An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System Sirithumgul, Pornpat Prasertsilp, Pimpaka Olfman, Lorne 2021-12-24T18:28:34Z 2021-12-24T18:28:34Z 2022-01-04
dc.description.abstract This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MCQs). The simulation of this research demonstrated an application of the algorithm in generating gap-fill MCQs about software testing. The simulation results revealed that by using 103 online documents as inputs, the algorithm could automatically produce more than 16 thousand valid gap-fill MCQs covering a variety of topics in the software testing domain. Finally, in the discussion section of this paper we suggest how the proposed algorithm should be applied to produce gap-fill MCQs being collected in a question pool used by a knowledge expert system.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.901
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Computational Intelligence and State-of-the-Art Data Analytics
dc.subject artificial intelligence (ai) for knowledge processing
dc.subject expert system
dc.subject natural language processing (nlp)
dc.subject ontology-based design
dc.subject text mining
dc.title An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System
dc.type.dcmi text
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