| A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention | | BIBAK | Full-Text | 1-30 | |
| Stephanie Elzer; Nancy Green; Sandra Carberry | |||
| This paper presents a model of perceptual task effort for use in
hypothesizing the message that a bar chart is intended to convey. It presents
our rules, based on research by cognitive psychologists, for estimating
perceptual task effort, and discusses the results of an eye tracking experiment
that demonstrates the validity of our model. These rules comprise a model that
captures the relative difficulty that a viewer would have performing one
perceptual task versus another on a specific bar chart. The paper outlines the
role of our model of relative perceptual task effort in recognizing the
intended message of an information graphic. Potential applications of this work
include using this message to provide (1) a more complete representation of the
content of the document to be used for searching and indexing in digital
libraries, and (2) alternative access to the information graphic for visually
impaired users or users of low-bandwidth environments. Keywords: Perceptual effort; Cognitive modeling; Diagrams; Plan recognition | |||
| The influence of personality factors on visitor attitudes towards adaptivity dimensions for mobile museum guides | | BIBAK | Full-Text | 31-62 | |
| Dina Goren-Bar; Ilenia Graziola | |||
| In this work, we present a study on adaptation in a mobile museum guide,
investigating the relationships between personality traits, and the attitudes
towards some basic dimensions of adaptivity. Each participant was exposed to
two simulated systems -- one adaptive, the other not -- on each of the
dimensions investigated. The study showed that the personality traits relating
to the notion of control (conscientiousness, neuroticism/emotional stability,
Locus of Control) have a selective effect on the acceptance of the adaptivity
dimensions. Keywords: Adaptivity; Mobile Information Presentation; HCI; Human Factors User
Evaluation | |||
| TV Program Recommendation for Multiple Viewers Based on user Profile Merging | | BIBAK | Full-Text | 63-82 | |
| Zhiwen Yu; Xingshe Zhou; Yanbin Hao | |||
| Since today's television can receive more and more programs, and televisions
are often viewed by groups of people, such as a family or a student dormitory,
this paper proposes a TV program recommendation strategy for multiple viewers
based on user profile merging. This paper first introduces three alternative
strategies to achieve program recommendation for multiple television viewers,
discusses, and analyzes their advantages and disadvantages respectively, and
then chooses the strategy based on user profile merging as our solution. The
selected strategy first merges all user profiles to construct a common user
profile, and then uses a recommendation approach to generate a common program
recommendation list for the group according to the merged user profile. This
paper then describes in detail the user profile merging scheme, the key
technology of the strategy, which is based on total distance minimization. The
evaluation results proved that the merging result can appropriately reflect the
preferences of the majority of members within the group, and the proposed
recommendation strategy is effective for multiple viewers watching TV together. Keywords: Digital television; Television program recommendation; Multiple viewers;
User profile merging; Total distance minimization | |||
| How do Experts Adapt their Explanations to a Layperson's Knowledge in Asynchronous Communication? An Experimental Study | | BIBAK | Full-Text | 87-127 | |
| Matthias Nückles; Alexandra Winter | |||
| Despite a plethora of recommendations for personalization techniques, such
approaches often lack empirical justification and their benefits to users
remain obscure. The study described in this paper takes a step towards filling
this gap by introducing an evidence-based approach for deriving adaptive
interaction techniques. In a dialogue experiment with 36 dyads of computer
experts and laypersons, we observed how experts tailored their written
explanations to laypersons' communicational needs. To support adaptation, the
experts in the experimental condition were provided with information about the
layperson's knowledge level. In the control condition, the experts had no
available information. During the composition of their answers, the experts in
both conditions articulated their planning activities. Compared with the
control condition, the experts in the experimental condition made a greater
attempt to form a mental model about the layperson's knowledge. As a result,
they varied the type and proportion of the information they provided depending
on the layperson's individual knowledge level. Accordingly, such adaptive
explanations helped laypersons reduce comprehension breakdowns and acquire new
