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Bernhardt, Victoria L. – Eye on Education, 2009
This book is an easy-to-read primer that describes what it takes to increase student achievement at every grade level, subject area, and student group. Readers will learn how to use data to drive their continuous improvement process as they develop an appreciation of the various types of data, uses for data, and how data are involved with the…
Descriptors: Self Evaluation (Individuals), Vision, Data Analysis, Educational Improvement
Bernhardt, Victoria L.; Geise, Bradley J. – Eye on Education, 2009
How do the best administrators find out how to improve their schools? They actively engage the people who work and study there. In "From Questions to Actions: Using Questionnaire Data for Continuous School Improvement," data experts Victoria L. Bernhardt and Bradley J. Geise explain how to collect and analyze data with an eye toward positive…
Descriptors: Attitude Measures, Questionnaires, Educational Change, Data Analysis
Bernhardt, Victoria L. – Eye on Education, 2007
In these times of high-stakes accountability, all professional educators must learn how to gather, analyze, and use data to improve teaching and learning, becoming efficient generators and consumers of data. The intended audiences for this book are school and district administrators and teacher leaders who recognize the need to use data to…
Descriptors: Audiences, Educational Change, Teacher Leadership, Educational Improvement
Bernhardt, Victoria L. – Eye on Education, 2004
With clear and concrete examples from elementary and high schools, this book helps you effectively gather, analyze, and use data to improve student learning. It demonstrates how to make better decisions, identify root causes of problems, and communicate and report results.
Descriptors: Educational Improvement, Data Analysis, Elementary Schools, High Schools
Bernhardt, Victoria L. – 1998
Data can help identify solutions to some of the biggest problems in schools. Yet few schools use data effectively. The purpose of this book is to help schools become aware of the advantages of collecting and using data (data analysis) for overall school improvement. The book begins with an overview of the current state of education and the…
Descriptors: Automation, Data Analysis, Data Collection, Data Interpretation
Bernhardt, Victoria L. – Eye on Education, 2004
This book helps you make sense of the data your school collects, including state student achievement results as well as other qualitative and quantitative data. Easy-to-use templates, tools, and examples are available on the accompanying CD-ROM.
Descriptors: Middle Schools, Student Evaluation, Data Analysis, Computer Uses in Education
Bernhardt, Victoria L. – Journal of Staff Development, 2000
Presents a model to help schools pose and answer data-based questions about teaching and learning, describing different types of data that educators can use to piece together pictures of their schools (e.g., student learning statistics, demographic data, and school process data) and discussing how educators can cross data from different categories…
Descriptors: Data Analysis, Data Collection, Educational Research, Elementary Secondary Education
Bernhardt, Victoria L. – Eye on Education, 2005
This book helps you make sense of the data your school collects, including state student achievement results as well as other qualitative and quantitative data. Easy-to-use templates, tools, and examples are available on the accompanying CD-ROM. High stakes accountability requires that you develop your understanding of who your students are and…
Descriptors: Data Analysis, Accountability, Academic Achievement, High Schools

Bernhardt, Victoria L. – Educational Leadership, 2003
A primer for schools attempting to analyze the data they collect. Describes ways schools can get a better picture of how to improve learning by gathering, intersecting, and organizing four categories of data more efficiently: (1) demographic data; (2) student-learning data; (3) perceptions data; and (4) school-processes data. (WFA)
Descriptors: Data Analysis, Data Collection, Data Interpretation, Data Processing