Abstract
Understanding and improving student engagement in higher education through data and technology has been the central motivation behind both my technical and STS research projects. The technical portion of my thesis focuses on analyzing student sentiment and performance across multiple semesters of the undergraduate computer science course under course number CS3205 and named ‘Human-Computer Interaction in Software Development.’ Adjacently, my STS research investigates the impact of generative AI on student engagement in computer science education. Although these projects approach the problem from different angles, they are deeply interconnected. Both examine how technological and structural systems shape student experience in academic environments. Together, they highlight that student outcomes are not determined solely by technical systems but are also significantly influenced by social and organizational factors embedded within university learning environments. This connection reflects the broader relevance of STS perspectives in engineering practice, where understanding the human context of technology is essential for responsible design. The technical portion of my thesis produced a structured analysis of student engagement and performance using real-world course data from the course HCI in Software Development. I began by analyzing student team projects across multiple semesters, extracting key attributes such as team size, project type, semester, and client involvement. A central question in this phase was whether the perceived impact of a project influenced overall student motivation; however, the results indicated no significant correlation. To expand my understanding of student engagement, I participated in a student-focused session at the Contemplative Practices in Higher Education (CPHE) conference where my mentor, Professor Panagiotis Apostolellis and I, presented our work briefly and strived to garner more feedback from students in technical courses. While this effort did not yield substantial quantitative data, it provided valuable qualitative insights from faculty across institutions regarding student motivation in technical courses. Building on these experiences, I returned to the HCI dataset and developed a spreadsheet-style analysis pipeline to process and analyze engagement data. This involved cleaning and integrating datasets across multiple formats, including CSV and Excel files, and applying aggregation and comparative analysis techniques. Using engagement categories derived from the Motivated Strategies for Learning Questionnaire (MSLQ), I analyzed patterns across teams to identify differences in engagement levels and explore potential contributing factors. Through this work, I designed a data-processing workflow that transforms complex educational data into interpretable patterns, contributing to a better understanding of how course structure and team dynamics influence student engagement. In my STS research, I examined how generative AI affects student engagement and flourishing within computer science education. Using a socio-technical systems framework and principles from virtue ethics, I analyzed how AI tools influence not only task completion but also the quality of student engagement. Drawing on literature and course-based data from CS3205, I explored constructs such as motivation, collaboration, and emotional engagement. My findings suggest that generative AI fundamentally reshapes how students interact with their coursework. Rather than serving as a neutral productivity tool, AI alters patterns of effort, redistributes cognitive labor, and changes how students collaborate with one another. These shifts have important implications for student flourishing, as they can both enhance efficiency and potentially diminish meaningful engagement depending on how the technology is used. Ultimately, my research argues that generative AI is not merely an assistive tool but a transformative force that restructures the learning environment itself. Considering these projects together reveals the importance of integrating technical, organizational, and cultural perspectives in engineering practice. While my technical work quantified student engagement through structured data, my STS research provided insight into underlying causes of those patterns. The engagement differences observed in my data are not solely the result of individual student behavior but are shaped by broader socio-technical factors such as AI usage, team dynamics, and course design. This synthesis highlights that data alone is insufficient without understanding of the context in which is produced, hence why my role as Teacher’s Assistant for the same course analyzed allows me to deeply engage with this project. From an ethical standpoint, this underscores the responsibility of engineers to design systems that account for their impact on human behavior. In the context of educational technology, this means recognizing that tools like generative AI can influence not only efficiency but also depth and quality of student learning. By applying STS perspectives, engineers can better anticipate these effects and design systems that promote meaningful engagement and support student flourishing. Finally, I would like to acknowledge several individuals and organizations who supported this work. I am especially grateful to Professor Panagiotis Apostolellis, whose mentorship extended beyond this capstone research and whose HCI course allowed me to connect data with real student experiences. I also thank Professor William Davis for introducing me to the concept of Giving Voice to Values in engineering ethics, which shaped my approach to ethical reasoning, and Professor Kelly Crace for providing a deeper understanding of student flourishing. Finally, I acknowledge Cavalier Funds supporting my travel to Virginia Tech for the CPHE conference, where I gained valuable perspectives that informed both my technical and STS research.
