This paper deals with the problem of selecting the number of changepoints in segmented regression models. The aim is to review selection criteria, namely information criteria and hypothesis testing, and to propose a novel application in the context of students' careers in higher education. The performance of the selection criteria is assessed through simulation studies. Furthermore, we investigate the relationship between University students' performance and one of its main determinants, finding out that this relationship is actually broken-line.Keywords Information criteria • Hypothesis testing • Segmented regression • Changepoints • Higher education RiassuntoQuesto articolo affronta il problema della selezione del numero di punti di svolta nei modelli di regressione segmentata. L'obiettivoè quello di offrire una panoramica dei metodi di selezione, in particolare criteri i di informazione e verifica di ipotesi, e proporre una nuova applicazione nel contesto della carriere degli studenti nell'istruzione superiore. La performance dei criteri di selezioneè valutata attraverso studi di simulazione. Inoltre, indaghiamo la relazione fra il successo degli studenti universitari e uno dei suoi principali predittori, scoprendo che questa relazioneè effettivamente segmentata.Parole chiave Criteri d'informazione -Test d'ipotesi -Regressione segmentata -Punti di svolta -Istruzione superiore

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.

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