OCCTIVE is a library of short, animated videos introducing foundational computing concepts. The primary goal is to support faculty outside of Computer Science who are teaching computing in their courses but often lack the class time or background to cover foundational computing concepts that underlie their course materials.

Studying computer science is a journey: people start at different times, travel at different paces, and pause along the way. In this experience report, we describe a peer-led, year-long program designed to welcome students to Computer Science and Engineering as a discipline, department, and academic program. We detail the logistical, curricular, and personnel structures of this program, highlighting design choices we made to (a) open multiple ways to join the program all year, (b) de-emphasize "getting ahead", (c) prioritize reflection, and (d) connect students to existing resources. Throughout, we emphasize the critical role of peer mentors in leading and shaping this space. We share our own lessons learned, as well as reflections from students and mentors on the value of this learning community outside of formal classroom structures.

Now that students in our introductory object-oriented programming course are more familiar with Zoom and screen sharing, we consider novel assessments that leverage these tools. We developed a style of assessment where students submit a recorded screencast of them tracing a submitted program. We describe the design of the assessment and tracing prompts, and we report on an analysis of 59 submitted student videos. Our findings include common mistakes and aspects of student understanding that were expressed in the video but not in the text and code submitted by students. For example, we observed different strategies that yielded the same correct trace of a loop, as well as incorrect traces of students' own correct recursive programs. These guide us towards ways to refine the assessment and prompts in future iterations.

There are many researchers in the Computer Science Education field who have moved into this area from a different field of research. This can present challenges for these researchers, who have not come from, and may not currently be in, CSEd research groups where knowledge of the area is plentiful. Key challenges are building networks and getting to know people, conventions, conferences and so on in the field. The proposed session is designed to help those who have moved into the field - whether recently or some years ago - to build connections and share knowledge about how to thrive and progress in the field. Our intention is to build this into an ongoing network that will support those currently in the field with this background as well as those wanting to move into the field in the future.

Concerns about academic misconduct are nearly ubiquitous among educators, and are especially prevalent in computer science. However most conversations relating to misconduct focus on how students cheat, how to detect when they do, and how to discipline offenders. This emphasis on "detect and punish" can have severe negative consequences, including toxic classroom cultures, adversarial student-staff relationships, and massive mental and emotional workloads for instructors. In this panel, we examine possible root causes for misconduct in CS courses and advocate for shifting the narrative to focus on designing and delivering courses that discourage misconduct by being inclusive and supportive to all students. We also offer concrete suggestions for approaches to reduce mis-conduct through non-punitive means.

At our large U.S. research-intensive university, Chicano/Latino and Black/African-American students have been disproportionately leaving the Computer Science and Engineering (CSE) majors at a higher rate than students without these identities. To uncover possible reasons for this, we invited students in these majors who identify as Chicano/Latino and Black/African-American to participate in focus groups. Twelve students, all identifying as Latinx/Hispanic, partici- pated in the focus groups. We identify several themes related to challenging aspects of the student experience, spanning physical campus environment, department curriculum and policies, and connections between students. We triangulate these findings with results from a survey measuring sense of belonging, confidence, and obstacles for thousands of students across eight introductory CSE courses. We discuss how these themes relate to actions that departments can take to address these challenges.

For six decades, ACM and the broader computing community has established guidelines for Computer Science (CS) curricula. In Spring 2021, a CS202X Steering Committee was formed with the goal of establishing new curricular guidelines that will lead CS education for the next decade. As part of CS202X, an Algorithms (AL) subcommittee is focused on revising the AL topics and learning outcomes specified in Computer Science Curricula 2013. As the CS body of knowledge continues to grow, a healthy debate has emerged regarding how to prioritize the discipline's expanding topics for potential inclusion in CS202X. Towards this end, the objective of this BoF session is to seek feedback from members of the SIGCSE community concerning the AL knowledge and competencies that should be included in the new CS202X guidelines. Possible discussions include: should all graduates know Turing Machines, the Halting Problem, Big-O Complexity, or be able to differentiate Divide-and-Conquer from Transform-and-Conquer strategies; should newer algorithmic approaches addressing bias, fairness, and privacy be included in the curriculum; what are we missing; and what is obsolete, and if we cannot include it all, what goes? The entire computing community must assist in deciding such questions and their answers. Attend this BoF and take pride in shaping the curricular guidelines of CS education for the next decade.

Are you the director of undergraduate programs for your department? Or do you want to be one in the future? In many departments, the role of shepherding the undergraduate program is a "middle management" position held by a single person, with pressures and responsibilities unique to the role. This Birds of a Feather session will be an opportunity to build community and learn from one another: what does it mean to effectively lead an undergraduate computing program? what challenges do we face? and how can this role help position us for other professional opportunities?

