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User Modeling and User-Adapted Interaction 5

Editors:Alfred Kobsa
Dates:1995
Volume:5
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:13
Links:link.springer.com | Table of Contents
  1. UMUAI 1995 Volume 5 Issue 1
  2. UMUAI 1995-0 Volume 5 Issue 2
  3. UMUAI 1995 Volume 5 Issue 3/4

UMUAI 1995 Volume 5 Issue 1

SMART: Student modeling approach for responsive tutoring BIBAKFull-Text 1-44
  Valerie J. Shute
This paper describes a new student modeling paradigm called SMART. The premise is that a single, principled approach to student modeling, involving both theoretical and empirical methods, can render automated instruction more efficacious across a broad array of instructional domains. After defining key terms and discussing limitations to previous student modeling paradigms, I describe the SMART approach, as embedded within a statistics tutor called Stat Lady (Shute and Gluck, 1994). SMART works in conjunction with a tutor design where low-level knowledge and skills (i.e., curricular elements) are identified and separated into three main outcome types. Throughout the tutor, curricular elements with values below a pre-set mastery criterion are instructed, evaluated, and remediated, if necessary. The diagnostic part of the student model is driven by a series of regression equations based on the level of assistance the computer gives each person, per curriculum element. Remediation on a given element occurs when a subject fails to achieve mastery during assessment, which follows instruction. Remediation is precise because each element knows its location within the tutor where it is instructed and assessed. I end with a summary of results from two controlled evaluations of SMART examining the following research issues: (a) diagnostic validity, (b) individual differences in learning from Stat Lady, (c) affective perceptions of the tutorial experience, and (d) contributions of mastery and remediation to learning outcome and efficiency. Comments about related and future research with this paradigm are offered.
Keywords: Aptitudes; cognitive diagnosis; learning outcomes; mastery learning; macroadaptation; microadaptation; remediation
Extending the scope of the student model BIBAKFull-Text 45-65
  Susan Bull; Paul Brna; Helen Pain
In this paper we maintain that there are benefits to extending the scope of student models to include additional information as part of the explicit student model. We illustrate our argument by describing a student model which focuses on 1. performance in the domain; 2. acquisition order of the target knowledge; 3. analogy; 4. learning strategies; 5. awareness and reflection. The first four of these issues are explicitly represented in the student model. Awareness and reflection should occur as the student model is transparent; it is used to promote learner reflection by encouraging the learner to view, and even negotiate changes to the model. Although the architecture is transferable across domains, each instantiation of the student model will necessarily be domain specific due to the importance of factors such as the relevant background knowledge for analogy, and typical progress through the target material. As an example of this approach we describe the student model of an intelligent computer assisted language learning system which was based on research findings on the above five topics in the field of second language acquisition. Throughout we address the issue of the generality of this model, with particular reference to the possibility of a similar architecture reflecting comparable issues in the domain of learning about electrical circuits.
Keywords: Student model; intelligent learning environment; reflection; learning strategies; analogy; second language acquisition
An adaptive Student Centered Curriculum for an intelligent training system BIBAKFull-Text 67-86
  Chris Eliot; Beverly Park Woolf
An intelligent tutoring system customizes its presentation of knowledge to the individual needs of each student based on a model of the student. Student models are more complex than other user models because the student is likely to have misconceptions. We have addressed several difficult issues in reasoning about a student's knowledge and skills within a real-time simulation-based training system. Our conceptual framework enables important aspects of the tutor's reasoning to be based upon simple, comprehensible representations that are the basis for a Student Centered Curriculum. We have built a system for teaching cardiac resuscitation techniques in which the decisions about how to teach are separated from the decisions about what to teach. The training context (i.e., choice of topics) is changed based on a tight interaction between student modeling techniques and simulation management. Although complex student models are still required to support detailed reasoning about how to teach, we argue that the decision about what to teach can be adequately supported by qualitatively simpler techniques, such as overlay models. This system was evaluated in formative studies involving medical school faculty and students. Construction of the student model involves monitoring student actions during a simulation and evaluating these actions in comparison with an expert model encoded as a multi-agent plan. The plan recognition techniques used in this system are novel and allow the expert knowledge to be expressed in a form that is natural for domain experts.
Keywords: Adaptive; planning; planning recognition; simulation; multi-agent; multimedia; tutoring; artificial intelligence; knowledge representation

UMUAI 1995-0 Volume 5 Issue 2

Student diagnosis in practice; bridging a gap BIBAKFull-Text 93-116
  Eva L. Ragnemalm
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.
Keywords: Student diagnosis; student modeling; intelligent tutoring systems
Feature Based Modelling: A methodology for producing coherent, consistent, dynamically changing models of agents' competencies BIBAKFull-Text 117-150
  Geoffrey I. Webb; Mark Kuzmycz
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.
Keywords: Student modelling; machine learning; modelling competency

Book Review

Student Modelling: The Key to Individualized Knowledge-based Instruction, Jim E. Greer and Gordon I. McCalla (eds) BIBFull-Text 151-155
  Judy Kay
Using dynamic user models in the recognition of the plans of the user BIBAKFull-Text 157-190
  Liliana Ardissono; Dario Sestero
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.
Keywords: User modeling; plan recognition; information-seeking dialogue; natural language; pragmatics

UMUAI 1995 Volume 5 Issue 3/4

Preface BIBFull-Text iii-iv
  Anthony Jameson
Numerical uncertainty management in user and student modeling: An overview of systems and issues BIBAKFull-Text 193-251
  Anthony Jameson
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.
Keywords: numerical uncertainty management; Bayesian networks; Dempster-Shafer theory; fuzzy logic; user modeling; student modeling
The role of probability-based inference in an intelligent tutoring system BIBAKFull-Text 253-282
  Robert J. Mislevy; Drew H. Gitomer
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 probability-based 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.
Keywords: Bayesian inference networks; cognitive diagnosis; HYDRIVE; intelligent tutoring systems; probability-based inference; student models
User-expertise modeling with empirically derived probabilistic implication networks BIBAKFull-Text 283-315
  Michel C. Desmarais; Ameen Maluf; Jiming Liu
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.
   The POKS technique is based on the cognitive theory of knowledge structures: a formalism for the representation of the order in which we learn knowledge units (KU). The technique permits the induction of knowledge structures from a small number of empirical data cases. It uses an evidence propagation scheme within these structures to infer an individual's knowledge state from a sample of KU. The empirical induction technique is based, in part, on statistical hypothesis testing over conditional probabilities that are determined by the KUs' learning order.
   Experiments with this approach show that the technique 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. However, the selective process, based on entropy minimization, is shown to be much more effective in reducing the standard error score of knowledge assessment than random sampling.
Keywords: knowledge assessment; knowledge spaces; automatic knowledge structure induction; Bayesian network inferences; computer assisted testing; user modeling
A Dempster-Shafer approach to modeling agent preferences for plan recognition BIBAKFull-Text 317-348
  Mathias Bauer
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 Dempster-Shafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.
Keywords: plan recognition; user preferences; quantitative user modeling; Dempster-Shafer Theory
Fuzzy techniques and user modeling in Sales Assistants BIBAKFull-Text 349-370
  Heribert Popp; Dieter Lödel
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.
Keywords: stereotypes; product rating; fuzzy techniques; soft computing; requirements analysis; sales assistants; user modeling