The student's interaction with Virtual Learning Environments (VLE) produces a large amount of data, known as learning traces, which is commonly used by the Learning Analytics (LA) domain to enhance the learning experience. Digital learning systems are generally based on the processing of these traces and must be able to adapt to different student profiles. However, the information provided in raw traces is diversified and can't be directly used for the profile identification task: it requires defining learning indicators pedagogically relevant, and measurable directly from learning traces, and then classify learners profiles according to these indicators. The paper's main contribution remains on the characterization of LA datasets both in terms of groups sizes and observed digital behaviors. It answers the lack of clearly stated information for LA systems developers, who need to ensure that their algorithms do not introduce bias, especially by disfavoring specific categories of students, which would only worsen existing inequalities in the student population. To go further, the embodiment of these identified profiles by translating them into learner personas also participates in the improvement of the explicability of LA outcomes by providing easy-to-interpret descriptions of students. These personas consist of fictitious representative student profiles, expressing different needs and learning objectives to which the LA systems must respond.