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Essential Tools for Data Analysis - The Data Coach Model

Adapted from

Adapted from: The Data Coach's Guide to Improving Learning for All Students, Nancy Love et al., by the EBP Project

Purpose

The Using Data Process and related tools are designed to help data coaches and data teams through the Collaborative Inquiry process. The Using Data Process offers a structured framework that supports the ongoing analysis of student achievement data, with the aim of improving outcomes for all students.

Description

The Using Data Process is the overall framework used to bridge the gap between data and results. It incorporates a number of  further tools and processes to help data teams remain focused on each step of the data analysis process and the end goal of improving teaching and learning.

The attached document includes the various tools used in The Data Coach’s Guide to Improving Learning for All Students.

The Using Data Process - Offers a stepped and guided process that begins with building the foundations for creating a high-performing using data culture and introducing data teams and school staff to the various components of the process. The process continues through analysis of data to identify possible student learning problems, investigating the possible causes of those problems, generating solutions, and finally implementation, monitoring & achieving results.

The Data Pyramid - The data pyramid offers a visual representation of the different types of data used in this process and also provides estimates of how often data should be collated and examined. Educators are taught how to drill down into the data and move through the sequential layers of analysis from the aggregate (results compiled at the largest level - general) down to detailed analysis of student work.

Data Driven Dialogue - Used throughout the Using Data Process, this four phase process is a core dialogue tool used by data team members to create meaningful connections to the data they are analysing. It uses vibrant visual displays to actively engage participants in open dialogue about assumptions to enable them to extract meaningful information the data.

The Verify Causes Tree - This rubric allows educators to list the possible causes of a chosen student learning problem, by gathering data about various school practices such as instructions, assessment and and equity. It also requires educators to look both at research findings and local data findings in order to verify causes of a student learning problem. Crucially, it takes the blame away from students and puts the responsibility for improving achievement back on the educators.

Core Competencies for High Capacity Data Use - Effective data users draw on four core competencies that form the basis for high-capacity data use resulting in long term significant improvements in teaching and learning. The 4 competencies are data literacy & Collaborative Inquiry skills, general pedagogical and content knowledge, cultural proficiency & leadership & facilitation skills. A table of high versus low capacity data use is also available for reference.

Context For Use

The EBP Project has incorporated these processes and tools into the Lead Data Team seminars and they are fundamental to the Data Analysis workshops run collaboratively between the EBP Project and CARE. The Frameworks and tools attached here and detailed in The Data Coach’s Guide can be used to support any kind of school team or collaborative inquiry process seeking to use data to find ways to improve student achievement.

Benefits

  • Knowledge management: This approach allows educators to comprehensively gather, organize, share and analyse their knowledge in terms of resources, documents, and people skills.
  • Knowledge acquisition: The Using Data Process guide introduces educators to the various frameworks and tools to support them through the collaborative inquiry data analysis process.
  • Knowledge mobilization: The tools help users make connections between student outcomes and their practices and utilises local data and research to support decision making.

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