November 10, 2015TESTING AND BEYOND:
The Future of College Math Placement in California

ACKNOWLEDGEMENTS

ADVISORY CIRCLEFUNDERS

3CSN
Career Ladders Project
CORE Districts
Education Insights Center
Educational Results Partnership
PACE
WestEdThe James Irvine Foundation
College Futures Foundation
California Education Policy Fund

DEGREES OF FREEDOM:
Three part series by Pamela Burdman“Decisions about math requirements
and expectations will have a major
impact on the academic opportunities
of millions of students nationally.”

COLLEGE MATH PLACEMENT:The California Context
PAMELA BURDMAN
Higher Education Policy Analyst

CHALLENGE:
Developmental Placements
NOT PROFICIENT
California Community
Colleges~ 85 percent of incoming students~ 234,000 students

California State University~ 33 percent of admitted highschool graduates~ 20,000 students

University of California

unknown(~ 18 percent UC-Riverside)

unknown(749 students UCR)OUTCOMES:Developmental Math
CCC
+

+

About 30 percent of developmental math students complete a “gatekeeper” math classrequired for transferring. (AA students don’t require transfer-level math.)(Alternative pathways for non-math-intensive majors have far better gatekeeper completion
rates, but only some are accepted for transfer.)

CSU+

About 5 percent of Cal State students leave un-remediated (in English, math, or both) and2 percent of students are permitted to re-enroll for a second year even though they are unremediated.

FOCUS FOR TODAY

Placing More Students Into
College-Level Math

Ensuring Students Have Quantitative
Skills for Success in College and Life

Improving College Readiness
and Completion

STATUS QUO:UC Admissions & Placement
Selective Admissions:++++

Top 1/8 of high school grads
Minimum high school of GPA or 3.0
C or better in a-g courses (Includes: Algebra 1, Geometry, Algebra 2)
Sufficiently high SAT or ACT scores

Presumed Readiness:
At most UC campuses students are not required to take placement exams unless theywish to enroll in calculus without taking a prerequisite course.

STATUS QUO:CSU Admissions Standards+

Moderately Selective Admissions:–

Top 1/3 of high school grades–

Minimum GPA of 2.0–

Completion of a-g courses (Includes: Algebra 1, Geometry, Algebra 2)–

High school diploma or equivalentDEV MATH PLACEMENT IN THE CSU:
Assessed Readiness Statewide
ENTRY LEVEL MATHEMATICS EXAMINATION (ELM)
EXEMPT FROM ELM – 51%

REQUIRED TO TAKE ELM – 49%

SAT math (550 or above)

Pass ELM (score ≥ 50) 16%

ACT math (23 or above)

Not proficient (score < 50) 33%

AP Statistics (3 or above)
AP Calculus (3 or above)
Early Assessment Program•EAP test (ready)•Conditionally ready + 12th grade mathTransferable college math course•C or better in approved course

Source: CSU Analytic Studies Proficiency Reports, 2014 data

STATUS QUO:
CCC Admissions Standards+

OPEN ADMISSIONS:–Top 100% of students.

–

High school graduation requires two years of mathematics, including Algebra 1–

High school graduation is not required for admission.

PLACEMENT IN THE CCC:
Assessed Readiness by College
UNTIL NOW

GOING FORWARD

MULTIPLE TESTSSINGLE TESTACCUPLACER (49%)
MDTP (35%)
Compass (13%)
Self-assessment (4%)
Locally developed (7%)

CCCAssess

CUT SCORES: Vary by college(In 2010 ACCUPLACER collegelevel score ranged from 43 to 63.)

CUT SCORES: Vary by college(may include enhanced multiple measures)

Below transfer-level: ~ 85%?????

Sources: Venezia et al, 2010. A One-Shot Deal?; Perry, M. et al, 2010. Coursetaking Patterns, Policies, and Practices in Developmental Education in the CCC.

PLACEMENT TESTING:
Efficacy and Equity?
+

Research on community college placement tests illustrates their limitations.

