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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
http://eprints.utm.my/id/eprint/11449/
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spelling my.utm.114492017-09-20T09:51:15Z http://eprints.utm.my/id/eprint/11449/ Feature reduction for neural network in determining the Bloom’s cognitive level of question items 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf Chai, Jing Hui (2009) Feature reduction for neural network in determining the Bloom’s cognitive level of question items. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2009
url http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf
http://eprints.utm.my/id/eprint/11449/
_version_ 1643645685476622336
score 13.211869

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