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Rafaël Van Durm

Bio: Rafaël Van Durm is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Adaptive learning & Educational technology. The author has an hindex of 6, co-authored 8 publications receiving 352 citations.

Papers
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Proceedings ArticleDOI
01 May 1998
TL;DR: Adaptive hypermedia systems build a model of a user model and then adapt the content and/or the links to be presented to that user with the goal of personalizing hypermedia.
Abstract: INTRODUCTION Hypermedia users with different goals and knowledge may be interested in different pieces of information and may use different links for navigation. Irrelevant information and links overload their working memories and screen [1]. In order to overcome this problem, it is possible to use information represented in a user model and then adapt the content and/or the links to be presented to that user. Adaptive hypermedia systems build such a model with the goal of personalizing hypermedia.

16 citations

Proceedings Article
01 Nov 1997

12 citations


Cited by
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Journal ArticleDOI
27 Mar 2001
TL;DR: Adaptive hypermedia as mentioned in this paper is a relatively new direction of research on the crossroads of hypermedia and user modeling, which builds a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user.
Abstract: Adaptive hypermedia is a relatively new direction of research on the crossroads of hypermedia and user modeling. Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. The goal of this paper is to present the state of the art in adaptive hypermedia at the eve of the year 2000, and to highlight some prospects for the future. This paper attempts to serve both the newcomers and the experts in the area of adaptive hypermedia by building on an earlier comprehensive review (Brusilovsky, 1996; Brusilovsky, 1998).

1,842 citations

Book ChapterDOI
01 Jan 2007
TL;DR: This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems by focusing on the overlay approach to user model representation and the uncertainty-based approach touser modeling.
Abstract: One distinctive feature of any adaptive system is the user model that represents essential information about each user This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling

869 citations

Proceedings ArticleDOI
01 Feb 1999
TL;DR: A reference model for adaptive hypermedia applications, called AHAM, is described, which encompasses most features supported by adaptive systems that exist today or that are being developed (and have been published about).
Abstract: Hypermedia applications offer users the impression that there are many meaningful ways to navigate through a large body of information nodes. This rich link structure not only creates orientation problems, it may also be a source of comprehension problems when users follow paths through the information which the author did not foresee. Adaptive techniques have been used by a number of researchers [1, 2, 4, 5, 6, 7, 8, 9, 10, 17, 19, 20, 22] in an attempt to offer guidance through and orientation support for rich link structures. The majority of these adaptive hypermedia systems (AHS) have been used in educational applications. The terminology used in this paper also has an educational “flavor”. However, there are some adaptive on-line information systems (or “kiosk”systems), adaptive information retrieval systems, and other adaptive hypermedia applications. In this paper we describe a reference model for adaptive hypermedia applications, called AHAM, which encompasses most features supported by adaptive systems that exist today or that are being developed (and have been published about). Our description of AHS is based on the Dexter model [15, 16], a widely used reference model for hypermedia. The description is kept somewhat informal in order to be able to explain AHAM rather than formally specify it. AHAM augments Dexter with features for doing adaptation based on a user model which persists beyond the duration of a session. Key aspects in AHAM are: Paul De Bra is also affiliated with the University of Antwerp, Belgium, and with the “Centrum voor Wiskunde en Informatica” (CWI) in Amsterdam. yGeert-Jan Houben is also affiliated with the University of Antwerp, Belgium, and with Origin in Eindhoven. The adaptation is based on a domain model, a user model and a teaching model which consists of pedagogical rules. We give a formal definition of each of these (sub)models (but only describe the pedagogical rules informally throughexamples). We distinguish the notions of concept, page and fragment. In some AHS these notions are confused. We provide a formalism which lets authors write pedagogical rules (about concepts) in such a way that they can be applied automatically. We illustrate various aspects of AHAM by means of some features of some well-known AHS [6, 10].

494 citations

Journal ArticleDOI
TL;DR: AHA is presented, an open Adaptive Hypermedia Architecture that is suitable for many different applications and concentrates on the adaptive hypermedia engine, which maintains the user model and which filters content pages and link structures accordingly.
Abstract: Hypermedia applications generate comprehension and orientation problems due to their rich link structure. Adaptive hypermedia tries to alleviate these problems by ensuring that the links that are offered and the content of the information pages are adapted to each individual user. This is done by maintaining a user model. Most adaptive hypermedia systems are aimed at one specific application. They provide an engine for maintaining the user model and for adapting content and link structure. They use a fixed screen layout that may include windows (HTML frames) for an annotated table of contents, an overview of known or missing knowledge, etc. Such systems are typically closed and difficult to reuse for very different applications. We present AHA, an open Adaptive Hypermedia Architecture that is suitable for many different applications. This paper concentrates on the adaptive hypermedia engine, which maintains the user model and which filters content pages and link structures accordingly. The engine...

419 citations

Journal ArticleDOI
TL;DR: This article reviews the published findings from empirical studies of hypermedia learning and classifies the research into five themes: nonlinear learning, learner control, navigation in hyperspace, matching and mismatching, and learning effectiveness.
Abstract: There has been an increased growth in the use of hypermedia to deliver learning and teaching material. However, much remains to be learned about how different learners perceive such systems. Therefore, it is essential to build robust learning models to illustrate how hypermedia features are experienced by different learners. Research into individual differences suggests cognitive styles have a significant effect on student learning in hypermedia systems. In particular, Witkin's Field Dependence has been extensively examined in previous studies. This article reviews the published findings from empirical studies of hypermedia learning. Specifically, the review classifies the research into five themes: nonlinear learning, learner control, navigation in hyperspace, matching and mismatching, and learning effectiveness. A learning model, developed from an analysis of findings of the previous studies, is presented. Finally, implications for the design of hypermedia learning systems are discussed.

336 citations