a new era of student access at california's community colleges, … · 2020. 11. 19. · marisol...

A New Era of Student Access at California’s Community Colleges

Technical Appendices

CONTENTS

Appendix A. Data and Methods Appendix B. Tables and Figures Appendix C. Assessment and Placement Analysis

Marisol Cuellar Mejia, Olga Rodriguez, and Hans Johnson with research support from Bonnie Brooks and Chidi Agu

Supported with funding from the Bill and Melinda Gates Foundation, the College Futures Foundation, the ECMC Foundation, the Evelyn & Walter Haas Jr. Fund, and the Sutton Family Fund

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PPIC.ORG Technical Appendices A New Era of Student Access at California’s Community Colleges 2

Appendix A. Data and Methods

Research Questions Our analyses utilize both quantitative and qualitative data to investigate five key questions:

1) How has access to and outcomes of transfer-level math and English courses changed in the first term of implementation of Assembly Bill 705 (AB 705)? Have those changes improve racial equity?

2) How have colleges modified their assessment and placement systems in response to AB 705? What institutional practices enable and inhibit the implementation of AB 705 with fidelity to its intents?

3) In what ways do the changes to assessment and placement function as a tool of racial equity in access to transfer-level courses?

4) What is the prevalence and characteristics of different corequisite support models offered across the system’s 114 colleges during fall 2019?

5) What are the outcomes of students in corequisite models, overall and across racial groups?

Data Sources We use three primary sources of data to answers these questions:

Student Longitudinal Data Our quantitative approach utilizes student-level longitudinal data from the California Community College Chancellor’s Office Management Information System (COMIS). The dataset includes students enrolled across the 114 community colleges that comprise the California Community College system, and includes demographic information, transcripts (grades and credits earned), and course elements (levels below transfer level, credit status, transfer status and minimum/maximum number of credits).

Please see the glossary of terms in the main report for description of key variables derived from the COMIS data.

College Scan We conducted a comprehensive scan of publically available college documents, including but not limited to college websites, catalogs, and class schedules. The purpose of the scan was to examine the structure of corequisite courses and assessment and placement policies in place during fall 2019, the first term of implementation of AB 705. On the corequisite side, the information we gathered included course names, course descriptions, type of corequsite structure (e.g. linked, enhanced, stretch, etc.), credit load, among other course details. On the assessment and placement front, we collected data on placement systems used, target populations for placement systems, placement measures used, data sources used for placement, placement rules used, information relating to prerequisite/corequisite placement, counselor involvement, and academic supports (see Appendix Table C1 for list of variables and definitions). We organized and coded this information on a secure Excel database to track patterns and themes. Upon this initial pass through the college documents, however, we found that there was still a lot of critical information missing, especially details regarding placement rules and guided self-placement. We contacted the California Community College Chancellor’s Office (CCCCO) to request access to the AB 705 implementation plans community colleges submitted, which detail how colleges are adjusting their assessment and placement policies in response to AB 705. Upon receiving the plans, we were able to fill in critical gaps in the college scan substantially. For any colleges where information remained missing, we

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followed up with key contacts cited in the AB 705 implementation plans. In cases where the information gathered from multiple sources diverged (e.g. college website and the AB 705 plans), the research team used information from the AB 705 implementation plans as the main data source. In all cases, the research team relied on the placement definitions found in Appendix Table C1 to code placement data. This was especially important in cases where data was confusing, such as when colleges called their placement system guided self-placement but in reviewing their placement documents we found that they were using high school records as the primary placement measure.

Interviews To help elucidate our quantitative findings, we conducted 38 semi-structured interviews with community college faculty, staff, and administrators at 21 colleges. Colleges were purposefully selected to be inclusive of the different approaches to assessment and placement (e.g. multiple measures and guided self-placement) and corequisite offerings (e.g. structure, unit load, etc.) as well as different scales of implementation. Interviews were conducted over the phone or Zoom over the course of three months (from March 2020 to May 2020) and were about one hour each. We asked each interviewee a variety of questions related to the implementation of AB 705, including but not limited to math/English sequence offerings, math/English assessment and placement details, structure and characteristics of corequisite courses, math/English outcomes and racial equity, professional development, alignment across reform efforts, and about planned changes moving forward. Importantly, because our interviews were in progress when the COVID-19 shelter-in-place orders began, we also asked about how the pandemic affected the implementation of AB 705, especially as it related to the online transition of courses and student services. We audio recorded and kept notes during each interview to accurately capture observations and thoughts of each interviewee, as well as to synthesize themes, observations, and insights to investigate further and inform other interviews.

In summary, the 38 interviews can be broken down into the following categories, by discipline and/or interviewer type and topics covered:

15 interviews with English instructors that focused on corequisites and assessment and placement practices

9 interviews with Mathematics instructors that focused on corequisites assessment and placement practices

3 interviews with college staff focused on math and English assessment and placement practices

11 interviews with college or district-level administrators and Guided Pathways regional coordinators focused on AB 705 implementation, resources needed, and alignment across reform efforts

Among the 15 English interviews, we find that the colleges represented in our sample are similar to the state average in various respects, including size, share of students with direct access to college composition, racial/ethnic demographics, and the share of students in corequisite course enrollment (Table A1). The main difference that emerges has to do with the fall 2019 outcomes, namely the interview sample had a slightly higher throughput rate in fall 2019 (65% vs. 61%) and a relatively higher success rate for corequisite courses (64% vs. 58%). In terms of placement policies used, all colleges in the system and the interview sample use multiple measures placement policies. However, we find that our interview sample is less likely to use guided self-placement than the system average (40% vs. 48%).

With respect to the math interviews, we find that the colleges represented in our sample enroll higher shares of Latinos and lower shares of white students, while enrolling similar shares of African American and Asian American students (Table A1). Key differences emerge when we examine transfer-level math access and outcomes: our interview sample (87%) has a higher share of students enrolling directly in transfer-level math courses compared to the state average (78%) and slightly higher overall throughput (44% vs. 40%). The interview



sample also has higher enrollment and success rates in corequisite courses than the state average. In terms of placement policies used, all colleges in the system and the interview sample use multiple measures placement policies. However, we find that our interview sample is less likely to use guided self-placement than the system average (11% vs. 39%).

