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June 16, 2003
Tools for Data-Driven Decision Making
Here are the three major types of products and services to help you manage your district's data.
By Todd McIntire
Student information systems log transactional data, such as students' contact information, attendance, grades, and demographic information. Some also offer standards-based assessments with reports and analysis.
Analytical and mining tools perform complex calculations to show relationships between student achievement and a multitude of selected variables. These are the thinking engines of decision making. They do the math so that the clearest information is available to the human decision maker. Basic analytical tools study relationships, patterns, and gaps in student data, while more advanced mining tools execute sophisticated statistical analyses. Some high-end mining tool operations include aggregation (or roll up), disaggregation (or drill down), selection, and pivot. A properly implemented data mining tool would allow a principal, for example, to compare the performance of students in various classes against their teachers' attendance and then to drill down in each group by other factors such as gender, primary home language, or number of years the student has been in the district.
Data warehouses store copies of data housed in student information systems. Aside from housing data, their highly specialized functions include combining disparate databases and distributing periodic or real-time reports.
For detailed examples of how districts and schools are imple-menting data warehousing and mining tools, see Digging for Data (Technology & Learning, March 2003).
Todd McIntire is the director of achievement for Edison Schools.
Editor's note: The above information was excerpted from "The Administrator's Guide to Data-Driven Decision Making" (Technology & Learning, June 2002), a Maggie Awards finalist for Best How-To Article.
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