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UMUAI Tables of Contents: 0809101112131415161718192021222324

User Modeling and User-Adapted Interaction 18

Editors:Alfred Kobsa
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Links:link.springer.com | Table of Contents
  1. UMUAI 2008-02 Volume 18 Issue 1/2
  2. UMUAI 2008-08 Volume 18 Issue 3
  3. UMUAI 2008-09 Volume 18 Issue 4
  4. UMUAI 2008-11 Volume 18 Issue 5

UMUAI 2008-02 Volume 18 Issue 1/2

Special Issue on Affective Modeling and Adaptation

Introduction to special Issue on 'Affective modeling and adaptation' BIBAFull-Text 1-9
  Sandra Carberry; Fiorella de Rosis
We are all ruled in what we do by impulses; and these impulses are so organized that our actions in general serve for our self preservation and that of the race. Hunger, love, pain, fear are some of those inner forces which rule the individual's instinct for self preservation. At the same time, as social beings, we are moved in the relations with our fellow beings by such feelings as sympathy, pride, hate, need for power, pity and so on. -- Albert Einstein, 1950
The relative impact of student affect on performance models in a spoken dialogue tutoring system BIBAKFull-Text 11-43
  Kate Forbes-Riley; Mihai Rotaru
We hypothesize that student affect is a useful predictor of spoken dialogue system performance, relative to other parameters. We test this hypothesis in the context of our spoken dialogue tutoring system, where student learning is the primary performance metric. We first present our system and corpora, which have been annotated with several student affective states, student correctness and discourse structure. We then discuss unigram and bigram parameters derived from these annotations. The unigram parameters represent each annotation type individually, as well as system-generic features. The bigram parameters represent annotation combinations, including student state sequences and student states in the discourse structure context. We then use these parameters to build learning models. First, we build simple models based on correlations between each of our parameters and learning. Our results suggest that our affect parameters are among our most useful predictors of learning, particularly in specific discourse structure contexts. Next, we use the PARADISE framework (multiple linear regression) to build complex learning models containing only the most useful subset of parameters. Our approach is a value-added one; we perform a number of model-building experiments, both with and without including our affect parameters, and then compare the performance of the models on the training and the test sets. Our results show that when included as inputs, our affect parameters are selected as predictors in most models, and many of these models show high generalizability in testing. Our results also show that overall, the affect-included models significantly outperform the affect-excluded models.
Keywords: Emotional speech; Discourse structure; Affective user modeling; Multiple linear regression; Adaptive spoken dialogue systems; Tutorial dialogue systems; System performance modeling
Automatic detection of learner's affect from conversational cues BIBAKFull-Text 45-80
  Sidney K. D'Mello; Scotty D. Craig
We explored the reliability of detecting a learner's affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by holding a conversation in natural language. Training data were collected in a learning session with AutoTutor, after which the affective states of the learner were rated by the learner, a peer, and two trained judges. Inter-rater reliability scores indicated that the classifications of the trained judges were more reliable than the novice judges. Seven data sets that temporally integrated the affective judgments with the dialogue features of each learner were constructed. The first four datasets corresponded to the judgments of the learner, a peer, and two trained judges, while the remaining three data sets combined judgments of two or more raters. Multiple regression analyses confirmed the hypothesis that dialogue features could significantly predict the affective states of boredom, confusion, flow, and frustration. Machine learning experiments indicated that standard classifiers were moderately successful in discriminating the affective states of boredom, confusion, flow, frustration, and neutral, yielding a peak accuracy of 42% with neutral (chance=20%) and 54% without neutral (chance=25%). Individual detections of boredom, confusion, flow, and frustration, when contrasted with neutral affect, had maximum accuracies of 69, 68, 71, and 78%, respectively (chance=50%). The classifiers that operated on the emotion judgments of the trained judges and combined models outperformed those based on judgments of the novices (i.e., the self and peer). Follow-up classification analyses that assessed the degree to which machine-generated affect labels correlated with affect judgments provided by humans revealed that human-machine agreement was on par with novice judges (self and peer) but quantitatively lower than trained judges. We discuss the prospects of extending AutoTutor into an affect-sensing ITS.
