Embracing digital messiness in education

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Manifesto

It’s not just cities, politics and civic life that suffer from an excess of technological determinism and techno-utopian visions. In education, too, ‘digital by default’ thinking tends to dominate decision-making, and technology is frequently looked at as either a cause of, or a solution to, many apparent ‘problems’ in schools, and in further, higher and continuing education.

Utopian and dystopian narratives of technology are widespread in discussions of digital education. Headlines about the recent massive open online course (MOOC) phenomenon indicate how this plays out with any given educational technology trend, with journalists asking whether MOOCs would:

The relationship of educators, institutions and researchers to technology has often been characterised by attempts at control and order. Indeed, advertising for educational technology is saturated with promises of speed, simplicity and efficiency.

The desire for these types of promises is shaped and spread by the ‘politics of complexity reduction’[1], which aims to map educational inputs to outputs as if there were a simple and linear relationship between the two[2]. This complexity reduction leads to ‘bad’ rather than ‘virtuous’ mess, because ‘seeking to force the inherently messy into a respectable tidy form can result in something that distorts, hides or falsifies the actual social world’[3].

Bad mess ignores diversity and inequities, and narrows definitions of learning and education to what can be understood and measured efficiently.

Teachers, students, researchers and instructional designers who value critical perspectives on digital education and emerging technologies need to break out of unhelpful extremes of ‘promise’ and ‘threat’ by turning our attention to the complexity and messiness of education.

Emerging digital technologies and practices contribute to the fruitful mess that characterises education, casting new light on issues of power, responsibility, sustainability, reach and contact. These areas of emergence offer ‘not-yetness’ – an acknowledgment and valuing of things we don’t yet understand, that we haven’t yet got under control.

Working with the not-yetness of education means engaging with complexity, uncertainty and risk, not as factors to be minimised or resolved, but as necessary dimensions of technologies and practices which are unknown and in flux.

This flies in the face of pressures to simplify, to speed up, to expand, to focus on the measurable and replicable, and to the demands for efficiency that often accompany institutional interest in digital learning.

It acknowledges how much we do not yet know, and the correct response to the unknown is not to narrow our vision to see only what we can account for. Nor is it to conclude that education is broken and that ‘fixing’ it is the next great computer science challenge. It is to keep looking for ways to broaden our view.

[1] Gough, N. (2012). Complexity, complexity reduction, and “methodological borrowing” in educational inquiry. Complicity: An International Journal of Complexity and Education, 9(1). Retrieved from http://ejournals.library.ualberta.ca/index.php/complicity/article/view/16532

[2] Biesta, G., & Osburg, D. (2010). Complexity, education and politics from the inside-out and outside-in: An introduction. In D. Osburg & G. Biesta (Eds.), Complexity Theory and the Politics of Education (pp. 2-3). Rotterdam: Sense Publishers.

[3] McArthur, J. (2012). Virtuous mess and wicked clarity: struggle in higher education research. Higher Education Research & Development, 31(3), 419–430.

Amy Collier is from Middlebury College and Jen Ross is from University of Edinburgh.

Illustration by Jean-Marc Côté. Source: http://publicdomainreview.org/collections/france-in-the-year-2000-1899-1910/

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5 Comments

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  2. Pingback: MOOC MOOC Instructional Design - Digital Pedagogy Lab

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  5. Openness in developing countries says

    Reblogged this on The OER World and commented:
    excellent reflection! the problem of quick fixes and being ruled only by analytics and tunnel vision…this is the reason, I appreciate so much the Finnish model…

    Like

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