We updated our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs). The course now includes explicit instruction on how LLMs work, exposure to current AI tools and ethical issues, and student re- flection on personal use of LLMs and the larger evolving landscape of AI. We demonstrate the use and verification of LLM outputs, guide students in the use of LLMs within a larger problem-solving loop, and require disclosure of the nature and extent of LLM as- sistance. Throughout the course, we discuss risks and benefits of LLMs across CS subfields. In our first iteration of the course, we collected and analyzed data from students' pre and post surveys. Student understanding of how LLMs work became more technical, and their verification and use of LLMs shifted to be more discerning and collaborative. These strategies can be used in other courses to prepare students for the AI-integrated future.

Decades of research on Peer Instruction (PI) have shown that it improves student engagement, retention, and learning in many fields, including computing. In PI, as defined by Eric Mazur of Harvard, the instructor displays a difficult multiple-choice question during lecture which students first answer individually, then discuss with nearby peers, and then answer individually again. We created a free tool, Peer+, for Peer Instruction and tested it over several semesters at multiple institutions (University of Michigan, Duke University, Virginia Tech, and Berea College) on over 1,000 students. Peer+ is freely available in the open-source ebook platform Runestone Academy. Instructors can use any of the 90+ free ebooks on that platform or use an empty ebook. There are ebooks for Advanced Placement Computer Science, CS1 (Python, Java, and C++), CS2 in C++ and Java, and Discrete Math. We added hundreds of existing PI questions to these ebooks, which instructors can use or they can create their own multiple-choice questions. In this tutorial you will learn best practices for Peer Instruction and gain hands-on experience creating PI assignments, browsing existing PI questions, writing new PI questions, and grading PI assignments. You will also experience PI sessions using three different modes for peer discussion: traditional verbal discussion with nearby peers, text-chat with assigned group members, and text-chat with an LLM for students answering questions after lecture.

In Peer Instruction (PI) an instructor displays a challenging multiple-choice question during lecture that students answer individually, discuss verbally with nearby peers, answer individually again, and finally, the instructor leads a discussion of the question. Peer Instruction typically increases student learning and motivation over traditional lecture. We added a text-chat mode to improve PI for remote synchronous learning. This feature assigns students to discussion groups to maximize the number of groups that have members with different answers. The tool was pilot tested in Winter 2022 and revised. In Fall 2022 and Winter 2023, it was tested at one institution. In Fall 2024, it was tested at four institutions. We conducted a log file analysis of student data from 1394 students and analyzed surveys with 848 student responses. We found that questions answered using the text-chat had a significantly higher improvement than those using traditional verbal discussion, although the two modes were tested with different questions. Interestingly, most of the students preferred to discuss the question verbally, although some preferred the text-chat discussion. These results inform efforts to improve the effectiveness of Peer Instruction and increase its adoption.

Computer science as a discipline is well-placed to research and adopt new pedagogical technologies as they are developed. Many popular software platforms that have been adopted across institutions have started as research projects in computer science programs. Additionally, because of the growth in demand for computer science as a program of study, CS instructors have been on the forefront of adopting new approaches and technologies to be able to meet the requirements of teaching at scale. However, being at this intersection creates challenges of its own. There are hundreds of tools and approaches, many with some level of research behind them. These tools come from vendors large and small, with costs both large and small, and with a wide variety of features that may or may not meet the needs of the instructor and the approach they want to pursue. From infinite diversity comes infinite combinations, with the attendant number of questions that instructors must answer when designing their courses. What tools do we use? How do we pick? How can we integrate them? Can we get support? Will my institution approve them? How can I adopt a technique like peer instruction in a class of 600 ? Will the tools I pick support 50,000 assignment submissions per term? How do I manage change? In this paper we will report our experience redesigning our CS 2 course from a mix of disparate tools to a fully-automated, integrated course delivery system. We combined multiple teaching platforms into a unified design, that is delivered to the student as a coherent whole, rather than piecemeal. We will discuss the design decisions we made, and some we didn't, and why. We will discuss our approach, the challenges we faced, and also provide instructors with a set of “lessons learned”, questions they should ask themselves and their colleagues as they embark on any course redesign.