TABLE A1 Descriptive statistics for interview sample compared to statewide average

English Math

Statewide Interviewees Statewide Interviewees

Number of colleges 114 15 114 9

Number of colleges using multiple measures 100 100 100 100

Number of colleges using guided self-placement 48 40 39 11

Average first-time English/math fall 2019 cohort 1,448 1,385 1,172 1,502

Share of first-time English/math students starting directly in transfer-level (%) 96 98 78 87

Share of first-time English/math students in corequisite models (%) 23 23 20 30

Success rate among first-time corequisite students (%) 58 64 45 49

Overall one-term throughput rate (%) 61 65 40 44

Share of Asian American students among first-time English/math students (%) 11 12 12 14

Share of African American students among first-time English/math students (%) 5 4 5 4

Share of Latino students among first-time English/math students (%) 55 57 52 60

Share of white students among first-time English/math students (%) 19 18 20 15

SOURCE: Authors’ calculations based on COMIS data and PPIC Scan of CCC Placement Policies.

NOTE: See the glossary of terms in the main report for definitions.

Caveats and Limitations of this study 1. The accuracy of our results relies on the accuracy with which colleges report their information to the

Chancellor’s Office. While we used various approaches to identify colleges with inconsistent data, it is possible that we missed colleges where the data discrepancies were not stark.

2. The focus of this report is on first-time English/math students because these students provide us with cleaner comparisons of pre- and post-implementation of AB 705. However, of course AB 705 implementation also impacted continuing students (i.e. students with previous enrollments in remedial courses or students with previous enrollment in transfer-level courses without success). This leads us to believe that the number of students who have benefited from this policy change is larger than the findings of this report indicate.



3. The MIS database do not include data on placement, so we are unable to identify who was referred to developmental education or to transfer-level with or without corequisite support. Neither do we do have information of students’ high school performance measures (i.e. course taking, grades or GPA). Our analysis is based exclusively on course-taking behavior.

4. A critical question is whether students who start in transfer-level courses with corequisite support have better outcomes than those who start in traditional developmental sequences. Since we do not have high school records or assessment and placement information, we cannot directly assess whether prior academic preparedness drives our results.

5. Even though our results do not include statistical controls, we believe our analysis uses the right comparison groups and counterfactuals. The evidence presented in this report is consistent or suggestive of a positive and strong relationship between increases in access and increases in throughput; however, it is not sufficient to infer causality.

6. Our focus in this report is on corequisite models because we are not yet able to consistently identify and measure participation in other forms of concurrent support (e.g., writing labs, tutoring centers, supplemental instruction).



Appendix B. Tables and Figures

TABLE B1 Access, throughput and racial equity in English by college

College name

Share of first-time English students starting in college composition (%)

One-term throughput rate (%)

One-year

throughput rate

(%)

