From Accountability to Algorithms: Interorganizational Learning and the Transformation of Quantification in Education

Abstract

Abstract While studies often explore the intended and unintended consequences of technologies, few have theorized how and why they change. One crucial transformation in quantitative technologies is the shift from evaluative accountability to predictive algorithms, such as in schools that use dropout prediction systems. Using the case of ninth-grade early warning indicators, I argue that the transformation of quantification resulted from interorganizational learning, or the acquisition of new knowledge through the interaction of different organizations. In particular, I show how technology changes gradually from organization-level evaluation to individual-based prediction to systems-focused improvement. Pivotal to such changes were new forms of knowledge that emerged (1) as “instructing” organizations directed changes and “receiving” organizations resisted them; (2) as organizations in various fields reciprocally collaborated; and (3) as similar organizations practiced networked learning. Although studies have traditionally highlighted the “discipline” of technologies, I illustrate the power of organizational agents to resist, adapt, and change them—with implications for the study of quantification, work, institutional change, and education.

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