For my final data visualisation, I decided to track my performance at work. The difficulty was to determine how to measure ‘performance’. Due to the nature of my job, it isn’t easy to say how many tasks I have completed each day. I’m working on big projects and am fairly autonomous as to what I’m doing each day. How would a machine measure how I did, I wondered? It’s easy to see how many meetings I attended, how many emails I sent and how much time I’m spending on my computer. But how can this information give insight into how well I’m doing my job?
I chose to explore this issue after reading how problematic performance measuring can be as part of the information infrastructure of test-based accountability. According to Anagnostopoulos et al. (2013), there are questions around how well performance measures can represent teaching, learning and schooling. ‘As [standardised tests, mathematical models, and computing technologies] define what kind of knowledge and ways of thinking matter and who counts as “good” teachers, students, and schools, these performance metrics shape how we practice, value, and think about education.’ The perceived objectivity of data, therefore leads to a shift of power away from traditional actors in educational governing.
Looking back at my visualisation, it seems as if Monday (top left) was my most productive day although I perceived Thursday (bottom right) as the day I achieved most. Although this is a very small sample, it shows how difficult it is to measure performance by purely looking at data.
Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (eds.) (2013). The Infrastructure of Accountability: Data use and the transformation of American education. Harvard Education Press.