Partnerships between academic institutions and industry for internships can benefit both parties. Experiential learning may help students prepare for their futures, while employers can help identify potential talent for jobs. Although the student perspective has been well explored, scholarship around employers remains limited. In this experience report, we describe a micro-internship program for (n=95) computing students that combined 7 weeks of university-led upskilling workshops with three weeks of practical experience with (n=18) employers to complete challenge projects in small groups. We elaborate further on the preparation and implementation required, as well as detail our qualitative evaluation of the program from the employer perspective. Situated learning theory (SLT) guided the investigation, which involved gathering feedback from employers through semi-structured interviews. Applying reflexive thematic analysis to employer interviews, we inductively examined: (1) how industry mentors integrated students into professional computing communities of practice (COP), (2) the mechanisms they employed to facilitate students' legitimate peripheral participation, (3) their observations of growth in students' technical and professional skills and identity formation, and (4) reciprocal learning that occurred during the internship period. Six resulting themes were then deductively mapped to SLT sub-constructs to further understand employers' impact during computing internships. Employers facilitated authentic problem solving and real-world learning experiences for students while gaining new perspectives and insights regarding new technology and problem-solving approaches in the process.

Making the transition from academia to industry can be a rite of passage for college graduates. Internships allow students to gain experience in small doses, and help shape their career goals, actions, and decisions. We sought to explore how employers perceived undergraduate computing students' performance and experience in a three-week micro-internship. We applied the boundary crossing (BC) framework and analyzed and interpreted n = 49 quotes extracted from semi-structured interviews with three industry mentors using the methodology of framework analysis. We examined the quotes and categorized them into one of four mechanisms of BC: identification, coordination, reflection, and transformation. The greatest number of BC mechanisms reported was that of coordination at the interpersonal level (33%), where the interns interacted with their mentors to navigate the differences in expectations and tasks that they had already identified. 51% of the BC mechanisms were reported to be at the interpersonal level, while six instances of transformation at the institutional level were also observed in the analysis. Our study's results can help administrators and industry mentors gain insight into how computing students may leverage mentorship to navigate professional dynamics.

The expanding applications of technology across sectors, coupled with the rising demand for qualified graduates, necessitate consideration of new ways to increase engagement with the discipline of computing. Towards this goal, we established a week-long program for non-majors to explore computing concepts (e.g., artificial intelligence) and aid in their professional development (e.g., through fostering presentation skills). We also sought to cultivate a community and incorporated peer and industry mentorship. In the experience report that follows, we detail the novel program and its evolution over five iterations across two institutions. We applied the Community of Inquiry framework to contextualize the programmatic design and its evaluation. Surveys collected daily gave insight into the student perspective on the various lessons and activities offered, with feedback from up to n = 141 students in total. Apart from including Likert-scale ratings to quantify preferences for each session, open-ended responses allowed greater understanding around what may have been viewed favorably or what could require further improvements. Based on the findings, we highlight how aspects of the experience may have contributed to the participants' engagement with the content, with others involved in the program, and with respect to learning outcomes. The session details and reflections presented are intended to inform as well as offer inspiration to other educators and administrators who may seek to introduce students from other majors to computing.

This study describes our current effort to expand our previous research to a national scale, exploring the implications of students' employability and salary between Bachelor of Arts (BA) and Bachelor of Sciences (BS) in Computer Science (CS) degrees. The literature on broadening participation has identified numerous barriers, but these challenges frequently burden students instead of addressing the underlying systemic issues within the curriculum. Previous research has identified bottleneck courses like calculus and physics as barriers to persistence in CS. Reimagining the curriculum and introducing alternative pathways, such as BA in CS, can eliminate bottlenecks and enhance access and retention without compromising essential skills in computing. To understand the implications of the BA pathway on employability and salary, we conducted a study at a large minority-serving institution (MSI) with a CS department serving around 2,000 students. Our research revealed that the BA degree offers comparable employment opportunities to the BS. Students choose BA due to broader career aspirations, a faster route to graduation, and view math courses as a significant barrier. Despite earning potential being important, we discovered salary differences between the two degrees, emphasizing the need for clear communication to help diverse students make informed decisions about their degree paths in CS. Given the findings from a single MSI, we recognize the importance of expanding this project nationally. This lightning talk will discuss background context, lessons learned, and potential collaborations for broader implementation.

