Board of Trustees Presentation

Trends in Student Enrollment and Demographics

Foothill-De Anza

Institutional Research & Planning

Spring 2015

Outline

Part I: The data – EnrollmentWhat are the trends?

What is (should be) our current reality?

Part II: How to think about dataPerspective on improvement strategies

What’s the problem with outcome rates?

Data Resources

FHDA Research Website

Factbook

Fast Facts

Strategic Planning Documents

College Institutional Research Websites

Completed Research Projects

Program Review

Active Division

CCCCOStudent Success Scorecard

Data Mart

Outline

Part I: The data – EnrollmentWhat are the trends?

What is (should be) our current reality?

Part II: How to think about dataPerspective on improvement strategies

What’s the problem with outcome rates?

Enrollment Trends by Race/Ethnicity

F08 F11 F12 F13 F140%5%

10%15%20%25%30%35%40%

African American Asian/PI FilipinoLatino/a White

Fall ‘08 Fall ‘11 Fall ‘12 Fall ‘13 Fall ‘14African American 2,036 1,767 1,902 1,781 1,826

Asian/PI 13,439 12,916 13,014 12,571 12,444Filipino 1,758 2,184 2,214 2,246 2,366Latino/a 6,131 7,511 8,086 8,623 9,008White 12,807 11,446 10,587 9,664 9,120Total 44,762 39,482 38,204 36,774 36,507 FHDA IRP

Enrollment Trends by Gender & FT/PT

F08 F11 F12 F13 F1447.0%

48.0%

49.0%

50.0%

51.0%

Male Female

F08 F11 F12 F13 F140%

20%

40%

60%

80%

Full-time Part-time

Fall ‘08

Fall ‘11

Fall ‘12

Fall ‘13

Fall ‘14

Full-time 14,031 14,921 15,574 15,990 16,222

Part-time 30,731 24,561 22,630 20,784 20,285

Fall ‘08

Fall ‘11

Fall ‘12

Fall ‘13

Fall ‘14

Male 22,089 19,451 18,551 18,109 18,089

Female 22,645 19,724 19,371 18,430 18,160

Unrecorded 28 307 282 235 258

FHDA IRPFHDA IRP

Enrollment Trends by Age Groups

F08 F11 F12 F13 F140%

10%

20%

30%

40%

50%

19 or less 20 - 24 25 - 29 30 - 3435 - 39 40 - 49 50 - 59 60+

Fall ‘08 Fall ‘11 Fall ‘12 Fall ‘13 Fall ‘1419 or less 13,629 8,769 8,651 6,896 7,122

20 - 24 11,883 14,050 14,627 16,359 16,28425 - 29 5,791 5,583 5,242 5,457 5,48330 - 34 3,185 2,986 2,707 2,550 2,48035 - 39 2,327 1,969 1,642 1,482 1,45240 - 49 3,507 2,799 2,471 1,948 1,82150 - 59 2,159 1,908 1,631 1,321 1,141

60+ 2,271 1,409 1,229 761 724FHDA IRP

Enrollment Trends by Enrollment Status

F08 F11 F12 F13 F140%

10%

20%

30%

40%

50%

60%

First-time Student First-time Transfer Returning StudentContinuing Special Admin (K-12)

Fall ‘08 Fall ‘11 Fall ‘12 Fall ‘13 Fall ‘14

First-time Student 6,200 5,463 5,367 5,543 5,653

First-time Transfer 6,448 4,797 4,392 5,198 4,472

Returning Student 9,055 6,317 6,712 7,780 9,035

Continuing 20,262 22,070 20,236 17,597 16,689

Special Admin (K-12) 2,787 832 825 654 658FHDA IRP

Enrollment Trends by Online Ed. (FTES) & International

Fall ‘08

Fall ‘11

Fall ‘12

Fall ‘13

Fall ‘14

Non-Foreign 41,878 36,068 34,526 32,917 32,456

Foreign - F1 and Other

2,884 3,414 3,678 3,857 4,051

Fall ‘08

Fall ‘11

Fall ‘12

Fall ‘13

Fall ‘14

Non-Online Ed.

