The Web has revolutionized the way information is delivered to people throughout the world. It did not take long for learning material to be delivered through the Web, by using electronic textbooks. The use of hypertext links gives the learner a lot of freedom to decide on an order in which to study the material. This leads to problems in understanding the textbook, which can be solved by using methods and techniques. In this chapter we describe how the field of educational hypermedia benefits from and. We also show that the information gathered about the learners and their learning process can be used to improve the quality of the electronic textbooks. 1

Adaptive Educational Hypermedia Systems (AEHS) have the potential of delivering instruction tailored to students ’ characteristics. However, despite of many years of research in the area, this kind of systems has been used only in a few real learning situations. Reasons for this are their use of proprietary semantics in the definition of adaptivity and educational elements, and their lack of interoperation among courses and applications. We claim that an option to define AEHS elements might be the IMS Learning Design specification and, in this way, define them using a common notational method and support their reusability and exchangeability. This paper, presents our current work with IMS LD to define “declaratively ” learning designs with adaptive characteristics. First, it introduces AEHS, their characterization, elements, taxonomy, techniques, and presents the elements that specific AEHS take into consideration for performing adaptivity. Then, it reviews briefly IMS LD and explains how the main characteristics of an AEHS can be modelled by means of this specification. Afterwards, it describes our research work towards the definition of adaptive learning designs compliant with IMS LD, and the authoring approaches we are developing for novice and expert users of the specification. Subsequently, it concludes pointing out some issues of utilizing IMS LD in AEHS, and exposes further work.

The mechanisms for personalisation used in web applications are currently the subject of much debate amongst researchers from many diverse subject areas. One of the most contemporary ideas for user modelling in web applications is that of cognitive styles, where a user’s psychological preferences are assessed stored in a database and then used to provide personalised content and/or links. We describe user trials of a case study that utilises visual-verbal preferences in an adaptive web-based educational system (AWBES). Students in this trial were assessed by the Felder-Solomon Inventory of Learning Styles (ILS) instrument, and their preferences were used as a means of content personalisation. Contrary to previous findings by other researchers, we found no significant differences in performance between matched and mismatched students. Conclusions are drawn about the value and validity of using cognitive styles as a way of modelling user preferences in educational web applications.

While the growing number of learning resources increases the choice for learners on how, what and when to learn, it also makes it more and more difficult to find the learning resources that best match the learners ’ preferences and needs. The same applies to learning systems that aim to adapt or recommend suitable courses and learning resources according to a learner’s wishes and requirements. Improved representations for a learner’s preferences as well as improved search capabilities that take these preferences into account leverage these issues. In this paper, we propose an approach for selecting optimal learning resources based on preference-enabled queries. A preferenceenabled query does not only allow for hard constraints (like ’return lectures about Mathematics’) but also for soft constraints (such as ’I prefer a course on Monday, but Tuesday is also fine’) and therefore allows for a more fine-grained representation of a learner’s requirements, interests, and wishes. We show how to exploit the representation of a learner’s wishes and interests with preferences and how to use preferences in order to find optimal learning resources. We present the Personal Preference Search Service (PPSS), which offers significantly enhanced search capabilities for learning resources by taking the learner’s detailed preferences into account.