Testing the effects of a social capital intervention on college student retention and academic success

Hersch, E., Werntz, A., Schwartz, S. E. O., Raposa, E. B., Hughes, J., Parnes, M. F., & Rhodes, J. (2025). Testing the effects of a social capital intervention on college student retention and academic success. American Journal of Community Psychology. https://doi.org/10.1002/ajcp.70027

Correspondence

Jean Rhodes, 617‐287‐5000, 100 Morrissey Blvd, Boston, MA 02125, USA.

Email: ude.bmu@sedohR.naeJ

INTRODUCTION

Abstract

Social capital, particularly in the form of supportive relationships and mentor ship, plays a crucial role in enhancing college students’ academic success and retention. However, disparities in access to these resources contribute to inequi ties in educational and career outcomes. This study examined the long‐term ef fects of a one‐credit course, Connected Scholars, which was designed to teach college students a variety of evidence‐based skills for building social capital and recruiting mentors. Drawing on longitudinal administrative data from a large, diverse public university, results demonstrated that passing Connected Scholars was associated with improved retention and an increased likelihood of graduating within 4 or 6 years. Connected Scholars was not associated with significant dif ferences in cumulative GPA at graduation. Results highlight the potential long‐ term impacts of this semester‐long social capital intervention. By increasing college retention and timely degree completion, Connected Scholars may help mitigate economic disparities often experienced by students from marginalized backgrounds.

KEYWORDS

academic retention, college students, help‐seeking, marginalized students, social capital

Research Highlights

Marginalized college students were more likely to enroll in a one‐semester social capital course.
Students who took the course were three times more likely to graduate within 4 years.
Social capital interventions reduce academic and economic disparities for minoritized students.

predictors of long‐term outcomes such as career satisfac

tion and earning potential (Ma & Pender, 2023).

Social capital refers to the resources, connections, support, and information that can be derived from one’s social re lationships (Bourdieu, 1986; Schwartz et al., 2023). It has been associated with a variety of positive health, economic, career, and academic outcomes, particularly for young adults (Chetty et al., 2022; Schwartz et al., 2023). In higher education, social capital has been linked to college students’ ability to navigate college and to key academic outcomes such as sense of belonging, retention, and performance (Baier et al., 2016; Baker, 2013; Mishra, 2020; Rios‐Aguilar & Deil‐Amen, 2012). These outcomes are, in turn, critical

Students from historically marginalized backgrounds, including racial and ethnic minority, low‐income, immi grant, and/or first‐generation students, have less access to social capital and are less likely to access it when it is available (Schwartz et al., 2023). Students with minoritized identities are less likely than their more privileged class mates to seek help from faculty and other campus supports and thus are less able to capitalize on the benefits of the social capital available in college settings (Hagler, 2023; Schwartz et al., 2023; White & Canning, 2023). The help‐ seeking barriers that historically marginalized students

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experience are due in part to societal and systems‐level factors that lead to greater stigma, judgment, and stereo type threat (Chang et al., 2020; Winograd & Rust, 2014). For example, students with minoritized identities may not seek help from college faculty or staff due, in part, to fear that doing so will confirm negative stereotypes held about individuals who have a shared identity or background (Massey & Fischer, 2005). Moreover, differences in skills and knowledge acquired through cultural capital, such as knowing how and when to access institutional support, can also shape students’ help‐seeking behaviors and their ability to build social capital (Richards, 2022). Cultural capital refers to the information, knowledge, and skills that enable individuals to navigate complex systems like higher edu cation and the workplace (Lareau, 2015). Students from historically marginalized backgrounds may enter college with less familiarity with these norms and unspoken rules, which can limit their access to key academic resources and support networks. The present study evaluated the long‐ term effects of a one‐credit undergraduate course through which racially diverse college students were provided with evidence‐based skills and opportunities to expand their social networks.

The importance of social capital in college

Sociologist Nan Lin (2001) conceptualized social capital as “resources embedded in a social structure which are accessed and/or mobilized in purposive actions” (p. 29). Social connections are an important community‐based asset that can facilitate access to networking and men toring relationships and career opportunities, which can be used to achieve social, academic, and career goals. Social capital theory suggests that differences in the quantity and quality of social networks contribute to inequities in social capital and resources (Gittell & Vidal, 1998; Granovetter, 1973; Lin, 2000; Putnam, 2000; Rajkumar et al., 2022; Schwartz et al., 2023).

College is an important period for social capital development, with the transition to college, specifically, being marked by substantial changes in many students’ support networks. As they transition to college, students must work to maintain relationships from their com munities outside of college, while also building new connections with nonfamilial, institutional agents that can support students within the college environment (Stanton‐Salazar, 2011). Social capital may therefore be an especially important predictor of academic and social outcomes among college students. Tinto (1993) posited that developing relationships with peers and faculty, socially integrating into the college community, and feeling a sense of belonging are critical for college stu dents’ persistence. Indeed, research shows that students with limited access to social capital may be less likely to attend college (Rios‐Aguilar & Deil‐Amen, 2012), and that greater access to social capital and support

throughout college is associated with improved educa tional and career outcomes, including retention, degree attainment, GPA, and sense of belonging and satisfac tion (Crisp & Cruz, 2009; Kniess et al., 2020; Kuh et al., 2006; Martin et al., 2020; Mishra, 2020; Rios‐ Aguilar & Deil‐Amen, 2012; Shiyuan et al., 2022; Stanton‐Salazar, 2011).

Social capital and college students with marginalized identities

Shifts in social capital during the transition to college are particularly significant for students from marginalized or underrepresented backgrounds, such as first‐generation college students and those with minoritized racial or ethnic identities (Hagler et al., 2021; Rios‐Aguilar & Deil‐Amen, 2012; Sánchez et al., 2011). Marginalized college students often face barriers to help‐seeking, including financial and family obligations that leave them with less time for networking and support‐seeking on campus (Chang et al., 2020). Underrepresented stu dents can also experience stigma around seeking support from nonfamily members and encounter difficulties navigating the “hidden curriculum” of college with respect to the knowledge and skills necessary to recruit social capital (Hagler et al., 2021; Werntz et al., 2023).

Institutional and systemic factors can make it espe cially difficult for students with minoritized identities to access social capital, which has been consistently tied to academic success in higher education (Mishra, 2020). For example, the dearth of diversity among faculty members can leave students of color feeling that there are few professors with whom they can identify culturally (Yosso et al., 2009). Cultural mistrust driven by faculty biases (Kozlowski, 2015) may make these students more hesi tant to seek out academic help from faculty who do not have a shared identity (Sloss, 2024). In fact, past research indicates that racially/ethnically minoritized youth may experience identity‐based stigma due to discrimination and concerns about stereotype threat, which in turn can create greater barriers to seeking help from faculty without a shared cultural background (Planey et al., 2019; Schmitz et al., 2020). In addition to the lack of representation among faculty in higher education (National Center for Education Statistics, 2024), the prevalence of microaggressions that marginalized stu dents face (Ogunyemi et al., 2020), and limited support ive services (Werntz et al., 2023), the cultural norms and bureaucratic structures embedded within higher educa tion institutions may also present significant challenges for underrepresented students. Universities often pro mote individualistic and Eurocentric norms, such as help‐seeking, self‐advocacy, assertiveness, and competi tiveness, which align with cultural values of the White middle class (Hagler et al., 2021). More privileged stu dents are often socialized into these norms and rules

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early in life, whereas first‐generation students and racial and ethnic minority groups may lack access to the information and networks needed to navigate these sys tems, putting them at greater risk for attrition and poor academic and career outcomes (Mishra, 2020).

