TY - JOUR
T1 - Misconceptions about data-based decision making in education
T2 - An exploration of the literature
AU - Mandinach, Ellen B.
AU - Schildkamp, Kim
N1 - Funding Information:
The authors wish to thank Amanda Datnow, Elizabeth Farley-Ripple, Edith Gummer, Jo Jimerson, Susan Mundry, and Diana Nunnaley for contributing ideas to this paper. The authors would also like to acknowledge Edith Gummer, Laura Hamilton, and Shazia Miller for their participation in the AERA session.
Publisher Copyright:
© 2020 The Authors
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - UT-Hybrid-D
KW - Continuous improvement
KW - Data literacy
KW - Data use
KW - Data-based decision making
KW - Data-driven decision making
KW - Misconceptions
KW - Teacher preparation
KW - Theory
KW - Accountability
UR - http://www.scopus.com/inward/record.url?scp=85078187101&partnerID=8YFLogxK
U2 - 10.1016/j.stueduc.2020.100842
DO - 10.1016/j.stueduc.2020.100842
M3 - Article
AN - SCOPUS:85078187101
SN - 0191-491X
VL - 69
JO - Studies in educational evaluation
JF - Studies in educational evaluation
M1 - 100842
ER -