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

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?

Data Can Be Meaningless SN 2, AHAAZ MN AAABR MN ABCAR EN AAARD SN AGRSE SN 2, AAARR SN AAXER SN 1, AYAAY SN 1, AAXAA SN XANRR SN 1, AGAAA SN 1, AANMS

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!

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

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

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

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

District TAKS Results Source: AEIS report for Wimberley ISD for school year

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

TAKS Passing Rate Achievement Gaps Closing 8 th Grade Reading TAKS Passing Rates, Central Texas Districts Source: E3 Alliance analysis of TEA TAKS data retrieved from 8 th Grade MathTAKS Passing Rates, Central Texas Districts

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

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

District Type & Rate of Growth Source: TEA AEIS, Growth from to Circle sizes are proportional to district sizes

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

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

Source: AEIS data for , plus GIS mapping data

Source: AEIS data for , plus GIS mapping data

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

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

Student Growth vs. Achievement State Average State Median

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

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

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?

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!