using data to inform our decisions

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Using Data to Inform Our Decisions Dan Crump American River College Jon Drinnon Merritt College With special thanks to Patrick Perry, Chancellor’s Office, Vice Chancellor--- Technology, Research & Information Systems (TRIS), for all his help and input.

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Using Data to Inform Our Decisions. Dan Crump American River College Jon Drinnon Merritt College With special thanks to Patrick Perry, Chancellor’s Office, Vice Chancellor---Technology, Research & Information Systems (TRIS), for all his help and input. Today’s Learning Outcomes:. - PowerPoint PPT Presentation

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Page 1: Using Data to Inform Our Decisions

Using Data to Inform Our Decisions

Dan CrumpAmerican River College

Jon Drinnon

Merritt CollegeWith special thanks to Patrick Perry, Chancellor’s

Office, Vice Chancellor---Technology, Research & Information Systems (TRIS), for all his help and input.

Page 2: Using Data to Inform Our Decisions

Today’s Learning Outcomes:

Learn how/why/where data are collected Learn how you can access this data See some “golden nuggets” of data

mining efforts Understand accountability reporting for

CCC’s

Page 3: Using Data to Inform Our Decisions

Some Sources of Data

Data Mart Accountability Data/Reporting

(ARCC) Transfer Data

At the core of this is the Chancellor’s Office MIS Data Collection system

Page 4: Using Data to Inform Our Decisions

Chancellor’s Office MIS Data

Source: submissions from all 112 campuses/72 districts

End of term Very detailed, unitary student and

enrollment data 1992-present Data Element Dictionary online

Page 5: Using Data to Inform Our Decisions

Data Uses Accreditation---Assessment New and Continuing Students Non-credit Matriculation EOPS / DSPS VTEA (Vocational and Technical

Education Act) BOG Waiver Federal Integrated Postsecondary

Education Data System (IPEDS) Reporting

Page 6: Using Data to Inform Our Decisions

Data Users Legislative Analyst Office (LAO) Department of Finance (DOF) California Postsecondary Education

Commission (CPEC) Public Policy Institutes/Think Tanks UC/CSU Legislature – Committees and individual

members Community College Organizations (e.g.

CCLC Newspapers Labor Unions

Page 7: Using Data to Inform Our Decisions

How Can I access the Data?

Data Mart – online Reports – online Ad-hoc report – call or email MIS

Page 8: Using Data to Inform Our Decisions

Data Mart (Chancellor’s Office---

Technology, Research and Information Systems)

www.cccco.edu

Tab to Community Colleges->Data Mart

Page 9: Using Data to Inform Our Decisions

Data Mart

Student Demographics – Term Student Demographics - Annual Full Time Equivalent

Students(FTES)  Full Time Equivalent Students(FTES)

- By Distance Education Status Student Program Awards Program Retention/Success Rates

Page 10: Using Data to Inform Our Decisions

• Program Retention/Success Rates• Program Retention/Success Rates - By Distance Education Status• Transfer Velocity Project Cohort •  Student Financial Aid•Student Services Programs (DSPS/EOPS/CalWORKs)•Student Matriculation Services•Student Assessment Services• Staffing Reports

