Both official statistics and student surveys show often dramatic increases in incidences of mental health conditions. Anxieties have been further raised due to incidences of student suicide. Whilst these remain low and relatively stable compared to previous decades (Office for National Statistics, 2018), each incidence is one too many.
A range of organizations have conducted research and produced practical guidance (Student Minds, UUK, Institute for Public Policy Research). However, significant challenges remain, particularly around resourcing student support at the start of a crisis, or at other times where it can be most effective. Finding the balance between supporting and ‘infantilizing’ students is also a real consideration.
Setting aside arguments around the appropriateness of proactively seeking to support students, this piece explores the question of whether or not existing learning analytics resources (perhaps with some modification) could be used to identify students most in need of mental health support at a time that is most likely to lead to successful outcomes.
Nottingham Trent University has embedded learning analytics into institutional practice to help students manage their own success, to help staff support them, to improve student/staff working relationships and to improve institutional insights into the student experience.
The Dashboard generates daily engagement ratings based only on a student’s learning interactions with university resources, such as the library or online tools, avoiding more contentious areas such as socio-economic background. In 2016/17 (the year used for this analysis) the engagement ratings were ‘Low’, ‘Partial’, ‘Good’, and ‘High’.
Engagement data is generated and displayed to both students and relevant university staff, alongside other contextual information, using Solutionpath’s StREAM tool. If a student has no engagement for 14 days the Dashboard sends tutors an email asking them to make contact with the student. As might be expected, there is a strong correlation between overall patterns of engagement and student success. Students with high average engagement are far more likely to progress and achieve higher grades than more lowly engaged peers, and the ‘no engagement’ alerts are a strong early indicator that a student is at risk of non-favourable outcomes.
Daily data can act as an early warning based on either low engagement or unexpected changes in engagement behaviour. Further to supporting the needs of the whole student population, we posit there is particular potential to provide support to students with mental health conditions at the point when a mental health incident may be starting.
At NTU, the majority of students with mental health conditions progress from the first to second year. For example, in 2016/17, 82% of NTU first years with mental health conditions progressed compared with 84% of first year students with no reported disabilities. However, there are differences in engagement patterns between the two groups from the very start of the year. For example,
The data demonstrated that students with mental health conditions are less likely to be highly engaged with their studies. This is important because engagement is such a strong predictor of the likelihood of success:
This 19% difference dwarfs the 2% progression gap between students with mental health conditions and their peers with no disabilities. Importantly, engagement data has the potential to target support to students who are most in need during a particular period of time, and to do some more effectively than using solely group characteristics.
Institutions need to develop strategies for supporting students with mental health at the point where they need it most. A range of tools already exist based on real world interaction, including tutor relationships with students and campaigns encouraging students to look out for their friends. Nonetheless, learning analytics gives a different set of insights to be used alongside existing options to spot students with mental health conditions at a point where they most need help.
Average engagement is perhaps most useful to provide contextual information to support tutors’ observations or improve the quality of a tutorial conversation. The ‘no engagement’ alerts appear to work as an effective early warning system at the point when a student with mental health conditions may be in most need of support. Clearly, challenges remain about the most effective way of supporting those students, but we feel there is potential to be explored.
One of the most-pressing challenges facing the UK higher education sector is how it responds to the increasing prevalence of mental health conditions amongst students. Article first published by WonkHE, 17/07/2019, written and contributed by Ed Foster, Nottingham Trent Univerity, Student Engagement Manager. Both official statistics and student surveys show often dramatic increases in incidences […]
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