knowledge. These results provide evidence for theoretical assumptions regarding
cognitive processes in text production and conversation. They empirically
ground and advance techniques for adaptation of content in adaptive hypermedia
systems. They are suggestive of ways in which explanations in recommender and
decision support systems could be effectively adapted to the user's knowledge
background and goals. Keywords: adaptive instructional explanations; advice-giving and recommender systems;
audience design; cognitive processes in writing and written communication;
computer-mediated communication; human experts' adaptation strategies; human
tutoring; natural language generation; personalization techniques; user-adapted
communication | |||
| An LDAP-based User Modeling Server and its Evaluation | | BIBAK | Full-Text | 129-169 | |
| Alfred Kobsa; Josef Fink | |||
| Representation components of user modeling servers have been traditionally
based on simple file structures and database systems. We propose directory
systems as an alternative, which offer numerous advantages over the more
traditional approaches: international vendor-independent standardization,
demonstrated performance and scalability, dynamic and transparent management of
distributed information, built-in replication and synchronization, a rich
number of pre-defined types of user information, and extensibility of the core
representation language for new information types and for data types with
associated semantics. Directories also allow for the virtual centralization of
distributed user models and their selective centralized replication if better
performance is needed. We present UMS, a user modeling server that is based on
the Lightweight Directory Access Protocol (LDAP). UMS allows for the
representation of different models (such as user and usage profiles, and system
and service models), and for the attachment of arbitrary components that
perform user modeling tasks upon these models. External clients such as
user-adaptive applications can submit and retrieve information about users. We
describe a simulation experiment to test the runtime performance of this
server, and present a theory of how the parameters of such an experiment can be
derived from empirical web usage research. The results show that the
performance of UMS meets the requirements of current small and medium websites
already on very modest hardware platforms, and those of very large websites in
an entry-level business server configuration. Keywords: User modeling server; Directory server; LDAP; Architecture; Evaluation;
Performance; Scalability | |||
| Preface to the special issue on user modeling to support groups, communities and collaboration | | BIB | Full-Text | 171-174 | |
| Elena Gaudioso; Amy Soller; Julita Vassileva | |||
| Creating cognitive tutors for collaborative learning: steps toward realization | | BIBAK | Full-Text | 175-209 | |
| Andreas Harrer; Bruce M. McLaren; Erin Walker | |||
| Our long-term research goal is to provide cognitive tutoring of
collaboration within a collaborative software environment. This is a
challenging goal, as intelligent tutors have traditionally focused on cognitive
skills, rather than on the skills necessary to collaborate successfully. In
this paper, we describe progress we have made toward this goal. Our first step
was to devise a process known as bootstrapping novice data (BND), in which
student problem-solving actions are collected and used to begin the development
of a tutor. Next, we implemented BND by integrating a collaborative software
tool, Cool Modes, with software designed to develop cognitive tutors (i.e., the
cognitive tutor authoring tools). Our initial implementation of BND provides a
means to directly capture data as a foundation for a collaboration tutor but
does not yet fully support tutoring. Our next step was to perform two
exploratory studies in which dyads of students used our integrated BND software
to collaborate in solving modeling tasks. The data collected from these studies
led us to identify five dimensions of collaborative and problem-solving
behavior that point to the need for abstraction of student actions to better
recognize, analyze, and provide feedback on collaboration. We also interviewed
a domain expert who provided evidence for the advantage of bootstrapping over
manual creation of a collaboration tutor. We discuss plans to use these
analyses to inform and extend our tools so that we can eventually reach our
goal of tutoring collaboration. Keywords: Intelligent tutoring systems; Collaborative learning; Collaboration
modeling; Action-based analysis | |||
| Modeling individual and collaborative problem-solving in medical problem-based learning | | BIBAK | Full-Text | 211-248 | |
| Siriwan Suebnukarn; Peter Haddawy | |||
| Today a great many medical schools have turned to a problem-based learning
(PBL) approach to teaching as an alternative to traditional didactic medical
