Collecting and analysing students’ opinions towards the learning experiences lived during their enrolment in an academic program is widely recognised as a key strategy to evaluate tertiary education quality. Academic institutions require students to participate every year in specific surveys, aiming at gathering their viewpoint about the organisation of the single courses, and the feelings about the traits and the effectiveness of the teaching activity. In the Italian university system, the surveys about students’ satisfaction are realised in accordance with the guidelines of the National Agency for the Evaluation of Universities and Research Institutes. Here we propose the implementation of a latent class analytical strategy to profile the satisfaction of students at a course level, taking into account the interest about each course, and the perceptions about the course organisation and the instructor performance. Since the items listed in the survey are expressed as 4-point balanced scales, we used the so-called Latent Profile Analysis (LPA) to identify unobserved clusters of courses (i.e., latent profiles) based on the responses of students to the continuous indicators concerning the different aspect related to course satisfaction. Differently from clustering approaches based on distance functions, LPA is a probabilistic model, which means that it models the probability of case belonging to a profile. An application of the strategy to the first-year courses delivered at the University of Calabria (Italy) in the academic year 2020/2021, during the second and third waves of the COVID-19 pandemic in Italy, is used to show the effectiveness of the approach.