To broaden participation in the computer science (CS) field and its workforce, it is important to consider how students from non-CS majors enter the field at various points along the educational pipeline. Gaining insight into these students' attitudes and interests toward CS requires a validated, reliable instrument that can capture the factors influencing their perceptions. While several tools have been developed to measure motivation, attitudes, knowledge, and self-efficacy in CS, few are specifically designed to focus on non-CS majors who may hold peripheral or emerging interests in the discipline. In this study, exploratory factor analysis was used to re-validate the Engineering Students' Attitudes towards CS survey initially created by Hoegh and Moskal using a population of non-CS majors. Results indicated that a 1-factor solution best fits the data for the Interest, Confidence, and Gender Equality Perceptions (GEP) constructs. Unique to this study, is support for a shortened 5-item GEP subscale. Results showed that the 5-item GEP performed as well as (and at times better than) the 10-item GEP. Based on these results, we recommend researchers wishing to examine Gender Equality Perceptions use a shortened version of the subscale utilizing only the 5 positively worded items. As a secondary interest of the work, results indicated women were nearly a full standard deviation higher on GEP subscales (Cohen's d = .961 and .837). This is considered a large effect size in social science research and indicates women had higher ratings of gender equality in CS than men did.

eam-based capstone projects are vital in preparing computer science students for real-world challenges by fostering teamwork, communication, and industry-relevant technical skills. However, their assessment presents challenges, such as aligning academic criteria with other stakeholders' expectations, evaluating individual contributions within teams, fairly addressing the diverse skills required, and determining the appropriate level of external partners' involvement in the evaluation process. Moreover, the high stakes of these projects necessitate transparent and equitable assessment methods that all stakeholders perceive as fair. Our working group (WG) aims to address the challenges of assessing capstone projects by examining the perspectives of instructors, students, and other stakeholders to ensure fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we will employ a mixed-methods approach to explore the issues faced by various stakeholders in assessing capstone projects and to capture both common challenges experienced (quantitative), and delve into nuanced individual experiences (qualitative). By conducting this research in a multi-national, multi-institutional context, we aim to capture a diverse range of global perspectives while accounting for the variation in capstone courses. Our goal is to provide actionable recommendations that enhance assessment practices, improve learning outcomes, and foster effective team collaboration in team-based capstone courses, ultimately preparing students for real-world challenges.

Transfer students play an important role in enhancing diversity within computer science programs. As first-generation college students, minorities, rural residents, and individuals from low-income backgrounds, transfer students represent a demographic critical to fostering innovation and inclusion in the field. However, while significant research has been conducted on transfer students in STEM disciplines, studies specifically addressing their experiences in computer science remain limited. This paper presents a preliminary systematic review of the literature to explore the challenges, successes, and gaps in the support of transfer students in computer science.

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.

Capstone courses are an integral part of undergraduate and postgraduate degrees in the computing fields. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self reflection as they transition into the real world. Prior research on capstone courses has primarily focused on the experiences of the students. The perspectives of instructors who teach these capstone courses has not been explored much. However, an instructor's motivation and expectancy can have a significant effect on a capstone course quality. In this working group, we plan to use a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation will be examined through a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across multiple continents. This global perspective will be used to develop a guiding framework on the different pedagogical approaches that can be used to enhance engagement and motivation for both staff and students in computing courses.

Algot is a newly developed visual programming language that seeks to bridge the syntax-semantics gap in programming via a novel implementation of programming by demonstration. Preliminary research, which will be presented separately at SIGCSE this year, suggests that Algot may be useful for teaching foundational computer science concepts at both secondary and tertiary levels. In this proposed SIGCSE demo session, attendees will have a chance to interact with Algot and learn about its potential benefits in their own classrooms.

The purpose of this workshop-designed for instructional and disciplinary STEM faculty interested in learning about qualitative research-is to (1) introduce participants to high-quality qualitative research design and (2) practice this design process alongside disciplinary STEM faculty to expand their STEM education research abilities and network. We will do so using the ProQual approach, a methodologically unencumbered and widely accessible way of thinking about qualitative research design that was deployed and refined over the last three years as part of the NSF-funded ProQual Institute for Research Methods [1]. This workshop will be conducted by ProQual Institute alumni, who are culturally sensitive to the challenges faced by disciplinary STEM faculty. Leveraging a propagation model of effecting academic change [2], the workshop leaders will serve as a community of practice to help participants move their educational research ideas forward during and after the workshop. In doing so, we strive to further FIE's mission to create a collaborative, supportive, and inclusive community of educational researchers.

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.

