This thesis focuses on three of the main components of an e-learning system: Infrastructure model, data integration and personalization. For the infrastructure model, our aim is to get best use of heterogeneously structured, geographically distributed data resources. Therefore, a detailed analysis of the available infrastructure models is carried out and an open source reference implementation based on grid technology is implemented. Furthermore, a simple data integration mechanism is proposed for the suggested reference implementation. For personalization, a statistical algorithm is proposed based on extracting and utilizing the learner model. The learner model based on Felder-Silverman learning style is extracted automatically using NBTree classification algorithm in conjunction with Binary relevance classifier for basic science learners. Experimental results show that the performance of the proposed automated learner modelling approach is consistent with the results, obtained by the questionnaires traditionally used for learning style assessment. In the thesis, the classification results are further utilized for providing the user with personalized queries. Keywords: Interactive learning environments / personalization in e-learning