A new report, this time from the Social Market Foundation (SMF), confirms that university drop-out rates in England are rising. Taken in context with a raft of other student data released this year and a picture emerges of increasing difficulties for disadvantaged students and those with mental health issues.
- A report from the Social Market Foundation (SMF) says that the national non-continuation rate for students at English universities rose from 6.6% in 2011-12 to 7.4% in 2014-15. The report goes on to point out that many of the disadvantaged groups targeted through widening access programmes are also the groups most likely to drop out.
- 2017 data from the Higher Education Statistics Agency (HESA) shows that a record number of 1,180 students left courses early in the 2014-15 academic year due to poor mental health – a 210% increase on 380 students in 2009-10.
- Statistics released by the Office for Fair Access in June 2017 showed that, in 2014-15, 8.8% of young, full-time, disadvantaged undergraduates did not continue their studies beyond the first year – up from 8.2% the year before.
What can universities do to reverse these trends?
For some struggling students, the university wellbeing department, with trained counsellors and GP services, can be a lifeline, but some services are better funded than others. A report in the Guardian says demand for these services is out-stripping supply at some universities, and the waiting time to see counsellors is therefore increasing.
But what about those students who don’t choose to seek help? In order to prevent them slipping into disaster, it is vital they can be identified, carefully approached and given the right support to enable them to stay well and continue on their course.
Identifying at-risk students
With thousands of learners on the roll, it’s not easy to keep personal tabs on everyone. Peer and tutor support has an important role to play, but technology can also spot signs that an individual student may be in trouble.
Using learning analytics will allow institutions to personalise interventions and uncover hidden patterns in their student data, reflect on how students are interacting, and make evidence-informed decisions about how best to support, or challenge, their students – Phil Richards, chief innovation officer at Jisc
Phil Richards, chief innovation officer at the UK’s education technology solutions not-for-profit, Jisc, explained: “Using learning analytics will allow institutions to personalise interventions and uncover hidden patterns in their student data, reflect on how students are interacting, and make evidence-informed decisions about how best to support, or challenge, their students.
“Imagine a student has not accessed the virtual learning environment, been to the library or engaged in the college community for a number of weeks and has missed their last couple of deadlines. Data about all this is already being collected (although it’s often geared toward internal admin needs) and can be used to ring an alarm bell.”
Boosting retention rates
The Office for Fair Access (OFFA) – which regulates fair access to higher education (HE) in England – encourages institutions to ensure that students from non-traditional backgrounds are successful following enrolment.
Analysis by Jisc of 2017/18 fair access agreements found that 14 institutions explicitly mention learning analytics. Buckinghamshire New University, for example, highlights that it ‘intends to introduce learning analytics to inform the support, learning, engagement, retention and success of its students’ as part of its efforts to establish a stronger culture and practice of data usage across the institution. Exeter University’s access agreement states that it is ‘developing effective learning analytics tools to enable both students and tutors to monitor performance more effectively and identify strategies to improve’.
Analytics data can also be rolled into a predictive model of student success or failure. For example, a model at the New York Institute of Technology successfully identified 74% of students who subsequently dropped out as having been ‘at risk’.
And, when learning analytics were applied at Columbus State University College in the US, retention rose by 4.2% overall, but 5.7% for low-income students. In Australia, a three-phase scheme at the University of New England saw drop-out rates fall from 18 to 12%.
The use of learning analytics is growing in the UK and Jisc has conducted considerable research and development in this field, culminating in the launch of an app for students in September.