Data use for equity: implications for teaching, leadership, and policy
| Pages | 354-360 |
| Published date | 03 July 2017 |
| Date | 03 July 2017 |
| DOI | https://doi.org/10.1108/JEA-04-2017-0040 |
| Author | Amanda Datnow,Jennifer C. Greene,Nora Gannon-Slater |
| Subject Matter | Education,Administration & policy in education,School administration/policy,Educational administration,Leadership in education |
Guest editorial
Data use for equity: implications for teaching, leadership, and policy
Introduction to data use and equity
The articles in this special issue provide cutting-edge research knowledge on the
intersection of two important policy priorities in the field of education: data-driven decision
making and equity. Both of these priorities have critical implications for the work of
teachers and school leaders, for school improvement initiatives, and ultimately for student
learning. Conceivably, data use could help accomplish goals of equity, and equity could
drive data use efforts. However, up to now, the field has had little knowledge about how
equity and data use come together in the process of educational improvement. This issue is
aimed at addressing this knowledge gap.
Providing an overview of data-driven decision making in education is a logical place to
start in this effort. First, what is data-driven decision making? Data-driven decision making
is the notion that important decisions will be anchored in data, rather than simply being
based on hunches about the right course of action. Originally deriving from the field of
management, the verve for data use is prevalent not just in education but across a wide
range of sectors. Organizations are expected to be data-driven, as are the individual decision
makers within them. What “data”means varies across contexts, but it typically refers to
systematically gathered information. Over time, researchers have questioned the notion that
data in fact drive (Dowd, 2005) and have argued that a more useful conception is that data
inform decision making (Datnow and Park, 2014). We clearly understand the limits of the
term data-driven, but we use the terms data-driven and data-informed somewhat
interchangeably here.
For more than a decade, data-driven decision making has been a prominent feature of
educational reform agendas across the globe, including in the USA, Canada, Spain,
The Netherlands, South Africa, and New Zealand, among many other countries. Data-driven
decisionmaking in education “refers to teachers,principals, and administratorssystematically
collecting and analyzing various types of data […]. To guide a range of decisions to help
improve the success of students in schools (Marsh et al., 2006, p. 1).
In education, Lai and Schildkamp (2016) explain that data use derives from two
often-competing agendas. On the one hand, data use is promoted as part of an external
accountability framework. On the other hand, data use is integral to an agenda of teacher
inquiry, contributing to internal accountability. In the first case, large scale assessment data
dominate, and in the second, a much wider range of data is used to inform instructional
decision making. Lai and Schildkamp explain that teachers often have to balance these two
competing agendas simultaneously.
While agendas for data use vary, the theory of action underlying these data use efforts in
education has similar contours across contexts (Ikemoto and Marsh, 2007). The common
idea is that the examination of data by educators will lead to decision making that is better
informed and ultimately more attuned to student and organizational needs. The process for
data use –at least in typical form –begins with setting a goal, gathering data, analyzing
data, using data to inform a plan of action, evaluating the results, and repeating the cycle
with refinements.
The part of the cycle that educators tend to struggle with the most is using data to inform
action. This is due to several reasons. First, time constraints often mean that educators
spend more time gathering and examining the data than they do planning on the basis of
data. They essentially run out of time and action plans tend to be cursory in nature.
Journal of Educational
Administration
Vol. 55 No. 4, 2017
pp. 354-360
© Emerald PublishingLimited
0957-8234
DOI 10.1108/JEA-04-2017-0040
354
JEA
55,4
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