We present the development of an asynchronous online module for teaching software documentation in an introductory software design course. Drawing on literature from industry practices and computing education, we designed a lightweight, self-paced module that introduces students to the principles of effective documentation. A central feature of the module is our ACCEU framework, which defines five key dimensions of documentation quality: accuracy, clarity, completeness, ease of use, and up-to-dateness. These dimensions are grounded in a synthesis of prior research and serve as a practical guide for students documenting both code and software projects. We describe the pedagogical motivations behind our design, the structure and content of the module, and initial student feedback. Our experience suggests that the module was well received by our students and met our goals. We conclude with recommendations for instructors interested in adopting or adapting our described approach.

Educational puzzles can be a powerful way to develop the situational interest of learners by presenting an authentic and challenging experience. This is important because situational interest has been shown to be a key predictor of engagement and performance. To this end, we developed CS Connections, inspired by the New York Times Connections puzzle game. CS Connections is a tool that provides educators with a flexible way to create custom interactive puzzles in a familiar format.

Introductory programming courses present a unique challenge for many students as a novel discipline, requiring significant time investment and featuring a steep learning curve, resulting in students experiencing high levels of failure while learning. Students' perspectives on failure are crucial in determining how they confront these challenges and, consequently, their learning outcomes. This study investigates the relationship between undergraduate students' views on failure--measured by validated scales about growth mindset, fear of failure, self-efficacy, and academic resilience--with their performance in introductory programming courses. While self-efficacy and growth mindset are well-studied in computing education, fear of failure and academic resilience remain understudied despite their prominence in other disciplines. We collected data from three universities to conduct a repeated measures study of 58 students' attitudes toward failure at the beginning and the end of the semester. Our results indicated self-efficacy and fear of failure uniquely predicted performance, with lower self-efficacy and higher fear of failure related to poorer outcomes. Furthermore, students with lower self-efficacy and higher fear of failure were four times more likely to withdraw from or fail the course. Our findings suggest that measuring self-efficacy and fear of failure at the beginning of the semester can help identify at-risk students who need support. Research and interventions related to academic fear of failure from other STEM fields should be examined in the context of computing education to improve outcomes for our students.

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.

In the context of problem sets in first- and second-year computer science theory courses, we investigate the factors influencing students' decisions to work individually or in a small group. Through analysis of open-ended survey responses from over 1,300 students, we have gained a more nuanced understanding of these factors. We observed three categories of factors: workload and time management, optimizing learning and assignment marks, and social and affective factors. We identified two modifiers, online learning and previous group experiences, that amplify the impact of factors in the three main categories. We highlight notable student quotations and discuss barriers to group formation.

In introductory CS theory courses, instructors sometimes give students the option to work in groups when completing assignments so they can discuss problems and gain teamwork skills. It is important, then, to ensure that diverse student populations, including those with different academic abilities, can all benefit from this optional group work. In this work, we investigate how lower- and higher-ability students, as measured by prerequisite grades, perform in self-selected, 1-3 person groups of different abilities. We find that lower-ability students can benefit from being in groups that are heterogeneous with respect to student ability.

We investigate the impact of allowing students to optionally work in small groups on problem sets in first- and second-year computer science theory courses. After each homework assignment, students reported on their experience working on that problem set either individually or in a group of two or three. Over 1,300 students from two courses participated. We explore who chooses to work in a group and why, how students work in groups, and differences in learning, drop-rates, help-seeking, and satisfaction with problem set submissions between groups and students working individually.