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UMUAI Tables of Contents: 0102030405060708091011121314

User Modeling and User-Adapted Interaction 4

Editors:Alfred Kobsa
Dates:1994
Volume:4
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:14
Links:link.springer.com | Table of Contents
  1. UMUAI 1994 Volume 4 Issue 1
  2. UMUAI 1994/1995 Volume 4 Issue 2
  3. UMUAI 1994/1995 Volume 4 Issue 3
  4. UMUAI 1994/1995 Volume 4 Issue 4

UMUAI 1994 Volume 4 Issue 1

Metadoc: An adaptive hypertext reading system BIBAKFull-Text 1-19
  Craig Boyle; Antonio O. Encarnacion
Presentation of textual information is undergoing rapid transition. Millennia of experience writing linear documents is gradually being discarded in favor of non-linear hypertext writing. In this paper, we investigate how hypertext -- in its current node-and-link form -- can be augmented by an adaptive, user-model-driven tool. Currently the reader of a document has to adapt to that document -- if the detail level is wrong the reader either skims the document or has to consult additional sources of information for clarification. The MetaDoc system not only has hypertext capabilities but also has knowledge about the documents it represents. This knowledge enables the document to modify its level of presentation to suit the user. MetaDoc builds and dynamically maintains a user model for each reader. The model tailors the presentation of the document to the reader. The three-dimensionality of MetaDoc allows the text presented to be changed either by the user model or through explicit user action. MetaDoc is more a documentation reading system rather than a hypertext navigation or reading tool. MetaDoc is a fully developed and debugged system that has been applied to technical documentation.
Keywords: Hypertext; adaptation; user expertise; stretchtext; evaluation; online documentation
User modelling in the interactive anatomy tutoring system ANATOM-TUTOR BIBAKFull-Text 21-45
  Ian H. Beaumont
This article is a comparative description of the user modelling component of ANATOM-TUTOR, an intelligent anatomy tutoring system for use at university level. We introduce ITSs in general, discussing some of the psychological and pedagogical issues involved in using computers in education, and ANATOM-TUTOR in particular, and locate ANATOM-TUTOR's user modelling component in the field of existing user models. Details of the user model's construction and maintenance, the knowledge representation techniques used in it, and its relation to the domain knowledge base are then discussed. Two applications of ANATOM-TUTOR's user model are described: (1) tailoring hypertext to the level of knowledge of the individual user; and (2) generating explanations and questions in a simulated examination situation, also taking into consideration the individual user's level of knowledge.
Keywords: User modelling; CAI; intelligent tutoring systems; hypertext; knowledge representation

Book Reviews

Anpaßbare Informationssysteme -- Auf dem Weg zu aufgaben- und benutzerorientierter Systemgestalung und Funktionalität BIBFull-Text 47-53
  Gerhard Peter; Uwe Malinowski

UMUAI 1994/1995 Volume 4 Issue 2

The user modeling shell system BGP-MS BIBAKFull-Text 59-106
  Alfred Kobsa; Wolfgang Pohl
BGP-MS is a user modeling shell system that can assist interactive software systems in adapting to their current users by taking the users' presumed knowledge, beliefs, and goals into account. It offers applications several methods for communicating observations concerning the user to BGP-MS, and for obtaining information on currently held assumptions about the user from BGP-MS. It provides a choice of two integrated formalisms for representing beliefs and goals, and includes several types of inferences for drawing additional assumptions based on an initial interview, observed user actions, and stereotypical knowledge about pre-defined user subgroups. BGP-MS is a customizable software system that is independent from applications, operates concurrently with them, and interacts with them through inter-process communication. For tailoring BGP-MS to a specific application domain, the developer must select those components of BGP-MS that are needed in this domain and fill them with relevant domain-dependent user modeling knowledge. This paper first summarizes the user modeling services that BGP-MS provides to application programs at runtime. It discusses the representational and inferential foundations that determine the scope and the limits of these services, and also gives a detailed example illustrating the interaction between the various system components. It describes interfaces that are available to application developers for tailoring BGP-MS to the specific user modeling needs of their application domains. Finally, it compares the system with all other major user modeling shell systems, and describes a first application that employs BGP-MS for adapting hypertext to users' terminological knowledge.
Keywords: user modeling shell system; belief and goal modeling; belief and goal representation; hybrid representation; stereotypes; adaptive hypertext; adaptive information presentation
Heterogeneous learning in the Doppelgänger user modeling system BIBAKFull-Text 107-130
  Jon Orwant
Doppelgänger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelgänger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates on Doppelgänger's learning techniques and their implementation in an application-independent, sensor-independent environment.
Keywords: User model; machine learning; server-client architecture; multivariate statistical analysis; Markov models; Beta distribution; linear prediction

Workshop Report

Abis-94: GI workshop on Adaptivity and User Modeling in Interactive Software Systems BIBFull-Text 131-138
  Christoph G. Thomas

Book Review

Intelligent Multimedia Interfaces, Mark T. Maybury (ed.) BIBFull-Text 139-141
  David Benyon

