california community colleges data resources patrick perry, vice chancellor of technology, research,...

California Community Colleges Data Resources

Patrick Perry, Vice Chancellor of Technology, Research, and Information SystemsCalifornia Community

Colleges Chancellor’s Office

Who is this guy? Why should we listen to you? Brad Pitt-like looks. Vin Diesel physique. And, I have an ENORMOUS…

…..database. I collect data and measure stuff for a living. I have all the data. Information Management & Institutional

Research: IM…therefore IR.

My Credo

I realize that I will not succeed in answering all of your questions. Indeed, I will not answer any of them completely. The answers I provide will only serve to raise a whole new set of questions that lead to more problems, some of which you weren’t aware of in the first place. When my work is complete, you will be as confused as ever, but hopefully, you will be confused on a higher level and about more important things.

Today’s Learning Outcomes:

Learn how, why, and 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 Know what new data tools are in the

works

Technology, Research & Information Systems Data Accountability Data/Reporting Transfer Data Data Mart

At the core of this is the MIS Data Collection system

MIS Data

Source: submissions from all 109 campuses/72 districts

End of term Very detailed, unitary student and

enrollment data 1992-present Data Element Dictionary online

Enrollments(SX)

StudentDemographics

(SB)Sections

CoursesFin.Aid

Assess.

PBS

VTEA

Matric.

Pgm.Awds.

Emp.Demo.

Sessions

Calendar Assignments

EOPSDSPS

Emp.Assign.

Database Relationships

Data Uses New and Continuing Students Non-credit Matriculation EOPS / DSPS Funding EOPS/ DSPS Program Justification VTEA (Vocational and Technical

Education Act) VTEA Core Indicator Reports VTEA Allocations

BOGW Administrative Funding Federal Integrated Postsecondary

Education Data System (IPEDS) Reporting

CCC Data Mart

Data Clients Legislative Analyst Office Department of Finance California Postsecondary Education

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

members Community College Organizations Newspapers Labor Unions Individuals

How Can I access the Data?

Data Mart – online Reports – online Ad-hoc report – call or email MIS Ad-hoc request for unitary dataset

Must be approved by system office Scrubbed of identifying fields Usage agreement

Ad-Hoc requests

CO can cut reports or datasets, provided: Student-identifiable information is not

given Request must have stated purpose

and focus Playing “what-if” is very time

consuming

Data Mart (TRIS)

Demographics, FTES (not apportionment), awards, finaid, matric, assessment, student svcs progs, program retention/success, staffing reports

Demo

Golden Nuggets: Student Demography

Headcount & FTES

Year Headcount FTES FTES per Head

1995-1996 2,118,747 827,135 0.390

1996-1997 2,241,557 923,395 0.412

1997-1998 2,306,923 960,069 0.416

1998-1999 2,437,610 996,188 0.409

1999-2000 2,546,643 1,036,691 0.407

2000-2001 2,648,581 1,053,237 0.398

2001-2002 2,812,023 1,136,210 0.404

2002-2003 2,829,860 1,159,744 0.410

2003-2004 2,545,443 1,114,661 0.438

2004-2005 2,515,550 1,095,089 0.435

2005-2006 2,550,247 1,121,779 0.440

2006-2007 2,621,388 1,133,924 0.433

What’s Going on in CCC?

Fee ImpactsBudget VolatilityCalifornia’s Changing Demography

CCC Trends

• CCC now coming out of early 2000’s budget cuts and fee increases…

• …headcounts are starting to creep back up…

• …fees are stable (this week, at least)…

• …and its all just in time for a demography crash.

•CCC Pipeline

• Coming in the door:• Early 2000’s:

• Fee increases from $11-$18-$26, now $20

• Budget cuts

• Pipeline issues now coming to fruition

•The Big Pipeline Factor: The State Budget• California has a volatile tax

revenue collection history• Very progressive taxation

• State budgets negotiated late• College schedules set early• College CBO’s need stability; State

provides little

•The Budget

• Downturns in revenue=• State:

• Raising of fees • Enrollment prioritization

• Local:• Expectation of cuts or no growth=

• Immediately become fiscally conservative; OR • burn up your reserves THEN become fiscally

conservative

•Local Budget Reaction

• Fall schedule set ~6 mo. beforehand• Budget frequently passed late, Fall term

already begun• If budget=good, then little chance to add

sections to capture• If budget=bad, then little chance to cut

sections

• In both cases, only Spring/Summer left to balance

•Early 2000’s

• Gray Davis came out with 10% budget reduction proposal in January 02

• CCC’s began creating Fall 02 schedules shortly thereafter• High anxiety and conservatism• Sections slashed

• Final budget late in 02• Cuts not nearly as drastic, but colleges

already acted

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

•Who Left?

