This paper presents a set of in-class activities and chemistry-based lab exercises developed for a CS1 course in Python that teaches programming through scientific contexts. The course pairs context-free in-class practice with a series of chemistry lab problems that increase in complexity, prompting students to apply computational thinking and form connections across course components. Survey results indicate that students responded positively to this approach.

Introductory computer science (CS1) courses are foundational to students' computing education, yet they often rely on abstract, decontextualized problems and introduce core concepts only once. This can lead to fragile learning, where students struggle to retain knowledge or apply it in new contexts. To address these challenges, we designed a CS1 course that introduces computing twice—first through a biology lens, then through a chemistry lens. This dual-introduction, dual-context structure aims to reinforce foundational computing concepts while highlighting their relevance across scientific domains. Our results indicate that the course structure effectively supported student learning, and end-of-semester survey responses reflected strong student engagement and appreciation for the interdisciplinary approach.

In an undergraduate CS1 course, we designed two projects to help students apply their learning: one in biology and one in chemistry. In this paper, we focus on the biology project, in which students identify and visualize gene mutations using real BRCA1 gene data. The projects received positive feedback, helping students see how coding applies to real-world problems in different scientific fields, understand real-world limitations, and reinforce their programming and problem-solving skills. This multi-context approach ensures a comprehensive understanding of programming concepts and their applications.

In an undergraduate CS1 course, we designed two projects to help students apply their learning: one in biology and one in chemistry. In this paper, we focus on the chemistry project, which centers on cheminformatics, the application of computational methods to analyze and interpret chemical data, which are particularly useful in drug discovery. In this project, students filter drug candidates using the PubChem database. The projects received positive feedback, helping students see how coding applies to real-world problems in different scientific fields, understand real-world limitations, and reinforce their programming and problem-solving skills. This multi-context approach ensures a comprehensive understanding of programming concepts and their applications.

In this paper, we present our experience with the use of breakout rooms in a second year undergraduate Software Design course at a large North American institution. Following the switch to remote instruction during the coronavirus pandemic, we revamped our in-person Software Design course to be delivered as a flipped online course, making extensive use of in-lecture exercises completed during breakout rooms. We report on the structure and logistics of this lecture design (for a large class of 300+ students). To gain insights into the impact of the use of breakout rooms on student experience, we conducted weekly student surveys asking for feedback on the lectures and specifically on the use of breakout rooms. Although many students had positive feelings regarding the use of breakout rooms, a significant percentage of students (an average of 47% of the survey responses each week) expressed negative feelings toward them. In an end-of-term survey, we specifically asked students about what they felt worked best for breakout rooms in terms of group size and pre-assigned versus randomized groups, and if there were any other areas that they felt needed improvement. Some of the patterns we observed were that most students liked smaller groups (2-5 people), preferred staying in the same group throughout the semester, and enjoyed the use of breakout rooms as long as others in their room were active participants. We share the details of these survey results as well as the tips and lessons that we learned through this experience.

We report our experience of preparing and conducting unplanned online exams in the unique half-physical, half-virtual semester of Winter 2020. The report covers four courses in a large university's computer science program, ranging from first-year to third-year. With the data generated by students taking both in-person and online exams in multiple courses, we perform analyses to evaluate the validity of the online exams (especially the unproctored ones) as an assessment of student understanding. With the fine-grained student activity data provided by the online exam platform, we are also able to investigate the patterns in student exam-taking behaviours and their correlations with student performance on the exam. In addition, we share, in detail, the tips and lessons that were learned throughout the process of designing, implementing, and hosting the online exams.

Recursion and iteration are two key topics taught in introductory Computer Science. This is especially so for CS2 students, as CS2 is the course where recursion is typically taught and where control-flow concepts are solidified. When asked to solve a problem that could feasibly be solved with recursion or iteration, what do CS2 students choose to do? And how does this choice relate to the correctness of their code? This paper provides one answer to these questions through an analysis of student exam responses to a problem on finding deepest common ancestors in trees.

Dynamic Programming (DP) is considered to be one of the most difficult topics for students to understand in theoretical CS. Prior work suggests that misconceptions arise even when students have completed a course in which there is considerable focus on learning how to solve DP problems. We conducted think-aloud interviews with students who have completed the DP portion of the Algorithms course at a top North American research university. We report on three themes and their misconceptions discovered through this process. The first theme delves into students' struggles defining the notion of a subproblem and identifying particular subproblems. The second theme focuses on the understanding and usage of DP solution techniques compared to other algorithmic approaches. The third theme is composed of misconceptions related to defining and using recurrences. Analysis of each misconception provides insight into student thinking and offers ideas for improving the education of DP to university students.

Taught and continually evolved since 2001, we discuss the critical path through a full stack, single term web development course including a discussion of topics and academic content. Our course design addresses the challenge of selecting a small collection of topics, technologies and classroom examples that provide the best pedagogical value in the rapidly changing area of web development. With the solid understanding of the fundamental Web/CS concepts learned in this course, the students are able to adapt to the favorite web frameworks of their employers and solve problems on the web.