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.

Our working group aims to better understand teamwork in undergraduate computing education through a systematic literature review and focus groups with instructors. Based on the strategies suggested and needs identified, we will also co-design instructional resources with the community to cultivate the skills, values, and virtues pertinent to teamwork. Through planned research and collaboration, we seek to inform and establish practical materials that can enhance teaming pedagogy and prepare graduates for the evolution of technology and the workforce.

The rapid expansion of artificial intelligence (AI) tools, including generative AI, is reshaping task completion and interaction, leading educational researchers to examine learning opportunities as well as emerging concerns in the present technological landscape. Despite ongoing discussions, it is less clear how students may envision such tools being integrated into computing education. We applied the Technology Acceptance Model and conducted a survey across three tertiary academic institutions in the United States, gathering quantitative and qualitative insights on n = 340 computing students' perspectives. Closed-ended items were examined with descriptive statistics, and an open-ended prompt around AI tool use in computing education was explored through reflexive thematic analysis. We found that 64.1% of students somewhat or strongly agreed that using AI tools (e.g., ChatGPT or GitHub Copilot) made them feel as though they were not truly doing the work themselves. Additionally, 48.8% of students somewhat or strongly agreed that they felt conflicted about using AI tools because competent computing/tech students should be able to figure things out without help. The qualitative themes spoke to instances where students may find adoption beneficial, like ''Expediting and enhancing learning, task completion, and organization,'' or problematic, such as ''Moral concerns and regard for social and environmental impact.'' Based on our results, we discuss strategies for teaching alongside AI tools and future considerations for computing educators and developers to support students' agency, ethical reasoning, and decision-making.

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.

Navigating the hiring process and workplace in computing can require a combination of professional and technical competencies. What is less clear are the approaches educators may take to cultivate students' development, prepare for technical interviews, and how the educational context might influence students' needs and learning experiences. In this paper, we focused on the perspectives of computing faculty in the context of supporting learners at Hispanic-Serving Institutions (HSIs) in the southeastern region of the United States. Data collection included semi-structured interviews with n = 20 participants, and we applied reflexive thematic analysis to examine the resultant transcripts. The findings suggested that critical thinking and decomposing problems could become even more relevant in light of the evolving landscape of generative artificial intelligence and its ability to create code. Faculty highlighted the value of taking more social approaches to computing instruction, such as enlisting pair programming and encouraging students to ideate on solutions with groups. Additionally, written and spoken language, as well as how they may pertain to the expression of technical concepts, were seen as important for students' long-term success in the field. For students whose first language was Spanish (or a language other than English), instructors suggested incorporating oral presentations and providing constructive feedback on how students expressed coding solutions. The outcomes from this work can serve to offer insight not only for faculty at HSIs but also for educators seeking to consider new ways to support students.

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.

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.

Understanding how tertiary academic institutions approach the integration of generative AI (GAI) into their course policies is crucial since AI technologies are rapidly transforming society. AI is being used and applied across sectors and industries, and it is important to do so with regard to ethics. This exploratory study sought to examine how GAI policies were discussed across academic institutions. The policies were analyzed using NLP techniques and utilized existing publicly available datasets, which consisted of a collection of over 100 university policies and syllabi policies. Unsupervised clustering techniques were applied to analyze patterns in how different institutions may express their policies and best practices. These findings illuminate how universities and colleges may approach topics and challenges around AI, and specifically GAI.

One challenge in technical interviews is the thinkaloud process, where candidates verbalize their thought processes while solving coding tasks. Despite its importance, opportunities for structured practice remain limited. Conversational AI offers potential assistance, but limited research explores user perceptions of its role in think-aloud practice. To address this gap, we conducted a study with 17 participants using an LLM-based technical interview practice tool. Participants valued AI’s role in simulation, feedback, and learning from generated examples. Key design recommendations include promoting social presence in conversational AI for technical interview simulation, providing feedback beyond verbal content analysis, and enabling crowdsourced think-aloud examples through humanAI collaboration. Beyond feature design, we examined broader considerations, including intersectional challenges and potential strategies to address them, how AI-driven interview preparation could promote equitable learning in computing careers, and the need to rethink AI’s role in interview practice by suggesting a research direction that integrates human-AI collaboration.

