ArticlePDF Available

Success with EASE: Who benefits from a STEM learning community?

Authors:

Abstract and Figures

During the past few decades, there has been a nationwide push to improve performance and persistence outcomes for STEM undergraduates. As part of this effort, recent research has emphasized the need for focus on not only improving the delivery of course content, but also addressing the social-psychological needs of students. One promising intervention type that has been proposed as a multifaceted way to address both cognitive and social-psychological aspects of the learning process is the learning community. Learning communities provide students with opportunities to build a strong support system in college and are generally associated with increased student engagement and integration with campus systems and cultures. In this study, we examine the impact of a learning community intervention for first-year biological sciences majors, the Enhanced Academic Success Experience (EASE) program. Incoming freshmen are assigned to EASE based on their SAT (or ACT equivalent) Math score, a metric demonstrated to be a key predictor of student success in the program. We find that enrollment in EASE is correlated with higher STEM course grades; an increase of 0.25 (on a 0–4 point scale) in cumulative first-year GPA; and gains in non-academic outcomes, such as measures of sense of belonging and academic integration. Further, these outcomes are more pronounced for particular subgroup populations. For example, whereas surveyed male students seemed to benefit academically from participating in a learning community, female students reported a greater sense of belonging in regard to the biological sciences major and reported higher values for behavioral indicators of academic integration. Lastly, we find that the EASE program is positively correlated with students’ intention to stay in the biological sciences major. And, among the three race-oriented groups, this impact is most pronounced for under-represented students. In light of these findings, we discuss the potential of discipline-specific learning community programs to improve academic outcomes for students most at risk of leaving STEM majors, such as students underprepared for college level coursework.
Content may be subject to copyright.
RESEARCH ARTICLE
Success with EASE: Who benefits from a STEM
learning community?
Sabrina Solanki
1
, Peter McPartlan
1
, Di Xu
1
, Brian K. SatoID
2
*
1School of Education, University of California, Irvine, United States of America, 2Department of Molecular
Biology and Biochemistry, University of California, Irvine, United States of America
*bsato@uci.edu
Abstract
During the past few decades, there has been a nationwide push to improve performance
and persistence outcomes for STEM undergraduates. As part of this effort, recent research
has emphasized the need for focus on not only improving the delivery of course content, but
also addressing the social-psychological needs of students. One promising intervention
type that has been proposed as a multifaceted way to address both cognitive and social-
psychological aspects of the learning process is the learning community. Learning commu-
nities provide students with opportunities to build a strong support system in college and are
generally associated with increased student engagement and integration with campus sys-
tems and cultures. In this study, we examine the impact of a learning community intervention
for first-year biological sciences majors, the Enhanced Academic Success Experience
(EASE) program. Incoming freshmen are assigned to EASE based on their SAT (or ACT
equivalent) Math score, a metric demonstrated to be a key predictor of student success in
the program. We find that enrollment in EASE is correlated with higher STEM course
grades; an increase of 0.25 (on a 0–4 point scale) in cumulative first-year GPA; and gains in
non-academic outcomes, such as measures of sense of belonging and academic integra-
tion. Further, these outcomes are more pronounced for particular subgroup populations. For
example, whereas surveyed male students seemed to benefit academically from participat-
ing in a learning community, female students reported a greater sense of belonging in regard
to the biological sciences major and reported higher values for behavioral indicators of aca-
demic integration. Lastly, we find that the EASE program is positively correlated with stu-
dents’ intention to stay in the biological sciences major. And, among the three race-oriented
groups, this impact is most pronounced for under-represented students. In light of these
findings, we discuss the potential of discipline-specific learning community programs to
improve academic outcomes for students most at risk of leaving STEM majors, such as stu-
dents underprepared for college level coursework.
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 1 / 20
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Solanki S, McPartlan P, Xu D, Sato BK
(2019) Success with EASE: Who benefits from a
STEM learning community? PLoS ONE 14(3):
e0213827. https://doi.org/10.1371/journal.
pone.0213827
Editor: Sara Rubinelli, Universitat Luzern,
SWITZERLAND
Received: May 8, 2018
Accepted: March 3, 2019
Published: March 22, 2019
Copyright: ©2019 Solanki et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
A considerable number of studies in science, technology, engineering, and mathematics
(STEM) education research fields have focused on improving outcomes for undergraduate stu-
dents using two intervention types: interventions that impact content delivery both inside and
outside the classroom and those that address social-psychological aspects of the student experi-
ence. Examples of interventions include the incorporation of active learning strategies [13],
the structuring of at-home activities that help students prepare for class [45], and values-affir-
mation writing exercises [67]. Interventions of this nature have been developed in response
to nationwide concern regarding the low persistence of STEM undergraduates in their aca-
demic majors—an issue that disproportionately impacts underrepresented minority (URM)
students, low-income students, and first-generation college-going students [8].
Another tactic used to improve STEM outcomes has been to alter the college student expe-
rience at the program or institution-wide level. Examples of interventions utilizing this model
include summer bridge programs, structured independent research experiences, and STEM
learning centers [913]. In the same vein are learning community programs, the focus of the
present study. Learning communities are intentionally designed to increase opportunities for
students to interact with peers, faculty, and the curriculum, which allows for the construction
of a strong support system. A number of studies have found a positive correlation between par-
ticipation in a learning community and traditional academic markers of success, also finding
positive outcomes for students most at risk for leaving college, such as students underprepared
for college-level coursework [1415]. Unlike the majority of studies about learning communi-
ties, the present study is unique in that it examines whether a learning community can be par-
ticularly beneficial within a specific discipline—the biological sciences—and therefore has the
potential to contribute to the small but growing body of work on learning communities in
STEM education.
Learning communities
Learning communities, at its core, promote peer-to-peer and student-faculty interaction and
provide students with a number of opportunities to build a strong support system. The major-
ity of learning communities incorporate active and collaborative learning activities (e.g., stu-
dents co-enrolling in courses) and promote involvement in complementary academic and
social activities that extend beyond the classroom (e.g., students meet weekly in a study skills
course and/or with a group mentor). Faculty involved in learning communities are encouraged
to use active pedagogical strategies that foster meaningful interaction between students and
instructors. They are also encouraged to engage with one another and think about ways to sup-
port student learning outcomes [15]. All of these components build institutionalized social
support networks that subsequently buttress academic support systems [1526].
The positive impacts of learning communities such as the Meyerhoff Scholars Program [27]
and Posse Foundation programs [28] exemplify the important role social integration plays in
academic success, which long-standing theories of college persistence have espoused [2930].
As documented in literature from the past two decades, a common measure of students’ social
integration is sense of belonging, which is based on perceived social support, connectedness,
and mattering [31]. The ways in which sense of belonging is associated with persistence have
become increasingly apparent, with research detailing that it is positively linked to students’
motivation [3133], engagement [34], and achievement [35]. Thus, it has become clear that
the potential of learning communities to further students’ sense of belonging is one of the
defining components of this intervention type [36].
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 2 / 20
Extant literature. Prior research has documented that first-year students who participate
in learning communities have higher grades, retention rates, and self-reported levels of
engagement than their peers who have not had a learning community experience. Further,
learning community students report studying more with peers outside of class and becoming
more involved in academic activities [19; 22–26].
Zhao and Kuh [26], for example, used the National Survey of Student Engagement—a sur-
vey widely used to assess the quality of the undergraduate college experience—to estimate the
impact of learning communities on a myriad of student outcomes. Learning community par-
ticipation was positively associated with a number of outcomes related to student engagement,
such as academic effort, academic integration, and collaborative learning. Learning commu-
nity participants were also more likely to interact with faculty members. Lastly, students in
learning communities reported being more satisfied with their college experience as compared
to students who did not participate in learning communities. For student outcomes, effect
sizes ranged from 0.23 to 0.60. Thus, even though the authors note limitations to the study, the
moderate correlations documented in their paper substantiate the idea that learning commu-
nities are a powerful support structure that can impact the overall student experience in
college.
The most rigorous evaluation of learning communities to date comes from a recent report
by MDRC [37]. Using random assignment, MDRC evaluated the impacts of a one-semester
learning community program on students assigned to developmental English classes at six dif-
ferent community colleges. In general, the study failed to find any consistent evidence that
learning communities positively influenced students’ college persistence and academic
performance.
A number of plausible reasons could explain these null effects. Students participating in the
learning community programs this study assessed came from a variety of fields. The lack of
common interests and goals represented could have substantially weakened the connections
between students and sense of belonging, a major component of learning communities that is
often cited as being highly correlated to student academic progress and retention decisions.
Also, all six programs’ interventions lasted only one semester, and programs included only
one component of a learning community: paired courses.
It is important to note, however, that the MRDC study makes a unique contribution to the
literature about learning communities, as it is the only learning community study to use the
gold standard in research design: randomization of participants for treatment and control con-
ditions. Findings can therefore be viewed as having a causal interpretation, which is important
because they therefore shed light on the possibility that correlational studies, most of which
have shown positive impacts, could be over-stating the benefits of participating in a learning
community program. The implications of using correlational research design in relation to the
present study is discussed in the last section of this paper.
Most published learning community studies were implemented for the general population
of first-year college students or for those in developmental education programs, such as
MDRC [37]. Only a small number were implemented in STEM programs. The nature of
STEM programs, however, makes students well-positioned to reap the benefits of a learning
community. This is because students in STEM programs often face discouragement and a loss
of confidence due to initially low grades; they experience the weakening of morale as a result
of competitive STEM culture and the generally unwelcoming atmosphere of STEM courses.
Further, students are often overwhelmed by STEM’s rigorous curriculum, fast-paced instruc-
tion, demand for independent work, and content overload in courses taught by often unenga-
ging STEM faculty [38,39]. In contrast, inclusive learning communities provide academic
support and are headed by faculty interested in effective instruction and strong student-
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 3 / 20
instructor relationships, indicating that these communities can foster positive student develop-
ment in STEM.
