This brief evolved from a larger Robert Wood Johnson Foundation–funded project to examine the intersection of and alignment between social and emotional learning (SEL) and school climate. It includes the ten key ideas that emerged from a two-day discussion of SEL and school climate with practice leaders in April 2016.

Creating Safe, Equitable, Engaging Schools helps school leaders make sense of the various evidence-based resources and frameworks, including MTSS, designed to support the whole child. Addressing critical topics like restorative practices, cultural responsiveness, social and emotional learning, and family engagement, this volume offers insights on how leaders can leverage school-based teams to assess student needs and select appropriate interventions.

AIR GBG is a universal classroom management strategy designed to teach increase on task behavior and reduce aggressive and disruptive behavior. An evidence-based intervention, AIR GBG has been linked to positive short-term behavior outcomes and long-term outcomes such as reduction of drug and alcohol use and suicide ideation and attempts

The purpose of this guide is to provide an overview of behavioral progress monitoring and goal setting to inform data-driven decision making within tiered support models and individualized education programs (IEPs).

In this guide, we explain how educators can establish IEP goals that are measurable, ambitious, and appropriate in light of the student's circumstances. Four important steps are required for setting a valid goal for individual student performance: selecting a measure, establishing baseline performance, choosing a strategy for setting the goal, and writing a measurable goal. Although this guide presents the steps that educators can take to set appropriate IEP goals, all members of the IEP team, including families, should be involved in discussions about setting the goal.

The purpose of this document is to provide content-specific examples of how to structure educator-level and/or systems-level coaching as a mechanism to ensure ongoing professional learning to support tiered intervention. This document provides examples of coaching supports, models, and functions within the context of tiered intervention (e.g., RtI, PBIS, MTSS) and data-based decision making (e.g., data-based individualization [DBI]) for educators who already have foundational knowledge and/or experience with coaching.

Few evidence-based resources exist for supporting elementary and secondary students who require intensive intervention—typically Tier 3 within a multi-tiered system of supports (MTSS). Filling a gap in the field, this book brings together leading experts to present data-based individualization (DBI), a systematic approach to providing intensive intervention which is applicable to reading, math, and behavior. Key components of the DBI process are explained in detail, including screening, progress monitoring, and the use and ongoing adaptation of validated interventions.

Data teams can use these checklists to monitor implementation of the initial planning meetings for individual students with intensive needs and ongoing review (progress monitoring) meetings.

If you are like most educators, you agree with the idea of providing intensive intervention for students with the most intractable academic and behavior problems. The question you may be asking is, how do I find the time? Intensive intervention requires time for planning and delivering individualized instruction, as well as time for collecting and analyzing progress monitoring and diagnostic data, developing intervention plans, and making changes to plans when needed.

This webinar, led by Dr. Lynn Fuchs of Vanderbilt University and Dr. Lee Kern of Lehigh University, addresses a challenge faced by many teachers: feeling inundated by data while struggling to find useful information to guide intervention decision-making. The webinar focuses on: (a) common progress monitoring measures in academics and behavior, (b) key considerations for optimizing data collection, and (c) structured questions for analyzing graphed progress monitoring data patterns.