Similarity measure for retrieval of question items with multi-variable data sets

In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. It has been realized that to carry out an effective case retrieval of questi...

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Main Author:	Che Hassan, Siti Hasrinafasya
Format:	Thesis
Language:	English
Published:	2008
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/9463/1/SitiHasrinafasyaFSKSM2008.pdf
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id	my-utm-ep.9463
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spelling	my-utm-ep.94632018-10-14T07:19:52Z Similarity measure for retrieval of question items with multi-variable data sets 2008-10 Che Hassan, Siti Hasrinafasya QA75 Electronic computers. Computer science In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. It has been realized that to carry out an effective case retrieval of question items, there must be selection criteria of questions’ features that considerably meet the specifications and requirements of learning outcomes as well as instructional objectives that are set by academician. In this case, each question item consists of multi-variables data type namely, Bloom level, question type, discrimination index and difficulty index. To retrieve the semantic similar question items, it strongly depends on the correct definition of the case representation as well as similarity measure. In other words, there presentation of data must reflect the characteristic of data type before the appropriate adapted similarity measure approach can be applied to ensure the degree of similarity values. In this case, Bloom was transformed into normalized rank data before Euclidean distance similarity measure was applied. Meanwhile, question type was converted into binary, 0 and 1 before Hamming distance was applied to calculate its similarity value. Both difficulty index and discrimination index used the concept of fuzzy similarity measure, where by their index ranges were adjusted and expressed in trapezoidal fuzzy numbers, respectively. Lastly, these approaches were aggregated together to produce one single similarity value of question item. 2008-10 Thesis http://eprints.utm.my/id/eprint/9463/ http://eprints.utm.my/id/eprint/9463/1/SitiHasrinafasyaFSKSM2008.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:685?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Che Hassan, Siti Hasrinafasya Similarity measure for retrieval of question items with multi-variable data sets
description	In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. It has been realized that to carry out an effective case retrieval of question items, there must be selection criteria of questions’ features that considerably meet the specifications and requirements of learning outcomes as well as instructional objectives that are set by academician. In this case, each question item consists of multi-variables data type namely, Bloom level, question type, discrimination index and difficulty index. To retrieve the semantic similar question items, it strongly depends on the correct definition of the case representation as well as similarity measure. In other words, there presentation of data must reflect the characteristic of data type before the appropriate adapted similarity measure approach can be applied to ensure the degree of similarity values. In this case, Bloom was transformed into normalized rank data before Euclidean distance similarity measure was applied. Meanwhile, question type was converted into binary, 0 and 1 before Hamming distance was applied to calculate its similarity value. Both difficulty index and discrimination index used the concept of fuzzy similarity measure, where by their index ranges were adjusted and expressed in trapezoidal fuzzy numbers, respectively. Lastly, these approaches were aggregated together to produce one single similarity value of question item.
format	Thesis
qualification_level	Master's degree
author	Che Hassan, Siti Hasrinafasya
author_facet	Che Hassan, Siti Hasrinafasya
author_sort	Che Hassan, Siti Hasrinafasya
title	Similarity measure for retrieval of question items with multi-variable data sets
title_short	Similarity measure for retrieval of question items with multi-variable data sets
title_full	Similarity measure for retrieval of question items with multi-variable data sets
title_fullStr	Similarity measure for retrieval of question items with multi-variable data sets
title_full_unstemmed	Similarity measure for retrieval of question items with multi-variable data sets
title_sort	similarity measure for retrieval of question items with multi-variable data sets
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2008
url	http://eprints.utm.my/id/eprint/9463/1/SitiHasrinafasyaFSKSM2008.pdf
_version_	1747814735995207680

Similarity measure for retrieval of question items with multi-variable data sets

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