I. Introduction
NOWADAYS, academic processes generate a huge amount of data, mainly pertaining to the interaction between students and teachers, as well as between students and their learning environment [1]. This information is being thoroughly explored to gain novel knowledge about how students learn in a variety of scenarios and then in the improvement of the quality of educative systems by providing timely support to learners, instructors, and administrators [2]. In fact, the application of well-known data mining techniques to educational data is an emerging research field, which is referred to as educational data mining (EDM) [1]. More precisely, the International Educational Data Mining Society defines EDM as “an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.” [3].