Proportionality index Latino students

Proportionality index African American

students

2015 2019 2015 2019 2015 2015 2019 2015 2019

Alameda 55 89 32 58 59 0.84 0.89 0.61 0.94

Allan Hancock 47 98 30 59 50 0.80 0.96 0.46 1.11

American River 37 100 26 65 44 0.73 0.92 0.42 0.76

Antelope Valley 45 93 31 62 49 0.94 1.01 0.62 0.72

Bakersfield 45 99 26 42 40 0.87 0.90 0.45 0.82

Barstow 20 99 14 63 50 0.72 1.02 0.22 0.85

Berkeley City 79 100 54 68 76 0.95 0.96 0.65 0.75

Butte 55 100 36 67 51 0.77 0.91 0.18 0.59

Cabrillo 38 96 28 60 56 0.59 0.92

Canada 55 99 40 66 54 0.83 0.92

Canyons 38 100 30 70 69 0.75 0.95 0.64 0.78

Cerritos 16 97 11 60 37 0.89 0.99 0.94 0.92

Cerro Coso 27 94 15 63 28 0.73 0.95

Chabot 35 83 25 52 54 0.89 0.94 0.40 1.04

Chaffey 40 73 28 49 48 0.84 0.94 0.75 0.76

Citrus 48 99 32 64 53 0.78 0.97 0.59 0.66

Clovis 42 100 29 65 43 0.73 0.96

Coalinga 52 100 28 59 40 0.81 0.99

Coastline 66 100 53 74 61 0.85 0.86 0.90 0.83

Columbia 40 100 29 71 50 0.69 0.90

Compton 15 84 9 37 25 1.16 1.05 0.64 0.83

Contra Costa 28 100 21 55 44 0.99 0.98 0.78 0.62

Copper Mountain 34 87 24 57 38 0.81 0.94

Cosumnes River 49 95 31 67 48 0.94 0.96 0.60 0.78

Crafton Hills 37 96 27 69 47 0.81 0.91 0.85 0.86

Cuesta 65 90 45 62 59 0.79 0.85

Cuyamaca 30 100 24 72 50 0.85 0.94 0.38 0.89

Cypress 33 93 24 65 47 0.64 0.91 0.64 0.90

De Anza 32 99 28 81 68 0.56 0.88 0.54 0.87

Desert 31 91 22 56 45 0.79 0.99 0.72 0.67

Diablo Valley 30 98 24 72 54 0.79 0.92 0.31 0.76



East L.A. 23 100 14 54 30 0.88 0.95 1.03 0.77

El Camino 38 96 29 63 49 0.70 0.86 0.48 0.82

Evergreen Valley 29 85 19 58 34 0.68 0.90 0.45 0.80

Feather River 57 77 42 46 65 0.80 0.87 0.80 0.82

Folsom Lake 32 100 24 74 49 0.77 0.93 0.21 0.88

Foothill 59 100 46 73 74 0.79 0.90 0.72 0.73

Fresno City 31 99 20 54 34 0.81 0.96 0.63 0.64

Fullerton 38 100 29 68 51 0.74 0.96 0.75 0.82

Gavilan 40 83 24 44 43 0.75 0.90

Glendale 56 92 42 64 67 0.58 0.75 0.49 0.86

Golden West 46 100 34 70 54 0.80 0.92

Grossmont 35 99 27 69 47 0.72 0.93 0.39 0.85

Hartnell 31 96 22 56 42 0.95 0.97

Imperial Valley 24 82 13 41 34 0.98 0.99

Irvine Valley 38 99 31 74 54 0.58 0.85 0.54 0.79

L.A. City 18 93 12 48 34 0.70 0.93 0.88 0.68

L.A. Harbor 26 93 20 60 46 0.81 0.98 0.84 0.77

L.A. Mission 24 98 16 50 25 0.88 0.94

L.A. Pierce 18 91 14 57 39 0.54 0.91 0.79 1.00

L.A. Trade-Tech 15 83 8 42 31 0.89 1.06 0.67 0.63

L.A. Valley 28 100 20 61 39 0.67 0.90 1.01 0.75

Lake Tahoe 52 91 35 66 57 0.75 1.01

Laney 44 96 30 61 54 0.72 0.98 0.47 0.87

Las Positas 44 96 35 70 70 0.83 0.88 0.83 0.84

Lassen 42 86 34 65 66 0.67 0.98

Lemoore 42 100 27 66 49 0.65 0.96

Long Beach City 25 70 16 45 35 0.79 0.95 0.72 0.78

Los Medanos 24 94 18 63 46 0.76 0.99 0.81 0.86

Marin 33 82 22 59 47 0.50 0.92

Mendocino 39 90 29 71 48 0.75 1.02

Merced 35 96 22 50 44 0.88 0.94 0.49 0.84

Merritt 43 92 27 59 54 0.80 0.90 0.74 0.87

Mira Costa 70 97 55 70 69 0.78 0.92 0.63 0.86

Mission 42 91 27 61 48 0.71 0.93

Modesto 35 94 22 54 40 0.77 0.94 0.78 0.71

Monterey 30 97 23 53 48 0.67 0.85 0.73 0.81

Moorpark 78 92 63 68 76 0.92 0.93 0.87 0.79

Moreno Valley 18 100 13 67 43 0.73 1.00 0.61 0.85

Mt. San Antonio 16 98 11 62 35 0.78 0.93 0.59 0.66

Mt. San Jacinto 25 97 19 62 47 0.80 0.97 0.78 0.68

Napa Valley 24 89 19 58 51 0.61 0.94 0.55 0.89



Norco 27 97 20 56 41 0.82 0.97 0.90 1.01

Ohlone 39 88 28 65 46 0.61 1.01 0.47 0.63

Orange Coast 58 94 41 58 63 0.67 0.84 0.29 0.83

Oxnard 48 81 38 53 58 0.92 0.98

Palo Verde 34 93 21 42 32 0.95 1.08 0.77 0.63

Palomar 44 100 31 69 50 0.73 0.92 0.86 1.02

Pasadena City 43 100 34 66 68 0.61 0.89 0.60 0.79

Porterville 14 100 10 55 34 0.89 0.98

Redwoods 43 100 31 68 51 0.64 0.89

Reedley 22 100 12 50 29 0.81 0.95 0.00 0.85

Rio Hondo 46 97 31 58 55 0.95 0.98

Riverside 29 98 19 57 39 0.91 0.98 0.37 0.78

Sacramento City 33 100 21 70 39 0.67 0.97 0.77 0.87

Saddleback 40 95 33 76 51 0.73 0.95 0.52 0.73

San Bernardino 25 96 14 51 28 0.83 0.97 0.53 0.83

San Diego City 22 91 16 65 40 0.80 0.99 0.86 0.92

San Diego Mesa 30 94 23 70 45 0.82 0.95 0.83 0.82

San Diego Miramar 31 93 22 75 43 0.59 0.96 0.79 0.93

San Francisco City 16 97 11 58 30 0.52 0.83 0.48 0.62

San Joaquin Delta 42 99 27 61 46 0.77 0.93 0.56 0.73

San Jose City 37 89 25 52 38 0.79 0.86 0.40 0.84

San Mateo 38 100 30 65 61 0.77 0.88 0.31 0.77

Santa Ana 44 100 29 50 52 0.84 0.94

Santa Barbara City 59 100 46 71 59 0.80 0.85 0.60 0.92

Santa Monica 50 99 39 65 52 0.56 0.87 0.49 0.78

Santa Rosa 51 94 36 63 60 0.64 0.90 0.64 0.94

Santiago Canyon 73 100 53 66 69 0.84 0.94

Sequoias 40 100 24 59 47 0.84 0.97 0.39 0.87

Shasta 61 90 41 50 60 0.90 0.99

Sierra 57 99 43 66 61 0.88 0.94 0.47 0.77

Siskiyous 49 100 35 61 59 0.98 1.05

Skyline 49 100 37 71 64 0.87 0.82

Solano 34 98 26 65 51 0.78 1.04 0.63 0.72

Southwest L.A. 12 97 7 34 21 1.43 1.07 0.56 0.86

Southwestern 63 98 46 72 55 0.94 1.00 0.92 0.83

Taft 38 85 24 50 43 0.76 0.93

Ventura 43 97 33 67 57 0.76 0.96 0.92 0.71

Victor Valley 23 97 16 54 38 0.81 0.97 0.24 0.76

West L.A. 31 100 20 54 42 0.93 0.94 0.65 0.84

West Valley 47 96 31 61 58 0.70 0.90 0.46 0.84

Woodland 29 99 19 68 37 0.74 0.98



Yuba 38 97 27 52 46 0.73 0.99 0.27 0.88

Total 38 96 27 61 48 0.73 0.92 0.54 0.78

SOURCE: Authors’ calculations using COMIS data.

NOTES: Fall of each year. The proportionality index in colleges with less than 20 students is not included in this table. See the glossary of terms in the main report for definitions.

TABLE B2 Racial equity measures: Direct access to college composition

Percentage point gap Proportionality index 80 percent rule

2015 2019 Diff 2015 2019 Diff 2015 2019 Diff

Asian American 5 0 -5 1.13 1.00 -0.13 79 99 19

White 16 1 -15 1.43 1.01 -0.42 100 100 0

Latino -8 0 8 0.79 1.00 0.21 55 99 43

African American -14 -2 12 0.63 0.97 0.35 44 96 53


NOTE: Fall of each year. See the glossary of terms in the main report for definitions.

FIGURE B1 Throughput rates for college composition over different windows of time


NOTE: Fall of each year. All 114 colleges included. Restricted to students who declared a transfer goal. See the glossary of terms in the main report for definitions.

2731

37

47

61

4852

56

63

5558 61

67

0

20

40

60

80

100

2015 2016 2017 2018 2019

Thro

ughp

ut ra

te (%

)

One-term

One-year

Fall-to-fall



TABLE B3 Racial equity measures: Successful completions of college composition


2015 2019 Diff 2015 2019 Diff 2015 2019 Diff

Asian American 7 12 5 1.27 1.20 -0.07 84 103 19

White 14 10 -4 1.51 1.16 -0.35 100 100 0

Latino -7 -5 2 0.73 0.92 0.18 48 79 31

African American -13 -14 -1 0.54 0.78 0.24 36 67 31



TABLE B4 Racial composition of first-time English students: Corequisite models versus standard college composition

Distribution of all first-

time English students (%)

Distribution first-time English students (%)

Distribution successful completions (%) Success rate (%)

Corequisite models Standard college

composition Corequisite

models Standard college



composition

Asian American 11 9 12 12 14 72 78

White 19 14 20 16 23 67 74

Latino 55 60 52 56 48 55 60

African American 5 6 5 5 4 48 52


NOTE: Based on data for the 98 of the 101 colleges that offered a corequisite model in fall 2019. In these colleges there 32,000 students in corequisite models and 100,700 students in standard college composition in fall 2019. See the glossary of terms in the main report for definitions.

