Commentary on Neil Selwyn’s LAK18 Keynote Address
Keywords:Critical Data Studies (CDS), ethics, learning analytics, power
In his keynote, Neil Selwyn not only acknowledged his role as ‘outsider’ to the field of learning analytics, but also intentionally assumed the role of “idiot”. In my commentary I assume that Selwyn’s embrace of being an idiot was more than just self-deprecating humour or a disclaimer aimed to prepare the audience for his provocations. In a Medieval carnival, the clown, fool or community idiot was crowned king, and for the duration of the carnival, could make fun of the royal household, blaspheme and provoke, all licenced by his or her role at that moment in time. Selwyn acknowledged that his own position was and continue to be informed by Critical Data Studies (CDS), an emerging research focus and discourse aimed at troubling much of current accepted and unquestioned assumptions and practices in the broader context of data science. I reflect and comment on Selwyn’s keynote by firstly mapping some of the key tenets of CDS, before addressing some aspects of the keynote and two aspect of his “learning analytics wish-list” namely “giving students control” and “seeing ethics in terms of power, not in terms of protection."
Bakhtin, M. (1984). Rabelais and his world. Trans. Helene Iswolsky. Bloomington, IN: Indiana University Press.
Beer, D. (2016). Metric power. London: MacMillan.
Bowker, G. C. (2005). Memory practices in the sciences. Cambridge, MA: MIT Press.
Dalton, C., & Thatcher, J. (2014, May 12). What does a critical data studies look like, and why do we care? Society and
Dalton, C. M., Taylor, L., & Thatcher, J. (2016). Critical data studies: A dialog on data and space. Big Data & Society, 3(1), 1–9. http://dx.doi.org/10.1177/2053951716648346
Iliadis, A., & Russo, F. (2016). Critical data studies: An introduction. Big Data & Society, 3(2), 1–7. http://dx.doi.org/10.1177/2053951716674238
Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 56–65). New York: ACM.
Gutiérrez, M., & Milan, S. (2019). Playing with data and its consequences. First Monday, 24(1). http://dx.doi.org/10.5210/fm.v24i1.9554
Haggerty, K. D., & Ericson, R. V. (2000). The surveillant assemblage. The British Journal of Sociology, 51(4), 605–622. https://dx.doi.org/10.1080/00071310020015280
Jones, K. M., & McCoy, C. (2018). Reconsidering data in learning analytics: Opportunities for critical research using a documentation studies framework. Learning, Media and Technology, 44(1), 52–63. https://dx.doi.org/10.1080/17439884.2018.1556216
Jones, K. M., McCoy, C., Crooks, R., & VanScoy, A. (2018). Contexts, critiques, and consequences: A discussion about educational data mining and learning analytics. Proceedings of the Association for Information Science and Technology, 55(1), 697–700. https://dx.doi.org/10.1002/pra2.2018.14505501085
Kitchin, R., & Lauriault, T. (2014). Towards critical data studies: Charting and unpacking data assemblages and their work. The Programmable City Working Paper, 29 July 2014. http://mural.maynoothuniversity.ie/5683/
Kitto, K., Buckingham Shum, S., & Gibson, A. (2018). Embracing imperfection in learning analytics. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 451–460). New York: ACM. https://dx.doi.org/10.1145/3170358.3170413
Popkewitz, T. (1987). The formation of school subjects: The struggle for creating an American institution. New York: Falmer Press
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182. https://dx.doi.org/10.18608/jla.2016.31.10
Prinsloo, P., & Slade, S. (2017). Big data, higher education and learning analytics: Beyond justice, toward an ethics of care. In B. Daniel (Ed.), Big data and learning analytics: Current theory and practice in higher education (pp. 109–124). Switzerland: Springer International Publishing. https://dx.doi.org/10.1007/978-3-319-06520-5_8
Selwyn, N., & Facer, K. (Eds.). (2013). The politics of education and technology: Conflicts, controversies, and connections. New York: Palgrave Macmillan.
Selwyn, N. (2014). Distrusting educational technology: Critical questions for changing times. New York: Routledge. https://dx.doi.org/10.4324/9781315886350
Selwyn, N. (2016). Is technology good for education? Malden, MA: Polity Press.
Selwyn, N. (2019). What’s the problem with learning analytics? Journal of Learning Analytics, 6(3), 11–19.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist 57(1), 1509–1528. https://dx.doi.org/10.1177/0002764213479366
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