Chris Ballard, of Tribal, is an ‘Innovation Consultant’ working on student administration and management systems, with a focus area on student retention modelling. Earlier this year, at the annual conference for their SITS:Vision student administration system, Chris co-presented with Paul Travill from the University of Wolverhampton on a research project being undertaken to see how they could be using learning analytics to improve student retention.
There is similar work going on in the Australian higher education marketplace, and I’ve had a number of discussions with universities here about student attrition and the ways to reduce it – driven by the fact that on average one in five students are leaving their higher education courses before the end of the first year. The factors which affect student attrition are made up of two key areas:
- The student’s circumstances before they arrive on campus (see ‘the biggest factor affecting student retention happens before the student arrives‘)
- Their activities and experiences on campus (for which there are a growing number of data sources to be able to analyse and model what’s happening)
Chris & Paul’s slides dig into these data, how to interpret them, and how to build a system which allows you to model and predict student attrition using them (which obviously leads to how to react to them). On Slide 8 there’s a really simple diagram of the key data sources:
If you’ve got an interest in student retention modelling, then I’d recommend taking a look at the full presentation slides from the SITS:Vision conference, on the Tribal Labs blog