Internships can allow computing students to cultivate valuable skills while offering them practical insight into industry. The aim of our study was to gain an understanding of undergraduate computing students' perceptions of a three-week micro-internship (called a ''Sprinternship'') program. We sought to explore their experiences throughout its duration, which included a priori professional and technical development training. We applied the methodology of phenomenography, conducting semi-structured interviews with n = 27 students and taking the developmental approach to the analysis. We noted cognitive, affective, interpersonal, and career-oriented factors often influenced students' views of the experience. In this work, we share the seven categories of description that emerged from the analysis and provide the implications. The findings of this investigation can offer guidance for educators and administrators looking to create similar short-term internship opportunities.

The demand for skilled workers in computing continues to outpace the supply of qualified graduates. Despite the need, hiring can be challenging, both for employers seeking prospective employees and for students who may be unsure where to apply, daunted by technical interviews, and/or feeling the effects of imposter phenomena. In this experience report, we describe a program established to reduce some of these hurdles by pairing (n = 63) undergraduate students with (n = 7) companies to offer short-term computing internships, called a Sprinternship. Sprinternships eliminated the hurdle of technical interviews, provided students with training beforehand to offer foundational knowledge, and placed them in teams to work on challenge projects. We describe the details of the program and our investigation of its impact. Social Cognitive Career Theory guided the inquiry as we took a mixed-methods approach to understand the students' experiences and the potential impact on their self-efficacy, outcome expectations, and career goals. Quantitative analysis revealed a statistically significant increase in students' confidence in computing, something echoed in their open-ended responses. Thematic analysis further yielded that Sprinternships were meaningful in two major areas: Goals and Learning Experiences. The program aided in students' self-discovery, made them feel accomplished, and strengthened their industry ambitions. Responses also revealed positive and negative programmatic aspects to consider for future iterations. We hope that our description of the Sprinternships, findings, and recommendations can be useful to other practitioners looking to engage students with practical learning and enhance their graduate employability.

Recently, a workshop was convened with the goal of creating a vision for computing education for the next 15 years. The first part of the workshop involved 43 members of the computing education community meeting virtually to enumerate themes within the discipline. Those themes included: diversity, equity, inclusion, ethics, broadening participation, teaching, learning, K-12, research to practice, computing's connection to other fields, and computing education research disciplinary issues. Within each of those areas, interesting and provocative questions emerged that led the organizers to dig deeper into some hard questions for the field and probe further into where the field should be heading and what difficult questions we need to tackle. Thus, a final report that probes the issues of curricular bloat, teaching and pedagogy, infusing humanities in computing, improving pathways into the discipline, and integrating social justice into the discipline has been created. This talk will introduce the main themes of the report and point the audience towards where it is available. This material is based upon work supported by the National Science Foundation under Grant No. 2039833 and 2039848. The report and other resources can be found at https://cerfutureworkshop.org.

The session will present an overview of findings of a recently funded NSF workshop that set out to examine the pressing issues for computing education research for the next 15 years. Based on dialogs for scholars working in this area, the workshop participants developed a series of themes and topics that they felt should become the focus of computing education research efforts for the next 15 years. Main themes that emerged and will be discussed in this session include: diversity, equity, inclusion, ethics, broadening participation, teaching, learning, K-12, research to practice, computing's connection to other fields, and computing education research disciplinary issues. The session will focus on interesting research questions from each of these areas that are ripe to be explored as well as enablers and blockers to the progress of this work.

