Computer Science (CS) students at the University of California, Irvine (UCI) have experienced academic probation rates higher than 50%. Particularly concerning, statistical analysis showed that students who self-identified as belonging to an underrepresented group (URG) experienced an even higher probation rate. Moreover, students who entered academic probation were twice as likely to leave the CS program. We designed and conducted a comprehensive survey involving 757 CS1 students at UCI to delve further into their past experiences, challenges, and perspectives to gain further insights into the factors contributing to these trends. Specifically, we studied (1) the role of socioeconomic factors such as mental health, academic preparedness, and computing participation in CS students' success, (2) to what extent these factors affect underrepresented group, first-generation, and female students, and (3) experiences that distinguish the most impacted minority groups. Our findings reveal significant correlations between underperformance in CS1 and socioeconomic factors, including satisfaction with course completion regardless of grade, mental health challenges, and insufficient pre-college math preparation. Many of these factors had strong associations with all minority groups. Moreover, our data shows that most URG students enter the program with weaker math preparation than their peers, often don't have prior programming experience, and once enrolled, they have limited interactions within the CS community. These insights highlight the urgency of redesigning academic support practices to support students with diverse backgrounds and experiences. There is a growing need to implement tailored interventions and support mechanisms for CS students, focusing on addressing the disparities in preparation, perspectives, and experiences. Our findings highlight the pressing need to reevaluate current academic support practices and provide a foundation for developing targeted support programs to guide struggling students toward greater success in CS.

With the mainstream adoption of Large Language Models (LLMs) over the last year, members of both academia and the media have raised concerns around the potential impact on student learning and pedagogy. Many students and educators wonder about the pedagogical fit of this emerging technology. We aim to measure the adoption and perception of LLMs among the CS education community in an R1 University to distinguish reality from hype. To this end, we conduct a large survey study targeting three populations participating in computing courses at the university: intro-sequence students (ISS), experienced students (ES), and faculty. Our survey seeks to gather insight around the different populations' perceptions of LLMs in education, as well as how these perceptions may be changing as LLMs improve. Our results show several significant differences across the views of 760 respondents. Most students report LLMs' un-paralleled potential for quick information access, yet many harbor concerns about their reliability and impact on academic integrity. Additionally, while ES rapidly integrate LLMs into their learning, ISS and faculty remain cautious, highlighting a stark contrast in adoption rates. Faculty are unconvinced of LLMs' educational benefits and are concerned about potential challenges in evaluating students' learning outcomes. LLMs are reshaping pedagogical approaches and student engagement. However, with the notable reservations expressed by certain segments, particularly by faculty and ISS, there is an imperative for careful, informed, and ethical integration to ensure that these tools enhance rather than compromise the educational experience.

More than 50% of Computer Science (CS) students at the University of California, Irvine (UCI) experienced academic probation over a 10-year span. Particularly concerning, underrepresented groups (URG) faced an even higher probation rate, and probation students were twice as likely to leave the CS program.

With the mainstream adoption of Large Language Models (LLMs), members of both academia and the media have raised concerns around their impact on student learning and pedagogy. Many students and educators wonder about the pedagogical fit of this emerging technology. We aim to measure the adoption of and attitudes toward LLMs among the CS student population at an R1 University to determine how students are using these new tools. To this end, we conducted a large survey study targeting two populations participating in computing courses at the university: intro-sequence students (ISS) and experienced students (ES).