SPACETIME Data

The metrics.

It is an unusual week for me because I am in an interview process for a job. I wanted to check how much it would take from my study time.
I continue to explore learning with ASD by including the place. One of the characteristics is the need to control the environment (or having a very stable, familiar environment), and relative inflexibility. I am used to studying in two main places: at my desk, or in the park. Together with that, I tracked how productive I felt the time used was to include another self-awareness, subjective tracking element like last week.

The design and methodology.

Following up on last week, I updated the design according to these considerations:

Last week I agonised over having coherent colour palettes, which is why I chose similar gradient shades. However, this created a problem that I also spotted in McCandless (2012, below): the scale that looks beautiful with its gradient shades, doesn’t end up looking as harmonious when the data is collated. Therefore I opted for colours that all work together.

I wanted to experiment with less rigid drawings. Both many of my classmates and Dear Data (Lupi&Posavec 2016) tend to be less concerned with filling squares and lines perfectly. This is not the case for me, as any major imprecisions are a trigger. However, I wanted to experiment with letting the rigidity go a bit, and opted for free brush strokes.

Using mixed techniques (stamp, tape, metallic and watercolour pens). Although the data is still on paper, I felt like experimenting with textures could add another dimension. The tape added neatness.

This time I took notes first, and then decided on the presentation.

The takeouts.

I can’t see any correlations between productivity and place.

The intervew task did indeed consume a lot of time.

The brush strokes extending to the right seem now like a wasted opportunity to add another scale (horizontal axis for data).

Sources.

Lupi, G. Posavec, S. (2016) Dear Data. Princeton Architectural Press

McCandless, D. (2012) Information is Beautiful. HarperCollins Publishers

Information processing in ASC and personalisation of learning via data gathering

Justification and background.

Hight Functioning (HF) Autism is associated with two main traits: difficulty in socialising, and exceptional ability to process patterns and understand systems (Baron Cohen 2008; 2016). Educational institutions have gone in great lengths to improve the inclusion of neurodiverse students. In the case of ASC the focus has been primarily on the first trait.

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Exploring ASD advantage in learning.

A common trait of autistic people is that they fixate on topics and dive extremely deep into study (Baron-Cohen 2008; Brosnan & Ashwin 2013). This is, of course, of benefit, especially in higher education. In practical terms, this means studying the topic far more extensively than required, even including optional sources. In the data gathering, I depicted the amount of sources I go through to show this.

It isn’t only memory, however. Autistic students can also be much better at processing various types of information (Mottron et al. 2013). This is partly due also to the exceptional attention to detail. Until recently, the common assumption was that this advantage came at an expense of being able to view the whole picture. Indeed, autistic people tend to display preference for the detail in some studies (Baron-Cohen 2008). This has not been my personal experience at all and I am pleased to see that newer research hast disproved this theory altogether (Johnson et al 2009). Nevertheless, processing information, linking and coming up with ideas based on the input is something that I find extremely easy. Therefore, this is depicted with the blue arrows.

Exploring ASD disadvantage in learning.

Although increasingly teachers are encouraged to focus on the ways ASD students learn, there are relatively few tools to help HF people with autism to make the most of their learning. The usual assumption is that, once the sensory and social obstacles are removed, the student with ASD will easily take care of their own learning, which isn’t necessarily the case. It could be especially false for women with autism, who display different traits. In particular, autistic women tend to struggle more with high executive functions. Another issue specific to females is their area of special interest or fixation, that is not typically associated with ASC. Males on the spectrum tend to gravitate towards formal systems such as mathematics, physics, engineering (Baron-Cohen 2016). Women, on the other hand, often choose literature, art, medicine, fashion, psychology etc (Rynkiewicz et al 2019).

The combination of the two clinical factors might cause issues with the output of the information. In the data I gathered, I specifically tracked feeling of block in output of the information, despite having a considerable amount of the input and ideas, as explained above.

The particular importance in depicting this data, for me, lies in the self-awareness and improvement that could help make the most out of the advantages of the autistic traits, while mitigating the disadvantages. Contrary to the popular belief, people with autism do not lack self-insight (Schriber et al 2014). Thus, a similar self-monitoring could be attempted by others with ASD, giving them more agency in helping themselves, instead of being helped.

