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Tech Equity: A Survival Analysis of an Undergraduate Computer Science Supplemental Education Program

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Title: Tech Equity: A Survival Analysis of an Undergraduate Computer Science Supplemental Education Program
Language: English
Authors: Ryan Creps (ORCID 0000-0002-8592-5381), Shadman Islem (ORCID 0000-0002-8320-1283), Bingran Zeng (ORCID 0000-0002-2039-8154), Angela Boatman (ORCID 0000-0003-3890-8164), Andrés Castro Samayoa (ORCID 0000-0002-9547-8482)
Source: Innovative Higher Education. 2025 50(4):1315-1334.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 20
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Undergraduate Study, Program Evaluation, Computer Science Education, Supplementary Education, Undergraduate Students, Academic Achievement, Nonprofit Organizations, Partnerships in Education, Racial Differences, Gender Differences, Program Design, Student Motivation
DOI: 10.1007/s10755-024-09779-5
ISSN: 0742-5627
1573-1758
Abstract: This study examines the success of undergraduate students in computer science supplementary courses offered by a non-profit organization in partnership with colleges and universities across the U.S. Using a novel dataset from the nonprofit organization, we present one of the first descriptive overviews of students enrolled in supplemental computer science programs. Moreover, we conduct a survival analysis finding that racial and gender disparities in traditional computer science programs exist in these supplemental courses. However, the study finds that when supplemental courses are taken for credit, students are much more likely to complete the course, offering an important insight into program design and student motivation.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480532
Database: ERIC
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  Value: <anid>AN0187381771;ihe01aug.25;2025Aug20.02:30;v2.2.500</anid> <title id="AN0187381771-1">Tech Equity: a Survival Analysis of an Undergraduate Computer Science Supplemental Education Program </title> <p>This study examines the success of undergraduate students in computer science supplementary courses offered by a non-profit organization in partnership with colleges and universities across the U.S. Using a novel dataset from the nonprofit organization, we present one of the first descriptive overviews of students enrolled in supplemental computer science programs. Moreover, we conduct a survival analysis finding that racial and gender disparities in traditional computer science programs exist in these supplemental courses. However, the study finds that when supplemental courses are taken for credit, students are much more likely to complete the course, offering an important insight into program design and student motivation.</p> <p>Keywords: Survival analysis; Computer science education; Supplemental instruction; Diversity in STEM</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0187381771-2">Introduction</hd> <p>Given the career prospect of yielding lucrative returns for graduates, computer science (CS) has become a popular major among college undergraduates (U.S. Department of Education, [<reflink idref="bib36" id="ref1">36</reflink>]). Graduates with computer programming skills continue to be in high demand within the workforce (CompTIA, [<reflink idref="bib8" id="ref2">8</reflink>]; U.S. Bureau of Labor Statistics, [<reflink idref="bib37" id="ref3">37</reflink>]). Not only has this resulted in the growing interest in CS as a major for students, but also in the proliferation and increasing popularity of supplementary training programs focused on developing individuals' computer coding skills—such as coding bootcamps, and CS massive open online courses (MOOCs). However, access to these supplemental programs is uneven and the quality and completion rates are often unknown (General Assembly, [<reflink idref="bib12" id="ref4">12</reflink>]; Jordan, [<reflink idref="bib17" id="ref5">17</reflink>]).</p> <p>The increase in supplemental programs represents a potential strategy toward a more equitable CS profession. These programs are intended to provide CS students additional support with their coursework, particularly given that a third of all college students enrolled in introductory computer science courses initially fail the course (Alvarez et al., [<reflink idref="bib4" id="ref6">4</reflink>]). The programs also offer alternative pathways into the tech industry, potentially widening access for diverse groups of students (Kaynak, [<reflink idref="bib19" id="ref7">19</reflink>]). By providing flexible learning options, such as part-time or online courses, these programs can accommodate individuals who may have family or work commitments that prevent them from pursuing a traditional four-year degree (Lyon & Green, [<reflink idref="bib24" id="ref8">24</reflink>]). Furthermore, some programs specifically target underrepresented groups, offering scholarships or support services to enhance diversity (Lyon & Green, [<reflink idref="bib24" id="ref9">24</reflink>]). Such emergent evidence underscores the need for additional empirical evidence examining whether and how these supplemental programs advance underrepresented groups' participation within CS majors and the CS profession. Indeed, while these programs have the potential to support individuals' persistence in CS majors, they may also give rise to potential inequity for students from underrepresented socioeconomic and racial groups.</p> <p>Framed through the conceptual affordances of scholarship on equitable learning in STEM education (Whitcomb et al., [<reflink idref="bib38" id="ref10">38</reflink>]), our study examines novel administrative evidence from Esplanade (a pseudonym), a non-profit organization that partners with colleges and universities to offer a range of computer science educational intervention courses to undergraduate students for no cost. A primary mission of Esplanade is to increase the number of women and students of color working in the tech industry.</p> <p>Our study examines the effects of covariates, such as demographic (race and gender) and contextual factors (college year), on the likelihood of students gaining admission to, enrolling in, completing, and earning honors in Esplanade's supplemental CS courses. As the number of third-party course providers grows in higher education, it is important to understand students' path from admission to learning outcomes, particularly the places in the pipeline where students are most likely to exit. Through our analysis, we seek a deeper understanding of the factors that shape student participation and success in undergraduate computer science education.</p> <p>Our study contributes to the nascent area of research by offering one of the first descriptive overviews of the types of students enrolled in supplemental CS programming. Furthermore, we employ a survival analysis approach to examine students' enrollment journey through supplemental courses, following students from application to course enrollment and completion. Our results indicate that White students persist through their supplemental courses at higher rates than their peers, but there are differences between Asian, Black and Hispanic students as to when they leave the course funnel. Moreover, we find that women demonstrate lower supplemental course completion rates compared to their male counterparts. Finally, one of the promising findings from this research is that students of all backgrounds are significantly more likely to complete a supplemental course when taken for credit instead of as a non-credit course. This offers important guidance for course providers to consider the incentive structures of their programs. We discuss the implications of these findings for students, administrators, and the workforce.