Towards a recommendation system for the learner from a semantic model of knowledge in a collaborative environment

Abstract

Collaboration is a common work between many people which generates the creation of a common task. A computing environment can foster collaboration among peers to exchange and share knowledge or skills for succeeding a common project. Therefore, when users interact among themselves and with an environment, they provide a lot of information. This information is recorded and classified in a model of traces to be used to enhance collaborative learning. In this paper, we propose (1) the refinement of a semantic model of traces with indicators calculated according to Bayes formulas and (2) the exploitation of these indicators to provide recommendations to the learner to reinforce learning points with learners, of his/her community of collaboration, identified as "experts".