Indeed, research suggests that first‐generation stu dents and students of color are more likely to endorse having close relationships with family and friends, but are less likely to have relationships with teachers and other adults that may support their career advancement (Raposa & Hurd, 2021; Raposa et al., 2018). Likewise, Rios‐Aguilar and Deil‐Amen (2012) found that while many first‐year Latino students had social ties in high school that helped them get into college, they struggled to develop relationships with institutional agents who could support their academic success and career aspira tions once enrolled. One survey of nearly 30,000 uni versity seniors found that first‐generation students and students of color are less likely to engage in social capital and career‐building activities such as networking, talking with faculty members about their interests, and sha dowing someone in their profession of interest (Center for Education Consumer Insights, 2021). In fact, only about 20 percent of first‐generation students reported having networked with a professional in their field, compared to approximately 33 percent of continuing‐ generation students.

As a result of these disparities, young adults with marginalized identities, such as first‐generation students, tend to receive lower levels of university support and are significantly more likely to drop out of college than their non‐marginalized peers (Mishra, 2020; Olmedo‐ Cifuentes & Martínez‐León, 2022; Werntz et al., 2023), which, in turn, undermines economic mobility. On average, individuals with a bachelor’s degree experience better economic outcomes than students who drop out of college, who may earn approximately 35% less and are up to 20% more likely to be unemployed (Hanson, 2023). In addition to diminished earning potential, college attrition may lead to the accumulation of personal debt and has been linked to broader societal costs (Schneider & Yin, 2011). Research shows that supportive faculty relationships are especially important in bolstering min oritized students’ academic success and retention (Rios‐ Aguilar & Deil‐Amen, 2012; Schwartz et al., 2016). Studies indicate that faculty support and the quality of student‐faculty relationships are strongly associated with GPA among students of color and first‐generation stu dents (Baker, 2013; Dika, 2012). Perceptions of men toring relationships also predict intentions to persist in college among racially and ethnically diverse students (Baier et al., 2016). Furthermore, one study found that more frequent faculty‐student interactions were associ ated with greater retention among first‐generation college students, with each additional advisor meeting associated with a 13% increase in retention likelihood (Swecker et al., 2013). Taken together, these studies highlight the

central role of social capital in supporting students’ persistence, degree completion, and success across the college career.

Social capital interventions for marginalized college students

There has been a growing focus on bolstering margin alized students’ social connections and capital, either through promoting supportive connections with faculty and peers or by supporting individuals’ support‐seeking attitudes and skills. One study found that participation in a second‐year experience program, which paired college students with a faculty mentor and taught strategies for building supportive peer, faculty, and staff relationships, was associated with higher third‐year retention, particu larly among Black and Latino participants (McDaniel et al., 2022). Similarly, Moschetti et al. (2018) demon strated that a peer mentoring intervention improved Latino students’ sense of integration and belonging in their university community. Participation in peer‐based social capital workforce or education programs has also been linked to greater progress towards young adults’ education and career goals (Boat et al., 2022).

Despite these promising findings, one concern about both traditional models of advising and mentoring pro grams is that students who are most in need of services may be the least likely to use those services (Alexitch, 2002). This could be due in part to such interventions relying on presumed skills and attitudes for engaging with adults in the college setting that may be less comfortable for first‐ generation college students than for their continuing‐ generation counterparts (Lareau & Cox, 2011). These interventions often do not address systemic barriers faced by historically marginalized students on college campuses. Ideally, individual interventions would be offered alongside efforts to promote institutional and systemic changes, for example, faculty and staff training on how to support and create a safe campus culture for historically marginalized students (Bunin et al., 2023). Community psychologists have pointed to the benefit of a broader network or “web” of support, rather than relying on a single relationship (Sánchez et al., 2008; Wallace et al., 2000).

Standardized curricula have also been developed to teach students skills for recruiting and retaining support. One such program is Connected Scholars (CS), a one‐ credit, semester‐long course through which college stu dents are taught skills to identify and recruit potential mentors to develop and expand their social networks. Through a series of lessons, students learn about the value of social capital and the attitudes and behaviors necessary to recruit mentors and cultivate a network of relationships with faculty and other professionals who can help them achieve their academic and career goals (Schwartz et al., 2016; Schwartz et al., 2023). Collect ively, these studies highlight the critical role of social

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capital interventions in supporting the academic and career access of marginalized students.

Given the well‐documented disparities in access to social capital on college campuses based on race and college student generation status, the CS course was designed as an individual‐level intervention aimed at equipping students with help‐seeking and support‐ building strategies. These efforts should be understood within the broader structural context and offered in addition to system‐level reforms. CS has various objec tives: (a) highlight the significance of mentoring re lationships and social capital in advancing academic and career aspirations, (b) help students identify existing and potential sources of support, and (c) foster help‐seeking behaviors and cultivating relationships with prospective mentors and other sources of social capital. The curric ulum acknowledges the value of diverse forms of social capital and cultural wealth (Guiffrida, 2006; Hagler et al., 2021; Yosso *, 2005), while moving away from a Eurocentric framing of social capital and exploring support systems both in college and at home. Through the course, a range of campus resources is introduced to help students identify and address individual and struc tural barriers to networking and building mentoring re lationships. The curriculum also encompasses setting personal and academic goals, identifying strengths and areas for growth, and teaching students to leverage their strengths and goals to effectively recruit diverse mentors from both institutional and community‐based environ ments (Schippers et al., 2015; Stephens et al., 2012). The pedagogical approaches employed throughout the cur riculum include student‐facilitated activities, reflective writing assignments, group discussions, role‐playing ex ercises, and opportunities to practice newly acquired skills in real‐world settings. For example, students create “eco‐maps” or graphical representations of their social connections, role‐play attending office hours, and write gratitude letters to individuals in their home support systems. Throughout these exercises, students are pro vided with scaffolded practice and real‐world assign ments, including supervised networking events, to cultivate a broad and diverse network of connections and support.

CS seeks to normalize challenges that emerge in college and the need to reach out for support. Role‐ playing exercises and real‐world assignments are intended to support students in navigating potential rejection or challenges faced when attempting to seek support, especially in seeking out mentorship from faculty and/or staff at college whose cultural back grounds and values may differ from those of the stu dents. These exercises are intended to help students attribute these challenges not to personal failure but to external factors (e.g., lack of availability, etc.) and identify alternative support‐seeking opportunities (e.g., referring back to their eco‐map to choose another potential social connection).

Multiple qualitative and quantitative studies have demonstrated the processes and effectiveness of the CS curriculum (Parnes et al., 2020; Schwartz et al., 2016; Schwartz et al., 2018; Schwartz et al., 2023). For ex ample, Schwartz et al. (2018) found that students who participated in a brief version of the social capital intervention as part of a summer remedial program before college had higher GPAs at the end of their first year, improved help‐seeking attitudes and behavior, and closer relationships with staff when compared to those who did not take the course. Another study ex ploring mechanisms of change underlying the impacts of CS on student outcomes found that decreases in help‐ seeking avoidance and increases in positive attitudes to wards help‐seeking partially accounted for the beneficial impact of CS on student‐instructor relationships and end‐of‐year‐GPA. Notably, this finding was strongest for first‐generation and Black students (Parnes et al., 2020). More recently, a rigorous, randomized controlled trial (RCT) of CS found that students who had taken the course reported increased academic self‐efficacy and help‐seeking attitudes and behavior 1 year after the course ended, as well as greater social capital and men toring support following the intervention (Schwartz et al., 2023). These positive changes in attitudes, behav iors, and support are promising, and have been associ ated in other studies with longer‐term college success (Afzal et al., 2024; Farruggia et al., 2018; Robbins et al., 2004). Nonetheless, the effects of CS on retention in college, years to graduation, and GPA at graduation have not yet been explored.

Our longitudinal study extends previous research by drawing on institutional data to examine the long‐term outcomes associated with enrollment in the CS course, which was offered to the general student body of a minority‐majority public university in the northeastern United States (U.S.) as an optional elective. Such work has the potential to inform systems‐level approaches to improving sense of belonging and connection to one’s college community among college students with mar ginalized identities, which, in turn, could play an important role in promoting their college success and redressing existing educational and economic disparities in the U.S.

