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Data-driven decision making for school improvement has become a part of the everyday vernacular of educators. However, saying that teachers, families and administrators need data to make decisions is one thing; understanding and using data well is another. In some schools, educators are conflicted about how to get started with the data. For others, once started with the data, questions surface about what to do and when to do it. In many cases teachers—the very people who can make the best use of student-learning data, have the least access to it.
The driving purpose for collecting and analyzing data is instructional improvement for all learners. There is no way to bridge the gap between data and results without changing what is taught, how it is taught, how it is assessed, and how we use assessments to plan instruction.
Yet in many instances, we look at and talk about disaggregated “achievement” data in ways that reinforce deficit thinking about students of color. A focus on underachieving students and remediation allows us to place blame on students, other teachers, and parents. When we look at data through the lens of “achievement gaps”, we often fail to look at the system, and in turn, fail to accept
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responsibility for the gap. Instead, we should use data to ask the questions that are capable of shaping equitable student learning such as: What do students need to know? How will we know if students have learned it? Who is successful? Who is not? What will we do if students have not learned what they need to know?
Action directed at improving equity, access, participation, and outcomes should be grounded in a thoughtful assessment of what is happening. In order to ensure that every student has the greatest opportunity to learn, enhanced by the resources and supports necessary, a school team should consider the types of data necessary for analysis. Because data are produced at the boundary of interaction between people, policies, and practices, it is imperative to have accurate and timely information about individual students: their capacities, experiences and knowledge base that can be activated in order for them to engage as fully as possible in new learning situations.
Some specific types of data to consider collecting and analyzing include:
- disaggregated achievement data;
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- demographic data for specialty courses such as special education, gifted, AP, technology, and extra-curricular involvement;
- data regarding student treatment, including the number of suspensions by gender and cultural background, and the number and types of students being recognized as outstanding;
- information regarding teacher qualifications, experience, and cultural background;
- budget information demonstrating equitable resource distribution supportive of diverse learners;
- information regarding school climate, student experiences, teacher beliefs, and community forces at play; and
- anecdotal information regarding stakeholders and the responsibility they share for all learners.
In this issue of Equity Matters, we take a look at how using data and other evidence of student performance can effectuate full access to quality education, qualified teachers, challenging curriculum, full opportunity to learn, and appropriate support for learning for all of the students in our schools.
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