The so-called Fourth Industrial Revolution has unleashed a tidal wave of data. It did not take long for human society to see this as an opportunity. Data is a thing and humans have always sought to own and colonise things. However, the action of domestication draws humans into entangled networks of humans and things and into care for those things (Hodder 2012: 62). Data, like all things, has agency of its own, for instance, in the ‘surveillance and structuring of human behaviour and action’ (Williamson 2017:64). While we seek to domesticate the rising volume of data, it ( through algorithms) domesticates us.
Williamson et. al. (2020) point out that culture is defined by data and that students are increasingly defined as data sets. In my first data visualisation, I created a tanglegram of technologies I use to teach during the covid crisis. I hoped to catch the behavioural chains and networks which form what Heidegger called a equipmental totality (Hodder 2012:28). By sketching the network of devices I use to teach and recording how much I used them, I hoped to surface, not raw usage data, but a sense of my dependence on these things. The visualisation also helps to understand where data is not spotlighting aspects of teaching (Williamson et. al. 2020, Brown 2020, Harrison et. al. 2020, Sander 2020). The dependency on things for education could not be starker now and also the dependency of things on us. For example, there is a fevered drive to collect data on students through LMS systems as we try to gauge the engagement of students in online environments. How much data and how many new algorithms will be created and depend on us to support this drive?
In my second visualisation, I was inspired by Brown (2020) and learning analytics dashboards to find out how much teachers depended on these dashboards. I happened to be doing a mini-ethnography on a Twitter community #hybridlearning on the culture and digital education course. For a week I counted the tweets in this community about dashboards, there were so few that I widened my search to edtech tweets. I found the community was all but drowned out by a deluge of edtech promotions and even a large number of retweets by teaching professionals. This community was in the midst of an, apparently very stressful, period of hybrid learning in the United States primary education sector. The community has widely embraced edtech as a kind silver bullet solution to many issues. There was very little sense that critical thought was being applied to the adoption of new technologies and platforms (Rhafaghelli et. al. 2020, Sander 2022, van Dijck et. al. 2020), but this may be understandable in the light of the crisis.
Finally I looked at how the way I teach distorts time for students (and myself) through teaching in different time zones and the extent to which I teach in real time or to future students (yet to log on and consume my materials). I found this interesting as it demonstrates how dependencies on things, including data, maps a network not just through real and virtual space but also through time. In fact, this time distortion is partly what marked human society’s transition into an entangled socio-material network (Hodder 2012:83). The transition to agricultural living forced humans to nurture and develop things over time and also to develop more complex social relationships (loc. cit.). Similarly, my dependence on technology and data to teach is now reaching into the future and ensuring my extended entanglement. Course evaluation data, student performance data, engagement statistics will all be used to develop future courses, and the turn in performance management based on shallow quantifications will have a significant role to play. As van Dijck et. al. (2020) point out data is transforming the curriculum (see also Williamson et.al. 2020) . There is also the risk here that pedagogy is driven by what we can measure (Brown 2020). This is the domestication of data and domestication by data in action.
Bibiography
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. 384-400Hodder, I., 2012. Entangled: An archaeology of the relationships between humans and things.
Harrison, M.J., Davies, C., Bell, H., Goodley, C., Fox, S & Downing, B. 2020. (Un)teaching the ‘datafied student subject’: perspectives from an education-based masters in an English university, Teaching in Higher Education, 25:4, 401-417
Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature, Teaching in Higher Education, 25:4, 435-455,
Sander, I. 2020. What is critical big data literacy and how can it be implemented? Internet Policy Review. 9(2)
van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press
Williamson, B., 2017. Big data in education: The digital future of learning, policy and practice. Sage.
Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.
I like these provocative ideas about domestication of and by data. A key issue in the wider data studies field is how data come to define who we are — what Deborah Lupton calls “data selves” or John Cheney Lippold calls “algorithmic identities”. But these are not just reductive representations — they affect how we are known, understood, treated and acted upon by others. In other words, these data selves ultimately act back to change the lives of the embodied humans from which the data were originally derived. These are interesting looping effects which, in the above, you’ve begun to query in relation to education specifically.
I also thought this was a significant line in your post: “Course evaluation data, student performance data, engagement statistics will all be used to develop future courses, and the turn in performance management based on shallow quantifications will have a significant role to play.” These are the kind of issues we have you’ll confront more in the governing with data block, as we explore how educational institutions use data (e.g. course evaluations) to inform their decision-making and planning, but also how policy authorities might use various forms of performance data (e.g. student outcomes, long-term graduate earnings) to assess, judge and hold institutions of education accountable too.