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There is too much testing in American Public Schools that is
robbing teachers of valuable instructional time. Some of it is
mandatory, but the vast majority is not, and under the control of
districts, schools and even teachers to reduce. Testing Too Much?
provides a rationale and set of three models to use in cutting back
on testing to reclaim valuable instructional time. It also includes
a high school case study describing how the themes proposed in the
book can be accomplished. Instructional time is a complex subject
that is discussed in detail and the underlying research why it is
so important, especially for disadvantaged children. To address how
best to cut back on non-mandated testing there is a chapter,
written in plain terms, on how to judge the value of a test. Three
models are then discussed on how to approach cutting back on
testing by as much as 25% that can recapture as much as ten days or
more of instruction during a typical school year. The goal of the
book is to help school leaders and teachers find ways, amongst
current local testing practices, to cut back, improve their
instruction and the educational experiences of their students.
There is too much testing in American Public Schools that is
robbing teachers of valuable instructional time. Some of it is
mandatory, but the vast majority is not, and under the control of
districts, schools and even teachers to reduce. Testing Too Much?
provides a rationale and set of three models to use in cutting back
on testing to reclaim valuable instructional time. It also includes
a high school case study describing how the themes proposed in the
book can be accomplished. Instructional time is a complex subject
that is discussed in detail and the underlying research why it is
so important, especially for disadvantaged children. To address how
best to cut back on non-mandated testing there is a chapter,
written in plain terms, on how to judge the value of a test. Three
models are then discussed on how to approach cutting back on
testing by as much as 25% that can recapture as much as ten days or
more of instruction during a typical school year. The goal of the
book is to help school leaders and teachers find ways, amongst
current local testing practices, to cut back, improve their
instruction and the educational experiences of their students.
With the new federal law, No Child Left Behind, there is ever
increasing pressure on schools to be accountable for improving
student achievement. That pressure is taking the form of focused
efforts around data-driven decision making. However, very little is
known about what data-driven decision making can really tell one
about improving achievement nor is there a full explanation
available about what it really takes to do this work. The few
examples that do exist, while proposing to get at some of these
issues, make huge assumptions about educators' knowledge base and
available resources necessary for success. In this book, Philip
Streifer fills the gaps by laying out how this work can be done and
then explains what is knowable when one actually conducts these
analyses and what follow-up steps are needed to make true
improvements. He provides readers with a comprehensive
understanding of what data-driven decision making can and cannot
tell educators about student achievement and addresses the related
issues for leadership, policy development, and accountability.
Senior level district administration for policy development, school
level administrators who have to put policy into practice, and
graduate college professors teaching data-driven decision making
will find this book most useful.
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