Feature reduction for neural network in determining the Bloom’s cognitive level of question items

The concept of Bloom’s taxonomy has broadly implemented as a guideline in designing a reasonable examination question paper that consist of question items belonging to various cognitive levels which are tolerate to the different capability of students. Currently, academician will identify the Bloom’...

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Main Author:	Chai, Jing Hui
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf
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id	my-utm-ep.11449
record_format	uketd_dc
spelling	my-utm-ep.114492017-09-20T09:51:15Z Feature reduction for neural network in determining the Bloom’s cognitive level of question items 2009-10 Chai, Jing Hui QA75 Electronic computers. Computer science The concept of Bloom’s taxonomy has broadly implemented as a guideline in designing a reasonable examination question paper that consist of question items belonging to various cognitive levels which are tolerate to the different capability of students. Currently, academician will identify the Bloom’s cognitive level of question items manually. However, most of them are not knowledgeable in identify the cognitive level and this situation will result to miss categorized of question items. To overcome this problem, this study has proposed a question classification model using artificial neural network trained by the scaled conjugate gradient backpropagation learning algorithm as question classifier to classify cognitive level of question items. 2009-10 Thesis http://eprints.utm.my/id/eprint/11449/ http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems 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 Chai, Jing Hui Feature reduction for neural network in determining the Bloom’s cognitive level of question items
description	The concept of Bloom’s taxonomy has broadly implemented as a guideline in designing a reasonable examination question paper that consist of question items belonging to various cognitive levels which are tolerate to the different capability of students. Currently, academician will identify the Bloom’s cognitive level of question items manually. However, most of them are not knowledgeable in identify the cognitive level and this situation will result to miss categorized of question items. To overcome this problem, this study has proposed a question classification model using artificial neural network trained by the scaled conjugate gradient backpropagation learning algorithm as question classifier to classify cognitive level of question items.
format	Thesis
qualification_level	Master's degree
author	Chai, Jing Hui
author_facet	Chai, Jing Hui
author_sort	Chai, Jing Hui
title	Feature reduction for neural network in determining the Bloom’s cognitive level of question items
title_short	Feature reduction for neural network in determining the Bloom’s cognitive level of question items
title_full	Feature reduction for neural network in determining the Bloom’s cognitive level of question items
title_fullStr	Feature reduction for neural network in determining the Bloom’s cognitive level of question items
title_full_unstemmed	Feature reduction for neural network in determining the Bloom’s cognitive level of question items
title_sort	feature reduction for neural network in determining the bloom’s cognitive level of question items
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2009
url	http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf
_version_	1747814856657993728

Feature reduction for neural network in determining the Bloom’s cognitive level of question items

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