Reflection activities in introductory programming courses help students assess their learning experience, but prior work overlooks how they translate struggles into improvement plans. We analyzed 906 open-ended reflections from two introductory programming courses on coding struggles (code design, debugging, concepts, timeliness) vs. improvement plans (timeliness, debugging, implementation, collaboration) aggregating these into themes. Some themes aligned strongly; others did not, revealing what students perceive as challenging yet actionable in their learning.

The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including a working understanding of these systems' fundamental concepts, their limitations, and implications for responsible use. However, misconceptions about GenAI, such as perceiving these systems as mere search engines or database lookup systems, are commonly observed among students, while the availability of teaching resources remains fragmented, and learning objectives lack alignment. This Working Group aims to design pedagogical resources for computing science instructors, enabling them to develop students' GenAI literacy. To achieve this, the Working Group will first identify a concise set of GenAI literacy learning objectives informed by instructor experience, research literature, and community input, and subsequently design pedagogical resources aligned with these objectives.

As use of generative AI grows across industries, educators must adapt curricula to prepare students for AI-augmented workplaces. While prior research has documented AI adoption among software developers, less is known about how domain-specific engineers (e.g., mechanical, electrical, civil) use GenAI for coding-related work. This pilot study surveyed and interviewed professional engineers to examine employer expectations and current GenAI coding workflows. Our findings suggest that domain-specific engineers use GenAI less frequently than software developers due to safety-critical contexts and legacy systems. Participants emphasized verification and ownership as the most critical professional skills.

When students leave office hours, what do they do next? This study introduces preliminary work on a behavior-based measure of help-seeking success by exploring the relationship between office hour (OH) interactions and subsequent coding progress. We ask: Can students' code changes following help-seeking interactions serve as evidence of forward progress?

Background and Context. Existing works in computing students' help-seeking and resource selection identified an expanding set of important dimensions that students consider when choosing a help resource. However, most works either assume a predefined list of help resources or focus on one specific help resource, while the landscape of help resources evolve at a faster speed.

This study explored the impact of learning flexibility and academic stress in a blended-flexible (BlendFlex) introductory computer science course. We delved into the fluctuation of students' choices between online and in-person attendance throughout the semester, considering factors such as gender and distance from campus. Additionally, we examined the impact of learning modality choices on course performance alongside academic stress, utilizing a combination of survey data and academic records for 412 students.

Students can have widely varying experiences while working on CS2 coding projects. Challenging experiences can lead to lower motivation and less success in completing these assignments. In this paper, we identify the common struggles CS2 students face while working on course projects and examine whether or not there is evidence of improvement in these areas of struggle between projects. While previous work has been conducted on understanding the importance of self-regulated learning to student success, it has not been fully investigated in the scope of CS2 coursework. We share our observations on investigating student struggles while working on coding projects through their self-reported response to a project reflection form. We apply emergent coding to identify student struggles at three points during the course and compare them against student actions in the course, such as project start times and office hours participation, to identify if students were overcoming these struggles. Through our coding and analysis we have found that while a majority of students encounter struggles with time management and debugging of failing tests, students tend to emphasize wanting to improve their time management skills in future coding assignments.