With new technologies come additional responsibilities. Examining Artificial Intelligence (AI) through an ethical lens has become increasingly important and significant. Advancements in AI have led numerous organizations, such as IEEE, to develop AI ethics guidelines for consideration in academia and industry. Additionally, higher education has an essential role in fostering innovation and developing skilled professionals who will work on topics that span social, philosophical, scientific, and technical spheres. To assess the content being covered in tertiary classrooms, we utilized a Natural Language Processing (NLP) approach for analysis. This study examines$(\mathrm{n}=45)$AI ethics syllabi that were publicly available online. The course description, topics, department, and year were some important features captured from each syllabus. Using various NLP tools for analysis, a general exploration of AI ethics curricula was conducted. Through supervised clustering, k-means clustering, and latent Dirichlet Allocation (LDA), various patterns in the contents of the AI ethics syllabus were found. Some of these include trends and patterns from syllabi across various academic departments, years, and the pre-post Chat-GPT era. Cluster evaluation was also done on the unsupervised clusters using various metrics to determine the viability of the clusters. The LDA analysis enabled a review of topics that are consistent among the clusters, which helped highlight salient areas of focus in AI ethics syllabi. The findings from this study can serve to inform administrators and educators, acting as a baseline for including language around AI ethics topics and uncovering potential topical gaps in the contents of AI ethics syllabi. They can also provide insight into how different academic departments, like computer science and philosophy, may approach the topic. Such understanding is critical to ensuring the next generation of graduates not only considers how to utilize AI but also promotes doing so responsibly and with regard to its societal implications

The ITiCSE '23 final keynote raised teaching soft skills, or professional dispositions, to help students face challenges in modern programming. This project addresses helping computing students develop professional dispositions through collaborative learning (CL) since some in the industry observe entry-level engineers struggling due to their fragile professional dispositions. We are motivated to understand professional expectations from entry-level engineers and present the academia-industry gap to support practitioners and researchers in advancing CL in Computing Education, encouraging positive curricula and policy changes that promote DEIA. We will present CL practices alongside their supported professional dispositions to assist practitioners in adoption. We will present the academia-industry gap in CL for future research opportunities, helping researchers advance CL practices to integrate professional dispositions the industry expects from entry-level engineers.

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.

Increasingly, interactive digital systems are being developed to offer learning opportunities with practical scenarios for individuals in fields ranging from health to the military. We created an application to provide those starting their careers with practice for job interviews. The platform, called Virtual Interview Ready (VI-Ready), allows engagement with 3D agents acting as hiring managers. To better understand the user experience and preferences of the (n = 5) agents available, we conducted an exploratory study with post-secondary students. The framework of impression management guided the inquiry as we employed a convergent parallel mixed-methods approach to data collection and analysis. Twenty undergraduate and graduate students (n = 20) interacted with the system, and then they completed a survey about VI-Ready and its agents. Among other findings, 80% of respondents agreed or strongly agreed that the virtual hiring manager was likeable. In addition, the qualitative analysis revealed three themes related to the agent interpretations: perceived personality, communications, and appearance. Students mentioned the voice of the agent enhanced the experience, and they found the agent competent and “approachable.” The responses also illustrated a mixture of predilections for engaging with a hiring manager who looked like the participant (based on concordance with the agent's perceived social identities) versus others who did not feel the appearance of the agent mattered. Several students mentioned that since they are not able to select who their hiring manager would be in real life, they did not think being able to choose the look of the agents would impact their practice experience. The outcomes of this research may be helpful for those developing new technologies to enhance students' career paths. Insights around user preferences can also be applied to inform future agent designs.

Although it is already established that computing education (CEd) is an emergent interdisciplinary field with scholars around the globe contributing to research in this area, it is less clear how and where this research occurs. To better understand the current state of computing education research (CEdR) and opportunities for those engaged in the field, we employed a data collection process involving training, information gathering, and reporting with institutional representatives. We sought to develop an overview of: 1) affiliations of graduate students and faculty conducting CEdR; 2) funding for graduate students and faculty conducting CEdR; 3) the academic degree options and the credit, coursework, and publication requirements for graduate students; and 4) institutions' current and future plans for CEd. Partnerships with contacts spanning 30 institutions spread across five continents provided insight into the pathways and possibilities that presently exist for researchers in the field, as well as a glimpse into future plans for expansion (or the lack thereof). The findings from this investigation offer valuable information for students and faculty seeking potential collaborations, thinking about their career trajectories, or when planning CEd initiatives; for educators trying to develop courses; and for administrators considering creating more formal tracks for those focused on CEd.

