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.
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In this video, Michele Walden-Doppke, M.A., CAGS, Response to Intervention (RTI) Technical Assistance Provider with Northern Rhode Island Collaborative for Rhode Island Department of Education (RIDE) and NCII Coach in Coventry Public Schools discusses infrastructure elements that support the implementation of intensive intervention.
Research tells us that ongoing coaching is essential for achieving practice change. And without ongoing coaching and practice opportunities, professional development is highly unlikely to lead to increased knowledge and skills to implement a new practice soundly. This rings especially true for complex processes like data-based individualization (DBI). DBI requires that educators commit to engaging in the iterative process of providing intervention, analyzing progress monitoring data, and making data-based decisions to adapt and individualize interventions when needed. To help schools effectively implement DBI, ongoing implementation support in the form of coaching that provides opportunities to learn critical information, apply and receive feedback, and troubleshoot problems when they occur is essential.
This is part 4 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part of the module is intended to provide participants with guidance for identifying skills to target in reading and math interventions.
This is part 3 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide participants with an introduction to error analysis of curriculum-based measures for the purpose of identifying skill deficits and providing examples of error analysis in reading and mathematics. Part 4, “Identifying Target Skills,” will further link these skill deficits to intervention.
This is part 2 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part includes examples of graphed data and is intended to provide participants with guidance for reviewing progress monitoring data to determine if the instructional plan is working or if a change is needed.
The purpose of this brief from the National Center for Systemic Improvement is to synthesize research on coaching and to offer a framework of effective coaching practices. Part 1 provides general information on coaching, including the need for coaching and the goals of coaching. Part 2 describes critical coaching practices that are linked to improvements in teacher practice and learner outcomes. As these practices are most associated with such improvements, they are the recommended practices that should be central to the every-day routine of coaches working in general education or special education settings, as well in environments (e.g., homes, schools, childcare centers) with learners of all ages. Appendix A contains information about various coaching models commonly cited in research and applied in the field (e.g., literacy coaching, behavior coaching, math coaching).
This worksheet and rubric can be used to collect information about the fidelity of coaching so that this information can be used by coaches and other educators to continuously improve upon how coaching occurs.
This training module demonstrates how academic progress monitoring fits into the Data-Based Individualization (DBI) process by (a) providing approaches and tools for academic progress monitoring and (b) showing how to use progress monitoring data to set ambitious goals, make instructional decisions, and plan programs for individual students with intensive needs.
This is part 1 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide an overview of common general outcome measures (GOM) used for progress monitoring in reading and mathematics, with guidance on selecting an appropriate measure.