Teams are a vital part of an effective multi-tiered system of supports (MTSS) across both academics and behavior as well as special education. Making connections across the various teams used in MTSS and special education can be challenging. This resource from NCII and the PBIS Center provides information about how DBI can support IEP implementation and a table with key considerations for teams working across the MTSS system.
We know that validity, reliability, classification accuracy, statistical bias, and sample representativeness are important considerations when reviewing screening tools, but what do they actually mean? These five screening one-page documents provide a brief overview of each of these areas that are used on the NCII Screening Tools Charts. These one-pagers include a definition, examples, and information on why that particular standard is important for understanding the quality of screening tools. Companion infographics are also available.
If we don’t implement critical components of an intervention with consistency, we cannot link student outcomes to the instruction provided. Fidelity can help us to determine the effectiveness of an intervention, and identify if a student requires more intensive supports. This resource outlines five elements of fidelity and provides guiding questions for each.
Research on professional development shows that teachers need long term support in order to improve their practice. Coaching can be one method for providing that support (Joyce & Showers, 2002; Kretlow & Bartholomew, 2010). However, not every form of coaching is effective. In fact, just four specific coaching practices are linked to improvements in teacher practice and learner outcomes.
Successful implementation of a multi-tiered system of supports (MTSS) and, specifically, intensive intervention through the data-based individualization (DBI) process, demands the collection and analysis of data. As teams consider data collection, challenges may occur with assessment administration, scoring, and data entry (Taylor, 2009). This resource reviews three data collection and entry challenges and strategies to ensure data about risk status and responsiveness accurately represent student performance and minimize measurement errors.