</p> <hd id="AN0187381771-3">Literature Review</hd> <p></p> <hd id="AN0187381771-4">Theoretical Framing</hd> <p>Past scholarship offers ample evidence on disparities in access, persistence, and completion by students of color and women in undergraduate computer science degrees, specifically, and science, technology, engineering & mathematics (STEM) more broadly. As Whitcomb et al. ([<reflink idref="bib38" id="ref11">38</reflink>]) assert, scholarship within this subfield must expand upon the conceptual premise that "inequitable outcomes are a result of inequitable access to resources, inadequate support and inequitable learning environments" (p. 1). Scholars of computer science education have carefully documented the interplay of structural, social, and psychological factors that create these inequitable learning environments. For example, Margolis et al. ([<reflink idref="bib25" id="ref12">25</reflink>]) describe the "preparatory privilege" of white, male, and affluent students as a conceptual shorthand to describe differential access to early computing courses, advanced courses, and parental support, all of which enhance these students' likelihood of persistence in the field. In contrast, students of minoritized racial, ethnic, and gender identities are more likely to face barriers in the forms of stereotype threats, overt discrimination, and limited role models (Sax et al., [<reflink idref="bib32" id="ref13">32</reflink>]).</p> <p>Compounded with these inequitable learning environments, Barker et al. ([<reflink idref="bib7" id="ref14">7</reflink>]) have shown how inadequate support systems through peer networks and faculty mentorships undermine students' persistence and success in computer science majors. Denner et al. ([<reflink idref="bib10" id="ref15">10</reflink>]) reaffirms this point by showing how targeted support services, such as learning communities and students' engagement in research opportunities, bolster students' integration into the computer science field.</p> <p>Responsive to such inequitable learning contexts and support systems, we conceptualize Esplanade as an external intervention redressing such inequities through supplemental educational opportunities for students of minoritized racial and gender identities. Rowan-Kenyon et al. ([<reflink idref="bib31" id="ref16">31</reflink>]) conducted a systematic review of institutional efforts to increase student success in computer science majors. They find the goal of most interventions is student retention as opposed to recruitment or career placement. Many interventions target introductory courses in computer science curricula because most attrition occurs early in computer science (Huang & Brainard, [<reflink idref="bib16" id="ref17">16</reflink>]; Ohland et al., [<reflink idref="bib30" id="ref18">30</reflink>]). As additional scholarship has examined, the robust evidence on attrition studies has primarily focused on students' persistence through their CS majors (Obaido et al., [<reflink idref="bib29" id="ref19">29</reflink>]). There is also a concerted effort to promote success among women and students of color (Kordaki & Berdousis, [<reflink idref="bib20" id="ref20">20</reflink>]; Stephenson et al., [<reflink idref="bib33" id="ref21">33</reflink>]).</p> <hd id="AN0187381771-5">Data & Methods</hd> <p>Our study builds upon prior literature by offering a detailed account of student trajectories and outcomes in supplemental computer science courses across multiple colleges and universities. In 2022 we partnered with Esplanade, a non-profit organization that was interested in studying the effects of their programs on student outcomes, to provide external research evidence to the organization. While Esplanade offers different supplemental CS courses, the most common three courses, considered the core courses of the program, are cybersecurity, iOS and Android app development. These courses are offered through colleges and universities, either in person or remotely, for credit or not for credit. The coding taught in these supplemental courses is often absent or limited to upper-level electives in traditional computer science curricula (Ahadi et al., [<reflink idref="bib1" id="ref22">1</reflink>]; Draus et al., [<reflink idref="bib11" id="ref23">11</reflink>]). Therefore, these supplemental courses offer students the opportunity to further develop and practice their coding skills earlier than they might in their traditional CS degree program. The motivation for this programmatic model is that teaching relevant coding skills in an inclusive environment will increase retention and graduation among women and students of color in computer science programs and contribute to the organization's larger goal of diversifying the population of software engineers.</p> <p>Longitudinal administrative data provided by Esplanade indicates that 36,569 students applied to an Esplanade core course from 2017–2022. This application process was relatively simple and involved setting up GitHub and LinkedIn accounts, self-reporting basic demographic and educational information, and the completion of pre-work tasks for some courses. These data track student progress through major course milestones, including enrollment, completion, and earning honors status in the course. The data includes student demographic information, course information (e.g., course name, delivery method, credit status, etc.) and institutional affiliation.</p> <hd id="AN0187381771-6">Methods</hd> <p>We conduct a survival analysis to examine the timing and likelihood of students exiting the course from the point of admission through completion of the course and the earning of honors. A survival analysis, also referred to as an event history analysis, is a statistical technique used to analyze the occurrence of events over time (Allison, [<reflink idref="bib2" id="ref24">2</reflink>]) and has been used in prior higher education research to study degree completion (Ampaw & Jaeger, [<reflink idref="bib5" id="ref25">5</reflink>]; Bahr, [<reflink idref="bib6" id="ref26">6</reflink>]; Gross et al., [<reflink idref="bib15" id="ref27">15</reflink>]). This technique involves modeling a hazard function, which estimates the probability of an event occurring, such as a student dropping out of a course, at a given point in time and accounts for the influence of covariates on the probability of an event occurring (Allison, [<reflink idref="bib3" id="ref28">3</reflink>]; Cox, [<reflink idref="bib9" id="ref29">9</reflink>]). Our analyses produce a Kaplan–Meier estimator, which we then plot to examine graphically the conditions associated with persistence through Esplanade's supplemental courses. A survival analysis can include three types of variables: event history variables, time-invariant covariates, and time-varying covariates (Allison, [<reflink idref="bib2" id="ref30">2</reflink>]).</p> <hd id="AN0187381771-7">Event History Variables</hd> <p></p> <hd id="AN0187381771-8">Dependent Variables</hd> <p>Our analysis starts with students who have applied for Esplanade and includes four successive critical moments that occur in these supplemental courses. These moments include: (<reflink idref="bib1" id="ref31">1</reflink>) gaining admission to the course, (<reflink idref="bib2" id="ref32">2</reflink>) enrolling in the course, (<reflink idref="bib3" id="ref33">3</reflink>) completing the course, and (<reflink idref="bib4" id="ref34">4</reflink>) earning an honors grade in the course. While gaining admission to a course is not common in most regular CS courses, stages 2–4 are typical of many required and elective college courses. The benchmarks required to complete a course and earn honors in a typical CS course vary across institutions, but, broadly speaking, are similar to the benchmarks used by Esplanade in their supplemental courses.</p> <p></p> <ulist> <item> Application: Students complete an initial application for the course of interest. They are asked for their demographic information and background information, such as gender, race/ ethnicity, and the institution where they are currently enrolled. They also answer short questions about their interest in the course topic, previous experience in the topic, and general career goals. For courses that involve prerequisite knowledge, applicants are sent a short preliminary assignment to assess their knowledge of this information. Applications are due approximately one month before the course begins.