This paper presents a new approach to arecommendation of learning activities adaptedto the spatial and temporal context of each mobile learner.
Indeed, the path traveled by the user during a field trip can be guided using the technique of passive
collaborative filtering. This recommendation is based on the ACO (Ant Colony Optimization) algorithm, which represents a good model for swarm intelligence.For this reas, the structure of our mobile scenario is described as a graph where POIs (Point Of Interest) are represented by nodes and the arcs indicate the probability of movingbetween them. This recommendation system allows the orchestration of mobile learn
ing according to thegeographical location of learners and the historic al of their activities.
Our contribution is devised in three parts:
(1) the creation of a mobile learning scenario based onPOIs, (2) the adaptation of the ACO algorithm for the orchestration of paths taken by learners, and (3) the development of a recommender system that helps learnersto better choose their paths during the field trip.