Study aims and hypotheses

This study examines the effects of CS on academic out comes such as retention and time‐to‐graduation in col lege. The effects of CS have been examined on proximal academic outcomes among college students (i.e., next semester’s GPA and academic engagement) using a rig orous RCT design; however, it is less clear whether there are positive distal effects on academic outcomes. This study, therefore, drew on university records to evaluate the longer‐term impact of CS by comparing academic

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outcomes for students who enrolled in the course, and those who passed it, to the same outcomes for all other students at the university. Our study aimed to (1) provide descriptive data and probe demographic differences between students who opted to take the one‐credit CS course (takers) and those who did not (non‐enrollers), and between those who passed the one‐credit course (passers) and those who enrolled but did not pass (non‐ passers); and (2) evaluate the effects of both course en rollment and successful course completion on a range of longer‐term academic outcomes. Analyses examining the first aim were exploratory, given the lack of previous research in this area using naturalistic, opt‐in designs for the course. Based on previous studies of proximal aca demic outcomes (Parnes et al., 2020; Schwartz et al., 2018), we hypothesized that taking or passing CS as a freshman or underclassman would be positively associated with academic retention (i.e., 1‐year, 2‐year college retention, respectively). We further hypothesized that taking or passing CS at any point during college would be positively associated with graduation outcomes (i.e., graduating within 4 and 6 years) and cumulative GPA at graduation among students who completed their degree.

MATERIALS AND METHODS

Participants and procedure

We analyzed academic and course enrollment data among undergraduate students attending a large, public, ethnically diverse research university in Massachusetts, located in the northeastern United States. All data were collected from institutional records and were deidentified to maintain student confidentiality. From 2018 to 2024, CS was offered approximately twice per year through five sections with 22 students per section, as a one‐credit, semester‐long course. The course was open to all undergraduate students, regardless of their year of study. Students could select CS from various other courses of fered by the university, including both major‐specific courses and those focused on career development and job skills. Students who completed CS attended roughly 12 class sessions across a 14‐week academic semester. All students who enrolled in (n = 912) or passed CS (n = 819) were included in the study, with data aggregated across all semesters from 2018 to 2022. Among those who passed the course, 226 (27.6%) were freshmen, 84 (10.3%) sophomores, 152 (18.6%) juniors, 317 (38.7%) seniors, and 40 (4.9%) were categorized as other or unknown. Enrollment in the course ranged from 80 to 170 students per semester (M = 101, SD = 28.79). Of students who enrolled in the course, 57% identified as female, 24% identified as White, 24% identified as African American, 22% identified as Asian, 20% identified as Hispanic, and 59% were eligible for a Pell Grant. Undergraduates

(n = 32,021) who entered the university from 2015 to 2022 but did not enroll in the course served as a com parison group in the analyses. Detailed demographic information is presented in Table S1 in the Supplemen tary Materials.

Connected scholars course

In response to early, encouraging results from trials testing CS, a brief summer orientation version of CS was expanded into a semester‐long course that was made available to all students to ensure broader access to course content (Schwartz et al., 2018). The curriculum used in the current evaluation was based on the original version of CS and integrated feedback from participants in pilot studies. The course underwent a thorough review by a team of undergraduates, graduate students, and postdoctoral scholars from diverse racial and ethnic backgrounds, as well as by a prevention scientist who specializes in developing evidence‐based curricula (Schwartz et al., 2023). Each iteration of the course was refined based on staff and student feedback. The current version of CS meets weekly for one academic semester, leading to approximately 10 total class sections being offered annually. CS is taught by university staff, who undergo 1‐day in‐person training before teaching the course. As described above, the course curriculum fo cuses on how to seek out mentorship and develop net working skills. Students’ grades in the course are based on attendance and participation in class activities, graded homework assignments, and a final exam, which consists of participation in a networking event. Students are graded on their effort and engagement in both structured activities and informal networking interactions with guests.

Measures

Demographics and other covariates

University records were used to collect information on students’ demographic characteristics. Demographic data were initially self‐reported by students during the ad missions process and could be subsequently updated at any time through the university’s student information system. Due to small sample sizes across some racial categories, race was coded as White, Black, Asian, His panic, and multi‐racial, with all other racial groups, including international students, as coded by the insti tution, collapsed into an “other” category for the pri mary analyses. Similarly, students who reported a gender other than male or female (n = 2) or did not report their gender (n = 49) were not included in the analyses. First‐ generation college student status was defined by the institution as a student, neither of whose parents had

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completed a college degree. This variable was derived from students’ responses on the Common Application, a standardized undergraduate admission application used by many U.S. colleges and universities. As part of the application, students reported the highest level of edu cation attained by each parent. Students who indicated that neither parent had completed a college degree were coded as first‐generation (1 = yes), and all others were coded as non‐first‐generation (0 = no).

Furthermore, to assess socioeconomic background, university records were used to identify whether students had in‐state or out‐of‐state residency for tuition pur poses, had attended a public high school in Boston or not, or were eligible for a Pell grant (a federally funded program based on financial need) in their first semester. These variables served as proxy indicators of socio economic status.

Academic variables, including cumulative high school GPA, were also obtained from institutional records. Several indicators were used to measure academic pre paredness. First‐time student status was assigned to students who had no prior postsecondary enrollment or transfer credits from a university. This was determined by institutional data collected during the admissions process. Students with previous enrollment at a public university within the state system were categorized as internal transfers, while those transferring from institu tions outside of this system were classified as external transfers.

Additionally, academic records were reviewed to determine whether students were enrolled in a mandatory preadmission summer program that prepares culturally and linguistically diverse undergraduate students for academic success in college. This program was required for students who did not meet standard admission requirements but demonstrated potential to succeed in college. Honors Program participation was also recorded for students who enrolled in the Honors College during their first year. Finally, we identified the academic college within the university in which the students’ majors were situated at the start of their first year (e.g., the College of Liberal Arts (CLA) and the College of Science and Mathematics (CSM)). Students in our sample were

TABLE 1 Sample definitions and primary analyses.

enrolled in a range of academic programs across the university.

Connected scholars final grade

To evaluate the impact of the CS course, we conducted analyses with both an intent‐to‐treat sample (i.e., all students who enrolled in CS regardless of whether they passed the course) and with a sample of course com pleters. All students who opted into CS were coded based on whether they passed the course. Passing was defined as receiving a grade of C‐ or higher, which corresponds to earning at least 68 out of 100 points. This cutoff is consistent with the university’s Institutional Research standard for academic evaluations, which groups grades of D, F, withdrawn, and incomplete together. A term GPA above this threshold is also required to remain in good academic standing at the university. Accordingly, students who earned a D or F, withdrew from the course, received an incomplete, or did not attend were coded as not passing (0), while all others were coded as passing (1). The intent‐to‐treat analysis compared all students who enrolled in CS (takers) to students who did not enroll in the course (non‐enrollers). The completer analysis com pared students who passed CS (passers) to all other students in the university, including non‐enrollers, as well as students who enrolled but withdrew or did not pass the course (non‐passers). Definitions of the analytic groups and corresponding sample sizes are provided in Table 1.

Outcome variables

Academic retention

University records were used to determine if students were retained 1 year after enrolling at the university (1 = yes, 0 = no). Similarly, 2‐year retention was assessed by determining if students were retained 2 years after becoming a student at the university (1 = yes, 0 = no). These metrics are widely used in institutional evaluations as early indicators of long‐term persistence and

Subgroup Definition Sample size Group comparisons

Takers All students who enrolled in Connected Scholars, regardless of whether they passed the course.

Passers Students who earned a passing grade of C‐ or higher in Connected Scholars.

Non‐Passers Students who enrolled in Connected Scholars but withdrew or did not earn a passing grade.