+

Nationally, community college students are 19 percent more likely to require remedial mathcourses than university students with similar records+

Research on SAT tests suggests similar limitations.

MISPLACEMENT:
Reasons & Responses
REASONSRESPONSES

Poor alignment of curriculum

Re-design tests/align curriculum

CCCAssess,
SBAC / CAASPP / EAP

Over-reliance on tests for
placing students

De-emphasize tests

CCC multiple measures
CCC differentiated placement

Need for better HS preparation

Strengthen high school mathcourses

CSU EAP/senior-year courses(See Burdman, 2012, Where to Begin? )CA EXAMPLERECOMMENDATION:
Intersegmental Dialogue"Intersegmental conversations areneeded to deepen alignment acrosssegments in math education."

TRADEOFFS & TENSIONS+

System-wide consistency vs. institutional autonomy(also system autonomy)+

Efficiency vs. effectiveness+Supporting student progression vs. enforcing standards(See: Jaggars, Hodara, 2013. The Opposing Forces That Shape Developmental Education)

FOR MORE INFORMATION:
DEGREES OF FREEDOM 1:
Diversifying Requirements for College
Readiness and Graduation
DEGREES OF FREEDOM 2:
Varying Routes to Math Readiness and theChallenge of Intersegmental Alignment
DEGREES OF FREEDOM 3:
Probing Placement Policies at California
Colleges and Universities

PRIOR LEARNINGWORKS REPORT:CHANGING EQUATIONS:How Community Colleges are Re-Thinking
College Readiness in MathALSO SEE (BY PAMELA BURDMAN):
WHERE TO BEGIN?
The Evolving Role of Placement Exams forStudents Starting College

PAMELA BURDMAN
info@changingequations.org

www.edpolicyinca.org
www.LearningWorksCA.org

PLACEMENT TESTS:
What Do We Know About Efficacy and Equity?

OLGA RODRIGUEZ
Public Policy Institute of California
Community College Research Center
Columbia University

TATIANA MELGUIZO
University of Southern California

TERRENCE WILLETT
Cabrillo College
Research and Planning GroupImproving the Accuracy of Remedial
Placement

Olga Rodriguez, Ph.D.
Research Fellow, PPIC
Research Affiliate, CCRC

Funding for this research was provided by the Bill & Melinda Gates Foundation20



Focus on the accuracy of the assignment mechanism—placement exam
scores—which determine whether someone receives remediation(Scott-Clayton, 2012; Belfield & Crosta, 2012; Scott-Clayton, Crosta & Belfield, 2014)

Using administrative data and a rich predictive model of college grades,
this study ask the following questions: How accurately do placement exams distinguish between thoselikely/unlikely to succeed?
 How much could assignment accuracy be improved by incorporating
information from high school transcripts into the screening process?

•

Would succeed atcollege-level

Would not succeed atcollege-level

Placed into remediation

Under-placed

Accurately placed

Placed into college-level

Accurately placed

Over-placed

We can’t directly observe potential outcomes in the top row, but we can:Estimate relationship between test scores & outcomes for those placed directly intocollege level using logistic regression, then Predict outcomes for those placed into remediation
 Use predicted outcomes to simulate overall accuracy & error rates under different
placement rules


Focus on placement error rates:

Severe Under-Placement: Proportion of students predicted to earn a B or better incollege-level but instead placed into remediation
 Severe Over-Placement: Proportion of students placed in to college-level but predicted
to fail there Severe Error Rate: Combines the severe under-placement rate with the severe underplacement rate

23

Figure 2 (Schematic). Percent Succeeding in College-Level Math, by Math Test Score
1[A]
Accurately Placedin Remediation

0.8

[E]
Severely Overplaced

[F]

0.7
0.6

[G]

[B]

0.5
0.4

[C]

0.3
0.2

[H]
Accurately Placedin College Level[D]
Severely
Underplaced

0.1

Math Placement Test Score (Cutoff=42)
B or better

C or better

Pass

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

0
15

Percent of Students Succeedingin First College-Level Math Course

0.9Placement

Student Ability

Developmental

Developmental

College Level

Under-placed(SWCCS: Math – 28%)
(LUCCS: Math – 19%)