Disclaimers.

I focus on HF autism exclusively in this blog. In particular, I am a female diagnosed later in life (as many of us are), so any findings are to be seen in this light. Apart from that, the data is gathered for one person only.

The same condition is often listed as Asperger’s Syndrome. Baron-Cohen (2008) differentiates between Asperger’s and HF Autism on the basis of language (the former is characterised by advanced language, the latter by initial language delay). For the sake of this project, I am using ‘autism’ exclusively based on the new DSM 5 diagnostic criteria that classify all these variation as Autism Spectrum Condition.

For describing people with autism, I interchangeably use terms ‘people with autism’ or ‘autistic people’. Personally, I am unbothered by either, but that might not be true for everyone. More on the meandres of ASC inclusive language here.

Sharing the sentiment of the majority of HF autistic people I do not refer to any sources by Autism Speaks for the following reasons.

Note that both the ideas and the block elements of this data gathering are subjective and difficult to quantify. Input and output can be measured in the number of pages or words, this, however, cannot. Therefore, I assessed it basically on the subjective amount of time and mental energy it consumed.

On terminology of Autism Spectrum Disorder vs Autism Spectrum Condition HERE. I use both interchangeably.

Sources.

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

Baron-Cohen, Simon. (2016) Autism: An evolutionary perspective. 1st Symposium of EPSIG. https://www.youtube.com/watch?v=0o1PXeFEcL0

Brosnan, M. Ashwin, C. (2013) Reasoning on the Autism Spectrum in Editor: Volkmar, F. R. Encyclopedia of Autism Spectrum Disorders. Springer NY

Johnson, S. A. Blaha, L. M. Houptb, J. W. Townsendb, J. T. (2008) Systems Factorial Technology provides new insights on global-local information processing in autism spectrum disorders. Journal of Mathematical Psychology

Mottron, L. Soulières, I. Dawson, M. (2013) Perception in Editor: Volkmar, F. R. Encyclopedia of Autism Spectrum Disorders. Springer NY

Rynkiewicz1, A. Janas-Kozik, M. Słopień, A. (2019) Girls and women with autism. Psychiatria Polska

Schriber, R. A. Robins, R. W. Solomon, M. (2014) Personality and self-insight in individuals with autism spectrum disorder. Journal of Personality and Social Psychology, 106(1), 112–130. https://doi.org/10.1037/a0034950

Data, learning, and students on the autistic spectrum

Learning in data.

There is an undeniable push towards personalised learning in education (Gasevic et al 2019). Few factors influence this. The enthusiasts of personalisation claim that it can make education more inclusive. Students will surely enjoy it and learn more (Bulger 2016). The other camp warns the change is driven by commercialisation of education (Biesta 2017). They claim the trend will negatively affect the social dimensions of learning (Bulger 2016). For students on the Autistic spectrum both the perceived advantages and disadvantages of datafication of education might look somewhat different.

Benefits.

Personalised data-driven learning is to open the door for exploring one’s own interests (Bulger 2016). This could be beneficial for ASD student, who fixate on topics of interest. Furthermore, they could also be spared studying subjects they find no interest in, or difficult to comprehending. Also, the move from summative assessment to competency (ibid.) – based education seems better for those students.

Another plus is that in a lot of data-driven learning the student sits in front of a machine, limiting their social interactions. ASD students struggle with social situations. In particular, they find looking at faces harder. This means that interacting with the teacher, e.g. to setup tasks or receive feedback, via computer is better for them.

Risks.

The main problem with datafication of education is that it often perpetuates existing inequalities (Tsai et al 2020). Broadly speaking, neurodiverse students are already a marginalised group, so they constitute the primary target here. It’s especially true for women with ASD, who are often undiagnoses so are neither receiving support nor showing on the radar of schools’ disability services.