The few studies about learning communities in STEM education show positive impacts. In
Dagley et al. [18], for example, researchers evaluated the EXCEL program, a first-year learning
community for STEM students at the University of Central Florida. In addition to offering tra-
ditional learning community components, EXCEL gives participants the option to live
together in on-campus residential housing, which appears to be beneficial since, in this partic-
ular context, a number of social interactions brought faculty and students to the residential
space to engage in informal activities.
When Dagley et al. [18] compared the learning outcomes of EXCEL participants to those of
a comparison group of students who had declared a STEM major and had the same standard-
ized test math score range, they found that first-year retention, long-term retention, and grad-
uation rates were higher for the EXCEL cohorts than for the comparison group. Specifically,
retention of students in a STEM major was 43% higher for program participants than for the
comparison group. Further, female, African-American, and Hispanic individuals in the pro-
gram were correlated with higher retention and graduation rates than similar comparison
students.
The present study
In this study, we evaluate the Enhanced Academic Success Experience (EASE) program, a
learning communities program implemented in the school of biological sciences at a large uni-
versity in the Western United States, the University of California, Irvine. Specifically, we
explore whether the benefits learning communities offer at the college level and for first-year
students can have similar impact when a learning community is instituted within a specific
field of study, such as biological sciences. Within learning community literature, only a few
programs have been implemented and evaluated in STEM fields, as noted earlier. However,
these studies have shown that learning communities can play an important role in fostering an
early sense of engagement and institutional identification [40], which can be especially impor-
tant as students face the challenges inherent in STEM courses of study [41].
We also focus our efforts on evaluating the ways in which the EASE program impacts cer-
tain subgroups. For example, learning communities may especially affect the academic perfor-
mance and persistence of underrepresented populations in college, such as first-generation
college students. First-generation college students have parents who have not attained a four-
year college degree and often come from families with fewer financial resources, in addition to
having attended lower quality high schools than their continuing-generation peers. As a result,
many begin their college career requiring additional academic support and are uncertain
about how to successfully navigate the college experience [42]. The support system a learning
community provides therefore has the potential to reduce socioeconomic achievement gaps in
college.
Learning communities also provide students with an environment that fosters feelings of
belonging. Indeed, upon their arrival in college underrepresented populations are prone to
feeling a lack of belongingness, which is an important component of social integration and
predictor of persistence [31,36,43]. Cultural Mismatch Theory has offered an explanation for
why underrepresented populations, such as first-generation students, are more prone to expe-
rience this phenomenon. First proposed by Stephens, Fryberg, Markus, Johnson, and Covarru-
bias [44], Cultural Mismatch Theory illuminates the stark contrast between the community-
oriented values typical of first-generation students and the values of the university environ-
ment. Specifically, it purports that individual performance is contingent upon whether people
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 4 / 20
experience a match or a mismatch between their own cultural norms and the norms that are
institutionalized in a given setting. First-generation and low-income students often come from
working-class communities that value interdependence and attention paid to others, yet higher
education culture emphasizes independence and competition [38,44]. This contrast is espe-
cially evident in competitive STEM disciplines and can make first-generation students espe-
cially likely to perform poorly and drop out. First-generation URM students must also combat
additional belongingness issues, as they are likely to react negatively when encountering chal-
lenges in college, interpreting them as evidence that they do not belong at their institution or
within their major [45]. In promoting social support, learning communities may be capable of
improving belongingness for underrepresented groups [36].
The EASE program. The EASE program at the University of California, Irvine follows a
learning communities model; it is a multi-faceted program that aims to improve freshman stu-
dents’ performance and persistence in biological sciences. Eligibility is determined by the
Department of Biological Sciences during the summer prior to the start of the school year. Spe-
cifically, all incoming freshmen with SAT (or ACT equivalent) Math scores of lower than 600
are enrolled in the EASE program. Administrators in the Biological Sciences department
assign students to cohorts (roughly 25 students per cohort group for a total of sixteen cohorts)
and adjust schedules accordingly so that student groups are enrolled in the same course section
for all first-year STEM courses. EASE students are made aware of their EASE status at the sum-
mer Student-Parent Orientation Program (SPOP).
EASE provides a number of ways for students to become integrated into campus culture
and campus systems, both academically and socially [2930]. Specifically, students in the
EASE program are provided the following resources:
1. Academic remediation: EASE students are required to take an additional developmental
chemistry course online the summer prior to college matriculation. This course is designed
to prepare biological sciences majors for college-level courses in chemistry and biology.
2. Academic and social support: Each cohort is enrolled in the same biology and chemistry
courses (lectures and discussion sections) for one year. The majority of EASE students co-
enrolled in 5 courses during their first year, one of which was a preparatory general chemis-
try course during the first academic quarter and was required in order to proceed with the
rest of the general chemistry curriculum. The main goal of co-enrollment is for cohorts to
engage in learning activities and develop strong relationships, which is hoped to increase
students’ sense of belonging in the biological sciences department and at the institution.
Each cohort is also matched with a senior biological sciences mentor. Mentors are upper-
classman biological sciences majors selected by the department; they have a tutoring back-
ground and have excelled in introductory biological sciences courses. The mentors provide
increased academic support and serve as students’ main guide to all campus resources and
opportunities. Lastly, EASE students participate in a weekly 50-minute seminar led by an
EASE mentor. Seminar topics are generally academic in nature and focus particularly on
study skills and metacognition. General first-year issues are also discussed, in addition to
advice about how to navigate the first-year experience. The personalized advising and guid-
ance students receive through the EASE program is intended to not only fill students’
knowledge gaps, but also provide support as students develop academic and social-emo-
tional skills during their first year in college.
In analyzing the EASE program, we expected social integration within the biological sci-
ences major to manifest itself in two particular ways. First, in keeping with other studies’ find-
ing that learning communities impact students’ sense of institution-specific belonging [36,21],
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 5 / 20
we expected learning communities to improve students’ sense of belonging within the biologi-
cal sciences major, thereby strengthening students’ belief that they fit the subject and that their
involvement is valued [36]. Second, as students often enter college with academic and social
concerns about the challenges they will face [33], we anticipated that the social support systems
in EASE would assuage these concerns, including those about being socially ostracized and
being judged for poor performance.
Evidence of the program’s effect on students’ academic integration was expected to mani-
fest itself in distinct ways as well. First, we anticipated that students would engage more in
course-related behaviors, such as visiting faculty during office hours, participating in student
study groups, and using other campus resources. Because the social support features of learn-
ing communities are designed to encourage academic collaboration, we hypothesized that we
would find evidence of improved academic integration in interactions with teachers, advisors,
and study groups [46]. We also expected that the program’s supplemental instruction and
advising would support students’ interest in the biological sciences, with academic interest
serving as an especially strong predictor of persistence among undergraduate students [47
48]. Consequently, we predicted that furthered course-related behaviors and interest in the
biological sciences major would correspond with higher grades in students’ biological science
courses.
The conceptual model in Fig 1 depicts the hypothesized mechanisms through which EASE
was expected to impact persistence. The model is based on popular theories of college persis-
tence that emphasize the complementary roles of academic integration and social integration;
the measures used in the present study are listed as indicators of academic and social integra-
tion. It is important to note that the present study measures only the total association of the
learning community, with academic and social integration functioning acting as independent
entities. However, as the vertical arrows in the model convey, we believe both that persistence
is generated through academic and social integration and that these factors are mutually rein-
forcing. For instance, a greater sense of belonging within the biological sciences major (social
integration) would be expected to enhance a student’s interest in biology (academic integra-
tion) [35]. By the same token, students who get higher grades (academic integration) as a result
of EASE’s supplementary instruction would be expected to have fewer academic and social
concerns (social integration) [49].
In this study, we address the following research questions:
1. Is enrollment in the EASE program correlated with improved academic outcomes?
2. Is enrollment in the EASE program correlated with social-psychological measures of the
student experience?
3. Are students in the EASE program more likely to remain in the biological sciences major
than students not enrolled in the program?
4. Do the impacts of the EASE program vary by student subgroup?
Methods
Study context
This study took place at the University of California, Irvine, a public research-intensive univer-
sity located in the Western United States. It focuses on first-year students in the biological sci-
ences (Bio Sci) major. This study was performed with approval from the University of
California, Irvine Institutional Review Board (HS# 2015–2310).
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 6 / 20
The EASE group consisted of 42.7% (N = 388) of first-year biological sciences majors,
grouped into sixteen cohorts. Descriptive data regarding the students who were and were not
placed in the EASE program can be found in Table 1. As shown in Table 1, female students
were more likely to get placed in the EASE program. The EASE program also included a larger
proportion of students traditionally marginalized in college: 55% of participants belong to a
URM group, 63.4% of participants are first-generation college students, and 54.6% come from
low-income family households. Lastly, EASE participants earned much lower average SAT-
reading and SAT-math scores.
Data collected
Data collection took a variety of forms, including an online survey instrument implemented at
the beginning and end of the fall quarter and a slightly modified version presented at the end
Fig 1. Conceptual model of EASE learning community program.
https://doi.org/10.1371/journal.pone.0213827.g001
Table 1. Demographic data for study population.
