Automatic Chinese Multiple Choice Question Generation Using Mixed Similarity Strategy

Liu, M; Rus, V; Liu, L

Automatic Chinese Multiple Choice Question Generation Using Mixed Similarity Strategy

Liu, M

Rus, V Liu, L

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Learning Technologies, 2018, 11 (2), pp. 193 - 202
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Liu, M https://orcid.org/0000-0003-4256-6531	en_US
dc.contributor.author	Rus, V	en_US
dc.contributor.author	Liu, L	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	IEEE Transactions on Learning Technologies, 2018, 11 (2), pp. 193 - 202	en_US
dc.identifier.issn	1939-1382	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131256
dc.description.abstract	© 2008-2011 IEEE. Automatic question generation can help teachers to save the time necessary for constructing examination papers. Several approaches were proposed to automatically generate multiple-choice questions for vocalbuary assessment or grammar exercises. However, most of these studies focused on generating questions in English with a certain similarity strategy. This paper presents a mixed similarity strategy which generates Chinese multiple choice distractors with a statistical regression model including orthographic, phonological and semantic features, i.e., features that were shown in previous psycholinguistics studies to contribute to character recognition. In a first experiment, we evaluated the predictive power of the proposed features in measuring Chinese character similarity. One of the significant experimental results showed that the combination of the four proposed categories of features (structure, semantic radical, stroke and meaning) accounts for 62.5 percent of the variance in the human judgments of character similarity. In the second experiment, a user study was conducted to evaluate the quality of system-generated questions using a test item analysis method. Two hundred ninety-six Chinese primary school students (10-11-year-old) participated in this study. We have compared the mixed strategy with another three common distractor generation strategies, orthographic strategy, semantic strategy, and phonological strategy. One of important findings suggested that the mixed strategy significantly outperformed other three strategies in terms of the distractor usefulness and has a highest discrimination power among four strategies.	en_US
dc.relation.ispartof	IEEE Transactions on Learning Technologies	en_US
dc.relation.isbasedon	10.1109/TLT.2017.2679009	en_US
dc.title	Automatic Chinese Multiple Choice Question Generation Using Mixed Similarity Strategy	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	11	en_US
utslib.for	1303 Specialist Studies in Education	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2008-2011 IEEE. Automatic question generation can help teachers to save the time necessary for constructing examination papers. Several approaches were proposed to automatically generate multiple-choice questions for vocalbuary assessment or grammar exercises. However, most of these studies focused on generating questions in English with a certain similarity strategy. This paper presents a mixed similarity strategy which generates Chinese multiple choice distractors with a statistical regression model including orthographic, phonological and semantic features, i.e., features that were shown in previous psycholinguistics studies to contribute to character recognition. In a first experiment, we evaluated the predictive power of the proposed features in measuring Chinese character similarity. One of the significant experimental results showed that the combination of the four proposed categories of features (structure, semantic radical, stroke and meaning) accounts for 62.5 percent of the variance in the human judgments of character similarity. In the second experiment, a user study was conducted to evaluate the quality of system-generated questions using a test item analysis method. Two hundred ninety-six Chinese primary school students (10-11-year-old) participated in this study. We have compared the mixed strategy with another three common distractor generation strategies, orthographic strategy, semantic strategy, and phonological strategy. One of important findings suggested that the mixed strategy significantly outperformed other three strategies in terms of the distractor usefulness and has a highest discrimination power among four strategies.

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http://hdl.handle.net/10453/131256