TY - JOUR

T1 - Do Predictive Analytics Dream of Risk-Free Education?

T2 - The Politics of Risk Mitigation

AU - Zakharova, Irina

AU - Jarke, Juliane

N1 - Publisher Copyright: © The Author(s) 2023.

PY - 2024/3

Y1 - 2024/3

N2 - The future is always shaped and, to some extent, haunted by design decisions of the present and their future residues. Predictive learning analytics, as increasingly used in education, is an example of a technology that is future-oriented by design. Particularly risk prediction concerned with the students at risk, risk factors hindering educational success, and their management has a long history in education. Currently, identification of students ‘at risk’, risk prediction, and mitigation are being automated through learning analytics. Understanding risk as integral both to modern society and the educational processes, our goal in this paper is to trace the politics of risk prediction and educational futures. We argue that the future orientation of educational technologies materialise in form of design features. To analyse these future making features, we examine the risk prediction-related design features of five globally used learning management systems. We consider their politics for future making through the ways in which they define what is problematic, what is thinkable, and what is desirable in education. We discuss (1) the promises and aspirations these learning management systems promote to educators about the possibilities of a risk-free educational future; (2) how risk prediction features work, e.g., on which different data categories they operate; and (3) the resulting politics of who is perceived as a carrier of risk in education and who is called upon to act. We close with a discussion on the politics (and risk) of aspiring for risk-free learning and risk mitigation in datafied education.

AB - The future is always shaped and, to some extent, haunted by design decisions of the present and their future residues. Predictive learning analytics, as increasingly used in education, is an example of a technology that is future-oriented by design. Particularly risk prediction concerned with the students at risk, risk factors hindering educational success, and their management has a long history in education. Currently, identification of students ‘at risk’, risk prediction, and mitigation are being automated through learning analytics. Understanding risk as integral both to modern society and the educational processes, our goal in this paper is to trace the politics of risk prediction and educational futures. We argue that the future orientation of educational technologies materialise in form of design features. To analyse these future making features, we examine the risk prediction-related design features of five globally used learning management systems. We consider their politics for future making through the ways in which they define what is problematic, what is thinkable, and what is desirable in education. We discuss (1) the promises and aspirations these learning management systems promote to educators about the possibilities of a risk-free educational future; (2) how risk prediction features work, e.g., on which different data categories they operate; and (3) the resulting politics of who is perceived as a carrier of risk in education and who is called upon to act. We close with a discussion on the politics (and risk) of aspiring for risk-free learning and risk mitigation in datafied education.

KW - Datafication

KW - Ed tech

KW - Learning analytics

KW - Learning management systems

KW - Predictive analytics

KW - Risk prediction

UR - http://www.scopus.com/inward/record.url?scp=85167729110&partnerID=8YFLogxK

U2 - 10.1007/s42438-023-00411-x

DO - 10.1007/s42438-023-00411-x

M3 - Article

VL - 6

SP - 32

EP - 51

JO - Postdigital Science and Education

JF - Postdigital Science and Education

IS - 1

ER -