In this article, we present the implementation, the experimentation and the evaluation of an approach for the recommendation of learning paths in an online course. The recommendation process is inspired by swarm intelligence and especially the ant colony optimization (ACO). In this context, we considered a differentiation of the learning paths based on the explored educational activity for learning a lesson. With the aim to recommend learning paths considered optimal and thus assess their impact on the learning of an online course, the proposed approach is based both on the recommendation of relevant paths by the teacher and the result stored progressively by learners on the paths taken. Our approach was validated experimentally and the results showed the emergence of a learning path promoting the success of a relatively considerable number of learners.