1. From Data to Information to Action: Leveraging Regional Data to Drive P-16 Goals Susan Dawson Jim Van Overschelde, PhD February 2, 2011

2. Why Better Data? Which Drop Out number do I believe? What keeps students beyond freshman year? Are we getting better or worse? Which program do I invest in? What practice is working best for students?

3. Data Can Be Meaningless 24690900111SN 2,6214.624.31AHAAZ7573777377 0.40 10590204111MN 5326.664.51AAABR6471698283 4.90 22790404311MN 9214.637.51ABCAR7579808689 3.50 22790117611EN 71754.590.81AAARD7783728481 0.90 02890200401SN 3431.253.91AGRSE5859858281 6.90 22791000101SN 2,1318.470.21AAARR47 525370 5.20 24690700101SN 2476.140.11AAXER6772638288 5.20 10590600501SN 1,6095.1471AYAAY4550556167 5.50 22790100701SN 1,40526.883.91AAXAA4142 5160 4.70 02890200101SN 9521.745.61XANRR6054657074 4.40 10590200101SN 1,9654.848.71AGAAA5357626462 2.50 22790100401SN 1,52533.584.11AANMS45 485055 2.50

4. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data More sophisticated may or may not be better!

5. Where Are The Data? TEA & THECB –AEIS, TPIER, LoneStar, Higher Ed(masked, free) –Submit adhoc data requests (masked, $) –TSDS (future, masked, free) –Most require knowledge of Excel or similar Data sharing agreement with each district –Unmasked, $$$ –Requires staff with research background

6. Education Research Centers UT Austin, UT Dallas, Texas A&M Support custom research –Submit research proposal to the Joint Advisory Board for approval –Get access to 20 years of TEA, THECB, and workforce wage data, plus ACT/SAT/NSC –Unmasked, $$$ –Requires staff with research background

7. Data to Information Grouped, categorized Interpreted –Requires little to extensive knowledge –Relative to a context –Inferences to larger population

8. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data

9. District TAKS Results Source: AEIS report for Wimberley ISD for 2009-10 school year

10. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data

11. TAKS Passing Rate Achievement Gaps Closing 8 th Grade Reading TAKS Passing Rates, Central Texas Districts 2004-08 Source: E3 Alliance analysis of TEA TAKS data retrieved from http://ritter.tea.state.tx.us/student.assessment/reporting/taksagg/dnload.html 8 th Grade MathTAKS Passing Rates, Central Texas Districts 2004-08

12. 9 th Grade “Bubble” Dropouts 21 st Century CTX Baby Boom Eligible but Not Attending Source: AEIS report for Region XIII for 2009-10 school year

13. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data

14. District Type & Rate of Growth Source: TEA AEIS, Growth from 1999-2000 to 2009-10 Circle sizes are proportional to district sizes

15. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data

16. Source: THECB Ad-Hoc Reports and TEA AEIS Reports District Low Income Rate 40% Won’t Graduate from College, Even With $ College Graduation Maps to Income

17. Source: AEIS data for 2008-09, plus GIS mapping data

18. Source: AEIS data for 1998-99, plus GIS mapping data

19. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data

20. Disciplinary Rates Triple at Middle School Source: EGS Research and Consulting (2010). Longitudinal analysis of a Central Texas cohort of student 2002-03 to 2007-08. Austin, TX: E3 Alliance.

21. Student Growth vs. Achievement State Average State Median

22. Data Refinement Continuum Difficulty and Cost Information Sophistication Aggregate Snapshot Data Multi-Dimensional Aggregate Data Multiple Data Sets Custom Data Sourcing Longitudinal Individual Student Data Longitudinal Linked Student Data Longitudinal Aggregate Data

23. PK Appears to Work Source: E 3 Alliance analysis of CTGSR assessment data, unweighted sample

24. Actionable Information “The mark of insanity is doing the same thing over and over again and expecting a different result.” -Albert Einstein (supposedly) Actionable information indicates what behavior needs to change? –And, if possible, how it needs to change?

25. Keys to Using Data/Information to Encourage Change Always be Objective Leverage existing data whenever possible Use more refined data only when needed Understand limits of data Tell a compelling story Make information actionable!

26. www.e3alliance.org