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