Retention analytics: more than getting ‘bums on seats’
Dave Kenworthy, director of digital services, CoSector – University of London, explains how simplified retention analytics can help universities ensure their students are happy on campus
Universities have invested a lot of time and money in student recruitment. There has been a huge drive in getting students through the door, but has there been less attention on retaining them?
Earlier this year, former education secretary Damian Hinds criticised universities with high student drop-out rates as being more interested in getting “bums on seats” than supporting undergraduates through their degrees. Hinds said that more must be done to reduce the number of students failing to complete their studies.
In business, a data-driven customer retention strategy can reap big rewards, if you do it right – and is 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 is a range of technical solutions to help spot students who might drop out, so that universities can intervene and do something. But universities may be complicating the issue more than they need to. They tend to want advanced statistical models like AI, which won’t address the problem, while retention analytics provide a straightforward option.
A simple formula
If you asked most people what the signs of a student likely to drop out of university are, 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 easily identify 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 get strong results issuing just six – poor attendance and reduced engagement, for example, are strong focus areas.
Hinds also stressed there is “no point” widening access without efforts to improve retention for students from disadvantaged backgrounds. Official figures have revealed that disadvantaged students are more likely to leave their degree in the first year than their more affluent peers, with 8.8% of poorer students quitting compared with 6%. Retention analytics allow you to put different levels of focus on the data you have, so if you know certain demographics are more likely to struggle, you can tailor the system to monitor these students. Retention analytics can produce automated lists of data daily.
Some universities say that even if they detect which students are at risk of dropping out, they haven’t 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?’
Manchester Metropolitan University has been developing a number of new approaches to monitor students’ engagement with their studies. Its attendance-monitoring system is linked to the university’s time-tabling 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 use virtual learning environment Moodle regularly. Notable periods of unplanned absences or low engagement with Moodle 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.
Times are changing
I am seeing an encouraging move towards the use of retention analytics. As funding pressures continue to increase, universities are reaching the stage where they can’t ignore retention problems. Universities need not be daunted by the technology available to help improve retention and doing some 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, and use the data we have to provide an even better service to those students in need.