Numerical uncertainty management in user and student modeling: An overview of systems and issues

Jameson, Anthony

doi:10.1007/bf01126111

Cited by 151 publications

(73 citation statements)

References 41 publications

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“…In an educational system where there is no direct interaction between the tutor and the student the collected data tend to be more haphazard, than those obtained through traditional face-to-face interaction. Furthermore, it is harder for these systems to rely upon background information and relevant experience, as human tutors can (Jameson, 1996). Especially in a web-based learning environment inaccurate measurements caused by technical difficulties, such as network congestion, cannot be ignored.…”

Section: The Problem Of Student Diagnosismentioning

confidence: 99%

Fuzzy Inference for Student Diagnosis in Adaptive Educational Hypermedia

Grigoriadou¹,

Kornilakis²,

Papanikolaou³

et al. 2002

Methods and Applications of Artificial Intelligence

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Abstract:In this paper we propose a method that implements student diagnosis in the context of the Adaptive Hypermedia Educational System INSPIRE -INtelligent System for Personalized Instruction in a Remote Environment. The method explores ideas from the fields of fuzzy logic and multicriteria decisionmaking in order to deal with uncertainty and incorporate in the system a more complete and accurate description of the expert's knowledge as well as flexibility in student's assessment. To be more precise, an inference system, using fuzzy logic and the Analytic Hierarchy Process to represent the knowledge of the teacher-expert on student's diagnosis, analyzes student's answers to questions of varying difficulty and importance, and estimates the student's knowledge level. Preliminary experiments with real students indicate that the method is characterized by effectiveness in handling the uncertainty of student diagnosis, and is found to be closer to the assessment performed by a human teacher, when compared to a more traditional method of assessment.

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Section: The Problem Of Student Diagnosismentioning

confidence: 99%

Fuzzy Inference for Student Diagnosis in Adaptive Educational Hypermedia

Grigoriadou¹,

Kornilakis²,

Papanikolaou³

et al. 2002

Methods and Applications of Artificial Intelligence

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“…First, the model uses a probabilistic framework to perform three kinds of assessment: (1) plan recognition, inferring the most likely strategy among possible alternatives the student is following, (2) prediction of students' goals and actions, and (3) long-term assessment of the student's domain knowledge. None of the existing systems that perform probabilistic user modeling seem to combine all three of these functions (Jameson, 1996). ANDES' Assessor evolves from POLA VanLehn, 1996a, 1996b), our first attempt at a student model for coached problem solving.…”

Section: The Andes Student Modeling Frameworkmentioning

confidence: 99%

On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

et al. 1997

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Abstract. This paper describes the student modeling component of ANDES, an Intelligent Tutoring System for Newtonian physics. ANDES' student model uses a Bayesian network to do long-term knowledge assessment, plan recognition and prediction of students' actions during problem solving. The network is updated in real time, using an approximate anytime algorithm based on stochastic sampling, as a student solves problems with ANDES. The information in the student model is used by ANDES' Help system to tailor its support when the student reaches impasses in the problem solving process. In this paper, we describe the knowledge structures represented in the student model and discuss the implementation of the Bayesian network assessor. We also present a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network.

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“…Still, there are also a number of systems that use other approaches like Fuzzy Logic [37; 98; 108] and, more recently, neuro-fuzzy approaches [182]. The interested reader can see [90] for an excellent review of former systems and issues in the field of user modeling, and [99] for some discussion about fuzzy user modeling in the context of adaptive web-based applications.…”

Section: Uncertainty-based User Modeling For Adaptive Hypermedia and mentioning

confidence: 99%

“…In the context of granularity relationships, examples of BNs with links defined in the diagnostic direction (o 4 ) are: [82; 132; 139], while [119; 122; 130; 195; 208] use links defined in the causal direction (o 3 ). The first theoretical comparison between both options can be found in [90], which discusses some implications but does not explicitly recommend any of them. The causal direction (o 3 ) is supported by theoretical studies such us [130], which compare both options in terms of the same criteria described above, and empirical studies such as [39], which evaluate three different course hierarchies in the context of adaptive testing, using quality measures such as test length and test coverage.…”

Section: Example 3 the Use Of The Two Different Options In User Modementioning

confidence: 99%

“…However, there are other systems where the structure has been specified or constrained by experts and the parameters are partially or totally derived from data [201], or adjusted to theoretical models, for example, models inspired by Item Response Theory [130] and [90]. Some approaches to simplify parameter specification in the context of student modeling have also been proposed [129].…”

Section: Development Of the Quantitative Modelmentioning

confidence: 99%

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User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

638

416

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Abstract. One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling. IntroductionAdaptive hypermedia and other adaptive Web systems (AWS) belong to the class of user-adaptive software systems [174]. One distinctive feature of an adaptive system is a user model. The user model is a representation of information about an individual user that is essential for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. For example, when the user searches for relevant information, the system can adaptively select and prioritize the most relevant items (see Chapter 6 of this book [125]). When the user navigates from one item to another, the system can manipulate the links (e.g., hide, sort, annotate) to provide adaptive navigation support (see Chapter 8 of this book [21]). When the user reaches a particular page, the system can present the content adaptively (see Chapter 13 of this book [28]). To create and maintain an up-to-date user model, an adaptive system collects data for the user model from various sources that may include implicitly observing user interaction and explicitly requesting direct input from the user. This process is known as user modeling. User modeling and adaptation are two sides of the same coin. The amount and the nature of the information represented in the user model depend to a large extent on the kind of adaptation effect that the system has to deliver. 4 P. Brusilovsky and E. Millán As mentioned in the introduction, Chapters 1 to 5 of this book are focused mostly on the modeling side of personalization, while the remaining chapters focus mostly on the adaptation side. Chapters 1 and 2 are specifically devoted to user models and user modeling. Beyond this, user modeling issues are discussed at different levels of detail in several other chapters. This chapter attempts to complement the remaining chapters in two ways. First, it provides an overview (a "big picture") of the user modeling side referring readers when necessary to additional information in chapters within this book. Second, it attempts to complement other chapters by presenting aspects that are either not covered in other chapters or covered insufficiently.To envision the big picture, this chapter follows Sleeman [175] who suggested classifying user models by the nature and form of information contained in the model as well as the methods of working with it. Following hi...

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Numerical uncertainty management in user and student modeling: An overview of systems and issues

Cited by 151 publications

References 41 publications

Fuzzy Inference for Student Diagnosis in Adaptive Educational Hypermedia

Fuzzy Inference for Student Diagnosis in Adaptive Educational Hypermedia

On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

User Models for Adaptive Hypermedia and Adaptive Educational Systems

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