</item> <p></p> <item> Admission: Applications are evaluated based on the course's eligibility requirements, successful completion of the preliminary assignment, if applicable, as well through an assessment of the student's interest in the course topic. Students are notified about their admission into the course no later than two weeks prior to the course start date and are asked to confirm their spots, which is a formal confirmation from the student that they have reviewed the course expectations and have time to participate in the course. There is no tuition or enrollment fees unless the student is taking the course for-credit through their university, in which case the university tuition and fees are paid by the student to the university just like any other course taken at the university.</item> <p></p> <item> Enrollment: Students engage in the course materials by attending lectures and completing assignments. Students may withdraw from the course at any time if they feel that they can not keep up with the assignments. Program staff also periodically review student progress and withdraw students who are not completing assignments or attending lectures. Students are not withdrawn based on the quality of their assignment submissions, but rather whether the assignments are completed at all. Absences and missing submissions threshold may vary by course. Students who are active from Week 4 onwards are considered enrolled.</item> <p></p> <item> Completion: Successful retention in the course only requires active participation throughout its duration by attending lectures and completing assignments. Students are considered to have passed the course, if they have met all course completion requirements, which may include getting a grade of at least 60% (applicability depends on the course).</item> <p></p> <item> Honors: As course retention is not judged on quality of assignments, Esplanade has developed an additional marker designating completion "with honors" if students score above a certain threshold in the course. Thresholds for receiving honors vary by course, with most having an 85% grade threshold.</item> </ulist> <hd id="AN0187381771-9">Independent Variables</hd> <p>The time-invariant covariates in our model include gender, race/ethnicity, class year, and course credit status. Race/ ethnicity is defined as the student's self-described race or ethnicity, and includes Asian, Black, Hispanic, Indigenous American, White, or unknown. Students have the option to not disclose this information or skip this question on the application. Gender is defined as the student's self-described gender identity with the options including female, male, non-binary, or unknown. The student's class year was defined as their class year at the time of application which included freshman, sophomore, junior, senior, post-graduate, not currently enrolled, or unknown. Credit status refers to whether the course would earn the student credit towards their college degree. The options for credit status were for-credit, non-credit, or unknown.</p> <hd id="AN0187381771-10">Limitations</hd> <p>While the majority of the demographic data is self-reported by students at the time of their application, many of these questions were optional. This means that there are a larger number of students who have unknown or missing values for their race/ ethnicity and gender than would be expected if this information was collected in a more comprehensive or systematic manner. Categorizing students as "unknown" allows them to be represented in the analysis, but may affect the calculation of the standard errors of each factor level and bias the estimates of their coefficients (Newman, [<reflink idref="bib28" id="ref35">28</reflink>]). Further, the self-reported nature of the demographic data affected our variable selection. We found that consistent or standardized definitions of "first-generation" or "low-income" were not provided to students at the time of application. As such, the reliability of these measures was questionable and we chose to exclude these variables from this study despite the fact that Esplanade's mission includes serving first-generation, low-income computing students.</p> <p>Other limitations of this study include that the outcome measures used in the analysis are short-term, and that the completion and honors designations may have different threshold criteria across courses at different colleges. Regardless of threshold, completing a course or receiving an honors designation does not guarantee future success in CS. Conversely, a student may not complete the course or not receive honors but still learn enough to materially improve their performance in other CS courses or improve their sense of belonging within CS. Without outcome measures such as completion of a degree or acquisition of a job, our analysis cannot report on the longer-term implications of this intervention. Differing criteria for the completion and honors designation across courses may create heterogeneity in the hazard ratios for that stage across the population that is more intrinsically related to the grading standards of the course instructor, compared to the other stages of our model where students were evaluated in a more standardized manner. However, the majority of courses across colleges require a completion grade of 60 percent and an honors grade of 85 percent, thus reducing some of the variability.</p> <hd id="AN0187381771-11">Results</hd> <p></p> <hd id="AN0187381771-12">Descriptive Overview</hd> <p>Before conducting the survival analysis, we provide a descriptive overview illustrating the application-to-honors pipeline of the undergraduate students included in this study. As shown in Table 1, we started with 36,569 applications to the core courses since 2017, of which 15,226 students were accepted. Applications increased steadily from 2,699 in 2017 to 3,712 in 2019, but jumped to 9,353 in 2020, the first year of the COVID-19 pandemic. Applications increased to 9,788 in 2021 before slightly decreasing to 8,624 in 2022, the first year that many college campuses had returned to fully in-person courses following the pandemic.</p> <p>Table 1 Descriptive Statistics by Year</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Applied</p></th><th align="left" colspan="2"><p>Admitted</p></th><th align="left" colspan="2"><p>Enrolled</p></th><th align="left" colspan="2"><p>Completed</p></th><th align="left" colspan="2"><p>Honors</p></th></tr><tr><th align="left" /><th align="left"><p><italic>n</italic><sup><italic>1</italic></sup></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic><sup><italic>2</italic></sup></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic></p></th></tr></thead><tbody><tr><td align="left" colspan="10"><p>YEAR</p></td></tr><tr><td align="left"><p> 2017</p></td><td align="left"><p>2,699</p></td><td align="left"><p>1,072</p></td><td align="left"><p>39.7%</p></td><td align="left"><p>876</p></td><td align="left"><p>81.7%</p></td><td align="left"><p>763</p></td><td align="left"><p>87.1%</p></td><td align="left"><p>343</p></td><td align="left"><p>45.0%</p></td></tr><tr><td align="left"><p> 2018</p></td><td align="left"><p>2,393</p></td><td align="left"><p>1,471</p></td><td align="left"><p>61.5%</p></td><td align="left"><p>1,139</p></td><td align="left"><p>77.4%</p></td><td align="left"><p>973</p></td><td align="left"><p>85.4%</p></td><td align="left"><p>604</p></td><td align="left"><p>62.1%</p></td></tr><tr><td align="left"><p> 2019</p></td><td align="left"><p>3,712</p></td><td align="left"><p>1,853</p></td><td align="left"><p>49.9%</p></td><td align="left"><p>1,475</p></td><td align="left"><p>79.6%</p></td><td align="left"><p>1,314</p></td><td align="left"><p>89.0%</p></td><td align="left"><p>951</p></td><td align="left"><p>72.4%</p></td></tr><tr><td align="left"><p> 2020</p></td><td align="left"><p>9,353</p></td><td align="left"><p>4,204</p></td><td align="left"><p>44.9%</p></td><td align="left"><p>3,270</p></td><td align="left"><p>77.8%</p></td><td align="left"><p>2,542</p></td><td align="left"><p>77.7%</p></td><td align="left"><p>1,959</p></td><td align="left"><p>77.1%</p></td></tr><tr><td align="left"><p> 2021</p></td><td align="left"><p>9,788</p></td><td align="left"><p>3,843</p></td><td align="left"><p>39.3%</p></td><td align="left"><p>3,258</p></td><td align="left"><p>84.8%</p></td><td align="left"><p>2,680</p></td><td align="left"><p>82.3%</p></td><td align="left"><p>1,549</p></td><td align="left"><p>57.8%</p></td></tr><tr><td align="left"><p> 2022</p></td><td align="left"><p>8,624</p></td><td align="left"><p>2,783</p></td><td