Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
University of Southern California Dissertations and Theses
/
To what extent does being a former high school English learner predict success in college mathematics? Evidence of Latinx students’ duality as math achievers
(USC Thesis Other)
To what extent does being a former high school English learner predict success in college mathematics? Evidence of Latinx students’ duality as math achievers
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
TO WHAT EXTENT DOES BEING A FORMER HIGH SCHOOL ENGLISH LEARNER
PREDICT SUCCESS IN COLLEGE MATHEMATICS? EVIDENCE OF LATINX
STUDENTS’ DUALITY AS MATH ACHIEVERS
by
David Velasquez
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(URBAN EDUCATION POLICY)
December 2022
Copyright 2022 David Velasquez
ii
Dedication
Josefina,
Chano,
&
Eztli.
iii
Table of Contents
Dedication ....................................................................................................................................... ii
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
Abstract ........................................................................................................................................... x
CHAPTER 1: Introduction ............................................................................................................. 1
Chapter Overview ....................................................................................................................... 3
Dissertation Motivation .............................................................................................................. 3
Purpose and Research Questions ................................................................................................ 7
The Value of Linguistic Capital .................................................................................................. 8
Structure of Dissertation ........................................................................................................... 10
Summary of Chapter 1 .............................................................................................................. 11
CHAPTER 2: Literature Review .................................................................................................. 14
Guiding Literature Review Questions ...................................................................................... 14
Criteria for Literature Inclusion ................................................................................................ 16
English Learner Terminology and Phenomena......................................................................... 19
Key Terms, Labels, and Designations .................................................................................. 19
Relevant Processes and Phenomena ..................................................................................... 28
iv
Latinx English Learners ............................................................................................................ 35
K-12 English Learners: Outcomes, Experiences, and College-Preparation ......................... 37
College English Learners: Outcomes, Experiences, and Assets for Latinx English
Learners................................................................................................................................. 43
Summary of Chapter 2 .............................................................................................................. 56
CHAPTER 3: Research Design .................................................................................................... 59
Purpose and Research Questions .............................................................................................. 59
Trends in Approaches to Studying English Learners ............................................................... 60
Trends in General Content Coverage: Most Quantitative Studies Focus on K-12, Few
on Higher Ed, Even Less Across Sectors.............................................................................. 61
Trends in Specific Content Coverage: A Focus on Language Programs.............................. 62
Trends in Methodological Techniques: Experimental and Quasi-Experimental Studies
Have Been Possible in K12 Much More Often Than Higher Education .............................. 65
Research Design: The Appropriateness of a Descriptive Approach ......................................... 67
Data and Setting .................................................................................................................... 68
Full Latinx Sample ................................................................................................................ 69
Group of Interest: Who are Latinx Former ELs and Latinx English Only students? ........... 70
Variables ............................................................................................................................... 71
Data Analysis ............................................................................................................................ 74
Phase 1: Disaggregate Points and Paths of Entry into College Math ................................... 75
v
Phase 2: Investigate First Math Course Enrollment, Noncompliance, and Success
in Math .................................................................................................................................. 75
Phase 3: Analyze Relationship between K-12 English Proficiency and Community
College Math. ........................................................................................................................ 76
Fixed Effects and Changes in Sample Sizes ......................................................................... 77
Limitations ................................................................................................................................ 79
Summary of Chapter 3 .............................................................................................................. 80
CHAPTER 4: Results ................................................................................................................... 83
Summary Statistics.................................................................................................................... 85
Background Characteristics .................................................................................................. 87
Academic Characteristics...................................................................................................... 90
Math Course-Taking Sequences ........................................................................................... 92
Summary of Summary Statistics ........................................................................................... 95
RQ1 Results: Educational Goals and Math Course Referrals .................................................. 97
Educational Goals ................................................................................................................. 97
Placement Referrals .............................................................................................................. 98
Summary of RQ1 Results ................................................................................................... 100
RQ2 Results: First Math Course Enrollment, Noncompliance, and First Math Success ....... 100
First Math Course Enrollment ............................................................................................ 101
vi
First Math Enrollment Relative to Referral ........................................................................ 103
First Math Enrollment Trends Among Noncompliers ........................................................ 106
First Math Pass Rates .......................................................................................................... 109
Summary of RQ2 Results. .................................................................................................. 110
RQ3: College Math Success and Long-Term Credit Accumulation ...................................... 112
Logistic Regression Results ................................................................................................ 114
Multivariate Regression Results ......................................................................................... 117
Summary of RQ3 Results ................................................................................................... 119
Summary of Chapter 4 ............................................................................................................ 121
CHAPTER 5: Discussion and Conclusion .................................................................................. 124
Summarizing Results .............................................................................................................. 126
Despite Challenges, Most ELs Perform Similar or Greater than Peers in High
School ................................................................................................................................. 127
Latinx Former ELs Report Lower College Goals but Score Higher on Placement
Exams .................................................................................................................................. 128
Latinx Former ELs Less Often Self-Place into Higher Math Courses Yet Perform
Better ................................................................................................................................... 129
Latinx Former ELs Outperform Peers in College Math and College Credit Attainment ... 131
Discussion of Findings with Extant Literature ....................................................................... 132
Aspirations, Self-Placement, and Performance .................................................................. 132
vii
Advantages and Assets of ELs ............................................................................................ 135
Revisiting EL Terminology ................................................................................................ 136
Implications and Recommendations for Policy and Practice ................................................. 138
Former EL Status as a Culturally and Linguistically Appropriate Measure ....................... 138
Addressing Inconsistencies Between Latinx Former ELs Achievement and Actions ........ 141
Future Research ...................................................................................................................... 144
Contribution, Significance, and Conclusion ........................................................................... 147
References ................................................................................................................................... 151
viii
List of Tables
Table 1. Demographic Characteristics for the Full Latinx Sample .............................................. 88
Table 2. Academic Characteristics for the Full Latinx Sample .................................................... 91
Table 3. Top 15 Most Common HS Math Course Sequences for Latinx Former EL and
Latinx English Only Students ....................................................................................................... 94
Table 4. Educational Goal for Latinx Former EL and Latinx English Only Students .................. 98
Table 5. Math Level Referral for Latinx Former EL and Latinx English Only Students ............. 99
Table 6. First Math Enrollment for Latinx Former EL and Latinx English Only Students ........ 102
Table 7. Math Enrollment Relative to Referral for Latinx Former Els and Latinx EOs ............ 103
Table 8. Noncomplier Enrollment Relative to Referral for Latinx Former El and Latinx
English Only Students................................................................................................................. 107
Table 9. First Math Taken Pass Raters for Latinx Former El and Latinx English Only
Students ....................................................................................................................................... 109
Table 10. Fixed Effects Logistic Regression Results in Odds Ratios by Groups of Interest ..... 115
Table 11. Fixed Effects OLS and Multiple Regression Models for Latinx Former EL
Students ....................................................................................................................................... 118
ix
List of Figures
Figure 1. Boote and Beile's (2005) Literature Review Scoring Rubric ........................................ 15
Figure 2. Literature Search Criteria and Outcomes ...................................................................... 18
Figure 3. English Learner Designation Flowchart ........................................................................ 22
Figure 4. Sample Overview for the Full Latinx Sample ............................................................... 86
Figure 5. First Math Enrollment by Referral for Latinx Former Els and Latinx EOs ................ 105
x
Abstract
Background: Partly because of data limitations, little research exists on English Learners (ELs)
in higher education. Extant research is typically bounded within K-12 and focuses on English
outcomes and analyzing factors that support English language proficiency. Scholarship indicates
that ELs in higher education are overrepresented in community colleges, which have historically
seen inequitable outcomes stemming from developmental coursework. Extant work in K-12
suggests ELs often trail behind their peers in early grades but, often, they catch up and
sometimes surpass their peers academically by later high school grades. Perhaps, such a trend
continues for former ELs (i.e., ELs that have demonstrated English proficiency).
Purpose: This study aims to examine the relationship between achieving English proficiency for
Latinx EL students and outcomes in credit attainment and math success. To capture different
points in community college, the three phases of analyses examine (1) educational goals and
math placement referrals, (2) course enrollment and (non)compliance trends, and (3) college
math course success and credit accumulation (total, degree-applicable, and transferable).
Method/Methodology: Considering the value of linguistic assets, this study leverages a student-
level, linked, longitudinal dataset to conduct descriptive research. The data reflect students’
trajectories between one community college district and its feeder high schools in California.
Conclusions: High school-to-college fixed effects regression indicates a 1.2 increase in the odds
of Latinx students passing their first college math course and completing 60-degree units. All
else being equal, Latinx former ELs were predicted to complete between 4 and 5 more total,
degree, and transfer units than their peers. These and other results suggest that EL status may be
an appropriate measure for use in community college decision-making related to assessment,
course placement, and referrals to developmental support (e.g., corequisite coursework).
1
CHAPTER 1: Introduction
English learners (ELs), a large and growing student population, are concentrated in large,
urban, public-school K-12 districts and disproportionately enrolled in community colleges (Hill,
2012; Zhang et al., 2021; Nuñez and Sparks, 2012; Wolf, Herman, Bachman Bailey, & Griffin,
2008). In California, home to the largest number and proportion of ELs, these students are
described as not yet having demonstrated proficiency in their ability to "speak, read, write, or
understand English as a result of English not being their home language (California Department
of Education, 2019).” Yet, extant studies indicate that for most students, enrollment in
community college has historically resulted in below-college level coursework placement and is
associated with little academic progress (Attewell, Lavin, Domina, & Levey, 2006; Chen, 2016;
Grub, 2013; Hodara & Xu, 2016). In particular, coursework in math has functioned as a more
significant barrier to student success than coursework in English and in other content areas
(Kosiewicz, Ngo, and Fong, 2016; Kurlaender & Larsen, 2013; Melguizo, Hagedorn, & Cypers,
2008; Melguizo et al., 2015). Larger achievement gaps in various areas, including certificate and
degree attainment and vertical transfer, have been associated with some groups more frequently
(Bailey, 2012; Chen, 2016; Hughes, 2012; Nora & Crisp, 2012; Shulock & Moore, 2010). More
specifically, scholars note that the student experience in these courses can be highly racialized,
discouraging, and impacted by deficit perspectives on students of color (Maldonado, 2019,
Roberts, 2019, Suarez-Orozco et al., 2015).
As a result, efforts to improve college and career outcomes for students emphasize the
importance of attention to specific state contexts. For example, the Every Student Succeeds Act
(ESSA) requires cross-sector collaboration between K-12 and higher education leaders to align
college-readiness standards with particular attention to each state’s unique student population. In
2
California, home to the largest community college and EL student population, leaders must
consider ELs as they constitute nearly 20% of their public-school population (ZHANG ET AL.,
2021). Yet, over 40% of all California public school students report speaking a language other
than English at home, indicating that nearly half of these students have already attained fluent
English proficiency (NCES, 2022). However, possibly because EL designation is primarily a K-
12 label, data limitations have posed problems for learning about ELs in higher education
(Núñez, Rios-Aguilar, Kanno & Flores, 2016). Thus, the research on ELs tends to be
concentrated within K-12, making it challenging to investigate ELs’ outcomes regarding college
readiness outlined in policies such as ESSA. Some longitudinal studies suggest that even though
ELs trail behind their peers academically in earlier grades, they catch up and sometimes surpass
them in high school (Umansky & Reardon, 2014). Thus, following ELs into higher education
may reveal comparable academic performance or a potentially widening lead relative to their
peers despite barriers limiting ELs’ progress.
Research on inter-sector misalignment, or placing students into below-college level
coursework despite already having demonstrated college-readiness in high school, documents the
prevalence of this pattern (Ngo & Melguizo, 2021; Melguizo, Flores, Velasquez, & Carroll,
2021). In a recent study, former ELs experienced rampant misalignment regarding college
English coursework. Three of every four former ELs who had already demonstrated college
readiness through high school measures were placed into-below college-level English courses
(Melguizo et al., 2021). Yet, after accounting for background characteristics, academic
achievement, and experiencing inter-sector misalignment, former ELs completed more college
credits than their monolingual, English-speaking peers. As such, scholars attributed this finding
to the “bilingual advantage” literature, which suggests bilingual or multilingual students may
3
possess certain assets that help acquire the institutional support necessary for academic success
and social mobility (Stanton-Salazar & Dornbusch, 1995). These findings are consistent with
Umansky and R Reardon’s (2014) notion that many ELs catch up with their peers in high school
and suggest that academic growth continues in English in college. The same may be true for
math. I elaborate on the rationale and motivation behind conducting a study on math outcomes
for California Latinx EL students who attended community colleges below. I begin by providing
a roadmap for the remainder of the chapter.
Chapter Overview
Given the history of inequitable college achievement for students of color and policy
efforts to improve long-term career and college outcomes through cross-sector collaboration and
attention to state-specific contexts and populations, I focus on Latinx EL students in this study.
In this chapter, I introduce the dissertation by elaborating on the rationale and motivation for this
study with more context on ELs, community college, developmental math, academic outcomes,
and relevant policies. I focus on California, a leader in community college policy. Next, I
summarize the purpose and list the guiding research questions. To follow, I discuss the value of
using linguistic capital as a framework for this study. I close by outlining the structure of the
dissertation.
Dissertation Motivation
According to the National Center of Education Statistics (NCES) (2021), roughly five
million students in public K-12 schools are classified as English learners (ELs), accounting for
approximately ten percent of the total student population nationwide. This population tends to
be concentrated within the largest urban school districts (Hill, 2012; ZHANG ET AL., 2021). As
the fastest growing K-12 group (Wolf, Herman, Bachman, Bailey, & Griffin, 2008), it is no
4
surprise that the share of EL students is also exhibiting rapid growth in college and university
enrollment (Acevedo-Gil, Santos, Solorzano, 2014). However, ELs are disproportionately
represented in community colleges relative to four-year universities (Nuñez and Sparks, 2012).
Thus, examining ELs’ college outcomes and experiences mandates a focus on community
colleges.
Historically, about two-thirds of community college students begin in below-college
courses (Chen, 2016; Grub, 2013). These “developmental” or “remedial” courses are intended to
help provide the skills and knowledge for success at the college level for students who have been
identified as underprepared (Cohen, Brawer, & Kisker, 2013). However, most students that begin
in developmental courses never reach a college-level course and discontinue their academic
careers before reaching college-level or making meaningful progress towards a degree (Hodara
& Xu, 2016). With these drawbacks in mind, scholars have described developmental courses as
the most prominent academic barrier to college student success (Attewell, Lavin, Domina, &
Levey, 2006). Additionally, in California Community Colleges, the most extensive community
college system serving roughly two million students, scholars have noted a “Latino problem”
given the prevalence and disproportionate development placement for this population (Dowd &
Bensimon, 2015).
Many studies note that math courses function as a more significant barrier to academic
progress than those in English and other content areas. (Kosiewicz, Ngo, and Fong, 2016;
Kurlaender & Larsen, 2013; Melguizo, Hagedorn, & Cypers, 2008; Melguizo et al., 2015).
Inequities in math coursework persist, with researchers reporting that developmental math
success rates are continually lower for Black, Latinx, female, first-generation, and low-income
students (Bailey, 2012; Chen, 2016; Hughes, 2012; Nora & Crisp, 2012; Shulock & Moore,
5
2010). Meanwhile, qualitative research describes the student experience related to
developmental math as highly racialized, discouraging, and shaped by deficit perspectives
(Maldonado, 2018, Roberts, 2019, Suarez-Orozco et al., 2015).
Because these shortcomings negatively impact some groups more than others, they pose
an issue for efforts toward equity— defined as parity in both access and outcomes among racial-
ethnic groups (Chase, Dowd, Pazich, & Bensimon, 2014). In response to these shortcomings,
policymakers have implemented policies directed at K-12 and higher education sectors to
improve academic outcomes. In K-12, the Every Student Succeeds Act (ESSA), passed in late
2015 under the Obama administration, expressed a recommitment to advancing equity and
protecting marginalized populations in K-12 to prepare them for college and careers. ESSA
prompted K-12 and higher education leaders to collaborate and identify college-readiness
standards that would support all student populations in the K-20 pipeline with particular attention
to each state’s unique context. California education leaders could not overlook ELs, given that
they constitute nearly 20% of their public K-12 student body (ZHANG ET AL., 2021). Of note,
few studies have been able to analyze whether Els’s long-term outcomes have lived up to
ESSA’s expectations of college readiness and career success.
While much research has examined EL student outcomes and experiences in the K-12
sector, far less focus has been placed on Els’s transitions and effects in higher education. Often,
scholars are limited within one industry due to data restraints. EL status is not typically tracked
in college individuals a K-12 designation is associated with individual rights and academic level
support services in elementary and secondary levels but not the higher education sector (Nunez
et al. 2016). However, tracking ELs for more extended periods may show promising results. For
example, one longitudinal study found that even though ELs often lag behind their peers
6
academically in elementary grades, they eventually catch up and sometimes surpass them in high
school (Umansky & Reardon, 2014). This work and research on inter-sector misalignment
suggest that such trends may continue through college.
Extant research has investigated inter-sector misalignment or the implications of
misalignment between K-12 and higher education sectors. For example, Ngo and Melguizo
(2021) explore disparities in inter-sector misalignment by race and gender in terms of
community college course completion and find that female students, Black students, and Latinx
students who had already demonstrated college readiness during high school are significantly
more likely to be placed into developmental math even after accounting for prior academic
achievement (Ngo & Melguizo, 2021).
Similarly, inter-sector misalignment is also prevalent in developmental English
(Melguizo et al., 2021). Former ELs experience the highest rates of inter-sector English
misalignment. More than 75% of former ELs were directed back to below-college level English
coursework even though they had demonstrated college readiness (Melguizo et al., 2021).
Notably, college-ready students who were Black, Latino, or reported Spanish as their home
language were also more likely to be misaligned than their white peers and those who spoke
English at home. While experiencing misalignment was associated with adverse college
outcomes (e.g., completing roughly five fewer transfer-applicable units), former ELs still
managed to complete between six more transfer-applicable units. Given research on inter-sector
misalignment, ESSA’s goals of college and career preparation, persistent shortcomings in
developmental education (specifical math), efforts towards equity in outcomes, and the
prevalence of Latinx and EL students in California, in this study, I focus on Latinx ELs and their
long -term outcomes in mathematics.
7
Purpose and Research Questions
As previously noted, even though ELs tend to trail in academics compared to their peers
in earlier grades, they often catch up and sometimes surpass them in high school (Umansky &
Reardon, 2014). Additionally, in college, after accounting for misalignment into below college-
level coursework, background characteristics, and academic performance, former ELs who were
deemed college-ready in high school outperformed their peers in college credit completion
(Melguizo et al, 2021). These findings are consistent with a story of ELs catching up during
high school and surpassing their peers in college, a trend best captured through a longitudinal
perspective. Such a trend might contribute to research on college readiness and speak to the
relationship between English language proficiency for Latinx ELs and academic achievement in
important non-English content, specifically math. This study aims to gauge the relationship
between high school EL classification and math outcomes in community colleges. The outcomes
analyzed cover success in college math and credit accumulation. Findings help contribute to the
shortage of research on ELs in higher education while also leading to implications and
recommendations for community college policies in various areas (e.g., assessment and
placement, multiple measures). Additionally, the study includes recommendations for
practitioners, programs, and curricula. The following questions guide this study.
RQ1. Where do Latinx former ELs begin their community college trajectories (including
educational goals and math placement referrals) and how do these rates compare to their
Latinx English Only peers?
RQ2. What math course do Latinx former ELs, as well as Latinx English Only students,
enroll into first and do these courses align with those they were referred to?
8
RQ3. Does achieving EL proficiency before high school graduation predict success in
college math courses and college credit accumulation for Latinx students?
The Value of Linguistic Capital
Linguistic capital has been defined through several related socio-cultural and
anthropological theories, including social reproduction theory (Bourdieu, 1977), community
cultural wealth (Yosso, 2005), and funds of knowledge (Moll, Amanti, Neff & Gonzalez, 1992).
However, in their comprehensive literature review of EL students’ transition to postsecondary
education, Núñez, Rios-Aguilar, Kanno, and Flores (2016) argue for the use of three conceptual
frameworks uniquely suited to study ELs in higher education: (1) Bourdieu’s (1977) theory of
social reproduction combined with integrated capitals and funds of knowledge (Rios-Aguilar,
Kiyama, Gravitt, & Moll, 2011), (2) Scarcella (2003) framework of academic English literacy
which emphasizes linguistic, cognitive, and sociocultural/psychological perspectives, and (3)
Leki’s (2007) framework of semi-academic relations that highlights the role of social and
cultural contexts affecting ELs college experience. While an argument can be made applying
one of these frameworks exclusively, I choose to frame this study centering on the notion of
linguistic capital.
Capital, the most used concept for studying success in higher education, is (in)tangible
resources needed for academic progress and includes several types (e.g., social capital, cultural
capital) (McDonough & Núñez, 2017). Linguistic capital focuses on proficiency and ease with
the dominant language of a particular context (Bourdieu, 1986). Scarcella’s (2003)
multidimensional framework of academic literacy further defines linguistic capital to help
scholars analyze the role of language as it relates to educational opportunities. To understand
linguistic capital in higher education, they explain it as having linguistic, cognitive, and
9
sociocultural/psychological factors. The linguistic dimension captures spoken language. That is,
different sounds, words, grammar, language genres, said strategies for maintaining conversation,
and discourse for communicating lines of thoughts. The linguistic aspect comes up in a context
where the appropriate spoken language is expected or required (e.g., debating, apologizing,
requesting). The cognitive element is internal, as an individual builds knowledge on concepts in
a particular topic, analyzes or evaluates ideas, and thinks about word choice or speech delivery
before speaking. The sociocultural/psychological dimensions talk about learning appropriate
patterns of discourse and communication in a specific community or setting. In context, a
student might attempt but fail to effectively communicate with a math instructor due to not
knowing or understanding office hour norms or the formal academic language.
Meanwhile, Yosso’s (2005) community cultural wealth pushes for a detailed anti-deficit
view of communities of color. Instead of focusing on cultural poverty disadvantages, this
perspective calls for scholars to learn from the assets that students and their families bring to the
classroom despite having experienced racialized cultural histories. Linguistic capital is one asset
identified as key. Yosso (2005) describes linguistic capital as an asset in multiple languages and
communication skills, partly the results of children’s experiences engaged in storytelling and oral
history traditions, cuentos (stories), dichos (proverbs), memorization, dramatic pauses, tone,
volume, rhythm, comedic timing, facial expressions, and translating for their parents or other
adults in “real world” settings (see Marjorie (Faulstich Orellana, 2003). Thus, students’
multilinguistic awareness is not solely a linguistic asset but also branches into other realms (e.g.,
social, cultural, economic). Of note, mathematics is considered a language or a register of words
and meanings that differ from those of language spoken in everyday situations (Secada, 1991).
10
EL linguistic assets are but one of the assets they lean on to navigate schooling. One
asset-based inquiry study on community college English language learners found that students
drew on a variety of personal and community assets (e.g., aspirational, linguistic capital) along
with institutional resources to motivate, inform, and support their attendance and persistence in
light of limiting assessment and placement policies (Mardock Uman, 2018). Kiyama’s (2010)
case study found that even when Mexican parents had an incomplete understanding of the
college process or the financial assistantship available, they helped their children in non-
traditional ways. For example, parents placed a high value on education, contributing to
students’ educational ideologies and positive educational aspirations.
In this study, focusing on ELs and linguistic assets means treating a former EL status as
an achievement. In other words, being a former EL represents attaining a particular degree of
linguistic capital that has been institutionally validated. However, demonstrating English
proficiency through achieving a former EL status has not been utilized in gauging college
readiness and placement into college-level coursework to the extent other traditional measures
have (e.g., GPA, course grades, or placement exams). By emphasizing linguistic capital in
studying the relationship between EL status among Latinx students and outcomes in
mathematics, scholars, practitioners, and policymakers can learn more about how to better align
college-readiness metrics for this special population.
Structure of Dissertation
This dissertation continues as follows. The second chapter reviews the literature on ELs
in higher education. This includes relevant terms, phenomena, and processes, evidence on
linguistic assets, contextualization of current policy, and reviews of K-12 and higher education
studies. The third chapter presents the research design. This includes trends in methodological
11
approaches to studying English learners, themes in content coverage, data, setting, explanation
and justification of variables used, a description of data analysis, and limitations. The results are
included in Chapter four. The results chapter first reports takeaways from summary statistics
and then results of analyses about the three guiding research questions (see Purpose and
Research Questions, above). A conclusion and discussion are included in the fifth and final
chapter. In particular, results are contextualized and situated in the extant literature, then
significance and recommendations are provided before discussing future research.
Summary of Chapter 1
While quantitative work on community colleges notes that math courses have functioned
as a significant barrier to student success (Kosiewicz, Ngo, and Fong, 2016; Kurlaender &
Larsen, 2013; Melguizo, Hagedorn, & Cypers, 2008; Melguizo et al., 2015), qualitative research
describes student experiences as discouraging and highly racialized (Maldonado, 2019, Roberts,
2019, Suarez-Orozco et al., 2015). In California, ELs are one key group impacted, as 40% of all
K-12 public school students in the state report speaking a language other than English at home
(ZHANG ET AL., 2021). However, researchers know less about ELs’ long-term outcomes in
college, possibly because EL status is a designation associated with K-12 support services
(Núñez et al., 2016). At the same time, scholars have noted a “Latino Problem” to describe these
students' prevalence and disproportionate developmental placement (Dowd & Bensimon, 2015).
Longitudinal studies suggest that EL may fall behind their peers in early grades but catch up and
sometimes surpass their peers academically in later stages (Umansky & Reardon, 2014). More
recent work has indicated that after accounting for placement levels, academic achievement, and
background characteristics, former ELs outperform their peers in college credit attainment
(Melguizo et al., 2021). These findings indicate that former EL students persist in college
12
despite experiencing rampant inter-sector misalignment, or placing students into below-college
level coursework despite already having demonstrated college readiness. Perhaps achieving
English language proficiency for Latinx former ELs, relative to Latinx English only students,
also benefits students in mathematics.
This study looks to the literature on linguistic capital (e.g., Bourdieu, 1977; Scarcella,
2003; Moll, Amanti, Neff & Gonzalez, 1992; Yosso, 2005) to help understand, interpret, and
frame assets, issues, and trends related to ELs. With linguistic capital in mind, former EL status
in this study is treated as an achievement that has partly been institutionally validated. While
ELs in K-12 that demonstrate proficiency in English may attain access to mainstream
coursework or no longer be assigned additional supports, EL status has not been used to assess
students’ college readiness or inform course placement. If EL status can be used to predict
success in college coursework, legitimizing and validating former EL status as an achievement in
college decision-making processes can help improve proper course placement and support
assignment (e.g., corequisite coursework).
To investigate the relationship between the achievement of English language proficiency
in high school and long-term outcomes in college math and credit attainment for Latinx ELs
(relative to English Only ELs), this study is guided by three questions. The first guiding research
question investigates educational goals and math placement referrals for Latinx former ELs. The
second research question examines the courses these students enroll in, compares enrollment to
referrals, and investigates trends among non-compliers or students that deviated from their math
course referral. The last question leads to an analysis of the relationship between being a Latinx
former EL and success in math and college credit accumulation. While this first chapter
introduced the motivation of this study, stated the purpose and research questions, and discussed
13
the value of linguistic capital, the remaining four chapters provide a review of the literature,
explain the research design, report the results, and discuss key takeaway from the analysis about
extant literature.
14
CHAPTER 2: Literature Review
Little scholarship exists on ELs in higher education, relative to what is known about ELs
in K-12. With the EL classification being a K-12 designation, this may come as no surprise.
Yet, work on ELs in higher education has been growing. In addition, understanding ELs in
higher education requires an understanding of K-12 literature. To provide ample coverage of
literature relevant to ELs, this review builds on Núñez, Rios-Aguilar, Kanno, & Flores’s (2016)
literature review of ELs in higher education. In addition, when appropriate, the literature review
in this chapter makes sure to pay special attention to Latinx students and math education.
To begin, this chapter explains how decisions were made about which questions to
consider and which studies to include to ensure an adequate review of Latinx ELs in higher
education. Then, the common terms and designations used in this literature on ELs are listed and
defined to prevent confusion given the lack of universal language on the topic. Processes and
phenomena related to ELs are also described. Next, themes in the literature on Latinx ELs,
beginning with takeaways from K-12 and then higher education, are discussed. The work on K-
12 spans EL programming and curriculum as well as teachers of ELs and ELs in K-12 Math.
Similarly, the section on college ELs captures outcomes and experiences organized into a section
on the overrepresentation of ELs in community colleges, college EL programming and
curriculum, professors of college ELs, while also including a section on developmental
coursework and policy.
Guiding Literature Review Questions
To conduct a substantive and sophisticated literature review that thoroughly synthesizes
and analyzes literature on Latinx ELs in higher education, this review is guided by Boote and
Beile’s (2005) criteria for conducting high-quality literature reviews. The twelve-criterion rubric
15
(listed as letters A-L) are recommended to be used in education research for evaluating the
quality of dissertation literature reviews (see Figure 1 below).
Figure 1. Boote and Beile's (2005) Literature Review Scoring Rubric
In sum, these twelve criteria address five key areas and aspects of a literature review:
coverage, synthesis, methodology, significance, and rhetoric. These five categories and twelve
16
criteria guided the creation of the following sets of questions which, in turn, guided the literature
search and structure of this literature review on Latinx ELs in higher education.
1. What will be included in this literature review? What will not? Why?
2. What has been researched and learned about Latinx ELs in higher education? What has
not?
3. How are Latinx ELs situated in the broader scholarly literature? (e.g., mixed results,
disagreements, consensus, future directions)
4. What are the important topics, variables, and/or phenomena important or relevant to ELs
in higher education? When we synthesize the literature what is the perspective or
narrative that emerges or that can be inferred?
5. What are the main methodologies that have been used to study ELs in higher
education? What research techniques have been used? What are their advantages and
disadvantages That is, what were scholars able to reveal or uncover and what were they
not able to reveal?
6. What are relevant ideas and theories in this topic that have been or could be used to frame
studies and how do they fit various research techniques/methods?
7. Why is the topic/research significant from a practical sense? Why is the topic/research
important from a scholarly sense? (e.g., consider educators, leaders, policymakers,
researchers, students, communities and think about short and long term)
Reflecting on these questions informed the organization of chapter two
Criteria for Literature Inclusion
Given that this study intends to analyze long term outcomes of English learners, the
identifying and review of articles in higher education was prioritized. In particular, this review
17
expands on Núñez, Rios-Aguilar, Kanno, & Flores (2016) systematic literature review of
literature from 1990 to 2015 on ELs in higher education. With so many labels used to describe
ELs, Núñez et al. (2016) used the following search terms to identify articles: limited English
proficient (LEP), English language proficiency (ELP), English as a Second Language (ESL),
English language learners (ELL), English learners (EL), linguistic/language minority (LM). For
the present study, the same terms are used with the addition of bilingual and multilingual. Also,
the time span of publications included publications between the years of 2016 and 2021.
Núñez and colleagues’ (2016) systematic literature review included relevant peer-
reviewed articles that were published in six journals that Bray and Major (2011) recognize as
high-impact journals. Likewise, the for the present study’s literature review, the same six
journals were included (four general higher education and two community college journals). In
addition, seven journals were added (see Figure 2 below).
18
Figure 2. Literature Search Criteria and Outcomes
For example, literature searches occurred within Journals of Latinos in Education and
Journal of Hispanics in Higher Education because of the journal’s focus on Latinx students.
Educational Evaluation and Policy Analysis was included given their quantitative approaches
and focus on policy. The “Literature Search Criteria and Outcomes” (Figure 2) also lists out the
criteria used to include articles relevant to ELs in higher education for review. While these
criteria guided a minimum inclusion of journal articles, in some instances, scholarship relevant to
Núñez, Rios-Aguilar, Kanno, & Flores
(2016) Velasquez (2021)
Timeframe 1990-2015 2016-2021
Search Terms Used
Limited English Proficient (LEP) x x
English Language Proficiency (ELP) x x
English as a Second Language (ESL) x x
English Language Learner (ELL) x x
English Learner (EL) x x
Linguistic/Language Minority (LM) x x
Bilingual x
Multilingual x
Included High Impact Journals* x
American Education Research Journal x
Community College Journal of Research and Practice x x
Community College Review x x
Educational Evaluation Policy Analysis x
Higher Education x x
Higher Education: Handbook of Theory and Research x
Journal of Hispanic Higher Education x
Journal of Latinos and Education x
New Directions for Community Colleges x
Research in Higher Education x x
Review of Higher Education x x
Studies in Higher Education x
The Journal of Higher Education x x
Search Results
Journal Articles Meeting Criteria 38 96
Largest Publishers CC-focuzed journals (76%) Latinx-focused Journals(49%)
Most Used Terms ESL, ELL, LEP Bilingual, Multilingual, EL, ELL
Least Used Terms Not Reported ELP, ESL, LM , LEP
Note: Given the time the literature search was conducted, some articles published in the last quarter of 2021 may not have been
included. Also, for more information on high impact journal selection see Bray and Major (2011).
19
higher education that fall outside the aforementioned criteria (e.g., articles published prior to
2016) were included to discuss trends, findings, and methodological approaches in extant
research.
English Learner Terminology and Phenomena
EL classification is associated with particular legal rights and academic support services
in both elementary and secondary levels (Nunez et al. 2016). As such, most terms used to
describe and identify students from homes where English is not the primary language derive
from K-12. However, processes for classification can vary between states and districts which
has prompted some scholars to call for standardizing terms, definitions, and processes for
classification and reclassification of ELs (Estrada & Wang, 2018). For clarity, commonly used
terms and phenomena related to this population are defined and explained below.
Key Terms, Labels, and Designations
To recap, six of the search terms used to identify studies on ELs in higher education were
modeled after Núñez and colleagues’ (2016) systematic literature review on the same topic.
These terms are limited English proficient (LEP), English language proficiency (ELP), English
as a Second Language (ESL), English language learners (ELL), English learners (EL),
linguistic/language minority (LM). Acknowledging EL students’ linguistic assets, the terms
bilingual and multilingual were also included. These search terms are all defined below and
listed in order of how frequently the term was used. For example, the most used terms (bilingual
and multilingual) were used 41 times to include a study. That is, among the studies included in
the literature review (per meeting the inclusion criteria), 41 of them used the term bilingual or
multilingual at least once in the journal article’s title, abstract, or list of keywords. After the
20
literature search was completed, the frequency of the terms was as follows: bilingual or
multilingual (41), EL (34), ELL (27), ELP (8), ESL, (6), LM (4), and LEP (2).
