ONTOLOGIES FOR LEARNER EVALUATION IN THE CONTEXT OF A SERIOUS GAME

The evolution of information and communication technologies impacts learning practices at every level and technology-enhanced learning impose transformation on multiple components of the learning process. As primary knowledge vehicle, the learning content is one of the first elements that should adapt to learner needs. Learning systems must take into account that learning doesn't occur in a single place or time. They also must adapt and integrate more functions that normally are assured by humans (e.g. evaluation). The variety of learning content combined with smart learning systems should provide an adapted and personalized learning experience for the learner. Such smart learning systems should be capable of identifying learner weaknesses and provide learning activities accordingly. This is the normal function of a feedback loop on a retroactive system. In the learning process, the evaluation of the learner constitutes the feedback of the entire learning system. It regulates the learning process providing the system, and specifically the learner, with skillful response according to the learning performance of the learner. In this paper we present an evaluation mechanism based on ontologies used for learner evaluation in the context of a serious game. We concentrate on the conception of these ontologies, which are used to represent competences as learning outcomes, learning tasks in the context of serious games, learner traces and other specific elements. The entire model makes highly use of semantic web technologies, notably the Web Ontology Language (OWL) and RDF(S) (Resource Description Framework Schema).