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).


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.


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.


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.


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.

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.