I am an academic working in a business school. There are two things I really enjoy: teaching and research about teaching (which is called scholarship). And like most people, I really don’t like admin. Is it because I am sensitive to admin that I think I am always doing it and that it distracts from teaching? I suppose you might say there is teeny bias at play here.
As a try-out data visualisation, I thought I would record my emails for a week to see how many are, in fact, admin e-mails and whether my hunch that I am doing way too much is actually true. Collecting data is very easy, just rummage through my inbox for a weeks emails. As it happens I colour code my emails as admin, scholarship and teaching. I have a system. I split my working day roughly three ways giving a certain amount of time for each category. I bias my hours towards teaching because I figure that’s mainly what I get paid for, I also really love teaching. I started to do this when I noticed that admin was taking over my working day.
So this fairly straightforward data collection is ideal for a first go at data visualisation using hand-drawn methods. In the week I chose (week 2 2021), I received 244 emails, of which 177 were admin, 53 teaching and 14 were scholarship. Ha ha! as I thought! I am still spending too much time on admin. Hand drawing the data gave me time to really think about the data itself. I was initially reminded of a time before digital, of hand-written notes and also memos and letters. In 1989 I joined the Royal Navy as an officer. On basic training, as well as the usual running around, we had to learn how to write various letters, such as memos, routine letters, official and demi-official letters. Official letters were signed:
I have the honour to be,
Your Obedient Servant
Can you imagine that? You couldn’t make mistakes on official letters, but in other kinds you could cross out the mistake neatly and then write it correctly. In the digital world we just edit, but then it took too long to start again every time. On the postcard, you can see I made a mistake on the total 244, which I wrote as 144, and then just wrote over it. I really couldn’t start again.
The visualisation shows an overwhelming number of admin emails, I could barely fit them in. The other types of email look pitiful. So job done?
Williams (2017:29) points out that the data we select should really be called capta because it is only the data we select not the whole data set that we observe. Selwyn and Gasevic (2020) discuss the limitations of data in learning analytics. The fact is, in learning analytics, and other data analysis we cannot escape our biases, something the analysts themselves freely admit (Ibid.). The limitations of these data are that all emails are not equal in terms of th time theye take to process or action, some are read and deleted, some are deleted and not read, others are the start of a huge project. There is no way from my analysis to tell which are for information and which for action. I also do a lot of teaching communication on MS Teams, running courses is an operational task and Teams is better suited for this than email. In short the visualisation has dramatic effect but the devil is in the data selection.