N = 912 Analysis 1. Takers versus Non‐Enrollers

N = 819 Analysis 2. Passers versus all other students (Non‐Passers + Non‐Enrollers)

N = 93 Analysis 3. Non‐Passers versus Non‐Enrollers

Non‐Enrollers All undergraduates who did not enroll in Connected Scholars. N = 32,021 ‐

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timely degree completion. This is particularly important, as most college dropouts occur during the first 2 years (Barefoot*, 2004; Engle & Tinto, 2008; Gardner et al., 2024). To examine whether CS is associated with academic retention, we tested whether taking or passing CS as a freshman is associated with 1‐year retention and whether taking or passing CS as an underclassman is predictive of 2‐year retention.

Graduation outcomes

In addition to retention, timely graduation is an impor tant indicator of student success and has been linked to long‐term outcomes such as career satisfaction, earning potential, and economic mobility (Ma & Pender, 2023; Schneider & Yin, 2011). Academic records were used to assess whether students graduated within 4 and 6 years. Each variable was coded using the categorical outcomes (“yes” or “no”), indicating whether the student com pleted their degree within the specified time frame.

Cumulative grade point average (GPA) at graduation GPA at graduation was determined by calculating the cumulative GPA of students at the time of graduation. GPAs were coded on a 4.0 scale, and only students who had graduated from the university were included in this analysis.

Data analysis

All analyses were conducted using SPSS (IBM Corp, 2023). We first examined the demographic char acteristics of undergraduate students who had enrolled in CS (takers) compared to all other undergraduate stu dents (non‐enrollers), and students who passed CS (passers) to those who enrolled in but did not pass the course (non‐passers), using chi‐squared tests for cate gorical variables and Mann–Whitney U tests for numeric variables. The Mann–Whitney U test was selected because the numeric variables were not normally dis tributed based on visual inspection of the data (Field, 2013). Cramer’s V is reported as the effect size for the Chi‐squared tests. Cramer’s V values range from 0 to 1, with standard cutoffs indicating that values of 0.1, 0.3, and 0.5 are interpreted as small, medium, and large ef fects, respectively (Cohen, 1988; Kim, 2017).

To test the hypothesis that the CS course would lead to improved academic outcomes relative to students who did not take or pass the class, backward stepwise multi ple logistic regression analyses were conducted for the dichotomous outcomes, examining the effects of taking and passing CS on 1‐ and 2‐year retention, and on whether students graduated within 4 and 6 years. This analytic approach begins by entering all covariates and independent variables into an initial full regression model. SPSS then iteratively removes predictors based on the significance values of the Wald statistic to identify the most parsimonious model (Field, 2013). At each step,

variables are eliminated based on SPSS’s default elim ination criterion (p > .10; IBM Corp, 2023). This con tinues until all remaining predictors have p values < .10. This approach aims to maximize the predictive power of the final model while minimizing the number of included variables. Standardized residuals were examined, and no values were found to be greater than |3|. The effect sizes for the logistic regression models are reported as odds ratios. Nagelkerke R2 values were also used to calculate the variance explained by each model.

Backward stepwise multiple linear regression analyses were used to test the effects of taking and passing CS on cumulative GPA at graduation. As with the logistic regression models, the elimination process began with all predictors included, and sequentially removed the least significant variable until only predictors with p values less than 0.10 remained (Field, 2013). The data tables reflect the final sample sizes and regression models, in which only predictors with p < .10 are retained. As a result, some predictors with p values between .05 and .10 are still shown in the tables. Variables tested but ex cluded from the final model are listed in the footnotes of each table. The adjusted R2 values are reported as the effect size for the multiple linear regression analyses.

To better understand the effects of course completion on student retention and academic performance and to account for potential self‐selection effects, exploratory analyses were conducted to compare academic outcomes between non‐passers and non‐enrollers. Backward step wise multiple logistic regression was used to examine the effects of taking CS as a freshman on 1‐year retention and as an underclassman on 2‐year retention, as well as the effects of taking CS on 4‐ and 6‐year graduation. Multiple linear regression was used to examine cumula tive GPA at graduation. Given that students who en rolled but did not pass the course (non‐passers) likely had little to no exposure to its content, we hypothesized that their academic outcomes would not differ signifi cantly from those of non‐enrollers.

Assumption checks were performed throughout this process. For each multiple regression analysis, a visual inspection of a plot of the studentized residuals versus standardized predicted values was performed to assess whether residuals were spread evenly across all levels of the predicted values. All multiple regression analyses showed evidence of homoscedasticity. Autocorrelation, or the independence of residuals, was assessed through the Durbin–Watson statistic. All Durbin‐Watson values were close to 2 and were considered within an acceptable range (1.5–2.5). VIF values were also examined to ensure there was no evidence of multicollinearity in the final model. VIF values across all analyses were less than 2, suggesting that no predictors were too highly correlated. Studentized deleted residuals greater than ±3 standard deviations were further investigated by using the Cook’s distance and leverage values to assess the impact of any outliers on the model. No leverage values were greater than 0.2 and

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Cook’s distance showed no influential cases with values above 1. Lastly, a visual inspection of a histogram of re siduals and Q–Q plot was performed and showed that residuals were relatively normally distributed.

Missing data

For our primary analyses, a listwise deletion approach was used to handle missing data (Allison, 2002; Meeyai, 2016). This method is commonly used in educational research, including studies using administrative datasets where sample sizes are sufficiently large to maintain statistical power despite case deletion. This method has been adopted in similar studies of college outcomes (Biwer et al., 2023; Guiffrida et al., 2013; Long & Perkins, 2007; Snel et al., 2022). All academic and demographic data were drawn from university registrar records, which collect information as part of standard university processes. To examine the effects of taking and passing CS on the likeli hood of graduating within 4 and 6‐years, only students who had the opportunity to graduate within these time periods were included in the analyses. Specifically, for the 4‐year graduation analysis, we included students who had ma triculated in Spring 2019 or earlier. For the 6‐year gradua tion analysis, we included students who had matriculated by Spring 2017. Students not yet eligible to graduate based on their matriculation year were excluded.

Covariates

Covariates for each model were selected based on previous studies demonstrating that factors such as age (Ferrão & Almeida, 2018), gender (Causey et al., 2022; Parker, 2021), first‐generation status (Mishra, 2020), socioeconomic sta tus (Raposa et al., 2018), and racial and ethnic identity (Banks & Dohy, 2019) are social cultural determinants of health shaped by structural and systemic inequities that perpetuate oppression and lack of access to resources and opportunities, thereby contributing to poor academic outcomes, reduced social capital, and/or increased risk of attrition. This study used Boston public high school attendance (Fletcher & Tienda, 2010), Pell eligibility status (Castleman & Long, 2013; Liu et al., 2023), and in‐state or out‐of‐state status (Castleman & Long, 2013; Feldblum et al., 2021) as proxy indicators of income. Research suggests that students from out‐of‐state and those who attended private school are more likely to pay higher tuition than resident students from public schools (Salazar et al., 2021). In addition to income, measures of academic preparedness have also been linked to college retention and academic performance. As a result, high school GPA (Komarraju et al., 2013), first‐time or transfer student status (Hanson, 2023), and whether students participated in the preadmission summer academic preparatory pro gram or enrolled in the Honors College during their

first year were selected as covariates (Bradford et al., 2021; Clark et al., 2018; Diaz et al., 2019; Howard & Flora, 2015). We also included, as a covariate, the aca demic college within the university in which students’ majors were situated at the time of matriculation (Byars‐ Winston et al., 2017; Sovero et al., 2021).

