Enhancing Bayesian Networks with Psychometric Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00599042" target="_blank" >RIV/67985556:_____/24:00599042 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/24:00602451

Výsledek na webu

<a href="https://proceedings.mlr.press/v246/perez24a.html" target="_blank" >https://proceedings.mlr.press/v246/perez24a.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Bayesian Networks with Psychometric Models

Popis výsledku v původním jazyce

Bayesian networks (BNs) are a popular framework in education and other fields. In this paper, we consider two-layer BNs, where the first layer consists of hidden binary variables that are assumed to be independent of each other, and the second layer consists of observed binary variables. The variables in the second layer depend on the variables in the first layer. The dependence is characterized by conditional probability tables, which represent Noisy-AND models. We refer to this class of models as BN2A models. We found that these models are also popular in the psychometric community, where they can be found under the name of Cognitive Diagnostic Models (CDMs), which are used to classify test takers into some latent classes according to the similarity of their responses to test questions. This paper shows the relation between some BN2A models and their corresponding CDMs. In particular, we compare the performance of these models on large-scale tests conducted in the Czech Republic in 2022. The BN2A model with general conditional probability tables produced the best absolute fit. However, when we added monotonic constraints to the General model, we obtained better predictive results.

Název v anglickém jazyce

Enhancing Bayesian Networks with Psychometric Models

Popis výsledku anglicky

Bayesian networks (BNs) are a popular framework in education and other fields. In this paper, we consider two-layer BNs, where the first layer consists of hidden binary variables that are assumed to be independent of each other, and the second layer consists of observed binary variables. The variables in the second layer depend on the variables in the first layer. The dependence is characterized by conditional probability tables, which represent Noisy-AND models. We refer to this class of models as BN2A models. We found that these models are also popular in the psychometric community, where they can be found under the name of Cognitive Diagnostic Models (CDMs), which are used to classify test takers into some latent classes according to the similarity of their responses to test questions. This paper shows the relation between some BN2A models and their corresponding CDMs. In particular, we compare the performance of these models on large-scale tests conducted in the Czech Republic in 2022. The BN2A model with general conditional probability tables produced the best absolute fit. However, when we added monotonic constraints to the General model, we obtained better predictive results.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/GA22-11101S" target="_blank" >GA22-11101S: Tenzorový rozklad v aktivní diagnostice poruch pro stochastické rozlehlé systémy</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models

ISBN

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ISSN

2640-3498

e-ISSN

2640-3498

Počet stran výsledku

14

Strana od-do

401-414

Název nakladatele

JMLR-JOURNAL MACHINE LEARNING RESEARCH

Místo vydání

San Diego

Místo konání akce

Nijmegen

Datum konání akce

11. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001347210900023