In Education, the bots patrol the data lake in the hope that ‘datafication’ of students, reconstructing people from data (Knox et. Al. 2019, Enyon 2015), will lead to penetrating insights about how they learn. The promises of AI and data are being used uncritically with a view to personalising learning (Tsai and Gašević 2020, Bulger 2016). The process seems to me to flow from the existence of the data lake itself, the promise of ‘datafication’ or perhaps quantification to ‘learnification’ (Knox et. al. 2019) and then to personalised learning (Ibid.). Learnification is the turn toward a more student-centered approach aimed at allowing the learner to choose her own road-map through the datascape. Combining the two developments only opens the doorway to personalised learning (Knox et. al. 2019) through a series of well-intentioned nudges and operant conditioning (Ibid.).
The ideal of personalised learning is not itself new, as Socrates himself often taught one-to-one, the golden ratio in Education (Friesen 2019). Friesen (2019) argues this ideal is imagined and unobtainable, essentially it cannot exist in Higher Education. Yet I feel Frank Smith offers some hope when he points out the intimate role of reading when it is linked with the power of learning identities (Smith 1998:23). In other words, motivated students can seek their own masters once clear of the white noise of information in Higher Education.
In my visualisations this week I have examined this white noise in the form of information I receive about my students’ learning and I also wondered just how personalised this is. And finally, I measured my own one-to-one’s with written educational material. In my working week, software that even promises personalisation does not yet exist, but I find the one-to-one’s with my students offer real personalisation and reading offers me something similar. Data technology has some way to go on this score. But it would be wrong to suggest data is not important. Edwin Hutchins, an anthropologist, observed US Navy navigation teams conducting navigational computation, he concluded the Navy personnel formed one part of a cybernetic system of humans, machines and data. He concluded the learning process is as follows:
- Building a structure based on the availability of data
- Describing the organisation of knowledge
- Dividing up knowledge by dividing computational tasks between people (modularising)
- Fitting the computational and social spaces
Hutchins (1995: 324)
This is somewhat like the process of data > datafication > learnification>personalisation (Knox et. al. 2019). Furthermore, I suggest the personalisation of learning is largely a matter of identity as suggested by Smith (1998). This identity is, of course, also a matter of social fit. Inevitably, machine learning attempts to mimic an established human behaviour. But since we know very little of how humans think AI has not moved from the Chinese Room argument, simulation not assimilation.
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper.
Eynon, R. (2015) The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411.
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.
Hutchins, E., 1995. Cognition in the Wild (No. 1995). MIT press.
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.
Smith, F., 1998. The book of learning and forgetting. Teachers College Press.
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, 554-567.