Data-driven decision making in the K-12 classroom

Abstract

During the past decade, data-driven decision making (DDDM) has been at the forefront of many discussions on how to improve public education in the USA. Professions such as medicine, business, politics, engineering, etc. have embraced a data culture and built tools to systematically collect and facilitate analysis of performance data, resulting in dramatic performance improvements. Every day the public depends on companies like Google that collect and aggregate data in ways that help us make decisions about everything from online purchases, to stock investments, to candidate selection. This chapter introduces current research undertaken to bring comparable advantages to education, with the goal of helping classroom- and school-level stakeholders incorporate DDDM as integral to their work. The chapter outlines several different theoretical perspectives currently applied to the DDDM challenge, including the lenses of cultural change, assessment, implementation/adoption, and technology. The bulk of the chapter focuses on research related to models of successful local DDDM implementation, including the design of technological tools and processes to facilitate collection and analysis of actionable data in ways previously not possible. The chapter concludes with implications for research and development that are relevant to those in the fields of instructional technology and learning sciences.