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Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge

In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important var...

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Main Authors: Idris, N., Hashim, S. Z. M., Samsudin, R., Ahmad, N. B. H.
Format: Article
Language:English
Published: Insight Society 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/81366/1/NorshamIdris2017_IntelligentLearningModelBasedOnSignificant.pdf
http://eprints.utm.my/id/eprint/81366/
http://dx.doi.org/10.18517/ijaseit.7.4-2.3408
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spelling my.utm.813662019-08-04T03:34:51Z http://eprints.utm.my/id/eprint/81366/ Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge Idris, N. Hashim, S. Z. M. Samsudin, R. Ahmad, N. B. H. QA75 Electronic computers. Computer science In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process. Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules. Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student's domain knowledge level. Insight Society 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/81366/1/NorshamIdris2017_IntelligentLearningModelBasedOnSignificant.pdf Idris, N. and Hashim, S. Z. M. and Samsudin, R. and Ahmad, N. B. H. (2017) Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge. International Journal on Advanced Science, Engineering and Information Technology, 10 (4-2). pp. 1486-1491. ISSN 2088-5334 http://dx.doi.org/10.18517/ijaseit.7.4-2.3408 DOI:10.18517/ijaseit.7.4-2.3408
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Idris, N.
Hashim, S. Z. M.
Samsudin, R.
Ahmad, N. B. H.
Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge
description In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process. Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules. Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student's domain knowledge level.
format Article
author Idris, N.
Hashim, S. Z. M.
Samsudin, R.
Ahmad, N. B. H.
author_facet Idris, N.
Hashim, S. Z. M.
Samsudin, R.
Ahmad, N. B. H.
author_sort Idris, N.
title Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge
title_short Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge
title_full Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge
title_fullStr Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge
title_full_unstemmed Intelligent Learning Model Based on Concept for Adaptive E-Learning Significant Weight of Domain Knowledge
title_sort intelligent learning model based on concept for adaptive e-learning significant weight of domain knowledge
publisher Insight Society
publishDate 2017
url http://eprints.utm.my/id/eprint/81366/1/NorshamIdris2017_IntelligentLearningModelBasedOnSignificant.pdf
http://eprints.utm.my/id/eprint/81366/
http://dx.doi.org/10.18517/ijaseit.7.4-2.3408
_version_ 1643658689223065600
score 13.211869

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