recommendations are transferable to other disciplines. Notably physics and engineering are not included, as we expect students in these areas to be highly numerate.

One of the key findings is that:

“Many students arrive at university with unrealistic expectations of the
mathematical and statistical demands of their subjects. Lack of confidence and
anxiety about Mathematics/Statistics are problems for many students.”

This is worrying if students are not aware of the importance of number in their studies. As a nation we are always happy to belittle the more numerate as “geeks” whereas an ability to write in (supposedly) perfect English is seen as a strength. This is dangerous thinking – being able to make proper inferences from numbers and data is a critical skill in so many roles.

The key recommendations are:

1. There should be clear signalling to the pre-university sector about the nature  and extent of mathematical and statistical knowledge and skills needed in undergraduate degree programmes.
2. As part of this signalling university tutors should consider recommending the  benefits of continuing with mathematical/statistical study beyond the age of 16.
They should be aware of the full range of post-16 Mathematics qualifications, in  particular the new “Core Maths” qualification.
3. Guidance documentation should be commissioned to provide university staff with a description of the range of knowledge and skills that students with
GCSE Mathematics at different grades can be expected to demonstrate when they start their undergraduate studies.
4. Key stakeholders within the disciplines should actively engage with current and future developments of discipline A-levels as well as those in post-16 Mathematics qualifications, (e.g. “Core Maths”).
5. University staff should consider the benefits of diagnostic testing of students’  mathematical and statistical knowledge and skills at the start of degree
programmes, and of using the results to inform feedback and other follow-up
6. Teaching staff should be made aware of the additional support in Mathematics  and Statistics that is available to students. Students should be actively  encouraged to make use of these resources and opportunities.


It’s important that we get to understand better how students transition into HE. For instance, according to the report only 13% of entrants to Psychology for example had an A-level in maths prior to entry in 2013. This will have an inevitable effect on those students’ ability to engage with any statistical tools or numerate analysis.

As well as considering the transition into HE, we should also consider how students with limited maths ability might struggle throughout their awards across a range of subjects and modules – one of the reasons that might explain low numbers of students gaining good degrees in some subjects may be their inability to engage fully with numerate modules or topics.


(from http://www.math.hope.edu/newsletter/2006-07/05-09.html)

The lack of numeracy does go further than students engaged in undergraduate study. This is a bit of a hobby horse of mine, but in years gone by, we created lists and mappings of skills that we expected to be attained by students. Communication was always included, maths never was. In our latest iteration of graduate attributes, again we recognise professionalism, team working and global citizenship, but still don’t give prominence to mathematical ability.

Long term this is concerning both for the individual and for employers – we (and other universities) might produce graduates who enter the workplace with only the flimsiest ideas of how to use number, and sometimes a too-trusting reliance on what Excel and other spreadsheets can produce.

For example, when I read a report that shows a log scale graph which the authors then claim demonstrates a linear relationship,  I worry about the kind of decision-making that will be made when the base data is presented in such a skewed way.

Maybe we could look to ensure that one ways in which we differentiate Staffordshire graduates is that in future they are more numerate and data literate.

We Can be Better Than This. Part 3

I keep returning to this theme, but since I have a role in academic enhancement, and specifically to look at ways of improving the attainment of individual students, with the resultant impact this should have on institutional success, then it’s really key for me.

Firstly, we now moving into the league table season (ignoring the THE World Rankings, as like most chippy northerners in million+  we don’t trouble them too much). The data is all in and is being counted and manipulated by the various compilers. Looking at some of the data through Heidi, then some of the work we did last year seems to be paying dividends. The next step really would be to be able to model all of the parameters used in the various tables, and develop some predictive tools, which will allow us to target specific areas, either academic subjects, or aspects of finance or staffing..

Secondly, we need to reinforce this message of student attainment and institutional success across our institution. I’ve given talks and presentations  on league tables and student attainment to 10 out of our 12 schools, where we look at how the input of academic practice impacts on league table performance,.I always ask where in a league table do we think we should be. The answer has never been lower than 70th. This is a huge strength we can tap into – we have a university plan that clearly states our ambition in this area, and we have huge numbers of staff who believe we can be there! We should not underestimate what a powerful engine for change this can be, if harnessed properly. To help reinforce the message, then this years Staff Fest Learning and Teaching Conference is on 1st July, and is all about student success. This is an area that everyone should be engaged in. A success for me will be if there are too many attendees for us to fit into the lecture theatre – take that as a challenge!

