Structural Learning of BN2A models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00639050" target="_blank" >RIV/67985556:_____/25:00639050 - isvavai.cz</a>

Alternative codes found

RIV/67985807:_____/25:00642661 RIV/00216208:11210/25:10508995 RIV/00216208:11320/25:10508995

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Structural Learning of BN2A models

Original language description

Bayesian networks are a popular framework for modeling probabilistic relationships between random variables and have been used successfully in educational tests. There is interest in a particular type of Bayesian networks we have called BN2A, which are characterized as bipartite networks, where the first layer consists of hidden variables (which commonly represent skills) and the second layer consists of observed variables (which represent questions in a test). In BN2A models all variables are assumed to be binary. The variables in the second layer depend on the variables in the first layer and this dependence is characterized by conditional probability tables representing Noisy-AND models. In this work, we propose an Expectation-Maximization (EM) algorithm for learning the structure of BN2A models, that is, for learning the relationship between hidden variables and observed variables. To test the structural learning algorithm, we conducted two experiments. For the first experiment, we used synthetic data generated from a BN2A model that we previously defined, while for the second experiment we used a well-known real-world dataset in the field of Cognitive Diagnostic Models, the Fraction Subtraction dataset. Our proposed algorithm has interesting potential use cases. One key application is to generate a reasonably accurate BN2A structure model for educational diagnosis, particularly in scenarios where no prior model exists. Depending on the required level of accuracy, the estimated model can be used directly to analyze skill profiles or serve as an initial framework for test designers, who can further refine it before implementation.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25)

ISBN

978-80-7378-525-3

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

176-187

Publisher name

MatfyzPress

Place of publication

Prague

Event location

Třešť

Event date

Jun 4, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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