So, am I data literate now? A reflection on this blog
Through this blog I’ve become more aware of the data I produce and how data is presented to me. Hand drawing and viewing my peers’ visualisations each week made me question: how data literate am I? How often do I assume that data is telling the truth?
I didn’t always tell the truth in my visualisations. There are reductions, conflations, and estimates in all of my work. I was interested in the affective nature of data, rather than providing truthful insights into an activity each week. My visualisations eschew accuracy and instead point to the anxieties I hold about an activity or experience. This un-truthfulness was augmented by the public nature of this blog – as much a public performance as it is a learning activity.
Given that data are social products (Williamson 2017), presenting data as truthful or objective is often a rhetorical act, as most data-driven practices try to change our perception, or to spur a course of action, through the measurement of something. With data-driven practices extending throughout education, part of a critical data literacy includes paying attention to acts of truth-telling rhetoric – especially when we are knowingly or unknowingly perpetuating that rhetoric ourselves as education workers.
Having control over my own visualisations allowed me to experiment with data methodologies. With no computer to help me collect or synthesise data, the choices I made were nominally my own – choosing what to measure, how to classify, how to draw, what to compare and what to leave out. I was sometimes frustrated by my lack of technical knowledge of statistical methods and data analysis. A comprehension of data methodologies is a key aspect of critical data literacy for education workers, but is challenging to learn given time and technical skill required, and the complexities of datafication in practice.
So, am I data literate now? I’ve gained a better understanding of how data operates in educational settings. How affective have these activities been? Below I synthesise some of the ideas that most interested me and discuss how my visualisations helped me explore these ideas in affective ways.
THE BODY LEARNING/TEACHING/GOVERNED THROUGH DATA
I was interested initially in the use of bodily data in learning analytics. For Derrida and Haraway, technologies must be understood as being inextricably entwined with the body (Barla 2018). As a turn towards bodily and emotional data in edtech occurs (Knox et al. 2020), the relationship between data, the body and learning is made explicit. There are rhetorical parallels between the promises of data-based personalised learning (Bulger 2016) and personalised health products like FitBits (Roberts at al. 2016), and material parallels as researchers use wearables and sensors to test the use of physiological data for learning analytics (Chang et al. 2018, Giannakos et al. 2020).
This work underscores how datafication increasingly extends towards and extracts from individual bodies in education. Datafication produces student and teacher subjectivities – we come to know and perform our learning encounters with reference to the data we produce (Harrison et al. 2020). Moreover, our data proxies are metricised and ranked against other students, teachers, institutions and countries (Anagnostopoulos et al. 2013, Williamson 2017). Our ideas about learning, teaching and governing are being shaped through our individual, sometimes bodily, encounters with data.
By centring the body in this assemblage, I start to comprehend how processes of datafication exist as something real, not just an abstract concept. I’m still thinking about my posture, my stretches, and my tinnitus, weeks after thinking ‘I can stop counting this now’. The affective power of data is so clear to me that it is literally ringing in my ears.
AGENCY & POWER
To me, agency in data-driven education means having an autonomous choice and voice over your data. Approaches like personalised learning, the use of data dashboards, or the development of critical data literacies all seek to empower individuals over their data in some way.
However, these approaches can’t be assumed to provide agency or empowerment to students and teachers. Individual agency in data-driven education is mediated through the asymmetrical power structures that exist in educational settings (Tsai et al. 2020). These structures are intensified through the expansion of datafication throughout education – and the cavalcade of tech industry voices that come with it. Industry actors have their own ideas about learning and teaching that they attempt to enact through the technologies they design and sell, which often tend toward a behaviourism that work against student agency and participation in their learning (Knox et al. 2020).
In these activities, I had agency over my data, but some of the visualisations reminded me of how little choice I have to engage with certain platforms at work (see Three and Four). Conversely, I have more agency than students who can’t easily opt out of having their activity datafied and may face serious consequences based on how an algorithm or teacher reads their online activity. Identifying the power and agency I actually have has made me think about how I use that power in my work.
Finally, I was interested in data as a concept, and the ‘data imaginary’ as enabler of datafication in education. Data is a powerful concept with a long history (Williamson 2017) which is specifically colonial and neoliberal (Prinsloo 2020). Data imaginaries – visions of the future where data can solve the problems of the present – serve to expand and intensify datafication (Beer 2019) and are enacted materially through data-driven educational spaces and policies. Data imaginaries may exist in tension or align with educational imaginaries, themselves widely held visions of solutions to educational problems (Friesen 2020).
I agree there is a dominant social imaginary that exists in education, but I prefer the plural to indicate that there are multitudes of imaginaries, existing interdependently and in resistance to the neoliberal ‘data imaginary’ emerging in critical data studies. My final visualisation attempted to explore alternative imaginaries but these remain undertheorised in educational literature (Selwyn 2020) and in this blog. Alternative critical data imaginaries exist – made visible through the efforts of data workers, in movements towards Indigenous data sovereignty (Kukutai & Taylor 2016) and Data for Black Lives (Watson-Daniels 2021), and theoretical approaches such as a queer futurity of data (Zeffiro 2019). It’s in alternative imaginaries that we might find space to assert our agency and resist the pervasive datafication – and encroaching neoliberal social imaginary – of our educational lives.
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Bulger, M. 2016, ‘Personalized learning: The conversations we’re not having’, Data and Society, 22(1), pp. 1-29.
Chang, C., Zhang, C., Chen, L. and Liu, Y. 2018, ‘An ensemble model using face and body tracking for engagement detection.’ In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 616-622).
Friesen, N. 2019, ‘The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.’ In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press, pp. 141-149.
Giannakos, M.N., Sharma, K., Papavlasopoulou, S., Pappas, I.O. and Kostakos, V. 2020, ‘Fitbit for learning: Towards capturing the learning experience using wearable sensing’, International Journal of Human-Computer Studies, 136, p.102384.
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), pp. 401-417.
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.
Kukutai, T. & Taylor, J. 2016. ‘Data sovereignty for Indigenous peoples: current practice and future needs’. In Kukutai , T. and Taylor, J. (eds.) Indigenous data sovereignty: Toward an agenda. ANU Press.
Prinsloo, P. 2020, ‘Data frontiers and frontiers of power in (higher) education: a view of/from the Global South’, Teaching in Higher Education, 25(4), pp.366-383.
Roberts, L.D., Howell, J.A., Seaman, K. & Gibson, D.C., 2016, ‘Student attitudes toward learning analytics in higher education: “The fitbit version of the learning world”’, Frontiers in Psychology, 7, p.1959.
Selwyn, N., 2020, ‘Re-imagining ‘Learning Analytics’… a case for starting again?’, The Internet and Higher Education, 46, p.100745.
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
Watson-Daniels, J. 2021, Introducing #NoMoreDataWeapons, 26 February, https://blog.d4bl.org/introducing-nomoredataweapons/
Williamson, B. 2017, Big Data in Education: The digital future of learning, policy and practice. Sage.
Zeffiro, A., 2019. ‘Towards a queer futurity of data’, Journal of Cultural Analytics, 1(1), DOI: 10.22148/16.038