Resulting Severe Error Rates:
 SWCCS: 34% = 28%+6%
 LUCCS: 24% = 19% + 5%

College Level

Over-placed(SWCCS: Math – 6%)(LUCCS: Math – 5%)



27

 Redesigned developmental math curricula into one-credit modules.
Arithmetic/
Pre-algebra(3-5 credits)

Whole
numbers(1 credit)

Factoring
Equations(1 credit)

Intermediate
Algebra(3-5 credits)

Beginning Algebra(3-5 credits)

Fractions
Decimals(1 credit)

Rational
Equations(1 credit)

RatiosPercents(1 credit)

LinearEquations(1 credit)

Radicals(1 credit)

2-variable
Equations(1 credit)

Quadratic
Equations(1 credit) Introduced a customized diagnostic assessment to place students into
individual modules.

 After the introduction of

the VPT-Math:

College Math placement morethan doubled
 College Math enrollment morethan doubled
 Among those who placed andenrolled, average pass rates
declined from 69% to 62%
 But overall, increasing access tocollege math resulted in morethan twice as many students
successfully completingcollege math within one-yearPlease visit us on the web at

http://ccrc.tc.columbia.edu,
where you can download presentations, reports,
CCRC Briefs, and sign-up for news announcements.
Community College Research Center
Institute on Education and the Economy, Teachers College, Columbia University
525 West 120th Street, Box 174, New York, NY 10027

E-mail: ccrc@columbia.edu
Telephone: 212.678.3091

CCRC is funded in part by: Alfred P. Sloan foundation, Bill & Melinda Gates Foundation, Lumina Foundation for
Education, The Ford Foundation, National Science Foundation (NSF), Institute of Education Sciences of the U.S.
Department of Education

30

LOST IN TRANSITIONDOCUMENTED PROBLEMS WITH ASSESSMENT AND PLACEMENTPOLICIES AND PRACTICES IN DEVELOPMENTAL MATH SEEM TODISPROPORTIONALLY AFFECT STUDENTS OF COLOR

Testing and Beyond: The Future of College Math Placement in California

Learning Works
Oakland, November 10, 2015

Tatiana Melguizo
Associate Professor, University of Southern California
melguizo@usc.edu
This research was funded by a grant from the U.S. Department of
Education’s Institute of Education Sciences (IES).

Problem Statement•

Every year about 80 percent of community college students in
California are placed into preparatory mathematics. This percentage
is higher than the national average.

• Community college students have widely varying initial skills levels• Colleges have to offer classes to meet these levels and have to keep
heterogeneity in the classrooms manageable• Placing students incorrectly can reduce the likelihood that students
succeedLiterature on Inequities in Placement byRace and Ethnicity•

Potential explanations for the over-representation of
students of color in basic skills courses
Students of color on average attend lower-quality highschools (Fryer & Levitt, 2004; Ladson-Billings & Tate, 1995)• Students lack awareness of the A&P process andconsequences of performance on tests (Bunch, Endris,
Panayotova, Romero, & Llosa, 2011; Venezia, Bracco, &Nodine, 2010)• Commercially developed tests are not placing students
correctly (Scott-Clayton, Crosta & Belfield, 2014; Melguizo etal., 2015; Ngo & Melguizo, 2015)•

Setting
Large Urban Community College District - a naturallaboratory
Diverse student population that varies by college
 Nine colleges with 130,000 plus students
 “Common data system”
 Large number of observations.
 Presumption of representativeness—likely to capture
the wide variation across community colleges in the
United States.
Developmental Math Sequence

Arithmetic

Pre-Alg.

Developmental MathElem. Alg.

Int. Alg.