Full Student Sample EASE—No EASE—Yes
Mean (%) SD Mean
(%)
SD Mean
(%)
SD P value
Female 0.684 (0.465) 0.613 (0.488) 0.780 (0.415) 0.000
White 0.125 (0.331) 0.131 (0.338) 0.116 (0.321) 0.518
URM 0.337 (0.473) 0.177 (0.381) 0.550 (0.498) 0.000
Asian 0.539 (0.499) 0.692 (0.462) 0.333 (0.472) 0.000
First-gen status 0.484 (0.500) 0.372 (0.484) 0.634 (0.482) 0.000
Low-income status 0.407 (0.492) 0.303 (0.460) 0.546 (0.498) 0.000
SAT Reading score (mean) 558.9 (76.45) 581.8 (77.42) 528.2 (63.33) 0.000
SAT Math score (mean) 598.0 (88.48) 653.4 (70.54) 523.9 (45.35) 0.000
N 907 519 388
Table 1. Demographic data of Bio Sci majors, including those that are and are not in the EASE program. URM consists of Hispanic, African-American, and Native-
American students. Differences between students in and not in the EASE program were determined using t-tests with the P value indicated.
https://doi.org/10.1371/journal.pone.0213827.t001
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 7 / 20
of the spring quarter. These surveys measured a variety of student attitudes and behaviors
regarding the field of biology, such as sense of belonging, academic and social concerns, aca-
demic integration, and interest. All three surveys are provided in the supporting information
(S1 File). We briefly describe each construct below.
Belonging in biology assesses the extent to which students feel they belong in the discipline
of biology at UCI. Items were adapted from Hoffman’s [36] Sense of Belonging Scale to ensure
they are specific to the biology discipline instead of to the university in general. This measure
includes 8 items; students were asked to indicate how true statements, such as “I have devel-
oped personal relationships with other students in my Bio Sci classes” and “I feel comfortable
seeking help from my Bio Sci teachers before or after class,” were true on a 1 (not at all true) to
7 (very true) Likert scale. Cronbach’s alpha for the 8 items is equal to 0.85.
Academic and social concerns conveys the extent to which participants worry that other stu-
dents will dislike them or unfairly evaluate their academic ability [49]. This measure includes 3
items—for example, “In college, I sometimes worry that people will dislike me.” Students were
asked to respond using the same Likert scale discussed above. Cronbach’s alpha for the three
items is equal to 0.73.
Academic integration indicates the frequency with which participants engaged in various
school-related activities—such as talking to faculty, planning with academic advisors, and
attending study groups—during their first term on campus. This measure included 5 items.
Cronbach’s alpha is equal to 0.60.
Interest in biology is a measure inspired by the Eccles’ [50] Expectancy-Value Model of
motivation, which is a critical means of evaluating how much students value a field of study.
Individual items were adapted from Harackiewicz’s [51] and include, for example, “I think the
field of Biology is very interesting.” This measure included 3 items. Cronbach’s alpha for the 3
items is equal to 0.91.
Student demographic data—including gender, ethnicity, first-generation status, low-
income status, SAT Reading score, and SAT (or ACT equivalent) Math score—was collected
from the campus registrar. We also collected student outcome data that includes information
about course performance in two first-year biology courses: Bio Sci 93, an introductory course
that covers biology basics, and Bio Sci 94, the follow-up course. Data regarding first-year over-
all GPA and retention within the biological sciences major at the end of the first year was also
collected and included in the analysis as student outcome measures.
Data analysis
To explore the relationship between the EASE program and student academic and nonaca-
demic outcomes, we use an ordinary least squares (OLS) estimation strategy
Yi¼b0þb1ðEASEiÞ þ Xiþmið1Þ
in which EASE is the key explanatory variable and is equal to 1 if the student participated in
the program; Χ
i
includes demographic characteristics (e.g., gender, race, first-generation sta-
tus, and low-income status) and academic preparedness characteristics (e.g., SAT section
scores). μ
i
is the error term.
Additionally, we explore whether gaps in academic achievement and in the social-psycho-
logical measures are wider or narrower for certain student subgroups. For this aspect of analy-
sis, we include an interaction term between a given individual attribute (such as ‘female’) and
EASE status in Eq 1. We present the formal equation below. For all analyses, robust standard
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 8 / 20
errors were used.
Yi¼b0þb1ðEASEiÞ þ b2ðAttributeiÞ þ b3ðEASEiAttributeiÞ þ Xiþmið2Þ
We report the β
1
coefficient, which indicates the impact of EASE on the reference group (for
example, male students), and the β
3
coefficient, which indicates whether EASE reduced the
academic achievement gap (or in the case of gender, the gender achievement gap). We run a
separate regression conditioning on the student attributes using Eq (1). Where noted, missing
values have been adjusted using a dummy variable approach [52].
Results
Impact of EASE on student performance outcomes
We first wanted to identify whether participation in the EASE program is correlated with
improved student outcomes. Table 2 provides the results for a number of these academic out-
comes, including performance in freshman biology courses, first-year cumulative GPA, and
retention within the biological sciences major for students who were and were not in the EASE
program. When controlling for a variety of demographic characteristics, we find that enroll-
ment in the EASE program is correlated with significantly higher grades in Bio Sci 94–0.38
grade points higher on a 0–4 point scale—and a 0.24 boost in first-year GPA.
As discussed earlier, the purpose of our study is to better understand whether certain stu-
dent subgroups (e.g., URM students, female students, and first-generation students) benefit
particularly from the learning community experience, with the results of our analysis found in
Table 3. Using gender as an example, both male and female EASE students earned higher Bio
Sci 94 grades than their non-EASE counterparts did (column 2). Specifically, male EASE stu-
dents and female EASE students earned grades 0.53 and 0.34 grade points higher (on a 0–4
point scale), respectively, than their non-EASE counterparts. However, the β
3
coefficient,
EASEFemale, is not significant. Thus, the gender achievement gap in the EASE sample is not
significantly different than that in the non-EASE sample.
In terms of race, all three student subgroup populations benefited from the EASE program.
White students, URM students, and Asian students earned Bio Sci 94 grades that were 0.48,
0.41, and 0.38 grade points higher, respectively, than those of their non-EASE counterparts.
Again, we do not find a significant interaction term (EASEURM) to indicate that the racial
achievement gap decreases given EASE involvement. Overall, whereas groups of students tra-
ditionally less-represented in STEM (e.g., female, URM, first generation, and low-income) saw
gains in Bio Sci 94 grades and first-year cumulative GPA, these gains are not greater than
those observed for their non-at-risk counterparts (Table 3).
Impact of EASE on social-psychological measures of the student experience
Learning communities are considered a way to not only alleviate academic issues common
among at-risk students, but also further students’ social psychological well-being, positively
affecting their sense of belonging, academic and social concerns, academic integration, and
interest in science. As shown in Table 4, we find that student involvement in EASE is corre-
lated with statistically significant higher levels of sense of belonging and academic integration
at the end of students’ first term in college. Specifically, EASE students reported values for
sense of belonging and academic integration that were 0.21 and 0.32 standard deviation units
larger, respectively, than those reported by non-EASE students.
Similar to our efforts to analyze academic outcomes, we explore the possibility of heteroge-
neous treatment effects. As shown in Table 5, we find significant differences in values
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 9 / 20
regarding sense of belonging reported by female students in EASE and female students not in
EASE. Specifically, female EASE students reported values for the sense of belonging measure
that were 0.18 standard deviation units higher than the values reported by female students not
in EASE. This impact is even more pronounced for the measure of academic integration, with
female EASE students reporting values that were 0.44 standard deviation units larger than
those reported by non-EASE female students. Additionally, for the measure of academic inte-
gration, the interaction term EASEFemale is positive and marginally significant, indicating
that the large increase for female students is responsible for a gender gap that favors female
students.
Male students reported social-psychological outcome measure values larger than those
reported by their non-EASE counterparts only for the measure of academic and social con-
cerns. The positive coefficient, however, indicates that male EASE students reported having
more concerns than their non-EASE counterparts.
Of the three different race groups, Asian students seemed most affected by EASE. Specifi-
cally, the coefficients indicate that Asian students in EASE experienced a greater sense of
belonging (β
1
= 0.32) and engaged in behaviors that indicated they were more academically
integrated within the major (β
1
= 0.43). EASE did not have similarly significant effects for
White and URM students. Lastly, first-generation students in EASE reported being more con-
cerned about their academic ability than their non-EASE counterparts. However, these same
students reported much higher values for the item measuring academic integration (β
1
= 0.41).
Low-income students in EASE also reported much higher values for the academic integration
measure than their non-EASE counterparts (β
1
= 0.48).
Table 2. Estimates for the impact of EASE on performance outcomes.
Bio Sci 93
course grade
Bio Sci 94
course grade
Year 1 GPA Retained
EASE -0.055 0.380��0.242��0.013
(0.085) (0.088) (0.057) (0.026)
Female -0.047 -0.144�� -0.005 -0.032
(0.065) (0.063) (0.044) (0.015)
URM -0.153 -0.225-0.163��-0.043
(0.094) (0.088) (0.063) (0.031)
Asian -0.185�� -0.202-0.142�� 0.021
(0.087) (0.080) (0.059) (0.025)
First-gen status 0.020 -0.037 -0.029 0.004
(0.066) (0.070) (0.046) (0.017)
Low-income status -0.027 0.010 -0.003 0.022
(0.067) (0.070) (0.046) (0.018)
SAT Reading score 0.003��0.003��0.002��0.000��
(0.000) (0.000) (0.000) (0.000)
SAT Math score 0.003��0.003��0.002��0.000
(0.001) (0.001) (0.000) (0.000)
N 903 853 839 899
Table 2. Robust standard errors in included in parentheses. The reference group White. Course grade and GPA estimates use a 0–4 point scale. Missing values have
been adjusted using a dummy variable approach.
p<0.10
�� p<0.05
��p<0.01.
https://doi.org/10.1371/journal.pone.0213827.t002
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 10 / 20
Impact of EASE on the desire to remain in the biological sciences major
The EASE program had little correlation with increases in major retention, as measured by the
number of students still declared as majoring in Bio Sci by the end of their freshman year. This
may be due, in part, to how uncommon it is for a student to be removed from a major in the
freshman year, as it is a multi-step process involving a probationary period that may have pre-
vented us from observing departure from the major within the ten-month time period of the
study.
