Learning systems are typically powered by biased algorithms [Noble, 2018] and limited types of behavioural data on student activity, rather than learning [Eynon, 2015]. Despite this, the learning system itself is permitted to define the research question [Eynon, 2013] based on the data it is able to collect, rather than the needs of teachers, and without teacher input.
That this data is presented on a dashboard to the teacher, suggests that it has innate use [Bulger, 2016] and value; the decisions behind what is presented and how, may not be made explicit [Williamson et al, 2020]. The dashboard is offered as a way to make a complex situation easy to understand for teachers [Williamson, 2017], but this should not be seen as innately positive. Simplification is transformation: through reductions and assumptions, data is changed. Hence, it has manipulated a situation it perceives as a problem, and made it appear solvable [Eynon, 2013]: a demonstration of the solutionism at its heart.
With limited data and little context, the dashboard presents students in a superficial, datafied form, based on proxies for their learning [Bulger, 2016], effectively misrepresented to their teachers. For those who see this situation only as an issue of too little data, this could lead to the call for more personal data collection [Knox et al, 2020], or a push towards more activity being done where data can be collected [Brown, 2020], positioning the learning system and the teachers using it, in a surveillance role.
If the means by which the dashboard arrives at pronouncements is known, this could help teachers, whose practice it appears to influence [Wise and Jung, 2019], understand how to judge its usefulness [Brown, 2020]. However, to do this, teachers need critical data literacy [Raffaghelli & Stewart, 2020; Sander, 2020]; but, if the teacher is disempowered, and the learning system uses its data to self-regulate, even this will not help [Williamson et al, 2020].
A dashboard may appear to offer personalisation to the teacher but, like personalised learning for students, this may be an illusion, and really no more than choice of pre-selected options. Teachers may have some control over the data gathered by the learning system, and whether students can see it, but they then have to deal with the effect that having this essentially surveillance role [Tsai, et al. 2020], has on their relationship with students [Williamson et al, 2020] and how it makes them see themselves as teachers [Harrison et al. 2020]. True empowerment may only be possible if teachers had control over the decision to have data collected at all.
If the dashboard is visible to the institution, this may impact how a teacher views it: a proxy for their teaching [Williamson et al, 2020] and a means whereby they are datafied [Williamson, 2016]. They may experience this as centralised control and surveillance of their performance, nudged towards ‘correct’ behaviour, like their students, [Knox et al, 2020] or to make changes in their teaching in order to influence the dashboard, rather than for pedagogic reasons [Brown, 2020; Harrison et al. 2020].
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Bulger, M., 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf
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.
Eynon, R., 2013. The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology, 38 (3), pp. 237-240.
Eynon, R., 2015. The quantified self for learning: critical questions for education. Learning, Media and Technology, 40 (4), pp. 407-411, DOI: 10.1080/17439884.2015.1100797
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, DOI: 10.1080/13562517.2019.1698541
Knox, J., Williamson, B. & Bayne, S., 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45 (1), pp. 1-15.
Noble, S. U. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble. NYU Press. doi:10.15713/ins.mmj.3.
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, DOI: 10.1080/13562517.2019.1696301
Sander, I. 2020. What is critical big data literacy and how can it be implemented? Internet Policy Review. 9(2) DOI: 10.14763/2020.2.1479
Tsai, Y-S. Perrotta, C. & Gašević, D., 2020. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45 (4), pp. 554-567, DOI: 10.1080/02602938.2019.1676396
Williamson, B. 2016. Digital Education Governance: Data Visualization, Predictive Analytics, and ‘Real-Time’ Policy Instruments. Journal of Education Policy. 31 (2), pp. 123–141. doi:10.1080/02680939.2015.1035758
Williamson, B., 2017. Conceptualising Digital Data in 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.
Wise A. F., and Jung Y., 2019. Teaching with Analytics: Towards a Situated Model of Instructional Decision-Making. Journal of Learning Analytics. 6(2), pp. 53-69. http://dx.doi.org/10.18608/jla.2019.62.4.