| Design and evaluation of an adaptive icon toolbar | | BIBAK | Full-Text | 1-21 | |
| Matjaz Debevc; Beth Meyer; Dali Donlagic | |||
| As information systems become increasingly important in many different
domains, the potential to adapt them to individual users and their needs also
becomes more important. Adaptive user interfaces offer many possible ways to
adjust displays and improve procedures for a user's individual patterns of
work. This paper describes an attempt to design an adaptive user interface in a
computer environment familiar to many users. According to one classification of
adaptive user interfaces, the adaptive bar described in this paper would be
classified as a user-controlled self-adaptation system.
At the user's convenience, the adaptive bar offers suggestions for adding or removing command icons, based on the frequency and probability of specific commands. It also implements these changes once the user has agreed to them. Beyond the adaptive bar, the general behavior of the whole user interface does not change, thereby allowing the user to maintain a clear general model of the system. This paper describes the decision-making algorithm implemented in the bar. It also describes the bar's self-adaptive behavior of displaying the frequency of each icon's use through the icon's size. Finally, we present some encouraging preliminary results of evaluations by users. Keywords: user interfaces; adaptive user interfaces; icon toolbars; software
ergonomics; user modelling; user-controlled self-adaptation; experimental
studies of adaptive interface use | |||
| Requirements for belief models in cooperative dialogue | | BIBAK | Full-Text | 23-68 | |
| Jasper A. Taylor; Jean Carletta | |||
| Models of rationality typically rely on underlying logics that allow
simulated agents to entertain beliefs about one another to any depth of
nesting. Such models seem to be overly complex when used for belief modelling
in environments in which cooperation between agents can be assumed, i.e., most
HCI contexts. We examine some existing dialogue systems and find that
deeply-nested beliefs are seldom supported, and that where present they appear
to be unnecessary except in some situations involving deception.
Use of nested beliefs is associated with nested reasoning (i.e., reasoning about other agents' reasoning). We argue that for cooperative dialogues, representations of individual nested beliefs of the third level (i.e., what A thinks B thinks A thinks B thinks) and beyond are in principle unnecessary unless directly available from the environment, because the corresponding nested reasoning is redundant. Since cooperation sometimes requires that agents reason about what is mutually believed, we propose a representation in which the second and all subsequent nesting levels are merged into a single category. In situations affording individual deeply-nested beliefs, such a representation restricts agents to human-like referring and repair strategies, where an unrestricted agent might make an unrealistic and perplexing utterance. Keywords: Belief modelling; cooperative dialogue; reference resolution; restricted
inference | |||
| ABIS-95 -- GI Workshop on Adaptivity and User Modeling in Interactive Software Systems | | BIB | Full-Text | 69-76 | |
| Uwe Malinowski | |||
| User Modeling in Text Generation, Cecile Paris | | BIB | Full-Text | 77-80 | |
| Julita Vassileva | |||
| Preface | | BIB | Full-Text | v-vi | |
| Peter Brusilovsky; Julita Vassileva | |||
| Methods and techniques of adaptive hypermedia | | BIBAK | Full-Text | 87-129 | |
| Peter Brusilovsky | |||
| 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. Keywords: Adaptive hypermedia; navigation support; collaborative user modeling;
adaptive text presentation; intelligent tutoring systems; student models | |||
| Hypadapter: An adaptive hypertext system for exploratory learning and programming | | BIBAK | Full-Text | 131-156 | |
| Hubertus Hohl; Heinz-Dieter Böcker | |||
| We have developed an adaptive hypertext system designed to individually
support exploratory learning and programming activities in the domain of Common
Lisp. Endowed with domain-specific knowledge represented in a hyperspace of
topics, the system builds up a detailed model of the user's expertise which it
utilizes to provide personalized assistance. Unlike other work emerging in the
field of adaptive hypertext systems, our approach exploits domain and user
modelling techniques to support individuals in different ways. The system not
only generates individualized presentations of topic nodes, but also provides
adaptive navigational assistance for link-based browsing. By identifying and
suggesting useful hyperlinks according to the user's knowledge state and
preferences, the system encourages and guides exploration. While browsing
through the hyperspace of topics, the system analyses the user's navigational
behaviour to infer the user's learning progress and to dynamically adapt
presentations of topics and links accordingly. Keywords: adaptive hypertext systems; adaptive navigational support; adaptive
presentation techniques; exploratory learning and programming; personal
assistants; user modelling; information exploration; information filtering;
Common Lisp | |||
| A glass box approach to adaptive hypermedia | | BIBAK | Full-Text | 157-184 | |
| Kristina Höök; Jussi Karlgren; Annika Wærn | |||
| Utilising adaptive interface techniques in the design of systems introduces
certain risks. An adaptive interface is not static, but will actively adapt to
the perceived needs of the user. Unless carefully designed, these changes may
lead to an unpredictable, obscure and uncontrollable interface. Therefore the
design of adaptive interfaces must ensure that users can inspect the adaptivity
mechanisms, and control their results. One way to do this is to rely on the
user's understanding of the application and the domain, and relate the
adaptivity mechanisms to domain-specific concepts. We present an example of an
adaptive hypertext help system POP, which is being built according to these
principles, and discuss the design considerations and empirical findings that
lead to this design. Keywords: adaptive hypermedia; plan inference; multimodality; user modelling | |||
| A task-centered approach for user modeling in a hypermedia office documentation system | | BIBAK | Full-Text | 185-223 | |
| Julita Vassileva | |||
| The development of user-adaptive systems is of increasing importance for
industrial applications. User modeling emerged from the need to represent in
the system knowledge about the user in order to allow informed decisions on how
to adapt to match the user's needs. Most of the research in this field,
however, has been theoretical, "top-down." Our approach, in contrast, was
driven by the needs of the application and shows features of bottom-up,
user-centered design.