• High headcount loss, not so much in FTES• We lost a lot of single course takers

• Enrollment priority to those already in system• Outsiders/first-timers-forget about

getting your course

• Fee Impact burden on older students

Population Projections

Year 15-24 yo

2000 4,850,103

2010 5,969,955

2020 5,953,842

2030 6,448,117

HS Grad Projections

Year HS Grads

2006 363,662

2008 374,877

2010 371,848

2012 366,720

2014 354,046

2016 348,000

Why The Drop?

• *The Children of Generation X

• Gen X influence defined the 80’s-early 90’s culture (new wave music, big hair and shoulder pads)

• Overeducated and underemployed, highly cynical and skeptical

• Burdened by the societal debt of boomers• Extremely entrepreneurial (tech & internet)

Gen X Parents

• More hands-on than Baby Boomer parents

• Value higher education as more important to success than Boomer parents

• Gen X is a much smaller cohort than Boomers; so are their offspring

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

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%

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%

1996-1997 12.2% 7.8% 22.9% 6.5% 44.7% 5.9%

1997-1998 12.1% 7.7% 23.3% 6.6% 43.9% 6.3%

1998-1999 12.2% 7.6% 23.9% 6.6% 42.5% 7.1%

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

2000-2001 12.1% 7.3% 25.2% 6.5% 40.3% 8.6%

2001-2002 12.3% 7.3% 26.3% 6.6% 40.1% 7.4%

2002-2003 12.3% 7.5% 26.5% 6.6% 39.2% 7.9%

2003-2004 12.5% 7.5% 27.2% 6.9% 37.9% 8.0%

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

2005-2006 12.2% 7.6% 28.5% 7.0% 36.1% 8.6%

2006-2007 12.3% 7.5% 28.8% 7.0% 35.4% 9.1%

Demography: Gender

• 55% Female, 45% Male

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

Annual Units Attempted

Year 0-11.9 (PT-Low) 12-23.9 (PT-Hi) 24+ (FT-Year)1995-1996 68.4% 18.8% 12.7%1996-1997 69.5% 18.3% 12.2%1997-1998 69.6% 18.1% 12.3%1998-1999 70.6% 17.5% 12.0%1999-2000 71.1% 17.2% 11.7%2000-2001 71.7% 16.9% 11.5%2001-2002 71.1% 17.0% 11.9%2002-2003 69.6% 17.8% 12.5%2003-2004 66.7% 19.5% 13.8%2004-2005 66.3% 19.6% 14.2%2005-2006 66.8% 19.0% 14.1%2006-2007 67.3% 18.9% 13.8%

Demography of Success

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

Demography of Success

• Does the group of students starting out or already in look like the students leaving with various outcomes?

• Demography in=demography out• = parity.

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%

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%

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%

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%

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%

Which Leads Us To…

Transfer Data

Located at CPEC website: “Transfer Pathways”

Also in Accountability Report (ARCC), Research website

Demo

•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

•Ten Years Ago…

• Ten Years Ago:• We served 2.44 million students

• 36% were underrepresented (AfrAm, Hisp/Latino, Filipino, Native Amer, Pac Isl)

• Today:• We serve 2.62 million students

• 42% are underrepresented (+6%)

• Headcount has grown only 7%• Not much…and one might expect similar

outcome parity…

•However...Transfer

• Ten Years Ago:• CSU Transfers: 44,943…UC: 10,177• CSU Underrepresented: 28%...UC: 20% (+6%)

• Today:• CSU Transfers: 54,379, UC: 13,874• CSU Underrepresented: 34%...UC: 26% (+6%)

• 24% increase in transfer volume (during a time when headcount went up only 7%) and achievement gap remained stable

•But…Times are a-Changing…

Measuring Transfer

•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?”

•Transfer Volumes

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

to CSU/UC• CSU: ~50,000 annually• UC: ~13,000 annually• In-State Private (ISP) and Out of

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

•Transfer Volumes

• Annual volume of Transfers• CSU=somewhat volatile• UC=somewhat stable

• Constrained by Enrollment Management at CSU/UC• 60/40, Fall/Spring admits, application deadlines• CSU/UC growth, FTES funding• CCC supply/pipeline• Functional barriers

• Unconstrained in the open Educational marketplace• Few barriers, ability to absorb and respond

Tracking Transfers

• Annual Volume of Transfers• CSU/UC: they provide these figures

based on their criteria• We didn’t want to redefine this

• In-State Private/Out of State: National Student Clearinghouse data match

• Added another 30% to annual volumes• ISP/OOS transfer not “traditional”

CCC Transfer Volumes

Sector 01-02 02-03 03-04 04-05 05-06 06-07

CSU 50,473 50,746 48,321 53,695 52,642 54,391

UC 12,291 12,780 12,580 13,211 13,462 13,874

ISP 17,070 15,541 18,100 18,365 17,840 18,752

OOS 10,762 10,540 11,150 11,709 11,726 11,825

Total 90,596 89,607 90,151 96,980 95,670 98,842

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

The Rise of The Phoenix96-97 2,166

97-98 2,829

98-99 3,374

99-00 4,194

00-01 5,055

01-02 5,586

02-03 6,515

03-04 8,222

04-05 8,585

05-06 8,134

06-07 9,216

Who Transfers to Phoenix?