RESULTS

Predictors of choosing to enroll in connected scholars

Chi‐squared analyses showed there were significant differ ences between students who opted into the course com pared to students who did not take the course (non‐ enrollers) based on racial identity. Students who took the course were more likely to identify as Black and Asian and were less likely to identify as White (χ2 (8) = 148.61, p < .001), Cramer’s V = 0.07. Significant differences were also found based on admission type, with first‐time stu dents taking the course more frequently than transfer stu dents (χ2 (1) = 79.11, p < .001), Cramer’s V = 0.05. Additionally, those taking the course were more likely to be Pell eligible (χ2 (1) = 81.08, p < .001), Cramer’s V = 0.05, and first‐generation students (χ2 (1) = 24.63, p < .001), Cramer’s V = 0.04. With respect to department/school in the university, students who took CS were more likely than other undergraduates to be from the College of Liberal Arts (χ2 (8) = 113.68, p < .001), Cramer’s V = 0.06, but there were no significant differences found across the other departments/schools. There were also no significant differ ences between students who had enrolled in CS and stu dents who had not taken the course based on gender (χ2 (1) = 1.48, p > .05). Mann–Whitney U tests showed that students who opted to take CS were significantly younger (M = 19.06, Mdn = 18) than other university students (M = 19.34, Mdn = 18), U = 12,456,947.00, z = −0.78, p < .001. In addition, those who took CS had significantly lower high school GPAs (Mdn = 3.12) than non‐enrollers (Mdn = 3.21), U = 7,390,029.50, z = −3.26, p < .01.

Comparing students who did and did not successfully pass connected scholars

Among students who enrolled in CS, there were no signifi cant differences between students who successfully passed the course (passers) and those who did not based on gender (χ2 (1) = 1.77, p > .05), Pell eligibility status (χ2 (1) = 0.23, p > .05), admission type (χ2 (1) = 0.14, p > .05), the initial college or school they were enrolled in within the university (χ2 (6) = 5.67, p > .05), age (U = 37,834.50, z = −0.11, p > .05), or high school GPA (U = 26,998.50, z = 1.85, p > .05). However, slightly fewer first‐generation students passed the course compared to their continuing‐generation peers (χ2 (1) = 5.08, p < .05), Cramer’s V = 0.09, and

AMERICAN JOURNAL OF COMMUNITY PSYCHOLOGY | 9

significantly fewer Hispanic students and more Asian stu dents passed the course compared to other racial/ethnic groups (χ2 (8) = 21.77, p < .01), Cramer’s V = 0.16.

Associations between connected scholars and students’ academic outcomes

Retention

First, logistic regressions were used to determine whether taking and/or passing CS as a freshman and underclassman predicts 1‐year and 2‐year retention in college, respectively.

Taking CS

For 1‐year retention, the final model was statistically sig nificant, χ2 (15, N = 11,284) = 334.52, p < .001, Nagelkerke R² = 0.04. However, taking CS as a freshman was not associated with whether students remained in college after 1 year (B = 0.33, Wald χ2 (1) = 3.55, p > .05; OR = 1.40; see Table 2). For 2‐year retention, the final model was also statistically significant χ2 (12, N = 10,979) = 564.38, p < .001, Nagelkerke R² = 0.07. Taking CS as an underclassman sig nificantly predicted 2‐year retention (B = 0.46, Wald χ2 (1) = 10.46, p < .01; OR = 1.59; see Table 3), with students

who took CS having 1.59 times the odds of retaining after 2 years compared to non‐enrollers.

Passing CS

The models predicting one‐ (χ2 (15, N = 11,284) = 340.02, p < .001, Nagelkerke R² = 0.04) and 2‐year retention (χ2 (12, N = 10,979) = 571.54, p < .001, Nagelkerke R² = 0.07) were both statistically significant. Passing CS as a freshman was associated with increased odds of 1‐year retention (B = 0.62, Wald χ2 (1) = 8.15, p < .01; OR = 1.86; see Table 2) com pared to all other undergraduate students at the university (non‐enrollers and non‐passers). The pattern of effects emerged more strongly for 2‐year retention. Passing CS as an underclassman was significantly associated with 2‐year retention (B = 0.67, Wald χ2 (1) = 16.58, p < .001; OR = 1.96; see Table 3), with students who passed CS having 1.96 times the odds of retaining after 2 years compared to students who did not enroll in or pass the course.

Four‐year graduation

Taking CS

Logistic regression was also performed to test if taking CS predicted whether students would graduate in four or

TABLE 2 The effects of taking and passing CS as a freshman on one‐year Retention.

Taking CS Passing CS

95% CI 95% CI

B SE (B) Wald OR Lower Upper B SE (B) Wald OR Lower Upper First‐Gen −0.17 0.05 13.29*** 0.84 0.77 0.92 −0.17 0.05 13.05*** 0.84 0.77 0.92 Female 0.17 0.05 14.20*** 1.19 1.09 1.29 0.17 0.05 14.22*** 1.19 1.09 1.29 Multiracial −0.18 0.11 2.84 0.83 0.68 1.03 −0.18 0.11 2.88 0.83 0.67 1.03 Black 0.25 0.06 15.37*** 1.29 1.13 1.46 0.25 0.06 15.07*** 1.28 1.13 1.45 Asian 0.44 0.07 43.66*** 1.56 1.37 1.78 0.44 0.07 43.15*** 1.56 1.36 1.78 Other Race 0.54 0.10 32.58*** 1.72 1.43 2.07 0.54 0.10 32.21*** 1.71 1.42 2.06 Summer Prep 0.20 0.07 7.19** 1.22 1.05 1.40 0.19 0.07 6.69* 1.21 1.05 1.39 First‐time −0.33 0.08 18.76*** 0.72 0.62 0.84 −0.33 0.08 18.83*** 0.72 0.62 0.83 In‐state 0.56 0.07 59.34*** 1.76 1.52 2.03 0.56 0.07 58.888*** 1.75 1.52 2.02 Pell 0.19 0.05 15.70*** 1.21 1.10 1.33 0.19 0.05 15.42*** 1.21 1.10 1.33 CLA −0.22 0.06 15.50*** 0.80 0.72 0.89 −0.22 0.06 15.35*** 0.80 0.72 0.90 CSM −0.15 0.06 6.85** 0.86 0.77 0.96 −0.15 0.06 6.65* 0.86 0.77 0.96 Honors 0.44 0.09 22.83*** 1.55 1.30 1.86 0.44 0.09 22.74*** 1.55 1.29 1.86 HS GPA 0.25 0.03 63.45*** 1.28 1.21 1.37 0.25 0.03 63.41*** 1.28 1.21 1.37 Took/Pass CS 0.33 0.18 3.55 1.40 0.99 1.98 0.62 0.22 8.15** 1.86 1.21 2.84 Constant −0.10 0.12 0.77 0.90 −0.10 0.12 0.75 0.91

Note: Model = “Backward Wald” method in SPSS (N = 11,284). White and male were the reference groups from race and gender, respectively. Variables included in the initial model but removed during stepwise selection (p > .10): Hispanic, Age, and Boston Public High School attendance.

*p < 0.05; **p < 0.01; ***p < 0.001.

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TABLE 3 Effects of Taking and passing CS on two‐year retention.

