Multiple-objective optimization applied in extracting multiple-choice tests

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248859" target="_blank" >RIV/61989100:27240/21:10248859 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0952197621002876?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197621002876?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2021.104439" target="_blank" >10.1016/j.engappai.2021.104439</a>

Result language

angličtina

Original language name

Multiple-objective optimization applied in extracting multiple-choice tests

Original language description

Student evaluation is an essential part of education and is usually done through examinations. These examinations generally use tests consisting of several questions as crucial factors to determine the quality of the students. Test-making can be thought of as a multi-constraint optimization problem. However, the test-making process that is done by either manually or randomly picking questions from question banks still consumes much time and effort. Besides, the quality of the tests generated is usually not good enough. The tests may not entirely satisfy the given multiple constraints such as required test durations, number of questions, and question difficulties. In this paper, we propose parallel strategies, in which parallel migration is based on Pareto optimums, and applyan improved genetic algorithm called a genetic algorithm combined with simulated annealing, GASA, which improves diversity and accuracy of the individuals by encoding schemes and a new mutation operator of GA to handle the multiple objectives while generating multiple choice-tests from a large question bank. The proposed algorithms can use the ability to exploit historical information structure in the discovered tests, and use this to construct desired tests later. Experimental results show that the proposed approaches are efficient and effective in generating valuable tests that satisfy specified requirements. In addition, the results, when compared with those from traditional genetic algorithms, are improved in several criteria including execution time, search speed, accuracy, solution diversity, and algorithm stability.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

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Volume of the periodical

105

Issue of the periodical within the volume

říjen 2021

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

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UT code for WoS article

000704650900013

EID of the result in the Scopus database

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