Background and Context. The prominence of Generative AI (GenAI) has led students to seek academic support from these tools, altering the help-seeking landscape. Since GenAI can operate as an on-demand help resource, understanding the unique value of human staff can help instructors better serve students.

Oral exams have been shown to have many benefits for both students and staff; however, there have been prior concerns about their scalability and fairness. In this experience report, we discuss the implementation of oral exams in a large-enrollment (N=628) introductory computing course designed for non-computer science majors. We report on the limited correlation between oral exam and computer-based exam scores, week-to-week variability in student performance, and the overwhelmingly positive feedback we received from students about their perceptions about the fairness of oral exams. Finally, we highlight the time required from instructional staff and provide recommendations and lessons learned for others considering implementing oral exams surrounding logistical hurdles, staff training, oral exam design, and grading.

Background and Context. An ongoing thread in computing education research is how to increase women's participation in computing. One potential way is to improve their sense of belonging, as prior research has found that individuals' sense of belonging is related to their persistence in computing. Research from other STEM fields has shown that students benefit from sharing a gender identity with their professor. However, there is limited work on how a teaching assistant's (TA) identity relates to students' outcomes, especially in computing contexts.

As enrollments in computer science courses continue to grow, with many now exceeding several hundred students, the logistics of running such large classes become increasingly complex. Tasks that instructors can complete quickly in small courses become significantly more time-consuming at scale. To address these challenges, our institution established a full-time teaching support staff position responsible for coordinating high-enrollment computer science courses. This experience report primarily documents the first author's experiences over the past three years in this role. Responsibilities include managing course logistics, hiring and supervising teaching assistants, serving as a liaison between students and course staff, and leading student outreach efforts. Shifting these responsibilities to a trained staff member enables instructors to focus more fully on pedagogy. Additionally, because the support staff member works across multiple courses, they are uniquely positioned to promote consistency in instructional policies and practices. This report presents a model for implementing similar roles at other institutions and provides guidance for its adoption.

This Birds of a Feather (BoF) session aims to build a community of graduate student researchers who study Computer Science Education (CSEd) during SIGCSE TS and continue beyond the conference. Given the interdisciplinary nature of CSEd research, graduate students may enter the field from varied departments such as computer science, education, information, and engineering education. Therefore, many graduate students lack a CSEd research community within their own institution. This BoF session will provide a space for SIGCSE TS graduate students to build their community across institutions, both during and after the conference. In this session, we will discuss key topics to improve graduate study experiences, including relationships with advisors, research topic selection, mentoring, and navigating conferences. The second half of the session will focus on discussing post-BoF activities to keep students engaged throughout the year, including future paper outlining and peer review sessions, as well as inviting former graduate students to share their career paths and experiences. During the BoF session, attendees will meet fellow graduate students in the same research field and brainstorm future activities. They will then have the opportunity to participate in follow-up sessions based on their ideas.

Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.

This experience report documents an attempt at embracing the "A's for all" and equitable grading frameworks in an introductory, proof writing-based discrete mathematics course for computer science majors (with N=138 students) at a medium-sized research-oriented university in the US. Unlike in introductory programming contexts, there is so far no reliable automated grading system that gives formative and adaptive feedback supporting the scope of a proof-based discrete mathematics course. We therefore faced the unique challenge of being unable to automate all assessments and directly offer all students unlimited attempts toward mastery.