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

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.

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Abstract

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.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Sains (Sains komputer)) - Universiti Teknologi Malaysia, 2009; Supervisor : Assoc. Prof. Dr. Norazah binti Yusof
Uncontrolled Keywords:Bloom’s cognitive level, neural network, examination question paper
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:11449
Deposited By: Ms Zalinda Shuratman
Deposited On:15 Dec 2010 09:47
Last Modified:23 Jul 2012 06:49

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