Refining RTI: A systems

approach to unifying scores

The Blenders

Peggy Jacobs

Greg Collier

Paul Cavender

Michele Burke

David Shaub

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What did we create and why is this important?

Problem

• Problem Statement – Data rich, Information poor:

• Results from numerous different assessments all in different formats.

• Time needed to cull out scores and interpret for areas of growth for each student.

• Determine appropriate next steps

Solution

• Taking numerous assessments, normalizing the data to create relevant student

cohorts in order maximize teaching and student growth.

• Making sure the remediation and/or extension teaching is research based with

learning targets being set

• Students will show growth based on: assessments

(formative, summative, anecdotal),

student responses, portfolio, student reflection

• Cohorts must be dynamic and fluid, based on student performance

Teachers’ Need

• Teachers need real time, normalized analysis of the data to create immediate cohorts

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How did we validate the problem?

Research:

•As teachers, we spend hours interpreting data to create quality cohorts

•57 years of combined experience in our group

Key Findings:

•Teachers need support in analyzing data to create cohort groups

•This would save MANY hours a year in data analysis and allow for more targeted

instruction

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Leveraging SLC

How SLC was used to bring the solution together:

•SLC will provide raw data from State and Vendor assessments

•Application will analyze data and create cohort groups

•Teachers will utilize information to create learning plans

•Application will write back cohort groups to SLC with new student data to create new

cohort groups

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Demonstration

• How we do it now:

• How we want it to be done: