1 Investigating Concurrent Enrollment Participation’s Effect on College Outcomes at Weber State University by Jeshua Van Sickle A proposal was submitted in partial fulfillment. of the requirements for the degree of MASTER OF EDUCATION with an emphasis in Curriculum and Instruction WEBER STATE UNIVERSITY Ogden, Utah December, 7th 2024 Approved Dustin Grote, Ph.D. Heather Chapman (Dec 16, 2024 15:11 MST) Heather Chapman, Ph.D. Megan Hamilton, Ph.D. 2 Abstract This study explores the impact of Concurrent Enrollment (CE) participation on college outcomes at Weber State University over an eight-year period. Analyzing student-level data, the research investigates how CE influences various metrics such as GPA, persistence, degree completion, and time-to-degree for both Associate and Bachelor degree students. The findings reveal that CE participants tend to demonstrate higher academic performance, increased persistence rates, and shorter time-to-degree completion in comparison to non-CE students. Specifically, CE enrollment is associated with a notable reduction in time spent pursuing degrees. Moreover, CE students exhibit higher likelihoods of degree attainment compared to their non-CE counterparts. These results underscore the significant advantages of CE programs in improving educational outcomes and suggest that targeted support for CE initiatives could enhance college readiness and overall success. 3 TABLE OF CONTENTS ABSTRACT.....................................................................................................................................2 INVESTIGATING CONCURRENT ENROLLMENT PARTICIPATION EFFECT ON COLLEGE OUTCOMES AT WEBER STATE UNIVERSITY ....................................................4 LITERATURE REVIEW............................................................................................................…6 HISTORICAL AND CURRENT CONTEXTS FOR CONCURRENT/DUAL ENROLLMENT IN THE U.S. ...........................................................................................7 CONCURRENT ENROLLMENT POLICIES & FUNDING…………………................ 9 SUCCESSFUL CONCURRENT ENROLLMENT PARTNERSHIPS........................... 10 RESEARCH ON THE IMPACTS OF CONCURRENT ENROLLMENT ON STUDENTS...................................................................................................................... 11 EQUAL OPPORTUNITY & ACCESS…….................................................................... 12 METHODS................................................................................................................................…13 DATA COLLECTION .............................................................................................................…13 DATA ANALYSIS....................................................................................................................…18 LIMITATIONS .........................................................................................................................…18 FINDINGS.................................................................................................................................…19 IMPLICATIONS.......................................................................................................................…38 CONCLUSION .........................................................................................................................…39 REFERENCES .........................................................................................................................…40 4 Investigating Concurrent Enrollment Participation Effect on College Outcomes at Weber State University Many high school students across various states are enrolled in Concurrent Enrollment (CE) programs (Nitzke, 2002). CE programs allow students to simultaneously involve themselves in high school and collegiate coursework. Instructors from an accredited college or a certified high school faculty member are tasked with developing a curriculum based on the requirements of the institution where the respective credits will transfer (Torres, 2019). Educators, parents, and policymakers agree that offering academically challenging high school courses plays a considerable role in the development and academic readiness of students for postsecondary studies (Gruman, 2013). McComas (2010) highlights the impacts of such programs, which are praised by school districts and colleges as innovative and instrumental components of modern-day education. They are seen as a phenomenon that will form the foundation for educational partnerships into the 21st century. Several of these programs are born out of government leaders pressuring universities to decrease the time of bachelor degree attainment and increase the rate of student completion (Campbell, 2016). Research shows that CE programs provide a much-needed academic opportunity for secondary school students (Loftin, 2012). Furthermore, the perceived benefits and reception of CE programs have led to a significant growth in their popularity. There has been a substantial increase in CE participation (+75%) between the 2002-2003 and 2010-2011 academic years with enrollment now exceeding two million students (Hughes, 2016). This growth is due in part to the qualitative evaluations that have shown CE participation to have a significant positive impact on students' on-campus experiences in high school (Foster, 2010) and baccalaureate degree completion in less than four years of college (e.g., Menzel, 2006). Given the swift integration of 5 innovative CE programs across various state and school districts, it is imperative to not only examine their expansion but also assess their outcomes. Research on CE programs is often conducted by their own governing body, potentially leading to a lack of objectivity in the reporting of outcomes (Swanson, 2008). Moreover, while there is substantial evidence of tuition savings for students, additional research is needed to explore additional benefits concerning student outcomes (Nitzke,2002). Specifically, the impact of participation in concurrent enrollment academic pathways on college performance, persistence, and completion, is not widely investigated, particularly to certain sub-populations such as the needs and characteristics of underserved students (McComas, 2010). Finally, due to the specific nature of concurrent enrollment programs at a state, regional, or institutional level, more localized research needs to be conducted. Sweeping studies in Utah broadly have found that students who participated in concurrent enrollment were more likely to graduate high school and more likely to attend and persist in college than their non-participating peers (Richardson & Hall, 2016). Furthermore, these positive outcomes were universal across student demographic groups, including race and/or ethnicity, income, and first-generation status. Overall, the research suggests that state-wide concurrent enrollment programs can have significant benefits for high school students in terms of academic achievement and college readiness, though we do not know CE outcomes specific to individual institutions in the state. Weber State’s concurrent enrollment program operates under the umbrella of the state of Utah but varies significantly to that of other public state universities (e.g., Utah State University) in key attributes such as CE participation and CE courses offered. Accordingly, this study closely investigates Weber State’s CE program and its impact on student’s academic outcomes after high school. Using student-level data across 8 years at WSU, 6 this study investigates how CE influences course performance (i.e., GPA), persistence, graduation, and time-to-degree outcomes for WSU students. Overall, the research suggests that state-wide concurrent enrollment programs can have significant benefits for high school students in terms of academic achievement and college readiness. But should we assume similar findings when we explore Weber State centric outcomes? Therefore, my research questions are as follows. 1. How does participation in CE coursework influence college GPA? 2. How does participation in CE coursework/programs influence college persistence and degree completion? 3. How does participation in CE coursework/programs influence time-to-degree completion for graduates? 4. How does participation in CE coursework/programs influence degree earned? Literature Review This literature review summarizes various aspects related to CE programing within the United States and provides insights into the current status of these programs, including their prevalence and popularity among high school students. The review also explores the policies and financial aspects governing these programs, along with successful collaborations that have contributed to their effectiveness. I review prior research that has investigated the impacts of CE programs including a student’s academic, social, and career-related outcomes. Lastly, I highlight literature that has focused on equal opportunity and access, exploring potential barriers and approaches to ensure fairness and inclusivity within these programs. Historical and Current Contexts for Concurrent/Dual Enrollment in the U.S. 7 Throughout the United States, high school students are engaging in CE programs (also called dual enrollment) where students take college-level courses for college credit that also simultaneously count as high school classes and credit for graduation (Campbell, 2016). CE programs have been used and developed for around forty years in the U.S. There is no definitive account of how or where CE programming started, however, Jamestown Community College is often thought of as the first innovative program in this regard dating back to 1978 (Hughes, 2016). The programs offered by Jamestown Community College in 1978 and other early CE programs were focused on providing access to higher education to individuals who might not have had the opportunity to pursue a college degree. They also helped to bridge the gap between high school and college, by offering college-level coursework and support services to students while they were still in high school (Nitzke, 2002). Since then, the prevalence of CE programs throughout high schools and colleges has only grown. These programs exist to encourage high school juniors and seniors, and more recently freshman and sophomores as well, to obtain college course credit prior to high school graduation while also enhancing students’ career planning and postsecondary degree attainment. High school counselors, along with CTE (Career technical education) coordinators, are tasked with creating a college-going culture and contributing to students’ transition into college by providing information about the college application process and exploration of major and career opportunities based on students’ interests and skills (Witkowsky & Clayton, 2020). Over a million high school students participate in CE programs in the United States with 86% of those students’ taking classes on their own high school campus taught by a college instructor or a credentialed high school teacher (National Center for Education Statistics - U.S. Department of Education [NCES-USDE], 2020). Though less common, another 17% of students 8 take courses on college campus and another 8% take them online (The Dual Enrollment Playbook, 2021). In the 2017-2018 school year, 82% of public schools with students enrolled in grades 9-12 offered CE opportunities in some form for students (NCES-USDE, 2020). Although still evolving and growing, education reform centered around CE gained prominence in national policy discussions and state education boards near the beginning of the 20th century with a motivation to devise new ways to make K-12 institutions better prepared to support high school graduates as they transition into successful postsecondary opportunities (Gruman, 2013). As secondary education and higher education institutions engage in CE partnerships it can assist both institutions in achieving each of their goals. Benefits for higher education institutions include increased enrollment through CE course enrollment numbers and recruitment pipelines of those students after high school graduation. Benefits for high schools to participate in CE partnerships include advanced academic preparation and college credit earning opportunities for students that participate in CE as well as opportunities for high school faculty to teach college level coursework. Furthermore, there are considerable benefits of participating in CE for students, including accelerated time to degree and improved likelihood of postsecondary degree completion (An & Han, 2019). Attaining a college degree is shown to improve an individual's lifetime earnings and provides several other helpful benefits, such as less negative health implications, more involvement in civic opportunities, and a better overall satisfaction in life (Campbell, 2016). Creating an effective educational system that effectively guides students from high school to postsecondary opportunities, prioritizing satisfaction, health, and income, not only fosters a sense of purpose and value following high school but also enhances access and preparation for underserved student groups, ultimately leading to improved college readiness and outcomes. 9 Concurrent Enrollment Policies & Funding Forty-six of 50 states have statewide policies governing at least one state CE program (Education Commission of the States (ECS), 2022). In the remaining four states, CE programs are still present, however, they are administered by institutional-level policies along with local district policies. The four states that do not have statewide policies in place are Alaska, New Hampshire, New York, and Rhode Island (ECS, 2022). However, many CE programs in the states are city-wide rather than state-wide. New York City’s College’s Now program, which is derived from the city of New York rather than the State of New York, is the nation’s largest CE initiative in an urban public-school setting. This initiative is evidence of the multiple ways that policies can drive CE program function, and in this case, that functional CE programs can operate despite lack of a state umbrella policy (Allen et al., 2012). Many states look to CE as an opportunity to bolster falling degree attainment and increase the local educated workforce (Campbell, 2017) Funding structures and costs for CE programs vary widely from state to state and program to program. In some CE programs, the student is required to pay all the program costs and in other programs the opportunity is nearly entirely covered for the student. For example, California fully funds its concurrent enrollment programs through the California College Promise Grant, making CE free for all eligible high school students, Texas students are required to pay for these courses themselves, resulting in a lower participation rate among students from low-income families who cannot afford the tuition fees (Smith, J. D,2023). Among districts with CE, a lower percentage of schools located in rural areas had reported that the CE programing was funded by the state, school, or district compared to other types of communities (NCES - U.S. DEPARTMENT OF EDUCATION. 2020) 10 Successful Concurrent Enrollment Partnerships Students in high school are not single beneficiaries of concurrent enrollment programs. Growing literature suggests that CE helps facilitates collaboration between many secondary and postsecondary institutions (Hughes, 2016). Most educators agree that improving high school completion is a priority, but equally significant is the need for high school graduates to be academically challenged and college-ready (Hughes, 2016). CE partnerships have been expanding steadily in the last decade, and each program can vary from the next. Establishing CE partnerships between high schools and local postsecondary institutions can be handled by a multitude of entities, including administration, school districts, or state education officials (Hagedorn, Maxwell, & Hampton, 2007). For example, at the state level, both Arizona and Kansas established advisory councils to provide professional development opportunities to high school instructors of whom are credentialed and teaching as CE adjuncts (Hughes, 2016). The University of Nebraska Omaha and Omaha Public Schools (OPS) have had a successful CE partnership since 2005. The partnership has increased access to college courses for OPS students, improved high school graduation rates within OPS, and increased college enrollment rates for graduates. Furthermore, the partnership facilitated communication and collaboration between the two institutions (Pettit & Aistrup, 2017). Lastly, the CE program between the University of Colorado Denver and Denver Public Schools has been in place since 2002 and has been successful in increasing access to college courses for Denver high school students (Kaye, 2016). The program has also been found to improve college readiness and success for students, especially for those from low-income families and historically underrepresented groups. Research on the Impacts of Concurrent Enrollment on Students 11 Research has shown that concurrent enrollment and dual credit programs have the ability to address a multitude of critical issues in education, including: 1. providing an incredible option to secondary schools to offer engaging programs to high honors students and also students that need to obtain vocational and technical skills for the postsecondary workforce, 2. an ever-increasing access to a wider array of course options, college-level instructors, and facilities, especially vital to small, rural located schools, 3. accelerating student progress toward degree completion, 4. reducing overall university/college costs for students, families of students, and states, 5. providing a simpler transition from secondary education to a college or university, and 6. increasing students' ability to envision themselves as "college material" (McComas, 2010, p. 30). All these factors create a very compelling argument for colleges, school districts, and states to implement and further CE partnerships and programs. In terms of the student experience, CE programs also allow high school students to experience postsecondary course rigor at an earlier time in their lives and additionally introduces them to the college environment (Torres, 2019). In some cases, this also grants the student potential to grow their social circle while attending courses on a college/university campus, experiencing social and professional interactions with college-level students along with their high school peers (Leonard, 2013). There is some research on CE programs and the implications that follow participation. Findings from these studies suggest that they offer students numerous benefits, including an increased likelihood of high school graduation, postsecondary education and training, and attainment of a postsecondary degree or credential (Allen, 2012). Beyond the individual advantages for participating students, early college high schools can also have a broader impact on the education system. They serve as a model for creating seamless transitions between high 12 schools and postsecondary institutions and may help reduce the need for remedial education in college (Dougherty, 2017). Swanson (2008) lends credence to the idea that CE program participation may create for students the "nest egg" effect: when students accumulate credits, it is harder to abandon their forward progress. Students who participate in CE programs may receive a psychological boost of confidence about their chances of college success while still within the more comfortable confines of high school. This evaluation makes sense given that the program is structured to allow and encourage every type of student to participate, especially students that would be considered a first-generation college student upon entry. However, there are isolated studies that indicate CE programs have no significant effect on degree attainment. Rather, there are variables besides CE enrollment that contributed to completion and acceleration, such as educational goals, major area of study, and accumulative credit requirements (Nitzke, 2002). Although CE courses perhaps serve as a conduit linking students to postsecondary institutions, specific research at the regional level needs to be conducted to better understand the persistence and academic achievement in college from these various diverse groups of degree seekers. Equal Opportunity & Access Other barriers to high school student participation in CE programs may include academic, financial, and transportation issues (Mullin & Honeyman, 2016). Many programs have requirements for eligibility such as age, GPA, and placement tests (The Dual Enrollment Playbook, 2021). As CE programs increased in popularity among students, parents, and school districts, the questions of eligibility and how to fund concurrent enrollment programs equitably for students and families will become a more debated subject for state legislators (McComas, 2010). A reoccurring theme amongst studies indicates that participation within these programs 13 often comes from students that have higher GPAs and/or come from a higher social class (Gruman, 2013). As concurrent enrollment grows, policymakers need to stay vigilant to ensure students can actively participate regardless of their academic, financial, or transportation circumstances (Foster, 2010). For instance, Torres (2019) found that Oklahoma high school and college representatives met to discuss student access to CE programs by addressing the qualifications such as academic standards, enrollment/book fees, and transportation limitations due to classes being on college campuses only. These policymakers found it imperative to schedule CE courses on high school campuses during the regular school day. As a result of addressing these issues, the EXCELerate program was created in Oklahoma, allowing more high schoolers to engage in CE opportunities while in high school and resulting in a significant increase in underserved student participation (Torres, 2019). Solutions such as this can help bridge the accessibility gap between students and actively include all student demographics in higher education opportunities. Methods This project analyzed student-level data for WSU students across seven years (2008 – 2015) to understand how enrollment in CE courses impacted completion and graduation outcomes. A linear regression analysis was conducted to investigate the relationships between Concurrent Enrollment (CE) enrollment and student outcomes. This section will outline the data collection process and the variables incorporated as well as how data were analyzed. Data Collection Data for this study was collected from Weber State University's data warehouse. Weber State’s data warehouse stores comprehensive information about students’ demographics and academic histories. The data was obtained securely via Box in excel file format and was 14 accounting for all students enrolled during the Fall 2008 academic semester through Fall of 2015. The project received approval from the Institutional Review Board (IRB), and in accordance with Weber State data-security standards, the data underwent aggregation, with personally identifiable markers removed before the researchers gained access to the data. Overall, 41 variables were included in the dataset (Table 1). Table 1 Study Variables and Descriptions Variable DIM_STUDENT_KEY FIRST_DEGREE_SEEK_TERM REG_STATUS AGE_TERM_START SEX_CODE RACE FIRST_GENERATION STU_HS_DESC STU_HS_GRADUATION_DATE PELL_RECEIVED DEGREE_CODE DEGREE_DESC DEGREE_PERCENT DEGREE_GPA COLLEGE_DESCRIPTION DEPARTMENT_DESC PROGRAM_DESC MAJOR_1_DEPT_DESC AVG_PELL_PGI TOTAL_AID_OFFERED TOTAL_AID_ACCEPTED TOTAL_AID_PAID LAST_DEGREESEEK_TERM ENROLLED_TERM_COUNT MAX_CREDITS CONC_CREDIT CONC_COURSES CONC_TERMS GRADUATED Description of Variable Student unique number First semester they started their degree Registration Status Age at the start of their first term Sex Race First generation student Y/N High school attended High school graduation year Pell grant received Y/N Degree code (BS, AA etc.) Degree description Degree percent completed Degree GPA Description of college Description of department Description of program Description of major Average Pell grant Total financial aid offered Total financial aid accepted Total financial aid paid Last term enrolled Total number of terms enrolled Total credits Total CE credits Total CE courses Total CE terms Graduated Y/N 15 GRADUATION_DATE EARNED_CERTIFICATE CERT_MAJOR CERT_GPA EARNED_ASSOCIATE ASSOC_MAJOR ASSOC_GPA EARNED_BACHELORS BACH_MAJOR BACH_GPA PERSISTENCE_YEAR_IND PERSISTENCE_GRAD_YEAR_IND Date graduated Certificate earned Y/N Certificate major Certificate GPA Associate degree earned Y/N Associate degree major Associate degree GPA Bachelor’s degree earned Y/N Bachelor’s degree major Bachelor’s degree GPA Persistence year over year Graduated within the year All cleaning and analysis of the raw excel file was conducted via R Studio. This ensures that while the source data remained static, I was able to manipulate and transform the data in a way that allows for charts, graphs, and regressions to express the most relevant and accurate information for our research questions. Several steps were taken to clean the data for analysis, beginning first with purposeful sampling procedures. Sampling The focus of this study was to understand how participating in CE impacted students’ academic outcomes later in college; accordingly, I used two purposeful sampling strategies. First, CE programming and policies within the state of Utah have grown considerably in the last 15 years, and I wanted to narrow the focus on outcomes for students who participated in CE during that most recent timeframe. To do so, all those who had graduated high school before the year 2000 were excluded from the final dataset used for analysis. Figure 1 summarizes the number of students in the sample by high school graduation year after removing students who graduated before 2000. Figure 1 16 Students Sorted by Graduation Year Next, rather than analyzing outcomes for all students in the entire dataset, which included students from outside of the state of Utah and schools in Utah far from WSU’s service area, I sought to narrow the focus on students from high schools that enrolled the largest numbers of students to WSU. Accordingly, I filtered the whole dataset by students’ high schools, and purposefully narrowed in on students from the 50 high schools with the highest counts of students (Table 2). To accomplish this, records were sorted from highest to lowest in terms of counts by students’ high school (STU_HS_DESC). These counts contain both students enrolled in CE and not enrolled. Table 2 Student Counts by High School Top 50 High Schools Count Weber High School 676 Davis High School 667 Northridge High School 627 Fremont High School 584 Bonneville High School 527 Clearfield High School 524 Layton High School 513 Roy High School 467 Ogden High School 386 Syracuse High School 370 Viewmont High School 289 17 Ben Lomond High School Box Elder High School Morgan High School Bountiful High School Woods Cross High School NUAMES High - Layton Home School George Washington High School Bear River High School Mountain High School Riverton High School Hunter Senior High West High School Mountain Crest High School DaVinci Academy Bingham High School Saint Joseph Catholic High Sky View High School Dorius Academy Hillcrest High School Copper Hills High School Layton Christian Academy West Jordan High School East High School Taylorsville Senior High Evanston High School Kearns Senior High Canyon Heights High School Cottonwood Senior High Christian Heritage School Tooele High School Lehi High School Highland High School North Summit High School Park City High School Uintah High School Cyprus Senior High Logan High School Stansbury High School 283 176 163 159 123 111 101 94 73 64 