The literature search yielded 96 peer-reviewed journal articles all of which contained at
least one of the seven search terms within their title, abstract, or listed keywords. Because
certain studies included more than one of the search terms, the sum of the inclusion term
frequencies exceeds 96. Of note, because the time in which the literature search was conducted,
articles published in the last quarter of 2021 may have not been included. Next, search terms are
defined.
Bilingual or Multilingual.
A number of definitions for bilingual or multilingual exist in the literature. Put simply,
bilingual or multilingual students speak a language other than English (Umansky & Dumont,
2021). Cavasoz (2015) uses the term bilingual to describe participants that possess knowledge of
a variation of Spanish and English (e.g., Spanglish, Spanglish, Tex-Mex, Chicano
English). Being bilingual functions as a linguistic asset that extends beyond academic
settings. Horner (2015) describes being bilingual or multilingual as a cross-language relations
skillset. This skillset allows students to work in their writing within, among, and across a
number of English registers and other languages. This means that students are not only
producing or reproducing standardized, academic English (or academic Spanish). More recently,
scholars have discussed translanguaging as a new term that views bilingualism as a valuable
community asset that is a resource for more than just transitioning to the majority language
(MacSwan, 2017). Some scholars opt to use the term “emergent bilingual” to refer to those
individuals that are in the early stages of acquiring English in order to emphasize these students’
bilingual development (García, 2009). Thus, “emergent bilingual” is sometimes used
21
synonymously with terms like EL, described below. The term emergent bilingual was initially
proposed to both (1) position language as a spectrum as opposed to rigidly defined categories
and also to (2) reframe language development itself as an asset rather than a deficit. However,
EL and ELL are terms used to refer to this population both when they’ve met English proficiency
metrics (e.g., reclassified EL, former EL) as well as when they haven’t (e.g., current EL). EL is
defined next.
English Learner (EL).
ELs are students with non-English speaking backgrounds (NESB) (Tann & Schott,
2021). In California, ELs are defined as students who have not yet demonstrated proficiency in
English in the areas of speaking, reading, writing, and listening (California Department of
Education, 2019). These students are described as requiring instruction in both the English
language and the academic content in order to learn to communicate in English effectively
(California School Dashboard, 2021). Thus, the EL designation is intended to be temporary. The
ELs represented in this study’s dataset were identified as such by a K-12 district protocol
included in Figure 3 in below. In sum, current ELs are students who both (1) report a language
other than English at home and (2) do not yet demonstrate English proficiency on their first
assessment or on subsequent annual assessments.
22
Figure 3. English Learner Designation Flowchart
Given the California context of this study, the term EL is used throughout this study, but
“former EL” is used most frequently to refer to students that were once current ELs who
eventually demonstrated English proficiency. Scholars have also used the term “ever EL”
(e.g.,Thompson, 2015; Umansky, Thompson & Diaz, 2017) to refer to two sets of students that
share the experience of ever being classified as an EL (Parish et al., 2006). Ever EL includes
both the students who are currently EL as well as students who once were but have managed to
demonstrate English proficiency (often referred to as former ELs, reclassified ELs).
Additionally, ELs in this study all attended the same K-12 district where ELs are
classified into four categories: English Only (EO), Initially Fluent English Proficient (IFEP),
Limited English Proficient (LEP), and Reclassified Fluent English Proficient (RFEP). For
clarity, Figure 3 (above) provides an English Learner Designation Flowchart. EOs can be
described as native-English speakers or never ELs (English spoken primarily at home). IFEPs
23
can be understood to be non-native English speakers assessed to be proficient in English upon
school entry. In other words, the IFEP label indicates a student was never EL as evidenced by
their performance on English proficiency assessments that occurred upon their entry into K-12
education, but they reported speaking a language other than English at home. ELs that never
attained an English proficiency status are current ELs or LEPs. Finally, former ELs are non-
native English speakers that demonstrated English proficiency prior to HS graduation (RFEP
students). The term EL in this study also refers to both current and former ELs given that both
populations begin with an EL designation upon entry into K-12. This is in line with Shin’s
(2017) study who used ELL instead to refer to students who are currently or once were ELs. In
some cases, the term Long-Term English Learner is used to describe students who have been
educated in the US for many years but have not yet met criteria to be considered proficient in
English (Thompson, 2015)
English Language Learner (ELL).
While the state of California uses the term EL, ELL is more commonly used across the
nation. Per the National Center for Education Statistics (NCES) (2020), students who are
identified as ELLs are eligible to participate in support programs that help develop English
proficiency and to meet the content and achievement standards expected of all students. ELLs
are students that have a native language other than English and, in some instances, immigrate to
the United States from other countries while having little or no proficiency in English (Kanno &
Varghese, 2010; Webster & Lu, 2012). Any student ever classified as an EL, regardless of
whether they reclassify to another status (e.g., former EL, RFEP), can be described as an ELL
(Shin, 2017). Thus, current ELs and former ELs are both considered ELLs. Similar to EL
designation across California’s school districts (see Figure 3), ELLs across the nation are
24
classified using context-specific criteria that typically include standardized test scores and home
language. For example, in Georgia eligibility for services for English for speakers of other
languages (ESOL) is determined using a home language survey and placement exam (Abedi,
Hofstetter, & Lord, 2004).
English Language Proficient (ELP).
The status of English language proficiency for ELs is reached by succeeding in
associated annual assessments that gauge students’ progress in English. Students must
demonstrate proficiency in various domains including listening, speaking, reading, and
writing. The scores that ELs receive on these ELP assessments are used for accountability
purposes and also for determining support services (Carroll & Bailey, 2016). In the district of
focus for this study, ELs engaged in a reclassification process intended to measure whether they
had acquired sufficient ELP to perform successfully in core academic subjects without additional
support services. To do so, students had to meet four criteria: (1) demonstrate proficiency in a
state English Language Arts (ELA) test, (2) demonstrate proficiency in the state's language
development exam, (3) receive teacher approval via grades, and (4) receive parental
approval.
English as a Second Language (ESL).
Whereas EL and ELL refer to a status that identifies a student population, ESL refers to
programs and coursework in both K-12 and higher education designed to provide explicit
instruction in English for EL students (Mardock Uman, 2008; Nunez et al., 2016).
In the context of higher education, education leaders identify instruction in ESL as
distinct from remediation in English. For example, in California’s AB 705 ESL coursework at
community colleges is described as coursework for (1) foreign language learners who require
25
additional training in English, (2) students who require support to complete degree and transfer
requirements in English, or both (Irwin, 2017). In K-12, ESL can be thought of as a support
service that schools can elect to provide to ELs to support their English development and
reclassification to attaining a status deeming them proficient in English. Most ELs in California
achieve Reclassified Fluent English Proficient status (aka former EL), an indicator of English
proficiency, before high school graduation (Betts, Hill, Bachofer, Hayes, Lee, & Zau, 2019). In
2019-2020 of 2.3 million ELs across K-12 in California, half were designated as RFEPs with
more expected to reclassify (CDE, 2021).
However, upon entry into community colleges, EL students (including former ELs who
have already demonstrated English proficiency), may be directed to below-college level ESL
coursework. Placement into ESL coursework may represent a stereotype threat, or a negative
stereotype about students’ language skills that negatively impacts students’ psychological
behaviors and hinders their college performance (Hodara, 2015; Steele & Aaronson, 1995). For
example, Hodara (2015) reports that at one college all second-generation students whose first
course placement was an ESL course started in the highest level of ESL and this placement had a
negative effect on their college credit accumulation. More specifically, placement into ESL
courses delayed enrollment into college-level coursework, limited transfer success, and deferred
students’ capacity to progress to their educational goals (Hodara, 2015). As such, Assembly Bill
705, implemented in 2019, tasked community colleges with maximizing the probability that
students that are directed to begin in ESL coursework enter and complete transfer-level English
within three years. With all of this in mind, community college ESL courses are not as relevant
to most U.S. educated ELs (since most reach English proficiency as evidenced by their RFEP
status) than they are for recent immigrants and international students who come to the U.S. for
26
academic advancement. While students in this study may have experienced ESL courses in K-12
or college, ESL courses are not a focus of this inquiry.
In community college ESL coursework, Park (2009) describes three broad
categorizations of students enrolled in these sequences: (1) students who are children of
immigrants that have done most, if not all, of their schooling in the United States, (2) recent
immigrants, and (3) international students who travel to the U.S. for educational
reasons. Because this study’s dataset tracks students who enrolled in community college and
attended high school in a local, feeder high school, the EL students in this study who also
experienced K-12 or community college ESL programming or coursework are better described
as being part of the Park’s (2019) first group.
ELs in community college ESL coursework can be recent immigrants and have received
some U.S. education in K-12. While some recent immigrants and international students who did
not receive any K-12 schooling in the U.S. may be considered learners of English, in this study
the EL designation does not refer to them since these students are never designated as such and
never experience the associated K-12 services and supports. Of note, prior to post-secondary
education, Lee, Kim and Wu (2018) find that ELs face similar challenges as their international
peers when considering both lack of adequate language and cultural preparation for college
success. ELs in community college ESL coursework may be native or foreign-born, are typically
proficient in conversational English but might have grammar and pronunciation errors as the
result of growing up in homes where English was not the primary language. Also, they are more
often first-generation college students that likely attended lower resourced schools (Núñez et al,
2016).
27
Linguistic Minority or Language Minority (LM).
Linguistic or Language Minority is a term more often used in research than in practice to
refer to a large group of students (e.g., LEP, ELL, EL, or those in ESL education) who began
their U.S. education in middle school or high school (Mardock Uman 2018; Rios Aguilar et al.
2016). Yet, the term is sometimes used to describe students who immigrated to the U.S. as
adults. While some scholars describe LMs to include all multilingual (including bilingual)
students who speak a language other than English at home (Kanno & Harklau, 2012). Others
state that LM does not provide any indication of English proficiency and may even include
students who grew up speaking English or primarily speak English at the moment (Callahan,
Wilkinson, & Mueller, 2010). Meanwhile, Wright (2019) shares that LM students may be fluent
in English and may even lack proficiency in their own language and complicates the use of LM
as a growing number of students are speakers of “minority” languages. Aside from the
confusion around using the LM term, many scholars refuse to use the term as it aligns with a
deficit orientation of students. The next section elaborates on this choice with a description of
LEP.
Limited English Proficient (LEP).
The federal government defines LEP as “individuals who do not speak English as their
primary language and who have a limited ability to read, speak, write, or understand English can
be limited English proficient, or "LEP." These individuals may be entitled language assistance
with respect to a particular type or service, benefit, or encounter (LEP, 2021).” In the realm of
education, LEP is one of the oldest terms used to refer to students but is now used more broadly
with the intent of improving language assistance services for LEPs in compliance with federal
law. While more recent federal policy, namely ESSA, uses the term ELL, LEP was a term more
28
commonly used in federal legislation preceding it (e.g., No Child Left Behind (2001)). This term
was used as early as 1974 when the supreme court declared a violation of civil rights due to
schools failing to provide supplemental language instruction to “limited English proficient
students” (Abedi, 2004; Lau v. Nichols, 1974)
When it comes to both LEP and LM, scholars explicitly note avoiding these terms due to
its problematic deficit framing. Mosqueda (2010) elects to use EL as opposed to LEP since EL
more appropriately describes students' process or developing English proficiency rather than as
having linguistic limitations. Meanwhile, Garcia (2019) encourages using “emergent bilingual”
to highlight students’ assets. Notably, like in policy, the term is no longer widely used in practice
(Gandara & Rumberger, 2007). This trend is reflected in how frequent LM and LEP populated
as key search words in my literature review on ELs in higher education. While words like
multilingual, bilingual EL, and ELL were common, LM only appeared in four articles’ title,
abstract, or keywords and LEP in two. Of note all journal articles that used LM or LEP, with the
exception of one literature review (i.e., Núñez et al., 2016), were quantitative studies. While this
may be due to quantitative researchers’ use of governmental data and terminology, this outdated
trend may also be due to an underuse of using explicit anti-deficit approaches to study ELs in
quantitative work.
Relevant Processes and Phenomena
Classification to English Learner or Initially Fluent English Proficient.
EL classification is based on two sources: a home language survey and an assessment of
English proficiency. In California, the California English Language Development Test (CELDT)
has typically been used to gauge English proficiency in the classification process (Shin, 2017).
Students who report a language other than English on the home language survey are directed to
29
take the CELDT. This assessment contains a speaking, listening, reading, and writing domain
and scores students from beginner (level 1) to advanced (level 5). A student is classified as EL if
their overall score falls below early advanced (level 4) or if any category falls below
intermediate (level 3) (CELDT; California Department of Education, 2013).
Students from non-English speaking homes that pass the CELDT are designated
IFEP. Yet, most of these students are classified as ELs. In 2019-2010, of the kindergarteners
that were directed to take the California English Language Development Test (CELDT) as a
result of primarily speaking a language other than English at home, only about six percent of
them passed to attain an IFEP status (Bedolla & Rodriguez, 2011). These trend leads scholars to
question the validity of the home language survey and CELDT as tools for identifying ELs in
California since being identified to take the CELDT almost guaranteed an EL classification.
Considering that EL classification is tightly linked to English Language Development
(ELD) instruction and services (Umansky, 2016), a student’s language classification early on is a
pivotal factor in their educational trajectory (Zarate & Pineada, 2014). For students near the EL
and IFEP cutoff, Shin (2017) found that being classified as an EL is beneficial. Suggesting that
English language education and services, do in fact, benefit students ELs who fell short of
attaining an IFEP status. However, this work directly contradicts Umansky’s (2016) inquiry that
found that students classified as EL near the EL-IFEP cusp had significantly lower test scores in
math and English in grades 2 through 10. Thus, the effectiveness of ELD resulting from early
classification may vary by district and school site. Students designated as ELs who fall further
from the EL-IFEP cutoff may have more to gain in terms of language skills.
Notably, the studies mentioned above look solely at students at the EL-IFEP
cusps. Schools and districts may offer different types of ELD programming and support. For
30
example, English immersion programs where students focus primarily on English, have shown
evidence of outperforming dual-language programs in the short term (Genesee, 2006). However,
in the medium-term, students in bilingual classrooms performed moderately better in English
literacy (August & Shanahan, 2006; Cheung & Salvin, 2012). Additional evidence supports the
trend of multilingual students in dual programs doing as well or better on measures of English
achievement compared to their multilingual peers in English-only ELD settings (Farver et al.,
2009; Raikes et al., 2019 (both in Goodrich et al. 2021). Next, the reclassification process that
students initially classified as ELs engage in annually intended to gauge whether they are ready
to reclassify to RFEP is discussed.
Reclassification: English Learner or Reclassified Fluent English Proficient.
The criteria guiding EL’s transition to fluent English proficiency, a process termed
reclassification, are heavily influenced by decisions made at the state-level. Yet, variability in
criteria is also present across districts (Cimpian, Thompson, & Makowski, 2017). Districts are
allowed to mandate their own reclassification policies as long as they follow minimum state law,
regulation, and guidance (Kim & Herman, 2012). For example, the Large Urban School District
(LUSD) in this study listed using the four state mandated criteria during the timespan in which
the data was collected. This included (1) scoring Basic or above on the California Content
Standards Test (CST), (2) scoring Intermediate or higher on the domains of listening, speaking,
reading and writing in the California English Language Development Test (CELDT), (3) parent
consultation and approval, and (4) teacher evaluation based on students’ grades. If students did
not meet criteria one and two, the district allowed each school’s Language Appraisal Team
(LAT) to meet to analyze other data that demonstrates grade-level proficiency (e.g., District ELA
assessment, report card grades, prior CST scores, work samples, and California High School Exit
31
Examination (CAHSEE) scores). Abedi (2008) indicates more so than students’ level of
proficiency in English, other factors including ethnicity, socioeconomic status, teacher, and
parent opinion were more determinant of EL classification. More specifically, less than 10% of
the variance of EL classification was explained by English proficiency. The English Learner
Designation Flowchart (see Figure 3) summarizes the classification and reclassification process
in LUSD.
Of note, since September of 2019, the updated criteria in California require
demonstration of English proficiency through (1) the state test of English Language Proficiency
for California, (2) teacher evaluation, (3) parent opinion and consultation, and (4) comparison of
student performance in basic skills of English with students of the same age (using instruments
such as the Smarter Balanced Assessment Consortium test). Local discretion is allowed on all
but the first criteria which has been standardized across the state in response to ESSA’s push
towards criterion standardization (Betts, Hill, Bachover, Hayes, Lee, & Zau, 2019; Hill, Lee,
Hayez, 2021). The California Department of Education is standardizing more criteria that may
benefit timing such as transition out of EL services and designation (Hill et al., 2021).
While reclassification criteria can vary based on state and district context, reclassification
rates themselves can also vary between districts. In their comparative, mixed method study of
two districts in California, Estrada and Wang (2018) found rates of not reclassifying being 2 to 5
times higher in one district compared to the other. In the district with the higher rates of not
reclassifying, school staff could exercise a formal recommendation role which could prevent
reclassification despite an EL meeting all criteria if teachers recommend students to not be
reclassified. Qualitative data revealed some teachers' inadequate understanding of second-
language learning. Many expressed expecting ELs to possess native English speaker skills in
32
listening, speaking, reading, and writing with no errors in order to recommend reclassification
(Estrada & Wang, 2018; Kibler & Valdés, 2016). Meanwhile the district with higher
reclassification rates showed a larger investment in clarifying policies, capacity building, and
monitoring. Of note, Mavrogordato and White’s (2017) study in the state of Texas also suggests
that practitioners' understanding of policy is linked to differences in rates of reclassification.
Immigrant, Bilingual, and Cognate Advantages.
The immigrant and bilingual advantage are two concepts that are often connected to EL
students. Scholars describe the immigrant advantage as an academic advantage attributed to first
and second-generation students relative to their native-born peers (Callahan & Humphries,
2016). But contradictory patterns remain as being a language minority is often considered a
disadvantage (Callahan & Humphries, 2016). Although having a higher English proficiency
would be assumed to lead to higher academic success, Insonio (1994) found that students whose
first language was one other than English had both higher college success and retention rates
than native English speakers. This success may be attributed to the fact that many students who
do not speak English as their first language immigrate to the U.S. for better educational
opportunities and with these clear goals they may have greater motivation to succeed and
prioritize academics (Hernandez & Lopez, 2004). For example, Park (2019) notes that among
students taking ESL in one community college district, international students and women who
were permanent residents had higher outcomes in English relative to U.S. citizens even after
accounting for relevant academic and background variables.
Meanwhile, Lee, Kim, and Wu (2018) state that native born ELs face the same challenges
as typical immigrant peers since both lack adequate language and cultural preparation for college
success. In fact, most ELs are not foreign-born. Among California ELs, roughly 82% report the
33
United States as their country of origin. Mexico, El Salvador, China, and Guatemala follow at 6,
1.7, 1.4, and 1.3 percent, respectively (CDE, 2018). While foreign-born students may be more
likely to miss years of school and have lower academic achievement than their peers, they are
just as or even more engaged in school (Potochnick, 2018). Despite country of origin, both
foreign and native-born ELs, can benefit from a bilingual advantage.
The bilingual advantage suggests bilingual, or multilingual, students may possess special
assets that help with acquiring the institutional support necessary for navigating academic and
social settings (Stanton-Salazar & Dornbusch, 1995). Evidence documents that being a
multilingual student, specifically one that has reclassified to fluent English proficient, may help
mitigate the negative effects of being placed into below-college English despite already having
demonstrated college-readiness in English (Melguizo, Flores, Velasquez, & Carroll,
2021). Supporting the bilingual assets may lead to greater achievement. Alvear (2018) found
evidence for the additive advantage hypothesis, which contends that EL students’ academics
benefit most from additive programs that promote dual language proficiency. English reading
results indicated that ELs in the most additive programs, those that promoted the fullest forms of
dual language proficiency, were associated with the highest levels of achievement (Alvear,
2018). Alternatively, Fukumine and Kennison (2016) note that when the characteristics of a
student’s first language and their second language differ, the first language can impede the
proper use of the second. However, when languages share characteristics, the acquisition of
students second language can be facilitated by their first. For example, evidence has shown that
bilingual students can acquire knowledge from a posed problem in one language and transfer it
to solve the problem in another (Bernardo, 1998; Francis, 1999 both in Fukumine & Kennison
34
(2016). Such a trend is likely to happen for ELs who first learn Spanish and then English because
the two languages share linguistic similarities which are further described next.
For Latinx ELs specifically, the notion of a bilingual advantage is further corroborated by
linguistic features shared between the Spanish and English languages. One study examining
cross-linguistic cognates, or words that share both form and meaning in two languages (e.g.,
helicopter in English and helicóptero in Spanish), found evidence that Spanish-speaking EL
scored higher on a standardized vocabulary test for items that were classified as cognates relative
to noncognates of similar difficulty (Kelley & Kohnert, 2012). Cognates in the Academic Word
List (see Coxhead 2000 in Lubliner & Hiebert, 2011) , which contains English academic terms
critical to reading academic texts in various disciplines, are actually more commonly and
colloquially used in the Spanish language than in English (Lubliner & Hiebert, 2011). These
works indicate that Spanish-speaking students leverage a cognate advantage when it comes to
comprehending and using English.
Bravo, Hiebert, and Pearson (2014) further substantiate the cognate advantage. Their
analysis of academic language in science texts revealed that the cognates used are more
frequently used in common Spanish speech than in English. They conclude that Spanish/English
bilinguals possess a linguistic resource that includes many words that are reserved for scientific
and academic registers (Bravo, Hiebert, and Pearson, 2014). With the exception of Tagalog, the
most spoken languages in California among ELs after Spanish (Vietnamese, Cantonese, Hmong,
and Korean) do not share these cognate advantage features (Hill, 2012). In this sense, the
cognate advantage as a linguistic asset is unique to many Spanish-speaking ELs and any other
ELs that speak languages using the Latin alphabet (e.g., Tagolog).
35
In addition to cognate similarities, the tendency for mathematics to often includes visuals
(e.g., graphs, coordinate axes, diagrams) might contribute ELs understandings in ways that other
content areas do not do so readily. One study on simplification of test items for ELs
demonstrated that adding visual representations, among other simplifications, to answer choices
had the largest positive effect on ELs performance (Noble et al., 2020).
Latinx English Learners
While some predictions state that the US Latinx population will double by 2050 and
make up 30% of the US population, other projections predict a proportion closer to 45% (Fink,
2017). Despite the specific percentage, significant growth is expected. However large, scholars
remind us that Latinx students are a heterogeneous group composed of individuals from a wide
variety of English-language proficiency, ethnicities, nationalities and social and economic
conditions (Campbell et al., 2012; Pino et al., 2012; Razfar & Simon, 2011). Of the five million
EL students enrolled in public schools across the country in Fall of 2018, roughly 3.8 million
were Latinx ELs making up about 78% of EL student enrollment (ZHANG ET AL.,
2021). Asian, White, and Black students followed at 10.7, 6.7, and 4.4 percent,
respectively. While the population of ELs in U.S. public schools speak over 400 languages,
Spanish is the most spoken language in 45 states and the District of Columbia with the next
being Arabic, Chinese, and Vietnamese (making up for only 2% of the population each) (US
Department of Education, 2017). California, home to the largest population of ELs, is of special
interest with over 80% of ELs in the state reporting Spanish as their home language (2021; Hill,
2018).
In 2015, Latinx students in California surpassed Whites as the largest demographic in the
state with public school enrollment reaching 54% (Panzar, 2015). California reported the highest
36
percentage of ELs (about 20% of their population) followed by other states in the southwest,
(i.e., Texas, New Mexico, Nevada) trailing shortly behind (ZHANG ET AL., 2021). Yet, almost
40% of California’s K-12 population are a current EL or former EL (Hayes, Lee, & Hayes,
2021). While roughly 82% of ELs in California indicate Spanish to be their home language (CA
Department of Education, 2021), roughly 80% are Latino Spanish-speakers. This population
makes up over 90% of all ELs in some California districts (LA School Report, 2021).
Evidence suggest that Latinx ELs face a number of challenges to academic advancement.
Research indicates that somewhere between 74 and 85 percent of California ELs are members of
low-income families (Hill, Weston, & Hayes, 2014). California also has been identified as the
most segregated state for Latinx students where 58% attend intensely segregated schools
(Frankenberg, Ee, Ayscue, & Orfield, 2019). Latinx students are also overrepresented in the EL
student population (Hill, Lee, & Hayes, 2021). This may help explained why the high school
and community college districts in the present study reported a higher share of Latinx students
compared to the state’s racial/ethnic distribution (Betts et al., 2019). With these statistics in
mind, Latinx ELs are a key subgroup for study, especially in a California context.
In addition to the large proportion of California Latinx students who are ELs, a focus on
this population can help address a problematic trend at the community college level. Scholars
have noted a “Latino problem” in California Community Colleges specifically given the
prevalence and disproportionate placement of this population into below-college level
coursework (Dowd & Bensimon, 2015). In what follows, literature relevant for studying Latinx
Els in higher education is reviewed. First literature on K-12 contexts that spans outcomes,
experiences, and college preparation are synthesized. In particular, the K-12 section discusses
trends related to EL programming, EL teachers, as well as math and ELs. Then, work on ELs in
37
community colleges is presented. This section includes review of literature on the
overrepresentation of ELs in community colleges, programming for college ELs, and
developmental math and other coursework for ELs.
K-12 English Learners: Outcomes, Experiences, and College-Preparation
Research on ELs has typically been bound within K-12 because EL designation stems
from policies related to legal rights and support services in K-12 schooling (Núñez, Rios-
Aguilar, Kanno, & Flores, 2016). As discussed previously, this has allowed for a dearth of
research on reclassification (e.g. e.g. Hill, Weston, & Hayes, 2014; Thompson, 2015; Umansky
& Reardon, 2014; Valentino & Reardon, 2016) as well as initial classification (e.g., Bedolla &
Rodriguez, 2011; Shin, 2017; Zarate & Pineada, 2014) with respect to math and English
outcomes within the K-12 sphere. Aside from focusing on classification and reclassification,
research relevant to ELs’ long-term outcomes can be organized into additional themes.
A synthesis of the literature revealed a focus on the effectiveness of English language
development programming for ELs, the teachers of ELs, as well as the connection between ELs
and math. In sum, these themes can be summarized into three parts. First, dual-language,
bilingual curriculum and instruction has been most beneficial compared to English only
programming. Second, ELs are often tracked to lower-level classrooms in English and other
subjects (e.g., math) where they receive instruction from less experienced teachers. Third, higher
English language proficiency can be linked to math success. These themes are elaborated on
below.
English Programs, Curriculum, and Instruction for English Learners.
While the programming provided to students classified as ELs can vary greatly between
schools, districts, and states, programs typically fall under two categories: bilingual or English-
38
only immersion. Valentino and Reardon (2016) summarize the undergirding ideology behind
these approaches. On the one hand, proponents of English-only immersion programs argue that
spending time developing another language detracts from exposure to English and ultimately
delays students’ opportunities to learn academic material and develop English proficiency. On
the other hand, those in favor of dual-language (i.e., bilingual) programming contend that
students benefit from a foundation in their home language as it facilitates the acquisition of
another language. Since students' knowledge base grows quicker when taught in a language they
already know, the lack of a solid foundation in a home language leads to more discrepancies in
understanding.
Evidence suggests that dual language immersion curriculum and instruction is more
effective compared to English-only immersion education. In fact, Steele, Slater, Li, Zamarro, and
Bacon (2018) estimate the causal effects of dual-language immersion enrollment on achievement
and found that every additional $100 US dollars spent per immersion student was associated with
an additional 8% of a standard deviation in English language arts performance. Umansky and
Reardon’s (2014) study indicated that Latino ELs in bilingual programs were slower to reclassify
in earlier grade levels with English-only (i.e., immersion) programs showing an advantage in
reclassification rate success. However, participants in dual-language programs had higher rates
of reclassification by the end of high school relative to those experiencing an immersion
curriculum. Not only did bilingual programming participants catch up academically in later
grades, but they also often surpassed their English immersion peers. Of note, some scholars have
indicated that Spanish home language, among other variables, is negatively associated with the
odds of reclassification between 3rd and 7th grade (Burke, Morita-Mullaney & Singh,
2016). Yet, if followed longer, we might expect academic growth. Other work has also shown
39
that English-only immersion outperforms dual-language programming but only for the short-
term (Genesee, 2006). However, in the medium-term, students in bilingual classroom settings
perform better in English literacy (August & Shanahan, 2006; Cheung & Salvin, 2012).
While the purpose of these programs aims to build English language proficiency,
academic gains can also be expected in other areas. Mosqueda’s (2010) study notes a strong
relationship between English proficiency and math performance (Mosqueda, 2010). Together,
these works tell a story of bilingual immersion approaches, while possibly trailing in academic
outcomes earlier on, have long-term gains. Thus, we might expect that this catch-up and
surpassing trend for ELs may continue into college in content areas outside of English, namely
mathematics. With all of this in mind, longitudinal analyses are more appropriate for studying
benefits of language assets. Also, since reclassification rates can be linked to practitioners’
understanding and approach to reclassification (Estrada & Wang, 2018; Mavrogordato & White,
2017) policy analyzing Latinx EL’s long term math outcomes at the district level may be more
appropriate in some instances than at the state or national level.
Teachers of English Learners.
English learner classification, while intended to function as a label for targeting support
services, can also hurt students’ access to quality instruction. Less experienced teachers tend to
be assigned to teaching ELs more so than their more experienced counterparts (Dabach,
2015). Being labeled as an EL may limit access to content since ELs are more often tracked into
lower level English coursework (Umansky, 2016a). Research also suggests that EL
classification leads to lower teacher perceptions of students’ academic ability (Umansky &
Dumont, 2021). However, in bilingual classrooms negative perceptions of ELs were lesser.
These deficit perspectives of ELs can be problematic since perceptions of students’ abilities are
40
linked to teachers’ expectations of students. In turn, these expectations inform the actions that
teachers take to help students reach those expectations. Bertrand and Marsh (2015) present
evidence demonstrating the link between low expectations and support. They note when
teachers attributed low achievement to subgroups of students these perceptions undermined
teachers’ motivation to improve their instruction. In addition, they report that stigmatized
groups, specifically ELs and students in special education, were more likely to be negatively
affected by low teacher expectations. In analyzing teachers’ sensemaking, they concluded that
teachers attributing low achievement to these stigmatized students further entrenches low
expectations (Bertrand and Marsh, 2015). Yet, teachers play a key role in facilitating ELs’
English proficiency and the research on quality EL instruction indicates that teachers’ need to
play an active role in reflecting on their instructional strategies.
One intervention for ELs in science classrooms introduced inquiry-based approaches to
content and found statistically significant, positive outcomes as measured by state exams and
other metrics (Llosa et al, 2016). Teachers in this study were provided with new strategies to
make science content more comprehensible for ELs as well as training in how to develop
academic language. Similarly, among students in classrooms with teachers who received a
pedagogical intervention designed to improve their instruction, ELs and those with the least
proficiency in English showed the largest gains (Portes, González Canché, Boada, & Whatley,
2018). Teachers were trained with an emphasis on supporting ELs, but all student groups showed
benefiting from participation in a culturally responsive dialogic model. More specifically,
teachers employed strategies to facilitate instructional discussion to develop students’ cognitive
and linguistic skills, they modeled academic conversation, gave multiple opportunities for
students to practice language with peers in joint productive activities, and more. Yet, Portes and
41
colleagues (2018) state that an intensive year of professional development may be required for
teachers to successfully employ these strategies. When it comes to math, EL teachers may have
more isolated math networks, be less likely to seek or provide advice in math instruction, and be
more likely to believe that math instruction for ELs need differentiated (Hopkins, Lowenhaupt,
& Sweet, 2015).
Catering instruction to support specific needs of ELs may also support relationship
building with families. Alvarez (2021) notes that Mexican immigrant parents attributed value
when school projects fostered their children’s agency and interests, which led to their excitement
and shared engagement with school. Teachers with training in bilingual education also show a
higher capacity to support ELs and other students in a number of ways. When compared to
teachers trained in an elementary generalist capacity, those trained as a bilingual generalist paid
more attention to being culturally responsive while developing a math lesson, scored higher on
overall teaching evaluations, drew connections between math and other contents more often, and
employed interdisciplinary, contextualized, themed approaches to instruction(An, Tillman,
Zhang, Robertson, & Tinajero, 2015). With this in mind, a synthesis of what research tells us
about ELs in K-12 mathematics is included next.
K-12 Math and English Learners.
Unsurprisingly, most research on ELs in a K-12 setting is focused on English-related
outcomes (e.g., reclassification, ELD program effectiveness). While this scholarship is
important for informing policy and practice for ELs, focusing on ELs’ outcomes in content areas
outside of English can also improve their academic success while also contributing to a
multidimensional perspective on ELs’ learning. A review of the literature revealed that among
the studies conducted on ELs in K12 that emphasized outcomes other than English, K-12 math
42
outcomes and course-taking experiences were a developing area of study (Jaquet & Fong, 2017;
Lewis et al., 2012; Mosqueda, 2010; Mosqueda & Maldonado, 2013; Robinson, 2016;
Thompson, 2017). Despite EL classification being a result of assessing English language
proficiency, some of the trends and themes that arise for ELs as it relates to English also occur in
math.
Aside from EL status being associated with tracking into lower-level English coursework,
English proficiency has been used, unjustifiably, to measure potential math success and justify
lower math placement (Mosqueda, 2010). ELs are more likely to be placed in low-level math
courses which was problematic since math achievement varies by academic track placement. In
Mosqueda’s (2010) study, placing students into lower-level math courses had a negative effect
on math success for both Latinx EL and non-EL Latinx students. Meanwhile, Mosqueda and
Maldonado (2013) note that maximizing Latinx students’ math achievement requires access to
rigorous math coursework and the provision of pedagogical and institutional supports that
develop students’ proficiency in both the mathematics register and ELP. Kangas and Cook
(2020) further highlighted the role of deficit perspectives and attributed the consistent placement
of ELs with disabilities into lower academic tracks to overreliance on high-stakes assessment
score.
