The global economic landscape is ever in flux, and as such, there is constant demand for highly skilled workers (Nager & Atkinson, 2017). This is especially true in the science, technology, engineering, and mathematics (STEM)-related industries which represent the fastest growing sector of the global labor force (Bureau of Labor Statistics, 2020; Smit et al., 2020). However, less than one-third (28%) of bachelor’s degree STEM graduates ultimately work in a STEM-designated field in the United States (Day & Martinez, 2021), and the share of STEM programs graduates has declined in Europe for the past two decades (Bacovic et al., 2022), suggesting a weak STEM college-to-career pathway for students. These pathways have also been shown to unequally sort students from under-represented groups out of STEM fields and/or into gendered STEM roles for women (Funk & Parker, 2018).

Countries across the world have dedicated significant resources to combatting employment shortages in fields of science, technology, engineering, and mathematics (STEM), especially given the demand for STEM-related skills is only expected to increase in years to come (OECD, 2017). In the U.S., the National Science Foundation and the National Institutes of Health have allocated significant financial support to undergraduate student success in STEM fields through undergraduate research programs. Such experiences equip students with research and critical thinking skills, support STEM major retention, and promote entrance to graduate school and research-based careers in STEM fields (NSB, 2018). Even non-STEM majors have reported that these experiences have increased their interest in STEM fields (Stanford et al., 2015). The design of undergraduate research experiences varies across and within institutions of higher education (IHEs), including summer research programs, volunteer lab positions, laboratory courses, and other “research-like” experiences.

Faculty-mentored undergraduate research experiences are standard in many colleges across the U.S. However, most of these experiences are highly selective and serve predominantly White male students from high-income backgrounds (Hu et al., 2008). Because these experiences are not widely accessible to most students (Balled et al., 2017; Bhattacharyya et al., 2020), there has been a recent push for universities to increase opportunities and access to undergraduate research opportunities through course-based undergraduate research (CUR). While the design of CUR varies across and within institutions, many have developed intensive, research-based courses that expose students to the process of research early in their college careers with faculty oversight (Auchincloss et al., 2014).

While researchers, universities, and policymakers widely accept CUR as an example of a scalable learning environment that can increase access to research opportunities, particularly for traditionally under-represented groups, there is limited research supporting this assertion. Moreover, the studies that do exist have data-related limitations, including narrow disciplinary focus, using data from a single institution, and relying on self-report measures of participation. The most striking limitation across many of the studies included the lack of addressing sources of selection bias. This issue arises because of the strong academic histories of students who are selected to participate in research experiences.

Given the recent push for universities to embed CUR in their curriculum, it is critical to understand which students are accessing such experiences. Additionally, understanding the characteristics of students participating int these experiences is important for understanding the sources of bias needed to model selection into undergraduate research. Drawing on the Texas statewide longitudinal data, the present study addresses the data-related shortcomings of previous studies to explore which student and institutional factors enable or constrain participation in CUR. Specifically, we ask:

1. What pre-college characteristics are related to the likelihood of participation in CUR?    

2. Do these relationships differ by field of research (e.g., STEM vs. non-STEM?)

This study uses administrative data from drawing from the Texas Statewide Longitudinal Data System provided by the University of Houston’s Education Research Center (ERC) from 2010-2022. The UH-ERC database is designed to track individual students attending Texas public institutions longitudinally from K-12 schools to postsecondary certificate and degree programs to the workforce. The UH-ERC contains a rich set of recorded high school student characteristics prior to attending IHEs, which are underexplored in the current literature on undergraduate research participation and the key variables of interest to address our research questions. Our sample will include students who graduated from Texas public high schools and attended a public, R1 university in Texas between 2010-2021. IHEs designated as R1 universities by the Carnegie Classification of IHEs are those with the highest research activity, measured by expenditures in research and development and doctoral degree conferrals. Each public 4-year university in Texas posts course catalogues, which contain course abbreviations and identifiers. Additionally, the ERC contains institution-specific course abbreviations and numbers, which can be linked to student data. Relying on course names, key words, and long-form descriptions, we identified all CUR offered available at a public, R1 university in Texas, and linked this to the students in the ERC database. We plan to expand this analysis to include 6 additional universities in the upcoming months. We define STEM fields in accordance with the National Science Foundation (NSF) to include fields in the physical and life sciences, technology, engineering, mathematics, as well as in psychology and other social sciences. The ERC contains a host of pre-college characteristics used in our analysis, including sex, race/ethnicity, disability, English proficiency, and immigrant-origin. From students’ high school data, we will also include measures of high school GPA, and number of advanced STEM or STEM-related credits earned. In the future analyses, we will also include several institutional measures, including acceptance rate, financial characteristics (e.g., endowment, research expenditures), urbanicity, retention rate, average time to graduation, number of CUR offerings, number of STEM degrees, and faculty research activity. We use a linear probability model with fixed effects for academic major declared at college-entry to identify what pre-college and institutional characteristics predict likelihood of participating in CUR.

Thus far, we have identified all CUR offered at a single, R1 university in Texas, and linked this to the students in the ERC database. We plan to expand this analysis to include 6 additional IHEs in the upcoming months. We focus our preliminary results on which student factors associate with participation in CUR. Our analysis illustrates that roughly 3% of all students participated in CUR from 2010-2021. The most popular subject areas of CURs were biological science (21%), communications (11%), and physical sciences (10%). We show that students who participated in gifted and talented programs (at any point in K-12) were 6.7% more likely (0.002 percentage points) to participate in CUR. Students whose mother earned an advanced degree beyond a bachelor’s were 20% more likely to participate in CUR (0.006 percentage points). When looking at differences across fields, students with limited English proficiency were 16.6% (0.005 percentage points) more likely to participate in non-STEM CURs. For students in STEM fields, the results mirrored those from research question 1. Namely, students identified as gifted and those whose mother earned an advanced degree had higher rates of participating in CUR experiences in STEM fields. The next steps in our analysis include expanding our data to include six additional R1, public universities in Texas for which we have compiled accurate CUR identifiers using course catalogues and syllabi. With the addition of more universities, we will include our set of institutional characteristics into the analysis along with institution-by-year fixed effects. The results from this study will provide university stakeholders and policymakers with information needed to make more informed decisions regarding the availability of CUR, which can be used to develop targeted interventions aimed and increasing participation for key underrepresented groups, including females, students of color, students from low-income households, and students with disabilities.

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