Modelling students’ career indicators via mixtures of parsimonious matrix-normal distributions

Abstract

The evaluation of the teaching efficiency, under different points of view, is an important aspect for the university system because it helps managers to improve more and more the quality of the education and helps students to achieve strong professional skills. In this framework, students’ careers as well as teachers’ qualification and quantity adequacy indicators are analysed based on data sets provided by the Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR) according to a mixture model approach. In particular, parsimonious mixtures of matrix-normal distributions are used to detect underlying grouping structures. The results show that the data present an underlying group structure of courses having different traits, thus providing useful information for the university policy makers.