TransferLevel

Remediation needs of LUCCD students

50% of students chose a test that could place them in a course
below the last math course they passed in high school

Diagnostic Tests such as MDTP
allow students to choose thesub-test to take instead of using abranching system as commerciallydeveloped tests
We analyzed the data for students
in one community college and foundthat over 50% chose a sub-test which
could place them at a lower-level maththan the one completed in high school

A substantial proportion of students are placed in developmental
math courses below the last course taken in high school
College Math Level Placement
vs. Highest Math Level Passed Prior to College
Colleges B, D, F, G, H
(Assessed 2005-2008, First Enrolled 2005-2011)
19.4

14.2

28.8

29.1

23.4

Higher thanPrior Passed

Percent

31.4
30.7
28.1

43.1

Asian

33.4

Same asPrior Passed

43.2

Lower thanPrior Passed

33.7

49.9

54.4
37.1

Black

Hispanic

White

Other

Note. Source: District's student background questionnaire and administrative data (Highest
level math completed with a C or better?). Assumes California math sequence order(www.cde.ca.gov/be/st/ss/documents/mathstandards.pdf):
Algebra I, Geometry, Algebra II,
Trigonometry, Calculus.  2(8, n = 42334) = 1296, p < .001 (Pearson's  2 test forindependence among categories).

There is substantial variation by college

College Math Level Placement
vs. Highest Math Level Passed Prior to College

College Math Level Placement
vs. Highest Math Level Passed Prior to College

College G(Assessed 2005-2008, First Enrolled 2005-2011)

College H(Assessed 2005-2008, First Enrolled 2005-2011)

7.5

8.7

23.1
24.3

15.9

24.2

11.8

Higher thanPrior Passed
Percent

26.4

67.1

Asian

Black

Hispanic

Higher thanPrior Passed

31.8

29.5

Same asPrior Passed

31.3

43.6

36.4

36.8

Same asPrior Passed

Lower thanPrior Passed

35.4

33.9

28.8

31.8

33.7

Lower thanPrior Passed

Asian

Black

Hispanic

White

Other

42.5

62.2
53.3

50.5

33.3

27.6

26.0
30.8

68.2

23.6

White

Other

Note. Source: District's student background questionnaire and administrative data (Highest
level math completed with a C or better?). Assumes California math sequence order(www.cde.ca.gov/be/st/ss/documents/mathstandards.pdf):
Algebra I, Geometry, Algebra II,
Trigonometry, Calculus. 2(8, n = 13371) = 387, p < .001 (Pearson's 2 test forindependence among categories).

Note. Source: District's student background questionnaire and administrative data (Highest
level math completed with a C or better?). Assumes California math sequence order(www.cde.ca.gov/be/st/ss/documents/mathstandards.pdf):
Algebra I, Geometry, Algebra II,
Trigonometry, Calculus. 2(8, n = 8264) = 83, p < .001 (Pearson's 2 test forindependence among categories).

In addition to low placements over 30% of the
non-compliers attempted a lower level course
Attempted Math Level Among Placement Non-CompliersColleges B, D, F, G, H
(Assessed 2005-2008, First Enrolled 2005-2011)

Percent

63.9

65.2

58.6
66.4

66.1

Higherthan Placed

Lower
than Placed
41.4

36.1

34.8

33.6

Asian

Black

Hispanic

33.9

White

Other

Note. Source: District's student background questionnaire and administrative data. Based on
first math enrollment after assessment. "Higher than placed" if student enrolled in a higher
level than placed. "Lower than placed" if student enrolled in a lower level than placed. Vast
majority of students were in compliance with placement. Large variation
among colleges.
2
Low statistical significancelevel
resulting
from
small
sample
size:

(4,
n
= 3385) = 12,
p < .05 (Pearson's  2 test for independence among categories).

