| Metadoc: An adaptive hypertext reading system | | BIBAK | Full-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 | | BIBAK | Full-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 | |||
| Anpaßbare Informationssysteme -- Auf dem Weg zu aufgaben- und benutzerorientierter Systemgestalung und Funktionalität | | BIB | Full-Text | 47-53 | |
| Gerhard Peter; Uwe Malinowski | |||
| The user modeling shell system BGP-MS | | BIBAK | Full-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 | | BIBAK | Full-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 | |||
| Abis-94: GI workshop on Adaptivity and User Modeling in Interactive Software Systems | | BIB | Full-Text | 131-138 | |
| Christoph G. Thomas | |||
| Intelligent Multimedia Interfaces, Mark T. Maybury (ed.) | | BIB | Full-Text | 139-141 | |
| David Benyon | |||
| The um toolkit for cooperative user modelling | | BIBAK | Full-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 | | BIBAK | Full-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 | |||
| Preface | | BIB | Full-Text | iii-vi | |
| Modelling the student in Pitagora 2.0 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | |||
| Adaptive User Support -- Ergonomic Design of Manually and Automatically Adaptable Software, Reinhard Oppermann (Ed.) | | BIB | Full-Text | 305-307 | |
| Uwe Malinowski | |||