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.