Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats-tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.

Over the past decade, Computer Science programs have seen increased interest, with classes often growing in size to accommodate this demand. While enrollment trends have recently stabilized or even declined slightly in some cases, many programs are still serving these large numbers of students. One common response has been to expand the number of course sections, which requires hiring additional faculty to handle the increased load. However, this solution is often constrained by budget limitations and the availability of qualified instructors.

As part of redesigning our introductory programming sequence, the University of Washington removed formal prerequisites from each course, allowing students to self-select into whichever course they believe best fits their experience level. To help facilitate these choices, we developed a guided self-placement tool that offers course recommendations based on students' previous experience and confidence with course topics. In this report, we describe the design and implementation of the self-placement tool and reflect on its first years of use. The tool has been effective, with most students reporting that they used the tool, followed its recommendation, and are confident in their enrollment decision. The rates of students switching or dropping courses within the introductory sequence have been low. In addition, results from a preliminary interview study show that all students who followed the tool's recommendation believed the suggested course was the right choice. Most students who opted for a different course were influenced by external factors, largely related to their confidence in the course content and perception of course difficulty levels. We conclude by reflecting on what we have learned so far and laying out next steps.