Multi-pronged programs that involve students in a combination of proven interventions (i.e., tutoring other students, building community, developing skills, etc.) constitute one pedagogical approach to increasing the number and diversity of computing professionals. In this manuscript, we evaluate the efficacy of one such multi-pronged program, the STARS Computing Corps, a Broadening Participation in Computing Alliance program funded by the National Science Foundation. These analyses improve upon previous efforts to assess the efficacy of STARS by examining dosage effects of the program, adding controls for students' initial intentions to pursue computing, and conducting these analyses at various points in a student's participation in STARS. We also conduct analyses to determine the efficacy of various STARS activities. Controlling for students' initial intentions to persist in computing, we find robust evidence that spending more time each week on STARS' activities positively predicts students' intentions to persist in a computing career, and that STARS has a heightened positive impact on Black and Hispanic students. We do not find evidence that the number of semesters a student spends in STARS is predictive of computing persistence, nor do we find differences in the efficacy of various STARS activities. In sum, these results suggest that STARS has a positive impact on students' intentions to persist in computing and that multi-pronged programs like STARS should focus on the intensity of participation (as opposed to the length of participation or a particular activity) to increase students' desire to persist in computing careers.

A key goal of many computer science education efforts is to increase the number and diversity of students who persist in the field of computer science and into computing careers. Many interventions have been developed in computer science designed to increase students' persistence in computing. However, it is often difficult to measure the efficacy of such interventions, as measuring actual persistence by tracking student enrollments and career placements after an intervention is difficult and time-consuming, and sometimes even impossible. In the social sciences, attitudinal research is often used to solve this problem, as attitudes can be collected in survey form around the same time that interventions are introduced and are predictive of behavior. This can allow researchers to assess the potential efficacy of an intervention before devoting the time and energy to conduct a longitudinal analysis. In this paper, we develop and validate a scale to measure intentions to persist in computing, and demonstrate its use in predicting actual persistence as defined by enrolling in another computer science course within two semesters. We conduct two analyses to do this: First, we develop a computing persistence index and test whether our scale has high alpha reliability and whether our scale predicts actual persistence in computing using students' course enrollments. Second, we conduct analyses to reduce the number of items in the scale, to make the scale easy for others to include in their own research. This paper contributes to research on computing education by developing and validating a novel measure of intentions to persist in computing, which can be used by computer science educators to evaluate potential interventions. This paper also creates a short version of the index, to ease implementation.

Are women less likely to persist in computer science because of gender differences in self-assessed computing ability? And why do gender differences exist in self-assessments among women and men who earn the same grades? We use a mixed-method research design to answer these questions, utilizing both quantitative survey data (n = 764) and qualitative interview data (n = 59) from students in introductory computing courses at a large U.S. state university. Quantitatively, we find that women self-assess their computing ability significantly lower than men who earn the same grades, and that these lower self-assessments reduce the likelihood that women enroll in future CS courses (relative to men who earn equivalent grades). Qualitatively, we explore how women and men perceive their own computing ability to understand why women self-assess their ability lower than men. Our interviews revealed that women were much less likely than men to make favorable comparative judgements about their ability relative to their classmates. Women also had higher personal performance standards than men. Lastly, women were more likely than men to experience disrespectful treatment, with an undertone of presumed incompetence, from their TAs and classmates. In sum, this research furthers our understanding of why gender differences exist in self-assessments of computing ability and how these differences can contribute to gender disparities in computing persistence. It also draws attention to the importance of feedback in computing courses and suggests that improving course feedback may reduce gender disparities in computing.

Research has shown that high self-assessment of ability, sense of belonging, and professional role confidence are crucial for students' persistence in computing. As grades in introductory computer science courses tend to be lower than other courses, it is essential to provide students with contextualized feedback about their performance in these courses. Giving students unambiguous and con- textualized feedback is especially important during COVID when many classes have moved online and instructors and students have fewer opportunities to interact. In this study, we investigate the effect of a lightweight, scalable intervention where students received personalized, contextualized feedback from their instructors after two major assignments during the semester. After each intervention, we collected survey data to assess students' self-assessment of computing ability, sense of belonging, intentions to persist in computing, professional role confidence, and the likelihood of stating intention to pursue a major in computer science. To analyze the effectiveness of our intervention, we conducted linear regression and mediation analysis on student survey responses. Our results have shown that providing students with personalized feedback can significantly improve their self-assessment of computing ability, which will significantly improve their intentions to persist in computing. Furthermore, our results have demonstrated that our intervention can significantly improve students' sense of belonging, professional role confidence, and the likelihood of stating an intention to pursue a major in computer science.

We have found that giving top-performing students in CS1 courses personalized feedback increases their intentions to persist in computing, especially among students who are women. This personalized feedback also appears to improve students' course experience and increases the likelihood that women apply to be CS1 TAs. Yet despite these benefits, giving personalized feedback may seem too impractical and time-intensive for faculty members to adopt in their own classrooms. In this workshop, we will reduce the burden of giving students personalized feedback by: 1) giving instructors empirically validated email templates to use in their own courses, and 2) guiding faculty how to send emails at-scale. We will also discuss how self-assessments influence students' career choices, how gender stereotypes bias self-assessments, and what faculty can do to counteract biased self-assessments of computing ability.

Test smells are commonly perceived as having a negative impact on software maintainability and correctness. Research has shown that Assertion Roulette is the most pervasive smell in industrial and open-source systems. However, some recent studies argue that the impact of Assertion Roulette is not as severe as previously believed, and developers usually consider it acceptable.The controversy over the impact of Assertion Roulette also exists in the area of testing education. To assess the impact of Assertion Roulette, we conducted a controlled empirical study with 42 CS students. We recruited participants from two populations, CS1 and a graduate testing course, to see what role experience may have in terms of this test smell’s impact. Participants were tasked with implementing a project in Java that passes provided JUnit tests. Through analysis of student-authored source code, we measured the impact of Assertion Roulette using code quality measures and testing behavior measures. Our findings show that the impact of Assertion Roulette on students in this study was minimal. Though students with exposure to the test smell began testing significantly later, they performed similarly in terms of programming quality measures. Thus, it would seem the Assertion Roulette smell is no longer a smell at all, even for less experienced populations like students.

Gender stereotypes about women's computing ability contribute to the dearth of women in computing by causing women to experience gender bias. These gender stereotypes are doubly disadvantaging to women because they create gender differences in self-assessments of computing ability, decreasing the likelihood that women will persist in Computer Science (CS). This is because students need to believe they have sufficient ability in a field in order to pursue it as a career.

Prior work suggests that novice programmers are greatly impacted by the feedback provided by their programming environments. While some research has examined the impact of feedback on student learning in programming, there is no work (to our knowledge) that examines the impact of adaptive immediate feedback within programming environments on students' desire to persist in computer science (CS). In this paper, we integrate an adaptive immediate feedback (AIF) system into a block-based programming environment. Our AIF system is novel because it provides personalized positive and corrective feedback to students in real time as they work. In a controlled pilot study with novice high-school programmers, we show that our AIF system significantly increased students' intentions to persist in CS, and that students using AIF had greater engagement (as measured by their lower idle time) compared to students in the control condition. Further, we found evidence that the AIF system may improve student learning, as measured by student performance in a subsequent task without AIF. In interviews, students found the system fun and helpful, and reported feeling more focused and engaged. We hope this paper spurs more research on adaptive immediate feedback and the impact of programming environments on students' intentions to persist in CS.