Open and Distance Learning platforms are more than system delivering pedagogical ressources. They require mechanisms for the enactment and coordination of pedagogical modules and learning activities. A common solution to express learning paths in learning management systems (LMS) can be the use of Educational Modelling Languages (EML). The next step will be the enactment of these models. For that purpose, workflow management system can be used. These systems formerly reserved for highly structured procedures can be used in dynamic and reactive environments such as virtual campuses platforms, thanks to a enhanced flexibility in the execution of models and in the management of exceptions. In this article we shall present COW our flexible workflow engine dedicated to open and distant learning. We will compare EML and workflow approach and see how to pass from a pedagogical modelisation to a workflow modelisation. We shall see how it is possible to organize the pedagogical modules and the learning paths to answer the expectation within the framework of individual courses (lifelong learning orientation) or within the framework of group courses (closer to the traditional face to face learning).

Mobile tourism or m-tourism can assist and help tourists anywhere and anytime face the overload of information that may appear in their smartphones. Indeed, these mobile users find difficulties in the choice of points of interest (POIs) that may interest them during their discovery of a new environment (a city, a university campus ...). In order to reduce the number of POIs to visit, the recommendation systems (RS) represent a good solution to guide each tourist towards personalized paths close to his instantaneous location during his visit. In this article, we focus on (1) the detection of the spatiotemporal context of the tourist to filter the POIs and (2) the use of the previous notations of the places. These two criteria make it possible to integrate the evolutionary context of the visit in order to predict incrementally the most relevant transitions to be borrowed by the tourists without profile. These predictions are calculated using collaborative filtering algorithms that require the collection of traces of tourists to better refine the recommendation of POIs. In our software prototype, we have adapted the SLOPE ONE algorithm to our context of discovering the city of Chlef to avoid problems like data scarcity, cold start and scalability. In order to validate the use of this prototype, we conducted experiments by tourists in order to calculate indicators to assess the relevance of the recommendations provided by our system.

We are in the transition to a new era where mobility extends to many aspects of our daily lives. Learning, for example, takes place throughout life and anywhere. One may wonder how the traditional orchestration of learning can be applied in a mobile context, for example, to better support students during field trips and museum visits. In this paper, we present a geographic orchestration of resources and activities associated to learning system. The objective is to consider collaboration in mobile learning scenario that depends heavily on the location of learners, their profiles and their activity logs. We also defined a software framework for the design and implementation of pedagogic scenarios of field trip. To validate our approach, we present the implementation of a mobile artifact dedicated to the support of new visitors in exploring the historic monuments of a city.

International audienceThis paper presents a new approach to a recommendation of learning activities adapted to 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 passivecollaborative filtering. This recommendation is based on the ACO (Ant Colony Optimization) algorithm, which represents a good model for swarm intelligence. For this reason, 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 moving between them. This recommendation system allows the orchestration of mobile learning according to the geographical location of learners and the historical of their activities. Our contribution is devised in three parts: (1) the creation of a mobile learning scenario based on POIs, (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 learners to better choose their paths during the field trip

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