During week 8, I began with readings on algorithmic cultures and education, several are discussed below. I also started ‘playing with algorithms’ task. I chose to look at Instagram and how the Instagram feed tells me what to like and who to follow. Lastly I looked at musings from the web that might help me understand discussions around algorithms and how they are shape our everyday learning lives.
Review of the main readings;
Williamson 2017 and Knox 2015 articles gave a good overview of the operation of web algorithms, and the ways these automated, non-human agents influence contemporary educational practices.
Both papers underlined how complex a job it is to understand how algorithims are critically analysed to see how they are influencing us, because there are so many interweaving agendas – “Businesses with products to sell, venture capital firms with return on investment to secure, think tanks with new ideas to promote, policy makers with problems to solve and politicians with agendas to set have all become key advocates for data driven education” Williamson 2017.
Kitchin follows up on this theme, explaining that algorithms are usually “woven together with hundreds of other algorithms to create algorithmic systems, and the rules generated by them are compressed and hidden”. They are “works of collective authorship, made, maintained, and revised by many people with different goals at different times”, (Kitchin 2017) and they are embedded in complex socio-technical assemblages. Therefore we do not encounter algorithmic generative rules in a clear manner and in a way that makes it easy to understand them.
In the paper on machine behaviourism: future visions of ‘learnification’ and ‘datafication’ (Knox et al, 2020) they explain the growing influence of behavioural psychology in the educational sector and how it interacts with datafication and machine learning to nudge education towards new forms of behavioural governance. The Knox et al paper did a good job in defining terms such as digital choice architectures, behavioural psychology, behavioural economics, machine learning, & learnification and these are now added to my terminology page. Behavioural governance can work “against notions of student autonomy and participation, seeking to intervene in educational conduct and shaping learner behaviour towards predefined aims”. (Knox et al 2020)
The Knox et al 2020 paper covers a huge amount of ground as it looks into the future of datification. They explained that Learnification theory (Biesta 2015), where the learner is the (potential) consumer, whose needs are being met by ‘education’, will soon be less dominant in education.
With the rise of datification, the learner is becoming more ‘modelled’ and therefore so too is the ability of machine learning to ‘predict’ the learner. When one can predict a human’s next steps, it becomes easier to manipulate those next steps. To compound the issue, learners come into education not really knowing what their preferences are, therefore they are easier to nudge towards the predetermined path as designed by the algorithm. Knox et al state “Here, learners are assumed to respond directly to what the dashboard reveals, rather than evoking some kind of consumerist desire. “
Knox et al (2020) describe this as a ‘crucial shift’ away from Biesta’s learnification model. In the future we will become more influenced by behavioural psychology and algorithmic generative rules (Kitchin 2017) that nudge us towards ‘correct’ forms of performance and conduct that have already been decided (Knox et al 2020).
Activity online- Critical research on machine learning can be negative, so it was nice to find this article on from “Data Science for Social good” which demonstrates a positive use of machine learning for social good and education. It describes the development of an algorithm that assigns a fire risk score to each property on the fire department’s inspection list.
The ‘bias of algorithm’s article here reminds me that “We must not assume the transparency and necessity of automation”. (Knox 2015), and to maintain a “more general, critical account of algorithms, their nature and how they perform work” (Kitchin 2017).
On the article about ‘kids growing up with algorithms’; Kitchin describes this kind of algorithmic play in his paper, discussing how he would like to see us “explore the ways in which people resist, subvert and transgress against the work of algorithms, and re-purpose and re-deploy them for purposes they were not originally intended”. Kids would be the best kind of subversive players I think.
References for the readings
Kitchin, R. (2017) Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 14-29, DOI: 10.1080/1369118X.2016.1154087
Knox, J. 2015. Algorithmic Cultures. Excerpt from Critical Education and Digital Cultures. In Encyclopedia of Educational Philosophy and Theory. M. A. Peters (ed.). DOI 10.1007/978-981-287-532-7_124-1
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, DOI: 10.1080/17439884.2019.1623251
Williamson, B. 2017. Introduction: Learning machines, digital data and the future of education (chapter 1). In Big Data and Education: the digital future of learning, policy, and practice
glean one thing;
involves them, i.e. when you tag or mention them. As for the other people who pop up in your suggestions, they are most likely coming from your friends, search histories, contacts, other social media accounts, and so on. If you friend somebody on Facebook, they will also appear as a follower suggestion on your Instagram.