Caroline Sih

Motivation : In computing, Impostor Phenomenon (IP) has been viewed as a problem for many years, but little research has been done to show its prevalence. In 2020, IP in computing began to be explored at single institutions [ 68 ]. The results showed that IP is prevalent among undergraduate and graduate students in computing courses and that the rates of IP are higher for women. In 2022, these results were reaffirmed with a replication study including two institutions [ 82 ]. This is concerning due to the negative effects correlated with people who experience IP such as low self-esteem [ 19 , 37 ] and anxiety [ 21 , 38 ]. Objectives : This study aims to replicate these previous findings at a considerably larger scale to determine whether similar results are observed across institutions. To support future work, we conduct an exploratory analysis of student demographics, course factors, and institutional factors to gain insight into factors that may be associated with higher levels of IP among students. Methods : A survey consisting of Clance’s IP scale (CIPS) and questions on students’ demographic and background information was given at 18 institutions. Higher CIPS scores indicate more IP experiences. Differences in CIPS scores were analyzed based on students’ demographics and background information (gender, race/ethnicity, transfer status, and chosen degree program), course factors (introductory computing courses vs. non-introductory computing courses, upper- vs. lower-division), and institutional factors (size of the institution, public vs. private, teaching- vs. research-centric). Results : Our results are consistent with previous findings that IP is prevalent among students in computing courses and that women have significantly higher CIPS scores of IP than men in computing, and that traditionally marginalized race/ethnicity status in computing and chosen degree program do not have an observable impact. In terms of course factors, we do not see a difference in scores based on whether students are enrolled in a lower- or upper-division course. We see that students enrolled in introductory computing (CS1) courses have statistically significant higher scores than students outside of CS1 courses. In terms of institutional factors, students in computing courses at public institutions have statistically significantly higher scores than students at private institutions. Students at medium-sized institutions have statistically significantly higher scores than students at small or large institutions. We do not find any difference based on whether an institution is teaching- or research-centric. Discussion : These results suggest that IP is prevalent in computing courses across the entire curriculum and across different types of institutions. Differences in demographic groups are consistent with prior work in computing, specifically higher rates among women, suggesting IP may be worth further inquiry as a potential factor in the gender participation gap in computing.

Online discussion boards, designed to facilitate learning from peers and instructors in an accessible space, are a vital part of course design, especially in large scale computer science classes. Previous work has shown that women in computer science tend to use anonymity more often than men on these boards, a trend not found in humanities, social science or business courses. In this work, we build on these findings using an intersectional lens, analyzing both gender and race/ethnicity. We find this combined analysis reveals differences in anonymity that are not apparent when examining gender alone. For example, we find a significantly greater difference in anonymity use between Hispanic men and women than would be expected from analyzing race/ethnicity and gender independently. We additionally analyze type of content (e.g., questions, answers), course, platform, and data source to characterize the many factors at play in measuring students’ choice to participate anonymously. In doing so, we show that different approaches used in prior work for eliciting information on gender — whether using registrar data, a survey, or imputing gender based on name — changes how over of students are classified, particularly affecting nonbinary students and Asian students. Understanding when students participate anonymously can help educators and platform designers to make students’ experience of online discussion boards more welcoming.