Technical interviews have long been used for assessing candidates for software engineering roles in industry, but given the recent advent of generative AI tools, a growing number of instructors have been investigating their role as an assessment in the computer science classroom. As a type of oral assessment, technical interviews dynamically evaluate students' ability to not only demonstrate technical skills but also communicate about their problem-solving approaches. However, there are some concerns about equity and accessibility in designing and implementing technical interviews, such as how language proficiency, neurodiversity, and familiarity with the interview format might affect a student's performance.

Alternative grading practices seek to improve learning and make education more equitable. For the past few years, our community has been exploring these techniques and contextualizing them for use in computing courses. In this panel, we will present various grading techniques with a particular attention to the experience implementing them and pitfalls to be avoided. The presenters all have extensive experience exploring alternative grading techniques. Furthermore, we hope to engage the audience in a conversation about the nuance of implementing these particular techniques in the variety of courses that are typical in computing programs.

A two-stage exam (TSE) is an exam format where students write an exam individually and rewrite the same exam in collaboration with a group of peers. This Working Group investigates TSEs in post-secondary computer science courses. By collecting data from a variety of post-secondary institutions, we aim to identify correlations between course environment indicated through the CALI inventory and students' perceptions and performance on TSEs, with consideration for students in under-represented groups. Students will be given surveys upon completion of TSEs which include open-ended questions regarding their group's dynamics and perceptions of participating in a TSE. Instructors will also provide their experiences running and observing TSEs. This research aims to understand best practices for the implementation of TSEs.

Demand for teaching faculty in the computing and information sciences is very high. To address this demand, many institutions of higher education in North America have been actively seeking and successfully recruiting full-time teaching faculty without requiring PhD. However, there remain many significant challenges around preparing, recruiting, and supporting these teaching faculty that are unique to their position and experience in academia. This Birds of a Feather session welcomes faculty on both sides of the hiring table as well as post-secondary students interested in teaching careers to discuss and envision how our community can better support diverse-degree pathways to full-time teaching faculty careers. We anticipate supporting questions and conversations from people with experience at all levels: conversation topics may include how to navigate the job market, how roles vary between institutions (with regards to degree requirements, workload, job security, service responsibilities, and expectations of scholarship), and how to better prepare and support students interested in this career track.

Generative artificial intelligence poses new challenges around assessment, increasingly driving introductory programming educators to employ invigilated exams. But exams do not afford more authentic programming experiences that involve planning, implementing, and debugging programs with computer interaction. In this experience report, we describe code interviews: a more authentic assessment method for take-home programming assignments. Through action research, we experimented with the number and type of questions as well as whether interviews were conducted individually or with groups of students. To scale the program, we converted most of our weekly teaching assistant (TA) sections to conduct code interviews on 5 major weekly take-home programming assignments. By triangulating data from 5 sources, we identified 4 themes. Code interviews (1) pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights; (2) enabled peer learning, reducing stress in some ways but increasing stress in other ways; (3) scaled with TA-led sections, replacing familiar practice with an unfamiliar assessment; (4) focused on student contributions, limiting opportunities for TAs to give guidance and feedback. We reflect on the design of code interviews for student experience, academic integrity, and teacher workload.

Elglaly et al. [2] argue for teaching accessible design not only in human–computer interaction courses but across the undergraduate computing curriculum. This poster provides a description and preliminary evaluation of a pedagogical approach to teaching accessible design in an undergraduate advanced Data Structures and Algorithms (DSA) course. Inspired by the idea that all technology is assistive, our approach aims to empower students to utilize their DSA content knowledge and skills to redesign software features to address design assumptions and improve technologies for all.

Prior work has argued for a more justice-centered approach to postsecondary computing education by emphasizing ethics, identity, and political vision. In this experience report, we examine how postsecondary students of diverse gender and racial identities experience a justice-centered Data Structures and Algorithms designed for undergraduate non-computer science majors. Through a quantitative and qualitative analysis of two quarters of student survey data collected at the start and end of each quarter, we report on student attitudes and expectations.

Why do instructors have the grading policies they do (e.g., rigid assignment deadlines, curved grading, exams with no "clobber" where a high performance on a later exam overwrites an earlier poor score, etc.)? Chances are, it's because that was the way (a) they were graded as a student, (b) the department expected, or (c) the previous instructor did it before they took over.

