Leadership magazine May/June 2018 V47 No. 5 | Page 24

Clarifying misconceptions about improvement science Here’s help with understanding the differences between improvement science, continuous improvement models, and the more traditional data- driven improvement methodologies that have been popular since the late 1990s. 24 Leadership Walk into any school or district office, and you will likely hear about a mul- titude of initiatives being implemented to improve processes and skills for adults and outcomes for students. Unfortunately, many initiatives are not being implemented with enough rigor or depth to have the desired impact. This can lead to frustration and the establishment of new initiatives, which sim- ply repeats the process. What if we could change this approach to school-improvement efforts so that we could get better at improving? A new field of thought – improvement science – has devel- oped to help schools do just that. Improve- ment science actually originated in health care, but in recent years has been applied to education, with the Carnegie Foundation for the Advancement of Teaching leading the way. As it relates to education, improvement science is the study of improvement efforts with the aim of determining which strate- gies work best for which schools and stu- dents, and for accelerating and spreading improvement efforts. Improvement science focuses on being thoughtful about both problems and solutions, and being system- atic, data-driven and collaborative. We are excited about the advent of im- provement science because our organiza- tions – Partners in School Innovation and WestEd – are dedicated to accelerating progress in schools and districts. We see promise in the greater rigor and reflection that this movement is bringing to school im- provement. And we are not alone. The field is garnering significant investments from the Bill & Melinda Gates Foundation and the California Department of Education, among others. Amidst the excitement is a significant amount of confusion, as is often the case with emerging approaches in any field. Practitioners are seeking to understand the differences between improvement science, continuous improvement models, and the more traditional data-driven improvement methodologies that have been popular since the late 1990s. Many ask the question, “Isn’t improvement science what we have already been doing?” This question indicates a need to clarify By Derek Mitchell, Chris Thorn and Brian Edwards