This study was designed to compare salary implications and employability of students who graduated with a Bachelor of Arts in Computer Science (BACS) - primarily distinguished by the removal of calculus and physics requirements from the traditional computer science curriculum versus those that graduated with a Bachelor of Science in Computer Science (BSCS). Given the numerous studies that identify gateway courses like calculus and physics as impediments to students' persistence in engineering and computer science AND their impact on women and people of color, the removal of this barrier has incredible potential for broadening participation in computing. One university's first cohort of BACS graduates (spring 2020) furnished a unique opportunity to compare student's self-reported employment and salary information to their BSCS peers. The study consisted of institutional data and a survey targeting spring 2020, summer 2020, fall 2020 graduates from computer science, with data fromn =134 recent graduates (BAn = 45, BSn =89). Preliminary results indicate there are no statistical significance in enrollment on the basis of gender nor job attainment; however, there is a statistical significance in enrollment on the basis of race/ethnicity and pay. The results of this work could either serve as a cautionary tale for institutions considering similar programs OR it could serve as the basis for a deeper, more critical review of the requirements currently in place in BSCS programs, nationally. Are calculus and physics courses required for prosperity in computing or are they simply a barrier to equity?

Despite increasing demands for skilled workers within the technological domain, there is still a deficit in the number of graduates in computing fields (computer science, information technology, and computer engineering). Understanding the factors that contribute to students’ motivation and persistence is critical to helping educators, administrators, and industry professionals better focus efforts to improve academic outcomes and job placement. This article examines how experiences contribute to a student’s computing identity, which we define by their interest, recognition, sense of belonging, and competence/performance beliefs. In particular, we consider groups underrepresented in these disciplines, women and minoritized racial/ethnic groups (Black/African American and Hispanic/Latinx). To delve into these relationships, a survey of more than 1,600 students in computing fields was conducted at three metropolitan public universities in Florida. Regression was used to elucidate which experiences predict computing identity and how social identification (i.e., as female, Black/African American, and/or Hispanic/Latinx) may interact with these experiences. Our results suggest that several types of experiences positively predict a student’s computing identity, such as mentoring others, having a job, or having friends in computing. Moreover, certain experiences have a different effect on computing identity for female and Hispanic/Latinx students. More specifically, receiving academic advice from teaching assistants was more positive for female students, receiving advice from industry professionals was more negative for Hispanic/Latinx students, and receiving help on classwork from students in their class was more positive for Hispanic/Latinx students. Other experiences, while having the same effect on computing identity across students, were experienced at significantly different rates by females, Black/African American students, and Hispanic/Latinx students. The findings highlight experiential ways in which computing programs can foster computing identity development, particularly for underrepresented and marginalized groups in computing.

Increasingly companies assess a computing candidate's capabilities using technical interviews (TIs). Yet students struggle to code on demand, and there is already an insufficient amount of computing graduates to meet industry needs. Therefore, it is important to understand students' perceptions of TIs, and other professional experiences (e.g., computing jobs). We surveyed 740 undergraduate computing students at three universities to examine their experiences with the hiring process, as well as the impact of professional and cultural experiences (e.g., familial support) on computing identity. We considered the interactions between these experiences and social identity for groups underrepresented in computing - women, Black/African American, and Hispanic/Latinx students. Among other findings, we observed that students that did not have positive experiences with TIs had a reduced computing identity, but that facing discrimination during technical interviews had the opposite effect. Social support may play a role. Having friends in computing bolsters computing identity for Hispanic/Latinx students, as does a supportive home environment for women. Also, freelance computing jobs increase computing identity for Black/African American students. Our findings are intended to raise awareness of the best way for educators to help diverse groups of students to succeed, and to inform them of the experiences that may influence students' engagement, resilience, and computing identity development.

The lack of diversity in computing fields in the United States is a known issue. Students enter the computing fields with the intention of graduating; however, a large number leave and do not persist after enrolling, due to discrimination and biases. This particularly concerns groups already underrepresented in computing fields, such as women, Black/African American students, and Hispanic/Latinx students. However, there are various experiences that can make students feel more included or excluded in the field. Some of these experiences include internships, undergraduate research, capstone courses, and projects, etc. Drawing on Astin's I-E-O model and applying a random forest algorithm, we measure the feature importance of 14 distinct experiences on 1650 students' feelings of inclusivity in the computing field. We observe that there are gender and racial differences in terms of the opinions of computing fields' inclusivity. For example, tutoring experience, job offers, and job experience are considered some of the most important factors for female's perceived inclusiveness of women. However, men perceived women's inclusivity differently, based on the experiences they engaged in. We also looked at the perceived inclusiveness of computing fields for ethnically and racially underrepresented groups, such as Hispanic/Latinx students. Understanding the effect of different experiences on students of both genders with different races and ethnicities on the perceived inclusion could assist the computing community to provide more cohesive experiences that benefits all students and helps them to feel more welcome.