Technical and ethical aspects of Computer Science (CS) are interdependent. Many CS departments teach ethical and social implications of technology in separate standalone courses. However, prior research shows that ethical issues are better taught in tandem with their related technical content as an integral required skill in CS curricula. In this experience report, we share our experience with embedding ethics assignments in 3 CS courses at different levels: a CS2 course in software design and data structures, a CS3 course in data structures and algorithms, and a Software Engineering capstone course, all taught at Virginia Tech (a large public R1 institution) in Spring 2024. Students from the 3 courses were surveyed at the beginning and end of Spring 2024. By comparing results from the pre and post surveys, we found that the embedded assignments for the CS2 and CS3 courses improved students' confidence in their knowledge about how ethical issues may come into play in their career, their confidence in their ability to address ethical issues arising from applying technology in real contexts, and their confidence in communicating and defending their positions on how to address these issues. For all 3 courses, students gave positive feedback on how the assignments were engaging and relevant to the course, and how it improved their ability in raising, and reasoning about, ethical implications of technology. We believe that the practices and results of our experience will be helpful to other CS instructors thinking of injecting ethical content into their technical courses.

This innovative practice full paper describes the impact of early exposure to practical computing skills on students' learning experiences. In a longitudinal study, we evaluated the effects of incorporating co-curricular activities and hands-on skills into the undergraduate Computer Science (CS) curriculum, while also tracking students' sustained interest and utilization of these skills over time. Over a three-year period, more than 900 students majoring in CS were surveyed at the end of a required introductory course (post-course survey) that introduced various computing practical skills. These skills, encompassing topics such as version control, SQL, command line tools, and web development, were strategically integrated into the course to enhance students' engagement and equip them for subsequent co-curricular computing endeavors. Students' interests in these topics were measured at the completion of the course and again re-evaluated during their senior year three years later (senior-year survey). In the senior year, over 500 students responded to the survey. Similar questions related to interest, motivation, and the use of skills were asked in this follow-up survey. The results of the post-course and senior-year surveys were compared at the aggregated level. We were also able to map the responses of 68 students individually and compare their post-course and senior-year survey responses. The results of the individual students were consistent with the overall aggregated data between post-course and senior-year data. Our data analysis offers information on the timing and occasion of students applying practical skills, their sentiments regarding co-curricular activities, and the favorable influence of practical skills on the overall student experience. As students mature and accumulate experiences in their academic journey, their perceptions about these early exposures and practical skills also evolve. By the time they reach their senior year, basic practical skills do not appear as crucial, given their acquired proficiency. Nevertheless, this observation is significant since educators may not always be attuned to the challenges faced by novice students, emphasizing the importance of early exposure. Additionally, our longitudinal results affirms that both the utility and interest levels persist over the span of their undergraduate degree, reinforcing sustained motivation.

The rise in multiprocessors has led to the incorporation of parallel processing in virtually all segments of industry. Creation of and maintenance for the software to run these systems, as well as for the applications using these systems, requires extensive knowledge of the concepts and skills of parallel and distributed computing (PDC). This will naturally lead to an increase in the demand for software developers familiar with PDC and an increase in the demand for universities to incorporate PDC concepts into all levels of their curricula including introductory courses. Because there are real and perceived difficulties in teaching PDC concepts, particularly early in the CS curriculum there is a need to produce educational materials to assist with this expansion. At the same time CS education is wrestling with the surge in the need for graduates with PDC skills, it is also attempting to overcome a gender imbalance in CS. The necessity to create the materials required for increasing PDC education provides an opportunity to make strides in increasing gender diversity in CS as well. Therefore, Parallel Islands was created as a tool to aid in introducing PDC concepts in introductory CS courses in a manner that appeals to all gender identities. It is a web-based educational video game; along with the associated pre- and post-gameplay introductory, review and discussion sections, that is designed to be used by students autonomously or with instructor oversight.

The Standard Performance Evaluation Corporation (SPEC) CPU benchmark has been widely used as a measure of computing performance for decades. The SPEC is an industry-standardized, CPU-intensive benchmark suite and the collective data provide a proxy for the history of worldwide CPU and system performance. Past efforts have not provided or enabled answers to questions such as, how has the SPEC benchmark suite evolved empirically over time and what micro-architecture artifacts have had the most influence on performance?—have any micro-benchmarks within the suite had undue influence on the results and comparisons among the codes?—can the answers to these questions provide insights to the future of computer system performance? To answer these questions, we detail our historical and statistical analysis of specific hardware artifacts (clock frequencies, core counts, etc.) on the performance of the SPEC benchmarks since 1995. We discuss in detail several methods to normalize across benchmark evolutions. We perform both isolated and collective sensitivity analyses for various hardware artifacts and we identify one benchmark (libquantum) that had somewhat undue influence on performance outcomes. We also present the use of SPEC data to predict future performance.