Table 3. Estimates for the impact of EASE on performance outcomes for the full sample and for student subgroups.
(1) (2) (3) (4)
Biology 93
course Grade
Biology 94
course Grade
Year 1 GPA Retained
Gender
Full-sample estimates
EASE (Male) -0.113 (0.169) 0.525 (0.168)��0.339 (0.114)��-0.037 (0.034)
EASEFemale 0.111 (0.196) -0.167 (0.199) -0.114 (0.133) 0.077 (0.048)
Subsample estimates
Male (N = 276) -0.113 (0.170) 0.525 (0.169)�� 0.339 (0.115)�� -0.037 (0.035)
Female (N = 602) -0.002 (0.099) 0.357 (0.106)��� 0.225 (0.067)�� 0.040 (0.034)
Race
Full-sample estimates
EASE (White) -0.014 (0.215) 0.475 (0.181)��0.367 (0.132)��-0.001 (0.071)
EASEURM 0.114 (0.248) -0.070 (0.233) -0.089 (0.160) 0.066 (0.086)
EASEAsian -0.121 (0.252) -0.093 (0.227) -0.141 (0.159) -0.008 (0.077)
Subsample estimates
White (N = 110) -0.014 (0.220) 0.475 (0.185)�� 0.367 (0.135)�� -0.001 (0.073)
URM (N = 297) 0.100 (0.124) 0.405 (0.147)��0.278 (0.091)��0.066 (0.049)
Asian (N = 471) -0.135 (0.131) 0.382 (0.137)��� 0.226 (0.088)-0.009 (0.030)
First-generation status
Full-sample estimates
EASE 0.104 (0.119) 0.593 (0.135)��0.334 (0.088)��0.043 (0.041)
EASEFirst-gen status -0.228 (0.173) -0.330 (0.182)-0.121 (0.118) -0.039 (0.054)
Subsample estimates
Continuing-gen status (N = 446) 0.104 (0.119) 0.593 (0.135)��0.334 (0.088)��0.043 (0.041)
First-gen status (N = 432) -0.124 (0.125) 0.263 (0.122)�� 0.214 (0.078)��0.005 (0.035)
Low-income status
Full-sample estimates
EASE 0.125 (0.103) 0.601 (0.117)��0.318 (0.076)��0.072 (0.037)
EASELow-income status -0.339 (0.172)�� -0.435 (0.180)-0.135 (0.118) -0.113 (0.051)
Subsample estimates
Low-income status = 0 (N = 514) 0.125 (0.103) 0.601 (0.117)��0.318 (0.076)��0.072 (0.037)
Low-income status = 1 (N = 364) -0.214 (0.137) 0.167 (0.136) 0.183 (0.090)�� -0.041 (0.035)
Table 3. Robust standard errors in included in parentheses. All models include the following student controls: female, URM, Asian, first-generation status, low-income
status, SAT Reading score, SAT Math score. The reference group is White. Course grade and GPA estimates are reported using a 0–4 point scale. Missing values have
been adjusted using a dummy variable approach.
p<0.10
�� p<0.05
��p<0.01.
https://doi.org/10.1371/journal.pone.0213827.t003
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 11 / 20
We also captured retention behavior using the following two survey questions: (1) “Are you
thinking about changing your major?” and (2) “How likely are you to change majors within
the next year?” These questions were presented to students at the end of the fall quarter as well
as at the end of the spring quarter. Overall, 27% and 34% of students, respectively, reported
that they were considering a major change.
We examine the Likert scale response to the second question in consideration of our demo-
graphic data and enrollment in the EASE program. As shown in Table 6, we find that students
placed in the EASE program reported lower values for this item, indicating that they were less
likely than non-EASE students to change their intended major. Specifically, EASE students
reported values 0.21 standard deviation units lower than those reported by their non-EASE
counterparts after the fall quarter and 0.12 standard deviation units lower than those reported
by their non-EASE counterparts at the end of the year, the latter of which is not statistically
significant.
When examining specific demographic groups, we find that the impact of EASE on intent
to leave the major is greatest for male and URM students, both groups of which reported val-
ues 0.37 and 0.47 standard deviation units lower, respectively, than their non-EASE counter-
parts when assessments were conducted at the end of students’ first term. It is important to
note that the interaction term EASEURM is negative and statistically significant. This indi-
cates that EASE has the potential to reduce the White-URM student racial gap in regard to stu-
dents’ intent to change majors.
Table 4. Estimates for the impact of EASE on social-psychological outcomes of the student experience.
(1) (2) (3) (4)
Sense of Belonging Academic & Social Concerns Academic Integration Academic
Interest
EASE 0.206�� 0.077 0.322�� -0.057
(0.080) (0.072) (0.110) (0.083)
Female 0.043 0.055 -0.052 -0.020
(0.063) (0.053) (0.076) (0.067)
URM -0.109 -0.048 -0.107 0.075
(0.105) (0.082) (0.130) (0.109)
Asian -0.046 0.056 -0.130 0.011
(0.094) (0.074) (0.113) (0.096)
First-gen status 0.054 -0.106-0.093 0.018
(0.067) (0.059) (0.080) (0.067)
Low-income status 0.027 -0.000 -0.023 0.117
(0.060) (0.057) (0.078) (0.065)
SAT Reading score -0.000 -0.000 -0.002��0.002��
(0.000) (0.000) (0.001) (0.001)
SAT Math score 0.002��� 0.000 0.001 -0.000
(0.000) (0.000) (0.001) (0.001)
N 832 834 864 829
Table 4. Robust standard errors in included in parentheses. Dummy variable approach to missing values used. All items measured at the end of the fall quarter and
standardized to have a mean of 0 and a standard deviation of 1. All models include a pre-score. For Academic and Social Concerns, higher values indicate more concern.
p<0.10
�� p<0.05
��p<0.01.
https://doi.org/10.1371/journal.pone.0213827.t004
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 12 / 20
Limitations
The present study is not without limitations. Most notable among them is that the research
design used is correlational, and we therefore cannot rule out the possibility that our results
are subject to omitted variable bias. Given that participation in EASE is correlated with a num-
ber of demographic variables—EASE participants are more likely to earn lower standardized
test scores, for example—we can assume that there are unobservable factors that are correlated
Table 5. Estimates for the impact of EASE on social-psychological outcome measures of the student experience for student subgroups.
(1) (2) (3) (4)
Sense of Belonging Academic and Social
Concerns
Academic Integration Academic
Interest
Gender
Full-sample estimates
EASE (Male) 0.243 (0.179) 0.280 (0.140)�� 0.003 (0.201) -0.011 (0.164)
EASEFemale -0.066 (0.200) -0.258 (0.164) 0.435 (0.242)-0.043 (0.191)
Subsample estimates
Male (N = 251) 0.243 (0.180) 0.280 (0.141)�� 0.003 (0.202) -0.011 (0.165)
Female (N = 558) 0.177 (0.089)�� 0.023 (0.085) 0.437 (0.134)��� -0.054 (0.097)
Race
Full-sample estimates
EASE (White) 0.145 (0.227) 0.262 (0.184) 0.436 (0.300) 0.043 (0.242)
EASEURM -0.047 (0.259) -0.243 (0.224) -0.297 (0.363) 0.073 (0.279)
EASEAsian 0.171 (0.257) -0.174 (0.214) -0.004 (0.332) -0.220 (0.270)
Subsample estimates
White (N = 100) 0.145 (0.233) 0.262 (0.189) 0.436 (0.307) 0.043 (0.249)
URM (N = 269) 0.098 (0.125) 0.019 (0.128) 0.139 (0.203) 0.115 (0.137)
Asian (N = 440) 0.316 (0.120)��0.088 (0.109) 0.431 (0.141)��� -0.178 (0.117)
First-generation status
Full-sample estimates
EASE 0.308 (0.133)-0.032 (0.111) 0.210 (0.160) -0.005 (0.132)
EASEFirst-gen status -0.222 (0.168) 0.223 (0.153) 0.202 (0.222) -0.053 (0.172)
Subsample estimates
Cont’ing-gen status (N = 410) 0.308 (0.133)�� -0.032 (0.111) 0.210 (0.160) -0.005 (0.132)
First-gen status (N = 399) 0.086 (0.103) 0.190 (0.105)0.412 (0.154)�� -0.059 (0.110)
Low-income (LI) status
Full-sample estimates
EASE 0.304 (0.116)�� -0.009 (0.101) 0.181 (0.147) -0.088 (0.117)
EASELow-income status -0.276 (0.161)-0.435 (0.180)0.301 (0.222) 0.090 (0.168)
Subsample estimates
LI status = 0 (N = 477) 0.304 (0.116)��-0.009 (0.101) 0.181 (0.147) -0.088 (0.117)
LI status = 1 (N = 332) 0.028 (0.112) 0.203 (0.110)0.483 (0.167)�� 0.002 (0.120)
Table 5. Robust standard errors in included in parentheses. All models include the following student controls: female, URM, Asian, first-generation status, low-income
status, SAT Reading score, SAT Math score. The reference group is White. All items measured at the end of fall quarter and standardized to have a mean of 0 and a
standard deviation of 1. All models include a pre-score. For Academic and Social Concerns, higher values indicate more concern. Missing values have been adjusted
using a dummy variable approach.
p<0.10
�� p<0.05
��p<0.01.
https://doi.org/10.1371/journal.pone.0213827.t005
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 13 / 20
with EASE participation and our outcome measures. For example, given that EASE partici-
pants are intentionally made aware of campus resources, they could very well be more likely to
use them. One could imagine a scenario where EASE participants interact with the campus’
peer tutors more often than non-EASE participants. If true, the treatment effects reported in
this study regarding course grades and GPA would be over-estimated.
