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Paint by Number:

Paint by Number:. Understanding Numerical Patterns as Images of Student Success. Temperature Check. Who’s in the room? How comfortable are you with data? What made you come to this session?. Big Picture.

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Paint by Number:

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  1. Paint by Number: Understanding Numerical Patterns as Images of Student Success

  2. Temperature Check • Who’s in the room? • How comfortable are you with data? • What made you come to this session?

  3. Big Picture “I am a deep believer in the power of data to drive our decisions. Data gives us the road map to reform. It tells us where we are, where we need to go, and who is most at risk.” Arni Duncan

  4. Big Picture • ESEA calls for the collection, analysis, and use of student achievement data to improve school outcomes • Includes requirement for state report cards • Examining implementation of data systems and the broader set of practices involving the use of data to improve instruction

  5. New Trend: Waivers • Good: Reduce unrealistic expectations • Bad: May be lower expectations for some at-risk groups of students & "make it easier for states to mask poor academic performance“ • Bad: Allowing states to combine small subgroups of students into a larger category of at-risk students may reduce the focus on the unique needs of smaller subgroups

  6. Three Challenges • Help the public understand data • Determine a better means to compare the performance of students • “Drive a national conversation that is above partisan policy disputes, beyond wars on math and reading, & instead focuses on the facts”

  7. Evolution • Find kids, get them in school • Find kids, get them in school, count them • Find kids, get them in school, count them, find out how they’re doing • Find kids, get them in school, count them, find out how they’re doing, actively help them grow

  8. Self-evaluation • Where are you in the data evolution? • What access do you have to data? • Do you know who your players are?

  9. Tips • Golden Rule: ALWAYS be nice to the data people • Find reasons or ways to do things for them • Review guidance, help train, field questions • Be mindful of their timelines • Find ways to help assure quality

  10. First Steps • Develop a plan • Identify foundations • Can change, but need a clear direction • Do your research Another tip: make your ask concrete

  11. Million Dollar Questions?

  12. Foundations • Count of students, by grade & housing • Diversity of HCY population • Where the students are • State testing performance • Special populations overview

  13. Comparisons Can compare multiple years to get general trend

  14. Comparisons Can compare multiple years to get general trend

  15. Comparisons Actual number vs. percentage

  16. Comparisons Actual number vs. percentage

  17. Comparisons: The Next Level Must compare the outcomes for homeless students to other student populations for true depth of growth and challenges

  18. Comparisons: The Next Level • Graduation rates • Special Education rates • Gifted and Talented • Suspensions

  19. Comparisons: The Next Level

  20. Comparisons: The Next Level Comparing the HCY Graduation Rate to the Graduation Rate of Other State Subgroups

  21. Comparisons: The Next Level Comparing outcomes for populations can give new meaning to data for HCY

  22. Comparisons: Top Level Once you’ve got multiple comparison groups and data points, you can mix and match

  23. Suspensions PCT of Student Population that Received a Suspension

  24. Suspensions

  25. In-School vs. Out of School

  26. Million Dollar Questions Revisited • Do you want to know the same things? • If not, what do you want to know now? • What data do you think you need to answer your question?

  27. It’s Alive!!!! • Data gives credibility • Vigo County • Data can help schools get resources • Mesa County • Data can help programs get resources • Montgomery County

  28. Quantitative vs. Qualitative • Qualitative does have its place • Can be harder to collect, analyze • Can tell you the story behind the numbers • What opportunities do you have to gather it • How can you make it reasonably standardized

  29. Data Quality • Consider requiring liaison verification • Consider tracking large changes • Consider comparison groups like free lunch, employment rates, census data • Consider n size: small group sizes skew

  30. Tips • When deciding what to look at, consider format for final report • Explain your findings

  31. Final Thoughts Data…"can basically take us out of the dark ages of just kinda teaching and hoping, which is what a lot of folks have done for a very long time. A lot of teachers have taught their hearts out and don't have a good way of telling who's learning what and what's working and what's not."

  32. Thank you! Christina Endres National Center for Homeless Education cendres@serve.org 336-315-7438 http://center.serve.org/nche/ibt/sc_data.php

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