Education, Privacy and Big Data Algorithms: Taking the Persons out of Personalized Learning

First Monday 2019

38 Pages Posted: 17 Jan 2023

See all articles by Priscilla Regan

Priscilla Regan

Independent

Valerie Steeves

University of Ottawa - Criminology

Date Written: 2019

Abstract

In this paper, we review the literature on philanthropy in education to provide a larger context for the role that technology company foundations, such as the Bill and Melinda Gates Foundation and Chan Zuckerberg Initiative, are playing with respect to the development and implementation of personalized learning. We then analyze the ways that education magazines and tech company foundation outreach discuss personalized learning, paying special attention to issues of privacy. Our findings suggest that competing discourses on personalized learning revolve around contested meanings about the type of expertise needed for twenty-first century learning, what self-directed learning should look like, whether education is about process or content, and the type of evidence that is required to establish whether or not personalized learning leads to better student outcomes. Throughout, privacy issues remain a hot spot of conflict between the desire for more efficient outcomes and a whole child approach that is reminiscent of John Dewey’s insight that public education plays a special role in creating citizens.

Suggested Citation

Regan, Priscilla and Steeves, Valerie, Education, Privacy and Big Data Algorithms: Taking the Persons out of Personalized Learning ( 2019). First Monday 2019, Available at SSRN: https://ssrn.com/abstract=4324013

Priscilla Regan

Independent

Valerie Steeves (Contact Author)

University of Ottawa - Criminology ( email )

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Ottawa, Ontario K1N 6N5
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