Data-Based Problem Solving and Decision Making
District and schoolwide data monitoring are at the core of MTSS. Data are used to develop highly effective school plans that inform supportive systems meant to engage students, prevent disparities, and provide optimal educational opportunities to all students.
The Use of Data Protocols
VIDEO: Dr. Elizabeth City of the Harvard Graduate School of Education talks about the use of data protocols to analyze data.
Data Protocols A-Z
from National School Reform Faculty - Harmony Education Center
Dictionary of Abbreviated Protocols
For successful planning and implementation of instruction
Data Principles and Safety Regulations
Principles of Effective Data Use
How to Use the 5 Whys Protocol
Root Cause Analysis
The 5 Whys Protocol
Root Cause Analysis - Step by Step Guide
The 5 Whys Problem Solving Tools
Root Cause Analysis - Benefits and Analysis
The 5 Whys Template
Root Cause Analysis Worksheet
Webinars: Data-Based Problem Solving
Webinar with Kathleen Lane: Part 1 of the Importance of Systematic Screening: Using Data to Support School Success
Webinar with Kathleen Lane: Part 2 of the Importance of Systematic Screening: Using Data to Support School Success
Using Data to Improve Student Outcomes: Dr. Chandra Williams Webinar
Using Data to Improve Student Outcomes: Dr. Chandra Williams Webinar Slides
SWIFT - My Brother's Keeper Task Force: Using Data to Promote Equity in Portland
Effective Use of Data Assumptions
from Data Coach's Guide to Improving Learning for All Students
By Nancy Love
Click HERE for Article
ASSUMPTION 1: Making significant progress in improving student learning and closing the achievement gaps is a moral responsibility and a real possibility in a relatively short amount of time – two to five years. It is not children's poverty or race or ethnic background that stands in the way of achievement; it is school practices and policies and the beliefs that underlie them that pose the biggest obstacles.
ASSUMPTION 2: Data have no meaning. Meaning is imposed through interpretation. Frames of reference – the way we see the world – influence the meaning we derive from data. Effective data users become aware of and critically examine their frames of reference and assumptions. Conversely, data themselves can also be catalysts for questioning assumptions and changing practices based on new ways of thinking.
ASSUMPTION 3: Collaborative inquiry – a process where teachers construct their understanding of student-learning problems and invent and test out solutions together through rigorous and frequent use of data and reflective dialogue – unleashes the resourcefulness and creativity to continuously improve instruction and student learning.
ASSUMPTION 4: A school culture characterized by collective responsibility for student learning, commitment to equity, and trust is the foundation for collaborative inquiry. In the absence of such a culture, schools may be unable to respond effectively to the data they have.
ASSUMPTION 5: Using data itself does not improve teaching. Improved teaching comes about when teachers implement sound teaching practices grounded in cultural proficiency – understanding of and respect for their students' cultures – and a thorough understanding of the subject matter and how to teach it, including understanding student thinking and ways of making content accessible to all students.
ASSUMPTION 6: Every member of a collaborative school community can act as a leader, dramatically impacting the quality of relationships, the school culture, and student learning.
Resources for Developing a Comprehensive Assessment System