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Students' learning style detection using tree augmented naive Bayes
Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Baye...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083720/ https://www.ncbi.nlm.nih.gov/pubmed/30109052 http://dx.doi.org/10.1098/rsos.172108 |
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author | Li, Ling Xiao Abdul Rahman, Siti Soraya |
author_facet | Li, Ling Xiao Abdul Rahman, Siti Soraya |
author_sort | Li, Ling Xiao |
collection | PubMed |
description | Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students’ learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network. |
format | Online Article Text |
id | pubmed-6083720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-60837202018-08-14 Students' learning style detection using tree augmented naive Bayes Li, Ling Xiao Abdul Rahman, Siti Soraya R Soc Open Sci Computer Science Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students’ learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network. The Royal Society Publishing 2018-07-25 /pmc/articles/PMC6083720/ /pubmed/30109052 http://dx.doi.org/10.1098/rsos.172108 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Computer Science Li, Ling Xiao Abdul Rahman, Siti Soraya Students' learning style detection using tree augmented naive Bayes |
title | Students' learning style detection using tree augmented naive Bayes |
title_full | Students' learning style detection using tree augmented naive Bayes |
title_fullStr | Students' learning style detection using tree augmented naive Bayes |
title_full_unstemmed | Students' learning style detection using tree augmented naive Bayes |
title_short | Students' learning style detection using tree augmented naive Bayes |
title_sort | students' learning style detection using tree augmented naive bayes |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083720/ https://www.ncbi.nlm.nih.gov/pubmed/30109052 http://dx.doi.org/10.1098/rsos.172108 |
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