Data-Driven Decision-Making Resources

A Practical Framework for Building a Data-Driven District or School

Article

Summary  
This 8-page white paper discusses a theory of action that links three critical factors [data quality, data capacity, and data culture] necessary for data use and to inform the types of decisions that might lead to improved student outcomes.  The white paper includes what effective data use might look like in practice.

  • District and building leaders might choose to read the white paper and complete the reflection tool in order to ascertain whether their district and/or school(s) have the conditions necessary to support data-driven decision making.

  • Reflection Tool

  • Data-driven decision-making is embedded in all practices and routines.

Ronka, D., Geier, R., & Marciniak, M. (2010). A practical framework for building a data-driven district or school: How a focus on data quality, capacity and culture supports data-driven action to improve student outcomes. PCG Education.

Guide for Teaming and Data Analysis

Guide

Summary
Data-based decision-making is inclusive of efficient data collection practices for multiple data sets and a formal continuous improvement process. This resource provides guidance for how districts and buildings might collect, analyze and respond to data at various levels of the organization.  Examples of teaming structures and their feedback loops, recommended data sets to use, as well as who, when, and how  data might be used and communicated are provided.

Data Quality Campaign. (2016, April 25). Data can help every student excel [Video]. YouTube. https://www.youtube.com/watch?v=ErE1QQvX8w8

Lipton, L., & Wellman, B. (2012). Got data? Now what? Creating and leading cultures of inquiry. Solution Tree.

Schildkamp, K., Lai, M. K., & Earl, L. (Eds.). (2012). Data-based decision making in education: Challenges and opportunities.

5 Whys Protocol

Guide & Worksheet

Summary  
The 5 Whys technique is a questioning process designed to drill down into the details of a problem or a solution and peel away the layers of symptoms. The technique was originally developed by Sakichi Toyoda who stated that “by repeating why five times, the nature of the problem as well as its solution becomes clear.”

The 5 Whys template was modified by the Utah Education Policy Center from the Residents First HQQ Initiative, available at 5-Whys Guide & Template

Fishbone Protocol

Protocol

Summary  
A fishbone diagram, also known as a cause-and-effect diagram or an Ishikawa diagram, is a visual tool used to identify and analyze the potential causes of a problem or an effect. It is named after its shape, which resembles the skeleton of a fish, with the problem or effect being represented as the "fish head" and the potential causes branching out like the "fishbones."

By using the fishbone diagram in education, educators and stakeholders can gain a systematic understanding of the multiple factors that contribute to a problem, enabling them to develop targeted solutions and improve the educational outcomes for students.

  • Collaborative teams at the state, ISD, district, school, and/or teacher team levels might choose to utilize the fishbone protocol in a variety of ways. For example:

    Problem identification: A fishbone diagram might be utilized to identify and understand the root causes of various issues or challenges faced in K-12 education. For example, if a school is experiencing a high dropout rate, the diagram is structured to help teams identify the underlying factors contributing to this problem.

    Categorization of causes: The fishbone diagram provides a framework for categorizing potential causes into different branches or categories. In the context of K-12 education, these categories might include factors related to curriculum, teaching methods, student engagement, parental involvement, school culture, resources, or external influences.

    Identifying root causes: The diagram facilitates a structured analysis of the causes by drilling down to more specific factors within each category. This process helps to identify the root causes that are contributing to the problem. For example, within the category of teaching methods, sub-causes might include outdated pedagogical approaches, inadequate professional development for teachers, or a lack of differentiated instruction.

  • How to Use the Fishbone Diagram {3:13}

    Example: Alpha Elementary School Final Fishbone Diagram

    Shorter Example: Fishbone Diagram for Low Success Rates in Developmental Mathematics

  • This protocol might support any readiness and planning activities

    Connections can be found through the research-based practices:

    Instructional System
    Student Support System
    Talent Management System
    Teacher Collaborative Routines

High Tech High Graduate School of Education (2008-2023).

Collaborative Learning Cycle (CLC)

CLC Protocol

Summary  
The collaborative learning cycle is a framework that establishes a learning forum for group exploration of data.  This field-tested protocol created by Laura Lipton and Bruce Wellman supports developing shared expertise and confidence with collaborative data work.

Lipton, L., & Wellman, B. (2012). Got data? Now what?: Creating and leading cultures of inquiry. Solution Tree Press.

Facilitation Guide: Modified Tuning Protocol

Facilitation Guide

Summary  
The Modified Tuning Protocol is a tool that might be used during a data-driven decision-making process in order to gather additional perspectives and/or collect new ideas around a problem of practice identified through data analysis.

National School Reform Faculty. NSRF Protocols and Activities. https://www.nsrfharmony.org/wp-content/uploads/2017/10/Tuning-N_0.pdf

Building Capacity for Data-Based Decision Making Through Effective School Leadership and Job-Embedded Learning

Podcast

Summary
This hour long edWeb podcast is sponsored by Illuminate Education. School leaders play a critical role in creating a culture of data-based decision making and empowering teachers to use data to impact future learning outcomes. During this edWeb podcast, research-based practices are shared that will help you to build capacity in your school for data-based decision making through effective leadership. You also learn how educational leaders can leverage data-based decision making as a platform for job-embedded professional learning.

Smith, C. C. & Smith-Peterson, M. (Hosts). (2022, November 10). Building capacity for data-based decision making through effective school leadership and job-embedded learning [Audio podcast]. edWebcasts. https://www.buzzsprout.com/1181414/11673849-building-capacity-for-data-based-decision-making-through-effective-school-leadership-and-job-embedded-learning

Data-Driven Instructional Decision-Making

Summary
The Practice Guide includes five recommendations that are intended to form a framework that examines data use at all levels of the system.

  • The first recommendation focuses on making data part of an ongoing cycle of instructional improvement.

  • The next recommendation focuses on students becoming partners in their own education, and teaching students to use data—to examine it and develop their own learning goals from it—can be one way to foster that partnership and help motivate students and make them really feel like they are part of the educational process.

  • The remaining three recommendations focus on what schools and districts need to do to create the conditions that are necessary to support data use and to establish a vision that everyone can get behind in terms of how they want to use data in the school.

Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., & Wayman, J. C. (2009). Using Student Achievement Data to Support Instructional Decision Making. IES Practice Guide. NCEE 2009-4067. National Center for Education Evaluation and Regional Assistance.

Institute of Education Sciences. (2017, November 30). Data-driven instructional decision making. [Video]. YouTube. https://www.youtube.com/watch?v=PU5_LYN_-ls&list=WL&index=1