This research project will investigate whether online courses provide increased access to college and STEM degrees, particularly for students typically underrepresented in STEM fields. Annually, between 28% and 36% of all post-secondary students in the U.S. enroll in at least one online course. The growth in online course enrollment contrasts with an overall decline in college enrollment. Thus, continued online course growth will have an escalating impact on degree progression and attainment. Online course-taking is widely adopted at community colleges, which have large populations of first-generation college students, low-income students, female students, students of color and non-traditional students. As a result, online courses at community colleges may have disproportionate impacts on the degree completion of students from groups that have been underrepresented in STEM fields. To support an innovative and productive national STEM workforce, access to STEM careers must be available to the entire, diverse U.S. talent pool. The aim of this research is to explore whether limited access to online courses hinders degree progress for STEM majors, particularly those from underrepresented groups.
The assumption that online courses enable non-traditional students with work and family responsibilities to enroll in more courses has never been rigorously tested. This project will: 1) explore the relationship between online course availability and academic momentum (the number of credits in which a student enrolls) and STEM persistence, with a particular focus on "non-traditional" students; 2) explore the role of student time poverty (i.e. quantity and quality of time available for college) in mediating these patterns; and 3) explore scarcity of alternate course sections as a potential moderating variable in explaining these patterns. No large-scale studies to date have explored whether the availability of online courses increases access to, or momentum through, college or STEM degrees. This project will collect data on 22,000 City University of New York (CUNY) students and will make causal inferences by using: 1) a simulated course registration system to create a within-subjects experimental design; and 2) student placement on course waitlists to conduct a regression discontinuity design. CUNY's student population mirrors the groups traditionally under-represented in STEM: largely non-white, female, and low income, as well as a large proportion who are non-native English speakers and first-generation college students. This project is designed to provide critical information to practitioners and policymakers about how to balance the dual concerns of access and retention when offering online courses.
The end product of this research will be a logistic regression equation (or a straightforward recipe for institutions to follow to create their own institution-specific equation) which can be used to pinpoint students at highest risk of dropping out of online STEM courses (or college subsequently), so that effective support services can be targeted at the most at-risk students. This research will not only advance STEM and higher education research, but it will also potentially transform educational practice and policy. These results will impact students considering online courses, faculty designing and teaching online courses, staff implementing online student support structures, administrators determining policies about student access to online courses, and policymakers determining how and when to include online courses in programs to increase student access to, and success in, STEM disciplines.
This project addresses the EHR Core Research (ECR) program’s goal to build a research foundation in STEM learning environments by investigating which factors predict poorer outcomes online vs. face-toface for STEM students, with a particular focus on traditionally underrepresented groups in STEM fields. Specifically, this research is motivated by the following questions:
The assumption that online courses enable non-traditional students with work and family responsibilities to enroll in more courses has never been rigorously tested. This project will: 1) explore the relationship between online course availability and academic momentum (the number of credits in which a student enrolls) and STEM persistence, with a particular focus on "non-traditional" students; 2) explore the role of student time poverty (i.e. quantity and quality of time available for college) in mediating these patterns; and 3) explore scarcity of alternate course sections as a potential moderating variable in explaining these patterns. No large-scale studies to date have explored whether the availability of online courses increases access to, or momentum through, college or STEM degrees. This project will collect data on 22,000 City University of New York (CUNY) students and will make causal inferences by using: 1) a simulated course registration system to create a within-subjects experimental design; and 2) student placement on course waitlists to conduct a regression discontinuity design. CUNY's student population mirrors the groups traditionally under-represented in STEM: largely non-white, female, and low income, as well as a large proportion who are non-native English speakers and first-generation college students. This project is designed to provide critical information to practitioners and policymakers about how to balance the dual concerns of access and retention when offering online courses.
This project is concerned with assessing factors that impact the course and college completion rates of students at BMCC and CUNY in order to inform eLearning policy. The research will support the creation of a CUNY dataset specific to online courses at the university, with information about the percentage of instruction conducted online, as well as variables related to online programs at each campus. This project will also support analysis on CUNY-wide data and some more general NCES data in order to identify factors that may be influencing online enrollment and course outcomes at BMCC specifically and further, to compare patterns at BMCC to national and CUNY-wide trends. The research will use logistic and ordinary linear regression models, along with propensity score matching and sensitivity analysis, to analyze the impact of student characteristics and eLearning program structures and policies on online course and subsequent college outcomes.
This project is concerned with assessing factors that impact the course and college completion rates of students at BMCC and CUNY in order to inform eLearning policy. The research will support the creation of a CUNY dataset specific to online courses at the university, with information about the percentage of instruction conducted online, as well as variables related to online programs at each campus. This project will also support analysis on CUNY-wide data and some more general NCES data in order to identify factors that may be influencing online enrollment and course outcomes at BMCC specifically and further, to compare patterns at BMCC to national and CUNY-wide trends. The research will use logistic and ordinary linear regression models, along with propensity score matching and sensitivity analysis, to analyze the impact of student characteristics and eLearning program structures and policies on online course and subsequent college outcomes.