Substantial variation by college that might berelated to counseling
Attempted Math Level Among Placement Non-Compliers

Attempted Math Level Among Placement Non-CompliersCollege H(Assessed 2005-2008, First Enrolled 2005-2011)

College G(Assessed 2005-2008, First Enrolled 2005-2011)

30.9

76.2

81.1

85.7

78.3

50.5

Lower
than Placed

Asian

23.8

Black

18.9

Hispanic

14.3

White

47.6

54.7

Higherthan Placed

Lower
than Placed

69.1
49.5

22.6

43.4

Percent

77.4

Higherthan Placed

45.3

56.6

52.4

White

Other

21.7

Other

Note. Source: District's student background questionnaire and administrative data. Based on
first math enrollment after assessment. "Higher than placed" if student enrolled in a higher
level than placed. "Lower than placed" if student enrolled in a lower level than placed. Vast
majority of students were in compliance with placement. Large variation
among colleges.
2
Low statistical significance level resulting
from
small
sample
size:

(4,
n
= 843) = 2,
p = .8200000000000001 (Pearson's 2 test for independence among categories).

Asian

Black

Hispanic

Note. Source: District's student background questionnaire and administrative data. Based on
first math enrollment after assessment. "Higher than placed" if student enrolled in a higher
level than placed. "Lower than placed" if student enrolled in a lower level than placed. Vast
majority of students were in compliance with placement. Large variation
among colleges.
2
Low statistical significancelevel
resulting
from
small
sample
size:

(4,
n
= 652) = 16,
p < .01 (Pearson's 2 test for independence among categories).

There were no differences in courses attemptedof non-compliers by gender

The inclusion of multiple measures can increase accessw/out decreasing student success (Ngo & Kwon, 2015)

Findings
Only 6% of the students benefitted from multiple
measures at the LUCCD
 Major benefits for African American and Latino
students who could enroll in higher-level math courses
 No evidence that “boosted” students were less likely tocomplete the course Performed at similar levels to similar-scoring and higher-scoring peers

ConclusionsThe state’s community colleges are moving in the right
direction in terms of using high school transcript
information to inform the assessment and placement
policies and practices in developmental math.
California colleges have been and can continue to leadthe way in terms of effectively using “multiple
measures” to improve placement in particular for
students of color

Other Relevant Work

Melguizo, T., Kosiewicz, H., Prather, G., & Bos, J. (2014). How are community college
students assessed and placed in developmental math? Grounding our understanding inreality. Journal of Higher Education, 85(5), 691-722.

Melguizo, T., Bos, H., Ngo, F., Mills, N., & Prather, G. (2015, available online). Using a
regression discontinuity design to estimate the impact of placement decisions indevelopmental math. Research in Higher Education.

Fong, K., Melguizo, T., & Prather, G. (2015). Increasing success rates in developmental
math: The complementary role of individual and institutional characteristics. Research inHigher Education.



Ngo, F. & Melguizo, T. (2015, available online). How can placement policy improve mathremediation outcomes? Evidence from experimentation in community colleges.
Educational Evaluation and Policy Analysis.

Policy Briefs and Working Papers available at:
http://www.uscrossier.org/pullias/research/projects/sc-community-college/

THANK YOU!
QuestionsTatiana Melguizo
melguizo@usc.edu
http://www.uscrossier.org/pullias/research/projects/s
c-community-college/

Enhanced Multiple
Measures for Math
Placement
Terrence Willett
Director of Planning and Research
Cabrillo College
November 10, 2015

𝑦=𝑓 𝑥Level of and Success (C or better) in First College Math forStudents whose Last High School Course was Algebra 2
with Grade of B or Better (n=35,806)
100%

80%

67%

63%

65%

URM=69%

URM=58%44%
URM=44%40%

Male=37%
24%

32%
Male=42%

Male=49%

20%

CST=275

CST=301

CST=3340%

Acc=57

Acc=84

Acc=9760%

Pre-Algebra/
Intermediate Algebra
Transfer Level
Elementary Algebra(repeating same level)(moved up 1+ levels)(back one or more levels)
Level of First Community College (CC) Course
Percent enrolled in course at community college
Success rate in course at community college

