Belonging uncertainty as predictor of dropout intentions among first-semester students of the computer sciences

Abstract

With the fast-growing sector of information technology in digitizing societies, the attraction and education of qualified recruits in computer science becomes a key task of tertiary education. Considering the high dropout rates and the continuing gender gap in computer science, the current study builds on the “leaky pipeline” phenomenon of women in STEM (science, technology, engineering, and mathematics) by investigating belonging uncertainty in computer science as a predictor of students’ dropout intentions. In a study with first-semester computer science students (N = 217) at two time points, we tested the hypotheses that female students experience a greater belonging uncertainty than male students and that this belonging uncertainty is predictive of students’ dropout intentions. Furthermore, we explored whether belonging uncertainty is a more relevant predictor of female than male students’ intentions to drop out of computer science. In line with our predictions, our results show that female students experienced greater uncertainty about their belonging within the domain of computer science than male students and that belonging uncertainty significantly predicted students’ dropout intentions above and beyond the pertinent predictors academic self-efficacy, expectancy of success, perceived future utility value of the subject, and previous academic performance. Belonging uncertainty, however, was a relevant predictor of both female and male computer science students’ dropout intentions.