This Research Full Paper presents the effects of computing identity sub-constructs on the persistence of computer science students. Computer science (CS) is one of the fastest growing disciplines in the world and an emerging critical field for all students to obtain vital skills to be successful in the 21st century. Despite the growing importance of computer science, many university and college programs suffer from low student persistence rates. Disciplinary identity is a theoretical framework that refers to how students see themselves with respect to a discipline and is related to long-term membership in a disciplinary community. The theory has been effectively applied in Science, Technology, Engineering, and Mathematics (STEM) to understand students' success and persistence. This study examines the effects of performance/competence, recognition, interest and sense of belonging on the academic persistence of computer science students. A survey of approximately 1,640 computing students as part of a National Science Foundation (NSF) funded project was developed and administered at three metropolitan public institutions. Confirmatory Factor Analysis (CFA) was performed to validate the sub-constructs of identity for use in a computing identity model. Then, a structural equation model (SEM) was constructed as a snapshot of the structural relationships for describing and quantifying the impact of the identity sub-constructs on persistence. The results indicated that our model for CS aligns with prior research on disciplinary identity but also adds the importance of sense of belonging. In addition, the findings indicate that students' academic persistence is directly influenced by their interest. A better understanding of these factors may leverage insight into students' academic persistence in computer science/engineering programs as well as a meaningful lens of analysis for further curriculum and extracurricular activities.

CircGR is a multi-touch non-symbolic gesture recognition algorithm, which utilizes circular statistic measures to implement linearithmic (O(n log n)) template-based matching. CircGR provides a solution to gesture designers, which allows for building complex multi-touch gestures with high-confidence accuracy. We demonstrated the algorithm and described a user study with 60 subjects and over 12,000 gestures collected for an original gesture set of 36. The accuracy is over 99% with the Matthews correlation coefficient of 0.95. In addition, early gesture detection was successful in CircGR as well.

The "Data Structures" course is arguably one of the most important for computer science majors. In this talk, I will discuss how this course has evolved over the last three decades, and discuss some topics we might want to start thinking about in the next decade.

With the Java language replacing C++ on the 2004 AP CS Exam, teachers need to be informed about the changes that must be implemented to support an OO approach to programming. This special session will include a retrospective look at the motivation behind the change to an object-oriented language, the process undertaken to select a testable language subset, the need to continue the development and classroom implementation of a Case Study, and a look at how the shift from an object-based approach to programming in C++ to an OO approach in Java leads to curriculum modification.The AP CS Development Committee's charge is to not only provide a comprehensive testing mechanism, but also advise, through various publications, a direction that high school teachers should take in preparing a foundation for more advanced student studies during college. This special session will bring together two college and two high school members of the AP CS Development Committee to share some of their insights into how the experts do it. Time will be provided to discuss participant's questions.

No abstract available.

No abstract available.

This paper describes our experiences incorporating Java in a Data Structures course. We describe the features of Java that made for a more interesting course, the difficulties that we encountered, and compare Java to the prior languages used in this course, Ada and C++. All in all, we found Java to be a reasonable, but not overwhelming better, alternative. Our students were particularly happy with the experiment. Introduction Among Computer Science educators, hardly any topic inspires more heated debate than the choice of programming language in the introductory sequence. In the late 80s, the uniformly accepted choice was Pascal, but since then, a host of alternatives have come into use. C++ seems to have emerged as the winner, while Pascal, C, Ada, Scheme, and Modula-3 split most of the remaining market. There appear to be two overriding reasons for C++'s emergence. First, principles such as encapsulation and information hiding, that are important to teach in the CS I/II curriculum...

