{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/135537"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/135537","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A Holistic Approach to Building Resilient Computer Science Pathways: Exploring Gender, Engagement, and Attrition in Computer Science from Academia to Industry","abstract":"In today's technology-driven society, computing knowledge and expertise are increasingly essential, yet the field of computer science (CS) continues to struggle with high attrition rates in both academia and industry. This persistent issue contributes to a growing shortage of qualified professionals, with millions of computer science positions in the U.S. remaining unfilled. Compounding this challenge is one of the widest gender disparities among STEM disciplines. This dissertation investigates factors contributing to both the gender gap and high attrition rates in computer science through three interconnected manuscripts. Manuscripts 1 and 2 examine why high-performing non-CS majors choose not to pursue a computer science minor, focusing separately on the experiences of women and men. Using thematic analysis of interview data framed by Ecological Systems Theory, these studies reveal significant gender-based differences in how students navigate various system layers to ultimately disengage from the field. Manuscript 3 explores the use of generative AI for large-scale thematic analysis of social media posts, identifying reasons individuals leave computer science across four stages of departure. Contextualized through Social Cognitive Career Theory, this study identifies six recurring themes present at every stage of disengagement. Collectively, these manuscripts offer a comprehensive view of the factors driving disengagement from computer science, amplifying underrepresented perspectives and generating actionable recommendations for both research and practice.","abstract_html":"In today's technology-driven society, computing knowledge and expertise are increasingly essential, yet the field of computer science (CS) continues to struggle with high attrition rates in both academia and industry. This persistent issue contributes to a growing shortage of qualified professionals, with millions of computer science positions in the U.S. remaining unfilled. Compounding this challenge is one of the widest gender disparities among STEM disciplines. This dissertation investigates factors contributing to both the gender gap and high attrition rates in computer science through three interconnected manuscripts. Manuscripts 1 and 2 examine why high-performing non-CS majors choose not to pursue a computer science minor, focusing separately on the experiences of women and men. Using thematic analysis of interview data framed by Ecological Systems Theory, these studies reveal significant gender-based differences in how students navigate various system layers to ultimately disengage from the field. Manuscript 3 explores the use of generative AI for large-scale thematic analysis of social media posts, identifying reasons individuals leave computer science across four stages of departure. Contextualized through Social Cognitive Career Theory, this study identifies six recurring themes present at every stage of disengagement. Collectively, these manuscripts offer a comprehensive view of the factors driving disengagement from computer science, amplifying underrepresented perspectives and generating actionable recommendations for both research and practice.","abstract_has_math":false,"creators":["Ross, Amanda Katherine"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Engineering Education","degree_department":"Engineering Education","school":null,"contributors":[],"advisors":[],"committee_chairs":["Katz, Andrew Scott","Hooshangi, Sara"],"committee_members":["Knight, David B.","Matusovich, Holly"],"year":2025,"date_issued":"2025-06-18","date_published":"2025-06-18","updated_at":"2026-07-22T22:20:40Z","subjects":["computer science","attrition","women"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44318"],"render_values":[{"text":"vt_gsexam:44318","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/135537","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Katz, Andrew Scott","Hooshangi, Sara"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Knight, David B.","Matusovich, Holly"]},{"key":"dc:contributor.department","label":"Department","values":["Engineering Education"]},{"key":"dc:creator","label":"Author","values":["Ross, Amanda Katherine"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-19T08:00:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-19T08:00:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-06-18"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Education"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science","attrition","women"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44318"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/135537"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In today's technology-driven society, computing knowledge and expertise are increasingly essential, yet the field of computer science (CS) continues to struggle with high attrition rates in both academia and industry. This persistent issue contributes to a growing shortage of qualified professionals, with millions of computer science positions in the U.S. remaining unfilled. Compounding this challenge is one of the widest gender disparities among STEM disciplines. This dissertation investigates factors contributing to both the gender gap and high attrition rates in computer science through three interconnected manuscripts. Manuscripts 1 and 2 examine why high-performing non-CS majors choose not to pursue a computer science minor, focusing separately on the experiences of women and men. Using thematic analysis of interview data framed by Ecological Systems Theory, these studies reveal significant gender-based differences in how students navigate various system layers to ultimately disengage from the field. Manuscript 3 explores the use of generative AI for large-scale thematic analysis of social media posts, identifying reasons individuals leave computer science across four stages of departure. Contextualized through Social Cognitive Career Theory, this study identifies six recurring themes present at every stage of disengagement. Collectively, these manuscripts offer a comprehensive view of the factors driving disengagement from computer science, amplifying underrepresented perspectives and generating actionable recommendations for both research and practice."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["In today's technology-driven society, computing knowledge and expertise are increasingly essential, yet the field of computer science (CS) continues to struggle with high attrition rates in both academia and industry. This persistent issue contributes to a growing shortage of qualified professionals, with millions of computer science positions in the U.S. remaining unfilled. Compounding this challenge is one of the widest gender disparities among STEM disciplines. This dissertation investigates factors contributing to both the gender gap and high attrition rates in computer science through three interconnected manuscripts. Manuscripts 1 and 2 examine why high-performing non-CS majors choose not to pursue a computer science minor, focusing separately on the experiences of women and men. By analyzing interview, these studies reveal significant gender-based differences in how students navigate classroom experiences and university policies to ultimately disengage from the field. Manuscript 3 explores the use of generative AI for large-scale analysis of social media posts, identifying reasons individuals leave computer science across four stages of departure. This study identifies six recurring reasons for leaving present at every stage of disengagement. 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