This week, I tracked sleep quantity and attempted to relate it to the amount of caffeine consumed throughout the day. Having a good night’s sleep can mean the world of difference in my own day to day, and correlates to whether or not I need more than one cup of coffee to feel human.
Shifting from the learning perspective to the teaching perspective when considering the importance of data, I was considering this week what teachers would be interested in knowing about their students. Sleep, I imagined would be one of them. It has been proven that lack of sleep, or poor quality of sleep, can negatively impact a student’s ability to focus and do well in school (Sharma, 2014). When interacting face-to-face, teachers would be able to pick up on body language and other clues easily to determine how well rested a student is feeling. In a virtual classroom, using the same clues becomes quite difficult, especially if the student is unable to (or doesn’t want to) use their camera. In the virtual class session this week, Ben Williamson shared a recent article in CNN Business (Chan, 2021) that stated emotion recognition AI may help teachers identify if students are happy, sad, angry, surprised, or fearful. With this advancement, it’s likely only a matter of time before we can add sleep deprived to the list.
When creating the visualisation, I put on my technology hat to put together an easily understood sleep diagram – something that a teacher could use to determined within a few seconds whether or not the student was well rested, or if lack of sleep could be a reason for their lack of motivation.
One thing that I do in my professional life is create dashboards for different data collected on a business’ physical locations, such as review ratings. One of the ways that we can visualise the ratings in the tool I work with is a geographical map that uses a RAG status to highlight which locations are doing well and which ones could use some improvements by coloring the location dot red, yellow, or green depending on the average review rating (1-5 scale, with 1 being the worst rating).
My first instinct was to use the same method for my own sleep pattern for the week, imagining that a teacher could look at the size and the color of the circle to determine within a split second whether or not I was well rested. As highlighted by Williamson, Bayne, and Shay (2020), teachers often have a limited view of students in large, or online, programs. The goal of this dashboard was to improve that view and be a window into one thing they would likely notice if interacting with the student face to face.
In reality, there are concerns with tracking sleep. One is that it ‘may change how teachers view [students]’ also highlighted by Williamson, Bayne and Shay (2020). I would argue that when it comes to sleep quality, the idea that a teacher views them differently could a positive thing in that the teacher may be able to adapt teaching methods, or dig deeper into why the students is not getting enough sleep. As an educator, I may be asking if this is my responsibility?
Another concern is data privacy. Should sleep quality be a data point that teachers have access to for their students? How would students report the data – self created sleep diaries, or through a wearable device? Should sleep data be considered under GDPR?
From a student’s perspective, if the teacher shares a clear correlation to sleep and their performance, could it contribute to additional sleep disturbance by giving the student anxiety? I’m sure we can all relate to a scenario where we lay in bed at night desperately wanting to fall asleep because we have a big day the following day.
In hindsight, while I can relate the increased caffeine consumption to my own lack of sleep, it wouldn’t necessarily be relevant for a teacher. Many children, I would hope, don’t consume caffeine to make up for the lack of sleep like adults do. Rather, I can imagine that they would be interested in knowing whether or not high stress events are directly contributing to the lack of sleep.
In a world where we are now hiding behind the Zoom camera, these data points could help the teacher understand the student in more context, even if the additional dashboard (or data point) may unfortunately contribute to additional ‘datafication’.
Chan, Milly. (2021, February 21). This AI reads children’s emotions as they learn. CNN Business. Retrieved from https://edition.cnn.com
Sharma M. G153(P) A Study of role of sleep on health and scholastic performance among children. Archives of Disease in Childhood 2014;99:A68.
Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.