Hence, deficit practices limiting access and opportunities for ELs have long-term
implications for learning in math and other contents along with developing English proficiency.
Research also indicates that ELs have an easier time switching into less rigorous courses as
opposed to more rigorous ones. Thompson (2017) reports that ELs in their study were able to
exercise more agency and deviate from course placement when they wanted to move to less
rigorous courses compared to when they attempted to switch to more rigorous courses
43
(Thompson, 2017). Using classification to track ELs into lower-level classes can minimize their
opportunities to learn in heterogeneous classrooms, which scholars suggest matters for academic
growth.
While EL status has been linked to reduced opportunities to learning math (Abedi,
Courtney, Leon, Kao, & Azzam, 2006), less content coverage in the classroom (Abedy &
Hernan, 2010), and a lower likelihood of taking advanced coursework (J. Wang & Goldschmidt,
1999). Garrot and Hong (2016) also find evidence noting that ELs are more likely than English
speakers of being in classrooms using homogeneous groupings. In fact, for EL kindergarteners'
math performance homogenous grouping is detrimental. When ELs were grouped
heterogeneously in Garrot and Hong’s (2016) study, they had the highest achievement despite
having less math instruction in terms of time. Among Latinx ELs, the highest achievement came
for those that experienced a combination of homogeneous and heterogeneous grouping. Thus,
these scholars recommend using heterogeneous or a combination of heterogeneous and
homogeneous for teaching math to ELs, specifically as it relates to kindergarten Latinx
ELs. This trend in homogeneous grouping may explain why EL classification in kindergarten
had a statistically significant negative effect on both math and English students for ELs just
below the classification threshold compared to those just above the cutoff who were labeled
Initial Fluent English Proficient students (Umansky, 2016b).
College English Learners: Outcomes, Experiences, and Assets for Latinx English Learners
Since EL designation stems from K-12 policies related to legal rights and support
services in K-12 schooling (Núñez, Rios-Aguilar, Kanno, & Flores, 2016) it’s no surprise that
the majority of research on Latinx ELs is centered around K-12 settings and English-related
achievement. Following ELs into higher education poses a data challenge because EL
44
designation does not typically follow students into college settings. Yet, a growing body of
scholarship has focused on post-secondary experiences and outcomes for this population. For
example, scholars have analyzed long-term, cross-sector outcomes for ELs (e.g., Flores & Drake,
2014; Flores, Batalova, & Fix 2012; Melguizo & Ngo, 2020). Yet, more work that both follows
ELs from secondary to postsecondary and examines math outcomes specifically is warranted
given the critical role that success in math courses play for academic achievement, which is
especially important for historically marginalized populations.
A review of the literature led to several themes for Latinx EL’s experiences and outcomes
in higher education. Four prevalent themes are covered below including the overrepresentation
of ELs in community colleges, trends in programming at the college level, trends from studies on
professors of ELs, and ELs experiences and outcomes in assessment, placement, as well as
developmental math and other college coursework.
Latinx EL Overrepresentation in Community Colleges
While I’ve noted that the majority of ELs are Latinx, Razfar and Simon (2011) remind us
that many Latinx students in higher education began their educational journey as ELs (Razfar &
Simon, 2011). ELs tend to come from lower socioeconomic backgrounds than other students
and have less access to dominant social, cultural, and linguistic resources considered necessary
for college success (McDonough & Nunez, 2007). Latinx ELs are disproportionately enrolled in
community colleges due to many of these factors including financial hardship and background in
basic skills (Bista, 2011; Razfar & Simon, 2011). Many Latinx EL’s English proficiency levels
prevent enrollment into four-year universities (Crisp & Nora, 2009). In addition, community
college’s open enrollment policies and higher availability of support in ESL may also help
explain the concentration of Latinx ELs in community colleges. Fink (2017) contends that while
45
more accessible and affordable, enrolling in community college ESL coursework means delaying
college-level coursework. This additional time to gain English proficiency may burden Latino
ELs with financial commitments and decrease chance of college completion. Nevertheless,
supporting Latinx EL students requires a focus on community colleges.
At the postsecondary level, the California Community College Chancellor's Office
(CCCO, 2012) reports that from 1992 to 2012 the percentage of Latinx students enrolled doubled
from 19% to 38% (Acevedo-Gil, Santos, Solorzano, 2014). Meanwhile, enrollment in California
community colleges declined from 50% to 30% for White students and remained relatively
stable for Asian students (14%-15%) and Black students (consistent at 7%). Keeping in mind
that most ELs are Latinx, the rapid growth in this population coincides with the national trend of
ELs being the both the largest and fastest growing population (Fink, 2017). Meanwhile, students
classified as EL are disproportionately represented in community colleges and typically placed
into and typically placed into below-college level coursework (Grubb & Gabriner, 2013). ELs in
community colleges face external challenges and must often balance additional work and family
responsibilities outside of school (Almon, 2015). For example, many ELs face financial
difficulties, are employed full-time, and care for children and/or elders. In one study, even
though community college EL students’ GPA was high enough to graduate and transfer (at 2.72
compared to 2.32 for non-ELs), only 13% graduated (Almon, 2012). Evidence suggests that
among students who have demonstrated college-readiness in California high schools, those that
are former ELs, Latinx, and/or report Spanish as a home language experience the highest rates of
being directed to below-college level English coursework relative to their peers (Melguizo et al.,
2021). Yet, after accounting for this phenomenon known as inter-sector English misalignment,
demographic characteristics, and prior achievement, scholars attributed former ELs higher
46
average transfer unit completion relative to their native English-speaking peers as resulting from
linguistic or multilingual assets. This study aims to build upon Melguizo and colleagues (2021)
prompt to explore the benefits of “multilingual and linguistic assets for long-term academic
outcomes in relation to assessment and placement in community colleges outcomes.
Half of the California’s first-time college Latinx students attend community colleges with
the majority intending to transfer to a four-year university, but these aspirations are met with
high rates of developmental mathematics placement (Acevedo-Gil, Santos, Alonso, & Solorzano,
2015; 2015; Bailey, 2012; Chen, 2016, Shulock & Moore, 2010). Considering (1) the important
role math plays as a gateway and gatekeeper to academic success, (2) the prevalent,
disproportionate developmental placement for Latinx students, and (3) the concentration of 1.5
million EL students in California schools (NCES, 2020), this study focuses primarily on Latinx,
EL students in one urban community college district who previously attended local, large, public
K-12. Additionally, a focus on college math outcomes for Latinx students formerly classified as
English learners in high school helps advance a positive portrayal of Latinx and EL students by
steering away from solely analyzing an academic challenge (English) that they are often defined
by.
English Programming, Curriculum, and Support for English Learners in College
Because of limited access to linked K-12 to college data, scholars looking at ELs in
community colleges have often focused on EL students’ taking courses within ESL
sequences. While more ESL support is offered in community colleges compared to four-year
universities, these below-college level courses require additional time to develop English
proficiency can add a burden to Latinx ELs. But US educated ELs are not the only groups that
may be found taking ESL coursework. Park (2019) describes three groups that typically take
47
ESL at community colleges: (1) students that are children of immigrants and have done all or
most of their schooling in the US, (2) recent immigrants, and (3) international students who have
traveled to the US (Center for Student Success, 2007).
For example, larger financial commitment, more time in college and less likelihood of
completion are all tied to ESL placement (Fink, 2017). This is of particular concern to ELs
because they must often balance additional work and family responsibilities outside of school
(Almon, 2015). Placement into these below-college level ESL courses can delay access to
college-level coursework which also limits vertical transfer and degree attainment (Hodara,
2015). In particular, employment and caring for children have been associated with lower gains
in writing on standardized tests compared to those who do not have them (Fink (2017). Even
upon successful completion of ESL or developmental coursework, Garrison-Fletcher (2019)
notes that ELs are still not finished with the process of acquiring English proficiency.
Aside from ELs experiences with placement into English and math courses, another
theme in the literature speaks to the role of Hispanic Serving Institutions (HSIs) in supporting
Latinx ELs. ELs in one HSI spent 2-3 times longer on reading assignments, struggled more in
earlier years, but managed to adapt, progress, and become more comfortable as time went
on. The HSI supported and sustained ELs by providing networks of bilingual speakers that could
support EL students (Arbel-Marrero & Millacci, 2015). Other scholars point out that because
HSI identity is connected to the unique institutional mission and characteristics so it will vary
across HSIs (Garcia, Ramirez, Patron, & Cristobal, 2017). The importance of sense of belonging
is also documented in Garza, Huerta, Garcia, and Lau’s (2020) study on EL college
students. These scholars found that learning communities had a significant and direct effect on
48
ELs sense of belonging and re enrollment rates. This finding aligns with Garrison-Fletcher’s
(2019) call for tailoring programs to support specific populations.
When it comes to contexts where ELs lack support, one multiple case study of ELs in a
community college bachelor’s of health program explored these students’ experiences. Li (2021)
found that five factors accounted for their shortcomings: (1) inadequate high school preparation
in science, (2) late enrollment and poor performance in their first program related course, (3)
linguistic insecurity, and (4) underdeveloped knowledge of their intended programs and transfer
process, and (5) underusing of advising services. This work elaborates on how EL linguistic
insecurities (e.g., anxiety and anxiousness as it relates to accents in their spoken English) is
important for ELs aspirations (Li, 2021).
Professors of English Learners
When it comes to teaching ELs at the community college level, one theme common in the
literature pertains to instructors’ difficulty with gauging ELs content understanding. Community
college instructors conscious of language challenges have demonstrated difficulty in identifying
whether their EL students’ challenges come from a lack of content understanding or an ability to
demonstrate that understanding in a way that is expected (e.g., essays). Meanwhile, those not
considering language challenges attribute ELs’ academic shortcomings to general academic
challenges including lacking critical thinking and problem-solving skills as well as having less
preparation, a lack of commitment, life circumstances, and the challenges of learning a new
discipline-specific language (a challenge they described prevalent for all students) (Bunch et al.,
2020). In Avni and Finn’s (2020) study, instructors also expressed challenges with assigning EL
students’ grades, specifically because they noted that many could discuss material with a high
degree of fluency but had challenges writing. Meanwhile, Tweedie and Chu (2017) report
49
significant differences between post-secondary English Language Proficiency assessments
suggesting that these instruments also struggle to measure ELs’ proficiency levels.
Another theme in the literature on college professors of ELs speaks to supporting
students in their writing. Support for EL’s writing skills and incorporating their background
knowledge appears promising for helping ELs succeed in all content areas. When instructor uses
students’ understanding of one field as a springboard for learning in another, ELs feel more
motivated to learn and are able to relate to knowledge about language to their subject area (Tann
& Scott, 2021). Other work suggests community college EL students are more likely to
internalize their instructors’ pedagogical offerings and recognize institutional supports compared
to their non-EL peers (Hartman, Callahan, & Yu, 2021). EL students in Hartman and colleagues
(2021) study benefited the most from academic engagement and instruction related to critical
thinking skills than their non-EL peers. ELs have reported preferring more writing support over
reading, although both were wanted (An, Tillman, Zhang, Robertson & Tinajero,
2015). Lambert’s (2020) study reported that ELs performed higher in writing when they spoke
some English at home and had less responsibilities (e.g., childcare, employment). But supporting
ELs in writing may only be something that ESL instructors get training for.
The general community college instructor population is likely not trained in the most
effective approaches to teaching ELs unless they teach ESL courses (Garrison-Fletcher,
2019). Yet, advocates of ELs urge teaching Language Across the Curriculum (LAC), a
promising practice mostly used in foreign language learning where instructors focus on and
scaffold academic language specific to their discipline in explicit ways (Bettencourt, 2011;
Cross, 2016). Garrison-Fletcher (2019) supports LAC, white noting that becoming academically
proficient in additional languages takes 7-10 years (Cummins, 1979; Fillmore & Snow, 2018;
50
Thomas & Collier, 1997) and different content areas have unique linguistic demands (DiCerbo,
Anstrom, Baker, & Rivera, 2014). Despite the need for linguistic support outside of ESL
coursework, analyses of interviews with community college instructors suggests they rarely
discuss the role of disciplinary courses supporting language and literacy support but instead
assume a belief that language and language and content learning should occur as a separate
sequential process (Avni & Finn 2020).
Developmental Coursework, Policy, College Math, Equity, and English Learners
Despite the abundance of research on ELs in community college ESL coursework (e.g.,
Almon, 2015; Delgado et. al., 2019; Fink, 2017; Garrison-Fletcher 2019; Hodara, 2015; Park,
2019), concentrating on non-ESL coursework is more appropriate for most ELs considering
English proficiency attainment prior to entering college. In 2020-2021, of the 2.1 million ELs in
public K-12 California schools across all grades, 1.1 million had already achieved fluent English
proficient status (CDE, 2021). The proportion of students achieving English proficiency is
consistently larger at higher grades, demonstrating growth in English proficiency for the EL
population as they approach high school graduation. In fact, most high school EL’s have already
reached RFEP designation by ninth grade (Hill, 2012). Given the concentration of Latinx ELs in
community colleges (see section “Latinx EL Overrepresentation in Community Colleges”) , a
history of placement into developmental, or below-college level, coursework at community
colleges(Attewell, Lavin, Domina, Levey, 2006; Chen, 2016; Grubb & Gabriner, 2013), and the
importance of mathematics in postsecondary success (Attewell et al., 2006; Hanford, 2016; Park,
Woods, Hu, Jones, & Tandberg, 2018; Stoelinga & Lynn, 2013) , a look at developmental
coursework is relevant for studying Latinx ELs’ postsecondary trajectories. Trends in
developmental coursework with a focus on math below are reviewed below.
51
The purpose of developmental courses is to help students labeled as “underprepared”
succeed in college-level work (Cohen, Brawer, & Kisker, 2013). Yet, scholars have described
developmental courses as the largest single academic barrier to college student success
(Attewell, Lavin, Domina, Levey, 2006). Developmental education has existed since the
colonial period (Cohen, Brawer, Kisker, 2013). Researchers have expended great effort on
studying developmental courses and their effects on higher education over the last thirty years
(Tierney & Garcia, 2011). Melguizo, Bos, and Prather’s (2011) review of developmental
education literature contends that evidence on basic skills math placement in the United States is
contradictory at best. A more recent meta-analysis of regression discontinuity studies suggests
that developmental placement has statistically significant and sizable negative impacts on
students’ academic outcomes (Valentine, Konstantopoulos, & Goldrick-Rab, 2017). Remediation
might increase persistence to a second year and total credits for those scoring just below the
college-placement cutoff but doesn’t increase the completion of college-level credits or eventual
degree completion (Calcagno & Long, 2008). Also, scholars note that roughly two-thirds of all
community college students start in developmental courses (Chen, 2016; Grub 2013), and most
students in these courses discontinue them before enrolling in a college-level course or making
meaningful progress towards attaining a degree (Hodara & Xu, 2016).
When it comes to assessment and placement policies and ELs, evidence suggests that
assessment and placement policies lead to disproportionate placement of Latinx ELs and former
ELs into developmental English. Developmental placement was found to be negatively
associated with degree-applicable credit as well as transfer-applicable credit accumulation
(Melguizo et al., 2021). Among college-ready ELs, only 24% were directed towards college-
level English courses. The rest were placed one level (43%), two levels (27%), three levels
52
(5%), or four levels below (1%) college-level English. Aside from developmental placement
being widespread for ELs, this trend was also prevalent for Latinx students and students from
Spanish speaking homes (relative to students of other races/ethnicities and home languages).
Even more disturbing, these trends were present among students that had already demonstrated
college-readiness in English. Still, college-ready ELs managed to accumulate more community
college credits compared to their native-English speaking peers even controlling for
misalignment in regard to English placement, background, and academic characteristics
(Melguizo et al., 2021). Whether this trend applies to misalignment in regard to math placement
is unknown. We might expect a different pattern since college-ready students have shown better
pass rates in English than in math (70 vs 47%) (Woods, Park, Hu, & Betrand Jones (2018). The
challenge math courses have typically posed are discussed next.
Traditional developmental sequences require students to pass algebra (Melguizo et al.,
2015), thus, developmental math’s role as a gatekeeper is no surprise to researchers and
practitioners at the high school level, where algebra is the most failed course (Hanford, 2016). In
both K-12 and higher education literature, algebra is considered a gatekeeper for students of
color and students from low-income backgrounds (Attewell et al., 2006; Park, Woods, Hu, Jones,
& Tandberg, 2018; Stoelinga & Lynn, 2013). Edley (2017) describes Algebra as a “course of
technical procedures that most college students will never use, either in college or in life,” and
argues that although it is necessary for most STEM majors, requiring it for all career paths tracks
capable students away from completing a college degree. At the community college level,
studies suggest students arrive having received less academic preparation to tackle math than
English (Melguizo, Hagedorn, & Cypers, 2008). Extant work provides context into students’
math course-taking histories at a large, urban California community college district and its feeder
53
high schools. The overwhelming majority of students (92%) repeated a math course at least
twice and most (53%) repeated a math course at least three times with algebra being the most
repeated course (Ngo & Velasquez, 2020). Students of color were more likely to experience
math traps, that is, instances where students never reach the highest level of math in community
college of which they had once taken in high school.
The shortcomings in outcomes resulting from developmental education negatively impact
some groups more than others, posing an issue in equity— defined as parity in both access and
outcomes among racial-ethnic groups (Chase, Dowd, Pazich, & Bensimon, 2014). In particular,
researchers report that rates of developmental math success are persistently lower for Black,
Latinx, female, first-generation, and low-income students (Bailey, 2012; Chen, 2016; Hughes,
2012; Nora & Crisp, 2012; Shulock & Moore, 2010). These trends hold true for other desirable
outcomes such as retention, successful degree attainment, and transfer to four-year
universities. Various quantitative studies indicate that courses in math function as a larger
barrier than those in other subjects (Kosiewicz, Ngo, and Fong, 2016; Kurlaender & Larsen,
2013; Melguizo, Hagedorn, & Cypers, 2008; Melguizo et al., 2015). Additionally, qualitative
research describes the student experience related to developmental math as one that is highly
racialized, discouraging, and shaped by deficit perspectives (Maldonado, 2019, Roberts, 2019,
Suárez-Orozco et al., 2015).
The discrepancy between the goals and outcomes of developmental coursework
generated a debate on the effectiveness and purpose of developmental education and prompted
action by policymakers. Scott-Clayton and Rodriguez (2015) categorize the various functions
that developmental coursework might serve as, described in extant research, into three
hypotheses: (1) skill development to prepare underprepared students for future college-level
54
courses, (2) a discouragement that stigmatizes students and sends a signal about their chances of
success, and (3) a diversion that steers students out of college-level courses and reduces
heterogeneity within classrooms. Policies across the nation aimed at reforming developmental
education have justified their mandates with rationale aligned with the diversion and
discouragement functions of developmental coursework as opposed to citing development.
One example of developmental reform, Assembly Bill 705 (AB 705) can be seen in
California, home to the largest system of higher education in the country (California Community
Colleges, 2019). California’s Assembly Bill 705 (AB 705) cites “serious implications for equity,
since students of color are more likely to be placed into remedial courses” and notes that such
placement carries “adverse consequences” like “discouraging some students from pursuing a
postsecondary education” and “burdening other students with higher educational costs and
delaying their degree plans (Irwin, 2017).” AB 705 mandates colleges to both (1) maximize the
probability that students will enter and complete transfer-level coursework within a one-year
timeframe and (2) use multiple measures (e.g., high school coursework, grades, GPA) for
placement into college courses.
Building off of AB 705, AB 1705 further supports direct placement into transfer-level
community college coursework. The law makes clarifications to assessment, placement, and the
use of multiple measures. More specifically, AB 1705 specifies certain circumstances for when
colleges can enroll students in below-transfer level coursework (Irwin & Medina, 2022). In the
past, college placement policies required students to prove they were prepared for these courses
by way of scoring well on placement exams. With AB 705 and AB 1705 in place, colleges must
now provide proof that developmental placement will benefit a student prior to placing them in
below-transfer coursework. Without such evidence, transfer-level placement for most students is
55
expected. AB 1705 also prohibits colleges from requiring students to take math courses they
have already passed in high school. With AB 705 and AB 1705 in place, colleges are left to
reform as well as eliminate most developmental courses. For example, most pre-requisite
development models, described below, no longer meet the law.
AB 705 and AB1705 are examples of widespread reform across the nation increasing
direct placement into college-level coursework in English and math. Previously, traditional
developmental course sequence followed a prerequisite model where students would take an
assessment exam and place into a course level based on their score. For example, at one college
students could place into a course called “World Numbers” (5 levels below transfer-level),
arithmetic (4 below), pre-algebra (3 below), elementary algebra (2 below), intermediate algebra
(1 below), and transfer-level math (Melguizo et al. 2015). Meanwhile, alternatives such as the
corequisite approach, allows students to enroll directly into transferable math while also taking a
concurrent support class (Bailey, Jaggars, & Scott‐Clayton, 2013; Cuellar Mejia, Rodriguez,
Johnson, & Brooks, 2018; Hern, 2019).
Early studies on the move from traditional course-sequencing models to AB 705-
compliant models suggest positive outcomes in terms of completion rates, but despite these
gains, equity gaps remain large between racial groups (Cuellar Mejia, Rodriguez, Johnson, &
Brooks, 2018). Ngo & Kwon (2014) found evidence suggesting that using multiple measures
from high school (specifically courses taken, course grades, and GPA) may reduce placement
errors and close racial gaps. These works expand the conversation on assessment and placement
by emphasizing the importance of more holistic metrics through multiple measures that gauge
academic potential. An opportunity emerges to explore the relevance of language proficiency in
relation to math placement and outcomes in such a policy context when appropriate data is
56
available. Metrics such as coursework, grades, and GPA attempt to measure students’ potential
to succeed in college math but might overlook potential strengths stemming from linguistic and
language assets. Prior work demonstrates that when students are provided the opportunity to
acquire two languages through dual-immersion programs, they demonstrate strengths in concept
formation, mental flexibility, and verbal problem-solving abilities relative to their monolingual
peers (Peal & Lambert, 1962: Umansky & Reardon, 2014). Therefore, investigating the
relationship between ELs’ classification at the high school level with college math outcomes
may contribute to the discussion on multiple measures and assessment and placement research.
Summary of Chapter 2
The purpose of this chapter was to present a review of the literature relevant to studying
ELs in higher education. With attention to Latinx students, community colleges, and math
experiences and outcomes, the literature review builds on Núñez and colleagues (2016)
systematic literature review of ELs in higher education. To do so, the review was informed and
guided by seven guiding questions that encompass five key aspects of strong literature reviews
literature. The questions guide the review of coverage, synthesis, methodology, significance,
and rhetoric (Boote & Beile, 2005). The criteria for literature inclusion are presented (see Figure
2 in Chapter 2 for an overview). For example, the search terms used, journal articles included,
and time span covered is described and justified. These parameters led to the identification of
key terms, designations, processes, and phenomena related to ELs. The terms bilingual,
multilingual, EL, ELL, ELP, ESP, LM, and LEP are defined. In addition, classification and
reclassification processes are explained along with advantages related to ELs (e.g., immigrant,
bilingual, and cognate advantages).
57
The rest of the chapter focuses on the literature on Latinx ELs in K-12 and higher
education. Review of literature on ELs for K-12 noted themes in the areas of EL programming
and instruction, teachers of ELs, and ELs’ experiences and outcomes with math. Similar themes
emerged for ELs in college settings (e.g., literature focused on EL programming, instruction as
well as professors of ELs), but also included trends in the overrepresentation of ELs in
community colleges. Finally, literature on developmental coursework, policy (e.g., AB 705, AB
1705), math, and equity for ELs.
The literature on ELs in K-12 suggests that dual language programs and instruction are
most beneficial to this population (Steele et al., 2018). While English-only education may
initially outperform dual-language programming early on (Genesee, 2006), in the medium-term,
bilingual students perform better (August & Shanahan, 2006; Cheung & Salvin, 2012). In the
longer-term, ELs tend to catch up to their peers in high school grades and sometimes surpass
them (Umansky & Reardon, 2014). Teachers of ELs play a vital role in their English
development. Of concern, studies indicate they often hold lower expectations of their EL
students (Bertrand & Marsh, 2015). The review further revealed that ELs tend to be tracked into
lower level coursework (Mosqueda, 2010; Thompson, 2017), are often less likely to take
advanced coursework (J. Wang & Goldschmidt, 1999), and are more often found in classrooms
with homogeneous grouping (Garrot and Hong, 2016). Such trends may help explain why EL
classification has been linked to negative effects in both math and English (Umansky, 2016b).
In higher education, Latinx ELs are disproportionately enrolled in community colleges
because of financial hardship and background in basic skills (Bista, 2011; Razfar & Simon,
2011). ELs are typically placed into and typically placed into below-college level coursework
(Grubb & Gabriner, 2013). Among Latinx students, about half of California’s first-time college
58
students attend community colleges with the majority intending to transfer to a four-year
university, but these aspirations are met with high rates of developmental mathematics placement
(Acevedo-Gil, Santos, Alonso, & Solorzano, 2015; 2015; Bailey, 2012; Chen, 2016, Shulock &
Moore, 2010). ESL coursework and HSIs support of Latinx ELs are two areas of research that
have focused on Latinx ELs. The literature on professors of ELs notes that professors often find
difficulty assessing ELs’ understanding (Avni & Finn, 2020; Bunch et al, 2020). Literature on
developmental coursework also helped learn more about ELs in higher education.
Given history of shortcomings resulting from developmental education and the
overrepresentation of ELs in community colleges, developmental coursework and assessment
and placement policy was also reviewed. California’s AB 705 and AB 1705 have called for
colleges to place students directly into transfer-level coursework. But, because pre-requisite
models have frequently been used, most colleges must reform their course sequences and course
offerings. Given that ELs academic advantages may materialize in later grades, understanding
the relationship between attaining English language proficiency and math success as well as
college credit attainment for Latinx former ELs may help inform decision-making related to
assessment, placement, and multiple measures.
59
CHAPTER 3: Research Design
The purpose of this study is to gauge the extent to which high school EL status predicts
success in college mathematics. In the previous chapter, I conducted a systematic literature
review focusing on the population of interest, community college Latinx ELs. In this chapter I
review the previous research methods used in the quantitative studies that resulted from the
literature search and enumerate on the steps I followed to address the research questions. I also
speak to the appropriateness of using descriptive and inferential statistical methods to answer this
study’s research questions.
This chapter is organized as follows: first I briefly list the purpose and research questions
of this study. Then I discuss the trends in approaches to studying ELs, in particular the
methodological techniques within and between K12 and higher education contexts. I provide a
rationale for using a descriptive research methods approach (Nassaji, 2015) and its
appropriateness in relation to the study’s purpose and research questions. Followed by a
discussion on the data sets and samples. I describe the three data analysis phases and the exact
steps that each phase takes to address the coinciding three research questions. Then I discuss
limitations. To close, I provide a summary of the chapter.
Purpose and Research Questions
In order to analyze the relationship between high school EL status and college outcomes
in mathematics I pose the following three research questions.
RQ1. Where do Latinx former ELs begin their community college trajectories (including
educational goals and math placement referrals) and how do these rates compare to their
Latinx English Only peers?
60
RQ2. What math course do Latinx former ELs, as well as Latinx English Only students,
enroll into first and do these courses align with those they were referred to?
RQ3. Does achieving EL proficiency before high school graduation predict success in
college math courses and college credit accumulation for Latinx students?
Trends in Approaches to Studying English Learners
Examining the methods used to conduct research relevant to ELs can be useful for
understanding not only how scholars have approached this area of research but also what
scholars have focused on, what they have found, and how they have been limited. While I have
previously shared a review of the literature focused on synthesizing research findings into
themes in the literature (see Chapter 2), next I share a methodological review of quantitative
studies. While qualitative work and mixed methods research is important, the previous chapter
provide useful information of these studies’ contributions. Given this study’s quantitative
approach, this review is limited to scholarship using quantitative methodology. Specifically, I
pay special attention to recently published work in top peer-reviewed journals identified using
specific search criteria suitable for reviewing literature relevant for Latinx ELs (see section
“Criteria for Literature Inclusion” in Chapter 2).
Examining the methods used to conduct research relevant to ELs can be useful for
understanding not only how scholars have approached this area of research but also what
scholars have focused on, what they have found, and how they have been limited. While I have
previously shared a review of the literature focused on synthesizing research findings into
themes in the literature (see Chapter 2), next I share a methodological review of quantitative
studies. While qualitative work and mixed methods research is important, the previous chapter
provide useful information of these studies’ contributions. Given this study’s quantitative
61
approach, this review is limited to scholarship using quantitative methodology. Specifically, I
pay special attention to recently published work in top peer-reviewed journals identified using
specific search criteria suitable for reviewing literature relevant for Latinx ELs (see section
“Criteria for Literature Inclusion” in Chapter 2).
I note trends among these studies in their area of focus as evidenced by the data and
sample scholars collected and analyzed while noting methodological approaches. My review of
recently published, peer-reviewed work on ELs reveals that about one third of the scholarship
employs quantitative methods, one third employ qualitative methods, and a final third are
roughly evenly mixed between empirical works employing mixed methods. In the following
sections, I synthesize takeaways as from quantitative then qualitative studies, the first and second
most used approaches in the literature base on ELs. Then I discuss trends in mixed method
studies and literature reviews on ELs. In particular, I note trends in research questions,
objectives, methods, coverage, findings, and limitations.
Trends in General Content Coverage: Most Quantitative Studies Focus on K-12, Few on
Higher Ed, Even Less Across Sectors
Quantitative studies account for 36 of the 96 peer-reviewed articles, or 37.5%, that met
the EL literature search criteria. When it comes to trends in content coverage, recent quantitative
studies related to Latinx ELs can be categorized into three groups: (1) K-12, (2) higher
education, or (3) parents and teachers of ELs. Of all the recent quantitative studies on ELs, the
majority (22 of 36, or 61%) of studies focus on K-12 contexts. Within these studies on ELs in K-
12, most scholars used data that spanned elementary and middle school grades (e.g., Burke,
Morita-Mullaney & Singh, 2016; Mavrogordato & White, 2017; Portes, González Canché,
Boada, & Whatley, 2018; Umansky, 2016; Umansky & Dumont, 2021; Valentino & Reardon,
62
2015). Fewer studies have examined trends from early elementary grades into high school (e.g.,
Johnson, 2020; Shin, 2017; Umansky, 2016).
Just under one third of all the quantitative studies focus on ELs in higher education (11 of
36, or 31%). Reflecting EL trajectories, the vast majority of these works (9 of 11) use data from
community college settings while the remaining focus on four-year universities. Similar to the
lack of longitudinal, cross-sector quantitative studies in K12, few quantitative studies that used
higher education data followed students from secondary to postsecondary education (see
Melguizo et al., 2021; Daugherty et al., 2021) while none looked at ELs student data after
college. A third group of studies emerged that did not fit perfectly into having ELs in K-12 or
higher education as central. Just three of the 36 quantitative studies in that met the literature
search criteria focused on outcomes and trends for either parents or teachers of ELs (e.g., Burke,
Morita-Mullaney & Singh, 2016; Rodriguez & Allen, 2018; Robles-Goodwin, Salazar, Garza,
Torres & Martinez, 2020).
Trends in Specific Content Coverage: A Focus on Language Programs
When distinguishing between higher education and K-12 studies, one area of focus
prevalent in both spans’ topics of evaluation and effectiveness of language programs. Among
higher education studies, specifically, the largest area of focus concerned immigrant or
international students in ESL courses. For example, Tweedie and Chu (2017) examined the
extent to which English language programs predicted success for international first year students.
Meanwhile, Park (2019) described immigrant student placement and progression in ESL in
relation to success in college-level English. At the same time, scholars have examined the effect
of ESL coursework in relation to long term outcomes (e.g., Hodara, 2015; Lambert, 2020). Yet,
while understanding the effects of ESL coursework is relevant for expanding knowledge on ELs,
63
this snapshot does not capture the more frequented EL community college path. Melguizo and
colleagues (2021) note that most students who were ELs in high school are placed into
developmental English coursework, not ESL. As such, EL’s enter into developmental courses
where English is the medium of instruction and courses are not designed with specific goals of
English language development for non-English speakers.
In regard to K-12 quantitative studies on ELs, the vast majority focus on evaluating the
effectiveness of programs supporting this population. While some studies compare the academic
outcomes resulting from EL participation in different types of programs (Alvear, 2018;
Butvilofsky, Hopewell, Escamilla, & Sparrow, 2016; Garza-Reyna, 2017), others measure the
impact of student participation in a specific program on EL achievement (Johnson, 2019; Steele,
2017). Similarly, scholars have examined the effect of teacher participation in programs aimed at
improving instruction and outcomes for ELs (Babinski et al., 2017; Llosa et al., 2016; Portes et
al., 2018). Yet, most of these works examine reading and writing measures to gauge language
and literacy gains. Fewer works emphasize non-English related outcomes.
While the most common area of focus for quantitative studies on ELs in higher education
(evaluating language program effectiveness) parallels the focus of K-12 EL studies, the second
most common focus differs between these contexts. On the one hand, quantitative studies on ELs
in higher education examine language factors and noncognitive outcomes. These include EL
persistence and enrollment (Garza et al., 2019), engagement and aspirations (Hartmant, Callahan,
& Yu, 2021), and motivation and retention (Fong et al., 2015). On the other hand, the second
most common area of focus among K-12 quantitative studies on ELs gauge the effects of EL
classification and reclassification. For example, scholars have estimated the effect of EL
classification and initial designation (Shin 2017; Umansky, 2016) as well as gauged outcomes in
64
light of reclassification impacts (Johnson, 2020) and reclassification differential policy
implementation (Mavrogordato & White, 2017).
Excluding studies in higher education that evaluate ESL program effectiveness or analyze
links between language factors and noncognitive outcomes, quantitative studies in higher
education that are relevant for Latinx ELs have focused on two areas. Scholars have used
assessment and placement data to measure the causal effect of placement into college-level
coursework for ELs (Daugherty, Gerber, Martorell, Miller, & Weisburst, 2021) and have also
examined the prevalence of misalignment into below-college level English coursework for
college-ready students (Melguizo et al, 2021). Finally, a couple of studies have examined the
transfer of language between contexts and the associated benefits. For example, Torres (2016)
examined the relationship between home language and learning styles while Fukumine and
Kennison (2016) investigated circumstances of language transfer between contexts. In the
present study, I leverage data on assessment and placement as well as high school and
community college course taking to examine the relationship between EL status and math
outcomes. This analysis can potentially shed light on the relationship between EL status and
math outcomes as well as contribute to the conversation on the transfer of language between
contexts (e.g., high school English and college math).
