Predicting Women's Persistence in Computer Science- and Technology-Related Majors from High School to College

Abstract

While demand for computer science and information technology skills grows, the proportion of women entering computer science (CS) fields has declined. One critical juncture is the transition from high school to college. In our study, we examined factors predicting college persistence in computer science- and technology-related majors from data collected from female high school students. We fielded a survey that asked about students’ interest and confidence in computing as well as their intentions to learn programming, game design, or invent new technology. The survey also asked about perceived social support from friends and family for pursuing computing as well as experiences with computing, including the CS Advanced Placement (AP) exam, out-of-school time activities such as clubs, and internships. Multinomial regression was used to predict persistence in computing and tech majors in college. Programming during high school, taking the CS Advanced Placement exam, and participation in the Aspirations awards program were the best predictors of persistence three years after the high school survey in both CS and other technology-related majors. Participation in tech-related work, internships, or after-school programs was negatively associated with persistence, and involvement with computing sub-domains of game design and inventing new applications were not associated with persistence. Our results suggest that efforts to broaden participation in computing should emphasize education in computer programming.

Re-Examining Inequalities in Computer Science Participation from a Bourdieusian Sociological Perspective

“…For example, Wang et al [58] found that regardless of the exposure (unstructured or structured), high school girls who had engaged with computer science classes were more likely to consider computer science-related degrees than girls with no such experience. Along the same lines, Weston, Dubow and Kaminsky [61] found that among the main predictors of women persistence in computer science was the programming experience in high school. A lot of other researchers highlighted previous knowledge, skills and participation in computer science practices as pre-requisite for persistence in computer science fields (e.g., [1], [60]).…”

Female Mechanical Engineering Students’ Career Decisions and Development: A Case Study of University Undergraduate Students

“…2) Social stigma: Women in mechanical engineering may be viewed as being rude because they work in the construction field with men (Strachan et al, 2018). 3) Academic stereotypes: peers, teachers, parents, school counsellors, and relatives may suggest that women should pursue liberal arts majors instead of vocation-oriented subjects, such as engineering and the sciences (Weston et al, 2020). 4) Earning differences: Even when both men and women hold the same position and title, women usually receive a lower salary and fewer benefits than their male co-workers do (White, 2018).…”

Impact of students evaluation of teaching: a text analysis of the teachers qualities by gender

“…They observed that adopting advanced network technologies for teaching can support the transformation and pave the way for in-depth construction of high-quality education (Crues et al, 2018). Also, Abu Zohair (2019) notes that accurate data analysis has not only become essential for improving the students' performance and experience (Crues et al, 2018;Kori et al, 2018;Weston, Dubow, & Kaminsky, 2019) but it also serves as a mechanism for elevating the university's ranking and reputation. The results of the study by Abu Zohair (2019) show that support vector machines or algorithms can be used to train and analyze the readily available datasets to produce acceptable learning-processclassification accuracy and reliability test rates (Liao et al, 2019).…”

“…On the other hand, Wen et al (2014) investigate what sentiment analysis can tell us about the students' opinions with regards to the learning experiences (Crues et al, 2018). Notably, Moshinskie (2001) states that it is essential to monitor the students' feelings or emotions because learners with a positive attitude have been seen as more confident and motivated to learn (Weston et al, 2019). Besides, considering the students as consumers of the educational systems and curricula is one of the main reasons to use digital learning strategies as a way to personalize learning experiences (Munro, 2018;Romero, Saucedo, Caliusco, & Gutiérrez, 2019).…”