No Schools Left Behind

In this eight-page article, the author describes and illustrates a specific process for using different types of data to improve student outcomes, including achievement. She defines and illustrates four types of data that schools should gather, including demographics, student learning, perception, and school processes data. She also describes different types of questions that may be answered through cross-section analysis of the data. The article concludes with recommendations for data collection, storage, and analysis.  

Content Comments 

The purposes of this article are effectively described in the article subtitle as follows: "Schools can get a better picture of how to improve learning for all students by gathering, intersecting, and organizing different categories of data more effectively." That purpose and others are effectively met through a thoughtfully presented, easy-to-understand article, from an author with considerable data use expertise.

The author supports her data use process recommendations with actual school examples, thereby increasing credibility and value. Readability is excellent. Although the overall graphic design is basic, it nicely supports the author's data use model. Utility should be reasonably high, especially for any school or district that either uses data now or is beginning their own data use system. Evidence of effectiveness is not provided, but the overall quality across all criteria suggests a potential impact on learning.