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