The topic of this block has proven a bit problematic to me, because at the moment I do not teach. But inspired by ‘Past Selves’ in Dear Data (Lupi&Posavec 2016, below), I mapped the total time of my life that I was a teacher.
Design.
The goal was to show time without a timeline. I copied the design from MCadless’s data visualisation of the cost of the 2008 world financial crisis (McCadless 2012). Putting data in context is key and this visualisation is powerful (McCadless 2010). As an editor with years in publishing, I like how it exploits the format of the book – the turn of the page delivers a real gut punch when the crisis cost takes up the entire two pages. This is the medium perfectly utilised to showcase its content.
Conclusions.
This time I let the data take me to the conclusions and it did. Here they are:
I am lucky to have had constant employment, since 85% of graduates on the spectrum remain unemployed. Surprisingly, the majority of my jobs were in teaching, which requires working with people – something autistic people aren’t meant to be good at. There’s a lesson here for the Disability and Careers services and profiling people based on their neurodiversity.
After the previous visualisation, the question on nature of teaching came up. It was about the immediate nature of the exchange between teachers and students as I understood it. But this data showed an interesting social aspect of teaching. Some time ago one of my private tutees I taught A1 English in 2006 contacted me. He had just graduated the University in the UK. 15 years on, I was still the teacher to him. For my students, I am always ‘teacher’. Seeing this visualisation presented like that made me realised why: indeed for most of my adult life, I was a teacher. It made me think of what such a visualisation would look like for my grandma, who was a primary math teacher for 42 years, longer than the total years she was a child, or was married, or spent rearing her children until they moved out. I said in the introduction this visualisation is of the times I held teaching positions. But it is not the total time I was considered to be the teacher. Perhaps this visualisation should be called ‘once a teacher, always a teacher’.
I wonder if commercialisation of education and seeing teaching as ‘just a job’ will change this?
Sources.
Lupi, G. Posavec, S. (2016) Dear Data. Princeton Architectural Press
This week’s data gathering is aimed to link the past block to this one. The central idea is around the notion of replacing teachers. Bulger’s (2016) extensive study mentions that teacher’s expertise in the subject and experience influence the outcomes. However, it is harder to do the more students a teacher has in the classroom. Data and dashboards are supposed to solve this problem. But the reality is that educators have been signalling for years that classes of 30+ pupils aren’t optimal. So even if this technology is not replacing existing teachers, it is to replace the extra teachers that should be hired. Naturally, this trend is self-perpetuating in the neoliberal economy (Williamson et al 2020). Once a metric system is developed for this purpose, it can be applied any number of times, further eliminating the need for teachers. Worse yet, in ‘society of rankings’ (Williamson et al 2020 p. 351) we can rank, as I did, how teachers fare as sources of knowledge against other means. It can put them at a clear, and quantifiable, disadvantage.
Another aspect has to do with availability of information. For centuries, teachers were the gatekeepers of knowledge. Lectures were the main source of information for students. Since the advent of the internet, this has changed. In contrast to the centuries of access obstacles, now we are bombarded with information. Even 10+ years ago, when I was studying, the university lecturers were who you turned to for recommendations of courses. Now, the online library search engines show if an article was peer reviewed. Any book and its authors an be verified for their academic merits after a quick Google search.
I deem the tendency to replace teachers with metrics negative for several reasons. The main one is that teachers left to interpret the information will inevitably have incomplete data. For example, any large deviation from the average performance will show the what, but not the why (Eynon 2013). In classroom a skilled and experienced teacher can pick up on non-verbal cues to assess this. Datasets won’t carry this information. This poses great risk for many students, for example, those who are neurodivergent. Those students especially will also fare poorly even if numerical data is accomapnied by face recognition technologies.
Metrics.
This time I set out to see where I learn from. I limited this study to reliable and valuable information tidbits that I could recall at the end of the day, like ‘the agricultural revolution started 12 000 years ago’ (Baron-Cohen 2020). The social media are only considered in the instances where I learned something from reliable channels (e.g. verified scientific profiles, as opposed to gossip pages). The books include only the two books I am currently reading (The Pattern Seekers, and The Ethical Algorithm).
I focused on all verifiable, valuable information, not just what I learn for the two courses I am taking this semester. However, this can be clearly extrapolated by contrasting the ‘Msc teachers’ circle to the ‘academic articles’ and ‘books’.
I continued to mark who I repeat the information to because I enjoyed it last week. it was easy to incorporate into this data, and did not obscure it visually.
Design.
The following designs from Dear Data inspired this look:
Sources.
Baron- Cohen, S. (2020) The Pattern Seekers. How Autism Drives Human Invention. Basic Books.
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper.
Lupi, G. Posavec, S. (2016) Dear Data. Princeton Architectural Press
Williamson, B. Bayne, S. & Shay, S. (2020) The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365
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.
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
There’s a famous saying that we learn in three steps: when we are taught, when we study on our own, and then, the final crucial step: when we teach others. Ever since starting this degree, I found myself sharing some of the knowledge gained with friends and family. This data could show the real circle of people that through me are reached indirectly by the university (Brown&Adler 2008). Exchanging knowledge is also essential to my relationships, because this is largely how I connect to other people due to the ASD.
I specified the metrics to the current courses, ASD (which I am researching for this course), and other (which can include extra reading I did on the general topic of digital education, or information from the previous semester). I focused on the four conversations I regularly have, since dialogue is central to exchanging knowledge in our culture (Friesen 2019). Best friend (Jo) and both groups of friends I contact on Whatsapp exclusively. My husband is the only person I talk to extensively in person.
Initially I planned to track it daily, but decided against this. Context, such as time, can put data in perspective (McCandles 2010). However, too many variables can also obscure data (Healy 2019). In this case, it seemed unnecessary to split it by weekdays. It was far more interesting to see what knowledge I pass onto whom.
Interestingly, flipping the format (ring for people and dot for knowledge) would bring the focus to the information, rather than the person.
Choice of design.
I wanted to experiment with round shapes I saw in many of my classmates’ blogs and McCandles (McCandles 2012, below in green). I also wanted to have evenly shaped, individual elements, inspired by the below Dear Data visualisation. I consistently chose neon colours for the subjects, but I was not as preoccupied with the tones this time.
I really enjoyed the simplicity of this presentation.
Sources.
Brown, J. S. Adler, R. P. (2008) Minds on Fire. Educause Review
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
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.
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 Speaksfor 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
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
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