Following (1) evaluating the effectiveness of language programs and (2) gauging
outcomes resulting from classification and reclassification, the third most common areas of focus
for studies on ELs in K-12 contexts can be summarized as studies that examine the relationship
between EL’s (or their teachers’) characteristics and access to content or academic outcomes. For
example, Umansky’s (2016) study on middle school ELs examines determinants of access to
academic content while Potochnick’s (2018) inquiry examines academic outcomes for high
65
school EL students who experience interrupted schooling. In their study focused on
interrogating how linguistic status relates to college-going and course-taking, Callahan and
Humphries’s (2016) study suggests that immigrant EL students who complete college-
preparatory math in high school are significantly more likely than their peers to go to a two-year
college and also significantly less likely to enroll in a four-year college. Their study suggests an
immigrant advantage among immigrant EL groups who are not in ESL while also indicating that
these students are overprepared compared to their peers. Considering most ELs are native born,
interrogating whether EL status predicts college math success may help expand the scholarly
discussion on the immigrant advantage and academic achievement to include math achievement
and linguistic assets for non-immigrant ELs.
Trends in Methodological Techniques: Experimental and Quasi-Experimental Studies
Have Been Possible in K12 Much More Often Than Higher Education
In regard to methodological techniques, the approaches scholars employed in these
studies can be grouped into randomized controlled trials, quasi-experimental methods, inferential
statistics or descriptive statistics. Of the 36 quantitative studies that emerged from the
systematics literature review, four employed randomized controlled trials (Babinski et al. 2017;
Borman et al. 2017; Llosa et al., 2016; Portes et al. 2018). All of these studies focused on
elementary school student data and were published by AERJ. Another 11 studies employed
quasi-experimental methods. Among these, regression discontinuity was most commonly used
(6 of 11) followed by difference in difference and matching (both used twice). One scholar used
Instrumental variables (Steele, 2017). Once more, a trend emerged in regard to coverage by
sector. Most quasi-experimental studies on ELs (9 of 11) were limited to using only K-12 data.
66
The most used quantitative approach in the EL literature falls under inferential statistics. Of the
36 quantitative studies that emerged from the literature review search, 17 used techniques
encompassed by this category. These techniques were distributed roughly evenly between
hierarchical or multilevel modeling, regression analyses, survey methods, and social network
analysis. More specifically, these 17 studies are focused K-12 ELs (9 studies) and ELs in higher
education (6 studies) with a couple of studies concentrated on teachers and parents of ELs.
Studies that used descriptive statistics alone, not in conjunction with inferential statistics or more
advanced statistical techniques, were just as common as randomized controlled studies. The four
studies that used descriptive statistics that are relevant to ELs can be found can be found in the
Journal of Latinos in Education (3 studies) and Studies in Higher Education (1 study).
When looking at these studies’ methodological approaches within area of concentration
more closely, specifically K-12 in comparison to higher education, additional trends emerge.
The most common category of methods used in both K12, and higher education fall under
inferential statistics with over half of higher education studies indicating using these types of
methods and a little under half of K-12 studies noting the same. Interestingly, among quantitative
studies relevant for Latinx ELs in higher education contexts, the overwhelming majority
(roughly two-thirds) failed to explicitly acknowledge a guiding theoretical or conceptual
framework. Most studies did not specify a framework outright, but instead presented as being
atheoretical or vaguely alluded to theory. The only studies to employ a framework explicitly all
happened to use methods falling under the inferential statistics category (e.g., Fong et al., 2015;
Garza et al. 2020; Melguizo et al., 2021).
Meanwhile, quasi-experimental methods have been used in over one third of K12 studies
relevant to Latinx ELs but have seldom been used in higher education studies. Nearly one third
67
of higher education studies have noted using descriptive statistics alone while only one K-12
study has done so. In sum, research relevant for Latinx ELs has most often used inferential
statistics. Whereas studies in K12 have more frequently used quasi-experimental methods in
conjunction with inferential statistics, studies in higher education have more often used
descriptive approaches. These trends may be a result of the limited access to data on ELs in
higher education. With EL status being a K-12 designation, following students across sectors
into higher education is much more difficult which may be why scholars have conducted no
experimental studies and few quasi-experimental studies on ELs in higher education. With such
limitations, the most rigorous quantitative methods employed to study the population of interest
has typically been limited to using inferential statistics.
Research Design: The Appropriateness of a Descriptive Approach
Descriptive approaches to research are common in education, psychology, and social
science and are beginning to be used in learning and second language teaching fields (Nassaji,
2015). This study aligns with what Nassaji (2015) describes as four properties of descriptive
research as they pertain to (1) purpose, (2) research questions, (3) data collection, and (4)
analysis. The purpose of descriptive research is to describe a phenomenon and its
characteristics. The research questions in descriptive research are more concerned with asking
“what” rather than explaining why something happens. In terms of data collection, survey tools
often play a crucial role in descriptive research studies. Finally, descriptive research analysis
often represents data quantitatively using frequencies, percentages, averages, and other statistical
measures to measure and model relationships. Next, I draw connections between the purpose
and research questions of this study to Nassaji’s (2015) four properties of descriptive analysis.
68
The purpose of this study is to unpack the relationship between high school EL
classification and outcomes in college. I particularly study the relationship between being a
Latinx former EL (in relation to being an English-only monolingual student) and community
college outcomes, with an emphasis on math. My three research questions do not intend to lead
to causal claims. Rather, the guiding questions prompt evidence for describing former, high
school Latinx EL students’ background characteristics, high school achievement, where they
begin their trajectories in college math, how they fare, and examining the extent to which EL
classification predicts college success, if at all. I leverage a unique, longitudinal dataset that
resulted from enrollment surveys, and administrative transcript data collection. Finally, my
analysis of quantitative student data uses frequencies, percentages, averages, which I report in
the form of tables and figures. Additionally, I use other descriptive and inferential statistics
techniques such as cross tabulations, mean-comparison tests, and regression analysis.
Data and Setting
In this study, I leverage a rich, longitudinal dataset created as part of a Research-Practice
Partnership in California between one research university, a large, urban community college
district, and its feeder high school district. The data was retrieved from enrollment surveys as
well as administrative and transcript records. A unique, common identifier variable allows for
the linking of students’ secondary to post-secondary records which captures EL status along with
other demographic characteristics and academic achievement. For example, variables include
socio-economic status, gender, ethnicity, parent education, home language, grade point average,
course grades, and test scores. The dataset follows every student in the local high school district
with an EL status and key academic and background characteristics that took a community
college math assessment and placement exam and subsequently enrolled in at least one of the
69
community colleges in the district between 2005 and 2014. College records extend through
2016.
Students in the dataset all attended high schools in the same, local school district where
roughly one quarter of the population enrolled is classified as an EL (around 150,000 ELs total).
To date, nearly three in four students in the high school district report being Latinx. All the
community colleges in this district are classified as Hispanic-Serving Institutions (Hispanic
Association of Colleges and Universities, n.d.) with nearly 60% of the district’s population being
Hispanic (California Community College Chancellor’s Office, 2018). In the 2019-2020 academic
year, 12% of students in the district completed transfer level math within a one-year time frame
(11% for those who reported their race/ethnicity as Hispanic) per Cal-PASS Plus’s Student
Success Metrics Dashboard. In comparison, 18% of students across the California Community
College system accomplished the same feat (16% for those who reported their race/ethnicity as
Hispanic).
Full Latinx Sample
The full Latinx sample was derived from combining a number of individual student data
from the community college district and the high school district. For the community college
data, I merged six files provided by the community college district with one another: a math
assessment, math assessment re-takes, English assessments, outcomes (includes unit, certificates,
and degrees attainment variables), term (includes citizenship, financial aid, first semester
variables), and enrollment files (includes variables such as course codes, grades, credit or
noncredit course status, attempted units). All of these files contained a community college ID
number and omitted student names.
70
Similarly, I merged seven high school files: California High School Exit Exam (included
variables such as CAHSEE Math scores, month taken), California Standards Test (included
variables such as CST Math Scores, performance levels), Early Assessment Program (included
information on EAP performance), Grade Point Average (by year, grade), Special Education
(included data on SPED status), Transcript (included data on courses taken, grades), and
Demographics (included EL status, gender, race/ethnicity, home language). All of these files
contained a high school ID number (different from the community college ID number) and also
omitted student names. A special file containing a linking ID variable allowed each unique high
school student ID to be linked to its respective community college student ID.
The merge explained above yielded a file with 118,649 rows or observations. Of these,
50,971 observations were excluded due to missing high school ID numbers, race/ethnicity,
gender, parent education, and/or EL status- all key variables mandatory for this study.
Of the remaining 67,697 observations, only those that had information on college math
placement, college math course grades, EL status, race/ethnicity, socioeconomic status, gender,
citizenship, GPA, and cohort were kept. These variables were critical given the purpose and
three research questions that guide this study (see Purpose and Research Questions section). The
full sample contained a total of 23,110 students. Of these students, roughly 80% were Latinx.
These 18,400 students referred to as the full Latinx sample throughout the paper.
Group of Interest: Who are Latinx Former ELs and Latinx English Only students?
I focus on Latinx Former ELs while using Latinx English Only students as a reference
group. The Latinx Former EL sample includes students who both reported being Latinx and
were initially ELs but attained a Reclassified Fluent English Proficient Students (RFEP) status at
some point before high school graduation (n=12,430 students). Figure 3 in Chapter 2 contains an
71
English Learner Flowchart a decision chart demonstrating the four EL designations used in the
high school district are provided along with a description of each group and indication of when
classification occurred. These four groups consist of English Only (EO), Initially Fluent English
Proficient (IFEP), RFEP, and Limited English Proficient (LEP). A complete description of these
designations can be found in Chapter 2 in the “Key Terms, Labels, and Designations” section
under the “English Learner (EL)” subsection.
While EO, IFEP, and EL designations are decided for a student upon entry, RFEP
designations result from student demonstrating English fluency at some point through their K-12
trajectory. As such, RFEPs are more commonly referred to as former ELs. A total of 1,734
students are included in the Latinx EO sample. Of note another 1,684 and 2,552 were classified
as IFEP or LEP, respectively.
Variables
In this section I describe key variables used in this study. I list the variables used in
summary statistics. I discuss the math and college outcomes in the dependent variables section.
I discuss covariates used in the regression models under independent variables. Since students in
the sample attend different high schools and select into different colleges, I accounted for this
self-selection by incorporating these variables as part of a high school-by-fixed effects estimation
strategy. Table 1 and Table 2 in Chapter 4 provide information on demographic characteristics
and academic achievement for the full Latinx sample and disaggregate by the four district
assigned EL designations.
Covariates.
In considering variables of focus for describing background and academic characteristics,
I kept in mind linguistic capital (Bourdieu, 1986; McDonough & Núñez, 2007; Scarcella, 2003).
72
Also, I sought to include variables that consider multiple social categories, or identities, to
disaggregate data as I looked at trends and outcomes. I describe the background and academic
achievement variables next.
Background Characteristics.
I use a variety of demographic and socioeconomic variables to describe the populations
of interest. I included race/ethnicity, a variable that indicates whether a student reported being
Asian, Black, Latinx, or White in the full sample. A number of binary variables were used such
as female, free-reduced-lunch eligible (proxy for socioeconomic status), and special education
participant to indicate whether or not these characteristics applied to a student. Home language
(English, Spanish, Asian Origin, and Other), immigration status, and parental education were
also considered. The original home language variable included 64 unique languages, for
reporting, these were condensed into four categories with 90% of all students falling into English
or Spanish. Asian Origin languages include languages such as Korean, Vietnamese, Thai,
Cantonese, and Cambodian. Languages such as Armenian, Russian, Arabic, Hebrew, and others
were categorized into the “Other” category. Immigration status indicated whether a student
reported being a U.S. citizen, permanent resident, refugee, a student or visitor with a
VISA. Also, the category “likely documented” was constructed following extant scholarship
(e.g., Flores, 2010; Ngo & Astudillo, 2018). This group includes students who self-selected
“Other Visa” or “Temporary-Resident, Amnesty” categories from the enrollment application
options. Other options on the enrollment application included "Student Visa-F1, M1" and the
"Visitor Visa-B1, B2" which were combined to form the Student or Visa Category.” In certain
analyses, (e.g., phase 2, described below) a variable capturing student’s educational goal was
included.
73
Academic Achievements.
In regard to academic performance in high school, I included EL designation, cumulative
GPA, top math course passed (B or better), standardized test scores in math and English (both
California Standards Test and Early Assessment Program) as well as whether a student passed
eight semesters of college preparatory English. Of note, a B or better in college preparatory
coursework grants students credit towards university in California (i.e., CSUs and UCs). In
addition, I examined students’ four-year math course sequence. The sequences capture the math
course a student was enrolled in the fall term of their 9
th
, 10
th
, 11
th
, and 12
th
grade years. The
California Standards Test, a math and English test administered in students’ 11
th
year, is reported
as “State’s Standardized Test for Math/English.” I refer the Early Assessment Program exam as
the “State University College Readiness Exam” in this paper.
Outside of the regression models, some college variables were used descriptively. These
included a math placement referral and first math course taken. These categorical variables
mirror one another with 6 categories: Transfer-Level Math as well as 1, 2, 3, 4, and 5 Levels
Below. Of note, the data used in this study predates policies such as AB 705 and AB 1705.
Whereas math placement referral indicates what course students were directed to take, the first
math course taken variable indicates their actual enrollment. Given different assessment and
placement policies across the district (e.g., self-placement), there is variation between the math
course students were referred to and what they actually took.
Outcomes
Throughout three phases of analysis (described below) I turned to a number of binary and
continuous variables. Binary variables captured whether a student passed the first community
college course they enrolled in during their first attempt, whether a student passed the first math
74
course they were referred to (by their assessment and placement exam) within a one-year
timeframe, and whether or not a student completed 60 units. In terms of continuous variables, I
looked at outcomes in regard to three types of credit accumulation: total, degree-applicable, and
transferrable. Total units included transfer, degree-applicable, and any other units completed. In
terms of covariates used for the regression analysis, I included race/ethnicity, gender, special
education participation, immigration status, parental education, free or reduced lunch eligibility,
high school cohort, and first math course taken as independent variables. High school campus
and community college campus variables were used as part of fixed effects specification, which I
describe in more depth in the data analysis section.
Data Analysis
Prior to addressing the research question summary statistics tables of background
characteristics and academic characteristics were created to get a sense of the full Latinx sample
(see Table 1 and 2 in Chapter 4). While informed by prior work on math course-taking and
sequence analysis (e.g., Abbot 1990, 1995; Ngo & Velasquez, 2020), I also look for patterns in
high school math course trajectories. I order a list of math courses, not by calendar year but by a
symbolic time (grade level) to look for trends. To do so, I tabulate the top 15 most common high
school course sequences for each group of interest. In other words, I rank the most common
math course-taking paths that Latinx former ELs experienced from their 9
th
to 12
th
grade and
compare to their Latinx EO peers and all students (see Table 3 in Chapter 4). These tables help
better understand the full Latinx sample as well as help compare between Latinx Former ELs and
their peers (e.g., Latinx English Only students).
75
Phase 1: Disaggregate Points and Paths of Entry into College Math
Similar to Razfar and Simon’s (2011) longitudinal study on course-taking patterns of
Latino students who begin in English as a Second Language (ESL) classes in community
colleges, I begin by identifying how Latinx former EL students begin their community college
trajectories but with a focus on math and English coursework. I ask, “Where do Latinx former
ELs begin their community college trajectories (including educational goals and math placement
referrals) and how do these rates compare to their Latinx English Only peers?” With a focus on
Latinx former ELs relative to Latinx EOs, I reported the proportion of students that indicated one
of seven different goals (see Table 3 in Chapter 4). I also note the difference between these two
groups and conduct mean comparisons test to indicate significant differences. In addition, I
conduct a similar analysis as it related to math course referrals. These are referrals that students
received based upon their performance on placement exams.
Phase 2: Investigate First Math Course Enrollment, Noncompliance, and Success in Math
Following phase 1, I am then able to compare students’ starting points to their math
course referrals. I ask, “What math course do Latinx former ELs, as well as Latinx English Only
students, enroll into first and do these courses align with those they were referred to?” In this
phase I note enrollment trends for Latinx former ELs, their Latinx English Only peers, and
examine differences. This allows me to note the extent to which Latinx students were referred to
below-college coursework as well as disaggregate between levels (e.g., five levels below, one
level below). I then compare students’ math course referrals to their actual enrollment to note
trends in compliance. For non-complying students, or those that enroll in a math course other
than the one they were referred to, I examine whether students enroll in higher or lower
coursework. To close, I examine success rates in the first math that students take.
76
Phase 3: Analyze Relationship between K-12 English Proficiency and Community College
Math.
Umansky and Reardon (2014) analysis of four language programs within K12 suggests
that even though ELs tend to trail behind their peers in earlier grades, they often catch up and
surpass them academically in later high school grades. This trend might persist into college.
While Thompson’s (2017) study gives us insight into current ELs, former ELs, and never ELs
math course taking, no study has yet to extend such an analysis from high school into higher
education. To answer the question, “does achieving EL proficiency before high school
graduation predict success in college math courses and college credit accumulation for Latinx
students?” I compare outcomes between groups including include passing their first math course,
passing the math course they were referred to within a one-year time frame, completing 60 units,
total units completed, total degree-applicable units completed, and transfer-applicable units
completed. Then I conduct logistic or multivariate regression analysis using Stata 14 depending
on the outcome of interest. Since students in the sample attend different high schools and select
into different colleges, I follow previous scholarship (e.g., Melguizo and Ngo, 2020; Melguizo et
al., 2021) and use a high school-by-college fixed effects estimation strategy in all regression
analyses.
I begin by looking at binary outcomes (passing first math course, passing referred math
within one year, completing 60 units). First, I conducted a logistic regression analysis using a
naïve model, which regressed the outcome on being a Latinx former EL student (relative to being
an EO student). The next models included covariates accounting for background and academic
characteristics (see Variables section for list of variables). I then determined whether the more
complex models were a better fit.
77
Next, I conduct regression analysis using continuous outcomes (total units, degree-
applicable, and transferable units completed). For each outcome, I ran a naive ordinary least
squares (OLS) regression model that predicted the relationship between being a Latinx former
EL (relative to an English only student) and the outcomes of interest. Like the logistic models, I
used models that included covariates accounting for background and academic characteristics
(see Variables section above for list) and determined if these, more complex, models were a
better fit. Below, I elaborate on the value of using fixed effects in the regression models.
Fixed Effects and Changes in Sample Sizes
When using traditional regression models (e.g., OLS, multiple regression, logistic
regression) the estimates produced account for observed factors that are included in the model. In
this study, observed variables include academic characteristics such as GPA and SPED status as
well as background characteristics such as socioeconomic status and parental education. As is
often the case, the dataset is limited in observing all the variables that would be ideal to control
for. For example, Latinx Former EL students did not experience the same supports and services
across school contexts but no variable in the dataset exist to indicate things such as the type,
quality, and amount of support and services received. Additionally, math course placement,
among other things, are not universal across colleges in this district. These key differences
between high schools (e.g., EL support services, programming, and resources) and between
colleges (e.g., assessment and placement policies), among other factors, are unobserved so
omitted variable bias is a concern. Luckily, this panel data nature of this study’s dataset contains
records of multiple students that experienced the same high school-to-college paths which can be
leveraged to account for the differences within these paths that can influence academic
outcomes.
78
Because the dataset observes the same high school-to-college path student trajectory,
multiple times, fixed effects analyses are appropriate to account for unobserved factors related to
high school-to-college path. Fixed effects help account for unobserved factors, or unobserved
heterogeneities, to improve estimates. In essence, fixed effects ignore the variation that exists
across different groups of students that had different high school-to-college paths. Fixed effects
can control for unobservable factors by analyzing only the variation that exists across each
student trajectory within the same high school-to-college path (Allison, 2009). In other words,
by focusing on within-group residuals, all unobserved factors that are fixed within that group
(i.e., high school-to-college path) are accounted for. In sum, fixed effects models help control
for omitted variable bias by having each high school-to-college path serve as their own control
(Wilson & Lorenz, 2015).
While the full Latinx sample contains 18,400 students, the sum of Latinx former ELs
(12,430) and Latinx English Only students (1,734) provides us with 14,164 students with
complete data on the outcomes and covariates used in the logistic and multivariate regression
analysis discussed below. These students experienced a total of 481 unique high school to
college paths. However, not all of the paths or observations within those paths aid the analysis.
Fixed effects regression analysis allows us to use all students within these high school-to-college
paths where variation exists across student trajectories. In other words, all observations within
the same path that have no variation are omitted. For example, because 154 of the high school-
to-college paths were only taken by one student each, all of these observations do not contribute
to any of the analysis. In addition, in cases where two or more students within the same path
show no variation in outcome (e.g., ten students in the same high school to college path all
passed their first math course) the observations are also omitted. Thus, all the logistic regression
79
and multiple regression models sample sizes are slightly lower than 14,164 with only
observations that have variation in student outcomes within each high school-to-college path
being included.
Limitations
Like most studies, this inquiry contains limitations that should be addressed. The study is
limited by the quantity and quality of the data provided by the community college and high
school district. For example, while the variables can capture a students’ EL designation by the
time they left high school, no data exists on when each student was classified or
reclassified. This is especially important since former ELs might include students who were able
to earn that designation right before graduation as well as those that achieved reclassification
early on in their high school trajectory. Additionally, some variables provided limited
information. For example, gauging socioeconomic status was done using free and reduced lunch
eligibility, and the majority of students were.
Generalizing outside of this large, urban community college district and its feeder high
school is not appropriate. Yet, these results may still provide insight to similar contexts with
large amounts of Latinx ELs. Of note, although fixed effects attempt to account for students’
high school to college paths, self-selection bias cannot be completely disregarded. Additionally,
Latinx ELs may be a more motivated group compared to Latinx EOs because they had to
successfully reclassify in high school. Thus, higher achievement for Latinx ELs might be tied to
other factors such as motivation (that are not part of the covariates) given that they succeeded in
reclassifying to English proficient in high school whereas Latinx EOs did not. As such, at each
stage, I also examine ever ELs (RFEPs and LEPs) and likely bilingual students (RFEP and IFEP)
in relation to Latinx EOs.
80
Summary of Chapter 3
In this chapter I reviewed the previous research methods used in the quantitative studies
that resulted from the systematic literature search while also describing the design and specific
steps I took to address the three guiding research questions of this study. In particular, these
questions span from Latinx former ELs college entry points to their outcomes in credit
attainment and college math success while also considering high school math course-taking and
placement into below college-level math for college ready populations. Before diving into the
specific steps used to address each question, I discuss the methods previously used to study this
population.
In the first half of the chapter, I described trends in approaches to studies relevant for a
Latinx EL population. In three subsections, I synthesized trends in general content coverage,
trends in specific topics of focus, and trends in methodological techniques. I note that most
quantitative studies on EL students are focused on K-12 settings, less focus on higher education,
and just track students across these two sectors. When distinguishing between the studies focused
on higher education and K-12, similarities exist between their topics of focus. Prevalent in these
studies was a focus on evaluation and effectiveness of language programs. For example, studies
in K-12 often examined differences in outcomes for EL students by language program (e.g.,
immersion vs bilingual education). Meanwhile, studies on effectiveness of ESL coursework
were popular for research on ELs in higher education. In terms of methodological techniques,
the quantitative studies used to study ELs can be grouped into four categories. First, studies
using inferential statistics were most common with over half of all higher education studies on
ELs using these kinds of methods and nearly half of K-12 studies doing the same. Of note, most
(about two-thirds) of studies using inferential statistics in higher education lacked explicit
81
theoretical or conceptual frameworks. Second most common were quasi-experimental
approaches. Fewer studies fell into the randomized control trial category or descriptive statistics
group. Perhaps because of the nature of EL data typically being more common in K-12, all
randomized controlled trials occurred in K-12 settings and most quasi-experimental studies did
so as well. Assuming that methodological rigor in quantitative studies ranks from descriptive
approaches to randomized controlled trials, the patterns among the techniques used suggests that
the most rigorous quantitative methods employed to study the population of interest have
typically been limited to inferential statistics. Thus, incorporating inferential quantitative
techniques into this study is important for expanding what is known about Latinx former ELs
and college math outcomes.
The second half of this chapter describes the research design, variables, data analysis, and
limitations. I begin by describing the appropriateness of using a descriptive approach that uses
both descriptive and inferential statistics techniques given the nature of the research questions. I
then describe the rich, longitudinal dataset leveraged from enrollment surveys and administrative
and transcript records stemming from a Research-Practice Partnership in California. I describe
the high school and community college district in regard to ethnicity and EL status and other
academic accomplishments. I then describe the observations included to conduct my analyses
and specify who are “all students,” “Latinx former ELs,” “Latinx EOs,” and other relevant
groups. I then list variables used as covariates that I group into background characteristics and
academic achievement as well as describe the outcomes and fixed effects estimation strategy. I
describe by data analysis in three distinct phases each corresponding to the previously listed
guiding research questions. I begin by describing how I disaggregate points and paths of entry
into college math. Then, I explain what it means to gauge inter-sector misalignment and math
82
referral compliance. Finally, I note how I analyze the relationship between K-12 proficiency and
community college math outcomes. To close I speak on the limitations of this inquiry.
83
CHAPTER 4: Results
Scholars have linked Latinx students’ attainment of reclassification to English
proficiency with positive academic outcomes such as improved probability of being on track to
graduate high school (Johnson, 2020) and high school completion (Zarate & Pineda, 2014).
Research also suggests that EL students in dual-language settings have less desirable English-
related outcomes in early grades compared to their English only peers (Conger, 2010; Thompson
2012; Umansky & Reardon, 2014). Yet, dual-language ELs tend to catch up and surpass their
peers in later grades in English proficiency in reading, writing, speaking, and listening. As such,
a comparison between Latinx ELs and Latinx English only speakers might demonstrate that
Latinx EL students who managed to achieve English language proficiency before high school
graduation outperform their English only peers beyond high school in English related outcomes
as well as overall academic achievement. Perhaps, this relationship also applies to mathematics,
a key content area for both high school and college success.
Still, data limitations, among other issues, have posed challenges to conducting research
that follows ELs into higher education. Thus, little is known about the relationship between
achieving reclassification before high school graduation and outcomes in college despite the
potential for such evidence to aid decision-making as it relates policies including classification
and reclassification in K12 and assessment, placement, and multiple measures in higher
education. Additional questions emerge about former ELs linguistic assets extending beyond
general measures of academic success (e.g., graduation) and measures related to English (e.g.,
reading, writing, speaking, listening) into other important content areas. In community colleges,
ample evidence suggests mathematics courses have functioned as a larger barrier for students
than English classes (Kosiewicz, Ngo, and Fong, 2016; Kurlaender & Larsen, 2013; Melguizo,
84
Hagedorn, & Cypers, 2008; Melguizo et al., 2015). Hence, this study examines the relationship
achieving reclassification to English proficiency before high school graduation and community
college success in math coursework and total college credits, degree-applicable, and transferrable
college credits.
This chapter contains results from analyses conducted to address the study purpose stated
above and the three guiding research questions. The first section of this chapter contains a
thorough description of summary statistics. The next three sections, report results for each of the
three guiding questions listed below. The fifth and concluding section includes a chapter
summary. Given the study’s purpose, most results in this chapter focus on Latinx former EL
students are presented relative to Latinx English Only students.
RQ1. Where do Latinx former ELs begin their community college trajectories (including
educational goals and math placement referrals) and how do these rates compare to their
Latinx English Only peers?
RQ2. What math course do Latinx former ELs, as well as Latinx English Only students,
enroll into first and do these courses align with those they were referred to?
RQ3. Does achieving EL proficiency before high school graduation predict success in
college math courses and college credit accumulation for Latinx students?
To elaborate, the first section on summary statistics presents demographic and
background descriptive statistics on the full Latinx sample. In addition to presenting
characteristics for Latinx former ELs and Latinx English only students, some summary statistics
in this chapter are provided for Latinx students who were not classified into either of these
groups. In other words, I also report summary statistics data on Initially Fluent English Proficient
(IFEP) and Limited English Proficient (LEP) students in order to describe the Latinx sample as a
85
whole. For definitions and more information on the four EL designations (RFEP, EO, LEP and
IFEP) see Chapter 2. Figure 3 in Chapter 2 also contains an English Learner Designation
Flowchart. As stated in earlier chapters, I treat the terms “Latinx Former EL” and “RFEP”
synonymously. I opt to use “Latinx Former EL” over “RFEP” in most cases for accessibility
purposes. Along with reporting background characteristics and academic achievement, the
summary statistics also includes trends for Latinx Former ELs and Latinx English Only students
as it pertains to their math course-taking sequences in high school.
The second section aligns with research question #1 which inquires about the educational
goals and math placement referrals for Latinx former ELs relative to Latinx English Only
students. The third section suits research question #2 as it interrogates the extent to which Latinx
students abide with their placement referral, examines how students may deviate from their
placement referral, and analyses their success in passing the first math course that they enrolled
into. It also includes the results on actual first math class enrollment, deviations from referrals,
trends among non-complying students (students that opted not to take their referred course), and
first math course pass rates. The fourth section corresponds to results from research question #3
as it gauges the relationship between EL status and academic achievement for Latinx students in
math and unit completion. Each of the sections include a corresponding section summary and
the chapter concludes with an overall summary of the results.
Summary Statistics
This section describes the background and academic characteristics for the full Latinx
sample (n=18,400) and disaggregates by their last recorded EL designation: RFEP (n= 12,430),
EO (1,734), LEP (2,552), and IFEP (1,684). Latinx Former ELs (RFEP) make up the majority of
the full Latinx sample (64.7%), followed by: Latinx LEP (16.5%), Latinx English Only (10.2%),
86
and Latinx IFEP students (8.6%). Thus, over 80% (RFEP + LEP) of the students in the full
Latinx sample were at one point or still are English Learners. Figure 4 (below) contains a pie
chart visualizing the sample size overall as well as divided into the four last recorded, district-
assigned designations.
Figure 4. Sample Overview for the Full Latinx Sample
The purpose of this study is to analyze long-term outcomes in college mathematics and
college credit attainment for Latinx former ELs. Latinx English Only students are used as the
reference group. Therefore, this section focuses on these two groups’ background characteristics
and academic achievement but also reports results for the other two district-assigned
designations: LEP, and IFEP. To further explore Latinx former ELs high school mathematics
g p p
Note. Reclassified Fluent English Proficient (RFEP) students are also referred to as
Former EL students.
64.67%
10.21%
8.617%
16.5%
RFEP EO
IFEP LEP
Full Latinx Sample
87
preparation and experience, the last part of this section reports the results of the math course
sequence analysis (see Abbot, 1990, 1995; Ngo and Velasquez, 2020), which are determined by
the math course each student was enrolled in at the beginning of the academic year throughout
high school. The most common math course sequences taken are reported for Latinx former ELs
and Latinx English only students.
Background Characteristics
Differences in background characteristics and academic achievement emerge when
comparing within the Latinx sample by the last recorded, four district-assigned designations
(Table 1). The full Latinx sample was composed of majority female students (56%) and the same
could be said for each of the four district assigned designations although percentages varied.
About 90% of all students in the full Latinx sample reported Spanish as their home language and
10% selected English. As expected, all Latinx English Only students indicated English as their
home language. All Latinx former EL students, along with Limited English Proficient students,
and all IFEP students selected Spanish as their home language.
88
Table 1. Demographic Characteristics for the Full Latinx Sample
Aside from language, Table 1 reports socioeconomic status, documented status, parental
education, and whether students were in Special Education (SPED). A higher proportion of
Latinx former ELs, compared to their Latinx EO peers, were eligible for free and reduced lunch
(94% vs 84%), were likely undocumented (12% vs. 1%), and had parents who did not graduate
high school (51% vs 19%). Meanwhile, a lower proportion of Latinx former ELs reported being
a U.S. Citizen (84% vs 99%) and indicated having parents who graduated college (9% vs 19%)
compared to their Latinx EO peers. For Latinx Former ELs (relative to Latinx English Only
students) these characteristics indicating lower SES, being less likely to be a U.S. Citizen, and
having parents and/or guardians with lower formal education attainment might imply that Latinx
Former ELs may not perform as well academically as their Latinx English Only peers.
LEP
mean sd mean sd mean sd mean sd mean sd
Gender
Female 0.56 0.50 0.57 0.50 0.55 0.50 0.53 0.50 0.53 0.50
Socioeconomic Status
Free or Reduced Lunch 0.93 0.26 0.94 0.23 0.84 0.36 0.95 0.23 0.89 0.32
Special Education
In SPED 0.09 0.28 0.05 0.21 0.12 0.32 0.28 0.45 0.07 0.25
Home Language
English 0.10 0.29 0.00 0.01 1.00 0.00 0.00 0.02 0.00 0.02
Spanish 0.90 0.29 1.00 0.03 0.00 0.00 1.00 0.06 1.00 0.05
Asian Origin 0.00 0.02 0.00 0.02 0.00 0.00 0.00 0.03 0.00 0.04
Other 0.00 0.03 0.00 0.02 0.00 0.00 0.00 0.04 0.00 0.02
Immigration Status
U.S. Citizen 0.86 0.35 0.84 0.36 0.99 0.10 0.76 0.43 0.97 0.17
Permanent Resident 0.03 0.18 0.04 0.19 0.00 0.06 0.06 0.24 0.01 0.09
Refugee 0.00 0.02 0.00 0.02 0.00 0.00 0.00 0.02 0.00 0.00
Likely Undocumented 0.11 0.31 0.12 0.33 0.01 0.08 0.18 0.38 0.02 0.14
Student or Visitor Visa 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02
Parent Education
Not HS Grad 0.48 0.50 0.51 0.50 0.19 0.40 0.57 0.49 0.32 0.47
HS Grad or Some College 0.42 0.49 0.40 0.49 0.62 0.49 0.35 0.48 0.52 0.50
College Grad or Higher 0.10 0.30 0.09 0.28 0.19 0.39 0.08 0.27 0.16 0.36
N 18400 12430 1734 2552 1684
Full Latinx Sample RFEP EO IFEP
(Former EL)
89
The background characteristics suggest Latinx Former EL students might be more likely
to encounter challenges related to poverty, navigating the legal system, and navigating U.S.
schooling. Historically, these challenges have been described as detrimental to academic
achievement. Yet, Latinx Former ELs did report having a smaller proportion of students in SPED
compared to Latinx English Only students (5% vs 12%). However, for ELs that never achieved
reclassification, 28% were in SPED. Considering the EL designations presented here are the last
recorded EL designations during students’ high school career, the magnitude of these rates for
Latinx Former ELs and LEPs are consistent with research indicating that students with
disabilities are far less likely to achieve reclassification (Umansky, Thompson, & Díaz, 2017).