Technology innovation requires a set of diverse employees with computing skills. Yet, it remains a challenge for the global digital labor market to obtain an equitable representation of women. This is particularly true for women who enter the computing workforce after obtaining an initial undergraduate degree in a non-computing field. To resolve discrepancies, it is important to learn more about the factors influencing career trajectories and the possible alternative pathways that may encourage participation in the field. We define alternative pathways as any post-baccalaureate program or training that meets the needs of bachelor's degree-holding women with computing aspirations. This research paper, guided by Schlossberg's Transition Theory, conducted a content analysis on publicly available job profiles to characterize the types and features of alternative pathways commonly chosen by women in the United States (U.S.) to aid in understanding their transitions into a computing career. Findings from this study provide guidance and suggestions to women who are interested in transitioning to computing later in their career paths. It further outlines potential avenues with actionable recommendations for the computing education community to attract and retain women in the computing workforce in an effort to build an inclusive ecosystem.

Despite the expansion and development of the field of computing education (CEd), a lack of formal programs means that researchers often exist as islands across and within a diverse range of departments. Given the broad nature of the discipline, trying to build a community typically occurs through more formal conferences. Another way to expand engagement with the field is through Research Experiences for Undergraduates (REU), programs that offer students the chance to get involved with research. REUs have been shown to strengthen disciplinary identity and encourage the pursuit of graduate degrees. However, such opportunities are not usually available at the graduate level. To address this gap, we present an experience report that describes our approach, Research Experiences for Graduate Students (REGS). In the work that follows, we detail our implementation and the partnerships created that allowed (n = 10) internationally dispersed graduate students from different institutions the chance to build connections, conduct CEd research, and develop a graduate-centric community. Leadership researchers, faculty, and staff supported the efforts, offering guidance and critical examination of the work to establish rigor over the course of the projects. Publications and presentations contributed to the CEd knowledge base, and interviews with the students upon completion of the experience illustrated the value of participation in the REGS.

This research full paper describes an eight-week, extra-curricular, multi-institutional program centered around sharing personal narratives. Over the course of the experience, we provided (n = 24) engineering students with scaffolding materials to develop their storytelling skills and opportunities for them to craft and present stories around specific prompts. The program culminated in a Story Slam where students orally delivered four-minute stories that communicated "who you are, what has shaped you, and the story you want to tell with your life’s work and why." The Kern Entrepreneurial Engineering Network (KEEN) framework of the 3Cs (Curiosity, Connections, and Creating value) for the development of an entrepreneurial mindset guided the programmatic development and shaped our inquiry to explore: 1) how storytelling prompts can encourage students’ consideration of their goals and potential impact on society; and 2) beneficial approaches to story-driven learning. To better understand students’ motivations and thoughts about the program, we qualitatively examined their open-ended responses. We also applied a rubric to quantify aspects of KEEN’s 3Cs observed in videos of students’ stories shared during the final Story Slam. The results suggest that storytelling can promote communication, allow students to reframe their experiences, and help them to think about their professional aspirations. Students’ curiosity not only shaped their desire to participate in the program, but for many students, was also a major contributor in their pathways into engineering. The content and materials offered in the program provided foundations that students connected with, as evidenced by the information and mentions they brought into their own stories. Furthermore, the stories presented illustrate that students want to create value both personally and professionally. Students preferred to share their stories in smaller groups and also appreciated positive feedback and comments from their peers and the facilitators. Additionally, they described how listening to other students’ stories helped them to grow and raised their awareness of others’ lived experiences. These insights serve to inform educators about the value of storytelling and to identify opportunities to support students when employing story-driven learning.

