GUEST BLOGGER: Mary Anne Mather, Using Data Senior Facilitator & Social Media Liaison on Twitter & FaceBook

If you want to tap one of the most powerful uses of data, disaggregate! Disaggregation means looking at how specific subgroups perform. Typically, formal student achievement data come “aggregated,” reported for the population as a whole—the whole state, school, grade level, or class. Disaggregating can bring to light critical problems and issues that might otherwise remain invisible.

For example, one district’s state test data indicated that eighth-grade math scores steadily improved over three years. When the data team disaggregated those data, they discovered that boys’ scores improved, while girls’ scores actually declined.different colored stick figures sorted into color-coordinated groups Another school noticed increased enrollment in their after-school science club. However, disaggregated data indicated that minority students, even those in more advanced classes, weren’t signing up. These are just some of the questions that disaggregated data can help answer:

• Is there an achievement gap among different demographic groups? Is it getting bigger or smaller?

• Are minority or female students enrolling in higher-level mathematics and science courses at the same rate as other students?

• Are poor or minority students over-represented in special education or under-represented in gifted and talented programs? (more…)

Guest Blogger: Dr. William L. Heller, Using Data Program Director, Teaching Matters*

There are often revelatory moments in the data inquiry process, where your analysis will lead to great insight and discovery in a way that challenges your assumptions and changes the way you think about teaching and learning in your school. There are other times when the data shows exactly what you werePen pointing to detail of bar graph showing flat results expecting, confirming your predictions and giving you valuable evidence in making your case to others. Many times, however, the data doesn’t show anything at all.

This can be somewhat dispiriting to an enthusiastic data team, but it doesn’t need to be. Sometimes the data may show nothing, but that’s still valuable information that puts you ahead of where you were before you looked. We don’t complain when our dentist finds no cavities, when the mechanic finds nothing wrong with our car, or when a medical test comes back negative. Similarly, in data inquiry, even a finding of nothing can really be something, if you know how to interpret what it means. (more…)