Table Of ContentADAPTIVE HYPERTEXT AND HYPERMEDIA
ADAPTIVE HYPERTEXT
AND
HYPERMEDIA
Edited by
Peter Brusilovsky
Carnegie Mellon University
Alfred Kobsa
GMDFIT
German National Research Centre for Information Technology
and
Julita Vassileva
University of Saskatchewan
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-90-481-4944-5 ISBN 978-94-017-0617-9 (eBook)
DOI 10.1007/978-94-017-0617-9
Printed on acid-free paper
All Rights Reserved
© 1998 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 1998
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner.
PREFACE
Hypertext/hypermedia systems and user-model based adaptive systems in
the areas of learning and information retrieval have for a long time been con
sidered as two mutually exclusive approaches to information access. Adaptive
systems cater information to the user and may guide the user in the informa
tion space to present the most relevant material, taking into account a model
of the user's goals, interests and preferences. Hypermedia systems, on the
other hand, are "user neutral"; they provide the user with the tools and the
freedom to explore an information space by browsing through a complex
network of information nodes. Adaptive hypertext and hypermedia systems
attempt to bridge the gap between these two approaches.
Adaptation of hypermedia systems to each individual user is increasing
ly needed. With the growing size, complexity and heterogeneity of current
hypermedia systems, such as the World Wide Web, it becomes virtually
impossible to impose guidelines on authors concerning the overall organiza
tion of hypermedia information. The networks therefore become so complex
and unstructured that the existing navigational tools are no longer powerful
enough to provide orientation on where to search for the needed information.
It is also not possible to identify appropriate pre-defined paths or subnets for
users with certain goals and knowledge backgrounds since the user communi
ty of hypermedia systems is usually quite inhomogeneous. This is particularly
true for Web-based applications which are expected to be used by a much
greater variety of users than any earlier standalone application. A Web-based
hypertext application which is designed with a particular class of users in
mind may not suit users of other classes.
A possible remedy for the negative effects of the traditional "one-size-fits
all" approach in the development of hypermedia systems is to equip them
with the ability to adapt to the needs of their individual users. A possible way
for achieving adaptivity is by modeling the users and tailoring the system's
interactions to their goals, tasks and interests. In this sense, the notion of
adaptive hypertext/hypermedia comes natural to denote a hypertext or hyper
media system which reflects some features of the user and/or characteristics
of his system usage in a user model, and utilizes this model in order to adapt
various behavioral aspects of the system to the user.
Adaptive hypertext and hypermedia (for brevity, we will henceforth use
the acronym AH) are a very new kind of user-model-based adaptive systems.
Though they are well grounded in research on Intelligent Tutoring Systems,
Help Systems and Information Retrieval Systems, real adaptive hypertext sys
tems appeared less than 5 years ago. However, quite a few have been developed
during the past four years. AH systems become increasingly important with
the growing commercial availability of hypermedia applications. More and
more users who have access to such systems are unfamiliar with them and/or
v
VI
their domains, and face difficulties in navigation and query formulation. The
need for AH systems has also been recently recognized by the industry, and
this field promises to become one of great commercial interest in the near
future.
This book is the first comprehensive publication on adaptive hypertext and
hypermedia. It is oriented towards researchers and practitioners in the fields of
hypertext and hypermedia, information systems, and personalized systems. It
is also an important resource for the numerous developers of Web-based appli
cations. The design decisions, adaptation methods, and experience presented
in this book are a unique source of ideas and techniques for developing more
usable and more intelligent Web-based systems suitable for a great variety
of users. The practitioners will find it important that many of the adaptation
techniques presented in this book proved to be efficient and are ready to be
used in various applications. From a research point of view it is important that
the papers in this book provide a topical state-of-the-art picture of adaptive
hypertext and hypermedia problems and solutions.
This book has its origin in the recently published special issue of User
Modeling and User Adapted Interaction: An International Journal [I}. Since
its publication, this special issue has become a source of creative ideas for
many researchers on hypermedia and adaptive systems. The five papers which
constitute the special issue are highly cited in many recent publications. User
Modeling and User Adapted Interaction is a journal known as the primary
source of high-quality papers on adaptive systems. It is not surprising that
three earlier important full-size papers on adaptive hypertext were also pub
lished in this journal. The goal of this book is to make all these excellent
papers available to a broader audience under a single cover.
Altogether, this book is a collection of the currently most influential papers
on adaptive hypertext and hypermedia.
Brusilovsky's article provides a comprehensive state-of-the-art review of
adaptive hypertext and hypermedia. This highly cited survey is centered
around methods and techniques used in existing AH systems. The other
papers go deeply into the characteristics of a particular AH system, attempt
to generalize the major concerns that define adaptivity, or describe in detail a
particular application.
The paper by Kaplan, Fenwick and Chen is one of the first and most often
referenced papers on adaptive navigation support .. It describes an on-line
information system, HYPERFLEX, which can suggest an adaptively sorted
list of relevant links, taking into account users' search goals and preferences.