MMAP Project Overview• Collaborative effort of CCCCO Common Assessment Initiative(CAI) designed to develop, pilot, and assess implementation ofplacement tool using multiple measures through joint efforts of
Cal-PASS Plus, RP Group and now 28 CCCs
• Develop multiple measures models for English and Mathematics
and, in 2015-2016, Reading and ESL• Identify, analyze and validate multiple measures data, including
high school transcript data, non cognitive variable data, and selfreported HS transcript data
• Engage pilot colleges to conduct local replications, test models
and pilot their use in placement, and provide feedback• bit.ly/MMAP2015

Tests Predict Tests,
Grades Predict Grades*
Simple correlations with community college success rates(grade of C or better)* with some caveats for higher level math

Statistics Tree – Direct Matriculants

Transfer Level Placement Rules for
Non-Direct Matriculants(delay of at least one year between high school and college)
Statistics• Cumulative high school GPA
through 11th grade ≥ 3.0• OR• Cumulative high school GPA
through 11th grade ≥ 2.3
AND C or better in highschool PreCalculus

Pre-Calculus• HS GPA>=3.3• OR• HS GPA>=3 AND
Algebra II CST>=340

Applying Multiple Measures
Disjunctive Placement

Conjunctive Placement

Test score
OR
High School Transcript
OR
AP scoreOR
EAP

Test score
AND(High School Transcript
OR
AP scoreOR
EAP)

Transfer Level Placement
Current

100%

Disjunctive MM

90%

80%
70%

61%

60%

50%
40%

42%

38%
31%

30%
20%

10%
0%
English(n=103,510)

Math(n=143,253)

Transfer Level Math Placement
Current

Disjunctive MM

100%
90%
80%
70%

65%

60%

53%

50%

41%

40%

32%

30%
20%

51%

22%

21%

15%

10%
0%
Afr AmLatino

Asian

White

For questions
Terrence Willett
Director of Planning and Research
Cabrillo Community College
terrence@cabrillo.edu
Multiple Measures Assessment Project
http://rpgroup.org/projects/multiple-measuresassessment-project/pilot-college-resources
Common Assessment Initiative
http://cccassess.org/

MATH READINESS:
Seeking a Shared Definition

KATHY BOOTH
WestEdDISCUSSANTS:EVELYN YOUNG SPATH
CSU-Bakersfield

BRUCE YOSHIWARA
Pierce College (retired)Alignment of Math
Competencies
WestEd Analysis of the Common
Core, ICAS, CSU, and CCC
Standards

November 2015

Comparing Descriptions ofStandards and Competencies•

Common Core State Standards Clusters, by Grade•

Intersegmental Committee for the Academic Senate(ICAS) Statements of Competencies•

California State University Entry Level Mathematics
Examination (ELM) Topics List•

California Community Colleges’ Common
Assessment Initiative (CCCAssess) Assessment
Competencies

Degrees of Consistency

Pre-High School•

Common Core, ELM, and CCCAssess focus on thesame core set of standards• Both college tests are more focused on basic
math skills and number sense than ICASHigh School•

Common Core, ICAS, ELM, and CCCAssess all put
a heavy emphasis on algebra and functions• Differences emerge in geometry and statistics

Areas of Inconsistency

Geometry•

Common Core and ICAS focus on a broader range of topics thanthe college tests•

ELM tests geometry concepts in more contexts than CCCAssess(geometric theorems in conjunction with the coordinate plane andalgebra, compared to application of volume formulas andtrigonometry)

Statistics•

Common Core and ICAS focus on a broader range of topics thanthe college tests•

The statistics standards for ICAS, ELM, and CCCAssess do not
align

•

ELM tests more statistics concepts than CCCAssess

Ongoing Discussion

CCCAssess•

The test is still under development

ELM•

CSU plans to re-evaluate the ELM beginning in 2016•

CSU also plans to update its quantitative reasoning requirement
over the next 2-3 years

BREAKOUT DISCUSSIONS:Math Readiness

ORANGE | toward entrance
Facilitator: KATHY BOOTH
WestEdGREEN | toward windows
Facilitator: ELISHA SMITH ARRILLAGA
Career Ladders Project