56 55 49 46 44 42 40 37 36 35 34 33 32 30 29 28 28 27 27 26 26 24 23 22 22 22 21 20 20 18 Table 3 then summarizes the summary counts (n = 7,911) for the final dataset by CE participation. The total sample size and subsample sizes for the dependent variable are large enough for multivariable regression analyses. Table 3 Student Counts within CE and Non-CE Group Count CE_students 4169 non_CE_students 3742 Data Analysis Dependent/Focal Variables For analysis, and to adequately address the research questions, I parceled the regression analyses based on the degree that students were seeking to complete. Grouping the analysis in this way helped to compare students seeking the same academic goals. Table 4 summarizes counts of students based on their intended degree and whether or not they participated in CE. Table 4 Student Counts by AA and BA Group Count AA_CE_students 2014 BA_CE_students 2101 AA_non_CE_students 1304 BA_non_CE_students 2364 The final regression analyses included a subset of variables that were key demographic and academic characteristics that offered interesting insights into how various factors, such as student background, academic involvement, and institutional context, may influence student outcomes. At the forefront, the CE involvement variable was the primary distinction between student records throughout the entire study. Furthermore, gender was another essential factor 19 included, as it allowed us to consider possible disparities in academic performance and results between females and male learners. Table 5 Independent Variables Concurrent Enrollment CE 4169 Non-CE 3742 Gender Male 3380 First Generation 1996 Female 4031 First Generation Status Unknown 1896 Race/Ethnicity Declined Hispani Multiracia to Answer c or l Latino American Indian Asian Black or African America n 49 152 106 594 Box Elder SD Cache Count y SD 83 Davis County SD 3272 Logan City SD Engineering, Applied Science, and Technology 1071 Health Profession s 1138 249 Educatio n 597 20 Not First Generation 4019 Internationa l White 933 171 43 School District Ogden Weber Charter and City SD County Private SD 669 2254 155 College Science Social & Behavioral Sciences 5863 361 Morga n 163 Salt Lak e 102 944 Business/Economics 900 713 In terms of institutional context, I found it particularly valuable to include high school information as one of the independent variables in the study. Factors such as the quality of instruction, the availability of CE, and class sizes are just a few of the many variables that could significantly influence student success, with notable variation across different high schools. Although this study only cites the high school attended, any demonstration of significance could be cause for further research. Othe r 20 Lastly, several additional valuable student data points were made available as part of this file request, including Pell Grant eligibility, school district, race, degree earned, and scholarship information. These variables offer a deeper understanding of the socio-economic backgrounds of students and how these factors intersect with the distribution of resources within higher education. By examining these elements, we gain insight into how financial aid, access to educational opportunities, and demographic characteristics influence students' ability to succeed and achieve their academic goals. Limitations Before sharing the results, I want to acknowledge some limitations to this study that should be considered when interpreting the results. There may be concerns regarding selection bias in the Weber State Concurrent Enrollment Program. The program might draw the brightest and most well-connected students, who might already be predisposed to succeed in college. Because of this, comparing the outcomes of former Weber State CE program participants with those of their non-participating peers may not be accurate or representative of the broader population. Additionally, it is possible that other social, economic, academic, and/or cultural characteristics that were not included in our data could have an impact on the outcomes. For example, the findings might also be influenced by young people serving Latter Day Saint missions as members of Utah’s mainstream and highly widespread religion. Some students enrolled in Weber State's concurrent enrollment programs may need to pause their academic pursuits to fulfill mission obligations. This pause could influence the timeline for degree completion and potentially affect GPA outcomes. Regrettably, due to the absence of data 21 regarding students' mission participation, we are unable to consider this potential influencing factor in our analysis. Several other factors might contribute to variations between the two groups in the study's research questions. Students who take part in the Weber State concurrent enrollment, for instance, might have different motivations and goals compared to their counterparts who do not take part in concurrent enrollment programming. For instance, CE students are advised to work toward an associate’s degree initially before bachelor consideration and are provided counseling resources to plan out their academic pathway. Additionally, there can be variations in the two groups' high school ranking, equality of access, and familial financial support that are not currently captures in this analysis. Lastly, CE adoption has only increased in the years following our dataset. Funding, infrastructure, and partnerships have grown significantly and the student outcomes could be influenced as a result. Findings in this study could answer interesting questions about the programs in past but likely won’t be reflective of modern trends in CE outcomes. Findings In accordance with the research questions, the results are arranged into four subsections. Each research question will be accompanied by a summary of the various findings, along with a supporting table and graph to visually illustrate the results. Additionally, each subsection will include a multivariate regression analysis table, detailing the statistical significance of each tested variable. How does participation in CE coursework influence college GPA? Table 4 provides a summary of GPAs for each group in my dataset. The results suggest that, on average, CE students had slightly lower GPAs (approximately 2.68 to 2.69 for AA and 22 BA) compared to non-CE students (around 2.71 to 2.73 for AA and BA). This difference was minimal, indicating that CE participation may not significantly impact GPAs. Table 6. GPA Breakdown by Group Group AA_CE_Students BA_CE_Students AA_non_CE_Students BA_non_CE_Students Min. 0.05 0.1 0.1 0.1 1st Qu. 2.09 2.1 2.14 2.19 Media n 2.91 2.88 2.89 2.93 3rd Qu. Mean 2.6 2.6 2.7 2.7 Max. 3.4 3.4 3.42 3.42 4 4 4 4 NA's 197 176 113 212 Moving to Tables 7 and 8, which summarize the results of multivariate regressions for AA and BA respectively, we observe a lack of statistical significance in both models as a whole. The R-squared and associated p-values for the associate’s degree model (Table 7) and bachelor’s degree model (Table 8) are extremely low (r = 0.001248, p =0.3714; and r = 0.01646, p =0.4095), indicating that the variables tested have little explanatory power over GPA variation. These findings collectively suggest that there are no meaningful differences in GPA between the CE and non-CE groups. Table 7. GPA Regression for Associate Degrees Predictor Age_ SEX Male Pell Received Either Degree CE Enrolled Aid Offered Per Term RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown Estimate Std Error 3.00E-02 -7.00E-03 -1.00E-01 -5.00E-02 1.00E-05 2.00E-02 9.00E-02 5.00E-02 5.00E-02 4.00E-02 -2.00E-01 1.30E+00 6.00E-02 5.00E-02 6.00E-02 5.00E-02 6.00E-02 1.00E-05 4.00E-01 2.00E-01 3.00E-01 9.00E-02 8.00E-02 2.00E-01 9.00E-01 1.00E-01 P 0.5934 0.9092 0.0445 0.415 0.4092 0.969 0.6328 0.8475 0.5501 0.6117 0.1563 0.1563 0.6304 23 FIRST_GENERATIONYes Box Elder School District Cache County School District Davis County Davis School District Logan City School District Ogden City School District Charter and private College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Intercept -5.00E-02 1.00E-01 5.00E-03 -7.00E-03 -1.1 -4.00E-02 -3.00E-01 -7.00E-02 -6.00E-02 -8.00E-02 4.00E-01 1.00E-01 5.00E-01 2.90E+00 6.00E-02 1.00E-01 2.00E-02 5.00E-02 4.00E-01 8.00E-02 4.00E-01 2.00E-01 8.00E-02 6.00E-02 4.00E-01 2.00E-02 5.00E-01 3.00E-01 0.354 0.4521 0.9808 0.8975 0.00168 0.5942 0.4295 0.739 0.4079 0.26 0.281 0.5958 0.2603 <0.001 Model Fit R-Squared F-Statistic P-Value N Table 8. GPA Regression for Bachelor Degrees Predictor SEX Male Pell Received Either Degree CE Enrolled Aid Offered Per Term RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown FIRST_GENERATIONYes Box Elder School District Cache County School District Davis County Davis School District Logan City School District 0.001248 1.067 0.3714 1498 Estimate -3.00E-03 -1.20E-01 4.00E-02 -3.00E-02 1.00E-05 -6.00E-01 2.00E-01 4.00E-02 2.30E-01 1.70E-01 1.00E-01 -6.00E-02 2.00E-03 6.00E-02 -3.00E-02 5.00E-02 5.00E-02 8.00E-02 Std Error 5.00E-02 5.00E-02 4.00E-02 5.00E-02 9.00E-06 3.00E-01 2.00E-01 2.00E-01 9.00E-02 7.00E-02 1.00E-01 9.00E-01 1.00E-01 5.00E-02 1.00E-01 2.00E-01 5.00E-02 3.00E-01 P 0.4173 0.0176 0.3348 0.5395 0.0587 0.4173 0.3065 0.8484 0.0096 0.0109 0.2907 0.9507 0.9822 0.2313 0.7678 0.717 0.3141 0.7771 24 Ogden City School District