Week 9 – Algorithms and the future

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This post brings me closer to the end of the block on Algorithmic Cultures. Most of my time this week was dedicated to the artefact, which brings together the literature, observations and experimentation with algorithms.  My interest over the past few days has been evaluating the socio-economical dimension of algorithms in popular platforms and education. Williamson (2017) describes the impetus of Silicon Valley enterprises and entrepreneurs and their interest in developing ‘incubators’ as prototypes for a new wave of education.

Williamson’s (2017) concept of sociotechnical imaginaries describes the way large corporations approach education…and ‘whose aspirations are therefore becoming part of how collectively and publicly shared visions of the future are accepted, implemented and taken up in daily life’. This begs the question of whether education within this vision can ever be free from the bias that exists when it is filtered through the strata of political, commercial and legislative machines. How unbiased can education be when the concept of learning and teaching becomes a set of data that can be studied, categorised and developed in a software lab?

Another case in point is the concept of nudging, also mentioned in a couple of my posts this week. While nudging can help students by providing them with timely feedback, support and content, one wonders whether this useful tool can be used to promote ideals that go beyond the educational aims, whose scope is to act as part of models ‘to which certain actors hope to make reality conform, serving as ‘distillations of practices’ for the shaping of behaviours and technologies for visualizing and governing particular ways of life ad forms of social order (Huxley, sited in Williamson, 2017).

 

References

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. Sage.

Is Education still in the hands of Educators?

Why is education so irresistible for Silicon Valley entrepreneus?

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The field of education has always been fertile ground for Silicon Valley entrepreneurs in search of new opportunities to develop hardware and software advertised as solutions for bettering education under the ‘liberal politics of the technology.’ (Williamson et el, 2018). The culture of a sense of freedom enjoyed by these companies in political and economic terms, as described by Williamson et al (2018)   is one of the driving forces behind the implementation of new technological trends in various sections of society.

Ferenstein (2015) terms the new Silicon Valley liberals ‘civicrats,’ or ‘techDemocrats,’ whose goal is to make everyone innovative, healthy, civic and educated, and see government’s role as an investor in maximizing people’s contribution to the economy and society.
(cited in Williamson et el, 2018)
What Ferenstein describes is a mentality which believes that anything can be solved having the right tools and resources…that humans are fundamentally ‘faulty’ and that issues concerning human limitations can be solved through technology. It is perhaps the same impetus that drives pioneers of technology to often redefine schooling in terms of what their technologies can ‘solve’ rather than how technologies can change pedagogies and design of syllabi. They flutter their banner of innovation based on the idea of ‘charter’ schools, independent centres that are funded to experiment freely from academic legislation governing other types of schools with the scope of showing how technologies are one big solution to most educational limitations (Williamson et el, 2018)

The idea of education as a capitalist goldmine has meant that any serious enterprise willing to invest in education has been required to break the learning process into quantifiable data (datafication). This is the same as a live update feeds on stock markets at Wall Street. Williamson (2017) describes how recent developments in technology have concerned themselves with the real-time collection of data pertaining to the way people learn in order to provide more personalised learning experiences. An example of this is the Silicon Schools Fund in America which promotes the creation of

 ‘laboratories of innovation and proof points for personalized learning (Williamson et al, 2018):

Schools that give each student a highly-personalized education, by combining the best of traditional education with the transformative power of technology

 Students gaining more control over the path and pace of their learning, creating better schools and better outcomes

Software and online courses that provide engaging curriculum, combined with real-time student data, giving teachers the information they need to support each student

Teachers developing flexibility to do what they do bestinspire, facilitate conversations, and encourage critical thinking

Personalised learning also advocates against standardized methods of testing and learning, pushing instead towards tailor-made solutions dependent on the collection of data similar to that experienced when shopping online, using GPS data, searching or booking flights. This personalization is possible because huge amounts of data can be collected, stored and kept while algorithms constantly check any developments in patterns and behaviour. While admirable in many ways, one questions how this data can be used in other ways that benefit companies to promote their goods.
References:
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. Sage.

Williamson, B., Means, A. & Saltman, K. (2018).Startup Schools, Fast Policies, and Full-Stack Education Companies. Available at: https://www.researchgate.net/publication/327404706_Startup_Schools_Fast_Policies_and_Full-Stack_Education_Companies. (Accessed: 11th March 2020).

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