Abstract
Research on data-based decision making has proliferated around the world, fueled by policy recommendations and the diverse data that are now available to educators to inform their practice. Yet, many misconceptions and concerns have been raised by researchers and practitioners. To better understand the issues, a session was convened at AERA’s annual convention in 2018, followed by an analysis of the literature based on misconceptions that emerged. This commentary is an outgrowth of that exploration by providing research, theoretical, and practical evidence to dispel some of the misconceptions. Our objective is to survey and synthesize the landscape of the data-based decision making literature to address the identified misconceptions and then to serve as a stimulus to changes in policy and practice as well as a roadmap for a research agenda.
Highlights
Data-based decision making (DBDM) has become important, in part, because policymakers have stressed the need for education to become an evidence-based field, causing educators to rely more on data and research evidence, and not just experience and intuition
Misconceptions about DBDM have arisen over the course of changing mandates from practice, policy, research, and theory
We argue here that it is crucial that data use starts with a certain school improvement goal and not a focus solely on accountability and/ or on the data available
Summary
Data-based decision making (DBDM; or data-driven decision making), data use for short, has emerged and evolved as a key field in education for nearly two decades. Studies in Educational Evaluation xxx (xxxx) xxxx and attaining specified targets (Wayman, Spikes, & Volonnino, 2013) This stimulated the use of data for informing teaching and learning in schools in the United States (Wayman, Jimerson, & Cho, 2012), but most often, for the purposes of accountability and compliance, rather than for continuous improvement (Hargreaves & Braun, 2013). Data use for accountability continues to be a prominent focus due to federal and state and/or national testing and compliance policies (Hargreaves & Braun, 2013; Nichols & Berliner, 2007) This focus did not take the variety of contexts in which the learning occurred into account, and led to a narrow focus on achievement data, as the sole source of important data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
