Giulia Alberini

Oral assessments can give students opportunities to demonstrate reasoning, communication, and depth of understanding in ways that are difficult to capture through written exams or programming projects alone. Technical interviews similarly emphasize live problem solving and communication, yet many students encounter them outside the curriculum, often with uneven access to preparation. This experience report describes the design and refinement of Problem Solver, an optional oral problem-solving assessment track modeled on technical interviews and implemented in two large undergraduate CS courses: CS2 (545 students) and Data Structures and Algorithms (DSA, 310 students). In both courses, students selected either a project-based track or Problem Solver, with about 10% choosing Problem Solver. The track was supported by a question bank, rubric, and optional low-stakes mini-interviews. Building on the CS2 deployment, the DSA implementation streamlined scheduling, adjusted the mini-interview format, and introduced a small set of unseen questions to reduce over-preparation while preserving authenticity. We report practical lessons about logistics, staffing, evaluator calibration, and structured support, and summarize student feedback on preparedness, stress, engagement, and fit. We also discuss limitations of the opt-in design, including self-selection and the difficulty of separating the effects of student choice from the oral assessment format itself. Overall, these design patterns and trade-offs may help instructors of large courses develop student-centered alternatives to traditional final assessments.

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.