align="left"><p>32.3%</p></td><td align="left"><p>1,951</p></td><td align="left"><p>70.1%</p></td><td align="left"><p>1,530</p></td><td align="left"><p>78.4%</p></td><td align="left"><p>1,010</p></td><td align="left"><p>66.0%</p></td></tr><tr><td align="left"><p>Total</p></td><td align="left"><p>36,569</p></td><td align="left"><p>15,226</p></td><td align="left"><p>41.6%</p></td><td align="left"><p>11,969</p></td><td align="left"><p>78.6%</p></td><td align="left"><p>9,802</p></td><td align="left"><p>81.9%</p></td><td align="left"><p>6,416</p></td><td align="left"><p>65.5%</p></td></tr></tbody></table> </ephtml> </p> <p>Courses include: Cyber security, iOS App Development, Android App Development <sups>1</sups><emph>n</emph> = number of students in each stage <sups>2</sups><emph>%</emph> = percentage of students from previous stage that moved into current stage</p> <p>As seen in Table 2, during this period, Asian students accounted for 15,529 (42.5%) of all applications. Black students were the next largest group of applicants (15.7%) closely followed by Hispanic students (14%), and then White students (11.6%). Males were 57.2% of the applicant pool and females were 31.4%. The largest class year represented in the applicant pool was seniors (24.7%) followed by juniors (21.8%), sophomores (14.5%), and freshmen (6.4%). The applicant pool also included people with college degrees (10.0%) and people that were not enrolled in college and did not have a college degree (2.9%). Since Espalande's model was to build relationships with universities by offering non-credit courses before establishing for-credit course options, we see many more applications to non-credit courses (<reflink idref="bib18" id="ref36">18</reflink>,<reflink idref="bib993" id="ref37">993</reflink>) than for-credit courses (<reflink idref="bib5" id="ref38">5</reflink>,<reflink idref="bib919" id="ref39">919</reflink>).[<reflink idref="bib1" id="ref40">1</reflink>]</p> <p>Table 2 Descriptive Statistics of Independent Variables</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Applied</p></th><th align="left" colspan="2"><p>Admitted</p></th><th align="left" colspan="2"><p>Enrolled</p></th><th align="left" colspan="2"><p>Completed</p></th><th align="left" colspan="2"><p>Honors</p></th></tr><tr><th align="left" /><th align="left"><p><italic>n</italic><sup><italic>1</italic></sup></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic><sup><italic>2</italic></sup></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic></p></th><th align="left"><p><italic>n</italic></p></th><th align="left"><p><italic>%</italic></p></th></tr></thead><tbody><tr><td align="left" colspan="10"><p>Race</p></td></tr><tr><td align="left"><p> Asian</p></td><td align="left"><p>15,529</p></td><td align="left"><p>6,185</p></td><td align="left"><p>39.8%</p></td><td align="left"><p>4,572</p></td><td align="left"><p>73.9%</p></td><td align="left"><p>3,613</p></td><td align="left"><p>79.0%</p></td><td align="left"><p>2,703</p></td><td align="left"><p>74.8%</p></td></tr><tr><td align="left"><p> Black</p></td><td align="left"><p>5,753</p></td><td align="left"><p>2,376</p></td><td align="left"><p>41.3%</p></td><td align="left"><p>1,929</p></td><td align="left"><p>81.2%</p></td><td align="left"><p>1,563</p></td><td align="left"><p>81.0%</p></td><td align="left"><p>851</p></td><td align="left"><p>54.4%</p></td></tr><tr><td align="left"><p> Hispanic</p></td><td align="left"><p>5,136</p></td><td align="left"><p>2,605</p></td><td align="left"><p>50.7%</p></td><td align="left"><p>2,139</p></td><td align="left"><p>82.1%</p></td><td align="left"><p>1,772</p></td><td align="left"><p>82.8%</p></td><td align="left"><p>1,128</p></td><td align="left"><p>63.7%</p></td></tr><tr><td align="left"><p> White</p></td><td align="left"><p>4,252</p></td><td align="left"><p>2,406</p></td><td align="left"><p>56.6%</p></td><td align="left"><p>2,004</p></td><td align="left"><p>83.3%</p></td><td align="left"><p>1,748</p></td><td align="left"><p>87.2%</p></td><td align="left"><p>1,155</p></td><td align="left"><p>66.1%</p></td></tr><tr><td align="left"><p> Native</p></td><td align="left"><p>172</p></td><td align="left"><p>71</p></td><td align="left"><p>41.3%</p></td><td align="left"><p>53</p></td><td align="left"><p>74.6%</p></td><td align="left"><p>33</p></td><td align="left"><p>62.3%</p></td><td align="left"><p>23</p></td><td align="left"><p>69.7%</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>5,727</p></td><td align="left"><p>1,583</p></td><td align="left"><p>27.6%</p></td><td align="left"><p>1,272</p></td><td align="left"><p>80.4%</p></td><td align="left"><p>1,073</p></td><td align="left"><p>84.4%</p></td><td align="left"><p>555</p></td><td align="left"><p>51.7%</p></td></tr><tr><td align="left" colspan="10"><p>Gender</p></td></tr><tr><td align="left"><p> Male</p></td><td align="left"><p>20,912</p></td><td align="left"><p>9,966</p></td><td align="left"><p>47.7%</p></td><td align="left"><p>7,955</p></td><td align="left"><p>79.8%</p></td><td align="left"><p>6,556</p></td><td align="left"><p>82.4%</p></td><td align="left"><p>4,327</p></td><td align="left"><p>66.0%</p></td></tr><tr><td align="left"><p> Female</p></td><td align="left"><p>11,500</p></td><td align="left"><p>4,405</p></td><td align="left"><p>38.3%</p></td><td align="left"><p>3,312</p></td><td align="left"><p>75.2%</p></td><td align="left"><p>2,626</p></td><td align="left"><p>79.3%</p></td><td align="left"><p>1,828</p></td><td align="left"><p>69.6%</p></td></tr><tr><td align="left"><p> Non-binary</p></td><td align="left"><p>371</p></td><td align="left"><p>144</p></td><td align="left"><p>38.8%</p></td><td align="left"><p>103</p></td><td align="left"><p>71.5%</p></td><td align="left"><p>84</p></td><td align="left"><p>81.6%</p></td><td align="left"><p>47</p></td><td align="left"><p>56.0%</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>3,786</p></td><td align="left"><p>711</p></td><td align="left"><p>18.8%</p></td><td align="left"><p>599</p></td><td align="left"><p>84.2%</p></td><td align="left"><p>536</p></td><td align="left"><p>89.5%</p></td><td align="left"><p>213</p></td><td align="left"><p>39.7%</p></td></tr><tr><td align="left" colspan="10"><p>Class Year</p></td></tr><tr><td align="left"><p> Freshmen</p></td><td align="left"><p>2,334</p></td><td align="left"><p>964</p></td><td align="left"><p>41.3%</p></td><td align="left"><p>706</p></td><td align="left"><p>40.0%</p></td><td align="left"><p>578</p></td><td align="left"><p>81.9%</p></td><td align="left"><p>384</p></td><td align="left"><p>66.4%</p></td></tr><tr><td align="left"><p> Sophomore</p></td><td align="left"><p>5,288</p></td><td align="left"><p>2,294</p></td><td align="left"><p>43.4%</p></td><td align="left"><p>1,764</p></td><td align="left"><p>76.9%</p></td><td align="left"><p>1,367</p></td><td align="left"><p>77.5%</p></td><td align="left"><p>989</p></td><td align="left"><p>72.3%</p></td></tr><tr><td align="left"><p> Junior</p></td><td align="left"><p>7,955</p></td><td align="left"><p>3,700</p></td><td align="left"><p>46.5%</p></td><td align="left"><p>2,844</p></td><td align="left"><p>76.9%</p></td><td align="left"><p>2,230</p></td><td align="left"><p>78.4%</p></td><td align="left"><p>1,517</p></td><td align="left"><p>68.0%</p></td></tr><tr><td align="left"><p> Senior</p></td><td align="left"><p>9,016</p></td><td align="left"><p>4,957</p></td><td align="left"><p>55.0%</p></td><td align="left"><p>4,149</p></td><td align="left"><p>83.7%</p></td><td align="left"><p>3,595</p></td><td align="left"><p>86.6%</p></td><td align="left"><p>2,291</p></td><td align="left"><p>63.7%</p></td></tr><tr><td align="left"><p> Post-grad</p></td><td align="left"><p>3,645</p></td><td align="left"><p>1,266</p></td><td align="left"><p>34.7%</p></td><td align="left"><p>893</p></td><td align="left"><p>70.5%</p></td><td align="left"><p>664</p></td><td align="left"><p>74.4%</p></td><td align="left"><p>486</p></td><td align="left"><p>73.2%</p></td></tr><tr><td align="left"><p> Non-student</p></td><td align="left"><p>1,067</p></td><td align="left"><p>172</p></td><td align="left"><p>16.1%</p></td><td align="left"><p>142</p></td><td align="left"><p>82.6%</p></td><td align="left"><p>123</p></td><td align="left"><p>86.6%</p></td><td align="left"><p>48</p></td><td