Solutionpath’s Michelle Craig discusses how universities are using student engagement analytics as a powerful proxy for identifying struggling students and why its an important element to include in student wellbeing and mental health initiatives. 

When it comes to students experiencing mental ill health, early warning is key.

The Office for Student’s  has said that full time students with a known mental health condition have lower continuation, attainment, and progression rates than full time students overall, and it is now widely recognised that mental health risks are linked with withdrawal and earlier intervention is needed if we want to help prevent such outcomes. 

Students suffering with mental ill health is a subject we unfortunately see all too often in the news, with universities, collectively and individually, trying to determine the best approach in supporting students during their time in higher education.  

In truth, there are a range of factors in and outside the university setting that can impact student’s mental health and wellbeing to varying degrees. From financial worries, family matters, bereavement, and loneliness to pressures of academic workloads, meaning mental health difficulties can surface at any time. Making it a challenging area for universities to navigate and not always easy for students to know where to look for support and ask for it at the time they need it most.

Over the past year we’ve seen increasing pressure from government on universities to do more to provide a duty of care for students by supporting student wellbeing, mental health, and suicide prevention initiatives. The use of student engagement analytics was recommended by Higher Education Minister Robert Halfon and the Mental Health Taskforce to help better identify students who need support and help to find new ways to provide proactive support intervention. ​How can the use of data help and how does it work?

​ The link between engagement, belonging and risk of withdrawal  

When Solutionpath explored this with our early adopter and development partner Nottingham Trent University (NTU), we found that academic engagement is the one thing that all students have common that is intrinsically linked to belonging and risk of withdrawal. ​ 

​Amongst NTU’s early findings they evidenced that students with high engagement were more likely to progress with a grade 2.1 or higher, and those with low engagement more likely to go on to withdraw. Making engagement a good predictor of continuation and completion and disengagement an indicator of risk.​ 

Through StREAM, we can visualise this risk in student and staff dashboards by codifying engagement into a single measure. We do this by collating data that represents educationally purposeful activity across a range of learning systems such as the VLE, lecture replay, library and even learning resources. Using our engagement algorithm, we then distribute each individual student into daily engagement categories from none through to very high. ​ 

​This allows staff to precisely pinpoint which students may be struggling and activate proactive support with a view to make early intervention that gets up stream of crisis.  

This insight also starts to help build a deeper understanding of different student characteristics. Although, the algorithm only looks at what students do academically, a key design principal that we believe reflects the most ethical use of data and that it can then be triangulated with contextual information about each student, to learn more about student profiles, trends and help inform future outreach and support initiatives. ​ 

​Ultimately, the goal here is to help tailor more personalised and timely support to help every student reach their potential. 

Adopting a whole university approach 

In the current climate, it’s especially important to create a whole university approach to mental health and wellbeing.  

We believe there is so much more potential for using analytics in this area and that tools like StREAM student engagement analytics platform can really help to create an ecosystem for students, person tutors and support services. To continually improve the student experience, quality of support, and encourage more meaningful and impactful conversations that help get the right support, to the right student at the right time.

Here’s just some of the benefits to using data to inform your support outreach: 

• Universal measure enables consistency and quality that can align to policy and processes 
• Promotes inclusion, focusing on the ‘do’ and not the ‘who’ ​ 
• Provides alerts to students starting to disengage, providing the opportunity for earlier intervention
• Data transparency opens up more meaningful conversations​ between students and staff 
• Support pastoral staff and the student with referrals to specialist services ​ 
• Provides insight and evidence for OfS Condition B3 regulation, continuation, and completion ​
• Informs what interventions are working and for whom​ 
• Fosters a sense of belonging and allows you to show that you care ​ 

And, students are in support of using student engagement analytics too!

​Solutionpath and Wonkhe commissioned Cybil to run a student survey in the Summer of this year to find out. We found that 80% of respondents were in support of the use of engagement analytics citing improving support for students – especially those who can’t or won’t ask for help – as the main reason for universities to use this kind of data.​ Read the full report.

To find out more, check out our 10 ways StREAM can help support mental health and wellbeing initiatives resource page – 10 ways StREAM can support student mental health and wellbeing initiatives – Solutionpath.

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