Coordinator of Multi-Tiered Systems of Support
Office of Academics
507.328.4490 | Email
Data is used to solve problems and make important decisions that impact student academic, behavior, and social-emotional well-being. Such decisions should create a continuous cycle of systems improvement involving educator support, policy enhancement and procedural / instructional improvement. Data sources might include existing academic and demographic records, surveys, interviews, observations, program / policy / process data and fidelity data.
Jill Dunn
Data System
Rochester Public Schools utilizes an accessible and integrated data system that allows users to document and access educational data and to disaggregate data to look at information for a variety of populations. This system allows for the discussion of the “whole child” including student academic, behavior and social emotional outcomes occurring across grade levels, content areas and tiers. This system is known as eduCLIMBER.
Decision Making Process
Rochester Public Schools use a structured process that guides decision-making. The Continuous Improvement Process employs four essential steps to help determine student needs and improve student outcomes. These cyclical steps help teams to engage in continuous school improvement as data are collected, and plans are monitored, refined and evaluated.
Determining Responsiveness to Tiered Instruction
Across all tiers, data are used to identify the difference or gap between expected and current student outcomes relative to academic, behavior and social-emotional goals. Both performance level and rate of growth are used for decision-making when determining responsiveness to instruction in all tiers. Using both of these indicators is advantageous because it affords making decisions based on understanding students’ responsiveness to instruction by considering performance at a single point in time (e.g., universal screening, benchmark cut-off) and performance over time (e.g., average weekly improvement). Considering both grade level expectations and growth provides a comprehensive understanding of students’ response to instruction in addition to providing multiple indices to determine if gains made by students are educationally meaningful. All decisions made regarding students’ responsiveness to tiered instruction should be documented in eduCLimber.
When reviewing performance over time, it is recommended that data teams use both the consecutive data rule and the trend line rule when analyzing progress monitoring graphs. The consecutive data rule allows for quicker decisions with less data. The trend line rule considers more data, making it less affected by short-term changes.