Post-secondary institutions are actively working to address gender imbalances within the field of computing. Although most of current research has focused on women, comparatively less attention has been paid to differences in motivational factors between various genders. Using Eccles's expectancy-value theory of motivation as theoretical framework, this study investigates the reasons students choose to pursue computer science through an analysis of their perceptions of cost—including personal effort, well-being, and sacrifices—as well as their expectations for success and the perceived utility of educational and career opportunities within the field. Interviews with 21 undergraduate students—including thirteen women (two identifying as transgender) and eight men—indicated that all groups viewed careers in computer science as highly valuable. However, women reported lower expectations of success and experienced greater negative effects related to their studies.

Team-based capstones are a cornerstone of computing education, designed to prepare students for professional computing practice. The rapid adoption of Generative AI (GenAI) is reshaping how students plan, implement, test, and document their capstone work, raising new questions about workflows, team dynamics, assessment practices, and graduate readiness for using GenAI in the workplace. This Working Group (WG) investigates how GenAI is influencing capstone design and practice from the perspectives of students, early-career graduates, instructors, and employers. Using a mixed-methods approach, including a scoping literature review, cross-institutional surveys, and semi-structured interviews, the WG will examine how GenAI is currently integrated into capstone courses, how students use and perceive GenAI across the project lifecycle, how instructors are adapting task design, supervision, feedback, and assessment, and how employer expectations for GenAI competencies align with university preparation. The ultimate goal is to produce evidence-based guidance that helps educators realign capstone experiences with the realities of GenAI-integrated professional computing practice.

Part of computer science disciplines, software engineering (SE) is concerned with the software lifecycle and the rigorous methods and processes required for designing, implementing, modifying and maintaining high-quality software systems. Project-based and experiential learning are core to developing SE competencies and skills. Besides technical competencies, soft skills including adaptability, communication, critical thinking and teamwork are required and highly valued by employers. Several aspects affect the success of a project: the student engagement and participation, the team's dynamics and diversity, the mentoring strategy and the peer feedback process. Important aspects of teamwork are achievement of a high-level of cohesiveness between team members, and effective communication. However, we found that these aspects are impacted by deterrents to diversity and inclusion, and are not always achieved. The purpose of this study is to explore instructional models for teaching SE project courses that foster diversity and inclusion in teamwork, and promote engagement.

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.

Computing science education has experienced low attendance and historic declines in registration from different minority groups. The past decade of enrollment surge in computer science undergraduate programs has increased the number of women and minorities in the field, but the improvements are inconsistent and less than expected. An increase in the use of computing science and in the demand of technology workforce is expected in the upcoming years. Thus, computing science is set to shape the future of technology for a diverse set of technology users. Therefore, it is important to analyze how undergraduate program admission procedures are affecting Equity, Diversity, and Inclusion of historically marginalized groups in computing science.

Computing education and careers are male dominated. Identifying strategies to reduce this gender gap would create a more diverse and inclusive workforce, and would respond to the growing importance of computing and technology in our society. This paper presents an intervention designed and conducted in a post-secondary polytechnic institution aimed at inspiring and motivating computer science education among high school girls. Grounded in theory and related research, this intervention was designed to address aspects recognized as having relevance to girls. Using the expectancy-value theory of motivation developed by Eccles as a theoretical foundation, this study explores the intervention's impact on participants' interest and attainment in computer science. Twenty five students (nineteen girls) from local high schools participated in this pre-questionnaire, intervention, post-questionnaire quasi-experimental study. Participants were mentored by post-secondary students (at least one mentor for each pair of participants) through activities including writing an algorithm, coding, exploring an AR/VR technology and practicing programming skills with an educational game. Analysis of resulting data revealed that girls who participated in this study experienced a high level of enjoyment, increased interest, perceived positive learning gains, and were inspired by their post-secondary mentors. Post-questionnaire responses indicated that girls improved their ability beliefs and reduced their stereotypical views.

This Working Group aims to take the current approved Computer Science curricula document, CS2013, and redevelop it into competency statements. The CC2020 project has designed and built a prototype of a visualization tool to compare and contrast current computing curricula. Three basic approaches were taken to portray the base data that will be used for the tool: the first being expert-defined competencies, the second based on mining, and the third based on expert-defined knowledge areas. The visualization tool takes competency statements from each of the current approved computing curricula and visually represents them. Using competency to frame curricula and describe educational outcomes in computing is not new. Since the CC2005 report was published several additional curricula have appeared and the information technology, information systems, and software engineering communities have developed three approaches to defining computing competency in the context of developing their curricula reports. In future the CC2020 report advocates that all new curricula will be written as competency statements. Currently the CS2013 curricula is expressed in learning outcomes rather than the competency statements, so it is essential to be able to demonstrate Computer Science curricula in these new terms to accommodate the new direction and demonstrate Computer Science in the new visualization tool.

This research study aims to identify design strategies, instructional models and technological tools (e.g., educational games) that can be used to motivate high school girls to pursue computer science (CS) education. Part of this study, an educational game CodeBlock intended to teach basic programming, was designed and implemented for HoloLens devices. This study evaluates to what extent participating in a coding workshop consisting of a set of coding exercises and the CodeBlock game play could increase the interest of high school girls in CS. The workshop is conducted by undergraduate students who were involved in the design and implementation of the game, which has an additional benefit of exposing young girls to technologies implemented by post-secondary students, thus increasing the chances they will pursue post-secondary education in CS related fields.

This working group asserts that Program Comprehension (PC) plays a critical part in the writing process. For example, this abstract is written from a basic draft that we have edited and revised until it clearly presents our idea. Similarly, a program is written in an incremental manner, with each step being tested, debugged and extended until the program achieves its goal. Novice programmers should develop their program comprehension as they learn to code, so that they are able to read and reason about code while they are writing it. To foster such competencies our group has identified two main goals: (1) to collect and define learning activities that explicitly cover key components of program comprehension and (2) to define possible learning trajectories that will guide teachers using those learning activities in their CS0/CS1 or K-12 courses.

Computer science and technology education should provide not only a strong theoretical foundation, but also problem solving, and communication and teamwork skills to prepare the students for careers. Including projects in curricula is a norm in many disciplines. However, projects are generally individual or based on small teams (two to five members). This paper presents my approach to teaching a capstone undergraduate computer technology course at the British Columbia Institute of Technology (BCIT) in the Computer System Technology (CST) Program in which a large class of students (maximum 22), organized into small teams work together and apply Agile software development practices to design, implement, integrate and test a large project. This model provides students with unique learning opportunities and experiences, as well as improving their soft skills, engagement and motivation.

Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area.

Drawing on the long debate about whether computer science (CS) and computational thinking skills are innate or learnable, this working group is based on the following hypothesis: The apparent innate ability of some CS learners who succeed in CS courses despite no prior exposure to computing is a manifestation of early childhood experiences and learning outside formal education.

We present the results of a study that explored the emotions experienced by students during interaction with an educational game for math (Heroes of Math Island). Starting from emotion frameworks in affective computing and education, we considered a larger set of emotions than in related research. For emotion labeling, we employed a standard method that relies on trained judges to report emotions over 20-second intervals. However, we asked judges to report all observed emotions in each interval, as opposed to only choosing one, as is standard practice. This variation allows us to discuss the appropriateness of this interval for emotion labeling. We present a detailed analysis of inter-coder reliability, both aggregated and over individual students, that considers not only the matching by judges over emotion type, but also the number of emotions detected.