Satisfiability, equivalence, and implication problems involving conjunctive queries are important and widely encountered problems in database management systems. These problems need to be efficiently and effectively solved. In this paper, we consider queries which are conjunctions of the inequalities of the form (X op C), (X op Y), and/or (X op Y+C), where X and Y are two attributes, C is a constant, and op /spl epsiv/ {, /spl ges/}. These types of inequalities are widely used in database systems, since the first type is a selection, the second type is a /spl theta/-join, and the third type is a very popular clause in a deductive database system. The satisfiability, equivalence, and implication problems in the integer domain (for attributes and constants) have been shown to be NP-hard. However, we show that these problems can be solved efficiently in the real domain. The incorporation of the real domain is significant, because the real domain is practically and widely used in a database. Necessary and sufficient conditions and algorithms are presented. A novel concept of the "module closure" and a set of sound and complete axioms with respect to the "module closure" are also proposed to infer all correct and necessary inequalities from a given query. The proposed axioms generalize Ullman's axioms (1989) where queries only consist of /spl theta/-joins.

Satisfiability, implication, and equivalence problems involving conjunctive inequalities are important and widely encountered database problems that need to be efficiently and effectively processed. In this article we consider two popular types of arithmetic inequalities, ( X op Y ) and ( X op C ), where X and Y are attributes, C is a constant of the domain or X , and op ∈{<, ≤, =, ≠, >, ≥). These inequalities are most frequently used in a database system, inasmuch as the former type of inequality represents a 0-join, and the latter is a selection. We study the satisfiability and implication problems under the integer domain and the real domain, as well as under two different operator sets ({<, ≤, =, ≥, >} and {<, ≤, =, ≠, ≥, >}). Our results show that solutions under different domains and/or different operator sets are quite different. Out of these eight cases, excluding two cases that had been shown to be NP-hard, we either report the first necessary and sufficient conditions for these problems as well as their efficient algorithms with complexity analysis (for four cases), or provide an improved algorithm (for two cases). These iff conditions and algorithms are essential to database designers, practitioners, and researchers. These algorithms have been implemented and an experimental study comparing the proposed algorithms and those previously known is conducted. Our experiments show that the proposed algorithms are more efficient than previously known algorithms even for small input. The C++ code can be obtained by an anonymous ftp from .

Implication testing of arithmetic inequalities has been widely used in different areas in database systems and has received extensive research as well. Klug and Ullman (A. Klug, 1988; and J.D. Ullman, 1989) proposed an algorithm that determines whether S implies T, where T and S consist of inequalities of form (X op Y), X and Y are two variables, and op/spl epsiv/ {=, /spl ges/}. The complexity of the algorithm is O(n/sup 3/), where n is the number of inequalities in S. We reduce the problem to matrix multiplication, thus improving the time bound to O(n/sup 2.376/). We also demonstrate an O(n/sup 2/) algorithm if the number of inequalities in T is bounded by O(n). Since matrix multiplication has been well studied, our reduction allows the possibility of directly adopting many practical results for managing matrices and their operations, such as parallel computation and efficient representation of sparse matrices.>

The preorder, inorder and postorder traversals of binary trees are standard topics in Data Structures courses. A common examples (or test question) which uses postorder traversal is finding the height of a randomly formed binary search tree (to verify experimentally that it is indeed O(log n)). The natural postorder implementation gives a linear algorithm, but frequently students manage, sometimes unintentionally, to add a third recursive call to the computation, resulting in an apparently drastic increase in running time. It is obvious that the worst case running time increases from linear to exponential, but the results on the average case running time are not as immediate. We show that the average running time increases from linear to cubic.

We present the results of a large empirical study of the running time of Shellsort. A previous study reported by Knuth for several increment sequences gave running times between Θ(N1.25) and Θ(NN1.28) or Θ(Nlog2N). but these forms are not very accurate especially for large permutations. Our results given a running time of Θ(N5/4) for all of the increment sequences suggested by Knuth and Θ(N7/6) for an increment sequence suggested by Sedgewick. Our fits are accurate to within 1% for 250 ≤N<100000 and about 0.1% for larger N. Much of the error in the fits appears to be related to the error in the measured data. These results are significant because they suggest that O(log N) increment sequences with Θ(Nk) worst-case running times have Θ(N(k+1)/2) average-case running times and that there is no increment sequence for which Shellsort is O(Nlog N), even on average.