Ethnicity UC CSU Phoenix

Asian 29.3% 14.2% 4.6%

African American 2.4% 5.2% 16.8%

Hispanic/Latino 13.6% 23.8% 28.6%

White 39.1% 43.6% 37.5%

•Who Transfers To Phoenix?

  CSUU of

PhxOther

ISP UC

Under 17 13.4% 5.3% 16.4% 31.2%

17 to 19 62.6% 45.2% 48.6% 53.3%

20 to 24 11.0% 20.7% 13.4% 8.6%

25 to 29 4.3% 11.3% 7.2% 2.6%

30 to 34 3.2% 7.7% 5.6% 1.7%

35 to 39 2.4% 5.3% 4.0% 1.0%

40 to 49 2.4% 3.8% 3.9% 1.0%

Over 49 0.7% 0.7% 0.9% 0.6%

• Start Age in CCC

•Transfers Out of StateUNIVERSITY OF NEVADA-LAS VEGAS 326

ARIZONA STATE UNIVERSITY 296

EMBRY RIDDLE UNIVERSITY* 262

UNIVERSITY OF NEVADA-RENO 215

UNIVERSITY OF MARYLAND* 200

BRIGHAM YOUNG UNIVERSITY 197

PORTLAND STATE UNIVERSITY 185

WESTERN GOVERNORS UNIVERSITY* 173

COLUMBIA COLLEGE* 171

UTAH VALLEY STATE COLLEGE 169

•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%

•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

•CCC Transfer Rate Methodology

• All first-timers, full year cohort• Behavioral intent to transfer:

• Did they ever attempt transfer level math OR English; and

• Completed any 12 units

• Tracked 6 years forward (10 is better)• Data match with CSU, UC, Nat’l

Student Clearinghouse

•Transfer Rates

• By Ethnicity:• Asian=56%• White=44%• Black/AfrAm=36%• Hispanic=31%

• Transfer Rates for older students are lower

•Assessing The Transfer “Pipeline” Effects• The loss in the early 2000’s will

now be seen for this much smaller group moving through• Smaller group, but greater % of

degree-seekers, younger students helps mitigate

•Adding to the Woes…

• Current year budget shortfall• CCC’s likely grew too much in 07-

08 (overcap)• Property tax shortfall

• Scenes of 2002 in the midst

•Back to The Pipeline…

• Coming Out The Other End:• Transfer Pool Proxies

•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

•Transfer Pool ProxiesDirected Prepared Ready

1997 76,872 61,752 44,4331998 77,599 66,316 47,9761999 77,700 62,122 45,9812000 75,996 63,022 46,7982001 77,907 64,803 48,6212002 81,796 69,375 51,8422003 85,351 75,201 55,5552004 83,576 77,818 56,2982005 85,066 82,239 57,5192006 81,863 82,462 52,873

•What Happens to them?

The Following Year:

Transfer Directed

(math+Eng)

Transfer Prepared

(60 units)

Transfer Ready

(math+Eng + 60 units)

Transferred or Earned Award 63.5% 77.0% 84.5%

Still Enrolled 30.9% 17.3% 10.6%

No transfer, award, or still enrolled 5.6% 5.7% 4.8%

Accountability Reporting

ARCC Report: annual “Dashboard” accountability report

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

demo

ARCC

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

Student Prog. & Achievement: Degree/Cert/Xfer 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

Student Prog. & Achievement: Voc/Occ/Wkforce 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

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

Participation

College: None yet…but coming.

System: Statewide Participation Rate (by

demographic)

Major Advancements of ARCC Creating participation rates. Creating a viable grad/transfer rate. Finding transfers to private/out of state

institutions. Doing a wage study. Geo-mapping district boundaries. Creating peer groups. All unitary datasets available.

