Using data to quantify students is a crucial part of education (Brown 2020). Education is also a hierarchical social structure so assessing and quantifying are key. In fact, most people would associate words such as ‘assessment’ and ‘grades’ with schooling. This week’s data focuses on the times that formalised quantification took place along my own path of education. These tests, especially those at the end of each school stage, have tremendous impact on one’s life both short and long term. Since I finished school, these assessments have become increasingly automatised. For example, ‘matura’ (Polish equivalent of A levels) went from a free writing task with lots of time to spare, to a test with multiple choice elements and fixed answers. There is, however, little publicly known on how the datafication of these gates is conceived (Kitchin 2017). I think a lot about the new rigidity the calculations wield on students these days, because if I had to be subject to them, I would have failed far more than I did. These evaluations and exams are becoming increasingly problematic for even the highly intelligent students on the autistic spectrum. Indeed more make it to the university, but they fare poorer than their neurotypical counterparts (Tops et al 2017). Datafication of education gives the governing bodies a new tool to rule, and in the new world order, some groups can be unintentionally disadvantaged.
I chose to draw a fairly traditional timeline. Due to its prevalence it is easy to understand and clear (Healy 2019).
Brown, M. (2020). Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education. 25(4), pp. 384-400
Healy, K. (2019) Data Visualisation. A practical introduction. Princeton Press
Kitchin, R. (2017). Thinking Critically about Researching Algorithms. Information, Communication and Society, 20(1), 14-29, DOI: 10.1080/1369118X.2016.1154087
Tops, W. Van Den Bergh, A. Noens, I. Baeyens, D. (2017) A multi-method assessment of study strategies in higher education students with an autism spectrum disorder; Learning and Individual Differences 59, pp. 141-148