Education, privacy, and big data algorithms: Taking the persons out of personalized learning

  • Priscilla M. Regan
  • Valerie Steeves

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.

Author Biographies

Priscilla M. Regan

Schar School of Policy and Government, George Mason University

Valerie Steeves

Department of Criminology, University of Ottawa, Ottawa, Canada

Published
2019-11-01
How to Cite
Regan, P. M., & Steeves, V. (2019). Education, privacy, and big data algorithms: Taking the persons out of personalized learning. First Monday, 24(11). https://doi.org/10.5210/fm.v24i11.10094