Journals Proceedings

Abstract

Human-computer interaction requires a software user interface to facilitate the communication to the user. The user interface plays a significant role in the user acceptance of software systems design. In Web-based e-learning systems, the need for adaptive personalization is increased because it is of particular importance in Web-based data accessing, providing relevant information and e-lectures effectively, and improving user performance and satisfaction. In this paper, we propose a framework for adaptive visualization for the interface of Web-based e-learning system. The important characteristics; that consist of user's preferences, background knowledge levels, needs, cognitive abilities, interface layout diversity, and interface structure are identified. These characteristics have a significant impact on the model to make the interface flexible, consistent, less complex, and more practical. The design is based on using weighting probabilistic scheme and artificial intelligence reasoning techniques to provide the users with relatively customized, reliable, and dynamic data to be used easily, while earlier were providing slow and static data. Short-term modeling and long-term modeling of learning algorithms, which are based on similarity-based visualization, are conceptually presented. They support e-learning users to assess, retrieve the right information, and facilitate the e-lectures classification.