There is also a concern with how much the initial input models actually accurately describe the learners from varied backgrounds (ibid.). For the algorithm to work, ‘signature behavioural patterns must be identify’ (Wilson et al 2017 p. 997). ASD behavioural patterns vary significantly from those of neurotypical students. Worse yet, they vary among individuals with autism, because ASD is not a linear scale, but a wide range spectrum (Baron-Cohen 2016). If the machine is taught to respond based on neutorypical students, it will fail when faced with atypical ones. This could lead to all sorts of problems with feedback loops (O’Neil 2016). For example, the unusual response could trigger an intervention (Bulger 2016), which would not only be unnecessary, but also stressful to someone with ASD. Or an intelligent tutor (Bulger 2016) might misinterpret their body language.

Bulger (2016) points out that one problem with personalised learning is the disregard for the social aspect of education. While autistic people are loners, many, especially high functioning ones, express the wish to socialise and the need for friends (Baron-Cohen 2016). Therefore, a complete removal of this aspect of learning isn’t necessarily optimal for them. Some students with ASD in the Tsai et al (2020) study confirmed this sentiment.

Conclusions.

All in all, data-driven learning might offer students with ASD some benefits, such as reducing social interactions or offering personalised paths of learning to harness their ability to hyperfocus. However, the risks associated with the trend are greater for this group than neurotypical students.

Sources.

Baron-Cohen, Simon. (2016) Autism: An evolutionary perspective. 1st Symposium of EPSIG. https://www.youtube.com/watch?v=0o1PXeFEcL0

Biesta, Gert. 2017. ”The Beautiful Risk of Education” : https://www.youtube.com/watch?v=QMqFcVoXnTI

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

Gasevic, D., Tsai, Y.-S., Dawson, S. and Pardo, A. (2019), “How do we start? An approach to learning analytics adoption in higher education”, International Journal of Information and Learning Technology, Vol. 36 No. 4, pp. 342-353. https://doi.org/10.1108/IJILT-02-2019-0024

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

Wilson. A. Watson, C. Thompson, T. L. Drew, V. & Doyle, S. (2017) Learning analytics: challenges and limitations, Teaching in Higher Education, 22:8, 991-1007, DOI: 10.1080/13562517.2017.1332026

Background to this blog

When I was applying to the Master’s degree I am currently studying for, I asked an old friend to have a look at my Personal Statement. For those not familiar with the UK higher education – it is a part of the application that describes how you are motivated and fit to take the course. I was quite methodical about writing the text: link to the course details, exemplify academic skills, show interest in the research the particular University department does. My friend had a read, and asked: OK, but why do you really want to learn more about education? I replied: because education has failed me. I disregarded his suggestion to rewrite the statement as a story about it. I was right. I got accepted. And now I am taking this Term 2 course.

Saying education has failed me sounds preposterous considering I finished one of the top high schools in the country, graduated from one of the world’s best universities, and hold three postgraduate certificates and diplomas.

But when I was 8, life circumstances lead to a change of primary school. I spent a month crying uncontrollably. I got good grades and was ok-ish socially, so it was written off by teachers as stress of the change. But going to school was daily internal torture of various sorts, so much so, that half way through high school, I basically stopped going to most of the classes. I hired a private tutor for a literature course, because that interested me. And I went to nearby university to study Sanskrit, because that was what interested me.

This scenario repeated, until I was out of full-time education. It took 15 years more to finally be diagnosed with Autism Spectrum Disorder. I was handed a book by Simon Baron-Cohen that describes a ‘poster patient’ with High Functioning Autism (formerly known as Asperger’s syndrome, which basically signifies anyone with Autism and an IQ over 70). Among other familiar characteristics, ‘Andrew’, the 19 year old with Asperger’s, quit school, because it was annoying and uninteresting. Then it hit.

Autism affects 1 in 54 children. As of now, boys are said to be four times more likely than girls to be on the spectrum. This might be an entirely erroneous divergence, however. Only recently have researchers started to realise that it manifests very differently in women. Still, women and girls often go undiagnosed, or are routinely refused the diagnosis, because the criteria do not fit them. They cannot. Autistic males are diagnosed by being compared to neurotypical males. Autistic females are diagnosed by…being compared to autistic males. It’s not hard to see where this goes wrong. There’s a long list of differences between how the disorder manifests in different genders, which I am not going to go into here.

The purpose of the first Data gathering for this blog is to focus on how ASD affects the way I intake, process, and output information.