AWSP
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Criterion Three: Planning with Data

Criterion three focuses on leading the development, implementation, and evaluation of a data-driven plan for increasing student achievement, including the use of multiple student data elements. Data includes both quantitative and qualitative information. Effective leaders use data to guide decisions across all aspects of school systems and the other seven leadership criteria.

Key Questions for Reflection

  • What systems do I have in place to collect both qualitative and quantitative data?

  • How am I involving staff in regularly analyzing and reflecting on multiple data sources?

  • In what ways are school improvement plans informed by student learning data?

  • How are our plans supporting historically underserved student groups?

  • What are the key data points that help me communicate progress to stakeholders?

Quick Wins

  • Set up a shared digital dashboard or data wall accessible to your leadership team and staff.

  • Schedule short data chats with teachers during PLCs to discuss student progress.

  • Survey students or families for perceptual data to complement academic data.

  • Host a "Data Day" where staff review and reflect on key school metrics.

  • Identify and communicate one or two priority data points to track consistently schoolwide.

AI Prompts


Illustration of a person at a laptop surrounded by AI, data, and analytics icons and graphs.

Tech Tips

  • Google Forms + Sheets: Easily collect and sort qualitative data from staff, students, or families.

  • Tableau or Google Data Studio: Create interactive dashboards for ongoing data analysis.

  • Edulastic/Illuminate/Schoology: Use formative tools to track classroom-level trends in real time.

  • Data protocols in Google Docs: Template protocols like “Data-Driven Dialogue” for PLCs.

  • Email automation with Mail Merge: Share individualized student data reports with families or staff.

Examples of Proficient Behaviors

A proficient leader uses both qualitative and quantitative data to guide schoolwide improvement.

In practice, they:

  • Establish regular data cycles with staff to reflect on student learning and guide instructional shifts.

  • Use surveys, interviews, and focus groups to collect perceptual data from students, families, and staff.

  • Disaggregate academic and behavior data to identify and respond to opportunity gaps.

  • Collaboratively develop measurable goals aligned to school improvement and district strategic plans.

  • Display progress visibly through dashboards or newsletters.

  • Monitor and revise improvement plans based on mid-year data reviews and stakeholder feedback.

Possible Evidence to Collect

  • School improvement plans and mid-year progress reviews.

  • Agendas or notes from PLCs and data team meetings.

  • Samples of teacher-created data walls, student tracking sheets, or goal-setting forms.

  • Surveys or focus group results from students, staff, or families.

  • Progress monitoring reports showing disaggregated student data and instructional adjustments.

  • SMART goals or school improvement plan targets aligned to specific student needs

  • Evidence of revisions made to plans based on data reviews or stakeholder input

  • PLC meeting agendas/minutes showing data analysis and resulting instructional decisions

Continued Learning


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The AWSP Learning Lab is our online learning platform with various courses, live and asynchronous options. Check out what is available to support you learning.

Explore the Learning Lab


  • Driven by Data 2.0: A Practical Guide to Improve Instruction – Paul Bambrick-Santoyo 
    Focuses on using systems and habits to improve teaching with data.

  • Street Data – Shane Safir & Jamila Dugan
    Introduces an equity-centered model for using qualitative, student-driven data to inform transformation and close opportunity gaps.

  • The K-12 Educator’s Data Guidebook: Reimagining Practical Data Use in Schools – Ryan A. Estrellado
    Validates the implicit challenges of learning to use data to empower educators and features original real-world examples from in-service educators to illustrate common problem-solving.

  • Untangling Data-Based Decision Making: A Problem-Solving Model to Enhance MTSS – Harlacher, Potter, & Collins
    Knowledge, strategies, and tools that will help guide your use of data to identify and solve problems that stand in the way of student achievement.