Teaching Computer Science in public, research-focussed institutions is often a team effort, with graduate students serving key roles as teaching assistants. These graduate students come from diverse backgrounds and educational experiences. Many of them have not taken the course they are TAing, or took it at their undergraduate institutions under different norms and practices. We developed a course for first-time Computer Science TAs to help them navigate the mechanics of their role while learning evidence-based teaching and reflective practices. After the recent pandemic-induced emergency shift to remote learning, we introduced and evaluated three new modalities of delivering this course: synchronous, fully self-paced, and hybrid pacing. We find that student satisfaction with all the alternate versions of the course was generally positive and is not correlated with modality. We conclude that Computer Science departments have significant flexibility in developing formats for delivering TA training courses, and that these courses are valued by first-time Computer Science graduate student TAs.

This work-in-progress paper presents an innovative practice of using oral exams to maintain academic integrity and promote student engagement in large-enrollment engineering courses during remote instruction. With the abrupt and widespread transition to distance learning and assessment brought on by the COVID-19 pandemic, there has been a registered upsurge in academic integrity violations globally. To address the challenge of compromised integrity, in the winter quarter of 2021 we have implemented oral exams across six mostly high-enrollment mechanical and electrical engineering undergraduate courses. We present our oral exam design parameters in each of the courses and discuss how oral exams relate to academic integrity, student engagement, stress, and implicit bias. We also address the challenge of scalability, as most of our oral exams were implemented in large classes, where academic integrity and student-instructor disconnection have generally gotten disproportionately worse during remote learning. Our survey results indicate that oral exams have positively contributed to academic integrity in our courses. Based on our preliminary study and experiences, we expect oral exams can be effectively leveraged to hinder cheating and foster academic honesty in students, even when in-person instruction and assessment resumes.

Although Massive Open Online Courses have the potential to reach a much broader audience and offer a lower cost education than traditional in-person classes, they have struggled with low completion rates and low diversity amongst those enrolled and completing the courses. In 2015, we built a series of online courses in computing with the specific goal of attracting and retaining students from groups underrepresented in computing. In our design, we incorporated a number of features aimed at improving the inclusive nature of the courses including: a project-centered course design; an online version of Peer Instruction ConceptTests; videos where students, faculty, and professionals report their struggles when they first learned computing concepts; videos by professional software engineers explaining how computing concepts from the course are used in industry; and videos aimed at providing additional support on the project to students who might be struggling. In this work, we report on the design of the courses and examine how successful our courses were at attracting and retaining women students. We find that compared to other computing courses offered by our institution on the same platform, our courses have: a higher percentage of women enrollment, higher rates of course completion for both men and women, and a slightly smaller gap between completion rates for men and women.

Summer internships present an opportunity for Computer Science (CS) students to expand and test their skills in “the real world.” These multi-faceted experiences call on students to use technical tools and critical thinking in collaboration with others to solve problems. There are many opportunities for learning and growth: which of these do students find most valuable? In this project, we collect and analyze open-ended reflections by undergraduate CS students at the conclusion of a summer internship. We see that students focus on technical skills, expanding professional networks, and the satisfaction of completing a product that will be of use to others. These insights help inform academic programs that support Computer Science students engaging in these internships and strengthen their connection to on-campus education.

Grading is a notoriously difficult and time-consuming part of teaching. For open-ended programming, mathematical, or design problems, assigning consistent scores and giving useful feedback can be very challenging. Large classes compound this difficulty. Adding TAs to the team can help parallelize the process but may impede grading consistency and quality. We present an adaptive rubric creation and application process to enable high-quality responses to student work, at scale. This process uses exploratory data analysis to discover common patterns in student responses to a problem, then tailors a rubric and feedback to address these patterns. Our method is supported by current grading tools, which allow calculation of the simple population-level statistics we need to extract meaningful features from a corpus of student work. In this case study, we describe using adaptive rubrics for a discrete math class for CS majors: the grading team found that this process produced concrete and transparent justifications of student scores and that it facilitated conversations around grading that were grounded in course learning objectives and values.

The undergraduate computer science curriculum is ever-changing but has seen particular turmoil recently. Topics such as machine learning, data science, and concurrency and parallelism have grown in importance over the last few years. As the content of our curriculum changes, so too does the mathematical foundations on which it rests. Do our current theoretical courses adequately support these foundations or must we consider new pedagogy that is more relevant to our students' needs? In this BoF, we will discuss what a modern mathematics curriculum for computer scientists should cover and how we should go about accomplishing this in our classrooms.

Although many educators acknowledge the importance of written communication in computer science education, there are a number of challenges to using writing assignments in classes. These include assessing writing effectively and efficiently, especially in larger classes. The workshop will consist of a combination of short presentations, hands-on exercises, and discussion of a number of items related to writing assessment: the role of writing in computer science classes and how different types of assessment apply to different types of writing; how clarifying the goals of writing assignments often leads to better assessment; different techniques, such as rubrics, for grading writing; and strategies --- such as peer review and breaking a substantial writing assignment into a sequence of subassignments --- for scaling writing and its assessment to larger classes.

