• Corpus ID: 245537545

ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks

@article{Bonesana2021ADAPQUESTAS,
  title={ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks},
  author={Claudio Bonesana and Francesca Mangili and Alessandro Antonucci},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.14476}
}
We introduce ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks. Adaptiveness is intended here as the dynamical choice of the question sequence on the basis of an evolving model of the skill level of the test taker. Bayesian networks offer a flexible and highly interpretable framework to describe such testing process, especially when coping with multiple skills. ADAPQUEST embeds dedicated elicitation strategies to simplify the… 
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References

SHOWING 1-10 OF 16 REFERENCES
Reliable Knowledge-Based Adaptive Tests by Credal Networks
TLDR
This work suggests the use of credal networks, a generalization of Bayesian networks based on sets of probability mass functions, to implement adaptive tests exploiting the knowledge of the test developer instead of training on databases of answers.
A New Score for Adaptive Tests in Bayesian and Credal Networks
TLDR
This work presents an alternative family of scores, based on the mode of the posterior probabilities, and hence easier to explain, that makes considerably simpler the evaluation in the credal case, without significantly affecting the quality of the adaptive process.
Bayesian Networks in Educational Assessment
TLDR
This critical review outlines the existing research on BN in educational assessment, providing an introduction to the ITS literature for the psychometric community, and points out several promising research paths.
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.
Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer‐adaptive practice
TLDR
A general response model is introduced that allows for several simple restrictions, resulting in other models such as the extended Rasch model, and a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values.
Monotonicity in practice of adaptive testing
TLDR
This article evaluates Bayesian network models used for computerized adaptive testing and learned with a recently proposed monotonicity gradient algorithm, which has a lower question prediction quality than unrestricted models but is better in the main target, which is the student score prediction.
Item Response Theory
During the past 30 years or so, a new theoretical basis for educational and psychological testing and measurement has emerged. It has been variously referred to as latent trait theory, item…
Probabilistic Graphical Models - Principles and Techniques
TLDR
The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.
CREMA: A Java Library for Credal Network Inference
TLDR
CREMA (Credal Models Algorithms), a Java library for inference in credal networks, is presented, which makes it the most advanced tool for credal network modelling and inference developed so far.
An empirical comparison of item response theory and classical test theory item/person statistics
An Empirical Comparison of Item Response Theory and Classical Test Theory Item/Person Statistics. (August 2004) Troy Gerard Courville, B.S., Louisiana State UniversityShreveport; M.S., Texas A&M…
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