Traditional grading prioritizes point accumulation over mastery, reinforcing strategies that hinder deep understanding. In large introductory courses, it can increase stress and inequities, particularly for students from diverse backgrounds. Competency-based grading (CBG) addresses these issues by focusing on mastery and providing multiple opportunities for improvement. Studies suggest CBG improves fairness, motivation, and transparency while reducing stress, yet its implementation in large CS courses remains underexplored. We present the implementation of CBG in a 400+ student introductory CS course. Unlike traditional grading, CBG evaluates students on structured competency levels, with multiple opportunities to demonstrate understanding. Challenges included student resistance, grading misconceptions, and institutional hurdles. We outline the CBG structure, student responses, and key challenges, along with refinements to improve clarity and engagement. By ITiCSE 2025, preliminary survey data on engagement and motivation will offer insights into its effectiveness.

Evidence supports offering research experiences for undergraduate computing science students as a means of broadening participation in computing[7, 10, 11]. However, student perceptions about computing science research, how students become interested in these research experiences, and the details of effective design and delivery of programs capable of attracting and retaining this interest are less explored. In this study, we investigate the design and delivery of undergraduate research programs. We expand on and explore several factors, including but not limited to cultural relevance, the presence of a cross-disciplinary high-level view, task assignment, entry point, and support elements of a program.

We describe the implementation of a flexible grading scheme in a large introductory computer science course, where students were given the option to be assessed through a traditional final project or a technical interview. The technical interview, modeled after real-world coding interviews, allowed students to practice and demonstrate both their problem-solving and communication skills under pressure. In this report, we outline the structure of the assessment, the preparation provided to students, and the opportunities for practice interviews aimed at reducing performance anxiety. We also present key observations regarding student performance and motivation, with data indicating higher engagement among non-CS majors and increased autonomy, involvement, and satisfaction overall. The experience highlights both the benefits and challenges of offering such an assessment in a large class setting, providing valuable insights for educators considering similar approaches to evaluation.

The instructor of a large, introductory computer science (CS) course at a public Canadian university implemented two interventions designed to support students' academic success and basic psychological needs as posited by self-determination theory (SDT). Interventions involved providing grading scheme choice for all students and sending targeted support emails to students who struggled on early term assessments. In keeping with SDT, we assessed the possible effect of these interventions on students' perceptions of competence (self-efficacy), autonomy, relatedness (via measures of instructor warmth), and final grades, by comparing the intervention cohort with a previous control cohort. Results indicate that all students in the intervention term may have benefited from grading scheme choice, as they earned higher final grades and felt more autonomous than the control group students. Moreover, struggling students who received support emails earned an average final grade 11.3% higher than struggling students in the control term. These students also performed closer to their non-struggling counterparts than those in the control group, reducing the achievement gap between early struggling and non-struggling students by 8.1%. Furthermore, even when controlling for past achievement, perceptions of self-efficacy and autonomy support positively predicted students' final grades across groups, with a small effect size. These results offer theoretical and practical insight into effective, light-touch teaching interventions which CS instructors can implement in large courses.