The Hybrid Flexible (HyFlex) instruction format provides learners with the flexibility to choose from in-person, online synchronous, or asynchronous learning. However, students' learning experiences with HyFlex has not been studied at scale. The primary goal of this study was to investigate how students' perceptions about the availability of learning resources relates to their course engagement and performance in a HyFlex learning environment. In Spring 2022, we administered an end-of-semester survey to one graduate and five undergraduate courses, each of which utilized the HyFlex instructional model. Courses were selected from three different colleges at a large public university in the United States. We investigated students' perceptions about the effectiveness, importance, and ease of use of all three learning modalities that were offered (in-person, online synchronous, and asynchronous) and the learning support options (instructor access outside of class, learning help resources, and flexibility to choose learning modality without restriction). With a sample size of 537, we found that 30% of surveyed students found in-person and online synchronous learning important for their learning whereas 60% found asynchronous learning and the flexibility to choose their learning modality important for their learning. When asked about their actual use of different modalities, students reported using asynchronous learning the most, followed by online synchronous learning. In-person learning was reportedly the least utilized. We found that non-real-time learning modalities contributed positively to overall student engagement. Students preferred to use asynchronous resources and have the flexibility to choose among learning modalities. Yet, results indicate that students who incorporated some real-time learning not only had higher performance-related engagement (e.g., confidence in their ability to succeed in the course) than those who relied primarily on non-real-time learning, but they also earned higher grades for the course. This suggests that utilizing some in-person or online synchronous modalities in conjunction with the student-preferred asynchronous options leads to improved course outcomes for both student engagement and course performance.

Are women less likely to persist in computer science because of gender differences in self-assessed computing ability? And why do gender differences exist in self-assessments among women and men who earn the same grades? We use a mixed-method research design to answer these questions, utilizing both quantitative survey data (n = 764) and qualitative interview data (n = 59) from students in introductory computing courses at a large U.S. state university. Quantitatively, we find that women self-assess their computing ability significantly lower than men who earn the same grades, and that these lower self-assessments reduce the likelihood that women enroll in future CS courses (relative to men who earn equivalent grades). Qualitatively, we explore how women and men perceive their own computing ability to understand why women self-assess their ability lower than men. Our interviews revealed that women were much less likely than men to make favorable comparative judgements about their ability relative to their classmates. Women also had higher personal performance standards than men. Lastly, women were more likely than men to experience disrespectful treatment, with an undertone of presumed incompetence, from their TAs and classmates. In sum, this research furthers our understanding of why gender differences exist in self-assessments of computing ability and how these differences can contribute to gender disparities in computing persistence. It also draws attention to the importance of feedback in computing courses and suggests that improving course feedback may reduce gender disparities in computing.

Research has shown that high self-assessment of ability, sense of belonging, and professional role confidence are crucial for students' persistence in computing. As grades in introductory computer science courses tend to be lower than other courses, it is essential to provide students with contextualized feedback about their performance in these courses. Giving students unambiguous and con- textualized feedback is especially important during COVID when many classes have moved online and instructors and students have fewer opportunities to interact. In this study, we investigate the effect of a lightweight, scalable intervention where students received personalized, contextualized feedback from their instructors after two major assignments during the semester. After each intervention, we collected survey data to assess students' self-assessment of computing ability, sense of belonging, intentions to persist in computing, professional role confidence, and the likelihood of stating intention to pursue a major in computer science. To analyze the effectiveness of our intervention, we conducted linear regression and mediation analysis on student survey responses. Our results have shown that providing students with personalized feedback can significantly improve their self-assessment of computing ability, which will significantly improve their intentions to persist in computing. Furthermore, our results have demonstrated that our intervention can significantly improve students' sense of belonging, professional role confidence, and the likelihood of stating an intention to pursue a major in computer science.

We have found that giving top-performing students in CS1 courses personalized feedback increases their intentions to persist in computing, especially among students who are women. This personalized feedback also appears to improve students' course experience and increases the likelihood that women apply to be CS1 TAs. Yet despite these benefits, giving personalized feedback may seem too impractical and time-intensive for faculty members to adopt in their own classrooms. In this workshop, we will reduce the burden of giving students personalized feedback by: 1) giving instructors empirically validated email templates to use in their own courses, and 2) guiding faculty how to send emails at-scale. We will also discuss how self-assessments influence students' career choices, how gender stereotypes bias self-assessments, and what faculty can do to counteract biased self-assessments of computing ability.

