Open AccessStudent evaluations of teaching and teacher (SET) have become the focus of extensive data collection, due to high levels of competition in education. Yet, analysis of the data collected from students has been relatively neglected in favor of evaluation. Furthermore, heterogeneity of student population with different academic background may require advanced statistical techniques, such as Latent Class Analysis (LCA), used in the context of Latent Variable Models, to disclose latent classes or structures by the manifest variables. The purpose of the study is to identify distinct groups of students based on their SET ratings and use the LCA method to discover whether there is a discrepancy in the identified classes in terms of level of success and gender. The study also aims to present a descriptive examination regarding the students who evaluated the instructors and which classes they belonged to. The following three conclusions have been drawn from this research; that different typological structures of student exist in the institution, that there are differences in the identified classes in terms of gender, and that, regardless of whether they were successful or not, students were generally positive about teachers.