Block: ‘Governing’ with data / Week 10

This week, I recorded the times I got out of bed each morning and the times I started work each day. I wanted to visualise the relationship between sleeping in and starting work on time while I work from home (Figure 1).

It looks like I’m more likely to sleep in when working from home. For me, this has an affect on the temporal and spatial aspects of work. The time and space of work feels more bendy, stretchy and relative (“I’ll just push back my start time” or “I’ll just answer these emails from bed”). On Friday, when I went to campus, it was like time and space ‘snapped back’ to something more fixed or intentional.

Figure 1: Get out of bed!

Links between sleep patterns and mental health are well documented. Monitoring sleep patterns like this might be a useful indicator for when an intervention into one’s health or sleep hygiene is required.

Mental health interventions were on my mind this week as I read about the University of Edinburgh’s investigation into the support provided to Romily Ulvestad prior to her death in 2020. This led me to read about ‘early indicator’ systems universities use to indicate when a student is in need of academic or mental health support. It is no surprise, after reading about the turn towards ‘future-tense’ governance and ‘fast policy’ (Williamson 2017), that data-driven ‘early indicator’ systems are being tested to track individual students’ mental health using multiple sources of continuous data, including learning analytics (Foster 2019, Office for Students 2019). I wonder how useful learning analytics are for this – academic participation is not always correlated to signs of worsening mental health (although this appeared to be an aspect of Ulvestad’s case). I am also wary of universities accessing personal data to drive these predictive systems. This week’s data is not something I would ongoingly share as a worker or a student (although others might be comfortable doing this, if they thought the collecting body would use that data responsibly).

Overall, I think it’s a mistake to turn to predictive data-driven systems to indicate when a student is in need of mental health support. These are expensive distractions from the difficult policy and governance work that’s needed to address the issues students encounter when trying to get help from under-resourced and over-burdened student support systems. 

References

Foster, E 2019 ‘Can learning analytics warn us early on mental health?’, WonkHE, 18th July https://wonkhe.com/blogs/can-learning-analytics-warn-us-early-on-mental-health/

Office for Students 2019, Innovation, partnership and data can help improve student mental health in new £14m drive, viewed 20th March 2021, https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/innovation-partnership-and-data-can-help-improve-student-mental-health-in-new-14m-drive.

Weale, S & Baldwin, J 2021 ‘Edinburgh University admits failings after student kills herself’, The Guardian, 16th March, https://www.theguardian.com/education/2021/mar/16/edinburgh-university-admits-failings-after-student-kills-herself-internal-review-support-mental-health.

Williamson, B 2017 ‘Digital education governance: political analytics, performativity and accountability’, in Big data in education: The digital future of learning, policy and practice, Sage.