This research full paper describes the development of a rubric to assess students’ empathy through their coursework. Engineering departments are increasingly striving to not just foster technical competencies but to also encourage professional skills like empathy and an other-centered service orientation. In this work, we apply Zaki’s model of empathy — which conceptualizes the construct in terms of its cognitive, affective, and action-oriented components — to guide the creation of a rubric to assess students’ empathy using artifacts produced. While existing tools to measure empathy typically rely on self-reports, we instead focus on a standardized approach to external evaluation. We describe the justification for the decisions made as well as the pilot assessment conducted in February of 2022. In this pilot, we applied the rubric to students’ assignments from four undergraduate biomedical engineering courses seeking to cultivate empathy at a large public institution in the Southeastern United States. These assignments include: 1) user stories from a problem-based learning class; 2) empathy maps from an introductory design class; 3) user needs assessment reports from a senior capstone experience; and 4) personal narratives submitted in a non-traditional storytelling course. Through understanding of the learning activities within our department, we hope to encourage others to explore the effects of their own curriculum on the multi-dimensional development of empathy as part of engineering students’ professional formation. We propose that the rubric developed in this investigation could be a useful tool applied to artifacts from other courses looking to promote empathy and to evaluate the presence of its different components.

Background and Context: Women remain minoritized in the global digital labor market [4], which is problematic since technology innovation requires a diverse set of employees with computing skills [6]. Existing studies have primarily focused on examining students at the K-12 and/or post-secondary levels [1, 2, 3], but less is known about women who are in the workforce and choose to enter a computing program or who transition into a computing role after obtaining an initial undergraduate degree(s) in a non-computing field. It is critical to learn more about the unique trajectories taken by those with non-traditional backgrounds to encourage the participation and retention of additional women in computing. Objectives: We conducted this pilot study to characterize the types and features of alternative pathways commonly chosen by women in the United States. The framework of Super’s Life Span and Life Space [5] guided our inquiry as we considered the correlation between life spaces for women and their career transitions into computing from other fields. This theory addresses different career development paths as a consequence of career development at distinct stages through progressive efforts in pursuing career growth [5]. It considers factors which may influence career development, such as social learning experiences, personality development, and one’s values, needs, and abilities. We use this theory to explore women’s life roles and the potential impact on career aspirations. Method: Data collection involved leveraging publicly available background information from profiles shared on a professional networking website. Specifically, we examined the profiles of women who entered computing later in their educational and/or career paths and who are currently working in computing-related positions. To characterize their trajectories, we conducted a content analysis, with a focus on their education, computing-related job information, and organizational affiliations. Findings: The exact alternative pathway programs selected by women from the targeted population varied on a case-by-case basis. We observed that women may obtain additional degrees in computing from higher education institutions, although they may also hone their skills through coding bootcamps and via self-learning through online resources. In particular, women switchers often have a background in mathematics, statistics, and electronic engineering. Moreover, we discovered that the majority of women, despite the field of their undergraduate majors, experienced exposure to computing through serving as research assistants. Implications: Exploring the backgrounds of these women, who may enter the field through alternative pathways, furthers our understanding of potential avenues to attract and retain an untapped talent pool. By examining different pathways, we seek to provide insight into ways to better support these women’s transitions and to find additional ways to encourage more women to join the profession. We recommend offering increased flexibility in coursework for learners and increased opportunities to gain exposure through undergraduate research. The results could not only be of interest to program administrators but could also offer suggestions for computing educators looking to make their lessons more inclusive.

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.

Computer science education (CSEd) is a growing interdisciplinary area that continues to gain momentum from students, researchers, and educators. Yet, there are few formal programs or degree options for students interested in pursuing graduate work in CSEd. This article explores the existing state of CSEd in the United States (U.S.) through semi-structured interviews with ( n = 15) faculty engaged in CSEd research. Thematic coding of the transcripts revealed the complexities involved in the development of formal programs, the distinct considerations for faculty, and the value of having strong ties to both computer science and education. The themes described positive aspects of support and cohesion within the larger community and opportunities to expand knowledge across fields. Applying Cornell and Parker’s principles of interdisciplinary science to the field of CSEd, we provide recommendations for ways forward and discuss the potential impact on institutional structures, research capacity, individual and group identities, and teaching and learning. The findings from this investigation not only inform on the present state of CSEd in the U.S., but also offer guidance for CSEd-focused graduate programs.