Table 6. Estimates for the impact of EASE on intent to change majors for the full sample and for student subgroups.
(1) (2)
End of fall quarter End of first year
Panel A.
EASE -0.207�� (0.105) -0.12 (0.102)
Panel B.
Gender
Full-sample estimates
EASE (Male) -0.365(0.192) -0.219 (0.231)
EASEFemale 0.241 (0.228) 0.037 (0.273)
Subsample estimates
Male (N = 263) -0.365(0.193) -0.219 (0.233)
Female (N = 571) -0.124 (0.123) -0.182 (0.146)
Race
Full-sample estimates
EASE (White) 0.286 (0.231) -0.399 (0.333)
EASEURM -0.758��(0.289) 0.052 (0.392)
EASEAsian -0.458(0.278) 0.280 (0.379)
Subsample estimates
White (N = 101) 0.286 (0.237) -0.399 (0.343)
URM (N = 284) -0.472��(0.173) -0.347(0.207)
Asian (N = 449) -0.172 (0.153) -0.119 (0.179)
First-generation status
Full-sample estimates
EASE -0.230 (0.167) -0.228 (0.172)
EASEFirst-gen status 0.070 (0.218) 0.004 (0.255)
Subsample estimates
Continuing-gen status (N = 424) -0.230 (0.167) -0.228 (0.172)
First-gen status (N = 410) -0.160 (0.141) -0.225 (0.188)
Low-income status
Full-sample estimates
EASE -0.349�� (0.141) -0.093 (0.168)
EASELow-income status 0.382(0.203) -0.149 (0.251)
Subsample estimates
Low-income status = 0 (N = 494) -0.349�� (0.141) -0.093 (0.168)
Low-income status = 1 (N = 340) 0.033 (0.146) -0.242 (0.187)
Table 6. Robust standard errors in included in parentheses. All models include the following student controls: female, URM, Asian, first-generation status, low-income
status, SAT Reading score, SAT Math score. The reference group is White. All items are standardized to have a mean of 0 and a standard deviation of 1. Higher values
indicate more likely to change majors. Missing values have been adjusted using a dummy variable approach.
p<0.10
�� p<0.05
��p<0.01.
https://doi.org/10.1371/journal.pone.0213827.t006
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 14 / 20
In another example, given the limited number of variables available in the dataset, we may
not have fully captured student ability and pre-college resources, both of which are correlated
with a number of our outcome measures. This is particularly important since the characteris-
tics associated with EASE participants indicate that they are more likely to come from lower
quality high schools and from family backgrounds with less financial resources. Adding addi-
tional control variables, such as high school location and a refined measure of socio-economic
status could provide a more precise, unbiased point estimate. It is important to note, however,
that without these variables our point estimates are actually under-estimated.
Additionally, our findings associated with academic integration must be interpreted some-
what cautiously, as Cronbach’s alpha was only 0.60. This could indicate that the individual
items in the scale have residual variance accounted for by different variables, other than aca-
demic integration. However, a student’s reported frequencies for “talking with faculty about
academic matters,” “meeting with an academic advisor,” “meeting with a student mentor,”
and “attending study groups outside of the classroom” could also be quite different from one
another because students who frequently engage in at least one of these forms of academic
help-seeking may not feel the need to engage in all of them. Although this would create low
reliability for the scale, a higher average overall would still represent a greater amount of aca-
demic integration.
Discussion
Our study has focused on a STEM learning community program, EASE, evaluating its impact
on student cognitive and social-psychological outcomes. We have sought to investigate who
benefits from learning community programs in order to better understand whether learning
communities are a viable way to reduce both academic and motivation gaps often present in
STEM disciplines. Overall, participation in the EASE program positively impacted both cogni-
tive and social-psychological outcome measures. EASE designation is correlated with higher
grades in Bio Sci 94, a key freshman level biology course, as well as with a nearly quarter-point
boost in first-year cumulative GPA. Additionally, EASE students indicated that they experi-
enced an improved sense of belonging and academic integration in addition to indicating that
they were less likely to consider a change in major after participating in the program.
In examining the impact of the EASE learning community program on different groups of
students, we find that students traditionally underrepresented in STEM exhibited the greatest
gains regarding the study’s social-psychological measures. For example, females participating
in EASE reported higher values for the sense of belonging measure than did non-EASE
females. Further, female students, first-generation students, and students from low-income
backgrounds all reported higher rates of engagement in behaviors indicative of academic inte-
gration relative to their non-EASE peers.
Surprisingly, however, the gains in social-psychological metrics do not correspond to dis-
proportionate increases in academic outcomes for these same student populations. Although
EASE status is associated with higher Bio Sci 94 course grades and first-year GPA, these effects
tend to be greater for EASE students from more traditionally-represented demographics
(males, White students, continuing-generation individuals, and non-low-income students).
These results suggest that EASE does not reduce gender or racial achievement gaps in first-
year biology courses. While much of the STEM education literature is focused on closing
achievement gaps, we argue that the observed gains for the entire EASE population highlights
the clear value of learning communities programs. The finding that EASE enrollment corre-
lates with a nearly quarter point increase in first-year GPA relative to non-EASE participants
means that an entire group of students may have increasing opportunities in STEM programs
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 15 / 20
and careers. This may also imply however that many of the barriers to success for students
underrepresented in STEM fields are also present in the EASE program. For example, while
attempts were made to recruit underrepresented students to act as EASE mentors, they ulti-
mately were predominately of Asian and White ethnicities. Thus, EASE students from under-
represented backgrounds may have had trouble connecting with their mentors or viewing
them as representations of their own success. This would be similar to the impact of the lack of
diversity in STEM faculty [5354].
It is important to note that the discussed findings for cognitive and social-psychological
measures point to the complex relationship between academic and social integration, as out-
lined in Fig 1. Academic and social integration are mutually reinforcing elements, and while
we do not find evidence that these forces are operating concurrently—as evidenced by, for
example, the idea that URM students experience disproportionate gains for a number of
social-psychological measures but not for performance markers—prior research has found
that treatment effects unfold over time [33,55]. As such, the social-psychological benefits expe-
rienced by students traditionally underrepresented in STEM may translate to positive long-
run academic performance outcomes, such as strong grades earned in the second year of col-
lege or even major persistence. The consistent positive coefficients for the academic measures,
although statistically insignificant, substantiate this conjecture. Our findings also suggest that
examining long-run impacts will be important for fully understanding the ways in which
learning communities improve student learning outcomes and the college experience as a
whole.
We also find that the results concerning subgroup populations (Tables 3and 5)—and for
first-generation college students in particular—do not necessarily support Culture Mismatch
Theory. As we note, first-generation students in EASE earned higher grades than their non-
EASE counterparts (Table 3, column 2). These treatment effects, however, were not signifi-
cantly different than continuing-generation students. In other words, EASE did not have a sig-
nificant impact on reducing the socio-economic achievement gap.
First-generation college students also reported values for one of our outcome variables, aca-
demic and social concerns, in the opposite direction of what was expected. For this particular
outcome, first-generation college students in EASE reported higher values, indicating that they
were relatively more concerned that other students disliked them or unfairly evaluated their
academic ability, as compared to non-EASE first-generation college students. One possible
explanation could be the idea that academic preparation programs, such as EASE, might
enhance feelings of stigmatization often felt among groups traditionally marginalized in col-
lege. Indeed, a separate study on EASE students found that both continuing- and first-genera-
tion students assigned to EASE felt somewhat stigmatized when learning of their assignment
to the EASE program [56]. Initial feelings of stigmatization, in turn, were predictive of greater
academic and social concerns during the school year. We do want to note that the point esti-
mate for the variable academic and social concerns in our study is significant at the p <.10
level and the standard error is quite big; compared to other associations that we discuss in our
paper, this one is relatively weak.
Lastly, even though the EASE program had no statistical impact on retention, we do find
that it is positively correlated with students’ intention to stay in the bio sci major, which is par-
ticularly important given that this measure is an early indicator of engagement. Further,
among the three race-oriented groups, this impact is most pronounced for URM students.
URM students who participated in EASE reported values 0.47 standard deviation units lower
than those reported by non-EASE URM students. This finding is particularly important given
the national agenda to improve STEM outcomes for students least represented in STEM. A
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 16 / 20
learning community certainly seems to have the potential to help URM students, in particular,
progress through the STEM pipeline.
Our results also suggest that there might be a connection between the cognitive effects of
participating in EASE and students’ intent to remain in the major, as outlined in Fig 1. For
example, male students in EASE reported significantly lower values for the item measuring
intent to leave the major than did non-EASE male students, whereas the difference for female
students is not significant. The disproportionate impact for male students might be attributed
to the large and significant impact that EASE had on male students’ academic performance. It
may be the case that doing well academically makes students feel more confident about their
future in the biological sciences.
Overall, our findings echo conclusions found in prior literature: learning communities ben-
efit students both academically and social-psychologically. We add to this body of work by
documenting the potential for learning communities to impact student learning and engage-
ment within a specific field of study. Further, our focus on estimating impacts for particular
student subgroups has resulted in evidence indicating that students respond to learning com-
munities differently. Ideally, this evidence can help researchers and practitioners design pro-
grams tailored to meet different needs, thereby enhancing the ability of learning communities
to positively impact the overall college experience.
Supporting information
S1 File. Items used for the social-psychological constructs evaluated by the survey instru-
ments, the complete beginning of fall survey, and the complete end of fall and spring quar-
ter surveys are presented in the supporting information.
(DOCX)
Acknowledgments
We would like to thank Dr. Michael Leon, Kristin Fung, and Jenna Bague-Sampson for their
support during this research project, as they have provided data and expert guidance on the
context for this research.
