Date of Award
Doctor of Philosophy in Computer Science
Computer Science and Stastistics
In recent years, women and underrepresented minority students have made significant advances in traditionally White male-dominated majors. Nevertheless, they are still underrepresented in most STEM majors. Consequently, many institutions in Higher Education are exploring ways to deliver environments that support those underrepresented students’ retention in science and engineering. This study sheds some light on women and minority students’ underrepresentation in computer science by exploring factors associated with their retention in the field.
This dissertation starts with an exploratory analysis that examines the attrition rate of underrepresented students who switched to other majors compared to their majority peers and investigates any gender/race disparities in students’ switching patterns in computer science.
Based on the exploratory results, we conduct a sequential mixed method to thoroughly investigate the factors that influence the underrepresented students’ decisions to stay/leave the program. Starting with a quantitative analysis, we use various statistical learning methods and techniques to show any significant results related to the retention rates among underrepresented students. Followed by a qualitative analysis, this dissertation shows a comprehensive reflection on the lived experience of underrepresented students who chose to either switch or persist in the program. The results show the similarities and differences between persisting and switching patterns.
Finally, we conduct a preliminary intervention experiment based on all the previous results in an effort to increase the retention rate of underrepresented students in CS. The results of this experiment show many promising outcomes.
Albarakati, Noura, "ANALYZING FACTORS THAT CONTRIBUTE TO ATTRITION OF UNDERREPRESENTED UNDERGRADUATE STUDENTS IN COMPUTER SCIENCE" (2022). Open Access Dissertations. Paper 1357.
Available for download on Friday, May 17, 2024