Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach
Personalized learning seek to provide each individual learner with the right and sufficient content they need according to learners level of knowledge, behavior and profile. One of the most important factors for improving the personalization methods of e-learning system is to app...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2013
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| Online Access: | http://eprints.unisza.edu.my/310/1/FH03-FIK-16-05753.jpg http://eprints.unisza.edu.my/310/ |
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| author | Fatma Susilawati, Mohamad Mumtazimah, Mohamad Engku Fadzli Hasan, Syed Abdullah |
| author_facet | Fatma Susilawati, Mohamad Mumtazimah, Mohamad Engku Fadzli Hasan, Syed Abdullah |
| author_sort | Fatma Susilawati, Mohamad |
| building | UNISZA Library |
| collection | Institutional Repository |
| content_provider | Universiti Sultan Zainal Abidin |
| content_source | UNISZA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Personalized learning seek to provide each individual learner with the right and sufficient content they need according to learners level of knowledge, behavior and profile. One of the most important factors for improving the personalization methods of e-learning system is to apply adaptive properties. The aim of adaptive personalized e-Learning system is to offer the most appropriate learning materials to learners by taking into account their background and profiles. However, most of the systems focused on users’ learning behaviors, interests and habits to provide personalized e-Learning services while ignoring course difficulty, users profile and user’s ability. Recent researchers focus on fuzzy implementation of item response theory to measure learner’s ability and course difficulty. This paper introduces an improved model by using a personal e-Learning by integrating Item Response Theory and Felder-Silverman's learning style theory as an attempt to obtain personal knowledge, background and learning style. These input will be verified and classified by an Artificial Neural Network as machine learning to model their behavior as whole. This technique will be able to estimate the ability of students towards improving the level of understanding to moderate until weak students in programming classes. Therefore, there will be suggestions for course materials suitable for students and course material difficulty can be adjusted automatically. It is hoped that this study will contribute towards higher education institution for an adaptive e-Learning rather than content-focus e-Learning. |
| format | Conference or Workshop Item |
| id | my.unisza.eprints-310 |
| institution | Universiti Sultan Zainal Abidin |
| language | en |
| publishDate | 2013 |
| record_format | eprints |
| spelling | my.unisza.eprints-3102020-10-21T03:14:53Z http://eprints.unisza.edu.my/310/ Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach Fatma Susilawati, Mohamad Mumtazimah, Mohamad Engku Fadzli Hasan, Syed Abdullah QA75 Electronic computers. Computer science QA76 Computer software Personalized learning seek to provide each individual learner with the right and sufficient content they need according to learners level of knowledge, behavior and profile. One of the most important factors for improving the personalization methods of e-learning system is to apply adaptive properties. The aim of adaptive personalized e-Learning system is to offer the most appropriate learning materials to learners by taking into account their background and profiles. However, most of the systems focused on users’ learning behaviors, interests and habits to provide personalized e-Learning services while ignoring course difficulty, users profile and user’s ability. Recent researchers focus on fuzzy implementation of item response theory to measure learner’s ability and course difficulty. This paper introduces an improved model by using a personal e-Learning by integrating Item Response Theory and Felder-Silverman's learning style theory as an attempt to obtain personal knowledge, background and learning style. These input will be verified and classified by an Artificial Neural Network as machine learning to model their behavior as whole. This technique will be able to estimate the ability of students towards improving the level of understanding to moderate until weak students in programming classes. Therefore, there will be suggestions for course materials suitable for students and course material difficulty can be adjusted automatically. It is hoped that this study will contribute towards higher education institution for an adaptive e-Learning rather than content-focus e-Learning. 2013 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/310/1/FH03-FIK-16-05753.jpg Fatma Susilawati, Mohamad and Mumtazimah, Mohamad and Engku Fadzli Hasan, Syed Abdullah (2013) Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach. In: The Second International Conference on Informatics & Applications (ICIA2013), 23 - 25 September 2013, Lodz, Poland. |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Fatma Susilawati, Mohamad Mumtazimah, Mohamad Engku Fadzli Hasan, Syed Abdullah Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach |
| title | Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach |
| title_full | Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach |
| title_fullStr | Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach |
| title_full_unstemmed | Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach |
| title_short | Integrating an e-Learning Model using IRT, Felder-Silverman and Neural Network Approach |
| title_sort | integrating an e-learning model using irt, felder-silverman and neural network approach |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.unisza.edu.my/310/1/FH03-FIK-16-05753.jpg http://eprints.unisza.edu.my/310/ |
| url_provider | https://eprints.unisza.edu.my/ |