Research in computing education has been steered towards understanding early indicators of what leads students to succeed in introductory programming courses (CS1). A major finding of these research efforts has been the impact that high school courses and prior programming experience have in predicting success in a post-secondary CS1. However, the socioeconomic status surrounding CS1 students has not been well explored as an indicator of performance. Specifically, a student's high school socioeconomic status (SES) has not been well investigated in this area, despite the intuition that more socioeconomically advantaged high schools will better prepare students for college computing courses. In this research, we propose a method to examine a student's prior high school regional socioeconomic status and determine whether this SES has a correlation to their post-secondary CS1 performance. This paper investigates the socioeconomic status of the neighborhood, census tract, and county the high school resides. To understand the socioeconomic statuses of these regions, we utilize multiple socioeconomic indices such as the Area Deprivation Index and the Social Deprivation Index. Some of the factors that create a deprivation index are the housing values of the region, poverty rate, adult educational completion, and household resources. After proposing a method to examine if there are any correlations between a student's attended high school regional SES and the student's performance in CS1, we perform a case study using seven years of CS1 student records from our institution. From the 4863 student records we use in this study, our initial findings indicate that students from more advantaged high school regions tend to pass CS1 more frequently across all surrounding region sizes we examined. Since our findings indicate that high school regional socioeconomic status may be a factor in a student's performance, we argue that future computing education researchers should consider a student's SES as a demographic factor of course performance in order to advocate for interventions that mitigate this disparity gap.

The process of sharing content among instructors at different institutions is not straightforward. In most contexts, "shared" material is unidirectional: a more experienced instructor shares their materials with a more novice instructor; a book publisher provides resources to instructors who have adopted their textbook. In a fully-realized sharing ecosystem, this flow is bi-directional. Materials can be shared, modified, corrected or edited, and then the changes are committed back to the repository for use by everyone who has adopted the material. There are many issues that must be addressed regarding the sharing process, related to both the actual content, as well as the context in which the sharing occurs. In fact, there is a wide opinion on what exactly "sharing" means. As such, we endeavoured to identify sharing opportunities and address issues that sharing course content raises. The primary goal of this initiative was to inform the development of ways in which shared courses and content can be made available online to share across higher education institutions in Virginia, as a potential model to be expanded to other localities and disciplines.

While many studies have focused on students' performance in CS1 courses, research related to the performance and persistence of students in CS2 classes is not as widely performed. In this work, we will extend our previous work to examine students' performance in CS2. We examined a data set that spanned over seven years on more than 5300 student records. In addition to typical factors studied by others (i.e. gender, race, CS1 performance), our work also took into account the relationship between various CS1 pathways to CS2, student major, and the number of previous college CS courses (including transfer credits) and student performance in CS2. CS1 grade is a good indicator of performance in CS2. Gender was not a significant factor in determining performance in CS2 and undeclared engineering majors stood out as high performers. CS majors passed the course at higher rates than other majors. Our large data set allowed for more granular analysis according to race and ethnicity and additional access to students' underserved status. Race and ethnicity had a significant correlation with performance, and so did the underserved status. Our large data set confirmed some of the findings of our previous work, while providing some new insight.

CS1 student success rates are a longstanding issue in the computer science community. Indicators of performance prior to CS1 continue to be investigated in research, especially concerning prior programming and math courses taken at the high school level. This study aims to take a look at students' high school socioeconomic neighborhood status and determines whether there is a correlation to CS1 performance. Specifically, we examine the Area Deprivation Index (ADI) of the high schools that CS1 students attended and the passing rates in CS1 based on the socioeconomic status of these high schools. The goal is to compare the performance of students from socioeconomic disadvantaged high schools to students from advantaged high schools. In this research, we find that students from the top 15% high schools ADI percentile pass CS1 at a higher rate with a significant difference.

Participation in co-curricular activities, such as hackathons, coding clubs, and undergraduate research has been shown to have a positive impact on the retention, persistence, and sense of belonging of students in the Computer Science (CS) field. In this paper, we will present the result of a study to assess the impact of integrating co-curricular activities and practical skills into the undergraduate CS curriculum. More than 500 senior CS students were surveyed over a span of four semesters about their comfort level, use of practical skills, and their experience in a sophomore-level required course which was redesigned a few years ago. The new course introduced practical skills such as version control, SQL, command line tools, and web development as a way to better engage the students and prepare them for co-curricular computing experiences. Our data analysis provides insight about when and where students use practical skills, how students feel about co-curricular activities, and the positive impact of the course redesign on the overall student experience.

