Any assessment is an objective driven process. In our case, the objective is to support training sessions by utilizing Technology-Enhanced approach, particularly ontologies and web semantic paradigms. Hence, we emphasize the idea that to provide a training which could fulfill individually the needs of all learners, we should establish an assessment of their prerequisites knowledge first. We call such method "Assessment Module". Such assessment is still a complex task and intellectually demanding for teachers. To suit our scenario of use and to meet the teacher' needs, we propose the modeling and the implementation of a new methodology for the learner's assessment in order to set-up an automated assessment for a better individualized training, particularly in Masters' training. Our approach is based on a conceptualization of knowledge and on a modeling of MCQ assessment added to a fuzzy-modeling method to refine the assessment's results. In this article, we present in detail our approach as well as two study cases we did as proof of concept, and the experimental results we got later on. The experimentations has validated our assumptions and has demonstrated that the framework we proposed and the way how it was designed provide a distinguished assessment of learners almost similar to when it is done manually by a human evaluator. Fulfilling this requirement is the prior step toward any Technology-Enhanced Individualization of training in higher education.