Academic computing departments are seeking ways to broaden participation in computing (BPC), and many are encouraging individual faculty, staff, and students to attend diversity-oriented conferences, like the Tapia and STARS Celebrations of Diversity in Computing and Grace Hopper Celebration of Women in Computing. The purpose of the STARS Ignite Workshop is to provide faculty and staff with a framework including the tools and knowledge needed to make such conference attendance beneficial both to the students attending the conference and to the sponsoring faculty and their department. In this workshop, participants will learn to recruit and lead a BPC purpose-driven student conference cohort and the implementation of a BPC event or program. Participants will be provided with opportunities to adapt sample materials (recruitment emails, applications, etc.) to their own needs, assess the BPC needs at their institutions, and learn how to determine if their BPC efforts are successful. A laptop and Google account are required for this event. Interested attendees should be willing to commit to leading a student conference cohort to a diversity-oriented computing conference and then implementing a BPC event or program with these students.

Gender stereotypes about women's computing ability contribute to the dearth of women in computing by causing women to experience gender bias. These gender stereotypes are doubly disadvantaging to women because they create gender differences in self-assessments of computing ability, decreasing the likelihood that women will persist in Computer Science (CS). This is because students need to believe they have sufficient ability in a field in order to pursue it as a career.

Academic computing departments are seeking ways to broaden participation, and many are encouraging individual faculty, staff, and students to attend diversity-oriented conferences, like the Tapia and STARS Celebrations of Diversity in Computing and Grace Hopper Celebration of Women in Computing. Such conferences present opportunities to meet a broader community of people for professional development and networking, to be inspired by leaders in computing, and to celebrate diversity. However, while many institutions sponsor these conferences and support individual student attendance, students may not know how to leverage these opportunities effectively. We argue that leading a cohort of faculty/staff and students who attend a conference with the shared goal of broadening participation can provide lasting benefits for computing departments. This workshop will prepare faculty and staff to recruit and lead a team of students and leverage conference attendance to ignite broadening participation efforts. Through a hands-on collaborative process, the workshop provides the knowledge and tools needed to successfully lead a cohort, and helps attendees tailor the provided tools to their local strengths and needs to broaden participation in computing.

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

Flipped courses often rely on pre-recorded videos that students are expected to watch before in-class time with the instructor. In this study, we investigated the video-watching behavior of students in a flipped CS1 programming course (n=490). We computed three behavioral metrics related to video watching: percentage of the videos watched, the number of times a video was opened to be watched, and when a video is watched with respect to the due date. We used k-medoids clustering on these metrics finding two distinct groups: 1) Low Video Engagement Group (53% of the students) watched 12% of the videos and 2) High Video Engagement Group (47% of the students) watched 75% of the videos. Analysis of these two different groups of engagement showed that students with prior programming experience watch fewer videos. We also found that students that watch more videos perform slightly better on summative assessments in the course. We discuss how regular video watching can be a key learning strategy for some but not all students in a flipped CS1 course, where some students can achieve good learning outcomes with minimal watching of the course videos.

Instructors are increasingly flipping their classrooms, where students are required to study on their own prior to in-class time with the instructor. We present preliminary results on identifying student online behavior patterns in a CS1 flipped course that correlate with students' test scores covering the material explained in the online videos. We found that clustering students based on how much of the online lecture videos they watched allows us to find significant differences in the average test scores of each cluster.

Automated grading has become crucial in supporting large introductory Computer Science courses by assisting instructors in reducing grading time and course costs. However, novice programmers are often frustrated by auto-grading tools as they often provide minimal feedback or the questions are too complex. We propose using Bloom's Taxonomy to gradually increase the complexity of the programming question in an auto-grading tool. The easiest questions are based on Knowledge and Comprehension. Next are questions that require Application and Analysis. The most complex questions require Design and Creativity. We have developed programming questions that fit these categories. For example, for debugging the students have to understand and Analyze code. However, in order to write a program with more complex logic the students have to be Creative. The main goal is to lead novice programmers to learn more effectively and efficiently. In this poster, we present examples of the developed auto-graded programming problems based on Bloom's Taxonomy and the results of a pilot study in a CS-1 non-majors course

