Sequencing Based on Choquet Fuzzy Integral and Item Response Theory

Personalization of learning path is an important research issue in current E-learning systems because learners differ from various aspects such as knowledge level, experience, and ability. Therefore, most personalized systems focus on learner preferences, and browsing behavior for providing adaptive learning path guidance. However, these systems usually neglect to consider the dependence among the learning concept difficulty and the learner model. Generally, a learning concept has varied difficulty for learners with different levels of knowledge. Considered the importance of learning path with learning concepts difficulty that are highly matched to the learner’s knowledge and ability, this paper proposes a system based on Choquet Fuzzy Integral and Item Response Theory. This system recommends appropriate learning contents to learner during the learning process. Keywords-E-learning; difficulty level; Choquet fuzzy integral; learning path; Item response theory