I have a number of reflections after an interesting journey with data and visualisations.
I now have a greater understanding of how complex, inter-related and ever-changing data are. Learning analytics and data allow for responsive information for learners, teachers and education systems (Thompson & Cook, 2017). Responsiveness is a key feature for learners and teachers, so that it can influence practice (Tsai et al, 2020). For example, during my reading (learner) week, being mindful of the data encouraged me to read more. Part of data’s complexities are the competing priorities of these groups, who view the value of data and learning analytics in different ways (Bulger, 2016).
Behaviour, or information, needs to be suitable for datafication (Williamson et al, 2020). I find it interesting that some the useful information from an educational and participatory perspective, remains difficult to measure without conversation with the learner. This was relevant to me during the ‘teaching’ block, where I started trying to measure complex behaviours and emotions such as procrastination, motivation and tiredness. The data that I collected on these measures were limited, and the inferences that I made from them followed suit.
Technology ‘easily’ collects information about us, but when you try to gather that manually you realise the effort that needs to go into that data collection. It highlighted to me the preciousness of our data. Especially, when we often share personal information for questionable benefit (Payne, 2014).
Although the educational shift to technology enabled personalisation is not new, there is a perceived veil of the unknown around data and data analyses (Bulger, 2017; Friesen, 2020). The needs and priorities of the learner, teacher and the governing agencies all co-exist, and ebb and flow amongst one another. Knox et al (2020) comment that it is increasingly important to reflect on the role of learners in a datafied education system. What once was information exclusive to data scientists can be made accessible to a wider audience through visualisation decisions (for example bar charts or scatter graphs or pom poms and concentric circles!) and importantly keys or descriptions on how to read the data.
The more I interacted with my own data, the more comfortable I became with the idea that data was everywhere, both found and created by ‘everyday living’ (Lupi & Posavec, 2016). It was a mindful activity, for example in the week that I tracked wildlife that I viewed in the garden and in the park.
Education is not neutral, and is always in service of an ideology (Apple, 2012). Similarly, technology is also a non-neutral entity. For learning analytics, technology and education are linked and shape one another and we must examine the impact of those influences (Halford & Savage, 2010). Data and learning analytics tend to reinforce a western pedagogy linked to personalisation for the learner (Friesen, 2020). Whereas some eastern pedagogies would encourage a collective approach to the education of the group over the education of the individual (Cambridge Assessment, 2014).
It follows that educational data and learning analytics are framed and constructed by ideologies. Further, alternative forces such as business/capitalist influences feature strongly in the use and understanding of data – manifested by global edtech businesses being sold for multibillion dollars (Williamson et al, 2020). Data analyses that track and potentially influence behaviours have transformed the commercial sector, making the value of the data accrued about learners valuable in that context. This was demonstrated in the week I tracked my steps and motivation, which was tracking behaviour and influencing behaviour at the same time.
A key feature of data as a tool of power has been the centralisation of education governance, and the associated move from the knowledge expertise to a more interdisciplinary problem-solving approach (Ozga, 2016). I use the word tool here, in the absence of anything better – the word tool feels instrumentalist and does not acknowledge the above points about the non-neutrality of data in education! Data policies can reproduce and reinforce existing regimes (Williamson et al, 2020). When I tracked my screen time is a good example here, where the reason that I opened my phone was part of the data and could be used to implement strategies to encourage me to use my phone more. Or alternatively, the opposite. Varying perspectives in educational data could be a tool for change, for example instead of the dominant neoliberal ideology in Higher Education – a liberal approach might use data and learning analytics to include marginalised learners and focus on inclusion (Prinsloo, 2020). Further, there is an argument that the visibility of learning data might shift power more to the hands of learners. Or that a stronger understanding, and less of a focus on quantifiable progress, might shift power from institutions to teachers.
My starting point was to view data as scientific. But as I’ve worked through the process I felt increasingly that there is a creativity involved in how we gather, analyse and feedback on our data. I was able to differently represent my data every week. Sometimes the inspiration for display stemmed from the type of data that was gathered, for example the questions week. Other times, the visual was less colourful and felt like a neat way to display the data (incomplete tasks week). Ways in conveying data forms part of it’s potential to sell progress in education (Sobe, 2013). Glossy visualisations of data, presented in a creative and engaging way in the public can influence their understanding and perceptions of the topic that the data represents (Williams, 2017; Prinsloo, 2020). Those who need to must understand the data, first and foremost. For example, there are ethical considerations about what data is collected about learners and the inferences that are made by that data by teachers and administrators in institutions and beyond (Bulger, 2016). Creative displays, such as learner dashboards can be a tool to support learners and teachers with reflection and next steps or intervention (Tsai et al, 2020).
Data and their infrastructures contain and wrestle with multiple conflicting tensions in education. This makes them intricate, and coupled with the instantaneous nature of data analyses that we have become used to – quickly changeable. Data are not as mysterious as I first thought, and are there for where we want to look – or mindfully reflect upon. To do this meaningfully, and accurately we must be careful not to attribute complete objectivity to data and their infrastructures. Considering which ideologies and motivations influence data and their analyses can help us understand them as tools of power, that could serve learners, teachers, or governance (institutions and agencies) best.
Apple, M. W. (2012) Education and Power. London: Routledge. [Google Scholar]
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
Cambridge Assessment (2014) Tim Oates – Assessment without levels. Podcast. (Accessed 04/04/2021)
Friesen, N. (2020) ‘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
Halford S., & Savage, M., (2010) Reconceptualizing Digital Social Inequality, Information, Communication & Society, 13(7), pp. 937-955, DOI: 10.1080/1369118X.2010.499956
Knox, J, Williamson, B & Bayne, S (2020) ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies’, Learning, Media and Technology, 45(1), pp. 1-15
Lupi, G. & Posavec, S. (2016) Dear Data. London: Particular Books
Ozga, J. (2016) Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81
Payne, R. (2014) “Frictionless Sharing and Digital Promiscuity.” Communication and Critical/Cultural Studies 11 (2): 85–102. [Taylor & Francis Online]
Thompson, G. and Cook, I. (2017) ‘The logic of data-sense: thinking through learning personalisation’. Discourse: Studies in the Cultural Politics of Education. 38(5), pp. 740-754
Tsai, Y., Perrotta, C. and Gasevic, D. (2020) ‘Empowering Learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics’. Assessment and learning in Higher Education (45:4) pp. 554-567
Williamson, B (2017) ‘Digital Education Governance: political analytics, performativity and accountability’. Chapter 4 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