Charter and private College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Intercept R-Squared F-Statistic P-Value N -8.00E-02 8.00E-02 9.00E-02 7.00E-02 8.00E-02 6.00E-02 8.00E-02 4.00E-02 2.50E+00 Model Fit 7.00E-02 2.00E-01 8.00E-02 8.00E-02 1.00E-01 9.00E-02 7.00E-02 8.00E-02 2.00E-01 0.2254 0.705 0.2158 0.3502 0.4283 0.4811 0.2605 0.5817 <0.001 0.01646 1.038 0.4095 1893 How does participation in CE influence persistence and degree completion? In Table 9, a display of persistence totals and percentages is presented for each research group. Notably, a discernible pattern of strong year-to-year persistence is observed within the groups of CE students, spanning both Associate and Bachelor degree seeking options. Specifically, students enrolled in Associate degree programs demonstrated a commendable persistence rate of 56.5%, surpassing their non-CE peers who maintained a persistence rate of 44.2%. This contrast marks a notable 13.3% disparity in persistence rates, underscoring a significantly greater likelihood of sustained academic engagement among CE participants. Likewise, Bachelor's degree seeking students showed a noteworthy persistence rate of 55.7%, whereas their non-CE peers recorded a slightly lower rate of 49.3%, a notable boost persistence rates for CE students. Such findings suggest that participation in CE programs may contribute positively to the academic persistence and continuity of students across different degree levels. Figure 2 presents these results in an easily comprehensible format, depicting the percentages of each group juxtaposed with the total student population separated by type. Table 9. Persistence Counts and Percentages 25 Group AA_CE_students BA_CE_students BA_non_CE_students AA_non_CE_students Total_students 2014 2101 2364 1304 Persisted 1138 1171 1165 577 Percentage_persisted 57 56 49 44 Figure 2. GPA Persistence Graph The regression models for persistence and completion did not exhibit a significant correlation between persistence and CE/non-CE participation; however, they did reveal significant correlations between various demographic variables and persistence for both the Associate and Bachelor models. The R-squared and associated p-values for the associate’s degree model (Table 10) and bachelor’s degree model (Table 11) are (r = 0.1646, p = <0.001; and r = 0.193, p = <0.001). Both Associate and Bachelor regression models showed a strong correlation between persistence and the starting age during the student’s first semester. Interestingly, males had a strong negative correlation with persistence rates within both cohorts tending to persist around 16% less often than females. Patterns were not consistent across all racial groups, however; Latino students persisted more often (~14%) than the comparison group (White), and Asians persisted more often when pursuing Bachelor’s degrees (~15%) compared to White students. Other elements such as funding played a factor suggesting that students who accepted a Pell grant were slightly more likely to persist than students who did not. Table 10. 26 Associate Degree Persistence Regression Predictor Estimate Std Error Starting Age 1.95E-01 9.29E-02 SEX Male -1.60E-01 3.00E-02 Pell Received 9.00E-02 3.00E-02 Either Degree 2.90E-01 2.00E-02 CE Enrolled 5.00E-02 3.00E-02 Aid Offered Per Term -3.70E-05 6.00E-06 RACEAmerican Indian 3.00E-01 2.00E-01 RACEAsian 7.00E-02 8.00E-02 RACEBlack or African American 8.00E-03 1.00E-01 RACEDeclined to Answer 8.00E-02 4.00E-02 RACEHispanic or Latino 1.40E-01 4.00E-02 RACEMultiracial 6.00E-02 8.00E-02 RACEInternational 4.00E-01 5.00E-01 FIRST_GENERATIONUnknown -2.00E-03 6.00E-02 FIRST_GENERATIONYes 2.00E-02 3.00E-02 Box Elder School District -7.00E-02 6.00E-02 Cache County School District -1.90E-01 9.00E-02 Davis County Davis School District -3.00E-02 3.00E-02 Logan City School District -1.00E-01 2.00E-02 Ogden City School District 9.60E-02 4.00E-02 Charter and private 2.00E-01 2.00E-01 College of Education 2.00E-01 1.00E-01 College of Engr Appl Sci Tech 4.00E-02 4.00E-02 College of Health Professions 1.00E-02 3.00E-02 College of Science 9.00E-03 2.00E-01 College of Social/Behav Scienc -3.00E-02 9.00E-02 College of Business/Economics 2.00E-01 2.00E-01 Intercept -2.90E-02 1.00E-01 Model Fit R-Squared 0.1646 F-Statistic 11.41 P-Value <0.001 N 1651 Table 11. Bachelor Degree Persistence Regression Predictor Starting Age SEX Male P 0.036 <0.001 0.0014 <0.001 0.1063 <0.001 0.1057 0.3916 0.9535 0.0508 0.0004 0.4966 0.3651 0.9743 0.5619 0.2252 0.0396 0.2046 0.5298 0.0119 0.2784 0.1091 0.2683 0.6136 0.9598 0.7447 0.4389 0.8312 Estimate Std Error 2.61E+00 2.63E-01 -1.60E-01 2.00E-02 P <2e-16 <0.001 27 Pell Received Either Degree CE Enrolled Aid Offered Per Term RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown FIRST_GENERATIONYes Box Elder School District Cache County School District Davis County Davis School District Logan City School District Ogden City School District Charter and private College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Intercept 5.00E-02 3.70E-01 -4.00E-02 -2.70E-05 3.00E-02 1.60E-01 -7.00E-03 5.00E-02 4.00E-02 -4.00E-02 4.00E-01 6.00E-02 -9.00E-03 -5.00E-02 -3.00E-02 -5.00E-02 -2.00E-02 -6.00E-02 -1.00E-01 -3.00E-02 -2.00E-02 1.00E-02 2.00E-02 -2.00E-02 -7.00E-03 3.00E-01 2.00E-02 2.00E-02 2.00E-02 4.00E-06 2.00E-01 7.00E-02 9.00E-02 4.00E-02 3.00E-02 7.00E-02 5.00E-01 5.00E-02 2.00E-02 5.00E-02 7.00E-02 2.00E-02 1.00E-02 3.00E-02 1.00E-01 4.00E-02 4.00E-02 6.00E-02 4.00E-02 3.00E-02 4.00E-02 1.00E-01 0.0282 <0.001 0.1124 <0.001 0.8384 0.0372 0.9382 0.2614 0.1692 0.5369 0.3608 0.2687 0.6789 0.2727 0.7071 0.0379 0.2239 0.0734 0.2214 0.4215 0.5149 0.8184 0.6672 0.5387 0.8396 0.01 Model Fit R-Squared F-Statistic P-Value N 0.193 16.31 <0.001 2079 How does participation in CE influence time-to-degree completion? Table 12 presents a descriptive analysis of the average years students took to complete a degree for students enrolled in CE compared to those not enrolled in CE, categorized by degree level (AA and BA). For Associate-seeking students, those enrolled in CE took an average of 2.39 years to complete their degree, while non-CE students took approximately 3.06 years. Similarly, 28 for Bachelors students, CE participants completed their degree in about 3.92 years on average, whereas non-CE students took around 4.57 years. These findings suggest that participation in CE programs may lead to shorter time-to-degree completion. Figure 3. displays the vertex of each student type with CE student groups displaying a tighter density overall that slightly trails their non-CE counterpart in time-to-degree values. Table 12. Time-to-Degree by Degree and CE Status Group Average_Years_Till_Degree AA_CE_students 2.39 BA_CE_students 3.92 AA_non_CE_students 3.06 BA_non_CE_students 4.57 Figure 3. Time-to-Degree Distribution The regression models showed several significant relationships of variables to time-to-degree completion. The model focused on Associate Degrees (Table 13) was statistically significant with an R-squared value of 0.1053 and a p-value of 0.0019. The model focused on Bachelor degrees (Table 14) was also statistically significant with an R-squared value of 0.1573 and a p-value of 0.04826. The regression analysis for Associate seeking students showed that CE enrollment significantly reduces the time-to-degree. Students enrolled in CE programs graduate 0.80 years faster on average than those not enrolled in CE programs. This finding is statistically 29 significant with a p-value of 0.0192. Moreover, Table 13 provides insights into how various factors influence time-to-degree completion beyond just CE enrollment. As with findings in the previous regression, sex is another significant variable to consider. Male students tend to take much longer to complete their Associates degree compared to female students. Additionally, students enrolled in the College of Health Professions tend to graduate, on average, much quicker than other students in their cohort. Lastly, students who are offered more financial aid per term take longer to graduate, suggesting that relying heavily on financial aid each subsequent term may reinforce prolonged time to graduation. Table 14 displays the findings for the Bachelor degree seeking students and indicates similar findings, however, the model as a whole is less strong and the only significant variables are sex and CE. CE enrollment also shortens the time-to-degree for Bachelor degree-seeking students. Those enrolled in CE programs graduate 1.00 year faster on average than those not enrolled, with a statistically significant p-value of 0.0043. Table 13. Associate Years Until Degree Regression Predictor SEX Male Pell Received Either Degree CE Enrolled Total_AID_PAID RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown FIRST_GENERATIONYes Box Elder School District Estimate Std Error P 7.00E-01 2.00E-01 0.0032 2.70E-05 8.00E-05 0.0018 n/a n/a n/a -8.00E-01 3.00E-01 0.0192 -2.70E-05 4.00E-06 <0.001 7.00E-01 2.00E+00 0.7504 -1.00E-01 8.00E-01 0.2182 -2.00E+00 1.00E+00 0.2283 -4.00E-01 1.00E-01 0.3691 -8.00E-01 4.00E-01 0.0663 3.00E-01 8.00E-01 0.7215 n/a n/a n/a 4.00E-01 7.00E-01 0.5236 3.00E-01 3.00E-01 0.3084 -3.00E-01 8.00E-01 0.6901 30 Cache County School District Davis County Davis School District Logan City School District Ogden City School District Charter and private