Bayesian Networks in Educational Assessment

@article{Culbertson2016BayesianNI,
  title={Bayesian Networks in Educational Assessment},
  author={Michael J. Culbertson},
  journal={Applied Psychological Measurement},
  year={2016},
  volume={40},
  pages={21 - 3}
}
Bayesian networks (BN) provide a convenient and intuitive framework for specifying complex joint probability distributions and are thus well suited for modeling content domains of educational assessments at a diagnostic level. BN have been used extensively in the artificial intelligence community as student models for intelligent tutoring systems (ITS) but have received less attention among psychometricians. This critical review outlines the existing research on BN in educational assessment… 

Figures from this paper

Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field
TLDR
A brief introduction to Dynamic Bayesian networks is offered, followed by a review of the existing literature on the use of DBNs in educational and psychological measurement with a focus on methodological investigations and novel applications that may provide guidance for practitioners wishing to deploy these models.
Student Skill Models in Adaptive Testing
TLDR
This paper provides a common framework, a generic model, for Computerized Adaptive Testing (CAT) for different model types, using three different types of models, Item Response Theory, Bayesian Networks, and Neural Networks, that instantiate the generic model.
Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks
TLDR
The methodology used in order to address student dropout in Engineering Education in the context of learning analytics revealed that the best model that fits the data is provided by the K2 algorithm although the great heterogeneity of the data studied did not permit the adjustment of the dropout profile of the student too accurately.
Probabilistic Models for Computerized Adaptive Testing
TLDR
This paper presents three different methods for CAT, one of them, the item response theory, is a well established method, while the other two, Bayesian and neural networks, are new in the area of educational testing.
An IRT-based Parameterization for Conditional Probability Tables
TLDR
A flexible parameterization for conditional probability tables based on item response theory (IRT) that preserves monotonicity is described, which is extensible because it rests on three auxiliary function.
ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks
TLDR
ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks, is introduced and an application of this tool for the diagnosis of mental disorders is discussed.
An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment
TLDR
This paper proposed to train the BN first based on the ideal response pattern data contained in every CDA and continue to estimate the parameters of BNbased on the EM or the GD algorithm regarding the parameters based onThe IRP training method as informative priors.
Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System
TLDR
A framework of a learning analytic method including an assessment design through evidence-centered design, a data mining method using social network analysis, and an analytic method using a Bayesian network is introduced.
The potential use of Bayesian Networks to support committee decisions in programmatic assessment
TLDR
The benefits of programmatic assessment are well‐established, while emphasis is often placed on data richness and considered input of qualified experts, committees reasonably wish for practical, defensible solutions to these challenges.
Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis
TLDR
This study found that the Bayesian Network provided diagnostic information about a learner’s level of cognitive load in the e-learning system and predicted the learners’ academic achievement in terms of their different cognitive load patterns.
...
...

References

SHOWING 1-10 OF 79 REFERENCES
Modeling Diagnostic Assessments with Bayesian Networks
TLDR
This paper discusses how Bayesian network models are set up with expert information, improved and calibrated from data, and deployed as evidence-based inference engines, and illustrates the flexibility and capabilities of Bayesian networks through a series of concrete examples, without extensive technical detail.
Adaptive Bayesian Networks for Multilevel Student Modelling
TLDR
An integrated theoretical approach for student modelling based on an Adaptive Bayesian Network is provided, and new question selection criteria presented, and a tool to assist in the diagnosis process has been implemented.
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
TLDR
A new approach to diagnosis in student modeling based on the use of Bayesian Networks and Computer Adaptive Tests is presented and a new integrated Bayesian student model is defined and then combined with an Adaptive Testing algorithm.
Bayes Nets in Educational Assessment: Where Do the Numbers Come from? CSE Technical Report.
TLDR
Details for the special cases of item response theory (IRT) and multivariate latent class modeling are given, with a numerical example of the latter.
Bayesian Student Modeling
  • C. Conati
  • Computer Science
    Advances in Intelligent Tutoring Systems
  • 2010
TLDR
This chapter will describe techniques and issues involved in building probabilistic student models based on Bayesian networks and their extensions, and discuss examples from existing Intelligent Tutoring Systems that rely onBayesian student models.
Using Bayesian Networks to Manage Uncertainty in Student Modeling
TLDR
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 are described.
Bayesian Networks In Educational Testing
  • J. Vomlel
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
TLDR
The experiments suggest that the test design can benefit from a Bayesian network that models relations between skills, not only in the case of an adaptive test but also when designing a fixed (non-adaptive) test.
Assessing Fit of Cognitive Diagnostic Models A Case Study
A cognitive diagnostic model uses information from educational experts to describe the relationships between item performances and posited proficiencies. When the cognitive relationships can be
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
TLDR
This paper shows that different course representations can be merged together and realized in a granularity hierarchy and Bayesian inference can be used to propagate knowledge throughout the hierarchy.
Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model
TLDR
An extension of a previously developed generic student model based on Bayesian Networks is presented, adding a new layer to include prerequisite relationships to improve the efficiency of the diagnosis process by allowing increased accuracy or reductions in the test length.
...
...