Performance in CS1 and introductory CS courses has been an area of active research in the CS education research community for more than four decades, but studies related to student performance in CS2 are not as widely available. Past studies have examined the impact of CS1 grade, prior math preparation, and other factors such as homework, test, and project grades, on the overall performance in CS2. In this work, we will build upon the existing research related to CS2 performance with an emphasis on a few factors that have not been previously considered for this course. In addition to typical factors studied by others (i.e. gender, race, CS1 performance), our work also takes into account the impact of various CS1 pathways to CS2 and the number of previous college CS courses (including transfer credits) on student performance in CS2. We also look into both persistence, by distinguishing students who stay in the course versus those who drop from the class before the mid-semester drop deadline, and performance. Gender and race were not significant factors in determining performance in CS2 but undeclared engineering majors stood out as high performers and students' CS pathway leading to CS2 was also significant. Notably, students with CS1 transfer credit had significantly lower pass rates. Students with only 1 previous CS course credit were less likely to drop or not pass the course.

Static analysis tools evaluate source code to identify problems beyond typical compiler errors. Prior work has shown a statistically significant relationship between correctness and static analysis results. This paper replicates and extends a prior study on FindBugs, a static analysis tool aimed at professional Java programmers. The prior study showed a strong link between certain FindBugs issues and problems with program correctness. It also showed they were significantly associated with struggling, as indicated by taking more time, making more submissions, and receiving lower scores. However, the study used small programming exercises involving only a handful of lines of code from one semester of a CS1 course. This replication study uses the same experimental approach, but applies it to full-scale programming assignments from hundreds to thousands of lines in length, across all sections of CS1, CS2, and CS3 at a large public university over a period of 4 academic semesters, and involving 4,244 students in 109 laboratory sections completing 255,222 submission attempts. The goal of this replication study is to confirm how prior results hold up, and to explore how the results apply to full-sized programming assignments. We find a set of FindBugs warnings that are inversely correlated with correctness and confirm that their presence is still significantly associated with struggling on larger programming assignments. However, a larger number of FindBugs issues were identified as valuable. We also discuss how student-friendly messages have been added to provided student-readable feedback for a tool where the native messages were written for professional programmers.

Very little research exists on the incorporation of co-curricular opportunities, specifically capture-the-flag (CTF) activities, and the impact on computer science students. However, some literature on underrepresented groups in the field of computer science indicates that co-curricular experiences, such as CTF activities, have a significant impact on efficacy and sense of belonging. This experience report seeks to identify and share teaching strategies that impact student learning, efficacy, and sense of self/belonging in the field of Computer Science, for all students. In an effort to design learning opportunities to help bridge the gap between the haves and have nots, and maximize learning for all students, this strategy uses CTF-based instruction as a way to generate and support culture-based, computer science experiences. The aim is to identify instructional strategies that build students' computing skills and improve their comfort participating in computing activities thus broadening participation in computing and cybersecurity. As students' comfort is related to their self-efficacy and sense of belonging, we explore the integration of co-curricular opportunities, specifically a capture-the-flag (CTF) approach to computer science instruction, as means of increasing student success, with regard to learning, efficacy, and sense of belonging/identity. This report involves a description of the class activities as well as data from a series of pre- and post- surveys of the student experience, attitudes, and gain in knowledge. Initial analysis indicates that low-stakes, entry-level exposure to computer science concepts has a positive effect on student comfort and skill acquisition in computing domains external to coursework.

COVID-19 caused universities to switch from traditional face-to-face (F2F) course delivery to completely online in Spring 2020. This transition took place on short notice in the middle of the semester. We present results from surveys of students in two CS courses offered at Virginia Tech. Results indicate differing perceptions in the two courses regarding the usefulness of course components before and after the transition for each course. A logistic regression model indicates that for each course, different course components both before and after the transition significantly affect students' preferences for course modality.

Driven by diverse needs of students, as well as increasing needs of other disciplines, the past two decades have seen a proliferation of emerging variations in how computer science programs approach a second course in the discipline. Approaches more focused on majors include adaptations to a traditional introduction to data structures course that may heavily incorporate object-oriented programming or emphasize using data structures from libraries. Paths that might be concurrent with a traditional CS2, or have greater appeal to non-majors include recent efforts with data science or secure computing. In a world of increasingly limited resources but ever-increasing needs and demands from inside and outside the major, how do we provide solutions? Bringing together educators who are concerned about intermediate CS education will allow participants to hear multiple viewpoints about what the needs are and how various institutions are currently, or are considering, addressing them. The discussion leaders will kick off the conversation with local expertise---one approach of a CS2 course for majors and non-majors that emphasizes data science and modern practical skills such as databases and HTTP, another with new curricula for multiple CS2 courses, and a third with novel approaches in a traditional data structures CS2 course. Attendees will be able to connect with other people interested in the future of course curricula following CS1 and share ideas, resources, successes, and failures. We will be soliciting participants' perspectives on preparing CS2 students for downstream paths, including (i) future courses, (ii) interviews, (iii) internships, and (iv) research projects.

