WordNet and cosine similarity based classifier of exam questions using bloom's taxonomy

Jayakodi, K; Bandara, M; Perera, I; Meedeniya, D

WordNet and cosine similarity based classifier of exam questions using bloom's taxonomy

Jayakodi, K Bandara, M Perera, I Meedeniya, D

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Publisher:: International Association of Online Engineering (IAOE)
Publication Type:: Journal Article
Citation:: International Journal of Emerging Technologies in Learning, 2016, 11, (4), pp. 142-149
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Jayakodi, K
dc.contributor.author	Bandara, M
dc.contributor.author	Perera, I
dc.contributor.author	Meedeniya, D
dc.date.accessioned	2022-08-13T07:46:17Z
dc.date.available	2022-08-13T07:46:17Z
dc.date.issued	2016-01-01
dc.identifier.citation	International Journal of Emerging Technologies in Learning, 2016, 11, (4), pp. 142-149
dc.identifier.issn	1868-8799
dc.identifier.issn	1863-0383
dc.identifier.uri	http://hdl.handle.net/10453/160080
dc.description.abstract	Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom's taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used prior to generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom's taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of this classification process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.
dc.language	en
dc.publisher	International Association of Online Engineering (IAOE)
dc.relation.ispartof	International Journal of Emerging Technologies in Learning
dc.relation.isbasedon	10.3991/ijet.v11i04.5654
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1303 Specialist Studies in Education
dc.title	WordNet and cosine similarity based classifier of exam questions using bloom's taxonomy
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	1303 Specialist Studies in Education
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-13T07:46:15Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	4

Abstract:

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom's taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used prior to generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom's taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of this classification process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.

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http://hdl.handle.net/10453/160080