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Aim
The Aim of the Data Collaborative is used as the widest constraint in designing the scope of the work. It tells us: "What are we trying to accomplish?" by how much, for whom, and by when. We use the aim to focus all of our strategy and scope of work. If a change idea is proposed, the last question we ask before accepting it is whether or not it will help the community achieve the Aim.
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The purpose of this page is to reflect the overall Aim and strategy of the Data Collaborative community. This section highlights our approach to achieving what you read in our Background section. We hope that parts of what is discussed in this section complement what is expressed in the Background section. The purpose is to go a layer deeper in understanding why it is important to the Data Collaborative project and what resources assisted us in arriving there. If you would prefer more in-depth support and understanding of the specific strategies used —Common Aim and Driver Diagram framework — we would highly recommend attending the Carnegie Foundation for Advancement of Teaching summit. It was inspirational and informative on how to create the framework for continuous improvement. it will also be valuable when reviewing the fourth pillar Improvement in the Core Concepts sections.
What you will find below:
- Aim: Defines our aim and its purpose.
- Driver Diagram: Defines our strategy and introduces our vision of success.
- Additional Supports for Driver Diagram: Resources that assisted with creating the Driver Diagram.
- Previous Strategies: Shares our previous approaches for strategy in accomplishing the Aim. The purpose of including previous approaches is to reflect back on the work from the past, highlight continuous successful themes, and potentially prevent repetition of mistakes made along the way.
Aim:
Aim:
To create a community of practitioners that depend on, understand and use data to continuously improve student language and literacy outcomes.
To create a community of practitioners that depend on, understand and use data to continuously improve student language and literacy outcomes.
The purpose of the Aim is ensure that all the work that is incorporated in the Data Collaborative fits within a single desired outcome. It is the guide that allows the team to say "yes" or "no" to a body of work based on whether or not it is critical to helping achieve the overall aim of the work. The Aim should have both leading and lagging indicators to measure whether or not they are successful. The Data Collaborative indicators can be found on the Driver Diagram (see below) which uses the Aim as the foundation to build the strategy on how to achieve it.
Theory of Improvement:
Theory of Improvement:
The Theory of Improvement represented below was adopted as the Data Collaborative's best assumption on how to catalyze the common Aim. We used the four behaviors, represented in the middle column, as the foundation for our primary drivers in the driver diagram — you will see these are reflected in the Four Pillars found in the Core Concepts section. Based on the experience of the first year of the Data Collaborative, we realized that these four areas were critical to develop to help achieve our Aim. Once we established these components, we were able to further define the secondary drivers and how we increased the community's capacity to achieve the drivers.
Driver Diagram
Driver Diagram
The Data Collaborative Driver Diagram represents the Theory of Improvement based on the three years implementing the project. We understand that the driver diagram likely has gaps and this represents our current best thinking. In the Insights section you can find the Data Collaborative's most recent learnings, as well as the perspectives and learnings that brought us to this current state.
Additional Supports for Driver Diagrams:
Additional Supports for Driver Diagrams:
Previous strategies used by the Data Collaborative Team:
Previous strategies used by the Data Collaborative Team:
The purpose of this section is to share our learnings with you with the hope that it helps you to move further faster in your efforts. We hope you can learn from our failures and build from our successes. This is an overview of two different frameworks we used, as well as focus areas that we circled around to achieve our ultimate Aim. The previous frameworks were critical for us to learn how to create a dynamic framework that allowed for evolution to occur with the community. We hope that this pathway and access to the raw documents allow you to understand and appreciate what became foundational versus reactive in the frameworks.
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