Thirdly, we could look again at some of the messages Gordon Tredgold proposed in his recent leadership workshop on FAST (focus, accountable, simple, transparent). Linking this to work on improving student and institutional success  means cutting through complex action plans, strategies, pilots projects etc and making a simple statement – “we want to be a top 50 university”, and then making sure our actions all relate to that, for instance:

  • focus on student success to improve degrees outcomes and help individual students to attain their goals;
  • recruit the best students possible – this might be a virtuous circle if we move up a league table
  • improve employability of our students mainly by making sure they get good degrees and ensuring our graduate attributes have a real impact
  • make sure we make favourable data returns
  • ensure we investigate and provide remedies for the outliers in the data (as always there are some)
  • develop an aspirational portfolio of undergraduate and postgraduate awards – top universities teach certain subjects
  • use portfolio perform ace measures to decide the shape of the portfolio, not just market information

We’re at an interesting time – the changing rules on student number controls, possible future changes in fee caps, consolidation of our campuses, changes in technology and estate redevelopments mean that now should be the time to have a clear focus and simple target.


After all, as Lou Reed sang, you’re going to reap just what you sow.

Digifest 14 – Keynotes

This year, JISC ran its annual event as a festival, rather than as a conventional conference, which meant lots of great branding and ideas that we could translate into some of our events in Staff Fest. This blog piece picks out some of the highlights from the main keynote sessions of Digifest 14.


Introducing day 1 was  Martin Harrow (Chief exec Jisc) who introduced us to exploiting potential of technology. Crucially, Jisc has been re-organised and is now more able to deliver its role better and at lower cost, recognising that in a fast moving digital world, the digital future is bigger than the digital past.

Diana Oblinger of Educause

The first keynote was by Diana Oblinger of Educause, and the talk can be seen here.

Why are we still talking about digital?

Why is digital a bolt-on rather than designed digital?

What would a true digital experience be for a student?

How do we translate from commercial space into education?

Being digital is not just about a PVC with an iPad instead of a piece of paper. (this is a quote, I didn’t make this up!)

Digital changes the nature of work, changes our society and is about man and machine working together.


Demographics drive new consumption patterns- in USA 82% students combine college and work. Students now come from non-traditional minorities or are first generation and unprepared for college, or have significant financial needs.

The changes lead to change in what we need from education.

There are 3 areas where we can add expertise

  • Engagement
  • Empowerment
  • Alternative models

 Engagement. Leads to more learning. Most powerful if face to face, but what if we used the best that technology can offer? Could be immersive and collaborative.

Higher order learning comes from complex challenges. Eg gamification. And students seek to want more of this!

Practice helps develop expertise and also generates data. With massive amounts of data patterns emerge that could lead to personalisation and adaptive learning systems

Empowerment. Lots of information is available but how do we empower students who don’t have this already in their heads. Must remember all students are different.

internal bars

external bars

Students need help with complex lives. Could developsStudent success plan with counseling and intervention software by drawing  all data together and creating early alert programs. Students are often unaware that their success is at risk, which could justify intrusive advising using predictive analytics and intervention

Too much choice can be enemy of student success. Students might choose courses they are not prepared for. we could use software to provide better informed module choices based on individual prior results etc and providing clearer pathways to graduation. This may be more suited to the North American system where a student may choose a major after 2 years of study.

Alternative models – Education is a tightly interconnected interdependent system. Eg market, mission and margin and changing any one will impact on the others. We live in a course rich world eg MOOCs. Credentialing MOOCs will change their the value proposition. Customers who are over-served may seek a  proposition that reflects their needs,  ie they are not after the same experience as before. In the US there is increasing assessment of  about competences now instead of credit hours, however IT systems are not set up to deal with this. Time is an opportunity cost for students which may drive changes to delivery systems. But if a student never goes to campus, how do you provide student support?

