Components, Infrastructures, and Capacity: The Quest for the Impact of Actionable Data Use on P–20 Educator Practice

Abstract

This chapter reviews actionable data use—both as an umbrella term and as a specific concept—developed in three different traditions that data/information can inform and guide P–20 educational practice toward better outcomes. The literatures reviewed are known as data-driven decision making (DDDM), education data mining (EDM), and learning analytics (LA). DDDM is grounded in K–12 settings, has a social orientation, and is shaped by policy. EDM and LA began in higher education using data provided by instructional tools. This review of more than 1,500 publications traced patterns in these communities revealing disciplinary disconnects between DDDM and EDM/LA. Recognizing information’s systemic nature, this review expanded the analysis from teacher practice to educator practice. While methodological progress has been made in all areas, studies of impact were concentrated in DDDM. EDM and LA focus on tools for current/future educational settings and leveraging data harvested for basic research while reconceiving learning practices. The DDDM impact studies did not support a directly beneficial model for data use. Rather, long timescale capacity factors, including cultural and organizational processes that impact data use were revealed. A complementary model of components, infrastructure, and capacity is advanced with recommendations for scholarship in education’s sociotechnical future.