align="left"><p>39.0%</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>7,264</p></td><td align="left"><p>1,873</p></td><td align="left"><p>25.8%</p></td><td align="left"><p>1,471</p></td><td align="left"><p>78.5%</p></td><td align="left"><p>1,245</p></td><td align="left"><p>84.6%</p></td><td align="left"><p>700</p></td><td align="left"><p>56.2%</p></td></tr><tr><td align="left" colspan="10"><p>Credit Status</p></td></tr><tr><td align="left"><p> For Credit</p></td><td align="left"><p>5,919</p></td><td align="left"><p>4,618</p></td><td align="left"><p>78.0%</p></td><td align="left"><p>4,301</p></td><td align="left"><p>93.1%</p></td><td align="left"><p>4,113</p></td><td align="left"><p>95.6%</p></td><td align="left"><p>2,399</p></td><td align="left"><p>58.3%</p></td></tr><tr><td align="left"><p> Non Credit</p></td><td align="left"><p>18,993</p></td><td align="left"><p>9,517</p></td><td align="left"><p>50.1%</p></td><td align="left"><p>6,904</p></td><td align="left"><p>72.5%</p></td><td align="left"><p>5,215</p></td><td align="left"><p>75.5%</p></td><td align="left"><p>3,623</p></td><td align="left"><p>69.5%</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>11,657</p></td><td align="left"><p>1,091</p></td><td align="left"><p>9.4%</p></td><td align="left"><p>764</p></td><td align="left"><p>70.0%</p></td><td align="left"><p>474</p></td><td align="left"><p>62.0%</p></td><td align="left"><p>393</p></td><td align="left"><p>82.9%</p></td></tr><tr><td align="left"><p>Total</p></td><td align="left"><p>36,569</p></td><td align="left"><p>15,226</p></td><td align="left"><p>41.6%</p></td><td align="left"><p>11,969</p></td><td align="left"><p>78.6%</p></td><td align="left"><p>9,802</p></td><td align="left"><p>81.9%</p></td><td align="left"><p>6,415</p></td><td align="left"><p>65.5%</p></td></tr></tbody></table> </ephtml> </p> <p>Courses include: Cyber security, iOS App Development, Android App Development <sups>1</sups><emph>n</emph> = number of students in each stage <sups>2</sups><emph>%</emph> = percentage of students from previous stage that moved into current stage</p> <p>As shown in Tables 1 and 2, the admission rate to these courses was 39.7% in 2017 and jumped to 61.5% in 2018 as Esplanade expanded both in the number of campuses and the number of students served. As the number of applications increased over time, the admission rate fell from 49.9% in 2019 to 39.3% in 2021 to 32.3% in 2022. The admission rate by race/ ethnicity was 56.6% among White students, 50.7% for Hispanic students, 41.3% for Black students, and 39.8% for Asian students. Although Asian students had the lowest rate of admission, they accounted for an outsized portion of the admitted pool (40.6%).[<reflink idref="bib2" id="ref41">2</reflink>] The admission rate for men was 47.7% compared to 38.3% for women. Men accounted for nearly two thirds (65.5%) of the admitted student population. Admission rates declined by class year with seniors admitted at 55%, juniors at 46.5%, sophomores at 43.4%, and freshmen at 41.3%. As expected, students applying for for-credit courses had a higher admission rate (78%) than non-credit applicants (50%). We would expect a higher acceptance rate because often students register for these courses through their university in the same way they register for other courses.</p> <p>Admitted students enrolled in the course at an overall rate between 70–85% over the six years in the study. Men enrolled at higher rates (79.8%) than women (75.2%). Since men applied, were admitted, and enrolled at higher rates than women, they took on a larger share in each round of our model accounting for 66.5% of enrolled students. Enrollment was higher for students admitted to for credit courses (93.1%) compared to non-credit courses (72.5%).</p> <p>Completion rates across all years ranged from 85.4% to 89% from 2017 to 2019. The completion rate declined to 77.7% in 2020, possibly due to a doubling of student enrollment as well as additional obstacles of completing coursework during the pandemic. Completion rates rose to 82.3% in 2021, but declined again to 78.4% in 2022. Completion rates were 87.2% for White students, 82.8% for Hispanic students, 81% for Black students, and 79% for Asian students. Completion rates were similar across gender and class year. Students taking courses for credit were much more likely to complete the course (95.6%) compared to students taking the course not-for-credit (75.5%).</p> <p>Forty-five percent of course completers earned honors in 2017, increasing to 62.1% in 2018, and 72.4% in 2019. The honors rate peaked in 2020 at 77.1% but dropped off sharply to 57.8% in 2021 before partially recovering to 66% in 2022. Black students had the lowest rate of honors at 54.4%, followed by Hispanic students (63.7%), and White students (66.1%). Asian students, despite having the lowest completion rates, had the highest rate of honors recognitions (74.8%) and accounted for 42.1% share of all honors students. Similarly with gender, while women had lower admission, enrollment, and completion rates, they outperformed men with 69.6% earning honors compared to 66% of men. Despite seniors having the highest admission, enrollment, and completion rates, they trailed all other classes in honors award (63.7%) which was led by sophomores (72.3%) who had the lowest completion rates for undergraduate cohorts. While for-credit courses had higher rates of admission, enrollment, and completion, these students had lower rates of honors recognitions (58.3%) than non-credit students (69.5%).</p> <hd id="AN0187381771-13">Survival Analysis</hd> <p>The descriptive information offers an overview to understand broad trends within our data. Building upon these trends, our survival analysis allows us to see whether the variations between subgroups are statistically significant at each stage in the student learning journey after controlling for covariates. White students are used as the reference group for ethnicity, men as the reference for gender, seniors as the reference for class year, and for-credit as the reference for credit status. Hazard ratios above one are associated with a higher likelihood of failing to move through each stage in the pipeline, while hazard ratios below one are more likely to persist to the next stage. The measure of risk is based on the percentage of the sample that achieves the final stage of the model. In this case, it is the percentage of applicants who earn honors. This is different from the descriptive statistics where the percentages are derived from the progression of students from one round to the next.</p> <p>We use a stepwise approach to build our model and show a comparison of the models in Table 3. Our first model includes race/ ethnicity and gender given Espalande's mission to reduce racial and gender inequalities in the tech workforce. We observe statistically significant results (<emph>p</emph> < 0.001) for all racial groups in comparison to white students. This means that each of these groups of students are less likely to persist through all stages of courses than White students. Asian and Black students report similar hazard ratios (1.31 and 1.32) while Hispanic students have a lower ratio (1.13). This means that of all the applicants, Asian students are at 31% higher risk of not earning honors compared to White students. This is despite having the highest percentage of completers earn honors (74.8%). This discrepancy is due to fewer Asian students persisting through other stages of the course. Black students report a 32% higher risk than White students of not persisting through to honors, and Hispanics students report a 13% higher risk. The difference between male and female students is also statistically significant (<emph>p</emph> < 0.001) indicating that a smaller share of female applicants would eventually earn honors as compared to the group of male applicants.