Many efforts are underway to have computing curricula prepare students to anticipate adverse social impacts of computing. Much of the attention currently focuses on introductory CS courses and machine learning courses, often framed around bias that arises around algorithmic decision-making systems. The presenters on this panel have instead focused on ways to weave responsible-computing content into data structures and introductory algorithms courses. They have done so at different levels, ranging from second-semester introductory courses (so-called CS2) up through upper-undergraduate or early graduate courses. Each panelist will describe their perspective on how responsible computing fits into their course and present an illustrative assignment or lecture from their course. The goal of the session is to inspire other CS faculty to work similar content into corresponding courses at their own institutions, while also fostering a community of practice for responsible computing in core CS courses beyond machine learning.

Justice-centered approaches to equitable computer science (CS) education frame CS learning as a means for advancing peace, antiracism, and social justice rather than war, empire, and corporations. However, most research in justice-centered approaches in CS education focus on K-12 learning environments. In this position paper, we review justice-centered approaches to CS education, problematize the lack of justice-centered approaches to CS in higher education in particular, and describe a justice-centered approach for undergraduate Data Structures and Algorithms. Our approach emphasizes three components: (1) ethics: critiques the sociopolitical values of data structure and algorithm design as well as the underlying logics of dominant computing culture; (2) identity: draws on culturally responsive-sustaining pedagogies to emphasize student identity as rooted in resistance to the dominant computing culture; and (3) political vision: ensures the rightful presence of political struggles by reauthoring rights to frame CS learning as a force for social justice. Through a case study of this Critical Comparative Data Structures and Algorithms pedagogy, we argue that justice-centered approaches to higher CS education can help all computing students not only learn about the ethical implications of nominally technical concepts, but also develop greater respect for diverse epistemologies, cultures, and experiences surrounding computing that are essential to creating the socially-just worlds we need.

Motivated by the need to develop equitable and just computer science education, we implemented a resubmission policy. We ran a large-scale, end-of-term survey of all students across two large introductory CS courses asking about their reasons for resubmission. Though some students were motivated primarily by grades, many responses suggested intrinsic motivation. We interviewed 9 students and found that in our competitive program, resubmissions take the pressure off the need to submit work that earns a perfect grade the first time. However, our findings suggest that resubmissions alone can't create space for equity and belonging.

Assessing student mastery is an increasingly important aspect of a computer science (CS) course. Recent discussions in the SIGCSE community have questioned traditional assessment and grading practices, such as the use of high-stakes exams and standardized programming assignments. As an alternative, authentic assessments of mastery have been proposed with the goal of creating more equitable and inclusive classrooms that support a diversity in student discourses and epistemologies. This Birds-of-a-Feather session will provide a forum for conversations around assessment of student mastery. Although conversations will likely draw on experiences from teaching remote courses, the discussions can also inspire assessment ideas and methods that work for in-person instruction as well. The discussion leaders will begin by sharing their experiences using formative, low-stakes quizzes; two-stage individual and group assessments; student-generated video problem solutions; written research papers; and creative projects. For each assessment, the discussion leaders expect to address questions such as: What were the goals? What classes was it used in? How did we grade it? How does it scale? This session is a space for participants to expand our collective understanding of how authentic assessments can be used in CS courses and share ideas to inform research and practice toward grading for equity. Afterward, discussion notes will be compiled and publicly archived at https://kevinl.info/authentic-assessments

While many students now interact with web apps across a variety of smart devices, the vast majority of our Nifty Assignments still present traditional user interfaces such as console input/output and desktop GUI. In this tutorial session, participants will learn to build simple web apps for programming assignments that execute student-written code to dynamically respond to user interactions resulting in a more modern app experience. Our approach requires up to 75% less code than similar desktop GUI apps while requiring few (if any) modifications to existing assignments. Instructors and students alike can run and modify these web apps on their own computers or deploy their apps online for access from any smart device at no cost. The tutorial presents examples from CS1 and CS2 courses in Python and Java, but the ideas apply generally.

