Showing papers in "User Modeling and User-adapted Interaction in 1996"
TL;DR: This paper is a review of existing work on adaptive hypermedia and introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.
Abstract: Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.
1,948 citations
TL;DR: This overview of the three major paradigms of Bayesian networks, the Dempster-Shafer theory of evidence, and fuzzy logic discusses several aspects of the usability of these techniques for user or student modeling, such as their knowledge engineering requirements, their need for computational resources, and the communicability of their results.
Abstract: A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the Dempster-Shafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections focuses on one of these paradigms. It first introduces the basic concepts by showing how they can be applied to a relatively simple user modeling problem. It then surveys systems that have applied techniques from the paradigm to user or student modeling, characterizing each system within a common framework. The final main section discusses several aspects of the usability of these techniques for user or student modeling, such as their knowledge engineering requirements, their need for computational resources, and the communicability of their results.
247 citations
TL;DR: The role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs) and the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based are highlighted.
Abstract: Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probability-based reasoning in a practical ITS context. The discussion revolves around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. HYDRIVE supports generalized claims about aspects of student proficiency through probabilitybased combination of rule-based evaluations of specific actions. The paper highlights the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based.
168 citations
TL;DR: This work presents an example of an adaptive hypertext help system POP, which is being built according to these principles, and discusses the design considerations and empirical findings that lead to this design.
Abstract: Utilising adaptive interface techniques in the design of systems introduces certain risks. An adaptive interface is not static, but will actively adapt to the perceived needs of the user. Unless carefully designed, these changes may lead to an unpredictable, obscure and uncontrollable interface. Therefore the design of adaptive interfaces must ensure that users can inspect the adaptivity mechanisms, and control their results. One way to do this is to rely on the user's understanding of the application and the domain, and relate the adaptivity mechanisms to domain-specific concepts. We present an example of an adaptive hypertext help system POP, which is being built according to these principles, and discuss the design considerations and empirical findings that lead to this design.
155 citations
TL;DR: This work’s approach was driven by the needs of the application and shows features of bottom-up, user-centered design.
Abstract: The development of user-adaptive systems is of increasing importance for industrial applications. User modeling emerged from the need to represent in the system knowledge about the user in order to allow informed decisions on how to adapt to match the user’s needs. Most of the research in this field, however, has been theoretical, “top-down.” Our approach, in contrast, was driven by the needs of the application and shows features of bottom-up, user-centered design.
121 citations
TL;DR: An adaptive hypertext system designed to individually support exploratory learning and programming activities in the domain of Common Lisp, endowed with domain-specific knowledge represented in a hyperspace of topics, builds up a detailed model of the user’s expertise which it utilizes to provide personalized assistance.
Abstract: We have developed an adaptive hypertext system designed to individually support exploratory learning and programming activities in the domain of Common Lisp. Endowed with domain-specific knowledge represented in a hyperspace of topics, the system builds up a detailed model of the user’s expertise which it utilizes to provide personalized assistance. Unlike other work emerging in the field of adaptive hypertext systems, our approach exploits domain and user modelling techniques to support individuals in different ways. The system not only generates individualized presentations of topic nodes, but also provides adaptive navigational assistance for link-based browsing. By identifying and suggesting useful hyperlinks according to the user’s knowledge state and preferences, the system encourages and guides exploration. While browsing through the hyperspace of topics, the system analyses the user’s navigational behaviour to infer the user’s learning progress and to dynamically adapt presentations of topics and links accordingly.
118 citations
TL;DR: A probabilistic user modeling approach, the POKS technique, which could serve as a standard user-expertise modeling tool and is successful in partially inferring an individual's knowledge state, either through the monitoring of a user's behavior, or through a selective questioning process.