TABLE B5 Racial equity measures: Successful completions in corequisite models versus standard college composition

Proportionality Index 80 percent rule Percentage point gap

Corequisite models Standard college





composition

Asian American 1.07 1.23 108 105 14 13

White 0.86 1.23 100 100 9 9

Latino 1.04 0.89 81 81 -3 -6

African American 1.05 0.71 72 70 -10 -14


NOTE: Based on data for the 98 of the 101 colleges that offered a corequisite model in fall 2019. In these colleges there 32,000 students in corequisite models and 100,700 students in standard college composition in fall 2019. See the glossary of terms in the main report for definitions.



TABLE B6 Access, throughput and racial equity in transfer-level math by college

College name Share of first-time

math students starting in transfer-

level (%)

One-term throughput rate (%)

Fall-to-fall throughout

rate (%)

Proportionality index Latino

students

Proportionality index African

American students

2015 2019 2015 2019 2015 2015 2019 2015 2019

Alameda 25 84 17 56 28 0.79 0.93 0.08 0.81

Allan Hancock 17 54 11 31 24 0.78 0.93

American River 16 63 12 38 24 0.82 0.90 0.30 0.73

Antelope Valley 12 60 8 34 25 0.88 0.97 0.60 0.76

Bakersfield 16 77 10 39 7 0.76 0.92 0.17 0.69

Barstow 0 89 0 51 26 0.87 0.77

Berkeley City 30 92 16 57 47 0.58 0.90 0.22 0.52

Butte 22 84 16 46 15 0.88 0.75 1.06 0.42

Cabrillo 22 74 14 32 34 0.58 0.71

Canada 33 73 21 44 38 0.67 0.77

Canyons 17 91 14 56 44 0.54 0.86 0.43 0.84

Cerritos 14 73 7 27 19 0.80 0.96 0.41 0.63

Cerro Coso 11 84 7 45 22 0.64 0.92 0.00 1.11

Chabot 27 85 15 38 30 0.44 0.83 0.38 0.76

Chaffey 14 76 9 28 25 0.71 0.90 0.86 0.82

Citrus 18 93 10 54 26 0.63 0.94 0.38 0.84

Clovis 39 85 28 51 41 0.74 0.88 0.90 0.86

Coalinga 13 87 6 55 20 0.85 1.02

Coastline 28 61 19 36 34 0.50 0.91 0.63 0.36

Columbia 17 72 12 52 26 1.05 1.09

Compton 5 42 3 17 14 0.69 0.93 0.27 1.26

Contra Costa 31 60 20 38 37 0.82 0.94 0.46 0.51

Copper Mountain 15 57 13 31 25 1.01 0.84

Cosumnes River 20 64 11 32 27 0.67 0.93 0.59 0.79

Crafton Hills 18 82 13 40 24 0.66 0.86 1.95 0.51

Cuesta 28 81 17 47 27 0.72 0.83

Cuyamaca 24 83 17 55 30 0.96 0.99 0.90 0.90

Cypress 25 80 15 38 24 0.58 0.76 0.49 0.64

De Anza 25 86 21 59 50 0.29 0.63 0.23 0.70

Desert 19 70 10 35 21 0.78 0.92

Diablo Valley 47 94 32 61 49 0.67 0.80 0.55 0.78

East L.A. 12 56 8 23 21 0.51 0.79 0.88 1.04

El Camino 13 85 10 35 31 0.54 0.81 0.49 0.60

Evergreen Valley 16 52 12 29 24 0.47 0.66 0.70 0.34

Feather River 29 54 21 22 43 0.62 0.56



Folsom Lake 21 84 15 51 29 0.70 0.89 0.60 0.99

Foothill 48 85 33 53 56 0.47 0.71 0.61 0.54

Fresno City 24 87 13 40 23 0.74 0.94 0.65 0.67

Fullerton 38 78 23 36 26 0.68 0.86 0.51 0.76

Gavilan 13 61 8 31 23 0.85 0.86

Glendale 25 49 14 28 25 0.48 0.49 0.93 0.36

Golden West 34 94 19 52 38 0.56 0.72

Grossmont 24 85 15 44 29 0.70 0.91 0.47 0.67

Hartnell 10 72 8 42 35 0.86 0.98

Imperial Valley 8 81 6 43 27 1.01 0.96

Irvine Valley 43 91 30 56 46 0.51 0.72 0.86 0.50

L.A. City 10 88 5 28 15 0.62 0.73 0.45 0.55

L.A. Harbor 15 79 11 37 23 0.77 0.83 0.37 0.76

L.A. Mission 11 85 7 29 17 0.93 0.94

L.A. Pierce 20 82 14 39 30 0.60 0.78 0.47 0.73

L.A. Trade-Tech 2 57 1 22 7 0.63 1.06 0.88 0.56

L.A. Valley 8 75 6 33 20 0.52 0.74 0.60 1.23

Lake Tahoe 13 42 8 26 18 0.73 0.54

Laney 24 71 19 37 49 0.92 0.94 0.54 0.81

Las Positas 29 82 18 44 26 0.74 0.76 0.55 0.64

Lassen 5 80 3 47 18 0.00 0.90 0.00 0.89

Lemoore 19 77 11 44 23 0.69 1.00 0.68 0.82

Long Beach City 17 53 8 21 10 0.75 0.79 0.64 0.60

Los Medanos 31 82 21 53 41 0.96 0.98 0.48 0.70

Marin 19 85 13 42 31 0.19 0.68

Mendocino 18 71 13 49 33 0.95 1.12

Merced 19 54 12 28 24 0.79 0.91

Merritt 16 88 13 50 41 0.53 1.01 0.47 0.61

Mira Costa 36 86 24 56 0.75 0.87 0.87 0.87

Mission 24 80 16 48 26 0.16 0.76

Modesto 6 83 5 38 18 0.71 0.88

Monterey 15 79 11 38 6 0.60 0.86 1.07 0.99

Moorpark 36 75 23 40 36 0.70 0.72 0.83 0.40

Moreno Valley 5 82 3 25 15 0.55 0.98 1.82 0.82

Mt. San Antonio 25 71 16 31 28 0.62 0.79 0.43 0.36

Mt. San Jacinto 15 89 11 46 24 0.85 0.92 0.89 0.59

Napa Valley 25 77 19 35 10 0.86 0.81 0.41 1.36

Norco 9 84 7 49 26 0.62 0.86 1.39 1.14

Ohlone 14 83 12 56 38 0.45 0.82

Orange Coast 37 92 24 51 45 0.56 0.73

Oxnard 18 11 25



Palo Verde 5 92 2 35 9 0.00 1.16 0.00 0.67

Palomar 20 62 12 29 30 0.55 0.74 0.65 0.46

Pasadena City 13 100 10 46 29 0.32 0.71 0.58 0.61

Porterville 21 100 16 60 30 0.89 0.98

Redwoods 29 95 18 52 42 0.71 0.90 0.31 0.86

Reedley 16 97 11 50 25 0.83 0.97 0.42 0.79

Rio Hondo 7 75 4 33 17 0.77 0.93

Riverside 9 91 6 37 21 0.73 0.92 0.10 0.82

Sacramento City 9 65 7 33 21 0.47 0.84 0.20 0.68

Saddleback 27 83 15 46 33 0.64 0.85

San Bernardino 6 57 4 22 19 0.89 0.97 0.37 0.63

San Diego City 21 67 13 31 23 0.75 0.86 1.05 1.00

