Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking
With the increase in the costs of providing education and concerns about financial responsibility, heightened consideration of accountability and results, elevated awareness of the range of teacher skills and student learning styles and needs, more focus is being placed on the promises offered by online software and educational technology. One of the most heavily marketed, exciting and controversial applications of edtech involves the varied educational programs to which different students are exposed based on how big data applications have evaluated their likely learning profiles. Characterized most often as ‘personalized learning,’ these programs raise a number of ethical concerns especially when used at the K-12 level. This paper analyzes the range of these ethical concerns arguing that characterizing them under the general rubric of ‘privacy’ oversimplifies the concerns and makes it too easy for advocates to dismiss or minimize them. Six distinct ethical concerns are identified: information privacy; anonymity; surveillance; autonomy; non-discrimination; and ownership of information. Particular attention is paid to whether personalized learning programs raise concerns similar to those raised about educational tracking in the 1950s. The paper closes with discussion of three themes that are important to consider in ethical and policy discussions.
KeywordsPrivacy Discrimination Big data Autonomy Education technology Personalized learning
The research for this paper was funded by the eQuality Partnership Grant from the Social Science and Humanities Research Council of Canada. For information on the grant, please see: http://www.equalityproject.ca/. An earlier version of this paper, “Big Data in the Education Arena: 21st Century Student Sorting and Tracking” co-authored by Priscilla M. Regan, Jolene Jesse and Elsa Talat Khwaja, was presented at the Surveillance Studies Network Conference in April 2016. The author acknowledges the assistance of Elsa Talat Khwaja in preparation of this paper. This material is based upon work supported while employed at the US National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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