Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model

Carmona, Cristina; Millán, Eva; Pérez-de-la-Cruz, José-Luís; Trella, Mónica; Conejo, Ricardo

doi:10.1007/11527886_46

Cited by 27 publications

(21 citation statements)

References 8 publications

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“…The goal is to make the whole approach more sound and applicable to real situations. For example, we plan to include: questions connected to compound concepts, prerequisite relationships (Carmona et al, 2005) and adaptive item selection criteria (Millán and Pérez de la Cruz, 2002) that will allow to increase the accuracy of the diagnosis while reducing the number of questions needed.…”

Section: Discussionmentioning

confidence: 99%

Using Bayesian networks to improve knowledge assessment

Millán

Descalço

Castillo

et al. 2013

Computers & Education

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In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE -Projecto Matemática Ensino) of the University of Aveiro. This generic Bayesian student model was previously evaluated with simulated students, but a real application was still missing. In the work presented here, we have used the GBSM to define Bayesian Student Models (BSMs) for a concrete domain: first degree equations. In order to test the diagnosis capabilities of such BSMs, an evaluation with 152 students has been performed. Each of the 152 students took both a computerized test within PMatE and a written exam, both of them designed to measure students knowledge in 12 concepts related to first degree equations. The written exam was graded by three experts. Then two BSMs were developed, one for the computer test and another one for the written exam. These BSMs were used to to obtain estimations of student's knowledge on the same 12 concepts, and the inter-rater agreement among the different measures was computed. Results show a high degree of agreement among the scores given by the experts and also among the diagnosis provided by the BSM in the written exam and the experts average, but a low degree of agreement among the diagnosis provided by the BSM in the computer test and expert's average.

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Section: Discussionmentioning

confidence: 99%

Using Bayesian networks to improve knowledge assessment

Millán

Descalço

Castillo

et al. 2013

Computers & Education

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“…However, the main difficulty of introducing this new kind of relationship into our model is that, as reported in [31], the meaning of the relationships between the nodes becomes somehow unclear and the specification of the parameters becomes more difficult. To illustrate this, let us consider the following example: in a basic arithmetic course, students are taught how to add and multiply natural numbers (N) and fractions (Q).…”

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

confidence: 99%

“…Just as an example, we would need to provide the probability of knowing how to multiply, given that "the student knows how to multiply natural numbers and fractions, but does not now how to add," which is quite improbable. As suggested in [31], a possible solution is to disregard that kind of relationship in the model (thus making a simplification of reality, in which prerequisite relationships do exist). Another possible solution is to make a different simplifying assumption: instead of not including prerequisites, consider that both relationships operate at different levels, i.e., to build a multi-layered student model as proposed by [200].…”

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

confidence: 99%

“…Another possible solution is to make a different simplifying assumption: instead of not including prerequisites, consider that both relationships operate at different levels, i.e., to build a multi-layered student model as proposed by [200]. In [31], Carmona et al present an empirical study comparing both alternatives. The study supports a conclusion previously established in [59]: "not considering valid prerequisites relationships does not lead to a wrong assessment of a student's knowledge state, but it renders the assessment less efficient in the sense that more answers than necessary have to be collected."…”

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

confidence: 99%

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User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

645

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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“…The 39 and five skill classifications were not tagged to the questions. Instead, the skills in the coarse-grained models were mapped to the finest-grained model in a "is a part of" type of hierarchy, as opposed to a prerequisite hierarchy [6]. The appropriate question-skill tagging for the WPI-5 and WPI-39 models could therefore be derived from this hierarchy as illustrated in Figure 2, …”

Section: Council Of Teachers Of Mathematics and The Massachusetts Depmentioning

confidence: 99%

The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks

et al.

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Abstract. A standing question in the field of Intelligent Tutoring Systems andUser Modeling in general is what is the appropriate level of model granularity (how many skills to model) and how is that granularity derived? In this paper we will explore models with varying levels of skill generality (1, 5, 39 and 106 skill models) and measure the accuracy of these models by predicting student performance within our tutoring system called ASSISTment as well as their performance on a state standardized test. We employ the use of Bayesian networks to model user knowledge and for prediction of student responses. Our results show that the finer the granularity of the skill model, the better we can predict student performance within the tutor. However, for the standardized test data we received, it was the 39 skill model that performed the best. We view this as support for fine-grained skill models despite the finest grain model not predicting the state test scores the best.1 This paper is an expansion of work presented at the workshop in

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Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model

Cited by 27 publications

References 8 publications

Using Bayesian networks to improve knowledge assessment

Using Bayesian networks to improve knowledge assessment

User Models for Adaptive Hypermedia and Adaptive Educational Systems

The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks

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