Case-based retrieval on question items generation

In the education, the purpose of the conducting test is to determine whether the instructional objectives have been achieved or not. It is a challenge to build a learning system that meet pedagogical aspect of learning. Test items should match to the learning outcomes and the conditions determine by...

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Main Author:	Subroto, Imam Much
Format:	Thesis
Language:	English
Published:	2007
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/6654/1/ImamMuchIbnuSubrotoMFSKSM2007.pdf
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spelling	my-utm-ep.66542018-08-26T04:49:49Z Case-based retrieval on question items generation 2007-07 Subroto, Imam Much QA75 Electronic computers. Computer science In the education, the purpose of the conducting test is to determine whether the instructional objectives have been achieved or not. It is a challenge to build a learning system that meet pedagogical aspect of learning. Test items should match to the learning outcomes and the conditions determine by the instructional objectives. Taxonomy Bloomâ€™s known is as the standard of the instructional objective level on the cognitive domain. This work is to study how effective the Case-based Reasoning (CBR) method is to solve the generation of question items problem. CBR is the artificial intelligent method that is suitable to solve the problem by finding similar cases from the past. Based on the similar case, the solution is to reuse the similar case and to revise its similar case solution. It is the fact that some question items or some test may be reused or revised for future situation. This work has been successfully implementing the CBR method on question items generation. Some retrieval techniques (Rule Base Reasoning and CBR) and similarity measure (Nearest neighborhood and Euclidean distance) has been experimented. From these experiments is that, CBR retrieval technique using Euclidean distance similarity and inductive indexing approach is the best performance. The experiment has given the similarity tolerance 0.7 is acceptable because it categorizes to high similarity and the recall is enough to give suggestion solution (in this experiment about 3 or 4 similar cases). Finally the overall results show that the complete task of CBR method has successfully solved the problem of matching the learning outcomes with the instructional objectives. 2007-07 Thesis http://eprints.utm.my/id/eprint/6654/ http://eprints.utm.my/id/eprint/6654/1/ImamMuchIbnuSubrotoMFSKSM2007.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62344 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 Subroto, Imam Much Case-based retrieval on question items generation
description	In the education, the purpose of the conducting test is to determine whether the instructional objectives have been achieved or not. It is a challenge to build a learning system that meet pedagogical aspect of learning. Test items should match to the learning outcomes and the conditions determine by the instructional objectives. Taxonomy Bloomâ€™s known is as the standard of the instructional objective level on the cognitive domain. This work is to study how effective the Case-based Reasoning (CBR) method is to solve the generation of question items problem. CBR is the artificial intelligent method that is suitable to solve the problem by finding similar cases from the past. Based on the similar case, the solution is to reuse the similar case and to revise its similar case solution. It is the fact that some question items or some test may be reused or revised for future situation. This work has been successfully implementing the CBR method on question items generation. Some retrieval techniques (Rule Base Reasoning and CBR) and similarity measure (Nearest neighborhood and Euclidean distance) has been experimented. From these experiments is that, CBR retrieval technique using Euclidean distance similarity and inductive indexing approach is the best performance. The experiment has given the similarity tolerance 0.7 is acceptable because it categorizes to high similarity and the recall is enough to give suggestion solution (in this experiment about 3 or 4 similar cases). Finally the overall results show that the complete task of CBR method has successfully solved the problem of matching the learning outcomes with the instructional objectives.
format	Thesis
qualification_level	Master's degree
author	Subroto, Imam Much
author_facet	Subroto, Imam Much
author_sort	Subroto, Imam Much
title	Case-based retrieval on question items generation
title_short	Case-based retrieval on question items generation
title_full	Case-based retrieval on question items generation
title_fullStr	Case-based retrieval on question items generation
title_full_unstemmed	Case-based retrieval on question items generation
title_sort	case-based retrieval on question items generation
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2007
url	http://eprints.utm.my/id/eprint/6654/1/ImamMuchIbnuSubrotoMFSKSM2007.pdf
_version_	1747814681261637632

Case-based retrieval on question items generation

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