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User Modeling and User-Adapted Interaction 12

Editors:Alfred Kobsa
Dates:2002
Volume:12
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:14
Links:link.springer.com | Table of Contents
  1. UMUAI 2002-02 Volume 12 Issue 1
  2. UMUAI 2002 Volume 12 Issue 2/3
  3. UMUAI 2002-11 Volume 12 Issue 4

UMUAI 2002-02 Volume 12 Issue 1

Assessment of User Affective and Belief States for Interface Adaptation: Application to an Air Force Pilot Task BIBAKFull-Text 1-47
  Eva Hudlicka; Michael D. McNeese
We describe an Affect and Belief Adaptive Interface System (ABAIS) designed to compensate for performance biases caused by users' affective states and active beliefs. The ABAIS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensatory strategy; and implementing this strategy in terms of specific GUI adaptations. ABAIS provides a generic adaptive framework for integrating a variety of user assessment methods (e.g. knowledge-based, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g. content- and format-based). The ABAIS performance bias prediction is based on empirical findings from emotion research combined with detailed knowledge of the task context. The initial ABAIS prototype was demonstrated in the context of an Air Force combat task, used a knowledge-based approach to assess the pilot's anxiety level, and adapted to the pilot's anxiety and belief states by modifying selected cockpit instrument displays in response to detected changes in these states.
Keywords: adaptive interface; affect adaptation; affect assessment; affective computing; aviation; human-computer interaction; user modeling
Modeling Multimodal Expression of User's Affective Subjective Experience BIBAKFull-Text 49-84
  Nadia Bianchi-Berthouze; Christine L. Lisetti
With the growing importance of information technology in our everyday life, new types of applications are appearing that require the understanding of information in a broad sense. Information that includes affective and subjective content plays a major role not only in an individual's cognitive processes but also in an individual's interaction with others. We identify three key points to be considered when developing systems that capture affective information: embodiment (experiencing physical reality), dynamics (mapping experience and emotional state with its label) and adaptive interaction (conveying emotive response, responding to a recognized emotional state). We present two computational systems that implement those principles: MOUE (Model Of User Emotions) is an emotion recognition system that recognizes the user's emotion from his/her facial expressions, and from it, adaptively builds semantic definitions of emotion concepts using the user's feedback; MIKE (Multimedia Interactive Environment for Kansei communication) is an interactive adaptive system that, along with the user, co-evolves a language for communicating over subjective impressions.
Keywords: affect; embodiment; emotion; interaction; perception; subjective experience

Book Review

Rosalind Picard: Affective Computing BIBFull-Text 85-89
  Annika Waern
Ana Paiva (ed.): Affective Interactions: Towards a New Generation of Computer Interfaces BIBFull-Text 91-96
  Kristina Höök

