A personalized courseware recommendation system based on fuzzy item response theory

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field currently. In past years, many researchers made efforts in developing e-learning systems with personalized learning mechanism to assist on-line learning. However, most of them focused on using learner's behaviors, interests, or habits to provide personalized e-learning services. These systems usually neglected to concern if learner's ability and the difficulty of courseware are matched each other. Generally, recommending an inappropriate courseware might result in learner's cognitive overhead or disorientation during a learning process. To promote learning efficiency and effectiveness, we present a personalized courseware recommendation system (PCRS) based on the proposed fuzzy item response theory (FIRT), which can recommend courseware with appropriate difficult level to learner through learner gives a fuzzy response of understanding percentage for the learned courseware. Experiment results show that applying the proposed fuzzy item response theory to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.