| Summary: | Although originally conceived as an educational intervention for at-risk students, modern career academies have expanded their scope to programs designed to promote critical thinking, problem solving, and analytical skills to be successful in an advanced career path. Through the integration of career and technical education courses and a rigorous, college preparatory academic curriculum, career academies serve as a key piece of a larger strategy for developing a well- prepared STEM workforce. This study focuses on the Virginia Governor's STEM Academies, a state-wide initiative containing programs designed to expand options for the general student population to acquire STEM literacy and other critical skills, knowledge and credentials that will prepare them for high-demand, high-wage, and high-skill careers. Currently, 22 Academies exist serving students across 36 Virginia School Divisions.
Using educational administrative data housed within the Virginia Longitudinal Data System, I examined the Virginia Governor's STEM Academies regarding characteristics of student participation and the relationship between Academy participation and high school and postsecondary outcomes. Using multi-level regression modeling, I found that male students, Asian and Hispanic students, and non-economically disadvantage students have a higher rate of Academy participation. After matching students with propensity score matching on demographic and early academic characteristics, I find that Academy participants are more likely to take Algebra II at an earlier grade, enroll in more Career and Technical Education and dual enrollment courses, and declare a STEM major after enrolling at a postsecondary institution.
This research provides a valuable new contribution to the study of career academies after such educational programs have undergone a paradigm shift to preparing students for high-demand, high-wage, and high-skill careers. By incorporating propensity score matching and multi-level regression model, I employ a statistically rigorous approach that can serve as important benchmarking of the enrollment and academic outcomes of the Virginia Governor's STEM Academies. === Doctor of Philosophy === Although originally conceived as an educational intervention for at-risk students, modern career academies have expanded their scope to programs designed to promote critical thinking, problem solving, and analytical skills to be successful in an advanced career path. Through the integration of career and technical education courses and a rigorous, college preparatory academic curriculum, career academies serve as a key piece of a larger strategy for developing a well- prepared STEM workforce. This study focuses on the Virginia Governor's STEM Academies, a state-wide initiative containing programs designed to expand options for the general student population to acquire STEM literacy and other critical skills, knowledge and credentials that will prepare them for high-demand, high-wage, and high-skill careers. Currently, 22 Academies exist serving students across 36 Virginia School Divisions.
Using educational administrative data housed within the Virginia Longitudinal Data System, I examined the Virginia Governor's STEM Academies regarding characteristics of student participation and the relationship between Academy participation and high school and postsecondary outcomes. Using multi-level regression modeling, I found that male students, Asian and Hispanic students, and non-economically disadvantage students have a higher rate of Academy participation. After matching students with propensity score matching on demographic and early academic characteristics, I find that Academy participants are more likely to take Algebra II at an earlier grade, enroll in more Career and Technical Education and dual enrollment courses, and declare a STEM major after enrolling at a postsecondary institution.
This research provides a valuable new contribution to the study of career academies after such educational programs have undergone a paradigm shift to preparing students for high-demand, high-wage, and high-skill careers. By incorporating propensity score matching and multi-level regression model, I employ a statistically rigorous approach that can serve as important benchmarking of the enrollment and academic outcomes of the Virginia Governor's STEM Academies.
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