Personalized support for learners becomes even more important, when e-Learning takes place in open and dynamic learning and information networks. This paper shows how to realize personalized learning support in distributed learning environments based on Semantic Web technologies. Our approach fills the existing gap between current adaptive educational systems with well-established personalization functionality, and open, dynamic learning repository networks. We propose a service-based architecture for establishing personalized e-Learning, where personalization functionality is provided by various web-services. A Personal Learning Assistant integrates personalization services and other supporting services, and provides the personalized access to learning resources in an e-Learning network.

The aim of this paper is to discuss how distributed learner modelling can benefit from semantic web technologies and which challenges have to be solved in this new environment. Heterogeneity of personalization techniques and their needs raise the question whether we can agree on one common data model for user profiles, which supports these techniques. In this paper we discuss an approach where a learner model can be distributed and can reflect features taken from several standards for a learner modelling. These features can be combined according to the requirements of specific personalization techniques, which can be provided as personalization services in a P2P learning network. RDF and RDFS as key tools of the semantic web allow us to handle such situations. We also sketch an architecture for such a network, where this approach can be realized. 1.

Traditional adaptive hypermedia systems have focused on providing adaptation functionality on a closed corpus, while Web search interfaces have delivered non-personalized information to users. In this paper, we show how we integrate closed corpus adaptation and global context provision in a Personal Reader environment. The local context consists of individually optimized recommendations to learning materials within the given corpus; the global context provides individually optimized recommendations to resources found on the Web, e. g., FAQs, student exercises, simulations, etc. The adaptive local context of a learning resource is generated by applying methods from adaptive educational hypermedia in a semantic web setting. The adaptive global context is generated by constructing appropriate queries, enrich them based on available user profile information, and, if necessary, relax them during the querying process according to available metadata.

This paper shows how Semantic Web technologies enable the design and implementation of advanced, personalized information systems. We demonstrate by means of an example application how personalized content syndication can be realized in the Semantic Web. Our approach consists of two main parts: The web data extraction part, providing the information system with real-time, dynamic data, and the personalization part, which deduces - with the aid of ontological domain knowledge - personalized views on the data. The prototype of the system has been realized using the Personal Reader Framework for designing, implementing, and maintaining Web content Readers .

Abstract. We propose a service–based architecture for bringing methods and techniques from the area of adaptive hypermedia to the Semantic Web. In our framework, personalization functionalities from adaptive hypermedia are available as web–services which a user can subscribe / un-subscribe as s/he likes. We have implemented our ideas in the Personal Reader, a framework for defining rule–based personalization algorithms for Semantic Web applications. In this paper, we present the basic architecture of the Personal Reader framework, and describe its realization in one example instance: A Personal Reader for displaying learning resources for Java programming. Other instances like e. g. a Personal Reader for publications, are currently under development.

Despite the fact that adaptive hypermedia techniques have proven their ability to provide user guidance and orientation in hyperspace, we do not currently see the widespread adoption of these techniques. A couple of reasons may explain this phenomenon. One of them is the current lack of re-usability and interoperability between adaptive techniques/systems, which – to some degree – originates in the so-called “open corpus problem” found in adaptive hypermedia. In this article, we analyze this problem in a popular arena: adaptive hypermedia systems with an emphasis on education. The origins and effects of the open corpus problem are discussed, and recent approaches are demonstrated that have – in one way or the other – developed as strategies for solving the open corpus problem. We summarize these findings and discuss how solution strategies can be successfully employed in the future, enabling adaptive hypermedia techniques within open, dynamic information spaces, such as the Semantic Web.

In this paper we show how personalization techniques from the area of adaptive hypermedia can be achieved in the semantic web. Our approach

Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers direct guidance or semi-automatic mechanisms for making the design process less demanding, still it requires significant effort to overcome the problems of inconsistency, confluence and insufficiency, introduced by the use of rules. Due to the problems of inconsistency and insufficiency of the defined rule sets in the Adaptation Model, conceptual “holes ” can be generated in the produced learning resource sequences (or learning paths). In this paper, we address the design problem of the Adaptation Model in AEHS proposing an alternative sequencing method that, instead of generating the learning path by populating a concept sequence with available learning resources based on pre-defined adaptation rules, it first generates all possible learning paths that match the learning goal in hand, and then, adaptively selects the desired one, based on the use of a decision model that estimates the suitability of learning resources for a targeted learner. In our simulations we compare the learning paths generated by the

The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioural information of users in learning contexts that can be used to deal with the information overload in user centric ways. We introduce a schema and framework for capturing and managing such contextualized attention metadata in this paper. Schema and framework are designed to enable collecting and merging observations about the attention users give to content and their contexts. The contextualized attention metadata schema enables the correlation of the observations, thus reflects the relationships that exists between the user, her context and the content she works with. We illustrate with a simple demo application how contextualized attention metadata can be collected from several tools, the merging of the data streams into a repository and finally the correlation of the data.

This paper describes our idea for personalized e-Learning in the Semantic Web which is based on configurable, re-usable personalization services. To realize our ideas, we have developed a framework for designing, implementing and maintaining personal learning object readers, which enable the learners to study learning objects in an embedding, personalized context. We describe the architecture of our Personal Reader framework, and discuss the implementation of personalization services in the Semantic Web. We have realized two Personal Readers for e-Learning: one for learning Java programming, and another for learning about the Semantic Web.