</p> <p>Table 3 Survival Analysis on the Risk of Not Earning Honors—Three Model Comparison of Independent Variables</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p>Model 1</p><p>Hazard Ratio</p><p>(Race & Gender)</p></th><th align="left"><p>Model 2</p><p>Hazard Ratio</p><p>(Race, Gender. & Class Year)</p></th><th align="left"><p>Model 3</p><p>Hazard Ratio</p><p>(Race, Gender, Class Year, & Credit Status)</p></th></tr></thead><tbody><tr><td align="left" colspan="4"><p>Race / Ethnicity</p></td></tr><tr><td align="left"><p> Asian</p></td><td align="left"><p>1.31 (0.03)***</p></td><td align="left"><p>1.26(0.03)***</p></td><td align="left"><p>1.11(0.02)***</p></td></tr><tr><td align="left"><p> Black</p></td><td align="left"><p>1.32(0.03)***</p></td><td align="left"><p>1.28(0.03)***</p></td><td align="left"><p>1.19(0.03)***</p></td></tr><tr><td align="left"><p> Hispanic</p></td><td align="left"><p>1.13(0.03)***</p></td><td align="left"><p>1.13(0.03)***</p></td><td align="left"><p>1.06(0.03)*</p></td></tr><tr><td align="left"><p> Native</p></td><td align="left"><p>1.38(0.12)***</p></td><td align="left"><p>1.34(0.11)***</p></td><td align="left"><p>1.22(0.10)*</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>1.28(0.04)***</p></td><td align="left"><p>1.24(0.04)***</p></td><td align="left"><p>1.13(0.03)***</p></td></tr><tr><td align="left" colspan="4"><p>Gender</p></td></tr><tr><td align="left"><p> Female</p></td><td align="left"><p>1.13(0.01)***</p></td><td align="left"><p>1.11(0.01)***</p></td><td align="left"><p>1.04(0.01)**</p></td></tr><tr><td align="left"><p> Non-binary</p></td><td align="left"><p>1.19(0.07)**</p></td><td align="left"><p>1.15(0.06)***</p></td><td align="left"><p>1.05(0.06)</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>1.41(0.04)***</p></td><td align="left"><p>1.28(0.04)*</p></td><td align="left"><p>1.30(0.04)***</p></td></tr><tr><td align="left" colspan="4"><p>Class Year</p></td></tr><tr><td align="left"><p> Freshmen</p></td><td align="left"><p>-</p></td><td align="left"><p>1.26(0.03)***</p></td><td align="left"><p>1.10(0.03)***</p></td></tr><tr><td align="left"><p> Sophomore</p></td><td align="left"><p>-</p></td><td align="left"><p>1.20(0.02)***</p></td><td align="left"><p>1.03(0.02)</p></td></tr><tr><td align="left"><p> Junior</p></td><td align="left"><p>-</p></td><td align="left"><p>1.18(0.02)***</p></td><td align="left"><p>1.05(0.02)**</p></td></tr><tr><td align="left"><p> Post Grad</p></td><td align="left"><p>-</p></td><td align="left"><p>1.34(0.03)***</p></td><td align="left"><p>1.12(0.02)***</p></td></tr><tr><td align="left"><p> Non Student</p></td><td align="left"><p>-</p></td><td align="left"><p>1.61(0.06)***</p></td><td align="left"><p>1.40(0.05)***</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>-</p></td><td align="left"><p>1.42(0.03)***</p></td><td align="left"><p>1.07(0.02)***</p></td></tr><tr><td align="left" colspan="4"><p>Credit Status</p></td></tr><tr><td align="left"><p> Non credit</p></td><td align="left"><p>-</p></td><td align="left"><p>-</p></td><td align="left"><p>1.81(0.04)***</p></td></tr><tr><td align="left"><p> Unknown</p></td><td align="left"><p>-</p></td><td align="left"><p>-</p></td><td align="left"><p>3.12(0.07)***</p></td></tr></tbody></table> </ephtml> </p> <p>Courses include: Cyber security, iOS App Development, Android App Development Significance levels: *** = 0.001, ** = 0.01, * = 0.05</p> <p>In Model 2 we add a variable for class year to race/ ethnicity and gender (Model 1). The model results in statistically significant differences in who receives Honors status by race/ ethnicity and gender as well as all class years. The ratios for Asian and Black students (1.26 and 1.28) slightly decline with the inclusion of class year in the model but remain similar to the results of the first model (1.31 and 1.32). The ratio for Hispanic students and female students remains similar to Model 1. Compared to seniors, freshmen report the highest hazard ratio (1.26) with a 26% higher risk of persisting through from enrollment to receiving honors, followed by sophomores (1.20) then juniors (1.18). Non-students had the highest overall ratio (1.61) in this group and post-graduate students report a higher ratio (1.34) than any undergraduate cohort.</p> <p>Our third and final model adds credit status to Model 2. The difference between non-credit and credit courses was statistically significant (<emph>p</emph> < 0.001) with a large hazard ratio (1.81). This indicates that students taking non-credit courses have an 81% higher risk of not earning honors. The ratio is even larger for unknown credit status (3.12) which is also statistically significant (<emph>p</emph> < 0.001). Since we can assume many, if not most, of the unknown credit status courses are non-credit courses, this model underestimates the negative effects of taking a non-credit course compared to a for-credit course on earning the honors designation. The addition of course credit also changes the magnitude and significance of several variables from the second model, including the hazard ratios by race, class year, and gender.</p> <p>In Fig. 1 we plot the estimates of our Kaplan–Meier analysis for Model 3. We observe that the largest decline in student participation occurs in the admission process and the smallest decline occurs in the course completion process. Declines in student participation in the enrollment stage and in earning honors are larger than declines in course completion.</p> <p>Graph: Fig. 1 Kaplan–Meier Survival Analysis for Model 3. Notes: Model 3 includes variables for race/ ethnicity, gender, class year, and course credit status</p> <p>Figure 2 plots the Kaplan–Meier analysis for Model 3 by race/ ethnicity. The figure reflects the descriptive results from Table 2. Asian and Black students report the lowest admission rates. Asian students are less likely to enroll after admission which is shown in the steeper drop off for Asian students than Black students in the enrolled period. A larger share of Black students persist through this model compared to Asian students until the final metric in which Black students report a large drop off from completion to earning honors. It is important to note that the Kaplan–Meier model does not reflect raw numbers, but rather the percent of the original applicant pool that exist in each of the subsequent rounds.</p> <p>Graph: Fig. 2 Kaplan–Meier Survival Analysis for Model 3 by Race/Ethnicity. Notes: Model 3 includes variables for race/ ethnicity, gender, class year, and course credit status</p> <p>In Fig. 3 we plot gender differences in Model 3. Men report higher admission, enrollment, and completion rates compared to women. Although women earn honors at a higher rate as a percent of course completers, men make up a larger percent of the original applicant pool earning honors. Similarly, in Fig. 4, we observe that seniors outperform all other class years in course admissions, enrollment, and completion. However, seniors report the lowest rate of honors, but like the situation with gender, because seniors outperform their peers in each of the earlier stages, a greater percent of the original applicant group of seniors earn honors compared to the other class years.</p> <p>Graph: Fig. 3 Kaplan–Meier Survival Analysis for Model 3 by Gender. Notes: Model 3 includes variables for race/ ethnicity, gender, class year, and course credit status</p> <p>Graph: Fig. 4 Kaplan–Meier Survival Analysis for Model 3 by Class Year. Notes: Model 3 includes variables for race/ ethnicity, gender, class year, and course credit status</p> <p>Finally, Fig. 5, shows a more extreme version of this trend. Students in for-credit courses drastically outperform non-credit students in earning admission, choosing to enroll, and completing the course. However, students in for-credit courses report a major drop off in the percentage of completers earning honors. Because students in for-credit courses persist through the rest of this process at such high rates, the percent of students in for-credit courses based on the original applicant pool is still much larger than the students in non-credit courses despite students in non-credit courses earning honors at a higher rate based on students that complete the course.</p> <p>Graph: Fig. 5 Kaplan–Meier Survival Analysis for Model 3 by Credit Status. Notes: Model 3 includes variables for race/ ethnicity, gender, class year, and course credit status</p> <hd id="AN0187381771-14">Discussion</hd> <p>Our analysis points to trends in persistence through supplemental computer science courses using data from a non-profit student support organization. We find that inequities in persistence rates exist across race/ gender and gender. We observe that students with more years in college are more likely to persist through a supplemental course and earn honors than students with fewer years in college. We also find that offering supplemental courses for credit offers a powerful incentive to enroll in and complete the course. Across three models, we observe an interesting trend in which the group with the highest honors rate (i.e., Asian, women, sophomores, and non-credit) is also the group that did not persist through the other parts of the process at as high of rates as the reference group.