Our Process

Our projects and partnerships span five phases. These phases were developed and tested through an iterative, design-based approach that combined insights from multiple research-practice partnerships. These partnerships have all aimed to develop data products and change ideas that support more equitable and effective learning environments. In working to achieve this aim, we identified the importance of four interrelated conditions: (1) trust and shared values among partners, (2) use of an explicit improvement method, (3) learning events that facilitate knowledge- and trust-building, and (4) data sharing infrastructure that promote joint interpretation of data products in a secure and ethical manner. The figure, below, illustrates our theory for how these conditions can be thought to support achieving the aim of using data intensive research techniques to improve learning environments. This theory is tentative, and we are working everyday to improve our understanding of how to engage in data intensive research with practitioners. Please contact us with ideas or suggestions.

Each one of the above primary drivers runs throughout the five phases of a CDIR project:

Phase I: Prepare
Clarify goals of the partnership
A crucial starting point for any partnership is to clarify what the partnership is trying to accomplish. We use improvement tools to jointly clarify goals and anchor those goals in the needs of frontline workers. Key tasks include the following:
1a. Identify project team members
1b. Clarify problem(s) partnership is trying to solve
1c. Specify goals of the partnership
1d. Develop memorandum of understanding

Phase II: Understand
Understand context and connections to prior research
Key to creating more equitable and effective learning environments is developing a working theory that helps to organize measures and change ideas. Prior research can help the partnership learn faster, identify potential data to collect, and serve as a source of potential change ideas to test. Key tasks include the following:
2a. Collect primary data from practitioner’s context
2b. Conduct rapid literature scan
2c. Co-develop working theory for improvement

Phase III Explore
Jointly develop and interpret data products
Data is crucial for improvement. We draw on the insights of both researchers and practitioners in using data to (1) generate questions and hypotheses, (2) develop evidence to take action on a small scale, and (3) use data as a tool to support taking action. Key tasks include the following:
3a. Access available data
3b. Engage in formal exploratory data analyses
3c. Jointly interpret data products and brainstorm change ideas

Phase IV: Co-Develop
Co-develop change strategies
Building off of the efforts to understand context and jointly interpret data products, we then work to co-develop concrete strategies that are explicit, scaffolded, and whose impact is testable. Key tasks include the following:
4a. Identify high leverage change ideas
4b. Make change ideas and opportunities to implement them explicit
4d. Develop scaffolds to support implementing change idea(s)

Phase V: Test
Launch and coordinate improvement cycles
We organize teams of researchers and practitioners to support one another and generate evidence related to co-developed change ideas. Key tasks include the following:
5a. Familiarize partnership with approach for testing change ideas
5b. Clarify measurement system
5c. Coordinate testing of change ideas
5d. Jointly reflect on results from multiple tests

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