School of Education


Doctor of Education in Educational Leadership (EdD)


Jame Zabloski


Gifted Education, Underrepresented Students, Social Dominance Theory, Poverty


Education | Elementary Education | Leadership Studies


The purpose of this multiple case study was to explore how Newland, Big Hills, and Edison Elementary, which are public elementary schools in North Carolina, identify gifted students who are historically underrepresented for placement in academically gifted classes. Based on statistical data from the Department of Education and the North Carolina Department of Public Instruction, there is a disparity between the identification of Whites, Blacks, and Hispanics, especially in impoverished communities. The study sought to understand the process of identification of gifted students and how it contributes to under-representation of Black, Hispanic, and impoverished students. The study revealed what social characteristics and demographic data are prioritized in the process and what factors and values influence the process of identification in these schools by asking: what factors of identification procedures result in higher than average identification rates of historically underrepresented gifted students? The theory guiding this study is Social Dominance Theory by Sidanius and Pratto (1999), which holds that possible oppression and discrimination is subconscious and upheld by society as a whole, whether or not it works in favor of society. This study focused on 3 elementary schools that have data that supports a higher than average enrollment of gifted students that are historically underrepresented. At each school interviews were conducted with 10 – 15 participants who have direct contact with the gifted program: principals, assistant principals, gifted coordinators, psychologists, counselors, and lead teachers. Data from the interviews were analyzed for categories and themes to connect important in this manner, and adds to the growing empirical research. Specific documents were analyzed for additional data.