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Retention analytics – more than getting “bums on seats”

(Image credit: Image source: Shutterstock/ESB Professional)

In recent years, we’ve seen universities invest a lot of time and money in student recruitment. There has been a huge drive in getting students through the door, but it is fair to say that there has been less attention on retaining them. Earlier this year, the previous Education Secretary, Damian Hinds said that more must be done to reduce the number of students failing to complete their studies, as it is damaging to the sector, and I couldn’t agree more.

Using the tech available – not reaching for AI

In business, a data-driven customer retention strategy can reap rewards in a big way, if you do it right. In fact, it’s proven to drive profit. In HE, the same can be applied. If retention is high, it is win, win. If you can help and support students who are disengaging, they ultimately go on to have a more successful outcome, and you as a university retain the revenue that you get from them.

There are a range of technical solutions out there to help spot students with potential to drop out of their studies, so that universities can intervene and do something about it. But the HE sector, on the whole, is not looking at the right solutions for their needs. In my experience, universities maybe overcomplicating the issue more than they need to. They tend to want advanced statistical models, like AI, which they may not need, and actually won’t address the problem. Simple solutions are staring universities in the face and retention analytics are a very straightforward option.

A simple formula

If you asked most people, what would be the sign of a student likely to drop out of university, I believe they would give you the same list:

  • Poor attendance
  • Grades getting worse
  • Not engaging with the system, or the people around them

Retention analytics are designed to help you measure and monitor the above metrics, so that you can identify, with ease, the students in need of additional support. CoSector – University of London, has worked and developed multiple systems to pull together various sets of existing data points, and to process those into simple lists of students at higher risk than others.

The systems we’ve worked on can accept around 35 data points, but you can actually get strong results issuing just six – poor attendance, and reduced engagement, for example, are strong focus areas.

Supporting disadvantaged students

Hinds also stressed that there is “no point” widening access without efforts to improve retention for students from disadvantaged backgrounds.

Official figures have revealed that disadvantaged students were more likely to leave their degree in the first year than their more affluent peers, with 8.8 per cent of poorer students quitting compared with 6 per cent. Retention analytics allow you to put different levels of focus on the data that you have, so if you know that certain demographics are more likely to struggle, then you can tailor the system to monitor these students. Retention analytics can produce automated lists of data on a daily basis.

Some universities say that even if they detect which students are at risk of dropping out, they haven’t got the tools to do anything about it. But it can be as simple as an automated set of emails to those names you’ve identified to say, ‘do you realise we have financial support services and we offer counselling support?’ It doesn’t require a huge student services team.

Manchester Metropolitan University has been developing a number of new approaches when it comes to monitoring students’ engagement with their studies.

Its attendance monitoring system is linked to the University’s timetabling software, so it can track and highlight where a lack of attendance may show a drop in engagement levels.

Automated ‘trigger’ emails are sent to students involved in attendance monitoring. If a student has notable unplanned absence in a two-week period an email will be triggered.

Students are also expected to make regular use of Moodle, its virtual learning environment (VLE). Notable periods of unplanned absences or very low engagement with Moodle, compared to other students on the course, will trigger an email as part of the intervention procedure.

The university uses data about study engagement to trigger interventions and support from a range of university services, including personal academic tutors, student wellbeing officers and academic skills advisors. The university is also piloting a student view of the data, designed in consultation with its students.

Times are changing

I am seeing an encouraging change towards the use of retention analytics. And as funding pressures continue to increase, universities are reaching the stage where they cannot ignore retention problems. 

There are actually some very powerful things you can do with minimal cost and effort. Universities are not to be daunted by the technology available to help improve retention, and doing some very simple things can make a huge difference to both the institution and its students. Let’s move away from the “bums on seats” mentality, and think about ‘heads engaged’ instead. We can use the data we’ve got to provide an even better service to those students in need.

Dave Kenworthy, director of digital services, CoSector - University of London