The Data Driven School Collaborating to Improve Student Outcomes 1st edition by Daniel Hyson, Joseph Kovaleski, Benjamin Silberglitt, Jason Pedersen ISBN 1462543103 9781462543106

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ISBN 10: 1462543103
ISBN 13: 9781462543106
Author: Daniel Hyson, Joseph Kovaleski, Benjamin Silberglitt, Jason Pedersen

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.

The Data Driven School Collaborating to Improve Student Outcomes 1st Table of contents:

Part I. The Engine for a Data-Driven School: Systems-Level Problem Solving

1. The Rationale and Context for a Data-Driven School

The Need for Data-Driven Schools

Key Tenets of a Data-Driven School

Strong Leadership with Buy-In from Key Stakeholders

A Comprehensive Assessment System

Easy Access to Appropriate Data for All Staff

The Time and Resources for All Staff to Examine the Data

Clear Connections between Data and Potential Interventions at the District, School, and Classroom Levels

The Data-Driven School and MTSS

2. Systems-Level Problem Identification

The Problem-Solving Model and Systems-Level Application

Establishing What Is Expected

Target Setting Using Backward Planning

Setting Targets on Other Key Variables

Understanding What Is Occurring

Different Approaches to Communicating Data

Communicating the Difference between What Is Expected and What Is Occurring

Four Purposes of Assessment

Conclusion

3. Systems-Level Problem Analysis

Problem Analysis: Understanding the “Why”

Data in Problem Analysis

Goal of Problem Analysis: Developing Alterable Hypotheses

Root Cause Analysis

Problem Analysis in Context

A Step-By-Step Guide to Problem Analysis Using Data Inquiry

Problem Analysis? Problem Analysis? We Don’t Need No Stinking Problem Analysis!

School Budgeting

One Size Fits All

Superficial Problem Analysis

What Happens When You Skip It

Using Problem Analysis to Address Achievement Gaps

4. Systems-Level Plan Development, Plan Implementation, and Plan Evaluation

Systems-Level Plan Development

Identifying Research-Based Practices and Resources

Planning How Progress Will Be Monitored

Systems-Level Plan Implementation

Systems-Level Plan Evaluation

A Practical Tool to Guide Systems-Level Problem Solving: The Data Book PowerPoint

Part 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

Team Format and Membership

Sources of Academic Data

Benchmark Assessments: Universal Screening

Diagnostic (Drill-Down) Assessments

Progress Monitoring

Outcomes Assessment

Sources of Behavioral Data

Office Discipline Referrals

Behavior Screeners

Classroom-Level Data

Progress Monitoring

Functional Behavioral Assessment

Psychoeducational Evaluations

6. Implementing Data Teaming at the School and Grade Levels for Academic Skills

Purposes and Aims of the Teaming Process at the School and Grade Levels

Sources of Data

Forms and Formats Used in Data-Analysis Teaming

The Data-Analysis Teaming Process for Academics

Beginning-of-Year Meetings

Formal Follow-Up Meetings

7. Implementing Data Teaming at the School and Grade Levels for Behavior and Social–Emotional Skills

General Considerations

The Data-Analysis Teaming Process for Behavior

Beginning-of-Year Meetings

Final Steps

Part III. Building the Capacity for a Data-Driven School

8. Data Management Using Technology

The Evolution of Education Technology

Technology for Data

Data Management

Data Management Plans

Interoperability

Bringing Your Data Together

Data Warehouse/Business Intelligence Tools

Single-Function Software

Statistical Software

Spreadsheet Software

Presentation Software

9. Developing Data Leaders

Characteristics of Successful Data Leaders

Data and Intervention Literacy

Belief in the Value of Systems-Level Data-Driven Decision Making

Leadership Skills

Relationships

Identifying and Appointing Data Leaders

Keys to Effective Training of Data Leaders

Framework for Assessing the Context for Data-Driven Leadership

Profiles of 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

About Guilford Press

Discover Related Guilford Books

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Tags: Daniel Hyson, Joseph Kovaleski, Benjamin Silberglitt, Jason Pedersen, The Data, Improve Student