</p> <p>Differences in persistence in CS courses across demographic groups could be due to structural barriers and/or stereotype threat (George et al., [<reflink idref="bib13" id="ref42">13</reflink>]; Lehman et al., [<reflink idref="bib23" id="ref43">23</reflink>]). Structural barriers, including access to resources and support systems, could be reflected in the institutions that these students attend (Giannakos et al., [<reflink idref="bib14" id="ref44">14</reflink>]; Kordaki & Berdousis, [<reflink idref="bib20" id="ref45">20</reflink>]). This analysis does not control for institutional characteristics, which could be an important factor for some demographic groups. Moreover, stereotype threat, which is the fear of confirming negative stereotypes about one's social group, may perpetuate disparities in STEM education (Master et al., [<reflink idref="bib26" id="ref46">26</reflink>]; Totonchi et al., [<reflink idref="bib35" id="ref47">35</reflink>]). Although Esplanade strives to create an inclusive learning experience where students develop a sense of belonging and a tech identity (Strayhorn, [<reflink idref="bib34" id="ref48">34</reflink>]), it is possible that the systemic barriers continue to persist at their institution and are difficult to overcome in a matter of a semester-long course. In effect, our conceptual framing at the outset of this study framed Esplanade as one of the growing auxiliary organizations targeting the enduring inadequate support systems and inequitable learning environments (Margolis et al., [<reflink idref="bib25" id="ref49">25</reflink>]). Yet, our analysis suggests that we cannot presume these supplemental interventions are immune to the very systemic issues that result in these enduring disparities. On the contrary, our evidence suggests that these inequitable outcomes may persist in supplementary learning environments.</p> <p>Seniors outperformed other students likely due to their continued engagement with computer science courses, and the development of foundational knowledge and technical skills (Katz et al., [<reflink idref="bib18" id="ref50">18</reflink>]). Since most students do not declare a major until their sophomore year and many computer science programs begin with several theoretical classes, it is likely that freshmen and sophomores do not have as much knowledge or technical experience as juniors and seniors. Additionally, seniors are more likely to have had a professional technical experience, whether an internship or research experience, that may have further developed their technical skills and solidified their interest in CS as an academic major (Lawrence-Fowler et al., [<reflink idref="bib22" id="ref51">22</reflink>]). Seniors are also more likely to have stronger institutional ties and social networks on campus. This gives them other people to turn to when they are struggling with a coding problem. Seniors may also have more motivation to complete these courses as it could show their proficiency in the workforce and prepare them for their first job.</p> <p>The finding that students in for-credit courses are more likely to complete the course than students in non-credit courses shows that incentives matter. It is also consistent with findings that for-credit MOOCs resulted in better student outcomes than non-credit MOOCs (Kursun, [<reflink idref="bib21" id="ref52">21</reflink>]). The added value of earning academic credit towards one's degree represents tangible progress toward an educational goal. The course and the subsequent grade appearing on their transcript creates additional incentive for students who plan to apply for graduate programs or jobs that require the submission of their transcript. GPAs also influence scholarship renewals and graduation honors. Moreover, for-credit courses offer a financial incentive to students as these courses count as part of their credit hours for the term, whereas non-credit courses are typically free.</p> <p>The most perplexing pattern in this analysis is the trend of particular groups of students earning honors at higher rates than their peers after lagging behind their peers in almost every other stage of the enrollment process. These higher achievers may be exceptional students who excel academically despite facing systemic barriers that impact the persistence of most people in their group. These students may possess a strong work ethic, perseverance, and grit in addition to the intellectual abilities to succeed in tech. Alternatively, these students may have high self-efficacy skills developed from years of working as the lone student from their demographic group, which taught them to seek out support systems, resources, and mentorship, possibly from others with a shared identity, who could help them mitigate these challenges and maximize their academic potential. While it may seem paradoxical to see groups of students earn honors at higher rates than their peers after lagging behind in all other measures, there appears to be more than a few explanations for this trend, mainly that the remaining students are likely the most motivated and most academically talented students in that group.</p> <hd id="AN0187381771-15">Implications</hd> <p>Our study offers novel insight into students' persistence in courses offered by a third-party educational provider, highlighting an area of higher education that has received limited attention from researchers. Despite the growth of university-third-party partnerships, few organizations have been willing to share their data with researchers, making our findings all the more significant. Moreover, our study suggests that there is no silver bullet to combat enduring barriers to student success in the field while also pinpointing pivotal moments in the student learning journey when students are most likely to drop out of courses. Finally, our research establishes the groundwork for future research on the effectiveness of partnerships between universities and third-party course providers, paving the way for a more comprehensive understanding of this consequential area of higher education.</p> <p>The expansion of university partnerships and student participation in CS supplementary courses signals the unbundling of higher education when the delivery of education is divided between universities and outside course providers, especially when students are taking courses for credit (McCowan, [<reflink idref="bib27" id="ref53">27</reflink>]). Partnering with outside providers to offer new supplementary courses can temporarily alleviate the growing demand for CS education nationwide, but our findings suggest that different rates of course completion persist across student groups, even when the courses are intentionally targeting underrepresented students in CS to support access and success. However, findings such as the higher completion rates among credit-seeking students may suggest that the stackability of credits and alternative pathways toward CS degrees could play an important role in incentivizing skills training opportunities in CS.</p> <p>While this study assesses student persistence in a CS course, it does not evaluate how the intervention affects student outcomes in their undergraduate coursework or in obtaining a job in the tech industry. Future research should work to establish a control group of computer science students who did not participate in a supplemental course to evaluate how participation and success in this program translate to larger educational goals. Furthermore, a qualitative investigation into the student experience across student demographic groups and universities may help to illuminate the reason why some groups of students are more successful than others in completing their CS courses.</p> <hd id="AN0187381771-16">Author Contributions</hd> <p>All authors contributed to the study conception and design.