Virtually Guided Personalized E-Learning

E-learning systems are tremendously popular. Lifelong learning, learning anytime and anywhere are one of most effective slogans for pushing distance learning. Unfortunately, the promises that all learners benefit from elearning could not be kept. The amount of frustrated, un-motivated users is quite huge proved by high drop out rates. Despite enormous efforts to provide high-level multimedia learning lessons, most e-learners complain about a "onesize-fits-all" philosophy, a resulting cognitive overload and consequently the lack of personalization of existing applications. The work presented in this paper introduces an approach of how to make e-learning systems more usercentric with respect to psychological differences and individualities. The big step from common to personalized e-learning is realized through the introduction of multi-level topic maps. Psychological factors such as cognitive styles, learning modalities, learning strategies, aspects relevant for managing the learning environment, and learning objects are represented as topics associated in networks to build individual learning sequences and surroundings. Single output depends on the chosen path taken in the topic maps, whereby this decision is supported through virtual guidance provided by the system.