Classifying modality learning styles based on Production-Fuzzy Rules

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..

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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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: AIWBES; Simple Rule Base; Fuzzy Logic; user model; Naive Bayes
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 16 Oct 2019 08:06
Last Modified: 16 Oct 2019 08:06
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