We aim to bring together colleagues who share a common interest in Computer Science and Engineering Bridge programs, which are designed to bridge the gap for less experienced and/or underserved students as they begin their university studies. We invite those interested in starting bridge programs at their institutions, as well as those knowledgeable about existing bridge programs, their best practices, and lessons learned. Together, we will discuss the most effective approaches to these programs and challenges that arise in their implementations, including exclusivity in the selection of admits, target audience, funding limitations, outreach, and more. We aim to have participants leave with resources, including information about other programs, points of contact for networking, a collaborative document from the session, as well as a website for hosting resources of existing programs. Our goal is to foster conversation and create a network of support so these programs can sustain despite general discouragement.

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.

University students, with their busy schedules and endless assignments, often struggle to remember due dates, upcoming tasks, and available course resources. Currently, courses use various educational platforms to navigate through coursework, causing confusion for students. We are developing AutoRemind, an opt-in notification tool that delivers personalized reminders for upcoming tasks and course resources via students' preferred communication platforms. AutoRemind is built to integrate with Learning Management Systems (LMS) to extract real-time data on student progress, assignment status, and available resources. This microservice, built for the GradeView platform, will serve as a centralized hub for students to gain access to all course deadlines and resources from a single source of truth, simplifying the process of tracking and completing tasks. This tool is not specific to computer science classes, so it potentially has very broad reach, hopefully leading to improved academic performance and increased course engagement. We aim to reduce administrative burdens on instructors and support mastery learning, enabling students to progress at their own pace while maintaining consistent academic performance.