We have implemented a user modeling component supporting a task-based interface to a hypermedia information system for hospitals and tested it under realistic conditions. A new architecture for user modeling has been developed which focuses on the tasks performed by users. It allows adaptive browsing support for users with different level of experience, and a level of adaptability. The requirements analysis shows that the differences in the information needs of users with different levels of experience are not only quantitative, but qualitative. Experienced users are not only able to cope with a wider browsing space, but sometimes prefer to organize their search in a different way. That is why the user model and the interface of the system are designed to support a smooth transition in the access options provided to novice users and to expert users. Keywords: adaptation; adaptive interfaces; hypermedia and hypertext navigation;
intelligent information retrieval; office/hospital documentation systems;
task-based context for information retrieval; task-structures | |||
| User-centered indexing for adaptive information access | | BIBAK | Full-Text | 225-261 | |
| Nathalie Mathé; James R. Chen | |||
| We are focusing on information access tasks characterized by large volume of
hypermedia connected technical documents, a need for rapid and effective access
to familiar information, and long-term interaction with evolving information.
The problem for technical users is to build and maintain a personalized
task-oriented model of the information to quickly access relevant information.
We propose a solution which provides user-centered adaptive information
retrieval and navigation. This solution supports users in customizing
information access over time. It is complementary to information discovery
methods which provide access to new information, since it lets users customize
future access to previously found information. It relies on a technique, called
Adaptive Relevance Network, which creates and maintains a complex indexing
structure to represent personal user's information access maps organized by
concepts. This technique is integrated within the Adaptive HyperMan system,
which helps NASA Space Shuttle flight controllers organize and access large
amount of information. It allows users to select and mark any part of a
document as interesting, and to index that part with user-defined concepts.
Users can then do subsequent retrieval of marked portions of documents. This
functionality allows users to define and access personal collections of
information, which are dynamically computed. The system also supports
collaborative review by letting users share group access maps. The adaptive
relevance network provides long-term adaptation based both on usage and on
explicit user input. The indexing structure is dynamic and evolves over time.
Learning and generalization support flexible retrieval of information under
similar concepts. The network is geared towards more recent information access,
and automatically manages its size in order to maintain rapid access when
scaling up to large hypermedia space. We present results of simulated learning
experiments. Keywords: user-centered indexing; long-term adaptation; adaptive information
retrieval; adaptive navigation; user feedback; shared information access | |||
| Spoken Natural Language Dialogue Systems: A Practical Approach | | BIB | Full-Text | 263-266 | |
| Karen Sparck Jones | |||
| INSTRUCT: Modeling students by asking questions | | BIBAK | Full-Text | 273-302 | |
| Antonija MitroviÄ | |||
| The paper reports an approach to inducing models of procedural skills from
observed student performance. The approach, referred to as INSTRUCT, builds on
two well-known techniques, reconstructive modeling and model tracing, at the
same time avoiding their major pitfalls. INSTRUCT does not require prior
empirical knowledge of student errors and is also neutral with respect to
pedagogy and reasoning strategies applied by the student. Pedagogical actions
and the student model are generated on-line, which allows for dynamic
adaptation of instruction, problem generation and immediate feedback on
student's errors. Furthermore, the approach is not only incremental but truly
active, since it involves students in explicit dialogues about problem-solving
decisions. Student behaviour is used as a source of information for user
modeling and to compensate for the unreliability of the student model. INSTRUCT
uses both implicit information about the steps the student performed or the
explanations he or she asked for, and explicit information gained from the
student's answers to direct question about operations being performed. Domain
knowledge and the user model are used to focus the search on the portion of the
problem space the student is likely to traverse while solving the problem at
hand. The approach presented is examined in the context of SINT, an ITS for the
domain of symbolic integration. Keywords: student modeling; intelligent tutoring systems; machine learning; procedure
induction from traces; model tracing; reconstructive modeling | |||
| Development of a model of user attributes and its implementation within an adaptive tutoring system | | BIBAK | Full-Text | 303-335 | |
| Sue Milne; Edward Shiu; Jean Cook | |||
| User modelling within tutoring systems often concentrates on the
representation of the learner's status with respect to the domain, paying
little attention to the user's individual characteristics in terms of
capabilities and preferences. A composite learner model, incorporating both
domain related data and information about personal attributes is useful in
determining not only which items should be presented, but how the student may
best be able to learn them. A model of users' individual characteristics has
been developed using multivariate statistical techniques as a means of
generating user stereotypes from empirical data. Each stereotype has an
associated profile in terms of attributes which are useful for the application
in which the model is used.
This paper describes the development of the model of learner attributes and its use within an adaptive tutoring system. The representation of the domain related information was in this case a basic overlay model. The results of experiments using the system with two classes of students in two successive academic years are discussed. The possibilities for application of the user model in other areas and the potential effects of combining an attribute learner model of this type with more sophisticated domain models are considered. Keywords: learner model; adaptive tutoring; multivariate statistics; individual
characteristics; learner attributes; user stereotypes | |||
| Participating in Explanatory Dialogues, Johanne Moore | | BIB | Full-Text | 337-340 | |
| Sandra Carberry | |||