Page 11: Using Data to Inform Our Decisions

Golden Nuggets: Student Demographics

Page 12: Using Data to Inform Our Decisions

Headcount & FTESYear Headcount FTES

1995-1996 2,118,747 827,135

2001-2002 2,812,023 1,136,210

2002-2003 2,829,860 1,159,744

2003-2004 2,545,443 1,114,661

2004-2005 2,515,550 1,095,089

2005-2006 2,550,247 1,121,779

2006-2007 2,621,388 1,133,924

2007-2008 2,739,846 1,226,006

2008-2009 2,894,166 1,316,305

2009-2010 2,758,832 1,347,210

Page 13: Using Data to Inform Our Decisions

TermSections Offered Enrollments

Average Section Size

Fall 2001 166,735 4,564,156 27.37

Spring 2002 172,811 4,674,836 27.05

Fall 2002 170,373 4,867,043 28.57

Spring 2003 164,597 4,676,951 28.41

Fall 2003 160,573 4,684,539 29.17

Spring 2004 165,261 4,580,776 27.71

Fall 2004 165,221 4,618,651 27.95

Spring 2005 171,295 4,542,878 26.52

Fall 2005 171,248 4,630,698 27.04

Spring 2006 175,445 4,519,494 25.76

Page 14: Using Data to Inform Our Decisions

Population Projections

Year 15-24 yo

2000 4,850,103

2010 5,969,955

2020 5,953,842

2030 6,448,117

Page 15: Using Data to Inform Our Decisions

HS Grad Projections

Year HS Grads

2006 363,662

2008 374,877

2010 371,848

2012 366,720

2014 354,046

2016 348,000

Page 16: Using Data to Inform Our Decisions

Enrollment StatusYear First-Time Returning Continuing

1995-1996 742,149 436,718 760,329

1996-1997 794,652 455,888 786,364

1997-1998 785,323 454,551 805,397

1998-1999 833,902 481,001 822,105

1999-2000 837,361 458,927 927,359

2000-2001 897,931 462,917 935,607

2001-2002 961,722 498,303 989,068

2002-2003 960,954 489,641 1,068,115

2003-2004 824,267 443,340 1,030,396

2004-2005 822,830 472,609 988,516

2005-2006 818,207 501,857 895,893

2006-2007 812,348 530,994 926,795

Page 17: Using Data to Inform Our Decisions

Demography: Age

Year 0-24 25+1995-1996 45% 55%1996-1997 44% 56%1997-1998 45% 55%1998-1999 46% 54%1999-2000 47% 53%2000-2001 48% 52%2001-2002 48% 52%2002-2003 49% 51%2003-2004 49% 51%2004-2005 50% 50%2005-2006 51% 49%2006-2007 51% 49%

Page 18: Using Data to Inform Our Decisions

Demography: Ethnicity/RaceYear Asian AfrAm Hisp/Lat Other-NonWht White Unk/DTS

1995-1996 12.3% 7.8% 22.5% 6.5% 45.8% 5.1%

1999-2000 12.1% 7.5% 24.5% 6.5% 41.6% 7.8%

2004-2005 12.2% 7.6% 27.9% 7.0% 37.1% 8.2%

2009-2010 14.9% 7.2% 30.4% 7.0% 31.9% 13.6%

Page 19: Using Data to Inform Our Decisions

Demography: Gender

• 55% Female, 45% Male

• Ratio hasn’t changed +/- 1% in 15 years

Page 20: Using Data to Inform Our Decisions

Demography of Success

• “It is not so important who starts the game but who finishes it.” –John Wooden

Page 21: Using Data to Inform Our Decisions

Demography of Parity (Example)

Demog (06-07) Input (Students)

Output (Outcome)

AfrAm 9% 9%

Asian 11% 11%

Hisp/Latino 35% 35%

White 29% 29%

     

F 55% 64%

M 45% 36%

Page 22: Using Data to Inform Our Decisions

Demography of ProcessDemog. (06-07)

FTF Students

Total Students

BOGWaiver Basic Skills

AfrAm 9% 8% 13% 9%Asian 11% 12% 12% 15%

Hsp/Latino 35% 29% 39% 43%White 29% 35% 23% 20%

         F 49% 55% 51% 64%M 49% 44% 49% 36%         

18-24 56% 44% 75% 57%25-39 20% 27% 9% 28%40+ 17% 22% 5% 12%

Page 23: Using Data to Inform Our Decisions

Demography of PersistenceDemog (06-07) FTF Students All Students

Fall-Spr Persist

AfrAm 9% 8% 8%Asian 11% 12% 12%

Hisp/Latino 35% 29% 33%White 29% 35% 34%

       F 49% 55% 51%M 49% 44% 49%       

18-24 56% 44% 75%25-39 20% 27% 9%40+ 17% 22% 5%

Page 24: Using Data to Inform Our Decisions

Demography of AA/AS/CertDemog (06-07) FTF Students All Students AA/AS/CertAfrAm 9% 8% 7%Asian 11% 12% 12%

Hisp/Latino 35% 29% 24%White 29% 35% 43%

       F 49% 55% 64%M 49% 44% 36%       

18-24 56% 44% 52%25-39 20% 27% 32%40+ 17% 22% 16%

Page 25: Using Data to Inform Our Decisions

Demography of Transfer

Demog (06-07)

FTF Stdents

All Stdents

XFER-CSU

XFER-UC

XFER-ISP

XFER-OOS

AfrAm 9% 8% 5% 3% 11% 13%

Asian 11% 12% 12% 26% 8% 7%

Hisp/Latino 35% 29% 23% 16% 23% 13%

White 29% 35% 37% 40% 44% 55%

Page 26: Using Data to Inform Our Decisions

Transfer Data

Located at CPEC website: www.cpec.ca.govTab to Detailed Data->Transfer Pathways

Also in Accountability Report (ARCC), Research website

www.cccco.edu Tab to Chancellor’s Office/

Divisions/TRIS/Research/ARCC

Page 27: Using Data to Inform Our Decisions

•Importance of Transfer in BA/BS Production• High dependence on CCC transfers

in BA/BS production at CSU/UC • CSU: 55%...and declining• UC: 28%...and steady• 45% of all BA/BS awarded from public

institutions were from CCC transferees

Page 28: Using Data to Inform Our Decisions

•But…Times are a-Changing…

Measuring Transfer

Page 29: Using Data to Inform Our Decisions

•Transfer Measurement 101

• Method #1: Volumes• “How many students transferred in

year X from CCC’s to other institutions?”