10,710 8,995 8,654 8,518 8,510

Online Ed. 1,222 1,482 1,379 1,542 1,633

F08 F11 F12 F13 F140%

20%

40%

60%

80%

100%

Foreign - F1 and OtherNon-Foreign

F08 F11 F12 F13 F140%

20%

40%

60%

80%

100%

Non-Online Ed. Online Ed.

FHDA IRPCCCCO Data Mart

Enrollment Trends by City

F08 F11 F12 F13 F140%

10%

20%

30%

40%

50%

San Jose Sunnyvale Cupertino Mountain ViewSanta Clara Palo Alto Los Altos/Los Altos Hills FremontMilpitas

F08 F11 F12 F13 F14San Jose 14,316 14,210 14,113 14,203 14,321Sunnyvale 4,436 3,613 3,421 3,839 3,905Cupertino 3,238 2,627 2,408 1,479 1,361Mountain View 2,915 2,486 2,294 1,808 1,648Santa Clara 2,294 2,050 2,001 2,102 2,170Palo Alto 2,298 1,561 1,382 1,155 1,108Los Altos/Los Altos Hills 1,616 1,153 996 693 606Fremont 960 1,012 1,022 883 849Milpitas 984 907 836 792 774Other 11,705 9,863 9,731 9,820 9,765 FHDA IRP

Enrollment Trends by City

F08 F11 F12 F13 F140%

5%

10%

15%

20%

25%

30%

35%

40%

45%

San Jose Sunnyvale Cupertino Mountain View

Santa Clara Palo Alto Los Altos/Los Altos Hills Fremont

MilpitasFHDA IRP

Enrollment Projections by CityAll Ages

2015 2020 2025 20300%

5%

10%

15%

20%

25%

30%

35%

San Jose Sunnyvale Cupertino

Santa Clara Palo Alto Los Altos/Los Altos Hills

Milpitas All other cities

Ages 15-30

2015 2020 2025 20300%

5%

10%

15%

20%

25%

30%

35%

San Jose Sunnyvale Cupertino

Santa Clara Palo Alto Los Altos/Los Altos Hills

Milpitas All other cities

2015 2020 2025 2030

San Jose 15,938 16,683 17,464 18,281

Sunnyvale 3,067 3,228 3,398 3,576

Cupertino 2,256 2,318 2,381 2,447

Santa Clara 1,994 2,096 2,204 2,317

Palo Alto 2,031 2,101 2,173 2,249

Los Altos/Los Altos Hills 802 818 833 849

Milpitas 892 963 1,040 1,123

All other cities 11,910 12,259 12,617 12,985

2015 2020 2025 2030

San Jose 12,954 13,560 14,194 14,858

Sunnyvale 2,216 2,332 2,454 2,583

Cupertino 1,648 1,693 1,740 1,788

Santa Clara 1,576 1,657 1,742 1,831

Palo Alto 1,294 1,338 1,385 1,432

Los Altos/Los Altos Hills 473 482 491 500

Milpitas 765 826 892 963

All other cities 9,128 9,396 9,671 9,955

Hanover ReportHanover Report

Enrollment Projections by CityAll Ages

2015 2020 2025 20300%

5%

10%

15%

20%

25%

30%

35%

San JoseSunnyvaleCupertinoSanta ClaraPalo AltoLos Altos/Los Altos HillsMilpitasAll other cities

Ages 15-30

2015 2020 2025 20300%5%

10%15%20%25%30%35%

San JoseSunnyvaleCupertinoSanta ClaraPalo AltoLos Altos/Los Altos HillsMilpitasAll other cities

Hanover Report Hanover Report

What Makes Sensefor Foothill-De Anza CCD?

Changes (percentage points) from F’08 – F’14Race/Ethnicity

Asian: +4

Latino/a: +11

White: -4

GenderVery little change

FT/PTFull-time: +13

Age Group20-24: +18

19 or less: -10

25-29: +2

Enrollment StatusContinuing: +1

Returning: +5

First-time Student: +1

Online EducationD.E.: +6

InternationalForeign: +5

CitySan Jose: +7

Cupertino: -3

Mountain View: -2

Outline

Part I: The data – EnrollmentWhat are the trends?

