Individualized e-learning systems enabled by a semantically determined adaptation of learning fragments

Nowadays e-learning systems are very popular. Several applications have already been implemented and many project initiatives have been started. Although such systems come with interesting advantages, there are still many unsolved problems. Enriching common learning content by applying multimedia did not meet the general expectation to decrease drop out rates of e-learners using such systems. Additionally, most e-learners complain about a "one-size-fits-all" philosophy, a resulting cognitive overload and consequently the lack of personalization of existing applications. In this paper, a user-centric approach is presented in order to improve the usability and acceptance, thus, making e-learning systems more successful. Focusing the e-learners\´ requirements learning fragments are introduced. Depending on the user\´s skills, learning styles and learning strategies these learning fragments are individually combined to an e-learning system. The necessary user profile is incrementally determined by observing the users learning activities. Based on these observation results the e-learning system is dynamically adapted to the current user\´s profile.