More specifically, Umansky and colleagues (2017) note that high school ELs who have not yet
reclassified to English proficient are overrepresented in SPED.
Former ELs and IFEP proportions of students in SPED were lower at 5% and 7%,
respectively, relative to the rate of 9% SPED for all Latinxs. LEP students also had the lowest
proportion of students who reported being U.S. Citizens (76%), the highest proportion of
students with parents that did not graduate high school (57%), and they tied with Latinx Former
ELs for the proportion of students eligible for free or reduced lunch (95%). Meanwhile, IFEP
students reported the lowest proportion of students eligible for free or reduced lunch status.
IFEPs had a higher proportion of students reporting being U.S. citizens (97%) than Latinx
Former ELs (86%) or LEP students (76%) but had a lower rate than Latinx English Only
students (99%). Latinx IFEP students had higher parental education relative to Latinx former EL
and Latinx LEPs but, once again, trailed behind Latinx English Only students. For Latinx EOs,
62% of their parents received a high school diploma or attended some college, while another
90
19% graduated from college. This compared to 52% of Latinx IFEPs graduating high school or
attending some college and 16% graduating college.
Academic Characteristics
Despite background characteristics potentially indicating that Latinx Former ELs would
trail Latinx English Only students academically, a look into academic preparation suggests that
Latinx Former ELs, on average, perform on par with and sometimes better than Latinx English
Only students. In Table 2 (see Chapter 4), In high school, Latinx former ELs attained a higher
GPA, on average, compared to English Only students (2.54 vs. 2.50). When considering the
highest high school math course passed with a B or better, a higher proportion of Latinx former
ELs succeeded in more advanced math courses (e.g., Calculus, Trig/Pre-Calc, Algebra 2)
compared to Latinx EO students (46% vs. 42%). Higher GPA and larger proportions of students
passing advanced math coursework for Latinx Former ELs suggests that Latinx Former ELs
entered college more prepared, on average, than their Latinx English Only peers. Other metrics,
though, do not show much of a difference between Latinx Former ELs and Latinx English Only
students.
91
Table 2. Academic Characteristics for the Full Latinx Sample
The performance on GPA and passing advanced math courses with a B or better was
highest for Latinx Former EL students when compared to the rest of the three district assigned
EL designations. The highest average GPA was achieved by Latinx Former ELs (2.54), then
Latinx IFEP students (2.52), followed by Latinx English Only students (2.50), and finally Latinx
mean sd mean sd mean sd mean sd mean sd
EL Status
EO 0.09 0.29 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00
IFEP 0.09 0.29 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00
LEP 0.14 0.35 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00
RFEP 0.68 0.47 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
High School GPA
Overall GPA 2.50 0.51 2.54 0.50 2.50 0.49 2.28 0.51 2.52 0.49
D Average 0.05 0.22 0.04 0.20 0.05 0.21 0.12 0.33 0.03 0.18
C Average 0.60 0.49 0.59 0.49 0.61 0.49 0.67 0.47 0.61 0.49
B Average 0.33 0.47 0.36 0.48 0.34 0.47 0.20 0.40 0.35 0.48
A Average 0.01 0.09 0.01 0.10 0.01 0.09 0.00 0.07 0.01 0.07
Highest HS Math Passed with B or Better
Algebra 1 0.26 0.44 0.24 0.43 0.25 0.43 0.37 0.48 0.25 0.43
Geometry 0.31 0.46 0.30 0.46 0.33 0.47 0.36 0.48 0.32 0.47
Algebra 2 0.28 0.45 0.28 0.45 0.28 0.45 0.22 0.41 0.28 0.45
Trig/Pre-Calc 0.11 0.32 0.13 0.34 0.09 0.29 0.04 0.20 0.10 0.30
Statistics 0.02 0.14 0.02 0.14 0.02 0.13 0.00 0.06 0.03 0.16
Calculus 0.02 0.15 0.03 0.16 0.03 0.16 0.00 0.05 0.02 0.14
State's Standardized Math Test (Grade 11)
Far Below Basic 0.33 0.47 0.30 0.46 0.31 0.46 0.50 0.50 0.28 0.45
Below Basic 0.44 0.50 0.45 0.50 0.41 0.49 0.43 0.50 0.43 0.50
Basic 0.17 0.38 0.18 0.39 0.20 0.40 0.07 0.25 0.20 0.40
Proficient 0.05 0.23 0.06 0.23 0.07 0.25 0.00 0.05 0.08 0.27
Advanced 0.01 0.08 0.01 0.08 0.01 0.09 0.00 0.03 0.01 0.10
State's Standardized English Test (Grade 11)
Far Below Basic 0.11 0.31 0.06 0.24 0.09 0.29 0.43 0.50 0.08 0.27
Below Basic 0.21 0.41 0.18 0.38 0.20 0.40 0.50 0.50 0.15 0.35
Basic 0.39 0.49 0.45 0.50 0.36 0.48 0.06 0.24 0.36 0.48
Proficient 0.22 0.41 0.24 0.43 0.25 0.43 0.00 0.07 0.30 0.46
Advanced 0.07 0.25 0.06 0.25 0.10 0.30 0.00 0.03 0.13 0.33
State University College Readiness Exam
Passed English 0.07 0.26 0.07 0.26 0.10 0.30 0.00 0.00 0.15 0.36
Passed Math 0.25 0.44 0.27 0.44 0.26 0.44 0.01 0.08 0.32 0.47
Four Years of College Prep English
Passed with C or Better 0.16 0.37 0.17 0.38 0.18 0.39 0.05 0.22 0.19 0.39
Passed with B or Better 0.03 0.17 0.04 0.19 0.03 0.17 0.00 0.07 0.04 0.20
N 18400 12430 1734 2552 1684
(Former EL)
Full Latinx Sample EO IFEP LEP RFEP
92
LEPs (2.28). Performance in passing advanced math courses ranked similarly with Latinx
Former ELs having the highest proportion of students passing (17%), followed by Latinx EO and
Latinx IFEP students (14%), and finally Latinx LEP (5%).
Differences in percentages between Latinx former ELs and their Latinx English Only
peers appeared in scores on standardized exams, but these differences were minor. Regarding
scores on an 11
th
grade state standardized math test, Latinx former ELs scored slightly lower
than English Only students in terms of the proportion that scored in the basic, proficient, or
advanced category (25% vs. 28%). A smaller proportion of Latinx former ELs passed the state
university system’s college readiness exam for Math but still outperformed English Only
students (27% vs 26%). In sum, when using math course-taking and GPA as metrics of
academic performance, Latinx former ELs outperformed EOs but when looking at standardized
state and university exam scores, there were no major differences. To further explore the Latinx
former EL and Latinx English Only students’ math preparation and experience, the next section
contains information on high school math course-taking.
Math Course-Taking Sequences
Prior scholarship has linked high school math course taking with academic outcomes
such as college enrollment and college success (Aughinbaugh, 2012; Byung, Irvin, & Bell, 2015;
Kim, Kim, DesJardins; Long, Conger, & Iatarola, 2012). Studies have also documented
inequalities in access to advanced math course sequences by race and social class (Kelly, 2009;
Bozick & Ingels, 2008; Domina & Saldana, 2012). With both access and the link between math
courses taking and college outcomes in mind, I further explore Latinx former ELs and Latinx
English Only students’ high school math course sequences to explore access to advanced math
courses and gauge preparation for college level math. To do so, I share results from a math
93
course sequence analysis (Abbot, 1990, 1995; Ngo and Velasquez, 2020) that ranks students’
most taken math course sequence and indicates whether math course sequences met college
preparatory criteria.
The analysis identified a total of 4963 different math course sequences taken by the full
Latinx sample. Latinx former ELs took a total of 454 different sequences while Latinx English
Only students took 219. In Table 3 (see Chapter 4) I report the 15 most common high school
math course sequences taken by Latinx Former ELs, Latinx English Only students, and the full
Latinx sample. Reporting the top 15 most common course sequences captures the trajectories of
the majority of the full Latinx sample. The same is true within Latinx Former ELs and Latinx
English Only students with over 54% of student’s trajectories concentrated in the top 15 most
common sequences. The 15
th
most common course sequence for EO students is not listed (see
note on Table 3). The remaining course sequences that are not listed were each taken by less
than 1.5% of students.
94
Table 3. Top 15 Most Common HS Math Course Sequences for Latinx Former EL and Latinx English Only
Students
To contextualize course sequences further, Table 3 indicates how many courses within
each sequence would be eligible to fulfill the state math course requirements for college
admission. Per the A-G requirements, California mandates success in a minimum of three years
of college preparatory math but recommends four years for admission into university school
(University of California, 2022; The California State University, 2022). The most taken math
course sequence for Latinx former ELs and Latinx English Only students, Alg1-Geo-Alg2-None,
met the three-year minimum college preparatory math requirement. These students started with
Algebra 1 in 9
th
grade, then Geometry in 10th, followed by Algebra 2 in 11
th
, and no math course
in 12
th
. This Alg1-Geo-Alg2-None college-preparatory math sequence was taken by a higher
proportion of Latinx former ELs (12.5%) than Latinx English Only students (11.5%).
On one hand, the second most common sequence for Latinx former ELs met the four-
year recommended college preparatory math requirement (Alg1-Geo-Alg2-PreCalc) and was
Years College
9th 10th 11th 12th Prep Math Percent Rank Percent Rank Percent Rank
Alg1 Geo Alg2 None 3 12.5 1st 11.5 1st 12.2 1st
Alg1 Geo Alg2 PreCalc 4 6.3 2nd 4.6 4th 5.4 2nd
Alg1 Geo Alg2 Alg2 3 5.0 3rd 5.7 2nd 4.8 3rd
Geo Alg2 PreCalc None 3 4.5 4th 3.7 7th 3.9 5th
Alg1 Geo None None 2 3.8 5th 4.4 5th 4.4 4th
Alg1 Geo Geo None 2 3.6 6th 4.7 3rd 3.8 6th
Alg1 Alg1 Geo Alg2 3 3.4 7th 3.5 8th 3.4 8th
Alg1 Alg1 Geo None 2 3.4 8th 4.0 6th 3.8 7th
Alg1 Geo Geo Alg2 3 2.6 9th 2.3 10th 2.5 9th
Geo Alg2 PreCalc Calc 4 2.2 10th 2.2 11th 1.9 12th
Alg1 Alg1 Geo Geo 2 2.0 11th 2.1 12th 2.2 10th
Alg1 Geo None Alg2 3 1.9 12th 1.4 16th* 1.8 13th
Alg1 Alg1 None None 2 1.8 13th 2.4 9th 2.0 11th
Geo Alg1 Alg2 None 3 1.7 14th 1.7 14th 1.5 14th
Geo Alg2 None None 2 1.7 15th 2.0 13th 1.5 15th
Math Course Sequence Latinx Former EL Latinx English Only Full Latinx Sample
Note. *The 15th most commonly taken math course sequence for EO students (taken by 1.6%) was Alg1-None-
None-None .
95
taken by 6.3% of them (4.6% for Latinx English Only students). On the other hand, the second
most common sequence for Latinx English Only (Alg1-Geo-Alg2-Alg2) students only met the
minimum, 3-year requirement and had 5.7% of them repeating Algebra two in their third and
fourth year of high school. A smaller proportion (5%) of Latinx former ELs took this repetitive
sequence. Overall, a larger percentage of Latinx former ELs took college preparatory math
course sequences. In addition, among Latinx students that took college-preparatory mathematics
sequences, Latinx former ELs took four years of math suitable for admission into the California
State University and University of California systems more often than Latinx English Only
students.
Within the top 15 most frequented sequences, two of them exceeded the minimum state
requirement for admission into university (Alg1-Geo-Alg2-PreCalc and Geo-Alg2-PreCalc-
Calc). For Latinx former ELs, 8.9% of students took these sequences which ranked 2
nd
and 10
th
within their group. Meanwhile, 6.8% of Latinx EO students took the same two sequences which
ranked 4
th
and 11
th
within their group. Considering these math course-taking trends, the data
suggest that both math preparation and course sequences for Latinx ELs may have been more
rigorous or advanced relative to their English Only student peers. These trends are consistent
with the academic achievement discussed previously that demonstrated that Latinx Former EL
students passed more advanced math courses in high school compared to their Latinx English
Only peers.
Summary of Summary Statistics
An exploration of the differences in the background characteristics between Latinx
former ELs relative to Latinx English Only students suggest that Latinx former ELs were more
likely to be eligible for free and reduced lunch, had lower proportions of students that indicated
96
being U.S. Citizens, and had parents who obtained less formal education. These results are
consistent with a narrative of Latinx Former ELs in this sample of being lower SES, being more
likely to be undocumented, and having less formally educated parents and guardians compared to
their Latinx English Only peers. These characteristics could indicate that Latinx Former ELs
would be more likely than Latinx English Only students to face challenges related to being low
income, navigating the legal system, attaining government services, and navigating U.S.
schooling. Thus, one might conclude that Latinx Former ELs are at a disadvantage due to facing
these challenges more frequently when it comes to performing academically compared to Latinx
English Only students.
In terms of academic preparation, however, Latinx ELs had equal or higher academic
performance than their Latinx English Only. In measures of GPA and advanced math course
success Latinx former ELs outperformed their peers, but performance on standardized math
exams were similar between Latinx former ELs and Latinx English Only students. English
success measured by completing four years of college preparatory English coursework during
high school was accomplished by similar proportions of Former Latinx EL and Latinx English
Only students (17% and 18% success, respectively).
A tabulation of the top fifteen most common math course sequences taking in high school
revealed that higher proportions of Latinx Former ELs took college preparatory math course
sequences relative to EO students. Also, among Latinx students that took college preparatory
math course sequences, Latinx former ELs more often took four years of these courses (as
opposed to the minimum, three) than Latinx English Only students. These results confirm
findings related to the academic preparation suggesting that Latinx Former ELs passed advanced
math courses in high school at higher rates than Latinx English Only students. I proceed to
97
describe the results for the first research question which asks about Latinx former EL students’
educational goals and math course placement referrals.
RQ1 Results: Educational Goals and Math Course Referrals
This section reports results corresponding to the first research question, “Where do
Latinx former ELs begin their community college trajectories (including educational goals and
math placement referrals) and how do these rates compare to their Latinx English Only peers?” I
begin by sharing results related to students’ educational goals and what levels of math they were
referred to in college after taking an assessment and placement exam. I report whether
differences for educational goals and math placement referrals were statistically significant
between Latinx former ELs and Latinx English Only students as indicated by means
comparisons tests.
Educational Goals
Upon entering community college, students indicated their educational goal by selecting
from one of seven options included in Table 4: (1) bachelor’s degree and/or transfer, (2)
associate degree, (3) high school diploma or GED, (4) advance or keep a current job credential,
(5) new career preparation or exploration, (6) personal development, or (7) unknown or
undecided. The majority of Latinx former EL students (55.7%) indicated and intention to
transfer and/or obtain a bachelor’s degree. Another 21.3% of them were undecided. There were
also Latinx former EL students that attended with new career goals (12.7%) and a small
percentage indicated seeking to earn an associate degree (4.8%), get a high school diploma or
GED (2.5%), or for reasons related to personal development (1.4%).
98
Table 4. Educational Goal for Latinx Former EL and Latinx English Only Students
Compared to Latinx English only students, means comparisons tests only indicated
statistically significant differences between groups as it pertained to selecting the BA/Transfer or
undecided options. Latinx Former ELs were slightly less likely to report bachelor’s degree
and/or transfer intentions than Latinx English Only students (55.7% vs 58.7%) and slightly more
likely to be undecided about their educational goal (21.3% vs19.6%). Table 4 captures the
distribution of students’ educational goals and compares between Latinx former ELs and Latinx
English Only. There was no evidence of a statistically significant difference between Latinx
former ELs and their Latinx English Only peers when it came to attending college to aid a new
career, intending to get an AA degree, obtain a HS diploma or GED, earn a job credential, begin
a new career, or attend college for personal development purposes.
Placement Referrals
Assessment and placement exams resulted in referral to math courses ranging from
Transfer level math all the way to 5 levels below transfer. I capture math placement referrals in
Table 5 and include results from mean comparison tests to indicate if differences between Latinx
former ELs and Latinx English Only students were statistically significant. Overall, more than
95% of Latinx former EL students were referred to below transfer-level math coursework. Over
half of all Latinx former EL students were referred to 2 or 3 levels below transfer (30.5% and
BA Degree
and/or
Transfer
Associate's
Degree
HS Diploma
or GED
Advance or
Keep a
Current Job
Credential
New Career
Preperation
or
Exploration
Personal
Development
Unknown or
Undecided
Language Status
Latinx Former EL 55.7 4.8 2.5 1.7 12.7 1.4 21.3
Latinx English Only 58.7 4.4 2.3 1.9 12.2 0.9 19.6
Difference -2.9* 0.3 0.2 -0.2 0.5 0.4 1.7*
Note. T-Tests determined statistically significant differences between the listed groups by Educational Goal. *, **, and ***
indicate P ≤ 0.05, P ≤ 0.01, P ≤ 0.001, respectively.
99
26.2%, respectively). Over a quarter of them were referred to 1 level below or to transfer level
(22.2% and 5.0%, respectively). The remaining were directed to 4 and 5 levels below transfer
(15.7% and 0.5%, respectively.
Table 5. Math Level Referral for Latinx Former EL and Latinx English Only Students
Thus, the distribution of referrals for Latinx Former EL students can be described as
being centered around 2 levels below transfer with the lowest frequencies being at 5 levels
below, Transfer, then 4 levels below. Outside of 2 levels below, the highest frequencies were at 3
and 1 level below. Meanwhile, the distribution for referrals for English Only students was more
centered around 3 levels below (as opposed to 2). The second and third most frequent referrals
for Latinx English Only students were to 2 and 4 levels below, respectively. At a glance, at the
distribution of placement referrals between the two groups suggests that Latinx EOs scored
higher, on average, in mathematics assessment and placement exams. These results are
consistent with their high school academic characteristics and math course taking sequences
presented previously that indicated that Latinx former ELs passed more advanced math
coursework and took college-preparatory mathematics sequences more often. Means
comparisons test corroborated the same narrative.
The difference in the proportion of students referred to each level between Latinx former
ELs and their English Only peers is statistically significant at the P ≤ 0.001 level for all math
course referral levels except for 5 levels below transfer. Notably, less than 1% of Latinx former
5LB 4LB 3LB 2LB 1LB Transfer
Language Status
Latinx Former EL 0.5 15.7 26.2 30.5 22.2 5.0
Latinx English Only 0.5 18.4 30.1 26.9 19.3 4.8
Difference 0.0 -2.8** -3.9*** 3.5** 2.9** 0.2
Note. 5LB indicates being referred to a math course five levels below transfer
level. 4LB indicates 4 levels below and so forth.
100
ELs or English Only students were referred to 5 levels below. The statistically significant
differences show higher proportions of Latinx former EL students enrolling in higher levels (1
and two levels below) and higher proportions of Latinx English Only students enrolling in the
lowest referral levels (3, 4, and levels below transfer). The biggest difference in referrals
between the two groups occurred at 3 levels below, with 26.2% of Latinx former ELs being
referred to this level compared to 30.1% of Latinx EO students. Even though placement referral
distributions for Latinx former ELs were better than Latinx English Only students, only 5% of
them were referred to transfer level mathematics.
Summary of RQ1 Results
A statistically significant difference emerged in the proportion of Latinx former EL
students and Latinx English Only students that intended to transfer and attain a bachelor’s
degree. A smaller proportion of Latinx Former EL students reported that their educational goal
was to transfer than Latinx English Only students. Instead, Latinx former EL students were
slightly more likely to be undecided. With respect to placement referrals, Latinx former EL
students scored higher on math assessment and placement exams and were referred to higher
levels of mathematics relative to Latinx English Only students. Only about 5% of Latinx former
EL students and 4.8% of Latinx English Only students were referred to take transfer level
mathematics upon completion of an assessment and placement exam. Thus, the vast majority of
Latinx former EL and Latinx English Only students (over 95%) were directed to take below-
transfer level mathematics courses.
RQ2 Results: First Math Course Enrollment, Noncompliance, and First Math Success
While assessment and placement exams and policies referred students to particular math
course levels, enrollment records of students' first community college math course enrollment
101
differ from these recommendations. This section includes results in response to the second
research question. “What math course do Latinx former ELs, as well as Latinx English Only
students, enroll into first and do these courses align with those they were referred to?” I first
share evidence capturing enrollment trends into students’ first math course for Latinx former EL
students and their Latinx English Only peers. Then, I compare the math courses that Latinx
former ELs and Latinx English Only students enrolled into with those which they were referred
to. In doing so, the results demonstrate the extent to which Latinx students did or did not enroll
in their referred course. I refer to these as compliance and noncompliance rates, respectively. I
also share the directionality of non-compliance by examining whether noncomplier students
enrolled in lower or higher-level math courses relative to their referral. To follow, I note success
rates in students first math course taken. I end with a section summary.
First Math Course Enrollment
Rates of first math enrollment by group are reported in Table 6. The trends in enrollment
for Latinx former ELs and Latinx English Only students, more or less, resemble the distributions
captured in Table 5, discussed previously, which captured students’ math level referrals. An
overwhelming majority of Latinx former EL students, like their Latinx English Only peers,
enrolled in below-transfer level coursework (95%). Similar to their placement referrals, the
largest proportions of Latinx Former EL student first math course enrollment was concentrated at
two (28.9%) and three levels below transfer (29.0%). The next largest enrollment group (22.2%)
started in coursework one level below transfer. The smallest proportion of enrollment for Latinx
Former EL was seen at four levels below transfer (14%), transfer level (5.6%), and five levels
below (0.3%).
102
Table 6. First Math Enrollment for Latinx Former EL and Latinx English Only Students
Along with capturing math enrollment for Latinx former EL and Latinx English Only
students, this table demonstrates the difference in enrollment between these groups. Means
comparisons tests indicate that the difference in the proportion of students enrolled into each
level between these two groups is statistically significant at one, two, three and four levels below
transfer. Latinx former EL students were more likely to enroll into one and two levels below
transfer level math coursework than their English Only peers and less likely to enroll into three
and four levels below. The largest differences between the groups emerged at three and two
levels below transfer.
The magnitude of enrollment proportions changed compared to placement referral
proportions, suggesting that students did not all enroll in line with their placement referral.
Although actual enrollment rates and group differences resembled the results in placement
referrals, there are other differences. For both groups, transfer level enrollment was slightly
higher relative to their placement referrals. Also, enrollment to the lowest levels (5 and 4 below)
was lower for each group when compared to their placement referral rates.
Relative to Latinx English Only students, a statistically significant higher proportion of
Latinx former ELs enrolled into one and two levels below and a statistically significant lower
proportion enrolled into three and four levels below. Yet, overall, the proportion of Latinx
former EL students that enrolled into two levels below transfer was lower compared to what was
5LB 4LB 3LB 2LB 1LB Transfer
Language Status
Latinx Former EL 0.3 14.0 29.0 28.9 22.2 5.6
Latinx English Only 0.4 16.1 32.9 24.7 20.1 5.7
Difference -0.1 -2.1** -3.9*** 4.2*** 2.1* -0.1
Note. 5LB indicates student enrolled into a math course five levels below
transfer. 4LB indicates 4 levels below and so forth.
103
advised by math placement referrals. Meanwhile, the proportion of Latinx former ELs students
that enrolled into three levels below was higher than their placement referral rates. To further
understand trends in noncompliance, the next section unpacks the prevalence and directionality
of enrollment relative to referral.
First Math Enrollment Relative to Referral
The results above suggest that students did not always enroll into the course they were
referred to. This result leads to additional questions. What percentage of students deviated from
their referred course? Did students comply more when directed to higher level courses? Also,
among students that deviated, did they enroll in higher- or lower-level courses relative to their
referral? Table 7 helps answer this question by providing enrollment rates relative to students’
referral level. The percentage of students that complied with their referral is included as well as
the percentage of students that enrolled in coursework higher or lower than their referral.
Table 7. Math Enrollment Relative to Referral for Latinx Former Els and Latinx EOs
Whereas compliance indicates that a student’s first math course they enrolled into was
the same level that they were referred to by assessment and placement procedures, being a non-
complier indicates that a student took a different level of math compared to what was
Total
Enrolled
Lower
Enrolled
in
Referred
Level
Total
Enrolled
Higher
Language Status
Latinx Former EL 6.1 87.7 6.3
Latinx English Only 6.0 87.1 6.9
Difference 0.1 0.5* -0.6*
Note. T-Tests determined statistically significant
differences between the listed groups by Educational
Goal. *, **, and *** indicate P ≤ 0.05, P ≤ 0.01, P
≤ 0.001, respectively.
104
recommended. Overall, 87.7% of Latinx former EL students complied with their placement
referrals. Of the remaining 12.3% of Latinx former ELs which deviated, about half enrolled into
lower coursework (6.1%) and another half enrolled into higher coursework (6.3%). Meanwhile,
a statistically significant smaller proportion of Latinx English Only (87.1%) students complied
with their placement referral. The proportion of Latinx former ELs that took coursework lower
than their referral was similar to their Latinx EO peers, (6.1% and 6.0%, respectively). A
statistically significant, smaller proportion of Latinx former EL students deviated from their
placement referral into higher coursework compared to Latinx English Only students (6.3% vs.
6.9%). When looking within the Latinx English Only group, a larger percentage (6.9%) of them
enrolled in higher levels of math as opposed to lower levels (6.0%). Among non-complying
Latinx English Only students, a modestly higher proportion enrolled in courses higher than their
referral as opposed to lower (6.1% vs 6.3%).
105
Figure 5. First Math Enrollment by Referral for Latinx Former Els and Latinx EOs
58.93
32.14
7.143
1.786 .0514
82.72
10.9
4.37
1.131 .8226
.092
2.239
90.86
3.62 1.595 1.595 .0264 .7658
10.22
85.03
3.301
.6602
.2535 1.231
5.867
90.98
1.666 .3221 .6441 1.127
7.568
90.34
100
75
50
25
0
100
75
50
25
0
100
75
50
25
0
5 Below
4 Below
3 Below
2 Below
1 Below
Transfer
5 Below
4 Below
3 Below
2 Below
1 Below
Transfer
5 Below 4 Below
3 Below 2 Below
1 Below Transfer
Percent
First Math Enrollment by Referral for RFEP Students
Graphs by Math Placement Level
62.5
37.5
.627
80.56
10.97
3.762 2.508 1.567
2.299
90.8
2.682 2.49 1.724 1.285
12.42
82.87
2.784
.6424
.2985 1.194
4.776
92.54
1.194
6.024
93.98
100
75
50
25
0
100
75
50
25
0
100
75
50
25
0
5 Below
4 Below
3 Below
2 Below
1 Below
Transfer
5 Below
4 Below
3 Below
2 Below
1 Below
Transfer
5 Below 4 Below
3 Below 2 Below
1 Below Transfer
Percent
First Math Enrollment by Referral for EO Students
Graphs by Math Placement Level
106
To further understand and visualize variation of enrollment given placement referrals,
Figure 5 (above) demonstrates compliance rates and deviation by referral level for both Latinx
former EL students and Latinx English Only students. As might be expected, students complied
at higher rates when referred to higher levels and deviated most when referred to lower
levels. When referred to transfer level or one level below, compliance rates for Latinx former
EL students exceeded 90%. When referred to four or five levels below, compliance rates
dropped to 58.9% and 82.7%, respectively. But the middling levels (2 and 3 below), did not
follow the trend at 85 and 90.8%. Latinx English Only students and Latinx Former EL students
deviated the most from their referral when directed to 5 levels below transfer level with Latinx
English Only students complying roughly 62.5% of the time and Latinx former EL students
complying 58.9% of the time. To get a better sense of trends among noncompliers and the
direction of their deviations (enrolling higher or lower), I examine only students that deviated
from their referral next.
First Math Enrollment Trends Among Noncompliers
Do any trends exist among noncompliers and the extent of their noncompliance? Table 8
addresses such a question by reporting student enrollment levels relative to their referral for
students that did not enroll in the math level they were referred to. The table provides the
proportion of Latinx Former EL and Latinx English Only noncompliers that deviated anywhere
from four levels higher than their referral to four levels lower than their referral. Also included
is a row indicating the difference in these groups’ proportions. Overall, the data suggest that a
similar proportion of noncompliers enrolled in higher and lower-level courses, but variation is
present when looking at the extent of deviation.
107
Table 8. Noncomplier Enrollment Relative to Referral for Latinx Former El and Latinx English Only Students
Table 7. Noncomplier Enrollment Relative to Referral for Latinx Former EL and Latinx English Only Students
Enrolled 4
Levels
Higher
Enrolled 3
Levels
Higher
Enrolled 2
Levels
Higher
Enrolled 1
Level
Lower
Enrolled
Lower
than
Referral
Enrolled
Higher
than
Referral
Enrolled 1
Level
Higher
Enrolled 2
Levels
Higher
Enrolled 3
Levesl
Higher
Enrolled 4
Levels
Higher
Language Status
Latinx Former EL 0.1 0.8 4.8 43.7 49.4 50.6 33.9 10.8 4.9 1.0
Latinx English Only 0.0 0.5 4.5 41.7 46.6 53.4 30.9 12.6 7.6 2.2
Difference 0.1 0.3 0.3 2.0 2.8 -2.7 2.9 -1.7 -2.7* -1.2
108
Noncomplier students mostly deviated just one or two levels. Unsurprisingly, among
those that deviated into lower courses, both Latinx Former ELs and Latinx English Only students
were concentrated at one level below their math placement referral (43.7% and 41.7%,
respectively). This was followed with roughly 4.8% of Latinx former EL noncompliers enrolling
into two math course levels below their referral and 4.5% of Latinx English Only noncompliers.
Small percentages enrolled three or more levels below their referral. Yet, overall, students more
often deviated towards higher level coursework in relation to their referral level. In addition,
among those enrolling into higher coursework, there was more variation. About one third of
Latinx Former EL noncompliers deviated one level higher (33.9%) and a smaller proportion
(30.9%) of Latinx English Only students, did the same.
The table indicates that deviating two or more levels was more common when enrolling
in higher courses than lower. For example, about 16.8% of noncomplying Latinx Former EL
students enrolled in two or more levels higher than their referral while only 5% of noncomplying
Latinx Former EL students enrolled in two or more levels lower relative to their referral. For
Latinx English Only students, a similar pattern emerged. Roughly 20% of noncomplying Latinx
English Only students enrolled two or more levels higher than their referral while only 5.7%
enrolled in two or more levels lower. Additional findings emerge when comparing between
Latinx former EL and Latinx English Only noncompliers.
On the one hand, about 50.6% of Latinx former ELs that did not comply with their
referral enrolled in courses that were higher than their referral. Alternatively, roughly 53.4%
Latinx English Only noncompliers were enrolled in higher levels. In fact, a statistically
significant, lower proportion of Latinx former EL noncompliers enrolled into courses higher than
their referral compared to Latinx EOs. Also, a statistically significant lower proportion of Latinx
109
Former ELs enrolled exactly 3 levels higher than their referral in relation to Latinx EO
noncompliers. After enrolling, did students manage to succeed in their first math course? The
next section reports passing rates.
First Math Pass Rates
The purpose of this study is to analyze the relationship between achieving reclassification
and outcomes in both college credit attainment and college math. Prior to investigating whether
achieving English proficiency predicts such outcomes even after controlling for background and
academic characteristics, this section reports students’ pass rates in their first enrolled math
course. In particular, Table 9 in Chapter 4 captures whether Latinx Former ELs and Latinx
English Only students passed their first math within a one-year time frame.
Table 9. First Math Taken Pass Raters for Latinx Former El and Latinx English Only Students
Overall, 62.6% of Latinx Former EL students passed the first course they were enrolled
into (55.7% for Latinx English Only students). For Latinx Former ELs, success rates ranged
from 60.6% to 78.9% while those for Latinx English Only students’ rates ranged from 42.9% to
62.6%. Latinx Former ELs saw the largest success rates at five levels below transfer (78.9%)
and at transfer level (66.7%). Latinx English Only students highest pass rates occurred at
transfer level (62.6%) and 3 levels below (58.0%). The differences between the two groups’
proportion of students that passed each level was statistically significant at all levels except for
transfer level. In sum, pass rates for Latinx Former EL students were higher at every level from
five levels below up to one level below transfer compared to Latinx English Only students.
Because Latinx former EL students’ higher success rates in math courses may be attributed to
5LB 4LB 3Lb 2LB 1LB Transfer Any
Language Status
Latinx Former EL 78.9 62.3 64.1 61.8 60.6 66.7 62.6
Latinx English Only 42.9 50.5 58.0 54.8 55.6 62.6 55.7
Difference 36.1* 11.8*** 6.1** 7.0** 5.0* 4.1 6.9***
110
having a stronger high school academic preparation, the next step in this analysis examines
whether math course success and other desirable academic outcomes persist for these students
relative to Latinx English Only students even after accounting for background characteristics and
academic preparation.
Summary of RQ2 Results.
In order to answer the second research question, “What math course do Latinx former
ELs, as well as Latinx English Only students, enroll into first and do these courses align with
those they were referred to?” this section reports results on students’ first math course
enrollment, enrollment rates relative to math placement referrals, trends in math course
enrollment among noncompliers, and pass rates in Latinx former ELs and Latinx English Only
students first enrolled math course.