San Diego Mesa 41 77 28 47 46 0.77 0.86 0.54 0.68

San Diego Miramar 41 69 30 45 48 0.63 0.77 0.61 0.77

San Francisco City 28 67 22 40 19 0.43 0.68 0.45 0.45

San Joaquin Delta 12 70 8 44 22 0.65 0.93 0.58 0.72

San Jose City 18 80 11 42 22 0.78 0.93 0.91 0.85

San Mateo 30 86 18 47 34 0.57 0.89 0.71 0.33

Santa Ana 29 90 17 31 37 0.75 0.89

Santa Barbara City 37 94 28 55 49 0.59 0.90 0.55 0.81

Santa Monica 31 70 16 29 27 0.43 0.62 0.51 0.55

Santa Rosa 28 76 18 42 36 0.61 0.80

Santiago Canyon 34 88 20 35 43 0.77 0.87

Sequoias 15 97 10 50 29 0.79 0.93 0.38 1.29

Shasta 32 61 18 35 39 0.98 1.02

Sierra 36 81 25 50 36 0.84 0.89 0.79 0.71

Siskiyous 12 89 10 63 34 0.27 0.98

Skyline 18 72 13 47 32 0.42 0.65

Solano 33 77 19 40 33 0.85 0.89 0.47 0.81

Southwest L.A. 7 80 4 26 15 1.32 1.07 0.67 0.85

Southwestern 8 75 5 38 12 0.78 0.95 1.17 0.58

Taft 18 64 12 36 33 0.73 0.90

Ventura 31 90 20 56 38 0.79 0.96

Victor Valley 5 90 4 36 0 0.78 0.94 0.32 0.58

West L.A. 15 79 9 32 14 0.69 1.00 0.48 0.62

West Valley 17 84 12 47 23 0.57 0.87

Woodland 10 72 6 40 18 0.84 0.95

Yuba 4 62 3 27 18 0.73 0.67

Total 21 78 14 40 27 0.61 0.83 0.48 0.67


NOTES: Fall of each year. The proportionality index in colleges with less than 20 students is not included in this table. See the glossary of terms in the main report for definitions. Fall 2019 excludes Oxnard college due to missing developmental math course data in COMIS.



TABLE B7 Racial/ethnic equity measures: Direct access to transfer-level math


2015 2019 Diff 2015 2019 Diff 2015 2019 Diff

Asian American 18 5 -13 1.86 1.06 -0.80 148 104 -44

White 5 2 -4 1.26 1.02 -0.23 100 100 0

Latino -6 -2 5 0.70 0.98 0.28 56 95 40

African American -9 -7 3 0.56 0.92 0.35 45 89 44



FIGURE B2 Throughput rates for transfer-level math over different windows of time



27 3034

41

22 2428

35

14 1517

22

40

0

20

40

60

80

100

2015 2016 2017 2018 2019

Thro

ughp

ut ra

te (%

)

Fall to fall

One-year

One-term



TABLE B8 Racial equity measures: Successful completions of transfer-level math


2015 2019 Diff 2015 2019 Diff 2015 2019 Diff

Asian American 16 17 1 2.14 1.43 -0.71 162 115 -47

White 4 10 5 1.32 1.24 -0.08 100 100 0

Latino -5 -7 -1 0.61 0.83 0.22 46 67 21

African American -7 -13 -6 0.48 0.67 0.19 36 54 18



TABLE B9 Racial equity measures: Successful completions in corequisite models versus transfer-level without support

SLAM pathway (14,850 students in corequisite models versus 43,800 students in transfer-level courses without support)

Distribution of all first-time math students

Distribution first-time transfer-level math

students (%) Distribution successful

completions (%) Success rate (%)

Corequisite models

Transfer-level

without support

Corequisite models

Transfer-level

without support

Corequisite models

Transfer-level

without support

Asian American 12 9 13 13 17 71 74

White 20 16 22 20 26 62 67

Latino 52 58 50 52 43 43 49


BSTEM pathway (8,900 students in corequisite models versus 21,500 students in transfer-level courses without support)

Distribution of all first-time math students

Distribution first-time transfer-level math

students (%) Distribution successful

completions (%) Success rate (%)

Corequisite models

Transfer-level

without support

Corequisite models

Transfer-level

without support

Corequisite models

Transfer-level

without support

Asian American 13 13 18 20 23 60 62

White 19 17 21 21 24 49 55

Latino 52 54 48 44 39 31 38



NOTE: The SLAM results are based on 90 colleges and the BSTEM results on 85 colleges. See the glossary of terms in the main report for definitions.



TABLE B10 Racial equity measures: Successful completions in corequisite models versus transfer-level without support

SLAM pathway (14,850 students in corequisite models versus 43,800 students in transfer-level courses without support)

Proportionality index 80 percent rule Percentage point gap

Corequisite models

Transfer-level course

without support

Corequisite models

Transfer-level without

support Corequisite

models Transfer-

level without support

Asian American 1.04 1.35 115 110 22 17

White 1.01 1.29 100 100 13 10

Latino 0.99 0.83 70 73 -6 -8


BSTEM pathway (8,900 students in corequisite models versus 21,500 students in transfer-level courses without support)

Proportionality index 80 percent rule Percentage point gap

Corequisite models

Transfer-level course

without support

Corequisite models

Transfer-level without

support Corequisite

models Transfer-

level without support

Asian American 1.53 1.75 121 113 21 15

White 1.11 1.27 100 100 11 8

Latino 0.84 0.74 63 68 -8 -9



NOTES: The SLAM results are based on 90 colleges and the BSTEM results on 85 colleges. See the glossary of terms in the main report for definitions.