</p> <hd id="AN0187381771-17">Funding</hd> <p>Partial financial support was received from <emph>Esplanade</emph> as part of a larger external evaluation of the organization.</p> <hd id="AN0187381771-18">Data Availability</hd> <p>The data that support the findings of this study are under restricted access and are not publicly available at present.</p> <hd id="AN0187381771-19">Declarations</hd> <p></p> <hd id="AN0187381771-20">Editorial Board Members and Editors</hd> <p>The authors declare they have no editorial interests.</p> <hd id="AN0187381771-21">Employment</hd> <p>The authors declare they have no employment interests.</p> <hd id="AN0187381771-22">Financial Interests</hd> <p>The authors declare they have no financial interests.</p> <hd id="AN0187381771-23">Non-financial Interests</hd> <p>The authors declare they have no non-financial interests.</p> <hd id="AN0187381771-24">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0187381771-25"> <title> References </title> <blist> <bibl id="bib1" idref="ref22" type="bt">1</bibl> <bibtext> Ahadi, A, Kitto, K, Rizoiu, M.-A, & Musial, K. 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These courses were likely non-credit courses given the amount of administrative requirements for for-credit courses, but for the sake of accuracy in this analysis we count these courses as unknown credit status. This likely underestimates the coefficient for non-credit courses in the model.</bibtext> </blist> <blist> <bibtext> Applicants whose race/ ethnicity was unknown had the lowest admission rates at 27.6%. This may be because Esplanade was able to collect race/ ethnicity data on admitted students who had not included their race/ ethnicity in their application through post-admission data collection. Due to this possibility, we refrain from making any interpretations of the unknown categories for race/ ethnicity as well as other demographic categories.</bibtext> </blist> </ref> <aug> <p>By Ryan Creps; Shadman Islem; Bingran Zeng; Angela Boatman and Andrés Castro Samayoa</p> <p>Reported by Author; Author; Author; Author; Author</p> <p></p> <p>Ryan Creps Ryan Creps recently completed his PhD in Higher Education at Boston College and will be joining the Department of Educational Leadership and Policy at the University at Buffalo as an Assistant Professor. His work analyzes college admission processes, enrollment trends, and their impacts on institutional outcomes and student access to higher education.</p> <p>Shadman Islem Shadman Islem is an Assistant Director of Institutional Research and doctoral candidate in the Higher Education PhD program at Boston College. His research interests center on postsecondary access and success, education policy, and the geography of opportunity.</p> <p>Bingran Zeng Bingran Zeng is a doctoral candidate in the Higher Education PhD program at the Lynch School of Education and Human Development at Boston College. Her research interests are international higher education partnerships (especially in developing contexts), STEM education, and student access and success.</p> <p>Angela Boatman Angela Boatman is an Associate Professor in the Lynch School of Education and Human Development at Boston College. She holds a doctoral degree in Higher Education from Harvard University. Her research focuses on theevaluation of college access and completion policies, particularly in the areas of remediation, financial aid, and community college student success.</p> <p>Andrés Castro Samayoa Andrés Castro Samayoa is an Associate Professor in Lynch School of Education and Human Development at Boston College. He holds a doctoral degree in Education from the University of Pennsylvania. His work enhancesexperiences for students of color from underresourced communities - specifically focusing on Hispanic-serving institutions.</p> </aug> <nolink nlid="nl1" bibid="bib36" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib37" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib12" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib17" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib19" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib24" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib38" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib25" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib32" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib10" firstref="ref15"></nolink> <nolink nlid="nl11" bibid="bib31" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib16" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib30" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib29" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib20" firstref="ref20"></nolink> <nolink nlid="nl16" bibid="bib33" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib11" firstref="ref23"></nolink> <nolink nlid="nl18" bibid="bib15" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib28" firstref="ref35"></nolink> <nolink nlid="nl20" bibid="bib18" firstref="ref36"></nolink> <nolink nlid="nl21" bibid="bib993" firstref="ref37"></nolink> <nolink nlid="nl22" bibid="bib919" firstref="ref39"></nolink> <nolink nlid="nl23" bibid="bib13" firstref="ref42"></nolink> <nolink nlid="nl24" bibid="bib23" firstref="ref43"></nolink> <nolink nlid="nl25" bibid="bib14" firstref="ref44"></nolink> <nolink nlid="nl26" bibid="bib26" firstref="ref46"></nolink> <nolink nlid="nl27" bibid="bib35" firstref="ref47"></nolink> <nolink nlid="nl28" bibid="bib34" firstref="ref48"></nolink> <nolink nlid="nl29" bibid="bib22" firstref="ref51"></nolink> <nolink nlid="nl30" bibid="bib21" firstref="ref52"></nolink> <nolink nlid="nl31" bibid="bib27" firstref="ref53"></nolink>
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  Data: Tech Equity: A Survival Analysis of an Undergraduate Computer Science Supplemental Education Program
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  Label: Abstract
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  Data: This study examines the success of undergraduate students in computer science supplementary courses offered by a non-profit organization in partnership with colleges and universities across the U.S. Using a novel dataset from the nonprofit organization, we present one of the first descriptive overviews of students enrolled in supplemental computer science programs. Moreover, we conduct a survival analysis finding that racial and gender disparities in traditional computer science programs exist in these supplemental courses. However, the study finds that when supplemental courses are taken for credit, students are much more likely to complete the course, offering an important insight into program design and student motivation.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1480532
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1480532
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10755-024-09779-5
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 1315
    Subjects:
      – SubjectFull: Undergraduate Study
        Type: general
      – SubjectFull: Program Evaluation
        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Supplementary Education
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Nonprofit Organizations
        Type: general
      – SubjectFull: Partnerships in Education
        Type: general
      – SubjectFull: Racial Differences
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Program Design
        Type: general
      – SubjectFull: Student Motivation
        Type: general
    Titles:
      – TitleFull: Tech Equity: A Survival Analysis of an Undergraduate Computer Science Supplemental Education Program
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ryan Creps
      – PersonEntity:
          Name:
            NameFull: Shadman Islem
      – PersonEntity:
          Name:
            NameFull: Bingran Zeng
      – PersonEntity:
          Name:
            NameFull: Angela Boatman
      – PersonEntity:
          Name:
            NameFull: Andrés Castro Samayoa
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0742-5627
            – Type: issn-electronic
              Value: 1573-1758
          Numbering:
            – Type: volume
              Value: 50
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Innovative Higher Education
              Type: main
ResultId 1