Transforming Higher Education with Analytics


The ability to adapt to change is an ability that higher education institutions will need now more than ever before. The good news is that much of what you need to help manage the challenges ahead may already be at your fingertips.    

Analytics has the potential to turn your data into your most valuable institutional asset. From gauging student wellbeing, to making the best use of your resources – innovations in analytics are enhancing the student experience and transforming universities for the better.

Here’s a closer look at how analytics can help you put your data to work.

Building a stronger student experience

Are we delivering genuine value for money? Can we provide the type of experience that our students demand? In an age of £9k annual fees and in a competitive global environment, these questions are vital.

Student satisfaction is quickly becoming something that no university can afford to ignore. It affects everything from reputation, to the type of talent that they attract, and can have a huge impact on student drop out rates.

One big problem with relying on surveys to understand student satisfaction is that they only focus on events that have already happened. The course content wasn’t as practically-focused as the student expected, or the facilities failed to meet the student’s needs. Simply put, the damage has already been done.

Learning analytics offers universities a more proactive approach to troubleshooting. From swipe cards and log-ins, through to information on how course content is being consumed, students create a huge data footprint as they interact with their environment.

Learning analytics involves collecting and analysing this data. So rather than waiting for the formal feedback to arrive and then scrambling to fix problems, universities can take a much more proactive approach. Behaviour can be analysed and interpreted and action can be taken before minor niggles become major problems.

Virtual Learning Environments (VLEs) include much of the data needed to obtain these rich insights about students. Data such as last log-in, time spent on each page, click-throughs, downloads and more can all be measured to provide a picture of levels of engagement and to better understand how students are interacting with course content and the university as a whole.  

If the VLE also features a facility for students to submit comments and a discussion forum, there may be scope for extracting further insights through sentiment analysis. This involves computationally assessing students’ submissions and identifying common themes and threads from these potentially vast amounts of data. Which aspects of the course and its delivery attract the most comments? What are the common attitudes to these? Are they positive, negative or neutral? Sentiment analysis can give you valuable intel on all of this.          

Optimising course content

It’s easy to see how analytics tools can be applied to the likes of VLEs and online courses. But it’s also possible to apply the same granular analysis to more traditional forms of teaching.

Take lectures, for instance. It’s now pretty standard practice for them to be recorded and made available to students for reference and revision purposes. Recent research carried out at Imperial College, London shows how this technology can also provide the opportunity to uncover actionable insights. 

Making these lecture videos available via a VLE or dedicated platform provides access to all manner of useful behavioural and activity data. Lecturers can see how often videos are viewed and at what times. Are certain sections being skipped over? Are particular segments being subjected to repeat views? If so, it could be a sign that students are struggling to understand something and that further explanation is needed. 

As well as helping the faculty to optimise course delivery, this data can also be valuable for HR. For instance, if a particular tutor’s content is prone to an unusually high level of views and rewinds, it may be worth scrutinising for performance measurement purposes. 

Student retention: analytics enables targeted action 

Regularly cited as one of the biggest challenges currently faced by Vice-Chancellors, UK university drop out rates remain stubbornly high, with disadvantaged students at particular risk. When it comes to tackling the problem, data could make all the difference. 

The big difficulty with student retention is that all too often, the institution only becomes aware that a student is struggling right on the brink of their departure. But effective, early intervention is possible, especially with the help of predictive analytics: in other words, using data relating to past behaviour and events to predict future outcomes. 

Charles Prince, Director of the Centre for Student Success at the University of East London (UEL) explained how that institution has put predictive analytics to work. As a starting point, all student data from across the institution is processed from a secure, central repository. Using an algorithm within the Civitas system, historic student records are analysed to identify the characteristics of students who successfully complete their courses and those who drop out. Those outcomes are then applied to currently enrolled students, using predictive analytics to identify which students are most at risk of falling behind or dropping out. 

The benefit of this approach is that it equips the student success team to make better decisions on where to focus its limited resources. For instance, if a student has a certain combination of characteristics relating to demographic background and previous academic attainment and they go on to display x behaviour relating to attendance and engagement, this could create a valuable trigger for the team to step in with targeted support.   

Making the best use of resources

Faced with seemingly endless political and economic uncertainty, the pressure is on to think creatively to find new ways of generating revenue. This is another area where analytics can perform a useful role.

For any organisation keen to do more with less, resource planning is an area of analytics that warrants especially close attention. At heart, it is concerned with assessing and understanding how a particular resource is being used in order to understand availability and capacity. 

Say, for example, one of your STEM faculties wants to invest in a major facility upgrade. A financial viability study suggests that this is possible - but only if it is done via a shared-use arrangement with an industry partner. Resource planning analysis can give you vital intel on how, when and to what extent the faculty will make use of the proposed new asset. You can then use this knowledge to negotiate the exact terms of the arrangement to make sure it delivers best value. 

On similar lines, generating revenue is, of course, one of the primary objectives of your alumni relations team. In an age of personalised marketing, sending out the university newsletter twice a year along with blanket ‘one-size-fits-all’ fundraising appeals is hardly a successful recipe for boosting engagement. Once again, analytics can help supercharge your approach. 

A better way of doing things may involve the computational analysis of past alumni department data. You can then identify the attributes of previous alumni who are most likely to donate. Drill down even further and you can find out which type of outreach media work best on which type of graduates, what type of content triggers the strongest engagement - and even the best time of day to send out your messages. All of this helps the outreach department assess where best to deploy limited resources. 

What’s next for your university?

From faculty level right up to strategic and financial administration, data analytics has the potential to bring benefits to all aspects of HEI activities. 

But transformation into a data-driven organisation is not something that happens overnight. As a start, it involves gaining a thorough understanding of what data you control, where it exists and how it ought to be consolidated. From here, it's useful to work with an expert to determine the solution that's best fit to meet your individual needs. 


Oletta Stewart

Content Writer

Oletta Stewart is a Content Writer for MHR Analytics.


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