Author Contributions
Conceptualization: Sabrina Solanki, Peter McPartlan, Di Xu, Brian K. Sato.
Data curation: Sabrina Solanki, Peter McPartlan, Di Xu, Brian K. Sato.
Formal analysis: Sabrina Solanki, Peter McPartlan, Di Xu, Brian K. Sato.
Methodology: Sabrina Solanki, Peter McPartlan, Di Xu.
Project administration: Brian K. Sato.
Writing – original draft: Sabrina Solanki, Peter McPartlan, Di Xu, Brian K. Sato.
Writing – review & editing: Sabrina Solanki, Peter McPartlan, Di Xu, Brian K. Sato.
References
1. Deslauriers L, Schelew E, Wieman C. Improved learning in a large-enrollment physics class. Science.
2011; 332(6031): 862–864. https://doi.org/10.1126/science.1201783 PMID: 21566198
2. Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, JordtH, Wenderoth MP. Active learning
increases student performance in science, engineering, and mathematics. Proceedings of the National
Academy of Sciences. 2014; 111(23): 8410–8415.
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 17 / 20
3. Handelsman J, Miller S, Pfund C. Scientific Teaching. New York: Freeman; 2007.
4. Eddy SL, Hogan KA. Getting under the hood: how and for whom does increasing course structure
work? CBE-Life Sciences Education. 2014; 13(3): 453–468. https://doi.org/10.1187/cbe.14-03-0050
PMID: 25185229
5. Freeman S, Haak D, Wenderoth MP. Increased course structure improves performance in introductor
biology. CBE-Life Sciences Education. 2011; 10(2): 175–186. https://doi.org/10.1187/cbe.10-08-0105
PMID: 21633066
6. Cohen GL, Garcia J, Apfel N, Master A. Reducing the racial achievement gap: A social-psychological
intervention. Science. 2006; 313: 1307–1310. https://doi.org/10.1126/science.1128317 PMID:
16946074
7. Harackiewicz JM, Canning EA, Tibbetts Y, Giffen CJ, Blair SS, Rouse DI, et al. Closing the social class
achievement gap for first-generation students in undergraduate biology. Journal of Educational Psy-
chology. 2014; 106: 375–389. https://doi.org/10.1037/a0034679 PMID: 25049437
8. National Science Foundation. Women, minorities, and persons with disabilities in science and engineer-
ing: 2011. Arlington, VA: National Science Foundation.
9. Hanauer DI, Jacobs-Sera D, Pedulla ML, Cresawn SG, Hendrix RW, Hatfull GF. Teaching scientific
inquiry. Science. 2006; 314: 1880–1881. https://doi.org/10.1126/science.1136796 PMID: 17185586
10. Auchincloss LC, Laursen SL, Branchaw JL, Eagan K, Graham M, Hanauer DI, et al. Assessment of
course-based undergraduate research experiences: a meeting report. CBE Life Sci Educ. 2014; 13:
29–40. https://doi.org/10.1187/cbe.14-01-0004 PMID: 24591501
11. Corwin LA, Graham M, Dolan EL. Modeling course-based undergraduate research experiences: an
agenda for future research and evaluation. CBE Life Sci Educ. 2015; 14: es1. https://doi.org/10.1187/
cbe.14-10-0167 PMID: 25687826
12. Garcia P. Summer bridge: Improving retention rates for underprepared students. Journal of the Fresh-
man Year Experience. 1991; 3(2): 91–105.
13. Kallison JM, Stader DL. Effectiveness of summer bridge programs in enhancing college readiness.
Community College Journal of Research and Practice. 2012; 36(5): 340–357.
14. Tinto V. Dropout from higher education: A theoretical synthesis of recent research. Review of Educa-
tional Research. 1975; 45: 89–125. http://doi.org/10.3102/00346543045001089
15. Smith BL, MacGregor J, Matthews RS, Gabelnick F. Learning Communities: Reforming Undergraduate
Education. San Francisco: Jossey-Bass; 2004.
16. Cohen J, Cook-Sather A, Lesnick A, Alter Z, Awkward R, Decius F, et al. Students as leaders and learn-
ers: Towards self-authorship and social change on a college campus. Innovations in Education and
Teaching International. 2013; 50(1): 3–13. https://doi.org/10.1080/14703297.2012.746511
17. Contreras F. Strengthening the bridge to higher education for academically promising underrepre-
sented students. Journal of Advanced Academics. 2011; 22(3): 500–526. https://doi.org/10.1177/
1932202X1102200306
18. Dagley M, Georgiopoulos M, Reece A, Young C. Increasing retention and graduation rates through a
STEM learning community. Journal of College Student Retention: Research, Theory & Practice. 2015;
18: 167–182.
19. Engstrom C, Tinto V. Pathways to student success: The impact of learning communities on the success
of academically under-prepared college students. 2007. Retrieved from the Lumina Foundation for Edu-
cation Website: https://www.luminafoundation.org
20. Lenning O, Ebbers, L. The powerful potential of learning communities: Improving education for the
future. ASHE-Higher Education Report. 1999; 26
21. Maton KI, Pollard SA, McDougall Weise TV, Hrabowski FA III. The Meyerhoff Scholars Program: A
strengths-based, institution-wide approach to increasing diversity in science, technology, engineering,
and mathematics. The Mount Sinai Journal of Medicine. 2012; 79(5): 610–623. https://doi.org/10.1002/
msj.21341 PMID: 22976367
22. Shapiro NS, Levine J. Creating learning communities: A practical guide to winning support, organizing
for change, and implementing programs. San Francisco, CA: Jossey-Bass; 1999.
23. Taylor K, Moore W, MacGregor J, Lindblad J. Learning community research and assessment: What we
know now. National Learning Communities Monograph Series. Olympia, WA: Washington Center for
Improving the Quality of Undergraduate Education; 2003.
24. Tinto V, Goodsell A. Freshman interest groups and the first year experience: Constructing student com-
munities in a large university. Paper presented at the annual meeting of the College Reading and Learn-
ing Association. Kansas City, MO; 1993.
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 18 / 20
25. Tinto V, Russo P. Coordinated studies programs: Their effect on student involvement at a community
college. Community College Review. 1994; 22: 16–25.
26. Zhao CM, Kuh GD. Adding value: Learning communities and student engagement. Research in Higher
Education. 2004; 45: 115–138.
27. Carter FD, Mandell M, Maton KI. The influence of on-campus, academic year undergraduate research
on STEM Ph.D. outcomes: Evidence from the Meyerhoff scholarship program. Educational Evaluation
and Policy Analysis. 2009; 31(4): 441–462. https://doi.org/10.3102/0162373709348584 PMID:
21785521
28. TCC Group. (2004). Evaluation of the national Posse program. Retrieved from http://www.
possefoundation.org
29. Spady WG. Dropouts from higher education: An interdisciplinary review and synthesis. Interchange.
1970; 1(1): 64–85.
30. Tinto V. Leaving college: Rethinking the causes and cures of student attrition ( 2nd ed.). Chicago, IL:
University of Chicago Press; 1993.
31. Strayhorn TL. College students’ sense of belonging: A key to educational success for all students. New
York: Routledge; 2012.
32. Freeman TM, Anderman LH, Jensen JM. Sense of belonging in college freshmen at the classroom and
campus levels. The Journal of Experimental Education. 2007; 75(753): 203–220. https://doi.org/10.
3200/JEXE.75.3.203–220
33. Murphy MC, Zirkel S. Race and belonging in school: How anticipated and experienced belonging affect
choice, persistence, and performance. Teachers College Record. 2015; 117(12): 1–40.
34. Wilson D, Jones D, Bocell F, Crawford J, Kim MJ, Veilleux N, et al. Belonging and academic engage-
ment among undergraduate STEM students: A multi-institutional study. Research in Higher Education.
2015; 56(7): 750–776. https://doi.org/10.1007/s11162-015-9367-x
35. Zumbrunn S, McKim C, Buhs E, Hawley LR. Support, belonging, motivation, and engagement in the col-
lege classroom: A mixed method study. Instructional Science. 2014; 42(5): 661–684. https://doi.org/10.
1007/s11251-014-9310-0
36. Hoffman M, Richmond J, Morrow J, Salomone K. Investigating “sense of belonging” in first-year college
students. Journal of College Student Retention. 2003; 4(3), 227–256.
37. MDRC (2012). The Effects of Learning Communities for Students in Developmental Education Report.
Web site: http://www.mdrc.org/publication/effects-learning-communities-students-developmental-
education
38. Seymour E, Hewitt NM. Taking about leaving: Why undergraduates leave the sciences. Boulder, CO:
Westview Press; 1997
39. President’s Council of Advisors on Science and Technology (PCAST). Engage to excel: Producing one
million additional college graduates with degrees in science, technology, engineering, and mathematics.
Washington, DC: Author; 2012.
40. Gabelnick F, MacGregor J, Matthews R, Smith BL. Learning communities: Creating connections
among students, faculty, and disciplines. San Francisco, CA: Jossey-Bass; 1990.
41. Dagley-Falls M. Psychological sense of community and retention: Rethinking the first-year experience
of students in STEM (Dissertation), University of Central Florida, Orlando, FL; 2009.
42. Warburton, E., Bugarin, R., & Nunez, A. Bridging the gap: Academic preparation and postsecondary
success of first-generation students (Report No. NCES 2001–153). Washington, DC: National Center
for Education Statistics, U.S. Government Printing Office; 2001.
43. Strayhorn TL. Sentido de pertenencia: A hierarchical analysis predicting sense of belonging among
Latino college students. Journal of Hispanic Higher Education. 2008; 7(4): 301–320.
