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Abstract. Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insuf¢cient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed. Key words: empirical evaluation, experimental design, covariant variables, e¡ect size, treatment magnitude, power, sensitivity. 1. What Is Empirical Evaluation? Empirical evaluation refers to the appraisal of a theory by observation in experiments. The key to good empirical evaluation is the proper design and execution of the experiments so that the particular factors to be tested can be easily separated from other confounding factors. For example, one may want to test whether a software system with a user model works better than the same system without a user model, test the effect of different levels of user modeling or different user model parameter settings, or test different user interfaces. These factors, which

This paper is devoted to evaluation of adaptive navigation support in educational context. We present an educational hypermedia system ISIS-Tutor that applies several ANS technologies -- adaptive annotation, adaptive hiding, and direct guidance -- and describe a study, which evaluates the first two technologies. The results show that adaptive navigation support is helpful and can reduce user navigation efforts

AHA!, the "Adaptive Hypermedia Architecture", was originally developed to support an on-line course with some user guidance through conditional (extra) explanations and conditional link hiding. This paper describes the many extensions and tools that have turned AHA! into a versatile adaptive hypermedia platform. It also shows how AHA! can be used to add different adaptive "features" to applications such as on-line courses, museum sites, encyclopedia, etc. The architecture of AHA! is heavily inspired by the AHAM reference model [6].

Abstract. In this paper we present an Adaptive Educational Hypermedia prototype, named INSPIRE. The approach employed in INSPIRE emphasizes the fact that learners perceive and process information in very different ways, and integrates ideas from theories of instructional design and learning styles. Our aim is to make a shift towards a more “learning-focused ” paradigm of instruction by providing a sequence of authentic and meaningful tasks that matches learners ’ preferred way of studying. INSPIRE, throughout its interaction with the learner, dynamically generates learner-tailored lessons that gradually lead to the accomplishment of learner’s learning goals. It supports several levels of adaptation: from full system-control to full learner-control, and offers learners the option to decide on the level of adaptation of the system by intervening in different stages of the lesson generation process and formulating the lesson contents and presentation. Both the adaptive and adaptable behavior of INSPIRE are guided by the learner model which provides information about the learner, such as knowledge level on the domain concepts and learning style. The learner model is exploited in multiple ways: curriculum sequencing, adaptive navigation support, adaptive presentation, and supports system’s adaptable behavior. An empirical study has been performed to evaluate the adaptation framework and assess learners ’ attitudes towards the proposed instructional design.

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.

Currently, adaptive educational hypermedia systems (AEHS) are described with nonuniform methods, depending on the specific view on the system, the application, or other parameters. There is no common language for expressing functionality of AEHS, hence these systems are difficult to compare and analyze.

Abstract. Closed corpus AH systems demonstrate what is possible to achieve with adaptive hypermedia technologies; however they are impractical for dealing with the large volume of open corpus resources. Our system, Knowledge Sea II, presented in this paper explores social adaptive navigation support, an approach for providing personalized guidance in the open corpus context. Following the ideas of social navigation, we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The social adaptive navigation support implemented in our system was considered quite useful by students participating in the classroom study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigation support. 1

this paper we address the Hera design methodology and specifically explain two models: the integration model that covers the di#erent aspects of integration, and the adaptation model that specifies how the generated presentations can be adaptable (e.g. based on device capabilities, user preferences) and adaptive (e.g. based on user browsing history). This detailed description includes an explanation of how the Hera software is constructed. This software provides a set of transformations that allow a WIS to go from integration to presentation generation

This paper presents INTRIGUE, a prototype tourist information server that presents information about the area around Torino city, on desktop and handset devices. This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children and elderly) and explains the recommendations by addressing the group members' requirements. Moreover the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.