• Corpus ID: 221703212

Monotonicity in practice of adaptive testing

@article{Plajner2020MonotonicityIP,
  title={Monotonicity in practice of adaptive testing},
  author={Martin Plajner and Jivr'i Vomlel},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.06981}
}
In our previous work we have shown how Bayesian networks can be used for adaptive testing of student skills. Later, we have taken the advantage of monotonicity restrictions in order to learn models fitting data better. This article provides a synergy between these two phases as it evaluates Bayesian network models used for computerized adaptive testing and learned with a recently proposed monotonicity gradient algorithm. This learning method is compared with another monotone method, the… 

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References

SHOWING 1-10 OF 19 REFERENCES
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 bipartite Bayesian networks under monotonicity restrictions
TLDR
An algorithm for Bayesian Networks parameter learning using monotonicity conditions outperforms other methods for small sets, and provides better or comparable results for larger sets.
Exploiting monotonicity via logistic regression in Bayesian network learning
TLDR
Two variants of the constrained logistic regression model, M2b CLR and M3 CLR, are presented, in which the number of constraints required to implement monotonicity does not grow exponentially with thenumber of parents hence providing a practicable method for estimating conditional probabilities with very sparse data.
Learning from incomplete data in Bayesian networks with qualitative influences
Learning from Sparse Data by Exploiting Monotonicity Constraints
TLDR
This paper shows how to interpret knowledge of qualitative influences, and in particular of monotonicities, as constraints on probability distributions, and to incorporate this knowledge into Bayesian network learning algorithms.
Computerized adaptive testing : theory and practice
TLDR
This chapter discusses item selection and ability estimation in adaptive testing, and methods of controlling the exposure of items in CAT, a flexible testing system in mathematics education for adults.
Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences
TLDR
This work presents a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables, and shows how the various signs translate into order constraints on the network parameters and how isotonic regression can be used to compute order-constrained estimates.
GRAPHICAL MODELS AND COMPUTERIZED ADAPTIVE TESTING
TLDR
This paper synthesizes ideas from the fields of graphical modeling and educational testing, particularly Item Response Theory (IRT) applied to Computerized Adaptive Testing (CAT), and extensions are discussed in the context of language proficiency testing.
Monotonicity in Bayesian Networks
TLDR
It is shown that establishing whether a network exhibits any of these properties of monotonicity is coNPPP-complete in general, and remains coNP-complete for poly-trees.
Probabilistic Models for Computerized Adaptive Testing: Experiments
TLDR
This paper presents models using Item Response Theory (IRT - standard CAT method), Bayesian networks, and neural networks for CAT, and conducted simulated CAT tests on empirical data.
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