Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00545875" target="_blank" >RIV/67985556:_____/21:00545875 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31160/21:00057566

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-86772-0_19" target="_blank" >http://dx.doi.org/10.1007/978-3-030-86772-0_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86772-0_19" target="_blank" >10.1007/978-3-030-86772-0_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam

Popis výsledku v původním jazyce

In this paper we study the problem of student knowledge level estimation. We use probabilistic models learned from collected data to model the tested students. We propose and compare experimentally several different Bayesian network models for the score prediction of student’s knowledge. The proposed scoring algorithm provides not only the expected value of the total score but the whole probability distribution of the total score. This means that confidence intervals of predicted total score can be provided along the expected value. The key that enabled efficient computations with the studied models is a newly proposed inference algorithm based on the CP tensor decomposition, which is used for the computation of the score distribution. The proposed algorithm is two orders of magnitude faster than a state of the art method. We report results of experimental comparisons on a large dataset from the Czech National Graduation Exam in Mathematics. In this evaluation the best performing model is an IRT model with one continuous normally distributed skill variable related to all items by the graded response models. The second best is a multidimensional IRT model with an expert structure of items-skills relations and a covariance matrix for the skills. This model has a higher improvement with larger training sets and seems to be the model of choice if a sufficiently large training dataset is available.

Název v anglickém jazyce

Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam

Popis výsledku anglicky

In this paper we study the problem of student knowledge level estimation. We use probabilistic models learned from collected data to model the tested students. We propose and compare experimentally several different Bayesian network models for the score prediction of student’s knowledge. The proposed scoring algorithm provides not only the expected value of the total score but the whole probability distribution of the total score. This means that confidence intervals of predicted total score can be provided along the expected value. The key that enabled efficient computations with the studied models is a newly proposed inference algorithm based on the CP tensor decomposition, which is used for the computation of the score distribution. The proposed algorithm is two orders of magnitude faster than a state of the art method. We report results of experimental comparisons on a large dataset from the Czech National Graduation Exam in Mathematics. In this evaluation the best performing model is an IRT model with one continuous normally distributed skill variable related to all items by the graded response models. The second best is a multidimensional IRT model with an expert structure of items-skills relations and a covariance matrix for the skills. This model has a higher improvement with larger training sets and seems to be the model of choice if a sufficiently large training dataset is available.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GA19-04579S" target="_blank" >GA19-04579S: Struktury podmíněné nezávislosti: metody polyedrální geometrie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2021.

ISBN

978-3-030-86771-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

13

Strana od-do

255-267

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Praha

Datum konání akce

21. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000711926000019