Hans Peter L’Orange State Higher Education Executive Officers
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State Higher Education Executive Officers State of State Postsecondary Data Systems Study www.sheeo.org Postsecondary and K-12 Data Sharing Results from SHEEO’s State of State Postsecondary Data Systems Study (and more) Hans Peter L’Orange State Higher Education Executive Officers
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Postsecondary and K-12 Data Sharing Results from SHEEO’s State of State Postsecondary Data Systems Study (and more). Hans Peter L’Orange State Higher Education Executive Officers. General Background. Starting assumption: state level data systems Monitor progress Assess effectiveness - PowerPoint PPT Presentation
Transcript of Hans Peter L’Orange State Higher Education Executive Officers
State Higher Education Executive Officers State of State Postsecondary Data Systems Study www.sheeo.org
Postsecondary and K-12 Data SharingResults from SHEEO’s State of State Postsecondary Data Systems Study
(and more)
Hans Peter L’OrangeState Higher Education Executive Officers
State Higher Education Executive Officers State of State Postsecondary Data Systems Study www.sheeo.org
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General Background• Starting assumption: state level data systems– Monitor progress– Assess effectiveness– All with ultimate goal of improved student
achievement• Examine and describe key characteristics of
state postsecondary data systems• Report designed to inform states and enhance
policy
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Not Apples to ApplesState postsecondary SUR data systems may be found within:– state boards of higher education (coordinating and
governing boards)– state boards of education– state financial aid agencies/entities– state budget agencies, and – state labor/workforce agencies
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Study Included 59 SURs in 45 States
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1 SUR 2 SURs 3 SURs 4 SURs
State Higher Education Executive Officers State of State Postsecondary Data Systems Study www.sheeo.org
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Age of Systems is Wide RangingSystem Year Est Cumulative # of SUR SystemsNY (NYSED) 2011 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 59PA (PDE) 2009 •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 58ME 2008 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 57WY (UWYO) 2006 •••••••••••••••••••••••••••••••••••••••••••••••••••••••• 56RI, TN 2004 ••••••••••••••••••••••••••••••••••••••••••••••••••••••• 55HI, KS, ND 2003 ••••••••••••••••••••••••••••••••••••••••••••••••••••• 53VT (VSC) 2002 •••••••••••••••••••••••••••••••••••••••••••••••••• 50MT 2001 ••••••••••••••••••••••••••••••••••••••••••••••••• 49DC, NY (CUNY) 2000 •••••••••••••••••••••••••••••••••••••••••••••••• 48CA (CPEC), MN (MNSCU) 1999 •••••••••••••••••••••••••••••••••••••••••••••• 46AL, AZ, NV, OH, UT, WA (OFM) 1998 •••••••••••••••••••••••••••••••••••••••••••• 44AK 1996 •••••••••••••••••••••••••••••••••••••• 38MA, VT (UVM) 1995 ••••••••••••••••••••••••••••••••••••• 37NM 1994 ••••••••••••••••••••••••••••••••••• 35OR (ODCC), SC 1993 •••••••••••••••••••••••••••••••••• 34AR, VA, WY (WCCC) 1992 •••••••••••••••••••••••••••••••• 32IL 1991 ••••••••••••••••••••••••••••• 29FL (DOE) 1989 •••••••••••••••••••••••••••• 28CO, MO, PA (PASSHE) 1988 ••••••••••••••••••••••••••• 27SD 1987 •••••••••••••••••••••••• 24MS 1986 ••••••••••••••••••••••• 23CA (UCS), NJ 1985 •••••••••••••••••••••• 22GA, WA (SBCTC) 1984 •••••••••••••••••••• 20MN (MOHE) 1983 •••••••••••••••••• 18CT 1982 ••••••••••••••••• 17CA (CCC), NC (NCCCS), NC (UNC), WV 1980 •••••••••••••••• 16IN, OR (OUS) 1979 •••••••••••• 12NY (SUNY) 1978 •••••••••• 10KY, MD 1977 ••••••••• 9LA, OK, TX 1976 ••••••• 7CA (CSU), WI 1973 •••• 4FL (BOG), WA (HECB) 1972 •• 2
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Reasons for Creating SURs
tracki
ng studen
t rete
ntion/grad
uation
tracki
ng studen
ts acr
oss insti
tutions
institutional
resource
allocati
on/funding f
ormula
legisla
tive m
andate
IPEDS r
eporti
ng
increasi
ng studen
t ach
ievem
ent
other rea
son
award
ing finan
cial a
id
audit c
ompliance
federa
l civil
rights
mandate
s
other fed
eral m
andate
s0
10
20
30
40
50
60
33 33
29
26
23
16
13 12
9 8
5
26
9
29
12
16
8 5
13
23
3333
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Types of Data Collected by # of SURs
37
46
58 58
24
44 45
54
37
43
29
17
demogra
phic
postseco
ndary en
rollm
ent
completi
ons
postseco
ndary ac
adem
ic hist
ory
financia
l aid
course
-leve
l inform
ation
academ
ic pro
gram in
ventory
K-12 acad
emic h
istory
non-cred
it instr
uctional
activit
y
faculty
/staff
finance
faciliti
es/cap
ital p
rojec
ts
Adult Basi
c Educati
on
institutional
chara
cteris
tics
labor/w
orkforce
/UI
other typ
e of d
ata0
10
20
30
40
50
60 58 58
54
46 45 44 43
37 37
29
24
21
18 17 17
3
3
1718
17
21
24
29
3737
4344
4546
54
58 58
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Existing links to K-12 and Labor
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K-12 Labor Both
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Matching between PS & K-12• Of those in states with linkages:–Almost half (42%) use both the Social
Security Number and a K-12 ID for matching–Almost all (92%) use an ID in conjunction
with demographic elements such as name, date of birth, and gender– Just under a third (27%) use SSN as the only
ID in conjunction with demographic elements
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Barriers to Linking
FERPA co
ncerns
resource
s
lack o
f common id
entifiers
/ cro
sswalk
s
coord
ination w
ith other
state
authoriti
es/ad
ministrat
ors
incompati
ble sys
tems
data quali
ty co
ncerns
legisla
tion
not applica
ble
lack o
f inter
est fr
om other ag
encie
s
attorn
ey ge
neral o
pinion/state
ment
other barr
ier0
10
20
30
40
50
60
3431
28
21
13 13 129 8
42
2 4
8 9
121313
21
2831
34
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Visit http://www.sheeo.org/sspds for
• Full report• Profiles of each responding agency or
entity• Data element tables
Wednesday, 3:00 pm, Meeting Room 602
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4 State Pilot Study• Gates funded, WICHE managed• WA, OR, ID, HI• Data from K-12, postsecondary and labor• “Where did they go and what did they do
when they got there?”• Issues:– Legal (FERPA, state law, AG-approval)– Technical (share or align data?)– Different starting points and value for different
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Postsecondary Data Advisory Committee K-12 / PSE feedback
• Lack of trust and personality conflicts are on-going challenges• Joint activity (e.g. P-20 data systems) can generate good will)• Find opportunities that have shared value for motivated individuals
– Demonstrate how data can be used– Identify what you need to (jointly) get to where you need to go– Start small to demonstrate results
• Data generated reports can actually challenge trust that has been created during the development process
• Trust is built on data integrity and willingness to address other’s needs• Foster the SHEEO/CCSSO relationship in each state• Institutionalize relationships• Lack of resources especially time• Shared interest is critical
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