Late enrollment in introductory computing is common yet consequential. Analyzing two offerings of a \course course (Term 1: n=204; Term 2: n=79), we find sharply elevated failure rates for Week~4 enrollees (Term~1: 41% F; Term~2: 100% F) versus on-time peers (Week~0 failures: 10% and 2%). These outcomes coincide with markedly lower engagement in support resources (office hours, exam reviews). While some subgroups (e.g., URM late enrollees) show pockets of resilience, performance by gender experiences dramatic changes. Results suggest late entry beyond Week~2 in cumulative, fast-paced courses is a high-risk policy point, and that structured catch-up and mandatory early support are critical.

Late enrollment in computer science courses presents unique challenges that may impact student performance, engagement, and retention. This paper investigates the academic trajectories of late enrollees compared to their on-time peers, focusing on performance across different assessment types, the cumulative impact of coursework, and engagement with course resources such as office hours, discussion sections, and review sessions.

This work examines how first-generation college status, ethnicity, technology access, and prior programming experience shape student performance in an introductory computer science course. Using data collected across two semesters, we find that first-generation students (FGS) earned fewer A grades (72%) than their non–first-generation peers (NFGS, 82%). Disparities were most pronounced among Hispanic FGS, who also reported the lowest rates of consistent computer access. Ordinal logistic regression showed that first-generation status predicted lower grade outcomes even when controlling for prior experience. These findings highlight persistent inequities in CS0 and underscore the need for instructional and structural supports that address preparation gaps and differential access to computing resources.

This Work-In-Progress Innovative Practice paper describes a new flexible, at-scale assignment extension policy and its implementation in large undergraduate computing class-rooms. While prior work has studied flexible deadlines and their effect on student learning, such policies are still under-utilized in post-secondary classrooms due to practical constraints on administrative workload, from managing hundreds to thousands of requests to reducing excessive grading overhead, especially in large post-secondary classrooms. The Flextensions tool-an automated flexible extension assignment software-promotes equitable learning opportunities in higher education by providing sufficient accommodations to each student's unique learning needs and life circumstances. Part of Flextensions is a scalable software tool implementation that facilitates instructor management of extension requests across thousands of students and a variety of course policies. We present Flextensions and describe initial experiences with adapting the tool to computer science and data science undergraduate courses at an R1 institution in the United States. This work shares the open-source software that enables and streamlines the management of extension requests across different course policies. Additionally, we provide an initial analysis of flexible extensions in three specific large-scale (500–1500 students) undergraduate computing courses. Overall, students tended not to take advantage of the policy but rather used it only when needed, with many citing extenuating circumstances-personal or otherwise. By analzing survey results, the policy was well-received, with positive impressions on well-being, learning outcomes, and overall academic experience. One student-reported benefit was that many felt valued as individuals in the classroom. Despite some students still reporting stigma towards requesting an extension, Flextensions has the promising ability to improve the quality and responsiveness of creating accommodations in large-scale classrooms.

In this work, we present the development of an automated extension tool that supports flexible extension policies. Students interact with a wide range of extension policies in similar ways; in particular, some students repeatedly request multi-day long extensions. When scaled to courses with hundreds or potentially thousands of students, course staff time is the limiting resource preventing adequate student support. We present a tool to help automate a range of extension processes. The use of this tool should reduce staff load while increasing individualized student support, through email communication and consequent recovery of student agency. Our early research questions are: Does the extension tool reduce barriers and stigma around asking for assistance? Does the tool lessen the wait time between requesting and receiving an extension, and how does the tool improve students' learning experience in the course? These questions will help inform us about how an automated tool for flexible extensions helps support growing course sizes and students who may not otherwise receive the support they need for their success and well-being in the course.

Equitable grading practices and flexible deadline policies have previously demonstrated positive student learning and well-being outcomes. In this poster, we contribute a framework for flexible extension policies that emphasize equitable grading. We then analyze extension requests and grades obtained by students in a Data Science course with a flexible extension policy. We present two research questions based on this data. RQ1: How does the length of an extension relate to student performance on the corresponding assignment? RQ2: How does student extension usage across the semester relate to students' learning of the content?