Evaluation based on personalization using optimized FIRT and MAS framework in engineering education in e-learning environment

The structure of learning domain and content should be presented take into account the learners'goals, experiences, knowledge, abilities. In recent years, e-learning environments, have been personalized with Intelligent Tutoring Systems that emphasis the importance of learning/tutoring model with their feedbacks. Identifying the learning style is the best way to obtain information about the learners. With considering the importance of learner model reliability, this paper has been proposed a frame work based on fuzzy learner model and Optimized Fuzzy Item Response Theory (OFIRT) in form of fuzzy pedagogical module. Based on these concurrent fuzzy systems and using Multi Agent System(MAS) for learners' monitoring, learners ability estimation and learners' evaluation have been done with less uncertainty. So generate better recommendations for learners' motivation. Examining the capability of the proposed system in a an e-learning engineering education indicated that the success rate of the learners higher than before and it's about more than 83%.