The Data Wise Improvement Process as Improvement Research (Poster 4)

• In Event: Mapping the Landscape of Improvement Research: Representing Diverse Approaches Using a Common Grammar

Abstract

Purpose

This poster illustrates one school’s use of the Data Wise Improvement Process (Boudett et al., 2013; Parrot-Sheffer et al., 2024) as an approach to improvement research in education.

Perspective

The Data Wise Improvement Process has roots in collaborative data inquiry and data-driven decision-making (Coburn & Turner, 2011, 2012; Nelson & Slavit, 2008, 2012; Schildkamp, 2019). Data Wise is a process for school educators working in teams to improve an area of student learning, and system leaders can use it to improve outcomes throughout a larger district. The Data Wise framework has eight steps and reflection questions about equity-oriented beliefs and behaviors throughout the inquiry cycle.

The concept of spreading and sustaining improvement is captured in our work on organizational symmetry and coherence. Organizational symmetry means replicating the same improvement process across all school teams, as well as having district administrators engage in improvement in a way that mirrors what they are asking of schools (Parrot-Sheffer et al., 2024). Promoting coherence involves orienting resources, personnel, and adult learning strategy around improving a few key priorities, ultimately leading to sustained attention on these goals (Author, 2017).

Modes of Inquiry

The poster features a case study (Yin, 2014) of Crown High School (pseudonym), a large, comprehensive high school in an urban area in the northeast United States. It shows an inquiry cycle in the English department, with an emphasis on students with disabilities.


Data Sources

We collected interview data and artifacts (e.g., professional learning materials) from three Crown High School educators who used Data Wise over two academic years (2022 through 2024)


Findings

Crown High School created foundational conditions by organizing inquiry teams and establishing collaborative routines. They decided to focus on improving “analysis” across content areas.

They mapped the improvement space in Steps 3 through 5, where the English department inquiry team examined data from large-scale state assessments, student work, interviews and surveys, lesson plans, and notes from classroom observations. They drew conclusions about what students did well and where they struggled, as well as how teachers currently taught analysis and what they needed to try next.

The English department inquiry team developed a theory of (and ideas for) improvement in Steps 6 and 7, where they chose writing strategies to build student skills in analysis and planned to measure strategy implementation and student learning.

In Step 8, the focus on analysis continued over the course of two academic years. The educators continued to spread and sustain the Data Wise process by using it with additional teams to tackle new problems.


Significance

This poster illustrates how educators engaged with the Data Wise Improvement Process in a school, and it demonstrates how this model maps onto the “grammar of improvement research” framework.

Author

• Candice Bocala, Harvard University