The Data-Driven School Collaborating to Improve Student Outcomes

This indispensable practitioner's guide helps to build the capacity of school psychologists, administrators, and teachers to use data in collaborative decision making. It presents an applied, step-by-step approach for creating and running effective data teams within a problem-solving framework. The authors describe innovative ways to improve academic and behavioral outcomes at the individual, class, grade, school, and district levels. Applications of readily available technology tools are highlighted. In a large-size format for easy photocopying, the book includes learning activities and helpful reproducible forms. The companion website provides downloadable copies of the reproducible forms as well as Excel spreadsheets, PowerPoint slides, and an online-only chapter on characteristics of effective teams.

This book is in The Guilford Practical Intervention in the Schools Series, edited by Sandra M. Chafouleas.

Daniel M. Hyson, PhD, NCSP, is Assistant Professor in the School Psychology Graduate Program at the University of Wisconsin–La Crosse. Previously, Dr. Hyson was a school psychologist in Minnesota public schools. He then served as Data Management Coordinator for Hiawatha Valley Education District, a consortium of 13 school districts in southeastern Minnesota. In that role, he consulted with teachers and administrators to help them access, interpret, and use data from academic and behavioral assessments to improve instruction for all students. Dr. Hyson's research interests include teacher–student relationships and their association with student engagement and achievement, and the school psychologist’s role in systems-level consultation and data-driven decision making.
 
Joseph F. Kovaleski, DEd, NCSP, is Professor Emeritus of Educational and School Psychology at Indiana University of Pennsylvania, where he was Director of the Doctoral Program in School Psychology from 2003 to 2017. He now consults with school districts and state departments of education. Dr. Kovaleski directed Pennsylvania’s Instructional Support Team Project and served as a university consultant for Pennsylvania’s Multi-Tiered System of Support Initiative. He has published numerous articles and book chapters on response to intervention and data-based decision making, and presents frequently at national and state conferences. He is coauthor (with Jason A. Pedersen) of a chapter on data-analysis teaming in Best Practices in School Psychology, Sixth Edition.
 
Benjamin Silberglitt, PhD, is Executive Director of Research, Outcomes, and Implementation at Intermediate District 287, a consortium of 11 school districts in the Twin Cities metropolitan area in Minnesota. He has founded, launched, and led the implementation of multiple education technology products since the early 2000s. Dr. Silberglitt is a cofounder of Cedar Labs, a universal data integration platform with statewide implementations in the United States and Australia. He regularly consults with school districts and presents on the effective use of data to support decision making.
 
Jason A. Pedersen, PhD, NCSP, is a school psychologist in the Derry Township School District in Hershey, Pennsylvania. He has worked with school staff to develop a comprehensive K–12 curriculum to foster and promote resilience, and he previously spearheaded schoolwide positive behavior support, response to intervention (RTI), and multi-tiered systems of support (MTSS) initiatives. Dr. Pedersen is coauthor (with Joseph F. Kovaleski) of a chapter on data-analysis teaming in Best Practices in School Psychology, Sixth Edition, as well as several articles in peer-reviewed journals. He has given numerous presentations on MTSS and RTI at the local, state, and national levels, and has consulted with school districts in Pennsylvania, New York, New Jersey, and Texas.

Introduction
I. The Engine for a Data-Driven School: Systems-Level Problem Solving
1. The Rationale and Context for a Data-Driven School
2. Systems-Level Problem Identification
3. Systems-Level Problem Analysis
4. Systems-Level Plan Development, Plan Implementation, and Plan Evaluation
II. The Roadmap for a Data-Driven School: Data-Analysis Teaming across Multiple Levels
5. Data-Driven Problem Solving at the Grade, Classroom, and Student Levels: Initial Considerations
6. Implementing Data Teaming at the School and Grade Level for Academic Skills
7. Implementing Data Teaming at the School and Grade Level for Behavior and Social–Emotional Skills
III. Building the Capacity for a Data-Driven School
8. Data Management Using Technology
9. Developing Data Leaders
Appendix 1. Identifying Gaps in Your Comprehensive Assessment System
Appendix 2. Case Example: Setting Your Own Target Scores
Appendix 3. Data Activity
References
Index