Academic co-curricular activities (e.g., programming contests, hackathons, student clubs, tutoring, internships, undergrad research) are popular with students, promote academic engagement and retention, and provide a competitive advantage to students applying for jobs and grad schools. This panel will continue a conversation started at a 2019 BOF session on this topic.

Solving problems in various current subfields of computer science improves student motivation and helps level the playing field for students who are less-likely to be exposed to such topics and tools outside of their coursework. The importance of student mindset is well studied in education, confident students who find course material useful and interesting are likely to be motivated and perform well. For several decades computer science educators have recognized the need for computer science students to enhance and practice their problem-solving skills. More recently, computer science educators have become attentive to barriers to student success, particularly barriers that impact students from groups that are underrepresented in the field. Students with more computing experience outside of their college coursework, such as high school coursework or extracurricular computing activities, have an advantage and the students who lack such experience are likely to be intimidated in addition to less skilled. This updated version of a Computer Science Problem Solving course still addresses the long-recognized need for undergraduates to improve metacognitive and problem-solving skills, but also improves student motivation and engagement by using current computer science tools and demystifying potentially intimidating topics such as version control, security, command line tools, web development and machine learning. In this poster the author reports on the philosophy and design of a newly updated Computer Science Problem Solving course for computer science majors and presents preliminary data regarding student mindset, metacognition and computing experience outside of coursework.

We share approaches for coordinating the use of many online educational tools within a CS2 course, including an eTextbook, automated grading system, programming practice website, diagramming tool, and debugger. These work with other commonly used tools such as a response system, forum, version control system, and our learning management system. We describe a number of approaches to deal with the potential negative effects of adopting so many tools. To improve student success we scaffold tool use by staging the addition of tools and by introducing individual tools in phases, we test tool assignments before student use, and we adapt tool use based on student feedback and performance. We streamline course management by consulting mentors who have used the tools before, starting small with room to grow, and choosing tools that simplify student account and grade management across multiple tools.

The current emphasis on cybersecurity worldwide, demonstrates the importance of this topic. This poster describes a unique NSF funded project that aims to create cybersecurity education opportunities at Virginia Tech (VT). It is a collaborative effort among faculty and graduate students in the Engineering Education, Computer Science (CS), Electrical and Computer Engineering (includes two majors, Electrical Engineering (EE) and Computer Engineering (CPE)) Departments, and the Hume Center in the College of Engineering at VT. The goal is to integrate cybersecurity modules into eight required CS and CPE courses, from freshman to junior levels, utilizing Jeremy Bruner's spiral-theory-based1,2 curriculum model. A spiraling theme of "handling threats to software for securing information" is chosen that will be returned to repeatedly as learners advance in their knowledge and intellectual capacity. Cybersecurity goals of the Confidentiality, Integrity, Availability, Authenticity, Anonymity, Assurance (CIA/AAA) triad, as appropriate for various academic levels, are adopted to develop the cybersecurity modules. Each module engages students in an authentic activity that reinforces the cybersecurity concepts. The project includes an engineering education research component, which is focused on evaluating the effectiveness of the curriculum in enhancing students' knowledge, skills, and motivation in cybersecurity concepts. The first year of the project has been completed by introducing cybersecurity modules into four courses (CS: Introduction to Software Design, and Software Design and Data Structures, and CPE: Engineering Problem Solving with C++, and Data Structures and Algorithms) impacting ~1600 students. The details of curriculum development, implementation and, preliminary findings of the research will be presented.

Understanding basic manipulation of object references is a conceptual prerequisite to understanding many data structures, beginning with linked lists and trees. Unfortunately, there is often a gap between introductory programming courses that introduce reference variables and creation of objects, and second-semester programming courses that immediately skip to introducing linked lists. As a consequence, students begin using references extensively during Data Structures and Algorithms courses without enough knowledge about how references work. To fill this gap, we created a tutorial about basic use of references and the manipulation of objects using references. The tutorial, implemented as part of the OpenDSA eTextbook system, focuses on learning through visualizations and interactive exercises. Students work a series of small code-writing exercises, inspired by the JhavePOP system. These exercises use simple program visualization to let students see the result of running their program. Manipulative exercises test student proficiency at reading and understanding small sections of code that use references to link objects. All exercises are automatically assessed, providing immediate feedback. Our evaluation of the tutorial's effectiveness will focus on identifying and correcting student misconceptions. Optional sections of the tutorial introduce the program stack and the dynamic memory heap.