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.

As AI becomes more widely used across a variety of disciplines, it is increasingly important to teach AI concepts to K-12 students in order to prepare them for an AI-driven future workforce. Hence, educators and researchers have been working to develop curricula that make these concepts accessible to K-12 students. We are designing and developing a comprehensive AI curriculum delivered through a series of carefully crafted activities in an adapted \emph{Snap!} environment for middle-grade students. In this work, we lay out the proposed content of our curriculum and present the design, development, and implementation results of the first unit of our curriculum that focuses on teaching the breadth-first search algorithm. The activities in this unit have been revised after being piloted with a single high-school student. These activities were further refined after a group of K-12 teachers examined and critiqued them during a two-week professional development workshop. Our teachers created a lesson plan around the activities and implemented that lesson in a summer workshop with 14 middle school students. Our results demonstrated that our activities were successful in helping many of the students in understanding and implementing the algorithm through block-based programming while extra supplementary material was needed to assist some other students. In this paper, we explain our curriculum and technology, the results of implementing the first unit of our curriculum in a summer camp, and lessons learned for future developments.

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.