Technical and ethical aspects of Computer Science (CS) are interdependent. Many CS departments teach ethical and social implications of technology in separate standalone courses. However, prior research shows that ethical issues are better taught in tandem with their related technical content as an integral required skill in CS curricula. In this experience report, we share our experience with embedding ethics assignments in 3 CS courses at different levels: a CS2 course in software design and data structures, a CS3 course in data structures and algorithms, and a Software Engineering capstone course, all taught at Virginia Tech (a large public R1 institution) in Spring 2024. Students from the 3 courses were surveyed at the beginning and end of Spring 2024. By comparing results from the pre and post surveys, we found that the embedded assignments for the CS2 and CS3 courses improved students' confidence in their knowledge about how ethical issues may come into play in their career, their confidence in their ability to address ethical issues arising from applying technology in real contexts, and their confidence in communicating and defending their positions on how to address these issues. For all 3 courses, students gave positive feedback on how the assignments were engaging and relevant to the course, and how it improved their ability in raising, and reasoning about, ethical implications of technology. We believe that the practices and results of our experience will be helpful to other CS instructors thinking of injecting ethical content into their technical courses.

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.

Computer Science programs have seen high enrollments in recent years, which contributed to widening the capacity gap. One way to address this problem is to hire more teaching-focused faculty at both research and non-doctoral granting institutions. Although this kind of hiring has already been taking place in several institutions, PhD-granting CS departments have not been able to produce enough PhDs to meet the increasing demand, especially for PhD holders with interest in - and capacity for - teaching. In this paper, we describe our experience with the initial phase of building a training program within our (large, land grant, R1) institution, targeting graduate students interested in pursuing an academic teaching-focused career in CS. Through a semester-long set of meetings, conversations, and activities, we worked with participants on improving their teaching skills and applying effective pedagogies in the classroom. At the end of the semester, we surveyed participants about the value of those meetings to them, ideas for improvement, and perspectives for future directions. Most participants rated the meetings positively in terms of content relevance and usefulness, and the opportunity to connect and interact with other participants and invited faculty members. We also discuss the lessons learned and best practices, which can be widely applied by other departments looking to better prepare their graduate students for a CS teaching-focused faculty position.

This working group concerns the adoption of computing education (CE) in undergraduate computer science (CS) programmes. Such adoption requires both arguments sufficient to persuade our departmental colleagues and our education committees, and also curricular outlines to assist our colleagues in delivery. The goal of the group is to develop examples of both arguments and curricular outlines, drawing on any prior experience available.

COVID-19 caused universities to switch from traditional face-to-face (F2F) course delivery to completely online in Spring 2020. This transition took place on short notice in the middle of the semester. We present results from surveys of students in two CS courses offered at Virginia Tech. Results indicate differing perceptions in the two courses regarding the usefulness of course components before and after the transition for each course. A logistic regression model indicates that for each course, different course components both before and after the transition significantly affect students' preferences for course modality.

We present initial results from our work towards developing a concept inventory for algorithm analysis (AACI) at the post-CS2 level. We used a Delphi process to identify a list of algorithm analysis topics that were considered both important and hard by surveying a panel of experienced instructors. Through a similar survey process, we identified a list of student misconceptions related to the identified topics. Based on this, a set of pilot AACI items were developed. We validated the misconceptions list by analyzing student responses to four administrations of the pilot AACI in two different universities during Fall 2015 and Spring 2016. Results revealed that a sufficient number of students held most of the misconceptions identified in the list.

Algorithm Visualizations (AVs) have been used for years as an interactive method to convey data structures and algorithms concepts. However, AVs have traditionally focused on illustrating the mechanics of how an algorithm works. We have developed visualizations that we name Algorithm Analysis Visualizations (AAVs), that focus on conveying algorithm analysis concepts. We present our findings from an initial evaluation study of the effectiveness of AAVs when applied to a semester long Data Structures course. AAVs were evaluated in terms of student engagement, student satisfaction, and student performance. Results indicate that the intervention group students spent significantly more time with the AAVs than did the control group students who used primarily textual content. Students gave positive feedback regarding the usefulness of the AAVs in illustrating algorithm analysis concepts. Students from the intervention group had better performance on the algorithm analysis part of the final exam than did control group students.

Data Structures and Algorithms (DSA) courses are considered critical in any computer science curriculum. DSA courses emphasize topics related to procedural dynamics (how an algorithm works) and algorithm analysis (the algorithm's efficiency). Historically, algorithm visualizations (AVs) have dealt almost exclusively with portraying algorithm dynamics, and there are few examples of visualizations related to algorithm analysis topics. We have developed a new generation of visualizations that we term Algorithm Analysis Visualizations (AAVs) to convey algorithm analysis concepts. We present the motivation behind AAVs, and outlines a methodology for their evaluation. We present results from student surveys and the analysis of student interaction logs from the OpenDSA eTextbook used by several CS3-level classes during the period of Fall 2014 through Fall 2015. Initial results from Fall 2014 revealed that students were not spending enough time reading the algorithm analysis material presented as textual content. Our results from a preliminary deployment of AAVs in Spring 2015 showed that students interacted with AAVs for significantly longer than the control group spent reading the previous text-based algorithm analysis material. We will present additional results from our ongoing experiment in Fall2015 (control group without AAVs) and Spring2016 (test group with additional AAVs).