# A New Score for Adaptive Tests in Bayesian and Credal Networks

arXiv: https://arxiv.org/abs/2105.12205 · HTML: https://mlsanity.com/paper/2105.12205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 2 of 2)

- [Monotonicity in practice of adaptive testing](https://mlsanity.com/paper/2009.06981/) (2020)
- [Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks](https://mlsanity.com/paper/1802.05639/) (2018)

## Built on by (most cited first, 3 of 3)

- [ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks](https://mlsanity.com/paper/2112.14476/) (2021)
- [Intelligent tutoring systems by Bayesian nets with noisy gates](https://mlsanity.com/paper/2409.04102/) (2024)
- [Causal Modelling of Support Interventions for Student Competency Assessment](https://mlsanity.com/paper/2608.24632/) (2026)

## Abstract (arXiv)

A test is adaptive when its sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. Graphical models, such as Bayesian networks, are used for adaptive tests as they allow to model the uncertainty about the questions and the skills in an explainable fashion, especially when coping with multiple skills. A better elicitation of the uncertainty in the question/skills relations can be achieved by interval probabilities. This turns the model into a credal network, thus making more challenging the inferential complexity of the queries required to select questions. This is especially the case for the information theoretic quantities used as scores to drive the adaptive mechanism. We present an alternative family of scores, based on the mode of the posterior probabilities, and hence easier to explain. This makes considerably simpler the evaluation in the credal case, without significantly affecting the quality of the adaptive process. Numerical tests on synthetic and real-world data are used to support this claim.