College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Intercept -1.00E+00 -3.00E-02 1.00E+00 6.00E-01 1.00E+00 -6.00E-02 -7.00E-02 1.30E+00 n/a 2.00E+00 n/a 3.00E+00 1.00E+00 2.00E-01 2.00E+00 3.00E-01 1.00E+00 2.00E+00 4.00E-01 4.00E-01 n/a 2.00E+00 n/a 2.00E+00 0.3624 9039 0.6461 0.0722 0.3196 0.9712 0.9026 0.003 n/a 0.3749 n/a 0.0557 Model Fit R-Squared F-Statistic P-Value N Table 14. Bachelor Years Until Degree Regression Predictor SEX Male Pell Received Either Degree CE Enrolled Avg_Aid_OF_Per_Term RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown FIRST_GENERATIONYes Box Elder School District Cache County School District Davis County Davis School District Logan City School District Ogden City School District Charter and private 0.1053 2.128 0.0019 441 Estimate Std Error P 8.00E-01 3.00E-01 0.0155 -4.00E-01 3.00E-01 0.2371 n/a n/a n/a -1.00E+00 3.00E-01 0.0043 -1.00E-04 9.00E-05 0.2919 n/a n/a n/a 1.00E+00 7.00E-01 0.1746 n/a n/a n/a 2.00E-01 6.00E-01 0.7555 -1.00E-01 5.00E-01 0.7834 -5.00E-01 1.00E+00 0.6939 n/a n/a n/a -1.00E-01 7.00E-01 0.8589 4.00E-02 3.00E-01 0.9153 -1.00E+00 8.00E-01 0.1229 -2.00E+00 1.00E+00 0.2143 -4.00E-02 3.00E-01 0.9014 3.00E+00 2.00E+00 0.2616 -3.00E-02 5.00E-01 0.9615 -2.00E+00 1.00E+00 0.1499 31 College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Intercept 4.00E-01 6.00E-01 3.00E-02 9.00E-01 6.00E-01 8.00E-01 7.00E+00 5.00E-01 6.00E-01 8.00E-01 6.00E-01 5.00E-01 6.00E-01 2.00E+00 0.4229 0.2883 0.9684 0.1714 0.1875 0.2372 <0.001 Model Fit R-Squared F-Statistic P-Value N 0.1573 1.578 0.04826 229 How does participation in CE influence degree earned? Table 15 displays any degree earned percentages (AA or BA) of students enrolled in CE compared to those not enrolled in CE, categorized by degree group. It is evident that participation in CE impacts degree attainment with 51.34% of Associate seeking CE students earning a degree compared to 27.76% of their non-CE counterparts. Similarly, the CE students seeking a Bachelor’s degree achieved that degree at 51.45% rates compared to 31.55% found in the non-CE group. Figure 4. visualizes these results in a palatable way and displays each group’s percentages against the total number of students by type. Table 15. Degree-Earned Percentage by Group Group Any_Degree_Percentage AA_CE_students 51.34 BA_CE_students 51.45 AA_non_CE_students 27.76 BA_non_CE_students 31.56 Figure 4. 32 Degree Earned Percentage by Group The regression analysis indicates that participation in CE programs significantly increases the likelihood of Associate's degree completion. Specifically, students in CE programs exhibited a 10.75% higher probability of obtaining their degrees compared to non-CE students. This notable increase suggests that CE programs provide valuable support and resources that enhance student persistence and success in achieving their academic goals. The R-squared and associated p-values for the associate’s degree model (Table 16) and bachelor’s degree model (Table 17) are very strong (r = 0.1202, p =<0.001; and r = 0.1104, p =<0.001). In the analysis of Table 16, several other significant factors beyond CE enrollment emerged. Notably, age at enrollment showed a positive association, with each one-unit increase in age at enrollment corresponding to a slight decrease in the likelihood of earning any degree. First-generation students were less likely to achieve an Associate's degree compared to their non-first-generation counterparts. Similarly, Pell Grant recipients were 13.9% less likely to complete any degree compared with non-Pell students. The next regression analysis reveals that students participating in CE programs are 14.29% more likely to complete a Bachelor's degree compared to those who do not participate. This significant improvement underscores the beneficial role of CE programs in enhancing outcomes regardless of 2-year or 4-year degree pursuit. The full analysis of the Bachelor's degree group in Table 17 revealed some similarities but also a few differences. Age at enrollment did 33 not significantly affect degree completion likelihood among Bachelor's students, unlike in the Associate's degree group. Also, similar to the Associate's group, first-generation students were ~10% less likely to earn a Bachelor's degree. Pell Grant recipients displayed a similar trend, being 12% less likely to attain any degree. These results underscore the importance of targeted support programs like CE in facilitating degree completion across different academic levels, despite the varying impact of demographic and socioeconomic factors. Table 16. Associate Degree Earned Regression Predictor Starting Age SEX Male Pell Received Either Degree CE Enrolled Avg_Aid_OF_Per_Term RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown FIRST_GENERATIONYes Box Elder School District Cache County School District Davis County Davis School District Logan City School District Ogden City School District Charter and private College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Estimate Std Error P 9.89E-01 1.38E-01 <0.001 -4.00E-02 3.00E-02 0.1258 -1.40E-01 3.00E-02 <0.001 n/a n/a n/a 1.20E-01 3.00E-02 0.0004 -2.60E-05 6.00E-06 <0.001 -2.00E-01 2.00E-01 0.3375 2.00E-02 9.00E-02 0.8229 -5.00E-02 1.00E-01 0.6979 -1.00E-01 4.00E-02 0.03201 -1.00E-02 4.00E-02 0.7304 -2.00E-02 8.00E-02 0.8562 5.00E-01 5.00E-01 0.3376 -8.00E-02 6.00E-02 0.1967 -1.20E-01 3.00E-02 <0.001 7.00E-03 6.00E-02 0.9071 3.00E-02 1.00E-01 0.7203 -1.00E-02 3.00E-02 0.5959 5.00E-02 2.00E-01 0.7888 7.00E-03 4.00E-02 0.8662 -2.00E-02 2.00E-02 0.9235 1.00E-01 1.00E-01 0.2009 -9.00E-02 4.00E-02 0.021 -8.00E-02 3.00E-02 0.0053 -6.00E-02 2.00E-01 0.7233 -1.00E-01 9.00E-02 0.1491 1.00E-01 2.00E-01 0.5076 34 Intercept 1.00E+00 1.00E-01 <0.001 Model Fit R-Squared F-Statistic P-Value N Table 17. Bachelor Degree Earned Regression Predictor SEX Male Pell Received Either Degree CE Enrolled Avg_Aid_OF_Per_Term RACEAmerican Indian RACEAsian RACEBlack or African American RACEDeclined to Answer RACEHispanic or Latino RACEMultiracial RACEInternational FIRST_GENERATIONUnknown FIRST_GENERATIONYes Box Elder School District Cache County School District Davis County Davis School District Logan City School District Ogden City School District Charter and private College of Education College of Engr Appl Sci Tech College of Health Professions College of Science College of Social/Behav Scienc College of Business/Economics Intercept R-Squared F-Statistic P-Value 0.1202 8.21 <0.001 1651 Estimate Std Error P 1.00E-01 2.00E-02 -1.20E-01 2.00E-02 n/a n/a 1.40E-01 2.00E-02 -1.80E-05 5.00E-06 2.00E-02 2.00E-01 6.00E-02 8.00E-02 -1.00E-01 1.00E-01 -5.00E-02 4.00E-02 -8.00E-02 3.00E-02 -1.00E-01 7.00E-02 5.00E-01 5.00E-01 8.00E-03 6.00E-02 -1.00E-02 2.00E-02 -7.00E-02 5.00E-02 -1.50E-01 8.00E-02 -4.00E-02 2.00E-02 -2.00E-01 1.00E-01 -8.00E-02 3.00E-02 -1.00E-01 1.00E-01 1.30E-01 4.00E-02 9.00E-02 4.00E-02 3.00E-02 5.00E-02 1.40E-01 4.00E-02 9.00E-02 4.00E-02 1.00E-01 4.00E-02 8.00E-01 1.00E-01 Model Fit 0.1104 8.767 <0.001 0.9531 <0.001 n/a <0.001 <0.001 0.8912 0.4462 0.2125 0.2835 0.02398 0.0878 0.2873 0.8822 <0.001 0.149 0.0599 0.1323 0.1465 0.0197 0.2932 0.0007 0.0312 0.5555 0.0013 0.0114 0.0114 <0.001 35 N 2079 Discussion This research contributes to the existing body of literature exploring the correlation between student participation in concurrent enrollment and postsecondary education outcomes. Many of the findings of this study align with prior research. The organization of the results around four different research questions provides interesting comparisons between a diverse group of studies. When examining student GPA, it became apparent that while there existed a slight variance between CE students and non-CE students, the difference was not statistically significant. Our finding suggests that CE students conversely tend to display a GPA slightly below than their non-CE (in BA programs) which aligns with previous research (e.g., An, 2013), which indicated that CE enrollment may not have a significant effect on GPA when you evaluate the cumulative college GPA over several years. Like An (2013) found, it could be that GPA for CE students in this study was stronger within the first year compared to non-CE students, but that the differences in GPA tapered with each subsequent year. It is also plausible that a more nuanced analysis of GPA was needed to see a significant difference in GPA between CE and non-CE students in this study; as one conducted by Swanson (2008), which looked at first year undergraduate GPA between CE and non-CE students within the same age range, found CE students to have significantly higher GPA than non-CE students. In terms of persistence, my study noted a sizable discrepancy between CE students and non-CE students across both Associate and Bachelor degree-seeking cohorts. As was true in Swanson (2008), it appears that CE programs are well suited to promote degree persistence into a student’s sophomore year of undergraduate study. Swanson’s study also found that CE students were far more likely to enter college immediately after high school implying a compounding 36 snowball effect of persistence from CE to freshmen year, sophomore year, and so on until graduation. Furthermore, The Dual Enrollment Playbook (2021) study has demonstrated that taking a college readiness or first year experience class can increase