ENHANCING PLACEMENT:
Using High School Transcripts

LONG BEACH

ROBERT TAGORDALong Beach Unified School District

LAUREN SOSENKOLong Beach City College
BAKERSFIELD

VICKIE SPANOS
Kern High School District

JANET FULKS
Bakersfield College

Promise Pathways:
Multiple Measures at Long
Beach City College
Lauren Sosenko
Director of Institutional Researchlsosenko@lbcc.edu

November 10, 2015

LBCC’s Promise Pathways: Background
Promise Pathways is a first year experience program forstudents matriculating directly from high school• Alternative assessment using multiple measures• Prescriptive scheduling emphasizing full-time enrollment and earlycompletion of basic skills courses• Priority registration• Achievement coaches
Note: Students may now get alternative placement without signing up for Promise Pathways

Alternative Assessment• Analysis revealed high school performance dramatically
predicts success in college courses.
• Traditional placement ignored high school performance
relying only on standardized assessment exam (Accuplacer).
• Devised a new assessment model that leveraged thepredictive utility of multiple measures of student
achievement.

Built upon partnerships

Critical faculty voiceMath Placement Criteria
Math alternative placement criteria has stayed relatively
stable over the four cohort years. Students’ alternative
placement is based upon:• high school GPA,
• highest-level math course in high school,• grade in the highest-level math course, and• California State Test (CST) proficiency level
Used highest score: Accuplacer assessment results or thealternative placement score% of all 1st time intent to complete students
participating in alternative placement by fall term
100%

46%

75%

56%
65%

50%

54%

25%

44%
35%

0%
Fall 12(n=2,700)

Promise Pathways

Fall 13(n=2,982)

Fall 14(n=2,989)

Other intent to complete 1st time students

Transfer math placements are slightly rising
100%

2%

1%
20%

75%

15%

15%

18%

25%
35%

18%

33%

30%

20%

16%

40%

50%
29%
18%

25%

0%

28%
30%

29%

32%

36%

P12

P13

P14

P15

10%
Fall 2012 without
alt place
Transfer Math

MATH130

MATH110

MATH815

MATH805

NoneNumber of students moved via alternative placementby the number of semesters in math
300

263
250
198

200

184
165

150
119
90

100

50

0

0

0

1

4 semesters

3

1

1

3 semesters
Cohort 1

2 semesters
Cohort 2

Cohort 3

1 semester

How can we best study thePromise Pathways outcomes?
Matched comparison group looks like Promise Pathways group• LBUSD students from fall 2011, 2010, and 2009••••

HS GPA
English CST proficiency
Algebra II in HS# of units attempted at LBCC during 1st term (at least 9 units)

Cohort 1 – 785
Cohort 2 – 891
Cohort 3 – 998
Only LBUSD students due todata availability

Promise Pathways students successfully complete
transfer-level math at similar rates as similar students
100%

75%

73%

75%

50%

25%

0%
Promise Pathways
n=348

Matched Cohort
n=259

% Promise Pathway and matched comparison
students who successfully completed transfer-levelcourses in math and English within three years
100%

75%
60%
50%
39%
32%
25%

25%

0%
Transfer Math Success
Promise Pathways

Transfer English Success
Matched Comparison

% of Promise Pathway and matched comparison students who
successfully completed transfer-level courses in math within three
years by ethnicity
100%

75%

50%

40%
28% 26%

24%

23%

25%
15%

14%

10%
0%
Asian

Black/African American
Promise Pathway

Hispanic/Latino

Matched ComparisonWhiteBy the end of their 3rd year, Pathway students are more
successful in achieving milestones than similar students
100%

75%
63%
50%

48%

45%

38%
30%
25%

21%

16%

14%

0%
Behavioral Intent to Transfer
Transfer Directed
On Track to 4-year completion(12+ transf. units and attempt (Complete transfer English and(45 units)transf. math and English)
math)

Promise Pathways

Matched Comparison

Transfer Prepared(60+ transf. units)