What is (should be) our current reality?

Part II: How to think about dataPerspective on improvement strategies

What’s the problem with outcome rates?

YOU’VE SEEN THE DATA: WHAT’S THE NEXT STEP?

HOW DO WE THINK ABOUT THE DATA?

An Equity Lens

AccessWho is enrolling on our campuses?

Ensuring diversity

Population projections

Outreach

OutcomesHow are we serving our students?

Are students achieving equal outcomes?

Measuring Student Outcomes

Many indicators of student success

Course success rates District Strategic Plan

Student Equity Plan

Institutional Goals (IEPI)

Let’s take a look….

Measuring Student Outcomes

Fall 2010 Fall 2011 Fall 2012 Fall 2013 Fall 20140%

10%20%30%40%50%60%70%80%90%

100%

Foothill-De Anza Community College DistrictCourse Success Rates

District

CCCCO Data Mart

Measuring Student Outcomes

District De Anza Foothill0%

10%20%30%40%50%60%70%80%90%

100%

Foothill-De Anza Community College DistrictFall 2014 Course Success Rates by Ethnicity

African American Asian Latino/a White

achievement gap at 18%

CCCCO Data Mart

Measuring Student Outcomes

2004-05 2005-06 2006-07 2007-08 2008-090%

10%20%30%40%50%60%70%80%90%

100%

Foothill-De Anza Community College DistrictCourse Success Rates

Overall Prepared Unprepared

Student Success Scorecard

2004-05 2005-06 2006-07 2007-08 2008-090%

10%20%30%40%50%60%70%80%90%

100%

Foothill-De Anza Community College DistrictCompletion/Student Progress and Attainment Rate (SPAR)

Prepared Unprepared

Measuring Student Outcomes

26% gap

Student Success Scorecard

HOW DO WE IMPROVE THE TREND?HOW DO WE MAKE DATA

ACTIONABLE?

Improving Course Success

Gap Increasing Growth Rising Tide

Bottom-up Win-Win

Gregory Stoup, Contra Costa CCD

Improving Student Outcomes

Consider an equity approach that would move as many as possible toward course success

As opposed to focusing only on increasing course success among specific groups

Win-WinBottom-up

STUDENT OUTCOMES - RATES

Student Outcomes – Rates: Language Review

Math LanguageIt’s a fraction

Bottom number (denominator) = total number possible

Top number (numerator) = total number actual

CC LanguageStill a fraction – but presented as a percent

Bottom number = # of students who could/want outcome

Top number = # off students who achieve outcome

Percent of students who achieve outcome.

Student Outcomes – Rates: Problem?

What’s the problem?It’s all about the denominator (could vs. want)

What’s the student’s intent?

Sometimes it’s also about the numerator

Do we even have good data?

Example…

Transfer Rates?

2007/08 CohortCompleters Overall (Scorecard)

Fall 2007 (Data Mart)

Annual Headcount (Data Mart)

Transfer Velocity Cohort (Data Mart)

First-Time Students (Data Mart)

Numerator is 1,930 (from Transfer Velocity Cohort)

Annual Headcount

Fall 07 Headcount

First-time Students

Completers Overall

Transfer Velocity Cohort

0%

10%

20%

30%

40%

50%

60%

70%

3% 4%

22%

32%

58%

Student Outcomes - Rates

Other areas where rates are discussedDenominator issues

(Retention Rates)

Persistence Rates

Completion Rates

Transfer Rates

Numerator issuesCTEl Employmentl Wage

Where do we talk about outcomes?

Internally (to name a few)Institutional Plans (E.g. EMP)

Program Review

Research Requests

Externally (to name a few)ACCJC

Grants

Institutional Effectiveness Partnership Initiative (IEPI)

Questions?

Thank You

“Not everything that can be counted counts, and not everything that counts can be counted.”

- Albert Einstein