In 2017, Edwards et al. studied a large corpus of Java programs collected through an automated submission and assessment system that integrated static analysis feedback. They found that errors reported were most commonly related to formatting, but that the frequency of errors they categorized as "Coding Flaws" correlated with program correctness grades. They argued that static analysis feedback could detect problems relating to code correctness and could therefore be useful beyond evaluating conformance to style rules, but that students may overlook non-cosmetic error messages because of the relative volume of formatting errors. In this paper we perform a conceptual replication of the Edwards et al. study with 1270 CS1 students learning Python. We confirm that almost a decade later and even after being instructed to use the auto-formatting options within their IDE, students still encounter mostly formatting errors when using a static analysis tool. We find that the second- most common category of errors detected are "Coding Flaws", and, like Edwards et al., that the frequency of coding flaws identified by the static analysis tool correlates to program correctness. When we examine trends based on levels of prior programming experience, we find that all students tend to make more formatting errors than other kinds of errors, but that students with no prior programming experience have more errors reported across all error categories.

Characterising code quality is a challenge that was addressed by Börstler et al. 's working group in 2017. As emerged from their study, educators, developers and students have different perceptions of the manifold aspects involved, and a major conclusion of that WG was that "code quality should be discussed more thoroughly in educational programs" [2, p. 70]. However, the lack of materials and the time constraints have slowed down progress in that regard.

Embedded ethics education integrates ethical considerations into computer sciences courses in support of ethics-informed design, development, and deployment of technology. Scholarly assessment has demonstrated that such modules can influence students' attitudes about the relevance and importance of ethics to their work, as well as their perceived ability to tackle ethical issues in the workplace. In this paper, we report on a study that investigates whether embedded ethics modules have an impact beyond the classroom. Specifically, we examine whether embedded ethics modules influence students to learn more about ethics on their own, whether students are better able to recognize ethical issues when they enter the workplace for an industrial or research work experience, and whether they report that the modules they participated in helped them to navigate the ethical situations they encountered at work. While further assessment is needed to investigate these questions fully, our results suggest that embedded ethics modules can indeed have this kind of positive impact beyond the classroom.

Embedding ethics modules in computer science (CS) courses is an approach to post-secondary ethics education that has been gaining traction. In contrast to dedicated courses on ethics in CS, embedding ethics modules into CS courses supports tight connections between ethical considerations and CS concepts, as well as enabling repeated exposure to ethics across multiple courses. Initial studies of the effectiveness of such modules suggest that this approach can increase both student interest in ethics and technology, and student self-efficacy towards incorporating ethical considerations in their computing work. Departments wishing to deploy embedded ethics (EE) modules need to decide how to invest resources, including class time, to maximize effectiveness while maintaining curriculum objectives. Such considerations include the number of EE module experiences a student has throughout their degree program, as well as the spacing of those experiences.

Students learning to program often rely on feedback from the compiler and from instructor-provided test cases to help them identify errors in their code. This feedback focuses on functional correctness, and the output, which is often phrased in technical language, may be difficult to for novices to understand or effectively use. Static analyses may be effective as a complementary aid, as they can highlight common errors that may be potential sources of problems. In this paper, we introduce PyTA, a wrapper for pylint that provides custom checks for common novice errors as well as improved messages to help students fix the errors that are found. We report on our experience integrating PyTA into an existing online system used to deliver programming exercises to CS1 students and evaluate it by comparing exercise submissions collected from the integrated system to previously collected data. This analysis demonstrates that, for students who chose to read the PyTA output, we observed a decrease in time to solve errors, occurrences of repeated errors, and submissions to complete a programming problem. This suggests that PyTA, and static analyses in general, may help students identify functional issues in their code not highlighted by compiler feedback and that static analysis output may help students more quickly identify debug their code.

The Nifty Assignments special session is all about promoting and sharing the ideas and ready-to-use materials of successful assignments. Each presenter will introduce their assignment, give a quick demo, and describe its niche in the curriculum and its strengths and weaknesses. The presentations (and the descriptions below) merely introduce the assignment. A key part of Nifty Assignments is the mundane but vital role of distributing the materials - handouts, data files, starter code, rubrics - that make each assignment ready to adopt. Each assignment presented has complete materials freely available on the Nifty Assignments home page nifty.stanford.edu. If you have an assignment that works well and would be of interest to the CSE community, please consider applying to present at Nifty Assignments.