Student performance data should be disaggregated by race, socioeconomic status, predominant language, gender, and rate of absenteeism, along with a host of other potentially important variables, such as participation in student activities and disciplinary problems. The data should then be further disaggregated by quartile or decile, and reviewed as three-year trends, to identify groups of students that are improving and those that are not. One district, for example, spent years and significant resources trying to improve its math program without success. Once it looked at the performance of students who were absent more than 20 days, as compared with those who attended regularly, the district discovered that the group attending, while burdened with some of the same negative influences as the group that was not, was able to perform at acceptable levels. This district then redirected its improvement efforts and focused on getting kids to school.
Information technologies make such ongoing disaggregation of data possible.
Fast-track program evaluation. Moreover, real-time performance benchmarking, a critical need in schools today, is simply not possible using traditional methods of evaluation. We are unable to manage our data because we lack the information-management tools that have transformed almost every sector except education. The database-decisionmaking process in schools needs to be a much more dynamic one, where hypotheses about relationships among data can be more quickly explored. This analysis would be overwhelming for most educators, but in an information-technology environment, it can be completed in an hour or so.