Showing papers in "User Modeling and User-adapted Interaction in 2006"

TV Program Recommendation for Multiple Viewers Based on user Profile Merging

Abstract: 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.

The impact of learning styles on student grouping for collaborative learning: a case study

Abstract: 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.

In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems

Abstract: 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.

Design and evaluation of an adaptive incentive mechanism for sustained educational online communities

Abstract: 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.

An LDAP-based User Modeling Server and its Evaluation

Abstract: 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.

The influence of personality factors on visitor attitudes towards adaptivity dimensions for mobile museum guides

Abstract: 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.

Modeling individual and collaborative problem-solving in medical problem-based learning

Abstract: 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 = 0774, Kappa = 0823)

Creating cognitive tutors for collaborative learning: steps toward realization

Abstract: 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.

Learned student models with item to item knowledge structures

Abstract: 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.

Coalescing individual and collaborative learning to model user linguistic competences

Abstract: 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'.

MASHA: A multi-agent system handling user and device adaptivity of Web sites

Abstract: 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.

How do Experts Adapt their Explanations to a Layperson's Knowledge in Asynchronous Communication? An Experimental Study

Abstract: 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.

A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention

Abstract: 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.

Using shared representations to improve coordination and intent inference

Abstract: In groupware, users must communicate about their intentions and aintain 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.

Preface to the special issue on user modeling to support groups, communities and collaboration

Abstract: Technology should support and enhanceour ability towork, learn, andmakedecisions. These activities naturally involve collaboration and communication, yet the personal computers we use to carry them out are traditionally individualized. This special issue addresses the design and extension of adaptive individual user technology to support complex collaborative processes involving information sharing, communication, and collaborative group work. Recent integration efforts have successfully combined collaborative and communication services with multi-user tools to create environments that enable individuals to communicate and share information. Examples of such environments include computer-supported collaborative workspaces, distributed learning management systems, online communities of interest, and peer-to-peer systems. While these environments enable collaboration, they provide no guarantee that timely productive collaboration occurs and proceeds effectively. Such assurance requires more substantial effort in understanding how the collaboration process is shaped by the individuals’ characteristics, behaviors, and relationships, and how to support their dynamic interaction. Models of groups, collaboration, and communities collect and structure the