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Dealing with Data: New IES Guide Offers Advice for Educators

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The Institute of Education Sciences is cranking out those popular practice guides. The latest one, published earlier this week, focuses on using student achievement data to plan instruction.

Data-driven decisionmaking is becoming increasingly popular among schools and districts, but, as this report points out, there is not much research out there to guide educators on how best to go about doing it. How would you evaluate the effects of a districtwide data system, anyway? It wouldn't be easy.

To fill in the gap, the institute assembled a panel of eight experts—seven researchers and one district administrator—to recommend some best-bet strategies districts could use to put student data to effective instructional use now.

A key thrust of the report is that effective data practices are interdependent among the classroom, school, and district levels. At the classroom level, the experts recommend that teachers make data part of an ongoing cycle of instructional improvement and teach students to analyze their own achievement data and set their own learning goals.

At the school level, the guide says, principals can work with teachers to set a clear vision for how schools should make use of data and provide teachers with supports, such as time and training, to help them do it. And districts, at the same time, should develop and maintain districtwide data systems to complement and support schools' efforts.

These recommendations are a little vague but the guide offers some concrete examples, too, as well as practical advice on overcoming some of the roadblocks that might crop up. You can download "Using Student Achievement Data to Support Instructional Decision Making" for free at the IES Website.

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We can attribute part of the report's vagueness to the fact that the kinds of data available to educators vary between district to district and school to school. In many cases, assessment data come in too late to be useful. Often, teachers are not authorized to see the actual questions their students aced or bombed. Data systems are critical, but much other work has to happen to improve the quality of the data.

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