The goal of this research methodology paper is to discuss the utility of employing a phenomenographic approach towards empirical examination of experiences in computing fields, and to argue for the application of theory within the process. Phenomenography is a qualitative technique applied to describe the variations in how populations perceive and conceptualize an observable fact, circumstance, or event. In our work, we discuss the benefits and challenges of this methodology, and examine the different approaches that can be employed. Although deviations exist in the styles of treating the data, its analysis, and the interpretation, ultimately, the goal of phenomenography is to develop an outcome space which consists of critical features for the object under investigation. Unlike many other qualitative techniques, the analysis itself is not guided using theoretical frameworks or a priori coding, since categorizations are meant to emerge from the data. While theoretical frameworks are not used during the evolution of the outcome space, we do suggest ways they can be applied in other stages of the process. Theoretical frameworks are valuable tools for limiting the scope of the relevant data by focusing on specific variables and defining a particular viewpoint. When employing phenomenography, we describe how theoretical grounding can be useful during planning and data collection - when establishing research questions, developing interview scripts, or during participant selection - or as part of interpretation and explanation of the results. This work is intended to encourage future researchers investigating experiences in computer science education to use phenomenography, and to assist in demonstrating how theoretical frameworks can enhance the protocol without compromising the integrity of the data-focused analysis.

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.

Although computer science education (CSEd) is growing rapidly as a discipline, presently there are a limited number of formal programs available for students to pursue graduate degrees. To explore what options exist, we sought to develop a better understanding of the researchers and institutions currently working in CSEd. We collected publication data between 2015 and 2020 from the Innovation and Technology in Computer Science Education (ITiCSE) and ACM International Computing Education Research (ICER) conferences, and from the ACM Transactions on Computing Education (TOCE) journal. Using a total of 1,099 publications, we analyzed the authorship blocks and their affiliations. We created a comprehensive database, used for analysis on recent contributions to CSEd research. Among other findings, we observed that 2,068 distinct authors contributed, spanning 578 global institutions. From these, 963 of the authors came from 236 distinct universities in the United States. Moreover, we found that most often, new growth from international contributions resulted from the participation of additional universities, whereas in the United States most growth was the result of new contributors from the same universities. The results of this research are intended to encourage global collaborations, to provide an informative guide about recent publications in the field, and also to serve as a guidepost for graduate recruitment and further exploration into CSEd research and programs.

Although computing occupations have some of the greatest projected growth rates, there remains a deficit of graduates in these fields. The struggle to engage enough students to meet demands is particularly pronounced for groups already underrepresented in computing, specifically, individuals that self-identify as a woman, or as Black, Hispanic/Latinx, or Native American. Prior studies have begun to examine issues surrounding engagement and retention, but more understanding is needed to close the gap, and to broaden participation. In this research, we provide quantitative evidence from the Multiple-Institution Database for Investigating Engineering Longitudinal Development—a longitudinal, multi-institutional database to describe participation trends of marginalized groups in computer science. Using descriptive statistics, we present the enrollment and graduation rates for those situated at the intersection of race/ethnicity and gender between 1987 and 2018. In this work, we observed periods of significant flux for Black men and women, and White women in particular, and consistently low participation of Hispanic/Latinx and Native American men and women, and Asian women. To provide framing for the evident peaks and valleys in participation, we applied historical context analysis to describe the political, economic, and social factors and events that may have impacted each group. These results put a spotlight on populations largely overlooked in statistical work and have the potential to inform educators, administrators, and researchers about how enrollments and graduation rates have changed over time in computing fields. In addition, they offer insight into potential causes for the vicissitudes, to encourage more equal access for all students going forward.

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 describes how web scraping and natural language processing can be utilized to answer complex questions in computer science education. In this work, we apply connectivism as the theoretical framework, and demonstrate how web scraping can be useful for extrapolating large amounts of data from publicly available web pages to pool data from a wider array of sources and to further knowledge in the field. In addition, we discuss how natural language processing can be used to reliably obtain salient information from textual data, and how it can complement qualitative analysis. To illustrate these techniques in practice, we provide a specific application in which we examine the current trends in the job market for computer science students. The information gathered in this example provides additional areas for educational consideration, such as offering students Python programming language and machine learning. Also, the job postings delineate a clear need for applicants to exhibit programming and testing skills. Although programming may be taught already, testing is widely considered a knowledge deficiency, which suggests that educators should consider placing an increased emphasis on this area to ensure their students are adequately prepared for their career endeavors, and able to transfer the knowledge taught to critically assess and debug their own programs.

Simulation-based training systems have proven effective in a variety of domains, both for facilitating the learning of skills as well for applying this knowledge to real life. Although difficulties managing students' disruptive behavior in classrooms has been identified as one of the main causes of teachers' turnover, only a handful of virtual training environments have focused on providing training to teachers, and still no clear methodologies exist for their design, their implementation, nor their evaluation.