education to teach clinical-reasoning skills at the early stages of medical
education. While PBL has many strengths, effective PBL tutoring is
time-intensive and requires the tutor to provide a high degree of personal
attention to the students, which is difficult in the current academic
environment of increasing demands on faculty time. This paper describes the
student modeling approach used in the COMET intelligent tutoring system for
collaborative medical PBL. To generate appropriate tutorial actions, COMET uses
a model of each student's clinical reasoning for the problem domain. In
addition, since problem solving in group PBL is a collaborative process, COMET
uses a group model that enables it to do things like focus the group
discussion, promote collaboration, and suggest peer helpers. Bayesian networks
are used to model individual student knowledge and activity, as well as that of
the group. The validity of the modeling approach has been tested with student
models in the areas of head injury, stroke, and heart attack. Receiver
operating characteristic (ROC) curve analysis shows that the models are highly
accurate in predicting individual student actions. Comparison with human tutors
shows that the focus of group activity determined by the model agrees with that
suggested by the majority of the human tutors with a high degree of statistical
agreement (McNemar test, p=0.774, Kappa=0.823). Keywords: Computer-supported collaborative learning; Intelligent tutoring systems;
Student modeling; Bayesian networks; Medical problem-based learning | |||
| Using shared representations to improve coordination and intent inference | | BIBAK | Full-Text | 249-280 | |
| Joshua Introne; Richard Alterman | |||
| In groupware, users must communicate about their intentions and maintain
common knowledge via communication channels that are explicitly designed into
the system. Depending upon the task, generic communication tools like chat or a
shared whiteboard may not be sufficient to support effective coordination. We
have previously reported on a methodology that helps the designer develop task
specific communication tools, called coordinating representations, for
groupware systems. Coordinating representations lend structure and persistence
to coordinating information. We have shown that coordinating representations
are readily adopted by a user population, reduce coordination errors, and
improve performance in a domain task. As we show in this article, coordinating
representations present a unique opportunity to acquire user information in
collaborative, user-adapted systems. Because coordinating representations
support the exchange of coordinating information, they offer a window onto task
and coordination-specific knowledge that is shared by users. Because they add
structure to communication, the information that passes through them can be
easily exploited by adaptive technology. This approach provides a simple
technique for acquiring user knowledge in collaborative, user-adapted systems.
We document our application of this approach to an existing groupware system.
Several empirical results are provided. First, we show how information that is
made available by a coordinating representation can be used to infer user
intentions. We also show how this information can be used to mine free text
chat for intent information, and show that this information further enhances
intent inference. Empirical data shows that an automatic plan generation
component, which is driven by information from a coordinating representation,
reduces coordination errors and cognitive effort for its users. Finally, our
methodology is summarized, and we present a framework for comparing our
approach to other strategies for user knowledge acquisition in adaptive
systems. Keywords: Groupware; Knowledge acquisition; Adaptive user interfaces; Coordinating
representations; Plan recognition | |||
| In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems | | BIBAK | Full-Text | 281-319 | |
| Judith Masthoff; Albert Gatt | |||
| This paper deals in depth with some of the emotions that play a role in a
group recommender system, which recommends sequences of items to a group of
users. First, it describes algorithms to model and predict the satisfaction
experienced by individuals. Satisfaction is treated as an affective state. In
particular, we model the decay of emotion over time and assimilation effects,
where the affective state produced by previous items influences the impact on
satisfaction of the next item. We compare the algorithms with each other, and
investigate the effect of parameter values by comparing the algorithms'
predictions with the results of an earlier empirical study. We discuss the
difficulty of evaluating affective models, and present an experiment in a
learning domain to show how some empirical evaluation can be done. Secondly,
