🎥 What does it mean? Our sources of data might include assessments, attendance, discipline, demographic records, surveys, interviews, observations, and fidelity data.
The amount of data that we could potentially collect and review is overwhelming — the key to effective data-based decision making is deliberately carving out routines and decision rules that allow staff to get into an effective rhythm.
Data Routines
The key to effective data-based decision making is deliberately carving out "lean, mean-ingful data routines" where the who, what, when, and how are clear. This is our objective at every level — district leadership, building leadership, grade-level teams, etc.
Data Protocols
When teams gather to discuss a set of data, a "data protocol" can provide structure and clarity for all participants. For example, when considering a set of reading data, the group might sequentially discuss: Observations, Reflections, Interpretations, and finally Decisions. Here are a few example data protocols. Note that data protocols sometimes blend into a team agenda.
Decision Rules
At what point do we know that we have a universal problem? At what point do we know a student needs a supplemental intervention? At what point do we need to change an intervention program or plan? Making these decision rules in advance is key to a healthy system.
There are many examples of decision rules at the Tier 1 level. We want to see 80% or more of our students to fall in the Low Risk range on our mySAEBRS SEL screener; that's indicative of a healthy school climate. Another indicator is 80% or more students in the range of 0-1 behavior referrals in a school year.
The Visual Analysis Handout covers decision rules for Tier 2, Tier 3, and SpEd interventions. It condenses guidance from the National Center on Intensive Intervention, the IRIS Center, and other notable experts.
It helps educators to objectively analyze the progress monitoring data that's gathered when a student receives a supplemental intervention. The Handout also helps answer the "Now what?" question by offering a menu of possible next steps.
Problem Solving Process
We use data to identify, understand, and solve problems. Whether we're solving a universal Tier 1 problem or a problem affecting a single student, the steps of the problem solving process are:
- Problem Identification: What is the discrepancy between what is expected and what is occurring?
- Problem Analysis: Why is the problem occurring?
- Plan Development: What is the goal, what is the plan to meet the goal, and how will progress be measured?
- Plan Implementation: How will fidelity be ensured?
- Plan Evaluation: Was the plan effective? What's next?
Learn more about how this helps schools to allocate resources efficiently.
Data Facilitator Project
Specific teachers receive a stipend to compensate them for taking on additional duties related to data-based decision making (see Requirements for Participants). Data Facilitators (DFs) are typically focused on grade- or department-level data. It's vital that building leaders determine how DFs will work in tandem with PLCs and teams that focus on building-wide data.
SCRED Services Coordinators use the Networking Google Classroom to help support DFs. DFs make heavy use of the eduCLIMBER website, the Universal Screening packet, and the Target Packet.
Meeting Agendas and Stipend Checklists
Schools use the folders linked below to house their running agendas (see templates for grade-level/dept. teams) and a Stipend Checklist (administrators complete and submit these each spring).
| Chisago Lakes |
East Central |
Hinckley-Finlayson |
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| North Branch |
Pine City |
Rush City |
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Connect
Many SCREDsters are deeply involved in supporting our member districts with data-based decision making, including all of our Services Coordinators.
 If you have questions or need support re: the Data Facilitator Project, please reach out to one of the Academic or SEL Services Coordinators below.
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