• Method #2: Rates• “Of all the students who started in

Year X, what % of them eventually transferred in X number of years?”

Page 30: Using Data to Inform Our Decisions

•Transfer Volumes

• Very common metrics:• Annual volume of transfers from CCC

to CSU/UC• CSU: ~37,000 annually• UC: ~14,000 annually• In-State Private (ISP) and Out of

State (OOS): ~13,000-15,000 annually each

Page 31: Using Data to Inform Our Decisions

Transfers: In State (not CSU/UC)UNIVERSITY OF PHOENIX 9,216

NATIONAL UNIVERSITY 1,250

DEVRY INSTITUTE OF TECHNOLOGY 975

CHAPMAN UNIVERSITY 849

UNIVERSITY OF SOUTHERN CALIFORNIA 587

ACADEMY OF ART UNIVERSITY 496

AZUSA PACIFIC UNIVERSITY 463

FRESNO PACIFIC UNIVERSITY 378

CALIFORNIA BAPTIST UNIVERSITY 375

UNIVERSITY OF SAN FRANCISCO 314

Page 32: Using Data to Inform Our Decisions

•Transfer: Sector of Choice

  % to UC% to CSU

% to Instate Private

% to Out of State

White 17.9% 60.7% 11.0% 10.4%

AfrAm 11.5% 51.2% 18.1% 19.2%

Hisp/Lat 15.1% 67.7% 12.1% 5.1%

Asian 37.0% 49.9% 9.2% 3.9%

Page 33: Using Data to Inform Our Decisions

•Measuring Transfer: Rates

• “Transfer Rate” is frequently mistaken for transfer volume

• Rates are ratios---percentages• “We transferred 352 people this year”

is not a transfer rate• “We transferred 38% of students with

transfer behavior within 6 years of their entrance” is a transfer rate

Page 34: Using Data to Inform Our Decisions

•Transfer Pool Proxies

• Transfer Directed• Completed Transfer Math and English

• Transfer Prepared• Completed 60 UC/CSU transferable units

• Transfer Ready• Completed Math, English, and 60 units• These are starting to go down

Page 35: Using Data to Inform Our Decisions

Accountability Reporting

ARCC Report: annual “Dashboard” accountability report

—not “pay for performance” Online: 800+ page .pdf AB 1417

Page 36: Using Data to Inform Our Decisions

ARCC – Accountability Reporting for Community Colleges The Model:

Measures 4 areas with 13 metrics: Student Progress &

Achievement-Degree/Certificate/Transfer Student Progress & Achievement-

Vocational/Occupational/Workforce Dev. Pre-collegiate improvement/basic skills/ESL Participation

“Process” is not measured

Page 37: Using Data to Inform Our Decisions

Student Progress & Achievement: Degree/Certificates/Transfer College:

Student Progress & Achievement Rate(s) (SPAR)

“30 units” Rate for SPAR cohort 1st year to 2nd year persistence rate

System: Annual volume of transfers Transfer Rate for 6-year cohort of FTF’s Annual % of BA/BS grads at CSU/UC who

attended a CCC

Page 38: Using Data to Inform Our Decisions

Student Progress & Achievement: Voc/Occ/Workforce Dev

College: Successful Course Completion rate:

vocational courses System:

Annual volume of degrees/certificates by program

Increase in total personal income as a result of receiving degree/certificate

Page 39: Using Data to Inform Our Decisions

Precollegiate Improvement/Basic Skills/ESL

College: Successful Course Completion rate:

basic skills courses ESL Improvement Rate Basic Skills Improvement Rate

System: Annual volume of basic skills

improvements

Page 40: Using Data to Inform Our Decisions

Participation

College: None yet…but coming.

System: Statewide Participation Rate (by

demographic)

Page 41: Using Data to Inform Our Decisions

Major Advancements of ARCC Creating participation rates. Creating a viable

graduation/transfer rate. Finding transfers to private/out of

state institutions. Doing a wage study. Creating peer groups.

Page 42: Using Data to Inform Our Decisions

Other Data

Program Approval Database Fiscal Data