UMUAI 1994/1995 Volume 4 Issue 3

The um toolkit for cooperative user modelling BIBAKFull-Text 149-196
  Judy Kay
This paper gives an overview of the um toolkit: the philosophy underlying its design, examples of its use and discussion of the way it deals with some major issues in creating user modelling shells. The um toolkit has been developed to provide support for a variety of cooperative agents. An important element of its cooperativeness is due to its capacity to give users an understanding of their own user models. This paper describes two substantial but very different uses of the toolkit. The first involves a collection of coaching systems that help users learn more about their text editor. Experimental results suggest that the user model is associated with users learning more. The second is a movie advisor that uses a range of tools to construct and refine the user model and to filter a database of movies. Both these systems are built from combining tools in um. The paper describes several of the tools for constructing and refining user models. In addition it describes the user-model viewing tools and the way that these help users ensure their user models are correct. The paper also discusses the two central themes of the um work, the application of a tools approach to the design of a user modelling toolkit and the implications of making the user model accessible to its owner, the person modelled.
Keywords: student model; user model; cooperative systems; accessible user models; visualisation of user models
TAGUS -- A user and learner modeling workbench BIBAKFull-Text 197-226
  Ana Paiva; John Self
In this paper we will describe, outline and exemplify the functionalities and architecture of a User and Learner Modeling System called TAGUS (within the project "Theory and Applications for General User/Learner-modeling Systems").
   TAGUS was developed with two main goals: (1) to develop a framework to represent models of users and learners where the meta-cognitive activities of learners were taken into account; and (2) to try to capture in a system some general mechanisms and techniques for user and learner modeling in the form of services.
   The basic idea of TAGUS is to achieve a kind of workbench where some techniques and approaches for user and learner modeling are implemented and applied. TAGUS provides a set of services, to be used by people testing methods or by applications using user models. These services, provided to external agents, embed some mechanisms for maintaining models of the users and learners. Thus, TAGUS plays a role of a user and learner modeling server.
   To achieve this goal, we first identified some basic mechanisms in user and learner modeling, and based on them we established a general modeling cycle. This cycle involves two main stages: the acquisition and the maintenance of the model. The different strategies and techniques are specified in separate modules or knowledge sources in TAGUS, which uses them to execute parts of that cycle. The architecture of TAGUS is composed of: a User or Learner Model (ULM); a set of maintenance functions; an acquisition engine; a reason maintenance system; a meta-reasoner and two interfaces.
   At the same time, TAGUS provides a language for defining the models of the users and learners, which can be used with different techniques, in order to test the models and simulate them in the system. This language is used not only to represent the models, but also as a way of establishing the communication between TAGUS and its environment.
   TAGUS was built incrementally around a set of core functions for the manipulation of the User or Learner Model (ULM). Other layers of this set were built where the last layer corresponds to the services TAGUS supplies.
Keywords: user modeling shells; learner modeling; reason maintenance; meta-reasoning; stereotypes; learner simulation

UMUAI 1994/1995 Volume 4 Issue 4

Special issue on student modeling

Preface BIBFull-Text iii-vi
 
Modelling the student in Pitagora 2.0 BIBAKFull-Text 233-251
  Antonella Carbonaro; Vittorio Maniezzo
With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a "classical" student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an "average student" which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.
Keywords: Student modelling; intelligent tutoring system; machine learning; explanation-based learning; Bayesian network; experimental studies of construction and use of student models
Knowledge tracing: Modeling the acquisition of procedural knowledge BIBAKFull-Text 253-278
  Albert T. Corbett; John R. Anderson
This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called the ideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process called knowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has 'mastered' each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.
Keywords: Student modeling; learning; empirical validity; procedural knowledge; intelligent tutoring systems; mastery learning; individual differences
A cognitive load application in tutoring BIBAKFull-Text 279-303
  Akihiro Kashihara; Tsukasa Hirashima
Research on intelligent tutoring systems has mainly concentrated on how to reduce a cognitive load which a student will bear in learning a domain. This load reduction approach contributes to facilitating his/her learning. However the approach often fails to reinforce the student's comprehension and retention. Another approach to tutoring is to apply a load to him/her purposefully. In this paper, we present a framework for cognitive load application and describe a demonstration system. The framework imposes a load on a student who tries to understand an explanation. The important point toward the load application is to provide the student with an optimal load that does not go beyond his/her capacity for understanding. This requires controlling the student's load by means of explanations. In order to implement such load control, it is necessary to estimate how much load the explanation imposes on his/her understanding process. The load estimate depends on his/her understanding capability since the same explanation imposes a different load according to the capability. Therefore a student model representing his/her capability is required. This paper shows how our system accomplishes a proper load application by generating explanations with the load estimate.
Keywords: Cognitive load; explanation; planning; ITS; self-explanation; student model; CHI

Book Review

Adaptive User Support -- Ergonomic Design of Manually and Automatically Adaptable Software, Reinhard Oppermann (Ed.) BIBFull-Text 305-307
  Uwe Malinowski