At a glance, first math course enrollment largely appears to mirror math course placement
referral distributions. Over 95% of Latinx former ELs began their math trajectory in below
transfer level courses with the largest proportions enrolling into two or three levels below (about
29% for both), followed by one level below transfer (22.2%), then four levels below (14.0%),
next transfer level (5.6%), and finally five levels below (0.3%). Statistically significant
differences in enrollment emerged between Latinx former ELs and Latinx English Only students
at all courses between one level to four levels below transfer. The statistically significant
differences indicated that Latinx former EL students were more likely to enroll into one and two
levels below transfer compared to their Latinx English only peers (by 2.1% and 4.2%,
respectively) and less likely to enroll into three and four levels below transfer (3.9% and 2.1%,
respectively. In sum, both placement referral and enrollment distributions were more desirable
111
for Latinx former ELs than Latinx EOs but there were differences in enrollment relative to
referral.
More specifically, 12.3% of Latinx former EL and 12.9% of Latinx English Only
students opted to take a different level of math course than the one they were referred to.
Overall, students complied at higher rates when referred to the highest level and deviated from
their referral the most when directed to the lowest levels. For example, Latinx English Only
students directed to five levels below transfer actually enrolled in that level 58.9% of the time
while Latinx former EL students did so at a rate of 62.5%. Meanwhile, over 90% of Latinx
Former ELs and Latinx English Only students directed to transfer level math or one level below
transfer enrolled in that level.
Examining noncompliers exclusively revealed that noncomplying Latinx former ELs less
frequently enrolled in courses higher than their referral compared to Latinx English Only
students. Latinx EOs enrolled in exactly three levels higher than their referral at a statistically
significant higher rate than their Latinx Former EL counterparts. Overall, the largest proportions
of noncomplying students enrolled into one level above their referred math level.
In terms of passing the first math course they were enrolled in, 62.6% of Latinx former
EL students and 55.7% of Latinx EO students succeeded within a one-year time frame. The
largest significant differences in pass rates occurred at the two lowest levels (5 and 4 below) with
Latinx former ELs outperforming Latinx EO students. In fact, Latinx former ELs had a
statistically significant, higher pass rate at all levels except for transfer level. Although 66.7% of
Latinx Former ELs who enrolled into transfer level as their first math course level compared to
62.6% of Latinx EOs, no evidence of a statistically significant between the two groups emerged
at this level.
112
While many of the background characteristics more prevalent in the Latinx former EL
sample (e.g., socioeconomic and citizenship status, parental education, home language) suggest
they may encounter more challenges in their academic endeavors, academic achievement
indicated that Latinx former ELs often had similar or stronger high school academic achievement
and preparation in mathematics compared to their Latinx English Only counterparts. With these
factors in mind as well as the fact that data limitations prevent this analysis from accounting for
additional factors such as student motivation, isolating the association between achieving
reclassification and long-term college math and college credit attainment is a logical next step.
To gauge the association between Latinx EL classification and math outcomes and account for
factors such as academic preparation and background characteristics, the next section reports
regression analysis results.
RQ3: College Math Success and Long-Term Credit Accumulation
While the majority of both Latinx former ELs and Latinx English only students passed
the first math course they enrolled into, a statistically significant higher proportion of Latinx
former ELs (62%) passed the first math course they enrolled into compared to their Latinx
English only peers (54%). However, summary statistics suggest that Latinx former ELs often
had higher stronger academic preparation, on average, than Latinx English Only students. Thus,
higher math course pass rates for Latinx Former EL over Latinx English only students might be
expected. A means comparison test, then, is insufficient to ascertain whether achieving English
proficiency in high school for Latinx ELs predicts higher math course success and higher college
credit attainment since academic achievement and background characteristics are not accounted
for. To gauge this relationship and adjust for academic preparation and background
characteristics, this section reports results from regression analyses. In particular, the analyses
113
address the third research question, “Does achieving EL proficiency before high school
graduation predict success in college math courses and college credit accumulation for Latinx
students?”
In other words, this research question interrogates whether being a Latinx Former EL,
relative to being a Latinx English Only student, is associated with college credit attainment and
college math success even after holding background characteristics and academic preparation
constant. Hence, regression analyses are appropriate since this technique can isolate the role of
achieving English proficiency from other variables (e.g., GPA, socioeconomic status) in relation
to success in credit attainment and math coursework (Frost, 2019). Logistic regression results are
shared first to note the relationship between attaining EL proficiency before high school
graduation and three binary outcomes: (1) passing their first enrolled math course within a one-
year time frame, (2) passing their referred math course within a one-year time frame, and (3)
completing 60-degree applicable units. Then relationships with continuous outcomes are
discussed as evidenced by OLS and multiple regression results. In particular, the college credit
attainment (continuous) outcomes of interests consist of total, degree, and transfer applicable
units.
The models used in the regression analyses follows scholarship that have identified
several demographic and academic factors associated with academic and college success for
English learners (e.g., Melguizo et al., 2021; Robinson, 2011; Thompson, 2015). Variables in
these models include whether a student achieved EL reclassification, high school GPA, gender,
special education designation, citizenship status, parental education, free and reduced lunch
program eligibility, and first math course enrollment. In addition, it is important to control for
the characteristics of the high schools and community colleges attended because students in the
114
sample were not randomly assigned and enrolled into local colleges. Instead, students in the
sample attended different high schools and self-selected into a community college within the
local district. Using high school-by-college fixed effects helps control for unobservable factors
such as differences in EL related policies at high schools and community college math placement
policies (see Andrews et al., 2006; Rabe-Hesketh & Skrondal, 2012). The benefit of using fixed
effects regression models in these analyses is explained in more detail in Chapter 3.
Logistic Regression Results
The fixed effects logistics regression models noted a positive, statistically significant
relationship between being a Latinx former EL and three outcomes: (1) passing the first math
course that students took in the first attempt, (2) passing the math that students were referred to
within a one-year time frame, and (3) completing 60-degree applicable units. These outcomes
coincide with Models A, B, and C in Table 10 which reflect fixed effects logistic regression
results in models with no covariates. For Latinx former EL students, the odds of both passing the
first math course taken in the first attempt (within a one-year time frame) were 1.34 times (or
34%) larger for this Latinx former EL student population compared to their Latinx English Only
peers. Similarly, estimates for passing their referred math course within a one-year time frame
yielded a statistically significant 1.25 odds ratio for Latinx former ELs relative to Latinx English
Only students. Model C demonstrates that Latinx former EL students were 1.26 times more
likely than their EO peers to successfully complete 60-degree applicable units
115
Table 10. Fixed Effects Logistic Regression Results in Odds Ratios by Groups of Interest
After controlling for background characteristics and academic achievement, statistical
significance at the 0.001 level remained for all three of the aforementioned binary
outcomes. Similar odd ratios resulted from Models D, E, and F in Table 10 which parallel
Models A, B, and C, with the inclusion of covariates. When accounting for which college math
Model A Model B Model C Model D Model E Model F
Passed 1st
Enrolled
Math
Passed
Referred
Math
60 Degree-
Applicable
Units
Passed 1st
Enrolled
Math
Passed
Referred
Math
60 Degree-
Applicable
Units
Latinx Former EL 1.34*** 1.25*** 1.26*** 1.27*** 1.21*** 1.32***
(0.07) (0.07) (0.07) (0.07) (0.07) (0.08)
HS GPA 3.29*** 2.69*** 3.05***
(0.15) (0.11) (0.14)
Female 0.88*** 0.91** 1.13**
(0.03) (0.03) (0.05)
SPED 0.73*** 0.80** 0.95
(0.06) (0.06) (0.09)
Permanent Resident 1.02 0.94 0.87
(0.11) (0.10) (0.09)
Refugee 0.18 0.00 0.42
(0.21) (0.00) (0.54)
Likely Undocumented 1.22** 1.14* 0.74***
(0.08) (0.07) (0.05)
Parent HS Grad or Some College 0.99 1.03 1.15***
(0.04) (0.04) (0.05)
Parent College Grad or Higher 1.05 1.12 1.17*
(0.07) (0.07) (0.08)
Free or Reduced Lunch Eligible 0.95 0.96 0.94
(0.07) (0.07) (0.07)
First Math 5 Below Transfer 2.33* 4.15*** 0.29***
(0.90) (1.54) (0.11)
4 Levels Below 1.70*** 1.65*** 0.37***
(0.18) (0.16) (0.04)
3 Levels Below 1.88*** 1.87*** 0.43***
(0.18) (0.17) (0.04)
2 Levels Below 1.41*** 1.53*** 0.54***
(0.13) (0.13) (0.05)
1 Level Below 1.06 1.14 0.71***
(0.10) (0.10) (0.06)
N 13633 13662 13602 13633 13662 13602
Note. Odds Ratios for Latinx Former EL students are reported in relation to English Only students.
116
course a student began in, their high school GPA, gender, whether they were previously in
special education, citizenship status, parent education, free or reduced lunch eligibility (a proxy
for socioeconomic status), and their graduating high school cohort year statistically significant
associations remained between achieving English proficiency in high school and college math
outcomes.
The odds ratios resulting from the fixed effects logistic regression models with covariates
were consistent with the fixed effects regression models without covariates in terms of direction,
significance, and magnitude. Overall, magnitudes decreased when introducing regressors. For
example, after controlling for other variables in the model, Latinx former EL students were 1.27
times more likely than Latinx English Only students to pass the first math they took, 1.21 times
more likely than Latinx English Only students to pass the math they were referred to, and 1.32
times more likely than Latinx English Only students to complete 60 degree-applicable units than
Latinx English Only students (simple fixed effects logistic regression models with no covariates
yielded odds ratios of 1.34, 1.25, and 1.26 respectively). Thus, relative to Latinx EO students,
Latinx former ELs were 27% more likely to pass the first math they took, 21% more likely to
pass the first math they were referred to, and 32% more likely to complete a 60 degree-
applicable units milestone even after accounting for academic achievement, background
characteristics, and high school-to-college path.
The fixed effects logistic regression analyses also include evidence or relationships
between the level of a student’s first math course, being likely undocumented, and being a Latinx
female student with outcomes in math coursework and unit completion. While Latinx students
who were likely undocumented, on average, outperform their documented peers in terms of
completing their first math course and the math course they were referred to, they are less likely
117
to complete 60 degree-applicable units even after controlling for background and academic
characteristics. Holding all else constant, Latinx females, on average, were less likely to
complete their first math course and course they were referred to. However, they were more
likely to complete 60 degree-applicable units than Latinx males. Evidence also emerged
indicating that students whose parents earned at least a high school diploma outperformed
students with parents who did not when it came to completing 60-degree applicable units, but no
evidence of a statistically significant relationship emerged regarding math course success.
Multivariate Regression Results
The previous analysis tells us that Latinx former EL students are more likely to succeed
in the first math they took, the math they were referred to and in completing a 60-degree
applicable unit benchmark. To gauge long term success beyond the first math course, fixed
effects multivariate regression analyses are included with a focus on three outcomes: total units
completed, degree applicable units completed, and transfer applicable units completed. The fixed
effects simple regression and multiple regression models with covariates yielded a positive,
statistically significant relationship between being a Latinx former EL student and units
completed when compared to English Only students. Models A, B, and C in Table 11 reflect
simple regression models.
118
Table 11. Fixed Effects OLS and Multiple Regression Models for Latinx Former EL Students
The models in Table 11 predicts that Latinx former EL students, on average, complete
4.7 total, 4.7 degree applicable, and 3.9 transfer applicable units relative to Latinx EO
students. When background characteristics, high school academic achievement, and math course
start point variables are added as covariates to the model this positive, statistically significant
Model A Model B Model C Model D Model E Model F
Total Units
Completed
Degree
Applicable
Units
Transfer
Applicable
Units
Total Units
Completed
Degree
Applicable
Units
Transfer
Applicable
Units
Latinx Former EL 4.7*** 4.7*** 3.9*** 5.2*** 5.0*** 4.2***
(0.94) (0.92) (0.82) (0.92) (0.90) (0.79)
HS GPA 19.2*** 18.9*** 15.9***
(0.64) (0.62) (0.55)
Female 2.4*** 1.9** 2.2***
(0.59) (0.58) (0.51)
SPED 1.0 -0.3 -0.4
(1.28) (1.25) (1.10)
Permanent Resident -2.2 -2.2 -2.8
(1.64) (1.60) (1.41)
Refugee -10.4 -7.8 -3.5
(15.33) (14.93) (13.15)
Likely Undocumented -5.1*** -4.9*** -4.5***
(0.94) (0.92) (0.81)
Parent HS Grad or Some College 2.6*** 2.7*** 2.5***
(0.62) (0.60) (0.53)
Parent College Grad or Higher 2.9** 3.1** 2.9***
(1.02) (0.99) (0.88)
Free or Reduced Lunch Eligible -1.3 -1.4 -1.4
(1.15) (1.12) (0.99)
First Math 5 Below Transfer -8.8 -17.3** -17.9***
(5.47) (5.32) (4.69)
4 Levels Below -7.7*** -13.9*** -19.0***
(1.57) (1.53) (1.34)
3 Levels Below -8.0*** -12.3*** -18.9***
(1.42) (1.38) (1.22)
2 Levels Below -7.2*** -8.3*** -16.8***
(1.38) (1.34) (1.18)
1 Level Below -3.3* -4.0** -10.2***
(1.37) (1.34) (1.18)
N 13937 13937 13937 13937 13937 13937
Note. Estimates for Latinx Former EL students are reported in relation to English Only students.
119
relationship between Latinx former ELs and units completed remains even though magnitudes
decrease slightly. At the 0.001 significance level, the multivariate regression analyses results (see
Model D, E, and F in Table 11) indicate that being a Latinx former EL is associated with
completing 5.2, 5.0, and 4.2 total, degree applicable, and transfer applicable units compared to
being a Latinx EO students. Given that one course in this context typically counts as four units,
the small decreases we see in magnitude between the simple regression models and those with
covariates is not very concerning. These models lead to the conclusion that when holding all
else equal, Latinx former EL students completed between 4-5 more total, degree, and transfer
units relative to their Latinx EO peers.
Additional relationships emerged as statistically significant in this analysis.
Unsurprisingly, after controlling for all other variables in the model, beginning in lower levels
was associated with completing less degree applicable and transfer applicable units. For
example, Latinx students who began at five, four, and three levels below transfer level completed
roughly 18 to 19 less transferable units than those beginning at transfer. Lower placement was
also associated with less total and degree units completed. The analyses also present evidence
that Latinx students with more educated parents and Latinx females completed more units all
around. Likely undocumented students, however, completed 4-5 less total units, degree
applicable units, and transfer units compared to likely documented students after controlling for
background characteristics and prior academic achievement.
Summary of RQ3 Results
To account for student self-selection into particular community colleges from specific
high schools I conducted fixed effects logistic and multivariate regression analyses. While
means comparisons provided evidence of a statistically significant difference between first
120
enrolled math pass rates (62.6% for Latinx Former EL students and 55.7% for Latinx English
Only students), fixed effects logistic regression models presented evidence of a positive,
statistically significant relationship between being a Latinx former EL student and academic
outcomes relative to being a Latinx EO student. On average, the odds of passing the first math
taken for Latinx former EL students were1.34 times larger than Latinx English Only
students. After controlling for background variables, academic background, and math course
entry levels these odds remained positive and statistically significant with a magnitude of
1.27. Meanwhile Latinx Former EL students were also 1.21 and 1.32 times more likely to pass
the math course they were referred to within a one-year time frame and also complete 60-degree
applicable units after controlling for academic achievement, background characteristics, and high
school-to-college path.
Fixed effects logistic regression results also indicated that likely undocumented Latinx
students, on average, outperformed their documented peers in terms of math course success but
they were less likely to complete 60-degree applicable units. With all else being equal, Latinx
female students performed better than their male peers as it related to completing 60-degree
applicable units but fared worse in math course success. With evidence of a positive relationship
between being a Latinx former EL and math outcomes, I also explored the extent to which this
relationship was associated with total, degree, and transfer applicable units.
Regression analyses displayed evidence of a positive, statistically significant relationship
between being a Latinx former EL students and total, degree, and transfer unit accumulation
relative to being a Latinx EO. This relationship remained when controlling for background
characteristics, academic achievement, and entry level math course. The models predict that
former Latinx EL students, on average, completed 4.7, 4.7, and 3.9 total, degree, and transfer
121
units, respectively, relative to their EO peers. Introducing covariates yielded an association
between being a Latinx former EL student and completing 5.2 more total units than Latinx
English Only students, 5.0 more-degree units, and 4.29 more transfer units.
The fixed effects regression models also revealed evidence suggesting that the lower
students began in mathematics, the less units they ultimately completed. The negative,
statistically significant association between lower-level math starts was harsher for completion of
transfer and degree-applicable units than total units. This is expected as completing more
developmental contributes to total units, but they are often not degree or transfer
applicable. Trends emerged demonstrating that Latinx students with more educated parents as
well as Latinx females completed more units all around relative to their peers. Meanwhile, likely
undocumented students completed 4-5 less total, degree, and transfer units than their likely
documented peers even after controlling for relevant background and academic variables.
Summary of Chapter 4
Summary statistics on the sample of Latinx students’ background characteristics and
academic preparation suggested some differences between Latinx former ELs, and Latinx
English Only students. Compared to Latinx EO students, Latinx Former EL students were more
often eligible for free or reduced lunch, more likely to be undocumented, and were more likely to
have parents that did not graduate high school. Such characteristics are often linked to lower
academic achievement. Latinx English Only students were more likely to report being a US
citizen, more often indicated that they had parents who graduated from college and had higher
proportion of students who were in special education relative to former EL students. Such
characteristics might suggest that higher proportions of former EL students face challenges that
may interfere with academic achievement.
122
However, when looking at high school academic performance, Latinx former EL students
outperformed EOs in high school when measuring GPA (2.54 vs. 2.50) and success in advanced
math courses (46% vs 42%). By other measures, Latinx former EL were a lot more similar than
their EO peers (e.g., state university math readiness exam, state standardized math test).
Consistent with their higher rates of success in advanced math courses, tabulating the most
frequently taken math course sequences indicated that a higher proportion of Latinx former EL
students more often took college preparatory math course sequences than their EO peers.
The first research question unveiled that a smaller proportion of Latinx former ELs have
an educational goal of transferring and attaining a BA compared to Latinx English Only students.
In fact, relative to their Latinx English Only peers, Latinx ELs had higher proportions of students
selected “Undecided” as their educational goal. These differences were found to be statistically
significant. Still, Latinx former EL students were more likely than Latinx English Only students
to be referred to higher level math courses. Unfortunately, the vast majority of all Latinx
students were directed to below transfer level math. More specifically, over 95% of Latinx
former EL students and EOs were directed to math courses below transfer level.
The second research question helped analyze patterns on the first math course students
enrolled in, whether that course was a deviation from their referral, trends among students who
did not comply with their referral, and student pass rates in their first math course. First math
enrollment rates mirror referral distributions most Latinx students (over 87%) taking their
referred course. A statistically significant, higher proportion of Latinx EO students enrolled in
their referred course. Compared to noncomplying Latinx Former EL students, noncomplying
Latinx EOs more often enrolled in math courses higher than their referral. Latinx EO
noncomplying students had a higher percentage of students deviating towards enrolling in
123
exactly three course higher than their referral compared to their Latinx Former EL peers.
Overall, 59% of all Latinx students passed their first enrolled math course within a one-year time
frame. Latinx former ELs passed at higher rates (62.6%) than EO students (55.7%).
The third research question further explored math success as well as credit accumulation
while accounting for background differences, academic achievement, and first math course
level. Using fixed effects logistic regression, I reported evidence on the relationship between EL
status and success in passing the first math taken, students’ referred math, and completing 60-
degree applicable units. Holding background characteristics, academic preparation, and math
course starting point constant, a positive, statistically significant relationship emerged between
Latinx former EL status and achievement. Latinx former ELs were between 1.2 and 1.3 times
more likely to (1) pass the first college math course they enrolled in within a one-year time
frame, (2) pass the math they were referred to in that same time frame, and (3) complete 60-
degree applicable units. Trends also emerged indicating that Latinx, likely undocumented
students outperformed their Latinx documented peers in terms of math course success but fared
worse in completing 60-degree applicable units.
Fixed effects multiple regression models identified a positive, statistically significant
associated between being a Latinx former EL students and total, degree, and transfer unit
completion. Compared to Latinx EO students, Latinx former EL students completed 5.2, 5.0,
and 4.2 total, degree, and transfer units, respectively, after controlling for background, academic,
and first math course level variables.
124
CHAPTER 5: Discussion and Conclusion
Ample research has linked EL status with reduced educational access, opportunities, and
achievement. For example, studies suggest being an EL student is associated with reduced
opportunities to learning math (Abedi et al., 2006), less content coverage in the classroom
(Abedy & Hernan, 2010), lower likelihood of taking advanced coursework (Wang &
Goldschmidt, 1999), lower teacher perception of students’ academic abilities (Umansky &
Dumont, 2021), and higher likelihood of being tracked into lower-level K12 math and English
coursework (Umansky, 2016a). In fact, research indicates that that being classified as an EL has
a negative effect on math and English outcomes for students just below the classification
threshold compared to those right above (Umansky, 2016b). Garrot and Hong (2016) suggest
that homogeneous classroom groupings may explain such negative effects. Despite these
barriers, roughly half of ELs in California K-12 public schools have already demonstrated
English proficiency (CDE, 2021).
While ELs have historically trailed English Only speakers academically, the vast majority
of ELs will achieve reclassification to English proficient before high school graduation. Unlike
English Only speakers whose English proficiency is assumed by virtue of reporting a home
language of English, all former EL students demonstrated English proficiency by meeting
reclassification criteria. The fact that former ELs are tasked with succeeding in such challenges
and their English Only peers are not, might also help explain why former ELs some of the
strongest K-12 are performing student populations which sometimes outperform English Only
students (Betts, et al. 2019). Indeed, scholars have provided consistent evidence documenting a
relationship between attaining English language proficiency and positive academic outcomes
(e.g., X, Y, Z). In addition, scholarship suggests that even though ELs tend to trail behind their
125
peers in earlier grades, they often catch up and sometimes surpass them in later high school
grades (Umansky & Reardon 2014). With such findings in mind, we may expect ELs, on
average, to trail their EO peers in early grades, catch up in high school grades, and surpass their
EO peers in post-secondary education.
Since the vast majority of ELs achieve reclassification before high school graduation,
ever EL outcomes are more heavily driven by former ELs in higher K-12 grades and more driven
by current ELs in earlier years. However, largely due to data limitations, most of the work
conducted on EL students have not expanded beyond K-12 into higher education and have
mostly focused on English-related outcomes. While some have also examined math outcomes,
very few have been able to track students’ EL status into post-secondary instructions. Given the
overrepresentation of EL students in community colleges and the role of math as a gatekeeper or
gateway course for Latinx student college success, community college credit attainment and
success in college mathematics are of special interest. More specifically, this study focuses on
Latinx former ELs in order to gauge the relationship between achieving English proficiency
before high school graduation and college math and credit attainment outcomes.
A connection between ELs’ K-12 success in English proficiency and success in
community college math and credit accumulation may lead higher education institution to
consider successful reclassification to inform decision making ((e.g., course placement,
determining types of support). By valuing their success in reclassification for Latinx former ELs,
colleges may save students more time and money. Meanwhile, honoring students’ linguistic
assets in these ways may combat stigma associated with being an EL and help circumvent the
stigma associated with placement into lower-level coursework and referrals into additional
support courses (e.g., math corequisites).
126
The previous chapter contained the results of this study’s analysis. The objective of this
chapter is to elaborate on the meaning of the results, discuss how the findings from this study
relate to the scholarship presented in the literature review, and note the study’s significance,
implications, contributions, recommendations as well as future directions of this line of work.
The chapter begins with an explanation and interpretation of results starting with the summary
statistics and followed by the results stemming from the three guiding research questions. Then, I
discuss these findings in relation extant literature. Afterwards, I note the study significance,
implications, contributions, recommendations, and discuss future directions. I close with a
conclusion and summary of the chapter.
Summarizing Results
Three research questions guided this study:
RQ1. Where do Latinx former ELs begin their community college trajectories (including
educational goals and math placement referrals) and how do these rates compare to their
Latinx English Only peers?
RQ2. What math course do Latinx former ELs, as well as Latinx English Only students,
enroll into first and do these courses align with those they were referred to?
RQ3. Does achieving EL proficiency before high school graduation predict success in
college math courses and college credit accumulation for Latinx students?
The analysis leveraged a rich, longitudinal dataset derived from a Research-Practice
Partnership in California between one research university, a large urban community college
district, and its feeder high school district. Given that a unique, common identifier allows linking
students from secondary to postsecondary, student trajectories and outcomes can be examined
while also considering their latest high school EL designation. Prior to engaging with the guiding
127
research questions, background and summary statistics were generated to examine attributes such
as demographic characteristics and academic achievement for all Latinx students in the sample.
That is, background characteristics and summary statistics were presented for all four district
designated EL groups prior to moving forward with the main analyses which center Latinx
former ELs in relation to English Only students. Characteristics including gender,
socioeconomic status, special education participation, parent education, immigration status, high
school GPA, standardized test scores, and high school math course taking trajectories were
presented with a focus on Latinx former EL students and Latinx English Only students. To
address each of the research questions, descriptive quantitative methods and regression analyses
were conducted. These methods included cross tabulations, means comparison tests, logistic and
multivariate fixed effects regression analyses.
Despite Challenges, Most ELs Perform Similar or Greater than Peers in High School
Over 80% of the Latinx students in this study were at one point in their K-12 trajectories
classified as ELs. Over 99% of these students indicated a home language of Spanish. Of these
14,164 students, 12,430 (or 87.8%) successfully attained English proficient status before high
school graduation. These former ELs, compose the majority of Latinx students while Latinx
English Only students constituted 1,734 (or 10.2%) of the sample and IFEP students composed
another 1,684 (or 8.6%).
Among the Latinx student population, background characteristics suggest that higher
proportions of Former ELs may face challenges related to being low income, navigating the legal
system, attaining governmental services, and navigating U.S. schooling. In particular, the data
indicate that Former ELs are more likely to be lower income, less likely to be U.S. Citizens, and
less likely to have parents or guardians who received high school or college education.
128
Consistent with literature indicating that ELs with disabilities are far less likely to achieve
reclassification (Umansky, Thompson, & Diaz, 2017), a smaller proportion of Former ELs were
in SPED (5%) compared to English Only students (12%). In fact, over one quarter of EL students
that never reclassified were in SPED.
Despite displaying characteristics that have historically been noted as less favorable for
the sake of academic success, Latinx former ELs had equal or higher academic performance than
their Latinx English Only peers. Measures of GPA and advanced math course success showed
that Latinx former ELs outperformed their EO peers while performance on standardized math
exams and rates of completion of college preparatory English sequences were similar.
Meanwhile, a math course sequence analysis demonstrated that Latinx former ELs took college
preparatory course sequences in high school more frequently than Latinx English Only students.
Latinx Former ELs Report Lower College Goals but Score Higher on Placement Exams
Educational goals and performance on assessment and placement exams help provide
insight into students’ intentions, aspirations, and preparation. A statistically significant difference
was noted between Latinx former EL students and Latinx English Only students when it came to
the proportion that reported a transfer and bachelor’s degree attainment intention. The same was
true for the proportion of students that indicated being undecided on their educational goal. In
particular, Latinx former EL students were more likely to be undecided and less likely to report a
transfer or bachelor’s degree attainment goal compared to their Latinx English Only Peers. Yet,
Latinx former EL students outperformed their Latinx English Only peers in assessment and
placement exams. Thus, these students were referred to take higher math course levels compared
to their peers. Still, only about 5% of Latinx former EL students were referred to take transfer
level mathematics compared to 4.8% of Latinx English Only students. In other words, over 95%
129
of Latinx and former EL students were directed to take below-transfer level mathematics
courses.
Perhaps, Latinx former ELs are led to underestimate their academic potential more so
than their Latinx EO peers. Prior research has already noted that historically marginalized
groups such as female, Black, and Latinx students tend to self-place into lower levels of
mathematics relative to their peers (Kosiewicz & Ngo, 2019). This study’s findings contribute to
the literature by suggesting that within the same racial group, specifically Latinx students, EL
status and linguistic identity play a role in students’ self-perceptions of their academic potential.
Research suggests that educators hold negative perceptions of EL students (Bertrand & Marsh,
2015; Umansky & Dumont, 2021). EL students may internalize such negative perceptions which
might explain why the data demonstrate that Latinx Former EL students doubt their academic
potential in comparison to Latinx English Only students. EL students may engage in a form of
stereotype threat, or the risk of confirming negative stereotypes about one’s group which
research has shown manifests itself in relation to race (Steele & Aaronson, 1995) and gender
(Spencer, Steele, & Quinn, 1999). Stereotype threat may manifest in relation to linguistic
identity as well. Of note, Latinx students in this study who at one point were EL differed from
Latinx students that never were when it came to educational goals, despite demonstrating greater
potential as evidenced by both assessment and placement exam performance and high school
academic achievement.
Latinx Former ELs Less Often Self-Place into Higher Math Courses Yet Perform Better
While students’ actual math course enrollment largely mirrored the referrals received
from completion of assessment and placement exams, over 12% of Latinx former EL and Latinx
English Only students deviated from their referral. Trends indicated that students complied with
130
their referrals at higher rates when referred to higher math course levels. For example, when
referred to the lowest level math course (five levels below transfer) 62.5% of Latinx former ELs
complied with their referral while 58.9% of Latinx English Only students did the same.
Alternatively, over 90% of students referred to either transfer level math or one level below
transfer level enrolled in that level. However, examining only non-complier students revealed
that Latinx former ELs less frequently enrolled in courses higher than their referral compared to
Latinx English Only students. While the largest proportion of noncomplying students enrolled in
one level above their referred math level, a statistically significant higher proportion of Latinx
English Only non-compliers enrolled in exactly three levels higher than their referral compared
to Latinx former ELs. Of note, this finding was consistent with data on educational goals.
Specifically, Latinx former EL non-compliers were more likely to self-place into higher
coursework compared to their referral and also indicate higher educational goals compared to
Latinx Former ELs.
Despite non-complier Latinx former ELs being less likely to self-place into higher
courses relative to their Latinx English Only peers, Latinx former ELs demonstrated higher
success in their first course. One year after enrollment into their first math course, 62.6% of
Latinx Former EL students passed their course compared to 55.7% of Latinx English Only
students. Once again, data suggest that Latinx Former ELs underestimate their potential
compared to their Latinx English Only peers. Yet again, Latinx former EL students outperformed
their Latinx English Only peers.
While assessment and placement policies have moved away lengthy developmental
course sequences, there are takeaways to be gleaned from these findings. The results suggests
that Latinx former ELs might be more likely to abide by course referrals and, alternatively, less
131
likely to deviate from placement policy recommendations. In California, AB 705 leaves room
for colleges to require students to take below-transfer level coursework as long as the intended
course sequence leads students to complete transfer-level math within a one-year time
frame. Unless there is ample evidence to suggest otherwise, colleges should remain cautious
about recommending developmental courses to historically marginalized populations, like Latinx
students and English learners, that can prolong students’ trajectories, demand more financial
costs, and discourage students.
Latinx Former ELs Outperform Peers in College Math and College Credit Attainment
While employing regression models (e.g., OLS, logistic, multivariate) produced estimates
that account for observed variables included in the models, fixed effects helped account for
unobserved characteristics. To elaborate, the dataset observes the same high-school to-college
student trajectory multiple times so a fixed effects strategy was able to ignore the variation that
exists across different groups of students that experienced different high school-to-college paths
while analyzing only the variation within each high school-to-college path (Allison, 2009). The
fixed effects regression results provide evidence to support the claim that former EL
classification predicts success in credit attainment and community college math courses for
Latinx students.
When controlling for background characteristics, high school academic achievement, and
the first math class students took in community college, the fixed effects logistic, OLS, and
multivariate regression models display a statistically significant, positive relationship between
being a Latinx former EL student and academic success as evidenced by outcomes in several
types of unit accumulation and math course success metrics. For example, the fixed effects
logistics regression models indicated a 1.27 increase in the odds of passing the first math course
132
taken for Latinx former ELs relative to English Only students. Additionally, while holding all
else constant, being a Latinx former EL student was associated with completing 5 more-degree
applicable units, and 4.2 more transfer applicable units relative to English Only students.
This finding suggests that Latinx students who manage to demonstrate English
proficiency before graduating high school outperform monolingual speakers with similar
academic achievement in subjects like math. Students’ linguistic assets may function as an asset
outside of language classes and spill over into other content areas. With regards to policy,
assessment and placement that utilizes multiple measures could benefit from considering EL
status for placement into math. Given that former EL students outperformed similarly,
academically prepared peers in credits attained and math outcomes, higher placement in math
and reducing course loads that may occupy students time might benefit Latinx former EL
students.
Discussion of Findings with Extant Literature
Aspirations, Self-Placement, and Performance
Studies have noted that Latinx families place high value on education (Kiyama, 2010).
For example, studies have found Latinx students’ aspirations to go to college to be higher than
the general population and that the overwhelming majority (over 90%) of Latinx parents expect
their children to go to college (Delgado-Gaitan 1992; Kao 2000; Pew Hispanic
Foundation/Kaiser Family Foundation 2004). Although research indicates Latinx students and
their families highly value education, researchers know less about differences in aspirations and
educational goals within the Latinx student population. One narrative consistent with this
study’s results posits that Latinx Former ELs express lower goals and lower perceptions of
academic ability compared to their Latinx former EL peers. But these lower goals and lower
133
perceptions are inconsistent with actual performance. The evidence presented in this study
indicated that Latinx Former ELs performed on par or surpassed their Latinx English Only peers
by both secondary and post-secondary measures.
Extant work has suggested that ELs may trail their peers in early grades but catch up in
post-secondary schooling (e.g., Conger, 2010; Umansky & Reardon, 2014). The evidence
presented in this study indicated that Latinx Former ELs perform on par or surpass their Latinx
English only peers by the end of high school, they outperform them on mathematics placement
exams and receive higher referrals, and they outperform them in math course success and college
credit completion. Therefore, the evidence implies that ELs’ may not just trail their peers in
early elementary and middle school grades and catch up in high school but they, on average,
surpass and continue making educational gains as evidenced by math course success and college
credit attainment. Still, smaller proportions of Latinx former ELs indicated transfer intentions
compared to Latinx English Only students. Additionally, among students that did not abide by
their placement referral, Latinx former ELs were less likely to actually enroll into higher levels
of mathematics compared to Latinx English Only students. Contextualizing this finding adds to
what research has already unveiled about self-placement and self-perceptions.