UMUAI 2002 Volume 12 Issue 2/3

Introduction to the Special Issue on Empirical Evaluation of User Models and User Modeling Systems BIBFull-Text 105-109
  David N. Chin; Martha E. Crosby
Designing and Evaluating an Adaptive Spoken Dialogue System BIBAKFull-Text 111-137
  Diane J. Litman; Shimei Pan
Spoken dialogue system performance can vary widely for different users, as well for the same user during different dialogues. This paper presents the design and evaluation of an adaptive version of TOOT, a spoken dialogue system for retrieving online train schedules. Based on rules learned from a set of training dialogues, adaptive TOOT constructs a user model representing whether the user is having speech recognition problems as a particular dialogue progresses. Adaptive TOOT then automatically adapts its dialogue strategies based on this dynamically changing user model. An empirical evaluation of the system demonstrates the utility of the approach.
Keywords: adaptive spoken dialogue systems; hypothesis testing for the effectiveness of adaptations; PARADISE for evaluating performance measures; speech recognition; user model acquisition via machine learning
User Models and User Physical Capability BIBAKFull-Text 139-169
  Simeon Keates; Patrick Langdon
Current interface design practices are based on user models and descriptions derived almost exclusively from studies of able-bodied users (Keates et al., 1999). However, such users are only one point on a wide and varied scale of physical capabilities.
   Users with a number of different physical impairment conditions have the same desire to use computers as able-bodied people (Busby, 1997), but cannot cope with most current computer access systems (Edwards, 1995).
   It is important to identify the differences in interaction for users of differing physical capability, because the border between the labels 'able-bodied' and 'motion-impaired' users is becoming increasingly blurred as the generation of computer users inexorably becomes older and physically less capable. If user models are to retain their relevance, then they have to be able to reflect users' physical capabilities (Stary, 1997).
   Through empirical studies, this paper will show that there are very important differences between those with motion-impairments, whether elderly or disabled, and able-bodied users when they interact with computers. It attempts to quantify where those differences occur in the interaction cycle with the use of a very straightforward user model, the Model Human Processor (MHP) (Card, Moran and Newell, 1983), which describes interaction purely in terms of perception, cognition and motor component times. Although this model is simplistic compared to the more recent sophisticated models, it affords a simple and valuable insight into interaction cycles and offers a building block on which to base more comprehensive models. This work is predicated on the idea that the use of this model in detailed analysis of the basic interaction cycle will provide a means for studying motion impairment at both an individual and general level.
Keywords: model human processor; motion-impaired users
Evaluating Comprehension-Based User Models: Predicting Individual User Planning and Action BIBAKFull-Text 171-205
  Young Woo Sohn; Stephanie M. Doane
Described is a program of research that uses rigorous methods to evaluate models of user cognition and action based on the construction-integration architecture of comprehension (Doane and Sohn, 2000; Kintsch, 1988; 1998). The models interrelate user environmental information, background knowledge, and current goals, and then spread activation throughout the interrelated information to simulate UNIX user command productions, aviation pilot eye fixations and control movements during flight, and army personnel intelligence planning. Models of individuals in the complex interactive environments are tested for descriptive as well as predictive validity. Comparisons of model and human empirical data have resulted in a high degree of agreement, validating the ability of the comprehension-based architecture to support models that can predict user performance. Evaluation methods are detailed and the importance of evaluative rigor is discussed.
Keywords: cognitive models; evaluations of models; goodness of fit; predictive validity
The Use of a Co-operative Student Model of Learner Characteristics to Configure a Multimedia Application BIBAKFull-Text 207-241
  Trevor Barker; Sara Jones; Carol Britton
This paper describes an investigation into the ways in which learning using a multimedia application can be supported and enhanced by means of a simple co-operative student model of learner characteristics. This paper reports the design, implementation and evaluation of an individually configurable multimedia learning application, based upon such a model.
   A multimedia learning application was developed that presented information differentially based upon the individual characteristics of learners, held in the student model. The characteristics employed in the model were language level, cognitive style, task and question levels, and help level. Small groups of learners followed the multimedia course in learning centres located in colleges in the UK. A Grounded Theory study was carried out in order to understand the many and complex interactions that took place between learners, tutors and the learning environment.
   Stages in the Grounded Theory method are described and some qualitative data is presented. It was possible to conclude from these, that the quality of learning for individuals was improved by the use of the co-operative student model. Quantitative data is presented to support this view and where possible, to relate performance on the multimedia learning application to the student model configuration.
Keywords: CAL; evaluation; global descriptors; Grounded Theory; multimedia; Student model
Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor BIBAKFull-Text 243-279
  Antonija Mitrovic; Brent Martin; Michael Mayo
This paper presents the results of three evaluation studies performed during 1998 and 1999 on SQL-Tutor, an intelligent tutoring system for the SQL database language. We have evaluated the system in the context of genuine courses, and used the results to further refine the system. The main goal of our research has been the exploration and extension of Constraint-Based Modeling (CBM), a student modeling approach proposed by Ohlsson (1994). SQL-Tutor provided us with experiences of using CBM, and we used it to extend the approach in several important ways. The main goal of all three evaluation studies was to determine how well CBM supported student learning. We have obtained positive results. The students who learnt with SQL-Tutor in the first study performed significantly better than those who did not when assessed by a subsequent classroom examination. Furthermore, the analysis of students' learning shows that CBM has a sound psychological foundation.
   Besides the evaluation of CBM, we also evaluated the improvements in terms of student assessments of the usefulness of the system and evaluated various techniques used in SQL-Tutor. In the second study, we evaluated the effectiveness of feedback provided to the students. This study showed that high-level advice is most beneficial to students' learning. The focus of the third study was different. We extended CBM to support long-term modeling of student knowledge, and used this extension to develop an adaptive problem-selection strategy. The study revealed the benefits of this strategy in comparison with a simple heuristic strategy. We also reflect on our experiences in evaluating SQL-Tutor.
Keywords: constraint based modeling; evaluation; intelligent tutoring systems; pedagogical decision making; probabilistic student model; student modeling
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation BIBAKFull-Text 281-330
  Eva Millán; José Luis Pérez-de-la-Cruz
In this paper, we present a new approach to diagnosis in student modeling based on the use of Bayesian Networks and Computer Adaptive Tests. A new integrated Bayesian student model is defined and then combined with an Adaptive Testing algorithm. The structural model defined has the advantage that it measures students' abilities at different levels of granularity, allows substantial simplifications when specifying the parameters (conditional probabilities) needed to construct the Bayesian Network that describes the student model, and supports the Adaptive Diagnosis algorithm. The validity of the approach has been tested intensively by using simulated students. The results obtained show that the Bayesian student model has excellent performance in terms of accuracy, and that the introduction of adaptive question selection methods improves its behavior both in terms of accuracy and efficiency.
Keywords: adaptive testing; Bayesian networks; student modeling

UMUAI 2002-11 Volume 12 Issue 4

Hybrid Recommender Systems: Survey and Experiments BIBAKFull-Text 331-370
  Robin Burke
Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
Keywords: case-based reasoning; collaborative filtering; recommender systems
Using Bayesian Networks to Manage Uncertainty in Student Modeling BIBAKFull-Text 371-417
  Cristina Conati; Abigail Gertner
When a tutoring system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. We use Bayesian networks as a comprehensive, sound formalism to handle this uncertainty. Using Bayesian networks, we have devised the probabilistic student models for Andes, a tutoring system for Newtonian physics whose philosophy is to maximize student initiative and freedom during the pedagogical interaction. Andes' models provide long-term knowledge assessment, plan recognition, and prediction of students' actions during problem solving, as well as assessment of students' knowledge and understanding as students read and explain worked out examples. In this paper, we describe the basic mechanisms that allow Andes' student models to soundly perform assessment and plan recognition, as well as the Bayesian network solutions to issues that arose in scaling up the model to a full-scale, field evaluated application. We also summarize the results of several evaluations of Andes which provide evidence on the accuracy of its student models.
Keywords: student modelling; Intelligent Tutoring Systems; dynamic Bayesian networks

Book Review

Book Review: User Interfaces for All: Concepts, Methods and Tools BIBFull-Text 419-420
  Bonnie A. Nardi