Inspecting and Visualizing Distributed Bayesian Student Models (original) (raw)

Visualizing and inspecting bayesian belief models

International …, 2001

Bayesian Belief Networks (BBNs) have become accepted and used widely to model uncertain reasoning and causal relationships. We have developed an interactive visualization tool (VisNet) that allows students and/or teachers to inspect BBNs. Using VisNet it is possible to experiment with concepts such as marginal probability, changes in probability, probability propagation and cause-effect relationships in BBNs using visualization techniques. ViSMod (Visualization of Bayesian Student Models), an extended version of VisNet, opens the internal representation of the student's knowledge to teachers and/or students interested in knowing more about the knowledge about them represented in the system. Both VisNet and ViSMod aim to support reflection processes in learning environments that rely on the use of Bayesian models.

Using Bayesian Networks to Manage Uncertainty in Student Modeling

User Modeling and User-adapted Interaction, 2002

When a tutoring system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. We use Bayesian networks as a comprehensive, sound formalism to handle this uncertainty. Using Bayesian networks, we have devised the probabilistic student models for Andes, a tutoring system for Newtonian physics whose philosophy is to maximize student initiative and freedom during the pedagogical interaction. Andes’ models provide long-term knowledge assessment, plan recognition, and prediction of students’ actions during problem solving, as well as assessment of students’ knowledge and understanding as students read and explain worked out examples. In this paper, we describe the basic mechanisms that allow Andes’ student models to soundly perform assessment and plan recognition, as well as the Bayesian network solutions to issues that arose in scaling up the model to a full-scale, field evaluated application. We also summarize the results of several evaluations of Andes which provide evidence on the accuracy of its student models.

Bayesian networks for student model engineering

Computers & Education, 2010

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.

Inspectable Bayesian student modelling servers in multi-agent tutoring systems

International Journal of Human-Computer Studies, 2004

User modelling shells and learner modelling servers (LMS) have been proposed in order to provide reusable user/student model information over different domains, common inference mechanisms, and mechanisms to handle consistency of beliefs from different sources. Open and inspectable student models have been investigated by several authors as a means to promote student's reflection, knowledge awareness, collaborative assessment, self-assessment, arrange groups of students, interactive diagnosis, and the use of students' models by the teacher. . This paper presents SModel, a Bayesian student modelling server used in distributed multi-agent environments. SModel server includes a student model database and a Bayesian student modelling component. SModel provides several services to a group of agents in a CORBA platform. Users can use ViSMod, a Bayesian student modelling visualization tool, and SMV, a student modelling database viewer, to visualize and inspect distributed Bayesian student models maintained by SModel server. SModel has been tested in a multi-agent tutoring system for teaching basic java programming.

Student model accuracy using inspectable bayesian student models

Proc. of AIED03, 2003

Inspectable student models (ISMs) have been used in a variety of applications. In order to create and fully deploy learning environments based on ISMs, issues such as how accurate are the student models when students and teachers interact with them should be further explored. This paper presents a framework for learning environments based on inspectable student models. The learning game, a learning environment based on this framework, has been implemented and used in a study focused on student model accuracy using inspectable Bayesian student models. Results of a study with the learning game suggest that Bayesian student models successfully integrate evidence from the student and the system/teacher producing an accurate aggregate view of the model.

Indirectly visible Bayesian student models

Proceedings of the 5th UAI Bayesian modelling …, 2007

Inspectable Bayesian student models have been used to support student reflection, knowledge awareness and communication among teacher, students and parents. This paper presents a new approach to interacting with inspectable Bayesian student models called indirectly visible Bayesian student models. In this approach, the student model is seen through the eyes of a pedagogical agent (e.g., a virtual student). This approach has been implemented in the context of an Assessment-Based Learning Environment for English grammar (English ABLE), where the student is asked to help a pedagogical agent find grammatical errors on various sentences. Since the pedagogical agent's knowledge levels, which are also the student's knowledge levels, are always visible, the student can see how much the pedagogical agent "knows" based on his/her actions. Initial reactions to this approach have been positive. We are planning on integrating it into assessment-based learning and gaming environments as indicators of progress that continuously change in light of new evidence.

Interacting with inspectable bayesian student models

… of Artificial Intelligence in Education, 2004

Inspectable student models focus on the idea of letting students and teachers interact with the representation of the student that the system maintains. Both humans and the system can benefit from this interaction. By externalizing the student model and making it an object for ...

Bayesian Student Modeling in the AC&NL Tutor

2020

The reasoning process about the level of student’s knowledge can be challenging even for experienced human tutors. The Bayesian networks are a formalism for reasoning under uncertainty, which has been successfully used for various artificial intelligence applications, including student modeling. While Bayesian networks are a highly flexible graphical and probabilistic modeling framework, its main challenges are related to the structural design and the definition of “a priori” and conditional probabilities. Since the AC&NL Tutor’s authoring tool automatically generates tutoring elements of different linguistic complexity, the generated sentences and questions fall into three difficulty levels. Based on these levels, the probability-based Bayesian student model is proposed for mastery-based learning in intelligent tutoring system. The Bayesian network structure is defined by generated questions related to the node representing knowledge in a sentence. Also, there are relations between...