For my last visualization, I decided to track my own efficiency during a working day. I work in education in the IT sector where reporting time and task progress in dedicated digital systems is a daily routine for most employees. However, our training department do not employ these task/time management practices yet. As an experiment, I logged the time for my work/non-work activities and ticked the task boxes for 5 days during regular working hours. The issues that concerned me in the process:

1)       I devote 1 hour of my working day to reading (professional development). As a rule, time for professional development is not counted as working hours, at least in my environment. Needless to say, professional development is essential for both employee motivation and quality teaching, so why educators need to sacrifice their sleeping or family hours to stay professional remains unclear.
2)       The numbers show that I am ‘underutilized’, as I don’t make 8 working hours a day. I must confess that it made me feel very uneasy, and the temptation to tweak the numbers was huge. It is not popular to be not busy these days.
3)       Some tasks weren’t completed for the reasons beyond my control. However, for the employer or the customer, it is usually a yes/no question (done/not done). To emphasize this message, I used black and white colours for the graphs with time and tasks.  As Anagnostopoulos noted, ‘data, in themselves, do not hold meaning’ (p.219). For the indicators on the right, for contrast, I chose colour coding to emphasize the fact that those who will look at my data or the algorithm that will process them will construct their meaning, in accordance with their values and priorities.

Thinking of education from this perspective, today teachers are turning into ‘data collectors’ and ‘data entry clerks’ (Williamson, 2017) that are also expected to log their daily activities, so that after their students test scores arrive, the interested parties could measure their effectiveness and define ‘best practices’ for further scaling. As Fontaine explains, ‘teaching and learning are increasingly being measured and quantified to enable analysis of the relationship between inputs (e.g., funding) and outputs (e.g., student performance) with the goal of maximizing economic growth and productivity and increasing human capital’ (p.2). It is noteworthy that ‘measuring teaching with the same ruler’ as IT work (e.g. code writing) is disputable practice. The same refers to insisting on meaningful causal relationships between teaching and students test scores. In reality, and this is proved by research, ’student performance is more closely linked to socioeconomic status’ (Fontaine, p.2) than teaching effort.