Abstract
This paper investigates gender differences in university performances in Science, Technology, Engineering and Mathematics (STEM) courses in Italy, proposing a novel application through the segmented regression models. The analysis concerns freshmen students enrolled at a 3-year STEM degree in Italian universities in the last decade, with a focus on the relationship between the number of university credits earned during the first year (a good predictor of the regularity of the career) and the probability of getting the bachelor degree within 4 years. Data is provided by the Italian Ministry of University and Research (MIUR). Our analysis confirms that first-year performance is strongly correlated to obtaining a degree within 4 years. Furthermore, our findings show that gender differences vary among STEM courses, in accordance with the care-oriented and technical-oriented dichotomy. Males outperform females in mathematics, physics, chemistry and computer science, while females are slightly better than males in biology. In engineering, female performance seems to follow the male stream. Finally, accounting for other important covariates regarding students, we point out the importance of high school background and students’ demographic characteristics.
Highlights
In the last years, studies on students’ university experiences have been increasingly common (Salanova et al 2010; Mega et al 2014; Freeman et al 2014)
Italian university students, as in most of the other western countries (Mostafa 2019), are not likely to enroll at Science, Technology, Engineering and Mathematics (STEM) courses
This paper aims at investigating the differences among STEM courses in terms of female and male performances
Summary
Studies on students’ university experiences have been increasingly common (Salanova et al 2010; Mega et al 2014; Freeman et al 2014). Segmented or broken-line models are regression models where the relationships between the response and one or more explanatory variables are piecewise linear and, as such, represented by two or more straight lines connected at unknown points. These models are a common tool in many fields, including epidemiology, occupational medicine, toxicology and ecology, where usually it is of interest to assess threshold values where the effect of the covariate changes (Ulm 1991; Betts et al 2007). Recent papers deal with applications of segmented regression models in higher education (Li et al 2019), but to the best of our knowledge, this paper represents the first application of segmented regression models applied to predict university success.
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