Scholars have noted that students of color and female students tend to self-place into
lower levels of mathematics relative to their peers (Kosiewicz & Ngo, 2019). We also know that
educators hold negative perceptions of ELs and that EL status and linguistic identity play a role
in students’ self-perceptions of their academic potential (Bertrand & Marsh, 2015; Umansky &
Dumont, 2021). Given the literature and the tendency noted in this study, it follows that within
the same racial group, specifically Latinx students, non-native English speakers would be more
likely to self-place lower than their English Only peers.
134
Acevedo’s (2018) study showed that Latinx students questioned their higher education
aspirations when faced with institutional microaggressions, or mechanisms of control that erode
self-confidence, among other things. Institutional microaggressions for Latinx students in that
study included anti-immigration policies, high stakes testing, as well as surveillance and
policing. As such, assessment exams and math course placement referrals for the Latinx former
EL students in this study could have functioned as institutionalized microaggression that
contributed to the cumulative effects of microaggression (see Acevedo, 2018) that Latinx
students have experienced. But institutionalized microaggressions alone do not explain the
difference in aspirations and placement between Latinx Former ELs and Latinx English Only
students. To account for this difference, it is important to consider other cumulative effects of
institutionalized microaggressions and other negative effects stemming from racism and other
forms of oppression.
In acknowledging intersectionality, or the interaction of multiple identities and
experiences of exclusion and subordination (Davis, 2008), we are reminded to disaggregate
within the Latinx population. This study contributes to such disaggregation by focusing on
Latinx students who, for the most part, either indicated Spanish (Latinx Former ELs) or English
(Latinx English Only) as their home language. Bear in mind that less than 1% of Latinx students
in this study indicated a language outside of Spanish or English. Consistent with work on
intersectionality (e.g., Crenshaw, 1990), the findings in this study imply that aside from facing
challenges associated with being Latinx, Former ELs’ aspirations may also be tempered by
linguistic identity. Racism, along with other forms of oppression (e.g., nativism) are likely
heightened for ELs. Researchers have already noted that ELs are more likely to be negatively
affected by low teacher expectations compared to other groups (Bertrand and Marsh, 2015). In
135
addition, Suárez-Orozco and colleagues (2015) notes that their inquiry on microaggressions in
the classroom found that most were delivered by instructors, undermined student intelligence,
and were most common in campuses with higher concentration of students of color. Thus,
educators’ negative perceptions of ELs and cumulative effects of microaggression potentially
contribute to a hostile, discouraging, invalidating experience for Former Latinx ELs which may
lead to lower self-perceptions and aspirations. This may explain why lower proportions of
Latinx Former ELs in this study expressed transfer intentions compared to their peers and also
make sense of why Latinx Former ELs were less likely to self-place into higher math coursework
relative to their Latinx English Only peers.
Advantages and Assets of ELs
Results from this study’s analysis corroborate previous scholarship that connects a
bilingual and immigrant advantage with EL students. First and second-generation students
demonstrate and immigrant advantage by outperforming their native-born peers academically
(Callahan and Humphries, 2016; Hernandez & Lopez, 2004). Meanwhile, the bilingual
advantage suggests that students learning English may carry special strengths that help them
acquire necessary support for navigating educational and social environments (Stanton-Salazar &
Dorbusch, 1995). While the dataset did not provide information indicating whether a student was
first or second generation, the citizenship variable indicated that 99% of English only students
were U.S. citizens (or native-born) compared to 84% of Latinx Former ELs. The remaining
Latinx Former ELs were likely undocumented (12%) and permanent residents (4%). Thus, the
data suggest Latinx former ELs were less likely to be native-born.
Latinx former EL students outperforming their Latinx former English Only peers in this
study is consistent with Insonio’s (1994) and Fong and colleagues (2015) work which found that
136
students whose language was one other than English had higher college success than native
speakers. Many Latinx former EL students may not have learned English as their first language
because they or their families immigrated to the U.S. in recent years or generations for better
educational opportunities and cultivated clear goals to succeed and prioritize academics
(Hernandez & Lopez, 2004). Yet, this study contributes results suggesting that Latinx Former
ELs were more likely than their English Only peers to indicate lower educational goals.
Considering Hartman, Callahan, and Yu’s (2021) work noting that ELs in community
colleges experienced the greatest academic gains when they had an intent to transfer, more
research into understanding why Latinx former ELs goals were lower than their Latinx English
Only peers may be beneficial. Additionally, this study indicated that among those Latinx former
ELs that deviated from their math placement referral, they were less likely to self-place into
coursework higher than their referral compared to their Latinx English Only peers. Still, Latinx
former ELs managed to outperform their Latinx English Only peers in mathematics courses even
after controlling for starting math course level, background characteristics, and academic
achievement. In contrast, Crisp and Nora’s (2009) study controlling for various demographic
and academic variables suggested that English as a first language for Latinx students was not a
significant predictor for college success. With this in mind, variables serving as a proxy for
bilingualism or linguistic assets (e.g., Former EL status), may be more accurate predictors of
college success.
Revisiting EL Terminology
The search terms used in this study to identify relevant literature built off of Nunez and
colleagues’ (2016) systematic literature review on ELs in higher education and, acknowledging
linguistic assets, added the search terms bilingual and multilingual. In sum, articles included in
137
the literature review each used the terms bilingual or multilingual, EL, ELL, ELP, ESL, LM,
and/or LEP in their title, abstract, or list of keywords. Of the 96 articles reviewed, each
contained one or more of these terms. The most frequently used terms were bilingual or
multilingual (41), EL (34), and ELL (27). Next, the terms ELP and ESL each were used in 8 and
6 research articles’ title, abstract, or list of key words. The least used terms were LM (4) and
LEP (2).
While federal and state entities continue to use terms such as Linguistic Minority,
Language Minority, and Limited English Proficient (LEP, 2021), the literature suggest that
scholarship on ELs has moved even further away from using such terms. Critiques on the deficit
orientation of such language and scholars calls to emphasize students’ development over
limitations (e.g., Garcia, 2019; Mosqueda, 2010) have potentially curbed widespread use
(Gandara & Rumberger, 2007). Since 2015, LM and LEP terms are most often used in
quantitative studies (e.g., Fong, Krause, Acee, & Weinstein, 2016; Garret and Hong, 2016;
Hodara, 2015; Daugherty et al., 2021) possibly because the language used in these studies is
aligned with state and federal terminology. Overall, the term bilingual or multilingual was most
used in studies since 2015, even more frequently than EL or ELL, which possibly notes a move
towards emphasizing linguistic assets over deficits. Keeping in mind that EL is intended to be a
temporary designation along with the fact that some scholars opt to use the term “emergent
bilingual” to refer to individuals that are still acquiring English (Garcia, 2019), an argument can
be made to consider ELs that demonstrate English proficiency (i.e., Former ELs) bilingual
students.
Among the journal articles identified using the systematic literature review search criteria
(see Chapter 2 for more information), twenty were empirical studies that focused on or included
138
data from higher education settings. Unsurprisingly, ESL was the most common terms used in
these studies and was used to refer to instruction in English for students whose primary language
was not English in agreement with previous scholarship’s definition (e.g., Mardock Uman, 2008;
Nunez et al., 2016). The same remained true when only looking at quantitative empirical studies
that spanned higher education. A review of these works noted a larger focus on ESL programs
as opposed to students who were once or remained ELs as a result of K12 classification and
reclassification processes. In fact, only two studies spanned K-12 to higher education
(Daugherty et al., 2021; Melguizo et al., 2021).
Implications and Recommendations for Policy and Practice
Former EL Status as a Culturally and Linguistically Appropriate Measure
Data from this study captures students’ trajectories at a time when course placement was
primarily determined by assessment and placement exams (not multiple measures) and pre-
requisite course sequences were rampant in California community colleges. Yet, findings from
this study are still relevant to policy contexts where direct transfer-level placement and timely
success in transfer-level coursework is mandated. More specifically, this study may be most
relevant to colleges implementing such policies in California and other states with a similar
Latinx EL population.
Transfer-level course access and academic support for success in these courses have been
two areas in which policymakers have focused on to address a history of academic shortcomings
associated with developmental coursework (e.g., transfer level math completion). Two of the
most, if not the most, critical and relevant policies in California (i.e., AB 705 and AB 1705)
emphasize the use of using multiple measures as assessment tools to inform decision making as
it relates to improving placement and identifying academic support. The text of both bills define
139
assessment as the process of gathering information about a student’s English language
proficiency, needs for special services, aptitudes, goals, career aspirations, academic
performance, and more. The policies also note that colleges’ assessment methods can include
interviews, standardized exam scores, surveys, high school transcripts, educational histories, and
many other measures. As such, former EL students’ educational history of successfully
demonstrating English language proficiency, is fit for use as a measure for the purpose of
assessment and placement.
However, AB 705 and AB 1705 never mention ELs, let alone Former ELs. The one-time
AB 1705 brings up the term ELL it is in relation to discussing college ESL. Both bills discuss
ESL programming, but these courses are not relevant for Latinx former ELs who are
overwhelmingly native born, US high school graduates that have already demonstrated English
proficiency. As a reminder, college ESL programming have historically been course sequences
aimed at supporting international students that are learning English. Terms such as bilingual,
multilingual, LM, LEP, or LEP are not used at all in the language of either AB 705 or AB 1705,
which is concerning given that 40% of California’s K-12 public school student population speaks
a language other than English at home (about 82% of these students report speaking Spanish)
(CDE, 2022). Yet, these mandates are aimed at supporting all students and do acknowledge the
need of accounting for different groups of students.
On the one hand, AB 705 intends to limit placement into non-credit, pre-requisite
coursework. In particular, the policy requires colleges to both improve course placement and
increase transfer level course enrollment and completion within a one-year timeframe. On the
other hand, AB 1705 (2022) includes additional amendments and clarifications to the education
code regarding assessment and placement in community colleges. The newer bill specifies that
140
the one-year time frame begins upon students’ initial attempt in a discipline (math or English),
mandates the course satisfies requirements aligned with a students’ academic goal, and requires
colleges who do not enroll students into transfer-level coursework to verify the benefit of their
placement. AB 1705 declares that measures used for assessment and placement ought-to “be
sensitive to cultural and language differences between students.” One way to place value and
honor Latinx former ELs cultural and linguistic assets would be to acknowledge the fact that
they managed to demonstrate English proficiency before high school graduation.
In this study, Latinx ELs that attained English proficiency before high school graduation
have demonstrated that, even after controlling for background characteristics and academic
achievement, they outperform their Latinx English Only peers in college math course success as
well as in accumulation of transferable, degree-applicable, and total college credits. Thus,
colleges may benefit from considering Former EL status as an additional metric for decision
making as it relates to course placement and determining academic support. The policies
explicitly mention high school coursework, grades, and grade point average as acceptable
multiple measures. In terms of implementation, colleges have adopted such measures. For
example, the CCC suggested colleges use their default placement rules, which centered on high
school GPA (Hope & Stanskas, 2018). Yet, the bills’ guide colleges to use measures to increase
(not decrease) student placement recommendations while also instructing them to allow
performance on any one measure to be offset by higher performance on another. With this in
mind, Former EL status can be a culturally and linguistically appropriate measure to be used that
could help Latinx and other students avoid lower placements.
Still, many colleges have moved towards direct college placement for most students. In
these cases, multiple measures are still to be used as an assessment tool to determine academic
141
support. These decisions determine whether a student might be made aware of, be
recommended, or be required services such as co-requisite course enrollment. AB 705 and AB
1705 both critique and emphasize the failure of pre-requisite courses sequences. They also
mandate colleges that continue to enroll students in pre-requisites to prove the effectiveness of
their courses.
In response, many colleges have designed concurrent or corequisite course models. But,
even though corequisites are intended to support students, these courses do come at a, potentially
unnecessary, time and financial cost. Instead of enrolling in additional corequisite coursework,
students could be taking other transfer-level courses. Because corequisite models have been less
common, less research exists on their effectiveness. Perhaps, co-requisite might negatively
impact progress for students who do not require them the way pre-requisites do. Cuellar Mejia
and colleagues (2018) note that while completion rates in corequisite and concurrent support
course sequences look promising, equity gaps persist. Additionally, less is known about which
specific types of transfer-level course support (e.g., tutoring, course contextualization, cohorts,
learning communities) might best suit different populations. By using Former EL status as a
measure to potentially require or recommend less students to a particular type of support,
colleges can focus such supports on students who are more likely to need them.
Addressing Inconsistencies Between Latinx Former ELs Achievement and Actions
One of the trends that arose from the analysis suggested that despite being equally or
more prepared than their Latinx English Only peers, Latinx former ELs had lower educational
goals as well as lower math course level self-placement. Considering previous scholarship (e.g.,
Acevedo, 2018; Bertrand & Marsh, 2015; Suárez-Orozco et al., 2015), negative expectations and
the cumulative effect of microaggressions might explain this trend (see above). To address the
142
inconsistencies between Latinx Former ELs achievement and actions, that is being more
prepared than Latinx English Only students but indicating lower goals and lower self-placement,
this section provides recommendations for policy and practice as it relates to educators,
programs, curriculum, and placement while grounding them in extant literature.
While educators can view ELs from deficit perspectives (Bertrand & Marsh, 2015),
scholarship has also noted that teachers and institutional agents can play an influential role in
helping Latinx students plan and prepare for college (Martinez & Castellanos, 2018). In fact,
institutional agents who emphasize high expectations, focus on social identities, and emphasized
improving academic skills have been able to provide academic validation to Latinx students in
developmental education coursework (Acevedo-Gil, 2015). Instructors cultivated a validating
environment by relating to students’ identities in numerous ways such as acknowledging social
positions such as race, ethnicity, neighborhood (Acevedo Gil et al., 2015). When considering the
extant literature and the findings from the present study, honoring students former EL status for
educators, might take the form of acknowledging their identities as it relates to both race and
language. ELs have shown the largest academic gains following interventions on teachers’
instruction (Portes, et al., 2018). Thus, providing teachers with training on culturally responsive
pedagogy with an emphasis on ELs can help educators better support ELs as well as former ELs
and, ultimately, encourage ELs’ academic self-perceptions and aspirations.
While using an asset-based framework, Yah (2013) found that utilizing various forms of
capital, including linguistic capital, helped Latinx male ELs that both completed high school and
attended college. On the one hand, traditional sociocultural theories emphasize how non-
dominant capital places students at a disadvantage. Take cultural capital, for example. Cultural
capital refers to qualities (e.g., language skills) derived from one’s family that defines a person’s
143
class position (Bordieu, 1986). A cultural capital lens would suggest that Latinx students are at a
disadvantage possibly because their sociocultural environment does not provide the cultural
capital that is valued in school such as dominant linguistic patterns (Rios-Aguilar and Kiyama,
2012). On the other hand, a focus on linguistic capital from asset-based perspectives such as
community cultural wealth (Yosso, 2005) and funds of knowledge (Moll 1988, 1992), assumes
that EL students, and others, arrive at school with valuable linguistic assets regardless of whether
these languages and linguistic patterns match that of the dominant class. In fact, this study links a
linguistic asset (former EL status) with math and college outcomes for Latinx students. With
these theories in mind, then, appropriate recommendations for programs and curriculums require
a focus on valuing Latinx former ELs linguistic assets. Of note, recent community college
policies (e.g., AB 1705) have noted an intention to be sensitive to cultural and language
differences of students in the process of assessment and placement. Honoring Latinx former ELs
linguistic assets throughout K-12 and college through programming and curriculum can help
combat oppression (e.g., racism, nativism) while also validating and encouraging students’
cultural backgrounds.
Targeted programs have been able to help Latinx students identify college requirements
and provide exposure to college (Martinez & Castellanos, 2018). College preparation programs
can also benefit students with non-economic forms of capitals (e.g., linguistic capital) that these
students and families already have (Dyce, Abold, & Long, 2012). In order to combat negative
academic self-perceptions that Latinx former ELs might take, programs and curriculum in K-12
and higher education can work to be more culturally sensitive. For example, programs such as
MESA (Mathematics, Engineering, Science Achievement), GEAR UP (Gaining Early
Awareness and Readiness for Undergraduate Programs) and High School Puente, have shown
144
success with Latinx students and their families (Rendón, 2002). Rendón (2002) noted that a
faculty, counselors, and students took all took part in validating students at the academic and
interpersonal levels. For example, in English classes, students were assigned to read essays such
as “From the Barrio to the Academy: Revelations of a Mexican American Scholarship Girl”
(Rendon, 1992), a familia or classroom community atmosphere was fostered to leverage
students’ familial assets, and guests who come from similar backgrounds as the students and
have achieved college success were invited to speak. Thus, targeted support programs can better
support Latinx former ELs by providing culturally validating environments that foster high
aspirations and self-perceptions of academic ability.
At the community college level, developmental placement has been experienced as
academic invalidation by many Latinx stude18nts (Acevedo-Gil et al., 2015). While policies
such as AB 705 and AB 1705 push for direct placement into college-level, many colleges
implementing reforms are now requiring co-requisite coursework as opposed to pre-requisite
coursework. Being referred to co-requisite coursework may also function as a form of academic
invalidation for Latinx former ELs. As such, community colleges should urge away from co-
requisite placement and requiring other supports that may do more harm than good. That is, co-
requisites and other referrals to forms of support may do more do more to undermine students’
aspirations and academic progress than help with developing math skills. Thus, amending policy
to consider former EL status as a measure in assessment for determining course placement and
referred or required support may help more Latinx former ELs avoid lower placement that can be
experienced as academic invalidation.
Future Research
145
Extant work on ELs in community college asserts that these students draw on different
assets (e.g., aspirational and linguistic capital) along with institutional resources to motivate and
support their persistence despite limiting assessment and placement policies (Mardock Uman,
2018). Such findings help explain why Latinx former EL students outperformed Latinx English
Only students as evidenced by math course success and credit attainment. Future qualitative
studies may inquire about how Latinx former EL students manage to succeed in their first
community college math course. In particular, researchers can explore the ways in which these
students leverage their assets, including linguistic assets, to make decisions about course
enrollment and to progress and succeed in transfer-level math courses. In doing so, scholars can
consider frameworks that acknowledge immigrant, bilingual, and cognate advantages that have
frequently been linked with EL students. Given a history of racial inequity in developmental
math and community college success, colleges can benefit from research that can identify
resources and supports that best support Latinx former EL students.
Future studies with access to data that indicates the time period in which a non-native
English-speaking student achieved classification or reclassification to English proficiency may
be able to explore if reaching such a milestone at one grade results in greater college outcomes
than at another (e.g., achieving proficiency in 9
th
vs 12
th
grade). With “emergent bilinguals”
becoming increasingly popular as an asset-based term for ELs (Garcia, 2019), one might argue
that Former Latinx EL students have attained a bilingual proficiency (e.g., English and Spanish
proficiency). Afterall, if students that indicate English as a home language are assumed English
proficient by virtue of not being mandated to demonstrate that aptitude, then students who
indicate Spanish as a home language can be assumed Spanish proficient in the same way. Yet, in
both cases we would likely find native English and native Spanish speakers failing to meet
146
criteria that demonstrate their respective language proficiency. Meanwhile, Latinx students in
the present study who indicated speaking Spanish at home but demonstrated English proficiency
immediately might also be considered bilingual. How might long term college outcomes
between these groups compare? Such work may provide insights into the importance of (initial)
classification over reclassification (or vice-versa).
The above critique of assuming English proficiency for some students leads to questions
about the English proficiency of students that indicate English as a home language and the
Spanish proficiency of students that indicate it a Spanish home language, assuming a traditional
definition of proficiency (e.g., passing formal assessments). As is, the classification and
reclassification process already attempts to identify subgroups within Latinx students that
indicate a language other than English as a home language, in order to determine EL supports
and services. With that in mind, scholars might explore whether subgroups of Latinx students
that indicate English as a home language might benefit from EL supports.
In some instances, Latinx English Only students in the present study performed lower or
the same as Latinx Former EL students in high school. One potential explanation is that unlike
Latinx Former ELs, Latinx English Only students never had to legitimize their English
proficiency through formal assessments. Instead, the act of having English reported as a home
language led to an assumption that all of these students were proficient in English. In addition,
another explanation for this difference is that the majority of ELs with disabilities never
reclassify to English proficient. Thus, Latinx students with disabilities from homes where
Spanish or another non-English language is primarily spoken may be overrepresented by the end
of high school as ELs and underrepresented as Former ELs. That is because to be considered
English proficient, students with disabilities that report speaking Spanish at home must achieve
147
reclassification while students with disabilities that report speaking English at home labeled as
proficient in English. It would follow that some, if not many, ELs never reclassify more so as a
result of other challenges (e.g., testing accommodations for students with disabilities) than as a
result of English proficiency itself. Nevertheless, these kinds of challenges help explain why the
present study saw a smaller proportion of students in SPED among Latinx former ELs than
within Latinx English Only students. Then, exploring long term college outcomes between ever
ELs and never ELs or likely bilingual and likely monolingual students may lead to additional
insights.
Contribution, Significance, and Conclusion
In this study, Latinx former ELs high school academic performance was on par and
sometimes greater than their Latinx English Only peers. For example, Latinx former ELs, on
average, had a higher high school GPA, success in advanced math coursework, and experienced
more rigorous math course sequences. At the same time, demographic characteristics for Latinx
former ELs indicated they, compared to their English only peers, were more likely to be eligible
for free or reduced lunch (an indicators of lower socioeconomic status), less likely to be U.S.
Citizens, and less likely to have parents or guardians that graduated high school, attended
college, or graduated from a higher education institution. While the characteristics mentioned
above have historically been associated with lower educational outcomes, Latinx former EL
students still managed to outperform their English Only peers even after accounting for high
school-to-college path, math course start level, background characteristics, and academic
achievement.
Among others, the analysis produced results indicating the odds of passing their first
enrolled math course was 34% higher for Latinx English only students relative to Latinx English
148
Only students. Additionally, Latinx Former EL students, on average, completed 1.27 more
transfer-applicable units than their peers. This study’s focus on math courses also contributes
results that suggest that linguistic assets may be relevant in the long-term not only for English
proficiency but for college outcomes in math. Additionally, the present study is also valuable for
disaggregating within the Latinx community college student population. Scholarship has noted
that students of color and women tend to self-place into lower coursework (Kosiewicz & Ngo,
2019). The findings presented in this study indicate that Latinx former ELs may hold lower self-
perceptions of ability and indicate lower educational goals compared to Latinx English Only
students. These contributions span high school and community college districts in California and
other similar context that serve large proportions of Latinx EL students. Aside from contributing
these findings to research on Latinx students, ELs, and higher education, this study also
contributes an extension to a systematic literature review.
Núñez and colleagues (2016) literature reviews on ELs in higher education systematically
reviewed scholarship from 1990-2015 found in six high impact journals all which were included
because they used six key terms relevant for ELs in higher education. The present study built
upon this work to extend the systematic literature review from 2016-2021. Given the focus on
Latinx students, this study’s search criteria expanded the list of high impact journals to include
journals focused on community colleges as well as Latinx students. In light of changing
terminology used to discuss EL students, this study’s literature review also incorporates
additional terms (e.g., bilingual, multilingual) to identify relevant articles. For a detailed
description and rationale of the search terms used, high impact journals included, and search
results see the literature review in Chapter 2 and Figure 2 in Chapter 2. Just under one third of
quantitative studies on ELs focused on higher education even though community college journals
149
were included. Only two of these studies were able to follow students longitudinally from
secondary to post-secondary education (Melguizo et al, 2021; Daugherty et al., 2021). These
trends reinforced the fact that little research focuses on ELs in higher education while also
emphasizing the value that the present study brings by using a rich, longitudinal, cross-sector
dataset to study ELs.
Extant work has noted that EL’s tend to trend behind their peers in earlier grades but
catch up and sometimes even surpass them in high school grades (Conger, 2010; Umansky &
Reardon, 2014). The present study contributes evidence that suggests that Latinx former ELs not
only catch up but eventually surpass and outperform their Latinx English only peers. This
finding matters as it combats negative narratives, often held by educators (Bertrand & Marsh,
2015; Maldonado, 2018; Suárez-Orozco et al., 2015) of Latinx EL students by noting that the
vast majority of Latinx ELs manage to achieve reclassification to English proficiency. Of these
students that attend community college, the study indicates that they, on average, surpass their
English only peers academically. Such a finding in conjunction with research noting benefits of
bilingual classrooms (e.g., August & Shanahan, 2006; Cheung & Salvin, 2012; Umansky &
Dumont, 2021) challenges the efficacy of policies that are often propelled by racist and nativist
rationales (e.g., English-only programming). Additionally, the evidence presented in this study
promotes honoring Latinx former ELs linguistic assets specifically by considering EL
classification as a multiple measure for assessment purposes that inform placement decisions and
decisions about referred or required supports (e.g., corequisite community college coursework).
In sum, the research aims of this study sought to explore the relationship between
achieving English language proficiency before high school graduation and math and college
outcomes for Latinx students. Results suggest that even though Latinx former ELs indicated
150
lower transfer-intentions and lower self-placement than their Latinx English Only peers, they
still managed to outperform them in math course success and college credit attainment. The
findings lead to recommendations for educators, programming, and curriculum as well as for
policy related to multiple measures, assessment, and placement.
151
References
AB 705. Seymour-Campbell Student Success Act of 2012: matriculation: assessment, 705,
Assembly, 2017-2018 (2017).
https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180AB705
AB 1705. An act to amend Section 78213 of, and to add Sections 78212.5 and 78213.1 to, the
Education Code, relating to community colleges, 1705, Senate, 2021-2022 Regular
Session (2022).
https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202120220AB1705
Abbott, A. (1995). Sequence analysis: new methods for old ideas. Annual Review of Sociology,
21(1), 93–113.
http://socsci2.ucsd.edu/~aronatas/project/academic/abbott%20on%20sequence%20analsy
s.pdf
Abedi, Courtney, Leon, Kao, & Azzam. (2006). English Language Learners and Math
Achievement: A Study of Opportunity to Learn and Language Accommodation.
Technical Report 702. Human Genome News / National Center for Human Genome
Research, National Institutes of Health. https://eric.ed.gov/?id=ED495848
Abedi, J. (2004). The No Child Left Behind Act and English Language Learners: Assessment
and Accountability Issues. In Educational Researcher (Vol. 33, Issue 1, pp. 4–14).
https://doi.org/10.3102/0013189x033001004
Abedi, J. (2008). Classification system for English language learners: Issues and
recommendations. Educational Measurement Issues and Practice, 27(3), 17–31.
https://doi.org/10.1111/j.1745-3992.2008.00125.x
Abedi, J., & Herman, J. (2010). Assessing English language learners’ opportunity to learn
mathematics: Issues and limitations. Teachers College Record, 112(3), 723–746.
https://doi.org/10.1177/016146811011200301
Abedi, J., Hofstetter, C. H., & Lord, C. (2004). Assessment Accommodations for English
Language Learners: Implications for Policy-Based Empirical Research. Review of
Educational Research, 74(1), 1–28. https://doi.org/10.3102/00346543074001001
Acevedo-Gil, N. (2018). New Juan Crow Education as a Context for Institutional
Microaggressions: Latina/o/x Students Maintaining College Aspirations. Urban
Education, 0042085918805152. https://doi.org/10.1177/0042085918805152
Acevedo-Gil, N., Santos, R. E., Alonso, L., & Solorzano, D. G. (2015). Latinas/os in Community
College Developmental Education: Increasing Moments of Academic and Interpersonal
152
Validation. Journal of Hispanic Higher Education, 14(2), 101–127.
https://doi.org/10.1177/1538192715572893
Acevedo-Gil, N., Santos, R., & Solorzano, D. (2014). Examining a Rupture in the Latina/o
College Pipeline: Developmental Education in California (No. 3). PERSPECTIVAS
Center for Research and Policy in Education.
Almon, C. (2012). Retention of English learner students at a community college. Linguistic
Minority Students Go to College: Preparation, Access, and Persistence, 184–200.
https://books.google.com/books?hl=en&lr=&id=Fw-
tAgAAQBAJ&oi=fnd&pg=PA184&dq=Retention+of+English+learner+students+at+a+c
ommunity+almon&ots=UCYZlZy5bL&sig=taLrFgjCHkIsuRwGwfTb_5Fz-X0
Almon, C. (2015). College Persistence and Engagement in Light of a Mature English Language
Learner (ELL) Student’s Voice. In Community College Journal of Research and Practice
(Vol. 39, Issue 5, pp. 461–472). https://doi.org/10.1080/10668926.2013.850757
Andrews, M., Schank, T., & Upward, R. (2006). Practical Fixed-Effects Estimation Methods for
the Three-Way Error-Components Model. The Stata Journal, 6(4), 461–481.
https://doi.org/10.1177/1536867X0600600402
Attewell, P., Lavin, D., Domina, T., & Levey, T. (2006). New Evidence on College
Remediation. The Journal of Higher Education, 77(5), 886–924.
http://www.jstor.org/stable/3838791
Aughinbaugh, A. (2012). The effects of high school math curriculum on college attendance:
Evidence from the NLSY97. Economics of Education Review, 31(6), 861–870.
https://doi.org/10.1016/j.econedurev.2012.06.004
August, D., & Shanahan, T. (2006). Developing literacy in second-language learners: Report of
the National Literacy Panel on Language-Minority Children and Youth.
https://www.taylorfrancis.com/books/mono/10.4324/9781315094922/developing-
literacy-second-language-learners-diane-august-timothy-shanahan
Bailey, T. (2012). Can community colleges achieve ambitious graduation goals. Getting to
Graduation: The Completion Agenda in Higher Education, 73–101.
http://ccrc.tc.columbia.edu/publications/can-community-colleges-achieve.html
Bailey, T., Jaggars, S. S., & Scott-Clayton, J. (2013). Characterizing the Effectiveness of
Developmental Education: A Response to Recent Criticism. Community College
Research Center, Columbia University. https://eric.ed.gov/?id=ED542142
153
Bertrand, M., & Marsh, J. A. (2015). Teachers’ Sensemaking of Data and Implications for
Equity. American Educational Research Journal, 52(5), 861–893.
https://doi.org/10.3102/0002831215599251
Betts, J., Hill, L., Bachofer, K., & Joseph Hayes: Andrew Lee: and Andrew Zau. (2019). English
Learner Trajectories and Reclassification. https://www.ppic.org/wp-
content/uploads/english-learner-trajectories-and-reclassification.pdf
Boote, D. N., & Beile, P. (2005). Scholars Before Researchers: On the Centrality of the
Dissertation Literature Review in Research Preparation. Educational Researcher , 34(6),
3–15. https://doi.org/10.3102/0013189X034006003
Bourdieu, P. (1977). The economics of linguistic exchanges. Social Sciences Information.
Information Sur Les Sciences Sociales, 16(6), 645–668.
https://doi.org/10.1177/053901847701600601
Bourdieu, P. (1986). The Forms of Capital. Handbook of Theory and Research for the Sociology
of Education, 241–258.
Bozick, R., & Ingels, S. J. (2008). Mathematics Coursetaking and Achievement at the End of
High School: Evidence from the Education Longitudinal Study of 2002 (ELS: 2002).
Statistical Analysis Report. NCES 2008-319. National Center for Education Statistics.
https://eric.ed.gov/?id=ED499546
Burke, A. M., Morita-Mullaney, T., & Singh, M. (2016). Indiana Emergent Bilingual Student
Time to Reclassification: A Survival Analysis. American Educational Research Journal,
53(5), 1310–1342. http://journals.sagepub.com/doi/abs/10.3102/0002831216667481
Byun, S.-Y., Irvin, M. J., & Bell, B. A. (2015). Advanced Math Course Taking: Effects on Math
Achievement and College Enrollment. Journal of Experimental Education, 83(4), 439–
468. https://doi.org/10.1080/00220973.2014.919570
Calcagno, J. C., & Long, B. T. (2008). The Impact of Postsecondary Remediation Using a
Regression Discontinuity Approach: Addressing Endogenous Sorting and
Noncompliance (No. 14194). National Bureau of Economic Research.
https://doi.org/10.3386/w14194
California Community Colleges Chancellor’s Office. (2007). Center for Student Success. Basic
Skills as a Foundation for Success in California Community Colleges.
http://www.cccbsi.org/publications
154
California Community Colleges Chancellor’s Office. (2018). Welcome to the California
Community Colleges Chancellor’s Office. California Community Colleges Chancellor’s
Office. http://www.cccco.edu/
California Department of Education. (2013). California English Language Development Test:
2013-14 information guide. https://1.cdn.edl.io/h8viYy9ljn2vnFzmB8f2R4vMbQTSm
TOPnlOsnfRoOE24P5r7.pdf
California Department of Education. (2018). English Learner, Immigrant, and Migratory
Students: 2017–18 Demographic Information Report.
California Department of Education. (2019). English Learners. California Department of
Education. https://www.cde.ca.gov/sp/el/index.asp
California Department of Education. (2022). English Learners in California Schools:
Information, resources, and educational outcomes for English Learner students.
California Department of Education.
https://www.cde.ca.gov/ds/sg/englishlearner.asp#:~:text=In%202019%2C%20reclassifie
d%20EL%20students%20scored%20an%20average%20of%2027.3,Balanced%20Summa
tive%20Assessment%20in%20mathematics.
