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.

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.