Abstract: The application of user-expertise modeling for adaptive interfaces is confronted with a number of difficult challenges, namely, efficiency and reliability, the cost-benefit ratio, and the practical usability of user modeling techniques. We argue that many of these obstacles can be overcome by standard, automatic means of performing knowledge assessment. Within this perspective, we present the basis of a probabilistic user modeling approach, the POKS technique, which could serve as a standard user-expertise modeling tool.
71 citations
TL;DR: The decision-making algorithm implemented in the bar is described and the bar's self-adaptive behavior of displaying the frequency of each icon's use through the icon's size is described, allowing the user to maintain a clear general model of the system.
Abstract: As information systems become increasingly important in many different domains, the potential to adapt them to individual users and their needs also becomes more important. Adaptive user interfaces offer many possible ways to adjust displays and improve procedures for a user's individual patterns of work. This paper describes an attempt to design an adaptive user interface in a computer environment familiar to many users. According to one classification of adaptive user interfaces, the adaptive bar described in this paper would be classified as a user-controlled self-adaptation system.
63 citations
TL;DR: An approach to the quantitative modeling of the required agent-related data and their use in plan recognition is presented that relies on the DempsterShafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.
Abstract: Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Most plan recognizers work by merely maintaining a set of equally plausible plan hypotheses each of which proved compatible with recent observations without taking into account individual preferences of the currently observed agent. Such additional information provides a basis for ranking the hypotheses so that the “best” one can be selected whenever the system is forced to react (e.g., to provide help to the user of a software system to accomplish his goals). Furthermore, hypotheses with low valuations can be excluded from considerations at an early stage. In this paper, an approach to the quantitative modeling of the required agent-related data and their use in plan recognition is presented. It relies on the DempsterShafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.
63 citations
TL;DR: This work proposes a solution which provides user-centered adaptive information retrieval and navigation which is complementary to information discovery methods which provide access to new information, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space.
Abstract: We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user’s information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.
61 citations
TL;DR: Techniques that have been developed for creating models and for extracting key information therefrom for Feature Based Modelling are described.
Abstract: Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90% when predicting student solutions. It also demonstrates the ability to identify and model student's buggy arithmetic procedures.
TL;DR: Since cooperation sometimes requires that agents reason about what is mutually believed, this work proposes a representation in which the second and all subsequent nesting levels are merged into a single category, which restricts agents to human-like referring and repair strategies.
Abstract: Models of rationality typically rely on underlying logics that allow simulated agents to entertain beliefs about one another to any depth of nesting. Such models seem to be overly complex when used for belief modelling in environments in which cooperation between agents can be assumed, i.e., most HCI contexts. We examine some existing dialogue systems and find that deeply-nested beliefs are seldom supported, and that where present they appear to be unnecessary except in some situations involving deception. Use of nested beliefs is associated with nested reasoning (i.e., reasoning about other agents' reasoning). We argue that for cooperative dialogues, representations of individual nested beliefs of the third level (i.e., what A thinks B thinks A thinks B thinks) and beyond are in principle unnecessary unless directly available from the environment, because the corresponding nested reasoning is redundant. Since cooperation sometimes requires that agents reason about what is mutually believed, we propose a representation in which the second and all subsequent nesting levels are merged into a single category. In situations affording individual deeply-nested beliefs, such a representation restricts agents to human-like referring and repair strategies, where an unrestricted agent might make an unrealistic and perplexing utterance.
TL;DR: The Sales Assistant employs user models in the problem solving and dialog control layers, and fuzzy techniques for the management of imprecision in a large hypertext-like information system.
Abstract: Uncertainty and fuzziness are ubiquitous in the field of computerized selling. Therefore the mastery of these domains might be a key factor for the success of electronic selling. In this paper the Sales Assistant is introduced. It employs user models in the problem solving and dialog control layers, and fuzzy techniques for the management of imprecision. Fuzzy Multiple Criteria Analysis has proven its usefulness in product evaluation if there are no severe interdependencies among the product attributes. The user model in the Sales Assistant is constructed unobtrusively on the basis of user behavior, and it uses short-term information. It increases the transparency and usability of a large hypertext-like information system.