Callahan, R. M., & Humphries, M. H. (2016). Undermatched? School-based Linguistic Status,
College Going, and the Immigrant Advantage. American Educational Research Journal,
53(2), 263–295. https://doi.org/10.3102/0002831215627857
Callahan, R., Wilkinson, L., & Muller, C. (2010). Academic achievement and course taking
among language minority youth in U.S. schools: Effects of ESL placement. Educational
Evaluation and Policy Analysis, 32(1), 84–117.
https://doi.org/10.3102/0162373709359805
Carroll, P. E., & Bailey, A. L. (2016). Do decision rules matter? A descriptive study of English
language proficiency assessment classifications for English-language learners and native
English speakers in fifth grade. Language Testing.
https://doi.org/10.1177/0265532215576380
Chase, M. M., Dowd, A. C., Pazich, L. B., & Bensimon, E. M. (2014). Transfer equity for
“minoritized” students: A critical policy analysis of seven states. Educational Policy ,
28(5), 669–717. http://journals.sagepub.com/doi/abs/10.1177/0895904812468227
Chen, X. (2016). Remedial Coursetaking at US Public 2-and 4-Year Institutions: Scope,
Experiences, and Outcomes. Statistical Analysis Report. NCES 2016-405. National
Center for Education Statistics. https://eric.ed.gov/?id=ED568682
155
Cheung, A. C. K., & Slavin, R. E. (2012). Effective Reading Programs for Spanish-Dominant
English Language Learners (ELLs) in the Elementary Grades. Review of Educational
Research. https://doi.org/10.3102/0034654312465472
Cimpian, J. R., Thompson, K. D., & Makowski, M. B. (2017). Evaluating English Learner
Reclassification Policy Effects Across Districts. American Educational Research
Journal, 54(1_suppl), 255S – 278S. https://doi.org/10.3102/0002831216635796
Cohen, A. M., Brawer, F. B., & Kisker, C. B. (2013). The American Community College. Wiley.
https://play.google.com/store/books/details?id=7XCTAAAAQBAJ
Crenshaw, K. (1990). Mapping the margins: Intersectionality, identity politics, and violence
against women of color. Stanford Law Review, 43, 1241. https://heinonline.org/hol-cgi-
bin/get_pdf.cgi?handle=hein.journals/stflr43§ion=52&casa_token=hIGQ8Ab2Da4A
AAAA:PBoZq411qzm8E4q8w2LzQ_32fIRHL-
28_m10VBjdcYB1O7tsEleIkRbL9OSevHmdM4niMdBv8c4
Crisp, G., & Nora, A. (2009). Hispanic Student Success: Factors Influencing the Persistence and
Transfer Decisions of Latino Community College Students Enrolled in Developmental
Education. Research in Higher Education, 51(2), 175–194.
https://doi.org/10.1007/s11162-009-9151-
Crisp, G., & Nora, A. (2012). Hispanic student participation and success in developmental
education. https://vtechworks.lib.vt.edu/handle/10919/83076
Cuellar Mejia, M., Rodriguez, O., Johnson, H., & Brooks, B. (2018). Reforming English
Pathways at California’s Community Colleges. Public Policy Institute of California.
http://www.ppic.org/wp-content/uploads/r-0217mcr.pdf
Dabach, D. B. (2015). Teacher Placement Into Immigrant English Learner Classrooms: Limiting
Access in Comprehensive High Schools. American Educational Research Journal, 52(2),
243–274. https://doi.org/10.3102/0002831215574725
Daugherty, L., Gerber, R., Martorell, F., Miller, T., & Weisburst, E. (2021). Heterogeneity in the
effects of college course placement. Research in Higher Education, 62(7), 1086–1111.
https://doi.org/10.1007/s11162-021-09630-2
Davis, K. (2008). Intersectionality as buzzword: A sociology of science perspective on what
makes a feminist theory successful. Feminist Theory, 9(1), 67–85.
https://doi.org/10.1177/1464700108086364
156
Delgado-Gaitan, C. (1992). School Matters in the Mexican-American Home: Socializing
Children to Education. American Educational Research Journal, 29(3), 495–513.
https://doi.org/10.3102/00028312029003495
DiCerbo, P. A., Anstrom, K. A., Baker, L. L., & Rivera, C. (2014). A Review of the Literature
on Teaching Academic English to English Language Learners. Review of Educational
Research. https://doi.org/10.3102/0034654314532695
Dowd, A. C., & Bensimon, E. M. (2015). Engaging the “Race Question”: Accountability and
Equity in U.S. Higher Education. Teachers College Press.
https://market.android.com/details?id=book-CAZ6BgAAQBAJ
Dyce, C. M., Albold, C., & Long, D. (2012). Moving From College Aspiration to Attainment:
Learning From One College Access Program. The High School Journal, 96(2), 152–165.
http://www.jstor.org/stable/23351967
Edley, C., Jr. (2017, June 5). At Cal State, algebra is a civil rights issue. Ed Source.
https://edsource.org/2017/at-cal-state-algebra-is-a-civil-rights-issue/582950
Estrada, P., & Wang, H. (2018). Making English Learner Reclassification to Fluent English
Proficient Attainable or Elusive: When Meeting Criteria Is and Is Not Enough. American
Educational Research Journal, 55(2), 207–242.
https://doi.org/10.3102/0002831217733543
Faulstich Orellana. (n.d.). In other words: En otras palabras: Learning from bilingual kids’
translating/interpreting experiences. Evanston, IL: School of Education and Social Policy.
Fink, J. (2017). What Do Students Think of Guided Pathways?
https://academiccommons.columbia.edu/doi/10.7916/D84M9D70
Flores, S. M. (2010). State Dream Acts: The Effect of In-State Resident Tuition Policies and
Undocumented Latino Students. The Review of Higher Education, 33(2), 239–283.
https://doi.org/10.1353/rhe.0.0134
Flores, S. M., Batalova, J., & Fix, M. (2012). The educational trajectories of English language
learners in Texas. Washington, DC: Migration Policy Institute, 220.
http://cccie.org/images/stories/TexasELLs.pdf
Flores, S. M., & Drake, T. A. (2014). Does English Language Learner (ELL) Identification
Predict College Remediation Designation?: A Comparison by Race and Ethnicity, and
ELL Waiver Status. The Review of Higher Education, 38(1), 1–36.
https://doi.org/10.1353/rhe.2014.0041
157
Fong, C. J., Krause, J. M., Acee, T. W., & Weinstein, C. E. (2016). Motivation for Staying in
College: Differences Between LEP (Limited English Proficiency) and Non-LEP Hispanic
Community College Students. Journal of Hispanic Higher Education, 15(4), 340–357.
https://doi.org/10.1177/1538192715607332
Fong, K. E., Melguizo, T., & Prather, G. (2015). Increasing success rates in developmental math:
The complementary role of individual and institutional characteristics. Research in
Higher Education, 56(7), 719–749. http://www.uscrossier.org/pullias/wp-
content/uploads/2014/11/FongMelguizoPrather_2014_Student-progression-through-
developmental-math1.pdf
Frankenberg, E., Ee, J., & Ayscue, J. B. (2019). Harming our common future: America’s
segregated schools 65 years after Brown. Www. Civilrightsproject.
https://escholarship.org/content/qt23j1b9nv/qt23j1b9nv.pdf
Frost, J. (2019). Regression analysis: An intuitive guide for using and interpreting linear models.
Statisics By Jim Publishing.
Fukumine, E., & Kennison, S. M. (2016). Analogical Transfer by Spanish–English Bilinguals:
Implications for Educational and Employment Settings. Journal of Latinos and
Education, 15(2), 134–139. https://doi.org/10.1080/15348431.2015.1099527
Gandara, P., & Rumberger, R. W. (2007). Defining an adequate education for English learners.
Education Finance and Policy. https://direct.mit.edu/edfp/article-abstract/3/1/130/10070
García Bedolla, L., & Rodriguez, R. (2011). Classifying California’s English Learners: Is the
CELDT too Blunt an Instrument? Center for Latino Policy Research.
http://www.escholarship.org/uc/item/2m74v93d
Garcia, G. A. (2019). Becoming Hispanic-Serving Institutions: Opportunities for Colleges and
Universities. JHU Press.
https://play.google.com/store/books/details?id=YxKEDwAAQBAJ
Genesee, F., Lindholm-Leary, K., Christian, D., Saunders, W., & Saunders, B. (2006). Educating
English Language Learners: A Synthesis of Research Evidence. Cambridge University
Press. https://play.google.com/store/books/details?id=60OpdH4q1VkC
González Canché, M., & Boada, D. (2018). Early evaluation findings from the instructional
conversation study: Culturally responsive teaching outcomes for diverse learners in
elementary school. American.
https://journals.sagepub.com/doi/abs/10.3102/0002831217741089
158
González Canché, M. S., & Rios-Aguilar, C. (2015). Critical Social Network Analysis in
Community Colleges: Peer Effects and Credit Attainment. New Directions for
Institutional Research, 2014(163), 75–91. https://doi.org/10.1002/ir.20087
Grubb, W. N., & Gabriner, R. (2013). Understanding the Quandry of Basic Skills: Framing the
Issue in Community Colleges. In Basic Skills Education in Community Colleges: Inside
and Outside of Classrooms (pp. 1–22). Routledge.
https://books.google.com/books?id=NlXNz836zk8C
Hanford, E. (2016, August 18). Stuck at Square One: The Remedial Education Trap from
Educate. APM Reports. http://www.apmreports.org/story/2016/08/18/remedial-
education-trap
Hartman, C., Callahan, R., & Yu, H. (2021). Optimizing Intent to Transfer: Engagement and
Community College English Learners. Research in Higher Education, 62(6), 789–828.
https://doi.org/10.1007/s11162-020-09619-3
Hern, K. (2019). Getting there: Are California Community Colleges maximizing student
completion of transfer-level math and English? A regional progress report on
implementation of AB 705. Campaign for College Opportunity.
http://files.eric.ed.gov/fulltext/ED598343.pdf
Hill, L. (2012). California’s English Learner Student. Public Policy Institute of California.
Hill, L. E., Weston, M., & Hayes, J. M. (2014). Reclassification of English learner students in
California. Public Policy Institute of California. Available at Www. Ppic.
Org/main/publication. Asp.
http://www.iicanet.orgwww.ppi.ppic.org/content/pubs/report/R_114LHR.pdf
Hill, L., Lee, A., & Hayes, J. (2021). Surveying the Landscape of California’s English Learner
Reclassification Policy. Public Policy Institute of California.
Hispanic Association of Colleges and Universities. (n.d.). HACU List of Hispanic Serving
Instututions (HSI) 2018-19. Retrieved July 17, 2020, from
https://www.hacu.net/hacu/hsis.asp
Hodara, M. (2015). The effects of English as a second language courses on language minority
community college students. Educational Evaluation and Policy Analysis.
https://journals.sagepub.com/doi/abs/10.3102/0162373714540321
Hodara, M., & Xu, D. (2016). Does Developmental Education Improve Labor Market
Outcomes? Evidence From Two States. American Educational Research Journal, 53(3),
781–813. https://doi.org/10.3102/0002831216647790
159
Hope, L. L., & Stanskas, J. (2018). Assembly Bill (AB) 705 Implementation.
https://asccc.org/sites/default/files/AA%2018-
40%20AB%20705%20Implementation%20Memorandum__0_0.pdf
Hopkins, M., Lowenhaupt, R., & Sweet, T. M. (2015). Organizing English Learner Instruction in
New Immigrant Destinations: District Infrastructure and Subject-Specific School
Practice. American Educational Research Journal, 52(3), 408–439.
https://doi.org/10.3102/0002831215584780
Hughes, K. (2012). The College Completion Agenda 2012 Progress Report. College Board
Advocacy & Policy Center.
http://media.collegeboard.com/digitalServices/pdf/advocacy/policycenter/college-
completion-agenda-2012-progress-report.pdf
Irwin, V., Zhang, J., Wang, X., Hein, S., Wang, K., Roberts, A., York, C., Barmer, A., Bullock
Mann, F., Dilig, R., & Others. (2021). Report on the Condition of Education 2021. NCES
2021-144. National Center for Education Statistics.
https://files.eric.ed.gov/fulltext/ED612942.pdf
Jaquet, K., & Fong, A. B. (2017). How Do Algebra I Course Repetition Rates Vary among
English Learner Students by Length of Time to Reclassification as English Proficient?
REL 2017-222. Regional Educational Laboratory West.
https://eric.ed.gov/?id=ED572903
Johnson, A. (2020). The Impact of English Learner Reclassification on High School Reading and
Academic Progress. Educational Evaluation and Policy Analysis, 42(1), 46–65.
https://doi.org/10.3102/0162373719877197
Kangas, S. E. N., & Cook, M. (2020). Academic Tracking of English Learners With Disabilities
in Middle School. American Educational Research Journal, 57(6), 2415–2449.
https://doi.org/10.3102/0002831220915702
Kanno, Y., & Harklau, L. (2012). Linguistic Minority Students Go to College: Preparation,
Access, and Persistence. Routledge. https://play.google.com/store/books/details?id=Fw-
tAgAAQBAJ
Kanno, Y., & Varghese, M. M. (2010). Immigrant and refugee ESL students’ challenges to
accessing four-year college education: From language policy to educational policy.
Journal of Language Identity & Education, 9(5), 310–328.
https://doi.org/10.1080/15348458.2010.517693
Kelley, A., & Kohnert, K. (2012). Is there a cognate advantage for typically developing Spanish-
speaking English-language learners? Language, Speech, and Hearing Services in
Schools, 43(2), 191–204. https://doi.org/10.1044/0161-1461(2011/10-0022)
160
Kelly, S. (2009). The Black-White Gap in Mathematics Course Taking. Sociology of Education,
82(1), 47–69. https://doi.org/10.1177/003804070908200103
Kim, & Herman. (2012). Understanding Patterns and Precursors of ELL Success Subsequent to
Reclassification. CRESST Report 818. National Center for Research on Evaluation,
Standards. https://eric.ed.gov/?id=ED540604
Kim, J., Kim, J., DesJardins, S. L., & McCall, B. P. (2015). Completing Algebra II in High
School: Does it Increase College Access and Success? The Journal of Higher Education,
86(4), 628–662. https://doi.org/10.1080/00221546.2015.11777377
Kiyama, J. M. (2010). College Aspirations and Limitations: The Role of Educational Ideologies
and Funds of Knowledge in Mexican American Families. American Educational
Research Journal, 47(2), 330–356. https://doi.org/10.3102/0002831209357468
Kosiewicz, H., & Ngo, F. (2019). Giving Community College Students Choice: The Impact of
Self-Placement in Math Courses. American Educational Research Journal, 57(3), 1358–
1391. https://doi.org/10.3102/0002831219872500
Kosiewicz, H., Ngo, F., & Fong, K. (2016). Alternative Models to Deliver Developmental Math:
Issues of Use and Student Access. Community College Review, 44(3), 205–231.
https://doi.org/10.1177/0091552116651490
Kurlaender, M., & Larsen, M. (2013). K–12 and Postsecondary Alignment: Racial/Ethnic
Differences in Freshmen Course-taking and Performance at California’s Community
Colleges. In education policy analysis archives (Vol. 21, p. 16).
https://doi.org/10.14507/epaa.v21n16.2013
Lambert, O. D. (2015). Learner characteristics and writing performance in a community college
English as a Second Language course: Some unexpected findings. Community College
Journal of Research and.
https://www.tandfonline.com/doi/abs/10.1080/10668926.2012.754731
Leki, I. (2017). Undergraduates in a second language challenges and complexities of academic
literacy development. Routledge.
https://www.taylorfrancis.com/books/mono/10.4324/9781315084442/undergraduates-
second-language-challenges-complexities-academic-literacy-development-ilona-leki
Lewis, J., Ream, R. K., Bocian, K. M., Cardullo, R. A., Hammond, K. A., & Fast, L. A. (2012).
Con cariño: Teacher caring, math self-efficacy, and math achievement among Hispanic
English learners. Teachers College Record, 114(7), 1–42.
http://facultyprofiles.ucr.edu/gsoe_dept/faculty/Robert_Ream/Lewis%20and%20Ream%
20et%20al.%202012%20TCR%20PDF.pdf
161
Lubliner, S., & Hiebert, E. H. (2011). An Analysis of English–Spanish Cognates as a Source of
General Academic Language. Bilingual Research Journal, 34(1), 76–93.
https://doi.org/10.1080/15235882.2011.568589
Maldonado, C. (2018). “Where Your Ethnic Kids Go”: How Counselors as First Responders
Legitimate Proper Course Placements for Community College Students. Community
College Journal of Research and Practice, 43(4), 280–294.
https://doi.org/10.1080/10668926.2018.1463303
Mardock Uman, N. (2018). A Resource-Oriented Investigation into the Community College
Matriculation and Persistence of US-Educated English Language Learners.
https://digitalcommons.unl.edu/cehsedaddiss/296/
Martinez, E., & Castellanos, M. (2018). Catching them early: An examination of Chicano/Latino
middle school boys’ early career aspirations. The Urban Review.
https://link.springer.com/article/10.1007/s11256-017-0438-5
Mavrogordato, M., & White, R. S. (2017). Reclassification Variation: How Policy
Implementation Guides the Process of Exiting Students From English Learner Status.
Educational Evaluation and Policy Analysis, 39(2), 281–310.
https://doi.org/10.3102/0162373716687075
McDonough, & Nunez. (2017). Bourdieu’s sociology of education: Identifying persistent
inequality, unmasking domination, and fighting social reproduction. In C. A. Torres & A.
Teodoro (Eds.), Critique and Utopia: New Developments in The Sociology of Education
in the Twenty-First Century. Rowman & Littlefield.
Melguizo, T., Flores, S., Velasquez, D., & Caroll, T. (2021). Lost Transitions: The Cost of Inter-
sector Misalignment for English Learners in Community Colleges (No. 4). Pullias Center
for Higher Education. https://pullias.usc.edu/download/lost-transitions/
Melguizo, T., Flores, S., Velasquez, D., & Carroll, T. (2021). Lost in the Transition: The Cost of
College-Readiness English Standards Misalignment for Students Initially Classified as
English Learners. The Journal of Higher Education.
https://doi.org/10.1080/00221546.2021.1888634
Melguizo, T., Hagedorn, L. S., & Cypers, S. (2008). Remedial/Developmental Education and the
Cost of Community College Transfer: A Los Angeles County Sample. The Review of
Higher Education, 31(4), 401–431. https://doi.org/10.1353/rhe.0.0008
Melguizo, T., & Ngo, F. (2020). Mis/Alignment Between High School and Community College
Standards. Educational Researcher , 49(2), 130–133.
https://doi.org/10.3102/0013189X19898697
162
Melguizo, T., Bos, H., Prather, G., Kosiewicz, H., Fong, K., Ngo, Federick, N. (2015).
Assessment and Placement Policies and Practices in Developmental Math: Evidence
from Experimentation in a Large Urban Community College District in California (E.
Park (ed.)). University of Southern California. http://www.uscrossier.org/pullias/wp-
content/uploads/2016/01/luccd-final-1.pdf
Moll, L. C., Amanti, C., Neff, D., & Gonzalez, N. (1992). Funds of knowledge for teaching:
Using a qualitative approach to connect homes and classrooms. Theory into Practice,
31(2), 132–141. https://doi.org/10.1080/00405849209543534
Mosqueda, E. (2010). Compounding Inequalities: English Proficiency and Tracking and Their
Relation to Mathematics Performance Among Latina/o Secondary School Youth. Journal
of Urban Mathematics Education, 3(1), 57–81. http://ed-
osprey.gsu.edu/ojs/index.php/JUME/article/view/47
Mosqueda, E., & Maldonado, S. I. (2013). The Effects of English Language Proficiency and
Curricular Pathways: Latina/os’ Mathematics Achievement in Secondary Schools. Equity
& Excellence in Education: University of Massachusetts School of Education Journal,
46(2), 202–219. https://doi.org/10.1080/10665684.2013.780647
Nassaji, H. (2015). Qualitative and descriptive research: Data type versus data analysis.
Language Teaching Research, 19(2), 129–132.
https://doi.org/10.1177/1362168815572747
National Center for Education Statistics. (2022). Fast Facts: English Learners. National Center
for Education Statistics. https://nces.ed.gov/fastfacts/display.asp?id=96
Ngo, F., & Astudillo, S. (2019). California DREAM: The Impact of Financial Aid for
Undocumented Community College Students. Educational Researcher , 48(1), 5–18.
https://doi.org/10.3102/0013189X18800047
Ngo, F., & Kwon, W. W. (2015). Using Multiple Measures to Make Math Placement Decisions:
Implications for Access and Success in Community Colleges. Research in Higher
Education, 56(5), 442–470. https://doi.org/10.1007/s11162-014-9352-9
Ngo, F., & Melguizo, T. (2021). The Equity Cost of Inter-Sector Math Misalignment: Racial and
Gender Disparities in Community College Student Outcomes. The Journal of Higher
Education, 92(3), 410–434. https://doi.org/10.1080/00221546.2020.1811570
Ngo, F., & Velasquez, D. (2020). Inside the Math Trap: Chronic Math Tracking from High
School to Community College. Urban Education, 29.
https://doi.org/0.1177/0042085920908912
163
Ngo, F., Velasquez, D., & Melguizo, T. (2021). Faculty Perspectives on Using High School Data
in an Era of Placement Testing Reform. Community College Review, 24.
https://doi.org/10.1177/00915521211002896
Nichols, L. v. (1974). 414 US 563.
Norton Grubb, W. (2013). Basic Skills Education in Community Colleges: Inside and Outside of
Classrooms. Routledge. https://play.google.com/store/books/details?id=NlXNz836zk8C
Núñez, A.-M., Rios-Aguilar, C., Kanno, Y., & Flores, S. M. (2016). English Learners and
Their Transition to Postsecondary Education. In M. B. Paulsen (Ed.), Higher Education:
Handbook of Theory and Research (pp. 41–90). Springer International Publishing.
https://doi.org/10.1007/978-3-319-26829-3_2
Nuñez, A.-M., & Sparks, P. J. (2012). Who are Linguistic Minority Students in Higher
Education?: An Analysis of the Beginning Postsecondary Students Study 2004. In
Linguistic Minority Students Go to College (pp. 120–139). Routledge.
https://www.taylorfrancis.com/books/e/9781136814952/chapters/10.4324/978020382938
7-13
Panzar, J. (2015). It’s official: Latinos now outnumber whites in California. Los Angeles Times.
https://www.latimes.com/local/california/la-me-census-latinos-20150708-story.html
Park, E. S. (2019). Examining community college students’ progression through the English as a
Second Language sequence. Community College Review, 47(4).
https://doi.org/10.1177/0091552119867467
Park, T., Woods, C. S., Hu, S., Jones, T. B., & Tandberg, D. (2018). What Happens to
Underprepared First-Time-in-College Students When Developmental Education is
Optional? The Case of Developmental Math and Intermediate Algebra in the First
Semester. In The Journal of Higher Education (Vol. 89, Issue 3, pp. 318–340).
https://doi.org/10.1080/00221546.2017.1390970
Peal, E., & Lambert, W. E. (1962). The relation of bilingualism to intelligence. Psychological
Monographs: General and Applied, 76(27), 1.
https://psycnet.apa.org/journals/mon/76/27/1/
Portes, P., Boada, D., Cabrera, L., & Pozo, T. (2017). EXAMINING CULTURAL
ADAPTATION ACROSS TWO CULTURAL CONTEXTS: HOW DIFFERENT ARE
YOUNG ADULTS IN SPAIN AND THE UNITED STATES? INTED2017.
https://library.iated.org/download/PORTES2017EXA
164
Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata,
Second Edition. Stata Press.
https://play.google.com/store/books/details?id=woi7AheOWSkC
Razfar, A., & Simon, J. (2011). Course-taking patterns of Latino ESL students: Mobility and
mainstreaming in urban community colleges in the United States. TESOL Quarterly,
45(4), 595–627. https://onlinelibrary.wiley.com/doi/abs/10.5054/tq.2011.268060
Rendón, L. I. (2002). Community College Puente: A Validating model of Education.
Educational Policy , 16(4), 642–667. https://doi.org/10.1177/0895904802016004010
Rios-Aguilar, C., & Kiyama, J. M. (2012). Funds of Knowledge: An Approach to Studying
Latina(o) Students’ Transition to College. Journal of Latinos and Education, 11(1), 2–16.
https://doi.org/10.1080/15348431.2012.631430
Rios-Aguilar, C., Kiyama, J. M., Gravitt, M., & Moll, L. C. (2011). Funds of knowledge for the
poor and forms of capital for the rich? A capital approach to examining funds of
knowledge. School Field, 9(2), 163–184. https://doi.org/10.1177/1477878511409776
Roberts, M. T. (2019). Racism in Remediation: How Black Students Navigate Stereotypes to
Achieve Success in Developmental Mathematics. In Community College Journal of
Research and Practice (pp. 1–21). https://doi.org/10.1080/10668926.2019.1640143
Robinson, J. P. (2011). Evaluating Criteria for English Learner Reclassification: A Causal-
Effects Approach Using a Binding-Score Regression Discontinuity Design With
Instrumental Variables. Educational Evaluation and Policy Analysis, 33(3), 267–292.
https://doi.org/10.3102/0162373711407912
Robinson, J. P. (2016). The Effects of Test Translation on Young English Learners’ Mathematics
Performance. Educational Researcher . https://doi.org/10.3102/0013189X10389811
Scarcella, R. (2003). Academic English: A Conceptual Framework.
https://escholarship.org/uc/item/6pd082d4
Scott-Clayton, J., & Rodriguez, O. (2015). Development, Discouragement, or Diversion? New
Evidence on the Effects of College Remediation Policy. Education Finance and Policy,
10(1), 4–45. https://doi.org/10.1162/EDFP_a_00150
Secada, W. G. (1991). Degree of Bilingualism and Arithmetic Problem Solving in Hispanic First
Graders. The Elementary School Journal, 92(2), 213–231. https://doi.org/10.1086/461689
165
Shin, N. (2017). The effects of the initial English language learner classification on students’
later academic outcomes. Educational Evaluation and Policy Analysis.
https://journals.sagepub.com/doi/abs/10.3102/0162373717737378
Shulock, N., & Moore, C. (2010). Divided We Fail: Improving Completion and Closing Racial
Gaps in California’s Community Colleges. California State University, Sacramento.
https://books.google.com/books?id=1dl4nQEACAAJ
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999/1). Stereotype Threat and Women’s Math
Performance. Journal of Experimental Social Psychology, 35(1), 4–28.
https://doi.org/10.1006/jesp.1998.1373
Stanton-Salazar, R. D., & Dornbusch, S. M. (1995). Social Capital and the Reproduction of
Inequality: Information Networks among Mexican-Origin High School Students.
Sociology of Education, 68(2), 116–135. https://doi.org/10.2307/2112778
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of
African Americans. Journal of Personality and Social Psychology, 69(5), 797–811.
https://www.ncbi.nlm.nih.gov/pubmed/7473032
Steele, J. L., Slater, R. O., Li, J., Zamarro, G., Miller, T., & Bacon, M. (2018). Dual-language
immersion education at scale: An analysis of program costs, mechanisms, and
moderators. Educational Evaluation and Policy Analysis, 40(3), 420–445.
https://doi.org/10.3102/0162373718779457
Stoelinga, T., & Lynn, J. (2013). Algebra and the Underprepared Learner. UIC Research on
Urban Education Policy Initiative Policy Brief, 2(3). http://c-stemec.org/wp-
content/uploads/2013/08/Algebra-and-Underprepared-Learner.pdf
Suárez-Orozco, C., Casanova, S., Martin, M., Katsiaficas, D., Cuellar, V., Smith, N. A., & Dias,
S. I. (2015). Toxic Rain in Class: Classroom Interpersonal Microaggressions.
Educational Researcher , 44(3), 151–160. https://doi.org/10.3102/0013189X15580314
Tann, K., & Scott, A. (2021). Bridging disciplinary knowledge: the challenge of integrating EAP
in business education. Higher Education, 81(3), 453–470.
https://doi.org/10.1007/s10734-020-00551-0
Thompson, K. D. (2015). English Learners’ Time to Reclassification. Educational Policy,
(Article In Press), 1–34. https://doi.org/10.1177/0895904815598394
Thompson, K. D. (2017). What Blocks the Gate? Exploring Current and Former English
Learners’ Math Course-Taking in Secondary School. American Educational Research
Journal, 54(4), 757–798. https://doi.org/10.3102/0002831217706687
166
Tierney, W. G., & Garcia, L. D. (2011). Remediation in Higher Education: The Role of
Information. The American Behavioral Scientist, 55(2), 102–120.
https://doi.org/10.1177/0002764210381869
U.S. Department of Education. (2017). Our Nation’s English Learners: Where Are English
Learners? U.S. Department of Education. https://www2.ed.gov/datastory/el-
characteristics/index.html#two
Umansky, I. M. (2016a). To Be or Not to Be EL. Educational Evaluation and Policy Analysis,
38(4), 714–737. https://doi.org/10.3102/0162373716664802
Umansky, I. M. (2016b). Leveled and exclusionary tracking. American Educational Research
Journal, 53(6), 1792–1833. https://doi.org/10.3102/0002831216675404
Umansky, I. M., & Dumont, H. (2021). English Learner Labeling: How English Learner
Classification in Kindergarten Shapes Teacher Perceptions of Student Skills and the
Moderating Role of Bilingual Instructional Settings. In American Educational Research
Journal (Vol. 58, Issue 5, pp. 993–1031). https://doi.org/10.3102/0002831221997571
Umansky, I. M., & Reardon, S. F. (2014). Reclassification Patterns Among Latino English
Learner Students in Bilingual, Dual Immersion, and English Immersion Classrooms.
American Educational Research Journal, 51(5), 879–912.
https://doi.org/10.3102/0002831214545110
Umansky, I. M., Thompson, K. D., & Díaz, G. (2017). Using an Ever–English Learner
Framework to Examine Disproportionality in Special Education. Exceptional Children,
84(1), 76–96. https://doi.org/10.1177/0014402917707470
University of California. (2022). Subject requirement (A-G). University of California
Admissions. https://admission.universityofcalifornia.edu/admission-
requirements/freshman-requirements/subject-requirement-a-g.html
Valentine, J. C., Konstantopoulos, S., & Goldrick-Rab, S. (2017). What Happens to Students
Placed Into Developmental Education? A Meta-Analysis of Regression Discontinuity
Studies. Review of Educational Research, 87(4), 806–833.
https://doi.org/10.3102/0034654317709237
Valentino, R. A., & Reardon, S. F. (2016). Effectiveness of Four Instructional Programs
Designed to Serve English Learners. Educational Evaluation and Policy Analysis.
https://doi.org/10.3102/0162373715573310
Wilson, J. R., & Lorenz, K. A. (2015). Modeling Binary Correlated Responses using SAS, SPSS
and R. Springer. https://play.google.com/store/books/details?id=XQK5CgAAQBAJ
167
Wolf, M. K., Herman, J. L., Bachman, L. F., Bailey, A. L., & Griffin, N. (2008).
Recommendations for Assessing English Language Learners: English Language
Proficiency Measures and Accommodation Uses. In PsycEXTRA Dataset.
https://doi.org/10.1037/e643112011-001
Wright. (2019). Foundations for Teaching English Language Learners: Research, Theory. Policy,
and Practice. Philadelphia: Caslon.
https://biurokarier.wum.edu.pl/sites/targi.wum.edu.pl/files/webform/pdf-foundations-for-
teaching-english-language-learners-research-the-wayne-e-wright-pdf-download-free-
book-5b6fe05.pdf
Yah, V. (2013). Una Cadena de Esperanza: How Latino Male English Language Learners Use
Community Cultural Wealth in Challenging Negative Educational Experiences.
California State University, Long Beach.
https://play.google.com/store/books/details?id=XvrOtwEACAAJ
Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community
cultural wealth. Race Ethnicity and Education, 8(1), 69–91.
https://doi.org/10.1080/1361332052000341006
Zarate, M. E., & Pineda, C. G. (2014). Effects of elementary school home language, immigrant
generation, language classification, and school’s English learner concentration on
Latinos’ high school completion. Teachers College Record, 116(2), 1–37.
http://www.academia.edu/download/39730516/ZaratePineda-HSCompletion-14.pdf
Abstract (if available)
Linked assets
University of Southern California Dissertations and Theses
Conceptually similar
PDF
Math and the making of college opportunity: persistent problems and possibilities for reform
PDF
Mathematics identity and sense of belonging in mathematics of successful African-American students in community college developmental mathematics courses
PDF
How extending time in developmental math impacts persistence and success: evidence from a regression discontinuity in community colleges
PDF
Essays on economics of education
PDF
Three essays on the high school to community college STEM pathway
PDF
Relationships between a community college student’s sense of belonging and student services engagement with completion of transfer gateway courses and persistence
PDF
A multi-perspective examination of developmental education: student progression, institutional assessment and placement policies, and statewide regulations
PDF
Essays on education: from Indonesia to Los Angeles, CA
PDF
Reforming developmental education in math: exploring the promise of self-placement and alternative delivery models
PDF
State policy as an opportunity to address Latinx transfer inequity in community college
PDF
Ready or not? Unprepared for community college mathematics: an exploration into the impact remedial mathematics has on preparation, persistence and educational goal attainment for first-time Cali...
PDF
AB 705: the equity policy – race and power in the implementation of a developmental education reform
PDF
Implications of a tracked mathematics curriculum in middle school
PDF
Preparing English language learners to be college and career ready for the 21st century: the leadership role of middle school principals in the support of English language learners
PDF
Institutional researchers as agents of organizational learning in hispanic-serving community colleges
PDF
Moving from great to greater: Math growth in high achieving elementary schools - A gap analysis
PDF
A phenomenological study of the impact of English language learner support services on students’ identity development
PDF
Math achievement and self-efficacy of linguistically and ethnically diverse high school students: their relationships with English reading and native language proficiency
PDF
The intersection of grit and social capital: a mixed methods examination of successful first-generation college students
PDF
Preparing English language learners to be college and career ready for the 21st century: the leadership role of secondary school principals in the support of English language learners
Asset Metadata
Creator
Velasquez, David
(author)
Core Title
To what extent does being a former high school English learner predict success in college mathematics? Evidence of Latinx students’ duality as math achievers
School
Rossier School of Education
Degree
Doctor of Philosophy
Degree Program
Urban Education Policy
Degree Conferral Date
2022-12
Publication Date
09/25/2022
Defense Date
08/16/2022
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
community college,credit accumulation,developmental courses,developmental education,ELs,English learners,fixed effects,High School,logistic regression,Math,mathematics,multiple regression,OAI-PMH Harvest,regression,Success,transfer
Format
177 pages
(extent)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Melguizo, Tatiana (
committee chair
), Bensimon, Estela Mara (
committee member
), Owens, Ann (
committee member
)
Creator Email
davidvelasquezphd@gmail.com,velasqud@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC112059629
Unique identifier
UC112059629
Legacy Identifier
etd-VelasquezD-11248
Document Type
Dissertation
Format
177 pages (extent)
Rights
Velasquez, David
Internet Media Type
application/pdf
Type
texts
Source
20221003-usctheses-batch-985
(batch),
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Repository Email
cisadmin@lib.usc.edu
Tags
community college
credit accumulation
developmental courses
developmental education
ELs
English learners
fixed effects
logistic regression
mathematics
multiple regression
regression