Classifying modality learning styles based on Production-Fuzzy Rules

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011
Pages154-159
Number of pages6
Volume1
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011 - Putrajaya
Duration: 28 Jun 201129 Jun 2011

Other

Other2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011
CityPutrajaya
Period28/6/1129/6/11

Fingerprint

Fuzzy rules
Education
Inference engines

Keywords

  • AIWBES
  • Fuzzy Logic
  • Naive Bayes
  • Simple Rule Base
  • user model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Mokhtar, R., Sheikh Abdullah, S. N. H., & Mat Zin, N. A. (2011). Classifying modality learning styles based on Production-Fuzzy Rules. In Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011 (Vol. 1, pp. 154-159). [5976887] https://doi.org/10.1109/ICPAIR.2011.5976887

Classifying modality learning styles based on Production-Fuzzy Rules. / Mokhtar, Rahmah; Sheikh Abdullah, Siti Norul Huda; Mat Zin, Nor Azan.

Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1 2011. p. 154-159 5976887.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mokhtar, R, Sheikh Abdullah, SNH & Mat Zin, NA 2011, Classifying modality learning styles based on Production-Fuzzy Rules. in Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. vol. 1, 5976887, pp. 154-159, 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011, Putrajaya, 28/6/11. https://doi.org/10.1109/ICPAIR.2011.5976887
Mokhtar R, Sheikh Abdullah SNH, Mat Zin NA. Classifying modality learning styles based on Production-Fuzzy Rules. In Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1. 2011. p. 154-159. 5976887 https://doi.org/10.1109/ICPAIR.2011.5976887
Mokhtar, Rahmah ; Sheikh Abdullah, Siti Norul Huda ; Mat Zin, Nor Azan. / Classifying modality learning styles based on Production-Fuzzy Rules. Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1 2011. pp. 154-159
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