Taking CS Passing CS

95% CI 95% CI

B SE (B) Wald OR Lower Upper B SE (B) Wald OR Lower Upper First‐Gen −0.11 0.04 6.73** 0.90 0.83 0.97 −0.11 0.04 6.61* 0.90 0.83 0.98 Female 0.22 0.04 27.45*** 1.24 1.15 1.35 0.22 0.04 27.47*** 1.24 1.14 1.35 Black 0.19 0.06 11.81** 1.21 1.09 1.36 0.19 0.06 11.69** 1.21 1.09 1.36 Asian 0.50 0.06 70.64*** 1.64 1.46 1.85 0.49 0.06 70.11*** 1.64 1.46 1.84 Other Race 0.43 0.08 25.93*** 1.53 1.30 1.81 0.43 0.08 25.81*** 1.53 1.30 1.81 First‐time −0.14 0.07 4.85* 0.87 0.76 0.98 −0.15 0.07 4.94* 0.86 0.76 0.98 In‐state 0.59 0.07 71.74*** 1.80 1.57 2.06 0.58 0.07 71.17*** 1.79 1.56 2.05 CLA −0.34 0.05 42.34*** 0.72 0.65 0.79 −0.33 0.05 42.02*** 0.72 0.65 0.79 CSM −0.37 0.05 48.44*** 0.69 0.63 0.77 −0.36 0.05 47.93*** 0.69 0.63 0.77 Honors 0.37 0.08 22.27*** 1.45 1.24 1.69 0.37 0.08 22.23*** 1.45 1.24 1.69 HS GPA 0.37 0.03 155.34*** 1.44 1.36 1.53 0.36 0.03 155.15*** 1.44 1.36 1.52 Took/Pass CS 0.46 0.14 10.46** 1.59 1.20 2.11 0.67 0.17 16.58*** 1.96 1.42 2.71 Constant −1.16 0.11 109.20*** 0.32 −1.15 0.11 108.97*** 0.32

Note: Model = “Backward Wald” method in SPSS (N = 10,979). White and male were the reference groups from race and gender, respectively. Variables included in the initial model but removed during stepwise selection (p > .10): Age, Multiracial, Hispanic, Boston Public High School attendance, Summer Prep, and Pell Eligibility Status. *p < 0.05; **p < 0.01; ***p < 0.001.

fewer years. The overall model was statistically signifi cant (χ2 (16, N = 13,392) = 1071.73, p < .001, Nagelkerke R² = 0.13). Taking CS significantly predicted 4‐year graduation (B = 1.17, Wald χ2 (1) = 109.48, p < .001; OR = 3.23; see Table 4). Students who took CS had 3.23 times the odds of graduating in 4 years compared to non‐ enrollers.

Passing CS

The final model significantly predicted whether students would graduate within 4 years (χ2 (16, N = 13,392) = 1075.22, p < .001, Nagelkerke R² = 0.13). Passing CS was significantly associated with 4‐year graduation (B = 1.23, Wald χ2 (1) = 113.54, p < .001; OR = 3.42; see Table 4). Students who passed CS had 3.42 times the odds of graduating within 4 years com pared to non‐enrollers and non‐passers.

Six‐year graduation

Logistic regressions were used to determine if taking and/ or passing CS predicted whether students graduated in 6 years or less.

Taking CS

The final model predicting 6‐year graduation was statis tically significant (χ2 (14, N = 13,392) = 755.22, p < .001, Nagelkerke R² = 0.08). Taking CS was significantly associated with whether students would graduate within

6 years (B = 0.95, Wald χ2 (1) = 91.53, p < .001; OR = 2.59; see Table 5). Students who took CS were found to have 2.59 times the odds of graduating in 6 years com pared to students who did not enroll in the course.

Passing CS

The overall model was also statistically significant (χ2 (14, N = 13,392) = 764.87, p < .001, Nagelkerke R² = 0.09). Passing CS was significantly associated with whether students graduated within 6 years (B = 1.04, Wald χ2 (1) = 101.89, p < .001; OR = 2.84; see Table 5), with students who passed CS having 2.84 times the odds of graduating in 6 years compared to non‐enrollers and non‐passers.

Cumulative GPA at graduation

Taking CS

A multiple linear regression was conducted to assess whether taking CS predicted students’ cumulative GPA at graduation. The model significantly predicted cumu lative GPA, F (12, 2907) = 49.32, p < .001, with an adjusted R2 of 0.17. However, taking CS was not sig nificantly associated with GPA compared to those who did not enroll in the course, p > .05.

Passing CS

Similarly, when testing the effects of passing CS on cumulative GPA, the model was statistically significant,

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TABLE 4 Effects of taking and passing CS on four‐year graduation.

Taking CS Passing CS

95% CI 95% CI

B SE (B) Wald OR Lower Upper B SE (B) Wald OR Lower Upper Age 0.07 0.01 22.44*** 1.07 1.04 1.10 0.07 0.01 22.43*** 1.07 1.04 1.10 First Gen −0.13 0.05 5.78* 0.88 0.79 0.98 −0.13 0.05 5.63* 0.88 0.79 0.98 Female 0.47 0.05 77.98*** 1.59 1.44 1.77 0.47 0.05 77.97*** 1.59 1.44 1.77 Multiracial −0.37 0.14 7.31** 0.69 0.53 0.90 −0.37 0.14 7.12** 0.69 0.53 0.91 Black −0.31 0.08 16.42*** 0.73 0.63 0.85 −0.31 0.08 16.15*** 0.73 0.63 0.85 Hispanic −0.32 0.07 20.66*** 0.73 0.64 0.84 −0.31 0.07 20.16*** 0.73 0.64 0.84 Public HS −0.20 0.08 6.71* 0.82 0.71 0.95 −0.19 0.08 6.54* 0.82 0.71 0.96 Summer Prep −0.92 0.12 59.09*** 0.40 0.32 0.51 −0.90 0.12 57.08*** 0.41 0.32 0.51 First‐time −1.07 0.08 185.73*** 0.34 0.30 0.40 −1.06 0.08 184.40*** 0.34 0.30 0.40 In‐state 0.30 0.08 13.78*** 1.35 1.15 1.59 0.30 0.08 13.63*** 1.35 1.15 1.58 Pell −0.10 0.06 3.24 0.91 0.81 1.01 −0.10 0.06 3.50 0.90 0.81 1.00 CLA −0.34 0.06 32.65*** 0.71 0.63 0.80 −0.34 0.06 32.56*** 0.71 0.63 0.80 CSM −0.46 0.06 54.43*** 0.63 0.56 0.71 −0.46 0.06 54.16*** 0.63 0.56 0.71 Honors 0.91 0.08 135.51*** 2.50 2.14 2.91 0.91 0.08 135.09*** 2.49 2.14 2.91 HS GPA 0.07 0.03 3.93* 1.07 1.00 1.14 0.06 0.03 3.79 1.07 1.00 1.14 Took/Pass CS 1.17 0.11 109.48*** 3.23 2.59 4.03 1.23 0.12 113.54*** 3.42 2.73 4.29 Constant −2.33 0.35 44.48 0.10 −2.33 0.35 44.40*** 0.10

Note: Model = “Backward Wald” method in SPSS (N = 13,392). White and male were the reference groups from race and gender, respectively. Variables included in the initial model but removed during stepwise selection (p > .10): Asian and Other Racial Group.

*p < 0.05; **p < 0.01; ***p < 0.001.

F(11, 2908) = 53.48, p < .001, with an adjusted R2 of 0.17; however, passing CS was not a significant predictor of students’ GPA, p > .05. Regression coefficients and standard errors for the final models can be found in Table 6.

Exploratory analyses

A series of regression analyses was conducted to com pare academic outcomes between students who enrolled in CS but did not pass the course (non‐passers) and students who never enrolled in CS (non‐enrollers). En rolling in CS without successfully passing the course did not significantly predict one‐ or 2‐year retention or graduating within 4 or 6 years (all ps > .05; see Sup plemental Tables S2–S5). The final model predicting cumulative GPA was statistically significant, F (13, 2722) = 43.16, p < .001, with an adjusted R2 of 0.17. Enrolling in but not passing the course was significantly associated with GPA (B = −0.33, SE = 0.13, β = −0.05, t (2722) = −2.55, p < .05). Non‐passers had cumulative GPAs that were on average 0.33 points lower than non‐ enrollers.

DISCUSSION

This study examined the long‐term effects of CS, a col lege course designed to teach students—particularly those holding marginalized identities—evidence‐based skills for building social capital and recruiting mentors. Using longitudinal administrative data from a large, public university that primarily serves students holding one or more marginalized identities, we found that passing CS was associated with improved retention and higher likelihood of graduating within 4 and 6 years. CS was not associated with significant differences in cumu lative GPA at graduation. Consistent with our hypoth eses, students who passed CS had significantly higher odds of being retained after 1 and 2 years. This is en couraging, particularly since the risk of academic strug gle and dropout is highest for marginalized students in the first 2 years (Barefoot*, 2004).

