This work investigates the impact of Large Language Models (LLMs) and the COVID-19 pandemic on student behavior with autograder systems in three programming-heavy courses. We examine whether the release of LLMs like ChatGPT and GitHub Copilot, along with post-pandemic effects, has modified student interactions with autograders. Using data from student submissions over five years, totalling over 4,500 students across over 420,000 submissions, we analyze trends in submission behaviors before and after these events. Our methodology involves tracking submission patterns, focusing on timing, frequency, and score.

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.

This full innovative practice paper describes a computational tool designed to optimally match students to industry-sponsored capstone projects in a software engineering capstone course for Computer Science undergraduates at an R1 University (R1U). In the context of these capstone courses, where students stand at the culmination of their academic journey, aligning students' personal learning goals and existing computing skills with team formations becomes critical. This paper presents the Student-Project Matching Tool (SPMT), created to help students find the best available industry-sponsored projects based on their desired learning outcomes, project requirements, and their interests in each project. To choose the learning outcomes they aim to achieve, students can select from a list of predefined software engineering categories and the skills needed to achieve proficiency in each category. The initial list of technical skills for each category was recorded from job postings on a variety of well-known job-search websites, and was further refined by the capstone program's industry partners. Allowing students to select the skills they will work on ensures that they have opportunities and exposure to the skill sets required for employment while still working on one of their most appealing projects. We have developed and piloted the SPMT, which utilizes student vectors to represent their interests and experiences across various software engineering skill sets. Similarly, this tool uses vectors to represent the skills required by each available project, aligning with the exact dimensions as those of the student vectors. The SPMT calculated Euclidean distances between the student interest and project requirement vectors. Next, the resulting Euclidean distances were multiplied with weights associated with students' level of interest in each industry-sponsored project. Subsequently, we framed the student-project matching process as a linear sum assignment problem, aiming to minimize the total sum of Euclidean distances between each student-project pair. The output of the SPMT process consistently matched students with teams that met their software engineering interests and project priorities. Our results reveal increased engagement and growth toward students' desired learning outcomes and computing skills. Specifically, after the first term of the capstone sequence, most students self-reported higher levels of proficiency growth in the skills within their desired software engineering category. This suggests that the SPMT effectively provides students with valuable learning experiences relevant to their career interests and representative of real-world settings.

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).

Pursuing higher education is a competitive process. Many students dropping out risk having less career opportunities. We implemented machine learning (ML) models to identify students at risk of probation and discover new factors correlated with probation cases. This would allow to proactively provide students at risk with support to maximize academic success, and also propose curricula changes.

Diverse classrooms are linked to enhanced intellectual engagement and understanding of different perspectives. College admissions decisions have traditionally relied heavily on academic characteristics like GPA and standardized testing. Universities started to adopt holistic strategies while attempting to increase diversity. Yet, increasing subjective assessment may increase risk of human bias. Machine Learning (ML) could assist in admitting a more diverse student body, but algorithmic bias could be introduced. Our goal is to develop software tools to minimize human bias in admissions while actively eliminating algorithmic bias. We will examine past admissions data and identify risks of use of possible privileged values, which have historically put certain groups at a disadvantage. This tool may help reduce bias risk and select a more diverse and academically prepared group for admission.