Keywords: Affect detection; Human-computer interaction; Human-computer dialogue; Dialogue features; Discourse markers; Conversational cues; Intelligent Tutoring Systems; AutoTutor
Modeling self-efficacy in intelligent tutoring systems: An inductive approach BIBAKFull-Text 81-123
  Scott W. McQuiggan; Bradford W. Mott
Self-efficacy is an individual's belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student's level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.
Keywords: Affective user modeling; Affective student modeling; Self-efficacy; Intelligent tutoring systems; Inductive learning; Human-computer interaction
Diagnosing and acting on student affect: the tutor's perspective BIBAKFull-Text 125-173
  KaÅ>ka Porayska-Pomsta; Manolis Mavrikis
In this paper we explore human tutors' inferences in relation to learners' affective states and the relationship between those inferences and the actions that tutors take as their consequence. At the core of the investigations presented in this paper lie fundamental questions associated with the role of affective considerations in computer-mediated educational interactions. Theory of linguistic politeness is used as the basis for determining the contextual factors relevant to human tutors's actions, with special attention being dedicated to learner affective states. A study was designed to determine what affective states of the learners are relevant to tutoring mathematics and to identify the mechanisms used by tutors to predict such states. Logs of tutor-student dialogues were recorded along with contextual factors taken into consideration by tutors in relation to their specific tutorial dialogue moves. The logs were annotated in order to determine the types and range of student and tutor actions. Machine learning techniques were then applied to those actions to predict the values of three factors: student confidence, interest and effort. Whilst due to limited size and sparsity of data the results are not conclusive, they are very valuable as the basis for empirically derived hypotheses to be tested in further studies. The potential implications of the hypotheses, if they were confirmed by further studies, are discussed in relation to the impact of tutor's ability to diagnose student affect on the nature of computer-mediated tutorial interactions.
Keywords: Situation modelling; Affect; Machine learning; Tutor feedback; Empirically based hypotheses generation; Computer based tutoring; Computer mediated communication
Private emotions versus social interaction: a data-driven approach towards analysing emotion in speech BIBAKFull-Text 175-206
  Anton Batliner; Stefan Steidl
The 'traditional' first two dimensions in emotion research are VALENCE and AROUSAL. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our 'AIBO' corpus (human-robot communication, children interacting with Sony's AIBO robot). The recordings were done in a Wizard-of-Oz scenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. Five labellers annotated each word as belonging to one of eleven emotion-related states; seven of these states which occurred frequently enough are dealt with in this paper. The confusion matrices of these labels were used in a Non-Metrical Multi-dimensional Scaling to display two dimensions; the first we interpret as VALENCE, the second, however, not as AROUSAL but as INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects' conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data. Two-dimensional solutions based on acoustic and linguistic features that were used for automatic classification of these emotional states are interpreted along the same lines.
Keywords: Emotion; Speech; Dimensions; Categories; Annotation; Data-driven; Non-metrical multi-dimensional scaling
Entertainment capture through heart rate activity in physical interactive playgrounds BIBAKFull-Text 207-243
  Georgios N. Yannakakis; John Hallam
An approach for capturing and modeling individual entertainment ("fun") preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children's physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children's heart rate (HR) signals, and their expressed preferences of how much "fun" particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children's reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p-value 6*10{sup:-5)). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed.
Keywords: Entertainment modeling; Biosignals; Intelligent interactive playgrounds; Mixed-reality games; Artificial neural networks

