An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System

Date
2022-01-04
Authors
Sirithumgul, Pornpat
Prasertsilp, Pimpaka
Olfman, Lorne
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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.
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Computational Intelligence and State-of-the-Art Data Analytics, artificial intelligence (ai) for knowledge processing, expert system, natural language processing (nlp), ontology-based design, text mining
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