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...
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2011
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| Online Access: | http://umpir.ump.edu.my/id/eprint/26122/1/Classifying%20modality%20learning%20styles%20based%20on%20Production-Fuzzy%20Rules.pdf |
| _version_ | 1796993599242502144 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:36:17Z |
| format | Conference or Workshop Item |
| id | UMPir26122 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:36:17Z |
| publishDate | 2011 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir261222019-10-16T08:06:28Z 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 |
| 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 |
| title | 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_short | Classifying modality learning styles based on Production-Fuzzy Rules |
| title_sort | classifying modality learning styles based on production fuzzy rules |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/26122/1/Classifying%20modality%20learning%20styles%20based%20on%20Production-Fuzzy%20Rules.pdf |
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