Images obtained and modified from https;//pixabay.com
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.