This block was all about ‘Learning with Data’. At the beginning of this block, I thought the data collection process and visual representation of the data would be a simple and easy task. However, ‘seeing’ reality through a distilled collection of data points was eye opening; not only did it provide a detailed snapshot of different aspects of my current life, but also highlighted areas and interactions I was not so aware were affecting my learning, teaching or personal life in general. The data visualisation process in block 1 was exciting, challenging and at times uncomfortable, as data, even when presented in a colourful and nice-looking format, is raw in essence.
The readings this block were very useful to frame and understand the key issues surrounding ‘data analytics’ and ‘personalisation in education’. Through the readings of this block we explored how new technologies offer the opportunity of ‘democratizing information and instruction’, where the classroom is a space where students can be creative and ‘pursuit their own paths’. (Bulger, 2016) The ‘myths of e-learning’ were explored by Friesen (2020), where personalised education can be better understood as a ‘dream’ that is still far from being achieved.
This block we also explored the different ways technology is ‘nudging’ learning in different directions, how ‘machine learning’ does not have a single definition and can be understood for many different angles (Knox, Williamson and Bayne, 2019), and how behaviours and emotions are at the centre of the educational paradigm we (I’m) are trying to make sense of.
An interesting finding for me this block was about how, it is argued, ‘learning analytics and ‘data technologies’ can support and improve ‘student agency’, at least in higher education. Tsai, Perrota & Gašević (2019) critically examined how personalised learning approaches can ‘empower learners’ through the adoption of ‘data technologies’. According to Tsai, Perrota & Gašević (2019), learner empowerment involves ‘interwoven power relationships in a complex educational system’, and should not be automatically assumed that it occurs only by adopting the ‘personalised data technologies’. In practice, I wonder, how well do teachers and academic leaders understand that ‘data technologies’ are not a one-stop solution? That by adapting a new ‘data analytics’ software, and that personalisation in education (can, should?) involves more human contact, not less.
Overall, this block I learned that data is not neutral. I understood while collecting and representing data, that I was curating data to represent my ‘ideal self’, the one I wanted others to see. By this I don’t mean that I falsified the data or invented the visualisation, but simply that even when choosing ‘what’ to track, I was pre-selecting aspects of data that I wanted others to see. If ‘data technologies’ work in a similar way, through the biased pre-selection and curation of data points, who’s ‘ideal self’ are we seeing when we look at educational data?
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Bulger, M. (2016) ‘Personalized Learning: The Conversation We Need’, Data & Society.
Friesen, N. (2020) ‘The Technological Imaginary in Education: Myth and Enlightenment in ‘Personalized Learning’’, in Matteo, S. (ed.) The Digital Age and Its Discontents: Helsinki University Press, pp. 141-141.
Knox, J., Williamson, B. and Bayne, S. (2019) ‘Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies’, Learning, media and technology, 45(1), pp. 31-45.
Tsai, Y.-S., Perrotta, C. and Gašević, D. (2019) ‘Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics’, Assessment and evaluation in higher education, 45(4), pp. 1-14.