A Tool Support for Web Applications Adaptation Using Navigation History

Currently the Web is a platform for performing complex tasks which involve dealing with different Web applications. However users still have to face these tasks in a handcrafted way. In this paper we present a novel approach that combines concern-sensitive adaptation and navigation history to improve the user experience while performing a task. We have developed some simple though powerful tools for applying this approach to some typical tasks such as trip planning and house rental. We illustrate the paper with a simple though realistic case study and compare our work with others in the same field.Publicado en Lecture Notes in Computer Science book series (vol. 6949).Laboratorio de Investigación y Formación en Informática Avanzad

A Tool Support for Web Applications Adaptation Using Navigation History

Currently the Web is a platform for performing complex tasks which involve dealing with different Web applications. However users still have to face these tasks in a handcrafted way. In this paper we present a novel approach that combines concern-sensitive adaptation and navigation history to improve the user experience while performing a task. We have developed some simple though powerful tools for applying this approach to some typical tasks such as trip planning and house rental. We illustrate the paper with a simple though realistic case study and compare our work with others in the same field.Publicado en Lecture Notes in Computer Science book series (vol. 6949).Laboratorio de Investigación y Formación en Informática Avanzad

Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

XML Document Adaptation Queries (XDAQ)

Adaptive web applications combine data retrieval on the web with reasoning so as to generate context dependent contents. The data is retrieved either as content or as context specifications. Content data is, for example, fragments of a textbook or e-commerce catalogue, whereas context data is, for example, a user model or a device profile. Current adaptive web applications are often implemented using ad hoc and heterogeneous techniques. This paper describes a novel approach called ”XML Document Adaptation Queries (XDAQ)” requiring less heterogeneous software components. The approach is based on using a web query language for data retrieval (content as well as context) and on a novel generic formalism to express adaptation. The approach is generic in the sense that it is applicable with all web query and transformation languages, for example with XQuery and XSLT

Perspectives for Electronic Books in the World Wide Web Age

While the World Wide Web (WWW or Web) is steadily expanding, electronic books (e-books) remain a niche market. In this article, it is first postulated that specialized contents and device independence can make Web-based e-books compete with paper prints; and that adaptive features that can be implemented by client-side computing are relevant for e-books, while more complex forms of adaptation requiring server-side computations are not. Then, enhancements of the WWW standards (specifically of XML, XHTML, of the style-sheet languages CSS and XSL, and of the linking language XLink) are proposed for a better support of client-side adaptation and device independent content modeling. Finally, advanced browsing functionalities desirable for e-books as well as their implementation in the WWW context are described

Adaptive Information Visualization for Personalized Access to Educational Digital Libraries

Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page

Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media

Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain. The use of neural networks or fuzzy logic filtering is a technique for future investigation. A prototype demonstrator is under construction