Passing CS was also associated with substantially higher odds of graduating within 4 and 6 years. Strik ingly, students who passed CS were more than three times as likely to graduate within 4 years and more than twice as likely to graduate within 6 years compared to all other students at the university, which comprised both

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TABLE 5 Effects of taking and passing CS on six‐year graduation.

Taking CS Passing CS

95% CI 95% CI

B SE (B) Wald OR Lower Upper B SE (B) Wald OR Lower Upper Age 0.06 0.01 18.84*** 1.06 1.03 1.09 0.06 0.01 18.91*** 1.06 1.03 1.09 Female 0.37 0.05 63.85*** 1.44 1.32 1.58 0.37 0.05 63.81*** 1.44 1.32 1.58 Multiracial −0.35 0.12 8.18** 0.71 0.56 0.90 −0.34 0.12 7.97** 0.71 0.56 0.90 Black −0.34 0.07 26.92*** 0.71 0.63 0.81 −0.34 0.07 26.58*** 0.71 0.63 0.81 Hispanic −0.40 0.06 45.08*** 0.67 0.60 0.76 −0.39 0.06 43.87*** 0.68 0.60 0.76 Other race 0.22 0.09 6.16* 1.24 1.05 1.48 0.22 0.09 6.23* 1.25 1.05 1.48 Summer Prep −0.28 0.08 11.73** 0.76 0.65 0.89 −0.27 0.08 11.09** 0.77 0.65 0.90 First‐time −0.79 0.07 112.80*** 0.45 0.39 0.53 −0.79 0.07 111.92*** 0.46 0.39 0.53 In‐state 0.36 0.08 21.99*** 1.44 1.23 1.67 0.36 0.08 21.77*** 1.43 1.23 1.67 CLA −0.29 0.05 29.54*** 0.75 0.67 0.83 −0.29 0.05 29.60*** 0.75 0.67 0.83 CSM −0.33 0.06 35.74*** 0.72 0.65 0.80 −0.33 0.06 35.42*** 0.72 0.65 0.80 Honors 0.82 0.07 131.53*** 2.28 1.98 2.63 0.82 0.07 131.40*** 2.28 1.98 2.63 HS GPA 0.06 0.03 3.71 1.06 1.00 1.13 0.06 0.03 3.58 1.06 1.00 1.13 Took/Pass CS 0.95 0.10 91.53*** 2.59 2.13 3.15 1.04 0.10 101.89*** 2.84 2.32 3.48 Constant −2.21 0.33 44.18*** 0.11 −2.21 0.33 44.25*** 0.11

TABLE 6 Effects of taking and passing CS on GPA at graduation.

Taking CS Passing CS

95% CI for B 95% CI for B

GPA B SE (B) LL UL β t B SE (B) LL UL β t Female 0.07 0.01 0.04 0.10 0.09 4.93*** 0.07 0.01 0.04 0.1 0.09 4.96*** Age 0.03 0.00 0.02 0.04 0.18 8.39*** 0.03 0.00 0.02 0.04 0.18 8.55*** Black −0.16 0.02 −0.20 −0.12 −0.14 −7.92*** −0.17 0.02 −0.21 −0.13 −0.14 −8.01*** Hispanic −0.08 0.02 −0.12 −0.05 −0.08 −4.38*** −0.09 0.02 −0.12 −0.05 −0.08 −4.37*** Other Race −0.09 0.03 −0.14 −0.05 −0.07 −3.74*** −0.09 0.03 −0.14 −0.04 −0.06 −3.68*** First‐Gen −0.04 0.01 −0.07 −0.01 −0.05 −2.81** −0.04 0.01 −0.07 −0.01 −0.05 −2.88** Public HS −0.07 0.02 −0.11 −0.04 −0.07 −3.88*** −0.08 0.02 −0.11 −0.04 −0.07 −3.95*** HS GPA 0.08 0.01 0.06 0.10 0.21 9.55*** 0.08 0.01 0.07 0.10 0.21 9.58*** Summer Prep −0.11 0.03 −0.16 −0.06 −0.08 −4.51*** −0.11 0.03 −0.16 −0.06 −0.08 −4.55*** CSM −0.01 0.02 −0.12 0.07 −0.12 −6.62*** −0.10 0.02 −0.13 −0.07 −0.11 −6.46*** Honors 0.22 0.02 0.17 0.26 0.19 10.24*** 0.22 0.02 0.18 0.26 0.19 10.28*** Constant 2.59 0.09 2.42 2.76 30.41*** 2.58 0.09 2.41 2.74 30.36***

Note: Model = “Backward” method in SPSS (N = 2,920). White and male were the reference groups from race and gender, respectively. Variables included in the initial model but removed during stepwise selection (p > .10): Take/Pass CS, Asian, Multiracial, First‐time Student, Pell Eligibility Status, In‐State Tuition, and CLA Enrollment.

*p < 0.05; **p < 0.01; ***p < 0.001.

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non‐enrollers and non‐passers. In fact, passing the course was the single strongest predictor of graduation during each of those time frames, ahead of being in the honors program, being female, and having a higher high school GPA, all of which are more commonly cited predictors of successful graduation (Diaz et al., 2019; Komarraju et al., 2013; Parker, 2021). These results underscore the potential long‐term impacts of this semester‐long social capital intervention. Graduating in a timely manner has important implications for students’ future career pros pects and earning potential (Witteveen & Attewell, 2021). Delays in graduation can lead to increased debt burdens and postponed entry into the workforce. By improving both college retention and timely degree completion, CS may contribute to reducing some of the economic dis parities often experienced by students from marginalized backgrounds.

These findings align with previous research linking social capital and mentoring relationships to college persistence (e.g., Crisp & Cruz, 2009; Kniess et al., 2020; Stanton‐Salazar, 2011), including previous evaluations of CS. A rigorous RCT that examined short‐term outcomes (Schwartz et al., 2023) found that CS improved students’ self‐efficacy, help‐seeking, and self‐advocacy behaviors. One year after the course ended, students in the treat ment group were still engaging in more help‐seeking behavior and reporting improved self‐efficacy for enlist ing support when compared to students in the control group. CS also appeared to lead to growth in students’ social capital, with students in the treatment group re porting greater college mentoring support and bonding social capital at the end of the course. These findings suggest that by encouraging help‐seeking, CS may help students build connections with staff and mentors that are critical for remaining enrolled and making timely progress towards graduation.

While CS was associated with key milestones like retention and graduation, it did not significantly predict students’ cumulative GPA at graduation. This is sur prising, given that the same mechanisms leading to higher retention might also be expected to enhance aca demic performance. One possible explanation is that the course’s emphasis on setting personal and professional goals, including postgraduation employment, may have shifted motivation away from earning high grades in favor of graduating on time and obtaining employment. It is also possible that GPA is a relatively less salient indicator of success to the participants in this study. Empirical studies suggest that GPA serves as a critical differentiator for students targeting prestige‐driven career pathways and elite graduate or professional pro grams, whereas its weight diminishes significantly among those prioritizing immediate postbaccalaureate employ ment. This divergence is particularly evident along socioeconomic lines, with working‐class students dis proportionately represented in cohorts prioritizing job placement over academic credentials due to structural

constraints and differential access to social capital (Hurst et al., 2024). Moreover, a course focused on the benefits of social capital may encourage students to place greater emphasis on cultivating professional networks over mastering coursework. Indeed, a rigorous evaluation of CS found no effects on GPA and actually found decreased cognitive academic engagement (Schwartz et al., 2023). This may in part reflect instructors’ experi ences working in Career Services and their tailoring of course content to emphasize career development and job attainment rather than academic outcomes or the tran sition to college (Schwartz et al., 2023). Finally, the inclusion of students from the first through the senior year of college may have shifted CS’s focus away from the types of skills that would boost grades towards building post‐graduate career connections. Future itera tions of the intervention could offer separate courses for first‐year students to ensure coverage of academic con tent. Additional research is needed to clarify the limited impact of CS on academic performance.

