Spring 2025 marks 12 years since Snap! was first used with students and the Snap! Cloud was developed for sharing projects. Since then, nearly 1 million students and educators have worked on almost 10 million Snap! projects. This BOF brings together the growing community of Snap! users, teachers, and core developers at SIGCSE.

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.

Large Language Models (LLMs) can be used to automate many aspects of the educational field. In this paper, we look into the benefits of automating responses to student questions in course discussion forums using our Retrieval-Augmented Generation (RAG)-based LLM pipeline (Edison). Our research questions are:

This year's Snap! 9 release represents 10 years since Snap! 4.0 was initially released as a web application. Version 9 includes hundreds of new features, focused on providing students and teachers with new cloud tools, continued development of tools for working with data, and ''quality of life'' enhancements.