Participation RatesState Partic. Rate Tuition/FeesCA 9,567 $ 806AZ 8,697 1,394NM 7,366 1,528WA 7,309 2,481IL 6,778 1,934OR 6,142 2,807NV 5,531 1,590FL 5,379 1,778NC 5,074 1,269TX 5,033 1,438MN 4,745 3,815CO 4,339 2,203NY 3,069 3,276MA 2,978 3,424PA 2,066 3,298

• (per 100k 18-44 year-olds)

Participation (and Fees)

Participation Rates: AgeAge 04-05 05-06 06-07

<18 14 16 16

18-19 353 352 354

20-24 253 249 249

25-29 122 122 125

30-34 76 75 77

35-39 60 60 60

40-49 49 48 48

50-64 34 34 35

Participation Rates: EthEth 04-05 05-06 06-07

Asian 91 90 90

AfrAm 74 75 74

Hisp/Lat 54 54 55

NatAm 77 72 69

PacIsl 125 127 130

White 56 56 57

Defining Grad/Transfer Rate

Student Progress & Achievement Rate (SPAR Rate)

CCC’s have multiple missions, students have multiple purposes for attending

For grad/xfer rates, we only want to count students here who want are degree-seeking Cohort denominator is key!

SPAR Rate

Defining the cohort: Scrub “first-time” by checking against

past records (CCC, UC, CSU, NSC)

SPAR Rate

Define “degree-seeking” behaviorally for CC populations Not by self-stated intent; this is a poor

indicator Behavior: did student ever attempt

transfer/deg-applicable level math OR English (at any point in academic history) Students don’t take this for “fun”

Defining Degree-Seeking Behaviorally Separates out remedial students not

yet at collegiate aptitude Measure remedial progression to this

threshold elsewhere Creates common measurement

“bar” of student aptitude between colleges Same students measured=viable

comparison

SPAR Rate-Unit Threshold

CCC provides a lot of CSU/UC remediation Lots of students take transfer math/Eng

and leave/take in summer Should not count these as success or “our”

student Set minimum unit completed threshold

(12) for cohort entrance Any 12 units in 6 years anywhere in system

SPAR Denominator:

First-Time (scrubbed) Degree-seeking (at any point in 6

years, attempt transfer/degree applicable math or English)

12 units (in 6 years)

This represents about 40% of students in our system

SPAR Numerator

Outcomes the State wants: Earned an AA/AS/certificate; OR Transfer: to a 4-yr institution; OR Become “transfer-prepared”;OR

Completed 60 xferable units Became “transfer-directed”:

Completed both xfer level math AND English

No double-counting, but any outcome counts SPAR Rate=51%

Wage Study

What was the economic value of the degrees (AA/AS/certificate) we were conferring?

Required data match with EDD Had to pass a bill changing EDD code

to allow match

Wage Study

Take all degree recipients in a given year Subtract out those still enrolled in a

CCC Subtract out those who transferred to

a 4-yr institution Match wage data 5 years

before/after degree

Wage Study

Separate out two groups: Those with wages of basically zero

before degree Those with >$0 pre wage

The result: The Smoking Gun of Success

Mapping Districts

CC Districts in CA are legally defined, have own elections, pass own bonds

We did not have a district mapping for all 72 districts So we couldn’t do district

participation rates

Mapping Project

Get a cheap copy of ESRI Suite Collect all legal district boundary

documents Find cheap labor—no budget for

this

Peer Grouping

“Peers” historically have been locally defined: My neighbor college Other colleges with similar demography Other colleges with similar size

Peer Grouping

Taking peering to another level: Peer on exogenous factors that

predict the accountability metric’s outcome (outside campus control)

Thus leaving the “endogenous” activity as the remaining variance (within campus control)

Peer Grouping: Example

Peering the SPAR Rate: 109 rates as outcomes Find data for all 109 that might

predict outcomes/explain variance Perform regression and other magical

SPSS things

Finding Data

What might affect a grad/transfer rate on an institutional level? Student academic preparedness levels Socioeconomic status of students First-gen status of students Distance to nearest transfer institution Student age/avg unit load

Finding Data

We had to create proxy indices for much of these (142 tried) GIS system: geocode student

zipcode/ZCTA Census: lots of data to be crossed by

zip/ZCTA Create college “service areas” based

on weighted zip/ZCTA values Different than district legal boundaries

Finding Data

The Killer Predictor “Bachelor Plus Index”, or what % of

service area population of college has a bachelor’s degree or higher

“Bachelor Plus Index” a proxy for: First gen Academic preparedness Socioeconomic status Distance to nearest transfer institution

Peering SPAR Rate

Exogenous factors that predict SPAR Rate: Bachelor Plus Index % older students % students in basic skills

R2 = .67 What’s left is implied institutional

variance

Peering

Campuses with similar exogenous profiles are clustered together to form peer groups

Other Data

Program Approval Database Fiscal Data

What’s in The Works:

New Perkins Reports and Reporting Portal Reports.cccco.edu

Program Evaluators Data Tool You upload the student ID’s, select

reports to get in return—tell me everything about this set of students

Thank You

Feel Free To Ask: Patrick Perry:

pperry@cccco.edu