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dc.contributor.authorBui, Toan
dc.contributor.authorNguyen, Tram
dc.contributor.authorVo, Bay
dc.contributor.authorNguyen, Thanh
dc.contributor.authorPedrycz, Witold
dc.contributor.authorSnášel, Václav
dc.date.accessioned2019-01-09T13:53:31Z
dc.date.available2019-01-09T13:53:31Z
dc.date.issued2018
dc.identifier.citationJournal of Information Science and Engineering. 2018, vol. 34, issue 6, p. 1405-1423.cs
dc.identifier.issn1016-2364
dc.identifier.urihttp://hdl.handle.net/10084/133512
dc.description.abstractGenerating tests from question banks by using manually extracted items or involving random method consumes a great deal of time and effort. At the same time, the quality of the resulting tests is often not high. The generated tests may not entirely meet the requirements formulated in advance. Therefore, this study develops innovative ways to enhance this process by optimizing the execution time and generating results that closely meet the extraction requirements. The paper proposes the use of Particle Swarm Optimization (PSO) to generate multiple-choice tests based on assumed objective levels of difficulty. The experimental results reveal that PSO speed-ups the extraction process, and improves the quality of tests in comparison with the results produced by previously used methods such as Random or Genetic Algorithm (GA) optimized methods. In addition, PSO shows to be more efficient than GA and random selection in most criteria, such as execution time, search space, stability, and standard deviation.cs
dc.language.isoencs
dc.publisherAcademia Sinica, Institute of Information Sciencecs
dc.relation.ispartofseriesJournal of Information Science and Engineeringcs
dc.relation.urihttp://doi.org/10.6688/JISE.201811_34(6).0004cs
dc.subjecttest question bankcs
dc.subjectmultiple-choice testscs
dc.subjectgenetic algorithmscs
dc.subjectparticle swarm optimizationcs
dc.subjectcreating testscs
dc.titleApplication of particle swarm optimization to create multiple-choice testscs
dc.typearticlecs
dc.identifier.doi10.6688/JISE.201811_34(6).0004
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume34cs
dc.description.issue6cs
dc.description.lastpage1423cs
dc.description.firstpage1405cs
dc.identifier.wos000451364100004


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