44. Stephens NM, Fryberg SA, Markus HR, Johnson CS, Covarrubias R. Unseen disadvantage: How
American universities’ focus on independence undermines the academic performance of first-genera-
tion college students. Journal of personality and social psychology. 2012; 102: 1178–1197. https://doi.
org/10.1037/a0027143 PMID: 22390227
45. Silverman A, Cohen G. Fostering positive narratives: Social-Psychological interventions to maximize
motivation in the classroom and beyond. In Karabenick S. A. & Urdan T. (Eds.). Motivational Interven-
tions. Bingley, UK: Emerald Group Publishing Limited; 2014. pp. 177–211
46. Flynn D. Baccalaureate attainment of college students at 4-year institutions as a function of student
engagement behaviors: Social and academic student engagement behaviors matter. Research in
Higher Education. 2014; 55(5): 467–493. http://doi.org/10.1007/s11162-013-9321-8
47. Astin AW. What matters in college? Four critical years revisited. San Francisco, CA: Jossey Bass;
1993.
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 19 / 20
48. Tracey T, Robbins S. The interest-major congruence and college success relation: A longitudinal study.
Journal of Vocational Behavior. 2006; 69(1): 64–89.
49. Sherman DK, Bunyan DP, Creswell JD, Jaremka LM. Psychological vulnerability and stress: The
effects of self-affirmation on sympathetic nervous system responses to naturalistic stressors. Health
Psychology. 2009; 28(5): 554–562. https://doi.org/10.1037/a0014663 PMID: 19751081
50. Eccles JS, Adler TF, Futterman R, Goff SB, Kaczala CM, Meece JL, Midgley C. Expectancies, values,
and academic behavior. In Spence J. T. (Ed.). Achievement and achievement motives: Psychological
and sociological approaches. San Francisco, CA: W.H. Freeman; 1983. pp. 75–146.
51. Harackiewicz JM, Canning EA, Tibbetts Y, Priniski SJ, Hyde JS. Closing achievement gaps with a util-
ity-value intervention: Disentangling race and social class. Journal of Personality and Social Psychol-
ogy. 2016; 111(5): 745–765. https://doi.org/10.1037/pspp0000075 PMID: 26524001
52. Cohen J. and Cohen P. Applied multiple regression and correlation analysis for the behavioral sciences
( 2nd edn.). Mahwah, NJ: Lawrence Erlbaum Associates; 1985.
53. Benitez M., James M., Joshua K., Perfetti L., & Vick S. Someone who looks like me: Promoting the suc-
cess of students of color by promoting the success of faculty of color. Liberal Education. 2017; 103.
54. Chang M., Sharkness J., Hurtado S., & Newman C. What matters in college for retaining aspiring scien-
tists and engineers from underrepresented racial groups. Journal of Research in Science Teaching.
2014; 51: 555–580.
55. Walton GM, Cohen GL. A brief social-belonging intervention improves academic and health outcomes
of minority students. Science. 2011; 331: 1447–1451. https://doi.org/10.1126/science.1198364 PMID:
21415354
56. McPartlan, P. (2018, August). Belonging in the blind spot: Do some students suffer negative effects in
learning community programs? Poster presented at the American Psychological Association annual
meeting, New York, NY.
Success in a STEM learning community
PLOS ONE | https://doi.org/10.1371/journal.pone.0213827 March 22, 2019 20 / 20

Supplementary resource (1)

... For example, most learning community research has focused on large first-year experience-based learning communities. But there has been limited study of increasingly popular disciplinespecific learning communities (Dagley et al., 2016;Solanki et al., 2019). Dagley et al. have reported that a residential learning community for STEM students led to greater firstyear retention, long-term retention, and graduation than a comparison group (Dagley et al., 2016). ...
... Dagley et al. have reported that a residential learning community for STEM students led to greater firstyear retention, long-term retention, and graduation than a comparison group (Dagley et al., 2016). Solanki and colleagues showed increased academic performance (first-year GPA) among participants in a bioscience-focused learning community (Solanki et al., 2019). Additionally, studies of learning communities have remained focused on quantifiable aggregate statistics such as GPA, retention, and graduation rates (Holt & Nielson, 2019;Lardner & Malnarich, 2009). ...
... Participating in learning communities provides students with learning opportunities to develop their knowledge and skills to solve real-world problems (Solanki et al., 2019). However, it is equally important for learners to build confidence in mastering a concept or skill, be self-motivated to learn, and apply the acquired knowledge in other contexts (Peters-Burton et al., 2015). ...
Article
Full-text available
Research on learning communities has primarily focused on identifying institutional outcomes such as student achievement and retention. However, more research is needed on how the learning community experience impacts the motivation, beliefs, and perceptions associated with student success. This study investigates the psychosocial effects of participating in a residential research-oriented learning community regarding students’ interest and motivation in pursuing research-oriented careers, research and data self-efficacy beliefs, sense of belongingness with the learning community, and socialization levels and career awareness in research-oriented fields. This study also investigated the mediating effects of students’ initial research self-efficacy beliefs on differential gains regarding career awareness, motivation and interest, and sense of belongingness and socialization after one year of participating in a residential research-oriented learning community. Participants of the study consisted of five cohorts of the learning community, each composed of twenty students. Students in each cohort participated in a pretest-posttest design survey study. Findings suggest that alignment of student interest with the learning community discipline is a key mediator of student growth in their self-efficacy beliefs, sense of belongingness with the learning community and levels of socialization, and career awareness in the selected field. Implications include recommendations for the thoughtful design of learning communities that promote cognitive apprenticeships by orchestrating the content, method, sequencing, and sociology of the learning environment.
... Reasons include the "chilly climate" of STEM classrooms and departments that makes it difficult to build sense of belonging, the fast-paced and content-heavy nature of the introductory curriculum, and a competitive culture that prioritizes independence (Hall and Sandler, 1982;Seymour and Hewitt, 1997;PCAST, 2012;Fabert, 2014). Not surprisingly, research has identified that retention in STEM relies on student social integration, fostered interest, and self-efficacy (Tinto, 1993;Chemers et al., 2001;Hoffman et al., 2002;Estrada et al., 2011;Solanki et al., 2019). However, given the public university context, which often includes noninteractive, large-enrollment courses, it is difficult to provide first-year students with intimate experiences that inculcate student experiences and traits that lead to improved retention (Stains et al., 2018). ...
... Student integration into the university community is a key determinant of student retention (Tinto, 1993). Although nearpeer mentorship programs have demonstrated promising qualitative results in influencing student perceptions of belonging (Yomtov et al., 2017;Zaniewski and Reinholz, 2016;Lim et al., 2017;Moschetti et al., 2018) and intentions to persist (Solanki et al., 2019), evidence of peer mentorship promoting STEM student retention beyond the program remains preliminary (Zaniewski and Reinholz, 2016). Given the results that BIOME mentees are approximately two times more likely to enroll in CHEM 1B, this result suggests that our peer mentorship course may promote student retention in the biology prerequisite course series. ...
Article
We examine the impact of Biology Mentoring and Engagement (BIOME) near-peer mentorship on 437 first-year undergraduate students over three cohort years. The BIOME course consists of ten, 50-minute meetings where groups of six first-year mentees meet with an upper-division student mentor to discuss topics including metacognition, growth mindset, and effective study strategies. We employed a mixed-methods approach to evaluate the impact of BIOME on mentee academic outcomes. Initial ethnographic analysis revealed that BIOME influenced student study methods, approaches to academic challenges, and use of campus learning communities. We then constructed a novel, program-specific instrument to measure the implementation of these habits, a construct we named "academic habit complexity." Regression analysis supported the hypothesis that enrollment in BIOME leads to students using more diverse approaches than their peers. Enrollment in BIOME, and the associated development of academic habit complexity, is related to higher course grades in General Chemistry, a biology major prerequisite. Finally, students participating in BIOME demonstrated improved short-term student retention as measured by increased enrollment in the subsequent prerequisite General Chemistry course. These results suggest that course-based near-peer mentorship may be an effective and scalable approach that can promote student academic success.
... To help students strengthen the foundation of their STEM major, PSI-supported learning is poised at a critical time, when students who enroll upon entry into college are most vulnerable to missing opportunities to understand, learn, and retain subject matter. This practice therefore targets the first-year learning experience for our students, which has been found through an analysis of National Survey of Student Engagement data to be a critical time for redirecting the trajectory of academic performance and promote social belonging for college students (Kuh 2008;Solanki et al. 2019;Stone and Jacobs, 2008). ...
... The innovations and adaptations described in our PSI model have been effective in enhancing STEM knowledge, transferable competencies, and confidence in both student participants and leaders. It is well-known that learning communities benefit students both academically and psychologically, so our future studies will examine the impact of this PSI learning community to promote social belonging, retention, and persistence, as has been observed by other first-year STEM learning communities (Solanki et al. 2019). ...
Article
Full-text available
Georgia Gwinnett College, an access institution serving the most diverse student body of southeast colleges, was awarded National Science Foundation and University System of Georgia STEM-Education Improvement grants to help our students meet the evolving needs of STEM education. One of the initiatives emerging from these resources is the Peer Supplemental Instruction (PSI) pro-gram, a modified model of the traditional SI program. SI is a well-documented, high-impact practice in higher education that engenders collaborative learning among students. Since SI was not available on campus, STEM faculty developed the current PSI program, with the aim to support students as they transition from high school to college. PSI is thus offered to students in the gateway courses for biology, chemistry, mathematics, and informa-tion technology majors and study sessions incorporate STEM skills, thereby increasing opportunities for students to engage in, and develop, STEM competencies. In the last year, attendance was recorded at 4,123 interactions. Assessment of academic performance of PSI students revealed that participation increased GPAs in PSI-supported courses, particularly in students entering college with low high school GPAs. Moreover, student attitudes towards STEM learning improved and peer students serving as leaders benefited, based on reports of their development of professional skills that are critical to success in college and STEM careers. We present an innovative adaptation of the SI program that can be adopted by STEM faculty, and may be particularly useful to institutions serving underprepared populations, in surmounting the academic success predictability of low high school GPA.