Using instructional videos - either as supplemental content or in a flipped classroom - has become increasingly popular among students and instructors in higher education CS courses. However, producing or finding appropriate videos can be expensive and time-consuming. This experience report describes a novel, relatively low-cost approach to creating customized video resources that requires very little instructor time. This approach leverages widely available screen capture and podcasting technologies to produce a suite of 2-5 minute "podcast highlights" videos from full-length live lecture footage. Podcast highlights distill lecture concepts into short segments, and supplementary annotations help illustrate these concepts further. Podcast highlights can be created quickly by undergraduates with just a small amount of instructor supervision. We created podcast highlights videos for four undergraduate CS courses. In a pilot study with two of these classes, many students used the highlights videos regularly and praised their usefulness for both previewing and reviewing class concepts. This report presents the podcast highlights production process, student reaction to and use of podcast highlights videos, and best practices for creation and deployment of these customized educational videos.

Many college computer science majors have little or no pre-college computing experience. Previous work has shown that inexperienced students under-perform their experienced peers when placed in the same introductory courses, and are more likely to drop out of the CS program. However, not much is known about what, if any, differences may persist beyond the introductory sequence for students who remain in the program. We conducted a study across all levels of a CS program at a large public university in the United States to determine whether grade differences exist between students with and without pre-college experience, and if so, for what types of experiences. We find significant grade differences in courses at all levels of the program. We further find that students who took AP Computer Science receive significantly higher average grades---by up to a half grade---in nearly all courses we studied. Pre-college experience appears to have a weaker relationship with retention and with low-stakes assessment grades. We discuss the limitations of these findings and implications for high school and college level CS courses and programs.

With surging enrollments in computer science, large classes are becoming standard, even at the upper division. Unfortunately, this new reality can leave students feeling anonymous and unsupported. This work examines the impact of several lightweight interventions on students' sense of connection with instructors and the class. These strategies were employed in a range of large courses at a public research-focused university. The implemented techniques include: opportunities for one-on-one tutoring, seating assignments with consistent teaching staff members seated in class, and assigned small discussion sections, among others. All strategies are lightweight and require only the usual staffing resources afforded to a class. In this report, we evaluate student sense of community and reflect on the benefits and challenges of these techniques.

The importance of written communication and critical thinking in Computer Science is widely acknowledged. It was called out specifically in the curriculum guidelines ACM CS2013 [6] and has been the topic of a number of previous SIGCSE papers, for example [1-4]. Moreover, writing as a pedagogical practice can help make CS more accessible for a broader population. However, special challenges may arise for students who are English-language learners or have writing-specific learning differences.

This paper presents results of a large-scale survey of students' experiences in CS classes at two institutions: a small liberal arts college and a large research-focused university. Our work provides a fine-grained view of students' feelings and behaviors in CS classes, from introductory through to upper division courses. We find significant differences between the reported behaviors and feelings of female students compared to male students: female students are less comfortable asking questions in class and interacting with their instructor, and come out of a class with lower confidence in their ability to tutor for the class, despite the fact that they perform just as well as male students. Furthermore, we find some of these differences are consistent or increase across course levels, and could potentially affect students' post-college trajectories. Focusing attention on the student experience in more advanced classes may impact gender differences seen in the transition to the CS workforce.

As class-sizes grow in computer science, the personal attention received by students tends to diminish. This work aims to replicate small-class community effects within a large class by creating "micro-classes"---small groups within the large class. These micro-classes consist of 20--30 students led by graduate teaching assistants and undergraduate tutors who are specifically trained in small-classroom instructional techniques. This paper studies the outcomes of the micro-classes framework in an upper-division data structures course and compares them to outcomes from the same class taught in a large lecture, active-learning format. Students report increased satisfaction and a higher perception of community in the micro-classes section, though there was no discernible difference in student academic performance.

Abstract We introduce martingales defined by probabilistic strategies, in which randomness is used to decide whether to bet. We show that different criteria for the success of computable probabilistic strategies can be used to characterize ML-randomness, computable randomness, and partial computable randomness. Our characterization of ML-randomness partially addresses a critique of Schnorr by formulating ML randomness in terms of a computable process rather than a computably enumerable function.

This paper studies infinite graphs produced from a natural unfolding operation applied to finite graphs. Graphs produced using such operations are of finite degree and automatic over the unary alphabet (that is, they can be described by finite automata over the unary alphabet). We investigate algorithmic properties of such unfolded graphs given their finite presentations. In particular, we ask whether a given node belongs to an infinite component, whether two given nodes in the graph are reachable from one another and whether the graph is connected. We give polynomial-time algorithms for each of these questions. For a fixed input graph, the algorithm for the first question is in constant time and the second question is decided using an automaton that recognises the reachability relation in a uniform way. Hence, we improve on previous work, in which non-elementary or non-uniform algorithms were found.