|Matt Offord

Learning with data is perhaps how we imagine the future will be. Perhaps a future where the teacher has been replaced completely by data, a landscape of knowledge where the teacher no longer shows the way (Biesta 2012). Perhaps in this landscape bots and web crawlers will assist the mythical self-directed learner to find what she needs. With 44 Zetabytes of data on the internet, much of it on social media this might be a good place to start if one wanted to pursue the dream of open and free learning. With this in mind, I considered four days’ worth of potential learning on social media for this week’s data visualisation. Since this week has included a Tweetorial, I thought this would be a good opportunity to incorporate this in my visualisation. I confess I am a reluctant Twitter user but I feel it is necessary to keep up with the more informal information around Higher Education, which I usually hoover up passively. I spend more time on LinkedIn and this is because academia is my second career, and in industry LinkedIn is more popular for professional learning and networking. I am yet to shake the habit. And finally, I included WhatsApp, which, again is used widely in business as a free way to coordinate and communicate across the Globe. Incidentally, many of my business friends are migrating to Signal and Telegraph in light of new data privacy arrangements at WhatsApp. I might have included Instagram but I only post pictures of sunsets and my family there, I haven’t looked at Facebook for several years.

I wrote down each time I used social media this week, categorising the data by platform, and whether I was learning directly, indirectly, purely socialising or networking, Finally I took note of which apps ‘nudged me in certain directions, one way which platforms might use one’s data (Tsai and Gasevic 2020, Knox 2019). These nudges could be towards useful data, which might assist me as a learner, or to commercial sites, or towards similarly minded people, potential friends. The data were hand drawn using the icons to record 10 minutes worth of social media time on each platform, categorised as explained above, with the extra notation of nudges received on that day by platform.

I have recreated the visualisation above with the key to decode the categories typed out, as my handwriting is so bad. In terms of direct learning, I spent an hour on Twitter on three of the four days. This is unusual actually as I do not use Twitter that often normally, perhaps a few minutes a day. However the Tweetorial was extremely educational and perhaps this is something I should do more often. Previously, I have used LinkedIn for this purpose but I would be learning what is happening in the world of business. Since I lecture at a business school, this makes sense, the 20 minutes I spent learning about business initiatives on Monday is more like my daily routine. Indirect learning is how I have labelled information I receive which indicates where to find learning resources, how to access something, or perhaps where I might find a good course on a certain subject for free, the logistics of learning, if you like. The majority of these data were from WhatsApp as I am a member of the MSc (Digital Education) group, established on IDEL. I spend a fair amount of time on there both helping and being helped. What little socialising I do on the internet is split between all the platforms. This highlights to me how little I enjoy the social aspect of these platforms. I would probably spend less time doing this if were not for the isolation of covid-19. I see social media as a resource, one to which I give and receive data, knowledge and even advice. This probably has a social aspect to it, but I find the purely social interaction awkward. Finally, I use social media for professional networking, mostly LinkedIn. Again I see this as making myself available to help people (mostly) and occasionally receiving support and advice. This is arguably social learning and altruistic collaboration, perhaps a far older method of learning that the dialectic method discussed by Friesen (2019).

I also noted the nudges in terms of recommended content. This was found only on Twitter and LinkedIn, it was generally commerical in nature and somewhat incompetent in actually making recommendations that suited me. We actually discussed this in the Tweetorial, the hype around AI would suggest we should be getting far more accurate nudges by now? I experience these nudges as noise. A constant tugging away from one’s purpose to follow this person or to a commercial website. Although social media is not expressly a learning platform, it is seen by many as having this capability. But a learning environment, for me, is quiet. Like a library. Both LinkedIn and Twitter are noisy places, there are many distractions. LinkedIn has a learning function, called LinkedIn learning, which I have used in the past. While I sat through endless videos, notifications streamed in, constantly urging me away from my purpose. I guess it is because learning from others (social learning) is actually quite noisy, there are gems of knowledge amongst this noise.

This might be an argument for the internet as a learning environment, that here we may learn from others or even bots in an imagined one to one environment. That the library and classroom are unatural constructs which sever us from social data in favour of decontextualised data. Human cognition, afterall, is believed to be built around social intelligence (Chudek at al 2012). In 1885 a man called Ebbinhaus demonstrated chopping up information into tiny decontextualised chunks made learning very difficult and forgetting very likely. The forefathers (for they were men) of the modern education system thought this was an excellent idea and built our education system on exactly the method Ebbinghaus had shown to be so problematic (Smith 1998). The information we tend to remember is usually tied up with social significance which is why the apprentice model of learning has been historically popular. But the one-to-one model has proved, unsurprisingly, to be ineffective for mass education (Friesen 2019).

So we hope to recreate the one-to-one scenario with electronic tutors and guides for our new digital landscape, even if this is a fantasy (Friesen 2019). The nudges are programmed using Bernard Skinner’s pidgeon box logic and behavioural economics are as ubiquitous on social media as they are in the supermarket (Friesen 2019, Knox 2019). Based on the rules of reinforcement or operant conditioning, we are offered treats for behaving correctly. Yet the AI is patently not up to the task, sending us on random errands, based on something we read last week, for example. Despite break throughs like the GPT-3 natural language algorithm of 2020, we remain in an AI Winter. Consequently, nudges are noise,not digital Platos. Perhaps one day AI will break the paradox of using Big Data to understand individual choice and autonomy. Whilst bots may predict behaviour on average, they cannot predict the behaviour of individuals. These mutually incompatible levels of analysis are what make personalised learning chimeric (Tsai and Gasevic 2020, Knox 2019, Bulger 2016). Nudges are noise.

Biesta, G. J. J. (2012). Giving Teaching back to education: responding to the disappearance of the teacher. Phenomenology and Practice, 6(2), 35–49. https://doi.org/10.1074/jbc.275.7.4949

Bulger, M. (2016). Personalized Learning: The Conversations We’re Not Having. Data & Society, 1–29. Retrieved from https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Chudek, M., Heller, S., Birch, S., & Henrich, J. (2012). Prestige-biased cultural learning: bystander’s differential attention to potential models influences children’s learning. Evolution and Human Behavior, 33(1), 46–56. https://doi.org/10.1016/j.evolhumbehav.2011.05.005

Friesen, N. (2020). The Technological Imaginary in Education: Myth and Enlightenment in ‘Personalized Learning’. In Stocchetti M. (Ed.), The Digital Age and Its Discontents: Critical Reflections in Education (pp. 141-160). Helsinki University Press. doi:10.2307/j.ctv16c9hdw.12

Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), 31–45. https://doi.org/10.1080/17439884.2019.1623251

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 and Evaluation in Higher Education, 45(4), 554–567. https://doi.org/10.1080/02602938.2019.1676396