At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center for Women & Information Technology (NCWIT) Engagement Practices framework. One of the report's recommendations was "Make connections from computing to your students' lives and interests (Make it Matter) but don't assume you know what those interests are; find out! " The goal of this 2023 working group is to find out what interests women students by bringing together data from our institutions on undergraduate module enrollment, seeing how they differ for women and men, and what drives those choices. We will code published module content based on ACM curriculum guidelines and combine these data to build a hierarchical statistical model of factors affecting student choice. This model should be able to tell us how interesting or valuable different topics are to women, and to what extent topic affects choice of module - as opposed to other factors such as the instructor, the timetable, or the mode of assessment. Equipped with this knowledge we can advise departments how to focus curriculum development on areas that are of value to women, and hence work towards making the discipline more inclusive.

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.

Abstract There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.

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.

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that are practical and compatible with this rapidly changing field. This is especially relevant as cloud services are becoming common computing resources for many new computational approaches and advanced subjects such as machine learning and data science. The ability to incorporate specific components of cloud computing teaching content into a variety of courses has become important. However, the lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts continues to be a challenge for instructors. This working group will try to address this challenge.

Code quality is a key issue in software development. The ability to develop software of high quality is therefore a key learning goal of computing programs. However, there are no universally accepted measures to assess the quality of code and current standards are consideredweak. Furthermore, there are many facets to code quality. Defining and explaining the concept of code quality is therefore a challenge faced by many educators. In this working group, we investigate the perceptions of code quality of students, teachers, and professional programmers. In particular, we are interested in the differences in views of code quality by students, educators, and professional programmers and which quality aspects they consider as more or less important. Furthermore, we are interested in which sources of information on code quality and its assessment are used by these groups. Eventually, this will help us to develop resources that can be used to broaden students' views on software quality.

Nationwide, only 17% of community college students go on to complete a bachelor's degree within six years of enrolling at a community college and even a fewer number in the STEM fields. Community college students are more likely to be non-traditional, who are coincidentally comprised mostly of members of underrepresented groups. In this work, we examined the academic development of a cohort of non-traditional students as they transferred to an elite four-year institution from local community colleges. Drawing from educational and psychological research, we evaluated student motivation, resilience, and self-regulated learning habits throughout the first year of their transfer. The broader goal of this project is to better understand the self-regulated learning skills and motivation of non-traditional students from the point of transfer to degree completion. This in turn will foster the development of pedagogical techniques and support systems that are better suited for non-traditional students, with the ultimate goal of on-time degree completion and entrance into the STEM workforce.

Writing secure code requires a programmer to think both as a defender and an attacker. One can draw a parallel between this model of thinking and techniques used in test-driven development, where students learn by thinking about how to effectively test their code and anticipate possible bugs. In this study, we analyzed the quality of both attack and defense code that students wrote for an assignment given in an introductory security class of 75 (both graduate and senior undergraduate levels) at NYU. We made several observations regarding students' behaviors and the quality of both their defensive and offensive code. We saw that student defensive programs (i.e., assignments) are highly unique and that their attack programs (i.e., test cases) are also relatively unique. In addition, we examined how student behaviors in writing defense programs correlated with their attack program's effectiveness. We found evidence that students who learn to write good defensive programs can write effective attack programs, but the converse is not true. While further exploration of causality is needed, our results indicate that a greater pedagogical emphasis on defensive security may benefit students more than one that emphasizes offense.

As the education landscape continues to evolve, non-traditional students are becoming a growing number in the demographics of college graduates. Many of these students, including working professionals, adult learners, and first generation college goers, start their education at a community college and later transfer to a four-year institution to finish their bachelor's degree. Juggling multiple commitments such as jobs and family obligations, lack of access to resources and role models, and a K-12 education that often does not reflect a solid foundation, collectively result in a learning experience that does not align or mirror the standard "college experience" of a traditional student. Yet teaching and instruction techniques have remained the same for this population. In this paper, we will share our experience over the past four years in running a bachelor's degree completion program for such non-traditional students. We will address some of the challenges faced by this group and also some of the innovative ways that we have constructed a curriculum that could connect, inspire, and motivate our students. Our long-term goal is to construct a pedagogical methodology that would better be suited for non-traditional students and to provide a support system that ensures their success.

Quorum sensing (QS) enables bacterial multicellularity and selective advantage for communicating populations. While genetic "switching" phenomena are a common feature, their mechanistic underpinnings have remained elusive. The interplay between circuit components and their regulation are intertwined and embedded. Observable phenotypes are complex and context dependent. We employed a combination of experimental work and mathematical models to decipher network connectivity and signal transduction in the autoinducer-2 (AI-2) quorum sensing system of E. coli. Negative and positive feedback mechanisms were examined by separating the network architecture into sub-networks. A new unreported negative feedback interaction was hypothesized and tested via a simple mathematical model. Also, the importance of the LsrR regulator and its determinant role in the E. coli QS "switch", normally masked by interfering regulatory loops, were revealed. Our simple model allowed mechanistic understanding of the interplay among regulatory sub-structures and their contributions to the overall native functioning network. This "bottom up" approach in understanding gene regulation will serve to unravel complex QS network architectures and lead to the directed coordination of emergent behaviors.