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This is the NCLB model. Schools are expected to collect data once a year, slice and dice them in various ways, set some goals based on the analyses, do some things differently, and then wait another whole year to see if their efforts were successful. Somehow, this model is supposed to get schools to 100% proficiency on key learning outcomes.

he key difference in this model is an emphasis on ongoing progress monitoring and continuous, useful data flow to teachers

Under this approach, schools have good baseline data available to them, which means that the data are useful for diagnostic purposes in the classroom and thus relevant to instruction. The data also are timely, meaning that teachers rarely have to wait more than a few days to get results. In an effective data-driven school, educators also are very clear about what essential instructional outcomes they are trying to achieve (this is actually much rarer than one would suppose) and set both short- and long-term measurable instructional goals from their data.

Harnessing more powerful data tools and greater amounts of data.

Granting teacher, parent and community access to data

Planning and utilizing interoperability standards

The scenarios that illustrate each chapter come from two case studies, one based on a K-8th grade scenario and the other a 9th-12th grade setting. Data Wise grounds its discussion in examples from those contexts, keeping the material accessible and focused on realistic problems and solutions. Data Wise's process depends on collaboration and full faculty participation. With a sympathetic understanding of the inevitable limits on staff time, the authors discuss the best ways to structure collaborative faculty time and include three protocols to involve faculty and staff in gaining insight from data.