Adaptive Exercises Generation Using an Automated Evaluation and a Domain Ontology: The ODALA+ Approach

Generating adapted learning contents is one of the most important activity that can give the best progression of a learner. This is why research on personalization of learning is growing to get learning environments able to adapt the learning activities and contents to the learner’s profile. This last contains information about knowledge, skills, behavior, of the learner, at a given time of the learning process and for given domains. This information, to be effective, must be collected directly from an evaluation module that the learning system will systematically integrate in particular with the learning by doing mode. After the satisfactory results of our research on teaching domain modeling and automated evaluation of learner’s in the case of learning by doing exercises, we propose in this paper the ODALA+ approach which is an extension of the already proposed approach called ODALA (Ontology Driven Auto-evaluation Learning Approach) by adding two steps for learner’s profile calculation using the evaluation results and for generating adapted exercises.