Decision Support Models for Personalized Course Composition with a Focus on Learning Styles

Various studies have indicated the value of customization and personalization in the context of e-learning systems and numerous efforts have been made in attempt to realize these goals. One significant research direction in this domain is finding effective methods for personalized course composition from a library of learning objects with respect to each learner´s characteristics, goals and preferences. In this paper we employ decision support models for providing course compositions tailored to the specific characteristics of each individual learner, with an emphasis on the learning style of the learner. We show how these models can formally accommodate various features and preferences of the learner as well as how they can assist the learner in evaluating each solution by different criteria. We also propose methods for incorporating social behavior of the learners to the model in order to make use of other learners´ experiences and provide higher levels of personalization for the target learner.