course completion and persistence in the first year of college for undergraduate students and is often required for CE students to take early on in their coursework. Factors such as this required class likely reinforce the beneficial nature of CE participation and its effect on post-secondary persistence. Weber State is beginning to implement a CE college preparedness course throughout its programing Nguyen, (2024). This implementation might have implications in future CE outcomes research and should be studied once the program has been fully launched. The discovery that Pell grant recipients are more likely to persist is supported by Bettinger (2004), who found Pell grants reduce college drop-out behavior. In fact, there is a large body of research surrounding financial aid and its effect on persistence along with other college success metrics. Castleman (2013), found that need based scholarships such as Pell not only significantly improved the early persistence in college, but also played a crucial role in maintaining enrollment momentum for the student. Unlike Pell status, gender, specifically comparing male with female students, did not share the same positive impact on persistence with male students trailing significantly behind female students in persistence rates. A potential explanation, Conger and Long (2010), revealed a stark reality: males face significant challenges in persisting through college compared to their female counterparts. Despite efforts to control for various factors such as demographics, educational needs, and high school quality, the gender gap in persistence remains evident (Conger & Long, 2010). Males not only earn fewer credits in their initial semesters but also continue to lag behind in subsequent semesters, resulting in a cumulative disadvantage in credits 37 earned (Conger & Long, 2010). Moreover, the choice of majors among male college entrants, such as Business, Engineering/Computer Science, and Natural and Physical Sciences, is associated with lower cumulative GPAs, credits, and rates of persistence. These findings underscore the complex interplay of academic, social, and institutional factors contributing to the persistence disparities between genders in higher education. In contrast, findings from this study demonstrate that Latino students who were seeking Associate’s degrees persisted at a higher rate than White students and Asians that were seeking a Bachelor’s degree also persisted at a higher rate than White students. Although literature does not provide potential explanations, this finding merits further research as it provides a potential avenue for higher education institutions to support minoritized students in the higher education system of the United States. Our findings also demonstrate that participation in CE can have a significant impact on the overall time it takes to graduate. This finding aligns with evidence from other studies such as Gruman (2013) which looked at the effect of advanced placement and dual enrollment had on time to degree completion and found that enrollment in just one dual enrollment course would, on average, decrease the total time to graduation by 1/4 of a year. As with persistence, males demonstrated a longer timeline for graduation compared to their female counterparts. Buchmann and DiPrete (2006), explain that this is just one of the many female-favorable trends in college outcomes that need to be addressed through community and family support. Specific to this study’s community and family contexts, males in Utah leave school at higher rates due to religious and military obligations. These variables, while needing more exploration, could contribute to the gap we see between men and women in WSU time to degree metrics. 38 Additionally, the findings from Glocker (2011) support the notion that an increase in financial aid per term corresponds to a longer time to graduation. This aligns with the finding in this study that a heavy reliance on financial aid each subsequent term may reinforce prolonged time to graduation. Students may feel less pressure to complete their studies quickly when financial assistance is readily available (Glocker, 2011). This highlights the importance of considering the impact of financial aid policies on students' academic trajectories and suggests the need for strategies that balance support with incentives for timely degree completion. Regarding degree attainment, the study revealed a substantially higher proportion of CE participants successfully earning degrees in comparison to non-CE participants, evident in both Associate and Bachelor degree-seeking cohorts. This discovery aligns closely with the findings of Allen (2012), whose research similarly emphasized the pronounced effect of CE programs in augmenting postsecondary education and training attainment and whose research underscored the substantial impact of CE programs on bolstering postsecondary education and training attainment. For instance, Allen's (2012) study highlighted the positive correlation between CE participation and increased degree completion rates, aligning with the patterns observed in our own research. By corroborating these outcomes, our study not only validates existing literature but also underscores the robustness of CE programs in facilitating degree completion rates. Implications This study holds significant implications for both policy and practice within higher education. It challenges colleges and universities to self-evaluate the outcomes CE programs have in their institutional frameworks. While the study reveals various positive advantages in educational achievement between CE and their non-CE peers, I encourage investment in comprehensive investigations to fully grasp the relationship between CE participation and 39 academic performance. This necessitates a strategic policy approach rooted in evidence-based decision-making, fostering a culture of continuous improvement in CE programming. On a practical level, the study emphasizes the necessity of customizing CE programs to meet the diverse needs of students. Despite the limited impact on GPAs, the notable disparities in persistence rates and time-to-degree completion underscore the potential benefits of CE initiatives. Practitioners are encouraged to integrate insights from this study into their programmatic strategies, ensuring that CE offerings are not only accessible but also conducive to student success. Targeted support mechanisms for underrepresented groups, such as first-generation students and Pell Grant recipients, are crucial for promoting equity in educational outcomes and optimizing the effectiveness of CE programming. From a research perspective, the study illuminates the intricate web of factors influencing student outcomes in CE programs. While participation in CE shows promise in enhancing persistence rates and time-to-degree completion, the multivariate regression analyses uncover nuanced relationships with demographic and socioeconomic variables. Further research is imperative to unpack these dynamics and elucidate the underlying mechanisms. Future studies should delve into additional factors such as program structure, student motivations, and policy frameworks to inform the development and implementation of effective CE initiatives. Interdisciplinary collaboration and methodological rigor are paramount in generating actionable knowledge that advances our understanding of CE's role in higher education. Conclusion This research adds to our knowledge of the benefits concurrent enrollment programs have for college student success. The results consistently show that, despite certain limitations, participation in CE is linked to higher persistence rates, shorter time-to-degree completion, and a higher likelihood of degree attainment. The significance of CE initiatives as beneficial routes to 40 postsecondary education, is highlighted by these results. Institutions can make the most of CE initiatives to support higher educational attainment and equity for all students by funding and growing CE programs, giving targeted support to underserved populations, incorporating CE participation into academic advising practices, and carrying out additional research. 