SEIZING THE 12TH GRADE:
Supporting Math Readiness
EAP SENIOR YEAR MATH COURSE

JOHN MONTGOMERY
Roseville Joint Union High School District

PAUL NEAL
Sierra College

RAVIN PAN
Sacramento State University
SOUTH LOS ANGELES (SLAM) PROJECTROBERT BOSLEY
Santee Education Complex, LAUSDKRISTIN WEBSTER
CSU Los Angeles

EAP Senior Year Mathematics Course• Origin• Content
Problem solving
LinearQuadratic
Systems of Equations and Inequalities
Exponential
Logarithm
Absolute Value and Piecewise
Math of Finance• Scaling Up

Participating Schools
Participating District

Participating High School

Placer Union HighDel Oro High School

Western Placer

Lincoln High School

Roseville Joint Union

Antelope Creek HighSchoolGranite Bay High

Oakmont High
Roseville HIgh
Woodcreek HighRocklin Unified

Whitney High School

High School EAP Data
Students who took the EAP Math course and enrolled
at Sierra College :2013-20142014-2015

Placer Union`

22

17

Rocklin Unified

5

19

Roseville Joint
Union

1

22

Western Placer
Unified52

Preliminary Outcomes Data

STEM math course = College Algebra, Trigonometry, Int Algebra, Pre-Calculus,
Calculus I or II
Non-STEM math course = Statistics, Concepts of Mathematics, Modern Business
Mathematics(italicized courses were added in year two

Persistence Rates at Sierra College On average, students enrolled in a course at Sierra College had a
74% persistence rate (i.e. persistence from first to second
semester).
 Students who completed the EAP Math course and enrolled in amath course at Sierra College had a 95% persistence rate.

 Students who completed the EAP Math course and did not enroll
in a math course, but still enrolled in a course at Sierra College,
had a 96% persistence rate.

Math Remediation Rates*
100%

83%

90%
80%

70%

70%
60%

58%

30%

10%
0

CSULA

20%

Wilson High School40%

Santee Education
Complex50%*Regular Incoming CSU Freshman, Fall 2013. Obtained from CSU Proficiency Reports
http://asd.calstate.edu/performance/proficiency.shtml

SLAM (MATH 109) Pass Rates
Concurrent* Cal State LA vs SLAM Students

100%
90%

67% (N = 332)

66% (N = 55)

SLAM Average

70%

Cal State LA Average

80%

60%
50%
40%
30%
20%
10%
0

*Aggregate pass rates for fall 2013 and fall 2014.

Remediation Rates

Cal State LA vs SLAM Students*
100%
90%
80%
70%
60%

58%

50%

20%
10%
0

34%

SLAM*

30%

Cal State LA

40%*Maximum possible remediation rate based on pass rate of MATH 109. Placement
test data not included.

SLAM Cohort 1

Actual College Access and Remediation Rates with
Preliminary Persistence Rates*

90%

89%

93%
86%

60%
50%
40%
30%
20%
10%
0

College Access – Winter 2015

70%

College Access – Fall 2014

80%

15%
Math
Remediation

Persistence from Year 1 to Year 2

100%*Persistence rate based on survey data with 93% of students reporting. NSC data
available in late November.

SLAM Cohorts 2 & 3

Preliminary College Access* & Remediation Rates**
100%
90%

60%
50%
40%
30%
20%
10%
0

39%
Math
Remediation

70%

73%
College Access – Fall 2015

80%

*Minimum matriculation rate based on survey data with 83% of students reporting.
NSC data available in late November.
**Maximum possible remediation rate based on pass rate of MATH 109. Placement
test data not included.

ALIGNING OPPORTUNITIES:An Intersegmental Dialogue

PHYLLIS BRAXTON
Los Angeles Harbor College

ROBERT GOULD
UCLAKATE STEVENSON
Cal State Northridge

PHIL TUCHER
Oakland Unified School District

MOVING FORWARD:
Next Steps

THANK YOU for your participation in

Testing and Beyond:
The Future of College Math Placement

Please shareyour feedback and recommendations!

www.LearningWorksCA.org