Most personal student interactions with instructional staff come through office hours. Particularly in large courses, office hours are predominantly run by teaching assistants (TAs). TAs are best advantaged by support and training from more senior instructional staff, especially faculty. This Birds-of-a-Feather session will provide a forum for discussing challenges and innovations in managing office hours towards improving the student learning experience and environment by discussing ideas around (1) personalized support and mentoring for TAs, (2) technological support such as online queues and internal wikis, (3) considerations for remote teaching and learning, and (4) methods for evaluating office hours. It is hoped that these conversations may inspire transformative changes to office hours structure that lead to future educational innovations in research and in practice. While this session will emphasize TA-supported office hours, the discussion may also inspire new ideas for managing instructor office hours as well. During our session we will create breakout rooms for each topic and use a shared document where each room can take notes. After the break out session, each group will report back and create a summary of their discussion notes, which will be publicly archived at https://kevinl.info/office-hours

Conference presentations usually focus on successful innovations: new ideas that yield significant improvements to current practice. Yet educators know that we often learn more from failure than from success. In this panel, we present four case studies of "good ideas" for improving CS education that resulted in failures. Each contributor will describe their "good idea", the failure that resulted, and wider lessons for the CS community.

There is overwhelming evidence that active learning is better than completely passive lecture. However, adoption of evidence-based teaching practices has been slow in part because creating new course materials is often a time-consuming and labor-intensive process. Inspired by prior work in the sciences, we describe our experiences deploying guided lecture notes to transition from peer instruction (10% of lecture time) to process oriented guided inquiry learning (POGIL, 50% of lecture time) over two offerings of data structures and algorithms in a large R1 university. In the first offering, we added metacognitive questions in the presentation speaker notes, providing additional scaffolding between pre-lecture reading and in-lecture peer instruction activities. At the beginning of each class session, we distributed guided lecture notes to students by printing the presentation speaker notes alongside lecture slide content. In this way, we were able to seamlessly integrate new supporting materials alongside lecture graphics and examples. In the second offering, we expanded guided lecture notes into POGIL worksheets by migrating most of the remaining passive lecture content to pre-lecture readings and consolidating lecture around three levels of process oriented guided inquiry: (1) metacognitive questions, (2) peer instruction activities scaffolded by the metacognitive questions, and (3) practice problems integrating multiple ideas. The resulting POGIL classroom leverages presentation software as a canvas for introducing problems with graphics and animations while structuring activities around active learning via process oriented guided inquiry.

Most personal student interactions with instructional staff come through office hours. Particularly in large courses, office hours are predominantly run by teaching assistants (TAs). TAs are best advantaged by support and training from more senior instructional staff, especially faculty. This Birds-of-a-Feather session will provide a forum for discussing challenges and innovations in managing office hours, particularly in computer science courses, with the aim of improving the student learning experience and environment. This session will have the opportunity for discussing the mentoring and personalized support of TAs, as well as what technological support exists, such as queue software or internal wikis, and additionally ideas for more significant changes to office hours structure that could lead to future collaborations or research. While this session will emphasize TA-supported office hours, the discussion should inspire new ideas and techniques for managing individual-instructor office hours as well.

Allyship in academia is critical for creating inclusive communities that are welcoming to all students, but the perception of its benefits and challenges can vary depending on a number of factors. This session will explore perspectives of allyship in academia by bringing together a diverse group of faculty and students who can share a wide range of experiences and insights, and aims to facilitate a discussion among all attendees that leads to an exchange of ideas, the strengthening of our community, and progress toward our common goal of inclusion in computing.

Educational researchers have increasingly drawn attention to how students develop computational thinking (CT) skills, including in science, math, and literacy contexts. A key component of CT is the process of abstraction, a particularly challenging concept for novice programmers, but one vital to problem solving. We propose a framework based on situated cognition that can be used to document how instructors and students communicate about abstractions during the problem solving process. We develop this framework in a multimodal interaction analysis of a 32-minute long excerpt of a middle school student working in the PixelBots JavaScript programming environment at a two-week summer programming workshop taught by undergraduate CS majors. Through a microgenetic analysis of the process of teaching and learning about abstraction in this excerpt, we document the extemporaneous prioritization of subgoals and the back-and-forth coordination of problem solving phases. In our case study, we identify that (a) problem solving phases are nested with several instances of context-switching within a single phase; (b) the introduction of new ideas and information creates bridges or opportunities to move between different problem solving phases; (c) planning to solve a problem is a non-linear process; and (d) pedagogical moves such as modeling and prompting highlight situated resources and advance problem solving. Future research should address how to help students structure subgoals and reflect on connections between problem solving phases, and how to help instructors reflect on their routes to supporting students in the problem solving process.