Eva Millán

User models for adaptive hypermedia and adaptive educational systems

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.

Bayesian networks for student model engineering

Bayesian networks are graphical modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement student models. The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical student modeling problems. A repertoire of models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed.

Introducing prerequisite relations in a multi-layered bayesian student model

In this paper we present an extension of a previously developed generic student model based on Bayesian Networks. A new layer has been added to the model to include prerequisite relationships. The need of this new layer is motivated from different points of view: in practice, this kind of relationships are very common in any educational setting, but also their use allows for improving efficiency of both adaptation mechanisms and the inference process. The new prerequisite layer has been evaluated using two different experiments: the first experiment uses a small toy example to show how the BN can emulate human reasoning in this context, while the second experiment with simulated students suggests that prerequisite relationships can improve the efficiency of the diagnosis process by allowing increased accuracy or reductions in the test length.

Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles

When using learning object repositories, it is interesting to have mechanisms to select the more adequate objects for each student. For this kind of adaptation, it is important to have sound models to estimate the relevant features. In this paper we present a student model to account for learning styles, based on the model defined by Felder and Sylverman and implemented using dynamic Bayesian networks. The model is initialized according to the results obtained by the student in the index of learning styles questionnaire, and then fine-tuned during the course of the interaction using the Bayesian model, The model is then used to classify objects in the repository as appropriate or not for a particular student.

Adaptive Bayesian Networks for Multilevel Student Modelling

In this paper we present an integrated theoretical approach for student modelling based on an Adaptive Bayesian Network. A mathematical formalization of the Adaptive Bayesian Network is provided, and new question selection criteria presented. Using this theoretical framework, a tool to assist in the diagnosis process has been implemented. This tool allows the definition of Bayesian Adaptive Tests in an easy way: the only specifications required are a curriculum-based structured domain (together with a set of weights) and a set of questions about the domain (the item pool), which will be internally converted into a Bayesian Network. In this way, we intend to make available this theoretically sound technology to educators, minimizing the knowledge engineering effort required.

An Empirical Approach to On-Line Learning in SIETTE

SIETTE is a web-based evaluation tool that implements CAT theory. With the help of a simulation program, different empirical experiments have been performed with SIETTE with two different goals: a) to study the influence of the parameters of characteristic item curves and selection criteria in test length and accuracy; and b) to study different learning strategies for these parameters. The results of the experiments are shown and interpreted.

Bayesian Student Modeling and the Problem of Parameter Specification.

In this paper, the application of Bayesian networks to student modeling is discussed. A review of related work is made, and then the structural model is defined. Two of the most commonly cited reasons for not using Bayesian networks in student modeling are the computational complexity of the algorithms and the difficulty of the knowledge acquisition process. We propose an approach to simplify knowledge acquisition. Our approach applies causal independence to factor the conditional probabilities and decrease the parameters required for each question to a number linear in the number of concepts. This also provides the new parameters with an intuitive meaning that makes their specification easier. Finally, we present an example to illustrate the use of our approach.

Dynamic versus Static Student Models Based on Bayesian Networks: An Empirical Study

In this paper, we present an empirical study with simulated students that allows to compare the accuracy of two models based on Bayesian networks in the context of student modelling: a dynamic model versus an static model. The results show that the performance of both models is very similar, being the dynamic much faster and easier to implement. A second study evaluates the use of adaptive item selection criteria, that can provide an increase on accuracy and a big reduction in test length.

TAPLI: An adaptive web-based learning environment for Linear Programming

In this paper we present TAPLI, an adaptive web-based learning environment for Linear Programming. TAPLI is in fact a set of adaptive tools offered in a web-based learning environment: a) an adaptive hypermedia component, that is responsible of presenting the learning contents; b) a testing component, based on the SIETTE system (that implements Computerized Adaptive Tests using Item Response Theory as inference machine to estimate the student’s knowledge level); and c) a drill-and-practice component, which generates exercises adapted to the student’s knowledge level, and which coaches students while solving the problems posed by the system, offering guidance, support, help and feedback. The estimation of the student’s knowledge level made by SIETTE is used by TAPLI as a basis to provide adaptation at all stages of the learning process: while learning the contents, while making tests, when being proposed an exercise and while solving it. Additionally the system provides an open student model that allows to inspect in detail the state of his/her knowledge at any time and to change the learning goals at any moment during the interaction with the system.

Layered shape grammars

Abstract In this article we propose a computer-aided conceptual design system to assist modelling at the early stages of design. More precisely, we address the problem of providing the designer with design alternatives that can be used as starting points of the design process. To guide the generation of such alternatives according to a given set of design requirements, the designer can express both visual knowledge in the form of basic geometric transformation rules, and also logic constraints that guide the modelling process. Our approach is based on the formalism of shape grammars, and supplements the basic algorithms with procedures that integrate logic design constraints and goals. Additionally, we introduce a layered scheme for shape grammars that can greatly reduce the computational cost of shape generation. Shape grammars, constraints, goals and layers can be handled through a graphic environment. We illustrate the functionalities of ShaDe through two use cases taken from the architectural design and video games domains, and also evaluate the performance of the system.