The results of two experiments with HYPERFLEX presented in the paper
show that this kind of adaptive navigation support leads to more efficient
navigation.
Boyle and Encarnacion discuss the problems of expertise-adapted presen
tation in hypermedia systems. They present MetaDoc, one of the earliest and
vii
most influential adaptive hypermedia systems. MetaDoc uses the stretchtext
technique to adapt the content of hypermedia manual pages to the experience
level of the user. Experiments with MetaDoc described in the paper show that
adaptive stretchtext can increase reading comprehension without increasing
document reading time.
The paper by Hohl, Bocker and Gunzenhauser is the first archive publi
cation of Hypadapter, an early adaptive hypertext system that gives individ
ualized support in exploratory learning and programming in the domain of
Common Lisp. The system employs domain and user modeling techniques
to provide two principled types of assistance: individualized presentations of
topic nodes and individualized navigation help.
Beaumont's paper provides an example of an intelligent tutoring systems
(ITS) with an adaptive hypermedia component. The system Anatom-Tutor
presented in the paper demonstrates how a traditional ITS student model can
be used to support hypermedia adaptation on the content level.
Hook et al. discuss principles for adaptive interface design using a hyper
text help system as an example, which is able to infer users' tasks and plans,
and to provide appropriate help.
Mathe and Chen describe an adaptive hypermedia information retrieval
system which maintains an individual model of users' tasks, preferences, his
tory, queries etc., in order to provide a complex secondary indexing scheme
called "Adaptive Relevance Network". This network provides long-term adap
tation based both on system usage and on explicit user input. Without any
a priori specialized structure or statistical knowledge, the system evolves
its dynamic indexing structure over time and allows users to quickly access
information relevant to specific tasks.
Vassileva takes the opposite approach of Mathe and Chen. She describes
a deployed application of an adaptive hypermedia system for hospital infor
mation (namely a large loosely-coupled office documentation systems with
underlying databases). The system limits the browsing space based on the cur
rent task performed by the user (tasks are defined a-priori by task-analysis),
thus achieving higher efficiency in task performance. It also adapts the size of
the browsing space and the direct search options available to the user's level
of experience by gradually allowing the use of alternative indexing schemes.
The editors hope that this book will shed new light on adaptation and user
modeling in AH systems and will become a landmark on the road towards
more user-friendly hypertext and hypermedia systems.
Peter Brusilovsky
Alfred Kobsa
Julita Vassileva
TABLE OF CONTENTS
PREFACE
v
1.
PETER BRUSILOVSKY
Methods and Techniques of Adaptive Hypermedia
2.
CRAIG KAPLAN, JUSTINE FENWICK and JAMES CHEN
Adaptive Hypertext Navigation based on User Goals and Context. 45
3.
CRAIG BOYLE and ANTONIO O. ENCARNACION
Mctadoc: An Adaptive Hypertext Reading System 71
4.
[AN H. BEAUMONT
User Modelling in the Interactive Anatomy Tutoring System
ANATOM-TUTOR 91
5.
HUBERTUS HOHL. HEINZ -DIETER BOCKER
and RUL GUNZENHAUSER
Hypadapter: An Adaptive Hypertext System for Exploratory
Learning and Programming 117
KRISTINA HOOK, JUSSI KARLGREN, ANNIKA WAERN,
6.
NILS DAHLBACK. CARL GUSTAF JANSSON, KLAS
KARLGREN and BENOiT LEMAIRE
A Glass Box Approach to Adaptive Hypermedia 143
NATHALIE MATHE and JAMES R. CHEN
7. User-Centered Indexing for Adaptive 111 formal ion Access 171
JULITA VASSILEVA
A Task-Centred Approach for User Modeling in a Hypermedia
8.
Office Documentation System 209
249
INDEX
Methods and Techniques of Adaptive Hypermedia
PETER BRUSILOVSKY
HCll, School o/Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, U,S.A.
E-mail: [email protected]
(Received 8 November 1995; in final form 17 March 1995)
Abstract. Adaptive hypermedia is a new direction of research within the area of adaptive and user
model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and
apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the
user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used
now in several application areas where the hyperspace is reasonably large and where a hypermedia
application is expected to be used by individuals with different goals, knowledge and backgrounds.
This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set
of identified methods and techniques of AH. It introduces several dimensions of classification of AH
systems, methods and techniques and describes the most important of them,
Key words: Adaptive hypermedia, navigation support, collaborative user modeling, adaptive text
presentation, intelligent tutoring systems, student models.
1. Introduction
Hypermedia systems have become increasingly popular in the last five years as tools
for user-driven access to information. Adaptive hypermedia is a new direction of
research within the area of user-adaptive systems. The goal of this research is
to increase the functionality of hypermedia by making it personalized. Adaptive
hypermedia (AH) systems build a model of the goals, preferences and knowledge
of the individual user and use this throughout the interaction for adaptation to the
needs of that user.