Appendix C. Assessment and Placement Analysis

TABLE C1 List of key variables used in our analysis of assessment and placement reforms

Variable Description

English/Math Placement

College Name Name of College

Placement System(s) Used

Multiple Measures (MM)

Placement system that relies primarily on high school records for placement. This approach uses more than one measure of prior academic achievement or measures of

non-academic factors to determine placement. These measures include high school grades, coursework, or achievement in standardized tests (i.e. SAT, ACT, AP, SBAC,

EAP, etc.), program of study, educational goals, etc.

Guided Self-Placement (GSP)

Under AB 705, colleges are allowed to use guided placement, including self-placement, if high school records are unavailable. We define a GSP placement system as one that

primarily relies on students’ self-evaluations of readiness for different levels of math and English coursework. GSP approaches primarily provide students with course

descriptions, samples of course specific tasks/assignments, and the opportunity to meet with a faculty/counselor to select the appropriate course placement.

Other Variable equal to 1 if other placement system is used.

MM: Target Population

All Students MM policy applies to all students

Recent High School Graduates MM policy applies to only students who are recent high school graduates. Colleges

sometimes specify the number of years between high school graduation and college enrollment they accept (e.g. graduated within 5 years)

Students with 4 years of U.S. HS data MM policy applies to only students who completed 4 years of high school in the United States. This might have happened because the default placement rules were computed

using this sample of students.

GED/Proficiency Students MM policy applies to only students who have a GED/Proficiency diploma.

Other Indicate if other target population is named

MM: Measures Used

HS GPA Placement uses information on HS Grade Point Average (GPA)

Highest HS English/Math course completed Placement uses information on highest course completed in math/English

Grade in last HS English/Math course completed Placement uses information on the grade in the last math/English course completed

SAT/ACT; AP/IB; SBAC/EAP Placement uses any of these standardized test scores for math/English placements

Intended Program of Study Placement uses a student’s intended program of study for math/English placements

Other MM Placement uses other MM

MM: Data Source

Self-reported using CCCApply Student self-reports data using the CCC application system.



Self-reported using online assessment/placement tool Student self-reports data using the online assessment/placement tool.

Self-reported using written assessment/placement form Student self-reports data using the written assessment/placement tool.

Self-reported via appointment with counselor Student self-reports data in a meeting with a counselor

Local Agreement with HS to share transcript data College obtains MM data as part of a local agreement with the high schools.

HS transcripts brought in by the student College obtains MM data by asking the student to submit their own transcript.

Other Indicate if other MM data source is used.

MM: Accessibility of Placement Tool

Open Access Variable equal to 1 if online tool is openly available to all on the college website.

Restricted Variable equal to 1 if online tool is restricted to students with a school ID.

MM: Placement Rules

Default Placement Rules

This variable is equal to 1 if the college uses the CCCCO default placement rules, 0 otherwise. We define colleges who offer optional prerequisite remediation as using the

default rules. Additionally, colleges who offer optional, recommended, or required corequisite remediation are marked as using the default rules.

Lower than default placement rules used This variable is equal to 1 if the college uses placement rules that are lower than the

default placement rules. This happens generally if the GPA or minimum grade rules are lower than the default.

Higher than default placement rules used This variable is equal to 1 if the college uses placement rules that are stricter/higher than the default placement rules. This can happen for example, if a college sets a higher GPA

cutoff or if it requires prerequisite remediation instead of making it optional.

Rules not directly comparable to default rules

This variable is equal to 1 if the college uses placement rules that are not directly comparable to the default rules. This is marked if rules are not necessarily more

strict/higher, but still are not directly comparable. This happens for example if prerequisite remediation is recommended, instead of optional.

GSP: Target Population

All Students GSP policy applies to all students

Students without HS Information GSP policy applies to students without HS information

Students who delayed college entry GSP policy applies to students who are delayed college entry/enrollment.

Students with lower GPA’s GSP policy applies to students who had lower GPA’s.

International Students without US HS info GSP policy applies to students who attended a foreign high school and thus have no U.S. HS information.

GED/Proficiency Students GSP policy applies to only students who have a GED/Proficiency diploma.

Other Indicate if other target population is named.

GSP: Measures Used

Course Description Placement uses course descriptions to help students select the appropriate course.

Sample Readings, Writing, or Math Problems

Placement uses sample readings, writing, or math problem samples to help students self-evaluate and select the appropriate course. As they denote, in English, this typically

includes reading passages and writing samples. In math, this typically includes sample math problems.

Assignment Samples Placement uses samples of course assignments to help students self-evaluate and select the appropriate course. This is only applicable to English because in math, assignments



are typically based on math problems. Writing prompts are most commonly used in this category.

High School Course taking Placement uses high school course taking as part of the GSP process.

Intended Program of Study Placement uses a students intended program of study for math/English placements

Self-Assessment of Academic or Study Skills Placement uses a series of questions and Likert scale responses intended to help a

student self-assess their readiness and confidence in their ability to succeed in various math or English courses.

Other measure Placement uses other GSP measure

GSP: Data Source

Self-reported using CCCApply Student self-reports data using the CCC application system.

Self-reported using online assessment/placement tool Student self-reports data using the online assessment/placement tool.

Self-reported using written assessment/placement form Student self-reports data using the written assessment/placement tool.

Self-reported via appointment with counselor Student self-reports data in a meeting with a counselor

Other Indicate if other MM data source is used and describe briefly.

GSP: Accessibility of Placement Tool

Open Access Variable equal to 1 if online tool is openly available to all on the college website.

Restricted Variable equal to 1 if online tool is restricted to students with a school ID.

MM or GSP: Corequisite Placement

Recommended for certain GPA band or GSP placement

Variable equal to 1 if corequisite is recommended for students falling in a certain GPA band or receiving a certain GSP placement.

Required for certain GPA band or GSP placement

Variable equal to 1 if corequisite is required for students falling in a certain GPA band or receiving a certain GSP placement.

Optional for everyone Variable equal to 1 if corequisite is optional for all students, regardless of placement.

Corequisite not offered Variable equal to 1 if corequisite courses are not offered at the college.

No Information Variable equal to 1 if no information provided about corequisite placement

MM or GSP: Prerequisite Placement

Required for certain GPA band or GSP placement

Variable equal to 1 if prerequisite remediation is required for students falling in a certain GPA band or receiving a certain GSP placement.

Recommended for certain GPA band or GSP placement

Variable equal to 1 if prerequisite remediation is recommended for students falling in a certain GPA band or receiving a certain GSP placement.

Optional for everyone Variable equal to 1 if prerequisite remediation is optional for all students, regardless of placement.

Prerequisite not offered Variable equal to 1 if prerequisite remediation courses are not offered at the college.

