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Computing self-efficacy is an important factor in shaping students' motivation, performance, and persistence in computer science (CS) courses. Therefore, investigating computing self-efficacy may help to improve the persistence of students from historically underrepresented groups in computing. Previous research has shown that computing self-efficacy is positively correlated with prior computing experience, but negatively correlated with some demographic identities (e.g., identifying as a woman). However, existing research has not demonstrated these patterns on a large scale while controlling for confounding variables and institutional context. In addition, there is a need to study the experiences of students with multiple marginalized identities through the lens of intersectionality. Our goal is to investigate the relationship between students' computing self-efficacy and their prior experience in computing, demographic identities, and institutional policies. We conduct this investigation using a large, recent, and multi-institutional dataset with survey responses from 31,425 students. Our findings confirm that more computing experience positively predicts computing self-efficacy. However, identifying as Asian, Black, Native, Hispanic, non-binary, and/or a woman were statistically significantly associated with lower computing self-efficacy. The results of our work point to several future avenues for self-efficacy research in computing. 

There has been an expansion of computer science (CS) in high schools in the USA. In most cases, initiatives seeking to expand high school CS offerings are not focused simply on increasing the number of students enrolled, but are focused on broadening participation in computing (BPC). Ideally we can evaluate and replicate effective BPC, or CSforAll, initiatives at the high school level. However, analyses of CS expansion frequently focus on the national landscape, despite the decentralized nature of public education in the USA. State-level analyses, when present, often do not take into account the demographics of high school students in the state, which are also changing over time. We propose three metrics for evaluating the impact of state-level initiatives to broaden participation in computing at the high school level. These metrics and our data set can be helpful benchmarks for evaluating high school CSforAll initiatives. 

Enrollment in computing at the college level has skyrocketed, and many institutions have responded by enacting competitive enrollment processes. However, little is known about the effects of enrollment policies on students' experiences. To identify relationships between those policies and students' experiences, we linked survey data from 1245 first-year students in 80 CS departments to a dataset of department policies. We found that competitive enrollment negatively predicts first-year students' perception of the computing department as welcoming, their sense of belonging, and their self-efficacy in computing. Both belonging and self-efficacy are known predictors of student retention in CS. In addition, these relationships are stronger for students without pre-college computing experience. Our classification of institutions as competitive is conservative, and false positives are likely. This biases our results and suggests that the negative relationships we found are an underestimation of the effects of competitive enrollment.