UMUAI 2008-08 Volume 18 Issue 3

Mediation of user models for enhanced personalization in recommender systems BIBAKFull-Text 245-286
  Shlomo Berkovsky; Tsvi Kuflik
Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.
Keywords: Recommender systems; Ubiquitous user modeling; Mediation of user modeling data
Developing a generalizable detector of when students game the system BIBAKFull-Text 287-314
  Ryan S. J. d. Baker; Albert T. Corbett
Some students, when working in interactive learning environments, attempt to "game the system", attempting to succeed in the environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we present a system that can accurately detect whether a student is gaming the system, within a Cognitive Tutor mathematics curricula. Our detector also distinguishes between two distinct types of gaming which are associated with different learning outcomes. We explore this detector's generalizability, and find that it transfers successfully to both new students and new tutor lessons.
Keywords: Gaming the system; Latent response models; Cognitive tutors; Behavior detection; Machine learning; Generalizable models; Student modeling; Interactive learning environments

UMUAI 2008-09 Volume 18 Issue 4

MUSIPER: a system for modeling music similarity perception based on objective feature subset selection BIBAKFull-Text 315-348
  Dionysios N. Sotiropoulos
We explore the use of objective audio signal features to model the individualized (subjective) perception of similarity between music files. We present MUSIPER, a content-based music retrieval system which constructs music similarity perception models of its users by associating different music similarity measures to different users. Specifically, a user-supplied relevance feedback procedure and related neural network-based incremental learning allows the system to determine which subset of a set of objective features approximates more accurately the subjective music similarity perception of a specific user. Our implementation and evaluation of MUSIPER verifies the relation between subsets of objective features and individualized music similarity perception and exhibits significant improvement in individualized perceived similarity in subsequent music retrievals.
Keywords: Music similarity perception; Relevance feedback; User model; User driven feature selection; Individualization; Content-based retrieval
A multifactor approach to student model evaluation BIBAKFull-Text 349-382
  Michael V. Yudelson; Olga P. Medvedeva
Creating student models for Intelligent Tutoring Systems (ITS) in novel domains is often a difficult task. In this study, we outline a multifactor approach to evaluating models that we developed in order to select an appropriate student model for our medical ITS. The combination of areas under the receiver-operator and precision-recall curves, with residual analysis, proved to be a useful and valid method for model selection. We improved on Bayesian Knowledge Tracing with models that treat help differently from mistakes, model all attempts, differentiate skill classes, and model forgetting. We discuss both the methodology we used and the insights we derived regarding student modeling in this novel domain.
Keywords: Student modeling; Intelligent tutoring systems; Knowledge Tracing; Methodology; Decision theory; Model evaluation; Model selection; Intelligent medical training systems; Machine learning; Probabilistic models; Bayesian models; Hidden Markov Models

UMUAI 2008-11 Volume 18 Issue 5

Special issue on Personalizing Cultural Heritage Exploration

Preface BIBFull-Text 383-387
  Liliana Ardissono; Daniela Petrelli
LISTEN: a user-adaptive audio-augmented museum guide BIBAKFull-Text 389-416
  Andreas Zimmermann; Andreas Lorenz
Modern personalized information systems have been proven to support the user with information at the appropriate level and in the appropriate form. In specific environments like museums and exhibitions, focusing on the control of such a system is contradictory to establishing a relationship with the artifacts and exhibits. Preferably, the technology becomes invisible to the user and the physical reality becomes the interface to an additional virtual layer: by naturally moving in the space and/or manipulating physical objects in our surroundings the user will access information and operate the virtual layer. The LISTEN project is an attempt to make use of the inherent "everyday" integration of aural and visual perception, developing a tailored, immersive audio-augmented environment for the visitors of art exhibitions. The challenge of the LISTEN project is to provide a personalized immersive augmented environment, an aim which goes beyond the guiding purpose. The visitors of the museum implicitly interact with the system because the audio presentation is adapted to the users' contexts (e.g. interests, preferences, motion, etc.), providing an intelligent audio-based environment. This article describes the realization and user evaluation of the LISTEN system focusing on the personalization component. As this system has been installed at the Kunstmuseum Bonn in the context of an exhibition comprising artworks of the painter August Macke, a detailed evaluation could be conducted.
Keywords: Audio information systems; User-adaptive systems; Context-awareness; User modeling; Personalization; Personalized audio environments; Acoustic information spaces; Motion styles; User tracking
A stroll with Carletto: adaptation in drama-based tours with virtual characters BIBAKFull-Text 417-453
  Rossana Damiano; Cristina Gena
In this paper, we present an application for character-based guided tours on mobile devices. The application is based on the Dramatour methodology for information presentation, which incorporates a dramatic attitude in character-based presentations. The application has been developed for a historical site and is based on a virtual character, "Carletto", a spider with an anthropomorphic aspect, who engages in a dramatized presentation of the site. Content items are delivered in a location-aware fashion, relying on a wireless network infrastructure, with visitors who can stroll freely. The selection of contents keeps track of user location and of the interaction history, in order to deliver the appropriate type and quantity of informative items, and to manage the given/new distinction in discourse. The communicative strategy of the character is designed to keep it believable along the interaction with the user, while enforcing dramatization effects. The design of the communicative strategy relies on the fact that the units of the presentation are tagged with metadata concerning their content and communicative function. The description of the application is accompanied by an evaluation study based on a sample of about 300 visitors, carried out in April 2006, when the installation was open to the public for 1 week.
Keywords: Interactive drama; Virtual characters; Cultural heritage; Mobile computing; Adaptive applications
The effects of transparency on trust in and acceptance of a content-based art recommender BIBAKFull-Text 455-496
  Henriette Cramer; Vanessa Evers
The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N=60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study's results.
Keywords: User-adaptivity; Human-computer interaction; Recommender systems; Transparency; Trust; Acceptance; Cultural heritage
Tag-based user modeling for social multi-device adaptive guides BIBAKFull-Text 497-538
  Francesca Carmagnola; Federica Cena
This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0's participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging.
Keywords: Web 2.0; Tag; Annotation; User model; Recommendations; Cultural events