AH systems can be useful in any application area where the system is expected
to be used by people with different goals and knowledge and where the hyperspace
is reasonably big. Users with different goals and knowledge may be interested in
different pieces of information presented on a hypermedia page and may use dif
ferent links for navigation. AH tries to overcome this problem by using knowledge
represented in the user model to adapt the information and links being presented to
the given user. Adaptation can also assist the user in a navigational sense, which is
particularly relevant for a large hyperspace. Knowing user goals and knowledge,
AH systems can support users in their navigation by limiting browsing space, sug
gesting most relevant links to follow, or providing adaptive comments to visible
links. The goal of this paper is to provide an overview of recent work on the
development of adaptive hypermedia systems.
P. Brusilovsky et al. (eds.), Adaptive Hypertext and Hypermedia, 1-43.
© 1998 Kluwer Academic Publishers.
2 PETER BRUSILOVSKY
Data about user
• User Modeling
.. Adaptation
Adaptation effect
Figure J. Classic loop "user modeling - adaptation" in adaptive systems.
Since this area of research is very new, the concept of adaptive hypermedia
systems has not been clearly defined yet. To make the scope of the review more
clear we use in this paper the following working definition:
by adaptive hypermedia systems we mean all hypertext and hypermedia sys
tems which reflect some features of the user in the user model and apply this
model to adapt various visible aspects of the system to the user.
In other words, the system should satisfy three criteria: it should be a hypertext
or hypermedia system, it should have a user model, and it should be able to adapt
the hypermedia using this model (i.e. the same system can look different to the
users with different models). We have identified more than 20 systems which can
be named as adaptive hypermedia systems according to our criteria (Appendix 1).
The analysis of these systems is the main content of our review. Note that not all
known systems which are named or referred to as adaptive hypermedia satisfy our
definition. Some of them are not full-fledged hypermedia systems (Brusilovsky,
1992b; Yetim, 1993; Andre & Rist, 1996); some of them are not really adaptive,
but rather adaptable (Waterworth, 1996) (this distinction will be made clearer later).
There are also some projects which suggest interesting relevant ideas but have not
yet reached the implementation stage (Tomek, Maurer & Nassar, 1993; Zyryanov,
1996). All these works, however, contain interesting ideas and we refer to them
when it is relevant to the main line of presentation.
In this paper, the critical feature of adaptive hypermedia systems is the possibility
of providing hypermedia adaptation on the basis of the user model. Therefore, the
paper is centered around the problems of adaptation, the second part of the overall
adaptation process in adaptive computer systems (Figure 1). The main content of the
paper (Sections 2-6) is a review of existing methods and techniques of adaptation
in AH systems. The problems of user modeling, i.e. building and updating the user
model in AH systems, are not a focus of the paper because they are not as critical for
AH systems as a subclass of adaptive computer systems. Specific problems of user
METHODS AND TECHNIQUES OF ADAPTIVE HYPERMEDIA 3
modeling in AH systems are discussed in Section 7 which provides a comparative
review of several methods of user modeling in AH systems. Special attention is
paid to collaborative user modeling which is especially important for AH systems.
The conclusion summarizes the content of the paper and discusses the prospects
for research in the area of adaptive hypermedia.
2. Methods and Techniques of Adaptive Hypermedia
Adaptation techniques refers to methods of providing adaptation in existing AH
systems. These techniques are a part of the implementation level of an AH system.
Each technique can be characterized by a specific kind of knowledge representation
and by a specific adaptation algorithm. Adaptive hypermedia is a new area of
research and most of the adaptation techniques are still unique in the sense that
each of them was suggested in conjunction with the development of an AH system.
However, some popular techniques were already implemented with minor variants
in several earlier systems.
Adaptation methods are defined as generalizations of existing adaptation tech
niques. Each method is based on a clear adaptation idea which can be presented
at the conceptual level. For example, " ... insert the comparison of the current
concept with another concept if this other concept is already known to the user", or
" ... hide the links to the concepts which are not yet ready to be learned". The same
conceptual method can be implemented by different techniques. At the same time,
some techniques are used to implement several methods using the same knowledge
representation.
The set of methods and techniques forms a tool kit or an "arsenal" of adaptive
hypermedia and can be used as a source of ideas for the designers and developers
of adaptive hypermedia systems.
To review AH systems it is first necessary to establish the basis for the clas
sification of adaptive hypermedia methods and techniques (Figure 2). The identi
fied dimensions are quite typical for the analysis of adaptive systems in general
(Dieterich et al., 1993).
• The first dimension considered is where adaptive hypermedia systems can be
helpful. The review identifies several application areas for AH systems (see
Table I) and for each area points the problems which can be partly solved by
applying adaptive hypermedia techniques (Section 3).
• The second dimension is what features of the user are used as a source oft he
adaptation, i.e. to what features of the user the system can adapt its behavior.
The review identifies several user features which are considered important
by existing AH systems and discusses the common ways to represent them
(Section 4).
• The third dimension is what can be adapted by a particular technique. Which
features of the system can be different for different users. Along this dimension
the review identifies seven ways to adapt hypermedia (see Figure 4). They can
