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Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose

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@article{IJASEIT1745,
   author = {Dadang Syarif Sihabudin Sahid and Lukito Edi Nugroho and Paulus Insap Santosa},
   title = {Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1708--1715},
   keywords = {literate based driven; learning style; personalized e-learning; stochastic approach; VARK learning style.},
   abstract = {This paper presents integrated stochastic and literate based driven approaches in learning style identification for personalized e-learning purpose. Shifting a paradigm in education from teacher learning to student learning center has encouraged that learning should follow and tailor learners’ characteristics in the form of personalized e-learning. There are several aspects to describe a condition of learners such as prior knowledge, learning goals, learning styles, cognitive ability, learning interest, and motivation. Even though, in many studies of the personalized e-learning, the learning style plays a significant role. In terms of e-learning, implementing several methods for identifying learner style becomes more challenging. Artificial intelligence and machine learning method give good accuracy, but they still have some issues in computation. Additionally, the stationary method is very hard to represent non-deterministic and dynamic data. Therefore, this research proposes the learning style identification by integrating stochastic and literate based driven approaches. Hidden Markov Model (HMM) and the Naïve Bayes as the Stochastic Approach have been implemented. Subsequently, learner behavior as the literate based data is used to get hints during accessing the learning objects. The proposed model has been implemented to VARK learning style. The accuracy is calculated by comparing the model results with the questionnaire results. When Using the HMM, the proposed model gives accuracy in the range of 95% up to 96.67%. Additionally, when using the Naïve Bayes; the accuracy is 93.33%. The results give better accuracy compared to previous studies. In conclusion, the proposed model is promising for modeling learner style in personalized e-learning.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1745},
   doi = {10.18517/ijaseit.7.5.1745}
}

EndNote

%A Sahid, Dadang Syarif Sihabudin
%A Nugroho, Lukito Edi
%A Santosa, Paulus Insap
%D 2017
%T Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose
%B 2017
%9 literate based driven; learning style; personalized e-learning; stochastic approach; VARK learning style.
%! Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose
%K literate based driven; learning style; personalized e-learning; stochastic approach; VARK learning style.
%X This paper presents integrated stochastic and literate based driven approaches in learning style identification for personalized e-learning purpose. Shifting a paradigm in education from teacher learning to student learning center has encouraged that learning should follow and tailor learners’ characteristics in the form of personalized e-learning. There are several aspects to describe a condition of learners such as prior knowledge, learning goals, learning styles, cognitive ability, learning interest, and motivation. Even though, in many studies of the personalized e-learning, the learning style plays a significant role. In terms of e-learning, implementing several methods for identifying learner style becomes more challenging. Artificial intelligence and machine learning method give good accuracy, but they still have some issues in computation. Additionally, the stationary method is very hard to represent non-deterministic and dynamic data. Therefore, this research proposes the learning style identification by integrating stochastic and literate based driven approaches. Hidden Markov Model (HMM) and the Naïve Bayes as the Stochastic Approach have been implemented. Subsequently, learner behavior as the literate based data is used to get hints during accessing the learning objects. The proposed model has been implemented to VARK learning style. The accuracy is calculated by comparing the model results with the questionnaire results. When Using the HMM, the proposed model gives accuracy in the range of 95% up to 96.67%. Additionally, when using the Naïve Bayes; the accuracy is 93.33%. The results give better accuracy compared to previous studies. In conclusion, the proposed model is promising for modeling learner style in personalized e-learning.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1745
%R doi:10.18517/ijaseit.7.5.1745
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 5
%@ 2088-5334

IEEE

Dadang Syarif Sihabudin Sahid,Lukito Edi Nugroho and Paulus Insap Santosa,"Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1708-1715, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.1745.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sahid, Dadang Syarif Sihabudin
AU  - Nugroho, Lukito Edi
AU  - Santosa, Paulus Insap
PY  - 2017
TI  - Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1708
EP  - 1715
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - literate based driven; learning style; personalized e-learning; stochastic approach; VARK learning style.
N2  - This paper presents integrated stochastic and literate based driven approaches in learning style identification for personalized e-learning purpose. Shifting a paradigm in education from teacher learning to student learning center has encouraged that learning should follow and tailor learners’ characteristics in the form of personalized e-learning. There are several aspects to describe a condition of learners such as prior knowledge, learning goals, learning styles, cognitive ability, learning interest, and motivation. Even though, in many studies of the personalized e-learning, the learning style plays a significant role. In terms of e-learning, implementing several methods for identifying learner style becomes more challenging. Artificial intelligence and machine learning method give good accuracy, but they still have some issues in computation. Additionally, the stationary method is very hard to represent non-deterministic and dynamic data. Therefore, this research proposes the learning style identification by integrating stochastic and literate based driven approaches. Hidden Markov Model (HMM) and the Naïve Bayes as the Stochastic Approach have been implemented. Subsequently, learner behavior as the literate based data is used to get hints during accessing the learning objects. The proposed model has been implemented to VARK learning style. The accuracy is calculated by comparing the model results with the questionnaire results. When Using the HMM, the proposed model gives accuracy in the range of 95% up to 96.67%. Additionally, when using the Naïve Bayes; the accuracy is 93.33%. The results give better accuracy compared to previous studies. In conclusion, the proposed model is promising for modeling learner style in personalized e-learning.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1745
DO  - 10.18517/ijaseit.7.5.1745

RefWorks

RT Journal Article
ID 1745
A1 Sahid, Dadang Syarif Sihabudin
A1 Nugroho, Lukito Edi
A1 Santosa, Paulus Insap
T1 Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1708
OP 1715
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 literate based driven; learning style; personalized e-learning; stochastic approach; VARK learning style.
AB This paper presents integrated stochastic and literate based driven approaches in learning style identification for personalized e-learning purpose. Shifting a paradigm in education from teacher learning to student learning center has encouraged that learning should follow and tailor learners’ characteristics in the form of personalized e-learning. There are several aspects to describe a condition of learners such as prior knowledge, learning goals, learning styles, cognitive ability, learning interest, and motivation. Even though, in many studies of the personalized e-learning, the learning style plays a significant role. In terms of e-learning, implementing several methods for identifying learner style becomes more challenging. Artificial intelligence and machine learning method give good accuracy, but they still have some issues in computation. Additionally, the stationary method is very hard to represent non-deterministic and dynamic data. Therefore, this research proposes the learning style identification by integrating stochastic and literate based driven approaches. Hidden Markov Model (HMM) and the Naïve Bayes as the Stochastic Approach have been implemented. Subsequently, learner behavior as the literate based data is used to get hints during accessing the learning objects. The proposed model has been implemented to VARK learning style. The accuracy is calculated by comparing the model results with the questionnaire results. When Using the HMM, the proposed model gives accuracy in the range of 95% up to 96.67%. Additionally, when using the Naïve Bayes; the accuracy is 93.33%. The results give better accuracy compared to previous studies. In conclusion, the proposed model is promising for modeling learner style in personalized e-learning.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1745
DO  - 10.18517/ijaseit.7.5.1745