from data to information to action: leveraging regional data to drive p-16 goals susan dawson jim...
TRANSCRIPT
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 Outnumber doI 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 Meaningless246909001 11S N 2,621 4.6 24.3 1A H A A Z 75 73 77 73 77 0.40
105902041 11MN 532 6.6 64.5 1A A A B R 64 71 69 82 83 4.90
227904043 11MN 921 4.6 37.5 1A B C A R 75 79 80 86 89 3.50
227901176 11E N 717 54.5 90.8 1A A A R D 77 83 72 84 81 0.90
028902004 01S N 343 1.2 53.9 1A G R S E 58 59 85 82 81 6.90
227910001 01S N 2,131 8.4 70.2 1A A A R R 47 47 52 53 70 5.20
246907001 01S N 247 6.1 40.1 1A A X E R 67 72 63 82 88 5.20
105906005 01S N 1,609 5.1 47 1A Y A A Y 45 50 55 61 67 5.50
227901007 01S N 1,405 26.8 83.9 1A A X A A 41 42 42 51 60 4.70
028902001 01S N 952 1.7 45.6 1X A N R R 60 54 65 70 74 4.40
105902001 01S N 1,965 4.8 48.7 1A G A A A 53 57 62 64 62 2.50
227901004 01S N 1,525 33.5 84.1 1A A N M S 45 45 48 50 55 2.50
Data Refinement ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData Sets
Custom Data Sourcing
Longitudinal Individual
Student Data
Longitudinal Linked Student
Data
Longitudinal Aggregate
DataMore sophisticated may or m
ay 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 ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData 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 2009-10 school year
Data Refinement ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData Sets
Custom Data Sourcing
Longitudinal Individual
Student Data
Longitudinal Linked Student
Data
Longitudinal Aggregate
Data
TAKS Passing RateAchievement Gaps Closing
8th 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
8th Grade MathTAKS Passing Rates,Central Texas Districts 2004-08
9th Grade “Bubble”
Dropouts
21st Century CTXBaby Boom
Eligible but Not Attending
Source: AEIS report for Region XIII for 2009-10 school year
Data Refinement ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData Sets
Custom Data Sourcing
Longitudinal Individual
Student Data
Longitudinal Linked Student
Data
Longitudinal Aggregate
Data
District Type & Rate of Growth
-100%
0%
100%
200%
300%
400%D
istr
ict G
row
th R
ate
Hutto
Austin
Manor
Leander
Hays
Georgetown
San Marcos
Bastrop Elgin
Liberty Hill
WimberleyLago Vista
Harper
Luling McDade
Urban Suburban Small Town Rural
Source: TEA AEIS, Growth from 1999-2000 to 2009-10Circle sizes are proportional to district sizes
Data Refinement ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData Sets
Custom Data Sourcing
Longitudinal Individual
Student Data
Longitudinal Linked Student
Data
Longitudinal Aggregate
Data
6-Year Texas Higher Ed Graduation Ratefor 2001 CT High School Graduates
San Marcos
Round Rock
Pflugerville
ManorLeander
Eanes
Del Valle
Bastrop
Austin
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
Gra
du
ati
on
Rate
GeorgetownHays
6-Year Texas Higher Ed Graduation Ratefor 2001 CT High School Graduates
San Marcos
Round Rock
Pflugerville
ManorLeander
Eanes
Del Valle
Bastrop
Austin
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
Gra
du
ati
on
Rate
GeorgetownHays
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 2008-09, plus GIS mapping data
Source: AEIS data for 1998-99, plus GIS mapping data
Data Refinement ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData Sets
Custom Data Sourcing
Longitudinal Individual
Student Data
Longitudinal Linked Student
Data
Longitudinal Aggregate
Data
Disciplinary Actions Per Year, 03-08
7.2%
26.7%27.8%
25.4%24.2%
20.4%
1.9
3.5
3.3
3.73.9
4.0
0%
5%
10%
15%
20%
25%
30%
5th 6th 7th 8th 9th 10th
Perc
enta
ge o
f Stu
dents
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Avera
ge #
Acti
ons p
er
Stu
dent
% Students # Actions Per Student
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.
Student Growth vs. Achievement
State Average
State Median
Data Refinement ContinuumD
iffic
ulty
and
Cos
t
Information Sophistication
AggregateSnapshot Data
Multi-Dimensional Aggregate Data
MultipleData Sets
Custom Data Sourcing
Longitudinal Individual
Student Data
Longitudinal Linked Student
Data
Longitudinal Aggregate
Data
PK Appears to Work
Source: E3 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 ObjectiveLeverage existing data whenever possibleUse more refined data only when neededUnderstand limits of dataTell a compelling storyMake information actionable!
www.e3alliance.org