TL;DR: A novel framework for looking at the problem of diagnosing a student's knowledge in an Intelligent Tutoring System is presented and it is indicated that the input and the conceptualisation of the student model are significant for the choice of modeling technique.
Abstract: This paper presents a novel framework for looking at the problem of diagnosing a student's knowledge in an Intelligent Tutoring System. It is indicated that the input and the conceptualisation of the student model are significant for the choice of modeling technique. The framework regards student diagnosis as the process of bridging the gap between the student's input to the tutoring system, and the system's conception and representation of correct knowledge. The process of bridging the gap can be subdivided into three phases, data acquisition, transformation and evaluation, which are studied further. A number of published student modeling techniques are studied with respect to how they bridge the gap.
TL;DR: The paper reports an approach to inducing models of procedural skills from observed student performance, referred to as INSTRUCT, which builds on two well-known techniques, reconstructive modeling and model tracing, at the same time avoiding their major pitfalls.
Abstract: The paper reports an approach to inducing models of procedural skills from observed student performance. The approach, referred to as INSTRUCT, builds on two well-known techniques, reconstructive modeling and model tracing, at the same time avoiding their major pitfalls. INSTRUCT does not require prior empirical knowledge of student errors and is also neutral with respect to pedagogy and reasoning strategies applied by the student. Pedagogical actions and the student model are generated on-line, which allows for dynamic adaptation of instruction, problem generation and immediate feedback on student's errors. Furthermore, the approach is not only incremental but truly active, since it involves students in explicit dialogues about problem-solving decisions. Student behaviour is used as a source of information for user modeling and to compensate for the unreliability of the student model. INSTRUCT uses both implicit information about the steps the student performed or the explanations he or she asked for, and explicit information gained from the student's answers to direct question about operations being performed. Domain knowledge and the user model are used to focus the search on the portion of the problem space the student is likely to traverse while solving the problem at hand. The approach presented is examined in the context of SINT, an ITS for the domain of symbolic integration.
TL;DR: This paper presents a framework where a plan recognition and a user modeling component are integrated to cooperate in the task of identifying the user's plans and goals and introduces some disambiguation rules that are applied to the information in the user model for restricting the set of ambiguous hypotheses on the users' plans and Goals to the most plausible ones.
Abstract: This paper is concerned with information-seeking dialogues in a restricted domain (we consider a consultation system for a Computer Science Department, delivering information about the various tasks that the users may want to perform: for example, how to access the library, get information about the courses of the Department, etc.) and presents a framework where a plan recognition and a user modeling component are integrated to cooperate in the task of identifying the user's plans and goals. The focus of the paper is centered on the techniques used for building the user model and exploiting it in the determination of the user's intentions. For this task, we use stereotypes and we propose some inference rules for expanding the user model by inferring the user's beliefs from both the sentences s/he utters and the information stored in the plan library of the system, that describes the actions in the domain. Moreover, we introduce some disambiguation rules that are applied to the information in the user model for restricting the set of ambiguous hypotheses on the user's plans and goals to the most plausible ones. This also simplifies a further clarification dialogue if it is necessary for a precise identification of the user's intentions.
TL;DR: The development of the model of learner attributes and its use within an adaptive tutoring system and the results of experiments using the system with two classes of students in two successive academic years are discussed.
Abstract: User modelling within tutoring systems often concentrates on the representation of the learner's status with respect to the domain, paying little attention to the user's individual characteristics in terms of capabilities and preferences. A composite learner model, incorporating both domain related data and information about personal attributes is useful in determining not only which items should be presented, but how the student may best be able to learn them. A model of users' individual characteristics has been developed using multivariate statistical techniques as a means of generating user stereotypes from empirical data. Each stereotype has an associated profile in terms of attributes which are useful for the application in which the model is used.