Collaborative Data Inquiry and the "Wicked" Problems of Inequity

• In Event: Adapting Continuous Improvement Methods to Advance Equity-Related Goals

Abstract

Collaborative data inquiry is one way that improvement science has been translated into practice at the school level. This study focuses on the Data Wise Improvement Process (Boudett, City, & Murnane, 2013), where teams examine data to identify a shared problem, investigate how the system (e.g., instructional practice) contributes to that problem, develop and implement an action plan, measure progress, and continuously adjust. Although previous studies have examined how educators use data for continuous improvement (Coburn & Turner, 2012; Schildkamp, 2019), there is increasing interest in how educators use data inquiry to advance equity or justice-oriented work (Bertrand & Marsh, 2015; Datnow, Greene, & Slater, 2017).

For this study, we draw on theories of organizational learning, or a “higher order of collective learning that extends beyond a single individual” (Higgins et al., 2012, p. 69). Scholars typically view organizational learning as either a quantitative, systematic process or a more qualitative, social process (Easerby-Smith & Lyles, 2011). Rather than viewing one approach as right or wrong, scholars have found that a systematic approach works well in stable contexts with known technologies, while social approaches are appropriate in unpredictable contexts with unknown technologies (Edmondson, 2012; Sitkin et al., 1994). Compared with problems like improving attendance or reading achievement, addressing institutionalized inequities is rife with uncertainty (Diamond & Lewis, 2015) and less amenable to more quantitative and systematic approaches (Bush-Mechenas, 2022; Turner, 2020). In this study, we therefore ask: How do educators adapt the routines and tools of collaborative data inquiry to address complex, undertain issues of equity?

We conducted a multi-site, descriptive case study of two schools that had been using Data Wise for several years: Forest Hills, an elementary school in the midwest, and Valley View, a high school in the southeast United States. The Forest Hills and Valley View principals decided to use Data Wise to advance what each school defined as “equity.” In 2021-2022, we interviewed educators at each school and collected relevant documents. We coded the qualitative data, drafted single-case and cross-case memos, and asked educators at the schools to review findings for accuracy.

We found that data inquiry posed unique challenges at Forest Hills: they chose a complex and “wicked” problem to tackle related to Black students and disciplinary policy. To address this higher level of uncertainty, this team adapted their Data Wise routine to include more work on establishing trust, agreeing on common language, finding appropriate measures for the problem, and supporting teacher learning through the discomfort of addressing implicit bias.

There are a wide variety of perspectives on how continuous improvement can better address equity and justice (Peurach et al., 2022). By using a lens of organizational learning, this paper supports more nuanced theorizing about how continuous improvement might be adapted to take on the complex and uncertain work of addressing institutionalized inequities.

Authors

• Candice Bocala, Harvard University
  Presenting Author

• Maxwell Yurkofsky, Radford University
  Presenting Author