Data-Based Decision Making

Data-based decision making (DBM) is a central tenet of an RtI framework. DBM occurs at each level of implementation (individual student, classroom, grade, building, and district) and at all levels of instruction (Tier 1, 2, and 3). Scroll down to access resources specific to DBM.


Curriculum–Based Measurement (CBM) is an assessment approach designed to measure the growth of student proficiency in core educational skills that are predictive of positive school success. This all-day workshop will provide an overview of CBM and discuss how to use CBM screening and progress monitoring data to make instructional decisions within a Response to Intervention (RtI) model. Steps involved in the RtI data-based decision making process will be outlined as well as procedures for establishing data-based decision making rules within a tiered intervention system.


Data-based decision making is a critical component of an RtI process that occurs across all tiers or levels within an RtI model. Data-based decision making involves using RTI data to address essential questions at a district, school, grade, class and individual student level. Prioritizing essential questions at each level, identifying measures to address them and creating an infrastructure so that data is actionable are key components to an effective, sustainable RTI process. This webinar strand will address data-based decision making at a district, school, grade and individual level including special education decision-making.

Data-based decision making is central to a highly functional RtI process and occurs at every level of tiered instruction and intervention. Data based decision making involves the use of student performance data from universal screening and progress monitoring measures to: (1) identify students who may be at-risk for academic failure and, (2) to determine individual student response to supplemental intervention. This webinar strand will highlight data team meeting protocol and logistical considerations that support effective and efficient RtI data team meetings. It will also provide a discussion and examples involving the use of RtI data to determine at-risk status and a student’s response to supplemental intervention.

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