No Information Variable equal to 1 if no information provided about prerequisite placement

MM or GSP: Meeting with Counselor or Discipline Faculty

Required Variable equal to 1 if the college requires students to meet with counselors or faculty as part of the MM or GSP process.



Optional/Recommended Variable equal to 1 if the meetings with counselors or faculty are optional as part of the MM or GSP process.

No information Variable equal to 1 if the college does not provide any information about this.

Math Pathways Definitions

Statistics Statistics courses often fulfill the math requirement for most social sciences, liberal arts and humanities majors.

Math for Educators These transferrable math courses are intended for education related majors, including child development and elementary education.

Liberal Arts Math These courses are typically designed for students in liberal arts majors—they often have the term “Liberal Arts” in their title.

Business or Applied Calculus Includes courses that are explicitly intended to prepare students for business, accounting, economics or other social science majors.

STEM Math Includes any math courses that provide a pathway to Calculus, including finite math, college algebra, trigonometry, and pre-calculus.

NOTES: Note: Colleges may use more than one placement system in the same discipline, oftentimes to address the needs of different student populations. For every section we also included flags to collect if the information was missing, if the information was updated from the AB 705 implementation plans, if the information was updated from additional follow-up with counselors/administrators, and if the information was still missing despite consulting all available documents.

TABLE C2 Characteristics of universal- and lower-access colleges, with mean comparison test

Universal Access Lower

Access P-value mean-

comparison test

Number of colleges 32 21

Number of first-time English students fall 2019 44,615 19,147

Average share of Latino students (%) 50 53 Pr(|T| > |t|) = 0.6356

Average share of African American students (%) 5 6 Pr(|T| > |t|) = 0.2954

PI for Latino students 1.00 0.99 Pr(|T| > |t|) = 0.3667

PI for African American students 1.00 0.94 Pr(|T| > |t|) = 0.0181

Average share of students 25 and older (%) 12 15 Pr(|T| > |t|) = 0.1542

Average GPA 2.78 2.80 Pr(|T| > |t|) = 0.5348

Average share of remedial English enrollment in total English enrollment (%), fall 2019 0 17 Pr(|T| > |t|) = 0.0000

One-term throughput rate (%), fall 2019 65 53 Pr(|T| > |t|) = 0.0000


Change in throughput rate 2015-19, pp 35 27 Pr(|T| > |t|) = 0.0055


NOTE: If the P-value is greater than the significance level (0.05), we cannot reject the null hypothesis that there is no difference between the two population means. See the glossary of terms in the main report for definitions.



TABLE C3 English Placement Policies in California’s Community Colleges, Fall 2019

English Placement Policies, Fall 2019

Systemwide Universal Access Lower Access

(n=114) (100% n=32) (89% or lower, n=21)

N % N % N %


Multiple Measures (MM) 114 100% 32 100% 21 100%

Guided Self-Placement (GSP)* 55 48% 16 50% 10 48%

Other 0 0% 0 0% 0 0%

No public information (see note below) 2 2% 1 3% 1 5%

No information found (public or otherwise) 0 0% 0 0% 0 0%

MM: Target Population (not mutually exclusive)

All students who provide MM placement data 81 71% 22 69% 18 86%

Recent High School Graduates 59 52% 18 56% 9 43%

GED/Proficiency Students 10 9% 5 16% 1 5%

Other 7 6% 1 3% 0 0%



MM: Measures Used (not mutually exclusive)

HS GPA 112 98% 31 97% 21 100%

Highest HS English course completed 53 46% 14 44% 16 76%

Grade in last HS English/Math course completed 46 40% 12 38% 14 67%

Intended Program of Study 10 9% 0 0% 4 19%

SAT/ACT; AP/IB; SBAC/EAP 29 25% 10 31% 6 29%

Other MM 10 9% 3 9% 2 10%



MM: Data Source (not mutually exclusive)

Self-reported (any) 103 90% 29 91% 20 95%

Self-reported using online assessment/placement tool 56 49% 14 44% 10 48%

Self-reported using CCCApply 48 42% 15 47% 4 19%

Self-reported via appointment with counselor 22 19% 4 13% 11 52%

Self-reported using written assessment/placement form 12 11% 3 9% 3 14%

Transcript Any 61 54% 20 63% 13 62%

Transcript brought in by the student 56 49% 17 53% 12 57%

Transcript using local data agreement/Cal-PASS 16 14% 5 16% 4 19%

Other 3 3% 1 3% 1 5%





MM: Accessibility of placement tool (mutually exclusive)

Restricted 97 85% 27 84% 19 90%

Open Access 16 14% 5 16% 2 10%



MM: Placement Rules (mutually exclusive)

Default placement rules 83 73% 28 88% 8 38%

Not directly comparable 12 11% 1 3% 6 29%

Lower than default placement rules used 4 4% 1 3% 0 0%

Higher than default placement rules used 9 8% 0 0% 7 33%



GSP: Target Population* (not mutually exclusive)

All students 3 5% 1 6% 1 10%

Students without HS Information 48 87% 15 94% 10 100%

Students who delayed college entry 38 69% 10 63% 6 60%

International Students without US HS info 36 65% 11 69% 4 40%


Other 5 9% 1 6% 1 10%


No public information 15 27% 4 25% 2 20%

GSP: Measures Used (not mutually exclusive)

Course Description 28 51% 7 44% 4 40%

Reading/Writing problems or sample assignments 48 87% 14 88% 9 90%

Self-assessment of academic/study skills 15 27% 3 19% 5 50%

High School Course taking 16 29% 6 38% 2 20%


Other measure 7 13% 2 13% 1 10%



GSP: Data Source (not mutually exclusive)







GSP: Online Assessment/Placement Tool (mutually exclusive)

Open Access 13 24% 3 19% 4 40%



Restricted 42 76% 13 81% 6 60%



MM or GSP: Corequisite Placement (mutually exclusive)

Required for certain GPA band or GSP placement 31 27% 8 25% 2 10%

Recommended for certain GPA band or GSP placement 50 44% 14 44% 12 57%

Optional for everyone 18 16% 7 22% 1 5%

Corequisite not offered 13 11% 2 6% 6 29%



MM or GSP: Prerequisite Remedial Placement (mutually exclusive)




Prerequisite not offered 28 25% 28 88% 0 0%



MM or GSP: Meeting with Counselor or Faculty (mutually exclusive)

Required 37 32% 10 31% 8 38%

Recommended/Optional 61 54% 16 50% 10 48%



SOURCE: Authors calculations using PPIC Scan of CCC Placement Policies.

