The student's interaction with Virtual Learning Environments 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. We propose to define personas, that are representative of subsets of students sharing common digital behaviors. The embodiment of the output of LA systems in the form of personas makes it possible to study the representativeness of the dataset with precision and act accordingly, but also to enhance the explicability to pedagogical experts who must manipulate these tools. These personas are defined from learning traces, which are processed to identify homogeneous subsets of learners. The presented methodology also allows to identify some outliers, that exhibit atypical behaviors, and thus makes it possible to represent the whole students, without privileging some of them.