41 References Allen, Drew, and Mina Dadgar. (2012) Does dual enrollment increase students’ success in college? Evidence from a quasi‐experimental analysis of dual enrollment in New York City. New Directions for Higher Education, 2012(158), 11-19. https://onlinelibrary.wiley.com/doi/abs/10.1002/he.20010 An, B. P., & Han, J. Y. (2019). Dual enrollment programs in high schools and the post-secondary outcomes of participants. Educational Research and Evaluation, 25(3-4), 136-153. doi: 10.1080/13803611.2019.1685577. An, B. P. (2013). The Influence of Dual Enrollment on Academic Performance and College Readiness: Differences by Socioeconomic Status. Research in Higher Education, 54(4), 407–432. http://www.jstor.org/stable/23470969 Campbell, J. L. (2016) A quantitative archival study on concurrent enrollment and time to degree Attainment. (Publication No. 10689664) [Doctoral dissertation, The University of the Rockies] ProQuest Dissertations Publishing https://www.proquest.com/docview/1985969942/abstract/C8C9E11BB7AA489CPQ/3?ac countid=14940&fbclid=IwAR21iKzO416Ga0pNJf6CT24GVC-JTxzS0B-Dki1EMBuNr9 OmlgFRu0UCxI0 Castleman, B. L., & Long, B. T. (2013). Looking beyond enrollment: The causal effect of needbased grants on college access, persistence, and graduation. National Bureau of Economic Research. https://doi.org/10.3386/w19306 Chapman, B. (2001). A model for implementing a concurrent enrollment program. New Directions for Community Colleges, 2001(113), 15. https://doi.org/10.1002/cc.4 Dougherty, K. J., Mellor, L., & Jian, S. (2017). Early College, Early Success: Early College High 42 School Initiative Impact Study. American Institutes for Research. Education Commission of the States - Dual Enrollment - All State Profiles. (2022). Retrieved April 16, 2022, from https://ecs.secure.force.com/mbdata/mbprofgroupall?Rep=DEA#:%7E:text=Statewide%2 0poli Foster, R. (2010). Cooperative and concurrent enrollment and college retention. Journal of Career and Technical Education, 25 (2), 38-45. https://journalcte.org/article/10.21061/jcte.v25i2.478/ Gaquin, D. A., & Ryan, M. M. (2017). Education and the American Workforce (County and City Extra Series). Bernan Press. Gruman, D. (2013) Advanced placement versus dual enrollment: making the best choice for college selection, persistence, early success, and time to completion. (Publication No. 552783) [Doctoral dissertation, University of Kansas]. ProQuest Dissertations Publishing. https://kuscholarworks.ku.edu/bitstream/handle/1808/15101/Gruman_ku_0099D_12641_ DATA_1.pdf;sequence=1?fbclid=IwAR1TGlyLKUdrv5-yDxZxUTzZKnom2Tg00srokD Rrj1ypH4UPXkMPorVum14 Hagedorn, L. S., Maxwell, W., & Hampton, S. (2007). Factors influencing the implementation of concurrent enrollment programs. Community College Journal of Research and Practice, 31(10), 779-791. doi:10.1080/10668920701647533 Hoffman, N., Vargas, J., & Santos, J. (2008). On ramp to college: A state policymaker's guide to dual enrollment. Boston: Jobs for the Future. https://jfforg-prod-new.s3.amazonaws.com/media/documents/OnRamp.pdf 43 Hoffman, N., & Vargas, J. (2004). State policies to support and sustain early college high schools. Policy Brief of The Early College High School Initiative at Jobs for the Future. https://files.eric.ed.gov/fulltext/ED486161.pdf Hughes, T. (2016) The impact of dual enrollment participation on degree attainment. (Publication No. 515939) [Doctoral dissertation, Old Dominion University] ProQuest Dissertations Publishing https://www.proquest.com/docview/854555458/8B96815616354E5CPQ/5?accountid=14 940&fbclid=IwAR3eYY-RvoGI86hN3_OSRjDhTJ1LrzYYyLEju4LaGzJjj6KA0kqWHh iUSWs Kaye, S. (2016). A study of the University of Colorado Denver's Concurrent Enrollment Program with Denver Public Schools: Access, equity, and effectiveness. Journal of Concurrent Enrollment, 2(1), 64-76. Ison, M. P. (2022). Dual enrollment, performance-based funding, and the completion agenda: An analysis of post-secondary credential outcomes of dual enrollment students by credential type. Community College Review, 50(1), 51–70. https://doi-org.hal.weber.edu/10.1177/00915521211047673 Jagesic, S., Ewing, M., Wyatt, J. N., & Feng, J. (2021). Unintended consequences: Understanding the relationship between dual enrollment participation, college undermatch, and bachelor’s degree attainment. Research in Higher Education, 1-21. https://link-springer-com.hal.weber.edu/article/10.1007/s11162-021-09643-x Leonard, J. (2013). Maximizing college readiness for all through parental support. School Community Journal, 23(1), 183–202. https://eric.ed.gov/?id=EJ1004338 44 Lewis, M., & Overman, L. (2008). Dual and concurrent enrollment and transition to postsecondary education. Career and Technical Education Research, 33(3), 189-202. Loftin, T. (2012) Concurrent and dual credit: the bridge to postsecondary education for first-generation college students. [Doctoral dissertation, University of Arkansas]. ProQuest Dissertations Publishing https://www.proquest.com/docview/1651831459/18F51AA544804FEDPQ/11?accountid =14940 McComas, M. (2010) Degree completion and acceleration rates of iowa community colleges' concurrent enrollment students. (Publication No. 516394) [Doctoral dissertation, Iowa State University]. ProQuest Dissertations & Theses Global. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2663&context=etd&fbclid=IwAR20 LwYsOVd4UpjwAUkg-LtTaPHvgYRqVUOLUZmVijjaSeKECM3eqsvsfhA Menzel, M. (2006) Type III concurrent enrollment program participation effect on time and cost to baccalaureate degree completion. (Publication No. 3205232) [Doctoral dissertation, University of Minnesota] ProQuest Dissertations & Theses Global. https://search.proquest.com/openview/1578b862517344db2c731fc668f19125/1?cbl=187 50&diss=y&fbclid=IwAR1zM1XOG_JlktW8jvqpjpd5zB5RNrU52emOeBc_tVxyqVDD S17Bb1mIm3M&pq-origsite=gscholar Mullin, C. M., & Honeyman, D. S. (2016). Dual enrollment participation and student success: An examination of the benefits and challenges of participating in a college-level program while in high school. Community College Journal of Research and Practice, 40(11), 914-926 45 NCES - U.S. DEPARTMENT OF EDUCATION. (2020, December). Dual or Concurrent Enrollment in Public Schools in the United States (NCES 2020–125). Institute of Education Sciences. https://nces.ed.gov/pubs2020/2020125.pdf Nitzke, J. (2002). A longitudinal study of dual credit enrollment of high school students as an accelerator for degree completion. [Doctoral dissertation, Iowa State University] ProQuest Information and Learning https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2016&context=rtd&fbclid=IwAR1jl M-zpik2nJIkZqUfe7__5g8ymS0vFwQsTDEvxs7Q_ZGas9X83yUDnXw Nguyen, T. (2024, June 24). Personal communication [Personal conversation] O’Brien, D. M., & Nelson, T. D. (2004). Strengthening college preparation and access through concurrent enrollment in high school and community college. Unpublished manuscript, University of Texas at Dallas. Pettit, J., & Aistrup, J. (2017). Developing and sustaining concurrent enrollment partnerships: A case study of the University of Nebraska Omaha and Omaha Public Schools. Journal of College Access, 3(2), 157-170. Richardson, J., & Hall, T. (2016). Concurrent enrollment in Utah: An analysis of student performance outcomes. Journal of Applied Research in the Community College, 23(1), 46-57. Swanson, J. (2008) An analysis of the impact of high school dual enrollment course participation on postsecondary academic success, persistence and degree completion. [Doctoral dissertation, University of Iowa] University of Iowa Campus Repository http://nacep.org/wp-content/uploads/2010/02/Dissertation-2008-Joni-L.-Swanson.pdf 46 Terranova, (2019) Susan. A Dual Enrollment Partnership Between Community Colleges and High Schools: A Case Study. Liberty University. https://www.proquest.com/openview/ff4b816425bfb0fca11fd1437ca34cdb/1?cbl=18750 &diss=y&pq-origsite=gscholar The Dual Enrollment Playbook: A Guide to Equitable Acceleration for Students. (2021, Fall). American Educator, 45(3), 46. https://ccrc.tc.columbia.edu/media/k2/attachments/dual-enrollment-playbook-equitable-a cceleration.pdf Torres, O. (2019) Determining the degree to which participation in dual credit and advanced placement programs during high school affects completion and success rates of community college graduates. (Publication No. 27546310) [Doctoral dissertation, University of La Verne] ProQuest Dissertations Publishing https://www.proquest.com/docview/2314065332/abstract/18F51AA544804FEDPQ/5?acc ountid=14940&fbclid=IwAR1jlM-zpik2nJIkZqUfe7__5g8ymS0vFwQsTDEvxs7Q_ZGa s9X83yUDnXw Webb, M. & Jobs for the Future. (2004). What Is the Cost of Planning and Implementing Early College High School? https://go.exlibris.link/K1xZCwQw. Witkowsky, P., & Clayton, G. (2020). What makes dual enrollment work? High school counselor perspectives. Community College Journal of Research and Practice, 44(6), 427-444. https://www-tandfonline-com.hal.weber.edu/doi/full/10.1080/10668926.2019.1610676 Jeshua VanSickle Final Thesis 12.7.24 Final Audit Report 2024-12-16 Created: 2024-12-16 By: Ellynn Raynor (ellynnraynor@weber.edu) Status: Signed Transaction ID: CBJCHBCAABAABd90CKap4cGV8jwq2w2rFZYr5zX88JXG "Jeshua VanSickle Final Thesis 12.7.24" History Document created by Ellynn Raynor (ellynnraynor@weber.edu) 2024-12-16 - 9:19:08 PM GMT- IP address: 137.190.70.233 Document emailed to Louise Moulding (lmoulding@weber.edu) for signature 2024-12-16 - 9:20:09 PM GMT Email viewed by Louise Moulding (lmoulding@weber.edu) 2024-12-16 - 9:22:27 PM GMT- IP address: 74.125.212.8 Document e-signed by Louise Moulding (lmoulding@weber.edu) Signature Date: 2024-12-16 - 9:23:00 PM GMT - Time Source: server- IP address: 172.59.152.60 Document emailed to Heather Chapman (heatherchapman@weber.edu) for signature 2024-12-16 - 9:23:02 PM GMT Email viewed by Heather Chapman (heatherchapman@weber.edu) 2024-12-16 - 10:11:25 PM GMT- IP address: 66.249.84.201 Document e-signed by Heather Chapman (heatherchapman@weber.edu) Signature Date: 2024-12-16 - 10:11:37 PM GMT - Time Source: server- IP address: 137.190.91.17 Document emailed to Dustin Grote (dustingrote@weber.edu) for signature 2024-12-16 - 10:11:38 PM GMT Email viewed by Dustin Grote (dustingrote@weber.edu) 2024-12-16 - 10:33:34 PM GMT- IP address: 74.125.212.3 Document e-signed by Dustin Grote (dustingrote@weber.edu) Signature Date: 2024-12-16 - 11:58:32 PM GMT - Time Source: server- IP address: 174.201.46.208 Agreement completed. 2024-12-16 - 11:58:32 PM GMT