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Week 9 – Algorithms and the future

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.

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What is Nudging and how can Nudging help Students to Enroll in College

This is a short video on nudging that presupposes that humans are irrational beings and that ‘most human decision-making is inherently irrational, habitual and predictable’ (Knox et al, 2020). It represents one side of the story. Below is another view of nudging, questioning the ethics behind nudging. Does the fact that people nowadays do not have the time and objectivity to reason logically, give carte blanche to industries to select for them? Is there really freedom of choice or is the concept just an illusion?

Below is a Soundcloud link that describes how nudging can be useful to students in order to help them make correct choices when enrolling in College. Knight (cited in Knox et al, 2020) also makes reference to this:

The UK higher education regulator, the Office for Students, has also adopted aspects of behavioural design to inform how it presents data to prospective university students – thus nudging them to make favourable choices about which degrees to study and which institutions to choose for application – while the Department for Education’s new ‘Education Lab’ positions behavioural science as a key source of scientific expertise  in policy design (Knight 2019).

https://ift.tt/3aVwJ2w

A growing body of research have found that small-scale behavioral nudge campaigns can get students to complete complex tasks, such as refiling for federal financial aid to attend college. But researchers don’t yet know enough about why certain nudges have worked in the past or whether they would still work on a larger scale.

On this episode of On the Evidence, we talk with Jenna Kramer, an associate policy researcher at RAND Corporation, and Kelly Ochs Rosinger, an assistant professor in the Department of Education Policy Studies at The Pennsylvania State University, about efforts to use large-scale nudges to increase college and financial aid applications, increase college enrollment, and bolster college students’ persistence in completing college.

This episode is part of a series produced by Mathematica in support of the Association for Public Policy Analysis and Management (APPAM) and its fall research conference.

Kramer and Rosinger participated in an APPAM panel about scaling nudge interventions in post-secondary education. A summary of the panel as well as links to papers discussed in the session is available here: https://ift.tt/2IHWash

To keep up with Kramer and Rosinger’s work, follow them on Twitter. Kramer is @j_w_kramer and Rosinger is @kelly_rosinger.
via IFTTT

References:

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