Gathering and keeping data about students isn’t new. In my career, most of the data was based on the CEFR framework, which is widely used. It is, however, not ideal as language skills are notoriously hard to quantify. Based on CEFR, I, the teacher was responsible for collecting, inputting and interpreting the data. The combination of those two unreliable factors demonstrate the issue of flawed data sets that then could serve either algorithms (Kearns&Roth 2020; O’Neil 2016) and inform decisions.
I calculated the rough number of students I taught at each of institution. ‚Profile‘ refers to a written paragraph about the student that is saved and passed on. Awareness of data gathering was based on explicity – if students were explicitly informed their data was being saved and processed.
I experimented with infographic format I saw in other blogs and McCadless (2012, below).
This data demonstrates a few worrying trends, apart from the aforementioned objectivity. Students were not explicitly told about the data gathering. Worse yet, as time progressed, there was more automated and saved data but less information. This coincides with the dawn of platforms – more platforms, less awareness (van Dijck et al 2018). Although not directly visible from this visualisation, it made me realise in the places where data was more standardised, processed, and formed basis for teaching, the faculty had no guidance or training on how to use it (Brown 2020).
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
Kearns, M. Roth K. (2020) The Ethical Algorithm; Audible
McCandless, D. (2012) Information is Beautiful. HarperCollins Publishers
O’Neil, C. (2016) Weapons of Math Destruction. Random House Audio (Audible release date 09-06-2016)
van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press