In this paper, we describe an automatic learning styles detection approach of learners in e-learning platform from observable indicators relating to their browsing and interactions. Based on the work in the fields of learning styles, user modelling, and track analysis, we propose a classification of learning style that encompasses different styles models proposed in the literature, and whose values are calculated by identifying dependent indicators at the different interaction levels identified.