this paper proposes modifications to the algorithms to deal with the effect on
an individual's satisfaction of that of others in the group. In particular, we
model emotional contagion and conformity, and consider the impact of different
relationship types. Thirdly, this paper explores the issue of privacy (feeling
safe, not accidentally disclosing private tastes to others in the group) which
is related to the emotion of embarrassment. It investigates the effect on
privacy of different group aggregation strategies and proposes to add a virtual
member to the group to further improve privacy. Keywords: Group modelling; Affective state; Satisfaction; Recommender systems; Privacy | |||
| Design and evaluation of an adaptive incentive mechanism for sustained educational online communities | | BIBAK | Full-Text | 321-348 | |
| Ran Cheng; Julita Vassileva | |||
| Most online communities, such as discussion forums, file-sharing
communities, e-learning communities, and others, suffer from insufficient user
participation in their initial phase of development. Therefore, it is important
to provide incentives to encourage participation, until the community reaches a
critical mass and "takes off". However, too much participation, especially of
low-quality can also be detrimental for the community, since it leads to
information overload, which makes users leave the community. Therefore, to
regulate the quality and the quantity of user contributions and ensure a
sustainable level of user participation in the online community, it is
important to adapt the rewards for particular forms of participation for
individual users depending on their reputation and the current needs of the
community. An incentive mechanism with these properties is proposed. The main
idea is to measure and reward the desirable user activities and compute a user
participation measure, then cluster the users based on their participation
measure into different classes, which have different status in the community
and enjoy special privileges. For each user, the reward for each type of
activity is computed dynamically based on a model of community needs and an
individual user model. The model of the community needs predicts what types of
contributions (e.g. more new papers or more ratings) are most valuable at the
current moment for the community. The individual model predicts the style of
contributions of the user based on her past performance (whether the user tends
to make high-quality contributions or not, whether she fairly rates the
contributions of others). The adaptive rewards are displayed to the user at the
beginning of each session and the user can decide what form of contribution to
make considering the rewards that she will earn. The mechanism was evaluated in
an online class resource-sharing system, Comtella. The results indicate that
the mechanism successfully encourages stable and active user participation; it
lowers the level of information overload and therefore enhances the
sustainability of the community. Keywords: Online communities; Virtual communities; Participation; Ratings; Incentive
mechanisms; Personalized rewards | |||
| Coalescing individual and collaborative learning to model user linguistic competences | | BIBAK | Full-Text | 349-376 | |
| Timothy Read; Beatriz Barros; Elena Bárcena | |||
| A linguistic, pedagogic and technological framework for an ICALL system
called COPPER is presented here, where individual and collaborative learning
are combined within a constructivist approach to facilitate second language
learning. Based upon the Common European Framework of Reference for Languages,
the ability to use language is viewed as one of several cognitive competences
that are mobilised and modified when individuals communicate. To combine the
different types of learning underlying the European Framework, a student model
has been developed for COPPER that represents linguistic competences in a
detailed way, combining high granularity expert-centric Bayesian networks with
multidimensional stereotypes, and is updated following student activities
semi-automatically. Instances of this model are used by an adaptive group
formation algorithm that dynamically generates communicative groups based upon
the linguistic capabilities of available students, and a collection of
collaborative activity templates. As well as the student model, which is a
representation of individual linguistic knowledge, preferences, etc., there is
a group model, which is a representation of how a set of students works
together. The results of a student's activity within a group are evaluated by a
student monitor, with more advanced linguistic competences, thereby
sidestepping the difficulties present when using NLP techniques to
automatically analyse non-restricted linguistic production. The monitor role
empowers students and further consolidates what has been previously learnt.