... They proposed that the headsand-hearts hypothesis explained the extensive variation in efficacy observed among studies; this hypothesis posits that meaningful reductions in achievement gaps only occur when course designs combine deliberate practice with inclusive teaching, as was the case in this study. Note that the headsand-heart hypothesis fits well within both Tinto's framework and SCCT and that other recent evidence also demonstrates that learning communities increase the success and retention for first generation and URM STEM students (Solanki et al., 2019;Van Sickle et al., 2020). ...
... Because these students self-selected into ESP and were better prepared academically, there are limitations to the conclusions that can be drawn from the positive results found in this study; however, similar results have been found by other researchers, suggesting that results from similar ESP-like programs would also be positive (Solanki et al., 2019;Van Sickle et al., 2020). In addition, it is important to study ESP students because they are highly motivated, high achieving students, to gain insight into methods for increasing success for other underrepresented students. ...
Article
Full-text available
Over the last several decades, Emerging Scholars Programs (ESPs) have incorporated active learning strategies and challenging problems into collegiate mathematics, resulting in students, underrepresented minority (URM) students in particular, earning at least half of a letter grade higher than other students in Calculus. In 2009, West Virginia University (WVU) adapted ESP models for use in Calculus I in an effort to support the success and retention of URM STEM students by embedding group and inquiry-based learning into a designated section of Calculus I. Seats in the class were reserved for URM and first-generation students. We anticipated that supporting students in courses in the calculus sequence, including Calculus I, would support URM Calculus I students in building learning communities and serve as a mechanism to provide a strong foundation for long-term retention. In this study we analyze the success of students that have progressed through our ESP Calculus courses and compare them to their non-ESP counterparts. Results show that ESP URM students succeed in the Calculus sequence at substantially higher rates than URM students in non-ESP sections of Calculus courses in the sequence (81% of URM students pass ESP Calculus I while only 50% of URM students pass non-ESP Calculus I). In addition, ESP URM and ESP non-URM (first-generation but not URM) students succeed at similar levels in the ESP Calculus sequence of courses (81% of URM students and 82% of non-URM students pass ESP Calculus I). Finally, ESP URM students’ one-year retention rates are similar to those of ESP non-URM students and significantly higher than those of URM students in non-ESP sections of Calculus (92% of ESP URM Calculus I students were retained after one year, while only 83% of URM non-ESP Calculus I students were retained). These results suggest that ESP is ideally suited for retaining and graduating URM STEM majors, helping them overcome obstacles and barriers in STEM, and increasing diversity, equity, and inclusion in Calculus.
... Academic benefits include improvements in student interactions with faculty, administrators, and peers. According to Solanki, McPartlan, Xu and Sato [19], learning communities are beneficial for students from underrepresented groups because learning communities provide high degrees of social support. ...
... This was most evident at the table conferences where there was direct communication between students. This exposes the concept of social integration because the attending students have unique qualities that may and should be shared among attendees [39]. Third,from discussions with students and advisors there is a lack of knowledge on graduate science/medical education requirements that which is also observed in the literature [21,22]. ...
Article
Full-text available
While the population of minorities is increasing in the USA, the numbers obtaining advanced degrees in science/engineering and medicine are minimal. Underrepresented groups make up 19% of the USA labor pool, but less than 6% of science (engineering and medicine) Ph.D.’s. Diversifying the universities and health-care institutions is important to improve the academic experience of faculty, staff, students and everyone regardless of race. To prepare for the approaching diverse environments, educational institutions must create programs that allow underrepresented groups thrive in higher education; and logistically to be sustaining, the recruitment programs must begin at the student level. One approach is the integration of the history (the linking of past-heroes with present-heroes) of science; which is an interesting and important paradigm that can be implemented. As such, a day-long symposium highlighting the life and accomplishments of an African American Scientist; Dr. Ernest Everett Just, Ph.D., is used as a working model to inspire, educate on admission requirements, and to recruit into graduate science and medical programs.
... However, the literature that does exist is in line with our estimates. For example, Solanki et al. (2019) find that participating in a STEMfocused learning community increases students' sense of belonging by about 21 percent of a standard deviation. Recent reviews of studies in higher education classify effect sizes between .12 and .20 as medium (Mayhew et al., 2016); however, more work is needed to understand the typical range of effects on psychosocial outcomes. ...
Article
This study examines the impact of a comprehensive college transition program, the Thompson Scholars Learning Communities (TSLC), on psychosocial outcomes using an experimental design. We estimate overall and heterogenous effects of program participation on students’ sense of belonging to campus, feelings of mattering to campus, sense of academic self-efficacy, and sense of social self-efficacy. Participation in TSLC, as compared to receiving a substantial college scholarship without comprehensive support, leads to large increases in both mattering and sense of belonging to campus during students’ two years in the program. We find no impact of TSLC participation on students’ academic or social self-efficacy. We demonstrate the program effects on students’ feelings of mattering to campus were largest for traditionally underrepresented student groups; however, we find no evidence of heterogeneous effects on students’ sense of belonging to campus, academic self-efficacy, or social self-efficacy. Our findings suggest that comprehensive college transition programs can promote students’ psychosocial outcomes.
... Both types of communities, which can be quite distinct depending on their specific model [19], are often used interchangeably to describe a community for sharing, developing, and/or maintaining knowledge, skills, and practices within which membership ranges from novices to seasoned experts. For students, participation in such communities has been shown to boost academic performance, self-efficacy, sense of belonging, STEM identity, retention, and graduation rates [20][21][22][23]. In the DIVAS Project, cohorts of novices work sideby-side with faculty mentors, and their more experienced student peers, to themselves become more advanced practitioners via legitimate peripheral participation [24]. ...
Article
Full-text available
In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling 'hook' for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and can finish the summer with independent research. Finally, scholars participate in a follow-up seminar the subsequent spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars identifying as members of an underrepresented minority group. Most scholars (82%) entered the program as first year students, with 94% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.
Preprint
Full-text available
In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling ‘hook’ for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and finish the summer with independent research. Finally, scholars participate in a follow-up seminar the following spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars being members of an underrepresented minority group. Most scholars (82%) entered the program as freshmen, with 89% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.
Article
Full-text available
This article reports summary findings from a statistical analysis report on high school preparation and postsecondary persistence of first-generation students--those students whose parents had no education beyond high school--and compares them with students whose parents went to college.
Article
Full-text available
A sense of belonging in school is a complex construct that relies heavily on students’ perceptions of the educational environment, especially their relationships with other students. Some research suggests that a sense of belonging in school is important to all students. However, we argue that the nature and meaning of belonging in school is different for students targeted by negative racial stereotypes—such as African American, Latino/a, Native American, and some Asian American students. Our conceptual framework draws upon stigma and stereotype threat theory and, specifically, the concept of belonging uncertainty, to explore how concerns about belonging in academic contexts may have different meaning for—and thus differentially affect the academic outcomes of—White students compared with underrepresented racial and ethnic minority students.
Article
Full-text available
Many college students abandon their goal of completing a degree in science, technology, engineering, or math (STEM) when confronted with challenging introductory-level science courses. In the U.S., this trend is more pronounced for underrepresented minority (URM) and first-generation (FG) students, and contributes to persisting racial and social-class achievement gaps in higher education. Previous intervention studies have focused exclusively on race or social class, but have not examined how the 2 may be confounded and interact. This research therefore investigates the independent and interactive effects of race and social class as moderators of an intervention designed to promote performance, measured by grade in the course. In a double-blind randomized experiment conducted over 4 semesters of an introductory biology course (N = 1,040), we tested the effectiveness of a utility-value intervention in which students wrote about the personal relevance of course material. The utility-value intervention was successful in reducing the achievement gap for FG-URM students by 61%: the performance gap for FG-URM students, relative to continuing generation (CG)-Majority students, was large in the control condition, .84 grade points (d = .98), and the treatment effect for FG-URM students was .51 grade points (d = 0.55). The UV intervention helped students from all groups find utility value in the course content, and mediation analyses showed that the process of writing about utility value was particularly powerful for FG-URM students. Results highlight the importance of intersectionality in examining the independent and interactive effects of race and social class when evaluating interventions to close achievement gaps and the mechanisms through which they may operate. (PsycINFO Database Record
Article
Belonging-with peers, in the classroom, or on campus-is a crucial part of the college experience. it can affect a student's degree of academic achievement, or even whether they stay in school. Although much is known about the causes and impact of sense of belonging in students, little is known about how belonging differs based on students' social identities, such as race, gender, or sexual orientation, or the conditions they encounter on campus.
Article
PurposeAchievement motivation is not a fixed quantity. Rather, it depends, in part, on one’s subjective construal of the learning environment and their place within it – their narrative. In this paper, we describe how brief interventions can maximize student motivation by changing the students’ narratives. ApproachWe review the recent field experiments testing the efficacy of social-psychological interventions in classroom settings. We focus our review on four types of interventions: ones that change students’ interpretations of setbacks, that reframe the learning environment as fair and nonthreatening, that remind students of their personal adequacy, or that clarify students’ purpose for learning. FindingsSuch interventions can have long-lasting benefits if changes in students’ narratives lead to initial achievement gains, which further propagate positive narratives, in a positive feedback loop. Yet social-psychological interventions are not magical panaceas for poor achievement. Rather, they must be targeted to specific populations, timed appropriately, and given in a context in which students have opportunities to act upon the messages they contain. Originality/valueSocial-psychological interventions can help many students realize their achievement potential if they are integrated within a supportive learning context.