Block 1: learning summary

What is learning? What is learning with ASD?

Gert Biesta’s definition of learning expressed across his work could be summed up as transforming social interaction:

”it is an ongoing dialogue between ‘self’ and ‘other’ (in the widest sense of the word ‘other’ in which both are formed and transformed—a process through which we come ‘into the world’ and the world comes into us.” (Biesta 2015). To him, both the teacher and the social aspect of learning are absolutely crucial. The sentiment echoed by the research presented by Bulger (2016). While this may appear true, little of it works for students on the autistic spectrum.

For an ASD brain learning is more acquiring and storing knowledge. Autistic people are known for going deep into the topic (known as fixating). This key element I intended to explore in week one of the data visualisation.

‘Meeting the other’ (Biesta 2017) is a challenge in itself for many on the spectrum, so despite Biesta’s diagnosis, it is not how we learn. However, autistic people often base most of their social interactions on communicating their vast residue of knowledge (Baron-Cohen 2008). I focused on portraying this in week 3 of data through the dialogues I regularly have (Friesen 2019).

How does Data impact learning?

Interestingly, these unique patterns of learning displayed by the autistic mind mean that learnification could be seen as beneficial. This is not because of the commercialisation of the big data, which tends to push discriminated groups more into marginalisation (O’Neil 2016), but because learnification could bring more personalisation. Bulger (2016) suggests personalisation does not actually help independence, but perhaps this is a generalisation that does not include neurodiverse individuals. Tsai et al (2020) bring an example of student on the spectrum who expressed optimism about being aided by learning analytics. While it is indeed still questionable how big data in education could benefit the autistic community as a whole, on a smaller scale collecting data is definitely useful. The data gathering in this course has helped me explore how my own learning is shaped by autism. It could be a great tool for others on the spectrum, given there is nobody more equipped to organise data, than people genetically wired to spot patterns (Baron-Cohen 2020).

Data visualisation.

Visualising data, in short, is the means to explore and understand data (Healy 2019). As McCandle points out, data without context is meaningless (McCandless 2010). This is why I intended to present sets of data where the relationship between variables is important (Healy 2019). For example, Week 1 relationship between input and output was a good way to visualise the block of meaningful production caused by poor high executive functions. Week two data was the least informative in that it showed no important correlation. While it was aesthetic, it wasn’t perceptual or substantive (ibid.) While it did help to debunk the assumptions I made about places where I study, it brought no further insight. This shows that, although we need data visualisation to be pleasant to the eye, it needs a balance of the thee aspects mentioned by Healy (2019).

Baron-Cohen, Simon. (2008) Autism and Asperger’s Syndrome. The Facts. OUP, Kindle Edition

Baron- Cohen, S. (2020) The Pattern Seekers. How Autism Drives Human Invention. Basic Books.

Biesta, Gert. 2012.  Giving teaching back to education: responding to the disappearance of the teacherPhenomenology & Practice, 6(2), pp. 35-49.

Biesta, Gert. 2015 What are schools for? An interview with Gert Biesta on the learnification of school buildings and education.

Biesta, Gert. 2017. ”The Beautiful Risk of Education” :

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. 

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.

Healy, K. (2019) Data Visualisation. A practical introduction. Princeton Press

McCandless, D. (2010) The beauty of data visualisation.

O’Neil, Cathy (2016) Weapons of Math Destruction. Random House Audio (Audible release date 09-06-2016)

Tsai, Yi-Shan. 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, DOI: 10.1080/02602938.2019.1676396

One Reply to “Block 1: learning summary”

  1. This is a useful conclusion to the block, and good to see you questioning some of the perspectives from the literature through the ASD perspective. It is especially interesting here to think about how ‘personalisation’ might bring benefits – it reminds me that as much as we critique the fallacy of assuming automated one-to-one teaching in education is suitable for all, it might be desired in some contexts.

Leave a Reply

Your email address will not be published. Required fields are marked *