Students therefore initially work individually in this framework on certain
linguistic concepts, and subsequently participate in authentic collaborative
communicative activities, where their linguistic competences can develop
approximately as they would in 'real foreign language immersion experiences'. Keywords: ICALL; Bayesian networks; Adaptive group formation; Collaborative activity
templates; European framework for languages | |||
| The impact of learning styles on student grouping for collaborative learning: a case study | | BIBAK | Full-Text | 377-401 | |
| Enrique Alfonseca; Rosa M. Carro | |||
| Learning style models constitute a valuable tool for improving individual
learning by the use of adaptation techniques based on them. In this paper, we
present how the benefit of considering learning styles with adaptation
purposes, as part of the user model, can be extended to the context of
collaborative learning as a key feature for group formation. We explore the
effects that the combination of students with different learning styles in
specific groups may have in the final results of the tasks accomplished by them
collaboratively. With this aim, a case study with 166 students of computer
science has been carried out, from which conclusions are drawn. We also
describe how an existing web-based system can take advantage of learning style
information in order to form more productive groups. Our ongoing work
concerning the automatic extraction of grouping rules starting from data about
previous interactions within the system is also outlined. Finally, we present
our challenges, related to the continuous improvement of collaboration by the
use and dynamic modification of automatic grouping rules. Keywords: Learning styles; Group formation; User modeling; adaptation; CSCL | |||
| Learned student models with item to item knowledge structures | | BIBAK | Full-Text | 403-434 | |
| Michel C. Desmarais; Peyman Meshkinfam | |||
| Probabilistic and learned approaches to student modeling are attractive
because of the uncertainty surrounding the student skills assessment and
because of the need to automatize the process. Item to item structures readily
lend themselves to probabilistic and fully learned models because they are
solely composed of observable nodes, like answers to test questions. Their
structure is also well grounded in the cognitive theory of knowledge spaces. We
study the effectiveness of two Bayesian frameworks to learn item to item
structures and to use the induced structures to predict item outcome from a
subset of evidence. One approach, Partial Order Knowledge Structures (POKS),
relies on a naive Bayes framework whereas the other is based on the Bayesian
network (BN) learning and inference framework. Both approaches are assessed
over their predictive ability and their computational efficiency in different
experimental simulations. The results from simulations over three data sets
show that they both can effectively perform accurate predictions, but POKS
generally displays higher predictive power than the BN. Moreover, the
simplicity of POKS translates to a time efficiency between one to three orders
of magnitude greater than the BN runs. We further explore the use of the item
to item approach for handling concepts mastery assessment. The approach
investigated consist in augmenting an initial set of observations, based on
inferences with the item to item structure, and feed the augmented set to a BN
containing a number of concepts. The results show that augmented set can
effectively improve predictive power of a BN for item outcome, but that
improvement does not transfer to the concept assessment in this particular
experiment. We discuss different explanations for the results and outline
future research avenues. Keywords: Student models; Probabilistic models; Bayesian networks; Bayesian inference;
POKS; Knowledge spaces; Knowledge assessment; Adaptive testing; CAT; Empirical
simulations | |||
| MASHA: A multi-agent system handling user and device adaptivity of Web sites | | BIBAK | Full-Text | 435-462 | |
| Domenico Rosaci; Giuseppe M. L. Sarné | |||
| A user that navigates on the Web using different devices should be
characterized by a global profile, which represents his behaviour when using
all these devices. Then, the user's profile could be usefully exploited when
interacting with a site agent that is able to provide useful recommendations on
the basis of the user's interests, on one hand, and to adapt the site
presentation to the device currently exploited by the user, on the other hand.
However, it is not suitable to construct such a global profile by a software
running on the exploited device since this device (e.g., a mobile phone or a
palmtop) may have limited resources. Therefore, in this paper, we propose a
multi-agent architecture, called MASHA, handling user and device adaptivity of
Web sites, in which each device is provided with a client agent that
autonomously collects information about the user's behaviour associated to just
that device. However, the user profile contained in this client is continuously
updated with information coming from a unique server agent, associated with the
user. Such information is collected by the server agent from the different
devices exploited by the user, and represents a global user profile. The third
component of this architecture, called adapter agent, is capable to generate a
personalized representation of the Web site, containing some useful
recommendations derived by both an analysis of the user profile and the
suggestions coming from other users exploiting the same device. Keywords: Information agents; Recommender systems; Web adaptivity; Device adaptivity | |||