Universities mine institutional data in search of gold

Analysis of information on staff and students can help to improve recruitment and retention

February 28, 2013

Source: Getty

Scanning for gold: universities are discovering that much of the big data collected for outside bodies can be used in their own service for recruitment and retention

Universities risk losing their competitive edge if they do not make better use of the information they are collecting from students and lecturers, experts have warned.

Institutions automatically collect data on how students and staff interact with campus services such as the library or the finance department. By analysing these “big data” sets, they can uncover patterns that might help them to improve their performance.

Universities also hold huge swathes of information on file in the form of Higher Education Funding Council for England Key Information Sets and Higher Education Achievement Reports, as well as data collected for both the Higher Education Statistics Agency and the Universities and Colleges Admissions Service, all of which can be used to improve performance.

Myles Danson, programme manager for technology-supported business change at the UK’s higher education IT consortium Jisc, said that big data were being used by some universities to give them an edge when targeting students or planning how to allocate resources more effectively.

“Universities are collecting stacks and stacks of information that could be used in different ways to that for which it was first intended,” he said. “As universities aspire to operate more like businesses they are picking up on business techniques, and businesses have been using analytics to gain a competitive edge. We should be seeing that in the university sector.”

At the University of East London, information improvement manager Gary Tindell is using big data sets to see how his institution compares with others in terms of performance, staff salaries, recruitment and retention rates. The university also uses the data to assess the potential market for courses it is considering developing.

“We’ve built a student life cycle application, which takes all the student data off our student record system and monitors how admissions are going and how enrolments are going,” Mr Tindell explained.

“We’ve also built a benchmarking application, and what we’ve done is taken a lot of the large data sets from Hesa and we compare ourselves on a whole range of stuff - whether that be awards, student numbers, staff-student ratios. We can compare ourselves with other institutions…and use that data to position ourselves.”

Local knowledge

Analysing the data allows UEL to see how many local residents in East London are going on to higher education, where they are enrolling and what they are studying.

The data also register if another university is recruiting students from the area to courses similar to those on offer at UEL. This information allows the recruitment team to look at ways to try to win them back. “It gives us a competitive advantage in recruitment,” Mr Tindell said.

According to Sheila MacNeill, assistant director at the Jisc Centre for Educational Technology and Interoperability Standards, many universities lack the staff who can make best use of big data sets.

“There is quite a large skills issue,” she said. “Many universities have business analysts - that’s quite a common role - but not these ‘data scientists’: people who can look at data from different sources, then work with other staff within institutions to develop questions and insights. There are some technologies that [universities] need to invest in, but it’s critical that staff development time is put in there as well.”

At the University of Derby, student experience project manager Jean Mutton has helped to introduce a student monitoring system that uses data to predict which students might be at risk of dropping out, allowing the university to intervene before it happens.

Her team examines factors such as how often students access mentoring and counselling services, and their interaction with student societies. By using these “engagement analytics” they can assess whether a particular student appears to be struggling.

“We weren’t just looking at what we call hard analytics - the stuff we capture like footfall in the library, or access to the virtual learning environment - but also softer indicators, like not only are they handing work in, but are they picking it up again? Are they receiving and acting on feedback? Some of this data is quite difficult to pin down and get hold of.”

Ms Mutton and her team realised that Derby was not exploiting fully information in its possession such as National Student Survey data, details on students’ achievement and feedback from student meeting groups.

“We asked ourselves, ‘Can we capture this information, do we capture it already, would it be easy to start capturing it, or is it not capturable at all?’ Then we talked to our lecturers and asked what would help them in their pastoral, personal tutor role. They came up with a range of factors they would like to know that would help them to enrich the dialogue they have when meeting students one on one.”

Traffic calming measures

Although some information could not be shared for reasons of confidentiality, the university has developed a data set that identifies students at risk of leaving their course. This “student experience traffic lighting”, the development of which was funded by Jisc, is helping Derby to improve student retention, progression and completion.

“It is also identifying students who aren’t fulfilling their full potential - perhaps they are on the cusp between one classification and another - or people who did well in one academic year [but] are not doing so well in the next one.”

Although there are beacons of best practice, there are still fundamental problems that need to be addressed if more universities are to start exploiting the data sets that they possess.

“Often it’s how you provide the data to foster analytical skills,” Mr Tindell said. “Universities aren’t terribly good at that. Often they won’t have the right people looking at the data. They’ll have people who don’t have a science background and…just aren’t that analytical.”

Another problem that needs to be tackled in many universities is a tendency to work in silos, Ms Mutton added.

“Even the hard analytics, such as who was swiping in to [visit] the library, was held across seven different systems that didn’t talk to each other. It’s about joining the dots about what we know about students, and how they are progressing with university life.”

chris.parr@tsleducation.com

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