Analyze the data
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Comprehensive Needs Assessment Professional Development. Analyze the Data. Session Questions. Why is data analysis a critical aspect of school/district improvement? How can data analysis focus the improvement work in a school/district?. 2. Data Analysis Can Help Us Answer Several Questions:.

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Analyze the data

Comprehensive Needs Assessment Professional Development

Analyze the Data


Session questions

Session Questions

  • Why is data analysis a critical aspect of school/district improvement?

  • How can data analysis focus the improvement work in a school/district?

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Data analysis can help us answer several questions

Data Analysis Can Help Us Answer Several Questions:

  • Who are we?

  • How do we do business?

  • Where are we now?

  • What are the gaps? What are the root [underlying] causes of the gaps?

  • Where do we want to be? How can we get to where we want to be?

    (Bernhardt, 2006)

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Delightful school scenario

Delightful School Scenario

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Questions about delightful school

Questions About Delightful School

1. What data are currently being collected by Delightful Intermediate School?

2. What additional data might the school want to collect?

3. What strengths and weaknesses do you notice as you read the scenario?

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Predictions and assumptions about what exists

Predictions and Assumptions About What Exists

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Possible predictions

Possible Predictions

  • Which is higher?

    • reading or math performance

    • district compared to school

  • Has performance increased or decreased over 3 years?

  • Which groups of students are performing high? Low?

    • Ethnicity

    • Economically disadvantaged

    • Special education

    • LEP

  • Other predictions?

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Prediction cards index cards

Prediction Cards (Index cards)

Prediction 2

Prediction 1

Prediction 3

Prediction 5

Prediction 4

This activity is adapted from Data-Driven Dialog: A Facilitators Guide to Collaborative Inquiry, by Bruce Wellman and Laura Lipton.

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Predictions and assumptions

Predictions and Assumptions

  • One person draws a card and reads a prediction.

  • Everyone speculates on assumption(s) underlying the prediction.

  • Next person draws a card and process continues.

  • Spend no more than 3 minutes on each prediction.

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Predictions and assumptions1

Predictions and Assumptions

Predictions

Assumptions

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Analyze the data

Why Assumptions Are Important

  • Accepted as fact

  • No actual proof that they are true

  • Serve as frames of reference for what we believe to be true

  • Underlie our predictions about future events

  • Usually unaware of our assumptions

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Analyze the data

AYP Indicators

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2009 adequate yearly progress guide

2009 Adequate Yearly Progress Guide

http://ritter.tea.state.tx.us/ayp/2009/guide.pdf

2009 AYP Indicators

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Ayp indicators

AYP Indicators

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Analyze the data

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Analyze the data

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Delightful school scenario data analysis

Delightful School Scenario: Data Analysis

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Rules of engagement

Rules of Engagement

  • Make sure everyone participates.

  • Respect each other’s ideas.

  • Stay focused on the task.

  • Avoid

    • “because” statements,

    • the “blame game,” and

    • being defensive.

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Analyze the data

BECAUSE

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Rules of engagement1

Rules of Engagement

  • Review one piece of data, with everyone at the table simultaneously looking at the same data.

  • While the data are important to understand, also important is the collective nature of this activity—everyone discussing the same data.

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District and school ayp data

District and School AYP Data

  • Three years of AYP reports for ABC District and Delightful Intermediate School

  • Graphs of these reports disaggregated by ethnicity, economically disadvantaged, special education, and LEP

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Adequate yearly progress guide

Adequate Yearly Progress Guide

http://ritter.tea.state.tx.us/ayp/2009/guide.pdf

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Additional data sets

Additional Data Sets

  • Demographic data

  • Attendance data

  • Content area survey results

  • Themes from focus groups and conversations with leaders

  • If available—Findings from system capacity rubric, additional relevant data

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Examining the data

Examining the Data

  • Use highlighters to indicate data that “pop out”

    • Differences among subgroups of students

    • Content areas in the greatest need for improvement

    • Patterns or trends in demographics or other data

    • Need for clarification or more information

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Examining the data1

Examining the Data

  • Recorder will chart group’s “pop outs”

  • Remember: No “becauses”

You have 30 minutes for this review.

Spend approximately 5–6 minutes on each data piece.

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Reflection

Reflection

  • What are the benefits and challenges of analyzing data collaboratively?

    5 minutes for table discussion, then report out

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References

References

  • Bernhardt, V. L. (2006). Using data to improve student learning in school districts. Larchmont, NY: Eye on Education.

  • Texas Education Agency. (2009). 2009 adequate yearly progress (AYP) guide for Texas public school districts and campuses. Austin, TX: Division of Performance Reporting.

  • U.S. Department of Education. (2006). Designing schoolwide programs: Non-regulatory guidance. Washington, DC: Author.

  • Wellman, B., & Lipton, L. (2004). Data-driven dialog: A facilitators guide to collaborative inquiry. Arlington, MA: MiraVia.

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