Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning, edited by Kathryn Parker Boudett, Elizabeth A. City, and Richard J. Murnane (Harvard Educational Publishing Group, 212 pages, $29.95)
Demystify that data! A powerful asset to data driven inquiry and improvement, Data Wise comes out of a work group of Boston Public School leaders and Harvard Graduate School of Education faculty and doctoral students and is informed by the development of a data system now used by all Boston Public Schools. Data Wise guides schools and school systems through the growth of comprehensive data systems that encompass classroom work samples as well as standardized tests.
Data Wise describes an eight-step system for using assessment outcomes in a collaborative professional learning community to improve a school’s pedagogy and learning results. Because it documents a tested process, it is usefully prescriptive, providing a concrete action plan that schools can adopt from the start or at various entry points.
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.
Teachers at small, autonomous schools committed to using data for schoolwide improvement would benefit greatly from the insight and experience of Data Wise’s authors. The Data Wise process trusts teachers, relying not just what the data says but on what teachers know and can say about student performance. The combination of teacher wisdom and assessment results helps a school’s staff members make connections among data, teaching, and learning.