Data Collaborative Portal - Improvement 4.0

Improvement Science is the adaptation of the Data Ladder of Inference and Improvement Cycles to facilitate continuous improvement in the Data Collaborative. The Data Ladder of Inference is a process we have adopted to ensure that we are using all relevant data in making our decisions and that we are using that data to learn and improve. The Data Ladder of Inference slows us down and prompts us to remember all the variation to look at before jumping to conclusions. Improvement Cycles brings us to be inclusive in our analysis and decision making process. The more inclusion we can have in our continuous learning will allows us to be better positioned to achieve the opportunities we discover in our data.

The Data Ladder of Inference is a cyclical process that generates insights from data, and uses these insights to inform our decisions and drive continuous improvement.

Improvement Cycles are a cyclical three-step process that drive change in practices and strategies through the inclusion of data, stakeholders, and experiences for improvement in outcomes.

Data Ladder of Inference

The Data Ladder of Inference is a cyclical process that generates insights from data, and uses these insights to inform our decisions and drive continuous improvement.

Further Explanation:

The Data Ladder of Inference guides us through analyzing data and identifying root causes to improve our decisions.

It will allow us to use data to check if the decisions we made are working as intended. We will answer "what is happening?" questions such as:

What is our progress to goal?
What went well? What are our bright spots?
What didn't go as well? What are our challenge areas?

The discipline incorporated into this process will help us move beyond the surface level “what is happening” to find deeper trends, valuable insights, and root causes. We will answer "why" for the "what is happening?" questions above.

Why is important?

The Data Ladder of Inference will allow us to continually improve by replicating and scaling the insights from brights spots and solving our challenge areas.

What does it look like in the Data Collaborative?

Within the Data Collaborative our decisions include setting our goals, processes, and actions associated with the Reading Success Pathway (RSP 1.0), Evidence Based Practices (EBP 2.0), and Implementation (IMP 3.0) pillars. We will use the Data Ladder of Inference in combination with Improvement Cycles to continuously improve our decisions and ultimately achieve better student literacy outcomes.

Key Concepts

Improvement Cycles

Improvement Cycles are a cyclical three-step process that drive change in practices and strategies through the inclusion of data, stakeholders, and experiences for improvement in outcomes.

Why is it important in the Data Collaborative?

The improvement cycle is the entry point for a group, team, program, or organization(s) to move forward within the Data Ladder of Inference, especially, when a rung is dependent on more than one person’s perspective. This is critical for the Data Collaborative because it drives quality insights, organization buy-in & awareness, and consensus throughout an organization.

The multiple perspectives -teacher assistants, teachers, site coordinators, directors, data managers, etc- help create clarity, generate alternative ideas, and craft new solutions within the Data Ladder of Inference process when individual data analysis and personal experience are not sufficient to move forward within the ladder. The Improvement Cycle allows for a targeted area within the Data Ladder of Inference to be done with quality, when quality data and/or secondary resources are not sufficient to move through the Data Ladder of Inference.

Improvement Cycles and Meetings, while are similar in some ways, have two major differences:

Data Ladder of Inference: Improvement Cycles focus on going through the Data Ladder of Inference. Traditional meetings may be missing one or more of the rungs and may be intended to only inform and or update staff, whereas Improvement Cycles will always go through the Data Ladder of Inference.
Inclusiveness: Improvement Cycles focus on capturing multiple voices and perspectives from different tiers in an organization. Meetings may be top-down, intended only to inform lower tiers in the organization of decisions that leadership have made, whereas Improvement Cycles will always be inclusive.
Consensus: Improvement Cycles focus on creating consensus and alignment throughout an organization. It does this by not forcing 100% agreement, but voicing over the direction the organization is going, where it is not going and name that the alternatives will be reviewed if this decision this time didn’t work.

Key Concepts

Improvement Cycles are a cyclical three-step process that drive change in practices and strategies through the inclusion of data, stakeholders, and experiences for improvement in outcomes.