This talk finished with three questions:

  • What will it take to exceed expectations in digital world?
  • Do we have capabilities required to deliver value from IT?
  • How can we optimize education for a digital future?

Not unlike a TED talk, but a great start.

Paul Curran of City University London

The VC of City talked about aligning university and IT strategy. The issues at City are similar to many others.

Staff and students needs and competences are changing.

City are aspiring of be in top 2% unis in world and so are investing in people, IT and estate. They have decided that some IT to be sector leading and some sector average.

Previously had a devolved cottage industry approach to IT so now had to align IT with strategic plan so that it was more responsive to student needs. The university bought into Moodle, Office365 and Sharepoint. These became core products

They are now spending less on IT with fewer staff but with more junior staff who can relate to students. At the same time they enhanced the skills of IT support staff.

In 2013-14 introduced a uni wide module evaluation system to collect student feedback and provide management reports.

Have provided Easy access to student records for staff, including student performance and metrics. All solutions are scalable to operate on different devices.

Challenges for staff were identified – for academic staff this was around developing ability to move between digital and real world. For  IT staff it was about relationship management, system integration and training.


Two very different keynote addresses, ranging from the inspirational TED style approach of asking lots of challenging questions, and the more prosaic, but hugely important explanation of why IT strategy needs to be aligned with overall strategy.

The message for me is this:

Our future is digital. It will be central to everything that we do, and the winners will be those who understand the changing needs and nature of students, and who can design their systems and change the skill base of their staff to respond. Putting this at the heart of a business is key.



All Watched Over by Machines of Loving Grace

This article tries to draw some distinctions between using management data or business intelligence, and the use of “big data”, with some caveats about the latter and the possible blind trust in numbers from the less-than-numerate.


Last week I went to a demonstration of a piece of a piece of software to help with student retention. There are some great things that the tool allows – integration with student information systems (including SITS), access to VLE analytics; the ability for any member of staff to flag a concern about a student. In addition to that however, the system looks at the last three years worth of retention data, looking at who withdraws and why and then  predicting correlations (if not causality).

So far, so good. I’m a big fan of exploiting data that we have available to us, to allow us to perform more effectively and successfully.

For example, looking at national data, we can identify how well we perform as an institution compared with others, either overall, or in individual subject areas. From this we could identify how successful we are in recruitment, or in degree outcomes

At a more granular level, ,we can look internally at portfolio performance information, to see how academic awards perform overall compared to each other – how overall retention rates or good degree outcomes compare between subjects. At a lower level of granularity, we look at the marks achieved on individual modules, their distribution, and how they compare to each other.

All of this provides simple and useful management information (or at the least granular level, business intelligence) which can help us to improve what we deliver, and improve the outcomes for our students.

What it does not do is provide a “big data” approach to education.

With enhanced student information, linked to personal tutoring or coaching we could start to look at how we could support individuals better, to identify their likely outcomes and to support them in achieving them. This is still a management information approach.

Going to eh next stage though, of profiling students, based on their various individual characteristics is where the water starts to be muddied.

We cloud provide information to tutors on information such as: entry qualifications; attendance; engagement with the VLE and marks obtained. In addition we also hold information on age, ethnicity, gender, socio-economic class, first generation HE, distance from home and many others. Individual staff may not be able to make any inferences from this themselves, but an algorithmic approach could.

Considering retention, the big data approach would look at all of this, and provide algorithms to identify a risk factor for students withdrawing. It could use a traffic light system – red, amber and green, with those scoring red as being most likely to withdraw.

Kate Crawford of MIT and writing a blog for the Harvard Business Review says:

But can big data really deliver on that promise? Can numbers actually speak for themselves?

Sadly, they can’t. Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves.

Depending on how the algorithm has been decided, we would then decide where to focus our interventions. Assuming that there will always be withdrawals, maybe we would’t intervene in studnets flagged as red, a their probability of withdrawing is high?

We’d need to look behind the algorithm, These are not as agnostic as the purveyors of technology might have us believe. If we found that students with BTEC entry qualifications were more likely to withdraw, we might flag them as a concern. However, we also know that students of a BME background are more likely to have a BTEC qualification. Our  algorithm might now have produced an unintended consequence of flagging these students as a high risk of withdrawal, and our policy might possibly even limit the interventions we might use.

