Student Modeling from Conventional Test Data: A Bayesian Approach without Priors

VanLehn, Kurt; Niu, Zhendong; Siler, Stephanie A.; Gertner, Abigail S.

doi:10.1007/3-540-68716-5_49

Cited by 51 publications

(41 citation statements)

References 6 publications

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“…Millán, de-la Cruz, & Suárez, 2000;Vomlel, 2004;VanLehn et al, 1998). In contrast to hierarchical structures, item to item structures build structures among observable knowledge item themselves (Falmagne, Koppen, Villano, Doignon, & Johannesen, 1990;Doignon & Falmagne, 1999), bypassing concept links.…”

Section: Item To Item Node Structures and The Theory Of Knowledge Spacesmentioning

confidence: 99%

“…There is considerable interest in the use of Bayesian networks (BN) for student modeling and cognitive assessment (for eg., Mislevy, Almond, Yan, & Steinberg, 1999;Conati, Gertner, & VanLehn, 2002;VanLehn, Niu, Siler, & Gertner, 1998;Vomlel, 2004). In part, this interest stems from the ability of a BN to model the uncertainty that is inherent to cognitive modeling.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Student Models Based on Item to Item Knowledge Structures

Desmarais

Gagnon

2006

Innovative Approaches for Learning and Knowledge Sharing

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Abstract. Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We explore their use for modeling item-item relationships, in accordance with the theory of knowledge spaces. We compare two Bayesian frameworks for that purpose, a standard Bayesian network approach and a more constrained framework that relies on a local independence assumption. Their performance is compared over their respective ability to predict item outcome and through simulations over two data sets. The simulation results show that both approaches can effectively perform accurate predictions, but the constrained approach shows higher predictive power than a Bayesian Network. We discuss the applications of item to item structure for cognitive modeling within different contexts.

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Section: Item To Item Node Structures and The Theory Of Knowledge Spacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Student Models Based on Item to Item Knowledge Structures

Desmarais

Gagnon

2006

Innovative Approaches for Learning and Knowledge Sharing

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“…To this end, we have used a Bayesian student model (BSM) based on Bayesian Networks (BNs). The BN paradigm was chosen because it has proven to be a sound methodology for the student modeling problem, and it has been used with this purpose in a number of existing applications (Collins et al, 1996;Conati et al, 1997;VanLehn et al, 1998;Jameson, 1996). This previous research has shown that a BSM allows for a sound and detailed evaluation of each student, according to the granularity level defined by teachers.…”

Section: Introductionmentioning

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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“…In [196] evidence is provided by problems that the student needs to solve and knowledge nodes represent the physics rules that the student needs to apply. The conclusion reached in the paper is that if links are defined in the diagnostic direction it "means that the rules are conditionally independent given the evidence, which just isn't true (see [196] for an example). Therefore, the causal direction seems preferable in this context.…”

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

confidence: 99%

User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

637

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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Student Modeling from Conventional Test Data: A Bayesian Approach without Priors

Cited by 51 publications

References 6 publications

Bayesian Student Models Based on Item to Item Knowledge Structures

Bayesian Student Models Based on Item to Item Knowledge Structures

Using Bayesian networks to improve knowledge assessment

User Models for Adaptive Hypermedia and Adaptive Educational Systems

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