Programming projects are widely used in CS1 classes to develop students' coding skills. To improve the learning impact of these projects, we propose and study a special project format named two-stage project in an introductory computer science course. For the first stage, students submit their programming projects individually followed by a second stage where they are paired to work on the same project in order to create an improved solution. Through peer collaboration, students review each other's work from the first stage, and write correctly-styled, well-documented, and more thoroughly tested code during the second stage. We used isomorphic assessments before and after the second stage of a project to measure students' understanding of the course material. Results indicate that two-stage projects tend to improve student understanding of course learning objectives. We also studied students' perceptions and experiences with two-stage projects, and their confidence toward computing. Students liked working on two-stage projects because they saw new ways to approach the same problem, and they liked discussions with their peers.

The New York Times published an op-ed by Anita Hill [3] suggesting that women in tech consider class action to remedy the gender bias that is increasingly being reported in the mass-media. This panel raises the question "what are we doing in undergraduate programs to reduce the 'Mad Men', 'Brogrammer' culture she describes that is increasingly being reported in the popular press. Part of our mission as educators is to develop professional behavior so that our students entering the workforce not only understand what it means to act professionally, but understand that it is their responsibility to actively push back on the existing bias within the tech culture. As moderator Ursula Wolz brings a depth of insight from 40 years of industrial and academic experience, including a National Science Foundation project to broaden participation in computing [5]. She does not believe this problem can be solved through quantitative data collection on who does well in computer science, but that SIGCSE needs to begin to collect good stories (ala Sally Fincher [2]) on what constitute best practices to support diversity. The panelists present a range of perspectives that have the potential to establish new cultural norms in the single most influential industry in our economy.

We propose a scale-out solution for deep packet processing (DPP) appliances, which uses a standard Ethernet switch in combination with a load balancing controller. The majority of the data-plane traffic is distributed via the switch's built-in traffic distribution function and no connection state is kept. If the load balancing controller detects a skew in the load of the DPP appliances, it updates the traffic rules in the switch to redirect new connections to less busy DPP appliances. This adaptive solution is beneficial for load-balancing at high data rates because state is kept for only the redirected connections. For typical traffic patterns, our solution reduces the packet drops with minimal connection redirection. We show an example capture, where we are able to improve the packet drop rate by 94.8% with only 3% of the connections being redirected.

An efficient analytical method is presented for the calculation of blocking probabilities in a tandem queuing network with simultaneous resource possession. This queuing network model is motivated from the need to model optical burst switching networks, where the size of the data bursts varies and the link distance between two adjacent network elements also varies depending on the network's topology. A fast single-node decomposition algorithm is developed to compute the blocking probabilities in the network. The algorithm extends the popular link-decomposition method from teletraffic theory by allowing dynamic simultaneous link possession. Simulation is used to validate the accuracy of the algorithm.

In this paper, we propose an edge reconfigurable optical network as an intermediate step toward fully dynamic optical core network. ERON is an overlay-control network created by installing GMPLS-enabled MEMs optical switches at the edge of a core optical network composed of static lightpaths. We propose a network design algorithm that takes as input the key resource locations dispersed globally and a realistic traffic matrix from end-users and outputs a virtual topology comprised of the minimum required static lightpaths interconnected via ERON switches. This paper describes the key concepts involved in the design of an efficient ERON network combined with routing and reservation algorithms to assure low blocking probabilities. We also describe the management and control architecture and protocols required to provide end-user/application control of the dynamic lightpaths.

Emerging high-end applications require a rich set of network provisioning services that go beyond the traditional source-destination, end-to-end path service. They also require high-bandwidth and high-quality circuit services that only optical networks could offer. In this paper, we first analyze some representative high-end Grid applications and abstract their needed generic set of network provisioning services. Then we provide some preliminary analysis on the routing, resource allocation, and survivability mechanisms of these generic network services. The focus is on the temporal and spatial extensions over the traditional network service definitions. We also introduce our implementation and experimental activities within the Enlightened Computing project and present ongoing research work.