If we adopt a big data approach, just to this simple aspect of HE, further questions arise for me:

  1. What information do you share with teaching staff – do they see the colour coding?
  2. What do you share with students – do they know how they have been categorised?
  3. How easy is it to change categorisation?

The HE sector has plenty of data to use, some of it could be treated as “big data”, and although  it might be useful to identify some correlations, unless we include human agency in our decisions then we cede control to a series of computer algorithms. We have to be prepared or able to challenge the outputs, and must not naively trust any set of numbers we are presented with.

I’ll finish with a couple of quotes from David Kernohan of JISC:

After all, if big data can reduce every problem to a bar chart, you don’t need people to choose the option that the machine tells you will make the numbers go up. – See more at: http://followersoftheapocalyp.se/9-things-to-watch-in-2014/#sthash.fvbekXiF.dpuf


those of us who wish to continue being knowledge workers need to start making sense of data (and for that matter finance, but that’s maybe another story). If every policy position is “justified” by a slew of numbers, we need more people that can make sense of these numbers. Maths – naturally – is hard and we’d all rather be shopping or watching cat videos. But if we want to understand the decisions that affect the world around us, we need to learn to read numbers and to be confident in disputing them. Policy is now quantitative – we need to get better at teaching people how to participate. – See more at: http://followersoftheapocalyp.se/9-things-to-watch-in-2014/#sthash.fvbekXiF.dpuf

My title, by the way, comes from a poem by Ricahrd Brautigan, and was used as the title of a series of BBC documentaries in 2011.



Some HESA Statistics

One of the joys of working in an organisation that is public sector (actually that’s a definitional can of worms that I’m not going to open in this post) is that plenty of data exists which is publicly available for anyone to read, both about the sector, and about individual institutions. Even without creating your own detailed reports, a quick view of the sector can be gained from the statistics that appear on the front page of the HESA (Higher Education Statistics Agency) website and in their iPhone app (yes, really!). a simple cut and paste into Excel and we can produce some simple headlines and compare individual institutions to either the overall sector to to a range of comparators or competitors.

Anyway, the most recent student and staff records have been added for the 2012-13 academic year, and we could use these to identify how we compare, and with a little bit of work, how we might perform in the next round of league tables.

Student Population

Over the last 5 years we’ve seen dramatic changes to the funding of HE, the shift in burden of fees and loans to the individual rather than the state, and changes to visas for overseas students.

The overall population has changed as follows:

sector student pop

showing a slight dip in the last year, although UCAS data would imply this may rise this year.

For us we see a decline in numbers overall:

su student pop

Looking in more detail at the undergraduate population for the sector as a whole:

sector student mode

We see that full tine numbers have an overall upward trend, but as has been highlighted many times by the sector’s mission groups, part time numbers are decreasing, and the policies for funding part time students have not been updated or considered to the same degree as those for full time students. There is untapped human potential here – and untapped markets for the universities that can get the right kind of part time offer.

For us the picture is not dissimilar, although we had previously significant growth in part time which was not replicated across the sector

su student mode

Academic Staffing

If we look at the staffing in higher education, across the sector the main change is the decrease in the number of non academic roles compared to academic. The overall number of academic posts has grown, with little change in number of part time roles.

sector staff popsector staff mode


Looking at our own institution, we see a similar trend in the balance of academic and non academic roles, but a noticeable difference in the balance between part time and full time academic posts.

su staff popsu staff mode


In conclusion, we can get an overview of any institution from this very publicly available data. To make more sense of it, and to go behind the headlines and reach a deeper meaning involves looking at what sits beyond the front page of the HESA website.

Those of us with HEIDI accounts can easily start to look in detail at all sorts of things – how subject areas are growing or declining compared with other universities or the sector as a whole; how well students achieve in different institutions; how well subject areas in individual institution compare in recruiting students locally, nationally or internationally.

I’m already linking this kind of information to very detailed internal portfolio performance data (which would only be available internal to an organisation since it contains materials protected under the Data Protection Act) for one of our faculties to develop some sophisticated pointers of how to develop their academic award portfolio.