NOTE: “No information found” is marked if no publically available information on this measure was available on college documents and website during summer and fall 2019. The gaps that we found in this information were later filled using AB 705 Implementation plans and GSP methods documents that colleges submitted to the Chancellor's Office in summer 2019 or by following-up with college to request documents. *GSP averages only for college that use that approach (See Guided Self-Placement row for sample size).

TABLE C4 Chancellor’s Office AB 705 default placement rules for English and math

High school performance metric Recommended AB 705 placement Throughput rates for

students enrolling directly into transfer-level

ENGLISH

GPA >=2.6 Transfer-level English composition

78.60% No additional academic and concurrent support required

GPA 1.9to 2.6 Transfer-level English composition

57.70% Additional academic and concurrent support recommended

GPA


MATH

GPA >=3.0 OR HSGPA ≥ 2.3 & C or better in

HS pre-calculus Course

Transfer-level statistics/ liberal arts math

75% No additional academic and concurrent support required

GPA 2.3 to 2.9 Transfer-level statistics/ liberal arts math

50% Additional academic and concurrent support recommended

GPA < 2.3 Transfer-level statistics/ liberal arts math

29% Additional academic and concurrent support strongly recommended

GPA>=3.4 OR Transfer-level BSTEM math 75%

GPA >=2.6 AND enrolled in HS calculus No additional academic and concurrent support required

GPA


FIGURE C1 Sample GSP self-assessment questions used at lower access math and English college



FIGURE C2 Sample self-assessment questions used at universal-access English AND higher access math colleges



FIGURE C3 Sample English readings, writing and assignments used at lower-access college



FIGURE C4 Sample writing assignment used at universal-access college



TABLE C5 Characteristics of higher- and lower access colleges, with mean comparison test

Higher Access Lower

Access P-value mean-

comparison test

Number of colleges 18 23

Number of first-time math students fall 2019 25,666 25,911

Average share of Latino students (%) 53 49 Pr(|T| > |t|) = 0.5028

Average share of African American students (%) 4 5 Pr(|T| > |t|) = 0.3815

PI for Latino students 1.00 0.95 Pr(|T| > |t|) = 0.0578

PI for African American students 1.00 0.85 Pr(|T| > |t|) = 0.0083

Average share of students 25 and older (%) 11 17 Pr(|T| > |t|) = 0.1367

Average GPA 2.79 2.72 Pr(|T| > |t|) = 0.1317

Average share of students in BSTEM 33 17 Pr(|T| > |t|) = 0.0001

Average share of remedial math enrollment in total math enrollment (%), fall 2019 9 39 Pr(|T| > |t|) = 0.0000



Change in throughput rate 2015-19, pp 34 18 Pr(|T| > |t|) = 0.0000


NOTE: If the P-value is greater than the significance level (0.05), we cannot reject the null hypothesis that there is no difference between the two population means. See the glossary of terms in the main report for definitions.



TABLE C6 Math Placement Policies in California’s Community Colleges, Fall 2019

Math Placement Policies, Fall 2019 Systemwide Higher Access Lower Access

(n=114) (90% or higher, n=18) (65% or lower, n=23)

N % N % N %


Multiple Measures (MM) 114 100% 18 100% 23 100%

Guided Self-Placement (GSP)* 44 39% 3 17% 10 43%

Other 0 0% 0 0% 0 0%



MM: Target Population (not mutually exclusive)

All students who provide MM placement data 80 70% 13 72% 14 61%

Recent High School Graduates 59 52% 7 39% 15 65%


Other 7 6% 0 0% 1 4%



MM: Measures Used (not mutually exclusive

HS GPA 113 99% 18 100% 23 100%

Highest HS Math course completed 104 91% 17 94% 21 91%

Grade in last HS Math course completed 84 74% 15 83% 15 65%


SAT/ACT; AP/IB; SBAC/EAP 37 32% 7 39% 6 26%

Other MM 12 11% 1 6% 4 17%



MM: Data Source (not mutually exclusive)

Self-reported (any) 104 91% 17 94% 21 91%





Transcript Any 56 49% 8 44% 9 39%

Transcript brought in by the student 54 47% 7 39% 9 39%

Transcript using local data agreement/Cal-PASS 12 11% 2 11% 1 4%

Other 3 3% 1 6% 2 9%





MM: Accessibility of placement tool (mutually exclusive)

Restricted 93 82% 16 89% 22 96%

Open Access 17 15% 2 11% 0 0%



MM: Placement Rules (mutually exclusive)

Default placement rules 50 44% 9 50% 7 30%

Not directly comparable 28 25% 5 28% 2 9%

Lower than default placement rules used 2 2% 1 6% 0 0%

Higher than default placement rules used 26 23% 1 6% 12 52%



GSP: Target Population* (not mutually exclusive)

All students 0 0% 0 0% 0 0%

Students without HS Information 38 86% 3 100% 7 70%

Students who delayed college entry 34 77% 2 67% 10 100%

International Students without US HS info 33 75% 2 67% 8 80%


Other 5 11% 0 0% 0 0%



GSP: Measures Used (not mutually exclusive)

Course Description 20 45% 2 67% 2 20%

Math problem samples 23 52% 0 0% 4 40%

Self-assessment of academic/study skills 26 59% 2 67% 8 80%

High School Course taking 25 57% 2 67% 8 80%


Other measure 5 11% 0 0% 2 20%



GSP: Data Source (not mutually exclusive)







GSP: Online Assessment/Placement Tool (mutually exclusive)

Open Access 7 16% 0 0% 0 0%



Restricted 37 84% 3 100% 10 100%



MM or GSP: Corequisite Placement (mutually exclusive)




Corequisite not offered 17 15% 1 6% 8 35%



MM or GSP: Prerequisite Remediation Placement (mutually exclusive)




Prerequisite not offered 2 2% 2 11% 0 0%



MM or GSP: Meeting with Counselor or Faculty (mutually exclusive)

Required 36 32% 4 22% 8 35%

Recommended/Optional 69 61% 14 78% 12 52%



SOURCE: Authors calculations using PPIC Scan of CCC Placement Policies.

NOTE: “No information found” is marked if no publically available information on this measure was available on college documents and website during summer and fall 2019. The gaps that we found in this information were later filled using AB 705 Implementation plans and GSP methods documents that colleges submitted to the Chancellor's Office in summer 2019 or by following-up with college to request documents. *GSP averages only for college that use that approach (See Guided Self-Placement row for sample size).



FIGURE C5 Sample math problems used at lower-access college



FIGURE C6 Map of college composition access across the CCC system

SOURCE: Authors calculations using COMIS fall 2019 data. NOTE: College composition access types are defined as follows: Universal=100%; Medium=99-91%; Lower=90% or less.

Access type

Universal access

Medium access

Lower access


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