Equality Julia Taylor Learner Experience

Student Minds- How can tech contribute to better mental health?

"" Risk, regulation, success and policy – the Universities UK’s case for making mental health a priority.  Technology has a clear role to play in supporting student mental health.

Julia Taylor considers the issues.

How big is the problem?

With 1 in 8 graduates saying they have a Mental Health condition, costs and demand for support with mental health are growing. The number of university ‘drop outs’ related to mental health has trebled and most worryingly, suicide rates amongst students are rising. This is a real cause for concern; a potential crisis in the sector. University Mental Health Advisors network and student minds advocate a whole university approach. Universities UK has made mental health a strategic imperative, urging institutions to take action under their Stepchange programme. Promoting open discussion and acknowledging mental health issues is a key strand of the UUK programme because it will help prevent problems developing.

Promoting well being

Approaching mental health pressures as a normal part of life that can be accommodated and managed is one reason many universities have introduced mindfulness courses and promoted well-being apps to students. However, this relies on students engaging independently. How much more effective would such resources be if they were built into the kind of bespoke university app that many HEIs are developing?

Monitoring progress

Many universities are exploring Learning Analytic dashboards and apps that track progress through a course but these may have unintended consequences. Jivet et al (2017) researched Learning Analytics dashboards and concluded that “current designs foster competition between learners rather than knowledge mastery, offering misguided frames of reference for comparison.”

Value could be added by blending the benefits of a well-being app with relevant study goals and tracking.  It might enable students to manage independently wherever possible and seek support when required.

Identifying those at risk and enabling early intervention is a crucial aspect of prevention. With consent, the data could also be used to flag problems. Jisc’s code of practice on Learning Analytics provides an ethical approach to gathering student data. With the right metrics you can identify patterns of disengagement. These may give early warning of mental health concerns, allowing timely responses. Has a student stopped attending? Or stopped using the VLE? Is there a declared disability or a known risk? Are there factors in course design or content that created stress and contributed to the change in behaviour? How best should it be responded to? How do known patterns of engagement correlate with well being?

Minimise barriers

The symptoms of depression, anxiety or stress can impact greatly on a students ability to engage with learning and also with people. Providing a personalised learning environment that allows a student to engage whenever and however they feel able and to contribute remotely through discussions and forums rather than face to face, can make the difference in maintaining contact and avoiding a deterioration in their condition.

On an everyday basis, technology is often an undervalued resource. It offers many options for personalising and customising the way we deliver learning. Many approaches that we recommend  for specific disabilities can make life easier for others facing additional challenges. Apps, productivity tools, even browser plugins, can help students to manage and prioritise their time and work. They can make reading and writing less challenging with text to speech or voice recognition. They can make it easier to capture learning activities or easier to review and revisit key learning points

Maximise opportunities

Student well-being data can inform discussions on flexible delivery. Mental health, well-being and success are all linked to opening up the diversity of delivery; students can keep in touch, catch up and stay engaged using media and methods that suit them – at times when they feel well enough. The more we know about the data the better we can respond to make learning more successful for the 1 in 8.







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