Consistency Verification of Learner Profiles in Adaptive Serious Games.

This article addresses issues of consistency verification of learner profiles in adaptive serious games. More precisely, our research objective is to propose models and tools that allow the user (learner, teacher or expert, depending on the context of application) to create coherent profiles consistent with domain knowledge. Our approach has been conceived and developed in the context of the platform GOALS. GOALS, as Generator Of Adaptive Learning Scenarios, is an online platform which allows the generation of learning scenarios, keeping into account the educational and entertaining aspects of serious games. For this, the knowledge in GOALS is organized into three layers: the domain concepts, the pedagogical resources, and the game resources. The profile is represented by a set of couples in the form < attribute, value >, where attribute corresponds to a concept, and value represents the learner competence in that concept. The profile is initialized by the user. During the game session, the profile is updated automatically according to dependencies among different domain concepts. In order to verify the learner profiles validity, we use a rule-based system which verifies, for every type of relation between concepts, the values between the source and the target concept. In this article, we present the formalization of our approach, as well as, its evaluation.