The MTSS teams at both the district and the school levels are charged with reviewing data regularly to guide decision-making and problem solving. One type of data that should be reviewed is student  outcome data. 

Student outcome data help us answer the question "How effective is what we're doing for our students?"

Like implementation data, student outcome data should also be utilized for problem solving, via a consistent data cycle across all tiers of support and pre-scheduled reviews using consistent routines for analysis. 

At Tier 1, student outcome data includes academic and social-emotional screening and school-wide data such as attendance and behavior referrals. If core supports are effective, at least 80% of students will be meeting the benchmark, whether that be academic, behavioral, or social-emotional. At Tier 1, the whole school is considered, since Tier 1 supports are provided to all students. Whole school data should be disaggregated by grade level and by subgroups, such as students with IEPs and multi-lingual learners, to ensure that specific groups of students are achieving at proportional rates to the whole school. For example, if 9% of all students have at least one major behavioral referral, teams would want to make sure that approximately 9% (or less!) of students with disabilities have one major referral. If more than 9% of students with disabilities have been referred to the office, that signals disproportionality, meaning students with disabilities are more likely to be referred to the office than their peers without disabilities. 

illustrated graphic of a chart with a line graph displayed At Tiers 2 and 3, students who have been identified as needing additional supports to meet benchmarks receive evidence-based interventions. Multiple data sources, called entrance criteria, are used to identify students for specific interventions to target the student need. Data is used to determine the student's baseline and goals are set using normed data to ensure gaps can be closed. While implementation data should be considered to make sure the intervention itself is being implemented correctly, measuring student outcomes includes using progress monitoring data to see if students are responding to the intervention. When students respond and meet pre-determined exit criteria, supports are faded and students are exited from the intervention. If a student is not responding to an otherwise effective intervention, data is used to intensify the intervention qualitatively and quantitatively to better meet the student's needs. 

The National Center on Intensive Intervention has a student progress monitoring tool "designed to help educators collect academic progress monitoring data across multiple measures as a part of the data-based individualization (DBI) process. This tool allows educators to store data for multiple students (across multiple measures), graph student progress, and set individualized goals for a student on specific measures" (NCII, 2016). 

Feeling confused by all the different data uses and meanings? Visit the RTI Action Network's Glossary to learn more about some of the terms here. 

Last modified: Wednesday, April 7, 2021, 1:22 PM