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Model Detecting Learning Styles with Artificial Neural Network

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Title: Model Detecting Learning Styles with Artificial Neural Network
Language: English
Authors: Hasibuan, Muhammad Said (ORCID 0000-0002-9542-1574), Nugroho, Lukito Edi (ORCID 0000-0001-9080-7622), Santosa, Paulus Insap (ORCID 0000-0002-0581-2521)
Source: Journal of Technology and Science Education. 2019 9(1):85-95.
Availability: Journal of Technology and Science Education. ESEIAAT, Department of Projectes d'Enginyeria c/Colom 11, 08222 Terrassa, Spain. e-mail: info@jotse.org; e-mail: info@omniascience.com; Web site: http://www.jotse.org/index.php/jotse
Peer Reviewed: Y
Page Count: 11
Publication Date: 2019
Document Type: Journal Articles
Reports - Research
Descriptors: Cognitive Style, Artificial Intelligence, Prior Learning, Identification, Natural Language Processing
ISSN: 2014-5349
Abstract: Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.
Abstractor: As Provided
Entry Date: 2019
Accession Number: EJ1204886
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1204886
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  Data: Model Detecting Learning Styles with Artificial Neural Network
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  Data: <searchLink fieldCode="AR" term="%22Hasibuan%2C+Muhammad+Said%22">Hasibuan, Muhammad Said</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9542-1574">0000-0002-9542-1574</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nugroho%2C+Lukito+Edi%22">Nugroho, Lukito Edi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9080-7622">0000-0001-9080-7622</externalLink>)<br /><searchLink fieldCode="AR" term="%22Santosa%2C+Paulus+Insap%22">Santosa, Paulus Insap</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0581-2521">0000-0002-0581-2521</externalLink>)
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  Data: Journal of Technology and Science Education. ESEIAAT, Department of Projectes d'Enginyeria c/Colom 11, 08222 Terrassa, Spain. e-mail: info@jotse.org; e-mail: info@omniascience.com; Web site: http://www.jotse.org/index.php/jotse
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  Data: Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.
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