Algorithmic culture, networked learning and the technological horizon of theory

ABSTRACT

Going via Bernard Stiegler’s theorisation of technology, and his response to Chris Anderson’s claim that the era of hyper-networked, algorithmically driven digital technologies signals the end of theory, this paper aims to place the educational practice of networked learning as a space to think the edge, excess or limit of this proposed algorithmic dominance. The author discusses how networked learning can negotiate the border between educational theory, the practice of teaching and learning, and the processes and systems of educational technology, but suggests that to do this it must engage these disciplines through a thinking of technology which does not decide upon its status in advance. He argues that affirming this particular relation to technology is increasingly urgent given we are at a moment in which educational institutions are asking how to prepare our students for an age of continuing technological disruption.

KEYWORDS:

• Bernard Stiegler
• networked learning
• algorithmic culture
• higher education
• algorithmic governmentality

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. For a particularly useful exploration and explanation of ‘originary technicity’ see Bradley (2011).

Additional information

Notes on contributors

Mark Dawson

Dr Mark Dawson is Director of Digital Education and a Senior Teaching Fellow at Lancaster University Management School. He is also a member of the Centre for Technological Futures, and Lancaster’s Centre for Technology Enhanced Learning. His research background takes in psychoanalysis, continental philosophy and literary theory, and he is particularly interested in how, at the intersection of these diverse intellectual traditions, there is a critically unique engagement with technology. His current research reads that engagement in the context of higher education, and addresses the impact of digital technologies on academic identity and practice, particularly in terms of automation, surveillance and the organisational structures of the university.