Graduation Year
2013
Document Type
Dissertation
Degree
Ph.D.
Degree Granting Department
Adult, Career and Higher Education
Major Professor
Victor M. Hernández-Gantes
Co-Major Professor
Edward C. Fletcher, Jr.
Keywords
CTE, minority, performance, trends, underrepresented
Abstract
The purpose of this study was to describe participation patterns at the district level of students enrolled in career academies and determine whether participation in career academies is a function of demographic and/or prior learning experience and prior performance variables. Ex-post facto data was used to determine six-year enrollment trends. In addition, both binary logistic regression and multinomial logistic regression methods were employed to determine the extent demographic along with prior learning experience and prior performance variables could be used to predict participation within career academies. Trend data results indicated slight increases in the proportions of students of color (including African American, Hispanic, and Multi-Racial) enrolling in career academies. However, Caucasian students continued to be overrepresented in career academies. Furthermore, female students, as well as students from economically advantaged families, enrolled to higher degrees in career academies. Moreover, students who enrolled in career academies were found to have taken more CTE coursework while enrolled in middle school and they demonstrated higher mean scores on the reading and mathematics portions of the state assessment during the school year prior to entering high school. The study is significant as it seeks to address a gap in the literature on career academy program participation, supporting the notion that the demographic (gender, race/ethnicity, and socio-economic status) make up of career academies mirror the demographics of the schools, districts, and the communities in which they operate.
Scholar Commons Citation
Cox, E. Daniel, "Predictors of Student Enrollment Patterns in High School Career Academies" (2013). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/4878