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User Modeling and User-Adapted Interaction 6

Editors:Alfred Kobsa
Dates:1996
Volume:6
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:14
Links:link.springer.com | Table of Contents
  1. UMUAI 1996 Volume 6 Issue 1
  2. UMUAI 1996-07 Volume 6 Issue 2/3
  3. UMUAI 1996-10 Volume 6 Issue 4

UMUAI 1996 Volume 6 Issue 1

Design and evaluation of an adaptive icon toolbar BIBAKFull-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 BIBAKFull-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

Workshop Report

ABIS-95 -- GI Workshop on Adaptivity and User Modeling in Interactive Software Systems BIBFull-Text 69-76
  Uwe Malinowski

Book Review

User Modeling in Text Generation, Cecile Paris BIBFull-Text 77-80
  Julita Vassileva

UMUAI 1996-07 Volume 6 Issue 2/3

Preface BIBFull-Text v-vi
  Peter Brusilovsky; Julita Vassileva
Methods and techniques of adaptive hypermedia BIBAKFull-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 BIBAKFull-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 BIBAKFull-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 BIBAKFull-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 BIBAKFull-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

Book Review

Spoken Natural Language Dialogue Systems: A Practical Approach BIBFull-Text 263-266
  Karen Sparck Jones

UMUAI 1996-10 Volume 6 Issue 4

INSTRUCT: Modeling students by asking questions BIBAKFull-Text 273-302
  Antonija Mitrovic; Slobodanka Djordjevic-Kajan; Leonid Stomenov
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 BIBAKFull-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

Book Review

Participating in Explanatory Dialogues, Johanne Moore BIBFull-Text 337-340
  Sandra Carberry