These findings suggest that the underlying mecha nisms that account for higher rates of timely graduation may stem from factors other than GPA. Previous research suggests that the course enhances students’ help‐ seeking attitudes and behaviors, as well as their connec tion with university staff. These improvements may, in turn, strengthen students’ sense of belonging to the uni versity, access to supportive mentors, and broader social capital, which could motivate continued enrollment each semester, independent of academic performance (Parnes et al., 2020; Schwartz et al., 2018; Tinto, 1993). For ex ample, students whose networking led to summer opportunities and internships connected to their majors may have held stronger beliefs in the value of completing their degrees, independent of their GPAs. Indeed, as long as it is above threshold, GPA appears to be only one of several factors that determine career success and eco nomic mobility after college. A growing body of research highlights the unique contributions of social skills, net working ability, and access to internships and job opportunities through one’s social connections, over and above one’s academic performance in college (Boat et al., 2022; Chetty et al., 2022; Hassan et al., 2023; Schalewski, 2021; Schwartz et al., 2018).

The findings have important implications for community psychology, as advancing our understanding of interven tions that enhance graduation outcomes among margin alized students contributes to our understanding of factors promoting educational equity and economic empowerment. What makes these findings particularly promising is that more traditionally marginalized (i.e., historically excluded or disadvantaged within educational systems due to factors like race, ethnicity, and socioeconomic status) and academically vulnerable students (i.e., those at increased risk for poor educational outcomes such as low academic achievement, disengagement, or dropout due to systemic and structural inequities) were more likely to enroll in CS (Harper &

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Newman, 2022). In particular, students who opted in to the course were more likely to identify as Black, to be Pell eli gible, to be first‐generation students, and to have signifi cantly lower high school GPAs, all of which are known risk factors for college attrition (Allensworth & Clark, 2020; Castleman & Long, 2013; Liu et al., 2023; Stephens et al., 2012). These groups often enter college with less access to career‐relevant networks compared to more privileged peers and are less likely to seek mentors and build connec tions with institutional agents who can share career advice and facilitate college retention (Raposa & Hurd, 2021; Raposa et al., 2018). It is encouraging that a course aimed at supporting youth with historically marginalized identities and less access to social capital not only attracted students who may face higher financial and academic barriers but also was associated with significantly higher retention and earlier degree completion.

By teaching networking and help‐seeking skills, CS could support students in navigating cultural barriers and stigma around accessing institutional support (Hagler et al., 2021). Making CS more widely available may help universities increase social capital among un derrepresented groups at scale. Interventions that aim to expand students’ social capital by connecting them with supportive mentors and helping them navigate the “hidden curriculum” of college could play an important role in reducing systemic barriers to academic success. This strengths‐based approach makes students active agents in cultivating their own networks rather than passive recipients of assigned mentors or advisors and may be particularly well‐suited for building social capital among historically marginalized groups within university settings. Further research should examine factors that motivate students to enroll in CS to guide strategies for increasing participation among such groups.

LIMITATIONS AND FUTURE DIRECTIONS

While offering valuable insights, this study has some key limitations. First, the self‐selection of students into CS could have biased our understanding of its impacts, as those who chose to enroll may have differed systemati cally, in ways that influenced the observed findings, from students who did not opt into the course. It was en couraging, however, that the exploratory analyses showed that students who enrolled in CS but did not pass the course did not differ significantly from non‐enrollers in terms of retention or graduation outcomes. This could suggest that completion of the CS curriculum, rather than merely self‐selection into the course, was associated with greater college persistence and more timely graduation.

Second, although the positive associations between CS and key academic outcomes remained even after controlling for demographic and academic variables linked to retention and performance, it will be important

to conduct more rigorously controlled experimental studies of the long‐term effects of CS on students’ tran sitions to and through college and employment path ways. While past work has documented mechanisms underlying the impact of CS on students’ academic out comes, including GPA and relationships with instructors, future research should examine whether these pathways also account for the impact of CS on retention and graduation outcomes. It is also important to note that social support is among many factors that may contrib ute to college students’ experiences and success, for ex ample, financial aid resources, class sizes, co‐curricular activities, and instructional approaches (Kinzie & Kuh, 2017; Millea et al., 2018).

Our reliance on the academic and demographic data routinely collected in this university system constrained the variables available for analysis. Additionally, we did not examine potential cohort effects, such as whether outcomes varied across semesters or cohorts in which CS was offered, which could reflect differences in student composition, institutional context, or broader external factors (e.g., COVID‐19, policy changes). Future longi tudinal research should draw on other sources, such as employment data and assessment tools to further explore whether interventions like CS impact more diverse student outcomes, including students’ occupational outcomes, job satisfaction, and number of mentors after graduation. Moreover, it will be important to explore what kinds of mentoring relationships CS supports, and which types of mentors are most closely related to student outcomes. Additional empirical work is needed to identify the com plementary roles of different types of mentors, including institutional, familial, and peer mentors, in supporting college student success (Mishra, 2020).

The lack of a true comparison group also limits our ability to isolate CS’s unique effects, as we compared students who took CS to all other undergraduates (non‐ enrollers) rather than a matched group enrolled in another one‐credit course. This naturalistic design, which relied on institutional indices of academic success, retention, and graduation, provides promising evidence that CS can benefit students in real‐world settings outside tightly controlled research contexts. However, additional research with rigorous controls, including experimental studies comparing CS to other retention and workforce development interventions, would help clarify its dis tinctive benefits.

It is also important to note that the university at which the intervention was delivered is an urban Northeast public 4‐year university designated as a Minority Serving Institution that is primarily a com muter campus. Participants came from diverse ethnic and racial backgrounds, and a large portion of the sample self‐identified as first‐generation students. Additional research is needed to assess whether CS demonstrates similar efficacy across other institutional contexts, such as community, residential, and more selective colleges,

AMERICAN JOURNAL OF COMMUNITY PSYCHOLOGY | 15

which differ in student composition and baseline gradu ation rates.

CONCLUSION

Given preliminary evidence that CS may improve key academic outcomes linked to long‐term academic success, expanding access to this social capital intervention across universities could be a powerful tool for supporting stu dent success. By equipping students with skills to cultivate mentoring relationships and professional networks, CS has the potential to improve college retention and increase upward mobility for marginalized groups. As calls for promoting equitable access and success intensify within higher education, initiatives like CS represent a scalable, strengths‐based approach that aligns with this critical goal. Additional studies are needed to evaluate its effec tiveness across diverse institutional contexts and to iden tify the underlying mechanisms through which it may influence long‐term academic outcomes.

Finally, although the findings demonstrate the value of equipping students with help‐seeking strategies, we note that this intervention targets individual‐level mechanisms and must be contextualized within broader structural inequities. Institutional reforms, including faculty training on equitable mentorship practices, pro active advising systems, and resource allocation to un derserved departments and colleges, are critical to ensuring that developing students’ agency occurs in tan dem with dismantling systemic barriers to support access.

IRB STATEMENT

This study was conducted as a program evaluation of a university course and was not considered human subjects research.

DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the first author, EH, upon request.

ORCID

Emily Hersch https://orcid.org/0000-0003-1573-9396 Alexandra Werntz https://orcid.org/0000-0002- 2679-8204

Elizabeth B. Raposa https://orcid.org/0000-0001- 8169-3862

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SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

How to cite this article: Hersch, E., Werntz, A., Schwartz, S. E. O., Raposa, E. B., Hughes, J., Parnes, M. F., & Rhodes, J. (2025). Testing the effects of a social capital intervention on college student retention and academic success. American Journal of Community Psychology, 1–18.

https://doi.org/10.1002/ajcp.70027