A Jisc report into a project that used analytics to help effectively target mental health and wellbeing support may offer a glimpse of the future. David Kernohan takes a look
Can you predict a student’s wellbeing? Using administrative data? To the extent that it allows you to target support and interventions to the students most in need?
Learning analytics – which uses similar data to deploy academic interventions – is already widespread within higher education.
Just about every large university provider is able to use information about student participation and characteristics to promote appropriate study support or intervention. So why not on an issue that could have far greater repercussions for students?
It should be a straightforward choice – a clear benefit to students, and a way to make the most effective use of scarce provider resources. And a recent OfS funded project has been a qualified success.
The project
In 2019 the Office for Students funded a project led by Northumbria University (with Buckinghamshire New University and the University of East London as partners to investigate the possibility of analytics for mental health. This was one of ten collaborative projects (at a total value of £6m) funded as part of the Mental Health Challenge Competition – a funding competition designed to foster collaborative projects to address common issues.
The final evaluation report for this programme of work – including for the Northumbria project – was published in October 2022. The impact of the restrictions in activity associated with Covid-19 meant that many of these results, although promising, could not be fully validated for applicability in a “normal” year. Depending on your own personal definition of “normal” we’ve now had a couple of these, resulting in a further report (out today) from project technology partner Jisc.
The Northumbria project deployed a number of indicators – disability information (including student support recommendations), personal extenuating circumstance information, change of circumstance information, care leaver status, and first language – finding that a combination of these could be used to predict where students would likely suffer from wellbeing issues.
And yet…
At the time the project was launched there was a considerable amount of concern expressed, particularly among university learning technology specialists, about the ethical basis of the project. Early publicity didn’t quite show the nuanced and human process that was eventually developed – and some believed the project would mean automated designation of students with mental health issues, to the detriment of students involved.
It is an iron rule of education technology that most “automated” systems are anything but – and in this case the incorporation of human decision making within the process has served to quell anxieties as well as increase the cost of the initiative.
But it does prompt the wider question more usually associated with diagnostic predictions: if your background, behaviour, or characteristics made you more likely to experience a life-changing health condition would you want to know?
For some parts of the population the answer would be an immediate “yes”. Data about us is gathered and interpreted nearly constantly – we carry sophisticated sensors in our pockets and on our wrists, while numerous systems track our behaviours and engagements with online tools. If we can use this information to make ourselves healthier and happier, why wouldn’t we?
The corollary is one of agency. Many identified risk factors (our genetics, our socio-economic background) are outside of our control – others (our behaviours, our diets) may technically be within our control but are in practice very difficult to alter. If you knew that something about you made you more prone to mental health issues (to anxiety, say), is there a risk that it would become a self-fulfilling prophecy?