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Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines
Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517283/ https://www.ncbi.nlm.nih.gov/pubmed/33286506 http://dx.doi.org/10.3390/e22070735 |
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author | Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Voyiatzis, Ioannis |
author_facet | Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Voyiatzis, Ioannis |
author_sort | Troussas, Christos |
collection | PubMed |
description | Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results. |
format | Online Article Text |
id | pubmed-7517283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75172832020-11-09 Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Voyiatzis, Ioannis Entropy (Basel) Article Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results. MDPI 2020-07-02 /pmc/articles/PMC7517283/ /pubmed/33286506 http://dx.doi.org/10.3390/e22070735 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Voyiatzis, Ioannis Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines |
title | Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines |
title_full | Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines |
title_fullStr | Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines |
title_full_unstemmed | Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines |
title_short | Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines |
title_sort | ensemble learning using fuzzy weights to improve learning style identification for adapted instructional routines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517283/ https://www.ncbi.nlm.nih.gov/pubmed/33286506 http://dx.doi.org/10.3390/e22070735 |
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