Classifying modality learning styles based on Production-Fuzzy Rules

Adaptive Intelligent Web Based Education System, (AIWBES) is an education technology which has been used world-wide. An Intelligent and adaptive AIWBES is materialized from the combination of Users' Model, Knowledge Based and Inference Engine. The development of adaptation or personalization in...

Full description

Bibliographic Details
Main Authors:	Rahmah, Mokhtar, Siti Norul Huda, Sheikh Abdullah, Nor Azan, Mat Zin
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2011
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/26122/ http://umpir.ump.edu.my/id/eprint/26122/ http://umpir.ump.edu.my/id/eprint/26122/1/Classifying%20modality%20learning%20styles%20based%20on%20Production-Fuzzy%20Rules.pdf

id	ump-26122
recordtype	eprints
spelling	ump-261222019-10-16T08:06:28Z http://umpir.ump.edu.my/id/eprint/26122/ Classifying modality learning styles based on Production-Fuzzy Rules Rahmah, Mokhtar Siti Norul Huda, Sheikh Abdullah Nor Azan, Mat Zin QA75 Electronic computers. Computer science Adaptive Intelligent Web Based Education System, (AIWBES) is an education technology which has been used world-wide. An Intelligent and adaptive AIWBES is materialized from the combination of Users' Model, Knowledge Based and Inference Engine. The development of adaptation or personalization in AIWBES will provide an Intelligence system for the users to obtain knowledge and information. This paper will focus on the user model to enhance AIWBES personalization based on its users' modality learning style. The objective of this paper is to compare the precision between Production-Fuzzy Rule and Naives Bayes for classifying modality learning styles in the user model. A prototype namely K-Stailo, is developed. These two different techniques were applied in K-Stailo. A test was carried out by the researcher to evaluate the precision between these two techniques. The results show that Production - Fuzzy Rule is the better technique when compared to Naives Bayes in user's modality learning style prediction. IEEE 2011 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26122/1/Classifying%20modality%20learning%20styles%20based%20on%20Production-Fuzzy%20Rules.pdf Rahmah, Mokhtar and Siti Norul Huda, Sheikh Abdullah and Nor Azan, Mat Zin (2011) Classifying modality learning styles based on Production-Fuzzy Rules. In: IEEE International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR 2011), 28-29 June 2011 , Putrajaya. pp. 154-159.. https://doi.org/10.1109/ICPAIR.2011.5976887
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Rahmah, Mokhtar Siti Norul Huda, Sheikh Abdullah Nor Azan, Mat Zin Classifying modality learning styles based on Production-Fuzzy Rules
description	Adaptive Intelligent Web Based Education System, (AIWBES) is an education technology which has been used world-wide. An Intelligent and adaptive AIWBES is materialized from the combination of Users' Model, Knowledge Based and Inference Engine. The development of adaptation or personalization in AIWBES will provide an Intelligence system for the users to obtain knowledge and information. This paper will focus on the user model to enhance AIWBES personalization based on its users' modality learning style. The objective of this paper is to compare the precision between Production-Fuzzy Rule and Naives Bayes for classifying modality learning styles in the user model. A prototype namely K-Stailo, is developed. These two different techniques were applied in K-Stailo. A test was carried out by the researcher to evaluate the precision between these two techniques. The results show that Production - Fuzzy Rule is the better technique when compared to Naives Bayes in user's modality learning style prediction.
format	Conference or Workshop Item
author	Rahmah, Mokhtar Siti Norul Huda, Sheikh Abdullah Nor Azan, Mat Zin
author_facet	Rahmah, Mokhtar Siti Norul Huda, Sheikh Abdullah Nor Azan, Mat Zin
author_sort	Rahmah, Mokhtar
title	Classifying modality learning styles based on Production-Fuzzy Rules
title_short	Classifying modality learning styles based on Production-Fuzzy Rules
title_full	Classifying modality learning styles based on Production-Fuzzy Rules
title_fullStr	Classifying modality learning styles based on Production-Fuzzy Rules
title_full_unstemmed	Classifying modality learning styles based on Production-Fuzzy Rules
title_sort	classifying modality learning styles based on production-fuzzy rules
publisher	IEEE
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/26122/ http://umpir.ump.edu.my/id/eprint/26122/ http://umpir.ump.edu.my/id/eprint/26122/1/Classifying%20modality%20learning%20styles%20based%20on%20Production-Fuzzy%20Rules.pdf
first_indexed	2023-09-18T22:40:29Z
last_indexed	2023-09-18T22:40:29Z
_version_	1777416881787895808

Classifying modality learning styles based on Production-Fuzzy Rules

Similar Items